[
  {
    "path": ".coveragerc",
    "content": "# .coveragerc to control coverage.py\n[run]\nbranch = True\nsource = pyrate\n#omit =\n    # omit everything in\n    # pyrate/tasks/*\n    # omit these files\n#    pyrate/pyratelog.py\n#    pyrate/scripts/main.py\n#    pyrate/pyaps.py\n#    pyrate/compat.py\n\n[report]\n# Regexes for lines to exclude from consideration\nexclude_lines =\n    # Have to re-enable the standard pragma\n    pragma: no cover\n\n    # Don't complain about missing debug-only code:\n    def __repr__\n    def __str__\n    if self\\.debug\n\n    # Don't complain if tests don't hit defensive assertion code:\n    raise AssertionError\n    raise NotImplementedError\n    raise ValueError\n    raise RuntimeError\n    raise IOError\n    except OSError\n    except ValueError\n    except IndexError\n    raise ImportError\n    except ImportError\n    raise ConfigException\n    raise ReferencePhaseError\n    raise RefPixelError\n    raise RoipacException\n    raise GeotiffException\n    raise RasterException\n    raise IfgException\n    raise GammaException\n    raise PreprocessError\n    raise TimeSeriesError\n    raise OrbitalError\n\n    # Don't complain if non-runnable code isn't run:\n    if 0:\n    if __name__ == .__main__.:\n\nignore_errors = True\n\n[html]\ndirectory = coverage_html_report\n"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/bug_report.md",
    "content": "---\nname: Bug report\nabout: Create a report to help us improve\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Describe the bug**\nA clear and concise description of what the bug is.\n\n**To Reproduce**\nSteps to reproduce the behavior:\n1. Go to '...'\n2. Click on '....'\n3. Scroll down to '....'\n4. See error\n\n**Expected behavior**\nA clear and concise description of what you expected to happen.\n\n**Screenshots**\nIf applicable, add screenshots to help explain your problem.\n\n**Desktop (please complete the following information):**\n - OS: [e.g. iOS]\n - Browser [e.g. chrome, safari]\n - Version [e.g. 22]\n\n**Smartphone (please complete the following information):**\n - Device: [e.g. iPhone6]\n - OS: [e.g. iOS8.1]\n - Browser [e.g. stock browser, safari]\n - Version [e.g. 22]\n\n**Additional context**\nAdd any other context about the problem here.\n"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/feature_request.md",
    "content": "---\nname: Feature request\nabout: Suggest an idea for this project\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Is your feature request related to a problem? Please describe.**\nA clear and concise description of what the problem is. Ex. I'm always frustrated when [...]\n\n**Describe the solution you'd like**\nA clear and concise description of what you want to happen.\n\n**Describe alternatives you've considered**\nA clear and concise description of any alternative solutions or features you've considered.\n\n**Additional context**\nAdd any other context or screenshots about the feature request here.\n"
  },
  {
    "path": ".github/workflows/build.yml",
    "content": "name: PyRate CI\n\n\non:\n  pull_request:\n    branches:\n      - 'master'\n  push:\n    branches:\n      - '**'\n\njobs:\n  build:\n    if: ${{ ! (contains(github.event.head_commit.message, 'ci skip') || contains(github.event.head_commit.message, 'skip ci'))}}\n    runs-on: ubuntu-18.04\n    name: Python ${{ matrix.python }}\n    strategy:\n      matrix:\n        include:\n          - build: 1\n            python-version: \"3.7\"\n            GDALVERSION: \"3.0.2\"\n            PROJVERSION: \"6.2.1\"\n          - build: 2\n            python-version: \"3.7\"\n            GDALVERSION: \"3.0.4\"\n            PROJVERSION: \"6.1.1\"\n          - build: 3\n            python-version: \"3.8\"\n            GDALVERSION: \"3.0.4\"\n            PROJVERSION: \"6.1.1\"\n          - build: 4\n            python-version: \"3.9\"\n            GDALVERSION: \"3.0.4\"\n            PROJVERSION: \"6.3.2\"\n\n    env:\n      PIP_WHEEL_DIR: \"/home/runner/.cache/pip/wheels\"\n      PIP_FIND_LINKS: \"file:///home/runner/.cache/pip/wheels\"\n      GDALINST: \"/home/runner/gdalinstall\"\n      GDALBUILD: \"/home/runner/gdalbuild\"\n      GDALVERSION: ${{ matrix.GDALVERSION }}\n      PROJINST: \"/home/runner/gdalinstall\"\n      PROJBUILD: \"/home/runner/projbuild\"\n      PROJVERSION: ${{ matrix.PROJVERSION }}\n      PYTHONVERSION: ${{ matrix.python-version }}\n\n    steps:\n      - uses: actions/checkout@v2\n      - name: Cache multiple paths\n        uses: actions/cache@v2\n        with:\n          path: |\n            /home/runner/gdalinstall\n          key: ${{ runner.os }}-cache-GDAL-${{ matrix.GDALVERSION }}-proj-${{ matrix.PROJVERSION }}\n      - name: Set up Python ${{ matrix.python-version }}\n        uses: actions/setup-python@v2\n        with:\n          python-version: ${{ matrix.python-version }}\n      - name: Common set up ${{ matrix.python-version }}\n        run: |\n          sudo apt update\n          sudo apt upgrade\n          python -m pip install -U pip wheel setuptools==58.0\n      - name: Install packages including openmpi\n        if: env.PYTHONVERSION != '3.9'\n        run: sudo apt install libhdf5-serial-dev libnetcdf13 libatlas-base-dev gfortran openmpi-bin libopenmpi-dev\n      - name: Install packages except openmpi libraries\n        if: env.PYTHONVERSION == '3.9'\n        run: sudo apt install libhdf5-serial-dev libnetcdf13 libatlas-base-dev gfortran\n      - name: Install proj ${{matrix.PROJVERSION}}\n        run: |\n          echo \"PATH=$GDALINST/gdal-$GDALVERSION/bin:$PATH\" >> $GITHUB_ENV\n          echo \"LD_LIBRARY_PATH=$GDALINST/gdal-$GDALVERSION/lib:$LD_LIBRARY_PATH\" >> $GITHUB_ENV\n          source ./scripts/ci_proj_install.sh\n      - name: Install GDAL ${{matrix.GDALVERSION}}\n        run: |\n          echo \"GDAL_DATA=$GDALINST/gdal-$GDALVERSION/share/gdal\" >> $GITHUB_ENV\n          echo \"PROJ_LIB=$GDALINST/gdal-$GDALVERSION/share/proj\" >> $GITHUB_ENV\n          echo \"LDFLAGS=-L$GDALINST/gdal-$GDALVERSION/lib -Wl,-rpath,$GDALINST/gdal-$GDALVERSION/lib\" >> $GITHUB_ENV\n          source ./scripts/ci_gdal_install.sh\n      - name: Python ${{ matrix.python-version }} with MPI\n        if: env.PYTHONVERSION != '3.9'\n        run: |\n          python setup.py install\n          rm -rf Py_Rate.egg-info  # remove the local egg\n          echo \"PYTHONPATH=$(pwd):$PYTHONPATH\" >> $GITHUB_ENV\n          chmod 444 tests/test_data/small_test/tif/geo_070709-070813_unw.tif  # makes the file readonly, used in a test\n          pip install -r requirements-dev.txt -r requirements-test.txt\n      - name: Python ${{ matrix.python-version }} without MPI\n        if: env.PYTHONVERSION == '3.9'\n        run: |\n          python setup.py install\n          rm -rf Py_Rate.egg-info  # remove the local egg\n          echo \"PYTHONPATH=$(pwd):$PYTHONPATH\" >> $GITHUB_ENV\n          chmod 444 tests/test_data/small_test/tif/geo_070709-070813_unw.tif  # makes the file readonly, used in a test\n          pip install -r requirements-test.txt\n      - name: Test Pyrate in Python ${{ matrix.python-version }} with MPI\n        if: env.PYTHONVERSION != '3.9'\n        run: |\n          pytest tests/ -m \"slow\"\n          mpirun -n 3 pytest tests/test_shared.py -k test_tiles_split -s\n          pytest --cov-config=.coveragerc --cov-report term-missing:skip-covered --cov=pyrate tests/ -m \"not slow\"\n        env:\n             OMP_NUM_THREADS: 1\n      - name: Test Pyrate in Python ${{ matrix.python-version }} without MPI\n        if: env.PYTHONVERSION == '3.9'\n        run: |\n          pytest tests/ -m \"not mpi and slow\"\n          pytest --cov-config=.coveragerc --cov-report term-missing:skip-covered --cov=pyrate tests/ -m \"not slow and not mpi\"\n        env:\n             OMP_NUM_THREADS: 1\n        \n"
  },
  {
    "path": ".gitignore",
    "content": "!*docs/_build/html/404.html\n!*tests/test_data/merge/linrate.tif\n!*tests/test_data/small_test/gamma_obs/20060619-20061002_utm_unw.tif\n*data/*\n*out*\n*.pyo\n*.pyc\n.project\n.pydevproject\n/tmp\n/.idea\n.spyderworkspace\ndoc\n.eggs/\n.tox/\n.coverage.*\n.vscode/\n*.egg-info/\n.installed.cfg\n*.egg\n*__pycache__/\n\n# files generated by doc build\ndocs/_build/\n\n# files generated by setup\n.cache/\nbuild/\ndist/\n# out\nPy_Rate.egg-info\n\n# files generated during tests\n*tests/test_data/prepifg/tif/0_1rlks_4cr.tif\n*tests/test_data/prepifg/tif/0_2rlks_4cr.tif\n*tests/test_data/prepifg/tif/1_1rlks_4cr.tif\n*tests/test_data/prepifg/tif/1_2rlks_4cr.tif\n\n*tests/test_data/merge/colormap.txt\n*tests/test_data/merge/linrate.kml\n*tests/test_data/merge/linrate.png\n*tests/test_data/merge/linrate.png.aux.xml\n*tests/test_data/merge/linrate.tif.aux.xml\n\n*tests/test_data/small_test/gamma_obs/*.tif\n"
  },
  {
    "path": ".pylintrc",
    "content": "[MASTER]\n\n# Specify a configuration file.\n#rcfile=\n\n# Python code to execute, usually for sys.path manipulation such as\n# pygtk.require().\n#init-hook=\n\n# Add files or directories to the blacklist. They should be base names, not\n# paths.\nignore=CVS\n\n# Add files or directories matching the regex patterns to the blacklist. The\n# regex matches against base names, not paths.\nignore-patterns=\n\n# Pickle collected data for later comparisons.\npersistent=yes\n\n# List of plugins (as comma separated values of python modules names) to load,\n# usually to register additional checkers.\nload-plugins=\n\n# Use multiple processes to speed up Pylint.\njobs=1\n\n# Allow loading of arbitrary C extensions. Extensions are imported into the\n# active Python interpreter and may run arbitrary code.\nunsafe-load-any-extension=no\n\n# A comma-separated list of package or module names from where C extensions may\n# be loaded. Extensions are loading into the active Python interpreter and may\n# run arbitrary code\nextension-pkg-whitelist=\n\n# Allow optimization of some AST trees. This will activate a peephole AST\n# optimizer, which will apply various small optimizations. For instance, it can\n# be used to obtain the result of joining multiple strings with the addition\n# operator. Joining a lot of strings can lead to a maximum recursion error in\n# Pylint and this flag can prevent that. It has one side effect, the resulting\n# AST will be different than the one from reality. This option is deprecated\n# and it will be removed in Pylint 2.0.\noptimize-ast=no\n\n\n[MESSAGES CONTROL]\n\n# Only show warnings with the listed confidence levels. Leave empty to show\n# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED\nconfidence=\n\n# Enable the message, report, category or checker with the given id(s). You can\n# either give multiple identifier separated by comma (,) or put this option\n# multiple time (only on the command line, not in the configuration file where\n# it should appear only once). See also the \"--disable\" option for examples.\n#enable=\n\n# Disable the message, report, category or checker with the given id(s). You\n# can either give multiple identifiers separated by comma (,) or put this\n# option multiple times (only on the command line, not in the configuration\n# file where it should appear only once).You can also use \"--disable=all\" to\n# disable everything first and then reenable specific checks. For example, if\n# you want to run only the similarities checker, you can use \"--disable=all\n# --enable=similarities\". If you want to run only the classes checker, but have\n# no Warning level messages displayed, use\"--disable=all --enable=classes\n# --disable=W\"\ndisable=logging-format-interpolation,too-few-public-methods,import-star-module-level,old-octal-literal,oct-method,print-statement,unpacking-in-except,parameter-unpacking,backtick,old-raise-syntax,old-ne-operator,long-suffix,dict-view-method,dict-iter-method,metaclass-assignment,next-method-called,raising-string,indexing-exception,raw_input-builtin,long-builtin,file-builtin,execfile-builtin,coerce-builtin,cmp-builtin,buffer-builtin,basestring-builtin,apply-builtin,filter-builtin-not-iterating,using-cmp-argument,useless-suppression,range-builtin-not-iterating,suppressed-message,no-absolute-import,old-division,cmp-method,reload-builtin,zip-builtin-not-iterating,intern-builtin,unichr-builtin,reduce-builtin,standarderror-builtin,unicode-builtin,xrange-builtin,coerce-method,delslice-method,getslice-method,setslice-method,input-builtin,round-builtin,hex-method,nonzero-method,map-builtin-not-iterating,fixme\n\n\n[REPORTS]\n\n# Set the output format. Available formats are text, parseable, colorized, msvs\n# (visual studio) and html. You can also give a reporter class, eg\n# mypackage.mymodule.MyReporterClass.\noutput-format=text\n\n# Put messages in a separate file for each module / package specified on the\n# command line instead of printing them on stdout. Reports (if any) will be\n# written in a file name \"pylint_global.[txt|html]\". This option is deprecated\n# and it will be removed in Pylint 2.0.\nfiles-output=no\n\n# Tells whether to display a full report or only the messages\nreports=yes\n\n# Python expression which should return a note less than 10 (10 is the highest\n# note). You have access to the variables errors warning, statement which\n# respectively contain the number of errors / warnings messages and the total\n# number of statements analyzed. This is used by the global evaluation report\n# (RP0004).\nevaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)\n\n# Template used to display messages. This is a python new-style format string\n# used to format the message information. See doc for all details\n#msg-template=\n\n\n[MISCELLANEOUS]\n\n# List of note tags to take in consideration, separated by a comma.\nnotes=FIXME,XXX,TODO\n\n\n[LOGGING]\n\n# Logging modules to check that the string format arguments are in logging\n# function parameter format\nlogging-modules=logging\n\n\n[SPELLING]\n\n# Spelling dictionary name. Available dictionaries: none. To make it working\n# install python-enchant package.\nspelling-dict=\n\n# List of comma separated words that should not be checked.\nspelling-ignore-words=\n\n# A path to a file that contains private dictionary; one word per line.\nspelling-private-dict-file=\n\n# Tells whether to store unknown words to indicated private dictionary in\n# --spelling-private-dict-file option instead of raising a message.\nspelling-store-unknown-words=no\n\n\n[TYPECHECK]\n\n# Tells whether missing members accessed in mixin class should be ignored. A\n# mixin class is detected if its name ends with \"mixin\" (case insensitive).\nignore-mixin-members=yes\n\n# List of module names for which member attributes should not be checked\n# (useful for modules/projects where namespaces are manipulated during runtime\n# and thus existing member attributes cannot be deduced by static analysis. It\n# supports qualified module names, as well as Unix pattern matching.\nignored-modules=numpy\n\n# List of class names for which member attributes should not be checked (useful\n# for classes with dynamically set attributes). This supports the use of\n# qualified names.\nignored-classes=optparse.Values,thread._local,_thread._local\n\n# List of members which are set dynamically and missed by pylint inference\n# system, and so shouldn't trigger E1101 when accessed. Python regular\n# expressions are accepted.\ngenerated-members=\n\n# List of decorators that produce context managers, such as\n# contextlib.contextmanager. Add to this list to register other decorators that\n# produce valid context managers.\ncontextmanager-decorators=contextlib.contextmanager\n\n\n[VARIABLES]\n\n# Tells whether we should check for unused import in __init__ files.\ninit-import=no\n\n# A regular expression matching the name of dummy variables (i.e. expectedly\n# not used).\ndummy-variables-rgx=(_+[a-zA-Z0-9]*?$)|dummy\n\n# List of additional names supposed to be defined in builtins. Remember that\n# you should avoid to define new builtins when possible.\nadditional-builtins=\n\n# List of strings which can identify a callback function by name. A callback\n# name must start or end with one of those strings.\ncallbacks=cb_,_cb\n\n# List of qualified module names which can have objects that can redefine\n# builtins.\nredefining-builtins-modules=six.moves,future.builtins\n\n\n[SIMILARITIES]\n\n# Minimum lines number of a similarity.\nmin-similarity-lines=4\n\n# Ignore comments when computing similarities.\nignore-comments=yes\n\n# Ignore docstrings when computing similarities.\nignore-docstrings=yes\n\n# Ignore imports when computing similarities.\nignore-imports=no\n\n\n[FORMAT]\n\n# Maximum number of characters on a single line.\nmax-line-length=100\n\n# Regexp for a line that is allowed to be longer than the limit.\nignore-long-lines=^\\s*(# )?<?https?://\\S+>?$\n\n# Allow the body of an if to be on the same line as the test if there is no\n# else.\nsingle-line-if-stmt=no\n\n# List of optional constructs for which whitespace checking is disabled. `dict-\n# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\\n222: 2}.\n# `trailing-comma` allows a space between comma and closing bracket: (a, ).\n# `empty-line` allows space-only lines.\nno-space-check=trailing-comma,dict-separator\n\n# Maximum number of lines in a module\nmax-module-lines=1000\n\n# String used as indentation unit. This is usually \"    \" (4 spaces) or \"\\t\" (1\n# tab).\nindent-string='    '\n\n# Number of spaces of indent required inside a hanging  or continued line.\nindent-after-paren=4\n\n# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.\nexpected-line-ending-format=\n\n\n[BASIC]\n\n# Good variable names which should always be accepted, separated by a comma\ngood-names=ts,i,j,k,ex,Run,_,x,w,f,n,v,t,r,d,c,cd,g,r,q,V,m,A,b,log,y,id,ds,gt,md\n\n# Bad variable names which should always be refused, separated by a comma\nbad-names=foo,bar,baz,toto,tutu,tata\n\n# Colon-delimited sets of names that determine each other's naming style when\n# the name regexes allow several styles.\nname-group=\n\n# Include a hint for the correct naming format with invalid-name\ninclude-naming-hint=no\n\n# List of decorators that produce properties, such as abc.abstractproperty. Add\n# to this list to register other decorators that produce valid properties.\nproperty-classes=abc.abstractproperty\n\n# Regular expression matching correct function names\nfunction-rgx=[a-z_][a-z0-9_]{2,30}$\n\n# Naming hint for function names\nfunction-name-hint=[a-z_][a-z0-9_]{2,30}$\n\n# Regular expression matching correct variable names\nvariable-rgx=[a-z_][a-z0-9_]{2,30}$\n\n# Naming hint for variable names\nvariable-name-hint=[a-z_][a-z0-9_]{2,30}$\n\n# Regular expression matching correct constant names\nconst-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$\n\n# Naming hint for constant names\nconst-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$\n\n# Regular expression matching correct attribute names\nattr-rgx=[a-z_][a-z0-9_]{2,30}$\n\n# Naming hint for attribute names\nattr-name-hint=[a-z_][a-z0-9_]{2,30}$\n\n# Regular expression matching correct argument names\nargument-rgx=[a-z_][a-z0-9_]{2,30}$\n\n# Naming hint for argument names\nargument-name-hint=[a-z_][a-z0-9_]{2,30}$\n\n# Regular expression matching correct class attribute names\nclass-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$\n\n# Naming hint for class attribute names\nclass-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$\n\n# Regular expression matching correct inline iteration names\ninlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$\n\n# Naming hint for inline iteration names\ninlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$\n\n# Regular expression matching correct class names\nclass-rgx=[A-Z_][a-zA-Z0-9]+$\n\n# Naming hint for class names\nclass-name-hint=[A-Z_][a-zA-Z0-9]+$\n\n# Regular expression matching correct module names\nmodule-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$\n\n# Naming hint for module names\nmodule-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$\n\n# Regular expression matching correct method names\nmethod-rgx=[a-z_][a-z0-9_]{2,30}$\n\n# Naming hint for method names\nmethod-name-hint=[a-z_][a-z0-9_]{2,30}$\n\n# Regular expression which should only match function or class names that do\n# not require a docstring.\nno-docstring-rgx=^_\n\n# Minimum line length for functions/classes that require docstrings, shorter\n# ones are exempt.\ndocstring-min-length=-1\n\n\n[ELIF]\n\n# Maximum number of nested blocks for function / method body\nmax-nested-blocks=5\n\n\n[DESIGN]\n\n# Maximum number of arguments for function / method\nmax-args=5\n\n# Argument names that match this expression will be ignored. Default to name\n# with leading underscore\nignored-argument-names=_.*\n\n# Maximum number of locals for function / method body\nmax-locals=15\n\n# Maximum number of return / yield for function / method body\nmax-returns=6\n\n# Maximum number of branch for function / method body\nmax-branches=12\n\n# Maximum number of statements in function / method body\nmax-statements=50\n\n# Maximum number of parents for a class (see R0901).\nmax-parents=7\n\n# Maximum number of attributes for a class (see R0902).\nmax-attributes=7\n\n# Minimum number of public methods for a class (see R0903).\nmin-public-methods=2\n\n# Maximum number of public methods for a class (see R0904).\nmax-public-methods=20\n\n# Maximum number of boolean expressions in a if statement\nmax-bool-expr=5\n\n\n[IMPORTS]\n\n# Deprecated modules which should not be used, separated by a comma\ndeprecated-modules=regsub,TERMIOS,Bastion,rexec\n\n# Create a graph of every (i.e. internal and external) dependencies in the\n# given file (report RP0402 must not be disabled)\nimport-graph=\n\n# Create a graph of external dependencies in the given file (report RP0402 must\n# not be disabled)\next-import-graph=\n\n# Create a graph of internal dependencies in the given file (report RP0402 must\n# not be disabled)\nint-import-graph=\n\n# Force import order to recognize a module as part of the standard\n# compatibility libraries.\nknown-standard-library=\n\n# Force import order to recognize a module as part of a third party library.\nknown-third-party=enchant\n\n# Analyse import fallback blocks. This can be used to support both Python 2 and\n# 3 compatible code, which means that the block might have code that exists\n# only in one or another interpreter, leading to false positives when analysed.\nanalyse-fallback-blocks=no\n\n\n[CLASSES]\n\n# List of method names used to declare (i.e. assign) instance attributes.\ndefining-attr-methods=__init__,__new__,setUp\n\n# List of valid names for the first argument in a class method.\nvalid-classmethod-first-arg=cls\n\n# List of valid names for the first argument in a metaclass class method.\nvalid-metaclass-classmethod-first-arg=mcs\n\n# List of member names, which should be excluded from the protected access\n# warning.\nexclude-protected=_asdict,_fields,_replace,_source,_make\n\n\n[EXCEPTIONS]\n\n# Exceptions that will emit a warning when being caught. Defaults to\n# \"Exception\"\novergeneral-exceptions=Exception\n"
  },
  {
    "path": "Dockerfile",
    "content": "FROM ubuntu:16.04\n\nENV LANG C.UTF-8\nENV LANG=en_AU.UTF-8\nENV WORKON_HOME=$HOME/venvs\nENV GDALINST=$HOME/gdalinstall\nENV GDALBUILD=$HOME/gdalbuild\nENV PROJINST=$HOME/gdalinstall\nENV GDALVERSION=\"3.0.2\"\nENV PROJVERSION=\"6.1.1\"\nENV PROJOPT=\"--with-proj=$GDALINST/gdal-$GDALVERSION\"\nENV PATH=$GDALINST/gdal-$GDALVERSION/bin:$PATH\nENV LD_LIBRARY_PATH=$GDALINST/gdal-$GDALVERSION/lib:$LD_LIBRARY_PATH\nENV PROJ_LIB=$GDALINST/gdal-$GDALVERSION/share/proj\nENV GDAL_DATA=$GDALINST/gdal-$GDALVERSION/share/gdal\n\n\nRUN apt-get update \\\n    && apt-get install -y build-essential checkinstall libreadline-gplv2-dev \\\n    libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev \\\n    libbz2-dev openssl curl libffi-dev\n\nRUN mkdir -p $HOME/opt\n\nRUN cd $HOME/opt \\\n    && curl -O https://www.python.org/ftp/python/3.7.7/Python-3.7.7.tgz \\\n    && tar -xzf Python-3.7.7.tgz \\\n    && cd Python-3.7.7 \\\n    && ./configure --enable-shared --enable-optimizations --prefix=/usr/local LDFLAGS=\"-Wl,--rpath=/usr/local/lib\" \\\n    && make altinstall\n\nRUN apt-get install -y build-essential python3-pip \\\n    apt-utils git libgdal-dev libatlas-base-dev openmpi-bin \\\n    libopenmpi-dev gfortran wget libhdf5-serial-dev sqlite3 vim\n\n# update pip\nRUN python3.7 -m pip install pip --upgrade\nRUN python3.7 -m pip install wheel\nRUN pip3 install --upgrade setuptools\n\n\nRUN mkdir -p $GDALBUILD $GDALINST $PROJBUILD $PROJINST\n\nENV GDALOPTS=\"  --with-geos \\\n            --with-expat \\\n            --without-libtool \\\n            --with-libz=internal \\\n            --with-libtiff=internal \\\n            --with-geotiff=internal \\\n            --without-gif \\\n            --without-pg \\\n            --without-grass \\\n            --without-libgrass \\\n            --without-cfitsio \\\n            --without-pcraster \\\n            --with-netcdf \\\n            --with-png=internal \\\n            --with-jpeg=internal \\\n            --without-gif \\\n            --without-ogdi \\\n            --without-fme \\\n            --without-hdf4 \\\n            --with-hdf5 \\\n            --without-jasper \\\n            --without-ecw \\\n            --without-kakadu \\\n            --without-mrsid \\\n            --without-jp2mrsid \\\n            --without-mysql \\\n            --without-ingres \\\n            --without-xerces \\\n            --without-odbc \\\n            --with-curl \\\n            --without-sqlite3 \\\n            --without-idb \\\n            --without-sde \\\n            --without-perl \\\n            --without-python\"\n\nRUN cd $PROJBUILD && wget -q https://download.osgeo.org/proj/proj-$PROJVERSION.tar.gz \\\n    && tar -xzf proj-$PROJVERSION.tar.gz \\\n    && cd proj-$PROJVERSION \\\n    && ./configure --prefix=$PROJINST/gdal-$GDALVERSION \\\n    && make -s -j 2 \\\n    && make install\n\nRUN  cd $GDALBUILD \\\n    && wget -q http://download.osgeo.org/gdal/$GDALVERSION/gdal-$GDALVERSION.tar.gz \\\n    && tar -xzf gdal-$GDALVERSION.tar.gz\n\nRUN cd $GDALBUILD/gdal-$GDALVERSION \\\n    && ./configure --prefix=$GDALINST/gdal-$GDALVERSION $GDALOPTS $PROJOPT\n\nRUN cd $GDALBUILD/gdal-$GDALVERSION && make\n\nRUN cd $GDALBUILD/gdal-$GDALVERSION  && make install\n\nRUN pip install virtualenv virtualenvwrapper\nENV VIRTUALENVWRAPPER_PYTHON=/usr/local/bin/python3.7\n\nADD . / PyRate/\nSHELL [\"/bin/bash\", \"-c\"]\nRUN source /usr/local/bin/virtualenvwrapper.sh \\\n    && mkvirtualenv -p python3.7 pyrate \\\n    && cd PyRate \\\n    && sed -i '/^GDAL/d' requirements.txt \\\n    && workon pyrate \\\n    && pip install -r requirements.txt -r requirements-dev.txt -r requirements-test.txt \\\n    && pip install GDAL==$(gdal-config --version) \\\n    && python setup.py install\n"
  },
  {
    "path": "LICENSE",
    "content": "\n                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "README.rst",
    "content": ".. image:: docs/PyRate_logo_50.png\n   :alt: PyRate logo\n\nPython tool for InSAR Rate and Time-series Estimation\n=====================================================\n\n.. image:: https://github.com/GeoscienceAustralia/PyRate/workflows/PyRate%20CI/badge.svg?branch=master\n   :target: https://github.com/GeoscienceAustralia/PyRate/actions\n.. image:: https://codecov.io/gh/GeoscienceAustralia/PyRate/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/GeoscienceAustralia/PyRate\n.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg\n   :target: https://opensource.org/licenses/Apache-2.0\n.. image:: https://img.shields.io/pypi/pyversions/Py-Rate \n   :target: https://pypi.org/project/Py-Rate/ \n\nPyRate is a Python tool for estimating the average displacement rate (velocity) and cumulative displacement time-series of surface movements for every pixel in a stack of geocoded unwrapped interferograms generated by Interferometric Synthetic Aperture Radar (InSAR) processing. PyRate uses a \"Small Baseline Subset\" (SBAS) processing strategy and currently supports input data in the GAMMA or ROI_PAC software formats.\nAdditionally, the European Space Agency `SNAP software <https://step.esa.int/main/download/snap-download/>`_ version 8 has a \"PyRate export\" capability that prepares SNAP output data in the GAMMA format for use with PyRate.\n\nThe PyRate project started in 2012 as a partial Python translation of \"Pirate\", a Matlab tool developed by the University of Leeds and the Guangdong University of Technology.\n\nThe full PyRate documentation is available at http://geoscienceaustralia.github.io/PyRate\n\nDependencies\n------------\n\nThe following system dependencies are required by PyRate:\n\n- `Python <https://www.python.org/downloads/>`_, versions 3.7, 3.8 or 3.9.\n- `GDAL <https://gdal.org/download.html>`_, versions 3.0.2 or 3.0.4\n\nThe following optional dependency is required for MPI processing capability:\n\n- `Open MPI <https://www.open-mpi.org/software/ompi/v4.0/>`_, versions 2.1.6, 3.0.4, 3.1.4 or 4.0.2\n\nThe versions of each package stated above have been tested to work using `GitHub Actions <https://github.com/GeoscienceAustralia/PyRate/actions>`_ continuous integration testing.\n\nPython dependencies for PyRate are::\n\n    joblib==1.0.0\n    mpi4py==3.0.3\n    networkx==2.5\n    numpy==1.19.4\n    pyproj==3.0.0\n    scipy==1.5.4\n    numexpr==2.7.2\n    nptyping==1.4.0\n\nInstall\n-------\n\nDetails of all install options are given in the `PyRate documentation <http://geoscienceaustralia.github.io/PyRate>`_.\n\nPyRate and its Python dependencies can be installed directly from the `Python Package Index (PyPI) <https://pypi.org/project/Py-Rate/>`_::\n\n    pip install Py-Rate\n\nAlternatively, to install from source and create an executable program in Linux, enter these commands in a terminal::\n\n    cd ~\n    git clone https://github.com/GeoscienceAustralia/PyRate.git\n    python3 -m venv ~/PyRateVenv\n    source ~/PyRateVenv/bin/activate\n    cd ~/PyRate\n    python3 setup.py install\n\nThis will install the above-listed Python dependencies and compile the executable program ``pyrate``.\nTo learn more about using PyRate, type ``pyrate`` command in the terminal::\n\n    >> pyrate --help\n    usage: pyrate [-h] [-v {DEBUG,INFO,WARNING,ERROR}]\n              {conv2tif,prepifg,correct,timeseries,stack,merge,workflow} ...\n\n    PyRate workflow:\n\n        Step 1: conv2tif\n        Step 2: prepifg\n        Step 3: correct\n        Step 4: timeseries\n        Step 5: stack\n        Step 6: merge\n\n    Refer to https://geoscienceaustralia.github.io/PyRate/usage.html for\n    more details.\n\n    positional arguments:\n      {conv2tif,prepifg,correct,timeseries,stack,merge,workflow}\n        conv2tif            Convert interferograms to geotiff.\n        prepifg             Perform multilooking, cropping and coherence masking to interferogram geotiffs.\n        correct             Calculate and apply corrections to interferogram phase data.\n        timeseries          Timeseries inversion of interferogram phase data.\n        stack               Stacking of interferogram phase data.\n        merge               Reassemble computed tiles and save as geotiffs.\n        workflow            Sequentially run all the PyRate processing steps.\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -v {DEBUG,INFO,WARNING,ERROR}, --verbosity {DEBUG,INFO,WARNING,ERROR}\n                            Increase output verbosity\n\nTest\n----\n\nTo run the test suite, enter these commands in the terminal::\n\n   pip install -r requirements-test.txt\n   python3 -m pytest -m \"not slow\" tests/\n\nTo run the tests for a single module (e.g. test_timeseries.py), use this command::\n\n   python3 -m pytest tests/test_timeseries.py\n\n"
  },
  {
    "path": "__init__.py",
    "content": ""
  },
  {
    "path": "docs/Makefile",
    "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS    =\nSPHINXBUILD   = sphinx-build\nSPHINXPROJ    = PyRate\nSOURCEDIR     = .\nBUILDDIR      = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n"
  },
  {
    "path": "docs/algorithm.rst",
    "content": "Algorithm Module\n================\n\n.. automodule:: pyrate.core.algorithm\n   :members:"
  },
  {
    "path": "docs/aps.rst",
    "content": "Atmospheric Phase Screen Module\n===============================\n\n.. automodule:: pyrate.core.aps\n   :members:\n"
  },
  {
    "path": "docs/authors.rst",
    "content": "=======\nCredits\n=======\n\nDevelopment Lead\n----------------\n\n`PyRate` has been developed by `Geoscience Australia <http://www.ga.gov.au>`__\nwith some initial assistance from the `National Computational Infrastructure <http://nci.org.au/>`__.\n\nContact: `Geoscience Australia InSAR team <mailto:insar@ga.gov.au>`__\n\nContributors\n------------\n\nThanks to `all the contributors`_ who helped to build the ship and raise the sail, Arrrr!\n\n.. _`all the contributors`: https://github.com/GeoscienceAustralia/PyRate/graphs/contributors\n\n* Sudipta Basak (GA)\n* Ben Davies (NCI)\n* Alistair Deane (GA)\n* Thomas Fuhrmann (GA)\n* Syed Sheece Raza Gardezi (GA)\n* Matt Garthwaite (GA)\n* Simon Knapp (GA)\n* Sarah Lawrie (GA)\n* Jonathan Mettes (GA)\n* Negin Moghaddam (GA)\n* Brenainn Moushall (GA)\n* Vanessa Newey (GA)\n* Chandrakanta Ojha (GA)\n* Garrick Paskos (GA)\n* Nahidul Samrat (GA)\n* Richard Taylor (GA)\n"
  },
  {
    "path": "docs/conf.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport os\nimport datetime\nimport sphinx_rtd_theme\n\n\n__version__ = \"0.6.0\"\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = ['sphinx.ext.autodoc',\n              'sphinx.ext.imgmath',\n              'sphinx.ext.viewcode',\n              'sphinx.ext.githubpages',\n              'sphinx.ext.mathjax',\n              'sphinx.ext.autosummary',\n              'sphinx.ext.napoleon',\n              'matplotlib.sphinxext.plot_directive',\n              'IPython.sphinxext.ipython_console_highlighting',\n              'IPython.sphinxext.ipython_directive',\n              'sphinxcontrib.programoutput',\n              'm2r2'\n              ]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\nfrom recommonmark.parser import CommonMarkParser\nsource_parsers = {'.md': CommonMarkParser}\nsource_suffix = ['.rst', '.md']\n# source_suffix = '.rst'\n\n# The main toctree document.\nmain_doc = 'index'\n\n# General information about the project.\nproject = 'PyRate'\ncopyright = str(datetime.datetime.now().year)+', Geoscience Australia'\nauthor = 'Geoscience Australia InSAR Team'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = __version__\n# The full version, including alpha/beta/rc tags.\nrelease = __version__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = 'en'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\nhtml_logo = \"PyRate_logo.png\"\nhtml_show_sourcelink = False\n# Theme options are theme-specific and customize the look and feel of a theme\n# further.  For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\nhtml_theme_options = {\n    'collapse_navigation': False,\n    'sticky_navigation': True,\n    'display_version': False,\n    # 'titles_only': True,\n    'navigation_depth': 2,\n    'logo_only': True,\n    'prev_next_buttons_location': 'top',\n    'style_nav_header_background': 'white'\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [os.path.join('_build', 'html', '_static')]\n\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'PyRatedoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    #\n    # 'papersize': 'letterpaper',\n\n    # The font size ('10pt', '11pt' or '12pt').\n    #\n    # 'pointsize': '10pt',\n\n    # Additional stuff for the LaTeX preamble.\n    #\n    # 'preamble': '',\n\n    # Latex figure (float) alignment\n    #\n    # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n    (main_doc, 'PyRate.tex', 'PyRate Documentation',\n     'Geoscience Australia InSAR team', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n    (main_doc, 'pyrate', 'PyRate Documentation',\n     [author], 1)\n]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n#  dir menu entry, description, category)\ntexinfo_documents = [\n    (main_doc, 'PyRate', 'PyRate Documentation',\n     author, 'PyRate', 'One line description of project.',\n     'Miscellaneous'),\n]\n\n\n\n# -- Options for Epub output ----------------------------------------------\n\n# Bibliographic Dublin Core info.\nepub_title = project\nepub_author = author\nepub_publisher = author\nepub_copyright = copyright\n\n# The unique identifier of the text. This can be a ISBN number\n# or the project homepage.\n#\n# epub_identifier = ''\n\n# A unique identification for the text.\n#\n# epub_uid = ''\n\n# A list of files that should not be packed into the epub file.\nepub_exclude_files = ['search.html']\n\n\n# link check ignore\nlinkcheck_ignore = [r'http://localhost:\\d+/',\n                    'https://github.com/Nekroze/PyRate/fork']\n\n\ndef setup(app):\n    app.add_stylesheet('css/custom.css')\n"
  },
  {
    "path": "docs/configuration.rst",
    "content": "Configuration Module\n====================\n\n.. automodule:: pyrate.configuration\n   :members:\n"
  },
  {
    "path": "docs/contributing.rst",
    "content": "============\nContributing\n============\n\nContributions to `PyRate` are welcome, and they are greatly appreciated! Every\nlittle bit helps, and credit will always be given.\n\nTypes of Contributions\n----------------------\n\nYou can contribute to `PyRate` in many ways:\n\nReport Bugs\n^^^^^^^^^^^\n\nReport bugs on the `Github issues`_ page. If you are reporting a bug, please include:\n\n.. _`Github issues`: https://github.com/GeoscienceAustralia/PyRate/issues\n\n* Your operating system name and version.\n* Any details about your local setup that might be helpful in troubleshooting.\n* Detailed steps to reproduce the bug.\n\nFor an example of how to report a bug please see: https://github.com/GeoscienceAustralia/PyRate/issues/146\n\nFix Bugs\n^^^^^^^^\n\nLook for any `GitHub issues`_ with the \"bug\" label.\n\nImplement Features\n^^^^^^^^^^^^^^^^^^\n\nLook for any `GitHub issues`_ with the \"enhancement\" label.\n\nWrite Documentation\n^^^^^^^^^^^^^^^^^^^\n\n`PyRate` could always use more documentation, whether as part of the\nofficial documentation, in docstrings, or even on the web in blog posts,\narticles etc.\n\nSubmit Feedback\n^^^^^^^^^^^^^^^\n\nGeneral feedback or questions about `PyRate` can be emailed to the\n`Geoscience Australia InSAR Team`_.\n\n.. _`Geoscience Australia InSAR Team`: mailto:insar@ga.gov.au\n\nThe best way to send feedback about a bug in the software is to lodge an Issue_.\n\n.. _Issue: https://github.com/GeoscienceAustralia/PyRate/issues\n\nIf you are proposing a feature:\n\n* Explain in detail how it would work.\n* Keep the scope as narrow as possible, to make it easier to implement.\n* Remember that this is a volunteer-driven project, and that contributions\n  are welcome :)\n\nGet Started!\n------------\n\nReady to contribute? Here's how to set up `PyRate` for local development.\n\n1. Fork_ the `PyRate` repository on GitHub.\n2. Clone your fork locally:\n\n::\n\n    cd ~\n    git clone git@github.com:<account_where_you_forked>/PyRate.git\n\n3. Create a branch for local development\n\n::\n\n    git checkout -b name-of-your-bugfix-or-feature\n\nNow you can make your changes locally.\n\n4. When you have finished making changes, check that your changes pass against unit\n   tests. A suite of tests have been developed for use in testing `PyRate` functionality\n   and for further code development. The tests use `pytest <http://doc.pytest.org/en/latest/>`__\n   and can be found in the ``PyRate/tests/`` directory. A small test dataset used by\n   the test suite is included in the repository in the ``PyRate/tests/test_data/small_test`` directory.\n\n::\n\n    cd ~/PyRate\n    # 'not slow' avoids running those tests marked as 'slow'\n    pytest tests/ -m \"not slow\"\n\n5. If the tests pass, commit your changes and push your branch to GitHub:\n\n::\n\n    git add .\n    git commit -m \"Short description of your changes.\"\n    git push origin name-of-your-bugfix-or-feature\n\n6. Submit a pull request through the GitHub website.\n\n.. _Fork: https://help.github.com/articles/fork-a-repo/\n\nPull Request Guidelines\n-----------------------\n\nBefore you submit a pull request, check that it meets these guidelines:\n\n1. The pull request should include tests that cover new functionality.\n2. If the pull request adds functionality, the documentation should be updated.\n   Put your new functionality into a function with a docstring.\n3. The pull request should work for Python 3.7+.\n\n   Check https://github.com/GeoscienceAustralia/PyRate/actions\n   for active pull requests or run the ``tox`` command and\n   make sure that the tests pass for all supported Python versions.\n"
  },
  {
    "path": "docs/covariance.rst",
    "content": "Covariance Module\n=====================\n\n.. automodule:: pyrate.core.covariance\n   :members:\n"
  },
  {
    "path": "docs/demerror.rst",
    "content": "DEM Error Correction Module\n===========================\n\n.. automodule:: pyrate.core.dem_error\n   :members:\n"
  },
  {
    "path": "docs/dependencies.rst",
    "content": "Dependencies\n------------\n\nThe following dependencies need to be on your system (or in your working\nenvironment) prior to `PyRate` installation:\n\n- Python_, versions 3.7, 3.8 or 3.9.\n- GDAL_, versions 3.0.2 or 3.0.4\n\nThe following optional dependency should be on your system if you want to make\nuse of MPI processing:\n\n- `Open MPI`_, versions 2.1.6, 3.0.4, 3.1.4 or 4.0.2\n\nThe versions of each package stated above have been tested to work using\n`GitHub Actions`_ continuous integration testing.\n\n.. _Python: https://www.python.org/downloads/\n.. _GDAL: https://gdal.org/download.html\n.. _`Open MPI`: https://www.open-mpi.org/software/ompi/v4.0/\n.. _`GitHub Actions`: https://github.com/GeoscienceAustralia/PyRate/actions\n\nOther Python dependencies that will be installed are listed in\n``PyRate/requirements.txt``.\n"
  },
  {
    "path": "docs/docker.rst",
    "content": "Docker\n^^^^^^\n\nDocker can be used to run `PyRate` on Linux, Windows and MacOS systems.\n\n.. note::\n\n    - Docker is the recommended method to use `PyRate` under Windows.\n\nThe only system pre-requisites for using docker are Git and Docker Desktop\n(e.g. for Windows https://docs.docker.com/docker-for-windows/ ).\nThe other system dependencies required by PyRate are installed in to the\ndocker container itself during the build operation.\nOnce Git and Docker Desktop are installed, and Docker Desktop is running,\nexecute the following at a command line prompt (e.g. PowerShell in Windows 10)::\n\n    git clone git@github.com:GeoscienceAustralia/PyRate.git\n    cd PyRate\n    docker build -t pyrate-image .\n    docker run -it pyrate-image\n\n.. note::\n\n    - The image name “pyrate-image” is not mandatory.\n      You are free to name the docker image whatever you choose.\n\nThe ``docker build`` command builds the docker container described in the\n``Dockerfile`` in the `PyRate` repo. It first starts by setting up an Ubuntu linux\nsystem image, and then installs the required system and python dependencies.\n\nOnce the docker container is running (you will see a different-looking command\nprompt in the PowerShell), execute the following commands::\n\n    source /usr/local/bin/virtualenvwrapper.sh\n    workon pyrate\n    cd PyRate\n    python3 setup.py install\n    pyrate --help\n    pyrate workflow -f input_parameters.conf\n\nThe ``pyrate`` executable program is built as part of the ``docker build`` step.\nIf the ``pyrate`` executable is reported as \"not found\", re-run the compilation::\n\n    cd PyRate\n    python3 setup.py install\n\n"
  },
  {
    "path": "docs/gamma.rst",
    "content": "GAMMA Module\n============\n\n.. automodule:: pyrate.core.gamma\n   :members:"
  },
  {
    "path": "docs/gdal_python.rst",
    "content": "GDAL-Python Bindings Module\n===========================\n\n.. automodule:: pyrate.core.gdal_python\n   :members:"
  },
  {
    "path": "docs/geometry.rst",
    "content": "Geometry Module\n===============\n\n.. automodule:: pyrate.core.geometry\n   :members:\n"
  },
  {
    "path": "docs/history.rst",
    "content": ".. :changelog:\n\nRelease History\n===============\n\n0.6.1 (2022-02-18)\n------------------\nAdded\n+++++\n- List generator in ``utils/listcreator.sh`` for easier input generation from GAMMA output to PyRate.\n\nFixed\n+++++\n- Fix wrong sign in Y-intercept output file.\n- Fix and simplify how user supplied reference pixel is validated and cropping issue.\n- Add metadata for reference pixel latitude and longitude to output files.\n\n0.6.0 (2021-10-18)\n------------------\nAdded\n+++++\n- Geometry and baseline calculations, making use of GAMMA software MLI metadata and baseline files.\n- DEM error estimation and correction functionality (untested prototype).\n- Unwrapping error detection and masking functionality, making use of phase closure loops.\n- Tests to check that the independent and network orbital methods are both able to recover the input parameters in synthetic examples.\n- Tests for temporal and spatial gaussian filter options. Compare PyRate code against ``scipy.ndimage`` equivalents.\n- More output visualisation scripts in the ``utils/`` directory, including an interferogram plotting script.\n- New \"Crop A\" unit test dataset derived from Sentinel-1 data over Mexico City.\n- Scaling factor parameter ``velerror_nsig`` for uncertainty/error output products.\n- Calculate and output coherence statistics files during ``prepifg``.\n- Add line-of-sight projection functionality for output products.\n- Add signal polarity switching functionality for output products.\n- Error handling for more than two MLI.par files in the header list matching an interferograms date-pair.\n\nFixed\n+++++\n- Fix bugs in the application of NaN masking and radian-to-millimetre conversion of interferogram data in ``correct`` step.\n- Fix bugs in the implementation of the orbital error network and independent methods.\n- Fix bugs in the implementation of spatial and temporal filters in the APS module.\n- Fix bug in the application of reference phase subtraction that resulted in NaNs in the reference window.\n- Fix file handling behaviour in ``prepifg`` so that a large number of files are not left open.\n\nChanged\n+++++++\n- Enabled multi-looking and parallel processing for the independent orbital method.\n- Simplify output filenames produced by PyRate.\n- Move output files to dedicated named sub-directories. \n- Use of the ``offset`` parameter in orbital module; now renamed to ``intercept``.\n- Input rasters to ``conv2tif`` can now have un-equal X and Y pixel resolutions.\n- Change units of temporal filter from years to days.\n- Moved Continuous Integration from Travis service to GitHub Actions.\n- Made MPI an optional system dependency for PyRate (previously required).\n\nRemoved\n+++++++\n- Remove unused cython, glob2 and pillow dependencies.\n- Remove support for Python 3.6.\n- Remove support for filter types other than Gaussian in the APS module, and associated config options ``slpfmethod``, ``slpforder``, ``tlpfmethod``.\n- ``config`` module deprecated; functionality moved to ``configuration``, ``shared`` and ``constants`` modules.\n- Deprecated ``obs_dir``, ``slc_dir`` and ``coh_file_dir`` configuration parameters.\n\n0.5.0 (2020-09-08)\n------------------\nAdded\n+++++\n- New functionality \"``linear_rate``\" to calculate linear regression of\n  cumulative displacement time series for every pixel as part of the ``timeseries`` step.\n- Script for plotting ``timeseries`` and ``linear_rate`` output geotiff products using Matplotlib.\n  To use, additional dependencies listed in ``requirements-plot.txt`` are required.\n- Correction data (except ``maxvar`` and ``vcmt``) applied to the ifg data is saved to disk\n  and re-used on subsequent repeat runs. Corrections are only re-calculated if config\n  parameters change between runs.\n- MPI parallelisation of APS spatio-temporal filter correction.\n- Unit test coverage for refpixel lat/lon to x/y conversion and ``aps`` module.\n\nFixed\n+++++\n- Re-enable ``ifglksx`` and ``ifglksy`` to be different values, resulting in different\n  resolutions in x and y dimensions in multi-looked interferograms.\n- Re-enable ``orbfitlksx`` and ``orbfitlksy`` to be different values, resulting in different\n  resolutions in x and y dimensions during network orbit correction.\n- Screen messages from main process only during MPI runs.\n\nChanged\n+++++++\n- ``process`` step has been renamed ``correct``. Stacking and timeseries have been removed from\n  this step and are now invoked by separate ``timeseries`` and ``stack`` command line options.\n- Processing of coherence files by ``conv2tif`` and ``prepifg`` is now triggered by the presence\n  of ``cohfilelist`` in the config file. If the list is present, multilooked/cropped coherence\n  files are saved to disk, regardless of whether ``cohmask`` is 0 or 1.\n- Parallelisation capability is refactored - MPI and multiprocessing both now use a common\n  tiling framework for ``stack``, ``timeseries`` and ``mst`` algorithms.\n- Introduced a simplified and standardised file naming format for files produced by the\n  ``prepifg`` step. Information previously saved in the filename (e.g. ``ifglksx``, ``ifglksy``,\n  and ``ifgcropopt`` values) is now saved to the geotiff header instead.\n\nRemoved\n+++++++\n- Redundant ``tscal`` config parameter was deprecated - not needed now there is a ``timeseries``\n  step invokable on the command line.\n- Unused ``Pillow``, ``cython`` and ``glob2`` dependencies.\n- Deprecated function ``pyrate.prepifg_helper.prepare_ifgs``, which is no longer needed.\n\n0.4.3 (2020-08-04)\n------------------\nAdded\n+++++\n- Ability to define the order of steps in the ``process`` workflow\n  (default order unchanged).\n  \nFixed\n+++++\n- Nil\n\nChanged\n+++++++\n- ``prepifg`` output interferograms are saved as read-only files.\n- ``process`` makes a writable copy of the ``prepifg`` output data\n  at the beginning of each run.\n- The selected reference pixel is saved to disk and re-used on subsequent\n  ``process`` runs.  \n- Saving of incremental time series (``tsincr``) products is optional,\n  controlled by the ``savetsincr`` configuration parameter (default is on).\n\nRemoved\n+++++++\n- Removed obsolete InSAR terminology from code, docs and test data\n  (changed to `first` and `second` images).\n- Stopped using ``unittest`` unit test framework in favour of exclusively\n  using ``pytest``.\n\n0.4.2 (2020-06-26)\n------------------\nAdded\n+++++\n- Save full-res coherence files to disk in ``conv2tif`` step if ``cohmask = 1``.\n- Save multi-looked coherence files to disk in ``prepifg`` step if ``cohmask = 1``.\n- Additional ``DATA_TYPE`` geotiff header metadata for above coherence files.\n- ``conv2tif`` and ``prepifg`` output files have a tag applied to filename dependent\n  on data type, i.e. ``_ifg.tif``, ``_coh.tif``, ``_dem.tif``.\n- Metadata about used reference pixel is added to interferogram geotiff headers:\n  lat/lon and x/y values; mean and standard deviation of reference window samples.\n- Quicklook PNG and KML files are generated for the ``Stack Rate`` error map by default.\n\nFixed\n+++++\n- Ensure ``prepifg`` treats input data files as `read only`.\n- Fix the way that the reference phase is subtracted from interferograms\n  during ``process`` step.\n- Manual entry of ``refx/y`` converted to type ``int``.\n\nChanged\n+++++++\n- User supplies latitude and longitude values when specifying a reference pixel in\n  the config file. Pixel x/y values are calculated and used internally.\n- Move ``Stack Rate`` masking to a standalone function ``pyrate.core.stack.mask_rate``,\n  applied during the ``merge`` step and add unit tests.\n- Skip ``Stack Rate`` masking if threshold parameter ``maxsig = 0``.\n- Provide log message indicating the percentage of pixels masked by \n  ``pyrate.core.stack.mask_rate``.\n- Refactor ``pyrate.core.stack`` module; expose two functions in documentation:\n  i) single pixel stacking algorithm, and\n  ii) loop function for processing full ifg array.\n- Refactor ``pyrate.merge`` script; remove duplicated code and create reusable\n  generic functions.\n- Colourmap used to render quicklook PNG images is calculated from min/max values of\n  the geotiff band.\n- Updated ``test`` and ``dev`` requirements.\n\nRemoved\n+++++++\n- Deprecate unused functions in ``pyrate.core.config`` and corresponding tests.\n- Static colourmap ``utils/colourmap.txt`` that was previously used to render\n  quicklook PNG images is removed. \n\n0.4.1 (2020-05-19)\n------------------\nAdded\n+++++\n- Python 3.8 support.\n- Algorithm to automatically calculate rows and columns for tiling.\n  User no longer specifies these as part of the CLI, but can optionally\n  specify ``rows`` and ``cols`` in the configuration file.\n- Improvements to the test suite, including systems-wide tests.\n- Improved logging.\n\nFixed\n+++++\n- Fixed bug in resampling/multi-looking when coherence masking is used.\n  This bugfix will result in significantly fewer ``nan`` pixels in the outputs.\n- Fixed a bug in how NaNs are handled during coherence masking and multi-looking.\n  Output rasters will contain ``nan`` as the nodata value.\n\nChanged\n+++++++\n- ``Linear Rate`` algorithm has been renamed ``Stack Rate``.\n- User supplies full paths to input files in respective file lists.\n- All files generated by `PyRate` saved to user-defined ``outdir`` directory.\n- Renamed ``slcfilelist`` parameter to ``hdrfilelist``.\n- Log files are generated in the ``outdir`` and every `PyRate` step produces independent log files.\n\nRemoved\n+++++++\n- Deprecate the use of ``obsdir``, ``slcfiledir`` and ``cohdir`` configuration variables.\n- Deprecate ``parallel = 2`` option; splitting image via rows for parallelisation.\n\n0.4.0 (2019-10-31)\n------------------\nAdded\n+++++\n- Python 3.7 support.\n- Optional ``conv2tif`` step.\n- Building of docs integrated with Travis CI.\n- Coherence masking, view coherence masking section in ``input_parameters.conf``\n  for options.\n- Input parameter validation.\n- SLC and coherence file lists for file discovery.\n- Create quick view png for rate map product.\n- Add support for reading interferogram in Geotiff format.\n- Add detailed validation and hints for configuration parameters\n- Add system tests for all 3 types of input formats\n\nChanged\n+++++++\n- ``linrate`` step has been renamed to ``process``.\n- ``postprocess`` step has been renamed to ``merge``.\n- ``converttogeotiff`` step has been renamed to ``conv2tif``.\n- CLI structure: config files now need to be provided with ``-f`` flag.\n- Reduced console output, default verbosity setting is now ``INFO``.\n- Restructure of code layout, src modules now in ``PyRate/pyrate/core`` directory\n  and scripts at ``PyRate/scripts``.\n- Reference pixel values are expected to be in latitude and longitude values.\n\nRemoved\n+++++++\n- Unused luigi code.\n- References to Matlab.\n- Unused tests for legacy api.\n\n0.3.0 (2019-07-26)\n------------------\nAdded\n+++++\n- ``utils/apt_install.sh`` script that lists Ubuntu/apt package requirements.\n- ``utils/load_modules.sh`` script that sets up NCI Raijin HPC environment.\n\nFixed\n+++++\n- Errors being caused by newer version of ``networkx``; v2.3 now supported.\n\nRemoved\n+++++++\n- Unused Python and OS packages.\n- environment.yml - conda env will now be installed using ``requirements.txt``.\n- HPC directory - hpc README.rst moved to docs.\n- setup.cfg - no longer needed.\n- Luigi functionality - hasn't been operational and is reported as vulnerable.\n  Single machine parallelism is achieved with joblib. \n\nChanged\n+++++++\n- Requirements now managed by ``requirements.txt`` file, parsed by ``setup.py``.\n- Requirements now split across base ``requirements.txt`` and separate files\n  for dev (``requirements-dev.txt``) and testing (``requirements-test.txt``).\n- Moved default config files to top level source directory.\n- Pinned Python dependencies to specific versions.\n- Travis build now installs GDAL from apt.\n- Travis only builds on master, develop and \\*-travis branches.\n- Consolidated documentation into ``PyRate/docs``.\n- Updated install instructions for Ubuntu and NCI.\n\n0.2.0 (2017-05-22)\n------------------\n- Stable beta release.\n\n0.1.0 (2017-01-31)\n------------------\n- First release on PyPI.\n"
  },
  {
    "path": "docs/hpc.rst",
    "content": "HPC\n^^^\n\nThese instructions have been written for the Gadi supercomputer of the \n`National Computational Infrastructure (NCI)`_. The process for other\nHPC platforms may differ. \n\n.. _`National Computational Infrastructure (NCI)`: https://nci.org.au/ \n\nLogin to Gadi and clone the `PyRate` repository::\n\n    ssh <user_name>@gadi.nci.org.au\n    cd ~\n    git clone https://github.com/GeoscienceAustralia/PyRate.git\n\nLoad the required Gadi modules (this will also remove the default NCI GDAL\nPython bindings so we can build and use our own)::\n\n    source ~/PyRate/scripts/nci_load_modules.sh\n\nCreate a Python virtual environment::\n\n    python3 -m venv ~/PyRateVenv\n    source ~/PyRateVenv/bin/activate\n\nInstall `PyRate`::\n\n    cd ~/PyRate\n    python3 setup.py install\n\nFollowing this, `PyRate` will be available for PBS jobs. To verify the \ninstallation, first run an interactive session::\n\n    qsub -I -q express -l walltime=01:00:00,mem=16Gb,ncpus=4,wd\n\nOnce the session has started, you will need to reactivate your virtual \nenvironment and reload the required modules. \nThere is a call to activate the virtual environment built into the\nscript below so this can be completed with a single command::\n\n    source ~/PyRate/scripts/nci_load_modules.sh\n"
  },
  {
    "path": "docs/ifgconstants.rst",
    "content": "Ifgconstants Module\n===================\n\n.. automodule:: pyrate.core.ifgconstants\n    :members:\n"
  },
  {
    "path": "docs/index.rst",
    "content": "PyRate documentation\n====================\n\nThis is the documentation for the `PyRate` software.\n\n`PyRate` is a Python tool for estimating the velocity and cumulative\ndisplacement time-series of surface movements for every pixel in a stack of\ngeocoded unwrapped interferograms generated by Interferometric Synthetic\nAperture Radar (InSAR) processing. `PyRate` exploits distributed scatterers\nusing a \"Small Baseline Subset\" (SBAS) processing strategy and currently\nsupports input data in the `GAMMA` or `ROI\\_PAC` software formats.\nAdditionally, the European Space Agency SNAP_ software version 8 has a\n\"PyRate export\" capability that prepares `SNAP` output data in the `GAMMA`\nformat for use with `PyRate`.\n\n.. _`SNAP`: https://step.esa.int/main/download/snap-download/\n\n.. note::\n\n    - Support and development of `ROI\\_PAC` has been discontinued.\n    - `ROI\\_PAC` support in `PyRate` will be deprecated in a future release.\n\nThe `PyRate` project started in 2012 as a partial Python translation of\n\"Pirate\", a Matlab tool developed by the University of Leeds and the Guangdong\nUniversity of Technology.\n\n* Link to `Github Repository`_\n* Link to `Issue tracking page`_\n\n.. _`Github Repository`: https://github.com/GeoscienceAustralia/PyRate\n.. _`Issue tracking page`: https://github.com/GeoscienceAustralia/PyRate/issues\n\nFeedback\n--------\n\nIf you have any suggestions or questions about `PyRate` please\nemail the `Geoscience Australia InSAR team`_.\n\n.. _`Geoscience Australia InSAR team`: mailto:insar@ga.gov.au\n\nIf you encounter any errors or problems with `PyRate`, please let us\nknow! Open an Issue_ in the GitHub repository.\n\n.. _Issue: https://github.com/GeoscienceAustralia/PyRate/issues\n\nContents:\n---------\n\n.. toctree::\n   :maxdepth: 3\n\n   installation\n   usage\n   scripts\n   modules\n   troubleshooting\n   contributing\n   authors\n   history\n\n.. _Home: https://geoscienceaustralia.github.io/PyRate/\n\n\nIndices and tables\n==================\n\n* :ref:`modindex`\n* :ref:`genindex`\n"
  },
  {
    "path": "docs/installation.rst",
    "content": "Installation\n============\n\nThis is an installation guide to get `PyRate` running on various platforms.\nFollow the instructions to install `PyRate` and run a small toy example using\nthe test dataset included in the repository.\n\n.. include:: dependencies.rst\n\nPlatforms\n---------\n\nPyPI\n^^^^\n\n`PyRate` and its Python dependencies can be installed directly from the\nPython Package Index (PyPI_)::\n\n    pip install Py-Rate\n\n.. _PyPI: https://pypi.org/project/Py-Rate/\n\n.. include:: ubuntu.rst\n.. include:: docker.rst\n.. include:: hpc.rst\n\n\nVerify Installation\n-------------------\n\nTo verify `PyRate` has been successfully installed, run the full processing\nworkflow with the example config file and the small dataset included\nin the repository. If you compiled the ``pyrate`` executable program::\n\n    pyrate workflow -f input_parameters.conf\n\nIf you installed from the Python Package Index (PyPI_), you won't have a\n``pyrate`` executable program. Instead use::\n\n    python3 pyrate/main.py workflow -f input_parameters.conf\n\nIf the installation has been successful, this workflow will complete without \nerrors and output geotiff files will be generated in::\n\n    ~/PyRate/out\n"
  },
  {
    "path": "docs/logger.rst",
    "content": "Logging Module\n==============\n\n.. automodule:: pyrate.core.logger\n   :members:\n"
  },
  {
    "path": "docs/make.bat",
    "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=.\r\nset BUILDDIR=_build\r\nset SPHINXPROJ=PyRate\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\r\n\r\n:end\r\npopd\r\n"
  },
  {
    "path": "docs/modules.rst",
    "content": "PyRate Modules\n==============\n\n.. toctree::\n   :maxdepth: 4\n\n   algorithm\n   aps\n   configuration\n   covariance\n   demerror\n   gamma\n   geometry\n   gdal_python\n   ifgconstants\n   logger\n   mpiops\n   mst\n   orbital\n   phase_closure\n   prepifg_helper\n   ref_phs_est\n   refpixel\n   roipac\n   shared\n   stack\n   timeseries\n"
  },
  {
    "path": "docs/mpiops.rst",
    "content": "MPI operations Module\n=====================\n\n.. automodule:: pyrate.core.mpiops\n    :members:\n"
  },
  {
    "path": "docs/mst.rst",
    "content": "Minimum Spanning Tree Module\n============================\n\n.. automodule:: pyrate.core.mst\n   :members:\n"
  },
  {
    "path": "docs/orbital.rst",
    "content": "Orbital Error Correction Module\n===============================\n\n.. automodule:: pyrate.core.orbital\n   :members:"
  },
  {
    "path": "docs/phase_closure.rst",
    "content": "Phase Closure Module\n====================\n\nThe phase closure functionality in PyRate is designed to identify and mitigate the \neffects of spatial unwrapping errors in the input interferograms (ifgs).\nIdentification is by forming \"closure loops\" between sets of ifgs and then\nsumming the phase in this closed loop for each pixel. Theoretically, the closure\nphase should be equal to zero, though in reality, close to zero is good enough.\nAn unwrapping error in one of the loop edges will manifest as a closure phase of\nn\\ *2*\\ pi radians.\nBy forming many loops amongst a network of ifgs, we attempt to identify\nthe individual ifgs that are contributing the unwrapping error.\nOnce identified, the unwrapping errors are mitigated by masking those pixels in\nthe necessary (but not all) ifgs.\n\nBy default, PyRate will do the phase closure step after orbital error, reference\nphase and DEM error correction steps.\nIf a user wants to change the order of the corrections during PyRate's ``correct``\nstep, copy and paste the following code-block in to the PyRate configuration file\nand re-order the steps. This will over-ride the default behaviour.\n\n.. code-block::\n\n   [correct]\n   steps =\n       orbfit\n       refphase\n       demerror\n       phase_closure\n       mst\n       apscorrect\n       maxvar\n\nFive parameters control the phase closure functionality in PyRate.\nThese are described in the example PyRate configuration file\n_input\\ *parameters.conf* as follows:\n\n.. code-block::\n\n   #------------------------------------\n   # Phase closure correction parameters\n\n   # closure_thr:         Closure threshold for each pixel in multiples of pi, e.g. 0.5 = pi/2, 1 = pi.\n   # ifg_drop_thr:        Ifgs with more than this fraction of pixels above the closure threshold in all\n   #                      loops it participates in, will be dropped entirely.\n   # min_loops_per_ifg:   Ifgs are dropped entirely if they do not participate in at least this many closure loops.\n   # max_loop_length:     Closure loops with up to this many edges will be used.\n   # max_loop_redundancy: A closure loop will be discarded if all constituent ifgs in that loop have\n   #                      already contributed to a number of loops equal to this parameter.\n   closure_thr:         0.5\n   ifg_drop_thr:        0.05\n   min_loops_per_ifg:   2\n   max_loop_length:     4\n   max_loop_redundancy: 2\n\nThe PyRate *phase closure* algorithm proceeds as follows:\n\n\n#. \n   Find the closed loops within the given ifg network having edges less than or\n   equal to the parameter ``max_loop_length``. This is done in the function\n   ``mst_closure.sort_loops_based_on_weights_and_date``.\n   We perform several steps in this stage:\n\n\n   * Find closed loops with edges numbering between 3 and ``max_loop_length``.\n     (function ``mst_closure.__find_closed_loops``\\ )\n   * Sort ifgs within each loop based on first date (earlier date first).\n     In case of a tie, we sort based on the second date. We then compute\n     weight of each ifg as the temporal baseline in days.\n     Then we sum the ifg weights in a loop to give the weight of each closure loop\n     (function ``mst_closure.__add_signs_and_weights_to_loops``\\ ). \n   * Sort the loops based on their weights, and in case of ties, further\n     sort by primary date, and secondary date.\n     (function ``mst_closure.sort_loops_based_on_weights_and_date``\\ )\n\n#. \n   Discard closure loops when all of the constituent ifgs have already contributed\n   to a number of loops equalling the ``max_loop_redundancy`` parameter.\n   (function ``closure_check.discard_loops_containing_max_ifg_count``\\ )\n\n#. \n   Drop ifgs from further PyRate processing when they are found to not form part\n   of any closed loop.\n   (function ``closure_check.__drop_ifgs_if_not_part_of_any_loop``\\ )\n\n#. \n   Compute phase closure sums for each pixel (in radians) in all the chosen loops\n   and flag when the resultant absolute sum exceeds the quantity <\\ ``closure_thr`` * pi>.\n   The median closure sum across all pixels is subtracted from the closure phase \n   (optional parameter ``subtract_median``\\ , which is on by default).\n   (function ``sum_closure.__compute_ifgs_breach_count``\\ )\n\n#. \n   Next, ifgs are dropped (removed from the processing list) if the fraction of\n   constituent pixels breaching the ``closure_thr`` parameter in all loops\n   the ifg participates in exceeds the parameter ``ifg_drop_thr``\\ , or the ifg\n   does not contribute to a number of loops at least equal to the parameter\n   ``min_loops_per_ifg``.\n   (function ``closure_check.__drop_ifgs_exceeding_threshold``\\ )\n\n#. \n   Steps 1-5 are repeated iteratively until a stable list of ifgs is returned.\n   The iteration is orchestrated by the function ``closure_check.iterative_closure_check``.\n\n#. \n   Once a stable list of ifgs is found, a new ifglist is written in the working\n   directory, and used for further PyRate processing.\n\n#. \n   The final step involves finding pixels in the ifg phase data that breach the\n   closure threshold defined by the ``closure_thr`` parameter. Those pixels in breach\n   are masked (changed to NaN value) for those pixels in those ifgs.\n   (function ``closure_check.mask_pixels_with_unwrapping_errors``\\ )\n\nExample usage\n-------------\n\nTo illustrate the PyRate phase closure functionality, we use a small example dataset\nof 8 Sentinel-1 ifgs which connect 5 common acquisition dates:\n\n.. code-block::\n\n   20160314-20160326\n   20160314-20160407\n   20160314-20160501\n   20160326-20160407\n   20160326-20160513\n   20160407-20160501\n   20160407-20160513\n   20160501-20160513\n\nThe below plots show the ifgs following ``prepifg``\\ :\n\n\n.. image:: ./phase_closure_images/ifg-phase-plot-1-before.png\n   :target: ./phase_closure_images/ifg-phase-plot-1-before.png\n   :alt: Ifgs before phase closure correction\n\n\nCircled in black are some visually obvious unwrapping errors.\n\nIn this example, we run the ``correct`` step with orbital correction and phase\nclosure correction enabled, and the following parameters:\n\n.. code-block::\n\n   orbfitmethod:  2\n   orbfitdegrees: 1\n   orbfitlksx:    10\n   orbfitlksy:    10\n\n   closure_thr:         0.5\n   ifg_drop_thr:        0.1\n   min_loops_per_ifg:   2\n   max_loop_length:     4\n   max_loop_redundancy: 2\n\nWe get the following log information from iteration #1:\n\n.. code-block::\n\n   16:40:45 closure_check:126 421776 INFO 0/7 Closure check iteration #1: working on 8 ifgs\n   16:40:45 closure_check:187 421776 INFO 0/7 Total number of selected closed loops with up to MAX_LOOP_LENGTH = 4 edges is 9\n   16:40:45 closure_check:202 421776 INFO 0/7 After applying MAX_LOOP_REDUNDANCY = 2 criteria, 8 loops are retained\n   16:40:52 plot_closure:76 421776 INFO 0/7 8 closure loops plotted in out/phase_closure_dir/closure_loops_iteration_1_fig_0.png\n\n\n.. image:: ./phase_closure_images/closure_loops_iteration_1_fig_0.png\n   :target: ./phase_closure_images/closure_loops_iteration_1_fig_0.png\n   :alt: Iteration #1 closure loops\n\n\nThe 8 plotted closure loops from iteration #1 show areas where the ``closure_thr``\nthreshold has been breached as either dark red or dark blue. The previously\ncircled unwrapping errors show up as breached areas in several closure loops.\n\nThe ``ifg_drop_thr`` parameter is set to 10% in this example. This is enough\nto detect the largest mis-closed area, which amounts to around 25% of the phase\ndata area spatially. The ifg introducing this mis-closure (20160407-20160513)\nis dropped and iteration #2 continues:\n\n.. code-block::\n\n   16:40:52 closure_check:126 421776 INFO 0/7 Closure check iteration #2: working on 7 ifgs\n   16:40:52 closure_check:187 421776 INFO 0/7 Total number of selected closed loops with up to MAX_LOOP_LENGTH = 4 edges is 5\n   16:40:52 closure_check:202 421776 INFO 0/7 After applying MAX_LOOP_REDUNDANCY = 2 criteria, 5 loops are retained\n   16:40:58 plot_closure:76 421776 INFO 0/7 5 closure loops plotted in out/phase_closure_dir/closure_loops_iteration_2_fig_0.png\n\n\n.. image:: ./phase_closure_images/closure_loops_iteration_2_fig_0.png\n   :target: ./phase_closure_images/closure_loops_iteration_2_fig_0.png\n   :alt: Iteration #2 closure loops\n\n\nNow the ifg network is smaller (7 ifgs) and less closure loops (5) are retained.\nThree smaller breached areas are evident: in the top left, centre bottom and\nbottom right of the image. The average breached area is now not greater than 10%\nso no further ifgs are fully dropped and no further iteration is required.\n\nFinally, pixels found to be breaching the ``closure_thr`` are masked in ifgs:\n\n\n.. image:: ./phase_closure_images/ifg-phase-plot-1-after.png\n   :target: ./phase_closure_images/ifg-phase-plot-1-after.png\n   :alt: Ifgs after phase closure correction\n\n\nThe previously circled unwrapping error in ifg 20160314-20160501 has now been\nmasked, but this spatial area has not been masked in other ifgs. In this case,\nthe algorithm has been successfully able to attribute the source of the unwrapping\nerror to this single ifg. The ability to do this depends on the parameter\nsettings chosen.\n\n.. automodule:: pyrate.core.phase_closure.closure_check\n   :members:\n.. automodule:: pyrate.core.phase_closure.collect_loops\n   :members:\n.. automodule:: pyrate.core.phase_closure.correct_phase\n   :members:\n.. automodule:: pyrate.core.phase_closure.mst_closure\n   :members:\n.. automodule:: pyrate.core.phase_closure.plot_closure\n   :members:\n.. automodule:: pyrate.core.phase_closure.sum_closure\n   :members:\n\n"
  },
  {
    "path": "docs/prepifg_helper.rst",
    "content": "Prepifg Helper Module\n=====================\n\n.. automodule:: pyrate.core.prepifg_helper\n    :members:\n"
  },
  {
    "path": "docs/pyrate.conv2tif.rst",
    "content": "PyRate Conv2tif Script\n======================\n\n.. automodule:: pyrate.conv2tif\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "docs/pyrate.correct.rst",
    "content": "PyRate Correct Script\n========================\n\n.. automodule:: pyrate.correct\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "docs/pyrate.main.rst",
    "content": "PyRate Main Script\n==================\n\n.. automodule:: pyrate.main\n\n   .. rubric:: Functions\n   .. autosummary::\n\n      conv2tif\n      prepifg\n      correct\n      timeseries\n      stack\n      merge\n"
  },
  {
    "path": "docs/pyrate.merge.rst",
    "content": "PyRate Merge Script\n===================\n\n.. automodule:: pyrate.merge\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "docs/pyrate.prepifg.rst",
    "content": "PyRate Prepifg Script\n=========================\n\n.. automodule:: pyrate.prepifg\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "docs/ref_phs_est.rst",
    "content": "Reference Phase Calculation Module\n==================================\n\n.. automodule:: pyrate.core.ref_phs_est\n   :members:"
  },
  {
    "path": "docs/refpixel.rst",
    "content": "Reference Pixel Calculation Module\n==================================\n\n.. automodule:: pyrate.core.refpixel\n   :members:"
  },
  {
    "path": "docs/roipac.rst",
    "content": "ROI_PAC Module\n==============\n\n.. automodule:: pyrate.core.roipac\n   :members:\n"
  },
  {
    "path": "docs/scripts.rst",
    "content": "PyRate Scripts\n==============\n\n.. toctree::\n   :maxdepth: 4\n\n   pyrate.conv2tif\n   pyrate.prepifg\n   pyrate.correct\n   pyrate.merge\n   pyrate.main\n"
  },
  {
    "path": "docs/shared.rst",
    "content": "Shared Module\n=============\n\n.. automodule:: pyrate.core.shared\n   :members:"
  },
  {
    "path": "docs/stack.rst",
    "content": "Stacking Module\n===============\n\n.. automodule:: pyrate.core.stack\n   :members:\n"
  },
  {
    "path": "docs/timeseries.rst",
    "content": "Time Series Module\n==================\n\n.. automodule:: pyrate.core.timeseries\n   :members:"
  },
  {
    "path": "docs/troubleshooting.rst",
    "content": "Troubleshooting\n===============\n\nSome common/known issues with `PyRate` that users may encounter are described below.\n\nIf your issue is not covered below, please contact the `Geoscience Australia InSAR Team`_ for help.\n\n.. _`Geoscience Australia InSAR Team`: mailto:insar@ga.gov.au\n\nValueError: too many values to unpack (expected 2)\n--------------------------------------------------\n**Problem**: During ``prepifg`` step, the following error is encountered:\n\n::\n\n    Traceback (most recent call last):\n      File \"pyrate/main.py\", line 131, in <module>\n        main()\n      File \"pyrate/main.py\", line 103, in main\n        prepifg.main(params)\n      File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/prepifg.py\", line 57, in main\n        do_prepifg(gtiff_paths, params)\n      File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/prepifg.py\", line 88, in do_prepifg\n        _prepifg_multiprocessing(gtiff_path, xlooks, ylooks, exts, thresh, crop, params)\n      File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/prepifg.py\", line 112, in _prepifg_multiprocessing\n        coherence_path=coherence_path, coherence_thresh=coherence_thresh)\n      File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/core/prepifg_helper.py\", line 187, in prepare_ifg\n        op = output_tiff_filename(raster.data_path, out_path)\n      File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/core/shared.py\", line 1222, in output_tiff_filename\n        fname, ext = os.path.basename(inpath).split('.')\n    ValueError: too many values to unpack (expected 2)\n\n**Reason**: This is caused by multiple double dots being used in the file names.\n\n**Solution**: Ensure only a single dot is given in the file names, delimiting the file extension.\nFor example, rename ``20151219-20160112.unw.tif`` to ``20151219-20160112_unw.tif``.\n\n\nStack Rate map appears to be blank/empty\n----------------------------------------\n**Problem**: Output of Stack Rate algorithm contains NaN values causing “merge“ to fail:\n\n::\n\n    Traceback (most recent call last):\n    File \"~/PyRateVenv/bin/pyrate\", line 11, in <module> load_entry_point('Py-Rate==0.4.0', 'console_scripts', 'pyrate')()\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py\", line 154, in main merge_handler(args.config_file)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py\", line 87, in merge_handler merge.main(config.__dict__)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py\", line 64, in main create_png_from_tif(output_folder_path)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py\", line 130, in create_png_from_tif minimum, maximum, mean, stddev = srcband.GetStatistics(True, True)\n    File \"/apps/gdal/3.0.2/lib/python3.7/site-packages/osgeo/gdal.py\", line 2610, in GetStatistics return _gdal.Band_GetStatistics(self, *args)\n    RuntimeError: ~/out/stack_rate.tif, band 1: Failed to compute statistics, no valid pixels found in sampling.\n    Traceback (most recent call last):\n    File \"~/PyRateVenv/bin/pyrate\", line 11, in <module> load_entry_point('Py-Rate==0.4.0', 'console_scripts', 'pyrate')()\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py\", line 154, in main merge_handler(args.config_file)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py\", line 87, in merge_handler merge.main(config.__dict__)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py\", line 64, in main create_png_from_tif(output_folder_path)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py\", line 130, in create_png_from_tif minimum, maximum, mean, stddev = srcband.GetStatistics(True, True)\n    File \"/apps/gdal/3.0.2/lib/python3.7/site-packages/osgeo/gdal.py\", line 2610, in GetStatistics return _gdal.Band_GetStatistics(self, *args)\n    RuntimeError: ~/out/stack_rate.tif, band 1: Failed to compute statistics, no valid pixels found in sampling.\n    Traceback (most recent call last):\n    File \"~/PyRateVenv/bin/pyrate\", line 11, in <module> load_entry_point('Py-Rate==0.4.0', 'console_scripts', 'pyrate')()\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py\", line 154, in main merge_handler(args.config_file)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py\", line 87, in merge_handler merge.main(config.__dict__)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py\", line 64, in main create_png_from_tif(output_folder_path)\n    File \"~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py\", line 130, in create_png_from_tif minimum, maximum, mean, stddev = srcband.GetStatistics(True, True)\n    File \"/apps/gdal/3.0.2/lib/python3.7/site-packages/osgeo/gdal.py\", line 2610, in GetStatistics return _gdal.Band_GetStatistics(self, *args)\n    RuntimeError: ~/out/stack_rate.tif, band 1: Failed to compute statistics, no valid pixels found in sampling.\n\n**Reason**: The ``maxsig`` parameter is too low, resulting in stack rate values being replaced by NaNs. ``maxsig`` is a threshold for masking stack rate pixels according to the corresponding stack error estimate saved in ``out/tmpdir/stack_error_*.npy``.\n\n**Solution**: Increase ``maxsig``, then re-run the ``merge`` step. Maximum permittable value for ``maxsig`` is 1000 mm.\n\n\nFailure of APS spatial low pass filter\n---------------------------------------\n**Problem**: Atmospheric corrections during ``correct`` step fails due to the interpolated grid used for correction being empty:\n\n::\n\n    +8s pyrate.aps:INFO Applying spatial low pass filter\n    Traceback (most recent call last):\n    File \"~/PyRateVenv/bin/pyrate\", line 11, in <module>\n    load_entry_point('Py-Rate==0.3.0.post3', 'console_scripts', 'pyrate')()\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py\", line 764, in __call__\n    return self.main(*args, **kwargs)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py\", line 717, in main\n    rv = self.invoke(ctx)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py\", line 1137, in invoke\n    return _process_result(sub_ctx.command.invoke(sub_ctx))\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py\", line 956, in invoke\n    return ctx.invoke(self.callback, **ctx.params)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py\", line 555, in invoke\n    return callback(*args, **kwargs)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/scripts/main.py\", line 69, in linrate\n    run_pyrate.process_ifgs(sorted(dest_paths), params, rows, cols)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/scripts/run_pyrate.py\", line 391, in process_ifgs\n    _wrap_spatio_temporal_filter(ifg_paths, params, tiles, preread_ifgs)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py\", line 63, in _wrap_spatio_temporal_filter\n    spatio_temporal_filter(tsincr, ifg, params, preread_ifgs)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py\", line 86, in spatio_temporal_filter\n    ts_aps = mpiops.run_once(spatial_low_pass_filter, ts_hp, ifg, params)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/mpiops.py\", line 54, in run_once\n    f_result = f(*args, **kwargs)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py\", line 192, in spatial_low_pass_filter\n    _interpolate_nans(ts_lp, params[cf.SLPF_NANFILL_METHOD])\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py\", line 208, in _interpolate_nans\n    _interpolate_nans_2d(a, rows, cols, method)\n    File \"~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py\", line 224, in _interpolate_nans_2d\n    method=method\n    File \"~/PyRateVenv/lib/python3.6/site-packages/scipy-1.3.0-py3.6-linux-x86_64.egg/scipy/interpolate/ndgriddata.py\", line 226, in griddata\n    rescale=rescale)\n    File \"interpnd.pyx\", line 846, in scipy.interpolate.interpnd.CloughTocher2DInterpolator.__init__\n    File \"qhull.pyx\", line 1836, in scipy.spatial.qhull.Delaunay.__init__\n    File \"qhull.pyx\", line 276, in scipy.spatial.qhull._Qhull.__init__\n    ValueError: No points given\n\n**Solution**: Use more interferograms as input and/or reduce the threshold parameters ``ts_pthr``, ``pthr``, ``tlpfpthr`` in the configuration file.\n\nIn general, users are advised to input a network of small-baseline interferograms\nthat has at least 2 interferometric connections per SAR image epoch. Furthermore,\nmake sure that ``ts_pthr``, ``pthr`` and ``tlpfpthr`` are smaller than the number\nof image epochs. To check that the spatio-temporal filters worked correctly, users can check that\nthe numpy arrays saved at ``/<outdir>/aps_error/*aps_error.npy`` contain numeric values and not NaNs.\n\n\nOut of memory errors\n--------------------\n**Problem**: `PyRate` is memory intensive. You may receive various out of memory errors if there is not enough memory to accommodate the images being processed.\n\n::\n\n    joblib.externals.loky.process_executor.TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker. The exit codes of the workers are {EXIT(1), EXIT(1), EXIT(1)}\n\n**Solution**: Increase the amount of memory available. On HPC systems this can be done by increasing the value provided to the ``mem`` argument when submitting a PBS job, e.g.::\n\n    mem=32Gb\n\nIf no more memory can be called upon, users can try running the job in serial, or reducing the size of the interferograms by increasing the multi-looking factors applied during ``prepifg`` (parameters ``ifglksx`` and ``ifglksy``).\n\nIncorrect modules loaded on Gadi\n----------------------------------\n**Problem**: `PyRate` requires certain versions of Python, GDAL and Open MPI to be loaded on Gadi and other HPC systems. While sourcing the `PyRate/scripts/nci_load_modules.sh` script will load the correct modules, you may need to unload previously unloaded modules.\n\nExample of errors caused by module conflicts::\n\n    ERROR:150: Module 'python3/3.7.2' conflicts with the currently loaded module(s) 'python3/3.4.3-matplotlib'\n    ERROR:150: Module 'gdal/2.2.2' conflicts with the currently loaded module(s) 'gdal/2.0.0'\n\n**Solution**: Purge the loaded modules and source the ``nci_load_modules.sh`` script:\n\n::\n\n    module purge\n    source ~/PyRate/scripts/nci_load_modules.sh\n"
  },
  {
    "path": "docs/ubuntu.rst",
    "content": "Linux\n^^^^^\n\nThese instructions have been tested using Ubuntu 18.04. If using another\nLinux distribution, you will have to install packages equivalent to those\nlisted in ``PyRate/scripts/apt_install.sh``.\n\nClone the repository, install required packages and install `PyRate`:\n\n::\n\n    git clone git@github.com:GeoscienceAustralia/PyRate.git\n    ./PyRate/scripts/apt_install.sh\n\n    # Add GDAL includes to C/CPLUS paths before building Python GDAL bindings.\n    export C_INCLUDE_PATH=$C_INCLUDE_PATH:/usr/include/gdal\n    export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/include/gdal\n\n    python3 setup.py install\n\nThese commands will compile the executable program ``pyrate``.\n"
  },
  {
    "path": "docs/usage.rst",
    "content": "Usage\n=====\n\nConfiguration\n-------------\n\nVarious parameters are controlled by values given in a text-based configuration file.\nThe user can choose any filename for this configuration file.\nExample parameters for running `PyRate` with `GAMMA`-format interferograms are\ncontained in the example configuration file ``PyRate/input_parameters.conf``.\n\n\nWorkflow\n--------\n\nFile Discovery\n^^^^^^^^^^^^^^\n\nTo allow flexibility in the file types that can be processed, `PyRate` requires\nfile lists to be provided. This allows `PyRate` to identify files of each\ntype without relying on file extensions. The file path to these lists are \nprovided under ``ifgfilelist``, ``hdrfilelist``, ``cohfilelist`` and \n``basefilelist`` keywords in the configuration file. These lists can be created manually \nor generated using the script ``list_creator.sh`` located in the ``utils`` folder.\nThis script may need to be slightly modified depending on the output paths of your input data.\n\n.. note::\n\n    - Filenames should be provided in these lists with their absolute system path,\n      with each name on a separate line.\n    - Filenames should only have a single period (\".\") delimiting the file extension.\n      Multiple periods in a filename will raise an error during runtime.\n\nThe ``ifgfilelist`` gives the list of interferograms to be processed.\nExample of an interferogram file list for `GAMMA` flat-binary files::\n\n    /absolute/path/to/20150702-20150920.unw\n    /absolute/path/to/20151219-20160109.unw\n    /absolute/path/to/20160202-20160415.unw\n\n.. note::\n\n    - Interferogram filenames must contain a pair of date epochs.\n      Any naming convention is appropriate as long as an epoch pair of format\n      ``YYYYMMDD-YYYYMMDD`` or ``YYMMDD-YYMMDD`` exists in the filename.\n\nThe ``hdrfilelist`` gives the list of `GAMMA` Multi-Look Intensity (MLI) header\ntext files. Example of a `GAMMA` MLI header file list::\n\n    /absolute/path/to/20150702_mli.par\n    /absolute/path/to/20150920_mli.par\n    /absolute/path/to/20151219_mli.par\n    /absolute/path/to/20160109_mli.par\n    /absolute/path/to/20160202_mli.par\n    /absolute/path/to/20160415_mli.par\n\n.. note::\n\n    - MLI header filenames must contain a single date epoch in the format\n      ``YYYYMMDD`` or ``YYMMDD``.\n    - `GAMMA` Single-Look Complex (SLC) header files could be used instead, but\n      the geometry calculations in ``prepifg`` will not work, and subsequently\n      the DEM Error correction step in ``correct`` will not work. Both require\n      parameters valid for the multi-looked radar-coded interferograms.\n\nThe ``cohfilelist`` is an optional list which contains the pool of all available\ncoherence files to be used for optional coherence masking.\nExample of a coherence file list for `GAMMA` flat-binary files::\n\n    /absolute/path/to/20150702-20150920.coh\n    /absolute/path/to/20151219-20160109.coh\n    /absolute/path/to/20160202-20160415.coh\n\n.. note::\n\n    - Like interferograms, coherence filenames must contain a pair of epochs.\n      The epoch pair must be in the format ``YYYYMMDD-YYYYMMDD`` or\n      ``YYMMDD-YYMMDD``. Otherwise, any naming convention is appropriate.\n\nThe ``basefilelist`` is an optional list which contains the pool of all\navailable GAMMA baseline vector files. These are only needed if the user\nwants to use the DEM error correction functionality.\nExample of a baseline file list for `GAMMA` baseline vector files::\n\n    /absolute/path/to/20150702-20150920_base.par\n    /absolute/path/to/20151219-20160109_base.par\n    /absolute/path/to/20160202-20160415_base.par\n\nThe date epochs in filenames are used to match the corresponding MLI header\nor coherence files to each interferogram. It is recommended to provide a\ncomplete list of available headers/coherence/baseline files for a stack in\ntheir respective lists, since only the necessary files will be used. This\nallows you to process a subset of interferograms by removing entries in\n``ifgfilelist`` without needing to modify all four lists.\n\n`PyRate` Workflow\n^^^^^^^^^^^^^^^^^\n\nAfter `Installation <installation.html>`__, an\nexecutable program ``pyrate`` is created in the system path::\n\n    >> pyrate --help\n    usage: pyrate [-h] [-v {DEBUG,INFO,WARNING,ERROR}]\n                  {conv2tif,prepifg,correct,timeseries,stack,merge,workflow} ...\n\n    PyRate workflow:\n\n        Step 1: conv2tif\n        Step 2: prepifg\n        Step 3: correct\n        Step 4: timeseries\n        Step 5: stack\n        Step 6: merge\n\n    Refer to https://geoscienceaustralia.github.io/PyRate/usage.html for\n    more details.\n\n    positional arguments:\n      {conv2tif,prepifg,correct,timeseries,stack,merge,workflow}\n        conv2tif            <Optional> Convert interferograms to geotiff.\n        prepifg             Perform multilooking, cropping and coherence masking to interferogram geotiffs.\n        correct             Calculate and apply corrections to interferogram phase data.\n        timeseries          <Optional> Timeseries inversion of interferogram phase data.\n        stack               <Optional> Stacking of interferogram phase data.\n        merge               Reassemble computed tiles and save as geotiffs.\n        workflow            <Optional> Sequentially run all the PyRate processing steps.\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -v {DEBUG,INFO,WARNING,ERROR}, --verbosity {DEBUG,INFO,WARNING,ERROR}\n                            Increase output verbosity\n\n .. note::\n\n    - If running on NCI, be sure to first load the correct modules and virtual environment:\n      ``source ~/PyRate/scripts/nci_load_modules.sh`` \n\nThe ``pyrate`` program has six command line options corresponding to\ndifferent steps in the `PyRate` workflow:\n\n1. ``conv2tif`` (optional)\n2. ``prepifg``\n3. ``correct``\n4. ``timeseries`` (optional)\n5. ``stack`` (optional)\n6. ``merge``\n\nNot all steps are required as indicated above. A seventh option, ``workflow``, is\navailable that will run all six of the above steps in the order shown.\n\nCommand line arguments for each step can be found using (e.g. for ``conv2tif``)::\n\n    >> pyrate conv2tif --help\n    usage: pyrate conv2tif [-h] -f CONFIG_FILE\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -f CONFIG_FILE, --config_file CONFIG_FILE\n                            Pass configuration file\n\nEach step can be run on the command line in one of the following two ways\n(e.g. for ``conv2tif``)::\n\n    >> pyrate conv2tif -f /path/to/config_file\n\nor::\n\n    >> python3 pyrate/main.py conv2tif -f /path/to/config_file\n\nIn the following sub-sections we describe each of the available steps.\n\n\n``conv2tif``: Converting flat-binary files to Geotiff format\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nBefore `PyRate` can process interferograms that are in flat-binary file format, they\nneed to be converted into geotiff format using the optional ``conv2tif`` step.\n\n.. note::\n\n    - Users of the `GAMMA` software can skip the ``conv2tif`` step if they have generated\n      geotiffs using the `GAMMA` program ``data2geotiff``, which is included in all\n      `GAMMA` software distributions.\n    - In this case, ``ifgfilelist`` and ``cohfilelist`` would contain the absolute\n      paths to these geotiff files. Even when using geotiff files, the MLI header files\n      are still required by ``prepifg``.\n    - If a DEM is to be processed by ``prepifg``, it's file format should match the\n      input interferograms (e.g. geotiff or flat-binary files).\n\nUpon completion of ``conv2tif`` geotiff formatted copies of the input files will be placed\nin the ``<outdir>`` directory defined in the configuration file.\n\n.. note::\n\n     - ``conv2tif`` will not perform the conversion if geotiffs for the provided\n       input files already exist.\n\n\n``prepifg``: Preparing input interferograms\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``prepifg`` is the second step of `PyRate`, which applys optional multi-looking\n(sub-sampling), cropping and coherence masking operations to the geotiff-format\ninput interferograms. This is a required step, which formats the input data in\na way expected by the rest of the `PyRate` workflow.\n\n**Coherence masking**\n\n``prepifg`` will perform optional coherence masking on the interferograms\nbefore multi-looking and cropping is performed. This requires corresponding\ncoherence images for each interferogram.\nThe purpose of coherence masking is to remove poor quality phase observations\nand leave a set of high-quality pixels for analysis.\n\nCoherence masking is enabled by setting ``cohmask: 1`` in\nthe configuration file. A threshold, ``cohthresh`` needs to be provided. \nPixels with coherence values below ``cohthresh`` will be set to Not-a-Number (NaN).\n\n.. note::\n\n    - The number of pixels with numeric phase values (i.e. pixels not equal to NaN)\n      will be different in each interferogram after coherence masking.\n\nThe available coherence files need to be specified in a list file as described above\nand defined in the ``cohfilelist`` parameter.\n\n.. note::\n\n    - Multi-looked and cropped versions of those coherence images found that match\n      the epochs of the input interferograms will be saved to disk in geotiff format,\n      even if coherence masking is not applied (i.e. ``cohmask: 0``).\n    - Additionally, the mean, median and standard deviation of the coherence for\n      each pixel is calculated and saved as part of ``prepifg``.\n    - All coherence files are saved to the ``<outdir>\\coherence_dir`` directory.\n\n**Multi-looking**\n\nThe ``prepifg`` step will perform optional multi-looking (image sub-sampling) \nof the input interferograms in geotiff format. The purpose of multi-looking is twofold:\n\n- Reduce the spatial resolution of the interferograms in order to improve the\n  computational efficiency of `PyRate` analysis.\n- Reduce the general phase noise in the interferograms in order to enhance the\n  signal-to-noise ratio in the output products.\n\nTo multi-look, set ``ifglksx`` and ``ifglksy`` to an integer subsampling factor greater\nthan one in the x (easting) and y (northing) dimensions respectively. Separate parameters\nfor x and y gives flexibility for users in case they want to achieve different spatial\nresolution in each dimension.\n\n.. note::\n\n    - For example, a value of ``2`` will reduce the resolution by half.\n      A value of ``1`` will keep the resolution the same as the input interferograms\n      (i.e. no multi-looking).\n    - It is recommended to try a large multi-look factor to start with (e.g. ``10``\n      or greater), and subsequently reduce the multi-looking factor once the user\n      has experience with processing a particular dataset.\n\n**Cropping**\n\nThe ``prepifg`` step will perform optional spatial cropping of the input interferograms.\nThis is useful if you are focussing on a specific area of interest within the full\nextent of the input interferograms. The advantage of cropping is that `PyRate`\nanalysis will be computationally more efficient.\n\nTo crop, set ``ifgcropopt`` to ``3`` and provide the geographic latitude and longitude\nbounds in the ``ifgxfirst`` (west), ``ifgxlast`` (east), ``ifgyfirst`` (north), and\n``ifgylast`` (south) parameters.\n\n**Geometry calculations**\n\nDuring the ``prepifg`` step, the radar viewing geometry for every pixel is\ncalculated using metadata from the `GAMMA` MLI parameter files.\n\n.. note::\n\n    - Geometry calculations are only implemented for `GAMMA` format input data.\n\nThe output arrays are saved to ``<outdir>/geometry_dir`` and contain as follows:\n\n- ``rdc_azimuth.tif``: azimuth coordinate in range-doppler system;\n- ``rdc_range.tif``: range coordinate in range-doppler system;\n- ``look_angle.tif``: look angle (vector between line-of-sight and satellite nadir);\n- ``incidence_angle.tif``: incidence angle (vector between line-of-sight and vector perpendicular to local ground surface);\n- ``azimuth_angle.tif``: azimuth angle (projection of line-of-sight on the surface);\n- ``range_dist.tif``: satellite to ground range distance;\n- ``dem.tif``: digital elevation model.\n\nUpon completion, ``prepifg`` will save a new set of interferogram files in the\n``<outdir>\\interferogram_dir`` (``*_ifg.tif``).\nIf provided as input, coherence files will be saved to ``<outdir>\\coherence_dir``\n(``*_coh.tif``).\n\n\n``correct``: Compute and apply interferometric phase corrections\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``correct`` is the third step in the `PyRate` processing workflow. This step\nwill perform a series of optional corrections to the interferogram phase data\nand apply a number of prepatory steps required prior to data inversion.\n\nThe sub-steps are performed in the following default order:\n\n- Search for a suitable reference pixel;\n- Residual Orbit error correction (optional, controlled by ``orbfit`` parameter);\n- Correction of reference phase in interferograms;\n- Residual DEM error correction (optional, controlled by ``demerror`` parameter);\n- Unwrapping error detection and masking (optional, controlled by ``phase_closure`` parameter); \n- Minimum Spanning Tree matrix formation;\n- Spatio-temporal filtering of the interferograms ((optional, controlled by ``apsest`` parameter);  \n- Calculation of interferogram spatial covariance functions;\n- Assembly of the variance-covariance matrix.\n\nThis default order of steps can be modified by the user by copying the\nfollowing code block in to the configuration file and switching the order of\nsteps as required::\n\n    [correct]\n    steps =\n        orbfit\n        refphase\n        demerror\n        phase_closure\n        mst\n        apscorrect\n        maxvar\n\n\nThe corrected interferogram phase is saved to copies of the ``prepifg``\ninterferograms in the directory ``<outdir>/temp_mlooked_dir/`` (the original\noutput from ``prepifg`` is retained as a read-only interferogram dataset in the\n``<outdir>/interferogram_dir``).\nAdditionally, copies of the phase corrections subtracted from interferograms\nare saved to disk as numpy array files (``*.npy``) for use in post-processing.\nThese can be found in labelled sub-directories in the ``<outdir>``.\n\n\n``timeseries``: Compute the displacement time series\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``timeseries`` is the optional fourth step in the `PyRate` processing workflow.\nThis step will perform a time series inversion to derive the cumulative displacement\ntime series from the stack of corrected interferograms.\nThe cumulative displacement time series (``tscuml*``) is saved by default.\nUsers can optionally save the incremental displacement time series (``tsincr*``)\nby setting parameter ``savetsincr: 1``.\n\nA linear regression of the cumulative displacement time series is also computed\nas part of the ``timeseries`` step. The resulting linear rate (velocity),\nstandard error, R-squared and y-intercept terms are all saved to disk.\n\n\n``stack``: Compute the average velocity via stacking\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``stack`` is the optional fifth step in the `PyRate` processing workflow.\nThis step will perform an iterative stacking of the phase data to derive a\nrobust velocity estimate for each pixel in the interferograms.\nThe velocity from stacking (``stack_rate*``) is saved by default.\n\n.. note::\n\n    - Both ``timeseries`` and ``stack`` are optional and independent steps\n      that can be computed in either order.\n\n\n``merge``: Reassemble the tiles\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``merge`` is the sixth and final step of the `PyRate` workflow, which produces\ngeotiff files containing the final time series, linear rate and stacking products.\n``merge`` will also re-assemble tiles that were generated during the previous\nsteps. Tiling is discussed in the :ref:`parallel_label` section below.\n\nThe final outputs contain signals in the line-of-sight (LOS) of the satellite.\nUsing the ``los_projection`` option, the user can project those signals to\neither the pseudo-vertical (set to 1) or pseudo-horizontal (set to 2). This\nprojection makes use of the per-pixel incidence angle image generated during\n``prepifg``.\n\n.. note::\n\n    - Users should be aware that the pseudo-vertical and pseudo-horizontal\n      signal projections do not necessarily represent the **true** vertical\n      or horizontal ground movement signal. The true signals cannot be\n      recovered with InSAR data from a single LOS viewing geometry.\n    - If the user has InSAR data from multiple viewing geometries (e.g. from\n      both ascending and descending orbits), signal decomposition can be\n      carried out after PyRate analysis as a post-processing step.\n\nIf necessary, the user can switch the polarity of the final output products\nby setting ``signal_polarity: -1``.\n\nThe error products produced by `PyRate` can be scaled by n-sigma using the\nparameter ``velerror_nsig``.\n\nAfter running the ``merge`` step, several geotiff products will appear in the\n``<outdir>/velocity_dir`` and ``<outdir>/timeseries_dir`` directories.\n\n\n``workflow``: Run the full PyRate workflow\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``workflow`` is an additional option that will run all the above six steps\nin order as a single job. This could be useful for batch processing runs.\n\n\nInput Files\n-----------\n\n`PyRate` currently supports input files generated by the `GAMMA`, `ROI\\_PAC`\nand `SNAP` interferometry softwares. `PyRate` will determine the input\nformat from the ``processor:`` parameter in the configuration file\n(``0``: `ROI\\_PAC`; ``1``: `GAMMA` (and `SNAP`)).\n\n`GAMMA`\n^^^^^^^\n\nEach `GAMMA` geocoded unwrapped interferogram requires three header files\nto extract metadata required for data formatting: a geocoded DEM header\nfile (``demHeaderFile`` keyword in the configuration file) and the relevant\nMLI image header files (``*mli.par``) found in the ``hdrfilelist``.\nThe header files for the first and second MLI images used in the formation\nof a particular interferogram are found automatically by date-string pattern\nmatching based on date epochs given in the filenames.\nA DEM with matching size and geometry to the interferograms can also be processed.\nThe DEM absolute path and filename are set with the ``demfile`` parameter.\n\n`SNAP`\n^^^^^^\n\nThe European Space Agency SNAP_ software has supported PyRate since version\n8.0.0. Users can convert their output data in to `GAMMA` format by\nusing the \"PyRate export\" function in `SNAP`. Then users should follow the\n`PyRate` instructions for `GAMMA`-formatted data.\n\n.. note::\n\n    - The capability to generate SBAS interferogram networks that are needed\n      for time series analysis of large InSAR stacks in `PyRate` is planned\n      for a future version of `SNAP`.\n\n.. _SNAP: https://step.esa.int/main/download/snap-download/\n\n\n`ROI\\_PAC`\n^^^^^^^^^^\n\nEach `ROI\\_PAC` geocoded unwrapped interferogram requires its own\nheader/resource file (``*.rsc``). These header files need to be\nlisted in the defined ``hdrfilelist``. In addition, the geocoded DEM\nheader file is required and its path and name are specified in the config file under\n``demHeaderFile``. The geographic projection in the parameter ``DATUM:`` is extracted\nfrom the DEM header file.\nA DEM with matching size and geometry to the interferograms can also be processed.\nThe DEM absolute path and filename are set with the ``demfile`` parameter.\n\n.. note::\n\n    - Support and development of `ROI\\_PAC` has been discontinued.\n    - `ROI\\_PAC` support in `PyRate` will be deprecated in a future release.\n\n.. _parallel_label:\n\nParallel Processing\n-------------------\n\nBy their very nature, interferograms are large files. This is particularly the case\nfor `Sentinel-1`_, which has an image swath of 250 km and a pixel resolution on the order\nof tens of metres in IW-mode.\nConsequently, InSAR processing can be computationally expensive and time consuming.\nIt therefore makes sense to parallelise processing operations wherever possible.\n\n.. _`Sentinel-1`: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar\n\n`PyRate` can be run in parallel using standard multi-threading simply by turning\n``parallel: 1`` in the configuration file to take advantage of multiple cores\non a single machine. The parameter ``processes`` sets the number of threads.\n\nAlternatively, `PyRate` can be parallelised on a system with an installed MPI library\nby using ``mpirun``::\n\n    # Modify '-n' based on the number of processors available.\n    mpirun -n 4 pyrate conv2tif -f path/to/config_file\n    mpirun -n 4 pyrate prepifg -f path/to/config_file\n    mpirun -n 4 pyrate correct -f path/to/config_file\n    mpirun -n 4 pyrate timeseries -f input_parameters.conf\n    mpirun -n 4 pyrate stack -f input_parameters.conf\n    mpirun -n 4 pyrate merge -f path/to/config_file\n\n.. note::\n\n    - In the case that `PyRate` is run using ``mpirun``, standard multi-threading is\n      automatically disabled (i.e. equivalent to setting ``parallel: 0``).\n\nDuring ``conv2tif`` and ``prepifg``, parallelism is achieved by sending sub-lists of input\nfiles to each process.\nParallelism in the ``correct``, ``timeseries`` and ``stack`` steps is achieved by splitting\nthe images in to a grid of tiles, where the number of tiles equals the number of processes\npassed with the ``-n`` argument to ``mpirun``, or the ``processes`` parameter for multi-threading.\nThe number of tiles in x and y dimension are automatically calculated by `PyRate`, ensuring\na roughly equivalent number in both dimensions. One of the functions of the ``merge`` step\nis to reassemble these tiles in to the full image for each output product.\n\n\nResults Visualisation\n---------------------\n\nSeveral plotting scripts are included in the ``utils/`` directory to help the\nuser visually inspect the output products of `PyRate`:\n\n- ``make_tscuml_animation.py``: Make an animated gif from cumulative time series data;\n- ``plot_linear_rate_profile.py``: Plot a profile through a linear rate map;\n- ``plot_time_series.py``: Map and graph view of cumulative time series data;\n- ``plot_correction_files.py``: Before and after viewing of interferogram corrections;\n- ``plot_sbas_network.py``: Baseline-time plot for the interferogram network.\n\n\nExample usage of ``plot_time_series.py`` with the included test data::\n\n    cd PyRate\n    source ~/PyRateVenv/bin/activate\n    pyrate workflow -f input_parameters.conf\n    pip install -r requirements-plot.txt\n    python3 utils/plot_time_series.py out/\n\n.. image:: PyRate_plot_screenshot.png \n   :alt: Screenshot of PyRate plotting tool\n   :scale: 30 %\n\n"
  },
  {
    "path": "input_parameters.conf",
    "content": "# PyRate configuration file for GAMMA-format interferograms\n#\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Optional Correction ON/OFF switches - ON = 1; OFF = 0\n\n# Coherence masking (PREPIFG)\ncohmask:       0\n\n# Orbital error correction (CORRECT)\norbfit:        0\n\n# DEM error (residual topography) correction (CORRECT)\ndemerror:      0\n\n# Phase Closure correction (CORRECT)\nphase_closure: 1\n\n# APS correction using spatio-temporal filter (CORRECT)\napsest:        0\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Integer parameters\n\n# LOS Projection of output products (MERGE)\n# Converts slanted (native) LOS signals to either \"pseudo-vertical\" or \"pseudo-horizontal\", \n# by dividing by the cosine or sine of the incidence angle for each pixel, respectively.\n# los_projection: 0 = LOS (no conversion); 1 = pseudo-vertical; 2 = pseudo-horizontal.\nlos_projection:  0\n\n# Sign convention for phase data (MERGE)\n# signal_polarity:  1 = retain sign convention of input interferograms\n# signal_polarity: -1 = reverse sign convention of input interferograms (default)\nsignal_polarity: -1\n\n# Number of sigma to report velocity error. Positive integer. Default: 2 (TIMESERIES/STACK)\nvelerror_nsig:   2\n\n# Optional save of numpy array files for output products (MERGE)\nsavenpy:         0\n\n# Optional save of incremental time series products (TIMESERIES/MERGE)\nsavetsincr:      0\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Multi-threading parameters (CORRECT/TIMESERIES/STACK)\n# parallel: 1 = parallel, 0 = serial\nparallel:      0\n# number of processes\nprocesses:     8\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Input/Output file locations\n#\n# File containing the list of interferograms to use.\nifgfilelist:   tests/test_data/cropA/ifg_30\n\n# The DEM file used in the InSAR processing\ndemfile:       tests/test_data/cropA/geotiffs/cropA_T005A_dem.tif\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/cropA/headers/cropA_20180106_VV_8rlks_eqa_dem.par\n\n# File listing the pool of available header files (GAMMA: *mli.par, ROI_PAC: *.rsc)\nhdrfilelist:   tests/test_data/cropA/headers_13\n\n# File listing the pool of available coherence files.\ncohfilelist:   tests/test_data/cropA/coherence_30\n\n# File listing the pool of available baseline files (GAMMA).\nbasefilelist:  tests/test_data/cropA/baseline_30\n\n# Look-up table containing radar-coded row and column for lat/lon pixels (GAMMA)\nltfile:        tests/test_data/cropA/geometry/20180106_VV_8rlks_eqa_to_rdc.lt\n\n# Directory to write the outputs to\noutdir:        out/\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# PREPIFG parameters\n#------------------------------------\n# Input data format: ROI_PAC = 0, GAMMA = 1\nprocessor:     1\n\n# Coherence threshold value for masking, between 0 and 1\ncohthresh:     0.3\n\n# Multi-look/subsampling factor in east (x) and north (y) dimension\nifglksx:       1\nifglksy:       1\n\n# Cropping options\n# ifgcropopt: 1 = minimum extent 2 = maximum extent 3 = crop 4 = no cropping\n# ifgxfirst,ifgyfirst: longitude (x) and latitude (y) of north-west corner\n# ifgxlast,ifgylast: longitude (x) and latitude (y) of south-east corner\nifgcropopt:    4\nifgxfirst:     150.92\nifgyfirst:     -34.18\nifgxlast:      150.94\nifgylast:      -34.22\n\n# No-data averaging threshold (0 = 0%; 1 = 100%)\nnoDataAveragingThreshold: 0.5\n\n# The No-data value used in the interferogram files\nnoDataValue:   0.0\n\n# Nan conversion flag. Set to 1 if missing No-data values are to be converted to NaN\nnan_conversion: 1\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# CORRECT parameters\n#------------------------------------\n# Reference pixel search parameters\n\n# refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed\n# refnx/y: number of search grid points in x/y image dimensions\n# refchipsize: size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:          -99.18\nrefy:          19.44\nrefnx:         5\nrefny:         5\nrefchipsize:   7\nrefminfrac:    0.01\n\n#------------------------------------\n# Reference phase correction method\n\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# Orbital error correction parameters\n\n# orbfitmethod = 1: interferograms corrected independently; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for network orbital correction\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    5\norbfitlksy:    5\n\n#------------------------------------\n# Phase closure correction parameters\n\n# closure_thr:         Closure threshold for each pixel in multiples of pi, e.g. 0.5 = pi/2, 1 = pi.\n# ifg_drop_thr:        Ifgs with more than this fraction of pixels above the closure threshold in all\n#                      loops it participates in, will be dropped entirely.\n# min_loops_per_ifg:   Ifgs are dropped entirely if they do not participate in at least this many closure loops.\n# max_loop_length:     Closure loops with up to this many edges will be used.\n# max_loop_redundancy: A closure loop will be discarded if all constituent ifgs in that loop have\n#                      already contributed to a number of loops equal to this parameter.\nclosure_thr:         0.5\nifg_drop_thr:        0.5\nmin_loops_per_ifg:   2\nmax_loop_length:     4\nmax_loop_redundancy: 2\n\n#------------------------------------\n# APS filter parameters\n\n# tlpfcutoff: cutoff t0 for temporal high-pass Gaussian filter in days (int);\n# tlpfpthr: valid pixel threshold;\n# slpfcutoff: spatial low-pass Gaussian filter cutoff in km (greater than zero).\n# slpfcutoff=0 triggers cutoff estimation from exponential covariance function\ntlpfcutoff:    30\ntlpfpthr:      1\nslpfcutoff:    1\n\n#------------------------------------\n# DEM error (residual topography) correction parameters\n\n# de_pthr: valid observations threshold;\nde_pthr:       20\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# TIMESERIES parameters\n#------------------------------------\n\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor)\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      2\nsmorder:       2\nsmfactor:      -0.25\nts_pthr:       10\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# STACK parameters\n#------------------------------------\n\n# pthr: threshold for minimum number of ifg observations for each pixel\n# nsig: threshold for iterative removal of observations\n# maxsig: maximum sigma (std dev; millimetres) used as an output masking threshold applied in Merge step. 0 = OFF.\npthr:          5\nnsig:          3\nmaxsig:        1000\n\n"
  },
  {
    "path": "pyrate/__init__.py",
    "content": ""
  },
  {
    "path": "pyrate/configuration.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains utilities to validate user input parameters\nparsed in a PyRate configuration file.\n\"\"\"\nimport re\nfrom configparser import ConfigParser\nfrom pathlib import Path, PurePath\nfrom typing import Union\n\nimport pyrate.constants as C\nfrom pyrate.constants import NO_OF_PARALLEL_PROCESSES, sixteen_digits_pattern, twelve_digits_pattern, ORB_ERROR_DIR, \\\n    DEM_ERROR_DIR, TEMP_MLOOKED_DIR\nfrom pyrate.core import ifgconstants as ifg\nfrom pyrate.default_parameters import PYRATE_DEFAULT_CONFIGURATION\nfrom pyrate.core.algorithm import factorise_integer\nfrom pyrate.core.shared import extract_epochs_from_filename, InputTypes, get_tiles\n\n\ndef set_parameter_value(data_type, input_value, default_value, required, input_name):\n    if len(input_value) < 1:\n        input_value = None\n        if required:  # pragma: no cover\n            raise ValueError(\"A required parameter is missing value in input configuration file: \" + str(input_name))\n\n    if input_value is not None:\n        if str(data_type) in \"path\":\n            return Path(input_value)\n        return data_type(input_value)\n    return default_value\n\n\ndef validate_parameter_value(input_name, input_value, min_value=None, max_value=None, possible_values=None):\n    if isinstance(input_value, PurePath):\n        if not Path.exists(input_value):  # pragma: no cover\n            raise ValueError(\"Given path: \" + str(input_value) + \" does not exist.\")\n    if input_value is not None:\n        if min_value is not None:\n            if input_value < min_value:  # pragma: no cover\n                raise ValueError(\n                    \"Invalid value for \" + str(input_name) + \" supplied: \" + str(\n                        input_value) + \". Provide a value greater than or equal to \" + str(min_value) + \".\")\n    if input_value is not None:\n        if max_value is not None:\n            if input_value > max_value:  # pragma: no cover\n                raise ValueError(\n                    \"Invalid value for \" + str(input_name) + \" supplied: \" + str(\n                        input_value) + \". Provide a value less than or equal to \" + str(max_value) + \".\")\n\n    if possible_values is not None:\n        if input_value not in possible_values:  # pragma: no cover\n            raise ValueError(\n                \"Invalid value for \" + str(input_name) + \" supplied: \" + str(\n                    input_value) + \". Provide one of these values: \" + str(possible_values) + \".\")\n    return True\n\n\ndef validate_file_list_values(file_list, no_of_epochs):\n    if file_list is None:  # pragma: no cover\n        raise ValueError(\"No value supplied for input file list: \" + str(file_list))\n\n    files = parse_namelist(file_list)\n\n    for f in files:\n        if not Path(f).exists():  # pragma: no cover\n            raise ConfigException(f\"{f} does not exist\")\n        else:\n            matches = extract_epochs_from_filename(filename_with_epochs=f)\n            if len(matches) < no_of_epochs:  # pragma: no cover\n                raise ConfigException(f\"the number of epochs in {f} names are less the required number: {no_of_epochs}\")\n\n\nclass MultiplePaths:\n    def __init__(self, file_name: str, params: dict, input_type: InputTypes = InputTypes.IFG):\n\n        self.input_type = input_type\n        out_dir = params[C.OUT_DIR]\n        tempdir = params[C.TEMP_MLOOKED_DIR]\n        if isinstance(tempdir, str):\n            tempdir = Path(tempdir)\n        b = Path(file_name)\n        if input_type in [InputTypes.IFG, InputTypes.COH]:\n            d = re.search(sixteen_digits_pattern, b.stem)\n            if d is None:  # could be 6 digit epoch dates\n                d = re.search(twelve_digits_pattern, b.stem)\n            if d is None:\n                raise ValueError(f\"{input_type.value} filename does not contain two 8- or 6-digit date strings\")\n            filestr = d.group() + '_'\n        else:\n            filestr = ''\n\n        dir_exists = input_type.value in InputTypes.dir_map.value.keys()\n        anchoring_dir = Path(out_dir).joinpath(InputTypes.dir_map.value[input_type.value]) \\\n            if dir_exists else Path(out_dir)\n\n        if b.suffix == \".tif\":\n            self.unwrapped_path = None\n            converted_path = b  # original file\n            self.sampled_path = anchoring_dir.joinpath(filestr + input_type.value + '.tif')\n        else:\n            self.unwrapped_path = b.as_posix()\n            converted_path = anchoring_dir.joinpath(b.stem.split('.')[0] + '_' + b.suffix[1:]).with_suffix('.tif')\n            self.sampled_path = converted_path.with_name(filestr + input_type.value + '.tif')\n\n        # tmp_sampled_paths are used after prepifg, during correct steps\n        self.tmp_sampled_path = tempdir.joinpath(self.sampled_path.name).as_posix()\n        self.converted_path = converted_path.as_posix()\n        self.sampled_path = self.sampled_path.as_posix()\n\n    @staticmethod\n    def orb_error_path(ifg_path: Union[str, Path], params) -> Path:\n        if isinstance(ifg_path, str):\n            ifg_path = Path(ifg_path)\n        return Path(params[C.OUT_DIR], C.ORB_ERROR_DIR,\n                    ifg_path.stem + '_' +\n                    '_'.join([str(params[C.ORBITAL_FIT_METHOD]),\n                              str(params[C.ORBITAL_FIT_DEGREE]),\n                              str(params[C.ORBITAL_FIT_LOOKS_X]),\n                              str(params[C.ORBITAL_FIT_LOOKS_Y])]) +\n                    '_orbfit.npy')\n\n    @staticmethod\n    def dem_error_path(ifg_path: Union[str, Path], params) -> Path:\n        if isinstance(ifg_path, str):\n            ifg_path = Path(ifg_path)\n        return Path(params[C.OUT_DIR], C.DEM_ERROR_DIR,\n                    ifg_path.stem + '_' + str(params[C.DE_PTHR]) + '_dem_error.npy')\n\n    @staticmethod\n    def aps_error_path(ifg_path: Union[str, Path], params) -> Path:\n        if isinstance(ifg_path, str):\n            ifg_path = Path(ifg_path)\n        return Path(params[C.OUT_DIR], C.APS_ERROR_DIR,\n                    ifg_path.stem + '_' +\n                    '_'.join([str(x) for x in [\n                        params[C.SLPF_CUTOFF],\n                        params[C.SLPF_NANFILL],\n                        params[C.SLPF_NANFILL_METHOD],\n                        params[C.TLPF_CUTOFF],\n                        params[C.TLPF_PTHR]\n                    ]\n                              ]) + '_aps_error.npy')\n\n    def __str__(self):  # pragma: no cover\n        st = \"\"\n        if self.unwrapped_path is not None:\n            st += \"\"\"\\nunwrapped_path = \"\"\" + self.unwrapped_path\n        else:\n            st += \"\"\"\\nunwrapped_path = None\"\"\"\n        st += \"\"\"\n            converted_path = \"\"\" + self.converted_path + \"\"\" \n            sampled_path = \"\"\" + self.sampled_path + \"\"\"    \n            tmp_sampled_path = \"\"\" + self.tmp_sampled_path + \"\"\"\n            \"\"\"\n        return st\n\n\nclass Configuration:\n\n    def __init__(self, config_file_path):\n\n        parser = ConfigParser()\n        parser.optionxform = str\n        # mimic header to fulfil the requirement for configparser\n        with open(config_file_path) as stream:\n            parser.read_string(\"[root]\\n\" + stream.read())\n\n        for key, value in parser[\"root\"].items():\n            self.__dict__[key] = value\n\n        # make output path, if not provided will error\n        Path(self.outdir).mkdir(exist_ok=True, parents=True)\n\n        # custom correct sequence if 'correct' section is provided in config\n        if 'correct' in parser and 'steps' in parser['correct']:\n            self.__dict__['correct'] = list(filter(None, parser['correct'].get('steps').splitlines()))\n        else:\n            self.__dict__['correct'] = [\n                'orbfit',\n                'refphase',\n                'demerror',\n                'phase_closure',\n                'mst',\n                'apscorrect',\n                'maxvar',\n            ]\n\n        # Validate required parameters exist.\n\n        required = {k for k, v in PYRATE_DEFAULT_CONFIGURATION.items() if v['Required']}\n\n        if not required.issubset(self.__dict__):  # pragma: no cover\n            raise ValueError(\"Required configuration parameters: \" + str(\n                required.difference(self.__dict__)) + \" are missing from input config file.\")\n\n        # handle control parameters\n        for parameter_name in PYRATE_DEFAULT_CONFIGURATION:\n            param_value = self.__dict__[parameter_name] if parameter_name in required or \\\n                                                           parameter_name in self.__dict__ else ''\n\n            self.__dict__[parameter_name] = set_parameter_value(\n                PYRATE_DEFAULT_CONFIGURATION[parameter_name][\"DataType\"],\n                param_value,\n                PYRATE_DEFAULT_CONFIGURATION[parameter_name][\"DefaultValue\"],\n                PYRATE_DEFAULT_CONFIGURATION[parameter_name][\"Required\"],\n                parameter_name)\n            validate_parameter_value(parameter_name, self.__dict__[parameter_name],\n                                     PYRATE_DEFAULT_CONFIGURATION[parameter_name][\"MinValue\"],\n                                     PYRATE_DEFAULT_CONFIGURATION[parameter_name][\"MaxValue\"],\n                                     PYRATE_DEFAULT_CONFIGURATION[parameter_name][\"PossibleValues\"])\n\n        # bespoke parameter validation\n        if self.refchipsize % 2 != 1:  # pragma: no cover\n            if self.refchipsize - 1 > 1:\n                # Configuration parameters refchipsize must be odd\n                # values too large (>101) will slow down the process without significant gains in results.\n                self.refchipsize = self.refchipsize - 1\n\n        # calculate rows and cols if not supplied\n        if hasattr(self, 'rows') and hasattr(self, 'cols'):\n            self.rows, self.cols = int(self.rows), int(self.cols)\n        else:\n            if NO_OF_PARALLEL_PROCESSES > 1:  # i.e. mpirun\n                self.rows, self.cols = [int(num) for num in factorise_integer(NO_OF_PARALLEL_PROCESSES)]\n            else:\n                if self.parallel:  # i.e. joblib parallelism\n                    self.rows, self.cols = [int(num) for num in factorise_integer(self.processes)]\n                else:  # i.e. serial\n                    self.rows, self.cols = 1, 1\n\n        # create a temporary directory if not supplied\n        if not hasattr(self, 'tmpdir'):\n            self.tmpdir = Path(self.outdir).joinpath(\"tmpdir\")\n        else:\n            self.tmpdir = Path(self.tmpdir)\n\n        self.tmpdir.mkdir(parents=True, exist_ok=True)\n\n        # create orbfit error dir\n        self.orb_error_dir = Path(self.outdir).joinpath(ORB_ERROR_DIR)\n        self.orb_error_dir.mkdir(parents=True, exist_ok=True)\n\n        self.interferogram_dir = Path(self.outdir).joinpath(C.INTERFEROGRAM_DIR)\n        self.interferogram_dir.mkdir(parents=True, exist_ok=True)\n\n        # create DEM error dir\n        self.dem_error_dir = Path(self.outdir).joinpath(DEM_ERROR_DIR)\n        self.dem_error_dir.mkdir(parents=True, exist_ok=True)\n\n        # create aps error dir\n        self.aps_error_dir = Path(self.outdir).joinpath(C.APS_ERROR_DIR)\n        self.aps_error_dir.mkdir(parents=True, exist_ok=True)\n\n        # create mst dir\n        self.mst_dir = Path(self.outdir).joinpath(C.MST_DIR)\n        self.mst_dir.mkdir(parents=True, exist_ok=True)\n\n        self.phase_closure_dir = Path(self.outdir).joinpath(C.PHASE_CLOSURE_DIR)\n        self.phase_closure_dir.mkdir(parents=True, exist_ok=True)\n\n        self.coherence_dir = Path(self.outdir).joinpath(C.COHERENCE_DIR)\n        self.coherence_dir.mkdir(parents=True, exist_ok=True)\n\n        self.geometry_dir = Path(self.outdir).joinpath(C.GEOMETRY_DIR)\n        self.geometry_dir.mkdir(parents=True, exist_ok=True)\n\n        self.timeseries_dir = Path(self.outdir).joinpath(C.TIMESERIES_DIR)\n        self.timeseries_dir.mkdir(parents=True, exist_ok=True)\n\n        self.velocity_dir = Path(self.outdir).joinpath(C.VELOCITY_DIR)\n        self.velocity_dir.mkdir(parents=True, exist_ok=True)\n\n        # create temp multilooked files dir\n        self.temp_mlooked_dir = Path(self.outdir).joinpath(TEMP_MLOOKED_DIR)\n        self.temp_mlooked_dir.mkdir(parents=True, exist_ok=True)\n\n        self.ref_pixel_file = self.ref_pixel_path(self.__dict__)\n\n        # var no longer used\n        self.APS_ELEVATION_EXT = None\n        self.APS_INCIDENCE_EXT = None\n        self.apscorrect = 0\n        self.apsmethod = 0\n        self.elevationmap = None\n        self.incidencemap = None\n\n        # define parallel processes that will run\n        self.NUMEXPR_MAX_THREADS = str(NO_OF_PARALLEL_PROCESSES)\n\n        # Validate file names supplied in list exist and contain correct epochs in file names\n        if self.cohfilelist is not None:\n            # if self.processor != 0:  # not roipac\n            validate_file_list_values(self.cohfilelist, 1)\n            self.coherence_file_paths = self.__get_files_from_attr('cohfilelist', input_type=InputTypes.COH)\n\n        if self.basefilelist is not None:\n            # if self.processor != 0:  # not roipac\n            validate_file_list_values(self.basefilelist, 1)\n            self.baseline_file_paths = self.__get_files_from_attr('basefilelist', input_type=InputTypes.BASE)\n\n        self.header_file_paths = self.__get_files_from_attr('hdrfilelist', input_type=InputTypes.HEADER)\n\n        self.interferogram_files = self.__get_files_from_attr('ifgfilelist')\n\n        self.dem_file = MultiplePaths(self.demfile, self.__dict__, input_type=InputTypes.DEM)\n\n        # backward compatibility for string paths\n        for key in self.__dict__:\n            if isinstance(self.__dict__[key], PurePath):\n                self.__dict__[key] = str(self.__dict__[key])\n\n    @staticmethod\n    def ref_pixel_path(params):\n        return Path(params[C.OUT_DIR]).joinpath(\n            '_'.join(\n                [str(x) for x in [\n                    'ref_pixel', params[C.REFX], params[C.REFY], params[\n                        C.REFNX], params[C.REFNY],\n                    params[C.REF_CHIP_SIZE], params[C.REF_MIN_FRAC], '.npy'\n                ]\n                 ]\n            )\n        )\n\n    @staticmethod\n    def mst_path(params, index) -> Path:\n        return Path(params[C.OUT_DIR], C.MST_DIR).joinpath(f'mst_mat_{index}.npy')\n\n    @staticmethod\n    def preread_ifgs(params: dict) -> Path:\n        return Path(params[C.TMPDIR], 'preread_ifgs.pk')\n\n    @staticmethod\n    def vcmt_path(params):\n        return Path(params[C.OUT_DIR], C.VCMT).with_suffix('.npy')\n\n    @staticmethod\n    def phase_closure_filtered_ifgs_list(params):\n        return Path(params[C.TEMP_MLOOKED_DIR]).joinpath('phase_closure_filtered_ifgs_list')\n\n    def refresh_ifg_list(self, params):  # update params dict\n        filtered_ifgs_list = self.phase_closure_filtered_ifgs_list(params)\n        files = parse_namelist(filtered_ifgs_list.as_posix())\n        params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.__dict__, input_type=InputTypes.IFG) for p in files]\n        return params\n\n    @staticmethod\n    def ref_phs_file(params):\n        ref_pixel_path = Configuration.ref_pixel_path(params)\n        # add ref pixel path as when ref pixel changes - ref phs path should also change\n        return Path(params[C.OUT_DIR]).joinpath(\n            ref_pixel_path.stem + '_' +\n            '_'.join(['ref_phs', str(params[C.REF_EST_METHOD]), '.npy'])\n        )\n\n    @staticmethod\n    def get_tiles(params):\n        ifg_path = params[C.INTERFEROGRAM_FILES][0].sampled_path\n        rows, cols = params['rows'], params['cols']\n        return get_tiles(ifg_path, rows, cols)\n\n    def __get_files_from_attr(self, attr, input_type=InputTypes.IFG):\n        val = self.__getattribute__(attr)\n        files = parse_namelist(val)\n        return [MultiplePaths(p, self.__dict__, input_type=input_type) for p in files]\n\n    def closure(self):\n        closure_d = Path(self.phase_closure_dir)\n\n        class Closure:\n            def __init__(self):\n                self.closure = closure_d.joinpath('closure.npy')\n                self.ifgs_breach_count = closure_d.joinpath('ifgs_breach_count.npy')\n                self.num_occurences_each_ifg = closure_d.joinpath('num_occurrences_each_ifg.npy')\n                self.loops = closure_d.joinpath('loops.npy')\n\n        return Closure()\n\n    @staticmethod\n    def coherence_stats(params):\n        coh_d = Path(params[C.COHERENCE_DIR])\n        return {k: coh_d.joinpath(k.lower() + '.tif').as_posix() for k in [ifg.COH_MEDIAN, ifg.COH_MEAN, ifg.COH_STD]}\n\n    @staticmethod\n    def geometry_files(params):\n        geom_dir = Path(params[C.GEOMETRY_DIR])\n        return {k: geom_dir.joinpath(k.lower() + '.tif').as_posix() for k in C.GEOMETRY_OUTPUT_TYPES}\n\n\ndef write_config_parser_file(conf: ConfigParser, output_conf_file: Union[str, Path]):\n    \"\"\"replacement function for write_config_file which uses dict instead of a ConfigParser instance\"\"\"\n    with open(output_conf_file, 'w') as configfile:\n        conf.write(configfile)\n\n\ndef write_config_file(params, output_conf_file):\n    \"\"\"\n    Takes a param object and writes the config file. Reverse of get_conf_params.\n\n    :param dict params: parameter dictionary\n    :param str output_conf_file: output file name\n\n    :return: config file\n    :rtype: list\n    \"\"\"\n    with open(output_conf_file, 'w') as f:\n        for k, v in params.items():\n            if v is not None:\n                if k == 'correct':\n                    f.write(''.join(['[', k, ']' ':\\t', '', '\\n']))\n                    f.write(''.join(['steps = ', '\\n']))\n                    for vv in v:\n                        f.write(''.join(['\\t' + str(vv), '\\n']))\n                elif isinstance(v, list):\n                    continue\n                else:\n                    if isinstance(v, MultiplePaths):\n                        if v.unwrapped_path is None:\n                            vv = v.converted_path\n                        else:\n                            vv = v.unwrapped_path\n                    else:\n                        vv = v\n                    f.write(''.join([k, ':\\t', str(vv), '\\n']))\n            else:\n                f.write(''.join([k, ':\\t', '', '\\n']))\n\n\ndef parse_namelist(nml):\n    \"\"\"\n    Parses name list file into array of paths\n\n    :param str nml: interferogram file list\n\n    :return: list of interferogram file names\n    :rtype: list\n    \"\"\"\n    with open(nml) as f_in:\n        lines = [line.rstrip() for line in f_in]\n    return filter(None, lines)\n\n\nclass ConfigException(Exception):\n    \"\"\"\n    Default exception class for configuration errors.\n    \"\"\"\n\n"
  },
  {
    "path": "pyrate/constants.py",
    "content": "import os\nimport re\nfrom pathlib import Path\nimport numpy as np\n\nPYRATEPATH = Path(__file__).parent.parent\n\n\n__version__ = \"0.6.0\"\nCLI_DESCRIPTION = \"\"\"\nPyRate workflow: \n\n    Step 1: conv2tif\n    Step 2: prepifg\n    Step 3: correct\n    Step 4: timeseries\n    Step 5: stack\n    Step 6: merge\n\nRefer to https://geoscienceaustralia.github.io/PyRate/usage.html for \nmore details.\n\"\"\"\n\nfrom pyrate.core.mpiops import comm\n\nNO_OF_PARALLEL_PROCESSES = comm.Get_size()\n\nCONV2TIF = 'conv2tif'\nPREPIFG = 'prepifg'\nCORRECT = 'correct'\nTIMESERIES = 'timeseries'\nSTACK = 'stack'\nMERGE = 'merge'\n\n# distance division factor of 1000 converts to km and is needed to match legacy output\nDISTFACT = 1000\n# mappings for metadata in header for interferogram\nGAMMA_DATE = 'date'\nGAMMA_TIME = 'center_time'\nGAMMA_WIDTH = 'width'\nGAMMA_NROWS = 'nlines'\nGAMMA_CORNER_LAT = 'corner_lat'\nGAMMA_CORNER_LONG = 'corner_lon'\nGAMMA_Y_STEP = 'post_lat'\nGAMMA_X_STEP = 'post_lon'\nGAMMA_DATUM = 'ellipsoid_name'\nGAMMA_FREQUENCY = 'radar_frequency'\nGAMMA_INCIDENCE = 'incidence_angle'\nGAMMA_HEADING = 'heading'\nGAMMA_AZIMUTH = 'azimuth_angle'\nGAMMA_RANGE_PIX = 'range_pixel_spacing'\nGAMMA_RANGE_N = 'range_samples'\nGAMMA_AZIMUTH_PIX = 'azimuth_pixel_spacing'\nGAMMA_AZIMUTH_N = 'azimuth_lines'\nGAMMA_AZIMUTH_LOOKS = 'azimuth_looks'\nGAMMA_PRF = 'prf'\nGAMMA_NEAR_RANGE = 'near_range_slc'\nGAMMA_SAR_EARTH = 'sar_to_earth_center'\nGAMMA_SEMI_MAJOR_AXIS = 'earth_semi_major_axis'\nGAMMA_SEMI_MINOR_AXIS = 'earth_semi_minor_axis'\nGAMMA_PRECISION_BASELINE = 'precision_baseline(TCN)'\nGAMMA_PRECISION_BASELINE_RATE = 'precision_baseline_rate'\n# RADIANS = 'RADIANS'\n# GAMMA = 'GAMMA'\n# value assigned to no-data-value\nLOW_FLOAT32 = np.finfo(np.float32).min*1e-10\n\nSIXTEEN_DIGIT_EPOCH_PAIR = r'\\d{8}-\\d{8}'\nsixteen_digits_pattern = re.compile(SIXTEEN_DIGIT_EPOCH_PAIR)\nTWELVE_DIGIT_EPOCH_PAIR = r'\\d{6}-\\d{6}'\ntwelve_digits_pattern = re.compile(TWELVE_DIGIT_EPOCH_PAIR)\n\n# general constants\n\nNO_MULTILOOKING = 1\nROIPAC = 0\nGAMMA = 1\nLOG_LEVEL = 'INFO'\n\n# constants for lookups\n#: STR; Name of input interferogram list file\nIFG_FILE_LIST = 'ifgfilelist'\n#: (0/1/2); The interferogram processor used (0==ROIPAC, 1==GAMMA, 2: GEOTIF)\nPROCESSOR = 'processor'\n\n#: STR; Name of directory for saving output products\nOUT_DIR = 'outdir'\n#: STR; Name of Digital Elevation Model file\nDEM_FILE = 'demfile'\n#: STR; Name of the DEM header file\nDEM_HEADER_FILE = 'demHeaderFile'\n\n#: STR; Name of the file list containing the pool of available header files\nHDR_FILE_LIST = 'hdrfilelist'\n\nINTERFEROGRAM_FILES = 'interferogram_files'\nHEADER_FILE_PATHS = 'header_file_paths'\nCOHERENCE_FILE_PATHS = 'coherence_file_paths'\nBASELINE_FILE_PATHS = 'baseline_file_paths'\nDEM_FILE_PATH = 'dem_file'\n\n\n# STR; The projection of the input interferograms.\n# TODO: only used in tests; deprecate?\nINPUT_IFG_PROJECTION = 'projection'\n#: FLOAT; The no data value in the interferogram files.\nNO_DATA_VALUE = 'noDataValue'\n#: FLOAT; No data averaging threshold for prepifg\nNO_DATA_AVERAGING_THRESHOLD = 'noDataAveragingThreshold'\n# BOOL (1/2/3); Re-project data from Line of sight, 1 = vertical, 2 = horizontal, 3 = no conversion\n# REPROJECTION = 'prjflag' # NOT CURRENTLY USED\n#: BOOL (0/1): Convert no data values to Nan\nNAN_CONVERSION = 'nan_conversion'\n\n# Prepifg parameters\n#: BOOL (1/2/3/4); Method for cropping interferograms, 1 = minimum overlapping area (intersection), 2 = maximum area (union), 3 = customised area, 4 = all ifgs already same size\nIFG_CROP_OPT = 'ifgcropopt'\n#: INT; Multi look factor for interferogram preparation in x dimension\nIFG_LKSX = 'ifglksx'\n#: INT; Multi look factor for interferogram preparation in y dimension\nIFG_LKSY = 'ifglksy'\n#: FLOAT; Minimum longitude for cropping with method 3\nIFG_XFIRST = 'ifgxfirst'\n#: FLOAT; Maximum longitude for cropping with method 3\nIFG_XLAST = 'ifgxlast'\n#: FLOAT; Minimum latitude for cropping with method 3\nIFG_YFIRST = 'ifgyfirst'\n#: FLOAT; Maximum latitude for cropping with method 3\nIFG_YLAST = 'ifgylast'\n\n# reference pixel parameters\n#: INT; Longitude (decimal degrees) of reference pixel, or if left blank a search will be performed\nREFX = 'refx'\nREFX_FOUND = 'refxfound'\n#: INT; Latitude (decimal degrees) of reference pixel, or if left blank a search will be performed\nREFY = 'refy'\nREFY_FOUND = 'refyfound'\n#: INT; Number of reference pixel grid search nodes in x dimension\nREFNX = \"refnx\"\n#: INT; Number of reference pixel grid search nodes in y dimension\nREFNY = \"refny\"\n#: INT; Dimension of reference pixel search window (in number of pixels)\nREF_CHIP_SIZE = 'refchipsize'\n#: FLOAT; Minimum fraction of observations required in search window for pixel to be a viable reference pixel\nREF_MIN_FRAC = 'refminfrac'\n#: BOOL (1/2); Reference phase estimation method (1: median of the whole interferogram, 2: median within the window surrounding the reference pixel)\nREF_EST_METHOD = 'refest'\n\nMAXVAR = 'maxvar'\nVCMT = 'vcmt'\nPREREAD_IFGS = 'preread_ifgs'\nTILES = 'tiles'\n\n# coherence masking parameters\n#: BOOL (0/1); Perform coherence masking (1: yes, 0: no)\nCOH_MASK = 'cohmask'\n#: FLOAT; Coherence threshold for masking\nCOH_THRESH = 'cohthresh'\n\n#: STR; Name of the file list containing the pool of available coherence files\nCOH_FILE_LIST = 'cohfilelist'\n\n# baseline parameters\n#: STR; Directory containing baseline files\nBASE_FILE_DIR = 'basefiledir'\n#: STR; Name of the file list containing the pool of available baseline files\nBASE_FILE_LIST = 'basefilelist'\n\n#: STR; Name of the file containing the GAMMA lookup table between lat/lon and radar coordinates (row/col)\nLT_FILE = 'ltfile'\n\n# atmospheric error correction parameters NOT CURRENTLY USED\nAPS_CORRECTION = 'apscorrect'\nAPS_METHOD = 'apsmethod'\nAPS_INCIDENCE_MAP = 'incidencemap'\nAPS_INCIDENCE_EXT = 'APS_INCIDENCE_EXT'\nAPS_ELEVATION_MAP = 'elevationmap'\nAPS_ELEVATION_EXT = 'APS_ELEVATION_EXT'\n\n\n# phase closure\nPHASE_CLOSURE = 'phase_closure'\nCLOSURE_THR = 'closure_thr'\nIFG_DROP_THR = 'ifg_drop_thr'\nMIN_LOOPS_PER_IFG = 'min_loops_per_ifg'\nMAX_LOOP_LENGTH = 'max_loop_length'\nMAX_LOOP_REDUNDANCY = 'max_loop_redundancy'\nSUBTRACT_MEDIAN = 'subtract_median'\n\n# orbital error correction/parameters\n#: BOOL (1/0); Perform orbital error correction (1: yes, 0: no)\nORBITAL_FIT = 'orbfit'\n#: BOOL (1/2); Method for orbital error correction (1: independent, 2: network)\nORBITAL_FIT_METHOD = 'orbfitmethod'\n#: BOOL (1/2/3) Polynomial order of orbital error model (1: planar in x and y - 2 parameter model, 2: quadratic in x and y - 5 parameter model, 3: quadratic in x and cubic in y - part-cubic 6 parameter model)\nORBITAL_FIT_DEGREE = 'orbfitdegrees'\n#: INT; Multi look factor for orbital error calculation in x dimension\nORBITAL_FIT_LOOKS_X = 'orbfitlksx'\n#: INT; Multi look factor for orbital error calculation in y dimension\nORBITAL_FIT_LOOKS_Y = 'orbfitlksy'\n#: BOOL (1/0); Estimate interferogram offsets during orbit correction design matrix (1: yes, 0: no)\nORBFIT_OFFSET = 'orbfitoffset'\n#: FLOAT; Scaling parameter for orbital correction design matrix\nORBFIT_SCALE = 'orbfitscale'\nORBFIT_INTERCEPT = 'orbfitintercept'\n\n# Stacking parameters\n#: FLOAT; Threshold ratio between 'model minus observation' residuals and a-priori observation standard deviations for stacking estimate acceptance (otherwise remove furthest outlier and re-iterate)\nLR_NSIG = 'nsig'\n#: INT; Number of required observations per pixel for stacking to occur\nLR_PTHRESH = 'pthr'\n#: FLOAT; Maximum allowable standard error for pixels in stacking\nLR_MAXSIG = 'maxsig'\n\n# atmospheric delay errors fitting parameters NOT CURRENTLY USED\n# atmfitmethod = 1: interferogram by interferogram; atmfitmethod = 2, epoch by epoch\n# ATM_FIT = 'atmfit'\n# ATM_FIT_METHOD = 'atmfitmethod'\n\n# APS correction parameters\n#: BOOL (0/1) Perform APS correction (1: yes, 0: no)\nAPSEST = 'apsest'\n# temporal low-pass filter parameters\n#: FLOAT; Cutoff time for gaussian filter in days;\nTLPF_CUTOFF = 'tlpfcutoff'\n#: INT; Number of required input observations per pixel for temporal filtering\nTLPF_PTHR = 'tlpfpthr'\n# spatially correlated noise low-pass filter parameters\n#: FLOAT; Cutoff  value for both butterworth and gaussian filters in km\nSLPF_CUTOFF = 'slpfcutoff'\n#: INT (1/0); Do spatial interpolation at NaN locations (1 for interpolation, 0 for zero fill)\nSLPF_NANFILL = 'slpnanfill'\n#: #: STR; Method for spatial interpolation (one of: linear, nearest, cubic), only used when slpnanfill=1\nSLPF_NANFILL_METHOD = 'slpnanfill_method'\n\n# DEM error correction parameters\n#: BOOL (0/1) Perform DEM error correction (1: yes, 0: no)\nDEMERROR = 'demerror'\n#: INT; Number of required input observations per pixel for DEM error estimation\nDE_PTHR = 'de_pthr'\n\n# Time series parameters\n#: INT (1/2); Method for time series inversion (1: Laplacian Smoothing; 2: SVD)\nTIME_SERIES_METHOD = 'tsmethod'\n#: INT; Number of required input observations per pixel for time series inversion\nTIME_SERIES_PTHRESH = 'ts_pthr'\n#: INT (1/2); Order of Laplacian smoothing operator, first or second order\nTIME_SERIES_SM_ORDER = 'smorder'\n#: FLOAT; Laplacian smoothing factor (values used is 10**smfactor)\nTIME_SERIES_SM_FACTOR = 'smfactor'\n# tsinterp is automatically assigned in the code; not needed in conf file\n# TIME_SERIES_INTERP = 'tsinterp'\n\n#: BOOL (0/1/2); Use parallelisation/Multi-threading (0: in serial, 1: in parallel by rows, 2: in parallel by pixel)\nPARALLEL = 'parallel'\n#: INT; Number of processes for multi-threading\nPROCESSES = 'processes'\nLARGE_TIFS = 'largetifs'\n# Orbital error correction constants for conversion to readable strings\nINDEPENDENT_METHOD = 1\nNETWORK_METHOD = 2\nPLANAR = 1\nQUADRATIC = 2\nPART_CUBIC = 3\n\n# Orbital error name look up for logging\nORB_METHOD_NAMES = {INDEPENDENT_METHOD: 'INDEPENDENT',\n                    NETWORK_METHOD: 'NETWORK'}\nORB_DEGREE_NAMES = {PLANAR: 'PLANAR',\n                    QUADRATIC: 'QUADRATIC',\n                    PART_CUBIC: 'PART CUBIC'}\n\n# geometry outputs\nGEOMETRY_OUTPUT_TYPES = ['rdc_azimuth', 'rdc_range', 'look_angle', 'incidence_angle', 'azimuth_angle', 'range_dist']\n\n# sign convention for phase data\nSIGNAL_POLARITY = 'signal_polarity'\n\n# LOS projection\nLOS_PROJECTION = 'los_projection'\n\n# Number of sigma to report velocity error\nVELERROR_NSIG = 'velerror_nsig'\n\n# dir for temp files\nTMPDIR = 'tmpdir'\n\n# Lookup to help convert args to correct type/defaults\n# format is\tkey : (conversion, default value)\n# None = no conversion\n\n# filenames reused in  many parts of the program\nREF_PIXEL_FILE = 'ref_pixel_file'\nORB_ERROR_DIR = 'orb_error_dir'\nDEM_ERROR_DIR = 'dem_error_dir'\nAPS_ERROR_DIR = 'aps_error_dir'\nPHASE_CLOSURE_DIR = 'phase_closure_dir'\nMST_DIR = 'mst_dir'\nTEMP_MLOOKED_DIR = 'temp_mlooked_dir'\nCOHERENCE_DIR = 'coherence_dir'\nINTERFEROGRAM_DIR = 'interferogram_dir'\nGEOMETRY_DIR = 'geometry_dir'\nTIMESERIES_DIR = 'timeseries_dir'\nVELOCITY_DIR = 'velocity_dir'\n\n"
  },
  {
    "path": "pyrate/conv2tif.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python script converts ROI_PAC or GAMMA format input interferograms \ninto geotiff format files\n\"\"\"\n# -*- coding: utf-8 -*-\nimport os\nfrom typing import Tuple, List\nfrom joblib import Parallel, delayed\nimport numpy as np\nfrom pathlib import Path\n\nimport pyrate.constants as C\nfrom pyrate.core.prepifg_helper import PreprocessError\nfrom pyrate.core import shared, mpiops, gamma, roipac\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import MultiplePaths\nfrom pyrate.core.shared import mpi_vs_multiprocess_logging\n\nGAMMA = 1\nROIPAC = 0\nGEOTIF = 2\n\n\ndef main(params):\n    \"\"\"\n    Parse parameters and prepare files for conversion.\n\n    :param dict params: Parameters dictionary read in from the config file\n    \"\"\"\n    # TODO: looks like base_ifg_paths are ordered according to ifg list\n    # This probably won't be a problem because input list won't be reordered\n    # and the original gamma generated list is ordered) this may not affect\n    # the important pyrate stuff anyway, but might affect gen_thumbs.py.\n    # Going to assume base_ifg_paths is ordered correcly\n    # pylint: disable=too-many-branches\n\n    if params[C.PROCESSOR] == 2:  # if geotif\n        log.warning(\"'conv2tif' step not required for geotiff!\")\n        return\n\n    mpi_vs_multiprocess_logging(\"conv2tif\", params)\n\n    base_ifg_paths = params[C.INTERFEROGRAM_FILES]\n\n    if params[C.COH_FILE_LIST] is not None:\n        base_ifg_paths.extend(params[C.COHERENCE_FILE_PATHS])\n\n    if params[C.DEM_FILE] is not None:  # optional DEM conversion\n        base_ifg_paths.append(params[C.DEM_FILE_PATH])\n\n    process_base_ifgs_paths = np.array_split(base_ifg_paths, mpiops.size)[mpiops.rank]\n    gtiff_paths = do_geotiff(process_base_ifgs_paths, params)\n    mpiops.comm.barrier()\n    log.info(\"Finished 'conv2tif' step\")\n    return gtiff_paths\n\n\ndef do_geotiff(unw_paths: List[MultiplePaths], params: dict) -> List[str]:\n    \"\"\"\n    Convert input interferograms to geotiff format.\n    \"\"\"\n    # pylint: disable=expression-not-assigned\n    log.info(\"Converting input interferograms to geotiff\")\n    parallel = params[C.PARALLEL]\n\n    if parallel:\n        log.info(\"Running geotiff conversion in parallel with {} processes\".format(params[C.PROCESSES]))\n        dest_base_ifgs = Parallel(n_jobs=params[C.PROCESSES], verbose=shared.joblib_log_level(\n            C.LOG_LEVEL))(\n            delayed(_geotiff_multiprocessing)(p, params) for p in unw_paths)\n    else:\n        log.info(\"Running geotiff conversion in serial\")\n        dest_base_ifgs = [_geotiff_multiprocessing(b, params) for b in unw_paths]\n    return dest_base_ifgs\n\n\ndef _geotiff_multiprocessing(unw_path: MultiplePaths, params: dict) -> Tuple[str, bool]:\n    \"\"\"\n    Multiprocessing wrapper for full-res geotiff conversion\n    \"\"\"\n    # TODO: Need a more robust method for identifying coherence files.\n    dest = unw_path.converted_path\n    processor = params[C.PROCESSOR]  # roipac or gamma\n\n    # Create full-res geotiff if not already on disk\n    if not os.path.exists(dest):\n        if processor == GAMMA:\n            header = gamma.gamma_header(unw_path.unwrapped_path, params)\n        elif processor == ROIPAC:\n            log.info(\"Warning: ROI_PAC support will be deprecated in a future PyRate release\")\n            header = roipac.roipac_header(unw_path.unwrapped_path, params)\n        else:\n            raise PreprocessError('Processor must be ROI_PAC (0) or GAMMA (1)')\n        header[ifc.INPUT_TYPE] = unw_path.input_type\n        shared.write_fullres_geotiff(header, unw_path.unwrapped_path, dest, nodata=params[\n            C.NO_DATA_VALUE])\n        Path(dest).chmod(0o444)  # readonly output\n        return dest, True\n    else:\n        log.warning(f\"Full-res geotiff already exists in {dest}! Returning existing geotiff!\")\n        return dest, False\n"
  },
  {
    "path": "pyrate/core/.coveragerc",
    "content": "[run]\nplugins = Cython.Coverage\nsource = pyrate\nomit = aps.py\nbranch = True\n\n[report]\nexclude_lines =\n    pragma: no cover\n    def __repr__\n    raise AssertionError\n    raise NotImplementedError\n    if __name__ == .__main__.:"
  },
  {
    "path": "pyrate/core/__init__.py",
    "content": ""
  },
  {
    "path": "pyrate/core/algorithm.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains a collection of generic algorithms used in PyRate\n\"\"\"\nfrom typing import Union, Iterable, Dict, Tuple\nfrom numpy import sin, cos, unique, histogram, diag, dot\nfrom scipy.linalg import qr, solve, lstsq\nfrom pyrate.core.shared import EpochList, IfgException, PrereadIfg\nfrom pyrate.core.ifgconstants import DAYS_PER_YEAR\n\n\ndef is_square(arr):\n    \"\"\"\n    Determines whether an array is square or not.\n\n    :param ndarray arr: numpy array\n\n    :return: condition\n    :rtype: bool\n    \"\"\"\n\n    shape = arr.shape\n    if len(shape) == 2 and (shape[0] == shape[1]):\n        return True\n    return False\n\n\ndef least_squares_covariance(A, b, v):\n    \"\"\"\n    Least squares solution in the presence of known covariance.\n\n    :param ndarray A: Design matrix\n    :param ndarray b: Observations (vector of phase values)\n    :param ndarray v: Covariances (vector of weights)\n\n    :return: solution\n    :rtype: ndarray\n    \"\"\"\n\n    # pylint: disable=too-many-locals\n    # X = LSCOV(A,b,V) returns the vector X that minimizes\n    # (A*X-b)'*inv(V)*(A*X-b) for the case in which length(b) > length(X).\n    # This is the over-determined least squares problem with covariance V.\n    # The solution is found without needing to invert V which is a square\n    # symmetric matrix with dimensions equal to length(b).\n    #\n    # The classical linear algebra solution to this problem is:\n    #\n    #     x = inv(A'*inv(V)*A)*A'*inv(V)*b\n    #\n    # Reference:\n    # G. Strang, \"Introduction to Applied Mathematics\",\n    # Wellesley-Cambridge, p. 398, 1986.\n    # L. S hure 3-31-89\n\n    # convert vectors to 2D singleton array\n    if len(A.shape) != 2:\n        raise ValueError('')\n\n    m, n = A.shape\n    if m <= n:\n        raise ValueError('Problem must be over-determined')\n\n    V = diag(1.0 / v.squeeze())\n    q, r = qr(A)  # Orthogonal-triangular Decomposition\n    efg = dot(q.T, dot(V, q))  # TODO: round it??\n    g = efg[n:, n:]  # modified to 0 indexing\n    cd = dot(q.T, b)  # q.T * b\n    f = efg[:n, n:]  # TODO: check +1/indexing\n    c = cd[:n]  # modified to 0 indexing\n    d = cd[n:]  # modified to 0 indexing\n    r = r[:n, :n]  # modified to 0 indexing\n\n    func = solve if is_square(g) else lstsq\n    tmp = func(g, d)\n    func = solve if is_square(r) else lstsq\n    return func(r, (c-f * tmp))\n\n\ndef los_conversion(phase_data, unit_vec):\n    \"\"\"\n    Converts phase from line-of-sight (LOS) to horizontal/vertical components.\n\n    :param ndarray phase_data: Phase band data array (eg. ifg.phase_data)\n    :param tuple unit_vec: 3 component unit vector e.g. (EW, NS, vertical)\n\n    :return: converted_phase\n    :rtype: ndarray\n    \"\"\"\n\n    # NB: currently not tested as implementation is too simple\n    return phase_data * unit_vec\n\n\ndef unit_vector(incidence, azimuth):\n    \"\"\"\n    Returns unit vector tuple (east_west, north_south, vertical).\n\n    :param float incidence: incidence angle w.r.t. nadir\n    :param float azimuth: azimuth of looking vector\n\n    :return: Unit vector (EW, NS, vertical).\n    :rtype: tuple\n    \"\"\"\n\n    vertical = cos(incidence)\n    north_south = sin(incidence) * cos(azimuth)\n    east_west = sin(incidence) * sin(azimuth)\n    return east_west, north_south, vertical\n\n\ndef ifg_date_lookup(ifgs, date_pair):\n    \"\"\"\n    Returns the Interferogram which has the first and second dates given\n    in 'date_pair'.\n\n    :param list ifgs: List of interferogram objects to search\n    :param tuple date_pair: A (datetime.date, datetime.date)\n\n    :return: interferogram list\n    :rtype: list\n    \"\"\"\n\n    if len(date_pair) != 2:\n        msg = \"Need (datetime.date, datetime.date) first/second image pair\"\n        raise IfgException(msg)\n\n    # check first/second dates are in order\n    try:\n        # TODO: Clarify: Is the comparison here for a different date?\n        # Then it should be written in a more pythonic way\n        # The if below is always true as long as the dates are different\n        # and not in order\n        if date_pair[0] > date_pair[1]:\n            date_pair = date_pair[1], date_pair[0]\n    except:\n        raise ValueError(\"Bad date_pair arg to ifg_date_lookup()\")\n\n    for i in ifgs:\n        if date_pair == (i.first, i.second):\n            return i\n\n    raise ValueError(\"Cannot find Ifg with first/second\"\n                     \"image dates of %s\" % str(date_pair))\n\n\ndef ifg_date_index_lookup(ifgs, date_pair):\n    \"\"\"\n    Returns the Interferogram index which has the first and second image\n    dates given in 'date_pair'.\n\n    :param list ifgs: List of interferogram objects to search\n    :param tuple date_pair: A (datetime.date, datetime.date)\n\n    :return: interferogram index\n    :rtype: int\n    \"\"\"\n\n    if len(date_pair) != 2:\n        msg = \"Need (datetime.date, datetime.date) first/second image date pair\"\n        raise IfgException(msg)\n\n    # check first/second image dates are in order\n    try:\n        if date_pair[0] > date_pair[1]:\n            date_pair = date_pair[1], date_pair[0]\n    except:\n        raise ValueError(\"Bad date_pair arg to ifg_date_lookup()\")\n\n    for i, _ in enumerate(ifgs):\n        if date_pair == (ifgs[i].first, ifgs[i].second):\n            return i\n\n    raise ValueError(\"Cannot find Ifg with first/second image dates of %s\" % str(date_pair))\n\n\ndef get_epochs(ifgs: Union[Iterable, Dict]) -> Tuple[EpochList, int]:\n    \"\"\"\n    Returns an EpochList derived from the given interferograms.\n\n    :param ifgs: List of interferogram objects\n\n    :return: EpochList\n    :rtype: list\n    \"\"\"\n\n    if isinstance(ifgs, dict):\n        ifgs = [v for v in ifgs.values() if isinstance(v, PrereadIfg)]\n    combined = get_all_epochs(ifgs)\n    dates, n = unique(combined, False, True)\n    repeat, _ = histogram(n, bins=len(set(n)))\n\n    # absolute span for each date from the zero/start point\n    span = [(dates[i] - dates[0]).days / DAYS_PER_YEAR for i in range(len(dates))]\n    return EpochList(dates, repeat, span), n\n\n\ndef get_all_epochs(ifgs):\n    \"\"\"\n    Returns a sequence of all image dates used to form given interferograms.\n\n    :param list ifgs: List of interferogram objects\n\n    :return: list of all image dates\n    :rtype: list\n    \"\"\"\n\n    return [ifg.first for ifg in ifgs] + [ifg.second for ifg in ifgs]\n\n\ndef first_second_ids(dates):\n    \"\"\"\n    Returns a dictionary of 'date:unique ID' for each date in 'dates'.\n    IDs are ordered from oldest to newest, starting at 0.\n\n    :param list dates: List of dates\n\n    :return: unique dates IDs\n    :rtype: dict\n    \"\"\"\n\n    dset = sorted(set(dates))\n    return dict([(date_, i) for i, date_ in enumerate(dset)])\n\n\ndef factorise_integer(n, memo={}, left=2):\n    \"\"\"\n    Returns two factors a and b of a supplied number n such that a * b = n.\n    The two factors are evaluated to be as close to each other in size as possible\n\n    :param int n: Number to factorise\n    :param dict memo: dictionary of candidate factors\n    :param int left: operation flag (default = 2)\n\n    :return: a, factor one\n    :rtype: int\n    :return: b, factor two\n    :rtype: int\n    \"\"\"\n    n = int(n)\n    if (n, left) in memo:\n        return memo[(n, left)]\n    if left == 1:\n        return n, [n]\n    i = 2\n    best = n\n    bestTuple = [n]\n    while i * i <= n:\n        if n % i == 0:\n            rem = factorise_integer(n / i, memo, left - 1)\n            if rem[0] + i < best:\n                best = rem[0] + i\n                bestTuple = [i] + rem[1]\n        i += 1\n\n    # handle edge case when only one processor is available\n    if bestTuple == [4]:\n        return 2, 2\n\n    if len(bestTuple) == 1:\n        bestTuple.append(1)\n\n    return int(bestTuple[0]), int(bestTuple[1])\n"
  },
  {
    "path": "pyrate/core/aps.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements a spatio-temporal filter method\nfor correcting interferograms for atmospheric phase screen (APS)\nsignals.\n\"\"\"\n# pylint: disable=invalid-name, too-many-locals, too-many-arguments\nimport os\nfrom copy import deepcopy\nfrom collections import OrderedDict\nfrom typing import List\nimport numpy as np\nfrom numpy import isnan\nfrom scipy.fftpack import fft2, ifft2, fftshift, ifftshift\nfrom scipy.interpolate import griddata\n\nimport pyrate.constants as C\nfrom pyrate.core.logger import pyratelogger as log\n\nfrom pyrate.core import shared, ifgconstants as ifc, mpiops\nfrom pyrate.core.covariance import cvd_from_phase, RDist\nfrom pyrate.core.algorithm import get_epochs\nfrom pyrate.core.shared import Ifg, Tile, EpochList, nan_and_mm_convert\nfrom pyrate.core.timeseries import time_series\nfrom pyrate.merge import assemble_tiles\nfrom pyrate.configuration import MultiplePaths, Configuration\n\n\ndef spatio_temporal_filter(params: dict) -> None:\n    \"\"\"\n    Applies a spatio-temporal filter to remove the atmospheric phase screen\n    (APS) and saves the corrected interferograms. Firstly the incremental\n    time series is computed using the SVD method, before a cascade of temporal\n    then spatial Gaussian filters is applied. The resulting APS corrections are\n    saved to disc before being subtracted from each interferogram.\n\n    :param params: Dictionary of PyRate configuration parameters.\n    \"\"\"\n    if params[C.APSEST]:\n        log.info('Doing APS spatio-temporal filtering')\n    else:\n        log.info('APS spatio-temporal filtering not required')\n        return\n    tiles = params[C.TILES]\n    preread_ifgs = params[C.PREREAD_IFGS]\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n\n    # perform some checks on existing ifgs\n    log.debug('Checking APS correction status')\n    if mpiops.run_once(shared.check_correction_status, ifg_paths, ifc.PYRATE_APS_ERROR):\n        log.debug('Finished APS correction')\n        return  # return if True condition returned\n\n    aps_paths = [MultiplePaths.aps_error_path(i, params) for i in ifg_paths]\n    if all(a.exists() for a in aps_paths):\n        log.warning('Reusing APS errors from previous run')\n        _apply_aps_correction(ifg_paths, aps_paths, params)\n        return\n\n    # obtain the incremental time series using SVD\n    tsincr = _calc_svd_time_series(ifg_paths, params, preread_ifgs, tiles)\n    mpiops.comm.barrier()\n\n    # get lists of epochs and ifgs\n    ifgs = list(OrderedDict(sorted(preread_ifgs.items())).values())\n    epochlist = mpiops.run_once(get_epochs, ifgs)[0]\n\n    # first perform temporal high pass filter\n    ts_hp = temporal_high_pass_filter(tsincr, epochlist, params)\n\n    # second perform spatial low pass filter to obtain APS correction in ts domain\n    ifg = Ifg(ifg_paths[0])  # just grab any for parameters in slpfilter\n    ifg.open()\n    ts_aps = spatial_low_pass_filter(ts_hp, ifg, params)\n    ifg.close()\n\n    # construct APS corrections for each ifg\n    _make_aps_corrections(ts_aps, ifgs, params)\n\n    # apply correction to ifgs and save ifgs to disc.\n    _apply_aps_correction(ifg_paths, aps_paths, params)\n\n    # update/save the phase_data in the tiled numpy files\n    shared.save_numpy_phase(ifg_paths, params)\n\n\ndef _calc_svd_time_series(ifg_paths: List[str], params: dict, preread_ifgs: dict,\n                          tiles: List[Tile]) -> np.ndarray:\n    \"\"\"\n    Helper function to obtain time series for spatio-temporal filter\n    using SVD method\n    \"\"\"\n    # Is there other existing functions that can perform this same job?\n    log.info('Calculating incremental time series via SVD method for APS '\n             'correction')\n    # copy params temporarily\n    new_params = deepcopy(params)\n    new_params[C.TIME_SERIES_METHOD] = 2  # use SVD method\n\n    process_tiles = mpiops.array_split(tiles)\n\n    nvels = None\n    for t in process_tiles:\n        log.debug(f'Calculating time series for tile {t.index} during APS '\n                  f'correction')\n        ifgp = [shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths]\n        mst_tile = np.load(Configuration.mst_path(params, t.index))\n        tsincr = time_series(ifgp, new_params, vcmt=None, mst=mst_tile)[0]\n        np.save(file=os.path.join(params[C.TMPDIR],\n                                  f'tsincr_aps_{t.index}.npy'), arr=tsincr)\n        nvels = tsincr.shape[2]\n\n    nvels = mpiops.comm.bcast(nvels, root=0)\n    mpiops.comm.barrier()\n    # need to assemble tsincr from all processes\n    tsincr_g = _assemble_tsincr(ifg_paths, params, preread_ifgs, tiles, nvels)\n    log.debug('Finished calculating time series for spatio-temporal filter')\n    return tsincr_g\n\n\ndef _assemble_tsincr(ifg_paths: List[str], params: dict, preread_ifgs: dict,\n                     tiles: List[Tile], nvels: np.float32) -> np.ndarray:\n    \"\"\"\n    Helper function to reconstruct time series images from tiles\n    \"\"\"\n    # pre-allocate dest 3D array\n    shape = preread_ifgs[ifg_paths[0]].shape\n    tsincr_p = {}\n    process_nvels = mpiops.array_split(range(nvels))\n    for i in process_nvels:\n        tsincr_p[i] = assemble_tiles(shape, params[C.TMPDIR], tiles,\n                                     out_type='tsincr_aps', index=i)\n    tsincr_g = shared.join_dicts(mpiops.comm.allgather(tsincr_p))\n    return np.dstack([v[1] for v in sorted(tsincr_g.items())])\n\n\ndef _make_aps_corrections(ts_aps: np.ndarray, ifgs: List[Ifg], params: dict) -> None:\n    \"\"\"\n    Function to convert the time series APS filter output into interferometric\n    phase corrections and save them to disc.\n\n    :param ts_aps: Incremental APS time series array.\n    :param ifgs:   List of Ifg class objects.\n    :param params: Dictionary of PyRate configuration parameters.\n    \"\"\"\n    log.debug('Reconstructing interferometric observations from time series')\n    # get first and second image indices \n    _ , n = mpiops.run_once(get_epochs, ifgs)\n    index_first, index_second = n[:len(ifgs)], n[len(ifgs):]\n\n    num_ifgs_tuples = mpiops.array_split(list(enumerate(ifgs)))\n    for i, ifg in [(int(num), ifg) for num, ifg in num_ifgs_tuples]:\n        # sum time slice data from first to second epoch\n        ifg_aps = np.sum(ts_aps[:, :, index_first[i]: index_second[i]], axis=2)\n        aps_error_on_disc = MultiplePaths.aps_error_path(ifg.tmp_path, params)\n        np.save(file=aps_error_on_disc, arr=ifg_aps) # save APS as numpy array\n\n    mpiops.comm.barrier()\n\n\ndef _apply_aps_correction(ifg_paths: List[str], aps_paths: List[str], params: dict) -> None:\n    \"\"\"\n    Function to read and apply (subtract) APS corrections from interferogram data.\n    \"\"\"\n    for ifg_path, aps_path in mpiops.array_split(list(zip(ifg_paths, aps_paths))):\n        # read the APS correction from numpy array\n        aps_corr = np.load(aps_path)\n        # open the Ifg object\n        ifg = Ifg(ifg_path)\n        ifg.open(readonly=False)\n        # convert NaNs and convert to mm\n        nan_and_mm_convert(ifg, params)\n        # subtract the correction from the ifg phase data\n        ifg.phase_data[~np.isnan(ifg.phase_data)] -= aps_corr[~np.isnan(ifg.phase_data)]\n        # set meta-data tags after aps error correction\n        ifg.dataset.SetMetadataItem(ifc.PYRATE_APS_ERROR, ifc.APS_REMOVED)\n        # write phase data to disc and close ifg.\n        ifg.write_modified_phase()\n        ifg.close()\n\n\ndef spatial_low_pass_filter(ts_hp: np.ndarray, ifg: Ifg, params: dict) -> np.ndarray:\n    \"\"\"\n    Filter time series data spatially using a Gaussian low-pass\n    filter defined by a cut-off distance. If the cut-off distance is\n    defined as zero in the parameters dictionary then it is calculated for\n    each time step using the pyrate.covariance.cvd_from_phase method.\n    :param ts_hp: Array of temporal high-pass time series data, shape (ifg.shape, n_epochs)\n    :param ifg: pyrate.core.shared.Ifg Class object.\n    :param params: Dictionary of PyRate configuration parameters.\n    :return: ts_lp: Low-pass filtered time series data of shape (ifg.shape, n_epochs).\n    \"\"\"\n    log.info('Applying spatial low-pass filter')\n\n    nvels = ts_hp.shape[2]\n    cutoff = params[C.SLPF_CUTOFF]\n    # nanfill = params[cf.SLPF_NANFILL]\n    # fillmethod = params[cf.SLPF_NANFILL_METHOD]\n    if cutoff == 0:\n        r_dist = RDist(ifg)()  # only needed for cvd_for_phase\n    else:\n        r_dist = None\n        log.info(f'Gaussian spatial filter cutoff is {cutoff:.3f} km for all '\n                 f'{nvels} time-series images')\n\n    process_nvel = mpiops.array_split(range(nvels))\n    process_ts_lp = {}\n\n    for i in process_nvel:\n        process_ts_lp[i] = _slpfilter(ts_hp[:, :, i], ifg, r_dist, params)\n\n    ts_lp_d = shared.join_dicts(mpiops.comm.allgather(process_ts_lp))\n    ts_lp = np.dstack([v[1] for v in sorted(ts_lp_d.items())])\n    log.debug('Finished applying spatial low pass filter')\n    return ts_lp\n\n\ndef _interpolate_nans_2d(arr: np.ndarray, method: str) -> None:\n    \"\"\"\n    In-place array interpolation and NaN-fill using scipy.interpolation.griddata.\n    :param arr: 2D ndarray to be interpolated.\n    :param method: Method; one of 'nearest', 'linear', and 'cubic'.\n    \"\"\"\n    log.debug(f'Interpolating array with \"{method}\" method')\n    r, c = np.indices(arr.shape)\n    arr[np.isnan(arr)] = griddata(\n        (r[~np.isnan(arr)], c[~np.isnan(arr)]),  # points we know\n        arr[~np.isnan(arr)],  # values we know\n        (r[np.isnan(arr)], c[np.isnan(arr)]),  # points to interpolate\n        method=method, fill_value=0)\n\n\ndef _slpfilter(phase: np.ndarray, ifg: Ifg, r_dist: float, params: dict) -> np.ndarray:\n    \"\"\"\n    Wrapper function for spatial low pass filter\n    \"\"\"\n    cutoff = params[C.SLPF_CUTOFF]\n    nanfill = params[C.SLPF_NANFILL]\n    fillmethod = params[C.SLPF_NANFILL_METHOD]\n\n    if np.all(np.isnan(phase)):  # return for nan matrix\n        return phase\n\n    if cutoff == 0:\n        _, alpha = cvd_from_phase(phase, ifg, r_dist, calc_alpha=True)\n        cutoff = 1.0 / alpha\n        log.info(f'Gaussian spatial filter cutoff is {cutoff:.3f} km')\n\n    return gaussian_spatial_filter(phase, cutoff, ifg.x_size, ifg.y_size, nanfill, fillmethod)\n\n\ndef gaussian_spatial_filter(image: np.ndarray, cutoff: float, x_size: float,\n                            y_size: float, nanfill: bool = True,\n                            fillmethod: str = 'nearest') -> np.ndarray:\n    \"\"\"\n    Function to apply a Gaussian spatial low-pass filter to a 2D image with\n    unequal pixel resolution in x and y dimensions. Performs filtering in the\n    Fourier domain. Any NaNs in the image are interpolated prior to Fourier\n    transformation, with NaNs being replaced in to the filtered output image.\n    :param image: 2D image to be filtered\n    :param cutoff: filter cutoff in kilometres\n    :param x_size: pixel size in x dimension, in metres\n    :param y_size: pixel size in y dimension, in metres\n    :param nanfill: interpolate image to fill NaNs\n    :param fillmethod: interpolation method ('nearest', 'cubic', or 'linear')\n    :return: filt: Gaussian low-pass filtered 2D image\n    \"\"\"\n    # create NaN mask of image\n    mask = np.isnan(image)\n    # in-place nearest-neighbour interpolation to fill NaNs\n    # nearest neighbour will fill values outside the convex hull\n    if nanfill:\n        _interpolate_nans_2d(image, fillmethod)\n\n    rows, cols = image.shape\n    pad = 4096\n    # pad the image to a large square array.\n    # TODO: implement variable padding dependent on image size\n    im = np.pad(image, ((0, pad - rows), (0, pad - cols)), 'constant')\n    # fast fourier transform of the input image\n    imf = fftshift(fft2(im))\n\n    # calculate centre coords of image\n    cx = np.floor(pad / 2)\n    cy = np.floor(pad / 2)\n    # calculate distance array\n    [xx, yy] = np.meshgrid(range(pad), range(pad))\n    xx = (xx - cx) * x_size  # these are in meters as x_size in metres\n    yy = (yy - cy) * y_size\n    dist = np.sqrt(xx ** 2 + yy ** 2) / ifc.METRE_PER_KM  # change m to km\n\n    # Estimate sigma value for Gaussian kernel function in spectral domain\n    # by converting cutoff distance to wavenumber and applying a scaling\n    # factor based on fixed kernel window size. \n    sigma = np.std(dist) * (1 / cutoff)\n    # Calculate kernel weights\n    wgt = _kernel(dist, sigma)\n    # Apply Gaussian smoothing kernel\n    outf = imf * wgt\n    # Inverse Fourier transform\n    out = np.real(ifft2(ifftshift(outf)))\n    filt = out[:rows, :cols]  # grab non-padded part\n    filt[mask] = np.nan  # re-insert nans in output image\n    return filt\n\n\n# TODO: use tiles here and distribute amongst processes\ndef temporal_high_pass_filter(tsincr: np.ndarray, epochlist: EpochList,\n                              params: dict) -> np.ndarray:\n    \"\"\"\n    Isolate high-frequency components of time series data by subtracting\n    low-pass components obtained using a Gaussian filter defined by a\n    cut-off time period (in days).\n    :param tsincr: Array of incremental time series data of shape (ifg.shape, n_epochs).\n    :param epochlist: A pyrate.core.shared.EpochList Class instance.\n    :param params: Dictionary of PyRate configuration parameters.\n    :return: ts_hp: Filtered high frequency time series data; shape (ifg.shape, nepochs).\n    \"\"\"\n    log.info('Applying temporal high-pass filter')\n    threshold = params[C.TLPF_PTHR]\n    cutoff_day = params[C.TLPF_CUTOFF]\n    if cutoff_day < 1 or type(cutoff_day) != int:\n        raise ValueError(f'tlpf_cutoff must be an integer greater than or '\n                         f'equal to 1 day. Value provided = {cutoff_day}')\n\n    # convert cutoff in days to years\n    cutoff_yr = cutoff_day / ifc.DAYS_PER_YEAR\n    log.info(f'Gaussian temporal filter cutoff is {cutoff_day} days '\n             f'({cutoff_yr:.4f} years)')\n\n    intv = np.diff(epochlist.spans)  # time interval for the neighboring epochs\n    span = epochlist.spans[: tsincr.shape[2]] + intv / 2  # accumulated time\n    rows, cols = tsincr.shape[:2]\n\n    tsfilt_row = {}\n    process_rows = mpiops.array_split(list(range(rows)))\n\n    for r in process_rows:\n        tsfilt_row[r] = np.empty(tsincr.shape[1:], dtype=np.float32) * np.nan\n        for j in range(cols):\n            # Result of gaussian filter is low frequency time series\n            tsfilt_row[r][j, :] = gaussian_temporal_filter(tsincr[r, j, :],\n                                                           cutoff_yr, span, threshold)\n\n    tsfilt_combined = shared.join_dicts(mpiops.comm.allgather(tsfilt_row))\n    tsfilt = np.array([v[1] for v in tsfilt_combined.items()])\n    log.debug(\"Finished applying temporal high-pass filter\")\n    # Return the high-pass time series by subtracting low-pass result from input\n    return tsincr - tsfilt\n\n\ndef gaussian_temporal_filter(tsincr: np.ndarray, cutoff: float, span: np.ndarray,\n                             thr: int) -> np.ndarray:\n    \"\"\"\n    Function to apply a Gaussian temporal low-pass filter to a 1D time-series\n    vector for one pixel with irregular temporal sampling.\n    :param tsincr: 1D time-series vector to be filtered.\n    :param cutoff: filter cutoff in years.\n    :param span: 1D vector of cumulative time spans, in years.\n    :param thr: threshold for non-NaN values in tsincr.\n    :return: ts_lp: Low-pass filtered time series vector.\n    \"\"\"\n    nanmat = ~isnan(tsincr)\n    sel = np.nonzero(nanmat)[0]  # don't select if nan\n    ts_lp = np.empty(tsincr.shape, dtype=np.float32) * np.nan\n    m = len(sel)\n    if m >= thr:\n        for k in range(m):\n            yr = span[sel] - span[sel[k]]\n            # apply Gaussian smoothing kernel\n            wgt = _kernel(yr, cutoff)\n            wgt /= np.sum(wgt)\n            ts_lp[sel[k]] = np.sum(tsincr[sel] * wgt)\n\n    return ts_lp\n\n\ndef _kernel(x: np.ndarray, sigma: float) -> np.ndarray:\n    \"\"\"\n    Gaussian low-pass filter kernel\n    \"\"\"\n    return np.exp(-0.5 * (x / sigma) ** 2)\n"
  },
  {
    "path": "pyrate/core/covariance.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements covariance calculation and\nVariance/Covariance matrix functionality.\n\"\"\"\n# coding: utf-8\nfrom os.path import basename, join\nfrom collections import OrderedDict\nfrom numpy import array, where, isnan, real, imag, sqrt, meshgrid\nfrom numpy import zeros, vstack, ceil, mean, exp, reshape\nfrom numpy.linalg import norm\nimport numpy as np\nfrom scipy.fftpack import fft2, ifft2, fftshift\nfrom scipy.optimize import fmin\n\nimport pyrate.constants as C\nfrom pyrate.core import shared, ifgconstants as ifc, mpiops\nfrom pyrate.core.shared import PrereadIfg, Ifg\nfrom pyrate.core.algorithm import first_second_ids\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration\n\n# pylint: disable=too-many-arguments\nMAIN_PROCESS = 0\n\n\ndef _pendiffexp(alphamod, cvdav):\n    \"\"\"\n    Fits an exponential model to data.\n\n    :param float alphamod: Exponential decay exponent.\n    :param ndarray cvdav: Function magnitude at 0 radius (2 col array of\n    radius, variance)\n    \"\"\"\n    # pylint: disable=invalid-name\n    # maxvar usually at zero lag\n    mx = cvdav[1, 0]\n    return norm(cvdav[1, :] - (mx * exp(-alphamod * cvdav[0, :])))\n\n\n# this is not used any more\ndef _unique_points(points):  # pragma: no cover\n    \"\"\"\n    Returns unique points from a list of coordinates.\n\n    :param points: Sequence of (y,x) or (x,y) tuples.\n    \"\"\"\n    return vstack([array(u) for u in set(points)])\n\n\ndef cvd(ifg_path, params, r_dist, calc_alpha=False, write_vals=False, save_acg=False):\n    \"\"\"\n    Calculate the 1D covariance function of an entire interferogram as the\n    radial average of its 2D autocorrelation.\n\n    :param str ifg_path: An interferogram file path. OR\n    :param dict params: Dictionary of configuration parameters\n    :param ndarray r_dist: Array of distance values from the image centre\n                (See Rdist class for more details)\n    :param bool calc_alpha: If True calculate alpha\n    :param bool write_vals: If True write maxvar and alpha values to\n                interferogram metadata\n    :param bool save_acg: If True write autocorrelation and radial distance\n                data to numpy array file on disk\n\n    :return: maxvar: The maximum variance (at zero lag)\n    :rtype: float\n    :return: alpha: the exponential length-scale of decay factor\n    :rtype: float\n    \"\"\"\n    ifg = shared.Ifg(ifg_path)\n    ifg.open()\n    shared.nan_and_mm_convert(ifg, params)\n    # calculate 2D auto-correlation of image using the\n    # spectral method (Wiener-Khinchin theorem)\n    if ifg.nan_converted:  # if nancoverted earlier, convert nans back to 0's\n        phase = where(isnan(ifg.phase_data), 0, ifg.phase_data)\n    else:\n        phase = ifg.phase_data\n\n    maxvar, alpha = cvd_from_phase(phase, ifg, r_dist, calc_alpha, save_acg=save_acg, params=params)\n    if write_vals:\n        ifg.add_metadata(**{\n            ifc.PYRATE_MAXVAR: str(maxvar),\n            ifc.PYRATE_ALPHA: str(alpha)\n        })\n\n    ifg.close()\n\n    return maxvar, alpha\n\n\ndef _save_cvd_data(acg, r_dist, ifg_path, outdir):\n    \"\"\"\n    Function to save numpy array of autocorrelation data to disk\n    \"\"\"\n    data = np.column_stack((acg, r_dist))\n    data_file = join(outdir, 'cvd_data_{b}.npy'.format(b=basename(ifg_path).split('.')[0]))\n    np.save(file=data_file, arr=data)\n\n\ndef cvd_from_phase(phase, ifg, r_dist, calc_alpha, save_acg=False, params=None):\n    \"\"\"\n    A convenience function used to compute radial autocovariance from phase\n    data\n\n    :param ndarray phase: An array of interferogram phase data\n    :param Ifg class ifg: A pyrate.shared.Ifg class instance\n    :param ndarray r_dist: Array of distance values from the image centre\n                (See Rdist class for more details)\n    :param bool calc_alpha: If True calculate alpha\n    :param bool save_acg: If True write autocorrelation and radial distance\n                data to numpy array file on disk\n    :param dict params: [optional] Dictionary of configuration parameters;\n                Must be provided if save_acg=True\n\n    :return: maxvar: The maximum variance (at zero lag)\n    :rtype: float\n    :return: alpha: the exponential length-scale of decay factor\n    :rtype: float\n    \"\"\"\n    # pylint: disable=invalid-name\n    # pylint: disable=too-many-locals\n\n    autocorr_grid = _get_autogrid(phase)\n    acg = reshape(autocorr_grid, phase.size, order='F')\n    # Symmetry in image; keep only unique points\n    # tmp = _unique_points(zip(acg, r_dist))\n    # Sudipta: Unlikely, as unique_point is a search/comparison,\n    # whereas keeping 1st half is just numpy indexing.\n    # If it is not faster, why was this done differently here?\n    # r_dist = r_dist[:int(ceil(phase.size / 2.0)) + nrows]\n    acg = acg[:len(r_dist)]\n    # Alternative method to remove duplicate cells\n    # r_dist = r_dist[:ceil(len(r_dist)/2)+nlines]\n    #  Reason for '+nlines' term unknown\n    # eg. array([x for x in set([(1,1), (2,2), (1,1)])])\n    # the above shortens r_dist by some number of cells\n\n    # pick the smallest axis to determine circle search radius\n    if (ifg.x_centre * ifg.x_size) < (ifg.y_centre * ifg.y_size):\n        maxdist = (ifg.x_centre+1) * ifg.x_size / ifc.METRE_PER_KM\n    else:\n        maxdist = (ifg.y_centre+1) * ifg.y_size / ifc.METRE_PER_KM\n\n    # filter out data where the of lag distance is greater than maxdist\n    # r_dist = array([e for e in rorig if e <= maxdist]) #\n    # MG: prefers to use all the data\n    # acg = array([e for e in rorig if e <= maxdist])\n    indices_to_keep = r_dist < maxdist\n    acg = acg[indices_to_keep]\n\n    # optionally save acg vs dist observations to disk\n    if save_acg:\n        _save_cvd_data(acg, r_dist[indices_to_keep],\n                       ifg.data_path, params[C.TMPDIR])\n\n    if calc_alpha:\n        # bin width for collecting data\n        bin_width = max(ifg.x_size, ifg.y_size) * 2 / ifc.METRE_PER_KM  # km\n        r_dist = r_dist[indices_to_keep]  # km\n        # classify values of r_dist according to bin number\n        rbin = ceil(r_dist / bin_width).astype(int)\n        maxbin = max(rbin) - 1  # consistent with Legacy data\n\n        cvdav = zeros(shape=(2, maxbin + 1))\n\n        # the following stays in numpy land\n        # distance instead of bin number\n        cvdav[0, :] = np.multiply(range(maxbin + 1), bin_width)\n        # mean variance for the bins\n        cvdav[1, :] = [mean(acg[rbin == b]) for b in range(maxbin + 1)]\n        # calculate best fit function maxvar*exp(-alpha*r_dist)\n        alphaguess = 2 / (maxbin * bin_width)\n        alpha = fmin(_pendiffexp, x0=alphaguess, args=(cvdav,), disp=False,\n                     xtol=1e-6, ftol=1e-6)\n        log.debug(\"1st guess alpha {}, converged \"\n                 \"alpha: {}\".format(alphaguess, alpha))\n        # maximum variance usually at the zero lag: max(acg[:len(r_dist)])\n        return np.max(acg), alpha[0]  # alpha unit 1/km\n    else:\n        return np.max(acg), None\n\n\nclass RDist():\n    \"\"\"\n    RDist class used for caching r_dist during maxvar/alpha computation\n    \"\"\"\n    # pylint: disable=invalid-name\n    def __init__(self, ifg):\n        self.r_dist = None\n        self.ifg = ifg\n        self.nrows, self.ncols = ifg.shape\n\n    def __call__(self):\n\n        if self.r_dist is None:\n            size = self.nrows * self.ncols\n            # pixel distances from pixel at zero lag (image centre).\n            xx, yy = meshgrid(range(self.ncols), range(self.nrows))\n            # r_dist is distance from the center\n            # doing np.divide and np.sqrt will improve performance as it keeps\n            # calculations in the numpy land\n            self.r_dist = np.divide(np.sqrt(((xx - self.ifg.x_centre) *\n                                             self.ifg.x_size) ** 2 +\n                                            ((yy - self.ifg.y_centre) *\n                                             self.ifg.y_size) ** 2),\n                                    ifc.METRE_PER_KM)  # km\n            self.r_dist = reshape(self.r_dist, size, order='F')\n            self.r_dist = self.r_dist[:int(ceil(size / 2.0)) + self.nrows]\n\n        return self.r_dist\n\n\ndef _get_autogrid(phase):\n    \"\"\"\n    Helper function to assist with memory re-allocation during FFT calculation\n    \"\"\"\n    autocorr_grid = _calc_autoc_grid(phase)\n    nzc = np.sum(np.sum(phase != 0))\n    autocorr_grid = fftshift(real(autocorr_grid)) / nzc\n    return autocorr_grid\n\n\ndef _calc_autoc_grid(phase):\n    \"\"\"\n    Helper function to assist with memory re-allocation during FFT calculation\n    \"\"\"\n    pspec = _calc_power_spectrum(phase)\n    autocorr_grid = ifft2(pspec)\n    return autocorr_grid.astype(dtype=np.complex64)\n\n\ndef _calc_power_spectrum(phase):\n    \"\"\"\n    Helper function to assist with memory re-allocation during FFT calculation\n    \"\"\"\n    fft_phase = fft2(phase)\n    pspec = real(fft_phase) ** 2 + imag(fft_phase) ** 2\n    return pspec.astype(dtype=np.float32)\n\n\ndef get_vcmt(ifgs, maxvar):\n    \"\"\"\n    Assembles a temporal variance/covariance matrix using the method\n    described by Biggs et al., Geophys. J. Int, 2007. Matrix elements are\n    evaluated according to sig_i * sig_j * C_ij where i and j are two\n    interferograms and C is a matrix of coefficients:\n\n    C = 1 if the first and second epochs of i and j are equal\n    C = 0.5 if have i and j share either a common first or second epoch\n    C = -0.5 if the first of i or j equals the second of the other\n    C = 0 otherwise\n\n    :param list ifgs: A list of pyrate.shared.Ifg class objects.\n    :param ndarray maxvar: numpy array of maximum variance values for the\n                interferograms.\n\n    :return: vcm_t: temporal variance-covariance matrix\n    :rtype: ndarray\n    \"\"\"\n    # pylint: disable=too-many-locals\n    # c=0.5 for common first or second; c=-0.5 if first\n    # of one matches second of another\n\n    if isinstance(ifgs, dict):\n        ifgs = {k: v for k, v in ifgs.items() if isinstance(v, PrereadIfg)}\n        ifgs = OrderedDict(sorted(ifgs.items()))\n        # pylint: disable=redefined-variable-type\n        ifgs = ifgs.values()\n\n    nifgs = len(ifgs)\n    vcm_pat = zeros((nifgs, nifgs))\n\n    dates = [ifg.first for ifg in ifgs] + [ifg.second for ifg in ifgs]\n    ids = first_second_ids(dates)\n\n    for i, ifg in enumerate(ifgs):\n        mas1, slv1 = ids[ifg.first], ids[ifg.second]\n\n        for j, ifg2 in enumerate(ifgs):\n            mas2, slv2 = ids[ifg2.first], ids[ifg2.second]\n            if mas1 == mas2 or slv1 == slv2:\n                vcm_pat[i, j] = 0.5\n\n            if mas1 == slv2 or slv1 == mas2:\n                vcm_pat[i, j] = -0.5\n\n            if mas1 == mas2 and slv1 == slv2:\n                vcm_pat[i, j] = 1.0  # diagonal elements\n\n    # make covariance matrix in time domain\n    std = sqrt(maxvar).reshape((nifgs, 1))\n    vcm_t = std * std.transpose()\n    return vcm_t * vcm_pat\n\n\ndef maxvar_vcm_calc_wrapper(params):\n    \"\"\"\n    MPI wrapper for maxvar and vcmt computation\n    \"\"\"\n    preread_ifgs = params[C.PREREAD_IFGS]\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    log.info('Calculating the temporal variance-covariance matrix')\n\n    def _get_r_dist(ifg_path):\n        \"\"\"\n        Get RDIst class object\n        \"\"\"\n        ifg = Ifg(ifg_path)\n        ifg.open()\n        r_dist = RDist(ifg)()\n        ifg.close()\n        return r_dist\n\n    r_dist = mpiops.run_once(_get_r_dist, ifg_paths[0])\n    prcs_ifgs = mpiops.array_split(list(enumerate(ifg_paths)))\n    process_maxvar = {}\n    for n, i in prcs_ifgs:\n        log.debug(f'Calculating maxvar for {n} of process ifgs {len(prcs_ifgs)} of total {len(ifg_paths)}')\n        process_maxvar[int(n)] = cvd(i, params, r_dist, calc_alpha=True, write_vals=True, save_acg=True)[0]\n    maxvar_d = shared.join_dicts(mpiops.comm.allgather(process_maxvar))\n    maxvar = [v[1] for v in sorted(maxvar_d.items(), key=lambda s: s[0])]\n\n    vcmt = mpiops.run_once(get_vcmt, preread_ifgs, maxvar)\n    log.debug(\"Finished maxvar and vcm calc!\")\n    params[C.MAXVAR], params[C.VCMT] = maxvar, vcmt\n    np.save(Configuration.vcmt_path(params), arr=vcmt)\n    return maxvar, vcmt\n"
  },
  {
    "path": "pyrate/core/dem_error.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements the calculation of correction for residual topographic effects (a.k.a. as DEM errors).\n\"\"\"\n# pylint: disable=invalid-name, too-many-locals, too-many-arguments\nimport os\nimport numpy as np\nfrom typing import Tuple, Optional\nfrom os.path import join\nfrom pathlib import Path\n\nimport pyrate.constants as C\nfrom pyrate.core import geometry, shared, mpiops, ifgconstants as ifc\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.core.shared import Ifg, Geometry, DEM, Tile, tiles_split\nfrom pyrate.core.timeseries import TimeSeriesError\nfrom pyrate.configuration import MultiplePaths, Configuration\nfrom pyrate.merge import assemble_tiles\n\n\ndef dem_error_calc_wrapper(params: dict) -> None:\n    \"\"\"\n    MPI wrapper for DEM error correction\n    :param params: Dictionary of PyRate configuration parameters.\n    \"\"\"\n    if not params[C.DEMERROR]:\n        log.info(\"DEM error correction not required\")\n        return\n\n    # geometry information needed to calculate Bperp for each pixel using first IFG in list\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n\n    # check if DEM error correction is already available\n    if mpiops.run_once(__check_and_apply_demerrors_found_on_disc, ifg_paths, params):\n        log.warning(\"Reusing DEM error correction from previous run!!!\")\n    else:\n        log.info(\"Calculating DEM error correction\")\n        # read and open the first IFG in list\n        ifg0_path = ifg_paths[0]\n        ifg0 = Ifg(ifg0_path)\n        ifg0.open(readonly=True)\n\n        # not currently implemented for ROIPAC data which breaks some tests\n        # if statement can be deleted once ROIPAC is deprecated from PyRate\n        if ifg0.meta_data[ifc.PYRATE_INSAR_PROCESSOR] == 'ROIPAC':\n            log.warning(\"Geometry calculations are not implemented for ROI_PAC\")\n            return\n\n        if params[C.BASE_FILE_LIST] is None:\n            log.warning(\"No baseline files supplied: DEM error has not been computed\")\n            return\n\n        # todo: subtract other corrections (e.g. orbital) from displacement phase before estimating the DEM error\n\n        # the following code is a quick way to do the bperp calculation, but is not identical to the GAMMA output\n        # where the near range of the first SLC is used for each pair.\n        # calculate look angle for interferograms (using the Near Range of the first SLC)\n        # look_angle = geometry.calc_local_geometry(ifg0, None, rg, lon, lat, params)\n        # bperp = geometry.calc_local_baseline(ifg0, az, look_angle)\n\n        # split full arrays in to tiles for parallel processing\n        tiles_split(_process_dem_error_per_tile, params)\n\n        preread_ifgs = params[C.PREREAD_IFGS]\n        # write dem error and correction values to file\n        mpiops.run_once(_write_dem_errors, ifg_paths, params, preread_ifgs)\n        shared.save_numpy_phase(ifg_paths, params)\n\n        log.debug('Finished DEM error correction step')\n\n\ndef _process_dem_error_per_tile(tile: Tile, params: dict) -> None:\n    \"\"\"\n    Convenience function for processing DEM error in tiles\n    :param tile: pyrate.core.shared.Tile Class object.\n    :param params: Dictionary of PyRate configuration parameters.\n    \"\"\"\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    ifg0_path = ifg_paths[0]\n    ifg0 = Ifg(ifg0_path)\n    ifg0.open(readonly=True)\n    # read lon and lat values of multi-looked ifg (first ifg only)\n    lon, lat = geometry.get_lonlat_coords(ifg0)\n    # read azimuth and range coords and DEM from tif files generated in prepifg\n    geom_files = Configuration.geometry_files(params)\n    rdc_az_file = geom_files['rdc_azimuth']\n    geom_az = Geometry(rdc_az_file)\n    rdc_rg_file = geom_files['rdc_range']\n    geom_rg = Geometry(rdc_rg_file)\n    dem_file = params[C.DEM_FILE_PATH].sampled_path\n    dem = DEM(dem_file)\n    preread_ifgs = params[C.PREREAD_IFGS]\n    threshold = params[C.DE_PTHR]\n    ifg_parts = [shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths]\n    lon_parts = lon(tile)\n    lat_parts = lat(tile)\n    az_parts = geom_az(tile)\n    rg_parts = geom_rg(tile)\n    dem_parts = dem(tile)\n    log.debug(f\"Calculating per-pixel baseline for tile {tile.index} during DEM error correction\")\n    bperp, look_angle, range_dist = _calculate_bperp_wrapper(ifg_paths, az_parts, rg_parts,\n                                                             lat_parts, lon_parts, dem_parts)\n    log.debug(f\"Calculating DEM error for tile {tile.index} during DEM error correction\")\n\n    # mst_tile = np.load(Configuration.mst_path(params, tile.index))\n    # calculate the DEM error estimate and the correction values for each IFG\n    # current implementation uses the look angle and range distance matrix of the primary SLC in the last IFG\n    # todo: check the impact of using the same information from another SLC\n    dem_error, dem_error_correction, _ = calc_dem_errors(ifg_parts, bperp, look_angle, range_dist, threshold)\n    # dem_error contains the estimated DEM error for each pixel (i.e. the topographic change relative to the DEM)\n    # size [row, col]\n    # dem_error_correction contains the correction value for each interferogram\n    # size [num_ifg, row, col]\n    # save tiled data in tmpdir\n    np.save(file=os.path.join(params[C.TMPDIR], 'dem_error_{}.npy'.format(tile.index)), arr=dem_error)\n    # swap the axes of 3D array to fit the style used in function assemble_tiles\n    tmp_array = np.moveaxis(dem_error_correction, 0, -1)\n    # new dimension is [row, col, num_ifg]\n    # save tiled data into tmpdir\n    np.save(file=os.path.join(params[C.TMPDIR], 'dem_error_correction_{}.npy'.format(tile.index)), arr=tmp_array)\n\n    # Calculate and save the average perpendicular baseline for the tile\n    bperp_avg =  np.nanmean(bperp, axis=(1, 2), dtype=np.float64)\n    np.save(file=os.path.join(params[C.TMPDIR], 'bperp_avg_{}.npy'.format(tile.index)), arr=bperp_avg)\n\n\ndef _calculate_bperp_wrapper(ifg_paths: list, az_parts: np.ndarray, rg_parts: np.ndarray,\n                             lat_parts: np.ndarray, lon_parts: np.ndarray, dem_parts: np.ndarray,\n                             ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Wrapper function to calculate the perpendicular baseline for each pixel in each interferogram.\n    :param ifg_paths: list of pyrate.core.shared.Ifg Class objects.\n    :param az_parts: azimuth coordinate (i.e. line) for each pixel.\n    :param rg_parts: range coordinate (i.e. column) for each pixel.\n    :param lat_parts: latitude for each pixel.\n    :param lon_parts: longitude for each pixel.\n    :param dem_parts: DEM height for each pixel.\n    :return: bperp: perpendicular baseline for each pixel and interferogram.\n    :return: look_angle: look angle for each pixel.\n    :return: range_dist: range distance measurement for each pixel.\n    \"\"\"\n    nifgs = len(ifg_paths)\n    bperp = np.empty((nifgs, lon_parts.shape[0], lon_parts.shape[1])) * np.nan\n    # calculate per-pixel perpendicular baseline for each IFG\n    # loop could be avoided by approximating the look angle for the first Ifg\n    for ifg_num, ifg_path in enumerate(ifg_paths):\n        ifg = Ifg(ifg_path)\n        ifg.open(readonly=True)\n        # calculate look angle for interferograms (using the Near Range of the primary SLC)\n        look_angle, _, _, range_dist = geometry.calc_pixel_geometry(ifg, rg_parts, lon_parts, lat_parts, dem_parts)\n        bperp[ifg_num, :, :] = geometry.calc_local_baseline(ifg, az_parts, look_angle)\n    return bperp, look_angle, range_dist\n\n\ndef calc_dem_errors(ifgs: list, bperp: np.ndarray, look_angle: np.ndarray, range_dist: np.ndarray,\n                    threshold: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Function to calculate the DEM error for each pixel using least-squares adjustment of phase data and\n    perpendicular baseline. The least-squares adjustment co-estimates the velocities.\n    - *nrows* is the number of rows in the ifgs, and\n    - *ncols* is the  number of columns in the ifgs.\n\n    :param ifgs: list of interferogram class objects.\n    :param bperp: Per-pixel perpendicular baseline for each interferogram\n    :param look_angle: Per-pixel look angle\n    :param range_dist: Per-pixel range distance measurement\n    :param threshold: minimum number of redundant phase values at pixel (config parameter de_pthr)\n    :return: dem_error: estimated per-pixel dem error (nrows x ncols)\n    :return: dem_error_correction: DEM error correction for each pixel and interferogram (nifgs x nrows x ncols)\n    :return: vel: velocity estimate for each pixel (nrows x ncols)\n    \"\"\"\n    ifg_data, mst, ncols, nrows, ifg_time_span = _per_tile_setup(ifgs)\n    if threshold < 4:\n        msg = f\"pixel threshold too low (i.e. <4) resulting in non-redundant DEM error estimation\"\n        raise DEMError(msg)\n    # pixel-by-pixel calculation\n    # preallocate empty arrays for results\n    dem_error = np.empty([nrows, ncols], dtype=np.float32) * np.nan\n    vel = np.empty([nrows, ncols], dtype=np.float32) * np.nan\n    # nested loops to loop over the 2 image dimensions\n    for row in range(nrows):\n        for col in range(ncols):\n            # calc DEM error for each pixel with valid Bperp and ifg phase data\n            # check pixel for non-redundant ifgs\n            sel = np.nonzero(mst[:, row, col])[0]  # trues in mst are chosen\n            if len(sel) >= threshold:  # given threshold for number of valid pixels in time series\n                # phase observations (in mm)\n                y = ifg_data[sel, row, col]\n                bperp_pix = bperp[sel, row, col]\n                # If NaN: skip pixel\n                if np.isnan(y).any() or np.isnan(bperp_pix).any():\n                    continue\n                # using the actual geometry of a particular IFG would be possible but is likely not signif. different\n                # geom = bperp_pix / (range_dist[row, col] * np.sin(look_angle[row, col]))\n                time_span = ifg_time_span[sel]\n                # new covariance matrix using actual number of observations\n                m = len(sel)\n                # Design matrix of least-squares system, velocities are co-estimated as done in StaMPS or MintPy\n                A = np.column_stack((np.ones(m), bperp_pix, time_span))\n                # solve ordinary least-squares system using numpy.linalg.lstsq function\n                xhat, res, rnk, s = np.linalg.lstsq(A, y, rcond=None)\n                # dem error estimate for the pixel is the second parameter (cf. design matrix)\n                dem_error[row][col] = xhat[1]\n                # velocity estimate for the pixel is the third parameter (cf. design matrix)\n                vel[row][col] = xhat[2]\n\n    # calculate correction value for each IFG by multiplying the least-squares estimate with the Bperp value\n    dem_error_correction = np.multiply(dem_error, bperp)\n    # calculate metric difference to DEM by multiplying the estimate with the per-pixel geometry\n    # (i.e. range distance and look angle, see Eq. (2.4.12) in Hanssen (2001))\n    # also scale by -0.001 since the phase observations are in mm with positive values away from the sensor\n    dem_error = np.multiply(dem_error, np.multiply(range_dist, np.sin(look_angle))) * (-0.001)\n\n    return dem_error, dem_error_correction, vel\n\n\ndef _per_tile_setup(ifgs: list) -> Tuple[np.ndarray, np.ndarray, int, int, np.ndarray]:\n    \"\"\"\n    Convenience function for setting up DEM error computation parameters\n    :param ifgs: list of interferogram class objects.\n    :return: ifg_data: phase observations for each pixel and interferogram\n    :return: mst: an array of booleans representing valid ifg connections (i.e. the minimum spanning tree)\n    :return: ncols: number of columns\n    :return: nrows: number of rows\n    :return: ifg_time_span: date difference for each interferogram\n    \"\"\"\n    if len(ifgs) < 1:\n        msg = 'Time series requires 2+ interferograms'\n        raise TimeSeriesError(msg)\n\n    nrows = ifgs[0].nrows\n    ncols = ifgs[0].ncols\n    nifgs = len(ifgs)\n    ifg_data = np.zeros((nifgs, nrows, ncols), dtype=np.float32)\n    for ifg_num in range(nifgs):\n        ifg_data[ifg_num] = ifgs[ifg_num].phase_data # phase data is read from numpy files\n    mst = ~np.isnan(ifg_data)\n    ifg_time_span = np.zeros((nifgs))\n    for ifg_num in range(nifgs):\n        ifg_time_span[ifg_num] = ifgs[ifg_num].time_span\n\n    return ifg_data, mst, ncols, nrows, ifg_time_span\n\n\ndef _write_dem_errors(ifg_paths: list, params: dict, preread_ifgs: dict) -> None:\n    \"\"\"\n    Convenience function for writing DEM error (one file) and DEM error correction for each IFG to disc\n    :param ifg_paths: List of interferogram class objects.\n    :param params: Dictionary of PyRate configuration parameters.\n    :param preread_ifgs: Dictionary of interferogram metadata.\n    \"\"\"\n    tiles = params[C.TILES]\n\n    # re-assemble tiles and save into dem_error dir\n    shape = preread_ifgs[ifg_paths[0]].shape\n\n    # save dem error as geotiff file in out directory\n    gt, md, wkt = shared.get_geotiff_header_info(ifg_paths[0])\n    md[ifc.DATA_TYPE] = ifc.DEM_ERROR\n    dem_error = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='dem_error')\n    dem_error_file = os.path.join(params[C.DEM_ERROR_DIR], 'dem_error.tif')\n    shared.remove_file_if_exists(dem_error_file)\n    shared.write_output_geotiff(md, gt, wkt, dem_error, dem_error_file, np.nan)\n\n    # read the average bperp vals for each ifg and each tile\n    bperp = np.empty(shape=(len(tiles), len(ifg_paths)), dtype=np.float64)\n    for t in tiles:\n        bperp_file = Path(join(params[C.TMPDIR], 'bperp_avg_' + str(t.index) + '.npy'))\n        arr = np.load(file=bperp_file)\n        bperp[t.index, :] = arr\n\n    # loop over all ifgs\n    idx = 0\n    for ifg_path in ifg_paths:\n        ifg = Ifg(ifg_path)\n        ifg.open()\n        shared.nan_and_mm_convert(ifg, params) # ensure we have phase data in mm\n        # read dem error correction file from tmpdir\n        dem_error_correction_ifg = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='dem_error_correction',\n                                                  index=idx)\n        # calculate average bperp value across all tiles for the ifg\n        bperp_val = np.nanmean(bperp[:, idx])\n        dem_error_correction_on_disc = MultiplePaths.dem_error_path(ifg.data_path, params)\n        np.save(file=dem_error_correction_on_disc, arr=dem_error_correction_ifg)\n        idx += 1\n\n        # subtract DEM error from the ifg\n        ifg.phase_data -= dem_error_correction_ifg\n        _save_dem_error_corrected_phase(ifg, bperp_val)\n\n\ndef __check_and_apply_demerrors_found_on_disc(ifg_paths: list, params: dict) -> bool:\n    \"\"\"\n    Convenience function to check if DEM error correction files have already been produced in a previous run\n    :param ifg_paths: List of interferogram class objects.\n    :param params: Dictionary of PyRate configuration parameters.\n    :return: bool value: True if dem error files found on disc, otherwise False.\n    \"\"\"\n    saved_dem_err_paths = [MultiplePaths.dem_error_path(ifg_path, params) for ifg_path in ifg_paths]\n    for d, i in zip(saved_dem_err_paths, ifg_paths):\n        if d.exists():\n            dem_corr = np.load(d)\n            if isinstance(i, str):\n                # are paths\n                ifg = Ifg(i)\n                ifg.open()\n            else:\n                ifg = i\n            ifg.phase_data -= dem_corr\n            # set geotiff meta tag and save phase to file\n            # TODO: calculate avg bperp and add to metadata even for reused DEM error correction\n            _save_dem_error_corrected_phase(ifg)\n\n    return all(d.exists() for d in saved_dem_err_paths)\n\n\ndef _save_dem_error_corrected_phase(ifg: Ifg, bperp: Optional[np.float64] = None) -> None:\n    \"\"\"\n    Convenience function to update metadata and save latest phase after DEM error correction\n    :param ifg: pyrate.core.shared.Ifg Class object\n    :param bperp: perpendicular baseline value for adding to geotiff metadata.\n    \"\"\"\n    # update geotiff tags after DEM error correction\n    ifg.dataset.SetMetadataItem(ifc.PYRATE_DEM_ERROR, ifc.DEM_ERROR_REMOVED)\n    if bperp is not None:\n        ifg.dataset.SetMetadataItem(ifc.PYRATE_BPERP, str(bperp))\n    ifg.write_modified_phase()\n    ifg.close()\n\n\nclass DEMError(Exception):\n    \"\"\"\n    Generic exception for DEM errors.\n    \"\"\"\n"
  },
  {
    "path": "pyrate/core/gamma.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tools for reading GAMMA format input data.\n\"\"\"\n# coding: utf-8\nimport re\nimport os\nfrom os.path import split\nfrom pathlib import Path\nfrom datetime import date, time, timedelta\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.configuration import ConfigException, parse_namelist\nimport pyrate.core.ifgconstants as ifc\nfrom pyrate.constants import sixteen_digits_pattern, BASELINE_FILE_PATHS, BASE_FILE_DIR\nfrom pyrate.core.shared import extract_epochs_from_filename, data_format\nfrom pyrate.core.logger import pyratelogger as log\nimport struct\n\n\n# constants\nGAMMA_DATE = 'date'\nGAMMA_TIME = 'center_time'\nGAMMA_WIDTH = 'width'\nGAMMA_NROWS = 'nlines'\nGAMMA_CORNER_LAT = 'corner_lat'\nGAMMA_CORNER_LONG = 'corner_lon'\nGAMMA_Y_STEP = 'post_lat'\nGAMMA_X_STEP = 'post_lon'\nGAMMA_DATUM = 'ellipsoid_name'\nGAMMA_FREQUENCY = 'radar_frequency'\nGAMMA_INCIDENCE = 'incidence_angle'\nGAMMA_HEADING = 'heading'\nGAMMA_AZIMUTH = 'azimuth_angle'\nGAMMA_RANGE_PIX = 'range_pixel_spacing'\nGAMMA_RANGE_N = 'range_samples'\nGAMMA_RANGE_LOOKS = 'range_looks'\nGAMMA_AZIMUTH_PIX = 'azimuth_pixel_spacing'\nGAMMA_AZIMUTH_N = 'azimuth_lines'\nGAMMA_AZIMUTH_LOOKS = 'azimuth_looks'\nGAMMA_PRF = 'prf'\nGAMMA_NEAR_RANGE = 'near_range_slc'\nGAMMA_SAR_EARTH = 'sar_to_earth_center'\nGAMMA_SEMI_MAJOR_AXIS = 'earth_semi_major_axis'\nGAMMA_SEMI_MINOR_AXIS = 'earth_semi_minor_axis'\nGAMMA_INITIAL_BASELINE = 'initial_baseline(TCN)'\nGAMMA_INITIAL_BASELINE_RATE = 'initial_baseline_rate'\nGAMMA_PRECISION_BASELINE = 'precision_baseline(TCN)'\nGAMMA_PRECISION_BASELINE_RATE = 'precision_baseline_rate'\nRADIANS = 'RADIANS'\nGAMMA = 'GAMMA'\n\n\ndef _parse_header(path):\n    \"\"\"Parses all GAMMA header file fields into a dictionary\"\"\"\n    with open(path) as f:\n        text = f.read().splitlines()\n        raw_segs = [line.split() for line in text if ':' in line]\n\n    # convert the content into a giant dict of all key, values\n    return dict((i[0][:-1], i[1:]) for i in raw_segs)\n\n\ndef parse_epoch_header(path):\n    \"\"\"\n    Returns dictionary of epoch metadata required for PyRate\n\n    :param str path: Full path to GAMMA mli.par file. Note that the mli.par is required as input since the baseline calculations require the input values valid for the GAMMA multi-looked products and also the GAMMA lookup table gives radar coordinates for the multi-looked geometry.\n\n    :return: subset: subset of full metadata\n    :rtype: dict\n    \"\"\"\n    lookup = _parse_header(path)\n    subset = _parse_date_time(lookup)\n\n    # handle conversion of radar frequency to wavelength\n    freq, unit = lookup[GAMMA_FREQUENCY]\n    if unit != \"Hz\":  # pragma: no cover\n        msg = 'Unrecognised unit field for radar_frequency: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_WAVELENGTH_METRES] = _frequency_to_wavelength(float(freq))\n\n    incidence, unit = lookup[GAMMA_INCIDENCE]\n    if unit != \"degrees\":  # pragma: no cover\n        msg = 'Unrecognised unit field for incidence_angle: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_INCIDENCE_DEGREES] = float(incidence)\n\n    sat_heading, unit = lookup[GAMMA_HEADING]\n    if unit != \"degrees\":  # pragma: no cover\n        msg = 'Unrecognised unit field for heading: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_HEADING_DEGREES] = float(sat_heading)\n\n    sat_azimuth, unit = lookup[GAMMA_AZIMUTH]\n    if unit != \"degrees\":  # pragma: no cover\n        msg = 'Unrecognised unit field for azimuth_angle: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_AZIMUTH_DEGREES] = float(sat_azimuth)\n\n    range_pix, unit = lookup[GAMMA_RANGE_PIX]\n    if unit != \"m\":  # pragma: no cover\n        msg = 'Unrecognised unit field for range_pixel_spacing: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_RANGE_PIX_METRES] = float(range_pix)\n\n    range_n = lookup[GAMMA_RANGE_N] # number without a unit in .par file\n    subset[ifc.PYRATE_RANGE_N] = int(range_n[0])\n\n    range_looks = lookup[GAMMA_RANGE_LOOKS]  # number without a unit in .par file\n    subset[ifc.PYRATE_RANGE_LOOKS] = int(range_looks[0])\n\n    azimuth_pix, unit = lookup[GAMMA_AZIMUTH_PIX]\n    if unit != \"m\":  # pragma: no cover\n        msg = 'Unrecognised unit field for azimuth_pixel_spacing: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_AZIMUTH_PIX_METRES] = float(azimuth_pix)\n\n    azimuth_n = lookup[GAMMA_AZIMUTH_N] # number without a unit in .par file\n    subset[ifc.PYRATE_AZIMUTH_N] = int(azimuth_n[0])\n\n    azimuth_looks = lookup[GAMMA_AZIMUTH_LOOKS]  # number without a unit in .par file\n    subset[ifc.PYRATE_AZIMUTH_LOOKS] = int(azimuth_looks[0])\n\n    pulse_rep_freq, unit = lookup[GAMMA_PRF]\n    if unit != \"Hz\":  # pragma: no cover\n        msg = 'Unrecognised unit field for prf: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_PRF_HERTZ] = float(pulse_rep_freq)\n\n    near_range, unit = lookup[GAMMA_NEAR_RANGE]\n    if unit != \"m\":  # pragma: no cover\n        msg = 'Unrecognised unit field for near_range_slc: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_NEAR_RANGE_METRES] = float(near_range)\n\n    sar_to_earth, unit = lookup[GAMMA_SAR_EARTH]\n    if unit != \"m\":  # pragma: no cover\n        msg = 'Unrecognised unit field for sar_to_earth_center: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_SAR_EARTH_METRES] = float(sar_to_earth)\n\n    semi_major_axis, unit = lookup[GAMMA_SEMI_MAJOR_AXIS]\n    if unit != \"m\":  # pragma: no cover\n        msg = 'Unrecognised unit field for earth_semi_major_axis: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES] = float(semi_major_axis)\n\n    semi_minor_axis, unit = lookup[GAMMA_SEMI_MINOR_AXIS]\n    if unit != \"m\":  # pragma: no cover\n        msg = 'Unrecognised unit field for earth_semi_minor_axis: %s'\n        raise GammaException(msg % unit)\n    subset[ifc.PYRATE_SEMI_MINOR_AXIS_METRES] = float(semi_minor_axis)\n\n    return subset\n\n\ndef _parse_date_time(lookup):\n    \"\"\"Grab date and time metadata and convert to datetime objects\"\"\"\n    subset = {}\n    if len(lookup[GAMMA_DATE]) == 3:  # pragma: no cover\n        year, month, day, = [int(float(i)) for i in lookup[GAMMA_DATE][:3]]\n        if lookup.get(GAMMA_TIME) is not None:\n            t = lookup[GAMMA_TIME][0]\n            h, m, s = str(timedelta(seconds=float(t))).split(\":\")\n            hour = int(h)\n            min = int(m)\n            sec = int(s.split(\".\")[0])\n        else:\n            # Occasionally GAMMA header has no time information - default to midnight\n            hour, min, sec = 0, 0, 0\n    elif len(lookup[GAMMA_DATE]) == 6:\n        year, month, day, hour, min, sec = [int(float(i)) for i in lookup[GAMMA_DATE][:6]]\n    else:  # pragma: no cover\n        msg = \"Date and time information not complete in GAMMA headers\"\n        raise GammaException(msg)\n\n    subset[ifc.FIRST_DATE] = date(year, month, day)\n    subset[ifc.FIRST_TIME] = time(hour, min, sec)\n\n    return subset\n\n\ndef parse_dem_header(path):\n    \"\"\"\n    Returns dictionary of DEM metadata required for PyRate\n\n    :param str path: `Full path to Gamma *dem.par file`\n\n    :return: subset: subset of full metadata\n    :rtype: dict\n    \"\"\"\n    lookup = _parse_header(path)\n\n    # NB: many lookup fields have multiple elements, eg ['1000', 'Hz']\n    subset = {ifc.PYRATE_NCOLS: int(lookup[GAMMA_WIDTH][0]), ifc.PYRATE_NROWS: int(lookup[GAMMA_NROWS][0])}\n\n    expected = ['decimal', 'degrees']\n    for k in [GAMMA_CORNER_LAT, GAMMA_CORNER_LONG, GAMMA_X_STEP, GAMMA_Y_STEP]:\n        units = lookup[GAMMA_CORNER_LAT][1:]\n        if units != expected:  # pragma: no cover\n            msg = \"Unrecognised units for GAMMA %s field\\n. Got %s, expected %s\"\n            raise GammaException(msg % (k, units, expected))\n\n    subset[ifc.PYRATE_LAT] = float(lookup[GAMMA_CORNER_LAT][0])\n    subset[ifc.PYRATE_LONG] = float(lookup[GAMMA_CORNER_LONG][0])\n    subset[ifc.PYRATE_Y_STEP] = float(lookup[GAMMA_Y_STEP][0])\n    subset[ifc.PYRATE_X_STEP] = float(lookup[GAMMA_X_STEP][0])\n    subset[ifc.PYRATE_DATUM] = \"\".join(lookup[GAMMA_DATUM])\n    subset[ifc.PYRATE_INSAR_PROCESSOR] = GAMMA\n    return subset\n\n\ndef parse_baseline_header(path: str) -> dict:\n    \"\"\"\n    Returns dictionary of Baseline metadata required for PyRate.\n    Will read the Precise baseline estimate, if available,\n    otherwise will read the Initial baseline estimate.\n\n    :param path: Full path to GAMMA base.par file\n\n    :return: bdict: Dictionary of baseline values\n    \"\"\"\n    lookup = _parse_header(path)  # read file contents in to a dict\n\n    # split the initial and precise baselines\n    initial = lookup[GAMMA_INITIAL_BASELINE]\n    initial_rate = lookup[GAMMA_INITIAL_BASELINE_RATE]\n    precise = lookup[GAMMA_PRECISION_BASELINE]\n    precise_rate = lookup[GAMMA_PRECISION_BASELINE_RATE]\n\n    # read the initial baseline if all precise components are zero\n    # (indicates that the precise baseline estimation was not ran in GAMMA workflow)\n    if float(precise[0]) == 0.0 and float(precise[1]) == 0.0 and float(precise[2]) == 0.0:\n        log.debug('Reading Initial GAMMA baseline values')\n        baseline, baseline_rate = initial, initial_rate\n    else:\n        log.debug('Reading Precise GAMMA baseline values')\n        baseline, baseline_rate = precise, precise_rate\n\n    # Extract and return a dict of baseline values\n    bdict = {}\n\n    # baseline vector (along Track, aCross track, Normal to the track)\n    bdict[ifc.PYRATE_BASELINE_T] = float(baseline[0])\n    bdict[ifc.PYRATE_BASELINE_C] = float(baseline[1])\n    bdict[ifc.PYRATE_BASELINE_N] = float(baseline[2])\n    bdict[ifc.PYRATE_BASELINE_RATE_T] = float(baseline_rate[0])\n    bdict[ifc.PYRATE_BASELINE_RATE_C] = float(baseline_rate[1])\n    bdict[ifc.PYRATE_BASELINE_RATE_N] = float(baseline_rate[2])\n\n    return bdict\n\n\ndef _frequency_to_wavelength(freq):\n    \"\"\"\n    Convert radar frequency to wavelength\n    \"\"\"\n    return ifc.SPEED_OF_LIGHT_METRES_PER_SECOND / freq\n\n\ndef combine_headers(hdr0, hdr1, dem_hdr, base_hdr=None):\n    \"\"\"\n    Combines metadata for first and second image epochs, DEM and baselines\n    into a single dictionary for an interferogram.\n\n    :param dict hdr0: Metadata for the first image\n    :param dict hdr1: Metadata for the second image\n    :param dict dem_hdr: Metadata for the DEM\n    :param dict base_hdr: Metadata for baselines (if available)\n\n    :return: chdr: combined metadata\n    :rtype: dict\n    \"\"\"\n    if not all([isinstance(a, dict) for a in [hdr0, hdr1, dem_hdr]]):\n        raise GammaException('Header args need to be dicts')\n\n    if base_hdr and not isinstance(base_hdr, dict):\n        raise GammaException('Header args need to be dicts')\n\n    date0, date1 = hdr0[ifc.FIRST_DATE], hdr1[ifc.FIRST_DATE]\n    if date0 == date1:\n        raise GammaException(\"Can't combine headers for the same day\")\n    elif date1 < date0:\n        raise GammaException(\"Wrong date order\")\n\n    chdr = {ifc.PYRATE_TIME_SPAN: (date1 - date0).days / ifc.DAYS_PER_YEAR,\n            ifc.FIRST_DATE: date0,\n            ifc.FIRST_TIME: hdr0[ifc.FIRST_TIME],\n            ifc.SECOND_DATE: date1,\n            ifc.SECOND_TIME: hdr1[ifc.FIRST_TIME],\n            ifc.DATA_UNITS: RADIANS,\n            ifc.PYRATE_INSAR_PROCESSOR: GAMMA}\n\n    # set incidence angle to mean of first amd second image values\n    inc_ang = hdr0[ifc.PYRATE_INCIDENCE_DEGREES]\n    if np.isclose(inc_ang, hdr1[ifc.PYRATE_INCIDENCE_DEGREES], atol=1e-1):\n        chdr[ifc.PYRATE_INCIDENCE_DEGREES] = (hdr0[ifc.PYRATE_INCIDENCE_DEGREES] + hdr1[\n            ifc.PYRATE_INCIDENCE_DEGREES]) / 2\n    else:\n        msg = \"Incidence angles differ by more than 0.1 degrees\"\n        raise GammaException(msg)\n\n    wavelen = hdr0[ifc.PYRATE_WAVELENGTH_METRES]\n    if np.isclose(wavelen, hdr1[ifc.PYRATE_WAVELENGTH_METRES], atol=1e-6):\n        chdr[ifc.PYRATE_WAVELENGTH_METRES] = wavelen\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Wavelength mismatch, check both header files for %s & %s\"\n        raise GammaException(msg % args)\n\n    # use parameter of first image (as done by GAMMA during interferometric processing)\n    heading_ang = hdr0[ifc.PYRATE_HEADING_DEGREES]\n    if np.isclose(heading_ang, hdr1[ifc.PYRATE_HEADING_DEGREES], atol=5e-1):\n        chdr[ifc.PYRATE_HEADING_DEGREES] = heading_ang\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Satellite heading angles differ by more than 0.5 degrees\"\n        raise GammaException(msg % args)\n\n    azimuth_ang = hdr0[ifc.PYRATE_AZIMUTH_DEGREES]\n    if np.isclose(azimuth_ang, hdr1[ifc.PYRATE_AZIMUTH_DEGREES], atol=1e-1):\n        chdr[ifc.PYRATE_AZIMUTH_DEGREES] = azimuth_ang\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Satellite azimuth angles differ by more than 0.1 degrees\"\n        raise GammaException(msg % args)\n\n    range_pix = hdr0[ifc.PYRATE_RANGE_PIX_METRES]\n    if np.isclose(range_pix, hdr1[ifc.PYRATE_RANGE_PIX_METRES], atol=1e-1):\n        chdr[ifc.PYRATE_RANGE_PIX_METRES] = range_pix\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Range pixel spacing differs by more than 0.001 metres\"\n        raise GammaException(msg % args)\n\n    range_n = hdr0[ifc.PYRATE_RANGE_N]\n    if range_n == hdr1[ifc.PYRATE_RANGE_N]:\n        chdr[ifc.PYRATE_RANGE_N] = range_n\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Number of range pixels mismatch, check both header files for %s & %s\"\n        raise GammaException(msg % args)\n\n    range_looks = hdr0[ifc.PYRATE_RANGE_LOOKS]\n    if range_looks == hdr1[ifc.PYRATE_RANGE_LOOKS]:\n        chdr[ifc.PYRATE_RANGE_LOOKS] = range_looks\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Number of range looks mismatch, check both header files for %s & %s\"\n        raise GammaException(msg % args)\n\n    azimuth_pix = hdr0[ifc.PYRATE_AZIMUTH_PIX_METRES]\n    if np.isclose(azimuth_pix, hdr1[ifc.PYRATE_AZIMUTH_PIX_METRES], atol=1e-1):\n        chdr[ifc.PYRATE_AZIMUTH_PIX_METRES] = azimuth_pix\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Azimuth pixel spacing differs by more than 0.001 metres\"\n        raise GammaException(msg % args)\n\n    azimuth_n = hdr0[ifc.PYRATE_AZIMUTH_N]\n    if azimuth_n == hdr1[ifc.PYRATE_AZIMUTH_N]:\n        chdr[ifc.PYRATE_AZIMUTH_N] = azimuth_n\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Number of azimuth pixels mismatch, check both header files for %s & %s\"\n        raise GammaException(msg % args)\n\n    azimuth_looks = hdr0[ifc.PYRATE_AZIMUTH_LOOKS]\n    if azimuth_looks == hdr1[ifc.PYRATE_AZIMUTH_LOOKS]:\n        chdr[ifc.PYRATE_AZIMUTH_LOOKS] = azimuth_looks\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Number of azimuth looks mismatch, check both header files for %s & %s\"\n        raise GammaException(msg % args)\n\n    prf_hertz = hdr0[ifc.PYRATE_PRF_HERTZ]\n    if np.isclose(prf_hertz, hdr1[ifc.PYRATE_PRF_HERTZ], atol=1e-6):\n        chdr[ifc.PYRATE_PRF_HERTZ] = prf_hertz\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Pulse repetition frequency mismatch, check both header files for %s & %s\"\n        raise GammaException(msg % args)\n\n    near_range = hdr0[ifc.PYRATE_NEAR_RANGE_METRES]\n    if np.isclose(near_range, hdr1[ifc.PYRATE_NEAR_RANGE_METRES], atol=1e3):\n        chdr[ifc.PYRATE_NEAR_RANGE_METRES] = near_range\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Near range differs by more than 1000 metres\"\n        raise GammaException(msg % args)\n\n    sar_earth = hdr0[ifc.PYRATE_SAR_EARTH_METRES]\n    if np.isclose(sar_earth, hdr1[ifc.PYRATE_SAR_EARTH_METRES], atol=1e3):\n        chdr[ifc.PYRATE_SAR_EARTH_METRES] = sar_earth\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"SAR to Earth Center differs by more than 1000 metres\"\n        raise GammaException(msg % args)\n\n    semi_major_axis = hdr0[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES]\n    if np.isclose(semi_major_axis, hdr1[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES], atol=1e-4):\n        chdr[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES] = semi_major_axis\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Earth semi major axis differs by more than 0.0001 metres\"\n        raise GammaException(msg % args)\n\n    semi_minor_axis = hdr0[ifc.PYRATE_SEMI_MINOR_AXIS_METRES]\n    if np.isclose(semi_minor_axis, hdr1[ifc.PYRATE_SEMI_MINOR_AXIS_METRES], atol=1e-4):\n        chdr[ifc.PYRATE_SEMI_MINOR_AXIS_METRES] = semi_minor_axis\n    else:\n        args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE])\n        msg = \"Earth semi minor axis differs by more than 0.0001 metres\"\n        raise GammaException(msg % args)\n\n    # non-cropped, non-multilooked geotif process step information added\n    chdr[ifc.DATA_TYPE] = ifc.ORIG\n\n    chdr.update(dem_hdr)  # add geographic data\n\n    if base_hdr:\n        chdr.update(base_hdr)  # add baseline information\n\n    return chdr\n\n\ndef manage_headers(dem_header_file, header_paths, baseline_paths=None):\n    \"\"\"\n    Manage and combine header files for GAMMA interferograms, DEM and\n    incidence files\n\n    :param str dem_header_file: DEM header path\n    :param list header_paths: List of first/second image header paths\n\n    :return: combined_header: Combined metadata dictionary\n    :rtype: dict\n    \"\"\"\n    dem_header = parse_dem_header(dem_header_file)\n    # find param files containing filename dates\n    if len(header_paths) == 2:\n        hdrs = [parse_epoch_header(hp) for hp in header_paths]\n        if baseline_paths is not None:\n            baseline_header = parse_baseline_header(baseline_paths)\n            combined_header = combine_headers(hdrs[0], hdrs[1], dem_header, baseline_header)\n        else:\n            combined_header = combine_headers(hdrs[0], hdrs[1], dem_header)\n    elif len(header_paths) > 2:\n        msg = f'There are too many parameter files for one interferogram; there should only be two. {len(header_paths)} parameter files have been given: {header_paths}.'\n        raise GammaException(msg)\n    else:\n        # probably have DEM or incidence file\n        combined_header = dem_header\n        combined_header[ifc.DATA_TYPE] = ifc.DEM\n\n    return combined_header\n\n\ndef get_header_paths(input_file, slc_file_list):\n    \"\"\"\n    Function that matches input GAMMA file names with GAMMA header file names\n\n    :param str input_file: input GAMMA image file.\n    :param slc_file_list: file listing the pool of available header files (GAMMA: slc.par, ROI_PAC: .rsc)\n    :return: list of matching header files\n    :rtype: list\n    \"\"\"\n    f = Path(input_file)\n    epochs = extract_epochs_from_filename(f.name)\n    header_names = parse_namelist(slc_file_list)\n    matches = [hdr for hdr in header_names if any(e in hdr for e in epochs)]\n    return matches\n\n\ndef gamma_header(ifg_file_path, params):\n    \"\"\"\n    Function to obtain combined Gamma headers for image file\n    \n    Args:\n        ifg_file_path: Path to interferogram file to find headers for.\n        params: PyRate parameters dictionary.\n\n    Returns:\n        A combined header dictionary containing metadata from matching\n        gamma headers and DEM header.   \n    \"\"\"\n    dem_hdr_path = params[C.DEM_HEADER_FILE]\n    header_paths = get_header_paths(ifg_file_path, params[C.HDR_FILE_LIST])\n    if len(header_paths) == 2 and params[C.BASE_FILE_LIST] is not None:\n        baseline_path = baseline_paths_for(ifg_file_path, params)\n    else:\n        baseline_path = None  # don't read baseline files for DEM\n\n    combined_headers = manage_headers(dem_hdr_path, header_paths, baseline_path)\n\n    if os.path.basename(ifg_file_path).split('.')[1] == \\\n            (params[C.APS_INCIDENCE_EXT] or params[C.APS_ELEVATION_EXT]):\n        # TODO: implement incidence class here\n        combined_headers['FILE_TYPE'] = 'Incidence'\n\n    return combined_headers\n\n\ndef read_lookup_table(head, data_path, xlooks, ylooks, xmin, xmax, ymin, ymax):\n    # pylint: disable = too - many - statements\n    \"\"\"\n    Creates a copy of input lookup table file in a numpy array and applies the ifg ML factors\n\n    :param IFG object head: first IFG in the list to read metadata\n    :param str data_path: Input file\n    :param int xlooks: multi-looking factor in x\n    :param int ylooks: multi-looking factor in y\n    :param int xmin: start pixel of cropped extent in x\n    :param int xmax: end pixel of cropped extent in x\n    :param int ymin: start pixel of cropped extent in y\n    :param int ymax: end pixel of cropped extent in y\n\n    :return: np-array lt_data_az: azimuth (i.e. row) of radar-coded MLI\n    :return: np-array lt_data_rg: range (i.e. column) of radar-coded MLI\n    \"\"\"\n    # pylint: disable=too-many-branches\n    # pylint: disable=too-many-locals\n\n    # read relevant metadata parameters\n    nrows_lt = int(head.meta_data[ifc.PYRATE_NROWS]) # number of rows of original geotiff files\n    ncols_lt = int(head.meta_data[ifc.PYRATE_NCOLS]) # number of columns of original geotiff files\n    nrows = head.nrows # number of rows in multi-looked and cropped data sets\n    ncols = head.ncols # number of columns in multi-looked and cropped data sets\n\n    ifg_proc = head.meta_data[ifc.PYRATE_INSAR_PROCESSOR]\n    # get dimensions of lookup table file\n    bytes_per_col, fmtstr = data_format(ifg_proc, True, ncols_lt*2) # float complex data set containing value tupels\n\n    # check if lookup table has the correct size\n    small_size = _check_raw_data(bytes_per_col * 2, data_path, ncols_lt, nrows_lt)\n    # todo: delete the following if condition once a suitable test data set has been included\n    if small_size: # this is a test data set without a corresponding lt-file\n        lt_data_az = np.empty((nrows, ncols)) * np.nan # nan array with size of input data set\n        lt_data_rg = np.empty((nrows, ncols)) * np.nan # nan array with size of input data set\n\n    else: # this is a real data set with an lt-file of correct size\n        row_bytes = ncols_lt * 2 * bytes_per_col\n        lt_data_az = np.empty((0, ncols))  # empty array with correct number of columns\n        lt_data_rg = np.empty((0, ncols))  # empty array with correct number of column\n        # for indexing: lookup table file contains value pairs (i.e. range, azimuth)\n        # value pair 0 would be index 0 and 1, value pair 1 would be index 2 and 3, and so on\n        # example: for a multi-looking factor of 10 we want value pair 4, 14, 24, ...\n        # this would be index 8 and 9, index 28 and 29, 48 and 49, ...\n        # start column needs to be added in case cropping is applied\n        if (xlooks % 2) == 0:  # for even ml factors\n            idx_start = xmin + int(xlooks / 2) - 1\n        else: # for odd ml factors\n            idx_start = xmin + int((xlooks - 1) / 2)\n        # indices of range info in lookup table for the cropped and multi-looked data set\n        idx_rg = np.arange(2 * idx_start, xmax * 2, 2 * xlooks)  # first value\n        idx_az = np.arange(2 * idx_start + 1, xmax * 2, 2 * xlooks)  # second value\n        # set up row idx, e.g. for ml=10 (without cropping): 4, 14, 24, ...\n        if (ylooks % 2) == 0: # for even ml factors\n            idx_start = ymin + int(ylooks / 2) - 1\n        else: # for odd ml factors\n            idx_start = ymin + int((ylooks - 1) / 2)\n        row_idx = np.arange(idx_start, ymax, ylooks)\n\n        # read the binary lookup table file and save the range/azimuth value pair for each position in the cropped and\n        # multi-looked data set\n        log.debug(f\"Reading lookup table file {data_path}\")\n        with open(data_path, 'rb') as f:\n            for y in range(nrows_lt): # loop through all lines in file\n                # this could potentially be made quicker by skipping unwanted bytes in the f.read command?\n                data = struct.unpack(fmtstr, f.read(row_bytes))\n                # but only read data from lines in row index:\n                if y in row_idx:\n                    row_data = np.array(data)\n                    row_data_ml_az = row_data[idx_az] # azimuth for PyRate\n                    row_data_ml_rg = row_data[idx_rg] # range for PyRate\n                    lt_data_az = np.append(lt_data_az, [row_data_ml_az], axis=0)\n                    lt_data_rg = np.append(lt_data_rg, [row_data_ml_rg], axis=0)\n\n    return lt_data_az, lt_data_rg\n\n\ndef _check_raw_data(bytes_per_col, data_path, ncols, nrows):\n    \"\"\"\n    Convenience function to check the file size is as expected\n    \"\"\"\n    size = ncols * nrows * bytes_per_col\n    act_size = os.stat(data_path).st_size\n    if act_size != size:\n        msg = '%s should have size %s, not %s. Is the correct file being used?'\n        if size < 28000:\n            # test data set doesn't currently fit the lookup table size, stop further calculation\n            # todo: delete this if statement once a new test data set has been introduced\n            return True\n        else:\n            raise GammaException(msg % (data_path, size, act_size))\n\n\nclass GammaException(Exception):\n    \"\"\"Gamma generic exception class\"\"\"\n\n\ndef baseline_paths_for(path: str, params: dict) -> str:\n    \"\"\"\n    Returns path to baseline file for given interferogram. Pattern matches\n    based on epoch in filename.\n\n    Example:\n        '20151025-20160501_base.par'\n        Date pair is the epoch.\n\n    Args:\n        path: Path to intergerogram to find baseline file for.\n        params: Parameter dictionary.\n        tif: Find converted tif if True (_cc.tif), else find .cc file.\n\n    Returns:\n        Path to baseline file.\n    \"\"\"\n\n    _, filename = split(path)\n    try:\n        epoch = re.search(sixteen_digits_pattern, filename).group(0)\n    except: # catch cases where filename does not have two epochs, e.g. DEM file\n        return None\n\n    base_file_paths = [f.unwrapped_path for f in params[BASELINE_FILE_PATHS] if epoch in f.unwrapped_path]\n\n    if len(base_file_paths) > 1:\n        raise ConfigException(f\"'{BASE_FILE_DIR}': found more than one baseline \"\n                              f\"file for '{path}'. There must be only one \"\n                              f\"baseline file per interferogram. Found {base_file_paths}.\")\n    return base_file_paths[0]\n"
  },
  {
    "path": "pyrate/core/gdal_python.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains bindings for the GDAL library\n\"\"\"\n# pylint: disable=too-many-arguments,R0914\nfrom typing import Union, List, Tuple\nfrom osgeo import gdal, gdalconst\nfrom osgeo.gdal import Dataset\nimport numpy as np\nimport numexpr as ne\nfrom pyrate.core import shared, ifgconstants as ifc\nfrom pyrate.core.logger import pyratelogger as log\n\n\ngdal.SetCacheMax(2**15)\nGDAL_WARP_MEMORY_LIMIT = 2**10\nLOW_FLOAT32 = np.finfo(np.float32).min*1e-10\nall_mlooked_types = [ifc.MLOOKED_COH_MASKED_IFG, ifc.MULTILOOKED, ifc.MULTILOOKED_COH,\n                     ifc.MLOOKED_DEM]\n\n\ndef coherence_masking(input_gdal_dataset: Dataset, coh_file_path: str,\n                      coh_thr: float) -> None:\n    \"\"\"\n    Perform coherence masking on raster in-place.\n\n    Based on gdal_calc formula provided by Nahidul:\n    gdal_calc.py -A 20151127-20151209_VV_8rlks_flat_eqa.cc.tif\n    -B 20151127-20151209_VV_8rlks_eqa.unw.tif\n    --outfile=test_v1.tif --calc=\"B*(A>=0.8)-999*(A<0.8)\"\n    --NoDataValue=-999\n    \"\"\"\n\n    coherence_ds = gdal.Open(coh_file_path, gdalconst.GA_ReadOnly)\n    coherence_band = coherence_ds.GetRasterBand(1)\n    src_band = input_gdal_dataset.GetRasterBand(1)\n    ndv = np.nan\n    coherence = coherence_band.ReadAsArray()\n    src = src_band.ReadAsArray()\n    var = {\"coh\": coherence, \"src\": src, \"t\": coh_thr, \"ndv\": ndv}\n    formula = \"where(coh>=t, src, ndv)\"\n    res = ne.evaluate(formula, local_dict=var)\n    src_band.WriteArray(res)\n    # update metadata\n    input_gdal_dataset.GetRasterBand(1).SetNoDataValue(ndv)\n    input_gdal_dataset.FlushCache()  # write on the disc\n    log.info(f\"Masking ifg using file {coh_file_path} and coherence threshold: {coh_thr}\")\n\n\ndef world_to_pixel(geo_transform, x, y):\n    \"\"\"\n    Uses a gdal geomatrix (gdal.GetGeoTransform()) to calculate\n    the pixel location of a geospatial coordinate;\n    see: http://pcjericks.github.io/py-gdalogr-cookbook/raster_layers.html\n\n    :param list geo_transform: Affine transformation coefficients\n    :param float x: longitude coordinate\n    :param float y: latitude coordinate\n\n    :return: col: pixel column number\n    :rtype: int\n    :return: line: pixel line number\n    :rtype: int\n    \"\"\"\n    ul_x = geo_transform[0]\n    ul_y = geo_transform[3]\n    xres = geo_transform[1]\n    yres = geo_transform[5]\n    col = int(np.round((x - ul_x) / xres))\n    line = int(np.round((ul_y - y) / abs(yres))) # yres has negative size\n\n    return col, line\n\n\ndef resample_nearest_neighbour(input_tif, extents, new_res, output_file):\n    \"\"\"\n    Nearest neighbor resampling and cropping of an image.\n\n    :param str input_tif: input geotiff file path\n    :param list extents: new extents for cropping\n    :param list[float] new_res: new resolution for resampling\n    :param str output_file: output geotiff file path\n\n    :return: dst: resampled image\n    :rtype: ndarray\n    \"\"\"\n    dst, resampled_proj, src, _ = _crop_resample_setup(extents, input_tif,\n                                                       new_res, output_file)\n    # Do the work\n    gdal.ReprojectImage(src, dst, '', resampled_proj,\n                        gdalconst.GRA_NearestNeighbour)\n    return dst.ReadAsArray()\n\n\ndef _crop_resample_setup(extents, input_tif, new_res, output_file,\n                         dst_driver_type='GTiff', out_bands=2):\n    \"\"\"\n    Convenience function for crop/resample setup\n    \"\"\"\n    # Source\n    src_ds = gdal.Open(input_tif, gdalconst.GA_ReadOnly)\n    src_proj = src_ds.GetProjection()\n\n    # source metadata to be copied into the output\n    meta_data = src_ds.GetMetadata()\n\n    # get the image extents\n    min_x, min_y, max_x, max_y = extents\n    geo_transform = src_ds.GetGeoTransform()  # tuple of 6 numbers\n\n    # Create a new geotransform for the image\n    gt2 = list(geo_transform)\n    gt2[0] = min_x\n    gt2[3] = max_y\n    # We want a section of source that matches this:\n    resampled_proj = src_proj\n    if new_res[0]:  # if new_res is not None, it can't be zero either\n        resampled_geotrans = gt2[:1] + [new_res[0]] + gt2[2:-1] + [new_res[1]]\n    else:\n        resampled_geotrans = gt2\n\n    px_height, px_width = _gdalwarp_width_and_height(max_x, max_y, min_x,\n                                                     min_y, resampled_geotrans)\n\n    # Output / destination\n    dst = gdal.GetDriverByName(dst_driver_type).Create(\n        output_file, px_width, px_height, out_bands, gdalconst.GDT_Float32)\n    dst.SetGeoTransform(resampled_geotrans)\n    dst.SetProjection(resampled_proj)\n\n    for k, v in meta_data.items():\n        dst.SetMetadataItem(k, v)\n\n    return dst, resampled_proj, src_ds, src_proj\n\n\ndef _gdalwarp_width_and_height(max_x, max_y, min_x, min_y, geo_trans):\n    \"\"\"\n    Modify pixel height and width\n    \"\"\"\n    # modified image extents\n    ul_x, ul_y = world_to_pixel(geo_trans, min_x, max_y)\n    lr_x, lr_y = world_to_pixel(geo_trans, max_x, min_y)\n    # Calculate the pixel size of the new image\n    px_width = int(lr_x - ul_x)\n    px_height = int(lr_y - ul_y)\n    return px_height, px_width  # this is the same as `gdalwarp`\n\n\ndef crop_resample_average(\n        input_tif, extents: Union[List, Tuple], new_res, output_file, thresh, hdr,\n        out_driver_type='GTiff', match_pyrate=False, coherence_path=None,\n        coherence_thresh=None):\n    \"\"\"\n    Crop, resample, and average a geotiff image.\n\n    :param str input_tif: Path to input geotiff to resample/crop\n    :param tuple extents: Cropping extents (xfirst, yfirst, xlast, ylast)\n    :param list new_res: [xres, yres] resolution of output image\n    :param str output_file: Path to output resampled/cropped geotiff\n    :param float thresh: NaN fraction threshold\n    :param str out_driver_type: The output driver; `MEM` or `GTiff` (optional)\n    :param bool match_pyrate: Match Legacy output (optional)\n    :param dict hdr: dictionary of metadata\n\n    :return: resampled_average: output cropped and resampled image\n    :rtype: ndarray\n    :return: out_ds: destination gdal dataset object\n    :rtype: gdal.Dataset\n    \"\"\"\n    dst_ds, _, _, _ = _crop_resample_setup(extents, input_tif, new_res, output_file,\n                                           out_bands=2, dst_driver_type='MEM')\n\n    # make a temporary copy of the dst_ds for PyRate style prepifg\n    if (match_pyrate and new_res[0]):\n        tmp_ds = gdal.GetDriverByName('MEM').CreateCopy('', dst_ds)\n    else:\n        tmp_ds = None\n\n    src_ds, src_ds_mem = _setup_source(input_tif)\n\n    if coherence_path and coherence_thresh:\n        coherence_masking(src_ds_mem, coherence_path, coherence_thresh)\n\n    elif coherence_path and not coherence_thresh:\n        raise ValueError(\"Coherence file provided without a coherence \"\n                         \"threshold. Please ensure you provide 'cohthresh' \"\n                         \"in your config if coherence masking is enabled.\")\n\n    resampled_average, src_ds_mem = gdal_average(dst_ds, src_ds, src_ds_mem, thresh)\n    src_ds = None\n    src_dtype = src_ds_mem.GetRasterBand(1).DataType\n    src_gt = src_ds_mem.GetGeoTransform()\n\n    # required to match Legacy output\n    if tmp_ds:\n        _alignment(input_tif, new_res, resampled_average, src_ds_mem, src_gt, tmp_ds)\n\n    # grab metadata from existing geotiff\n    gt = dst_ds.GetGeoTransform()\n    wkt = dst_ds.GetProjection()\n\n    # insert metadata from the header\n    md = shared.collate_metadata(hdr)\n\n    # update metadata for output\n\n    # TODO: Metadata should be updated immediately as a prepifg/process step is applied\n    # move this into the respective steps\n    for k, v in md.items():\n        if k == ifc.DATA_TYPE:\n            # update data type metadata\n            if (v == ifc.ORIG) and (coherence_path is not None):\n                md.update({ifc.DATA_TYPE: ifc.MLOOKED_COH_MASKED_IFG})\n            elif (v == ifc.ORIG) and (coherence_path is None):\n                md.update({ifc.DATA_TYPE: ifc.MULTILOOKED})\n            elif v == ifc.COH:\n                md.update({ifc.DATA_TYPE: ifc.MULTILOOKED_COH})\n            elif v == ifc.DEM:\n                md.update({ifc.DATA_TYPE: ifc.MLOOKED_DEM})\n            else:\n                raise TypeError(f'Data Type metadata {v} not recognised')\n\n    _add_looks_and_crop_from_header(hdr, md, coherence_thresh)\n\n    # In-memory GDAL driver doesn't support compression so turn it off.\n    creation_opts = ['compress=packbits'] if out_driver_type != 'MEM' else []\n    out_ds = shared.gdal_dataset(output_file, dst_ds.RasterXSize, dst_ds.RasterYSize,\n                                 driver=out_driver_type, bands=1, dtype=src_dtype, metadata=md,\n                                 crs=wkt, geotransform=gt, creation_opts=creation_opts)\n\n    if out_driver_type != 'MEM':\n        log.info(f\"Writing geotiff: {output_file}\")\n        shared.write_geotiff(resampled_average, out_ds, np.nan)\n    else:\n        out_ds.GetRasterBand(1).WriteArray(resampled_average)\n    return resampled_average, out_ds\n\n\ndef _add_looks_and_crop_from_header(hdr, md, coh_thr):\n    \"\"\"\n    function to add prepfig options to geotiff metadata\n    \"\"\"\n    # insert prepifg mlook and crop params as metadata\n    if any(m in md.values() for m in all_mlooked_types):\n        if ifc.IFG_LKSX in hdr:\n            md[ifc.IFG_LKSX] = hdr[ifc.IFG_LKSX]\n        if ifc.IFG_LKSY in hdr:\n            md[ifc.IFG_LKSY] = hdr[ifc.IFG_LKSY]\n        if ifc.IFG_CROP in hdr:\n            md[ifc.IFG_CROP] = hdr[ifc.IFG_CROP]\n\n    # add coherence threshold to metadata, if used for masking ifgs\n    if md[ifc.DATA_TYPE] == ifc.MLOOKED_COH_MASKED_IFG:\n        md[ifc.COH_THRESH] = coh_thr\n\n\ndef _alignment(input_tif, new_res, resampled_average, src_ds_mem,\n                      src_gt, tmp_ds):\n    \"\"\"\n    Correction step to match python multi-look/crop output to match that of\n    Legacy data. Modifies the resampled_average array in place.\n    \"\"\"\n    src_ds = gdal.Open(input_tif)\n    data = src_ds.GetRasterBand(1).ReadAsArray()\n    xlooks = ylooks = int(new_res[0] / src_gt[1])\n    xres, yres = _get_resampled_data_size(xlooks, ylooks, data)\n    nrows, ncols = resampled_average.shape\n    # Legacy nearest neighbor resampling for the last\n    # [yres:nrows, xres:ncols] cells without nan_conversion\n    # turn off nan-conversion\n    src_ds_mem.GetRasterBand(1).SetNoDataValue(LOW_FLOAT32)\n    # nearest neighbor resapling\n    gdal.ReprojectImage(src_ds_mem, tmp_ds, '', '', gdal.GRA_NearestNeighbour)\n    # only take the [yres:nrows, xres:ncols] slice\n    if nrows > yres or ncols > xres:\n        resampled_nearest_neighbor = tmp_ds.GetRasterBand(1).ReadAsArray()\n        resampled_average[yres - nrows:, xres - ncols:] = \\\n            resampled_nearest_neighbor[yres - nrows:, xres - ncols:]\n\n\ndef gdal_average(dst_ds, src_ds, src_ds_mem, thresh):\n    \"\"\"\n    Perform subsampling of an image by averaging values\n\n    :param gdal.Dataset dst_ds: Destination gdal dataset object\n    :param str input_tif: Input geotif\n    :param float thresh: NaN fraction threshold\n\n    :return resampled_average: resampled image data\n    :rtype: ndarray\n    :return src_ds_mem: Modified in memory src_ds with nan_fraction in Band2. The nan_fraction\n        is computed efficiently here in gdal in the same step as the that of\n        the resampled average (band 1). This results is huge memory and\n        computational efficiency\n    :rtype: gdal.Dataset\n    \"\"\"\n    src_gt = src_ds.GetGeoTransform()\n    src_ds_mem.SetGeoTransform(src_gt)\n    data = src_ds_mem.GetRasterBand(1).ReadAsArray()\n    # update nan_matrix\n    # if data==nan, then 1, else 0\n    nan_matrix = np.isnan(data)  # all nans due to phase data + coh masking if used\n    src_ds_mem.GetRasterBand(2).WriteArray(nan_matrix)\n    gdal.ReprojectImage(src_ds_mem, dst_ds, '', '', gdal.GRA_Average)\n    # dst_ds band2 average is our nan_fraction matrix\n    nan_frac = dst_ds.GetRasterBand(2).ReadAsArray()\n    resampled_average = dst_ds.GetRasterBand(1).ReadAsArray()\n    resampled_average[nan_frac >= thresh] = np.nan\n    return resampled_average, src_ds_mem\n\n\ndef _setup_source(input_tif):\n    \"\"\"convenience setup function for gdal_average\"\"\"\n    src_ds = gdal.Open(input_tif)\n    data = src_ds.GetRasterBand(1).ReadAsArray()\n    src_dtype = src_ds.GetRasterBand(1).DataType\n    mem_driver = gdal.GetDriverByName('MEM')\n    src_ds_mem = mem_driver.Create('', src_ds.RasterXSize, src_ds.RasterYSize, 2, src_dtype)\n    if isinstance(shared.dem_or_ifg(data_path=input_tif), shared.Ifg):\n        data[np.isclose(data, 0, atol=1e-6)] = np.nan   # nan conversion of phase data\n    src_ds_mem.GetRasterBand(1).WriteArray(data)\n    src_ds_mem.GetRasterBand(1).SetNoDataValue(np.nan)\n    src_ds_mem.GetRasterBand(2).SetNoDataValue(np.nan)\n    src_ds_mem.SetGeoTransform(src_ds.GetGeoTransform())\n    return src_ds, src_ds_mem\n\n\ndef _get_resampled_data_size(xscale, yscale, data):\n    \"\"\"convenience function mimicking the Legacy output size\"\"\"\n    xscale = int(xscale)\n    yscale = int(yscale)\n    ysize, xsize = data.shape\n    xres, yres = int(xsize / xscale), int(ysize / yscale)\n    return xres, yres\n"
  },
  {
    "path": "pyrate/core/geometry.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements the calculation and output of the per-pixel vector of the radar viewing geometry\n(i.e. local angles, incidence angles and azimuth angles) as well as the calculation of per-pixel baseline values\nused for correcting interferograms for residual topographic effects (a.k.a. DEM errors).\n\"\"\"\n# pylint: disable=invalid-name, too-many-locals, too-many-arguments\nimport numpy as np\nfrom typing import Tuple, Union\n\nimport pyrate.constants as C\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.core.gamma import read_lookup_table\nfrom pyrate.core.shared import Ifg, IfgPart, MemGeometry\n\n\ndef get_lonlat_coords(ifg: Ifg) -> Tuple[MemGeometry, MemGeometry]:\n    \"\"\"\n    Function to get longitude and latitude coordinates for each pixel in the multi-looked.\n    interferogram dataset. Coordinates are identical for each interferogram in the stack.\n    :param ifg: pyrate.core.shared.Ifg Class object.\n    :return: lon: Longitude for each pixel (decimal degrees)\n    :return: lat: Latitude for each pixel (decimal degrees)\n    \"\"\"\n    # assume all interferograms have same projection and will share the same transform\n    transform = ifg.dataset.GetGeoTransform()\n    # number of rows and columns in dataset\n    nrows, ncols = ifg.shape\n    yOrigin = transform[3]\n    pixelHeight = -transform[5]\n    xOrigin = transform[0]\n    pixelWidth = transform[1]\n\n    lons = np.arange(0, ncols) * pixelWidth + xOrigin\n    lats = yOrigin - np.arange(0, nrows) * pixelHeight\n    lon, lat = np.meshgrid(lons, lats)\n\n    return MemGeometry(lon), MemGeometry(lat)\n\n\ndef calc_radar_coords(ifg: Ifg, params: dict, xmin: int, xmax: int,\n                      ymin: int, ymax: int) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Function to calculate radar coordinates for each pixel in the multi-looked\n    interferogram dataset. Radar coordinates are identical for each interferogram\n    in the stack. Uses the Gamma lookup table defined in the configuration file.\n    :param ifg: pyrate.core.shared.Ifg Class object.\n    :param params: Dictionary of PyRate configuration parameters.\n    :param xmin: Minimum longitude of cropped image (decimal degrees).\n    :param xmax: Maximum longitude of cropped image (decimal degrees)\n    :param ymin: Minimum latitude of cropped image (decimal degrees).\n    :param ymax: Maximum latitude of cropped image (decimal degrees)\n    :return: lt_az: Radar geometry azimuth coordinate for each pixel.\n    :return: lt_rg: Radar geometry range coordinate for each pixel.\n    \"\"\"\n    # lookup table file:\n    lookup_table = params[C.LT_FILE]\n\n    if lookup_table is None:\n        msg = f\"No lookup table file supplied: Geometry cannot be computed\"\n        raise FileNotFoundError(msg)\n\n    # PyRate IFG multi-looking factors\n    ifglksx = params[C.IFG_LKSX]\n    ifglksy = params[C.IFG_LKSY]\n    # transform float lookup table file to np array, min/max pixel coordinates are required for cropping\n    lt_az, lt_rg = read_lookup_table(ifg, lookup_table, ifglksx, ifglksy, xmin, xmax, ymin, ymax)\n    # replace 0.0 with NaN\n    lt_az[lt_az == 0.0] = np.nan\n    lt_rg[lt_rg == 0.0] = np.nan\n\n    return lt_az, lt_rg\n\n\ndef get_sat_positions(lat: np.ndarray, lon: np.ndarray, look_angle: np.ndarray, inc_angle: np.ndarray,\n                      heading: np.float64, look_dir: np.float64) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Function to calculate the lon/lat position of the satellite for each pixel.\n    :param lat: Ground latitude for each pixel (decimal degrees).\n    :param lon: Ground point longitude for each pixel (decimal degrees).\n    :param look_angle: Look angle (between nadir and look vector) for each pixel (radians).\n    :param inc_angle: Local incidence angle (between vertical and look vector) for each pixel (radians).\n    :param heading: Satellite flight heading (radians).\n    :param look_dir: Look direction w.r.t. satellite heading; +ve = right looking (radians).\n    :return: sat_lat: Satellite position latitude for each pixel (decimal degrees).\n    :return: sat_lon: Satellite position longitude for each pixel (decimal degrees).\n    \"\"\"\n    # note that the accuracy of satellite lat/lon positions code could be improved by calculating satellite positions\n    # for each azimuth row from orbital state vectors given in .par file, using the following workflow:\n    # 1. read orbital state vectors and start/stop times from mli.par\n    # 2. for each pixel get the corresponding radar row (from matrix az)\n    # 3. get the corresponding radar time for that row (using linear interpolation)\n    # 4. calculate the satellite XYZ position for that time by interpolating the time and velocity state vectors\n\n    # angle at the Earth's center between se and re\n    epsilon = np.pi - look_angle - (np.pi - inc_angle)\n    # azimuth of satellite look vector (satellite heading + look direction (+90 deg for right-looking SAR)\n    sat_azi = heading + look_dir\n    # the following equations are adapted from Section 4.4 (page 4-16) in EARTH-REFERENCED AIRCRAFT NAVIGATION AND\n    # SURVEILLANCE ANALYSIS (https://ntlrepository.blob.core.windows.net/lib/59000/59300/59358/DOT-VNTSC-FAA-16-12.pdf)\n    sat_lon = np.divide(np.arcsin(-(np.multiply(np.sin(epsilon), np.sin(sat_azi)))), np.cos(lat)) + lon  # Eq. 103\n    temp = np.multiply(np.divide(np.cos(0.5 * (sat_azi + sat_lon - lon)), np.cos(0.5 * (sat_azi - sat_lon + lon))), \\\n                       np.tan(0.5 * (np.pi / 2 + lat - epsilon)))  # Eq. 104\n    sat_lat = -np.pi / 2 + 2 * np.arctan(temp)\n\n    return np.real(sat_lat), np.real(sat_lon)\n\n\ndef calc_pixel_geometry(ifg: Union[Ifg, IfgPart], rg: np.ndarray, lon: np.ndarray, lat: np.ndarray,\n                        dem_height: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Function to calculate angular satellite to ground geometries and distance for each pixel.\n    :param ifg: pyrate.core.shared.Ifg Class object.\n    :param rg: Range image coordinate for each pixel.\n    :param lon: Longitude for each pixel (decimal degrees).\n    :param lat: Latitude for each pixel (decimal degrees).\n    :param dem_height: Height from DEM for each pixel (metres).\n    :return: look_angle: Look angle (between nadir and look vector) for each pixel (radians).\n    :return: incidence_angle: Local incidence angle (between vertical and look vector) for each pixel (radians).\n    :return: azimuth_angle: Geodetic azimuth for each pixel (radians).\n    :return: range_dist: Distance from satellite to ground for each pixel (metres).\n    \"\"\"\n    # read relevant metadata from first IFG\n    a = float(ifg.meta_data[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES])\n    b = float(ifg.meta_data[ifc.PYRATE_SEMI_MINOR_AXIS_METRES])\n    se = float(ifg.meta_data[ifc.PYRATE_SAR_EARTH_METRES])\n    # near range of primary image is stored in the interferogram metadata\n    near_range = float(ifg.meta_data[ifc.PYRATE_NEAR_RANGE_METRES])\n    rps = float(ifg.meta_data[ifc.PYRATE_RANGE_PIX_METRES])\n    heading = float(ifg.meta_data[ifc.PYRATE_HEADING_DEGREES])\n    # direction of look vector w.r.t. satellite heading. \n    # Gamma convention: +ve = right; -ve = left.\n    look_dir = float(ifg.meta_data[ifc.PYRATE_AZIMUTH_DEGREES])\n\n    # convert to radians\n    lon = np.radians(lon)\n    lat = np.radians(lat)\n    heading = np.radians(heading)\n    look_dir = np.radians(look_dir)\n\n    # Earth radius at given latitude\n    re = np.sqrt(np.divide(np.square(a ** 2 * np.cos(lat)) + np.square(b ** 2 * np.sin(lat)),\n                           np.square(a * np.cos(lat)) + np.square(b * np.sin(lat))))\n\n    # range measurement at pixel ij\n    range_dist = near_range + rps * rg\n\n    # look angle at pixel ij -> law of cosines in \"satellite - Earth centre - ground pixel\" triangle\n    # see e.g. Section 2 in https://www.cs.uaf.edu/~olawlor/ref/asf/sar_equations_2006_08_17.pdf\n    look_angle = np.arccos(np.divide(se ** 2 + np.square(range_dist) - np.square(re), 2 * se * range_dist))\n\n    # add per-pixel height to the earth radius to obtain a more accurate ground pixel position for\n    # incidence angle calculation\n    re = re + dem_height\n\n    # incidence angle at pixel ij -> law of cosines in \"satellite - Earth centre - ground pixel\" triangle\n    # see e.g. Section 2 in https://www.cs.uaf.edu/~olawlor/ref/asf/sar_equations_2006_08_17.pdf\n    incidence_angle = np.pi - np.arccos(np.divide(np.square(range_dist) + np.square(re) - se ** 2,\n                                                  2 * np.multiply(range_dist, re)))\n\n    # calculate satellite positions for each pixel\n    sat_lat, sat_lon = get_sat_positions(lat, lon, look_angle, incidence_angle, heading, look_dir)\n\n    # # calc azimuth angle using Vincenty's equations\n    azimuth_angle = vincinv(lat, lon, sat_lat, sat_lon, a, b)\n\n    return look_angle, incidence_angle, azimuth_angle, range_dist\n\n\ndef calc_local_baseline(ifg: Ifg, az: np.ndarray, look_angle: np.ndarray) -> np.ndarray:\n    \"\"\"\n    Function to calculate perpendicular baseline values for each pixel.\n    :param ifg: pyrate.core.shared.Ifg Class object\n    :param az: Azimuth image coordinate for each pixel\n    :param look_angle: Look angle for each pixel (radians)\n    :return: bperp: Perpendicular baseline for each pixel (metres)\n    \"\"\"\n    # open ifg object\n    if not ifg.is_open:\n        ifg.open()\n\n    # read relevant metadata from IFG\n    prf = float(ifg.meta_data[ifc.PYRATE_PRF_HERTZ])\n    az_looks = int(ifg.meta_data[ifc.PYRATE_AZIMUTH_LOOKS])\n    az_n = int(ifg.meta_data[ifc.PYRATE_AZIMUTH_N])\n    base_C = float(ifg.meta_data[ifc.PYRATE_BASELINE_C])\n    base_N = float(ifg.meta_data[ifc.PYRATE_BASELINE_N])\n    baserate_C = float(ifg.meta_data[ifc.PYRATE_BASELINE_RATE_C])\n    baserate_N = float(ifg.meta_data[ifc.PYRATE_BASELINE_RATE_N])\n\n    # calculate per pixel baseline vectors across track (C) and normal to the track (N)\n    mean_az = az_n / 2 - 0.5  # mean azimuth line\n    prf = prf / az_looks  # Pulse Repetition Frequency needs to be adjusted according to GAMMA azimuth looks\n    base_C_local = base_C + baserate_C * (az - mean_az) / prf\n    base_N_local = base_N + baserate_N * (az - mean_az) / prf\n\n    # calculate the per-pixel perpendicular baseline (see Eq. 3.5 in Baehr, 2012 available here:\n    # http://www.dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-719.pdf)\n    bperp = np.multiply(base_C_local, np.cos(look_angle)) - np.multiply(base_N_local, np.sin(look_angle))\n\n    return bperp\n\n\ndef vincinv(lat1: np.ndarray, lon1: np.ndarray, lat2: np.ndarray, lon2: np.ndarray,\n            semimaj: float, semimin: float) -> np.ndarray:\n    \"\"\"\n    Vincenty's Inverse Formula, adapted from GeodePy function vincinv\n    (see https://github.com/GeoscienceAustralia/GeodePy/blob/master/geodepy/geodesy.py)\n    - only relevant parts of the Geodepy to retrieve the azimuth angle have been used\n    - vectorised the function for use with numpy arrays\n    :param lat1: Latitude of Point 1 (radians)\n    :param lon1: Longitude of Point 1 (radians)\n    :param lat2: Latitude of Point 2 (radians)\n    :param lon2: Longitude of Point 2 (radians)\n    :param semimaj: semi-major axis of ellipsoid\n    :param semimin: semi-minor axis of ellipsoid\n    :return: azimuth1to2: Azimuth from Point 1 to 2 (Decimal Degrees)\n    \"\"\"\n    # Exit if any of the two sets of input points are the same\n    if np.any(lat1 == lat2) and np.any(lon1 == lon2):\n        return 0\n    # calculate flattening\n    f = (semimaj - semimin) / semimaj\n    # Equation numbering is from the GDA2020 Tech Manual v1.0\n    # Eq. 71\n    u1 = np.arctan((1 - f) * np.tan(lat1))\n    # Eq. 72\n    u2 = np.arctan((1 - f) * np.tan(lat2))\n    # Eq. 73; initial approximation\n    lon = lon2 - lon1\n    omega = lon\n    # Iterate until the change in lambda, lambda_sigma, is insignificant\n    # (< 1e-12) or after 1000 iterations have been completed\n    for i in range(1000):\n        # Eq. 74\n        sin_sigma = np.sqrt(\n            (np.cos(u2) * np.sin(lon)) ** 2 + (np.cos(u1) * np.sin(u2) - np.sin(u1) * np.cos(u2) * np.cos(lon)) ** 2)\n        # Eq. 75\n        cos_sigma = np.sin(u1) * np.sin(u2) + np.cos(u1) * np.cos(u2) * np.cos(lon)\n        # Eq. 76\n        sigma = np.arctan2(sin_sigma, cos_sigma)\n        # Eq. 77\n        alpha = np.arcsin((np.cos(u1) * np.cos(u2) * np.sin(lon)) / sin_sigma)\n        # Eq. 78\n        cos_two_sigma_m = np.cos(sigma) - (2 * np.sin(u1) * np.sin(u2) / np.cos(alpha) ** 2)\n        # Eq. 79\n        c = (f / 16) * np.cos(alpha) ** 2 * (4 + f * (4 - 3 * np.cos(alpha) ** 2))\n        # Eq. 80\n        new_lon = omega + (1 - c) * f * np.sin(alpha) * (\n                sigma + c * np.sin(sigma) * (cos_two_sigma_m + c * np.cos(sigma) * (-1 + 2 * (cos_two_sigma_m ** 2)))\n        )\n        delta_lon = new_lon - lon\n        lon = new_lon\n        if np.all(np.absolute(delta_lon) < 1e-12):\n            break\n    # Calculate the azimuth from point 1 to point 2\n    azimuth1to2 = np.arctan2((np.cos(u2) * np.sin(lon)),\n                             (np.cos(u1) * np.sin(u2) - np.sin(u1) * np.cos(u2) * np.cos(lon)))\n\n    # add 2 pi in case an angle is below zero\n    for azi in np.nditer(azimuth1to2, op_flags=['readwrite']):\n        if azi < 0:\n            azi[...] = azi + 2 * np.pi\n\n    return np.round(azimuth1to2, 9)\n"
  },
  {
    "path": "pyrate/core/ifgconstants.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains a collection of constants used in\nvarious components of the PyRate software\n\"\"\"\n\n# lookup keys for the metadata fields in PyRate GeoTIFF files\nPYRATE_NCOLS = 'NCOLS'\nPYRATE_NROWS = 'NROWS'\nPYRATE_X_STEP = 'X_STEP'\nPYRATE_Y_STEP = 'Y_STEP'\nPYRATE_LAT = 'LAT'\nPYRATE_LONG = 'LONG'\nFIRST_DATE = 'FIRST_DATE'\nFIRST_TIME = 'FIRST_TIME'\nSECOND_DATE = 'SECOND_DATE'\nSECOND_TIME = 'SECOND_TIME'\nEPOCH_DATE = 'EPOCH_DATE'\nPYRATE_DATUM = 'DATUM'\nPYRATE_TIME_SPAN = 'TIME_SPAN_YEAR'\nPYRATE_WAVELENGTH_METRES = 'WAVELENGTH_METRES'\nPYRATE_INCIDENCE_DEGREES = 'INCIDENCE_DEGREES'\nPYRATE_HEADING_DEGREES = 'HEADING_DEGREES'\nPYRATE_AZIMUTH_DEGREES = 'AZIMUTH_DEGREES'\nPYRATE_RANGE_PIX_METRES = 'RANGE_PIX_METRES'\nPYRATE_RANGE_N = 'RANGE_N'\nPYRATE_RANGE_LOOKS = 'RANGE_LOOKS'\nPYRATE_AZIMUTH_PIX_METRES = 'AZIMUTH_PIX_METRES'\nPYRATE_AZIMUTH_N = 'AZIMUTH_N'\nPYRATE_AZIMUTH_LOOKS = 'AZIMUTH_LOOKS'\nPYRATE_PRF_HERTZ = 'PRF_HERTZ'\nPYRATE_NEAR_RANGE_METRES = 'NEAR_RANGE_METRES'\nPYRATE_SAR_EARTH_METRES = 'SAR_EARTH_METRES'\nPYRATE_SEMI_MAJOR_AXIS_METRES = 'SEMI_MAJOR_AXIS_METRES'\nPYRATE_SEMI_MINOR_AXIS_METRES = 'SEMI_MINOR_AXIS_METRES'\nPYRATE_BASELINE_T = 'BASELINE_T'\nPYRATE_BASELINE_C = 'BASELINE_C'\nPYRATE_BASELINE_N = 'BASELINE_N'\nPYRATE_BASELINE_RATE_T = 'BASELINE_RATE_T'\nPYRATE_BASELINE_RATE_C = 'BASELINE_RATE_C'\nPYRATE_BASELINE_RATE_N = 'BASELINE_RATE_N'\nPYRATE_BPERP = 'BASELINE_PERP_METRES'\nPYRATE_INSAR_PROCESSOR = 'INSAR_PROCESSOR'\nPYRATE_WEATHER_ERROR = 'WEATHER_ERROR'\nPYRATE_APS_ERROR = 'APS_ERROR'\nPYRATE_DEM_ERROR = 'DEM_ERROR'\nPYRATE_MAXVAR = 'CVD_MAXVAR'\nPYRATE_ALPHA = 'CVD_ALPHA'\nIFG_LKSX = 'IFG_MULTILOOK_X'\nIFG_LKSY = 'IFG_MULTILOOK_Y'\nIFG_CROP = 'IFG_CROP_OPT'\nCOH_THRESH = 'COH_MASK_THRESHOLD'\nMLOOKED_COH_MASKED_IFG = 'COHERENCE_MASKED_MULTILOOKED_IFG'\nMULTILOOKED = 'MULTILOOKED_IFG'\nMULTILOOKED_COH = 'MULTILOOKED_COH'\nORIG = 'ORIGINAL_IFG'\nCOH = 'ORIGINAL_COH'\nDEM = 'ORIGINAL_DEM'\nMLOOKED_DEM = 'MULTILOOKED_DEM'\nRDC_AZIMUTH = 'RDC_AZIMUTH_MAP'\nRDC_RANGE = 'RDC_RANGE_MAP'\nLOOK = 'LOOK_ANGLE_MAP'\nINCIDENCE = 'INCIDENCE_ANGLE_MAP'\nAZIMUTH = 'AZIMUTH_ANGLE_MAP'\nRANGE_DIST = 'RANGE_DIST_MAP'\n\n# coherence stats\nCOH_MEDIAN = 'COH_MEDIAN'\nCOH_MEAN = 'COH_MEAN'\nCOH_STD = 'COH_STD'\n\nBPERP = 'PERPENDICULAR_BASELINE_MAP'\nDEM_ERROR = 'DEM_ERROR_MAP'\nINCR = 'INCREMENTAL_TIME_SLICE'\nCUML = 'CUMULATIVE_TIME_SLICE'\nSTACKRATE = 'STACKED_RATE_MAP'\nSTACKERROR = 'STACKED_RATE_ERROR'\nSTACKSAMP = 'STACKED_RATE_SAMPLES'\nLINRATE = 'LINEAR_RATE_MAP'\nLINERROR = 'LINEAR_RATE_ERROR'\nLINSAMP = 'LINEAR_RATE_SAMPLES'\nLINRSQ = 'LINEAR_RATE_RSQUARED'\nLINICPT = 'LINEAR_RATE_INTERCEPT'\nPYRATE_ORBITAL_ERROR = 'ORBITAL_ERROR'\nORB_REMOVED = 'REMOVED'\nAPS_REMOVED = 'REMOVED'\nDEM_ERROR_REMOVED = 'REMOVED'\nPYRATE_REF_PHASE = 'REFERENCE_PHASE'\nREF_PHASE_REMOVED = 'REMOVED'\nNAN_STATUS = 'NAN_STATUS'\nNAN_CONVERTED = 'CONVERTED'\nDATA_TYPE = 'DATA_TYPE'\nDATA_UNITS = 'DATA_UNITS'\nINPUT_TYPE = 'INPUT_TYPE'\n\nPYRATE_REFPIX_X = 'REF_PIX_X'\nPYRATE_REFPIX_Y = 'REF_PIX_Y'\nPYRATE_REFPIX_LAT = 'REF_PIX_LAT'\nPYRATE_REFPIX_LON = 'REF_PIX_LON'\nPYRATE_MEAN_REF_AREA = 'REF_AREA_MEAN'\nPYRATE_STDDEV_REF_AREA = 'REF_AREA_STDDEV'\nSEQUENCE_POSITION = 'SEQUENCE_POSITION'\n\nPYRATE_ORB_METHOD = 'ORB_METHOD'\nPYRATE_ORB_DEG = 'ORB_DEGREES'\nPYRATE_ORB_XLOOKS = 'ORB_MULTILOOK_X'\nPYRATE_ORB_YLOOKS = 'ORB_MULTILOOK_Y'\nPYRATE_ORB_PLANAR = 'PLANAR'\nPYRATE_ORB_QUADRATIC = 'QUADRATIC'\nPYRATE_ORB_PART_CUBIC = 'PART-CUBIC'\nPYRATE_ORB_INDEPENDENT = 'INDEPENDENT'\nPYRATE_ORB_NETWORK = 'NETWORK'\n\nDAYS_PER_YEAR = 365.25  # span of year, not a calendar year\nYEARS_PER_DAY = 1 / DAYS_PER_YEAR\nSPEED_OF_LIGHT_METRES_PER_SECOND = 3e8\nMM_PER_METRE = 1000\nMETRE_PER_KM = 1000\n\nLINE_OF_SIGHT = 0\nPSEUDO_VERTICAL = 1\nPSEUDO_HORIZONTAL = 2\n\nLOS_PROJECTION_OPTION = {\n    LINE_OF_SIGHT: 'LINE-OF-SIGHT',\n    PSEUDO_VERTICAL: 'PSEUDO-VERTICAL',\n    PSEUDO_HORIZONTAL: 'PSEUDO-HORIZONTAL'\n}\n"
  },
  {
    "path": "pyrate/core/logger.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains functions to control PyRate log outputs\n\"\"\"\nimport sys\nimport logging\nimport warnings\nimport traceback\nfrom datetime import datetime\nfrom pyrate.core.mpiops import size, rank, run_once\n\n\npyratelogger = logging.getLogger(__name__)\nformatter = logging.Formatter(\"%(asctime)s %(module)s:%(lineno)d %(process)d %(levelname)s \" + str(rank) + \"/\" + str(\n    size-1)+\" %(message)s\", \"%H:%M:%S\")\n\n\ndef configure_stage_log(verbosity, step_name, log_file_name='pyrate.log.'):\n\n    log_file_name = run_once(str.__add__, log_file_name, step_name + '.' + datetime.now().isoformat())\n\n    ch = MPIStreamHandler()\n    ch.setLevel(verbosity)\n    ch.setFormatter(formatter)\n\n    fh = logging.FileHandler(log_file_name)\n    fh.setLevel(verbosity)\n    fh.setFormatter(formatter)\n\n    pyratelogger.addHandler(ch)\n    pyratelogger.addHandler(fh)\n\n\ndef warn_with_traceback(message, category, filename, lineno, line=None):\n    \"\"\"\n    copied from:\n    http://stackoverflow.com/questions/22373927/get-traceback-of-warnings\n    \"\"\"\n    traceback.print_stack()\n    log = sys.stderr\n    log.write(warnings.formatwarning(message, category, filename, lineno, line))\n\n\nclass MPIStreamHandler(logging.StreamHandler):\n    \"\"\"\n    Only logs messages from Node 0\n    \"\"\"\n    def emit(self, record):\n        if rank == 0:\n            super().emit(record)\n"
  },
  {
    "path": "pyrate/core/mpiops.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains MPI convenience functions for PyRate\n\"\"\"\n# pylint: disable=no-member\n# pylint: disable=invalid-name\nimport logging\nimport pickle\nfrom typing import Callable, Any, Iterable\nimport numpy as np\n\n\n\"\"\"For MPI compatibility\"\"\"\ntry:\n    from mpi4py import MPI\n    MPI_INSTALLED = True\n    # # We're having trouble with the MPI pickling and 64bit integers\n    # MPI.pickle.__init__(pickle.dumps, pickle.loads)\n\n    # module-level MPI 'world' object representing all connected nodes\n    comm = MPI.COMM_WORLD\n\n    # int: the total number of nodes in the MPI world\n    size = comm.Get_size()\n\n    # int: the index (from zero) of this node in the MPI world. Also known as\n    # the rank of the node.\n    rank = comm.Get_rank()\nexcept ImportError:\n    MPI_INSTALLED = False\n    size = 1\n    rank = 0\n\n    class MPI:\n        SUM = np.sum\n\n    class comm:\n        \"\"\"\n        the mpi simulators that are used in a non-mpi environment\n        \"\"\"\n\n        @staticmethod\n        def barrier():\n            pass\n\n        @staticmethod\n        def reduce(arr, op, root=0):\n            return op(arr)\n\n        @staticmethod\n        def Get_size():\n            return 1\n\n        @staticmethod\n        def allgather(* args):\n            return args\n\n        @staticmethod\n        def gather(* args, **kwargs):\n            return args\n\n        @staticmethod\n        def allreduce(arr, op):\n            return op(arr)\n\n        @staticmethod\n        def Bcast(arr, root=0):\n            return\n\n        @staticmethod\n        def bcast(arr, root=0):\n            return arr\n\n\nclass MPIException(Exception):\n    pass\n\n\ndef validate_mpi():\n    if not MPI_INSTALLED:\n        raise MPIException(\"MPI needs to be installed in order to use this module\")\n\n\ndef run_once(f: Callable, *args, **kwargs) -> Any:\n    \"\"\"\n    Run a function on one node and then broadcast result to all.\n\n    :param Callable f: The function to be evaluated. Can take arbitrary arguments\n                and return anything or nothing\n    :param list args: Other positional arguments to pass on to f (optional)\n    :param dict kwargs: Other named arguments to pass on to f (optional)\n\n    :return: The value returned by f.\n    :rtype: unknown\n    \"\"\"\n    if MPI_INSTALLED:\n        if rank == 0:\n            # without the try except, any error in this step hangs the other mpi threads\n            # and these processes never exit, i.e., this MPI call hangs in case process 0 errors.\n            try:\n                f_result = f(*args, **kwargs)\n            except Exception as e:\n                f_result = e\n        else:\n            f_result = None\n        result = comm.bcast(f_result, root=0)\n        if isinstance(result, Exception):\n            raise result\n        else:\n            return result\n    else:\n        return f(*args, **kwargs)\n\n\ndef array_split(arr: Iterable, process: int = None) -> Iterable:\n    \"\"\"\n    Convenience function for splitting array elements across MPI processes\n\n    :param ndarray arr: Numpy array\n    :param int process: Process for which array members are required.\n                If None, MPI.comm.rank is used instead. (optional)\n\n    :return List corresponding to array members in a process.\n    :rtype: list\n    \"\"\"\n    if MPI_INSTALLED:\n        r = process if process else rank\n        return np.array_split(np.array(arr, dtype=object), size)[r]\n    else:\n        return np.array(arr)\n\n\ndef sum_vars(x, y, dtype):\n    s = np.sum([x, y], axis=0)\n    return s\n\n\ndef sum_axis_0(x, y, dtype):\n    s = np.sum(np.stack((x, y)), axis=0)\n    return s\n\n\nif MPI_INSTALLED:\n    sum_op = MPI.Op.Create(sum_vars, commute=True)\n    sum0_op = MPI.Op.Create(sum_axis_0, commute=True)\nelse:\n    sum_op = lambda arr: np.sum(np.expand_dims(arr, 0), axis=0)\n    sum0_op = lambda arr: np.sum(np.stack(np.expand_dims(arr, 0), axis=0), axis=0)\n"
  },
  {
    "path": "pyrate/core/mst.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module implements the minimum spanning tree\nfunctionality for selecting interferometric observations.\n\"\"\"\n# pylint: disable=invalid-name\nfrom pathlib import Path\nfrom itertools import product\nfrom numpy import array, nan, isnan, float32, empty, sum as nsum\nimport numpy as np\nfrom networkx.classes.reportviews import EdgeView\nimport networkx as nx\nfrom joblib import Parallel, delayed\n\nimport pyrate.constants as C\nfrom pyrate.core.algorithm import ifg_date_lookup\nfrom pyrate.core.algorithm import ifg_date_index_lookup\nfrom pyrate.core.shared import IfgPart, create_tiles, tiles_split\nfrom pyrate.core.shared import joblib_log_level, Tile\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration\n\nnp.seterr(invalid='ignore')  # stops RuntimeWarning in nan conversion\n\n\n# TODO: may need to implement memory saving row-by-row access\n# TODO: document weighting by either Nan fraction OR variance\n\n\ndef mst_from_ifgs(ifgs):\n    \"\"\"\n    Returns a Minimum Spanning Tree (MST) network from the given interferograms\n    using Kruskal's algorithm. The MST is calculated using a weighting based\n    on the number of NaN cells in the phase band.\n\n    :param list ifgs: List of interferogram objects (Ifg class)\n\n    :return: edges: The number of network connections\n    :rtype: int\n    :return: is_tree: A boolean that is True if network is a tree\n    :rtype: bool\n    :return: ntrees: The number of disconnected trees\n    :rtype: int\n    :return: mst_ifgs: Minimum Spanning Tree network of interferograms\n    :rtype: list\n    \"\"\"\n\n    edges_with_weights_for_networkx = [(i.first, i.second, i.nan_fraction) for i in ifgs]\n    g_nx = _build_graph_networkx(edges_with_weights_for_networkx)\n    mst = nx.minimum_spanning_tree(g_nx)\n    # mst_edges, is tree?, number of trees\n    edges = mst.edges()\n    ifg_sub = [ifg_date_index_lookup(ifgs, d) for d in edges]\n    mst_ifgs = [i for k, i in enumerate(ifgs) if k in ifg_sub]\n    return mst.edges(), nx.is_tree(mst), nx.number_connected_components(mst), mst_ifgs\n\n\ndef mst_parallel(ifgs, params):\n    \"\"\"\n    Wrapper function for calculating MST matrix in non-MPI runs.\n\n    :param list ifgs: List of interferogram objects (Ifg class)\n    :param dict params: Dictionary of parameters\n\n    :return: result: 3-dimensional Minimum Spanning Tree matrix\n    :rtype: ndarray\n    \"\"\"\n\n    log.info('Calculating MST in tiles')\n    ncpus = params[C.PROCESSES]\n    no_ifgs = len(ifgs)\n    no_y, no_x = ifgs[0].phase_data.shape\n    tiles = create_tiles(ifgs[0].shape)\n    no_tiles = len(tiles)\n    # need to break up the ifg class as multiprocessing does not allow pickling\n    # don't read in all the phase data at once\n    # use data paths and locally read in each core/process,\n    # gdal read is very fast\n    ifg_paths = [i.data_path for i in ifgs]\n    result = empty(shape=(no_ifgs, no_y, no_x), dtype=np.bool)\n\n    if params[C.PARALLEL]:\n        log.info('Calculating MST using {} tiles in parallel using {} ' \\\n                 'processes'.format(no_tiles, ncpus))\n        t_msts = Parallel(n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL))(\n            delayed(mst_multiprocessing)(t, ifg_paths, params=params) for t in tiles\n        )\n        for k, tile in enumerate(tiles):\n            result[:, tile.top_left_y:tile.bottom_right_y, tile.top_left_x: tile.bottom_right_x] = t_msts[k]\n    else:\n        log.info('Calculating MST using {} tiles in serial'.format(no_tiles))\n        for k, tile in enumerate(tiles):\n            result[:, tile.top_left_y:tile.bottom_right_y, tile.top_left_x: tile.bottom_right_x] = \\\n                mst_multiprocessing(tile, ifg_paths, params=params)\n\n    return result\n\n\ndef mst_multiprocessing(tile, ifgs_or_paths, preread_ifgs=None, params=None):\n    \"\"\"\n    Wrapper function for calculating MST matrix for a tile\n\n    :param ndarray tile: Tile class instance\n    :param list ifgs_or_paths: All interferograms paths of the problem.\n        List of strings\n    :param dict preread_ifgs: Dictionary of interferogram metadata\n\n    :return: mst_tile: MST matrix tile. An array of booleans representing\n        valid ifg connections\n    :rtype: ndarray\n    \"\"\"\n    # The memory requirement during MPI MST computation is determined by the\n    # number of interferograms times size of IfgPart. Note that we need all\n    # interferogram header information (like first/second image dates) for MST\n    # computation. To manage memory we need smaller tiles (IfgPart) as number\n    # of interferograms increases\n\n    ifg_parts = [IfgPart(p, tile, preread_ifgs, params) for p in ifgs_or_paths]\n    return mst_boolean_array(ifg_parts)\n\n\ndef _build_graph_networkx(edges_with_weights):\n    \"\"\"\n    Convenience graph builder function: returns a new graph object.\n    \"\"\"\n    g = nx.Graph()\n    g.add_weighted_edges_from(edges_with_weights)\n    return g\n\n\ndef mst_boolean_array(ifgs):\n    \"\"\"\n    Returns a 3D array of booleans constituting valid interferogram connections\n    in the Minimum Spanning Tree matrix.\n\n    :param list ifgs: Sequence of interferogram objects\n\n    :return: result: Array of booleans representing valid ifg connections\n    :rtype: ndarray\n    \"\"\"\n    # The MSTs are stripped of connecting edge info, leaving just the ifgs.\n    nifgs = len(ifgs)\n    ny, nx = ifgs[0].phase_data.shape\n    result = empty(shape=(nifgs, ny, nx), dtype=np.bool)\n\n    for y, x, mst in mst_matrix_networkx(ifgs):\n        # mst is a list of datetime.date tuples\n        if isinstance(mst, EdgeView):\n            ifg_sub = [ifg_date_index_lookup(ifgs, d) for d in mst]\n            ifg_sub_bool = [True if i in ifg_sub else False\n                            for i in range(nifgs)]  # boolean conversion\n            result[:, y, x] = np.array(ifg_sub_bool)\n        else:\n            result[:, y, x] = np.zeros(nifgs, dtype=bool)\n    return result\n\n\ndef _mst_matrix_ifgs_only(ifgs):\n    \"\"\"\n    Alternative method for producing 3D MST array\n    \"\"\"\n    # Currently not used\n    # The MSTs are stripped of connecting edge info, leaving just the ifgs.\n    result = empty(shape=ifgs[0].phase_data.shape, dtype=object)\n\n    for y, x, mst in mst_matrix_networkx(ifgs):\n        if isinstance(mst, EdgeView):\n            ifg_sub = [ifg_date_lookup(ifgs, d) for d in mst]\n            result[(y, x)] = tuple(ifg_sub)\n        else:\n            result[(y, x)] = mst  # usually NaN\n    return result\n\n\ndef _mst_matrix_as_array(ifgs):\n    \"\"\"\n    Alternative method for producing 3D MST array\n    \"\"\"\n    # Currently not used\n    # Each pixel contains an MST (with connecting edges etc).\n    mst_result = empty(shape=ifgs[0].phase_data.shape, dtype=object)\n\n    for y, x, mst in mst_matrix_networkx(ifgs):\n        mst_result[y, x] = mst\n    return mst_result\n\n\n# TODO: custom edge weighting could be included with an additional\n# 'weights' arg if some other weighting criterion is required later\ndef mst_matrix_networkx(ifgs):\n    \"\"\"\n    Generates MST network for a single pixel for the given ifgs using\n    NetworkX-package algorithms.\n\n    :param list ifgs: Sequence of interferogram objects\n\n    :return: y: pixel y coordinate\n    :rtype: int\n    :return: x: pixel x coordinate\n    :rtype: int\n    :return: mst: list of tuples for edges in the minimum spanning tree\n    :rtype: list\n    \"\"\"\n    # make default MST to optimise result when no Ifg cells in a stack are nans\n    edges_with_weights = [(i.first, i.second, i.nan_fraction) for i in ifgs]\n    edges, g_nx = _minimum_spanning_edges_from_mst(edges_with_weights)\n    # TODO: memory efficiencies can be achieved here with tiling\n\n    list_of_phase_data = [i.phase_data for i in ifgs]\n    data_stack = array(list_of_phase_data, dtype=float32)\n\n    # create MSTs for each pixel in the ifg data stack\n    nifgs = len(ifgs)\n\n    for y, x in product(range(ifgs[0].nrows), range(ifgs[0].ncols)):\n        values = data_stack[:, y, x]  # vertical stack of ifg values for a pixel\n        nan_count = nsum(isnan(values))\n\n        # optimisations: use pre-created results for all nans/no nans\n        if nan_count == 0:\n            yield y, x, edges\n            continue\n        elif nan_count == nifgs:\n            yield y, x, nan\n            continue\n\n        # dynamically modify graph to reuse a single graph: this should avoid\n        # repeatedly creating new graph objs & reduce RAM use\n        ebunch_add = []\n        ebunch_delete = []\n        for value, edge in zip(values, edges_with_weights):\n            if not isnan(value):\n                if not g_nx.has_edge(edge[0], edge[1]):\n                    ebunch_add.append(edge)\n            else:\n                if g_nx.has_edge(edge[0], edge[1]):\n                    ebunch_delete.append(edge)\n        if ebunch_add:\n            g_nx.add_weighted_edges_from(ebunch_add)\n        if ebunch_delete:\n            g_nx.remove_edges_from(ebunch_delete)\n        yield y, x, nx.minimum_spanning_tree(g_nx).edges()\n\n\ndef _minimum_spanning_edges_from_mst(edges):\n    \"\"\"\n    Convenience function to determine MST edges\n    \"\"\"\n    g_nx = _build_graph_networkx(edges)\n    T = nx.minimum_spanning_tree(g_nx)  # step ifglist_mst in make_mstmat.m\n    edges = T.edges()\n    return edges, g_nx\n\n\ndef mst_calc_wrapper(params):\n    \"\"\"\n    MPI wrapper function for MST calculation\n    \"\"\"\n\n    log.info('Calculating minimum spanning tree matrix')\n\n    def _save_mst_tile(tile: Tile, params: dict) -> None:\n        \"\"\"\n        Convenient inner loop for mst tile saving\n        \"\"\"\n        preread_ifgs = params[C.PREREAD_IFGS]\n        dest_tifs = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n        mst_file_process_n = Configuration.mst_path(params, index=tile.index)\n        if mst_file_process_n.exists():\n            return\n        mst_tile = mst_multiprocessing(tile, dest_tifs, preread_ifgs, params)\n        # locally save the mst_mat\n        np.save(file=mst_file_process_n, arr=mst_tile)\n\n    tiles_split(_save_mst_tile, params)\n\n    log.debug('Finished minimum spanning tree calculation')\n"
  },
  {
    "path": "pyrate/core/orbital.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements residual orbital corrections for interferograms.\n\"\"\"\n# pylint: disable=invalid-name\nimport tempfile\nfrom typing import Optional, List, Dict, Iterable\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom numpy import empty, isnan, reshape, float32, squeeze\nfrom numpy import dot, vstack, zeros, meshgrid\nimport numpy as np\nfrom numpy.linalg import pinv, cond\n\nimport pyrate.constants as C\nfrom pyrate.core.algorithm import first_second_ids, get_all_epochs\nfrom pyrate.core import shared, ifgconstants as ifc, prepifg_helper, mst, mpiops\nfrom pyrate.core.shared import nanmedian, Ifg, InputTypes, iterable_split\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.prepifg import find_header\nfrom pyrate.configuration import MultiplePaths\n# Orbital correction tasks\n#\n# TODO: options for multilooking\n# 1) do the 2nd stage mlook at prepifg.py/generate up front, then delete in\n#    workflow afterward\n# 2) refactor prep_ifgs() call to take input filenames and params & generate\n#    mlooked versions from that\n#    this needs to be more generic to call at any point in the runtime.\n\n\n# Design notes:\n# The orbital correction code includes several enhancements. PyRate creates\n# sparse arrays for the linear inversion, which contain many empty cells.\n# This is unnecessary for the independent method, and temporarily wastes\n# potentially a lot of memory.\n#\n# For the independent method, PyRate makes individual small design matrices and\n# corrects the Ifgs one by one. If required in the correction, the intercept\n# option adds an extra column of ones to include in the inversion.\n#\n# Network method design matrices are mostly empty, and intercept terms are handled\n# differently. Individual design matrices (== independent method DMs) are\n# placed in the sparse network design matrix. Intercept terms are not included in the\n# smaller DMs to prevent unwanted cols being inserted. This is why some funcs\n# appear to ignore the intercept parameter in the networked method. Network DM\n# intercept terms are cols of 1s in a diagonal line on the RHS of the sparse array.\n\nMAIN_PROCESS = 0\n\n# ORBITAL ERROR correction constants\nINDEPENDENT_METHOD = C.INDEPENDENT_METHOD\nNETWORK_METHOD = C.NETWORK_METHOD\n\nPLANAR = C.PLANAR\nQUADRATIC = C.QUADRATIC\nPART_CUBIC = C.PART_CUBIC\n\n\ndef remove_orbital_error(ifgs: List, params: dict) -> None:\n    \"\"\"\n    Wrapper function for PyRate orbital error removal functionality.\n\n    NB: the ifg data is modified in situ, rather than create intermediate\n    files. The network method assumes the given ifgs have already been reduced\n    to a minimum spanning tree network.\n    \"\"\"\n    ifg_paths = [i.data_path for i in ifgs] if isinstance(ifgs[0], Ifg) else ifgs\n    degree = params[C.ORBITAL_FIT_DEGREE]\n    method = params[C.ORBITAL_FIT_METHOD]\n    orbfitlksx = params[C.ORBITAL_FIT_LOOKS_X]\n    orbfitlksy = params[C.ORBITAL_FIT_LOOKS_Y]\n\n    # Sanity check of the orbital params\n    if type(orbfitlksx) != int or type(orbfitlksy) != int:\n        msg = f\"Multi-look factors for orbital correction should be of type: int\"\n        raise OrbitalError(msg)\n    if degree not in [PLANAR, QUADRATIC, PART_CUBIC]:\n        msg = \"Invalid degree of %s for orbital correction\" % C.ORB_DEGREE_NAMES.get(degree)\n        raise OrbitalError(msg)\n    if method not in [NETWORK_METHOD, INDEPENDENT_METHOD]:\n        msg = \"Invalid method of %s for orbital correction\" % C.ORB_METHOD_NAMES.get(method)\n        raise OrbitalError(msg)\n\n    # Give informative log messages based on selected options\n    log.info(f'Calculating {__degrees_as_string(degree)} orbital correction using '\n             f'{__methods_as_string(method)} method')\n    if orbfitlksx > 1 or orbfitlksy > 1:\n        log.info(f'Multi-looking interferograms for orbital correction with '\n                 f'factors of X = {orbfitlksx} and Y = {orbfitlksy}')\n\n    if method == INDEPENDENT_METHOD:\n        iterable_split(independent_orbital_correction, ifg_paths, params)\n\n    elif method == NETWORK_METHOD:\n        # Here we do all the multilooking in one process, but in memory\n        # could use multiple processes if we write data to disc during\n        # remove_orbital_error step\n        # TODO: performance comparison of saving multilooked files on\n        # disc vs in-memory single-process multilooking\n        #\n        # The gdal swig bindings prevent us from doing multi-looking in parallel\n        # when using multiprocessing because the multilooked ifgs are held in\n        # memory using in-memory tifs. Parallelism using MPI is possible.\n        # TODO: Use a flag to select mpi parallel vs multiprocessing in the\n        # iterable_split function, which will use mpi but can fall back on\n        # single process based on the flag for the multiprocessing side.\n        if mpiops.rank == MAIN_PROCESS:\n            mlooked = __create_multilooked_datasets(params)\n            _validate_mlooked(mlooked, ifg_paths)\n            network_orbital_correction(ifg_paths, params, mlooked)\n    else:\n        raise OrbitalError(\"Unrecognised orbital correction method\")\n\n\ndef __create_multilooked_datasets(params):\n    exts, ifg_paths, multi_paths = __extents_from_params(params)\n    mlooked_datasets = [_create_mlooked_dataset(m, i, exts, params) for m, i in zip(multi_paths, ifg_paths)]\n\n    mlooked = [Ifg(m) for m in mlooked_datasets]\n    for m in mlooked:\n        m.initialize()\n        shared.nan_and_mm_convert(m, params)\n    return mlooked\n\n\ndef __extents_from_params(params):\n    multi_paths = params[C.INTERFEROGRAM_FILES]\n    ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n    rasters = [shared.dem_or_ifg(r) for r in ifg_paths]\n    crop_opt = prepifg_helper.ALREADY_SAME_SIZE\n    xlooks = params[C.ORBITAL_FIT_LOOKS_X]\n    ylooks = params[C.ORBITAL_FIT_LOOKS_Y]\n    exts = prepifg_helper.get_analysis_extent(crop_opt, rasters, xlooks, ylooks, None)\n    return exts, ifg_paths, multi_paths\n\n\ndef _create_mlooked_dataset(multi_path, ifg_path, exts, params):\n    '''\n    Wrapper to generate a multi-looked dataset for a single ifg\n    '''\n    header = find_header(multi_path, params)\n    thresh = params[C.NO_DATA_AVERAGING_THRESHOLD]\n    crop_opt = prepifg_helper.ALREADY_SAME_SIZE\n    xlooks = params[C.ORBITAL_FIT_LOOKS_X]\n    ylooks = params[C.ORBITAL_FIT_LOOKS_Y]\n    out_path = tempfile.mktemp()\n    log.debug(f'Multi-looking {ifg_path} with factors X = {xlooks} and Y = {ylooks} for orbital correction')\n    resampled_data, out_ds = prepifg_helper.prepare_ifg(\n        ifg_path, xlooks, ylooks, exts, thresh, crop_opt, header, False, out_path\n    )\n    return out_ds\n\n\ndef _validate_mlooked(mlooked, ifgs):\n    '''\n    Basic sanity checking of the multilooked ifgs.\n    '''\n\n    if len(mlooked) != len(ifgs):\n        msg = \"Mismatching # ifgs and # multilooked ifgs\"\n        raise OrbitalError(msg)\n\n    if not all([hasattr(i, 'phase_data') for i in mlooked]):\n        msg = \"Mismatching types in multilooked ifgs arg:\\n%s\" % mlooked\n        raise OrbitalError(msg)\n\n\ndef _get_num_params(degree, intercept: Optional[bool] = False):\n    '''\n    Returns number of model parameters from string parameter\n    '''\n\n    if degree == PLANAR:\n        nparams = 2\n    elif degree == QUADRATIC:\n        nparams = 5\n    elif degree == PART_CUBIC:\n        nparams = 6\n    else:\n        msg = \"Invalid orbital model degree: %s\" \\\n              % C.ORB_DEGREE_NAMES.get(degree)\n        raise OrbitalError(msg)\n\n    # NB: independent method only, network method handles intercept terms differently\n    if intercept:\n        nparams += 1  # c in y = mx + c\n    return nparams\n\n\ndef independent_orbital_correction(ifg_path, params):\n    \"\"\"\n    Calculates and removes an orbital error surface from a single independent\n    interferogram.\n\n    Warning: This will write orbital error corrected phase_data to the ifg.\n\n    :param Ifg class instance ifg: the interferogram to be corrected\n    :param dict params: dictionary of configuration parameters\n\n    :return: None - interferogram phase data is updated and saved to disk\n    \"\"\"\n    log.debug(f\"Orbital correction of {ifg_path}\")\n    degree = params[C.ORBITAL_FIT_DEGREE]\n    offset = params[C.ORBFIT_OFFSET]\n    intercept = params[C.ORBFIT_INTERCEPT]\n    xlooks = params[C.ORBITAL_FIT_LOOKS_X]\n    ylooks = params[C.ORBITAL_FIT_LOOKS_Y]\n\n    ifg0 = shared.Ifg(ifg_path) if isinstance(ifg_path, str) else ifg_path\n\n    # get full-resolution design matrix\n    fullres_dm = get_design_matrix(ifg0, degree, intercept=intercept)\n\n    ifg = shared.dem_or_ifg(ifg_path) if isinstance(ifg_path, str) else ifg_path\n    multi_path = MultiplePaths(ifg.data_path, params)\n    orb_on_disc = MultiplePaths.orb_error_path(ifg.data_path, params)\n\n    if not ifg.is_open:\n        ifg.open()\n    shared.nan_and_mm_convert(ifg, params)\n    fullres_ifg = ifg  # keep a backup\n    fullres_phase = fullres_ifg.phase_data\n\n    if orb_on_disc.exists():\n        log.info(f'Reusing already computed orbital fit correction: {orb_on_disc}')\n        orbital_correction = np.load(file=orb_on_disc)\n    else:\n        # Multi-look the ifg data if either X or Y is greater than 1\n        if (xlooks > 1) or (ylooks > 1):\n            exts, _, _ = __extents_from_params(params)\n            mlooked = _create_mlooked_dataset(multi_path, ifg.data_path, exts, params)\n            ifg = Ifg(mlooked) # multi-looked Ifg object\n            ifg.initialize()\n            shared.nan_and_mm_convert(ifg, params)\n\n        # vectorise phase data, keeping NODATA\n        vphase = reshape(ifg.phase_data, ifg.num_cells)\n\n        # compute design matrix for multi-looked data\n        mlooked_dm = get_design_matrix(ifg, degree, intercept=intercept)\n\n        # invert to obtain the correction image (forward model) at full-res\n        orbital_correction = __orb_correction(fullres_dm, mlooked_dm, fullres_phase, vphase, offset=offset)\n\n        # save correction to disc\n        if not orb_on_disc.parent.exists():\n            shared.mkdir_p(orb_on_disc.parent)\n\n        np.save(file=orb_on_disc, arr=orbital_correction)\n\n    # subtract orbital correction from the full-res ifg\n    fullres_ifg.phase_data -= orbital_correction\n\n    # set orbfit meta tag and save phase to file\n    _save_orbital_error_corrected_phase(fullres_ifg, params)\n\n\ndef __orb_correction(fullres_dm, mlooked_dm, fullres_phase, mlooked_phase, offset=False):\n    \"\"\"\n    Function to perform the inversion to obtain orbital model parameters\n    and return the orbital correction as the full resolution forward model.\n    \"\"\"\n    # perform inversion using pseudoinverse of DM\n    orbparams = __orb_inversion(mlooked_dm, mlooked_phase)\n\n    # compute forward model at full resolution\n    fullorb = reshape(dot(fullres_dm, orbparams), fullres_phase.shape)\n\n    # Estimate the offset of the interferogram as the median of ifg minus model\n    # Only needed if reference phase correction has already been applied?\n    if offset:\n        offset_removal = nanmedian(np.ravel(fullres_phase - fullorb))\n    else:\n        offset_removal = 0\n    orbital_correction = fullorb - offset_removal\n    return orbital_correction\n\n\ndef __orb_inversion(design_matrix, data):\n    \"\"\"Inversion using pseudoinverse of design matrix\"\"\"\n    # remove NaN elements before inverting to get the model\n    B = design_matrix[~isnan(data)]\n    d = data[~isnan(data)]\n\n    return pinv(B) @ d\n\n\ndef network_orbital_correction(ifg_paths, params, m_ifgs: Optional[List] = None):\n    \"\"\"\n    This algorithm implements a network inversion to determine orbital\n    corrections for a set of interferograms forming a connected network.\n\n    Warning: This will write orbital error corrected phase_data to the ifgs.\n\n    :param list ifg_paths: List of Ifg class objects reduced to a minimum spanning tree network\n    :param dict params: dictionary of configuration parameters\n    :param list m_ifgs: list of multilooked Ifg class objects (sequence must be multilooked versions of 'ifgs' arg)\n\n    :return: None - interferogram phase data is updated and saved to disk\n    \"\"\"\n    # pylint: disable=too-many-locals, too-many-arguments\n    offset = params[C.ORBFIT_OFFSET]\n    degree = params[C.ORBITAL_FIT_DEGREE]\n    preread_ifgs = params[C.PREREAD_IFGS]\n    intercept = params[C.ORBFIT_INTERCEPT]\n    scale = params[C.ORBFIT_SCALE]\n\n    # all orbit corrections available?\n    if isinstance(ifg_paths[0], str):\n        if __check_and_apply_orberrors_found_on_disc(ifg_paths, params):\n            log.warning(\"Reusing orbfit errors from previous run!!!\")\n            return\n        # all corrections are available in numpy files already saved - return\n        ifgs = [shared.Ifg(i) for i in ifg_paths]\n    else:  # alternate test paths # TODO: improve\n        ifgs = ifg_paths\n    src_ifgs = ifgs if m_ifgs is None else m_ifgs\n    src_ifgs = mst.mst_from_ifgs(src_ifgs)[3]  # use networkx mst\n\n    if preread_ifgs:\n        temp_ifgs = OrderedDict(sorted(preread_ifgs.items())).values()\n        ids = first_second_ids(get_all_epochs(temp_ifgs))\n    else:\n        ids = first_second_ids(get_all_epochs(ifgs))\n\n    nepochs = len(set(ids))\n\n    # call the actual inversion routine\n    coefs = calc_network_orb_correction(src_ifgs, degree, scale, nepochs, intercept=intercept)\n\n    # create full res DM to expand determined coefficients into full res\n    # orbital correction (eg. expand coarser model to full size)\n    if preread_ifgs:\n        temp_ifg = Ifg(ifg_paths[0])  # ifgs here are paths\n        temp_ifg.open()\n        dm = get_design_matrix(temp_ifg, degree, intercept=intercept, scale=scale)\n        temp_ifg.close()\n    else:\n        ifg = ifgs[0]\n        dm = get_design_matrix(ifg, degree, intercept=intercept, scale=scale)\n\n    for i in ifg_paths:\n        # open if not Ifg instance\n        if isinstance(i, str):  # pragma: no cover\n            # are paths\n            i = Ifg(i)\n            i.open(readonly=False)\n            shared.nan_and_mm_convert(i, params)\n        _remove_network_orb_error(coefs, dm, i, ids, offset, params)\n\ndef calc_network_orb_correction(src_ifgs, degree, scale, nepochs, intercept=False):\n    \"\"\"\n    Calculate and return coefficients for the network orbital correction model\n    given a set of ifgs:\n    :param list src_ifgs: iterable of Ifg objects\n    :param str degree: the degree of the orbital fit (planar, quadratic or part-cubic)\n    :param int scale: Scale factor for design matrix to improve inversion robustness\n    :param int nepochs: The number of epochs in the network\n    :param intercept: whether to include a constant offset to fit to each ifg. This\n    intercept is discarded and not returned.\n\n    :return coefs: a list of coefficient lists, indexed by epoch. The coefficient lists are in the following order:\n\n    PLANAR - x, y\n    QUADRATIC - x^2, y^2, x*y, x, y\n    PART_CUBIC - x*y^2, x^2, y^2, x*y, x, y\n    \"\"\"\n    vphase = vstack([i.phase_data.reshape((i.num_cells, 1)) for i in src_ifgs])\n    vphase = squeeze(vphase)\n    B = get_network_design_matrix(src_ifgs, degree, scale, intercept=intercept)\n    orbparams = __orb_inversion(B, vphase)\n    ncoef = _get_num_params(degree) + intercept\n    # extract all params except intercept terms\n    coefs = [orbparams[i:i+ncoef] for i in range(0, nepochs * ncoef, ncoef)]\n    return coefs\n\ndef __check_and_apply_orberrors_found_on_disc(ifg_paths, params):\n    saved_orb_err_paths = [MultiplePaths.orb_error_path(ifg_path, params) for ifg_path in ifg_paths]\n    for p, i in zip(saved_orb_err_paths, ifg_paths):\n        if p.exists():\n            orb = np.load(p)\n            if isinstance(i, str):\n                # are paths\n                ifg = Ifg(i)\n                ifg.open(readonly=False)\n                shared.nan_and_mm_convert(ifg, params)\n            else:\n                ifg = i\n            ifg.phase_data -= orb\n            # set orbfit meta tag and save phase to file\n            _save_orbital_error_corrected_phase(ifg, params)\n    return all(p.exists() for p in saved_orb_err_paths)\n\n\ndef _remove_network_orb_error(coefs, dm, ifg, ids, offset, params):\n    \"\"\"\n    remove network orbital error from input interferograms\n    \"\"\"\n    saved_orb_err_path = MultiplePaths.orb_error_path(ifg.data_path, params)\n    orb = dm.dot(coefs[ids[ifg.second]] - coefs[ids[ifg.first]])\n    orb = orb.reshape(ifg.shape)\n    # Estimate the offset of the interferogram as the median of ifg minus model\n    # Only needed if reference phase correction has already been applied?\n    if offset:\n        # brings all ifgs to same reference level\n        orb -= nanmedian(np.ravel(ifg.phase_data - orb))\n    # subtract orbital error from the ifg\n    ifg.phase_data -= orb\n\n    # save orb error on disc\n    np.save(file=saved_orb_err_path, arr=orb)\n    # set orbfit meta tag and save phase to file\n    _save_orbital_error_corrected_phase(ifg, params)\n\n\ndef _save_orbital_error_corrected_phase(ifg, params):\n    \"\"\"\n    Convenience function to update metadata and save latest phase after\n    orbital fit correction\n    \"\"\"\n    # set orbfit tags after orbital error correction\n    ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_METHOD, __methods_as_string(params[C.ORBITAL_FIT_METHOD]))\n    ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_DEG, __degrees_as_string(params[C.ORBITAL_FIT_DEGREE]))\n    ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_XLOOKS, str(params[C.ORBITAL_FIT_LOOKS_X]))\n    ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_YLOOKS, str(params[C.ORBITAL_FIT_LOOKS_Y]))\n    ifg.dataset.SetMetadataItem(ifc.PYRATE_ORBITAL_ERROR, ifc.ORB_REMOVED)\n    ifg.write_modified_phase()\n    ifg.close()\n\n\ndef __methods_as_string(method):\n    \"\"\"Look up table to get orbital method string names\"\"\"\n    meth = {1:ifc.PYRATE_ORB_INDEPENDENT, 2:ifc.PYRATE_ORB_NETWORK}\n    return str(meth[method])\n\n\ndef __degrees_as_string(degree):\n    \"\"\"Look up table to get orbital degree string names\"\"\"\n    deg = {1: ifc.PYRATE_ORB_PLANAR, 2: ifc.PYRATE_ORB_QUADRATIC, 3: ifc.PYRATE_ORB_PART_CUBIC}\n    return str(deg[degree])\n\n\n# TODO: subtract reference pixel coordinate from x and y\ndef get_design_matrix(ifg, degree, intercept: Optional[bool] = True, scale: Optional[int] = 1):\n    \"\"\"\n    Returns orbital error design matrix with columns for model parameters.\n\n    :param Ifg class instance ifg: interferogram to get design matrix for\n    :param str degree: model to fit (PLANAR / QUADRATIC / PART_CUBIC)\n    :param bool intercept: whether to include column for the intercept term.\n    :param int scale: Scale factor for design matrix to improve inversion robustness\n\n    :return: dm: design matrix\n    :rtype: ndarray\n    \"\"\"\n    if not ifg.is_open:\n        ifg.open()\n\n    if degree not in [PLANAR, QUADRATIC, PART_CUBIC]:\n        raise OrbitalError(\"Invalid degree argument\")\n\n    if scale < 1:\n        raise OrbitalError(\"Scale argument must be greater or equal to 1\")\n\n    # scaling required with higher degree models to help with param estimation\n    xsize = ifg.x_size / scale if scale else ifg.x_size\n    ysize = ifg.y_size / scale if scale else ifg.y_size\n\n    # mesh needs to start at 1, otherwise first cell resolves to 0 and ignored\n    xg, yg = [g+1 for g in meshgrid(range(ifg.ncols), range(ifg.nrows))]\n    x = xg.reshape(ifg.num_cells) * xsize\n    y = yg.reshape(ifg.num_cells) * ysize\n\n    # TODO: performance test this vs np.concatenate (n by 1 cols)??\n    dm = empty((ifg.num_cells, _get_num_params(degree, intercept)), dtype=float32)\n\n    # apply positional parameter values, multiply pixel coordinate by cell size\n    # to get distance (a coord by itself doesn't tell us distance from origin)\n    if degree == PLANAR:\n        dm[:, 0] = x\n        dm[:, 1] = y\n    elif degree == QUADRATIC:\n        dm[:, 0] = x**2\n        dm[:, 1] = y**2\n        dm[:, 2] = x * y\n        dm[:, 3] = x\n        dm[:, 4] = y\n    elif degree == PART_CUBIC:\n        dm[:, 0] = x * (y**2)\n        dm[:, 1] = x**2\n        dm[:, 2] = y**2\n        dm[:, 3] = x * y\n        dm[:, 4] = x\n        dm[:, 5] = y\n    if intercept:\n        dm[:, -1] = np.ones(ifg.num_cells) # estimate the intercept term\n\n    # report condition number of the design matrix - L2-norm computed using SVD\n    log.debug(f'The condition number of the design matrix is {cond(dm)}')\n\n    return dm\n\n\ndef get_network_design_matrix(ifgs, degree, scale, intercept=True):\n    # pylint: disable=too-many-locals\n    \"\"\"\n    Returns larger-format design matrix for network error correction. The\n    network design matrix includes rows which relate to those of NaN cells.\n\n    :param list ifgs: List of Ifg class objects\n    :param str degree: model to fit (PLANAR / QUADRATIC / PART_CUBIC)\n    :param int scale: Scale factor for design matrix to improve inversion robustness\n    :param bool intercept: whether to include columns for intercept estimation.\n\n    :return: netdm: network design matrix\n    :rtype: ndarray\n    \"\"\"\n\n    if degree not in [PLANAR, QUADRATIC, PART_CUBIC]:\n        raise OrbitalError(\"Invalid degree argument\")\n\n    if scale < 1:\n        raise OrbitalError(\"Scale argument must be greater or equal to 1\")\n\n    nifgs = len(ifgs)\n    if nifgs < 1:\n        # can feasibly do correction on a single Ifg/2 epochs\n        raise OrbitalError(\"Invalid number of Ifgs: %s\" % nifgs)\n\n    # init sparse network design matrix\n    nepochs = len(set(get_all_epochs(ifgs)))\n\n    # no intercepts here; they are included separately below\n    ncoef = _get_num_params(degree)\n    shape = [ifgs[0].num_cells * nifgs, ncoef * nepochs]\n\n    if intercept:\n        shape[1] += nepochs  # add extra space for intercepts\n\n    netdm = zeros(shape, dtype=float32)\n\n    # calc location for individual design matrices\n    dates = [ifg.first for ifg in ifgs] + [ifg.second for ifg in ifgs]\n    ids = first_second_ids(dates)\n    tmpdm = get_design_matrix(ifgs[0], degree, intercept=intercept, scale=scale)\n\n    # iteratively build up sparse matrix\n    for i, ifg in enumerate(ifgs):\n        rs = i * ifg.num_cells  # starting row\n        m = ids[ifg.first] * (ncoef + intercept)  # start col for first\n        s = ids[ifg.second] * (ncoef + intercept)  # start col for second\n        netdm[rs:rs + ifg.num_cells, m:m + ncoef + intercept] = -tmpdm\n        netdm[rs:rs + ifg.num_cells, s:s + ncoef + intercept] = tmpdm\n\n    return netdm\n\n\nclass OrbitalError(Exception):\n    \"\"\"\n    Generic class for errors in orbital correction.\n    \"\"\"\n\n\ndef orb_fit_calc_wrapper(params: dict) -> None:\n    \"\"\"\n    MPI wrapper for orbital fit correction\n    \"\"\"\n    multi_paths = params[C.INTERFEROGRAM_FILES]\n    if not params[C.ORBITAL_FIT]:\n        log.info('Orbital correction not required!')\n        return\n    ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n    remove_orbital_error(ifg_paths, params)\n    mpiops.comm.barrier()\n    # update phase_data saved in tiled numpy files\n    shared.save_numpy_phase(ifg_paths, params)\n    log.debug('Finished Orbital error correction')\n"
  },
  {
    "path": "pyrate/core/phase_closure/__init__.py",
    "content": ""
  },
  {
    "path": "pyrate/core/phase_closure/closure_check.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\nfrom collections import defaultdict\nfrom typing import List, Dict, Tuple, Any\nfrom nptyping import NDArray, UInt16, Float32\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core import mpiops\nfrom pyrate.core.phase_closure.mst_closure import sort_loops_based_on_weights_and_date, WeightedLoop, Edge\nfrom pyrate.configuration import Configuration, MultiplePaths\nfrom pyrate.core.phase_closure.sum_closure import sum_phase_closures\nfrom pyrate.core.phase_closure.plot_closure import plot_closure\nfrom pyrate.core.shared import Ifg, nan_and_mm_convert\nfrom pyrate.core.logger import pyratelogger as log\n\n\ndef mask_pixels_with_unwrapping_errors(ifgs_breach_count: NDArray[(Any, Any, Any), UInt16],\n                                       num_occurrences_each_ifg: NDArray[(Any,), UInt16],\n                                       params: dict) -> None:\n    \"\"\"\n    Find pixels in the phase data that breach closure_thr, and mask\n    (assign NaNs) to those pixels in those ifgs.\n    :param ifgs_breach_count: unwrapping issues at pixels in all loops\n    :param num_occurrences_each_ifg:  frequency of ifgs appearing in all loops\n    :param params: params dict\n    \"\"\"\n    log.debug(\"Masking phase data of retained ifgs\")\n\n    for i, m_p in enumerate(params[C.INTERFEROGRAM_FILES]):\n        pix_index = ifgs_breach_count[:, :, i] == num_occurrences_each_ifg[i]\n        ifg = Ifg(m_p.tmp_sampled_path)\n        ifg.open()\n        nan_and_mm_convert(ifg, params)\n        ifg.phase_data[pix_index] = np.nan\n        ifg.write_modified_phase()\n\n    log.info(f\"Masked phase data of {i + 1} retained ifgs after phase closure\")\n    return None\n\n\ndef __drop_ifgs_if_not_part_of_any_loop(ifg_files: List[str], loops: List[WeightedLoop], params: dict) -> List[str]:\n    \"\"\"\n    Check if an ifg is part of any of the loops, otherwise drop it from the list of interferograms for further PyRate\n    processing.\n    \"\"\"\n    loop_ifgs = set()\n    for weighted_loop in loops:\n        for edge in weighted_loop.loop:\n            loop_ifgs.add(Edge(edge.first, edge.second))\n\n    ifgs = [Ifg(i) for i in ifg_files]\n    for i in ifgs:\n        i.open()\n        i.nodata_value = params[C.NO_DATA_VALUE]\n    selected_ifg_files = []\n    for i, f in zip(ifgs, ifg_files):\n        if Edge(i.first, i.second) in loop_ifgs:\n            selected_ifg_files.append(f)\n    if len(ifg_files) != len(selected_ifg_files):\n        log.info(f'Only {len(selected_ifg_files)} (out of {len(ifg_files)}) ifgs participate in '\n                 f'one or more closure loops, and are selected for further PyRate analysis')\n    return selected_ifg_files\n\n\ndef __drop_ifgs_exceeding_threshold(orig_ifg_files: List[str], ifgs_breach_count, num_occurences_each_ifg, params):\n    \"\"\"\n    Function to identify and drop ifgs, based on two thresholds.\n    We demand two thresholds to be breached before an ifg is dropped:\n    1. min_loops_per_ifg: the basic ifg loop participation count check: does the ifg participate in\n                          enough loops to accurately check for unwrapping errors?\n    2. The second threshold is an average check of pixels breached taking all loops into account.\n       It is evaluated as follows:\n        (i) ifgs_breach_count contains the number of loops where this pixel in this ifg had a closure exceeding closure_thr.\n        (b) sum(ifgs_breach_count[:, :, i]) is the number of pixels in ifg exceeding closure_thr over all loops\n        (c) divide by loop_count_of_this_ifg and num of cells (nrows x ncols) for a normalised  measure of threshold.\n    \"\"\"\n    orig_ifg_files.sort()\n    nrows, ncols, n_ifgs = ifgs_breach_count.shape\n    selected_ifg_files = []\n    for i, ifg_file in enumerate(orig_ifg_files):\n        loop_count_of_this_ifg = num_occurences_each_ifg[i]\n        if loop_count_of_this_ifg:  # if the ifg participated in at least one loop\n            ifg_remove_threshold_breached = \\\n                np.sum(ifgs_breach_count[:, :, i] == loop_count_of_this_ifg) / (nrows * ncols) > params[\n                    C.IFG_DROP_THR]\n\n            if not (\n                    # min loops count # check 1\n                    (num_occurences_each_ifg[i] > params[C.MIN_LOOPS_PER_IFG])\n                    and\n                    ifg_remove_threshold_breached  # and breached threshold\n            ):\n                selected_ifg_files.append(ifg_file)\n\n    return selected_ifg_files\n\n\ndef iterative_closure_check(config, interactive_plot=True) -> \\\n        Tuple[List[str], NDArray[(Any, Any, Any), UInt16], NDArray[(Any,), UInt16]]:\n    \"\"\"\n    This function iterates the closure check until a stable list of interferogram files is returned.\n    :param config: Configuration class instance\n    :param interactive_plot: bool, whether to plot sum closures of loops\n    :return: stable list of ifg files, their ifgs_breach_count, and number of occurrences of ifgs in loops\n    \"\"\"\n    params = config.__dict__\n    ifg_files = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    i = 1  # iteration counter\n\n    while True:  # iterate till ifgs/loops are stable\n        log.info(f\"Closure check iteration #{i}: working on {len(ifg_files)} ifgs\")\n        rets = __wrap_closure_check(config)\n        if rets is None:\n            return\n        new_ifg_files, closure, ifgs_breach_count, num_occurences_each_ifg, loops = rets\n        if interactive_plot:\n            if mpiops.rank == 0:\n                plot_closure(closure=closure, loops=loops, config=config,\n                             thr=params[C.CLOSURE_THR], iteration=i)\n        if len(ifg_files) == len(new_ifg_files):\n            break\n        else:\n            i += 1\n            ifg_files = new_ifg_files  # exit condition could be some other check like number_of_loops\n\n    mpiops.comm.barrier()\n\n    log.info(f\"Stable list of ifgs achieved after iteration #{i}. {len(ifg_files)} ifgs are retained\")\n    return ifg_files, ifgs_breach_count, num_occurences_each_ifg\n\n\ndef discard_loops_containing_max_ifg_count(loops: List[WeightedLoop], params) -> List[WeightedLoop]:\n    \"\"\"\n    This function will discard loops when each ifg participating in a loop has met the max loop count criteria.\n\n    :param loops: list of loops\n    :param params: params dict\n    :return: selected loops satisfying MAX_LOOP_REDUNDANCY criteria\n    \"\"\"\n    selected_loops = []\n    ifg_counter = defaultdict(int)\n    for loop in loops:\n        edge_appearances = np.array([ifg_counter[e] for e in loop.edges])\n        if not np.all(edge_appearances > params[C.MAX_LOOP_REDUNDANCY]):\n            selected_loops.append(loop)\n            for e in loop.edges:\n                ifg_counter[e] += 1\n        else:\n            log.debug(f\"Loop {loop.loop} ignored: all constituent ifgs have been in a loop \"\n                      f\"{params[C.MAX_LOOP_REDUNDANCY]} times or more\")\n    return selected_loops\n\n\ndef __wrap_closure_check(config: Configuration) -> \\\n        Tuple[\n            List[str],\n            NDArray[(Any, Any), Float32],\n            NDArray[(Any, Any, Any), UInt16],\n            NDArray[(Any,), UInt16],\n            List[WeightedLoop]]:\n    \"\"\"\n    This wrapper function returns the closure check outputs for a single iteration of closure check.\n\n    :param config: Configuration class instance\n    For return variables see docstring in `sum_phase_closures`.\n    \"\"\"\n    params = config.__dict__\n    ifg_files = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    ifg_files.sort()\n    log.debug(f\"The number of ifgs in the list is {len(ifg_files)}\")\n    sorted_signed_loops = mpiops.run_once(sort_loops_based_on_weights_and_date, params)\n    log.info(f\"Total number of selected closed loops with up to MAX_LOOP_LENGTH = \"\n             f\"{params[C.MAX_LOOP_LENGTH]} edges is {len(sorted_signed_loops)}\")\n\n    if len(sorted_signed_loops) < 1:\n        return None\n\n    retained_loops = mpiops.run_once(discard_loops_containing_max_ifg_count,\n                                     sorted_signed_loops, params)\n    ifgs_with_loops = mpiops.run_once(__drop_ifgs_if_not_part_of_any_loop, ifg_files, retained_loops, params)\n\n    msg = f\"After applying MAX_LOOP_REDUNDANCY = {params[C.MAX_LOOP_REDUNDANCY]} criteria, \" \\\n          f\"{len(retained_loops)} loops are retained\"\n    if len(retained_loops) < 1:\n        return None\n    else:\n        log.info(msg)\n\n    closure, ifgs_breach_count, num_occurences_each_ifg = sum_phase_closures(ifgs_with_loops, retained_loops, params)\n\n    if mpiops.rank == 0:\n        closure_ins = config.closure()\n        np.save(closure_ins.closure, closure)\n        np.save(closure_ins.ifgs_breach_count, ifgs_breach_count)\n        np.save(closure_ins.num_occurences_each_ifg, num_occurences_each_ifg)\n        np.save(closure_ins.loops, retained_loops, allow_pickle=True)\n\n    selected_ifg_files = mpiops.run_once(__drop_ifgs_exceeding_threshold,\n                                         ifgs_with_loops, ifgs_breach_count, num_occurences_each_ifg, params)\n\n    # update the ifg list in the parameters dictionary\n    params[C.INTERFEROGRAM_FILES] = \\\n        mpiops.run_once(update_ifg_list, selected_ifg_files, params[C.INTERFEROGRAM_FILES])\n    return selected_ifg_files, closure, ifgs_breach_count, num_occurences_each_ifg, retained_loops\n\n\n# TODO: Consider whether this helper function is better homed in a generic module and used more widely\ndef update_ifg_list(ifg_files: List[str], multi_paths: List[MultiplePaths]) -> List[MultiplePaths]:\n    \"\"\"\n    Function to extract full paths for a subsetted list of interferograms\n    :param ifg_files: list of interferograms to subset\n    :param multi_paths: list of full paths for list of original interferograms\n    :return: filtered_multi_paths: list of full paths for the subset\n    \"\"\"\n    filtered_multi_paths = []\n    for m_p in multi_paths:\n        if m_p.tmp_sampled_path in ifg_files:\n            filtered_multi_paths.append(m_p)\n    return filtered_multi_paths\n"
  },
  {
    "path": "pyrate/core/phase_closure/collect_loops.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\nfrom typing import List\nfrom pyrate.core.logger import pyratelogger as log\n\n\ndef dfs(graph, marked, n, vert, start, count, loop, all_loops):\n    \"\"\"\n    Depth First Search algorithm to count loops of length n in a given graph.\n    Adapted from https://www.geeksforgeeks.org/print-all-the-cycles-in-an-undirected-graph/\n    \"\"\"\n    # Number of vertices\n    V = graph.shape[0] \n\n    # mark the vertex vert as visited\n    marked[vert] = True\n\n    # if the path of length (n-1) is found\n    if n == 0:\n\n        # mark vert as un-visited to make it usable again.\n        marked[vert] = False\n\n        # Check if vertex vert can end with vertex start\n        if graph[vert][start] == 1:\n            count += 1\n            all_loops.append(loop)\n            return count\n        else:\n            return count\n\n    # For searching every possible path of length (n-1)\n    for i in range(V):\n        if (not marked[i]) and (graph[vert][i] == 1):\n            next_path = loop[:]\n            next_path.append(i)\n            # DFS for searching path by decreasing length by 1\n            count = dfs(graph, marked, n - 1, i, start, count, next_path, all_loops)\n\n    # marking vert as unvisited to make it usable again.\n    marked[vert] = False\n    return count\n\n\ndef find_loops(graph, loop_length):\n    \"\"\"Counts and collects loops of a defined length in an undirected and connected graph\"\"\"\n    V = graph.shape[0]\n    all_loops = []\n    # all vertex are marked un-visited initially.\n    marked = [False] * V\n    # Searching for loop by using v-n+1 vertices\n    count = 0\n    for i in range(V - (loop_length - 1)):\n        count = dfs(graph, marked, loop_length - 1, i, i, count, [i], all_loops)\n\n        # ith vertex is marked as visited and will not be visited again.\n        marked[i] = True\n    log.debug(f\"Total possible number of loops of length {loop_length} is {count}\")\n    return count, all_loops\n\n\ndef dedupe_loops(loops: List[List]) -> List:\n    \"\"\"\n    Deduplication of loops with same members.\n\n    For example: for nodes 1, 2, 3 in the graph below\n\n        [\n            [0, 1, 1],\n            [1, 0, 1],\n            [1, 1, 0],\n        ]\n\n    Loops of length 3 are 0->1->2 and 0->2->1. We only retain 1 of these loops after dedupe.\n\n    Example 2:\n    For the following graph:\n            [\n                [0, 1, 1, 1],\n                [1, 0, 1, 1],\n                [1, 1, 0, 1],\n                [1, 1, 1, 0],\n            ]\n\n    Pictorially this is the network\n    1 ---------2\n    | *      * |\n    |    *     |\n    |  *    *  |\n    4----------3\n\n    Loops of length 4 are:\n        [[0, 1, 2, 3], [0, 1, 3, 2], [0, 2, 1, 3], [0, 2, 3, 1], [0, 3, 1, 2], [0, 3, 2, 1]]\n\n    After deduplication we will only retain [0, 1, 2, 3]\n\n    \"\"\"\n    seen_sets = set()\n    filtered = []\n    for l in loops:\n        loop = l[:]\n        l.sort()\n        l = tuple(l)\n        if l not in seen_sets:\n            seen_sets.add(l)\n            filtered.append(loop)\n    return filtered\n"
  },
  {
    "path": "pyrate/core/phase_closure/correct_phase.py",
    "content": ""
  },
  {
    "path": "pyrate/core/phase_closure/mst_closure.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\nfrom collections import namedtuple\nfrom typing import List, Union\nfrom datetime import date\nimport numpy as np\nimport networkx as nx\nfrom pyrate.core.shared import dem_or_ifg\nimport pyrate.constants as C\nfrom pyrate.core.phase_closure.collect_loops import find_loops, dedupe_loops\nfrom pyrate.core.logger import pyratelogger as log\n\nEdge = namedtuple('Edge', ['first', 'second'])\n\n\nclass SignedEdge:\n\n    def __init__(self, edge: Edge, sign: int):\n        self.edge = edge\n        self.sign = sign\n        self.first = self.edge.first\n        self.second = self.edge.second\n\n    def __repr__(self):\n        return f'({self.first}, {self.second})'\n\n\nclass SignedWeightedEdge(SignedEdge):\n\n    def __init__(self, signed_edge: SignedEdge, weight: int):\n        super().__init__(signed_edge.edge, sign=signed_edge.sign)\n        self.signed_edge = signed_edge\n        self.weight = weight\n\n\nclass WeightedLoop:\n    \"\"\"\n    Loop with weight equal to the sum of the edge weights, where edge weights are the ifg duration in days\n    \"\"\"\n\n    def __init__(self, loop: List[SignedWeightedEdge]):\n        self.loop = loop\n\n    @property\n    def weight(self):\n        return sum([swe.weight for swe in self.loop])\n\n    @property\n    def earliest_date(self):\n        return min({swe.first for swe in self.loop})\n\n    @property\n    def primary_dates(self):\n        first_dates = [swe.first for swe in self.loop]\n        first_dates.sort()\n        st = ''.join([str(d) for d in first_dates])\n        return st\n\n    @property\n    def secondary_dates(self):\n        first_dates = [swe.second for swe in self.loop]\n        first_dates.sort()\n        st = ''.join([str(d) for d in first_dates])\n        return st\n\n    def __len__(self):\n        return len(self.loop)\n\n    @property\n    def edges(self):\n        return [Edge(swe.first, swe.edge.second) for swe in self.loop]\n\n\ndef __find_closed_loops(edges: List[Edge], max_loop_length: int) -> List[List[date]]:\n    g = nx.Graph()\n    edges = [(we.first, we.second) for we in edges]\n    g.add_edges_from(edges)\n    A = nx.adjacency_matrix(g)\n    graph = np.asarray(A.todense())\n\n    loops = []\n\n    for n in range(3, max_loop_length + 1):\n        log.debug(f\"Searching for loops of length {n} using Depth First Search\")\n        _, all_loops = find_loops(graph=graph, loop_length=n)\n        loops_ = dedupe_loops(all_loops)\n        log.debug(f\"Selected number of loops of length {n} after deduplication is {len(loops_)}\")\n        loops.extend(loops_)\n\n    node_list = g.nodes()\n    node_list_dict = {i: n for i, n in enumerate(node_list)}\n    loop_subset = []\n    for l in loops:\n        loop = []\n        for ll in l:\n            loop.append(node_list_dict[ll])\n        loop_subset.append(loop)\n\n    log.debug(f\"Total number of loops is {len(loop_subset)}\")\n\n    return loop_subset\n\n\ndef __add_signs_and_weights_to_loops(loops: List[List[date]], available_edges: List[Edge]) -> List[WeightedLoop]:\n    \"\"\"\n    add signs and weights to loops.\n    Additionally, sort the loops (change order of ifgs appearing in loop) by weight and date\n    \"\"\"\n    weighted_signed_loops = []\n    available_edges = set(available_edges)  # hash it once for O(1) lookup\n    for i, l in enumerate(loops):\n        weighted_signed_loop = []\n        l.append(l[0])  # add the closure loop\n        for ii, ll in enumerate(l[:-1]):\n            if l[ii+1] > ll:\n                edge = Edge(ll, l[ii+1])\n                assert edge in available_edges\n                signed_edge = SignedEdge(edge, 1)  # opposite direction of ifg\n            else:\n                edge = Edge(l[ii+1], ll)\n                assert edge in available_edges\n                signed_edge = SignedEdge(edge, -1)  # in direction of ifg\n            weighted_signed_edge = SignedWeightedEdge(\n                signed_edge,\n                (signed_edge.second - signed_edge.first).days # weight in days\n            )\n            weighted_signed_loop.append(weighted_signed_edge)\n\n        # sort the loops by minimum first date, and then second date if there are ties\n        weighted_signed_loop.sort(key=lambda x: (x.first, x.second, x.sign))\n        weighted_loop = WeightedLoop(weighted_signed_loop)\n        weighted_signed_loops.append(weighted_loop)\n\n    return weighted_signed_loops\n\n\ndef __setup_edges(ifg_files: List[str]) -> List[Edge]:\n    ifg_files.sort()\n    ifgs = [dem_or_ifg(i) for i in ifg_files]\n    for i in ifgs:\n        i.open()\n        i.nodata_value = 0\n    return [Edge(i.first, i.second) for i in ifgs]\n\n\ndef __find_signed_closed_loops(params: dict) -> List[WeightedLoop]:\n    ifg_files = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    ifg_files.sort()\n    log.debug(f\"The number of ifgs in the list is {len(ifg_files)}\")\n    available_edges = __setup_edges(ifg_files)\n    all_loops = __find_closed_loops(available_edges, max_loop_length=params[C.MAX_LOOP_LENGTH])  # find loops with weights\n    signed_weighted_loops = __add_signs_and_weights_to_loops(all_loops, available_edges)\n    return signed_weighted_loops\n\n\ndef sort_loops_based_on_weights_and_date(params: dict) -> List[WeightedLoop]:\n    \"\"\"\n    :param params: dict of params\n    :return: list of sorted, signed, and weighted loops\n    \"\"\"\n    signed_weighted_loops = __find_signed_closed_loops(params)\n    # sort based on weights and dates\n    signed_weighted_loops.sort(key=lambda x: [x.weight, x.primary_dates, x.secondary_dates])\n    return signed_weighted_loops\n"
  },
  {
    "path": "pyrate/core/phase_closure/plot_closure.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\nfrom typing import List\nimport numpy as np\nfrom pathlib import Path\n\nfrom pyrate.core.phase_closure.mst_closure import WeightedLoop\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration\n\n\ndef plot_closure(closure: np.ndarray, loops: List[WeightedLoop],\n                    config: Configuration, thr: float, iteration: int):\n    thr = thr * np.pi\n    try:\n        import matplotlib.pyplot as plt\n        import matplotlib as mpl\n        from mpl_toolkits.axes_grid1 import make_axes_locatable\n        cmap = mpl.cm.Spectral\n    except ImportError as e:\n        log.warn(ImportError(e))\n        log.warn(\"Required plotting packages are not found in environment. \"\n                 \"Closure loop plot will not be generated!!!\")\n        return\n\n    nrows, ncols, n_loops = closure.shape\n\n    # 49 ifgs per fig\n    plt_rows = 7 \n    plt_cols = 7\n    plots_per_fig = plt_rows * plt_cols\n    n_figs = n_loops // plots_per_fig + (n_loops % plots_per_fig > 0)\n\n    all_fig_plots = 1\n    for fig_i in range(n_figs):\n        fig = plt.figure(figsize=(9*plt_rows, 6*plt_cols))\n        this_fig_plots = 0\n        for p_r in range(plt_rows):\n            for p_c in range(plt_cols):\n                if all_fig_plots == n_loops + 1:   \n                    break\n                    \n                ax = fig.add_subplot(plt_rows, plt_cols, plt_cols * p_r + p_c + 1)\n                data = closure[:, :, plt_cols * p_r + p_c + fig_i * plots_per_fig]\n                loop = loops[plt_cols * p_r + p_c + fig_i * plots_per_fig]\n                title = ',\\n'.join([repr(l) for l in loop.loop])\n                im = ax.imshow(data, vmin=-thr, vmax=thr, cmap=cmap)\n                plt.tick_params(axis='both', which='major', labelsize=12)\n                text = ax.set_title(title)\n                text.set_fontsize(16)\n                #text.set_fontsize(min(20, int(n_loops/3)))\n\n                divider = make_axes_locatable(ax)\n                cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n                plt.colorbar(im, cax=cax)\n                this_fig_plots += 1\n                all_fig_plots += 1\n\n        fig.tight_layout()\n\n        closure_plot_file = Path(config.phase_closure_dir).joinpath(f'closure_loops_iteration_{iteration}_fig_{fig_i}.png')\n        plt.savefig(closure_plot_file, dpi=100)\n        plt.close(fig)\n        log.info(f'{this_fig_plots} closure loops plotted in {closure_plot_file}')\n"
  },
  {
    "path": "pyrate/core/phase_closure/sum_closure.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\nimport resource\nfrom collections import namedtuple\nfrom typing import List, Dict, Tuple, Any\nfrom nptyping import NDArray, Float32, UInt16\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core import mpiops\nfrom pyrate.core.shared import Ifg, join_dicts\nfrom pyrate.core.phase_closure.mst_closure import Edge, WeightedLoop\nfrom pyrate.core.logger import pyratelogger as log\n\nIndexedIfg = namedtuple('IndexedIfg', ['index', 'IfgPhase'])\n\n\nclass IfgPhase:\n    \"\"\"\n    workaround class to only hold phase data for mpi SwigPyObject pickle error\n    \"\"\"\n\n    def __init__(self, phase_data):\n        self.phase_data = phase_data\n\n\ndef __create_ifg_edge_dict(ifg_files: List[str], params: dict) -> Dict[Edge, IndexedIfg]:\n    \"\"\"Returns a dictionary of indexed ifg 'edges'\"\"\"\n    ifg_files.sort()\n    ifgs = [Ifg(i) for i in ifg_files]\n\n    def _func(ifg, index):\n        ifg.open()\n        ifg.nodata_value = params[C.NO_DATA_VALUE]\n        ifg.convert_to_nans()\n        ifg.convert_to_radians()\n        idx_ifg = IndexedIfg(index, IfgPhase(ifg.phase_data))\n        return idx_ifg\n\n    process_ifgs = mpiops.array_split(list(enumerate(ifgs)))\n    ret_combined = {}\n    for idx, _ifg in process_ifgs:\n        ret_combined[Edge(_ifg.first, _ifg.second)] = _func(_ifg, idx)\n        _ifg.close()\n\n    ret_combined = join_dicts(mpiops.comm.allgather(ret_combined))\n    return ret_combined\n\n\ndef sum_phase_closures(ifg_files: List[str], loops: List[WeightedLoop], params: dict) -> \\\n        Tuple[NDArray[(Any, Any, Any), Float32], NDArray[(Any, Any, Any), UInt16], NDArray[(Any,), UInt16]]:\n    \"\"\"\n    Compute the closure sum for each pixel in each loop, and count the number of times a pixel\n    contributes to a failed closure loop (where the summed closure is above/below the \n    CLOSURE_THR threshold).\n    :param ifg_files: list of ifg files\n    :param loops: list of loops\n    :param params: params dict\n    :return: Tuple of closure, ifgs_breach_count, num_occurrences_each_ifg\n    closure: summed closure for each loop.\n    ifgs_breach_count: shape=(ifg.shape, n_ifgs) number of times a pixel in an ifg fails the closure\n    check (i.e., has unwrapping error) in all loops under investigation.\n    num_occurrences_each_ifg: frequency of ifg appearance in all loops.\n    \"\"\"\n    edge_to_indexed_ifgs = __create_ifg_edge_dict(ifg_files, params)\n    ifgs = [v.IfgPhase for v in edge_to_indexed_ifgs.values()]\n    n_ifgs = len(ifgs)\n\n    if params[C.PARALLEL]:\n        # rets = Parallel(n_jobs=params[cf.PROCESSES], verbose=joblib_log_level(cf.LOG_LEVEL))(\n        #     delayed(__compute_ifgs_breach_count)(ifg0, n_ifgs, weighted_loop, edge_to_indexed_ifgs, params)\n        #     for weighted_loop in loops\n        # )\n        # for k, r in enumerate(rets):\n        #     closure_dict[k], ifgs_breach_count_dict[k] = r\n        # TODO: enable multiprocessing - needs pickle error workaround\n        closure = np.zeros(shape=(* ifgs[0].phase_data.shape, len(loops)), dtype=np.float32)\n        ifgs_breach_count = np.zeros(shape=(ifgs[0].phase_data.shape + (n_ifgs,)), dtype=np.uint16)\n        for k, weighted_loop in enumerate(loops):\n            closure[:, :, k], ifgs_breach_count_l = __compute_ifgs_breach_count(weighted_loop, edge_to_indexed_ifgs,\n                                                                                params)\n            ifgs_breach_count += ifgs_breach_count_l\n    else:\n        process_loops = mpiops.array_split(loops)\n        closure_process = np.zeros(shape=(* ifgs[0].phase_data.shape, len(process_loops)), dtype=np.float32)\n        ifgs_breach_count_process = np.zeros(shape=(ifgs[0].phase_data.shape + (n_ifgs,)), dtype=np.uint16)\n        for k, weighted_loop in enumerate(process_loops):\n            closure_process[:, :, k], ifgs_breach_count_l = \\\n                __compute_ifgs_breach_count(weighted_loop, edge_to_indexed_ifgs, params)\n            ifgs_breach_count_process += ifgs_breach_count_l  # process\n\n        total_gb = mpiops.comm.allreduce(ifgs_breach_count_process.nbytes / 1e9, op=mpiops.MPI.SUM)\n        log.debug(f\"Memory usage to compute ifgs_breach_count_process was {total_gb} GB\")\n        log.debug(f\"shape of ifgs_breach_count_process is {ifgs_breach_count_process.shape}\")\n        log.debug(f\"dtype of ifgs_breach_count_process is {ifgs_breach_count_process.dtype}\")\n\n        total_gb = mpiops.comm.allreduce(closure_process.nbytes / 1e9, op=mpiops.MPI.SUM)\n        log.debug(f\"Memory usage to compute closure_process was {total_gb} GB\")\n        if mpiops.rank == 0:\n            ifgs_breach_count = np.zeros(shape=(ifgs[0].phase_data.shape + (n_ifgs,)), dtype=np.uint16)\n\n            # closure\n            closure = np.zeros(shape=(* ifgs[0].phase_data.shape, len(loops)), dtype=np.float32)\n            main_process_indices = mpiops.array_split(range(len(loops))).astype(np.uint16)\n            closure[:, :, main_process_indices] = closure_process\n            for rank in range(1, mpiops.size):\n                rank_indices = mpiops.array_split(range(len(loops)), rank).astype(np.uint16)\n                this_rank_closure = np.zeros(shape=(* ifgs[0].phase_data.shape, len(rank_indices)), dtype=np.float32)\n                mpiops.comm.Recv(this_rank_closure, source=rank, tag=rank)\n                closure[:, :, rank_indices] = this_rank_closure\n        else:\n            closure = None\n            ifgs_breach_count = None\n            mpiops.comm.Send(closure_process, dest=0, tag=mpiops.rank)\n\n        if mpiops.MPI_INSTALLED:\n            mpiops.comm.Reduce([ifgs_breach_count_process, mpiops.MPI.UINT16_T],\n                               [ifgs_breach_count, mpiops.MPI.UINT16_T], op=mpiops.MPI.SUM, root=0)  # global\n        else:\n            ifgs_breach_count = mpiops.comm.reduce(ifgs_breach_count_process, op=mpiops.sum0_op, root=0)\n\n        log.debug(f\"successfully summed phase closure breach array\")\n\n    num_occurrences_each_ifg = None\n    if mpiops.rank == 0:\n        num_occurrences_each_ifg = _find_num_occurrences_each_ifg(loops, edge_to_indexed_ifgs, n_ifgs)\n\n    return closure, ifgs_breach_count, num_occurrences_each_ifg\n\n\ndef _find_num_occurrences_each_ifg(loops: List[WeightedLoop],\n                                   edge_to_indexed_ifgs: Dict[Edge, IndexedIfg],\n                                   n_ifgs: int) -> NDArray[(Any,), UInt16]:\n    \"\"\"find how many times each ifg appears in total in all loops\"\"\"\n    num_occurrences_each_ifg = np.zeros(shape=n_ifgs, dtype=np.uint16)\n    for weighted_loop in loops:\n        for signed_edge in weighted_loop.loop:\n            indexed_ifg = edge_to_indexed_ifgs[signed_edge.edge]\n            ifg_index = indexed_ifg.index\n            num_occurrences_each_ifg[ifg_index] += 1\n    return num_occurrences_each_ifg\n\n\ndef __compute_ifgs_breach_count(weighted_loop: WeightedLoop,\n                                edge_to_indexed_ifgs: Dict[Edge, IndexedIfg], params: dict) \\\n        -> Tuple[NDArray[(Any, Any), Float32], NDArray[(Any, Any, Any), UInt16]]:\n    \"\"\"Compute summed `closure` of each loop, and compute `ifgs_breach_count` for each pixel.\"\"\"\n    n_ifgs = len(edge_to_indexed_ifgs)\n    indexed_ifg = list(edge_to_indexed_ifgs.values())[0]\n    ifg = indexed_ifg.IfgPhase\n    closure_thr = params[C.CLOSURE_THR] * np.pi\n    use_median = params[C.SUBTRACT_MEDIAN]\n\n    closure = np.zeros(shape=ifg.phase_data.shape, dtype=np.float32)\n    # initiate variable for check of unwrapping issues at the same pixels in all loops\n    ifgs_breach_count = np.zeros(shape=(ifg.phase_data.shape + (n_ifgs,)), dtype=np.uint16)\n\n    for signed_edge in weighted_loop.loop:\n        indexed_ifg = edge_to_indexed_ifgs[signed_edge.edge]\n        ifg = indexed_ifg.IfgPhase\n        closure += signed_edge.sign * ifg.phase_data\n    if use_median:\n        closure -= np.nanmedian(closure)  # optionally subtract the median closure phase\n\n    # this will deal with nans in `closure`, i.e., nans are not selected in indices_breaching_threshold\n    indices_breaching_threshold = np.absolute(closure) > closure_thr\n\n    for signed_edge in weighted_loop.loop:\n        ifg_index = edge_to_indexed_ifgs[signed_edge.edge].index\n        # the variable 'ifgs_breach_count' is increased by 1 for that pixel\n        # make sure we are not incrementing the nan positions in the closure\n        # as we don't know the phase of these pixels and also they were converted to zero before closure check\n        # Therefore, we leave them out of ifgs_breach_count, i.e., we don't increment their ifgs_breach_count values\n        ifgs_breach_count[indices_breaching_threshold, ifg_index] += 1\n    return closure, ifgs_breach_count\n"
  },
  {
    "path": "pyrate/core/prepifg_helper.py",
    "content": "#   This Python module is part of the PyRate software package\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module converts interferogram input files to a common geotiff\nformat with PyRate specific metadata headers. The module also implements\nmultilooking/downsampling and cropping operations to reduce the size of\nthe computational problem.\n\"\"\"\n# pylint: disable=too-many-arguments,invalid-name\nimport re\nfrom collections import namedtuple\nfrom os.path import split\n\nfrom math import modf\nfrom numbers import Number\nfrom decimal import Decimal\nfrom typing import List, Tuple, Union, Callable\nfrom numpy import array, nan, isnan, nanmean, float32, zeros, sum as nsum\n\nfrom pyrate.constants import sixteen_digits_pattern, COHERENCE_FILE_PATHS, IFG_CROP_OPT, IFG_LKSX, IFG_LKSY\nfrom pyrate.configuration import ConfigException\nfrom pyrate.core.gdal_python import crop_resample_average\nfrom pyrate.core.shared import dem_or_ifg, Ifg, DEM\nfrom pyrate.core.logger import pyratelogger as log\n\nCustomExts = namedtuple('CustExtents', ['xfirst', 'yfirst', 'xlast', 'ylast'])\n\n# Constants\nMINIMUM_CROP = 1\nMAXIMUM_CROP = 2\nCUSTOM_CROP = 3\nALREADY_SAME_SIZE = 4\nCROP_OPTIONS = [MINIMUM_CROP, MAXIMUM_CROP, CUSTOM_CROP, ALREADY_SAME_SIZE]\n\nGRID_TOL = 1e-6\n\n\ndef get_analysis_extent(crop_opt: int, rasters: List[Union[Ifg, DEM]], xlooks: int, ylooks: int,\n                        user_exts: Tuple[float, float, float, float]) -> Tuple[float, float, float, float]:\n    \"\"\"\n    Function checks prepifg parameters and returns extents/bounding box.\n\n    :param int crop_opt: Cropping option\n    :param list rasters: List of either Ifg or DEM class objects\n    :param int xlooks: Number of multi-looks in x\n    :param int ylooks: Number of multi-looks in y\n    :param tuple user_exts: Tuple of user defined cropping coordinates\n\n    :return: extents: tuple of four bounding coordinates\n    :rtype: tuple\n    \"\"\"\n\n    if crop_opt not in CROP_OPTIONS:\n        raise PreprocessError(\"Unrecognised crop option: %s\" % crop_opt)\n\n    if crop_opt == CUSTOM_CROP:\n        if not user_exts:\n            raise PreprocessError('No custom cropping extents specified')\n        elif len(user_exts) != 4:  # check for required numbers\n            raise PreprocessError('Custom extents must have all 4 values')\n        elif len(user_exts) == 4:  # check for non floats\n            if not all([_is_number(z) for z in user_exts]):\n                raise PreprocessError('Custom extents must be 4 numbers')\n\n    _check_looks(xlooks, ylooks)\n    _check_resolution(rasters)\n\n    return _get_extents(rasters, crop_opt, user_exts)\n\n\ndef _is_number(s):\n    \"\"\"\n    Check whether string can be converted to float\n    \"\"\"\n    try:\n        float(s)\n        return True\n    except TypeError:  # for example if 'None' is sent\n        return False\n    except ValueError:  # for example if a 'string' is sent\n        return False\n\n\ndef _check_looks(xlooks, ylooks):\n    \"\"\"\n    Convenience function to verify that looks parameters are valid.\n    \"\"\"\n\n    if not (isinstance(xlooks, Number) and\n            isinstance(ylooks, Number)):  # pragma: no cover\n        msg = \"Non-numeric looks parameter(s), x: %s, y: %s\" % (xlooks, ylooks)\n        raise PreprocessError(msg)\n\n    if not (xlooks > 0 and ylooks > 0):  # pragma: no cover\n        msg = \"Invalid looks parameter(s), x: %s, y: %s. \" \\\n              \"Looks must be an integer greater than zero\" % (xlooks, ylooks)\n        raise PreprocessError(msg)\n\n    if xlooks != ylooks:\n        log.warning('X and Y multi-look factors are not equal')\n\n\ndef _check_resolution(ifgs: List[Union[Ifg, DEM]]):\n    \"\"\"\n    Convenience function to verify Ifg resolutions are equal.\n    \"\"\"\n\n    for var in ['x_step', 'y_step']:\n        values = []\n        for i in ifgs:\n            i.open()\n            values.append(getattr(i, var))\n            i.close()\n        values = array(values)\n        if not (values == values[0]).all():  # pragma: no cover\n            msg = \"Grid resolution does not match for %s\" % var\n            raise PreprocessError(msg)\n\n\ndef _get_extents(ifgs, crop_opt, user_exts=None):\n    \"\"\"\n    Convenience function that returns extents/bounding box.\n    MINIMUM_CROP = 1\n    MAXIMUM_CROP = 2\n    CUSTOM_CROP = 3\n    ALREADY_SAME_SIZE = 4\n    \"\"\"\n    if crop_opt == MINIMUM_CROP:\n        extents = __bounds(ifgs, min)\n    elif crop_opt == MAXIMUM_CROP:\n        extents = __bounds(ifgs, max)\n    elif crop_opt == CUSTOM_CROP:\n        extents = _custom_bounds(ifgs, *user_exts)\n        # only need to check crop coords when custom bounds are supplied\n        _check_crop_coords(ifgs, *extents)\n    else:\n        extents = _get_same_bounds(ifgs)\n\n    return extents\n\n\ndef prepare_ifg(raster_path, xlooks, ylooks, exts, thresh, crop_opt, header, write_to_disk=True, out_path=None,\n                coherence_path=None, coherence_thresh=None):\n    \"\"\"\n    Open, resample, crop and optionally save to disk an interferogram or DEM.\n    Returns are only given if write_to_disk=False\n\n    :param str raster_path: Input raster file path name\n    :param int xlooks: Number of multi-looks in x; 5 is 5 times smaller,\n        1 is no change\n    :param int ylooks: Number of multi-looks in y\n    :param tuple exts: Tuple of user defined georeferenced extents for\n        new file: (xfirst, yfirst, xlast, ylast)cropping coordinates\n    :param float thresh: NaN fraction threshold below which resampled_data is assigned NaNs\n    :param int crop_opt: Crop option\n    :param bool write_to_disk: Write new data to disk\n    :param str out_path: Path for output file\n    :param dict header: dictionary of metadata from header file\n\n    :return: resampled_data: output cropped and resampled image\n    :rtype: ndarray\n    :return: out_ds: destination gdal dataset object\n    :rtype: gdal.Dataset\n    \"\"\"\n    do_multilook = xlooks > 1 or ylooks > 1\n    # resolution=None completes faster for non-multilooked layers in gdalwarp\n    resolution = [None, None]\n    raster = dem_or_ifg(raster_path)\n    if not raster.is_open:\n        raster.open()\n    if do_multilook:\n        resolution = [xlooks * raster.x_step, ylooks * raster.y_step]\n\n    #     # Add missing/updated metadata to resampled ifg/DEM\n    #     new_lyr = type(ifg)(looks_path)\n    #     new_lyr.open(readonly=True)\n    #     # for non-DEMs, phase bands need extra metadata & conversions\n    #     if hasattr(new_lyr, \"phase_band\"):\n    #         # TODO: LOS conversion to vertical/horizontal (projection)\n    #         # TODO: push out to workflow\n    #         #if params.has_key(REPROJECTION_FLAG):\n    #         #    reproject()\n    driver_type = 'GTiff' if write_to_disk else 'MEM'\n    resampled_data, out_ds = crop_resample_average(\n        input_tif=raster.data_path, extents=exts, new_res=resolution, output_file=out_path, thresh=thresh,\n        out_driver_type=driver_type, hdr=header, coherence_path=coherence_path, coherence_thresh=coherence_thresh\n    )\n\n    return resampled_data, out_ds\n\n\n# TODO: Not being used. Remove in future?\ndef _resample(data, xscale, yscale, thresh):\n    \"\"\"\n    Resamples/averages 'data' to return an array from the averaging of blocks\n    of several tiles in 'data'. NB: Assumes incoherent cells are NaNs.\n\n    :param data: source array to resample to different size\n    :param xscale: number of cells to average along X axis\n    :param yscale: number of Y axis cells to average\n    :param thresh: minimum allowable\n        proportion of NaN cells (range from 0.0-1.0), eg. 0.25 = 1/4 or\n        more as NaNs results in a NaN value for the output cell.\n    \"\"\"\n    # TODO: make more efficient\n    if thresh < 0 or thresh > 1:\n        raise ValueError(\"threshold must be >= 0 and <= 1\")\n\n    xscale = int(xscale)\n    yscale = int(yscale)\n    ysize, xsize = data.shape\n    xres, yres = int(xsize / xscale), int(ysize / yscale)\n    dest = zeros((yres, xres), dtype=float32) * nan\n    tile_cell_count = xscale * yscale\n\n    # calc mean without nans (fractional threshold ignores tiles\n    # with excess NaNs)\n    for x in range(xres):\n        for y in range(yres):\n            tile = data[y * yscale: (y + 1) * yscale, x * xscale: (x + 1) * xscale]\n            nan_fraction = nsum(isnan(tile)) / float(tile_cell_count)\n            if nan_fraction < thresh or (nan_fraction == 0 and thresh == 0):\n                dest[y, x] = nanmean(tile)\n    return dest\n\n\ndef __bounds(ifgs: List[Ifg], func: Callable) -> Tuple[float, float, float, float]:\n    \"\"\"\n    Returns bounds for the total area covered by the given interferograms.\n    \"\"\"\n    assert func in [min, max]  # no other func allowed\n    opposite_func = min if func is max else max\n    x_firsts = []\n    y_firsts = []\n    x_lasts = []\n    y_lasts = []\n    for i in ifgs:\n        i.open()\n        x_firsts.append(i.x_first)\n        y_firsts.append(i.y_first)\n        x_lasts.append(i.x_last)\n        y_lasts.append(i.y_last)\n        i.close()\n\n    xmin = opposite_func(x_firsts)\n    ymax = func(y_firsts)\n    xmax = func(x_lasts)\n    ymin = opposite_func(y_lasts)\n    return xmin, ymin, xmax, ymax\n\n\ndef _get_same_bounds(ifgs: List[Ifg]) -> Tuple[float, float, float, float]:\n    \"\"\"\n    Check and return bounding box for ALREADY_SAME_SIZE option.\n    \"\"\"\n    tfs = []\n    for i in ifgs:\n        i.open()\n        tfs.append(i.dataset.GetGeoTransform())\n        i.close()\n\n    equal = []\n\n    for t in tfs[1:]:\n        for i, tf in enumerate(tfs[0]):\n\n            if round(Decimal(tf), 4) == round(Decimal(t[i]), 4):\n                equal.append(True)\n            else:\n                equal.append(False)\n\n    if not all(equal):\n        msg = 'Ifgs do not have the same bounding box for crop option: %s'\n        raise PreprocessError(msg % ALREADY_SAME_SIZE)\n    ifg = ifgs[0]\n    ifg.open()\n    xmin, xmax = ifg.x_first, ifg.x_last\n    ymin, ymax = ifg.y_first, ifg.y_last\n    ifg.close()\n    # swap y_first & y_last when using southern hemisphere -ve coords\n    if ymin > ymax:\n        ymin, ymax = ymax, ymin\n\n    return xmin, ymin, xmax, ymax\n\n\ndef _custom_bounds(ifgs, xw, ytop, xe, ybot):\n    \"\"\"\n    Check and modify input custom crop bounds to line up with grid interval\n    \"\"\"\n    # pylint: disable=too-many-locals\n    # pylint: disable=too-many-branches\n    msg = 'Cropped image bounds are outside the original image bounds'\n    i = ifgs[0]\n    i.open()\n\n    if ytop < ybot:\n        raise PreprocessError('ERROR Custom crop bounds: '\n                              'ifgyfirst must be greater than ifgylast')\n\n    if xe < xw:\n        raise PreprocessError('ERROR Custom crop bounds: '\n                              'ifgxfirst must be greater than ifgxlast')\n\n    for par, crop, orig, step in zip(['x_first', 'x_last', 'y_first', 'y_last'],\n                                     [xw, xe, ytop, ybot],\n                                     [i.x_first, i.x_last, i.y_first, i.y_last],\n                                     [i.x_step, i.x_step, i.y_step, i.y_step]):\n        diff = crop - orig\n        nint = round(diff / step)\n\n        if par == 'x_first':\n            if diff < 0:\n                raise PreprocessError(msg)\n            xmin = orig + (nint * step)\n\n        elif par == 'x_last':\n            if diff > 0:\n                raise PreprocessError(msg)\n            xmax = orig + (nint * step)\n\n        elif par == 'y_first':\n            if diff > 0:\n                raise PreprocessError(msg)\n            y1 = orig + (nint * step)\n\n        elif par == 'y_last':\n            if diff < 0:\n                raise PreprocessError(msg)\n            y2 = orig + (nint * step)\n        else:\n            raise ValueError('Value error in supplied custom bounds')\n\n    i.close()\n\n    if y2 > y1:\n        ymin = y1\n        ymax = y2\n    else:\n        ymin = y2\n        ymax = y1\n\n    return xmin, ymin, xmax, ymax\n\n\ndef _check_crop_coords(ifgs, xmin, ymin, xmax, ymax):\n    \"\"\"\n    Ensures cropping coords line up with grid system within tolerance.\n    \"\"\"\n\n    # NB: assumption is the first Ifg is correct, so only test against it\n    i = ifgs[0]\n    i.open()\n\n    for par, crop, step in zip(['x_first', 'x_last', 'y_first', 'y_last'],\n                               [xmin, xmax, ymax, ymin],\n                               [i.x_step, i.x_step, i.y_step, i.y_step]):\n\n        # is diff of the given extent from grid a multiple of X|Y_STEP ?\n        param = getattr(i, par)\n        diff = abs(crop - param)\n        remainder = abs(modf(diff / step)[0])\n\n        # handle cases where division gives remainder near zero, or just < 1\n        if (remainder > GRID_TOL) and (remainder < (1 - GRID_TOL)):  # pragma: no cover\n            msg = \"%s crop extent not within %s of grid coordinate\"\n            raise PreprocessError(msg % (par, GRID_TOL))\n    i.close()\n\n\nclass PreprocessError(Exception):\n    \"\"\"\n    Preprocess exception\n    \"\"\"\n\n\ndef transform_params(params):\n    \"\"\"\n    Returns subset of all parameters for cropping and multilooking.\n\n    :param dict params: Parameter dictionary\n\n    :return: xlooks, ylooks, crop\n    :rtype: int\n    \"\"\"\n\n    t_params = [IFG_LKSX, IFG_LKSY, IFG_CROP_OPT]\n    xlooks, ylooks, crop = [params[k] for k in t_params]\n    return xlooks, ylooks, crop\n\n\ndef coherence_paths_for(path: str, params: dict, tif=False) -> str:\n    \"\"\"\n    Returns path to coherence file for given interferogram. Pattern matches\n    based on epoch in filename.\n\n    Example:\n        '20151025-20160501_eqa_filt.cc'\n        Date pair is the epoch.\n\n    Args:\n        path: Path to intergerogram to find coherence file for.\n        params: Parameter dictionary.\n        tif: Find converted tif if True (_cc.tif), else find .cc file.\n\n    Returns:\n        Path to coherence file.\n    \"\"\"\n    _, filename = split(path)\n    epoch = re.search(sixteen_digits_pattern, filename).group(0)\n    if tif:\n        coh_file_paths = [f.converted_path for f in params[COHERENCE_FILE_PATHS] if epoch in f.converted_path]\n    else:\n        coh_file_paths = [f.unwrapped_path for f in params[COHERENCE_FILE_PATHS] if epoch in f.unwrapped_path]\n\n    if len(coh_file_paths) > 1:\n        raise ConfigException(f\"found more than one coherence \"\n                              f\"file for '{path}'. There must be only one \"\n                              f\"coherence file per interferogram. Found {coh_file_paths}.\")\n    return coh_file_paths[0]\n"
  },
  {
    "path": "pyrate/core/ref_phs_est.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module implements a reference phase estimation algorithm.\n\"\"\"\nfrom pathlib import Path\nfrom typing import List\nfrom joblib import Parallel, delayed\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core import ifgconstants as ifc, shared\nfrom pyrate.core.shared import joblib_log_level, nanmedian, Ifg, nan_and_mm_convert\nfrom pyrate.core import mpiops\nfrom pyrate.configuration import Configuration\nfrom pyrate.core.logger import pyratelogger as log\n\nMAIN_PROCESS = 0\n\n\ndef est_ref_phase_patch_median(ifg_paths, params, refpx, refpy):\n    \"\"\"\n    Reference phase estimation, calculated as the median within a patch around\n    the supplied reference pixel.\n\n    :param list ifg_paths: List of interferogram paths or objects.\n    :param dict params: Dictionary of configuration parameters\n    :param int refpx: Reference pixel X found by ref pixel method\n    :param int refpy: Reference pixel Y found by ref pixel method\n\n    :return: ref_phs: Numpy array of reference phase values of size (nifgs, 1)\n    :rtype: ndarray\n    :return: ifgs: Reference phase data is removed interferograms in place\n    \"\"\"\n    half_chip_size = int(np.floor(params[C.REF_CHIP_SIZE] / 2.0))\n    chipsize = 2 * half_chip_size + 1\n    thresh = chipsize * chipsize * params[C.REF_MIN_FRAC]\n\n    def _inner(ifg_paths):\n        if isinstance(ifg_paths[0], Ifg):\n            ifgs = ifg_paths\n        else:\n            ifgs = [Ifg(ifg_path) for ifg_path in ifg_paths]\n\n        for ifg in ifgs:\n            if not ifg.is_open:\n                ifg.open(readonly=False)\n\n        phase_data = [i.phase_data for i in ifgs]\n        if params[C.PARALLEL]:\n            ref_phs = Parallel(n_jobs=params[C.PROCESSES],\n                               verbose=joblib_log_level(C.LOG_LEVEL))(\n                delayed(_est_ref_phs_patch_median)(p, half_chip_size, refpx, refpy, thresh)\n                for p in phase_data)\n        else:\n            ref_phs = np.zeros(len(ifgs))\n            for n, ifg in enumerate(ifgs):\n                ref_phs[n] = _est_ref_phs_patch_median(phase_data[n], half_chip_size, refpx, refpy, thresh)\n\n        return ref_phs\n    \n    process_ifgs_paths = mpiops.array_split(ifg_paths)\n    ref_phs = _inner(process_ifgs_paths)\n    return ref_phs   \n\n\ndef _est_ref_phs_patch_median(phase_data, half_chip_size, refpx, refpy, thresh):\n    \"\"\"\n    Convenience function for ref phs estimate method 2 parallelisation\n    \"\"\"\n    patch = phase_data[refpy - half_chip_size: refpy + half_chip_size + 1,\n                       refpx - half_chip_size: refpx + half_chip_size + 1]\n    patch = np.reshape(patch, newshape=(-1, 1), order='F')\n    nanfrac = np.sum(~np.isnan(patch))\n    if nanfrac < thresh:\n        raise ReferencePhaseError('The data window at the reference pixel '\n                                  'does not have enough valid observations. '\n                                  'Actual = {}, Threshold = {}.'.format(\n                                          nanfrac, thresh))\n    ref_ph = nanmedian(patch)\n    return ref_ph\n\n\ndef est_ref_phase_ifg_median(ifg_paths, params):\n    \"\"\"\n    Reference phase estimation, calculated as the median of the whole\n    interferogram image.\n\n    :param list ifg_paths: List of interferogram paths or objects\n    :param dict params: Dictionary of configuration parameters\n\n    :return: ref_phs: Numpy array of reference phase values of size (nifgs, 1)\n    :rtype: ndarray\n    :return: ifgs: Reference phase data is removed interferograms in place\n    \"\"\"\n    def _process_phase_sum(ifg_paths):\n        if isinstance(ifg_paths[0], Ifg):\n            proc_ifgs = ifg_paths\n        else:\n            proc_ifgs = [Ifg(ifg_path) for ifg_path in ifg_paths]\n\n        for ifg in proc_ifgs:\n            if not ifg.is_open:\n                ifg.open(readonly=False)\n\n        ifg_phase_data_sum = np.zeros(proc_ifgs[0].shape, dtype=np.float32)\n\n        for ifg in proc_ifgs:\n            ifg_phase_data_sum += ifg.phase_data\n\n        return ifg_phase_data_sum\n\n    def _inner(proc_ifgs, phase_data_sum):\n        if isinstance(proc_ifgs[0], Ifg):\n            proc_ifgs = proc_ifgs\n        else:\n            proc_ifgs = [Ifg(ifg_path) for ifg_path in proc_ifgs]\n\n        for ifg in proc_ifgs:\n            if not ifg.is_open:\n                ifg.open(readonly=False)\n\n        comp = np.isnan(phase_data_sum)\n        comp = np.ravel(comp, order='F')\n\n        if params[C.PARALLEL]:\n            log.debug(\"Calculating ref phase using multiprocessing\")\n            ref_phs = Parallel(n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL))(\n                delayed(_est_ref_phs_ifg_median)(p.phase_data, comp) for p in proc_ifgs\n            )\n        else:\n            log.debug(\"Calculating ref phase\")\n            ref_phs = np.zeros(len(proc_ifgs))\n            for n, ifg in enumerate(proc_ifgs):\n                ref_phs[n] = _est_ref_phs_ifg_median(ifg.phase_data, comp)\n\n        return ref_phs\n\n    process_ifg_paths = mpiops.array_split(ifg_paths)\n    ifg_phase_data_sum = mpiops.comm.allreduce(_process_phase_sum(process_ifg_paths), mpiops.sum_op)\n    ref_phs = _inner(process_ifg_paths, ifg_phase_data_sum)\n\n    return ref_phs\n\n\ndef _est_ref_phs_ifg_median(phase_data, comp):\n    \"\"\"\n    Convenience function for ref phs estimate method 1 parallelisation\n    \"\"\"\n    ifgv = np.ravel(phase_data, order='F')\n    ifgv[comp == 1] = np.nan\n    return nanmedian(ifgv)\n\n\ndef _update_phase_and_metadata(ifgs, ref_phs, params):\n    \"\"\"\n    Function that applies the reference phase correction and updates ifg metadata\n    \"\"\"\n    def __inner(ifg, ref_ph):\n        ifg.open()\n        # nan-convert and mm-convert before subtracting ref phase\n        nan_and_mm_convert(ifg, params)\n        # add 1e-20 to avoid 0.0 values being converted to NaN downstream (Github issue #310)\n        # TODO: implement a more robust way of avoiding this issue, e.g. using numpy masked\n        #       arrays to mark invalid pixel values rather than directly changing values to NaN\n        ifg.phase_data -= ref_ph + 1e-20\n        ifg.meta_data[ifc.PYRATE_REF_PHASE] = ifc.REF_PHASE_REMOVED\n        ifg.write_modified_phase()\n        log.debug(f\"Reference phase corrected for {ifg.data_path}\")\n        ifg.close()\n\n    log.info(\"Correcting ifgs by subtracting reference phase\")\n    for i, rp in zip(mpiops.array_split(ifgs), mpiops.array_split(ref_phs)):\n        __inner(i, rp)\n\n\nclass ReferencePhaseError(Exception):\n    \"\"\"\n    Generic class for errors in reference phase estimation.\n    \"\"\"\n    pass\n\n\ndef ref_phase_est_wrapper(params):\n    \"\"\"\n    Wrapper for reference phase estimation.\n    \"\"\"\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    refpx, refpy = params[C.REFX_FOUND], params[C.REFY_FOUND]\n    if len(ifg_paths) < 2:\n        raise ReferencePhaseError(\n            \"At least two interferograms required for reference phase correction ({len_ifg_paths} \"\n            \"provided).\".format(len_ifg_paths=len(ifg_paths))\n        )\n\n    # this is not going to be true as we now start with fresh multilooked ifg copies - remove?\n    if mpiops.run_once(shared.check_correction_status, ifg_paths, ifc.PYRATE_REF_PHASE):\n        log.warning('Reference phase correction already applied to ifgs; returning')\n        return\n\n    ifgs = [Ifg(ifg_path) for ifg_path in ifg_paths]\n    # Save reference phase numpy arrays to disk.\n    ref_phs_file = Configuration.ref_phs_file(params)\n\n    # If ref phase file exists on disk, then reuse - subtract ref_phase from ifgs and return\n    if ref_phs_file.exists():\n        ref_phs = np.load(ref_phs_file)\n        _update_phase_and_metadata(ifgs, ref_phs, params)\n        shared.save_numpy_phase(ifg_paths, params)\n        return ref_phs, ifgs\n\n    # determine the reference phase for each ifg\n    if params[C.REF_EST_METHOD] == 1:\n        log.info(\"Calculating reference phase as median of interferogram\")\n        ref_phs = est_ref_phase_ifg_median(ifg_paths, params)\n    elif params[C.REF_EST_METHOD] == 2:\n        log.info('Calculating reference phase in a patch surrounding pixel (x, y): ({}, {})'.format(refpx, refpy))\n        ref_phs = est_ref_phase_patch_median(ifg_paths, params, refpx, refpy)\n    else:\n        raise ReferencePhaseError(\"No such option, set parameter 'refest' to '1' or '2'.\")\n\n    # gather all reference phases from distributed processes and save to disk\n    if mpiops.rank == MAIN_PROCESS:\n        collected_ref_phs = np.zeros(len(ifg_paths), dtype=np.float64)\n        process_indices = mpiops.array_split(range(len(ifg_paths))).astype(np.uint16)\n        collected_ref_phs[process_indices] = ref_phs\n        for r in range(1, mpiops.size):\n            process_indices = mpiops.array_split(range(len(ifg_paths)), r).astype(np.uint16)\n            this_process_ref_phs = np.zeros(shape=len(process_indices),\n                                            dtype=np.float64)\n            mpiops.comm.Recv(this_process_ref_phs, source=r, tag=r)\n            collected_ref_phs[process_indices] = this_process_ref_phs\n        np.save(file=ref_phs_file, arr=collected_ref_phs)\n    else:\n        collected_ref_phs = np.empty(len(ifg_paths), dtype=np.float64)\n        mpiops.comm.Send(ref_phs, dest=MAIN_PROCESS, tag=mpiops.rank)\n\n    mpiops.comm.Bcast(collected_ref_phs, root=0)\n\n    # subtract ref_phase from ifgs\n    _update_phase_and_metadata(ifgs, collected_ref_phs, params)\n\n    mpiops.comm.barrier()\n    shared.save_numpy_phase(ifg_paths, params)\n\n    log.debug(\"Finished reference phase correction\")\n\n    # Preserve old return value so tests don't break.\n    return ref_phs, ifgs\n"
  },
  {
    "path": "pyrate/core/refpixel.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements an algorithm to search for the location\nof the interferometric reference pixel\n\"\"\"\nimport os\nfrom os.path import join\nfrom typing import Tuple\n\nfrom itertools import product\n\nimport numpy as np\nfrom numpy import isnan, std, mean, sum as nsum\nfrom joblib import Parallel, delayed\n\nimport pyrate.constants as C\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.core import mpiops\nfrom pyrate.core.shared import Ifg, nan_and_mm_convert\nfrom pyrate.core.shared import joblib_log_level\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.core import prepifg_helper\nfrom pyrate.configuration import Configuration, ConfigException\n\nMAIN_PROCESS = 0\n\n\ndef update_refpix_metadata(ifg_paths, refx, refy, transform, params):\n    \"\"\"\n    Function that adds metadata about the chosen reference pixel to each interferogram.\n    \"\"\"\n    pyrate_refpix_lon, pyrate_refpix_lat = mpiops.run_once(convert_pixel_value_to_geographic_coordinate, refx, refy, transform)\n\n\n    process_ifgs_paths = mpiops.array_split(ifg_paths)\n\n    for ifg_file in process_ifgs_paths:\n        log.debug(\"Updating metadata for: \"+ifg_file)\n        ifg = Ifg(ifg_file)\n        log.debug(\"Open dataset\")\n        ifg.open(readonly=True)\n        nan_and_mm_convert(ifg, params)\n        half_patch_size = params[\"refchipsize\"] // 2\n        x, y = refx, refy\n        log.debug(\"Extract reference pixel windows\")\n        data = ifg.phase_data[y - half_patch_size: y + half_patch_size + 1,\n                              x - half_patch_size: x + half_patch_size + 1]\n        log.debug(\"Calculate standard deviation for reference window\")\n        stddev_ref_area = np.nanstd(data)\n        log.debug(\"Calculate mean for reference window\")\n        mean_ref_area = np.nanmean(data)\n        ifg.add_metadata(**{\n            ifc.PYRATE_REFPIX_X: str(refx),\n            ifc.PYRATE_REFPIX_Y: str(refy),\n            ifc.PYRATE_REFPIX_LAT: str(pyrate_refpix_lat),\n            ifc.PYRATE_REFPIX_LON: str(pyrate_refpix_lon),\n            ifc.PYRATE_MEAN_REF_AREA: str(mean_ref_area),\n            ifc.PYRATE_STDDEV_REF_AREA: str(stddev_ref_area)\n        })\n        ifg.write_modified_phase()\n        ifg.close()\n\n\ndef convert_pixel_value_to_geographic_coordinate(refx, refy, transform):\n    \"\"\"\n    Converts a pixel coordinate to a latitude/longitude coordinate given the\n    geotransform of the image.\n    Args:\n        refx: The pixel x coordinate.\n        refy: The pixel ye coordinate.\n        transform: The geotransform array of the image.\n    Returns:\n        Tuple of lon, lat geographic coordinate.\n    \"\"\"\n\n    lon = lon_from_pixel_coordinate(refx, transform)\n\n    lat = lat_from_pixel_coordinate(refy, transform)\n\n    return lon, lat\n\n\ndef lat_from_pixel_coordinate(refy, transform):\n    yOrigin = transform[3]\n    pixelHeight = -transform[5]\n    lat = yOrigin - refy * pixelHeight\n    return lat\n\n\ndef lon_from_pixel_coordinate(refx, transform):\n    xOrigin = transform[0]\n    pixelWidth = transform[1]\n    lon = refx * pixelWidth + xOrigin\n    return lon\n\n\ndef convert_geographic_coordinate_to_pixel_value(lon, lat, transform):\n    \"\"\"\n    Converts a latitude/longitude coordinate to a pixel coordinate given the\n    geotransform of the image.\n    Args:\n        lon: Pixel longitude.\n        lat: Pixel latitude.\n        transform: The geotransform array of the image.\n    Returns:\n        Tuple of refx, refy pixel coordinates.\n    \"\"\"\n\n    xOrigin = transform[0]\n    yOrigin = transform[3]\n    pixelWidth = transform[1]\n    pixelHeight = -transform[5]\n\n    refx = round((lon - xOrigin) / pixelWidth)\n    refy = round((yOrigin - lat) / pixelHeight)\n\n    return int(refx), int(refy)\n\n\n# TODO: move error checking to config step (for fail fast)\n# TODO: this function is not used. Plan removal\ndef ref_pixel(ifgs, params):\n    \"\"\"\n    Determines the most appropriate reference pixel coordinate by conducting\n    a grid search and calculating the mean standard deviation with patches\n    around candidate pixels from the given interferograms.\n\n    If the config file REFX or REFY values are empty or negative, the search\n    for the reference pixel is performed. If the REFX|Y values are within the\n    bounds of the raster, a search is not performed. REFX|Y values outside\n    the upper bounds cause an exception.\n\n    :param list ifgs: List of interferogram objects\n    :param dict params: Dictionary of configuration parameters\n\n    :return: tuple of best REFX and REFY coordinates\n    :rtype: tuple\n    \"\"\"\n    half_patch_size, thresh, grid = ref_pixel_setup(ifgs, params)\n    parallel = params[C.PARALLEL]\n    if parallel:\n        phase_data = [i.phase_data for i in ifgs]\n        mean_sds = Parallel(n_jobs=params[C.PROCESSES],\n                            verbose=joblib_log_level(C.LOG_LEVEL))(\n            delayed(_ref_pixel_multi)(g, half_patch_size, phase_data,\n                                     thresh, params) for g in grid)\n        refxy = find_min_mean(mean_sds, grid)\n    else:\n        phase_data = [i.phase_data for i in ifgs]\n        mean_sds = []\n        for g in grid:\n            mean_sds.append(_ref_pixel_multi(g, half_patch_size, phase_data, thresh, params))\n        refxy = find_min_mean(mean_sds, grid)\n\n    if isinstance(refxy, RefPixelError):\n        raise RefPixelError('Refpixel calculation not possible!')\n\n    refy, refx = refxy\n\n    if refy and refx:\n        return refy, refx\n\n    raise RefPixelError(\"Could not find a reference pixel\")\n\n\ndef find_min_mean(mean_sds, grid):\n    \"\"\"\n    Determine the ref pixel block with minimum mean value\n\n    :param list mean_sds: List of mean standard deviations from each\n        reference pixel grid\n    :param list grid: List of ref pixel coordinates tuples\n\n    :return: Tuple of (refy, refx) with minimum mean\n    :rtype: tuple    \n    \"\"\"\n    log.debug('Ranking ref pixel candidates based on mean values')\n    try:\n        refp_index = np.nanargmin(mean_sds)\n        return grid[refp_index]\n    except RefPixelError as v:\n        log.error(v)\n        return v\n\n\ndef ref_pixel_setup(ifgs_or_paths, params):\n    \"\"\"\n    Sets up the grid for reference pixel computation and saves numpy files\n    to disk for later use during ref pixel computation.\n        \n    :param list ifgs_or_paths: List of interferogram filenames or Ifg objects\n    :param dict params: Dictionary of configuration parameters\n    \n    :return: half_patch_size: size of patch\n    :rtype: float\n    :return: thresh\n    :rtype: float\n    :return: list(product(ysteps, xsteps))\n    :rtype: list\n    \"\"\"\n    log.debug('Setting up ref pixel computation')\n    refnx, refny, chipsize, min_frac = params[C.REFNX], \\\n                                       params[C.REFNY], \\\n                                       params[C.REF_CHIP_SIZE], \\\n                                       params[C.REF_MIN_FRAC]\n    if len(ifgs_or_paths) < 1:\n        msg = 'Reference pixel search requires 2+ interferograms'\n        raise RefPixelError(msg)\n\n    if isinstance(ifgs_or_paths[0], str):\n        head = Ifg(ifgs_or_paths[0])\n        head.open(readonly=True)\n    else:\n        head = ifgs_or_paths[0]\n\n    # sanity check inputs\n    _validate_chipsize(chipsize, head)\n    _validate_minimum_fraction(min_frac)\n    _validate_search_win(refnx, refny, chipsize, head)\n    # pre-calculate useful amounts\n    half_patch_size = chipsize // 2\n    chipsize = half_patch_size * 2 + 1\n    thresh = min_frac * chipsize * chipsize\n    # do window searches across dataset, central pixel of stack with smallest\n    # mean is the reference pixel\n    rows, cols = head.shape\n    ysteps = _step(rows, refny, half_patch_size)\n    xsteps = _step(cols, refnx, half_patch_size)\n    log.debug('Ref pixel setup finished')\n    return half_patch_size, thresh, list(product(ysteps, xsteps))\n\n\ndef save_ref_pixel_blocks(grid, half_patch_size, ifg_paths, params):\n    \"\"\"\n    Save reference pixel grid blocks to numpy array files on disk\n\n    :param list grid: List of tuples (y, x) corresponding to ref pixel grids\n    :param int half_patch_size: patch size in pixels\n    :param list ifg_paths: list of interferogram paths\n    :param dict params: Dictionary of configuration parameters\n\n    :return: None, file saved to disk\n    \"\"\"\n    log.debug('Saving ref pixel blocks')\n    outdir = params[C.TMPDIR]\n    for pth in ifg_paths:\n        ifg = Ifg(pth)\n        ifg.open(readonly=True)\n        ifg.nodata_value = params[C.NO_DATA_VALUE]\n        ifg.convert_to_nans()\n        ifg.convert_to_mm()\n        for y, x in grid:\n            data = ifg.phase_data[y - half_patch_size:y + half_patch_size + 1,\n                                  x - half_patch_size:x + half_patch_size + 1]\n\n            data_file = join(outdir, 'ref_phase_data_{b}_{y}_{x}.npy'.format(\n                    b=os.path.basename(pth).split('.')[0], y=y, x=x))\n            np.save(file=data_file, arr=data)\n        ifg.close()\n    log.debug('Saved ref pixel blocks')\n\n\ndef _ref_pixel_mpi(process_grid, half_patch_size, ifgs, thresh, params):\n    \"\"\"\n    Convenience function for MPI-enabled ref pixel calculation\n    \"\"\"\n    log.debug('Ref pixel calculation started')\n    mean_sds = []\n    for g in process_grid:\n        mean_sds.append(_ref_pixel_multi(g, half_patch_size, ifgs, thresh, params))\n    return mean_sds\n\n\ndef _ref_pixel_multi(g, half_patch_size, phase_data_or_ifg_paths,\n                    thresh, params):\n    \"\"\"\n    Convenience function for ref pixel optimisation\n    \"\"\"\n    # pylint: disable=invalid-name\n    # phase_data_or_ifg is list of ifgs\n    y, x, = g\n    if isinstance(phase_data_or_ifg_paths[0], str):\n        # this consumes a lot less memory\n        # one ifg.phase_data in memory at any time\n        data = []\n        output_dir = params[C.TMPDIR]\n        for p in phase_data_or_ifg_paths:\n            data_file = os.path.join(output_dir,\n                                     'ref_phase_data_{b}_{y}_{x}.npy'.format(\n                                         b=os.path.basename(p).split('.')[0],\n                                         y=y, x=x))\n            data.append(np.load(file=data_file))\n    else:  # phase_data_or_ifg is phase_data list\n        data = [p[y - half_patch_size:y + half_patch_size + 1,\n                  x - half_patch_size:x + half_patch_size + 1]\n                for p in phase_data_or_ifg_paths]\n    valid = [nsum(~isnan(d)) > thresh for d in data]\n    if all(valid):  # ignore if 1+ ifgs have too many incoherent cells\n        sd = [std(i[~isnan(i)]) for i in data]\n        return mean(sd)\n    else:\n        return np.nan\n\n\ndef _step(dim, ref, radius):\n    \"\"\"\n    Helper: returns range object of axis indices for a search window.\n\n    :param int dim: Total length of the grid dimension\n    :param int ref: The desired number of steps\n    :param int radius: The number of cells from the centre of the chip eg.\n        (chipsize / 2)\n\n    :return: range object of axis indices\n    :rtype: range\n    \"\"\"\n\n    # if ref == 1:\n    #     # centre a single search step\n    #     return xrange(dim // 2, dim, dim)  # fake step to ensure single xrange value\n\n    # if ref == 2: # handle 2 search windows, method below doesn't cover the case\n    #     return [radius, dim-radius-1]\n    # max_dim = dim - (2*radius)  # max possible number for refn(x|y)\n    # step = max_dim // (ref-1)\n    step_size = dim // ref\n    return range(radius, dim-radius, step_size)\n\n\ndef _validate_chipsize(chipsize, head):\n    \"\"\"\n    Sanity check min chipsize\n    \"\"\"\n    if chipsize is None:\n        raise ConfigException('Chipsize is None')\n\n    if chipsize < 3 or chipsize > head.ncols or (chipsize % 2 == 0):\n        msg = \"Chipsize setting must be >=3 and at least <= grid width\"\n        raise RefPixelError(msg)\n    log.debug('Chipsize validation successful')\n\n\ndef _validate_minimum_fraction(min_frac):\n    \"\"\"\n    Sanity check min fraction\n    \"\"\"\n    if min_frac is None:\n        raise ConfigException('Minimum fraction is None')\n\n    if min_frac < 0.0 or min_frac > 1.0:\n        raise RefPixelError(\"Minimum fraction setting must be >= 0.0 and <= 1.0 \")\n\n\ndef _validate_search_win(refnx, refny, chipsize, head):\n    \"\"\"\n    Sanity check X|Y steps\n    \"\"\"\n    if refnx is None:\n        raise ConfigException('refnx is None')\n\n    max_width = (head.ncols - (chipsize-1))\n    if refnx < 1 or refnx > max_width:\n        msg = \"Invalid refnx setting, must be > 0 and <= %s\"\n        raise RefPixelError(msg % max_width)\n\n    if refny is None:\n        raise ConfigException('refny is None')\n\n    max_rows = (head.nrows - (chipsize-1))\n    if refny < 1 or refny > max_rows:\n        msg = \"Invalid refny setting, must be > 0 and <= %s\"\n        raise RefPixelError(msg % max_rows)\n\n\ndef __validate_supplied_lat_lon(params: dict) -> None:\n    \"\"\"\n    Function to validate that the user supplied lat/lon values sit within image bounds\n    \"\"\"\n    lon, lat = params[C.REFX], params[C.REFY]\n    if lon == -1 or lat == -1:\n        return\n    xmin, ymin, xmax, ymax = prepifg_helper.get_analysis_extent(\n        crop_opt=params[C.IFG_CROP_OPT],\n        rasters=[prepifg_helper.dem_or_ifg(p.sampled_path) for p in params[C.INTERFEROGRAM_FILES]],\n        xlooks=params[C.IFG_LKSX], ylooks=params[C.IFG_LKSY],\n        user_exts=(params[C.IFG_XFIRST], params[C.IFG_YFIRST], params[\n            C.IFG_XLAST], params[C.IFG_YLAST])\n    )\n    msg = \"Supplied {} value is outside the bounds of the interferogram data\"\n    lat_lon_txt = ''\n    if (lon < xmin) or (lon > xmax):\n        lat_lon_txt += 'longitude'\n    if (lat < ymin) or (lat > ymax):\n        lat_lon_txt += ' and latitude' if lat_lon_txt else 'latitude'\n    if lat_lon_txt:\n        raise RefPixelError(msg.format(lat_lon_txt))\n\n\nclass RefPixelError(Exception):\n    \"\"\"\n    Generic exception for reference pixel errors.\n    \"\"\"\n\n\ndef ref_pixel_calc_wrapper(params: dict) -> Tuple[int, int]:\n    \"\"\"\n    Wrapper for reference pixel calculation\n    \"\"\"\n    __validate_supplied_lat_lon(params)\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    lon = params[C.REFX]\n    lat = params[C.REFY]\n\n    ifg = Ifg(ifg_paths[0])\n    ifg.open(readonly=True)\n    # assume all interferograms have same projection and will share the same transform\n    transform = ifg.dataset.GetGeoTransform()\n\n    ref_pixel_file = Configuration.ref_pixel_path(params)\n\n    def __reuse_ref_pixel_file_if_exists():\n        if ref_pixel_file.exists():\n            refx, refy = np.load(ref_pixel_file)\n            log.info('Reusing pre-calculated ref-pixel values: ({}, {}) from file {}'.format(\n                refx, refy, ref_pixel_file.as_posix()))\n            log.warning(\"Reusing ref-pixel values from previous run!!!\")\n            params[C.REFX_FOUND], params[C.REFY_FOUND] = int(refx), int(refy)\n            return int(refx), int(refy)\n        else:\n            return None, None\n\n    # read and return\n    refx, refy = mpiops.run_once(__reuse_ref_pixel_file_if_exists)\n    if (refx is not None) and (refy is not None):\n        update_refpix_metadata(ifg_paths, int(refx), int(refy), transform, params)\n        return refx, refy\n\n    if lon == -1 or lat == -1:\n        log.info('Searching for best reference pixel location')\n\n        half_patch_size, thresh, grid = ref_pixel_setup(ifg_paths, params)\n        process_grid = mpiops.array_split(grid)\n        save_ref_pixel_blocks(process_grid, half_patch_size, ifg_paths, params)\n        mean_sds = _ref_pixel_mpi(process_grid, half_patch_size, ifg_paths, thresh, params)\n        mean_sds = mpiops.comm.gather(mean_sds, root=0)\n        if mpiops.rank == MAIN_PROCESS:\n            mean_sds = np.hstack(mean_sds)\n\n        refpixel_returned = mpiops.run_once(find_min_mean, mean_sds, grid)\n\n        if isinstance(refpixel_returned, ValueError):\n            raise RefPixelError(\n                \"Reference pixel calculation returned an all nan slice!\\n\"\n                \"Cannot continue downstream computation. Please change reference pixel algorithm used before \"\n                \"continuing.\")\n        refy, refx = refpixel_returned   # row first means first value is latitude\n        log.info('Selected reference pixel coordinate (x, y): ({}, {})'.format(refx, refy))\n        lon, lat = convert_pixel_value_to_geographic_coordinate(refx, refy, transform)\n        log.info('Selected reference pixel coordinate (lon, lat): ({}, {})'.format(lon, lat))\n    else:\n        log.info('Using reference pixel from config file (lon, lat): ({}, {})'.format(lon, lat))\n        log.warning(\"Ensure user supplied reference pixel values are in lon/lat\")\n        refx, refy = convert_geographic_coordinate_to_pixel_value(lon, lat, transform)\n        log.info('Converted reference pixel coordinate (x, y): ({}, {})'.format(refx, refy))\n\n    np.save(file=ref_pixel_file, arr=[int(refx), int(refy)])\n    update_refpix_metadata(ifg_paths, refx, refy, transform, params)\n\n    log.debug(\"refpx, refpy: \"+str(refx) + \" \" + str(refy))\n    ifg.close()\n    params[C.REFX_FOUND], params[C.REFY_FOUND] = int(refx), int(refy)\n    return int(refx), int(refy)\n"
  },
  {
    "path": "pyrate/core/roipac.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tools for reading ROI_PAC format input data.\n\"\"\"\nimport os\nfrom pathlib import Path\nimport re\nimport datetime\n\nimport pyrate.constants as C\nimport pyrate.core.ifgconstants as ifc\nfrom pyrate.core.shared import extract_epochs_from_filename\n\n# ROIPAC RSC header file constants\nWIDTH = \"WIDTH\"\nFILE_LENGTH = \"FILE_LENGTH\"\nXMIN = \"XMIN\"\nXMAX = \"XMAX\"\nYMIN = \"YMIN\"\nYMAX = \"YMAX\"\nX_FIRST = \"X_FIRST\"\nX_STEP = \"X_STEP\"\nX_UNIT = \"X_UNIT\"\nY_FIRST = \"Y_FIRST\"\nY_STEP = \"Y_STEP\"\nY_UNIT = \"Y_UNIT\"\nTIME_SPAN_YEAR = \"TIME_SPAN_YEAR\"\n\n# Old ROIPAC headers (may not be needed)\nORBIT_NUMBER = \"ORBIT_NUMBER\"\nVELOCITY = \"VELOCITY\"\nHEIGHT = \"HEIGHT\"\nEARTH_RADIUS = \"EARTH_RADIUS\"\nWAVELENGTH = \"WAVELENGTH\"\nDATE = \"DATE\"\nDATE12 = \"DATE12\"\nHEADING_DEG = \"HEADING_DEG\"\n\n# DEM specific\nZ_OFFSET = \"Z_OFFSET\"\nZ_SCALE = \"Z_SCALE\"\nPROJECTION = \"PROJECTION\"\nDATUM = \"DATUM\"\n\n# custom header aliases\nFIRST = \"FIRST\"\nSECOND = \"SECOND\"\nX_LAST = \"X_LAST\"\nY_LAST = \"Y_LAST\"\nRADIANS = \"RADIANS\"\nROIPAC = \"ROIPAC\"\n\n# store type for each of the header items\nINT_HEADERS = [WIDTH, FILE_LENGTH, XMIN, XMAX, YMIN, YMAX, Z_OFFSET, Z_SCALE]\nSTR_HEADERS = [X_UNIT, Y_UNIT, ORBIT_NUMBER, DATUM, PROJECTION]\nFLOAT_HEADERS = [X_FIRST, X_STEP, Y_FIRST, Y_STEP, TIME_SPAN_YEAR,\n                 VELOCITY, HEIGHT, EARTH_RADIUS, WAVELENGTH, HEADING_DEG]\nDATE_HEADERS = [DATE, DATE12]\n\nROIPAC_HEADER_LEFT_JUSTIFY = 18\nROI_PAC_HEADER_FILE_EXT = \".rsc\"\n\ndef parse_date(dstr):\n    \"\"\"\n    Parses ROI_PAC 'yymmdd' or 'yymmdd-yymmdd' format string to datetime.\n\n    :param str dstr: 'date' or 'date1-date2' string\n\n    :return: dstr: datetime string or tuple\n    :rtype: str or tuple\n    \"\"\"\n    def to_date(date_str):\n        \"\"\"convert string to datetime\"\"\"\n        year, month, day = [int(date_str[i:i+2]) for i in range(0, 6, 2)]\n        year += 1900 if ((year <= 99) and (year >= 50)) else 2000\n        return datetime.date(year, month, day)\n\n    if \"-\" in dstr:  # ranged date\n        return tuple([to_date(d) for d in dstr.split(\"-\")])\n    else:\n        return to_date(dstr)\n\n\ndef parse_header(hdr_file):\n    \"\"\"\n    Parses ROI_PAC header file metadata to a dictionary.\n\n    :param str hdr_file: `path to ROI_PAC *.rsc file`\n\n    :return: subset: subset of metadata\n    :rtype: dict\n    \"\"\"\n    with open(hdr_file, encoding=\"utf8\", errors='ignore') as f:\n        text = f.read()\n\n    try:\n        lines = [e.split() for e in text.split(\"\\n\") if e != \"\"]\n        headers = dict(lines)\n        is_dem = True if DATUM in headers or Z_SCALE in headers \\\n                         or PROJECTION in headers else False\n        if is_dem and DATUM not in headers:\n            msg = 'No \"DATUM\" parameter in DEM header/resource file'\n            raise RoipacException(msg)\n    except ValueError:\n        msg = \"Unable to parse content of %s. Is it a ROIPAC header file?\"\n        raise RoipacException(msg % hdr_file)\n\n    for k in headers.keys():\n        if k in INT_HEADERS:\n            headers[k] = int(headers[k])\n        elif k in STR_HEADERS:\n            headers[k] = str(headers[k])\n        elif k in FLOAT_HEADERS:\n            headers[k] = float(headers[k])\n        elif k in DATE_HEADERS:\n            headers[k] = parse_date(headers[k])\n        else:  # pragma: no cover\n            pass  # ignore other headers\n\n    # grab a subset for GeoTIFF conversion\n    subset = {ifc.PYRATE_NCOLS: headers[WIDTH],\n              ifc.PYRATE_NROWS: headers[FILE_LENGTH],\n              ifc.PYRATE_LAT: headers[Y_FIRST],\n              ifc.PYRATE_LONG: headers[X_FIRST],\n              ifc.PYRATE_X_STEP: headers[X_STEP],\n              ifc.PYRATE_Y_STEP: headers[Y_STEP]}\n\n    if is_dem:\n        subset[ifc.PYRATE_DATUM] = headers[DATUM]\n    else:\n        subset[ifc.PYRATE_WAVELENGTH_METRES] = headers[WAVELENGTH]\n\n        # grab first/second dates from header, or the filename\n        has_dates = True if DATE in headers and DATE12 in headers else False\n        dates = headers[DATE12] if has_dates else _parse_dates_from(hdr_file)\n        subset[ifc.FIRST_DATE], subset[ifc.SECOND_DATE] = dates\n\n        # replace time span as ROIPAC is ~4 hours different to (second minus first)\n        timespan = (subset[ifc.SECOND_DATE] - subset[ifc.FIRST_DATE]).days / ifc.DAYS_PER_YEAR\n        subset[ifc.PYRATE_TIME_SPAN] = timespan\n\n        # Add data units of interferogram\n        subset[ifc.DATA_UNITS] = RADIANS\n\n    # Add InSAR processor flag\n    subset[ifc.PYRATE_INSAR_PROCESSOR] = ROIPAC\n\n    # add custom X|Y_LAST for convenience\n    subset[X_LAST] = headers[X_FIRST] + (headers[X_STEP] * (headers[WIDTH]))\n    subset[Y_LAST] = headers[Y_FIRST] + (headers[Y_STEP] * (headers[FILE_LENGTH]))\n\n    return subset\n\n\ndef _parse_dates_from(filename):\n    \"\"\"Determine dates from file name\"\"\"\n    # pylint: disable=invalid-name\n    # process dates from filename if rsc file doesn't have them (skip for DEMs)\n    p = re.compile(r'\\d{6}-\\d{6}')  # match 2 sets of 6 digits separated by '-'\n    m = p.search(filename)\n\n    if m:\n        s = m.group()\n        min_date_len = 13  # assumes \"nnnnnn-nnnnnn\" format\n        if len(s) == min_date_len:\n            return parse_date(s)\n    else:  # pragma: no cover\n        msg = \"Filename does not include first/second image dates: %s\"\n        raise RoipacException(msg % filename)\n\n\ndef manage_header(header_file, projection):\n    \"\"\"\n    Manage header files for ROI_PAC interferograms and DEM files.\n    NB: projection = roipac.parse_header(dem_file)[ifc.PYRATE_DATUM]\n\n    :param str header_file: `ROI_PAC *.rsc header file path`\n    :param projection: Projection obtained from dem header.\n\n    :return: combined_header: Combined metadata dictionary\n    :rtype: dict\n    \"\"\"\n    header = parse_header(header_file)\n    if ifc.PYRATE_DATUM not in header:  # DEM already has DATUM\n        header[ifc.PYRATE_DATUM] = projection\n    header[ifc.DATA_TYPE] = ifc.ORIG  # non-cropped, non-multilooked geotiff\n    return header\n\n\ndef roipac_header(file_path, params):\n    \"\"\"\n    Function to obtain a header for roipac interferogram file or converted\n    geotiff.\n    \"\"\"\n    rsc_file = params[C.DEM_HEADER_FILE]\n    p = Path(file_path)\n    if rsc_file is not None:\n        projection = parse_header(rsc_file)[ifc.PYRATE_DATUM]\n    else:\n        raise RoipacException('No DEM resource/header file is provided')\n    if file_path.endswith('_dem.tif'):\n        header_file = os.path.join(params[C.DEM_HEADER_FILE])\n    elif file_path.endswith('unw_ifg.tif') or file_path.endswith('unw.tif'):\n        # TODO: improve this\n        interferogram_epoches = extract_epochs_from_filename(p.name)\n        for header_path in params[C.HEADER_FILE_PATHS]:\n            h = Path(header_path.unwrapped_path)\n            header_epochs = extract_epochs_from_filename(h.name)\n            if set(header_epochs).__eq__(set(interferogram_epoches)):\n                header_file = header_path.unwrapped_path\n                break\n    else:\n        header_file = \"%s%s\" % (file_path, ROI_PAC_HEADER_FILE_EXT)\n\n    header = manage_header(header_file, projection)\n\n    return header\n\n\nclass RoipacException(Exception):\n    \"\"\"\n    Convenience class for throwing exception\n    \"\"\"\n"
  },
  {
    "path": "pyrate/core/shared.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module contains utilities and classes shared by\nall other PyRate modules\n\"\"\"\n# pylint: disable=too-many-lines\nimport re\nfrom typing import List, Union, Optional, Iterable, Callable, Dict\n\nimport errno\nimport math\nfrom joblib import Parallel, delayed\nfrom math import floor\nimport os\nfrom os.path import basename, join\nfrom pathlib import Path\nimport struct\nfrom datetime import date\nfrom itertools import product\nfrom enum import Enum\nimport numpy as np\nfrom numpy import where, nan, isnan, sum as nsum, isclose\nimport pyproj\nimport pkg_resources\n\nimport pyrate.constants as C\n\nfrom osgeo import osr, gdal\nfrom osgeo.gdalconst import GA_Update, GA_ReadOnly\n\nfrom pyrate.core import ifgconstants as ifc, mpiops\nfrom pyrate.core.logger import pyratelogger as log\n\n\ngdal.UseExceptions()\n\nVERBOSE = True\n\n# Constants\nPHASE_BAND = 1\nRADIANS = 'RADIANS'\nMILLIMETRES = 'MILLIMETRES'\nGAMMA = 'GAMMA'\nROIPAC = 'ROIPAC'\n\n# GDAL projection list\nGDAL_X_CELLSIZE = 1\nGDAL_Y_CELLSIZE = 5\nGDAL_X_FIRST = 0\nGDAL_Y_FIRST = 3\n\n\nclass InputTypes(Enum):\n    IFG = 'ifg'\n    COH = 'coh'\n    BASE = 'base'\n    LT = 'lt'\n    DEM = 'dem'\n    HEADER = 'header'\n    dir_map = {\n        IFG: C.INTERFEROGRAM_DIR,\n        COH: C.COHERENCE_DIR,\n        DEM: C.GEOMETRY_DIR,\n    }\n\n\ndef joblib_log_level(level: str) -> int:\n    \"\"\"\n    Convert python log level to joblib int verbosity.\n    \"\"\" \n    if level == 'INFO':\n        return 0\n    else:\n        return 60\n\ndef mkdir_p(path):\n    \"\"\"\n    Make new directory and create parent directories as necessary.\n    Copied from stackoverflow:\n    http://stackoverflow.com/questions/600268/mkdir-p-functionality-in-python\n\n    :param str path: Path name for new directory\n    \"\"\"\n    try:\n        os.makedirs(path)\n    except OSError as exc:  # Python >2.5\n        if exc.errno == errno.EEXIST and os.path.isdir(path):\n            pass\n        else:\n            raise\n\n\nclass RasterBase(object):\n    \"\"\"\n    Base class for PyRate GeoTIFF based raster datasets.\n    \"\"\"\n    # pylint: disable=missing-docstring\n    # pylint: disable=too-many-instance-attributes\n    def __init__(self, path: Union[gdal.Dataset, str, Path]):\n        if isinstance(path, gdal.Dataset):\n            self.dataset = path  # path will be Dataset in this case\n            self.data_path = self.dataset  # data_path dummy\n            self.add_geographic_data()\n        else:\n            path = path.as_posix() if isinstance(path, Path) else path\n            self.data_path = path\n            self.dataset = None  # for GDAL dataset obj\n            self._readonly = not os.access(path, os.R_OK | os.W_OK)\n\n            if self._readonly is None:\n                raise NotImplementedError  # os.access() has failed?\n\n    def __str__(self):\n        name = self.__class__.__name__\n        return \"%s('%s')\" % (name, self.data_path)\n\n    def __repr__(self):\n        name = self.__class__.__name__\n        return \"%s('%s')\" % (name, self.data_path)\n\n    def open(self, readonly=None):\n        \"\"\"\n        Opens generic raster dataset.\n        \"\"\"\n        if self.dataset is not None:\n            msg = \"open() already called for %s\" % self\n            raise RasterException(msg)\n\n        if not os.path.exists(self.data_path):\n            raise IOError('The file {path} does not exist. Consider first running prepifg'.format(path=self.data_path))\n\n        # unless read only, by default open files as writeable\n        if readonly not in [True, False, None]:\n            raise ValueError(\"readonly must be True, False or None\")\n\n        if readonly is False and self._readonly is True:\n            raise IOError(\"Cannot open write protected file for writing\")\n\n        flag = GA_ReadOnly if self._readonly else GA_Update\n        self.dataset = gdal.Open(self.data_path, flag)\n        if self.dataset is None:\n            raise RasterException(\"Error opening %s\" % self.data_path)\n\n        self.add_geographic_data()\n\n    def add_geographic_data(self):\n        \"\"\"\n        Determine and add geographic data to object\n        \"\"\"\n        # add some geographic data\n        self.x_centre = int(self.ncols / 2)\n        self.y_centre = int(self.nrows / 2)\n        self.lat_centre = self.y_first + (self.y_step * self.y_centre)\n        self.long_centre = self.x_first + (self.x_step * self.x_centre)\n        # use cell size from centre of scene\n        self.x_size, self.y_size = cell_size(self.lat_centre, self.long_centre, self.x_step, self.y_step)\n\n    @property\n    def ncols(self):\n        \"\"\"\n        Number of raster columns\n        \"\"\"\n        return self.dataset.RasterXSize\n\n    @property\n    def nrows(self):\n        \"\"\"\n        Number of raster rows\n        \"\"\"\n        return self.dataset.RasterYSize\n\n    @property\n    def x_step(self):\n        \"\"\"\n        Raster pixel size in X (easting) dimension\n        \"\"\"\n        return float(self.dataset.GetGeoTransform()[GDAL_X_CELLSIZE])\n\n    @property\n    def y_step(self):\n        \"\"\"\n        Raster pixel size in Y (northing) dimension\n        \"\"\"\n        return float(self.dataset.GetGeoTransform()[GDAL_Y_CELLSIZE])\n\n    @property\n    def x_first(self):\n        \"\"\"\n        Raster western bounding coordinate\n        \"\"\"\n        return float(self.dataset.GetGeoTransform()[GDAL_X_FIRST])\n\n    @property\n    def x_last(self):\n        \"\"\"\n        Raster eastern bounding coordinate\n        \"\"\"\n        return self.x_first + (self.x_step * self.ncols)\n\n    @property\n    def y_first(self):\n        \"\"\"\n        Raster northern bounding coordinate\n        \"\"\"\n        return float(self.dataset.GetGeoTransform()[GDAL_Y_FIRST])\n\n    @property\n    def y_last(self):\n        \"\"\"\n        Raster southern bounding coordinate\n        \"\"\"\n        return self.y_first + (self.y_step * self.nrows)\n\n    @property\n    def shape(self):\n        \"\"\"\n        Returns tuple of (Y,X) shape of the raster (as per numpy.shape).\n        \"\"\"\n        return self.dataset.RasterYSize, self.dataset.RasterXSize\n\n    @property\n    def num_cells(self):\n        \"\"\"\n        Total number of pixels in raster dataset\n        \"\"\"\n        if self.is_open:\n            return self.dataset.RasterXSize * self.dataset.RasterYSize\n        else:\n            raise RasterException('Dataset not open')\n\n    @property\n    def is_open(self):\n        \"\"\"\n        Returns True if the underlying dataset has been opened by GDAL.\n        \"\"\"\n        return self.dataset is not None\n\n    def close(self):\n        \"\"\"\n        Explicitly closes file opened by gdal.Open()\n        This is required in windows, otherwise opened files can not be removed,\n        because windows locks open files.\n        \"\"\"\n        if self.is_open:\n            self.dataset = None\n\n    @property\n    def is_read_only(self):\n        \"\"\"\n        Determines file permissions\n        \"\"\"\n        return self._readonly\n\n    def _get_band(self, band):\n        \"\"\"\n        Wrapper (with error checking) for GDAL's Band.GetRasterBand() method.\n\n        :param int band: number of band, starting at 1\n        \"\"\"\n        if self.dataset is not None:\n            return self.dataset.GetRasterBand(band)\n        else:\n            raise RasterException(\"Raster %s has not been opened\" % self.data_path)\n\n\nclass Ifg(RasterBase):\n    \"\"\"\n    Interferogram (Ifg) class objects; double as a container for\n    interferometric phase raster band data and related data.\n    \"\"\"\n    # pylint: disable=too-many-instance-attributes\n    def __init__(self, path: Union[str, Path, gdal.Dataset]):\n        \"\"\"\n        Interferogram constructor, for 2-band Ifg raster datasets.\n\n        :param str path: Path to interferogram file\n        \"\"\"\n        RasterBase.__init__(self, path)\n        self._phase_band = None\n        self._phase_data = None\n        self.first = None\n        self.second = None\n        self.nan_converted = False\n        self.mm_converted = False\n        self.meta_data = None\n        self.wavelength = None\n        self._nodata_value = None\n        self.time_span = None\n\n    def open(self, readonly=None):\n        \"\"\"\n        Open raster file.\n\n        :param bool readonly: True/False, or None to open as underlying file setting\n        \"\"\"\n        RasterBase.open(self, readonly)\n        self.initialize()\n\n    def initialize(self):\n        \"\"\"\n        Read basic interferogram properties upon opening interferogram.\n        \"\"\"\n        self._init_dates()\n        md = self.dataset.GetMetadata()\n        self.wavelength = float(md[ifc.PYRATE_WAVELENGTH_METRES])\n        self.meta_data = md\n        self.nan_converted = False  # This flag set True after NaN conversion\n\n    def _init_dates(self):\n        \"\"\"\n        Determine first and second image dates, and interferogram timespan\n        \"\"\"\n        def _to_date(datestr):\n            year, month, day = [int(i) for i in datestr.split('-')]\n            return date(year, month, day)\n\n        md = self.dataset.GetMetadata()\n        datestrs = [md[k] for k in [ifc.FIRST_DATE, ifc.SECOND_DATE]]\n\n        if all(datestrs):\n            self.first, self.second = [_to_date(s) for s in datestrs]\n            self.time_span = (self.second - self.first).days/ifc.DAYS_PER_YEAR\n        else:\n            msg = 'Missing first and/or second date in %s' % self.data_path\n            raise IfgException(msg)\n\n    def convert_to_nans(self):\n        \"\"\"\n        Convert phase data of given value to NaN\n        \"\"\"\n        if (self._nodata_value is None) \\\n                or (self.dataset is None):  # pragma: no cover\n            msg = 'nodata value needs to be set for nan conversion.' \\\n                  'Use ifg.nodata_value = NoDataValue to set nodata_value'\n            log.warning(msg)\n            raise RasterException(msg)\n        if ((self.dataset.GetMetadataItem(ifc.NAN_STATUS) == ifc.NAN_CONVERTED)\n                or self.nan_converted):\n            self.phase_data = self.phase_data\n            self.nan_converted = True\n            msg = '{}: ignored as previous nan ' \\\n                  'conversion detected'.format(self.data_path)\n            log.debug(msg)\n            return\n        else:\n            self.phase_data = where(\n                isclose(self.phase_data, self._nodata_value, atol=1e-6),\n                nan,\n                self.phase_data)\n            self.meta_data[ifc.NAN_STATUS] = ifc.NAN_CONVERTED\n            self.nan_converted = True\n\n        # self.write_modified_phase(self.phase_data)\n\n    @property\n    def phase_band(self):\n        \"\"\"\n        Returns a GDAL Band object for the phase band.\n        \"\"\"\n        if self._phase_band is None:\n            self._phase_band = self._get_band(PHASE_BAND)\n        return self._phase_band\n\n    @property\n    def nodata_value(self):\n        \"\"\"\n        Determine the no-data value in phase band.\n        \"\"\"\n        return self._nodata_value\n\n    @nodata_value.setter\n    def nodata_value(self, val):\n        \"\"\"\n        Set the no-data value for phase band.\n        \"\"\"\n        self._nodata_value = val\n\n    @property\n    def phase_data(self):\n        \"\"\"\n        Returns phase band as an array.\n        \"\"\"\n        # TODO: enhance this to use x/y offset and size\n        if self._phase_data is None:\n            self._phase_data = self.phase_band.ReadAsArray()\n        return self._phase_data\n\n    def convert_to_mm(self):\n        \"\"\"\n        Convert phase_data units from radians to millimetres.\n        Note: converted phase_data held in memory and not written to disc\n        (see shared.write_modified_phase)\n        \"\"\"\n        if self.dataset.GetMetadataItem(ifc.DATA_UNITS) == MILLIMETRES:\n            self.mm_converted = True\n            msg = '{}: ignored as phase units are already ' \\\n                  'millimetres'.format(self.data_path)\n            log.debug(msg)\n            return\n        elif self.dataset.GetMetadataItem(ifc.DATA_UNITS) == RADIANS:\n            self.phase_data = convert_radians_to_mm(self.phase_data, self.wavelength)\n            self.meta_data[ifc.DATA_UNITS] = MILLIMETRES\n            self.mm_converted = True\n            # self.write_modified_phase()\n            # otherwise NaN's don't write to bytecode properly\n            # and numpy complains\n            # self.dataset.FlushCache()\n            msg = '{}: converted phase units to millimetres'.format(self.data_path)\n            log.debug(msg)\n            return\n        else:  # pragma: no cover\n            msg = 'Phase units are not millimetres or radians'\n            raise IfgException(msg)\n\n    def convert_to_radians(self):\n        \"\"\"\n        Convert phase_data units from millimetres to radians.\n        Note: converted phase_data held in memory and not written to disc\n        (see shared.write_modified_phase)\n        \"\"\"\n        if self.meta_data[ifc.DATA_UNITS] == MILLIMETRES:\n            self.phase_data = convert_mm_to_radians(self.phase_data, wavelength=self.wavelength)\n            self.meta_data[ifc.DATA_UNITS] = RADIANS\n            self.mm_converted = False\n            msg = '{}: converted phase units to radians'.format(self.data_path)\n            log.debug(msg)\n            return\n        elif self.meta_data[ifc.DATA_UNITS] == RADIANS:\n            self.mm_converted = False\n            msg = '{}: ignored as phase units are already ' \\\n                  'radians'.format(self.data_path)\n            log.debug(msg)\n            return\n        else:  # pragma: no cover\n            msg = 'Phase units are not millimetres or radians'\n            raise IfgException(msg)\n\n    @phase_data.setter\n    def phase_data(self, data):\n        \"\"\"\n        Set phase data value\n        \"\"\"\n        self._phase_data = data\n\n    @property\n    def phase_rows(self):\n        \"\"\"\n        Generator returning each row of the phase data.\n        \"\"\"\n        for y in range(self.nrows):\n            r = self.phase_band.ReadAsArray(yoff=y,\n                                            win_xsize=self.ncols, win_ysize=1)\n            yield r[0] # squeezes row from (1, WIDTH) to 1D array\n\n    @property\n    def nan_count(self):\n        \"\"\"\n        Returns total number of NaN cells in the phase data.\n        \"\"\"\n        return nsum(isnan(self.phase_data))\n\n    @property\n    def nan_fraction(self):\n        \"\"\"\n        Returns decimal fraction of NaN cells in the phase band.\n        \"\"\"\n        if (self._nodata_value is None) or (self.dataset is None):\n            msg = 'nodata_value needs to be set for nan fraction calc.' \\\n                  'Use ifg.nondata = NoDataValue to set nodata'\n            raise RasterException(msg)\n        # don't cache nan_count as client code may modify phase data\n        nan_count = self.nan_count\n        # handle datasets with no 0 -> NaN replacement\n        if not self.nan_converted and (nan_count == 0):\n            nan_count = nsum(np.isclose(self.phase_data,\n                                        self._nodata_value, atol=1e-6))\n        return nan_count / float(self.num_cells)\n\n    def write_modified_phase(self, data=None):\n        \"\"\"\n        Write updated phase data to file on disk.\n        \"\"\"\n        if self.is_read_only:\n            raise IOError(\"Cannot write to read only Ifg\")\n\n        # keep this block\n        # if new_data_path is None:\n        #     self.dataset = gdal.Open(self.data_path, GA_Update)\n        # else:\n        #     self.dataset = gdal.Open(new_data_path, GA_Update)\n        # self._phase_band = None\n\n        if data is not None:\n            assert isinstance(data, np.ndarray)\n            data_r, data_c = data.shape\n            assert data_r == self.nrows and data_c == self.ncols\n            self.phase_data = data\n        self.phase_band.WriteArray(self.phase_data)\n        for k, v in self.meta_data.items():\n            self.dataset.SetMetadataItem(k, v)\n        self.dataset.FlushCache()\n\n    def add_metadata(self, **kwargs):\n        if (not self.is_open) or self.is_read_only:\n            raise IOError(\"Ifg not open or readonly. Cannot write!\")\n\n        for k, v in kwargs.items():\n            self.dataset.SetMetadataItem(k, v)\n        self.dataset.FlushCache()  # write to disc\n\n\nclass Tile:\n    \"\"\"\n    Tile class for containing a sub-part of an interferogram\n    \"\"\"\n    def __init__(self, index, top_left, bottom_right):\n        \"\"\"\n        Parameters\n        ----------\n        index: int\n            identifying index of a tile\n        top_left: tuple\n            ifg index of top left of tile\n        bottom_right: tuple\n            ifg index of bottom right of tile\n        \"\"\"\n\n        self.index = index\n        self.top_left = top_left\n        self.bottom_right = bottom_right\n        self.top_left_y, self.top_left_x = top_left\n        self.bottom_right_y, self.bottom_right_x = bottom_right\n\n    def __str__(self):\n        return \"Convenience Tile class containing tile co-ordinates\"\n\n\nclass IfgPart(object):\n    \"\"\"\n    Create a tile (subset) of an Ifg data object\n    \"\"\"\n    def __init__(self, ifg_or_path, tile: Tile, ifg_dict=None, params=None):\n        \"\"\"\n        Interferogram tile constructor.\n\n        :param str path: Path to interferogram file\n        \"\"\"\n        self.tile = tile\n        self.r_start = self.tile.top_left_y\n        self.r_end = self.tile.bottom_right_y\n        self.c_start = self.tile.top_left_x\n        self.c_end = self.tile.bottom_right_x\n        if ifg_dict is not None:  # should be used with MPI\n            ifg = ifg_dict[ifg_or_path]\n            self.nan_fraction = ifg.nan_fraction\n            self.first = ifg.first\n            self.second = ifg.second\n            self.time_span = ifg.time_span\n            phase_file = 'phase_data_{}_{}.npy'.format(basename(ifg_or_path).split('.')[0], tile.index)\n            self.phase_data = np.load(join(params[C.TMPDIR], phase_file))\n        else:\n            # check if Ifg was sent.\n            if isinstance(ifg_or_path, Ifg):\n                ifg = ifg_or_path\n            else:\n                self.data_path = ifg_or_path\n                ifg = Ifg(ifg_or_path)\n            self.phase_data = None\n            self.nan_fraction = None\n            self.first = None\n            self.second = None\n            self.time_span = None\n        if isinstance(ifg, Ifg):\n            self.read_tile(ifg)\n\n    def read_tile(self, ifg: Ifg):\n        \"\"\"\n        Read interferogram file if not already open.\n        \"\"\"\n        if not ifg.is_open:\n            ifg.open(readonly=True)\n        ifg.nodata_value = 0\n        self.phase_data = ifg.phase_data[self.r_start:self.r_end,\n                                         self.c_start:self.c_end]\n        self.nan_fraction = ifg.nan_fraction\n        self.first = ifg.first\n        self.second = ifg.second\n        self.time_span = ifg.time_span\n        ifg.phase_data = None\n        ifg.close()  # close base ifg\n\n    @property\n    def nrows(self):\n        \"\"\"\n        Determine number of rows in tile.\n        \"\"\"\n        return self.r_end - self.r_start\n\n    @property\n    def ncols(self):\n        \"\"\"\n        Determine number of columns in tile.\n        \"\"\"\n        return self.c_end - self.c_start\n\n\nclass Incidence(RasterBase):   # pragma: no cover\n    \"\"\"\n    Class for storing viewing geometry data.\n    e.g. incidence and azimuth raster values\n    \"\"\"\n\n    def __init__(self, path):\n        \"\"\"\n        Incidence object constructor.\n        \"\"\"\n        RasterBase.__init__(self, path)\n        self._incidence_band = None\n        self._azimuth_band = None\n        self._incidence_data = None\n        self._azimuth_data = None\n\n    @property\n    def incidence_band(self):\n        \"\"\"\n        Returns the GDALBand for the incidence angle layer.\n        \"\"\"\n\n        if self._incidence_band is None:\n            self._incidence_band = self._get_band(1)\n        return self._incidence_band\n\n    @property\n    def incidence_data(self):\n        \"\"\"\n        Returns the incidence band as an array.\n        \"\"\"\n        if self._incidence_data is None:\n            self._incidence_data = self.incidence_band.ReadAsArray()\n        return self._incidence_data\n\n    @property\n    def azimuth_band(self):\n        \"\"\"\n        Returns the GDALBand for the azimuth layer.\n        \"\"\"\n        if self._azimuth_band is None:\n            self._azimuth_band = self._get_band(2)\n        return self._azimuth_band\n\n    @property\n    def azimuth_data(self):\n        \"\"\"\n        Returns the azimuth band as an array.\n        \"\"\"\n        if self._azimuth_data is None:\n            self._azimuth_data = self.azimuth_band.ReadAsArray()\n        return self._azimuth_data\n\n\nclass TileMixin:\n    def __call__(self, tile: Tile):\n        t = tile\n        return self.data[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x]\n\n\nclass DEM(RasterBase, TileMixin):\n    \"\"\"\n    Generic raster class for single band DEM/Geometry files.\n    \"\"\"\n\n    def __init__(self, path):\n        \"\"\"\n        DEM constructor.\n        \"\"\"\n        RasterBase.__init__(self, path)\n        self._band = None\n        self._data = None\n\n    @property\n    def band(self):\n        \"\"\"\n        Returns the GDALBand for the elevation layer.\n        \"\"\"\n        if not self.is_open:\n            self.open()\n        if self._band is None:\n            self._band = self._get_band(1)\n        return self._band\n\n    @property\n    def data(self):\n        \"\"\"\n        Returns the geometry band as an array.\n        \"\"\"\n        if self._data is None:\n            self._data = self.band.ReadAsArray()\n        return self._data\n\n\nclass MemGeometry(TileMixin):\n\n    def __init__(self, data):\n        \"\"\"\n        Set phase data value\n        \"\"\"\n        self._data = data\n\n    @property\n    def data(self):\n        return self._data\n\n\nGeometry = DEM\n\n\nclass IfgException(Exception):\n    \"\"\"\n    Generic exception class for interferogram errors.\n    \"\"\"\n\n\nclass RasterException(Exception):\n    \"\"\"\n    Generic exception for raster errors.\n    \"\"\"\n\n\nclass EpochList(object):\n    \"\"\"\n    Metadata container for epoch related information.\n    \"\"\"\n\n    def __init__(self, dates=None, repeat=None, spans=None):\n        \"\"\"\n        Construct epochlist object\n        \"\"\"\n        self.dates = dates # list of unique dates from all the ifgs\n        self.repeat = repeat\n        self.spans = spans  # time span from earliest ifg\n\n    def __str__(self):\n        return \"EpochList: %s\" % str(self.dates)\n\n    def __repr__(self):\n        return \"EpochList: %s\" % repr(self.dates)\n\n\ndef convert_radians_to_mm(data, wavelength):\n    \"\"\"\n    Function to translates phase in units of radians to units in millimetres.\n\n    :param ndarray data: Interferogram phase data array\n    :param float wavelength: Radar wavelength in metres\n\n    :return: data: converted phase data\n    :rtype: ndarray\n    \"\"\"\n    return data * ifc.MM_PER_METRE * (wavelength / (4 * math.pi))\n\n\ndef convert_mm_to_radians(data, wavelength):\n    \"\"\"\n    Function to translates phase in units of radians to units in millimetres.\n\n    :param ndarray data: Interferogram phase data array\n    :param float wavelength: Radar wavelength in metres\n\n    :return: data: converted phase data\n    :rtype: ndarray\n    \"\"\"\n    return data / ifc.MM_PER_METRE * ((4 * math.pi) / wavelength)\n\n\ndef nanmedian(x):\n    \"\"\"\n    Determine the median of values excluding nan values.\n    Use different numpy algorithm dependent on numpy version.\n\n    :param ndarray x: array of numeric data.\n\n    :return: y: median value\n    :rtype: float\n    \"\"\"\n    # pylint: disable=no-member\n    version = [int(i) for i in pkg_resources.get_distribution(\"numpy\").version.split('.')[:2]]\n    if version[0] == 1 and version[1] > 9:\n        return np.nanmedian(x)\n    else:   # pragma: no cover\n        return np.median(x[~np.isnan(x)])\n\n\ndef _is_interferogram(hdr):\n    \"\"\"\n    Convenience function to determine if file is interferogram\n    \"\"\"\n    return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \\\n           (hdr[ifc.INPUT_TYPE] == InputTypes.IFG if ifc.INPUT_TYPE in hdr else True)\n\n\ndef _is_coherence(hdr):\n    \"\"\"\n    Convenience function to determine if file is interferogram\n    \"\"\"\n    return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \\\n           (hdr[ifc.INPUT_TYPE] == InputTypes.COH if ifc.INPUT_TYPE in hdr else False)\n\n\ndef _is_baseline(hdr):\n    \"\"\"\n    Convenience function to determine if file is baseline file\n    \"\"\"\n    return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \\\n           (hdr[ifc.INPUT_TYPE] == InputTypes.BASE if ifc.INPUT_TYPE in hdr else False)\n\n\ndef _is_lookuptable(hdr):\n    \"\"\"\n    Convenience function to determine if file is lookup table file\n    \"\"\"\n    return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \\\n           (hdr[ifc.INPUT_TYPE] == InputTypes.LT if ifc.INPUT_TYPE in hdr else False)\n\n\ndef _is_incidence(hdr):\n    \"\"\"\n    Convenience function to determine if incidence file\n    \"\"\"\n    return 'FILE_TYPE' in hdr\n\n\ndef write_fullres_geotiff(header, data_path, dest, nodata):\n    # pylint: disable=too-many-statements\n    \"\"\"\n    Creates a copy of input image data (interferograms, DEM, incidence maps\n    etc) in GeoTIFF format with PyRate metadata.\n\n    :param dict header: Interferogram metadata dictionary\n    :param str data_path: Input file\n    :param str dest: Output destination file\n    :param float nodata: No-data value\n\n    :return: None, file saved to disk\n    \"\"\"\n    # pylint: disable=too-many-branches\n    # pylint: disable=too-many-locals\n    ifg_proc = header[ifc.PYRATE_INSAR_PROCESSOR]\n    ncols = header[ifc.PYRATE_NCOLS]\n    nrows = header[ifc.PYRATE_NROWS]\n    bytes_per_col, fmtstr = data_format(ifg_proc, _is_interferogram(header), ncols)\n    if _is_interferogram(header) and ifg_proc == ROIPAC:\n        # roipac ifg has 2 bands\n        _check_raw_data(bytes_per_col*2, data_path, ncols, nrows)\n    else:\n        _check_raw_data(bytes_per_col, data_path, ncols, nrows)\n\n    # position and projection data\n    gt = [header[ifc.PYRATE_LONG], header[ifc.PYRATE_X_STEP], 0, header[ifc.PYRATE_LAT], 0, header[ifc.PYRATE_Y_STEP]]\n    srs = osr.SpatialReference()\n    res = srs.SetWellKnownGeogCS(header[ifc.PYRATE_DATUM])\n    if res:\n        msg = 'Unrecognised projection: %s' % header[ifc.PYRATE_DATUM]\n        raise GeotiffException(msg)\n\n    wkt = srs.ExportToWkt()\n    dtype = 'float32' if (_is_interferogram(header) or _is_incidence(header) or _is_coherence(header) or \\\n                          _is_baseline(header) or _is_lookuptable(header)) else 'int16'\n\n    # get subset of metadata relevant to PyRate\n    md = collate_metadata(header)\n\n    # create GDAL object\n    ds = gdal_dataset(\n        dest, ncols, nrows, driver=\"GTiff\", bands=1, dtype=dtype, metadata=md, crs=wkt, geotransform=gt,\n        creation_opts=[\"compress=packbits\"]\n    )\n\n    # copy data from the binary file\n    band = ds.GetRasterBand(1)\n    band.SetNoDataValue(nodata)\n\n    row_bytes = ncols * bytes_per_col\n\n    with open(data_path, 'rb') as f:\n        for y in range(nrows):\n            if ifg_proc == ROIPAC:\n                if _is_interferogram(header):\n                    f.seek(row_bytes, 1)  # skip interleaved band 1\n\n            data = struct.unpack(fmtstr, f.read(row_bytes))\n            band.WriteArray(np.array(data).reshape(1, ncols), yoff=y)\n\n    ds = None  # manual close\n    del ds\n\n\ndef gdal_dataset(out_fname, columns, rows, driver=\"GTiff\", bands=1,\n                 dtype='float32', metadata=None, crs=None,\n                 geotransform=None, creation_opts=None):\n    \"\"\"\n    Initialises a py-GDAL dataset object for writing image data.\n    \"\"\"\n    if dtype == 'float32':\n        gdal_dtype = gdal.GDT_Float32\n    elif dtype == 'int16':\n        gdal_dtype = gdal.GDT_Int16\n    else:\n        # assume gdal.GDT val is passed to function\n        gdal_dtype = dtype\n\n    # create output dataset\n    driver = gdal.GetDriverByName(driver)\n    outds = driver.Create(out_fname, columns, rows, bands, gdal_dtype, options=creation_opts)\n\n    # geospatial info\n    outds.SetGeoTransform(geotransform)\n    outds.SetProjection(crs)\n\n    # add metadata\n    if metadata is not None:\n        for k, v in metadata.items():\n            outds.SetMetadataItem(k, str(v))\n\n    return outds\n\n\ndef collate_metadata(header):\n    \"\"\"\n    Grab metadata relevant to PyRate from input metadata\n\n    :param dict header: Input file metadata dictionary\n\n    :return: dict of relevant metadata for PyRate\n    \"\"\"\n    md = dict()\n\n    def __common_ifg_coh_update(header, md):\n        for k in [ifc.PYRATE_WAVELENGTH_METRES, ifc.PYRATE_TIME_SPAN,\n                  ifc.PYRATE_INSAR_PROCESSOR,\n                  ifc.FIRST_DATE, ifc.SECOND_DATE,\n                  ifc.DATA_UNITS]:\n            md.update({k: str(header[k])})\n        if header[ifc.PYRATE_INSAR_PROCESSOR] == GAMMA:\n            for k in [ifc.FIRST_TIME, ifc.SECOND_TIME,\n                      ifc.PYRATE_NROWS, ifc.PYRATE_NCOLS,\n                      ifc.PYRATE_INCIDENCE_DEGREES, ifc.PYRATE_HEADING_DEGREES,\n                      ifc.PYRATE_AZIMUTH_DEGREES, ifc.PYRATE_RANGE_PIX_METRES,\n                      ifc.PYRATE_RANGE_N, ifc.PYRATE_RANGE_LOOKS,\n                      ifc.PYRATE_AZIMUTH_PIX_METRES, ifc.PYRATE_AZIMUTH_N,\n                      ifc.PYRATE_AZIMUTH_LOOKS, ifc.PYRATE_PRF_HERTZ,\n                      ifc.PYRATE_NEAR_RANGE_METRES, ifc.PYRATE_SAR_EARTH_METRES,\n                      ifc.PYRATE_SEMI_MAJOR_AXIS_METRES, ifc.PYRATE_SEMI_MINOR_AXIS_METRES]:\n                md.update({k: str(header[k])})\n            if ifc.PYRATE_BASELINE_T in header:\n                for k in [ifc.PYRATE_BASELINE_T, ifc.PYRATE_BASELINE_C,\n                          ifc.PYRATE_BASELINE_N, ifc.PYRATE_BASELINE_RATE_T,\n                          ifc.PYRATE_BASELINE_RATE_C, ifc.PYRATE_BASELINE_RATE_N]:\n                    md.update({k: str(header[k])})\n\n    if _is_coherence(header):\n        __common_ifg_coh_update(header, md)\n        md.update({ifc.DATA_TYPE: ifc.COH})\n    elif _is_baseline(header):\n        __common_ifg_coh_update(header, md)\n        md.update({ifc.DATA_TYPE: ifc.BASE})\n    elif _is_interferogram(header):\n        __common_ifg_coh_update(header, md)\n        md.update({ifc.DATA_TYPE: ifc.ORIG})\n    elif _is_lookuptable(header):\n        md.update({ifc.DATA_TYPE: ifc.LT})\n    elif _is_incidence(header):\n        md.update({ifc.DATA_TYPE: ifc.INCIDENCE})\n    else:  # must be dem\n        md.update({ifc.DATA_TYPE: ifc.DEM})\n\n    return md\n\n\ndef data_format(ifg_proc, is_ifg, ncols):\n    \"\"\"\n    Convenience function to determine the bytesize and format of input files\n    \"\"\"\n    if ifg_proc == GAMMA:\n        fmtstr = '!' + ('f' * ncols)  # data format is big endian float32s\n        bytes_per_col = 4\n    elif ifg_proc == ROIPAC:\n        if is_ifg:\n            fmtstr = '<' + ('f' * ncols)  # roipac ifgs are little endian float32s\n            bytes_per_col = 4\n        else:\n            fmtstr = '<' + ('h' * ncols)  # roipac DEM is little endian signed int16\n            bytes_per_col = 2\n    else:  # pragma: no cover\n        msg = 'Unrecognised InSAR Processor: %s' % ifg_proc\n        raise GeotiffException(msg)\n    return bytes_per_col, fmtstr\n\n\ndef _check_raw_data(bytes_per_col, data_path, ncols, nrows):\n    \"\"\"\n    Convenience function to check the file size is as expected\n    \"\"\"\n    size = ncols * nrows * bytes_per_col\n    act_size = os.stat(data_path).st_size\n    if act_size != size:\n        msg = '%s should have size %s, not %s. Is the correct file being used?'\n        raise GeotiffException(msg % (data_path, size, act_size))\n\n\ndef write_unw_from_data_or_geotiff(geotif_or_data, dest_unw, ifg_proc):\n    \"\"\"\n    Function to write numpy array data or a geotiff to a GAMMA-format\n    big-endian float32 .unw file\n\n    :param str geotif_or_data: path name of geotiff file to convert\n        OR\n    :param ndarray geotif_or_data: numpy array of data to convert\n    :param str dest_unw: destination unw file\n    :param int ifg_proc: processor type, GAMMA=1, ROIPAC=0\n\n    :return: None, file saved to disk\n    \"\"\"\n    if ifg_proc != 1:\n        raise NotImplementedError('only supports GAMMA format for now')\n    if isinstance(geotif_or_data, str):\n        assert os.path.exists(geotif_or_data), 'make sure geotif exists'\n        ds = gdal.Open(geotif_or_data)\n        data = ds.ReadAsArray()\n        ds = None\n    else:\n        data = geotif_or_data\n\n    nrows, ncols = data.shape\n    fmtstr = '!' + ('f' * ncols)  # data format is big endian float32s\n\n    with open(dest_unw, 'wb') as f:\n        for y in range(nrows):\n            col_data = struct.pack(fmtstr, *data[y, :])\n            f.write(col_data)\n\n\ndef write_output_geotiff(md, gt, wkt, data, dest, nodata):\n    # pylint: disable=too-many-arguments\n    \"\"\"\n    Writes PyRate output data to a GeoTIFF file.\n\n    :param dict md: Dictionary containing PyRate metadata\n    :param list gt: GDAL geotransform for the data\n    :param list wkt: GDAL projection information for the data\n    :param ndarray data: Output data array to save\n    :param str dest: Destination file name\n    :param float nodata: No data value of data\n\n    :return None, file saved to disk\n    \"\"\"\n\n    driver = gdal.GetDriverByName(\"GTiff\")\n    nrows, ncols = data.shape\n    ds = driver.Create(dest, ncols, nrows, 1, gdal.GDT_Float32, options=['compress=packbits'])\n    # set spatial reference for geotiff\n    ds.SetGeoTransform(gt)\n    ds.SetProjection(wkt)\n\n    # set data type metadata\n    ds.SetMetadataItem(ifc.DATA_TYPE, str(md[ifc.DATA_TYPE]))\n\n    # set other metadata\n    for k in [ifc.SEQUENCE_POSITION, ifc.PYRATE_REFPIX_X, ifc.PYRATE_REFPIX_Y, ifc.PYRATE_REFPIX_LAT,\n              ifc.PYRATE_REFPIX_LON, ifc.PYRATE_MEAN_REF_AREA, ifc.PYRATE_STDDEV_REF_AREA,\n              ifc.EPOCH_DATE, C.LOS_PROJECTION.upper(), C.SIGNAL_POLARITY.upper(),\n              C.VELERROR_NSIG.upper()]:\n        if k in md:\n            ds.SetMetadataItem(k, str(md[k]))\n\n    # write data to geotiff\n    band = ds.GetRasterBand(1)\n    band.SetNoDataValue(nodata)\n    band.WriteArray(data, 0, 0)\n\n    del ds\n\n\ndef write_geotiff(data, outds, nodata):\n    # pylint: disable=too-many-arguments\n    \"\"\"\n    A generic routine for writing a NumPy array to a geotiff.\n\n    :param ndarray data: Output data array to save\n    :param obj outds: GDAL destination object\n    :param float nodata: No data value of data\n\n    :return None, file saved to disk\n    \"\"\"\n    # only support \"2 <= dims <= 3\"\n    if data.ndim == 3:\n        count, height, width = data.shape\n    elif data.ndim == 2:\n        height, width = data.shape\n    else:\n        msg = \"Only support dimensions of '2 <= dims <= 3'.\"\n        raise GeotiffException(msg)\n\n    # write data to geotiff\n    band = outds.GetRasterBand(1)\n    band.SetNoDataValue(nodata)\n    band.WriteArray(data, 0, 0)\n\n    outds = None\n    band = None\n    del outds\n    del band\n\n\nclass GeotiffException(Exception):\n    \"\"\"\n    Geotiff exception class\n    \"\"\"\n\n\ndef create_tiles(shape, nrows=2, ncols=2):\n    \"\"\"\n    Return a list of tiles containing nrows x ncols with each tile preserving\n    the physical layout of original array. The number of rows can be changed\n    (increased) such that the resulting tiles with float32's do not exceed\n    500MB in memory. When the array shape (rows, columns) are not divisible\n    by (nrows, ncols) then some of the array dimensions can change according\n    to numpy.array_split.\n\n    :param tuple shape: Shape tuple (2-element) of interferogram.\n    :param int nrows: Number of rows of tiles\n    :param int ncols: Number of columns of tiles\n\n    :return: List of Tile class instances.\n    :rtype: list\n    \"\"\"\n\n    if len(shape) != 2:\n        raise ValueError('shape must be a length 2 tuple')\n\n    no_y, no_x = shape\n\n    if ncols > no_x or nrows > no_y:\n        raise ValueError('nrows/cols must be greater than ifg dimensions')\n    col_arr = np.array_split(range(no_x), ncols)\n    row_arr = np.array_split(range(no_y), nrows)\n    return [Tile(i, (r[0], c[0]), (r[-1]+1, c[-1]+1)) for i, (r, c) in enumerate(product(row_arr, col_arr))]\n\n\ndef get_tiles(ifg_path, rows, cols) -> List[Tile]:\n    \"\"\"\n    Break up the interferograms into smaller tiles based on user supplied\n    rows and columns.\n\n    :param list ifg_path: List of destination geotiff file names\n    :param int rows: Number of rows to break each interferogram into\n    :param int cols: Number of columns to break each interferogram into\n\n    :return: tiles: List of shared.Tile instances\n    :rtype: list\n    \"\"\"\n    ifg = Ifg(ifg_path)\n    ifg.open(readonly=True)\n    tiles = create_tiles(ifg.shape, nrows=rows, ncols=cols)\n    ifg.close()\n    return tiles\n\n\ndef nan_and_mm_convert(ifg, params):\n    \"\"\"\n    Perform millimetre and nan conversion on interferogram data\n\n    :param Ifg instance ifg: Interferogram class instance\n    :param dict params: Dictionary of parameters\n\n    :return: None, data modified internally\n    \"\"\"\n    nan_conversion = params[C.NAN_CONVERSION]\n    if nan_conversion:  # nan conversion happens here in networkx mst\n        # if not ifg.nan_converted:\n        ifg.nodata_value = params[C.NO_DATA_VALUE]\n        ifg.convert_to_nans()\n    if not ifg.mm_converted:\n        ifg.convert_to_mm()\n\n\ndef cell_size(lat, lon, x_step, y_step):\n    # pylint: disable=invalid-name\n    \"\"\"\n    Converts X|Y_STEP in degrees to X & Y cell size in metres.\n    This function depends on PyProj/PROJ4 to implement the function\n\n    :param float lat: Latitude in degrees\n    :param float lon: Longitude in degrees\n    :param float x_step: Horizontal step size in degrees\n    :param float y_step: Vertical step size in degrees\n\n    :return: tuple of X and Y cell size floats\n    :rtype: tuple\n    \"\"\"\n    if lat > 84.0 or lat < -80:\n        msg = \"No UTM zone for polar region: > 84 degrees N or < 80 degrees S. \" \\\n              \"Provided values are lat: \"+str(lat) +\" long: \" +str(lon)\n        raise ValueError(msg)\n\n    zone = _utm_zone(lon)\n    p0 = pyproj.Proj(proj='latlong', ellps='WGS84')\n    p1 = pyproj.Proj(proj='utm', zone=zone, ellps='WGS84')\n\n    x0, y0 = pyproj.transform(p0, p1, lon, lat, errcheck=True)\n    x1, y1 = pyproj.transform(p0, p1, lon + x_step, lat + y_step, errcheck=True)\n\n    return tuple(abs(e) for e in (x1 - x0, y1 - y0))\n\n\ndef _utm_zone(longitude):\n    \"\"\"\n    Returns basic UTM zone for given longitude in degrees.\n    Currently does NOT handle the sub-zoning around Scandanavian countries.\n    See http://www.dmap.co.uk/utmworld.htm\n    \"\"\"\n    if longitude == 180:\n        return 60.0\n    return floor((longitude + 180) / 6.0) + 1\n\n\nclass PrereadIfg:\n    \"\"\"\n    Convenience class for handling pre-calculated ifg params\n    \"\"\"\n    # pylint: disable=too-many-arguments\n    # pylint: disable=too-many-instance-attributes\n    def __init__(self, path, tmp_path, nan_fraction, first, second, time_span,\n                 nrows, ncols, metadata):\n        self.path = path\n        self.tmp_path = tmp_path\n        self.nan_fraction = nan_fraction\n        self.first = first\n        self.second = second\n        self.time_span = time_span\n        self.nrows = nrows\n        self.ncols = ncols\n        self.shape = (nrows, ncols)\n        self.metadata = metadata\n\n\ndef save_numpy_phase(ifg_paths, params):\n    \"\"\"\n    Split interferogram phase data in to tiles (if they exist in the params\n    dict) and save as numpy array files on disk.\n\n    :param list ifg_paths: List of strings for interferogram paths\n    :param dict params: Dictionary of configuration parameters\n\n    :return: None, numpy file saved to disk\n    \"\"\"\n    tiles = params['tiles']\n    outdir = params[C.TMPDIR]\n    if not os.path.exists(outdir):\n        mkdir_p(outdir)\n    for ifg_path in mpiops.array_split(ifg_paths):\n        ifg = Ifg(ifg_path)\n        ifg.open()\n        phase_data = ifg.phase_data\n        bname = basename(ifg_path).split('.')[0]\n        for t in tiles:\n            p_data = phase_data[t.top_left_y:t.bottom_right_y,\n                                t.top_left_x:t.bottom_right_x]\n            phase_file = 'phase_data_{}_{}.npy'.format(bname, t.index)\n            np.save(file=join(outdir, phase_file),\n                    arr=p_data)\n        ifg.close()\n    mpiops.comm.barrier()\n    log.debug(f'Finished writing phase_data to numpy files in {outdir}')\n\n\ndef get_geotiff_header_info(ifg_path):\n    \"\"\"\n    Return information from a geotiff interferogram header using GDAL methods.\n\n    :param str ifg_path: path to interferogram geotiff file\n\n    :return: md: PyRate metadata\n    :rtype: list\n    :return: gt: GDAL geotransform for the data\n    :rtype: list\n    :return: wkt: GDAL projection information for the data\n    :rtype: list\n    \"\"\"\n    ds = gdal.Open(ifg_path)\n    md = ds.GetMetadata()  # get metadata for writing on output tifs\n    gt = ds.GetGeoTransform()  # get geographical bounds of data\n    wkt = ds.GetProjection()  # get projection of data\n    ds = None  # close dataset\n    return gt, md, wkt\n\n\ndef warp_required(xlooks, ylooks, crop):\n    \"\"\"\n    Check if a crop or multi-look operation is required.\n\n    :param int xlooks: Resampling/multi-looking in x dir\n    :param int ylooks: Resampling/multilooking in y dir\n    :param int crop: Interferogram crop option\n\n    :return: True if params show rasters need to be cropped and/or resized\n    :rtype: bool\n    \"\"\"\n    if xlooks > 1 or ylooks > 1:\n        return True\n    if crop is None:\n        return False\n    return True\n\n\ndef check_correction_status(ifgs, meta):  # pragma: no cover\n    \"\"\"\n    Generic function for checking if a correction has already been performed\n    in a previous run by interrogating PyRate meta data entries\n\n    :param preread_ifgs: Dictionary of pre-read interferogram information\n    :param str meta: Meta data flag to check for\n\n    :return: True if correction has been performed, otherwise False\n    :rtype: bool\n    \"\"\"\n    def close_all(ifgs):\n        for ifg in ifgs:\n            ifg.close()\n    \n    if not isinstance(ifgs[0], Ifg):\n        ifgs = [Ifg(ifg_path) for ifg_path in ifgs]\n\n    for ifg in ifgs:\n        if not ifg.is_open:\n            ifg.open()\n\n    flags = [meta in ifg.meta_data for ifg in ifgs]\n    if all(flags):\n        log.info('Skipped: interferograms already corrected')\n        return True\n    elif not all(flags) and any(flags):\n        log.debug('Detected mix of corrected and uncorrected interferograms')\n        for i, flag in zip(ifgs, flags):\n            if flag:\n                msg = '{}: correction detected'.format(i.data_path)\n            else:\n                msg = '{}: correction NOT detected'.format(i.data_path)\n            log.debug(msg)\n            close_all(ifgs)\n            raise CorrectionStatusError(msg)\n    else:\n        log.debug('Calculating corrections')\n        close_all(ifgs)\n        return False\n\n\nclass CorrectionStatusError(Exception):\n    \"\"\"\n    Generic class for correction status errors.\n    \"\"\"\n\n\ndef extract_epochs_from_filename(filename_with_epochs: str) -> List[str]:\n    src_epochs = re.findall(r\"(\\d{8})\", str(filename_with_epochs))\n    if not len(src_epochs) > 0:\n        src_epochs = re.findall(r\"(\\d{6})\", str(filename_with_epochs))\n    return src_epochs\n\n\ndef mpi_vs_multiprocess_logging(step, params):\n    if mpiops.size > 1:  # Over-ride input options if this is an MPI job\n        log.info(f\"Running '{step}' step with MPI using {mpiops.size} processes\")\n        log.warning(\"Disabling joblib parallel processing (setting parallel = 0)\")\n        params[C.PARALLEL] = 0\n    else:\n        if params[C.PARALLEL] == 1:\n            log.info(f\"Running '{step}' step in parallel using {params[C.PROCESSES]} processes\")\n        else:\n            log.info(f\"Running '{step}' step in serial\")\n\n\ndef dem_or_ifg(data_path: str) -> Union[Ifg, DEM]:\n    \"\"\"\n    Returns an Ifg or DEM class object from input geotiff file.\n\n    :param data_path: file path name\n\n    :return: Interferogram or DEM object from input file\n    :rtype: Ifg or DEM class object\n    \"\"\"\n    ds = gdal.Open(data_path)\n    md = ds.GetMetadata()\n    if ifc.FIRST_DATE in md:  # ifg\n        return Ifg(data_path)\n    else:\n        return DEM(data_path)\n    ds = None  # close the dataset\n\n\ndef join_dicts(dicts: List[dict]) -> dict:\n    \"\"\"\n    Function to concatenate a list of dictionaries of distinct keys.\n    \"\"\"\n    if dicts is None:  # pragma: no cover\n        return {}\n    assembled_dict = {k: v for D in dicts for k, v in D.items()}\n    return assembled_dict\n\n\ndef iterable_split(func: Callable, iterable: Iterable, params: dict, *args, **kwargs) -> np.ndarray:\n    \"\"\"\n    # TODO: a faster version using buffer-provider objects via the uppercase communication method\n    A faster version of iterable/tiles_split is possible when the return values from each process is of the same size\n    and will be addressed in future. In this case a buffer-provider object can be sent between processes using the\n    uppercase communication (like Gather instead of gather) methods which can be significantly faster.\n    \"\"\"\n    if params[C.PARALLEL]:\n        ret_combined = {}\n        rets = Parallel(\n            n_jobs=params[C.PROCESSES],\n            verbose=joblib_log_level(C.LOG_LEVEL)\n        )(delayed(func)(t, params, *args, **kwargs) for t in iterable)\n        for i, r in enumerate(rets):\n            ret_combined[i] = r\n    else:\n        iterable_with_index = list(enumerate(iterable))\n        process_iterables = mpiops.array_split(iterable_with_index)\n        ret_combined = {}\n        for i, t in process_iterables:\n            ret_combined[i] = func(t, params, *args, **kwargs)\n        ret_combined = join_dicts(mpiops.comm.allgather(ret_combined))\n    ret = np.array([v[1] for v in ret_combined.items()], dtype=object)\n    mpiops.comm.barrier()\n    return ret\n\n\ndef tiles_split(func: Callable, params: dict, *args, **kwargs) -> np.ndarray:\n    \"\"\"\n    Function to pass tiles of a full array to an array processing function call.\n    :param func: Name of function to pass tiles to.\n    :param params: Dictionary of PyRate configuration parameters; must contain a 'tiles' list\n    \"\"\"\n    tiles = params[C.TILES]\n    return iterable_split(func, tiles, params, *args, **kwargs)\n\n\ndef output_tiff_filename(inpath: str, outpath: str) -> str:\n    \"\"\"\n    Output geotiff filename for a given input filename.\n\n    :param inpath: Path of input file location.\n    :param outpath: Path of output file location.\n    :return: name: Geotiff filename for the given file.\n    \"\"\"\n    fname, ext = os.path.basename(inpath).split('.')\n    outpath = os.path.dirname(inpath) if outpath is None else outpath\n    if ext == 'tif':\n        name = os.path.join(outpath, fname + '.tif')\n    else:\n        name = os.path.join(outpath, fname + '_' + ext + '.tif')\n    return name\n\n\ndef remove_file_if_exists(filename: str) -> None:\n    \"\"\"\n    Function to remove a file if it already exists.\n    :param filename: Name of file to be removed.\n    \"\"\"\n    try:\n        os.remove(filename)\n    except OSError:\n        pass\n"
  },
  {
    "path": "pyrate/core/stack.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module implements pixel-by-pixel rate\n(velocity) estimation using an iterative weighted least-squares\nstacking method.\n\"\"\"\nimport os\nimport pickle as cp\nfrom scipy.linalg import solve, cholesky, qr, inv\nfrom numpy import nan, isnan, sqrt, diag, delete, array, float32, size\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core import shared\nfrom pyrate.core.shared import tiles_split\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration\n\n\ndef stack_rate_array(ifgs, params, vcmt, mst=None):\n    \"\"\"\n    This function loops over all interferogram pixels in a 3-dimensional array and estimates\n    the pixel rate (velocity) by applying the iterative weighted least-squares stacking\n    algorithm 'pyrate.core.stack.stack_rate_pixel'.\n\n    :param Ifg.object ifgs: Sequence of objects containing the interferometric observations\n    :param dict params: Configuration parameters\n    :param ndarray vcmt: Derived positive definite temporal variance covariance matrix\n    :param ndarray mst: Pixel-wise matrix describing the minimum spanning tree network\n\n    :return: rate: Rate (velocity) map\n    :rtype: ndarray\n    :return: error: Standard deviation of the rate map\n    :rtype: ndarray\n    :return: samples: Number of observations used in rate calculation for each pixel\n    :rtype: ndarray\n    \"\"\"\n    nsig, pthresh, cols, error, mst, obs, rate, rows, samples, span = _stack_setup(ifgs, mst, params)\n\n    # pixel-by-pixel calculation.\n    # nested loops to loop over the 2 image dimensions\n    for i in range(rows):\n        for j in range(cols):\n            rate[i, j], error[i, j], samples[i, j] = stack_rate_pixel(obs[:, i, j], mst[:, i, j], vcmt, span, nsig, pthresh)\n\n    return rate, params[C.VELERROR_NSIG]*error, samples\n\ndef mask_rate(rate, error, maxsig):\n    \"\"\"\n    Function to mask pixels in the rate and error arrays when the error\n    is greater than the error threshold 'maxsig'.\n\n    :param ndarray rate: array of pixel rates derived by stacking\n    :param ndarray error: array of errors for the pixel rates\n    :param int maxsig: error threshold for masking (in millimetres).\n\n    :return: rate: Masked rate (velocity) map\n    :rtype: ndarray\n    :return: error: Masked error (standard deviation) map\n    :rtype: ndarray\n    \"\"\"\n    # initialise mask array with existing NaNs\n    mask = ~isnan(error)\n    # original Nan count\n    orig = np.count_nonzero(mask)\n    # determine where error is larger than the maximum sigma threshold\n    mask[mask] &= error[mask] > maxsig\n    # replace values with NaNs\n    rate[mask] = nan\n    error[mask] = nan\n    # calculate percentage of masked pixels\n    nummasked = int(np.count_nonzero(mask)/orig*100)\n    log.info('Percentage of pixels masked = {}%'.format(nummasked))\n\n    return rate, error\n\n\ndef stack_rate_pixel(obs, mst, vcmt, span, nsig, pthresh):\n    \"\"\"\n    Algorithm to estimate the rate (velocity) for a single pixel using iterative\n    weighted least-squares stacking method.\n\n    :param ndarray obs: Vector of interferometric phase observations for the pixel\n    :param ndarray mst: Vector describing the minimum spanning tree network for the pixel\n    :param ndarray vcmt: Derived positive definite temporal variance covariance matrix\n    :param ndarray span: Vector of interferometric time spans\n    :param int nsig: Threshold for iterative removal of interferometric observations\n    :param int pthresh: Threshold for minimum number of observations for the pixel\n\n    :return: rate: Estimated rate (velocity) for the pixel\n    :rtype: float64\n    :return: error: Standard deviation of the observations with respect to the estimated rate\n    :rtype: float64\n    :return: samples: Number of observations used in the rate estimation for the pixel\n    :rtype: int\n    \"\"\"\n\n    # find the indices of independent ifgs from MST\n    ind = np.nonzero(mst)[0]  # only True's in mst are chosen\n    # iterative loop to calculate 'robust' velocity for pixel\n    default_no_samples = len(ind)\n\n    while len(ind) >= pthresh:\n        # select ifg observations\n        ifgv = obs[ind]\n\n        # form design matrix from appropriate ifg time spans\n        B = span[:, ind]\n\n        # Subset of full VCM matrix for selected observations\n        vcm_temp = vcmt[ind, np.vstack(ind)]\n\n        # Get the lower triangle cholesky decomposition.\n        # V must be positive definite (symmetrical and square)\n        T = cholesky(vcm_temp, 1)\n\n        # Incorporate inverse of VCM into the design matrix\n        # and observations vector\n        A = solve(T, B.transpose())\n        b = solve(T, ifgv.transpose())\n\n        # Factor the design matrix, incorporate covariances or weights into the\n        # system of equations, and transform the response vector.\n        Q, R, _ = qr(A, mode='economic', pivoting=True)\n        z = Q.conj().transpose().dot(b)\n\n        # Compute the Lstsq coefficient for the velocity\n        v = solve(R, z)\n\n        # Compute the model errors\n        err1 = inv(vcm_temp).dot(B.conj().transpose())\n        err2 = B.dot(err1)\n        err = sqrt(diag(inv(err2)))\n\n        # Compute the residuals (model minus observations)\n        r = (B * v) - ifgv\n\n        # determine the ratio of residuals and apriori variances\n        w = cholesky(inv(vcm_temp))\n        wr = abs(np.dot(w, r.transpose()))\n\n        # test if maximum ratio is greater than user threshold.\n        max_val = wr.max()\n        if max_val > nsig:\n            # if yes, discard and re-do the calculation.\n            ind = delete(ind, wr.argmax())\n        else:\n            # if no, save estimate, exit the while loop and go to next pixel\n            return v[0], err[0], ifgv.shape[0]\n    # dummy return for no change\n    return np.nan, np.nan, default_no_samples\n\n\ndef _stack_setup(ifgs, mst, params):\n    \"\"\"\n    Convenience function for stack rate setup\n    \"\"\"\n    # stack rate parameters from config file\n    # n-sigma ratio used to threshold 'model minus observation' residuals\n    nsig = params[C.LR_NSIG]\n    # Pixel threshold; minimum number of observations for a pixel\n    pthresh = params[C.LR_PTHRESH]\n    rows, cols = ifgs[0].phase_data.shape\n    # make 3D block of observations\n    obs = array([np.where(isnan(x.phase_data), 0, x.phase_data) for x in ifgs])\n    span = array([[x.time_span for x in ifgs]])\n    # Update MST in case additional NaNs generated by APS filtering\n    if mst is None:  # dummy mst if none is passed in\n        mst = ~isnan(obs)\n    else:\n        mst[isnan(obs)] = 0\n\n    # preallocate empty arrays (no need to preallocate NaNs)\n    error = np.empty([rows, cols], dtype=float32)\n    rate = np.empty([rows, cols], dtype=float32)\n    samples = np.empty([rows, cols], dtype=np.float32)\n    return nsig, pthresh, cols, error, mst, obs, rate, rows, samples, span\n\n\ndef stack_calc_wrapper(params):\n    \"\"\"\n    Wrapper for stacking on a set of tiles.\n    \"\"\"\n    log.info('Calculating rate map via stacking')\n    if not Configuration.vcmt_path(params).exists():\n        raise FileNotFoundError(\"VCMT is not found on disc. Have you run the 'correct' step?\")\n    params[C.PREREAD_IFGS] = cp.load(open(Configuration.preread_ifgs(params), 'rb'))\n    params[C.VCMT] = np.load(Configuration.vcmt_path(params))\n    params[C.TILES] = Configuration.get_tiles(params)\n    tiles_split(_stacking_for_tile, params)\n    log.debug(\"Finished stacking calc!\")\n\n\ndef _stacking_for_tile(tile, params):\n    \"\"\"\n    Wrapper for stacking calculation on a single tile\n    \"\"\"\n    preread_ifgs = params[C.PREREAD_IFGS]\n    vcmt = params[C.VCMT]\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    output_dir = params[C.TMPDIR]\n    log.debug(f\"Stacking of tile {tile.index}\")\n    ifg_parts = [shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths]\n    mst_tile = np.load(Configuration.mst_path(params, tile.index))\n    rate, error, samples = stack_rate_array(ifg_parts, params, vcmt, mst_tile)\n    np.save(file=os.path.join(output_dir, 'stack_rate_{}.npy'.format(tile.index)), arr=rate)\n    np.save(file=os.path.join(output_dir, 'stack_error_{}.npy'.format(tile.index)), arr=error)\n    np.save(file=os.path.join(output_dir, 'stack_samples_{}.npy'.format(tile.index)), arr=samples)\n"
  },
  {
    "path": "pyrate/core/timeseries.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains functions for computing a time series\ninversion in PyRate.\n\"\"\"\n# pylint: disable=too-many-locals\n# pylint: disable=too-many-arguments\nimport os\n\nimport pickle as cp\nfrom numpy import (where, isnan, nan, diff, zeros,\n                   float32, cumsum, dot, delete, asarray)\nfrom numpy.linalg import matrix_rank, pinv, cholesky\nimport numpy as np\nfrom scipy.linalg import qr\nfrom scipy.stats import linregress\n\nimport pyrate.constants as C\nfrom pyrate.core.shared import tiles_split\nfrom pyrate.core.algorithm import first_second_ids, get_epochs\nfrom pyrate.core import mst as mst_module, shared\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration, ConfigException\n\n\ndef _time_series_setup(ifgs, params, mst=None):\n    \"\"\"\n    Convenience function for setting up time series computation parameters\n    \"\"\"\n    if len(ifgs) < 1:\n        msg = 'Time series requires 2+ interferograms'\n        raise TimeSeriesError(msg)\n\n    # if mst is not a single tree then do interpolation\n    interp = 0 if mst_module.mst_from_ifgs(ifgs)[1] else 1\n\n    # Time Series parameters\n    tsmethod = params[C.TIME_SERIES_METHOD]\n\n    pthresh, smfactor, smorder = _validate_params(params, tsmethod)\n\n    epochlist = get_epochs(ifgs)[0]\n    nrows = ifgs[0].nrows\n    ncols = ifgs[0].ncols\n    nifgs = len(ifgs)\n    span = diff(epochlist.spans)\n    nepoch = len(epochlist.dates)  # epoch number\n    nvelpar = nepoch - 1  # velocity parameters number\n    # nlap = nvelpar - smorder  # Laplacian observations number\n    mast_second_ids = first_second_ids(epochlist.dates)\n    ifirst = [mast_second_ids[ifg.first] for ifg in ifgs]\n    isecond = [mast_second_ids[ifg.second] for ifg in ifgs]\n    ifirst = min(ifirst, isecond)\n    isecond = max(ifirst, isecond)\n    b0_mat = zeros((nifgs, nvelpar))\n    for i in range(nifgs):\n        b0_mat[i, ifirst[i]:isecond[i]] = span[ifirst[i]:isecond[i]]\n\n    # change the sign if second is earlier than first\n    isign = where(np.atleast_1d(ifirst) > np.atleast_1d(isecond))\n    b0_mat[isign[0], :] = -b0_mat[isign[0], :]\n    tsvel_matrix = np.empty(shape=(nrows, ncols, nvelpar),\n                            dtype=float32)\n    ifg_data = np.zeros((nifgs, nrows, ncols), dtype=float32)\n    for ifg_num in range(nifgs):\n        ifg_data[ifg_num] = ifgs[ifg_num].phase_data\n    if mst is None:\n        mst = ~isnan(ifg_data)\n    return b0_mat, interp, pthresh, smfactor, smorder, tsmethod, ifg_data, \\\n        mst, ncols, nrows, nvelpar, span, tsvel_matrix\n\n\ndef _validate_params(params, tsmethod):\n    \"\"\"\n    Helper function to validate supplied time series parameters\n    \"\"\"\n    if tsmethod == 1 and params[C.TIME_SERIES_SM_ORDER] is None:\n        _missing_option_error(C.TIME_SERIES_SM_ORDER)\n    else:\n        smorder = params[C.TIME_SERIES_SM_ORDER]\n    if tsmethod == 1 and params[C.TIME_SERIES_SM_FACTOR] is None:\n        _missing_option_error(C.TIME_SERIES_SM_FACTOR)\n    else:\n        smfactor = np.power(10, params[C.TIME_SERIES_SM_FACTOR])\n\n    if params[C.TIME_SERIES_PTHRESH] is None:\n        _missing_option_error(C.TIME_SERIES_PTHRESH)\n    else:\n        pthresh = params[C.TIME_SERIES_PTHRESH]\n        if pthresh < 0.0 or pthresh > 1000:\n            raise TimeSeriesError(\n                \"minimum number of coherent observations for a pixel\"\n                \" TIME_SERIES_PTHRESH setting must be >= 0.0 and <= 1000\")\n    return pthresh, smfactor, smorder\n\n\ndef time_series(ifgs, params, vcmt=None, mst=None):\n    \"\"\"\n    Calculates the displacement time series from the given interferogram\n    network. Solves the linear least squares system using either the SVD\n    method (similar to the SBAS method implemented by Berardino et al. 2002)\n    or a Finite Difference method using a Laplacian Smoothing operator\n    (similar to the method implemented by Schmidt and Burgmann 2003).\n    The returned outputs are multi-dimensional arrays of\n    size(nrows, ncols, nepochs-1), where:\n\n        - *nrows* is the number of rows in the ifgs,\n        - *ncols* is the  number of columns in the ifgs, and\n        - *nepochs* is the number of unique epochs (dates)\n\n    :param list ifgs: list of interferogram class objects.\n    :param dict params: Dictionary of configuration parameters\n    :param ndarray vcmt: Positive definite temporal variance covariance matrix\n    :param ndarray mst: [optional] Minimum spanning tree array.\n\n    :return: tsincr: incremental displacement time series.\n    :rtype: ndarray\n    :return: tscuml: cumulative displacement time series.\n    :rtype: ndarray\n    :return: tsvel_matrix: velocity for each epoch interval.\n    :rtype: ndarray\n    \"\"\"\n    b0_mat, interp, p_thresh, sm_factor, sm_order, ts_method, ifg_data, mst, \\\n        ncols, nrows, nvelpar, span, tsvel_matrix = \\\n        _time_series_setup(ifgs, params, mst)\n\n    # pixel-by-pixel calculation.\n    # nested loops to loop over the 2 image dimensions\n    for row in range(nrows):\n        for col in range(ncols):\n            tsvel_matrix[row, col] = _time_series_pixel(\n                row, col, b0_mat, sm_factor, sm_order, ifg_data, mst,\n                nvelpar, p_thresh, interp, vcmt, ts_method)\n\n    tsvel_matrix = where(tsvel_matrix == 0, nan, tsvel_matrix)\n    # SB: do the span multiplication as a numpy linalg operation, MUCH faster\n    #  not even this is necessary here, perform late for performance\n    tsincr = tsvel_matrix * span\n    tscuml = cumsum(tsincr, 2)\n    # SB: perform this after tsvel_matrix has been nan converted,\n    # saves the step of comparing a large matrix (tsincr) to zero.\n    # tscuml = where(tscuml == 0, nan, tscuml)\n    return tsincr, tscuml, tsvel_matrix\n\n\ndef _remove_rank_def_rows(b_mat, nvelpar, ifgv, sel):\n    \"\"\"\n    Remove rank deficient rows of design matrix\n    \"\"\"\n    _, _, e_var = qr(b_mat, mode='economic', pivoting=True)\n    licols = e_var[matrix_rank(b_mat):nvelpar]\n    [rmrow, _] = where(b_mat[:, licols] != 0)\n    b_mat = delete(b_mat, rmrow, axis=0)\n    ifgv = delete(ifgv, rmrow)\n    sel = delete(sel, rmrow)\n    return b_mat, ifgv, sel, rmrow\n\n\ndef _time_series_pixel(row, col, b0_mat, sm_factor, sm_order, ifg_data, mst,\n                          nvelpar, p_thresh, interp, vcmt, method):\n    \"\"\"\n    Wrapper function to compute time series for single pixel.\n    \"\"\"\n    # check pixel for non-redundant ifgs\n    sel = np.nonzero(mst[:, row, col])[0]  # trues in mst are chosen\n    if len(sel) >= p_thresh:\n        ifgv = ifg_data[sel, row, col]\n        # make design matrix, b_mat\n        b_mat = b0_mat[sel, :]\n        if interp == 0:\n            # remove rank deficient rows\n            rmrow = asarray([0])  # dummy\n\n            while len(rmrow) > 0:\n                # if b_mat.shape[0] <=1 then we return nans\n                if b_mat.shape[0] > 1:\n                    b_mat, ifgv, sel, rmrow = _remove_rank_def_rows(\n                        b_mat, nvelpar, ifgv, sel)\n                else:\n                    return np.empty(nvelpar) * np.nan\n\n            # Some epochs have been deleted; get valid epoch indices\n            velflag = sum(abs(b_mat), 0)\n            # remove corresponding columns in design matrix\n            b_mat = b_mat[:, ~np.isclose(velflag, 0.0)]\n        else:\n            velflag = np.ones(nvelpar)\n        if method == 1: # Use Laplacian smoothing method\n            tsvel = _solve_ts_lap(nvelpar, velflag, ifgv, b_mat,\n                                  sm_order, sm_factor, sel, vcmt)\n        elif method == 2: # Use SVD method\n            tsvel = _solve_ts_svd(nvelpar, velflag, ifgv, b_mat)\n        else:\n            raise TimeSeriesError(\"Unrecognised time series method\")\n        return tsvel\n    else:\n        return np.empty(nvelpar) * np.nan\n\n\ndef _solve_ts_svd(nvelpar, velflag, ifgv, b_mat):\n    \"\"\"\n    Solve the linear least squares system using the SVD method.\n    \"\"\"\n    # pre-allocate the velocity matrix\n    tsvel = np.empty(nvelpar, dtype=float32) * np.nan\n    # solve least squares equation using Moore-Penrose pseudoinverse\n    tsvel[velflag != 0] = dot(pinv(b_mat), ifgv)\n    return tsvel\n\n\ndef _solve_ts_lap(nvelpar, velflag, ifgv, mat_b, smorder, smfactor, sel, vcmt):\n    \"\"\"\n    Solve the linear least squares system using the Finite Difference\n    method using a Laplacian Smoothing operator.\n    \"\"\"\n    # pylint: disable=invalid-name\n    # Laplacian observations number\n    nlap = nvelpar - smorder\n    #  Laplacian smoothing coefficient matrix\n    b_lap0 = np.zeros(shape=(nlap, nvelpar))\n\n    for i in range(nlap):\n        if smorder == 1:\n            b_lap0[i, i:i+2] = [-1, 1]\n        else:\n            b_lap0[i, i:i+3] = [1, -2, 1]\n\n    # Scale the coefficients by Laplacian smoothing factor\n    b_lap0 *= smfactor\n\n    # Laplacian smoothing design matrix\n    nvelleft = np.count_nonzero(velflag)\n    nlap = nvelleft - smorder\n\n    # constrain for the first and the last incremental\n    b_lap1 = - np.divide(np.ones(shape=nvelleft), nvelleft - 1)\n    b_lap1[0] = 1.0\n    b_lapn = - np.divide(np.ones(shape=nvelleft), nvelleft - 1)\n    b_lapn[-1] = 1.0\n\n    b_lap = np.empty(shape=(nlap + 2, nvelleft))\n    b_lap[0, :] = b_lap1\n    b_lap[1:nlap + 1, :] = b_lap0[0:nlap, 0:nvelleft]\n    b_lap[-1, :] = b_lapn\n\n    nlap += 2\n\n    # add laplacian design matrix to existing design matrix\n    mat_b = np.concatenate((mat_b, b_lap), axis=0)\n\n    # combine ifg and Laplacian smooth vector\n    v_lap = np.zeros(nlap)\n    obsv = np.concatenate((ifgv, v_lap), axis=0)\n\n    # make variance-covariance matrix\n    # new covariance matrix, adding the laplacian equations\n    m = len(sel)\n    nobs = m + nlap\n    vcm_tmp = np.eye(nobs)\n    vcm_tmp[:m, :m] = vcmt[sel, np.vstack(sel)]\n\n    # solve the equation by least-squares\n    # calculate velocities\n    # we get the lower triangle in numpy\n    w = cholesky(pinv(vcm_tmp)).T\n    wb = dot(w, mat_b)\n    wl = dot(w, obsv)\n    x = dot(pinv(wb, rcond=1e-8), wl)\n\n    # TODO: implement residuals and roughness calculations\n    tsvel = np.empty(nvelpar, dtype=float32) * np.nan\n    tsvel[~np.isclose(velflag, 0.0, atol=1e-8)] = x[:nvelleft]\n\n    # TODO: implement uncertainty estimates (tserror)\n    return tsvel\n\n\ndef linear_rate_pixel(y, t):\n    \"\"\"\n    Calculate best fitting linear rate to cumulative displacement\n    time series for one pixel using linear regression.\n\n    :param ndarray y: 1-dimensional vector of cumulative displacement time series\n    :param ndarray t: 1-dimensional vector of cumulative time at each epoch\n\n    :return: linrate: Linear rate; gradient of fitted line.\n    :rtype: float\n    :return intercept: Y-axis intercept of fitted line at t = 0\n    :rtype: float\n    :return: rsquared: R-squared value of the linear regression\n    :rtype: float\n    :return: error: Standard error of the linear regression\n    :rtype: float\n    :return: samples: Number of observations used in linear regression\n    :rtype: int\n    \"\"\"\n\n    # Mask to exclude nan elements\n    mask = ~isnan(y)\n    # remove nan elements from both arrays\n    y = y[mask]    \n    try:\n        t = t[mask]\n    except IndexError:\n        raise TimeSeriesError(\"linear_rate_pixel: y and t are not equal length\")\n\n    # break out of func if not enough time series obs for line fitting\n    nsamp = len(y)\n    if nsamp < 2:\n        return nan, nan, nan, nan, nan\n\n    # compute linear regression of tscuml \n    linrate, intercept, r_value, p_value, std_err = linregress(t, y)\n\n    return linrate, intercept, r_value**2, std_err, int(nsamp)\n\n\ndef linear_rate_array(tscuml, ifgs, params):\n    \"\"\"\n    This function loops over all pixels in a 3-dimensional cumulative\n    time series array and calculates the linear rate (line of best fit)\n    for each pixel using linear regression.\n\n    :param ndarray tscuml: 3-dimensional cumulative time series array\n    :param list ifgs: list of interferogram class objects.\n    :param dict params: Configuration parameters\n\n    :return: linrate: Linear rate map from linear regression\n    :rtype: ndarray\n    :return: intercept: Array of Y-axis intercepts from linear regression\n    :rtype: ndarray\n    :return: rsquared: Array of R-squared value of the linear regression\n    :rtype: ndarray\n    :return: error: Array of standard errors of the linear regression\n    :rtype: ndarray\n    :return: samples: Number of observations used in linear regression for each pixel\n    :rtype: ndarray\n    \"\"\"\n    b0_mat, interp, p_thresh, sm_factor, sm_order, ts_method, ifg_data, mst, \\\n        ncols, nrows, nvelpar, span, tsvel_matrix = \\\n        _time_series_setup(ifgs, params)\n\n    epochlist = get_epochs(ifgs)[0]\n    # get cumulative time per epoch\n    t = asarray(epochlist.spans)\n\n    # test for equal length of input vectors\n    if tscuml.shape[2] != len(t):\n        raise TimeSeriesError(\"linear_rate_array: tscuml and nepochs are not equal length\")\n\n    # preallocate empty arrays for results\n    linrate = np.empty([nrows, ncols], dtype=float32)\n    rsquared = np.empty([nrows, ncols], dtype=float32)\n    error = np.empty([nrows, ncols], dtype=float32)\n    intercept = np.empty([nrows, ncols], dtype=np.float32)\n    samples = np.empty([nrows, ncols], dtype=np.float32)\n\n    # pixel-by-pixel calculation.\n    # nested loops to loop over the 2 image dimensions\n    for i in range(nrows):\n        for j in range(ncols):\n            linrate[i, j], intercept[i, j], rsquared[i, j], error[i, j], samples[i, j] = \\\n                linear_rate_pixel(tscuml[i, j, :], t)\n\n    return linrate, intercept, rsquared, params[C.VELERROR_NSIG]*error, samples\n\n\ndef _missing_option_error(option):\n    \"\"\"\n    Convenience function for raising similar missing option errors.\n    \"\"\"\n    msg = \"Missing '%s' option in config file\" % option\n    raise ConfigException(msg)\n\n\nclass TimeSeriesError(Exception):\n    \"\"\"\n    Generic exception for time series errors.\n    \"\"\"\n\n\ndef timeseries_calc_wrapper(params):\n    \"\"\"\n    Wrapper for time series calculation on a set of tiles.\n    \"\"\"\n    if params[C.TIME_SERIES_METHOD] == 1:\n        log.info('Calculating time series using Laplacian Smoothing method')\n    elif params[C.TIME_SERIES_METHOD] == 2:\n        log.info('Calculating time series using SVD method')\n    if not Configuration.vcmt_path(params).exists():\n        raise FileNotFoundError(\"VCMT is not found on disc. Have you run the 'correct' step?\")\n    params[C.PREREAD_IFGS] = cp.load(open(Configuration.preread_ifgs(params), 'rb'))\n    params[C.VCMT] = np.load(Configuration.vcmt_path(params))\n    params[C.TILES] = Configuration.get_tiles(params)\n    tiles_split(__calc_time_series_for_tile, params)\n    log.debug(\"Finished timeseries calc!\")\n\n\ndef __calc_time_series_for_tile(tile, params):\n    \"\"\"\n    Wrapper for time series calculation on a single tile\n    \"\"\"\n    preread_ifgs = params[C.PREREAD_IFGS]\n    vcmt = params[C.VCMT]\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    output_dir = params[C.TMPDIR]\n    log.debug(f\"Calculating time series for tile {tile.index}\")\n    ifg_parts = [shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths]\n    mst_tile = np.load(Configuration.mst_path(params, tile.index))\n    tsincr, tscuml, _ = time_series(ifg_parts, params, vcmt, mst_tile)\n    np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(tile.index)), arr=tscuml)\n    # optional save of tsincr npy tiles\n    if params[\"savetsincr\"] == 1:\n        np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(tile.index)), arr=tsincr)\n    tscuml = np.insert(tscuml, 0, 0, axis=2)  # add zero epoch to tscuml 3D array\n    log.info('Calculating linear regression of cumulative time series')\n    linrate, intercept, r_squared, std_err, samples = linear_rate_array(tscuml, ifg_parts, params)\n    np.save(file=os.path.join(output_dir, 'linear_rate_{}.npy'.format(tile.index)), arr=linrate)\n    np.save(file=os.path.join(output_dir, 'linear_intercept_{}.npy'.format(tile.index)), arr=intercept)\n    np.save(file=os.path.join(output_dir, 'linear_rsquared_{}.npy'.format(tile.index)), arr=r_squared)\n    np.save(file=os.path.join(output_dir, 'linear_error_{}.npy'.format(tile.index)), arr=std_err)\n    np.save(file=os.path.join(output_dir, 'linear_samples_{}.npy'.format(tile.index)), arr=samples)\n\n"
  },
  {
    "path": "pyrate/correct.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module runs the main PyRate correction workflow\n\"\"\"\nimport shutil\nimport os\nfrom pathlib import Path\nimport pickle as cp\nfrom typing import List\nimport sys\n\nimport pyrate.constants as C\nfrom pyrate.core import (shared, algorithm, mpiops)\nfrom pyrate.core.aps import spatio_temporal_filter\nfrom pyrate.core.covariance import maxvar_vcm_calc_wrapper\nfrom pyrate.core.mst import mst_calc_wrapper\nfrom pyrate.core.orbital import orb_fit_calc_wrapper\nfrom pyrate.core.dem_error import dem_error_calc_wrapper\nfrom pyrate.core.phase_closure.closure_check import iterative_closure_check, mask_pixels_with_unwrapping_errors, \\\n    update_ifg_list\nfrom pyrate.core.ref_phs_est import ref_phase_est_wrapper\nfrom pyrate.core.refpixel import ref_pixel_calc_wrapper\nfrom pyrate.core.shared import PrereadIfg, Ifg, get_tiles, mpi_vs_multiprocess_logging, join_dicts, \\\n        nan_and_mm_convert, save_numpy_phase\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration, MultiplePaths, ConfigException\n\nMAIN_PROCESS = 0\n\n\ndef _create_ifg_dict(params):\n    \"\"\"\n    Save the preread_ifgs dict with information about the ifgs that are\n    later used for fast loading of Ifg files in IfgPart class\n\n    :param list dest_tifs: List of destination tifs\n    :param dict params: Config dictionary\n    :param list tiles: List of all Tile instances\n\n    :return: preread_ifgs: Dictionary containing information regarding\n                interferograms that are used later in workflow\n    :rtype: dict\n    \"\"\"\n    dest_tifs = [ifg_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    ifgs_dict = {}\n    process_tifs = mpiops.array_split(dest_tifs)\n    for d in process_tifs:\n        ifg = Ifg(d.tmp_sampled_path) # get the writable copy\n        ifg.open()\n        nan_and_mm_convert(ifg, params)\n        ifgs_dict[d.tmp_sampled_path] = PrereadIfg(\n            path=d.sampled_path,\n            tmp_path=d.tmp_sampled_path,\n            nan_fraction=ifg.nan_fraction,\n            first=ifg.first,\n            second=ifg.second,\n            time_span=ifg.time_span,\n            nrows=ifg.nrows,\n            ncols=ifg.ncols,\n            metadata=ifg.meta_data\n        )\n        ifg.write_modified_phase() # update phase converted to mm\n        ifg.close()\n    ifgs_dict = join_dicts(mpiops.comm.allgather(ifgs_dict))\n\n    ifgs_dict = mpiops.run_once(__save_ifgs_dict_with_headers_and_epochs, dest_tifs, ifgs_dict, params, process_tifs)\n\n    params[C.PREREAD_IFGS] = ifgs_dict\n    return ifgs_dict\n\n\ndef __save_ifgs_dict_with_headers_and_epochs(dest_tifs, ifgs_dict, params, process_tifs):\n    tmpdir = params[C.TMPDIR]\n    if not os.path.exists(tmpdir):\n        shared.mkdir_p(tmpdir)\n\n    preread_ifgs_file = Configuration.preread_ifgs(params)\n    nifgs = len(dest_tifs)\n    # add some extra information that's also useful later\n    gt, md, wkt = shared.get_geotiff_header_info(process_tifs[0].tmp_sampled_path)\n    epochlist = algorithm.get_epochs(ifgs_dict)[0]\n    log.info('Found {} unique epochs in the {} interferogram network'.format(len(epochlist.dates), nifgs))\n    ifgs_dict['epochlist'] = epochlist\n    ifgs_dict['gt'] = gt\n    ifgs_dict['md'] = md\n    ifgs_dict['wkt'] = wkt\n    # dump ifgs_dict file for later use\n    cp.dump(ifgs_dict, open(preread_ifgs_file, 'wb'))\n\n    for k in ['gt', 'epochlist', 'md', 'wkt']:\n        ifgs_dict.pop(k)\n\n    return ifgs_dict\n\n\ndef _copy_mlooked(params):\n    \"\"\"\n    Make a copy of the multi-looked files in the 'tmp_sampled_path'\n    for manipulation during correct steps\n    \"\"\"\n    log.info(\"Copying input files into tempdir for manipulation during 'correct' steps\")\n    mpaths = params[C.INTERFEROGRAM_FILES]\n    process_mpaths = mpiops.array_split(mpaths)\n    for p in process_mpaths:\n        shutil.copy(p.sampled_path, p.tmp_sampled_path)\n        Path(p.tmp_sampled_path).chmod(0o664)  # assign write permission as prepifg output is readonly\n\n\ndef main(config):\n    \"\"\"\n    Top level function to perform PyRate workflow on given interferograms\n\n    :param dict params: Dictionary of configuration parameters\n\n    :return: refpt: tuple of reference pixel x and y position\n    :rtype: tuple\n    :return: maxvar: array of maximum variance values of interferograms\n    :rtype: ndarray\n    :return: vcmt: Variance-covariance matrix array\n    :rtype: ndarray\n    \"\"\"\n    params = config.__dict__\n    mpi_vs_multiprocess_logging(\"correct\", params)\n\n    _copy_mlooked(params)\n\n    return correct_ifgs(config)\n\n\ndef _update_params_with_tiles(params: dict) -> None:\n    ifg_path = params[C.INTERFEROGRAM_FILES][0].tmp_sampled_path\n    rows, cols = params[\"rows\"], params[\"cols\"]\n    tiles = mpiops.run_once(get_tiles, ifg_path, rows, cols)\n    # add tiles to params\n    params[C.TILES] = tiles\n\n\ndef phase_closure_wrapper(params: dict, config: Configuration) -> dict:\n    \"\"\"\n    This wrapper will run the iterative phase closure check to return a stable\n    list of checked interferograms, and then mask pixels in interferograms that\n    exceed the unwrapping error threshold.\n    :param params: Dictionary of PyRate configuration parameters. \n    :param config: Configuration class instance.\n    :return: params: Updated dictionary of PyRate configuration parameters.\n    \"\"\"\n\n    if not params[C.PHASE_CLOSURE]:\n        log.info(\"Phase closure correction is not required!\")\n        return\n\n    rets = iterative_closure_check(config)\n    if rets is None:\n        log.info(\"Zero loops are returned from the iterative closure check.\")\n        log.warning(\"Abandoning phase closure correction without modifying the interferograms.\")\n        return\n        \n    ifg_files, ifgs_breach_count, num_occurences_each_ifg = rets\n\n    # update params with closure checked ifg list\n    params[C.INTERFEROGRAM_FILES] = \\\n        mpiops.run_once(update_ifg_list, ifg_files, params[C.INTERFEROGRAM_FILES])\n\n    if mpiops.rank == 0:\n        with open(config.phase_closure_filtered_ifgs_list(params), 'w') as f:\n            lines = [p.converted_path + '\\n' for p in params[C.INTERFEROGRAM_FILES]]\n            f.writelines(lines)\n\n    # mask ifgs with nans where phase unwrap threshold is breached\n    if mpiops.rank == 0:\n        mask_pixels_with_unwrapping_errors(ifgs_breach_count, num_occurences_each_ifg, params)\n\n    _create_ifg_dict(params) # update the preread_ifgs dict\n\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n    # update/save the phase_data in the tiled numpy files\n    save_numpy_phase(ifg_paths, params)\n\n    return params\n\n\ncorrect_steps = {\n    'orbfit': orb_fit_calc_wrapper,\n    'refphase': ref_phase_est_wrapper,\n    'phase_closure': phase_closure_wrapper,\n    'demerror': dem_error_calc_wrapper,\n    'mst': mst_calc_wrapper,\n    'apscorrect': spatio_temporal_filter,\n    'maxvar': maxvar_vcm_calc_wrapper,\n}\n\n\ndef correct_ifgs(config: Configuration) -> None:\n    \"\"\"\n    Top level function to perform PyRate workflow on given interferograms\n    \"\"\"\n    params = config.__dict__\n    __validate_correct_steps(params)\n\n    # work out the tiling and add to params dict\n    _update_params_with_tiles(params)\n\n    # create the preread_ifgs dict for use with tiled data\n    _create_ifg_dict(params)\n\n    ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]]\n\n    # create initial tiled phase_data numpy files on disc\n    save_numpy_phase(ifg_paths, params)\n\n    params[C.REFX_FOUND], params[C.REFY_FOUND] = ref_pixel_calc_wrapper(params)\n\n    # run through the correct steps in user specified sequence\n    for step in params['correct']:\n        if step == 'phase_closure':\n            correct_steps[step](params, config)\n        else:\n            correct_steps[step](params)\n    log.info(\"Finished 'correct' step\")\n\n\ndef __validate_correct_steps(params):\n    for step in params['correct']:\n        if step not in correct_steps.keys():\n            raise ConfigException(f\"{step} is not a supported 'correct' step. \\n\"\n                                  f\"Supported steps are {correct_steps.keys()}\")\n"
  },
  {
    "path": "pyrate/default_parameters.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License,\n#   Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing,\n#   software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n#   either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\nPYRATE_DEFAULT_CONFIGURATION = {\n    \"ifgfilelist\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": True\n    },\n    \"demfile\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": True\n    },\n    \"demHeaderFile\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": True\n    },\n    \"hdrfilelist\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": True\n    },\n    \"cohfilelist\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"basefilelist\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ltfile\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"outdir\": {\n        \"DataType\": \"path\",\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": True\n    },\n    \"processor\": {\n        \"DataType\": int,\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1, 2],\n        \"Required\": False\n    },\n    \"noDataAveragingThreshold\": {\n        \"DataType\": float,\n        \"DefaultValue\": 0.0,\n        \"MinValue\": 0,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"noDataValue\": {\n        \"DataType\": float,\n        \"DefaultValue\": 0.0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"nan_conversion\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"largetifs\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"parallel\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"processes\": {\n        \"DataType\": int,\n        \"DefaultValue\": 8,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"cohmask\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"cohthresh\": {\n        \"DataType\": float,\n        \"DefaultValue\": 0.3,\n        \"MinValue\": 0.0,\n        \"MaxValue\": 1.0,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifgcropopt\": {\n        \"DataType\": int,\n        \"DefaultValue\": 4,\n        \"MinValue\": 1,\n        \"MaxValue\": 4,\n        \"PossibleValues\": [1, 2, 3, 4],\n        \"Required\": False\n    },\n    \"ifglksx\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifglksy\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifgxfirst\": {\n        \"DataType\": float,\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifgxlast\": {\n        \"DataType\": float,\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifgyfirst\": {\n        \"DataType\": float,\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifgylast\": {\n        \"DataType\": float,\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"refx\": {\n        \"DataType\": float,\n        \"DefaultValue\": -1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"refy\": {\n        \"DataType\": float,\n        \"DefaultValue\": -1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"refnx\": {\n        \"DataType\": int,\n        \"DefaultValue\": 10,\n        \"MinValue\": 1,\n        \"MaxValue\": 50,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"refny\": {\n        \"DataType\": int,\n        \"DefaultValue\": 10,\n        \"MinValue\": 1,\n        \"MaxValue\": 50,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"refchipsize\": {\n        \"DataType\": int,\n        \"DefaultValue\": 21,\n        \"MinValue\": 1,\n        \"MaxValue\": 101,\n        \"PossibleValues\": None,\n        \"Note\": \"Must be an odd number.\",\n        \"Required\": False\n    },\n    \"refminfrac\": {\n        \"DataType\": float,\n        \"DefaultValue\": 0.5,\n        \"MinValue\": 0.0,\n        \"MaxValue\": 1.0,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"refest\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [1, 2],\n        \"Required\": False\n    },\n    \"orbfit\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"orbfitmethod\": {\n        \"DataType\": int,\n        \"DefaultValue\": 2,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [1, 2],\n        \"Required\": False\n    },\n    \"orbfitdegrees\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [1, 2, 3],\n        \"Required\": False\n    },\n    \"orbfitlksx\": {\n        \"DataType\": int,\n        \"DefaultValue\": 10,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"orbfitlksy\": {\n        \"DataType\": int,\n        \"DefaultValue\": 10,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"orbfitintercept\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"orbfitoffset\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"orbfitscale\": {\n        \"DataType\": int,\n        \"DefaultValue\": 100,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"apsest\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"slpfcutoff\": {\n        \"DataType\": float,\n        \"DefaultValue\": 1.0,\n        \"MinValue\": 0,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"slpnanfill\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"slpnanfill_method\": {\n        \"DataType\": str,\n        \"DefaultValue\": \"nearest\",\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [\"linear\", \"nearest\", \"cubic\"],\n        \"Required\": False\n    },\n    \"tlpfcutoff\": {\n        \"DataType\": int,\n        \"DefaultValue\": 30,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"tlpfpthr\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"phase_closure\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"closure_thr\": {\n        \"DataType\": float,\n        \"DefaultValue\": 0.5,\n        \"MinValue\": 0.01,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ifg_drop_thr\": {\n        \"DataType\": float,\n        \"DefaultValue\": 0.5,\n        \"MinValue\": 0.01,\n        \"MaxValue\": 1.0,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"min_loops_per_ifg\": {\n        \"DataType\": int,\n        \"DefaultValue\": 2,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"max_loop_length\": {\n        \"DataType\": int,\n        \"DefaultValue\": 4,\n        \"MinValue\": 3,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"max_loop_redundancy\": {\n        \"DataType\": int,\n        \"DefaultValue\": 2,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"subtract_median\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n\n    \"demerror\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [0, 1],\n        \"Required\": False\n    },\n    \"de_pthr\": {\n        \"DataType\": int,\n        \"DefaultValue\": 10,\n        \"MinValue\": 4,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"tsmethod\": {\n        \"DataType\": int,\n        \"DefaultValue\": 2,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [1, 2],\n        \"Required\": False\n    },\n    \"smorder\": {\n        \"DataType\": int,\n        \"DefaultValue\": None,\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": [1, 2],\n        \"Required\": False\n    },\n    \"smfactor\": {\n        \"DataType\": float,\n        \"DefaultValue\": -1.0,\n        \"MinValue\": -5.0,\n        \"MaxValue\": 0,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"ts_pthr\": {\n        \"DataType\": int,\n        \"DefaultValue\": 3,\n        \"MinValue\": 1,\n        \"MaxValue\": 1000,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"nsig\": {\n        \"DataType\": int,\n        \"DefaultValue\": 2,\n        \"MinValue\": 1,\n        \"MaxValue\": 10,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"pthr\": {\n        \"DataType\": int,\n        \"DefaultValue\": 3,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"maxsig\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1000,\n        \"MinValue\": 0,\n        \"MaxValue\": 1000,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"savenpy\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": 0,\n        \"MaxValue\": 1,\n        \"PossibleValues\": [1, 0],\n        \"Required\": False\n    },\n    \"savetsincr\": {\n        \"DataType\": int,\n        \"DefaultValue\": 1,\n        \"MinValue\": 0,\n        \"MaxValue\": 1,\n        \"PossibleValues\": [1, 0],\n        \"Required\": False\n    },\n    \"los_projection\": {\n        \"DataType\": int,\n        \"DefaultValue\": 0,\n        \"MinValue\": 0,\n        \"MaxValue\": 2,\n        \"PossibleValues\": [2, 1, 0],\n        \"Required\": False\n    },\n    \"signal_polarity\": {\n        \"DataType\": int,\n        \"DefaultValue\": -1,\n        \"MinValue\": -1,\n        \"MaxValue\": 1,\n        \"PossibleValues\": [1, -1],\n        \"Required\": False\n    },\n    \"velerror_nsig\": {\n        \"DataType\": int,\n        \"DefaultValue\": 2,\n        \"MinValue\": 1,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    },\n    \"correct\": {\n        \"DataType\": list,\n        \"DefaultValue\": ['orbfit', 'refphase', 'demerror', 'mst', 'apscorrect', 'maxvar', 'timeseries', 'stack'],\n        \"MinValue\": None,\n        \"MaxValue\": None,\n        \"PossibleValues\": None,\n        \"Required\": False\n    }\n}\n"
  },
  {
    "path": "pyrate/main.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module defines executable run configuration for the PyRate software\n\"\"\"\n\nimport os\nimport argparse\nfrom argparse import RawTextHelpFormatter\nimport time\nfrom pathlib import Path\n\nimport pyrate.constants as C\nfrom pyrate.constants import CLI_DESCRIPTION\nfrom pyrate import conv2tif, prepifg, correct, merge\nfrom pyrate.core.logger import pyratelogger as log, configure_stage_log\nfrom pyrate.core import mpiops\nfrom pyrate.configuration import Configuration\nfrom pyrate.core.shared import mpi_vs_multiprocess_logging\nfrom pyrate.core.stack import stack_calc_wrapper\nfrom pyrate.core.timeseries import timeseries_calc_wrapper\n\n\ndef _params_from_conf(config_file):\n    config_file = os.path.abspath(config_file)\n    config = Configuration(config_file)\n    params = config.__dict__\n    return params\n\n\ndef update_params_due_to_ifg_selection(config):\n    params = config.__dict__\n    if config.phase_closure_filtered_ifgs_list(params).exists():\n        params = config.refresh_ifg_list(params)\n        correct._create_ifg_dict(params)\n        correct._update_params_with_tiles(params)\n    return params\n\n\ndef main():\n    start_time = time.time()\n\n    parser = argparse.ArgumentParser(prog='pyrate', description=CLI_DESCRIPTION, add_help=True,\n                                     formatter_class=RawTextHelpFormatter)\n    parser.add_argument('-v', '--verbosity', type=str, default='INFO', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],\n                        help=\"Increase output verbosity\")\n\n    subparsers = parser.add_subparsers(dest='command')\n    subparsers.required = True\n\n    parser_conv2tif = subparsers.add_parser('conv2tif', help='<Optional> Convert interferograms to geotiff.',\n                                            add_help=True)\n\n    parser_prepifg = subparsers.add_parser(\n        'prepifg', help='Perform multilooking, cropping and coherence masking to interferogram geotiffs.',\n        add_help=True)\n\n    parser_correct = subparsers.add_parser(\n        'correct', help='Calculate and apply corrections to interferogram phase data.',\n        add_help=True)\n\n    parser_ts = subparsers.add_parser(\n        'timeseries', help='<Optional> Timeseries inversion of interferogram phase data.', add_help=True\n    )\n\n    parser_stack = subparsers.add_parser('stack', help='<Optional> Stacking of interferogram phase data.',\n                                         add_help=True)\n\n    parser_merge = subparsers.add_parser(\n        'merge', help=\"Reassemble computed tiles and save as geotiffs.\",\n        add_help=True)\n\n    parser_workflow = subparsers.add_parser(\n        'workflow', help=\"<Optional> Sequentially run all the PyRate processing steps.\",\n        add_help=True)\n    for p in [parser_conv2tif, parser_prepifg, parser_correct, parser_merge, parser_ts, parser_stack, parser_workflow]:\n        p.add_argument('-f', '--config_file', action=\"store\", type=str, default=None,\n                       help=\"Pass configuration file\", required=False)\n\n    args = parser.parse_args()\n\n    params = mpiops.run_once(_params_from_conf, args.config_file)\n\n    configure_stage_log(args.verbosity, args.command, Path(params[C.OUT_DIR]).joinpath('pyrate.log.').as_posix())\n\n    log.debug(\"Starting PyRate\")\n    log.debug(\"Arguments supplied at command line: \")\n    log.debug(args)\n\n    if args.verbosity:\n        log.setLevel(args.verbosity)\n        log.info(\"Verbosity set to \" + str(args.verbosity) + \".\")\n\n    if args.command == \"conv2tif\":\n        conv2tif.main(params)\n\n    if args.command == \"prepifg\":\n        prepifg.main(params)\n\n    if args.command == \"correct\":\n        config_file = os.path.abspath(args.config_file)\n        config = Configuration(config_file)\n        correct.main(config)\n\n    if args.command == \"timeseries\":\n        config_file = os.path.abspath(args.config_file)\n        config = Configuration(config_file)\n        timeseries(config)\n\n    if args.command == \"stack\":\n        config_file = os.path.abspath(args.config_file)\n        config = Configuration(config_file)\n        stack(config)\n\n    if args.command == \"merge\":\n        merge.main(params)\n\n    if args.command == \"workflow\":\n        log.info(\"***********CONV2TIF**************\")\n        conv2tif.main(params)\n\n        log.info(\"***********PREPIFG**************\")\n        params = mpiops.run_once(_params_from_conf, args.config_file)\n        prepifg.main(params)\n\n        log.info(\"***********CORRECT**************\")\n        # reset params as prepifg modifies params\n        config_file = os.path.abspath(args.config_file)\n        config = Configuration(config_file)\n        correct.main(config)\n\n        log.info(\"***********TIMESERIES**************\")\n        config = Configuration(config_file)\n        timeseries(config)\n\n        log.info(\"***********STACK**************\")\n        config = Configuration(config_file)\n        stack(config)\n\n        log.info(\"***********MERGE**************\")\n        params = mpiops.run_once(_params_from_conf, args.config_file)\n        merge.main(params)\n\n    log.info(\"--- Runtime = %s seconds ---\" % (time.time() - start_time))\n\n\ndef timeseries(config: Configuration) -> None:\n    params = config.__dict__\n    mpi_vs_multiprocess_logging(\"timeseries\", params)\n    params = update_params_due_to_ifg_selection(config=config)\n    timeseries_calc_wrapper(params)\n\n\ndef stack(config: Configuration) -> None:\n    params = config.__dict__\n    mpi_vs_multiprocess_logging(\"stack\", params)\n    params = update_params_due_to_ifg_selection(config=config)\n    stack_calc_wrapper(params)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "pyrate/merge.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module does post-processing steps to assemble the\nstack rate and time series outputs and save as geotiff files\n\"\"\"\nfrom os.path import join, isfile, exists\nimport pickle\nimport numpy as np\nfrom osgeo import gdal\nimport subprocess\nfrom pathlib import Path\nfrom typing import Optional, Tuple\n\nimport pyrate.constants as C\nfrom pyrate.core import shared, stack, ifgconstants as ifc, mpiops\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import Configuration\nfrom pyrate.core.shared import Tile\n\ngdal.SetCacheMax(64)\n\n\ndef main(params: dict) -> None:\n    \"\"\"\n    PyRate merge main function. Assembles product tiles in to\n    single geotiff files\n    \"\"\"\n    if params[C.SIGNAL_POLARITY] == 1:\n        log.info(f\"Saving output products with the same sign convention as input data\")\n    elif params[C.SIGNAL_POLARITY] == -1:\n        log.info(f\"Saving output products with reversed sign convention compared to the input data\")\n    else:\n        log.warning(f\"Check the value of signal_polarity parameter\")\n\n    out_types = []\n\n    tsfile = join(params[C.TMPDIR], 'tscuml_0.npy')\n    if exists(tsfile):\n        _merge_timeseries(params, 'tscuml')\n        _merge_linrate(params)\n        out_types += ['linear_rate', 'linear_error', 'linear_rsquared']\n\n        # optional save of merged tsincr products\n        if params[\"savetsincr\"] == 1:\n            _merge_timeseries(params, 'tsincr')\n    else:\n        log.warning('Not merging time series products; {} does not exist'.format(tsfile))\n\n    stfile = join(params[C.TMPDIR], 'stack_rate_0.npy')\n    if exists(stfile):\n        # setup paths\n        mpiops.run_once(_merge_stack, params)\n        out_types += ['stack_rate', 'stack_error']\n    else:\n        log.warning('Not merging stack products; {} does not exist'.format(stfile))\n\n    if len(out_types) > 0:\n        process_out_types = mpiops.array_split(out_types)\n        for out_type in process_out_types:\n            create_png_and_kml_from_tif(params[C.VELOCITY_DIR], output_type=out_type)\n    else:\n        log.warning('Exiting: no products to merge')\n\n\ndef _merge_stack(params: dict) -> None:\n    \"\"\"\n    Merge stacking outputs\n    \"\"\"\n    shape, tiles, ifgs_dict = __merge_setup(params)\n\n    log.info('Merging and writing Stack Rate product geotiffs')\n\n    # read and assemble tile outputs\n    rate = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='stack_rate')\n    error = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='stack_error')\n    samples = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='stack_samples')\n\n    # mask pixels according to threshold\n    if params[C.LR_MAXSIG] > 0:\n        log.info(f\"Masking stack_rate and stack_error maps where error is greater than {params[C.LR_MAXSIG]} millimetres\")\n        rate, error = stack.mask_rate(rate, error, params[C.LR_MAXSIG])\n    else:\n        log.info('Skipping stack product masking (maxsig = 0)')\n\n    # save geotiff and numpy array files\n    for out, ot in zip([rate, error, samples], ['stack_rate', 'stack_error', 'stack_samples']):\n        __save_merged_files(ifgs_dict, params, out, ot, savenpy=params[\"savenpy\"])\n\n\ndef _merge_linrate(params: dict) -> None:\n    \"\"\"\n    Merge linear rate outputs\n    \"\"\"\n    shape, tiles, ifgs_dict = mpiops.run_once(__merge_setup, params)\n\n    log.info('Merging and writing Linear Rate product geotiffs')\n\n    # read and assemble tile outputs\n    out_types = ['linear_' + x for x in ['rate', 'rsquared', 'error', 'intercept', 'samples']]\n    process_out_types = mpiops.array_split(out_types)\n    for p_out_type in process_out_types:\n        out = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type=p_out_type)\n        __save_merged_files(ifgs_dict, params, out, p_out_type, savenpy=params[\"savenpy\"])\n    mpiops.comm.barrier()\n\n\ndef _merge_timeseries(params: dict, tstype: str) -> None:\n    \"\"\"\n    Merge tiled time series outputs\n    \"\"\"\n    log.info('Merging {} time series outputs'.format(tstype))\n    shape, tiles, ifgs_dict = __merge_setup(params)\n\n    # load the first time series file to determine the number of time series tifs\n    ts_file = join(params[C.TMPDIR], tstype + '_0.npy')\n    ts = np.load(file=ts_file)\n    # pylint: disable=no-member\n    no_ts_tifs = ts.shape[2]\n    process_tifs = mpiops.array_split(range(no_ts_tifs))\n    # depending on nvelpar, this will not fit in memory\n    # e.g. nvelpar=100, nrows=10000, ncols=10000, 32bit floats need 40GB memory\n    # 32 * 100 * 10000 * 10000 / 8 bytes = 4e10 bytes = 40 GB\n    # the double for loop helps us overcome the memory limit\n    log.info('Process {} writing {} {} time series tifs of '\n             'total {}'.format(mpiops.rank, len(process_tifs), tstype, no_ts_tifs))\n    for i in process_tifs:\n        ts_arr = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type=tstype, index=i)\n        __save_merged_files(ifgs_dict, params, ts_arr, out_type=tstype, index=i, savenpy=params[\"savenpy\"])\n\n    mpiops.comm.barrier()\n    log.debug('Process {} finished writing {} {} time series tifs of '\n              'total {}'.format(mpiops.rank, len(process_tifs), tstype, no_ts_tifs))\n\n\ndef create_png_and_kml_from_tif(output_folder_path: str, output_type: str) -> None:\n    \"\"\"\n    Function to create a preview PNG format image from a geotiff, and a KML file\n    \"\"\"\n    log.info(f'Creating quicklook image for {output_type}')\n    # open raster and choose band to find min, max\n    raster_path = join(output_folder_path, f\"{output_type}.tif\")\n    if not isfile(raster_path):\n        raise Exception(f\"{output_type}.tif file not found at: \" + raster_path)\n    gtif = gdal.Open(raster_path)\n    # find bounds of image\n    west, north, east, south = \"\", \"\", \"\", \"\"\n    for line in gdal.Info(gtif).split('\\n'):\n        if \"Upper Left\" in line:\n            west, north = line.split(\")\")[0].split(\"(\")[1].split(\",\")\n        if \"Lower Right\" in line:\n            east, south = line.split(\")\")[0].split(\"(\")[1].split(\",\")\n    # write KML file\n    kml_file_path = join(output_folder_path, f\"{output_type}.kml\")\n    kml_file_content = f\"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<kml xmlns=\"http://earth.google.com/kml/2.1\">\n  <Document>\n    <name>{output_type}.kml</name>\n    <GroundOverlay>\n      <name>{output_type}.png</name>\n      <Icon>\n        <href>{output_type}.png</href>\n      </Icon>\n      <LatLonBox>\n        <north> \"\"\" + north + \"\"\" </north>\n        <south> \"\"\" + south + \"\"\" </south>\n        <east>  \"\"\" + east + \"\"\" </east>\n        <west>  \"\"\" + west + \"\"\" </west>\n      </LatLonBox>\n    </GroundOverlay>\n  </Document>\n</kml>\"\"\"\n    with open(kml_file_path, \"w\") as f:\n        f.write(kml_file_content)\n\n    # Get raster statistics\n    srcband = gtif.GetRasterBand(1)\n    minimum, maximum, _, _ = srcband.GetStatistics(True, True)\n    del gtif  # close geotiff (used to calculate statistics)\n    # steps used for the colourmap, must be even (currently hard-coded to 254 resulting in 255 values)\n    no_of_steps = 254\n    # slightly different code required for rate map and rate error map\n    if output_type in ('stack_rate', 'linear_rate'):\n        # minimum value might be negative\n        maximum = max(abs(minimum), abs(maximum))\n        minimum = -1 * maximum\n        # colours: blue -> white -> red (white==0) \n        # note that an extra value will be added for zero (i.e. white: 255 255 255)\n        # generate a colourmap for odd number of values (currently hard-coded to 255)\n        mid = int(no_of_steps * 0.5)\n        # allocate RGB values to three numpy arrays r, g, b\n        r = np.arange(0, mid) / mid\n        g = r\n        r = np.concatenate((r, np.ones(mid + 1)))\n        g = np.concatenate((g, np.array([1]), np.flipud(g)))\n        b = np.flipud(r)\n        # change direction of colours (blue: positve, red: negative)\n        r = np.flipud(r) * 255\n        g = np.flipud(g) * 255\n        b = np.flipud(b) * 255\n    if output_type in ('stack_error', 'linear_error', 'linear_rsquared'):\n        # colours: white -> red (minimum error -> maximum error  \n        # allocate RGB values to three numpy arrays r, g, b\n        r = np.ones(no_of_steps + 1) * 255\n        g = np.arange(0, no_of_steps + 1) / (no_of_steps)\n        g = np.flipud(g) * 255\n        b = g\n        # generate the colourmap file in the output folder\n    color_map_path = join(output_folder_path, f\"colourmap_{output_type}.txt\")\n    log.info('Saving colour map to file {}; min/max values: {:.2f}/{:.2f}'.format(\n        color_map_path, minimum, maximum))\n\n    # write colourmap as text file\n    with open(color_map_path, \"w\") as f:\n        f.write(\"nan 0 0 0 0\\n\")\n        for i, value in enumerate(np.linspace(minimum, maximum, no_of_steps + 1)):\n            f.write(\"%f %f %f %f 255\\n\" % (value, r[i], g[i], b[i]))\n\n    # create PNG image file\n    input_tif_path = join(output_folder_path, f\"{output_type}.tif\")\n    output_png_path = join(output_folder_path, f\"{output_type}.png\")\n    subprocess.check_call([\"gdaldem\", \"color-relief\", \"-of\", \"PNG\", input_tif_path, \"-alpha\",\n                           color_map_path, output_png_path, \"-nearest_color_entry\"])\n    log.debug(f'Finished creating quicklook image for {output_type}')\n\n\ndef assemble_tiles(s: Tuple, dir: str, tiles: Tile, out_type: str, index: Optional[int] = None) -> np.ndarray:\n    \"\"\"\n    Function to reassemble tiles from numpy files in to a merged array\n\n    :param s: shape for merged array.\n    :param dir: path to directory containing numpy tile files.\n    :param tiles: pyrate.core.shared.Tile Class object.\n    :param out_type: product type string, used to construct numpy tile file name.\n    :param index: array third dimension index to extract from 3D time series array tiles.\n    :return: merged_array: array assembled from all tiles.\n    \"\"\"\n    log.debug('Re-assembling tiles for {}'.format(out_type))\n    # pre-allocate dest array\n    merged_array = np.empty(shape=s, dtype=np.float32)\n\n    # loop over each tile, load and slot in to correct spot\n    for t in tiles:\n        tile_file = Path(join(dir, out_type + '_' + str(t.index) + '.npy'))\n        tile = np.load(file=tile_file)\n        if index is None:  # 2D array\n            merged_array[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x] = tile\n        else:  # 3D array\n            merged_array[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x] = tile[:, :, index]\n\n    log.debug('Finished assembling tiles for {}'.format(out_type))\n    return merged_array\n\n\nout_type_md_dict = {\n    'stack_rate': ifc.STACKRATE,\n    'stack_error': ifc.STACKERROR,\n    'stack_samples': ifc.STACKSAMP,\n    'linear_rate': ifc.LINRATE,\n    'linear_error': ifc.LINERROR,\n    'linear_samples': ifc.LINSAMP,\n    'linear_intercept': ifc.LINICPT,\n    'linear_rsquared': ifc.LINRSQ,\n    'tsincr': ifc.INCR,\n    'tscuml': ifc.CUML\n}\n\nerror_out_types = {'linear_error', 'stack_error'}\nlos_projection_out_types = {'tsincr', 'tscuml', 'linear_rate', 'stack_rate'}\nlos_projection_divisors = {\n    ifc.LINE_OF_SIGHT: lambda data: 1,\n    ifc.PSEUDO_VERTICAL: lambda data: np.cos(data),\n    ifc.PSEUDO_HORIZONTAL: lambda data: np.sin(data)\n}\n\n\ndef __save_merged_files(ifgs_dict, params, array, out_type, index=None, savenpy=None):\n    \"\"\"\n    Convenience function to save PyRate geotiff and numpy array files\n    \"\"\"\n    outdir = params[C.OUT_DIR]\n    ts_dir = params[C.TIMESERIES_DIR]\n    vel_dir = params[C.VELOCITY_DIR]\n    log.debug('Saving PyRate outputs {}'.format(out_type))\n    gt, md, wkt = ifgs_dict['gt'], ifgs_dict['md'], ifgs_dict['wkt']\n    epochlist = ifgs_dict['epochlist']\n\n    if out_type in ('tsincr', 'tscuml'):\n        epoch = epochlist.dates[index + 1]\n        dest = join(ts_dir, out_type + \"_\" + str(epoch) + \".tif\")\n        npy_file = join(ts_dir, out_type + \"_\" + str(epoch) + \".npy\")\n        # sequence position; first time slice is #0\n        md['SEQUENCE_POSITION'] = index + 1\n        md[ifc.EPOCH_DATE] = epoch\n    else:\n        dest = join(vel_dir, out_type + \".tif\")\n        npy_file = join(vel_dir, out_type + '.npy')\n        md[ifc.EPOCH_DATE] = [d.strftime('%Y-%m-%d') for d in epochlist.dates]\n\n    md[ifc.DATA_TYPE] = out_type_md_dict[out_type]\n\n    if out_type in los_projection_out_types:  # apply LOS projection and sign conversion for these outputs\n        incidence_path = Path(Configuration.geometry_files(params)['incidence_angle'])\n        if incidence_path.exists():  # We can do LOS projection\n            if params[C.LOS_PROJECTION] == ifc.LINE_OF_SIGHT:\n                log.info(f\"Retaining Line-of-sight signal projection for file {dest}\")\n            elif params[C.LOS_PROJECTION] != ifc.LINE_OF_SIGHT:\n                log.info(f\"Projecting Line-of-sight signal into \"\n                         f\"{ifc.LOS_PROJECTION_OPTION[params[C.LOS_PROJECTION]]} for file {dest}\")\n            incidence = shared.Geometry(incidence_path)\n            incidence.open()\n            array /= los_projection_divisors[params[C.LOS_PROJECTION]](incidence.data) * params[C.SIGNAL_POLARITY]\n            md[C.LOS_PROJECTION.upper()] = ifc.LOS_PROJECTION_OPTION[params[C.LOS_PROJECTION]]\n            md[C.SIGNAL_POLARITY.upper()] = params[C.SIGNAL_POLARITY]\n\n    if out_type in error_out_types:  # add n-sigma value to error file metadata\n        # TODO: move the multiplication of nsigma and error data here?\n        md[C.VELERROR_NSIG.upper()] = params[C.VELERROR_NSIG]\n        log.info(f\"Saving file {dest} with {params[C.VELERROR_NSIG]}-sigma uncertainties\")\n\n    shared.write_output_geotiff(md, gt, wkt, array, dest, np.nan)\n\n    # clear the extra metadata so it does not mess up other images\n    if C.LOS_PROJECTION.upper() in md:\n        md.pop(C.LOS_PROJECTION.upper())\n    if C.SIGNAL_POLARITY.upper() in md:\n        md.pop(C.SIGNAL_POLARITY.upper())\n    if C.VELERROR_NSIG.upper() in md:\n        md.pop(C.VELERROR_NSIG.upper())\n\n    if savenpy:\n        np.save(file=npy_file, arr=array)\n\n    log.debug('Finished saving {}'.format(out_type))\n\n\ndef __merge_setup(params):\n    \"\"\"\n    Convenience function for Merge set up steps\n    \"\"\"\n    # load previously saved preread_ifgs dict\n    preread_ifgs_file = Configuration.preread_ifgs(params)\n    ifgs_dict = pickle.load(open(preread_ifgs_file, 'rb'))\n    ifgs = [v for v in ifgs_dict.values() if isinstance(v, shared.PrereadIfg)]\n    shape = ifgs[0].shape\n    tiles = Configuration.get_tiles(params)\n    return shape, tiles, ifgs_dict\n"
  },
  {
    "path": "pyrate/prepifg.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python script applies optional multilooking and cropping to input\ninterferogram geotiff files.\n\nThere are two modes of running prepifg using pyrate:\n1. A python/numpy version, which is also the default version, and\n2. a `largetifs` option which can be activated using the config option largetifs: 1\n\nThe python/numpy version is recommended when the both the input interferogram and multilooked interferogram can fit\ninto the memory allocated to the process.\n\nWhen dealing with relatively large (compared to memory available to the process) interferogram, the largetif option\ncan be used. This option uses a slightly modified version of the `gdal_calc.py` included with pyrate. Arbitrarily\nlarge interferograms can be multilooked using largetifs.\n\nThe `largetifs` option uses system calls to gdal utilities and avoid loading large chunks of memory. Our tests\n(tests/test_prepifg_largetifs_vs_python.py::test_prepifg_largetifs_vs_python) indicate that for two different small\ndatasets included in our test suite, the `largetifs` option exactly match the output of the numpy version. However,\non large practical datasets we have observed numerical differences in the multilooked output in the 3rd and 4th decimal\nplaces (in sub mm range).\n\n\"\"\"\n# -*- coding: utf-8 -*-\nimport os\nfrom subprocess import check_call\nimport warnings\nfrom typing import List, Tuple, Dict\nfrom pathlib import Path\nfrom joblib import Parallel, delayed\nimport numpy as np\nfrom osgeo import gdal\n\nimport pyrate.constants as C\nfrom pyrate.core import shared, geometry, mpiops, prepifg_helper, gamma, roipac, ifgconstants as ifc\nfrom pyrate.core.prepifg_helper import PreprocessError, coherence_paths_for, transform_params\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.configuration import MultiplePaths, Configuration\nfrom pyrate.core.shared import Ifg, DEM\nfrom pyrate.core.refpixel import convert_geographic_coordinate_to_pixel_value\nfrom pyrate.core import ifgconstants as ifg\n\n\nGAMMA = 1\nROIPAC = 0\nGEOTIF = 2\n\n\ndef main(params):\n    \"\"\"\n    Main workflow function for preparing interferograms for PyRate.\n\n    :param dict params: Parameters dictionary read in from the config file\n    \"\"\"\n    # TODO: looks like ifg_paths are ordered according to ifg list\n    # This probably won't be a problem because input list won't be reordered\n    # and the original gamma generated list is ordered) this may not affect\n    # the important pyrate stuff anyway, but might affect gen_thumbs.py.\n    # Going to assume ifg_paths is ordered correcly\n    # pylint: disable=too-many-branches\n    shared.mpi_vs_multiprocess_logging(\"prepifg\", params)\n\n    ifg_paths = params[C.INTERFEROGRAM_FILES]\n    if params[C.DEM_FILE] is not None:  # optional DEM conversion\n        ifg_paths.append(params[C.DEM_FILE_PATH])\n\n    if params[C.COH_FILE_LIST] is not None:\n        ifg_paths.extend(params[C.COHERENCE_FILE_PATHS])\n\n    if params[C.COH_FILE_LIST] is None and params[C.COH_MASK]:\n        raise FileNotFoundError(\"Cannot apply coherence masking: no coherence file list \"\n                                \"supplied (parameter 'cohfilelist')\")\n\n    shared.mkdir_p(params[C.OUT_DIR])  # create output dir\n\n    user_exts = (params[C.IFG_XFIRST], params[C.IFG_YFIRST], params[\n        C.IFG_XLAST], params[C.IFG_YLAST])\n    xlooks, ylooks, crop = transform_params(params)\n    ifgs = [prepifg_helper.dem_or_ifg(p.converted_path) for p in ifg_paths]\n    exts = prepifg_helper.get_analysis_extent(crop, ifgs, xlooks, ylooks, user_exts=user_exts)\n\n    ifg0 = ifgs[0]\n    ifg0.open()\n\n    transform = ifg0.dataset.GetGeoTransform()\n\n    process_ifgs_paths = np.array_split(ifg_paths, mpiops.size)[mpiops.rank]\n    do_prepifg(process_ifgs_paths, exts, params)\n\n    if params[C.LT_FILE] is not None:\n        log.info(\"Calculating and writing geometry files\")\n        __write_geometry_files(params, exts, transform, ifg_paths[0].sampled_path)\n    else:\n        log.info(\"Skipping geometry calculations: Lookup table not provided\")\n\n    if params[C.COH_FILE_LIST] is not None:\n        log.info(\"Calculating and writing coherence statistics\")\n        mpiops.run_once(__calc_coherence_stats, params, ifg_paths[0].sampled_path)\n    else:\n        log.info(\"Skipping coherence file statistics computation.\")\n    log.info(\"Finished 'prepifg' step\")\n    ifg0.close()\n\n\ndef __calc_coherence_stats(params, ifg_path):\n    coherence_files_multi_paths = params[C.COHERENCE_FILE_PATHS]\n    sampled_paths = [c.sampled_path for c in coherence_files_multi_paths]\n    ifgs = [Ifg(s) for s in sampled_paths]\n    for i in ifgs:\n        i.open()\n    phase_data = np.stack([i.phase_data for i in ifgs])\n    coh_stats = Configuration.coherence_stats(params)\n\n    for stat_func, out_type in zip([np.nanmedian, np.nanmean, np.nanstd], [ifg.COH_MEDIAN, ifg.COH_MEAN, ifg.COH_STD]):\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n            arr = stat_func(phase_data, axis=0)\n        arr[arr==0.0] = np.nan # convert exact zeros (no-data) to NaN\n        dest = coh_stats[out_type]\n        __save_geom_files(ifg_path, dest, arr, out_type)\n\n\ndef do_prepifg(multi_paths: List[MultiplePaths], exts: Tuple[float, float, float, float], params: dict) -> None:\n    \"\"\"\n    Prepare interferograms by applying multilooking/cropping operations.\n\n    \"\"\"\n    # pylint: disable=expression-not-assigned\n    parallel = params[C.PARALLEL]\n    if mpiops.size > 1:\n        parallel = False\n\n    for f in multi_paths:\n        if not os.path.isfile(f.converted_path):\n            raise FileNotFoundError(\"Can not find geotiff: \" + str(f) + \". Ensure you have converted your \"\n                                    \"interferograms to geotiffs.\")\n\n    if params[C.LARGE_TIFS]:\n        log.info(\"Using gdal system calls to execute 'prepifg' step\")\n        ifg = prepifg_helper.dem_or_ifg(multi_paths[0].converted_path)\n        ifg.open()\n        xlooks, ylooks = params[C.IFG_LKSX], params[C.IFG_LKSY]\n        res_str = [xlooks * ifg.x_step, ylooks * ifg.y_step]\n        res_str = ' '.join([str(e) for e in res_str])\n        if parallel:\n            Parallel(n_jobs=params[C.PROCESSES], verbose=50)(\n                delayed(__prepifg_system)(exts, gtiff_path, params, res_str) for gtiff_path in multi_paths)\n        else:\n            for m_path in multi_paths:\n                __prepifg_system(exts, m_path, params, res_str)\n    else:\n        if parallel:\n            Parallel(n_jobs=params[C.PROCESSES], verbose=50)(\n                delayed(_prepifg_multiprocessing)(p, exts, params) for p in multi_paths\n            )\n        else:\n            for m_path in multi_paths:\n                _prepifg_multiprocessing(m_path, exts, params)\n    mpiops.comm.barrier()\n\n\nCOMMON_OPTIONS = \"-co BLOCKXSIZE=256 -co BLOCKYSIZE=256 -co TILED=YES --config GDAL_CACHEMAX=64 -q\"\nCOMMON_OPTIONS2 = \"--co BLOCKXSIZE=256 --co BLOCKYSIZE=256 --co TILED=YES --quiet\"\nGDAL_CALC = 'gdal_calc_local.py'\n\n\ndef __prepifg_system(exts, gtiff, params, res):\n    thresh = params[C.NO_DATA_AVERAGING_THRESHOLD]\n    p, c, l = _prepifg_multiprocessing(gtiff, exts, params)\n    log.info(\"Multilooking {p} into {l}\".format(p=p, l=l))\n    extents = ' '.join([str(e) for e in exts])\n\n    if isinstance(prepifg_helper.dem_or_ifg(p), shared.DEM):\n        check_call('gdalwarp {co} -te\\t{extents}\\t-tr\\t{res}\\t-r\\taverage \\t{p}\\t{l}\\n'.format(\n            co=COMMON_OPTIONS, extents=extents, res=res, p=p, l=l), shell=True)\n        __update_meta_data(p, c, l, params)\n        return\n\n    p_unset = Path(params[C.OUT_DIR]).joinpath(Path(p).name).with_suffix('.unset.tif')\n    # change nodataval from zero, also leave input geotifs unchanged if one supplies conv2tif output/geotifs\n    check_call('gdal_translate {co} -a_nodata nan\\t{p}\\t{q}'.format(co=COMMON_OPTIONS, p=p, q=p_unset),\n               shell=True)\n\n    # calculate nan-fraction\n    # TODO: use output options and datatypes to reduce size of the next two tifs\n    nan_frac = Path(l).with_suffix('.nanfrac.tif')\n    nan_frac_avg = Path(l).with_suffix('.nanfrac.avg.tif')\n    corrected_p = Path(p_unset).with_suffix('.corrected.tif')\n\n    if c is not None:\n        # find all the nans\n        log.info(f\"applying coherence + nodata masking on {p}\")\n        check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} -A {p_unset} -B {c} --outfile={nan_frac}\\t'\n                   f'--calc=\\\"logical_or((B<{params[C.COH_THRESH]}), isclose(A,0,atol=0.000001))\\\"\\t'\n                   f'--NoDataValue=nan', shell=True)\n\n        # coh masking\n        check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {p_unset} -B {c}\\t'\n                   f'--calc=\\\"A*(B>={params[C.COH_THRESH]}) - '\n                   f'99999*logical_or((B<{params[C.COH_THRESH]}), isclose(A,0,atol=0.000001))\\\"\\t'\n                   f'--outfile={corrected_p}\\t'\n                   f'--NoDataValue=nan', shell=True)\n    else:\n        log.info(f\"applying nodata masking on {p}\")\n        check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {p_unset}\\t'\n                   f'--calc=\\\"isclose(A,0,atol=0.000001)\\\"\\t'\n                   f'--outfile={nan_frac}\\t'\n                   f'--NoDataValue=nan', shell=True)\n        check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {p_unset}\\t'\n                   f'--calc=\\\"A - 99999*isclose(A, 0, atol=0.000001)\\\"\\t'\n                   f'--outfile={corrected_p}\\t'\n                   f'--NoDataValue=nan', shell=True)\n\n    # crop resample/average multilooking of nan-fraction\n    check_call('gdalwarp {co} -te\\t{extents}\\t-tr\\t{res}\\t-r\\taverage\\t{p}\\t{out_file}'.format(\n        co=COMMON_OPTIONS, extents=extents, res=res, p=nan_frac, out_file=nan_frac_avg), shell=True)\n\n    # crop resample/average multilooking of raster\n    check_call('gdalwarp {co} -te\\t{extents}\\t-tr\\t{res}\\t-r\\taverage \\t{p}\\t{l}'.format(\n        co=COMMON_OPTIONS, extents=extents, res=res, p=corrected_p, l=l), shell=True)\n\n    check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {nan_frac_avg}\\t-B {l}\\t'\n               f'--calc=\\\"B*(A < {thresh}) -99999*(A >= {thresh})\\\"\\t'\n               f'--outfile={l}\\t'\n               f'--NoDataValue=nan', shell=True)\n\n    __update_meta_data(p_unset.as_posix(), c, l, params)\n\n    # clean up\n    nan_frac_avg.unlink()\n    nan_frac.unlink()\n    corrected_p.unlink()\n    p_unset.unlink()\n\n\ndef __update_meta_data(p_unset, c, l, params):\n    # update metadata\n    ds = gdal.Open(p_unset)\n    md = ds.GetMetadata()\n    # remove data type\n    v = md.pop(ifc.DATA_TYPE)\n    md[ifc.IFG_LKSX] = str(params[C.IFG_LKSX])\n    md[ifc.IFG_LKSY] = str(params[C.IFG_LKSY])\n    md[ifc.IFG_CROP] = str(params[C.IFG_CROP_OPT])\n    # update data type\n    if c is not None:  # it's a interferogram when COH_MASK=1\n        md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MLOOKED_COH_MASKED_IFG)\n        md[ifc.COH_THRESH] = str(params[C.COH_THRESH])\n    else:\n        if v == ifc.DEM:  # it's a dem\n            md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MLOOKED_DEM)\n        elif v == ifc.COH:\n            md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MULTILOOKED_COH)\n        else:  # it's an ifg\n            md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MULTILOOKED)\n    for k, v in md.items():\n        md_str += ' -mo {k}={v}'.format(k=k.replace(' ', '_'), v=v.replace(' ', '_'))\n    check_call('gdal_edit.py -unsetmd {md} {f}'.format(md=md_str, f=l), shell=True)\n    ds = None\n\n    # make prepifg output readonly\n    Path(l).chmod(0o444)  # readonly output\n\n\ndef _prepifg_multiprocessing(m_path: MultiplePaths, exts: Tuple[float, float, float, float], params: dict):\n    \"\"\"\n    Multiprocessing wrapper for prepifg\n    \"\"\"\n    thresh = params[C.NO_DATA_AVERAGING_THRESHOLD]\n    hdr = find_header(m_path, params)\n    hdr[ifc.INPUT_TYPE] = m_path.input_type\n    xlooks, ylooks, crop = transform_params(params)\n    hdr[ifc.IFG_LKSX] = xlooks\n    hdr[ifc.IFG_LKSY] = ylooks\n    hdr[ifc.IFG_CROP] = crop\n\n    # If we're performing coherence masking, find the coherence file for this IFG.\n    if params[C.COH_MASK] and shared._is_interferogram(hdr):\n        coherence_path = coherence_paths_for(m_path.converted_path, params, tif=True)\n        coherence_thresh = params[C.COH_THRESH]\n    else:\n        coherence_path = None\n        coherence_thresh = None\n\n    if params[C.LARGE_TIFS]:\n        return m_path.converted_path, coherence_path, m_path.sampled_path\n    else:\n        prepifg_helper.prepare_ifg(m_path.converted_path, xlooks, ylooks, exts, thresh, crop,\n                                   out_path=m_path.sampled_path, header=hdr, coherence_path=coherence_path,\n                                   coherence_thresh=coherence_thresh)\n        Path(m_path.sampled_path).chmod(0o444)  # readonly output\n\n\ndef find_header(path: MultiplePaths, params: dict) -> Dict[str, str]:\n    processor = params[C.PROCESSOR]  # roipac, gamma or geotif\n    tif_path = path.converted_path\n    if (processor == GAMMA) or (processor == GEOTIF):\n        header = gamma.gamma_header(tif_path, params)\n    elif processor == ROIPAC:\n        import warnings\n        warnings.warn(\"Warning: ROI_PAC support will be deprecated in a future PyRate release\",\n                      category=DeprecationWarning)\n        header = roipac.roipac_header(tif_path, params)\n    else:\n        raise PreprocessError('Processor must be ROI_PAC (0) or GAMMA (1)')\n    header[ifc.INPUT_TYPE] = path.input_type\n    return header\n\n\ndef __write_geometry_files(params: dict, exts: Tuple[float, float, float, float],\n                           transform, ifg_path: str) -> None:\n    \"\"\"\n    Calculate geometry and save to geotiff files using the information in the\n    first interferogram in the stack, i.e.:\n    - rdc_azimuth.tif (azimuth radar coordinate at each pixel)\n    - rdc_range.tif (range radar coordinate at each pixel)\n    - azimuth_angle.tif (satellite azimuth angle at each pixel)\n    - incidence_angle.tif (incidence angle at each pixel)\n    - look_angle.tif (look angle at each pixel)\n    \"\"\"\n    ifg = Ifg(ifg_path)\n    ifg.open(readonly=True)\n\n    # calculate per-pixel lon/lat\n    lon, lat = geometry.get_lonlat_coords(ifg)\n\n    # not currently implemented for ROIPAC data which breaks some tests\n    # if statement can be deleted once ROIPAC is deprecated from PyRate\n    if ifg.meta_data[ifc.PYRATE_INSAR_PROCESSOR] == 'ROIPAC':\n        log.warning(\"Geometry calculations are not implemented for ROI_PAC\")\n        return\n\n    # get geometry information and save radar coordinates and angles to tif files\n    # using metadata of the first image in the stack\n    # get pixel values of crop (needed to crop lookup table file)\n    # pixel extent of cropped area (original IFG input)\n    xmin, ymax = convert_geographic_coordinate_to_pixel_value(exts[0], exts[1], transform)\n    xmax, ymin = convert_geographic_coordinate_to_pixel_value(exts[2], exts[3], transform)\n    # xmin, xmax: columns of crop\n    # ymin, ymax: rows of crop\n\n    # calculate per-pixel radar coordinates\n    az, rg = geometry.calc_radar_coords(ifg, params, xmin, xmax, ymin, ymax)\n\n    # Read height data from DEM\n    dem_file = params[C.DEM_FILE_PATH].sampled_path\n    dem = DEM(dem_file)\n    # calculate per-pixel look angle (also calculates and saves incidence and azimuth angles)\n    lk_ang, inc_ang, az_ang, rg_dist = geometry.calc_pixel_geometry(ifg, rg, lon.data, lat.data, dem.data)\n\n    # save radar coordinates and angles to geotiff files\n    combinations = zip([az, rg, lk_ang, inc_ang, az_ang, rg_dist], C.GEOMETRY_OUTPUT_TYPES)\n    shared.iterable_split(__parallelly_write, combinations, params, ifg_path)\n\n\ndef __parallelly_write(tup, params, ifg_path):\n    array, output_type = tup\n    dest = Configuration.geometry_files(params)[output_type]\n    if mpiops.size > 0:\n        log.debug(f\"Writing {dest} using process {mpiops.rank}\")\n    __save_geom_files(ifg_path, dest, array, output_type)\n\n\nout_type_md_dict = {\n    'rdc_azimuth': ifc.RDC_AZIMUTH,\n    'rdc_range': ifc.RDC_RANGE,\n    'look_angle': ifc.LOOK,\n    'incidence_angle': ifc.INCIDENCE,\n    'azimuth_angle': ifc.AZIMUTH,\n    'range_dist': ifc.RANGE_DIST,\n    ifc.COH_MEAN: ifc.COH_MEAN,\n    ifc.COH_MEDIAN: ifc.COH_MEDIAN,\n    ifc.COH_STD: ifc.COH_STD + '_1SIGMA'\n}\n\n\ndef __save_geom_files(ifg_path, dest, array, out_type):\n    \"\"\"\n    Convenience function to save geometry geotiff files\n    \"\"\"\n    log.debug('Saving PyRate outputs {}'.format(out_type))\n    gt, md, wkt = shared.get_geotiff_header_info(ifg_path)\n    md[ifc.DATA_TYPE] = out_type_md_dict[out_type]\n    shared.remove_file_if_exists(dest)\n    log.info(f\"Writing geotiff: {dest}\")\n    shared.write_output_geotiff(md, gt, wkt, array, dest, np.nan)\n"
  },
  {
    "path": "pytest.ini",
    "content": "[pytest]\nfilterwarnings =\n    ignore::DeprecationWarning\n\nmarkers =\n    slow: marks tests as slow (deselect with '-m \"not slow\"')\n    mpi: marks tests that require mpi (deselect with '-m \"not mpi\"')\n"
  },
  {
    "path": "requirements-dev.txt",
    "content": "ghp-import==0.5.5\nsphinx==3.0.3\nsphinx_rtd_theme==0.4.3\nsphinxcontrib-programoutput==0.16\nrecommonmark==0.6.0\nm2r2\nmpi4py==3.0.3\nmatplotlib\nipython\nsetuptools\ntwine\nwheel\npylint\npytest\n"
  },
  {
    "path": "requirements-plot.txt",
    "content": "rasterio\nmatplotlib \nstatsmodels\nxarray\npylab\n"
  },
  {
    "path": "requirements-test.txt",
    "content": "pytest-cov==2.8.1\ncoverage==5.1\ncodecov==2.1.0\ntox==3.15.0\npytest==5.4.2\n"
  },
  {
    "path": "requirements.txt",
    "content": "GDAL\njoblib==1.0.0\nmpi4py==3.0.3\nnetworkx==2.5\nnumpy==1.19.4\npyproj==3.0.0\nscipy==1.5.4\nnumexpr==2.7.2\nnptyping==1.4.0\n"
  },
  {
    "path": "scripts/apt_install.sh",
    "content": "echo \"This script will install packages required by PyRate. Continue?\" \n    select yn in \"Yes\" \"No\"; do\n    case $yn in\n        Yes ) break;;\n        No ) exit;; \n    esac\ndone\n\n# OS package requirements for Ubuntu 18.04\nsudo apt-get update\nsudo apt-get -y install \\\n    gdal-bin \\\n    libgdal-dev \\\n    openmpi-bin \\\n    libopenmpi-dev \n\n"
  },
  {
    "path": "scripts/ci_gdal_install.sh",
    "content": "#!/bin/bash\n#\n# originally contributed by @rbuffat to Toblerity/Fiona\nset -e\n\nGDALOPTS=\"  --with-geos \\\n            --with-expat \\\n            --without-libtool \\\n            --with-libz=internal \\\n            --with-libtiff=internal \\\n            --with-geotiff=internal \\\n            --without-gif \\\n            --without-pg \\\n            --without-grass \\\n            --without-libgrass \\\n            --without-cfitsio \\\n            --without-pcraster \\\n            --with-netcdf \\\n            --with-png=internal \\\n            --with-jpeg=internal \\\n            --without-gif \\\n            --without-ogdi \\\n            --without-fme \\\n            --without-hdf4 \\\n            --with-hdf5 \\\n            --without-jasper \\\n            --without-ecw \\\n            --without-kakadu \\\n            --without-mrsid \\\n            --without-jp2mrsid \\\n            --without-mysql \\\n            --without-ingres \\\n            --without-xerces \\\n            --without-odbc \\\n            --with-curl \\\n            --without-sqlite3 \\\n            --without-idb \\\n            --without-sde \\\n            --without-perl \\\n            --without-python\"\n\n# Create build dir if not exists\nif [ ! -d \"$GDALBUILD\" ]; then\n  mkdir $GDALBUILD;\nfi\n\nif [ ! -d \"$GDALINST\" ]; then\n  mkdir $GDALINST;\nfi\n\nls -l $GDALINST\n\nif [ \"$GDALVERSION\" = \"master\" ]; then\n    PROJOPT=\"--with-proj=$GDALINST/gdal-$GDALVERSION\"\n    cd $GDALBUILD\n    git clone --depth 1 https://github.com/OSGeo/gdal gdal-$GDALVERSION\n    cd gdal-$GDALVERSION/gdal\n    git rev-parse HEAD > newrev.txt\n    BUILD=no\n    # Only build if nothing cached or if the GDAL revision changed\n    if test ! -f $GDALINST/gdal-$GDALVERSION/rev.txt; then\n        BUILD=yes\n    elif ! diff newrev.txt $GDALINST/gdal-$GDALVERSION/rev.txt >/dev/null; then\n        BUILD=yes\n    fi\n    if test \"$BUILD\" = \"yes\"; then\n        mkdir -p $GDALINST/gdal-$GDALVERSION\n        cp newrev.txt $GDALINST/gdal-$GDALVERSION/rev.txt\n        ./configure --prefix=$GDALINST/gdal-$GDALVERSION $GDALOPTS $PROJOPT\n        make -s -j 2\n        make install\n    fi\nelse\n    case \"$GDALVERSION\" in\n        3*)\n            PROJOPT=\"--with-proj=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n        2.4*)\n            PROJOPT=\"--with-proj=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n        2.3*)\n            PROJOPT=\"--with-proj=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n        2.2*)\n            PROJOPT=\"--with-static-proj4=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n        2.1*)\n            PROJOPT=\"--with-static-proj4=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n        2.0*)\n            PROJOPT=\"--with-static-proj4=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n        1*)\n            PROJOPT=\"--with-static-proj4=$GDALINST/gdal-$GDALVERSION\"\n            ;;\n    esac\n\n    if [ ! -d \"$GDALINST/gdal-$GDALVERSION/share/gdal\" ]; then\n        cd $GDALBUILD\n        gdalver=$(expr \"$GDALVERSION\" : '\\([0-9]*.[0-9]*.[0-9]*\\)')\n        wget -q http://download.osgeo.org/gdal/$gdalver/gdal-$GDALVERSION.tar.gz\n        tar -xzf gdal-$GDALVERSION.tar.gz\n        cd gdal-$gdalver\n        ./configure --prefix=$GDALINST/gdal-$GDALVERSION $GDALOPTS $PROJOPT\n        make -s -j 2\n        make install\n    fi\nfi\n"
  },
  {
    "path": "scripts/ci_proj_install.sh",
    "content": "#!/bin/sh\nset -e\n\n# Create build dir if not exists\nif [ ! -d \"$PROJBUILD\" ]; then\n  mkdir $PROJBUILD;\nfi\n\nif [ ! -d \"$PROJINST\" ]; then\n  mkdir $PROJINST;\nfi\n\nls -l $PROJINST\n\necho \"PROJ VERSION: $PROJVERSION\"\n\nif [ ! -d \"$PROJINST/gdal-$GDALVERSION/share/proj\" ]; then\n    cd $PROJBUILD\n    wget -q https://download.osgeo.org/proj/proj-$PROJVERSION.tar.gz\n    tar -xzf proj-$PROJVERSION.tar.gz\n    cd proj-$PROJVERSION\n    ./configure --prefix=$PROJINST/gdal-$GDALVERSION\n    make -s -j 2\n    make install\nfi\n"
  },
  {
    "path": "scripts/create_html_documentation.sh",
    "content": "#!/bin/bash\nmodule purge\nmodule load python3/3.7.4\nmodule load gdal/3.0.2\nmodule load openmpi/4.0.2\nexport PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH\nsource ~/PyRateVenv/bin/activate\ncd ~/PyRate/docs\nmake html\necho \"Use following cmd to copy file to local machine:\"\ncd ~\necho \"scp -r `whoami`@gadi.nci.org.au:`pwd ~`/PyRate/docs/_build/html C:/preview\"\n"
  },
  {
    "path": "scripts/gdal_calc_local.py",
    "content": "#! /usr/bin/python\n# -*- coding: utf-8 -*-\n#******************************************************************************\n#\n#  Project:  GDAL\n#  Purpose:  Command line raster calculator with numpy syntax\n#  Author:   Chris Yesson, chris.yesson@ioz.ac.uk\n#\n#******************************************************************************\n#  Copyright (c) 2010, Chris Yesson <chris.yesson@ioz.ac.uk>\n#  Copyright (c) 2010-2011, Even Rouault <even dot rouault at mines-paris dot org>\n#  Copyright (c) 2016, Piers Titus van der Torren <pierstitus@gmail.com>\n#\n#  Permission is hereby granted, free of charge, to any person obtaining a\n#  copy of this software and associated documentation files (the \"Software\"),\n#  to deal in the Software without restriction, including without limitation\n#  the rights to use, copy, modify, merge, publish, distribute, sublicense,\n#  and/or sell copies of the Software, and to permit persons to whom the\n#  Software is furnished to do so, subject to the following conditions:\n#\n#  The above copyright notice and this permission notice shall be included\n#  in all copies or substantial portions of the Software.\n#\n#  THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n#  OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n#  THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n#  FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n#  DEALINGS IN THE SOFTWARE.\n#******************************************************************************\n\n################################################################\n# Command line raster calculator with numpy syntax. Use any basic arithmetic supported by numpy arrays such as +-*\\ along with logical operators such as >.  Note that all files must have the same dimensions, but no projection checking is performed.  Use gdal_calc.py --help for list of options.\n\n# This script is a copy of gdal_calc.py included in the GDAL library, modified for use with PyRate.\n\n################################################################\n\nfrom optparse import OptionParser, Values\nimport os\nimport sys\n\nimport numpy\n\nfrom osgeo import gdal\nfrom osgeo import gdalnumeric\n\n\n# create alphabetic list for storing input layers\nAlphaList=[\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\",\"K\",\"L\",\"M\",\n           \"N\",\"O\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"U\",\"V\",\"W\",\"X\",\"Y\",\"Z\"]\n\n# set up some default nodatavalues for each datatype\nDefaultNDVLookup={'Byte':255, 'UInt16':65535, 'Int16':-32767, 'UInt32':4294967293, 'Int32':-2147483647, 'Float32':3.402823466E+38, 'Float64':1.7976931348623158E+308}\n\n################################################################\ndef doit(opts, args):\n    if opts.debug:\n        print(\"gdal_calc.py starting calculation %s\" %(opts.calc))\n\n    # set up global namespace for eval with all functions of gdalnumeric\n    global_namespace = dict([(key, getattr(gdalnumeric, key))\n        for key in dir(gdalnumeric) if not key.startswith('__')])\n\n    if not opts.calc:\n        raise Exception(\"No calculation provided.\")\n    elif not opts.outF:\n        raise Exception(\"No output file provided.\")\n\n    ################################################################\n    # fetch details of input layers\n    ################################################################\n\n    # set up some lists to store data for each band\n    myFiles=[]\n    myBands=[]\n    myAlphaList=[]\n    myDataType=[]\n\n    myDataTypeNum=[]\n    myNDV=[]\n    DimensionsCheck=None\n\n    # loop through input files - checking dimensions\n    for myI, myF in opts.input_files.items():\n        if not myI.endswith(\"_band\"):\n            # check if we have asked for a specific band...\n            if \"%s_band\" % myI in opts.input_files:\n                myBand = opts.input_files[\"%s_band\" % myI]\n            else:\n                myBand = 1\n\n            myFile = gdal.Open(myF, gdal.GA_ReadOnly)\n            if not myFile:\n                raise IOError(\"No such file or directory: '%s'\" % myF)\n\n            myFiles.append(myFile)\n            myBands.append(myBand)\n            myAlphaList.append(myI)\n            myDataType.append(gdal.GetDataTypeName(myFile.GetRasterBand(myBand).DataType))\n            myDataTypeNum.append(myFile.GetRasterBand(myBand).DataType)\n            myNDV.append(myFile.GetRasterBand(myBand).GetNoDataValue())\n            # check that the dimensions of each layer are the same\n            if DimensionsCheck:\n                if DimensionsCheck != [myFile.RasterXSize, myFile.RasterYSize]:\n                    raise Exception(\"Error! Dimensions of file %s (%i, %i) are different from other files (%i, %i).  Cannot proceed\" % \\\n                            (myF, myFile.RasterXSize, myFile.RasterYSize, DimensionsCheck[0], DimensionsCheck[1]))\n            else:\n                DimensionsCheck = [myFile.RasterXSize, myFile.RasterYSize]\n\n            if opts.debug:\n                print(\"file %s: %s, dimensions: %s, %s, type: %s\" %(myI,myF,DimensionsCheck[0],DimensionsCheck[1],myDataType[-1]))\n\n    # process allBands option\n    allBandsIndex=None\n    allBandsCount=1\n    if opts.allBands:\n        try:\n            allBandsIndex=myAlphaList.index(opts.allBands)\n        except ValueError:\n            raise Exception(\"Error! allBands option was given but Band %s not found.  Cannot proceed\" % (opts.allBands))\n        allBandsCount=myFiles[allBandsIndex].RasterCount\n        if allBandsCount <= 1:\n            allBandsIndex=None\n\n    ################################################################\n    # set up output file\n    ################################################################\n\n    # open output file exists\n    if os.path.isfile(opts.outF) and not opts.overwrite:\n        if allBandsIndex is not None:\n            raise Exception(\"Error! allBands option was given but Output file exists, must use --overwrite option!\")\n        if opts.debug:\n            print(\"Output file %s exists - filling in results into file\" %(opts.outF))\n        myOut=gdal.Open(opts.outF, gdal.GA_Update)\n        if [myOut.RasterXSize,myOut.RasterYSize] != DimensionsCheck:\n            raise Exception(\"Error! Output exists, but is the wrong size.  Use the --overwrite option to automatically overwrite the existing file\")\n        myOutB=myOut.GetRasterBand(1)\n        myOutNDV=myOutB.GetNoDataValue()\n        myOutType=gdal.GetDataTypeName(myOutB.DataType)\n\n    else:\n        # remove existing file and regenerate\n        if os.path.isfile(opts.outF):\n            os.remove(opts.outF)\n        # create a new file\n        if opts.debug:\n            print(\"Generating output file %s\" %(opts.outF))\n\n        # find data type to use\n        if not opts.type:\n            # use the largest type of the input files\n            myOutType=gdal.GetDataTypeName(max(myDataTypeNum))\n        else:\n            myOutType=opts.type\n\n        # create file\n        myOutDrv = gdal.GetDriverByName(opts.format)\n        myOut = myOutDrv.Create(\n            opts.outF, DimensionsCheck[0], DimensionsCheck[1], allBandsCount,\n            gdal.GetDataTypeByName(myOutType), opts.creation_options)\n\n        # set output geo info based on first input layer\n        myOut.SetGeoTransform(myFiles[0].GetGeoTransform())\n        myOut.SetProjection(myFiles[0].GetProjection())\n\n        if opts.NoDataValue!=None:\n            myOutNDV=opts.NoDataValue\n        else:\n            myOutNDV=DefaultNDVLookup[myOutType]\n\n        for i in range(1,allBandsCount+1):\n            myOutB=myOut.GetRasterBand(i)\n            myOutB.SetNoDataValue(myOutNDV)\n            # write to band\n            myOutB=None\n\n    if opts.debug:\n        print(\"output file: %s, dimensions: %s, %s, type: %s\" %(opts.outF,myOut.RasterXSize,myOut.RasterYSize,myOutType))\n\n    ################################################################\n    # find block size to chop grids into bite-sized chunks\n    ################################################################\n\n    # use the block size of the first layer to read efficiently\n    myBlockSize=myFiles[0].GetRasterBand(myBands[0]).GetBlockSize();\n    # store these numbers in variables that may change later\n    nXValid = myBlockSize[0]\n    nYValid = myBlockSize[1]\n    # find total x and y blocks to be read\n    nXBlocks = (int)((DimensionsCheck[0] + myBlockSize[0] - 1) / myBlockSize[0]);\n    nYBlocks = (int)((DimensionsCheck[1] + myBlockSize[1] - 1) / myBlockSize[1]);\n    myBufSize = myBlockSize[0]*myBlockSize[1]\n\n    if opts.debug:\n        print(\"using blocksize %s x %s\" %(myBlockSize[0], myBlockSize[1]))\n\n    # variables for displaying progress\n    ProgressCt=-1\n    ProgressMk=-1\n    ProgressEnd=nXBlocks*nYBlocks*allBandsCount\n\n    ################################################################\n    # start looping through each band in allBandsCount\n    ################################################################\n\n    for bandNo in range(1,allBandsCount+1):\n\n        ################################################################\n        # start looping through blocks of data\n        ################################################################\n\n        # loop through X-lines\n        for X in range(0,nXBlocks):\n\n            # in the rare (impossible?) case that the blocks don't fit perfectly\n            # change the block size of the final piece\n            if X==nXBlocks-1:\n                nXValid = DimensionsCheck[0] - X * myBlockSize[0]\n                myBufSize = nXValid*nYValid\n\n            # find X offset\n            myX=X*myBlockSize[0]\n\n            # reset buffer size for start of Y loop\n            nYValid = myBlockSize[1]\n            myBufSize = nXValid*nYValid\n\n            # loop through Y lines\n            for Y in range(0,nYBlocks):\n                ProgressCt+=1\n                if 10*ProgressCt/ProgressEnd%10!=ProgressMk and not opts.quiet:\n                    ProgressMk=10*ProgressCt/ProgressEnd%10\n                    from sys import version_info\n                    if version_info >= (3,0,0):\n                        exec('print(\"%d..\" % (10*ProgressMk), end=\" \")')\n                    else:\n                        exec('print 10*ProgressMk, \"..\",')\n\n                # change the block size of the final piece\n                if Y==nYBlocks-1:\n                    nYValid = DimensionsCheck[1] - Y * myBlockSize[1]\n                    myBufSize = nXValid*nYValid\n\n                # find Y offset\n                myY=Y*myBlockSize[1]\n\n                # create empty buffer to mark where nodata occurs\n                myNDVs = None\n\n                # make local namespace for calculation\n                local_namespace = {}\n\n                # fetch data for each input layer\n\n                for i, Alpha in enumerate(myAlphaList):\n\n                    # populate lettered arrays with values\n                    if allBandsIndex is not None and allBandsIndex==i:\n                        myBandNo=bandNo\n                    else:\n                        myBandNo=myBands[i]\n                    myval=gdalnumeric.BandReadAsArray(myFiles[i].GetRasterBand(myBandNo),\n                                          xoff=myX, yoff=myY,\n                                          win_xsize=nXValid, win_ysize=nYValid)\n\n                    # fill in nodata values\n                    if myNDV[i] is not None:\n                        if myNDVs is None:\n                            myNDVs = numpy.zeros(myBufSize)\n                            myNDVs.shape=(nYValid,nXValid)\n                        myNDVs=1*numpy.logical_or(myNDVs==1, myval==myNDV[i])\n\n                    # add an array of values for this block to the eval namespace\n                    local_namespace[Alpha] = myval\n                    myval=None\n\n\n                # try the calculation on the array blocks\n                try:\n                    myResult = eval(opts.calc, global_namespace, local_namespace)\n                except:\n                    print(\"evaluation of calculation %s failed\" %(opts.calc))\n                    raise\n\n                if myNDVs is not None:\n                    myResult[numpy.isclose(myResult, -99999, atol=1e-5)] = myOutNDV\n                elif not isinstance(myResult, numpy.ndarray):\n                    myResult = numpy.ones( (nYValid,nXValid) ) * myResult\n\n                # write data block to the output file\n                myOutB=myOut.GetRasterBand(bandNo)\n                gdalnumeric.BandWriteArray(myOutB, myResult, xoff=myX, yoff=myY)\n\n    if not opts.quiet:\n        print(\"100 - Done\")\n    #print(\"Finished - Results written to %s\" %opts.outF)\n\n    return\n\n################################################################\ndef Calc(calc, outfile, NoDataValue=None, type=None, format='GTiff', creation_options=[], allBands='', overwrite=False, debug=False, quiet=False, **input_files):\n    \"\"\" Perform raster calculations with numpy syntax.\n    Use any basic arithmetic supported by numpy arrays such as +-*\\ along with logical\n    operators such as >. Note that all files must have the same dimensions, but no projection checking is performed.\n\n    Keyword arguments:\n        [A-Z]: input files\n        [A_band - Z_band]: band to use for respective input file\n\n    Examples:\n    add two files together:\n        Calc(\"A+B\", A=\"input1.tif\", B=\"input2.tif\", outfile=\"result.tif\")\n\n    average of two layers:\n        Calc(calc=\"(A+B)/2\", A=\"input1.tif\", B=\"input2.tif\", outfile=\"result.tif\")\n\n    set values of zero and below to null:\n        Calc(calc=\"A*(A>0)\", A=\"input.tif\", A_Band=2, outfile=\"result.tif\", NoDataValue=0)\n    \"\"\"\n    opts = Values()\n    opts.input_files = input_files\n    opts.calc = calc\n    opts.outF = outfile\n    opts.NoDataValue = NoDataValue\n    opts.type = type\n    opts.format = format\n    opts.creation_options = creation_options\n    opts.allBands = allBands\n    opts.overwrite = overwrite\n    opts.debug = debug\n    opts.quiet = quiet\n\n    doit(opts, None)\n\ndef store_input_file(option, opt_str, value, parser):\n    if not hasattr(parser.values, 'input_files'):\n        parser.values.input_files = {}\n    parser.values.input_files[opt_str.lstrip('-')] = value\n\ndef main():\n    usage = \"\"\"usage: %prog --calc=expression --outfile=out_filename [-A filename]\n                    [--A_band=n] [-B...-Z filename] [other_options]\"\"\"\n    parser = OptionParser(usage)\n\n    # define options\n    parser.add_option(\"--calc\", dest=\"calc\", help=\"calculation in gdalnumeric syntax using +-/* or any numpy array functions (i.e. log10())\", metavar=\"expression\")\n    # limit the input file options to the ones in the argument list\n    given_args = set([a[1] for a in sys.argv if a[1:2] in AlphaList] + ['A'])\n    for myAlpha in given_args:\n        parser.add_option(\"-%s\" % myAlpha, action=\"callback\", callback=store_input_file, type=str, help=\"input gdal raster file, you can use any letter (A-Z)\", metavar='filename')\n        parser.add_option(\"--%s_band\" % myAlpha, action=\"callback\", callback=store_input_file, type=int, help=\"number of raster band for file %s (default 1)\" % myAlpha, metavar='n')\n\n    parser.add_option(\"--outfile\", dest=\"outF\", help=\"output file to generate or fill\", metavar=\"filename\")\n    parser.add_option(\"--NoDataValue\", dest=\"NoDataValue\", type=float, help=\"output nodata value (default datatype specific value)\", metavar=\"value\")\n    parser.add_option(\"--type\", dest=\"type\", help=\"output datatype, must be one of %s\" % list(DefaultNDVLookup.keys()), metavar=\"datatype\")\n    parser.add_option(\"--format\", dest=\"format\", default=\"GTiff\", help=\"GDAL format for output file (default 'GTiff')\", metavar=\"gdal_format\")\n    parser.add_option(\n        \"--creation-option\", \"--co\", dest=\"creation_options\", default=[], action=\"append\",\n        help=\"Passes a creation option to the output format driver. Multiple \"\n        \"options may be listed. See format specific documentation for legal \"\n        \"creation options for each format.\", metavar=\"option\")\n    parser.add_option(\"--allBands\", dest=\"allBands\", default=\"\", help=\"process all bands of given raster (A-Z)\", metavar=\"[A-Z]\")\n    parser.add_option(\"--overwrite\", dest=\"overwrite\", action=\"store_true\", help=\"overwrite output file if it already exists\")\n    parser.add_option(\"--debug\", dest=\"debug\", action=\"store_true\", help=\"print debugging information\")\n    parser.add_option(\"--quiet\", dest=\"quiet\", action=\"store_true\", help=\"suppress progress messages\")\n\n    (opts, args) = parser.parse_args()\n    if not hasattr(opts, \"input_files\"):\n        opts.input_files = {}\n\n    if len(sys.argv) == 1:\n        parser.print_help()\n        sys.exit(1)\n    elif not opts.calc:\n        print(\"No calculation provided. Nothing to do!\")\n        parser.print_help()\n        sys.exit(1)\n    elif not opts.outF:\n        print(\"No output file provided. Cannot proceed.\")\n        parser.print_help()\n        sys.exit(1)\n    else:\n        try:\n            doit(opts, args)\n        except IOError as e:\n            print(e)\n            sys.exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/nci_install.sh",
    "content": "#!/bin/bash\nrm -rf  ~/PyRateVenv\nrm -rf  ~/PyRate\ngit clone https://github.com/GeoscienceAustralia/PyRate.git\nmodule purge\nmodule load python3/3.7.4\nmodule load gdal/3.0.2\nmodule load openmpi/4.0.2\nexport PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH\npython3 -m venv ~/PyRateVenv\nsource ~/PyRateVenv/bin/activate\ncd ~/PyRate\npython setup.py install\n"
  },
  {
    "path": "scripts/nci_load_modules.sh",
    "content": "#!bin/bash\nmodule purge\nmodule load python3/3.7.4\nmodule load gdal/3.0.2\nmodule load openmpi/4.0.2\n# Remove the prebuilt GDAL Python bindings that get added by the NCI module.\nexport PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH\n# Required by Click\nexport LC_ALL=en_AU.UTF-8\nexport LANG=en_AU.UTF-8\nsource ${PYRATE_VENV:-${HOME}/PyRateVenv}/bin/activate\n"
  },
  {
    "path": "scripts/nci_run_tests.sh",
    "content": "#!/bin/bash\nmodule purge\nmodule load python3/3.7.4\nmodule load gdal/3.0.2\nmodule load openmpi/4.0.2\nexport PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH\nsource ~/PyRateVenv/bin/activate\npip install -U pytest\ncd ~/PyRate\npytest tests/\n"
  },
  {
    "path": "scripts/plot_ifgs.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module plots the input interferograms to the PyRate software\n\"\"\"\n\nimport argparse\nfrom argparse import RawTextHelpFormatter\nfrom pathlib import Path\nimport math\nimport numpy as np\nimport pyrate.constants as C\nfrom pyrate.core.logger import pyratelogger as log, configure_stage_log\nfrom pyrate.core.shared import Ifg, InputTypes, nan_and_mm_convert\nfrom pyrate.main import _params_from_conf\n\n\ntry:\n    import matplotlib.pyplot as plt\n    import matplotlib as mpl\n    from mpl_toolkits.axes_grid1 import make_axes_locatable\n    cmap = mpl.cm.Spectral_r\n    cmap.set_bad(color='grey')\nexcept ImportError as e:\n    log.warn(ImportError(e))\n    log.warn(\"Required plotting packages are not found in environment. \"\n             \"Please install matplotlib in your environment to continue plotting!!!\")\n    raise ImportError(e)\n\n\ndef main():\n\n    parser = argparse.ArgumentParser(prog='plot_ifgs', description=\"Python script to plot interferograms\",\n                                     add_help=True,\n                                     formatter_class=RawTextHelpFormatter)\n    parser.add_argument('-v', '--verbosity', type=str, default='INFO', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],\n                        help=\"Increase output verbosity\")\n\n    parser.add_argument('-d', '--directory', action=\"store\", type=str, default=None,\n                        help=\"Pass path to directory containing ifgs\", required=True)\n\n    parser.add_argument('-f', '--config_file', action=\"store\", type=str, default=None,\n                        help=\"Pass configuration file\", required=True)\n\n    parser.add_argument('-n', '--ifgs_per_plot', type=int, default=50, help='number of ifgs per plot', required=False)\n\n    args = parser.parse_args()\n\n    params = _params_from_conf(args.config_file)\n\n    configure_stage_log(args.verbosity, 'plot_ifgs', Path(params[C.OUT_DIR]).joinpath('pyrate.log.').as_posix())\n\n    if args.verbosity:\n        log.setLevel(args.verbosity)\n        log.info(\"Verbosity set to \" + str(args.verbosity) + \".\")\n\n    log.debug(\"Arguments supplied at command line: \")\n    log.debug(args)\n\n    ifgs = sorted(list(Path(args.directory).glob('*_ifg.tif')))\n\n    num_ifgs = len(ifgs)\n    if num_ifgs == 0:\n        log.warning(f'No interferograms with extension *_ifg.tif were found in {args.directory}')\n        return\n\n    log.info(f'Plotting {num_ifgs} interferograms found in {args.directory}')\n\n    ifgs_per_plot = args.ifgs_per_plot\n\n    plt_rows = np.int(np.sqrt(ifgs_per_plot))\n    plt_cols = ifgs_per_plot//plt_rows\n\n    if ifgs_per_plot % plt_rows:\n        plt_cols += 1\n\n    tot_plots = 0\n    num_of_figs = math.ceil(num_ifgs / ifgs_per_plot)\n\n    fig_no = 0\n    for i in range(num_of_figs):\n        fig_no += 1\n        fig = plt.figure(figsize=(12*plt_rows, 8*plt_cols))\n        fig_plots = 0\n        for p_r in range(plt_rows):\n            for p_c in range(plt_cols):\n                ax = fig.add_subplot(plt_rows, plt_cols, fig_plots + 1)\n                ifg_num = plt_cols * p_r + p_c + ifgs_per_plot * i\n                file = ifgs[ifg_num]\n                log.info(f'Plotting {file}')\n                __plot_ifg(file, cmap, ax, num_ifgs, params)\n                tot_plots += 1\n                fig_plots += 1\n                log.debug(f'Plotted interferogram #{tot_plots}')\n                if (fig_plots == ifgs_per_plot) or (tot_plots == num_ifgs):\n                    f_name = Path(args.directory).joinpath('ifg-phase-plot-' + str(fig_no) + '.png').as_posix()\n                    plt.savefig(f_name, dpi=50)\n                    log.info(f'{fig_plots} interferograms plotted in {f_name}')\n                    plt.close(fig)\n                    break\n            if tot_plots == num_ifgs:\n                break\n\n\ndef __plot_ifg(file, cmap, ax, num_ifgs, params):\n    try:\n        ifg = Ifg(file)\n        ifg.open()\n    except:\n        raise AttributeError(f'Cannot open interferogram geotiff: {file}')\n\n    # change nodata values to NaN for display; convert units to mm (if in radians)\n    nan_and_mm_convert(ifg, params)\n\n    im = ax.imshow(ifg.phase_data, cmap=cmap)\n    text = ax.set_title(Path(ifg.data_path).stem)\n    text.set_fontsize(20)\n#    text.set_fontsize(min(20, int(num_ifgs / 2)))\n    divider = make_axes_locatable(ax)\n    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n    plt.colorbar(im, cax=cax, label='mm')\n    ifg.close()\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/prepifg.sh",
    "content": "#!/usr/bin/env bash\n# inputs are extents, thresh, coherence file, coherence_threshold\n# coherence masking\n# gdal average\n# gdal average has two steps\n# create a raster with the input raster nan_matrix, then compute the nan_frac of the input raster\n# take average of the input raster, and set nan_frac > thresh = nan\n\ninputdir=/home/sudipta/repos/PyRate/out/gamma/out\noutdir_crop=/home/sudipta/repos/PyRate/out/gamma/out/cropped\noutdir_coh=/home/sudipta/repos/PyRate/out/gamma/out/coherence\n\nmkdir -p ${outdir_crop}\nmkdir -p ${outdir_coh}\n\nfunction crop_resample_average {\n    f=$1\n    echo ${f}\n    gdalwarp -overwrite ${f}\n}\n\nfunction nan_matrix {\n    f=$1\n    echo ${f}\n    python gdal_calc_local.py --overwrite ${f}\n}\n\nfunction resampled_average {\n    f=$1\n    echo ${f}\n    python gdal_calc_local.py --overwrite ${f}\n}\n\nexport -f crop_resample_average\n#export -f mask\n#export -f multilook\nexport -f nan_matrix\nexport -f resampled_average\n\n#ls ${inputdir}/*.tif | parallel crop ${outdir_crop}\n\n# while IFS= read -r lineA && IFS= read -r lineB <&3; do\n#  echo \"$lineA\"; echo \"$lineB\"\n# done <fileA 3<fileB\n\n\n# outfile.txt is tab/space delimited file with `ifg - coh mask` pairs in each line\n\n# 1. apply coh mask at the very beginning\n#cat mask.txt | parallel mask\n\n# 2. crop, average, resample\n#cat crop.txt | parallel crop_resample_average\n\n# 3. create nan fractions\n#cat nan_fraction.txt | parallel nan_matrix\n\n# 4. crop average resample nan fractions\n# cat crop_average_nan_fraction.txt | parallel crop_resample_average\n\n# 5. now resampled\n# cat resampled_average.txt | parallel resampled_average\n\n"
  },
  {
    "path": "setup.cfg",
    "content": "# Inside of setup.cfg\n[metadata]\ndescription-file = README.rst\n\n[aliases]\ntest = pytest\n\n[tool:pytest]\nmarkers:\n  slow: Skipped unless --runslow passed\n"
  },
  {
    "path": "setup.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\nfrom setuptools.command.install import install\nfrom setuptools.command.develop import develop\nfrom subprocess import check_output, run\nimport platform\nimport setuptools\n__version__ = \"0.6.1\"\n\n# Get requirements (and dev requirements for testing) from requirements\n#  txt files. Also ensure we are using correct GDAL version.\nwith open('requirements.txt') as f:\n    requirements_lines = f.read().splitlines()\nwith open('requirements-test.txt') as f:\n    test_requirements = f.read().splitlines()\nwith open('requirements-dev.txt') as f:\n    dev_requirements = f.read().splitlines()\n\nif platform.system() in 'Windows':\n    GDAL_VERSION = check_output([\"gdalinfo\", \"--version\"]).decode(encoding=\"utf-8\").strip().split(\" \")[1][:-1]\nelse:\n    GDAL_VERSION = check_output([\"gdal-config\", \"--version\"]).decode(encoding=\"utf-8\").split('\\n')[0]\n\nrequirements = []\nfor r in requirements_lines:\n    if r.startswith('GDAL'):\n        requirements.append('GDAL' + '=={GDAL_VERSION}'.format(GDAL_VERSION=GDAL_VERSION))\n    elif r.startswith('mpi4py'):\n        if run(args=['which', 'mpirun']).returncode == 0:\n            requirements.append(r)\n    else:\n        requirements.append(r)\n\n\ndef update_reqs_based_on_envs(reqs):\n    with open('requirements.txt', 'w') as f:\n        for r in reqs:\n            f.write('{}\\n'.format(r))\n\n\nupdate_reqs_based_on_envs(requirements)\n\nsetup_requirements = [r for r in requirements if \"numpy==\" in r]\n\n\nclass CustomInstall(install):\n    def run(self):\n        self.install_setup_requirements()\n        # numpy becomes available after this line. Test it\n        import numpy\n        print(numpy.__version__)\n        self.install_requirements()\n        super().run()\n        # run(args=['git', 'checkout', 'HEAD', '--', 'requirements.txt'])\n\n    @staticmethod\n    def install_setup_requirements():\n        for s in setup_requirements:\n            print(f'installing {s}')\n            run(args=['pip', 'install', s])\n\n    @staticmethod\n    def install_requirements():\n        for s in requirements:\n            print(f'installing {s}')\n            run(args=['pip', 'install', s])\n\n#\n#\n# class CustomDevelop(develop, InstallDevelopMixin):\n#     \"mod of install\"\n\n\nclass PyTest(TestCommand, object):\n\n    def initialize_options(self):\n        super(PyTest, self).initialize_options()\n        self.pytest_args = []\n\n    def finalize_options(self):\n        super(PyTest, self).finalize_options()\n        self.test_suite = True\n        self.test_args = []\n\n    def run_tests(self):\n        # import here, cause outside the eggs aren't loaded\n        import pytest\n        exit(pytest.main(self.pytest_args))\n\n\nreadme = open('README.rst').read()\ndoclink = \"\"\"\nDocumentation\n-------------\n\nThe full documentation is at http://geoscienceaustralia.github.io/PyRate/\"\"\"\nhistory = open('docs/history.rst').read().replace('.. :changelog:', '')\n\nsetup(\n    name='Py-Rate',\n    version=__version__,\n    license=\"Apache Software License 2.0\",\n    description='A Python tool for estimating velocity and cumulative displacement '\n                'time-series from Interferometric Synthetic Aperture Radar (InSAR) data.',\n    long_description=readme + '\\n\\n' + doclink + '\\n\\n' + history,\n    author='Geoscience Australia InSAR team',\n    author_email='insar@ga.gov.au',\n    url='https://github.com/GeoscienceAustralia/PyRate',\n    download_url='https://github.com/GeoscienceAustralia/PyRate/archive/'+__version__+'.tar.gz',\n    packages=setuptools.find_packages(),\n    package_dir={'PyRate': 'pyrate'},\n    package_data={\n        'utils': ['colourmap.txt']\n    },\n    scripts=[\n        'scripts/gdal_calc_local.py',\n        'scripts/plot_ifgs.py',\n        ],\n    entry_points={\n          'console_scripts': [\n              'pyrate = pyrate.main:main',\n              'plot_ifgs = scripts.plot_ifgs:main',\n          ]\n      },\n    setup_requires=setup_requirements,\n    install_requires=requirements,\n    extras_require={\n        'dev': dev_requirements\n    },\n    tests_require=test_requirements,\n    zip_safe=False,\n    keywords='PyRate, Python, InSAR, Geodesy, Remote Sensing, Image Processing',\n    classifiers=[\n        'Development Status :: 4 - Beta',\n        \"Operating System :: POSIX\",\n        \"License :: OSI Approved :: Apache Software License\",\n        \"Natural Language :: English\",\n        \"Programming Language :: Python\",\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.7\",\n        \"Programming Language :: Python :: 3.8\",\n        \"Programming Language :: Python :: 3.9\",\n        \"Intended Audience :: Science/Research\",\n        \"Intended Audience :: Developers\",\n        \"Topic :: Software Development :: Libraries :: Python Modules\",\n        \"Topic :: Scientific/Engineering :: Information Analysis\"\n    ],\n    cmdclass={\n        'test': PyTest,\n        'install': CustomInstall,\n        # 'develop': CustomDevelop,\n    }\n)\n"
  },
  {
    "path": "tests/__init__.py",
    "content": ""
  },
  {
    "path": "tests/common.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains generic utilities and mock objects for use in the\nPyRate test suite.\n\"\"\"\n\nimport glob\nimport os\nimport shutil\nimport stat\nimport itertools\nimport tempfile\nfrom decimal import Decimal\nimport pytest\nfrom typing import Iterable, Union\nfrom os.path import join\nfrom subprocess import check_output, run\nfrom pathlib import Path\n\nimport numpy as np\nfrom numpy import isnan, sum as nsum\nfrom osgeo import gdal\n\nimport pyrate.constants as C\nfrom pyrate.constants import PYRATEPATH\nfrom pyrate.core import algorithm, ifgconstants as ifc, timeseries, mst, stack\nfrom pyrate.core.shared import (Ifg, nan_and_mm_convert, get_geotiff_header_info,\n                                write_output_geotiff, dem_or_ifg)\nfrom pyrate.core import ifgconstants as ifg\nfrom pyrate.core import roipac\nfrom pyrate.configuration import Configuration, parse_namelist\n\nPYTHON_VERSION = check_output([\"python\", \"--version\"]).decode(encoding=\"utf-8\").strip().split(\" \")[1][:3]\n\nPYTHON3P7 = True if PYTHON_VERSION == '3.7' else False\nPYTHON3P8 = True if PYTHON_VERSION == '3.8' else False\nPYTHON3P9 = True if PYTHON_VERSION == '3.9' else False\n\nGDAL_VERSION = check_output([\"gdal-config\", \"--version\"]).decode(encoding=\"utf-8\").split('\\n')[0]\nGITHUB_ACTIONS = True if ('GITHUB_ACTIONS' in os.environ) else False\n\n# python3.7 and gdal3.0.2\nPY37GDAL302 = PYTHON3P7 and (GDAL_VERSION == '3.0.2')\n# python3.7 and gdal3.0.4\nPY37GDAL304 = PYTHON3P7 and (GDAL_VERSION == '3.0.4')\n\n\nTEMPDIR = tempfile.gettempdir()\nTESTDIR = join(PYRATEPATH, 'tests')\nBASE_TEST = join(PYRATEPATH, \"tests\", \"test_data\")\nSML_TEST_DIR = join(BASE_TEST, \"small_test\")\nROIPAC_SML_TEST_DIR = join(SML_TEST_DIR, 'roipac_obs')  # roipac processed unws\nSML_TEST_OUT = join(SML_TEST_DIR, 'out')\nSML_TEST_TIF = join(SML_TEST_DIR, 'tif')\nGAMMA_SML_TEST_DIR = join(SML_TEST_DIR, 'gamma_obs')  # gamma processed unws\nSML_TEST_CONF = join(SML_TEST_DIR, 'conf')\nSML_TEST_LINRATE = join(SML_TEST_DIR, 'linrate')\nSML_TEST_GAMMA_HEADER_LIST = join(GAMMA_SML_TEST_DIR, 'headers')\nSML_TEST_ROIPAC_HEADER_LIST = join(ROIPAC_SML_TEST_DIR, 'headers')\n\nSML_TEST_DEM_DIR = join(SML_TEST_DIR, 'dem')\nSML_TEST_LEGACY_PREPIFG_DIR = join(SML_TEST_DIR, 'prepifg_output')\nSML_TEST_LEGACY_ORBITAL_DIR = join(SML_TEST_DIR, 'orbital_error_correction')\nSML_TEST_DEM_ROIPAC = join(ROIPAC_SML_TEST_DIR, 'roipac_test_trimmed.dem')\nSML_TEST_DEM_GAMMA = join(GAMMA_SML_TEST_DIR, '20060619_utm.dem')\nSML_TEST_INCIDENCE = join(GAMMA_SML_TEST_DIR, '20060619_utm.inc')\nSML_TEST_ELEVATION = join(GAMMA_SML_TEST_DIR, '20060619_utm.lv_theta')\nSML_TEST_DEM_HDR_GAMMA = join(GAMMA_SML_TEST_DIR, '20060619_utm_dem.par')\nSML_TEST_DEM_HDR = join(ROIPAC_SML_TEST_DIR, 'roipac_test_trimmed.dem.rsc')\nSML_TEST_DEM_TIF = join(SML_TEST_DEM_DIR, 'roipac_test_trimmed.tif')\n\nSML_TEST_COH_DIR = join(SML_TEST_DIR, 'coherence')\nSML_TEST_COH_LIST = join(SML_TEST_COH_DIR, 'coherence_17')\n\nSML_TEST_BASE_LIST = join(GAMMA_SML_TEST_DIR, 'baseline_17')\n\nSML_TEST_LT_FILE = join(GAMMA_SML_TEST_DIR, 'cropped_lookup_table.lt')\n\nTEST_CONF_ROIPAC = join(SML_TEST_CONF, 'pyrate_roipac_test.conf')\nTEST_CONF_GAMMA = join(SML_TEST_CONF, 'pyrate_gamma_test.conf')\n\nsystem_test_dir = PYRATEPATH.joinpath(\"tests\", \"test_data\", \"system\")\nROIPAC_SYSTEM_FILES = system_test_dir.joinpath(\"roipac\")\nROIPAC_SYSTEM_CONF = ROIPAC_SYSTEM_FILES.joinpath(\"input_parameters.conf\")\n\nGAMMA_SYSTEM_FILES = system_test_dir.joinpath(\"gamma\")\nGAMMA_SYSTEM_CONF = GAMMA_SYSTEM_FILES.joinpath(\"input_parameters.conf\")\n\nGEOTIF_SYSTEM_FILES = system_test_dir.joinpath(\"geotiff\")\nGEOTIF_SYSTEM_CONF = GEOTIF_SYSTEM_FILES.joinpath(\"input_parameters.conf\")\n\nPREP_TEST_DIR = join(BASE_TEST, 'prepifg')\nPREP_TEST_OBS = join(PREP_TEST_DIR, 'obs')\nPREP_TEST_TIF = join(PREP_TEST_DIR, 'tif')\n\nHEADERS_TEST_DIR = join(BASE_TEST, 'headers')\nINCID_TEST_DIR = join(BASE_TEST, 'incidence')\n\nGAMMA_TEST_DIR = join(BASE_TEST, \"gamma\")\n\nMEXICO_CROPA_DIR = join(BASE_TEST, \"cropA\", \"geotiffs\")\nMEXICO_CROPA_DIR_GEOMETRY = join(BASE_TEST, \"cropA\", \"geometry\")\nMEXICO_CROPA_DIR_HEADERS = join(BASE_TEST, \"cropA\", \"headers\")\nMEXICO_CROPA_DIR_DEM_ERROR = join(BASE_TEST, \"cropA\", \"dem_error_result\")\nMEXICO_CROPA_CONF = PYRATEPATH.joinpath(\"tests\", \"test_data\", \"cropA\", \"pyrate_mexico_cropa.conf\")\n\n#: STR; Name of directory containing input interferograms for certian tests\nWORKING_DIR = 'working_dir'\n\n# small dummy ifg list to limit overall # of ifgs\nIFMS5 = \"\"\"geo_060828-061211_unw.tif\ngeo_061106-061211_unw.tif\ngeo_061106-070115_unw.tif\ngeo_061106-070326_unw.tif\ngeo_070326-070917_unw.tif\n\"\"\"\n\nUNWS5 = \"\"\"geo_060828-061211.unw\ngeo_061106-061211.unw\ngeo_061106-070115.unw\ngeo_061106-070326.unw\ngeo_070326-070917.unw\n\"\"\"\n\nIFMS16 = [\n    \"geo_060619-061002_unw.tif\",\n    \"geo_060828-061211_unw.tif\",\n    \"geo_061002-070219_unw.tif\",\n    \"geo_061002-070430_unw.tif\",\n    \"geo_061106-061211_unw.tif\",\n    \"geo_061106-070115_unw.tif\",\n    \"geo_061106-070326_unw.tif\",\n    \"geo_061211-070709_unw.tif\",\n    \"geo_061211-070813_unw.tif\",\n    \"geo_070115-070326_unw.tif\",\n    \"geo_070115-070917_unw.tif\",\n    \"geo_070219-070430_unw.tif\",\n    \"geo_070219-070604_unw.tif\",\n    \"geo_070326-070917_unw.tif\",\n    \"geo_070430-070604_unw.tif\",\n    \"geo_070604-070709_unw.tif\",\n]\n\n\ndef remove_tifs(path):\n    tifs = glob.glob(os.path.join(path, '*.tif'))\n    for tif in tifs:\n        os.remove(tif)\n\n\ndef small_data_setup(datafiles=None, is_dir=False):\n    \"\"\"Returns Ifg objs for the files in the small test dir\n    input phase data is in radians; these ifgs are in radians - not converted to mm\"\"\"\n    if is_dir:\n        datafiles = glob.glob(join(datafiles, \"*.tif\"))\n    else:\n        if datafiles:\n            for i, d in enumerate(datafiles):\n                datafiles[i] = os.path.join(SML_TEST_TIF, d)\n        else:\n            datafiles = glob.glob(join(SML_TEST_TIF, \"*.tif\"))\n    datafiles.sort()\n    ifgs = [dem_or_ifg(i) for i in datafiles]\n    \n    for i in ifgs: \n        i.open()\n        i.nodata_value = 0\n\n    return ifgs\n\n\ndef assert_tifs_equal(tif1, tif2):\n    mds = gdal.Open(tif1)\n    sds = gdal.Open(tif2)\n\n    md_mds = mds.GetMetadata()\n    md_sds = sds.GetMetadata()\n    # meta data equal\n    for k, v in md_sds.items():\n        if k in [ifg.PYRATE_ALPHA, ifg.PYRATE_MAXVAR]:\n            print(k, v, md_mds[k])\n            assert round(eval(md_sds[k]), 1) == round(eval(md_mds[k]), 1)\n        else:\n            assert md_sds[k] == md_mds[k]\n    # assert md_mds == md_sds\n    d1 = mds.ReadAsArray()\n    d2 = sds.ReadAsArray()\n    # phase equal\n    np.testing.assert_array_almost_equal(d1,  d2, decimal=3)\n\n    mds = None  # close datasets\n    sds = None\n\n\ndef copy_small_ifg_file_list():\n    temp_dir = tempfile.mkdtemp()\n    move_files(SML_TEST_TIF, temp_dir, file_type='*.tif', copy=True)\n    datafiles = glob.glob(join(temp_dir, \"*.tif\"))\n    for d in datafiles:\n        Path(d).chmod(0o664)  # assign write permission as conv2tif output is readonly\n    return temp_dir, datafiles\n\n\ndef copy_and_setup_small_data():\n    temp_dir, datafiles = copy_small_ifg_file_list()\n    datafiles.sort()\n    ifgs = [dem_or_ifg(i) for i in datafiles]\n\n    for i in ifgs:\n        i.open()\n        i.nodata_value = 0\n    return temp_dir, ifgs\n\n\ndef small_ifg_file_list(datafiles=None):\n    \"\"\"Returns the file list of all the .tif files after prepifg conversion\n    input phase data is in radians; these ifgs are in radians - not converted to mm\"\"\"\n    if datafiles:\n        for i, d in enumerate(datafiles):\n            datafiles[i] = os.path.join(SML_TEST_TIF, d)\n    else:\n        datafiles = glob.glob(join(SML_TEST_TIF, \"*.tif\"))\n    datafiles.sort()\n    return datafiles\n\n\ndef small_data_roipac_unws():\n    \"\"\"Returns unw file list before prepifg operation\n    input phase data is in radians; these ifgs are in radians - not converted to mm\"\"\"\n    return glob.glob(join(ROIPAC_SML_TEST_DIR, \"*.unw\"))\n\n\ndef small_data_setup_gamma_unws():\n    \"\"\"Returns unw file list before prepifg operation\n    input phase data is in radians; these ifgs are in radians - not converted to mm\"\"\"\n    return glob.glob(join(GAMMA_SML_TEST_DIR, \"*.unw\"))\n\n\ndef small5_ifgs():\n    \"\"\"Convenience func to return a subset of 5 linked Ifgs from the testdata\"\"\"\n    new_data_paths = small5_ifg_paths()\n\n    return [Ifg(p) for p in new_data_paths]\n\n\ndef small5_ifg_paths():\n    BASE_DIR = tempfile.mkdtemp()\n    data_paths = [os.path.join(SML_TEST_TIF, p) for p in IFMS5.split()]\n    new_data_paths = [os.path.join(BASE_DIR, os.path.basename(d))\n                      for d in data_paths]\n    for d in data_paths:\n        shutil.copy(d, os.path.join(BASE_DIR, os.path.basename(d)))\n    return new_data_paths\n\n\ndef small5_mock_ifgs(xs=3, ys=4):\n    '''Returns smaller mocked version of small Ifgs for testing'''\n    ifgs = small5_ifgs()\n    for i in ifgs:\n        i.open()\n        i.nodata_value = 0\n\n    return [MockIfg(i, xs, ys) for i in ifgs]\n\n\nclass MockIfg(object):\n    \"\"\"Mock Ifg for detailed testing\"\"\"\n\n    def __init__(self, ifg, xsize=None, ysize=None):\n        \"\"\"\n        Creates mock Ifg based on a given interferogram. Size args specify the\n        dimensions of the phase band (so the mock ifg can be resized differently\n        to the source interferogram for smaller test datasets).\n        \"\"\"\n        self.dataset = ifg.dataset\n        self.first = ifg.first\n        self.second = ifg.second\n        self.data_path = ifg.data_path\n        self.nrows = ysize\n        self.ncols = xsize\n        self.x_size = ifg.x_size\n        self.y_size = ifg.y_size\n        self.x_step = ifg.x_step\n        self.y_step = ifg.y_step\n        self.num_cells = self.ncols * self.nrows\n        self.phase_data = ifg.phase_data[:ysize, :xsize]\n        self.nan_fraction = ifg.nan_fraction # use existing overall nan fraction\n        self.is_open = False\n\n    def __repr__(self, *args, **kwargs):\n        return 'MockIfg: %s -> %s' % (self.first, self.second)\n\n    def open(self):\n        # TODO: could move some of the init code here to mimic Ifgs\n        pass  # can't actually open anything!\n\n    @property\n    def nan_count(self):\n        return nsum(isnan(self.phase_data))\n\n    @property\n    def shape(self):\n        return self.nrows, self.ncols\n\n    def write_modified_phase(self):  #dummy\n        pass\n\n    def close(self):  # dummy\n        pass\n\n\ndef reconstruct_stack_rate(shape, tiles, output_dir, out_type):\n    rate = np.zeros(shape=shape, dtype=np.float32)\n    for t in tiles:\n        rate_file = os.path.join(output_dir, out_type +\n                                 '_{}.npy'.format(t.index))\n        rate_tile = np.load(file=rate_file)\n        rate[t.top_left_y:t.bottom_right_y,\n             t.top_left_x:t.bottom_right_x] = rate_tile\n    return rate\n\n\ndef reconstruct_mst(shape, tiles, output_dir):\n    mst_file_0 = os.path.join(output_dir, C.MST_DIR, 'mst_mat_{}.npy'.format(0))\n    shape0 = np.load(mst_file_0).shape[0]\n\n    mst = np.empty(shape=((shape0,) + shape), dtype=np.float32)\n    for i, t in enumerate(tiles):\n        mst_file_n = os.path.join(output_dir, C.MST_DIR, 'mst_mat_{}.npy'.format(i))\n        mst[:, t.top_left_y:t.bottom_right_y,\n                t.top_left_x: t.bottom_right_x] = np.load(mst_file_n)\n    return mst\n\n\ndef move_files(source_dir, dest_dir, file_type='*.tif', copy=False):\n    for filename in glob.glob(os.path.join(source_dir, file_type)):\n        if copy:\n            shutil.copy(filename, dest_dir)\n        else:\n            shutil.move(filename, dest_dir)\n\n\ndef assert_ifg_phase_equal(ifg_path1, ifg_path2):\n    ds1 = gdal.Open(ifg_path1)\n    ds2 = gdal.Open(ifg_path2)\n    np.testing.assert_array_almost_equal(ds1.ReadAsArray(), ds2.ReadAsArray())\n    ds1 = None\n    ds2 = None\n\n\ndef prepare_ifgs_without_phase(ifg_paths, params):\n    ifgs = [Ifg(p) for p in ifg_paths]\n    for i in ifgs:\n        i.open(readonly=False)\n        nan_conversion = params[C.NAN_CONVERSION]\n        if nan_conversion:  # nan conversion happens here in networkx mst\n            # if not ifg.nan_converted:\n            i.nodata_value = params[C.NO_DATA_VALUE]\n            i.convert_to_nans()\n    return ifgs\n\n\ndef mst_calculation(ifg_paths_or_instance, params):\n    if isinstance(ifg_paths_or_instance, list):\n        ifgs = pre_prepare_ifgs(ifg_paths_or_instance, params)\n        mst_grid = mst.mst_parallel(ifgs, params)\n        # write mst output to a file\n        mst_mat_binary_file = join(params[C.OUT_DIR], 'mst_mat')\n        np.save(file=mst_mat_binary_file, arr=mst_grid)\n\n        for i in ifgs:\n            i.close()\n        return mst_grid\n    return None\n\n\ndef get_nml(ifg_list_instance, nodata_value, nan_conversion=False):\n    \"\"\"\n    :param xxx(eg str, tuple, int, float...) ifg_list_instance: xxxx\n    :param float nodata_value: No data value in image\n    :param bool nan_conversion: Convert NaNs\n    \n    :return: ifg_list_instance: replaces in place\n    :rtype: list\n    :return: _epoch_list: list of epochs\n    :rtype: list\n    \"\"\"\n    _epoch_list, n = algorithm.get_epochs(ifg_list_instance.ifgs)\n    ifg_list_instance.reshape_n(n)\n    if nan_conversion:\n        ifg_list_instance.update_nan_frac(nodata_value)\n        # turn on for nan conversion\n        ifg_list_instance.convert_nans(nan_conversion=nan_conversion)\n    ifg_list_instance.make_data_stack()\n    return ifg_list_instance, _epoch_list\n\n\ndef compute_time_series(ifgs, mst_grid, params, vcmt):\n    # Calculate time series\n    tsincr, tscum, tsvel = calculate_time_series(\n        ifgs, params, vcmt=vcmt, mst=mst_grid)\n\n    # tsvel_file = join(params[cf.OUT_DIR], 'tsvel.npy')\n    tsincr_file = join(params[C.OUT_DIR], 'tsincr.npy')\n    tscum_file = join(params[C.OUT_DIR], 'tscum.npy')\n    np.save(file=tsincr_file, arr=tsincr)\n    np.save(file=tscum_file, arr=tscum)\n    # np.save(file=tsvel_file, arr=tsvel)\n\n    # TODO: write tests for these functions\n    write_timeseries_geotiff(ifgs, params, tsincr, pr_type='tsincr')\n    write_timeseries_geotiff(ifgs, params, tscum, pr_type='tscuml')\n    # write_timeseries_geotiff(ifgs, params, tsvel, pr_type='tsvel')\n    return tsincr, tscum, tsvel\n\n\ndef calculate_time_series(ifgs, params, vcmt, mst):\n    res = timeseries.time_series(ifgs, params, vcmt, mst)\n    for r in res:\n        if len(r.shape) != 3:\n            raise timeseries.TimeSeriesError\n\n    tsincr, tscum, tsvel = res\n    return tsincr, tscum, tsvel\n\n\ndef write_timeseries_geotiff(ifgs, params, tsincr, pr_type):\n    # setup metadata for writing into result files\n    gt, md, wkt = get_geotiff_header_info(ifgs[0].data_path)\n    epochlist = algorithm.get_epochs(ifgs)[0]\n\n    for i in range(tsincr.shape[2]):\n        md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]\n        md['SEQUENCE_POSITION'] = i+1  # sequence position\n\n        data = tsincr[:, :, i]\n        dest = join(params[C.OUT_DIR], pr_type + \"_\" +\n                    str(epochlist.dates[i + 1]) + \".tif\")\n        md[ifc.DATA_TYPE] = pr_type\n        write_output_geotiff(md, gt, wkt, data, dest, np.nan)\n\n\ndef calculate_stack_rate(ifgs, params, vcmt, mst_mat=None):\n    # log.info('Calculating stacked rate')\n    res = stack.stack_rate_array(ifgs, params, vcmt, mst_mat)\n    for r in res:\n        if r is None:\n            raise ValueError('TODO: bad value')\n\n    r, e, samples = res\n    rate, error = stack.mask_rate(r, e, params['maxsig'])\n    write_stackrate_tifs(ifgs, params, res)\n    # log.info('Stacked rate calculated')\n    return rate, error, samples\n\n\ndef write_stackrate_tifs(ifgs, params, res):\n    rate, error, samples = res\n    gt, md, wkt = get_geotiff_header_info(ifgs[0].data_path)\n    epochlist = algorithm.get_epochs(ifgs)[0]\n    dest = join(params[C.OUT_DIR], \"stack_rate.tif\")\n    md[ifc.EPOCH_DATE] = epochlist.dates\n    md[ifc.DATA_TYPE] = ifc.STACKRATE\n    write_output_geotiff(md, gt, wkt, rate, dest, np.nan)\n    dest = join(params[C.OUT_DIR], \"stack_error.tif\")\n    md[ifc.DATA_TYPE] = ifc.STACKERROR\n    write_output_geotiff(md, gt, wkt, error, dest, np.nan)\n    dest = join(params[C.OUT_DIR], \"stack_samples.tif\")\n    md[ifc.DATA_TYPE] = ifc.STACKSAMP\n    write_output_geotiff(md, gt, wkt, samples, dest, np.nan)\n    write_stackrate_numpy_files(error, rate, samples, params)\n\n\ndef write_stackrate_numpy_files(error, rate, samples, params):\n    rate_file = join(params[C.OUT_DIR], 'rate.npy')\n    error_file = join(params[C.OUT_DIR], 'error.npy')\n    samples_file = join(params[C.OUT_DIR], 'samples.npy')\n    np.save(file=rate_file, arr=rate)\n    np.save(file=error_file, arr=error)\n    np.save(file=samples_file, arr=samples)\n\n\ndef copytree(src: Union[str, bytes, os.PathLike], dst: Union[str, bytes, os.PathLike], symlinks=False, ignore=None):\n    # pylint: disable=line-too-long\n    \"\"\"\n    Copy entire contents of src directory into dst directory.\n    See: http://stackoverflow.com/questions/1868714/how-do-i-copy-an-entire-directory-of-files-into-an-existing-directory-using-pyth?lq=1\n\n    :param str src: source directory path\n    :param str dst: destination directory path (created if does not exist)\n    :param bool symlinks: Whether to copy symlink or not\n    :param bool ignore:\n    \"\"\"\n    # pylint: disable=invalid-name\n    if not os.path.exists(dst):  # pragma: no cover\n        os.makedirs(dst)\n    shutil.copystat(src, dst)\n    lst = os.listdir(src)\n    if ignore:\n        excl = ignore(src, lst)\n        lst = [x for x in lst if x not in excl]\n    for item in lst:\n        s = os.path.join(src, item)\n        d = os.path.join(dst, item)\n        if symlinks and os.path.islink(s):  # pragma: no cover\n            if os.path.lexists(d):\n                os.remove(d)\n            os.symlink(os.readlink(s), d)\n            try:\n                st = os.lstat(s)\n                mode = stat.S_IMODE(st.st_mode)\n                os.lchmod(d, mode)\n            except AttributeError:\n                pass  # lchmod not available\n        elif os.path.isdir(s):  # pragma: no cover\n            copytree(s, d, symlinks, ignore)\n        else:\n            shutil.copy2(s, d)\n\n\ndef repair_params_for_correct_tests(out_dir, params):\n    base_ifg_paths = [d.unwrapped_path for d in params[C.INTERFEROGRAM_FILES]]\n    headers = [roipac.roipac_header(i, params) for i in base_ifg_paths]\n    params[C.INTERFEROGRAM_FILES] = params[C.INTERFEROGRAM_FILES][:-2]\n    dest_paths = [Path(out_dir).joinpath(Path(d.sampled_path).name).as_posix()\n                  for d in params[C.INTERFEROGRAM_FILES]]\n    for p, d in zip(params[C.INTERFEROGRAM_FILES], dest_paths):  # hack\n        p.sampled_path = d\n    return dest_paths, headers\n\n\ndef pre_prepare_ifgs(ifg_paths, params):\n    \"\"\"\n    nan and mm convert ifgs\n    \"\"\"\n    ifgs = [Ifg(p) for p in ifg_paths]\n    for i in ifgs:\n        i.open(readonly=False)\n        nan_and_mm_convert(i, params)\n    return ifgs\n\n\ndef assert_two_dirs_equal(dir1, dir2, ext, num_files=None):\n    if not isinstance(ext, list):\n        ext = [ext]\n    dir1_files = list(itertools.chain(* [list(Path(dir1).glob(ex)) for ex in ext]))\n    dir2_files = list(itertools.chain(* [list(Path(dir2).glob(ex)) for ex in ext]))\n    dir1_files.sort()\n    dir2_files.sort()\n    # 17 unwrapped geotifs\n    # 17 cropped multilooked tifs + 1 dem\n    if num_files is not None:\n        assert len(dir1_files) == num_files\n        assert len(dir2_files) == num_files\n    else:\n        assert len(dir1_files) == len(dir2_files)\n    if dir1_files[0].suffix == '.tif':\n        for m_f, s_f in zip(dir1_files, dir2_files):\n            assert m_f.name == s_f.name\n            assert_tifs_equal(m_f.as_posix(), s_f.as_posix())\n\n    elif dir1_files[0].suffix == '.npy':\n        for m_f, s_f in zip(dir1_files, dir2_files):\n            assert m_f.name == s_f.name\n            np.testing.assert_array_almost_equal(np.load(m_f), np.load(s_f), decimal=3)\n    elif dir1_files[0].suffix in {'.kml', '.png'}:\n        return\n    else:\n        raise\n\n\ndef assert_same_files_produced(dir1, dir2, dir3, ext, num_files=None):\n    assert_two_dirs_equal(dir1, dir2, ext, num_files)\n    assert_two_dirs_equal(dir1, dir3, ext, num_files)\n\n\nworking_dirs = {\n    GAMMA_SYSTEM_CONF: GAMMA_SYSTEM_FILES,\n    ROIPAC_SYSTEM_CONF: ROIPAC_SYSTEM_FILES,\n    GEOTIF_SYSTEM_CONF: GEOTIF_SYSTEM_FILES,\n    Path(TEST_CONF_ROIPAC).name: ROIPAC_SML_TEST_DIR,\n    Path(TEST_CONF_GAMMA).name: GAMMA_SML_TEST_DIR\n}\n\n\ndef manipulate_test_conf(conf_file, work_dir: Path):\n    params = Configuration(conf_file).__dict__\n    if conf_file == MEXICO_CROPA_CONF:\n        copytree(MEXICO_CROPA_DIR, work_dir)\n        copytree(MEXICO_CROPA_DIR_HEADERS, work_dir)\n        copytree(MEXICO_CROPA_DIR_GEOMETRY, work_dir)\n        copytree(MEXICO_CROPA_DIR_DEM_ERROR, work_dir)\n        shutil.copy2(params[C.IFG_FILE_LIST], work_dir)\n        shutil.copy2(params[C.HDR_FILE_LIST], work_dir)\n        shutil.copy2(params[C.COH_FILE_LIST], work_dir)\n        shutil.copy2(params[C.BASE_FILE_LIST], work_dir)\n        for m_path in params[C.INTERFEROGRAM_FILES]:\n            m_path.converted_path = work_dir.joinpath(Path(m_path.converted_path).name).as_posix()\n    else:  # legacy unit test data\n        params[WORKING_DIR] = working_dirs[Path(conf_file).name]\n        copytree(params[WORKING_DIR], work_dir)\n\n    params[WORKING_DIR] = work_dir.as_posix()\n    # manipulate params\n    outdir = work_dir.joinpath('out')\n    outdir.mkdir(exist_ok=True)\n    params[C.OUT_DIR] = outdir.as_posix()\n    params[C.TEMP_MLOOKED_DIR] = outdir.joinpath(C.TEMP_MLOOKED_DIR).as_posix()\n    params[C.DEM_FILE] = work_dir.joinpath(Path(params[C.DEM_FILE]).name).as_posix()\n    params[C.DEM_HEADER_FILE] = work_dir.joinpath(Path(params[C.DEM_HEADER_FILE]).name).as_posix()\n    params[C.HDR_FILE_LIST] = work_dir.joinpath(Path(params[C.HDR_FILE_LIST]).name).as_posix()\n    params[C.IFG_FILE_LIST] = work_dir.joinpath(Path(params[C.IFG_FILE_LIST]).name).as_posix()\n    params[C.TMPDIR] = outdir.joinpath(C.TMPDIR).as_posix()\n    params[C.COHERENCE_DIR] = outdir.joinpath(C.COHERENCE_DIR).as_posix()\n    params[C.GEOMETRY_DIR] = outdir.joinpath(C.GEOMETRY_DIR).as_posix()\n    params[C.APS_ERROR_DIR] = outdir.joinpath(C.APS_ERROR_DIR).as_posix()\n    params[C.MST_DIR] = outdir.joinpath(C.MST_DIR).as_posix()\n    params[C.ORB_ERROR_DIR] = outdir.joinpath(C.ORB_ERROR_DIR).as_posix()\n    params[C.PHASE_CLOSURE_DIR] = outdir.joinpath(C.PHASE_CLOSURE_DIR).as_posix()\n    params[C.DEM_ERROR_DIR] = outdir.joinpath(C.DEM_ERROR_DIR).as_posix()\n    params[C.INTERFEROGRAM_DIR] = outdir.joinpath(C.INTERFEROGRAM_DIR).as_posix()\n    params[C.VELOCITY_DIR] = outdir.joinpath(C.VELOCITY_DIR).as_posix()\n    params[C.TIMESERIES_DIR] = outdir.joinpath(C.TIMESERIES_DIR).as_posix()\n\n    return params\n\n\nclass UnitTestAdaptation:\n    @staticmethod\n    def assertEqual(arg1, arg2):\n        assert arg1 == arg2\n\n    @staticmethod\n    def assertTrue(arg, msg=''):\n        assert arg, msg\n\n    @staticmethod\n    def assertFalse(arg, msg=''):\n        assert ~ arg, msg\n\n    @staticmethod\n    def assertIsNotNone(arg, msg=''):\n        assert arg is not None, msg\n\n    @staticmethod\n    def assertIsNone(arg, msg=''):\n        assert arg is None, msg\n\n    @staticmethod\n    def assertDictEqual(d1: dict, d2: dict):\n        assert d1 == d2\n\n    @staticmethod\n    def assertRaises(excpt: Exception, func, *args, **kwargs):\n        with pytest.raises(excpt):\n            func(*args, **kwargs)\n\n    @staticmethod\n    def assertIn(item, s: Iterable):\n        assert item in s\n\n    @staticmethod\n    def assertAlmostEqual(arg1, arg2, places=7):\n        places *= -1\n        num = Decimal((0, (1, ), places))\n        assert arg1 == pytest.approx(arg2, abs=num)\n\n\ndef min_params(out_dir):\n    params = {}\n    params[C.OUT_DIR] = out_dir\n    params[C.IFG_LKSX] = 1\n    params[C.IFG_LKSY] = 1\n    params[C.IFG_CROP_OPT] = 4\n    params[C.TEMP_MLOOKED_DIR] = Path(tempfile.mkdtemp())\n    params[C.ORBFIT_OFFSET] = 1\n    params[C.ORBITAL_FIT_METHOD] = 1\n    params[C.ORBITAL_FIT_DEGREE] = 2\n    params[C.ORBITAL_FIT_LOOKS_X] = 1\n    params[C.ORBITAL_FIT_LOOKS_Y] = 1\n    return params\n\n\ndef sub_process_run(cmd, *args, **kwargs):\n    return run(cmd, *args, shell=True, check=True, **kwargs)\n\n\ndef original_ifg_paths(ifglist_path, working_dir):\n    \"\"\"\n    Returns sequence of paths to files in given ifglist file.\n\n    Args:\n        ifglist_path: Absolute path to interferogram file list.\n        working_dir: Absolute path to observations directory.\n\n    Returns:\n        list: List of full paths to interferogram files.\n    \"\"\"\n    ifglist = parse_namelist(ifglist_path)\n    return [os.path.join(working_dir, p) for p in ifglist]\n"
  },
  {
    "path": "tests/conftest.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains fixtures for use in the PyRate test suite.\n\"\"\"\nimport shutil\nimport random\nimport string\nimport tempfile\nimport pytest\n\nimport pyrate.constants as C\nfrom pyrate.constants import PYRATEPATH\nfrom pyrate.core import mpiops, shared\nfrom pyrate.configuration import Configuration\nfrom tests.common import TEST_CONF_ROIPAC, TEST_CONF_GAMMA, SML_TEST_DEM_TIF, MEXICO_CROPA_CONF\nfrom tests.common import ROIPAC_SYSTEM_CONF, GAMMA_SYSTEM_CONF, GEOTIF_SYSTEM_CONF, SML_TEST_COH_LIST\n\n\n@pytest.fixture\ndef tempdir():\n    \"\"\"\n    tempdir for tests\n    \"\"\"\n    def tmpdir():\n        return tempfile.mkdtemp()\n    return tmpdir\n\n\n@pytest.fixture(params=[ROIPAC_SYSTEM_CONF, GEOTIF_SYSTEM_CONF, GAMMA_SYSTEM_CONF])\ndef system_conf(request):\n    params = Configuration(request.param).__dict__\n    yield request.param\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n\n\n@pytest.fixture\ndef random_filename(tmp_path_factory):\n    def make_random_filename(ext=''):\n        fname = ''.join(random.choice(string.ascii_lowercase) for _ in range(10))\n        return tmp_path_factory.mktemp(basename='pyrate-').joinpath(fname + ext)\n    return make_random_filename\n\n\n# Make sure all MPI tests use this fixure\n@pytest.fixture()\ndef mpisync(request):\n    mpiops.comm.barrier()\n\n    def fin():\n        mpiops.comm.barrier()\n\n    request.addfinalizer(fin)\n    return mpiops.comm\n\n\n@pytest.fixture(params=[0, 1])\ndef coh_mask(request):\n    return request.param\n\n\n@pytest.fixture(params=[1, 2])\ndef ref_est_method(request):\n    return request.param\n\n\n@pytest.fixture(params=[(-1, -1), (150.941666654, -34.218333314)])\ndef ref_pixel(request):\n    return request.param\n\n\n@pytest.fixture(params=[1, 3])\ndef orbfit_lks(request):\n    return request.param\n\n\n@pytest.fixture(params=C.ORB_METHOD_NAMES.keys())\ndef orbfit_method(request):\n    return request.param\n\n\n@pytest.fixture(params=C.ORB_DEGREE_NAMES.keys())\ndef orbfit_degrees(request):\n    return request.param\n\n\n@pytest.fixture(params=[1, 3])\ndef get_lks(request):\n    return request.param\n\n\n@pytest.fixture(params=[1, 4])\ndef get_crop(request):\n    return request.param\n\n\n@pytest.fixture()\ndef get_config():\n    def params(conf_file):\n        params_ = Configuration(conf_file).__dict__\n        return params_\n    return params\n\n\n@pytest.fixture\ndef gamma_params():\n    params = Configuration(TEST_CONF_GAMMA).__dict__\n    shared.mkdir_p(params[C.OUT_DIR])\n    yield params\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n\n\n@pytest.fixture\ndef roipac_params():\n    params = Configuration(TEST_CONF_ROIPAC).__dict__\n    yield params\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n\n\n@pytest.fixture\ndef mexico_cropa_params():\n    params = Configuration(MEXICO_CROPA_CONF).__dict__\n    yield params\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n\n\n@pytest.fixture(params=[TEST_CONF_GAMMA, TEST_CONF_ROIPAC])\ndef roipac_or_gamma_conf(request):\n    return request.param\n\n\n@pytest.fixture(params=[TEST_CONF_GAMMA], scope='session')\ndef gamma_conf(request):\n    params = Configuration(TEST_CONF_GAMMA).__dict__\n    yield request.param\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n\n\n@pytest.fixture\ndef coh_list_file():\n    return SML_TEST_COH_LIST\n\n\n@pytest.fixture\ndef dem():\n    d = shared.dem_or_ifg(SML_TEST_DEM_TIF)\n    d.open()\n    return d\n\n\n@pytest.fixture(params=[TEST_CONF_GAMMA, MEXICO_CROPA_CONF], scope='session')\ndef gamma_or_mexicoa_conf(request):\n    params = Configuration(request.param).__dict__\n    yield request.param\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n\n\n@pytest.fixture(params=range(5))\ndef run_number(request):\n    return request.param\n\n\nGEOTIFF = PYRATEPATH.joinpath('tests', 'test_data', 'geotiffs')\n\n\n@pytest.fixture\ndef geotiffs():\n    tifs = [u.as_posix() for u in GEOTIFF.glob('*_unw.tif')]\n    tifs.sort()\n    return tifs\n\n\n@pytest.fixture\ndef ten_geotiffs():\n    tifs = [u.as_posix() for u in GEOTIFF.glob('*_unw.tif')]\n    tifs.sort()\n    return tifs[:10]\n\n\n@pytest.fixture\ndef cropa_geotifs(mexico_cropa_params):\n    m_paths = mexico_cropa_params[C.INTERFEROGRAM_FILES]\n    return [m.converted_path for m in m_paths]\n"
  },
  {
    "path": "tests/phase_closure/__init__.py",
    "content": ""
  },
  {
    "path": "tests/phase_closure/common.py",
    "content": "class IfgDummy:\n    def __init__(self, ifg_path):\n        self.tmp_sampled_path = ifg_path"
  },
  {
    "path": "tests/phase_closure/conftest.py",
    "content": "from pathlib import Path\nimport pytest\nfrom pyrate import constants as C\nfrom tests.phase_closure.common import IfgDummy\nfrom tests.common import MEXICO_CROPA_DIR\n\n\n@pytest.fixture()\ndef closure_params(geotiffs):\n    ifg_files = [IfgDummy(ifg_path) for ifg_path in geotiffs]\n    params = {\n        C.INTERFEROGRAM_FILES: ifg_files,\n        C.MAX_LOOP_LENGTH: 100,\n        'geotiffs': geotiffs\n    }\n    return params\n\n\n@pytest.fixture(scope='module')\ndef cropa_geotifs():\n    tifs = [u.as_posix() for u in Path(MEXICO_CROPA_DIR).glob('*_unw.tif')]\n    tifs.sort()\n    return tifs\n"
  },
  {
    "path": "tests/phase_closure/test_closure_check.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core.phase_closure.mst_closure import (\n    sort_loops_based_on_weights_and_date,\n)\nfrom pyrate.core.phase_closure.closure_check import (\n    discard_loops_containing_max_ifg_count,\n    __drop_ifgs_if_not_part_of_any_loop\n)\n\n\ndef test_discard_loops_containing_max_ifg_count(closure_params):\n    params = closure_params\n    loops1 = retain_loops(params)\n    loops2 = retain_loops(params)\n    m_weights = [m.weight for m in loops1]\n    s_weights = [m.weight for m in loops2]\n    np.testing.assert_array_equal(m_weights, s_weights)\n\n\ndef retain_loops(params):\n    sorted_loops = sort_loops_based_on_weights_and_date(params)\n    params[C.MAX_LOOP_REDUNDANCY] = 2\n    params[C.MAX_LOOP_LENGTH] = 3\n    retained_loops_meeting_max_loop_criteria = [sl for sl in sorted_loops\n                                                if len(sl) <= params[C.MAX_LOOP_LENGTH]]\n    msg = f\"After applying MAX_LOOP_LENGTH={params[C.MAX_LOOP_LENGTH]} criteria, \" \\\n          f\"{len(retained_loops_meeting_max_loop_criteria)} loops are retained\"\n    print(msg)\n    retained_loops = discard_loops_containing_max_ifg_count(retained_loops_meeting_max_loop_criteria, params)\n    msg = f\"After applying MAX_LOOP_REDUNDANCY={params[C.MAX_LOOP_REDUNDANCY]} criteria, \" \\\n          f\"{len(retained_loops)} loops are retained\"\n    print(msg)\n    return retained_loops\n\n\ndef test_drop_ifgs_if_not_part_of_any_loop(closure_params):\n    params = closure_params\n    params[C.NO_DATA_VALUE] = 0.0\n    geotiffs = params['geotiffs']\n\n    loops1 = retain_loops(params)\n    selected_tifs1 = __drop_ifgs_if_not_part_of_any_loop(geotiffs, loops1, params)\n\n    loops2 = retain_loops(params)\n    selected_tifs2 = __drop_ifgs_if_not_part_of_any_loop(geotiffs, loops2, params)\n    assert all([a == b for a, b in zip(selected_tifs1, selected_tifs2)])\n"
  },
  {
    "path": "tests/phase_closure/test_collect_loops.py",
    "content": "from typing import List\nfrom datetime import date\nimport pytest\nimport numpy as np\nimport networkx as nx\nfrom pyrate.core.phase_closure.mst_closure import Edge, __setup_edges, __find_closed_loops\nfrom pyrate.core.phase_closure.collect_loops import dedupe_loops, find_loops\n\n\ndef test_collect_loops():\n    graph = np.array(\n        [\n            [0, 1, 0, 1, 0],\n            [1, 0, 1, 0, 1],\n            [0, 1, 0, 1, 0],\n            [1, 0, 1, 0, 1],\n            [0, 1, 0, 1, 0]\n        ]\n    )\n\n    n = 4\n    count, all_loops = find_loops(graph, n)\n    assert count == 6\n    deduped_loops = dedupe_loops(all_loops)\n    np.testing.assert_array_equal(deduped_loops, [[0, 1, 2, 3], [0, 1, 4, 3], [1, 2, 3, 4]])\n\n\ndef test_count_loops():\n    graph = np.array(\n            [\n                [0, 1, 1, 1],\n                [1, 0, 1, 1],\n                [1, 1, 0, 1],\n                [1, 1, 1, 0],\n            ]\n    )\n\n    n = 4\n    count, all_loops = find_loops(graph, n)\n    assert len(all_loops) == 6\n    np.testing.assert_array_equal(all_loops, [[0, 1, 2, 3], [0, 1, 3, 2], [0, 2, 1, 3], [0, 2, 3, 1], [0, 3, 1, 2],\n                                              [0, 3, 2, 1]])\n    deduped_loops = dedupe_loops(all_loops)\n    np.testing.assert_array_equal(deduped_loops, [all_loops[0]])\n\n\ndef __find_closed_loops_nx(edges: List[Edge], max_loop_length: int) -> List[List[date]]:\n    g = nx.Graph()\n    edges = [(we.first, we.second) for we in edges]\n    g.add_edges_from(edges)\n    dg = nx.DiGraph(g)\n    print(f\"Evaluating all possible loops using NetworkX simple_cycles function\")\n    simple_cycles = list(nx.simple_cycles(dg))\n    print(f\"Total number of possible loops is {len(simple_cycles)}\")\n    print(f\"Discarding loops with less than 3 edges and more than {max_loop_length} edges\")\n    loop_subset = [scc for scc in simple_cycles\n                   if\n                   (len(scc) > 2)  # three or more edges reqd for closed loop\n                   and\n                   (len(scc) <= max_loop_length)  # discard loops exceeding max loop length\n                   ]\n    print(f\"Number of remaining loops is {len(loop_subset)}\")\n\n    # also discard loops when the loop members are the same\n    return dedupe_loops(loop_subset)\n\n\n@pytest.fixture(scope='module')\ndef available_edges(cropa_geotifs):\n    available_edges = __setup_edges(cropa_geotifs)\n    return available_edges\n\n\ndef max_loop_length(available_edges):\n    all_possible_loops = __find_closed_loops_nx(available_edges, max_loop_length=100)\n    max_length = max([len(l) for l in all_possible_loops])\n    return max_length\n\n\ndef test_find_closed_loops_vs_networkx(available_edges):\n    max_length = max_loop_length(available_edges)\n\n    for n in range(max_length + 1):\n        print(f'====checking for max_loop_length {n}=====')\n        networkx_loops = __find_closed_loops_nx(available_edges, max_loop_length=n)\n        networkx_loops = [sorted(l) for l in networkx_loops]\n        networkx_set = {tuple(l) for l in networkx_loops}\n        our_loops = __find_closed_loops(available_edges, max_loop_length=n)\n        our_loops = [sorted(l) for l in our_loops]\n        our_set = {tuple(l) for l in our_loops}\n        assert networkx_set == our_set\n"
  },
  {
    "path": "tests/phase_closure/test_mst_closure.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\nfrom datetime import date\nimport numpy as np\nimport pytest\nfrom pyrate.constants import PYRATEPATH\nfrom pyrate.core.phase_closure.mst_closure import (\n    __find_closed_loops, Edge, SignedWeightedEdge, SignedEdge, __setup_edges,\n    __add_signs_and_weights_to_loops, sort_loops_based_on_weights_and_date, WeightedLoop,\n    __find_signed_closed_loops\n)\nimport pyrate.constants as C\nfrom tests.phase_closure.common import IfgDummy\n\nGEOTIFF = PYRATEPATH.joinpath('tests', 'test_data', 'geotiffs')\n\n\n@pytest.fixture\ndef geotiffs():\n    tifs = [u.as_posix() for u in GEOTIFF.glob('*_unw.tif')]\n    tifs.sort()\n    return tifs\n\n\n@pytest.fixture\ndef all_loops(geotiffs):\n    edges = __setup_edges(geotiffs)\n    loops = __find_closed_loops(edges, max_loop_length=100)\n    assert len(loops) == 541\n    return loops\n\n\n@pytest.fixture\ndef edges(geotiffs):\n    all_edges = __setup_edges(geotiffs)\n    return all_edges\n\n\n@pytest.fixture\ndef signed_loops(all_loops, edges):\n    loops = __add_signs_and_weights_to_loops(all_loops, edges)\n    return loops\n\n\n@pytest.fixture(params=[True, False])\ndef weight(request):\n    return request.param\n\n\ndef test_setup_edges(geotiffs):\n    edges = __setup_edges(geotiffs)\n    assert len(edges) == len(geotiffs) == 30\n    assert isinstance(edges[0], Edge)\n\n\ndef test_associate_ifgs_with_loops(signed_loops, geotiffs):\n    assert len(geotiffs) == 30\n    assert len(signed_loops) == 541\n    assert isinstance(signed_loops[0], WeightedLoop)\n    swe = signed_loops[0].loop[0]\n    assert isinstance(swe, SignedWeightedEdge)\n    assert isinstance(swe.signed_edge, SignedEdge)\n    assert isinstance(swe.edge, Edge)\n    assert isinstance(swe.first, date)\n    assert isinstance(swe.second, date)\n\n\ndef test_sort_loops_based_on_weights_and_date(geotiffs):\n    ifg_files = [IfgDummy(ifg_path) for ifg_path in geotiffs]\n    params = {\n        C.INTERFEROGRAM_FILES: ifg_files,\n        C.MAX_LOOP_LENGTH: 100\n    }\n    weighted_loops = sort_loops_based_on_weights_and_date(params)\n    assert len(weighted_loops) == 541\n    # order\n    weights = [w.weight for w in weighted_loops]\n    earliest_dates = [w.earliest_date for w in weighted_loops]\n    assert np.all(np.diff(weights) >= 0)\n\n    for i, (w, d, wl) in enumerate(zip(weights[:-1], earliest_dates[:-1], weighted_loops[:-1])):\n        sub_list = [d]\n        if w == weights[i+1]:\n            sub_list.append(earliest_dates[i+1])\n        if len(sub_list) > 1:\n            tds = np.array([td.days for td in np.diff(sub_list)])\n            assert np.all(tds >= 0)  # assert all dates are increasing for same weights\n\n\ndef test_add_signs_and_weights_to_loops(closure_params):\n    geotiffs = closure_params[\"geotiffs\"]\n\n    \"\"\"also tests find_signed_closed_loops\"\"\"\n    all_edges = __setup_edges(geotiffs)\n    all_loops = __find_closed_loops(all_edges, max_loop_length=closure_params[C.MAX_LOOP_LENGTH])\n    loops1 = __add_signs_and_weights_to_loops(all_loops, all_edges)\n\n    all_edges = __setup_edges(geotiffs)\n    all_loops = __find_closed_loops(all_edges, closure_params[C.MAX_LOOP_LENGTH])\n    loops2 = __add_signs_and_weights_to_loops(all_loops, all_edges)\n\n    compare_loops(loops1, loops2)\n\n\ndef compare_loops(loops1, loops2):\n    for i, (l1, l2) in enumerate(zip(loops1, loops2)):\n        assert l1.weight == l2.weight\n        for ll1, ll2 in zip(l1.loop, l2.loop):\n            assert ll1.weight == ll2.weight\n            assert ll1.first == ll2.first\n            assert ll1.second == ll2.second\n            assert ll1.sign == ll2.sign\n    assert i == len(loops1) - 1\n    assert i == 540\n\n\ndef test_find_signed_closed_loops(closure_params):\n    loops1 = __find_signed_closed_loops(closure_params)\n    loops2 = __find_signed_closed_loops(closure_params)\n    compare_loops(loops1, loops2)\n\n\ndef test_sort_loops_based_on_weights_and_date_2(closure_params):\n    sorted_loops1 = sort_loops_based_on_weights_and_date(closure_params)\n    sorted_loops2 = sort_loops_based_on_weights_and_date(closure_params)\n    compare_loops(sorted_loops1, sorted_loops2)\n"
  },
  {
    "path": "tests/phase_closure/test_plot_closure.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\nimport shutil\nfrom pathlib import Path\nfrom subprocess import check_call\nimport pytest\n\nimport pyrate.constants as C\nfrom pyrate import correct\nfrom pyrate.configuration import Configuration\nfrom tests.common import MEXICO_CROPA_CONF\n\n\ntry:\n    import matplotlib.pyplot as plt\n    import matplotlib as mpl\n    from mpl_toolkits.axes_grid1 import make_axes_locatable\n    cmap = mpl.cm.Spectral\n    PLOT = True\nexcept ImportError as e:\n    PLOT = False\n\n\nsteps = ['orbfit',  'refphase',  'phase_closure']\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif((not PLOT),\n                    reason='skipped as plotting packages are missing')\ndef test_plot_closure(mexico_cropa_params):\n    config = Configuration(MEXICO_CROPA_CONF)\n    params = config.__dict__\n    check_call(f\"mpirun -n 3 pyrate prepifg -f {MEXICO_CROPA_CONF}\", shell=True)\n\n    correct._copy_mlooked(params)\n    correct.__validate_correct_steps(params)\n    # house keeping\n    correct._update_params_with_tiles(params)\n    correct._create_ifg_dict(params)\n    params[C.REFX_FOUND], params[C.REFY_FOUND] = correct.ref_pixel_calc_wrapper(params)\n\n    # run through the correct steps in user specified sequence\n    for step in steps:\n        if step == 'phase_closure':\n            correct.correct_steps[step](params, config)\n        else:\n            correct.correct_steps[step](params)\n\n    closure_plot_file = Path(config.phase_closure_dir).joinpath(f'closure_loops_iteration_1_fig_0.png')\n    assert closure_plot_file.exists()\n    shutil.rmtree(params[C.OUT_DIR], ignore_errors=True)\n"
  },
  {
    "path": "tests/phase_closure/test_sum_closure.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\nimport pytest\nfrom pathlib import Path\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.configuration import Configuration, write_config_file\nfrom tests.common import MEXICO_CROPA_CONF, manipulate_test_conf, PY37GDAL302, sub_process_run\n\n\n@pytest.fixture\ndef modified_config(tempdir, get_lks=1, get_crop=1, orbfit_lks=2, orbfit_method=1, orbfit_degrees=1, ref_est_method=1):\n    def modify_params(conf_file, parallel_vs_serial, output_conf_file):\n        tdir = Path(tempdir())\n        params = manipulate_test_conf(conf_file, tdir)\n\n        if params[C.PROCESSOR] == 1:  # turn on coherence for gamma\n            params[C.COH_MASK] = 1\n\n        params[C.PARALLEL] = parallel_vs_serial\n        params[C.PROCESSES] = 4\n        params[C.APSEST] = 1\n        params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks\n        params[C.REFNX], params[C.REFNY] = 2, 2\n\n        params[C.IFG_CROP_OPT] = get_crop\n        params[C.ORBITAL_FIT_LOOKS_X], params[\n            C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks\n        params[C.ORBITAL_FIT] = 1\n        params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        params[C.REF_EST_METHOD] = ref_est_method\n        params[C.MAX_LOOP_LENGTH] = 3\n        params[\"rows\"], params[\"cols\"] = 3, 2\n        params[\"savenpy\"] = 1\n        params[\"notiles\"] = params[\"rows\"] * params[\"cols\"]  # number of tiles\n\n        # write new temp config\n        output_conf = tdir.joinpath(output_conf_file)\n        write_config_file(params=params, output_conf_file=output_conf)\n\n        return output_conf, params\n\n    return modify_params\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif((not PY37GDAL302), reason=\"Only run in one CI env\")\ndef test_mpi_vs_single_process(modified_config):\n    mpi_conf, m_params = modified_config(MEXICO_CROPA_CONF, 0, 'mpi_conf.conf')\n    sub_process_run(f\"mpirun -n 3 pyrate conv2tif -f {mpi_conf}\")\n    sub_process_run(f\"mpirun -n 3 pyrate prepifg -f {mpi_conf}\")\n    sub_process_run(f\"mpirun -n 3 pyrate correct -f {mpi_conf}\")\n\n    serial_conf, s_params = modified_config(MEXICO_CROPA_CONF, 0, 'single_conf.conf')\n    sub_process_run(f\"pyrate conv2tif -f {serial_conf}\")\n    sub_process_run(f\"pyrate prepifg -f {serial_conf}\")\n    sub_process_run(f\"pyrate correct -f {serial_conf}\")\n\n    parallel_conf, p_params = modified_config(MEXICO_CROPA_CONF, 1, 'parallel_conf.conf')\n    sub_process_run(f\"pyrate conv2tif -f {parallel_conf}\")\n    sub_process_run(f\"pyrate prepifg -f {parallel_conf}\")\n    sub_process_run(f\"pyrate correct -f {parallel_conf}\")\n\n    m_config = Configuration(mpi_conf)\n    s_config = Configuration(serial_conf)\n    p_config = Configuration(parallel_conf)\n    m_closure = np.load(m_config.closure().closure)\n    s_closure = np.load(s_config.closure().closure)\n    p_closure = np.load(p_config.closure().closure)\n\n    # loops\n    m_loops = np.load(m_config.closure().loops, allow_pickle=True)\n    s_loops = np.load(s_config.closure().loops, allow_pickle=True)\n    p_loops = np.load(p_config.closure().loops, allow_pickle=True)\n    m_weights = [m.weight for m in m_loops]\n    s_weights = [m.weight for m in s_loops]\n    p_weights = [m.weight for m in p_loops]\n    np.testing.assert_array_equal(m_weights, s_weights)\n    np.testing.assert_array_equal(m_weights, p_weights)\n\n    for i, (m, s) in enumerate(zip(m_loops, s_loops)):\n        assert all(m_e == s_e for m_e, s_e in zip(m.edges, s.edges))\n\n    # closure\n    np.testing.assert_array_almost_equal(np.abs(m_closure), np.abs(s_closure), decimal=4)\n    np.testing.assert_array_almost_equal(np.abs(m_closure), np.abs(p_closure), decimal=4)\n\n    # num_occurrences_each_ifg\n    m_num_occurrences_each_ifg = np.load(m_config.closure().num_occurences_each_ifg, allow_pickle=True)\n    s_num_occurrences_each_ifg = np.load(s_config.closure().num_occurences_each_ifg, allow_pickle=True)\n    p_num_occurrences_each_ifg = np.load(p_config.closure().num_occurences_each_ifg, allow_pickle=True)\n    np.testing.assert_array_equal(m_num_occurrences_each_ifg, s_num_occurrences_each_ifg)\n    np.testing.assert_array_equal(m_num_occurrences_each_ifg, p_num_occurrences_each_ifg)\n\n    # check ps\n    m_ifgs_breach_count = np.load(m_config.closure().ifgs_breach_count)\n    s_ifgs_breach_count = np.load(s_config.closure().ifgs_breach_count)\n    p_ifgs_breach_count = np.load(p_config.closure().ifgs_breach_count)\n    np.testing.assert_array_equal(m_ifgs_breach_count, s_ifgs_breach_count)\n    np.testing.assert_array_equal(m_ifgs_breach_count, p_ifgs_breach_count)\n"
  },
  {
    "path": "tests/test_algorithm.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the algorithm.py PyRate module.\n\"\"\"\nfrom datetime import date\nfrom math import pi, cos, sin, radians\nfrom numpy import array, reshape, squeeze\nfrom os.path import join\n\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal, assert_allclose\n\nfrom pyrate.core.algorithm import (least_squares_covariance,\n                                   is_square,\n                                   unit_vector,\n                                   ifg_date_lookup,\n                                   get_all_epochs,\n                                   get_epochs,\n                                   first_second_ids,\n                                   factorise_integer,\n                                   )\n\nfrom pyrate.configuration import parse_namelist\nfrom pyrate.core.shared import Ifg, convert_radians_to_mm\nfrom tests.common import small5_mock_ifgs, SML_TEST_TIF, UnitTestAdaptation\n\n\nclass TestLeastSquaresTests(UnitTestAdaptation):\n    \"\"\"\n    Unit tests for the PyRate least_squares_covariance() implementation.\n    \"\"\"\n\n    @staticmethod\n    def test_least_squares_covariance():\n        b = array([[13, 7.2, 5.7]]).T\n        A = array([[1, 0.4, 0.3], [1, 1, 1]]).T\n        v = array([[1, 1, 1]]).T\n        r = least_squares_covariance(A, b, v)\n        exp = [10.1628, 2.8744]\n        assert_array_almost_equal(r.T.squeeze(), exp, decimal=4)\n\n    def test_least_squares_covariance_overdetermined(self):\n        # must be overdetermined, ie. more observations than params\n        b = array([[10]]).T\n        A = array([[1]]).T\n        v = array([[1]]).T\n        self.assertRaises(ValueError, least_squares_covariance, A, b, v)\n\n        # try non transposed style\n        b = array([[10]])\n        A = array([[1]])\n        v = array([[1]])\n        self.assertRaises(ValueError, least_squares_covariance, A, b, v)\n\n\nclass TestAlgorithmTests(UnitTestAdaptation):\n    \"\"\"\n    Misc unittests for functions in the algorithm module.\n    \"\"\"\n\n    def test_factorise(self):\n        self.assertEqual(factorise_integer(1), (1, 1))\n        self.assertEqual(factorise_integer(2), (2, 1))\n        self.assertEqual(factorise_integer(4), (2, 2))\n        self.assertEqual(factorise_integer(9), (3, 3))\n        self.assertEqual(factorise_integer(76), (4, 19))\n        self.assertEqual(factorise_integer(76.5), (4, 19))\n        a, b = factorise_integer(12)\n        self.assertEqual(type(a), int)\n        self.assertEqual(type(b), int)\n    \n    def test_is_square(self):\n        self.assertTrue(is_square(np.empty((2, 2))))\n\n    def test_is_not_square(self):\n        for shape in [(3, 2), (2, 3)]:\n            self.assertFalse(is_square(np.empty(shape)))\n\n    @staticmethod\n    def test_phase_conversion():\n        # ROIPAC interferograms in units of radians, verify conversion to mm\n        xs, ys = 5, 7\n        data = (np.arange(xs * ys) - 1.7) * 0.1 # fake a range of values\n        data = np.where(data == 0, np.nan, data)\n        wavelen = 0.0562356424\n        exp = (data * wavelen * 1000) / (4 * pi)\n        act = convert_radians_to_mm(data, wavelen)\n        assert_allclose(exp, act)\n\n    def test_unit_vector(self):\n        # last values here simulate a descending pass\n        incidence = [radians(x) for x in (34.3, 39.3, 29.3, 34.3)]\n        azimuth = [radians(x) for x in (77.8, 77.9, 80.0, 282.2)]\n\n        vert, ns, ew = [], [], []\n        for i, a in zip(incidence, azimuth):\n            vert.append(cos(i))\n            ns.append(sin(i) * cos(a))\n            ew.append(sin(i) * sin(a))\n\n        sh = 4\n        unitv = [array(ew), array(ns), array(vert)]\n        unitv = [a.reshape(sh) for a in unitv]\n\n        # NB: assumes radian inputs\n        act = unit_vector(reshape(incidence, sh), reshape(azimuth, sh))\n        for a, e in zip(act, unitv):\n            assert_array_almost_equal(squeeze(a), e)\n\n        # check unit vec components have correct signs\n        E, N, V = act\n        # test E/W component of ascending is +ve\n        self.assertTrue((E[:-2]).all() > 0)\n        self.assertTrue(E[-1] < 0)  # test E/W component of descending is -ve\n        self.assertTrue((N > 0).all())  # ensure all north values are positive\n\n        # check unit vec components have correct magnitudes\n        self.assertTrue((abs(V) > abs(E)).all())\n        self.assertTrue((abs(V) > abs(N)).all())\n        self.assertTrue((abs(E) > abs(N)).all())\n\n\nclass TestDateLookup(UnitTestAdaptation):\n    \"\"\"\n    Tests for the algorithm.ifg_date_lookup() function.\n    \"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        cls.ifgs = small5_mock_ifgs()\n\n    def test_ifg_date_lookup(self):\n        # check reverse lookup of ifg given a first and second date tuple\n        date_pair = (date(2006, 8, 28), date(2006, 12, 11))\n        i = ifg_date_lookup(self.ifgs, date_pair)\n        self.assertEqual(self.ifgs[0], i)\n\n        # test with reversed date tuple, should reorder it according to age\n        date_pair = (date(2006, 12, 11), date(2006, 11, 6))\n        i = ifg_date_lookup(self.ifgs, date_pair)\n        self.assertEqual(self.ifgs[1], i)\n\n    def test_ifg_date_lookup_failure(self):\n        # error when lookup cannot find an ifg given a date pair\n        dates = (date(2006, 12, 11), date(2007, 3, 26))\n        self.assertRaises(ValueError, ifg_date_lookup, self.ifgs, dates)\n\n    def test_date_lookup_bad_inputs(self):\n        # test some bad inputs to date lookup\n        inputs = [(None, None), (1, 10), (34.56, 345.93),\n                  (date(2007, 3, 26), \"\"), (date(2007, 3, 26), None)]\n\n        for d in inputs:\n            self.assertRaises(ValueError, ifg_date_lookup, self.ifgs, d)\n\n\n# TODO: InitialModelTests\n#class InitialModelTests(unittest.TestCase):\n\n#    def test_initial_model(self):\n        # 1. fake an RSC file with coords\n        # 2. fake a ones(shape)  # could also make a ramp etc\n        # data is single band of DISPLACEMENT\n        #raise NotImplementedError\n\n\nclass TestEpochs(UnitTestAdaptation):\n    \"\"\"\n    Unittests for the EpochList class.\n    \"\"\"\n\n    def test_get_epochs(self):\n        def str2date(s):\n            segs = s[:4], s[4:6], s[6:] # year, month, day\n            return date(*[int(sg) for sg in segs])\n\n        raw_date = ['20060619', '20060828', '20061002', '20061106', '20061211',\n                    '20070115', '20070219', '20070326', '20070430', '20070604',\n                    '20070709', '20070813', '20070917']\n\n        exp_dates = [str2date(d) for d in raw_date]\n        exp_repeat = [1, 1, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 2]\n        exp_spans = [0, 0.1916, 0.2875, 0.3833, 0.4791, 0.5749, 0.6708, 0.7666,\n                            0.8624, 0.9582, 1.0541, 1.1499, 1.2457]\n\n        ifms = join(SML_TEST_TIF, \"ifms_17\")\n        ifgs = [Ifg(join(SML_TEST_TIF, p)) for p in parse_namelist(ifms)]\n        for i in ifgs:\n            i.open()\n\n        epochs = get_epochs(ifgs)[0]\n\n        self.assertTrue((exp_dates == epochs.dates).all())\n        self.assertTrue((exp_repeat == epochs.repeat).all())\n        assert_array_almost_equal(exp_spans, epochs.spans, decimal=4)\n\n    def test_get_all_epochs(self):\n        # test function to extract all dates from sequence of ifgs\n        ifgs = small5_mock_ifgs()\n        for i in ifgs:\n            i.nodata_value = 0\n        dates = [date(2006, 8, 28), date(2006, 11, 6), date(2006, 12, 11),\n                 date(2007, 1, 15), date(2007, 3, 26), date(2007, 9, 17)]\n\n        self.assertEqual(dates, sorted(set(get_all_epochs(ifgs))))\n\n    def test_get_epoch_count(self):\n        self.assertEqual(6, len(set(get_all_epochs(small5_mock_ifgs()))))\n\n    def test_first_second_ids(self):\n        d0 = date(2006, 6, 19)\n        d1 = date(2006, 8, 28)\n        d2 = date(2006, 10, 2)\n        d3 = date(2006, 11, 6)\n        exp = {d0: 0, d1: 1, d2: 2, d3: 3}\n\n        # test unordered and with duplicates\n        self.assertEqual(exp, first_second_ids([d3, d0, d2, d1]))\n        self.assertEqual(exp, first_second_ids([d3, d0, d2, d1, d3, d0]))\n"
  },
  {
    "path": "tests/test_aps.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the APS PyRate module.\n\"\"\"\nimport os\nimport shutil\nimport pytest\nimport numpy as np\nfrom os.path import join\nfrom scipy.ndimage import gaussian_filter1d, gaussian_filter\nfrom numpy.testing import assert_array_almost_equal\n\nimport pyrate.constants as C\nfrom pyrate import conv2tif, prepifg, correct\nfrom pyrate.configuration import Configuration, MultiplePaths\nfrom pyrate.core import shared\nfrom pyrate.core.aps import spatio_temporal_filter, _interpolate_nans_2d, _kernel\nfrom pyrate.core.aps import gaussian_temporal_filter as tlpfilter, gaussian_spatial_filter as slpfilter\nfrom pyrate.core.shared import Ifg\nfrom pyrate.core.ifgconstants import DAYS_PER_YEAR\nfrom tests import common\nfrom tests.common import BASE_TEST, PY37GDAL304\n\n\n@pytest.fixture(params=[\"linear\", \"nearest\", \"cubic\"])\ndef slpnanfill_method(request):\n    return request.param\n\n\n@pytest.fixture(params=[0.1, 0.5, 1, 5])\ndef slpfcutoff_method(request):\n    return request.param\n\n\n@pytest.fixture(params=[0.001, 0.01])\ndef slpfcutoff(request):\n    return request.param\n\n\ndef test_interpolate_nans_2d(slpnanfill_method):\n    arr = np.random.rand(200, 100)\n    arr[arr < 0.1] = np.nan  # insert some nans\n    assert np.sum(np.isnan(arr)) != 0  # some nans present\n    _interpolate_nans_2d(arr, method=slpnanfill_method)\n    assert np.sum(np.isnan(arr)) == 0  # should not be any nans\n\n\nclass TestSpatialFilter:\n    \"\"\"\n    Test the implementation of Gaussian spatial filter\n    \"\"\"\n\n    def setup_method(self):\n        ifg_path = join(str(BASE_TEST), 'cropB', '20180106-20180130_ifg.tif')\n        ifg = Ifg(ifg_path)\n        ifg.open()\n        p = ifg.phase_data\n        self.x_size = ifg.x_size\n        self.y_size = ifg.y_size\n        # convert zeros to NaNs\n        p[p == 0] = np.nan\n        self.phase = p\n\n    def test_gaussian_filter(self, slpfcutoff_method):\n        \"\"\"\n        Compare against scipy.ndimage.gaussian_filter,\n        that operates in the spatial domain on data with equal resolution in x and y\n        \"\"\"\n        e = gaussian_filter(self.phase, sigma=slpfcutoff_method)\n        r = slpfilter(self.phase, cutoff=slpfcutoff_method,\n                      x_size=self.x_size, y_size=self.y_size)\n        # pull data from middle of images; away from potential edge effects\n        exp = e[50:150, 50:150]\n        res = r[50:150, 50:150]\n        assert_array_almost_equal(res, exp, 0)\n\n\ndef test_gaussian_kernel():\n    \"\"\"\n    Test the Gaussian smoothing kernel\n    \"\"\"\n    x = np.arange(1, 10, 2)\n    res = _kernel(x, 3)\n    exp = np.array([0.94595947, 0.60653066, 0.24935221, 0.06572853, 0.011109])\n    np.testing.assert_array_almost_equal(res, exp, decimal=6)\n\n    res = _kernel(x, 4)\n    exp = np.array([0.96923323, 0.7548396, 0.45783336, 0.21626517, 0.07955951])\n    np.testing.assert_array_almost_equal(res, exp, decimal=6)\n\n    res = _kernel(x, 0.001)\n    exp = np.array([0, 0, 0, 0, 0])\n    np.testing.assert_array_equal(res, exp)\n\n\nclass TestTemporalFilter:\n    \"\"\"\n    Tests for the temporal filter with synthetic data for a single pixel\n    \"\"\"\n\n    def setup_method(self):\n        self.thr = 1  # no nans in these test cases, threshold = 1\n        # instance of normally distributed noise\n        n = np.array([-0.36427456, 0.69539061, 0.42181139, -2.56306134,\n                      0.55844095, -0.65562626, 0.65607911, 1.19431637,\n                      -1.43837395, -0.91656358])\n        # synthetic incremental displacement\n        d = np.array([1., 1., 0.7, 0.3, 0., 0.1, 0.2, 0.6, 1., 1.])\n        # incremental displacement + noise\n        self.tsincr = d * 2 + n\n        # regular time series, every 12 days\n        self.interval = 12 / DAYS_PER_YEAR  # 0.03285 years\n        intv = np.ones(d.shape, dtype=np.float32) * self.interval\n        self.span = np.cumsum(intv)\n\n    def test_tlpfilter_repeatability(self):\n        \"\"\"\n        TEST 1: check for repeatability against expected result from\n        tlpfilter. Cutoff equal to sampling interval (sigma=1)\n        \"\"\"\n        res = tlpfilter(self.tsincr, self.interval, self.span, self.thr)\n        exp = np.array([1.9936507, 1.9208364, 1.0252733, -0.07402889,\n                        -0.1842336, 0.24325351, 0.94737214, 1.3890865,\n                        1.1903466, 1.0036403])\n        np.testing.assert_array_almost_equal(res, exp, decimal=6)\n\n    def test_tlpfilter_scipy_sig1(self):\n        \"\"\"\n        TEST 2: compare tlpfilter to scipy.ndimage.gaussian_filter1d. Works for\n        regularly sampled data. Cutoff equal to sampling interval (sigma=1)\n        \"\"\"\n        res = tlpfilter(self.tsincr, self.interval, self.span, self.thr)\n        exp = gaussian_filter1d(self.tsincr, sigma=1)\n        np.testing.assert_array_almost_equal(res, exp, decimal=1)\n\n    def test_tlpfilter_scipy_sig2(self):\n        \"\"\"\n        TEST 3: compare tlpfilter to scipy.ndimage.gaussian_filter1d. Works for\n        regularly sampled data. Cutoff equal to twice the sampling interval (sigma=2)\n        \"\"\"\n        res = tlpfilter(self.tsincr, self.interval * 2, self.span, self.thr)\n        exp = gaussian_filter1d(self.tsincr, sigma=2)\n        np.testing.assert_array_almost_equal(res, exp, decimal=1)\n\n    def test_tlpfilter_scipy_sig05(self):\n        \"\"\"\n        TEST 4: compare tlpfilter to scipy.ndimage.gaussian_filter1d. Works for\n        regularly sampled data. Cutoff equal to half the sampling interval (sigma=0.5)\n        \"\"\"\n        res = tlpfilter(self.tsincr, self.interval * 0.5, self.span, self.thr)\n        exp = gaussian_filter1d(self.tsincr, sigma=0.5)\n        np.testing.assert_array_almost_equal(res, exp, decimal=2)\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\nclass TestAPSErrorCorrectionsOnDiscReused:\n\n    @classmethod\n    def setup_method(cls):\n        cls.conf = common.TEST_CONF_GAMMA\n        params = Configuration(cls.conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(cls.conf).__dict__\n        prepifg.main(params)\n        cls.params = Configuration(cls.conf).__dict__\n        correct._copy_mlooked(cls.params)\n        correct._update_params_with_tiles(cls.params)\n        correct._create_ifg_dict(cls.params)\n        multi_paths = cls.params[C.INTERFEROGRAM_FILES]\n        cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n        cls.ifgs = [shared.Ifg(i) for i in cls.ifg_paths]\n        for i in cls.ifgs:\n            i.open()\n        shared.save_numpy_phase(cls.ifg_paths, cls.params)\n        correct.mst_calc_wrapper(cls.params)\n\n    @classmethod\n    def teardown_method(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_aps_error_files_on_disc(self, slpnanfill_method, slpfcutoff):\n        self.params[C.SLPF_NANFILL_METHOD] = slpnanfill_method\n        self.params[C.SLPF_CUTOFF] = slpfcutoff\n        spatio_temporal_filter(self.params)\n\n        # test_orb_errors_written\n        aps_error_files = [MultiplePaths.aps_error_path(i, self.params) for i in self.ifg_paths]\n        assert all(p.exists() for p in aps_error_files)\n        last_mod_times = [os.stat(o).st_mtime for o in aps_error_files]\n        phase_prev = [i.phase_data for i in self.ifgs]\n\n        # run aps error removal again\n        spatio_temporal_filter(self.params)\n        aps_error_files2 = [MultiplePaths.aps_error_path(i, self.params) for i in self.ifg_paths]\n        # if files are written again - times will change\n        last_mod_times_2 = [os.stat(o).st_mtime for o in aps_error_files2]\n        # test_aps_error_reused_if_params_unchanged\n        assert all(a == b for a, b in zip(last_mod_times, last_mod_times_2))\n        phase_now = [i.phase_data for i in self.ifgs]\n\n        # run aps error correction once mroe\n        spatio_temporal_filter(self.params)\n        aps_error_files3 = [MultiplePaths.aps_error_path(i, self.params) for i in self.ifg_paths]\n        last_mod_times_3 = [os.stat(o).st_mtime for o in aps_error_files3]\n        assert all(a == b for a, b in zip(last_mod_times, last_mod_times_3))\n        phase_again = [i.phase_data for i in self.ifgs]\n        np.testing.assert_array_equal(phase_prev, phase_now)\n        np.testing.assert_array_equal(phase_prev, phase_again)\n"
  },
  {
    "path": "tests/test_constants.py",
    "content": "import re\nfrom pathlib import Path\nimport pytest\nfrom pyrate.constants import twelve_digits_pattern, sixteen_digits_pattern\nfrom pyrate.configuration import parse_namelist\nfrom tests.common import IFMS16, GAMMA_SML_TEST_DIR\n\n\n@pytest.fixture\ndef six_digits_filenames():\n    return IFMS16\n\n\n@pytest.fixture\ndef eight_digits_filenames():\n    files = list(parse_namelist(Path(GAMMA_SML_TEST_DIR).joinpath('ifms_17').as_posix()))\n    return files\n\n\n@pytest.mark.parametrize(\n    \"regex_pattern,extracted_string_length\",\n    [\n        (twelve_digits_pattern, 12+1),  # +1 due to the internal joining `-`\n        (sixteen_digits_pattern, 16+1)\n     ]\n)\ndef test_file_patterns(regex_pattern, extracted_string_length, six_digits_filenames, eight_digits_filenames):\n\n    if regex_pattern == twelve_digits_pattern:\n        for f in six_digits_filenames:\n            m = re.search(twelve_digits_pattern, Path(f).stem).group()\n            assert len(m) == extracted_string_length\n    if regex_pattern == sixteen_digits_pattern:\n        for f in eight_digits_filenames:\n            m = re.search(sixteen_digits_pattern, Path(f).stem).group()\n            assert len(m) == extracted_string_length\n\n"
  },
  {
    "path": "tests/test_conv2tif.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the PyRate conv2tif script.\n\"\"\"\nimport os\nimport shutil\nimport pytest\nimport glob\nimport copy\nimport itertools\nfrom pathlib import Path\n\nimport pyrate.configuration\nimport pyrate.constants as C\nfrom pyrate.core.shared import Ifg, DEM\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate import conv2tif, prepifg, configuration\nfrom tests.common import manipulate_test_conf, GAMMA_SML_TEST_DIR\n\n\ndef test_dem_and_incidence_not_converted(gamma_params):\n    gp_copy = copy.deepcopy(gamma_params)\n    gp_copy[C.DEM_FILE] = None\n    gp_copy[C.APS_INCIDENCE_MAP] = None\n    conv2tif.main(gp_copy)\n    inc_tif = glob.glob(os.path.join(GAMMA_SML_TEST_DIR, '*inc.tif'))\n    assert len(inc_tif) == 0\n    dem_tif = glob.glob(os.path.join(GAMMA_SML_TEST_DIR, '*dem.tif'))\n    assert len(dem_tif) == 0\n\n\ndef test_conv2tif_file_types(tempdir, gamma_conf):\n    tdir = Path(tempdir())\n    params = manipulate_test_conf(gamma_conf, tdir)\n    params[C.COH_MASK] = 1\n    output_conf_file = 'conf.conf'\n    output_conf = tdir.joinpath(output_conf_file)\n    pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n    params_s = configuration.Configuration(output_conf).__dict__\n    conv2tif.main(params_s)\n    ifg_files = list(Path(tdir.joinpath(params_s[C.OUT_DIR])).glob('*_ifg.tif'))\n    coh_files = list(Path(tdir.joinpath(params_s[C.OUT_DIR])).glob('*_coh.tif'))\n    dem_file = list(Path(tdir.joinpath(params_s[C.GEOMETRY_DIR])).glob('*_dem.tif'))[0]\n    # assert coherence and ifgs have correct metadata\n    for i in itertools.chain(*[ifg_files, coh_files]):\n        ifg = Ifg(i)\n        ifg.open()\n        md = ifg.meta_data\n        if i.name.endswith('_ifg.tif'):\n            assert md[ifc.DATA_TYPE] == ifc.ORIG\n            continue\n        if i.name.endswith('_coh.tif'):\n            assert md[ifc.DATA_TYPE] == ifc.COH\n            continue\n\n    # assert dem has correct metadata\n    dem = DEM(dem_file.as_posix())\n    dem.open()\n    md = dem.dataset.GetMetadata()\n    assert md[ifc.DATA_TYPE] == ifc.DEM\n    shutil.rmtree(tdir)\n\n\ndef test_tifs_placed_in_out_dir(gamma_params):\n    # Test no tifs in obs dir\n    tifs = glob.glob(os.path.join(gamma_params[C.INTERFEROGRAM_DIR], '*.tif'))\n    assert len(tifs) == 0\n    # Test tifs in obs dir\n    conv2tif.main(gamma_params)\n    tifs = glob.glob(os.path.join(gamma_params[C.INTERFEROGRAM_DIR], '*.tif')) + \\\n        glob.glob(os.path.join(gamma_params[C.COHERENCE_DIR], '*.tif')) + \\\n        glob.glob(os.path.join(gamma_params[C.GEOMETRY_DIR], '*.tif')) \\\n\n    assert len(tifs) == 35\n\n\ndef test_num_gamma_tifs_equals_num_unws(gamma_params):\n    gtifs = conv2tif.main(gamma_params)\n    # 17 unws + dem + 17 cohfiles\n    assert len(gtifs) == 35\n\n    for g, _ in gtifs:   # assert all output from conv2tfi are readonly\n        assert Path(g).stat().st_mode == 33060\n\ndef test_num_roipac_tifs_equals_num_unws(roipac_params):\n    gtifs = conv2tif.main(roipac_params)\n    # 17 unws + dem\n    assert len(gtifs) == 18\n\n\ndef test_conversion_not_recomputed(gamma_params):\n\n    \"\"\"\n    If a gtif already exists, conv2tif will return None instead\n    of path to the gtif.\n    \"\"\"\n    conv2tif.main(gamma_params)\n    gtifs_and_conversion_bool = conv2tif.main(gamma_params)\n    assert all([not b for gt, b in gtifs_and_conversion_bool])\n\n\ndef test_no_tifs_exits(gamma_params):\n    with pytest.raises(Exception):\n        prepifg.main(gamma_params)\n\n\ndef test_tifs_succeeds(gamma_params):\n    conv2tif.main(gamma_params)\n    prepifg.main(gamma_params)\n"
  },
  {
    "path": "tests/test_correct.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the correct.py PyRate module.\n\"\"\"\nimport shutil\nfrom pathlib import Path\nimport pytest\n\nimport pyrate.constants as C\nfrom pyrate.configuration import ConfigException, Configuration, write_config_file\nfrom pyrate import correct, prepifg, conv2tif\nfrom tests import common\n\n\ndef test_unsupported_process_steps_raises(gamma_conf):\n    config = Configuration(gamma_conf)\n    gamma_params = config.__dict__\n    gamma_params['correct'] = ['orbfit2', 'something_other_step']\n    with pytest.raises(ConfigException):\n        correct.correct_ifgs(config)\n\n\ndef test_supported_process_steps_dont_raise(gamma_params):\n    supported_stpes = ['orbfit', 'refphase', 'mst', 'apscorrect', 'maxvar', 'demerror', 'phase_closure']\n    assert all([s in gamma_params['correct'] for s in supported_stpes])\n    correct.__validate_correct_steps(params=gamma_params)\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not common.PYTHON3P9, reason=\"Only run in one CI env\")\ndef test_process_treats_prepif_outputs_readonly(gamma_conf, tempdir, coh_mask):\n    from pyrate.configuration import Configuration\n    tdir = Path(tempdir())\n    params = common.manipulate_test_conf(gamma_conf, tdir)\n    params[C.COH_MASK] = coh_mask\n    params[C.PARALLEL] = 0\n    output_conf = tdir.joinpath('conf.cfg')\n    write_config_file(params=params, output_conf_file=output_conf)\n    params = Configuration(output_conf).__dict__\n    conv2tif.main(params)\n    tifs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_unw.tif'))\n    assert len(tifs) == 17\n\n    if params[C.COH_FILE_LIST] is not None:\n        coh_tifs = list(Path(params[C.COHERENCE_DIR]).glob('*_cc.tif'))\n        assert len(coh_tifs) == 17\n\n    params = Configuration(output_conf).__dict__\n    prepifg.main(params)\n    cropped_coh = list(Path(params[C.COHERENCE_DIR]).glob('*_coh.tif'))\n    cropped_ifgs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_ifg.tif'))\n    dem_ifgs = list(Path(params[C.GEOMETRY_DIR]).glob('*_dem.tif'))\n\n    if params[C.COH_FILE_LIST] is not None:  # 17 + 1 dem + 17 coh files\n        assert len(cropped_coh) + len(cropped_ifgs) + len(dem_ifgs) == 35\n    else:  # 17 + 1 dem\n        assert len(cropped_coh) + len(cropped_ifgs) + len(dem_ifgs) == 18\n    # check all tifs from conv2tif are still readonly\n    for t in tifs:\n        assert t.stat().st_mode == 33060\n\n    # check all prepifg outputs are readonly\n    for c in cropped_coh + cropped_ifgs:\n        assert c.stat().st_mode == 33060\n\n    config = Configuration(output_conf)\n    correct.main(config)\n\n    # check all after correct steps multilooked files are still readonly\n    for c in cropped_coh + cropped_ifgs + dem_ifgs:\n        assert c.stat().st_mode == 33060\n    shutil.rmtree(params[C.OUT_DIR])\n"
  },
  {
    "path": "tests/test_covariance.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the covariance.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nfrom pathlib import Path\nfrom numpy import array\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal\n\nimport pyrate.constants as C\nimport pyrate.core.ref_phs_est\nimport pyrate.core.refpixel\nfrom pyrate.core import shared, ifgconstants as ifc\nfrom pyrate import correct, prepifg, conv2tif\nfrom pyrate.core.covariance import cvd, get_vcmt, RDist\nfrom pyrate.configuration import Configuration, MultiplePaths\nimport pyrate.core.orbital\nfrom tests import common\nfrom tests.common import (\n    small5_mock_ifgs,\n    small5_ifg_paths,\n    TEST_CONF_ROIPAC,\n    small_data_setup,\n    prepare_ifgs_without_phase\n)\n\n\nclass TestCovariance:\n\n    @classmethod\n    def setup_class(cls):\n        cls.ifgs = small_data_setup()\n        for i in cls.ifgs:\n            i.mm_converted = True\n        params = dict()\n        params[C.NO_DATA_VALUE] = 0\n        params[C.NAN_CONVERSION] = True\n        cls.params = params\n        cls.r_dist = RDist(cls.ifgs[0])()\n\n    def test_covariance_basic(self):\n        for i in small5_ifg_paths():\n\n            maxvar, alpha = cvd(i, self.params, self.r_dist, calc_alpha=True)\n            assert maxvar is not None\n            assert alpha is not None\n            print(\"maxvar: %s, alpha: %s\" % (maxvar, alpha))\n\n    def test_covariance_17ifgs(self):\n        # After raw data import\n        # (no reference pixel correction and units in radians)\n        exp_maxvar = [5.6149, 8.7710, 2.9373, 0.3114, 12.9931, 2.0459, 0.4236,\n                      2.1243, 0.4745, 0.6725, 0.8333, 3.8232, 3.3052, 2.4925,\n                      16.0159, 2.8025, 1.4345]\n\n        exp_alpha = [0.0356, 0.1601, 0.5128, 0.5736, 0.0691, 0.1337, 0.2333,\n                    0.3202, 1.2338, 0.4273, 0.9024, 0.1280, 0.3585, 0.1599,\n                    0.0110, 0.1287, 0.0676]\n\n        act_maxvar = []\n        act_alpha = []\n        for i in self.ifgs:\n\n            maxvar, alpha = cvd(i.data_path, self.params, self.r_dist, calc_alpha=True)\n            assert maxvar is not None\n            assert alpha is not None\n           \n            act_maxvar.append(maxvar / 20.02) # rough conversion factor back to radians\n            act_alpha.append(alpha)\n\n        # below tests fails at 3 d.p. on account of above conversion factor not being accurate enough\n        assert_array_almost_equal(act_maxvar, exp_maxvar, decimal=2)\n\n        # This test fails for greater than 1 decimal place.\n        # Discrepancies observed in distance calculations.\n        assert_array_almost_equal(act_alpha, exp_alpha, decimal=1)\n\n\nclass TestVCMT:\n\n    def setup_class(cls):\n        cls.ifgs = small_data_setup()\n\n    def test_vcm_basic(self):\n        ifgs = small5_mock_ifgs(5, 9)\n        maxvar = [8.486, 12.925, 6.313, 0.788, 0.649]\n\n        exp = array([[8.486, 5.2364, 0.0, 0.0, 0.0],\n            [5.2364, 12.925,  4.5165,  1.5957,  0.0],\n            [0.0, 4.5165, 6.313, 1.1152, 0.0],\n            [0.0, 1.5957, 1.1152, 0.788, -0.3576],\n            [0.0, 0.0, 0.0, -0.3576, 0.649]])\n\n        act = get_vcmt(ifgs, maxvar)\n        assert_array_almost_equal(act, exp, decimal=3)\n\n    def test_vcm_17ifgs(self):\n        # TODO: maxvar should be calculated by vcm.cvd\n        maxvar = [2.879, 4.729, 22.891, 4.604, 3.290, 6.923, 2.519, 13.177,\n                  7.548, 6.190, 12.565, 9.822, 18.484, 7.776, 2.734, 6.411,\n                  4.754]\n\n        exp = array([\n            [2.879, 0.0, -4.059, -1.820, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n             0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n            [0.0, 4.729, 0.0, 0.0, 1.972, 0.0, 0.0, -3.947, -2.987, 0.0,\n             0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n            [-4.059, 0.0, 22.891, 5.133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n             -7.497, -10.285, 0.0, 0.0, 0.0, 0.0],\n            [-1.820, 0.0, 5.133, 4.604, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n             3.362, 0.0, 0.0, -1.774, 0.0, 0.0],\n            [0.0, 1.972, 0.0, 0.0, 3.290, 2.386, 1.439, -3.292, -2.492, 0.0,\n             0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n            [0.0, 0.0, 0.0, 0.0, 2.386, 6.923, 2.088, 0.0, 0.0, -3.273,\n             -4.663, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n            [0.0, 0.0, 0.0, 0.0, 1.439, 2.088, 2.519, 0.0, 0.0, 1.974, 0.0,\n             0.0, 0.0, -2.213, 0.0, 0.0, 0.0],\n            [0.0, -3.947, 0.0, 0.0, -3.292, 0.0, 0.0, 13.177, 4.986, 0.0, 0.0,\n             0.0, 0.0, 0.0, 0.0, 4.596, -3.957],\n            [0.0, -2.987, 0.0, 0.0, -2.492, 0.0, 0.0, 4.986, 7.548, 0.0, 0.0,\n             0.0, 0.0, 0.0, 0.0, 0.0, 2.995],\n            [0.0, 0.0, 0.0, 0.0, 0.0, -3.273, 1.974, 0.0, 0.0, 6.190, 4.410,\n             0.0, 0.0, -3.469, 0.0, 0.0, 0.0],\n            [0.0, 0.0, 0.0, 0.0, 0.0, -4.663, 0.0, 0.0, 0.0, 4.410, 12.565,\n             0.0, 0.0, 4.942, 0.0, 0.0, 0.0],\n            [0.0, 0.0, -7.497, 3.362, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n             9.8221, 6.737, 0.0, -2.591, 0.0, 0.0],\n            [0.0, 0.0, -10.285, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.737,\n             18.484, 0.0, 3.554, -5.443, 0.0],\n            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.213, 0.0, 0.0, -3.469, 4.942,\n             0.0, 0.0, 7.776, 0.0, 0.0, 0.0],\n            [0.0, 0.0, 0.0, -1.774, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.591,\n             3.554, 0.0, 2.734, -2.093, 0.0],\n            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.596, 0.0, 0.0, 0.0, 0.0,\n             -5.443, 0.0, -2.093, 6.411, -2.760],\n            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -3.957, 2.995, 0.0, 0.0, 0.0,\n             0.0, 0.0, 0.0, -2.760, 4.754]\n        ])\n\n        act = get_vcmt(self.ifgs, maxvar)\n        assert_array_almost_equal(act, exp, decimal=3)\n\n\nlegacy_maxvar = [15.4156637191772,\n                 2.85829424858093,\n                 34.3486289978027,\n                 2.59190344810486,\n                 3.18510007858276,\n                 3.61054635047913,\n                 1.64398515224457,\n                 14.9226036071777,\n                 5.13451862335205,\n                 6.82901763916016,\n                 10.9644861221313,\n                 14.5026779174805,\n                 29.3710079193115,\n                 8.00364685058594,\n                 2.06328082084656,\n                 5.66661834716797,\n                 5.62802362442017]\n\n\nclass TestLegacyEquality:\n    @classmethod\n    def setup_class(cls):\n        roipac_params = Configuration(TEST_CONF_ROIPAC).__dict__\n        from copy import deepcopy\n        params = deepcopy(roipac_params)\n        shared.mkdir_p(params[C.TMPDIR])\n        params[C.REF_EST_METHOD] = 2\n        conv2tif.main(params)\n        params = deepcopy(roipac_params)\n        prepifg.main(params)\n        params = deepcopy(roipac_params)\n        base_ifg_paths = [c.unwrapped_path for c in params[C.INTERFEROGRAM_FILES]]\n        dest_paths = [c.converted_path for c in params[C.INTERFEROGRAM_FILES]]\n        params[C.INTERFEROGRAM_FILES] = [MultiplePaths(d, params) for d in dest_paths]\n        for p in params[C.INTERFEROGRAM_FILES]:  # hack\n            p.sampled_path = p.converted_path\n\n        for i in dest_paths:\n            Path(i).chmod(0o664)  # assign write permission as conv2tif output is readonly\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n        correct._copy_mlooked(params)\n        correct._update_params_with_tiles(params)\n        correct._create_ifg_dict(params)\n        pyrate.core.refpixel.ref_pixel_calc_wrapper(params)\n        params[C.ORBFIT_OFFSET] = True\n        pyrate.core.orbital.remove_orbital_error(ifgs, params)\n        ifgs = prepare_ifgs_without_phase(dest_paths, params)\n        for ifg in ifgs:\n            ifg.close()\n\n        for p in params[C.INTERFEROGRAM_FILES]:  # hack\n            p.tmp_sampled_path = p.sampled_path\n        _, cls.ifgs = pyrate.core.ref_phs_est.ref_phase_est_wrapper(params)\n        ifgs[0].open()\n        r_dist = RDist(ifgs[0])()\n        ifgs[0].close()\n        # Calculate interferogram noise\n        cls.maxvar = [cvd(i, params, r_dist, calc_alpha=True, save_acg=True, write_vals=True)[0] for i in dest_paths]\n        cls.vcmt = get_vcmt(ifgs, cls.maxvar)\n        for ifg in ifgs:\n            ifg.close()\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_legacy_maxvar_equality_small_test_files(self):\n        np.testing.assert_array_almost_equal(self.maxvar, legacy_maxvar, decimal=3)\n\n    def test_legacy_vcmt_equality_small_test_files(self):\n        from tests.common import SML_TEST_DIR\n        LEGACY_VCM_DIR = os.path.join(SML_TEST_DIR, 'vcm')\n        legacy_vcm = np.genfromtxt(os.path.join(LEGACY_VCM_DIR, 'vcmt.csv'), delimiter=',')\n        np.testing.assert_array_almost_equal(legacy_vcm, self.vcmt, decimal=3)\n\n    def test_metadata(self):\n        for ifg in self.ifgs:\n            if not ifg.is_open:\n                ifg.open()\n            assert ifc.PYRATE_MAXVAR in ifg.meta_data\n            assert ifc.PYRATE_ALPHA in ifg.meta_data\n\n    def test_save_cvd_data(self):\n        from os.path import join, basename, isfile\n        for ifg in self.ifgs:\n            if not ifg.is_open:\n                ifg.open()\n            data_file = join(self.params[C.TMPDIR],\n                             'cvd_data_{b}.npy'.format(b=basename(ifg.data_path).split('.')[0]))\n            assert isfile(data_file)\n"
  },
  {
    "path": "tests/test_data/cropA/baseline_30",
    "content": "tests/test_data/cropA/geometry/20180106-20180130_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180106-20180319_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180106-20180412_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180106-20180518_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180130-20180307_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180130-20180412_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180307-20180319_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180307-20180331_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180307-20180506_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180307-20180530_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180307-20180611_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180319-20180331_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180319-20180506_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180319-20180518_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180319-20180530_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180319-20180623_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180331-20180412_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180331-20180506_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180331-20180518_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180331-20180530_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180331-20180623_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180331-20180717_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180412-20180506_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180412-20180518_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180506-20180518_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180506-20180530_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180506-20180611_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180506-20180623_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180506-20180705_VV_8rlks_base.par\ntests/test_data/cropA/geometry/20180506-20180717_VV_8rlks_base.par\n"
  },
  {
    "path": "tests/test_data/cropA/coherence_30",
    "content": "tests/test_data/cropA/geotiffs/cropA_20180106-20180130_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180106-20180319_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180106-20180412_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180106-20180518_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180130-20180307_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180130-20180412_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180319_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180331_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180506_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180530_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180611_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180331_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180506_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180518_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180530_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180623_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180412_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180506_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180518_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180530_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180623_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180717_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180412-20180506_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180412-20180518_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180518_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180530_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180611_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180623_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180705_VV_8rlks_flat_eqa_cc.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180717_VV_8rlks_flat_eqa_cc.tif\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180130_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.1536945       40.1145103        4.5090025   m   m   m\ninitial_baseline_rate:          0.0000000        0.0636107        0.0126167   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       40.1010426        4.5164084   m   m   m\nprecision_baseline_rate:        0.0000000        0.0703755        0.0082572   m/s m/s m/s\nunwrap_phase_constant:           -0.00001     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180130_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.154     40.115      4.509\norbit baseline rate   (TCN) (m/s):  0.000e+00  6.361e-02  1.262e-02\n\nbaseline vector (TCN)   (m):      0.000     40.101      4.516\nbaseline rate   (TCN) (m/s):  0.000e+00  7.038e-02  8.257e-03\n\nSLC-1 center baseline length (m):    40.3546\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    39.4441     4.4393    27.496918     22.1492     32.9386     39.6932\n     0    200     0.0000    39.4441     4.4393    27.939152     22.4028     32.7667     39.6932\n     0    400     0.0000    39.4441     4.4393    28.370753     22.6490     32.5970     39.6932\n     0    600     0.0000    39.4441     4.4393    28.792243     22.8881     32.4295     39.6932\n     0    800     0.0000    39.4441     4.4393    29.204102     23.1207     32.2642     39.6932\n     0   1000     0.0000    39.4441     4.4393    29.606772     23.3468     32.1009     39.6932\n     0   1200     0.0000    39.4441     4.4393    30.000665     23.5670     31.9396     39.6932\n     0   1400     0.0000    39.4441     4.4393    30.386160     23.7813     31.7803     39.6932\n     0   1600     0.0000    39.4441     4.4393    30.763612     23.9902     31.6230     39.6932\n     0   1800     0.0000    39.4441     4.4393    31.133349     24.1937     31.4675     39.6932\n     0   2000     0.0000    39.4441     4.4393    31.495679     24.3923     31.3139     39.6932\n     0   2200     0.0000    39.4441     4.4393    31.850890     24.5859     31.1621     39.6932\n     0   2400     0.0000    39.4441     4.4393    32.199251     24.7749     31.0120     39.6932\n     0   2600     0.0000    39.4441     4.4393    32.541017     24.9595     30.8637     39.6932\n     0   2800     0.0000    39.4441     4.4393    32.876424     25.1397     30.7170     39.6932\n     0   3000     0.0000    39.4441     4.4393    33.205696     25.3158     30.5721     39.6932\n     0   3200     0.0000    39.4441     4.4393    33.529047     25.4880     30.4287     39.6932\n     0   3400     0.0000    39.4441     4.4393    33.846674     25.6563     30.2869     39.6932\n     0   3600     0.0000    39.4441     4.4393    34.158768     25.8209     30.1467     39.6932\n     0   3800     0.0000    39.4441     4.4393    34.465506     25.9819     30.0081     39.6932\n     0   4000     0.0000    39.4441     4.4393    34.767059     26.1395     29.8709     39.6932\n     0   4200     0.0000    39.4441     4.4393    35.063588     26.2937     29.7352     39.6932\n     0   4400     0.0000    39.4441     4.4393    35.355245     26.4447     29.6010     39.6932\n     0   4600     0.0000    39.4441     4.4393    35.642177     26.5926     29.4682     39.6932\n     0   4800     0.0000    39.4441     4.4393    35.924522     26.7375     29.3368     39.6932\n     0   5000     0.0000    39.4441     4.4393    36.202412     26.8795     29.2068     39.6932\n     0   5200     0.0000    39.4441     4.4393    36.475974     27.0186     29.0781     39.6932\n     0   5400     0.0000    39.4441     4.4393    36.745327     27.1550     28.9508     39.6932\n     0   5600     0.0000    39.4441     4.4393    37.010588     27.2888     28.8247     39.6932\n     0   5800     0.0000    39.4441     4.4393    37.271866     27.4199     28.7000     39.6932\n     0   6000     0.0000    39.4441     4.4393    37.529266     27.5486     28.5765     39.6932\n     0   6200     0.0000    39.4441     4.4393    37.782890     27.6748     28.4543     39.6932\n     0   6400     0.0000    39.4441     4.4393    38.032835     27.7987     28.3333     39.6932\n     0   6600     0.0000    39.4441     4.4393    38.279194     27.9203     28.2135     39.6932\n     0   6800     0.0000    39.4441     4.4393    38.522056     28.0396     28.0949     39.6932\n     0   7000     0.0000    39.4441     4.4393    38.761507     28.1568     27.9775     39.6932\n     0   7200     0.0000    39.4441     4.4393    38.997629     28.2718     27.8612     39.6932\n     0   7400     0.0000    39.4441     4.4393    39.230502     28.3848     27.7461     39.6932\n     0   7600     0.0000    39.4441     4.4393    39.460203     28.4958     27.6321     39.6932\n     0   7800     0.0000    39.4441     4.4393    39.686804     28.6049     27.5192     39.6932\n     0   8000     0.0000    39.4441     4.4393    39.910378     28.7121     27.4073     39.6932\n     0   8200     0.0000    39.4441     4.4393    40.130991     28.8174     27.2966     39.6932\n     0   8400     0.0000    39.4441     4.4393    40.348712     28.9209     27.1869     39.6932\n   500      0     0.0000    39.5888     4.4563    27.495955     22.2305     33.0595     39.8388\n   500    200     0.0000    39.5888     4.4563    27.938206     22.4850     32.8869     39.8388\n   500    400     0.0000    39.5888     4.4563    28.369822     22.7321     32.7166     39.8388\n   500    600     0.0000    39.5888     4.4563    28.791327     22.9722     32.5485     39.8388\n   500    800     0.0000    39.5888     4.4563    29.203199     23.2056     32.3825     39.8388\n   500   1000     0.0000    39.5888     4.4563    29.605883     23.4326     32.2186     39.8388\n   500   1200     0.0000    39.5888     4.4563    29.999788     23.6535     32.0568     39.8388\n   500   1400     0.0000    39.5888     4.4563    30.385296     23.8687     31.8969     39.8388\n   500   1600     0.0000    39.5888     4.4563    30.762759     24.0783     31.7390     39.8388\n   500   1800     0.0000    39.5888     4.4563    31.132507     24.2826     31.5829     39.8388\n   500   2000     0.0000    39.5888     4.4563    31.494848     24.4819     31.4287     39.8388\n   500   2200     0.0000    39.5888     4.4563    31.850069     24.6762     31.2763     39.8388\n   500   2400     0.0000    39.5888     4.4563    32.198441     24.8659     31.1257     39.8388\n   500   2600     0.0000    39.5888     4.4563    32.540215     25.0512     30.9768     39.8388\n   500   2800     0.0000    39.5888     4.4563    32.875632     25.2321     30.8297     39.8388\n   500   3000     0.0000    39.5888     4.4563    33.204913     25.4088     30.6841     39.8388\n   500   3200     0.0000    39.5888     4.4563    33.528272     25.5816     30.5403     39.8388\n   500   3400     0.0000    39.5888     4.4563    33.845908     25.7505     30.3980     39.8388\n   500   3600     0.0000    39.5888     4.4563    34.158010     25.9157     30.2573     39.8388\n   500   3800     0.0000    39.5888     4.4563    34.464756     26.0773     30.1181     39.8388\n   500   4000     0.0000    39.5888     4.4563    34.766316     26.2355     29.9804     39.8388\n   500   4200     0.0000    39.5888     4.4563    35.062852     26.3903     29.8442     39.8388\n   500   4400     0.0000    39.5888     4.4563    35.354517     26.5419     29.7095     39.8388\n   500   4600     0.0000    39.5888     4.4563    35.641456     26.6903     29.5762     39.8388\n   500   4800     0.0000    39.5888     4.4563    35.923807     26.8358     29.4443     39.8388\n   500   5000     0.0000    39.5888     4.4563    36.201704     26.9783     29.3138     39.8388\n   500   5200     0.0000    39.5888     4.4563    36.475272     27.1179     29.1847     39.8388\n   500   5400     0.0000    39.5888     4.4563    36.744632     27.2548     29.0569     39.8388\n   500   5600     0.0000    39.5888     4.4563    37.009898     27.3891     28.9304     39.8388\n   500   5800     0.0000    39.5888     4.4563    37.271182     27.5207     28.8052     39.8388\n   500   6000     0.0000    39.5888     4.4563    37.528588     27.6498     28.6812     39.8388\n   500   6200     0.0000    39.5888     4.4563    37.782218     27.7765     28.5586     39.8388\n   500   6400     0.0000    39.5888     4.4563    38.032168     27.9009     28.4371     39.8388\n   500   6600     0.0000    39.5888     4.4563    38.278532     28.0229     28.3169     39.8388\n   500   6800     0.0000    39.5888     4.4563    38.521399     28.1427     28.1979     39.8388\n   500   7000     0.0000    39.5888     4.4563    38.760855     28.2603     28.0800     39.8388\n   500   7200     0.0000    39.5888     4.4563    38.996982     28.3757     27.9633     39.8388\n   500   7400     0.0000    39.5888     4.4563    39.229860     28.4892     27.8477     39.8388\n   500   7600     0.0000    39.5888     4.4563    39.459566     28.6006     27.7333     39.8388\n   500   7800     0.0000    39.5888     4.4563    39.686172     28.7100     27.6200     39.8388\n   500   8000     0.0000    39.5888     4.4563    39.909750     28.8176     27.5077     39.8388\n   500   8200     0.0000    39.5888     4.4563    40.130368     28.9233     27.3965     39.8388\n   500   8400     0.0000    39.5888     4.4563    40.348092     29.0272     27.2864     39.8388\n  1000      0     0.0000    39.7335     4.4733    27.494972     22.3118     33.1803     39.9845\n  1000    200     0.0000    39.7335     4.4733    27.937239     22.5672     33.0071     39.9845\n  1000    400     0.0000    39.7335     4.4733    28.368871     22.8152     32.8362     39.9845\n  1000    600     0.0000    39.7335     4.4733    28.790391     23.0562     32.6675     39.9845\n  1000    800     0.0000    39.7335     4.4733    29.202277     23.2904     32.5009     39.9845\n  1000   1000     0.0000    39.7335     4.4733    29.604975     23.5183     32.3364     39.9845\n  1000   1200     0.0000    39.7335     4.4733    29.998893     23.7400     32.1739     39.9845\n  1000   1400     0.0000    39.7335     4.4733    30.384413     23.9560     32.0135     39.9845\n  1000   1600     0.0000    39.7335     4.4733    30.761888     24.1664     31.8550     39.9845\n  1000   1800     0.0000    39.7335     4.4733    31.131648     24.3715     31.6983     39.9845\n  1000   2000     0.0000    39.7335     4.4733    31.494000     24.5714     31.5436     39.9845\n  1000   2200     0.0000    39.7335     4.4733    31.849231     24.7665     31.3906     39.9845\n  1000   2400     0.0000    39.7335     4.4733    32.197613     24.9569     31.2395     39.9845\n  1000   2600     0.0000    39.7335     4.4733    32.539398     25.1428     31.0900     39.9845\n  1000   2800     0.0000    39.7335     4.4733    32.874823     25.3244     30.9423     39.9845\n  1000   3000     0.0000    39.7335     4.4733    33.204114     25.5018     30.7962     39.9845\n  1000   3200     0.0000    39.7335     4.4733    33.527482     25.6752     30.6518     39.9845\n  1000   3400     0.0000    39.7335     4.4733    33.845126     25.8448     30.5090     39.9845\n  1000   3600     0.0000    39.7335     4.4733    34.157236     26.0106     30.3678     39.9845\n  1000   3800     0.0000    39.7335     4.4733    34.463990     26.1728     30.2281     39.9845\n  1000   4000     0.0000    39.7335     4.4733    34.765558     26.3315     30.0899     39.9845\n  1000   4200     0.0000    39.7335     4.4733    35.062102     26.4869     29.9532     39.9845\n  1000   4400     0.0000    39.7335     4.4733    35.353774     26.6391     29.8180     39.9845\n  1000   4600     0.0000    39.7335     4.4733    35.640720     26.7881     29.6842     39.9845\n  1000   4800     0.0000    39.7335     4.4733    35.923078     26.9340     29.5519     39.9845\n  1000   5000     0.0000    39.7335     4.4733    36.200982     27.0770     29.4209     39.9845\n  1000   5200     0.0000    39.7335     4.4733    36.474556     27.2172     29.2912     39.9845\n  1000   5400     0.0000    39.7335     4.4733    36.743922     27.3546     29.1630     39.9845\n  1000   5600     0.0000    39.7335     4.4733    37.009195     27.4893     29.0360     39.9845\n  1000   5800     0.0000    39.7335     4.4733    37.270484     27.6215     28.9103     39.9845\n  1000   6000     0.0000    39.7335     4.4733    37.527896     27.7511     28.7860     39.9845\n  1000   6200     0.0000    39.7335     4.4733    37.781532     27.8782     28.6628     39.9845\n  1000   6400     0.0000    39.7335     4.4733    38.031488     28.0030     28.5409     39.9845\n  1000   6600     0.0000    39.7335     4.4733    38.277857     28.1255     28.4203     39.9845\n  1000   6800     0.0000    39.7335     4.4733    38.520730     28.2457     28.3008     39.9845\n  1000   7000     0.0000    39.7335     4.4733    38.760191     28.3637     28.1825     39.9845\n  1000   7200     0.0000    39.7335     4.4733    38.996323     28.4796     28.0654     39.9845\n  1000   7400     0.0000    39.7335     4.4733    39.229206     28.5935     27.9494     39.9845\n  1000   7600     0.0000    39.7335     4.4733    39.458916     28.7053     27.8345     39.9845\n  1000   7800     0.0000    39.7335     4.4733    39.685527     28.8152     27.7208     39.9845\n  1000   8000     0.0000    39.7335     4.4733    39.909109     28.9231     27.6081     39.9845\n  1000   8200     0.0000    39.7335     4.4733    40.129732     29.0292     27.4965     39.9845\n  1000   8400     0.0000    39.7335     4.4733    40.347461     29.1335     27.3860     39.9845\n  1500      0     0.0000    39.8781     4.4903    27.493968     22.3930     33.3012     40.1301\n  1500    200     0.0000    39.8781     4.4903    27.936252     22.6494     33.1274     40.1301\n  1500    400     0.0000    39.8781     4.4903    28.367900     22.8983     32.9558     40.1301\n  1500    600     0.0000    39.8781     4.4903    28.789435     23.1402     32.7865     40.1301\n  1500    800     0.0000    39.8781     4.4903    29.201336     23.3753     32.6193     40.1301\n  1500   1000     0.0000    39.8781     4.4903    29.604048     23.6040     32.4542     40.1301\n  1500   1200     0.0000    39.8781     4.4903    29.997980     23.8266     32.2911     40.1301\n  1500   1400     0.0000    39.8781     4.4903    30.383512     24.0433     32.1301     40.1301\n  1500   1600     0.0000    39.8781     4.4903    30.760999     24.2545     31.9710     40.1301\n  1500   1800     0.0000    39.8781     4.4903    31.130771     24.4603     31.8138     40.1301\n  1500   2000     0.0000    39.8781     4.4903    31.493134     24.6610     31.6584     40.1301\n  1500   2200     0.0000    39.8781     4.4903    31.848377     24.8568     31.5049     40.1301\n  1500   2400     0.0000    39.8781     4.4903    32.196769     25.0479     31.3532     40.1301\n  1500   2600     0.0000    39.8781     4.4903    32.538563     25.2345     31.2032     40.1301\n  1500   2800     0.0000    39.8781     4.4903    32.873999     25.4168     31.0550     40.1301\n  1500   3000     0.0000    39.8781     4.4903    33.203299     25.5948     30.9084     40.1301\n  1500   3200     0.0000    39.8781     4.4903    33.526676     25.7689     30.7634     40.1301\n  1500   3400     0.0000    39.8781     4.4903    33.844329     25.9390     30.6201     40.1301\n  1500   3600     0.0000    39.8781     4.4903    34.156447     26.1054     30.4783     40.1301\n  1500   3800     0.0000    39.8781     4.4903    34.463209     26.2682     30.3381     40.1301\n  1500   4000     0.0000    39.8781     4.4903    34.764785     26.4276     30.1994     40.1301\n  1500   4200     0.0000    39.8781     4.4903    35.061337     26.5835     30.0622     40.1301\n  1500   4400     0.0000    39.8781     4.4903    35.353016     26.7362     29.9265     40.1301\n  1500   4600     0.0000    39.8781     4.4903    35.639969     26.8858     29.7923     40.1301\n  1500   4800     0.0000    39.8781     4.4903    35.922335     27.0322     29.6594     40.1301\n  1500   5000     0.0000    39.8781     4.4903    36.200245     27.1758     29.5279     40.1301\n  1500   5200     0.0000    39.8781     4.4903    36.473826     27.3165     29.3978     40.1301\n  1500   5400     0.0000    39.8781     4.4903    36.743198     27.4544     29.2691     40.1301\n  1500   5600     0.0000    39.8781     4.4903    37.008477     27.5896     29.1417     40.1301\n  1500   5800     0.0000    39.8781     4.4903    37.269773     27.7222     29.0155     40.1301\n  1500   6000     0.0000    39.8781     4.4903    37.527191     27.8523     28.8907     40.1301\n  1500   6200     0.0000    39.8781     4.4903    37.780832     27.9799     28.7671     40.1301\n  1500   6400     0.0000    39.8781     4.4903    38.030794     28.1052     28.6448     40.1301\n  1500   6600     0.0000    39.8781     4.4903    38.277169     28.2281     28.5237     40.1301\n  1500   6800     0.0000    39.8781     4.4903    38.520047     28.3487     28.4037     40.1301\n  1500   7000     0.0000    39.8781     4.4903    38.759513     28.4672     28.2850     40.1301\n  1500   7200     0.0000    39.8781     4.4903    38.995651     28.5835     28.1674     40.1301\n  1500   7400     0.0000    39.8781     4.4903    39.228539     28.6978     28.0510     40.1301\n  1500   7600     0.0000    39.8781     4.4903    39.458254     28.8100     27.9357     40.1301\n  1500   7800     0.0000    39.8781     4.4903    39.684869     28.9203     27.8216     40.1301\n  1500   8000     0.0000    39.8781     4.4903    39.908457     29.0286     27.7085     40.1301\n  1500   8200     0.0000    39.8781     4.4903    40.129084     29.1351     27.5965     40.1301\n  1500   8400     0.0000    39.8781     4.4903    40.346818     29.2398     27.4856     40.1301\n  2000      0     0.0000    40.0228     4.5072    27.492944     22.4743     33.4221     40.2758\n  2000    200     0.0000    40.0228     4.5072    27.935245     22.7316     33.2476     40.2758\n  2000    400     0.0000    40.0228     4.5072    28.366910     22.9814     33.0754     40.2758\n  2000    600     0.0000    40.0228     4.5072    28.788460     23.2242     32.9055     40.2758\n  2000    800     0.0000    40.0228     4.5072    29.200376     23.4601     32.7376     40.2758\n  2000   1000     0.0000    40.0228     4.5072    29.603102     23.6897     32.5719     40.2758\n  2000   1200     0.0000    40.0228     4.5072    29.997048     23.9131     32.4083     40.2758\n  2000   1400     0.0000    40.0228     4.5072    30.382593     24.1306     32.2466     40.2758\n  2000   1600     0.0000    40.0228     4.5072    30.760093     24.3425     32.0870     40.2758\n  2000   1800     0.0000    40.0228     4.5072    31.129876     24.5491     31.9292     40.2758\n  2000   2000     0.0000    40.0228     4.5072    31.492251     24.7506     31.7733     40.2758\n  2000   2200     0.0000    40.0228     4.5072    31.847505     24.9471     31.6192     40.2758\n  2000   2400     0.0000    40.0228     4.5072    32.195907     25.1389     31.4669     40.2758\n  2000   2600     0.0000    40.0228     4.5072    32.537712     25.3262     31.3164     40.2758\n  2000   2800     0.0000    40.0228     4.5072    32.873158     25.5091     31.1676     40.2758\n  2000   3000     0.0000    40.0228     4.5072    33.202467     25.6878     31.0205     40.2758\n  2000   3200     0.0000    40.0228     4.5072    33.525853     25.8625     30.8750     40.2758\n  2000   3400     0.0000    40.0228     4.5072    33.843515     26.0332     30.7311     40.2758\n  2000   3600     0.0000    40.0228     4.5072    34.155642     26.2003     30.5889     40.2758\n  2000   3800     0.0000    40.0228     4.5072    34.462413     26.3637     30.4482     40.2758\n  2000   4000     0.0000    40.0228     4.5072    34.763997     26.5236     30.3090     40.2758\n  2000   4200     0.0000    40.0228     4.5072    35.060556     26.6801     30.1713     40.2758\n  2000   4400     0.0000    40.0228     4.5072    35.352243     26.8333     30.0351     40.2758\n  2000   4600     0.0000    40.0228     4.5072    35.639204     26.9834     29.9003     40.2758\n  2000   4800     0.0000    40.0228     4.5072    35.921577     27.1305     29.7669     40.2758\n  2000   5000     0.0000    40.0228     4.5072    36.199494     27.2745     29.6350     40.2758\n  2000   5200     0.0000    40.0228     4.5072    36.473082     27.4157     29.5044     40.2758\n  2000   5400     0.0000    40.0228     4.5072    36.742461     27.5541     29.3752     40.2758\n  2000   5600     0.0000    40.0228     4.5072    37.007746     27.6899     29.2473     40.2758\n  2000   5800     0.0000    40.0228     4.5072    37.269048     27.8230     29.1207     40.2758\n  2000   6000     0.0000    40.0228     4.5072    37.526472     27.9535     28.9954     40.2758\n  2000   6200     0.0000    40.0228     4.5072    37.780120     28.0816     28.8714     40.2758\n  2000   6400     0.0000    40.0228     4.5072    38.030087     28.2073     28.7486     40.2758\n  2000   6600     0.0000    40.0228     4.5072    38.276468     28.3307     28.6270     40.2758\n  2000   6800     0.0000    40.0228     4.5072    38.519351     28.4518     28.5067     40.2758\n  2000   7000     0.0000    40.0228     4.5072    38.758823     28.5707     28.3875     40.2758\n  2000   7200     0.0000    40.0228     4.5072    38.994966     28.6874     28.2695     40.2758\n  2000   7400     0.0000    40.0228     4.5072    39.227859     28.8021     28.1527     40.2758\n  2000   7600     0.0000    40.0228     4.5072    39.457579     28.9147     28.0370     40.2758\n  2000   7800     0.0000    40.0228     4.5072    39.684200     29.0254     27.9224     40.2758\n  2000   8000     0.0000    40.0228     4.5072    39.907792     29.1341     27.8089     40.2758\n  2000   8200     0.0000    40.0228     4.5072    40.128424     29.2410     27.6965     40.2758\n  2000   8400     0.0000    40.0228     4.5072    40.346162     29.3461     27.5852     40.2758\n  2500      0     0.0000    40.1674     4.5242    27.491899     22.5555     33.5430     40.4214\n  2500    200     0.0000    40.1674     4.5242    27.934218     22.8138     33.3679     40.4214\n  2500    400     0.0000    40.1674     4.5242    28.365900     23.0645     33.1951     40.4214\n  2500    600     0.0000    40.1674     4.5242    28.787466     23.3081     33.0245     40.4214\n  2500    800     0.0000    40.1674     4.5242    29.199398     23.5450     32.8560     40.4214\n  2500   1000     0.0000    40.1674     4.5242    29.602138     23.7753     32.6897     40.4214\n  2500   1200     0.0000    40.1674     4.5242    29.996097     23.9995     32.5255     40.4214\n  2500   1400     0.0000    40.1674     4.5242    30.381656     24.2179     32.3633     40.4214\n  2500   1600     0.0000    40.1674     4.5242    30.759169     24.4306     32.2030     40.4214\n  2500   1800     0.0000    40.1674     4.5242    31.128964     24.6379     32.0446     40.4214\n  2500   2000     0.0000    40.1674     4.5242    31.491351     24.8401     31.8882     40.4214\n  2500   2200     0.0000    40.1674     4.5242    31.846616     25.0373     31.7335     40.4214\n  2500   2400     0.0000    40.1674     4.5242    32.195030     25.2298     31.5807     40.4214\n  2500   2600     0.0000    40.1674     4.5242    32.536845     25.4178     31.4296     40.4214\n  2500   2800     0.0000    40.1674     4.5242    32.872300     25.6014     31.2803     40.4214\n  2500   3000     0.0000    40.1674     4.5242    33.201620     25.7807     31.1326     40.4214\n  2500   3200     0.0000    40.1674     4.5242    33.525015     25.9561     30.9866     40.4214\n  2500   3400     0.0000    40.1674     4.5242    33.842686     26.1275     30.8422     40.4214\n  2500   3600     0.0000    40.1674     4.5242    34.154822     26.2951     30.6994     40.4214\n  2500   3800     0.0000    40.1674     4.5242    34.461601     26.4591     30.5582     40.4214\n  2500   4000     0.0000    40.1674     4.5242    34.763194     26.6196     30.4185     40.4214\n  2500   4200     0.0000    40.1674     4.5242    35.059761     26.7767     30.2803     40.4214\n  2500   4400     0.0000    40.1674     4.5242    35.351456     26.9305     30.1436     40.4214\n  2500   4600     0.0000    40.1674     4.5242    35.638424     27.0811     30.0083     40.4214\n  2500   4800     0.0000    40.1674     4.5242    35.920804     27.2287     29.8745     40.4214\n  2500   5000     0.0000    40.1674     4.5242    36.198729     27.3733     29.7421     40.4214\n  2500   5200     0.0000    40.1674     4.5242    36.472323     27.5150     29.6110     40.4214\n  2500   5400     0.0000    40.1674     4.5242    36.741709     27.6539     29.4813     40.4214\n  2500   5600     0.0000    40.1674     4.5242    37.007001     27.7901     29.3530     40.4214\n  2500   5800     0.0000    40.1674     4.5242    37.268310     27.9237     29.2259     40.4214\n  2500   6000     0.0000    40.1674     4.5242    37.525740     28.0547     29.1002     40.4214\n  2500   6200     0.0000    40.1674     4.5242    37.779394     28.1833     28.9757     40.4214\n  2500   6400     0.0000    40.1674     4.5242    38.029367     28.3094     28.8525     40.4214\n  2500   6600     0.0000    40.1674     4.5242    38.275754     28.4332     28.7305     40.4214\n  2500   6800     0.0000    40.1674     4.5242    38.518643     28.5548     28.6097     40.4214\n  2500   7000     0.0000    40.1674     4.5242    38.758120     28.6741     28.4901     40.4214\n  2500   7200     0.0000    40.1674     4.5242    38.994268     28.7913     28.3716     40.4214\n  2500   7400     0.0000    40.1674     4.5242    39.227167     28.9064     28.2544     40.4214\n  2500   7600     0.0000    40.1674     4.5242    39.456892     29.0194     28.1382     40.4214\n  2500   7800     0.0000    40.1674     4.5242    39.683518     29.1305     28.0232     40.4214\n  2500   8000     0.0000    40.1674     4.5242    39.907115     29.2396     27.9094     40.4214\n  2500   8200     0.0000    40.1674     4.5242    40.127752     29.3469     27.7965     40.4214\n  2500   8400     0.0000    40.1674     4.5242    40.345495     29.4523     27.6848     40.4214\n  3000      0     0.0000    40.3121     4.5412    27.490833     22.6367     33.6639     40.5671\n  3000    200     0.0000    40.3121     4.5412    27.933171     22.8959     33.4882     40.5671\n  3000    400     0.0000    40.3121     4.5412    28.364870     23.1476     33.3147     40.5671\n  3000    600     0.0000    40.3121     4.5412    28.786452     23.3921     33.1435     40.5671\n  3000    800     0.0000    40.3121     4.5412    29.198400     23.6298     32.9745     40.5671\n  3000   1000     0.0000    40.3121     4.5412    29.601155     23.8610     32.8075     40.5671\n  3000   1200     0.0000    40.3121     4.5412    29.995129     24.0860     32.6427     40.5671\n  3000   1400     0.0000    40.3121     4.5412    30.380701     24.3051     32.4799     40.5671\n  3000   1600     0.0000    40.3121     4.5412    30.758227     24.5186     32.3190     40.5671\n  3000   1800     0.0000    40.3121     4.5412    31.128035     24.7267     32.1601     40.5671\n  3000   2000     0.0000    40.3121     4.5412    31.490433     24.9296     32.0031     40.5671\n  3000   2200     0.0000    40.3121     4.5412    31.845710     25.1276     31.8479     40.5671\n  3000   2400     0.0000    40.3121     4.5412    32.194135     25.3208     31.6945     40.5671\n  3000   2600     0.0000    40.3121     4.5412    32.535961     25.5094     31.5428     40.5671\n  3000   2800     0.0000    40.3121     4.5412    32.871427     25.6937     31.3930     40.5671\n  3000   3000     0.0000    40.3121     4.5412    33.200756     25.8737     31.2448     40.5671\n  3000   3200     0.0000    40.3121     4.5412    33.524162     26.0496     31.0982     40.5671\n  3000   3400     0.0000    40.3121     4.5412    33.841842     26.2217     30.9533     40.5671\n  3000   3600     0.0000    40.3121     4.5412    34.153987     26.3899     30.8100     40.5671\n  3000   3800     0.0000    40.3121     4.5412    34.460775     26.5545     30.6682     40.5671\n  3000   4000     0.0000    40.3121     4.5412    34.762376     26.7156     30.5280     40.5671\n  3000   4200     0.0000    40.3121     4.5412    35.058951     26.8732     30.3893     40.5671\n  3000   4400     0.0000    40.3121     4.5412    35.350654     27.0276     30.2521     40.5671\n  3000   4600     0.0000    40.3121     4.5412    35.637630     27.1788     30.1164     40.5671\n  3000   4800     0.0000    40.3121     4.5412    35.920018     27.3269     29.9821     40.5671\n  3000   5000     0.0000    40.3121     4.5412    36.197949     27.4720     29.8492     40.5671\n  3000   5200     0.0000    40.3121     4.5412    36.471551     27.6142     29.7176     40.5671\n  3000   5400     0.0000    40.3121     4.5412    36.740944     27.7536     29.5875     40.5671\n  3000   5600     0.0000    40.3121     4.5412    37.006243     27.8903     29.4586     40.5671\n  3000   5800     0.0000    40.3121     4.5412    37.267558     28.0244     29.3311     40.5671\n  3000   6000     0.0000    40.3121     4.5412    37.524995     28.1559     29.2049     40.5671\n  3000   6200     0.0000    40.3121     4.5412    37.778654     28.2849     29.0800     40.5671\n  3000   6400     0.0000    40.3121     4.5412    38.028634     28.4115     28.9563     40.5671\n  3000   6600     0.0000    40.3121     4.5412    38.275026     28.5358     28.8339     40.5671\n  3000   6800     0.0000    40.3121     4.5412    38.517921     28.6578     28.7126     40.5671\n  3000   7000     0.0000    40.3121     4.5412    38.757404     28.7775     28.5926     40.5671\n  3000   7200     0.0000    40.3121     4.5412    38.993558     28.8952     28.4737     40.5671\n  3000   7400     0.0000    40.3121     4.5412    39.226462     29.0107     28.3561     40.5671\n  3000   7600     0.0000    40.3121     4.5412    39.456192     29.1241     28.2395     40.5671\n  3000   7800     0.0000    40.3121     4.5412    39.682823     29.2356     28.1241     40.5671\n  3000   8000     0.0000    40.3121     4.5412    39.906425     29.3451     28.0098     40.5671\n  3000   8200     0.0000    40.3121     4.5412    40.127067     29.4528     27.8966     40.5671\n  3000   8400     0.0000    40.3121     4.5412    40.344815     29.5586     27.7844     40.5671\n  3500      0     0.0000    40.4568     4.5581    27.489747     22.7179     33.7848     40.7127\n  3500    200     0.0000    40.4568     4.5581    27.932104     22.9780     33.6084     40.7127\n  3500    400     0.0000    40.4568     4.5581    28.363820     23.2306     33.4344     40.7127\n  3500    600     0.0000    40.4568     4.5581    28.785420     23.4760     33.2625     40.7127\n  3500    800     0.0000    40.4568     4.5581    29.197383     23.7146     33.0929     40.7127\n  3500   1000     0.0000    40.4568     4.5581    29.600153     23.9466     32.9254     40.7127\n  3500   1200     0.0000    40.4568     4.5581    29.994142     24.1725     32.7599     40.7127\n  3500   1400     0.0000    40.4568     4.5581    30.379728     24.3924     32.5965     40.7127\n  3500   1600     0.0000    40.4568     4.5581    30.757267     24.6066     32.4351     40.7127\n  3500   1800     0.0000    40.4568     4.5581    31.127088     24.8155     32.2756     40.7127\n  3500   2000     0.0000    40.4568     4.5581    31.489499     25.0191     32.1180     40.7127\n  3500   2200     0.0000    40.4568     4.5581    31.844788     25.2178     31.9622     40.7127\n  3500   2400     0.0000    40.4568     4.5581    32.193224     25.4117     31.8083     40.7127\n  3500   2600     0.0000    40.4568     4.5581    32.535061     25.6010     31.6561     40.7127\n  3500   2800     0.0000    40.4568     4.5581    32.870537     25.7860     31.5056     40.7127\n  3500   3000     0.0000    40.4568     4.5581    33.199877     25.9666     31.3569     40.7127\n  3500   3200     0.0000    40.4568     4.5581    33.523292     26.1432     31.2098     40.7127\n  3500   3400     0.0000    40.4568     4.5581    33.840982     26.3159     31.0644     40.7127\n  3500   3600     0.0000    40.4568     4.5581    34.153136     26.4847     30.9206     40.7127\n  3500   3800     0.0000    40.4568     4.5581    34.459933     26.6499     30.7783     40.7127\n  3500   4000     0.0000    40.4568     4.5581    34.761543     26.8115     30.6376     40.7127\n  3500   4200     0.0000    40.4568     4.5581    35.058127     26.9698     30.4984     40.7127\n  3500   4400     0.0000    40.4568     4.5581    35.349838     27.1247     30.3607     40.7127\n  3500   4600     0.0000    40.4568     4.5581    35.636821     27.2764     30.2245     40.7127\n  3500   4800     0.0000    40.4568     4.5581    35.919217     27.4251     30.0896     40.7127\n  3500   5000     0.0000    40.4568     4.5581    36.197156     27.5707     29.9563     40.7127\n  3500   5200     0.0000    40.4568     4.5581    36.470765     27.7135     29.8243     40.7127\n  3500   5400     0.0000    40.4568     4.5581    36.740165     27.8534     29.6936     40.7127\n  3500   5600     0.0000    40.4568     4.5581    37.005471     27.9906     29.5643     40.7127\n  3500   5800     0.0000    40.4568     4.5581    37.266792     28.1251     29.4364     40.7127\n  3500   6000     0.0000    40.4568     4.5581    37.524236     28.2571     29.3097     40.7127\n  3500   6200     0.0000    40.4568     4.5581    37.777902     28.3866     29.1843     40.7127\n  3500   6400     0.0000    40.4568     4.5581    38.027888     28.5137     29.0602     40.7127\n  3500   6600     0.0000    40.4568     4.5581    38.274286     28.6384     28.9373     40.7127\n  3500   6800     0.0000    40.4568     4.5581    38.517187     28.7608     28.8156     40.7127\n  3500   7000     0.0000    40.4568     4.5581    38.756676     28.8810     28.6951     40.7127\n  3500   7200     0.0000    40.4568     4.5581    38.992835     28.9990     28.5759     40.7127\n  3500   7400     0.0000    40.4568     4.5581    39.225745     29.1149     28.4577     40.7127\n  3500   7600     0.0000    40.4568     4.5581    39.455480     29.2288     28.3408     40.7127\n  3500   7800     0.0000    40.4568     4.5581    39.682117     29.3407     28.2249     40.7127\n  3500   8000     0.0000    40.4568     4.5581    39.905724     29.4506     28.1102     40.7127\n  3500   8200     0.0000    40.4568     4.5581    40.126371     29.5586     27.9966     40.7127\n  3500   8400     0.0000    40.4568     4.5581    40.344124     29.6648     27.8841     40.7127\n  4000      0     0.0000    40.6014     4.5751    27.488641     22.7991     33.9058     40.8584\n  4000    200     0.0000    40.6014     4.5751    27.931016     23.0602     33.7287     40.8584\n  4000    400     0.0000    40.6014     4.5751    28.362751     23.3137     33.5540     40.8584\n  4000    600     0.0000    40.6014     4.5751    28.784368     23.5599     33.3816     40.8584\n  4000    800     0.0000    40.6014     4.5751    29.196347     23.7994     33.2113     40.8584\n  4000   1000     0.0000    40.6014     4.5751    29.599133     24.0322     33.0432     40.8584\n  4000   1200     0.0000    40.6014     4.5751    29.993137     24.2589     32.8771     40.8584\n  4000   1400     0.0000    40.6014     4.5751    30.378737     24.4796     32.7131     40.8584\n  4000   1600     0.0000    40.6014     4.5751    30.756290     24.6946     32.5511     40.8584\n  4000   1800     0.0000    40.6014     4.5751    31.126124     24.9042     32.3910     40.8584\n  4000   2000     0.0000    40.6014     4.5751    31.488547     25.1086     32.2329     40.8584\n  4000   2200     0.0000    40.6014     4.5751    31.843848     25.3080     32.0765     40.8584\n  4000   2400     0.0000    40.6014     4.5751    32.192296     25.5026     31.9220     40.8584\n  4000   2600     0.0000    40.6014     4.5751    32.534144     25.6926     31.7693     40.8584\n  4000   2800     0.0000    40.6014     4.5751    32.869631     25.8782     31.6183     40.8584\n  4000   3000     0.0000    40.6014     4.5751    33.198982     26.0595     31.4691     40.8584\n  4000   3200     0.0000    40.6014     4.5751    33.522407     26.2368     31.3215     40.8584\n  4000   3400     0.0000    40.6014     4.5751    33.840106     26.4100     31.1755     40.8584\n  4000   3600     0.0000    40.6014     4.5751    34.152270     26.5795     31.0311     40.8584\n  4000   3800     0.0000    40.6014     4.5751    34.459076     26.7453     30.8884     40.8584\n  4000   4000     0.0000    40.6014     4.5751    34.760695     26.9075     30.7472     40.8584\n  4000   4200     0.0000    40.6014     4.5751    35.057287     27.0663     30.6075     40.8584\n  4000   4400     0.0000    40.6014     4.5751    35.349006     27.2218     30.4693     40.8584\n  4000   4600     0.0000    40.6014     4.5751    35.635998     27.3741     30.3325     40.8584\n  4000   4800     0.0000    40.6014     4.5751    35.918401     27.5233     30.1972     40.8584\n  4000   5000     0.0000    40.6014     4.5751    36.196348     27.6694     30.0634     40.8584\n  4000   5200     0.0000    40.6014     4.5751    36.469965     27.8127     29.9309     40.8584\n  4000   5400     0.0000    40.6014     4.5751    36.739372     27.9531     29.7998     40.8584\n  4000   5600     0.0000    40.6014     4.5751    37.004685     28.0908     29.6700     40.8584\n  4000   5800     0.0000    40.6014     4.5751    37.266013     28.2258     29.5416     40.8584\n  4000   6000     0.0000    40.6014     4.5751    37.523463     28.3583     29.4145     40.8584\n  4000   6200     0.0000    40.6014     4.5751    37.777136     28.4882     29.2886     40.8584\n  4000   6400     0.0000    40.6014     4.5751    38.027128     28.6158     29.1640     40.8584\n  4000   6600     0.0000    40.6014     4.5751    38.273533     28.7409     29.0407     40.8584\n  4000   6800     0.0000    40.6014     4.5751    38.516440     28.8638     28.9186     40.8584\n  4000   7000     0.0000    40.6014     4.5751    38.755934     28.9844     28.7977     40.8584\n  4000   7200     0.0000    40.6014     4.5751    38.992100     29.1029     28.6780     40.8584\n  4000   7400     0.0000    40.6014     4.5751    39.225015     29.2192     28.5594     40.8584\n  4000   7600     0.0000    40.6014     4.5751    39.454756     29.3335     28.4421     40.8584\n  4000   7800     0.0000    40.6014     4.5751    39.681398     29.4458     28.3258     40.8584\n  4000   8000     0.0000    40.6014     4.5751    39.905010     29.5561     28.2107     40.8584\n  4000   8200     0.0000    40.6014     4.5751    40.125663     29.6645     28.0966     40.8584\n  4000   8400     0.0000    40.6014     4.5751    40.343421     29.7711     27.9837     40.8584\n  4500      0     0.0000    40.7461     4.5921    27.487515     22.8802     34.0267     41.0040\n  4500    200     0.0000    40.7461     4.5921    27.929909     23.1423     33.8490     41.0040\n  4500    400     0.0000    40.7461     4.5921    28.361662     23.3967     33.6737     41.0040\n  4500    600     0.0000    40.7461     4.5921    28.783297     23.6438     33.5006     41.0040\n  4500    800     0.0000    40.7461     4.5921    29.195293     23.8841     33.3297     41.0040\n  4500   1000     0.0000    40.7461     4.5921    29.598095     24.1179     33.1610     41.0040\n  4500   1200     0.0000    40.7461     4.5921    29.992113     24.3453     32.9944     41.0040\n  4500   1400     0.0000    40.7461     4.5921    30.377729     24.5668     32.8298     41.0040\n  4500   1600     0.0000    40.7461     4.5921    30.755295     24.7826     32.6672     41.0040\n  4500   1800     0.0000    40.7461     4.5921    31.125143     24.9930     32.5065     41.0040\n  4500   2000     0.0000    40.7461     4.5921    31.487579     25.1981     32.3478     41.0040\n  4500   2200     0.0000    40.7461     4.5921    31.842892     25.3982     32.1909     41.0040\n  4500   2400     0.0000    40.7461     4.5921    32.191352     25.5935     32.0358     41.0040\n  4500   2600     0.0000    40.7461     4.5921    32.533211     25.7842     31.8826     41.0040\n  4500   2800     0.0000    40.7461     4.5921    32.868710     25.9705     31.7310     41.0040\n  4500   3000     0.0000    40.7461     4.5921    33.198070     26.1525     31.5812     41.0040\n  4500   3200     0.0000    40.7461     4.5921    33.521506     26.3303     31.4331     41.0040\n  4500   3400     0.0000    40.7461     4.5921    33.839215     26.5042     31.2866     41.0040\n  4500   3600     0.0000    40.7461     4.5921    34.151388     26.6743     31.1417     41.0040\n  4500   3800     0.0000    40.7461     4.5921    34.458204     26.8407     30.9985     41.0040\n  4500   4000     0.0000    40.7461     4.5921    34.759832     27.0035     30.8567     41.0040\n  4500   4200     0.0000    40.7461     4.5921    35.056433     27.1628     30.7165     41.0040\n  4500   4400     0.0000    40.7461     4.5921    35.348161     27.3189     30.5778     41.0040\n  4500   4600     0.0000    40.7461     4.5921    35.635161     27.4717     30.4406     41.0040\n  4500   4800     0.0000    40.7461     4.5921    35.917572     27.6214     30.3048     41.0040\n  4500   5000     0.0000    40.7461     4.5921    36.195527     27.7681     30.1705     41.0040\n  4500   5200     0.0000    40.7461     4.5921    36.469151     27.9119     30.0375     41.0040\n  4500   5400     0.0000    40.7461     4.5921    36.738565     28.0528     29.9059     41.0040\n  4500   5600     0.0000    40.7461     4.5921    37.003885     28.1910     29.7757     41.0040\n  4500   5800     0.0000    40.7461     4.5921    37.265221     28.3265     29.6468     41.0040\n  4500   6000     0.0000    40.7461     4.5921    37.522678     28.4594     29.5192     41.0040\n  4500   6200     0.0000    40.7461     4.5921    37.776357     28.5899     29.3930     41.0040\n  4500   6400     0.0000    40.7461     4.5921    38.026356     28.7178     29.2679     41.0040\n  4500   6600     0.0000    40.7461     4.5921    38.272767     28.8435     29.1442     41.0040\n  4500   6800     0.0000    40.7461     4.5921    38.515680     28.9668     29.0216     41.0040\n  4500   7000     0.0000    40.7461     4.5921    38.755181     29.0878     28.9003     41.0040\n  4500   7200     0.0000    40.7461     4.5921    38.991352     29.2067     28.7801     41.0040\n  4500   7400     0.0000    40.7461     4.5921    39.224272     29.3234     28.6612     41.0040\n  4500   7600     0.0000    40.7461     4.5921    39.454020     29.4381     28.5433     41.0040\n  4500   7800     0.0000    40.7461     4.5921    39.680667     29.5508     28.4267     41.0040\n  4500   8000     0.0000    40.7461     4.5921    39.904285     29.6615     28.3111     41.0040\n  4500   8200     0.0000    40.7461     4.5921    40.124942     29.7704     28.1967     41.0040\n  4500   8400     0.0000    40.7461     4.5921    40.342705     29.8773     28.0833     41.0040\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180319_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.0425811        3.6031274       -0.5126463   m   m   m\ninitial_baseline_rate:          0.0000000        0.0521367        0.0726505   m/s m/s m/s\nprecision_baseline(TCN):       -0.0425811        3.6031274       -0.5126463   m   m   m\nprecision_baseline_rate:        0.0000000        0.0521367        0.0726505   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180319_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.043      3.603     -0.513\norbit baseline rate   (TCN) (m/s):  0.000e+00  5.214e-02  7.265e-02\n\nbaseline vector (TCN)   (m):     -0.043      3.603     -0.513\nbaseline rate   (TCN) (m/s):  0.000e+00  5.214e-02  7.265e-02\n\nSLC-1 center baseline length (m):     3.6397\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.0426     3.1165    -1.1908    27.496918      0.3826      3.3143      3.3365\n     0    200    -0.0426     3.1165    -1.1908    27.939152      0.4082      3.3112      3.3365\n     0    400    -0.0426     3.1165    -1.1908    28.370753      0.4331      3.3081      3.3365\n     0    600    -0.0426     3.1165    -1.1908    28.792243      0.4575      3.3048      3.3365\n     0    800    -0.0426     3.1165    -1.1908    29.204102      0.4812      3.3014      3.3365\n     0   1000    -0.0426     3.1165    -1.1908    29.606772      0.5044      3.2979      3.3365\n     0   1200    -0.0426     3.1165    -1.1908    30.000665      0.5271      3.2944      3.3365\n     0   1400    -0.0426     3.1165    -1.1908    30.386160      0.5492      3.2908      3.3365\n     0   1600    -0.0426     3.1165    -1.1908    30.763612      0.5709      3.2871      3.3365\n     0   1800    -0.0426     3.1165    -1.1908    31.133349      0.5921      3.2833      3.3365\n     0   2000    -0.0426     3.1165    -1.1908    31.495679      0.6128      3.2795      3.3365\n     0   2200    -0.0426     3.1165    -1.1908    31.850890      0.6332      3.2757      3.3365\n     0   2400    -0.0426     3.1165    -1.1908    32.199251      0.6531      3.2717      3.3365\n     0   2600    -0.0426     3.1165    -1.1908    32.541017      0.6726      3.2678      3.3365\n     0   2800    -0.0426     3.1165    -1.1908    32.876424      0.6917      3.2638      3.3365\n     0   3000    -0.0426     3.1165    -1.1908    33.205696      0.7104      3.2598      3.3365\n     0   3200    -0.0426     3.1165    -1.1908    33.529047      0.7288      3.2557      3.3365\n     0   3400    -0.0426     3.1165    -1.1908    33.846674      0.7468      3.2516      3.3365\n     0   3600    -0.0426     3.1165    -1.1908    34.158768      0.7645      3.2475      3.3365\n     0   3800    -0.0426     3.1165    -1.1908    34.465506      0.7819      3.2434      3.3365\n     0   4000    -0.0426     3.1165    -1.1908    34.767059      0.7990      3.2392      3.3365\n     0   4200    -0.0426     3.1165    -1.1908    35.063588      0.8157      3.2350      3.3365\n     0   4400    -0.0426     3.1165    -1.1908    35.355245      0.8322      3.2308      3.3365\n     0   4600    -0.0426     3.1165    -1.1908    35.642177      0.8484      3.2266      3.3365\n     0   4800    -0.0426     3.1165    -1.1908    35.924522      0.8642      3.2224      3.3365\n     0   5000    -0.0426     3.1165    -1.1908    36.202412      0.8799      3.2182      3.3365\n     0   5200    -0.0426     3.1165    -1.1908    36.475974      0.8952      3.2139      3.3365\n     0   5400    -0.0426     3.1165    -1.1908    36.745327      0.9103      3.2097      3.3365\n     0   5600    -0.0426     3.1165    -1.1908    37.010588      0.9252      3.2054      3.3365\n     0   5800    -0.0426     3.1165    -1.1908    37.271866      0.9398      3.2012      3.3365\n     0   6000    -0.0426     3.1165    -1.1908    37.529266      0.9541      3.1969      3.3365\n     0   6200    -0.0426     3.1165    -1.1908    37.782890      0.9683      3.1927      3.3365\n     0   6400    -0.0426     3.1165    -1.1908    38.032835      0.9822      3.1884      3.3365\n     0   6600    -0.0426     3.1165    -1.1908    38.279194      0.9959      3.1842      3.3365\n     0   6800    -0.0426     3.1165    -1.1908    38.522056      1.0094      3.1799      3.3365\n     0   7000    -0.0426     3.1165    -1.1908    38.761507      1.0227      3.1757      3.3365\n     0   7200    -0.0426     3.1165    -1.1908    38.997629      1.0358      3.1714      3.3365\n     0   7400    -0.0426     3.1165    -1.1908    39.230502      1.0486      3.1672      3.3365\n     0   7600    -0.0426     3.1165    -1.1908    39.460203      1.0613      3.1630      3.3365\n     0   7800    -0.0426     3.1165    -1.1908    39.686804      1.0738      3.1587      3.3365\n     0   8000    -0.0426     3.1165    -1.1908    39.910378      1.0861      3.1545      3.3365\n     0   8200    -0.0426     3.1165    -1.1908    40.130991      1.0983      3.1503      3.3365\n     0   8400    -0.0426     3.1165    -1.1908    40.348712      1.1102      3.1461      3.3365\n   500      0    -0.0426     3.2236    -1.0414    27.495955      0.5645      3.3404      3.3880\n   500    200    -0.0426     3.2236    -1.0414    27.938206      0.5903      3.3359      3.3880\n   500    400    -0.0426     3.2236    -1.0414    28.369822      0.6154      3.3314      3.3880\n   500    600    -0.0426     3.2236    -1.0414    28.791327      0.6399      3.3268      3.3880\n   500    800    -0.0426     3.2236    -1.0414    29.203199      0.6638      3.3221      3.3880\n   500   1000    -0.0426     3.2236    -1.0414    29.605883      0.6871      3.3174      3.3880\n   500   1200    -0.0426     3.2236    -1.0414    29.999788      0.7099      3.3125      3.3880\n   500   1400    -0.0426     3.2236    -1.0414    30.385296      0.7322      3.3077      3.3880\n   500   1600    -0.0426     3.2236    -1.0414    30.762759      0.7540      3.3028      3.3880\n   500   1800    -0.0426     3.2236    -1.0414    31.132507      0.7753      3.2979      3.3880\n   500   2000    -0.0426     3.2236    -1.0414    31.494848      0.7961      3.2929      3.3880\n   500   2200    -0.0426     3.2236    -1.0414    31.850069      0.8165      3.2879      3.3880\n   500   2400    -0.0426     3.2236    -1.0414    32.198441      0.8365      3.2829      3.3880\n   500   2600    -0.0426     3.2236    -1.0414    32.540215      0.8561      3.2778      3.3880\n   500   2800    -0.0426     3.2236    -1.0414    32.875632      0.8752      3.2728      3.3880\n   500   3000    -0.0426     3.2236    -1.0414    33.204913      0.8940      3.2677      3.3880\n   500   3200    -0.0426     3.2236    -1.0414    33.528272      0.9124      3.2626      3.3880\n   500   3400    -0.0426     3.2236    -1.0414    33.845908      0.9305      3.2575      3.3880\n   500   3600    -0.0426     3.2236    -1.0414    34.158010      0.9482      3.2523      3.3880\n   500   3800    -0.0426     3.2236    -1.0414    34.464756      0.9656      3.2472      3.3880\n   500   4000    -0.0426     3.2236    -1.0414    34.766316      0.9827      3.2421      3.3880\n   500   4200    -0.0426     3.2236    -1.0414    35.062852      0.9995      3.2370      3.3880\n   500   4400    -0.0426     3.2236    -1.0414    35.354517      1.0160      3.2318      3.3880\n   500   4600    -0.0426     3.2236    -1.0414    35.641456      1.0321      3.2267      3.3880\n   500   4800    -0.0426     3.2236    -1.0414    35.923807      1.0480      3.2216      3.3880\n   500   5000    -0.0426     3.2236    -1.0414    36.201704      1.0636      3.2165      3.3880\n   500   5200    -0.0426     3.2236    -1.0414    36.475272      1.0790      3.2113      3.3880\n   500   5400    -0.0426     3.2236    -1.0414    36.744632      1.0941      3.2062      3.3880\n   500   5600    -0.0426     3.2236    -1.0414    37.009898      1.1089      3.2011      3.3880\n   500   5800    -0.0426     3.2236    -1.0414    37.271182      1.1235      3.1960      3.3880\n   500   6000    -0.0426     3.2236    -1.0414    37.528588      1.1378      3.1910      3.3880\n   500   6200    -0.0426     3.2236    -1.0414    37.782218      1.1519      3.1859      3.3880\n   500   6400    -0.0426     3.2236    -1.0414    38.032168      1.1658      3.1808      3.3880\n   500   6600    -0.0426     3.2236    -1.0414    38.278532      1.1795      3.1758      3.3880\n   500   6800    -0.0426     3.2236    -1.0414    38.521399      1.1929      3.1708      3.3880\n   500   7000    -0.0426     3.2236    -1.0414    38.760855      1.2062      3.1658      3.3880\n   500   7200    -0.0426     3.2236    -1.0414    38.996982      1.2192      3.1608      3.3880\n   500   7400    -0.0426     3.2236    -1.0414    39.229860      1.2321      3.1558      3.3880\n   500   7600    -0.0426     3.2236    -1.0414    39.459566      1.2447      3.1508      3.3880\n   500   7800    -0.0426     3.2236    -1.0414    39.686172      1.2571      3.1459      3.3880\n   500   8000    -0.0426     3.2236    -1.0414    39.909750      1.2694      3.1409      3.3880\n   500   8200    -0.0426     3.2236    -1.0414    40.130368      1.2815      3.1360      3.3880\n   500   8400    -0.0426     3.2236    -1.0414    40.348092      1.2934      3.1311      3.3880\n  1000      0    -0.0426     3.3308    -0.8921    27.494972      0.7464      3.3665      3.4485\n  1000    200    -0.0426     3.3308    -0.8921    27.937239      0.7724      3.3607      3.4485\n  1000    400    -0.0426     3.3308    -0.8921    28.368871      0.7977      3.3548      3.4485\n  1000    600    -0.0426     3.3308    -0.8921    28.790391      0.8223      3.3488      3.4485\n  1000    800    -0.0426     3.3308    -0.8921    29.202277      0.8464      3.3428      3.4485\n  1000   1000    -0.0426     3.3308    -0.8921    29.604975      0.8699      3.3368      3.4485\n  1000   1200    -0.0426     3.3308    -0.8921    29.998893      0.8928      3.3307      3.4485\n  1000   1400    -0.0426     3.3308    -0.8921    30.384413      0.9152      3.3246      3.4485\n  1000   1600    -0.0426     3.3308    -0.8921    30.761888      0.9371      3.3185      3.4485\n  1000   1800    -0.0426     3.3308    -0.8921    31.131648      0.9585      3.3124      3.4485\n  1000   2000    -0.0426     3.3308    -0.8921    31.494000      0.9794      3.3063      3.4485\n  1000   2200    -0.0426     3.3308    -0.8921    31.849231      0.9999      3.3001      3.4485\n  1000   2400    -0.0426     3.3308    -0.8921    32.197613      1.0199      3.2940      3.4485\n  1000   2600    -0.0426     3.3308    -0.8921    32.539398      1.0395      3.2879      3.4485\n  1000   2800    -0.0426     3.3308    -0.8921    32.874823      1.0588      3.2817      3.4485\n  1000   3000    -0.0426     3.3308    -0.8921    33.204114      1.0776      3.2756      3.4485\n  1000   3200    -0.0426     3.3308    -0.8921    33.527482      1.0961      3.2694      3.4485\n  1000   3400    -0.0426     3.3308    -0.8921    33.845126      1.1142      3.2633      3.4485\n  1000   3600    -0.0426     3.3308    -0.8921    34.157236      1.1320      3.2572      3.4485\n  1000   3800    -0.0426     3.3308    -0.8921    34.463990      1.1494      3.2511      3.4485\n  1000   4000    -0.0426     3.3308    -0.8921    34.765558      1.1665      3.2450      3.4485\n  1000   4200    -0.0426     3.3308    -0.8921    35.062102      1.1832      3.2389      3.4485\n  1000   4400    -0.0426     3.3308    -0.8921    35.353774      1.1997      3.2328      3.4485\n  1000   4600    -0.0426     3.3308    -0.8921    35.640720      1.2159      3.2268      3.4485\n  1000   4800    -0.0426     3.3308    -0.8921    35.923078      1.2318      3.2208      3.4485\n  1000   5000    -0.0426     3.3308    -0.8921    36.200982      1.2474      3.2147      3.4485\n  1000   5200    -0.0426     3.3308    -0.8921    36.474556      1.2627      3.2088      3.4485\n  1000   5400    -0.0426     3.3308    -0.8921    36.743922      1.2778      3.2028      3.4485\n  1000   5600    -0.0426     3.3308    -0.8921    37.009195      1.2926      3.1968      3.4485\n  1000   5800    -0.0426     3.3308    -0.8921    37.270484      1.3072      3.1909      3.4485\n  1000   6000    -0.0426     3.3308    -0.8921    37.527896      1.3215      3.1850      3.4485\n  1000   6200    -0.0426     3.3308    -0.8921    37.781532      1.3356      3.1791      3.4485\n  1000   6400    -0.0426     3.3308    -0.8921    38.031488      1.3494      3.1733      3.4485\n  1000   6600    -0.0426     3.3308    -0.8921    38.277857      1.3631      3.1674      3.4485\n  1000   6800    -0.0426     3.3308    -0.8921    38.520730      1.3765      3.1616      3.4485\n  1000   7000    -0.0426     3.3308    -0.8921    38.760191      1.3897      3.1558      3.4485\n  1000   7200    -0.0426     3.3308    -0.8921    38.996323      1.4027      3.1501      3.4485\n  1000   7400    -0.0426     3.3308    -0.8921    39.229206      1.4155      3.1444      3.4485\n  1000   7600    -0.0426     3.3308    -0.8921    39.458916      1.4281      3.1387      3.4485\n  1000   7800    -0.0426     3.3308    -0.8921    39.685527      1.4405      3.1330      3.4485\n  1000   8000    -0.0426     3.3308    -0.8921    39.909109      1.4527      3.1273      3.4485\n  1000   8200    -0.0426     3.3308    -0.8921    40.129732      1.4647      3.1217      3.4485\n  1000   8400    -0.0426     3.3308    -0.8921    40.347461      1.4766      3.1161      3.4485\n  1500      0    -0.0426     3.4380    -0.7428    27.493968      0.9283      3.3927      3.5176\n  1500    200    -0.0426     3.4380    -0.7428    27.936252      0.9545      3.3854      3.5176\n  1500    400    -0.0426     3.4380    -0.7428    28.367900      0.9800      3.3781      3.5176\n  1500    600    -0.0426     3.4380    -0.7428    28.789435      1.0048      3.3708      3.5176\n  1500    800    -0.0426     3.4380    -0.7428    29.201336      1.0290      3.3635      3.5176\n  1500   1000    -0.0426     3.4380    -0.7428    29.604048      1.0526      3.3562      3.5176\n  1500   1200    -0.0426     3.4380    -0.7428    29.997980      1.0756      3.3489      3.5176\n  1500   1400    -0.0426     3.4380    -0.7428    30.383512      1.0982      3.3416      3.5176\n  1500   1600    -0.0426     3.4380    -0.7428    30.760999      1.1201      3.3342      3.5176\n  1500   1800    -0.0426     3.4380    -0.7428    31.130771      1.1416      3.3269      3.5176\n  1500   2000    -0.0426     3.4380    -0.7428    31.493134      1.1627      3.3197      3.5176\n  1500   2200    -0.0426     3.4380    -0.7428    31.848377      1.1832      3.3124      3.5176\n  1500   2400    -0.0426     3.4380    -0.7428    32.196769      1.2033      3.3051      3.5176\n  1500   2600    -0.0426     3.4380    -0.7428    32.538563      1.2230      3.2979      3.5176\n  1500   2800    -0.0426     3.4380    -0.7428    32.873999      1.2423      3.2907      3.5176\n  1500   3000    -0.0426     3.4380    -0.7428    33.203299      1.2612      3.2835      3.5176\n  1500   3200    -0.0426     3.4380    -0.7428    33.526676      1.2797      3.2763      3.5176\n  1500   3400    -0.0426     3.4380    -0.7428    33.844329      1.2979      3.2692      3.5176\n  1500   3600    -0.0426     3.4380    -0.7428    34.156447      1.3157      3.2620      3.5176\n  1500   3800    -0.0426     3.4380    -0.7428    34.463209      1.3331      3.2550      3.5176\n  1500   4000    -0.0426     3.4380    -0.7428    34.764785      1.3502      3.2479      3.5176\n  1500   4200    -0.0426     3.4380    -0.7428    35.061337      1.3670      3.2409      3.5176\n  1500   4400    -0.0426     3.4380    -0.7428    35.353016      1.3835      3.2339      3.5176\n  1500   4600    -0.0426     3.4380    -0.7428    35.639969      1.3997      3.2269      3.5176\n  1500   4800    -0.0426     3.4380    -0.7428    35.922335      1.4156      3.2200      3.5176\n  1500   5000    -0.0426     3.4380    -0.7428    36.200245      1.4312      3.2130      3.5176\n  1500   5200    -0.0426     3.4380    -0.7428    36.473826      1.4465      3.2062      3.5176\n  1500   5400    -0.0426     3.4380    -0.7428    36.743198      1.4615      3.1993      3.5176\n  1500   5600    -0.0426     3.4380    -0.7428    37.008477      1.4763      3.1925      3.5176\n  1500   5800    -0.0426     3.4380    -0.7428    37.269773      1.4909      3.1858      3.5176\n  1500   6000    -0.0426     3.4380    -0.7428    37.527191      1.5052      3.1790      3.5176\n  1500   6200    -0.0426     3.4380    -0.7428    37.780832      1.5192      3.1723      3.5176\n  1500   6400    -0.0426     3.4380    -0.7428    38.030794      1.5331      3.1657      3.5176\n  1500   6600    -0.0426     3.4380    -0.7428    38.277169      1.5467      3.1591      3.5176\n  1500   6800    -0.0426     3.4380    -0.7428    38.520047      1.5600      3.1525      3.5176\n  1500   7000    -0.0426     3.4380    -0.7428    38.759513      1.5732      3.1459      3.5176\n  1500   7200    -0.0426     3.4380    -0.7428    38.995651      1.5861      3.1394      3.5176\n  1500   7400    -0.0426     3.4380    -0.7428    39.228539      1.5989      3.1329      3.5176\n  1500   7600    -0.0426     3.4380    -0.7428    39.458254      1.6114      3.1265      3.5176\n  1500   7800    -0.0426     3.4380    -0.7428    39.684869      1.6238      3.1201      3.5176\n  1500   8000    -0.0426     3.4380    -0.7428    39.908457      1.6360      3.1138      3.5176\n  1500   8200    -0.0426     3.4380    -0.7428    40.129084      1.6479      3.1074      3.5176\n  1500   8400    -0.0426     3.4380    -0.7428    40.346818      1.6597      3.1011      3.5176\n  2000      0    -0.0426     3.5451    -0.5934    27.492944      1.1102      3.4188      3.5947\n  2000    200    -0.0426     3.5451    -0.5934    27.935245      1.1366      3.4101      3.5947\n  2000    400    -0.0426     3.5451    -0.5934    28.366910      1.1622      3.4015      3.5947\n  2000    600    -0.0426     3.5451    -0.5934    28.788460      1.1872      3.3928      3.5947\n  2000    800    -0.0426     3.5451    -0.5934    29.200376      1.2116      3.3842      3.5947\n  2000   1000    -0.0426     3.5451    -0.5934    29.603102      1.2353      3.3756      3.5947\n  2000   1200    -0.0426     3.5451    -0.5934    29.997048      1.2585      3.3670      3.5947\n  2000   1400    -0.0426     3.5451    -0.5934    30.382593      1.2811      3.3585      3.5947\n  2000   1600    -0.0426     3.5451    -0.5934    30.760093      1.3032      3.3500      3.5947\n  2000   1800    -0.0426     3.5451    -0.5934    31.129876      1.3248      3.3415      3.5947\n  2000   2000    -0.0426     3.5451    -0.5934    31.492251      1.3459      3.3331      3.5947\n  2000   2200    -0.0426     3.5451    -0.5934    31.847505      1.3666      3.3246      3.5947\n  2000   2400    -0.0426     3.5451    -0.5934    32.195907      1.3868      3.3163      3.5947\n  2000   2600    -0.0426     3.5451    -0.5934    32.537712      1.4065      3.3079      3.5947\n  2000   2800    -0.0426     3.5451    -0.5934    32.873158      1.4259      3.2996      3.5947\n  2000   3000    -0.0426     3.5451    -0.5934    33.202467      1.4448      3.2914      3.5947\n  2000   3200    -0.0426     3.5451    -0.5934    33.525853      1.4634      3.2832      3.5947\n  2000   3400    -0.0426     3.5451    -0.5934    33.843515      1.4815      3.2750      3.5947\n  2000   3600    -0.0426     3.5451    -0.5934    34.155642      1.4994      3.2669      3.5947\n  2000   3800    -0.0426     3.5451    -0.5934    34.462413      1.5168      3.2588      3.5947\n  2000   4000    -0.0426     3.5451    -0.5934    34.763997      1.5340      3.2508      3.5947\n  2000   4200    -0.0426     3.5451    -0.5934    35.060556      1.5508      3.2428      3.5947\n  2000   4400    -0.0426     3.5451    -0.5934    35.352243      1.5673      3.2349      3.5947\n  2000   4600    -0.0426     3.5451    -0.5934    35.639204      1.5834      3.2270      3.5947\n  2000   4800    -0.0426     3.5451    -0.5934    35.921577      1.5993      3.2191      3.5947\n  2000   5000    -0.0426     3.5451    -0.5934    36.199494      1.6149      3.2114      3.5947\n  2000   5200    -0.0426     3.5451    -0.5934    36.473082      1.6302      3.2036      3.5947\n  2000   5400    -0.0426     3.5451    -0.5934    36.742461      1.6453      3.1959      3.5947\n  2000   5600    -0.0426     3.5451    -0.5934    37.007746      1.6601      3.1882      3.5947\n  2000   5800    -0.0426     3.5451    -0.5934    37.269048      1.6746      3.1806      3.5947\n  2000   6000    -0.0426     3.5451    -0.5934    37.526472      1.6888      3.1731      3.5947\n  2000   6200    -0.0426     3.5451    -0.5934    37.780120      1.7029      3.1656      3.5947\n  2000   6400    -0.0426     3.5451    -0.5934    38.030087      1.7167      3.1581      3.5947\n  2000   6600    -0.0426     3.5451    -0.5934    38.276468      1.7302      3.1507      3.5947\n  2000   6800    -0.0426     3.5451    -0.5934    38.519351      1.7436      3.1433      3.5947\n  2000   7000    -0.0426     3.5451    -0.5934    38.758823      1.7567      3.1360      3.5947\n  2000   7200    -0.0426     3.5451    -0.5934    38.994966      1.7696      3.1288      3.5947\n  2000   7400    -0.0426     3.5451    -0.5934    39.227859      1.7823      3.1215      3.5947\n  2000   7600    -0.0426     3.5451    -0.5934    39.457579      1.7948      3.1144      3.5947\n  2000   7800    -0.0426     3.5451    -0.5934    39.684200      1.8071      3.1072      3.5947\n  2000   8000    -0.0426     3.5451    -0.5934    39.907792      1.8192      3.1002      3.5947\n  2000   8200    -0.0426     3.5451    -0.5934    40.128424      1.8312      3.0931      3.5947\n  2000   8400    -0.0426     3.5451    -0.5934    40.346162      1.8429      3.0862      3.5947\n  2500      0    -0.0426     3.6523    -0.4441    27.491899      1.2921      3.4450      3.6795\n  2500    200    -0.0426     3.6523    -0.4441    27.934218      1.3186      3.4349      3.6795\n  2500    400    -0.0426     3.6523    -0.4441    28.365900      1.3445      3.4249      3.6795\n  2500    600    -0.0426     3.6523    -0.4441    28.787466      1.3696      3.4149      3.6795\n  2500    800    -0.0426     3.6523    -0.4441    29.199398      1.3942      3.4049      3.6795\n  2500   1000    -0.0426     3.6523    -0.4441    29.602138      1.4181      3.3950      3.6795\n  2500   1200    -0.0426     3.6523    -0.4441    29.996097      1.4414      3.3852      3.6795\n  2500   1400    -0.0426     3.6523    -0.4441    30.381656      1.4641      3.3754      3.6795\n  2500   1600    -0.0426     3.6523    -0.4441    30.759169      1.4863      3.3657      3.6795\n  2500   1800    -0.0426     3.6523    -0.4441    31.128964      1.5080      3.3561      3.6795\n  2500   2000    -0.0426     3.6523    -0.4441    31.491351      1.5292      3.3465      3.6795\n  2500   2200    -0.0426     3.6523    -0.4441    31.846616      1.5499      3.3369      3.6795\n  2500   2400    -0.0426     3.6523    -0.4441    32.195030      1.5702      3.3274      3.6795\n  2500   2600    -0.0426     3.6523    -0.4441    32.536845      1.5900      3.3180      3.6795\n  2500   2800    -0.0426     3.6523    -0.4441    32.872300      1.6094      3.3086      3.6795\n  2500   3000    -0.0426     3.6523    -0.4441    33.201620      1.6284      3.2993      3.6795\n  2500   3200    -0.0426     3.6523    -0.4441    33.525015      1.6470      3.2901      3.6795\n  2500   3400    -0.0426     3.6523    -0.4441    33.842686      1.6652      3.2809      3.6795\n  2500   3600    -0.0426     3.6523    -0.4441    34.154822      1.6831      3.2718      3.6795\n  2500   3800    -0.0426     3.6523    -0.4441    34.461601      1.7006      3.2627      3.6795\n  2500   4000    -0.0426     3.6523    -0.4441    34.763194      1.7177      3.2537      3.6795\n  2500   4200    -0.0426     3.6523    -0.4441    35.059761      1.7345      3.2448      3.6795\n  2500   4400    -0.0426     3.6523    -0.4441    35.351456      1.7510      3.2359      3.6795\n  2500   4600    -0.0426     3.6523    -0.4441    35.638424      1.7672      3.2271      3.6795\n  2500   4800    -0.0426     3.6523    -0.4441    35.920804      1.7831      3.2183      3.6795\n  2500   5000    -0.0426     3.6523    -0.4441    36.198729      1.7987      3.2097      3.6795\n  2500   5200    -0.0426     3.6523    -0.4441    36.472323      1.8140      3.2010      3.6795\n  2500   5400    -0.0426     3.6523    -0.4441    36.741709      1.8290      3.1925      3.6795\n  2500   5600    -0.0426     3.6523    -0.4441    37.007001      1.8438      3.1840      3.6795\n  2500   5800    -0.0426     3.6523    -0.4441    37.268310      1.8583      3.1755      3.6795\n  2500   6000    -0.0426     3.6523    -0.4441    37.525740      1.8725      3.1671      3.6795\n  2500   6200    -0.0426     3.6523    -0.4441    37.779394      1.8865      3.1588      3.6795\n  2500   6400    -0.0426     3.6523    -0.4441    38.029367      1.9003      3.1506      3.6795\n  2500   6600    -0.0426     3.6523    -0.4441    38.275754      1.9138      3.1424      3.6795\n  2500   6800    -0.0426     3.6523    -0.4441    38.518643      1.9271      3.1342      3.6795\n  2500   7000    -0.0426     3.6523    -0.4441    38.758120      1.9402      3.1261      3.6795\n  2500   7200    -0.0426     3.6523    -0.4441    38.994268      1.9531      3.1181      3.6795\n  2500   7400    -0.0426     3.6523    -0.4441    39.227167      1.9657      3.1101      3.6795\n  2500   7600    -0.0426     3.6523    -0.4441    39.456892      1.9782      3.1022      3.6795\n  2500   7800    -0.0426     3.6523    -0.4441    39.683518      1.9904      3.0944      3.6795\n  2500   8000    -0.0426     3.6523    -0.4441    39.907115      2.0025      3.0866      3.6795\n  2500   8200    -0.0426     3.6523    -0.4441    40.127752      2.0144      3.0789      3.6795\n  2500   8400    -0.0426     3.6523    -0.4441    40.345495      2.0261      3.0712      3.6795\n  3000      0    -0.0426     3.7595    -0.2948    27.490833      1.4740      3.4711      3.7713\n  3000    200    -0.0426     3.7595    -0.2948    27.933171      1.5007      3.4596      3.7713\n  3000    400    -0.0426     3.7595    -0.2948    28.364870      1.5267      3.4482      3.7713\n  3000    600    -0.0426     3.7595    -0.2948    28.786452      1.5521      3.4369      3.7713\n  3000    800    -0.0426     3.7595    -0.2948    29.198400      1.5767      3.4257      3.7713\n  3000   1000    -0.0426     3.7595    -0.2948    29.601155      1.6008      3.4145      3.7713\n  3000   1200    -0.0426     3.7595    -0.2948    29.995129      1.6242      3.4034      3.7713\n  3000   1400    -0.0426     3.7595    -0.2948    30.380701      1.6471      3.3924      3.7713\n  3000   1600    -0.0426     3.7595    -0.2948    30.758227      1.6694      3.3815      3.7713\n  3000   1800    -0.0426     3.7595    -0.2948    31.128035      1.6912      3.3706      3.7713\n  3000   2000    -0.0426     3.7595    -0.2948    31.490433      1.7125      3.3599      3.7713\n  3000   2200    -0.0426     3.7595    -0.2948    31.845710      1.7333      3.3492      3.7713\n  3000   2400    -0.0426     3.7595    -0.2948    32.194135      1.7536      3.3386      3.7713\n  3000   2600    -0.0426     3.7595    -0.2948    32.535961      1.7735      3.3280      3.7713\n  3000   2800    -0.0426     3.7595    -0.2948    32.871427      1.7930      3.3176      3.7713\n  3000   3000    -0.0426     3.7595    -0.2948    33.200756      1.8120      3.3072      3.7713\n  3000   3200    -0.0426     3.7595    -0.2948    33.524162      1.8306      3.2970      3.7713\n  3000   3400    -0.0426     3.7595    -0.2948    33.841842      1.8489      3.2868      3.7713\n  3000   3600    -0.0426     3.7595    -0.2948    34.153987      1.8668      3.2766      3.7713\n  3000   3800    -0.0426     3.7595    -0.2948    34.460775      1.8843      3.2666      3.7713\n  3000   4000    -0.0426     3.7595    -0.2948    34.762376      1.9014      3.2566      3.7713\n  3000   4200    -0.0426     3.7595    -0.2948    35.058951      1.9183      3.2467      3.7713\n  3000   4400    -0.0426     3.7595    -0.2948    35.350654      1.9348      3.2369      3.7713\n  3000   4600    -0.0426     3.7595    -0.2948    35.637630      1.9510      3.2272      3.7713\n  3000   4800    -0.0426     3.7595    -0.2948    35.920018      1.9669      3.2176      3.7713\n  3000   5000    -0.0426     3.7595    -0.2948    36.197949      1.9824      3.2080      3.7713\n  3000   5200    -0.0426     3.7595    -0.2948    36.471551      1.9977      3.1985      3.7713\n  3000   5400    -0.0426     3.7595    -0.2948    36.740944      2.0127      3.1890      3.7713\n  3000   5600    -0.0426     3.7595    -0.2948    37.006243      2.0275      3.1797      3.7713\n  3000   5800    -0.0426     3.7595    -0.2948    37.267558      2.0420      3.1704      3.7713\n  3000   6000    -0.0426     3.7595    -0.2948    37.524995      2.0562      3.1612      3.7713\n  3000   6200    -0.0426     3.7595    -0.2948    37.778654      2.0702      3.1521      3.7713\n  3000   6400    -0.0426     3.7595    -0.2948    38.028634      2.0839      3.1430      3.7713\n  3000   6600    -0.0426     3.7595    -0.2948    38.275026      2.0974      3.1340      3.7713\n  3000   6800    -0.0426     3.7595    -0.2948    38.517921      2.1107      3.1251      3.7713\n  3000   7000    -0.0426     3.7595    -0.2948    38.757404      2.1237      3.1162      3.7713\n  3000   7200    -0.0426     3.7595    -0.2948    38.993558      2.1365      3.1075      3.7713\n  3000   7400    -0.0426     3.7595    -0.2948    39.226462      2.1492      3.0987      3.7713\n  3000   7600    -0.0426     3.7595    -0.2948    39.456192      2.1616      3.0901      3.7713\n  3000   7800    -0.0426     3.7595    -0.2948    39.682823      2.1738      3.0815      3.7713\n  3000   8000    -0.0426     3.7595    -0.2948    39.906425      2.1858      3.0730      3.7713\n  3000   8200    -0.0426     3.7595    -0.2948    40.127067      2.1976      3.0646      3.7713\n  3000   8400    -0.0426     3.7595    -0.2948    40.344815      2.2092      3.0562      3.7713\n  3500      0    -0.0426     3.8667    -0.1454    27.489747      1.6558      3.4973      3.8696\n  3500    200    -0.0426     3.8667    -0.1454    27.932104      1.6828      3.4844      3.8696\n  3500    400    -0.0426     3.8667    -0.1454    28.363820      1.7090      3.4716      3.8696\n  3500    600    -0.0426     3.8667    -0.1454    28.785420      1.7345      3.4590      3.8696\n  3500    800    -0.0426     3.8667    -0.1454    29.197383      1.7593      3.4464      3.8696\n  3500   1000    -0.0426     3.8667    -0.1454    29.600153      1.7835      3.4339      3.8696\n  3500   1200    -0.0426     3.8667    -0.1454    29.994142      1.8071      3.4216      3.8696\n  3500   1400    -0.0426     3.8667    -0.1454    30.379728      1.8301      3.4094      3.8696\n  3500   1600    -0.0426     3.8667    -0.1454    30.757267      1.8525      3.3972      3.8696\n  3500   1800    -0.0426     3.8667    -0.1454    31.127088      1.8744      3.3852      3.8696\n  3500   2000    -0.0426     3.8667    -0.1454    31.489499      1.8957      3.3733      3.8696\n  3500   2200    -0.0426     3.8667    -0.1454    31.844788      1.9166      3.3614      3.8696\n  3500   2400    -0.0426     3.8667    -0.1454    32.193224      1.9370      3.3497      3.8696\n  3500   2600    -0.0426     3.8667    -0.1454    32.535061      1.9570      3.3381      3.8696\n  3500   2800    -0.0426     3.8667    -0.1454    32.870537      1.9765      3.3266      3.8696\n  3500   3000    -0.0426     3.8667    -0.1454    33.199877      1.9956      3.3152      3.8696\n  3500   3200    -0.0426     3.8667    -0.1454    33.523292      2.0143      3.3039      3.8696\n  3500   3400    -0.0426     3.8667    -0.1454    33.840982      2.0326      3.2926      3.8696\n  3500   3600    -0.0426     3.8667    -0.1454    34.153136      2.0505      3.2815      3.8696\n  3500   3800    -0.0426     3.8667    -0.1454    34.459933      2.0680      3.2705      3.8696\n  3500   4000    -0.0426     3.8667    -0.1454    34.761543      2.0852      3.2596      3.8696\n  3500   4200    -0.0426     3.8667    -0.1454    35.058127      2.1020      3.2487      3.8696\n  3500   4400    -0.0426     3.8667    -0.1454    35.349838      2.1185      3.2380      3.8696\n  3500   4600    -0.0426     3.8667    -0.1454    35.636821      2.1347      3.2273      3.8696\n  3500   4800    -0.0426     3.8667    -0.1454    35.919217      2.1506      3.2168      3.8696\n  3500   5000    -0.0426     3.8667    -0.1454    36.197156      2.1662      3.2063      3.8696\n  3500   5200    -0.0426     3.8667    -0.1454    36.470765      2.1815      3.1959      3.8696\n  3500   5400    -0.0426     3.8667    -0.1454    36.740165      2.1965      3.1856      3.8696\n  3500   5600    -0.0426     3.8667    -0.1454    37.005471      2.2112      3.1754      3.8696\n  3500   5800    -0.0426     3.8667    -0.1454    37.266792      2.2257      3.1653      3.8696\n  3500   6000    -0.0426     3.8667    -0.1454    37.524236      2.2399      3.1553      3.8696\n  3500   6200    -0.0426     3.8667    -0.1454    37.777902      2.2538      3.1453      3.8696\n  3500   6400    -0.0426     3.8667    -0.1454    38.027888      2.2675      3.1354      3.8696\n  3500   6600    -0.0426     3.8667    -0.1454    38.274286      2.2810      3.1257      3.8696\n  3500   6800    -0.0426     3.8667    -0.1454    38.517187      2.2942      3.1160      3.8696\n  3500   7000    -0.0426     3.8667    -0.1454    38.756676      2.3072      3.1064      3.8696\n  3500   7200    -0.0426     3.8667    -0.1454    38.992835      2.3200      3.0968      3.8696\n  3500   7400    -0.0426     3.8667    -0.1454    39.225745      2.3326      3.0874      3.8696\n  3500   7600    -0.0426     3.8667    -0.1454    39.455480      2.3449      3.0780      3.8696\n  3500   7800    -0.0426     3.8667    -0.1454    39.682117      2.3571      3.0687      3.8696\n  3500   8000    -0.0426     3.8667    -0.1454    39.905724      2.3690      3.0595      3.8696\n  3500   8200    -0.0426     3.8667    -0.1454    40.126371      2.3808      3.0503      3.8696\n  3500   8400    -0.0426     3.8667    -0.1454    40.344124      2.3924      3.0412      3.8696\n  4000      0    -0.0426     3.9738     0.0039    27.488641      1.8377      3.5235      3.9741\n  4000    200    -0.0426     3.9738     0.0039    27.931016      1.8649      3.5092      3.9741\n  4000    400    -0.0426     3.9738     0.0039    28.362751      1.8913      3.4950      3.9741\n  4000    600    -0.0426     3.9738     0.0039    28.784368      1.9169      3.4810      3.9741\n  4000    800    -0.0426     3.9738     0.0039    29.196347      1.9419      3.4671      3.9741\n  4000   1000    -0.0426     3.9738     0.0039    29.599133      1.9662      3.4534      3.9741\n  4000   1200    -0.0426     3.9738     0.0039    29.993137      1.9899      3.4398      3.9741\n  4000   1400    -0.0426     3.9738     0.0039    30.378737      2.0130      3.4263      3.9741\n  4000   1600    -0.0426     3.9738     0.0039    30.756290      2.0356      3.4130      3.9741\n  4000   1800    -0.0426     3.9738     0.0039    31.126124      2.0576      3.3998      3.9741\n  4000   2000    -0.0426     3.9738     0.0039    31.488547      2.0790      3.3867      3.9741\n  4000   2200    -0.0426     3.9738     0.0039    31.843848      2.1000      3.3737      3.9741\n  4000   2400    -0.0426     3.9738     0.0039    32.192296      2.1205      3.3609      3.9741\n  4000   2600    -0.0426     3.9738     0.0039    32.534144      2.1405      3.3482      3.9741\n  4000   2800    -0.0426     3.9738     0.0039    32.869631      2.1600      3.3356      3.9741\n  4000   3000    -0.0426     3.9738     0.0039    33.198982      2.1792      3.3231      3.9741\n  4000   3200    -0.0426     3.9738     0.0039    33.522407      2.1979      3.3108      3.9741\n  4000   3400    -0.0426     3.9738     0.0039    33.840106      2.2162      3.2985      3.9741\n  4000   3600    -0.0426     3.9738     0.0039    34.152270      2.2342      3.2864      3.9741\n  4000   3800    -0.0426     3.9738     0.0039    34.459076      2.2517      3.2744      3.9741\n  4000   4000    -0.0426     3.9738     0.0039    34.760695      2.2689      3.2625      3.9741\n  4000   4200    -0.0426     3.9738     0.0039    35.057287      2.2858      3.2507      3.9741\n  4000   4400    -0.0426     3.9738     0.0039    35.349006      2.3023      3.2390      3.9741\n  4000   4600    -0.0426     3.9738     0.0039    35.635998      2.3185      3.2274      3.9741\n  4000   4800    -0.0426     3.9738     0.0039    35.918401      2.3344      3.2160      3.9741\n  4000   5000    -0.0426     3.9738     0.0039    36.196348      2.3500      3.2046      3.9741\n  4000   5200    -0.0426     3.9738     0.0039    36.469965      2.3652      3.1934      3.9741\n  4000   5400    -0.0426     3.9738     0.0039    36.739372      2.3802      3.1822      3.9741\n  4000   5600    -0.0426     3.9738     0.0039    37.004685      2.3949      3.1711      3.9741\n  4000   5800    -0.0426     3.9738     0.0039    37.266013      2.4094      3.1602      3.9741\n  4000   6000    -0.0426     3.9738     0.0039    37.523463      2.4235      3.1493      3.9741\n  4000   6200    -0.0426     3.9738     0.0039    37.777136      2.4375      3.1386      3.9741\n  4000   6400    -0.0426     3.9738     0.0039    38.027128      2.4511      3.1279      3.9741\n  4000   6600    -0.0426     3.9738     0.0039    38.273533      2.4646      3.1173      3.9741\n  4000   6800    -0.0426     3.9738     0.0039    38.516440      2.4778      3.1069      3.9741\n  4000   7000    -0.0426     3.9738     0.0039    38.755934      2.4907      3.0965      3.9741\n  4000   7200    -0.0426     3.9738     0.0039    38.992100      2.5035      3.0862      3.9741\n  4000   7400    -0.0426     3.9738     0.0039    39.225015      2.5160      3.0760      3.9741\n  4000   7600    -0.0426     3.9738     0.0039    39.454756      2.5283      3.0659      3.9741\n  4000   7800    -0.0426     3.9738     0.0039    39.681398      2.5404      3.0558      3.9741\n  4000   8000    -0.0426     3.9738     0.0039    39.905010      2.5523      3.0459      3.9741\n  4000   8200    -0.0426     3.9738     0.0039    40.125663      2.5640      3.0360      3.9741\n  4000   8400    -0.0426     3.9738     0.0039    40.343421      2.5755      3.0263      3.9741\n  4500      0    -0.0426     4.0810     0.1532    27.487515      2.0196      3.5496      4.0841\n  4500    200    -0.0426     4.0810     0.1532    27.929909      2.0469      3.5339      4.0841\n  4500    400    -0.0426     4.0810     0.1532    28.361662      2.0735      3.5184      4.0841\n  4500    600    -0.0426     4.0810     0.1532    28.783297      2.0993      3.5031      4.0841\n  4500    800    -0.0426     4.0810     0.1532    29.195293      2.1245      3.4879      4.0841\n  4500   1000    -0.0426     4.0810     0.1532    29.598095      2.1489      3.4729      4.0841\n  4500   1200    -0.0426     4.0810     0.1532    29.992113      2.1728      3.4580      4.0841\n  4500   1400    -0.0426     4.0810     0.1532    30.377729      2.1960      3.4433      4.0841\n  4500   1600    -0.0426     4.0810     0.1532    30.755295      2.2186      3.4287      4.0841\n  4500   1800    -0.0426     4.0810     0.1532    31.125143      2.2407      3.4143      4.0841\n  4500   2000    -0.0426     4.0810     0.1532    31.487579      2.2623      3.4001      4.0841\n  4500   2200    -0.0426     4.0810     0.1532    31.842892      2.2833      3.3860      4.0841\n  4500   2400    -0.0426     4.0810     0.1532    32.191352      2.3039      3.3721      4.0841\n  4500   2600    -0.0426     4.0810     0.1532    32.533211      2.3240      3.3583      4.0841\n  4500   2800    -0.0426     4.0810     0.1532    32.868710      2.3436      3.3446      4.0841\n  4500   3000    -0.0426     4.0810     0.1532    33.198070      2.3628      3.3311      4.0841\n  4500   3200    -0.0426     4.0810     0.1532    33.521506      2.3815      3.3177      4.0841\n  4500   3400    -0.0426     4.0810     0.1532    33.839215      2.3999      3.3044      4.0841\n  4500   3600    -0.0426     4.0810     0.1532    34.151388      2.4179      3.2913      4.0841\n  4500   3800    -0.0426     4.0810     0.1532    34.458204      2.4354      3.2783      4.0841\n  4500   4000    -0.0426     4.0810     0.1532    34.759832      2.4527      3.2654      4.0841\n  4500   4200    -0.0426     4.0810     0.1532    35.056433      2.4695      3.2527      4.0841\n  4500   4400    -0.0426     4.0810     0.1532    35.348161      2.4861      3.2401      4.0841\n  4500   4600    -0.0426     4.0810     0.1532    35.635161      2.5023      3.2276      4.0841\n  4500   4800    -0.0426     4.0810     0.1532    35.917572      2.5181      3.2152      4.0841\n  4500   5000    -0.0426     4.0810     0.1532    36.195527      2.5337      3.2029      4.0841\n  4500   5200    -0.0426     4.0810     0.1532    36.469151      2.5490      3.1908      4.0841\n  4500   5400    -0.0426     4.0810     0.1532    36.738565      2.5640      3.1788      4.0841\n  4500   5600    -0.0426     4.0810     0.1532    37.003885      2.5786      3.1669      4.0841\n  4500   5800    -0.0426     4.0810     0.1532    37.265221      2.5931      3.1551      4.0841\n  4500   6000    -0.0426     4.0810     0.1532    37.522678      2.6072      3.1434      4.0841\n  4500   6200    -0.0426     4.0810     0.1532    37.776357      2.6211      3.1318      4.0841\n  4500   6400    -0.0426     4.0810     0.1532    38.026356      2.6347      3.1204      4.0841\n  4500   6600    -0.0426     4.0810     0.1532    38.272767      2.6481      3.1090      4.0841\n  4500   6800    -0.0426     4.0810     0.1532    38.515680      2.6613      3.0977      4.0841\n  4500   7000    -0.0426     4.0810     0.1532    38.755181      2.6742      3.0866      4.0841\n  4500   7200    -0.0426     4.0810     0.1532    38.991352      2.6869      3.0755      4.0841\n  4500   7400    -0.0426     4.0810     0.1532    39.224272      2.6994      3.0646      4.0841\n  4500   7600    -0.0426     4.0810     0.1532    39.454020      2.7117      3.0537      4.0841\n  4500   7800    -0.0426     4.0810     0.1532    39.680667      2.7237      3.0430      4.0841\n  4500   8000    -0.0426     4.0810     0.1532    39.904285      2.7356      3.0323      4.0841\n  4500   8200    -0.0426     4.0810     0.1532    40.124942      2.7472      3.0218      4.0841\n  4500   8400    -0.0426     4.0810     0.1532    40.342705      2.7587      3.0113      4.0841\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180412_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -3.3824897      -65.5423278       36.9516791   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0300356        0.1047678   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -65.6149034       36.9707023   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0194198        0.0946307   m/s m/s m/s\nunwrap_phase_constant:           -0.00012     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180412_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -3.382    -65.542     36.952\norbit baseline rate   (TCN) (m/s):  0.000e+00 -3.004e-02  1.048e-01\n\nbaseline vector (TCN)   (m):      0.000    -65.615     36.971\nbaseline rate   (TCN) (m/s):  0.000e+00 -1.942e-02  9.463e-02\n\nSLC-1 center baseline length (m):    75.3137\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -65.4336    36.0874    27.496918      1.8001    -74.7034     74.7252\n     0    200     0.0000   -65.4336    36.0874    27.939152      1.2235    -74.7151     74.7252\n     0    400     0.0000   -65.4336    36.0874    28.370753      0.6606    -74.7222     74.7252\n     0    600     0.0000   -65.4336    36.0874    28.792243      0.1109    -74.7250     74.7252\n     0    800     0.0000   -65.4336    36.0874    29.204102     -0.4262    -74.7239     74.7252\n     0   1000     0.0000   -65.4336    36.0874    29.606772     -0.9514    -74.7191     74.7252\n     0   1200     0.0000   -65.4336    36.0874    30.000665     -1.4650    -74.7107     74.7252\n     0   1400     0.0000   -65.4336    36.0874    30.386160     -1.9676    -74.6992     74.7252\n     0   1600     0.0000   -65.4336    36.0874    30.763612     -2.4597    -74.6846     74.7252\n     0   1800     0.0000   -65.4336    36.0874    31.133349     -2.9416    -74.6672     74.7252\n     0   2000     0.0000   -65.4336    36.0874    31.495679     -3.4137    -74.6471     74.7252\n     0   2200     0.0000   -65.4336    36.0874    31.850890     -3.8764    -74.6245     74.7252\n     0   2400     0.0000   -65.4336    36.0874    32.199251     -4.3301    -74.5996     74.7252\n     0   2600     0.0000   -65.4336    36.0874    32.541017     -4.7750    -74.5724     74.7252\n     0   2800     0.0000   -65.4336    36.0874    32.876424     -5.2115    -74.5432     74.7252\n     0   3000     0.0000   -65.4336    36.0874    33.205696     -5.6398    -74.5120     74.7252\n     0   3200     0.0000   -65.4336    36.0874    33.529047     -6.0602    -74.4790     74.7252\n     0   3400     0.0000   -65.4336    36.0874    33.846674     -6.4730    -74.4443     74.7252\n     0   3600     0.0000   -65.4336    36.0874    34.158768     -6.8784    -74.4079     74.7252\n     0   3800     0.0000   -65.4336    36.0874    34.465506     -7.2766    -74.3700     74.7252\n     0   4000     0.0000   -65.4336    36.0874    34.767059     -7.6679    -74.3307     74.7252\n     0   4200     0.0000   -65.4336    36.0874    35.063588     -8.0525    -74.2900     74.7252\n     0   4400     0.0000   -65.4336    36.0874    35.355245     -8.4306    -74.2480     74.7252\n     0   4600     0.0000   -65.4336    36.0874    35.642177     -8.8023    -74.2049     74.7252\n     0   4800     0.0000   -65.4336    36.0874    35.924522     -9.1679    -74.1606     74.7252\n     0   5000     0.0000   -65.4336    36.0874    36.202412     -9.5274    -74.1153     74.7252\n     0   5200     0.0000   -65.4336    36.0874    36.475974     -9.8812    -74.0690     74.7252\n     0   5400     0.0000   -65.4336    36.0874    36.745327    -10.2293    -74.0217     74.7252\n     0   5600     0.0000   -65.4336    36.0874    37.010588    -10.5719    -73.9735     74.7252\n     0   5800     0.0000   -65.4336    36.0874    37.271866    -10.9091    -73.9246     74.7252\n     0   6000     0.0000   -65.4336    36.0874    37.529266    -11.2411    -73.8748     74.7252\n     0   6200     0.0000   -65.4336    36.0874    37.782890    -11.5680    -73.8243     74.7252\n     0   6400     0.0000   -65.4336    36.0874    38.032835    -11.8899    -73.7732     74.7252\n     0   6600     0.0000   -65.4336    36.0874    38.279194    -12.2070    -73.7214     74.7252\n     0   6800     0.0000   -65.4336    36.0874    38.522056    -12.5194    -73.6689     74.7252\n     0   7000     0.0000   -65.4336    36.0874    38.761507    -12.8272    -73.6160     74.7252\n     0   7200     0.0000   -65.4336    36.0874    38.997629    -13.1305    -73.5625     74.7252\n     0   7400     0.0000   -65.4336    36.0874    39.230502    -13.4293    -73.5085     74.7252\n     0   7600     0.0000   -65.4336    36.0874    39.460203    -13.7239    -73.4541     74.7252\n     0   7800     0.0000   -65.4336    36.0874    39.686804    -14.0143    -73.3992     74.7252\n     0   8000     0.0000   -65.4336    36.0874    39.910378    -14.3006    -73.3440     74.7252\n     0   8200     0.0000   -65.4336    36.0874    40.130991    -14.5829    -73.2884     74.7252\n     0   8400     0.0000   -65.4336    36.0874    40.348712    -14.8613    -73.2325     74.7252\n   500      0     0.0000   -65.4736    36.2819    27.495955      1.9555    -74.8286     74.8543\n   500    200     0.0000   -65.4736    36.2819    27.938206      1.3778    -74.8415     74.8543\n   500    400     0.0000   -65.4736    36.2819    28.369822      0.8140    -74.8497     74.8543\n   500    600     0.0000   -65.4736    36.2819    28.791327      0.2634    -74.8537     74.8543\n   500    800     0.0000   -65.4736    36.2819    29.203199     -0.2747    -74.8536     74.8543\n   500   1000     0.0000   -65.4736    36.2819    29.605883     -0.8008    -74.8499     74.8543\n   500   1200     0.0000   -65.4736    36.2819    29.999788     -1.3154    -74.8426     74.8543\n   500   1400     0.0000   -65.4736    36.2819    30.385296     -1.8189    -74.8321     74.8543\n   500   1600     0.0000   -65.4736    36.2819    30.762759     -2.3119    -74.8185     74.8543\n   500   1800     0.0000   -65.4736    36.2819    31.132507     -2.7946    -74.8020     74.8543\n   500   2000     0.0000   -65.4736    36.2819    31.494848     -3.2676    -74.7828     74.8543\n   500   2200     0.0000   -65.4736    36.2819    31.850069     -3.7312    -74.7611     74.8543\n   500   2400     0.0000   -65.4736    36.2819    32.198441     -4.1857    -74.7371     74.8543\n   500   2600     0.0000   -65.4736    36.2819    32.540215     -4.6314    -74.7108     74.8543\n   500   2800     0.0000   -65.4736    36.2819    32.875632     -5.0687    -74.6824     74.8543\n   500   3000     0.0000   -65.4736    36.2819    33.204913     -5.4978    -74.6520     74.8543\n   500   3200     0.0000   -65.4736    36.2819    33.528272     -5.9191    -74.6198     74.8543\n   500   3400     0.0000   -65.4736    36.2819    33.845908     -6.3326    -74.5858     74.8543\n   500   3600     0.0000   -65.4736    36.2819    34.158010     -6.7388    -74.5502     74.8543\n   500   3800     0.0000   -65.4736    36.2819    34.464756     -7.1379    -74.5131     74.8543\n   500   4000     0.0000   -65.4736    36.2819    34.766316     -7.5299    -74.4745     74.8543\n   500   4200     0.0000   -65.4736    36.2819    35.062852     -7.9153    -74.4345     74.8543\n   500   4400     0.0000   -65.4736    36.2819    35.354517     -8.2941    -74.3933     74.8543\n   500   4600     0.0000   -65.4736    36.2819    35.641456     -8.6666    -74.3508     74.8543\n   500   4800     0.0000   -65.4736    36.2819    35.923807     -9.0328    -74.3072     74.8543\n   500   5000     0.0000   -65.4736    36.2819    36.201704     -9.3931    -74.2625     74.8543\n   500   5200     0.0000   -65.4736    36.2819    36.475272     -9.7476    -74.2168     74.8543\n   500   5400     0.0000   -65.4736    36.2819    36.744632    -10.0964    -74.1702     74.8543\n   500   5600     0.0000   -65.4736    36.2819    37.009898    -10.4397    -74.1226     74.8543\n   500   5800     0.0000   -65.4736    36.2819    37.271182    -10.7776    -74.0743     74.8543\n   500   6000     0.0000   -65.4736    36.2819    37.528588    -11.1103    -74.0251     74.8543\n   500   6200     0.0000   -65.4736    36.2819    37.782218    -11.4379    -73.9752     74.8543\n   500   6400     0.0000   -65.4736    36.2819    38.032168    -11.7605    -73.9246     74.8543\n   500   6600     0.0000   -65.4736    36.2819    38.278532    -12.0782    -73.8733     74.8543\n   500   6800     0.0000   -65.4736    36.2819    38.521399    -12.3912    -73.8215     74.8543\n   500   7000     0.0000   -65.4736    36.2819    38.760855    -12.6997    -73.7690     74.8543\n   500   7200     0.0000   -65.4736    36.2819    38.996982    -13.0036    -73.7161     74.8543\n   500   7400     0.0000   -65.4736    36.2819    39.229860    -13.3031    -73.6626     74.8543\n   500   7600     0.0000   -65.4736    36.2819    39.459566    -13.5983    -73.6087     74.8543\n   500   7800     0.0000   -65.4736    36.2819    39.686172    -13.8893    -73.5543     74.8543\n   500   8000     0.0000   -65.4736    36.2819    39.909750    -14.1762    -73.4996     74.8543\n   500   8200     0.0000   -65.4736    36.2819    40.130368    -14.4591    -73.4444     74.8543\n   500   8400     0.0000   -65.4736    36.2819    40.348092    -14.7381    -73.3890     74.8543\n  1000      0     0.0000   -65.5135    36.4764    27.494972      2.1109    -74.9538     74.9836\n  1000    200     0.0000   -65.5135    36.4764    27.937239      1.5323    -74.9678     74.9836\n  1000    400     0.0000   -65.5135    36.4764    28.368871      0.9675    -74.9773     74.9836\n  1000    600     0.0000   -65.5135    36.4764    28.790391      0.4158    -74.9823     74.9836\n  1000    800     0.0000   -65.5135    36.4764    29.202277     -0.1232    -74.9834     74.9836\n  1000   1000     0.0000   -65.5135    36.4764    29.604975     -0.6502    -74.9807     74.9836\n  1000   1200     0.0000   -65.5135    36.4764    29.998893     -1.1657    -74.9744     74.9836\n  1000   1400     0.0000   -65.5135    36.4764    30.384413     -1.6701    -74.9649     74.9836\n  1000   1600     0.0000   -65.5135    36.4764    30.761888     -2.1640    -74.9523     74.9836\n  1000   1800     0.0000   -65.5135    36.4764    31.131648     -2.6477    -74.9368     74.9836\n  1000   2000     0.0000   -65.5135    36.4764    31.494000     -3.1215    -74.9185     74.9836\n  1000   2200     0.0000   -65.5135    36.4764    31.849231     -3.5859    -74.8977     74.9836\n  1000   2400     0.0000   -65.5135    36.4764    32.197613     -4.0413    -74.8745     74.9836\n  1000   2600     0.0000   -65.5135    36.4764    32.539398     -4.4879    -74.8491     74.9836\n  1000   2800     0.0000   -65.5135    36.4764    32.874823     -4.9260    -74.8216     74.9836\n  1000   3000     0.0000   -65.5135    36.4764    33.204114     -5.3559    -74.7920     74.9836\n  1000   3200     0.0000   -65.5135    36.4764    33.527482     -5.7779    -74.7606     74.9836\n  1000   3400     0.0000   -65.5135    36.4764    33.845126     -6.1923    -74.7274     74.9836\n  1000   3600     0.0000   -65.5135    36.4764    34.157236     -6.5993    -74.6926     74.9836\n  1000   3800     0.0000   -65.5135    36.4764    34.463990     -6.9991    -74.6562     74.9836\n  1000   4000     0.0000   -65.5135    36.4764    34.765558     -7.3919    -74.6183     74.9836\n  1000   4200     0.0000   -65.5135    36.4764    35.062102     -7.7780    -74.5790     74.9836\n  1000   4400     0.0000   -65.5135    36.4764    35.353774     -8.1576    -74.5385     74.9836\n  1000   4600     0.0000   -65.5135    36.4764    35.640720     -8.5308    -74.4967     74.9836\n  1000   4800     0.0000   -65.5135    36.4764    35.923078     -8.8978    -74.4538     74.9836\n  1000   5000     0.0000   -65.5135    36.4764    36.200982     -9.2588    -74.4097     74.9836\n  1000   5200     0.0000   -65.5135    36.4764    36.474556     -9.6140    -74.3647     74.9836\n  1000   5400     0.0000   -65.5135    36.4764    36.743922     -9.9635    -74.3186     74.9836\n  1000   5600     0.0000   -65.5135    36.4764    37.009195    -10.3075    -74.2717     74.9836\n  1000   5800     0.0000   -65.5135    36.4764    37.270484    -10.6461    -74.2239     74.9836\n  1000   6000     0.0000   -65.5135    36.4764    37.527896    -10.9794    -74.1754     74.9836\n  1000   6200     0.0000   -65.5135    36.4764    37.781532    -11.3077    -74.1260     74.9836\n  1000   6400     0.0000   -65.5135    36.4764    38.031488    -11.6310    -74.0760     74.9836\n  1000   6600     0.0000   -65.5135    36.4764    38.277857    -11.9494    -74.0253     74.9836\n  1000   6800     0.0000   -65.5135    36.4764    38.520730    -12.2631    -73.9740     74.9836\n  1000   7000     0.0000   -65.5135    36.4764    38.760191    -12.5721    -73.9221     74.9836\n  1000   7200     0.0000   -65.5135    36.4764    38.996323    -12.8767    -73.8697     74.9836\n  1000   7400     0.0000   -65.5135    36.4764    39.229206    -13.1768    -73.8167     74.9836\n  1000   7600     0.0000   -65.5135    36.4764    39.458916    -13.4726    -73.7633     74.9836\n  1000   7800     0.0000   -65.5135    36.4764    39.685527    -13.7643    -73.7094     74.9836\n  1000   8000     0.0000   -65.5135    36.4764    39.909109    -14.0518    -73.6552     74.9836\n  1000   8200     0.0000   -65.5135    36.4764    40.129732    -14.3353    -73.6005     74.9836\n  1000   8400     0.0000   -65.5135    36.4764    40.347461    -14.6149    -73.5455     74.9836\n  1500      0     0.0000   -65.5534    36.6709    27.493968      2.2663    -75.0789     75.1133\n  1500    200     0.0000   -65.5534    36.6709    27.936252      1.6867    -75.0942     75.1133\n  1500    400     0.0000   -65.5534    36.6709    28.367900      1.1209    -75.1048     75.1133\n  1500    600     0.0000   -65.5534    36.6709    28.789435      0.5683    -75.1110     75.1133\n  1500    800     0.0000   -65.5534    36.6709    29.201336      0.0283    -75.1132     75.1133\n  1500   1000     0.0000   -65.5534    36.6709    29.604048     -0.4996    -75.1115     75.1133\n  1500   1200     0.0000   -65.5534    36.6709    29.997980     -1.0160    -75.1063     75.1133\n  1500   1400     0.0000   -65.5534    36.6709    30.383512     -1.5214    -75.0978     75.1133\n  1500   1600     0.0000   -65.5534    36.6709    30.760999     -2.0161    -75.0861     75.1133\n  1500   1800     0.0000   -65.5534    36.6709    31.130771     -2.5006    -75.0715     75.1133\n  1500   2000     0.0000   -65.5534    36.6709    31.493134     -2.9754    -75.0542     75.1133\n  1500   2200     0.0000   -65.5534    36.6709    31.848377     -3.4407    -75.0343     75.1133\n  1500   2400     0.0000   -65.5534    36.6709    32.196769     -3.8968    -75.0120     75.1133\n  1500   2600     0.0000   -65.5534    36.6709    32.538563     -4.3443    -74.9874     75.1133\n  1500   2800     0.0000   -65.5534    36.6709    32.873999     -4.7832    -74.9607     75.1133\n  1500   3000     0.0000   -65.5534    36.6709    33.203299     -5.2139    -74.9320     75.1133\n  1500   3200     0.0000   -65.5534    36.6709    33.526676     -5.6368    -74.9014     75.1133\n  1500   3400     0.0000   -65.5534    36.6709    33.844329     -6.0519    -74.8690     75.1133\n  1500   3600     0.0000   -65.5534    36.6709    34.156447     -6.4597    -74.8349     75.1133\n  1500   3800     0.0000   -65.5534    36.6709    34.463209     -6.8603    -74.7993     75.1133\n  1500   4000     0.0000   -65.5534    36.6709    34.764785     -7.2539    -74.7621     75.1133\n  1500   4200     0.0000   -65.5534    36.6709    35.061337     -7.6407    -74.7236     75.1133\n  1500   4400     0.0000   -65.5534    36.6709    35.353016     -8.0210    -74.6837     75.1133\n  1500   4600     0.0000   -65.5534    36.6709    35.639969     -8.3950    -74.6426     75.1133\n  1500   4800     0.0000   -65.5534    36.6709    35.922335     -8.7627    -74.6003     75.1133\n  1500   5000     0.0000   -65.5534    36.6709    36.200245     -9.1245    -74.5569     75.1133\n  1500   5200     0.0000   -65.5534    36.6709    36.473826     -9.4804    -74.5125     75.1133\n  1500   5400     0.0000   -65.5534    36.6709    36.743198     -9.8306    -74.4671     75.1133\n  1500   5600     0.0000   -65.5534    36.6709    37.008477    -10.1752    -74.4208     75.1133\n  1500   5800     0.0000   -65.5534    36.6709    37.269773    -10.5145    -74.3736     75.1133\n  1500   6000     0.0000   -65.5534    36.6709    37.527191    -10.8486    -74.3257     75.1133\n  1500   6200     0.0000   -65.5534    36.6709    37.780832    -11.1775    -74.2769     75.1133\n  1500   6400     0.0000   -65.5534    36.6709    38.030794    -11.5014    -74.2274     75.1133\n  1500   6600     0.0000   -65.5534    36.6709    38.277169    -11.8205    -74.1773     75.1133\n  1500   6800     0.0000   -65.5534    36.6709    38.520047    -12.1348    -74.1265     75.1133\n  1500   7000     0.0000   -65.5534    36.6709    38.759513    -12.4446    -74.0752     75.1133\n  1500   7200     0.0000   -65.5534    36.6709    38.995651    -12.7497    -74.0232     75.1133\n  1500   7400     0.0000   -65.5534    36.6709    39.228539    -13.0505    -73.9708     75.1133\n  1500   7600     0.0000   -65.5534    36.6709    39.458254    -13.3470    -73.9179     75.1133\n  1500   7800     0.0000   -65.5534    36.6709    39.684869    -13.6392    -73.8645     75.1133\n  1500   8000     0.0000   -65.5534    36.6709    39.908457    -13.9274    -73.8107     75.1133\n  1500   8200     0.0000   -65.5534    36.6709    40.129084    -14.2115    -73.7566     75.1133\n  1500   8400     0.0000   -65.5534    36.6709    40.346818    -14.4917    -73.7020     75.1133\n  2000      0     0.0000   -65.5933    36.8655    27.492944      2.4218    -75.2041     75.2432\n  2000    200     0.0000   -65.5933    36.8655    27.935245      1.8412    -75.2206     75.2432\n  2000    400     0.0000   -65.5933    36.8655    28.366910      1.2744    -75.2323     75.2432\n  2000    600     0.0000   -65.5933    36.8655    28.788460      0.7209    -75.2397     75.2432\n  2000    800     0.0000   -65.5933    36.8655    29.200376      0.1799    -75.2429     75.2432\n  2000   1000     0.0000   -65.5933    36.8655    29.603102     -0.3489    -75.2423     75.2432\n  2000   1200     0.0000   -65.5933    36.8655    29.997048     -0.8663    -75.2381     75.2432\n  2000   1400     0.0000   -65.5933    36.8655    30.382593     -1.3725    -75.2306     75.2432\n  2000   1600     0.0000   -65.5933    36.8655    30.760093     -1.8682    -75.2199     75.2432\n  2000   1800     0.0000   -65.5933    36.8655    31.129876     -2.3536    -75.2063     75.2432\n  2000   2000     0.0000   -65.5933    36.8655    31.492251     -2.8292    -75.1899     75.2432\n  2000   2200     0.0000   -65.5933    36.8655    31.847505     -3.2953    -75.1709     75.2432\n  2000   2400     0.0000   -65.5933    36.8655    32.195907     -3.7524    -75.1495     75.2432\n  2000   2600     0.0000   -65.5933    36.8655    32.537712     -4.2006    -75.1258     75.2432\n  2000   2800     0.0000   -65.5933    36.8655    32.873158     -4.6404    -75.0999     75.2432\n  2000   3000     0.0000   -65.5933    36.8655    33.202467     -5.0719    -75.0720     75.2432\n  2000   3200     0.0000   -65.5933    36.8655    33.525853     -5.4956    -75.0422     75.2432\n  2000   3400     0.0000   -65.5933    36.8655    33.843515     -5.9115    -75.0106     75.2432\n  2000   3600     0.0000   -65.5933    36.8655    34.155642     -6.3201    -74.9772     75.2432\n  2000   3800     0.0000   -65.5933    36.8655    34.462413     -6.7214    -74.9423     75.2432\n  2000   4000     0.0000   -65.5933    36.8655    34.763997     -7.1158    -74.9059     75.2432\n  2000   4200     0.0000   -65.5933    36.8655    35.060556     -7.5034    -74.8681     75.2432\n  2000   4400     0.0000   -65.5933    36.8655    35.352243     -7.8845    -74.8289     75.2432\n  2000   4600     0.0000   -65.5933    36.8655    35.639204     -8.2591    -74.7885     75.2432\n  2000   4800     0.0000   -65.5933    36.8655    35.921577     -8.6276    -74.7469     75.2432\n  2000   5000     0.0000   -65.5933    36.8655    36.199494     -8.9901    -74.7042     75.2432\n  2000   5200     0.0000   -65.5933    36.8655    36.473082     -9.3467    -74.6604     75.2432\n  2000   5400     0.0000   -65.5933    36.8655    36.742461     -9.6976    -74.6156     75.2432\n  2000   5600     0.0000   -65.5933    36.8655    37.007746    -10.0430    -74.5699     75.2432\n  2000   5800     0.0000   -65.5933    36.8655    37.269048    -10.3830    -74.5233     75.2432\n  2000   6000     0.0000   -65.5933    36.8655    37.526472    -10.7177    -74.4759     75.2432\n  2000   6200     0.0000   -65.5933    36.8655    37.780120    -11.0473    -74.4278     75.2432\n  2000   6400     0.0000   -65.5933    36.8655    38.030087    -11.3719    -74.3789     75.2432\n  2000   6600     0.0000   -65.5933    36.8655    38.276468    -11.6916    -74.3293     75.2432\n  2000   6800     0.0000   -65.5933    36.8655    38.519351    -12.0066    -74.2790     75.2432\n  2000   7000     0.0000   -65.5933    36.8655    38.758823    -12.3170    -74.2282     75.2432\n  2000   7200     0.0000   -65.5933    36.8655    38.994966    -12.6228    -74.1768     75.2432\n  2000   7400     0.0000   -65.5933    36.8655    39.227859    -12.9242    -74.1249     75.2432\n  2000   7600     0.0000   -65.5933    36.8655    39.457579    -13.2213    -74.0725     75.2432\n  2000   7800     0.0000   -65.5933    36.8655    39.684200    -13.5142    -74.0196     75.2432\n  2000   8000     0.0000   -65.5933    36.8655    39.907792    -13.8029    -73.9663     75.2432\n  2000   8200     0.0000   -65.5933    36.8655    40.128424    -14.0876    -73.9126     75.2432\n  2000   8400     0.0000   -65.5933    36.8655    40.346162    -14.3684    -73.8585     75.2432\n  2500      0     0.0000   -65.6332    37.0600    27.491899      2.5773    -75.3293     75.3735\n  2500    200     0.0000   -65.6332    37.0600    27.934218      1.9957    -75.3469     75.3735\n  2500    400     0.0000   -65.6332    37.0600    28.365900      1.4279    -75.3598     75.3735\n  2500    600     0.0000   -65.6332    37.0600    28.787466      0.8734    -75.3683     75.3735\n  2500    800     0.0000   -65.6332    37.0600    29.199398      0.3315    -75.3726     75.3735\n  2500   1000     0.0000   -65.6332    37.0600    29.602138     -0.1983    -75.3731     75.3735\n  2500   1200     0.0000   -65.6332    37.0600    29.996097     -0.7165    -75.3700     75.3735\n  2500   1400     0.0000   -65.6332    37.0600    30.381656     -1.2237    -75.3634     75.3735\n  2500   1600     0.0000   -65.6332    37.0600    30.759169     -1.7202    -75.3537     75.3735\n  2500   1800     0.0000   -65.6332    37.0600    31.128964     -2.2065    -75.3411     75.3735\n  2500   2000     0.0000   -65.6332    37.0600    31.491351     -2.6830    -75.3256     75.3735\n  2500   2200     0.0000   -65.6332    37.0600    31.846616     -3.1500    -75.3075     75.3735\n  2500   2400     0.0000   -65.6332    37.0600    32.195030     -3.6079    -75.2870     75.3735\n  2500   2600     0.0000   -65.6332    37.0600    32.536845     -4.0570    -75.2641     75.3735\n  2500   2800     0.0000   -65.6332    37.0600    32.872300     -4.4976    -75.2391     75.3735\n  2500   3000     0.0000   -65.6332    37.0600    33.201620     -4.9299    -75.2120     75.3735\n  2500   3200     0.0000   -65.6332    37.0600    33.525015     -5.3544    -75.1830     75.3735\n  2500   3400     0.0000   -65.6332    37.0600    33.842686     -5.7711    -75.1521     75.3735\n  2500   3600     0.0000   -65.6332    37.0600    34.154822     -6.1805    -75.1196     75.3735\n  2500   3800     0.0000   -65.6332    37.0600    34.461601     -6.5826    -75.0854     75.3735\n  2500   4000     0.0000   -65.6332    37.0600    34.763194     -6.9777    -75.0497     75.3735\n  2500   4200     0.0000   -65.6332    37.0600    35.059761     -7.3661    -75.0126     75.3735\n  2500   4400     0.0000   -65.6332    37.0600    35.351456     -7.7479    -74.9741     75.3735\n  2500   4600     0.0000   -65.6332    37.0600    35.638424     -8.1233    -74.9344     75.3735\n  2500   4800     0.0000   -65.6332    37.0600    35.920804     -8.4925    -74.8934     75.3735\n  2500   5000     0.0000   -65.6332    37.0600    36.198729     -8.8557    -74.8514     75.3735\n  2500   5200     0.0000   -65.6332    37.0600    36.472323     -9.2130    -74.8082     75.3735\n  2500   5400     0.0000   -65.6332    37.0600    36.741709     -9.5646    -74.7641     75.3735\n  2500   5600     0.0000   -65.6332    37.0600    37.007001     -9.9107    -74.7190     75.3735\n  2500   5800     0.0000   -65.6332    37.0600    37.268310    -10.2514    -74.6730     75.3735\n  2500   6000     0.0000   -65.6332    37.0600    37.525740    -10.5868    -74.6262     75.3735\n  2500   6200     0.0000   -65.6332    37.0600    37.779394    -10.9171    -74.5786     75.3735\n  2500   6400     0.0000   -65.6332    37.0600    38.029367    -11.2423    -74.5303     75.3735\n  2500   6600     0.0000   -65.6332    37.0600    38.275754    -11.5627    -74.4812     75.3735\n  2500   6800     0.0000   -65.6332    37.0600    38.518643    -11.8784    -74.4316     75.3735\n  2500   7000     0.0000   -65.6332    37.0600    38.758120    -12.1894    -74.3813     75.3735\n  2500   7200     0.0000   -65.6332    37.0600    38.994268    -12.4958    -74.3304     75.3735\n  2500   7400     0.0000   -65.6332    37.0600    39.227167    -12.7979    -74.2790     75.3735\n  2500   7600     0.0000   -65.6332    37.0600    39.456892    -13.0956    -74.2271     75.3735\n  2500   7800     0.0000   -65.6332    37.0600    39.683518    -13.3891    -74.1747     75.3735\n  2500   8000     0.0000   -65.6332    37.0600    39.907115    -13.6784    -74.1219     75.3735\n  2500   8200     0.0000   -65.6332    37.0600    40.127752    -13.9638    -74.0687     75.3735\n  2500   8400     0.0000   -65.6332    37.0600    40.345495    -14.2452    -74.0151     75.3735\n  3000      0     0.0000   -65.6731    37.2545    27.490833      2.7328    -75.4544     75.5040\n  3000    200     0.0000   -65.6731    37.2545    27.933171      2.1502    -75.4733     75.5040\n  3000    400     0.0000   -65.6731    37.2545    28.364870      1.5815    -75.4873     75.5040\n  3000    600     0.0000   -65.6731    37.2545    28.786452      1.0260    -75.4969     75.5040\n  3000    800     0.0000   -65.6731    37.2545    29.198400      0.4832    -75.5024     75.5040\n  3000   1000     0.0000   -65.6731    37.2545    29.601155     -0.0476    -75.5039     75.5040\n  3000   1200     0.0000   -65.6731    37.2545    29.995129     -0.5667    -75.5018     75.5040\n  3000   1400     0.0000   -65.6731    37.2545    30.380701     -1.0748    -75.4963     75.5040\n  3000   1600     0.0000   -65.6731    37.2545    30.758227     -1.5722    -75.4876     75.5040\n  3000   1800     0.0000   -65.6731    37.2545    31.128035     -2.0594    -75.4758     75.5040\n  3000   2000     0.0000   -65.6731    37.2545    31.490433     -2.5368    -75.4613     75.5040\n  3000   2200     0.0000   -65.6731    37.2545    31.845710     -3.0046    -75.4441     75.5040\n  3000   2400     0.0000   -65.6731    37.2545    32.194135     -3.4634    -75.4245     75.5040\n  3000   2600     0.0000   -65.6731    37.2545    32.535961     -3.9133    -75.4025     75.5040\n  3000   2800     0.0000   -65.6731    37.2545    32.871427     -4.3547    -75.3783     75.5040\n  3000   3000     0.0000   -65.6731    37.2545    33.200756     -4.7879    -75.3520     75.5040\n  3000   3200     0.0000   -65.6731    37.2545    33.524162     -5.2131    -75.3238     75.5040\n  3000   3400     0.0000   -65.6731    37.2545    33.841842     -5.6307    -75.2937     75.5040\n  3000   3600     0.0000   -65.6731    37.2545    34.153987     -6.0408    -75.2619     75.5040\n  3000   3800     0.0000   -65.6731    37.2545    34.460775     -6.4437    -75.2285     75.5040\n  3000   4000     0.0000   -65.6731    37.2545    34.762376     -6.8396    -75.1935     75.5040\n  3000   4200     0.0000   -65.6731    37.2545    35.058951     -7.2287    -75.1571     75.5040\n  3000   4400     0.0000   -65.6731    37.2545    35.350654     -7.6113    -75.1193     75.5040\n  3000   4600     0.0000   -65.6731    37.2545    35.637630     -7.9874    -75.0803     75.5040\n  3000   4800     0.0000   -65.6731    37.2545    35.920018     -8.3574    -75.0400     75.5040\n  3000   5000     0.0000   -65.6731    37.2545    36.197949     -8.7213    -74.9986     75.5040\n  3000   5200     0.0000   -65.6731    37.2545    36.471551     -9.0793    -74.9561     75.5040\n  3000   5400     0.0000   -65.6731    37.2545    36.740944     -9.4316    -74.9126     75.5040\n  3000   5600     0.0000   -65.6731    37.2545    37.006243     -9.7784    -74.8681     75.5040\n  3000   5800     0.0000   -65.6731    37.2545    37.267558    -10.1198    -74.8227     75.5040\n  3000   6000     0.0000   -65.6731    37.2545    37.524995    -10.4559    -74.7765     75.5040\n  3000   6200     0.0000   -65.6731    37.2545    37.778654    -10.7868    -74.7295     75.5040\n  3000   6400     0.0000   -65.6731    37.2545    38.028634    -11.1127    -74.6817     75.5040\n  3000   6600     0.0000   -65.6731    37.2545    38.275026    -11.4338    -74.6332     75.5040\n  3000   6800     0.0000   -65.6731    37.2545    38.517921    -11.7501    -74.5841     75.5040\n  3000   7000     0.0000   -65.6731    37.2545    38.757404    -12.0617    -74.5343     75.5040\n  3000   7200     0.0000   -65.6731    37.2545    38.993558    -12.3688    -74.4840     75.5040\n  3000   7400     0.0000   -65.6731    37.2545    39.226462    -12.6715    -74.4331     75.5040\n  3000   7600     0.0000   -65.6731    37.2545    39.456192    -12.9698    -74.3817     75.5040\n  3000   7800     0.0000   -65.6731    37.2545    39.682823    -13.2640    -74.3298     75.5040\n  3000   8000     0.0000   -65.6731    37.2545    39.906425    -13.5539    -74.2775     75.5040\n  3000   8200     0.0000   -65.6731    37.2545    40.127067    -13.8399    -74.2247     75.5040\n  3000   8400     0.0000   -65.6731    37.2545    40.344815    -14.1219    -74.1716     75.5040\n  3500      0     0.0000   -65.7131    37.4490    27.489747      2.8884    -75.5796     75.6349\n  3500    200     0.0000   -65.7131    37.4490    27.932104      2.3048    -75.5996     75.6349\n  3500    400     0.0000   -65.7131    37.4490    28.363820      1.7351    -75.6148     75.6349\n  3500    600     0.0000   -65.7131    37.4490    28.785420      1.1786    -75.6256     75.6349\n  3500    800     0.0000   -65.7131    37.4490    29.197383      0.6349    -75.6321     75.6349\n  3500   1000     0.0000   -65.7131    37.4490    29.600153      0.1032    -75.6347     75.6349\n  3500   1200     0.0000   -65.7131    37.4490    29.994142     -0.4169    -75.6336     75.6349\n  3500   1400     0.0000   -65.7131    37.4490    30.379728     -0.9259    -75.6291     75.6349\n  3500   1600     0.0000   -65.7131    37.4490    30.757267     -1.4242    -75.6214     75.6349\n  3500   1800     0.0000   -65.7131    37.4490    31.127088     -1.9123    -75.6106     75.6349\n  3500   2000     0.0000   -65.7131    37.4490    31.489499     -2.3905    -75.5970     75.6349\n  3500   2200     0.0000   -65.7131    37.4490    31.844788     -2.8592    -75.5807     75.6349\n  3500   2400     0.0000   -65.7131    37.4490    32.193224     -3.3188    -75.5619     75.6349\n  3500   2600     0.0000   -65.7131    37.4490    32.535061     -3.7696    -75.5408     75.6349\n  3500   2800     0.0000   -65.7131    37.4490    32.870537     -4.2118    -75.5174     75.6349\n  3500   3000     0.0000   -65.7131    37.4490    33.199877     -4.6458    -75.4920     75.6349\n  3500   3200     0.0000   -65.7131    37.4490    33.523292     -5.0719    -75.4645     75.6349\n  3500   3400     0.0000   -65.7131    37.4490    33.840982     -5.4902    -75.4353     75.6349\n  3500   3600     0.0000   -65.7131    37.4490    34.153136     -5.9011    -75.4042     75.6349\n  3500   3800     0.0000   -65.7131    37.4490    34.459933     -6.3048    -75.3716     75.6349\n  3500   4000     0.0000   -65.7131    37.4490    34.761543     -6.7015    -75.3373     75.6349\n  3500   4200     0.0000   -65.7131    37.4490    35.058127     -7.0914    -75.3016     75.6349\n  3500   4400     0.0000   -65.7131    37.4490    35.349838     -7.4747    -75.2646     75.6349\n  3500   4600     0.0000   -65.7131    37.4490    35.636821     -7.8515    -75.2262     75.6349\n  3500   4800     0.0000   -65.7131    37.4490    35.919217     -8.2222    -75.1866     75.6349\n  3500   5000     0.0000   -65.7131    37.4490    36.197156     -8.5868    -75.1458     75.6349\n  3500   5200     0.0000   -65.7131    37.4490    36.470765     -8.9456    -75.1039     75.6349\n  3500   5400     0.0000   -65.7131    37.4490    36.740165     -9.2986    -75.0610     75.6349\n  3500   5600     0.0000   -65.7131    37.4490    37.005471     -9.6461    -75.0172     75.6349\n  3500   5800     0.0000   -65.7131    37.4490    37.266792     -9.9881    -74.9724     75.6349\n  3500   6000     0.0000   -65.7131    37.4490    37.524236    -10.3249    -74.9268     75.6349\n  3500   6200     0.0000   -65.7131    37.4490    37.777902    -10.6565    -74.8803     75.6349\n  3500   6400     0.0000   -65.7131    37.4490    38.027888    -10.9831    -74.8331     75.6349\n  3500   6600     0.0000   -65.7131    37.4490    38.274286    -11.3049    -74.7852     75.6349\n  3500   6800     0.0000   -65.7131    37.4490    38.517187    -11.6218    -74.7366     75.6349\n  3500   7000     0.0000   -65.7131    37.4490    38.756676    -11.9341    -74.6874     75.6349\n  3500   7200     0.0000   -65.7131    37.4490    38.992835    -12.2418    -74.6375     75.6349\n  3500   7400     0.0000   -65.7131    37.4490    39.225745    -12.5451    -74.5872     75.6349\n  3500   7600     0.0000   -65.7131    37.4490    39.455480    -12.8441    -74.5363     75.6349\n  3500   7800     0.0000   -65.7131    37.4490    39.682117    -13.1388    -74.4849     75.6349\n  3500   8000     0.0000   -65.7131    37.4490    39.905724    -13.4294    -74.4330     75.6349\n  3500   8200     0.0000   -65.7131    37.4490    40.126371    -13.7160    -74.3808     75.6349\n  3500   8400     0.0000   -65.7131    37.4490    40.344124    -13.9986    -74.3281     75.6349\n  4000      0     0.0000   -65.7530    37.6435    27.488641      3.0440    -75.7047     75.7660\n  4000    200     0.0000   -65.7530    37.6435    27.931016      2.4594    -75.7260     75.7660\n  4000    400     0.0000   -65.7530    37.6435    28.362751      1.8887    -75.7423     75.7660\n  4000    600     0.0000   -65.7530    37.6435    28.784368      1.3313    -75.7542     75.7660\n  4000    800     0.0000   -65.7530    37.6435    29.196347      0.7866    -75.7618     75.7660\n  4000   1000     0.0000   -65.7530    37.6435    29.599133      0.2539    -75.7655     75.7660\n  4000   1200     0.0000   -65.7530    37.6435    29.993137     -0.2671    -75.7654     75.7660\n  4000   1400     0.0000   -65.7530    37.6435    30.378737     -0.7770    -75.7619     75.7660\n  4000   1600     0.0000   -65.7530    37.6435    30.756290     -1.2762    -75.7552     75.7660\n  4000   1800     0.0000   -65.7530    37.6435    31.126124     -1.7651    -75.7454     75.7660\n  4000   2000     0.0000   -65.7530    37.6435    31.488547     -2.2442    -75.7327     75.7660\n  4000   2200     0.0000   -65.7530    37.6435    31.843848     -2.7138    -75.7173     75.7660\n  4000   2400     0.0000   -65.7530    37.6435    32.192296     -3.1743    -75.6994     75.7660\n  4000   2600     0.0000   -65.7530    37.6435    32.534144     -3.6258    -75.6791     75.7660\n  4000   2800     0.0000   -65.7530    37.6435    32.869631     -4.0689    -75.6566     75.7660\n  4000   3000     0.0000   -65.7530    37.6435    33.198982     -4.5037    -75.6320     75.7660\n  4000   3200     0.0000   -65.7530    37.6435    33.522407     -4.9306    -75.6053     75.7660\n  4000   3400     0.0000   -65.7530    37.6435    33.840106     -5.3497    -75.5768     75.7660\n  4000   3600     0.0000   -65.7530    37.6435    34.152270     -5.7614    -75.5466     75.7660\n  4000   3800     0.0000   -65.7530    37.6435    34.459076     -6.1659    -75.5146     75.7660\n  4000   4000     0.0000   -65.7530    37.6435    34.760695     -6.5633    -75.4811     75.7660\n  4000   4200     0.0000   -65.7530    37.6435    35.057287     -6.9540    -75.4461     75.7660\n  4000   4400     0.0000   -65.7530    37.6435    35.349006     -7.3380    -75.4098     75.7660\n  4000   4600     0.0000   -65.7530    37.6435    35.635998     -7.7156    -75.3721     75.7660\n  4000   4800     0.0000   -65.7530    37.6435    35.918401     -8.0870    -75.3331     75.7660\n  4000   5000     0.0000   -65.7530    37.6435    36.196348     -8.4524    -75.2930     75.7660\n  4000   5200     0.0000   -65.7530    37.6435    36.469965     -8.8118    -75.2518     75.7660\n  4000   5400     0.0000   -65.7530    37.6435    36.739372     -9.1656    -75.2095     75.7660\n  4000   5600     0.0000   -65.7530    37.6435    37.004685     -9.5138    -75.1663     75.7660\n  4000   5800     0.0000   -65.7530    37.6435    37.266013     -9.8565    -75.1221     75.7660\n  4000   6000     0.0000   -65.7530    37.6435    37.523463    -10.1939    -75.0770     75.7660\n  4000   6200     0.0000   -65.7530    37.6435    37.777136    -10.5262    -75.0312     75.7660\n  4000   6400     0.0000   -65.7530    37.6435    38.027128    -10.8535    -74.9845     75.7660\n  4000   6600     0.0000   -65.7530    37.6435    38.273533    -11.1759    -74.9372     75.7660\n  4000   6800     0.0000   -65.7530    37.6435    38.516440    -11.4935    -74.8891     75.7660\n  4000   7000     0.0000   -65.7530    37.6435    38.755934    -11.8064    -74.8404     75.7660\n  4000   7200     0.0000   -65.7530    37.6435    38.992100    -12.1148    -74.7911     75.7660\n  4000   7400     0.0000   -65.7530    37.6435    39.225015    -12.4187    -74.7413     75.7660\n  4000   7600     0.0000   -65.7530    37.6435    39.454756    -12.7183    -74.6909     75.7660\n  4000   7800     0.0000   -65.7530    37.6435    39.681398    -13.0137    -74.6400     75.7660\n  4000   8000     0.0000   -65.7530    37.6435    39.905010    -13.3049    -74.5886     75.7660\n  4000   8200     0.0000   -65.7530    37.6435    40.125663    -13.5920    -74.5368     75.7660\n  4000   8400     0.0000   -65.7530    37.6435    40.343421    -13.8752    -74.4846     75.7660\n  4500      0     0.0000   -65.7929    37.8381    27.487515      3.1996    -75.8298     75.8975\n  4500    200     0.0000   -65.7929    37.8381    27.929909      2.6140    -75.8523     75.8975\n  4500    400     0.0000   -65.7929    37.8381    28.361662      2.0423    -75.8698     75.8975\n  4500    600     0.0000   -65.7929    37.8381    28.783297      1.4840    -75.8828     75.8975\n  4500    800     0.0000   -65.7929    37.8381    29.195293      0.9383    -75.8915     75.8975\n  4500   1000     0.0000   -65.7929    37.8381    29.598095      0.4047    -75.8963     75.8975\n  4500   1200     0.0000   -65.7929    37.8381    29.992113     -0.1172    -75.8972     75.8975\n  4500   1400     0.0000   -65.7929    37.8381    30.377729     -0.6280    -75.8947     75.8975\n  4500   1600     0.0000   -65.7929    37.8381    30.755295     -1.1281    -75.8890     75.8975\n  4500   1800     0.0000   -65.7929    37.8381    31.125143     -1.6180    -75.8801     75.8975\n  4500   2000     0.0000   -65.7929    37.8381    31.487579     -2.0979    -75.8684     75.8975\n  4500   2200     0.0000   -65.7929    37.8381    31.842892     -2.5684    -75.8539     75.8975\n  4500   2400     0.0000   -65.7929    37.8381    32.191352     -3.0297    -75.8369     75.8975\n  4500   2600     0.0000   -65.7929    37.8381    32.533211     -3.4821    -75.8174     75.8975\n  4500   2800     0.0000   -65.7929    37.8381    32.868710     -3.9260    -75.7958     75.8975\n  4500   3000     0.0000   -65.7929    37.8381    33.198070     -4.3616    -75.7719     75.8975\n  4500   3200     0.0000   -65.7929    37.8381    33.521506     -4.7893    -75.7461     75.8975\n  4500   3400     0.0000   -65.7929    37.8381    33.839215     -5.2092    -75.7184     75.8975\n  4500   3600     0.0000   -65.7929    37.8381    34.151388     -5.6217    -75.6889     75.8975\n  4500   3800     0.0000   -65.7929    37.8381    34.458204     -6.0269    -75.6577     75.8975\n  4500   4000     0.0000   -65.7929    37.8381    34.759832     -6.4251    -75.6249     75.8975\n  4500   4200     0.0000   -65.7929    37.8381    35.056433     -6.8165    -75.5906     75.8975\n  4500   4400     0.0000   -65.7929    37.8381    35.348161     -7.2013    -75.5550     75.8975\n  4500   4600     0.0000   -65.7929    37.8381    35.635161     -7.5797    -75.5179     75.8975\n  4500   4800     0.0000   -65.7929    37.8381    35.917572     -7.9518    -75.4797     75.8975\n  4500   5000     0.0000   -65.7929    37.8381    36.195527     -8.3179    -75.4402     75.8975\n  4500   5200     0.0000   -65.7929    37.8381    36.469151     -8.6781    -75.3996     75.8975\n  4500   5400     0.0000   -65.7929    37.8381    36.738565     -9.0325    -75.3580     75.8975\n  4500   5600     0.0000   -65.7929    37.8381    37.003885     -9.3814    -75.3154     75.8975\n  4500   5800     0.0000   -65.7929    37.8381    37.265221     -9.7248    -75.2718     75.8975\n  4500   6000     0.0000   -65.7929    37.8381    37.522678    -10.0629    -75.2273     75.8975\n  4500   6200     0.0000   -65.7929    37.8381    37.776357    -10.3959    -75.1820     75.8975\n  4500   6400     0.0000   -65.7929    37.8381    38.026356    -10.7239    -75.1360     75.8975\n  4500   6600     0.0000   -65.7929    37.8381    38.272767    -11.0469    -75.0891     75.8975\n  4500   6800     0.0000   -65.7929    37.8381    38.515680    -11.3652    -75.0416     75.8975\n  4500   7000     0.0000   -65.7929    37.8381    38.755181    -11.6787    -74.9935     75.8975\n  4500   7200     0.0000   -65.7929    37.8381    38.991352    -11.9878    -74.9447     75.8975\n  4500   7400     0.0000   -65.7929    37.8381    39.224272    -12.2923    -74.8953     75.8975\n  4500   7600     0.0000   -65.7929    37.8381    39.454020    -12.5925    -74.8455     75.8975\n  4500   7800     0.0000   -65.7929    37.8381    39.680667    -12.8885    -74.7951     75.8975\n  4500   8000     0.0000   -65.7929    37.8381    39.904285    -13.1803    -74.7442     75.8975\n  4500   8200     0.0000   -65.7929    37.8381    40.124942    -13.4681    -74.6929     75.8975\n  4500   8400     0.0000   -65.7929    37.8381    40.342705    -13.7519    -74.6411     75.8975\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180518_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.8185852      -15.9799853       28.0671376   m   m   m\ninitial_baseline_rate:          0.0000000        0.0487790        0.1002578   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -15.6884185       27.9327136   m   m   m\nprecision_baseline_rate:        0.0000000        0.0490924        0.1006091   m/s m/s m/s\nunwrap_phase_constant:            0.00028     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180106-20180518_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.819    -15.980     28.067\norbit baseline rate   (TCN) (m/s):  0.000e+00  4.878e-02  1.003e-01\n\nbaseline vector (TCN)   (m):      0.000    -15.688     27.933\nbaseline rate   (TCN) (m/s):  0.000e+00  4.909e-02  1.006e-01\n\nSLC-1 center baseline length (m):    32.0369\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -16.1467    26.9936    27.496918     16.4894    -26.7856     31.4542\n     0    200     0.0000   -16.1467    26.9936    27.939152     16.2821    -26.9121     31.4542\n     0    400     0.0000   -16.1467    26.9936    28.370753     16.0790    -27.0340     31.4542\n     0    600     0.0000   -16.1467    26.9936    28.792243     15.8797    -27.1515     31.4542\n     0    800     0.0000   -16.1467    26.9936    29.204102     15.6841    -27.2650     31.4542\n     0   1000     0.0000   -16.1467    26.9936    29.606772     15.4921    -27.3745     31.4542\n     0   1200     0.0000   -16.1467    26.9936    30.000665     15.3035    -27.4804     31.4542\n     0   1400     0.0000   -16.1467    26.9936    30.386160     15.1183    -27.5827     31.4542\n     0   1600     0.0000   -16.1467    26.9936    30.763612     14.9362    -27.6817     31.4542\n     0   1800     0.0000   -16.1467    26.9936    31.133349     14.7573    -27.7775     31.4542\n     0   2000     0.0000   -16.1467    26.9936    31.495679     14.5813    -27.8703     31.4542\n     0   2200     0.0000   -16.1467    26.9936    31.850890     14.4083    -27.9601     31.4542\n     0   2400     0.0000   -16.1467    26.9936    32.199251     14.2380    -28.0472     31.4542\n     0   2600     0.0000   -16.1467    26.9936    32.541017     14.0705    -28.1317     31.4542\n     0   2800     0.0000   -16.1467    26.9936    32.876424     13.9055    -28.2135     31.4542\n     0   3000     0.0000   -16.1467    26.9936    33.205696     13.7432    -28.2930     31.4542\n     0   3200     0.0000   -16.1467    26.9936    33.529047     13.5833    -28.3701     31.4542\n     0   3400     0.0000   -16.1467    26.9936    33.846674     13.4258    -28.4450     31.4542\n     0   3600     0.0000   -16.1467    26.9936    34.158768     13.2707    -28.5177     31.4542\n     0   3800     0.0000   -16.1467    26.9936    34.465506     13.1178    -28.5883     31.4542\n     0   4000     0.0000   -16.1467    26.9936    34.767059     12.9671    -28.6570     31.4542\n     0   4200     0.0000   -16.1467    26.9936    35.063588     12.8187    -28.7237     31.4542\n     0   4400     0.0000   -16.1467    26.9936    35.355245     12.6723    -28.7886     31.4542\n     0   4600     0.0000   -16.1467    26.9936    35.642177     12.5280    -28.8517     31.4542\n     0   4800     0.0000   -16.1467    26.9936    35.924522     12.3856    -28.9131     31.4542\n     0   5000     0.0000   -16.1467    26.9936    36.202412     12.2452    -28.9728     31.4542\n     0   5200     0.0000   -16.1467    26.9936    36.475974     12.1068    -29.0309     31.4542\n     0   5400     0.0000   -16.1467    26.9936    36.745327     11.9702    -29.0875     31.4542\n     0   5600     0.0000   -16.1467    26.9936    37.010588     11.8354    -29.1426     31.4542\n     0   5800     0.0000   -16.1467    26.9936    37.271866     11.7024    -29.1963     31.4542\n     0   6000     0.0000   -16.1467    26.9936    37.529266     11.5711    -29.2486     31.4542\n     0   6200     0.0000   -16.1467    26.9936    37.782890     11.4415    -29.2995     31.4542\n     0   6400     0.0000   -16.1467    26.9936    38.032835     11.3136    -29.3491     31.4542\n     0   6600     0.0000   -16.1467    26.9936    38.279194     11.1873    -29.3975     31.4542\n     0   6800     0.0000   -16.1467    26.9936    38.522056     11.0626    -29.4447     31.4542\n     0   7000     0.0000   -16.1467    26.9936    38.761507     10.9394    -29.4906     31.4542\n     0   7200     0.0000   -16.1467    26.9936    38.997629     10.8178    -29.5355     31.4542\n     0   7400     0.0000   -16.1467    26.9936    39.230502     10.6976    -29.5792     31.4542\n     0   7600     0.0000   -16.1467    26.9936    39.460203     10.5790    -29.6218     31.4542\n     0   7800     0.0000   -16.1467    26.9936    39.686804     10.4617    -29.6635     31.4542\n     0   8000     0.0000   -16.1467    26.9936    39.910378     10.3459    -29.7041     31.4542\n     0   8200     0.0000   -16.1467    26.9936    40.130991     10.2315    -29.7437     31.4542\n     0   8400     0.0000   -16.1467    26.9936    40.348712     10.1184    -29.7823     31.4542\n   500      0     0.0000   -16.0457    27.2004    27.495955     16.7199    -26.7913     31.5805\n   500    200     0.0000   -16.0457    27.2004    27.938206     16.5126    -26.9195     31.5805\n   500    400     0.0000   -16.0457    27.2004    28.369822     16.3093    -27.0432     31.5805\n   500    600     0.0000   -16.0457    27.2004    28.791327     16.1099    -27.1624     31.5805\n   500    800     0.0000   -16.0457    27.2004    29.203199     15.9143    -27.2775     31.5805\n   500   1000     0.0000   -16.0457    27.2004    29.605883     15.7222    -27.3887     31.5805\n   500   1200     0.0000   -16.0457    27.2004    29.999788     15.5335    -27.4961     31.5805\n   500   1400     0.0000   -16.0457    27.2004    30.385296     15.3481    -27.6000     31.5805\n   500   1600     0.0000   -16.0457    27.2004    30.762759     15.1660    -27.7005     31.5805\n   500   1800     0.0000   -16.0457    27.2004    31.132507     14.9869    -27.7978     31.5805\n   500   2000     0.0000   -16.0457    27.2004    31.494848     14.8108    -27.8921     31.5805\n   500   2200     0.0000   -16.0457    27.2004    31.850069     14.6376    -27.9834     31.5805\n   500   2400     0.0000   -16.0457    27.2004    32.198441     14.4672    -28.0718     31.5805\n   500   2600     0.0000   -16.0457    27.2004    32.540215     14.2995    -28.1576     31.5805\n   500   2800     0.0000   -16.0457    27.2004    32.875632     14.1344    -28.2409     31.5805\n   500   3000     0.0000   -16.0457    27.2004    33.204913     13.9719    -28.3216     31.5805\n   500   3200     0.0000   -16.0457    27.2004    33.528272     13.8118    -28.4000     31.5805\n   500   3400     0.0000   -16.0457    27.2004    33.845908     13.6541    -28.4762     31.5805\n   500   3600     0.0000   -16.0457    27.2004    34.158010     13.4988    -28.5501     31.5805\n   500   3800     0.0000   -16.0457    27.2004    34.464756     13.3458    -28.6220     31.5805\n   500   4000     0.0000   -16.0457    27.2004    34.766316     13.1950    -28.6918     31.5805\n   500   4200     0.0000   -16.0457    27.2004    35.062852     13.0463    -28.7597     31.5805\n   500   4400     0.0000   -16.0457    27.2004    35.354517     12.8997    -28.8258     31.5805\n   500   4600     0.0000   -16.0457    27.2004    35.641456     12.7552    -28.8900     31.5805\n   500   4800     0.0000   -16.0457    27.2004    35.923807     12.6127    -28.9525     31.5805\n   500   5000     0.0000   -16.0457    27.2004    36.201704     12.4721    -29.0133     31.5805\n   500   5200     0.0000   -16.0457    27.2004    36.475272     12.3334    -29.0726     31.5805\n   500   5400     0.0000   -16.0457    27.2004    36.744632     12.1966    -29.1302     31.5805\n   500   5600     0.0000   -16.0457    27.2004    37.009898     12.0616    -29.1864     31.5805\n   500   5800     0.0000   -16.0457    27.2004    37.271182     11.9284    -29.2411     31.5805\n   500   6000     0.0000   -16.0457    27.2004    37.528588     11.7969    -29.2944     31.5805\n   500   6200     0.0000   -16.0457    27.2004    37.782218     11.6671    -29.3463     31.5805\n   500   6400     0.0000   -16.0457    27.2004    38.032168     11.5390    -29.3969     31.5805\n   500   6600     0.0000   -16.0457    27.2004    38.278532     11.4125    -29.4463     31.5805\n   500   6800     0.0000   -16.0457    27.2004    38.521399     11.2875    -29.4944     31.5805\n   500   7000     0.0000   -16.0457    27.2004    38.760855     11.1642    -29.5413     31.5805\n   500   7200     0.0000   -16.0457    27.2004    38.996982     11.0423    -29.5871     31.5805\n   500   7400     0.0000   -16.0457    27.2004    39.229860     10.9220    -29.6317     31.5805\n   500   7600     0.0000   -16.0457    27.2004    39.459566     10.8031    -29.6753     31.5805\n   500   7800     0.0000   -16.0457    27.2004    39.686172     10.6857    -29.7177     31.5805\n   500   8000     0.0000   -16.0457    27.2004    39.909750     10.5696    -29.7592     31.5805\n   500   8200     0.0000   -16.0457    27.2004    40.130368     10.4549    -29.7997     31.5805\n   500   8400     0.0000   -16.0457    27.2004    40.348092     10.3416    -29.8392     31.5805\n  1000      0     0.0000   -15.9448    27.4072    27.494972     16.9504    -26.7970     31.7079\n  1000    200     0.0000   -15.9448    27.4072    27.937239     16.7430    -26.9270     31.7079\n  1000    400     0.0000   -15.9448    27.4072    28.368871     16.5397    -27.0524     31.7079\n  1000    600     0.0000   -15.9448    27.4072    28.790391     16.3402    -27.1733     31.7079\n  1000    800     0.0000   -15.9448    27.4072    29.202277     16.1445    -27.2901     31.7079\n  1000   1000     0.0000   -15.9448    27.4072    29.604975     15.9523    -27.4029     31.7079\n  1000   1200     0.0000   -15.9448    27.4072    29.998893     15.7635    -27.5119     31.7079\n  1000   1400     0.0000   -15.9448    27.4072    30.384413     15.5780    -27.6174     31.7079\n  1000   1600     0.0000   -15.9448    27.4072    30.761888     15.3957    -27.7194     31.7079\n  1000   1800     0.0000   -15.9448    27.4072    31.131648     15.2165    -27.8182     31.7079\n  1000   2000     0.0000   -15.9448    27.4072    31.494000     15.0403    -27.9138     31.7079\n  1000   2200     0.0000   -15.9448    27.4072    31.849231     14.8669    -28.0066     31.7079\n  1000   2400     0.0000   -15.9448    27.4072    32.197613     14.6964    -28.0964     31.7079\n  1000   2600     0.0000   -15.9448    27.4072    32.539398     14.5285    -28.1836     31.7079\n  1000   2800     0.0000   -15.9448    27.4072    32.874823     14.3633    -28.2682     31.7079\n  1000   3000     0.0000   -15.9448    27.4072    33.204114     14.2006    -28.3503     31.7079\n  1000   3200     0.0000   -15.9448    27.4072    33.527482     14.0403    -28.4299     31.7079\n  1000   3400     0.0000   -15.9448    27.4072    33.845126     13.8825    -28.5073     31.7079\n  1000   3600     0.0000   -15.9448    27.4072    34.157236     13.7270    -28.5825     31.7079\n  1000   3800     0.0000   -15.9448    27.4072    34.463990     13.5738    -28.6556     31.7079\n  1000   4000     0.0000   -15.9448    27.4072    34.765558     13.4228    -28.7267     31.7079\n  1000   4200     0.0000   -15.9448    27.4072    35.062102     13.2739    -28.7958     31.7079\n  1000   4400     0.0000   -15.9448    27.4072    35.353774     13.1271    -28.8630     31.7079\n  1000   4600     0.0000   -15.9448    27.4072    35.640720     12.9824    -28.9283     31.7079\n  1000   4800     0.0000   -15.9448    27.4072    35.923078     12.8397    -28.9920     31.7079\n  1000   5000     0.0000   -15.9448    27.4072    36.200982     12.6989    -29.0539     31.7079\n  1000   5200     0.0000   -15.9448    27.4072    36.474556     12.5601    -29.1142     31.7079\n  1000   5400     0.0000   -15.9448    27.4072    36.743922     12.4231    -29.1729     31.7079\n  1000   5600     0.0000   -15.9448    27.4072    37.009195     12.2879    -29.2301     31.7079\n  1000   5800     0.0000   -15.9448    27.4072    37.270484     12.1544    -29.2859     31.7079\n  1000   6000     0.0000   -15.9448    27.4072    37.527896     12.0227    -29.3402     31.7079\n  1000   6200     0.0000   -15.9448    27.4072    37.781532     11.8927    -29.3931     31.7079\n  1000   6400     0.0000   -15.9448    27.4072    38.031488     11.7644    -29.4447     31.7079\n  1000   6600     0.0000   -15.9448    27.4072    38.277857     11.6377    -29.4950     31.7079\n  1000   6800     0.0000   -15.9448    27.4072    38.520730     11.5125    -29.5441     31.7079\n  1000   7000     0.0000   -15.9448    27.4072    38.760191     11.3890    -29.5920     31.7079\n  1000   7200     0.0000   -15.9448    27.4072    38.996323     11.2669    -29.6386     31.7079\n  1000   7400     0.0000   -15.9448    27.4072    39.229206     11.1463    -29.6842     31.7079\n  1000   7600     0.0000   -15.9448    27.4072    39.458916     11.0272    -29.7286     31.7079\n  1000   7800     0.0000   -15.9448    27.4072    39.685527     10.9096    -29.7720     31.7079\n  1000   8000     0.0000   -15.9448    27.4072    39.909109     10.7933    -29.8144     31.7079\n  1000   8200     0.0000   -15.9448    27.4072    40.129732     10.6784    -29.8557     31.7079\n  1000   8400     0.0000   -15.9448    27.4072    40.347461     10.5649    -29.8961     31.7079\n  1500      0     0.0000   -15.8439    27.6140    27.493968     17.1809    -26.8026     31.8365\n  1500    200     0.0000   -15.8439    27.6140    27.936252     16.9735    -26.9345     31.8365\n  1500    400     0.0000   -15.8439    27.6140    28.367900     16.7701    -27.0616     31.8365\n  1500    600     0.0000   -15.8439    27.6140    28.789435     16.5705    -27.1842     31.8365\n  1500    800     0.0000   -15.8439    27.6140    29.201336     16.3747    -27.3026     31.8365\n  1500   1000     0.0000   -15.8439    27.6140    29.604048     16.1824    -27.4170     31.8365\n  1500   1200     0.0000   -15.8439    27.6140    29.997980     15.9935    -27.5277     31.8365\n  1500   1400     0.0000   -15.8439    27.6140    30.383512     15.8079    -27.6347     31.8365\n  1500   1600     0.0000   -15.8439    27.6140    30.760999     15.6255    -27.7382     31.8365\n  1500   1800     0.0000   -15.8439    27.6140    31.130771     15.4461    -27.8385     31.8365\n  1500   2000     0.0000   -15.8439    27.6140    31.493134     15.2698    -27.9356     31.8365\n  1500   2200     0.0000   -15.8439    27.6140    31.848377     15.0963    -28.0297     31.8365\n  1500   2400     0.0000   -15.8439    27.6140    32.196769     14.9255    -28.1210     31.8365\n  1500   2600     0.0000   -15.8439    27.6140    32.538563     14.7575    -28.2095     31.8365\n  1500   2800     0.0000   -15.8439    27.6140    32.873999     14.5921    -28.2955     31.8365\n  1500   3000     0.0000   -15.8439    27.6140    33.203299     14.4293    -28.3789     31.8365\n  1500   3200     0.0000   -15.8439    27.6140    33.526676     14.2689    -28.4598     31.8365\n  1500   3400     0.0000   -15.8439    27.6140    33.844329     14.1109    -28.5385     31.8365\n  1500   3600     0.0000   -15.8439    27.6140    34.156447     13.9552    -28.6150     31.8365\n  1500   3800     0.0000   -15.8439    27.6140    34.463209     13.8018    -28.6893     31.8365\n  1500   4000     0.0000   -15.8439    27.6140    34.764785     13.6506    -28.7615     31.8365\n  1500   4200     0.0000   -15.8439    27.6140    35.061337     13.5015    -28.8318     31.8365\n  1500   4400     0.0000   -15.8439    27.6140    35.353016     13.3546    -28.9001     31.8365\n  1500   4600     0.0000   -15.8439    27.6140    35.639969     13.2097    -28.9667     31.8365\n  1500   4800     0.0000   -15.8439    27.6140    35.922335     13.0668    -29.0314     31.8365\n  1500   5000     0.0000   -15.8439    27.6140    36.200245     12.9258    -29.0945     31.8365\n  1500   5200     0.0000   -15.8439    27.6140    36.473826     12.7867    -29.1558     31.8365\n  1500   5400     0.0000   -15.8439    27.6140    36.743198     12.6495    -29.2156     31.8365\n  1500   5600     0.0000   -15.8439    27.6140    37.008477     12.5141    -29.2739     31.8365\n  1500   5800     0.0000   -15.8439    27.6140    37.269773     12.3805    -29.3307     31.8365\n  1500   6000     0.0000   -15.8439    27.6140    37.527191     12.2486    -29.3860     31.8365\n  1500   6200     0.0000   -15.8439    27.6140    37.780832     12.1184    -29.4399     31.8365\n  1500   6400     0.0000   -15.8439    27.6140    38.030794     11.9898    -29.4925     31.8365\n  1500   6600     0.0000   -15.8439    27.6140    38.277169     11.8629    -29.5438     31.8365\n  1500   6800     0.0000   -15.8439    27.6140    38.520047     11.7375    -29.5938     31.8365\n  1500   7000     0.0000   -15.8439    27.6140    38.759513     11.6138    -29.6426     31.8365\n  1500   7200     0.0000   -15.8439    27.6140    38.995651     11.4915    -29.6902     31.8365\n  1500   7400     0.0000   -15.8439    27.6140    39.228539     11.3707    -29.7367     31.8365\n  1500   7600     0.0000   -15.8439    27.6140    39.458254     11.2514    -29.7820     31.8365\n  1500   7800     0.0000   -15.8439    27.6140    39.684869     11.1335    -29.8263     31.8365\n  1500   8000     0.0000   -15.8439    27.6140    39.908457     11.0170    -29.8695     31.8365\n  1500   8200     0.0000   -15.8439    27.6140    40.129084     10.9019    -29.9117     31.8365\n  1500   8400     0.0000   -15.8439    27.6140    40.346818     10.7882    -29.9529     31.8365\n  2000      0     0.0000   -15.7430    27.8208    27.492944     17.4114    -26.8083     31.9662\n  2000    200     0.0000   -15.7430    27.8208    27.935245     17.2039    -26.9419     31.9662\n  2000    400     0.0000   -15.7430    27.8208    28.366910     17.0005    -27.0707     31.9662\n  2000    600     0.0000   -15.7430    27.8208    28.788460     16.8008    -27.1951     31.9662\n  2000    800     0.0000   -15.7430    27.8208    29.200376     16.6049    -27.3152     31.9662\n  2000   1000     0.0000   -15.7430    27.8208    29.603102     16.4125    -27.4312     31.9662\n  2000   1200     0.0000   -15.7430    27.8208    29.997048     16.2235    -27.5434     31.9662\n  2000   1400     0.0000   -15.7430    27.8208    30.382593     16.0378    -27.6519     31.9662\n  2000   1600     0.0000   -15.7430    27.8208    30.760093     15.8552    -27.7570     31.9662\n  2000   1800     0.0000   -15.7430    27.8208    31.129876     15.6758    -27.8588     31.9662\n  2000   2000     0.0000   -15.7430    27.8208    31.492251     15.4993    -27.9573     31.9662\n  2000   2200     0.0000   -15.7430    27.8208    31.847505     15.3256    -28.0529     31.9662\n  2000   2400     0.0000   -15.7430    27.8208    32.195907     15.1547    -28.1456     31.9662\n  2000   2600     0.0000   -15.7430    27.8208    32.537712     14.9866    -28.2355     31.9662\n  2000   2800     0.0000   -15.7430    27.8208    32.873158     14.8210    -28.3227     31.9662\n  2000   3000     0.0000   -15.7430    27.8208    33.202467     14.6580    -28.4075     31.9662\n  2000   3200     0.0000   -15.7430    27.8208    33.525853     14.4974    -28.4897     31.9662\n  2000   3400     0.0000   -15.7430    27.8208    33.843515     14.3392    -28.5697     31.9662\n  2000   3600     0.0000   -15.7430    27.8208    34.155642     14.1834    -28.6474     31.9662\n  2000   3800     0.0000   -15.7430    27.8208    34.462413     14.0298    -28.7229     31.9662\n  2000   4000     0.0000   -15.7430    27.8208    34.763997     13.8784    -28.7964     31.9662\n  2000   4200     0.0000   -15.7430    27.8208    35.060556     13.7292    -28.8678     31.9662\n  2000   4400     0.0000   -15.7430    27.8208    35.352243     13.5820    -28.9373     31.9662\n  2000   4600     0.0000   -15.7430    27.8208    35.639204     13.4369    -29.0050     31.9662\n  2000   4800     0.0000   -15.7430    27.8208    35.921577     13.2938    -29.0709     31.9662\n  2000   5000     0.0000   -15.7430    27.8208    36.199494     13.1527    -29.1350     31.9662\n  2000   5200     0.0000   -15.7430    27.8208    36.473082     13.0134    -29.1975     31.9662\n  2000   5400     0.0000   -15.7430    27.8208    36.742461     12.8760    -29.2583     31.9662\n  2000   5600     0.0000   -15.7430    27.8208    37.007746     12.7404    -29.3176     31.9662\n  2000   5800     0.0000   -15.7430    27.8208    37.269048     12.6065    -29.3754     31.9662\n  2000   6000     0.0000   -15.7430    27.8208    37.526472     12.4744    -29.4318     31.9662\n  2000   6200     0.0000   -15.7430    27.8208    37.780120     12.3440    -29.4867     31.9662\n  2000   6400     0.0000   -15.7430    27.8208    38.030087     12.2152    -29.5403     31.9662\n  2000   6600     0.0000   -15.7430    27.8208    38.276468     12.0881    -29.5925     31.9662\n  2000   6800     0.0000   -15.7430    27.8208    38.519351     11.9626    -29.6435     31.9662\n  2000   7000     0.0000   -15.7430    27.8208    38.758823     11.8386    -29.6933     31.9662\n  2000   7200     0.0000   -15.7430    27.8208    38.994966     11.7161    -29.7418     31.9662\n  2000   7400     0.0000   -15.7430    27.8208    39.227859     11.5951    -29.7892     31.9662\n  2000   7600     0.0000   -15.7430    27.8208    39.457579     11.4756    -29.8354     31.9662\n  2000   7800     0.0000   -15.7430    27.8208    39.684200     11.3575    -29.8806     31.9662\n  2000   8000     0.0000   -15.7430    27.8208    39.907792     11.2408    -29.9247     31.9662\n  2000   8200     0.0000   -15.7430    27.8208    40.128424     11.1254    -29.9677     31.9662\n  2000   8400     0.0000   -15.7430    27.8208    40.346162     11.0115    -30.0098     31.9662\n  2500      0     0.0000   -15.6421    28.0276    27.491899     17.6419    -26.8139     32.0971\n  2500    200     0.0000   -15.6421    28.0276    27.934218     17.4344    -26.9493     32.0971\n  2500    400     0.0000   -15.6421    28.0276    28.365900     17.2308    -27.0799     32.0971\n  2500    600     0.0000   -15.6421    28.0276    28.787466     17.0311    -27.2059     32.0971\n  2500    800     0.0000   -15.6421    28.0276    29.199398     16.8351    -27.3277     32.0971\n  2500   1000     0.0000   -15.6421    28.0276    29.602138     16.6426    -27.4453     32.0971\n  2500   1200     0.0000   -15.6421    28.0276    29.996097     16.4535    -27.5591     32.0971\n  2500   1400     0.0000   -15.6421    28.0276    30.381656     16.2677    -27.6692     32.0971\n  2500   1600     0.0000   -15.6421    28.0276    30.759169     16.0850    -27.7758     32.0971\n  2500   1800     0.0000   -15.6421    28.0276    31.128964     15.9054    -27.8790     32.0971\n  2500   2000     0.0000   -15.6421    28.0276    31.491351     15.7288    -27.9791     32.0971\n  2500   2200     0.0000   -15.6421    28.0276    31.846616     15.5550    -28.0761     32.0971\n  2500   2400     0.0000   -15.6421    28.0276    32.195030     15.3840    -28.1701     32.0971\n  2500   2600     0.0000   -15.6421    28.0276    32.536845     15.2156    -28.2614     32.0971\n  2500   2800     0.0000   -15.6421    28.0276    32.872300     15.0499    -28.3500     32.0971\n  2500   3000     0.0000   -15.6421    28.0276    33.201620     14.8867    -28.4361     32.0971\n  2500   3200     0.0000   -15.6421    28.0276    33.525015     14.7260    -28.5196     32.0971\n  2500   3400     0.0000   -15.6421    28.0276    33.842686     14.5676    -28.6008     32.0971\n  2500   3600     0.0000   -15.6421    28.0276    34.154822     14.4116    -28.6798     32.0971\n  2500   3800     0.0000   -15.6421    28.0276    34.461601     14.2578    -28.7565     32.0971\n  2500   4000     0.0000   -15.6421    28.0276    34.763194     14.1063    -28.8312     32.0971\n  2500   4200     0.0000   -15.6421    28.0276    35.059761     13.9568    -28.9038     32.0971\n  2500   4400     0.0000   -15.6421    28.0276    35.351456     13.8095    -28.9745     32.0971\n  2500   4600     0.0000   -15.6421    28.0276    35.638424     13.6642    -29.0433     32.0971\n  2500   4800     0.0000   -15.6421    28.0276    35.920804     13.5209    -29.1103     32.0971\n  2500   5000     0.0000   -15.6421    28.0276    36.198729     13.3795    -29.1755     32.0971\n  2500   5200     0.0000   -15.6421    28.0276    36.472323     13.2401    -29.2391     32.0971\n  2500   5400     0.0000   -15.6421    28.0276    36.741709     13.1025    -29.3010     32.0971\n  2500   5600     0.0000   -15.6421    28.0276    37.007001     12.9666    -29.3614     32.0971\n  2500   5800     0.0000   -15.6421    28.0276    37.268310     12.8326    -29.4202     32.0971\n  2500   6000     0.0000   -15.6421    28.0276    37.525740     12.7003    -29.4775     32.0971\n  2500   6200     0.0000   -15.6421    28.0276    37.779394     12.5697    -29.5335     32.0971\n  2500   6400     0.0000   -15.6421    28.0276    38.029367     12.4407    -29.5880     32.0971\n  2500   6600     0.0000   -15.6421    28.0276    38.275754     12.3133    -29.6413     32.0971\n  2500   6800     0.0000   -15.6421    28.0276    38.518643     12.1876    -29.6932     32.0971\n  2500   7000     0.0000   -15.6421    28.0276    38.758120     12.0634    -29.7439     32.0971\n  2500   7200     0.0000   -15.6421    28.0276    38.994268     11.9407    -29.7933     32.0971\n  2500   7400     0.0000   -15.6421    28.0276    39.227167     11.8195    -29.8416     32.0971\n  2500   7600     0.0000   -15.6421    28.0276    39.456892     11.6997    -29.8888     32.0971\n  2500   7800     0.0000   -15.6421    28.0276    39.683518     11.5814    -29.9348     32.0971\n  2500   8000     0.0000   -15.6421    28.0276    39.907115     11.4645    -29.9798     32.0971\n  2500   8200     0.0000   -15.6421    28.0276    40.127752     11.3490    -30.0237     32.0971\n  2500   8400     0.0000   -15.6421    28.0276    40.345495     11.2348    -30.0666     32.0971\n  3000      0     0.0000   -15.5412    28.2344    27.490833     17.8724    -26.8195     32.2291\n  3000    200     0.0000   -15.5412    28.2344    27.933171     17.6649    -26.9567     32.2291\n  3000    400     0.0000   -15.5412    28.2344    28.364870     17.4613    -27.0890     32.2291\n  3000    600     0.0000   -15.5412    28.2344    28.786452     17.2615    -27.2168     32.2291\n  3000    800     0.0000   -15.5412    28.2344    29.198400     17.0653    -27.3402     32.2291\n  3000   1000     0.0000   -15.5412    28.2344    29.601155     16.8727    -27.4595     32.2291\n  3000   1200     0.0000   -15.5412    28.2344    29.995129     16.6835    -27.5748     32.2291\n  3000   1400     0.0000   -15.5412    28.2344    30.380701     16.4976    -27.6865     32.2291\n  3000   1600     0.0000   -15.5412    28.2344    30.758227     16.3148    -27.7946     32.2291\n  3000   1800     0.0000   -15.5412    28.2344    31.128035     16.1351    -27.8993     32.2291\n  3000   2000     0.0000   -15.5412    28.2344    31.490433     15.9583    -28.0008     32.2291\n  3000   2200     0.0000   -15.5412    28.2344    31.845710     15.7843    -28.0992     32.2291\n  3000   2400     0.0000   -15.5412    28.2344    32.194135     15.6132    -28.1947     32.2291\n  3000   2600     0.0000   -15.5412    28.2344    32.535961     15.4447    -28.2873     32.2291\n  3000   2800     0.0000   -15.5412    28.2344    32.871427     15.2788    -28.3773     32.2291\n  3000   3000     0.0000   -15.5412    28.2344    33.200756     15.1154    -28.4646     32.2291\n  3000   3200     0.0000   -15.5412    28.2344    33.524162     14.9545    -28.5495     32.2291\n  3000   3400     0.0000   -15.5412    28.2344    33.841842     14.7960    -28.6320     32.2291\n  3000   3600     0.0000   -15.5412    28.2344    34.153987     14.6398    -28.7122     32.2291\n  3000   3800     0.0000   -15.5412    28.2344    34.460775     14.4858    -28.7901     32.2291\n  3000   4000     0.0000   -15.5412    28.2344    34.762376     14.3341    -28.8660     32.2291\n  3000   4200     0.0000   -15.5412    28.2344    35.058951     14.1845    -28.9398     32.2291\n  3000   4400     0.0000   -15.5412    28.2344    35.350654     14.0370    -29.0116     32.2291\n  3000   4600     0.0000   -15.5412    28.2344    35.637630     13.8915    -29.0816     32.2291\n  3000   4800     0.0000   -15.5412    28.2344    35.920018     13.7480    -29.1497     32.2291\n  3000   5000     0.0000   -15.5412    28.2344    36.197949     13.6064    -29.2160     32.2291\n  3000   5200     0.0000   -15.5412    28.2344    36.471551     13.4668    -29.2807     32.2291\n  3000   5400     0.0000   -15.5412    28.2344    36.740944     13.3289    -29.3437     32.2291\n  3000   5600     0.0000   -15.5412    28.2344    37.006243     13.1929    -29.4051     32.2291\n  3000   5800     0.0000   -15.5412    28.2344    37.267558     13.0587    -29.4649     32.2291\n  3000   6000     0.0000   -15.5412    28.2344    37.524995     12.9262    -29.5233     32.2291\n  3000   6200     0.0000   -15.5412    28.2344    37.778654     12.7953    -29.5803     32.2291\n  3000   6400     0.0000   -15.5412    28.2344    38.028634     12.6661    -29.6358     32.2291\n  3000   6600     0.0000   -15.5412    28.2344    38.275026     12.5386    -29.6900     32.2291\n  3000   6800     0.0000   -15.5412    28.2344    38.517921     12.4126    -29.7429     32.2291\n  3000   7000     0.0000   -15.5412    28.2344    38.757404     12.2882    -29.7945     32.2291\n  3000   7200     0.0000   -15.5412    28.2344    38.993558     12.1653    -29.8449     32.2291\n  3000   7400     0.0000   -15.5412    28.2344    39.226462     12.0438    -29.8941     32.2291\n  3000   7600     0.0000   -15.5412    28.2344    39.456192     11.9239    -29.9422     32.2291\n  3000   7800     0.0000   -15.5412    28.2344    39.682823     11.8054    -29.9891     32.2291\n  3000   8000     0.0000   -15.5412    28.2344    39.906425     11.6882    -30.0349     32.2291\n  3000   8200     0.0000   -15.5412    28.2344    40.127067     11.5725    -30.0797     32.2291\n  3000   8400     0.0000   -15.5412    28.2344    40.344815     11.4581    -30.1235     32.2291\n  3500      0     0.0000   -15.4403    28.4413    27.489747     18.1030    -26.8251     32.3621\n  3500    200     0.0000   -15.4403    28.4413    27.932104     17.8953    -26.9641     32.3621\n  3500    400     0.0000   -15.4403    28.4413    28.363820     17.6917    -27.0982     32.3621\n  3500    600     0.0000   -15.4403    28.4413    28.785420     17.4918    -27.2276     32.3621\n  3500    800     0.0000   -15.4403    28.4413    29.197383     17.2956    -27.3527     32.3621\n  3500   1000     0.0000   -15.4403    28.4413    29.600153     17.1029    -27.4736     32.3621\n  3500   1200     0.0000   -15.4403    28.4413    29.994142     16.9135    -27.5905     32.3621\n  3500   1400     0.0000   -15.4403    28.4413    30.379728     16.7275    -27.7037     32.3621\n  3500   1600     0.0000   -15.4403    28.4413    30.757267     16.5446    -27.8134     32.3621\n  3500   1800     0.0000   -15.4403    28.4413    31.127088     16.3647    -27.9196     32.3621\n  3500   2000     0.0000   -15.4403    28.4413    31.489499     16.1878    -28.0225     32.3621\n  3500   2200     0.0000   -15.4403    28.4413    31.844788     16.0137    -28.1224     32.3621\n  3500   2400     0.0000   -15.4403    28.4413    32.193224     15.8424    -28.2192     32.3621\n  3500   2600     0.0000   -15.4403    28.4413    32.535061     15.6737    -28.3132     32.3621\n  3500   2800     0.0000   -15.4403    28.4413    32.870537     15.5077    -28.4045     32.3621\n  3500   3000     0.0000   -15.4403    28.4413    33.199877     15.3442    -28.4932     32.3621\n  3500   3200     0.0000   -15.4403    28.4413    33.523292     15.1831    -28.5794     32.3621\n  3500   3400     0.0000   -15.4403    28.4413    33.840982     15.0244    -28.6631     32.3621\n  3500   3600     0.0000   -15.4403    28.4413    34.153136     14.8680    -28.7445     32.3621\n  3500   3800     0.0000   -15.4403    28.4413    34.459933     14.7139    -28.8237     32.3621\n  3500   4000     0.0000   -15.4403    28.4413    34.761543     14.5620    -28.9008     32.3621\n  3500   4200     0.0000   -15.4403    28.4413    35.058127     14.4122    -28.9758     32.3621\n  3500   4400     0.0000   -15.4403    28.4413    35.349838     14.2644    -29.0488     32.3621\n  3500   4600     0.0000   -15.4403    28.4413    35.636821     14.1188    -29.1199     32.3621\n  3500   4800     0.0000   -15.4403    28.4413    35.919217     13.9751    -29.1891     32.3621\n  3500   5000     0.0000   -15.4403    28.4413    36.197156     13.8333    -29.2566     32.3621\n  3500   5200     0.0000   -15.4403    28.4413    36.470765     13.6934    -29.3223     32.3621\n  3500   5400     0.0000   -15.4403    28.4413    36.740165     13.5554    -29.3863     32.3621\n  3500   5600     0.0000   -15.4403    28.4413    37.005471     13.4192    -29.4488     32.3621\n  3500   5800     0.0000   -15.4403    28.4413    37.266792     13.2848    -29.5097     32.3621\n  3500   6000     0.0000   -15.4403    28.4413    37.524236     13.1520    -29.5691     32.3621\n  3500   6200     0.0000   -15.4403    28.4413    37.777902     13.0210    -29.6270     32.3621\n  3500   6400     0.0000   -15.4403    28.4413    38.027888     12.8916    -29.6835     32.3621\n  3500   6600     0.0000   -15.4403    28.4413    38.274286     12.7638    -29.7387     32.3621\n  3500   6800     0.0000   -15.4403    28.4413    38.517187     12.6376    -29.7926     32.3621\n  3500   7000     0.0000   -15.4403    28.4413    38.756676     12.5130    -29.8451     32.3621\n  3500   7200     0.0000   -15.4403    28.4413    38.992835     12.3899    -29.8964     32.3621\n  3500   7400     0.0000   -15.4403    28.4413    39.225745     12.2682    -29.9466     32.3621\n  3500   7600     0.0000   -15.4403    28.4413    39.455480     12.1481    -29.9955     32.3621\n  3500   7800     0.0000   -15.4403    28.4413    39.682117     12.0293    -30.0433     32.3621\n  3500   8000     0.0000   -15.4403    28.4413    39.905724     11.9120    -30.0900     32.3621\n  3500   8200     0.0000   -15.4403    28.4413    40.126371     11.7960    -30.1357     32.3621\n  3500   8400     0.0000   -15.4403    28.4413    40.344124     11.6814    -30.1803     32.3621\n  4000      0     0.0000   -15.3394    28.6481    27.488641     18.3335    -26.8307     32.4963\n  4000    200     0.0000   -15.3394    28.6481    27.931016     18.1258    -26.9715     32.4963\n  4000    400     0.0000   -15.3394    28.6481    28.362751     17.9221    -27.1073     32.4963\n  4000    600     0.0000   -15.3394    28.6481    28.784368     17.7221    -27.2384     32.4963\n  4000    800     0.0000   -15.3394    28.6481    29.196347     17.5258    -27.3652     32.4963\n  4000   1000     0.0000   -15.3394    28.6481    29.599133     17.3330    -27.4877     32.4963\n  4000   1200     0.0000   -15.3394    28.6481    29.993137     17.1436    -27.6062     32.4963\n  4000   1400     0.0000   -15.3394    28.6481    30.378737     16.9574    -27.7210     32.4963\n  4000   1600     0.0000   -15.3394    28.6481    30.756290     16.7744    -27.8321     32.4963\n  4000   1800     0.0000   -15.3394    28.6481    31.126124     16.5944    -27.9398     32.4963\n  4000   2000     0.0000   -15.3394    28.6481    31.488547     16.4173    -28.0442     32.4963\n  4000   2200     0.0000   -15.3394    28.6481    31.843848     16.2431    -28.1455     32.4963\n  4000   2400     0.0000   -15.3394    28.6481    32.192296     16.0716    -28.2438     32.4963\n  4000   2600     0.0000   -15.3394    28.6481    32.534144     15.9028    -28.3391     32.4963\n  4000   2800     0.0000   -15.3394    28.6481    32.869631     15.7366    -28.4318     32.4963\n  4000   3000     0.0000   -15.3394    28.6481    33.198982     15.5729    -28.5218     32.4963\n  4000   3200     0.0000   -15.3394    28.6481    33.522407     15.4117    -28.6092     32.4963\n  4000   3400     0.0000   -15.3394    28.6481    33.840106     15.2528    -28.6942     32.4963\n  4000   3600     0.0000   -15.3394    28.6481    34.152270     15.0962    -28.7769     32.4963\n  4000   3800     0.0000   -15.3394    28.6481    34.459076     14.9419    -28.8573     32.4963\n  4000   4000     0.0000   -15.3394    28.6481    34.760695     14.7898    -28.9356     32.4963\n  4000   4200     0.0000   -15.3394    28.6481    35.057287     14.6398    -29.0118     32.4963\n  4000   4400     0.0000   -15.3394    28.6481    35.349006     14.4919    -29.0859     32.4963\n  4000   4600     0.0000   -15.3394    28.6481    35.635998     14.3461    -29.1581     32.4963\n  4000   4800     0.0000   -15.3394    28.6481    35.918401     14.2022    -29.2285     32.4963\n  4000   5000     0.0000   -15.3394    28.6481    36.196348     14.0602    -29.2971     32.4963\n  4000   5200     0.0000   -15.3394    28.6481    36.469965     13.9201    -29.3639     32.4963\n  4000   5400     0.0000   -15.3394    28.6481    36.739372     13.7819    -29.4290     32.4963\n  4000   5600     0.0000   -15.3394    28.6481    37.004685     13.6455    -29.4925     32.4963\n  4000   5800     0.0000   -15.3394    28.6481    37.266013     13.5108    -29.5544     32.4963\n  4000   6000     0.0000   -15.3394    28.6481    37.523463     13.3779    -29.6148     32.4963\n  4000   6200     0.0000   -15.3394    28.6481    37.777136     13.2467    -29.6738     32.4963\n  4000   6400     0.0000   -15.3394    28.6481    38.027128     13.1171    -29.7313     32.4963\n  4000   6600     0.0000   -15.3394    28.6481    38.273533     12.9891    -29.7874     32.4963\n  4000   6800     0.0000   -15.3394    28.6481    38.516440     12.8627    -29.8422     32.4963\n  4000   7000     0.0000   -15.3394    28.6481    38.755934     12.7378    -29.8957     32.4963\n  4000   7200     0.0000   -15.3394    28.6481    38.992100     12.6145    -29.9480     32.4963\n  4000   7400     0.0000   -15.3394    28.6481    39.225015     12.4926    -29.9990     32.4963\n  4000   7600     0.0000   -15.3394    28.6481    39.454756     12.3723    -30.0489     32.4963\n  4000   7800     0.0000   -15.3394    28.6481    39.681398     12.2533    -30.0976     32.4963\n  4000   8000     0.0000   -15.3394    28.6481    39.905010     12.1357    -30.1452     32.4963\n  4000   8200     0.0000   -15.3394    28.6481    40.125663     12.0196    -30.1917     32.4963\n  4000   8400     0.0000   -15.3394    28.6481    40.343421     11.9047    -30.2371     32.4963\n  4500      0     0.0000   -15.2385    28.8549    27.487515     18.5641    -26.8363     32.6315\n  4500    200     0.0000   -15.2385    28.8549    27.929909     18.3564    -26.9788     32.6315\n  4500    400     0.0000   -15.2385    28.8549    28.361662     18.1525    -27.1164     32.6315\n  4500    600     0.0000   -15.2385    28.8549    28.783297     17.9525    -27.2492     32.6315\n  4500    800     0.0000   -15.2385    28.8549    29.195293     17.7561    -27.3776     32.6315\n  4500   1000     0.0000   -15.2385    28.8549    29.598095     17.5632    -27.5018     32.6315\n  4500   1200     0.0000   -15.2385    28.8549    29.992113     17.3736    -27.6219     32.6315\n  4500   1400     0.0000   -15.2385    28.8549    30.377729     17.1873    -27.7382     32.6315\n  4500   1600     0.0000   -15.2385    28.8549    30.755295     17.0042    -27.8509     32.6315\n  4500   1800     0.0000   -15.2385    28.8549    31.125143     16.8241    -27.9600     32.6315\n  4500   2000     0.0000   -15.2385    28.8549    31.487579     16.6469    -28.0659     32.6315\n  4500   2200     0.0000   -15.2385    28.8549    31.842892     16.4725    -28.1686     32.6315\n  4500   2400     0.0000   -15.2385    28.8549    32.191352     16.3009    -28.2683     32.6315\n  4500   2600     0.0000   -15.2385    28.8549    32.533211     16.1319    -28.3650     32.6315\n  4500   2800     0.0000   -15.2385    28.8549    32.868710     15.9655    -28.4590     32.6315\n  4500   3000     0.0000   -15.2385    28.8549    33.198070     15.8017    -28.5503     32.6315\n  4500   3200     0.0000   -15.2385    28.8549    33.521506     15.6403    -28.6390     32.6315\n  4500   3400     0.0000   -15.2385    28.8549    33.839215     15.4812    -28.7253     32.6315\n  4500   3600     0.0000   -15.2385    28.8549    34.151388     15.3245    -28.8093     32.6315\n  4500   3800     0.0000   -15.2385    28.8549    34.458204     15.1700    -28.8909     32.6315\n  4500   4000     0.0000   -15.2385    28.8549    34.759832     15.0177    -28.9704     32.6315\n  4500   4200     0.0000   -15.2385    28.8549    35.056433     14.8675    -29.0477     32.6315\n  4500   4400     0.0000   -15.2385    28.8549    35.348161     14.7194    -29.1230     32.6315\n  4500   4600     0.0000   -15.2385    28.8549    35.635161     14.5734    -29.1964     32.6315\n  4500   4800     0.0000   -15.2385    28.8549    35.917572     14.4293    -29.2679     32.6315\n  4500   5000     0.0000   -15.2385    28.8549    36.195527     14.2871    -29.3375     32.6315\n  4500   5200     0.0000   -15.2385    28.8549    36.469151     14.1469    -29.4054     32.6315\n  4500   5400     0.0000   -15.2385    28.8549    36.738565     14.0084    -29.4716     32.6315\n  4500   5600     0.0000   -15.2385    28.8549    37.003885     13.8718    -29.5362     32.6315\n  4500   5800     0.0000   -15.2385    28.8549    37.265221     13.7369    -29.5992     32.6315\n  4500   6000     0.0000   -15.2385    28.8549    37.522678     13.6038    -29.6606     32.6315\n  4500   6200     0.0000   -15.2385    28.8549    37.776357     13.4723    -29.7205     32.6315\n  4500   6400     0.0000   -15.2385    28.8549    38.026356     13.3425    -29.7790     32.6315\n  4500   6600     0.0000   -15.2385    28.8549    38.272767     13.2143    -29.8361     32.6315\n  4500   6800     0.0000   -15.2385    28.8549    38.515680     13.0877    -29.8919     32.6315\n  4500   7000     0.0000   -15.2385    28.8549    38.755181     12.9627    -29.9463     32.6315\n  4500   7200     0.0000   -15.2385    28.8549    38.991352     12.8391    -29.9995     32.6315\n  4500   7400     0.0000   -15.2385    28.8549    39.224272     12.7171    -30.0515     32.6315\n  4500   7600     0.0000   -15.2385    28.8549    39.454020     12.5965    -30.1022     32.6315\n  4500   7800     0.0000   -15.2385    28.8549    39.680667     12.4773    -30.1518     32.6315\n  4500   8000     0.0000   -15.2385    28.8549    39.904285     12.3595    -30.2003     32.6315\n  4500   8200     0.0000   -15.2385    28.8549    40.124942     12.2431    -30.2476     32.6315\n  4500   8400     0.0000   -15.2385    28.8549    40.342705     12.1281    -30.2940     32.6315\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180130-20180307_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.6939756      -40.1971021       -5.6555867   m   m   m\ninitial_baseline_rate:          0.0000000       -0.1938983        0.0418034   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -40.2128077       -5.6388078   m   m   m\nprecision_baseline_rate:        0.0000000       -0.1881275        0.0374671   m/s m/s m/s\nunwrap_phase_constant:            0.00002     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180130-20180307_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.694    -40.197     -5.656\norbit baseline rate   (TCN) (m/s):  0.000e+00 -1.939e-01  4.180e-02\n\nbaseline vector (TCN)   (m):      0.000    -40.213     -5.639\nbaseline rate   (TCN) (m/s):  0.000e+00 -1.881e-01  3.747e-02\n\nSLC-1 center baseline length (m):    40.6062\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -38.4568    -5.9885    27.494719    -23.0663    -31.3484     38.9203\n     0    200     0.0000   -38.4568    -5.9885    27.936991    -23.3076    -31.1695     38.9203\n     0    400     0.0000   -38.4568    -5.9885    28.368628    -23.5418    -30.9930     38.9203\n     0    600     0.0000   -38.4568    -5.9885    28.790152    -23.7692    -30.8190     38.9203\n     0    800     0.0000   -38.4568    -5.9885    29.202043    -23.9901    -30.6473     38.9203\n     0   1000     0.0000   -38.4568    -5.9885    29.604744    -24.2049    -30.4779     38.9203\n     0   1200     0.0000   -38.4568    -5.9885    29.998667    -24.4139    -30.3108     38.9203\n     0   1400     0.0000   -38.4568    -5.9885    30.384190    -24.6173    -30.1458     38.9203\n     0   1600     0.0000   -38.4568    -5.9885    30.761669    -24.8153    -29.9830     38.9203\n     0   1800     0.0000   -38.4568    -5.9885    31.131432    -25.0083    -29.8222     38.9203\n     0   2000     0.0000   -38.4568    -5.9885    31.493787    -25.1964    -29.6635     38.9203\n     0   2200     0.0000   -38.4568    -5.9885    31.849022    -25.3799    -29.5067     38.9203\n     0   2400     0.0000   -38.4568    -5.9885    32.197407    -25.5588    -29.3518     38.9203\n     0   2600     0.0000   -38.4568    -5.9885    32.539195    -25.7334    -29.1988     38.9203\n     0   2800     0.0000   -38.4568    -5.9885    32.874623    -25.9039    -29.0477     38.9203\n     0   3000     0.0000   -38.4568    -5.9885    33.203917    -26.0705    -28.8983     38.9203\n     0   3200     0.0000   -38.4568    -5.9885    33.527288    -26.2331    -28.7507     38.9203\n     0   3400     0.0000   -38.4568    -5.9885    33.844935    -26.3921    -28.6049     38.9203\n     0   3600     0.0000   -38.4568    -5.9885    34.157047    -26.5476    -28.4607     38.9203\n     0   3800     0.0000   -38.4568    -5.9885    34.463804    -26.6996    -28.3181     38.9203\n     0   4000     0.0000   -38.4568    -5.9885    34.765375    -26.8482    -28.1772     38.9203\n     0   4200     0.0000   -38.4568    -5.9885    35.061921    -26.9937    -28.0379     38.9203\n     0   4400     0.0000   -38.4568    -5.9885    35.353595    -27.1361    -27.9001     38.9203\n     0   4600     0.0000   -38.4568    -5.9885    35.640543    -27.2755    -27.7638     38.9203\n     0   4800     0.0000   -38.4568    -5.9885    35.922904    -27.4120    -27.6291     38.9203\n     0   5000     0.0000   -38.4568    -5.9885    36.200810    -27.5457    -27.4958     38.9203\n     0   5200     0.0000   -38.4568    -5.9885    36.474387    -27.6766    -27.3640     38.9203\n     0   5400     0.0000   -38.4568    -5.9885    36.743755    -27.8050    -27.2335     38.9203\n     0   5600     0.0000   -38.4568    -5.9885    37.009030    -27.9308    -27.1045     38.9203\n     0   5800     0.0000   -38.4568    -5.9885    37.270321    -28.0541    -26.9769     38.9203\n     0   6000     0.0000   -38.4568    -5.9885    37.527736    -28.1750    -26.8506     38.9203\n     0   6200     0.0000   -38.4568    -5.9885    37.781373    -28.2936    -26.7256     38.9203\n     0   6400     0.0000   -38.4568    -5.9885    38.031331    -28.4099    -26.6019     38.9203\n     0   6600     0.0000   -38.4568    -5.9885    38.277703    -28.5240    -26.4795     38.9203\n     0   6800     0.0000   -38.4568    -5.9885    38.520577    -28.6360    -26.3583     38.9203\n     0   7000     0.0000   -38.4568    -5.9885    38.760041    -28.7459    -26.2384     38.9203\n     0   7200     0.0000   -38.4568    -5.9885    38.996175    -28.8538    -26.1197     38.9203\n     0   7400     0.0000   -38.4568    -5.9885    39.229060    -28.9598    -26.0022     38.9203\n     0   7600     0.0000   -38.4568    -5.9885    39.458772    -29.0638    -25.8859     38.9203\n     0   7800     0.0000   -38.4568    -5.9885    39.685385    -29.1659    -25.7708     38.9203\n     0   8000     0.0000   -38.4568    -5.9885    39.908969    -29.2663    -25.6567     38.9203\n     0   8200     0.0000   -38.4568    -5.9885    40.129594    -29.3649    -25.5439     38.9203\n     0   8400     0.0000   -38.4568    -5.9885    40.347325    -29.4617    -25.4321     38.9203\n   500      0     0.0000   -38.8435    -5.9115    27.493760    -23.1760    -31.7274     39.2907\n   500    200     0.0000   -38.8435    -5.9115    27.936048    -23.4202    -31.5476     39.2907\n   500    400     0.0000   -38.8435    -5.9115    28.367700    -23.6572    -31.3702     39.2907\n   500    600     0.0000   -38.8435    -5.9115    28.789239    -23.8874    -31.1953     39.2907\n   500    800     0.0000   -38.8435    -5.9115    29.201143    -24.1111    -31.0228     39.2907\n   500   1000     0.0000   -38.8435    -5.9115    29.603858    -24.3285    -30.8526     39.2907\n   500   1200     0.0000   -38.8435    -5.9115    29.997793    -24.5401    -30.6846     39.2907\n   500   1400     0.0000   -38.8435    -5.9115    30.383329    -24.7460    -30.5188     39.2907\n   500   1600     0.0000   -38.8435    -5.9115    30.760819    -24.9465    -30.3551     39.2907\n   500   1800     0.0000   -38.8435    -5.9115    31.130593    -25.1419    -30.1934     39.2907\n   500   2000     0.0000   -38.8435    -5.9115    31.492959    -25.3323    -30.0338     39.2907\n   500   2200     0.0000   -38.8435    -5.9115    31.848205    -25.5181    -29.8762     39.2907\n   500   2400     0.0000   -38.8435    -5.9115    32.196599    -25.6993    -29.7205     39.2907\n   500   2600     0.0000   -38.8435    -5.9115    32.538396    -25.8761    -29.5666     39.2907\n   500   2800     0.0000   -38.8435    -5.9115    32.873834    -26.0488    -29.4146     39.2907\n   500   3000     0.0000   -38.8435    -5.9115    33.203137    -26.2174    -29.2644     39.2907\n   500   3200     0.0000   -38.8435    -5.9115    33.526516    -26.3821    -29.1160     39.2907\n   500   3400     0.0000   -38.8435    -5.9115    33.844171    -26.5432    -28.9693     39.2907\n   500   3600     0.0000   -38.8435    -5.9115    34.156292    -26.7006    -28.8243     39.2907\n   500   3800     0.0000   -38.8435    -5.9115    34.463056    -26.8545    -28.6809     39.2907\n   500   4000     0.0000   -38.8435    -5.9115    34.764635    -27.0051    -28.5391     39.2907\n   500   4200     0.0000   -38.8435    -5.9115    35.061188    -27.1525    -28.3990     39.2907\n   500   4400     0.0000   -38.8435    -5.9115    35.352869    -27.2967    -28.2604     39.2907\n   500   4600     0.0000   -38.8435    -5.9115    35.639824    -27.4379    -28.1233     39.2907\n   500   4800     0.0000   -38.8435    -5.9115    35.922192    -27.5761    -27.9878     39.2907\n   500   5000     0.0000   -38.8435    -5.9115    36.200104    -27.7116    -27.8537     39.2907\n   500   5200     0.0000   -38.8435    -5.9115    36.473687    -27.8443    -27.7210     39.2907\n   500   5400     0.0000   -38.8435    -5.9115    36.743061    -27.9743    -27.5898     39.2907\n   500   5600     0.0000   -38.8435    -5.9115    37.008342    -28.1017    -27.4600     39.2907\n   500   5800     0.0000   -38.8435    -5.9115    37.269640    -28.2267    -27.3316     39.2907\n   500   6000     0.0000   -38.8435    -5.9115    37.527060    -28.3492    -27.2045     39.2907\n   500   6200     0.0000   -38.8435    -5.9115    37.780703    -28.4693    -27.0787     39.2907\n   500   6400     0.0000   -38.8435    -5.9115    38.030667    -28.5872    -26.9543     39.2907\n   500   6600     0.0000   -38.8435    -5.9115    38.277043    -28.7028    -26.8311     39.2907\n   500   6800     0.0000   -38.8435    -5.9115    38.519923    -28.8163    -26.7092     39.2907\n   500   7000     0.0000   -38.8435    -5.9115    38.759391    -28.9277    -26.5885     39.2907\n   500   7200     0.0000   -38.8435    -5.9115    38.995530    -29.0370    -26.4691     39.2907\n   500   7400     0.0000   -38.8435    -5.9115    39.228420    -29.1444    -26.3508     39.2907\n   500   7600     0.0000   -38.8435    -5.9115    39.458137    -29.2498    -26.2337     39.2907\n   500   7800     0.0000   -38.8435    -5.9115    39.684754    -29.3533    -26.1179     39.2907\n   500   8000     0.0000   -38.8435    -5.9115    39.908343    -29.4550    -26.0031     39.2907\n   500   8200     0.0000   -38.8435    -5.9115    40.128972    -29.5549    -25.8895     39.2907\n   500   8400     0.0000   -38.8435    -5.9115    40.346708    -29.6531    -25.7770     39.2907\n  1000      0     0.0000   -39.2302    -5.8345    27.492780    -23.2857    -32.1064     39.6617\n  1000    200     0.0000   -39.2302    -5.8345    27.935085    -23.5328    -31.9257     39.6617\n  1000    400     0.0000   -39.2302    -5.8345    28.366753    -23.7727    -31.7475     39.6617\n  1000    600     0.0000   -39.2302    -5.8345    28.788306    -24.0056    -31.5717     39.6617\n  1000    800     0.0000   -39.2302    -5.8345    29.200225    -24.2320    -31.3983     39.6617\n  1000   1000     0.0000   -39.2302    -5.8345    29.602953    -24.4521    -31.2272     39.6617\n  1000   1200     0.0000   -39.2302    -5.8345    29.996901    -24.6662    -31.0584     39.6617\n  1000   1400     0.0000   -39.2302    -5.8345    30.382449    -24.8746    -30.8917     39.6617\n  1000   1600     0.0000   -39.2302    -5.8345    30.759951    -25.0776    -30.7271     39.6617\n  1000   1800     0.0000   -39.2302    -5.8345    31.129737    -25.2754    -30.5646     39.6617\n  1000   2000     0.0000   -39.2302    -5.8345    31.492114    -25.4682    -30.4042     39.6617\n  1000   2200     0.0000   -39.2302    -5.8345    31.847370    -25.6563    -30.2457     39.6617\n  1000   2400     0.0000   -39.2302    -5.8345    32.195774    -25.8397    -30.0891     39.6617\n  1000   2600     0.0000   -39.2302    -5.8345    32.537581    -26.0187    -29.9344     39.6617\n  1000   2800     0.0000   -39.2302    -5.8345    32.873029    -26.1936    -29.7816     39.6617\n  1000   3000     0.0000   -39.2302    -5.8345    33.202340    -26.3643    -29.6305     39.6617\n  1000   3200     0.0000   -39.2302    -5.8345    33.525728    -26.5311    -29.4813     39.6617\n  1000   3400     0.0000   -39.2302    -5.8345    33.843392    -26.6942    -29.3337     39.6617\n  1000   3600     0.0000   -39.2302    -5.8345    34.155520    -26.8536    -29.1879     39.6617\n  1000   3800     0.0000   -39.2302    -5.8345    34.462293    -27.0095    -29.0437     39.6617\n  1000   4000     0.0000   -39.2302    -5.8345    34.763879    -27.1620    -28.9011     39.6617\n  1000   4200     0.0000   -39.2302    -5.8345    35.060440    -27.3112    -28.7601     39.6617\n  1000   4400     0.0000   -39.2302    -5.8345    35.352129    -27.4572    -28.6207     39.6617\n  1000   4600     0.0000   -39.2302    -5.8345    35.639091    -27.6002    -28.4828     39.6617\n  1000   4800     0.0000   -39.2302    -5.8345    35.921465    -27.7403    -28.3465     39.6617\n  1000   5000     0.0000   -39.2302    -5.8345    36.199384    -27.8775    -28.2116     39.6617\n  1000   5200     0.0000   -39.2302    -5.8345    36.472974    -28.0119    -28.0781     39.6617\n  1000   5400     0.0000   -39.2302    -5.8345    36.742354    -28.1436    -27.9461     39.6617\n  1000   5600     0.0000   -39.2302    -5.8345    37.007641    -28.2726    -27.8155     39.6617\n  1000   5800     0.0000   -39.2302    -5.8345    37.268945    -28.3992    -27.6863     39.6617\n  1000   6000     0.0000   -39.2302    -5.8345    37.526370    -28.5233    -27.5584     39.6617\n  1000   6200     0.0000   -39.2302    -5.8345    37.780019    -28.6450    -27.4319     39.6617\n  1000   6400     0.0000   -39.2302    -5.8345    38.029988    -28.7644    -27.3066     39.6617\n  1000   6600     0.0000   -39.2302    -5.8345    38.276371    -28.8816    -27.1827     39.6617\n  1000   6800     0.0000   -39.2302    -5.8345    38.519255    -28.9966    -27.0600     39.6617\n  1000   7000     0.0000   -39.2302    -5.8345    38.758729    -29.1094    -26.9386     39.6617\n  1000   7200     0.0000   -39.2302    -5.8345    38.994873    -29.2202    -26.8184     39.6617\n  1000   7400     0.0000   -39.2302    -5.8345    39.227768    -29.3290    -26.6994     39.6617\n  1000   7600     0.0000   -39.2302    -5.8345    39.457489    -29.4358    -26.5816     39.6617\n  1000   7800     0.0000   -39.2302    -5.8345    39.684111    -29.5407    -26.4650     39.6617\n  1000   8000     0.0000   -39.2302    -5.8345    39.907705    -29.6437    -26.3495     39.6617\n  1000   8200     0.0000   -39.2302    -5.8345    40.128339    -29.7450    -26.2351     39.6617\n  1000   8400     0.0000   -39.2302    -5.8345    40.346078    -29.8445    -26.1219     39.6617\n  1500      0     0.0000   -39.6169    -5.7575    27.491780    -23.3953    -32.4854     40.0331\n  1500    200     0.0000   -39.6169    -5.7575    27.934101    -23.6454    -32.3038     40.0331\n  1500    400     0.0000   -39.6169    -5.7575    28.365785    -23.8881    -32.1248     40.0331\n  1500    600     0.0000   -39.6169    -5.7575    28.787354    -24.1238    -31.9481     40.0331\n  1500    800     0.0000   -39.6169    -5.7575    29.199287    -24.3529    -31.7739     40.0331\n  1500   1000     0.0000   -39.6169    -5.7575    29.602029    -24.5756    -31.6019     40.0331\n  1500   1200     0.0000   -39.6169    -5.7575    29.995991    -24.7923    -31.4322     40.0331\n  1500   1400     0.0000   -39.6169    -5.7575    30.381551    -25.0033    -31.2646     40.0331\n  1500   1600     0.0000   -39.6169    -5.7575    30.759066    -25.2088    -31.0992     40.0331\n  1500   1800     0.0000   -39.6169    -5.7575    31.128863    -25.4090    -30.9359     40.0331\n  1500   2000     0.0000   -39.6169    -5.7575    31.491251    -25.6041    -30.7745     40.0331\n  1500   2200     0.0000   -39.6169    -5.7575    31.846518    -25.7944    -30.6152     40.0331\n  1500   2400     0.0000   -39.6169    -5.7575    32.194933    -25.9801    -30.4578     40.0331\n  1500   2600     0.0000   -39.6169    -5.7575    32.536750    -26.1614    -30.3022     40.0331\n  1500   2800     0.0000   -39.6169    -5.7575    32.872207    -26.3383    -30.1485     40.0331\n  1500   3000     0.0000   -39.6169    -5.7575    33.201528    -26.5112    -29.9967     40.0331\n  1500   3200     0.0000   -39.6169    -5.7575    33.524924    -26.6801    -29.8465     40.0331\n  1500   3400     0.0000   -39.6169    -5.7575    33.842597    -26.8451    -29.6982     40.0331\n  1500   3600     0.0000   -39.6169    -5.7575    34.154734    -27.0065    -29.5515     40.0331\n  1500   3800     0.0000   -39.6169    -5.7575    34.461515    -27.1644    -29.4065     40.0331\n  1500   4000     0.0000   -39.6169    -5.7575    34.763109    -27.3188    -29.2631     40.0331\n  1500   4200     0.0000   -39.6169    -5.7575    35.059677    -27.4699    -29.1213     40.0331\n  1500   4400     0.0000   -39.6169    -5.7575    35.351373    -27.6178    -28.9810     40.0331\n  1500   4600     0.0000   -39.6169    -5.7575    35.638343    -27.7626    -28.8424     40.0331\n  1500   4800     0.0000   -39.6169    -5.7575    35.920724    -27.9044    -28.7052     40.0331\n  1500   5000     0.0000   -39.6169    -5.7575    36.198650    -28.0433    -28.5695     40.0331\n  1500   5200     0.0000   -39.6169    -5.7575    36.472246    -28.1794    -28.4352     40.0331\n  1500   5400     0.0000   -39.6169    -5.7575    36.741633    -28.3128    -28.3024     40.0331\n  1500   5600     0.0000   -39.6169    -5.7575    37.006926    -28.4436    -28.1710     40.0331\n  1500   5800     0.0000   -39.6169    -5.7575    37.268236    -28.5717    -28.0410     40.0331\n  1500   6000     0.0000   -39.6169    -5.7575    37.525667    -28.6974    -27.9124     40.0331\n  1500   6200     0.0000   -39.6169    -5.7575    37.779322    -28.8207    -27.7851     40.0331\n  1500   6400     0.0000   -39.6169    -5.7575    38.029297    -28.9417    -27.6590     40.0331\n  1500   6600     0.0000   -39.6169    -5.7575    38.275685    -29.0604    -27.5343     40.0331\n  1500   6800     0.0000   -39.6169    -5.7575    38.518575    -29.1768    -27.4109     40.0331\n  1500   7000     0.0000   -39.6169    -5.7575    38.758054    -29.2911    -27.2887     40.0331\n  1500   7200     0.0000   -39.6169    -5.7575    38.994203    -29.4034    -27.1678     40.0331\n  1500   7400     0.0000   -39.6169    -5.7575    39.227103    -29.5135    -27.0480     40.0331\n  1500   7600     0.0000   -39.6169    -5.7575    39.456829    -29.6218    -26.9295     40.0331\n  1500   7800     0.0000   -39.6169    -5.7575    39.683456    -29.7280    -26.8121     40.0331\n  1500   8000     0.0000   -39.6169    -5.7575    39.907054    -29.8325    -26.6959     40.0331\n  1500   8200     0.0000   -39.6169    -5.7575    40.127693    -29.9350    -26.5808     40.0331\n  1500   8400     0.0000   -39.6169    -5.7575    40.345437    -30.0358    -26.4668     40.0331\n  2000      0     0.0000   -40.0036    -5.6805    27.490758    -23.5049    -32.8644     40.4049\n  2000    200     0.0000   -40.0036    -5.6805    27.933098    -23.7579    -32.6820     40.4049\n  2000    400     0.0000   -40.0036    -5.6805    28.364798    -24.0035    -32.5020     40.4049\n  2000    600     0.0000   -40.0036    -5.6805    28.786382    -24.2420    -32.3245     40.4049\n  2000    800     0.0000   -40.0036    -5.6805    29.198330    -24.4738    -32.1494     40.4049\n  2000   1000     0.0000   -40.0036    -5.6805    29.601087    -24.6992    -31.9766     40.4049\n  2000   1200     0.0000   -40.0036    -5.6805    29.995062    -24.9185    -31.8060     40.4049\n  2000   1400     0.0000   -40.0036    -5.6805    30.380636    -25.1319    -31.6376     40.4049\n  2000   1600     0.0000   -40.0036    -5.6805    30.758162    -25.3399    -31.4713     40.4049\n  2000   1800     0.0000   -40.0036    -5.6805    31.127971    -25.5425    -31.3071     40.4049\n  2000   2000     0.0000   -40.0036    -5.6805    31.490371    -25.7400    -31.1449     40.4049\n  2000   2200     0.0000   -40.0036    -5.6805    31.845649    -25.9326    -30.9847     40.4049\n  2000   2400     0.0000   -40.0036    -5.6805    32.194074    -26.1205    -30.8264     40.4049\n  2000   2600     0.0000   -40.0036    -5.6805    32.535902    -26.3040    -30.6701     40.4049\n  2000   2800     0.0000   -40.0036    -5.6805    32.871368    -26.4831    -30.5155     40.4049\n  2000   3000     0.0000   -40.0036    -5.6805    33.200699    -26.6581    -30.3628     40.4049\n  2000   3200     0.0000   -40.0036    -5.6805    33.524105    -26.8290    -30.2118     40.4049\n  2000   3400     0.0000   -40.0036    -5.6805    33.841786    -26.9961    -30.0626     40.4049\n  2000   3600     0.0000   -40.0036    -5.6805    34.153932    -27.1595    -29.9151     40.4049\n  2000   3800     0.0000   -40.0036    -5.6805    34.460721    -27.3193    -29.7693     40.4049\n  2000   4000     0.0000   -40.0036    -5.6805    34.762323    -27.4756    -29.6250     40.4049\n  2000   4200     0.0000   -40.0036    -5.6805    35.058899    -27.6286    -29.4824     40.4049\n  2000   4400     0.0000   -40.0036    -5.6805    35.350603    -27.7783    -29.3414     40.4049\n  2000   4600     0.0000   -40.0036    -5.6805    35.637580    -27.9249    -29.2019     40.4049\n  2000   4800     0.0000   -40.0036    -5.6805    35.919968    -28.0685    -29.0639     40.4049\n  2000   5000     0.0000   -40.0036    -5.6805    36.197901    -28.2092    -28.9274     40.4049\n  2000   5200     0.0000   -40.0036    -5.6805    36.471504    -28.3470    -28.7924     40.4049\n  2000   5400     0.0000   -40.0036    -5.6805    36.740898    -28.4821    -28.6588     40.4049\n  2000   5600     0.0000   -40.0036    -5.6805    37.006197    -28.6145    -28.5266     40.4049\n  2000   5800     0.0000   -40.0036    -5.6805    37.267513    -28.7443    -28.3958     40.4049\n  2000   6000     0.0000   -40.0036    -5.6805    37.524951    -28.8716    -28.2663     40.4049\n  2000   6200     0.0000   -40.0036    -5.6805    37.778612    -28.9964    -28.1382     40.4049\n  2000   6400     0.0000   -40.0036    -5.6805    38.028592    -29.1189    -28.0115     40.4049\n  2000   6600     0.0000   -40.0036    -5.6805    38.274986    -29.2391    -27.8860     40.4049\n  2000   6800     0.0000   -40.0036    -5.6805    38.517882    -29.3571    -27.7618     40.4049\n  2000   7000     0.0000   -40.0036    -5.6805    38.757365    -29.4728    -27.6388     40.4049\n  2000   7200     0.0000   -40.0036    -5.6805    38.993520    -29.5865    -27.5171     40.4049\n  2000   7400     0.0000   -40.0036    -5.6805    39.226425    -29.6981    -27.3966     40.4049\n  2000   7600     0.0000   -40.0036    -5.6805    39.456157    -29.8077    -27.2773     40.4049\n  2000   7800     0.0000   -40.0036    -5.6805    39.682788    -29.9154    -27.1592     40.4049\n  2000   8000     0.0000   -40.0036    -5.6805    39.906392    -30.0212    -27.0423     40.4049\n  2000   8200     0.0000   -40.0036    -5.6805    40.127035    -30.1251    -26.9264     40.4049\n  2000   8400     0.0000   -40.0036    -5.6805    40.344783    -30.2272    -26.8118     40.4049\n  2500      0     0.0000   -40.3903    -5.6035    27.489717    -23.6145    -33.2434     40.7771\n  2500    200     0.0000   -40.3903    -5.6035    27.932074    -23.8704    -33.0601     40.7771\n  2500    400     0.0000   -40.3903    -5.6035    28.363791    -24.1189    -32.8793     40.7771\n  2500    600     0.0000   -40.3903    -5.6035    28.785391    -24.3602    -32.7010     40.7771\n  2500    800     0.0000   -40.3903    -5.6035    29.197355    -24.5946    -32.5250     40.7771\n  2500   1000     0.0000   -40.3903    -5.6035    29.600126    -24.8227    -32.3513     40.7771\n  2500   1200     0.0000   -40.3903    -5.6035    29.994114    -25.0446    -32.1798     40.7771\n  2500   1400     0.0000   -40.3903    -5.6035    30.379702    -25.2605    -32.0105     40.7771\n  2500   1600     0.0000   -40.3903    -5.6035    30.757241    -25.4709    -31.8434     40.7771\n  2500   1800     0.0000   -40.3903    -5.6035    31.127062    -25.6759    -31.6783     40.7771\n  2500   2000     0.0000   -40.3903    -5.6035    31.489474    -25.8758    -31.5153     40.7771\n  2500   2200     0.0000   -40.3903    -5.6035    31.844763    -26.0707    -31.3542     40.7771\n  2500   2400     0.0000   -40.3903    -5.6035    32.193199    -26.2609    -31.1951     40.7771\n  2500   2600     0.0000   -40.3903    -5.6035    32.535037    -26.4466    -31.0379     40.7771\n  2500   2800     0.0000   -40.3903    -5.6035    32.870514    -26.6278    -30.8825     40.7771\n  2500   3000     0.0000   -40.3903    -5.6035    33.199854    -26.8049    -30.7289     40.7771\n  2500   3200     0.0000   -40.3903    -5.6035    33.523270    -26.9779    -30.5772     40.7771\n  2500   3400     0.0000   -40.3903    -5.6035    33.840960    -27.1471    -30.4271     40.7771\n  2500   3600     0.0000   -40.3903    -5.6035    34.153114    -27.3124    -30.2787     40.7771\n  2500   3800     0.0000   -40.3903    -5.6035    34.459912    -27.4742    -30.1321     40.7771\n  2500   4000     0.0000   -40.3903    -5.6035    34.761522    -27.6324    -29.9870     40.7771\n  2500   4200     0.0000   -40.3903    -5.6035    35.058107    -27.7873    -29.8436     40.7771\n  2500   4400     0.0000   -40.3903    -5.6035    35.349818    -27.9388    -29.7017     40.7771\n  2500   4600     0.0000   -40.3903    -5.6035    35.636803    -28.0873    -29.5614     40.7771\n  2500   4800     0.0000   -40.3903    -5.6035    35.919199    -28.2326    -29.4226     40.7771\n  2500   5000     0.0000   -40.3903    -5.6035    36.197138    -28.3750    -29.2853     40.7771\n  2500   5200     0.0000   -40.3903    -5.6035    36.470748    -28.5145    -29.1495     40.7771\n  2500   5400     0.0000   -40.3903    -5.6035    36.740149    -28.6513    -29.0151     40.7771\n  2500   5600     0.0000   -40.3903    -5.6035    37.005455    -28.7853    -28.8821     40.7771\n  2500   5800     0.0000   -40.3903    -5.6035    37.266777    -28.9168    -28.7505     40.7771\n  2500   6000     0.0000   -40.3903    -5.6035    37.524221    -29.0457    -28.6203     40.7771\n  2500   6200     0.0000   -40.3903    -5.6035    37.777888    -29.1721    -28.4914     40.7771\n  2500   6400     0.0000   -40.3903    -5.6035    38.027874    -29.2961    -28.3639     40.7771\n  2500   6600     0.0000   -40.3903    -5.6035    38.274274    -29.4178    -28.2376     40.7771\n  2500   6800     0.0000   -40.3903    -5.6035    38.517175    -29.5373    -28.1127     40.7771\n  2500   7000     0.0000   -40.3903    -5.6035    38.756665    -29.6545    -27.9890     40.7771\n  2500   7200     0.0000   -40.3903    -5.6035    38.992825    -29.7696    -27.8665     40.7771\n  2500   7400     0.0000   -40.3903    -5.6035    39.225735    -29.8827    -27.7453     40.7771\n  2500   7600     0.0000   -40.3903    -5.6035    39.455472    -29.9937    -27.6252     40.7771\n  2500   7800     0.0000   -40.3903    -5.6035    39.682108    -30.1027    -27.5064     40.7771\n  2500   8000     0.0000   -40.3903    -5.6035    39.905717    -30.2098    -27.3887     40.7771\n  2500   8200     0.0000   -40.3903    -5.6035    40.126364    -30.3151    -27.2721     40.7771\n  2500   8400     0.0000   -40.3903    -5.6035    40.344118    -30.4185    -27.1567     40.7771\n  3000      0     0.0000   -40.7770    -5.5264    27.488655    -23.7240    -33.6225     41.1498\n  3000    200     0.0000   -40.7770    -5.5264    27.931030    -23.9829    -33.4383     41.1498\n  3000    400     0.0000   -40.7770    -5.5264    28.362764    -24.2342    -33.2566     41.1498\n  3000    600     0.0000   -40.7770    -5.5264    28.784381    -24.4783    -33.0774     41.1498\n  3000    800     0.0000   -40.7770    -5.5264    29.196360    -24.7155    -32.9005     41.1498\n  3000   1000     0.0000   -40.7770    -5.5264    29.599146    -24.9462    -32.7260     41.1498\n  3000   1200     0.0000   -40.7770    -5.5264    29.993149    -25.1706    -32.5537     41.1498\n  3000   1400     0.0000   -40.7770    -5.5264    30.378750    -25.3891    -32.3835     41.1498\n  3000   1600     0.0000   -40.7770    -5.5264    30.756302    -25.6020    -32.2155     41.1498\n  3000   1800     0.0000   -40.7770    -5.5264    31.126136    -25.8094    -32.0496     41.1498\n  3000   2000     0.0000   -40.7770    -5.5264    31.488559    -26.0116    -31.8857     41.1498\n  3000   2200     0.0000   -40.7770    -5.5264    31.843860    -26.2088    -31.7238     41.1498\n  3000   2400     0.0000   -40.7770    -5.5264    32.192308    -26.4013    -31.5638     41.1498\n  3000   2600     0.0000   -40.7770    -5.5264    32.534156    -26.5891    -31.4057     41.1498\n  3000   2800     0.0000   -40.7770    -5.5264    32.869643    -26.7726    -31.2495     41.1498\n  3000   3000     0.0000   -40.7770    -5.5264    33.198993    -26.9517    -31.0951     41.1498\n  3000   3200     0.0000   -40.7770    -5.5264    33.522418    -27.1268    -30.9425     41.1498\n  3000   3400     0.0000   -40.7770    -5.5264    33.840118    -27.2980    -30.7916     41.1498\n  3000   3600     0.0000   -40.7770    -5.5264    34.152282    -27.4654    -30.6424     41.1498\n  3000   3800     0.0000   -40.7770    -5.5264    34.459088    -27.6290    -30.4949     41.1498\n  3000   4000     0.0000   -40.7770    -5.5264    34.760707    -27.7892    -30.3490     41.1498\n  3000   4200     0.0000   -40.7770    -5.5264    35.057299    -27.9459    -30.2048     41.1498\n  3000   4400     0.0000   -40.7770    -5.5264    35.349019    -28.0993    -30.0621     41.1498\n  3000   4600     0.0000   -40.7770    -5.5264    35.636011    -28.2496    -29.9210     41.1498\n  3000   4800     0.0000   -40.7770    -5.5264    35.918414    -28.3967    -29.7814     41.1498\n  3000   5000     0.0000   -40.7770    -5.5264    36.196361    -28.5408    -29.6433     41.1498\n  3000   5200     0.0000   -40.7770    -5.5264    36.469978    -28.6821    -29.5066     41.1498\n  3000   5400     0.0000   -40.7770    -5.5264    36.739386    -28.8205    -29.3714     41.1498\n  3000   5600     0.0000   -40.7770    -5.5264    37.004699    -28.9562    -29.2377     41.1498\n  3000   5800     0.0000   -40.7770    -5.5264    37.266027    -29.0893    -29.1053     41.1498\n  3000   6000     0.0000   -40.7770    -5.5264    37.523478    -29.2197    -28.9743     41.1498\n  3000   6200     0.0000   -40.7770    -5.5264    37.777151    -29.3477    -28.8446     41.1498\n  3000   6400     0.0000   -40.7770    -5.5264    38.027143    -29.4733    -28.7163     41.1498\n  3000   6600     0.0000   -40.7770    -5.5264    38.273549    -29.5965    -28.5893     41.1498\n  3000   6800     0.0000   -40.7770    -5.5264    38.516456    -29.7175    -28.4636     41.1498\n  3000   7000     0.0000   -40.7770    -5.5264    38.755951    -29.8362    -28.3391     41.1498\n  3000   7200     0.0000   -40.7770    -5.5264    38.992117    -29.9528    -28.2159     41.1498\n  3000   7400     0.0000   -40.7770    -5.5264    39.225032    -30.0672    -28.0939     41.1498\n  3000   7600     0.0000   -40.7770    -5.5264    39.454774    -30.1796    -27.9731     41.1498\n  3000   7800     0.0000   -40.7770    -5.5264    39.681416    -30.2900    -27.8535     41.1498\n  3000   8000     0.0000   -40.7770    -5.5264    39.905029    -30.3985    -27.7351     41.1498\n  3000   8200     0.0000   -40.7770    -5.5264    40.125682    -30.5051    -27.6178     41.1498\n  3000   8400     0.0000   -40.7770    -5.5264    40.343441    -30.6098    -27.5017     41.1498\n  3500      0     0.0000   -41.1637    -5.4494    27.487573    -23.8336    -34.0015     41.5229\n  3500    200     0.0000   -41.1637    -5.4494    27.929966    -24.0954    -33.8165     41.5229\n  3500    400     0.0000   -41.1637    -5.4494    28.361718    -24.3495    -33.6339     41.5229\n  3500    600     0.0000   -41.1637    -5.4494    28.783351    -24.5964    -33.4538     41.5229\n  3500    800     0.0000   -41.1637    -5.4494    29.195346    -24.8363    -33.2761     41.5229\n  3500   1000     0.0000   -41.1637    -5.4494    29.598148    -25.0696    -33.1007     41.5229\n  3500   1200     0.0000   -41.1637    -5.4494    29.992165    -25.2967    -32.9275     41.5229\n  3500   1400     0.0000   -41.1637    -5.4494    30.377780    -25.5177    -32.7565     41.5229\n  3500   1600     0.0000   -41.1637    -5.4494    30.755345    -25.7330    -32.5877     41.5229\n  3500   1800     0.0000   -41.1637    -5.4494    31.125192    -25.9428    -32.4209     41.5229\n  3500   2000     0.0000   -41.1637    -5.4494    31.487628    -26.1474    -32.2561     41.5229\n  3500   2200     0.0000   -41.1637    -5.4494    31.842940    -26.3469    -32.0934     41.5229\n  3500   2400     0.0000   -41.1637    -5.4494    32.191399    -26.5416    -31.9325     41.5229\n  3500   2600     0.0000   -41.1637    -5.4494    32.533258    -26.7317    -31.7736     41.5229\n  3500   2800     0.0000   -41.1637    -5.4494    32.868756    -26.9173    -31.6165     41.5229\n  3500   3000     0.0000   -41.1637    -5.4494    33.198117    -27.0986    -31.4613     41.5229\n  3500   3200     0.0000   -41.1637    -5.4494    33.521551    -27.2757    -31.3078     41.5229\n  3500   3400     0.0000   -41.1637    -5.4494    33.839261    -27.4489    -31.1561     41.5229\n  3500   3600     0.0000   -41.1637    -5.4494    34.151433    -27.6183    -31.0061     41.5229\n  3500   3800     0.0000   -41.1637    -5.4494    34.458249    -27.7839    -30.8577     41.5229\n  3500   4000     0.0000   -41.1637    -5.4494    34.759876    -27.9460    -30.7110     41.5229\n  3500   4200     0.0000   -41.1637    -5.4494    35.056477    -28.1046    -30.5660     41.5229\n  3500   4400     0.0000   -41.1637    -5.4494    35.348205    -28.2598    -30.4225     41.5229\n  3500   4600     0.0000   -41.1637    -5.4494    35.635205    -28.4119    -30.2805     41.5229\n  3500   4800     0.0000   -41.1637    -5.4494    35.917616    -28.5608    -30.1401     41.5229\n  3500   5000     0.0000   -41.1637    -5.4494    36.195570    -28.7067    -30.0012     41.5229\n  3500   5200     0.0000   -41.1637    -5.4494    36.469194    -28.8496    -29.8638     41.5229\n  3500   5400     0.0000   -41.1637    -5.4494    36.738609    -28.9897    -29.7278     41.5229\n  3500   5600     0.0000   -41.1637    -5.4494    37.003929    -29.1271    -29.5932     41.5229\n  3500   5800     0.0000   -41.1637    -5.4494    37.265264    -29.2617    -29.4601     41.5229\n  3500   6000     0.0000   -41.1637    -5.4494    37.522721    -29.3938    -29.3283     41.5229\n  3500   6200     0.0000   -41.1637    -5.4494    37.776401    -29.5234    -29.1979     41.5229\n  3500   6400     0.0000   -41.1637    -5.4494    38.026399    -29.6505    -29.0688     41.5229\n  3500   6600     0.0000   -41.1637    -5.4494    38.272811    -29.7752    -28.9410     41.5229\n  3500   6800     0.0000   -41.1637    -5.4494    38.515724    -29.8977    -28.8145     41.5229\n  3500   7000     0.0000   -41.1637    -5.4494    38.755225    -30.0179    -28.6893     41.5229\n  3500   7200     0.0000   -41.1637    -5.4494    38.991396    -30.1359    -28.5653     41.5229\n  3500   7400     0.0000   -41.1637    -5.4494    39.224317    -30.2517    -28.4425     41.5229\n  3500   7600     0.0000   -41.1637    -5.4494    39.454064    -30.3655    -28.3210     41.5229\n  3500   7800     0.0000   -41.1637    -5.4494    39.680712    -30.4773    -28.2007     41.5229\n  3500   8000     0.0000   -41.1637    -5.4494    39.904330    -30.5872    -28.0815     41.5229\n  3500   8200     0.0000   -41.1637    -5.4494    40.124988    -30.6951    -27.9635     41.5229\n  3500   8400     0.0000   -41.1637    -5.4494    40.342751    -30.8011    -27.8466     41.5229\n  4000      0     0.0000   -41.5504    -5.3724    27.486470    -23.9431    -34.3806     41.8963\n  4000    200     0.0000   -41.5504    -5.3724    27.928882    -24.2078    -34.1947     41.8963\n  4000    400     0.0000   -41.5504    -5.3724    28.360652    -24.4648    -34.0113     41.8963\n  4000    600     0.0000   -41.5504    -5.3724    28.782302    -24.7145    -33.8303     41.8963\n  4000    800     0.0000   -41.5504    -5.3724    29.194314    -24.9571    -33.6517     41.8963\n  4000   1000     0.0000   -41.5504    -5.3724    29.597131    -25.1931    -33.4754     41.8963\n  4000   1200     0.0000   -41.5504    -5.3724    29.991163    -25.4227    -33.3014     41.8963\n  4000   1400     0.0000   -41.5504    -5.3724    30.376792    -25.6462    -33.1295     41.8963\n  4000   1600     0.0000   -41.5504    -5.3724    30.754371    -25.8640    -32.9598     41.8963\n  4000   1800     0.0000   -41.5504    -5.3724    31.124231    -26.0762    -32.7922     41.8963\n  4000   2000     0.0000   -41.5504    -5.3724    31.486679    -26.2832    -32.6265     41.8963\n  4000   2200     0.0000   -41.5504    -5.3724    31.842003    -26.4850    -32.4629     41.8963\n  4000   2400     0.0000   -41.5504    -5.3724    32.190474    -26.6819    -32.3012     41.8963\n  4000   2600     0.0000   -41.5504    -5.3724    32.532345    -26.8742    -32.1415     41.8963\n  4000   2800     0.0000   -41.5504    -5.3724    32.867853    -27.0619    -31.9836     41.8963\n  4000   3000     0.0000   -41.5504    -5.3724    33.197224    -27.2454    -31.8275     41.8963\n  4000   3200     0.0000   -41.5504    -5.3724    33.520669    -27.4246    -31.6731     41.8963\n  4000   3400     0.0000   -41.5504    -5.3724    33.838388    -27.5998    -31.5206     41.8963\n  4000   3600     0.0000   -41.5504    -5.3724    34.150570    -27.7711    -31.3697     41.8963\n  4000   3800     0.0000   -41.5504    -5.3724    34.457394    -27.9387    -31.2206     41.8963\n  4000   4000     0.0000   -41.5504    -5.3724    34.759031    -28.1027    -31.0731     41.8963\n  4000   4200     0.0000   -41.5504    -5.3724    35.055640    -28.2632    -30.9272     41.8963\n  4000   4400     0.0000   -41.5504    -5.3724    35.347376    -28.4203    -30.7828     41.8963\n  4000   4600     0.0000   -41.5504    -5.3724    35.634384    -28.5741    -30.6401     41.8963\n  4000   4800     0.0000   -41.5504    -5.3724    35.916803    -28.7248    -30.4989     41.8963\n  4000   5000     0.0000   -41.5504    -5.3724    36.194765    -28.8724    -30.3592     41.8963\n  4000   5200     0.0000   -41.5504    -5.3724    36.468397    -29.0171    -30.2209     41.8963\n  4000   5400     0.0000   -41.5504    -5.3724    36.737818    -29.1589    -30.0842     41.8963\n  4000   5600     0.0000   -41.5504    -5.3724    37.003145    -29.2979    -29.9488     41.8963\n  4000   5800     0.0000   -41.5504    -5.3724    37.264488    -29.4342    -29.8149     41.8963\n  4000   6000     0.0000   -41.5504    -5.3724    37.521951    -29.5679    -29.6823     41.8963\n  4000   6200     0.0000   -41.5504    -5.3724    37.775637    -29.6990    -29.5511     41.8963\n  4000   6400     0.0000   -41.5504    -5.3724    38.025642    -29.8277    -29.4212     41.8963\n  4000   6600     0.0000   -41.5504    -5.3724    38.272060    -29.9539    -29.2927     41.8963\n  4000   6800     0.0000   -41.5504    -5.3724    38.514979    -30.0778    -29.1654     41.8963\n  4000   7000     0.0000   -41.5504    -5.3724    38.754486    -30.1995    -29.0394     41.8963\n  4000   7200     0.0000   -41.5504    -5.3724    38.990663    -30.3189    -28.9147     41.8963\n  4000   7400     0.0000   -41.5504    -5.3724    39.223589    -30.4362    -28.7912     41.8963\n  4000   7600     0.0000   -41.5504    -5.3724    39.453342    -30.5514    -28.6689     41.8963\n  4000   7800     0.0000   -41.5504    -5.3724    39.679995    -30.6646    -28.5478     41.8963\n  4000   8000     0.0000   -41.5504    -5.3724    39.903618    -30.7758    -28.4279     41.8963\n  4000   8200     0.0000   -41.5504    -5.3724    40.124281    -30.8851    -28.3092     41.8963\n  4000   8400     0.0000   -41.5504    -5.3724    40.342050    -30.9924    -28.1916     41.8963\n  4500      0     0.0000   -41.9371    -5.2954    27.485347    -24.0526    -34.7596     42.2701\n  4500    200     0.0000   -41.9371    -5.2954    27.927779    -24.3203    -34.5729     42.2701\n  4500    400     0.0000   -41.9371    -5.2954    28.359567    -24.5801    -34.3886     42.2701\n  4500    600     0.0000   -41.9371    -5.2954    28.781235    -24.8325    -34.2068     42.2701\n  4500    800     0.0000   -41.9371    -5.2954    29.193263    -25.0779    -34.0273     42.2701\n  4500   1000     0.0000   -41.9371    -5.2954    29.596095    -25.3165    -33.8502     42.2701\n  4500   1200     0.0000   -41.9371    -5.2954    29.990143    -25.5487    -33.6753     42.2701\n  4500   1400     0.0000   -41.9371    -5.2954    30.375786    -25.7748    -33.5025     42.2701\n  4500   1600     0.0000   -41.9371    -5.2954    30.753379    -25.9950    -33.3320     42.2701\n  4500   1800     0.0000   -41.9371    -5.2954    31.123252    -26.2096    -33.1635     42.2701\n  4500   2000     0.0000   -41.9371    -5.2954    31.485713    -26.4189    -32.9970     42.2701\n  4500   2200     0.0000   -41.9371    -5.2954    31.841050    -26.6230    -32.8325     42.2701\n  4500   2400     0.0000   -41.9371    -5.2954    32.189533    -26.8222    -32.6700     42.2701\n  4500   2600     0.0000   -41.9371    -5.2954    32.531414    -27.0167    -32.5094     42.2701\n  4500   2800     0.0000   -41.9371    -5.2954    32.866934    -27.2066    -32.3506     42.2701\n  4500   3000     0.0000   -41.9371    -5.2954    33.196315    -27.3921    -32.1936     42.2701\n  4500   3200     0.0000   -41.9371    -5.2954    33.519771    -27.5734    -32.0385     42.2701\n  4500   3400     0.0000   -41.9371    -5.2954    33.837500    -27.7507    -31.8851     42.2701\n  4500   3600     0.0000   -41.9371    -5.2954    34.149691    -27.9240    -31.7334     42.2701\n  4500   3800     0.0000   -41.9371    -5.2954    34.456525    -28.0935    -31.5834     42.2701\n  4500   4000     0.0000   -41.9371    -5.2954    34.758170    -28.2594    -31.4351     42.2701\n  4500   4200     0.0000   -41.9371    -5.2954    35.054788    -28.4218    -31.2884     42.2701\n  4500   4400     0.0000   -41.9371    -5.2954    35.346533    -28.5807    -31.1432     42.2701\n  4500   4600     0.0000   -41.9371    -5.2954    35.633549    -28.7364    -30.9997     42.2701\n  4500   4800     0.0000   -41.9371    -5.2954    35.915976    -28.8888    -30.8577     42.2701\n  4500   5000     0.0000   -41.9371    -5.2954    36.193946    -29.0382    -30.7171     42.2701\n  4500   5200     0.0000   -41.9371    -5.2954    36.467585    -29.1846    -30.5781     42.2701\n  4500   5400     0.0000   -41.9371    -5.2954    36.737014    -29.3280    -30.4405     42.2701\n  4500   5600     0.0000   -41.9371    -5.2954    37.002348    -29.4687    -30.3044     42.2701\n  4500   5800     0.0000   -41.9371    -5.2954    37.263697    -29.6066    -30.1697     42.2701\n  4500   6000     0.0000   -41.9371    -5.2954    37.521168    -29.7419    -30.0363     42.2701\n  4500   6200     0.0000   -41.9371    -5.2954    37.774860    -29.8746    -29.9043     42.2701\n  4500   6400     0.0000   -41.9371    -5.2954    38.024872    -30.0048    -29.7737     42.2701\n  4500   6600     0.0000   -41.9371    -5.2954    38.271296    -30.1326    -29.6444     42.2701\n  4500   6800     0.0000   -41.9371    -5.2954    38.514221    -30.2580    -29.5163     42.2701\n  4500   7000     0.0000   -41.9371    -5.2954    38.753734    -30.3811    -29.3896     42.2701\n  4500   7200     0.0000   -41.9371    -5.2954    38.989917    -30.5020    -29.2641     42.2701\n  4500   7400     0.0000   -41.9371    -5.2954    39.222849    -30.6207    -29.1399     42.2701\n  4500   7600     0.0000   -41.9371    -5.2954    39.452608    -30.7373    -29.0168     42.2701\n  4500   7800     0.0000   -41.9371    -5.2954    39.679266    -30.8519    -28.8950     42.2701\n  4500   8000     0.0000   -41.9371    -5.2954    39.902895    -30.9644    -28.7744     42.2701\n  4500   8200     0.0000   -41.9371    -5.2954    40.123563    -31.0750    -28.6549     42.2701\n  4500   8400     0.0000   -41.9371    -5.2954    40.341337    -31.1837    -28.5366     42.2701\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180130-20180412_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.7080870     -105.5946431       32.4031046   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0986267        0.0940323   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000     -105.7019368       32.4448897   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0898244        0.0863512   m/s m/s m/s\nunwrap_phase_constant:           -0.00013     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180130-20180412_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.708   -105.595     32.403\norbit baseline rate   (TCN) (m/s):  0.000e+00 -9.863e-02  9.403e-02\n\nbaseline vector (TCN)   (m):      0.000   -105.702     32.445\nbaseline rate   (TCN) (m/s):  0.000e+00 -8.982e-02  8.635e-02\n\nSLC-1 center baseline length (m):   110.5693\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000  -104.8635    31.6389    27.494719    -20.3465   -107.6260    109.5325\n     0    200     0.0000  -104.8635    31.6389    27.936991    -21.1767   -107.4657    109.5325\n     0    400     0.0000  -104.8635    31.6389    28.368628    -21.9857   -107.3031    109.5325\n     0    600     0.0000  -104.8635    31.6389    28.790152    -22.7745   -107.1385    109.5325\n     0    800     0.0000  -104.8635    31.6389    29.202043    -23.5441   -106.9720    109.5325\n     0   1000     0.0000  -104.8635    31.6389    29.604744    -24.2954   -106.8039    109.5325\n     0   1200     0.0000  -104.8635    31.6389    29.998667    -25.0291   -106.6343    109.5325\n     0   1400     0.0000  -104.8635    31.6389    30.384190    -25.7461   -106.4635    109.5325\n     0   1600     0.0000  -104.8635    31.6389    30.761669    -26.4469   -106.2916    109.5325\n     0   1800     0.0000  -104.8635    31.6389    31.131432    -27.1323   -106.1187    109.5325\n     0   2000     0.0000  -104.8635    31.6389    31.493787    -27.8029   -105.9450    109.5325\n     0   2200     0.0000  -104.8635    31.6389    31.849022    -28.4592   -105.7706    109.5325\n     0   2400     0.0000  -104.8635    31.6389    32.197407    -29.1018   -105.5956    109.5325\n     0   2600     0.0000  -104.8635    31.6389    32.539195    -29.7312   -105.4201    109.5325\n     0   2800     0.0000  -104.8635    31.6389    32.874623    -30.3479   -105.2442    109.5325\n     0   3000     0.0000  -104.8635    31.6389    33.203917    -30.9522   -105.0681    109.5325\n     0   3200     0.0000  -104.8635    31.6389    33.527288    -31.5447   -104.8917    109.5325\n     0   3400     0.0000  -104.8635    31.6389    33.844935    -32.1258   -104.7152    109.5325\n     0   3600     0.0000  -104.8635    31.6389    34.157047    -32.6957   -104.5387    109.5325\n     0   3800     0.0000  -104.8635    31.6389    34.463804    -33.2549   -104.3621    109.5325\n     0   4000     0.0000  -104.8635    31.6389    34.765375    -33.8038   -104.1857    109.5325\n     0   4200     0.0000  -104.8635    31.6389    35.061921    -34.3425   -104.0093    109.5325\n     0   4400     0.0000  -104.8635    31.6389    35.353595    -34.8716   -103.8331    109.5325\n     0   4600     0.0000  -104.8635    31.6389    35.640543    -35.3912   -103.6572    109.5325\n     0   4800     0.0000  -104.8635    31.6389    35.922904    -35.9016   -103.4815    109.5325\n     0   5000     0.0000  -104.8635    31.6389    36.200810    -36.4031   -103.3062    109.5325\n     0   5200     0.0000  -104.8635    31.6389    36.474387    -36.8959   -103.1312    109.5325\n     0   5400     0.0000  -104.8635    31.6389    36.743755    -37.3804   -102.9566    109.5325\n     0   5600     0.0000  -104.8635    31.6389    37.009030    -37.8566   -102.7824    109.5325\n     0   5800     0.0000  -104.8635    31.6389    37.270321    -38.3250   -102.6087    109.5325\n     0   6000     0.0000  -104.8635    31.6389    37.527736    -38.7856   -102.4355    109.5325\n     0   6200     0.0000  -104.8635    31.6389    37.781373    -39.2387   -102.2628    109.5325\n     0   6400     0.0000  -104.8635    31.6389    38.031331    -39.6844   -102.0906    109.5325\n     0   6600     0.0000  -104.8635    31.6389    38.277703    -40.1230   -101.9190    109.5325\n     0   6800     0.0000  -104.8635    31.6389    38.520577    -40.5547   -101.7480    109.5325\n     0   7000     0.0000  -104.8635    31.6389    38.760041    -40.9796   -101.5777    109.5325\n     0   7200     0.0000  -104.8635    31.6389    38.996175    -41.3979   -101.4079    109.5325\n     0   7400     0.0000  -104.8635    31.6389    39.229060    -41.8097   -101.2388    109.5325\n     0   7600     0.0000  -104.8635    31.6389    39.458772    -42.2153   -101.0704    109.5325\n     0   7800     0.0000  -104.8635    31.6389    39.685385    -42.6147   -100.9026    109.5325\n     0   8000     0.0000  -104.8635    31.6389    39.908969    -43.0081   -100.7356    109.5325\n     0   8200     0.0000  -104.8635    31.6389    40.129594    -43.3957   -100.5692    109.5325\n     0   8400     0.0000  -104.8635    31.6389    40.347325    -43.7776   -100.4036    109.5325\n   500      0     0.0000  -105.0481    31.8164    27.493760    -20.2725   -107.8720    109.7606\n   500    200     0.0000  -105.0481    31.8164    27.936048    -21.1046   -107.7123    109.7606\n   500    400     0.0000  -105.0481    31.8164    28.367700    -21.9155   -107.5503    109.7606\n   500    600     0.0000  -105.0481    31.8164    28.789239    -22.7062   -107.3861    109.7606\n   500    800     0.0000  -105.0481    31.8164    29.201143    -23.4776   -107.2201    109.7606\n   500   1000     0.0000  -105.0481    31.8164    29.603858    -24.2306   -107.0525    109.7606\n   500   1200     0.0000  -105.0481    31.8164    29.997793    -24.9661   -106.8833    109.7606\n   500   1400     0.0000  -105.0481    31.8164    30.383329    -25.6847   -106.7129    109.7606\n   500   1600     0.0000  -105.0481    31.8164    30.760819    -26.3872   -106.5414    109.7606\n   500   1800     0.0000  -105.0481    31.8164    31.130593    -27.0743   -106.3689    109.7606\n   500   2000     0.0000  -105.0481    31.8164    31.492959    -27.7465   -106.1955    109.7606\n   500   2200     0.0000  -105.0481    31.8164    31.848205    -28.4044   -106.0215    109.7606\n   500   2400     0.0000  -105.0481    31.8164    32.196599    -29.0485   -105.8468    109.7606\n   500   2600     0.0000  -105.0481    31.8164    32.538396    -29.6794   -105.6716    109.7606\n   500   2800     0.0000  -105.0481    31.8164    32.873834    -30.2976   -105.4961    109.7606\n   500   3000     0.0000  -105.0481    31.8164    33.203137    -30.9034   -105.3202    109.7606\n   500   3200     0.0000  -105.0481    31.8164    33.526516    -31.4973   -105.1441    109.7606\n   500   3400     0.0000  -105.0481    31.8164    33.844171    -32.0798   -104.9679    109.7606\n   500   3600     0.0000  -105.0481    31.8164    34.156292    -32.6511   -104.7916    109.7606\n   500   3800     0.0000  -105.0481    31.8164    34.463056    -33.2117   -104.6152    109.7606\n   500   4000     0.0000  -105.0481    31.8164    34.764635    -33.7619   -104.4390    109.7606\n   500   4200     0.0000  -105.0481    31.8164    35.061188    -34.3020   -104.2628    109.7606\n   500   4400     0.0000  -105.0481    31.8164    35.352869    -34.8323   -104.0869    109.7606\n   500   4600     0.0000  -105.0481    31.8164    35.639824    -35.3532   -103.9111    109.7606\n   500   4800     0.0000  -105.0481    31.8164    35.922192    -35.8649   -103.7356    109.7606\n   500   5000     0.0000  -105.0481    31.8164    36.200104    -36.3676   -103.5604    109.7606\n   500   5200     0.0000  -105.0481    31.8164    36.473687    -36.8617   -103.3856    109.7606\n   500   5400     0.0000  -105.0481    31.8164    36.743061    -37.3473   -103.2112    109.7606\n   500   5600     0.0000  -105.0481    31.8164    37.008342    -37.8248   -103.0371    109.7606\n   500   5800     0.0000  -105.0481    31.8164    37.269640    -38.2943   -102.8636    109.7606\n   500   6000     0.0000  -105.0481    31.8164    37.527060    -38.7561   -102.6905    109.7606\n   500   6200     0.0000  -105.0481    31.8164    37.780703    -39.2103   -102.5179    109.7606\n   500   6400     0.0000  -105.0481    31.8164    38.030667    -39.6572   -102.3459    109.7606\n   500   6600     0.0000  -105.0481    31.8164    38.277043    -40.0969   -102.1744    109.7606\n   500   6800     0.0000  -105.0481    31.8164    38.519923    -40.5297   -102.0035    109.7606\n   500   7000     0.0000  -105.0481    31.8164    38.759391    -40.9556   -101.8332    109.7606\n   500   7200     0.0000  -105.0481    31.8164    38.995530    -41.3750   -101.6636    109.7606\n   500   7400     0.0000  -105.0481    31.8164    39.228420    -41.7879   -101.4946    109.7606\n   500   7600     0.0000  -105.0481    31.8164    39.458137    -42.1945   -101.3262    109.7606\n   500   7800     0.0000  -105.0481    31.8164    39.684754    -42.5949   -101.1585    109.7606\n   500   8000     0.0000  -105.0481    31.8164    39.908343    -42.9893   -100.9915    109.7606\n   500   8200     0.0000  -105.0481    31.8164    40.128972    -43.3779   -100.8252    109.7606\n   500   8400     0.0000  -105.0481    31.8164    40.346708    -43.7607   -100.6597    109.7606\n  1000      0     0.0000  -105.2328    31.9939    27.492780    -20.1985   -108.1181    109.9888\n  1000    200     0.0000  -105.2328    31.9939    27.935085    -21.0325   -107.9590    109.9888\n  1000    400     0.0000  -105.2328    31.9939    28.366753    -21.8453   -107.7974    109.9888\n  1000    600     0.0000  -105.2328    31.9939    28.788306    -22.6378   -107.6338    109.9888\n  1000    800     0.0000  -105.2328    31.9939    29.200225    -23.4110   -107.4683    109.9888\n  1000   1000     0.0000  -105.2328    31.9939    29.602953    -24.1658   -107.3011    109.9888\n  1000   1200     0.0000  -105.2328    31.9939    29.996901    -24.9030   -107.1324    109.9888\n  1000   1400     0.0000  -105.2328    31.9939    30.382449    -25.6233   -106.9624    109.9888\n  1000   1600     0.0000  -105.2328    31.9939    30.759951    -26.3275   -106.7913    109.9888\n  1000   1800     0.0000  -105.2328    31.9939    31.129737    -27.0162   -106.6191    109.9888\n  1000   2000     0.0000  -105.2328    31.9939    31.492114    -27.6900   -106.4461    109.9888\n  1000   2200     0.0000  -105.2328    31.9939    31.847370    -28.3495   -106.2724    109.9888\n  1000   2400     0.0000  -105.2328    31.9939    32.195774    -28.9952   -106.0980    109.9888\n  1000   2600     0.0000  -105.2328    31.9939    32.537581    -29.6276   -105.9232    109.9888\n  1000   2800     0.0000  -105.2328    31.9939    32.873029    -30.2472   -105.7479    109.9888\n  1000   3000     0.0000  -105.2328    31.9939    33.202340    -30.8545   -105.5723    109.9888\n  1000   3200     0.0000  -105.2328    31.9939    33.525728    -31.4499   -105.3965    109.9888\n  1000   3400     0.0000  -105.2328    31.9939    33.843392    -32.0338   -105.2205    109.9888\n  1000   3600     0.0000  -105.2328    31.9939    34.155520    -32.6065   -105.0444    109.9888\n  1000   3800     0.0000  -105.2328    31.9939    34.462293    -33.1684   -104.8684    109.9888\n  1000   4000     0.0000  -105.2328    31.9939    34.763879    -33.7200   -104.6923    109.9888\n  1000   4200     0.0000  -105.2328    31.9939    35.060440    -34.2614   -104.5164    109.9888\n  1000   4400     0.0000  -105.2328    31.9939    35.352129    -34.7930   -104.3406    109.9888\n  1000   4600     0.0000  -105.2328    31.9939    35.639091    -35.3152   -104.1650    109.9888\n  1000   4800     0.0000  -105.2328    31.9939    35.921465    -35.8281   -103.9897    109.9888\n  1000   5000     0.0000  -105.2328    31.9939    36.199384    -36.3321   -103.8147    109.9888\n  1000   5200     0.0000  -105.2328    31.9939    36.472974    -36.8274   -103.6401    109.9888\n  1000   5400     0.0000  -105.2328    31.9939    36.742354    -37.3143   -103.4658    109.9888\n  1000   5600     0.0000  -105.2328    31.9939    37.007641    -37.7929   -103.2919    109.9888\n  1000   5800     0.0000  -105.2328    31.9939    37.268945    -38.2636   -103.1185    109.9888\n  1000   6000     0.0000  -105.2328    31.9939    37.526370    -38.7265   -102.9455    109.9888\n  1000   6200     0.0000  -105.2328    31.9939    37.780019    -39.1819   -102.7731    109.9888\n  1000   6400     0.0000  -105.2328    31.9939    38.029988    -39.6299   -102.6011    109.9888\n  1000   6600     0.0000  -105.2328    31.9939    38.276371    -40.0707   -102.4298    109.9888\n  1000   6800     0.0000  -105.2328    31.9939    38.519255    -40.5046   -102.2590    109.9888\n  1000   7000     0.0000  -105.2328    31.9939    38.758729    -40.9316   -102.0888    109.9888\n  1000   7200     0.0000  -105.2328    31.9939    38.994873    -41.3521   -101.9192    109.9888\n  1000   7400     0.0000  -105.2328    31.9939    39.227768    -41.7660   -101.7503    109.9888\n  1000   7600     0.0000  -105.2328    31.9939    39.457489    -42.1736   -101.5820    109.9888\n  1000   7800     0.0000  -105.2328    31.9939    39.684111    -42.5751   -101.4144    109.9888\n  1000   8000     0.0000  -105.2328    31.9939    39.907705    -42.9705   -101.2475    109.9888\n  1000   8200     0.0000  -105.2328    31.9939    40.128339    -43.3601   -101.0813    109.9888\n  1000   8400     0.0000  -105.2328    31.9939    40.346078    -43.7439   -100.9158    109.9888\n  1500      0     0.0000  -105.4174    32.1714    27.491780    -20.1244   -108.3642    110.2172\n  1500    200     0.0000  -105.4174    32.1714    27.934101    -20.9603   -108.2056    110.2172\n  1500    400     0.0000  -105.4174    32.1714    28.365785    -21.7750   -108.0446    110.2172\n  1500    600     0.0000  -105.4174    32.1714    28.787354    -22.5693   -107.8815    110.2172\n  1500    800     0.0000  -105.4174    32.1714    29.199287    -23.3444   -107.7164    110.2172\n  1500   1000     0.0000  -105.4174    32.1714    29.602029    -24.1010   -107.5497    110.2172\n  1500   1200     0.0000  -105.4174    32.1714    29.995991    -24.8399   -107.3814    110.2172\n  1500   1400     0.0000  -105.4174    32.1714    30.381551    -25.5619   -107.2118    110.2172\n  1500   1600     0.0000  -105.4174    32.1714    30.759066    -26.2678   -107.0411    110.2172\n  1500   1800     0.0000  -105.4174    32.1714    31.128863    -26.9581   -106.8693    110.2172\n  1500   2000     0.0000  -105.4174    32.1714    31.491251    -27.6335   -106.6967    110.2172\n  1500   2200     0.0000  -105.4174    32.1714    31.846518    -28.2945   -106.5233    110.2172\n  1500   2400     0.0000  -105.4174    32.1714    32.194933    -28.9418   -106.3493    110.2172\n  1500   2600     0.0000  -105.4174    32.1714    32.536750    -29.5757   -106.1747    110.2172\n  1500   2800     0.0000  -105.4174    32.1714    32.872207    -30.1968   -105.9998    110.2172\n  1500   3000     0.0000  -105.4174    32.1714    33.201528    -30.8056   -105.8244    110.2172\n  1500   3200     0.0000  -105.4174    32.1714    33.524924    -31.4024   -105.6489    110.2172\n  1500   3400     0.0000  -105.4174    32.1714    33.842597    -31.9877   -105.4732    110.2172\n  1500   3600     0.0000  -105.4174    32.1714    34.154734    -32.5618   -105.2973    110.2172\n  1500   3800     0.0000  -105.4174    32.1714    34.461515    -33.1251   -105.1215    110.2172\n  1500   4000     0.0000  -105.4174    32.1714    34.763109    -33.6780   -104.9457    110.2172\n  1500   4200     0.0000  -105.4174    32.1714    35.059677    -34.2208   -104.7699    110.2172\n  1500   4400     0.0000  -105.4174    32.1714    35.351373    -34.7537   -104.5944    110.2172\n  1500   4600     0.0000  -105.4174    32.1714    35.638343    -35.2772   -104.4190    110.2172\n  1500   4800     0.0000  -105.4174    32.1714    35.920724    -35.7914   -104.2439    110.2172\n  1500   5000     0.0000  -105.4174    32.1714    36.198650    -36.2966   -104.0690    110.2172\n  1500   5200     0.0000  -105.4174    32.1714    36.472246    -36.7931   -103.8945    110.2172\n  1500   5400     0.0000  -105.4174    32.1714    36.741633    -37.2812   -103.7204    110.2172\n  1500   5600     0.0000  -105.4174    32.1714    37.006926    -37.7610   -103.5466    110.2172\n  1500   5800     0.0000  -105.4174    32.1714    37.268236    -38.2329   -103.3734    110.2172\n  1500   6000     0.0000  -105.4174    32.1714    37.525667    -38.6970   -103.2005    110.2172\n  1500   6200     0.0000  -105.4174    32.1714    37.779322    -39.1535   -103.0282    110.2172\n  1500   6400     0.0000  -105.4174    32.1714    38.029297    -39.6026   -102.8564    110.2172\n  1500   6600     0.0000  -105.4174    32.1714    38.275685    -40.0445   -102.6852    110.2172\n  1500   6800     0.0000  -105.4174    32.1714    38.518575    -40.4795   -102.5145    110.2172\n  1500   7000     0.0000  -105.4174    32.1714    38.758054    -40.9076   -102.3444    110.2172\n  1500   7200     0.0000  -105.4174    32.1714    38.994203    -41.3291   -102.1749    110.2172\n  1500   7400     0.0000  -105.4174    32.1714    39.227103    -41.7441   -102.0061    110.2172\n  1500   7600     0.0000  -105.4174    32.1714    39.456829    -42.1527   -101.8379    110.2172\n  1500   7800     0.0000  -105.4174    32.1714    39.683456    -42.5552   -101.6704    110.2172\n  1500   8000     0.0000  -105.4174    32.1714    39.907054    -42.9517   -101.5035    110.2172\n  1500   8200     0.0000  -105.4174    32.1714    40.127693    -43.3422   -101.3374    110.2172\n  1500   8400     0.0000  -105.4174    32.1714    40.345437    -43.7270   -101.1719    110.2172\n  2000      0     0.0000  -105.6020    32.3489    27.490758    -20.0502   -108.6103    110.4456\n  2000    200     0.0000  -105.6020    32.3489    27.933098    -20.8881   -108.4522    110.4456\n  2000    400     0.0000  -105.6020    32.3489    28.364798    -21.7046   -108.2918    110.4456\n  2000    600     0.0000  -105.6020    32.3489    28.786382    -22.5009   -108.1292    110.4456\n  2000    800     0.0000  -105.6020    32.3489    29.198330    -23.2777   -107.9646    110.4456\n  2000   1000     0.0000  -105.6020    32.3489    29.601087    -24.0361   -107.7983    110.4456\n  2000   1200     0.0000  -105.6020    32.3489    29.995062    -24.7767   -107.6305    110.4456\n  2000   1400     0.0000  -105.6020    32.3489    30.380636    -25.5005   -107.4613    110.4456\n  2000   1600     0.0000  -105.6020    32.3489    30.758162    -26.2080   -107.2910    110.4456\n  2000   1800     0.0000  -105.6020    32.3489    31.127971    -26.8999   -107.1196    110.4456\n  2000   2000     0.0000  -105.6020    32.3489    31.490371    -27.5769   -106.9473    110.4456\n  2000   2200     0.0000  -105.6020    32.3489    31.845649    -28.2396   -106.7742    110.4456\n  2000   2400     0.0000  -105.6020    32.3489    32.194074    -28.8883   -106.6005    110.4456\n  2000   2600     0.0000  -105.6020    32.3489    32.535902    -29.5238   -106.4263    110.4456\n  2000   2800     0.0000  -105.6020    32.3489    32.871368    -30.1464   -106.2516    110.4456\n  2000   3000     0.0000  -105.6020    32.3489    33.200699    -30.7566   -106.0766    110.4456\n  2000   3200     0.0000  -105.6020    32.3489    33.524105    -31.3549   -105.9013    110.4456\n  2000   3400     0.0000  -105.6020    32.3489    33.841786    -31.9416   -105.7258    110.4456\n  2000   3600     0.0000  -105.6020    32.3489    34.153932    -32.5171   -105.5502    110.4456\n  2000   3800     0.0000  -105.6020    32.3489    34.460721    -33.0818   -105.3746    110.4456\n  2000   4000     0.0000  -105.6020    32.3489    34.762323    -33.6360   -105.1990    110.4456\n  2000   4200     0.0000  -105.6020    32.3489    35.058899    -34.1801   -105.0235    110.4456\n  2000   4400     0.0000  -105.6020    32.3489    35.350603    -34.7144   -104.8481    110.4456\n  2000   4600     0.0000  -105.6020    32.3489    35.637580    -35.2391   -104.6729    110.4456\n  2000   4800     0.0000  -105.6020    32.3489    35.919968    -35.7546   -104.4980    110.4456\n  2000   5000     0.0000  -105.6020    32.3489    36.197901    -36.2610   -104.3233    110.4456\n  2000   5200     0.0000  -105.6020    32.3489    36.471504    -36.7588   -104.1490    110.4456\n  2000   5400     0.0000  -105.6020    32.3489    36.740898    -37.2481   -103.9750    110.4456\n  2000   5600     0.0000  -105.6020    32.3489    37.006197    -37.7291   -103.8014    110.4456\n  2000   5800     0.0000  -105.6020    32.3489    37.267513    -38.2021   -103.6283    110.4456\n  2000   6000     0.0000  -105.6020    32.3489    37.524951    -38.6674   -103.4556    110.4456\n  2000   6200     0.0000  -105.6020    32.3489    37.778612    -39.1250   -103.2834    110.4456\n  2000   6400     0.0000  -105.6020    32.3489    38.028592    -39.5753   -103.1117    110.4456\n  2000   6600     0.0000  -105.6020    32.3489    38.274986    -40.0183   -102.9405    110.4456\n  2000   6800     0.0000  -105.6020    32.3489    38.517882    -40.4544   -102.7700    110.4456\n  2000   7000     0.0000  -105.6020    32.3489    38.757365    -40.8836   -102.6000    110.4456\n  2000   7200     0.0000  -105.6020    32.3489    38.993520    -41.3061   -102.4306    110.4456\n  2000   7400     0.0000  -105.6020    32.3489    39.226425    -41.7221   -102.2619    110.4456\n  2000   7600     0.0000  -105.6020    32.3489    39.456157    -42.1318   -102.0937    110.4456\n  2000   7800     0.0000  -105.6020    32.3489    39.682788    -42.5353   -101.9263    110.4456\n  2000   8000     0.0000  -105.6020    32.3489    39.906392    -42.9328   -101.7595    110.4456\n  2000   8200     0.0000  -105.6020    32.3489    40.127035    -43.3243   -101.5934    110.4456\n  2000   8400     0.0000  -105.6020    32.3489    40.344783    -43.7101   -101.4281    110.4456\n  2500      0     0.0000  -105.7867    32.5264    27.489717    -19.9760   -108.8563    110.6742\n  2500    200     0.0000  -105.7867    32.5264    27.932074    -20.8158   -108.6989    110.6742\n  2500    400     0.0000  -105.7867    32.5264    28.363791    -21.6343   -108.5390    110.6742\n  2500    600     0.0000  -105.7867    32.5264    28.785391    -22.4323   -108.3768    110.6742\n  2500    800     0.0000  -105.7867    32.5264    29.197355    -23.2110   -108.2128    110.6742\n  2500   1000     0.0000  -105.7867    32.5264    29.600126    -23.9711   -108.0469    110.6742\n  2500   1200     0.0000  -105.7867    32.5264    29.994114    -24.7135   -107.8795    110.6742\n  2500   1400     0.0000  -105.7867    32.5264    30.379702    -25.4390   -107.7108    110.6742\n  2500   1600     0.0000  -105.7867    32.5264    30.757241    -26.1481   -107.5408    110.6742\n  2500   1800     0.0000  -105.7867    32.5264    31.127062    -26.8417   -107.3698    110.6742\n  2500   2000     0.0000  -105.7867    32.5264    31.489474    -27.5203   -107.1979    110.6742\n  2500   2200     0.0000  -105.7867    32.5264    31.844763    -28.1845   -107.0252    110.6742\n  2500   2400     0.0000  -105.7867    32.5264    32.193199    -28.8349   -106.8518    110.6742\n  2500   2600     0.0000  -105.7867    32.5264    32.535037    -29.4719   -106.6779    110.6742\n  2500   2800     0.0000  -105.7867    32.5264    32.870514    -30.0960   -106.5035    110.6742\n  2500   3000     0.0000  -105.7867    32.5264    33.199854    -30.7077   -106.3287    110.6742\n  2500   3200     0.0000  -105.7867    32.5264    33.523270    -31.3074   -106.1537    110.6742\n  2500   3400     0.0000  -105.7867    32.5264    33.840960    -31.8955   -105.9785    110.6742\n  2500   3600     0.0000  -105.7867    32.5264    34.153114    -32.4724   -105.8031    110.6742\n  2500   3800     0.0000  -105.7867    32.5264    34.459912    -33.0384   -105.6278    110.6742\n  2500   4000     0.0000  -105.7867    32.5264    34.761522    -33.5940   -105.4524    110.6742\n  2500   4200     0.0000  -105.7867    32.5264    35.058107    -34.1394   -105.2771    110.6742\n  2500   4400     0.0000  -105.7867    32.5264    35.349818    -34.6750   -105.1019    110.6742\n  2500   4600     0.0000  -105.7867    32.5264    35.636803    -35.2010   -104.9269    110.6742\n  2500   4800     0.0000  -105.7867    32.5264    35.919199    -35.7177   -104.7521    110.6742\n  2500   5000     0.0000  -105.7867    32.5264    36.197138    -36.2254   -104.5776    110.6742\n  2500   5200     0.0000  -105.7867    32.5264    36.470748    -36.7244   -104.4035    110.6742\n  2500   5400     0.0000  -105.7867    32.5264    36.740149    -37.2149   -104.2296    110.6742\n  2500   5600     0.0000  -105.7867    32.5264    37.005455    -37.6972   -104.0562    110.6742\n  2500   5800     0.0000  -105.7867    32.5264    37.266777    -38.1714   -103.8832    110.6742\n  2500   6000     0.0000  -105.7867    32.5264    37.524221    -38.6377   -103.7106    110.6742\n  2500   6200     0.0000  -105.7867    32.5264    37.777888    -39.0965   -103.5385    110.6742\n  2500   6400     0.0000  -105.7867    32.5264    38.027874    -39.5479   -103.3670    110.6742\n  2500   6600     0.0000  -105.7867    32.5264    38.274274    -39.9921   -103.1959    110.6742\n  2500   6800     0.0000  -105.7867    32.5264    38.517175    -40.4292   -103.0255    110.6742\n  2500   7000     0.0000  -105.7867    32.5264    38.756665    -40.8595   -102.8556    110.6742\n  2500   7200     0.0000  -105.7867    32.5264    38.992825    -41.2831   -102.6863    110.6742\n  2500   7400     0.0000  -105.7867    32.5264    39.225735    -41.7002   -102.5176    110.6742\n  2500   7600     0.0000  -105.7867    32.5264    39.455472    -42.1109   -102.3496    110.6742\n  2500   7800     0.0000  -105.7867    32.5264    39.682108    -42.5154   -102.1822    110.6742\n  2500   8000     0.0000  -105.7867    32.5264    39.905717    -42.9139   -102.0155    110.6742\n  2500   8200     0.0000  -105.7867    32.5264    40.126364    -43.3064   -101.8495    110.6742\n  2500   8400     0.0000  -105.7867    32.5264    40.344118    -43.6932   -101.6842    110.6742\n  3000      0     0.0000  -105.9713    32.7039    27.488655    -19.9017   -109.1024    110.9030\n  3000    200     0.0000  -105.9713    32.7039    27.931030    -20.7435   -108.9455    110.9030\n  3000    400     0.0000  -105.9713    32.7039    28.362764    -21.5638   -108.7861    110.9030\n  3000    600     0.0000  -105.9713    32.7039    28.784381    -22.3638   -108.6245    110.9030\n  3000    800     0.0000  -105.9713    32.7039    29.196360    -23.1442   -108.4609    110.9030\n  3000   1000     0.0000  -105.9713    32.7039    29.599146    -23.9061   -108.2955    110.9030\n  3000   1200     0.0000  -105.9713    32.7039    29.993149    -24.6503   -108.1286    110.9030\n  3000   1400     0.0000  -105.9713    32.7039    30.378750    -25.3774   -107.9603    110.9030\n  3000   1600     0.0000  -105.9713    32.7039    30.756302    -26.0883   -107.7907    110.9030\n  3000   1800     0.0000  -105.9713    32.7039    31.126136    -26.7835   -107.6201    110.9030\n  3000   2000     0.0000  -105.9713    32.7039    31.488559    -27.4637   -107.4485    110.9030\n  3000   2200     0.0000  -105.9713    32.7039    31.843860    -28.1295   -107.2761    110.9030\n  3000   2400     0.0000  -105.9713    32.7039    32.192308    -28.7814   -107.1031    110.9030\n  3000   2600     0.0000  -105.9713    32.7039    32.534156    -29.4199   -106.9294    110.9030\n  3000   2800     0.0000  -105.9713    32.7039    32.869643    -30.0455   -106.7554    110.9030\n  3000   3000     0.0000  -105.9713    32.7039    33.198993    -30.6586   -106.5809    110.9030\n  3000   3200     0.0000  -105.9713    32.7039    33.522418    -31.2598   -106.4061    110.9030\n  3000   3400     0.0000  -105.9713    32.7039    33.840118    -31.8493   -106.2312    110.9030\n  3000   3600     0.0000  -105.9713    32.7039    34.152282    -32.4276   -106.0561    110.9030\n  3000   3800     0.0000  -105.9713    32.7039    34.459088    -32.9950   -105.8809    110.9030\n  3000   4000     0.0000  -105.9713    32.7039    34.760707    -33.5520   -105.7057    110.9030\n  3000   4200     0.0000  -105.9713    32.7039    35.057299    -34.0987   -105.5306    110.9030\n  3000   4400     0.0000  -105.9713    32.7039    35.349019    -34.6356   -105.3557    110.9030\n  3000   4600     0.0000  -105.9713    32.7039    35.636011    -35.1629   -105.1809    110.9030\n  3000   4800     0.0000  -105.9713    32.7039    35.918414    -35.6809   -105.0063    110.9030\n  3000   5000     0.0000  -105.9713    32.7039    36.196361    -36.1898   -104.8320    110.9030\n  3000   5200     0.0000  -105.9713    32.7039    36.469978    -36.6900   -104.6579    110.9030\n  3000   5400     0.0000  -105.9713    32.7039    36.739386    -37.1817   -104.4843    110.9030\n  3000   5600     0.0000  -105.9713    32.7039    37.004699    -37.6652   -104.3110    110.9030\n  3000   5800     0.0000  -105.9713    32.7039    37.266027    -38.1405   -104.1381    110.9030\n  3000   6000     0.0000  -105.9713    32.7039    37.523478    -38.6081   -103.9657    110.9030\n  3000   6200     0.0000  -105.9713    32.7039    37.777151    -39.0680   -103.7937    110.9030\n  3000   6400     0.0000  -105.9713    32.7039    38.027143    -39.5205   -103.6223    110.9030\n  3000   6600     0.0000  -105.9713    32.7039    38.273549    -39.9658   -103.4513    110.9030\n  3000   6800     0.0000  -105.9713    32.7039    38.516456    -40.4040   -103.2810    110.9030\n  3000   7000     0.0000  -105.9713    32.7039    38.755951    -40.8354   -103.1112    110.9030\n  3000   7200     0.0000  -105.9713    32.7039    38.992117    -41.2600   -102.9420    110.9030\n  3000   7400     0.0000  -105.9713    32.7039    39.225032    -41.6782   -102.7734    110.9030\n  3000   7600     0.0000  -105.9713    32.7039    39.454774    -42.0899   -102.6055    110.9030\n  3000   7800     0.0000  -105.9713    32.7039    39.681416    -42.4955   -102.4382    110.9030\n  3000   8000     0.0000  -105.9713    32.7039    39.905029    -42.8949   -102.2716    110.9030\n  3000   8200     0.0000  -105.9713    32.7039    40.125682    -43.2885   -102.1056    110.9030\n  3000   8400     0.0000  -105.9713    32.7039    40.343441    -43.6762   -101.9403    110.9030\n  3500      0     0.0000  -106.1560    32.8814    27.487573    -19.8274   -109.3485    111.1318\n  3500    200     0.0000  -106.1560    32.8814    27.929966    -20.6711   -109.1922    111.1318\n  3500    400     0.0000  -106.1560    32.8814    28.361718    -21.4934   -109.0333    111.1318\n  3500    600     0.0000  -106.1560    32.8814    28.783351    -22.2951   -108.8722    111.1318\n  3500    800     0.0000  -106.1560    32.8814    29.195346    -23.0774   -108.7091    111.1318\n  3500   1000     0.0000  -106.1560    32.8814    29.598148    -23.8411   -108.5442    111.1318\n  3500   1200     0.0000  -106.1560    32.8814    29.992165    -24.5870   -108.3777    111.1318\n  3500   1400     0.0000  -106.1560    32.8814    30.377780    -25.3158   -108.2097    111.1318\n  3500   1600     0.0000  -106.1560    32.8814    30.755345    -26.0284   -108.0406    111.1318\n  3500   1800     0.0000  -106.1560    32.8814    31.125192    -26.7252   -107.8703    111.1318\n  3500   2000     0.0000  -106.1560    32.8814    31.487628    -27.4070   -107.6991    111.1318\n  3500   2200     0.0000  -106.1560    32.8814    31.842940    -28.0744   -107.5271    111.1318\n  3500   2400     0.0000  -106.1560    32.8814    32.191399    -28.7278   -107.3543    111.1318\n  3500   2600     0.0000  -106.1560    32.8814    32.533258    -29.3678   -107.1810    111.1318\n  3500   2800     0.0000  -106.1560    32.8814    32.868756    -29.9949   -107.0072    111.1318\n  3500   3000     0.0000  -106.1560    32.8814    33.198117    -30.6096   -106.8330    111.1318\n  3500   3200     0.0000  -106.1560    32.8814    33.521551    -31.2122   -106.6586    111.1318\n  3500   3400     0.0000  -106.1560    32.8814    33.839261    -31.8031   -106.4838    111.1318\n  3500   3600     0.0000  -106.1560    32.8814    34.151433    -32.3828   -106.3090    111.1318\n  3500   3800     0.0000  -106.1560    32.8814    34.458249    -32.9516   -106.1341    111.1318\n  3500   4000     0.0000  -106.1560    32.8814    34.759876    -33.5099   -105.9591    111.1318\n  3500   4200     0.0000  -106.1560    32.8814    35.056477    -34.0579   -105.7842    111.1318\n  3500   4400     0.0000  -106.1560    32.8814    35.348205    -34.5961   -105.6095    111.1318\n  3500   4600     0.0000  -106.1560    32.8814    35.635205    -35.1247   -105.4348    111.1318\n  3500   4800     0.0000  -106.1560    32.8814    35.917616    -35.6439   -105.2604    111.1318\n  3500   5000     0.0000  -106.1560    32.8814    36.195570    -36.1542   -105.0863    111.1318\n  3500   5200     0.0000  -106.1560    32.8814    36.469194    -36.6556   -104.9124    111.1318\n  3500   5400     0.0000  -106.1560    32.8814    36.738609    -37.1485   -104.7389    111.1318\n  3500   5600     0.0000  -106.1560    32.8814    37.003929    -37.6331   -104.5658    111.1318\n  3500   5800     0.0000  -106.1560    32.8814    37.265264    -38.1097   -104.3930    111.1318\n  3500   6000     0.0000  -106.1560    32.8814    37.522721    -38.5784   -104.2207    111.1318\n  3500   6200     0.0000  -106.1560    32.8814    37.776401    -39.0395   -104.0489    111.1318\n  3500   6400     0.0000  -106.1560    32.8814    38.026399    -39.4931   -103.8776    111.1318\n  3500   6600     0.0000  -106.1560    32.8814    38.272811    -39.9395   -103.7068    111.1318\n  3500   6800     0.0000  -106.1560    32.8814    38.515724    -40.3788   -103.5365    111.1318\n  3500   7000     0.0000  -106.1560    32.8814    38.755225    -40.8112   -103.3668    111.1318\n  3500   7200     0.0000  -106.1560    32.8814    38.991396    -41.2369   -103.1977    111.1318\n  3500   7400     0.0000  -106.1560    32.8814    39.224317    -41.6561   -103.0292    111.1318\n  3500   7600     0.0000  -106.1560    32.8814    39.454064    -42.0689   -102.8614    111.1318\n  3500   7800     0.0000  -106.1560    32.8814    39.680712    -42.4755   -102.6941    111.1318\n  3500   8000     0.0000  -106.1560    32.8814    39.904330    -42.8760   -102.5276    111.1318\n  3500   8200     0.0000  -106.1560    32.8814    40.124988    -43.2705   -102.3617    111.1318\n  3500   8400     0.0000  -106.1560    32.8814    40.342751    -43.6592   -102.1965    111.1318\n  4000      0     0.0000  -106.3406    33.0589    27.486470    -19.7531   -109.5946    111.3607\n  4000    200     0.0000  -106.3406    33.0589    27.928882    -20.5987   -109.4389    111.3607\n  4000    400     0.0000  -106.3406    33.0589    28.360652    -21.4229   -109.2805    111.3607\n  4000    600     0.0000  -106.3406    33.0589    28.782302    -22.2265   -109.1199    111.3607\n  4000    800     0.0000  -106.3406    33.0589    29.194314    -23.0106   -108.9573    111.3607\n  4000   1000     0.0000  -106.3406    33.0589    29.597131    -23.7760   -108.7928    111.3607\n  4000   1200     0.0000  -106.3406    33.0589    29.991163    -24.5236   -108.6267    111.3607\n  4000   1400     0.0000  -106.3406    33.0589    30.376792    -25.2542   -108.4592    111.3607\n  4000   1600     0.0000  -106.3406    33.0589    30.754371    -25.9684   -108.2904    111.3607\n  4000   1800     0.0000  -106.3406    33.0589    31.124231    -26.6669   -108.1206    111.3607\n  4000   2000     0.0000  -106.3406    33.0589    31.486679    -27.3503   -107.9497    111.3607\n  4000   2200     0.0000  -106.3406    33.0589    31.842003    -28.0193   -107.7780    111.3607\n  4000   2400     0.0000  -106.3406    33.0589    32.190474    -28.6742   -107.6056    111.3607\n  4000   2600     0.0000  -106.3406    33.0589    32.532345    -29.3158   -107.4326    111.3607\n  4000   2800     0.0000  -106.3406    33.0589    32.867853    -29.9444   -107.2591    111.3607\n  4000   3000     0.0000  -106.3406    33.0589    33.197224    -30.5605   -107.0852    111.3607\n  4000   3200     0.0000  -106.3406    33.0589    33.520669    -31.1645   -106.9110    111.3607\n  4000   3400     0.0000  -106.3406    33.0589    33.838388    -31.7569   -106.7365    111.3607\n  4000   3600     0.0000  -106.3406    33.0589    34.150570    -32.3380   -106.5619    111.3607\n  4000   3800     0.0000  -106.3406    33.0589    34.457394    -32.9081   -106.3872    111.3607\n  4000   4000     0.0000  -106.3406    33.0589    34.759031    -33.4678   -106.2125    111.3607\n  4000   4200     0.0000  -106.3406    33.0589    35.055640    -34.0172   -106.0378    111.3607\n  4000   4400     0.0000  -106.3406    33.0589    35.347376    -34.5566   -105.8632    111.3607\n  4000   4600     0.0000  -106.3406    33.0589    35.634384    -35.0865   -105.6888    111.3607\n  4000   4800     0.0000  -106.3406    33.0589    35.916803    -35.6070   -105.5146    111.3607\n  4000   5000     0.0000  -106.3406    33.0589    36.194765    -36.1185   -105.3406    111.3607\n  4000   5200     0.0000  -106.3406    33.0589    36.468397    -36.6212   -105.1669    111.3607\n  4000   5400     0.0000  -106.3406    33.0589    36.737818    -37.1153   -104.9936    111.3607\n  4000   5600     0.0000  -106.3406    33.0589    37.003145    -37.6011   -104.8206    111.3607\n  4000   5800     0.0000  -106.3406    33.0589    37.264488    -38.0788   -104.6480    111.3607\n  4000   6000     0.0000  -106.3406    33.0589    37.521951    -38.5487   -104.4758    111.3607\n  4000   6200     0.0000  -106.3406    33.0589    37.775637    -39.0109   -104.3041    111.3607\n  4000   6400     0.0000  -106.3406    33.0589    38.025642    -39.4656   -104.1329    111.3607\n  4000   6600     0.0000  -106.3406    33.0589    38.272060    -39.9131   -103.9622    111.3607\n  4000   6800     0.0000  -106.3406    33.0589    38.514979    -40.3535   -103.7920    111.3607\n  4000   7000     0.0000  -106.3406    33.0589    38.754486    -40.7870   -103.6224    111.3607\n  4000   7200     0.0000  -106.3406    33.0589    38.990663    -41.2138   -103.4534    111.3607\n  4000   7400     0.0000  -106.3406    33.0589    39.223589    -41.6341   -103.2850    111.3607\n  4000   7600     0.0000  -106.3406    33.0589    39.453342    -42.0479   -103.1173    111.3607\n  4000   7800     0.0000  -106.3406    33.0589    39.679995    -42.4555   -102.9501    111.3607\n  4000   8000     0.0000  -106.3406    33.0589    39.903618    -42.8570   -102.7836    111.3607\n  4000   8200     0.0000  -106.3406    33.0589    40.124281    -43.2525   -102.6178    111.3607\n  4000   8400     0.0000  -106.3406    33.0589    40.342050    -43.6422   -102.4527    111.3607\n  4500      0     0.0000  -106.5252    33.2364    27.485347    -19.6787   -109.8407    111.5898\n  4500    200     0.0000  -106.5252    33.2364    27.927779    -20.5263   -109.6855    111.5898\n  4500    400     0.0000  -106.5252    33.2364    28.359567    -21.3523   -109.5277    111.5898\n  4500    600     0.0000  -106.5252    33.2364    28.781235    -22.1578   -109.3676    111.5898\n  4500    800     0.0000  -106.5252    33.2364    29.193263    -22.9437   -109.2055    111.5898\n  4500   1000     0.0000  -106.5252    33.2364    29.596095    -23.7109   -109.0415    111.5898\n  4500   1200     0.0000  -106.5252    33.2364    29.990143    -24.4603   -108.8758    111.5898\n  4500   1400     0.0000  -106.5252    33.2364    30.375786    -25.1925   -108.7087    111.5898\n  4500   1600     0.0000  -106.5252    33.2364    30.753379    -25.9084   -108.5403    111.5898\n  4500   1800     0.0000  -106.5252    33.2364    31.123252    -26.6085   -108.3708    111.5898\n  4500   2000     0.0000  -106.5252    33.2364    31.485713    -27.2936   -108.2003    111.5898\n  4500   2200     0.0000  -106.5252    33.2364    31.841050    -27.9641   -108.0290    111.5898\n  4500   2400     0.0000  -106.5252    33.2364    32.189533    -28.6206   -107.8569    111.5898\n  4500   2600     0.0000  -106.5252    33.2364    32.531414    -29.2637   -107.6842    111.5898\n  4500   2800     0.0000  -106.5252    33.2364    32.866934    -29.8938   -107.5110    111.5898\n  4500   3000     0.0000  -106.5252    33.2364    33.196315    -30.5113   -107.3374    111.5898\n  4500   3200     0.0000  -106.5252    33.2364    33.519771    -31.1168   -107.1634    111.5898\n  4500   3400     0.0000  -106.5252    33.2364    33.837500    -31.7106   -106.9892    111.5898\n  4500   3600     0.0000  -106.5252    33.2364    34.149691    -32.2931   -106.8149    111.5898\n  4500   3800     0.0000  -106.5252    33.2364    34.456525    -32.8646   -106.6404    111.5898\n  4500   4000     0.0000  -106.5252    33.2364    34.758170    -33.4256   -106.4659    111.5898\n  4500   4200     0.0000  -106.5252    33.2364    35.054788    -33.9763   -106.2914    111.5898\n  4500   4400     0.0000  -106.5252    33.2364    35.346533    -34.5171   -106.1170    111.5898\n  4500   4600     0.0000  -106.5252    33.2364    35.633549    -35.0483   -105.9428    111.5898\n  4500   4800     0.0000  -106.5252    33.2364    35.915976    -35.5700   -105.7688    111.5898\n  4500   5000     0.0000  -106.5252    33.2364    36.193946    -36.0828   -105.5950    111.5898\n  4500   5200     0.0000  -106.5252    33.2364    36.467585    -36.5867   -105.4214    111.5898\n  4500   5400     0.0000  -106.5252    33.2364    36.737014    -37.0820   -105.2482    111.5898\n  4500   5600     0.0000  -106.5252    33.2364    37.002348    -37.5690   -105.0754    111.5898\n  4500   5800     0.0000  -106.5252    33.2364    37.263697    -38.0479   -104.9029    111.5898\n  4500   6000     0.0000  -106.5252    33.2364    37.521168    -38.5189   -104.7309    111.5898\n  4500   6200     0.0000  -106.5252    33.2364    37.774860    -38.9823   -104.5593    111.5898\n  4500   6400     0.0000  -106.5252    33.2364    38.024872    -39.4381   -104.3882    111.5898\n  4500   6600     0.0000  -106.5252    33.2364    38.271296    -39.8867   -104.2176    111.5898\n  4500   6800     0.0000  -106.5252    33.2364    38.514221    -40.3282   -104.0476    111.5898\n  4500   7000     0.0000  -106.5252    33.2364    38.753734    -40.7628   -103.8781    111.5898\n  4500   7200     0.0000  -106.5252    33.2364    38.989917    -41.1907   -103.7092    111.5898\n  4500   7400     0.0000  -106.5252    33.2364    39.222849    -41.6120   -103.5408    111.5898\n  4500   7600     0.0000  -106.5252    33.2364    39.452608    -42.0268   -103.3732    111.5898\n  4500   7800     0.0000  -106.5252    33.2364    39.679266    -42.4355   -103.2061    111.5898\n  4500   8000     0.0000  -106.5252    33.2364    39.902895    -42.8380   -103.0397    111.5898\n  4500   8200     0.0000  -106.5252    33.2364    40.123563    -43.2345   -102.8739    111.5898\n  4500   8400     0.0000  -106.5252    33.2364    40.341337    -43.6252   -102.7089    111.5898\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180319_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.0000004        4.1283381        0.3230800   m   m   m\ninitial_baseline_rate:          0.0000000        0.1863333        0.0175918   m/s m/s m/s\nprecision_baseline(TCN):       -0.0000004        4.1283381        0.3230800   m   m   m\nprecision_baseline_rate:        0.0000000        0.1863333        0.0175918   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180319_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.000      4.128      0.323\norbit baseline rate   (TCN) (m/s):  0.000e+00  1.863e-01  1.759e-02\n\nbaseline vector (TCN)   (m):     -0.000      4.128      0.323\nbaseline rate   (TCN) (m/s):  0.000e+00  1.863e-01  1.759e-02\n\nSLC-1 center baseline length (m):     4.1410\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.0000     2.3891     0.1589    27.496815      1.2439      2.0458      2.3943\n     0    200    -0.0000     2.3891     0.1589    27.939050      1.2597      2.0362      2.3943\n     0    400    -0.0000     2.3891     0.1589    28.370652      1.2750      2.0266      2.3943\n     0    600    -0.0000     2.3891     0.1589    28.792142      1.2899      2.0172      2.3943\n     0    800    -0.0000     2.3891     0.1589    29.204001      1.3043      2.0079      2.3943\n     0   1000    -0.0000     2.3891     0.1589    29.606672      1.3184      1.9986      2.3943\n     0   1200    -0.0000     2.3891     0.1589    30.000566      1.3321      1.9895      2.3943\n     0   1400    -0.0000     2.3891     0.1589    30.386062      1.3455      1.9805      2.3943\n     0   1600    -0.0000     2.3891     0.1589    30.763514      1.3585      1.9716      2.3943\n     0   1800    -0.0000     2.3891     0.1589    31.133251      1.3712      1.9628      2.3943\n     0   2000    -0.0000     2.3891     0.1589    31.495582      1.3836      1.9541      2.3943\n     0   2200    -0.0000     2.3891     0.1589    31.850793      1.3957      1.9455      2.3943\n     0   2400    -0.0000     2.3891     0.1589    32.199155      1.4075      1.9370      2.3943\n     0   2600    -0.0000     2.3891     0.1589    32.540921      1.4190      1.9285      2.3943\n     0   2800    -0.0000     2.3891     0.1589    32.876328      1.4303      1.9202      2.3943\n     0   3000    -0.0000     2.3891     0.1589    33.205602      1.4413      1.9119      2.3943\n     0   3200    -0.0000     2.3891     0.1589    33.528952      1.4520      1.9038      2.3943\n     0   3400    -0.0000     2.3891     0.1589    33.846580      1.4626      1.8957      2.3943\n     0   3600    -0.0000     2.3891     0.1589    34.158674      1.4729      1.8877      2.3943\n     0   3800    -0.0000     2.3891     0.1589    34.465413      1.4830      1.8798      2.3943\n     0   4000    -0.0000     2.3891     0.1589    34.766966      1.4928      1.8720      2.3943\n     0   4200    -0.0000     2.3891     0.1589    35.063495      1.5025      1.8642      2.3943\n     0   4400    -0.0000     2.3891     0.1589    35.355153      1.5120      1.8565      2.3943\n     0   4600    -0.0000     2.3891     0.1589    35.642085      1.5213      1.8489      2.3943\n     0   4800    -0.0000     2.3891     0.1589    35.924431      1.5304      1.8414      2.3943\n     0   5000    -0.0000     2.3891     0.1589    36.202321      1.5393      1.8340      2.3943\n     0   5200    -0.0000     2.3891     0.1589    36.475883      1.5480      1.8266      2.3943\n     0   5400    -0.0000     2.3891     0.1589    36.745237      1.5566      1.8193      2.3943\n     0   5600    -0.0000     2.3891     0.1589    37.010498      1.5650      1.8121      2.3943\n     0   5800    -0.0000     2.3891     0.1589    37.271776      1.5732      1.8049      2.3943\n     0   6000    -0.0000     2.3891     0.1589    37.529176      1.5813      1.7978      2.3943\n     0   6200    -0.0000     2.3891     0.1589    37.782801      1.5893      1.7908      2.3943\n     0   6400    -0.0000     2.3891     0.1589    38.032746      1.5971      1.7839      2.3943\n     0   6600    -0.0000     2.3891     0.1589    38.279105      1.6047      1.7770      2.3943\n     0   6800    -0.0000     2.3891     0.1589    38.521968      1.6122      1.7702      2.3943\n     0   7000    -0.0000     2.3891     0.1589    38.761419      1.6196      1.7634      2.3943\n     0   7200    -0.0000     2.3891     0.1589    38.997541      1.6269      1.7567      2.3943\n     0   7400    -0.0000     2.3891     0.1589    39.230415      1.6340      1.7501      2.3943\n     0   7600    -0.0000     2.3891     0.1589    39.460116      1.6410      1.7435      2.3943\n     0   7800    -0.0000     2.3891     0.1589    39.686717      1.6479      1.7370      2.3943\n     0   8000    -0.0000     2.3891     0.1589    39.910291      1.6546      1.7306      2.3943\n     0   8200    -0.0000     2.3891     0.1589    40.130905      1.6613      1.7242      2.3943\n     0   8400    -0.0000     2.3891     0.1589    40.348626      1.6678      1.7179      2.3943\n   500      0    -0.0000     2.7721     0.1950    27.495868      1.4528      2.3689      2.7789\n   500    200    -0.0000     2.7721     0.1950    27.938119      1.4711      2.3576      2.7789\n   500    400    -0.0000     2.7721     0.1950    28.369736      1.4888      2.3465      2.7789\n   500    600    -0.0000     2.7721     0.1950    28.791241      1.5060      2.3354      2.7789\n   500    800    -0.0000     2.7721     0.1950    29.203113      1.5228      2.3246      2.7789\n   500   1000    -0.0000     2.7721     0.1950    29.605798      1.5391      2.3138      2.7789\n   500   1200    -0.0000     2.7721     0.1950    29.999704      1.5549      2.3032      2.7789\n   500   1400    -0.0000     2.7721     0.1950    30.385211      1.5704      2.2927      2.7789\n   500   1600    -0.0000     2.7721     0.1950    30.762675      1.5855      2.2823      2.7789\n   500   1800    -0.0000     2.7721     0.1950    31.132423      1.6002      2.2720      2.7789\n   500   2000    -0.0000     2.7721     0.1950    31.494764      1.6145      2.2618      2.7789\n   500   2200    -0.0000     2.7721     0.1950    31.849986      1.6285      2.2518      2.7789\n   500   2400    -0.0000     2.7721     0.1950    32.198358      1.6421      2.2418      2.7789\n   500   2600    -0.0000     2.7721     0.1950    32.540133      1.6555      2.2320      2.7789\n   500   2800    -0.0000     2.7721     0.1950    32.875549      1.6685      2.2223      2.7789\n   500   3000    -0.0000     2.7721     0.1950    33.204831      1.6813      2.2126      2.7789\n   500   3200    -0.0000     2.7721     0.1950    33.528190      1.6937      2.2031      2.7789\n   500   3400    -0.0000     2.7721     0.1950    33.845826      1.7059      2.1937      2.7789\n   500   3600    -0.0000     2.7721     0.1950    34.157928      1.7178      2.1844      2.7789\n   500   3800    -0.0000     2.7721     0.1950    34.464675      1.7295      2.1751      2.7789\n   500   4000    -0.0000     2.7721     0.1950    34.766235      1.7409      2.1660      2.7789\n   500   4200    -0.0000     2.7721     0.1950    35.062772      1.7521      2.1570      2.7789\n   500   4400    -0.0000     2.7721     0.1950    35.354436      1.7631      2.1480      2.7789\n   500   4600    -0.0000     2.7721     0.1950    35.641375      1.7738      2.1392      2.7789\n   500   4800    -0.0000     2.7721     0.1950    35.923727      1.7843      2.1304      2.7789\n   500   5000    -0.0000     2.7721     0.1950    36.201624      1.7946      2.1217      2.7789\n   500   5200    -0.0000     2.7721     0.1950    36.475192      1.8048      2.1131      2.7789\n   500   5400    -0.0000     2.7721     0.1950    36.744552      1.8147      2.1046      2.7789\n   500   5600    -0.0000     2.7721     0.1950    37.009819      1.8244      2.0962      2.7789\n   500   5800    -0.0000     2.7721     0.1950    37.271103      1.8339      2.0878      2.7789\n   500   6000    -0.0000     2.7721     0.1950    37.528509      1.8433      2.0796      2.7789\n   500   6200    -0.0000     2.7721     0.1950    37.782139      1.8525      2.0714      2.7789\n   500   6400    -0.0000     2.7721     0.1950    38.032090      1.8615      2.0633      2.7789\n   500   6600    -0.0000     2.7721     0.1950    38.278454      1.8704      2.0553      2.7789\n   500   6800    -0.0000     2.7721     0.1950    38.521321      1.8790      2.0473      2.7789\n   500   7000    -0.0000     2.7721     0.1950    38.760777      1.8876      2.0395      2.7789\n   500   7200    -0.0000     2.7721     0.1950    38.996905      1.8960      2.0317      2.7789\n   500   7400    -0.0000     2.7721     0.1950    39.229783      1.9042      2.0239      2.7789\n   500   7600    -0.0000     2.7721     0.1950    39.459488      1.9123      2.0163      2.7789\n   500   7800    -0.0000     2.7721     0.1950    39.686095      1.9203      2.0087      2.7789\n   500   8000    -0.0000     2.7721     0.1950    39.909673      1.9281      2.0012      2.7789\n   500   8200    -0.0000     2.7721     0.1950    40.130291      1.9358      1.9938      2.7789\n   500   8400    -0.0000     2.7721     0.1950    40.348016      1.9434      1.9864      2.7789\n  1000      0    -0.0000     3.1551     0.2312    27.494901      1.6617      2.6920      3.1635\n  1000    200    -0.0000     3.1551     0.2312    27.937168      1.6824      2.6791      3.1635\n  1000    400    -0.0000     3.1551     0.2312    28.368800      1.7026      2.6663      3.1635\n  1000    600    -0.0000     3.1551     0.2312    28.790320      1.7221      2.6537      3.1635\n  1000    800    -0.0000     3.1551     0.2312    29.202206      1.7412      2.6413      3.1635\n  1000   1000    -0.0000     3.1551     0.2312    29.604904      1.7597      2.6290      3.1635\n  1000   1200    -0.0000     3.1551     0.2312    29.998823      1.7777      2.6168      3.1635\n  1000   1400    -0.0000     3.1551     0.2312    30.384343      1.7953      2.6048      3.1635\n  1000   1600    -0.0000     3.1551     0.2312    30.761818      1.8124      2.5929      3.1635\n  1000   1800    -0.0000     3.1551     0.2312    31.131578      1.8291      2.5812      3.1635\n  1000   2000    -0.0000     3.1551     0.2312    31.493929      1.8454      2.5695      3.1635\n  1000   2200    -0.0000     3.1551     0.2312    31.849161      1.8613      2.5581      3.1635\n  1000   2400    -0.0000     3.1551     0.2312    32.197543      1.8768      2.5467      3.1635\n  1000   2600    -0.0000     3.1551     0.2312    32.539328      1.8920      2.5354      3.1635\n  1000   2800    -0.0000     3.1551     0.2312    32.874754      1.9068      2.5243      3.1635\n  1000   3000    -0.0000     3.1551     0.2312    33.204045      1.9212      2.5133      3.1635\n  1000   3200    -0.0000     3.1551     0.2312    33.527412      1.9354      2.5024      3.1635\n  1000   3400    -0.0000     3.1551     0.2312    33.845057      1.9492      2.4917      3.1635\n  1000   3600    -0.0000     3.1551     0.2312    34.157166      1.9628      2.4810      3.1635\n  1000   3800    -0.0000     3.1551     0.2312    34.463921      1.9760      2.4705      3.1635\n  1000   4000    -0.0000     3.1551     0.2312    34.765489      1.9890      2.4600      3.1635\n  1000   4200    -0.0000     3.1551     0.2312    35.062033      2.0017      2.4497      3.1635\n  1000   4400    -0.0000     3.1551     0.2312    35.353705      2.0142      2.4395      3.1635\n  1000   4600    -0.0000     3.1551     0.2312    35.640651      2.0264      2.4294      3.1635\n  1000   4800    -0.0000     3.1551     0.2312    35.923009      2.0383      2.4194      3.1635\n  1000   5000    -0.0000     3.1551     0.2312    36.200913      2.0500      2.4094      3.1635\n  1000   5200    -0.0000     3.1551     0.2312    36.474487      2.0615      2.3996      3.1635\n  1000   5400    -0.0000     3.1551     0.2312    36.743853      2.0728      2.3899      3.1635\n  1000   5600    -0.0000     3.1551     0.2312    37.009126      2.0838      2.3803      3.1635\n  1000   5800    -0.0000     3.1551     0.2312    37.270416      2.0946      2.3708      3.1635\n  1000   6000    -0.0000     3.1551     0.2312    37.527828      2.1053      2.3613      3.1635\n  1000   6200    -0.0000     3.1551     0.2312    37.781464      2.1157      2.3520      3.1635\n  1000   6400    -0.0000     3.1551     0.2312    38.031420      2.1259      2.3427      3.1635\n  1000   6600    -0.0000     3.1551     0.2312    38.277789      2.1360      2.3336      3.1635\n  1000   6800    -0.0000     3.1551     0.2312    38.520662      2.1459      2.3245      3.1635\n  1000   7000    -0.0000     3.1551     0.2312    38.760123      2.1556      2.3155      3.1635\n  1000   7200    -0.0000     3.1551     0.2312    38.996255      2.1651      2.3066      3.1635\n  1000   7400    -0.0000     3.1551     0.2312    39.229138      2.1744      2.2978      3.1635\n  1000   7600    -0.0000     3.1551     0.2312    39.458848      2.1836      2.2891      3.1635\n  1000   7800    -0.0000     3.1551     0.2312    39.685459      2.1927      2.2804      3.1635\n  1000   8000    -0.0000     3.1551     0.2312    39.909042      2.2016      2.2718      3.1635\n  1000   8200    -0.0000     3.1551     0.2312    40.129665      2.2103      2.2633      3.1635\n  1000   8400    -0.0000     3.1551     0.2312    40.347394      2.2189      2.2549      3.1635\n  1500      0    -0.0000     3.5381     0.2674    27.493913      1.8705      3.0151      3.5482\n  1500    200    -0.0000     3.5381     0.2674    27.936197      1.8938      3.0005      3.5482\n  1500    400    -0.0000     3.5381     0.2674    28.367845      1.9163      2.9862      3.5482\n  1500    600    -0.0000     3.5381     0.2674    28.789379      1.9382      2.9720      3.5482\n  1500    800    -0.0000     3.5381     0.2674    29.201280      1.9595      2.9580      3.5482\n  1500   1000    -0.0000     3.5381     0.2674    29.603992      1.9803      2.9442      3.5482\n  1500   1200    -0.0000     3.5381     0.2674    29.997923      2.0005      2.9305      3.5482\n  1500   1400    -0.0000     3.5381     0.2674    30.383456      2.0202      2.9170      3.5482\n  1500   1600    -0.0000     3.5381     0.2674    30.760943      2.0393      2.9036      3.5482\n  1500   1800    -0.0000     3.5381     0.2674    31.130714      2.0580      2.8904      3.5482\n  1500   2000    -0.0000     3.5381     0.2674    31.493077      2.0763      2.8773      3.5482\n  1500   2200    -0.0000     3.5381     0.2674    31.848320      2.0941      2.8644      3.5482\n  1500   2400    -0.0000     3.5381     0.2674    32.196712      2.1114      2.8516      3.5482\n  1500   2600    -0.0000     3.5381     0.2674    32.538506      2.1284      2.8389      3.5482\n  1500   2800    -0.0000     3.5381     0.2674    32.873942      2.1450      2.8264      3.5482\n  1500   3000    -0.0000     3.5381     0.2674    33.203242      2.1612      2.8140      3.5482\n  1500   3200    -0.0000     3.5381     0.2674    33.526618      2.1771      2.8018      3.5482\n  1500   3400    -0.0000     3.5381     0.2674    33.844271      2.1926      2.7897      3.5482\n  1500   3600    -0.0000     3.5381     0.2674    34.156389      2.2077      2.7777      3.5482\n  1500   3800    -0.0000     3.5381     0.2674    34.463152      2.2226      2.7658      3.5482\n  1500   4000    -0.0000     3.5381     0.2674    34.764728      2.2371      2.7541      3.5482\n  1500   4200    -0.0000     3.5381     0.2674    35.061279      2.2513      2.7425      3.5482\n  1500   4400    -0.0000     3.5381     0.2674    35.352959      2.2652      2.7310      3.5482\n  1500   4600    -0.0000     3.5381     0.2674    35.639912      2.2789      2.7196      3.5482\n  1500   4800    -0.0000     3.5381     0.2674    35.922277      2.2923      2.7083      3.5482\n  1500   5000    -0.0000     3.5381     0.2674    36.200187      2.3054      2.6972      3.5482\n  1500   5200    -0.0000     3.5381     0.2674    36.473768      2.3182      2.6862      3.5482\n  1500   5400    -0.0000     3.5381     0.2674    36.743141      2.3308      2.6752      3.5482\n  1500   5600    -0.0000     3.5381     0.2674    37.008420      2.3432      2.6644      3.5482\n  1500   5800    -0.0000     3.5381     0.2674    37.269715      2.3553      2.6537      3.5482\n  1500   6000    -0.0000     3.5381     0.2674    37.527133      2.3672      2.6431      3.5482\n  1500   6200    -0.0000     3.5381     0.2674    37.780775      2.3789      2.6326      3.5482\n  1500   6400    -0.0000     3.5381     0.2674    38.030736      2.3904      2.6222      3.5482\n  1500   6600    -0.0000     3.5381     0.2674    38.277111      2.4016      2.6119      3.5482\n  1500   6800    -0.0000     3.5381     0.2674    38.519989      2.4127      2.6017      3.5482\n  1500   7000    -0.0000     3.5381     0.2674    38.759456      2.4235      2.5916      3.5482\n  1500   7200    -0.0000     3.5381     0.2674    38.995593      2.4342      2.5816      3.5482\n  1500   7400    -0.0000     3.5381     0.2674    39.228481      2.4446      2.5716      3.5482\n  1500   7600    -0.0000     3.5381     0.2674    39.458196      2.4549      2.5618      3.5482\n  1500   7800    -0.0000     3.5381     0.2674    39.684812      2.4651      2.5521      3.5482\n  1500   8000    -0.0000     3.5381     0.2674    39.908399      2.4750      2.5425      3.5482\n  1500   8200    -0.0000     3.5381     0.2674    40.129027      2.4848      2.5329      3.5482\n  1500   8400    -0.0000     3.5381     0.2674    40.346760      2.4944      2.5234      3.5482\n  2000      0    -0.0000     3.9211     0.3035    27.492904      2.0794      3.3382      3.9329\n  2000    200    -0.0000     3.9211     0.3035    27.935205      2.1051      3.3220      3.9329\n  2000    400    -0.0000     3.9211     0.3035    28.366870      2.1301      3.3061      3.9329\n  2000    600    -0.0000     3.9211     0.3035    28.788420      2.1543      3.2903      3.9329\n  2000    800    -0.0000     3.9211     0.3035    29.200335      2.1779      3.2747      3.9329\n  2000   1000    -0.0000     3.9211     0.3035    29.603061      2.2009      3.2594      3.9329\n  2000   1200    -0.0000     3.9211     0.3035    29.997006      2.2232      3.2441      3.9329\n  2000   1400    -0.0000     3.9211     0.3035    30.382551      2.2450      3.2291      3.9329\n  2000   1600    -0.0000     3.9211     0.3035    30.760051      2.2662      3.2142      3.9329\n  2000   1800    -0.0000     3.9211     0.3035    31.129834      2.2869      3.1996      3.9329\n  2000   2000    -0.0000     3.9211     0.3035    31.492208      2.3071      3.1850      3.9329\n  2000   2200    -0.0000     3.9211     0.3035    31.847461      2.3268      3.1707      3.9329\n  2000   2400    -0.0000     3.9211     0.3035    32.195864      2.3461      3.1565      3.9329\n  2000   2600    -0.0000     3.9211     0.3035    32.537668      2.3649      3.1424      3.9329\n  2000   2800    -0.0000     3.9211     0.3035    32.873113      2.3832      3.1285      3.9329\n  2000   3000    -0.0000     3.9211     0.3035    33.202423      2.4012      3.1148      3.9329\n  2000   3200    -0.0000     3.9211     0.3035    33.525809      2.4187      3.1012      3.9329\n  2000   3400    -0.0000     3.9211     0.3035    33.843470      2.4359      3.0877      3.9329\n  2000   3600    -0.0000     3.9211     0.3035    34.155597      2.4526      3.0744      3.9329\n  2000   3800    -0.0000     3.9211     0.3035    34.462368      2.4691      3.0612      3.9329\n  2000   4000    -0.0000     3.9211     0.3035    34.763952      2.4852      3.0482      3.9329\n  2000   4200    -0.0000     3.9211     0.3035    35.060511      2.5009      3.0353      3.9329\n  2000   4400    -0.0000     3.9211     0.3035    35.352197      2.5163      3.0225      3.9329\n  2000   4600    -0.0000     3.9211     0.3035    35.639158      2.5314      3.0099      3.9329\n  2000   4800    -0.0000     3.9211     0.3035    35.921530      2.5462      2.9973      3.9329\n  2000   5000    -0.0000     3.9211     0.3035    36.199447      2.5607      2.9850      3.9329\n  2000   5200    -0.0000     3.9211     0.3035    36.473035      2.5750      2.9727      3.9329\n  2000   5400    -0.0000     3.9211     0.3035    36.742414      2.5889      2.9606      3.9329\n  2000   5600    -0.0000     3.9211     0.3035    37.007699      2.6026      2.9485      3.9329\n  2000   5800    -0.0000     3.9211     0.3035    37.269001      2.6160      2.9366      3.9329\n  2000   6000    -0.0000     3.9211     0.3035    37.526425      2.6292      2.9249      3.9329\n  2000   6200    -0.0000     3.9211     0.3035    37.780072      2.6421      2.9132      3.9329\n  2000   6400    -0.0000     3.9211     0.3035    38.030040      2.6548      2.9016      3.9329\n  2000   6600    -0.0000     3.9211     0.3035    38.276421      2.6672      2.8902      3.9329\n  2000   6800    -0.0000     3.9211     0.3035    38.519304      2.6795      2.8789      3.9329\n  2000   7000    -0.0000     3.9211     0.3035    38.758775      2.6915      2.8676      3.9329\n  2000   7200    -0.0000     3.9211     0.3035    38.994918      2.7033      2.8565      3.9329\n  2000   7400    -0.0000     3.9211     0.3035    39.227811      2.7149      2.8455      3.9329\n  2000   7600    -0.0000     3.9211     0.3035    39.457531      2.7262      2.8346      3.9329\n  2000   7800    -0.0000     3.9211     0.3035    39.684152      2.7374      2.8238      3.9329\n  2000   8000    -0.0000     3.9211     0.3035    39.907744      2.7484      2.8131      3.9329\n  2000   8200    -0.0000     3.9211     0.3035    40.128376      2.7592      2.8025      3.9329\n  2000   8400    -0.0000     3.9211     0.3035    40.346114      2.7699      2.7920      3.9329\n  2500      0    -0.0000     4.3041     0.3397    27.491875      2.2882      3.6613      4.3175\n  2500    200    -0.0000     4.3041     0.3397    27.934194      2.3164      3.6435      4.3175\n  2500    400    -0.0000     4.3041     0.3397    28.365875      2.3438      3.6260      4.3175\n  2500    600    -0.0000     4.3041     0.3397    28.787440      2.3704      3.6086      4.3175\n  2500    800    -0.0000     4.3041     0.3397    29.199371      2.3963      3.5915      4.3175\n  2500   1000    -0.0000     4.3041     0.3397    29.602111      2.4215      3.5746      4.3175\n  2500   1200    -0.0000     4.3041     0.3397    29.996070      2.4460      3.5578      4.3175\n  2500   1400    -0.0000     4.3041     0.3397    30.381628      2.4699      3.5413      4.3175\n  2500   1600    -0.0000     4.3041     0.3397    30.759140      2.4932      3.5249      4.3175\n  2500   1800    -0.0000     4.3041     0.3397    31.128935      2.5159      3.5088      4.3175\n  2500   2000    -0.0000     4.3041     0.3397    31.491321      2.5380      3.4928      4.3175\n  2500   2200    -0.0000     4.3041     0.3397    31.846586      2.5596      3.4770      4.3175\n  2500   2400    -0.0000     4.3041     0.3397    32.194999      2.5807      3.4613      4.3175\n  2500   2600    -0.0000     4.3041     0.3397    32.536814      2.6013      3.4459      4.3175\n  2500   2800    -0.0000     4.3041     0.3397    32.872269      2.6214      3.4306      4.3175\n  2500   3000    -0.0000     4.3041     0.3397    33.201588      2.6411      3.4155      4.3175\n  2500   3200    -0.0000     4.3041     0.3397    33.524983      2.6603      3.4005      4.3175\n  2500   3400    -0.0000     4.3041     0.3397    33.842654      2.6792      3.3857      4.3175\n  2500   3600    -0.0000     4.3041     0.3397    34.154789      2.6976      3.3711      4.3175\n  2500   3800    -0.0000     4.3041     0.3397    34.461568      2.7156      3.3566      4.3175\n  2500   4000    -0.0000     4.3041     0.3397    34.763160      2.7332      3.3422      4.3175\n  2500   4200    -0.0000     4.3041     0.3397    35.059727      2.7505      3.3280      4.3175\n  2500   4400    -0.0000     4.3041     0.3397    35.351422      2.7674      3.3140      4.3175\n  2500   4600    -0.0000     4.3041     0.3397    35.638389      2.7839      3.3001      4.3175\n  2500   4800    -0.0000     4.3041     0.3397    35.920769      2.8002      3.2863      4.3175\n  2500   5000    -0.0000     4.3041     0.3397    36.198693      2.8161      3.2727      4.3175\n  2500   5200    -0.0000     4.3041     0.3397    36.472288      2.8317      3.2592      4.3175\n  2500   5400    -0.0000     4.3041     0.3397    36.741674      2.8470      3.2459      4.3175\n  2500   5600    -0.0000     4.3041     0.3397    37.006965      2.8620      3.2327      4.3175\n  2500   5800    -0.0000     4.3041     0.3397    37.268273      2.8767      3.2196      4.3175\n  2500   6000    -0.0000     4.3041     0.3397    37.525704      2.8911      3.2066      4.3175\n  2500   6200    -0.0000     4.3041     0.3397    37.779357      2.9053      3.1938      4.3175\n  2500   6400    -0.0000     4.3041     0.3397    38.029330      2.9192      3.1811      4.3175\n  2500   6600    -0.0000     4.3041     0.3397    38.275717      2.9328      3.1685      4.3175\n  2500   6800    -0.0000     4.3041     0.3397    38.518605      2.9462      3.1560      4.3175\n  2500   7000    -0.0000     4.3041     0.3397    38.758083      2.9594      3.1437      4.3175\n  2500   7200    -0.0000     4.3041     0.3397    38.994231      2.9723      3.1315      4.3175\n  2500   7400    -0.0000     4.3041     0.3397    39.227129      2.9850      3.1194      4.3175\n  2500   7600    -0.0000     4.3041     0.3397    39.456854      2.9975      3.1074      4.3175\n  2500   7800    -0.0000     4.3041     0.3397    39.683480      3.0098      3.0955      4.3175\n  2500   8000    -0.0000     4.3041     0.3397    39.907076      3.0219      3.0837      4.3175\n  2500   8200    -0.0000     4.3041     0.3397    40.127713      3.0337      3.0721      4.3175\n  2500   8400    -0.0000     4.3041     0.3397    40.345456      3.0454      3.0605      4.3175\n  3000      0    -0.0000     4.6872     0.3758    27.490826      2.4970      3.9844      4.7022\n  3000    200    -0.0000     4.6872     0.3758    27.933162      2.5277      3.9650      4.7022\n  3000    400    -0.0000     4.6872     0.3758    28.364860      2.5575      3.9459      4.7022\n  3000    600    -0.0000     4.6872     0.3758    28.786442      2.5865      3.9269      4.7022\n  3000    800    -0.0000     4.6872     0.3758    29.198388      2.6146      3.9082      4.7022\n  3000   1000    -0.0000     4.6872     0.3758    29.601143      2.6420      3.8898      4.7022\n  3000   1200    -0.0000     4.6872     0.3758    29.995115      2.6687      3.8715      4.7022\n  3000   1400    -0.0000     4.6872     0.3758    30.380687      2.6947      3.8535      4.7022\n  3000   1600    -0.0000     4.6872     0.3758    30.758212      2.7201      3.8356      4.7022\n  3000   1800    -0.0000     4.6872     0.3758    31.128020      2.7448      3.8180      4.7022\n  3000   2000    -0.0000     4.6872     0.3758    31.490417      2.7688      3.8005      4.7022\n  3000   2200    -0.0000     4.6872     0.3758    31.845693      2.7924      3.7833      4.7022\n  3000   2400    -0.0000     4.6872     0.3758    32.194117      2.8153      3.7662      4.7022\n  3000   2600    -0.0000     4.6872     0.3758    32.535943      2.8377      3.7494      4.7022\n  3000   2800    -0.0000     4.6872     0.3758    32.871408      2.8596      3.7327      4.7022\n  3000   3000    -0.0000     4.6872     0.3758    33.200737      2.8810      3.7162      4.7022\n  3000   3200    -0.0000     4.6872     0.3758    33.524142      2.9020      3.6999      4.7022\n  3000   3400    -0.0000     4.6872     0.3758    33.841821      2.9224      3.6837      4.7022\n  3000   3600    -0.0000     4.6872     0.3758    34.153966      2.9425      3.6678      4.7022\n  3000   3800    -0.0000     4.6872     0.3758    34.460753      2.9621      3.6520      4.7022\n  3000   4000    -0.0000     4.6872     0.3758    34.762354      2.9813      3.6363      4.7022\n  3000   4200    -0.0000     4.6872     0.3758    35.058929      3.0000      3.6208      4.7022\n  3000   4400    -0.0000     4.6872     0.3758    35.350631      3.0184      3.6055      4.7022\n  3000   4600    -0.0000     4.6872     0.3758    35.637607      3.0365      3.5904      4.7022\n  3000   4800    -0.0000     4.6872     0.3758    35.919994      3.0541      3.5753      4.7022\n  3000   5000    -0.0000     4.6872     0.3758    36.197925      3.0714      3.5605      4.7022\n  3000   5200    -0.0000     4.6872     0.3758    36.471527      3.0884      3.5458      4.7022\n  3000   5400    -0.0000     4.6872     0.3758    36.740919      3.1050      3.5312      4.7022\n  3000   5600    -0.0000     4.6872     0.3758    37.006218      3.1213      3.5168      4.7022\n  3000   5800    -0.0000     4.6872     0.3758    37.267532      3.1373      3.5025      4.7022\n  3000   6000    -0.0000     4.6872     0.3758    37.524969      3.1531      3.4884      4.7022\n  3000   6200    -0.0000     4.6872     0.3758    37.778628      3.1685      3.4744      4.7022\n  3000   6400    -0.0000     4.6872     0.3758    38.028607      3.1836      3.4606      4.7022\n  3000   6600    -0.0000     4.6872     0.3758    38.275000      3.1984      3.4468      4.7022\n  3000   6800    -0.0000     4.6872     0.3758    38.517894      3.2130      3.4332      4.7022\n  3000   7000    -0.0000     4.6872     0.3758    38.757377      3.2274      3.4198      4.7022\n  3000   7200    -0.0000     4.6872     0.3758    38.993530      3.2414      3.4064      4.7022\n  3000   7400    -0.0000     4.6872     0.3758    39.226434      3.2552      3.3932      4.7022\n  3000   7600    -0.0000     4.6872     0.3758    39.456164      3.2688      3.3802      4.7022\n  3000   7800    -0.0000     4.6872     0.3758    39.682795      3.2822      3.3672      4.7022\n  3000   8000    -0.0000     4.6872     0.3758    39.906397      3.2953      3.3544      4.7022\n  3000   8200    -0.0000     4.6872     0.3758    40.127039      3.3082      3.3417      4.7022\n  3000   8400    -0.0000     4.6872     0.3758    40.344786      3.3208      3.3291      4.7022\n  3500      0    -0.0000     5.0702     0.4120    27.489756      2.7058      4.3075      5.0869\n  3500    200    -0.0000     5.0702     0.4120    27.932111      2.7390      4.2865      5.0869\n  3500    400    -0.0000     5.0702     0.4120    28.363826      2.7712      4.2658      5.0869\n  3500    600    -0.0000     5.0702     0.4120    28.785424      2.8025      4.2453      5.0869\n  3500    800    -0.0000     5.0702     0.4120    29.197386      2.8330      4.2250      5.0869\n  3500   1000    -0.0000     5.0702     0.4120    29.600156      2.8626      4.2050      5.0869\n  3500   1200    -0.0000     5.0702     0.4120    29.994143      2.8915      4.1852      5.0869\n  3500   1400    -0.0000     5.0702     0.4120    30.379728      2.9196      4.1656      5.0869\n  3500   1600    -0.0000     5.0702     0.4120    30.757266      2.9469      4.1463      5.0869\n  3500   1800    -0.0000     5.0702     0.4120    31.127086      2.9736      4.1272      5.0869\n  3500   2000    -0.0000     5.0702     0.4120    31.489496      2.9997      4.1083      5.0869\n  3500   2200    -0.0000     5.0702     0.4120    31.844784      3.0251      4.0896      5.0869\n  3500   2400    -0.0000     5.0702     0.4120    32.193219      3.0499      4.0712      5.0869\n  3500   2600    -0.0000     5.0702     0.4120    32.535056      3.0742      4.0529      5.0869\n  3500   2800    -0.0000     5.0702     0.4120    32.870531      3.0978      4.0348      5.0869\n  3500   3000    -0.0000     5.0702     0.4120    33.199870      3.1210      4.0169      5.0869\n  3500   3200    -0.0000     5.0702     0.4120    33.523284      3.1436      3.9993      5.0869\n  3500   3400    -0.0000     5.0702     0.4120    33.840974      3.1657      3.9818      5.0869\n  3500   3600    -0.0000     5.0702     0.4120    34.153127      3.1874      3.9645      5.0869\n  3500   3800    -0.0000     5.0702     0.4120    34.459923      3.2086      3.9473      5.0869\n  3500   4000    -0.0000     5.0702     0.4120    34.761533      3.2293      3.9304      5.0869\n  3500   4200    -0.0000     5.0702     0.4120    35.058116      3.2496      3.9136      5.0869\n  3500   4400    -0.0000     5.0702     0.4120    35.349826      3.2695      3.8970      5.0869\n  3500   4600    -0.0000     5.0702     0.4120    35.636809      3.2890      3.8806      5.0869\n  3500   4800    -0.0000     5.0702     0.4120    35.919204      3.3080      3.8644      5.0869\n  3500   5000    -0.0000     5.0702     0.4120    36.197143      3.3267      3.8483      5.0869\n  3500   5200    -0.0000     5.0702     0.4120    36.470752      3.3451      3.8323      5.0869\n  3500   5400    -0.0000     5.0702     0.4120    36.740151      3.3631      3.8166      5.0869\n  3500   5600    -0.0000     5.0702     0.4120    37.005456      3.3807      3.8010      5.0869\n  3500   5800    -0.0000     5.0702     0.4120    37.266777      3.3980      3.7855      5.0869\n  3500   6000    -0.0000     5.0702     0.4120    37.524220      3.4150      3.7702      5.0869\n  3500   6200    -0.0000     5.0702     0.4120    37.777886      3.4316      3.7550      5.0869\n  3500   6400    -0.0000     5.0702     0.4120    38.027871      3.4480      3.7400      5.0869\n  3500   6600    -0.0000     5.0702     0.4120    38.274270      3.4640      3.7252      5.0869\n  3500   6800    -0.0000     5.0702     0.4120    38.517170      3.4798      3.7104      5.0869\n  3500   7000    -0.0000     5.0702     0.4120    38.756658      3.4953      3.6959      5.0869\n  3500   7200    -0.0000     5.0702     0.4120    38.992817      3.5105      3.6814      5.0869\n  3500   7400    -0.0000     5.0702     0.4120    39.225727      3.5254      3.6671      5.0869\n  3500   7600    -0.0000     5.0702     0.4120    39.455462      3.5401      3.6530      5.0869\n  3500   7800    -0.0000     5.0702     0.4120    39.682098      3.5545      3.6389      5.0869\n  3500   8000    -0.0000     5.0702     0.4120    39.905705      3.5687      3.6250      5.0869\n  3500   8200    -0.0000     5.0702     0.4120    40.126352      3.5826      3.6113      5.0869\n  3500   8400    -0.0000     5.0702     0.4120    40.344104      3.5963      3.5976      5.0869\n  4000      0    -0.0000     5.4532     0.4482    27.488666      2.9146      4.6307      5.4716\n  4000    200    -0.0000     5.4532     0.4482    27.931039      2.9503      4.6080      5.4716\n  4000    400    -0.0000     5.4532     0.4482    28.362772      2.9849      4.5857      5.4716\n  4000    600    -0.0000     5.4532     0.4482    28.784387      3.0186      4.5636      5.4716\n  4000    800    -0.0000     5.4532     0.4482    29.196366      3.0513      4.5418      5.4716\n  4000   1000    -0.0000     5.4532     0.4482    29.599150      3.0832      4.5202      5.4716\n  4000   1200    -0.0000     5.4532     0.4482    29.993152      3.1142      4.4989      5.4716\n  4000   1400    -0.0000     5.4532     0.4482    30.378752      3.1444      4.4778      5.4716\n  4000   1600    -0.0000     5.4532     0.4482    30.756303      3.1738      4.4570      5.4716\n  4000   1800    -0.0000     5.4532     0.4482    31.126136      3.2025      4.4364      5.4716\n  4000   2000    -0.0000     5.4532     0.4482    31.488558      3.2305      4.4161      5.4716\n  4000   2200    -0.0000     5.4532     0.4482    31.843858      3.2578      4.3960      5.4716\n  4000   2400    -0.0000     5.4532     0.4482    32.192305      3.2845      4.3761      5.4716\n  4000   2600    -0.0000     5.4532     0.4482    32.534152      3.3106      4.3564      5.4716\n  4000   2800    -0.0000     5.4532     0.4482    32.869638      3.3360      4.3369      5.4716\n  4000   3000    -0.0000     5.4532     0.4482    33.198987      3.3609      4.3177      5.4716\n  4000   3200    -0.0000     5.4532     0.4482    33.522412      3.3852      4.2987      5.4716\n  4000   3400    -0.0000     5.4532     0.4482    33.840110      3.4090      4.2798      5.4716\n  4000   3600    -0.0000     5.4532     0.4482    34.152273      3.4323      4.2612      5.4716\n  4000   3800    -0.0000     5.4532     0.4482    34.459078      3.4550      4.2427      5.4716\n  4000   4000    -0.0000     5.4532     0.4482    34.760696      3.4773      4.2245      5.4716\n  4000   4200    -0.0000     5.4532     0.4482    35.057288      3.4991      4.2064      5.4716\n  4000   4400    -0.0000     5.4532     0.4482    35.349007      3.5205      4.1886      5.4716\n  4000   4600    -0.0000     5.4532     0.4482    35.635998      3.5414      4.1709      5.4716\n  4000   4800    -0.0000     5.4532     0.4482    35.918400      3.5620      4.1534      5.4716\n  4000   5000    -0.0000     5.4532     0.4482    36.196346      3.5821      4.1361      5.4716\n  4000   5200    -0.0000     5.4532     0.4482    36.469963      3.6018      4.1189      5.4716\n  4000   5400    -0.0000     5.4532     0.4482    36.739369      3.6211      4.1019      5.4716\n  4000   5600    -0.0000     5.4532     0.4482    37.004681      3.6401      4.0851      5.4716\n  4000   5800    -0.0000     5.4532     0.4482    37.266009      3.6587      4.0685      5.4716\n  4000   6000    -0.0000     5.4532     0.4482    37.523459      3.6769      4.0520      5.4716\n  4000   6200    -0.0000     5.4532     0.4482    37.777131      3.6948      4.0357      5.4716\n  4000   6400    -0.0000     5.4532     0.4482    38.027122      3.7124      4.0195      5.4716\n  4000   6600    -0.0000     5.4532     0.4482    38.273527      3.7296      4.0035      5.4716\n  4000   6800    -0.0000     5.4532     0.4482    38.516433      3.7466      3.9877      5.4716\n  4000   7000    -0.0000     5.4532     0.4482    38.755927      3.7632      3.9720      5.4716\n  4000   7200    -0.0000     5.4532     0.4482    38.992092      3.7795      3.9564      5.4716\n  4000   7400    -0.0000     5.4532     0.4482    39.225007      3.7956      3.9410      5.4716\n  4000   7600    -0.0000     5.4532     0.4482    39.454748      3.8114      3.9258      5.4716\n  4000   7800    -0.0000     5.4532     0.4482    39.681389      3.8269      3.9107      5.4716\n  4000   8000    -0.0000     5.4532     0.4482    39.905001      3.8421      3.8957      5.4716\n  4000   8200    -0.0000     5.4532     0.4482    40.125653      3.8571      3.8809      5.4716\n  4000   8400    -0.0000     5.4532     0.4482    40.343411      3.8718      3.8662      5.4716\n  4500      0    -0.0000     5.8362     0.4843    27.487555      3.1234      4.9538      5.8563\n  4500    200    -0.0000     5.8362     0.4843    27.929948      3.1615      4.9296      5.8563\n  4500    400    -0.0000     5.8362     0.4843    28.361699      3.1986      4.9056      5.8563\n  4500    600    -0.0000     5.8362     0.4843    28.783331      3.2346      4.8819      5.8563\n  4500    800    -0.0000     5.8362     0.4843    29.195326      3.2696      4.8585      5.8563\n  4500   1000    -0.0000     5.8362     0.4843    29.598126      3.3037      4.8354      5.8563\n  4500   1200    -0.0000     5.8362     0.4843    29.992143      3.3369      4.8126      5.8563\n  4500   1400    -0.0000     5.8362     0.4843    30.377757      3.3692      4.7900      5.8563\n  4500   1600    -0.0000     5.8362     0.4843    30.755322      3.4007      4.7677      5.8563\n  4500   1800    -0.0000     5.8362     0.4843    31.125168      3.4314      4.7457      5.8563\n  4500   2000    -0.0000     5.8362     0.4843    31.487603      3.4613      4.7239      5.8563\n  4500   2200    -0.0000     5.8362     0.4843    31.842915      3.4906      4.7023      5.8563\n  4500   2400    -0.0000     5.8362     0.4843    32.191373      3.5191      4.6810      5.8563\n  4500   2600    -0.0000     5.8362     0.4843    32.533232      3.5470      4.6599      5.8563\n  4500   2800    -0.0000     5.8362     0.4843    32.868729      3.5742      4.6391      5.8563\n  4500   3000    -0.0000     5.8362     0.4843    33.198089      3.6008      4.6185      5.8563\n  4500   3200    -0.0000     5.8362     0.4843    33.521523      3.6268      4.5981      5.8563\n  4500   3400    -0.0000     5.8362     0.4843    33.839232      3.6523      4.5779      5.8563\n  4500   3600    -0.0000     5.8362     0.4843    34.151404      3.6771      4.5579      5.8563\n  4500   3800    -0.0000     5.8362     0.4843    34.458218      3.7015      4.5381      5.8563\n  4500   4000    -0.0000     5.8362     0.4843    34.759845      3.7253      4.5186      5.8563\n  4500   4200    -0.0000     5.8362     0.4843    35.056446      3.7487      4.4993      5.8563\n  4500   4400    -0.0000     5.8362     0.4843    35.348172      3.7715      4.4801      5.8563\n  4500   4600    -0.0000     5.8362     0.4843    35.635172      3.7939      4.4612      5.8563\n  4500   4800    -0.0000     5.8362     0.4843    35.917582      3.8159      4.4424      5.8563\n  4500   5000    -0.0000     5.8362     0.4843    36.195536      3.8374      4.4238      5.8563\n  4500   5200    -0.0000     5.8362     0.4843    36.469160      3.8585      4.4055      5.8563\n  4500   5400    -0.0000     5.8362     0.4843    36.738573      3.8791      4.3873      5.8563\n  4500   5600    -0.0000     5.8362     0.4843    37.003892      3.8994      4.3693      5.8563\n  4500   5800    -0.0000     5.8362     0.4843    37.265227      3.9193      4.3514      5.8563\n  4500   6000    -0.0000     5.8362     0.4843    37.522684      3.9388      4.3338      5.8563\n  4500   6200    -0.0000     5.8362     0.4843    37.776362      3.9580      4.3163      5.8563\n  4500   6400    -0.0000     5.8362     0.4843    38.026360      3.9768      4.2990      5.8563\n  4500   6600    -0.0000     5.8362     0.4843    38.272771      3.9952      4.2818      5.8563\n  4500   6800    -0.0000     5.8362     0.4843    38.515683      4.0133      4.2649      5.8563\n  4500   7000    -0.0000     5.8362     0.4843    38.755184      4.0311      4.2481      5.8563\n  4500   7200    -0.0000     5.8362     0.4843    38.991354      4.0486      4.2314      5.8563\n  4500   7400    -0.0000     5.8362     0.4843    39.224274      4.0658      4.2149      5.8563\n  4500   7600    -0.0000     5.8362     0.4843    39.454021      4.0826      4.1986      5.8563\n  4500   7800    -0.0000     5.8362     0.4843    39.680668      4.0992      4.1824      5.8563\n  4500   8000    -0.0000     5.8362     0.4843    39.904285      4.1155      4.1664      5.8563\n  4500   8200    -0.0000     5.8362     0.4843    40.124942      4.1315      4.1505      5.8563\n  4500   8400    -0.0000     5.8362     0.4843    40.342705      4.1473      4.1347      5.8563\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180331_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.1112816       -1.2495340        5.0924280   m   m   m\ninitial_baseline_rate:          0.0000000        0.2291424        0.0401035   m/s m/s m/s\nprecision_baseline(TCN):       -0.1112816       -1.2495340        5.0924280   m   m   m\nprecision_baseline_rate:        0.0000000        0.2291424        0.0401035   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180331_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.111     -1.250      5.092\norbit baseline rate   (TCN) (m/s):  0.000e+00  2.291e-01  4.010e-02\n\nbaseline vector (TCN)   (m):     -0.111     -1.250      5.092\nbaseline rate   (TCN) (m/s):  0.000e+00  2.291e-01  4.010e-02\n\nSLC-1 center baseline length (m):     5.2447\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.1113    -3.3884     4.7181    27.496815      2.6208     -5.1837      5.8098\n     0    200    -0.1113    -3.3884     4.7181    27.939050      2.5807     -5.2038      5.8098\n     0    400    -0.1113    -3.3884     4.7181    28.370652      2.5415     -5.2231      5.8098\n     0    600    -0.1113    -3.3884     4.7181    28.792142      2.5030     -5.2417      5.8098\n     0    800    -0.1113    -3.3884     4.7181    29.204001      2.4652     -5.2595      5.8098\n     0   1000    -0.1113    -3.3884     4.7181    29.606672      2.4282     -5.2767      5.8098\n     0   1200    -0.1113    -3.3884     4.7181    30.000566      2.3919     -5.2933      5.8098\n     0   1400    -0.1113    -3.3884     4.7181    30.386062      2.3562     -5.3093      5.8098\n     0   1600    -0.1113    -3.3884     4.7181    30.763514      2.3212     -5.3247      5.8098\n     0   1800    -0.1113    -3.3884     4.7181    31.133251      2.2867     -5.3396      5.8098\n     0   2000    -0.1113    -3.3884     4.7181    31.495582      2.2529     -5.3539      5.8098\n     0   2200    -0.1113    -3.3884     4.7181    31.850793      2.2197     -5.3678      5.8098\n     0   2400    -0.1113    -3.3884     4.7181    32.199155      2.1870     -5.3812      5.8098\n     0   2600    -0.1113    -3.3884     4.7181    32.540921      2.1549     -5.3941      5.8098\n     0   2800    -0.1113    -3.3884     4.7181    32.876328      2.1233     -5.4067      5.8098\n     0   3000    -0.1113    -3.3884     4.7181    33.205602      2.0922     -5.4188      5.8098\n     0   3200    -0.1113    -3.3884     4.7181    33.528952      2.0615     -5.4305      5.8098\n     0   3400    -0.1113    -3.3884     4.7181    33.846580      2.0314     -5.4418      5.8098\n     0   3600    -0.1113    -3.3884     4.7181    34.158674      2.0017     -5.4528      5.8098\n     0   3800    -0.1113    -3.3884     4.7181    34.465413      1.9725     -5.4635      5.8098\n     0   4000    -0.1113    -3.3884     4.7181    34.766966      1.9437     -5.4738      5.8098\n     0   4200    -0.1113    -3.3884     4.7181    35.063495      1.9154     -5.4838      5.8098\n     0   4400    -0.1113    -3.3884     4.7181    35.355153      1.8874     -5.4934      5.8098\n     0   4600    -0.1113    -3.3884     4.7181    35.642085      1.8599     -5.5028      5.8098\n     0   4800    -0.1113    -3.3884     4.7181    35.924431      1.8328     -5.5119      5.8098\n     0   5000    -0.1113    -3.3884     4.7181    36.202321      1.8060     -5.5208      5.8098\n     0   5200    -0.1113    -3.3884     4.7181    36.475883      1.7796     -5.5293      5.8098\n     0   5400    -0.1113    -3.3884     4.7181    36.745237      1.7536     -5.5376      5.8098\n     0   5600    -0.1113    -3.3884     4.7181    37.010498      1.7279     -5.5457      5.8098\n     0   5800    -0.1113    -3.3884     4.7181    37.271776      1.7026     -5.5535      5.8098\n     0   6000    -0.1113    -3.3884     4.7181    37.529176      1.6777     -5.5611      5.8098\n     0   6200    -0.1113    -3.3884     4.7181    37.782801      1.6530     -5.5685      5.8098\n     0   6400    -0.1113    -3.3884     4.7181    38.032746      1.6287     -5.5756      5.8098\n     0   6600    -0.1113    -3.3884     4.7181    38.279105      1.6047     -5.5826      5.8098\n     0   6800    -0.1113    -3.3884     4.7181    38.521968      1.5811     -5.5893      5.8098\n     0   7000    -0.1113    -3.3884     4.7181    38.761419      1.5577     -5.5959      5.8098\n     0   7200    -0.1113    -3.3884     4.7181    38.997541      1.5346     -5.6023      5.8098\n     0   7400    -0.1113    -3.3884     4.7181    39.230415      1.5118     -5.6085      5.8098\n     0   7600    -0.1113    -3.3884     4.7181    39.460116      1.4893     -5.6145      5.8098\n     0   7800    -0.1113    -3.3884     4.7181    39.686717      1.4671     -5.6203      5.8098\n     0   8000    -0.1113    -3.3884     4.7181    39.910291      1.4452     -5.6260      5.8098\n     0   8200    -0.1113    -3.3884     4.7181    40.130905      1.4235     -5.6315      5.8098\n     0   8400    -0.1113    -3.3884     4.7181    40.348626      1.4021     -5.6369      5.8098\n   500      0    -0.1113    -2.9174     4.8005    27.495868      2.9115     -4.8039      5.6186\n   500    200    -0.1113    -2.9174     4.8005    27.938119      2.8743     -4.8263      5.6186\n   500    400    -0.1113    -2.9174     4.8005    28.369736      2.8379     -4.8478      5.6186\n   500    600    -0.1113    -2.9174     4.8005    28.791241      2.8021     -4.8685      5.6186\n   500    800    -0.1113    -2.9174     4.8005    29.203113      2.7671     -4.8886      5.6186\n   500   1000    -0.1113    -2.9174     4.8005    29.605798      2.7326     -4.9079      5.6186\n   500   1200    -0.1113    -2.9174     4.8005    29.999704      2.6988     -4.9266      5.6186\n   500   1400    -0.1113    -2.9174     4.8005    30.385211      2.6656     -4.9446      5.6186\n   500   1600    -0.1113    -2.9174     4.8005    30.762675      2.6330     -4.9621      5.6186\n   500   1800    -0.1113    -2.9174     4.8005    31.132423      2.6009     -4.9790      5.6186\n   500   2000    -0.1113    -2.9174     4.8005    31.494764      2.5694     -4.9953      5.6186\n   500   2200    -0.1113    -2.9174     4.8005    31.849986      2.5383     -5.0112      5.6186\n   500   2400    -0.1113    -2.9174     4.8005    32.198358      2.5078     -5.0265      5.6186\n   500   2600    -0.1113    -2.9174     4.8005    32.540133      2.4778     -5.0414      5.6186\n   500   2800    -0.1113    -2.9174     4.8005    32.875549      2.4482     -5.0558      5.6186\n   500   3000    -0.1113    -2.9174     4.8005    33.204831      2.4191     -5.0698      5.6186\n   500   3200    -0.1113    -2.9174     4.8005    33.528190      2.3905     -5.0834      5.6186\n   500   3400    -0.1113    -2.9174     4.8005    33.845826      2.3623     -5.0965      5.6186\n   500   3600    -0.1113    -2.9174     4.8005    34.157928      2.3345     -5.1093      5.6186\n   500   3800    -0.1113    -2.9174     4.8005    34.464675      2.3071     -5.1217      5.6186\n   500   4000    -0.1113    -2.9174     4.8005    34.766235      2.2801     -5.1338      5.6186\n   500   4200    -0.1113    -2.9174     4.8005    35.062772      2.2535     -5.1456      5.6186\n   500   4400    -0.1113    -2.9174     4.8005    35.354436      2.2273     -5.1570      5.6186\n   500   4600    -0.1113    -2.9174     4.8005    35.641375      2.2014     -5.1681      5.6186\n   500   4800    -0.1113    -2.9174     4.8005    35.923727      2.1759     -5.1788      5.6186\n   500   5000    -0.1113    -2.9174     4.8005    36.201624      2.1508     -5.1893      5.6186\n   500   5200    -0.1113    -2.9174     4.8005    36.475192      2.1260     -5.1995      5.6186\n   500   5400    -0.1113    -2.9174     4.8005    36.744552      2.1015     -5.2095      5.6186\n   500   5600    -0.1113    -2.9174     4.8005    37.009819      2.0774     -5.2192      5.6186\n   500   5800    -0.1113    -2.9174     4.8005    37.271103      2.0535     -5.2286      5.6186\n   500   6000    -0.1113    -2.9174     4.8005    37.528509      2.0300     -5.2378      5.6186\n   500   6200    -0.1113    -2.9174     4.8005    37.782139      2.0068     -5.2467      5.6186\n   500   6400    -0.1113    -2.9174     4.8005    38.032090      1.9839     -5.2554      5.6186\n   500   6600    -0.1113    -2.9174     4.8005    38.278454      1.9613     -5.2639      5.6186\n   500   6800    -0.1113    -2.9174     4.8005    38.521321      1.9390     -5.2721      5.6186\n   500   7000    -0.1113    -2.9174     4.8005    38.760777      1.9169     -5.2802      5.6186\n   500   7200    -0.1113    -2.9174     4.8005    38.996905      1.8951     -5.2881      5.6186\n   500   7400    -0.1113    -2.9174     4.8005    39.229783      1.8736     -5.2957      5.6186\n   500   7600    -0.1113    -2.9174     4.8005    39.459488      1.8524     -5.3032      5.6186\n   500   7800    -0.1113    -2.9174     4.8005    39.686095      1.8314     -5.3105      5.6186\n   500   8000    -0.1113    -2.9174     4.8005    39.909673      1.8107     -5.3176      5.6186\n   500   8200    -0.1113    -2.9174     4.8005    40.130291      1.7902     -5.3245      5.6186\n   500   8400    -0.1113    -2.9174     4.8005    40.348016      1.7699     -5.3313      5.6186\n  1000      0    -0.1113    -2.4464     4.8830    27.494901      3.2021     -4.4241      5.4626\n  1000    200    -0.1113    -2.4464     4.8830    27.937168      3.1679     -4.4487      5.4626\n  1000    400    -0.1113    -2.4464     4.8830    28.368800      3.1343     -4.4725      5.4626\n  1000    600    -0.1113    -2.4464     4.8830    28.790320      3.1013     -4.4954      5.4626\n  1000    800    -0.1113    -2.4464     4.8830    29.202206      3.0689     -4.5176      5.4626\n  1000   1000    -0.1113    -2.4464     4.8830    29.604904      3.0371     -4.5391      5.4626\n  1000   1200    -0.1113    -2.4464     4.8830    29.998823      3.0058     -4.5598      5.4626\n  1000   1400    -0.1113    -2.4464     4.8830    30.384343      2.9750     -4.5800      5.4626\n  1000   1600    -0.1113    -2.4464     4.8830    30.761818      2.9448     -4.5995      5.4626\n  1000   1800    -0.1113    -2.4464     4.8830    31.131578      2.9151     -4.6184      5.4626\n  1000   2000    -0.1113    -2.4464     4.8830    31.493929      2.8858     -4.6367      5.4626\n  1000   2200    -0.1113    -2.4464     4.8830    31.849161      2.8570     -4.6545      5.4626\n  1000   2400    -0.1113    -2.4464     4.8830    32.197543      2.8286     -4.6718      5.4626\n  1000   2600    -0.1113    -2.4464     4.8830    32.539328      2.8007     -4.6886      5.4626\n  1000   2800    -0.1113    -2.4464     4.8830    32.874754      2.7732     -4.7049      5.4626\n  1000   3000    -0.1113    -2.4464     4.8830    33.204045      2.7461     -4.7208      5.4626\n  1000   3200    -0.1113    -2.4464     4.8830    33.527412      2.7194     -4.7362      5.4626\n  1000   3400    -0.1113    -2.4464     4.8830    33.845057      2.6931     -4.7512      5.4626\n  1000   3600    -0.1113    -2.4464     4.8830    34.157166      2.6672     -4.7658      5.4626\n  1000   3800    -0.1113    -2.4464     4.8830    34.463921      2.6417     -4.7800      5.4626\n  1000   4000    -0.1113    -2.4464     4.8830    34.765489      2.6165     -4.7939      5.4626\n  1000   4200    -0.1113    -2.4464     4.8830    35.062033      2.5916     -4.8073      5.4626\n  1000   4400    -0.1113    -2.4464     4.8830    35.353705      2.5671     -4.8205      5.4626\n  1000   4600    -0.1113    -2.4464     4.8830    35.640651      2.5429     -4.8333      5.4626\n  1000   4800    -0.1113    -2.4464     4.8830    35.923009      2.5191     -4.8457      5.4626\n  1000   5000    -0.1113    -2.4464     4.8830    36.200913      2.4956     -4.8579      5.4626\n  1000   5200    -0.1113    -2.4464     4.8830    36.474487      2.4723     -4.8698      5.4626\n  1000   5400    -0.1113    -2.4464     4.8830    36.743853      2.4494     -4.8813      5.4626\n  1000   5600    -0.1113    -2.4464     4.8830    37.009126      2.4268     -4.8926      5.4626\n  1000   5800    -0.1113    -2.4464     4.8830    37.270416      2.4044     -4.9036      5.4626\n  1000   6000    -0.1113    -2.4464     4.8830    37.527828      2.3824     -4.9144      5.4626\n  1000   6200    -0.1113    -2.4464     4.8830    37.781464      2.3606     -4.9249      5.4626\n  1000   6400    -0.1113    -2.4464     4.8830    38.031420      2.3391     -4.9352      5.4626\n  1000   6600    -0.1113    -2.4464     4.8830    38.277789      2.3179     -4.9452      5.4626\n  1000   6800    -0.1113    -2.4464     4.8830    38.520662      2.2969     -4.9550      5.4626\n  1000   7000    -0.1113    -2.4464     4.8830    38.760123      2.2761     -4.9645      5.4626\n  1000   7200    -0.1113    -2.4464     4.8830    38.996255      2.2557     -4.9738      5.4626\n  1000   7400    -0.1113    -2.4464     4.8830    39.229138      2.2354     -4.9830      5.4626\n  1000   7600    -0.1113    -2.4464     4.8830    39.458848      2.2154     -4.9919      5.4626\n  1000   7800    -0.1113    -2.4464     4.8830    39.685459      2.1957     -5.0006      5.4626\n  1000   8000    -0.1113    -2.4464     4.8830    39.909042      2.1761     -5.0092      5.4626\n  1000   8200    -0.1113    -2.4464     4.8830    40.129665      2.1568     -5.0175      5.4626\n  1000   8400    -0.1113    -2.4464     4.8830    40.347394      2.1377     -5.0257      5.4626\n  1500      0    -0.1113    -1.9754     4.9654    27.493913      3.4928     -4.0443      5.3451\n  1500    200    -0.1113    -1.9754     4.9654    27.936197      3.4615     -4.0712      5.3451\n  1500    400    -0.1113    -1.9754     4.9654    28.367845      3.4307     -4.0971      5.3451\n  1500    600    -0.1113    -1.9754     4.9654    28.789379      3.4005     -4.1223      5.3451\n  1500    800    -0.1113    -1.9754     4.9654    29.201280      3.3707     -4.1466      5.3451\n  1500   1000    -0.1113    -1.9754     4.9654    29.603992      3.3415     -4.1702      5.3451\n  1500   1200    -0.1113    -1.9754     4.9654    29.997923      3.3128     -4.1931      5.3451\n  1500   1400    -0.1113    -1.9754     4.9654    30.383456      3.2845     -4.2153      5.3451\n  1500   1600    -0.1113    -1.9754     4.9654    30.760943      3.2566     -4.2368      5.3451\n  1500   1800    -0.1113    -1.9754     4.9654    31.130714      3.2292     -4.2578      5.3451\n  1500   2000    -0.1113    -1.9754     4.9654    31.493077      3.2022     -4.2781      5.3451\n  1500   2200    -0.1113    -1.9754     4.9654    31.848320      3.1756     -4.2979      5.3451\n  1500   2400    -0.1113    -1.9754     4.9654    32.196712      3.1494     -4.3171      5.3451\n  1500   2600    -0.1113    -1.9754     4.9654    32.538506      3.1236     -4.3358      5.3451\n  1500   2800    -0.1113    -1.9754     4.9654    32.873942      3.0982     -4.3540      5.3451\n  1500   3000    -0.1113    -1.9754     4.9654    33.203242      3.0731     -4.3718      5.3451\n  1500   3200    -0.1113    -1.9754     4.9654    33.526618      3.0484     -4.3890      5.3451\n  1500   3400    -0.1113    -1.9754     4.9654    33.844271      3.0240     -4.4059      5.3451\n  1500   3600    -0.1113    -1.9754     4.9654    34.156389      3.0000     -4.4223      5.3451\n  1500   3800    -0.1113    -1.9754     4.9654    34.463152      2.9762     -4.4383      5.3451\n  1500   4000    -0.1113    -1.9754     4.9654    34.764728      2.9528     -4.4539      5.3451\n  1500   4200    -0.1113    -1.9754     4.9654    35.061279      2.9297     -4.4691      5.3451\n  1500   4400    -0.1113    -1.9754     4.9654    35.352959      2.9069     -4.4840      5.3451\n  1500   4600    -0.1113    -1.9754     4.9654    35.639912      2.8844     -4.4985      5.3451\n  1500   4800    -0.1113    -1.9754     4.9654    35.922277      2.8622     -4.5126      5.3451\n  1500   5000    -0.1113    -1.9754     4.9654    36.200187      2.8403     -4.5265      5.3451\n  1500   5200    -0.1113    -1.9754     4.9654    36.473768      2.8187     -4.5400      5.3451\n  1500   5400    -0.1113    -1.9754     4.9654    36.743141      2.7973     -4.5532      5.3451\n  1500   5600    -0.1113    -1.9754     4.9654    37.008420      2.7762     -4.5661      5.3451\n  1500   5800    -0.1113    -1.9754     4.9654    37.269715      2.7553     -4.5787      5.3451\n  1500   6000    -0.1113    -1.9754     4.9654    37.527133      2.7347     -4.5910      5.3451\n  1500   6200    -0.1113    -1.9754     4.9654    37.780775      2.7144     -4.6031      5.3451\n  1500   6400    -0.1113    -1.9754     4.9654    38.030736      2.6943     -4.6149      5.3451\n  1500   6600    -0.1113    -1.9754     4.9654    38.277111      2.6744     -4.6264      5.3451\n  1500   6800    -0.1113    -1.9754     4.9654    38.519989      2.6548     -4.6377      5.3451\n  1500   7000    -0.1113    -1.9754     4.9654    38.759456      2.6354     -4.6488      5.3451\n  1500   7200    -0.1113    -1.9754     4.9654    38.995593      2.6162     -4.6596      5.3451\n  1500   7400    -0.1113    -1.9754     4.9654    39.228481      2.5972     -4.6702      5.3451\n  1500   7600    -0.1113    -1.9754     4.9654    39.458196      2.5785     -4.6806      5.3451\n  1500   7800    -0.1113    -1.9754     4.9654    39.684812      2.5599     -4.6908      5.3451\n  1500   8000    -0.1113    -1.9754     4.9654    39.908399      2.5416     -4.7007      5.3451\n  1500   8200    -0.1113    -1.9754     4.9654    40.129027      2.5235     -4.7105      5.3451\n  1500   8400    -0.1113    -1.9754     4.9654    40.346760      2.5056     -4.7200      5.3451\n  2000      0    -0.1113    -1.5044     5.0478    27.492904      3.7834     -3.6645      5.2684\n  2000    200    -0.1113    -1.5044     5.0478    27.935205      3.7550     -3.6936      5.2684\n  2000    400    -0.1113    -1.5044     5.0478    28.366870      3.7271     -3.7218      5.2684\n  2000    600    -0.1113    -1.5044     5.0478    28.788420      3.6996     -3.7491      5.2684\n  2000    800    -0.1113    -1.5044     5.0478    29.200335      3.6726     -3.7756      5.2684\n  2000   1000    -0.1113    -1.5044     5.0478    29.603061      3.6459     -3.8013      5.2684\n  2000   1200    -0.1113    -1.5044     5.0478    29.997006      3.6197     -3.8263      5.2684\n  2000   1400    -0.1113    -1.5044     5.0478    30.382551      3.5939     -3.8506      5.2684\n  2000   1600    -0.1113    -1.5044     5.0478    30.760051      3.5684     -3.8742      5.2684\n  2000   1800    -0.1113    -1.5044     5.0478    31.129834      3.5433     -3.8971      5.2684\n  2000   2000    -0.1113    -1.5044     5.0478    31.492208      3.5186     -3.9195      5.2684\n  2000   2200    -0.1113    -1.5044     5.0478    31.847461      3.4942     -3.9412      5.2684\n  2000   2400    -0.1113    -1.5044     5.0478    32.195864      3.4702     -3.9624      5.2684\n  2000   2600    -0.1113    -1.5044     5.0478    32.537668      3.4465     -3.9830      5.2684\n  2000   2800    -0.1113    -1.5044     5.0478    32.873113      3.4231     -4.0031      5.2684\n  2000   3000    -0.1113    -1.5044     5.0478    33.202423      3.4001     -4.0227      5.2684\n  2000   3200    -0.1113    -1.5044     5.0478    33.525809      3.3773     -4.0419      5.2684\n  2000   3400    -0.1113    -1.5044     5.0478    33.843470      3.3548     -4.0605      5.2684\n  2000   3600    -0.1113    -1.5044     5.0478    34.155597      3.3327     -4.0788      5.2684\n  2000   3800    -0.1113    -1.5044     5.0478    34.462368      3.3108     -4.0965      5.2684\n  2000   4000    -0.1113    -1.5044     5.0478    34.763952      3.2892     -4.1139      5.2684\n  2000   4200    -0.1113    -1.5044     5.0478    35.060511      3.2678     -4.1309      5.2684\n  2000   4400    -0.1113    -1.5044     5.0478    35.352197      3.2468     -4.1475      5.2684\n  2000   4600    -0.1113    -1.5044     5.0478    35.639158      3.2260     -4.1637      5.2684\n  2000   4800    -0.1113    -1.5044     5.0478    35.921530      3.2054     -4.1795      5.2684\n  2000   5000    -0.1113    -1.5044     5.0478    36.199447      3.1851     -4.1950      5.2684\n  2000   5200    -0.1113    -1.5044     5.0478    36.473035      3.1650     -4.2102      5.2684\n  2000   5400    -0.1113    -1.5044     5.0478    36.742414      3.1452     -4.2250      5.2684\n  2000   5600    -0.1113    -1.5044     5.0478    37.007699      3.1256     -4.2395      5.2684\n  2000   5800    -0.1113    -1.5044     5.0478    37.269001      3.1062     -4.2538      5.2684\n  2000   6000    -0.1113    -1.5044     5.0478    37.526425      3.0871     -4.2677      5.2684\n  2000   6200    -0.1113    -1.5044     5.0478    37.780072      3.0682     -4.2813      5.2684\n  2000   6400    -0.1113    -1.5044     5.0478    38.030040      3.0494     -4.2946      5.2684\n  2000   6600    -0.1113    -1.5044     5.0478    38.276421      3.0309     -4.3077      5.2684\n  2000   6800    -0.1113    -1.5044     5.0478    38.519304      3.0127     -4.3205      5.2684\n  2000   7000    -0.1113    -1.5044     5.0478    38.758775      2.9946     -4.3331      5.2684\n  2000   7200    -0.1113    -1.5044     5.0478    38.994918      2.9767     -4.3454      5.2684\n  2000   7400    -0.1113    -1.5044     5.0478    39.227811      2.9590     -4.3575      5.2684\n  2000   7600    -0.1113    -1.5044     5.0478    39.457531      2.9415     -4.3693      5.2684\n  2000   7800    -0.1113    -1.5044     5.0478    39.684152      2.9242     -4.3809      5.2684\n  2000   8000    -0.1113    -1.5044     5.0478    39.907744      2.9071     -4.3923      5.2684\n  2000   8200    -0.1113    -1.5044     5.0478    40.128376      2.8901     -4.4034      5.2684\n  2000   8400    -0.1113    -1.5044     5.0478    40.346114      2.8734     -4.4144      5.2684\n  2500      0    -0.1113    -1.0333     5.1303    27.491875      4.0741     -3.2847      5.2345\n  2500    200    -0.1113    -1.0333     5.1303    27.934194      4.0486     -3.3160      5.2345\n  2500    400    -0.1113    -1.0333     5.1303    28.365875      4.0235     -3.3464      5.2345\n  2500    600    -0.1113    -1.0333     5.1303    28.787440      3.9987     -3.3759      5.2345\n  2500    800    -0.1113    -1.0333     5.1303    29.199371      3.9744     -3.4046      5.2345\n  2500   1000    -0.1113    -1.0333     5.1303    29.602111      3.9503     -3.4325      5.2345\n  2500   1200    -0.1113    -1.0333     5.1303    29.996070      3.9266     -3.4595      5.2345\n  2500   1400    -0.1113    -1.0333     5.1303    30.381628      3.9033     -3.4859      5.2345\n  2500   1600    -0.1113    -1.0333     5.1303    30.759140      3.8802     -3.5115      5.2345\n  2500   1800    -0.1113    -1.0333     5.1303    31.128935      3.8575     -3.5365      5.2345\n  2500   2000    -0.1113    -1.0333     5.1303    31.491321      3.8350     -3.5608      5.2345\n  2500   2200    -0.1113    -1.0333     5.1303    31.846586      3.8129     -3.5846      5.2345\n  2500   2400    -0.1113    -1.0333     5.1303    32.194999      3.7910     -3.6077      5.2345\n  2500   2600    -0.1113    -1.0333     5.1303    32.536814      3.7694     -3.6302      5.2345\n  2500   2800    -0.1113    -1.0333     5.1303    32.872269      3.7481     -3.6522      5.2345\n  2500   3000    -0.1113    -1.0333     5.1303    33.201588      3.7270     -3.6737      5.2345\n  2500   3200    -0.1113    -1.0333     5.1303    33.524983      3.7062     -3.6947      5.2345\n  2500   3400    -0.1113    -1.0333     5.1303    33.842654      3.6857     -3.7152      5.2345\n  2500   3600    -0.1113    -1.0333     5.1303    34.154789      3.6654     -3.7352      5.2345\n  2500   3800    -0.1113    -1.0333     5.1303    34.461568      3.6453     -3.7548      5.2345\n  2500   4000    -0.1113    -1.0333     5.1303    34.763160      3.6255     -3.7739      5.2345\n  2500   4200    -0.1113    -1.0333     5.1303    35.059727      3.6059     -3.7926      5.2345\n  2500   4400    -0.1113    -1.0333     5.1303    35.351422      3.5866     -3.8110      5.2345\n  2500   4600    -0.1113    -1.0333     5.1303    35.638389      3.5675     -3.8289      5.2345\n  2500   4800    -0.1113    -1.0333     5.1303    35.920769      3.5485     -3.8464      5.2345\n  2500   5000    -0.1113    -1.0333     5.1303    36.198693      3.5298     -3.8636      5.2345\n  2500   5200    -0.1113    -1.0333     5.1303    36.472288      3.5113     -3.8804      5.2345\n  2500   5400    -0.1113    -1.0333     5.1303    36.741674      3.4931     -3.8969      5.2345\n  2500   5600    -0.1113    -1.0333     5.1303    37.006965      3.4750     -3.9130      5.2345\n  2500   5800    -0.1113    -1.0333     5.1303    37.268273      3.4571     -3.9288      5.2345\n  2500   6000    -0.1113    -1.0333     5.1303    37.525704      3.4394     -3.9443      5.2345\n  2500   6200    -0.1113    -1.0333     5.1303    37.779357      3.4219     -3.9595      5.2345\n  2500   6400    -0.1113    -1.0333     5.1303    38.029330      3.4046     -3.9744      5.2345\n  2500   6600    -0.1113    -1.0333     5.1303    38.275717      3.3875     -3.9890      5.2345\n  2500   6800    -0.1113    -1.0333     5.1303    38.518605      3.3705     -4.0033      5.2345\n  2500   7000    -0.1113    -1.0333     5.1303    38.758083      3.3538     -4.0174      5.2345\n  2500   7200    -0.1113    -1.0333     5.1303    38.994231      3.3372     -4.0312      5.2345\n  2500   7400    -0.1113    -1.0333     5.1303    39.227129      3.3208     -4.0447      5.2345\n  2500   7600    -0.1113    -1.0333     5.1303    39.456854      3.3045     -4.0580      5.2345\n  2500   7800    -0.1113    -1.0333     5.1303    39.683480      3.2885     -4.0710      5.2345\n  2500   8000    -0.1113    -1.0333     5.1303    39.907076      3.2725     -4.0838      5.2345\n  2500   8200    -0.1113    -1.0333     5.1303    40.127713      3.2568     -4.0964      5.2345\n  2500   8400    -0.1113    -1.0333     5.1303    40.345456      3.2412     -4.1087      5.2345\n  3000      0    -0.1113    -0.5623     5.2127    27.490826      4.3647     -2.9048      5.2441\n  3000    200    -0.1113    -0.5623     5.2127    27.933162      4.3421     -2.9384      5.2441\n  3000    400    -0.1113    -0.5623     5.2127    28.364860      4.3198     -2.9711      5.2441\n  3000    600    -0.1113    -0.5623     5.2127    28.786442      4.2979     -3.0028      5.2441\n  3000    800    -0.1113    -0.5623     5.2127    29.198388      4.2762     -3.0336      5.2441\n  3000   1000    -0.1113    -0.5623     5.2127    29.601143      4.2547     -3.0636      5.2441\n  3000   1200    -0.1113    -0.5623     5.2127    29.995115      4.2336     -3.0928      5.2441\n  3000   1400    -0.1113    -0.5623     5.2127    30.380687      4.2126     -3.1212      5.2441\n  3000   1600    -0.1113    -0.5623     5.2127    30.758212      4.1920     -3.1489      5.2441\n  3000   1800    -0.1113    -0.5623     5.2127    31.128020      4.1716     -3.1759      5.2441\n  3000   2000    -0.1113    -0.5623     5.2127    31.490417      4.1514     -3.2022      5.2441\n  3000   2200    -0.1113    -0.5623     5.2127    31.845693      4.1315     -3.2279      5.2441\n  3000   2400    -0.1113    -0.5623     5.2127    32.194117      4.1118     -3.2529      5.2441\n  3000   2600    -0.1113    -0.5623     5.2127    32.535943      4.0923     -3.2774      5.2441\n  3000   2800    -0.1113    -0.5623     5.2127    32.871408      4.0730     -3.3013      5.2441\n  3000   3000    -0.1113    -0.5623     5.2127    33.200737      4.0540     -3.3247      5.2441\n  3000   3200    -0.1113    -0.5623     5.2127    33.524142      4.0351     -3.3475      5.2441\n  3000   3400    -0.1113    -0.5623     5.2127    33.841821      4.0165     -3.3698      5.2441\n  3000   3600    -0.1113    -0.5623     5.2127    34.153966      3.9981     -3.3917      5.2441\n  3000   3800    -0.1113    -0.5623     5.2127    34.460753      3.9799     -3.4130      5.2441\n  3000   4000    -0.1113    -0.5623     5.2127    34.762354      3.9619     -3.4339      5.2441\n  3000   4200    -0.1113    -0.5623     5.2127    35.058929      3.9440     -3.4544      5.2441\n  3000   4400    -0.1113    -0.5623     5.2127    35.350631      3.9264     -3.4744      5.2441\n  3000   4600    -0.1113    -0.5623     5.2127    35.637607      3.9089     -3.4941      5.2441\n  3000   4800    -0.1113    -0.5623     5.2127    35.919994      3.8917     -3.5133      5.2441\n  3000   5000    -0.1113    -0.5623     5.2127    36.197925      3.8746     -3.5321      5.2441\n  3000   5200    -0.1113    -0.5623     5.2127    36.471527      3.8577     -3.5506      5.2441\n  3000   5400    -0.1113    -0.5623     5.2127    36.740919      3.8409     -3.5687      5.2441\n  3000   5600    -0.1113    -0.5623     5.2127    37.006218      3.8244     -3.5864      5.2441\n  3000   5800    -0.1113    -0.5623     5.2127    37.267532      3.8080     -3.6038      5.2441\n  3000   6000    -0.1113    -0.5623     5.2127    37.524969      3.7917     -3.6209      5.2441\n  3000   6200    -0.1113    -0.5623     5.2127    37.778628      3.7757     -3.6377      5.2441\n  3000   6400    -0.1113    -0.5623     5.2127    38.028607      3.7598     -3.6541      5.2441\n  3000   6600    -0.1113    -0.5623     5.2127    38.275000      3.7440     -3.6702      5.2441\n  3000   6800    -0.1113    -0.5623     5.2127    38.517894      3.7284     -3.6861      5.2441\n  3000   7000    -0.1113    -0.5623     5.2127    38.757377      3.7130     -3.7016      5.2441\n  3000   7200    -0.1113    -0.5623     5.2127    38.993530      3.6977     -3.7169      5.2441\n  3000   7400    -0.1113    -0.5623     5.2127    39.226434      3.6825     -3.7319      5.2441\n  3000   7600    -0.1113    -0.5623     5.2127    39.456164      3.6676     -3.7466      5.2441\n  3000   7800    -0.1113    -0.5623     5.2127    39.682795      3.6527     -3.7611      5.2441\n  3000   8000    -0.1113    -0.5623     5.2127    39.906397      3.6380     -3.7753      5.2441\n  3000   8200    -0.1113    -0.5623     5.2127    40.127039      3.6234     -3.7893      5.2441\n  3000   8400    -0.1113    -0.5623     5.2127    40.344786      3.6090     -3.8031      5.2441\n  3500      0    -0.1113    -0.0913     5.2951    27.489756      4.6553     -2.5249      5.2971\n  3500    200    -0.1113    -0.0913     5.2951    27.932111      4.6356     -2.5608      5.2971\n  3500    400    -0.1113    -0.0913     5.2951    28.363826      4.6162     -2.5957      5.2971\n  3500    600    -0.1113    -0.0913     5.2951    28.785424      4.5970     -2.6296      5.2971\n  3500    800    -0.1113    -0.0913     5.2951    29.197386      4.5779     -2.6626      5.2971\n  3500   1000    -0.1113    -0.0913     5.2951    29.600156      4.5591     -2.6947      5.2971\n  3500   1200    -0.1113    -0.0913     5.2951    29.994143      4.5405     -2.7260      5.2971\n  3500   1400    -0.1113    -0.0913     5.2951    30.379728      4.5220     -2.7565      5.2971\n  3500   1600    -0.1113    -0.0913     5.2951    30.757266      4.5038     -2.7862      5.2971\n  3500   1800    -0.1113    -0.0913     5.2951    31.127086      4.4857     -2.8152      5.2971\n  3500   2000    -0.1113    -0.0913     5.2951    31.489496      4.4678     -2.8435      5.2971\n  3500   2200    -0.1113    -0.0913     5.2951    31.844784      4.4501     -2.8712      5.2971\n  3500   2400    -0.1113    -0.0913     5.2951    32.193219      4.4325     -2.8982      5.2971\n  3500   2600    -0.1113    -0.0913     5.2951    32.535056      4.4151     -2.9246      5.2971\n  3500   2800    -0.1113    -0.0913     5.2951    32.870531      4.3979     -2.9504      5.2971\n  3500   3000    -0.1113    -0.0913     5.2951    33.199870      4.3809     -2.9756      5.2971\n  3500   3200    -0.1113    -0.0913     5.2951    33.523284      4.3640     -3.0003      5.2971\n  3500   3400    -0.1113    -0.0913     5.2951    33.840974      4.3473     -3.0245      5.2971\n  3500   3600    -0.1113    -0.0913     5.2951    34.153127      4.3308     -3.0481      5.2971\n  3500   3800    -0.1113    -0.0913     5.2951    34.459923      4.3144     -3.0713      5.2971\n  3500   4000    -0.1113    -0.0913     5.2951    34.761533      4.2982     -3.0939      5.2971\n  3500   4200    -0.1113    -0.0913     5.2951    35.058116      4.2821     -3.1161      5.2971\n  3500   4400    -0.1113    -0.0913     5.2951    35.349826      4.2662     -3.1379      5.2971\n  3500   4600    -0.1113    -0.0913     5.2951    35.636809      4.2504     -3.1592      5.2971\n  3500   4800    -0.1113    -0.0913     5.2951    35.919204      4.2348     -3.1801      5.2971\n  3500   5000    -0.1113    -0.0913     5.2951    36.197143      4.2193     -3.2006      5.2971\n  3500   5200    -0.1113    -0.0913     5.2951    36.470752      4.2040     -3.2208      5.2971\n  3500   5400    -0.1113    -0.0913     5.2951    36.740151      4.1888     -3.2405      5.2971\n  3500   5600    -0.1113    -0.0913     5.2951    37.005456      4.1737     -3.2599      5.2971\n  3500   5800    -0.1113    -0.0913     5.2951    37.266777      4.1588     -3.2789      5.2971\n  3500   6000    -0.1113    -0.0913     5.2951    37.524220      4.1441     -3.2975      5.2971\n  3500   6200    -0.1113    -0.0913     5.2951    37.777886      4.1294     -3.3158      5.2971\n  3500   6400    -0.1113    -0.0913     5.2951    38.027871      4.1149     -3.3338      5.2971\n  3500   6600    -0.1113    -0.0913     5.2951    38.274270      4.1005     -3.3515      5.2971\n  3500   6800    -0.1113    -0.0913     5.2951    38.517170      4.0863     -3.3688      5.2971\n  3500   7000    -0.1113    -0.0913     5.2951    38.756658      4.0722     -3.3859      5.2971\n  3500   7200    -0.1113    -0.0913     5.2951    38.992817      4.0582     -3.4026      5.2971\n  3500   7400    -0.1113    -0.0913     5.2951    39.225727      4.0443     -3.4191      5.2971\n  3500   7600    -0.1113    -0.0913     5.2951    39.455462      4.0306     -3.4353      5.2971\n  3500   7800    -0.1113    -0.0913     5.2951    39.682098      4.0169     -3.4512      5.2971\n  3500   8000    -0.1113    -0.0913     5.2951    39.905705      4.0034     -3.4669      5.2971\n  3500   8200    -0.1113    -0.0913     5.2951    40.126352      3.9901     -3.4823      5.2971\n  3500   8400    -0.1113    -0.0913     5.2951    40.344104      3.9768     -3.4974      5.2971\n  4000      0    -0.1113     0.3797     5.3776    27.488666      4.9458     -2.1451      5.3921\n  4000    200    -0.1113     0.3797     5.3776    27.931039      4.9291     -2.1832      5.3921\n  4000    400    -0.1113     0.3797     5.3776    28.362772      4.9125     -2.2203      5.3921\n  4000    600    -0.1113     0.3797     5.3776    28.784387      4.8961     -2.2564      5.3921\n  4000    800    -0.1113     0.3797     5.3776    29.196366      4.8797     -2.2915      5.3921\n  4000   1000    -0.1113     0.3797     5.3776    29.599150      4.8635     -2.3258      5.3921\n  4000   1200    -0.1113     0.3797     5.3776    29.993152      4.8474     -2.3592      5.3921\n  4000   1400    -0.1113     0.3797     5.3776    30.378752      4.8314     -2.3917      5.3921\n  4000   1600    -0.1113     0.3797     5.3776    30.756303      4.8155     -2.4235      5.3921\n  4000   1800    -0.1113     0.3797     5.3776    31.126136      4.7998     -2.4545      5.3921\n  4000   2000    -0.1113     0.3797     5.3776    31.488558      4.7841     -2.4849      5.3921\n  4000   2200    -0.1113     0.3797     5.3776    31.843858      4.7686     -2.5145      5.3921\n  4000   2400    -0.1113     0.3797     5.3776    32.192305      4.7533     -2.5434      5.3921\n  4000   2600    -0.1113     0.3797     5.3776    32.534152      4.7380     -2.5718      5.3921\n  4000   2800    -0.1113     0.3797     5.3776    32.869638      4.7229     -2.5995      5.3921\n  4000   3000    -0.1113     0.3797     5.3776    33.198987      4.7078     -2.6266      5.3921\n  4000   3200    -0.1113     0.3797     5.3776    33.522412      4.6929     -2.6531      5.3921\n  4000   3400    -0.1113     0.3797     5.3776    33.840110      4.6782     -2.6791      5.3921\n  4000   3600    -0.1113     0.3797     5.3776    34.152273      4.6635     -2.7045      5.3921\n  4000   3800    -0.1113     0.3797     5.3776    34.459078      4.6489     -2.7295      5.3921\n  4000   4000    -0.1113     0.3797     5.3776    34.760696      4.6345     -2.7539      5.3921\n  4000   4200    -0.1113     0.3797     5.3776    35.057288      4.6202     -2.7779      5.3921\n  4000   4400    -0.1113     0.3797     5.3776    35.349007      4.6060     -2.8013      5.3921\n  4000   4600    -0.1113     0.3797     5.3776    35.635998      4.5919     -2.8244      5.3921\n  4000   4800    -0.1113     0.3797     5.3776    35.918400      4.5779     -2.8470      5.3921\n  4000   5000    -0.1113     0.3797     5.3776    36.196346      4.5640     -2.8692      5.3921\n  4000   5200    -0.1113     0.3797     5.3776    36.469963      4.5503     -2.8909      5.3921\n  4000   5400    -0.1113     0.3797     5.3776    36.739369      4.5366     -2.9123      5.3921\n  4000   5600    -0.1113     0.3797     5.3776    37.004681      4.5231     -2.9333      5.3921\n  4000   5800    -0.1113     0.3797     5.3776    37.266009      4.5097     -2.9539      5.3921\n  4000   6000    -0.1113     0.3797     5.3776    37.523459      4.4964     -2.9741      5.3921\n  4000   6200    -0.1113     0.3797     5.3776    37.777131      4.4832     -2.9940      5.3921\n  4000   6400    -0.1113     0.3797     5.3776    38.027122      4.4700     -3.0135      5.3921\n  4000   6600    -0.1113     0.3797     5.3776    38.273527      4.4570     -3.0327      5.3921\n  4000   6800    -0.1113     0.3797     5.3776    38.516433      4.4441     -3.0516      5.3921\n  4000   7000    -0.1113     0.3797     5.3776    38.755927      4.4314     -3.0701      5.3921\n  4000   7200    -0.1113     0.3797     5.3776    38.992092      4.4187     -3.0884      5.3921\n  4000   7400    -0.1113     0.3797     5.3776    39.225007      4.4061     -3.1063      5.3921\n  4000   7600    -0.1113     0.3797     5.3776    39.454748      4.3936     -3.1240      5.3921\n  4000   7800    -0.1113     0.3797     5.3776    39.681389      4.3812     -3.1413      5.3921\n  4000   8000    -0.1113     0.3797     5.3776    39.905001      4.3689     -3.1584      5.3921\n  4000   8200    -0.1113     0.3797     5.3776    40.125653      4.3567     -3.1752      5.3921\n  4000   8400    -0.1113     0.3797     5.3776    40.343411      4.3446     -3.1917      5.3921\n  4500      0    -0.1113     0.8507     5.4600    27.487555      5.2364     -1.7652      5.5270\n  4500    200    -0.1113     0.8507     5.4600    27.929948      5.2226     -1.8056      5.5270\n  4500    400    -0.1113     0.8507     5.4600    28.361699      5.2089     -1.8449      5.5270\n  4500    600    -0.1113     0.8507     5.4600    28.783331      5.1951     -1.8831      5.5270\n  4500    800    -0.1113     0.8507     5.4600    29.195326      5.1815     -1.9205      5.5270\n  4500   1000    -0.1113     0.8507     5.4600    29.598126      5.1678     -1.9568      5.5270\n  4500   1200    -0.1113     0.8507     5.4600    29.992143      5.1543     -1.9923      5.5270\n  4500   1400    -0.1113     0.8507     5.4600    30.377757      5.1407     -2.0270      5.5270\n  4500   1600    -0.1113     0.8507     5.4600    30.755322      5.1273     -2.0608      5.5270\n  4500   1800    -0.1113     0.8507     5.4600    31.125168      5.1139     -2.0939      5.5270\n  4500   2000    -0.1113     0.8507     5.4600    31.487603      5.1005     -2.1262      5.5270\n  4500   2200    -0.1113     0.8507     5.4600    31.842915      5.0872     -2.1578      5.5270\n  4500   2400    -0.1113     0.8507     5.4600    32.191373      5.0740     -2.1887      5.5270\n  4500   2600    -0.1113     0.8507     5.4600    32.533232      5.0609     -2.2189      5.5270\n  4500   2800    -0.1113     0.8507     5.4600    32.868729      5.0478     -2.2485      5.5270\n  4500   3000    -0.1113     0.8507     5.4600    33.198089      5.0348     -2.2775      5.5270\n  4500   3200    -0.1113     0.8507     5.4600    33.521523      5.0218     -2.3059      5.5270\n  4500   3400    -0.1113     0.8507     5.4600    33.839232      5.0090     -2.3337      5.5270\n  4500   3600    -0.1113     0.8507     5.4600    34.151404      4.9962     -2.3609      5.5270\n  4500   3800    -0.1113     0.8507     5.4600    34.458218      4.9835     -2.3877      5.5270\n  4500   4000    -0.1113     0.8507     5.4600    34.759845      4.9708     -2.4139      5.5270\n  4500   4200    -0.1113     0.8507     5.4600    35.056446      4.9582     -2.4396      5.5270\n  4500   4400    -0.1113     0.8507     5.4600    35.348172      4.9458     -2.4648      5.5270\n  4500   4600    -0.1113     0.8507     5.4600    35.635172      4.9334     -2.4895      5.5270\n  4500   4800    -0.1113     0.8507     5.4600    35.917582      4.9210     -2.5138      5.5270\n  4500   5000    -0.1113     0.8507     5.4600    36.195536      4.9088     -2.5377      5.5270\n  4500   5200    -0.1113     0.8507     5.4600    36.469160      4.8966     -2.5611      5.5270\n  4500   5400    -0.1113     0.8507     5.4600    36.738573      4.8845     -2.5841      5.5270\n  4500   5600    -0.1113     0.8507     5.4600    37.003892      4.8725     -2.6067      5.5270\n  4500   5800    -0.1113     0.8507     5.4600    37.265227      4.8605     -2.6289      5.5270\n  4500   6000    -0.1113     0.8507     5.4600    37.522684      4.8487     -2.6507      5.5270\n  4500   6200    -0.1113     0.8507     5.4600    37.776362      4.8369     -2.6721      5.5270\n  4500   6400    -0.1113     0.8507     5.4600    38.026360      4.8252     -2.6932      5.5270\n  4500   6600    -0.1113     0.8507     5.4600    38.272771      4.8136     -2.7139      5.5270\n  4500   6800    -0.1113     0.8507     5.4600    38.515683      4.8020     -2.7343      5.5270\n  4500   7000    -0.1113     0.8507     5.4600    38.755184      4.7905     -2.7544      5.5270\n  4500   7200    -0.1113     0.8507     5.4600    38.991354      4.7791     -2.7741      5.5270\n  4500   7400    -0.1113     0.8507     5.4600    39.224274      4.7678     -2.7935      5.5270\n  4500   7600    -0.1113     0.8507     5.4600    39.454021      4.7566     -2.8126      5.5270\n  4500   7800    -0.1113     0.8507     5.4600    39.680668      4.7454     -2.8314      5.5270\n  4500   8000    -0.1113     0.8507     5.4600    39.904285      4.7343     -2.8499      5.5270\n  4500   8200    -0.1113     0.8507     5.4600    40.124942      4.7233     -2.8681      5.5270\n  4500   8400    -0.1113     0.8507     5.4600    40.342705      4.7124     -2.8860      5.5270\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180506_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.0000004       -2.5497431       28.2483011   m   m   m\ninitial_baseline_rate:          0.0000000        0.1254563        0.0490788   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       -1.9353785       27.9022509   m   m   m\nprecision_baseline_rate:        0.0000000        0.1116583        0.0582004   m/s m/s m/s\nunwrap_phase_constant:            0.00038     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180506_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.000     -2.550     28.248\norbit baseline rate   (TCN) (m/s):  0.000e+00  1.255e-01  4.908e-02\n\nbaseline vector (TCN)   (m):      0.000     -1.935     27.902\nbaseline rate   (TCN) (m/s):  0.000e+00  1.117e-01  5.820e-02\n\nSLC-1 center baseline length (m):    27.9693\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    -2.9776    27.3590    27.496815     22.8937    -15.2729     27.5206\n     0    200     0.0000    -2.9776    27.3590    27.939050     22.7751    -15.4491     27.5206\n     0    400     0.0000    -2.9776    27.3590    28.370652     22.6581    -15.6203     27.5206\n     0    600     0.0000    -2.9776    27.3590    28.792142     22.5426    -15.7865     27.5206\n     0    800     0.0000    -2.9776    27.3590    29.204001     22.4285    -15.9481     27.5206\n     0   1000     0.0000    -2.9776    27.3590    29.606672     22.3159    -16.1054     27.5206\n     0   1200     0.0000    -2.9776    27.3590    30.000566     22.2046    -16.2584     27.5206\n     0   1400     0.0000    -2.9776    27.3590    30.386062     22.0947    -16.4074     27.5206\n     0   1600     0.0000    -2.9776    27.3590    30.763514     21.9862    -16.5526     27.5206\n     0   1800     0.0000    -2.9776    27.3590    31.133251     21.8789    -16.6942     27.5206\n     0   2000     0.0000    -2.9776    27.3590    31.495582     21.7729    -16.8322     27.5206\n     0   2200     0.0000    -2.9776    27.3590    31.850793     21.6681    -16.9669     27.5206\n     0   2400     0.0000    -2.9776    27.3590    32.199155     21.5645    -17.0983     27.5206\n     0   2600     0.0000    -2.9776    27.3590    32.540921     21.4622    -17.2266     27.5206\n     0   2800     0.0000    -2.9776    27.3590    32.876328     21.3610    -17.3520     27.5206\n     0   3000     0.0000    -2.9776    27.3590    33.205602     21.2609    -17.4744     27.5206\n     0   3200     0.0000    -2.9776    27.3590    33.528952     21.1619    -17.5941     27.5206\n     0   3400     0.0000    -2.9776    27.3590    33.846580     21.0641    -17.7112     27.5206\n     0   3600     0.0000    -2.9776    27.3590    34.158674     20.9673    -17.8257     27.5206\n     0   3800     0.0000    -2.9776    27.3590    34.465413     20.8715    -17.9376     27.5206\n     0   4000     0.0000    -2.9776    27.3590    34.766966     20.7769    -18.0472     27.5206\n     0   4200     0.0000    -2.9776    27.3590    35.063495     20.6832    -18.1545     27.5206\n     0   4400     0.0000    -2.9776    27.3590    35.355153     20.5905    -18.2596     27.5206\n     0   4600     0.0000    -2.9776    27.3590    35.642085     20.4988    -18.3625     27.5206\n     0   4800     0.0000    -2.9776    27.3590    35.924431     20.4081    -18.4633     27.5206\n     0   5000     0.0000    -2.9776    27.3590    36.202321     20.3183    -18.5620     27.5206\n     0   5200     0.0000    -2.9776    27.3590    36.475883     20.2294    -18.6588     27.5206\n     0   5400     0.0000    -2.9776    27.3590    36.745237     20.1415    -18.7537     27.5206\n     0   5600     0.0000    -2.9776    27.3590    37.010498     20.0544    -18.8468     27.5206\n     0   5800     0.0000    -2.9776    27.3590    37.271776     19.9683    -18.9380     27.5206\n     0   6000     0.0000    -2.9776    27.3590    37.529176     19.8830    -19.0275     27.5206\n     0   6200     0.0000    -2.9776    27.3590    37.782801     19.7986    -19.1154     27.5206\n     0   6400     0.0000    -2.9776    27.3590    38.032746     19.7150    -19.2015     27.5206\n     0   6600     0.0000    -2.9776    27.3590    38.279105     19.6323    -19.2861     27.5206\n     0   6800     0.0000    -2.9776    27.3590    38.521968     19.5503    -19.3692     27.5206\n     0   7000     0.0000    -2.9776    27.3590    38.761419     19.4692    -19.4507     27.5206\n     0   7200     0.0000    -2.9776    27.3590    38.997541     19.3889    -19.5308     27.5206\n     0   7400     0.0000    -2.9776    27.3590    39.230415     19.3093    -19.6094     27.5206\n     0   7600     0.0000    -2.9776    27.3590    39.460116     19.2306    -19.6867     27.5206\n     0   7800     0.0000    -2.9776    27.3590    39.686717     19.1526    -19.7626     27.5206\n     0   8000     0.0000    -2.9776    27.3590    39.910291     19.0753    -19.8372     27.5206\n     0   8200     0.0000    -2.9776    27.3590    40.130905     18.9988    -19.9105     27.5206\n     0   8400     0.0000    -2.9776    27.3590    40.348626     18.9230    -19.9825     27.5206\n   500      0     0.0000    -2.7481    27.4786    27.495868     23.1060    -15.1241     27.6157\n   500    200     0.0000    -2.7481    27.4786    27.938119     22.9886    -15.3020     27.6157\n   500    400     0.0000    -2.7481    27.4786    28.369736     22.8726    -15.4748     27.6157\n   500    600     0.0000    -2.7481    27.4786    28.791241     22.7582    -15.6426     27.6157\n   500    800     0.0000    -2.7481    27.4786    29.203113     22.6452    -15.8058     27.6157\n   500   1000     0.0000    -2.7481    27.4786    29.605798     22.5335    -15.9646     27.6157\n   500   1200     0.0000    -2.7481    27.4786    29.999704     22.4232    -16.1191     27.6157\n   500   1400     0.0000    -2.7481    27.4786    30.385211     22.3143    -16.2696     27.6157\n   500   1600     0.0000    -2.7481    27.4786    30.762675     22.2066    -16.4163     27.6157\n   500   1800     0.0000    -2.7481    27.4786    31.132423     22.1002    -16.5592     27.6157\n   500   2000     0.0000    -2.7481    27.4786    31.494764     21.9950    -16.6987     27.6157\n   500   2200     0.0000    -2.7481    27.4786    31.849986     21.8911    -16.8347     27.6157\n   500   2400     0.0000    -2.7481    27.4786    32.198358     21.7883    -16.9675     27.6157\n   500   2600     0.0000    -2.7481    27.4786    32.540133     21.6867    -17.0972     27.6157\n   500   2800     0.0000    -2.7481    27.4786    32.875549     21.5863    -17.2238     27.6157\n   500   3000     0.0000    -2.7481    27.4786    33.204831     21.4869    -17.3476     27.6157\n   500   3200     0.0000    -2.7481    27.4786    33.528190     21.3887    -17.4686     27.6157\n   500   3400     0.0000    -2.7481    27.4786    33.845826     21.2915    -17.5869     27.6157\n   500   3600     0.0000    -2.7481    27.4786    34.157928     21.1954    -17.7026     27.6157\n   500   3800     0.0000    -2.7481    27.4786    34.464675     21.1003    -17.8158     27.6157\n   500   4000     0.0000    -2.7481    27.4786    34.766235     21.0062    -17.9267     27.6157\n   500   4200     0.0000    -2.7481    27.4786    35.062772     20.9132    -18.0351     27.6157\n   500   4400     0.0000    -2.7481    27.4786    35.354436     20.8211    -18.1414     27.6157\n   500   4600     0.0000    -2.7481    27.4786    35.641375     20.7300    -18.2454     27.6157\n   500   4800     0.0000    -2.7481    27.4786    35.923727     20.6398    -18.3473     27.6157\n   500   5000     0.0000    -2.7481    27.4786    36.201624     20.5506    -18.4472     27.6157\n   500   5200     0.0000    -2.7481    27.4786    36.475192     20.4623    -18.5451     27.6157\n   500   5400     0.0000    -2.7481    27.4786    36.744552     20.3749    -18.6411     27.6157\n   500   5600     0.0000    -2.7481    27.4786    37.009819     20.2883    -18.7353     27.6157\n   500   5800     0.0000    -2.7481    27.4786    37.271103     20.2027    -18.8276     27.6157\n   500   6000     0.0000    -2.7481    27.4786    37.528509     20.1179    -18.9182     27.6157\n   500   6200     0.0000    -2.7481    27.4786    37.782139     20.0340    -19.0070     27.6157\n   500   6400     0.0000    -2.7481    27.4786    38.032090     19.9509    -19.0942     27.6157\n   500   6600     0.0000    -2.7481    27.4786    38.278454     19.8686    -19.1799     27.6157\n   500   6800     0.0000    -2.7481    27.4786    38.521321     19.7871    -19.2639     27.6157\n   500   7000     0.0000    -2.7481    27.4786    38.760777     19.7064    -19.3464     27.6157\n   500   7200     0.0000    -2.7481    27.4786    38.996905     19.6265    -19.4275     27.6157\n   500   7400     0.0000    -2.7481    27.4786    39.229783     19.5474    -19.5071     27.6157\n   500   7600     0.0000    -2.7481    27.4786    39.459488     19.4690    -19.5853     27.6157\n   500   7800     0.0000    -2.7481    27.4786    39.686095     19.3914    -19.6621     27.6157\n   500   8000     0.0000    -2.7481    27.4786    39.909673     19.3145    -19.7377     27.6157\n   500   8200     0.0000    -2.7481    27.4786    40.130291     19.2384    -19.8119     27.6157\n   500   8400     0.0000    -2.7481    27.4786    40.348016     19.1630    -19.8849     27.6157\n  1000      0     0.0000    -2.5186    27.5983    27.494901     23.3183    -14.9754     27.7129\n  1000    200     0.0000    -2.5186    27.5983    27.937168     23.2020    -15.1549     27.7129\n  1000    400     0.0000    -2.5186    27.5983    28.368800     23.0872    -15.3293     27.7129\n  1000    600     0.0000    -2.5186    27.5983    28.790320     22.9738    -15.4987     27.7129\n  1000    800     0.0000    -2.5186    27.5983    29.202206     22.8618    -15.6635     27.7129\n  1000   1000     0.0000    -2.5186    27.5983    29.604904     22.7512    -15.8238     27.7129\n  1000   1200     0.0000    -2.5186    27.5983    29.998823     22.6418    -15.9798     27.7129\n  1000   1400     0.0000    -2.5186    27.5983    30.384343     22.5338    -16.1318     27.7129\n  1000   1600     0.0000    -2.5186    27.5983    30.761818     22.4270    -16.2799     27.7129\n  1000   1800     0.0000    -2.5186    27.5983    31.131578     22.3215    -16.4243     27.7129\n  1000   2000     0.0000    -2.5186    27.5983    31.493929     22.2172    -16.5651     27.7129\n  1000   2200     0.0000    -2.5186    27.5983    31.849161     22.1141    -16.7026     27.7129\n  1000   2400     0.0000    -2.5186    27.5983    32.197543     22.0121    -16.8367     27.7129\n  1000   2600     0.0000    -2.5186    27.5983    32.539328     21.9113    -16.9677     27.7129\n  1000   2800     0.0000    -2.5186    27.5983    32.874754     21.8116    -17.0957     27.7129\n  1000   3000     0.0000    -2.5186    27.5983    33.204045     21.7129    -17.2208     27.7129\n  1000   3200     0.0000    -2.5186    27.5983    33.527412     21.6154    -17.3431     27.7129\n  1000   3400     0.0000    -2.5186    27.5983    33.845057     21.5189    -17.4626     27.7129\n  1000   3600     0.0000    -2.5186    27.5983    34.157166     21.4235    -17.5796     27.7129\n  1000   3800     0.0000    -2.5186    27.5983    34.463921     21.3291    -17.6940     27.7129\n  1000   4000     0.0000    -2.5186    27.5983    34.765489     21.2356    -17.8060     27.7129\n  1000   4200     0.0000    -2.5186    27.5983    35.062033     21.1432    -17.9157     27.7129\n  1000   4400     0.0000    -2.5186    27.5983    35.353705     21.0517    -18.0231     27.7129\n  1000   4600     0.0000    -2.5186    27.5983    35.640651     20.9612    -18.1283     27.7129\n  1000   4800     0.0000    -2.5186    27.5983    35.923009     20.8716    -18.2314     27.7129\n  1000   5000     0.0000    -2.5186    27.5983    36.200913     20.7829    -18.3324     27.7129\n  1000   5200     0.0000    -2.5186    27.5983    36.474487     20.6951    -18.4314     27.7129\n  1000   5400     0.0000    -2.5186    27.5983    36.743853     20.6083    -18.5285     27.7129\n  1000   5600     0.0000    -2.5186    27.5983    37.009126     20.5223    -18.6237     27.7129\n  1000   5800     0.0000    -2.5186    27.5983    37.270416     20.4371    -18.7171     27.7129\n  1000   6000     0.0000    -2.5186    27.5983    37.527828     20.3528    -18.8088     27.7129\n  1000   6200     0.0000    -2.5186    27.5983    37.781464     20.2694    -18.8987     27.7129\n  1000   6400     0.0000    -2.5186    27.5983    38.031420     20.1867    -18.9869     27.7129\n  1000   6600     0.0000    -2.5186    27.5983    38.277789     20.1049    -19.0736     27.7129\n  1000   6800     0.0000    -2.5186    27.5983    38.520662     20.0239    -19.1586     27.7129\n  1000   7000     0.0000    -2.5186    27.5983    38.760123     19.9436    -19.2421     27.7129\n  1000   7200     0.0000    -2.5186    27.5983    38.996255     19.8641    -19.3242     27.7129\n  1000   7400     0.0000    -2.5186    27.5983    39.229138     19.7854    -19.4047     27.7129\n  1000   7600     0.0000    -2.5186    27.5983    39.458848     19.7075    -19.4839     27.7129\n  1000   7800     0.0000    -2.5186    27.5983    39.685459     19.6303    -19.5617     27.7129\n  1000   8000     0.0000    -2.5186    27.5983    39.909042     19.5538    -19.6382     27.7129\n  1000   8200     0.0000    -2.5186    27.5983    40.129665     19.4780    -19.7133     27.7129\n  1000   8400     0.0000    -2.5186    27.5983    40.347394     19.4030    -19.7872     27.7129\n  1500      0     0.0000    -2.2891    27.7179    27.493913     23.5307    -14.8266     27.8123\n  1500    200     0.0000    -2.2891    27.7179    27.936197     23.4155    -15.0078     27.8123\n  1500    400     0.0000    -2.2891    27.7179    28.367845     23.3018    -15.1838     27.8123\n  1500    600     0.0000    -2.2891    27.7179    28.789379     23.1895    -15.3548     27.8123\n  1500    800     0.0000    -2.2891    27.7179    29.201280     23.0785    -15.5211     27.8123\n  1500   1000     0.0000    -2.2891    27.7179    29.603992     22.9688    -15.6830     27.8123\n  1500   1200     0.0000    -2.2891    27.7179    29.997923     22.8604    -15.8405     27.8123\n  1500   1400     0.0000    -2.2891    27.7179    30.383456     22.7533    -15.9940     27.8123\n  1500   1600     0.0000    -2.2891    27.7179    30.760943     22.6475    -16.1435     27.8123\n  1500   1800     0.0000    -2.2891    27.7179    31.130714     22.5428    -16.2894     27.8123\n  1500   2000     0.0000    -2.2891    27.7179    31.493077     22.4393    -16.4316     27.8123\n  1500   2200     0.0000    -2.2891    27.7179    31.848320     22.3370    -16.5704     27.8123\n  1500   2400     0.0000    -2.2891    27.7179    32.196712     22.2359    -16.7059     27.8123\n  1500   2600     0.0000    -2.2891    27.7179    32.538506     22.1358    -16.8383     27.8123\n  1500   2800     0.0000    -2.2891    27.7179    32.873942     22.0369    -16.9676     27.8123\n  1500   3000     0.0000    -2.2891    27.7179    33.203242     21.9390    -17.0939     27.8123\n  1500   3200     0.0000    -2.2891    27.7179    33.526618     21.8421    -17.2175     27.8123\n  1500   3400     0.0000    -2.2891    27.7179    33.844271     21.7464    -17.3383     27.8123\n  1500   3600     0.0000    -2.2891    27.7179    34.156389     21.6516    -17.4565     27.8123\n  1500   3800     0.0000    -2.2891    27.7179    34.463152     21.5578    -17.5722     27.8123\n  1500   4000     0.0000    -2.2891    27.7179    34.764728     21.4650    -17.6854     27.8123\n  1500   4200     0.0000    -2.2891    27.7179    35.061279     21.3732    -17.7963     27.8123\n  1500   4400     0.0000    -2.2891    27.7179    35.352959     21.2823    -17.9049     27.8123\n  1500   4600     0.0000    -2.2891    27.7179    35.639912     21.1924    -18.0112     27.8123\n  1500   4800     0.0000    -2.2891    27.7179    35.922277     21.1034    -18.1155     27.8123\n  1500   5000     0.0000    -2.2891    27.7179    36.200187     21.0152    -18.2176     27.8123\n  1500   5200     0.0000    -2.2891    27.7179    36.473768     20.9280    -18.3177     27.8123\n  1500   5400     0.0000    -2.2891    27.7179    36.743141     20.8417    -18.4159     27.8123\n  1500   5600     0.0000    -2.2891    27.7179    37.008420     20.7562    -18.5122     27.8123\n  1500   5800     0.0000    -2.2891    27.7179    37.269715     20.6715    -18.6067     27.8123\n  1500   6000     0.0000    -2.2891    27.7179    37.527133     20.5877    -18.6994     27.8123\n  1500   6200     0.0000    -2.2891    27.7179    37.780775     20.5048    -18.7903     27.8123\n  1500   6400     0.0000    -2.2891    27.7179    38.030736     20.4226    -18.8796     27.8123\n  1500   6600     0.0000    -2.2891    27.7179    38.277111     20.3412    -18.9672     27.8123\n  1500   6800     0.0000    -2.2891    27.7179    38.519989     20.2606    -19.0533     27.8123\n  1500   7000     0.0000    -2.2891    27.7179    38.759456     20.1808    -19.1378     27.8123\n  1500   7200     0.0000    -2.2891    27.7179    38.995593     20.1018    -19.2208     27.8123\n  1500   7400     0.0000    -2.2891    27.7179    39.228481     20.0235    -19.3024     27.8123\n  1500   7600     0.0000    -2.2891    27.7179    39.458196     19.9459    -19.3825     27.8123\n  1500   7800     0.0000    -2.2891    27.7179    39.684812     19.8691    -19.4612     27.8123\n  1500   8000     0.0000    -2.2891    27.7179    39.908399     19.7930    -19.5386     27.8123\n  1500   8200     0.0000    -2.2891    27.7179    40.129027     19.7176    -19.6147     27.8123\n  1500   8400     0.0000    -2.2891    27.7179    40.346760     19.6430    -19.6895     27.8123\n  2000      0     0.0000    -2.0595    27.8375    27.492904     23.7430    -14.6778     27.9136\n  2000    200     0.0000    -2.0595    27.8375    27.935205     23.6290    -14.8607     27.9136\n  2000    400     0.0000    -2.0595    27.8375    28.366870     23.5164    -15.0383     27.9136\n  2000    600     0.0000    -2.0595    27.8375    28.788420     23.4051    -15.2109     27.9136\n  2000    800     0.0000    -2.0595    27.8375    29.200335     23.2951    -15.3788     27.9136\n  2000   1000     0.0000    -2.0595    27.8375    29.603061     23.1865    -15.5421     27.9136\n  2000   1200     0.0000    -2.0595    27.8375    29.997006     23.0791    -15.7012     27.9136\n  2000   1400     0.0000    -2.0595    27.8375    30.382551     22.9729    -15.8561     27.9136\n  2000   1600     0.0000    -2.0595    27.8375    30.760051     22.8679    -16.0071     27.9136\n  2000   1800     0.0000    -2.0595    27.8375    31.129834     22.7641    -16.1544     27.9136\n  2000   2000     0.0000    -2.0595    27.8375    31.492208     22.6615    -16.2980     27.9136\n  2000   2200     0.0000    -2.0595    27.8375    31.847461     22.5600    -16.4382     27.9136\n  2000   2400     0.0000    -2.0595    27.8375    32.195864     22.4596    -16.5751     27.9136\n  2000   2600     0.0000    -2.0595    27.8375    32.537668     22.3604    -16.7088     27.9136\n  2000   2800     0.0000    -2.0595    27.8375    32.873113     22.2622    -16.8394     27.9136\n  2000   3000     0.0000    -2.0595    27.8375    33.202423     22.1650    -16.9671     27.9136\n  2000   3200     0.0000    -2.0595    27.8375    33.525809     22.0689    -17.0919     27.9136\n  2000   3400     0.0000    -2.0595    27.8375    33.843470     21.9738    -17.2140     27.9136\n  2000   3600     0.0000    -2.0595    27.8375    34.155597     21.8797    -17.3335     27.9136\n  2000   3800     0.0000    -2.0595    27.8375    34.462368     21.7866    -17.4504     27.9136\n  2000   4000     0.0000    -2.0595    27.8375    34.763952     21.6944    -17.5648     27.9136\n  2000   4200     0.0000    -2.0595    27.8375    35.060511     21.6032    -17.6769     27.9136\n  2000   4400     0.0000    -2.0595    27.8375    35.352197     21.5129    -17.7866     27.9136\n  2000   4600     0.0000    -2.0595    27.8375    35.639158     21.4236    -17.8941     27.9136\n  2000   4800     0.0000    -2.0595    27.8375    35.921530     21.3351    -17.9995     27.9136\n  2000   5000     0.0000    -2.0595    27.8375    36.199447     21.2476    -18.1028     27.9136\n  2000   5200     0.0000    -2.0595    27.8375    36.473035     21.1609    -18.2040     27.9136\n  2000   5400     0.0000    -2.0595    27.8375    36.742414     21.0751    -18.3033     27.9136\n  2000   5600     0.0000    -2.0595    27.8375    37.007699     20.9901    -18.4007     27.9136\n  2000   5800     0.0000    -2.0595    27.8375    37.269001     20.9060    -18.4962     27.9136\n  2000   6000     0.0000    -2.0595    27.8375    37.526425     20.8227    -18.5900     27.9136\n  2000   6200     0.0000    -2.0595    27.8375    37.780072     20.7402    -18.6820     27.9136\n  2000   6400     0.0000    -2.0595    27.8375    38.030040     20.6584    -18.7723     27.9136\n  2000   6600     0.0000    -2.0595    27.8375    38.276421     20.5775    -18.8609     27.9136\n  2000   6800     0.0000    -2.0595    27.8375    38.519304     20.4974    -18.9480     27.9136\n  2000   7000     0.0000    -2.0595    27.8375    38.758775     20.4180    -19.0335     27.9136\n  2000   7200     0.0000    -2.0595    27.8375    38.994918     20.3394    -19.1175     27.9136\n  2000   7400     0.0000    -2.0595    27.8375    39.227811     20.2615    -19.2000     27.9136\n  2000   7600     0.0000    -2.0595    27.8375    39.457531     20.1844    -19.2811     27.9136\n  2000   7800     0.0000    -2.0595    27.8375    39.684152     20.1080    -19.3608     27.9136\n  2000   8000     0.0000    -2.0595    27.8375    39.907744     20.0323    -19.4391     27.9136\n  2000   8200     0.0000    -2.0595    27.8375    40.128376     19.9573    -19.5161     27.9136\n  2000   8400     0.0000    -2.0595    27.8375    40.346114     19.8829    -19.5918     27.9136\n  2500      0     0.0000    -1.8300    27.9572    27.491875     23.9554    -14.5290     28.0170\n  2500    200     0.0000    -1.8300    27.9572    27.934194     23.8425    -14.7135     28.0170\n  2500    400     0.0000    -1.8300    27.9572    28.365875     23.7309    -14.8927     28.0170\n  2500    600     0.0000    -1.8300    27.9572    28.787440     23.6207    -15.0670     28.0170\n  2500    800     0.0000    -1.8300    27.9572    29.199371     23.5118    -15.2364     28.0170\n  2500   1000     0.0000    -1.8300    27.9572    29.602111     23.4041    -15.4013     28.0170\n  2500   1200     0.0000    -1.8300    27.9572    29.996070     23.2977    -15.5618     28.0170\n  2500   1400     0.0000    -1.8300    27.9572    30.381628     23.1924    -15.7183     28.0170\n  2500   1600     0.0000    -1.8300    27.9572    30.759140     23.0884    -15.8707     28.0170\n  2500   1800     0.0000    -1.8300    27.9572    31.128935     22.9854    -16.0194     28.0170\n  2500   2000     0.0000    -1.8300    27.9572    31.491321     22.8837    -16.1645     28.0170\n  2500   2200     0.0000    -1.8300    27.9572    31.846586     22.7830    -16.3060     28.0170\n  2500   2400     0.0000    -1.8300    27.9572    32.194999     22.6834    -16.4443     28.0170\n  2500   2600     0.0000    -1.8300    27.9572    32.536814     22.5849    -16.5793     28.0170\n  2500   2800     0.0000    -1.8300    27.9572    32.872269     22.4875    -16.7113     28.0170\n  2500   3000     0.0000    -1.8300    27.9572    33.201588     22.3910    -16.8402     28.0170\n  2500   3200     0.0000    -1.8300    27.9572    33.524983     22.2956    -16.9663     28.0170\n  2500   3400     0.0000    -1.8300    27.9572    33.842654     22.2012    -17.0897     28.0170\n  2500   3600     0.0000    -1.8300    27.9572    34.154789     22.1078    -17.2104     28.0170\n  2500   3800     0.0000    -1.8300    27.9572    34.461568     22.0153    -17.3285     28.0170\n  2500   4000     0.0000    -1.8300    27.9572    34.763160     21.9238    -17.4442     28.0170\n  2500   4200     0.0000    -1.8300    27.9572    35.059727     21.8332    -17.5574     28.0170\n  2500   4400     0.0000    -1.8300    27.9572    35.351422     21.7435    -17.6683     28.0170\n  2500   4600     0.0000    -1.8300    27.9572    35.638389     21.6548    -17.7770     28.0170\n  2500   4800     0.0000    -1.8300    27.9572    35.920769     21.5669    -17.8835     28.0170\n  2500   5000     0.0000    -1.8300    27.9572    36.198693     21.4799    -17.9879     28.0170\n  2500   5200     0.0000    -1.8300    27.9572    36.472288     21.3938    -18.0903     28.0170\n  2500   5400     0.0000    -1.8300    27.9572    36.741674     21.3085    -18.1907     28.0170\n  2500   5600     0.0000    -1.8300    27.9572    37.006965     21.2240    -18.2891     28.0170\n  2500   5800     0.0000    -1.8300    27.9572    37.268273     21.1404    -18.3858     28.0170\n  2500   6000     0.0000    -1.8300    27.9572    37.525704     21.0576    -18.4805     28.0170\n  2500   6200     0.0000    -1.8300    27.9572    37.779357     20.9755    -18.5736     28.0170\n  2500   6400     0.0000    -1.8300    27.9572    38.029330     20.8943    -18.6649     28.0170\n  2500   6600     0.0000    -1.8300    27.9572    38.275717     20.8139    -18.7546     28.0170\n  2500   6800     0.0000    -1.8300    27.9572    38.518605     20.7342    -18.8427     28.0170\n  2500   7000     0.0000    -1.8300    27.9572    38.758083     20.6552    -18.9292     28.0170\n  2500   7200     0.0000    -1.8300    27.9572    38.994231     20.5770    -19.0141     28.0170\n  2500   7400     0.0000    -1.8300    27.9572    39.227129     20.4996    -19.0976     28.0170\n  2500   7600     0.0000    -1.8300    27.9572    39.456854     20.4228    -19.1797     28.0170\n  2500   7800     0.0000    -1.8300    27.9572    39.683480     20.3468    -19.2603     28.0170\n  2500   8000     0.0000    -1.8300    27.9572    39.907076     20.2715    -19.3395     28.0170\n  2500   8200     0.0000    -1.8300    27.9572    40.127713     20.1969    -19.4175     28.0170\n  2500   8400     0.0000    -1.8300    27.9572    40.345456     20.1229    -19.4941     28.0170\n  3000      0     0.0000    -1.6005    28.0768    27.490826     24.1677    -14.3802     28.1224\n  3000    200     0.0000    -1.6005    28.0768    27.933162     24.0560    -14.5664     28.1224\n  3000    400     0.0000    -1.6005    28.0768    28.364860     23.9455    -14.7472     28.1224\n  3000    600     0.0000    -1.6005    28.0768    28.786442     23.8364    -14.9230     28.1224\n  3000    800     0.0000    -1.6005    28.0768    29.198388     23.7285    -15.0940     28.1224\n  3000   1000     0.0000    -1.6005    28.0768    29.601143     23.6218    -15.2604     28.1224\n  3000   1200     0.0000    -1.6005    28.0768    29.995115     23.5163    -15.4225     28.1224\n  3000   1400     0.0000    -1.6005    28.0768    30.380687     23.4120    -15.5804     28.1224\n  3000   1600     0.0000    -1.6005    28.0768    30.758212     23.3088    -15.7343     28.1224\n  3000   1800     0.0000    -1.6005    28.0768    31.128020     23.2068    -15.8844     28.1224\n  3000   2000     0.0000    -1.6005    28.0768    31.490417     23.1058    -16.0309     28.1224\n  3000   2200     0.0000    -1.6005    28.0768    31.845693     23.0060    -16.1738     28.1224\n  3000   2400     0.0000    -1.6005    28.0768    32.194117     22.9072    -16.3134     28.1224\n  3000   2600     0.0000    -1.6005    28.0768    32.535943     22.8095    -16.4498     28.1224\n  3000   2800     0.0000    -1.6005    28.0768    32.871408     22.7128    -16.5831     28.1224\n  3000   3000     0.0000    -1.6005    28.0768    33.200737     22.6171    -16.7134     28.1224\n  3000   3200     0.0000    -1.6005    28.0768    33.524142     22.5224    -16.8408     28.1224\n  3000   3400     0.0000    -1.6005    28.0768    33.841821     22.4287    -16.9654     28.1224\n  3000   3600     0.0000    -1.6005    28.0768    34.153966     22.3359    -17.0873     28.1224\n  3000   3800     0.0000    -1.6005    28.0768    34.460753     22.2441    -17.2067     28.1224\n  3000   4000     0.0000    -1.6005    28.0768    34.762354     22.1532    -17.3235     28.1224\n  3000   4200     0.0000    -1.6005    28.0768    35.058929     22.0632    -17.4379     28.1224\n  3000   4400     0.0000    -1.6005    28.0768    35.350631     21.9742    -17.5500     28.1224\n  3000   4600     0.0000    -1.6005    28.0768    35.637607     21.8860    -17.6599     28.1224\n  3000   4800     0.0000    -1.6005    28.0768    35.919994     21.7987    -17.7675     28.1224\n  3000   5000     0.0000    -1.6005    28.0768    36.197925     21.7122    -17.8731     28.1224\n  3000   5200     0.0000    -1.6005    28.0768    36.471527     21.6266    -17.9766     28.1224\n  3000   5400     0.0000    -1.6005    28.0768    36.740919     21.5419    -18.0780     28.1224\n  3000   5600     0.0000    -1.6005    28.0768    37.006218     21.4579    -18.1776     28.1224\n  3000   5800     0.0000    -1.6005    28.0768    37.267532     21.3748    -18.2753     28.1224\n  3000   6000     0.0000    -1.6005    28.0768    37.524969     21.2925    -18.3711     28.1224\n  3000   6200     0.0000    -1.6005    28.0768    37.778628     21.2109    -18.4652     28.1224\n  3000   6400     0.0000    -1.6005    28.0768    38.028607     21.1302    -18.5576     28.1224\n  3000   6600     0.0000    -1.6005    28.0768    38.275000     21.0502    -18.6483     28.1224\n  3000   6800     0.0000    -1.6005    28.0768    38.517894     20.9709    -18.7373     28.1224\n  3000   7000     0.0000    -1.6005    28.0768    38.757377     20.8924    -18.8248     28.1224\n  3000   7200     0.0000    -1.6005    28.0768    38.993530     20.8147    -18.9108     28.1224\n  3000   7400     0.0000    -1.6005    28.0768    39.226434     20.7376    -18.9952     28.1224\n  3000   7600     0.0000    -1.6005    28.0768    39.456164     20.6613    -19.0782     28.1224\n  3000   7800     0.0000    -1.6005    28.0768    39.682795     20.5857    -19.1598     28.1224\n  3000   8000     0.0000    -1.6005    28.0768    39.906397     20.5107    -19.2400     28.1224\n  3000   8200     0.0000    -1.6005    28.0768    40.127039     20.4365    -19.3188     28.1224\n  3000   8400     0.0000    -1.6005    28.0768    40.344786     20.3629    -19.3964     28.1224\n  3500      0     0.0000    -1.3710    28.1964    27.489756     24.3800    -14.2314     28.2297\n  3500    200     0.0000    -1.3710    28.1964    27.932111     24.2694    -14.4192     28.2297\n  3500    400     0.0000    -1.3710    28.1964    28.363826     24.1601    -14.6016     28.2297\n  3500    600     0.0000    -1.3710    28.1964    28.785424     24.0520    -14.7790     28.2297\n  3500    800     0.0000    -1.3710    28.1964    29.197386     23.9451    -14.9516     28.2297\n  3500   1000     0.0000    -1.3710    28.1964    29.600156     23.8394    -15.1195     28.2297\n  3500   1200     0.0000    -1.3710    28.1964    29.994143     23.7349    -15.2831     28.2297\n  3500   1400     0.0000    -1.3710    28.1964    30.379728     23.6315    -15.4425     28.2297\n  3500   1600     0.0000    -1.3710    28.1964    30.757266     23.5292    -15.5979     28.2297\n  3500   1800     0.0000    -1.3710    28.1964    31.127086     23.4281    -15.7494     28.2297\n  3500   2000     0.0000    -1.3710    28.1964    31.489496     23.3280    -15.8973     28.2297\n  3500   2200     0.0000    -1.3710    28.1964    31.844784     23.2290    -16.0416     28.2297\n  3500   2400     0.0000    -1.3710    28.1964    32.193219     23.1310    -16.1826     28.2297\n  3500   2600     0.0000    -1.3710    28.1964    32.535056     23.0340    -16.3203     28.2297\n  3500   2800     0.0000    -1.3710    28.1964    32.870531     22.9381    -16.4549     28.2297\n  3500   3000     0.0000    -1.3710    28.1964    33.199870     22.8431    -16.5865     28.2297\n  3500   3200     0.0000    -1.3710    28.1964    33.523284     22.7491    -16.7151     28.2297\n  3500   3400     0.0000    -1.3710    28.1964    33.840974     22.6561    -16.8410     28.2297\n  3500   3600     0.0000    -1.3710    28.1964    34.153127     22.5640    -16.9642     28.2297\n  3500   3800     0.0000    -1.3710    28.1964    34.459923     22.4728    -17.0848     28.2297\n  3500   4000     0.0000    -1.3710    28.1964    34.761533     22.3826    -17.2028     28.2297\n  3500   4200     0.0000    -1.3710    28.1964    35.058116     22.2932    -17.3185     28.2297\n  3500   4400     0.0000    -1.3710    28.1964    35.349826     22.2048    -17.4318     28.2297\n  3500   4600     0.0000    -1.3710    28.1964    35.636809     22.1172    -17.5428     28.2297\n  3500   4800     0.0000    -1.3710    28.1964    35.919204     22.0305    -17.6515     28.2297\n  3500   5000     0.0000    -1.3710    28.1964    36.197143     21.9446    -17.7582     28.2297\n  3500   5200     0.0000    -1.3710    28.1964    36.470752     21.8595    -17.8628     28.2297\n  3500   5400     0.0000    -1.3710    28.1964    36.740151     21.7753    -17.9654     28.2297\n  3500   5600     0.0000    -1.3710    28.1964    37.005456     21.6919    -18.0660     28.2297\n  3500   5800     0.0000    -1.3710    28.1964    37.266777     21.6092    -18.1648     28.2297\n  3500   6000     0.0000    -1.3710    28.1964    37.524220     21.5274    -18.2617     28.2297\n  3500   6200     0.0000    -1.3710    28.1964    37.777886     21.4464    -18.3568     28.2297\n  3500   6400     0.0000    -1.3710    28.1964    38.027871     21.3661    -18.4502     28.2297\n  3500   6600     0.0000    -1.3710    28.1964    38.274270     21.2865    -18.5419     28.2297\n  3500   6800     0.0000    -1.3710    28.1964    38.517170     21.2077    -18.6320     28.2297\n  3500   7000     0.0000    -1.3710    28.1964    38.756658     21.1297    -18.7205     28.2297\n  3500   7200     0.0000    -1.3710    28.1964    38.992817     21.0523    -18.8074     28.2297\n  3500   7400     0.0000    -1.3710    28.1964    39.225727     20.9757    -18.8928     28.2297\n  3500   7600     0.0000    -1.3710    28.1964    39.455462     20.8998    -18.9768     28.2297\n  3500   7800     0.0000    -1.3710    28.1964    39.682098     20.8245    -19.0593     28.2297\n  3500   8000     0.0000    -1.3710    28.1964    39.905705     20.7500    -19.1404     28.2297\n  3500   8200     0.0000    -1.3710    28.1964    40.126352     20.6761    -19.2202     28.2297\n  3500   8400     0.0000    -1.3710    28.1964    40.344104     20.6029    -19.2986     28.2297\n  4000      0     0.0000    -1.1415    28.3161    27.488666     24.5924    -14.0825     28.3391\n  4000    200     0.0000    -1.1415    28.3161    27.931039     24.4829    -14.2720     28.3391\n  4000    400     0.0000    -1.1415    28.3161    28.362772     24.3747    -14.4561     28.3391\n  4000    600     0.0000    -1.1415    28.3161    28.784387     24.2676    -14.6350     28.3391\n  4000    800     0.0000    -1.1415    28.3161    29.196366     24.1618    -14.8091     28.3391\n  4000   1000     0.0000    -1.1415    28.3161    29.599150     24.0571    -14.9786     28.3391\n  4000   1200     0.0000    -1.1415    28.3161    29.993152     23.9535    -15.1437     28.3391\n  4000   1400     0.0000    -1.1415    28.3161    30.378752     23.8511    -15.3046     28.3391\n  4000   1600     0.0000    -1.1415    28.3161    30.756303     23.7497    -15.4614     28.3391\n  4000   1800     0.0000    -1.1415    28.3161    31.126136     23.6494    -15.6144     28.3391\n  4000   2000     0.0000    -1.1415    28.3161    31.488558     23.5501    -15.7637     28.3391\n  4000   2200     0.0000    -1.1415    28.3161    31.843858     23.4519    -15.9094     28.3391\n  4000   2400     0.0000    -1.1415    28.3161    32.192305     23.3548    -16.0517     28.3391\n  4000   2600     0.0000    -1.1415    28.3161    32.534152     23.2586    -16.1908     28.3391\n  4000   2800     0.0000    -1.1415    28.3161    32.869638     23.1634    -16.3267     28.3391\n  4000   3000     0.0000    -1.1415    28.3161    33.198987     23.0691    -16.4596     28.3391\n  4000   3200     0.0000    -1.1415    28.3161    33.522412     22.9759    -16.5895     28.3391\n  4000   3400     0.0000    -1.1415    28.3161    33.840110     22.8835    -16.7167     28.3391\n  4000   3600     0.0000    -1.1415    28.3161    34.152273     22.7921    -16.8411     28.3391\n  4000   3800     0.0000    -1.1415    28.3161    34.459078     22.7016    -16.9629     28.3391\n  4000   4000     0.0000    -1.1415    28.3161    34.760696     22.6120    -17.0822     28.3391\n  4000   4200     0.0000    -1.1415    28.3161    35.057288     22.5233    -17.1990     28.3391\n  4000   4400     0.0000    -1.1415    28.3161    35.349007     22.4354    -17.3134     28.3391\n  4000   4600     0.0000    -1.1415    28.3161    35.635998     22.3484    -17.4256     28.3391\n  4000   4800     0.0000    -1.1415    28.3161    35.918400     22.2622    -17.5355     28.3391\n  4000   5000     0.0000    -1.1415    28.3161    36.196346     22.1769    -17.6433     28.3391\n  4000   5200     0.0000    -1.1415    28.3161    36.469963     22.0924    -17.7490     28.3391\n  4000   5400     0.0000    -1.1415    28.3161    36.739369     22.0087    -17.8527     28.3391\n  4000   5600     0.0000    -1.1415    28.3161    37.004681     21.9258    -17.9544     28.3391\n  4000   5800     0.0000    -1.1415    28.3161    37.266009     21.8437    -18.0543     28.3391\n  4000   6000     0.0000    -1.1415    28.3161    37.523459     21.7623    -18.1522     28.3391\n  4000   6200     0.0000    -1.1415    28.3161    37.777131     21.6818    -18.2484     28.3391\n  4000   6400     0.0000    -1.1415    28.3161    38.027122     21.6019    -18.3428     28.3391\n  4000   6600     0.0000    -1.1415    28.3161    38.273527     21.5228    -18.4356     28.3391\n  4000   6800     0.0000    -1.1415    28.3161    38.516433     21.4445    -18.5266     28.3391\n  4000   7000     0.0000    -1.1415    28.3161    38.755927     21.3669    -18.6161     28.3391\n  4000   7200     0.0000    -1.1415    28.3161    38.992092     21.2899    -18.7040     28.3391\n  4000   7400     0.0000    -1.1415    28.3161    39.225007     21.2137    -18.7904     28.3391\n  4000   7600     0.0000    -1.1415    28.3161    39.454748     21.1382    -18.8753     28.3391\n  4000   7800     0.0000    -1.1415    28.3161    39.681389     21.0634    -18.9588     28.3391\n  4000   8000     0.0000    -1.1415    28.3161    39.905001     20.9892    -19.0409     28.3391\n  4000   8200     0.0000    -1.1415    28.3161    40.125653     20.9158    -19.1215     28.3391\n  4000   8400     0.0000    -1.1415    28.3161    40.343411     20.8429    -19.2009     28.3391\n  4500      0     0.0000    -0.9119    28.4357    27.487555     24.8047    -13.9337     28.4503\n  4500    200     0.0000    -0.9119    28.4357    27.929948     24.6964    -14.1248     28.4503\n  4500    400     0.0000    -0.9119    28.4357    28.361699     24.5893    -14.3105     28.4503\n  4500    600     0.0000    -0.9119    28.4357    28.783331     24.4833    -14.4910     28.4503\n  4500    800     0.0000    -0.9119    28.4357    29.195326     24.3784    -14.6667     28.4503\n  4500   1000     0.0000    -0.9119    28.4357    29.598126     24.2747    -14.8377     28.4503\n  4500   1200     0.0000    -0.9119    28.4357    29.992143     24.1721    -15.0043     28.4503\n  4500   1400     0.0000    -0.9119    28.4357    30.377757     24.0706    -15.1666     28.4503\n  4500   1600     0.0000    -0.9119    28.4357    30.755322     23.9701    -15.3249     28.4503\n  4500   1800     0.0000    -0.9119    28.4357    31.125168     23.8707    -15.4793     28.4503\n  4500   2000     0.0000    -0.9119    28.4357    31.487603     23.7723    -15.6300     28.4503\n  4500   2200     0.0000    -0.9119    28.4357    31.842915     23.6749    -15.7772     28.4503\n  4500   2400     0.0000    -0.9119    28.4357    32.191373     23.5785    -15.9208     28.4503\n  4500   2600     0.0000    -0.9119    28.4357    32.533232     23.4831    -16.0612     28.4503\n  4500   2800     0.0000    -0.9119    28.4357    32.868729     23.3887    -16.1985     28.4503\n  4500   3000     0.0000    -0.9119    28.4357    33.198089     23.2952    -16.3327     28.4503\n  4500   3200     0.0000    -0.9119    28.4357    33.521523     23.2026    -16.4639     28.4503\n  4500   3400     0.0000    -0.9119    28.4357    33.839232     23.1110    -16.5923     28.4503\n  4500   3600     0.0000    -0.9119    28.4357    34.151404     23.0202    -16.7180     28.4503\n  4500   3800     0.0000    -0.9119    28.4357    34.458218     22.9304    -16.8410     28.4503\n  4500   4000     0.0000    -0.9119    28.4357    34.759845     22.8414    -16.9615     28.4503\n  4500   4200     0.0000    -0.9119    28.4357    35.056446     22.7533    -17.0795     28.4503\n  4500   4400     0.0000    -0.9119    28.4357    35.348172     22.6660    -17.1951     28.4503\n  4500   4600     0.0000    -0.9119    28.4357    35.635172     22.5796    -17.3084     28.4503\n  4500   4800     0.0000    -0.9119    28.4357    35.917582     22.4940    -17.4195     28.4503\n  4500   5000     0.0000    -0.9119    28.4357    36.195536     22.4092    -17.5284     28.4503\n  4500   5200     0.0000    -0.9119    28.4357    36.469160     22.3253    -17.6353     28.4503\n  4500   5400     0.0000    -0.9119    28.4357    36.738573     22.2421    -17.7400     28.4503\n  4500   5600     0.0000    -0.9119    28.4357    37.003892     22.1597    -17.8429     28.4503\n  4500   5800     0.0000    -0.9119    28.4357    37.265227     22.0781    -17.9437     28.4503\n  4500   6000     0.0000    -0.9119    28.4357    37.522684     21.9973    -18.0428     28.4503\n  4500   6200     0.0000    -0.9119    28.4357    37.776362     21.9172    -18.1400     28.4503\n  4500   6400     0.0000    -0.9119    28.4357    38.026360     21.8378    -18.2354     28.4503\n  4500   6600     0.0000    -0.9119    28.4357    38.272771     21.7592    -18.3292     28.4503\n  4500   6800     0.0000    -0.9119    28.4357    38.515683     21.6813    -18.4213     28.4503\n  4500   7000     0.0000    -0.9119    28.4357    38.755184     21.6041    -18.5117     28.4503\n  4500   7200     0.0000    -0.9119    28.4357    38.991354     21.5276    -18.6006     28.4503\n  4500   7400     0.0000    -0.9119    28.4357    39.224274     21.4518    -18.6880     28.4503\n  4500   7600     0.0000    -0.9119    28.4357    39.454021     21.3767    -18.7739     28.4503\n  4500   7800     0.0000    -0.9119    28.4357    39.680668     21.3023    -18.8583     28.4503\n  4500   8000     0.0000    -0.9119    28.4357    39.904285     21.2285    -18.9413     28.4503\n  4500   8200     0.0000    -0.9119    28.4357    40.124942     21.1554    -19.0229     28.4503\n  4500   8400     0.0000    -0.9119    28.4357    40.342705     21.0829    -19.1032     28.4503\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180530_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.6454202       33.7680109       43.0365483   m   m   m\ninitial_baseline_rate:          0.0000000        0.1681843        0.0413736   m/s m/s m/s\nprecision_baseline(TCN):       -0.6454202       33.7680109       43.0365483   m   m   m\nprecision_baseline_rate:        0.0000000        0.1681843        0.0413736   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180530_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.645     33.768     43.037\norbit baseline rate   (TCN) (m/s):  0.000e+00  1.682e-01  4.137e-02\n\nbaseline vector (TCN)   (m):     -0.645     33.768     43.037\nbaseline rate   (TCN) (m/s):  0.000e+00  1.682e-01  4.137e-02\n\nSLC-1 center baseline length (m):    54.7069\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.6454    32.1981    42.6504    27.496815     52.6990      8.8706     53.4433\n     0    200    -0.6454    32.1981    42.6504    27.939050     52.7658      8.4635     53.4433\n     0    400    -0.6454    32.1981    42.6504    28.370652     52.8281      8.0658     53.4433\n     0    600    -0.6454    32.1981    42.6504    28.792142     52.8860      7.6770     53.4433\n     0    800    -0.6454    32.1981    42.6504    29.204001     52.9398      7.2966     53.4433\n     0   1000    -0.6454    32.1981    42.6504    29.606672     52.9898      6.9243     53.4433\n     0   1200    -0.6454    32.1981    42.6504    30.000566     53.0361      6.5599     53.4433\n     0   1400    -0.6454    32.1981    42.6504    30.386062     53.0790      6.2029     53.4433\n     0   1600    -0.6454    32.1981    42.6504    30.763514     53.1187      5.8531     53.4433\n     0   1800    -0.6454    32.1981    42.6504    31.133251     53.1554      5.5101     53.4433\n     0   2000    -0.6454    32.1981    42.6504    31.495582     53.1892      5.1739     53.4433\n     0   2200    -0.6454    32.1981    42.6504    31.850793     53.2202      4.8440     53.4433\n     0   2400    -0.6454    32.1981    42.6504    32.199155     53.2487      4.5203     53.4433\n     0   2600    -0.6454    32.1981    42.6504    32.540921     53.2747      4.2026     53.4433\n     0   2800    -0.6454    32.1981    42.6504    32.876328     53.2984      3.8907     53.4433\n     0   3000    -0.6454    32.1981    42.6504    33.205602     53.3198      3.5843     53.4433\n     0   3200    -0.6454    32.1981    42.6504    33.528952     53.3392      3.2833     53.4433\n     0   3400    -0.6454    32.1981    42.6504    33.846580     53.3566      2.9876     53.4433\n     0   3600    -0.6454    32.1981    42.6504    34.158674     53.3721      2.6969     53.4433\n     0   3800    -0.6454    32.1981    42.6504    34.465413     53.3857      2.4111     53.4433\n     0   4000    -0.6454    32.1981    42.6504    34.766966     53.3977      2.1301     53.4433\n     0   4200    -0.6454    32.1981    42.6504    35.063495     53.4080      1.8537     53.4433\n     0   4400    -0.6454    32.1981    42.6504    35.355153     53.4167      1.5818     53.4433\n     0   4600    -0.6454    32.1981    42.6504    35.642085     53.4240      1.3143     53.4433\n     0   4800    -0.6454    32.1981    42.6504    35.924431     53.4298      1.0510     53.4433\n     0   5000    -0.6454    32.1981    42.6504    36.202321     53.4342      0.7918     53.4433\n     0   5200    -0.6454    32.1981    42.6504    36.475883     53.4374      0.5367     53.4433\n     0   5400    -0.6454    32.1981    42.6504    36.745237     53.4393      0.2855     53.4433\n     0   5600    -0.6454    32.1981    42.6504    37.010498     53.4401      0.0380     53.4433\n     0   5800    -0.6454    32.1981    42.6504    37.271776     53.4397     -0.2057     53.4433\n     0   6000    -0.6454    32.1981    42.6504    37.529176     53.4382     -0.4457     53.4433\n     0   6200    -0.6454    32.1981    42.6504    37.782801     53.4357     -0.6823     53.4433\n     0   6400    -0.6454    32.1981    42.6504    38.032746     53.4322     -0.9154     53.4433\n     0   6600    -0.6454    32.1981    42.6504    38.279105     53.4278     -1.1451     53.4433\n     0   6800    -0.6454    32.1981    42.6504    38.521968     53.4225     -1.3716     53.4433\n     0   7000    -0.6454    32.1981    42.6504    38.761419     53.4163     -1.5949     53.4433\n     0   7200    -0.6454    32.1981    42.6504    38.997541     53.4092     -1.8150     53.4433\n     0   7400    -0.6454    32.1981    42.6504    39.230415     53.4014     -2.0321     53.4433\n     0   7600    -0.6454    32.1981    42.6504    39.460116     53.3928     -2.2461     53.4433\n     0   7800    -0.6454    32.1981    42.6504    39.686717     53.3835     -2.4573     53.4433\n     0   8000    -0.6454    32.1981    42.6504    39.910291     53.3735     -2.6656     53.4433\n     0   8200    -0.6454    32.1981    42.6504    40.130905     53.3629     -2.8711     53.4433\n     0   8400    -0.6454    32.1981    42.6504    40.348626     53.3516     -3.0738     53.4433\n   500      0    -0.6454    32.5438    42.7354    27.495868     52.9339      9.1388     53.7199\n   500    200    -0.6454    32.5438    42.7354    27.938119     53.0028      8.7300     53.7199\n   500    400    -0.6454    32.5438    42.7354    28.369736     53.0671      8.3304     53.7199\n   500    600    -0.6454    32.5438    42.7354    28.791241     53.1269      7.9398     53.7199\n   500    800    -0.6454    32.5438    42.7354    29.203113     53.1826      7.5577     53.7199\n   500   1000    -0.6454    32.5438    42.7354    29.605798     53.2344      7.1837     53.7199\n   500   1200    -0.6454    32.5438    42.7354    29.999704     53.2825      6.8175     53.7199\n   500   1400    -0.6454    32.5438    42.7354    30.385211     53.3272      6.4589     53.7199\n   500   1600    -0.6454    32.5438    42.7354    30.762675     53.3685      6.1074     53.7199\n   500   1800    -0.6454    32.5438    42.7354    31.132423     53.4068      5.7629     53.7199\n   500   2000    -0.6454    32.5438    42.7354    31.494764     53.4422      5.4250     53.7199\n   500   2200    -0.6454    32.5438    42.7354    31.849986     53.4748      5.0935     53.7199\n   500   2400    -0.6454    32.5438    42.7354    32.198358     53.5048      4.7683     53.7199\n   500   2600    -0.6454    32.5438    42.7354    32.540133     53.5323      4.4490     53.7199\n   500   2800    -0.6454    32.5438    42.7354    32.875549     53.5574      4.1356     53.7199\n   500   3000    -0.6454    32.5438    42.7354    33.204831     53.5803      3.8277     53.7199\n   500   3200    -0.6454    32.5438    42.7354    33.528190     53.6010      3.5252     53.7199\n   500   3400    -0.6454    32.5438    42.7354    33.845826     53.6197      3.2280     53.7199\n   500   3600    -0.6454    32.5438    42.7354    34.157928     53.6365      2.9359     53.7199\n   500   3800    -0.6454    32.5438    42.7354    34.464675     53.6514      2.6487     53.7199\n   500   4000    -0.6454    32.5438    42.7354    34.766235     53.6646      2.3663     53.7199\n   500   4200    -0.6454    32.5438    42.7354    35.062772     53.6762      2.0885     53.7199\n   500   4400    -0.6454    32.5438    42.7354    35.354436     53.6861      1.8152     53.7199\n   500   4600    -0.6454    32.5438    42.7354    35.641375     53.6945      1.5463     53.7199\n   500   4800    -0.6454    32.5438    42.7354    35.923727     53.7015      1.2817     53.7199\n   500   5000    -0.6454    32.5438    42.7354    36.201624     53.7070      1.0212     53.7199\n   500   5200    -0.6454    32.5438    42.7354    36.475192     53.7113      0.7648     53.7199\n   500   5400    -0.6454    32.5438    42.7354    36.744552     53.7143      0.5122     53.7199\n   500   5600    -0.6454    32.5438    42.7354    37.009819     53.7161      0.2635     53.7199\n   500   5800    -0.6454    32.5438    42.7354    37.271103     53.7167      0.0186     53.7199\n   500   6000    -0.6454    32.5438    42.7354    37.528509     53.7163     -0.2228     53.7199\n   500   6200    -0.6454    32.5438    42.7354    37.782139     53.7148     -0.4606     53.7199\n   500   6400    -0.6454    32.5438    42.7354    38.032090     53.7122     -0.6949     53.7199\n   500   6600    -0.6454    32.5438    42.7354    38.278454     53.7087     -0.9258     53.7199\n   500   6800    -0.6454    32.5438    42.7354    38.521321     53.7043     -1.1535     53.7199\n   500   7000    -0.6454    32.5438    42.7354    38.760777     53.6990     -1.3779     53.7199\n   500   7200    -0.6454    32.5438    42.7354    38.996905     53.6929     -1.5992     53.7199\n   500   7400    -0.6454    32.5438    42.7354    39.229783     53.6859     -1.8175     53.7199\n   500   7600    -0.6454    32.5438    42.7354    39.459488     53.6782     -2.0327     53.7199\n   500   7800    -0.6454    32.5438    42.7354    39.686095     53.6698     -2.2450     53.7199\n   500   8000    -0.6454    32.5438    42.7354    39.909673     53.6606     -2.4544     53.7199\n   500   8200    -0.6454    32.5438    42.7354    40.130291     53.6507     -2.6610     53.7199\n   500   8400    -0.6454    32.5438    42.7354    40.348016     53.6402     -2.8649     53.7199\n  1000      0    -0.6454    32.8896    42.8204    27.494901     53.1687      9.4071     53.9975\n  1000    200    -0.6454    32.8896    42.8204    27.937168     53.2398      8.9964     53.9975\n  1000    400    -0.6454    32.8896    42.8204    28.368800     53.3060      8.5951     53.9975\n  1000    600    -0.6454    32.8896    42.8204    28.790320     53.3678      8.2027     53.9975\n  1000    800    -0.6454    32.8896    42.8204    29.202206     53.4254      7.8188     53.9975\n  1000   1000    -0.6454    32.8896    42.8204    29.604904     53.4790      7.4431     53.9975\n  1000   1200    -0.6454    32.8896    42.8204    29.998823     53.5289      7.0752     53.9975\n  1000   1400    -0.6454    32.8896    42.8204    30.384343     53.5753      6.7149     53.9975\n  1000   1600    -0.6454    32.8896    42.8204    30.761818     53.6184      6.3618     53.9975\n  1000   1800    -0.6454    32.8896    42.8204    31.131578     53.6583      6.0156     53.9975\n  1000   2000    -0.6454    32.8896    42.8204    31.493929     53.6953      5.6761     53.9975\n  1000   2200    -0.6454    32.8896    42.8204    31.849161     53.7294      5.3431     53.9975\n  1000   2400    -0.6454    32.8896    42.8204    32.197543     53.7609      5.0163     53.9975\n  1000   2600    -0.6454    32.8896    42.8204    32.539328     53.7899      4.6955     53.9975\n  1000   2800    -0.6454    32.8896    42.8204    32.874754     53.8164      4.3805     53.9975\n  1000   3000    -0.6454    32.8896    42.8204    33.204045     53.8407      4.0711     53.9975\n  1000   3200    -0.6454    32.8896    42.8204    33.527412     53.8628      3.7672     53.9975\n  1000   3400    -0.6454    32.8896    42.8204    33.845057     53.8829      3.4685     53.9975\n  1000   3600    -0.6454    32.8896    42.8204    34.157166     53.9009      3.1749     53.9975\n  1000   3800    -0.6454    32.8896    42.8204    34.463921     53.9172      2.8863     53.9975\n  1000   4000    -0.6454    32.8896    42.8204    34.765489     53.9316      2.6025     53.9975\n  1000   4200    -0.6454    32.8896    42.8204    35.062033     53.9443      2.3233     53.9975\n  1000   4400    -0.6454    32.8896    42.8204    35.353705     53.9555      2.0487     53.9975\n  1000   4600    -0.6454    32.8896    42.8204    35.640651     53.9650      1.7784     53.9975\n  1000   4800    -0.6454    32.8896    42.8204    35.923009     53.9732      1.5124     53.9975\n  1000   5000    -0.6454    32.8896    42.8204    36.200913     53.9798      1.2506     53.9975\n  1000   5200    -0.6454    32.8896    42.8204    36.474487     53.9852      0.9929     53.9975\n  1000   5400    -0.6454    32.8896    42.8204    36.743853     53.9893      0.7390     53.9975\n  1000   5600    -0.6454    32.8896    42.8204    37.009126     53.9921      0.4891     53.9975\n  1000   5800    -0.6454    32.8896    42.8204    37.270416     53.9938      0.2428     53.9975\n  1000   6000    -0.6454    32.8896    42.8204    37.527828     53.9943      0.0002     53.9975\n  1000   6200    -0.6454    32.8896    42.8204    37.781464     53.9938     -0.2388     53.9975\n  1000   6400    -0.6454    32.8896    42.8204    38.031420     53.9922     -0.4743     53.9975\n  1000   6600    -0.6454    32.8896    42.8204    38.277789     53.9897     -0.7065     53.9975\n  1000   6800    -0.6454    32.8896    42.8204    38.520662     53.9862     -0.9354     53.9975\n  1000   7000    -0.6454    32.8896    42.8204    38.760123     53.9818     -1.1610     53.9975\n  1000   7200    -0.6454    32.8896    42.8204    38.996255     53.9766     -1.3835     53.9975\n  1000   7400    -0.6454    32.8896    42.8204    39.229138     53.9705     -1.6028     53.9975\n  1000   7600    -0.6454    32.8896    42.8204    39.458848     53.9636     -1.8192     53.9975\n  1000   7800    -0.6454    32.8896    42.8204    39.685459     53.9560     -2.0326     53.9975\n  1000   8000    -0.6454    32.8896    42.8204    39.909042     53.9476     -2.2432     53.9975\n  1000   8200    -0.6454    32.8896    42.8204    40.129665     53.9386     -2.4509     53.9975\n  1000   8400    -0.6454    32.8896    42.8204    40.347394     53.9289     -2.6559     53.9975\n  1500      0    -0.6454    33.2353    42.9055    27.493913     53.4036      9.6755     54.2760\n  1500    200    -0.6454    33.2353    42.9055    27.936197     53.4767      9.2629     54.2760\n  1500    400    -0.6454    33.2353    42.9055    28.367845     53.5450      8.8598     54.2760\n  1500    600    -0.6454    33.2353    42.9055    28.789379     53.6087      8.4656     54.2760\n  1500    800    -0.6454    33.2353    42.9055    29.201280     53.6682      8.0799     54.2760\n  1500   1000    -0.6454    33.2353    42.9055    29.603992     53.7236      7.7025     54.2760\n  1500   1200    -0.6454    33.2353    42.9055    29.997923     53.7753      7.3330     54.2760\n  1500   1400    -0.6454    33.2353    42.9055    30.383456     53.8234      6.9709     54.2760\n  1500   1600    -0.6454    33.2353    42.9055    30.760943     53.8682      6.6162     54.2760\n  1500   1800    -0.6454    33.2353    42.9055    31.130714     53.9097      6.2684     54.2760\n  1500   2000    -0.6454    33.2353    42.9055    31.493077     53.9483      5.9273     54.2760\n  1500   2200    -0.6454    33.2353    42.9055    31.848320     53.9840      5.5927     54.2760\n  1500   2400    -0.6454    33.2353    42.9055    32.196712     54.0170      5.2643     54.2760\n  1500   2600    -0.6454    33.2353    42.9055    32.538506     54.0474      4.9420     54.2760\n  1500   2800    -0.6454    33.2353    42.9055    32.873942     54.0754      4.6255     54.2760\n  1500   3000    -0.6454    33.2353    42.9055    33.203242     54.1011      4.3146     54.2760\n  1500   3200    -0.6454    33.2353    42.9055    33.526618     54.1246      4.0092     54.2760\n  1500   3400    -0.6454    33.2353    42.9055    33.844271     54.1460      3.7090     54.2760\n  1500   3600    -0.6454    33.2353    42.9055    34.156389     54.1654      3.4140     54.2760\n  1500   3800    -0.6454    33.2353    42.9055    34.463152     54.1829      3.1239     54.2760\n  1500   4000    -0.6454    33.2353    42.9055    34.764728     54.1986      2.8387     54.2760\n  1500   4200    -0.6454    33.2353    42.9055    35.061279     54.2125      2.5581     54.2760\n  1500   4400    -0.6454    33.2353    42.9055    35.352959     54.2248      2.2821     54.2760\n  1500   4600    -0.6454    33.2353    42.9055    35.639912     54.2356      2.0105     54.2760\n  1500   4800    -0.6454    33.2353    42.9055    35.922277     54.2448      1.7432     54.2760\n  1500   5000    -0.6454    33.2353    42.9055    36.200187     54.2526      1.4800     54.2760\n  1500   5200    -0.6454    33.2353    42.9055    36.473768     54.2591      1.2210     54.2760\n  1500   5400    -0.6454    33.2353    42.9055    36.743141     54.2642      0.9659     54.2760\n  1500   5600    -0.6454    33.2353    42.9055    37.008420     54.2681      0.7146     54.2760\n  1500   5800    -0.6454    33.2353    42.9055    37.269715     54.2708      0.4671     54.2760\n  1500   6000    -0.6454    33.2353    42.9055    37.527133     54.2723      0.2233     54.2760\n  1500   6200    -0.6454    33.2353    42.9055    37.780775     54.2728     -0.0170     54.2760\n  1500   6400    -0.6454    33.2353    42.9055    38.030736     54.2722     -0.2538     54.2760\n  1500   6600    -0.6454    33.2353    42.9055    38.277111     54.2706     -0.4872     54.2760\n  1500   6800    -0.6454    33.2353    42.9055    38.519989     54.2680     -0.7172     54.2760\n  1500   7000    -0.6454    33.2353    42.9055    38.759456     54.2646     -0.9440     54.2760\n  1500   7200    -0.6454    33.2353    42.9055    38.995593     54.2602     -1.1677     54.2760\n  1500   7400    -0.6454    33.2353    42.9055    39.228481     54.2550     -1.3882     54.2760\n  1500   7600    -0.6454    33.2353    42.9055    39.458196     54.2490     -1.6057     54.2760\n  1500   7800    -0.6454    33.2353    42.9055    39.684812     54.2422     -1.8203     54.2760\n  1500   8000    -0.6454    33.2353    42.9055    39.908399     54.2347     -2.0319     54.2760\n  1500   8200    -0.6454    33.2353    42.9055    40.129027     54.2265     -2.2408     54.2760\n  1500   8400    -0.6454    33.2353    42.9055    40.346760     54.2176     -2.4468     54.2760\n  2000      0    -0.6454    33.5810    42.9905    27.492904     53.6385      9.9438     54.5553\n  2000    200    -0.6454    33.5810    42.9905    27.935205     53.7136      9.5294     54.5553\n  2000    400    -0.6454    33.5810    42.9905    28.366870     53.7839      9.1245     54.5553\n  2000    600    -0.6454    33.5810    42.9905    28.788420     53.8496      8.7285     54.5553\n  2000    800    -0.6454    33.5810    42.9905    29.200335     53.9109      8.3411     54.5553\n  2000   1000    -0.6454    33.5810    42.9905    29.603061     53.9682      7.9620     54.5553\n  2000   1200    -0.6454    33.5810    42.9905    29.997006     54.0217      7.5907     54.5553\n  2000   1400    -0.6454    33.5810    42.9905    30.382551     54.0715      7.2270     54.5553\n  2000   1600    -0.6454    33.5810    42.9905    30.760051     54.1179      6.8706     54.5553\n  2000   1800    -0.6454    33.5810    42.9905    31.129834     54.1612      6.5212     54.5553\n  2000   2000    -0.6454    33.5810    42.9905    31.492208     54.2013      6.1785     54.5553\n  2000   2200    -0.6454    33.5810    42.9905    31.847461     54.2386      5.8423     54.5553\n  2000   2400    -0.6454    33.5810    42.9905    32.195864     54.2731      5.5123     54.5553\n  2000   2600    -0.6454    33.5810    42.9905    32.537668     54.3050      5.1885     54.5553\n  2000   2800    -0.6454    33.5810    42.9905    32.873113     54.3344      4.8704     54.5553\n  2000   3000    -0.6454    33.5810    42.9905    33.202423     54.3615      4.5581     54.5553\n  2000   3200    -0.6454    33.5810    42.9905    33.525809     54.3864      4.2512     54.5553\n  2000   3400    -0.6454    33.5810    42.9905    33.843470     54.4091      3.9495     54.5553\n  2000   3600    -0.6454    33.5810    42.9905    34.155597     54.4298      3.6531     54.5553\n  2000   3800    -0.6454    33.5810    42.9905    34.462368     54.4486      3.3616     54.5553\n  2000   4000    -0.6454    33.5810    42.9905    34.763952     54.4655      3.0749     54.5553\n  2000   4200    -0.6454    33.5810    42.9905    35.060511     54.4807      2.7930     54.5553\n  2000   4400    -0.6454    33.5810    42.9905    35.352197     54.4942      2.5156     54.5553\n  2000   4600    -0.6454    33.5810    42.9905    35.639158     54.5061      2.2426     54.5553\n  2000   4800    -0.6454    33.5810    42.9905    35.921530     54.5165      1.9740     54.5553\n  2000   5000    -0.6454    33.5810    42.9905    36.199447     54.5254      1.7095     54.5553\n  2000   5200    -0.6454    33.5810    42.9905    36.473035     54.5330      1.4491     54.5553\n  2000   5400    -0.6454    33.5810    42.9905    36.742414     54.5392      1.1927     54.5553\n  2000   5600    -0.6454    33.5810    42.9905    37.007699     54.5441      0.9402     54.5553\n  2000   5800    -0.6454    33.5810    42.9905    37.269001     54.5478      0.6914     54.5553\n  2000   6000    -0.6454    33.5810    42.9905    37.526425     54.5504      0.4463     54.5553\n  2000   6200    -0.6454    33.5810    42.9905    37.780072     54.5518      0.2048     54.5553\n  2000   6400    -0.6454    33.5810    42.9905    38.030040     54.5522     -0.0332     54.5553\n  2000   6600    -0.6454    33.5810    42.9905    38.276421     54.5515     -0.2678     54.5553\n  2000   6800    -0.6454    33.5810    42.9905    38.519304     54.5499     -0.4990     54.5553\n  2000   7000    -0.6454    33.5810    42.9905    38.758775     54.5473     -0.7270     54.5553\n  2000   7200    -0.6454    33.5810    42.9905    38.994918     54.5439     -0.9519     54.5553\n  2000   7400    -0.6454    33.5810    42.9905    39.227811     54.5395     -1.1736     54.5553\n  2000   7600    -0.6454    33.5810    42.9905    39.457531     54.5344     -1.3922     54.5553\n  2000   7800    -0.6454    33.5810    42.9905    39.684152     54.5284     -1.6079     54.5553\n  2000   8000    -0.6454    33.5810    42.9905    39.907744     54.5218     -1.8207     54.5553\n  2000   8200    -0.6454    33.5810    42.9905    40.128376     54.5143     -2.0306     54.5553\n  2000   8400    -0.6454    33.5810    42.9905    40.346114     54.5062     -2.2378     54.5553\n  2500      0    -0.6454    33.9267    43.0756    27.491875     53.8733     10.2122     54.8356\n  2500    200    -0.6454    33.9267    43.0756    27.934194     53.9506      9.7960     54.8356\n  2500    400    -0.6454    33.9267    43.0756    28.365875     54.0228      9.3892     54.8356\n  2500    600    -0.6454    33.9267    43.0756    28.787440     54.0904      8.9915     54.8356\n  2500    800    -0.6454    33.9267    43.0756    29.199371     54.1537      8.6023     54.8356\n  2500   1000    -0.6454    33.9267    43.0756    29.602111     54.2128      8.2214     54.8356\n  2500   1200    -0.6454    33.9267    43.0756    29.996070     54.2680      7.8485     54.8356\n  2500   1400    -0.6454    33.9267    43.0756    30.381628     54.3196      7.4831     54.8356\n  2500   1600    -0.6454    33.9267    43.0756    30.759140     54.3677      7.1250     54.8356\n  2500   1800    -0.6454    33.9267    43.0756    31.128935     54.4126      6.7740     54.8356\n  2500   2000    -0.6454    33.9267    43.0756    31.491321     54.4543      6.4297     54.8356\n  2500   2200    -0.6454    33.9267    43.0756    31.846586     54.4931      6.0919     54.8356\n  2500   2400    -0.6454    33.9267    43.0756    32.194999     54.5292      5.7604     54.8356\n  2500   2600    -0.6454    33.9267    43.0756    32.536814     54.5626      5.4350     54.8356\n  2500   2800    -0.6454    33.9267    43.0756    32.872269     54.5934      5.1154     54.8356\n  2500   3000    -0.6454    33.9267    43.0756    33.201588     54.6219      4.8016     54.8356\n  2500   3200    -0.6454    33.9267    43.0756    33.524983     54.6482      4.4932     54.8356\n  2500   3400    -0.6454    33.9267    43.0756    33.842654     54.6722      4.1901     54.8356\n  2500   3600    -0.6454    33.9267    43.0756    34.154789     54.6942      3.8922     54.8356\n  2500   3800    -0.6454    33.9267    43.0756    34.461568     54.7143      3.5993     54.8356\n  2500   4000    -0.6454    33.9267    43.0756    34.763160     54.7325      3.3112     54.8356\n  2500   4200    -0.6454    33.9267    43.0756    35.059727     54.7489      3.0279     54.8356\n  2500   4400    -0.6454    33.9267    43.0756    35.351422     54.7636      2.7491     54.8356\n  2500   4600    -0.6454    33.9267    43.0756    35.638389     54.7766      2.4748     54.8356\n  2500   4800    -0.6454    33.9267    43.0756    35.920769     54.7882      2.2048     54.8356\n  2500   5000    -0.6454    33.9267    43.0756    36.198693     54.7982      1.9390     54.8356\n  2500   5200    -0.6454    33.9267    43.0756    36.472288     54.8068      1.6773     54.8356\n  2500   5400    -0.6454    33.9267    43.0756    36.741674     54.8141      1.4196     54.8356\n  2500   5600    -0.6454    33.9267    43.0756    37.006965     54.8201      1.1657     54.8356\n  2500   5800    -0.6454    33.9267    43.0756    37.268273     54.8248      0.9157     54.8356\n  2500   6000    -0.6454    33.9267    43.0756    37.525704     54.8284      0.6694     54.8356\n  2500   6200    -0.6454    33.9267    43.0756    37.779357     54.8308      0.4266     54.8356\n  2500   6400    -0.6454    33.9267    43.0756    38.029330     54.8321      0.1874     54.8356\n  2500   6600    -0.6454    33.9267    43.0756    38.275717     54.8324     -0.0484     54.8356\n  2500   6800    -0.6454    33.9267    43.0756    38.518605     54.8317     -0.2809     54.8356\n  2500   7000    -0.6454    33.9267    43.0756    38.758083     54.8301     -0.5100     54.8356\n  2500   7200    -0.6454    33.9267    43.0756    38.994231     54.8275     -0.7360     54.8356\n  2500   7400    -0.6454    33.9267    43.0756    39.227129     54.8241     -0.9589     54.8356\n  2500   7600    -0.6454    33.9267    43.0756    39.456854     54.8198     -1.1787     54.8356\n  2500   7800    -0.6454    33.9267    43.0756    39.683480     54.8147     -1.3955     54.8356\n  2500   8000    -0.6454    33.9267    43.0756    39.907076     54.8088     -1.6094     54.8356\n  2500   8200    -0.6454    33.9267    43.0756    40.127713     54.8022     -1.8205     54.8356\n  2500   8400    -0.6454    33.9267    43.0756    40.345456     54.7949     -2.0287     54.8356\n  3000      0    -0.6454    34.2724    43.1606    27.490826     54.1082     10.4806     55.1167\n  3000    200    -0.6454    34.2724    43.1606    27.933162     54.1875     10.0625     55.1167\n  3000    400    -0.6454    34.2724    43.1606    28.364860     54.2617      9.6540     55.1167\n  3000    600    -0.6454    34.2724    43.1606    28.786442     54.3313      9.2544     55.1167\n  3000    800    -0.6454    34.2724    43.1606    29.198388     54.3964      8.8635     55.1167\n  3000   1000    -0.6454    34.2724    43.1606    29.601143     54.4574      8.4809     55.1167\n  3000   1200    -0.6454    34.2724    43.1606    29.995115     54.5144      8.1063     55.1167\n  3000   1400    -0.6454    34.2724    43.1606    30.380687     54.5677      7.7392     55.1167\n  3000   1600    -0.6454    34.2724    43.1606    30.758212     54.6175      7.3795     55.1167\n  3000   1800    -0.6454    34.2724    43.1606    31.128020     54.6640      7.0268     55.1167\n  3000   2000    -0.6454    34.2724    43.1606    31.490417     54.7073      6.6809     55.1167\n  3000   2200    -0.6454    34.2724    43.1606    31.845693     54.7477      6.3416     55.1167\n  3000   2400    -0.6454    34.2724    43.1606    32.194117     54.7852      6.0085     55.1167\n  3000   2600    -0.6454    34.2724    43.1606    32.535943     54.8201      5.6815     55.1167\n  3000   2800    -0.6454    34.2724    43.1606    32.871408     54.8524      5.3605     55.1167\n  3000   3000    -0.6454    34.2724    43.1606    33.200737     54.8823      5.0451     55.1167\n  3000   3200    -0.6454    34.2724    43.1606    33.524142     54.9099      4.7352     55.1167\n  3000   3400    -0.6454    34.2724    43.1606    33.841821     54.9353      4.4307     55.1167\n  3000   3600    -0.6454    34.2724    43.1606    34.153966     54.9586      4.1313     55.1167\n  3000   3800    -0.6454    34.2724    43.1606    34.460753     54.9800      3.8370     55.1167\n  3000   4000    -0.6454    34.2724    43.1606    34.762354     54.9994      3.5475     55.1167\n  3000   4200    -0.6454    34.2724    43.1606    35.058929     55.0170      3.2628     55.1167\n  3000   4400    -0.6454    34.2724    43.1606    35.350631     55.0329      2.9826     55.1167\n  3000   4600    -0.6454    34.2724    43.1606    35.637607     55.0472      2.7069     55.1167\n  3000   4800    -0.6454    34.2724    43.1606    35.919994     55.0598      2.4356     55.1167\n  3000   5000    -0.6454    34.2724    43.1606    36.197925     55.0710      2.1685     55.1167\n  3000   5200    -0.6454    34.2724    43.1606    36.471527     55.0807      1.9055     55.1167\n  3000   5400    -0.6454    34.2724    43.1606    36.740919     55.0890      1.6465     55.1167\n  3000   5600    -0.6454    34.2724    43.1606    37.006218     55.0961      1.3913     55.1167\n  3000   5800    -0.6454    34.2724    43.1606    37.267532     55.1018      1.1400     55.1167\n  3000   6000    -0.6454    34.2724    43.1606    37.524969     55.1064      0.8924     55.1167\n  3000   6200    -0.6454    34.2724    43.1606    37.778628     55.1098      0.6485     55.1167\n  3000   6400    -0.6454    34.2724    43.1606    38.028607     55.1121      0.4080     55.1167\n  3000   6600    -0.6454    34.2724    43.1606    38.275000     55.1134      0.1710     55.1167\n  3000   6800    -0.6454    34.2724    43.1606    38.517894     55.1136     -0.0626     55.1167\n  3000   7000    -0.6454    34.2724    43.1606    38.757377     55.1128     -0.2930     55.1167\n  3000   7200    -0.6454    34.2724    43.1606    38.993530     55.1111     -0.5202     55.1167\n  3000   7400    -0.6454    34.2724    43.1606    39.226434     55.1086     -0.7442     55.1167\n  3000   7600    -0.6454    34.2724    43.1606    39.456164     55.1051     -0.9652     55.1167\n  3000   7800    -0.6454    34.2724    43.1606    39.682795     55.1009     -1.1831     55.1167\n  3000   8000    -0.6454    34.2724    43.1606    39.906397     55.0958     -1.3982     55.1167\n  3000   8200    -0.6454    34.2724    43.1606    40.127039     55.0900     -1.6103     55.1167\n  3000   8400    -0.6454    34.2724    43.1606    40.344786     55.0835     -1.8197     55.1167\n  3500      0    -0.6454    34.6181    43.2457    27.489756     54.3430     10.7490     55.3987\n  3500    200    -0.6454    34.6181    43.2457    27.932111     54.4244     10.3291     55.3987\n  3500    400    -0.6454    34.6181    43.2457    28.363826     54.5006      9.9187     55.3987\n  3500    600    -0.6454    34.6181    43.2457    28.785424     54.5721      9.5174     55.3987\n  3500    800    -0.6454    34.6181    43.2457    29.197386     54.6391      9.1248     55.3987\n  3500   1000    -0.6454    34.6181    43.2457    29.600156     54.7019      8.7405     55.3987\n  3500   1200    -0.6454    34.6181    43.2457    29.994143     54.7607      8.3641     55.3987\n  3500   1400    -0.6454    34.6181    43.2457    30.379728     54.8158      7.9954     55.3987\n  3500   1600    -0.6454    34.6181    43.2457    30.757266     54.8673      7.6340     55.3987\n  3500   1800    -0.6454    34.6181    43.2457    31.127086     54.9154      7.2797     55.3987\n  3500   2000    -0.6454    34.6181    43.2457    31.489496     54.9603      6.9322     55.3987\n  3500   2200    -0.6454    34.6181    43.2457    31.844784     55.0022      6.5912     55.3987\n  3500   2400    -0.6454    34.6181    43.2457    32.193219     55.0413      6.2566     55.3987\n  3500   2600    -0.6454    34.6181    43.2457    32.535056     55.0776      5.9281     55.3987\n  3500   2800    -0.6454    34.6181    43.2457    32.870531     55.1114      5.6055     55.3987\n  3500   3000    -0.6454    34.6181    43.2457    33.199870     55.1427      5.2886     55.3987\n  3500   3200    -0.6454    34.6181    43.2457    33.523284     55.1717      4.9773     55.3987\n  3500   3400    -0.6454    34.6181    43.2457    33.840974     55.1984      4.6713     55.3987\n  3500   3600    -0.6454    34.6181    43.2457    34.153127     55.2230      4.3705     55.3987\n  3500   3800    -0.6454    34.6181    43.2457    34.459923     55.2456      4.0747     55.3987\n  3500   4000    -0.6454    34.6181    43.2457    34.761533     55.2663      3.7838     55.3987\n  3500   4200    -0.6454    34.6181    43.2457    35.058116     55.2852      3.4977     55.3987\n  3500   4400    -0.6454    34.6181    43.2457    35.349826     55.3023      3.2162     55.3987\n  3500   4600    -0.6454    34.6181    43.2457    35.636809     55.3177      2.9391     55.3987\n  3500   4800    -0.6454    34.6181    43.2457    35.919204     55.3315      2.6664     55.3987\n  3500   5000    -0.6454    34.6181    43.2457    36.197143     55.3437      2.3980     55.3987\n  3500   5200    -0.6454    34.6181    43.2457    36.470752     55.3546      2.1337     55.3987\n  3500   5400    -0.6454    34.6181    43.2457    36.740151     55.3640      1.8734     55.3987\n  3500   5600    -0.6454    34.6181    43.2457    37.005456     55.3721      1.6170     55.3987\n  3500   5800    -0.6454    34.6181    43.2457    37.266777     55.3788      1.3644     55.3987\n  3500   6000    -0.6454    34.6181    43.2457    37.524220     55.3844      1.1155     55.3987\n  3500   6200    -0.6454    34.6181    43.2457    37.777886     55.3888      0.8703     55.3987\n  3500   6400    -0.6454    34.6181    43.2457    38.027871     55.3921      0.6287     55.3987\n  3500   6600    -0.6454    34.6181    43.2457    38.274270     55.3943      0.3904     55.3987\n  3500   6800    -0.6454    34.6181    43.2457    38.517170     55.3954      0.1556     55.3987\n  3500   7000    -0.6454    34.6181    43.2457    38.756658     55.3956     -0.0760     55.3987\n  3500   7200    -0.6454    34.6181    43.2457    38.992817     55.3948     -0.3043     55.3987\n  3500   7400    -0.6454    34.6181    43.2457    39.225727     55.3931     -0.5295     55.3987\n  3500   7600    -0.6454    34.6181    43.2457    39.455462     55.3905     -0.7516     55.3987\n  3500   7800    -0.6454    34.6181    43.2457    39.682098     55.3871     -0.9707     55.3987\n  3500   8000    -0.6454    34.6181    43.2457    39.905705     55.3829     -1.1868     55.3987\n  3500   8200    -0.6454    34.6181    43.2457    40.126352     55.3779     -1.4001     55.3987\n  3500   8400    -0.6454    34.6181    43.2457    40.344104     55.3722     -1.6106     55.3987\n  4000      0    -0.6454    34.9638    43.3307    27.488666     54.5778     11.0175     55.6816\n  4000    200    -0.6454    34.9638    43.3307    27.931039     54.6612     10.5958     55.6816\n  4000    400    -0.6454    34.9638    43.3307    28.362772     54.7395     10.1836     55.6816\n  4000    600    -0.6454    34.9638    43.3307    28.784387     54.8130      9.7805     55.6816\n  4000    800    -0.6454    34.9638    43.3307    29.196366     54.8819      9.3861     55.6816\n  4000   1000    -0.6454    34.9638    43.3307    29.599150     54.9465      9.0000     55.6816\n  4000   1200    -0.6454    34.9638    43.3307    29.993152     55.0071      8.6219     55.6816\n  4000   1400    -0.6454    34.9638    43.3307    30.378752     55.0638      8.2515     55.6816\n  4000   1600    -0.6454    34.9638    43.3307    30.756303     55.1170      7.8885     55.6816\n  4000   1800    -0.6454    34.9638    43.3307    31.126136     55.1668      7.5326     55.6816\n  4000   2000    -0.6454    34.9638    43.3307    31.488558     55.2133      7.1835     55.6816\n  4000   2200    -0.6454    34.9638    43.3307    31.843858     55.2568      6.8409     55.6816\n  4000   2400    -0.6454    34.9638    43.3307    32.192305     55.2974      6.5047     55.6816\n  4000   2600    -0.6454    34.9638    43.3307    32.534152     55.3352      6.1747     55.6816\n  4000   2800    -0.6454    34.9638    43.3307    32.869638     55.3704      5.8506     55.6816\n  4000   3000    -0.6454    34.9638    43.3307    33.198987     55.4031      5.5322     55.6816\n  4000   3200    -0.6454    34.9638    43.3307    33.522412     55.4334      5.2193     55.6816\n  4000   3400    -0.6454    34.9638    43.3307    33.840110     55.4615      4.9119     55.6816\n  4000   3600    -0.6454    34.9638    43.3307    34.152273     55.4874      4.6096     55.6816\n  4000   3800    -0.6454    34.9638    43.3307    34.459078     55.5113      4.3124     55.6816\n  4000   4000    -0.6454    34.9638    43.3307    34.760696     55.5332      4.0202     55.6816\n  4000   4200    -0.6454    34.9638    43.3307    35.057288     55.5533      3.7326     55.6816\n  4000   4400    -0.6454    34.9638    43.3307    35.349007     55.5716      3.4497     55.6816\n  4000   4600    -0.6454    34.9638    43.3307    35.635998     55.5882      3.1713     55.6816\n  4000   4800    -0.6454    34.9638    43.3307    35.918400     55.6031      2.8973     55.6816\n  4000   5000    -0.6454    34.9638    43.3307    36.196346     55.6165      2.6275     55.6816\n  4000   5200    -0.6454    34.9638    43.3307    36.469963     55.6284      2.3619     55.6816\n  4000   5400    -0.6454    34.9638    43.3307    36.739369     55.6389      2.1003     55.6816\n  4000   5600    -0.6454    34.9638    43.3307    37.004681     55.6480      1.8426     55.6816\n  4000   5800    -0.6454    34.9638    43.3307    37.266009     55.6558      1.5888     55.6816\n  4000   6000    -0.6454    34.9638    43.3307    37.523459     55.6624      1.3387     55.6816\n  4000   6200    -0.6454    34.9638    43.3307    37.777131     55.6678      1.0922     55.6816\n  4000   6400    -0.6454    34.9638    43.3307    38.027122     55.6720      0.8493     55.6816\n  4000   6600    -0.6454    34.9638    43.3307    38.273527     55.6752      0.6099     55.6816\n  4000   6800    -0.6454    34.9638    43.3307    38.516433     55.6772      0.3738     55.6816\n  4000   7000    -0.6454    34.9638    43.3307    38.755927     55.6783      0.1411     55.6816\n  4000   7200    -0.6454    34.9638    43.3307    38.992092     55.6784     -0.0884     55.6816\n  4000   7400    -0.6454    34.9638    43.3307    39.225007     55.6776     -0.3148     55.6816\n  4000   7600    -0.6454    34.9638    43.3307    39.454748     55.6759     -0.5380     55.6816\n  4000   7800    -0.6454    34.9638    43.3307    39.681389     55.6733     -0.7582     55.6816\n  4000   8000    -0.6454    34.9638    43.3307    39.905001     55.6699     -0.9755     55.6816\n  4000   8200    -0.6454    34.9638    43.3307    40.125653     55.6657     -1.1899     55.6816\n  4000   8400    -0.6454    34.9638    43.3307    40.343411     55.6608     -1.4015     55.6816\n  4500      0    -0.6454    35.3095    43.4158    27.487555     54.8126     11.2860     55.9652\n  4500    200    -0.6454    35.3095    43.4158    27.929948     54.8981     10.8624     55.9652\n  4500    400    -0.6454    35.3095    43.4158    28.361699     54.9784     10.4484     55.9652\n  4500    600    -0.6454    35.3095    43.4158    28.783331     55.0538     10.0435     55.9652\n  4500    800    -0.6454    35.3095    43.4158    29.195326     55.1246      9.6474     55.9652\n  4500   1000    -0.6454    35.3095    43.4158    29.598126     55.1910      9.2596     55.9652\n  4500   1200    -0.6454    35.3095    43.4158    29.992143     55.2534      8.8798     55.9652\n  4500   1400    -0.6454    35.3095    43.4158    30.377757     55.3119      8.5077     55.9652\n  4500   1600    -0.6454    35.3095    43.4158    30.755322     55.3667      8.1431     55.9652\n  4500   1800    -0.6454    35.3095    43.4158    31.125168     55.4181      7.7855     55.9652\n  4500   2000    -0.6454    35.3095    43.4158    31.487603     55.4663      7.4348     55.9652\n  4500   2200    -0.6454    35.3095    43.4158    31.842915     55.5113      7.0906     55.9652\n  4500   2400    -0.6454    35.3095    43.4158    32.191373     55.5534      6.7529     55.9652\n  4500   2600    -0.6454    35.3095    43.4158    32.533232     55.5927      6.4213     55.9652\n  4500   2800    -0.6454    35.3095    43.4158    32.868729     55.6293      6.0957     55.9652\n  4500   3000    -0.6454    35.3095    43.4158    33.198089     55.6635      5.7758     55.9652\n  4500   3200    -0.6454    35.3095    43.4158    33.521523     55.6952      5.4615     55.9652\n  4500   3400    -0.6454    35.3095    43.4158    33.839232     55.7246      5.1525     55.9652\n  4500   3600    -0.6454    35.3095    43.4158    34.151404     55.7518      4.8488     55.9652\n  4500   3800    -0.6454    35.3095    43.4158    34.458218     55.7770      4.5502     55.9652\n  4500   4000    -0.6454    35.3095    43.4158    34.759845     55.8002      4.2565     55.9652\n  4500   4200    -0.6454    35.3095    43.4158    35.056446     55.8214      3.9676     55.9652\n  4500   4400    -0.6454    35.3095    43.4158    35.348172     55.8409      3.6833     55.9652\n  4500   4600    -0.6454    35.3095    43.4158    35.635172     55.8587      3.4035     55.9652\n  4500   4800    -0.6454    35.3095    43.4158    35.917582     55.8747      3.1282     55.9652\n  4500   5000    -0.6454    35.3095    43.4158    36.195536     55.8893      2.8571     55.9652\n  4500   5200    -0.6454    35.3095    43.4158    36.469160     55.9023      2.5901     55.9652\n  4500   5400    -0.6454    35.3095    43.4158    36.738573     55.9138      2.3272     55.9652\n  4500   5600    -0.6454    35.3095    43.4158    37.003892     55.9240      2.0683     55.9652\n  4500   5800    -0.6454    35.3095    43.4158    37.265227     55.9328      1.8132     55.9652\n  4500   6000    -0.6454    35.3095    43.4158    37.522684     55.9404      1.5618     55.9652\n  4500   6200    -0.6454    35.3095    43.4158    37.776362     55.9468      1.3141     55.9652\n  4500   6400    -0.6454    35.3095    43.4158    38.026360     55.9520      1.0700     55.9652\n  4500   6600    -0.6454    35.3095    43.4158    38.272771     55.9561      0.8293     55.9652\n  4500   6800    -0.6454    35.3095    43.4158    38.515683     55.9591      0.5921     55.9652\n  4500   7000    -0.6454    35.3095    43.4158    38.755184     55.9610      0.3582     55.9652\n  4500   7200    -0.6454    35.3095    43.4158    38.991354     55.9620      0.1275     55.9652\n  4500   7400    -0.6454    35.3095    43.4158    39.224274     55.9621     -0.1000     55.9652\n  4500   7600    -0.6454    35.3095    43.4158    39.454021     55.9612     -0.3244     55.9652\n  4500   7800    -0.6454    35.3095    43.4158    39.680668     55.9595     -0.5458     55.9652\n  4500   8000    -0.6454    35.3095    43.4158    39.904285     55.9569     -0.7642     55.9652\n  4500   8200    -0.6454    35.3095    43.4158    40.124942     55.9536     -0.9797     55.9652\n  4500   8400    -0.6454    35.3095    43.4158    40.342705     55.9495     -1.1923     55.9652\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180611_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -1.0202033      -42.5608037       28.9838632   m   m   m\ninitial_baseline_rate:          0.0000000        0.1856629        0.0418846   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -42.7289394       29.0749561   m   m   m\nprecision_baseline_rate:        0.0000000        0.1888319        0.0419065   m/s m/s m/s\nunwrap_phase_constant:           -0.00012     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180307-20180611_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -1.020    -42.561     28.984\norbit baseline rate   (TCN) (m/s):  0.000e+00  1.857e-01  4.188e-02\n\nbaseline vector (TCN)   (m):      0.000    -42.729     29.075\nbaseline rate   (TCN) (m/s):  0.000e+00  1.888e-01  4.191e-02\n\nSLC-1 center baseline length (m):    51.6828\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -44.4915    28.6838    27.496815      4.9019    -52.7088     52.9364\n     0    200     0.0000   -44.4915    28.6838    27.939050      4.4949    -52.7451     52.9364\n     0    400     0.0000   -44.4915    28.6838    28.370652      4.0975    -52.7774     52.9364\n     0    600     0.0000   -44.4915    28.6838    28.792142      3.7091    -52.8062     52.9364\n     0    800     0.0000   -44.4915    28.6838    29.204001      3.3294    -52.8315     52.9364\n     0   1000     0.0000   -44.4915    28.6838    29.606672      2.9581    -52.8535     52.9364\n     0   1200     0.0000   -44.4915    28.6838    30.000566      2.5946    -52.8726     52.9364\n     0   1400     0.0000   -44.4915    28.6838    30.386062      2.2389    -52.8889     52.9364\n     0   1600     0.0000   -44.4915    28.6838    30.763514      1.8904    -52.9025     52.9364\n     0   1800     0.0000   -44.4915    28.6838    31.133251      1.5490    -52.9136     52.9364\n     0   2000     0.0000   -44.4915    28.6838    31.495582      1.2143    -52.9223     52.9364\n     0   2200     0.0000   -44.4915    28.6838    31.850793      0.8862    -52.9289     52.9364\n     0   2400     0.0000   -44.4915    28.6838    32.199155      0.5644    -52.9333     52.9364\n     0   2600     0.0000   -44.4915    28.6838    32.540921      0.2486    -52.9357     52.9364\n     0   2800     0.0000   -44.4915    28.6838    32.876328     -0.0613    -52.9362     52.9364\n     0   3000     0.0000   -44.4915    28.6838    33.205602     -0.3655    -52.9350     52.9364\n     0   3200     0.0000   -44.4915    28.6838    33.528952     -0.6642    -52.9321     52.9364\n     0   3400     0.0000   -44.4915    28.6838    33.846580     -0.9577    -52.9276     52.9364\n     0   3600     0.0000   -44.4915    28.6838    34.158674     -1.2459    -52.9216     52.9364\n     0   3800     0.0000   -44.4915    28.6838    34.465413     -1.5292    -52.9142     52.9364\n     0   4000     0.0000   -44.4915    28.6838    34.766966     -1.8077    -52.9054     52.9364\n     0   4200     0.0000   -44.4915    28.6838    35.063495     -2.0815    -52.8953     52.9364\n     0   4400     0.0000   -44.4915    28.6838    35.355153     -2.3507    -52.8841     52.9364\n     0   4600     0.0000   -44.4915    28.6838    35.642085     -2.6155    -52.8716     52.9364\n     0   4800     0.0000   -44.4915    28.6838    35.924431     -2.8760    -52.8581     52.9364\n     0   5000     0.0000   -44.4915    28.6838    36.202321     -3.1324    -52.8435     52.9364\n     0   5200     0.0000   -44.4915    28.6838    36.475883     -3.3846    -52.8280     52.9364\n     0   5400     0.0000   -44.4915    28.6838    36.745237     -3.6330    -52.8115     52.9364\n     0   5600     0.0000   -44.4915    28.6838    37.010498     -3.8774    -52.7941     52.9364\n     0   5800     0.0000   -44.4915    28.6838    37.271776     -4.1181    -52.7759     52.9364\n     0   6000     0.0000   -44.4915    28.6838    37.529176     -4.3552    -52.7568     52.9364\n     0   6200     0.0000   -44.4915    28.6838    37.782801     -4.5887    -52.7370     52.9364\n     0   6400     0.0000   -44.4915    28.6838    38.032746     -4.8187    -52.7165     52.9364\n     0   6600     0.0000   -44.4915    28.6838    38.279105     -5.0453    -52.6953     52.9364\n     0   6800     0.0000   -44.4915    28.6838    38.521968     -5.2686    -52.6735     52.9364\n     0   7000     0.0000   -44.4915    28.6838    38.761419     -5.4887    -52.6510     52.9364\n     0   7200     0.0000   -44.4915    28.6838    38.997541     -5.7057    -52.6279     52.9364\n     0   7400     0.0000   -44.4915    28.6838    39.230415     -5.9195    -52.6043     52.9364\n     0   7600     0.0000   -44.4915    28.6838    39.460116     -6.1303    -52.5801     52.9364\n     0   7800     0.0000   -44.4915    28.6838    39.686717     -6.3383    -52.5555     52.9364\n     0   8000     0.0000   -44.4915    28.6838    39.910291     -6.5433    -52.5304     52.9364\n     0   8200     0.0000   -44.4915    28.6838    40.130905     -6.7455    -52.5048     52.9364\n     0   8400     0.0000   -44.4915    28.6838    40.348626     -6.9450    -52.4788     52.9364\n   500      0     0.0000   -44.1034    28.7699    27.495868      5.1584    -52.4042     52.6576\n   500    200     0.0000   -44.1034    28.7699    27.938119      4.7538    -52.4424     52.6576\n   500    400     0.0000   -44.1034    28.7699    28.369736      4.3586    -52.4768     52.6576\n   500    600     0.0000   -44.1034    28.7699    28.791241      3.9724    -52.5074     52.6576\n   500    800     0.0000   -44.1034    28.7699    29.203113      3.5948    -52.5346     52.6576\n   500   1000     0.0000   -44.1034    28.7699    29.605798      3.2255    -52.5586     52.6576\n   500   1200     0.0000   -44.1034    28.7699    29.999704      2.8641    -52.5795     52.6576\n   500   1400     0.0000   -44.1034    28.7699    30.385211      2.5103    -52.5976     52.6576\n   500   1600     0.0000   -44.1034    28.7699    30.762675      2.1637    -52.6130     52.6576\n   500   1800     0.0000   -44.1034    28.7699    31.132423      1.8241    -52.6259     52.6576\n   500   2000     0.0000   -44.1034    28.7699    31.494764      1.4913    -52.6364     52.6576\n   500   2200     0.0000   -44.1034    28.7699    31.849986      1.1649    -52.6446     52.6576\n   500   2400     0.0000   -44.1034    28.7699    32.198358      0.8448    -52.6507     52.6576\n   500   2600     0.0000   -44.1034    28.7699    32.540133      0.5307    -52.6548     52.6576\n   500   2800     0.0000   -44.1034    28.7699    32.875549      0.2225    -52.6570     52.6576\n   500   3000     0.0000   -44.1034    28.7699    33.204831     -0.0801    -52.6574     52.6576\n   500   3200     0.0000   -44.1034    28.7699    33.528190     -0.3773    -52.6561     52.6576\n   500   3400     0.0000   -44.1034    28.7699    33.845826     -0.6692    -52.6532     52.6576\n   500   3600     0.0000   -44.1034    28.7699    34.157928     -0.9560    -52.6488     52.6576\n   500   3800     0.0000   -44.1034    28.7699    34.464675     -1.2379    -52.6429     52.6576\n   500   4000     0.0000   -44.1034    28.7699    34.766235     -1.5149    -52.6357     52.6576\n   500   4200     0.0000   -44.1034    28.7699    35.062772     -1.7873    -52.6271     52.6576\n   500   4400     0.0000   -44.1034    28.7699    35.354436     -2.0552    -52.6174     52.6576\n   500   4600     0.0000   -44.1034    28.7699    35.641375     -2.3187    -52.6064     52.6576\n   500   4800     0.0000   -44.1034    28.7699    35.923727     -2.5779    -52.5944     52.6576\n   500   5000     0.0000   -44.1034    28.7699    36.201624     -2.8330    -52.5812     52.6576\n   500   5200     0.0000   -44.1034    28.7699    36.475192     -3.0840    -52.5671     52.6576\n   500   5400     0.0000   -44.1034    28.7699    36.744552     -3.3311    -52.5520     52.6576\n   500   5600     0.0000   -44.1034    28.7699    37.009819     -3.5744    -52.5360     52.6576\n   500   5800     0.0000   -44.1034    28.7699    37.271103     -3.8139    -52.5192     52.6576\n   500   6000     0.0000   -44.1034    28.7699    37.528509     -4.0498    -52.5015     52.6576\n   500   6200     0.0000   -44.1034    28.7699    37.782139     -4.2822    -52.4831     52.6576\n   500   6400     0.0000   -44.1034    28.7699    38.032090     -4.5111    -52.4639     52.6576\n   500   6600     0.0000   -44.1034    28.7699    38.278454     -4.7366    -52.4440     52.6576\n   500   6800     0.0000   -44.1034    28.7699    38.521321     -4.9589    -52.4235     52.6576\n   500   7000     0.0000   -44.1034    28.7699    38.760777     -5.1779    -52.4023     52.6576\n   500   7200     0.0000   -44.1034    28.7699    38.996905     -5.3939    -52.3805     52.6576\n   500   7400     0.0000   -44.1034    28.7699    39.229783     -5.6067    -52.3582     52.6576\n   500   7600     0.0000   -44.1034    28.7699    39.459488     -5.8166    -52.3353     52.6576\n   500   7800     0.0000   -44.1034    28.7699    39.686095     -6.0235    -52.3119     52.6576\n   500   8000     0.0000   -44.1034    28.7699    39.909673     -6.2276    -52.2880     52.6576\n   500   8200     0.0000   -44.1034    28.7699    40.130291     -6.4289    -52.2636     52.6576\n   500   8400     0.0000   -44.1034    28.7699    40.348016     -6.6275    -52.2388     52.6576\n  1000      0     0.0000   -43.7152    28.8561    27.494901      5.4149    -52.0995     52.3803\n  1000    200     0.0000   -43.7152    28.8561    27.937168      5.0126    -52.1398     52.3803\n  1000    400     0.0000   -43.7152    28.8561    28.368800      4.6196    -52.1761     52.3803\n  1000    600     0.0000   -43.7152    28.8561    28.790320      4.2357    -52.2087     52.3803\n  1000    800     0.0000   -43.7152    28.8561    29.202206      3.8602    -52.2378     52.3803\n  1000   1000     0.0000   -43.7152    28.8561    29.604904      3.4930    -52.2636     52.3803\n  1000   1200     0.0000   -43.7152    28.8561    29.998823      3.1336    -52.2864     52.3803\n  1000   1400     0.0000   -43.7152    28.8561    30.384343      2.7817    -52.3063     52.3803\n  1000   1600     0.0000   -43.7152    28.8561    30.761818      2.4371    -52.3235     52.3803\n  1000   1800     0.0000   -43.7152    28.8561    31.131578      2.0993    -52.3381     52.3803\n  1000   2000     0.0000   -43.7152    28.8561    31.493929      1.7683    -52.3504     52.3803\n  1000   2200     0.0000   -43.7152    28.8561    31.849161      1.4437    -52.3603     52.3803\n  1000   2400     0.0000   -43.7152    28.8561    32.197543      1.1253    -52.3681     52.3803\n  1000   2600     0.0000   -43.7152    28.8561    32.539328      0.8129    -52.3739     52.3803\n  1000   2800     0.0000   -43.7152    28.8561    32.874754      0.5063    -52.3778     52.3803\n  1000   3000     0.0000   -43.7152    28.8561    33.204045      0.2052    -52.3798     52.3803\n  1000   3200     0.0000   -43.7152    28.8561    33.527412     -0.0904    -52.3801     52.3803\n  1000   3400     0.0000   -43.7152    28.8561    33.845057     -0.3808    -52.3788     52.3803\n  1000   3600     0.0000   -43.7152    28.8561    34.157166     -0.6661    -52.3760     52.3803\n  1000   3800     0.0000   -43.7152    28.8561    34.463921     -0.9465    -52.3717     52.3803\n  1000   4000     0.0000   -43.7152    28.8561    34.765489     -1.2222    -52.3660     52.3803\n  1000   4200     0.0000   -43.7152    28.8561    35.062033     -1.4932    -52.3589     52.3803\n  1000   4400     0.0000   -43.7152    28.8561    35.353705     -1.7597    -52.3507     52.3803\n  1000   4600     0.0000   -43.7152    28.8561    35.640651     -2.0218    -52.3412     52.3803\n  1000   4800     0.0000   -43.7152    28.8561    35.923009     -2.2798    -52.3306     52.3803\n  1000   5000     0.0000   -43.7152    28.8561    36.200913     -2.5336    -52.3189     52.3803\n  1000   5200     0.0000   -43.7152    28.8561    36.474487     -2.7833    -52.3062     52.3803\n  1000   5400     0.0000   -43.7152    28.8561    36.743853     -3.0292    -52.2926     52.3803\n  1000   5600     0.0000   -43.7152    28.8561    37.009126     -3.2713    -52.2780     52.3803\n  1000   5800     0.0000   -43.7152    28.8561    37.270416     -3.5097    -52.2625     52.3803\n  1000   6000     0.0000   -43.7152    28.8561    37.527828     -3.7444    -52.2462     52.3803\n  1000   6200     0.0000   -43.7152    28.8561    37.781464     -3.9757    -52.2291     52.3803\n  1000   6400     0.0000   -43.7152    28.8561    38.031420     -4.2035    -52.2113     52.3803\n  1000   6600     0.0000   -43.7152    28.8561    38.277789     -4.4280    -52.1927     52.3803\n  1000   6800     0.0000   -43.7152    28.8561    38.520662     -4.6492    -52.1735     52.3803\n  1000   7000     0.0000   -43.7152    28.8561    38.760123     -4.8672    -52.1536     52.3803\n  1000   7200     0.0000   -43.7152    28.8561    38.996255     -5.0821    -52.1331     52.3803\n  1000   7400     0.0000   -43.7152    28.8561    39.229138     -5.2939    -52.1120     52.3803\n  1000   7600     0.0000   -43.7152    28.8561    39.458848     -5.5028    -52.0904     52.3803\n  1000   7800     0.0000   -43.7152    28.8561    39.685459     -5.7088    -52.0682     52.3803\n  1000   8000     0.0000   -43.7152    28.8561    39.909042     -5.9119    -52.0455     52.3803\n  1000   8200     0.0000   -43.7152    28.8561    40.129665     -6.1123    -52.0224     52.3803\n  1000   8400     0.0000   -43.7152    28.8561    40.347394     -6.3099    -51.9988     52.3803\n  1500      0     0.0000   -43.3271    28.9422    27.493913      5.6714    -51.7949     52.1046\n  1500    200     0.0000   -43.3271    28.9422    27.936197      5.2714    -51.8371     52.1046\n  1500    400     0.0000   -43.3271    28.9422    28.367845      4.8807    -51.8754     52.1046\n  1500    600     0.0000   -43.3271    28.9422    28.789379      4.4989    -51.9099     52.1046\n  1500    800     0.0000   -43.3271    28.9422    29.201280      4.1257    -51.9409     52.1046\n  1500   1000     0.0000   -43.3271    28.9422    29.603992      3.7605    -51.9686     52.1046\n  1500   1200     0.0000   -43.3271    28.9422    29.997923      3.4031    -51.9932     52.1046\n  1500   1400     0.0000   -43.3271    28.9422    30.383456      3.0532    -52.0150     52.1046\n  1500   1600     0.0000   -43.3271    28.9422    30.760943      2.7104    -52.0340     52.1046\n  1500   1800     0.0000   -43.3271    28.9422    31.130714      2.3745    -52.0504     52.1046\n  1500   2000     0.0000   -43.3271    28.9422    31.493077      2.0453    -52.0643     52.1046\n  1500   2200     0.0000   -43.3271    28.9422    31.848320      1.7224    -52.0760     52.1046\n  1500   2400     0.0000   -43.3271    28.9422    32.196712      1.4058    -52.0855     52.1046\n  1500   2600     0.0000   -43.3271    28.9422    32.538506      1.0950    -52.0930     52.1046\n  1500   2800     0.0000   -43.3271    28.9422    32.873942      0.7900    -52.0985     52.1046\n  1500   3000     0.0000   -43.3271    28.9422    33.203242      0.4906    -52.1022     52.1046\n  1500   3200     0.0000   -43.3271    28.9422    33.526618      0.1965    -52.1041     52.1046\n  1500   3400     0.0000   -43.3271    28.9422    33.844271     -0.0924    -52.1044     52.1046\n  1500   3600     0.0000   -43.3271    28.9422    34.156389     -0.3762    -52.1032     52.1046\n  1500   3800     0.0000   -43.3271    28.9422    34.463152     -0.6551    -52.1004     52.1046\n  1500   4000     0.0000   -43.3271    28.9422    34.764728     -0.9294    -52.0962     52.1046\n  1500   4200     0.0000   -43.3271    28.9422    35.061279     -1.1990    -52.0907     52.1046\n  1500   4400     0.0000   -43.3271    28.9422    35.352959     -1.4642    -52.0839     52.1046\n  1500   4600     0.0000   -43.3271    28.9422    35.639912     -1.7250    -52.0760     52.1046\n  1500   4800     0.0000   -43.3271    28.9422    35.922277     -1.9816    -52.0668     52.1046\n  1500   5000     0.0000   -43.3271    28.9422    36.200187     -2.2341    -52.0566     52.1046\n  1500   5200     0.0000   -43.3271    28.9422    36.473768     -2.4827    -52.0453     52.1046\n  1500   5400     0.0000   -43.3271    28.9422    36.743141     -2.7273    -52.0331     52.1046\n  1500   5600     0.0000   -43.3271    28.9422    37.008420     -2.9682    -52.0199     52.1046\n  1500   5800     0.0000   -43.3271    28.9422    37.269715     -3.2054    -52.0058     52.1046\n  1500   6000     0.0000   -43.3271    28.9422    37.527133     -3.4390    -51.9909     52.1046\n  1500   6200     0.0000   -43.3271    28.9422    37.780775     -3.6692    -51.9752     52.1046\n  1500   6400     0.0000   -43.3271    28.9422    38.030736     -3.8959    -51.9587     52.1046\n  1500   6600     0.0000   -43.3271    28.9422    38.277111     -4.1193    -51.9414     52.1046\n  1500   6800     0.0000   -43.3271    28.9422    38.519989     -4.3394    -51.9235     52.1046\n  1500   7000     0.0000   -43.3271    28.9422    38.759456     -4.5564    -51.9049     52.1046\n  1500   7200     0.0000   -43.3271    28.9422    38.995593     -4.7703    -51.8857     52.1046\n  1500   7400     0.0000   -43.3271    28.9422    39.228481     -4.9811    -51.8659     52.1046\n  1500   7600     0.0000   -43.3271    28.9422    39.458196     -5.1890    -51.8455     52.1046\n  1500   7800     0.0000   -43.3271    28.9422    39.684812     -5.3940    -51.8246     52.1046\n  1500   8000     0.0000   -43.3271    28.9422    39.908399     -5.5962    -51.8031     52.1046\n  1500   8200     0.0000   -43.3271    28.9422    40.129027     -5.7957    -51.7812     52.1046\n  1500   8400     0.0000   -43.3271    28.9422    40.346760     -5.9924    -51.7588     52.1046\n  2000      0     0.0000   -42.9389    29.0284    27.492904      5.9279    -51.4903     51.8305\n  2000    200     0.0000   -42.9389    29.0284    27.935205      5.5302    -51.5345     51.8305\n  2000    400     0.0000   -42.9389    29.0284    28.366870      5.1418    -51.5747     51.8305\n  2000    600     0.0000   -42.9389    29.0284    28.788420      4.7622    -51.6111     51.8305\n  2000    800     0.0000   -42.9389    29.0284    29.200335      4.3911    -51.6440     51.8305\n  2000   1000     0.0000   -42.9389    29.0284    29.603061      4.0280    -51.6736     51.8305\n  2000   1200     0.0000   -42.9389    29.0284    29.997006      3.6726    -51.7001     51.8305\n  2000   1400     0.0000   -42.9389    29.0284    30.382551      3.3246    -51.7236     51.8305\n  2000   1600     0.0000   -42.9389    29.0284    30.760051      2.9837    -51.7444     51.8305\n  2000   1800     0.0000   -42.9389    29.0284    31.129834      2.6497    -51.7626     51.8305\n  2000   2000     0.0000   -42.9389    29.0284    31.492208      2.3223    -51.7783     51.8305\n  2000   2200     0.0000   -42.9389    29.0284    31.847461      2.0012    -51.7917     51.8305\n  2000   2400     0.0000   -42.9389    29.0284    32.195864      1.6862    -51.8030     51.8305\n  2000   2600     0.0000   -42.9389    29.0284    32.537668      1.3772    -51.8121     51.8305\n  2000   2800     0.0000   -42.9389    29.0284    32.873113      1.0738    -51.8193     51.8305\n  2000   3000     0.0000   -42.9389    29.0284    33.202423      0.7760    -51.8246     51.8305\n  2000   3200     0.0000   -42.9389    29.0284    33.525809      0.4834    -51.8281     51.8305\n  2000   3400     0.0000   -42.9389    29.0284    33.843470      0.1961    -51.8300     51.8305\n  2000   3600     0.0000   -42.9389    29.0284    34.155597     -0.0863    -51.8303     51.8305\n  2000   3800     0.0000   -42.9389    29.0284    34.462368     -0.3638    -51.8291     51.8305\n  2000   4000     0.0000   -42.9389    29.0284    34.763952     -0.6366    -51.8265     51.8305\n  2000   4200     0.0000   -42.9389    29.0284    35.060511     -0.9048    -51.8225     51.8305\n  2000   4400     0.0000   -42.9389    29.0284    35.352197     -1.1686    -51.8172     51.8305\n  2000   4600     0.0000   -42.9389    29.0284    35.639158     -1.4281    -51.8107     51.8305\n  2000   4800     0.0000   -42.9389    29.0284    35.921530     -1.6835    -51.8031     51.8305\n  2000   5000     0.0000   -42.9389    29.0284    36.199447     -1.9347    -51.7943     51.8305\n  2000   5200     0.0000   -42.9389    29.0284    36.473035     -2.1820    -51.7845     51.8305\n  2000   5400     0.0000   -42.9389    29.0284    36.742414     -2.4254    -51.7736     51.8305\n  2000   5600     0.0000   -42.9389    29.0284    37.007699     -2.6651    -51.7618     51.8305\n  2000   5800     0.0000   -42.9389    29.0284    37.269001     -2.9012    -51.7491     51.8305\n  2000   6000     0.0000   -42.9389    29.0284    37.526425     -3.1336    -51.7356     51.8305\n  2000   6200     0.0000   -42.9389    29.0284    37.780072     -3.3626    -51.7212     51.8305\n  2000   6400     0.0000   -42.9389    29.0284    38.030040     -3.5883    -51.7061     51.8305\n  2000   6600     0.0000   -42.9389    29.0284    38.276421     -3.8106    -51.6901     51.8305\n  2000   6800     0.0000   -42.9389    29.0284    38.519304     -4.0297    -51.6735     51.8305\n  2000   7000     0.0000   -42.9389    29.0284    38.758775     -4.2456    -51.6562     51.8305\n  2000   7200     0.0000   -42.9389    29.0284    38.994918     -4.4585    -51.6383     51.8305\n  2000   7400     0.0000   -42.9389    29.0284    39.227811     -4.6683    -51.6198     51.8305\n  2000   7600     0.0000   -42.9389    29.0284    39.457531     -4.8752    -51.6006     51.8305\n  2000   7800     0.0000   -42.9389    29.0284    39.684152     -5.0793    -51.5809     51.8305\n  2000   8000     0.0000   -42.9389    29.0284    39.907744     -5.2806    -51.5607     51.8305\n  2000   8200     0.0000   -42.9389    29.0284    40.128376     -5.4791    -51.5400     51.8305\n  2000   8400     0.0000   -42.9389    29.0284    40.346114     -5.6749    -51.5188     51.8305\n  2500      0     0.0000   -42.5508    29.1145    27.491875      6.1844    -51.1856     51.5580\n  2500    200     0.0000   -42.5508    29.1145    27.934194      5.7891    -51.2318     51.5580\n  2500    400     0.0000   -42.5508    29.1145    28.365875      5.4029    -51.2740     51.5580\n  2500    600     0.0000   -42.5508    29.1145    28.787440      5.0255    -51.3123     51.5580\n  2500    800     0.0000   -42.5508    29.1145    29.199371      4.6565    -51.3472     51.5580\n  2500   1000     0.0000   -42.5508    29.1145    29.602111      4.2955    -51.3786     51.5580\n  2500   1200     0.0000   -42.5508    29.1145    29.996070      3.9421    -51.4069     51.5580\n  2500   1400     0.0000   -42.5508    29.1145    30.381628      3.5961    -51.4323     51.5580\n  2500   1600     0.0000   -42.5508    29.1145    30.759140      3.2571    -51.4549     51.5580\n  2500   1800     0.0000   -42.5508    29.1145    31.128935      2.9249    -51.4748     51.5580\n  2500   2000     0.0000   -42.5508    29.1145    31.491321      2.5993    -51.4923     51.5580\n  2500   2200     0.0000   -42.5508    29.1145    31.846586      2.2800    -51.5074     51.5580\n  2500   2400     0.0000   -42.5508    29.1145    32.194999      1.9667    -51.5204     51.5580\n  2500   2600     0.0000   -42.5508    29.1145    32.536814      1.6593    -51.5312     51.5580\n  2500   2800     0.0000   -42.5508    29.1145    32.872269      1.3576    -51.5400     51.5580\n  2500   3000     0.0000   -42.5508    29.1145    33.201588      1.0613    -51.5470     51.5580\n  2500   3200     0.0000   -42.5508    29.1145    33.524983      0.7704    -51.5521     51.5580\n  2500   3400     0.0000   -42.5508    29.1145    33.842654      0.4845    -51.5556     51.5580\n  2500   3600     0.0000   -42.5508    29.1145    34.154789      0.2037    -51.5575     51.5580\n  2500   3800     0.0000   -42.5508    29.1145    34.461568     -0.0724    -51.5578     51.5580\n  2500   4000     0.0000   -42.5508    29.1145    34.763160     -0.3438    -51.5567     51.5580\n  2500   4200     0.0000   -42.5508    29.1145    35.059727     -0.6106    -51.5543     51.5580\n  2500   4400     0.0000   -42.5508    29.1145    35.351422     -0.8731    -51.5505     51.5580\n  2500   4600     0.0000   -42.5508    29.1145    35.638389     -1.1313    -51.5455     51.5580\n  2500   4800     0.0000   -42.5508    29.1145    35.920769     -1.3853    -51.5393     51.5580\n  2500   5000     0.0000   -42.5508    29.1145    36.198693     -1.6353    -51.5320     51.5580\n  2500   5200     0.0000   -42.5508    29.1145    36.472288     -1.8813    -51.5236     51.5580\n  2500   5400     0.0000   -42.5508    29.1145    36.741674     -2.1235    -51.5141     51.5580\n  2500   5600     0.0000   -42.5508    29.1145    37.006965     -2.3620    -51.5038     51.5580\n  2500   5800     0.0000   -42.5508    29.1145    37.268273     -2.5969    -51.4925     51.5580\n  2500   6000     0.0000   -42.5508    29.1145    37.525704     -2.8282    -51.4803     51.5580\n  2500   6200     0.0000   -42.5508    29.1145    37.779357     -3.0561    -51.4672     51.5580\n  2500   6400     0.0000   -42.5508    29.1145    38.029330     -3.2806    -51.4534     51.5580\n  2500   6600     0.0000   -42.5508    29.1145    38.275717     -3.5019    -51.4388     51.5580\n  2500   6800     0.0000   -42.5508    29.1145    38.518605     -3.7199    -51.4235     51.5580\n  2500   7000     0.0000   -42.5508    29.1145    38.758083     -3.9348    -51.4075     51.5580\n  2500   7200     0.0000   -42.5508    29.1145    38.994231     -4.1466    -51.3909     51.5580\n  2500   7400     0.0000   -42.5508    29.1145    39.227129     -4.3555    -51.3736     51.5580\n  2500   7600     0.0000   -42.5508    29.1145    39.456854     -4.5615    -51.3557     51.5580\n  2500   7800     0.0000   -42.5508    29.1145    39.683480     -4.7645    -51.3373     51.5580\n  2500   8000     0.0000   -42.5508    29.1145    39.907076     -4.9649    -51.3183     51.5580\n  2500   8200     0.0000   -42.5508    29.1145    40.127713     -5.1624    -51.2988     51.5580\n  2500   8400     0.0000   -42.5508    29.1145    40.345456     -5.3574    -51.2788     51.5580\n  3000      0     0.0000   -42.1626    29.2006    27.490826      6.4410    -50.8809     51.2871\n  3000    200     0.0000   -42.1626    29.2006    27.933162      6.0480    -50.9291     51.2871\n  3000    400     0.0000   -42.1626    29.2006    28.364860      5.6641    -50.9733     51.2871\n  3000    600     0.0000   -42.1626    29.2006    28.786442      5.2888    -51.0136     51.2871\n  3000    800     0.0000   -42.1626    29.2006    29.198388      4.9219    -51.0503     51.2871\n  3000   1000     0.0000   -42.1626    29.2006    29.601143      4.5630    -51.0836     51.2871\n  3000   1200     0.0000   -42.1626    29.2006    29.995115      4.2116    -51.1138     51.2871\n  3000   1400     0.0000   -42.1626    29.2006    30.380687      3.8675    -51.1410     51.2871\n  3000   1600     0.0000   -42.1626    29.2006    30.758212      3.5305    -51.1653     51.2871\n  3000   1800     0.0000   -42.1626    29.2006    31.128020      3.2002    -51.1871     51.2871\n  3000   2000     0.0000   -42.1626    29.2006    31.490417      2.8763    -51.2063     51.2871\n  3000   2200     0.0000   -42.1626    29.2006    31.845693      2.5588    -51.2231     51.2871\n  3000   2400     0.0000   -42.1626    29.2006    32.194117      2.2472    -51.2377     51.2871\n  3000   2600     0.0000   -42.1626    29.2006    32.535943      1.9415    -51.2502     51.2871\n  3000   2800     0.0000   -42.1626    29.2006    32.871408      1.6414    -51.2607     51.2871\n  3000   3000     0.0000   -42.1626    29.2006    33.200737      1.3467    -51.2693     51.2871\n  3000   3200     0.0000   -42.1626    29.2006    33.524142      1.0573    -51.2761     51.2871\n  3000   3400     0.0000   -42.1626    29.2006    33.841821      0.7730    -51.2812     51.2871\n  3000   3600     0.0000   -42.1626    29.2006    34.153966      0.4936    -51.2846     51.2871\n  3000   3800     0.0000   -42.1626    29.2006    34.460753      0.2190    -51.2865     51.2871\n  3000   4000     0.0000   -42.1626    29.2006    34.762354     -0.0510    -51.2870     51.2871\n  3000   4200     0.0000   -42.1626    29.2006    35.058929     -0.3164    -51.2860     51.2871\n  3000   4400     0.0000   -42.1626    29.2006    35.350631     -0.5775    -51.2838     51.2871\n  3000   4600     0.0000   -42.1626    29.2006    35.637607     -0.8344    -51.2802     51.2871\n  3000   4800     0.0000   -42.1626    29.2006    35.919994     -1.0871    -51.2755     51.2871\n  3000   5000     0.0000   -42.1626    29.2006    36.197925     -1.3358    -51.2696     51.2871\n  3000   5200     0.0000   -42.1626    29.2006    36.471527     -1.5806    -51.2627     51.2871\n  3000   5400     0.0000   -42.1626    29.2006    36.740919     -1.8216    -51.2547     51.2871\n  3000   5600     0.0000   -42.1626    29.2006    37.006218     -2.0590    -51.2457     51.2871\n  3000   5800     0.0000   -42.1626    29.2006    37.267532     -2.2927    -51.2357     51.2871\n  3000   6000     0.0000   -42.1626    29.2006    37.524969     -2.5228    -51.2249     51.2871\n  3000   6200     0.0000   -42.1626    29.2006    37.778628     -2.7496    -51.2133     51.2871\n  3000   6400     0.0000   -42.1626    29.2006    38.028607     -2.9730    -51.2008     51.2871\n  3000   6600     0.0000   -42.1626    29.2006    38.275000     -3.1932    -51.1875     51.2871\n  3000   6800     0.0000   -42.1626    29.2006    38.517894     -3.4101    -51.1735     51.2871\n  3000   7000     0.0000   -42.1626    29.2006    38.757377     -3.6240    -51.1588     51.2871\n  3000   7200     0.0000   -42.1626    29.2006    38.993530     -3.8348    -51.1435     51.2871\n  3000   7400     0.0000   -42.1626    29.2006    39.226434     -4.0427    -51.1274     51.2871\n  3000   7600     0.0000   -42.1626    29.2006    39.456164     -4.2477    -51.1108     51.2871\n  3000   7800     0.0000   -42.1626    29.2006    39.682795     -4.4498    -51.0936     51.2871\n  3000   8000     0.0000   -42.1626    29.2006    39.906397     -4.6492    -51.0759     51.2871\n  3000   8200     0.0000   -42.1626    29.2006    40.127039     -4.8458    -51.0576     51.2871\n  3000   8400     0.0000   -42.1626    29.2006    40.344786     -5.0398    -51.0388     51.2871\n  3500      0     0.0000   -41.7745    29.2868    27.489756      6.6975    -50.5762     51.0179\n  3500    200     0.0000   -41.7745    29.2868    27.932111      6.3068    -50.6264     51.0179\n  3500    400     0.0000   -41.7745    29.2868    28.363826      5.9252    -50.6725     51.0179\n  3500    600     0.0000   -41.7745    29.2868    28.785424      5.5522    -50.7147     51.0179\n  3500    800     0.0000   -41.7745    29.2868    29.197386      5.1874    -50.7534     51.0179\n  3500   1000     0.0000   -41.7745    29.2868    29.600156      4.8305    -50.7886     51.0179\n  3500   1200     0.0000   -41.7745    29.2868    29.994143      4.4811    -50.8206     51.0179\n  3500   1400     0.0000   -41.7745    29.2868    30.379728      4.1390    -50.8496     51.0179\n  3500   1600     0.0000   -41.7745    29.2868    30.757266      3.8038    -50.8758     51.0179\n  3500   1800     0.0000   -41.7745    29.2868    31.127086      3.4754    -50.8993     51.0179\n  3500   2000     0.0000   -41.7745    29.2868    31.489496      3.1534    -50.9202     51.0179\n  3500   2200     0.0000   -41.7745    29.2868    31.844784      2.8376    -50.9388     51.0179\n  3500   2400     0.0000   -41.7745    29.2868    32.193219      2.5277    -50.9551     51.0179\n  3500   2600     0.0000   -41.7745    29.2868    32.535056      2.2237    -50.9693     51.0179\n  3500   2800     0.0000   -41.7745    29.2868    32.870531      1.9252    -50.9814     51.0179\n  3500   3000     0.0000   -41.7745    29.2868    33.199870      1.6321    -50.9917     51.0179\n  3500   3200     0.0000   -41.7745    29.2868    33.523284      1.3443    -51.0001     51.0179\n  3500   3400     0.0000   -41.7745    29.2868    33.840974      1.0615    -51.0067     51.0179\n  3500   3600     0.0000   -41.7745    29.2868    34.153127      0.7836    -51.0118     51.0179\n  3500   3800     0.0000   -41.7745    29.2868    34.459923      0.5104    -51.0152     51.0179\n  3500   4000     0.0000   -41.7745    29.2868    34.761533      0.2419    -51.0172     51.0179\n  3500   4200     0.0000   -41.7745    29.2868    35.058116     -0.0222    -51.0178     51.0179\n  3500   4400     0.0000   -41.7745    29.2868    35.349826     -0.2820    -51.0170     51.0179\n  3500   4600     0.0000   -41.7745    29.2868    35.636809     -0.5375    -51.0150     51.0179\n  3500   4800     0.0000   -41.7745    29.2868    35.919204     -0.7889    -51.0117     51.0179\n  3500   5000     0.0000   -41.7745    29.2868    36.197143     -1.0364    -51.0073     51.0179\n  3500   5200     0.0000   -41.7745    29.2868    36.470752     -1.2800    -51.0017     51.0179\n  3500   5400     0.0000   -41.7745    29.2868    36.740151     -1.5197    -50.9952     51.0179\n  3500   5600     0.0000   -41.7745    29.2868    37.005456     -1.7559    -50.9876     51.0179\n  3500   5800     0.0000   -41.7745    29.2868    37.266777     -1.9884    -50.9790     51.0179\n  3500   6000     0.0000   -41.7745    29.2868    37.524220     -2.2174    -50.9696     51.0179\n  3500   6200     0.0000   -41.7745    29.2868    37.777886     -2.4431    -50.9593     51.0179\n  3500   6400     0.0000   -41.7745    29.2868    38.027871     -2.6654    -50.9481     51.0179\n  3500   6600     0.0000   -41.7745    29.2868    38.274270     -2.8845    -50.9362     51.0179\n  3500   6800     0.0000   -41.7745    29.2868    38.517170     -3.1004    -50.9235     51.0179\n  3500   7000     0.0000   -41.7745    29.2868    38.756658     -3.3132    -50.9101     51.0179\n  3500   7200     0.0000   -41.7745    29.2868    38.992817     -3.5230    -50.8960     51.0179\n  3500   7400     0.0000   -41.7745    29.2868    39.225727     -3.7299    -50.8813     51.0179\n  3500   7600     0.0000   -41.7745    29.2868    39.455462     -3.9339    -50.8659     51.0179\n  3500   7800     0.0000   -41.7745    29.2868    39.682098     -4.1350    -50.8500     51.0179\n  3500   8000     0.0000   -41.7745    29.2868    39.905705     -4.3334    -50.8334     51.0179\n  3500   8200     0.0000   -41.7745    29.2868    40.126352     -4.5292    -50.8164     51.0179\n  3500   8400     0.0000   -41.7745    29.2868    40.344104     -4.7223    -50.7988     51.0179\n  4000      0     0.0000   -41.3863    29.3729    27.488666      6.9540    -50.2715     50.7503\n  4000    200     0.0000   -41.3863    29.3729    27.931039      6.5657    -50.3237     50.7503\n  4000    400     0.0000   -41.3863    29.3729    28.362772      6.1863    -50.3718     50.7503\n  4000    600     0.0000   -41.3863    29.3729    28.784387      5.8155    -50.4159     50.7503\n  4000    800     0.0000   -41.3863    29.3729    29.196366      5.4528    -50.4564     50.7503\n  4000   1000     0.0000   -41.3863    29.3729    29.599150      5.0980    -50.4935     50.7503\n  4000   1200     0.0000   -41.3863    29.3729    29.993152      4.7506    -50.5274     50.7503\n  4000   1400     0.0000   -41.3863    29.3729    30.378752      4.4105    -50.5582     50.7503\n  4000   1600     0.0000   -41.3863    29.3729    30.756303      4.0772    -50.5862     50.7503\n  4000   1800     0.0000   -41.3863    29.3729    31.126136      3.7506    -50.6115     50.7503\n  4000   2000     0.0000   -41.3863    29.3729    31.488558      3.4304    -50.6342     50.7503\n  4000   2200     0.0000   -41.3863    29.3729    31.843858      3.1163    -50.6545     50.7503\n  4000   2400     0.0000   -41.3863    29.3729    32.192305      2.8082    -50.6725     50.7503\n  4000   2600     0.0000   -41.3863    29.3729    32.534152      2.5059    -50.6883     50.7503\n  4000   2800     0.0000   -41.3863    29.3729    32.869638      2.2090    -50.7022     50.7503\n  4000   3000     0.0000   -41.3863    29.3729    33.198987      1.9175    -50.7140     50.7503\n  4000   3200     0.0000   -41.3863    29.3729    33.522412      1.6312    -50.7240     50.7503\n  4000   3400     0.0000   -41.3863    29.3729    33.840110      1.3499    -50.7323     50.7503\n  4000   3600     0.0000   -41.3863    29.3729    34.152273      1.0735    -50.7389     50.7503\n  4000   3800     0.0000   -41.3863    29.3729    34.459078      0.8018    -50.7439     50.7503\n  4000   4000     0.0000   -41.3863    29.3729    34.760696      0.5347    -50.7474     50.7503\n  4000   4200     0.0000   -41.3863    29.3729    35.057288      0.2720    -50.7495     50.7503\n  4000   4400     0.0000   -41.3863    29.3729    35.349007      0.0136    -50.7503     50.7503\n  4000   4600     0.0000   -41.3863    29.3729    35.635998     -0.2406    -50.7497     50.7503\n  4000   4800     0.0000   -41.3863    29.3729    35.918400     -0.4908    -50.7479     50.7503\n  4000   5000     0.0000   -41.3863    29.3729    36.196346     -0.7369    -50.7449     50.7503\n  4000   5200     0.0000   -41.3863    29.3729    36.469963     -0.9793    -50.7408     50.7503\n  4000   5400     0.0000   -41.3863    29.3729    36.739369     -1.2178    -50.7357     50.7503\n  4000   5600     0.0000   -41.3863    29.3729    37.004681     -1.4528    -50.7295     50.7503\n  4000   5800     0.0000   -41.3863    29.3729    37.266009     -1.6841    -50.7223     50.7503\n  4000   6000     0.0000   -41.3863    29.3729    37.523459     -1.9120    -50.7142     50.7503\n  4000   6200     0.0000   -41.3863    29.3729    37.777131     -2.1365    -50.7053     50.7503\n  4000   6400     0.0000   -41.3863    29.3729    38.027122     -2.3577    -50.6955     50.7503\n  4000   6600     0.0000   -41.3863    29.3729    38.273527     -2.5757    -50.6849     50.7503\n  4000   6800     0.0000   -41.3863    29.3729    38.516433     -2.7906    -50.6735     50.7503\n  4000   7000     0.0000   -41.3863    29.3729    38.755927     -3.0024    -50.6614     50.7503\n  4000   7200     0.0000   -41.3863    29.3729    38.992092     -3.2112    -50.6486     50.7503\n  4000   7400     0.0000   -41.3863    29.3729    39.225007     -3.4170    -50.6351     50.7503\n  4000   7600     0.0000   -41.3863    29.3729    39.454748     -3.6201    -50.6210     50.7503\n  4000   7800     0.0000   -41.3863    29.3729    39.681389     -3.8203    -50.6063     50.7503\n  4000   8000     0.0000   -41.3863    29.3729    39.905001     -4.0177    -50.5910     50.7503\n  4000   8200     0.0000   -41.3863    29.3729    40.125653     -4.2125    -50.5751     50.7503\n  4000   8400     0.0000   -41.3863    29.3729    40.343411     -4.4047    -50.5588     50.7503\n  4500      0     0.0000   -40.9982    29.4591    27.487555      7.2106    -49.9668     50.4845\n  4500    200     0.0000   -40.9982    29.4591    27.929948      6.8246    -50.0210     50.4845\n  4500    400     0.0000   -40.9982    29.4591    28.361699      6.4474    -50.0710     50.4845\n  4500    600     0.0000   -40.9982    29.4591    28.783331      6.0788    -50.1171     50.4845\n  4500    800     0.0000   -40.9982    29.4591    29.195326      5.7183    -50.1595     50.4845\n  4500   1000     0.0000   -40.9982    29.4591    29.598126      5.3655    -50.1985     50.4845\n  4500   1200     0.0000   -40.9982    29.4591    29.992143      5.0202    -50.2342     50.4845\n  4500   1400     0.0000   -40.9982    29.4591    30.377757      4.6820    -50.2669     50.4845\n  4500   1600     0.0000   -40.9982    29.4591    30.755322      4.3506    -50.2966     50.4845\n  4500   1800     0.0000   -40.9982    29.4591    31.125168      4.0259    -50.3237     50.4845\n  4500   2000     0.0000   -40.9982    29.4591    31.487603      3.7074    -50.3481     50.4845\n  4500   2200     0.0000   -40.9982    29.4591    31.842915      3.3951    -50.3701     50.4845\n  4500   2400     0.0000   -40.9982    29.4591    32.191373      3.0887    -50.3899     50.4845\n  4500   2600     0.0000   -40.9982    29.4591    32.533232      2.7880    -50.4074     50.4845\n  4500   2800     0.0000   -40.9982    29.4591    32.868729      2.4928    -50.4229     50.4845\n  4500   3000     0.0000   -40.9982    29.4591    33.198089      2.2029    -50.4364     50.4845\n  4500   3200     0.0000   -40.9982    29.4591    33.521523      1.9182    -50.4480     50.4845\n  4500   3400     0.0000   -40.9982    29.4591    33.839232      1.6384    -50.4578     50.4845\n  4500   3600     0.0000   -40.9982    29.4591    34.151404      1.3635    -50.4660     50.4845\n  4500   3800     0.0000   -40.9982    29.4591    34.458218      1.0932    -50.4726     50.4845\n  4500   4000     0.0000   -40.9982    29.4591    34.759845      0.8275    -50.4777     50.4845\n  4500   4200     0.0000   -40.9982    29.4591    35.056446      0.5662    -50.4813     50.4845\n  4500   4400     0.0000   -40.9982    29.4591    35.348172      0.3091    -50.4835     50.4845\n  4500   4600     0.0000   -40.9982    29.4591    35.635172      0.0563    -50.4844     50.4845\n  4500   4800     0.0000   -40.9982    29.4591    35.917582     -0.1926    -50.4841     50.4845\n  4500   5000     0.0000   -40.9982    29.4591    36.195536     -0.4375    -50.4826     50.4845\n  4500   5200     0.0000   -40.9982    29.4591    36.469160     -0.6786    -50.4799     50.4845\n  4500   5400     0.0000   -40.9982    29.4591    36.738573     -0.9159    -50.4761     50.4845\n  4500   5600     0.0000   -40.9982    29.4591    37.003892     -1.1496    -50.4714     50.4845\n  4500   5800     0.0000   -40.9982    29.4591    37.265227     -1.3798    -50.4656     50.4845\n  4500   6000     0.0000   -40.9982    29.4591    37.522684     -1.6066    -50.4589     50.4845\n  4500   6200     0.0000   -40.9982    29.4591    37.776362     -1.8300    -50.4513     50.4845\n  4500   6400     0.0000   -40.9982    29.4591    38.026360     -2.0501    -50.4428     50.4845\n  4500   6600     0.0000   -40.9982    29.4591    38.272771     -2.2670    -50.4335     50.4845\n  4500   6800     0.0000   -40.9982    29.4591    38.515683     -2.4808    -50.4235     50.4845\n  4500   7000     0.0000   -40.9982    29.4591    38.755184     -2.6916    -50.4127     50.4845\n  4500   7200     0.0000   -40.9982    29.4591    38.991354     -2.8993    -50.4011     50.4845\n  4500   7400     0.0000   -40.9982    29.4591    39.224274     -3.1042    -50.3889     50.4845\n  4500   7600     0.0000   -40.9982    29.4591    39.454021     -3.3062    -50.3761     50.4845\n  4500   7800     0.0000   -40.9982    29.4591    39.680668     -3.5055    -50.3626     50.4845\n  4500   8000     0.0000   -40.9982    29.4591    39.904285     -3.7020    -50.3485     50.4845\n  4500   8200     0.0000   -40.9982    29.4591    40.124942     -3.8959    -50.3339     50.4845\n  4500   8400     0.0000   -40.9982    29.4591    40.342705     -4.0872    -50.3187     50.4845\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180331_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.0940080       -4.4095585        4.0878617   m   m   m\ninitial_baseline_rate:          0.0000000        0.0564518        0.0226244   m/s m/s m/s\nprecision_baseline(TCN):        0.0940080       -4.4095585        4.0878617   m   m   m\nprecision_baseline_rate:        0.0000000        0.0564518        0.0226244   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180331_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.094     -4.410      4.088\norbit baseline rate   (TCN) (m/s):  0.000e+00  5.645e-02  2.262e-02\n\nbaseline vector (TCN)   (m):      0.094     -4.410      4.088\nbaseline rate   (TCN) (m/s):  0.000e+00  5.645e-02  2.262e-02\n\nSLC-1 center baseline length (m):     6.0136\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0940    -4.9365     3.8767    27.496626      1.1595     -6.1689      6.2775\n     0    200     0.0940    -4.9365     3.8767    27.938864      1.1119     -6.1777      6.2775\n     0    400     0.0940    -4.9365     3.8767    28.370470      1.0653     -6.1859      6.2775\n     0    600     0.0940    -4.9365     3.8767    28.791963      1.0198     -6.1935      6.2775\n     0    800     0.0940    -4.9365     3.8767    29.203825      0.9752     -6.2007      6.2775\n     0   1000     0.0940    -4.9365     3.8767    29.606500      0.9316     -6.2074      6.2775\n     0   1200     0.0940    -4.9365     3.8767    30.000396      0.8889     -6.2136      6.2775\n     0   1400     0.0940    -4.9365     3.8767    30.385895      0.8471     -6.2195      6.2775\n     0   1600     0.0940    -4.9365     3.8767    30.763349      0.8061     -6.2249      6.2775\n     0   1800     0.0940    -4.9365     3.8767    31.133089      0.7659     -6.2300      6.2775\n     0   2000     0.0940    -4.9365     3.8767    31.495422      0.7265     -6.2347      6.2775\n     0   2200     0.0940    -4.9365     3.8767    31.850636      0.6878     -6.2391      6.2775\n     0   2400     0.0940    -4.9365     3.8767    32.199000      0.6499     -6.2432      6.2775\n     0   2600     0.0940    -4.9365     3.8767    32.540768      0.6126     -6.2469      6.2775\n     0   2800     0.0940    -4.9365     3.8767    32.876178      0.5761     -6.2504      6.2775\n     0   3000     0.0940    -4.9365     3.8767    33.205453      0.5401     -6.2536      6.2775\n     0   3200     0.0940    -4.9365     3.8767    33.528805      0.5048     -6.2566      6.2775\n     0   3400     0.0940    -4.9365     3.8767    33.846435      0.4701     -6.2593      6.2775\n     0   3600     0.0940    -4.9365     3.8767    34.158531      0.4360     -6.2617      6.2775\n     0   3800     0.0940    -4.9365     3.8767    34.465271      0.4025     -6.2640      6.2775\n     0   4000     0.0940    -4.9365     3.8767    34.766827      0.3695     -6.2660      6.2775\n     0   4200     0.0940    -4.9365     3.8767    35.063357      0.3371     -6.2678      6.2775\n     0   4400     0.0940    -4.9365     3.8767    35.355017      0.3052     -6.2695      6.2775\n     0   4600     0.0940    -4.9365     3.8767    35.641950      0.2738     -6.2709      6.2775\n     0   4800     0.0940    -4.9365     3.8767    35.924297      0.2429     -6.2722      6.2775\n     0   5000     0.0940    -4.9365     3.8767    36.202189      0.2125     -6.2733      6.2775\n     0   5200     0.0940    -4.9365     3.8767    36.475752      0.1825     -6.2742      6.2775\n     0   5400     0.0940    -4.9365     3.8767    36.745108      0.1530     -6.2750      6.2775\n     0   5600     0.0940    -4.9365     3.8767    37.010370      0.1240     -6.2757      6.2775\n     0   5800     0.0940    -4.9365     3.8767    37.271649      0.0953     -6.2762      6.2775\n     0   6000     0.0940    -4.9365     3.8767    37.529051      0.0672     -6.2765      6.2775\n     0   6200     0.0940    -4.9365     3.8767    37.782677      0.0394     -6.2768      6.2775\n     0   6400     0.0940    -4.9365     3.8767    38.032623      0.0120     -6.2769      6.2775\n     0   6600     0.0940    -4.9365     3.8767    38.278984     -0.0150     -6.2769      6.2775\n     0   6800     0.0940    -4.9365     3.8767    38.521847     -0.0416     -6.2767      6.2775\n     0   7000     0.0940    -4.9365     3.8767    38.761299     -0.0678     -6.2765      6.2775\n     0   7200     0.0940    -4.9365     3.8767    38.997423     -0.0937     -6.2762      6.2775\n     0   7400     0.0940    -4.9365     3.8767    39.230297     -0.1192     -6.2757      6.2775\n     0   7600     0.0940    -4.9365     3.8767    39.459999     -0.1444     -6.2752      6.2775\n     0   7800     0.0940    -4.9365     3.8767    39.686602     -0.1692     -6.2746      6.2775\n     0   8000     0.0940    -4.9365     3.8767    39.910176     -0.1937     -6.2739      6.2775\n     0   8200     0.0940    -4.9365     3.8767    40.130792     -0.2178     -6.2731      6.2775\n     0   8400     0.0940    -4.9365     3.8767    40.348513     -0.2417     -6.2722      6.2775\n   500      0     0.0940    -4.8205     3.9232    27.495684      1.2544     -6.0874      6.2159\n   500    200     0.0940    -4.8205     3.9232    27.937938      1.2074     -6.0969      6.2159\n   500    400     0.0940    -4.8205     3.9232    28.369559      1.1614     -6.1058      6.2159\n   500    600     0.0940    -4.8205     3.9232    28.791066      1.1165     -6.1142      6.2159\n   500    800     0.0940    -4.8205     3.9232    29.202942      1.0725     -6.1221      6.2159\n   500   1000     0.0940    -4.8205     3.9232    29.605629      1.0295     -6.1295      6.2159\n   500   1200     0.0940    -4.8205     3.9232    29.999538      0.9873     -6.1364      6.2159\n   500   1400     0.0940    -4.8205     3.9232    30.385049      0.9460     -6.1429      6.2159\n   500   1600     0.0940    -4.8205     3.9232    30.762515      0.9055     -6.1490      6.2159\n   500   1800     0.0940    -4.8205     3.9232    31.132265      0.8658     -6.1547      6.2159\n   500   2000     0.0940    -4.8205     3.9232    31.494609      0.8269     -6.1601      6.2159\n   500   2200     0.0940    -4.8205     3.9232    31.849833      0.7887     -6.1651      6.2159\n   500   2400     0.0940    -4.8205     3.9232    32.198207      0.7512     -6.1697      6.2159\n   500   2600     0.0940    -4.8205     3.9232    32.539984      0.7143     -6.1741      6.2159\n   500   2800     0.0940    -4.8205     3.9232    32.875402      0.6782     -6.1782      6.2159\n   500   3000     0.0940    -4.8205     3.9232    33.204686      0.6427     -6.1820      6.2159\n   500   3200     0.0940    -4.8205     3.9232    33.528047      0.6078     -6.1855      6.2159\n   500   3400     0.0940    -4.8205     3.9232    33.845685      0.5735     -6.1888      6.2159\n   500   3600     0.0940    -4.8205     3.9232    34.157789      0.5398     -6.1918      6.2159\n   500   3800     0.0940    -4.8205     3.9232    34.464537      0.5066     -6.1946      6.2159\n   500   4000     0.0940    -4.8205     3.9232    34.766099      0.4740     -6.1972      6.2159\n   500   4200     0.0940    -4.8205     3.9232    35.062637      0.4419     -6.1996      6.2159\n   500   4400     0.0940    -4.8205     3.9232    35.354304      0.4103     -6.2017      6.2159\n   500   4600     0.0940    -4.8205     3.9232    35.641244      0.3793     -6.2037      6.2159\n   500   4800     0.0940    -4.8205     3.9232    35.923597      0.3487     -6.2055      6.2159\n   500   5000     0.0940    -4.8205     3.9232    36.201496      0.3186     -6.2071      6.2159\n   500   5200     0.0940    -4.8205     3.9232    36.475065      0.2890     -6.2086      6.2159\n   500   5400     0.0940    -4.8205     3.9232    36.744426      0.2598     -6.2098      6.2159\n   500   5600     0.0940    -4.8205     3.9232    37.009694      0.2310     -6.2110      6.2159\n   500   5800     0.0940    -4.8205     3.9232    37.270979      0.2027     -6.2120      6.2159\n   500   6000     0.0940    -4.8205     3.9232    37.528387      0.1748     -6.2128      6.2159\n   500   6200     0.0940    -4.8205     3.9232    37.782018      0.1473     -6.2135      6.2159\n   500   6400     0.0940    -4.8205     3.9232    38.031970      0.1202     -6.2141      6.2159\n   500   6600     0.0940    -4.8205     3.9232    38.278335      0.0935     -6.2146      6.2159\n   500   6800     0.0940    -4.8205     3.9232    38.521204      0.0671     -6.2149      6.2159\n   500   7000     0.0940    -4.8205     3.9232    38.760661      0.0411     -6.2151      6.2159\n   500   7200     0.0940    -4.8205     3.9232    38.996789      0.0155     -6.2153      6.2159\n   500   7400     0.0940    -4.8205     3.9232    39.229668     -0.0097     -6.2153      6.2159\n   500   7600     0.0940    -4.8205     3.9232    39.459375     -0.0346     -6.2152      6.2159\n   500   7800     0.0940    -4.8205     3.9232    39.685982     -0.0592     -6.2150      6.2159\n   500   8000     0.0940    -4.8205     3.9232    39.909561     -0.0835     -6.2147      6.2159\n   500   8200     0.0940    -4.8205     3.9232    40.130181     -0.1074     -6.2143      6.2159\n   500   8400     0.0940    -4.8205     3.9232    40.347906     -0.1310     -6.2139      6.2159\n  1000      0     0.0940    -4.7044     3.9697    27.494721      1.3494     -6.0059      6.1562\n  1000    200     0.0940    -4.7044     3.9697    27.936992      1.3030     -6.0162      6.1562\n  1000    400     0.0940    -4.7044     3.9697    28.368628      1.2576     -6.0258      6.1562\n  1000    600     0.0940    -4.7044     3.9697    28.790150      1.2132     -6.0349      6.1562\n  1000    800     0.0940    -4.7044     3.9697    29.202040      1.1698     -6.0435      6.1562\n  1000   1000     0.0940    -4.7044     3.9697    29.604740      1.1273     -6.0515      6.1562\n  1000   1200     0.0940    -4.7044     3.9697    29.998662      1.0857     -6.0591      6.1562\n  1000   1400     0.0940    -4.7044     3.9697    30.384184      1.0449     -6.0663      6.1562\n  1000   1600     0.0940    -4.7044     3.9697    30.761662      1.0049     -6.0730      6.1562\n  1000   1800     0.0940    -4.7044     3.9697    31.131424      0.9657     -6.0794      6.1562\n  1000   2000     0.0940    -4.7044     3.9697    31.493778      0.9272     -6.0854      6.1562\n  1000   2200     0.0940    -4.7044     3.9697    31.849012      0.8895     -6.0910      6.1562\n  1000   2400     0.0940    -4.7044     3.9697    32.197396      0.8524     -6.0963      6.1562\n  1000   2600     0.0940    -4.7044     3.9697    32.539183      0.8161     -6.1013      6.1562\n  1000   2800     0.0940    -4.7044     3.9697    32.874611      0.7803     -6.1060      6.1562\n  1000   3000     0.0940    -4.7044     3.9697    33.203904      0.7452     -6.1103      6.1562\n  1000   3200     0.0940    -4.7044     3.9697    33.527273      0.7107     -6.1144      6.1562\n  1000   3400     0.0940    -4.7044     3.9697    33.844919      0.6768     -6.1183      6.1562\n  1000   3600     0.0940    -4.7044     3.9697    34.157031      0.6435     -6.1219      6.1562\n  1000   3800     0.0940    -4.7044     3.9697    34.463787      0.6107     -6.1252      6.1562\n  1000   4000     0.0940    -4.7044     3.9697    34.765357      0.5784     -6.1284      6.1562\n  1000   4200     0.0940    -4.7044     3.9697    35.061902      0.5467     -6.1313      6.1562\n  1000   4400     0.0940    -4.7044     3.9697    35.353576      0.5155     -6.1340      6.1562\n  1000   4600     0.0940    -4.7044     3.9697    35.640523      0.4848     -6.1365      6.1562\n  1000   4800     0.0940    -4.7044     3.9697    35.922883      0.4545     -6.1388      6.1562\n  1000   5000     0.0940    -4.7044     3.9697    36.200788      0.4247     -6.1409      6.1562\n  1000   5200     0.0940    -4.7044     3.9697    36.474364      0.3954     -6.1429      6.1562\n  1000   5400     0.0940    -4.7044     3.9697    36.743731      0.3665     -6.1447      6.1562\n  1000   5600     0.0940    -4.7044     3.9697    37.009005      0.3381     -6.1463      6.1562\n  1000   5800     0.0940    -4.7044     3.9697    37.270296      0.3101     -6.1478      6.1562\n  1000   6000     0.0940    -4.7044     3.9697    37.527709      0.2824     -6.1491      6.1562\n  1000   6200     0.0940    -4.7044     3.9697    37.781346      0.2552     -6.1503      6.1562\n  1000   6400     0.0940    -4.7044     3.9697    38.031303      0.2284     -6.1514      6.1562\n  1000   6600     0.0940    -4.7044     3.9697    38.277674      0.2019     -6.1523      6.1562\n  1000   6800     0.0940    -4.7044     3.9697    38.520547      0.1758     -6.1531      6.1562\n  1000   7000     0.0940    -4.7044     3.9697    38.760010      0.1501     -6.1538      6.1562\n  1000   7200     0.0940    -4.7044     3.9697    38.996143      0.1248     -6.1543      6.1562\n  1000   7400     0.0940    -4.7044     3.9697    39.229027      0.0997     -6.1548      6.1562\n  1000   7600     0.0940    -4.7044     3.9697    39.458738      0.0751     -6.1551      6.1562\n  1000   7800     0.0940    -4.7044     3.9697    39.685350      0.0507     -6.1554      6.1562\n  1000   8000     0.0940    -4.7044     3.9697    39.908934      0.0267     -6.1555      6.1562\n  1000   8200     0.0940    -4.7044     3.9697    40.129557      0.0030     -6.1556      6.1562\n  1000   8400     0.0940    -4.7044     3.9697    40.347287     -0.0204     -6.1556      6.1562\n  1500      0     0.0940    -4.5884     4.0162    27.493738      1.4443     -5.9244      6.0985\n  1500    200     0.0940    -4.5884     4.0162    27.936026      1.3985     -5.9354      6.0985\n  1500    400     0.0940    -4.5884     4.0162    28.367677      1.3538     -5.9458      6.0985\n  1500    600     0.0940    -4.5884     4.0162    28.789215      1.3100     -5.9556      6.0985\n  1500    800     0.0940    -4.5884     4.0162    29.201119      1.2671     -5.9648      6.0985\n  1500   1000     0.0940    -4.5884     4.0162    29.603833      1.2252     -5.9736      6.0985\n  1500   1200     0.0940    -4.5884     4.0162    29.997767      1.1841     -5.9819      6.0985\n  1500   1400     0.0940    -4.5884     4.0162    30.383302      1.1438     -5.9897      6.0985\n  1500   1600     0.0940    -4.5884     4.0162    30.760792      1.1043     -5.9971      6.0985\n  1500   1800     0.0940    -4.5884     4.0162    31.130565      1.0656     -6.0041      6.0985\n  1500   2000     0.0940    -4.5884     4.0162    31.492930      1.0276     -6.0107      6.0985\n  1500   2200     0.0940    -4.5884     4.0162    31.848175      0.9903     -6.0170      6.0985\n  1500   2400     0.0940    -4.5884     4.0162    32.196569      0.9537     -6.0229      6.0985\n  1500   2600     0.0940    -4.5884     4.0162    32.538366      0.9178     -6.0285      6.0985\n  1500   2800     0.0940    -4.5884     4.0162    32.873803      0.8825     -6.0337      6.0985\n  1500   3000     0.0940    -4.5884     4.0162    33.203105      0.8478     -6.0387      6.0985\n  1500   3200     0.0940    -4.5884     4.0162    33.526483      0.8137     -6.0434      6.0985\n  1500   3400     0.0940    -4.5884     4.0162    33.844138      0.7801     -6.0478      6.0985\n  1500   3600     0.0940    -4.5884     4.0162    34.156258      0.7472     -6.0520      6.0985\n  1500   3800     0.0940    -4.5884     4.0162    34.463022      0.7148     -6.0559      6.0985\n  1500   4000     0.0940    -4.5884     4.0162    34.764599      0.6829     -6.0596      6.0985\n  1500   4200     0.0940    -4.5884     4.0162    35.061152      0.6515     -6.0630      6.0985\n  1500   4400     0.0940    -4.5884     4.0162    35.352833      0.6207     -6.0662      6.0985\n  1500   4600     0.0940    -4.5884     4.0162    35.639787      0.5903     -6.0693      6.0985\n  1500   4800     0.0940    -4.5884     4.0162    35.922154      0.5603     -6.0721      6.0985\n  1500   5000     0.0940    -4.5884     4.0162    36.200066      0.5309     -6.0748      6.0985\n  1500   5200     0.0940    -4.5884     4.0162    36.473648      0.5019     -6.0772      6.0985\n  1500   5400     0.0940    -4.5884     4.0162    36.743022      0.4733     -6.0795      6.0985\n  1500   5600     0.0940    -4.5884     4.0162    37.008302      0.4451     -6.0816      6.0985\n  1500   5800     0.0940    -4.5884     4.0162    37.269599      0.4174     -6.0836      6.0985\n  1500   6000     0.0940    -4.5884     4.0162    37.527018      0.3901     -6.0854      6.0985\n  1500   6200     0.0940    -4.5884     4.0162    37.780660      0.3631     -6.0871      6.0985\n  1500   6400     0.0940    -4.5884     4.0162    38.030623      0.3366     -6.0886      6.0985\n  1500   6600     0.0940    -4.5884     4.0162    38.276999      0.3104     -6.0900      6.0985\n  1500   6800     0.0940    -4.5884     4.0162    38.519878      0.2846     -6.0913      6.0985\n  1500   7000     0.0940    -4.5884     4.0162    38.759345      0.2591     -6.0924      6.0985\n  1500   7200     0.0940    -4.5884     4.0162    38.995484      0.2340     -6.0934      6.0985\n  1500   7400     0.0940    -4.5884     4.0162    39.228373      0.2092     -6.0943      6.0985\n  1500   7600     0.0940    -4.5884     4.0162    39.458089      0.1848     -6.0951      6.0985\n  1500   7800     0.0940    -4.5884     4.0162    39.684705      0.1607     -6.0958      6.0985\n  1500   8000     0.0940    -4.5884     4.0162    39.908294      0.1369     -6.0964      6.0985\n  1500   8200     0.0940    -4.5884     4.0162    40.128922      0.1134     -6.0968      6.0985\n  1500   8400     0.0940    -4.5884     4.0162    40.346656      0.0903     -6.0972      6.0985\n  2000      0     0.0940    -4.4723     4.0627    27.492735      1.5392     -5.8429      6.0429\n  2000    200     0.0940    -4.4723     4.0627    27.935039      1.4941     -5.8547      6.0429\n  2000    400     0.0940    -4.4723     4.0627    28.366707      1.4499     -5.8657      6.0429\n  2000    600     0.0940    -4.4723     4.0627    28.788259      1.4067     -5.8762      6.0429\n  2000    800     0.0940    -4.4723     4.0627    29.200178      1.3644     -5.8862      6.0429\n  2000   1000     0.0940    -4.4723     4.0627    29.602906      1.3230     -5.8956      6.0429\n  2000   1200     0.0940    -4.4723     4.0627    29.996854      1.2825     -5.9046      6.0429\n  2000   1400     0.0940    -4.4723     4.0627    30.382402      1.2427     -5.9131      6.0429\n  2000   1600     0.0940    -4.4723     4.0627    30.759903      1.2037     -5.9212      6.0429\n  2000   1800     0.0940    -4.4723     4.0627    31.129689      1.1655     -5.9288      6.0429\n  2000   2000     0.0940    -4.4723     4.0627    31.492065      1.1280     -5.9361      6.0429\n  2000   2200     0.0940    -4.4723     4.0627    31.847321      1.0911     -5.9429      6.0429\n  2000   2400     0.0940    -4.4723     4.0627    32.195725      1.0550     -5.9495      6.0429\n  2000   2600     0.0940    -4.4723     4.0627    32.537532      1.0195     -5.9556      6.0429\n  2000   2800     0.0940    -4.4723     4.0627    32.872978      0.9846     -5.9615      6.0429\n  2000   3000     0.0940    -4.4723     4.0627    33.202290      0.9503     -5.9671      6.0429\n  2000   3200     0.0940    -4.4723     4.0627    33.525677      0.9166     -5.9723      6.0429\n  2000   3400     0.0940    -4.4723     4.0627    33.843341      0.8835     -5.9773      6.0429\n  2000   3600     0.0940    -4.4723     4.0627    34.155469      0.8509     -5.9820      6.0429\n  2000   3800     0.0940    -4.4723     4.0627    34.462241      0.8189     -5.9865      6.0429\n  2000   4000     0.0940    -4.4723     4.0627    34.763827      0.7873     -5.9907      6.0429\n  2000   4200     0.0940    -4.4723     4.0627    35.060387      0.7563     -5.9947      6.0429\n  2000   4400     0.0940    -4.4723     4.0627    35.352075      0.7258     -5.9985      6.0429\n  2000   4600     0.0940    -4.4723     4.0627    35.639037      0.6958     -6.0021      6.0429\n  2000   4800     0.0940    -4.4723     4.0627    35.921411      0.6662     -6.0054      6.0429\n  2000   5000     0.0940    -4.4723     4.0627    36.199329      0.6370     -6.0086      6.0429\n  2000   5200     0.0940    -4.4723     4.0627    36.472918      0.6083     -6.0115      6.0429\n  2000   5400     0.0940    -4.4723     4.0627    36.742298      0.5801     -6.0143      6.0429\n  2000   5600     0.0940    -4.4723     4.0627    37.007585      0.5522     -6.0170      6.0429\n  2000   5800     0.0940    -4.4723     4.0627    37.268888      0.5248     -6.0194      6.0429\n  2000   6000     0.0940    -4.4723     4.0627    37.526313      0.4977     -6.0217      6.0429\n  2000   6200     0.0940    -4.4723     4.0627    37.779961      0.4710     -6.0239      6.0429\n  2000   6400     0.0940    -4.4723     4.0627    38.029930      0.4448     -6.0259      6.0429\n  2000   6600     0.0940    -4.4723     4.0627    38.276312      0.4188     -6.0277      6.0429\n  2000   6800     0.0940    -4.4723     4.0627    38.519196      0.3933     -6.0294      6.0429\n  2000   7000     0.0940    -4.4723     4.0627    38.758669      0.3681     -6.0310      6.0429\n  2000   7200     0.0940    -4.4723     4.0627    38.994812      0.3432     -6.0325      6.0429\n  2000   7400     0.0940    -4.4723     4.0627    39.227706      0.3187     -6.0338      6.0429\n  2000   7600     0.0940    -4.4723     4.0627    39.457427      0.2945     -6.0351      6.0429\n  2000   7800     0.0940    -4.4723     4.0627    39.684049      0.2706     -6.0362      6.0429\n  2000   8000     0.0940    -4.4723     4.0627    39.907642      0.2471     -6.0372      6.0429\n  2000   8200     0.0940    -4.4723     4.0627    40.128274      0.2238     -6.0381      6.0429\n  2000   8400     0.0940    -4.4723     4.0627    40.346013      0.2009     -6.0389      6.0429\n  2500      0     0.0940    -4.3563     4.1092    27.491711      1.6341     -5.7615      5.9893\n  2500    200     0.0940    -4.3563     4.1092    27.934033      1.5896     -5.7739      5.9893\n  2500    400     0.0940    -4.3563     4.1092    28.365717      1.5461     -5.7857      5.9893\n  2500    600     0.0940    -4.3563     4.1092    28.787285      1.5035     -5.7969      5.9893\n  2500    800     0.0940    -4.3563     4.1092    29.199219      1.4617     -5.8076      5.9893\n  2500   1000     0.0940    -4.3563     4.1092    29.601961      1.4209     -5.8177      5.9893\n  2500   1200     0.0940    -4.3563     4.1092    29.995922      1.3808     -5.8273      5.9893\n  2500   1400     0.0940    -4.3563     4.1092    30.381483      1.3416     -5.8365      5.9893\n  2500   1600     0.0940    -4.3563     4.1092    30.758997      1.3031     -5.8452      5.9893\n  2500   1800     0.0940    -4.3563     4.1092    31.128795      1.2654     -5.8535      5.9893\n  2500   2000     0.0940    -4.3563     4.1092    31.491183      1.2283     -5.8614      5.9893\n  2500   2200     0.0940    -4.3563     4.1092    31.846449      1.1920     -5.8689      5.9893\n  2500   2400     0.0940    -4.3563     4.1092    32.194864      1.1562     -5.8760      5.9893\n  2500   2600     0.0940    -4.3563     4.1092    32.536681      1.1212     -5.8828      5.9893\n  2500   2800     0.0940    -4.3563     4.1092    32.872138      1.0867     -5.8893      5.9893\n  2500   3000     0.0940    -4.3563     4.1092    33.201459      1.0528     -5.8954      5.9893\n  2500   3200     0.0940    -4.3563     4.1092    33.524856      1.0196     -5.9013      5.9893\n  2500   3400     0.0940    -4.3563     4.1092    33.842528      0.9868     -5.9068      5.9893\n  2500   3600     0.0940    -4.3563     4.1092    34.154665      0.9546     -5.9121      5.9893\n  2500   3800     0.0940    -4.3563     4.1092    34.461445      0.9230     -5.9171      5.9893\n  2500   4000     0.0940    -4.3563     4.1092    34.763039      0.8918     -5.9219      5.9893\n  2500   4200     0.0940    -4.3563     4.1092    35.059607      0.8611     -5.9265      5.9893\n  2500   4400     0.0940    -4.3563     4.1092    35.351303      0.8310     -5.9308      5.9893\n  2500   4600     0.0940    -4.3563     4.1092    35.638272      0.8012     -5.9348      5.9893\n  2500   4800     0.0940    -4.3563     4.1092    35.920653      0.7720     -5.9387      5.9893\n  2500   5000     0.0940    -4.3563     4.1092    36.198579      0.7432     -5.9424      5.9893\n  2500   5200     0.0940    -4.3563     4.1092    36.472175      0.7148     -5.9459      5.9893\n  2500   5400     0.0940    -4.3563     4.1092    36.741561      0.6868     -5.9492      5.9893\n  2500   5600     0.0940    -4.3563     4.1092    37.006854      0.6593     -5.9523      5.9893\n  2500   5800     0.0940    -4.3563     4.1092    37.268164      0.6321     -5.9552      5.9893\n  2500   6000     0.0940    -4.3563     4.1092    37.525595      0.6054     -5.9580      5.9893\n  2500   6200     0.0940    -4.3563     4.1092    37.779249      0.5790     -5.9606      5.9893\n  2500   6400     0.0940    -4.3563     4.1092    38.029223      0.5530     -5.9631      5.9893\n  2500   6600     0.0940    -4.3563     4.1092    38.275611      0.5273     -5.9654      5.9893\n  2500   6800     0.0940    -4.3563     4.1092    38.518501      0.5020     -5.9676      5.9893\n  2500   7000     0.0940    -4.3563     4.1092    38.757979      0.4771     -5.9696      5.9893\n  2500   7200     0.0940    -4.3563     4.1092    38.994128      0.4525     -5.9716      5.9893\n  2500   7400     0.0940    -4.3563     4.1092    39.227027      0.4282     -5.9733      5.9893\n  2500   7600     0.0940    -4.3563     4.1092    39.456753      0.4042     -5.9750      5.9893\n  2500   7800     0.0940    -4.3563     4.1092    39.683379      0.3806     -5.9766      5.9893\n  2500   8000     0.0940    -4.3563     4.1092    39.906977      0.3573     -5.9780      5.9893\n  2500   8200     0.0940    -4.3563     4.1092    40.127615      0.3343     -5.9793      5.9893\n  2500   8400     0.0940    -4.3563     4.1092    40.345358      0.3115     -5.9806      5.9893\n  3000      0     0.0940    -4.2403     4.1557    27.490666      1.7291     -5.6800      5.9379\n  3000    200     0.0940    -4.2403     4.1557    27.933006      1.6852     -5.6931      5.9379\n  3000    400     0.0940    -4.2403     4.1557    28.364707      1.6422     -5.7057      5.9379\n  3000    600     0.0940    -4.2403     4.1557    28.786291      1.6002     -5.7176      5.9379\n  3000    800     0.0940    -4.2403     4.1557    29.198240      1.5590     -5.7289      5.9379\n  3000   1000     0.0940    -4.2403     4.1557    29.600997      1.5187     -5.7398      5.9379\n  3000   1200     0.0940    -4.2403     4.1557    29.994972      1.4792     -5.7501      5.9379\n  3000   1400     0.0940    -4.2403     4.1557    30.380547      1.4405     -5.7599      5.9379\n  3000   1600     0.0940    -4.2403     4.1557    30.758074      1.4025     -5.7693      5.9379\n  3000   1800     0.0940    -4.2403     4.1557    31.127883      1.3653     -5.7782      5.9379\n  3000   2000     0.0940    -4.2403     4.1557    31.490283      1.3287     -5.7867      5.9379\n  3000   2200     0.0940    -4.2403     4.1557    31.845561      1.2928     -5.7948      5.9379\n  3000   2400     0.0940    -4.2403     4.1557    32.193987      1.2575     -5.8026      5.9379\n  3000   2600     0.0940    -4.2403     4.1557    32.535814      1.2229     -5.8100      5.9379\n  3000   2800     0.0940    -4.2403     4.1557    32.871281      1.1888     -5.8170      5.9379\n  3000   3000     0.0940    -4.2403     4.1557    33.200612      1.1554     -5.8238      5.9379\n  3000   3200     0.0940    -4.2403     4.1557    33.524018      1.1225     -5.8302      5.9379\n  3000   3400     0.0940    -4.2403     4.1557    33.841699      1.0902     -5.8363      5.9379\n  3000   3600     0.0940    -4.2403     4.1557    34.153845      1.0583     -5.8422      5.9379\n  3000   3800     0.0940    -4.2403     4.1557    34.460634      1.0270     -5.8478      5.9379\n  3000   4000     0.0940    -4.2403     4.1557    34.762236      0.9963     -5.8531      5.9379\n  3000   4200     0.0940    -4.2403     4.1557    35.058813      0.9659     -5.8582      5.9379\n  3000   4400     0.0940    -4.2403     4.1557    35.350516      0.9361     -5.8630      5.9379\n  3000   4600     0.0940    -4.2403     4.1557    35.637493      0.9067     -5.8676      5.9379\n  3000   4800     0.0940    -4.2403     4.1557    35.919882      0.8778     -5.8720      5.9379\n  3000   5000     0.0940    -4.2403     4.1557    36.197814      0.8493     -5.8762      5.9379\n  3000   5200     0.0940    -4.2403     4.1557    36.471417      0.8212     -5.8802      5.9379\n  3000   5400     0.0940    -4.2403     4.1557    36.740810      0.7936     -5.8840      5.9379\n  3000   5600     0.0940    -4.2403     4.1557    37.006110      0.7663     -5.8876      5.9379\n  3000   5800     0.0940    -4.2403     4.1557    37.267426      0.7395     -5.8910      5.9379\n  3000   6000     0.0940    -4.2403     4.1557    37.524863      0.7130     -5.8943      5.9379\n  3000   6200     0.0940    -4.2403     4.1557    37.778524      0.6869     -5.8974      5.9379\n  3000   6400     0.0940    -4.2403     4.1557    38.028504      0.6612     -5.9003      5.9379\n  3000   6600     0.0940    -4.2403     4.1557    38.274897      0.6358     -5.9031      5.9379\n  3000   6800     0.0940    -4.2403     4.1557    38.517793      0.6107     -5.9058      5.9379\n  3000   7000     0.0940    -4.2403     4.1557    38.757276      0.5861     -5.9083      5.9379\n  3000   7200     0.0940    -4.2403     4.1557    38.993431      0.5617     -5.9106      5.9379\n  3000   7400     0.0940    -4.2403     4.1557    39.226335      0.5377     -5.9129      5.9379\n  3000   7600     0.0940    -4.2403     4.1557    39.456066      0.5140     -5.9150      5.9379\n  3000   7800     0.0940    -4.2403     4.1557    39.682698      0.4906     -5.9170      5.9379\n  3000   8000     0.0940    -4.2403     4.1557    39.906301      0.4675     -5.9188      5.9379\n  3000   8200     0.0940    -4.2403     4.1557    40.126943      0.4447     -5.9206      5.9379\n  3000   8400     0.0940    -4.2403     4.1557    40.344692      0.4222     -5.9222      5.9379\n  3500      0     0.0940    -4.1242     4.2022    27.489602      1.8240     -5.5984      5.8887\n  3500    200     0.0940    -4.1242     4.2022    27.931959      1.7807     -5.6124      5.8887\n  3500    400     0.0940    -4.1242     4.2022    28.363677      1.7384     -5.6256      5.8887\n  3500    600     0.0940    -4.1242     4.2022    28.785278      1.6969     -5.6383      5.8887\n  3500    800     0.0940    -4.1242     4.2022    29.197243      1.6563     -5.6503      5.8887\n  3500   1000     0.0940    -4.1242     4.2022    29.600015      1.6166     -5.6618      5.8887\n  3500   1200     0.0940    -4.1242     4.2022    29.994004      1.5776     -5.6728      5.8887\n  3500   1400     0.0940    -4.1242     4.2022    30.379592      1.5394     -5.6833      5.8887\n  3500   1600     0.0940    -4.1242     4.2022    30.757132      1.5019     -5.6933      5.8887\n  3500   1800     0.0940    -4.1242     4.2022    31.126954      1.4651     -5.7029      5.8887\n  3500   2000     0.0940    -4.1242     4.2022    31.489366      1.4290     -5.7120      5.8887\n  3500   2200     0.0940    -4.1242     4.2022    31.844656      1.3936     -5.7208      5.8887\n  3500   2400     0.0940    -4.1242     4.2022    32.193093      1.3588     -5.7291      5.8887\n  3500   2600     0.0940    -4.1242     4.2022    32.534931      1.3246     -5.7371      5.8887\n  3500   2800     0.0940    -4.1242     4.2022    32.870408      1.2910     -5.7448      5.8887\n  3500   3000     0.0940    -4.1242     4.2022    33.199749      1.2579     -5.7521      5.8887\n  3500   3200     0.0940    -4.1242     4.2022    33.523165      1.2254     -5.7591      5.8887\n  3500   3400     0.0940    -4.1242     4.2022    33.840856      1.1935     -5.7658      5.8887\n  3500   3600     0.0940    -4.1242     4.2022    34.153010      1.1621     -5.7722      5.8887\n  3500   3800     0.0940    -4.1242     4.2022    34.459808      1.1311     -5.7784      5.8887\n  3500   4000     0.0940    -4.1242     4.2022    34.761419      1.1007     -5.7843      5.8887\n  3500   4200     0.0940    -4.1242     4.2022    35.058003      1.0707     -5.7899      5.8887\n  3500   4400     0.0940    -4.1242     4.2022    35.349715      1.0413     -5.7953      5.8887\n  3500   4600     0.0940    -4.1242     4.2022    35.636699      1.0122     -5.8004      5.8887\n  3500   4800     0.0940    -4.1242     4.2022    35.919095      0.9836     -5.8053      5.8887\n  3500   5000     0.0940    -4.1242     4.2022    36.197035      0.9554     -5.8100      5.8887\n  3500   5200     0.0940    -4.1242     4.2022    36.470645      0.9277     -5.8145      5.8887\n  3500   5400     0.0940    -4.1242     4.2022    36.740046      0.9003     -5.8188      5.8887\n  3500   5600     0.0940    -4.1242     4.2022    37.005352      0.8734     -5.8229      5.8887\n  3500   5800     0.0940    -4.1242     4.2022    37.266674      0.8468     -5.8268      5.8887\n  3500   6000     0.0940    -4.1242     4.2022    37.524118      0.8206     -5.8306      5.8887\n  3500   6200     0.0940    -4.1242     4.2022    37.777785      0.7948     -5.8342      5.8887\n  3500   6400     0.0940    -4.1242     4.2022    38.027771      0.7693     -5.8376      5.8887\n  3500   6600     0.0940    -4.1242     4.2022    38.274170      0.7442     -5.8408      5.8887\n  3500   6800     0.0940    -4.1242     4.2022    38.517072      0.7195     -5.8439      5.8887\n  3500   7000     0.0940    -4.1242     4.2022    38.756561      0.6950     -5.8469      5.8887\n  3500   7200     0.0940    -4.1242     4.2022    38.992721      0.6709     -5.8497      5.8887\n  3500   7400     0.0940    -4.1242     4.2022    39.225631      0.6471     -5.8524      5.8887\n  3500   7600     0.0940    -4.1242     4.2022    39.455367      0.6237     -5.8549      5.8887\n  3500   7800     0.0940    -4.1242     4.2022    39.682004      0.6005     -5.8573      5.8887\n  3500   8000     0.0940    -4.1242     4.2022    39.905612      0.5777     -5.8596      5.8887\n  3500   8200     0.0940    -4.1242     4.2022    40.126259      0.5551     -5.8618      5.8887\n  3500   8400     0.0940    -4.1242     4.2022    40.344013      0.5328     -5.8639      5.8887\n  4000      0     0.0940    -4.0082     4.2487    27.488516      1.9189     -5.5169      5.8417\n  4000    200     0.0940    -4.0082     4.2487    27.930893      1.8763     -5.5316      5.8417\n  4000    400     0.0940    -4.0082     4.2487    28.362629      1.8345     -5.5456      5.8417\n  4000    600     0.0940    -4.0082     4.2487    28.784246      1.7937     -5.5589      5.8417\n  4000    800     0.0940    -4.0082     4.2487    29.196227      1.7536     -5.5717      5.8417\n  4000   1000     0.0940    -4.0082     4.2487    29.599014      1.7144     -5.5839      5.8417\n  4000   1200     0.0940    -4.0082     4.2487    29.993018      1.6760     -5.5955      5.8417\n  4000   1400     0.0940    -4.0082     4.2487    30.378620      1.6383     -5.6067      5.8417\n  4000   1600     0.0940    -4.0082     4.2487    30.756173      1.6013     -5.6173      5.8417\n  4000   1800     0.0940    -4.0082     4.2487    31.126008      1.5650     -5.6275      5.8417\n  4000   2000     0.0940    -4.0082     4.2487    31.488432      1.5294     -5.6373      5.8417\n  4000   2200     0.0940    -4.0082     4.2487    31.843734      1.4944     -5.6467      5.8417\n  4000   2400     0.0940    -4.0082     4.2487    32.192182      1.4601     -5.6557      5.8417\n  4000   2600     0.0940    -4.0082     4.2487    32.534031      1.4263     -5.6643      5.8417\n  4000   2800     0.0940    -4.0082     4.2487    32.869519      1.3931     -5.6726      5.8417\n  4000   3000     0.0940    -4.0082     4.2487    33.198870      1.3605     -5.6805      5.8417\n  4000   3200     0.0940    -4.0082     4.2487    33.522296      1.3284     -5.6881      5.8417\n  4000   3400     0.0940    -4.0082     4.2487    33.839996      1.2968     -5.6953      5.8417\n  4000   3600     0.0940    -4.0082     4.2487    34.152160      1.2658     -5.7023      5.8417\n  4000   3800     0.0940    -4.0082     4.2487    34.458967      1.2352     -5.7090      5.8417\n  4000   4000     0.0940    -4.0082     4.2487    34.760586      1.2052     -5.7154      5.8417\n  4000   4200     0.0940    -4.0082     4.2487    35.057179      1.1756     -5.7216      5.8417\n  4000   4400     0.0940    -4.0082     4.2487    35.348899      1.1464     -5.7275      5.8417\n  4000   4600     0.0940    -4.0082     4.2487    35.635891      1.1177     -5.7332      5.8417\n  4000   4800     0.0940    -4.0082     4.2487    35.918295      1.0894     -5.7386      5.8417\n  4000   5000     0.0940    -4.0082     4.2487    36.196242      1.0616     -5.7438      5.8417\n  4000   5200     0.0940    -4.0082     4.2487    36.469860      1.0341     -5.7488      5.8417\n  4000   5400     0.0940    -4.0082     4.2487    36.739267      1.0071     -5.7536      5.8417\n  4000   5600     0.0940    -4.0082     4.2487    37.004580      0.9804     -5.7582      5.8417\n  4000   5800     0.0940    -4.0082     4.2487    37.265909      0.9542     -5.7626      5.8417\n  4000   6000     0.0940    -4.0082     4.2487    37.523360      0.9283     -5.7669      5.8417\n  4000   6200     0.0940    -4.0082     4.2487    37.777033      0.9027     -5.7709      5.8417\n  4000   6400     0.0940    -4.0082     4.2487    38.027025      0.8775     -5.7748      5.8417\n  4000   6600     0.0940    -4.0082     4.2487    38.273431      0.8527     -5.7785      5.8417\n  4000   6800     0.0940    -4.0082     4.2487    38.516338      0.8282     -5.7821      5.8417\n  4000   7000     0.0940    -4.0082     4.2487    38.755833      0.8040     -5.7855      5.8417\n  4000   7200     0.0940    -4.0082     4.2487    38.991999      0.7802     -5.7888      5.8417\n  4000   7400     0.0940    -4.0082     4.2487    39.224914      0.7566     -5.7919      5.8417\n  4000   7600     0.0940    -4.0082     4.2487    39.454656      0.7334     -5.7949      5.8417\n  4000   7800     0.0940    -4.0082     4.2487    39.681298      0.7105     -5.7977      5.8417\n  4000   8000     0.0940    -4.0082     4.2487    39.904911      0.6878     -5.8004      5.8417\n  4000   8200     0.0940    -4.0082     4.2487    40.125563      0.6655     -5.8031      5.8417\n  4000   8400     0.0940    -4.0082     4.2487    40.343322      0.6434     -5.8055      5.8417\n  4500      0     0.0940    -3.8921     4.2952    27.487411      2.0138     -5.4354      5.7971\n  4500    200     0.0940    -3.8921     4.2952    27.929806      1.9718     -5.4508      5.7971\n  4500    400     0.0940    -3.8921     4.2952    28.361560      1.9307     -5.4655      5.7971\n  4500    600     0.0940    -3.8921     4.2952    28.783195      1.8904     -5.4796      5.7971\n  4500    800     0.0940    -3.8921     4.2952    29.195192      1.8509     -5.4930      5.7971\n  4500   1000     0.0940    -3.8921     4.2952    29.597995      1.8123     -5.5059      5.7971\n  4500   1200     0.0940    -3.8921     4.2952    29.992014      1.7744     -5.5182      5.7971\n  4500   1400     0.0940    -3.8921     4.2952    30.377630      1.7372     -5.5300      5.7971\n  4500   1600     0.0940    -3.8921     4.2952    30.755197      1.7007     -5.5414      5.7971\n  4500   1800     0.0940    -3.8921     4.2952    31.125045      1.6649     -5.5522      5.7971\n  4500   2000     0.0940    -3.8921     4.2952    31.487481      1.6298     -5.5626      5.7971\n  4500   2200     0.0940    -3.8921     4.2952    31.842795      1.5952     -5.5726      5.7971\n  4500   2400     0.0940    -3.8921     4.2952    32.191255      1.5613     -5.5822      5.7971\n  4500   2600     0.0940    -3.8921     4.2952    32.533115      1.5280     -5.5915      5.7971\n  4500   2800     0.0940    -3.8921     4.2952    32.868614      1.4952     -5.6003      5.7971\n  4500   3000     0.0940    -3.8921     4.2952    33.197975      1.4630     -5.6088      5.7971\n  4500   3200     0.0940    -3.8921     4.2952    33.521411      1.4313     -5.6170      5.7971\n  4500   3400     0.0940    -3.8921     4.2952    33.839121      1.4002     -5.6248      5.7971\n  4500   3600     0.0940    -3.8921     4.2952    34.151295      1.3695     -5.6324      5.7971\n  4500   3800     0.0940    -3.8921     4.2952    34.458111      1.3393     -5.6396      5.7971\n  4500   4000     0.0940    -3.8921     4.2952    34.759739      1.3096     -5.6466      5.7971\n  4500   4200     0.0940    -3.8921     4.2952    35.056340      1.2804     -5.6533      5.7971\n  4500   4400     0.0940    -3.8921     4.2952    35.348068      1.2516     -5.6597      5.7971\n  4500   4600     0.0940    -3.8921     4.2952    35.635069      1.2232     -5.6659      5.7971\n  4500   4800     0.0940    -3.8921     4.2952    35.917480      1.1952     -5.6719      5.7971\n  4500   5000     0.0940    -3.8921     4.2952    36.195435      1.1677     -5.6776      5.7971\n  4500   5200     0.0940    -3.8921     4.2952    36.469060      1.1406     -5.6831      5.7971\n  4500   5400     0.0940    -3.8921     4.2952    36.738475      1.1139     -5.6884      5.7971\n  4500   5600     0.0940    -3.8921     4.2952    37.003795      1.0875     -5.6935      5.7971\n  4500   5800     0.0940    -3.8921     4.2952    37.265131      1.0615     -5.6984      5.7971\n  4500   6000     0.0940    -3.8921     4.2952    37.522588      1.0359     -5.7031      5.7971\n  4500   6200     0.0940    -3.8921     4.2952    37.776268      1.0106     -5.7077      5.7971\n  4500   6400     0.0940    -3.8921     4.2952    38.026267      0.9857     -5.7120      5.7971\n  4500   6600     0.0940    -3.8921     4.2952    38.272678      0.9612     -5.7162      5.7971\n  4500   6800     0.0940    -3.8921     4.2952    38.515591      0.9369     -5.7202      5.7971\n  4500   7000     0.0940    -3.8921     4.2952    38.755092      0.9130     -5.7241      5.7971\n  4500   7200     0.0940    -3.8921     4.2952    38.991264      0.8894     -5.7278      5.7971\n  4500   7400     0.0940    -3.8921     4.2952    39.224185      0.8661     -5.7314      5.7971\n  4500   7600     0.0940    -3.8921     4.2952    39.453932      0.8431     -5.7348      5.7971\n  4500   7800     0.0940    -3.8921     4.2952    39.680580      0.8204     -5.7381      5.7971\n  4500   8000     0.0940    -3.8921     4.2952    39.904198      0.7980     -5.7413      5.7971\n  4500   8200     0.0940    -3.8921     4.2952    40.124856      0.7759     -5.7443      5.7971\n  4500   8400     0.0940    -3.8921     4.2952    40.342619      0.7541     -5.7472      5.7971\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180506_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.9353410       -5.3971995       27.0244745   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0603629        0.0321722   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       -5.3217217       27.0078715   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0556305        0.0294090   m/s m/s m/s\nunwrap_phase_constant:            0.00014     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180506_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.935     -5.397     27.024\norbit baseline rate   (TCN) (m/s):  0.000e+00 -6.036e-02  3.217e-02\n\nbaseline vector (TCN)   (m):      0.000     -5.322     27.008\nbaseline rate   (TCN) (m/s):  0.000e+00 -5.563e-02  2.941e-02\n\nSLC-1 center baseline length (m):    27.5272\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    -4.8025    26.7334    27.496626     21.4962    -16.6026     27.1613\n     0    200     0.0000    -4.8025    26.7334    27.938864     21.3675    -16.7681     27.1613\n     0    400     0.0000    -4.8025    26.7334    28.370470     21.2405    -16.9285     27.1613\n     0    600     0.0000    -4.8025    26.7334    28.791963     21.1154    -17.0843     27.1613\n     0    800     0.0000    -4.8025    26.7334    29.203825     20.9921    -17.2357     27.1613\n     0   1000     0.0000    -4.8025    26.7334    29.606500     20.8704    -17.3828     27.1613\n     0   1200     0.0000    -4.8025    26.7334    30.000396     20.7504    -17.5259     27.1613\n     0   1400     0.0000    -4.8025    26.7334    30.385895     20.6320    -17.6651     27.1613\n     0   1600     0.0000    -4.8025    26.7334    30.763349     20.5152    -17.8006     27.1613\n     0   1800     0.0000    -4.8025    26.7334    31.133089     20.3999    -17.9326     27.1613\n     0   2000     0.0000    -4.8025    26.7334    31.495422     20.2861    -18.0613     27.1613\n     0   2200     0.0000    -4.8025    26.7334    31.850636     20.1737    -18.1867     27.1613\n     0   2400     0.0000    -4.8025    26.7334    32.199000     20.0628    -18.3090     27.1613\n     0   2600     0.0000    -4.8025    26.7334    32.540768     19.9532    -18.4284     27.1613\n     0   2800     0.0000    -4.8025    26.7334    32.876178     19.8450    -18.5449     27.1613\n     0   3000     0.0000    -4.8025    26.7334    33.205453     19.7381    -18.6586     27.1613\n     0   3200     0.0000    -4.8025    26.7334    33.528805     19.6325    -18.7697     27.1613\n     0   3400     0.0000    -4.8025    26.7334    33.846435     19.5281    -18.8782     27.1613\n     0   3600     0.0000    -4.8025    26.7334    34.158531     19.4250    -18.9843     27.1613\n     0   3800     0.0000    -4.8025    26.7334    34.465271     19.3231    -19.0881     27.1613\n     0   4000     0.0000    -4.8025    26.7334    34.766827     19.2224    -19.1895     27.1613\n     0   4200     0.0000    -4.8025    26.7334    35.063357     19.1228    -19.2887     27.1613\n     0   4400     0.0000    -4.8025    26.7334    35.355017     19.0244    -19.3858     27.1613\n     0   4600     0.0000    -4.8025    26.7334    35.641950     18.9270    -19.4808     27.1613\n     0   4800     0.0000    -4.8025    26.7334    35.924297     18.8308    -19.5739     27.1613\n     0   5000     0.0000    -4.8025    26.7334    36.202189     18.7357    -19.6650     27.1613\n     0   5200     0.0000    -4.8025    26.7334    36.475752     18.6416    -19.7542     27.1613\n     0   5400     0.0000    -4.8025    26.7334    36.745108     18.5485    -19.8416     27.1613\n     0   5600     0.0000    -4.8025    26.7334    37.010370     18.4564    -19.9273     27.1613\n     0   5800     0.0000    -4.8025    26.7334    37.271649     18.3654    -20.0112     27.1613\n     0   6000     0.0000    -4.8025    26.7334    37.529051     18.2753    -20.0935     27.1613\n     0   6200     0.0000    -4.8025    26.7334    37.782677     18.1861    -20.1742     27.1613\n     0   6400     0.0000    -4.8025    26.7334    38.032623     18.0980    -20.2534     27.1613\n     0   6600     0.0000    -4.8025    26.7334    38.278984     18.0107    -20.3310     27.1613\n     0   6800     0.0000    -4.8025    26.7334    38.521847     17.9244    -20.4072     27.1613\n     0   7000     0.0000    -4.8025    26.7334    38.761299     17.8389    -20.4819     27.1613\n     0   7200     0.0000    -4.8025    26.7334    38.997423     17.7544    -20.5553     27.1613\n     0   7400     0.0000    -4.8025    26.7334    39.230297     17.6707    -20.6272     27.1613\n     0   7600     0.0000    -4.8025    26.7334    39.459999     17.5878    -20.6979     27.1613\n     0   7800     0.0000    -4.8025    26.7334    39.686602     17.5058    -20.7673     27.1613\n     0   8000     0.0000    -4.8025    26.7334    39.910176     17.4247    -20.8355     27.1613\n     0   8200     0.0000    -4.8025    26.7334    40.130792     17.3443    -20.9024     27.1613\n     0   8400     0.0000    -4.8025    26.7334    40.348513     17.2648    -20.9682     27.1613\n   500      0     0.0000    -4.9168    26.7938    27.495684     21.4973    -16.7316     27.2412\n   500    200     0.0000    -4.9168    26.7938    27.937938     21.3676    -16.8971     27.2412\n   500    400     0.0000    -4.9168    26.7938    28.369559     21.2397    -17.0576     27.2412\n   500    600     0.0000    -4.9168    26.7938    28.791066     21.1136    -17.2133     27.2412\n   500    800     0.0000    -4.9168    26.7938    29.202942     20.9893    -17.3647     27.2412\n   500   1000     0.0000    -4.9168    26.7938    29.605629     20.8668    -17.5118     27.2412\n   500   1200     0.0000    -4.9168    26.7938    29.999538     20.7459    -17.6548     27.2412\n   500   1400     0.0000    -4.9168    26.7938    30.385049     20.6266    -17.7940     27.2412\n   500   1600     0.0000    -4.9168    26.7938    30.762515     20.5089    -17.9295     27.2412\n   500   1800     0.0000    -4.9168    26.7938    31.132265     20.3928    -18.0615     27.2412\n   500   2000     0.0000    -4.9168    26.7938    31.494609     20.2782    -18.1901     27.2412\n   500   2200     0.0000    -4.9168    26.7938    31.849833     20.1650    -18.3154     27.2412\n   500   2400     0.0000    -4.9168    26.7938    32.198207     20.0533    -18.4377     27.2412\n   500   2600     0.0000    -4.9168    26.7938    32.539984     19.9429    -18.5570     27.2412\n   500   2800     0.0000    -4.9168    26.7938    32.875402     19.8340    -18.6734     27.2412\n   500   3000     0.0000    -4.9168    26.7938    33.204686     19.7263    -18.7871     27.2412\n   500   3200     0.0000    -4.9168    26.7938    33.528047     19.6200    -18.8981     27.2412\n   500   3400     0.0000    -4.9168    26.7938    33.845685     19.5149    -19.0066     27.2412\n   500   3600     0.0000    -4.9168    26.7938    34.157789     19.4111    -19.1126     27.2412\n   500   3800     0.0000    -4.9168    26.7938    34.464537     19.3085    -19.2163     27.2412\n   500   4000     0.0000    -4.9168    26.7938    34.766099     19.2071    -19.3177     27.2412\n   500   4200     0.0000    -4.9168    26.7938    35.062637     19.1068    -19.4168     27.2412\n   500   4400     0.0000    -4.9168    26.7938    35.354304     19.0077    -19.5138     27.2412\n   500   4600     0.0000    -4.9168    26.7938    35.641244     18.9098    -19.6088     27.2412\n   500   4800     0.0000    -4.9168    26.7938    35.923597     18.8129    -19.7017     27.2412\n   500   5000     0.0000    -4.9168    26.7938    36.201496     18.7171    -19.7927     27.2412\n   500   5200     0.0000    -4.9168    26.7938    36.475065     18.6224    -19.8819     27.2412\n   500   5400     0.0000    -4.9168    26.7938    36.744426     18.5287    -19.9692     27.2412\n   500   5600     0.0000    -4.9168    26.7938    37.009694     18.4361    -20.0548     27.2412\n   500   5800     0.0000    -4.9168    26.7938    37.270979     18.3444    -20.1386     27.2412\n   500   6000     0.0000    -4.9168    26.7938    37.528387     18.2538    -20.2208     27.2412\n   500   6200     0.0000    -4.9168    26.7938    37.782018     18.1641    -20.3014     27.2412\n   500   6400     0.0000    -4.9168    26.7938    38.031970     18.0754    -20.3805     27.2412\n   500   6600     0.0000    -4.9168    26.7938    38.278335     17.9876    -20.4580     27.2412\n   500   6800     0.0000    -4.9168    26.7938    38.521204     17.9007    -20.5341     27.2412\n   500   7000     0.0000    -4.9168    26.7938    38.760661     17.8147    -20.6087     27.2412\n   500   7200     0.0000    -4.9168    26.7938    38.996789     17.7296    -20.6820     27.2412\n   500   7400     0.0000    -4.9168    26.7938    39.229668     17.6454    -20.7539     27.2412\n   500   7600     0.0000    -4.9168    26.7938    39.459375     17.5621    -20.8244     27.2412\n   500   7800     0.0000    -4.9168    26.7938    39.685982     17.4796    -20.8937     27.2412\n   500   8000     0.0000    -4.9168    26.7938    39.909561     17.3979    -20.9618     27.2412\n   500   8200     0.0000    -4.9168    26.7938    40.130181     17.3171    -21.0286     27.2412\n   500   8400     0.0000    -4.9168    26.7938    40.347906     17.2370    -21.0943     27.2412\n  1000      0     0.0000    -5.0312    26.8543    27.494721     21.4985    -16.8606     27.3215\n  1000    200     0.0000    -5.0312    26.8543    27.936992     21.3677    -17.0261     27.3215\n  1000    400     0.0000    -5.0312    26.8543    28.368628     21.2388    -17.1865     27.3215\n  1000    600     0.0000    -5.0312    26.8543    28.790150     21.1118    -17.3423     27.3215\n  1000    800     0.0000    -5.0312    26.8543    29.202040     20.9866    -17.4937     27.3215\n  1000   1000     0.0000    -5.0312    26.8543    29.604740     20.8631    -17.6407     27.3215\n  1000   1200     0.0000    -5.0312    26.8543    29.998662     20.7413    -17.7837     27.3215\n  1000   1400     0.0000    -5.0312    26.8543    30.384184     20.6212    -17.9229     27.3215\n  1000   1600     0.0000    -5.0312    26.8543    30.761662     20.5027    -18.0584     27.3215\n  1000   1800     0.0000    -5.0312    26.8543    31.131424     20.3857    -18.1903     27.3215\n  1000   2000     0.0000    -5.0312    26.8543    31.493778     20.2702    -18.3189     27.3215\n  1000   2200     0.0000    -5.0312    26.8543    31.849012     20.1563    -18.4442     27.3215\n  1000   2400     0.0000    -5.0312    26.8543    32.197396     20.0438    -18.5664     27.3215\n  1000   2600     0.0000    -5.0312    26.8543    32.539183     19.9327    -18.6856     27.3215\n  1000   2800     0.0000    -5.0312    26.8543    32.874611     19.8229    -18.8020     27.3215\n  1000   3000     0.0000    -5.0312    26.8543    33.203904     19.7145    -18.9156     27.3215\n  1000   3200     0.0000    -5.0312    26.8543    33.527273     19.6075    -19.0266     27.3215\n  1000   3400     0.0000    -5.0312    26.8543    33.844919     19.5017    -19.1350     27.3215\n  1000   3600     0.0000    -5.0312    26.8543    34.157031     19.3972    -19.2410     27.3215\n  1000   3800     0.0000    -5.0312    26.8543    34.463787     19.2939    -19.3445     27.3215\n  1000   4000     0.0000    -5.0312    26.8543    34.765357     19.1918    -19.4458     27.3215\n  1000   4200     0.0000    -5.0312    26.8543    35.061902     19.0909    -19.5449     27.3215\n  1000   4400     0.0000    -5.0312    26.8543    35.353576     18.9911    -19.6418     27.3215\n  1000   4600     0.0000    -5.0312    26.8543    35.640523     18.8925    -19.7367     27.3215\n  1000   4800     0.0000    -5.0312    26.8543    35.922883     18.7950    -19.8295     27.3215\n  1000   5000     0.0000    -5.0312    26.8543    36.200788     18.6986    -19.9205     27.3215\n  1000   5200     0.0000    -5.0312    26.8543    36.474364     18.6033    -20.0095     27.3215\n  1000   5400     0.0000    -5.0312    26.8543    36.743731     18.5090    -20.0968     27.3215\n  1000   5600     0.0000    -5.0312    26.8543    37.009005     18.4158    -20.1822     27.3215\n  1000   5800     0.0000    -5.0312    26.8543    37.270296     18.3235    -20.2660     27.3215\n  1000   6000     0.0000    -5.0312    26.8543    37.527709     18.2323    -20.3481     27.3215\n  1000   6200     0.0000    -5.0312    26.8543    37.781346     18.1421    -20.4286     27.3215\n  1000   6400     0.0000    -5.0312    26.8543    38.031303     18.0528    -20.5076     27.3215\n  1000   6600     0.0000    -5.0312    26.8543    38.277674     17.9644    -20.5850     27.3215\n  1000   6800     0.0000    -5.0312    26.8543    38.520547     17.8770    -20.6610     27.3215\n  1000   7000     0.0000    -5.0312    26.8543    38.760010     17.7905    -20.7355     27.3215\n  1000   7200     0.0000    -5.0312    26.8543    38.996143     17.7049    -20.8087     27.3215\n  1000   7400     0.0000    -5.0312    26.8543    39.229027     17.6202    -20.8805     27.3215\n  1000   7600     0.0000    -5.0312    26.8543    39.458738     17.5363    -20.9509     27.3215\n  1000   7800     0.0000    -5.0312    26.8543    39.685350     17.4533    -21.0201     27.3215\n  1000   8000     0.0000    -5.0312    26.8543    39.908934     17.3711    -21.0881     27.3215\n  1000   8200     0.0000    -5.0312    26.8543    40.129557     17.2898    -21.1548     27.3215\n  1000   8400     0.0000    -5.0312    26.8543    40.347287     17.2093    -21.2204     27.3215\n  1500      0     0.0000    -5.1455    26.9147    27.493738     21.4996    -16.9896     27.4022\n  1500    200     0.0000    -5.1455    26.9147    27.936026     21.3678    -17.1550     27.4022\n  1500    400     0.0000    -5.1455    26.9147    28.367677     21.2379    -17.3155     27.4022\n  1500    600     0.0000    -5.1455    26.9147    28.789215     21.1100    -17.4713     27.4022\n  1500    800     0.0000    -5.1455    26.9147    29.201119     20.9838    -17.6226     27.4022\n  1500   1000     0.0000    -5.1455    26.9147    29.603833     20.8594    -17.7697     27.4022\n  1500   1200     0.0000    -5.1455    26.9147    29.997767     20.7368    -17.9127     27.4022\n  1500   1400     0.0000    -5.1455    26.9147    30.383302     20.6158    -18.0518     27.4022\n  1500   1600     0.0000    -5.1455    26.9147    30.760792     20.4964    -18.1872     27.4022\n  1500   1800     0.0000    -5.1455    26.9147    31.130565     20.3786    -18.3191     27.4022\n  1500   2000     0.0000    -5.1455    26.9147    31.492930     20.2623    -18.4477     27.4022\n  1500   2200     0.0000    -5.1455    26.9147    31.848175     20.1476    -18.5729     27.4022\n  1500   2400     0.0000    -5.1455    26.9147    32.196569     20.0343    -18.6951     27.4022\n  1500   2600     0.0000    -5.1455    26.9147    32.538366     19.9224    -18.8143     27.4022\n  1500   2800     0.0000    -5.1455    26.9147    32.873803     19.8119    -18.9306     27.4022\n  1500   3000     0.0000    -5.1455    26.9147    33.203105     19.7028    -19.0441     27.4022\n  1500   3200     0.0000    -5.1455    26.9147    33.526483     19.5950    -19.1550     27.4022\n  1500   3400     0.0000    -5.1455    26.9147    33.844138     19.4885    -19.2634     27.4022\n  1500   3600     0.0000    -5.1455    26.9147    34.156258     19.3832    -19.3693     27.4022\n  1500   3800     0.0000    -5.1455    26.9147    34.463022     19.2793    -19.4728     27.4022\n  1500   4000     0.0000    -5.1455    26.9147    34.764599     19.1765    -19.5740     27.4022\n  1500   4200     0.0000    -5.1455    26.9147    35.061152     19.0749    -19.6730     27.4022\n  1500   4400     0.0000    -5.1455    26.9147    35.352833     18.9745    -19.7698     27.4022\n  1500   4600     0.0000    -5.1455    26.9147    35.639787     18.8753    -19.8646     27.4022\n  1500   4800     0.0000    -5.1455    26.9147    35.922154     18.7771    -19.9574     27.4022\n  1500   5000     0.0000    -5.1455    26.9147    36.200066     18.6801    -20.0482     27.4022\n  1500   5200     0.0000    -5.1455    26.9147    36.473648     18.5842    -20.1372     27.4022\n  1500   5400     0.0000    -5.1455    26.9147    36.743022     18.4893    -20.2243     27.4022\n  1500   5600     0.0000    -5.1455    26.9147    37.008302     18.3955    -20.3097     27.4022\n  1500   5800     0.0000    -5.1455    26.9147    37.269599     18.3027    -20.3934     27.4022\n  1500   6000     0.0000    -5.1455    26.9147    37.527018     18.2108    -20.4754     27.4022\n  1500   6200     0.0000    -5.1455    26.9147    37.780660     18.1200    -20.5558     27.4022\n  1500   6400     0.0000    -5.1455    26.9147    38.030623     18.0302    -20.6347     27.4022\n  1500   6600     0.0000    -5.1455    26.9147    38.276999     17.9413    -20.7120     27.4022\n  1500   6800     0.0000    -5.1455    26.9147    38.519878     17.8533    -20.7879     27.4022\n  1500   7000     0.0000    -5.1455    26.9147    38.759345     17.7663    -20.8623     27.4022\n  1500   7200     0.0000    -5.1455    26.9147    38.995484     17.6801    -20.9354     27.4022\n  1500   7400     0.0000    -5.1455    26.9147    39.228373     17.5949    -21.0071     27.4022\n  1500   7600     0.0000    -5.1455    26.9147    39.458089     17.5105    -21.0775     27.4022\n  1500   7800     0.0000    -5.1455    26.9147    39.684705     17.4270    -21.1465     27.4022\n  1500   8000     0.0000    -5.1455    26.9147    39.908294     17.3444    -21.2144     27.4022\n  1500   8200     0.0000    -5.1455    26.9147    40.128922     17.2626    -21.2810     27.4022\n  1500   8400     0.0000    -5.1455    26.9147    40.346656     17.1816    -21.3465     27.4022\n  2000      0     0.0000    -5.2599    26.9752    27.492735     21.5007    -17.1186     27.4832\n  2000    200     0.0000    -5.2599    26.9752    27.935039     21.3679    -17.2840     27.4832\n  2000    400     0.0000    -5.2599    26.9752    28.366707     21.2371    -17.4445     27.4832\n  2000    600     0.0000    -5.2599    26.9752    28.788259     21.1082    -17.6003     27.4832\n  2000    800     0.0000    -5.2599    26.9752    29.200178     20.9811    -17.7516     27.4832\n  2000   1000     0.0000    -5.2599    26.9752    29.602906     20.8558    -17.8986     27.4832\n  2000   1200     0.0000    -5.2599    26.9752    29.996854     20.7322    -18.0416     27.4832\n  2000   1400     0.0000    -5.2599    26.9752    30.382402     20.6104    -18.1807     27.4832\n  2000   1600     0.0000    -5.2599    26.9752    30.759903     20.4901    -18.3161     27.4832\n  2000   1800     0.0000    -5.2599    26.9752    31.129689     20.3715    -18.4480     27.4832\n  2000   2000     0.0000    -5.2599    26.9752    31.492065     20.2544    -18.5764     27.4832\n  2000   2200     0.0000    -5.2599    26.9752    31.847321     20.1389    -18.7017     27.4832\n  2000   2400     0.0000    -5.2599    26.9752    32.195725     20.0248    -18.8238     27.4832\n  2000   2600     0.0000    -5.2599    26.9752    32.537532     19.9121    -18.9429     27.4832\n  2000   2800     0.0000    -5.2599    26.9752    32.872978     19.8009    -19.0592     27.4832\n  2000   3000     0.0000    -5.2599    26.9752    33.202290     19.6910    -19.1726     27.4832\n  2000   3200     0.0000    -5.2599    26.9752    33.525677     19.5825    -19.2835     27.4832\n  2000   3400     0.0000    -5.2599    26.9752    33.843341     19.4753    -19.3918     27.4832\n  2000   3600     0.0000    -5.2599    26.9752    34.155469     19.3693    -19.4976     27.4832\n  2000   3800     0.0000    -5.2599    26.9752    34.462241     19.2647    -19.6010     27.4832\n  2000   4000     0.0000    -5.2599    26.9752    34.763827     19.1612    -19.7021     27.4832\n  2000   4200     0.0000    -5.2599    26.9752    35.060387     19.0590    -19.8010     27.4832\n  2000   4400     0.0000    -5.2599    26.9752    35.352075     18.9579    -19.8978     27.4832\n  2000   4600     0.0000    -5.2599    26.9752    35.639037     18.8580    -19.9925     27.4832\n  2000   4800     0.0000    -5.2599    26.9752    35.921411     18.7593    -20.0852     27.4832\n  2000   5000     0.0000    -5.2599    26.9752    36.199329     18.6616    -20.1760     27.4832\n  2000   5200     0.0000    -5.2599    26.9752    36.472918     18.5651    -20.2648     27.4832\n  2000   5400     0.0000    -5.2599    26.9752    36.742298     18.4696    -20.3519     27.4832\n  2000   5600     0.0000    -5.2599    26.9752    37.007585     18.3752    -20.4372     27.4832\n  2000   5800     0.0000    -5.2599    26.9752    37.268888     18.2818    -20.5208     27.4832\n  2000   6000     0.0000    -5.2599    26.9752    37.526313     18.1894    -20.6027     27.4832\n  2000   6200     0.0000    -5.2599    26.9752    37.779961     18.0980    -20.6830     27.4832\n  2000   6400     0.0000    -5.2599    26.9752    38.029930     18.0076    -20.7618     27.4832\n  2000   6600     0.0000    -5.2599    26.9752    38.276312     17.9181    -20.8390     27.4832\n  2000   6800     0.0000    -5.2599    26.9752    38.519196     17.8296    -20.9148     27.4832\n  2000   7000     0.0000    -5.2599    26.9752    38.758669     17.7421    -20.9892     27.4832\n  2000   7200     0.0000    -5.2599    26.9752    38.994812     17.6554    -21.0621     27.4832\n  2000   7400     0.0000    -5.2599    26.9752    39.227706     17.5697    -21.1337     27.4832\n  2000   7600     0.0000    -5.2599    26.9752    39.457427     17.4848    -21.2040     27.4832\n  2000   7800     0.0000    -5.2599    26.9752    39.684049     17.4008    -21.2730     27.4832\n  2000   8000     0.0000    -5.2599    26.9752    39.907642     17.3176    -21.3407     27.4832\n  2000   8200     0.0000    -5.2599    26.9752    40.128274     17.2353    -21.4072     27.4832\n  2000   8400     0.0000    -5.2599    26.9752    40.346013     17.1538    -21.4726     27.4832\n  2500      0     0.0000    -5.3742    27.0356    27.491711     21.5019    -17.2475     27.5646\n  2500    200     0.0000    -5.3742    27.0356    27.934033     21.3681    -17.4130     27.5646\n  2500    400     0.0000    -5.3742    27.0356    28.365717     21.2363    -17.5735     27.5646\n  2500    600     0.0000    -5.3742    27.0356    28.787285     21.1064    -17.7293     27.5646\n  2500    800     0.0000    -5.3742    27.0356    29.199219     20.9784    -17.8806     27.5646\n  2500   1000     0.0000    -5.3742    27.0356    29.601961     20.8522    -18.0276     27.5646\n  2500   1200     0.0000    -5.3742    27.0356    29.995922     20.7277    -18.1705     27.5646\n  2500   1400     0.0000    -5.3742    27.0356    30.381483     20.6050    -18.3096     27.5646\n  2500   1600     0.0000    -5.3742    27.0356    30.758997     20.4839    -18.4450     27.5646\n  2500   1800     0.0000    -5.3742    27.0356    31.128795     20.3644    -18.5768     27.5646\n  2500   2000     0.0000    -5.3742    27.0356    31.491183     20.2465    -18.7052     27.5646\n  2500   2200     0.0000    -5.3742    27.0356    31.846449     20.1302    -18.8304     27.5646\n  2500   2400     0.0000    -5.3742    27.0356    32.194864     20.0153    -18.9525     27.5646\n  2500   2600     0.0000    -5.3742    27.0356    32.536681     19.9018    -19.0715     27.5646\n  2500   2800     0.0000    -5.3742    27.0356    32.872138     19.7898    -19.1877     27.5646\n  2500   3000     0.0000    -5.3742    27.0356    33.201459     19.6792    -19.3011     27.5646\n  2500   3200     0.0000    -5.3742    27.0356    33.524856     19.5700    -19.4119     27.5646\n  2500   3400     0.0000    -5.3742    27.0356    33.842528     19.4621    -19.5201     27.5646\n  2500   3600     0.0000    -5.3742    27.0356    34.154665     19.3554    -19.6259     27.5646\n  2500   3800     0.0000    -5.3742    27.0356    34.461445     19.2501    -19.7292     27.5646\n  2500   4000     0.0000    -5.3742    27.0356    34.763039     19.1459    -19.8303     27.5646\n  2500   4200     0.0000    -5.3742    27.0356    35.059607     19.0430    -19.9291     27.5646\n  2500   4400     0.0000    -5.3742    27.0356    35.351303     18.9413    -20.0258     27.5646\n  2500   4600     0.0000    -5.3742    27.0356    35.638272     18.8408    -20.1204     27.5646\n  2500   4800     0.0000    -5.3742    27.0356    35.920653     18.7414    -20.2130     27.5646\n  2500   5000     0.0000    -5.3742    27.0356    36.198579     18.6431    -20.3037     27.5646\n  2500   5200     0.0000    -5.3742    27.0356    36.472175     18.5460    -20.3925     27.5646\n  2500   5400     0.0000    -5.3742    27.0356    36.741561     18.4499    -20.4795     27.5646\n  2500   5600     0.0000    -5.3742    27.0356    37.006854     18.3549    -20.5647     27.5646\n  2500   5800     0.0000    -5.3742    27.0356    37.268164     18.2609    -20.6482     27.5646\n  2500   6000     0.0000    -5.3742    27.0356    37.525595     18.1679    -20.7300     27.5646\n  2500   6200     0.0000    -5.3742    27.0356    37.779249     18.0760    -20.8102     27.5646\n  2500   6400     0.0000    -5.3742    27.0356    38.029223     17.9850    -20.8889     27.5646\n  2500   6600     0.0000    -5.3742    27.0356    38.275611     17.8950    -20.9660     27.5646\n  2500   6800     0.0000    -5.3742    27.0356    38.518501     17.8060    -21.0417     27.5646\n  2500   7000     0.0000    -5.3742    27.0356    38.757979     17.7179    -21.1160     27.5646\n  2500   7200     0.0000    -5.3742    27.0356    38.994128     17.6307    -21.1888     27.5646\n  2500   7400     0.0000    -5.3742    27.0356    39.227027     17.5444    -21.2603     27.5646\n  2500   7600     0.0000    -5.3742    27.0356    39.456753     17.4590    -21.3305     27.5646\n  2500   7800     0.0000    -5.3742    27.0356    39.683379     17.3745    -21.3994     27.5646\n  2500   8000     0.0000    -5.3742    27.0356    39.906977     17.2909    -21.4670     27.5646\n  2500   8200     0.0000    -5.3742    27.0356    40.127615     17.2081    -21.5334     27.5646\n  2500   8400     0.0000    -5.3742    27.0356    40.345358     17.1261    -21.5987     27.5646\n  3000      0     0.0000    -5.4886    27.0961    27.490666     21.5030    -17.3765     27.6464\n  3000    200     0.0000    -5.4886    27.0961    27.933006     21.3682    -17.5420     27.6464\n  3000    400     0.0000    -5.4886    27.0961    28.364707     21.2354    -17.7025     27.6464\n  3000    600     0.0000    -5.4886    27.0961    28.786291     21.1046    -17.8582     27.6464\n  3000    800     0.0000    -5.4886    27.0961    29.198240     20.9757    -18.0095     27.6464\n  3000   1000     0.0000    -5.4886    27.0961    29.600997     20.8486    -18.1565     27.6464\n  3000   1200     0.0000    -5.4886    27.0961    29.994972     20.7232    -18.2994     27.6464\n  3000   1400     0.0000    -5.4886    27.0961    30.380547     20.5996    -18.4385     27.6464\n  3000   1600     0.0000    -5.4886    27.0961    30.758074     20.4777    -18.5738     27.6464\n  3000   1800     0.0000    -5.4886    27.0961    31.127883     20.3574    -18.7056     27.6464\n  3000   2000     0.0000    -5.4886    27.0961    31.490283     20.2386    -18.8340     27.6464\n  3000   2200     0.0000    -5.4886    27.0961    31.845561     20.1215    -18.9591     27.6464\n  3000   2400     0.0000    -5.4886    27.0961    32.193987     20.0058    -19.0811     27.6464\n  3000   2600     0.0000    -5.4886    27.0961    32.535814     19.8916    -19.2001     27.6464\n  3000   2800     0.0000    -5.4886    27.0961    32.871281     19.7788    -19.3163     27.6464\n  3000   3000     0.0000    -5.4886    27.0961    33.200612     19.6675    -19.4296     27.6464\n  3000   3200     0.0000    -5.4886    27.0961    33.524018     19.5575    -19.5403     27.6464\n  3000   3400     0.0000    -5.4886    27.0961    33.841699     19.4489    -19.6485     27.6464\n  3000   3600     0.0000    -5.4886    27.0961    34.153845     19.3415    -19.7542     27.6464\n  3000   3800     0.0000    -5.4886    27.0961    34.460634     19.2355    -19.8574     27.6464\n  3000   4000     0.0000    -5.4886    27.0961    34.762236     19.1307    -19.9584     27.6464\n  3000   4200     0.0000    -5.4886    27.0961    35.058813     19.0271    -20.0572     27.6464\n  3000   4400     0.0000    -5.4886    27.0961    35.350516     18.9248    -20.1538     27.6464\n  3000   4600     0.0000    -5.4886    27.0961    35.637493     18.8236    -20.2483     27.6464\n  3000   4800     0.0000    -5.4886    27.0961    35.919882     18.7236    -20.3408     27.6464\n  3000   5000     0.0000    -5.4886    27.0961    36.197814     18.6247    -20.4314     27.6464\n  3000   5200     0.0000    -5.4886    27.0961    36.471417     18.5269    -20.5201     27.6464\n  3000   5400     0.0000    -5.4886    27.0961    36.740810     18.4302    -20.6070     27.6464\n  3000   5600     0.0000    -5.4886    27.0961    37.006110     18.3346    -20.6921     27.6464\n  3000   5800     0.0000    -5.4886    27.0961    37.267426     18.2400    -20.7755     27.6464\n  3000   6000     0.0000    -5.4886    27.0961    37.524863     18.1465    -20.8573     27.6464\n  3000   6200     0.0000    -5.4886    27.0961    37.778524     18.0540    -20.9374     27.6464\n  3000   6400     0.0000    -5.4886    27.0961    38.028504     17.9625    -21.0160     27.6464\n  3000   6600     0.0000    -5.4886    27.0961    38.274897     17.8719    -21.0930     27.6464\n  3000   6800     0.0000    -5.4886    27.0961    38.517793     17.7823    -21.1686     27.6464\n  3000   7000     0.0000    -5.4886    27.0961    38.757276     17.6937    -21.2428     27.6464\n  3000   7200     0.0000    -5.4886    27.0961    38.993431     17.6060    -21.3155     27.6464\n  3000   7400     0.0000    -5.4886    27.0961    39.226335     17.5192    -21.3869     27.6464\n  3000   7600     0.0000    -5.4886    27.0961    39.456066     17.4333    -21.4570     27.6464\n  3000   7800     0.0000    -5.4886    27.0961    39.682698     17.3483    -21.5258     27.6464\n  3000   8000     0.0000    -5.4886    27.0961    39.906301     17.2642    -21.5933     27.6464\n  3000   8200     0.0000    -5.4886    27.0961    40.126943     17.1809    -21.6596     27.6464\n  3000   8400     0.0000    -5.4886    27.0961    40.344692     17.0984    -21.7248     27.6464\n  3500      0     0.0000    -5.6029    27.1565    27.489602     21.5042    -17.5054     27.7285\n  3500    200     0.0000    -5.6029    27.1565    27.931959     21.3684    -17.6709     27.7285\n  3500    400     0.0000    -5.6029    27.1565    28.363677     21.2346    -17.8314     27.7285\n  3500    600     0.0000    -5.6029    27.1565    28.785278     21.1028    -17.9872     27.7285\n  3500    800     0.0000    -5.6029    27.1565    29.197243     20.9730    -18.1385     27.7285\n  3500   1000     0.0000    -5.6029    27.1565    29.600015     20.8449    -18.2854     27.7285\n  3500   1200     0.0000    -5.6029    27.1565    29.994004     20.7187    -18.4283     27.7285\n  3500   1400     0.0000    -5.6029    27.1565    30.379592     20.5942    -18.5674     27.7285\n  3500   1600     0.0000    -5.6029    27.1565    30.757132     20.4714    -18.7027     27.7285\n  3500   1800     0.0000    -5.6029    27.1565    31.126954     20.3503    -18.8344     27.7285\n  3500   2000     0.0000    -5.6029    27.1565    31.489366     20.2308    -18.9627     27.7285\n  3500   2200     0.0000    -5.6029    27.1565    31.844656     20.1128    -19.0878     27.7285\n  3500   2400     0.0000    -5.6029    27.1565    32.193093     19.9963    -19.2098     27.7285\n  3500   2600     0.0000    -5.6029    27.1565    32.534931     19.8814    -19.3288     27.7285\n  3500   2800     0.0000    -5.6029    27.1565    32.870408     19.7678    -19.4448     27.7285\n  3500   3000     0.0000    -5.6029    27.1565    33.199749     19.6558    -19.5581     27.7285\n  3500   3200     0.0000    -5.6029    27.1565    33.523165     19.5450    -19.6688     27.7285\n  3500   3400     0.0000    -5.6029    27.1565    33.840856     19.4357    -19.7768     27.7285\n  3500   3600     0.0000    -5.6029    27.1565    34.153010     19.3276    -19.8824     27.7285\n  3500   3800     0.0000    -5.6029    27.1565    34.459808     19.2209    -19.9856     27.7285\n  3500   4000     0.0000    -5.6029    27.1565    34.761419     19.1154    -20.0865     27.7285\n  3500   4200     0.0000    -5.6029    27.1565    35.058003     19.0112    -20.1852     27.7285\n  3500   4400     0.0000    -5.6029    27.1565    35.349715     18.9082    -20.2818     27.7285\n  3500   4600     0.0000    -5.6029    27.1565    35.636699     18.8064    -20.3762     27.7285\n  3500   4800     0.0000    -5.6029    27.1565    35.919095     18.7057    -20.4687     27.7285\n  3500   5000     0.0000    -5.6029    27.1565    36.197035     18.6062    -20.5592     27.7285\n  3500   5200     0.0000    -5.6029    27.1565    36.470645     18.5078    -20.6478     27.7285\n  3500   5400     0.0000    -5.6029    27.1565    36.740046     18.4105    -20.7346     27.7285\n  3500   5600     0.0000    -5.6029    27.1565    37.005352     18.3143    -20.8196     27.7285\n  3500   5800     0.0000    -5.6029    27.1565    37.266674     18.2192    -20.9029     27.7285\n  3500   6000     0.0000    -5.6029    27.1565    37.524118     18.1251    -20.9846     27.7285\n  3500   6200     0.0000    -5.6029    27.1565    37.777785     18.0320    -21.0646     27.7285\n  3500   6400     0.0000    -5.6029    27.1565    38.027771     17.9399    -21.1431     27.7285\n  3500   6600     0.0000    -5.6029    27.1565    38.274170     17.8488    -21.2200     27.7285\n  3500   6800     0.0000    -5.6029    27.1565    38.517072     17.7587    -21.2955     27.7285\n  3500   7000     0.0000    -5.6029    27.1565    38.756561     17.6695    -21.3695     27.7285\n  3500   7200     0.0000    -5.6029    27.1565    38.992721     17.5813    -21.4422     27.7285\n  3500   7400     0.0000    -5.6029    27.1565    39.225631     17.4940    -21.5135     27.7285\n  3500   7600     0.0000    -5.6029    27.1565    39.455367     17.4076    -21.5835     27.7285\n  3500   7800     0.0000    -5.6029    27.1565    39.682004     17.3221    -21.6521     27.7285\n  3500   8000     0.0000    -5.6029    27.1565    39.905612     17.2374    -21.7196     27.7285\n  3500   8200     0.0000    -5.6029    27.1565    40.126259     17.1537    -21.7858     27.7285\n  3500   8400     0.0000    -5.6029    27.1565    40.344013     17.0707    -21.8508     27.7285\n  4000      0     0.0000    -5.7173    27.2170    27.488516     21.5054    -17.6344     27.8110\n  4000    200     0.0000    -5.7173    27.2170    27.930893     21.3686    -17.7999     27.8110\n  4000    400     0.0000    -5.7173    27.2170    28.362629     21.2338    -17.9604     27.8110\n  4000    600     0.0000    -5.7173    27.2170    28.784246     21.1011    -18.1161     27.8110\n  4000    800     0.0000    -5.7173    27.2170    29.196227     20.9703    -18.2674     27.8110\n  4000   1000     0.0000    -5.7173    27.2170    29.599014     20.8413    -18.4144     27.8110\n  4000   1200     0.0000    -5.7173    27.2170    29.993018     20.7142    -18.5572     27.8110\n  4000   1400     0.0000    -5.7173    27.2170    30.378620     20.5889    -18.6962     27.8110\n  4000   1600     0.0000    -5.7173    27.2170    30.756173     20.4652    -18.8315     27.8110\n  4000   1800     0.0000    -5.7173    27.2170    31.126008     20.3432    -18.9632     27.8110\n  4000   2000     0.0000    -5.7173    27.2170    31.488432     20.2229    -19.0915     27.8110\n  4000   2200     0.0000    -5.7173    27.2170    31.843734     20.1041    -19.2165     27.8110\n  4000   2400     0.0000    -5.7173    27.2170    32.192182     19.9869    -19.3385     27.8110\n  4000   2600     0.0000    -5.7173    27.2170    32.534031     19.8711    -19.4574     27.8110\n  4000   2800     0.0000    -5.7173    27.2170    32.869519     19.7569    -19.5734     27.8110\n  4000   3000     0.0000    -5.7173    27.2170    33.198870     19.6440    -19.6866     27.8110\n  4000   3200     0.0000    -5.7173    27.2170    33.522296     19.5326    -19.7972     27.8110\n  4000   3400     0.0000    -5.7173    27.2170    33.839996     19.4225    -19.9052     27.8110\n  4000   3600     0.0000    -5.7173    27.2170    34.152160     19.3138    -20.0107     27.8110\n  4000   3800     0.0000    -5.7173    27.2170    34.458967     19.2063    -20.1139     27.8110\n  4000   4000     0.0000    -5.7173    27.2170    34.760586     19.1002    -20.2147     27.8110\n  4000   4200     0.0000    -5.7173    27.2170    35.057179     18.9953    -20.3133     27.8110\n  4000   4400     0.0000    -5.7173    27.2170    35.348899     18.8916    -20.4097     27.8110\n  4000   4600     0.0000    -5.7173    27.2170    35.635891     18.7892    -20.5041     27.8110\n  4000   4800     0.0000    -5.7173    27.2170    35.918295     18.6879    -20.5965     27.8110\n  4000   5000     0.0000    -5.7173    27.2170    36.196242     18.5877    -20.6869     27.8110\n  4000   5200     0.0000    -5.7173    27.2170    36.469860     18.4887    -20.7754     27.8110\n  4000   5400     0.0000    -5.7173    27.2170    36.739267     18.3908    -20.8621     27.8110\n  4000   5600     0.0000    -5.7173    27.2170    37.004580     18.2940    -20.9470     27.8110\n  4000   5800     0.0000    -5.7173    27.2170    37.265909     18.1983    -21.0303     27.8110\n  4000   6000     0.0000    -5.7173    27.2170    37.523360     18.1036    -21.1118     27.8110\n  4000   6200     0.0000    -5.7173    27.2170    37.777033     18.0100    -21.1918     27.8110\n  4000   6400     0.0000    -5.7173    27.2170    38.027025     17.9173    -21.2702     27.8110\n  4000   6600     0.0000    -5.7173    27.2170    38.273431     17.8257    -21.3470     27.8110\n  4000   6800     0.0000    -5.7173    27.2170    38.516338     17.7350    -21.4224     27.8110\n  4000   7000     0.0000    -5.7173    27.2170    38.755833     17.6453    -21.4963     27.8110\n  4000   7200     0.0000    -5.7173    27.2170    38.991999     17.5566    -21.5689     27.8110\n  4000   7400     0.0000    -5.7173    27.2170    39.224914     17.4688    -21.6401     27.8110\n  4000   7600     0.0000    -5.7173    27.2170    39.454656     17.3819    -21.7100     27.8110\n  4000   7800     0.0000    -5.7173    27.2170    39.681298     17.2958    -21.7785     27.8110\n  4000   8000     0.0000    -5.7173    27.2170    39.904911     17.2107    -21.8459     27.8110\n  4000   8200     0.0000    -5.7173    27.2170    40.125563     17.1265    -21.9120     27.8110\n  4000   8400     0.0000    -5.7173    27.2170    40.343322     17.0430    -21.9769     27.8110\n  4500      0     0.0000    -5.8316    27.2774    27.487411     21.5065    -17.7633     27.8938\n  4500    200     0.0000    -5.8316    27.2774    27.929806     21.3687    -17.9288     27.8938\n  4500    400     0.0000    -5.8316    27.2774    28.361560     21.2330    -18.0893     27.8938\n  4500    600     0.0000    -5.8316    27.2774    28.783195     21.0993    -18.2451     27.8938\n  4500    800     0.0000    -5.8316    27.2774    29.195192     20.9676    -18.3963     27.8938\n  4500   1000     0.0000    -5.8316    27.2774    29.597995     20.8378    -18.5433     27.8938\n  4500   1200     0.0000    -5.8316    27.2774    29.992014     20.7097    -18.6861     27.8938\n  4500   1400     0.0000    -5.8316    27.2774    30.377630     20.5835    -18.8251     27.8938\n  4500   1600     0.0000    -5.8316    27.2774    30.755197     20.4590    -18.9603     27.8938\n  4500   1800     0.0000    -5.8316    27.2774    31.125045     20.3362    -19.0920     27.8938\n  4500   2000     0.0000    -5.8316    27.2774    31.487481     20.2150    -19.2203     27.8938\n  4500   2200     0.0000    -5.8316    27.2774    31.842795     20.0954    -19.3452     27.8938\n  4500   2400     0.0000    -5.8316    27.2774    32.191255     19.9774    -19.4671     27.8938\n  4500   2600     0.0000    -5.8316    27.2774    32.533115     19.8609    -19.5860     27.8938\n  4500   2800     0.0000    -5.8316    27.2774    32.868614     19.7459    -19.7019     27.8938\n  4500   3000     0.0000    -5.8316    27.2774    33.197975     19.6323    -19.8151     27.8938\n  4500   3200     0.0000    -5.8316    27.2774    33.521411     19.5201    -19.9256     27.8938\n  4500   3400     0.0000    -5.8316    27.2774    33.839121     19.4093    -20.0335     27.8938\n  4500   3600     0.0000    -5.8316    27.2774    34.151295     19.2999    -20.1390     27.8938\n  4500   3800     0.0000    -5.8316    27.2774    34.458111     19.1918    -20.2421     27.8938\n  4500   4000     0.0000    -5.8316    27.2774    34.759739     19.0850    -20.3428     27.8938\n  4500   4200     0.0000    -5.8316    27.2774    35.056340     18.9794    -20.4413     27.8938\n  4500   4400     0.0000    -5.8316    27.2774    35.348068     18.8751    -20.5377     27.8938\n  4500   4600     0.0000    -5.8316    27.2774    35.635069     18.7720    -20.6320     27.8938\n  4500   4800     0.0000    -5.8316    27.2774    35.917480     18.6700    -20.7243     27.8938\n  4500   5000     0.0000    -5.8316    27.2774    36.195435     18.5693    -20.8146     27.8938\n  4500   5200     0.0000    -5.8316    27.2774    36.469060     18.4697    -20.9030     27.8938\n  4500   5400     0.0000    -5.8316    27.2774    36.738475     18.3712    -20.9897     27.8938\n  4500   5600     0.0000    -5.8316    27.2774    37.003795     18.2738    -21.0745     27.8938\n  4500   5800     0.0000    -5.8316    27.2774    37.265131     18.1775    -21.1576     27.8938\n  4500   6000     0.0000    -5.8316    27.2774    37.522588     18.0822    -21.2391     27.8938\n  4500   6200     0.0000    -5.8316    27.2774    37.776268     17.9880    -21.3190     27.8938\n  4500   6400     0.0000    -5.8316    27.2774    38.026267     17.8948    -21.3972     27.8938\n  4500   6600     0.0000    -5.8316    27.2774    38.272678     17.8026    -21.4740     27.8938\n  4500   6800     0.0000    -5.8316    27.2774    38.515591     17.7114    -21.5493     27.8938\n  4500   7000     0.0000    -5.8316    27.2774    38.755092     17.6212    -21.6231     27.8938\n  4500   7200     0.0000    -5.8316    27.2774    38.991264     17.5319    -21.6956     27.8938\n  4500   7400     0.0000    -5.8316    27.2774    39.224185     17.4436    -21.7667     27.8938\n  4500   7600     0.0000    -5.8316    27.2774    39.453932     17.3561    -21.8364     27.8938\n  4500   7800     0.0000    -5.8316    27.2774    39.680580     17.2696    -21.9049     27.8938\n  4500   8000     0.0000    -5.8316    27.2774    39.904198     17.1840    -21.9722     27.8938\n  4500   8200     0.0000    -5.8316    27.2774    40.124856     17.0993    -22.0382     27.8938\n  4500   8400     0.0000    -5.8316    27.2774    40.342619     17.0154    -22.1030     27.8938\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180518_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -1.0633968      -19.5288097       28.5389506   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0082175        0.0277582   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -19.4409579       28.4937618   m   m   m\nprecision_baseline_rate:        0.0000000        0.0020898        0.0233207   m/s m/s m/s\nunwrap_phase_constant:            0.00007     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180518_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -1.063    -19.529     28.539\norbit baseline rate   (TCN) (m/s):  0.000e+00 -8.218e-03  2.776e-02\n\nbaseline vector (TCN)   (m):      0.000    -19.441     28.494\nbaseline rate   (TCN) (m/s):  0.000e+00  2.090e-03  2.332e-02\n\nSLC-1 center baseline length (m):    34.4941\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -19.4605    28.2761    27.496626     16.0972    -30.3171     34.3256\n     0    200     0.0000   -19.4605    28.2761    27.938864     15.8627    -30.4404     34.3256\n     0    400     0.0000   -19.4605    28.2761    28.370470     15.6329    -30.5591     34.3256\n     0    600     0.0000   -19.4605    28.2761    28.791963     15.4077    -30.6732     34.3256\n     0    800     0.0000   -19.4605    28.2761    29.203825     15.1868    -30.7832     34.3256\n     0   1000     0.0000   -19.4605    28.2761    29.606500     14.9701    -30.8892     34.3256\n     0   1200     0.0000   -19.4605    28.2761    30.000396     14.7574    -30.9914     34.3256\n     0   1400     0.0000   -19.4605    28.2761    30.385895     14.5485    -31.0899     34.3256\n     0   1600     0.0000   -19.4605    28.2761    30.763349     14.3434    -31.1851     34.3256\n     0   1800     0.0000   -19.4605    28.2761    31.133089     14.1419    -31.2770     34.3256\n     0   2000     0.0000   -19.4605    28.2761    31.495422     13.9438    -31.3658     34.3256\n     0   2200     0.0000   -19.4605    28.2761    31.850636     13.7491    -31.4517     34.3256\n     0   2400     0.0000   -19.4605    28.2761    32.199000     13.5576    -31.5347     34.3256\n     0   2600     0.0000   -19.4605    28.2761    32.540768     13.3692    -31.6150     34.3256\n     0   2800     0.0000   -19.4605    28.2761    32.876178     13.1839    -31.6927     34.3256\n     0   3000     0.0000   -19.4605    28.2761    33.205453     13.0016    -31.7680     34.3256\n     0   3200     0.0000   -19.4605    28.2761    33.528805     12.8221    -31.8408     34.3256\n     0   3400     0.0000   -19.4605    28.2761    33.846435     12.6454    -31.9114     34.3256\n     0   3600     0.0000   -19.4605    28.2761    34.158531     12.4714    -31.9798     34.3256\n     0   3800     0.0000   -19.4605    28.2761    34.465271     12.3000    -32.0461     34.3256\n     0   4000     0.0000   -19.4605    28.2761    34.766827     12.1311    -32.1104     34.3256\n     0   4200     0.0000   -19.4605    28.2761    35.063357     11.9648    -32.1728     34.3256\n     0   4400     0.0000   -19.4605    28.2761    35.355017     11.8009    -32.2333     34.3256\n     0   4600     0.0000   -19.4605    28.2761    35.641950     11.6393    -32.2920     34.3256\n     0   4800     0.0000   -19.4605    28.2761    35.924297     11.4800    -32.3489     34.3256\n     0   5000     0.0000   -19.4605    28.2761    36.202189     11.3230    -32.4042     34.3256\n     0   5200     0.0000   -19.4605    28.2761    36.475752     11.1681    -32.4579     34.3256\n     0   5400     0.0000   -19.4605    28.2761    36.745108     11.0154    -32.5101     34.3256\n     0   5600     0.0000   -19.4605    28.2761    37.010370     10.8648    -32.5607     34.3256\n     0   5800     0.0000   -19.4605    28.2761    37.271649     10.7162    -32.6099     34.3256\n     0   6000     0.0000   -19.4605    28.2761    37.529051     10.5696    -32.6578     34.3256\n     0   6200     0.0000   -19.4605    28.2761    37.782677     10.4249    -32.7042     34.3256\n     0   6400     0.0000   -19.4605    28.2761    38.032623     10.2822    -32.7494     34.3256\n     0   6600     0.0000   -19.4605    28.2761    38.278984     10.1413    -32.7933     34.3256\n     0   6800     0.0000   -19.4605    28.2761    38.521847     10.0022    -32.8360     34.3256\n     0   7000     0.0000   -19.4605    28.2761    38.761299      9.8648    -32.8775     34.3256\n     0   7200     0.0000   -19.4605    28.2761    38.997423      9.7293    -32.9179     34.3256\n     0   7400     0.0000   -19.4605    28.2761    39.230297      9.5954    -32.9571     34.3256\n     0   7600     0.0000   -19.4605    28.2761    39.459999      9.4632    -32.9954     34.3256\n     0   7800     0.0000   -19.4605    28.2761    39.686602      9.3326    -33.0325     34.3256\n     0   8000     0.0000   -19.4605    28.2761    39.910176      9.2037    -33.0687     34.3256\n     0   8200     0.0000   -19.4605    28.2761    40.130792      9.0763    -33.1039     34.3256\n     0   8400     0.0000   -19.4605    28.2761    40.348513      8.9504    -33.1381     34.3256\n   500      0     0.0000   -19.4562    28.3240    27.495684     16.1422    -30.3351     34.3627\n   500    200     0.0000   -19.4562    28.3240    27.937938     15.9075    -30.4588     34.3627\n   500    400     0.0000   -19.4562    28.3240    28.369559     15.6776    -30.5778     34.3627\n   500    600     0.0000   -19.4562    28.3240    28.791066     15.4523    -30.6923     34.3627\n   500    800     0.0000   -19.4562    28.3240    29.202942     15.2312    -30.8026     34.3627\n   500   1000     0.0000   -19.4562    28.3240    29.605629     15.0144    -30.9089     34.3627\n   500   1200     0.0000   -19.4562    28.3240    29.999538     14.8015    -31.0114     34.3627\n   500   1400     0.0000   -19.4562    28.3240    30.385049     14.5925    -31.1103     34.3627\n   500   1600     0.0000   -19.4562    28.3240    30.762515     14.3872    -31.2057     34.3627\n   500   1800     0.0000   -19.4562    28.3240    31.132265     14.1856    -31.2979     34.3627\n   500   2000     0.0000   -19.4562    28.3240    31.494609     13.9873    -31.3870     34.3627\n   500   2200     0.0000   -19.4562    28.3240    31.849833     13.7925    -31.4731     34.3627\n   500   2400     0.0000   -19.4562    28.3240    32.198207     13.6009    -31.5564     34.3627\n   500   2600     0.0000   -19.4562    28.3240    32.539984     13.4124    -31.6370     34.3627\n   500   2800     0.0000   -19.4562    28.3240    32.875402     13.2270    -31.7150     34.3627\n   500   3000     0.0000   -19.4562    28.3240    33.204686     13.0445    -31.7905     34.3627\n   500   3200     0.0000   -19.4562    28.3240    33.528047     12.8648    -31.8636     34.3627\n   500   3400     0.0000   -19.4562    28.3240    33.845685     12.6880    -31.9344     34.3627\n   500   3600     0.0000   -19.4562    28.3240    34.157789     12.5139    -32.0030     34.3627\n   500   3800     0.0000   -19.4562    28.3240    34.464537     12.3423    -32.0696     34.3627\n   500   4000     0.0000   -19.4562    28.3240    34.766099     12.1734    -32.1341     34.3627\n   500   4200     0.0000   -19.4562    28.3240    35.062637     12.0069    -32.1967     34.3627\n   500   4400     0.0000   -19.4562    28.3240    35.354304     11.8429    -32.2574     34.3627\n   500   4600     0.0000   -19.4562    28.3240    35.641244     11.6812    -32.3163     34.3627\n   500   4800     0.0000   -19.4562    28.3240    35.923597     11.5218    -32.3734     34.3627\n   500   5000     0.0000   -19.4562    28.3240    36.201496     11.3646    -32.4290     34.3627\n   500   5200     0.0000   -19.4562    28.3240    36.475065     11.2096    -32.4828     34.3627\n   500   5400     0.0000   -19.4562    28.3240    36.744426     11.0568    -32.5352     34.3627\n   500   5600     0.0000   -19.4562    28.3240    37.009694     10.9061    -32.5860     34.3627\n   500   5800     0.0000   -19.4562    28.3240    37.270979     10.7573    -32.6354     34.3627\n   500   6000     0.0000   -19.4562    28.3240    37.528387     10.6106    -32.6834     34.3627\n   500   6200     0.0000   -19.4562    28.3240    37.782018     10.4658    -32.7301     34.3627\n   500   6400     0.0000   -19.4562    28.3240    38.031970     10.3229    -32.7754     34.3627\n   500   6600     0.0000   -19.4562    28.3240    38.278335     10.1819    -32.8195     34.3627\n   500   6800     0.0000   -19.4562    28.3240    38.521204     10.0427    -32.8624     34.3627\n   500   7000     0.0000   -19.4562    28.3240    38.760661      9.9053    -32.9041     34.3627\n   500   7200     0.0000   -19.4562    28.3240    38.996789      9.7696    -32.9446     34.3627\n   500   7400     0.0000   -19.4562    28.3240    39.229668      9.6356    -32.9840     34.3627\n   500   7600     0.0000   -19.4562    28.3240    39.459375      9.5033    -33.0224     34.3627\n   500   7800     0.0000   -19.4562    28.3240    39.685982      9.3726    -33.0597     34.3627\n   500   8000     0.0000   -19.4562    28.3240    39.909561      9.2435    -33.0960     34.3627\n   500   8200     0.0000   -19.4562    28.3240    40.130181      9.1160    -33.1314     34.3627\n   500   8400     0.0000   -19.4562    28.3240    40.347906      8.9901    -33.1658     34.3627\n  1000      0     0.0000   -19.4519    28.3720    27.494721     16.1872    -30.3532     34.3998\n  1000    200     0.0000   -19.4519    28.3720    27.936992     15.9524    -30.4772     34.3998\n  1000    400     0.0000   -19.4519    28.3720    28.368628     15.7223    -30.5966     34.3998\n  1000    600     0.0000   -19.4519    28.3720    28.790150     15.4968    -30.7114     34.3998\n  1000    800     0.0000   -19.4519    28.3720    29.202040     15.2756    -30.8220     34.3998\n  1000   1000     0.0000   -19.4519    28.3720    29.604740     15.0586    -30.9286     34.3998\n  1000   1200     0.0000   -19.4519    28.3720    29.998662     14.8456    -31.0314     34.3998\n  1000   1400     0.0000   -19.4519    28.3720    30.384184     14.6365    -31.1306     34.3998\n  1000   1600     0.0000   -19.4519    28.3720    30.761662     14.4311    -31.2263     34.3998\n  1000   1800     0.0000   -19.4519    28.3720    31.131424     14.2293    -31.3188     34.3998\n  1000   2000     0.0000   -19.4519    28.3720    31.493778     14.0309    -31.4082     34.3998\n  1000   2200     0.0000   -19.4519    28.3720    31.849012     13.8359    -31.4946     34.3998\n  1000   2400     0.0000   -19.4519    28.3720    32.197396     13.6442    -31.5781     34.3998\n  1000   2600     0.0000   -19.4519    28.3720    32.539183     13.4556    -31.6590     34.3998\n  1000   2800     0.0000   -19.4519    28.3720    32.874611     13.2700    -31.7372     34.3998\n  1000   3000     0.0000   -19.4519    28.3720    33.203904     13.0874    -31.8129     34.3998\n  1000   3200     0.0000   -19.4519    28.3720    33.527273     12.9076    -31.8863     34.3998\n  1000   3400     0.0000   -19.4519    28.3720    33.844919     12.7306    -31.9574     34.3998\n  1000   3600     0.0000   -19.4519    28.3720    34.157031     12.5564    -32.0262     34.3998\n  1000   3800     0.0000   -19.4519    28.3720    34.463787     12.3847    -32.0930     34.3998\n  1000   4000     0.0000   -19.4519    28.3720    34.765357     12.2156    -32.1577     34.3998\n  1000   4200     0.0000   -19.4519    28.3720    35.061902     12.0490    -32.2205     34.3998\n  1000   4400     0.0000   -19.4519    28.3720    35.353576     11.8848    -32.2815     34.3998\n  1000   4600     0.0000   -19.4519    28.3720    35.640523     11.7230    -32.3406     34.3998\n  1000   4800     0.0000   -19.4519    28.3720    35.922883     11.5635    -32.3980     34.3998\n  1000   5000     0.0000   -19.4519    28.3720    36.200788     11.4062    -32.4537     34.3998\n  1000   5200     0.0000   -19.4519    28.3720    36.474364     11.2511    -32.5078     34.3998\n  1000   5400     0.0000   -19.4519    28.3720    36.743731     11.0982    -32.5603     34.3998\n  1000   5600     0.0000   -19.4519    28.3720    37.009005     10.9473    -32.6113     34.3998\n  1000   5800     0.0000   -19.4519    28.3720    37.270296     10.7985    -32.6609     34.3998\n  1000   6000     0.0000   -19.4519    28.3720    37.527709     10.6516    -32.7091     34.3998\n  1000   6200     0.0000   -19.4519    28.3720    37.781346     10.5067    -32.7559     34.3998\n  1000   6400     0.0000   -19.4519    28.3720    38.031303     10.3637    -32.8014     34.3998\n  1000   6600     0.0000   -19.4519    28.3720    38.277674     10.2226    -32.8457     34.3998\n  1000   6800     0.0000   -19.4519    28.3720    38.520547     10.0833    -32.8887     34.3998\n  1000   7000     0.0000   -19.4519    28.3720    38.760010      9.9457    -32.9306     34.3998\n  1000   7200     0.0000   -19.4519    28.3720    38.996143      9.8099    -32.9713     34.3998\n  1000   7400     0.0000   -19.4519    28.3720    39.229027      9.6758    -33.0109     34.3998\n  1000   7600     0.0000   -19.4519    28.3720    39.458738      9.5434    -33.0494     34.3998\n  1000   7800     0.0000   -19.4519    28.3720    39.685350      9.4126    -33.0869     34.3998\n  1000   8000     0.0000   -19.4519    28.3720    39.908934      9.2834    -33.1234     34.3998\n  1000   8200     0.0000   -19.4519    28.3720    40.129557      9.1558    -33.1589     34.3998\n  1000   8400     0.0000   -19.4519    28.3720    40.347287      9.0297    -33.1935     34.3998\n  1500      0     0.0000   -19.4476    28.4199    27.493738     16.2322    -30.3712     34.4369\n  1500    200     0.0000   -19.4476    28.4199    27.936026     15.9973    -30.4956     34.4369\n  1500    400     0.0000   -19.4476    28.4199    28.367677     15.7671    -30.6153     34.4369\n  1500    600     0.0000   -19.4476    28.4199    28.789215     15.5414    -30.7305     34.4369\n  1500    800     0.0000   -19.4476    28.4199    29.201119     15.3201    -30.8414     34.4369\n  1500   1000     0.0000   -19.4476    28.4199    29.603833     15.1029    -30.9483     34.4369\n  1500   1200     0.0000   -19.4476    28.4199    29.997767     14.8898    -31.0514     34.4369\n  1500   1400     0.0000   -19.4476    28.4199    30.383302     14.6805    -31.1509     34.4369\n  1500   1600     0.0000   -19.4476    28.4199    30.760792     14.4750    -31.2470     34.4369\n  1500   1800     0.0000   -19.4476    28.4199    31.130565     14.2730    -31.3397     34.4369\n  1500   2000     0.0000   -19.4476    28.4199    31.492930     14.0745    -31.4294     34.4369\n  1500   2200     0.0000   -19.4476    28.4199    31.848175     13.8794    -31.5160     34.4369\n  1500   2400     0.0000   -19.4476    28.4199    32.196569     13.6875    -31.5998     34.4369\n  1500   2600     0.0000   -19.4476    28.4199    32.538366     13.4987    -31.6809     34.4369\n  1500   2800     0.0000   -19.4476    28.4199    32.873803     13.3130    -31.7594     34.4369\n  1500   3000     0.0000   -19.4476    28.4199    33.203105     13.1303    -31.8354     34.4369\n  1500   3200     0.0000   -19.4476    28.4199    33.526483     12.9504    -31.9090     34.4369\n  1500   3400     0.0000   -19.4476    28.4199    33.844138     12.7733    -31.9803     34.4369\n  1500   3600     0.0000   -19.4476    28.4199    34.156258     12.5989    -32.0494     34.4369\n  1500   3800     0.0000   -19.4476    28.4199    34.463022     12.4271    -32.1164     34.4369\n  1500   4000     0.0000   -19.4476    28.4199    34.764599     12.2579    -32.1814     34.4369\n  1500   4200     0.0000   -19.4476    28.4199    35.061152     12.0912    -32.2444     34.4369\n  1500   4400     0.0000   -19.4476    28.4199    35.352833     11.9268    -32.3055     34.4369\n  1500   4600     0.0000   -19.4476    28.4199    35.639787     11.7649    -32.3649     34.4369\n  1500   4800     0.0000   -19.4476    28.4199    35.922154     11.6053    -32.4224     34.4369\n  1500   5000     0.0000   -19.4476    28.4199    36.200066     11.4479    -32.4784     34.4369\n  1500   5200     0.0000   -19.4476    28.4199    36.473648     11.2926    -32.5327     34.4369\n  1500   5400     0.0000   -19.4476    28.4199    36.743022     11.1396    -32.5854     34.4369\n  1500   5600     0.0000   -19.4476    28.4199    37.008302     10.9886    -32.6366     34.4369\n  1500   5800     0.0000   -19.4476    28.4199    37.269599     10.8396    -32.6864     34.4369\n  1500   6000     0.0000   -19.4476    28.4199    37.527018     10.6927    -32.7348     34.4369\n  1500   6200     0.0000   -19.4476    28.4199    37.780660     10.5476    -32.7818     34.4369\n  1500   6400     0.0000   -19.4476    28.4199    38.030623     10.4045    -32.8275     34.4369\n  1500   6600     0.0000   -19.4476    28.4199    38.276999     10.2633    -32.8719     34.4369\n  1500   6800     0.0000   -19.4476    28.4199    38.519878     10.1238    -32.9151     34.4369\n  1500   7000     0.0000   -19.4476    28.4199    38.759345      9.9862    -32.9571     34.4369\n  1500   7200     0.0000   -19.4476    28.4199    38.995484      9.8503    -32.9980     34.4369\n  1500   7400     0.0000   -19.4476    28.4199    39.228373      9.7161    -33.0378     34.4369\n  1500   7600     0.0000   -19.4476    28.4199    39.458089      9.5835    -33.0765     34.4369\n  1500   7800     0.0000   -19.4476    28.4199    39.684705      9.4526    -33.1141     34.4369\n  1500   8000     0.0000   -19.4476    28.4199    39.908294      9.3233    -33.1508     34.4369\n  1500   8200     0.0000   -19.4476    28.4199    40.128922      9.1956    -33.1864     34.4369\n  1500   8400     0.0000   -19.4476    28.4199    40.346656      9.0694    -33.2211     34.4369\n  2000      0     0.0000   -19.4433    28.4678    27.492735     16.2772    -30.3893     34.4740\n  2000    200     0.0000   -19.4433    28.4678    27.935039     16.0422    -30.5140     34.4740\n  2000    400     0.0000   -19.4433    28.4678    28.366707     15.8118    -30.6340     34.4740\n  2000    600     0.0000   -19.4433    28.4678    28.788259     15.5860    -30.7495     34.4740\n  2000    800     0.0000   -19.4433    28.4678    29.200178     15.3645    -30.8608     34.4740\n  2000   1000     0.0000   -19.4433    28.4678    29.602906     15.1472    -30.9680     34.4740\n  2000   1200     0.0000   -19.4433    28.4678    29.996854     14.9340    -31.0714     34.4740\n  2000   1400     0.0000   -19.4433    28.4678    30.382402     14.7245    -31.1712     34.4740\n  2000   1600     0.0000   -19.4433    28.4678    30.759903     14.5188    -31.2676     34.4740\n  2000   1800     0.0000   -19.4433    28.4678    31.129689     14.3167    -31.3606     34.4740\n  2000   2000     0.0000   -19.4433    28.4678    31.492065     14.1181    -31.4505     34.4740\n  2000   2200     0.0000   -19.4433    28.4678    31.847321     13.9228    -31.5375     34.4740\n  2000   2400     0.0000   -19.4433    28.4678    32.195725     13.7308    -31.6215     34.4740\n  2000   2600     0.0000   -19.4433    28.4678    32.537532     13.5419    -31.7029     34.4740\n  2000   2800     0.0000   -19.4433    28.4678    32.872978     13.3561    -31.7816     34.4740\n  2000   3000     0.0000   -19.4433    28.4678    33.202290     13.1732    -31.8579     34.4740\n  2000   3200     0.0000   -19.4433    28.4678    33.525677     12.9932    -31.9317     34.4740\n  2000   3400     0.0000   -19.4433    28.4678    33.843341     12.8159    -32.0033     34.4740\n  2000   3600     0.0000   -19.4433    28.4678    34.155469     12.6414    -32.0726     34.4740\n  2000   3800     0.0000   -19.4433    28.4678    34.462241     12.4695    -32.1398     34.4740\n  2000   4000     0.0000   -19.4433    28.4678    34.763827     12.3001    -32.2050     34.4740\n  2000   4200     0.0000   -19.4433    28.4678    35.060387     12.1333    -32.2683     34.4740\n  2000   4400     0.0000   -19.4433    28.4678    35.352075     11.9689    -32.3296     34.4740\n  2000   4600     0.0000   -19.4433    28.4678    35.639037     11.8068    -32.3891     34.4740\n  2000   4800     0.0000   -19.4433    28.4678    35.921411     11.6470    -32.4469     34.4740\n  2000   5000     0.0000   -19.4433    28.4678    36.199329     11.4895    -32.5031     34.4740\n  2000   5200     0.0000   -19.4433    28.4678    36.472918     11.3342    -32.5575     34.4740\n  2000   5400     0.0000   -19.4433    28.4678    36.742298     11.1810    -32.6105     34.4740\n  2000   5600     0.0000   -19.4433    28.4678    37.007585     11.0299    -32.6619     34.4740\n  2000   5800     0.0000   -19.4433    28.4678    37.268888     10.8808    -32.7119     34.4740\n  2000   6000     0.0000   -19.4433    28.4678    37.526313     10.7337    -32.7604     34.4740\n  2000   6200     0.0000   -19.4433    28.4678    37.779961     10.5886    -32.8076     34.4740\n  2000   6400     0.0000   -19.4433    28.4678    38.029930     10.4453    -32.8535     34.4740\n  2000   6600     0.0000   -19.4433    28.4678    38.276312     10.3040    -32.8981     34.4740\n  2000   6800     0.0000   -19.4433    28.4678    38.519196     10.1644    -32.9415     34.4740\n  2000   7000     0.0000   -19.4433    28.4678    38.758669     10.0266    -32.9837     34.4740\n  2000   7200     0.0000   -19.4433    28.4678    38.994812      9.8906    -33.0247     34.4740\n  2000   7400     0.0000   -19.4433    28.4678    39.227706      9.7563    -33.0647     34.4740\n  2000   7600     0.0000   -19.4433    28.4678    39.457427      9.6236    -33.1035     34.4740\n  2000   7800     0.0000   -19.4433    28.4678    39.684049      9.4926    -33.1413     34.4740\n  2000   8000     0.0000   -19.4433    28.4678    39.907642      9.3632    -33.1781     34.4740\n  2000   8200     0.0000   -19.4433    28.4678    40.128274      9.2354    -33.2139     34.4740\n  2000   8400     0.0000   -19.4433    28.4678    40.346013      9.1091    -33.2488     34.4740\n  2500      0     0.0000   -19.4390    28.5158    27.491711     16.3223    -30.4073     34.5112\n  2500    200     0.0000   -19.4390    28.5158    27.934033     16.0871    -30.5324     34.5112\n  2500    400     0.0000   -19.4390    28.5158    28.365717     15.8566    -30.6527     34.5112\n  2500    600     0.0000   -19.4390    28.5158    28.787285     15.6306    -30.7686     34.5112\n  2500    800     0.0000   -19.4390    28.5158    29.199219     15.4090    -30.8802     34.5112\n  2500   1000     0.0000   -19.4390    28.5158    29.601961     15.1915    -30.9877     34.5112\n  2500   1200     0.0000   -19.4390    28.5158    29.995922     14.9781    -31.0914     34.5112\n  2500   1400     0.0000   -19.4390    28.5158    30.381483     14.7686    -31.1915     34.5112\n  2500   1600     0.0000   -19.4390    28.5158    30.758997     14.5627    -31.2882     34.5112\n  2500   1800     0.0000   -19.4390    28.5158    31.128795     14.3605    -31.3815     34.5112\n  2500   2000     0.0000   -19.4390    28.5158    31.491183     14.1617    -31.4717     34.5112\n  2500   2200     0.0000   -19.4390    28.5158    31.846449     13.9663    -31.5589     34.5112\n  2500   2400     0.0000   -19.4390    28.5158    32.194864     13.7741    -31.6432     34.5112\n  2500   2600     0.0000   -19.4390    28.5158    32.536681     13.5851    -31.7249     34.5112\n  2500   2800     0.0000   -19.4390    28.5158    32.872138     13.3991    -31.8038     34.5112\n  2500   3000     0.0000   -19.4390    28.5158    33.201459     13.2161    -31.8803     34.5112\n  2500   3200     0.0000   -19.4390    28.5158    33.524856     13.0360    -31.9544     34.5112\n  2500   3400     0.0000   -19.4390    28.5158    33.842528     12.8586    -32.0262     34.5112\n  2500   3600     0.0000   -19.4390    28.5158    34.154665     12.6839    -32.0958     34.5112\n  2500   3800     0.0000   -19.4390    28.5158    34.461445     12.5119    -32.1632     34.5112\n  2500   4000     0.0000   -19.4390    28.5158    34.763039     12.3424    -32.2287     34.5112\n  2500   4200     0.0000   -19.4390    28.5158    35.059607     12.1754    -32.2921     34.5112\n  2500   4400     0.0000   -19.4390    28.5158    35.351303     12.0109    -32.3537     34.5112\n  2500   4600     0.0000   -19.4390    28.5158    35.638272     11.8487    -32.4134     34.5112\n  2500   4800     0.0000   -19.4390    28.5158    35.920653     11.6888    -32.4714     34.5112\n  2500   5000     0.0000   -19.4390    28.5158    36.198579     11.5311    -32.5278     34.5112\n  2500   5200     0.0000   -19.4390    28.5158    36.472175     11.3757    -32.5824     34.5112\n  2500   5400     0.0000   -19.4390    28.5158    36.741561     11.2224    -32.6356     34.5112\n  2500   5600     0.0000   -19.4390    28.5158    37.006854     11.0711    -32.6872     34.5112\n  2500   5800     0.0000   -19.4390    28.5158    37.268164     10.9219    -32.7373     34.5112\n  2500   6000     0.0000   -19.4390    28.5158    37.525595     10.7747    -32.7861     34.5112\n  2500   6200     0.0000   -19.4390    28.5158    37.779249     10.6295    -32.8335     34.5112\n  2500   6400     0.0000   -19.4390    28.5158    38.029223     10.4861    -32.8795     34.5112\n  2500   6600     0.0000   -19.4390    28.5158    38.275611     10.3447    -32.9243     34.5112\n  2500   6800     0.0000   -19.4390    28.5158    38.518501     10.2050    -32.9679     34.5112\n  2500   7000     0.0000   -19.4390    28.5158    38.757979     10.0671    -33.0102     34.5112\n  2500   7200     0.0000   -19.4390    28.5158    38.994128      9.9310    -33.0514     34.5112\n  2500   7400     0.0000   -19.4390    28.5158    39.227027      9.7965    -33.0915     34.5112\n  2500   7600     0.0000   -19.4390    28.5158    39.456753      9.6638    -33.1306     34.5112\n  2500   7800     0.0000   -19.4390    28.5158    39.683379      9.5327    -33.1685     34.5112\n  2500   8000     0.0000   -19.4390    28.5158    39.906977      9.4031    -33.2055     34.5112\n  2500   8200     0.0000   -19.4390    28.5158    40.127615      9.2752    -33.2414     34.5112\n  2500   8400     0.0000   -19.4390    28.5158    40.345358      9.1488    -33.2764     34.5112\n  3000      0     0.0000   -19.4347    28.5637    27.490666     16.3674    -30.4253     34.5484\n  3000    200     0.0000   -19.4347    28.5637    27.933006     16.1320    -30.5508     34.5484\n  3000    400     0.0000   -19.4347    28.5637    28.364707     15.9013    -30.6715     34.5484\n  3000    600     0.0000   -19.4347    28.5637    28.786291     15.6752    -30.7876     34.5484\n  3000    800     0.0000   -19.4347    28.5637    29.198240     15.4535    -30.8995     34.5484\n  3000   1000     0.0000   -19.4347    28.5637    29.600997     15.2359    -31.0074     34.5484\n  3000   1200     0.0000   -19.4347    28.5637    29.994972     15.0223    -31.1114     34.5484\n  3000   1400     0.0000   -19.4347    28.5637    30.380547     14.8126    -31.2118     34.5484\n  3000   1600     0.0000   -19.4347    28.5637    30.758074     14.6066    -31.3087     34.5484\n  3000   1800     0.0000   -19.4347    28.5637    31.127883     14.4042    -31.4024     34.5484\n  3000   2000     0.0000   -19.4347    28.5637    31.490283     14.2053    -31.4928     34.5484\n  3000   2200     0.0000   -19.4347    28.5637    31.845561     14.0098    -31.5803     34.5484\n  3000   2400     0.0000   -19.4347    28.5637    32.193987     13.8175    -31.6649     34.5484\n  3000   2600     0.0000   -19.4347    28.5637    32.535814     13.6283    -31.7468     34.5484\n  3000   2800     0.0000   -19.4347    28.5637    32.871281     13.4422    -31.8261     34.5484\n  3000   3000     0.0000   -19.4347    28.5637    33.200612     13.2590    -31.9028     34.5484\n  3000   3200     0.0000   -19.4347    28.5637    33.524018     13.0788    -31.9771     34.5484\n  3000   3400     0.0000   -19.4347    28.5637    33.841699     12.9013    -32.0492     34.5484\n  3000   3600     0.0000   -19.4347    28.5637    34.153845     12.7265    -32.1190     34.5484\n  3000   3800     0.0000   -19.4347    28.5637    34.460634     12.5543    -32.1866     34.5484\n  3000   4000     0.0000   -19.4347    28.5637    34.762236     12.3847    -32.2523     34.5484\n  3000   4200     0.0000   -19.4347    28.5637    35.058813     12.2176    -32.3160     34.5484\n  3000   4400     0.0000   -19.4347    28.5637    35.350516     12.0529    -32.3777     34.5484\n  3000   4600     0.0000   -19.4347    28.5637    35.637493     11.8906    -32.4377     34.5484\n  3000   4800     0.0000   -19.4347    28.5637    35.919882     11.7306    -32.4959     34.5484\n  3000   5000     0.0000   -19.4347    28.5637    36.197814     11.5728    -32.5524     34.5484\n  3000   5200     0.0000   -19.4347    28.5637    36.471417     11.4172    -32.6073     34.5484\n  3000   5400     0.0000   -19.4347    28.5637    36.740810     11.2638    -32.6607     34.5484\n  3000   5600     0.0000   -19.4347    28.5637    37.006110     11.1124    -32.7125     34.5484\n  3000   5800     0.0000   -19.4347    28.5637    37.267426     10.9631    -32.7628     34.5484\n  3000   6000     0.0000   -19.4347    28.5637    37.524863     10.8158    -32.8117     34.5484\n  3000   6200     0.0000   -19.4347    28.5637    37.778524     10.6704    -32.8593     34.5484\n  3000   6400     0.0000   -19.4347    28.5637    38.028504     10.5270    -32.9055     34.5484\n  3000   6600     0.0000   -19.4347    28.5637    38.274897     10.3854    -32.9505     34.5484\n  3000   6800     0.0000   -19.4347    28.5637    38.517793     10.2456    -32.9942     34.5484\n  3000   7000     0.0000   -19.4347    28.5637    38.757276     10.1076    -33.0368     34.5484\n  3000   7200     0.0000   -19.4347    28.5637    38.993431      9.9713    -33.0781     34.5484\n  3000   7400     0.0000   -19.4347    28.5637    39.226335      9.8368    -33.1184     34.5484\n  3000   7600     0.0000   -19.4347    28.5637    39.456066      9.7039    -33.1576     34.5484\n  3000   7800     0.0000   -19.4347    28.5637    39.682698      9.5727    -33.1957     34.5484\n  3000   8000     0.0000   -19.4347    28.5637    39.906301      9.4431    -33.2328     34.5484\n  3000   8200     0.0000   -19.4347    28.5637    40.126943      9.3150    -33.2689     34.5484\n  3000   8400     0.0000   -19.4347    28.5637    40.344692      9.1885    -33.3041     34.5484\n  3500      0     0.0000   -19.4304    28.6116    27.489602     16.4124    -30.4433     34.5856\n  3500    200     0.0000   -19.4304    28.6116    27.931959     16.1769    -30.5691     34.5856\n  3500    400     0.0000   -19.4304    28.6116    28.363677     15.9461    -30.6902     34.5856\n  3500    600     0.0000   -19.4304    28.6116    28.785278     15.7198    -30.8067     34.5856\n  3500    800     0.0000   -19.4304    28.6116    29.197243     15.4979    -30.9189     34.5856\n  3500   1000     0.0000   -19.4304    28.6116    29.600015     15.2802    -31.0271     34.5856\n  3500   1200     0.0000   -19.4304    28.6116    29.994004     15.0665    -31.1314     34.5856\n  3500   1400     0.0000   -19.4304    28.6116    30.379592     14.8566    -31.2321     34.5856\n  3500   1600     0.0000   -19.4304    28.6116    30.757132     14.6505    -31.3293     34.5856\n  3500   1800     0.0000   -19.4304    28.6116    31.126954     14.4480    -31.4232     34.5856\n  3500   2000     0.0000   -19.4304    28.6116    31.489366     14.2489    -31.5140     34.5856\n  3500   2200     0.0000   -19.4304    28.6116    31.844656     14.0533    -31.6017     34.5856\n  3500   2400     0.0000   -19.4304    28.6116    32.193093     13.8608    -31.6866     34.5856\n  3500   2600     0.0000   -19.4304    28.6116    32.534931     13.6715    -31.7688     34.5856\n  3500   2800     0.0000   -19.4304    28.6116    32.870408     13.4853    -31.8483     34.5856\n  3500   3000     0.0000   -19.4304    28.6116    33.199749     13.3020    -31.9253     34.5856\n  3500   3200     0.0000   -19.4304    28.6116    33.523165     13.1216    -31.9998     34.5856\n  3500   3400     0.0000   -19.4304    28.6116    33.840856     12.9439    -32.0721     34.5856\n  3500   3600     0.0000   -19.4304    28.6116    34.153010     12.7690    -32.1421     34.5856\n  3500   3800     0.0000   -19.4304    28.6116    34.459808     12.5967    -32.2101     34.5856\n  3500   4000     0.0000   -19.4304    28.6116    34.761419     12.4270    -32.2759     34.5856\n  3500   4200     0.0000   -19.4304    28.6116    35.058003     12.2598    -32.3398     34.5856\n  3500   4400     0.0000   -19.4304    28.6116    35.349715     12.0949    -32.4018     34.5856\n  3500   4600     0.0000   -19.4304    28.6116    35.636699     11.9325    -32.4620     34.5856\n  3500   4800     0.0000   -19.4304    28.6116    35.919095     11.7724    -32.5204     34.5856\n  3500   5000     0.0000   -19.4304    28.6116    36.197035     11.6145    -32.5771     34.5856\n  3500   5200     0.0000   -19.4304    28.6116    36.470645     11.4588    -32.6322     34.5856\n  3500   5400     0.0000   -19.4304    28.6116    36.740046     11.3052    -32.6857     34.5856\n  3500   5600     0.0000   -19.4304    28.6116    37.005352     11.1537    -32.7377     34.5856\n  3500   5800     0.0000   -19.4304    28.6116    37.266674     11.0043    -32.7883     34.5856\n  3500   6000     0.0000   -19.4304    28.6116    37.524118     10.8569    -32.8374     34.5856\n  3500   6200     0.0000   -19.4304    28.6116    37.777785     10.7114    -32.8851     34.5856\n  3500   6400     0.0000   -19.4304    28.6116    38.027771     10.5678    -32.9315     34.5856\n  3500   6600     0.0000   -19.4304    28.6116    38.274170     10.4261    -32.9767     34.5856\n  3500   6800     0.0000   -19.4304    28.6116    38.517072     10.2862    -33.0206     34.5856\n  3500   7000     0.0000   -19.4304    28.6116    38.756561     10.1481    -33.0633     34.5856\n  3500   7200     0.0000   -19.4304    28.6116    38.992721     10.0117    -33.1048     34.5856\n  3500   7400     0.0000   -19.4304    28.6116    39.225631      9.8770    -33.1453     34.5856\n  3500   7600     0.0000   -19.4304    28.6116    39.455367      9.7441    -33.1846     34.5856\n  3500   7800     0.0000   -19.4304    28.6116    39.682004      9.6127    -33.2229     34.5856\n  3500   8000     0.0000   -19.4304    28.6116    39.905612      9.4830    -33.2602     34.5856\n  3500   8200     0.0000   -19.4304    28.6116    40.126259      9.3548    -33.2964     34.5856\n  3500   8400     0.0000   -19.4304    28.6116    40.344013      9.2282    -33.3317     34.5856\n  4000      0     0.0000   -19.4261    28.6596    27.488516     16.4575    -30.4613     34.6229\n  4000    200     0.0000   -19.4261    28.6596    27.930893     16.2218    -30.5875     34.6229\n  4000    400     0.0000   -19.4261    28.6596    28.362629     15.9909    -30.7089     34.6229\n  4000    600     0.0000   -19.4261    28.6596    28.784246     15.7645    -30.8257     34.6229\n  4000    800     0.0000   -19.4261    28.6596    29.196227     15.5424    -30.9383     34.6229\n  4000   1000     0.0000   -19.4261    28.6596    29.599014     15.3245    -31.0468     34.6229\n  4000   1200     0.0000   -19.4261    28.6596    29.993018     15.1107    -31.1514     34.6229\n  4000   1400     0.0000   -19.4261    28.6596    30.378620     14.9007    -31.2524     34.6229\n  4000   1600     0.0000   -19.4261    28.6596    30.756173     14.6944    -31.3499     34.6229\n  4000   1800     0.0000   -19.4261    28.6596    31.126008     14.4918    -31.4441     34.6229\n  4000   2000     0.0000   -19.4261    28.6596    31.488432     14.2926    -31.5351     34.6229\n  4000   2200     0.0000   -19.4261    28.6596    31.843734     14.0968    -31.6232     34.6229\n  4000   2400     0.0000   -19.4261    28.6596    32.192182     13.9042    -31.7083     34.6229\n  4000   2600     0.0000   -19.4261    28.6596    32.534031     13.7147    -31.7907     34.6229\n  4000   2800     0.0000   -19.4261    28.6596    32.869519     13.5284    -31.8705     34.6229\n  4000   3000     0.0000   -19.4261    28.6596    33.198870     13.3449    -31.9477     34.6229\n  4000   3200     0.0000   -19.4261    28.6596    33.522296     13.1644    -32.0225     34.6229\n  4000   3400     0.0000   -19.4261    28.6596    33.839996     12.9866    -32.0950     34.6229\n  4000   3600     0.0000   -19.4261    28.6596    34.152160     12.8116    -32.1653     34.6229\n  4000   3800     0.0000   -19.4261    28.6596    34.458967     12.6392    -32.2334     34.6229\n  4000   4000     0.0000   -19.4261    28.6596    34.760586     12.4693    -32.2995     34.6229\n  4000   4200     0.0000   -19.4261    28.6596    35.057179     12.3019    -32.3637     34.6229\n  4000   4400     0.0000   -19.4261    28.6596    35.348899     12.1370    -32.4259     34.6229\n  4000   4600     0.0000   -19.4261    28.6596    35.635891     11.9744    -32.4863     34.6229\n  4000   4800     0.0000   -19.4261    28.6596    35.918295     11.8141    -32.5449     34.6229\n  4000   5000     0.0000   -19.4261    28.6596    36.196242     11.6561    -32.6018     34.6229\n  4000   5200     0.0000   -19.4261    28.6596    36.469860     11.5003    -32.6571     34.6229\n  4000   5400     0.0000   -19.4261    28.6596    36.739267     11.3466    -32.7108     34.6229\n  4000   5600     0.0000   -19.4261    28.6596    37.004580     11.1950    -32.7630     34.6229\n  4000   5800     0.0000   -19.4261    28.6596    37.265909     11.0455    -32.8137     34.6229\n  4000   6000     0.0000   -19.4261    28.6596    37.523360     10.8979    -32.8630     34.6229\n  4000   6200     0.0000   -19.4261    28.6596    37.777033     10.7523    -32.9110     34.6229\n  4000   6400     0.0000   -19.4261    28.6596    38.027025     10.6086    -32.9576     34.6229\n  4000   6600     0.0000   -19.4261    28.6596    38.273431     10.4668    -33.0029     34.6229\n  4000   6800     0.0000   -19.4261    28.6596    38.516338     10.3268    -33.0470     34.6229\n  4000   7000     0.0000   -19.4261    28.6596    38.755833     10.1886    -33.0898     34.6229\n  4000   7200     0.0000   -19.4261    28.6596    38.991999     10.0521    -33.1315     34.6229\n  4000   7400     0.0000   -19.4261    28.6596    39.224914      9.9173    -33.1721     34.6229\n  4000   7600     0.0000   -19.4261    28.6596    39.454656      9.7842    -33.2116     34.6229\n  4000   7800     0.0000   -19.4261    28.6596    39.681298      9.6528    -33.2501     34.6229\n  4000   8000     0.0000   -19.4261    28.6596    39.904911      9.5229    -33.2875     34.6229\n  4000   8200     0.0000   -19.4261    28.6596    40.125563      9.3947    -33.3239     34.6229\n  4000   8400     0.0000   -19.4261    28.6596    40.343322      9.2679    -33.3594     34.6229\n  4500      0     0.0000   -19.4218    28.7075    27.487411     16.5026    -30.4793     34.6602\n  4500    200     0.0000   -19.4218    28.7075    27.929806     16.2668    -30.6058     34.6602\n  4500    400     0.0000   -19.4218    28.7075    28.361560     16.0357    -30.7276     34.6602\n  4500    600     0.0000   -19.4218    28.7075    28.783195     15.8091    -30.8447     34.6602\n  4500    800     0.0000   -19.4218    28.7075    29.195192     15.5869    -30.9576     34.6602\n  4500   1000     0.0000   -19.4218    28.7075    29.597995     15.3689    -31.0664     34.6602\n  4500   1200     0.0000   -19.4218    28.7075    29.992014     15.1549    -31.1714     34.6602\n  4500   1400     0.0000   -19.4218    28.7075    30.377630     14.9448    -31.2727     34.6602\n  4500   1600     0.0000   -19.4218    28.7075    30.755197     14.7384    -31.3705     34.6602\n  4500   1800     0.0000   -19.4218    28.7075    31.125045     14.5356    -31.4650     34.6602\n  4500   2000     0.0000   -19.4218    28.7075    31.487481     14.3362    -31.5563     34.6602\n  4500   2200     0.0000   -19.4218    28.7075    31.842795     14.1403    -31.6446     34.6602\n  4500   2400     0.0000   -19.4218    28.7075    32.191255     13.9475    -31.7300     34.6602\n  4500   2600     0.0000   -19.4218    28.7075    32.533115     13.7580    -31.8126     34.6602\n  4500   2800     0.0000   -19.4218    28.7075    32.868614     13.5715    -31.8927     34.6602\n  4500   3000     0.0000   -19.4218    28.7075    33.197975     13.3879    -31.9701     34.6602\n  4500   3200     0.0000   -19.4218    28.7075    33.521411     13.2072    -32.0452     34.6602\n  4500   3400     0.0000   -19.4218    28.7075    33.839121     13.0293    -32.1180     34.6602\n  4500   3600     0.0000   -19.4218    28.7075    34.151295     12.8541    -32.1885     34.6602\n  4500   3800     0.0000   -19.4218    28.7075    34.458111     12.6816    -32.2568     34.6602\n  4500   4000     0.0000   -19.4218    28.7075    34.759739     12.5116    -32.3232     34.6602\n  4500   4200     0.0000   -19.4218    28.7075    35.056340     12.3441    -32.3875     34.6602\n  4500   4400     0.0000   -19.4218    28.7075    35.348068     12.1790    -32.4499     34.6602\n  4500   4600     0.0000   -19.4218    28.7075    35.635069     12.0163    -32.5105     34.6602\n  4500   4800     0.0000   -19.4218    28.7075    35.917480     11.8560    -32.5694     34.6602\n  4500   5000     0.0000   -19.4218    28.7075    36.195435     11.6978    -32.6265     34.6602\n  4500   5200     0.0000   -19.4218    28.7075    36.469060     11.5419    -32.6820     34.6602\n  4500   5400     0.0000   -19.4218    28.7075    36.738475     11.3881    -32.7359     34.6602\n  4500   5600     0.0000   -19.4218    28.7075    37.003795     11.2364    -32.7883     34.6602\n  4500   5800     0.0000   -19.4218    28.7075    37.265131     11.0867    -32.8392     34.6602\n  4500   6000     0.0000   -19.4218    28.7075    37.522588     10.9390    -32.8887     34.6602\n  4500   6200     0.0000   -19.4218    28.7075    37.776268     10.7933    -32.9368     34.6602\n  4500   6400     0.0000   -19.4218    28.7075    38.026267     10.6495    -32.9836     34.6602\n  4500   6600     0.0000   -19.4218    28.7075    38.272678     10.5075    -33.0291     34.6602\n  4500   6800     0.0000   -19.4218    28.7075    38.515591     10.3674    -33.0733     34.6602\n  4500   7000     0.0000   -19.4218    28.7075    38.755092     10.2291    -33.1164     34.6602\n  4500   7200     0.0000   -19.4218    28.7075    38.991264     10.0925    -33.1582     34.6602\n  4500   7400     0.0000   -19.4218    28.7075    39.224185      9.9576    -33.1990     34.6602\n  4500   7600     0.0000   -19.4218    28.7075    39.453932      9.8244    -33.2387     34.6602\n  4500   7800     0.0000   -19.4218    28.7075    39.680580      9.6928    -33.2773     34.6602\n  4500   8000     0.0000   -19.4218    28.7075    39.904198      9.5629    -33.3148     34.6602\n  4500   8200     0.0000   -19.4218    28.7075    40.124856      9.4345    -33.3514     34.6602\n  4500   8400     0.0000   -19.4218    28.7075    40.342619      9.3077    -33.3870     34.6602\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180530_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.6579066       30.3023419       42.2468260   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0223397        0.0234496   m/s m/s m/s\nprecision_baseline(TCN):       -0.6579066       30.3023419       42.2468260   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0223397        0.0234496   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180530_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.658     30.302     42.247\norbit baseline rate   (TCN) (m/s):  0.000e+00 -2.234e-02  2.345e-02\n\nbaseline vector (TCN)   (m):     -0.658     30.302     42.247\nbaseline rate   (TCN) (m/s):  0.000e+00 -2.234e-02  2.345e-02\n\nSLC-1 center baseline length (m):    51.9948\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.6579    30.5109    42.0279    27.496626     51.3678      7.6614     51.9393\n     0    200    -0.6579    30.5109    42.0279    27.938864     51.4254      7.2647     51.9393\n     0    400    -0.6579    30.5109    42.0279    28.370470     51.4787      6.8771     51.9393\n     0    600    -0.6579    30.5109    42.0279    28.791963     51.5278      6.4982     51.9393\n     0    800    -0.6579    30.5109    42.0279    29.203825     51.5732      6.1276     51.9393\n     0   1000    -0.6579    30.5109    42.0279    29.606500     51.6150      5.7650     51.9393\n     0   1200    -0.6579    30.5109    42.0279    30.000396     51.6534      5.4100     51.9393\n     0   1400    -0.6579    30.5109    42.0279    30.385895     51.6886      5.0624     51.9393\n     0   1600    -0.6579    30.5109    42.0279    30.763349     51.7209      4.7217     51.9393\n     0   1800    -0.6579    30.5109    42.0279    31.133089     51.7502      4.3878     51.9393\n     0   2000    -0.6579    30.5109    42.0279    31.495422     51.7769      4.0605     51.9393\n     0   2200    -0.6579    30.5109    42.0279    31.850636     51.8011      3.7394     51.9393\n     0   2400    -0.6579    30.5109    42.0279    32.199000     51.8229      3.4244     51.9393\n     0   2600    -0.6579    30.5109    42.0279    32.540768     51.8424      3.1152     51.9393\n     0   2800    -0.6579    30.5109    42.0279    32.876178     51.8597      2.8116     51.9393\n     0   3000    -0.6579    30.5109    42.0279    33.205453     51.8750      2.5135     51.9393\n     0   3200    -0.6579    30.5109    42.0279    33.528805     51.8884      2.2207     51.9393\n     0   3400    -0.6579    30.5109    42.0279    33.846435     51.8999      1.9330     51.9393\n     0   3600    -0.6579    30.5109    42.0279    34.158531     51.9096      1.6503     51.9393\n     0   3800    -0.6579    30.5109    42.0279    34.465271     51.9177      1.3724     51.9393\n     0   4000    -0.6579    30.5109    42.0279    34.766827     51.9242      1.0991     51.9393\n     0   4200    -0.6579    30.5109    42.0279    35.063357     51.9292      0.8303     51.9393\n     0   4400    -0.6579    30.5109    42.0279    35.355017     51.9328      0.5660     51.9393\n     0   4600    -0.6579    30.5109    42.0279    35.641950     51.9349      0.3059     51.9393\n     0   4800    -0.6579    30.5109    42.0279    35.924297     51.9358      0.0500     51.9393\n     0   5000    -0.6579    30.5109    42.0279    36.202189     51.9354     -0.2020     51.9393\n     0   5200    -0.6579    30.5109    42.0279    36.475752     51.9339     -0.4499     51.9393\n     0   5400    -0.6579    30.5109    42.0279    36.745108     51.9312     -0.6941     51.9393\n     0   5600    -0.6579    30.5109    42.0279    37.010370     51.9274     -0.9345     51.9393\n     0   5800    -0.6579    30.5109    42.0279    37.271649     51.9226     -1.1713     51.9393\n     0   6000    -0.6579    30.5109    42.0279    37.529051     51.9168     -1.4045     51.9393\n     0   6200    -0.6579    30.5109    42.0279    37.782677     51.9101     -1.6344     51.9393\n     0   6400    -0.6579    30.5109    42.0279    38.032623     51.9024     -1.8608     51.9393\n     0   6600    -0.6579    30.5109    42.0279    38.278984     51.8940     -2.0840     51.9393\n     0   6800    -0.6579    30.5109    42.0279    38.521847     51.8847     -2.3039     51.9393\n     0   7000    -0.6579    30.5109    42.0279    38.761299     51.8746     -2.5207     51.9393\n     0   7200    -0.6579    30.5109    42.0279    38.997423     51.8637     -2.7345     51.9393\n     0   7400    -0.6579    30.5109    42.0279    39.230297     51.8522     -2.9453     51.9393\n     0   7600    -0.6579    30.5109    42.0279    39.459999     51.8400     -3.1531     51.9393\n     0   7800    -0.6579    30.5109    42.0279    39.686602     51.8271     -3.3581     51.9393\n     0   8000    -0.6579    30.5109    42.0279    39.910176     51.8136     -3.5604     51.9393\n     0   8200    -0.6579    30.5109    42.0279    40.130792     51.7995     -3.7598     51.9393\n     0   8400    -0.6579    30.5109    42.0279    40.348513     51.7848     -3.9567     51.9393\n   500      0    -0.6579    30.4649    42.0761    27.495684     51.3892      7.5993     51.9514\n   500    200    -0.6579    30.4649    42.0761    27.937938     51.4464      7.2024     51.9514\n   500    400    -0.6579    30.4649    42.0761    28.369559     51.4991      6.8146     51.9514\n   500    600    -0.6579    30.4649    42.0761    28.791066     51.5479      6.4356     51.9514\n   500    800    -0.6579    30.4649    42.0761    29.202942     51.5928      6.0648     51.9514\n   500   1000    -0.6579    30.4649    42.0761    29.605629     51.6341      5.7021     51.9514\n   500   1200    -0.6579    30.4649    42.0761    29.999538     51.6721      5.3469     51.9514\n   500   1400    -0.6579    30.4649    42.0761    30.385049     51.7069      4.9991     51.9514\n   500   1600    -0.6579    30.4649    42.0761    30.762515     51.7387      4.6584     51.9514\n   500   1800    -0.6579    30.4649    42.0761    31.132265     51.7677      4.3244     51.9514\n   500   2000    -0.6579    30.4649    42.0761    31.494609     51.7940      3.9969     51.9514\n   500   2200    -0.6579    30.4649    42.0761    31.849833     51.8178      3.6757     51.9514\n   500   2400    -0.6579    30.4649    42.0761    32.198207     51.8392      3.3605     51.9514\n   500   2600    -0.6579    30.4649    42.0761    32.539984     51.8583      3.0512     51.9514\n   500   2800    -0.6579    30.4649    42.0761    32.875402     51.8752      2.7476     51.9514\n   500   3000    -0.6579    30.4649    42.0761    33.204686     51.8902      2.4494     51.9514\n   500   3200    -0.6579    30.4649    42.0761    33.528047     51.9032      2.1565     51.9514\n   500   3400    -0.6579    30.4649    42.0761    33.845685     51.9143      1.8687     51.9514\n   500   3600    -0.6579    30.4649    42.0761    34.157789     51.9237      1.5859     51.9514\n   500   3800    -0.6579    30.4649    42.0761    34.464537     51.9315      1.3079     51.9514\n   500   4000    -0.6579    30.4649    42.0761    34.766099     51.9376      1.0345     51.9514\n   500   4200    -0.6579    30.4649    42.0761    35.062637     51.9423      0.7657     51.9514\n   500   4400    -0.6579    30.4649    42.0761    35.354304     51.9455      0.5013     51.9514\n   500   4600    -0.6579    30.4649    42.0761    35.641244     51.9473      0.2411     51.9514\n   500   4800    -0.6579    30.4649    42.0761    35.923597     51.9479     -0.0149     51.9514\n   500   5000    -0.6579    30.4649    42.0761    36.201496     51.9472     -0.2668     51.9514\n   500   5200    -0.6579    30.4649    42.0761    36.475065     51.9453     -0.5149     51.9514\n   500   5400    -0.6579    30.4649    42.0761    36.744426     51.9423     -0.7591     51.9514\n   500   5600    -0.6579    30.4649    42.0761    37.009694     51.9383     -0.9996     51.9514\n   500   5800    -0.6579    30.4649    42.0761    37.270979     51.9332     -1.2364     51.9514\n   500   6000    -0.6579    30.4649    42.0761    37.528387     51.9271     -1.4697     51.9514\n   500   6200    -0.6579    30.4649    42.0761    37.782018     51.9201     -1.6996     51.9514\n   500   6400    -0.6579    30.4649    42.0761    38.031970     51.9121     -1.9261     51.9514\n   500   6600    -0.6579    30.4649    42.0761    38.278335     51.9034     -2.1493     51.9514\n   500   6800    -0.6579    30.4649    42.0761    38.521204     51.8938     -2.3693     51.9514\n   500   7000    -0.6579    30.4649    42.0761    38.760661     51.8834     -2.5861     51.9514\n   500   7200    -0.6579    30.4649    42.0761    38.996789     51.8723     -2.7999     51.9514\n   500   7400    -0.6579    30.4649    42.0761    39.229668     51.8605     -3.0108     51.9514\n   500   7600    -0.6579    30.4649    42.0761    39.459375     51.8480     -3.2187     51.9514\n   500   7800    -0.6579    30.4649    42.0761    39.685982     51.8349     -3.4237     51.9514\n   500   8000    -0.6579    30.4649    42.0761    39.909561     51.8211     -3.6259     51.9514\n   500   8200    -0.6579    30.4649    42.0761    40.130181     51.8068     -3.8255     51.9514\n   500   8400    -0.6579    30.4649    42.0761    40.347906     51.7919     -4.0223     51.9514\n  1000      0    -0.6579    30.4190    42.1243    27.494721     51.4107      7.5372     51.9635\n  1000    200    -0.6579    30.4190    42.1243    27.936992     51.4673      7.1401     51.9635\n  1000    400    -0.6579    30.4190    42.1243    28.368628     51.5196      6.7521     51.9635\n  1000    600    -0.6579    30.4190    42.1243    28.790150     51.5679      6.3729     51.9635\n  1000    800    -0.6579    30.4190    42.1243    29.202040     51.6124      6.0020     51.9635\n  1000   1000    -0.6579    30.4190    42.1243    29.604740     51.6533      5.6391     51.9635\n  1000   1200    -0.6579    30.4190    42.1243    29.998662     51.6908      5.2839     51.9635\n  1000   1400    -0.6579    30.4190    42.1243    30.384184     51.7252      4.9359     51.9635\n  1000   1600    -0.6579    30.4190    42.1243    30.761662     51.7566      4.5950     51.9635\n  1000   1800    -0.6579    30.4190    42.1243    31.131424     51.7851      4.2609     51.9635\n  1000   2000    -0.6579    30.4190    42.1243    31.493778     51.8111      3.9333     51.9635\n  1000   2200    -0.6579    30.4190    42.1243    31.849012     51.8344      3.6120     51.9635\n  1000   2400    -0.6579    30.4190    42.1243    32.197396     51.8554      3.2967     51.9635\n  1000   2600    -0.6579    30.4190    42.1243    32.539183     51.8742      2.9873     51.9635\n  1000   2800    -0.6579    30.4190    42.1243    32.874611     51.8908      2.6836     51.9635\n  1000   3000    -0.6579    30.4190    42.1243    33.203904     51.9053      2.3853     51.9635\n  1000   3200    -0.6579    30.4190    42.1243    33.527273     51.9180      2.0923     51.9635\n  1000   3400    -0.6579    30.4190    42.1243    33.844919     51.9288      1.8044     51.9635\n  1000   3600    -0.6579    30.4190    42.1243    34.157031     51.9378      1.5215     51.9635\n  1000   3800    -0.6579    30.4190    42.1243    34.463787     51.9452      1.2434     51.9635\n  1000   4000    -0.6579    30.4190    42.1243    34.765357     51.9510      0.9700     51.9635\n  1000   4200    -0.6579    30.4190    42.1243    35.061902     51.9553      0.7011     51.9635\n  1000   4400    -0.6579    30.4190    42.1243    35.353576     51.9582      0.4366     51.9635\n  1000   4600    -0.6579    30.4190    42.1243    35.640523     51.9598      0.1764     51.9635\n  1000   4800    -0.6579    30.4190    42.1243    35.922883     51.9600     -0.0797     51.9635\n  1000   5000    -0.6579    30.4190    42.1243    36.200788     51.9590     -0.3317     51.9635\n  1000   5200    -0.6579    30.4190    42.1243    36.474364     51.9568     -0.5798     51.9635\n  1000   5400    -0.6579    30.4190    42.1243    36.743731     51.9535     -0.8241     51.9635\n  1000   5600    -0.6579    30.4190    42.1243    37.009005     51.9491     -1.0646     51.9635\n  1000   5800    -0.6579    30.4190    42.1243    37.270296     51.9437     -1.3015     51.9635\n  1000   6000    -0.6579    30.4190    42.1243    37.527709     51.9373     -1.5349     51.9635\n  1000   6200    -0.6579    30.4190    42.1243    37.781346     51.9300     -1.7648     51.9635\n  1000   6400    -0.6579    30.4190    42.1243    38.031303     51.9218     -1.9913     51.9635\n  1000   6600    -0.6579    30.4190    42.1243    38.277674     51.9128     -2.2146     51.9635\n  1000   6800    -0.6579    30.4190    42.1243    38.520547     51.9029     -2.4346     51.9635\n  1000   7000    -0.6579    30.4190    42.1243    38.760010     51.8923     -2.6515     51.9635\n  1000   7200    -0.6579    30.4190    42.1243    38.996143     51.8809     -2.8654     51.9635\n  1000   7400    -0.6579    30.4190    42.1243    39.229027     51.8688     -3.0762     51.9635\n  1000   7600    -0.6579    30.4190    42.1243    39.458738     51.8561     -3.2842     51.9635\n  1000   7800    -0.6579    30.4190    42.1243    39.685350     51.8427     -3.4892     51.9635\n  1000   8000    -0.6579    30.4190    42.1243    39.908934     51.8287     -3.6915     51.9635\n  1000   8200    -0.6579    30.4190    42.1243    40.129557     51.8141     -3.8911     51.9635\n  1000   8400    -0.6579    30.4190    42.1243    40.347287     51.7989     -4.0880     51.9635\n  1500      0    -0.6579    30.3731    42.1725    27.493738     51.4321      7.4751     51.9758\n  1500    200    -0.6579    30.3731    42.1725    27.936026     51.4883      7.0778     51.9758\n  1500    400    -0.6579    30.3731    42.1725    28.367677     51.5401      6.6897     51.9758\n  1500    600    -0.6579    30.3731    42.1725    28.789215     51.5879      6.3103     51.9758\n  1500    800    -0.6579    30.3731    42.1725    29.201119     51.6319      5.9393     51.9758\n  1500   1000    -0.6579    30.3731    42.1725    29.603833     51.6724      5.5762     51.9758\n  1500   1200    -0.6579    30.3731    42.1725    29.997767     51.7095      5.2208     51.9758\n  1500   1400    -0.6579    30.3731    42.1725    30.383302     51.7435      4.8727     51.9758\n  1500   1600    -0.6579    30.3731    42.1725    30.760792     51.7744      4.5317     51.9758\n  1500   1800    -0.6579    30.3731    42.1725    31.130565     51.8026      4.1974     51.9758\n  1500   2000    -0.6579    30.3731    42.1725    31.492930     51.8281      3.8697     51.9758\n  1500   2200    -0.6579    30.3731    42.1725    31.848175     51.8511      3.5483     51.9758\n  1500   2400    -0.6579    30.3731    42.1725    32.196569     51.8717      3.2329     51.9758\n  1500   2600    -0.6579    30.3731    42.1725    32.538366     51.8901      2.9234     51.9758\n  1500   2800    -0.6579    30.3731    42.1725    32.873803     51.9063      2.6196     51.9758\n  1500   3000    -0.6579    30.3731    42.1725    33.203105     51.9205      2.3212     51.9758\n  1500   3200    -0.6579    30.3731    42.1725    33.526483     51.9327      2.0281     51.9758\n  1500   3400    -0.6579    30.3731    42.1725    33.844138     51.9432      1.7402     51.9758\n  1500   3600    -0.6579    30.3731    42.1725    34.156258     51.9519      1.4572     51.9758\n  1500   3800    -0.6579    30.3731    42.1725    34.463022     51.9589      1.1790     51.9758\n  1500   4000    -0.6579    30.3731    42.1725    34.764599     51.9644      0.9055     51.9758\n  1500   4200    -0.6579    30.3731    42.1725    35.061152     51.9684      0.6365     51.9758\n  1500   4400    -0.6579    30.3731    42.1725    35.352833     51.9710      0.3719     51.9758\n  1500   4600    -0.6579    30.3731    42.1725    35.639787     51.9722      0.1116     51.9758\n  1500   4800    -0.6579    30.3731    42.1725    35.922154     51.9721     -0.1445     51.9758\n  1500   5000    -0.6579    30.3731    42.1725    36.200066     51.9708     -0.3966     51.9758\n  1500   5200    -0.6579    30.3731    42.1725    36.473648     51.9683     -0.6448     51.9758\n  1500   5400    -0.6579    30.3731    42.1725    36.743022     51.9647     -0.8891     51.9758\n  1500   5600    -0.6579    30.3731    42.1725    37.008302     51.9600     -1.1297     51.9758\n  1500   5800    -0.6579    30.3731    42.1725    37.269599     51.9543     -1.3666     51.9758\n  1500   6000    -0.6579    30.3731    42.1725    37.527018     51.9476     -1.6000     51.9758\n  1500   6200    -0.6579    30.3731    42.1725    37.780660     51.9400     -1.8300     51.9758\n  1500   6400    -0.6579    30.3731    42.1725    38.030623     51.9315     -2.0566     51.9758\n  1500   6600    -0.6579    30.3731    42.1725    38.276999     51.9222     -2.2799     51.9758\n  1500   6800    -0.6579    30.3731    42.1725    38.519878     51.9121     -2.5000     51.9758\n  1500   7000    -0.6579    30.3731    42.1725    38.759345     51.9012     -2.7169     51.9758\n  1500   7200    -0.6579    30.3731    42.1725    38.995484     51.8895     -2.9308     51.9758\n  1500   7400    -0.6579    30.3731    42.1725    39.228373     51.8772     -3.1417     51.9758\n  1500   7600    -0.6579    30.3731    42.1725    39.458089     51.8642     -3.3497     51.9758\n  1500   7800    -0.6579    30.3731    42.1725    39.684705     51.8505     -3.5548     51.9758\n  1500   8000    -0.6579    30.3731    42.1725    39.908294     51.8362     -3.7571     51.9758\n  1500   8200    -0.6579    30.3731    42.1725    40.128922     51.8214     -3.9567     51.9758\n  1500   8400    -0.6579    30.3731    42.1725    40.346656     51.8060     -4.1536     51.9758\n  2000      0    -0.6579    30.3272    42.2207    27.492735     51.4535      7.4130     51.9881\n  2000    200    -0.6579    30.3272    42.2207    27.935039     51.5092      7.0155     51.9881\n  2000    400    -0.6579    30.3272    42.2207    28.366707     51.5606      6.6273     51.9881\n  2000    600    -0.6579    30.3272    42.2207    28.788259     51.6080      6.2477     51.9881\n  2000    800    -0.6579    30.3272    42.2207    29.200178     51.6515      5.8765     51.9881\n  2000   1000    -0.6579    30.3272    42.2207    29.602906     51.6915      5.5133     51.9881\n  2000   1200    -0.6579    30.3272    42.2207    29.996854     51.7282      5.1577     51.9881\n  2000   1400    -0.6579    30.3272    42.2207    30.382402     51.7618      4.8095     51.9881\n  2000   1600    -0.6579    30.3272    42.2207    30.759903     51.7923      4.4684     51.9881\n  2000   1800    -0.6579    30.3272    42.2207    31.129689     51.8201      4.1340     51.9881\n  2000   2000    -0.6579    30.3272    42.2207    31.492065     51.8452      3.8062     51.9881\n  2000   2200    -0.6579    30.3272    42.2207    31.847321     51.8678      3.4846     51.9881\n  2000   2400    -0.6579    30.3272    42.2207    32.195725     51.8880      3.1692     51.9881\n  2000   2600    -0.6579    30.3272    42.2207    32.537532     51.9060      2.8596     51.9881\n  2000   2800    -0.6579    30.3272    42.2207    32.872978     51.9218      2.5556     51.9881\n  2000   3000    -0.6579    30.3272    42.2207    33.202290     51.9356      2.2571     51.9881\n  2000   3200    -0.6579    30.3272    42.2207    33.525677     51.9475      1.9640     51.9881\n  2000   3400    -0.6579    30.3272    42.2207    33.843341     51.9576      1.6759     51.9881\n  2000   3600    -0.6579    30.3272    42.2207    34.155469     51.9660      1.3928     51.9881\n  2000   3800    -0.6579    30.3272    42.2207    34.462241     51.9727      1.1146     51.9881\n  2000   4000    -0.6579    30.3272    42.2207    34.763827     51.9778      0.8410     51.9881\n  2000   4200    -0.6579    30.3272    42.2207    35.060387     51.9815      0.5719     51.9881\n  2000   4400    -0.6579    30.3272    42.2207    35.352075     51.9837      0.3073     51.9881\n  2000   4600    -0.6579    30.3272    42.2207    35.639037     51.9846      0.0469     51.9881\n  2000   4800    -0.6579    30.3272    42.2207    35.921411     51.9842     -0.2093     51.9881\n  2000   5000    -0.6579    30.3272    42.2207    36.199329     51.9826     -0.4615     51.9881\n  2000   5200    -0.6579    30.3272    42.2207    36.472918     51.9798     -0.7097     51.9881\n  2000   5400    -0.6579    30.3272    42.2207    36.742298     51.9758     -0.9541     51.9881\n  2000   5600    -0.6579    30.3272    42.2207    37.007585     51.9709     -1.1947     51.9881\n  2000   5800    -0.6579    30.3272    42.2207    37.268888     51.9649     -1.4317     51.9881\n  2000   6000    -0.6579    30.3272    42.2207    37.526313     51.9579     -1.6652     51.9881\n  2000   6200    -0.6579    30.3272    42.2207    37.779961     51.9500     -1.8952     51.9881\n  2000   6400    -0.6579    30.3272    42.2207    38.029930     51.9412     -2.1218     51.9881\n  2000   6600    -0.6579    30.3272    42.2207    38.276312     51.9316     -2.3452     51.9881\n  2000   6800    -0.6579    30.3272    42.2207    38.519196     51.9212     -2.5653     51.9881\n  2000   7000    -0.6579    30.3272    42.2207    38.758669     51.9100     -2.7823     51.9881\n  2000   7200    -0.6579    30.3272    42.2207    38.994812     51.8981     -2.9962     51.9881\n  2000   7400    -0.6579    30.3272    42.2207    39.227706     51.8855     -3.2071     51.9881\n  2000   7600    -0.6579    30.3272    42.2207    39.457427     51.8722     -3.4152     51.9881\n  2000   7800    -0.6579    30.3272    42.2207    39.684049     51.8583     -3.6203     51.9881\n  2000   8000    -0.6579    30.3272    42.2207    39.907642     51.8438     -3.8227     51.9881\n  2000   8200    -0.6579    30.3272    42.2207    40.128274     51.8287     -4.0223     51.9881\n  2000   8400    -0.6579    30.3272    42.2207    40.346013     51.8130     -4.2192     51.9881\n  2500      0    -0.6579    30.2813    42.2690    27.491711     51.4750      7.3509     52.0005\n  2500    200    -0.6579    30.2813    42.2690    27.934033     51.5302      6.9533     52.0005\n  2500    400    -0.6579    30.2813    42.2690    28.365717     51.5811      6.5648     52.0005\n  2500    600    -0.6579    30.2813    42.2690    28.787285     51.6280      6.1851     52.0005\n  2500    800    -0.6579    30.2813    42.2690    29.199219     51.6711      5.8138     52.0005\n  2500   1000    -0.6579    30.2813    42.2690    29.601961     51.7107      5.4504     52.0005\n  2500   1200    -0.6579    30.2813    42.2690    29.995922     51.7469      5.0947     52.0005\n  2500   1400    -0.6579    30.2813    42.2690    30.381483     51.7800      4.7464     52.0005\n  2500   1600    -0.6579    30.2813    42.2690    30.758997     51.8102      4.4051     52.0005\n  2500   1800    -0.6579    30.2813    42.2690    31.128795     51.8375      4.0706     52.0005\n  2500   2000    -0.6579    30.2813    42.2690    31.491183     51.8622      3.7426     52.0005\n  2500   2200    -0.6579    30.2813    42.2690    31.846449     51.8844      3.4210     52.0005\n  2500   2400    -0.6579    30.2813    42.2690    32.194864     51.9043      3.1054     52.0005\n  2500   2600    -0.6579    30.2813    42.2690    32.536681     51.9219      2.7957     52.0005\n  2500   2800    -0.6579    30.2813    42.2690    32.872138     51.9373      2.4916     52.0005\n  2500   3000    -0.6579    30.2813    42.2690    33.201459     51.9508      2.1931     52.0005\n  2500   3200    -0.6579    30.2813    42.2690    33.524856     51.9623      1.8998     52.0005\n  2500   3400    -0.6579    30.2813    42.2690    33.842528     51.9721      1.6117     52.0005\n  2500   3600    -0.6579    30.2813    42.2690    34.154665     51.9801      1.3285     52.0005\n  2500   3800    -0.6579    30.2813    42.2690    34.461445     51.9864      1.0502     52.0005\n  2500   4000    -0.6579    30.2813    42.2690    34.763039     51.9912      0.7765     52.0005\n  2500   4200    -0.6579    30.2813    42.2690    35.059607     51.9945      0.5074     52.0005\n  2500   4400    -0.6579    30.2813    42.2690    35.351303     51.9964      0.2426     52.0005\n  2500   4600    -0.6579    30.2813    42.2690    35.638272     51.9970     -0.0178     52.0005\n  2500   4800    -0.6579    30.2813    42.2690    35.920653     51.9963     -0.2741     52.0005\n  2500   5000    -0.6579    30.2813    42.2690    36.198579     51.9943     -0.5263     52.0005\n  2500   5200    -0.6579    30.2813    42.2690    36.472175     51.9912     -0.7746     52.0005\n  2500   5400    -0.6579    30.2813    42.2690    36.741561     51.9870     -1.0190     52.0005\n  2500   5600    -0.6579    30.2813    42.2690    37.006854     51.9817     -1.2597     52.0005\n  2500   5800    -0.6579    30.2813    42.2690    37.268164     51.9754     -1.4968     52.0005\n  2500   6000    -0.6579    30.2813    42.2690    37.525595     51.9682     -1.7303     52.0005\n  2500   6200    -0.6579    30.2813    42.2690    37.779249     51.9600     -1.9604     52.0005\n  2500   6400    -0.6579    30.2813    42.2690    38.029223     51.9510     -2.1870     52.0005\n  2500   6600    -0.6579    30.2813    42.2690    38.275611     51.9411     -2.4104     52.0005\n  2500   6800    -0.6579    30.2813    42.2690    38.518501     51.9304     -2.6306     52.0005\n  2500   7000    -0.6579    30.2813    42.2690    38.757979     51.9189     -2.8476     52.0005\n  2500   7200    -0.6579    30.2813    42.2690    38.994128     51.9067     -3.0616     52.0005\n  2500   7400    -0.6579    30.2813    42.2690    39.227027     51.8939     -3.2726     52.0005\n  2500   7600    -0.6579    30.2813    42.2690    39.456753     51.8803     -3.4806     52.0005\n  2500   7800    -0.6579    30.2813    42.2690    39.683379     51.8661     -3.6858     52.0005\n  2500   8000    -0.6579    30.2813    42.2690    39.906977     51.8514     -3.8882     52.0005\n  2500   8200    -0.6579    30.2813    42.2690    40.127615     51.8360     -4.0879     52.0005\n  2500   8400    -0.6579    30.2813    42.2690    40.345358     51.8201     -4.2848     52.0005\n  3000      0    -0.6579    30.2353    42.3172    27.490666     51.4964      7.2889     52.0130\n  3000    200    -0.6579    30.2353    42.3172    27.933006     51.5511      6.8911     52.0130\n  3000    400    -0.6579    30.2353    42.3172    28.364707     51.6016      6.5024     52.0130\n  3000    600    -0.6579    30.2353    42.3172    28.786291     51.6480      6.1226     52.0130\n  3000    800    -0.6579    30.2353    42.3172    29.198240     51.6907      5.7511     52.0130\n  3000   1000    -0.6579    30.2353    42.3172    29.600997     51.7298      5.3876     52.0130\n  3000   1200    -0.6579    30.2353    42.3172    29.994972     51.7656      5.0317     52.0130\n  3000   1400    -0.6579    30.2353    42.3172    30.380547     51.7983      4.6832     52.0130\n  3000   1600    -0.6579    30.2353    42.3172    30.758074     51.8280      4.3418     52.0130\n  3000   1800    -0.6579    30.2353    42.3172    31.127883     51.8550      4.0072     52.0130\n  3000   2000    -0.6579    30.2353    42.3172    31.490283     51.8793      3.6791     52.0130\n  3000   2200    -0.6579    30.2353    42.3172    31.845561     51.9011      3.3573     52.0130\n  3000   2400    -0.6579    30.2353    42.3172    32.193987     51.9205      3.0417     52.0130\n  3000   2600    -0.6579    30.2353    42.3172    32.535814     51.9378      2.7318     52.0130\n  3000   2800    -0.6579    30.2353    42.3172    32.871281     51.9529      2.4277     52.0130\n  3000   3000    -0.6579    30.2353    42.3172    33.200612     51.9659      2.1290     52.0130\n  3000   3200    -0.6579    30.2353    42.3172    33.524018     51.9771      1.8357     52.0130\n  3000   3400    -0.6579    30.2353    42.3172    33.841699     51.9865      1.5474     52.0130\n  3000   3600    -0.6579    30.2353    42.3172    34.153845     51.9942      1.2642     52.0130\n  3000   3800    -0.6579    30.2353    42.3172    34.460634     52.0002      0.9858     52.0130\n  3000   4000    -0.6579    30.2353    42.3172    34.762236     52.0046      0.7120     52.0130\n  3000   4200    -0.6579    30.2353    42.3172    35.058813     52.0076      0.4428     52.0130\n  3000   4400    -0.6579    30.2353    42.3172    35.350516     52.0092      0.1780     52.0130\n  3000   4600    -0.6579    30.2353    42.3172    35.637493     52.0094     -0.0825     52.0130\n  3000   4800    -0.6579    30.2353    42.3172    35.919882     52.0084     -0.3388     52.0130\n  3000   5000    -0.6579    30.2353    42.3172    36.197814     52.0061     -0.5911     52.0130\n  3000   5200    -0.6579    30.2353    42.3172    36.471417     52.0027     -0.8395     52.0130\n  3000   5400    -0.6579    30.2353    42.3172    36.740810     51.9982     -1.0840     52.0130\n  3000   5600    -0.6579    30.2353    42.3172    37.006110     51.9926     -1.3247     52.0130\n  3000   5800    -0.6579    30.2353    42.3172    37.267426     51.9860     -1.5619     52.0130\n  3000   6000    -0.6579    30.2353    42.3172    37.524863     51.9785     -1.7954     52.0130\n  3000   6200    -0.6579    30.2353    42.3172    37.778524     51.9700     -2.0255     52.0130\n  3000   6400    -0.6579    30.2353    42.3172    38.028504     51.9607     -2.2523     52.0130\n  3000   6600    -0.6579    30.2353    42.3172    38.274897     51.9505     -2.4757     52.0130\n  3000   6800    -0.6579    30.2353    42.3172    38.517793     51.9395     -2.6959     52.0130\n  3000   7000    -0.6579    30.2353    42.3172    38.757276     51.9278     -2.9130     52.0130\n  3000   7200    -0.6579    30.2353    42.3172    38.993431     51.9153     -3.1270     52.0130\n  3000   7400    -0.6579    30.2353    42.3172    39.226335     51.9022     -3.3380     52.0130\n  3000   7600    -0.6579    30.2353    42.3172    39.456066     51.8884     -3.5461     52.0130\n  3000   7800    -0.6579    30.2353    42.3172    39.682698     51.8740     -3.7513     52.0130\n  3000   8000    -0.6579    30.2353    42.3172    39.906301     51.8589     -3.9537     52.0130\n  3000   8200    -0.6579    30.2353    42.3172    40.126943     51.8433     -4.1534     52.0130\n  3000   8400    -0.6579    30.2353    42.3172    40.344692     51.8271     -4.3504     52.0130\n  3500      0    -0.6579    30.1894    42.3654    27.489602     51.5178      7.2268     52.0255\n  3500    200    -0.6579    30.1894    42.3654    27.931959     51.5721      6.8289     52.0255\n  3500    400    -0.6579    30.1894    42.3654    28.363677     51.6220      6.4401     52.0255\n  3500    600    -0.6579    30.1894    42.3654    28.785278     51.6680      6.0600     52.0255\n  3500    800    -0.6579    30.1894    42.3654    29.197243     51.7103      5.6884     52.0255\n  3500   1000    -0.6579    30.1894    42.3654    29.600015     51.7490      5.3247     52.0255\n  3500   1200    -0.6579    30.1894    42.3654    29.994004     51.7843      4.9687     52.0255\n  3500   1400    -0.6579    30.1894    42.3654    30.379592     51.8166      4.6201     52.0255\n  3500   1600    -0.6579    30.1894    42.3654    30.757132     51.8459      4.2786     52.0255\n  3500   1800    -0.6579    30.1894    42.3654    31.126954     51.8724      3.9438     52.0255\n  3500   2000    -0.6579    30.1894    42.3654    31.489366     51.8963      3.6156     52.0255\n  3500   2200    -0.6579    30.1894    42.3654    31.844656     51.9178      3.2937     52.0255\n  3500   2400    -0.6579    30.1894    42.3654    32.193093     51.9368      2.9779     52.0255\n  3500   2600    -0.6579    30.1894    42.3654    32.534931     51.9537      2.6680     52.0255\n  3500   2800    -0.6579    30.1894    42.3654    32.870408     51.9684      2.3637     52.0255\n  3500   3000    -0.6579    30.1894    42.3654    33.199749     51.9811      2.0650     52.0255\n  3500   3200    -0.6579    30.1894    42.3654    33.523165     51.9919      1.7715     52.0255\n  3500   3400    -0.6579    30.1894    42.3654    33.840856     52.0009      1.4832     52.0255\n  3500   3600    -0.6579    30.1894    42.3654    34.153010     52.0082      1.1999     52.0255\n  3500   3800    -0.6579    30.1894    42.3654    34.459808     52.0139      0.9214     52.0255\n  3500   4000    -0.6579    30.1894    42.3654    34.761419     52.0180      0.6475     52.0255\n  3500   4200    -0.6579    30.1894    42.3654    35.058003     52.0207      0.3783     52.0255\n  3500   4400    -0.6579    30.1894    42.3654    35.349715     52.0219      0.1134     52.0255\n  3500   4600    -0.6579    30.1894    42.3654    35.636699     52.0218     -0.1472     52.0255\n  3500   4800    -0.6579    30.1894    42.3654    35.919095     52.0205     -0.4036     52.0255\n  3500   5000    -0.6579    30.1894    42.3654    36.197035     52.0179     -0.6559     52.0255\n  3500   5200    -0.6579    30.1894    42.3654    36.470645     52.0142     -0.9043     52.0255\n  3500   5400    -0.6579    30.1894    42.3654    36.740046     52.0093     -1.1489     52.0255\n  3500   5600    -0.6579    30.1894    42.3654    37.005352     52.0035     -1.3897     52.0255\n  3500   5800    -0.6579    30.1894    42.3654    37.266674     51.9966     -1.6269     52.0255\n  3500   6000    -0.6579    30.1894    42.3654    37.524118     51.9887     -1.8605     52.0255\n  3500   6200    -0.6579    30.1894    42.3654    37.777785     51.9800     -2.0907     52.0255\n  3500   6400    -0.6579    30.1894    42.3654    38.027771     51.9704     -2.3175     52.0255\n  3500   6600    -0.6579    30.1894    42.3654    38.274170     51.9599     -2.5409     52.0255\n  3500   6800    -0.6579    30.1894    42.3654    38.517072     51.9487     -2.7612     52.0255\n  3500   7000    -0.6579    30.1894    42.3654    38.756561     51.9367     -2.9783     52.0255\n  3500   7200    -0.6579    30.1894    42.3654    38.992721     51.9240     -3.1924     52.0255\n  3500   7400    -0.6579    30.1894    42.3654    39.225631     51.9105     -3.4034     52.0255\n  3500   7600    -0.6579    30.1894    42.3654    39.455367     51.8965     -3.6116     52.0255\n  3500   7800    -0.6579    30.1894    42.3654    39.682004     51.8818     -3.8168     52.0255\n  3500   8000    -0.6579    30.1894    42.3654    39.905612     51.8665     -4.0193     52.0255\n  3500   8200    -0.6579    30.1894    42.3654    40.126259     51.8506     -4.2190     52.0255\n  3500   8400    -0.6579    30.1894    42.3654    40.344013     51.8342     -4.4160     52.0255\n  4000      0    -0.6579    30.1435    42.4136    27.488516     51.5392      7.1648     52.0382\n  4000    200    -0.6579    30.1435    42.4136    27.930893     51.5930      6.7667     52.0382\n  4000    400    -0.6579    30.1435    42.4136    28.362629     51.6425      6.3777     52.0382\n  4000    600    -0.6579    30.1435    42.4136    28.784246     51.6881      5.9975     52.0382\n  4000    800    -0.6579    30.1435    42.4136    29.196227     51.7298      5.6257     52.0382\n  4000   1000    -0.6579    30.1435    42.4136    29.599014     51.7681      5.2619     52.0382\n  4000   1200    -0.6579    30.1435    42.4136    29.993018     51.8030      4.9057     52.0382\n  4000   1400    -0.6579    30.1435    42.4136    30.378620     51.8349      4.5570     52.0382\n  4000   1600    -0.6579    30.1435    42.4136    30.756173     51.8638      4.2153     52.0382\n  4000   1800    -0.6579    30.1435    42.4136    31.126008     51.8899      3.8804     52.0382\n  4000   2000    -0.6579    30.1435    42.4136    31.488432     51.9134      3.5521     52.0382\n  4000   2200    -0.6579    30.1435    42.4136    31.843734     51.9344      3.2301     52.0382\n  4000   2400    -0.6579    30.1435    42.4136    32.192182     51.9531      2.9142     52.0382\n  4000   2600    -0.6579    30.1435    42.4136    32.534031     51.9696      2.6042     52.0382\n  4000   2800    -0.6579    30.1435    42.4136    32.869519     51.9839      2.2998     52.0382\n  4000   3000    -0.6579    30.1435    42.4136    33.198870     51.9963      2.0010     52.0382\n  4000   3200    -0.6579    30.1435    42.4136    33.522296     52.0067      1.7074     52.0382\n  4000   3400    -0.6579    30.1435    42.4136    33.839996     52.0154      1.4190     52.0382\n  4000   3600    -0.6579    30.1435    42.4136    34.152160     52.0223      1.1356     52.0382\n  4000   3800    -0.6579    30.1435    42.4136    34.458967     52.0277      0.8570     52.0382\n  4000   4000    -0.6579    30.1435    42.4136    34.760586     52.0315      0.5831     52.0382\n  4000   4200    -0.6579    30.1435    42.4136    35.057179     52.0338      0.3137     52.0382\n  4000   4400    -0.6579    30.1435    42.4136    35.348899     52.0347      0.0488     52.0382\n  4000   4600    -0.6579    30.1435    42.4136    35.635891     52.0343     -0.2119     52.0382\n  4000   4800    -0.6579    30.1435    42.4136    35.918295     52.0326     -0.4683     52.0382\n  4000   5000    -0.6579    30.1435    42.4136    36.196242     52.0297     -0.7207     52.0382\n  4000   5200    -0.6579    30.1435    42.4136    36.469860     52.0257     -0.9692     52.0382\n  4000   5400    -0.6579    30.1435    42.4136    36.739267     52.0205     -1.2138     52.0382\n  4000   5600    -0.6579    30.1435    42.4136    37.004580     52.0143     -1.4547     52.0382\n  4000   5800    -0.6579    30.1435    42.4136    37.265909     52.0072     -1.6919     52.0382\n  4000   6000    -0.6579    30.1435    42.4136    37.523360     51.9990     -1.9256     52.0382\n  4000   6200    -0.6579    30.1435    42.4136    37.777033     51.9900     -2.1558     52.0382\n  4000   6400    -0.6579    30.1435    42.4136    38.027025     51.9801     -2.3827     52.0382\n  4000   6600    -0.6579    30.1435    42.4136    38.273431     51.9693     -2.6062     52.0382\n  4000   6800    -0.6579    30.1435    42.4136    38.516338     51.9578     -2.8265     52.0382\n  4000   7000    -0.6579    30.1435    42.4136    38.755833     51.9456     -3.0437     52.0382\n  4000   7200    -0.6579    30.1435    42.4136    38.991999     51.9326     -3.2577     52.0382\n  4000   7400    -0.6579    30.1435    42.4136    39.224914     51.9189     -3.4688     52.0382\n  4000   7600    -0.6579    30.1435    42.4136    39.454656     51.9046     -3.6770     52.0382\n  4000   7800    -0.6579    30.1435    42.4136    39.681298     51.8896     -3.8823     52.0382\n  4000   8000    -0.6579    30.1435    42.4136    39.904911     51.8740     -4.0848     52.0382\n  4000   8200    -0.6579    30.1435    42.4136    40.125563     51.8579     -4.2845     52.0382\n  4000   8400    -0.6579    30.1435    42.4136    40.343322     51.8413     -4.4816     52.0382\n  4500      0    -0.6579    30.0976    42.4618    27.487411     51.5607      7.1028     52.0509\n  4500    200    -0.6579    30.0976    42.4618    27.929806     51.6140      6.7045     52.0509\n  4500    400    -0.6579    30.0976    42.4618    28.361560     51.6630      6.3154     52.0509\n  4500    600    -0.6579    30.0976    42.4618    28.783195     51.7081      5.9350     52.0509\n  4500    800    -0.6579    30.0976    42.4618    29.195192     51.7494      5.5630     52.0509\n  4500   1000    -0.6579    30.0976    42.4618    29.597995     51.7872      5.1991     52.0509\n  4500   1200    -0.6579    30.0976    42.4618    29.992014     51.8218      4.8428     52.0509\n  4500   1400    -0.6579    30.0976    42.4618    30.377630     51.8532      4.4939     52.0509\n  4500   1600    -0.6579    30.0976    42.4618    30.755197     51.8816      4.1521     52.0509\n  4500   1800    -0.6579    30.0976    42.4618    31.125045     51.9074      3.8171     52.0509\n  4500   2000    -0.6579    30.0976    42.4618    31.487481     51.9305      3.4887     52.0509\n  4500   2200    -0.6579    30.0976    42.4618    31.842795     51.9511      3.1665     52.0509\n  4500   2400    -0.6579    30.0976    42.4618    32.191255     51.9694      2.8505     52.0509\n  4500   2600    -0.6579    30.0976    42.4618    32.533115     51.9855      2.5404     52.0509\n  4500   2800    -0.6579    30.0976    42.4618    32.868614     51.9994      2.2359     52.0509\n  4500   3000    -0.6579    30.0976    42.4618    33.197975     52.0114      1.9370     52.0509\n  4500   3200    -0.6579    30.0976    42.4618    33.521411     52.0215      1.6433     52.0509\n  4500   3400    -0.6579    30.0976    42.4618    33.839121     52.0298      1.3548     52.0509\n  4500   3600    -0.6579    30.0976    42.4618    34.151295     52.0364      1.0713     52.0509\n  4500   3800    -0.6579    30.0976    42.4618    34.458111     52.0414      0.7926     52.0509\n  4500   4000    -0.6579    30.0976    42.4618    34.759739     52.0449      0.5186     52.0509\n  4500   4200    -0.6579    30.0976    42.4618    35.056340     52.0468      0.2492     52.0509\n  4500   4400    -0.6579    30.0976    42.4618    35.348068     52.0474     -0.0158     52.0509\n  4500   4600    -0.6579    30.0976    42.4618    35.635069     52.0467     -0.2765     52.0509\n  4500   4800    -0.6579    30.0976    42.4618    35.917480     52.0447     -0.5331     52.0509\n  4500   5000    -0.6579    30.0976    42.4618    36.195435     52.0415     -0.7855     52.0509\n  4500   5200    -0.6579    30.0976    42.4618    36.469060     52.0371     -1.0341     52.0509\n  4500   5400    -0.6579    30.0976    42.4618    36.738475     52.0317     -1.2787     52.0509\n  4500   5600    -0.6579    30.0976    42.4618    37.003795     52.0252     -1.5197     52.0509\n  4500   5800    -0.6579    30.0976    42.4618    37.265131     52.0177     -1.7570     52.0509\n  4500   6000    -0.6579    30.0976    42.4618    37.522588     52.0093     -1.9907     52.0509\n  4500   6200    -0.6579    30.0976    42.4618    37.776268     52.0000     -2.2210     52.0509\n  4500   6400    -0.6579    30.0976    42.4618    38.026267     51.9898     -2.4478     52.0509\n  4500   6600    -0.6579    30.0976    42.4618    38.272678     51.9788     -2.6714     52.0509\n  4500   6800    -0.6579    30.0976    42.4618    38.515591     51.9670     -2.8918     52.0509\n  4500   7000    -0.6579    30.0976    42.4618    38.755092     51.9544     -3.1090     52.0509\n  4500   7200    -0.6579    30.0976    42.4618    38.991264     51.9412     -3.3231     52.0509\n  4500   7400    -0.6579    30.0976    42.4618    39.224185     51.9272     -3.5342     52.0509\n  4500   7600    -0.6579    30.0976    42.4618    39.453932     51.9126     -3.7424     52.0509\n  4500   7800    -0.6579    30.0976    42.4618    39.680580     51.8974     -3.9478     52.0509\n  4500   8000    -0.6579    30.0976    42.4618    39.904198     51.8816     -4.1503     52.0509\n  4500   8200    -0.6579    30.0976    42.4618    40.124856     51.8652     -4.3501     52.0509\n  4500   8400    -0.6579    30.0976    42.4618    40.342619     51.8483     -4.5472     52.0509\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180623_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.2737791      -19.7407601       42.8055396   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0412166        0.0250700   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -19.9138798       42.8891912   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0633762        0.0375154   m/s m/s m/s\nunwrap_phase_constant:           -0.00015     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180319-20180623_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.274    -19.741     42.806\norbit baseline rate   (TCN) (m/s):  0.000e+00 -4.122e-02  2.507e-02\n\nbaseline vector (TCN)   (m):      0.000    -19.914     42.889\nbaseline rate   (TCN) (m/s):  0.000e+00 -6.338e-02  3.752e-02\n\nSLC-1 center baseline length (m):    47.2868\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -19.3223    42.5390    27.496626     28.8127    -36.7797     46.7217\n     0    200     0.0000   -19.3223    42.5390    27.938864     28.5280    -37.0010     46.7217\n     0    400     0.0000   -19.3223    42.5390    28.370470     28.2484    -37.2148     46.7217\n     0    600     0.0000   -19.3223    42.5390    28.791963     27.9739    -37.4216     46.7217\n     0    800     0.0000   -19.3223    42.5390    29.203825     27.7042    -37.6217     46.7217\n     0   1000     0.0000   -19.3223    42.5390    29.606500     27.4391    -37.8155     46.7217\n     0   1200     0.0000   -19.3223    42.5390    30.000396     27.1785    -38.0033     46.7217\n     0   1400     0.0000   -19.3223    42.5390    30.385895     26.9222    -38.1853     46.7217\n     0   1600     0.0000   -19.3223    42.5390    30.763349     26.6700    -38.3618     46.7217\n     0   1800     0.0000   -19.3223    42.5390    31.133089     26.4219    -38.5331     46.7217\n     0   2000     0.0000   -19.3223    42.5390    31.495422     26.1777    -38.6994     46.7217\n     0   2200     0.0000   -19.3223    42.5390    31.850636     25.9373    -38.8610     46.7217\n     0   2400     0.0000   -19.3223    42.5390    32.199000     25.7005    -39.0180     46.7217\n     0   2600     0.0000   -19.3223    42.5390    32.540768     25.4673    -39.1706     46.7217\n     0   2800     0.0000   -19.3223    42.5390    32.876178     25.2376    -39.3190     46.7217\n     0   3000     0.0000   -19.3223    42.5390    33.205453     25.0112    -39.4634     46.7217\n     0   3200     0.0000   -19.3223    42.5390    33.528805     24.7881    -39.6039     46.7217\n     0   3400     0.0000   -19.3223    42.5390    33.846435     24.5682    -39.7407     46.7217\n     0   3600     0.0000   -19.3223    42.5390    34.158531     24.3513    -39.8739     46.7217\n     0   3800     0.0000   -19.3223    42.5390    34.465271     24.1375    -40.0037     46.7217\n     0   4000     0.0000   -19.3223    42.5390    34.766827     23.9266    -40.1302     46.7217\n     0   4200     0.0000   -19.3223    42.5390    35.063357     23.7186    -40.2535     46.7217\n     0   4400     0.0000   -19.3223    42.5390    35.355017     23.5134    -40.3737     46.7217\n     0   4600     0.0000   -19.3223    42.5390    35.641950     23.3109    -40.4910     46.7217\n     0   4800     0.0000   -19.3223    42.5390    35.924297     23.1111    -40.6054     46.7217\n     0   5000     0.0000   -19.3223    42.5390    36.202189     22.9139    -40.7170     46.7217\n     0   5200     0.0000   -19.3223    42.5390    36.475752     22.7192    -40.8259     46.7217\n     0   5400     0.0000   -19.3223    42.5390    36.745108     22.5270    -40.9323     46.7217\n     0   5600     0.0000   -19.3223    42.5390    37.010370     22.3373    -41.0361     46.7217\n     0   5800     0.0000   -19.3223    42.5390    37.271649     22.1499    -41.1376     46.7217\n     0   6000     0.0000   -19.3223    42.5390    37.529051     21.9649    -41.2366     46.7217\n     0   6200     0.0000   -19.3223    42.5390    37.782677     21.7821    -41.3335     46.7217\n     0   6400     0.0000   -19.3223    42.5390    38.032623     21.6016    -41.4281     46.7217\n     0   6600     0.0000   -19.3223    42.5390    38.278984     21.4233    -41.5206     46.7217\n     0   6800     0.0000   -19.3223    42.5390    38.521847     21.2471    -41.6110     46.7217\n     0   7000     0.0000   -19.3223    42.5390    38.761299     21.0730    -41.6995     46.7217\n     0   7200     0.0000   -19.3223    42.5390    38.997423     20.9010    -41.7860     46.7217\n     0   7400     0.0000   -19.3223    42.5390    39.230297     20.7310    -41.8706     46.7217\n     0   7600     0.0000   -19.3223    42.5390    39.459999     20.5630    -41.9533     46.7217\n     0   7800     0.0000   -19.3223    42.5390    39.686602     20.3969    -42.0343     46.7217\n     0   8000     0.0000   -19.3223    42.5390    39.910176     20.2327    -42.1136     46.7217\n     0   8200     0.0000   -19.3223    42.5390    40.130792     20.0704    -42.1912     46.7217\n     0   8400     0.0000   -19.3223    42.5390    40.348513     19.9099    -42.2672     46.7217\n   500      0     0.0000   -19.4526    42.6161    27.495684     28.8216    -36.9304     46.8459\n   500    200     0.0000   -19.4526    42.6161    27.937938     28.5357    -37.1517     46.8459\n   500    400     0.0000   -19.4526    42.6161    28.369559     28.2550    -37.3656     46.8459\n   500    600     0.0000   -19.4526    42.6161    28.791066     27.9793    -37.5725     46.8459\n   500    800     0.0000   -19.4526    42.6161    29.202942     27.7085    -37.7727     46.8459\n   500   1000     0.0000   -19.4526    42.6161    29.605629     27.4424    -37.9665     46.8459\n   500   1200     0.0000   -19.4526    42.6161    29.999538     27.1807    -38.1542     46.8459\n   500   1400     0.0000   -19.4526    42.6161    30.385049     26.9234    -38.3363     46.8459\n   500   1600     0.0000   -19.4526    42.6161    30.762515     26.6702    -38.5128     46.8459\n   500   1800     0.0000   -19.4526    42.6161    31.132265     26.4211    -38.6841     46.8459\n   500   2000     0.0000   -19.4526    42.6161    31.494609     26.1760    -38.8504     46.8459\n   500   2200     0.0000   -19.4526    42.6161    31.849833     25.9346    -39.0120     46.8459\n   500   2400     0.0000   -19.4526    42.6161    32.198207     25.6969    -39.1689     46.8459\n   500   2600     0.0000   -19.4526    42.6161    32.539984     25.4628    -39.3215     46.8459\n   500   2800     0.0000   -19.4526    42.6161    32.875402     25.2322    -39.4699     46.8459\n   500   3000     0.0000   -19.4526    42.6161    33.204686     25.0049    -39.6143     46.8459\n   500   3200     0.0000   -19.4526    42.6161    33.528047     24.7809    -39.7548     46.8459\n   500   3400     0.0000   -19.4526    42.6161    33.845685     24.5602    -39.8915     46.8459\n   500   3600     0.0000   -19.4526    42.6161    34.157789     24.3425    -40.0247     46.8459\n   500   3800     0.0000   -19.4526    42.6161    34.464537     24.1279    -40.1545     46.8459\n   500   4000     0.0000   -19.4526    42.6161    34.766099     23.9162    -40.2809     46.8459\n   500   4200     0.0000   -19.4526    42.6161    35.062637     23.7074    -40.4041     46.8459\n   500   4400     0.0000   -19.4526    42.6161    35.354304     23.5014    -40.5243     46.8459\n   500   4600     0.0000   -19.4526    42.6161    35.641244     23.2982    -40.6415     46.8459\n   500   4800     0.0000   -19.4526    42.6161    35.923597     23.0976    -40.7558     46.8459\n   500   5000     0.0000   -19.4526    42.6161    36.201496     22.8997    -40.8674     46.8459\n   500   5200     0.0000   -19.4526    42.6161    36.475065     22.7043    -40.9762     46.8459\n   500   5400     0.0000   -19.4526    42.6161    36.744426     22.5114    -41.0825     46.8459\n   500   5600     0.0000   -19.4526    42.6161    37.009694     22.3209    -41.1863     46.8459\n   500   5800     0.0000   -19.4526    42.6161    37.270979     22.1329    -41.2877     46.8459\n   500   6000     0.0000   -19.4526    42.6161    37.528387     21.9472    -41.3867     46.8459\n   500   6200     0.0000   -19.4526    42.6161    37.782018     21.7638    -41.4834     46.8459\n   500   6400     0.0000   -19.4526    42.6161    38.031970     21.5826    -41.5780     46.8459\n   500   6600     0.0000   -19.4526    42.6161    38.278335     21.4036    -41.6704     46.8459\n   500   6800     0.0000   -19.4526    42.6161    38.521204     21.2268    -41.7607     46.8459\n   500   7000     0.0000   -19.4526    42.6161    38.760661     21.0521    -41.8491     46.8459\n   500   7200     0.0000   -19.4526    42.6161    38.996789     20.8794    -41.9355     46.8459\n   500   7400     0.0000   -19.4526    42.6161    39.229668     20.7088    -42.0200     46.8459\n   500   7600     0.0000   -19.4526    42.6161    39.459375     20.5402    -42.1027     46.8459\n   500   7800     0.0000   -19.4526    42.6161    39.685982     20.3735    -42.1836     46.8459\n   500   8000     0.0000   -19.4526    42.6161    39.909561     20.2087    -42.2628     46.8459\n   500   8200     0.0000   -19.4526    42.6161    40.130181     20.0458    -42.3403     46.8459\n   500   8400     0.0000   -19.4526    42.6161    40.347906     19.8848    -42.4162     46.8459\n  1000      0     0.0000   -19.5829    42.6932    27.494721     28.8305    -37.0810     46.9702\n  1000    200     0.0000   -19.5829    42.6932    27.936992     28.5434    -37.3025     46.9702\n  1000    400     0.0000   -19.5829    42.6932    28.368628     28.2615    -37.5165     46.9702\n  1000    600     0.0000   -19.5829    42.6932    28.790150     27.9848    -37.7234     46.9702\n  1000    800     0.0000   -19.5829    42.6932    29.202040     27.7129    -37.9236     46.9702\n  1000   1000     0.0000   -19.5829    42.6932    29.604740     27.4456    -38.1174     46.9702\n  1000   1200     0.0000   -19.5829    42.6932    29.998662     27.1829    -38.3052     46.9702\n  1000   1400     0.0000   -19.5829    42.6932    30.384184     26.9246    -38.4872     46.9702\n  1000   1600     0.0000   -19.5829    42.6932    30.761662     26.6704    -38.6638     46.9702\n  1000   1800     0.0000   -19.5829    42.6932    31.131424     26.4203    -38.8351     46.9702\n  1000   2000     0.0000   -19.5829    42.6932    31.493778     26.1742    -39.0014     46.9702\n  1000   2200     0.0000   -19.5829    42.6932    31.849012     25.9319    -39.1629     46.9702\n  1000   2400     0.0000   -19.5829    42.6932    32.197396     25.6933    -39.3199     46.9702\n  1000   2600     0.0000   -19.5829    42.6932    32.539183     25.4583    -39.4725     46.9702\n  1000   2800     0.0000   -19.5829    42.6932    32.874611     25.2268    -39.6208     46.9702\n  1000   3000     0.0000   -19.5829    42.6932    33.203904     24.9986    -39.7652     46.9702\n  1000   3200     0.0000   -19.5829    42.6932    33.527273     24.7738    -39.9056     46.9702\n  1000   3400     0.0000   -19.5829    42.6932    33.844919     24.5522    -40.0423     46.9702\n  1000   3600     0.0000   -19.5829    42.6932    34.157031     24.3337    -40.1755     46.9702\n  1000   3800     0.0000   -19.5829    42.6932    34.463787     24.1183    -40.3052     46.9702\n  1000   4000     0.0000   -19.5829    42.6932    34.765357     23.9058    -40.4316     46.9702\n  1000   4200     0.0000   -19.5829    42.6932    35.061902     23.6962    -40.5548     46.9702\n  1000   4400     0.0000   -19.5829    42.6932    35.353576     23.4895    -40.6749     46.9702\n  1000   4600     0.0000   -19.5829    42.6932    35.640523     23.2855    -40.7920     46.9702\n  1000   4800     0.0000   -19.5829    42.6932    35.922883     23.0841    -40.9063     46.9702\n  1000   5000     0.0000   -19.5829    42.6932    36.200788     22.8855    -41.0177     46.9702\n  1000   5200     0.0000   -19.5829    42.6932    36.474364     22.6893    -41.1266     46.9702\n  1000   5400     0.0000   -19.5829    42.6932    36.743731     22.4957    -41.2328     46.9702\n  1000   5600     0.0000   -19.5829    42.6932    37.009005     22.3046    -41.3365     46.9702\n  1000   5800     0.0000   -19.5829    42.6932    37.270296     22.1159    -41.4378     46.9702\n  1000   6000     0.0000   -19.5829    42.6932    37.527709     21.9295    -41.5367     46.9702\n  1000   6200     0.0000   -19.5829    42.6932    37.781346     21.7454    -41.6334     46.9702\n  1000   6400     0.0000   -19.5829    42.6932    38.031303     21.5635    -41.7278     46.9702\n  1000   6600     0.0000   -19.5829    42.6932    38.277674     21.3839    -41.8202     46.9702\n  1000   6800     0.0000   -19.5829    42.6932    38.520547     21.2065    -41.9105     46.9702\n  1000   7000     0.0000   -19.5829    42.6932    38.760010     21.0311    -41.9987     46.9702\n  1000   7200     0.0000   -19.5829    42.6932    38.996143     20.8578    -42.0850     46.9702\n  1000   7400     0.0000   -19.5829    42.6932    39.229027     20.6866    -42.1695     46.9702\n  1000   7600     0.0000   -19.5829    42.6932    39.458738     20.5174    -42.2521     46.9702\n  1000   7800     0.0000   -19.5829    42.6932    39.685350     20.3501    -42.3329     46.9702\n  1000   8000     0.0000   -19.5829    42.6932    39.908934     20.1848    -42.4120     46.9702\n  1000   8200     0.0000   -19.5829    42.6932    40.129557     20.0213    -42.4894     46.9702\n  1000   8400     0.0000   -19.5829    42.6932    40.347287     19.8597    -42.5652     46.9702\n  1500      0     0.0000   -19.7131    42.7704    27.493738     28.8394    -37.2317     47.0947\n  1500    200     0.0000   -19.7131    42.7704    27.936026     28.5511    -37.4532     47.0947\n  1500    400     0.0000   -19.7131    42.7704    28.367677     28.2681    -37.6673     47.0947\n  1500    600     0.0000   -19.7131    42.7704    28.789215     27.9902    -37.8742     47.0947\n  1500    800     0.0000   -19.7131    42.7704    29.201119     27.7172    -38.0745     47.0947\n  1500   1000     0.0000   -19.7131    42.7704    29.603833     27.4489    -38.2683     47.0947\n  1500   1200     0.0000   -19.7131    42.7704    29.997767     27.1852    -38.4561     47.0947\n  1500   1400     0.0000   -19.7131    42.7704    30.383302     26.9258    -38.6382     47.0947\n  1500   1600     0.0000   -19.7131    42.7704    30.760792     26.6706    -38.8148     47.0947\n  1500   1800     0.0000   -19.7131    42.7704    31.130565     26.4196    -38.9861     47.0947\n  1500   2000     0.0000   -19.7131    42.7704    31.492930     26.1725    -39.1524     47.0947\n  1500   2200     0.0000   -19.7131    42.7704    31.848175     25.9292    -39.3139     47.0947\n  1500   2400     0.0000   -19.7131    42.7704    32.196569     25.6897    -39.4709     47.0947\n  1500   2600     0.0000   -19.7131    42.7704    32.538366     25.4538    -39.6234     47.0947\n  1500   2800     0.0000   -19.7131    42.7704    32.873803     25.2214    -39.7717     47.0947\n  1500   3000     0.0000   -19.7131    42.7704    33.203105     24.9924    -39.9160     47.0947\n  1500   3200     0.0000   -19.7131    42.7704    33.526483     24.7667    -40.0565     47.0947\n  1500   3400     0.0000   -19.7131    42.7704    33.844138     24.5442    -40.1932     47.0947\n  1500   3600     0.0000   -19.7131    42.7704    34.156258     24.3249    -40.3263     47.0947\n  1500   3800     0.0000   -19.7131    42.7704    34.463022     24.1087    -40.4559     47.0947\n  1500   4000     0.0000   -19.7131    42.7704    34.764599     23.8954    -40.5823     47.0947\n  1500   4200     0.0000   -19.7131    42.7704    35.061152     23.6850    -40.7054     47.0947\n  1500   4400     0.0000   -19.7131    42.7704    35.352833     23.4775    -40.8254     47.0947\n  1500   4600     0.0000   -19.7131    42.7704    35.639787     23.2727    -40.9425     47.0947\n  1500   4800     0.0000   -19.7131    42.7704    35.922154     23.0707    -41.0567     47.0947\n  1500   5000     0.0000   -19.7131    42.7704    36.200066     22.8713    -41.1681     47.0947\n  1500   5200     0.0000   -19.7131    42.7704    36.473648     22.6744    -41.2769     47.0947\n  1500   5400     0.0000   -19.7131    42.7704    36.743022     22.4801    -41.3830     47.0947\n  1500   5600     0.0000   -19.7131    42.7704    37.008302     22.2883    -41.4867     47.0947\n  1500   5800     0.0000   -19.7131    42.7704    37.269599     22.0988    -41.5879     47.0947\n  1500   6000     0.0000   -19.7131    42.7704    37.527018     21.9118    -41.6867     47.0947\n  1500   6200     0.0000   -19.7131    42.7704    37.780660     21.7270    -41.7833     47.0947\n  1500   6400     0.0000   -19.7131    42.7704    38.030623     21.5445    -41.8777     47.0947\n  1500   6600     0.0000   -19.7131    42.7704    38.276999     21.3642    -41.9700     47.0947\n  1500   6800     0.0000   -19.7131    42.7704    38.519878     21.1861    -42.0602     47.0947\n  1500   7000     0.0000   -19.7131    42.7704    38.759345     21.0102    -42.1483     47.0947\n  1500   7200     0.0000   -19.7131    42.7704    38.995484     20.8363    -42.2346     47.0947\n  1500   7400     0.0000   -19.7131    42.7704    39.228373     20.6644    -42.3189     47.0947\n  1500   7600     0.0000   -19.7131    42.7704    39.458089     20.4946    -42.4014     47.0947\n  1500   7800     0.0000   -19.7131    42.7704    39.684705     20.3267    -42.4822     47.0947\n  1500   8000     0.0000   -19.7131    42.7704    39.908294     20.1608    -42.5612     47.0947\n  1500   8200     0.0000   -19.7131    42.7704    40.128922     19.9968    -42.6385     47.0947\n  1500   8400     0.0000   -19.7131    42.7704    40.346656     19.8346    -42.7142     47.0947\n  2000      0     0.0000   -19.8434    42.8475    27.492735     28.8483    -37.3824     47.2193\n  2000    200     0.0000   -19.8434    42.8475    27.935039     28.5588    -37.6040     47.2193\n  2000    400     0.0000   -19.8434    42.8475    28.366707     28.2747    -37.8180     47.2193\n  2000    600     0.0000   -19.8434    42.8475    28.788259     27.9957    -38.0251     47.2193\n  2000    800     0.0000   -19.8434    42.8475    29.200178     27.7216    -38.2253     47.2193\n  2000   1000     0.0000   -19.8434    42.8475    29.602906     27.4522    -38.4192     47.2193\n  2000   1200     0.0000   -19.8434    42.8475    29.996854     27.1874    -38.6071     47.2193\n  2000   1400     0.0000   -19.8434    42.8475    30.382402     26.9270    -38.7892     47.2193\n  2000   1600     0.0000   -19.8434    42.8475    30.759903     26.6709    -38.9657     47.2193\n  2000   1800     0.0000   -19.8434    42.8475    31.129689     26.4188    -39.1371     47.2193\n  2000   2000     0.0000   -19.8434    42.8475    31.492065     26.1708    -39.3034     47.2193\n  2000   2200     0.0000   -19.8434    42.8475    31.847321     25.9266    -39.4649     47.2193\n  2000   2400     0.0000   -19.8434    42.8475    32.195725     25.6861    -39.6218     47.2193\n  2000   2600     0.0000   -19.8434    42.8475    32.537532     25.4493    -39.7743     47.2193\n  2000   2800     0.0000   -19.8434    42.8475    32.872978     25.2160    -39.9226     47.2193\n  2000   3000     0.0000   -19.8434    42.8475    33.202290     24.9861    -40.0669     47.2193\n  2000   3200     0.0000   -19.8434    42.8475    33.525677     24.7596    -40.2073     47.2193\n  2000   3400     0.0000   -19.8434    42.8475    33.843341     24.5363    -40.3440     47.2193\n  2000   3600     0.0000   -19.8434    42.8475    34.155469     24.3162    -40.4770     47.2193\n  2000   3800     0.0000   -19.8434    42.8475    34.462241     24.0991    -40.6066     47.2193\n  2000   4000     0.0000   -19.8434    42.8475    34.763827     23.8850    -40.7329     47.2193\n  2000   4200     0.0000   -19.8434    42.8475    35.060387     23.6739    -40.8560     47.2193\n  2000   4400     0.0000   -19.8434    42.8475    35.352075     23.4656    -40.9760     47.2193\n  2000   4600     0.0000   -19.8434    42.8475    35.639037     23.2600    -41.0930     47.2193\n  2000   4800     0.0000   -19.8434    42.8475    35.921411     23.0572    -41.2071     47.2193\n  2000   5000     0.0000   -19.8434    42.8475    36.199329     22.8571    -41.3185     47.2193\n  2000   5200     0.0000   -19.8434    42.8475    36.472918     22.6595    -41.4272     47.2193\n  2000   5400     0.0000   -19.8434    42.8475    36.742298     22.4645    -41.5333     47.2193\n  2000   5600     0.0000   -19.8434    42.8475    37.007585     22.2720    -41.6368     47.2193\n  2000   5800     0.0000   -19.8434    42.8475    37.268888     22.0818    -41.7380     47.2193\n  2000   6000     0.0000   -19.8434    42.8475    37.526313     21.8941    -41.8368     47.2193\n  2000   6200     0.0000   -19.8434    42.8475    37.779961     21.7087    -41.9333     47.2193\n  2000   6400     0.0000   -19.8434    42.8475    38.029930     21.5255    -42.0276     47.2193\n  2000   6600     0.0000   -19.8434    42.8475    38.276312     21.3446    -42.1198     47.2193\n  2000   6800     0.0000   -19.8434    42.8475    38.519196     21.1658    -42.2099     47.2193\n  2000   7000     0.0000   -19.8434    42.8475    38.758669     20.9892    -42.2980     47.2193\n  2000   7200     0.0000   -19.8434    42.8475    38.994812     20.8147    -42.3841     47.2193\n  2000   7400     0.0000   -19.8434    42.8475    39.227706     20.6423    -42.4684     47.2193\n  2000   7600     0.0000   -19.8434    42.8475    39.457427     20.4718    -42.5508     47.2193\n  2000   7800     0.0000   -19.8434    42.8475    39.684049     20.3034    -42.6314     47.2193\n  2000   8000     0.0000   -19.8434    42.8475    39.907642     20.1369    -42.7103     47.2193\n  2000   8200     0.0000   -19.8434    42.8475    40.128274     19.9722    -42.7876     47.2193\n  2000   8400     0.0000   -19.8434    42.8475    40.346013     19.8095    -42.8631     47.2193\n  2500      0     0.0000   -19.9737    42.9246    27.491711     28.8572    -37.5330     47.3441\n  2500    200     0.0000   -19.9737    42.9246    27.934033     28.5666    -37.7547     47.3441\n  2500    400     0.0000   -19.9737    42.9246    28.365717     28.2813    -37.9688     47.3441\n  2500    600     0.0000   -19.9737    42.9246    28.787285     28.0012    -38.1759     47.3441\n  2500    800     0.0000   -19.9737    42.9246    29.199219     27.7260    -38.3762     47.3441\n  2500   1000     0.0000   -19.9737    42.9246    29.601961     27.4556    -38.5702     47.3441\n  2500   1200     0.0000   -19.9737    42.9246    29.995922     27.1897    -38.7580     47.3441\n  2500   1400     0.0000   -19.9737    42.9246    30.381483     26.9283    -38.9401     47.3441\n  2500   1600     0.0000   -19.9737    42.9246    30.758997     26.6711    -39.1167     47.3441\n  2500   1800     0.0000   -19.9737    42.9246    31.128795     26.4181    -39.2880     47.3441\n  2500   2000     0.0000   -19.9737    42.9246    31.491183     26.1691    -39.4543     47.3441\n  2500   2200     0.0000   -19.9737    42.9246    31.846449     25.9240    -39.6158     47.3441\n  2500   2400     0.0000   -19.9737    42.9246    32.194864     25.6826    -39.7727     47.3441\n  2500   2600     0.0000   -19.9737    42.9246    32.536681     25.4448    -39.9253     47.3441\n  2500   2800     0.0000   -19.9737    42.9246    32.872138     25.2107    -40.0735     47.3441\n  2500   3000     0.0000   -19.9737    42.9246    33.201459     24.9799    -40.2178     47.3441\n  2500   3200     0.0000   -19.9737    42.9246    33.524856     24.7525    -40.3581     47.3441\n  2500   3400     0.0000   -19.9737    42.9246    33.842528     24.5284    -40.4948     47.3441\n  2500   3600     0.0000   -19.9737    42.9246    34.154665     24.3074    -40.6278     47.3441\n  2500   3800     0.0000   -19.9737    42.9246    34.461445     24.0895    -40.7574     47.3441\n  2500   4000     0.0000   -19.9737    42.9246    34.763039     23.8746    -40.8836     47.3441\n  2500   4200     0.0000   -19.9737    42.9246    35.059607     23.6627    -41.0066     47.3441\n  2500   4400     0.0000   -19.9737    42.9246    35.351303     23.4536    -41.1266     47.3441\n  2500   4600     0.0000   -19.9737    42.9246    35.638272     23.2474    -41.2435     47.3441\n  2500   4800     0.0000   -19.9737    42.9246    35.920653     23.0438    -41.3576     47.3441\n  2500   5000     0.0000   -19.9737    42.9246    36.198579     22.8429    -41.4689     47.3441\n  2500   5200     0.0000   -19.9737    42.9246    36.472175     22.6446    -41.5775     47.3441\n  2500   5400     0.0000   -19.9737    42.9246    36.741561     22.4489    -41.6835     47.3441\n  2500   5600     0.0000   -19.9737    42.9246    37.006854     22.2557    -41.7870     47.3441\n  2500   5800     0.0000   -19.9737    42.9246    37.268164     22.0649    -41.8881     47.3441\n  2500   6000     0.0000   -19.9737    42.9246    37.525595     21.8764    -41.9868     47.3441\n  2500   6200     0.0000   -19.9737    42.9246    37.779249     21.6903    -42.0832     47.3441\n  2500   6400     0.0000   -19.9737    42.9246    38.029223     21.5065    -42.1774     47.3441\n  2500   6600     0.0000   -19.9737    42.9246    38.275611     21.3249    -42.2695     47.3441\n  2500   6800     0.0000   -19.9737    42.9246    38.518501     21.1456    -42.3596     47.3441\n  2500   7000     0.0000   -19.9737    42.9246    38.757979     20.9683    -42.4476     47.3441\n  2500   7200     0.0000   -19.9737    42.9246    38.994128     20.7932    -42.5336     47.3441\n  2500   7400     0.0000   -19.9737    42.9246    39.227027     20.6201    -42.6178     47.3441\n  2500   7600     0.0000   -19.9737    42.9246    39.456753     20.4491    -42.7001     47.3441\n  2500   7800     0.0000   -19.9737    42.9246    39.683379     20.2800    -42.7807     47.3441\n  2500   8000     0.0000   -19.9737    42.9246    39.906977     20.1129    -42.8595     47.3441\n  2500   8200     0.0000   -19.9737    42.9246    40.127615     19.9477    -42.9366     47.3441\n  2500   8400     0.0000   -19.9737    42.9246    40.345358     19.7844    -43.0121     47.3441\n  3000      0     0.0000   -20.1039    43.0017    27.490666     28.8662    -37.6836     47.4691\n  3000    200     0.0000   -20.1039    43.0017    27.933006     28.5744    -37.9054     47.4691\n  3000    400     0.0000   -20.1039    43.0017    28.364707     28.2880    -38.1196     47.4691\n  3000    600     0.0000   -20.1039    43.0017    28.786291     28.0067    -38.3267     47.4691\n  3000    800     0.0000   -20.1039    43.0017    29.198240     27.7304    -38.5271     47.4691\n  3000   1000     0.0000   -20.1039    43.0017    29.600997     27.4589    -38.7211     47.4691\n  3000   1200     0.0000   -20.1039    43.0017    29.994972     27.1920    -38.9090     47.4691\n  3000   1400     0.0000   -20.1039    43.0017    30.380547     26.9296    -39.0911     47.4691\n  3000   1600     0.0000   -20.1039    43.0017    30.758074     26.6714    -39.2677     47.4691\n  3000   1800     0.0000   -20.1039    43.0017    31.127883     26.4174    -39.4390     47.4691\n  3000   2000     0.0000   -20.1039    43.0017    31.490283     26.1674    -39.6053     47.4691\n  3000   2200     0.0000   -20.1039    43.0017    31.845561     25.9213    -39.7668     47.4691\n  3000   2400     0.0000   -20.1039    43.0017    32.193987     25.6790    -39.9237     47.4691\n  3000   2600     0.0000   -20.1039    43.0017    32.535814     25.4404    -40.0762     47.4691\n  3000   2800     0.0000   -20.1039    43.0017    32.871281     25.2053    -40.2244     47.4691\n  3000   3000     0.0000   -20.1039    43.0017    33.200612     24.9737    -40.3687     47.4691\n  3000   3200     0.0000   -20.1039    43.0017    33.524018     24.7454    -40.5090     47.4691\n  3000   3400     0.0000   -20.1039    43.0017    33.841699     24.5205    -40.6456     47.4691\n  3000   3600     0.0000   -20.1039    43.0017    34.153845     24.2987    -40.7785     47.4691\n  3000   3800     0.0000   -20.1039    43.0017    34.460634     24.0800    -40.9081     47.4691\n  3000   4000     0.0000   -20.1039    43.0017    34.762236     23.8643    -41.0342     47.4691\n  3000   4200     0.0000   -20.1039    43.0017    35.058813     23.6516    -41.1572     47.4691\n  3000   4400     0.0000   -20.1039    43.0017    35.350516     23.4417    -41.2771     47.4691\n  3000   4600     0.0000   -20.1039    43.0017    35.637493     23.2347    -41.3940     47.4691\n  3000   4800     0.0000   -20.1039    43.0017    35.919882     23.0304    -41.5080     47.4691\n  3000   5000     0.0000   -20.1039    43.0017    36.197814     22.8288    -41.6192     47.4691\n  3000   5200     0.0000   -20.1039    43.0017    36.471417     22.6298    -41.7278     47.4691\n  3000   5400     0.0000   -20.1039    43.0017    36.740810     22.4333    -41.8337     47.4691\n  3000   5600     0.0000   -20.1039    43.0017    37.006110     22.2394    -41.9371     47.4691\n  3000   5800     0.0000   -20.1039    43.0017    37.267426     22.0479    -42.0381     47.4691\n  3000   6000     0.0000   -20.1039    43.0017    37.524863     21.8588    -42.1368     47.4691\n  3000   6200     0.0000   -20.1039    43.0017    37.778524     21.6720    -42.2331     47.4691\n  3000   6400     0.0000   -20.1039    43.0017    38.028504     21.4875    -42.3273     47.4691\n  3000   6600     0.0000   -20.1039    43.0017    38.274897     21.3053    -42.4193     47.4691\n  3000   6800     0.0000   -20.1039    43.0017    38.517793     21.1253    -42.5092     47.4691\n  3000   7000     0.0000   -20.1039    43.0017    38.757276     20.9474    -42.5972     47.4691\n  3000   7200     0.0000   -20.1039    43.0017    38.993431     20.7717    -42.6831     47.4691\n  3000   7400     0.0000   -20.1039    43.0017    39.226335     20.5980    -42.7672     47.4691\n  3000   7600     0.0000   -20.1039    43.0017    39.456066     20.4264    -42.8495     47.4691\n  3000   7800     0.0000   -20.1039    43.0017    39.682698     20.2567    -42.9299     47.4691\n  3000   8000     0.0000   -20.1039    43.0017    39.906301     20.0890    -43.0087     47.4691\n  3000   8200     0.0000   -20.1039    43.0017    40.126943     19.9233    -43.0857     47.4691\n  3000   8400     0.0000   -20.1039    43.0017    40.344692     19.7594    -43.1611     47.4691\n  3500      0     0.0000   -20.2342    43.0788    27.489602     28.8751    -37.8343     47.5942\n  3500    200     0.0000   -20.2342    43.0788    27.931959     28.5822    -38.0561     47.5942\n  3500    400     0.0000   -20.2342    43.0788    28.363677     28.2946    -38.2704     47.5942\n  3500    600     0.0000   -20.2342    43.0788    28.785278     28.0123    -38.4775     47.5942\n  3500    800     0.0000   -20.2342    43.0788    29.197243     27.7349    -38.6779     47.5942\n  3500   1000     0.0000   -20.2342    43.0788    29.600015     27.4623    -38.8719     47.5942\n  3500   1200     0.0000   -20.2342    43.0788    29.994004     27.1944    -39.0599     47.5942\n  3500   1400     0.0000   -20.2342    43.0788    30.379592     26.9309    -39.2420     47.5942\n  3500   1600     0.0000   -20.2342    43.0788    30.757132     26.6717    -39.4186     47.5942\n  3500   1800     0.0000   -20.2342    43.0788    31.126954     26.4167    -39.5899     47.5942\n  3500   2000     0.0000   -20.2342    43.0788    31.489366     26.1658    -39.7562     47.5942\n  3500   2200     0.0000   -20.2342    43.0788    31.844656     25.9187    -39.9177     47.5942\n  3500   2400     0.0000   -20.2342    43.0788    32.193093     25.6755    -40.0746     47.5942\n  3500   2600     0.0000   -20.2342    43.0788    32.534931     25.4360    -40.2271     47.5942\n  3500   2800     0.0000   -20.2342    43.0788    32.870408     25.2000    -40.3753     47.5942\n  3500   3000     0.0000   -20.2342    43.0788    33.199749     24.9675    -40.5195     47.5942\n  3500   3200     0.0000   -20.2342    43.0788    33.523165     24.7384    -40.6598     47.5942\n  3500   3400     0.0000   -20.2342    43.0788    33.840856     24.5126    -40.7963     47.5942\n  3500   3600     0.0000   -20.2342    43.0788    34.153010     24.2899    -40.9293     47.5942\n  3500   3800     0.0000   -20.2342    43.0788    34.459808     24.0704    -41.0588     47.5942\n  3500   4000     0.0000   -20.2342    43.0788    34.761419     23.8540    -41.1849     47.5942\n  3500   4200     0.0000   -20.2342    43.0788    35.058003     23.6404    -41.3078     47.5942\n  3500   4400     0.0000   -20.2342    43.0788    35.349715     23.4298    -41.4276     47.5942\n  3500   4600     0.0000   -20.2342    43.0788    35.636699     23.2220    -41.5445     47.5942\n  3500   4800     0.0000   -20.2342    43.0788    35.919095     23.0170    -41.6584     47.5942\n  3500   5000     0.0000   -20.2342    43.0788    36.197035     22.8146    -41.7696     47.5942\n  3500   5200     0.0000   -20.2342    43.0788    36.470645     22.6149    -41.8781     47.5942\n  3500   5400     0.0000   -20.2342    43.0788    36.740046     22.4177    -41.9839     47.5942\n  3500   5600     0.0000   -20.2342    43.0788    37.005352     22.2231    -42.0873     47.5942\n  3500   5800     0.0000   -20.2342    43.0788    37.266674     22.0309    -42.1882     47.5942\n  3500   6000     0.0000   -20.2342    43.0788    37.524118     21.8411    -42.2868     47.5942\n  3500   6200     0.0000   -20.2342    43.0788    37.777785     21.6537    -42.3831     47.5942\n  3500   6400     0.0000   -20.2342    43.0788    38.027771     21.4686    -42.4771     47.5942\n  3500   6600     0.0000   -20.2342    43.0788    38.274170     21.2857    -42.5691     47.5942\n  3500   6800     0.0000   -20.2342    43.0788    38.517072     21.1050    -42.6589     47.5942\n  3500   7000     0.0000   -20.2342    43.0788    38.756561     20.9265    -42.7468     47.5942\n  3500   7200     0.0000   -20.2342    43.0788    38.992721     20.7502    -42.8327     47.5942\n  3500   7400     0.0000   -20.2342    43.0788    39.225631     20.5759    -42.9167     47.5942\n  3500   7600     0.0000   -20.2342    43.0788    39.455367     20.4036    -42.9988     47.5942\n  3500   7800     0.0000   -20.2342    43.0788    39.682004     20.2334    -43.0792     47.5942\n  3500   8000     0.0000   -20.2342    43.0788    39.905612     20.0651    -43.1578     47.5942\n  3500   8200     0.0000   -20.2342    43.0788    40.126259     19.8988    -43.2348     47.5942\n  3500   8400     0.0000   -20.2342    43.0788    40.344013     19.7343    -43.3101     47.5942\n  4000      0     0.0000   -20.3645    43.1559    27.488516     28.8841    -37.9849     47.7195\n  4000    200     0.0000   -20.3645    43.1559    27.930893     28.5900    -38.2068     47.7195\n  4000    400     0.0000   -20.3645    43.1559    28.362629     28.3013    -38.4211     47.7195\n  4000    600     0.0000   -20.3645    43.1559    28.784246     28.0178    -38.6283     47.7195\n  4000    800     0.0000   -20.3645    43.1559    29.196227     27.7393    -38.8288     47.7195\n  4000   1000     0.0000   -20.3645    43.1559    29.599014     27.4657    -39.0228     47.7195\n  4000   1200     0.0000   -20.3645    43.1559    29.993018     27.1967    -39.2108     47.7195\n  4000   1400     0.0000   -20.3645    43.1559    30.378620     26.9322    -39.3929     47.7195\n  4000   1600     0.0000   -20.3645    43.1559    30.756173     26.6720    -39.5695     47.7195\n  4000   1800     0.0000   -20.3645    43.1559    31.126008     26.4160    -39.7409     47.7195\n  4000   2000     0.0000   -20.3645    43.1559    31.488432     26.1641    -39.9072     47.7195\n  4000   2200     0.0000   -20.3645    43.1559    31.843734     25.9162    -40.0687     47.7195\n  4000   2400     0.0000   -20.3645    43.1559    32.192182     25.6720    -40.2255     47.7195\n  4000   2600     0.0000   -20.3645    43.1559    32.534031     25.4316    -40.3780     47.7195\n  4000   2800     0.0000   -20.3645    43.1559    32.869519     25.1947    -40.5262     47.7195\n  4000   3000     0.0000   -20.3645    43.1559    33.198870     24.9613    -40.6704     47.7195\n  4000   3200     0.0000   -20.3645    43.1559    33.522296     24.7313    -40.8106     47.7195\n  4000   3400     0.0000   -20.3645    43.1559    33.839996     24.5047    -40.9471     47.7195\n  4000   3600     0.0000   -20.3645    43.1559    34.152160     24.2812    -41.0800     47.7195\n  4000   3800     0.0000   -20.3645    43.1559    34.458967     24.0609    -41.2095     47.7195\n  4000   4000     0.0000   -20.3645    43.1559    34.760586     23.8436    -41.3355     47.7195\n  4000   4200     0.0000   -20.3645    43.1559    35.057179     23.6293    -41.4584     47.7195\n  4000   4400     0.0000   -20.3645    43.1559    35.348899     23.4179    -41.5782     47.7195\n  4000   4600     0.0000   -20.3645    43.1559    35.635891     23.2094    -41.6950     47.7195\n  4000   4800     0.0000   -20.3645    43.1559    35.918295     23.0036    -41.8089     47.7195\n  4000   5000     0.0000   -20.3645    43.1559    36.196242     22.8005    -41.9200     47.7195\n  4000   5200     0.0000   -20.3645    43.1559    36.469860     22.6001    -42.0284     47.7195\n  4000   5400     0.0000   -20.3645    43.1559    36.739267     22.4022    -42.1342     47.7195\n  4000   5600     0.0000   -20.3645    43.1559    37.004580     22.2068    -42.2374     47.7195\n  4000   5800     0.0000   -20.3645    43.1559    37.265909     22.0140    -42.3383     47.7195\n  4000   6000     0.0000   -20.3645    43.1559    37.523360     21.8235    -42.4368     47.7195\n  4000   6200     0.0000   -20.3645    43.1559    37.777033     21.6354    -42.5330     47.7195\n  4000   6400     0.0000   -20.3645    43.1559    38.027025     21.4496    -42.6270     47.7195\n  4000   6600     0.0000   -20.3645    43.1559    38.273431     21.2661    -42.7188     47.7195\n  4000   6800     0.0000   -20.3645    43.1559    38.516338     21.0848    -42.8086     47.7195\n  4000   7000     0.0000   -20.3645    43.1559    38.755833     20.9057    -42.8964     47.7195\n  4000   7200     0.0000   -20.3645    43.1559    38.991999     20.7287    -42.9822     47.7195\n  4000   7400     0.0000   -20.3645    43.1559    39.224914     20.5538    -43.0661     47.7195\n  4000   7600     0.0000   -20.3645    43.1559    39.454656     20.3809    -43.1482     47.7195\n  4000   7800     0.0000   -20.3645    43.1559    39.681298     20.2101    -43.2284     47.7195\n  4000   8000     0.0000   -20.3645    43.1559    39.904911     20.0412    -43.3070     47.7195\n  4000   8200     0.0000   -20.3645    43.1559    40.125563     19.8743    -43.3838     47.7195\n  4000   8400     0.0000   -20.3645    43.1559    40.343322     19.7093    -43.4591     47.7195\n  4500      0     0.0000   -20.4948    43.2330    27.487411     28.8932    -38.1355     47.8449\n  4500    200     0.0000   -20.4948    43.2330    27.929806     28.5978    -38.3574     47.8449\n  4500    400     0.0000   -20.4948    43.2330    28.361560     28.3080    -38.5718     47.8449\n  4500    600     0.0000   -20.4948    43.2330    28.783195     28.0234    -38.7791     47.8449\n  4500    800     0.0000   -20.4948    43.2330    29.195192     27.7438    -38.9796     47.8449\n  4500   1000     0.0000   -20.4948    43.2330    29.597995     27.4691    -39.1737     47.8449\n  4500   1200     0.0000   -20.4948    43.2330    29.992014     27.1990    -39.3617     47.8449\n  4500   1400     0.0000   -20.4948    43.2330    30.377630     26.9335    -39.5438     47.8449\n  4500   1600     0.0000   -20.4948    43.2330    30.755197     26.6723    -39.7205     47.8449\n  4500   1800     0.0000   -20.4948    43.2330    31.125045     26.4154    -39.8918     47.8449\n  4500   2000     0.0000   -20.4948    43.2330    31.487481     26.1625    -40.0581     47.8449\n  4500   2200     0.0000   -20.4948    43.2330    31.842795     25.9136    -40.2196     47.8449\n  4500   2400     0.0000   -20.4948    43.2330    32.191255     25.6685    -40.3764     47.8449\n  4500   2600     0.0000   -20.4948    43.2330    32.533115     25.4272    -40.5289     47.8449\n  4500   2800     0.0000   -20.4948    43.2330    32.868614     25.1894    -40.6771     47.8449\n  4500   3000     0.0000   -20.4948    43.2330    33.197975     24.9552    -40.8212     47.8449\n  4500   3200     0.0000   -20.4948    43.2330    33.521411     24.7243    -40.9614     47.8449\n  4500   3400     0.0000   -20.4948    43.2330    33.839121     24.4968    -41.0979     47.8449\n  4500   3600     0.0000   -20.4948    43.2330    34.151295     24.2725    -41.2308     47.8449\n  4500   3800     0.0000   -20.4948    43.2330    34.458111     24.0514    -41.3601     47.8449\n  4500   4000     0.0000   -20.4948    43.2330    34.759739     23.8333    -41.4862     47.8449\n  4500   4200     0.0000   -20.4948    43.2330    35.056340     23.6182    -41.6090     47.8449\n  4500   4400     0.0000   -20.4948    43.2330    35.348068     23.4061    -41.7287     47.8449\n  4500   4600     0.0000   -20.4948    43.2330    35.635069     23.1968    -41.8454     47.8449\n  4500   4800     0.0000   -20.4948    43.2330    35.917480     22.9902    -41.9593     47.8449\n  4500   5000     0.0000   -20.4948    43.2330    36.195435     22.7864    -42.0703     47.8449\n  4500   5200     0.0000   -20.4948    43.2330    36.469060     22.5852    -42.1786     47.8449\n  4500   5400     0.0000   -20.4948    43.2330    36.738475     22.3866    -42.2844     47.8449\n  4500   5600     0.0000   -20.4948    43.2330    37.003795     22.1906    -42.3876     47.8449\n  4500   5800     0.0000   -20.4948    43.2330    37.265131     21.9970    -42.4884     47.8449\n  4500   6000     0.0000   -20.4948    43.2330    37.522588     21.8059    -42.5868     47.8449\n  4500   6200     0.0000   -20.4948    43.2330    37.776268     21.6171    -42.6829     47.8449\n  4500   6400     0.0000   -20.4948    43.2330    38.026267     21.4307    -42.7768     47.8449\n  4500   6600     0.0000   -20.4948    43.2330    38.272678     21.2465    -42.8686     47.8449\n  4500   6800     0.0000   -20.4948    43.2330    38.515591     21.0646    -42.9583     47.8449\n  4500   7000     0.0000   -20.4948    43.2330    38.755092     20.8848    -43.0460     47.8449\n  4500   7200     0.0000   -20.4948    43.2330    38.991264     20.7072    -43.1317     47.8449\n  4500   7400     0.0000   -20.4948    43.2330    39.224185     20.5317    -43.2155     47.8449\n  4500   7600     0.0000   -20.4948    43.2330    39.453932     20.3582    -43.2975     47.8449\n  4500   7800     0.0000   -20.4948    43.2330    39.680580     20.1868    -43.3777     47.8449\n  4500   8000     0.0000   -20.4948    43.2330    39.904198     20.0174    -43.4561     47.8449\n  4500   8200     0.0000   -20.4948    43.2330    40.124856     19.8499    -43.5329     47.8449\n  4500   8400     0.0000   -20.4948    43.2330    40.342619     19.6843    -43.6080     47.8449\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180412_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.5207146      -64.6524710       33.3215888   m   m   m\ninitial_baseline_rate:          0.0000000       -0.1463346        0.0086984   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -64.8239215       33.4257189   m   m   m\nprecision_baseline_rate:        0.0000000       -0.1516492        0.0110900   m/s m/s m/s\nunwrap_phase_constant:           -0.00008     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180412_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.521    -64.652     33.322\norbit baseline rate   (TCN) (m/s):  0.000e+00 -1.463e-01  8.698e-03\n\nbaseline vector (TCN)   (m):      0.000    -64.824     33.426\nbaseline rate   (TCN) (m/s):  0.000e+00 -1.516e-01  1.109e-02\n\nSLC-1 center baseline length (m):    72.9344\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -63.4084    33.3222    27.497013      0.2822    -71.6302     71.6309\n     0    200     0.0000   -63.4084    33.3222    27.939249     -0.2706    -71.6303     71.6309\n     0    400     0.0000   -63.4084    33.3222    28.370851     -0.8102    -71.6262     71.6309\n     0    600     0.0000   -63.4084    33.3222    28.792342     -1.3371    -71.6183     71.6309\n     0    800     0.0000   -63.4084    33.3222    29.204202     -1.8519    -71.6069     71.6309\n     0   1000     0.0000   -63.4084    33.3222    29.606873     -2.3551    -71.5921     71.6309\n     0   1200     0.0000   -63.4084    33.3222    30.000767     -2.8472    -71.5742     71.6309\n     0   1400     0.0000   -63.4084    33.3222    30.386264     -3.3287    -71.5534     71.6309\n     0   1600     0.0000   -63.4084    33.3222    30.763716     -3.8000    -71.5300     71.6309\n     0   1800     0.0000   -63.4084    33.3222    31.133454     -4.2615    -71.5039     71.6309\n     0   2000     0.0000   -63.4084    33.3222    31.495785     -4.7136    -71.4756     71.6309\n     0   2200     0.0000   -63.4084    33.3222    31.850997     -5.1566    -71.4450     71.6309\n     0   2400     0.0000   -63.4084    33.3222    32.199360     -5.5909    -71.4123     71.6309\n     0   2600     0.0000   -63.4084    33.3222    32.541126     -6.0168    -71.3777     71.6309\n     0   2800     0.0000   -63.4084    33.3222    32.876534     -6.4345    -71.3412     71.6309\n     0   3000     0.0000   -63.4084    33.3222    33.205808     -6.8444    -71.3031     71.6309\n     0   3200     0.0000   -63.4084    33.3222    33.529159     -7.2467    -71.2633     71.6309\n     0   3400     0.0000   -63.4084    33.3222    33.846787     -7.6417    -71.2221     71.6309\n     0   3600     0.0000   -63.4084    33.3222    34.158881     -8.0295    -71.1794     71.6309\n     0   3800     0.0000   -63.4084    33.3222    34.465620     -8.4105    -71.1354     71.6309\n     0   4000     0.0000   -63.4084    33.3222    34.767174     -8.7847    -71.0901     71.6309\n     0   4200     0.0000   -63.4084    33.3222    35.063703     -9.1525    -71.0437     71.6309\n     0   4400     0.0000   -63.4084    33.3222    35.355362     -9.5141    -70.9962     71.6309\n     0   4600     0.0000   -63.4084    33.3222    35.642294     -9.8695    -70.9477     71.6309\n     0   4800     0.0000   -63.4084    33.3222    35.924640    -10.2190    -70.8982     71.6309\n     0   5000     0.0000   -63.4084    33.3222    36.202531    -10.5627    -70.8478     71.6309\n     0   5200     0.0000   -63.4084    33.3222    36.476093    -10.9009    -70.7965     71.6309\n     0   5400     0.0000   -63.4084    33.3222    36.745447    -11.2336    -70.7445     71.6309\n     0   5600     0.0000   -63.4084    33.3222    37.010708    -11.5610    -70.6918     71.6309\n     0   5800     0.0000   -63.4084    33.3222    37.271986    -11.8832    -70.6383     71.6309\n     0   6000     0.0000   -63.4084    33.3222    37.529387    -12.2004    -70.5842     71.6309\n     0   6200     0.0000   -63.4084    33.3222    37.783012    -12.5128    -70.5295     71.6309\n     0   6400     0.0000   -63.4084    33.3222    38.032957    -12.8203    -70.4742     71.6309\n     0   6600     0.0000   -63.4084    33.3222    38.279317    -13.1232    -70.4185     71.6309\n     0   6800     0.0000   -63.4084    33.3222    38.522179    -13.4216    -70.3622     71.6309\n     0   7000     0.0000   -63.4084    33.3222    38.761630    -13.7155    -70.3055     71.6309\n     0   7200     0.0000   -63.4084    33.3222    38.997753    -14.0052    -70.2484     71.6309\n     0   7400     0.0000   -63.4084    33.3222    39.230627    -14.2906    -70.1909     71.6309\n     0   7600     0.0000   -63.4084    33.3222    39.460328    -14.5719    -70.1330     71.6309\n     0   7800     0.0000   -63.4084    33.3222    39.686930    -14.8491    -70.0749     71.6309\n     0   8000     0.0000   -63.4084    33.3222    39.910504    -15.1224    -70.0164     71.6309\n     0   8200     0.0000   -63.4084    33.3222    40.131118    -15.3919    -69.9576     71.6309\n     0   8400     0.0000   -63.4084    33.3222    40.348838    -15.6576    -69.8986     71.6309\n   500      0     0.0000   -63.7201    33.3450    27.496077      0.1597    -71.9173     71.9176\n   500    200     0.0000   -63.7201    33.3450    27.938329     -0.3954    -71.9164     71.9176\n   500    400     0.0000   -63.7201    33.3450    28.369946     -0.9371    -71.9113     71.9176\n   500    600     0.0000   -63.7201    33.3450    28.791451     -1.4661    -71.9025     71.9176\n   500    800     0.0000   -63.7201    33.3450    29.203324     -1.9830    -71.8901     71.9176\n   500   1000     0.0000   -63.7201    33.3450    29.606009     -2.4882    -71.8744     71.9176\n   500   1200     0.0000   -63.7201    33.3450    29.999915     -2.9823    -71.8556     71.9176\n   500   1400     0.0000   -63.7201    33.3450    30.385423     -3.4657    -71.8339     71.9176\n   500   1600     0.0000   -63.7201    33.3450    30.762887     -3.9388    -71.8095     71.9176\n   500   1800     0.0000   -63.7201    33.3450    31.132636     -4.4022    -71.7826     71.9176\n   500   2000     0.0000   -63.7201    33.3450    31.494978     -4.8560    -71.7533     71.9176\n   500   2200     0.0000   -63.7201    33.3450    31.850200     -5.3008    -71.7219     71.9176\n   500   2400     0.0000   -63.7201    33.3450    32.198572     -5.7368    -71.6883     71.9176\n   500   2600     0.0000   -63.7201    33.3450    32.540347     -6.1643    -71.6528     71.9176\n   500   2800     0.0000   -63.7201    33.3450    32.875764     -6.5836    -71.6155     71.9176\n   500   3000     0.0000   -63.7201    33.3450    33.205046     -6.9951    -71.5765     71.9176\n   500   3200     0.0000   -63.7201    33.3450    33.528405     -7.3990    -71.5359     71.9176\n   500   3400     0.0000   -63.7201    33.3450    33.846042     -7.7954    -71.4938     71.9176\n   500   3600     0.0000   -63.7201    33.3450    34.158144     -8.1848    -71.4502     71.9176\n   500   3800     0.0000   -63.7201    33.3450    34.464890     -8.5672    -71.4054     71.9176\n   500   4000     0.0000   -63.7201    33.3450    34.766452     -8.9429    -71.3593     71.9176\n   500   4200     0.0000   -63.7201    33.3450    35.062988     -9.3121    -71.3121     71.9176\n   500   4400     0.0000   -63.7201    33.3450    35.354653     -9.6750    -71.2638     71.9176\n   500   4600     0.0000   -63.7201    33.3450    35.641592    -10.0317    -71.2144     71.9176\n   500   4800     0.0000   -63.7201    33.3450    35.923944    -10.3826    -71.1641     71.9176\n   500   5000     0.0000   -63.7201    33.3450    36.201842    -10.7276    -71.1129     71.9176\n   500   5200     0.0000   -63.7201    33.3450    36.475410    -11.0670    -71.0609     71.9176\n   500   5400     0.0000   -63.7201    33.3450    36.744770    -11.4010    -71.0081     71.9176\n   500   5600     0.0000   -63.7201    33.3450    37.010037    -11.7296    -70.9545     71.9176\n   500   5800     0.0000   -63.7201    33.3450    37.271321    -12.0530    -70.9003     71.9176\n   500   6000     0.0000   -63.7201    33.3450    37.528727    -12.3714    -70.8454     71.9176\n   500   6200     0.0000   -63.7201    33.3450    37.782357    -12.6849    -70.7900     71.9176\n   500   6400     0.0000   -63.7201    33.3450    38.032308    -12.9936    -70.7340     71.9176\n   500   6600     0.0000   -63.7201    33.3450    38.278673    -13.2977    -70.6774     71.9176\n   500   6800     0.0000   -63.7201    33.3450    38.521540    -13.5971    -70.6204     71.9176\n   500   7000     0.0000   -63.7201    33.3450    38.760996    -13.8922    -70.5630     71.9176\n   500   7200     0.0000   -63.7201    33.3450    38.997124    -14.1828    -70.5052     71.9176\n   500   7400     0.0000   -63.7201    33.3450    39.230002    -14.4693    -70.4469     71.9176\n   500   7600     0.0000   -63.7201    33.3450    39.459708    -14.7516    -70.3884     71.9176\n   500   7800     0.0000   -63.7201    33.3450    39.686314    -15.0299    -70.3295     71.9176\n   500   8000     0.0000   -63.7201    33.3450    39.909892    -15.3042    -70.2703     71.9176\n   500   8200     0.0000   -63.7201    33.3450    40.130511    -15.5747    -70.2108     71.9176\n   500   8400     0.0000   -63.7201    33.3450    40.348236    -15.8414    -70.1511     71.9176\n  1000      0     0.0000   -64.0318    33.3678    27.495122      0.0372    -72.2043     72.2045\n  1000    200     0.0000   -64.0318    33.3678    27.937389     -0.5201    -72.2024     72.2045\n  1000    400     0.0000   -64.0318    33.3678    28.369022     -1.0640    -72.1965     72.2045\n  1000    600     0.0000   -64.0318    33.3678    28.790541     -1.5952    -72.1867     72.2045\n  1000    800     0.0000   -64.0318    33.3678    29.202428     -2.1140    -72.1734     72.2045\n  1000   1000     0.0000   -64.0318    33.3678    29.605126     -2.6213    -72.1567     72.2045\n  1000   1200     0.0000   -64.0318    33.3678    29.999045     -3.1173    -72.1370     72.2045\n  1000   1400     0.0000   -64.0318    33.3678    30.384565     -3.6026    -72.1144     72.2045\n  1000   1600     0.0000   -64.0318    33.3678    30.762040     -4.0776    -72.0891     72.2045\n  1000   1800     0.0000   -64.0318    33.3678    31.131800     -4.5428    -72.0613     72.2045\n  1000   2000     0.0000   -64.0318    33.3678    31.494152     -4.9984    -72.0311     72.2045\n  1000   2200     0.0000   -64.0318    33.3678    31.849385     -5.4449    -71.9988     72.2045\n  1000   2400     0.0000   -64.0318    33.3678    32.197766     -5.8826    -71.9643     72.2045\n  1000   2600     0.0000   -64.0318    33.3678    32.539551     -6.3117    -71.9280     72.2045\n  1000   2800     0.0000   -64.0318    33.3678    32.874977     -6.7327    -71.8898     72.2045\n  1000   3000     0.0000   -64.0318    33.3678    33.204268     -7.1458    -71.8499     72.2045\n  1000   3200     0.0000   -64.0318    33.3678    33.527636     -7.5512    -71.8084     72.2045\n  1000   3400     0.0000   -64.0318    33.3678    33.845281     -7.9492    -71.7655     72.2045\n  1000   3600     0.0000   -64.0318    33.3678    34.157391     -8.3400    -71.7211     72.2045\n  1000   3800     0.0000   -64.0318    33.3678    34.464145     -8.7238    -71.6754     72.2045\n  1000   4000     0.0000   -64.0318    33.3678    34.765714     -9.1010    -71.6285     72.2045\n  1000   4200     0.0000   -64.0318    33.3678    35.062258     -9.4716    -71.5804     72.2045\n  1000   4400     0.0000   -64.0318    33.3678    35.353930     -9.8358    -71.5313     72.2045\n  1000   4600     0.0000   -64.0318    33.3678    35.640876    -10.1940    -71.4811     72.2045\n  1000   4800     0.0000   -64.0318    33.3678    35.923235    -10.5461    -71.4300     72.2045\n  1000   5000     0.0000   -64.0318    33.3678    36.201138    -10.8924    -71.3781     72.2045\n  1000   5200     0.0000   -64.0318    33.3678    36.474713    -11.2331    -71.3252     72.2045\n  1000   5400     0.0000   -64.0318    33.3678    36.744079    -11.5683    -71.2716     72.2045\n  1000   5600     0.0000   -64.0318    33.3678    37.009352    -11.8982    -71.2173     72.2045\n  1000   5800     0.0000   -64.0318    33.3678    37.270642    -12.2228    -71.1623     72.2045\n  1000   6000     0.0000   -64.0318    33.3678    37.528054    -12.5424    -71.1067     72.2045\n  1000   6200     0.0000   -64.0318    33.3678    37.781689    -12.8571    -71.0505     72.2045\n  1000   6400     0.0000   -64.0318    33.3678    38.031646    -13.1669    -70.9937     72.2045\n  1000   6600     0.0000   -64.0318    33.3678    38.278015    -13.4721    -70.9364     72.2045\n  1000   6800     0.0000   -64.0318    33.3678    38.520888    -13.7726    -70.8787     72.2045\n  1000   7000     0.0000   -64.0318    33.3678    38.760349    -14.0687    -70.8205     72.2045\n  1000   7200     0.0000   -64.0318    33.3678    38.996482    -14.3605    -70.7619     72.2045\n  1000   7400     0.0000   -64.0318    33.3678    39.229365    -14.6480    -70.7030     72.2045\n  1000   7600     0.0000   -64.0318    33.3678    39.459075    -14.9313    -70.6437     72.2045\n  1000   7800     0.0000   -64.0318    33.3678    39.685686    -15.2106    -70.5841     72.2045\n  1000   8000     0.0000   -64.0318    33.3678    39.909268    -15.4859    -70.5242     72.2045\n  1000   8200     0.0000   -64.0318    33.3678    40.129891    -15.7574    -70.4640     72.2045\n  1000   8400     0.0000   -64.0318    33.3678    40.347621    -16.0250    -70.4036     72.2045\n  1500      0     0.0000   -64.3436    33.3906    27.494145     -0.0852    -72.4913     72.4915\n  1500    200     0.0000   -64.3436    33.3906    27.936429     -0.6448    -72.4885     72.4915\n  1500    400     0.0000   -64.3436    33.3906    28.368077     -1.1909    -72.4816     72.4915\n  1500    600     0.0000   -64.3436    33.3906    28.789612     -1.7241    -72.4709     72.4915\n  1500    800     0.0000   -64.3436    33.3906    29.201513     -2.2451    -72.4566     72.4915\n  1500   1000     0.0000   -64.3436    33.3906    29.604224     -2.7543    -72.4391     72.4915\n  1500   1200     0.0000   -64.3436    33.3906    29.998156     -3.2523    -72.4184     72.4915\n  1500   1400     0.0000   -64.3436    33.3906    30.383688     -3.7395    -72.3949     72.4915\n  1500   1600     0.0000   -64.3436    33.3906    30.761176     -4.2164    -72.3687     72.4915\n  1500   1800     0.0000   -64.3436    33.3906    31.130947     -4.6833    -72.3400     72.4915\n  1500   2000     0.0000   -64.3436    33.3906    31.493310     -5.1407    -72.3089     72.4915\n  1500   2200     0.0000   -64.3436    33.3906    31.848553     -5.5890    -72.2757     72.4915\n  1500   2400     0.0000   -64.3436    33.3906    32.196945     -6.0283    -72.2403     72.4915\n  1500   2600     0.0000   -64.3436    33.3906    32.538739     -6.4592    -72.2031     72.4915\n  1500   2800     0.0000   -64.3436    33.3906    32.874174     -6.8818    -72.1640     72.4915\n  1500   3000     0.0000   -64.3436    33.3906    33.203475     -7.2964    -72.1233     72.4915\n  1500   3200     0.0000   -64.3436    33.3906    33.526851     -7.7034    -72.0810     72.4915\n  1500   3400     0.0000   -64.3436    33.3906    33.844504     -8.1029    -72.0372     72.4915\n  1500   3600     0.0000   -64.3436    33.3906    34.156623     -8.4952    -71.9920     72.4915\n  1500   3800     0.0000   -64.3436    33.3906    34.463385     -8.8805    -71.9454     72.4915\n  1500   4000     0.0000   -64.3436    33.3906    34.764961     -9.2591    -71.8977     72.4915\n  1500   4200     0.0000   -64.3436    33.3906    35.061513     -9.6311    -71.8488     72.4915\n  1500   4400     0.0000   -64.3436    33.3906    35.353192     -9.9967    -71.7989     72.4915\n  1500   4600     0.0000   -64.3436    33.3906    35.640145    -10.3562    -71.7479     72.4915\n  1500   4800     0.0000   -64.3436    33.3906    35.922510    -10.7096    -71.6960     72.4915\n  1500   5000     0.0000   -64.3436    33.3906    36.200421    -11.0573    -71.6432     72.4915\n  1500   5200     0.0000   -64.3436    33.3906    36.474002    -11.3992    -71.5896     72.4915\n  1500   5400     0.0000   -64.3436    33.3906    36.743374    -11.7357    -71.5352     72.4915\n  1500   5600     0.0000   -64.3436    33.3906    37.008653    -12.0667    -71.4801     72.4915\n  1500   5800     0.0000   -64.3436    33.3906    37.269949    -12.3926    -71.4243     72.4915\n  1500   6000     0.0000   -64.3436    33.3906    37.527367    -12.7134    -71.3679     72.4915\n  1500   6200     0.0000   -64.3436    33.3906    37.781008    -13.0292    -71.3110     72.4915\n  1500   6400     0.0000   -64.3436    33.3906    38.030970    -13.3402    -71.2534     72.4915\n  1500   6600     0.0000   -64.3436    33.3906    38.277345    -13.6464    -71.1954     72.4915\n  1500   6800     0.0000   -64.3436    33.3906    38.520223    -13.9481    -71.1369     72.4915\n  1500   7000     0.0000   -64.3436    33.3906    38.759689    -14.2453    -71.0780     72.4915\n  1500   7200     0.0000   -64.3436    33.3906    38.995827    -14.5381    -71.0187     72.4915\n  1500   7400     0.0000   -64.3436    33.3906    39.228715    -14.8267    -70.9590     72.4915\n  1500   7600     0.0000   -64.3436    33.3906    39.458429    -15.1110    -70.8990     72.4915\n  1500   7800     0.0000   -64.3436    33.3906    39.685045    -15.3913    -70.8387     72.4915\n  1500   8000     0.0000   -64.3436    33.3906    39.908632    -15.6677    -70.7781     72.4915\n  1500   8200     0.0000   -64.3436    33.3906    40.129260    -15.9401    -70.7172     72.4915\n  1500   8400     0.0000   -64.3436    33.3906    40.346993    -16.2087    -70.6562     72.4915\n  2000      0     0.0000   -64.6553    33.4134    27.493148     -0.2077    -72.7784     72.7788\n  2000    200     0.0000   -64.6553    33.4134    27.935449     -0.7695    -72.7746     72.7788\n  2000    400     0.0000   -64.6553    33.4134    28.367113     -1.3177    -72.7668     72.7788\n  2000    600     0.0000   -64.6553    33.4134    28.788663     -1.8531    -72.7551     72.7788\n  2000    800     0.0000   -64.6553    33.4134    29.200578     -2.3761    -72.7399     72.7788\n  2000   1000     0.0000   -64.6553    33.4134    29.603304     -2.8873    -72.7214     72.7788\n  2000   1200     0.0000   -64.6553    33.4134    29.997248     -3.3872    -72.6998     72.7788\n  2000   1400     0.0000   -64.6553    33.4134    30.382794     -3.8763    -72.6754     72.7788\n  2000   1600     0.0000   -64.6553    33.4134    30.760293     -4.3551    -72.6483     72.7788\n  2000   1800     0.0000   -64.6553    33.4134    31.130076     -4.8239    -72.6187     72.7788\n  2000   2000     0.0000   -64.6553    33.4134    31.492450     -5.2831    -72.5867     72.7788\n  2000   2200     0.0000   -64.6553    33.4134    31.847704     -5.7330    -72.5526     72.7788\n  2000   2400     0.0000   -64.6553    33.4134    32.196106     -6.1741    -72.5164     72.7788\n  2000   2600     0.0000   -64.6553    33.4134    32.537910     -6.6066    -72.4782     72.7788\n  2000   2800     0.0000   -64.6553    33.4134    32.873355     -7.0308    -72.4383     72.7788\n  2000   3000     0.0000   -64.6553    33.4134    33.202665     -7.4470    -72.3967     72.7788\n  2000   3200     0.0000   -64.6553    33.4134    33.526051     -7.8555    -72.3535     72.7788\n  2000   3400     0.0000   -64.6553    33.4134    33.843712     -8.2565    -72.3089     72.7788\n  2000   3600     0.0000   -64.6553    33.4134    34.155839     -8.6503    -72.2628     72.7788\n  2000   3800     0.0000   -64.6553    33.4134    34.462609     -9.0371    -72.2155     72.7788\n  2000   4000     0.0000   -64.6553    33.4134    34.764193     -9.4171    -72.1669     72.7788\n  2000   4200     0.0000   -64.6553    33.4134    35.060752     -9.7905    -72.1172     72.7788\n  2000   4400     0.0000   -64.6553    33.4134    35.352439    -10.1575    -72.0664     72.7788\n  2000   4600     0.0000   -64.6553    33.4134    35.639399    -10.5183    -72.0147     72.7788\n  2000   4800     0.0000   -64.6553    33.4134    35.921772    -10.8731    -71.9619     72.7788\n  2000   5000     0.0000   -64.6553    33.4134    36.199689    -11.2220    -71.9084     72.7788\n  2000   5200     0.0000   -64.6553    33.4134    36.473276    -11.5653    -71.8540     72.7788\n  2000   5400     0.0000   -64.6553    33.4134    36.742655    -11.9030    -71.7988     72.7788\n  2000   5600     0.0000   -64.6553    33.4134    37.007941    -12.2353    -71.7429     72.7788\n  2000   5800     0.0000   -64.6553    33.4134    37.269242    -12.5623    -71.6864     72.7788\n  2000   6000     0.0000   -64.6553    33.4134    37.526666    -12.8843    -71.6292     72.7788\n  2000   6200     0.0000   -64.6553    33.4134    37.780313    -13.2013    -71.5715     72.7788\n  2000   6400     0.0000   -64.6553    33.4134    38.030281    -13.5134    -71.5132     72.7788\n  2000   6600     0.0000   -64.6553    33.4134    38.276661    -13.8208    -71.4544     72.7788\n  2000   6800     0.0000   -64.6553    33.4134    38.519545    -14.1236    -71.3952     72.7788\n  2000   7000     0.0000   -64.6553    33.4134    38.759016    -14.4218    -71.3355     72.7788\n  2000   7200     0.0000   -64.6553    33.4134    38.995159    -14.7157    -71.2755     72.7788\n  2000   7400     0.0000   -64.6553    33.4134    39.228052    -15.0053    -71.2151     72.7788\n  2000   7600     0.0000   -64.6553    33.4134    39.457772    -15.2907    -71.1544     72.7788\n  2000   7800     0.0000   -64.6553    33.4134    39.684392    -15.5720    -71.0933     72.7788\n  2000   8000     0.0000   -64.6553    33.4134    39.907984    -15.8494    -71.0320     72.7788\n  2000   8200     0.0000   -64.6553    33.4134    40.128616    -16.1228    -70.9705     72.7788\n  2000   8400     0.0000   -64.6553    33.4134    40.346354    -16.3924    -70.9087     72.7788\n  2500      0     0.0000   -64.9670    33.4362    27.492131     -0.3300    -73.0654     73.0663\n  2500    200     0.0000   -64.9670    33.4362    27.934449     -0.8941    -73.0607     73.0663\n  2500    400     0.0000   -64.9670    33.4362    28.366129     -1.4445    -73.0519     73.0663\n  2500    600     0.0000   -64.9670    33.4362    28.787694     -1.9820    -73.0393     73.0663\n  2500    800     0.0000   -64.9670    33.4362    29.199625     -2.5070    -73.0232     73.0663\n  2500   1000     0.0000   -64.9670    33.4362    29.602364     -3.0203    -73.0037     73.0663\n  2500   1200     0.0000   -64.9670    33.4362    29.996323     -3.5222    -72.9813     73.0663\n  2500   1400     0.0000   -64.9670    33.4362    30.381881     -4.0132    -72.9559     73.0663\n  2500   1600     0.0000   -64.9670    33.4362    30.759393     -4.4938    -72.9279     73.0663\n  2500   1800     0.0000   -64.9670    33.4362    31.129187     -4.9644    -72.8974     73.0663\n  2500   2000     0.0000   -64.9670    33.4362    31.491573     -5.4253    -72.8645     73.0663\n  2500   2200     0.0000   -64.9670    33.4362    31.846838     -5.8770    -72.8295     73.0663\n  2500   2400     0.0000   -64.9670    33.4362    32.195250     -6.3198    -72.7924     73.0663\n  2500   2600     0.0000   -64.9670    33.4362    32.537065     -6.7539    -72.7534     73.0663\n  2500   2800     0.0000   -64.9670    33.4362    32.872520     -7.1798    -72.7126     73.0663\n  2500   3000     0.0000   -64.9670    33.4362    33.201839     -7.5976    -72.6701     73.0663\n  2500   3200     0.0000   -64.9670    33.4362    33.525234     -8.0076    -72.6261     73.0663\n  2500   3400     0.0000   -64.9670    33.4362    33.842904     -8.4102    -72.5806     73.0663\n  2500   3600     0.0000   -64.9670    33.4362    34.155039     -8.8055    -72.5337     73.0663\n  2500   3800     0.0000   -64.9670    33.4362    34.461818     -9.1937    -72.4855     73.0663\n  2500   4000     0.0000   -64.9670    33.4362    34.763411     -9.5751    -72.4361     73.0663\n  2500   4200     0.0000   -64.9670    33.4362    35.059977     -9.9499    -72.3856     73.0663\n  2500   4400     0.0000   -64.9670    33.4362    35.351672    -10.3183    -72.3340     73.0663\n  2500   4600     0.0000   -64.9670    33.4362    35.638639    -10.6805    -72.2814     73.0663\n  2500   4800     0.0000   -64.9670    33.4362    35.921019    -11.0366    -72.2279     73.0663\n  2500   5000     0.0000   -64.9670    33.4362    36.198943    -11.3868    -72.1735     73.0663\n  2500   5200     0.0000   -64.9670    33.4362    36.472537    -11.7313    -72.1183     73.0663\n  2500   5400     0.0000   -64.9670    33.4362    36.741923    -12.0703    -72.0624     73.0663\n  2500   5600     0.0000   -64.9670    33.4362    37.007214    -12.4038    -72.0057     73.0663\n  2500   5800     0.0000   -64.9670    33.4362    37.268522    -12.7321    -71.9484     73.0663\n  2500   6000     0.0000   -64.9670    33.4362    37.525952    -13.0552    -71.8905     73.0663\n  2500   6200     0.0000   -64.9670    33.4362    37.779605    -13.3733    -71.8320     73.0663\n  2500   6400     0.0000   -64.9670    33.4362    38.029579    -13.6866    -71.7729     73.0663\n  2500   6600     0.0000   -64.9670    33.4362    38.275965    -13.9951    -71.7134     73.0663\n  2500   6800     0.0000   -64.9670    33.4362    38.518854    -14.2990    -71.6534     73.0663\n  2500   7000     0.0000   -64.9670    33.4362    38.758331    -14.5984    -71.5931     73.0663\n  2500   7200     0.0000   -64.9670    33.4362    38.994478    -14.8933    -71.5323     73.0663\n  2500   7400     0.0000   -64.9670    33.4362    39.227377    -15.1840    -71.4712     73.0663\n  2500   7600     0.0000   -64.9670    33.4362    39.457102    -15.4704    -71.4097     73.0663\n  2500   7800     0.0000   -64.9670    33.4362    39.683727    -15.7527    -71.3480     73.0663\n  2500   8000     0.0000   -64.9670    33.4362    39.907324    -16.0310    -71.2859     73.0663\n  2500   8200     0.0000   -64.9670    33.4362    40.127960    -16.3054    -71.2237     73.0663\n  2500   8400     0.0000   -64.9670    33.4362    40.345703    -16.5760    -71.1612     73.0663\n  3000      0     0.0000   -65.2787    33.4590    27.491093     -0.4524    -73.3525     73.3540\n  3000    200     0.0000   -65.2787    33.4590    27.933428     -1.0187    -73.3468     73.3540\n  3000    400     0.0000   -65.2787    33.4590    28.365126     -1.5713    -73.3371     73.3540\n  3000    600     0.0000   -65.2787    33.4590    28.786707     -2.1108    -73.3235     73.3540\n  3000    800     0.0000   -65.2787    33.4590    29.198653     -2.6380    -73.3064     73.3540\n  3000   1000     0.0000   -65.2787    33.4590    29.601406     -3.1532    -73.2861     73.3540\n  3000   1200     0.0000   -65.2787    33.4590    29.995379     -3.6570    -73.2627     73.3540\n  3000   1400     0.0000   -65.2787    33.4590    30.380950     -4.1500    -73.2364     73.3540\n  3000   1600     0.0000   -65.2787    33.4590    30.758475     -4.6325    -73.2075     73.3540\n  3000   1800     0.0000   -65.2787    33.4590    31.128282     -5.1049    -73.1761     73.3540\n  3000   2000     0.0000   -65.2787    33.4590    31.490679     -5.5676    -73.1423     73.3540\n  3000   2200     0.0000   -65.2787    33.4590    31.845955     -6.0210    -73.1064     73.3540\n  3000   2400     0.0000   -65.2787    33.4590    32.194378     -6.4655    -73.0684     73.3540\n  3000   2600     0.0000   -65.2787    33.4590    32.536204     -6.9013    -73.0286     73.3540\n  3000   2800     0.0000   -65.2787    33.4590    32.871668     -7.3288    -72.9869     73.3540\n  3000   3000     0.0000   -65.2787    33.4590    33.200997     -7.7481    -72.9436     73.3540\n  3000   3200     0.0000   -65.2787    33.4590    33.524401     -8.1598    -72.8987     73.3540\n  3000   3400     0.0000   -65.2787    33.4590    33.842081     -8.5638    -72.8523     73.3540\n  3000   3600     0.0000   -65.2787    33.4590    34.154225     -8.9606    -72.8046     73.3540\n  3000   3800     0.0000   -65.2787    33.4590    34.461012     -9.3503    -72.7556     73.3540\n  3000   4000     0.0000   -65.2787    33.4590    34.762613     -9.7331    -72.7053     73.3540\n  3000   4200     0.0000   -65.2787    33.4590    35.059187    -10.1093    -72.6540     73.3540\n  3000   4400     0.0000   -65.2787    33.4590    35.350889    -10.4791    -72.6016     73.3540\n  3000   4600     0.0000   -65.2787    33.4590    35.637865    -10.8426    -72.5482     73.3540\n  3000   4800     0.0000   -65.2787    33.4590    35.920252    -11.2000    -72.4939     73.3540\n  3000   5000     0.0000   -65.2787    33.4590    36.198183    -11.5516    -72.4387     73.3540\n  3000   5200     0.0000   -65.2787    33.4590    36.471784    -11.8973    -72.3827     73.3540\n  3000   5400     0.0000   -65.2787    33.4590    36.741176    -12.2375    -72.3260     73.3540\n  3000   5600     0.0000   -65.2787    33.4590    37.006474    -12.5723    -72.2685     73.3540\n  3000   5800     0.0000   -65.2787    33.4590    37.267789    -12.9018    -72.2104     73.3540\n  3000   6000     0.0000   -65.2787    33.4590    37.525225    -13.2261    -72.1517     73.3540\n  3000   6200     0.0000   -65.2787    33.4590    37.778884    -13.5454    -72.0925     73.3540\n  3000   6400     0.0000   -65.2787    33.4590    38.028863    -13.8598    -72.0327     73.3540\n  3000   6600     0.0000   -65.2787    33.4590    38.275255    -14.1694    -71.9724     73.3540\n  3000   6800     0.0000   -65.2787    33.4590    38.518150    -14.4744    -71.9117     73.3540\n  3000   7000     0.0000   -65.2787    33.4590    38.757632    -14.7749    -71.8506     73.3540\n  3000   7200     0.0000   -65.2787    33.4590    38.993785    -15.0709    -71.7891     73.3540\n  3000   7400     0.0000   -65.2787    33.4590    39.226689    -15.3626    -71.7272     73.3540\n  3000   7600     0.0000   -65.2787    33.4590    39.456419    -15.6500    -71.6651     73.3540\n  3000   7800     0.0000   -65.2787    33.4590    39.683049    -15.9334    -71.6026     73.3540\n  3000   8000     0.0000   -65.2787    33.4590    39.906651    -16.2127    -71.5399     73.3540\n  3000   8200     0.0000   -65.2787    33.4590    40.127293    -16.4881    -71.4769     73.3540\n  3000   8400     0.0000   -65.2787    33.4590    40.345040    -16.7596    -71.4137     73.3540\n  3500      0     0.0000   -65.5904    33.4818    27.490034     -0.5747    -73.6395     73.6419\n  3500    200     0.0000   -65.5904    33.4818    27.932388     -1.1432    -73.6329     73.6419\n  3500    400     0.0000   -65.5904    33.4818    28.364103     -1.6980    -73.6222     73.6419\n  3500    600     0.0000   -65.5904    33.4818    28.785700     -2.2397    -73.6077     73.6419\n  3500    800     0.0000   -65.5904    33.4818    29.197661     -2.7689    -73.5897     73.6419\n  3500   1000     0.0000   -65.5904    33.4818    29.600430     -3.2861    -73.5684     73.6419\n  3500   1200     0.0000   -65.5904    33.4818    29.994416     -3.7919    -73.5441     73.6419\n  3500   1400     0.0000   -65.5904    33.4818    30.380001     -4.2868    -73.5169     73.6419\n  3500   1600     0.0000   -65.5904    33.4818    30.757539     -4.7711    -73.4871     73.6419\n  3500   1800     0.0000   -65.5904    33.4818    31.127358     -5.2453    -73.4548     73.6419\n  3500   2000     0.0000   -65.5904    33.4818    31.489768     -5.7098    -73.4201     73.6419\n  3500   2200     0.0000   -65.5904    33.4818    31.845055     -6.1650    -73.3833     73.6419\n  3500   2400     0.0000   -65.5904    33.4818    32.193490     -6.6111    -73.3445     73.6419\n  3500   2600     0.0000   -65.5904    33.4818    32.535326     -7.0486    -73.3037     73.6419\n  3500   2800     0.0000   -65.5904    33.4818    32.870801     -7.4777    -73.2612     73.6419\n  3500   3000     0.0000   -65.5904    33.4818    33.200139     -7.8987    -73.2170     73.6419\n  3500   3200     0.0000   -65.5904    33.4818    33.523553     -8.3118    -73.1713     73.6419\n  3500   3400     0.0000   -65.5904    33.4818    33.841242     -8.7174    -73.1240     73.6419\n  3500   3600     0.0000   -65.5904    33.4818    34.153395     -9.1157    -73.0755     73.6419\n  3500   3800     0.0000   -65.5904    33.4818    34.460191     -9.5068    -73.0256     73.6419\n  3500   4000     0.0000   -65.5904    33.4818    34.761800     -9.8911    -72.9746     73.6419\n  3500   4200     0.0000   -65.5904    33.4818    35.058383    -10.2687    -72.9224     73.6419\n  3500   4400     0.0000   -65.5904    33.4818    35.350093    -10.6399    -72.8692     73.6419\n  3500   4600     0.0000   -65.5904    33.4818    35.637076    -11.0047    -72.8150     73.6419\n  3500   4800     0.0000   -65.5904    33.4818    35.919470    -11.3635    -72.7598     73.6419\n  3500   5000     0.0000   -65.5904    33.4818    36.197408    -11.7163    -72.7039     73.6419\n  3500   5200     0.0000   -65.5904    33.4818    36.471017    -12.0633    -72.6471     73.6419\n  3500   5400     0.0000   -65.5904    33.4818    36.740416    -12.4048    -72.5896     73.6419\n  3500   5600     0.0000   -65.5904    33.4818    37.005721    -12.7408    -72.5313     73.6419\n  3500   5800     0.0000   -65.5904    33.4818    37.267042    -13.0714    -72.4725     73.6419\n  3500   6000     0.0000   -65.5904    33.4818    37.524484    -13.3969    -72.4130     73.6419\n  3500   6200     0.0000   -65.5904    33.4818    37.778150    -13.7174    -72.3530     73.6419\n  3500   6400     0.0000   -65.5904    33.4818    38.028135    -14.0330    -72.2925     73.6419\n  3500   6600     0.0000   -65.5904    33.4818    38.274533    -14.3437    -72.2314     73.6419\n  3500   6800     0.0000   -65.5904    33.4818    38.517433    -14.6498    -72.1700     73.6419\n  3500   7000     0.0000   -65.5904    33.4818    38.756921    -14.9513    -72.1081     73.6419\n  3500   7200     0.0000   -65.5904    33.4818    38.993080    -15.2484    -72.0459     73.6419\n  3500   7400     0.0000   -65.5904    33.4818    39.225988    -15.5412    -71.9833     73.6419\n  3500   7600     0.0000   -65.5904    33.4818    39.455724    -15.8297    -71.9204     73.6419\n  3500   7800     0.0000   -65.5904    33.4818    39.682359    -16.1140    -71.8572     73.6419\n  3500   8000     0.0000   -65.5904    33.4818    39.905966    -16.3943    -71.7938     73.6419\n  3500   8200     0.0000   -65.5904    33.4818    40.126613    -16.6707    -71.7301     73.6419\n  3500   8400     0.0000   -65.5904    33.4818    40.344365    -16.9432    -71.6663     73.6419\n  4000      0     0.0000   -65.9022    33.5046    27.488955     -0.6970    -73.9266     73.9301\n  4000    200     0.0000   -65.9022    33.5046    27.931328     -1.2677    -73.9190     73.9301\n  4000    400     0.0000   -65.9022    33.5046    28.363060     -1.8247    -73.9074     73.9301\n  4000    600     0.0000   -65.9022    33.5046    28.784674     -2.3685    -73.8919     73.9301\n  4000    800     0.0000   -65.9022    33.5046    29.196651     -2.8997    -73.8730     73.9301\n  4000   1000     0.0000   -65.9022    33.5046    29.599435     -3.4190    -73.8508     73.9301\n  4000   1200     0.0000   -65.9022    33.5046    29.993436     -3.9267    -73.8256     73.9301\n  4000   1400     0.0000   -65.9022    33.5046    30.379035     -4.4235    -73.7975     73.9301\n  4000   1600     0.0000   -65.9022    33.5046    30.756585     -4.9097    -73.7667     73.9301\n  4000   1800     0.0000   -65.9022    33.5046    31.126418     -5.3857    -73.7335     73.9301\n  4000   2000     0.0000   -65.9022    33.5046    31.488839     -5.8520    -73.6979     73.9301\n  4000   2200     0.0000   -65.9022    33.5046    31.844138     -6.3089    -73.6602     73.9301\n  4000   2400     0.0000   -65.9022    33.5046    32.192584     -6.7568    -73.6205     73.9301\n  4000   2600     0.0000   -65.9022    33.5046    32.534431     -7.1959    -73.5789     73.9301\n  4000   2800     0.0000   -65.9022    33.5046    32.869917     -7.6266    -73.5355     73.9301\n  4000   3000     0.0000   -65.9022    33.5046    33.199265     -8.0492    -73.4905     73.9301\n  4000   3200     0.0000   -65.9022    33.5046    33.522689     -8.4639    -73.4438     73.9301\n  4000   3400     0.0000   -65.9022    33.5046    33.840387     -8.8710    -73.3958     73.9301\n  4000   3600     0.0000   -65.9022    33.5046    34.152550     -9.2707    -73.3464     73.9301\n  4000   3800     0.0000   -65.9022    33.5046    34.459354     -9.6633    -73.2957     73.9301\n  4000   4000     0.0000   -65.9022    33.5046    34.760972    -10.0491    -73.2438     73.9301\n  4000   4200     0.0000   -65.9022    33.5046    35.057563    -10.4281    -73.1908     73.9301\n  4000   4400     0.0000   -65.9022    33.5046    35.349281    -10.8006    -73.1368     73.9301\n  4000   4600     0.0000   -65.9022    33.5046    35.636272    -11.1668    -73.0817     73.9301\n  4000   4800     0.0000   -65.9022    33.5046    35.918674    -11.5269    -73.0258     73.9301\n  4000   5000     0.0000   -65.9022    33.5046    36.196620    -11.8810    -72.9690     73.9301\n  4000   5200     0.0000   -65.9022    33.5046    36.470236    -12.2293    -72.9115     73.9301\n  4000   5400     0.0000   -65.9022    33.5046    36.739642    -12.5720    -72.8532     73.9301\n  4000   5600     0.0000   -65.9022    33.5046    37.004953    -12.9092    -72.7942     73.9301\n  4000   5800     0.0000   -65.9022    33.5046    37.266281    -13.2411    -72.7345     73.9301\n  4000   6000     0.0000   -65.9022    33.5046    37.523730    -13.5678    -72.6743     73.9301\n  4000   6200     0.0000   -65.9022    33.5046    37.777402    -13.8894    -72.6135     73.9301\n  4000   6400     0.0000   -65.9022    33.5046    38.027393    -14.2061    -72.5522     73.9301\n  4000   6600     0.0000   -65.9022    33.5046    38.273797    -14.5180    -72.4905     73.9301\n  4000   6800     0.0000   -65.9022    33.5046    38.516703    -14.8252    -72.4283     73.9301\n  4000   7000     0.0000   -65.9022    33.5046    38.756197    -15.1278    -72.3657     73.9301\n  4000   7200     0.0000   -65.9022    33.5046    38.992361    -15.4259    -72.3027     73.9301\n  4000   7400     0.0000   -65.9022    33.5046    39.225276    -15.7197    -72.2394     73.9301\n  4000   7600     0.0000   -65.9022    33.5046    39.455016    -16.0093    -72.1758     73.9301\n  4000   7800     0.0000   -65.9022    33.5046    39.681657    -16.2946    -72.1119     73.9301\n  4000   8000     0.0000   -65.9022    33.5046    39.905269    -16.5760    -72.0478     73.9301\n  4000   8200     0.0000   -65.9022    33.5046    40.125921    -16.8533    -71.9834     73.9301\n  4000   8400     0.0000   -65.9022    33.5046    40.343678    -17.1268    -71.9188     73.9301\n  4500      0     0.0000   -66.2139    33.5274    27.487857     -0.8192    -74.2137     74.2184\n  4500    200     0.0000   -66.2139    33.5274    27.930248     -1.3922    -74.2051     74.2184\n  4500    400     0.0000   -66.2139    33.5274    28.361998     -1.9513    -74.1925     74.2184\n  4500    600     0.0000   -66.2139    33.5274    28.783629     -2.4973    -74.1762     74.2184\n  4500    800     0.0000   -66.2139    33.5274    29.195622     -3.0306    -74.1563     74.2184\n  4500   1000     0.0000   -66.2139    33.5274    29.598421     -3.5518    -74.1332     74.2184\n  4500   1200     0.0000   -66.2139    33.5274    29.992437     -4.0615    -74.1070     74.2184\n  4500   1400     0.0000   -66.2139    33.5274    30.378050     -4.5602    -74.0780     74.2184\n  4500   1600     0.0000   -66.2139    33.5274    30.755614     -5.0483    -74.0463     74.2184\n  4500   1800     0.0000   -66.2139    33.5274    31.125460     -5.5261    -74.0122     74.2184\n  4500   2000     0.0000   -66.2139    33.5274    31.487894     -5.9942    -73.9758     74.2184\n  4500   2200     0.0000   -66.2139    33.5274    31.843205     -6.4528    -73.9372     74.2184\n  4500   2400     0.0000   -66.2139    33.5274    32.191662     -6.9024    -73.8966     74.2184\n  4500   2600     0.0000   -66.2139    33.5274    32.533520     -7.3431    -73.8541     74.2184\n  4500   2800     0.0000   -66.2139    33.5274    32.869017     -7.7755    -73.8098     74.2184\n  4500   3000     0.0000   -66.2139    33.5274    33.198376     -8.1996    -73.7639     74.2184\n  4500   3200     0.0000   -66.2139    33.5274    33.521810     -8.6159    -73.7164     74.2184\n  4500   3400     0.0000   -66.2139    33.5274    33.839518     -9.0245    -73.6675     74.2184\n  4500   3600     0.0000   -66.2139    33.5274    34.151689     -9.4258    -73.6173     74.2184\n  4500   3800     0.0000   -66.2139    33.5274    34.458503     -9.8198    -73.5657     74.2184\n  4500   4000     0.0000   -66.2139    33.5274    34.760129    -10.2070    -73.5130     74.2184\n  4500   4200     0.0000   -66.2139    33.5274    35.056729    -10.5874    -73.4592     74.2184\n  4500   4400     0.0000   -66.2139    33.5274    35.348455    -10.9613    -73.4044     74.2184\n  4500   4600     0.0000   -66.2139    33.5274    35.635454    -11.3288    -73.3485     74.2184\n  4500   4800     0.0000   -66.2139    33.5274    35.917864    -11.6902    -73.2918     74.2184\n  4500   5000     0.0000   -66.2139    33.5274    36.195818    -12.0456    -73.2342     74.2184\n  4500   5200     0.0000   -66.2139    33.5274    36.469441    -12.3952    -73.1759     74.2184\n  4500   5400     0.0000   -66.2139    33.5274    36.738854    -12.7392    -73.1168     74.2184\n  4500   5600     0.0000   -66.2139    33.5274    37.004173    -13.0776    -73.0570     74.2184\n  4500   5800     0.0000   -66.2139    33.5274    37.265507    -13.4107    -72.9966     74.2184\n  4500   6000     0.0000   -66.2139    33.5274    37.522963    -13.7386    -72.9356     74.2184\n  4500   6200     0.0000   -66.2139    33.5274    37.776641    -14.0614    -72.8741     74.2184\n  4500   6400     0.0000   -66.2139    33.5274    38.026639    -14.3792    -72.8120     74.2184\n  4500   6600     0.0000   -66.2139    33.5274    38.273049    -14.6922    -72.7495     74.2184\n  4500   6800     0.0000   -66.2139    33.5274    38.515961    -15.0005    -72.6866     74.2184\n  4500   7000     0.0000   -66.2139    33.5274    38.755460    -15.3042    -72.6232     74.2184\n  4500   7200     0.0000   -66.2139    33.5274    38.991631    -15.6034    -72.5595     74.2184\n  4500   7400     0.0000   -66.2139    33.5274    39.224551    -15.8983    -72.4955     74.2184\n  4500   7600     0.0000   -66.2139    33.5274    39.454297    -16.1888    -72.4312     74.2184\n  4500   7800     0.0000   -66.2139    33.5274    39.680943    -16.4752    -72.3666     74.2184\n  4500   8000     0.0000   -66.2139    33.5274    39.904560    -16.7576    -72.3017     74.2184\n  4500   8200     0.0000   -66.2139    33.5274    40.125217    -17.0359    -72.2366     74.2184\n  4500   8400     0.0000   -66.2139    33.5274    40.342979    -17.3103    -72.1714     74.2184\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180506_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.4776116       -0.7573620       22.7725658   m   m   m\ninitial_baseline_rate:          0.0000000       -0.1235698        0.0091351   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       -0.7275811       22.8105130   m   m   m\nprecision_baseline_rate:        0.0000000       -0.1397472        0.0220372   m/s m/s m/s\nunwrap_phase_constant:            0.00021     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180506_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.478     -0.757     22.773\norbit baseline rate   (TCN) (m/s):  0.000e+00 -1.236e-01  9.135e-03\n\nbaseline vector (TCN)   (m):      0.000     -0.728     22.811\nbaseline rate   (TCN) (m/s):  0.000e+00 -1.397e-01  2.204e-02\n\nSLC-1 center baseline length (m):    22.8221\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000     0.5769    22.6048    27.497013     20.3176     -9.9250     22.6122\n     0    200     0.0000     0.5769    22.6048    27.939249     20.2404    -10.0815     22.6122\n     0    400     0.0000     0.5769    22.6048    28.370851     20.1639    -10.2337     22.6122\n     0    600     0.0000     0.5769    22.6048    28.792342     20.0880    -10.3818     22.6122\n     0    800     0.0000     0.5769    22.6048    29.204202     20.0129    -10.5259     22.6122\n     0   1000     0.0000     0.5769    22.6048    29.606873     19.9384    -10.6663     22.6122\n     0   1200     0.0000     0.5769    22.6048    30.000767     19.8646    -10.8031     22.6122\n     0   1400     0.0000     0.5769    22.6048    30.386264     19.7915    -10.9365     22.6122\n     0   1600     0.0000     0.5769    22.6048    30.763716     19.7190    -11.0666     22.6122\n     0   1800     0.0000     0.5769    22.6048    31.133454     19.6472    -11.1937     22.6122\n     0   2000     0.0000     0.5769    22.6048    31.495785     19.5760    -11.3177     22.6122\n     0   2200     0.0000     0.5769    22.6048    31.850997     19.5055    -11.4388     22.6122\n     0   2400     0.0000     0.5769    22.6048    32.199360     19.4356    -11.5572     22.6122\n     0   2600     0.0000     0.5769    22.6048    32.541126     19.3663    -11.6729     22.6122\n     0   2800     0.0000     0.5769    22.6048    32.876534     19.2976    -11.7861     22.6122\n     0   3000     0.0000     0.5769    22.6048    33.205808     19.2296    -11.8968     22.6122\n     0   3200     0.0000     0.5769    22.6048    33.529159     19.1621    -12.0052     22.6122\n     0   3400     0.0000     0.5769    22.6048    33.846787     19.0953    -12.1112     22.6122\n     0   3600     0.0000     0.5769    22.6048    34.158881     19.0290    -12.2150     22.6122\n     0   3800     0.0000     0.5769    22.6048    34.465620     18.9633    -12.3167     22.6122\n     0   4000     0.0000     0.5769    22.6048    34.767174     18.8983    -12.4164     22.6122\n     0   4200     0.0000     0.5769    22.6048    35.063703     18.8337    -12.5140     22.6122\n     0   4400     0.0000     0.5769    22.6048    35.355362     18.7698    -12.6097     22.6122\n     0   4600     0.0000     0.5769    22.6048    35.642294     18.7064    -12.7035     22.6122\n     0   4800     0.0000     0.5769    22.6048    35.924640     18.6436    -12.7956     22.6122\n     0   5000     0.0000     0.5769    22.6048    36.202531     18.5813    -12.8858     22.6122\n     0   5200     0.0000     0.5769    22.6048    36.476093     18.5196    -12.9744     22.6122\n     0   5400     0.0000     0.5769    22.6048    36.745447     18.4584    -13.0613     22.6122\n     0   5600     0.0000     0.5769    22.6048    37.010708     18.3977    -13.1467     22.6122\n     0   5800     0.0000     0.5769    22.6048    37.271986     18.3376    -13.2304     22.6122\n     0   6000     0.0000     0.5769    22.6048    37.529387     18.2779    -13.3127     22.6122\n     0   6200     0.0000     0.5769    22.6048    37.783012     18.2188    -13.3934     22.6122\n     0   6400     0.0000     0.5769    22.6048    38.032957     18.1602    -13.4728     22.6122\n     0   6600     0.0000     0.5769    22.6048    38.279317     18.1021    -13.5508     22.6122\n     0   6800     0.0000     0.5769    22.6048    38.522179     18.0445    -13.6274     22.6122\n     0   7000     0.0000     0.5769    22.6048    38.761630     17.9874    -13.7027     22.6122\n     0   7200     0.0000     0.5769    22.6048    38.997753     17.9308    -13.7767     22.6122\n     0   7400     0.0000     0.5769    22.6048    39.230627     17.8747    -13.8494     22.6122\n     0   7600     0.0000     0.5769    22.6048    39.460328     17.8190    -13.9210     22.6122\n     0   7800     0.0000     0.5769    22.6048    39.686930     17.7638    -13.9913     22.6122\n     0   8000     0.0000     0.5769    22.6048    39.910504     17.7091    -14.0605     22.6122\n     0   8200     0.0000     0.5769    22.6048    40.131118     17.6548    -14.1286     22.6122\n     0   8400     0.0000     0.5769    22.6048    40.348838     17.6010    -14.1956     22.6122\n   500      0     0.0000     0.2896    22.6501    27.496077     20.2253    -10.2004     22.6520\n   500    200     0.0000     0.2896    22.6501    27.938329     20.1460    -10.3562     22.6520\n   500    400     0.0000     0.2896    22.6501    28.369946     20.0674    -10.5077     22.6520\n   500    600     0.0000     0.2896    22.6501    28.791451     19.9895    -10.6550     22.6520\n   500    800     0.0000     0.2896    22.6501    29.203324     19.9124    -10.7984     22.6520\n   500   1000     0.0000     0.2896    22.6501    29.606009     19.8361    -10.9381     22.6520\n   500   1200     0.0000     0.2896    22.6501    29.999915     19.7604    -11.0742     22.6520\n   500   1400     0.0000     0.2896    22.6501    30.385423     19.6854    -11.2069     22.6520\n   500   1600     0.0000     0.2896    22.6501    30.762887     19.6112    -11.3364     22.6520\n   500   1800     0.0000     0.2896    22.6501    31.132636     19.5376    -11.4627     22.6520\n   500   2000     0.0000     0.2896    22.6501    31.494978     19.4647    -11.5860     22.6520\n   500   2200     0.0000     0.2896    22.6501    31.850200     19.3925    -11.7065     22.6520\n   500   2400     0.0000     0.2896    22.6501    32.198572     19.3210    -11.8242     22.6520\n   500   2600     0.0000     0.2896    22.6501    32.540347     19.2501    -11.9392     22.6520\n   500   2800     0.0000     0.2896    22.6501    32.875764     19.1799    -12.0517     22.6520\n   500   3000     0.0000     0.2896    22.6501    33.205046     19.1103    -12.1617     22.6520\n   500   3200     0.0000     0.2896    22.6501    33.528405     19.0414    -12.2694     22.6520\n   500   3400     0.0000     0.2896    22.6501    33.846042     18.9731    -12.3748     22.6520\n   500   3600     0.0000     0.2896    22.6501    34.158144     18.9054    -12.4779     22.6520\n   500   3800     0.0000     0.2896    22.6501    34.464890     18.8383    -12.5790     22.6520\n   500   4000     0.0000     0.2896    22.6501    34.766452     18.7718    -12.6779     22.6520\n   500   4200     0.0000     0.2896    22.6501    35.062988     18.7060    -12.7749     22.6520\n   500   4400     0.0000     0.2896    22.6501    35.354653     18.6407    -12.8700     22.6520\n   500   4600     0.0000     0.2896    22.6501    35.641592     18.5760    -12.9632     22.6520\n   500   4800     0.0000     0.2896    22.6501    35.923944     18.5119    -13.0545     22.6520\n   500   5000     0.0000     0.2896    22.6501    36.201842     18.4484    -13.1442     22.6520\n   500   5200     0.0000     0.2896    22.6501    36.475410     18.3854    -13.2321     22.6520\n   500   5400     0.0000     0.2896    22.6501    36.744770     18.3230    -13.3184     22.6520\n   500   5600     0.0000     0.2896    22.6501    37.010037     18.2611    -13.4031     22.6520\n   500   5800     0.0000     0.2896    22.6501    37.271321     18.1998    -13.4862     22.6520\n   500   6000     0.0000     0.2896    22.6501    37.528727     18.1390    -13.5679     22.6520\n   500   6200     0.0000     0.2896    22.6501    37.782357     18.0788    -13.6480     22.6520\n   500   6400     0.0000     0.2896    22.6501    38.032308     18.0191    -13.7268     22.6520\n   500   6600     0.0000     0.2896    22.6501    38.278673     17.9599    -13.8041     22.6520\n   500   6800     0.0000     0.2896    22.6501    38.521540     17.9012    -13.8801     22.6520\n   500   7000     0.0000     0.2896    22.6501    38.760996     17.8431    -13.9548     22.6520\n   500   7200     0.0000     0.2896    22.6501    38.997124     17.7854    -14.0282     22.6520\n   500   7400     0.0000     0.2896    22.6501    39.230002     17.7282    -14.1004     22.6520\n   500   7600     0.0000     0.2896    22.6501    39.459708     17.6716    -14.1714     22.6520\n   500   7800     0.0000     0.2896    22.6501    39.686314     17.6154    -14.2411     22.6520\n   500   8000     0.0000     0.2896    22.6501    39.909892     17.5597    -14.3098     22.6520\n   500   8200     0.0000     0.2896    22.6501    40.130511     17.5044    -14.3773     22.6520\n   500   8400     0.0000     0.2896    22.6501    40.348236     17.4497    -14.4437     22.6520\n  1000      0     0.0000     0.0023    22.6954    27.495122     20.1330    -10.4758     22.6954\n  1000    200     0.0000     0.0023    22.6954    27.937389     20.0516    -10.6309     22.6954\n  1000    400     0.0000     0.0023    22.6954    28.369022     19.9709    -10.7816     22.6954\n  1000    600     0.0000     0.0023    22.6954    28.790541     19.8911    -10.9283     22.6954\n  1000    800     0.0000     0.0023    22.6954    29.202428     19.8120    -11.0710     22.6954\n  1000   1000     0.0000     0.0023    22.6954    29.605126     19.7337    -11.2099     22.6954\n  1000   1200     0.0000     0.0023    22.6954    29.999045     19.6562    -11.3453     22.6954\n  1000   1400     0.0000     0.0023    22.6954    30.384565     19.5794    -11.4773     22.6954\n  1000   1600     0.0000     0.0023    22.6954    30.762040     19.5033    -11.6061     22.6954\n  1000   1800     0.0000     0.0023    22.6954    31.131800     19.4280    -11.7317     22.6954\n  1000   2000     0.0000     0.0023    22.6954    31.494152     19.3535    -11.8543     22.6954\n  1000   2200     0.0000     0.0023    22.6954    31.849385     19.2796    -11.9741     22.6954\n  1000   2400     0.0000     0.0023    22.6954    32.197766     19.2064    -12.0911     22.6954\n  1000   2600     0.0000     0.0023    22.6954    32.539551     19.1340    -12.2055     22.6954\n  1000   2800     0.0000     0.0023    22.6954    32.874977     19.0622    -12.3173     22.6954\n  1000   3000     0.0000     0.0023    22.6954    33.204268     18.9911    -12.4266     22.6954\n  1000   3200     0.0000     0.0023    22.6954    33.527636     18.9206    -12.5336     22.6954\n  1000   3400     0.0000     0.0023    22.6954    33.845281     18.8509    -12.6383     22.6954\n  1000   3600     0.0000     0.0023    22.6954    34.157391     18.7817    -12.7408     22.6954\n  1000   3800     0.0000     0.0023    22.6954    34.464145     18.7132    -12.8412     22.6954\n  1000   4000     0.0000     0.0023    22.6954    34.765714     18.6454    -12.9395     22.6954\n  1000   4200     0.0000     0.0023    22.6954    35.062258     18.5782    -13.0358     22.6954\n  1000   4400     0.0000     0.0023    22.6954    35.353930     18.5116    -13.1302     22.6954\n  1000   4600     0.0000     0.0023    22.6954    35.640876     18.4456    -13.2228     22.6954\n  1000   4800     0.0000     0.0023    22.6954    35.923235     18.3802    -13.3135     22.6954\n  1000   5000     0.0000     0.0023    22.6954    36.201138     18.3154    -13.4025     22.6954\n  1000   5200     0.0000     0.0023    22.6954    36.474713     18.2512    -13.4898     22.6954\n  1000   5400     0.0000     0.0023    22.6954    36.744079     18.1876    -13.5755     22.6954\n  1000   5600     0.0000     0.0023    22.6954    37.009352     18.1245    -13.6595     22.6954\n  1000   5800     0.0000     0.0023    22.6954    37.270642     18.0621    -13.7420     22.6954\n  1000   6000     0.0000     0.0023    22.6954    37.528054     18.0001    -13.8230     22.6954\n  1000   6200     0.0000     0.0023    22.6954    37.781689     17.9388    -13.9026     22.6954\n  1000   6400     0.0000     0.0023    22.6954    38.031646     17.8779    -13.9807     22.6954\n  1000   6600     0.0000     0.0023    22.6954    38.278015     17.8177    -14.0575     22.6954\n  1000   6800     0.0000     0.0023    22.6954    38.520888     17.7579    -14.1329     22.6954\n  1000   7000     0.0000     0.0023    22.6954    38.760349     17.6987    -14.2070     22.6954\n  1000   7200     0.0000     0.0023    22.6954    38.996482     17.6400    -14.2798     22.6954\n  1000   7400     0.0000     0.0023    22.6954    39.229365     17.5818    -14.3514     22.6954\n  1000   7600     0.0000     0.0023    22.6954    39.459075     17.5241    -14.4217     22.6954\n  1000   7800     0.0000     0.0023    22.6954    39.685686     17.4670    -14.4909     22.6954\n  1000   8000     0.0000     0.0023    22.6954    39.909268     17.4103    -14.5590     22.6954\n  1000   8200     0.0000     0.0023    22.6954    40.129891     17.3541    -14.6259     22.6954\n  1000   8400     0.0000     0.0023    22.6954    40.347621     17.2984    -14.6918     22.6954\n  1500      0     0.0000    -0.2849    22.7407    27.494145     20.0408    -10.7512     22.7425\n  1500    200     0.0000    -0.2849    22.7407    27.936429     19.9572    -10.9055     22.7425\n  1500    400     0.0000    -0.2849    22.7407    28.368077     19.8745    -11.0556     22.7425\n  1500    600     0.0000    -0.2849    22.7407    28.789612     19.7926    -11.2015     22.7425\n  1500    800     0.0000    -0.2849    22.7407    29.201513     19.7116    -11.3435     22.7425\n  1500   1000     0.0000    -0.2849    22.7407    29.604224     19.6314    -11.4818     22.7425\n  1500   1200     0.0000    -0.2849    22.7407    29.998156     19.5519    -11.6165     22.7425\n  1500   1400     0.0000    -0.2849    22.7407    30.383688     19.4733    -11.7478     22.7425\n  1500   1600     0.0000    -0.2849    22.7407    30.761176     19.3955    -11.8758     22.7425\n  1500   1800     0.0000    -0.2849    22.7407    31.130947     19.3185    -12.0007     22.7425\n  1500   2000     0.0000    -0.2849    22.7407    31.493310     19.2422    -12.1227     22.7425\n  1500   2200     0.0000    -0.2849    22.7407    31.848553     19.1667    -12.2417     22.7425\n  1500   2400     0.0000    -0.2849    22.7407    32.196945     19.0919    -12.3581     22.7425\n  1500   2600     0.0000    -0.2849    22.7407    32.538739     19.0178    -12.4717     22.7425\n  1500   2800     0.0000    -0.2849    22.7407    32.874174     18.9445    -12.5829     22.7425\n  1500   3000     0.0000    -0.2849    22.7407    33.203475     18.8718    -12.6915     22.7425\n  1500   3200     0.0000    -0.2849    22.7407    33.526851     18.7999    -12.7978     22.7425\n  1500   3400     0.0000    -0.2849    22.7407    33.844504     18.7287    -12.9019     22.7425\n  1500   3600     0.0000    -0.2849    22.7407    34.156623     18.6581    -13.0037     22.7425\n  1500   3800     0.0000    -0.2849    22.7407    34.463385     18.5882    -13.1034     22.7425\n  1500   4000     0.0000    -0.2849    22.7407    34.764961     18.5190    -13.2011     22.7425\n  1500   4200     0.0000    -0.2849    22.7407    35.061513     18.4504    -13.2967     22.7425\n  1500   4400     0.0000    -0.2849    22.7407    35.353192     18.3825    -13.3905     22.7425\n  1500   4600     0.0000    -0.2849    22.7407    35.640145     18.3152    -13.4824     22.7425\n  1500   4800     0.0000    -0.2849    22.7407    35.922510     18.2485    -13.5725     22.7425\n  1500   5000     0.0000    -0.2849    22.7407    36.200421     18.1825    -13.6608     22.7425\n  1500   5200     0.0000    -0.2849    22.7407    36.474002     18.1170    -13.7475     22.7425\n  1500   5400     0.0000    -0.2849    22.7407    36.743374     18.0522    -13.8325     22.7425\n  1500   5600     0.0000    -0.2849    22.7407    37.008653     17.9880    -13.9160     22.7425\n  1500   5800     0.0000    -0.2849    22.7407    37.269949     17.9243    -13.9978     22.7425\n  1500   6000     0.0000    -0.2849    22.7407    37.527367     17.8612    -14.0782     22.7425\n  1500   6200     0.0000    -0.2849    22.7407    37.781008     17.7988    -14.1572     22.7425\n  1500   6400     0.0000    -0.2849    22.7407    38.030970     17.7368    -14.2347     22.7425\n  1500   6600     0.0000    -0.2849    22.7407    38.277345     17.6754    -14.3108     22.7425\n  1500   6800     0.0000    -0.2849    22.7407    38.520223     17.6146    -14.3856     22.7425\n  1500   7000     0.0000    -0.2849    22.7407    38.759689     17.5543    -14.4591     22.7425\n  1500   7200     0.0000    -0.2849    22.7407    38.995827     17.4946    -14.5313     22.7425\n  1500   7400     0.0000    -0.2849    22.7407    39.228715     17.4354    -14.6023     22.7425\n  1500   7600     0.0000    -0.2849    22.7407    39.458429     17.3767    -14.6721     22.7425\n  1500   7800     0.0000    -0.2849    22.7407    39.685045     17.3185    -14.7407     22.7425\n  1500   8000     0.0000    -0.2849    22.7407    39.908632     17.2609    -14.8082     22.7425\n  1500   8200     0.0000    -0.2849    22.7407    40.129260     17.2037    -14.8746     22.7425\n  1500   8400     0.0000    -0.2849    22.7407    40.346993     17.1471    -14.9398     22.7425\n  2000      0     0.0000    -0.5722    22.7860    27.493148     19.9486    -11.0265     22.7932\n  2000    200     0.0000    -0.5722    22.7860    27.935449     19.8628    -11.1802     22.7932\n  2000    400     0.0000    -0.5722    22.7860    28.367113     19.7780    -11.3295     22.7932\n  2000    600     0.0000    -0.5722    22.7860    28.788663     19.6942    -11.4747     22.7932\n  2000    800     0.0000    -0.5722    22.7860    29.200578     19.6112    -11.6160     22.7932\n  2000   1000     0.0000    -0.5722    22.7860    29.603304     19.5290    -11.7536     22.7932\n  2000   1200     0.0000    -0.5722    22.7860    29.997248     19.4477    -11.8876     22.7932\n  2000   1400     0.0000    -0.5722    22.7860    30.382794     19.3673    -12.0182     22.7932\n  2000   1600     0.0000    -0.5722    22.7860    30.760293     19.2877    -12.1455     22.7932\n  2000   1800     0.0000    -0.5722    22.7860    31.130076     19.2089    -12.2697     22.7932\n  2000   2000     0.0000    -0.5722    22.7860    31.492450     19.1309    -12.3910     22.7932\n  2000   2200     0.0000    -0.5722    22.7860    31.847704     19.0537    -12.5094     22.7932\n  2000   2400     0.0000    -0.5722    22.7860    32.196106     18.9773    -12.6250     22.7932\n  2000   2600     0.0000    -0.5722    22.7860    32.537910     18.9017    -12.7380     22.7932\n  2000   2800     0.0000    -0.5722    22.7860    32.873355     18.8268    -12.8484     22.7932\n  2000   3000     0.0000    -0.5722    22.7860    33.202665     18.7526    -12.9564     22.7932\n  2000   3200     0.0000    -0.5722    22.7860    33.526051     18.6792    -13.0621     22.7932\n  2000   3400     0.0000    -0.5722    22.7860    33.843712     18.6065    -13.1654     22.7932\n  2000   3600     0.0000    -0.5722    22.7860    34.155839     18.5345    -13.2666     22.7932\n  2000   3800     0.0000    -0.5722    22.7860    34.462609     18.4632    -13.3656     22.7932\n  2000   4000     0.0000    -0.5722    22.7860    34.764193     18.3926    -13.4626     22.7932\n  2000   4200     0.0000    -0.5722    22.7860    35.060752     18.3227    -13.5576     22.7932\n  2000   4400     0.0000    -0.5722    22.7860    35.352439     18.2534    -13.6508     22.7932\n  2000   4600     0.0000    -0.5722    22.7860    35.639399     18.1848    -13.7420     22.7932\n  2000   4800     0.0000    -0.5722    22.7860    35.921772     18.1169    -13.8315     22.7932\n  2000   5000     0.0000    -0.5722    22.7860    36.199689     18.0495    -13.9192     22.7932\n  2000   5200     0.0000    -0.5722    22.7860    36.473276     17.9829    -14.0052     22.7932\n  2000   5400     0.0000    -0.5722    22.7860    36.742655     17.9168    -14.0896     22.7932\n  2000   5600     0.0000    -0.5722    22.7860    37.007941     17.8514    -14.1724     22.7932\n  2000   5800     0.0000    -0.5722    22.7860    37.269242     17.7866    -14.2537     22.7932\n  2000   6000     0.0000    -0.5722    22.7860    37.526666     17.7224    -14.3334     22.7932\n  2000   6200     0.0000    -0.5722    22.7860    37.780313     17.6587    -14.4117     22.7932\n  2000   6400     0.0000    -0.5722    22.7860    38.030281     17.5957    -14.4886     22.7932\n  2000   6600     0.0000    -0.5722    22.7860    38.276661     17.5332    -14.5642     22.7932\n  2000   6800     0.0000    -0.5722    22.7860    38.519545     17.4713    -14.6384     22.7932\n  2000   7000     0.0000    -0.5722    22.7860    38.759016     17.4100    -14.7113     22.7932\n  2000   7200     0.0000    -0.5722    22.7860    38.995159     17.3492    -14.7829     22.7932\n  2000   7400     0.0000    -0.5722    22.7860    39.228052     17.2890    -14.8533     22.7932\n  2000   7600     0.0000    -0.5722    22.7860    39.457772     17.2293    -14.9225     22.7932\n  2000   7800     0.0000    -0.5722    22.7860    39.684392     17.1701    -14.9905     22.7932\n  2000   8000     0.0000    -0.5722    22.7860    39.907984     17.1115    -15.0574     22.7932\n  2000   8200     0.0000    -0.5722    22.7860    40.128616     17.0534    -15.1232     22.7932\n  2000   8400     0.0000    -0.5722    22.7860    40.346354     16.9958    -15.1879     22.7932\n  2500      0     0.0000    -0.8594    22.8313    27.492131     19.8563    -11.3019     22.8475\n  2500    200     0.0000    -0.8594    22.8313    27.934449     19.7685    -11.4549     22.8475\n  2500    400     0.0000    -0.8594    22.8313    28.366129     19.6816    -11.6035     22.8475\n  2500    600     0.0000    -0.8594    22.8313    28.787694     19.5957    -11.7480     22.8475\n  2500    800     0.0000    -0.8594    22.8313    29.199625     19.5107    -11.8886     22.8475\n  2500   1000     0.0000    -0.8594    22.8313    29.602364     19.4267    -12.0254     22.8475\n  2500   1200     0.0000    -0.8594    22.8313    29.996323     19.3436    -12.1587     22.8475\n  2500   1400     0.0000    -0.8594    22.8313    30.381881     19.2613    -12.2886     22.8475\n  2500   1600     0.0000    -0.8594    22.8313    30.759393     19.1799    -12.4152     22.8475\n  2500   1800     0.0000    -0.8594    22.8313    31.129187     19.0994    -12.5388     22.8475\n  2500   2000     0.0000    -0.8594    22.8313    31.491573     19.0197    -12.6593     22.8475\n  2500   2200     0.0000    -0.8594    22.8313    31.846838     18.9408    -12.7770     22.8475\n  2500   2400     0.0000    -0.8594    22.8313    32.195250     18.8628    -12.8919     22.8475\n  2500   2600     0.0000    -0.8594    22.8313    32.537065     18.7855    -13.0042     22.8475\n  2500   2800     0.0000    -0.8594    22.8313    32.872520     18.7091    -13.1140     22.8475\n  2500   3000     0.0000    -0.8594    22.8313    33.201839     18.6334    -13.2213     22.8475\n  2500   3200     0.0000    -0.8594    22.8313    33.525234     18.5585    -13.3263     22.8475\n  2500   3400     0.0000    -0.8594    22.8313    33.842904     18.4843    -13.4290     22.8475\n  2500   3600     0.0000    -0.8594    22.8313    34.155039     18.4109    -13.5295     22.8475\n  2500   3800     0.0000    -0.8594    22.8313    34.461818     18.3382    -13.6279     22.8475\n  2500   4000     0.0000    -0.8594    22.8313    34.763411     18.2662    -13.7242     22.8475\n  2500   4200     0.0000    -0.8594    22.8313    35.059977     18.1949    -13.8186     22.8475\n  2500   4400     0.0000    -0.8594    22.8313    35.351672     18.1243    -13.9110     22.8475\n  2500   4600     0.0000    -0.8594    22.8313    35.638639     18.0544    -14.0016     22.8475\n  2500   4800     0.0000    -0.8594    22.8313    35.921019     17.9852    -14.0904     22.8475\n  2500   5000     0.0000    -0.8594    22.8313    36.198943     17.9166    -14.1775     22.8475\n  2500   5200     0.0000    -0.8594    22.8313    36.472537     17.8487    -14.2629     22.8475\n  2500   5400     0.0000    -0.8594    22.8313    36.741923     17.7815    -14.3466     22.8475\n  2500   5600     0.0000    -0.8594    22.8313    37.007214     17.7149    -14.4288     22.8475\n  2500   5800     0.0000    -0.8594    22.8313    37.268522     17.6489    -14.5095     22.8475\n  2500   6000     0.0000    -0.8594    22.8313    37.525952     17.5835    -14.5886     22.8475\n  2500   6200     0.0000    -0.8594    22.8313    37.779605     17.5187    -14.6663     22.8475\n  2500   6400     0.0000    -0.8594    22.8313    38.029579     17.4546    -14.7426     22.8475\n  2500   6600     0.0000    -0.8594    22.8313    38.275965     17.3910    -14.8175     22.8475\n  2500   6800     0.0000    -0.8594    22.8313    38.518854     17.3281    -14.8911     22.8475\n  2500   7000     0.0000    -0.8594    22.8313    38.758331     17.2657    -14.9634     22.8475\n  2500   7200     0.0000    -0.8594    22.8313    38.994478     17.2038    -15.0345     22.8475\n  2500   7400     0.0000    -0.8594    22.8313    39.227377     17.1426    -15.1043     22.8475\n  2500   7600     0.0000    -0.8594    22.8313    39.457102     17.0819    -15.1729     22.8475\n  2500   7800     0.0000    -0.8594    22.8313    39.683727     17.0217    -15.2403     22.8475\n  2500   8000     0.0000    -0.8594    22.8313    39.907324     16.9621    -15.3066     22.8475\n  2500   8200     0.0000    -0.8594    22.8313    40.127960     16.9031    -15.3718     22.8475\n  2500   8400     0.0000    -0.8594    22.8313    40.345703     16.8445    -15.4360     22.8475\n  3000      0     0.0000    -1.1467    22.8766    27.491093     19.7641    -11.5773     22.9053\n  3000    200     0.0000    -1.1467    22.8766    27.933428     19.6741    -11.7295     22.9053\n  3000    400     0.0000    -1.1467    22.8766    28.365126     19.5852    -11.8774     22.9053\n  3000    600     0.0000    -1.1467    22.8766    28.786707     19.4973    -12.0212     22.9053\n  3000    800     0.0000    -1.1467    22.8766    29.198653     19.4104    -12.1611     22.9053\n  3000   1000     0.0000    -1.1467    22.8766    29.601406     19.3244    -12.2972     22.9053\n  3000   1200     0.0000    -1.1467    22.8766    29.995379     19.2394    -12.4298     22.9053\n  3000   1400     0.0000    -1.1467    22.8766    30.380950     19.1553    -12.5590     22.9053\n  3000   1600     0.0000    -1.1467    22.8766    30.758475     19.0721    -12.6849     22.9053\n  3000   1800     0.0000    -1.1467    22.8766    31.128282     18.9899    -12.8078     22.9053\n  3000   2000     0.0000    -1.1467    22.8766    31.490679     18.9085    -12.9276     22.9053\n  3000   2200     0.0000    -1.1467    22.8766    31.845955     18.8279    -13.0446     22.9053\n  3000   2400     0.0000    -1.1467    22.8766    32.194378     18.7483    -13.1589     22.9053\n  3000   2600     0.0000    -1.1467    22.8766    32.536204     18.6694    -13.2705     22.9053\n  3000   2800     0.0000    -1.1467    22.8766    32.871668     18.5914    -13.3796     22.9053\n  3000   3000     0.0000    -1.1467    22.8766    33.200997     18.5142    -13.4862     22.9053\n  3000   3200     0.0000    -1.1467    22.8766    33.524401     18.4378    -13.5905     22.9053\n  3000   3400     0.0000    -1.1467    22.8766    33.842081     18.3622    -13.6925     22.9053\n  3000   3600     0.0000    -1.1467    22.8766    34.154225     18.2873    -13.7924     22.9053\n  3000   3800     0.0000    -1.1467    22.8766    34.461012     18.2132    -13.8901     22.9053\n  3000   4000     0.0000    -1.1467    22.8766    34.762613     18.1398    -13.9858     22.9053\n  3000   4200     0.0000    -1.1467    22.8766    35.059187     18.0672    -14.0795     22.9053\n  3000   4400     0.0000    -1.1467    22.8766    35.350889     17.9953    -14.1713     22.9053\n  3000   4600     0.0000    -1.1467    22.8766    35.637865     17.9240    -14.2612     22.9053\n  3000   4800     0.0000    -1.1467    22.8766    35.920252     17.8535    -14.3494     22.9053\n  3000   5000     0.0000    -1.1467    22.8766    36.198183     17.7837    -14.4358     22.9053\n  3000   5200     0.0000    -1.1467    22.8766    36.471784     17.7146    -14.5206     22.9053\n  3000   5400     0.0000    -1.1467    22.8766    36.741176     17.6461    -14.6037     22.9053\n  3000   5600     0.0000    -1.1467    22.8766    37.006474     17.5783    -14.6853     22.9053\n  3000   5800     0.0000    -1.1467    22.8766    37.267789     17.5112    -14.7653     22.9053\n  3000   6000     0.0000    -1.1467    22.8766    37.525225     17.4446    -14.8438     22.9053\n  3000   6200     0.0000    -1.1467    22.8766    37.778884     17.3787    -14.9209     22.9053\n  3000   6400     0.0000    -1.1467    22.8766    38.028863     17.3135    -14.9966     22.9053\n  3000   6600     0.0000    -1.1467    22.8766    38.275255     17.2488    -15.0709     22.9053\n  3000   6800     0.0000    -1.1467    22.8766    38.518150     17.1848    -15.1439     22.9053\n  3000   7000     0.0000    -1.1467    22.8766    38.757632     17.1213    -15.2156     22.9053\n  3000   7200     0.0000    -1.1467    22.8766    38.993785     17.0585    -15.2860     22.9053\n  3000   7400     0.0000    -1.1467    22.8766    39.226689     16.9962    -15.3552     22.9053\n  3000   7600     0.0000    -1.1467    22.8766    39.456419     16.9345    -15.4232     22.9053\n  3000   7800     0.0000    -1.1467    22.8766    39.683049     16.8734    -15.4901     22.9053\n  3000   8000     0.0000    -1.1467    22.8766    39.906651     16.8128    -15.5558     22.9053\n  3000   8200     0.0000    -1.1467    22.8766    40.127293     16.7528    -15.6205     22.9053\n  3000   8400     0.0000    -1.1467    22.8766    40.345040     16.6933    -15.6840     22.9053\n  3500      0     0.0000    -1.4339    22.9219    27.490034     19.6719    -11.8527     22.9667\n  3500    200     0.0000    -1.4339    22.9219    27.932388     19.5798    -12.0042     22.9667\n  3500    400     0.0000    -1.4339    22.9219    28.364103     19.4888    -12.1514     22.9667\n  3500    600     0.0000    -1.4339    22.9219    28.785700     19.3989    -12.2944     22.9667\n  3500    800     0.0000    -1.4339    22.9219    29.197661     19.3100    -12.4336     22.9667\n  3500   1000     0.0000    -1.4339    22.9219    29.600430     19.2221    -12.5690     22.9667\n  3500   1200     0.0000    -1.4339    22.9219    29.994416     19.1352    -12.7009     22.9667\n  3500   1400     0.0000    -1.4339    22.9219    30.380001     19.0493    -12.8294     22.9667\n  3500   1600     0.0000    -1.4339    22.9219    30.757539     18.9644    -12.9546     22.9667\n  3500   1800     0.0000    -1.4339    22.9219    31.127358     18.8803    -13.0768     22.9667\n  3500   2000     0.0000    -1.4339    22.9219    31.489768     18.7973    -13.1959     22.9667\n  3500   2200     0.0000    -1.4339    22.9219    31.845055     18.7151    -13.3123     22.9667\n  3500   2400     0.0000    -1.4339    22.9219    32.193490     18.6338    -13.4258     22.9667\n  3500   2600     0.0000    -1.4339    22.9219    32.535326     18.5533    -13.5367     22.9667\n  3500   2800     0.0000    -1.4339    22.9219    32.870801     18.4738    -13.6451     22.9667\n  3500   3000     0.0000    -1.4339    22.9219    33.200139     18.3950    -13.7511     22.9667\n  3500   3200     0.0000    -1.4339    22.9219    33.523553     18.3171    -13.8547     22.9667\n  3500   3400     0.0000    -1.4339    22.9219    33.841242     18.2400    -13.9561     22.9667\n  3500   3600     0.0000    -1.4339    22.9219    34.153395     18.1637    -14.0552     22.9667\n  3500   3800     0.0000    -1.4339    22.9219    34.460191     18.0882    -14.1523     22.9667\n  3500   4000     0.0000    -1.4339    22.9219    34.761800     18.0134    -14.2473     22.9667\n  3500   4200     0.0000    -1.4339    22.9219    35.058383     17.9394    -14.3404     22.9667\n  3500   4400     0.0000    -1.4339    22.9219    35.350093     17.8662    -14.4315     22.9667\n  3500   4600     0.0000    -1.4339    22.9219    35.637076     17.7937    -14.5208     22.9667\n  3500   4800     0.0000    -1.4339    22.9219    35.919470     17.7219    -14.6083     22.9667\n  3500   5000     0.0000    -1.4339    22.9219    36.197408     17.6508    -14.6941     22.9667\n  3500   5200     0.0000    -1.4339    22.9219    36.471017     17.5805    -14.7783     22.9667\n  3500   5400     0.0000    -1.4339    22.9219    36.740416     17.5108    -14.8608     22.9667\n  3500   5600     0.0000    -1.4339    22.9219    37.005721     17.4418    -14.9417     22.9667\n  3500   5800     0.0000    -1.4339    22.9219    37.267042     17.3735    -15.0211     22.9667\n  3500   6000     0.0000    -1.4339    22.9219    37.524484     17.3058    -15.0990     22.9667\n  3500   6200     0.0000    -1.4339    22.9219    37.778150     17.2388    -15.1755     22.9667\n  3500   6400     0.0000    -1.4339    22.9219    38.028135     17.1724    -15.2505     22.9667\n  3500   6600     0.0000    -1.4339    22.9219    38.274533     17.1067    -15.3242     22.9667\n  3500   6800     0.0000    -1.4339    22.9219    38.517433     17.0415    -15.3966     22.9667\n  3500   7000     0.0000    -1.4339    22.9219    38.756921     16.9770    -15.4677     22.9667\n  3500   7200     0.0000    -1.4339    22.9219    38.993080     16.9131    -15.5376     22.9667\n  3500   7400     0.0000    -1.4339    22.9219    39.225988     16.8498    -15.6062     22.9667\n  3500   7600     0.0000    -1.4339    22.9219    39.455724     16.7871    -15.6736     22.9667\n  3500   7800     0.0000    -1.4339    22.9219    39.682359     16.7250    -15.7399     22.9667\n  3500   8000     0.0000    -1.4339    22.9219    39.905966     16.6634    -15.8051     22.9667\n  3500   8200     0.0000    -1.4339    22.9219    40.126613     16.6024    -15.8691     22.9667\n  3500   8400     0.0000    -1.4339    22.9219    40.344365     16.5420    -15.9321     22.9667\n  4000      0     0.0000    -1.7212    22.9672    27.488955     19.5797    -12.1280     23.0316\n  4000    200     0.0000    -1.7212    22.9672    27.931328     19.4855    -12.2788     23.0316\n  4000    400     0.0000    -1.7212    22.9672    28.363060     19.3924    -12.4253     23.0316\n  4000    600     0.0000    -1.7212    22.9672    28.784674     19.3005    -12.5677     23.0316\n  4000    800     0.0000    -1.7212    22.9672    29.196651     19.2096    -12.7061     23.0316\n  4000   1000     0.0000    -1.7212    22.9672    29.599435     19.1198    -12.8409     23.0316\n  4000   1200     0.0000    -1.7212    22.9672    29.993436     19.0311    -12.9720     23.0316\n  4000   1400     0.0000    -1.7212    22.9672    30.379035     18.9433    -13.0998     23.0316\n  4000   1600     0.0000    -1.7212    22.9672    30.756585     18.8566    -13.2244     23.0316\n  4000   1800     0.0000    -1.7212    22.9672    31.126418     18.7708    -13.3458     23.0316\n  4000   2000     0.0000    -1.7212    22.9672    31.488839     18.6861    -13.4643     23.0316\n  4000   2200     0.0000    -1.7212    22.9672    31.844138     18.6022    -13.5799     23.0316\n  4000   2400     0.0000    -1.7212    22.9672    32.192584     18.5193    -13.6928     23.0316\n  4000   2600     0.0000    -1.7212    22.9672    32.534431     18.4372    -13.8030     23.0316\n  4000   2800     0.0000    -1.7212    22.9672    32.869917     18.3561    -13.9107     23.0316\n  4000   3000     0.0000    -1.7212    22.9672    33.199265     18.2758    -14.0160     23.0316\n  4000   3200     0.0000    -1.7212    22.9672    33.522689     18.1964    -14.1189     23.0316\n  4000   3400     0.0000    -1.7212    22.9672    33.840387     18.1179    -14.2196     23.0316\n  4000   3600     0.0000    -1.7212    22.9672    34.152550     18.0401    -14.3181     23.0316\n  4000   3800     0.0000    -1.7212    22.9672    34.459354     17.9632    -14.4145     23.0316\n  4000   4000     0.0000    -1.7212    22.9672    34.760972     17.8871    -14.5089     23.0316\n  4000   4200     0.0000    -1.7212    22.9672    35.057563     17.8117    -14.6013     23.0316\n  4000   4400     0.0000    -1.7212    22.9672    35.349281     17.7372    -14.6918     23.0316\n  4000   4600     0.0000    -1.7212    22.9672    35.636272     17.6633    -14.7804     23.0316\n  4000   4800     0.0000    -1.7212    22.9672    35.918674     17.5903    -14.8673     23.0316\n  4000   5000     0.0000    -1.7212    22.9672    36.196620     17.5179    -14.9525     23.0316\n  4000   5200     0.0000    -1.7212    22.9672    36.470236     17.4463    -15.0360     23.0316\n  4000   5400     0.0000    -1.7212    22.9672    36.739642     17.3755    -15.1178     23.0316\n  4000   5600     0.0000    -1.7212    22.9672    37.004953     17.3053    -15.1981     23.0316\n  4000   5800     0.0000    -1.7212    22.9672    37.266281     17.2358    -15.2769     23.0316\n  4000   6000     0.0000    -1.7212    22.9672    37.523730     17.1669    -15.3542     23.0316\n  4000   6200     0.0000    -1.7212    22.9672    37.777402     17.0988    -15.4300     23.0316\n  4000   6400     0.0000    -1.7212    22.9672    38.027393     17.0313    -15.5045     23.0316\n  4000   6600     0.0000    -1.7212    22.9672    38.273797     16.9645    -15.5776     23.0316\n  4000   6800     0.0000    -1.7212    22.9672    38.516703     16.8983    -15.6494     23.0316\n  4000   7000     0.0000    -1.7212    22.9672    38.756197     16.8327    -15.7199     23.0316\n  4000   7200     0.0000    -1.7212    22.9672    38.992361     16.7678    -15.7891     23.0316\n  4000   7400     0.0000    -1.7212    22.9672    39.225276     16.7035    -15.8572     23.0316\n  4000   7600     0.0000    -1.7212    22.9672    39.455016     16.6397    -15.9240     23.0316\n  4000   7800     0.0000    -1.7212    22.9672    39.681657     16.5766    -15.9897     23.0316\n  4000   8000     0.0000    -1.7212    22.9672    39.905269     16.5141    -16.0543     23.0316\n  4000   8200     0.0000    -1.7212    22.9672    40.125921     16.4521    -16.1177     23.0316\n  4000   8400     0.0000    -1.7212    22.9672    40.343678     16.3908    -16.1802     23.0316\n  4500      0     0.0000    -2.0085    23.0125    27.487857     19.4876    -12.4034     23.1000\n  4500    200     0.0000    -2.0085    23.0125    27.930248     19.3912    -12.5535     23.1000\n  4500    400     0.0000    -2.0085    23.0125    28.361998     19.2961    -12.6992     23.1000\n  4500    600     0.0000    -2.0085    23.0125    28.783629     19.2021    -12.8409     23.1000\n  4500    800     0.0000    -2.0085    23.0125    29.195622     19.1093    -12.9786     23.1000\n  4500   1000     0.0000    -2.0085    23.0125    29.598421     19.0176    -13.1127     23.1000\n  4500   1200     0.0000    -2.0085    23.0125    29.992437     18.9269    -13.2431     23.1000\n  4500   1400     0.0000    -2.0085    23.0125    30.378050     18.8374    -13.3702     23.1000\n  4500   1600     0.0000    -2.0085    23.0125    30.755614     18.7489    -13.4941     23.1000\n  4500   1800     0.0000    -2.0085    23.0125    31.125460     18.6614    -13.6148     23.1000\n  4500   2000     0.0000    -2.0085    23.0125    31.487894     18.5749    -13.7326     23.1000\n  4500   2200     0.0000    -2.0085    23.0125    31.843205     18.4893    -13.8475     23.1000\n  4500   2400     0.0000    -2.0085    23.0125    32.191662     18.4048    -13.9597     23.1000\n  4500   2600     0.0000    -2.0085    23.0125    32.533520     18.3212    -14.0692     23.1000\n  4500   2800     0.0000    -2.0085    23.0125    32.869017     18.2385    -14.1763     23.1000\n  4500   3000     0.0000    -2.0085    23.0125    33.198376     18.1567    -14.2809     23.1000\n  4500   3200     0.0000    -2.0085    23.0125    33.521810     18.0758    -14.3832     23.1000\n  4500   3400     0.0000    -2.0085    23.0125    33.839518     17.9957    -14.4832     23.1000\n  4500   3600     0.0000    -2.0085    23.0125    34.151689     17.9166    -14.5810     23.1000\n  4500   3800     0.0000    -2.0085    23.0125    34.458503     17.8382    -14.6767     23.1000\n  4500   4000     0.0000    -2.0085    23.0125    34.760129     17.7607    -14.7704     23.1000\n  4500   4200     0.0000    -2.0085    23.0125    35.056729     17.6840    -14.8622     23.1000\n  4500   4400     0.0000    -2.0085    23.0125    35.348455     17.6081    -14.9520     23.1000\n  4500   4600     0.0000    -2.0085    23.0125    35.635454     17.5330    -15.0400     23.1000\n  4500   4800     0.0000    -2.0085    23.0125    35.917864     17.4587    -15.1263     23.1000\n  4500   5000     0.0000    -2.0085    23.0125    36.195818     17.3851    -15.2108     23.1000\n  4500   5200     0.0000    -2.0085    23.0125    36.469441     17.3122    -15.2936     23.1000\n  4500   5400     0.0000    -2.0085    23.0125    36.738854     17.2401    -15.3749     23.1000\n  4500   5600     0.0000    -2.0085    23.0125    37.004173     17.1688    -15.4545     23.1000\n  4500   5800     0.0000    -2.0085    23.0125    37.265507     17.0981    -15.5327     23.1000\n  4500   6000     0.0000    -2.0085    23.0125    37.522963     17.0281    -15.6094     23.1000\n  4500   6200     0.0000    -2.0085    23.0125    37.776641     16.9588    -15.6846     23.1000\n  4500   6400     0.0000    -2.0085    23.0125    38.026639     16.8902    -15.7585     23.1000\n  4500   6600     0.0000    -2.0085    23.0125    38.273049     16.8223    -15.8309     23.1000\n  4500   6800     0.0000    -2.0085    23.0125    38.515961     16.7550    -15.9021     23.1000\n  4500   7000     0.0000    -2.0085    23.0125    38.755460     16.6884    -15.9720     23.1000\n  4500   7200     0.0000    -2.0085    23.0125    38.991631     16.6224    -16.0407     23.1000\n  4500   7400     0.0000    -2.0085    23.0125    39.224551     16.5571    -16.1081     23.1000\n  4500   7600     0.0000    -2.0085    23.0125    39.454297     16.4924    -16.1744     23.1000\n  4500   7800     0.0000    -2.0085    23.0125    39.680943     16.4283    -16.2395     23.1000\n  4500   8000     0.0000    -2.0085    23.0125    39.904560     16.3648    -16.3035     23.1000\n  4500   8200     0.0000    -2.0085    23.0125    40.125217     16.3019    -16.3664     23.1000\n  4500   8400     0.0000    -2.0085    23.0125    40.342979     16.2395    -16.4282     23.1000\n\nuser time (s):         0.010\nsystem time (s):       0.010\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180518_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.2959757      -15.0330123       24.3922942   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0699261        0.0056151   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -14.8908566       24.3852158   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0836758        0.0170065   m/s m/s m/s\nunwrap_phase_constant:            0.00035     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180518_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.296    -15.033     24.392\norbit baseline rate   (TCN) (m/s):  0.000e+00 -6.993e-02  5.615e-03\n\nbaseline vector (TCN)   (m):      0.000    -14.891     24.385\nbaseline rate   (TCN) (m/s):  0.000e+00 -8.368e-02  1.701e-02\n\nSLC-1 center baseline length (m):    28.5723\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -14.1098    24.2265    27.497013     14.9752    -23.7013     28.0358\n     0    200     0.0000   -14.1098    24.2265    27.939249     14.7918    -23.8162     28.0358\n     0    400     0.0000   -14.1098    24.2265    28.370851     14.6120    -23.9269     28.0358\n     0    600     0.0000   -14.1098    24.2265    28.792342     14.4356    -24.0337     28.0358\n     0    800     0.0000   -14.1098    24.2265    29.204202     14.2625    -24.1369     28.0358\n     0   1000     0.0000   -14.1098    24.2265    29.606873     14.0925    -24.2365     28.0358\n     0   1200     0.0000   -14.1098    24.2265    30.000767     13.9255    -24.3328     28.0358\n     0   1400     0.0000   -14.1098    24.2265    30.386264     13.7615    -24.4260     28.0358\n     0   1600     0.0000   -14.1098    24.2265    30.763716     13.6003    -24.5161     28.0358\n     0   1800     0.0000   -14.1098    24.2265    31.133454     13.4418    -24.6034     28.0358\n     0   2000     0.0000   -14.1098    24.2265    31.495785     13.2859    -24.6879     28.0358\n     0   2200     0.0000   -14.1098    24.2265    31.850997     13.1326    -24.7698     28.0358\n     0   2400     0.0000   -14.1098    24.2265    32.199360     12.9818    -24.8492     28.0358\n     0   2600     0.0000   -14.1098    24.2265    32.541126     12.8333    -24.9262     28.0358\n     0   2800     0.0000   -14.1098    24.2265    32.876534     12.6872    -25.0009     28.0358\n     0   3000     0.0000   -14.1098    24.2265    33.205808     12.5433    -25.0734     28.0358\n     0   3200     0.0000   -14.1098    24.2265    33.529159     12.4016    -25.1437     28.0358\n     0   3400     0.0000   -14.1098    24.2265    33.846787     12.2620    -25.2121     28.0358\n     0   3600     0.0000   -14.1098    24.2265    34.158881     12.1245    -25.2785     28.0358\n     0   3800     0.0000   -14.1098    24.2265    34.465620     11.9890    -25.3431     28.0358\n     0   4000     0.0000   -14.1098    24.2265    34.767174     11.8555    -25.4058     28.0358\n     0   4200     0.0000   -14.1098    24.2265    35.063703     11.7238    -25.4668     28.0358\n     0   4400     0.0000   -14.1098    24.2265    35.355362     11.5940    -25.5262     28.0358\n     0   4600     0.0000   -14.1098    24.2265    35.642294     11.4661    -25.5839     28.0358\n     0   4800     0.0000   -14.1098    24.2265    35.924640     11.3398    -25.6401     28.0358\n     0   5000     0.0000   -14.1098    24.2265    36.202531     11.2154    -25.6948     28.0358\n     0   5200     0.0000   -14.1098    24.2265    36.476093     11.0925    -25.7481     28.0358\n     0   5400     0.0000   -14.1098    24.2265    36.745447     10.9714    -25.7999     28.0358\n     0   5600     0.0000   -14.1098    24.2265    37.010708     10.8518    -25.8505     28.0358\n     0   5800     0.0000   -14.1098    24.2265    37.271986     10.7338    -25.8997     28.0358\n     0   6000     0.0000   -14.1098    24.2265    37.529387     10.6174    -25.9476     28.0358\n     0   6200     0.0000   -14.1098    24.2265    37.783012     10.5024    -25.9944     28.0358\n     0   6400     0.0000   -14.1098    24.2265    38.032957     10.3889    -26.0399     28.0358\n     0   6600     0.0000   -14.1098    24.2265    38.279317     10.2768    -26.0844     28.0358\n     0   6800     0.0000   -14.1098    24.2265    38.522179     10.1662    -26.1277     28.0358\n     0   7000     0.0000   -14.1098    24.2265    38.761630     10.0569    -26.1700     28.0358\n     0   7200     0.0000   -14.1098    24.2265    38.997753      9.9490    -26.2112     28.0358\n     0   7400     0.0000   -14.1098    24.2265    39.230627      9.8423    -26.2514     28.0358\n     0   7600     0.0000   -14.1098    24.2265    39.460328      9.7370    -26.2907     28.0358\n     0   7800     0.0000   -14.1098    24.2265    39.686930      9.6330    -26.3290     28.0358\n     0   8000     0.0000   -14.1098    24.2265    39.910504      9.5302    -26.3663     28.0358\n     0   8200     0.0000   -14.1098    24.2265    40.131118      9.4286    -26.4028     28.0358\n     0   8400     0.0000   -14.1098    24.2265    40.348838      9.3282    -26.4385     28.0358\n   500      0     0.0000   -14.2818    24.2614    27.496077     14.9272    -23.8697     28.1529\n   500    200     0.0000   -14.2818    24.2614    27.938329     14.7425    -23.9842     28.1529\n   500    400     0.0000   -14.2818    24.2614    28.369946     14.5614    -24.0946     28.1529\n   500    600     0.0000   -14.2818    24.2614    28.791451     14.3838    -24.2011     28.1529\n   500    800     0.0000   -14.2818    24.2614    29.203324     14.2094    -24.3039     28.1529\n   500   1000     0.0000   -14.2818    24.2614    29.606009     14.0383    -24.4031     28.1529\n   500   1200     0.0000   -14.2818    24.2614    29.999915     13.8702    -24.4991     28.1529\n   500   1400     0.0000   -14.2818    24.2614    30.385423     13.7050    -24.5918     28.1529\n   500   1600     0.0000   -14.2818    24.2614    30.762887     13.5427    -24.6816     28.1529\n   500   1800     0.0000   -14.2818    24.2614    31.132636     13.3831    -24.7685     28.1529\n   500   2000     0.0000   -14.2818    24.2614    31.494978     13.2262    -24.8526     28.1529\n   500   2200     0.0000   -14.2818    24.2614    31.850200     13.0719    -24.9341     28.1529\n   500   2400     0.0000   -14.2818    24.2614    32.198572     12.9201    -25.0132     28.1529\n   500   2600     0.0000   -14.2818    24.2614    32.540347     12.7706    -25.0898     28.1529\n   500   2800     0.0000   -14.2818    24.2614    32.875764     12.6235    -25.1641     28.1529\n   500   3000     0.0000   -14.2818    24.2614    33.205046     12.4787    -25.2362     28.1529\n   500   3200     0.0000   -14.2818    24.2614    33.528405     12.3361    -25.3063     28.1529\n   500   3400     0.0000   -14.2818    24.2614    33.846042     12.1956    -25.3743     28.1529\n   500   3600     0.0000   -14.2818    24.2614    34.158144     12.0572    -25.4403     28.1529\n   500   3800     0.0000   -14.2818    24.2614    34.464890     11.9208    -25.5045     28.1529\n   500   4000     0.0000   -14.2818    24.2614    34.766452     11.7864    -25.5669     28.1529\n   500   4200     0.0000   -14.2818    24.2614    35.062988     11.6539    -25.6276     28.1529\n   500   4400     0.0000   -14.2818    24.2614    35.354653     11.5233    -25.6866     28.1529\n   500   4600     0.0000   -14.2818    24.2614    35.641592     11.3946    -25.7439     28.1529\n   500   4800     0.0000   -14.2818    24.2614    35.923944     11.2675    -25.7998     28.1529\n   500   5000     0.0000   -14.2818    24.2614    36.201842     11.1423    -25.8541     28.1529\n   500   5200     0.0000   -14.2818    24.2614    36.475410     11.0187    -25.9070     28.1529\n   500   5400     0.0000   -14.2818    24.2614    36.744770     10.8968    -25.9585     28.1529\n   500   5600     0.0000   -14.2818    24.2614    37.010037     10.7765    -26.0087     28.1529\n   500   5800     0.0000   -14.2818    24.2614    37.271321     10.6578    -26.0576     28.1529\n   500   6000     0.0000   -14.2818    24.2614    37.528727     10.5406    -26.1052     28.1529\n   500   6200     0.0000   -14.2818    24.2614    37.782357     10.4249    -26.1516     28.1529\n   500   6400     0.0000   -14.2818    24.2614    38.032308     10.3108    -26.1968     28.1529\n   500   6600     0.0000   -14.2818    24.2614    38.278673     10.1980    -26.2409     28.1529\n   500   6800     0.0000   -14.2818    24.2614    38.521540     10.0867    -26.2839     28.1529\n   500   7000     0.0000   -14.2818    24.2614    38.760996      9.9768    -26.3259     28.1529\n   500   7200     0.0000   -14.2818    24.2614    38.997124      9.8682    -26.3667     28.1529\n   500   7400     0.0000   -14.2818    24.2614    39.230002      9.7609    -26.4066     28.1529\n   500   7600     0.0000   -14.2818    24.2614    39.459708      9.6550    -26.4456     28.1529\n   500   7800     0.0000   -14.2818    24.2614    39.686314      9.5503    -26.4835     28.1529\n   500   8000     0.0000   -14.2818    24.2614    39.909892      9.4469    -26.5206     28.1529\n   500   8200     0.0000   -14.2818    24.2614    40.130511      9.3447    -26.5568     28.1529\n   500   8400     0.0000   -14.2818    24.2614    40.348236      9.2437    -26.5921     28.1529\n  1000      0     0.0000   -14.4538    24.2964    27.495122     14.8792    -24.0382     28.2706\n  1000    200     0.0000   -14.4538    24.2964    27.937389     14.6932    -24.1523     28.2706\n  1000    400     0.0000   -14.4538    24.2964    28.369022     14.5108    -24.2623     28.2706\n  1000    600     0.0000   -14.4538    24.2964    28.790541     14.3320    -24.3684     28.2706\n  1000    800     0.0000   -14.4538    24.2964    29.202428     14.1564    -24.4708     28.2706\n  1000   1000     0.0000   -14.4538    24.2964    29.605126     13.9841    -24.5697     28.2706\n  1000   1200     0.0000   -14.4538    24.2964    29.999045     13.8148    -24.6653     28.2706\n  1000   1400     0.0000   -14.4538    24.2964    30.384565     13.6485    -24.7577     28.2706\n  1000   1600     0.0000   -14.4538    24.2964    30.762040     13.4851    -24.8471     28.2706\n  1000   1800     0.0000   -14.4538    24.2964    31.131800     13.3245    -24.9336     28.2706\n  1000   2000     0.0000   -14.4538    24.2964    31.494152     13.1666    -25.0174     28.2706\n  1000   2200     0.0000   -14.4538    24.2964    31.849385     13.0112    -25.0985     28.2706\n  1000   2400     0.0000   -14.4538    24.2964    32.197766     12.8583    -25.1772     28.2706\n  1000   2600     0.0000   -14.4538    24.2964    32.539551     12.7079    -25.2534     28.2706\n  1000   2800     0.0000   -14.4538    24.2964    32.874977     12.5599    -25.3274     28.2706\n  1000   3000     0.0000   -14.4538    24.2964    33.204268     12.4141    -25.3991     28.2706\n  1000   3200     0.0000   -14.4538    24.2964    33.527636     12.2706    -25.4688     28.2706\n  1000   3400     0.0000   -14.4538    24.2964    33.845281     12.1292    -25.5364     28.2706\n  1000   3600     0.0000   -14.4538    24.2964    34.157391     11.9899    -25.6021     28.2706\n  1000   3800     0.0000   -14.4538    24.2964    34.464145     11.8526    -25.6660     28.2706\n  1000   4000     0.0000   -14.4538    24.2964    34.765714     11.7174    -25.7280     28.2706\n  1000   4200     0.0000   -14.4538    24.2964    35.062258     11.5841    -25.7883     28.2706\n  1000   4400     0.0000   -14.4538    24.2964    35.353930     11.4526    -25.8469     28.2706\n  1000   4600     0.0000   -14.4538    24.2964    35.640876     11.3231    -25.9039     28.2706\n  1000   4800     0.0000   -14.4538    24.2964    35.923235     11.1953    -25.9594     28.2706\n  1000   5000     0.0000   -14.4538    24.2964    36.201138     11.0692    -26.0134     28.2706\n  1000   5200     0.0000   -14.4538    24.2964    36.474713     10.9449    -26.0660     28.2706\n  1000   5400     0.0000   -14.4538    24.2964    36.744079     10.8222    -26.1172     28.2706\n  1000   5600     0.0000   -14.4538    24.2964    37.009352     10.7012    -26.1670     28.2706\n  1000   5800     0.0000   -14.4538    24.2964    37.270642     10.5817    -26.2155     28.2706\n  1000   6000     0.0000   -14.4538    24.2964    37.528054     10.4639    -26.2628     28.2706\n  1000   6200     0.0000   -14.4538    24.2964    37.781689     10.3475    -26.3089     28.2706\n  1000   6400     0.0000   -14.4538    24.2964    38.031646     10.2326    -26.3537     28.2706\n  1000   6600     0.0000   -14.4538    24.2964    38.278015     10.1192    -26.3975     28.2706\n  1000   6800     0.0000   -14.4538    24.2964    38.520888     10.0072    -26.4402     28.2706\n  1000   7000     0.0000   -14.4538    24.2964    38.760349      9.8966    -26.4817     28.2706\n  1000   7200     0.0000   -14.4538    24.2964    38.996482      9.7874    -26.5223     28.2706\n  1000   7400     0.0000   -14.4538    24.2964    39.229365      9.6795    -26.5619     28.2706\n  1000   7600     0.0000   -14.4538    24.2964    39.459075      9.5730    -26.6005     28.2706\n  1000   7800     0.0000   -14.4538    24.2964    39.685686      9.4677    -26.6381     28.2706\n  1000   8000     0.0000   -14.4538    24.2964    39.909268      9.3637    -26.6749     28.2706\n  1000   8200     0.0000   -14.4538    24.2964    40.129891      9.2609    -26.7107     28.2706\n  1000   8400     0.0000   -14.4538    24.2964    40.347621      9.1593    -26.7457     28.2706\n  1500      0     0.0000   -14.6258    24.3313    27.494145     14.8312    -24.2067     28.3889\n  1500    200     0.0000   -14.6258    24.3313    27.936429     14.6439    -24.3204     28.3889\n  1500    400     0.0000   -14.6258    24.3313    28.368077     14.4603    -24.4301     28.3889\n  1500    600     0.0000   -14.6258    24.3313    28.789612     14.2802    -24.5358     28.3889\n  1500    800     0.0000   -14.6258    24.3313    29.201513     14.1034    -24.6378     28.3889\n  1500   1000     0.0000   -14.6258    24.3313    29.604224     13.9299    -24.7363     28.3889\n  1500   1200     0.0000   -14.6258    24.3313    29.998156     13.7595    -24.8315     28.3889\n  1500   1400     0.0000   -14.6258    24.3313    30.383688     13.5921    -24.9235     28.3889\n  1500   1600     0.0000   -14.6258    24.3313    30.761176     13.4276    -25.0125     28.3889\n  1500   1800     0.0000   -14.6258    24.3313    31.130947     13.2659    -25.0987     28.3889\n  1500   2000     0.0000   -14.6258    24.3313    31.493310     13.1069    -25.1821     28.3889\n  1500   2200     0.0000   -14.6258    24.3313    31.848553     12.9505    -25.2629     28.3889\n  1500   2400     0.0000   -14.6258    24.3313    32.196945     12.7966    -25.3411     28.3889\n  1500   2600     0.0000   -14.6258    24.3313    32.538739     12.6452    -25.4170     28.3889\n  1500   2800     0.0000   -14.6258    24.3313    32.874174     12.4962    -25.4906     28.3889\n  1500   3000     0.0000   -14.6258    24.3313    33.203475     12.3495    -25.5620     28.3889\n  1500   3200     0.0000   -14.6258    24.3313    33.526851     12.2050    -25.6313     28.3889\n  1500   3400     0.0000   -14.6258    24.3313    33.844504     12.0628    -25.6986     28.3889\n  1500   3600     0.0000   -14.6258    24.3313    34.156623     11.9226    -25.7639     28.3889\n  1500   3800     0.0000   -14.6258    24.3313    34.463385     11.7845    -25.8274     28.3889\n  1500   4000     0.0000   -14.6258    24.3313    34.764961     11.6484    -25.8891     28.3889\n  1500   4200     0.0000   -14.6258    24.3313    35.061513     11.5142    -25.9490     28.3889\n  1500   4400     0.0000   -14.6258    24.3313    35.353192     11.3820    -26.0073     28.3889\n  1500   4600     0.0000   -14.6258    24.3313    35.640145     11.2516    -26.0640     28.3889\n  1500   4800     0.0000   -14.6258    24.3313    35.922510     11.1230    -26.1191     28.3889\n  1500   5000     0.0000   -14.6258    24.3313    36.200421     10.9962    -26.1727     28.3889\n  1500   5200     0.0000   -14.6258    24.3313    36.474002     10.8711    -26.2249     28.3889\n  1500   5400     0.0000   -14.6258    24.3313    36.743374     10.7477    -26.2758     28.3889\n  1500   5600     0.0000   -14.6258    24.3313    37.008653     10.6259    -26.3252     28.3889\n  1500   5800     0.0000   -14.6258    24.3313    37.269949     10.5057    -26.3734     28.3889\n  1500   6000     0.0000   -14.6258    24.3313    37.527367     10.3871    -26.4204     28.3889\n  1500   6200     0.0000   -14.6258    24.3313    37.781008     10.2701    -26.4661     28.3889\n  1500   6400     0.0000   -14.6258    24.3313    38.030970     10.1545    -26.5106     28.3889\n  1500   6600     0.0000   -14.6258    24.3313    38.277345     10.0404    -26.5541     28.3889\n  1500   6800     0.0000   -14.6258    24.3313    38.520223      9.9278    -26.5964     28.3889\n  1500   7000     0.0000   -14.6258    24.3313    38.759689      9.8165    -26.6376     28.3889\n  1500   7200     0.0000   -14.6258    24.3313    38.995827      9.7066    -26.6779     28.3889\n  1500   7400     0.0000   -14.6258    24.3313    39.228715      9.5981    -26.7171     28.3889\n  1500   7600     0.0000   -14.6258    24.3313    39.458429      9.4909    -26.7554     28.3889\n  1500   7800     0.0000   -14.6258    24.3313    39.685045      9.3850    -26.7927     28.3889\n  1500   8000     0.0000   -14.6258    24.3313    39.908632      9.2804    -26.8291     28.3889\n  1500   8200     0.0000   -14.6258    24.3313    40.129260      9.1770    -26.8647     28.3889\n  1500   8400     0.0000   -14.6258    24.3313    40.346993      9.0749    -26.8993     28.3889\n  2000      0     0.0000   -14.7978    24.3663    27.493148     14.7832    -24.3751     28.5077\n  2000    200     0.0000   -14.7978    24.3663    27.935449     14.5946    -24.4885     28.5077\n  2000    400     0.0000   -14.7978    24.3663    28.367113     14.4097    -24.5978     28.5077\n  2000    600     0.0000   -14.7978    24.3663    28.788663     14.2284    -24.7031     28.5077\n  2000    800     0.0000   -14.7978    24.3663    29.200578     14.0504    -24.8048     28.5077\n  2000   1000     0.0000   -14.7978    24.3663    29.603304     13.8757    -24.9029     28.5077\n  2000   1200     0.0000   -14.7978    24.3663    29.997248     13.7042    -24.9977     28.5077\n  2000   1400     0.0000   -14.7978    24.3663    30.382794     13.5356    -25.0894     28.5077\n  2000   1600     0.0000   -14.7978    24.3663    30.760293     13.3700    -25.1780     28.5077\n  2000   1800     0.0000   -14.7978    24.3663    31.130076     13.2073    -25.2638     28.5077\n  2000   2000     0.0000   -14.7978    24.3663    31.492450     13.0472    -25.3468     28.5077\n  2000   2200     0.0000   -14.7978    24.3663    31.847704     12.8898    -25.4272     28.5077\n  2000   2400     0.0000   -14.7978    24.3663    32.196106     12.7350    -25.5051     28.5077\n  2000   2600     0.0000   -14.7978    24.3663    32.537910     12.5826    -25.5807     28.5077\n  2000   2800     0.0000   -14.7978    24.3663    32.873355     12.4326    -25.6539     28.5077\n  2000   3000     0.0000   -14.7978    24.3663    33.202665     12.2849    -25.7249     28.5077\n  2000   3200     0.0000   -14.7978    24.3663    33.526051     12.1395    -25.7938     28.5077\n  2000   3400     0.0000   -14.7978    24.3663    33.843712     11.9964    -25.8607     28.5077\n  2000   3600     0.0000   -14.7978    24.3663    34.155839     11.8553    -25.9257     28.5077\n  2000   3800     0.0000   -14.7978    24.3663    34.462609     11.7163    -25.9888     28.5077\n  2000   4000     0.0000   -14.7978    24.3663    34.764193     11.5794    -26.0501     28.5077\n  2000   4200     0.0000   -14.7978    24.3663    35.060752     11.4444    -26.1097     28.5077\n  2000   4400     0.0000   -14.7978    24.3663    35.352439     11.3113    -26.1676     28.5077\n  2000   4600     0.0000   -14.7978    24.3663    35.639399     11.1801    -26.2240     28.5077\n  2000   4800     0.0000   -14.7978    24.3663    35.921772     11.0507    -26.2787     28.5077\n  2000   5000     0.0000   -14.7978    24.3663    36.199689     10.9231    -26.3320     28.5077\n  2000   5200     0.0000   -14.7978    24.3663    36.473276     10.7973    -26.3839     28.5077\n  2000   5400     0.0000   -14.7978    24.3663    36.742655     10.6731    -26.4344     28.5077\n  2000   5600     0.0000   -14.7978    24.3663    37.007941     10.5506    -26.4835     28.5077\n  2000   5800     0.0000   -14.7978    24.3663    37.269242     10.4297    -26.5313     28.5077\n  2000   6000     0.0000   -14.7978    24.3663    37.526666     10.3104    -26.5779     28.5077\n  2000   6200     0.0000   -14.7978    24.3663    37.780313     10.1926    -26.6233     28.5077\n  2000   6400     0.0000   -14.7978    24.3663    38.030281     10.0764    -26.6675     28.5077\n  2000   6600     0.0000   -14.7978    24.3663    38.276661      9.9616    -26.7106     28.5077\n  2000   6800     0.0000   -14.7978    24.3663    38.519545      9.8483    -26.7526     28.5077\n  2000   7000     0.0000   -14.7978    24.3663    38.759016      9.7364    -26.7935     28.5077\n  2000   7200     0.0000   -14.7978    24.3663    38.995159      9.6259    -26.8334     28.5077\n  2000   7400     0.0000   -14.7978    24.3663    39.228052      9.5167    -26.8723     28.5077\n  2000   7600     0.0000   -14.7978    24.3663    39.457772      9.4089    -26.9103     28.5077\n  2000   7800     0.0000   -14.7978    24.3663    39.684392      9.3024    -26.9473     28.5077\n  2000   8000     0.0000   -14.7978    24.3663    39.907984      9.1972    -26.9834     28.5077\n  2000   8200     0.0000   -14.7978    24.3663    40.128616      9.0932    -27.0186     28.5077\n  2000   8400     0.0000   -14.7978    24.3663    40.346354      8.9905    -27.0530     28.5077\n  2500      0     0.0000   -14.9698    24.4013    27.492131     14.7353    -24.5436     28.6272\n  2500    200     0.0000   -14.9698    24.4013    27.934449     14.5454    -24.6566     28.6272\n  2500    400     0.0000   -14.9698    24.4013    28.366129     14.3592    -24.7655     28.6272\n  2500    600     0.0000   -14.9698    24.4013    28.787694     14.1766    -24.8705     28.6272\n  2500    800     0.0000   -14.9698    24.4013    29.199625     13.9974    -24.9717     28.6272\n  2500   1000     0.0000   -14.9698    24.4013    29.602364     13.8215    -25.0695     28.6272\n  2500   1200     0.0000   -14.9698    24.4013    29.996323     13.6488    -25.1640     28.6272\n  2500   1400     0.0000   -14.9698    24.4013    30.381881     13.4792    -25.2552     28.6272\n  2500   1600     0.0000   -14.9698    24.4013    30.759393     13.3125    -25.3435     28.6272\n  2500   1800     0.0000   -14.9698    24.4013    31.129187     13.1487    -25.4289     28.6272\n  2500   2000     0.0000   -14.9698    24.4013    31.491573     12.9876    -25.5115     28.6272\n  2500   2200     0.0000   -14.9698    24.4013    31.846838     12.8291    -25.5916     28.6272\n  2500   2400     0.0000   -14.9698    24.4013    32.195250     12.6733    -25.6691     28.6272\n  2500   2600     0.0000   -14.9698    24.4013    32.537065     12.5199    -25.7443     28.6272\n  2500   2800     0.0000   -14.9698    24.4013    32.872520     12.3690    -25.8171     28.6272\n  2500   3000     0.0000   -14.9698    24.4013    33.201839     12.2204    -25.8878     28.6272\n  2500   3200     0.0000   -14.9698    24.4013    33.525234     12.0741    -25.9564     28.6272\n  2500   3400     0.0000   -14.9698    24.4013    33.842904     11.9300    -26.0229     28.6272\n  2500   3600     0.0000   -14.9698    24.4013    34.155039     11.7880    -26.0875     28.6272\n  2500   3800     0.0000   -14.9698    24.4013    34.461818     11.6482    -26.1503     28.6272\n  2500   4000     0.0000   -14.9698    24.4013    34.763411     11.5104    -26.2112     28.6272\n  2500   4200     0.0000   -14.9698    24.4013    35.059977     11.3745    -26.2704     28.6272\n  2500   4400     0.0000   -14.9698    24.4013    35.351672     11.2406    -26.3280     28.6272\n  2500   4600     0.0000   -14.9698    24.4013    35.638639     11.1086    -26.3840     28.6272\n  2500   4800     0.0000   -14.9698    24.4013    35.921019     10.9785    -26.4384     28.6272\n  2500   5000     0.0000   -14.9698    24.4013    36.198943     10.8501    -26.4913     28.6272\n  2500   5200     0.0000   -14.9698    24.4013    36.472537     10.7235    -26.5429     28.6272\n  2500   5400     0.0000   -14.9698    24.4013    36.741923     10.5986    -26.5930     28.6272\n  2500   5600     0.0000   -14.9698    24.4013    37.007214     10.4753    -26.6418     28.6272\n  2500   5800     0.0000   -14.9698    24.4013    37.268522     10.3537    -26.6893     28.6272\n  2500   6000     0.0000   -14.9698    24.4013    37.525952     10.2337    -26.7355     28.6272\n  2500   6200     0.0000   -14.9698    24.4013    37.779605     10.1152    -26.7806     28.6272\n  2500   6400     0.0000   -14.9698    24.4013    38.029579      9.9983    -26.8244     28.6272\n  2500   6600     0.0000   -14.9698    24.4013    38.275965      9.8828    -26.8672     28.6272\n  2500   6800     0.0000   -14.9698    24.4013    38.518854      9.7689    -26.9088     28.6272\n  2500   7000     0.0000   -14.9698    24.4013    38.758331      9.6563    -26.9494     28.6272\n  2500   7200     0.0000   -14.9698    24.4013    38.994478      9.5452    -26.9890     28.6272\n  2500   7400     0.0000   -14.9698    24.4013    39.227377      9.4354    -27.0276     28.6272\n  2500   7600     0.0000   -14.9698    24.4013    39.457102      9.3269    -27.0652     28.6272\n  2500   7800     0.0000   -14.9698    24.4013    39.683727      9.2198    -27.1019     28.6272\n  2500   8000     0.0000   -14.9698    24.4013    39.907324      9.1140    -27.1376     28.6272\n  2500   8200     0.0000   -14.9698    24.4013    40.127960      9.0094    -27.1725     28.6272\n  2500   8400     0.0000   -14.9698    24.4013    40.345703      8.9061    -27.2066     28.6272\n  3000      0     0.0000   -15.1418    24.4362    27.491093     14.6873    -24.7120     28.7472\n  3000    200     0.0000   -15.1418    24.4362    27.933428     14.4961    -24.8247     28.7472\n  3000    400     0.0000   -15.1418    24.4362    28.365126     14.3087    -24.9332     28.7472\n  3000    600     0.0000   -15.1418    24.4362    28.786707     14.1248    -25.0378     28.7472\n  3000    800     0.0000   -15.1418    24.4362    29.198653     13.9444    -25.1387     28.7472\n  3000   1000     0.0000   -15.1418    24.4362    29.601406     13.7674    -25.2361     28.7472\n  3000   1200     0.0000   -15.1418    24.4362    29.995379     13.5935    -25.3302     28.7472\n  3000   1400     0.0000   -15.1418    24.4362    30.380950     13.4228    -25.4211     28.7472\n  3000   1600     0.0000   -15.1418    24.4362    30.758475     13.2550    -25.5090     28.7472\n  3000   1800     0.0000   -15.1418    24.4362    31.128282     13.0901    -25.5940     28.7472\n  3000   2000     0.0000   -15.1418    24.4362    31.490679     12.9279    -25.6763     28.7472\n  3000   2200     0.0000   -15.1418    24.4362    31.845955     12.7685    -25.7559     28.7472\n  3000   2400     0.0000   -15.1418    24.4362    32.194378     12.6116    -25.8331     28.7472\n  3000   2600     0.0000   -15.1418    24.4362    32.536204     12.4573    -25.9079     28.7472\n  3000   2800     0.0000   -15.1418    24.4362    32.871668     12.3054    -25.9804     28.7472\n  3000   3000     0.0000   -15.1418    24.4362    33.200997     12.1558    -26.0507     28.7472\n  3000   3200     0.0000   -15.1418    24.4362    33.524401     12.0086    -26.1189     28.7472\n  3000   3400     0.0000   -15.1418    24.4362    33.842081     11.8636    -26.1851     28.7472\n  3000   3600     0.0000   -15.1418    24.4362    34.154225     11.7208    -26.2493     28.7472\n  3000   3800     0.0000   -15.1418    24.4362    34.461012     11.5800    -26.3117     28.7472\n  3000   4000     0.0000   -15.1418    24.4362    34.762613     11.4414    -26.3723     28.7472\n  3000   4200     0.0000   -15.1418    24.4362    35.059187     11.3047    -26.4312     28.7472\n  3000   4400     0.0000   -15.1418    24.4362    35.350889     11.1700    -26.4884     28.7472\n  3000   4600     0.0000   -15.1418    24.4362    35.637865     11.0372    -26.5440     28.7472\n  3000   4800     0.0000   -15.1418    24.4362    35.920252     10.9062    -26.5981     28.7472\n  3000   5000     0.0000   -15.1418    24.4362    36.198183     10.7771    -26.6506     28.7472\n  3000   5200     0.0000   -15.1418    24.4362    36.471784     10.6497    -26.7018     28.7472\n  3000   5400     0.0000   -15.1418    24.4362    36.741176     10.5240    -26.7516     28.7472\n  3000   5600     0.0000   -15.1418    24.4362    37.006474     10.4001    -26.8000     28.7472\n  3000   5800     0.0000   -15.1418    24.4362    37.267789     10.2777    -26.8472     28.7472\n  3000   6000     0.0000   -15.1418    24.4362    37.525225     10.1570    -26.8931     28.7472\n  3000   6200     0.0000   -15.1418    24.4362    37.778884     10.0378    -26.9378     28.7472\n  3000   6400     0.0000   -15.1418    24.4362    38.028863      9.9202    -26.9813     28.7472\n  3000   6600     0.0000   -15.1418    24.4362    38.275255      9.8041    -27.0237     28.7472\n  3000   6800     0.0000   -15.1418    24.4362    38.518150      9.6894    -27.0651     28.7472\n  3000   7000     0.0000   -15.1418    24.4362    38.757632      9.5762    -27.1053     28.7472\n  3000   7200     0.0000   -15.1418    24.4362    38.993785      9.4644    -27.1446     28.7472\n  3000   7400     0.0000   -15.1418    24.4362    39.226689      9.3540    -27.1828     28.7472\n  3000   7600     0.0000   -15.1418    24.4362    39.456419      9.2449    -27.2201     28.7472\n  3000   7800     0.0000   -15.1418    24.4362    39.683049      9.1372    -27.2565     28.7472\n  3000   8000     0.0000   -15.1418    24.4362    39.906651      9.0308    -27.2919     28.7472\n  3000   8200     0.0000   -15.1418    24.4362    40.127293      8.9256    -27.3265     28.7472\n  3000   8400     0.0000   -15.1418    24.4362    40.345040      8.8217    -27.3602     28.7472\n  3500      0     0.0000   -15.3138    24.4712    27.490034     14.6394    -24.8805     28.8678\n  3500    200     0.0000   -15.3138    24.4712    27.932388     14.4469    -24.9927     28.8678\n  3500    400     0.0000   -15.3138    24.4712    28.364103     14.2582    -25.1009     28.8678\n  3500    600     0.0000   -15.3138    24.4712    28.785700     14.0731    -25.2051     28.8678\n  3500    800     0.0000   -15.3138    24.4712    29.197661     13.8915    -25.3057     28.8678\n  3500   1000     0.0000   -15.3138    24.4712    29.600430     13.7133    -25.4027     28.8678\n  3500   1200     0.0000   -15.3138    24.4712    29.994416     13.5383    -25.4964     28.8678\n  3500   1400     0.0000   -15.3138    24.4712    30.380001     13.3664    -25.5869     28.8678\n  3500   1600     0.0000   -15.3138    24.4712    30.757539     13.1975    -25.6744     28.8678\n  3500   1800     0.0000   -15.3138    24.4712    31.127358     13.0315    -25.7591     28.8678\n  3500   2000     0.0000   -15.3138    24.4712    31.489768     12.8683    -25.8410     28.8678\n  3500   2200     0.0000   -15.3138    24.4712    31.845055     12.7078    -25.9203     28.8678\n  3500   2400     0.0000   -15.3138    24.4712    32.193490     12.5500    -25.9971     28.8678\n  3500   2600     0.0000   -15.3138    24.4712    32.535326     12.3946    -26.0715     28.8678\n  3500   2800     0.0000   -15.3138    24.4712    32.870801     12.2418    -26.1436     28.8678\n  3500   3000     0.0000   -15.3138    24.4712    33.200139     12.0913    -26.2136     28.8678\n  3500   3200     0.0000   -15.3138    24.4712    33.523553     11.9431    -26.2814     28.8678\n  3500   3400     0.0000   -15.3138    24.4712    33.841242     11.7972    -26.3472     28.8678\n  3500   3600     0.0000   -15.3138    24.4712    34.153395     11.6535    -26.4111     28.8678\n  3500   3800     0.0000   -15.3138    24.4712    34.460191     11.5119    -26.4731     28.8678\n  3500   4000     0.0000   -15.3138    24.4712    34.761800     11.3724    -26.5334     28.8678\n  3500   4200     0.0000   -15.3138    24.4712    35.058383     11.2349    -26.5919     28.8678\n  3500   4400     0.0000   -15.3138    24.4712    35.350093     11.0994    -26.6487     28.8678\n  3500   4600     0.0000   -15.3138    24.4712    35.637076     10.9658    -26.7040     28.8678\n  3500   4800     0.0000   -15.3138    24.4712    35.919470     10.8340    -26.7577     28.8678\n  3500   5000     0.0000   -15.3138    24.4712    36.197408     10.7041    -26.8099     28.8678\n  3500   5200     0.0000   -15.3138    24.4712    36.471017     10.5759    -26.8608     28.8678\n  3500   5400     0.0000   -15.3138    24.4712    36.740416     10.4495    -26.9102     28.8678\n  3500   5600     0.0000   -15.3138    24.4712    37.005721     10.3248    -26.9583     28.8678\n  3500   5800     0.0000   -15.3138    24.4712    37.267042     10.2017    -27.0051     28.8678\n  3500   6000     0.0000   -15.3138    24.4712    37.524484     10.0803    -27.0507     28.8678\n  3500   6200     0.0000   -15.3138    24.4712    37.778150      9.9604    -27.0950     28.8678\n  3500   6400     0.0000   -15.3138    24.4712    38.028135      9.8421    -27.1382     28.8678\n  3500   6600     0.0000   -15.3138    24.4712    38.274533      9.7253    -27.1803     28.8678\n  3500   6800     0.0000   -15.3138    24.4712    38.517433      9.6100    -27.2213     28.8678\n  3500   7000     0.0000   -15.3138    24.4712    38.756921      9.4961    -27.2612     28.8678\n  3500   7200     0.0000   -15.3138    24.4712    38.993080      9.3837    -27.3001     28.8678\n  3500   7400     0.0000   -15.3138    24.4712    39.225988      9.2726    -27.3380     28.8678\n  3500   7600     0.0000   -15.3138    24.4712    39.455724      9.1630    -27.3750     28.8678\n  3500   7800     0.0000   -15.3138    24.4712    39.682359      9.0546    -27.4110     28.8678\n  3500   8000     0.0000   -15.3138    24.4712    39.905966      8.9476    -27.4462     28.8678\n  3500   8200     0.0000   -15.3138    24.4712    40.126613      8.8418    -27.4804     28.8678\n  3500   8400     0.0000   -15.3138    24.4712    40.344365      8.7373    -27.5138     28.8678\n  4000      0     0.0000   -15.4858    24.5061    27.488955     14.5915    -25.0489     28.9890\n  4000    200     0.0000   -15.4858    24.5061    27.931328     14.3977    -25.1608     28.9890\n  4000    400     0.0000   -15.4858    24.5061    28.363060     14.2077    -25.2686     28.9890\n  4000    600     0.0000   -15.4858    24.5061    28.784674     14.0214    -25.3724     28.9890\n  4000    800     0.0000   -15.4858    24.5061    29.196651     13.8386    -25.4726     28.9890\n  4000   1000     0.0000   -15.4858    24.5061    29.599435     13.6591    -25.5693     28.9890\n  4000   1200     0.0000   -15.4858    24.5061    29.993436     13.4830    -25.6626     28.9890\n  4000   1400     0.0000   -15.4858    24.5061    30.379035     13.3100    -25.7528     28.9890\n  4000   1600     0.0000   -15.4858    24.5061    30.756585     13.1400    -25.8399     28.9890\n  4000   1800     0.0000   -15.4858    24.5061    31.126418     12.9729    -25.9242     28.9890\n  4000   2000     0.0000   -15.4858    24.5061    31.488839     12.8087    -26.0057     28.9890\n  4000   2200     0.0000   -15.4858    24.5061    31.844138     12.6472    -26.0846     28.9890\n  4000   2400     0.0000   -15.4858    24.5061    32.192584     12.4883    -26.1611     28.9890\n  4000   2600     0.0000   -15.4858    24.5061    32.534431     12.3320    -26.2351     28.9890\n  4000   2800     0.0000   -15.4858    24.5061    32.869917     12.1782    -26.3069     28.9890\n  4000   3000     0.0000   -15.4858    24.5061    33.199265     12.0268    -26.3764     28.9890\n  4000   3200     0.0000   -15.4858    24.5061    33.522689     11.8777    -26.4439     28.9890\n  4000   3400     0.0000   -15.4858    24.5061    33.840387     11.7309    -26.5094     28.9890\n  4000   3600     0.0000   -15.4858    24.5061    34.152550     11.5863    -26.5729     28.9890\n  4000   3800     0.0000   -15.4858    24.5061    34.459354     11.4438    -26.6346     28.9890\n  4000   4000     0.0000   -15.4858    24.5061    34.760972     11.3034    -26.6944     28.9890\n  4000   4200     0.0000   -15.4858    24.5061    35.057563     11.1651    -26.7526     28.9890\n  4000   4400     0.0000   -15.4858    24.5061    35.349281     11.0287    -26.8091     28.9890\n  4000   4600     0.0000   -15.4858    24.5061    35.636272     10.8943    -26.8640     28.9890\n  4000   4800     0.0000   -15.4858    24.5061    35.918674     10.7618    -26.9174     28.9890\n  4000   5000     0.0000   -15.4858    24.5061    36.196620     10.6311    -26.9692     28.9890\n  4000   5200     0.0000   -15.4858    24.5061    36.470236     10.5022    -27.0197     28.9890\n  4000   5400     0.0000   -15.4858    24.5061    36.739642     10.3750    -27.0688     28.9890\n  4000   5600     0.0000   -15.4858    24.5061    37.004953     10.2495    -27.1165     28.9890\n  4000   5800     0.0000   -15.4858    24.5061    37.266281     10.1258    -27.1630     28.9890\n  4000   6000     0.0000   -15.4858    24.5061    37.523730     10.0036    -27.2082     28.9890\n  4000   6200     0.0000   -15.4858    24.5061    37.777402      9.8830    -27.2523     28.9890\n  4000   6400     0.0000   -15.4858    24.5061    38.027393      9.7640    -27.2951     28.9890\n  4000   6600     0.0000   -15.4858    24.5061    38.273797      9.6466    -27.3369     28.9890\n  4000   6800     0.0000   -15.4858    24.5061    38.516703      9.5306    -27.3775     28.9890\n  4000   7000     0.0000   -15.4858    24.5061    38.756197      9.4161    -27.4171     28.9890\n  4000   7200     0.0000   -15.4858    24.5061    38.992361      9.3030    -27.4557     28.9890\n  4000   7400     0.0000   -15.4858    24.5061    39.225276      9.1913    -27.4933     28.9890\n  4000   7600     0.0000   -15.4858    24.5061    39.455016      9.0810    -27.5299     28.9890\n  4000   7800     0.0000   -15.4858    24.5061    39.681657      8.9720    -27.5656     28.9890\n  4000   8000     0.0000   -15.4858    24.5061    39.905269      8.8644    -27.6004     28.9890\n  4000   8200     0.0000   -15.4858    24.5061    40.125921      8.7580    -27.6344     28.9890\n  4000   8400     0.0000   -15.4858    24.5061    40.343678      8.6529    -27.6674     28.9890\n  4500      0     0.0000   -15.6578    24.5411    27.487857     14.5436    -25.2173     29.1107\n  4500    200     0.0000   -15.6578    24.5411    27.930248     14.3485    -25.3289     29.1107\n  4500    400     0.0000   -15.6578    24.5411    28.361998     14.1572    -25.4363     29.1107\n  4500    600     0.0000   -15.6578    24.5411    28.783629     13.9696    -25.5398     29.1107\n  4500    800     0.0000   -15.6578    24.5411    29.195622     13.7856    -25.6396     29.1107\n  4500   1000     0.0000   -15.6578    24.5411    29.598421     13.6050    -25.7358     29.1107\n  4500   1200     0.0000   -15.6578    24.5411    29.992437     13.4277    -25.8288     29.1107\n  4500   1400     0.0000   -15.6578    24.5411    30.378050     13.2536    -25.9186     29.1107\n  4500   1600     0.0000   -15.6578    24.5411    30.755614     13.0825    -26.0054     29.1107\n  4500   1800     0.0000   -15.6578    24.5411    31.125460     12.9144    -26.0893     29.1107\n  4500   2000     0.0000   -15.6578    24.5411    31.487894     12.7491    -26.1704     29.1107\n  4500   2200     0.0000   -15.6578    24.5411    31.843205     12.5866    -26.2490     29.1107\n  4500   2400     0.0000   -15.6578    24.5411    32.191662     12.4267    -26.3251     29.1107\n  4500   2600     0.0000   -15.6578    24.5411    32.533520     12.2694    -26.3987     29.1107\n  4500   2800     0.0000   -15.6578    24.5411    32.869017     12.1146    -26.4701     29.1107\n  4500   3000     0.0000   -15.6578    24.5411    33.198376     11.9622    -26.5393     29.1107\n  4500   3200     0.0000   -15.6578    24.5411    33.521810     11.8122    -26.6064     29.1107\n  4500   3400     0.0000   -15.6578    24.5411    33.839518     11.6645    -26.6715     29.1107\n  4500   3600     0.0000   -15.6578    24.5411    34.151689     11.5190    -26.7347     29.1107\n  4500   3800     0.0000   -15.6578    24.5411    34.458503     11.3757    -26.7960     29.1107\n  4500   4000     0.0000   -15.6578    24.5411    34.760129     11.2345    -26.8555     29.1107\n  4500   4200     0.0000   -15.6578    24.5411    35.056729     11.0953    -26.9133     29.1107\n  4500   4400     0.0000   -15.6578    24.5411    35.348455     10.9581    -26.9694     29.1107\n  4500   4600     0.0000   -15.6578    24.5411    35.635454     10.8229    -27.0240     29.1107\n  4500   4800     0.0000   -15.6578    24.5411    35.917864     10.6896    -27.0770     29.1107\n  4500   5000     0.0000   -15.6578    24.5411    36.195818     10.5581    -27.1285     29.1107\n  4500   5200     0.0000   -15.6578    24.5411    36.469441     10.4284    -27.1787     29.1107\n  4500   5400     0.0000   -15.6578    24.5411    36.738854     10.3005    -27.2274     29.1107\n  4500   5600     0.0000   -15.6578    24.5411    37.004173     10.1743    -27.2748     29.1107\n  4500   5800     0.0000   -15.6578    24.5411    37.265507     10.0498    -27.3209     29.1107\n  4500   6000     0.0000   -15.6578    24.5411    37.522963      9.9269    -27.3658     29.1107\n  4500   6200     0.0000   -15.6578    24.5411    37.776641      9.8057    -27.4095     29.1107\n  4500   6400     0.0000   -15.6578    24.5411    38.026639      9.6860    -27.4520     29.1107\n  4500   6600     0.0000   -15.6578    24.5411    38.273049      9.5678    -27.4934     29.1107\n  4500   6800     0.0000   -15.6578    24.5411    38.515961      9.4512    -27.5337     29.1107\n  4500   7000     0.0000   -15.6578    24.5411    38.755460      9.3360    -27.5730     29.1107\n  4500   7200     0.0000   -15.6578    24.5411    38.991631      9.2223    -27.6112     29.1107\n  4500   7400     0.0000   -15.6578    24.5411    39.224551      9.1100    -27.6485     29.1107\n  4500   7600     0.0000   -15.6578    24.5411    39.454297      8.9990    -27.6848     29.1107\n  4500   7800     0.0000   -15.6578    24.5411    39.680943      8.8894    -27.7202     29.1107\n  4500   8000     0.0000   -15.6578    24.5411    39.904560      8.7812    -27.7547     29.1107\n  4500   8200     0.0000   -15.6578    24.5411    40.125217      8.6742    -27.7883     29.1107\n  4500   8400     0.0000   -15.6578    24.5411    40.342979      8.5686    -27.8211     29.1107\n\nuser time (s):         0.010\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180530_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.6160465       34.5715540       38.2579471   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0803903        0.0012571   m/s m/s m/s\nprecision_baseline(TCN):       -0.6160465       34.5715540       38.2579471   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0803903        0.0012571   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180530_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.616     34.572     38.258\norbit baseline rate   (TCN) (m/s):  0.000e+00 -8.039e-02  1.257e-03\n\nbaseline vector (TCN)   (m):     -0.616     34.572     38.258\nbaseline rate   (TCN) (m/s):  0.000e+00 -8.039e-02  1.257e-03\n\nSLC-1 center baseline length (m):    51.5678\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.6160    35.3219    38.2462    27.497013     50.2346     13.6746     52.0653\n     0    200    -0.6160    35.3219    38.2462    27.939249     50.3386     13.2865     52.0653\n     0    400    -0.6160    35.3219    38.2462    28.370851     50.4373     12.9069     52.0653\n     0    600    -0.6160    35.3219    38.2462    28.792342     50.5308     12.5355     52.0653\n     0    800    -0.6160    35.3219    38.2462    29.204202     50.6196     12.1719     52.0653\n     0   1000    -0.6160    35.3219    38.2462    29.606873     50.7039     11.8159     52.0653\n     0   1200    -0.6160    35.3219    38.2462    30.000767     50.7840     11.4670     52.0653\n     0   1400    -0.6160    35.3219    38.2462    30.386264     50.8599     11.1251     52.0653\n     0   1600    -0.6160    35.3219    38.2462    30.763716     50.9321     10.7898     52.0653\n     0   1800    -0.6160    35.3219    38.2462    31.133454     51.0007     10.4608     52.0653\n     0   2000    -0.6160    35.3219    38.2462    31.495785     51.0658     10.1381     52.0653\n     0   2200    -0.6160    35.3219    38.2462    31.850997     51.1277      9.8213     52.0653\n     0   2400    -0.6160    35.3219    38.2462    32.199360     51.1864      9.5103     52.0653\n     0   2600    -0.6160    35.3219    38.2462    32.541126     51.2422      9.2048     52.0653\n     0   2800    -0.6160    35.3219    38.2462    32.876534     51.2952      8.9046     52.0653\n     0   3000    -0.6160    35.3219    38.2462    33.205808     51.3456      8.6097     52.0653\n     0   3200    -0.6160    35.3219    38.2462    33.529159     51.3933      8.3198     52.0653\n     0   3400    -0.6160    35.3219    38.2462    33.846787     51.4387      8.0347     52.0653\n     0   3600    -0.6160    35.3219    38.2462    34.158881     51.4817      7.7544     52.0653\n     0   3800    -0.6160    35.3219    38.2462    34.465620     51.5224      7.4787     52.0653\n     0   4000    -0.6160    35.3219    38.2462    34.767174     51.5611      7.2074     52.0653\n     0   4200    -0.6160    35.3219    38.2462    35.063703     51.5977      6.9404     52.0653\n     0   4400    -0.6160    35.3219    38.2462    35.355362     51.6323      6.6777     52.0653\n     0   4600    -0.6160    35.3219    38.2462    35.642294     51.6651      6.4190     52.0653\n     0   4800    -0.6160    35.3219    38.2462    35.924640     51.6961      6.1644     52.0653\n     0   5000    -0.6160    35.3219    38.2462    36.202531     51.7254      5.9135     52.0653\n     0   5200    -0.6160    35.3219    38.2462    36.476093     51.7530      5.6665     52.0653\n     0   5400    -0.6160    35.3219    38.2462    36.745447     51.7791      5.4231     52.0653\n     0   5600    -0.6160    35.3219    38.2462    37.010708     51.8036      5.1834     52.0653\n     0   5800    -0.6160    35.3219    38.2462    37.271986     51.8267      4.9471     52.0653\n     0   6000    -0.6160    35.3219    38.2462    37.529387     51.8484      4.7142     52.0653\n     0   6200    -0.6160    35.3219    38.2462    37.783012     51.8688      4.4846     52.0653\n     0   6400    -0.6160    35.3219    38.2462    38.032957     51.8879      4.2583     52.0653\n     0   6600    -0.6160    35.3219    38.2462    38.279317     51.9057      4.0351     52.0653\n     0   6800    -0.6160    35.3219    38.2462    38.522179     51.9223      3.8151     52.0653\n     0   7000    -0.6160    35.3219    38.2462    38.761630     51.9378      3.5981     52.0653\n     0   7200    -0.6160    35.3219    38.2462    38.997753     51.9522      3.3840     52.0653\n     0   7400    -0.6160    35.3219    38.2462    39.230627     51.9655      3.1728     52.0653\n     0   7600    -0.6160    35.3219    38.2462    39.460328     51.9778      2.9644     52.0653\n     0   7800    -0.6160    35.3219    38.2462    39.686930     51.9891      2.7588     52.0653\n     0   8000    -0.6160    35.3219    38.2462    39.910504     51.9995      2.5559     52.0653\n     0   8200    -0.6160    35.3219    38.2462    40.131118     52.0089      2.3557     52.0653\n     0   8400    -0.6160    35.3219    38.2462    40.348838     52.0175      2.1580     52.0653\n   500      0    -0.6160    35.1567    38.2488    27.496077     50.1604     13.5277     51.9552\n   500    200    -0.6160    35.1567    38.2488    27.938329     50.2633     13.1401     51.9552\n   500    400    -0.6160    35.1567    38.2488    28.369946     50.3608     12.7611     51.9552\n   500    600    -0.6160    35.1567    38.2488    28.791451     50.4533     12.3902     51.9552\n   500    800    -0.6160    35.1567    38.2488    29.203324     50.5411     12.0272     51.9552\n   500   1000    -0.6160    35.1567    38.2488    29.606009     50.6244     11.6717     51.9552\n   500   1200    -0.6160    35.1567    38.2488    29.999915     50.7034     11.3234     51.9552\n   500   1400    -0.6160    35.1567    38.2488    30.385423     50.7784     10.9819     51.9552\n   500   1600    -0.6160    35.1567    38.2488    30.762887     50.8497     10.6472     51.9552\n   500   1800    -0.6160    35.1567    38.2488    31.132636     50.9173     10.3188     51.9552\n   500   2000    -0.6160    35.1567    38.2488    31.494978     50.9815      9.9966     51.9552\n   500   2200    -0.6160    35.1567    38.2488    31.850200     51.0425      9.6803     51.9552\n   500   2400    -0.6160    35.1567    38.2488    32.198572     51.1004      9.3698     51.9552\n   500   2600    -0.6160    35.1567    38.2488    32.540347     51.1554      9.0648     51.9552\n   500   2800    -0.6160    35.1567    38.2488    32.875764     51.2076      8.7651     51.9552\n   500   3000    -0.6160    35.1567    38.2488    33.205046     51.2571      8.4707     51.9552\n   500   3200    -0.6160    35.1567    38.2488    33.528405     51.3041      8.1813     51.9552\n   500   3400    -0.6160    35.1567    38.2488    33.846042     51.3487      7.8967     51.9552\n   500   3600    -0.6160    35.1567    38.2488    34.158144     51.3909      7.6169     51.9552\n   500   3800    -0.6160    35.1567    38.2488    34.464890     51.4309      7.3416     51.9552\n   500   4000    -0.6160    35.1567    38.2488    34.766452     51.4689      7.0708     51.9552\n   500   4200    -0.6160    35.1567    38.2488    35.062988     51.5048      6.8043     51.9552\n   500   4400    -0.6160    35.1567    38.2488    35.354653     51.5387      6.5421     51.9552\n   500   4600    -0.6160    35.1567    38.2488    35.641592     51.5708      6.2839     51.9552\n   500   4800    -0.6160    35.1567    38.2488    35.923944     51.6012      6.0296     51.9552\n   500   5000    -0.6160    35.1567    38.2488    36.201842     51.6298      5.7793     51.9552\n   500   5200    -0.6160    35.1567    38.2488    36.475410     51.6568      5.5327     51.9552\n   500   5400    -0.6160    35.1567    38.2488    36.744770     51.6822      5.2898     51.9552\n   500   5600    -0.6160    35.1567    38.2488    37.010037     51.7062      5.0504     51.9552\n   500   5800    -0.6160    35.1567    38.2488    37.271321     51.7287      4.8146     51.9552\n   500   6000    -0.6160    35.1567    38.2488    37.528727     51.7498      4.5821     51.9552\n   500   6200    -0.6160    35.1567    38.2488    37.782357     51.7695      4.3530     51.9552\n   500   6400    -0.6160    35.1567    38.2488    38.032308     51.7880      4.1271     51.9552\n   500   6600    -0.6160    35.1567    38.2488    38.278673     51.8053      3.9044     51.9552\n   500   6800    -0.6160    35.1567    38.2488    38.521540     51.8214      3.6848     51.9552\n   500   7000    -0.6160    35.1567    38.2488    38.760996     51.8363      3.4682     51.9552\n   500   7200    -0.6160    35.1567    38.2488    38.997124     51.8502      3.2545     51.9552\n   500   7400    -0.6160    35.1567    38.2488    39.230002     51.8630      3.0437     51.9552\n   500   7600    -0.6160    35.1567    38.2488    39.459708     51.8747      2.8358     51.9552\n   500   7800    -0.6160    35.1567    38.2488    39.686314     51.8856      2.6306     51.9552\n   500   8000    -0.6160    35.1567    38.2488    39.909892     51.8954      2.4281     51.9552\n   500   8200    -0.6160    35.1567    38.2488    40.130511     51.9044      2.2282     51.9552\n   500   8400    -0.6160    35.1567    38.2488    40.348236     51.9125      2.0310     51.9552\n  1000      0    -0.6160    34.9914    38.2514    27.495122     50.0861     13.3808     51.8454\n  1000    200    -0.6160    34.9914    38.2514    27.937389     50.1879     12.9937     51.8454\n  1000    400    -0.6160    34.9914    38.2514    28.369022     50.2844     12.6153     51.8454\n  1000    600    -0.6160    34.9914    38.2514    28.790541     50.3758     12.2450     51.8454\n  1000    800    -0.6160    34.9914    38.2514    29.202428     50.4625     11.8825     51.8454\n  1000   1000    -0.6160    34.9914    38.2514    29.605126     50.5448     11.5275     51.8454\n  1000   1200    -0.6160    34.9914    38.2514    29.999045     50.6228     11.1797     51.8454\n  1000   1400    -0.6160    34.9914    38.2514    30.384565     50.6969     10.8389     51.8454\n  1000   1600    -0.6160    34.9914    38.2514    30.762040     50.7672     10.5046     51.8454\n  1000   1800    -0.6160    34.9914    38.2514    31.131800     50.8339     10.1767     51.8454\n  1000   2000    -0.6160    34.9914    38.2514    31.494152     50.8973      9.8551     51.8454\n  1000   2200    -0.6160    34.9914    38.2514    31.849385     50.9574      9.5393     51.8454\n  1000   2400    -0.6160    34.9914    38.2514    32.197766     51.0144      9.2293     51.8454\n  1000   2600    -0.6160    34.9914    38.2514    32.539551     51.0686      8.9248     51.8454\n  1000   2800    -0.6160    34.9914    38.2514    32.874977     51.1199      8.6256     51.8454\n  1000   3000    -0.6160    34.9914    38.2514    33.204268     51.1687      8.3317     51.8454\n  1000   3200    -0.6160    34.9914    38.2514    33.527636     51.2149      8.0428     51.8454\n  1000   3400    -0.6160    34.9914    38.2514    33.845281     51.2587      7.7587     51.8454\n  1000   3600    -0.6160    34.9914    38.2514    34.157391     51.3002      7.4794     51.8454\n  1000   3800    -0.6160    34.9914    38.2514    34.464145     51.3395      7.2046     51.8454\n  1000   4000    -0.6160    34.9914    38.2514    34.765714     51.3767      6.9343     51.8454\n  1000   4200    -0.6160    34.9914    38.2514    35.062258     51.4119      6.6683     51.8454\n  1000   4400    -0.6160    34.9914    38.2514    35.353930     51.4451      6.4064     51.8454\n  1000   4600    -0.6160    34.9914    38.2514    35.640876     51.4766      6.1487     51.8454\n  1000   4800    -0.6160    34.9914    38.2514    35.923235     51.5062      5.8950     51.8454\n  1000   5000    -0.6160    34.9914    38.2514    36.201138     51.5342      5.6451     51.8454\n  1000   5200    -0.6160    34.9914    38.2514    36.474713     51.5606      5.3989     51.8454\n  1000   5400    -0.6160    34.9914    38.2514    36.744079     51.5854      5.1565     51.8454\n  1000   5600    -0.6160    34.9914    38.2514    37.009352     51.6087      4.9176     51.8454\n  1000   5800    -0.6160    34.9914    38.2514    37.270642     51.6306      4.6821     51.8454\n  1000   6000    -0.6160    34.9914    38.2514    37.528054     51.6511      4.4501     51.8454\n  1000   6200    -0.6160    34.9914    38.2514    37.781689     51.6703      4.2214     51.8454\n  1000   6400    -0.6160    34.9914    38.2514    38.031646     51.6882      3.9960     51.8454\n  1000   6600    -0.6160    34.9914    38.2514    38.278015     51.7049      3.7737     51.8454\n  1000   6800    -0.6160    34.9914    38.2514    38.520888     51.7204      3.5545     51.8454\n  1000   7000    -0.6160    34.9914    38.2514    38.760349     51.7348      3.3383     51.8454\n  1000   7200    -0.6160    34.9914    38.2514    38.996482     51.7482      3.1250     51.8454\n  1000   7400    -0.6160    34.9914    38.2514    39.229365     51.7604      2.9147     51.8454\n  1000   7600    -0.6160    34.9914    38.2514    39.459075     51.7717      2.7071     51.8454\n  1000   7800    -0.6160    34.9914    38.2514    39.685686     51.7820      2.5023     51.8454\n  1000   8000    -0.6160    34.9914    38.2514    39.909268     51.7914      2.3002     51.8454\n  1000   8200    -0.6160    34.9914    38.2514    40.129891     51.7998      2.1008     51.8454\n  1000   8400    -0.6160    34.9914    38.2514    40.347621     51.8074      1.9039     51.8454\n  1500      0    -0.6160    34.8262    38.2540    27.494145     50.0119     13.2338     51.7360\n  1500    200    -0.6160    34.8262    38.2540    27.936429     50.1126     12.8474     51.7360\n  1500    400    -0.6160    34.8262    38.2540    28.368077     50.2079     12.4695     51.7360\n  1500    600    -0.6160    34.8262    38.2540    28.789612     50.2983     12.0997     51.7360\n  1500    800    -0.6160    34.8262    38.2540    29.201513     50.3840     11.7378     51.7360\n  1500   1000    -0.6160    34.8262    38.2540    29.604224     50.4652     11.3834     51.7360\n  1500   1200    -0.6160    34.8262    38.2540    29.998156     50.5423     11.0361     51.7360\n  1500   1400    -0.6160    34.8262    38.2540    30.383688     50.6154     10.6958     51.7360\n  1500   1600    -0.6160    34.8262    38.2540    30.761176     50.6848     10.3621     51.7360\n  1500   1800    -0.6160    34.8262    38.2540    31.130947     50.7506     10.0347     51.7360\n  1500   2000    -0.6160    34.8262    38.2540    31.493310     50.8130      9.7135     51.7360\n  1500   2200    -0.6160    34.8262    38.2540    31.848553     50.8722      9.3983     51.7360\n  1500   2400    -0.6160    34.8262    38.2540    32.196945     50.9284      9.0888     51.7360\n  1500   2600    -0.6160    34.8262    38.2540    32.538739     50.9818      8.7848     51.7360\n  1500   2800    -0.6160    34.8262    38.2540    32.874174     51.0323      8.4862     51.7360\n  1500   3000    -0.6160    34.8262    38.2540    33.203475     51.0802      8.1927     51.7360\n  1500   3200    -0.6160    34.8262    38.2540    33.526851     51.1256      7.9043     51.7360\n  1500   3400    -0.6160    34.8262    38.2540    33.844504     51.1687      7.6207     51.7360\n  1500   3600    -0.6160    34.8262    38.2540    34.156623     51.2094      7.3419     51.7360\n  1500   3800    -0.6160    34.8262    38.2540    34.463385     51.2480      7.0676     51.7360\n  1500   4000    -0.6160    34.8262    38.2540    34.764961     51.2845      6.7977     51.7360\n  1500   4200    -0.6160    34.8262    38.2540    35.061513     51.3190      6.5322     51.7360\n  1500   4400    -0.6160    34.8262    38.2540    35.353192     51.3516      6.2708     51.7360\n  1500   4600    -0.6160    34.8262    38.2540    35.640145     51.3823      6.0136     51.7360\n  1500   4800    -0.6160    34.8262    38.2540    35.922510     51.4113      5.7603     51.7360\n  1500   5000    -0.6160    34.8262    38.2540    36.200421     51.4386      5.5108     51.7360\n  1500   5200    -0.6160    34.8262    38.2540    36.474002     51.4644      5.2651     51.7360\n  1500   5400    -0.6160    34.8262    38.2540    36.743374     51.4886      5.0231     51.7360\n  1500   5600    -0.6160    34.8262    38.2540    37.008653     51.5113      4.7847     51.7360\n  1500   5800    -0.6160    34.8262    38.2540    37.269949     51.5325      4.5497     51.7360\n  1500   6000    -0.6160    34.8262    38.2540    37.527367     51.5524      4.3181     51.7360\n  1500   6200    -0.6160    34.8262    38.2540    37.781008     51.5711      4.0899     51.7360\n  1500   6400    -0.6160    34.8262    38.2540    38.030970     51.5884      3.8648     51.7360\n  1500   6600    -0.6160    34.8262    38.2540    38.277345     51.6045      3.6430     51.7360\n  1500   6800    -0.6160    34.8262    38.2540    38.520223     51.6195      3.4242     51.7360\n  1500   7000    -0.6160    34.8262    38.2540    38.759689     51.6334      3.2084     51.7360\n  1500   7200    -0.6160    34.8262    38.2540    38.995827     51.6461      2.9956     51.7360\n  1500   7400    -0.6160    34.8262    38.2540    39.228715     51.6579      2.7856     51.7360\n  1500   7600    -0.6160    34.8262    38.2540    39.458429     51.6686      2.5785     51.7360\n  1500   7800    -0.6160    34.8262    38.2540    39.685045     51.6784      2.3741     51.7360\n  1500   8000    -0.6160    34.8262    38.2540    39.908632     51.6873      2.1724     51.7360\n  1500   8200    -0.6160    34.8262    38.2540    40.129260     51.6953      1.9733     51.7360\n  1500   8400    -0.6160    34.8262    38.2540    40.346993     51.7024      1.7769     51.7360\n  2000      0    -0.6160    34.6610    38.2565    27.493148     49.9377     13.0869     51.6268\n  2000    200    -0.6160    34.6610    38.2565    27.935449     50.0372     12.7010     51.6268\n  2000    400    -0.6160    34.6610    38.2565    28.367113     50.1315     12.3237     51.6268\n  2000    600    -0.6160    34.6610    38.2565    28.788663     50.2208     11.9545     51.6268\n  2000    800    -0.6160    34.6610    38.2565    29.200578     50.3054     11.5931     51.6268\n  2000   1000    -0.6160    34.6610    38.2565    29.603304     50.3857     11.2392     51.6268\n  2000   1200    -0.6160    34.6610    38.2565    29.997248     50.4617     10.8925     51.6268\n  2000   1400    -0.6160    34.6610    38.2565    30.382794     50.5339     10.5527     51.6268\n  2000   1600    -0.6160    34.6610    38.2565    30.760293     50.6023     10.2195     51.6268\n  2000   1800    -0.6160    34.6610    38.2565    31.130076     50.6672      9.8927     51.6268\n  2000   2000    -0.6160    34.6610    38.2565    31.492450     50.7287      9.5721     51.6268\n  2000   2200    -0.6160    34.6610    38.2565    31.847704     50.7871      9.2573     51.6268\n  2000   2400    -0.6160    34.6610    38.2565    32.196106     50.8425      8.9483     51.6268\n  2000   2600    -0.6160    34.6610    38.2565    32.537910     50.8949      8.6448     51.6268\n  2000   2800    -0.6160    34.6610    38.2565    32.873355     50.9447      8.3467     51.6268\n  2000   3000    -0.6160    34.6610    38.2565    33.202665     50.9918      8.0538     51.6268\n  2000   3200    -0.6160    34.6610    38.2565    33.526051     51.0364      7.7658     51.6268\n  2000   3400    -0.6160    34.6610    38.2565    33.843712     51.0787      7.4827     51.6268\n  2000   3600    -0.6160    34.6610    38.2565    34.155839     51.1187      7.2044     51.6268\n  2000   3800    -0.6160    34.6610    38.2565    34.462609     51.1565      6.9306     51.6268\n  2000   4000    -0.6160    34.6610    38.2565    34.764193     51.1923      6.6612     51.6268\n  2000   4200    -0.6160    34.6610    38.2565    35.060752     51.2261      6.3961     51.6268\n  2000   4400    -0.6160    34.6610    38.2565    35.352439     51.2580      6.1352     51.6268\n  2000   4600    -0.6160    34.6610    38.2565    35.639399     51.2880      5.8784     51.6268\n  2000   4800    -0.6160    34.6610    38.2565    35.921772     51.3164      5.6256     51.6268\n  2000   5000    -0.6160    34.6610    38.2565    36.199689     51.3431      5.3766     51.6268\n  2000   5200    -0.6160    34.6610    38.2565    36.473276     51.3682      5.1314     51.6268\n  2000   5400    -0.6160    34.6610    38.2565    36.742655     51.3917      4.8898     51.6268\n  2000   5600    -0.6160    34.6610    38.2565    37.007941     51.4138      4.6518     51.6268\n  2000   5800    -0.6160    34.6610    38.2565    37.269242     51.4345      4.4173     51.6268\n  2000   6000    -0.6160    34.6610    38.2565    37.526666     51.4538      4.1861     51.6268\n  2000   6200    -0.6160    34.6610    38.2565    37.780313     51.4718      3.9583     51.6268\n  2000   6400    -0.6160    34.6610    38.2565    38.030281     51.4886      3.7337     51.6268\n  2000   6600    -0.6160    34.6610    38.2565    38.276661     51.5042      3.5123     51.6268\n  2000   6800    -0.6160    34.6610    38.2565    38.519545     51.5186      3.2939     51.6268\n  2000   7000    -0.6160    34.6610    38.2565    38.759016     51.5319      3.0785     51.6268\n  2000   7200    -0.6160    34.6610    38.2565    38.995159     51.5441      2.8661     51.6268\n  2000   7400    -0.6160    34.6610    38.2565    39.228052     51.5554      2.6566     51.6268\n  2000   7600    -0.6160    34.6610    38.2565    39.457772     51.5656      2.4498     51.6268\n  2000   7800    -0.6160    34.6610    38.2565    39.684392     51.5749      2.2459     51.6268\n  2000   8000    -0.6160    34.6610    38.2565    39.907984     51.5832      2.0446     51.6268\n  2000   8200    -0.6160    34.6610    38.2565    40.128616     51.5907      1.8459     51.6268\n  2000   8400    -0.6160    34.6610    38.2565    40.346354     51.5974      1.6498     51.6268\n  2500      0    -0.6160    34.4957    38.2591    27.492131     49.8635     12.9400     51.5179\n  2500    200    -0.6160    34.4957    38.2591    27.934449     49.9619     12.5547     51.5179\n  2500    400    -0.6160    34.4957    38.2591    28.366129     50.0550     12.1779     51.5179\n  2500    600    -0.6160    34.4957    38.2591    28.787694     50.1433     11.8093     51.5179\n  2500    800    -0.6160    34.4957    38.2591    29.199625     50.2269     11.4484     51.5179\n  2500   1000    -0.6160    34.4957    38.2591    29.602364     50.3061     11.0951     51.5179\n  2500   1200    -0.6160    34.4957    38.2591    29.996323     50.3812     10.7489     51.5179\n  2500   1400    -0.6160    34.4957    38.2591    30.381881     50.4524     10.4096     51.5179\n  2500   1600    -0.6160    34.4957    38.2591    30.759393     50.5198     10.0770     51.5179\n  2500   1800    -0.6160    34.4957    38.2591    31.129187     50.5838      9.7507     51.5179\n  2500   2000    -0.6160    34.4957    38.2591    31.491573     50.6445      9.4306     51.5179\n  2500   2200    -0.6160    34.4957    38.2591    31.846838     50.7020      9.1164     51.5179\n  2500   2400    -0.6160    34.4957    38.2591    32.195250     50.7565      8.8079     51.5179\n  2500   2600    -0.6160    34.4957    38.2591    32.537065     50.8081      8.5049     51.5179\n  2500   2800    -0.6160    34.4957    38.2591    32.872520     50.8570      8.2073     51.5179\n  2500   3000    -0.6160    34.4957    38.2591    33.201839     50.9033      7.9148     51.5179\n  2500   3200    -0.6160    34.4957    38.2591    33.525234     50.9472      7.6274     51.5179\n  2500   3400    -0.6160    34.4957    38.2591    33.842904     50.9887      7.3448     51.5179\n  2500   3600    -0.6160    34.4957    38.2591    34.155039     51.0280      7.0669     51.5179\n  2500   3800    -0.6160    34.4957    38.2591    34.461818     51.0651      6.7936     51.5179\n  2500   4000    -0.6160    34.4957    38.2591    34.763411     51.1001      6.5247     51.5179\n  2500   4200    -0.6160    34.4957    38.2591    35.059977     51.1332      6.2601     51.5179\n  2500   4400    -0.6160    34.4957    38.2591    35.351672     51.1644      5.9997     51.5179\n  2500   4600    -0.6160    34.4957    38.2591    35.638639     51.1938      5.7433     51.5179\n  2500   4800    -0.6160    34.4957    38.2591    35.921019     51.2215      5.4909     51.5179\n  2500   5000    -0.6160    34.4957    38.2591    36.198943     51.2475      5.2424     51.5179\n  2500   5200    -0.6160    34.4957    38.2591    36.472537     51.2719      4.9976     51.5179\n  2500   5400    -0.6160    34.4957    38.2591    36.741923     51.2949      4.7565     51.5179\n  2500   5600    -0.6160    34.4957    38.2591    37.007214     51.3163      4.5189     51.5179\n  2500   5800    -0.6160    34.4957    38.2591    37.268522     51.3364      4.2849     51.5179\n  2500   6000    -0.6160    34.4957    38.2591    37.525952     51.3551      4.0541     51.5179\n  2500   6200    -0.6160    34.4957    38.2591    37.779605     51.3726      3.8267     51.5179\n  2500   6400    -0.6160    34.4957    38.2591    38.029579     51.3888      3.6026     51.5179\n  2500   6600    -0.6160    34.4957    38.2591    38.275965     51.4038      3.3816     51.5179\n  2500   6800    -0.6160    34.4957    38.2591    38.518854     51.4177      3.1636     51.5179\n  2500   7000    -0.6160    34.4957    38.2591    38.758331     51.4304      2.9487     51.5179\n  2500   7200    -0.6160    34.4957    38.2591    38.994478     51.4421      2.7367     51.5179\n  2500   7400    -0.6160    34.4957    38.2591    39.227377     51.4528      2.5275     51.5179\n  2500   7600    -0.6160    34.4957    38.2591    39.457102     51.4625      2.3212     51.5179\n  2500   7800    -0.6160    34.4957    38.2591    39.683727     51.4713      2.1176     51.5179\n  2500   8000    -0.6160    34.4957    38.2591    39.907324     51.4792      1.9167     51.5179\n  2500   8200    -0.6160    34.4957    38.2591    40.127960     51.4862      1.7185     51.5179\n  2500   8400    -0.6160    34.4957    38.2591    40.345703     51.4923      1.5228     51.5179\n  3000      0    -0.6160    34.3305    38.2617    27.491093     49.7892     12.7932     51.4093\n  3000    200    -0.6160    34.3305    38.2617    27.933428     49.8865     12.4084     51.4093\n  3000    400    -0.6160    34.3305    38.2617    28.365126     49.9786     12.0321     51.4093\n  3000    600    -0.6160    34.3305    38.2617    28.786707     50.0658     11.6641     51.4093\n  3000    800    -0.6160    34.3305    38.2617    29.198653     50.1483     11.3038     51.4093\n  3000   1000    -0.6160    34.3305    38.2617    29.601406     50.2265     10.9510     51.4093\n  3000   1200    -0.6160    34.3305    38.2617    29.995379     50.3006     10.6054     51.4093\n  3000   1400    -0.6160    34.3305    38.2617    30.380950     50.3709     10.2666     51.4093\n  3000   1600    -0.6160    34.3305    38.2617    30.758475     50.4374      9.9345     51.4093\n  3000   1800    -0.6160    34.3305    38.2617    31.128282     50.5005      9.6087     51.4093\n  3000   2000    -0.6160    34.3305    38.2617    31.490679     50.5602      9.2891     51.4093\n  3000   2200    -0.6160    34.3305    38.2617    31.845955     50.6168      8.9754     51.4093\n  3000   2400    -0.6160    34.3305    38.2617    32.194378     50.6705      8.6674     51.4093\n  3000   2600    -0.6160    34.3305    38.2617    32.536204     50.7213      8.3650     51.4093\n  3000   2800    -0.6160    34.3305    38.2617    32.871668     50.7694      8.0678     51.4093\n  3000   3000    -0.6160    34.3305    38.2617    33.200997     50.8149      7.7759     51.4093\n  3000   3200    -0.6160    34.3305    38.2617    33.524401     50.8580      7.4889     51.4093\n  3000   3400    -0.6160    34.3305    38.2617    33.842081     50.8987      7.2068     51.4093\n  3000   3600    -0.6160    34.3305    38.2617    34.154225     50.9372      6.9294     51.4093\n  3000   3800    -0.6160    34.3305    38.2617    34.461012     50.9736      6.6566     51.4093\n  3000   4000    -0.6160    34.3305    38.2617    34.762613     51.0079      6.3881     51.4093\n  3000   4200    -0.6160    34.3305    38.2617    35.059187     51.0403      6.1240     51.4093\n  3000   4400    -0.6160    34.3305    38.2617    35.350889     51.0708      5.8641     51.4093\n  3000   4600    -0.6160    34.3305    38.2617    35.637865     51.0995      5.6082     51.4093\n  3000   4800    -0.6160    34.3305    38.2617    35.920252     51.1265      5.3563     51.4093\n  3000   5000    -0.6160    34.3305    38.2617    36.198183     51.1519      5.1082     51.4093\n  3000   5200    -0.6160    34.3305    38.2617    36.471784     51.1757      4.8639     51.4093\n  3000   5400    -0.6160    34.3305    38.2617    36.741176     51.1980      4.6232     51.4093\n  3000   5600    -0.6160    34.3305    38.2617    37.006474     51.2189      4.3861     51.4093\n  3000   5800    -0.6160    34.3305    38.2617    37.267789     51.2383      4.1524     51.4093\n  3000   6000    -0.6160    34.3305    38.2617    37.525225     51.2565      3.9222     51.4093\n  3000   6200    -0.6160    34.3305    38.2617    37.778884     51.2733      3.6952     51.4093\n  3000   6400    -0.6160    34.3305    38.2617    38.028863     51.2890      3.4715     51.4093\n  3000   6600    -0.6160    34.3305    38.2617    38.275255     51.3034      3.2509     51.4093\n  3000   6800    -0.6160    34.3305    38.2617    38.518150     51.3167      3.0333     51.4093\n  3000   7000    -0.6160    34.3305    38.2617    38.757632     51.3290      2.8188     51.4093\n  3000   7200    -0.6160    34.3305    38.2617    38.993785     51.3401      2.6072     51.4093\n  3000   7400    -0.6160    34.3305    38.2617    39.226689     51.3503      2.3985     51.4093\n  3000   7600    -0.6160    34.3305    38.2617    39.456419     51.3595      2.1926     51.4093\n  3000   7800    -0.6160    34.3305    38.2617    39.683049     51.3678      1.9894     51.4093\n  3000   8000    -0.6160    34.3305    38.2617    39.906651     51.3751      1.7889     51.4093\n  3000   8200    -0.6160    34.3305    38.2617    40.127293     51.3816      1.5911     51.4093\n  3000   8400    -0.6160    34.3305    38.2617    40.345040     51.3873      1.3958     51.4093\n  3500      0    -0.6160    34.1652    38.2643    27.490034     49.7150     12.6463     51.3011\n  3500    200    -0.6160    34.1652    38.2643    27.932388     49.8112     12.2621     51.3011\n  3500    400    -0.6160    34.1652    38.2643    28.364103     49.9021     11.8864     51.3011\n  3500    600    -0.6160    34.1652    38.2643    28.785700     49.9882     11.5189     51.3011\n  3500    800    -0.6160    34.1652    38.2643    29.197661     50.0698     11.1591     51.3011\n  3500   1000    -0.6160    34.1652    38.2643    29.600430     50.1470     10.8069     51.3011\n  3500   1200    -0.6160    34.1652    38.2643    29.994416     50.2201     10.4618     51.3011\n  3500   1400    -0.6160    34.1652    38.2643    30.380001     50.2893     10.1236     51.3011\n  3500   1600    -0.6160    34.1652    38.2643    30.757539     50.3549      9.7920     51.3011\n  3500   1800    -0.6160    34.1652    38.2643    31.127358     50.4171      9.4667     51.3011\n  3500   2000    -0.6160    34.1652    38.2643    31.489768     50.4760      9.1476     51.3011\n  3500   2200    -0.6160    34.1652    38.2643    31.845055     50.5317      8.8345     51.3011\n  3500   2400    -0.6160    34.1652    38.2643    32.193490     50.5845      8.5270     51.3011\n  3500   2600    -0.6160    34.1652    38.2643    32.535326     50.6345      8.2250     51.3011\n  3500   2800    -0.6160    34.1652    38.2643    32.870801     50.6817      7.9284     51.3011\n  3500   3000    -0.6160    34.1652    38.2643    33.200139     50.7265      7.6370     51.3011\n  3500   3200    -0.6160    34.1652    38.2643    33.523553     50.7688      7.3505     51.3011\n  3500   3400    -0.6160    34.1652    38.2643    33.841242     50.8087      7.0689     51.3011\n  3500   3600    -0.6160    34.1652    38.2643    34.153395     50.8465      6.7919     51.3011\n  3500   3800    -0.6160    34.1652    38.2643    34.460191     50.8821      6.5196     51.3011\n  3500   4000    -0.6160    34.1652    38.2643    34.761800     50.9157      6.2516     51.3011\n  3500   4200    -0.6160    34.1652    38.2643    35.058383     50.9474      5.9880     51.3011\n  3500   4400    -0.6160    34.1652    38.2643    35.350093     50.9772      5.7285     51.3011\n  3500   4600    -0.6160    34.1652    38.2643    35.637076     51.0053      5.4731     51.3011\n  3500   4800    -0.6160    34.1652    38.2643    35.919470     51.0316      5.2216     51.3011\n  3500   5000    -0.6160    34.1652    38.2643    36.197408     51.0563      4.9740     51.3011\n  3500   5200    -0.6160    34.1652    38.2643    36.471017     51.0795      4.7301     51.3011\n  3500   5400    -0.6160    34.1652    38.2643    36.740416     51.1012      4.4899     51.3011\n  3500   5600    -0.6160    34.1652    38.2643    37.005721     51.1214      4.2532     51.3011\n  3500   5800    -0.6160    34.1652    38.2643    37.267042     51.1403      4.0200     51.3011\n  3500   6000    -0.6160    34.1652    38.2643    37.524484     51.1578      3.7902     51.3011\n  3500   6200    -0.6160    34.1652    38.2643    37.778150     51.1741      3.5637     51.3011\n  3500   6400    -0.6160    34.1652    38.2643    38.028135     51.1892      3.3404     51.3011\n  3500   6600    -0.6160    34.1652    38.2643    38.274533     51.2030      3.1202     51.3011\n  3500   6800    -0.6160    34.1652    38.2643    38.517433     51.2158      2.9031     51.3011\n  3500   7000    -0.6160    34.1652    38.2643    38.756921     51.2275      2.6890     51.3011\n  3500   7200    -0.6160    34.1652    38.2643    38.993080     51.2381      2.4778     51.3011\n  3500   7400    -0.6160    34.1652    38.2643    39.225988     51.2478      2.2695     51.3011\n  3500   7600    -0.6160    34.1652    38.2643    39.455724     51.2565      2.0640     51.3011\n  3500   7800    -0.6160    34.1652    38.2643    39.682359     51.2642      1.8612     51.3011\n  3500   8000    -0.6160    34.1652    38.2643    39.905966     51.2711      1.6611     51.3011\n  3500   8200    -0.6160    34.1652    38.2643    40.126613     51.2771      1.4637     51.3011\n  3500   8400    -0.6160    34.1652    38.2643    40.344365     51.2823      1.2688     51.3011\n  4000      0    -0.6160    34.0000    38.2669    27.488955     49.6408     12.4995     51.1931\n  4000    200    -0.6160    34.0000    38.2669    27.931328     49.7358     12.1158     51.1931\n  4000    400    -0.6160    34.0000    38.2669    28.363060     49.8257     11.7407     51.1931\n  4000    600    -0.6160    34.0000    38.2669    28.784674     49.9107     11.3737     51.1931\n  4000    800    -0.6160    34.0000    38.2669    29.196651     49.9912     11.0145     51.1931\n  4000   1000    -0.6160    34.0000    38.2669    29.599435     50.0674     10.6628     51.1931\n  4000   1200    -0.6160    34.0000    38.2669    29.993436     50.1395     10.3182     51.1931\n  4000   1400    -0.6160    34.0000    38.2669    30.379035     50.2078      9.9806     51.1931\n  4000   1600    -0.6160    34.0000    38.2669    30.756585     50.2725      9.6495     51.1931\n  4000   1800    -0.6160    34.0000    38.2669    31.126418     50.3337      9.3248     51.1931\n  4000   2000    -0.6160    34.0000    38.2669    31.488839     50.3917      9.0062     51.1931\n  4000   2200    -0.6160    34.0000    38.2669    31.844138     50.4466      8.6935     51.1931\n  4000   2400    -0.6160    34.0000    38.2669    32.192584     50.4985      8.3866     51.1931\n  4000   2600    -0.6160    34.0000    38.2669    32.534431     50.5476      8.0851     51.1931\n  4000   2800    -0.6160    34.0000    38.2669    32.869917     50.5941      7.7890     51.1931\n  4000   3000    -0.6160    34.0000    38.2669    33.199265     50.6380      7.4980     51.1931\n  4000   3200    -0.6160    34.0000    38.2669    33.522689     50.6795      7.2121     51.1931\n  4000   3400    -0.6160    34.0000    38.2669    33.840387     50.7188      6.9309     51.1931\n  4000   3600    -0.6160    34.0000    38.2669    34.152550     50.7558      6.6545     51.1931\n  4000   3800    -0.6160    34.0000    38.2669    34.459354     50.7907      6.3826     51.1931\n  4000   4000    -0.6160    34.0000    38.2669    34.760972     50.8235      6.1151     51.1931\n  4000   4200    -0.6160    34.0000    38.2669    35.057563     50.8545      5.8520     51.1931\n  4000   4400    -0.6160    34.0000    38.2669    35.349281     50.8836      5.5930     51.1931\n  4000   4600    -0.6160    34.0000    38.2669    35.636272     50.9110      5.3380     51.1931\n  4000   4800    -0.6160    34.0000    38.2669    35.918674     50.9367      5.0870     51.1931\n  4000   5000    -0.6160    34.0000    38.2669    36.196620     50.9608      4.8398     51.1931\n  4000   5200    -0.6160    34.0000    38.2669    36.470236     50.9833      4.5964     51.1931\n  4000   5400    -0.6160    34.0000    38.2669    36.739642     51.0043      4.3566     51.1931\n  4000   5600    -0.6160    34.0000    38.2669    37.004953     51.0240      4.1204     51.1931\n  4000   5800    -0.6160    34.0000    38.2669    37.266281     51.0422      3.8876     51.1931\n  4000   6000    -0.6160    34.0000    38.2669    37.523730     51.0592      3.6582     51.1931\n  4000   6200    -0.6160    34.0000    38.2669    37.777402     51.0749      3.4321     51.1931\n  4000   6400    -0.6160    34.0000    38.2669    38.027393     51.0893      3.2093     51.1931\n  4000   6600    -0.6160    34.0000    38.2669    38.273797     51.1027      2.9895     51.1931\n  4000   6800    -0.6160    34.0000    38.2669    38.516703     51.1149      2.7728     51.1931\n  4000   7000    -0.6160    34.0000    38.2669    38.756197     51.1260      2.5591     51.1931\n  4000   7200    -0.6160    34.0000    38.2669    38.992361     51.1361      2.3484     51.1931\n  4000   7400    -0.6160    34.0000    38.2669    39.225276     51.1452      2.1405     51.1931\n  4000   7600    -0.6160    34.0000    38.2669    39.455016     51.1534      1.9354     51.1931\n  4000   7800    -0.6160    34.0000    38.2669    39.681657     51.1607      1.7330     51.1931\n  4000   8000    -0.6160    34.0000    38.2669    39.905269     51.1670      1.5333     51.1931\n  4000   8200    -0.6160    34.0000    38.2669    40.125921     51.1726      1.3363     51.1931\n  4000   8400    -0.6160    34.0000    38.2669    40.343678     51.1773      1.1418     51.1931\n  4500      0    -0.6160    33.8347    38.2695    27.487857     49.5666     12.3526     51.0854\n  4500    200    -0.6160    33.8347    38.2695    27.930248     49.6605     11.9695     51.0854\n  4500    400    -0.6160    33.8347    38.2695    28.361998     49.7493     11.5950     51.0854\n  4500    600    -0.6160    33.8347    38.2695    28.783629     49.8332     11.2285     51.0854\n  4500    800    -0.6160    33.8347    38.2695    29.195622     49.9127     10.8699     51.0854\n  4500   1000    -0.6160    33.8347    38.2695    29.598421     49.9878     10.5187     51.0854\n  4500   1200    -0.6160    33.8347    38.2695    29.992437     50.0590     10.1747     51.0854\n  4500   1400    -0.6160    33.8347    38.2695    30.378050     50.1263      9.8376     51.0854\n  4500   1600    -0.6160    33.8347    38.2695    30.755614     50.1901      9.5070     51.0854\n  4500   1800    -0.6160    33.8347    38.2695    31.125460     50.2504      9.1828     51.0854\n  4500   2000    -0.6160    33.8347    38.2695    31.487894     50.3074      8.8648     51.0854\n  4500   2200    -0.6160    33.8347    38.2695    31.843205     50.3614      8.5526     51.0854\n  4500   2400    -0.6160    33.8347    38.2695    32.191662     50.4125      8.2462     51.0854\n  4500   2600    -0.6160    33.8347    38.2695    32.533520     50.4608      7.9452     51.0854\n  4500   2800    -0.6160    33.8347    38.2695    32.869017     50.5065      7.6496     51.0854\n  4500   3000    -0.6160    33.8347    38.2695    33.198376     50.5496      7.3591     51.0854\n  4500   3200    -0.6160    33.8347    38.2695    33.521810     50.5903      7.0737     51.0854\n  4500   3400    -0.6160    33.8347    38.2695    33.839518     50.6288      6.7930     51.0854\n  4500   3600    -0.6160    33.8347    38.2695    34.151689     50.6650      6.5171     51.0854\n  4500   3800    -0.6160    33.8347    38.2695    34.458503     50.6992      6.2457     51.0854\n  4500   4000    -0.6160    33.8347    38.2695    34.760129     50.7314      5.9787     51.0854\n  4500   4200    -0.6160    33.8347    38.2695    35.056729     50.7616      5.7160     51.0854\n  4500   4400    -0.6160    33.8347    38.2695    35.348455     50.7901      5.4574     51.0854\n  4500   4600    -0.6160    33.8347    38.2695    35.635454     50.8168      5.2029     51.0854\n  4500   4800    -0.6160    33.8347    38.2695    35.917864     50.8418      4.9524     51.0854\n  4500   5000    -0.6160    33.8347    38.2695    36.195818     50.8652      4.7057     51.0854\n  4500   5200    -0.6160    33.8347    38.2695    36.469441     50.8871      4.4627     51.0854\n  4500   5400    -0.6160    33.8347    38.2695    36.738854     50.9075      4.2234     51.0854\n  4500   5600    -0.6160    33.8347    38.2695    37.004173     50.9265      3.9876     51.0854\n  4500   5800    -0.6160    33.8347    38.2695    37.265507     50.9442      3.7553     51.0854\n  4500   6000    -0.6160    33.8347    38.2695    37.522963     50.9605      3.5263     51.0854\n  4500   6200    -0.6160    33.8347    38.2695    37.776641     50.9756      3.3006     51.0854\n  4500   6400    -0.6160    33.8347    38.2695    38.026639     50.9895      3.0782     51.0854\n  4500   6600    -0.6160    33.8347    38.2695    38.273049     51.0023      2.8588     51.0854\n  4500   6800    -0.6160    33.8347    38.2695    38.515961     51.0140      2.6426     51.0854\n  4500   7000    -0.6160    33.8347    38.2695    38.755460     51.0246      2.4293     51.0854\n  4500   7200    -0.6160    33.8347    38.2695    38.991631     51.0341      2.2190     51.0854\n  4500   7400    -0.6160    33.8347    38.2695    39.224551     51.0427      2.0115     51.0854\n  4500   7600    -0.6160    33.8347    38.2695    39.454297     51.0504      1.8068     51.0854\n  4500   7800    -0.6160    33.8347    38.2695    39.680943     51.0571      1.6048     51.0854\n  4500   8000    -0.6160    33.8347    38.2695    39.904560     51.0630      1.4055     51.0854\n  4500   8200    -0.6160    33.8347    38.2695    40.125217     51.0680      1.2089     51.0854\n  4500   8400    -0.6160    33.8347    38.2695    40.342979     51.0722      1.0148     51.0854\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180623_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.2764284      -15.6218708       38.9224121   m   m   m\ninitial_baseline_rate:          0.0000000       -0.1012466        0.0028457   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -15.6997972       38.9643539   m   m   m\nprecision_baseline_rate:        0.0000000       -0.1035150        0.0023676   m/s m/s m/s\nunwrap_phase_constant:           -0.00005     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180623_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.276    -15.622     38.922\norbit baseline rate   (TCN) (m/s):  0.000e+00 -1.012e-01  2.846e-03\n\nbaseline vector (TCN)   (m):      0.000    -15.700     38.964\nbaseline rate   (TCN) (m/s):  0.000e+00 -1.035e-01  2.368e-03\n\nSLC-1 center baseline length (m):    42.0084\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -14.7336    38.9423    27.497013     27.7406    -31.0489     41.6362\n     0    200     0.0000   -14.7336    38.9423    27.939249     27.5002    -31.2621     41.6362\n     0    400     0.0000   -14.7336    38.9423    28.370851     27.2639    -31.4683     41.6362\n     0    600     0.0000   -14.7336    38.9423    28.792342     27.0317    -31.6680     41.6362\n     0    800     0.0000   -14.7336    38.9423    29.204202     26.8033    -31.8615     41.6362\n     0   1000     0.0000   -14.7336    38.9423    29.606873     26.5787    -32.0491     41.6362\n     0   1200     0.0000   -14.7336    38.9423    30.000767     26.3578    -32.2311     41.6362\n     0   1400     0.0000   -14.7336    38.9423    30.386264     26.1403    -32.4077     41.6362\n     0   1600     0.0000   -14.7336    38.9423    30.763716     25.9263    -32.5792     41.6362\n     0   1800     0.0000   -14.7336    38.9423    31.133454     25.7155    -32.7458     41.6362\n     0   2000     0.0000   -14.7336    38.9423    31.495785     25.5079    -32.9078     41.6362\n     0   2200     0.0000   -14.7336    38.9423    31.850997     25.3034    -33.0653     41.6362\n     0   2400     0.0000   -14.7336    38.9423    32.199360     25.1019    -33.2185     41.6362\n     0   2600     0.0000   -14.7336    38.9423    32.541126     24.9033    -33.3677     41.6362\n     0   2800     0.0000   -14.7336    38.9423    32.876534     24.7075    -33.5129     41.6362\n     0   3000     0.0000   -14.7336    38.9423    33.205808     24.5145    -33.6543     41.6362\n     0   3200     0.0000   -14.7336    38.9423    33.529159     24.3242    -33.7921     41.6362\n     0   3400     0.0000   -14.7336    38.9423    33.846787     24.1365    -33.9265     41.6362\n     0   3600     0.0000   -14.7336    38.9423    34.158881     23.9514    -34.0574     41.6362\n     0   3800     0.0000   -14.7336    38.9423    34.465620     23.7687    -34.1852     41.6362\n     0   4000     0.0000   -14.7336    38.9423    34.767174     23.5884    -34.3098     41.6362\n     0   4200     0.0000   -14.7336    38.9423    35.063703     23.4106    -34.4314     41.6362\n     0   4400     0.0000   -14.7336    38.9423    35.355362     23.2350    -34.5501     41.6362\n     0   4600     0.0000   -14.7336    38.9423    35.642294     23.0617    -34.6661     41.6362\n     0   4800     0.0000   -14.7336    38.9423    35.924640     22.8906    -34.7793     41.6362\n     0   5000     0.0000   -14.7336    38.9423    36.202531     22.7216    -34.8899     41.6362\n     0   5200     0.0000   -14.7336    38.9423    36.476093     22.5548    -34.9980     41.6362\n     0   5400     0.0000   -14.7336    38.9423    36.745447     22.3900    -35.1036     41.6362\n     0   5600     0.0000   -14.7336    38.9423    37.010708     22.2272    -35.2069     41.6362\n     0   5800     0.0000   -14.7336    38.9423    37.271986     22.0664    -35.3079     41.6362\n     0   6000     0.0000   -14.7336    38.9423    37.529387     21.9076    -35.4067     41.6362\n     0   6200     0.0000   -14.7336    38.9423    37.783012     21.7507    -35.5033     41.6362\n     0   6400     0.0000   -14.7336    38.9423    38.032957     21.5956    -35.5979     41.6362\n     0   6600     0.0000   -14.7336    38.9423    38.279317     21.4423    -35.6904     41.6362\n     0   6800     0.0000   -14.7336    38.9423    38.522179     21.2908    -35.7810     41.6362\n     0   7000     0.0000   -14.7336    38.9423    38.761630     21.1411    -35.8696     41.6362\n     0   7200     0.0000   -14.7336    38.9423    38.997753     20.9931    -35.9564     41.6362\n     0   7400     0.0000   -14.7336    38.9423    39.230627     20.8468    -36.0415     41.6362\n     0   7600     0.0000   -14.7336    38.9423    39.460328     20.7021    -36.1248     41.6362\n     0   7800     0.0000   -14.7336    38.9423    39.686930     20.5591    -36.2063     41.6362\n     0   8000     0.0000   -14.7336    38.9423    39.910504     20.4177    -36.2863     41.6362\n     0   8200     0.0000   -14.7336    38.9423    40.131118     20.2778    -36.3646     41.6362\n     0   8400     0.0000   -14.7336    38.9423    40.348838     20.1395    -36.4414     41.6362\n   500      0     0.0000   -14.9463    38.9471    27.496077     27.6472    -31.2394     41.7166\n   500    200     0.0000   -14.9463    38.9471    27.938329     27.4053    -31.4519     41.7166\n   500    400     0.0000   -14.9463    38.9471    28.369946     27.1676    -31.6574     41.7166\n   500    600     0.0000   -14.9463    38.9471    28.791451     26.9339    -31.8564     41.7166\n   500    800     0.0000   -14.9463    38.9471    29.203324     26.7042    -32.0492     41.7166\n   500   1000     0.0000   -14.9463    38.9471    29.606009     26.4783    -32.2361     41.7166\n   500   1200     0.0000   -14.9463    38.9471    29.999915     26.2561    -32.4174     41.7166\n   500   1400     0.0000   -14.9463    38.9471    30.385423     26.0374    -32.5933     41.7166\n   500   1600     0.0000   -14.9463    38.9471    30.762887     25.8221    -32.7642     41.7166\n   500   1800     0.0000   -14.9463    38.9471    31.132636     25.6101    -32.9301     41.7166\n   500   2000     0.0000   -14.9463    38.9471    31.494978     25.4014    -33.0914     41.7166\n   500   2200     0.0000   -14.9463    38.9471    31.850200     25.1957    -33.2483     41.7166\n   500   2400     0.0000   -14.9463    38.9471    32.198572     24.9931    -33.4008     41.7166\n   500   2600     0.0000   -14.9463    38.9471    32.540347     24.7934    -33.5493     41.7166\n   500   2800     0.0000   -14.9463    38.9471    32.875764     24.5966    -33.6939     41.7166\n   500   3000     0.0000   -14.9463    38.9471    33.205046     24.4025    -33.8347     41.7166\n   500   3200     0.0000   -14.9463    38.9471    33.528405     24.2112    -33.9719     41.7166\n   500   3400     0.0000   -14.9463    38.9471    33.846042     24.0225    -34.1056     41.7166\n   500   3600     0.0000   -14.9463    38.9471    34.158144     23.8364    -34.2359     41.7166\n   500   3800     0.0000   -14.9463    38.9471    34.464890     23.6527    -34.3631     41.7166\n   500   4000     0.0000   -14.9463    38.9471    34.766452     23.4715    -34.4871     41.7166\n   500   4200     0.0000   -14.9463    38.9471    35.062988     23.2927    -34.6081     41.7166\n   500   4400     0.0000   -14.9463    38.9471    35.354653     23.1163    -34.7262     41.7166\n   500   4600     0.0000   -14.9463    38.9471    35.641592     22.9421    -34.8415     41.7166\n   500   4800     0.0000   -14.9463    38.9471    35.923944     22.7701    -34.9542     41.7166\n   500   5000     0.0000   -14.9463    38.9471    36.201842     22.6003    -35.0642     41.7166\n   500   5200     0.0000   -14.9463    38.9471    36.475410     22.4326    -35.1717     41.7166\n   500   5400     0.0000   -14.9463    38.9471    36.744770     22.2670    -35.2768     41.7166\n   500   5600     0.0000   -14.9463    38.9471    37.010037     22.1034    -35.3795     41.7166\n   500   5800     0.0000   -14.9463    38.9471    37.271321     21.9419    -35.4799     41.7166\n   500   6000     0.0000   -14.9463    38.9471    37.528727     21.7823    -35.5781     41.7166\n   500   6200     0.0000   -14.9463    38.9471    37.782357     21.6245    -35.6742     41.7166\n   500   6400     0.0000   -14.9463    38.9471    38.032308     21.4687    -35.7682     41.7166\n   500   6600     0.0000   -14.9463    38.9471    38.278673     21.3147    -35.8602     41.7166\n   500   6800     0.0000   -14.9463    38.9471    38.521540     21.1625    -35.9502     41.7166\n   500   7000     0.0000   -14.9463    38.9471    38.760996     21.0121    -36.0384     41.7166\n   500   7200     0.0000   -14.9463    38.9471    38.997124     20.8634    -36.1246     41.7166\n   500   7400     0.0000   -14.9463    38.9471    39.230002     20.7164    -36.2091     41.7166\n   500   7600     0.0000   -14.9463    38.9471    39.459708     20.5711    -36.2919     41.7166\n   500   7800     0.0000   -14.9463    38.9471    39.686314     20.4274    -36.3730     41.7166\n   500   8000     0.0000   -14.9463    38.9471    39.909892     20.2853    -36.4524     41.7166\n   500   8200     0.0000   -14.9463    38.9471    40.130511     20.1448    -36.5303     41.7166\n   500   8400     0.0000   -14.9463    38.9471    40.348236     20.0058    -36.6065     41.7166\n  1000      0     0.0000   -15.1591    38.9520    27.495122     27.5538    -31.4299     41.7978\n  1000    200     0.0000   -15.1591    38.9520    27.937389     27.3104    -31.6417     41.7978\n  1000    400     0.0000   -15.1591    38.9520    28.369022     27.0713    -31.8465     41.7978\n  1000    600     0.0000   -15.1591    38.9520    28.790541     26.8362    -32.0448     41.7978\n  1000    800     0.0000   -15.1591    38.9520    29.202428     26.6052    -32.2369     41.7978\n  1000   1000     0.0000   -15.1591    38.9520    29.605126     26.3779    -32.4231     41.7978\n  1000   1200     0.0000   -15.1591    38.9520    29.999045     26.1544    -32.6037     41.7978\n  1000   1400     0.0000   -15.1591    38.9520    30.384565     25.9344    -32.7790     41.7978\n  1000   1600     0.0000   -15.1591    38.9520    30.762040     25.7179    -32.9491     41.7978\n  1000   1800     0.0000   -15.1591    38.9520    31.131800     25.5048    -33.1144     41.7978\n  1000   2000     0.0000   -15.1591    38.9520    31.494152     25.2948    -33.2750     41.7978\n  1000   2200     0.0000   -15.1591    38.9520    31.849385     25.0880    -33.4312     41.7978\n  1000   2400     0.0000   -15.1591    38.9520    32.197766     24.8843    -33.5831     41.7978\n  1000   2600     0.0000   -15.1591    38.9520    32.539551     24.6835    -33.7310     41.7978\n  1000   2800     0.0000   -15.1591    38.9520    32.874977     24.4856    -33.8749     41.7978\n  1000   3000     0.0000   -15.1591    38.9520    33.204268     24.2905    -34.0151     41.7978\n  1000   3200     0.0000   -15.1591    38.9520    33.527636     24.0982    -34.1516     41.7978\n  1000   3400     0.0000   -15.1591    38.9520    33.845281     23.9085    -34.2847     41.7978\n  1000   3600     0.0000   -15.1591    38.9520    34.157391     23.7214    -34.4144     41.7978\n  1000   3800     0.0000   -15.1591    38.9520    34.464145     23.5368    -34.5409     41.7978\n  1000   4000     0.0000   -15.1591    38.9520    34.765714     23.3546    -34.6643     41.7978\n  1000   4200     0.0000   -15.1591    38.9520    35.062258     23.1749    -34.7847     41.7978\n  1000   4400     0.0000   -15.1591    38.9520    35.353930     22.9975    -34.9023     41.7978\n  1000   4600     0.0000   -15.1591    38.9520    35.640876     22.8225    -35.0170     41.7978\n  1000   4800     0.0000   -15.1591    38.9520    35.923235     22.6496    -35.1291     41.7978\n  1000   5000     0.0000   -15.1591    38.9520    36.201138     22.4790    -35.2385     41.7978\n  1000   5200     0.0000   -15.1591    38.9520    36.474713     22.3104    -35.3454     41.7978\n  1000   5400     0.0000   -15.1591    38.9520    36.744079     22.1440    -35.4499     41.7978\n  1000   5600     0.0000   -15.1591    38.9520    37.009352     21.9797    -35.5521     41.7978\n  1000   5800     0.0000   -15.1591    38.9520    37.270642     21.8173    -35.6519     41.7978\n  1000   6000     0.0000   -15.1591    38.9520    37.528054     21.6569    -35.7496     41.7978\n  1000   6200     0.0000   -15.1591    38.9520    37.781689     21.4984    -35.8451     41.7978\n  1000   6400     0.0000   -15.1591    38.9520    38.031646     21.3419    -35.9386     41.7978\n  1000   6600     0.0000   -15.1591    38.9520    38.278015     21.1871    -36.0300     41.7978\n  1000   6800     0.0000   -15.1591    38.9520    38.520888     21.0342    -36.1195     41.7978\n  1000   7000     0.0000   -15.1591    38.9520    38.760349     20.8831    -36.2071     41.7978\n  1000   7200     0.0000   -15.1591    38.9520    38.996482     20.7337    -36.2928     41.7978\n  1000   7400     0.0000   -15.1591    38.9520    39.229365     20.5860    -36.3768     41.7978\n  1000   7600     0.0000   -15.1591    38.9520    39.459075     20.4400    -36.4591     41.7978\n  1000   7800     0.0000   -15.1591    38.9520    39.685686     20.2956    -36.5396     41.7978\n  1000   8000     0.0000   -15.1591    38.9520    39.909268     20.1529    -36.6185     41.7978\n  1000   8200     0.0000   -15.1591    38.9520    40.129891     20.0117    -36.6959     41.7978\n  1000   8400     0.0000   -15.1591    38.9520    40.347621     19.8721    -36.7716     41.7978\n  1500      0     0.0000   -15.3719    38.9569    27.494145     27.4604    -31.6205     41.8800\n  1500    200     0.0000   -15.3719    38.9569    27.936429     27.2155    -31.8315     41.8800\n  1500    400     0.0000   -15.3719    38.9569    28.368077     26.9750    -32.0356     41.8800\n  1500    600     0.0000   -15.3719    38.9569    28.789612     26.7385    -32.2332     41.8800\n  1500    800     0.0000   -15.3719    38.9569    29.201513     26.5061    -32.4246     41.8800\n  1500   1000     0.0000   -15.3719    38.9569    29.604224     26.2776    -32.6101     41.8800\n  1500   1200     0.0000   -15.3719    38.9569    29.998156     26.0527    -32.7900     41.8800\n  1500   1400     0.0000   -15.3719    38.9569    30.383688     25.8315    -32.9646     41.8800\n  1500   1600     0.0000   -15.3719    38.9569    30.761176     25.6138    -33.1340     41.8800\n  1500   1800     0.0000   -15.3719    38.9569    31.130947     25.3994    -33.2987     41.8800\n  1500   2000     0.0000   -15.3719    38.9569    31.493310     25.1883    -33.4586     41.8800\n  1500   2200     0.0000   -15.3719    38.9569    31.848553     24.9804    -33.6142     41.8800\n  1500   2400     0.0000   -15.3719    38.9569    32.196945     24.7755    -33.7654     41.8800\n  1500   2600     0.0000   -15.3719    38.9569    32.538739     24.5737    -33.9126     41.8800\n  1500   2800     0.0000   -15.3719    38.9569    32.874174     24.3747    -34.0559     41.8800\n  1500   3000     0.0000   -15.3719    38.9569    33.203475     24.1786    -34.1954     41.8800\n  1500   3200     0.0000   -15.3719    38.9569    33.526851     23.9852    -34.3314     41.8800\n  1500   3400     0.0000   -15.3719    38.9569    33.844504     23.7945    -34.4638     41.8800\n  1500   3600     0.0000   -15.3719    38.9569    34.156623     23.6064    -34.5929     41.8800\n  1500   3800     0.0000   -15.3719    38.9569    34.463385     23.4208    -34.7188     41.8800\n  1500   4000     0.0000   -15.3719    38.9569    34.764961     23.2378    -34.8416     41.8800\n  1500   4200     0.0000   -15.3719    38.9569    35.061513     23.0571    -34.9614     41.8800\n  1500   4400     0.0000   -15.3719    38.9569    35.353192     22.8788    -35.0783     41.8800\n  1500   4600     0.0000   -15.3719    38.9569    35.640145     22.7029    -35.1925     41.8800\n  1500   4800     0.0000   -15.3719    38.9569    35.922510     22.5292    -35.3039     41.8800\n  1500   5000     0.0000   -15.3719    38.9569    36.200421     22.3577    -35.4128     41.8800\n  1500   5200     0.0000   -15.3719    38.9569    36.474002     22.1883    -35.5191     41.8800\n  1500   5400     0.0000   -15.3719    38.9569    36.743374     22.0211    -35.6231     41.8800\n  1500   5600     0.0000   -15.3719    38.9569    37.008653     21.8559    -35.7246     41.8800\n  1500   5800     0.0000   -15.3719    38.9569    37.269949     21.6928    -35.8240     41.8800\n  1500   6000     0.0000   -15.3719    38.9569    37.527367     21.5316    -35.9210     41.8800\n  1500   6200     0.0000   -15.3719    38.9569    37.781008     21.3724    -36.0160     41.8800\n  1500   6400     0.0000   -15.3719    38.9569    38.030970     21.2150    -36.1089     41.8800\n  1500   6600     0.0000   -15.3719    38.9569    38.277345     21.0596    -36.1998     41.8800\n  1500   6800     0.0000   -15.3719    38.9569    38.520223     20.9059    -36.2888     41.8800\n  1500   7000     0.0000   -15.3719    38.9569    38.759689     20.7541    -36.3758     41.8800\n  1500   7200     0.0000   -15.3719    38.9569    38.995827     20.6040    -36.4610     41.8800\n  1500   7400     0.0000   -15.3719    38.9569    39.228715     20.4556    -36.5445     41.8800\n  1500   7600     0.0000   -15.3719    38.9569    39.458429     20.3089    -36.6262     41.8800\n  1500   7800     0.0000   -15.3719    38.9569    39.685045     20.1639    -36.7062     41.8800\n  1500   8000     0.0000   -15.3719    38.9569    39.908632     20.0205    -36.7846     41.8800\n  1500   8200     0.0000   -15.3719    38.9569    40.129260     19.8787    -36.8615     41.8800\n  1500   8400     0.0000   -15.3719    38.9569    40.346993     19.7385    -36.9367     41.8800\n  2000      0     0.0000   -15.5847    38.9617    27.493148     27.3671    -31.8110     41.9631\n  2000    200     0.0000   -15.5847    38.9617    27.935449     27.1207    -32.0213     41.9631\n  2000    400     0.0000   -15.5847    38.9617    28.367113     26.8787    -32.2247     41.9631\n  2000    600     0.0000   -15.5847    38.9617    28.788663     26.6409    -32.4216     41.9631\n  2000    800     0.0000   -15.5847    38.9617    29.200578     26.4071    -32.6123     41.9631\n  2000   1000     0.0000   -15.5847    38.9617    29.603304     26.1772    -32.7971     41.9631\n  2000   1200     0.0000   -15.5847    38.9617    29.997248     25.9511    -32.9763     41.9631\n  2000   1400     0.0000   -15.5847    38.9617    30.382794     25.7286    -33.1502     41.9631\n  2000   1600     0.0000   -15.5847    38.9617    30.760293     25.5096    -33.3190     41.9631\n  2000   1800     0.0000   -15.5847    38.9617    31.130076     25.2941    -33.4829     41.9631\n  2000   2000     0.0000   -15.5847    38.9617    31.492450     25.0818    -33.6422     41.9631\n  2000   2200     0.0000   -15.5847    38.9617    31.847704     24.8727    -33.7971     41.9631\n  2000   2400     0.0000   -15.5847    38.9617    32.196106     24.6668    -33.9477     41.9631\n  2000   2600     0.0000   -15.5847    38.9617    32.537910     24.4638    -34.0943     41.9631\n  2000   2800     0.0000   -15.5847    38.9617    32.873355     24.2638    -34.2369     41.9631\n  2000   3000     0.0000   -15.5847    38.9617    33.202665     24.0666    -34.3758     41.9631\n  2000   3200     0.0000   -15.5847    38.9617    33.526051     23.8722    -34.5111     41.9631\n  2000   3400     0.0000   -15.5847    38.9617    33.843712     23.6805    -34.6429     41.9631\n  2000   3600     0.0000   -15.5847    38.9617    34.155839     23.4914    -34.7714     41.9631\n  2000   3800     0.0000   -15.5847    38.9617    34.462609     23.3049    -34.8967     41.9631\n  2000   4000     0.0000   -15.5847    38.9617    34.764193     23.1209    -35.0189     41.9631\n  2000   4200     0.0000   -15.5847    38.9617    35.060752     22.9393    -35.1381     41.9631\n  2000   4400     0.0000   -15.5847    38.9617    35.352439     22.7602    -35.2544     41.9631\n  2000   4600     0.0000   -15.5847    38.9617    35.639399     22.5833    -35.3679     41.9631\n  2000   4800     0.0000   -15.5847    38.9617    35.921772     22.4087    -35.4788     41.9631\n  2000   5000     0.0000   -15.5847    38.9617    36.199689     22.2364    -35.5871     41.9631\n  2000   5200     0.0000   -15.5847    38.9617    36.473276     22.0662    -35.6929     41.9631\n  2000   5400     0.0000   -15.5847    38.9617    36.742655     21.8981    -35.7962     41.9631\n  2000   5600     0.0000   -15.5847    38.9617    37.007941     21.7322    -35.8972     41.9631\n  2000   5800     0.0000   -15.5847    38.9617    37.269242     21.5682    -35.9960     41.9631\n  2000   6000     0.0000   -15.5847    38.9617    37.526666     21.4063    -36.0925     41.9631\n  2000   6200     0.0000   -15.5847    38.9617    37.780313     21.2463    -36.1869     41.9631\n  2000   6400     0.0000   -15.5847    38.9617    38.030281     21.0882    -36.2793     41.9631\n  2000   6600     0.0000   -15.5847    38.9617    38.276661     20.9320    -36.3696     41.9631\n  2000   6800     0.0000   -15.5847    38.9617    38.519545     20.7776    -36.4580     41.9631\n  2000   7000     0.0000   -15.5847    38.9617    38.759016     20.6251    -36.5445     41.9631\n  2000   7200     0.0000   -15.5847    38.9617    38.995159     20.4743    -36.6292     41.9631\n  2000   7400     0.0000   -15.5847    38.9617    39.228052     20.3252    -36.7122     41.9631\n  2000   7600     0.0000   -15.5847    38.9617    39.457772     20.1779    -36.7933     41.9631\n  2000   7800     0.0000   -15.5847    38.9617    39.684392     20.0322    -36.8729     41.9631\n  2000   8000     0.0000   -15.5847    38.9617    39.907984     19.8881    -36.9508     41.9631\n  2000   8200     0.0000   -15.5847    38.9617    40.128616     19.7457    -37.0271     41.9631\n  2000   8400     0.0000   -15.5847    38.9617    40.346354     19.6049    -37.1018     41.9631\n  2500      0     0.0000   -15.7975    38.9666    27.492131     27.2737    -32.0015     42.0471\n  2500    200     0.0000   -15.7975    38.9666    27.934449     27.0259    -32.2111     42.0471\n  2500    400     0.0000   -15.7975    38.9666    28.366129     26.7824    -32.4138     42.0471\n  2500    600     0.0000   -15.7975    38.9666    28.787694     26.5432    -32.6100     42.0471\n  2500    800     0.0000   -15.7975    38.9666    29.199625     26.3081    -32.8000     42.0471\n  2500   1000     0.0000   -15.7975    38.9666    29.602364     26.0769    -32.9841     42.0471\n  2500   1200     0.0000   -15.7975    38.9666    29.996323     25.8495    -33.1626     42.0471\n  2500   1400     0.0000   -15.7975    38.9666    30.381881     25.6257    -33.3358     42.0471\n  2500   1600     0.0000   -15.7975    38.9666    30.759393     25.4055    -33.5039     42.0471\n  2500   1800     0.0000   -15.7975    38.9666    31.129187     25.1888    -33.6672     42.0471\n  2500   2000     0.0000   -15.7975    38.9666    31.491573     24.9753    -33.8258     42.0471\n  2500   2200     0.0000   -15.7975    38.9666    31.846838     24.7651    -33.9800     42.0471\n  2500   2400     0.0000   -15.7975    38.9666    32.195250     24.5580    -34.1300     42.0471\n  2500   2600     0.0000   -15.7975    38.9666    32.537065     24.3540    -34.2759     42.0471\n  2500   2800     0.0000   -15.7975    38.9666    32.872520     24.1529    -34.4179     42.0471\n  2500   3000     0.0000   -15.7975    38.9666    33.201839     23.9546    -34.5562     42.0471\n  2500   3200     0.0000   -15.7975    38.9666    33.525234     23.7592    -34.6908     42.0471\n  2500   3400     0.0000   -15.7975    38.9666    33.842904     23.5665    -34.8220     42.0471\n  2500   3600     0.0000   -15.7975    38.9666    34.155039     23.3765    -34.9499     42.0471\n  2500   3800     0.0000   -15.7975    38.9666    34.461818     23.1890    -35.0746     42.0471\n  2500   4000     0.0000   -15.7975    38.9666    34.763411     23.0041    -35.1961     42.0471\n  2500   4200     0.0000   -15.7975    38.9666    35.059977     22.8216    -35.3147     42.0471\n  2500   4400     0.0000   -15.7975    38.9666    35.351672     22.6415    -35.4305     42.0471\n  2500   4600     0.0000   -15.7975    38.9666    35.638639     22.4637    -35.5434     42.0471\n  2500   4800     0.0000   -15.7975    38.9666    35.921019     22.2883    -35.6537     42.0471\n  2500   5000     0.0000   -15.7975    38.9666    36.198943     22.1151    -35.7614     42.0471\n  2500   5200     0.0000   -15.7975    38.9666    36.472537     21.9441    -35.8666     42.0471\n  2500   5400     0.0000   -15.7975    38.9666    36.741923     21.7752    -35.9694     42.0471\n  2500   5600     0.0000   -15.7975    38.9666    37.007214     21.6084    -36.0698     42.0471\n  2500   5800     0.0000   -15.7975    38.9666    37.268522     21.4437    -36.1680     42.0471\n  2500   6000     0.0000   -15.7975    38.9666    37.525952     21.2810    -36.2640     42.0471\n  2500   6200     0.0000   -15.7975    38.9666    37.779605     21.1202    -36.3578     42.0471\n  2500   6400     0.0000   -15.7975    38.9666    38.029579     20.9614    -36.4496     42.0471\n  2500   6600     0.0000   -15.7975    38.9666    38.275965     20.8045    -36.5394     42.0471\n  2500   6800     0.0000   -15.7975    38.9666    38.518854     20.6494    -36.6273     42.0471\n  2500   7000     0.0000   -15.7975    38.9666    38.758331     20.4961    -36.7133     42.0471\n  2500   7200     0.0000   -15.7975    38.9666    38.994478     20.3446    -36.7974     42.0471\n  2500   7400     0.0000   -15.7975    38.9666    39.227377     20.1949    -36.8798     42.0471\n  2500   7600     0.0000   -15.7975    38.9666    39.457102     20.0468    -36.9605     42.0471\n  2500   7800     0.0000   -15.7975    38.9666    39.683727     19.9005    -37.0395     42.0471\n  2500   8000     0.0000   -15.7975    38.9666    39.907324     19.7558    -37.1169     42.0471\n  2500   8200     0.0000   -15.7975    38.9666    40.127960     19.6127    -37.1927     42.0471\n  2500   8400     0.0000   -15.7975    38.9666    40.345703     19.4712    -37.2669     42.0471\n  3000      0     0.0000   -16.0102    38.9715    27.491093     27.1804    -32.1920     42.1320\n  3000    200     0.0000   -16.0102    38.9715    27.933428     26.9311    -32.4009     42.1320\n  3000    400     0.0000   -16.0102    38.9715    28.365126     26.6862    -32.6029     42.1320\n  3000    600     0.0000   -16.0102    38.9715    28.786707     26.4456    -32.7984     42.1320\n  3000    800     0.0000   -16.0102    38.9715    29.198653     26.2091    -32.9876     42.1320\n  3000   1000     0.0000   -16.0102    38.9715    29.601406     25.9766    -33.1711     42.1320\n  3000   1200     0.0000   -16.0102    38.9715    29.995379     25.7479    -33.3489     42.1320\n  3000   1400     0.0000   -16.0102    38.9715    30.380950     25.5229    -33.5214     42.1320\n  3000   1600     0.0000   -16.0102    38.9715    30.758475     25.3014    -33.6889     42.1320\n  3000   1800     0.0000   -16.0102    38.9715    31.128282     25.0835    -33.8515     42.1320\n  3000   2000     0.0000   -16.0102    38.9715    31.490679     24.8688    -34.0094     42.1320\n  3000   2200     0.0000   -16.0102    38.9715    31.845955     24.6575    -34.1630     42.1320\n  3000   2400     0.0000   -16.0102    38.9715    32.194378     24.4493    -34.3123     42.1320\n  3000   2600     0.0000   -16.0102    38.9715    32.536204     24.2441    -34.4575     42.1320\n  3000   2800     0.0000   -16.0102    38.9715    32.871668     24.0420    -34.5989     42.1320\n  3000   3000     0.0000   -16.0102    38.9715    33.200997     23.8427    -34.7365     42.1320\n  3000   3200     0.0000   -16.0102    38.9715    33.524401     23.6463    -34.8706     42.1320\n  3000   3400     0.0000   -16.0102    38.9715    33.842081     23.4526    -35.0011     42.1320\n  3000   3600     0.0000   -16.0102    38.9715    34.154225     23.2615    -35.1284     42.1320\n  3000   3800     0.0000   -16.0102    38.9715    34.461012     23.0731    -35.2524     42.1320\n  3000   4000     0.0000   -16.0102    38.9715    34.762613     22.8872    -35.3734     42.1320\n  3000   4200     0.0000   -16.0102    38.9715    35.059187     22.7038    -35.4914     42.1320\n  3000   4400     0.0000   -16.0102    38.9715    35.350889     22.5228    -35.6065     42.1320\n  3000   4600     0.0000   -16.0102    38.9715    35.637865     22.3442    -35.7189     42.1320\n  3000   4800     0.0000   -16.0102    38.9715    35.920252     22.1679    -35.8286     42.1320\n  3000   5000     0.0000   -16.0102    38.9715    36.198183     21.9938    -35.9357     42.1320\n  3000   5200     0.0000   -16.0102    38.9715    36.471784     21.8220    -36.0403     42.1320\n  3000   5400     0.0000   -16.0102    38.9715    36.741176     21.6523    -36.1425     42.1320\n  3000   5600     0.0000   -16.0102    38.9715    37.006474     21.4847    -36.2424     42.1320\n  3000   5800     0.0000   -16.0102    38.9715    37.267789     21.3192    -36.3400     42.1320\n  3000   6000     0.0000   -16.0102    38.9715    37.525225     21.1557    -36.4354     42.1320\n  3000   6200     0.0000   -16.0102    38.9715    37.778884     20.9942    -36.5287     42.1320\n  3000   6400     0.0000   -16.0102    38.9715    38.028863     20.8346    -36.6199     42.1320\n  3000   6600     0.0000   -16.0102    38.9715    38.275255     20.6769    -36.7092     42.1320\n  3000   6800     0.0000   -16.0102    38.9715    38.518150     20.5211    -36.7965     42.1320\n  3000   7000     0.0000   -16.0102    38.9715    38.757632     20.3671    -36.8820     42.1320\n  3000   7200     0.0000   -16.0102    38.9715    38.993785     20.2150    -36.9656     42.1320\n  3000   7400     0.0000   -16.0102    38.9715    39.226689     20.0645    -37.0475     42.1320\n  3000   7600     0.0000   -16.0102    38.9715    39.456419     19.9158    -37.1276     42.1320\n  3000   7800     0.0000   -16.0102    38.9715    39.683049     19.7688    -37.2061     42.1320\n  3000   8000     0.0000   -16.0102    38.9715    39.906651     19.6235    -37.2830     42.1320\n  3000   8200     0.0000   -16.0102    38.9715    40.127293     19.4797    -37.3583     42.1320\n  3000   8400     0.0000   -16.0102    38.9715    40.345040     19.3376    -37.4320     42.1320\n  3500      0     0.0000   -16.2230    38.9763    27.490034     27.0871    -32.3825     42.2178\n  3500    200     0.0000   -16.2230    38.9763    27.932388     26.8363    -32.5907     42.2178\n  3500    400     0.0000   -16.2230    38.9763    28.364103     26.5900    -32.7920     42.2178\n  3500    600     0.0000   -16.2230    38.9763    28.785700     26.3480    -32.9867     42.2178\n  3500    800     0.0000   -16.2230    38.9763    29.197661     26.1101    -33.1753     42.2178\n  3500   1000     0.0000   -16.2230    38.9763    29.600430     25.8763    -33.3580     42.2178\n  3500   1200     0.0000   -16.2230    38.9763    29.994416     25.6463    -33.5352     42.2178\n  3500   1400     0.0000   -16.2230    38.9763    30.380001     25.4200    -33.7070     42.2178\n  3500   1600     0.0000   -16.2230    38.9763    30.757539     25.1973    -33.8738     42.2178\n  3500   1800     0.0000   -16.2230    38.9763    31.127358     24.9782    -34.0357     42.2178\n  3500   2000     0.0000   -16.2230    38.9763    31.489768     24.7624    -34.1930     42.2178\n  3500   2200     0.0000   -16.2230    38.9763    31.845055     24.5499    -34.3459     42.2178\n  3500   2400     0.0000   -16.2230    38.9763    32.193490     24.3406    -34.4946     42.2178\n  3500   2600     0.0000   -16.2230    38.9763    32.535326     24.1343    -34.6392     42.2178\n  3500   2800     0.0000   -16.2230    38.9763    32.870801     23.9311    -34.7799     42.2178\n  3500   3000     0.0000   -16.2230    38.9763    33.200139     23.7308    -34.9169     42.2178\n  3500   3200     0.0000   -16.2230    38.9763    33.523553     23.5333    -35.0503     42.2178\n  3500   3400     0.0000   -16.2230    38.9763    33.841242     23.3386    -35.1802     42.2178\n  3500   3600     0.0000   -16.2230    38.9763    34.153395     23.1466    -35.3069     42.2178\n  3500   3800     0.0000   -16.2230    38.9763    34.460191     22.9572    -35.4303     42.2178\n  3500   4000     0.0000   -16.2230    38.9763    34.761800     22.7704    -35.5506     42.2178\n  3500   4200     0.0000   -16.2230    38.9763    35.058383     22.5861    -35.6680     42.2178\n  3500   4400     0.0000   -16.2230    38.9763    35.350093     22.4042    -35.7826     42.2178\n  3500   4600     0.0000   -16.2230    38.9763    35.637076     22.2247    -35.8943     42.2178\n  3500   4800     0.0000   -16.2230    38.9763    35.919470     22.0475    -36.0034     42.2178\n  3500   5000     0.0000   -16.2230    38.9763    36.197408     21.8726    -36.1100     42.2178\n  3500   5200     0.0000   -16.2230    38.9763    36.471017     21.6999    -36.2140     42.2178\n  3500   5400     0.0000   -16.2230    38.9763    36.740416     21.5294    -36.3156     42.2178\n  3500   5600     0.0000   -16.2230    38.9763    37.005721     21.3610    -36.4149     42.2178\n  3500   5800     0.0000   -16.2230    38.9763    37.267042     21.1947    -36.5120     42.2178\n  3500   6000     0.0000   -16.2230    38.9763    37.524484     21.0304    -36.6068     42.2178\n  3500   6200     0.0000   -16.2230    38.9763    37.778150     20.8681    -36.6996     42.2178\n  3500   6400     0.0000   -16.2230    38.9763    38.028135     20.7078    -36.7903     42.2178\n  3500   6600     0.0000   -16.2230    38.9763    38.274533     20.5494    -36.8790     42.2178\n  3500   6800     0.0000   -16.2230    38.9763    38.517433     20.3929    -36.9658     42.2178\n  3500   7000     0.0000   -16.2230    38.9763    38.756921     20.2382    -37.0507     42.2178\n  3500   7200     0.0000   -16.2230    38.9763    38.993080     20.0853    -37.1338     42.2178\n  3500   7400     0.0000   -16.2230    38.9763    39.225988     19.9342    -37.2151     42.2178\n  3500   7600     0.0000   -16.2230    38.9763    39.455724     19.7848    -37.2948     42.2178\n  3500   7800     0.0000   -16.2230    38.9763    39.682359     19.6371    -37.3727     42.2178\n  3500   8000     0.0000   -16.2230    38.9763    39.905966     19.4911    -37.4491     42.2178\n  3500   8200     0.0000   -16.2230    38.9763    40.126613     19.3468    -37.5239     42.2178\n  3500   8400     0.0000   -16.2230    38.9763    40.344365     19.2040    -37.5971     42.2178\n  4000      0     0.0000   -16.4358    38.9812    27.488955     26.9938    -32.5730     42.3045\n  4000    200     0.0000   -16.4358    38.9812    27.931328     26.7415    -32.7804     42.3045\n  4000    400     0.0000   -16.4358    38.9812    28.363060     26.4938    -32.9810     42.3045\n  4000    600     0.0000   -16.4358    38.9812    28.784674     26.2504    -33.1751     42.3045\n  4000    800     0.0000   -16.4358    38.9812    29.196651     26.0111    -33.3630     42.3045\n  4000   1000     0.0000   -16.4358    38.9812    29.599435     25.7760    -33.5450     42.3045\n  4000   1200     0.0000   -16.4358    38.9812    29.993436     25.5447    -33.7215     42.3045\n  4000   1400     0.0000   -16.4358    38.9812    30.379035     25.3172    -33.8926     42.3045\n  4000   1600     0.0000   -16.4358    38.9812    30.756585     25.0933    -34.0587     42.3045\n  4000   1800     0.0000   -16.4358    38.9812    31.126418     24.8729    -34.2200     42.3045\n  4000   2000     0.0000   -16.4358    38.9812    31.488839     24.6560    -34.3766     42.3045\n  4000   2200     0.0000   -16.4358    38.9812    31.844138     24.4423    -34.5288     42.3045\n  4000   2400     0.0000   -16.4358    38.9812    32.192584     24.2319    -34.6769     42.3045\n  4000   2600     0.0000   -16.4358    38.9812    32.534431     24.0245    -34.8208     42.3045\n  4000   2800     0.0000   -16.4358    38.9812    32.869917     23.8202    -34.9609     42.3045\n  4000   3000     0.0000   -16.4358    38.9812    33.199265     23.6189    -35.0972     42.3045\n  4000   3200     0.0000   -16.4358    38.9812    33.522689     23.4204    -35.2300     42.3045\n  4000   3400     0.0000   -16.4358    38.9812    33.840387     23.2247    -35.3593     42.3045\n  4000   3600     0.0000   -16.4358    38.9812    34.152550     23.0317    -35.4853     42.3045\n  4000   3800     0.0000   -16.4358    38.9812    34.459354     22.8414    -35.6081     42.3045\n  4000   4000     0.0000   -16.4358    38.9812    34.760972     22.6536    -35.7279     42.3045\n  4000   4200     0.0000   -16.4358    38.9812    35.057563     22.4683    -35.8447     42.3045\n  4000   4400     0.0000   -16.4358    38.9812    35.349281     22.2856    -35.9586     42.3045\n  4000   4600     0.0000   -16.4358    38.9812    35.636272     22.1052    -36.0698     42.3045\n  4000   4800     0.0000   -16.4358    38.9812    35.918674     21.9271    -36.1783     42.3045\n  4000   5000     0.0000   -16.4358    38.9812    36.196620     21.7513    -36.2842     42.3045\n  4000   5200     0.0000   -16.4358    38.9812    36.470236     21.5778    -36.3877     42.3045\n  4000   5400     0.0000   -16.4358    38.9812    36.739642     21.4065    -36.4888     42.3045\n  4000   5600     0.0000   -16.4358    38.9812    37.004953     21.2373    -36.5875     42.3045\n  4000   5800     0.0000   -16.4358    38.9812    37.266281     21.0702    -36.6840     42.3045\n  4000   6000     0.0000   -16.4358    38.9812    37.523730     20.9052    -36.7783     42.3045\n  4000   6200     0.0000   -16.4358    38.9812    37.777402     20.7421    -36.8705     42.3045\n  4000   6400     0.0000   -16.4358    38.9812    38.027393     20.5810    -36.9606     42.3045\n  4000   6600     0.0000   -16.4358    38.9812    38.273797     20.4219    -37.0488     42.3045\n  4000   6800     0.0000   -16.4358    38.9812    38.516703     20.2647    -37.1350     42.3045\n  4000   7000     0.0000   -16.4358    38.9812    38.756197     20.1093    -37.2194     42.3045\n  4000   7200     0.0000   -16.4358    38.9812    38.992361     19.9557    -37.3020     42.3045\n  4000   7400     0.0000   -16.4358    38.9812    39.225276     19.8039    -37.3828     42.3045\n  4000   7600     0.0000   -16.4358    38.9812    39.455016     19.6538    -37.4619     42.3045\n  4000   7800     0.0000   -16.4358    38.9812    39.681657     19.5055    -37.5394     42.3045\n  4000   8000     0.0000   -16.4358    38.9812    39.905269     19.3588    -37.6152     42.3045\n  4000   8200     0.0000   -16.4358    38.9812    40.125921     19.2138    -37.6895     42.3045\n  4000   8400     0.0000   -16.4358    38.9812    40.343678     19.0704    -37.7622     42.3045\n  4500      0     0.0000   -16.6486    38.9861    27.487857     26.9006    -32.7635     42.3921\n  4500    200     0.0000   -16.6486    38.9861    27.930248     26.6468    -32.9702     42.3921\n  4500    400     0.0000   -16.6486    38.9861    28.361998     26.3976    -33.1701     42.3921\n  4500    600     0.0000   -16.6486    38.9861    28.783629     26.1528    -33.3634     42.3921\n  4500    800     0.0000   -16.6486    38.9861    29.195622     25.9122    -33.5506     42.3921\n  4500   1000     0.0000   -16.6486    38.9861    29.598421     25.6757    -33.7320     42.3921\n  4500   1200     0.0000   -16.6486    38.9861    29.992437     25.4431    -33.9077     42.3921\n  4500   1400     0.0000   -16.6486    38.9861    30.378050     25.2143    -34.0782     42.3921\n  4500   1600     0.0000   -16.6486    38.9861    30.755614     24.9892    -34.2436     42.3921\n  4500   1800     0.0000   -16.6486    38.9861    31.125460     24.7677    -34.4042     42.3921\n  4500   2000     0.0000   -16.6486    38.9861    31.487894     24.5495    -34.5602     42.3921\n  4500   2200     0.0000   -16.6486    38.9861    31.843205     24.3347    -34.7118     42.3921\n  4500   2400     0.0000   -16.6486    38.9861    32.191662     24.1232    -34.8591     42.3921\n  4500   2600     0.0000   -16.6486    38.9861    32.533520     23.9148    -35.0024     42.3921\n  4500   2800     0.0000   -16.6486    38.9861    32.869017     23.7094    -35.1419     42.3921\n  4500   3000     0.0000   -16.6486    38.9861    33.198376     23.5070    -35.2776     42.3921\n  4500   3200     0.0000   -16.6486    38.9861    33.521810     23.3075    -35.4097     42.3921\n  4500   3400     0.0000   -16.6486    38.9861    33.839518     23.1108    -35.5384     42.3921\n  4500   3600     0.0000   -16.6486    38.9861    34.151689     22.9168    -35.6638     42.3921\n  4500   3800     0.0000   -16.6486    38.9861    34.458503     22.7255    -35.7860     42.3921\n  4500   4000     0.0000   -16.6486    38.9861    34.760129     22.5368    -35.9051     42.3921\n  4500   4200     0.0000   -16.6486    38.9861    35.056729     22.3506    -36.0213     42.3921\n  4500   4400     0.0000   -16.6486    38.9861    35.348455     22.1669    -36.1347     42.3921\n  4500   4600     0.0000   -16.6486    38.9861    35.635454     21.9857    -36.2452     42.3921\n  4500   4800     0.0000   -16.6486    38.9861    35.917864     21.8067    -36.3532     42.3921\n  4500   5000     0.0000   -16.6486    38.9861    36.195818     21.6301    -36.4585     42.3921\n  4500   5200     0.0000   -16.6486    38.9861    36.469441     21.4558    -36.5614     42.3921\n  4500   5400     0.0000   -16.6486    38.9861    36.738854     21.2836    -36.6619     42.3921\n  4500   5600     0.0000   -16.6486    38.9861    37.004173     21.1136    -36.7601     42.3921\n  4500   5800     0.0000   -16.6486    38.9861    37.265507     20.9457    -36.8560     42.3921\n  4500   6000     0.0000   -16.6486    38.9861    37.522963     20.7799    -36.9497     42.3921\n  4500   6200     0.0000   -16.6486    38.9861    37.776641     20.6161    -37.0414     42.3921\n  4500   6400     0.0000   -16.6486    38.9861    38.026639     20.4543    -37.1310     42.3921\n  4500   6600     0.0000   -16.6486    38.9861    38.273049     20.2944    -37.2186     42.3921\n  4500   6800     0.0000   -16.6486    38.9861    38.515961     20.1364    -37.3043     42.3921\n  4500   7000     0.0000   -16.6486    38.9861    38.755460     19.9803    -37.3881     42.3921\n  4500   7200     0.0000   -16.6486    38.9861    38.991631     19.8260    -37.4702     42.3921\n  4500   7400     0.0000   -16.6486    38.9861    39.224551     19.6736    -37.5505     42.3921\n  4500   7600     0.0000   -16.6486    38.9861    39.454297     19.5228    -37.6291     42.3921\n  4500   7800     0.0000   -16.6486    38.9861    39.680943     19.3738    -37.7060     42.3921\n  4500   8000     0.0000   -16.6486    38.9861    39.904560     19.2265    -37.7813     42.3921\n  4500   8200     0.0000   -16.6486    38.9861    40.125217     19.0809    -37.8551     42.3921\n  4500   8400     0.0000   -16.6486    38.9861    40.342979     18.9369    -37.9273     42.3921\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180717_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.6757929       -6.1269114       33.0701755   m   m   m\ninitial_baseline_rate:          0.0000000       -0.1067184        0.0085317   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       -6.0583173       33.0501396   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0985459       -0.0011029   m/s m/s m/s\nunwrap_phase_constant:            0.00011     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180331-20180717_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.676     -6.127     33.070\norbit baseline rate   (TCN) (m/s):  0.000e+00 -1.067e-01  8.532e-03\n\nbaseline vector (TCN)   (m):      0.000     -6.058     33.050\nbaseline rate   (TCN) (m/s):  0.000e+00 -9.855e-02 -1.103e-03\n\nSLC-1 center baseline length (m):    33.6008\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    -5.1385    33.0604    27.497013     26.9533    -19.8221     33.4574\n     0    200     0.0000    -5.1385    33.0604    27.939249     26.7995    -20.0295     33.4574\n     0    400     0.0000    -5.1385    33.0604    28.370851     26.6479    -20.2308     33.4574\n     0    600     0.0000    -5.1385    33.0604    28.792342     26.4983    -20.4263     33.4574\n     0    800     0.0000    -5.1385    33.0604    29.204202     26.3508    -20.6163     33.4574\n     0   1000     0.0000    -5.1385    33.0604    29.606873     26.2053    -20.8009     33.4574\n     0   1200     0.0000    -5.1385    33.0604    30.000767     26.0617    -20.9806     33.4574\n     0   1400     0.0000    -5.1385    33.0604    30.386264     25.9199    -21.1555     33.4574\n     0   1600     0.0000    -5.1385    33.0604    30.763716     25.7800    -21.3258     33.4574\n     0   1800     0.0000    -5.1385    33.0604    31.133454     25.6418    -21.4917     33.4574\n     0   2000     0.0000    -5.1385    33.0604    31.495785     25.5054    -21.6534     33.4574\n     0   2200     0.0000    -5.1385    33.0604    31.850997     25.3707    -21.8111     33.4574\n     0   2400     0.0000    -5.1385    33.0604    32.199360     25.2376    -21.9650     33.4574\n     0   2600     0.0000    -5.1385    33.0604    32.541126     25.1061    -22.1151     33.4574\n     0   2800     0.0000    -5.1385    33.0604    32.876534     24.9762    -22.2617     33.4574\n     0   3000     0.0000    -5.1385    33.0604    33.205808     24.8479    -22.4049     33.4574\n     0   3200     0.0000    -5.1385    33.0604    33.529159     24.7211    -22.5447     33.4574\n     0   3400     0.0000    -5.1385    33.0604    33.846787     24.5957    -22.6814     33.4574\n     0   3600     0.0000    -5.1385    33.0604    34.158881     24.4718    -22.8151     33.4574\n     0   3800     0.0000    -5.1385    33.0604    34.465620     24.3493    -22.9458     33.4574\n     0   4000     0.0000    -5.1385    33.0604    34.767174     24.2282    -23.0736     33.4574\n     0   4200     0.0000    -5.1385    33.0604    35.063703     24.1084    -23.1987     33.4574\n     0   4400     0.0000    -5.1385    33.0604    35.355362     23.9900    -23.3211     33.4574\n     0   4600     0.0000    -5.1385    33.0604    35.642294     23.8730    -23.4409     33.4574\n     0   4800     0.0000    -5.1385    33.0604    35.924640     23.7572    -23.5583     33.4574\n     0   5000     0.0000    -5.1385    33.0604    36.202531     23.6426    -23.6733     33.4574\n     0   5200     0.0000    -5.1385    33.0604    36.476093     23.5293    -23.7859     33.4574\n     0   5400     0.0000    -5.1385    33.0604    36.745447     23.4172    -23.8962     33.4574\n     0   5600     0.0000    -5.1385    33.0604    37.010708     23.3064    -24.0044     33.4574\n     0   5800     0.0000    -5.1385    33.0604    37.271986     23.1966    -24.1104     33.4574\n     0   6000     0.0000    -5.1385    33.0604    37.529387     23.0881    -24.2144     33.4574\n     0   6200     0.0000    -5.1385    33.0604    37.783012     22.9807    -24.3163     33.4574\n     0   6400     0.0000    -5.1385    33.0604    38.032957     22.8744    -24.4164     33.4574\n     0   6600     0.0000    -5.1385    33.0604    38.279317     22.7692    -24.5145     33.4574\n     0   6800     0.0000    -5.1385    33.0604    38.522179     22.6651    -24.6108     33.4574\n     0   7000     0.0000    -5.1385    33.0604    38.761630     22.5620    -24.7053     33.4574\n     0   7200     0.0000    -5.1385    33.0604    38.997753     22.4600    -24.7981     33.4574\n     0   7400     0.0000    -5.1385    33.0604    39.230627     22.3590    -24.8891     33.4574\n     0   7600     0.0000    -5.1385    33.0604    39.460328     22.2591    -24.9786     33.4574\n     0   7800     0.0000    -5.1385    33.0604    39.686930     22.1601    -25.0664     33.4574\n     0   8000     0.0000    -5.1385    33.0604    39.910504     22.0621    -25.1527     33.4574\n     0   8200     0.0000    -5.1385    33.0604    40.131118     21.9651    -25.2375     33.4574\n     0   8400     0.0000    -5.1385    33.0604    40.348838     21.8691    -25.3207     33.4574\n   500      0     0.0000    -5.3410    33.0582    27.496077     26.8581    -20.0003     33.4868\n   500    200     0.0000    -5.3410    33.0582    27.938329     26.7029    -20.2070     33.4868\n   500    400     0.0000    -5.3410    33.0582    28.369946     26.5500    -20.4076     33.4868\n   500    600     0.0000    -5.3410    33.0582    28.791451     26.3991    -20.6023     33.4868\n   500    800     0.0000    -5.3410    33.0582    29.203324     26.2503    -20.7916     33.4868\n   500   1000     0.0000    -5.3410    33.0582    29.606009     26.1036    -20.9755     33.4868\n   500   1200     0.0000    -5.3410    33.0582    29.999915     25.9587    -21.1545     33.4868\n   500   1400     0.0000    -5.3410    33.0582    30.385423     25.8158    -21.3287     33.4868\n   500   1600     0.0000    -5.3410    33.0582    30.762887     25.6747    -21.4983     33.4868\n   500   1800     0.0000    -5.3410    33.0582    31.132636     25.5355    -21.6635     33.4868\n   500   2000     0.0000    -5.3410    33.0582    31.494978     25.3980    -21.8246     33.4868\n   500   2200     0.0000    -5.3410    33.0582    31.850200     25.2622    -21.9816     33.4868\n   500   2400     0.0000    -5.3410    33.0582    32.198572     25.1280    -22.1348     33.4868\n   500   2600     0.0000    -5.3410    33.0582    32.540347     24.9956    -22.2843     33.4868\n   500   2800     0.0000    -5.3410    33.0582    32.875764     24.8647    -22.4303     33.4868\n   500   3000     0.0000    -5.3410    33.0582    33.205046     24.7354    -22.5728     33.4868\n   500   3200     0.0000    -5.3410    33.0582    33.528405     24.6076    -22.7120     33.4868\n   500   3400     0.0000    -5.3410    33.0582    33.846042     24.4813    -22.8481     33.4868\n   500   3600     0.0000    -5.3410    33.0582    34.158144     24.3565    -22.9811     33.4868\n   500   3800     0.0000    -5.3410    33.0582    34.464890     24.2331    -23.1112     33.4868\n   500   4000     0.0000    -5.3410    33.0582    34.766452     24.1111    -23.2384     33.4868\n   500   4200     0.0000    -5.3410    33.0582    35.062988     23.9905    -23.3629     33.4868\n   500   4400     0.0000    -5.3410    33.0582    35.354653     23.8713    -23.4847     33.4868\n   500   4600     0.0000    -5.3410    33.0582    35.641592     23.7534    -23.6040     33.4868\n   500   4800     0.0000    -5.3410    33.0582    35.923944     23.6368    -23.7207     33.4868\n   500   5000     0.0000    -5.3410    33.0582    36.201842     23.5214    -23.8351     33.4868\n   500   5200     0.0000    -5.3410    33.0582    36.475410     23.4074    -23.9471     33.4868\n   500   5400     0.0000    -5.3410    33.0582    36.744770     23.2945    -24.0569     33.4868\n   500   5600     0.0000    -5.3410    33.0582    37.010037     23.1829    -24.1645     33.4868\n   500   5800     0.0000    -5.3410    33.0582    37.271321     23.0725    -24.2700     33.4868\n   500   6000     0.0000    -5.3410    33.0582    37.528727     22.9632    -24.3734     33.4868\n   500   6200     0.0000    -5.3410    33.0582    37.782357     22.8551    -24.4748     33.4868\n   500   6400     0.0000    -5.3410    33.0582    38.032308     22.7481    -24.5743     33.4868\n   500   6600     0.0000    -5.3410    33.0582    38.278673     22.6422    -24.6718     33.4868\n   500   6800     0.0000    -5.3410    33.0582    38.521540     22.5374    -24.7676     33.4868\n   500   7000     0.0000    -5.3410    33.0582    38.760996     22.4337    -24.8616     33.4868\n   500   7200     0.0000    -5.3410    33.0582    38.997124     22.3311    -24.9538     33.4868\n   500   7400     0.0000    -5.3410    33.0582    39.230002     22.2295    -25.0444     33.4868\n   500   7600     0.0000    -5.3410    33.0582    39.459708     22.1289    -25.1333     33.4868\n   500   7800     0.0000    -5.3410    33.0582    39.686314     22.0293    -25.2206     33.4868\n   500   8000     0.0000    -5.3410    33.0582    39.909892     21.9307    -25.3064     33.4868\n   500   8200     0.0000    -5.3410    33.0582    40.130511     21.8331    -25.3906     33.4868\n   500   8400     0.0000    -5.3410    33.0582    40.348236     21.7365    -25.4734     33.4868\n  1000      0     0.0000    -5.5436    33.0559    27.495122     26.7629    -20.1785     33.5175\n  1000    200     0.0000    -5.5436    33.0559    27.937389     26.6064    -20.3844     33.5175\n  1000    400     0.0000    -5.5436    33.0559    28.369022     26.4520    -20.5843     33.5175\n  1000    600     0.0000    -5.5436    33.0559    28.790541     26.2999    -20.7783     33.5175\n  1000    800     0.0000    -5.5436    33.0559    29.202428     26.1498    -20.9669     33.5175\n  1000   1000     0.0000    -5.5436    33.0559    29.605126     26.0018    -21.1501     33.5175\n  1000   1200     0.0000    -5.5436    33.0559    29.999045     25.8558    -21.3284     33.5175\n  1000   1400     0.0000    -5.5436    33.0559    30.384565     25.7117    -21.5019     33.5175\n  1000   1600     0.0000    -5.5436    33.0559    30.762040     25.5695    -21.6708     33.5175\n  1000   1800     0.0000    -5.5436    33.0559    31.131800     25.4291    -21.8354     33.5175\n  1000   2000     0.0000    -5.5436    33.0559    31.494152     25.2905    -21.9958     33.5175\n  1000   2200     0.0000    -5.5436    33.0559    31.849385     25.1537    -22.1521     33.5175\n  1000   2400     0.0000    -5.5436    33.0559    32.197766     25.0185    -22.3047     33.5175\n  1000   2600     0.0000    -5.5436    33.0559    32.539551     24.8850    -22.4535     33.5175\n  1000   2800     0.0000    -5.5436    33.0559    32.874977     24.7531    -22.5988     33.5175\n  1000   3000     0.0000    -5.5436    33.0559    33.204268     24.6228    -22.7407     33.5175\n  1000   3200     0.0000    -5.5436    33.0559    33.527636     24.4941    -22.8793     33.5175\n  1000   3400     0.0000    -5.5436    33.0559    33.845281     24.3669    -23.0147     33.5175\n  1000   3600     0.0000    -5.5436    33.0559    34.157391     24.2412    -23.1471     33.5175\n  1000   3800     0.0000    -5.5436    33.0559    34.464145     24.1169    -23.2766     33.5175\n  1000   4000     0.0000    -5.5436    33.0559    34.765714     23.9940    -23.4032     33.5175\n  1000   4200     0.0000    -5.5436    33.0559    35.062258     23.8726    -23.5271     33.5175\n  1000   4400     0.0000    -5.5436    33.0559    35.353930     23.7525    -23.6483     33.5175\n  1000   4600     0.0000    -5.5436    33.0559    35.640876     23.6338    -23.7670     33.5175\n  1000   4800     0.0000    -5.5436    33.0559    35.923235     23.5164    -23.8831     33.5175\n  1000   5000     0.0000    -5.5436    33.0559    36.201138     23.4002    -23.9969     33.5175\n  1000   5200     0.0000    -5.5436    33.0559    36.474713     23.2854    -24.1084     33.5175\n  1000   5400     0.0000    -5.5436    33.0559    36.744079     23.1718    -24.2176     33.5175\n  1000   5600     0.0000    -5.5436    33.0559    37.009352     23.0594    -24.3246     33.5175\n  1000   5800     0.0000    -5.5436    33.0559    37.270642     22.9483    -24.4295     33.5175\n  1000   6000     0.0000    -5.5436    33.0559    37.528054     22.8383    -24.5324     33.5175\n  1000   6200     0.0000    -5.5436    33.0559    37.781689     22.7295    -24.6332     33.5175\n  1000   6400     0.0000    -5.5436    33.0559    38.031646     22.6218    -24.7321     33.5175\n  1000   6600     0.0000    -5.5436    33.0559    38.278015     22.5152    -24.8292     33.5175\n  1000   6800     0.0000    -5.5436    33.0559    38.520888     22.4098    -24.9244     33.5175\n  1000   7000     0.0000    -5.5436    33.0559    38.760349     22.3054    -25.0179     33.5175\n  1000   7200     0.0000    -5.5436    33.0559    38.996482     22.2021    -25.1096     33.5175\n  1000   7400     0.0000    -5.5436    33.0559    39.229365     22.0999    -25.1996     33.5175\n  1000   7600     0.0000    -5.5436    33.0559    39.459075     21.9987    -25.2880     33.5175\n  1000   7800     0.0000    -5.5436    33.0559    39.685686     21.8985    -25.3748     33.5175\n  1000   8000     0.0000    -5.5436    33.0559    39.909268     21.7993    -25.4601     33.5175\n  1000   8200     0.0000    -5.5436    33.0559    40.129891     21.7011    -25.5438     33.5175\n  1000   8400     0.0000    -5.5436    33.0559    40.347621     21.6039    -25.6261     33.5175\n  1500      0     0.0000    -5.7462    33.0536    27.494145     26.6677    -20.3566     33.5494\n  1500    200     0.0000    -5.7462    33.0536    27.936429     26.5098    -20.5619     33.5494\n  1500    400     0.0000    -5.7462    33.0536    28.368077     26.3541    -20.7610     33.5494\n  1500    600     0.0000    -5.7462    33.0536    28.789612     26.2007    -20.9543     33.5494\n  1500    800     0.0000    -5.7462    33.0536    29.201513     26.0494    -21.1422     33.5494\n  1500   1000     0.0000    -5.7462    33.0536    29.604224     25.9001    -21.3247     33.5494\n  1500   1200     0.0000    -5.7462    33.0536    29.998156     25.7529    -21.5023     33.5494\n  1500   1400     0.0000    -5.7462    33.0536    30.383688     25.6076    -21.6751     33.5494\n  1500   1600     0.0000    -5.7462    33.0536    30.761176     25.4643    -21.8433     33.5494\n  1500   1800     0.0000    -5.7462    33.0536    31.130947     25.3228    -22.0072     33.5494\n  1500   2000     0.0000    -5.7462    33.0536    31.493310     25.1831    -22.1669     33.5494\n  1500   2200     0.0000    -5.7462    33.0536    31.848553     25.0452    -22.3226     33.5494\n  1500   2400     0.0000    -5.7462    33.0536    32.196945     24.9090    -22.4745     33.5494\n  1500   2600     0.0000    -5.7462    33.0536    32.538739     24.7745    -22.6227     33.5494\n  1500   2800     0.0000    -5.7462    33.0536    32.874174     24.6416    -22.7674     33.5494\n  1500   3000     0.0000    -5.7462    33.0536    33.203475     24.5103    -22.9086     33.5494\n  1500   3200     0.0000    -5.7462    33.0536    33.526851     24.3806    -23.0466     33.5494\n  1500   3400     0.0000    -5.7462    33.0536    33.844504     24.2525    -23.1814     33.5494\n  1500   3600     0.0000    -5.7462    33.0536    34.156623     24.1259    -23.3132     33.5494\n  1500   3800     0.0000    -5.7462    33.0536    34.463385     24.0007    -23.4420     33.5494\n  1500   4000     0.0000    -5.7462    33.0536    34.764961     23.8770    -23.5680     33.5494\n  1500   4200     0.0000    -5.7462    33.0536    35.061513     23.7547    -23.6913     33.5494\n  1500   4400     0.0000    -5.7462    33.0536    35.353192     23.6338    -23.8119     33.5494\n  1500   4600     0.0000    -5.7462    33.0536    35.640145     23.5142    -23.9300     33.5494\n  1500   4800     0.0000    -5.7462    33.0536    35.922510     23.3960    -24.0456     33.5494\n  1500   5000     0.0000    -5.7462    33.0536    36.200421     23.2791    -24.1588     33.5494\n  1500   5200     0.0000    -5.7462    33.0536    36.474002     23.1635    -24.2696     33.5494\n  1500   5400     0.0000    -5.7462    33.0536    36.743374     23.0491    -24.3783     33.5494\n  1500   5600     0.0000    -5.7462    33.0536    37.008653     22.9360    -24.4847     33.5494\n  1500   5800     0.0000    -5.7462    33.0536    37.269949     22.8241    -24.5891     33.5494\n  1500   6000     0.0000    -5.7462    33.0536    37.527367     22.7134    -24.6914     33.5494\n  1500   6200     0.0000    -5.7462    33.0536    37.781008     22.6039    -24.7917     33.5494\n  1500   6400     0.0000    -5.7462    33.0536    38.030970     22.4955    -24.8900     33.5494\n  1500   6600     0.0000    -5.7462    33.0536    38.277345     22.3882    -24.9865     33.5494\n  1500   6800     0.0000    -5.7462    33.0536    38.520223     22.2821    -25.0812     33.5494\n  1500   7000     0.0000    -5.7462    33.0536    38.759689     22.1771    -25.1741     33.5494\n  1500   7200     0.0000    -5.7462    33.0536    38.995827     22.0732    -25.2653     33.5494\n  1500   7400     0.0000    -5.7462    33.0536    39.228715     21.9703    -25.3548     33.5494\n  1500   7600     0.0000    -5.7462    33.0536    39.458429     21.8685    -25.4427     33.5494\n  1500   7800     0.0000    -5.7462    33.0536    39.685045     21.7677    -25.5290     33.5494\n  1500   8000     0.0000    -5.7462    33.0536    39.908632     21.6679    -25.6138     33.5494\n  1500   8200     0.0000    -5.7462    33.0536    40.129260     21.5691    -25.6970     33.5494\n  1500   8400     0.0000    -5.7462    33.0536    40.346993     21.4713    -25.7788     33.5494\n  2000      0     0.0000    -5.9487    33.0514    27.493148     26.5726    -20.5348     33.5824\n  2000    200     0.0000    -5.9487    33.0514    27.935449     26.4133    -20.7393     33.5824\n  2000    400     0.0000    -5.9487    33.0514    28.367113     26.2563    -20.9377     33.5824\n  2000    600     0.0000    -5.9487    33.0514    28.788663     26.1015    -21.1303     33.5824\n  2000    800     0.0000    -5.9487    33.0514    29.200578     25.9489    -21.3175     33.5824\n  2000   1000     0.0000    -5.9487    33.0514    29.603304     25.7984    -21.4993     33.5824\n  2000   1200     0.0000    -5.9487    33.0514    29.997248     25.6500    -21.6762     33.5824\n  2000   1400     0.0000    -5.9487    33.0514    30.382794     25.5036    -21.8483     33.5824\n  2000   1600     0.0000    -5.9487    33.0514    30.760293     25.3591    -22.0159     33.5824\n  2000   1800     0.0000    -5.9487    33.0514    31.130076     25.2164    -22.1791     33.5824\n  2000   2000     0.0000    -5.9487    33.0514    31.492450     25.0757    -22.3381     33.5824\n  2000   2200     0.0000    -5.9487    33.0514    31.847704     24.9367    -22.4931     33.5824\n  2000   2400     0.0000    -5.9487    33.0514    32.196106     24.7994    -22.6444     33.5824\n  2000   2600     0.0000    -5.9487    33.0514    32.537910     24.6639    -22.7919     33.5824\n  2000   2800     0.0000    -5.9487    33.0514    32.873355     24.5301    -22.9359     33.5824\n  2000   3000     0.0000    -5.9487    33.0514    33.202665     24.3978    -23.0765     33.5824\n  2000   3200     0.0000    -5.9487    33.0514    33.526051     24.2672    -23.2139     33.5824\n  2000   3400     0.0000    -5.9487    33.0514    33.843712     24.1381    -23.3480     33.5824\n  2000   3600     0.0000    -5.9487    33.0514    34.155839     24.0106    -23.4792     33.5824\n  2000   3800     0.0000    -5.9487    33.0514    34.462609     23.8845    -23.6074     33.5824\n  2000   4000     0.0000    -5.9487    33.0514    34.764193     23.7599    -23.7328     33.5824\n  2000   4200     0.0000    -5.9487    33.0514    35.060752     23.6368    -23.8555     33.5824\n  2000   4400     0.0000    -5.9487    33.0514    35.352439     23.5150    -23.9755     33.5824\n  2000   4600     0.0000    -5.9487    33.0514    35.639399     23.3946    -24.0930     33.5824\n  2000   4800     0.0000    -5.9487    33.0514    35.921772     23.2756    -24.2080     33.5824\n  2000   5000     0.0000    -5.9487    33.0514    36.199689     23.1579    -24.3206     33.5824\n  2000   5200     0.0000    -5.9487    33.0514    36.473276     23.0415    -24.4309     33.5824\n  2000   5400     0.0000    -5.9487    33.0514    36.742655     22.9264    -24.5389     33.5824\n  2000   5600     0.0000    -5.9487    33.0514    37.007941     22.8126    -24.6448     33.5824\n  2000   5800     0.0000    -5.9487    33.0514    37.269242     22.6999    -24.7486     33.5824\n  2000   6000     0.0000    -5.9487    33.0514    37.526666     22.5885    -24.8503     33.5824\n  2000   6200     0.0000    -5.9487    33.0514    37.780313     22.4783    -24.9501     33.5824\n  2000   6400     0.0000    -5.9487    33.0514    38.030281     22.3692    -25.0479     33.5824\n  2000   6600     0.0000    -5.9487    33.0514    38.276661     22.2613    -25.1439     33.5824\n  2000   6800     0.0000    -5.9487    33.0514    38.519545     22.1545    -25.2380     33.5824\n  2000   7000     0.0000    -5.9487    33.0514    38.759016     22.0488    -25.3304     33.5824\n  2000   7200     0.0000    -5.9487    33.0514    38.995159     21.9442    -25.4211     33.5824\n  2000   7400     0.0000    -5.9487    33.0514    39.228052     21.8407    -25.5101     33.5824\n  2000   7600     0.0000    -5.9487    33.0514    39.457772     21.7383    -25.5974     33.5824\n  2000   7800     0.0000    -5.9487    33.0514    39.684392     21.6369    -25.6832     33.5824\n  2000   8000     0.0000    -5.9487    33.0514    39.907984     21.5365    -25.7674     33.5824\n  2000   8200     0.0000    -5.9487    33.0514    40.128616     21.4371    -25.8502     33.5824\n  2000   8400     0.0000    -5.9487    33.0514    40.346354     21.3387    -25.9315     33.5824\n  2500      0     0.0000    -6.1513    33.0491    27.492131     26.4774    -20.7130     33.6167\n  2500    200     0.0000    -6.1513    33.0491    27.934449     26.3167    -20.9168     33.6167\n  2500    400     0.0000    -6.1513    33.0491    28.366129     26.1584    -21.1145     33.6167\n  2500    600     0.0000    -6.1513    33.0491    28.787694     26.0023    -21.3063     33.6167\n  2500    800     0.0000    -6.1513    33.0491    29.199625     25.8485    -21.4927     33.6167\n  2500   1000     0.0000    -6.1513    33.0491    29.602364     25.6968    -21.6739     33.6167\n  2500   1200     0.0000    -6.1513    33.0491    29.996323     25.5471    -21.8501     33.6167\n  2500   1400     0.0000    -6.1513    33.0491    30.381881     25.3995    -22.0215     33.6167\n  2500   1600     0.0000    -6.1513    33.0491    30.759393     25.2539    -22.1884     33.6167\n  2500   1800     0.0000    -6.1513    33.0491    31.129187     25.1101    -22.3509     33.6167\n  2500   2000     0.0000    -6.1513    33.0491    31.491573     24.9683    -22.5093     33.6167\n  2500   2200     0.0000    -6.1513    33.0491    31.846838     24.8282    -22.6636     33.6167\n  2500   2400     0.0000    -6.1513    33.0491    32.195250     24.6899    -22.8142     33.6167\n  2500   2600     0.0000    -6.1513    33.0491    32.537065     24.5534    -22.9611     33.6167\n  2500   2800     0.0000    -6.1513    33.0491    32.872520     24.4185    -23.1045     33.6167\n  2500   3000     0.0000    -6.1513    33.0491    33.201839     24.2853    -23.2444     33.6167\n  2500   3200     0.0000    -6.1513    33.0491    33.525234     24.1538    -23.3811     33.6167\n  2500   3400     0.0000    -6.1513    33.0491    33.842904     24.0238    -23.5147     33.6167\n  2500   3600     0.0000    -6.1513    33.0491    34.155039     23.8953    -23.6452     33.6167\n  2500   3800     0.0000    -6.1513    33.0491    34.461818     23.7683    -23.7728     33.6167\n  2500   4000     0.0000    -6.1513    33.0491    34.763411     23.6429    -23.8976     33.6167\n  2500   4200     0.0000    -6.1513    33.0491    35.059977     23.5189    -24.0197     33.6167\n  2500   4400     0.0000    -6.1513    33.0491    35.351672     23.3963    -24.1391     33.6167\n  2500   4600     0.0000    -6.1513    33.0491    35.638639     23.2751    -24.2560     33.6167\n  2500   4800     0.0000    -6.1513    33.0491    35.921019     23.1553    -24.3704     33.6167\n  2500   5000     0.0000    -6.1513    33.0491    36.198943     23.0368    -24.4824     33.6167\n  2500   5200     0.0000    -6.1513    33.0491    36.472537     22.9196    -24.5921     33.6167\n  2500   5400     0.0000    -6.1513    33.0491    36.741923     22.8037    -24.6996     33.6167\n  2500   5600     0.0000    -6.1513    33.0491    37.007214     22.6891    -24.8049     33.6167\n  2500   5800     0.0000    -6.1513    33.0491    37.268522     22.5758    -24.9082     33.6167\n  2500   6000     0.0000    -6.1513    33.0491    37.525952     22.4636    -25.0093     33.6167\n  2500   6200     0.0000    -6.1513    33.0491    37.779605     22.3527    -25.1085     33.6167\n  2500   6400     0.0000    -6.1513    33.0491    38.029579     22.2429    -25.2058     33.6167\n  2500   6600     0.0000    -6.1513    33.0491    38.275965     22.1343    -25.3012     33.6167\n  2500   6800     0.0000    -6.1513    33.0491    38.518854     22.0269    -25.3948     33.6167\n  2500   7000     0.0000    -6.1513    33.0491    38.758331     21.9205    -25.4867     33.6167\n  2500   7200     0.0000    -6.1513    33.0491    38.994478     21.8153    -25.5768     33.6167\n  2500   7400     0.0000    -6.1513    33.0491    39.227377     21.7112    -25.6653     33.6167\n  2500   7600     0.0000    -6.1513    33.0491    39.457102     21.6081    -25.7521     33.6167\n  2500   7800     0.0000    -6.1513    33.0491    39.683727     21.5061    -25.8374     33.6167\n  2500   8000     0.0000    -6.1513    33.0491    39.907324     21.4051    -25.9211     33.6167\n  2500   8200     0.0000    -6.1513    33.0491    40.127960     21.3051    -26.0034     33.6167\n  2500   8400     0.0000    -6.1513    33.0491    40.345703     21.2061    -26.0841     33.6167\n  3000      0     0.0000    -6.3539    33.0468    27.491093     26.3823    -20.8912     33.6521\n  3000    200     0.0000    -6.3539    33.0468    27.933428     26.2202    -21.0942     33.6521\n  3000    400     0.0000    -6.3539    33.0468    28.365126     26.0605    -21.2912     33.6521\n  3000    600     0.0000    -6.3539    33.0468    28.786707     25.9032    -21.4823     33.6521\n  3000    800     0.0000    -6.3539    33.0468    29.198653     25.7480    -21.6680     33.6521\n  3000   1000     0.0000    -6.3539    33.0468    29.601406     25.5951    -21.8485     33.6521\n  3000   1200     0.0000    -6.3539    33.0468    29.995379     25.4442    -22.0240     33.6521\n  3000   1400     0.0000    -6.3539    33.0468    30.380950     25.2955    -22.1947     33.6521\n  3000   1600     0.0000    -6.3539    33.0468    30.758475     25.1487    -22.3609     33.6521\n  3000   1800     0.0000    -6.3539    33.0468    31.128282     25.0038    -22.5227     33.6521\n  3000   2000     0.0000    -6.3539    33.0468    31.490679     24.8609    -22.6804     33.6521\n  3000   2200     0.0000    -6.3539    33.0468    31.845955     24.7198    -22.8341     33.6521\n  3000   2400     0.0000    -6.3539    33.0468    32.194378     24.5804    -22.9840     33.6521\n  3000   2600     0.0000    -6.3539    33.0468    32.536204     24.4429    -23.1303     33.6521\n  3000   2800     0.0000    -6.3539    33.0468    32.871668     24.3070    -23.2730     33.6521\n  3000   3000     0.0000    -6.3539    33.0468    33.200997     24.1729    -23.4123     33.6521\n  3000   3200     0.0000    -6.3539    33.0468    33.524401     24.0403    -23.5484     33.6521\n  3000   3400     0.0000    -6.3539    33.0468    33.842081     23.9094    -23.6813     33.6521\n  3000   3600     0.0000    -6.3539    33.0468    34.154225     23.7800    -23.8112     33.6521\n  3000   3800     0.0000    -6.3539    33.0468    34.461012     23.6522    -23.9382     33.6521\n  3000   4000     0.0000    -6.3539    33.0468    34.762613     23.5259    -24.0624     33.6521\n  3000   4200     0.0000    -6.3539    33.0468    35.059187     23.4010    -24.1838     33.6521\n  3000   4400     0.0000    -6.3539    33.0468    35.350889     23.2776    -24.3027     33.6521\n  3000   4600     0.0000    -6.3539    33.0468    35.637865     23.1555    -24.4189     33.6521\n  3000   4800     0.0000    -6.3539    33.0468    35.920252     23.0349    -24.5328     33.6521\n  3000   5000     0.0000    -6.3539    33.0468    36.198183     22.9156    -24.6442     33.6521\n  3000   5200     0.0000    -6.3539    33.0468    36.471784     22.7977    -24.7534     33.6521\n  3000   5400     0.0000    -6.3539    33.0468    36.741176     22.6811    -24.8603     33.6521\n  3000   5600     0.0000    -6.3539    33.0468    37.006474     22.5657    -24.9650     33.6521\n  3000   5800     0.0000    -6.3539    33.0468    37.267789     22.4516    -25.0677     33.6521\n  3000   6000     0.0000    -6.3539    33.0468    37.525225     22.3388    -25.1683     33.6521\n  3000   6200     0.0000    -6.3539    33.0468    37.778884     22.2271    -25.2670     33.6521\n  3000   6400     0.0000    -6.3539    33.0468    38.028863     22.1167    -25.3637     33.6521\n  3000   6600     0.0000    -6.3539    33.0468    38.275255     22.0074    -25.4586     33.6521\n  3000   6800     0.0000    -6.3539    33.0468    38.518150     21.8993    -25.5517     33.6521\n  3000   7000     0.0000    -6.3539    33.0468    38.757632     21.7923    -25.6430     33.6521\n  3000   7200     0.0000    -6.3539    33.0468    38.993785     21.6864    -25.7326     33.6521\n  3000   7400     0.0000    -6.3539    33.0468    39.226689     21.5816    -25.8205     33.6521\n  3000   7600     0.0000    -6.3539    33.0468    39.456419     21.4779    -25.9068     33.6521\n  3000   7800     0.0000    -6.3539    33.0468    39.683049     21.3753    -25.9916     33.6521\n  3000   8000     0.0000    -6.3539    33.0468    39.906651     21.2737    -26.0748     33.6521\n  3000   8200     0.0000    -6.3539    33.0468    40.127293     21.1731    -26.1565     33.6521\n  3000   8400     0.0000    -6.3539    33.0468    40.345040     21.0735    -26.2368     33.6521\n  3500      0     0.0000    -6.5564    33.0446    27.490034     26.2871    -21.0693     33.6887\n  3500    200     0.0000    -6.5564    33.0446    27.932388     26.1237    -21.2716     33.6887\n  3500    400     0.0000    -6.5564    33.0446    28.364103     25.9627    -21.4679     33.6887\n  3500    600     0.0000    -6.5564    33.0446    28.785700     25.8040    -21.6583     33.6887\n  3500    800     0.0000    -6.5564    33.0446    29.197661     25.6476    -21.8433     33.6887\n  3500   1000     0.0000    -6.5564    33.0446    29.600430     25.4934    -22.0231     33.6887\n  3500   1200     0.0000    -6.5564    33.0446    29.994416     25.3414    -22.1978     33.6887\n  3500   1400     0.0000    -6.5564    33.0446    30.380001     25.1914    -22.3679     33.6887\n  3500   1600     0.0000    -6.5564    33.0446    30.757539     25.0435    -22.5334     33.6887\n  3500   1800     0.0000    -6.5564    33.0446    31.127358     24.8975    -22.6946     33.6887\n  3500   2000     0.0000    -6.5564    33.0446    31.489768     24.7535    -22.8516     33.6887\n  3500   2200     0.0000    -6.5564    33.0446    31.845055     24.6113    -23.0046     33.6887\n  3500   2400     0.0000    -6.5564    33.0446    32.193490     24.4710    -23.1539     33.6887\n  3500   2600     0.0000    -6.5564    33.0446    32.535326     24.3324    -23.2995     33.6887\n  3500   2800     0.0000    -6.5564    33.0446    32.870801     24.1955    -23.4415     33.6887\n  3500   3000     0.0000    -6.5564    33.0446    33.200139     24.0604    -23.5802     33.6887\n  3500   3200     0.0000    -6.5564    33.0446    33.523553     23.9269    -23.7157     33.6887\n  3500   3400     0.0000    -6.5564    33.0446    33.841242     23.7951    -23.8480     33.6887\n  3500   3600     0.0000    -6.5564    33.0446    34.153395     23.6648    -23.9772     33.6887\n  3500   3800     0.0000    -6.5564    33.0446    34.460191     23.5360    -24.1036     33.6887\n  3500   4000     0.0000    -6.5564    33.0446    34.761800     23.4088    -24.2272     33.6887\n  3500   4200     0.0000    -6.5564    33.0446    35.058383     23.2831    -24.3480     33.6887\n  3500   4400     0.0000    -6.5564    33.0446    35.350093     23.1589    -24.4662     33.6887\n  3500   4600     0.0000    -6.5564    33.0446    35.637076     23.0360    -24.5819     33.6887\n  3500   4800     0.0000    -6.5564    33.0446    35.919470     22.9146    -24.6952     33.6887\n  3500   5000     0.0000    -6.5564    33.0446    36.197408     22.7945    -24.8060     33.6887\n  3500   5200     0.0000    -6.5564    33.0446    36.471017     22.6758    -24.9146     33.6887\n  3500   5400     0.0000    -6.5564    33.0446    36.740416     22.5584    -25.0210     33.6887\n  3500   5600     0.0000    -6.5564    33.0446    37.005721     22.4423    -25.1251     33.6887\n  3500   5800     0.0000    -6.5564    33.0446    37.267042     22.3275    -25.2272     33.6887\n  3500   6000     0.0000    -6.5564    33.0446    37.524484     22.2139    -25.3273     33.6887\n  3500   6200     0.0000    -6.5564    33.0446    37.778150     22.1016    -25.4254     33.6887\n  3500   6400     0.0000    -6.5564    33.0446    38.028135     21.9904    -25.5216     33.6887\n  3500   6600     0.0000    -6.5564    33.0446    38.274533     21.8805    -25.6159     33.6887\n  3500   6800     0.0000    -6.5564    33.0446    38.517433     21.7717    -25.7085     33.6887\n  3500   7000     0.0000    -6.5564    33.0446    38.756921     21.6640    -25.7992     33.6887\n  3500   7200     0.0000    -6.5564    33.0446    38.993080     21.5575    -25.8883     33.6887\n  3500   7400     0.0000    -6.5564    33.0446    39.225988     21.4521    -25.9757     33.6887\n  3500   7600     0.0000    -6.5564    33.0446    39.455724     21.3477    -26.0615     33.6887\n  3500   7800     0.0000    -6.5564    33.0446    39.682359     21.2445    -26.1458     33.6887\n  3500   8000     0.0000    -6.5564    33.0446    39.905966     21.1423    -26.2285     33.6887\n  3500   8200     0.0000    -6.5564    33.0446    40.126613     21.0411    -26.3097     33.6887\n  3500   8400     0.0000    -6.5564    33.0446    40.344365     20.9410    -26.3895     33.6887\n  4000      0     0.0000    -6.7590    33.0423    27.488955     26.1920    -21.2475     33.7265\n  4000    200     0.0000    -6.7590    33.0423    27.931328     26.0272    -21.4491     33.7265\n  4000    400     0.0000    -6.7590    33.0423    28.363060     25.8648    -21.6446     33.7265\n  4000    600     0.0000    -6.7590    33.0423    28.784674     25.7049    -21.8343     33.7265\n  4000    800     0.0000    -6.7590    33.0423    29.196651     25.5472    -22.0186     33.7265\n  4000   1000     0.0000    -6.7590    33.0423    29.599435     25.3918    -22.1976     33.7265\n  4000   1200     0.0000    -6.7590    33.0423    29.993436     25.2385    -22.3717     33.7265\n  4000   1400     0.0000    -6.7590    33.0423    30.379035     25.0874    -22.5411     33.7265\n  4000   1600     0.0000    -6.7590    33.0423    30.756585     24.9383    -22.7059     33.7265\n  4000   1800     0.0000    -6.7590    33.0423    31.126418     24.7913    -22.8664     33.7265\n  4000   2000     0.0000    -6.7590    33.0423    31.488839     24.6461    -23.0227     33.7265\n  4000   2200     0.0000    -6.7590    33.0423    31.844138     24.5029    -23.1751     33.7265\n  4000   2400     0.0000    -6.7590    33.0423    32.192584     24.3615    -23.3237     33.7265\n  4000   2600     0.0000    -6.7590    33.0423    32.534431     24.2219    -23.4686     33.7265\n  4000   2800     0.0000    -6.7590    33.0423    32.869917     24.0841    -23.6101     33.7265\n  4000   3000     0.0000    -6.7590    33.0423    33.199265     23.9480    -23.7481     33.7265\n  4000   3200     0.0000    -6.7590    33.0423    33.522689     23.8135    -23.8829     33.7265\n  4000   3400     0.0000    -6.7590    33.0423    33.840387     23.6807    -24.0146     33.7265\n  4000   3600     0.0000    -6.7590    33.0423    34.152550     23.5495    -24.1433     33.7265\n  4000   3800     0.0000    -6.7590    33.0423    34.459354     23.4199    -24.2690     33.7265\n  4000   4000     0.0000    -6.7590    33.0423    34.760972     23.2918    -24.3920     33.7265\n  4000   4200     0.0000    -6.7590    33.0423    35.057563     23.1653    -24.5122     33.7265\n  4000   4400     0.0000    -6.7590    33.0423    35.349281     23.0402    -24.6298     33.7265\n  4000   4600     0.0000    -6.7590    33.0423    35.636272     22.9165    -24.7449     33.7265\n  4000   4800     0.0000    -6.7590    33.0423    35.918674     22.7943    -24.8576     33.7265\n  4000   5000     0.0000    -6.7590    33.0423    36.196620     22.6734    -24.9679     33.7265\n  4000   5200     0.0000    -6.7590    33.0423    36.470236     22.5539    -25.0759     33.7265\n  4000   5400     0.0000    -6.7590    33.0423    36.739642     22.4358    -25.1816     33.7265\n  4000   5600     0.0000    -6.7590    33.0423    37.004953     22.3189    -25.2852     33.7265\n  4000   5800     0.0000    -6.7590    33.0423    37.266281     22.2033    -25.3868     33.7265\n  4000   6000     0.0000    -6.7590    33.0423    37.523730     22.0891    -25.4863     33.7265\n  4000   6200     0.0000    -6.7590    33.0423    37.777402     21.9760    -25.5838     33.7265\n  4000   6400     0.0000    -6.7590    33.0423    38.027393     21.8642    -25.6795     33.7265\n  4000   6600     0.0000    -6.7590    33.0423    38.273797     21.7535    -25.7733     33.7265\n  4000   6800     0.0000    -6.7590    33.0423    38.516703     21.6441    -25.8653     33.7265\n  4000   7000     0.0000    -6.7590    33.0423    38.756197     21.5358    -25.9555     33.7265\n  4000   7200     0.0000    -6.7590    33.0423    38.992361     21.4286    -26.0441     33.7265\n  4000   7400     0.0000    -6.7590    33.0423    39.225276     21.3225    -26.1309     33.7265\n  4000   7600     0.0000    -6.7590    33.0423    39.455016     21.2176    -26.2162     33.7265\n  4000   7800     0.0000    -6.7590    33.0423    39.681657     21.1137    -26.3000     33.7265\n  4000   8000     0.0000    -6.7590    33.0423    39.905269     21.0109    -26.3822     33.7265\n  4000   8200     0.0000    -6.7590    33.0423    40.125921     20.9092    -26.4629     33.7265\n  4000   8400     0.0000    -6.7590    33.0423    40.343678     20.8084    -26.5422     33.7265\n  4500      0     0.0000    -6.9616    33.0400    27.487857     26.0969    -21.4256     33.7655\n  4500    200     0.0000    -6.9616    33.0400    27.930248     25.9307    -21.6265     33.7655\n  4500    400     0.0000    -6.9616    33.0400    28.361998     25.7670    -21.8213     33.7655\n  4500    600     0.0000    -6.9616    33.0400    28.783629     25.6057    -22.0103     33.7655\n  4500    800     0.0000    -6.9616    33.0400    29.195622     25.4468    -22.1938     33.7655\n  4500   1000     0.0000    -6.9616    33.0400    29.598421     25.2902    -22.3722     33.7655\n  4500   1200     0.0000    -6.9616    33.0400    29.992437     25.1357    -22.5456     33.7655\n  4500   1400     0.0000    -6.9616    33.0400    30.378050     24.9834    -22.7142     33.7655\n  4500   1600     0.0000    -6.9616    33.0400    30.755614     24.8332    -22.8784     33.7655\n  4500   1800     0.0000    -6.9616    33.0400    31.125460     24.6850    -23.0382     33.7655\n  4500   2000     0.0000    -6.9616    33.0400    31.487894     24.5388    -23.1939     33.7655\n  4500   2200     0.0000    -6.9616    33.0400    31.843205     24.3945    -23.3456     33.7655\n  4500   2400     0.0000    -6.9616    33.0400    32.191662     24.2520    -23.4935     33.7655\n  4500   2600     0.0000    -6.9616    33.0400    32.533520     24.1114    -23.6378     33.7655\n  4500   2800     0.0000    -6.9616    33.0400    32.869017     23.9726    -23.7786     33.7655\n  4500   3000     0.0000    -6.9616    33.0400    33.198376     23.8355    -23.9160     33.7655\n  4500   3200     0.0000    -6.9616    33.0400    33.521810     23.7001    -24.0502     33.7655\n  4500   3400     0.0000    -6.9616    33.0400    33.839518     23.5664    -24.1812     33.7655\n  4500   3600     0.0000    -6.9616    33.0400    34.151689     23.4343    -24.3093     33.7655\n  4500   3800     0.0000    -6.9616    33.0400    34.458503     23.3038    -24.4344     33.7655\n  4500   4000     0.0000    -6.9616    33.0400    34.760129     23.1748    -24.5567     33.7655\n  4500   4200     0.0000    -6.9616    33.0400    35.056729     23.0474    -24.6764     33.7655\n  4500   4400     0.0000    -6.9616    33.0400    35.348455     22.9215    -24.7934     33.7655\n  4500   4600     0.0000    -6.9616    33.0400    35.635454     22.7970    -24.9079     33.7655\n  4500   4800     0.0000    -6.9616    33.0400    35.917864     22.6739    -25.0200     33.7655\n  4500   5000     0.0000    -6.9616    33.0400    36.195818     22.5523    -25.1297     33.7655\n  4500   5200     0.0000    -6.9616    33.0400    36.469441     22.4320    -25.2371     33.7655\n  4500   5400     0.0000    -6.9616    33.0400    36.738854     22.3131    -25.3423     33.7655\n  4500   5600     0.0000    -6.9616    33.0400    37.004173     22.1955    -25.4453     33.7655\n  4500   5800     0.0000    -6.9616    33.0400    37.265507     22.0792    -25.5463     33.7655\n  4500   6000     0.0000    -6.9616    33.0400    37.522963     21.9642    -25.6453     33.7655\n  4500   6200     0.0000    -6.9616    33.0400    37.776641     21.8505    -25.7423     33.7655\n  4500   6400     0.0000    -6.9616    33.0400    38.026639     21.7379    -25.8374     33.7655\n  4500   6600     0.0000    -6.9616    33.0400    38.273049     21.6266    -25.9306     33.7655\n  4500   6800     0.0000    -6.9616    33.0400    38.515961     21.5165    -26.0221     33.7655\n  4500   7000     0.0000    -6.9616    33.0400    38.755460     21.4075    -26.1118     33.7655\n  4500   7200     0.0000    -6.9616    33.0400    38.991631     21.2997    -26.1998     33.7655\n  4500   7400     0.0000    -6.9616    33.0400    39.224551     21.1930    -26.2862     33.7655\n  4500   7600     0.0000    -6.9616    33.0400    39.454297     21.0875    -26.3709     33.7655\n  4500   7800     0.0000    -6.9616    33.0400    39.680943     20.9830    -26.4541     33.7655\n  4500   8000     0.0000    -6.9616    33.0400    39.904560     20.8796    -26.5358     33.7655\n  4500   8200     0.0000    -6.9616    33.0400    40.125217     20.7772    -26.6161     33.7655\n  4500   8400     0.0000    -6.9616    33.0400    40.342979     20.6759    -26.6948     33.7655\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180412-20180506_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.8598882       63.9985273      -10.6224613   m   m   m\ninitial_baseline_rate:          0.0000000        0.0255046        0.0007563   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       63.9442366      -10.5557685   m   m   m\nprecision_baseline_rate:        0.0000000        0.0078066        0.0132544   m/s m/s m/s\nunwrap_phase_constant:            0.00010     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180412-20180506_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.860     63.999    -10.622\norbit baseline rate   (TCN) (m/s):  0.000e+00  2.550e-02  7.563e-04\n\nbaseline vector (TCN)   (m):      0.000     63.944    -10.556\nbaseline rate   (TCN) (m/s):  0.000e+00  7.807e-03  1.325e-02\n\nSLC-1 center baseline length (m):    64.8096\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    63.8714   -10.6795    27.501881     20.0216     61.5850     64.7580\n     0    200     0.0000    63.8714   -10.6795    27.944066     20.4963     61.4287     64.7580\n     0    400     0.0000    63.8714   -10.6795    28.375620     20.9584     61.2726     64.7580\n     0    600     0.0000    63.8714   -10.6795    28.797066     21.4086     61.1168     64.7580\n     0    800     0.0000    63.8714   -10.6795    29.208882     21.8473     60.9613     64.7580\n     0   1000     0.0000    63.8714   -10.6795    29.611513     22.2751     60.8063     64.7580\n     0   1200     0.0000    63.8714   -10.6795    30.005367     22.6926     60.6517     64.7580\n     0   1400     0.0000    63.8714   -10.6795    30.390826     23.1001     60.4977     64.7580\n     0   1600     0.0000    63.8714   -10.6795    30.768243     23.4981     60.3442     64.7580\n     0   1800     0.0000    63.8714   -10.6795    31.137946     23.8870     60.1914     64.7580\n     0   2000     0.0000    63.8714   -10.6795    31.500244     24.2671     60.0391     64.7580\n     0   2200     0.0000    63.8714   -10.6795    31.855425     24.6389     59.8875     64.7580\n     0   2400     0.0000    63.8714   -10.6795    32.203756     25.0025     59.7366     64.7580\n     0   2600     0.0000    63.8714   -10.6795    32.545493     25.3583     59.5864     64.7580\n     0   2800     0.0000    63.8714   -10.6795    32.880872     25.7067     59.4370     64.7580\n     0   3000     0.0000    63.8714   -10.6795    33.210118     26.0478     59.2883     64.7580\n     0   3200     0.0000    63.8714   -10.6795    33.533443     26.3820     59.1404     64.7580\n     0   3400     0.0000    63.8714   -10.6795    33.851045     26.7094     58.9932     64.7580\n     0   3600     0.0000    63.8714   -10.6795    34.163114     27.0303     58.8469     64.7580\n     0   3800     0.0000    63.8714   -10.6795    34.469829     27.3449     58.7013     64.7580\n     0   4000     0.0000    63.8714   -10.6795    34.771360     27.6535     58.5566     64.7580\n     0   4200     0.0000    63.8714   -10.6795    35.067866     27.9561     58.4127     64.7580\n     0   4400     0.0000    63.8714   -10.6795    35.359502     28.2531     58.2697     64.7580\n     0   4600     0.0000    63.8714   -10.6795    35.646413     28.5445     58.1275     64.7580\n     0   4800     0.0000    63.8714   -10.6795    35.928737     28.8306     57.9861     64.7580\n     0   5000     0.0000    63.8714   -10.6795    36.206607     29.1115     57.8456     64.7580\n     0   5200     0.0000    63.8714   -10.6795    36.480149     29.3873     57.7060     64.7580\n     0   5400     0.0000    63.8714   -10.6795    36.749483     29.6583     57.5672     64.7580\n     0   5600     0.0000    63.8714   -10.6795    37.014725     29.9244     57.4293     64.7580\n     0   5800     0.0000    63.8714   -10.6795    37.275984     30.1860     57.2922     64.7580\n     0   6000     0.0000    63.8714   -10.6795    37.533367     30.4431     57.1560     64.7580\n     0   6200     0.0000    63.8714   -10.6795    37.786973     30.6957     57.0207     64.7580\n     0   6400     0.0000    63.8714   -10.6795    38.036901     30.9442     56.8863     64.7580\n     0   6600     0.0000    63.8714   -10.6795    38.283243     31.1885     56.7527     64.7580\n     0   6800     0.0000    63.8714   -10.6795    38.526088     31.4287     56.6200     64.7580\n     0   7000     0.0000    63.8714   -10.6795    38.765522     31.6651     56.4882     64.7580\n     0   7200     0.0000    63.8714   -10.6795    39.001629     31.8976     56.3572     64.7580\n     0   7400     0.0000    63.8714   -10.6795    39.234486     32.1264     56.2271     64.7580\n     0   7600     0.0000    63.8714   -10.6795    39.464171     32.3515     56.0979     64.7580\n     0   7800     0.0000    63.8714   -10.6795    39.690757     32.5731     55.9695     64.7580\n     0   8000     0.0000    63.8714   -10.6795    39.914315     32.7912     55.8420     64.7580\n     0   8200     0.0000    63.8714   -10.6795    40.134914     33.0060     55.7153     64.7580\n     0   8400     0.0000    63.8714   -10.6795    40.352620     33.2175     55.5895     64.7580\n   500      0     0.0000    63.8874   -10.6522    27.500948     20.0522     61.5870     64.7694\n   500    200     0.0000    63.8874   -10.6522    27.943149     20.5269     61.4304     64.7694\n   500    400     0.0000    63.8874   -10.6522    28.374718     20.9891     61.2741     64.7694\n   500    600     0.0000    63.8874   -10.6522    28.796177     21.4392     61.1180     64.7694\n   500    800     0.0000    63.8874   -10.6522    29.208007     21.8780     60.9624     64.7694\n   500   1000     0.0000    63.8874   -10.6522    29.610651     22.3058     60.8071     64.7694\n   500   1200     0.0000    63.8874   -10.6522    30.004518     22.7233     60.6523     64.7694\n   500   1400     0.0000    63.8874   -10.6522    30.389988     23.1308     60.4981     64.7694\n   500   1600     0.0000    63.8874   -10.6522    30.767416     23.5289     60.3444     64.7694\n   500   1800     0.0000    63.8874   -10.6522    31.137130     23.9178     60.1913     64.7694\n   500   2000     0.0000    63.8874   -10.6522    31.499439     24.2979     60.0389     64.7694\n   500   2200     0.0000    63.8874   -10.6522    31.854629     24.6696     59.8871     64.7694\n   500   2400     0.0000    63.8874   -10.6522    32.202970     25.0333     59.7360     64.7694\n   500   2600     0.0000    63.8874   -10.6522    32.544716     25.3891     59.5857     64.7694\n   500   2800     0.0000    63.8874   -10.6522    32.880104     25.7375     59.4360     64.7694\n   500   3000     0.0000    63.8874   -10.6522    33.209359     26.0786     59.2871     64.7694\n   500   3200     0.0000    63.8874   -10.6522    33.532692     26.4128     59.1390     64.7694\n   500   3400     0.0000    63.8874   -10.6522    33.850302     26.7402     58.9917     64.7694\n   500   3600     0.0000    63.8874   -10.6522    34.162379     27.0611     58.8452     64.7694\n   500   3800     0.0000    63.8874   -10.6522    34.469102     27.3757     58.6995     64.7694\n   500   4000     0.0000    63.8874   -10.6522    34.770639     27.6843     58.5546     64.7694\n   500   4200     0.0000    63.8874   -10.6522    35.067153     27.9869     58.4105     64.7694\n   500   4400     0.0000    63.8874   -10.6522    35.358795     28.2839     58.2673     64.7694\n   500   4600     0.0000    63.8874   -10.6522    35.645713     28.5753     58.1250     64.7694\n   500   4800     0.0000    63.8874   -10.6522    35.928043     28.8614     57.9835     64.7694\n   500   5000     0.0000    63.8874   -10.6522    36.205920     29.1423     57.8428     64.7694\n   500   5200     0.0000    63.8874   -10.6522    36.479468     29.4181     57.7030     64.7694\n   500   5400     0.0000    63.8874   -10.6522    36.748808     29.6890     57.5641     64.7694\n   500   5600     0.0000    63.8874   -10.6522    37.014055     29.9552     57.4260     64.7694\n   500   5800     0.0000    63.8874   -10.6522    37.275320     30.2167     57.2888     64.7694\n   500   6000     0.0000    63.8874   -10.6522    37.532708     30.4738     57.1525     64.7694\n   500   6200     0.0000    63.8874   -10.6522    37.786320     30.7265     57.0171     64.7694\n   500   6400     0.0000    63.8874   -10.6522    38.036253     30.9749     56.8825     64.7694\n   500   6600     0.0000    63.8874   -10.6522    38.282600     31.2192     56.7488     64.7694\n   500   6800     0.0000    63.8874   -10.6522    38.525450     31.4594     56.6160     64.7694\n   500   7000     0.0000    63.8874   -10.6522    38.764890     31.6957     56.4840     64.7694\n   500   7200     0.0000    63.8874   -10.6522    39.001001     31.9282     56.3529     64.7694\n   500   7400     0.0000    63.8874   -10.6522    39.233863     32.1570     56.2227     64.7694\n   500   7600     0.0000    63.8874   -10.6522    39.463552     32.3821     56.0933     64.7694\n   500   7800     0.0000    63.8874   -10.6522    39.690143     32.6037     55.9648     64.7694\n   500   8000     0.0000    63.8874   -10.6522    39.913705     32.8218     55.8372     64.7694\n   500   8200     0.0000    63.8874   -10.6522    40.134309     33.0366     55.7104     64.7694\n   500   8400     0.0000    63.8874   -10.6522    40.352019     33.2480     55.5845     64.7694\n  1000      0     0.0000    63.9035   -10.6250    27.499995     20.0828     61.5890     64.7807\n  1000    200     0.0000    63.9035   -10.6250    27.942212     20.5575     61.4322     64.7807\n  1000    400     0.0000    63.9035   -10.6250    28.373796     21.0197     61.2756     64.7807\n  1000    600     0.0000    63.9035   -10.6250    28.795270     21.4699     61.1193     64.7807\n  1000    800     0.0000    63.9035   -10.6250    29.207113     21.9086     60.9634     64.7807\n  1000   1000     0.0000    63.9035   -10.6250    29.609770     22.3365     60.8079     64.7807\n  1000   1200     0.0000    63.9035   -10.6250    30.003649     22.7540     60.6530     64.7807\n  1000   1400     0.0000    63.9035   -10.6250    30.389132     23.1616     60.4985     64.7807\n  1000   1600     0.0000    63.9035   -10.6250    30.766571     23.5596     60.3446     64.7807\n  1000   1800     0.0000    63.9035   -10.6250    31.136297     23.9485     60.1913     64.7807\n  1000   2000     0.0000    63.9035   -10.6250    31.498616     24.3287     60.0387     64.7807\n  1000   2200     0.0000    63.9035   -10.6250    31.853816     24.7004     59.8867     64.7807\n  1000   2400     0.0000    63.9035   -10.6250    32.202167     25.0640     59.7354     64.7807\n  1000   2600     0.0000    63.9035   -10.6250    32.543923     25.4199     59.5849     64.7807\n  1000   2800     0.0000    63.9035   -10.6250    32.879320     25.7683     59.4351     64.7807\n  1000   3000     0.0000    63.9035   -10.6250    33.208583     26.1094     59.2860     64.7807\n  1000   3200     0.0000    63.9035   -10.6250    33.531925     26.4435     59.1377     64.7807\n  1000   3400     0.0000    63.9035   -10.6250    33.849543     26.7710     58.9902     64.7807\n  1000   3600     0.0000    63.9035   -10.6250    34.161628     27.0919     58.8435     64.7807\n  1000   3800     0.0000    63.9035   -10.6250    34.468358     27.4065     58.6976     64.7807\n  1000   4000     0.0000    63.9035   -10.6250    34.769903     27.7151     58.5526     64.7807\n  1000   4200     0.0000    63.9035   -10.6250    35.066424     28.0177     58.4084     64.7807\n  1000   4400     0.0000    63.9035   -10.6250    35.358074     28.3147     58.2650     64.7807\n  1000   4600     0.0000    63.9035   -10.6250    35.644998     28.6061     58.1225     64.7807\n  1000   4800     0.0000    63.9035   -10.6250    35.927335     28.8921     57.9808     64.7807\n  1000   5000     0.0000    63.9035   -10.6250    36.205218     29.1730     57.8400     64.7807\n  1000   5200     0.0000    63.9035   -10.6250    36.478772     29.4488     57.7001     64.7807\n  1000   5400     0.0000    63.9035   -10.6250    36.748119     29.7198     57.5610     64.7807\n  1000   5600     0.0000    63.9035   -10.6250    37.013372     29.9859     57.4228     64.7807\n  1000   5800     0.0000    63.9035   -10.6250    37.274643     30.2475     57.2855     64.7807\n  1000   6000     0.0000    63.9035   -10.6250    37.532037     30.5045     57.1490     64.7807\n  1000   6200     0.0000    63.9035   -10.6250    37.785654     30.7572     57.0134     64.7807\n  1000   6400     0.0000    63.9035   -10.6250    38.035592     31.0056     56.8787     64.7807\n  1000   6600     0.0000    63.9035   -10.6250    38.281945     31.2498     56.7449     64.7807\n  1000   6800     0.0000    63.9035   -10.6250    38.524800     31.4901     56.6119     64.7807\n  1000   7000     0.0000    63.9035   -10.6250    38.764244     31.7264     56.4798     64.7807\n  1000   7200     0.0000    63.9035   -10.6250    39.000360     31.9589     56.3486     64.7807\n  1000   7400     0.0000    63.9035   -10.6250    39.233227     32.1876     56.2182     64.7807\n  1000   7600     0.0000    63.9035   -10.6250    39.462921     32.4128     56.0887     64.7807\n  1000   7800     0.0000    63.9035   -10.6250    39.689516     32.6343     55.9601     64.7807\n  1000   8000     0.0000    63.9035   -10.6250    39.913083     32.8524     55.8324     64.7807\n  1000   8200     0.0000    63.9035   -10.6250    40.133691     33.0672     55.7054     64.7807\n  1000   8400     0.0000    63.9035   -10.6250    40.351405     33.2786     55.5794     64.7807\n  1500      0     0.0000    63.9195   -10.5978    27.499021     20.1133     61.5910     64.7921\n  1500    200     0.0000    63.9195   -10.5978    27.941254     20.5881     61.4339     64.7921\n  1500    400     0.0000    63.9195   -10.5978    28.372854     21.0503     61.2771     64.7921\n  1500    600     0.0000    63.9195   -10.5978    28.794343     21.5005     61.1206     64.7921\n  1500    800     0.0000    63.9195   -10.5978    29.206200     21.9393     60.9645     64.7921\n  1500   1000     0.0000    63.9195   -10.5978    29.608871     22.3672     60.8088     64.7921\n  1500   1200     0.0000    63.9195   -10.5978    30.002763     22.7847     60.6536     64.7921\n  1500   1400     0.0000    63.9195   -10.5978    30.388258     23.1923     60.4989     64.7921\n  1500   1600     0.0000    63.9195   -10.5978    30.765709     23.5903     60.3448     64.7921\n  1500   1800     0.0000    63.9195   -10.5978    31.135446     23.9792     60.1913     64.7921\n  1500   2000     0.0000    63.9195   -10.5978    31.497775     24.3594     60.0385     64.7921\n  1500   2200     0.0000    63.9195   -10.5978    31.852986     24.7311     59.8863     64.7921\n  1500   2400     0.0000    63.9195   -10.5978    32.201348     25.0948     59.7349     64.7921\n  1500   2600     0.0000    63.9195   -10.5978    32.543112     25.4507     59.5841     64.7921\n  1500   2800     0.0000    63.9195   -10.5978    32.878519     25.7990     59.4341     64.7921\n  1500   3000     0.0000    63.9195   -10.5978    33.207792     26.1402     59.2849     64.7921\n  1500   3200     0.0000    63.9195   -10.5978    33.531142     26.4743     59.1364     64.7921\n  1500   3400     0.0000    63.9195   -10.5978    33.848769     26.8017     58.9887     64.7921\n  1500   3600     0.0000    63.9195   -10.5978    34.160862     27.1227     58.8419     64.7921\n  1500   3800     0.0000    63.9195   -10.5978    34.467600     27.4373     58.6958     64.7921\n  1500   4000     0.0000    63.9195   -10.5978    34.769152     27.7458     58.5506     64.7921\n  1500   4200     0.0000    63.9195   -10.5978    35.065681     28.0485     58.4062     64.7921\n  1500   4400     0.0000    63.9195   -10.5978    35.357338     28.3454     58.2627     64.7921\n  1500   4600     0.0000    63.9195   -10.5978    35.644269     28.6368     58.1200     64.7921\n  1500   4800     0.0000    63.9195   -10.5978    35.926613     28.9229     57.9782     64.7921\n  1500   5000     0.0000    63.9195   -10.5978    36.204502     29.2037     57.8372     64.7921\n  1500   5200     0.0000    63.9195   -10.5978    36.478063     29.4796     57.6971     64.7921\n  1500   5400     0.0000    63.9195   -10.5978    36.747416     29.7505     57.5579     64.7921\n  1500   5600     0.0000    63.9195   -10.5978    37.012675     30.0166     57.4196     64.7921\n  1500   5800     0.0000    63.9195   -10.5978    37.273952     30.2782     57.2821     64.7921\n  1500   6000     0.0000    63.9195   -10.5978    37.531351     30.5352     57.1455     64.7921\n  1500   6200     0.0000    63.9195   -10.5978    37.784974     30.7879     57.0098     64.7921\n  1500   6400     0.0000    63.9195   -10.5978    38.034918     31.0363     56.8749     64.7921\n  1500   6600     0.0000    63.9195   -10.5978    38.281276     31.2805     56.7409     64.7921\n  1500   6800     0.0000    63.9195   -10.5978    38.524136     31.5207     56.6079     64.7921\n  1500   7000     0.0000    63.9195   -10.5978    38.763586     31.7570     56.4756     64.7921\n  1500   7200     0.0000    63.9195   -10.5978    38.999707     31.9895     56.3443     64.7921\n  1500   7400     0.0000    63.9195   -10.5978    39.232578     32.2183     56.2138     64.7921\n  1500   7600     0.0000    63.9195   -10.5978    39.462277     32.4434     56.0842     64.7921\n  1500   7800     0.0000    63.9195   -10.5978    39.688877     32.6649     55.9554     64.7921\n  1500   8000     0.0000    63.9195   -10.5978    39.912449     32.8830     55.8275     64.7921\n  1500   8200     0.0000    63.9195   -10.5978    40.133061     33.0977     55.7005     64.7921\n  1500   8400     0.0000    63.9195   -10.5978    40.350779     33.3091     55.5744     64.7921\n  2000      0     0.0000    63.9356   -10.5705    27.498027     20.1438     61.5930     64.8035\n  2000    200     0.0000    63.9356   -10.5705    27.940276     20.6186     61.4357     64.8035\n  2000    400     0.0000    63.9356   -10.5705    28.371892     21.0808     61.2786     64.8035\n  2000    600     0.0000    63.9356   -10.5705    28.793396     21.5311     61.1219     64.8035\n  2000    800     0.0000    63.9356   -10.5705    29.205268     21.9699     60.9655     64.8035\n  2000   1000     0.0000    63.9356   -10.5705    29.607952     22.3978     60.8096     64.8035\n  2000   1200     0.0000    63.9356   -10.5705    30.001858     22.8154     60.6542     64.8035\n  2000   1400     0.0000    63.9356   -10.5705    30.387365     23.2229     60.4993     64.8035\n  2000   1600     0.0000    63.9356   -10.5705    30.764828     23.6210     60.3450     64.8035\n  2000   1800     0.0000    63.9356   -10.5705    31.134577     24.0099     60.1913     64.8035\n  2000   2000     0.0000    63.9356   -10.5705    31.496918     24.3901     60.0383     64.8035\n  2000   2200     0.0000    63.9356   -10.5705    31.852139     24.7619     59.8859     64.8035\n  2000   2400     0.0000    63.9356   -10.5705    32.200511     25.1255     59.7343     64.8035\n  2000   2600     0.0000    63.9356   -10.5705    32.542286     25.4814     59.5833     64.8035\n  2000   2800     0.0000    63.9356   -10.5705    32.877702     25.8298     59.4332     64.8035\n  2000   3000     0.0000    63.9356   -10.5705    33.206984     26.1709     59.2837     64.8035\n  2000   3200     0.0000    63.9356   -10.5705    33.530343     26.5051     59.1351     64.8035\n  2000   3400     0.0000    63.9356   -10.5705    33.847979     26.8325     58.9872     64.8035\n  2000   3600     0.0000    63.9356   -10.5705    34.160080     27.1534     58.8402     64.8035\n  2000   3800     0.0000    63.9356   -10.5705    34.466826     27.4680     58.6940     64.8035\n  2000   4000     0.0000    63.9356   -10.5705    34.768386     27.7766     58.5486     64.8035\n  2000   4200     0.0000    63.9356   -10.5705    35.064922     28.0792     58.4041     64.8035\n  2000   4400     0.0000    63.9356   -10.5705    35.356587     28.3762     58.2604     64.8035\n  2000   4600     0.0000    63.9356   -10.5705    35.643525     28.6676     58.1175     64.8035\n  2000   4800     0.0000    63.9356   -10.5705    35.925876     28.9536     57.9756     64.8035\n  2000   5000     0.0000    63.9356   -10.5705    36.203772     29.2345     57.8345     64.8035\n  2000   5200     0.0000    63.9356   -10.5705    36.477340     29.5103     57.6942     64.8035\n  2000   5400     0.0000    63.9356   -10.5705    36.746699     29.7812     57.5548     64.8035\n  2000   5600     0.0000    63.9356   -10.5705    37.011964     30.0473     57.4164     64.8035\n  2000   5800     0.0000    63.9356   -10.5705    37.273247     30.3089     57.2787     64.8035\n  2000   6000     0.0000    63.9356   -10.5705    37.530653     30.5659     57.1420     64.8035\n  2000   6200     0.0000    63.9356   -10.5705    37.784281     30.8185     57.0061     64.8035\n  2000   6400     0.0000    63.9356   -10.5705    38.034231     31.0669     56.8711     64.8035\n  2000   6600     0.0000    63.9356   -10.5705    38.280594     31.3112     56.7370     64.8035\n  2000   6800     0.0000    63.9356   -10.5705    38.523460     31.5514     56.6038     64.8035\n  2000   7000     0.0000    63.9356   -10.5705    38.762915     31.7877     56.4715     64.8035\n  2000   7200     0.0000    63.9356   -10.5705    38.999041     32.0201     56.3400     64.8035\n  2000   7400     0.0000    63.9356   -10.5705    39.231917     32.2489     56.2094     64.8035\n  2000   7600     0.0000    63.9356   -10.5705    39.461621     32.4739     56.0796     64.8035\n  2000   7800     0.0000    63.9356   -10.5705    39.688226     32.6955     55.9508     64.8035\n  2000   8000     0.0000    63.9356   -10.5705    39.911802     32.9136     55.8227     64.8035\n  2000   8200     0.0000    63.9356   -10.5705    40.132419     33.1283     55.6956     64.8035\n  2000   8400     0.0000    63.9356   -10.5705    40.350142     33.3397     55.5693     64.8035\n  2500      0     0.0000    63.9516   -10.5433    27.497012     20.1743     61.5950     64.8149\n  2500    200     0.0000    63.9516   -10.5433    27.939279     20.6491     61.4375     64.8149\n  2500    400     0.0000    63.9516   -10.5433    28.370911     21.1114     61.2802     64.8149\n  2500    600     0.0000    63.9516   -10.5433    28.792430     21.5616     61.1232     64.8149\n  2500    800     0.0000    63.9516   -10.5433    29.204317     22.0005     60.9666     64.8149\n  2500   1000     0.0000    63.9516   -10.5433    29.607015     22.4284     60.8105     64.8149\n  2500   1200     0.0000    63.9516   -10.5433    30.000934     22.8460     60.6549     64.8149\n  2500   1400     0.0000    63.9516   -10.5433    30.386455     23.2536     60.4998     64.8149\n  2500   1600     0.0000    63.9516   -10.5433    30.763930     23.6517     60.3453     64.8149\n  2500   1800     0.0000    63.9516   -10.5433    31.133691     24.0406     60.1914     64.8149\n  2500   2000     0.0000    63.9516   -10.5433    31.496043     24.4208     60.0381     64.8149\n  2500   2200     0.0000    63.9516   -10.5433    31.851275     24.7926     59.8856     64.8149\n  2500   2400     0.0000    63.9516   -10.5433    32.199658     25.1562     59.7337     64.8149\n  2500   2600     0.0000    63.9516   -10.5433    32.541443     25.5121     59.5826     64.8149\n  2500   2800     0.0000    63.9516   -10.5433    32.876869     25.8605     59.4322     64.8149\n  2500   3000     0.0000    63.9516   -10.5433    33.206160     26.2016     59.2826     64.8149\n  2500   3200     0.0000    63.9516   -10.5433    33.529528     26.5358     59.1338     64.8149\n  2500   3400     0.0000    63.9516   -10.5433    33.847173     26.8632     58.9858     64.8149\n  2500   3600     0.0000    63.9516   -10.5433    34.159283     27.1841     58.8386     64.8149\n  2500   3800     0.0000    63.9516   -10.5433    34.466037     27.4988     58.6922     64.8149\n  2500   4000     0.0000    63.9516   -10.5433    34.767605     27.8073     58.5466     64.8149\n  2500   4200     0.0000    63.9516   -10.5433    35.064149     28.1099     58.4019     64.8149\n  2500   4400     0.0000    63.9516   -10.5433    35.355821     28.4069     58.2581     64.8149\n  2500   4600     0.0000    63.9516   -10.5433    35.642767     28.6983     58.1151     64.8149\n  2500   4800     0.0000    63.9516   -10.5433    35.925125     28.9843     57.9730     64.8149\n  2500   5000     0.0000    63.9516   -10.5433    36.203028     29.2652     57.8317     64.8149\n  2500   5200     0.0000    63.9516   -10.5433    36.476602     29.5410     57.6913     64.8149\n  2500   5400     0.0000    63.9516   -10.5433    36.745968     29.8119     57.5518     64.8149\n  2500   5600     0.0000    63.9516   -10.5433    37.011240     30.0780     57.4131     64.8149\n  2500   5800     0.0000    63.9516   -10.5433    37.272529     30.3395     57.2754     64.8149\n  2500   6000     0.0000    63.9516   -10.5433    37.529940     30.5965     57.1385     64.8149\n  2500   6200     0.0000    63.9516   -10.5433    37.783575     30.8492     57.0025     64.8149\n  2500   6400     0.0000    63.9516   -10.5433    38.033530     31.0976     56.8674     64.8149\n  2500   6600     0.0000    63.9516   -10.5433    38.279899     31.3418     56.7331     64.8149\n  2500   6800     0.0000    63.9516   -10.5433    38.522770     31.5820     56.5998     64.8149\n  2500   7000     0.0000    63.9516   -10.5433    38.762231     31.8183     56.4673     64.8149\n  2500   7200     0.0000    63.9516   -10.5433    38.998362     32.0507     56.3357     64.8149\n  2500   7400     0.0000    63.9516   -10.5433    39.231244     32.2794     56.2049     64.8149\n  2500   7600     0.0000    63.9516   -10.5433    39.460952     32.5045     56.0751     64.8149\n  2500   7800     0.0000    63.9516   -10.5433    39.687562     32.7261     55.9461     64.8149\n  2500   8000     0.0000    63.9516   -10.5433    39.911143     32.9441     55.8180     64.8149\n  2500   8200     0.0000    63.9516   -10.5433    40.131765     33.1588     55.6907     64.8149\n  2500   8400     0.0000    63.9516   -10.5433    40.349492     33.3702     55.5643     64.8149\n  3000      0     0.0000    63.9676   -10.5160    27.495976     20.2048     61.5970     64.8263\n  3000    200     0.0000    63.9676   -10.5160    27.938261     20.6796     61.4392     64.8263\n  3000    400     0.0000    63.9676   -10.5160    28.369910     21.1419     61.2817     64.8263\n  3000    600     0.0000    63.9676   -10.5160    28.791445     21.5922     61.1245     64.8263\n  3000    800     0.0000    63.9676   -10.5160    29.203347     22.0311     60.9677     64.8263\n  3000   1000     0.0000    63.9676   -10.5160    29.606060     22.4590     60.8114     64.8263\n  3000   1200     0.0000    63.9676   -10.5160    29.999993     22.8766     60.6555     64.8263\n  3000   1400     0.0000    63.9676   -10.5160    30.385526     23.2842     60.5002     64.8263\n  3000   1600     0.0000    63.9676   -10.5160    30.763014     23.6823     60.3455     64.8263\n  3000   1800     0.0000    63.9676   -10.5160    31.132787     24.0713     60.1914     64.8263\n  3000   2000     0.0000    63.9676   -10.5160    31.495151     24.4515     60.0380     64.8263\n  3000   2200     0.0000    63.9676   -10.5160    31.850395     24.8233     59.8852     64.8263\n  3000   2400     0.0000    63.9676   -10.5160    32.198788     25.1869     59.7332     64.8263\n  3000   2600     0.0000    63.9676   -10.5160    32.540583     25.5428     59.5818     64.8263\n  3000   2800     0.0000    63.9676   -10.5160    32.876019     25.8912     59.4313     64.8263\n  3000   3000     0.0000    63.9676   -10.5160    33.205320     26.2323     59.2815     64.8263\n  3000   3200     0.0000    63.9676   -10.5160    33.528698     26.5665     59.1325     64.8263\n  3000   3400     0.0000    63.9676   -10.5160    33.846351     26.8939     58.9843     64.8263\n  3000   3600     0.0000    63.9676   -10.5160    34.158470     27.2149     58.8369     64.8263\n  3000   3800     0.0000    63.9676   -10.5160    34.465233     27.5295     58.6904     64.8263\n  3000   4000     0.0000    63.9676   -10.5160    34.766809     27.8380     58.5447     64.8263\n  3000   4200     0.0000    63.9676   -10.5160    35.063361     28.1407     58.3998     64.8263\n  3000   4400     0.0000    63.9676   -10.5160    35.355041     28.4376     58.2558     64.8263\n  3000   4600     0.0000    63.9676   -10.5160    35.641994     28.7290     58.1126     64.8263\n  3000   4800     0.0000    63.9676   -10.5160    35.924359     29.0150     57.9704     64.8263\n  3000   5000     0.0000    63.9676   -10.5160    36.202270     29.2959     57.8289     64.8263\n  3000   5200     0.0000    63.9676   -10.5160    36.475851     29.5717     57.6884     64.8263\n  3000   5400     0.0000    63.9676   -10.5160    36.745223     29.8426     57.5487     64.8263\n  3000   5600     0.0000    63.9676   -10.5160    37.010502     30.1087     57.4099     64.8263\n  3000   5800     0.0000    63.9676   -10.5160    37.271797     30.3702     57.2720     64.8263\n  3000   6000     0.0000    63.9676   -10.5160    37.529215     30.6272     57.1350     64.8263\n  3000   6200     0.0000    63.9676   -10.5160    37.782855     30.8798     56.9989     64.8263\n  3000   6400     0.0000    63.9676   -10.5160    38.032817     31.1282     56.8636     64.8263\n  3000   6600     0.0000    63.9676   -10.5160    38.279191     31.3724     56.7292     64.8263\n  3000   6800     0.0000    63.9676   -10.5160    38.522068     31.6126     56.5957     64.8263\n  3000   7000     0.0000    63.9676   -10.5160    38.761534     31.8489     56.4631     64.8263\n  3000   7200     0.0000    63.9676   -10.5160    38.997670     32.0813     56.3314     64.8263\n  3000   7400     0.0000    63.9676   -10.5160    39.230557     32.3100     56.2005     64.8263\n  3000   7600     0.0000    63.9676   -10.5160    39.460271     32.5351     56.0705     64.8263\n  3000   7800     0.0000    63.9676   -10.5160    39.686886     32.7566     55.9414     64.8263\n  3000   8000     0.0000    63.9676   -10.5160    39.910472     32.9747     55.8132     64.8263\n  3000   8200     0.0000    63.9676   -10.5160    40.131098     33.1893     55.6858     64.8263\n  3000   8400     0.0000    63.9676   -10.5160    40.348830     33.4007     55.5593     64.8263\n  3500      0     0.0000    63.9837   -10.4888    27.494920     20.2352     61.5991     64.8377\n  3500    200     0.0000    63.9837   -10.4888    27.937223     20.7101     61.4410     64.8377\n  3500    400     0.0000    63.9837   -10.4888    28.368889     21.1724     61.2833     64.8377\n  3500    600     0.0000    63.9837   -10.4888    28.790441     21.6227     61.1258     64.8377\n  3500    800     0.0000    63.9837   -10.4888    29.202358     22.0616     60.9688     64.8377\n  3500   1000     0.0000    63.9837   -10.4888    29.605086     22.4896     60.8122     64.8377\n  3500   1200     0.0000    63.9837   -10.4888    29.999033     22.9072     60.6562     64.8377\n  3500   1400     0.0000    63.9837   -10.4888    30.384580     23.3149     60.5007     64.8377\n  3500   1600     0.0000    63.9837   -10.4888    30.762081     23.7130     60.3457     64.8377\n  3500   1800     0.0000    63.9837   -10.4888    31.131866     24.1019     60.1914     64.8377\n  3500   2000     0.0000    63.9837   -10.4888    31.494242     24.4821     60.0378     64.8377\n  3500   2200     0.0000    63.9837   -10.4888    31.849497     24.8539     59.8848     64.8377\n  3500   2400     0.0000    63.9837   -10.4888    32.197901     25.2176     59.7326     64.8377\n  3500   2600     0.0000    63.9837   -10.4888    32.539707     25.5735     59.5811     64.8377\n  3500   2800     0.0000    63.9837   -10.4888    32.875153     25.9219     59.4304     64.8377\n  3500   3000     0.0000    63.9837   -10.4888    33.204464     26.2630     59.2804     64.8377\n  3500   3200     0.0000    63.9837   -10.4888    33.527851     26.5972     59.1312     64.8377\n  3500   3400     0.0000    63.9837   -10.4888    33.845514     26.9246     58.9829     64.8377\n  3500   3600     0.0000    63.9837   -10.4888    34.157642     27.2456     58.8353     64.8377\n  3500   3800     0.0000    63.9837   -10.4888    34.464413     27.5602     58.6886     64.8377\n  3500   4000     0.0000    63.9837   -10.4888    34.765998     27.8687     58.5427     64.8377\n  3500   4200     0.0000    63.9837   -10.4888    35.062558     28.1714     58.3977     64.8377\n  3500   4400     0.0000    63.9837   -10.4888    35.354246     28.4683     58.2535     64.8377\n  3500   4600     0.0000    63.9837   -10.4888    35.641207     28.7597     58.1102     64.8377\n  3500   4800     0.0000    63.9837   -10.4888    35.923580     29.0457     57.9678     64.8377\n  3500   5000     0.0000    63.9837   -10.4888    36.201497     29.3266     57.8262     64.8377\n  3500   5200     0.0000    63.9837   -10.4888    36.475085     29.6024     57.6855     64.8377\n  3500   5400     0.0000    63.9837   -10.4888    36.744464     29.8732     57.5457     64.8377\n  3500   5600     0.0000    63.9837   -10.4888    37.009750     30.1394     57.4068     64.8377\n  3500   5800     0.0000    63.9837   -10.4888    37.271052     30.4009     57.2687     64.8377\n  3500   6000     0.0000    63.9837   -10.4888    37.528475     30.6579     57.1315     64.8377\n  3500   6200     0.0000    63.9837   -10.4888    37.782123     30.9105     56.9953     64.8377\n  3500   6400     0.0000    63.9837   -10.4888    38.032090     31.1588     56.8599     64.8377\n  3500   6600     0.0000    63.9837   -10.4888    38.278470     31.4030     56.7254     64.8377\n  3500   6800     0.0000    63.9837   -10.4888    38.521353     31.6432     56.5917     64.8377\n  3500   7000     0.0000    63.9837   -10.4888    38.760824     31.8795     56.4590     64.8377\n  3500   7200     0.0000    63.9837   -10.4888    38.996966     32.1119     56.3271     64.8377\n  3500   7400     0.0000    63.9837   -10.4888    39.229859     32.3406     56.1961     64.8377\n  3500   7600     0.0000    63.9837   -10.4888    39.459578     32.5656     56.0660     64.8377\n  3500   7800     0.0000    63.9837   -10.4888    39.686197     32.7871     55.9368     64.8377\n  3500   8000     0.0000    63.9837   -10.4888    39.909789     33.0052     55.8084     64.8377\n  3500   8200     0.0000    63.9837   -10.4888    40.130420     33.2198     55.6809     64.8377\n  3500   8400     0.0000    63.9837   -10.4888    40.348157     33.4312     55.5542     64.8377\n  4000      0     0.0000    63.9997   -10.4615    27.493844     20.2656     61.6011     64.8491\n  4000    200     0.0000    63.9997   -10.4615    27.936165     20.7406     61.4428     64.8491\n  4000    400     0.0000    63.9997   -10.4615    28.367849     21.2029     61.2848     64.8491\n  4000    600     0.0000    63.9997   -10.4615    28.789417     21.6532     61.1272     64.8491\n  4000    800     0.0000    63.9997   -10.4615    29.201351     22.0922     60.9699     64.8491\n  4000   1000     0.0000    63.9997   -10.4615    29.604093     22.5202     60.8131     64.8491\n  4000   1200     0.0000    63.9997   -10.4615    29.998054     22.9378     60.6568     64.8491\n  4000   1400     0.0000    63.9997   -10.4615    30.383615     23.3455     60.5011     64.8491\n  4000   1600     0.0000    63.9997   -10.4615    30.761130     23.7436     60.3460     64.8491\n  4000   1800     0.0000    63.9997   -10.4615    31.130927     24.1326     60.1915     64.8491\n  4000   2000     0.0000    63.9997   -10.4615    31.493315     24.5128     60.0376     64.8491\n  4000   2200     0.0000    63.9997   -10.4615    31.848582     24.8846     59.8845     64.8491\n  4000   2400     0.0000    63.9997   -10.4615    32.196998     25.2483     59.7321     64.8491\n  4000   2600     0.0000    63.9997   -10.4615    32.538815     25.6042     59.5804     64.8491\n  4000   2800     0.0000    63.9997   -10.4615    32.874271     25.9526     59.4295     64.8491\n  4000   3000     0.0000    63.9997   -10.4615    33.203592     26.2937     59.2793     64.8491\n  4000   3200     0.0000    63.9997   -10.4615    33.526989     26.6279     59.1300     64.8491\n  4000   3400     0.0000    63.9997   -10.4615    33.844662     26.9553     58.9814     64.8491\n  4000   3600     0.0000    63.9997   -10.4615    34.156798     27.2763     58.8337     64.8491\n  4000   3800     0.0000    63.9997   -10.4615    34.463579     27.5909     58.6868     64.8491\n  4000   4000     0.0000    63.9997   -10.4615    34.765172     27.8994     58.5408     64.8491\n  4000   4200     0.0000    63.9997   -10.4615    35.061741     28.2020     58.3956     64.8491\n  4000   4400     0.0000    63.9997   -10.4615    35.353436     28.4990     58.2512     64.8491\n  4000   4600     0.0000    63.9997   -10.4615    35.640405     28.7904     58.1078     64.8491\n  4000   4800     0.0000    63.9997   -10.4615    35.922786     29.0764     57.9652     64.8491\n  4000   5000     0.0000    63.9997   -10.4615    36.200710     29.3572     57.8235     64.8491\n  4000   5200     0.0000    63.9997   -10.4615    36.474306     29.6330     57.6826     64.8491\n  4000   5400     0.0000    63.9997   -10.4615    36.743692     29.9039     57.5427     64.8491\n  4000   5600     0.0000    63.9997   -10.4615    37.008984     30.1700     57.4036     64.8491\n  4000   5800     0.0000    63.9997   -10.4615    37.270293     30.4315     57.2654     64.8491\n  4000   6000     0.0000    63.9997   -10.4615    37.527723     30.6885     57.1281     64.8491\n  4000   6200     0.0000    63.9997   -10.4615    37.781376     30.9411     56.9917     64.8491\n  4000   6400     0.0000    63.9997   -10.4615    38.031350     31.1894     56.8561     64.8491\n  4000   6600     0.0000    63.9997   -10.4615    38.277736     31.4337     56.7215     64.8491\n  4000   6800     0.0000    63.9997   -10.4615    38.520625     31.6738     56.5877     64.8491\n  4000   7000     0.0000    63.9997   -10.4615    38.760102     31.9101     56.4548     64.8491\n  4000   7200     0.0000    63.9997   -10.4615    38.996249     32.1425     56.3228     64.8491\n  4000   7400     0.0000    63.9997   -10.4615    39.229147     32.3712     56.1917     64.8491\n  4000   7600     0.0000    63.9997   -10.4615    39.458872     32.5962     56.0615     64.8491\n  4000   7800     0.0000    63.9997   -10.4615    39.685497     32.8177     55.9321     64.8491\n  4000   8000     0.0000    63.9997   -10.4615    39.909093     33.0357     55.8036     64.8491\n  4000   8200     0.0000    63.9997   -10.4615    40.129729     33.2503     55.6760     64.8491\n  4000   8400     0.0000    63.9997   -10.4615    40.347471     33.4617     55.5492     64.8491\n  4500      0     0.0000    64.0158   -10.4343    27.492748     20.2960     61.6031     64.8606\n  4500    200     0.0000    64.0158   -10.4343    27.935088     20.7710     61.4446     64.8606\n  4500    400     0.0000    64.0158   -10.4343    28.366789     21.2334     61.2864     64.8606\n  4500    600     0.0000    64.0158   -10.4343    28.788374     21.6837     61.1285     64.8606\n  4500    800     0.0000    64.0158   -10.4343    29.200324     22.1227     60.9710     64.8606\n  4500   1000     0.0000    64.0158   -10.4343    29.603082     22.5507     60.8140     64.8606\n  4500   1200     0.0000    64.0158   -10.4343    29.997058     22.9684     60.6575     64.8606\n  4500   1400     0.0000    64.0158   -10.4343    30.382633     23.3760     60.5016     64.8606\n  4500   1600     0.0000    64.0158   -10.4343    30.760161     23.7742     60.3462     64.8606\n  4500   1800     0.0000    64.0158   -10.4343    31.129971     24.1632     60.1915     64.8606\n  4500   2000     0.0000    64.0158   -10.4343    31.492372     24.5434     60.0375     64.8606\n  4500   2200     0.0000    64.0158   -10.4343    31.847651     24.9152     59.8842     64.8606\n  4500   2400     0.0000    64.0158   -10.4343    32.196078     25.2789     59.7315     64.8606\n  4500   2600     0.0000    64.0158   -10.4343    32.537906     25.6348     59.5797     64.8606\n  4500   2800     0.0000    64.0158   -10.4343    32.873373     25.9832     59.4286     64.8606\n  4500   3000     0.0000    64.0158   -10.4343    33.202705     26.3244     59.2782     64.8606\n  4500   3200     0.0000    64.0158   -10.4343    33.526111     26.6586     59.1287     64.8606\n  4500   3400     0.0000    64.0158   -10.4343    33.843793     26.9860     58.9800     64.8606\n  4500   3600     0.0000    64.0158   -10.4343    34.155940     27.3069     58.8321     64.8606\n  4500   3800     0.0000    64.0158   -10.4343    34.462729     27.6216     58.6850     64.8606\n  4500   4000     0.0000    64.0158   -10.4343    34.764332     27.9301     58.5388     64.8606\n  4500   4200     0.0000    64.0158   -10.4343    35.060908     28.2327     58.3935     64.8606\n  4500   4400     0.0000    64.0158   -10.4343    35.352612     28.5296     58.2490     64.8606\n  4500   4600     0.0000    64.0158   -10.4343    35.639589     28.8210     58.1053     64.8606\n  4500   4800     0.0000    64.0158   -10.4343    35.921977     29.1071     57.9626     64.8606\n  4500   5000     0.0000    64.0158   -10.4343    36.199910     29.3879     57.8207     64.8606\n  4500   5200     0.0000    64.0158   -10.4343    36.473512     29.6637     57.6797     64.8606\n  4500   5400     0.0000    64.0158   -10.4343    36.742906     29.9345     57.5396     64.8606\n  4500   5600     0.0000    64.0158   -10.4343    37.008205     30.2006     57.4004     64.8606\n  4500   5800     0.0000    64.0158   -10.4343    37.269520     30.4621     57.2621     64.8606\n  4500   6000     0.0000    64.0158   -10.4343    37.526957     30.7191     57.1246     64.8606\n  4500   6200     0.0000    64.0158   -10.4343    37.780617     30.9717     56.9881     64.8606\n  4500   6400     0.0000    64.0158   -10.4343    38.030597     31.2200     56.8524     64.8606\n  4500   6600     0.0000    64.0158   -10.4343    38.276989     31.4642     56.7176     64.8606\n  4500   6800     0.0000    64.0158   -10.4343    38.519884     31.7044     56.5837     64.8606\n  4500   7000     0.0000    64.0158   -10.4343    38.759367     31.9406     56.4507     64.8606\n  4500   7200     0.0000    64.0158   -10.4343    38.995520     32.1730     56.3186     64.8606\n  4500   7400     0.0000    64.0158   -10.4343    39.228424     32.4017     56.1873     64.8606\n  4500   7600     0.0000    64.0158   -10.4343    39.458154     32.6267     56.0570     64.8606\n  4500   7800     0.0000    64.0158   -10.4343    39.684784     32.8482     55.9275     64.8606\n  4500   8000     0.0000    64.0158   -10.4343    39.908385     33.0662     55.7989     64.8606\n  4500   8200     0.0000    64.0158   -10.4343    40.129027     33.2808     55.6711     64.8606\n  4500   8400     0.0000    64.0158   -10.4343    40.346774     33.4922     55.5442     64.8606\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180412-20180518_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.2932894       49.7232694       -9.0026781   m   m   m\ninitial_baseline_rate:          0.0000000        0.0835121       -0.0035232   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       49.7313924       -8.9840098   m   m   m\nprecision_baseline_rate:        0.0000000        0.0801545       -0.0014154   m/s m/s m/s\nunwrap_phase_constant:            0.00009     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180412-20180518_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.293     49.723     -9.003\norbit baseline rate   (TCN) (m/s):  0.000e+00  8.351e-02 -3.523e-03\n\nbaseline vector (TCN)   (m):      0.000     49.731     -8.984\nbaseline rate   (TCN) (m/s):  0.000e+00  8.015e-02 -1.415e-03\n\nSLC-1 center baseline length (m):    50.5364\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    48.9832    -8.9708    27.501881     14.6622     47.5903     49.7979\n     0    200     0.0000    48.9832    -8.9708    27.944066     15.0291     47.4757     49.7979\n     0    400     0.0000    48.9832    -8.9708    28.375620     15.3862     47.3612     49.7979\n     0    600     0.0000    48.9832    -8.9708    28.797066     15.7342     47.2467     49.7979\n     0    800     0.0000    48.9832    -8.9708    29.208882     16.0734     47.1324     49.7979\n     0   1000     0.0000    48.9832    -8.9708    29.611513     16.4042     47.0183     49.7979\n     0   1200     0.0000    48.9832    -8.9708    30.005367     16.7270     46.9044     49.7979\n     0   1400     0.0000    48.9832    -8.9708    30.390826     17.0422     46.7909     49.7979\n     0   1600     0.0000    48.9832    -8.9708    30.768243     17.3500     46.6776     49.7979\n     0   1800     0.0000    48.9832    -8.9708    31.137946     17.6509     46.5647     49.7979\n     0   2000     0.0000    48.9832    -8.9708    31.500244     17.9449     46.4521     49.7979\n     0   2200     0.0000    48.9832    -8.9708    31.855425     18.2326     46.3400     49.7979\n     0   2400     0.0000    48.9832    -8.9708    32.203756     18.5139     46.2283     49.7979\n     0   2600     0.0000    48.9832    -8.9708    32.545493     18.7893     46.1170     49.7979\n     0   2800     0.0000    48.9832    -8.9708    32.880872     19.0590     46.0063     49.7979\n     0   3000     0.0000    48.9832    -8.9708    33.210118     19.3230     45.8960     49.7979\n     0   3200     0.0000    48.9832    -8.9708    33.533443     19.5817     45.7862     49.7979\n     0   3400     0.0000    48.9832    -8.9708    33.851045     19.8352     45.6770     49.7979\n     0   3600     0.0000    48.9832    -8.9708    34.163114     20.0837     45.5683     49.7979\n     0   3800     0.0000    48.9832    -8.9708    34.469829     20.3273     45.4601     49.7979\n     0   4000     0.0000    48.9832    -8.9708    34.771360     20.5663     45.3525     49.7979\n     0   4200     0.0000    48.9832    -8.9708    35.067866     20.8007     45.2455     49.7979\n     0   4400     0.0000    48.9832    -8.9708    35.359502     21.0308     45.1390     49.7979\n     0   4600     0.0000    48.9832    -8.9708    35.646413     21.2565     45.0331     49.7979\n     0   4800     0.0000    48.9832    -8.9708    35.928737     21.4782     44.9278     49.7979\n     0   5000     0.0000    48.9832    -8.9708    36.206607     21.6958     44.8232     49.7979\n     0   5200     0.0000    48.9832    -8.9708    36.480149     21.9096     44.7191     49.7979\n     0   5400     0.0000    48.9832    -8.9708    36.749483     22.1195     44.6156     49.7979\n     0   5600     0.0000    48.9832    -8.9708    37.014725     22.3258     44.5127     49.7979\n     0   5800     0.0000    48.9832    -8.9708    37.275984     22.5286     44.4104     49.7979\n     0   6000     0.0000    48.9832    -8.9708    37.533367     22.7278     44.3088     49.7979\n     0   6200     0.0000    48.9832    -8.9708    37.786973     22.9237     44.2078     49.7979\n     0   6400     0.0000    48.9832    -8.9708    38.036901     23.1164     44.1073     49.7979\n     0   6600     0.0000    48.9832    -8.9708    38.283243     23.3058     44.0075     49.7979\n     0   6800     0.0000    48.9832    -8.9708    38.526088     23.4921     43.9084     49.7979\n     0   7000     0.0000    48.9832    -8.9708    38.765522     23.6754     43.8098     49.7979\n     0   7200     0.0000    48.9832    -8.9708    39.001629     23.8557     43.7119     49.7979\n     0   7400     0.0000    48.9832    -8.9708    39.234486     24.0332     43.6146     49.7979\n     0   7600     0.0000    48.9832    -8.9708    39.464171     24.2078     43.5179     49.7979\n     0   7800     0.0000    48.9832    -8.9708    39.690757     24.3797     43.4218     49.7979\n     0   8000     0.0000    48.9832    -8.9708    39.914315     24.5490     43.3264     49.7979\n     0   8200     0.0000    48.9832    -8.9708    40.134914     24.7156     43.2315     49.7979\n     0   8400     0.0000    48.9832    -8.9708    40.352620     24.8797     43.1373     49.7979\n   500      0     0.0000    49.1480    -8.9737    27.500948     14.7350     47.7380     49.9605\n   500    200     0.0000    49.1480    -8.9737    27.943149     15.1030     47.6229     49.9605\n   500    400     0.0000    49.1480    -8.9737    28.374718     15.4612     47.5078     49.9605\n   500    600     0.0000    49.1480    -8.9737    28.796177     15.8103     47.3928     49.9605\n   500    800     0.0000    49.1480    -8.9737    29.208007     16.1505     47.2779     49.9605\n   500   1000     0.0000    49.1480    -8.9737    29.610651     16.4824     47.1632     49.9605\n   500   1200     0.0000    49.1480    -8.9737    30.004518     16.8062     47.0488     49.9605\n   500   1400     0.0000    49.1480    -8.9737    30.389988     17.1223     46.9347     49.9605\n   500   1600     0.0000    49.1480    -8.9737    30.767416     17.4311     46.8209     49.9605\n   500   1800     0.0000    49.1480    -8.9737    31.137130     17.7329     46.7074     49.9605\n   500   2000     0.0000    49.1480    -8.9737    31.499439     18.0279     46.5944     49.9605\n   500   2200     0.0000    49.1480    -8.9737    31.854629     18.3164     46.4817     49.9605\n   500   2400     0.0000    49.1480    -8.9737    32.202970     18.5987     46.3695     49.9605\n   500   2600     0.0000    49.1480    -8.9737    32.544716     18.8749     46.2578     49.9605\n   500   2800     0.0000    49.1480    -8.9737    32.880104     19.1453     46.1465     49.9605\n   500   3000     0.0000    49.1480    -8.9737    33.209359     19.4102     46.0357     49.9605\n   500   3200     0.0000    49.1480    -8.9737    33.532692     19.6697     45.9254     49.9605\n   500   3400     0.0000    49.1480    -8.9737    33.850302     19.9240     45.8157     49.9605\n   500   3600     0.0000    49.1480    -8.9737    34.162379     20.1732     45.7065     49.9605\n   500   3800     0.0000    49.1480    -8.9737    34.469102     20.4176     45.5978     49.9605\n   500   4000     0.0000    49.1480    -8.9737    34.770639     20.6573     45.4898     49.9605\n   500   4200     0.0000    49.1480    -8.9737    35.067153     20.8924     45.3823     49.9605\n   500   4400     0.0000    49.1480    -8.9737    35.358795     21.1232     45.2753     49.9605\n   500   4600     0.0000    49.1480    -8.9737    35.645713     21.3496     45.1690     49.9605\n   500   4800     0.0000    49.1480    -8.9737    35.928043     21.5720     45.0632     49.9605\n   500   5000     0.0000    49.1480    -8.9737    36.205920     21.7902     44.9581     49.9605\n   500   5200     0.0000    49.1480    -8.9737    36.479468     22.0046     44.8535     49.9605\n   500   5400     0.0000    49.1480    -8.9737    36.748808     22.2153     44.7496     49.9605\n   500   5600     0.0000    49.1480    -8.9737    37.014055     22.4222     44.6463     49.9605\n   500   5800     0.0000    49.1480    -8.9737    37.275320     22.6255     44.5436     49.9605\n   500   6000     0.0000    49.1480    -8.9737    37.532708     22.8254     44.4415     49.9605\n   500   6200     0.0000    49.1480    -8.9737    37.786320     23.0219     44.3400     49.9605\n   500   6400     0.0000    49.1480    -8.9737    38.036253     23.2151     44.2392     49.9605\n   500   6600     0.0000    49.1480    -8.9737    38.282600     23.4051     44.1389     49.9605\n   500   6800     0.0000    49.1480    -8.9737    38.525450     23.5920     44.0393     49.9605\n   500   7000     0.0000    49.1480    -8.9737    38.764890     23.7758     43.9404     49.9605\n   500   7200     0.0000    49.1480    -8.9737    39.001001     23.9567     43.8420     49.9605\n   500   7400     0.0000    49.1480    -8.9737    39.233863     24.1347     43.7443     49.9605\n   500   7600     0.0000    49.1480    -8.9737    39.463552     24.3098     43.6472     49.9605\n   500   7800     0.0000    49.1480    -8.9737    39.690143     24.4822     43.5507     49.9605\n   500   8000     0.0000    49.1480    -8.9737    39.913705     24.6520     43.4549     49.9605\n   500   8200     0.0000    49.1480    -8.9737    40.134309     24.8191     43.3596     49.9605\n   500   8400     0.0000    49.1480    -8.9737    40.352019     24.9837     43.2650     49.9605\n  1000      0     0.0000    49.3127    -8.9766    27.499995     14.8077     47.8858     50.1231\n  1000    200     0.0000    49.3127    -8.9766    27.942212     15.1768     47.7701     50.1231\n  1000    400     0.0000    49.3127    -8.9766    28.373796     15.5362     47.6544     50.1231\n  1000    600     0.0000    49.3127    -8.9766    28.795270     15.8863     47.5388     50.1231\n  1000    800     0.0000    49.3127    -8.9766    29.207113     16.2276     47.4234     50.1231\n  1000   1000     0.0000    49.3127    -8.9766    29.609770     16.5605     47.3082     50.1231\n  1000   1200     0.0000    49.3127    -8.9766    30.003649     16.8853     47.1932     50.1231\n  1000   1400     0.0000    49.3127    -8.9766    30.389132     17.2025     47.0786     50.1231\n  1000   1600     0.0000    49.3127    -8.9766    30.766571     17.5122     46.9642     50.1231\n  1000   1800     0.0000    49.3127    -8.9766    31.136297     17.8149     46.8502     50.1231\n  1000   2000     0.0000    49.3127    -8.9766    31.498616     18.1108     46.7366     50.1231\n  1000   2200     0.0000    49.3127    -8.9766    31.853816     18.4002     46.6235     50.1231\n  1000   2400     0.0000    49.3127    -8.9766    32.202167     18.6833     46.5107     50.1231\n  1000   2600     0.0000    49.3127    -8.9766    32.543923     18.9604     46.3985     50.1231\n  1000   2800     0.0000    49.3127    -8.9766    32.879320     19.2317     46.2867     50.1231\n  1000   3000     0.0000    49.3127    -8.9766    33.208583     19.4974     46.1754     50.1231\n  1000   3200     0.0000    49.3127    -8.9766    33.531925     19.7577     46.0646     50.1231\n  1000   3400     0.0000    49.3127    -8.9766    33.849543     20.0127     45.9544     50.1231\n  1000   3600     0.0000    49.3127    -8.9766    34.161628     20.2627     45.8447     50.1231\n  1000   3800     0.0000    49.3127    -8.9766    34.468358     20.5079     45.7356     50.1231\n  1000   4000     0.0000    49.3127    -8.9766    34.769903     20.7483     45.6270     50.1231\n  1000   4200     0.0000    49.3127    -8.9766    35.066424     20.9841     45.5190     50.1231\n  1000   4400     0.0000    49.3127    -8.9766    35.358074     21.2156     45.4116     50.1231\n  1000   4600     0.0000    49.3127    -8.9766    35.644998     21.4427     45.3048     50.1231\n  1000   4800     0.0000    49.3127    -8.9766    35.927335     21.6657     45.1986     50.1231\n  1000   5000     0.0000    49.3127    -8.9766    36.205218     21.8847     45.0930     50.1231\n  1000   5200     0.0000    49.3127    -8.9766    36.478772     22.0997     44.9880     50.1231\n  1000   5400     0.0000    49.3127    -8.9766    36.748119     22.3110     44.8836     50.1231\n  1000   5600     0.0000    49.3127    -8.9766    37.013372     22.5185     44.7799     50.1231\n  1000   5800     0.0000    49.3127    -8.9766    37.274643     22.7225     44.6767     50.1231\n  1000   6000     0.0000    49.3127    -8.9766    37.532037     22.9229     44.5742     50.1231\n  1000   6200     0.0000    49.3127    -8.9766    37.785654     23.1200     44.4723     50.1231\n  1000   6400     0.0000    49.3127    -8.9766    38.035592     23.3138     44.3710     50.1231\n  1000   6600     0.0000    49.3127    -8.9766    38.281945     23.5044     44.2703     50.1231\n  1000   6800     0.0000    49.3127    -8.9766    38.524800     23.6918     44.1703     50.1231\n  1000   7000     0.0000    49.3127    -8.9766    38.764244     23.8762     44.0709     50.1231\n  1000   7200     0.0000    49.3127    -8.9766    39.000360     24.0576     43.9722     50.1231\n  1000   7400     0.0000    49.3127    -8.9766    39.233227     24.2361     43.8740     50.1231\n  1000   7600     0.0000    49.3127    -8.9766    39.462921     24.4118     43.7765     50.1231\n  1000   7800     0.0000    49.3127    -8.9766    39.689516     24.5848     43.6796     50.1231\n  1000   8000     0.0000    49.3127    -8.9766    39.913083     24.7550     43.5834     50.1231\n  1000   8200     0.0000    49.3127    -8.9766    40.133691     24.9226     43.4877     50.1231\n  1000   8400     0.0000    49.3127    -8.9766    40.351405     25.0877     43.3927     50.1231\n  1500      0     0.0000    49.4775    -8.9795    27.499021     14.8804     48.0335     50.2857\n  1500    200     0.0000    49.4775    -8.9795    27.941254     15.2506     47.9172     50.2857\n  1500    400     0.0000    49.4775    -8.9795    28.372854     15.6112     47.8010     50.2857\n  1500    600     0.0000    49.4775    -8.9795    28.794343     15.9624     47.6849     50.2857\n  1500    800     0.0000    49.4775    -8.9795    29.206200     16.3047     47.5689     50.2857\n  1500   1000     0.0000    49.4775    -8.9795    29.608871     16.6386     47.4531     50.2857\n  1500   1200     0.0000    49.4775    -8.9795    30.002763     16.9645     47.3376     50.2857\n  1500   1400     0.0000    49.4775    -8.9795    30.388258     17.2826     47.2224     50.2857\n  1500   1600     0.0000    49.4775    -8.9795    30.765709     17.5933     47.1075     50.2857\n  1500   1800     0.0000    49.4775    -8.9795    31.135446     17.8969     46.9930     50.2857\n  1500   2000     0.0000    49.4775    -8.9795    31.497775     18.1937     46.8789     50.2857\n  1500   2200     0.0000    49.4775    -8.9795    31.852986     18.4840     46.7652     50.2857\n  1500   2400     0.0000    49.4775    -8.9795    32.201348     18.7680     46.6520     50.2857\n  1500   2600     0.0000    49.4775    -8.9795    32.543112     19.0460     46.5392     50.2857\n  1500   2800     0.0000    49.4775    -8.9795    32.878519     19.3181     46.4269     50.2857\n  1500   3000     0.0000    49.4775    -8.9795    33.207792     19.5846     46.3151     50.2857\n  1500   3200     0.0000    49.4775    -8.9795    33.531142     19.8456     46.2039     50.2857\n  1500   3400     0.0000    49.4775    -8.9795    33.848769     20.1015     46.0931     50.2857\n  1500   3600     0.0000    49.4775    -8.9795    34.160862     20.3522     45.9830     50.2857\n  1500   3800     0.0000    49.4775    -8.9795    34.467600     20.5981     45.8734     50.2857\n  1500   4000     0.0000    49.4775    -8.9795    34.769152     20.8393     45.7643     50.2857\n  1500   4200     0.0000    49.4775    -8.9795    35.065681     21.0758     45.6558     50.2857\n  1500   4400     0.0000    49.4775    -8.9795    35.357338     21.3080     45.5480     50.2857\n  1500   4600     0.0000    49.4775    -8.9795    35.644269     21.5358     45.4407     50.2857\n  1500   4800     0.0000    49.4775    -8.9795    35.926613     21.7595     45.3340     50.2857\n  1500   5000     0.0000    49.4775    -8.9795    36.204502     21.9791     45.2280     50.2857\n  1500   5200     0.0000    49.4775    -8.9795    36.478063     22.1948     45.1225     50.2857\n  1500   5400     0.0000    49.4775    -8.9795    36.747416     22.4067     45.0177     50.2857\n  1500   5600     0.0000    49.4775    -8.9795    37.012675     22.6148     44.9134     50.2857\n  1500   5800     0.0000    49.4775    -8.9795    37.273952     22.8194     44.8099     50.2857\n  1500   6000     0.0000    49.4775    -8.9795    37.531351     23.0205     44.7069     50.2857\n  1500   6200     0.0000    49.4775    -8.9795    37.784974     23.2182     44.6045     50.2857\n  1500   6400     0.0000    49.4775    -8.9795    38.034918     23.4125     44.5028     50.2857\n  1500   6600     0.0000    49.4775    -8.9795    38.281276     23.6036     44.4018     50.2857\n  1500   6800     0.0000    49.4775    -8.9795    38.524136     23.7916     44.3013     50.2857\n  1500   7000     0.0000    49.4775    -8.9795    38.763586     23.9766     44.2015     50.2857\n  1500   7200     0.0000    49.4775    -8.9795    38.999707     24.1585     44.1023     50.2857\n  1500   7400     0.0000    49.4775    -8.9795    39.232578     24.3376     44.0038     50.2857\n  1500   7600     0.0000    49.4775    -8.9795    39.462277     24.5138     43.9058     50.2857\n  1500   7800     0.0000    49.4775    -8.9795    39.688877     24.6872     43.8085     50.2857\n  1500   8000     0.0000    49.4775    -8.9795    39.912449     24.8580     43.7119     50.2857\n  1500   8200     0.0000    49.4775    -8.9795    40.133061     25.0261     43.6158     50.2857\n  1500   8400     0.0000    49.4775    -8.9795    40.350779     25.1917     43.5204     50.2857\n  2000      0     0.0000    49.6423    -8.9824    27.498027     14.9530     48.1813     50.4484\n  2000    200     0.0000    49.6423    -8.9824    27.940276     15.3245     48.0644     50.4484\n  2000    400     0.0000    49.6423    -8.9824    28.371892     15.6861     47.9476     50.4484\n  2000    600     0.0000    49.6423    -8.9824    28.793396     16.0384     47.8309     50.4484\n  2000    800     0.0000    49.6423    -8.9824    29.205268     16.3818     47.7144     50.4484\n  2000   1000     0.0000    49.6423    -8.9824    29.607952     16.7168     47.5981     50.4484\n  2000   1200     0.0000    49.6423    -8.9824    30.001858     17.0436     47.4820     50.4484\n  2000   1400     0.0000    49.6423    -8.9824    30.387365     17.3627     47.3663     50.4484\n  2000   1600     0.0000    49.6423    -8.9824    30.764828     17.6744     47.2509     50.4484\n  2000   1800     0.0000    49.6423    -8.9824    31.134577     17.9789     47.1358     50.4484\n  2000   2000     0.0000    49.6423    -8.9824    31.496918     18.2766     47.0212     50.4484\n  2000   2200     0.0000    49.6423    -8.9824    31.852139     18.5678     46.9070     50.4484\n  2000   2400     0.0000    49.6423    -8.9824    32.200511     18.8527     46.7932     50.4484\n  2000   2600     0.0000    49.6423    -8.9824    32.542286     19.1315     46.6799     50.4484\n  2000   2800     0.0000    49.6423    -8.9824    32.877702     19.4044     46.5671     50.4484\n  2000   3000     0.0000    49.6423    -8.9824    33.206984     19.6717     46.4549     50.4484\n  2000   3200     0.0000    49.6423    -8.9824    33.530343     19.9336     46.3431     50.4484\n  2000   3400     0.0000    49.6423    -8.9824    33.847979     20.1902     46.2319     50.4484\n  2000   3600     0.0000    49.6423    -8.9824    34.160080     20.4417     46.1212     50.4484\n  2000   3800     0.0000    49.6423    -8.9824    34.466826     20.6883     46.0111     50.4484\n  2000   4000     0.0000    49.6423    -8.9824    34.768386     20.9302     45.9016     50.4484\n  2000   4200     0.0000    49.6423    -8.9824    35.064922     21.1675     45.7927     50.4484\n  2000   4400     0.0000    49.6423    -8.9824    35.356587     21.4003     45.6843     50.4484\n  2000   4600     0.0000    49.6423    -8.9824    35.643525     21.6289     45.5766     50.4484\n  2000   4800     0.0000    49.6423    -8.9824    35.925876     21.8532     45.4694     50.4484\n  2000   5000     0.0000    49.6423    -8.9824    36.203772     22.0735     45.3629     50.4484\n  2000   5200     0.0000    49.6423    -8.9824    36.477340     22.2898     45.2570     50.4484\n  2000   5400     0.0000    49.6423    -8.9824    36.746699     22.5023     45.1517     50.4484\n  2000   5600     0.0000    49.6423    -8.9824    37.011964     22.7111     45.0470     50.4484\n  2000   5800     0.0000    49.6423    -8.9824    37.273247     22.9163     44.9430     50.4484\n  2000   6000     0.0000    49.6423    -8.9824    37.530653     23.1180     44.8396     50.4484\n  2000   6200     0.0000    49.6423    -8.9824    37.784281     23.3163     44.7368     50.4484\n  2000   6400     0.0000    49.6423    -8.9824    38.034231     23.5112     44.6347     50.4484\n  2000   6600     0.0000    49.6423    -8.9824    38.280594     23.7029     44.5332     50.4484\n  2000   6800     0.0000    49.6423    -8.9824    38.523460     23.8915     44.4323     50.4484\n  2000   7000     0.0000    49.6423    -8.9824    38.762915     24.0769     44.3321     50.4484\n  2000   7200     0.0000    49.6423    -8.9824    38.999041     24.2594     44.2325     50.4484\n  2000   7400     0.0000    49.6423    -8.9824    39.231917     24.4390     44.1335     50.4484\n  2000   7600     0.0000    49.6423    -8.9824    39.461621     24.6158     44.0352     50.4484\n  2000   7800     0.0000    49.6423    -8.9824    39.688226     24.7897     43.9375     50.4484\n  2000   8000     0.0000    49.6423    -8.9824    39.911802     24.9610     43.8404     50.4484\n  2000   8200     0.0000    49.6423    -8.9824    40.132419     25.1296     43.7440     50.4484\n  2000   8400     0.0000    49.6423    -8.9824    40.350142     25.2957     43.6482     50.4484\n  2500      0     0.0000    49.8070    -8.9853    27.497012     15.0256     48.3290     50.6110\n  2500    200     0.0000    49.8070    -8.9853    27.939279     15.3982     48.2116     50.6110\n  2500    400     0.0000    49.8070    -8.9853    28.370911     15.7610     48.0942     50.6110\n  2500    600     0.0000    49.8070    -8.9853    28.792430     16.1144     47.9770     50.6110\n  2500    800     0.0000    49.8070    -8.9853    29.204317     16.4589     47.8599     50.6110\n  2500   1000     0.0000    49.8070    -8.9853    29.607015     16.7949     47.7430     50.6110\n  2500   1200     0.0000    49.8070    -8.9853    30.000934     17.1227     47.6264     50.6110\n  2500   1400     0.0000    49.8070    -8.9853    30.386455     17.4428     47.5102     50.6110\n  2500   1600     0.0000    49.8070    -8.9853    30.763930     17.7554     47.3942     50.6110\n  2500   1800     0.0000    49.8070    -8.9853    31.133691     18.0609     47.2786     50.6110\n  2500   2000     0.0000    49.8070    -8.9853    31.496043     18.3595     47.1635     50.6110\n  2500   2200     0.0000    49.8070    -8.9853    31.851275     18.6516     47.0488     50.6110\n  2500   2400     0.0000    49.8070    -8.9853    32.199658     18.9373     46.9345     50.6110\n  2500   2600     0.0000    49.8070    -8.9853    32.541443     19.2170     46.8207     50.6110\n  2500   2800     0.0000    49.8070    -8.9853    32.876869     19.4907     46.7074     50.6110\n  2500   3000     0.0000    49.8070    -8.9853    33.206160     19.7588     46.5946     50.6110\n  2500   3200     0.0000    49.8070    -8.9853    33.529528     20.0215     46.4823     50.6110\n  2500   3400     0.0000    49.8070    -8.9853    33.847173     20.2789     46.3706     50.6110\n  2500   3600     0.0000    49.8070    -8.9853    34.159283     20.5312     46.2595     50.6110\n  2500   3800     0.0000    49.8070    -8.9853    34.466037     20.7786     46.1489     50.6110\n  2500   4000     0.0000    49.8070    -8.9853    34.767605     21.0212     46.0389     50.6110\n  2500   4200     0.0000    49.8070    -8.9853    35.064149     21.2592     45.9295     50.6110\n  2500   4400     0.0000    49.8070    -8.9853    35.355821     21.4927     45.8207     50.6110\n  2500   4600     0.0000    49.8070    -8.9853    35.642767     21.7219     45.7124     50.6110\n  2500   4800     0.0000    49.8070    -8.9853    35.925125     21.9469     45.6048     50.6110\n  2500   5000     0.0000    49.8070    -8.9853    36.203028     22.1679     45.4979     50.6110\n  2500   5200     0.0000    49.8070    -8.9853    36.476602     22.3848     45.3915     50.6110\n  2500   5400     0.0000    49.8070    -8.9853    36.745968     22.5980     45.2858     50.6110\n  2500   5600     0.0000    49.8070    -8.9853    37.011240     22.8074     45.1806     50.6110\n  2500   5800     0.0000    49.8070    -8.9853    37.272529     23.0132     45.0762     50.6110\n  2500   6000     0.0000    49.8070    -8.9853    37.529940     23.2155     44.9723     50.6110\n  2500   6200     0.0000    49.8070    -8.9853    37.783575     23.4144     44.8691     50.6110\n  2500   6400     0.0000    49.8070    -8.9853    38.033530     23.6099     44.7665     50.6110\n  2500   6600     0.0000    49.8070    -8.9853    38.279899     23.8022     44.6646     50.6110\n  2500   6800     0.0000    49.8070    -8.9853    38.522770     23.9913     44.5633     50.6110\n  2500   7000     0.0000    49.8070    -8.9853    38.762231     24.1773     44.4627     50.6110\n  2500   7200     0.0000    49.8070    -8.9853    38.998362     24.3603     44.3626     50.6110\n  2500   7400     0.0000    49.8070    -8.9853    39.231244     24.5405     44.2633     50.6110\n  2500   7600     0.0000    49.8070    -8.9853    39.460952     24.7177     44.1645     50.6110\n  2500   7800     0.0000    49.8070    -8.9853    39.687562     24.8922     44.0664     50.6110\n  2500   8000     0.0000    49.8070    -8.9853    39.911143     25.0640     43.9689     50.6110\n  2500   8200     0.0000    49.8070    -8.9853    40.131765     25.2331     43.8721     50.6110\n  2500   8400     0.0000    49.8070    -8.9853    40.349492     25.3996     43.7759     50.6110\n  3000      0     0.0000    49.9718    -8.9883    27.495976     15.0983     48.4768     50.7737\n  3000    200     0.0000    49.9718    -8.9883    27.938261     15.4720     48.3588     50.7737\n  3000    400     0.0000    49.9718    -8.9883    28.369910     15.8359     48.2409     50.7737\n  3000    600     0.0000    49.9718    -8.9883    28.791445     16.1904     48.1231     50.7737\n  3000    800     0.0000    49.9718    -8.9883    29.203347     16.5359     48.0054     50.7737\n  3000   1000     0.0000    49.9718    -8.9883    29.606060     16.8729     47.8880     50.7737\n  3000   1200     0.0000    49.9718    -8.9883    29.999993     17.2018     47.7709     50.7737\n  3000   1400     0.0000    49.9718    -8.9883    30.385526     17.5228     47.6540     50.7737\n  3000   1600     0.0000    49.9718    -8.9883    30.763014     17.8364     47.5376     50.7737\n  3000   1800     0.0000    49.9718    -8.9883    31.132787     18.1428     47.4215     50.7737\n  3000   2000     0.0000    49.9718    -8.9883    31.495151     18.4424     47.3058     50.7737\n  3000   2200     0.0000    49.9718    -8.9883    31.850395     18.7353     47.1905     50.7737\n  3000   2400     0.0000    49.9718    -8.9883    32.198788     19.0219     47.0757     50.7737\n  3000   2600     0.0000    49.9718    -8.9883    32.540583     19.3024     46.9614     50.7737\n  3000   2800     0.0000    49.9718    -8.9883    32.876019     19.5770     46.8476     50.7737\n  3000   3000     0.0000    49.9718    -8.9883    33.205320     19.8460     46.7343     50.7737\n  3000   3200     0.0000    49.9718    -8.9883    33.528698     20.1094     46.6216     50.7737\n  3000   3400     0.0000    49.9718    -8.9883    33.846351     20.3676     46.5094     50.7737\n  3000   3600     0.0000    49.9718    -8.9883    34.158470     20.6206     46.3977     50.7737\n  3000   3800     0.0000    49.9718    -8.9883    34.465233     20.8687     46.2867     50.7737\n  3000   4000     0.0000    49.9718    -8.9883    34.766809     21.1121     46.1762     50.7737\n  3000   4200     0.0000    49.9718    -8.9883    35.063361     21.3508     46.0663     50.7737\n  3000   4400     0.0000    49.9718    -8.9883    35.355041     21.5850     45.9570     50.7737\n  3000   4600     0.0000    49.9718    -8.9883    35.641994     21.8149     45.8483     50.7737\n  3000   4800     0.0000    49.9718    -8.9883    35.924359     22.0406     45.7403     50.7737\n  3000   5000     0.0000    49.9718    -8.9883    36.202270     22.2622     45.6328     50.7737\n  3000   5200     0.0000    49.9718    -8.9883    36.475851     22.4799     45.5260     50.7737\n  3000   5400     0.0000    49.9718    -8.9883    36.745223     22.6936     45.4198     50.7737\n  3000   5600     0.0000    49.9718    -8.9883    37.010502     22.9037     45.3143     50.7737\n  3000   5800     0.0000    49.9718    -8.9883    37.271797     23.1101     45.2093     50.7737\n  3000   6000     0.0000    49.9718    -8.9883    37.529215     23.3130     45.1050     50.7737\n  3000   6200     0.0000    49.9718    -8.9883    37.782855     23.5124     45.0014     50.7737\n  3000   6400     0.0000    49.9718    -8.9883    38.032817     23.7085     44.8984     50.7737\n  3000   6600     0.0000    49.9718    -8.9883    38.279191     23.9014     44.7960     50.7737\n  3000   6800     0.0000    49.9718    -8.9883    38.522068     24.0911     44.6943     50.7737\n  3000   7000     0.0000    49.9718    -8.9883    38.761534     24.2776     44.5932     50.7737\n  3000   7200     0.0000    49.9718    -8.9883    38.997670     24.4612     44.4928     50.7737\n  3000   7400     0.0000    49.9718    -8.9883    39.230557     24.6419     44.3930     50.7737\n  3000   7600     0.0000    49.9718    -8.9883    39.460271     24.8197     44.2939     50.7737\n  3000   7800     0.0000    49.9718    -8.9883    39.686886     24.9947     44.1954     50.7737\n  3000   8000     0.0000    49.9718    -8.9883    39.910472     25.1669     44.0975     50.7737\n  3000   8200     0.0000    49.9718    -8.9883    40.131098     25.3365     44.0002     50.7737\n  3000   8400     0.0000    49.9718    -8.9883    40.348830     25.5036     43.9036     50.7737\n  3500      0     0.0000    50.1365    -8.9912    27.494920     15.1709     48.6246     50.9364\n  3500    200     0.0000    50.1365    -8.9912    27.937223     15.5458     48.5060     50.9364\n  3500    400     0.0000    50.1365    -8.9912    28.368889     15.9108     48.3875     50.9364\n  3500    600     0.0000    50.1365    -8.9912    28.790441     16.2663     48.2691     50.9364\n  3500    800     0.0000    50.1365    -8.9912    29.202358     16.6129     48.1509     50.9364\n  3500   1000     0.0000    50.1365    -8.9912    29.605086     16.9510     48.0330     50.9364\n  3500   1200     0.0000    50.1365    -8.9912    29.999033     17.2808     47.9153     50.9364\n  3500   1400     0.0000    50.1365    -8.9912    30.384580     17.6029     47.7979     50.9364\n  3500   1600     0.0000    50.1365    -8.9912    30.762081     17.9174     47.6809     50.9364\n  3500   1800     0.0000    50.1365    -8.9912    31.131866     18.2248     47.5643     50.9364\n  3500   2000     0.0000    50.1365    -8.9912    31.494242     18.5252     47.4481     50.9364\n  3500   2200     0.0000    50.1365    -8.9912    31.849497     18.8191     47.3323     50.9364\n  3500   2400     0.0000    50.1365    -8.9912    32.197901     19.1065     47.2170     50.9364\n  3500   2600     0.0000    50.1365    -8.9912    32.539707     19.3879     47.1022     50.9364\n  3500   2800     0.0000    50.1365    -8.9912    32.875153     19.6633     46.9879     50.9364\n  3500   3000     0.0000    50.1365    -8.9912    33.204464     19.9330     46.8741     50.9364\n  3500   3200     0.0000    50.1365    -8.9912    33.527851     20.1973     46.7608     50.9364\n  3500   3400     0.0000    50.1365    -8.9912    33.845514     20.4562     46.6481     50.9364\n  3500   3600     0.0000    50.1365    -8.9912    34.157642     20.7101     46.5360     50.9364\n  3500   3800     0.0000    50.1365    -8.9912    34.464413     20.9589     46.4245     50.9364\n  3500   4000     0.0000    50.1365    -8.9912    34.765998     21.2030     46.3135     50.9364\n  3500   4200     0.0000    50.1365    -8.9912    35.062558     21.4424     46.2031     50.9364\n  3500   4400     0.0000    50.1365    -8.9912    35.354246     21.6774     46.0934     50.9364\n  3500   4600     0.0000    50.1365    -8.9912    35.641207     21.9079     45.9842     50.9364\n  3500   4800     0.0000    50.1365    -8.9912    35.923580     22.1343     45.8757     50.9364\n  3500   5000     0.0000    50.1365    -8.9912    36.201497     22.3566     45.7678     50.9364\n  3500   5200     0.0000    50.1365    -8.9912    36.475085     22.5749     45.6605     50.9364\n  3500   5400     0.0000    50.1365    -8.9912    36.744464     22.7893     45.5539     50.9364\n  3500   5600     0.0000    50.1365    -8.9912    37.009750     23.0000     45.4479     50.9364\n  3500   5800     0.0000    50.1365    -8.9912    37.271052     23.2070     45.3425     50.9364\n  3500   6000     0.0000    50.1365    -8.9912    37.528475     23.4105     45.2378     50.9364\n  3500   6200     0.0000    50.1365    -8.9912    37.782123     23.6105     45.1337     50.9364\n  3500   6400     0.0000    50.1365    -8.9912    38.032090     23.8072     45.0303     50.9364\n  3500   6600     0.0000    50.1365    -8.9912    38.278470     24.0006     44.9275     50.9364\n  3500   6800     0.0000    50.1365    -8.9912    38.521353     24.1908     44.8253     50.9364\n  3500   7000     0.0000    50.1365    -8.9912    38.760824     24.3780     44.7238     50.9364\n  3500   7200     0.0000    50.1365    -8.9912    38.996966     24.5621     44.6230     50.9364\n  3500   7400     0.0000    50.1365    -8.9912    39.229859     24.7433     44.5228     50.9364\n  3500   7600     0.0000    50.1365    -8.9912    39.459578     24.9216     44.4232     50.9364\n  3500   7800     0.0000    50.1365    -8.9912    39.686197     25.0971     44.3243     50.9364\n  3500   8000     0.0000    50.1365    -8.9912    39.909789     25.2699     44.2260     50.9364\n  3500   8200     0.0000    50.1365    -8.9912    40.130420     25.4400     44.1284     50.9364\n  3500   8400     0.0000    50.1365    -8.9912    40.348157     25.6075     44.0314     50.9364\n  4000      0     0.0000    50.3013    -8.9941    27.493844     15.2434     48.7723     51.0991\n  4000    200     0.0000    50.3013    -8.9941    27.936165     15.6195     48.6532     51.0991\n  4000    400     0.0000    50.3013    -8.9941    28.367849     15.9856     48.5341     51.0991\n  4000    600     0.0000    50.3013    -8.9941    28.789417     16.3423     48.4152     51.0991\n  4000    800     0.0000    50.3013    -8.9941    29.201351     16.6899     48.2965     51.0991\n  4000   1000     0.0000    50.3013    -8.9941    29.604093     17.0290     48.1780     51.0991\n  4000   1200     0.0000    50.3013    -8.9941    29.998054     17.3599     48.0597     51.0991\n  4000   1400     0.0000    50.3013    -8.9941    30.383615     17.6829     47.9418     51.0991\n  4000   1600     0.0000    50.3013    -8.9941    30.761130     17.9984     47.8243     51.0991\n  4000   1800     0.0000    50.3013    -8.9941    31.130927     18.3067     47.7071     51.0991\n  4000   2000     0.0000    50.3013    -8.9941    31.493315     18.6080     47.5904     51.0991\n  4000   2200     0.0000    50.3013    -8.9941    31.848582     18.9028     47.4741     51.0991\n  4000   2400     0.0000    50.3013    -8.9941    32.196998     19.1911     47.3583     51.0991\n  4000   2600     0.0000    50.3013    -8.9941    32.538815     19.4733     47.2429     51.0991\n  4000   2800     0.0000    50.3013    -8.9941    32.874271     19.7496     47.1281     51.0991\n  4000   3000     0.0000    50.3013    -8.9941    33.203592     20.0201     47.0138     51.0991\n  4000   3200     0.0000    50.3013    -8.9941    33.526989     20.2852     46.9001     51.0991\n  4000   3400     0.0000    50.3013    -8.9941    33.844662     20.5449     46.7869     51.0991\n  4000   3600     0.0000    50.3013    -8.9941    34.156798     20.7995     46.6743     51.0991\n  4000   3800     0.0000    50.3013    -8.9941    34.463579     21.0491     46.5622     51.0991\n  4000   4000     0.0000    50.3013    -8.9941    34.765172     21.2939     46.4508     51.0991\n  4000   4200     0.0000    50.3013    -8.9941    35.061741     21.5340     46.3400     51.0991\n  4000   4400     0.0000    50.3013    -8.9941    35.353436     21.7697     46.2297     51.0991\n  4000   4600     0.0000    50.3013    -8.9941    35.640405     22.0009     46.1201     51.0991\n  4000   4800     0.0000    50.3013    -8.9941    35.922786     22.2280     46.0111     51.0991\n  4000   5000     0.0000    50.3013    -8.9941    36.200710     22.4509     45.9028     51.0991\n  4000   5200     0.0000    50.3013    -8.9941    36.474306     22.6698     45.7950     51.0991\n  4000   5400     0.0000    50.3013    -8.9941    36.743692     22.8849     45.6880     51.0991\n  4000   5600     0.0000    50.3013    -8.9941    37.008984     23.0962     45.5815     51.0991\n  4000   5800     0.0000    50.3013    -8.9941    37.270293     23.3038     45.4757     51.0991\n  4000   6000     0.0000    50.3013    -8.9941    37.527723     23.5079     45.3705     51.0991\n  4000   6200     0.0000    50.3013    -8.9941    37.781376     23.7086     45.2660     51.0991\n  4000   6400     0.0000    50.3013    -8.9941    38.031350     23.9058     45.1622     51.0991\n  4000   6600     0.0000    50.3013    -8.9941    38.277736     24.0998     45.0589     51.0991\n  4000   6800     0.0000    50.3013    -8.9941    38.520625     24.2906     44.9564     51.0991\n  4000   7000     0.0000    50.3013    -8.9941    38.760102     24.4783     44.8544     51.0991\n  4000   7200     0.0000    50.3013    -8.9941    38.996249     24.6630     44.7532     51.0991\n  4000   7400     0.0000    50.3013    -8.9941    39.229147     24.8447     44.6526     51.0991\n  4000   7600     0.0000    50.3013    -8.9941    39.458872     25.0235     44.5526     51.0991\n  4000   7800     0.0000    50.3013    -8.9941    39.685497     25.1995     44.4533     51.0991\n  4000   8000     0.0000    50.3013    -8.9941    39.909093     25.3728     44.3546     51.0991\n  4000   8200     0.0000    50.3013    -8.9941    40.129729     25.5434     44.2565     51.0991\n  4000   8400     0.0000    50.3013    -8.9941    40.347471     25.7114     44.1592     51.0991\n  4500      0     0.0000    50.4661    -8.9970    27.492748     15.3160     48.9201     51.2618\n  4500    200     0.0000    50.4661    -8.9970    27.935088     15.6932     48.8004     51.2618\n  4500    400     0.0000    50.4661    -8.9970    28.366789     16.0604     48.6808     51.2618\n  4500    600     0.0000    50.4661    -8.9970    28.788374     16.4182     48.5613     51.2618\n  4500    800     0.0000    50.4661    -8.9970    29.200324     16.7669     48.4420     51.2618\n  4500   1000     0.0000    50.4661    -8.9970    29.603082     17.1070     48.3230     51.2618\n  4500   1200     0.0000    50.4661    -8.9970    29.997058     17.4389     48.2042     51.2618\n  4500   1400     0.0000    50.4661    -8.9970    30.382633     17.7629     48.0857     51.2618\n  4500   1600     0.0000    50.4661    -8.9970    30.760161     18.0793     47.9677     51.2618\n  4500   1800     0.0000    50.4661    -8.9970    31.129971     18.3886     47.8500     51.2618\n  4500   2000     0.0000    50.4661    -8.9970    31.492372     18.6909     47.7327     51.2618\n  4500   2200     0.0000    50.4661    -8.9970    31.847651     18.9865     47.6159     51.2618\n  4500   2400     0.0000    50.4661    -8.9970    32.196078     19.2757     47.4996     51.2618\n  4500   2600     0.0000    50.4661    -8.9970    32.537906     19.5587     47.3837     51.2618\n  4500   2800     0.0000    50.4661    -8.9970    32.873373     19.8358     47.2684     51.2618\n  4500   3000     0.0000    50.4661    -8.9970    33.202705     20.1072     47.1536     51.2618\n  4500   3200     0.0000    50.4661    -8.9970    33.526111     20.3730     47.0394     51.2618\n  4500   3400     0.0000    50.4661    -8.9970    33.843793     20.6335     46.9257     51.2618\n  4500   3600     0.0000    50.4661    -8.9970    34.155940     20.8889     46.8126     51.2618\n  4500   3800     0.0000    50.4661    -8.9970    34.462729     21.1392     46.7001     51.2618\n  4500   4000     0.0000    50.4661    -8.9970    34.764332     21.3848     46.5881     51.2618\n  4500   4200     0.0000    50.4661    -8.9970    35.060908     21.6256     46.4768     51.2618\n  4500   4400     0.0000    50.4661    -8.9970    35.352612     21.8620     46.3661     51.2618\n  4500   4600     0.0000    50.4661    -8.9970    35.639589     22.0939     46.2560     51.2618\n  4500   4800     0.0000    50.4661    -8.9970    35.921977     22.3216     46.1466     51.2618\n  4500   5000     0.0000    50.4661    -8.9970    36.199910     22.5452     46.0378     51.2618\n  4500   5200     0.0000    50.4661    -8.9970    36.473512     22.7648     45.9296     51.2618\n  4500   5400     0.0000    50.4661    -8.9970    36.742906     22.9805     45.8220     51.2618\n  4500   5600     0.0000    50.4661    -8.9970    37.008205     23.1924     45.7151     51.2618\n  4500   5800     0.0000    50.4661    -8.9970    37.269520     23.4007     45.6089     51.2618\n  4500   6000     0.0000    50.4661    -8.9970    37.526957     23.6054     45.5033     51.2618\n  4500   6200     0.0000    50.4661    -8.9970    37.780617     23.8066     45.3983     51.2618\n  4500   6400     0.0000    50.4661    -8.9970    38.030597     24.0044     45.2940     51.2618\n  4500   6600     0.0000    50.4661    -8.9970    38.276989     24.1990     45.1904     51.2618\n  4500   6800     0.0000    50.4661    -8.9970    38.519884     24.3904     45.0874     51.2618\n  4500   7000     0.0000    50.4661    -8.9970    38.759367     24.5786     44.9851     51.2618\n  4500   7200     0.0000    50.4661    -8.9970    38.995520     24.7638     44.8834     51.2618\n  4500   7400     0.0000    50.4661    -8.9970    39.228424     24.9461     44.7823     51.2618\n  4500   7600     0.0000    50.4661    -8.9970    39.458154     25.1254     44.6820     51.2618\n  4500   7800     0.0000    50.4661    -8.9970    39.684784     25.3019     44.5822     51.2618\n  4500   8000     0.0000    50.4661    -8.9970    39.908385     25.4757     44.4831     51.2618\n  4500   8200     0.0000    50.4661    -8.9970    40.129027     25.6469     44.3847     51.2618\n  4500   8400     0.0000    50.4661    -8.9970    40.346774     25.8153     44.2869     51.2618\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180518_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.4958283      -14.4402710        1.7338821   m   m   m\ninitial_baseline_rate:          0.0000000        0.0527872       -0.0035656   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -14.1367727        1.5899685   m   m   m\nprecision_baseline_rate:        0.0000000        0.0555532       -0.0044993   m/s m/s m/s\nunwrap_phase_constant:            0.00028     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180518_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.496    -14.440      1.734\norbit baseline rate   (TCN) (m/s):  0.000e+00  5.279e-02 -3.566e-03\n\nbaseline vector (TCN)   (m):      0.000    -14.137      1.590\nbaseline rate   (TCN) (m/s):  0.000e+00  5.555e-02 -4.499e-03\n\nSLC-1 center baseline length (m):    14.2259\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -14.6553     1.6320    27.497816     -5.3190    -13.7532     14.7459\n     0    200     0.0000   -14.6553     1.6320    27.940057     -5.4250    -13.7117     14.7459\n     0    400     0.0000   -14.6553     1.6320    28.371664     -5.5281    -13.6704     14.7459\n     0    600     0.0000   -14.6553     1.6320    28.793160     -5.6285    -13.6294     14.7459\n     0    800     0.0000   -14.6553     1.6320    29.205025     -5.7263    -13.5886     14.7459\n     0   1000     0.0000   -14.6553     1.6320    29.607701     -5.8217    -13.5480     14.7459\n     0   1200     0.0000   -14.6553     1.6320    30.001600     -5.9147    -13.5077     14.7459\n     0   1400     0.0000   -14.6553     1.6320    30.387101     -6.0054    -13.4676     14.7459\n     0   1600     0.0000   -14.6553     1.6320    30.764557     -6.0940    -13.4277     14.7459\n     0   1800     0.0000   -14.6553     1.6320    31.134300     -6.1806    -13.3881     14.7459\n     0   2000     0.0000   -14.6553     1.6320    31.496635     -6.2651    -13.3488     14.7459\n     0   2200     0.0000   -14.6553     1.6320    31.851850     -6.3477    -13.3097     14.7459\n     0   2400     0.0000   -14.6553     1.6320    32.200217     -6.4285    -13.2708     14.7459\n     0   2600     0.0000   -14.6553     1.6320    32.541986     -6.5076    -13.2322     14.7459\n     0   2800     0.0000   -14.6553     1.6320    32.877397     -6.5849    -13.1939     14.7459\n     0   3000     0.0000   -14.6553     1.6320    33.206674     -6.6607    -13.1559     14.7459\n     0   3200     0.0000   -14.6553     1.6320    33.530029     -6.7348    -13.1181     14.7459\n     0   3400     0.0000   -14.6553     1.6320    33.847660     -6.8074    -13.0805     14.7459\n     0   3600     0.0000   -14.6553     1.6320    34.159757     -6.8786    -13.0432     14.7459\n     0   3800     0.0000   -14.6553     1.6320    34.466499     -6.9483    -13.0062     14.7459\n     0   4000     0.0000   -14.6553     1.6320    34.768056     -7.0167    -12.9695     14.7459\n     0   4200     0.0000   -14.6553     1.6320    35.064588     -7.0837    -12.9330     14.7459\n     0   4400     0.0000   -14.6553     1.6320    35.356248     -7.1494    -12.8968     14.7459\n     0   4600     0.0000   -14.6553     1.6320    35.643183     -7.2139    -12.8608     14.7459\n     0   4800     0.0000   -14.6553     1.6320    35.925531     -7.2772    -12.8251     14.7459\n     0   5000     0.0000   -14.6553     1.6320    36.203424     -7.3393    -12.7897     14.7459\n     0   5200     0.0000   -14.6553     1.6320    36.476989     -7.4003    -12.7545     14.7459\n     0   5400     0.0000   -14.6553     1.6320    36.746345     -7.4602    -12.7195     14.7459\n     0   5600     0.0000   -14.6553     1.6320    37.011608     -7.5190    -12.6849     14.7459\n     0   5800     0.0000   -14.6553     1.6320    37.272888     -7.5768    -12.6504     14.7459\n     0   6000     0.0000   -14.6553     1.6320    37.530291     -7.6335    -12.6163     14.7459\n     0   6200     0.0000   -14.6553     1.6320    37.783917     -7.6893    -12.5824     14.7459\n     0   6400     0.0000   -14.6553     1.6320    38.033864     -7.7441    -12.5487     14.7459\n     0   6600     0.0000   -14.6553     1.6320    38.280225     -7.7980    -12.5153     14.7459\n     0   6800     0.0000   -14.6553     1.6320    38.523089     -7.8510    -12.4821     14.7459\n     0   7000     0.0000   -14.6553     1.6320    38.762542     -7.9031    -12.4492     14.7459\n     0   7200     0.0000   -14.6553     1.6320    38.998666     -7.9543    -12.4165     14.7459\n     0   7400     0.0000   -14.6553     1.6320    39.231541     -8.0047    -12.3841     14.7459\n     0   7600     0.0000   -14.6553     1.6320    39.461243     -8.0543    -12.3519     14.7459\n     0   7800     0.0000   -14.6553     1.6320    39.687846     -8.1031    -12.3199     14.7459\n     0   8000     0.0000   -14.6553     1.6320    39.911421     -8.1511    -12.2882     14.7459\n     0   8200     0.0000   -14.6553     1.6320    40.132036     -8.1984    -12.2568     14.7459\n     0   8400     0.0000   -14.6553     1.6320    40.349758     -8.2449    -12.2255     14.7459\n   500      0     0.0000   -14.5411     1.6227    27.496883     -5.2742    -13.6477     14.6314\n   500    200     0.0000   -14.5411     1.6227    27.939140     -5.3794    -13.6066     14.6314\n   500    400     0.0000   -14.5411     1.6227    28.370762     -5.4818    -13.5657     14.6314\n   500    600     0.0000   -14.5411     1.6227    28.792272     -5.5814    -13.5250     14.6314\n   500    800     0.0000   -14.5411     1.6227    29.204150     -5.6785    -13.4845     14.6314\n   500   1000     0.0000   -14.5411     1.6227    29.606840     -5.7731    -13.4442     14.6314\n   500   1200     0.0000   -14.5411     1.6227    30.000750     -5.8654    -13.4042     14.6314\n   500   1400     0.0000   -14.5411     1.6227    30.386263     -5.9555    -13.3645     14.6314\n   500   1600     0.0000   -14.5411     1.6227    30.763731     -6.0434    -13.3249     14.6314\n   500   1800     0.0000   -14.5411     1.6227    31.133484     -6.1292    -13.2857     14.6314\n   500   2000     0.0000   -14.5411     1.6227    31.495829     -6.2131    -13.2466     14.6314\n   500   2200     0.0000   -14.5411     1.6227    31.851055     -6.2952    -13.2079     14.6314\n   500   2400     0.0000   -14.5411     1.6227    32.199431     -6.3753    -13.1693     14.6314\n   500   2600     0.0000   -14.5411     1.6227    32.541209     -6.4538    -13.1311     14.6314\n   500   2800     0.0000   -14.5411     1.6227    32.876630     -6.5305    -13.0931     14.6314\n   500   3000     0.0000   -14.5411     1.6227    33.205915     -6.6057    -13.0553     14.6314\n   500   3200     0.0000   -14.5411     1.6227    33.529278     -6.6793    -13.0178     14.6314\n   500   3400     0.0000   -14.5411     1.6227    33.846917     -6.7513    -12.9806     14.6314\n   500   3600     0.0000   -14.5411     1.6227    34.159022     -6.8219    -12.9436     14.6314\n   500   3800     0.0000   -14.5411     1.6227    34.465771     -6.8911    -12.9069     14.6314\n   500   4000     0.0000   -14.5411     1.6227    34.767335     -6.9590    -12.8705     14.6314\n   500   4200     0.0000   -14.5411     1.6227    35.063874     -7.0255    -12.8343     14.6314\n   500   4400     0.0000   -14.5411     1.6227    35.355542     -7.0907    -12.7984     14.6314\n   500   4600     0.0000   -14.5411     1.6227    35.642483     -7.1547    -12.7627     14.6314\n   500   4800     0.0000   -14.5411     1.6227    35.924838     -7.2176    -12.7273     14.6314\n   500   5000     0.0000   -14.5411     1.6227    36.202737     -7.2792    -12.6921     14.6314\n   500   5200     0.0000   -14.5411     1.6227    36.476308     -7.3397    -12.6572     14.6314\n   500   5400     0.0000   -14.5411     1.6227    36.745670     -7.3991    -12.6226     14.6314\n   500   5600     0.0000   -14.5411     1.6227    37.010938     -7.4575    -12.5882     14.6314\n   500   5800     0.0000   -14.5411     1.6227    37.272224     -7.5148    -12.5541     14.6314\n   500   6000     0.0000   -14.5411     1.6227    37.529632     -7.5712    -12.5202     14.6314\n   500   6200     0.0000   -14.5411     1.6227    37.783264     -7.6265    -12.4865     14.6314\n   500   6400     0.0000   -14.5411     1.6227    38.033217     -7.6809    -12.4531     14.6314\n   500   6600     0.0000   -14.5411     1.6227    38.279583     -7.7344    -12.4200     14.6314\n   500   6800     0.0000   -14.5411     1.6227    38.522452     -7.7870    -12.3871     14.6314\n   500   7000     0.0000   -14.5411     1.6227    38.761909     -7.8387    -12.3545     14.6314\n   500   7200     0.0000   -14.5411     1.6227    38.998038     -7.8895    -12.3220     14.6314\n   500   7400     0.0000   -14.5411     1.6227    39.230918     -7.9395    -12.2899     14.6314\n   500   7600     0.0000   -14.5411     1.6227    39.460624     -7.9887    -12.2579     14.6314\n   500   7800     0.0000   -14.5411     1.6227    39.687232     -8.0372    -12.2263     14.6314\n   500   8000     0.0000   -14.5411     1.6227    39.910811     -8.0848    -12.1948     14.6314\n   500   8200     0.0000   -14.5411     1.6227    40.131431     -8.1317    -12.1636     14.6314\n   500   8400     0.0000   -14.5411     1.6227    40.349156     -8.1779    -12.1326     14.6314\n  1000      0     0.0000   -14.4269     1.6135    27.495930     -5.2295    -13.5422     14.5169\n  1000    200     0.0000   -14.4269     1.6135    27.938203     -5.3339    -13.5014     14.5169\n  1000    400     0.0000   -14.4269     1.6135    28.369840     -5.4354    -13.4609     14.5169\n  1000    600     0.0000   -14.4269     1.6135    28.791365     -5.5343    -13.4205     14.5169\n  1000    800     0.0000   -14.4269     1.6135    29.203256     -5.6306    -13.3804     14.5169\n  1000   1000     0.0000   -14.4269     1.6135    29.605959     -5.7245    -13.3405     14.5169\n  1000   1200     0.0000   -14.4269     1.6135    29.999882     -5.8161    -13.3008     14.5169\n  1000   1400     0.0000   -14.4269     1.6135    30.385407     -5.9055    -13.2614     14.5169\n  1000   1600     0.0000   -14.4269     1.6135    30.762886     -5.9927    -13.2222     14.5169\n  1000   1800     0.0000   -14.4269     1.6135    31.132650     -6.0779    -13.1832     14.5169\n  1000   2000     0.0000   -14.4269     1.6135    31.495006     -6.1612    -13.1445     14.5169\n  1000   2200     0.0000   -14.4269     1.6135    31.850242     -6.2426    -13.1061     14.5169\n  1000   2400     0.0000   -14.4269     1.6135    32.198628     -6.3221    -13.0679     14.5169\n  1000   2600     0.0000   -14.4269     1.6135    32.540416     -6.4000    -13.0299     14.5169\n  1000   2800     0.0000   -14.4269     1.6135    32.875845     -6.4762    -12.9922     14.5169\n  1000   3000     0.0000   -14.4269     1.6135    33.205140     -6.5507    -12.9548     14.5169\n  1000   3200     0.0000   -14.4269     1.6135    33.528511     -6.6237    -12.9176     14.5169\n  1000   3400     0.0000   -14.4269     1.6135    33.846158     -6.6952    -12.8807     14.5169\n  1000   3600     0.0000   -14.4269     1.6135    34.158271     -6.7653    -12.8441     14.5169\n  1000   3800     0.0000   -14.4269     1.6135    34.465028     -6.8340    -12.8076     14.5169\n  1000   4000     0.0000   -14.4269     1.6135    34.766600     -6.9013    -12.7715     14.5169\n  1000   4200     0.0000   -14.4269     1.6135    35.063146     -6.9673    -12.7356     14.5169\n  1000   4400     0.0000   -14.4269     1.6135    35.354821     -7.0320    -12.7000     14.5169\n  1000   4600     0.0000   -14.4269     1.6135    35.641769     -7.0956    -12.6646     14.5169\n  1000   4800     0.0000   -14.4269     1.6135    35.924130     -7.1579    -12.6295     14.5169\n  1000   5000     0.0000   -14.4269     1.6135    36.202036     -7.2191    -12.5946     14.5169\n  1000   5200     0.0000   -14.4269     1.6135    36.475612     -7.2791    -12.5600     14.5169\n  1000   5400     0.0000   -14.4269     1.6135    36.744981     -7.3381    -12.5256     14.5169\n  1000   5600     0.0000   -14.4269     1.6135    37.010255     -7.3960    -12.4915     14.5169\n  1000   5800     0.0000   -14.4269     1.6135    37.271547     -7.4529    -12.4577     14.5169\n  1000   6000     0.0000   -14.4269     1.6135    37.528961     -7.5088    -12.4241     14.5169\n  1000   6200     0.0000   -14.4269     1.6135    37.782598     -7.5637    -12.3907     14.5169\n  1000   6400     0.0000   -14.4269     1.6135    38.032556     -7.6177    -12.3576     14.5169\n  1000   6600     0.0000   -14.4269     1.6135    38.278927     -7.6708    -12.3247     14.5169\n  1000   6800     0.0000   -14.4269     1.6135    38.521801     -7.7229    -12.2921     14.5169\n  1000   7000     0.0000   -14.4269     1.6135    38.761264     -7.7742    -12.2597     14.5169\n  1000   7200     0.0000   -14.4269     1.6135    38.997398     -7.8247    -12.2276     14.5169\n  1000   7400     0.0000   -14.4269     1.6135    39.230282     -7.8743    -12.1957     14.5169\n  1000   7600     0.0000   -14.4269     1.6135    39.459993     -7.9232    -12.1640     14.5169\n  1000   7800     0.0000   -14.4269     1.6135    39.686605     -7.9712    -12.1326     14.5169\n  1000   8000     0.0000   -14.4269     1.6135    39.910189     -8.0185    -12.1014     14.5169\n  1000   8200     0.0000   -14.4269     1.6135    40.130813     -8.0650    -12.0704     14.5169\n  1000   8400     0.0000   -14.4269     1.6135    40.348543     -8.1108    -12.0397     14.5169\n  1500      0     0.0000   -14.3127     1.6042    27.494956     -5.1847    -13.4367     14.4024\n  1500    200     0.0000   -14.3127     1.6042    27.937245     -5.2883    -13.3963     14.4024\n  1500    400     0.0000   -14.3127     1.6042    28.368898     -5.3891    -13.3561     14.4024\n  1500    600     0.0000   -14.3127     1.6042    28.790438     -5.4872    -13.3161     14.4024\n  1500    800     0.0000   -14.3127     1.6042    29.202344     -5.5828    -13.2763     14.4024\n  1500   1000     0.0000   -14.3127     1.6042    29.605059     -5.6760    -13.2367     14.4024\n  1500   1200     0.0000   -14.3127     1.6042    29.998996     -5.7668    -13.1974     14.4024\n  1500   1400     0.0000   -14.3127     1.6042    30.384532     -5.8555    -13.1583     14.4024\n  1500   1600     0.0000   -14.3127     1.6042    30.762024     -5.9421    -13.1194     14.4024\n  1500   1800     0.0000   -14.3127     1.6042    31.131799     -6.0266    -13.0808     14.4024\n  1500   2000     0.0000   -14.3127     1.6042    31.494166     -6.1092    -13.0424     14.4024\n  1500   2200     0.0000   -14.3127     1.6042    31.849412     -6.1900    -13.0043     14.4024\n  1500   2400     0.0000   -14.3127     1.6042    32.197808     -6.2689    -12.9664     14.4024\n  1500   2600     0.0000   -14.3127     1.6042    32.539606     -6.3462    -12.9288     14.4024\n  1500   2800     0.0000   -14.3127     1.6042    32.875045     -6.4218    -12.8914     14.4024\n  1500   3000     0.0000   -14.3127     1.6042    33.204348     -6.4957    -12.8543     14.4024\n  1500   3200     0.0000   -14.3127     1.6042    33.527728     -6.5682    -12.8174     14.4024\n  1500   3400     0.0000   -14.3127     1.6042    33.845384     -6.6391    -12.7808     14.4024\n  1500   3600     0.0000   -14.3127     1.6042    34.157505     -6.7087    -12.7445     14.4024\n  1500   3800     0.0000   -14.3127     1.6042    34.464270     -6.7768    -12.7084     14.4024\n  1500   4000     0.0000   -14.3127     1.6042    34.765849     -6.8436    -12.6725     14.4024\n  1500   4200     0.0000   -14.3127     1.6042    35.062403     -6.9091    -12.6369     14.4024\n  1500   4400     0.0000   -14.3127     1.6042    35.354084     -6.9733    -12.6016     14.4024\n  1500   4600     0.0000   -14.3127     1.6042    35.641040     -7.0364    -12.5665     14.4024\n  1500   4800     0.0000   -14.3127     1.6042    35.923408     -7.0982    -12.5317     14.4024\n  1500   5000     0.0000   -14.3127     1.6042    36.201320     -7.1589    -12.4971     14.4024\n  1500   5200     0.0000   -14.3127     1.6042    36.474903     -7.2185    -12.4628     14.4024\n  1500   5400     0.0000   -14.3127     1.6042    36.744278     -7.2770    -12.4287     14.4024\n  1500   5600     0.0000   -14.3127     1.6042    37.009558     -7.3345    -12.3949     14.4024\n  1500   5800     0.0000   -14.3127     1.6042    37.270856     -7.3909    -12.3613     14.4024\n  1500   6000     0.0000   -14.3127     1.6042    37.528276     -7.4464    -12.3280     14.4024\n  1500   6200     0.0000   -14.3127     1.6042    37.781919     -7.5009    -12.2949     14.4024\n  1500   6400     0.0000   -14.3127     1.6042    38.031882     -7.5545    -12.2620     14.4024\n  1500   6600     0.0000   -14.3127     1.6042    38.278259     -7.6071    -12.2294     14.4024\n  1500   6800     0.0000   -14.3127     1.6042    38.521138     -7.6589    -12.1971     14.4024\n  1500   7000     0.0000   -14.3127     1.6042    38.760605     -7.7098    -12.1650     14.4024\n  1500   7200     0.0000   -14.3127     1.6042    38.996744     -7.7599    -12.1331     14.4024\n  1500   7400     0.0000   -14.3127     1.6042    39.229633     -7.8091    -12.1014     14.4024\n  1500   7600     0.0000   -14.3127     1.6042    39.459350     -7.8576    -12.0700     14.4024\n  1500   7800     0.0000   -14.3127     1.6042    39.685966     -7.9053    -12.0389     14.4024\n  1500   8000     0.0000   -14.3127     1.6042    39.909555     -7.9522    -12.0079     14.4024\n  1500   8200     0.0000   -14.3127     1.6042    40.130183     -7.9984    -11.9772     14.4024\n  1500   8400     0.0000   -14.3127     1.6042    40.347918     -8.0438    -11.9467     14.4024\n  2000      0     0.0000   -14.1986     1.5950    27.493962     -5.1400    -13.3313     14.2879\n  2000    200     0.0000   -14.1986     1.5950    27.936268     -5.2427    -13.2912     14.2879\n  2000    400     0.0000   -14.1986     1.5950    28.367937     -5.3427    -13.2513     14.2879\n  2000    600     0.0000   -14.1986     1.5950    28.789491     -5.4401    -13.2116     14.2879\n  2000    800     0.0000   -14.1986     1.5950    29.201412     -5.5349    -13.1722     14.2879\n  2000   1000     0.0000   -14.1986     1.5950    29.604141     -5.6274    -13.1330     14.2879\n  2000   1200     0.0000   -14.1986     1.5950    29.998091     -5.7175    -13.0940     14.2879\n  2000   1400     0.0000   -14.1986     1.5950    30.383640     -5.8055    -13.0552     14.2879\n  2000   1600     0.0000   -14.1986     1.5950    30.761143     -5.8914    -13.0167     14.2879\n  2000   1800     0.0000   -14.1986     1.5950    31.130930     -5.9753    -12.9784     14.2879\n  2000   2000     0.0000   -14.1986     1.5950    31.493308     -6.0573    -12.9403     14.2879\n  2000   2200     0.0000   -14.1986     1.5950    31.848565     -6.1374    -12.9025     14.2879\n  2000   2400     0.0000   -14.1986     1.5950    32.196971     -6.2157    -12.8649     14.2879\n  2000   2600     0.0000   -14.1986     1.5950    32.538779     -6.2924    -12.8276     14.2879\n  2000   2800     0.0000   -14.1986     1.5950    32.874228     -6.3674    -12.7906     14.2879\n  2000   3000     0.0000   -14.1986     1.5950    33.203540     -6.4408    -12.7538     14.2879\n  2000   3200     0.0000   -14.1986     1.5950    33.526929     -6.5126    -12.7172     14.2879\n  2000   3400     0.0000   -14.1986     1.5950    33.844594     -6.5831    -12.6809     14.2879\n  2000   3600     0.0000   -14.1986     1.5950    34.156723     -6.6520    -12.6449     14.2879\n  2000   3800     0.0000   -14.1986     1.5950    34.463496     -6.7196    -12.6091     14.2879\n  2000   4000     0.0000   -14.1986     1.5950    34.765083     -6.7859    -12.5735     14.2879\n  2000   4200     0.0000   -14.1986     1.5950    35.061644     -6.8509    -12.5382     14.2879\n  2000   4400     0.0000   -14.1986     1.5950    35.353334     -6.9147    -12.5032     14.2879\n  2000   4600     0.0000   -14.1986     1.5950    35.640296     -6.9772    -12.4684     14.2879\n  2000   4800     0.0000   -14.1986     1.5950    35.922671     -7.0386    -12.4339     14.2879\n  2000   5000     0.0000   -14.1986     1.5950    36.200590     -7.0988    -12.3996     14.2879\n  2000   5200     0.0000   -14.1986     1.5950    36.474180     -7.1579    -12.3655     14.2879\n  2000   5400     0.0000   -14.1986     1.5950    36.743561     -7.2160    -12.3317     14.2879\n  2000   5600     0.0000   -14.1986     1.5950    37.008848     -7.2730    -12.2982     14.2879\n  2000   5800     0.0000   -14.1986     1.5950    37.270151     -7.3290    -12.2649     14.2879\n  2000   6000     0.0000   -14.1986     1.5950    37.527577     -7.3840    -12.2319     14.2879\n  2000   6200     0.0000   -14.1986     1.5950    37.781226     -7.4381    -12.1990     14.2879\n  2000   6400     0.0000   -14.1986     1.5950    38.031195     -7.4913    -12.1665     14.2879\n  2000   6600     0.0000   -14.1986     1.5950    38.277577     -7.5435    -12.1342     14.2879\n  2000   6800     0.0000   -14.1986     1.5950    38.520461     -7.5949    -12.1021     14.2879\n  2000   7000     0.0000   -14.1986     1.5950    38.759934     -7.6454    -12.0702     14.2879\n  2000   7200     0.0000   -14.1986     1.5950    38.996078     -7.6951    -12.0386     14.2879\n  2000   7400     0.0000   -14.1986     1.5950    39.228972     -7.7439    -12.0072     14.2879\n  2000   7600     0.0000   -14.1986     1.5950    39.458693     -7.7920    -11.9761     14.2879\n  2000   7800     0.0000   -14.1986     1.5950    39.685315     -7.8393    -11.9452     14.2879\n  2000   8000     0.0000   -14.1986     1.5950    39.908908     -7.8859    -11.9145     14.2879\n  2000   8200     0.0000   -14.1986     1.5950    40.129541     -7.9317    -11.8840     14.2879\n  2000   8400     0.0000   -14.1986     1.5950    40.347280     -7.9768    -11.8538     14.2879\n  2500      0     0.0000   -14.0844     1.5857    27.492947     -5.0952    -13.2258     14.1733\n  2500    200     0.0000   -14.0844     1.5857    27.935270     -5.1972    -13.1860     14.1733\n  2500    400     0.0000   -14.0844     1.5857    28.366955     -5.2964    -13.1465     14.1733\n  2500    600     0.0000   -14.0844     1.5857    28.788525     -5.3930    -13.1072     14.1733\n  2500    800     0.0000   -14.0844     1.5857    29.200461     -5.4871    -13.0681     14.1733\n  2500   1000     0.0000   -14.0844     1.5857    29.603204     -5.5788    -13.0292     14.1733\n  2500   1200     0.0000   -14.0844     1.5857    29.997167     -5.6682    -12.9905     14.1733\n  2500   1400     0.0000   -14.0844     1.5857    30.382730     -5.7555    -12.9521     14.1733\n  2500   1600     0.0000   -14.0844     1.5857    30.760245     -5.8407    -12.9139     14.1733\n  2500   1800     0.0000   -14.0844     1.5857    31.130044     -5.9240    -12.8759     14.1733\n  2500   2000     0.0000   -14.0844     1.5857    31.492434     -6.0053    -12.8382     14.1733\n  2500   2200     0.0000   -14.0844     1.5857    31.847702     -6.0848    -12.8007     14.1733\n  2500   2400     0.0000   -14.0844     1.5857    32.196118     -6.1625    -12.7635     14.1733\n  2500   2600     0.0000   -14.0844     1.5857    32.537936     -6.2385    -12.7265     14.1733\n  2500   2800     0.0000   -14.0844     1.5857    32.873394     -6.3130    -12.6897     14.1733\n  2500   3000     0.0000   -14.0844     1.5857    33.202716     -6.3858    -12.6532     14.1733\n  2500   3200     0.0000   -14.0844     1.5857    33.526114     -6.4571    -12.6170     14.1733\n  2500   3400     0.0000   -14.0844     1.5857    33.843788     -6.5270    -12.5810     14.1733\n  2500   3600     0.0000   -14.0844     1.5857    34.155926     -6.5954    -12.5453     14.1733\n  2500   3800     0.0000   -14.0844     1.5857    34.462707     -6.6625    -12.5098     14.1733\n  2500   4000     0.0000   -14.0844     1.5857    34.764302     -6.7282    -12.4745     14.1733\n  2500   4200     0.0000   -14.0844     1.5857    35.060871     -6.7927    -12.4395     14.1733\n  2500   4400     0.0000   -14.0844     1.5857    35.352568     -6.8560    -12.4048     14.1733\n  2500   4600     0.0000   -14.0844     1.5857    35.639538     -6.9180    -12.3703     14.1733\n  2500   4800     0.0000   -14.0844     1.5857    35.921920     -6.9789    -12.3361     14.1733\n  2500   5000     0.0000   -14.0844     1.5857    36.199846     -7.0386    -12.3021     14.1733\n  2500   5200     0.0000   -14.0844     1.5857    36.473442     -7.0973    -12.2683     14.1733\n  2500   5400     0.0000   -14.0844     1.5857    36.742830     -7.1549    -12.2348     14.1733\n  2500   5600     0.0000   -14.0844     1.5857    37.008123     -7.2115    -12.2015     14.1733\n  2500   5800     0.0000   -14.0844     1.5857    37.269433     -7.2671    -12.1685     14.1733\n  2500   6000     0.0000   -14.0844     1.5857    37.526865     -7.3217    -12.1358     14.1733\n  2500   6200     0.0000   -14.0844     1.5857    37.780519     -7.3753    -12.1032     14.1733\n  2500   6400     0.0000   -14.0844     1.5857    38.030494     -7.4280    -12.0709     14.1733\n  2500   6600     0.0000   -14.0844     1.5857    38.276882     -7.4799    -12.0389     14.1733\n  2500   6800     0.0000   -14.0844     1.5857    38.519772     -7.5309    -12.0071     14.1733\n  2500   7000     0.0000   -14.0844     1.5857    38.759250     -7.5810    -11.9755     14.1733\n  2500   7200     0.0000   -14.0844     1.5857    38.995399     -7.6303    -11.9441     14.1733\n  2500   7400     0.0000   -14.0844     1.5857    39.228299     -7.6788    -11.9130     14.1733\n  2500   7600     0.0000   -14.0844     1.5857    39.458025     -7.7265    -11.8821     14.1733\n  2500   7800     0.0000   -14.0844     1.5857    39.684651     -7.7734    -11.8515     14.1733\n  2500   8000     0.0000   -14.0844     1.5857    39.908249     -7.8196    -11.8211     14.1733\n  2500   8200     0.0000   -14.0844     1.5857    40.128887     -7.8650    -11.7909     14.1733\n  2500   8400     0.0000   -14.0844     1.5857    40.346630     -7.9098    -11.7609     14.1733\n  3000      0     0.0000   -13.9702     1.5765    27.491911     -5.0505    -13.1203     14.0588\n  3000    200     0.0000   -13.9702     1.5765    27.934252     -5.1516    -13.0809     14.0588\n  3000    400     0.0000   -13.9702     1.5765    28.365954     -5.2500    -13.0417     14.0588\n  3000    600     0.0000   -13.9702     1.5765    28.787540     -5.3459    -13.0028     14.0588\n  3000    800     0.0000   -13.9702     1.5765    29.199491     -5.4392    -12.9640     14.0588\n  3000   1000     0.0000   -13.9702     1.5765    29.602249     -5.5302    -12.9254     14.0588\n  3000   1200     0.0000   -13.9702     1.5765    29.996226     -5.6190    -12.8871     14.0588\n  3000   1400     0.0000   -13.9702     1.5765    30.381801     -5.7055    -12.8490     14.0588\n  3000   1600     0.0000   -13.9702     1.5765    30.759330     -5.7901    -12.8111     14.0588\n  3000   1800     0.0000   -13.9702     1.5765    31.129141     -5.8727    -12.7735     14.0588\n  3000   2000     0.0000   -13.9702     1.5765    31.491542     -5.9533    -12.7361     14.0588\n  3000   2200     0.0000   -13.9702     1.5765    31.846821     -6.0322    -12.6989     14.0588\n  3000   2400     0.0000   -13.9702     1.5765    32.195248     -6.1093    -12.6620     14.0588\n  3000   2600     0.0000   -13.9702     1.5765    32.537077     -6.1847    -12.6253     14.0588\n  3000   2800     0.0000   -13.9702     1.5765    32.872545     -6.2586    -12.5889     14.0588\n  3000   3000     0.0000   -13.9702     1.5765    33.201877     -6.3308    -12.5527     14.0588\n  3000   3200     0.0000   -13.9702     1.5765    33.525284     -6.4016    -12.5168     14.0588\n  3000   3400     0.0000   -13.9702     1.5765    33.842966     -6.4709    -12.4811     14.0588\n  3000   3600     0.0000   -13.9702     1.5765    34.155113     -6.5388    -12.4457     14.0588\n  3000   3800     0.0000   -13.9702     1.5765    34.461903     -6.6053    -12.4105     14.0588\n  3000   4000     0.0000   -13.9702     1.5765    34.763506     -6.6705    -12.3755     14.0588\n  3000   4200     0.0000   -13.9702     1.5765    35.060083     -6.7345    -12.3408     14.0588\n  3000   4400     0.0000   -13.9702     1.5765    35.351788     -6.7973    -12.3064     14.0588\n  3000   4600     0.0000   -13.9702     1.5765    35.638765     -6.8588    -12.2722     14.0588\n  3000   4800     0.0000   -13.9702     1.5765    35.921154     -6.9192    -12.2382     14.0588\n  3000   5000     0.0000   -13.9702     1.5765    36.199088     -6.9785    -12.2045     14.0588\n  3000   5200     0.0000   -13.9702     1.5765    36.472691     -7.0367    -12.1711     14.0588\n  3000   5400     0.0000   -13.9702     1.5765    36.742085     -7.0938    -12.1379     14.0588\n  3000   5600     0.0000   -13.9702     1.5765    37.007385     -7.1500    -12.1049     14.0588\n  3000   5800     0.0000   -13.9702     1.5765    37.268701     -7.2051    -12.0721     14.0588\n  3000   6000     0.0000   -13.9702     1.5765    37.526139     -7.2593    -12.0396     14.0588\n  3000   6200     0.0000   -13.9702     1.5765    37.779800     -7.3125    -12.0074     14.0588\n  3000   6400     0.0000   -13.9702     1.5765    38.029781     -7.3648    -11.9754     14.0588\n  3000   6600     0.0000   -13.9702     1.5765    38.276174     -7.4163    -11.9436     14.0588\n  3000   6800     0.0000   -13.9702     1.5765    38.519070     -7.4668    -11.9120     14.0588\n  3000   7000     0.0000   -13.9702     1.5765    38.758554     -7.5165    -11.8807     14.0588\n  3000   7200     0.0000   -13.9702     1.5765    38.994708     -7.5655    -11.8496     14.0588\n  3000   7400     0.0000   -13.9702     1.5765    39.227613     -7.6136    -11.8188     14.0588\n  3000   7600     0.0000   -13.9702     1.5765    39.457344     -7.6609    -11.7882     14.0588\n  3000   7800     0.0000   -13.9702     1.5765    39.683975     -7.7075    -11.7578     14.0588\n  3000   8000     0.0000   -13.9702     1.5765    39.907578     -7.7533    -11.7276     14.0588\n  3000   8200     0.0000   -13.9702     1.5765    40.128221     -7.7984    -11.6977     14.0588\n  3000   8400     0.0000   -13.9702     1.5765    40.345969     -7.8428    -11.6679     14.0588\n  3500      0     0.0000   -13.8560     1.5672    27.490855     -5.0057    -13.0148     13.9443\n  3500    200     0.0000   -13.8560     1.5672    27.933214     -5.1061    -12.9758     13.9443\n  3500    400     0.0000   -13.8560     1.5672    28.364934     -5.2037    -12.9370     13.9443\n  3500    600     0.0000   -13.8560     1.5672    28.786536     -5.2987    -12.8983     13.9443\n  3500    800     0.0000   -13.8560     1.5672    29.198502     -5.3914    -12.8599     13.9443\n  3500   1000     0.0000   -13.8560     1.5672    29.601275     -5.4816    -12.8217     13.9443\n  3500   1200     0.0000   -13.8560     1.5672    29.995266     -5.5697    -12.7837     13.9443\n  3500   1400     0.0000   -13.8560     1.5672    30.380855     -5.6556    -12.7459     13.9443\n  3500   1600     0.0000   -13.8560     1.5672    30.758396     -5.7394    -12.7084     13.9443\n  3500   1800     0.0000   -13.8560     1.5672    31.128219     -5.8213    -12.6710     13.9443\n  3500   2000     0.0000   -13.8560     1.5672    31.490633     -5.9014    -12.6340     13.9443\n  3500   2200     0.0000   -13.8560     1.5672    31.845923     -5.9796    -12.5971     13.9443\n  3500   2400     0.0000   -13.8560     1.5672    32.194362     -6.0561    -12.5605     13.9443\n  3500   2600     0.0000   -13.8560     1.5672    32.536201     -6.1309    -12.5242     13.9443\n  3500   2800     0.0000   -13.8560     1.5672    32.871679     -6.2041    -12.4881     13.9443\n  3500   3000     0.0000   -13.8560     1.5672    33.201021     -6.2758    -12.4522     13.9443\n  3500   3200     0.0000   -13.8560     1.5672    33.524438     -6.3460    -12.4166     13.9443\n  3500   3400     0.0000   -13.8560     1.5672    33.842129     -6.4148    -12.3812     13.9443\n  3500   3600     0.0000   -13.8560     1.5672    34.154285     -6.4821    -12.3461     13.9443\n  3500   3800     0.0000   -13.8560     1.5672    34.461084     -6.5481    -12.3112     13.9443\n  3500   4000     0.0000   -13.8560     1.5672    34.762695     -6.6129    -12.2765     13.9443\n  3500   4200     0.0000   -13.8560     1.5672    35.059281     -6.6763    -12.2422     13.9443\n  3500   4400     0.0000   -13.8560     1.5672    35.350993     -6.7386    -12.2080     13.9443\n  3500   4600     0.0000   -13.8560     1.5672    35.637978     -6.7996    -12.1741     13.9443\n  3500   4800     0.0000   -13.8560     1.5672    35.920375     -6.8595    -12.1404     13.9443\n  3500   5000     0.0000   -13.8560     1.5672    36.198315     -6.9184    -12.1070     13.9443\n  3500   5200     0.0000   -13.8560     1.5672    36.471925     -6.9761    -12.0738     13.9443\n  3500   5400     0.0000   -13.8560     1.5672    36.741326     -7.0328    -12.0409     13.9443\n  3500   5600     0.0000   -13.8560     1.5672    37.006633     -7.0885    -12.0082     13.9443\n  3500   5800     0.0000   -13.8560     1.5672    37.267956     -7.1432    -11.9758     13.9443\n  3500   6000     0.0000   -13.8560     1.5672    37.525400     -7.1969    -11.9435     13.9443\n  3500   6200     0.0000   -13.8560     1.5672    37.779067     -7.2497    -11.9116     13.9443\n  3500   6400     0.0000   -13.8560     1.5672    38.029054     -7.3016    -11.8798     13.9443\n  3500   6600     0.0000   -13.8560     1.5672    38.275453     -7.3526    -11.8483     13.9443\n  3500   6800     0.0000   -13.8560     1.5672    38.518355     -7.4028    -11.8170     13.9443\n  3500   7000     0.0000   -13.8560     1.5672    38.757844     -7.4521    -11.7860     13.9443\n  3500   7200     0.0000   -13.8560     1.5672    38.994004     -7.5006    -11.7552     13.9443\n  3500   7400     0.0000   -13.8560     1.5672    39.226914     -7.5484    -11.7246     13.9443\n  3500   7600     0.0000   -13.8560     1.5672    39.456650     -7.5953    -11.6942     13.9443\n  3500   7800     0.0000   -13.8560     1.5672    39.683287     -7.6415    -11.6641     13.9443\n  3500   8000     0.0000   -13.8560     1.5672    39.906895     -7.6870    -11.6342     13.9443\n  3500   8200     0.0000   -13.8560     1.5672    40.127542     -7.7317    -11.6045     13.9443\n  3500   8400     0.0000   -13.8560     1.5672    40.345295     -7.7758    -11.5750     13.9443\n  4000      0     0.0000   -13.7418     1.5580    27.489779     -4.9610    -12.9094     13.8298\n  4000    200     0.0000   -13.7418     1.5580    27.932157     -5.0605    -12.8707     13.8298\n  4000    400     0.0000   -13.7418     1.5580    28.363894     -5.1573    -12.8322     13.8298\n  4000    600     0.0000   -13.7418     1.5580    28.785512     -5.2516    -12.7939     13.8298\n  4000    800     0.0000   -13.7418     1.5580    29.197494     -5.3435    -12.7558     13.8298\n  4000   1000     0.0000   -13.7418     1.5580    29.600282     -5.4330    -12.7179     13.8298\n  4000   1200     0.0000   -13.7418     1.5580    29.994287     -5.5204    -12.6802     13.8298\n  4000   1400     0.0000   -13.7418     1.5580    30.379890     -5.6056    -12.6428     13.8298\n  4000   1600     0.0000   -13.7418     1.5580    30.757445     -5.6888    -12.6056     13.8298\n  4000   1800     0.0000   -13.7418     1.5580    31.127281     -5.7700    -12.5686     13.8298\n  4000   2000     0.0000   -13.7418     1.5580    31.489706     -5.8494    -12.5319     13.8298\n  4000   2200     0.0000   -13.7418     1.5580    31.845009     -5.9270    -12.4953     13.8298\n  4000   2400     0.0000   -13.7418     1.5580    32.193458     -6.0029    -12.4591     13.8298\n  4000   2600     0.0000   -13.7418     1.5580    32.535308     -6.0771    -12.4230     13.8298\n  4000   2800     0.0000   -13.7418     1.5580    32.870797     -6.1497    -12.3872     13.8298\n  4000   3000     0.0000   -13.7418     1.5580    33.200149     -6.2209    -12.3517     13.8298\n  4000   3200     0.0000   -13.7418     1.5580    33.523576     -6.2905    -12.3164     13.8298\n  4000   3400     0.0000   -13.7418     1.5580    33.841277     -6.3587    -12.2813     13.8298\n  4000   3600     0.0000   -13.7418     1.5580    34.153442     -6.4255    -12.2465     13.8298\n  4000   3800     0.0000   -13.7418     1.5580    34.460249     -6.4910    -12.2119     13.8298\n  4000   4000     0.0000   -13.7418     1.5580    34.761869     -6.5552    -12.1776     13.8298\n  4000   4200     0.0000   -13.7418     1.5580    35.058463     -6.6181    -12.1435     13.8298\n  4000   4400     0.0000   -13.7418     1.5580    35.350183     -6.6799    -12.1096     13.8298\n  4000   4600     0.0000   -13.7418     1.5580    35.637176     -6.7404    -12.0760     13.8298\n  4000   4800     0.0000   -13.7418     1.5580    35.919581     -6.7999    -12.0426     13.8298\n  4000   5000     0.0000   -13.7418     1.5580    36.197528     -6.8582    -12.0095     13.8298\n  4000   5200     0.0000   -13.7418     1.5580    36.471146     -6.9155    -11.9766     13.8298\n  4000   5400     0.0000   -13.7418     1.5580    36.740554     -6.9717    -11.9440     13.8298\n  4000   5600     0.0000   -13.7418     1.5580    37.005868     -7.0270    -11.9116     13.8298\n  4000   5800     0.0000   -13.7418     1.5580    37.267197     -7.0812    -11.8794     13.8298\n  4000   6000     0.0000   -13.7418     1.5580    37.524648     -7.1345    -11.8474     13.8298\n  4000   6200     0.0000   -13.7418     1.5580    37.778321     -7.1869    -11.8157     13.8298\n  4000   6400     0.0000   -13.7418     1.5580    38.028314     -7.2384    -11.7843     13.8298\n  4000   6600     0.0000   -13.7418     1.5580    38.274719     -7.2890    -11.7530     13.8298\n  4000   6800     0.0000   -13.7418     1.5580    38.517627     -7.3388    -11.7220     13.8298\n  4000   7000     0.0000   -13.7418     1.5580    38.757122     -7.3877    -11.6912     13.8298\n  4000   7200     0.0000   -13.7418     1.5580    38.993287     -7.4358    -11.6607     13.8298\n  4000   7400     0.0000   -13.7418     1.5580    39.226203     -7.4832    -11.6304     13.8298\n  4000   7600     0.0000   -13.7418     1.5580    39.455945     -7.5297    -11.6003     13.8298\n  4000   7800     0.0000   -13.7418     1.5580    39.682586     -7.5756    -11.5704     13.8298\n  4000   8000     0.0000   -13.7418     1.5580    39.906199     -7.6207    -11.5407     13.8298\n  4000   8200     0.0000   -13.7418     1.5580    40.126852     -7.6651    -11.5113     13.8298\n  4000   8400     0.0000   -13.7418     1.5580    40.344610     -7.7088    -11.4821     13.8298\n  4500      0     0.0000   -13.6276     1.5487    27.488683     -4.9162    -12.8039     13.7153\n  4500    200     0.0000   -13.6276     1.5487    27.931079     -5.0149    -12.7655     13.7153\n  4500    400     0.0000   -13.6276     1.5487    28.362834     -5.1110    -12.7274     13.7153\n  4500    600     0.0000   -13.6276     1.5487    28.784470     -5.2045    -12.6894     13.7153\n  4500    800     0.0000   -13.6276     1.5487    29.196467     -5.2956    -12.6517     13.7153\n  4500   1000     0.0000   -13.6276     1.5487    29.599271     -5.3844    -12.6141     13.7153\n  4500   1200     0.0000   -13.6276     1.5487    29.993291     -5.4711    -12.5768     13.7153\n  4500   1400     0.0000   -13.6276     1.5487    30.378908     -5.5556    -12.5397     13.7153\n  4500   1600     0.0000   -13.6276     1.5487    30.756476     -5.6381    -12.5028     13.7153\n  4500   1800     0.0000   -13.6276     1.5487    31.126325     -5.7187    -12.4662     13.7153\n  4500   2000     0.0000   -13.6276     1.5487    31.488763     -5.7974    -12.4297     13.7153\n  4500   2200     0.0000   -13.6276     1.5487    31.844077     -5.8744    -12.3936     13.7153\n  4500   2400     0.0000   -13.6276     1.5487    32.192539     -5.9497    -12.3576     13.7153\n  4500   2600     0.0000   -13.6276     1.5487    32.534400     -6.0233    -12.3219     13.7153\n  4500   2800     0.0000   -13.6276     1.5487    32.869899     -6.0953    -12.2864     13.7153\n  4500   3000     0.0000   -13.6276     1.5487    33.199262     -6.1659    -12.2512     13.7153\n  4500   3200     0.0000   -13.6276     1.5487    33.522698     -6.2349    -12.2162     13.7153\n  4500   3400     0.0000   -13.6276     1.5487    33.840409     -6.3026    -12.1814     13.7153\n  4500   3600     0.0000   -13.6276     1.5487    34.152583     -6.3689    -12.1469     13.7153\n  4500   3800     0.0000   -13.6276     1.5487    34.459400     -6.4338    -12.1126     13.7153\n  4500   4000     0.0000   -13.6276     1.5487    34.761029     -6.4975    -12.0786     13.7153\n  4500   4200     0.0000   -13.6276     1.5487    35.057631     -6.5599    -12.0448     13.7153\n  4500   4400     0.0000   -13.6276     1.5487    35.349359     -6.6212    -12.0112     13.7153\n  4500   4600     0.0000   -13.6276     1.5487    35.636360     -6.6812    -11.9779     13.7153\n  4500   4800     0.0000   -13.6276     1.5487    35.918772     -6.7402    -11.9448     13.7153\n  4500   5000     0.0000   -13.6276     1.5487    36.196728     -6.7981    -11.9120     13.7153\n  4500   5200     0.0000   -13.6276     1.5487    36.470353     -6.8549    -11.8794     13.7153\n  4500   5400     0.0000   -13.6276     1.5487    36.739768     -6.9107    -11.8470     13.7153\n  4500   5600     0.0000   -13.6276     1.5487    37.005088     -6.9655    -11.8149     13.7153\n  4500   5800     0.0000   -13.6276     1.5487    37.266425     -7.0193    -11.7830     13.7153\n  4500   6000     0.0000   -13.6276     1.5487    37.523882     -7.0721    -11.7513     13.7153\n  4500   6200     0.0000   -13.6276     1.5487    37.777562     -7.1241    -11.7199     13.7153\n  4500   6400     0.0000   -13.6276     1.5487    38.027561     -7.1752    -11.6887     13.7153\n  4500   6600     0.0000   -13.6276     1.5487    38.273972     -7.2254    -11.6577     13.7153\n  4500   6800     0.0000   -13.6276     1.5487    38.516886     -7.2747    -11.6270     13.7153\n  4500   7000     0.0000   -13.6276     1.5487    38.756387     -7.3233    -11.5965     13.7153\n  4500   7200     0.0000   -13.6276     1.5487    38.992558     -7.3710    -11.5662     13.7153\n  4500   7400     0.0000   -13.6276     1.5487    39.225479     -7.4180    -11.5362     13.7153\n  4500   7600     0.0000   -13.6276     1.5487    39.455227     -7.4642    -11.5063     13.7153\n  4500   7800     0.0000   -13.6276     1.5487    39.681874     -7.5096    -11.4767     13.7153\n  4500   8000     0.0000   -13.6276     1.5487    39.905492     -7.5544    -11.4473     13.7153\n  4500   8200     0.0000   -13.6276     1.5487    40.126149     -7.5984    -11.4181     13.7153\n  4500   8400     0.0000   -13.6276     1.5487    40.343913     -7.6417    -11.3892     13.7153\n\nuser time (s):         0.010\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180530_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -1.1414793       35.2035164       15.5722874   m   m   m\ninitial_baseline_rate:          0.0000000        0.0459441       -0.0078633   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       35.4681793       15.4120456   m   m   m\nprecision_baseline_rate:        0.0000000        0.0377316       -0.0043365   m/s m/s m/s\nunwrap_phase_constant:            0.00012     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180530_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -1.141     35.204     15.572\norbit baseline rate   (TCN) (m/s):  0.000e+00  4.594e-02 -7.863e-03\n\nbaseline vector (TCN)   (m):      0.000     35.468     15.412\nbaseline rate   (TCN) (m/s):  0.000e+00  3.773e-02 -4.337e-03\n\nSLC-1 center baseline length (m):    38.6720\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    35.1160    15.4525    27.497816     29.9204     24.0141     38.3655\n     0    200     0.0000    35.1160    15.4525    27.940057     30.1048     23.7825     38.3655\n     0    400     0.0000    35.1160    15.4525    28.371664     30.2831     23.5550     38.3655\n     0    600     0.0000    35.1160    15.4525    28.793160     30.4556     23.3316     38.3655\n     0    800     0.0000    35.1160    15.4525    29.205025     30.6225     23.1121     38.3655\n     0   1000     0.0000    35.1160    15.4525    29.607701     30.7842     22.8963     38.3655\n     0   1200     0.0000    35.1160    15.4525    30.001600     30.9409     22.6841     38.3655\n     0   1400     0.0000    35.1160    15.4525    30.387101     31.0928     22.4754     38.3655\n     0   1600     0.0000    35.1160    15.4525    30.764557     31.2402     22.2701     38.3655\n     0   1800     0.0000    35.1160    15.4525    31.134300     31.3832     22.0681     38.3655\n     0   2000     0.0000    35.1160    15.4525    31.496635     31.5222     21.8692     38.3655\n     0   2200     0.0000    35.1160    15.4525    31.851850     31.6571     21.6733     38.3655\n     0   2400     0.0000    35.1160    15.4525    32.200217     31.7883     21.4804     38.3655\n     0   2600     0.0000    35.1160    15.4525    32.541986     31.9159     21.2904     38.3655\n     0   2800     0.0000    35.1160    15.4525    32.877397     32.0400     21.1032     38.3655\n     0   3000     0.0000    35.1160    15.4525    33.206674     32.1607     20.9188     38.3655\n     0   3200     0.0000    35.1160    15.4525    33.530029     32.2783     20.7369     38.3655\n     0   3400     0.0000    35.1160    15.4525    33.847660     32.3927     20.5577     38.3655\n     0   3600     0.0000    35.1160    15.4525    34.159757     32.5042     20.3809     38.3655\n     0   3800     0.0000    35.1160    15.4525    34.466499     32.6129     20.2066     38.3655\n     0   4000     0.0000    35.1160    15.4525    34.768056     32.7188     20.0347     38.3655\n     0   4200     0.0000    35.1160    15.4525    35.064588     32.8220     19.8651     38.3655\n     0   4400     0.0000    35.1160    15.4525    35.356248     32.9227     19.6978     38.3655\n     0   4600     0.0000    35.1160    15.4525    35.643183     33.0210     19.5326     38.3655\n     0   4800     0.0000    35.1160    15.4525    35.925531     33.1168     19.3697     38.3655\n     0   5000     0.0000    35.1160    15.4525    36.203424     33.2104     19.2088     38.3655\n     0   5200     0.0000    35.1160    15.4525    36.476989     33.3017     19.0500     38.3655\n     0   5400     0.0000    35.1160    15.4525    36.746345     33.3909     18.8933     38.3655\n     0   5600     0.0000    35.1160    15.4525    37.011608     33.4780     18.7385     38.3655\n     0   5800     0.0000    35.1160    15.4525    37.272888     33.5631     18.5856     38.3655\n     0   6000     0.0000    35.1160    15.4525    37.530291     33.6463     18.4347     38.3655\n     0   6200     0.0000    35.1160    15.4525    37.783917     33.7276     18.2855     38.3655\n     0   6400     0.0000    35.1160    15.4525    38.033864     33.8070     18.1382     38.3655\n     0   6600     0.0000    35.1160    15.4525    38.280225     33.8847     17.9927     38.3655\n     0   6800     0.0000    35.1160    15.4525    38.523089     33.9606     17.8489     38.3655\n     0   7000     0.0000    35.1160    15.4525    38.762542     34.0349     17.7068     38.3655\n     0   7200     0.0000    35.1160    15.4525    38.998666     34.1076     17.5664     38.3655\n     0   7400     0.0000    35.1160    15.4525    39.231541     34.1787     17.4276     38.3655\n     0   7600     0.0000    35.1160    15.4525    39.461243     34.2483     17.2905     38.3655\n     0   7800     0.0000    35.1160    15.4525    39.687846     34.3165     17.1549     38.3655\n     0   8000     0.0000    35.1160    15.4525    39.911421     34.3831     17.0209     38.3655\n     0   8200     0.0000    35.1160    15.4525    40.132036     34.4484     16.8883     38.3655\n     0   8400     0.0000    35.1160    15.4525    40.349758     34.5123     16.7573     38.3655\n   500      0     0.0000    35.1935    15.4436    27.496883     29.9479     24.0875     38.4329\n   500    200     0.0000    35.1935    15.4436    27.939140     30.1329     23.8557     38.4329\n   500    400     0.0000    35.1935    15.4436    28.370762     30.3118     23.6280     38.4329\n   500    600     0.0000    35.1935    15.4436    28.792272     30.4848     23.4044     38.4329\n   500    800     0.0000    35.1935    15.4436    29.204150     30.6522     23.1846     38.4329\n   500   1000     0.0000    35.1935    15.4436    29.606840     30.8144     22.9686     38.4329\n   500   1200     0.0000    35.1935    15.4436    30.000750     30.9716     22.7562     38.4329\n   500   1400     0.0000    35.1935    15.4436    30.386263     31.1240     22.5473     38.4329\n   500   1600     0.0000    35.1935    15.4436    30.763731     31.2719     22.3418     38.4329\n   500   1800     0.0000    35.1935    15.4436    31.133484     31.4154     22.1395     38.4329\n   500   2000     0.0000    35.1935    15.4436    31.495829     31.5548     21.9404     38.4329\n   500   2200     0.0000    35.1935    15.4436    31.851055     31.6902     21.7443     38.4329\n   500   2400     0.0000    35.1935    15.4436    32.199431     31.8218     21.5513     38.4329\n   500   2600     0.0000    35.1935    15.4436    32.541209     31.9498     21.3611     38.4329\n   500   2800     0.0000    35.1935    15.4436    32.876630     32.0743     21.1736     38.4329\n   500   3000     0.0000    35.1935    15.4436    33.205915     32.1955     20.9890     38.4329\n   500   3200     0.0000    35.1935    15.4436    33.529278     32.3134     20.8069     38.4329\n   500   3400     0.0000    35.1935    15.4436    33.846917     32.4283     20.6275     38.4329\n   500   3600     0.0000    35.1935    15.4436    34.159022     32.5402     20.4505     38.4329\n   500   3800     0.0000    35.1935    15.4436    34.465771     32.6492     20.2760     38.4329\n   500   4000     0.0000    35.1935    15.4436    34.767335     32.7554     20.1039     38.4329\n   500   4200     0.0000    35.1935    15.4436    35.063874     32.8591     19.9341     38.4329\n   500   4400     0.0000    35.1935    15.4436    35.355542     32.9601     19.7666     38.4329\n   500   4600     0.0000    35.1935    15.4436    35.642483     33.0587     19.6013     38.4329\n   500   4800     0.0000    35.1935    15.4436    35.924838     33.1549     19.4381     38.4329\n   500   5000     0.0000    35.1935    15.4436    36.202737     33.2488     19.2771     38.4329\n   500   5200     0.0000    35.1935    15.4436    36.476308     33.3404     19.1181     38.4329\n   500   5400     0.0000    35.1935    15.4436    36.745670     33.4299     18.9611     38.4329\n   500   5600     0.0000    35.1935    15.4436    37.010938     33.5174     18.8062     38.4329\n   500   5800     0.0000    35.1935    15.4436    37.272224     33.6028     18.6531     38.4329\n   500   6000     0.0000    35.1935    15.4436    37.529632     33.6863     18.5020     38.4329\n   500   6200     0.0000    35.1935    15.4436    37.783264     33.7678     18.3527     38.4329\n   500   6400     0.0000    35.1935    15.4436    38.033217     33.8476     18.2052     38.4329\n   500   6600     0.0000    35.1935    15.4436    38.279583     33.9255     18.0595     38.4329\n   500   6800     0.0000    35.1935    15.4436    38.522452     34.0018     17.9155     38.4329\n   500   7000     0.0000    35.1935    15.4436    38.761909     34.0764     17.7733     38.4329\n   500   7200     0.0000    35.1935    15.4436    38.998038     34.1493     17.6327     38.4329\n   500   7400     0.0000    35.1935    15.4436    39.230918     34.2207     17.4937     38.4329\n   500   7600     0.0000    35.1935    15.4436    39.460624     34.2906     17.3564     38.4329\n   500   7800     0.0000    35.1935    15.4436    39.687232     34.3589     17.2206     38.4329\n   500   8000     0.0000    35.1935    15.4436    39.910811     34.4259     17.0864     38.4329\n   500   8200     0.0000    35.1935    15.4436    40.131431     34.4914     16.9537     38.4329\n   500   8400     0.0000    35.1935    15.4436    40.349156     34.5556     16.8226     38.4329\n  1000      0     0.0000    35.2711    15.4347    27.495930     29.9754     24.1609     38.5004\n  1000    200     0.0000    35.2711    15.4347    27.938203     30.1610     23.9288     38.5004\n  1000    400     0.0000    35.2711    15.4347    28.369840     30.3404     23.7010     38.5004\n  1000    600     0.0000    35.2711    15.4347    28.791365     30.5139     23.4771     38.5004\n  1000    800     0.0000    35.2711    15.4347    29.203256     30.6819     23.2571     38.5004\n  1000   1000     0.0000    35.2711    15.4347    29.605959     30.8446     23.0409     38.5004\n  1000   1200     0.0000    35.2711    15.4347    29.999882     31.0023     22.8283     38.5004\n  1000   1400     0.0000    35.2711    15.4347    30.385407     31.1552     22.6192     38.5004\n  1000   1600     0.0000    35.2711    15.4347    30.762886     31.3035     22.4134     38.5004\n  1000   1800     0.0000    35.2711    15.4347    31.132650     31.4475     22.2110     38.5004\n  1000   2000     0.0000    35.2711    15.4347    31.495006     31.5874     22.0116     38.5004\n  1000   2200     0.0000    35.2711    15.4347    31.850242     31.7232     21.8154     38.5004\n  1000   2400     0.0000    35.2711    15.4347    32.198628     31.8553     21.6221     38.5004\n  1000   2600     0.0000    35.2711    15.4347    32.540416     31.9837     21.4317     38.5004\n  1000   2800     0.0000    35.2711    15.4347    32.875845     32.1086     21.2441     38.5004\n  1000   3000     0.0000    35.2711    15.4347    33.205140     32.2302     21.0592     38.5004\n  1000   3200     0.0000    35.2711    15.4347    33.528511     32.3486     20.8769     38.5004\n  1000   3400     0.0000    35.2711    15.4347    33.846158     32.4638     20.6973     38.5004\n  1000   3600     0.0000    35.2711    15.4347    34.158271     32.5761     20.5201     38.5004\n  1000   3800     0.0000    35.2711    15.4347    34.465028     32.6855     20.3454     38.5004\n  1000   4000     0.0000    35.2711    15.4347    34.766600     32.7921     20.1731     38.5004\n  1000   4200     0.0000    35.2711    15.4347    35.063146     32.8961     20.0031     38.5004\n  1000   4400     0.0000    35.2711    15.4347    35.354821     32.9975     19.8354     38.5004\n  1000   4600     0.0000    35.2711    15.4347    35.641769     33.0964     19.6699     38.5004\n  1000   4800     0.0000    35.2711    15.4347    35.924130     33.1929     19.5066     38.5004\n  1000   5000     0.0000    35.2711    15.4347    36.202036     33.2872     19.3453     38.5004\n  1000   5200     0.0000    35.2711    15.4347    36.475612     33.3791     19.1862     38.5004\n  1000   5400     0.0000    35.2711    15.4347    36.744981     33.4690     19.0290     38.5004\n  1000   5600     0.0000    35.2711    15.4347    37.010255     33.5567     18.8739     38.5004\n  1000   5800     0.0000    35.2711    15.4347    37.271547     33.6424     18.7206     38.5004\n  1000   6000     0.0000    35.2711    15.4347    37.528961     33.7262     18.5693     38.5004\n  1000   6200     0.0000    35.2711    15.4347    37.782598     33.8081     18.4198     38.5004\n  1000   6400     0.0000    35.2711    15.4347    38.032556     33.8881     18.2722     38.5004\n  1000   6600     0.0000    35.2711    15.4347    38.278927     33.9664     18.1263     38.5004\n  1000   6800     0.0000    35.2711    15.4347    38.521801     34.0429     17.9821     38.5004\n  1000   7000     0.0000    35.2711    15.4347    38.761264     34.1178     17.8397     38.5004\n  1000   7200     0.0000    35.2711    15.4347    38.997398     34.1910     17.6989     38.5004\n  1000   7400     0.0000    35.2711    15.4347    39.230282     34.2627     17.5598     38.5004\n  1000   7600     0.0000    35.2711    15.4347    39.459993     34.3328     17.4223     38.5004\n  1000   7800     0.0000    35.2711    15.4347    39.686605     34.4014     17.2864     38.5004\n  1000   8000     0.0000    35.2711    15.4347    39.910189     34.4686     17.1520     38.5004\n  1000   8200     0.0000    35.2711    15.4347    40.130813     34.5344     17.0192     38.5004\n  1000   8400     0.0000    35.2711    15.4347    40.348543     34.5988     16.8878     38.5004\n  1500      0     0.0000    35.3487    15.4258    27.494956     30.0029     24.2344     38.5679\n  1500    200     0.0000    35.3487    15.4258    27.937245     30.1890     24.0020     38.5679\n  1500    400     0.0000    35.3487    15.4258    28.368898     30.3690     23.7739     38.5679\n  1500    600     0.0000    35.3487    15.4258    28.790438     30.5431     23.5499     38.5679\n  1500    800     0.0000    35.3487    15.4258    29.202344     30.7116     23.3297     38.5679\n  1500   1000     0.0000    35.3487    15.4258    29.605059     30.8748     23.1132     38.5679\n  1500   1200     0.0000    35.3487    15.4258    29.998996     31.0330     22.9004     38.5679\n  1500   1400     0.0000    35.3487    15.4258    30.384532     31.1864     22.6911     38.5679\n  1500   1600     0.0000    35.3487    15.4258    30.762024     31.3352     22.4851     38.5679\n  1500   1800     0.0000    35.3487    15.4258    31.131799     31.4797     22.2824     38.5679\n  1500   2000     0.0000    35.3487    15.4258    31.494166     31.6200     22.0829     38.5679\n  1500   2200     0.0000    35.3487    15.4258    31.849412     31.7563     21.8864     38.5679\n  1500   2400     0.0000    35.3487    15.4258    32.197808     31.8888     21.6929     38.5679\n  1500   2600     0.0000    35.3487    15.4258    32.539606     32.0176     21.5023     38.5679\n  1500   2800     0.0000    35.3487    15.4258    32.875045     32.1430     21.3145     38.5679\n  1500   3000     0.0000    35.3487    15.4258    33.204348     32.2649     21.1294     38.5679\n  1500   3200     0.0000    35.3487    15.4258    33.527728     32.3837     20.9470     38.5679\n  1500   3400     0.0000    35.3487    15.4258    33.845384     32.4993     20.7671     38.5679\n  1500   3600     0.0000    35.3487    15.4258    34.157505     32.6120     20.5898     38.5679\n  1500   3800     0.0000    35.3487    15.4258    34.464270     32.7217     20.4149     38.5679\n  1500   4000     0.0000    35.3487    15.4258    34.765849     32.8287     20.2423     38.5679\n  1500   4200     0.0000    35.3487    15.4258    35.062403     32.9331     20.0722     38.5679\n  1500   4400     0.0000    35.3487    15.4258    35.354084     33.0348     19.9042     38.5679\n  1500   4600     0.0000    35.3487    15.4258    35.641040     33.1341     19.7385     38.5679\n  1500   4800     0.0000    35.3487    15.4258    35.923408     33.2310     19.5750     38.5679\n  1500   5000     0.0000    35.3487    15.4258    36.201320     33.3255     19.4136     38.5679\n  1500   5200     0.0000    35.3487    15.4258    36.474903     33.4178     19.2542     38.5679\n  1500   5400     0.0000    35.3487    15.4258    36.744278     33.5080     19.0969     38.5679\n  1500   5600     0.0000    35.3487    15.4258    37.009558     33.5961     18.9416     38.5679\n  1500   5800     0.0000    35.3487    15.4258    37.270856     33.6821     18.7882     38.5679\n  1500   6000     0.0000    35.3487    15.4258    37.528276     33.7662     18.6367     38.5679\n  1500   6200     0.0000    35.3487    15.4258    37.781919     33.8483     18.4870     38.5679\n  1500   6400     0.0000    35.3487    15.4258    38.031882     33.9287     18.3391     38.5679\n  1500   6600     0.0000    35.3487    15.4258    38.278259     34.0072     18.1931     38.5679\n  1500   6800     0.0000    35.3487    15.4258    38.521138     34.0840     18.0488     38.5679\n  1500   7000     0.0000    35.3487    15.4258    38.760605     34.1592     17.9062     38.5679\n  1500   7200     0.0000    35.3487    15.4258    38.996744     34.2327     17.7652     38.5679\n  1500   7400     0.0000    35.3487    15.4258    39.229633     34.3046     17.6259     38.5679\n  1500   7600     0.0000    35.3487    15.4258    39.459350     34.3750     17.4882     38.5679\n  1500   7800     0.0000    35.3487    15.4258    39.685966     34.4439     17.3522     38.5679\n  1500   8000     0.0000    35.3487    15.4258    39.909555     34.5113     17.2176     38.5679\n  1500   8200     0.0000    35.3487    15.4258    40.130183     34.5774     17.0846     38.5679\n  1500   8400     0.0000    35.3487    15.4258    40.347918     34.6421     16.9531     38.5679\n  2000      0     0.0000    35.4262    15.4169    27.493962     30.0303     24.3078     38.6354\n  2000    200     0.0000    35.4262    15.4169    27.936268     30.2171     24.0753     38.6354\n  2000    400     0.0000    35.4262    15.4169    28.367937     30.3976     23.8469     38.6354\n  2000    600     0.0000    35.4262    15.4169    28.789491     30.5722     23.6226     38.6354\n  2000    800     0.0000    35.4262    15.4169    29.201412     30.7413     23.4022     38.6354\n  2000   1000     0.0000    35.4262    15.4169    29.604141     30.9050     23.1856     38.6354\n  2000   1200     0.0000    35.4262    15.4169    29.998091     31.0637     22.9725     38.6354\n  2000   1400     0.0000    35.4262    15.4169    30.383640     31.2176     22.7630     38.6354\n  2000   1600     0.0000    35.4262    15.4169    30.761143     31.3669     22.5568     38.6354\n  2000   1800     0.0000    35.4262    15.4169    31.130930     31.5118     22.3539     38.6354\n  2000   2000     0.0000    35.4262    15.4169    31.493308     31.6526     22.1542     38.6354\n  2000   2200     0.0000    35.4262    15.4169    31.848565     31.7893     21.9575     38.6354\n  2000   2400     0.0000    35.4262    15.4169    32.196971     31.9223     21.7638     38.6354\n  2000   2600     0.0000    35.4262    15.4169    32.538779     32.0515     21.5729     38.6354\n  2000   2800     0.0000    35.4262    15.4169    32.874228     32.1773     21.3849     38.6354\n  2000   3000     0.0000    35.4262    15.4169    33.203540     32.2997     21.1996     38.6354\n  2000   3200     0.0000    35.4262    15.4169    33.526929     32.4188     21.0170     38.6354\n  2000   3400     0.0000    35.4262    15.4169    33.844594     32.5348     20.8369     38.6354\n  2000   3600     0.0000    35.4262    15.4169    34.156723     32.6478     20.6594     38.6354\n  2000   3800     0.0000    35.4262    15.4169    34.463496     32.7580     20.4843     38.6354\n  2000   4000     0.0000    35.4262    15.4169    34.765083     32.8654     20.3116     38.6354\n  2000   4200     0.0000    35.4262    15.4169    35.061644     32.9701     20.1412     38.6354\n  2000   4400     0.0000    35.4262    15.4169    35.353334     33.0722     19.9731     38.6354\n  2000   4600     0.0000    35.4262    15.4169    35.640296     33.1718     19.8072     38.6354\n  2000   4800     0.0000    35.4262    15.4169    35.922671     33.2690     19.6435     38.6354\n  2000   5000     0.0000    35.4262    15.4169    36.200590     33.3639     19.4819     38.6354\n  2000   5200     0.0000    35.4262    15.4169    36.474180     33.4565     19.3223     38.6354\n  2000   5400     0.0000    35.4262    15.4169    36.743561     33.5470     19.1648     38.6354\n  2000   5600     0.0000    35.4262    15.4169    37.008848     33.6354     19.0093     38.6354\n  2000   5800     0.0000    35.4262    15.4169    37.270151     33.7217     18.8557     38.6354\n  2000   6000     0.0000    35.4262    15.4169    37.527577     33.8061     18.7040     38.6354\n  2000   6200     0.0000    35.4262    15.4169    37.781226     33.8886     18.5542     38.6354\n  2000   6400     0.0000    35.4262    15.4169    38.031195     33.9692     18.4061     38.6354\n  2000   6600     0.0000    35.4262    15.4169    38.277577     34.0480     18.2599     38.6354\n  2000   6800     0.0000    35.4262    15.4169    38.520461     34.1251     18.1154     38.6354\n  2000   7000     0.0000    35.4262    15.4169    38.759934     34.2006     17.9726     38.6354\n  2000   7200     0.0000    35.4262    15.4169    38.996078     34.2743     17.8315     38.6354\n  2000   7400     0.0000    35.4262    15.4169    39.228972     34.3465     17.6920     38.6354\n  2000   7600     0.0000    35.4262    15.4169    39.458693     34.4172     17.5542     38.6354\n  2000   7800     0.0000    35.4262    15.4169    39.685315     34.4864     17.4179     38.6354\n  2000   8000     0.0000    35.4262    15.4169    39.908908     34.5541     17.2832     38.6354\n  2000   8200     0.0000    35.4262    15.4169    40.129541     34.6204     17.1500     38.6354\n  2000   8400     0.0000    35.4262    15.4169    40.347280     34.6853     17.0183     38.6354\n  2500      0     0.0000    35.5038    15.4080    27.492947     30.0578     24.3812     38.7030\n  2500    200     0.0000    35.5038    15.4080    27.935270     30.2451     24.1485     38.7030\n  2500    400     0.0000    35.5038    15.4080    28.366955     30.4262     23.9199     38.7030\n  2500    600     0.0000    35.5038    15.4080    28.788525     30.6014     23.6954     38.7030\n  2500    800     0.0000    35.5038    15.4080    29.200461     30.7710     23.4748     38.7030\n  2500   1000     0.0000    35.5038    15.4080    29.603204     30.9352     23.2579     38.7030\n  2500   1200     0.0000    35.5038    15.4080    29.997167     31.0944     23.0447     38.7030\n  2500   1400     0.0000    35.5038    15.4080    30.382730     31.2488     22.8349     38.7030\n  2500   1600     0.0000    35.5038    15.4080    30.760245     31.3985     22.6285     38.7030\n  2500   1800     0.0000    35.5038    15.4080    31.130044     31.5439     22.4254     38.7030\n  2500   2000     0.0000    35.5038    15.4080    31.492434     31.6851     22.2254     38.7030\n  2500   2200     0.0000    35.5038    15.4080    31.847702     31.8223     22.0285     38.7030\n  2500   2400     0.0000    35.5038    15.4080    32.196118     31.9557     21.8346     38.7030\n  2500   2600     0.0000    35.5038    15.4080    32.537936     32.0854     21.6436     38.7030\n  2500   2800     0.0000    35.5038    15.4080    32.873394     32.2116     21.4554     38.7030\n  2500   3000     0.0000    35.5038    15.4080    33.202716     32.3344     21.2699     38.7030\n  2500   3200     0.0000    35.5038    15.4080    33.526114     32.4539     21.0870     38.7030\n  2500   3400     0.0000    35.5038    15.4080    33.843788     32.5703     20.9068     38.7030\n  2500   3600     0.0000    35.5038    15.4080    34.155926     32.6837     20.7290     38.7030\n  2500   3800     0.0000    35.5038    15.4080    34.462707     32.7943     20.5537     38.7030\n  2500   4000     0.0000    35.5038    15.4080    34.764302     32.9020     20.3808     38.7030\n  2500   4200     0.0000    35.5038    15.4080    35.060871     33.0070     20.2102     38.7030\n  2500   4400     0.0000    35.5038    15.4080    35.352568     33.1095     20.0419     38.7030\n  2500   4600     0.0000    35.5038    15.4080    35.639538     33.2095     19.8759     38.7030\n  2500   4800     0.0000    35.5038    15.4080    35.921920     33.3070     19.7120     38.7030\n  2500   5000     0.0000    35.5038    15.4080    36.199846     33.4023     19.5502     38.7030\n  2500   5200     0.0000    35.5038    15.4080    36.473442     33.4952     19.3904     38.7030\n  2500   5400     0.0000    35.5038    15.4080    36.742830     33.5860     19.2327     38.7030\n  2500   5600     0.0000    35.5038    15.4080    37.008123     33.6747     19.0770     38.7030\n  2500   5800     0.0000    35.5038    15.4080    37.269433     33.7614     18.9232     38.7030\n  2500   6000     0.0000    35.5038    15.4080    37.526865     33.8461     18.7714     38.7030\n  2500   6200     0.0000    35.5038    15.4080    37.780519     33.9288     18.6213     38.7030\n  2500   6400     0.0000    35.5038    15.4080    38.030494     34.0097     18.4731     38.7030\n  2500   6600     0.0000    35.5038    15.4080    38.276882     34.0889     18.3267     38.7030\n  2500   6800     0.0000    35.5038    15.4080    38.519772     34.1663     18.1820     38.7030\n  2500   7000     0.0000    35.5038    15.4080    38.759250     34.2420     18.0391     38.7030\n  2500   7200     0.0000    35.5038    15.4080    38.995399     34.3160     17.8978     38.7030\n  2500   7400     0.0000    35.5038    15.4080    39.228299     34.3885     17.7582     38.7030\n  2500   7600     0.0000    35.5038    15.4080    39.458025     34.4594     17.6201     38.7030\n  2500   7800     0.0000    35.5038    15.4080    39.684651     34.5288     17.4837     38.7030\n  2500   8000     0.0000    35.5038    15.4080    39.908249     34.5968     17.3488     38.7030\n  2500   8200     0.0000    35.5038    15.4080    40.128887     34.6634     17.2155     38.7030\n  2500   8400     0.0000    35.5038    15.4080    40.346630     34.7285     17.0836     38.7030\n  3000      0     0.0000    35.5813    15.3990    27.491911     30.0853     24.4547     38.7706\n  3000    200     0.0000    35.5813    15.3990    27.934252     30.2732     24.2217     38.7706\n  3000    400     0.0000    35.5813    15.3990    28.365954     30.4548     23.9929     38.7706\n  3000    600     0.0000    35.5813    15.3990    28.787540     30.6305     23.7682     38.7706\n  3000    800     0.0000    35.5813    15.3990    29.199491     30.8006     23.5474     38.7706\n  3000   1000     0.0000    35.5813    15.3990    29.602249     30.9654     23.3303     38.7706\n  3000   1200     0.0000    35.5813    15.3990    29.996226     31.1251     23.1168     38.7706\n  3000   1400     0.0000    35.5813    15.3990    30.381801     31.2799     22.9068     38.7706\n  3000   1600     0.0000    35.5813    15.3990    30.759330     31.4302     22.7002     38.7706\n  3000   1800     0.0000    35.5813    15.3990    31.129141     31.5760     22.4969     38.7706\n  3000   2000     0.0000    35.5813    15.3990    31.491542     31.7177     22.2967     38.7706\n  3000   2200     0.0000    35.5813    15.3990    31.846821     31.8554     22.0996     38.7706\n  3000   2400     0.0000    35.5813    15.3990    32.195248     31.9892     21.9055     38.7706\n  3000   2600     0.0000    35.5813    15.3990    32.537077     32.1193     21.7143     38.7706\n  3000   2800     0.0000    35.5813    15.3990    32.872545     32.2459     21.5258     38.7706\n  3000   3000     0.0000    35.5813    15.3990    33.201877     32.3691     21.3401     38.7706\n  3000   3200     0.0000    35.5813    15.3990    33.525284     32.4890     21.1571     38.7706\n  3000   3400     0.0000    35.5813    15.3990    33.842966     32.6058     20.9766     38.7706\n  3000   3600     0.0000    35.5813    15.3990    34.155113     32.7196     20.7987     38.7706\n  3000   3800     0.0000    35.5813    15.3990    34.461903     32.8305     20.6232     38.7706\n  3000   4000     0.0000    35.5813    15.3990    34.763506     32.9386     20.4501     38.7706\n  3000   4200     0.0000    35.5813    15.3990    35.060083     33.0440     20.2793     38.7706\n  3000   4400     0.0000    35.5813    15.3990    35.351788     33.1468     20.1108     38.7706\n  3000   4600     0.0000    35.5813    15.3990    35.638765     33.2472     19.9445     38.7706\n  3000   4800     0.0000    35.5813    15.3990    35.921154     33.3450     19.7804     38.7706\n  3000   5000     0.0000    35.5813    15.3990    36.199088     33.4406     19.6185     38.7706\n  3000   5200     0.0000    35.5813    15.3990    36.472691     33.5339     19.4585     38.7706\n  3000   5400     0.0000    35.5813    15.3990    36.742085     33.6250     19.3007     38.7706\n  3000   5600     0.0000    35.5813    15.3990    37.007385     33.7140     19.1448     38.7706\n  3000   5800     0.0000    35.5813    15.3990    37.268701     33.8010     18.9908     38.7706\n  3000   6000     0.0000    35.5813    15.3990    37.526139     33.8860     18.8387     38.7706\n  3000   6200     0.0000    35.5813    15.3990    37.779800     33.9691     18.6885     38.7706\n  3000   6400     0.0000    35.5813    15.3990    38.029781     34.0503     18.5401     38.7706\n  3000   6600     0.0000    35.5813    15.3990    38.276174     34.1297     18.3935     38.7706\n  3000   6800     0.0000    35.5813    15.3990    38.519070     34.2074     18.2487     38.7706\n  3000   7000     0.0000    35.5813    15.3990    38.758554     34.2833     18.1056     38.7706\n  3000   7200     0.0000    35.5813    15.3990    38.994708     34.3577     17.9641     38.7706\n  3000   7400     0.0000    35.5813    15.3990    39.227613     34.4304     17.8243     38.7706\n  3000   7600     0.0000    35.5813    15.3990    39.457344     34.5016     17.6861     38.7706\n  3000   7800     0.0000    35.5813    15.3990    39.683975     34.5713     17.5495     38.7706\n  3000   8000     0.0000    35.5813    15.3990    39.907578     34.6395     17.4144     38.7706\n  3000   8200     0.0000    35.5813    15.3990    40.128221     34.7063     17.2809     38.7706\n  3000   8400     0.0000    35.5813    15.3990    40.345969     34.7717     17.1489     38.7706\n  3500      0     0.0000    35.6589    15.3901    27.490855     30.1127     24.5282     38.8383\n  3500    200     0.0000    35.6589    15.3901    27.933214     30.3012     24.2950     38.8383\n  3500    400     0.0000    35.6589    15.3901    28.364934     30.4834     24.0660     38.8383\n  3500    600     0.0000    35.6589    15.3901    28.786536     30.6596     23.8410     38.8383\n  3500    800     0.0000    35.6589    15.3901    29.198502     30.8303     23.6199     38.8383\n  3500   1000     0.0000    35.6589    15.3901    29.601275     30.9955     23.4026     38.8383\n  3500   1200     0.0000    35.6589    15.3901    29.995266     31.1557     23.1889     38.8383\n  3500   1400     0.0000    35.6589    15.3901    30.380855     31.3111     22.9788     38.8383\n  3500   1600     0.0000    35.6589    15.3901    30.758396     31.4618     22.7719     38.8383\n  3500   1800     0.0000    35.6589    15.3901    31.128219     31.6081     22.5684     38.8383\n  3500   2000     0.0000    35.6589    15.3901    31.490633     31.7503     22.3680     38.8383\n  3500   2200     0.0000    35.6589    15.3901    31.845923     31.8884     22.1707     38.8383\n  3500   2400     0.0000    35.6589    15.3901    32.194362     32.0226     21.9764     38.8383\n  3500   2600     0.0000    35.6589    15.3901    32.536201     32.1531     21.7849     38.8383\n  3500   2800     0.0000    35.6589    15.3901    32.871679     32.2801     21.5963     38.8383\n  3500   3000     0.0000    35.6589    15.3901    33.201021     32.4038     21.4104     38.8383\n  3500   3200     0.0000    35.6589    15.3901    33.524438     32.5241     21.2271     38.8383\n  3500   3400     0.0000    35.6589    15.3901    33.842129     32.6413     21.0465     38.8383\n  3500   3600     0.0000    35.6589    15.3901    34.154285     32.7555     20.8683     38.8383\n  3500   3800     0.0000    35.6589    15.3901    34.461084     32.8667     20.6926     38.8383\n  3500   4000     0.0000    35.6589    15.3901    34.762695     32.9752     20.5193     38.8383\n  3500   4200     0.0000    35.6589    15.3901    35.059281     33.0810     20.3484     38.8383\n  3500   4400     0.0000    35.6589    15.3901    35.350993     33.1842     20.1797     38.8383\n  3500   4600     0.0000    35.6589    15.3901    35.637978     33.2848     20.0132     38.8383\n  3500   4800     0.0000    35.6589    15.3901    35.920375     33.3831     19.8489     38.8383\n  3500   5000     0.0000    35.6589    15.3901    36.198315     33.4790     19.6868     38.8383\n  3500   5200     0.0000    35.6589    15.3901    36.471925     33.5726     19.5267     38.8383\n  3500   5400     0.0000    35.6589    15.3901    36.741326     33.6640     19.3686     38.8383\n  3500   5600     0.0000    35.6589    15.3901    37.006633     33.7534     19.2125     38.8383\n  3500   5800     0.0000    35.6589    15.3901    37.267956     33.8406     19.0584     38.8383\n  3500   6000     0.0000    35.6589    15.3901    37.525400     33.9259     18.9061     38.8383\n  3500   6200     0.0000    35.6589    15.3901    37.779067     34.0093     18.7557     38.8383\n  3500   6400     0.0000    35.6589    15.3901    38.029054     34.0908     18.6072     38.8383\n  3500   6600     0.0000    35.6589    15.3901    38.275453     34.1705     18.4604     38.8383\n  3500   6800     0.0000    35.6589    15.3901    38.518355     34.2485     18.3154     38.8383\n  3500   7000     0.0000    35.6589    15.3901    38.757844     34.3247     18.1720     38.8383\n  3500   7200     0.0000    35.6589    15.3901    38.994004     34.3993     18.0304     38.8383\n  3500   7400     0.0000    35.6589    15.3901    39.226914     34.4723     17.8904     38.8383\n  3500   7600     0.0000    35.6589    15.3901    39.456650     34.5438     17.7521     38.8383\n  3500   7800     0.0000    35.6589    15.3901    39.683287     34.6137     17.6153     38.8383\n  3500   8000     0.0000    35.6589    15.3901    39.906895     34.6822     17.4801     38.8383\n  3500   8200     0.0000    35.6589    15.3901    40.127542     34.7493     17.3464     38.8383\n  3500   8400     0.0000    35.6589    15.3901    40.345295     34.8150     17.2142     38.8383\n  4000      0     0.0000    35.7365    15.3812    27.489779     30.1401     24.6017     38.9060\n  4000    200     0.0000    35.7365    15.3812    27.932157     30.3292     24.3682     38.9060\n  4000    400     0.0000    35.7365    15.3812    28.363894     30.5119     24.1390     38.9060\n  4000    600     0.0000    35.7365    15.3812    28.785512     30.6887     23.9138     38.9060\n  4000    800     0.0000    35.7365    15.3812    29.197494     30.8599     23.6925     38.9060\n  4000   1000     0.0000    35.7365    15.3812    29.600282     31.0257     23.4750     38.9060\n  4000   1200     0.0000    35.7365    15.3812    29.994287     31.1864     23.2611     38.9060\n  4000   1400     0.0000    35.7365    15.3812    30.379890     31.3422     23.0507     38.9060\n  4000   1600     0.0000    35.7365    15.3812    30.757445     31.4934     22.8437     38.9060\n  4000   1800     0.0000    35.7365    15.3812    31.127281     31.6402     22.6399     38.9060\n  4000   2000     0.0000    35.7365    15.3812    31.489706     31.7828     22.4393     38.9060\n  4000   2200     0.0000    35.7365    15.3812    31.845009     31.9214     22.2418     38.9060\n  4000   2400     0.0000    35.7365    15.3812    32.193458     32.0560     22.0473     38.9060\n  4000   2600     0.0000    35.7365    15.3812    32.535308     32.1870     21.8556     38.9060\n  4000   2800     0.0000    35.7365    15.3812    32.870797     32.3144     21.6668     38.9060\n  4000   3000     0.0000    35.7365    15.3812    33.200149     32.4384     21.4807     38.9060\n  4000   3200     0.0000    35.7365    15.3812    33.523576     32.5592     21.2972     38.9060\n  4000   3400     0.0000    35.7365    15.3812    33.841277     32.6768     21.1163     38.9060\n  4000   3600     0.0000    35.7365    15.3812    34.153442     32.7913     20.9380     38.9060\n  4000   3800     0.0000    35.7365    15.3812    34.460249     32.9030     20.7621     38.9060\n  4000   4000     0.0000    35.7365    15.3812    34.761869     33.0118     20.5886     38.9060\n  4000   4200     0.0000    35.7365    15.3812    35.058463     33.1180     20.4175     38.9060\n  4000   4400     0.0000    35.7365    15.3812    35.350183     33.2215     20.2486     38.9060\n  4000   4600     0.0000    35.7365    15.3812    35.637176     33.3225     20.0819     38.9060\n  4000   4800     0.0000    35.7365    15.3812    35.919581     33.4211     19.9174     38.9060\n  4000   5000     0.0000    35.7365    15.3812    36.197528     33.5173     19.7551     38.9060\n  4000   5200     0.0000    35.7365    15.3812    36.471146     33.6113     19.5948     38.9060\n  4000   5400     0.0000    35.7365    15.3812    36.740554     33.7030     19.4365     38.9060\n  4000   5600     0.0000    35.7365    15.3812    37.005868     33.7927     19.2803     38.9060\n  4000   5800     0.0000    35.7365    15.3812    37.267197     33.8802     19.1259     38.9060\n  4000   6000     0.0000    35.7365    15.3812    37.524648     33.9658     18.9735     38.9060\n  4000   6200     0.0000    35.7365    15.3812    37.778321     34.0495     18.8229     38.9060\n  4000   6400     0.0000    35.7365    15.3812    38.028314     34.1313     18.6742     38.9060\n  4000   6600     0.0000    35.7365    15.3812    38.274719     34.2113     18.5272     38.9060\n  4000   6800     0.0000    35.7365    15.3812    38.517627     34.2896     18.3820     38.9060\n  4000   7000     0.0000    35.7365    15.3812    38.757122     34.3661     18.2385     38.9060\n  4000   7200     0.0000    35.7365    15.3812    38.993287     34.4410     18.0967     38.9060\n  4000   7400     0.0000    35.7365    15.3812    39.226203     34.5143     17.9566     38.9060\n  4000   7600     0.0000    35.7365    15.3812    39.455945     34.5860     17.8180     38.9060\n  4000   7800     0.0000    35.7365    15.3812    39.682586     34.6562     17.6811     38.9060\n  4000   8000     0.0000    35.7365    15.3812    39.906199     34.7249     17.5457     38.9060\n  4000   8200     0.0000    35.7365    15.3812    40.126852     34.7922     17.4118     38.9060\n  4000   8400     0.0000    35.7365    15.3812    40.344610     34.8582     17.2795     38.9060\n  4500      0     0.0000    35.8140    15.3723    27.488683     30.1675     24.6752     38.9737\n  4500    200     0.0000    35.8140    15.3723    27.931079     30.3572     24.4415     38.9737\n  4500    400     0.0000    35.8140    15.3723    28.362834     30.5405     24.2120     38.9737\n  4500    600     0.0000    35.8140    15.3723    28.784470     30.7178     23.9866     38.9737\n  4500    800     0.0000    35.8140    15.3723    29.196467     30.8895     23.7651     38.9737\n  4500   1000     0.0000    35.8140    15.3723    29.599271     31.0558     23.5474     38.9737\n  4500   1200     0.0000    35.8140    15.3723    29.993291     31.2170     23.3333     38.9737\n  4500   1400     0.0000    35.8140    15.3723    30.378908     31.3734     23.1227     38.9737\n  4500   1600     0.0000    35.8140    15.3723    30.756476     31.5251     22.9154     38.9737\n  4500   1800     0.0000    35.8140    15.3723    31.126325     31.6723     22.7114     38.9737\n  4500   2000     0.0000    35.8140    15.3723    31.488763     31.8154     22.5106     38.9737\n  4500   2200     0.0000    35.8140    15.3723    31.844077     31.9543     22.3129     38.9737\n  4500   2400     0.0000    35.8140    15.3723    32.192539     32.0895     22.1182     38.9737\n  4500   2600     0.0000    35.8140    15.3723    32.534400     32.2209     21.9263     38.9737\n  4500   2800     0.0000    35.8140    15.3723    32.869899     32.3487     21.7373     38.9737\n  4500   3000     0.0000    35.8140    15.3723    33.199262     32.4731     21.5509     38.9737\n  4500   3200     0.0000    35.8140    15.3723    33.522698     32.5943     21.3673     38.9737\n  4500   3400     0.0000    35.8140    15.3723    33.840409     32.7122     21.1862     38.9737\n  4500   3600     0.0000    35.8140    15.3723    34.152583     32.8272     21.0077     38.9737\n  4500   3800     0.0000    35.8140    15.3723    34.459400     32.9392     20.8316     38.9737\n  4500   4000     0.0000    35.8140    15.3723    34.761029     33.0484     20.6579     38.9737\n  4500   4200     0.0000    35.8140    15.3723    35.057631     33.1549     20.4865     38.9737\n  4500   4400     0.0000    35.8140    15.3723    35.349359     33.2588     20.3175     38.9737\n  4500   4600     0.0000    35.8140    15.3723    35.636360     33.3601     20.1506     38.9737\n  4500   4800     0.0000    35.8140    15.3723    35.918772     33.4591     19.9859     38.9737\n  4500   5000     0.0000    35.8140    15.3723    36.196728     33.5556     19.8234     38.9737\n  4500   5200     0.0000    35.8140    15.3723    36.470353     33.6499     19.6629     38.9737\n  4500   5400     0.0000    35.8140    15.3723    36.739768     33.7420     19.5045     38.9737\n  4500   5600     0.0000    35.8140    15.3723    37.005088     33.8320     19.3480     38.9737\n  4500   5800     0.0000    35.8140    15.3723    37.266425     33.9199     19.1935     38.9737\n  4500   6000     0.0000    35.8140    15.3723    37.523882     34.0058     19.0409     38.9737\n  4500   6200     0.0000    35.8140    15.3723    37.777562     34.0897     18.8901     38.9737\n  4500   6400     0.0000    35.8140    15.3723    38.027561     34.1718     18.7412     38.9737\n  4500   6600     0.0000    35.8140    15.3723    38.273972     34.2521     18.5941     38.9737\n  4500   6800     0.0000    35.8140    15.3723    38.516886     34.3306     18.4487     38.9737\n  4500   7000     0.0000    35.8140    15.3723    38.756387     34.4075     18.3050     38.9737\n  4500   7200     0.0000    35.8140    15.3723    38.992558     34.4826     18.1631     38.9737\n  4500   7400     0.0000    35.8140    15.3723    39.225479     34.5562     18.0227     38.9737\n  4500   7600     0.0000    35.8140    15.3723    39.455227     34.6282     17.8840     38.9737\n  4500   7800     0.0000    35.8140    15.3723    39.681874     34.6986     17.7469     38.9737\n  4500   8000     0.0000    35.8140    15.3723    39.905492     34.7676     17.6113     38.9737\n  4500   8200     0.0000    35.8140    15.3723    40.126149     34.8352     17.4773     38.9737\n  4500   8400     0.0000    35.8140    15.3723    40.343913     34.9014     17.3448     38.9737\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180611_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          0.4118799      -40.7471207        1.2559498   m   m   m\ninitial_baseline_rate:          0.0000000        0.0719436       -0.0067777   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -41.0589267        1.3893527   m   m   m\nprecision_baseline_rate:        0.0000000        0.0980549       -0.0290264   m/s m/s m/s\nunwrap_phase_constant:           -0.00034     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180611_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      0.412    -40.747      1.256\norbit baseline rate   (TCN) (m/s):  0.000e+00  7.194e-02 -6.778e-03\n\nbaseline vector (TCN)   (m):      0.000    -41.059      1.389\nbaseline rate   (TCN) (m/s):  0.000e+00  9.805e-02 -2.903e-02\n\nSLC-1 center baseline length (m):    41.0824\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -41.9742     1.6603    27.497816    -17.9073    -37.9988     42.0070\n     0    200     0.0000   -41.9742     1.6603    27.940057    -18.2001    -37.8594     42.0070\n     0    400     0.0000   -41.9742     1.6603    28.371664    -18.4848    -37.7213     42.0070\n     0    600     0.0000   -41.9742     1.6603    28.793160    -18.7618    -37.5843     42.0070\n     0    800     0.0000   -41.9742     1.6603    29.205025    -19.0314    -37.4484     42.0070\n     0   1000     0.0000   -41.9742     1.6603    29.607701    -19.2942    -37.3138     42.0070\n     0   1200     0.0000   -41.9742     1.6603    30.001600    -19.5502    -37.1802     42.0070\n     0   1400     0.0000   -41.9742     1.6603    30.387101    -19.7999    -37.0479     42.0070\n     0   1600     0.0000   -41.9742     1.6603    30.764557    -20.0436    -36.9166     42.0070\n     0   1800     0.0000   -41.9742     1.6603    31.134300    -20.2814    -36.7865     42.0070\n     0   2000     0.0000   -41.9742     1.6603    31.496635    -20.5136    -36.6575     42.0070\n     0   2200     0.0000   -41.9742     1.6603    31.851850    -20.7405    -36.5296     42.0070\n     0   2400     0.0000   -41.9742     1.6603    32.200217    -20.9622    -36.4029     42.0070\n     0   2600     0.0000   -41.9742     1.6603    32.541986    -21.1790    -36.2772     42.0070\n     0   2800     0.0000   -41.9742     1.6603    32.877397    -21.3910    -36.1526     42.0070\n     0   3000     0.0000   -41.9742     1.6603    33.206674    -21.5984    -36.0290     42.0070\n     0   3200     0.0000   -41.9742     1.6603    33.530029    -21.8014    -35.9066     42.0070\n     0   3400     0.0000   -41.9742     1.6603    33.847660    -22.0001    -35.7852     42.0070\n     0   3600     0.0000   -41.9742     1.6603    34.159757    -22.1947    -35.6648     42.0070\n     0   3800     0.0000   -41.9742     1.6603    34.466499    -22.3853    -35.5455     42.0070\n     0   4000     0.0000   -41.9742     1.6603    34.768056    -22.5721    -35.4272     42.0070\n     0   4200     0.0000   -41.9742     1.6603    35.064588    -22.7552    -35.3099     42.0070\n     0   4400     0.0000   -41.9742     1.6603    35.356248    -22.9346    -35.1936     42.0070\n     0   4600     0.0000   -41.9742     1.6603    35.643183    -23.1106    -35.0783     42.0070\n     0   4800     0.0000   -41.9742     1.6603    35.925531    -23.2831    -34.9640     42.0070\n     0   5000     0.0000   -41.9742     1.6603    36.203424    -23.4525    -34.8506     42.0070\n     0   5200     0.0000   -41.9742     1.6603    36.476989    -23.6186    -34.7383     42.0070\n     0   5400     0.0000   -41.9742     1.6603    36.746345    -23.7816    -34.6268     42.0070\n     0   5600     0.0000   -41.9742     1.6603    37.011608    -23.9417    -34.5164     42.0070\n     0   5800     0.0000   -41.9742     1.6603    37.272888    -24.0988    -34.4068     42.0070\n     0   6000     0.0000   -41.9742     1.6603    37.530291    -24.2532    -34.2982     42.0070\n     0   6200     0.0000   -41.9742     1.6603    37.783917    -24.4048    -34.1905     42.0070\n     0   6400     0.0000   -41.9742     1.6603    38.033864    -24.5537    -34.0837     42.0070\n     0   6600     0.0000   -41.9742     1.6603    38.280225    -24.7000    -33.9779     42.0070\n     0   6800     0.0000   -41.9742     1.6603    38.523089    -24.8438    -33.8729     42.0070\n     0   7000     0.0000   -41.9742     1.6603    38.762542    -24.9852    -33.7687     42.0070\n     0   7200     0.0000   -41.9742     1.6603    38.998666    -25.1241    -33.6655     42.0070\n     0   7400     0.0000   -41.9742     1.6603    39.231541    -25.2607    -33.5631     42.0070\n     0   7600     0.0000   -41.9742     1.6603    39.461243    -25.3951    -33.4615     42.0070\n     0   7800     0.0000   -41.9742     1.6603    39.687846    -25.5272    -33.3608     42.0070\n     0   8000     0.0000   -41.9742     1.6603    39.911421    -25.6572    -33.2610     42.0070\n     0   8200     0.0000   -41.9742     1.6603    40.132036    -25.7851    -33.1619     42.0070\n     0   8400     0.0000   -41.9742     1.6603    40.349758    -25.9109    -33.0637     42.0070\n   500      0     0.0000   -41.7726     1.6006    27.496883    -17.8666    -37.7927     41.8033\n   500    200     0.0000   -41.7726     1.6006    27.939140    -18.1578    -37.6537     41.8033\n   500    400     0.0000   -41.7726     1.6006    28.370762    -18.4409    -37.5159     41.8033\n   500    600     0.0000   -41.7726     1.6006    28.792272    -18.7164    -37.3792     41.8033\n   500    800     0.0000   -41.7726     1.6006    29.204150    -18.9846    -37.2437     41.8033\n   500   1000     0.0000   -41.7726     1.6006    29.606840    -19.2459    -37.1093     41.8033\n   500   1200     0.0000   -41.7726     1.6006    30.000750    -19.5006    -36.9761     41.8033\n   500   1400     0.0000   -41.7726     1.6006    30.386263    -19.7489    -36.8441     41.8033\n   500   1600     0.0000   -41.7726     1.6006    30.763731    -19.9912    -36.7132     41.8033\n   500   1800     0.0000   -41.7726     1.6006    31.133484    -20.2277    -36.5834     41.8033\n   500   2000     0.0000   -41.7726     1.6006    31.495829    -20.4587    -36.4548     41.8033\n   500   2200     0.0000   -41.7726     1.6006    31.851055    -20.6843    -36.3272     41.8033\n   500   2400     0.0000   -41.7726     1.6006    32.199431    -20.9048    -36.2008     41.8033\n   500   2600     0.0000   -41.7726     1.6006    32.541209    -21.1204    -36.0755     41.8033\n   500   2800     0.0000   -41.7726     1.6006    32.876630    -21.3312    -35.9512     41.8033\n   500   3000     0.0000   -41.7726     1.6006    33.205915    -21.5375    -35.8280     41.8033\n   500   3200     0.0000   -41.7726     1.6006    33.529278    -21.7393    -35.7059     41.8033\n   500   3400     0.0000   -41.7726     1.6006    33.846917    -21.9369    -35.5848     41.8033\n   500   3600     0.0000   -41.7726     1.6006    34.159022    -22.1305    -35.4648     41.8033\n   500   3800     0.0000   -41.7726     1.6006    34.465771    -22.3200    -35.3458     41.8033\n   500   4000     0.0000   -41.7726     1.6006    34.767335    -22.5057    -35.2278     41.8033\n   500   4200     0.0000   -41.7726     1.6006    35.063874    -22.6878    -35.1109     41.8033\n   500   4400     0.0000   -41.7726     1.6006    35.355542    -22.8662    -34.9949     41.8033\n   500   4600     0.0000   -41.7726     1.6006    35.642483    -23.0412    -34.8800     41.8033\n   500   4800     0.0000   -41.7726     1.6006    35.924838    -23.2128    -34.7660     41.8033\n   500   5000     0.0000   -41.7726     1.6006    36.202737    -23.3811    -34.6530     41.8033\n   500   5200     0.0000   -41.7726     1.6006    36.476308    -23.5463    -34.5410     41.8033\n   500   5400     0.0000   -41.7726     1.6006    36.745670    -23.7084    -34.4299     41.8033\n   500   5600     0.0000   -41.7726     1.6006    37.010938    -23.8676    -34.3198     41.8033\n   500   5800     0.0000   -41.7726     1.6006    37.272224    -24.0239    -34.2106     41.8033\n   500   6000     0.0000   -41.7726     1.6006    37.529632    -24.1773    -34.1023     41.8033\n   500   6200     0.0000   -41.7726     1.6006    37.783264    -24.3280    -33.9950     41.8033\n   500   6400     0.0000   -41.7726     1.6006    38.033217    -24.4761    -33.8885     41.8033\n   500   6600     0.0000   -41.7726     1.6006    38.279583    -24.6216    -33.7829     41.8033\n   500   6800     0.0000   -41.7726     1.6006    38.522452    -24.7646    -33.6783     41.8033\n   500   7000     0.0000   -41.7726     1.6006    38.761909    -24.9051    -33.5745     41.8033\n   500   7200     0.0000   -41.7726     1.6006    38.998038    -25.0433    -33.4716     41.8033\n   500   7400     0.0000   -41.7726     1.6006    39.230918    -25.1791    -33.3695     41.8033\n   500   7600     0.0000   -41.7726     1.6006    39.460624    -25.3127    -33.2683     41.8033\n   500   7800     0.0000   -41.7726     1.6006    39.687232    -25.4441    -33.1679     41.8033\n   500   8000     0.0000   -41.7726     1.6006    39.910811    -25.5733    -33.0684     41.8033\n   500   8200     0.0000   -41.7726     1.6006    40.131431    -25.7004    -32.9697     41.8033\n   500   8400     0.0000   -41.7726     1.6006    40.349156    -25.8255    -32.8718     41.8033\n  1000      0     0.0000   -41.5711     1.5410    27.495930    -17.8258    -37.5867     41.5996\n  1000    200     0.0000   -41.5711     1.5410    27.938203    -18.1154    -37.4480     41.5996\n  1000    400     0.0000   -41.5711     1.5410    28.369840    -18.3970    -37.3105     41.5996\n  1000    600     0.0000   -41.5711     1.5410    28.791365    -18.6710    -37.1741     41.5996\n  1000    800     0.0000   -41.5711     1.5410    29.203256    -18.9378    -37.0389     41.5996\n  1000   1000     0.0000   -41.5711     1.5410    29.605959    -19.1976    -36.9049     41.5996\n  1000   1200     0.0000   -41.5711     1.5410    29.999882    -19.4509    -36.7721     41.5996\n  1000   1400     0.0000   -41.5711     1.5410    30.385407    -19.6979    -36.6403     41.5996\n  1000   1600     0.0000   -41.5711     1.5410    30.762886    -19.9389    -36.5098     41.5996\n  1000   1800     0.0000   -41.5711     1.5410    31.132650    -20.1741    -36.3803     41.5996\n  1000   2000     0.0000   -41.5711     1.5410    31.495006    -20.4037    -36.2520     41.5996\n  1000   2200     0.0000   -41.5711     1.5410    31.850242    -20.6281    -36.1248     41.5996\n  1000   2400     0.0000   -41.5711     1.5410    32.198628    -20.8474    -35.9987     41.5996\n  1000   2600     0.0000   -41.5711     1.5410    32.540416    -21.0618    -35.8737     41.5996\n  1000   2800     0.0000   -41.5711     1.5410    32.875845    -21.2714    -35.7498     41.5996\n  1000   3000     0.0000   -41.5711     1.5410    33.205140    -21.4765    -35.6270     41.5996\n  1000   3200     0.0000   -41.5711     1.5410    33.528511    -21.6773    -35.5052     41.5996\n  1000   3400     0.0000   -41.5711     1.5410    33.846158    -21.8738    -35.3845     41.5996\n  1000   3600     0.0000   -41.5711     1.5410    34.158271    -22.0662    -35.2648     41.5996\n  1000   3800     0.0000   -41.5711     1.5410    34.465028    -22.2547    -35.1462     41.5996\n  1000   4000     0.0000   -41.5711     1.5410    34.766600    -22.4394    -35.0285     41.5996\n  1000   4200     0.0000   -41.5711     1.5410    35.063146    -22.6204    -34.9119     41.5996\n  1000   4400     0.0000   -41.5711     1.5410    35.354821    -22.7978    -34.7963     41.5996\n  1000   4600     0.0000   -41.5711     1.5410    35.641769    -22.9718    -34.6817     41.5996\n  1000   4800     0.0000   -41.5711     1.5410    35.924130    -23.1424    -34.5681     41.5996\n  1000   5000     0.0000   -41.5711     1.5410    36.202036    -23.3098    -34.4554     41.5996\n  1000   5200     0.0000   -41.5711     1.5410    36.475612    -23.4741    -34.3437     41.5996\n  1000   5400     0.0000   -41.5711     1.5410    36.744981    -23.6353    -34.2330     41.5996\n  1000   5600     0.0000   -41.5711     1.5410    37.010255    -23.7935    -34.1232     41.5996\n  1000   5800     0.0000   -41.5711     1.5410    37.271547    -23.9489    -34.0143     41.5996\n  1000   6000     0.0000   -41.5711     1.5410    37.528961    -24.1014    -33.9064     41.5996\n  1000   6200     0.0000   -41.5711     1.5410    37.782598    -24.2513    -33.7994     41.5996\n  1000   6400     0.0000   -41.5711     1.5410    38.032556    -24.3985    -33.6933     41.5996\n  1000   6600     0.0000   -41.5711     1.5410    38.278927    -24.5432    -33.5880     41.5996\n  1000   6800     0.0000   -41.5711     1.5410    38.521801    -24.6853    -33.4837     41.5996\n  1000   7000     0.0000   -41.5711     1.5410    38.761264    -24.8251    -33.3802     41.5996\n  1000   7200     0.0000   -41.5711     1.5410    38.997398    -24.9624    -33.2776     41.5996\n  1000   7400     0.0000   -41.5711     1.5410    39.230282    -25.0975    -33.1759     41.5996\n  1000   7600     0.0000   -41.5711     1.5410    39.459993    -25.2303    -33.0750     41.5996\n  1000   7800     0.0000   -41.5711     1.5410    39.686605    -25.3609    -32.9750     41.5996\n  1000   8000     0.0000   -41.5711     1.5410    39.910189    -25.4894    -32.8758     41.5996\n  1000   8200     0.0000   -41.5711     1.5410    40.130813    -25.6158    -32.7774     41.5996\n  1000   8400     0.0000   -41.5711     1.5410    40.348543    -25.7402    -32.6798     41.5996\n  1500      0     0.0000   -41.3695     1.4813    27.494956    -17.7851    -37.3807     41.3960\n  1500    200     0.0000   -41.3695     1.4813    27.937245    -18.0731    -37.2423     41.3960\n  1500    400     0.0000   -41.3695     1.4813    28.368898    -18.3531    -37.1051     41.3960\n  1500    600     0.0000   -41.3695     1.4813    28.790438    -18.6256    -36.9690     41.3960\n  1500    800     0.0000   -41.3695     1.4813    29.202344    -18.8909    -36.8342     41.3960\n  1500   1000     0.0000   -41.3695     1.4813    29.605059    -19.1494    -36.7005     41.3960\n  1500   1200     0.0000   -41.3695     1.4813    29.998996    -19.4012    -36.5680     41.3960\n  1500   1400     0.0000   -41.3695     1.4813    30.384532    -19.6469    -36.4366     41.3960\n  1500   1600     0.0000   -41.3695     1.4813    30.762024    -19.8865    -36.3064     41.3960\n  1500   1800     0.0000   -41.3695     1.4813    31.131799    -20.1204    -36.1773     41.3960\n  1500   2000     0.0000   -41.3695     1.4813    31.494166    -20.3488    -36.0493     41.3960\n  1500   2200     0.0000   -41.3695     1.4813    31.849412    -20.5719    -35.9224     41.3960\n  1500   2400     0.0000   -41.3695     1.4813    32.197808    -20.7900    -35.7967     41.3960\n  1500   2600     0.0000   -41.3695     1.4813    32.539606    -21.0031    -35.6720     41.3960\n  1500   2800     0.0000   -41.3695     1.4813    32.875045    -21.2116    -35.5485     41.3960\n  1500   3000     0.0000   -41.3695     1.4813    33.204348    -21.4156    -35.4260     41.3960\n  1500   3200     0.0000   -41.3695     1.4813    33.527728    -21.6152    -35.3045     41.3960\n  1500   3400     0.0000   -41.3695     1.4813    33.845384    -21.8106    -35.1841     41.3960\n  1500   3600     0.0000   -41.3695     1.4813    34.157505    -22.0019    -35.0648     41.3960\n  1500   3800     0.0000   -41.3695     1.4813    34.464270    -22.1894    -34.9465     41.3960\n  1500   4000     0.0000   -41.3695     1.4813    34.765849    -22.3730    -34.8292     41.3960\n  1500   4200     0.0000   -41.3695     1.4813    35.062403    -22.5530    -34.7130     41.3960\n  1500   4400     0.0000   -41.3695     1.4813    35.354084    -22.7294    -34.5977     41.3960\n  1500   4600     0.0000   -41.3695     1.4813    35.641040    -22.9024    -34.4834     41.3960\n  1500   4800     0.0000   -41.3695     1.4813    35.923408    -23.0720    -34.3702     41.3960\n  1500   5000     0.0000   -41.3695     1.4813    36.201320    -23.2385    -34.2578     41.3960\n  1500   5200     0.0000   -41.3695     1.4813    36.474903    -23.4018    -34.1465     41.3960\n  1500   5400     0.0000   -41.3695     1.4813    36.744278    -23.5621    -34.0361     41.3960\n  1500   5600     0.0000   -41.3695     1.4813    37.009558    -23.7194    -33.9266     41.3960\n  1500   5800     0.0000   -41.3695     1.4813    37.270856    -23.8739    -33.8181     41.3960\n  1500   6000     0.0000   -41.3695     1.4813    37.528276    -24.0256    -33.7105     41.3960\n  1500   6200     0.0000   -41.3695     1.4813    37.781919    -24.1746    -33.6038     41.3960\n  1500   6400     0.0000   -41.3695     1.4813    38.031882    -24.3210    -33.4980     41.3960\n  1500   6600     0.0000   -41.3695     1.4813    38.278259    -24.4648    -33.3931     41.3960\n  1500   6800     0.0000   -41.3695     1.4813    38.521138    -24.6061    -33.2891     41.3960\n  1500   7000     0.0000   -41.3695     1.4813    38.760605    -24.7450    -33.1860     41.3960\n  1500   7200     0.0000   -41.3695     1.4813    38.996744    -24.8816    -33.0837     41.3960\n  1500   7400     0.0000   -41.3695     1.4813    39.229633    -25.0159    -32.9823     41.3960\n  1500   7600     0.0000   -41.3695     1.4813    39.459350    -25.1479    -32.8818     41.3960\n  1500   7800     0.0000   -41.3695     1.4813    39.685966    -25.2777    -32.7820     41.3960\n  1500   8000     0.0000   -41.3695     1.4813    39.909555    -25.4055    -32.6832     41.3960\n  1500   8200     0.0000   -41.3695     1.4813    40.130183    -25.5311    -32.5851     41.3960\n  1500   8400     0.0000   -41.3695     1.4813    40.347918    -25.6548    -32.4878     41.3960\n  2000      0     0.0000   -41.1680     1.4216    27.493962    -17.7443    -37.1746     41.1925\n  2000    200     0.0000   -41.1680     1.4216    27.936268    -18.0307    -37.0366     41.1925\n  2000    400     0.0000   -41.1680     1.4216    28.367937    -18.3092    -36.8997     41.1925\n  2000    600     0.0000   -41.1680     1.4216    28.789491    -18.5802    -36.7640     41.1925\n  2000    800     0.0000   -41.1680     1.4216    29.201412    -18.8441    -36.6294     41.1925\n  2000   1000     0.0000   -41.1680     1.4216    29.604141    -19.1011    -36.4961     41.1925\n  2000   1200     0.0000   -41.1680     1.4216    29.998091    -19.3516    -36.3639     41.1925\n  2000   1400     0.0000   -41.1680     1.4216    30.383640    -19.5958    -36.2328     41.1925\n  2000   1600     0.0000   -41.1680     1.4216    30.761143    -19.8341    -36.1030     41.1925\n  2000   1800     0.0000   -41.1680     1.4216    31.130930    -20.0667    -35.9742     41.1925\n  2000   2000     0.0000   -41.1680     1.4216    31.493308    -20.2938    -35.8466     41.1925\n  2000   2200     0.0000   -41.1680     1.4216    31.848565    -20.5157    -35.7200     41.1925\n  2000   2400     0.0000   -41.1680     1.4216    32.196971    -20.7325    -35.5946     41.1925\n  2000   2600     0.0000   -41.1680     1.4216    32.538779    -20.9445    -35.4703     41.1925\n  2000   2800     0.0000   -41.1680     1.4216    32.874228    -21.1518    -35.3471     41.1925\n  2000   3000     0.0000   -41.1680     1.4216    33.203540    -21.3546    -35.2249     41.1925\n  2000   3200     0.0000   -41.1680     1.4216    33.526929    -21.5531    -35.1038     41.1925\n  2000   3400     0.0000   -41.1680     1.4216    33.844594    -21.7474    -34.9838     41.1925\n  2000   3600     0.0000   -41.1680     1.4216    34.156723    -21.9377    -34.8648     41.1925\n  2000   3800     0.0000   -41.1680     1.4216    34.463496    -22.1240    -34.7469     41.1925\n  2000   4000     0.0000   -41.1680     1.4216    34.765083    -22.3066    -34.6299     41.1925\n  2000   4200     0.0000   -41.1680     1.4216    35.061644    -22.4855    -34.5140     41.1925\n  2000   4400     0.0000   -41.1680     1.4216    35.353334    -22.6610    -34.3991     41.1925\n  2000   4600     0.0000   -41.1680     1.4216    35.640296    -22.8330    -34.2852     41.1925\n  2000   4800     0.0000   -41.1680     1.4216    35.922671    -23.0017    -34.1722     41.1925\n  2000   5000     0.0000   -41.1680     1.4216    36.200590    -23.1671    -34.0602     41.1925\n  2000   5200     0.0000   -41.1680     1.4216    36.474180    -23.3295    -33.9492     41.1925\n  2000   5400     0.0000   -41.1680     1.4216    36.743561    -23.4889    -33.8392     41.1925\n  2000   5600     0.0000   -41.1680     1.4216    37.008848    -23.6453    -33.7301     41.1925\n  2000   5800     0.0000   -41.1680     1.4216    37.270151    -23.7989    -33.6219     41.1925\n  2000   6000     0.0000   -41.1680     1.4216    37.527577    -23.9497    -33.5146     41.1925\n  2000   6200     0.0000   -41.1680     1.4216    37.781226    -24.0978    -33.4083     41.1925\n  2000   6400     0.0000   -41.1680     1.4216    38.031195    -24.2434    -33.3028     41.1925\n  2000   6600     0.0000   -41.1680     1.4216    38.277577    -24.3864    -33.1982     41.1925\n  2000   6800     0.0000   -41.1680     1.4216    38.520461    -24.5269    -33.0946     41.1925\n  2000   7000     0.0000   -41.1680     1.4216    38.759934    -24.6650    -32.9918     41.1925\n  2000   7200     0.0000   -41.1680     1.4216    38.996078    -24.8007    -32.8898     41.1925\n  2000   7400     0.0000   -41.1680     1.4216    39.228972    -24.9342    -32.7888     41.1925\n  2000   7600     0.0000   -41.1680     1.4216    39.458693    -25.0655    -32.6885     41.1925\n  2000   7800     0.0000   -41.1680     1.4216    39.685315    -25.1946    -32.5891     41.1925\n  2000   8000     0.0000   -41.1680     1.4216    39.908908    -25.3216    -32.4906     41.1925\n  2000   8200     0.0000   -41.1680     1.4216    40.129541    -25.4465    -32.3928     41.1925\n  2000   8400     0.0000   -41.1680     1.4216    40.347280    -25.5694    -32.2959     41.1925\n  2500      0     0.0000   -40.9664     1.3620    27.492947    -17.7035    -36.9686     40.9890\n  2500    200     0.0000   -40.9664     1.3620    27.935270    -17.9884    -36.8308     40.9890\n  2500    400     0.0000   -40.9664     1.3620    28.366955    -18.2654    -36.6943     40.9890\n  2500    600     0.0000   -40.9664     1.3620    28.788525    -18.5348    -36.5589     40.9890\n  2500    800     0.0000   -40.9664     1.3620    29.200461    -18.7972    -36.4247     40.9890\n  2500   1000     0.0000   -40.9664     1.3620    29.603204    -19.0528    -36.2917     40.9890\n  2500   1200     0.0000   -40.9664     1.3620    29.997167    -19.3019    -36.1598     40.9890\n  2500   1400     0.0000   -40.9664     1.3620    30.382730    -19.5448    -36.0291     40.9890\n  2500   1600     0.0000   -40.9664     1.3620    30.760245    -19.7817    -35.8995     40.9890\n  2500   1800     0.0000   -40.9664     1.3620    31.130044    -20.0130    -35.7711     40.9890\n  2500   2000     0.0000   -40.9664     1.3620    31.492434    -20.2389    -35.6438     40.9890\n  2500   2200     0.0000   -40.9664     1.3620    31.847702    -20.4595    -35.5177     40.9890\n  2500   2400     0.0000   -40.9664     1.3620    32.196118    -20.6751    -35.3926     40.9890\n  2500   2600     0.0000   -40.9664     1.3620    32.537936    -20.8859    -35.2686     40.9890\n  2500   2800     0.0000   -40.9664     1.3620    32.873394    -21.0920    -35.1457     40.9890\n  2500   3000     0.0000   -40.9664     1.3620    33.202716    -21.2937    -35.0239     40.9890\n  2500   3200     0.0000   -40.9664     1.3620    33.526114    -21.4910    -34.9032     40.9890\n  2500   3400     0.0000   -40.9664     1.3620    33.843788    -21.6842    -34.7835     40.9890\n  2500   3600     0.0000   -40.9664     1.3620    34.155926    -21.8734    -34.6648     40.9890\n  2500   3800     0.0000   -40.9664     1.3620    34.462707    -22.0587    -34.5472     40.9890\n  2500   4000     0.0000   -40.9664     1.3620    34.764302    -22.2402    -34.4306     40.9890\n  2500   4200     0.0000   -40.9664     1.3620    35.060871    -22.4181    -34.3151     40.9890\n  2500   4400     0.0000   -40.9664     1.3620    35.352568    -22.5925    -34.2005     40.9890\n  2500   4600     0.0000   -40.9664     1.3620    35.639538    -22.7636    -34.0869     40.9890\n  2500   4800     0.0000   -40.9664     1.3620    35.921920    -22.9313    -33.9743     40.9890\n  2500   5000     0.0000   -40.9664     1.3620    36.199846    -23.0958    -33.8627     40.9890\n  2500   5200     0.0000   -40.9664     1.3620    36.473442    -23.2572    -33.7520     40.9890\n  2500   5400     0.0000   -40.9664     1.3620    36.742830    -23.4157    -33.6423     40.9890\n  2500   5600     0.0000   -40.9664     1.3620    37.008123    -23.5712    -33.5335     40.9890\n  2500   5800     0.0000   -40.9664     1.3620    37.269433    -23.7239    -33.4256     40.9890\n  2500   6000     0.0000   -40.9664     1.3620    37.526865    -23.8738    -33.3187     40.9890\n  2500   6200     0.0000   -40.9664     1.3620    37.780519    -24.0211    -33.2127     40.9890\n  2500   6400     0.0000   -40.9664     1.3620    38.030494    -24.1658    -33.1076     40.9890\n  2500   6600     0.0000   -40.9664     1.3620    38.276882    -24.3079    -33.0034     40.9890\n  2500   6800     0.0000   -40.9664     1.3620    38.519772    -24.4476    -32.9000     40.9890\n  2500   7000     0.0000   -40.9664     1.3620    38.759250    -24.5849    -32.7975     40.9890\n  2500   7200     0.0000   -40.9664     1.3620    38.995399    -24.7199    -32.6959     40.9890\n  2500   7400     0.0000   -40.9664     1.3620    39.228299    -24.8526    -32.5952     40.9890\n  2500   7600     0.0000   -40.9664     1.3620    39.458025    -24.9831    -32.4953     40.9890\n  2500   7800     0.0000   -40.9664     1.3620    39.684651    -25.1114    -32.3962     40.9890\n  2500   8000     0.0000   -40.9664     1.3620    39.908249    -25.2377    -32.2980     40.9890\n  2500   8200     0.0000   -40.9664     1.3620    40.128887    -25.3618    -32.2005     40.9890\n  2500   8400     0.0000   -40.9664     1.3620    40.346630    -25.4840    -32.1039     40.9890\n  3000      0     0.0000   -40.7649     1.3023    27.491911    -17.6627    -36.7626     40.7857\n  3000    200     0.0000   -40.7649     1.3023    27.934252    -17.9460    -36.6251     40.7857\n  3000    400     0.0000   -40.7649     1.3023    28.365954    -18.2215    -36.4889     40.7857\n  3000    600     0.0000   -40.7649     1.3023    28.787540    -18.4894    -36.3538     40.7857\n  3000    800     0.0000   -40.7649     1.3023    29.199491    -18.7503    -36.2200     40.7857\n  3000   1000     0.0000   -40.7649     1.3023    29.602249    -19.0045    -36.0873     40.7857\n  3000   1200     0.0000   -40.7649     1.3023    29.996226    -19.2522    -35.9557     40.7857\n  3000   1400     0.0000   -40.7649     1.3023    30.381801    -19.4937    -35.8254     40.7857\n  3000   1600     0.0000   -40.7649     1.3023    30.759330    -19.7293    -35.6961     40.7857\n  3000   1800     0.0000   -40.7649     1.3023    31.129141    -19.9593    -35.5681     40.7857\n  3000   2000     0.0000   -40.7649     1.3023    31.491542    -20.1839    -35.4411     40.7857\n  3000   2200     0.0000   -40.7649     1.3023    31.846821    -20.4033    -35.3153     40.7857\n  3000   2400     0.0000   -40.7649     1.3023    32.195248    -20.6177    -35.1905     40.7857\n  3000   2600     0.0000   -40.7649     1.3023    32.537077    -20.8272    -35.0669     40.7857\n  3000   2800     0.0000   -40.7649     1.3023    32.872545    -21.0322    -34.9444     40.7857\n  3000   3000     0.0000   -40.7649     1.3023    33.201877    -21.2327    -34.8229     40.7857\n  3000   3200     0.0000   -40.7649     1.3023    33.525284    -21.4289    -34.7025     40.7857\n  3000   3400     0.0000   -40.7649     1.3023    33.842966    -21.6210    -34.5832     40.7857\n  3000   3600     0.0000   -40.7649     1.3023    34.155113    -21.8091    -34.4649     40.7857\n  3000   3800     0.0000   -40.7649     1.3023    34.461903    -21.9933    -34.3476     40.7857\n  3000   4000     0.0000   -40.7649     1.3023    34.763506    -22.1738    -34.2313     40.7857\n  3000   4200     0.0000   -40.7649     1.3023    35.060083    -22.3507    -34.1161     40.7857\n  3000   4400     0.0000   -40.7649     1.3023    35.351788    -22.5241    -34.0019     40.7857\n  3000   4600     0.0000   -40.7649     1.3023    35.638765    -22.6941    -33.8886     40.7857\n  3000   4800     0.0000   -40.7649     1.3023    35.921154    -22.8609    -33.7764     40.7857\n  3000   5000     0.0000   -40.7649     1.3023    36.199088    -23.0245    -33.6651     40.7857\n  3000   5200     0.0000   -40.7649     1.3023    36.472691    -23.1850    -33.5547     40.7857\n  3000   5400     0.0000   -40.7649     1.3023    36.742085    -23.3425    -33.4454     40.7857\n  3000   5600     0.0000   -40.7649     1.3023    37.007385    -23.4971    -33.3369     40.7857\n  3000   5800     0.0000   -40.7649     1.3023    37.268701    -23.6489    -33.2294     40.7857\n  3000   6000     0.0000   -40.7649     1.3023    37.526139    -23.7980    -33.1228     40.7857\n  3000   6200     0.0000   -40.7649     1.3023    37.779800    -23.9444    -33.0171     40.7857\n  3000   6400     0.0000   -40.7649     1.3023    38.029781    -24.0882    -32.9124     40.7857\n  3000   6600     0.0000   -40.7649     1.3023    38.276174    -24.2295    -32.8085     40.7857\n  3000   6800     0.0000   -40.7649     1.3023    38.519070    -24.3684    -32.7055     40.7857\n  3000   7000     0.0000   -40.7649     1.3023    38.758554    -24.5049    -32.6033     40.7857\n  3000   7200     0.0000   -40.7649     1.3023    38.994708    -24.6390    -32.5020     40.7857\n  3000   7400     0.0000   -40.7649     1.3023    39.227613    -24.7710    -32.4016     40.7857\n  3000   7600     0.0000   -40.7649     1.3023    39.457344    -24.9007    -32.3020     40.7857\n  3000   7800     0.0000   -40.7649     1.3023    39.683975    -25.0282    -32.2033     40.7857\n  3000   8000     0.0000   -40.7649     1.3023    39.907578    -25.1537    -32.1054     40.7857\n  3000   8200     0.0000   -40.7649     1.3023    40.128221    -25.2772    -32.0083     40.7857\n  3000   8400     0.0000   -40.7649     1.3023    40.345969    -25.3986    -31.9120     40.7857\n  3500      0     0.0000   -40.5633     1.2426    27.490855    -17.6219    -36.5566     40.5823\n  3500    200     0.0000   -40.5633     1.2426    27.933214    -17.9036    -36.4194     40.5823\n  3500    400     0.0000   -40.5633     1.2426    28.364934    -18.1776    -36.2835     40.5823\n  3500    600     0.0000   -40.5633     1.2426    28.786536    -18.4440    -36.1488     40.5823\n  3500    800     0.0000   -40.5633     1.2426    29.198502    -18.7035    -36.0152     40.5823\n  3500   1000     0.0000   -40.5633     1.2426    29.601275    -18.9562    -35.8829     40.5823\n  3500   1200     0.0000   -40.5633     1.2426    29.995266    -19.2025    -35.7517     40.5823\n  3500   1400     0.0000   -40.5633     1.2426    30.380855    -19.4427    -35.6216     40.5823\n  3500   1600     0.0000   -40.5633     1.2426    30.758396    -19.6770    -35.4927     40.5823\n  3500   1800     0.0000   -40.5633     1.2426    31.128219    -19.9056    -35.3650     40.5823\n  3500   2000     0.0000   -40.5633     1.2426    31.490633    -20.1289    -35.2384     40.5823\n  3500   2200     0.0000   -40.5633     1.2426    31.845923    -20.3471    -35.1129     40.5823\n  3500   2400     0.0000   -40.5633     1.2426    32.194362    -20.5602    -34.9885     40.5823\n  3500   2600     0.0000   -40.5633     1.2426    32.536201    -20.7686    -34.8652     40.5823\n  3500   2800     0.0000   -40.5633     1.2426    32.871679    -20.9724    -34.7430     40.5823\n  3500   3000     0.0000   -40.5633     1.2426    33.201021    -21.1717    -34.6219     40.5823\n  3500   3200     0.0000   -40.5633     1.2426    33.524438    -21.3668    -34.5018     40.5823\n  3500   3400     0.0000   -40.5633     1.2426    33.842129    -21.5578    -34.3828     40.5823\n  3500   3600     0.0000   -40.5633     1.2426    34.154285    -21.7448    -34.2649     40.5823\n  3500   3800     0.0000   -40.5633     1.2426    34.461084    -21.9280    -34.1480     40.5823\n  3500   4000     0.0000   -40.5633     1.2426    34.762695    -22.1074    -34.0320     40.5823\n  3500   4200     0.0000   -40.5633     1.2426    35.059281    -22.2833    -33.9172     40.5823\n  3500   4400     0.0000   -40.5633     1.2426    35.350993    -22.4557    -33.8033     40.5823\n  3500   4600     0.0000   -40.5633     1.2426    35.637978    -22.6247    -33.6904     40.5823\n  3500   4800     0.0000   -40.5633     1.2426    35.920375    -22.7905    -33.5784     40.5823\n  3500   5000     0.0000   -40.5633     1.2426    36.198315    -22.9531    -33.4675     40.5823\n  3500   5200     0.0000   -40.5633     1.2426    36.471925    -23.1127    -33.3575     40.5823\n  3500   5400     0.0000   -40.5633     1.2426    36.741326    -23.2693    -33.2485     40.5823\n  3500   5600     0.0000   -40.5633     1.2426    37.006633    -23.4230    -33.1404     40.5823\n  3500   5800     0.0000   -40.5633     1.2426    37.267956    -23.5739    -33.0332     40.5823\n  3500   6000     0.0000   -40.5633     1.2426    37.525400    -23.7221    -32.9269     40.5823\n  3500   6200     0.0000   -40.5633     1.2426    37.779067    -23.8676    -32.8216     40.5823\n  3500   6400     0.0000   -40.5633     1.2426    38.029054    -24.0106    -32.7171     40.5823\n  3500   6600     0.0000   -40.5633     1.2426    38.275453    -24.1511    -32.6136     40.5823\n  3500   6800     0.0000   -40.5633     1.2426    38.518355    -24.2891    -32.5109     40.5823\n  3500   7000     0.0000   -40.5633     1.2426    38.757844    -24.4248    -32.4091     40.5823\n  3500   7200     0.0000   -40.5633     1.2426    38.994004    -24.5582    -32.3081     40.5823\n  3500   7400     0.0000   -40.5633     1.2426    39.226914    -24.6893    -32.2080     40.5823\n  3500   7600     0.0000   -40.5633     1.2426    39.456650    -24.8183    -32.1088     40.5823\n  3500   7800     0.0000   -40.5633     1.2426    39.683287    -24.9451    -32.0104     40.5823\n  3500   8000     0.0000   -40.5633     1.2426    39.906895    -25.0698    -31.9128     40.5823\n  3500   8200     0.0000   -40.5633     1.2426    40.127542    -25.1925    -31.8160     40.5823\n  3500   8400     0.0000   -40.5633     1.2426    40.345295    -25.3133    -31.7200     40.5823\n  4000      0     0.0000   -40.3617     1.1830    27.489779    -17.5811    -36.3506     40.3791\n  4000    200     0.0000   -40.3617     1.1830    27.932157    -17.8613    -36.2138     40.3791\n  4000    400     0.0000   -40.3617     1.1830    28.363894    -18.1336    -36.0781     40.3791\n  4000    600     0.0000   -40.3617     1.1830    28.785512    -18.3986    -35.9437     40.3791\n  4000    800     0.0000   -40.3617     1.1830    29.197494    -18.6566    -35.8105     40.3791\n  4000   1000     0.0000   -40.3617     1.1830    29.600282    -18.9079    -35.6785     40.3791\n  4000   1200     0.0000   -40.3617     1.1830    29.994287    -19.1528    -35.5476     40.3791\n  4000   1400     0.0000   -40.3617     1.1830    30.379890    -19.3916    -35.4179     40.3791\n  4000   1600     0.0000   -40.3617     1.1830    30.757445    -19.6246    -35.2894     40.3791\n  4000   1800     0.0000   -40.3617     1.1830    31.127281    -19.8519    -35.1619     40.3791\n  4000   2000     0.0000   -40.3617     1.1830    31.489706    -20.0740    -35.0357     40.3791\n  4000   2200     0.0000   -40.3617     1.1830    31.845009    -20.2908    -34.9105     40.3791\n  4000   2400     0.0000   -40.3617     1.1830    32.193458    -20.5028    -34.7865     40.3791\n  4000   2600     0.0000   -40.3617     1.1830    32.535308    -20.7100    -34.6635     40.3791\n  4000   2800     0.0000   -40.3617     1.1830    32.870797    -20.9126    -34.5417     40.3791\n  4000   3000     0.0000   -40.3617     1.1830    33.200149    -21.1108    -34.4209     40.3791\n  4000   3200     0.0000   -40.3617     1.1830    33.523576    -21.3047    -34.3012     40.3791\n  4000   3400     0.0000   -40.3617     1.1830    33.841277    -21.4946    -34.1825     40.3791\n  4000   3600     0.0000   -40.3617     1.1830    34.153442    -21.6805    -34.0649     40.3791\n  4000   3800     0.0000   -40.3617     1.1830    34.460249    -21.8626    -33.9483     40.3791\n  4000   4000     0.0000   -40.3617     1.1830    34.761869    -22.0410    -33.8328     40.3791\n  4000   4200     0.0000   -40.3617     1.1830    35.058463    -22.2159    -33.7182     40.3791\n  4000   4400     0.0000   -40.3617     1.1830    35.350183    -22.3873    -33.6047     40.3791\n  4000   4600     0.0000   -40.3617     1.1830    35.637176    -22.5553    -33.4921     40.3791\n  4000   4800     0.0000   -40.3617     1.1830    35.919581    -22.7201    -33.3805     40.3791\n  4000   5000     0.0000   -40.3617     1.1830    36.197528    -22.8818    -33.2699     40.3791\n  4000   5200     0.0000   -40.3617     1.1830    36.471146    -23.0404    -33.1603     40.3791\n  4000   5400     0.0000   -40.3617     1.1830    36.740554    -23.1961    -33.0516     40.3791\n  4000   5600     0.0000   -40.3617     1.1830    37.005868    -23.3489    -32.9438     40.3791\n  4000   5800     0.0000   -40.3617     1.1830    37.267197    -23.4989    -32.8370     40.3791\n  4000   6000     0.0000   -40.3617     1.1830    37.524648    -23.6462    -32.7310     40.3791\n  4000   6200     0.0000   -40.3617     1.1830    37.778321    -23.7909    -32.6260     40.3791\n  4000   6400     0.0000   -40.3617     1.1830    38.028314    -23.9330    -32.5219     40.3791\n  4000   6600     0.0000   -40.3617     1.1830    38.274719    -24.0727    -32.4187     40.3791\n  4000   6800     0.0000   -40.3617     1.1830    38.517627    -24.2099    -32.3164     40.3791\n  4000   7000     0.0000   -40.3617     1.1830    38.757122    -24.3447    -32.2149     40.3791\n  4000   7200     0.0000   -40.3617     1.1830    38.993287    -24.4773    -32.1143     40.3791\n  4000   7400     0.0000   -40.3617     1.1830    39.226203    -24.6077    -32.0145     40.3791\n  4000   7600     0.0000   -40.3617     1.1830    39.455945    -24.7358    -31.9156     40.3791\n  4000   7800     0.0000   -40.3617     1.1830    39.682586    -24.8619    -31.8175     40.3791\n  4000   8000     0.0000   -40.3617     1.1830    39.906199    -24.9859    -31.7202     40.3791\n  4000   8200     0.0000   -40.3617     1.1830    40.126852    -25.1079    -31.6237     40.3791\n  4000   8400     0.0000   -40.3617     1.1830    40.344610    -25.2279    -31.5281     40.3791\n  4500      0     0.0000   -40.1602     1.1233    27.488683    -17.5403    -36.1446     40.1759\n  4500    200     0.0000   -40.1602     1.1233    27.931079    -17.8189    -36.0081     40.1759\n  4500    400     0.0000   -40.1602     1.1233    28.362834    -18.0897    -35.8728     40.1759\n  4500    600     0.0000   -40.1602     1.1233    28.784470    -18.3532    -35.7387     40.1759\n  4500    800     0.0000   -40.1602     1.1233    29.196467    -18.6097    -35.6058     40.1759\n  4500   1000     0.0000   -40.1602     1.1233    29.599271    -18.8596    -35.4741     40.1759\n  4500   1200     0.0000   -40.1602     1.1233    29.993291    -19.1031    -35.3435     40.1759\n  4500   1400     0.0000   -40.1602     1.1233    30.378908    -19.3405    -35.2142     40.1759\n  4500   1600     0.0000   -40.1602     1.1233    30.756476    -19.5722    -35.0860     40.1759\n  4500   1800     0.0000   -40.1602     1.1233    31.126325    -19.7982    -34.9589     40.1759\n  4500   2000     0.0000   -40.1602     1.1233    31.488763    -20.0190    -34.8330     40.1759\n  4500   2200     0.0000   -40.1602     1.1233    31.844077    -20.2346    -34.7081     40.1759\n  4500   2400     0.0000   -40.1602     1.1233    32.192539    -20.4453    -34.5844     40.1759\n  4500   2600     0.0000   -40.1602     1.1233    32.534400    -20.6513    -34.4618     40.1759\n  4500   2800     0.0000   -40.1602     1.1233    32.869899    -20.8528    -34.3403     40.1759\n  4500   3000     0.0000   -40.1602     1.1233    33.199262    -21.0498    -34.2199     40.1759\n  4500   3200     0.0000   -40.1602     1.1233    33.522698    -21.2426    -34.1005     40.1759\n  4500   3400     0.0000   -40.1602     1.1233    33.840409    -21.4314    -33.9822     40.1759\n  4500   3600     0.0000   -40.1602     1.1233    34.152583    -21.6162    -33.8649     40.1759\n  4500   3800     0.0000   -40.1602     1.1233    34.459400    -21.7973    -33.7487     40.1759\n  4500   4000     0.0000   -40.1602     1.1233    34.761029    -21.9746    -33.6335     40.1759\n  4500   4200     0.0000   -40.1602     1.1233    35.057631    -22.1485    -33.5193     40.1759\n  4500   4400     0.0000   -40.1602     1.1233    35.349359    -22.3188    -33.4061     40.1759\n  4500   4600     0.0000   -40.1602     1.1233    35.636360    -22.4859    -33.2939     40.1759\n  4500   4800     0.0000   -40.1602     1.1233    35.918772    -22.6497    -33.1826     40.1759\n  4500   5000     0.0000   -40.1602     1.1233    36.196728    -22.8104    -33.0724     40.1759\n  4500   5200     0.0000   -40.1602     1.1233    36.470353    -22.9681    -32.9630     40.1759\n  4500   5400     0.0000   -40.1602     1.1233    36.739768    -23.1229    -32.8547     40.1759\n  4500   5600     0.0000   -40.1602     1.1233    37.005088    -23.2748    -32.7473     40.1759\n  4500   5800     0.0000   -40.1602     1.1233    37.266425    -23.4239    -32.6408     40.1759\n  4500   6000     0.0000   -40.1602     1.1233    37.523882    -23.5703    -32.5352     40.1759\n  4500   6200     0.0000   -40.1602     1.1233    37.777562    -23.7141    -32.4305     40.1759\n  4500   6400     0.0000   -40.1602     1.1233    38.027561    -23.8554    -32.3267     40.1759\n  4500   6600     0.0000   -40.1602     1.1233    38.273972    -23.9942    -32.2238     40.1759\n  4500   6800     0.0000   -40.1602     1.1233    38.516886    -24.1306    -32.1218     40.1759\n  4500   7000     0.0000   -40.1602     1.1233    38.756387    -24.2647    -32.0207     40.1759\n  4500   7200     0.0000   -40.1602     1.1233    38.992558    -24.3965    -31.9204     40.1759\n  4500   7400     0.0000   -40.1602     1.1233    39.225479    -24.5260    -31.8209     40.1759\n  4500   7600     0.0000   -40.1602     1.1233    39.455227    -24.6534    -31.7223     40.1759\n  4500   7800     0.0000   -40.1602     1.1233    39.681874    -24.7787    -31.6246     40.1759\n  4500   8000     0.0000   -40.1602     1.1233    39.905492    -24.9020    -31.5276     40.1759\n  4500   8200     0.0000   -40.1602     1.1233    40.126149    -25.0232    -31.4315     40.1759\n  4500   8400     0.0000   -40.1602     1.1233    40.343913    -25.1425    -31.3361     40.1759\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180623_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.5613989      -14.6282589       15.9816550   m   m   m\ninitial_baseline_rate:          0.0000000        0.0085490       -0.0055608   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000      -14.8438160       16.0763524   m   m   m\nprecision_baseline_rate:        0.0000000        0.0187805       -0.0088620   m/s m/s m/s\nunwrap_phase_constant:           -0.00022     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180623_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.561    -14.628     15.982\norbit baseline rate   (TCN) (m/s):  0.000e+00  8.549e-03 -5.561e-03\n\nbaseline vector (TCN)   (m):      0.000    -14.844     16.076\nbaseline rate   (TCN) (m/s):  0.000e+00  1.878e-02 -8.862e-03\n\nSLC-1 center baseline length (m):    21.8812\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000   -15.0191    16.1591    27.497816      7.3990    -20.7832     22.0610\n     0    200     0.0000   -15.0191    16.1591    27.940057      7.2384    -20.8397     22.0610\n     0    400     0.0000   -15.0191    16.1591    28.371664      7.0812    -20.8937     22.0610\n     0    600     0.0000   -15.0191    16.1591    28.793160      6.9273    -20.9452     22.0610\n     0    800     0.0000   -15.0191    16.1591    29.205025      6.7766    -20.9944     22.0610\n     0   1000     0.0000   -15.0191    16.1591    29.607701      6.6289    -21.0415     22.0610\n     0   1200     0.0000   -15.0191    16.1591    30.001600      6.4840    -21.0866     22.0610\n     0   1400     0.0000   -15.0191    16.1591    30.387101      6.3420    -21.1298     22.0610\n     0   1600     0.0000   -15.0191    16.1591    30.764557      6.2027    -21.1711     22.0610\n     0   1800     0.0000   -15.0191    16.1591    31.134300      6.0659    -21.2107     22.0610\n     0   2000     0.0000   -15.0191    16.1591    31.496635      5.9317    -21.2486     22.0610\n     0   2200     0.0000   -15.0191    16.1591    31.851850      5.7998    -21.2850     22.0610\n     0   2400     0.0000   -15.0191    16.1591    32.200217      5.6703    -21.3199     22.0610\n     0   2600     0.0000   -15.0191    16.1591    32.541986      5.5430    -21.3533     22.0610\n     0   2800     0.0000   -15.0191    16.1591    32.877397      5.4179    -21.3854     22.0610\n     0   3000     0.0000   -15.0191    16.1591    33.206674      5.2949    -21.4162     22.0610\n     0   3200     0.0000   -15.0191    16.1591    33.530029      5.1740    -21.4457     22.0610\n     0   3400     0.0000   -15.0191    16.1591    33.847660      5.0550    -21.4741     22.0610\n     0   3600     0.0000   -15.0191    16.1591    34.159757      4.9380    -21.5013     22.0610\n     0   3800     0.0000   -15.0191    16.1591    34.466499      4.8228    -21.5274     22.0610\n     0   4000     0.0000   -15.0191    16.1591    34.768056      4.7094    -21.5525     22.0610\n     0   4200     0.0000   -15.0191    16.1591    35.064588      4.5978    -21.5766     22.0610\n     0   4400     0.0000   -15.0191    16.1591    35.356248      4.4879    -21.5997     22.0610\n     0   4600     0.0000   -15.0191    16.1591    35.643183      4.3797    -21.6219     22.0610\n     0   4800     0.0000   -15.0191    16.1591    35.925531      4.2731    -21.6432     22.0610\n     0   5000     0.0000   -15.0191    16.1591    36.203424      4.1681    -21.6637     22.0610\n     0   5200     0.0000   -15.0191    16.1591    36.476989      4.0646    -21.6834     22.0610\n     0   5400     0.0000   -15.0191    16.1591    36.746345      3.9626    -21.7022     22.0610\n     0   5600     0.0000   -15.0191    16.1591    37.011608      3.8621    -21.7203     22.0610\n     0   5800     0.0000   -15.0191    16.1591    37.272888      3.7630    -21.7377     22.0610\n     0   6000     0.0000   -15.0191    16.1591    37.530291      3.6653    -21.7544     22.0610\n     0   6200     0.0000   -15.0191    16.1591    37.783917      3.5690    -21.7704     22.0610\n     0   6400     0.0000   -15.0191    16.1591    38.033864      3.4740    -21.7858     22.0610\n     0   6600     0.0000   -15.0191    16.1591    38.280225      3.3803    -21.8005     22.0610\n     0   6800     0.0000   -15.0191    16.1591    38.523089      3.2878    -21.8147     22.0610\n     0   7000     0.0000   -15.0191    16.1591    38.762542      3.1966    -21.8282     22.0610\n     0   7200     0.0000   -15.0191    16.1591    38.998666      3.1066    -21.8412     22.0610\n     0   7400     0.0000   -15.0191    16.1591    39.231541      3.0178    -21.8536     22.0610\n     0   7600     0.0000   -15.0191    16.1591    39.461243      2.9302    -21.8656     22.0610\n     0   7800     0.0000   -15.0191    16.1591    39.687846      2.8437    -21.8770     22.0610\n     0   8000     0.0000   -15.0191    16.1591    39.911421      2.7583    -21.8879     22.0610\n     0   8200     0.0000   -15.0191    16.1591    40.132036      2.6740    -21.8984     22.0610\n     0   8400     0.0000   -15.0191    16.1591    40.349758      2.5908    -21.9084     22.0610\n   500      0     0.0000   -14.9805    16.1409    27.496883      7.4010    -20.7405     22.0214\n   500    200     0.0000   -14.9805    16.1409    27.939140      7.2407    -20.7970     22.0214\n   500    400     0.0000   -14.9805    16.1409    28.370762      7.0838    -20.8509     22.0214\n   500    600     0.0000   -14.9805    16.1409    28.792272      6.9303    -20.9025     22.0214\n   500    800     0.0000   -14.9805    16.1409    29.204150      6.7798    -20.9518     22.0214\n   500   1000     0.0000   -14.9805    16.1409    29.606840      6.6324    -20.9989     22.0214\n   500   1200     0.0000   -14.9805    16.1409    30.000750      6.4879    -21.0440     22.0214\n   500   1400     0.0000   -14.9805    16.1409    30.386263      6.3461    -21.0872     22.0214\n   500   1600     0.0000   -14.9805    16.1409    30.763731      6.2071    -21.1285     22.0214\n   500   1800     0.0000   -14.9805    16.1409    31.133484      6.0706    -21.1681     22.0214\n   500   2000     0.0000   -14.9805    16.1409    31.495829      5.9366    -21.2061     22.0214\n   500   2200     0.0000   -14.9805    16.1409    31.851055      5.8050    -21.2425     22.0214\n   500   2400     0.0000   -14.9805    16.1409    32.199431      5.6757    -21.2774     22.0214\n   500   2600     0.0000   -14.9805    16.1409    32.541209      5.5487    -21.3109     22.0214\n   500   2800     0.0000   -14.9805    16.1409    32.876630      5.4239    -21.3430     22.0214\n   500   3000     0.0000   -14.9805    16.1409    33.205915      5.3011    -21.3738     22.0214\n   500   3200     0.0000   -14.9805    16.1409    33.529278      5.1804    -21.4034     22.0214\n   500   3400     0.0000   -14.9805    16.1409    33.846917      5.0617    -21.4318     22.0214\n   500   3600     0.0000   -14.9805    16.1409    34.159022      4.9449    -21.4590     22.0214\n   500   3800     0.0000   -14.9805    16.1409    34.465771      4.8299    -21.4852     22.0214\n   500   4000     0.0000   -14.9805    16.1409    34.767335      4.7167    -21.5103     22.0214\n   500   4200     0.0000   -14.9805    16.1409    35.063874      4.6054    -21.5345     22.0214\n   500   4400     0.0000   -14.9805    16.1409    35.355542      4.4957    -21.5576     22.0214\n   500   4600     0.0000   -14.9805    16.1409    35.642483      4.3877    -21.5799     22.0214\n   500   4800     0.0000   -14.9805    16.1409    35.924838      4.2813    -21.6012     22.0214\n   500   5000     0.0000   -14.9805    16.1409    36.202737      4.1764    -21.6217     22.0214\n   500   5200     0.0000   -14.9805    16.1409    36.476308      4.0732    -21.6414     22.0214\n   500   5400     0.0000   -14.9805    16.1409    36.745670      3.9714    -21.6603     22.0214\n   500   5600     0.0000   -14.9805    16.1409    37.010938      3.8710    -21.6785     22.0214\n   500   5800     0.0000   -14.9805    16.1409    37.272224      3.7721    -21.6959     22.0214\n   500   6000     0.0000   -14.9805    16.1409    37.529632      3.6746    -21.7127     22.0214\n   500   6200     0.0000   -14.9805    16.1409    37.783264      3.5785    -21.7287     22.0214\n   500   6400     0.0000   -14.9805    16.1409    38.033217      3.4837    -21.7441     22.0214\n   500   6600     0.0000   -14.9805    16.1409    38.279583      3.3901    -21.7589     22.0214\n   500   6800     0.0000   -14.9805    16.1409    38.522452      3.2979    -21.7731     22.0214\n   500   7000     0.0000   -14.9805    16.1409    38.761909      3.2068    -21.7867     22.0214\n   500   7200     0.0000   -14.9805    16.1409    38.998038      3.1170    -21.7997     22.0214\n   500   7400     0.0000   -14.9805    16.1409    39.230918      3.0284    -21.8122     22.0214\n   500   7600     0.0000   -14.9805    16.1409    39.460624      2.9409    -21.8241     22.0214\n   500   7800     0.0000   -14.9805    16.1409    39.687232      2.8546    -21.8356     22.0214\n   500   8000     0.0000   -14.9805    16.1409    39.910811      2.7693    -21.8466     22.0214\n   500   8200     0.0000   -14.9805    16.1409    40.131431      2.6852    -21.8571     22.0214\n   500   8400     0.0000   -14.9805    16.1409    40.349156      2.6021    -21.8671     22.0214\n  1000      0     0.0000   -14.9419    16.1226    27.495930      7.4030    -20.6977     21.9818\n  1000    200     0.0000   -14.9419    16.1226    27.938203      7.2431    -20.7542     21.9818\n  1000    400     0.0000   -14.9419    16.1226    28.369840      7.0865    -20.8082     21.9818\n  1000    600     0.0000   -14.9419    16.1226    28.791365      6.9332    -20.8598     21.9818\n  1000    800     0.0000   -14.9419    16.1226    29.203256      6.7831    -20.9091     21.9818\n  1000   1000     0.0000   -14.9419    16.1226    29.605959      6.6360    -20.9562     21.9818\n  1000   1200     0.0000   -14.9419    16.1226    29.999882      6.4917    -21.0013     21.9818\n  1000   1400     0.0000   -14.9419    16.1226    30.385407      6.3503    -21.0446     21.9818\n  1000   1600     0.0000   -14.9419    16.1226    30.762886      6.2115    -21.0859     21.9818\n  1000   1800     0.0000   -14.9419    16.1226    31.132650      6.0753    -21.1256     21.9818\n  1000   2000     0.0000   -14.9419    16.1226    31.495006      5.9415    -21.1636     21.9818\n  1000   2200     0.0000   -14.9419    16.1226    31.850242      5.8102    -21.2000     21.9818\n  1000   2400     0.0000   -14.9419    16.1226    32.198628      5.6812    -21.2350     21.9818\n  1000   2600     0.0000   -14.9419    16.1226    32.540416      5.5544    -21.2685     21.9818\n  1000   2800     0.0000   -14.9419    16.1226    32.875845      5.4298    -21.3006     21.9818\n  1000   3000     0.0000   -14.9419    16.1226    33.205140      5.3073    -21.3315     21.9818\n  1000   3200     0.0000   -14.9419    16.1226    33.528511      5.1868    -21.3611     21.9818\n  1000   3400     0.0000   -14.9419    16.1226    33.846158      5.0683    -21.3895     21.9818\n  1000   3600     0.0000   -14.9419    16.1226    34.158271      4.9517    -21.4168     21.9818\n  1000   3800     0.0000   -14.9419    16.1226    34.465028      4.8370    -21.4430     21.9818\n  1000   4000     0.0000   -14.9419    16.1226    34.766600      4.7241    -21.4682     21.9818\n  1000   4200     0.0000   -14.9419    16.1226    35.063146      4.6129    -21.4923     21.9818\n  1000   4400     0.0000   -14.9419    16.1226    35.354821      4.5034    -21.5155     21.9818\n  1000   4600     0.0000   -14.9419    16.1226    35.641769      4.3956    -21.5378     21.9818\n  1000   4800     0.0000   -14.9419    16.1226    35.924130      4.2894    -21.5592     21.9818\n  1000   5000     0.0000   -14.9419    16.1226    36.202036      4.1848    -21.5798     21.9818\n  1000   5200     0.0000   -14.9419    16.1226    36.475612      4.0817    -21.5995     21.9818\n  1000   5400     0.0000   -14.9419    16.1226    36.744981      3.9801    -21.6185     21.9818\n  1000   5600     0.0000   -14.9419    16.1226    37.010255      3.8800    -21.6367     21.9818\n  1000   5800     0.0000   -14.9419    16.1226    37.271547      3.7813    -21.6541     21.9818\n  1000   6000     0.0000   -14.9419    16.1226    37.528961      3.6840    -21.6709     21.9818\n  1000   6200     0.0000   -14.9419    16.1226    37.782598      3.5880    -21.6870     21.9818\n  1000   6400     0.0000   -14.9419    16.1226    38.032556      3.4933    -21.7024     21.9818\n  1000   6600     0.0000   -14.9419    16.1226    38.278927      3.4000    -21.7173     21.9818\n  1000   6800     0.0000   -14.9419    16.1226    38.521801      3.3079    -21.7315     21.9818\n  1000   7000     0.0000   -14.9419    16.1226    38.761264      3.2170    -21.7451     21.9818\n  1000   7200     0.0000   -14.9419    16.1226    38.997398      3.1274    -21.7582     21.9818\n  1000   7400     0.0000   -14.9419    16.1226    39.230282      3.0389    -21.7707     21.9818\n  1000   7600     0.0000   -14.9419    16.1226    39.459993      2.9516    -21.7827     21.9818\n  1000   7800     0.0000   -14.9419    16.1226    39.686605      2.8654    -21.7942     21.9818\n  1000   8000     0.0000   -14.9419    16.1226    39.910189      2.7804    -21.8053     21.9818\n  1000   8200     0.0000   -14.9419    16.1226    40.130813      2.6964    -21.8158     21.9818\n  1000   8400     0.0000   -14.9419    16.1226    40.348543      2.6135    -21.8259     21.9818\n  1500      0     0.0000   -14.9033    16.1044    27.494956      7.4051    -20.6549     21.9422\n  1500    200     0.0000   -14.9033    16.1044    27.937245      7.2454    -20.7114     21.9422\n  1500    400     0.0000   -14.9033    16.1044    28.368898      7.0892    -20.7654     21.9422\n  1500    600     0.0000   -14.9033    16.1044    28.790438      6.9362    -20.8170     21.9422\n  1500    800     0.0000   -14.9033    16.1044    29.202344      6.7864    -20.8664     21.9422\n  1500   1000     0.0000   -14.9033    16.1044    29.605059      6.6395    -20.9136     21.9422\n  1500   1200     0.0000   -14.9033    16.1044    29.998996      6.4956    -20.9587     21.9422\n  1500   1400     0.0000   -14.9033    16.1044    30.384532      6.3544    -21.0019     21.9422\n  1500   1600     0.0000   -14.9033    16.1044    30.762024      6.2159    -21.0434     21.9422\n  1500   1800     0.0000   -14.9033    16.1044    31.131799      6.0800    -21.0830     21.9422\n  1500   2000     0.0000   -14.9033    16.1044    31.494166      5.9465    -21.1211     21.9422\n  1500   2200     0.0000   -14.9033    16.1044    31.849412      5.8154    -21.1575     21.9422\n  1500   2400     0.0000   -14.9033    16.1044    32.197808      5.6867    -21.1925     21.9422\n  1500   2600     0.0000   -14.9033    16.1044    32.539606      5.5601    -21.2260     21.9422\n  1500   2800     0.0000   -14.9033    16.1044    32.875045      5.4358    -21.2582     21.9422\n  1500   3000     0.0000   -14.9033    16.1044    33.204348      5.3135    -21.2891     21.9422\n  1500   3200     0.0000   -14.9033    16.1044    33.527728      5.1933    -21.3188     21.9422\n  1500   3400     0.0000   -14.9033    16.1044    33.845384      5.0750    -21.3472     21.9422\n  1500   3600     0.0000   -14.9033    16.1044    34.157505      4.9586    -21.3746     21.9422\n  1500   3800     0.0000   -14.9033    16.1044    34.464270      4.8441    -21.4008     21.9422\n  1500   4000     0.0000   -14.9033    16.1044    34.765849      4.7314    -21.4260     21.9422\n  1500   4200     0.0000   -14.9033    16.1044    35.062403      4.6204    -21.4502     21.9422\n  1500   4400     0.0000   -14.9033    16.1044    35.354084      4.5112    -21.4735     21.9422\n  1500   4600     0.0000   -14.9033    16.1044    35.641040      4.4036    -21.4958     21.9422\n  1500   4800     0.0000   -14.9033    16.1044    35.923408      4.2976    -21.5172     21.9422\n  1500   5000     0.0000   -14.9033    16.1044    36.201320      4.1932    -21.5378     21.9422\n  1500   5200     0.0000   -14.9033    16.1044    36.474903      4.0903    -21.5576     21.9422\n  1500   5400     0.0000   -14.9033    16.1044    36.744278      3.9889    -21.5766     21.9422\n  1500   5600     0.0000   -14.9033    16.1044    37.009558      3.8889    -21.5948     21.9422\n  1500   5800     0.0000   -14.9033    16.1044    37.270856      3.7904    -21.6123     21.9422\n  1500   6000     0.0000   -14.9033    16.1044    37.528276      3.6933    -21.6291     21.9422\n  1500   6200     0.0000   -14.9033    16.1044    37.781919      3.5975    -21.6453     21.9422\n  1500   6400     0.0000   -14.9033    16.1044    38.031882      3.5030    -21.6608     21.9422\n  1500   6600     0.0000   -14.9033    16.1044    38.278259      3.4099    -21.6756     21.9422\n  1500   6800     0.0000   -14.9033    16.1044    38.521138      3.3179    -21.6899     21.9422\n  1500   7000     0.0000   -14.9033    16.1044    38.760605      3.2273    -21.7036     21.9422\n  1500   7200     0.0000   -14.9033    16.1044    38.996744      3.1378    -21.7167     21.9422\n  1500   7400     0.0000   -14.9033    16.1044    39.229633      3.0495    -21.7293     21.9422\n  1500   7600     0.0000   -14.9033    16.1044    39.459350      2.9623    -21.7413     21.9422\n  1500   7800     0.0000   -14.9033    16.1044    39.685966      2.8763    -21.7529     21.9422\n  1500   8000     0.0000   -14.9033    16.1044    39.909555      2.7914    -21.7639     21.9422\n  1500   8200     0.0000   -14.9033    16.1044    40.130183      2.7076    -21.7745     21.9422\n  1500   8400     0.0000   -14.9033    16.1044    40.347918      2.6248    -21.7846     21.9422\n  2000      0     0.0000   -14.8647    16.0862    27.493962      7.4071    -20.6121     21.9026\n  2000    200     0.0000   -14.8647    16.0862    27.936268      7.2477    -20.6687     21.9026\n  2000    400     0.0000   -14.8647    16.0862    28.367937      7.0918    -20.7227     21.9026\n  2000    600     0.0000   -14.8647    16.0862    28.789491      6.9392    -20.7743     21.9026\n  2000    800     0.0000   -14.8647    16.0862    29.201412      6.7896    -20.8237     21.9026\n  2000   1000     0.0000   -14.8647    16.0862    29.604141      6.6431    -20.8709     21.9026\n  2000   1200     0.0000   -14.8647    16.0862    29.998091      6.4994    -20.9161     21.9026\n  2000   1400     0.0000   -14.8647    16.0862    30.383640      6.3585    -20.9593     21.9026\n  2000   1600     0.0000   -14.8647    16.0862    30.761143      6.2203    -21.0008     21.9026\n  2000   1800     0.0000   -14.8647    16.0862    31.130930      6.0846    -21.0405     21.9026\n  2000   2000     0.0000   -14.8647    16.0862    31.493308      5.9514    -21.0785     21.9026\n  2000   2200     0.0000   -14.8647    16.0862    31.848565      5.8206    -21.1150     21.9026\n  2000   2400     0.0000   -14.8647    16.0862    32.196971      5.6921    -21.1500     21.9026\n  2000   2600     0.0000   -14.8647    16.0862    32.538779      5.5659    -21.1836     21.9026\n  2000   2800     0.0000   -14.8647    16.0862    32.874228      5.4417    -21.2158     21.9026\n  2000   3000     0.0000   -14.8647    16.0862    33.203540      5.3197    -21.2468     21.9026\n  2000   3200     0.0000   -14.8647    16.0862    33.526929      5.1997    -21.2765     21.9026\n  2000   3400     0.0000   -14.8647    16.0862    33.844594      5.0817    -21.3050     21.9026\n  2000   3600     0.0000   -14.8647    16.0862    34.156723      4.9655    -21.3323     21.9026\n  2000   3800     0.0000   -14.8647    16.0862    34.463496      4.8512    -21.3586     21.9026\n  2000   4000     0.0000   -14.8647    16.0862    34.765083      4.7387    -21.3838     21.9026\n  2000   4200     0.0000   -14.8647    16.0862    35.061644      4.6280    -21.4081     21.9026\n  2000   4400     0.0000   -14.8647    16.0862    35.353334      4.5189    -21.4314     21.9026\n  2000   4600     0.0000   -14.8647    16.0862    35.640296      4.4116    -21.4537     21.9026\n  2000   4800     0.0000   -14.8647    16.0862    35.922671      4.3058    -21.4752     21.9026\n  2000   5000     0.0000   -14.8647    16.0862    36.200590      4.2015    -21.4959     21.9026\n  2000   5200     0.0000   -14.8647    16.0862    36.474180      4.0989    -21.5157     21.9026\n  2000   5400     0.0000   -14.8647    16.0862    36.743561      3.9977    -21.5347     21.9026\n  2000   5600     0.0000   -14.8647    16.0862    37.008848      3.8979    -21.5530     21.9026\n  2000   5800     0.0000   -14.8647    16.0862    37.270151      3.7996    -21.5705     21.9026\n  2000   6000     0.0000   -14.8647    16.0862    37.527577      3.7026    -21.5874     21.9026\n  2000   6200     0.0000   -14.8647    16.0862    37.781226      3.6070    -21.6036     21.9026\n  2000   6400     0.0000   -14.8647    16.0862    38.031195      3.5127    -21.6191     21.9026\n  2000   6600     0.0000   -14.8647    16.0862    38.277577      3.4197    -21.6340     21.9026\n  2000   6800     0.0000   -14.8647    16.0862    38.520461      3.3280    -21.6483     21.9026\n  2000   7000     0.0000   -14.8647    16.0862    38.759934      3.2375    -21.6620     21.9026\n  2000   7200     0.0000   -14.8647    16.0862    38.996078      3.1482    -21.6752     21.9026\n  2000   7400     0.0000   -14.8647    16.0862    39.228972      3.0600    -21.6878     21.9026\n  2000   7600     0.0000   -14.8647    16.0862    39.458693      2.9731    -21.6999     21.9026\n  2000   7800     0.0000   -14.8647    16.0862    39.685315      2.8872    -21.7115     21.9026\n  2000   8000     0.0000   -14.8647    16.0862    39.908908      2.8025    -21.7226     21.9026\n  2000   8200     0.0000   -14.8647    16.0862    40.129541      2.7188    -21.7332     21.9026\n  2000   8400     0.0000   -14.8647    16.0862    40.347280      2.6362    -21.7434     21.9026\n  2500      0     0.0000   -14.8261    16.0680    27.492947      7.4091    -20.5693     21.8631\n  2500    200     0.0000   -14.8261    16.0680    27.935270      7.2501    -20.6259     21.8631\n  2500    400     0.0000   -14.8261    16.0680    28.366955      7.0945    -20.6800     21.8631\n  2500    600     0.0000   -14.8261    16.0680    28.788525      6.9421    -20.7316     21.8631\n  2500    800     0.0000   -14.8261    16.0680    29.200461      6.7929    -20.7810     21.8631\n  2500   1000     0.0000   -14.8261    16.0680    29.603204      6.6467    -20.8282     21.8631\n  2500   1200     0.0000   -14.8261    16.0680    29.997167      6.5033    -20.8734     21.8631\n  2500   1400     0.0000   -14.8261    16.0680    30.382730      6.3627    -20.9167     21.8631\n  2500   1600     0.0000   -14.8261    16.0680    30.760245      6.2247    -20.9582     21.8631\n  2500   1800     0.0000   -14.8261    16.0680    31.130044      6.0893    -20.9979     21.8631\n  2500   2000     0.0000   -14.8261    16.0680    31.492434      5.9564    -21.0360     21.8631\n  2500   2200     0.0000   -14.8261    16.0680    31.847702      5.8258    -21.0725     21.8631\n  2500   2400     0.0000   -14.8261    16.0680    32.196118      5.6976    -21.1076     21.8631\n  2500   2600     0.0000   -14.8261    16.0680    32.537936      5.5716    -21.1412     21.8631\n  2500   2800     0.0000   -14.8261    16.0680    32.873394      5.4477    -21.1735     21.8631\n  2500   3000     0.0000   -14.8261    16.0680    33.202716      5.3259    -21.2044     21.8631\n  2500   3200     0.0000   -14.8261    16.0680    33.526114      5.2061    -21.2341     21.8631\n  2500   3400     0.0000   -14.8261    16.0680    33.843788      5.0883    -21.2627     21.8631\n  2500   3600     0.0000   -14.8261    16.0680    34.155926      4.9724    -21.2901     21.8631\n  2500   3800     0.0000   -14.8261    16.0680    34.462707      4.8583    -21.3164     21.8631\n  2500   4000     0.0000   -14.8261    16.0680    34.764302      4.7461    -21.3417     21.8631\n  2500   4200     0.0000   -14.8261    16.0680    35.060871      4.6355    -21.3660     21.8631\n  2500   4400     0.0000   -14.8261    16.0680    35.352568      4.5267    -21.3893     21.8631\n  2500   4600     0.0000   -14.8261    16.0680    35.639538      4.4195    -21.4117     21.8631\n  2500   4800     0.0000   -14.8261    16.0680    35.921920      4.3139    -21.4332     21.8631\n  2500   5000     0.0000   -14.8261    16.0680    36.199846      4.2099    -21.4539     21.8631\n  2500   5200     0.0000   -14.8261    16.0680    36.473442      4.1074    -21.4737     21.8631\n  2500   5400     0.0000   -14.8261    16.0680    36.742830      4.0064    -21.4928     21.8631\n  2500   5600     0.0000   -14.8261    16.0680    37.008123      3.9069    -21.5111     21.8631\n  2500   5800     0.0000   -14.8261    16.0680    37.269433      3.8087    -21.5287     21.8631\n  2500   6000     0.0000   -14.8261    16.0680    37.526865      3.7120    -21.5456     21.8631\n  2500   6200     0.0000   -14.8261    16.0680    37.780519      3.6165    -21.5619     21.8631\n  2500   6400     0.0000   -14.8261    16.0680    38.030494      3.5224    -21.5774     21.8631\n  2500   6600     0.0000   -14.8261    16.0680    38.276882      3.4296    -21.5924     21.8631\n  2500   6800     0.0000   -14.8261    16.0680    38.519772      3.3380    -21.6067     21.8631\n  2500   7000     0.0000   -14.8261    16.0680    38.759250      3.2477    -21.6205     21.8631\n  2500   7200     0.0000   -14.8261    16.0680    38.995399      3.1586    -21.6337     21.8631\n  2500   7400     0.0000   -14.8261    16.0680    39.228299      3.0706    -21.6463     21.8631\n  2500   7600     0.0000   -14.8261    16.0680    39.458025      2.9838    -21.6585     21.8631\n  2500   7800     0.0000   -14.8261    16.0680    39.684651      2.8981    -21.6701     21.8631\n  2500   8000     0.0000   -14.8261    16.0680    39.908249      2.8135    -21.6813     21.8631\n  2500   8200     0.0000   -14.8261    16.0680    40.128887      2.7300    -21.6919     21.8631\n  2500   8400     0.0000   -14.8261    16.0680    40.346630      2.6475    -21.7022     21.8631\n  3000      0     0.0000   -14.7875    16.0498    27.491911      7.4111    -20.5265     21.8235\n  3000    200     0.0000   -14.7875    16.0498    27.934252      7.2524    -20.5831     21.8235\n  3000    400     0.0000   -14.7875    16.0498    28.365954      7.0972    -20.6372     21.8235\n  3000    600     0.0000   -14.7875    16.0498    28.787540      6.9451    -20.6889     21.8235\n  3000    800     0.0000   -14.7875    16.0498    29.199491      6.7962    -20.7383     21.8235\n  3000   1000     0.0000   -14.7875    16.0498    29.602249      6.6502    -20.7855     21.8235\n  3000   1200     0.0000   -14.7875    16.0498    29.996226      6.5072    -20.8308     21.8235\n  3000   1400     0.0000   -14.7875    16.0498    30.381801      6.3668    -20.8741     21.8235\n  3000   1600     0.0000   -14.7875    16.0498    30.759330      6.2291    -20.9156     21.8235\n  3000   1800     0.0000   -14.7875    16.0498    31.129141      6.0940    -20.9554     21.8235\n  3000   2000     0.0000   -14.7875    16.0498    31.491542      5.9614    -20.9935     21.8235\n  3000   2200     0.0000   -14.7875    16.0498    31.846821      5.8311    -21.0300     21.8235\n  3000   2400     0.0000   -14.7875    16.0498    32.195248      5.7031    -21.0651     21.8235\n  3000   2600     0.0000   -14.7875    16.0498    32.537077      5.5773    -21.0988     21.8235\n  3000   2800     0.0000   -14.7875    16.0498    32.872545      5.4537    -21.1311     21.8235\n  3000   3000     0.0000   -14.7875    16.0498    33.201877      5.3321    -21.1621     21.8235\n  3000   3200     0.0000   -14.7875    16.0498    33.525284      5.2126    -21.1918     21.8235\n  3000   3400     0.0000   -14.7875    16.0498    33.842966      5.0950    -21.2204     21.8235\n  3000   3600     0.0000   -14.7875    16.0498    34.155113      4.9793    -21.2478     21.8235\n  3000   3800     0.0000   -14.7875    16.0498    34.461903      4.8655    -21.2742     21.8235\n  3000   4000     0.0000   -14.7875    16.0498    34.763506      4.7534    -21.2995     21.8235\n  3000   4200     0.0000   -14.7875    16.0498    35.060083      4.6431    -21.3238     21.8235\n  3000   4400     0.0000   -14.7875    16.0498    35.351788      4.5345    -21.3472     21.8235\n  3000   4600     0.0000   -14.7875    16.0498    35.638765      4.4275    -21.3696     21.8235\n  3000   4800     0.0000   -14.7875    16.0498    35.921154      4.3221    -21.3912     21.8235\n  3000   5000     0.0000   -14.7875    16.0498    36.199088      4.2183    -21.4119     21.8235\n  3000   5200     0.0000   -14.7875    16.0498    36.472691      4.1160    -21.4318     21.8235\n  3000   5400     0.0000   -14.7875    16.0498    36.742085      4.0152    -21.4509     21.8235\n  3000   5600     0.0000   -14.7875    16.0498    37.007385      3.9158    -21.4693     21.8235\n  3000   5800     0.0000   -14.7875    16.0498    37.268701      3.8179    -21.4869     21.8235\n  3000   6000     0.0000   -14.7875    16.0498    37.526139      3.7213    -21.5039     21.8235\n  3000   6200     0.0000   -14.7875    16.0498    37.779800      3.6261    -21.5201     21.8235\n  3000   6400     0.0000   -14.7875    16.0498    38.029781      3.5321    -21.5358     21.8235\n  3000   6600     0.0000   -14.7875    16.0498    38.276174      3.4395    -21.5507     21.8235\n  3000   6800     0.0000   -14.7875    16.0498    38.519070      3.3481    -21.5651     21.8235\n  3000   7000     0.0000   -14.7875    16.0498    38.758554      3.2579    -21.5789     21.8235\n  3000   7200     0.0000   -14.7875    16.0498    38.994708      3.1690    -21.5922     21.8235\n  3000   7400     0.0000   -14.7875    16.0498    39.227613      3.0812    -21.6049     21.8235\n  3000   7600     0.0000   -14.7875    16.0498    39.457344      2.9945    -21.6171     21.8235\n  3000   7800     0.0000   -14.7875    16.0498    39.683975      2.9090    -21.6287     21.8235\n  3000   8000     0.0000   -14.7875    16.0498    39.907578      2.8245    -21.6399     21.8235\n  3000   8200     0.0000   -14.7875    16.0498    40.128221      2.7412    -21.6506     21.8235\n  3000   8400     0.0000   -14.7875    16.0498    40.345969      2.6589    -21.6609     21.8235\n  3500      0     0.0000   -14.7489    16.0316    27.490855      7.4132    -20.4838     21.7840\n  3500    200     0.0000   -14.7489    16.0316    27.933214      7.2548    -20.5404     21.7840\n  3500    400     0.0000   -14.7489    16.0316    28.364934      7.0998    -20.5945     21.7840\n  3500    600     0.0000   -14.7489    16.0316    28.786536      6.9481    -20.6461     21.7840\n  3500    800     0.0000   -14.7489    16.0316    29.198502      6.7995    -20.6956     21.7840\n  3500   1000     0.0000   -14.7489    16.0316    29.601275      6.6538    -20.7429     21.7840\n  3500   1200     0.0000   -14.7489    16.0316    29.995266      6.5110    -20.7881     21.7840\n  3500   1400     0.0000   -14.7489    16.0316    30.380855      6.3710    -20.8315     21.7840\n  3500   1600     0.0000   -14.7489    16.0316    30.758396      6.2336    -20.8730     21.7840\n  3500   1800     0.0000   -14.7489    16.0316    31.128219      6.0987    -20.9128     21.7840\n  3500   2000     0.0000   -14.7489    16.0316    31.490633      5.9663    -20.9510     21.7840\n  3500   2200     0.0000   -14.7489    16.0316    31.845923      5.8363    -20.9876     21.7840\n  3500   2400     0.0000   -14.7489    16.0316    32.194362      5.7085    -21.0227     21.7840\n  3500   2600     0.0000   -14.7489    16.0316    32.536201      5.5830    -21.0563     21.7840\n  3500   2800     0.0000   -14.7489    16.0316    32.871679      5.4596    -21.0887     21.7840\n  3500   3000     0.0000   -14.7489    16.0316    33.201021      5.3383    -21.1197     21.7840\n  3500   3200     0.0000   -14.7489    16.0316    33.524438      5.2190    -21.1495     21.7840\n  3500   3400     0.0000   -14.7489    16.0316    33.842129      5.1017    -21.1781     21.7840\n  3500   3600     0.0000   -14.7489    16.0316    34.154285      4.9862    -21.2056     21.7840\n  3500   3800     0.0000   -14.7489    16.0316    34.461084      4.8726    -21.2320     21.7840\n  3500   4000     0.0000   -14.7489    16.0316    34.762695      4.7608    -21.2573     21.7840\n  3500   4200     0.0000   -14.7489    16.0316    35.059281      4.6507    -21.2817     21.7840\n  3500   4400     0.0000   -14.7489    16.0316    35.350993      4.5423    -21.3051     21.7840\n  3500   4600     0.0000   -14.7489    16.0316    35.637978      4.4355    -21.3276     21.7840\n  3500   4800     0.0000   -14.7489    16.0316    35.920375      4.3303    -21.3492     21.7840\n  3500   5000     0.0000   -14.7489    16.0316    36.198315      4.2267    -21.3700     21.7840\n  3500   5200     0.0000   -14.7489    16.0316    36.471925      4.1246    -21.3899     21.7840\n  3500   5400     0.0000   -14.7489    16.0316    36.741326      4.0240    -21.4091     21.7840\n  3500   5600     0.0000   -14.7489    16.0316    37.006633      3.9248    -21.4275     21.7840\n  3500   5800     0.0000   -14.7489    16.0316    37.267956      3.8270    -21.4451     21.7840\n  3500   6000     0.0000   -14.7489    16.0316    37.525400      3.7306    -21.4621     21.7840\n  3500   6200     0.0000   -14.7489    16.0316    37.779067      3.6356    -21.4784     21.7840\n  3500   6400     0.0000   -14.7489    16.0316    38.029054      3.5418    -21.4941     21.7840\n  3500   6600     0.0000   -14.7489    16.0316    38.275453      3.4494    -21.5091     21.7840\n  3500   6800     0.0000   -14.7489    16.0316    38.518355      3.3581    -21.5235     21.7840\n  3500   7000     0.0000   -14.7489    16.0316    38.757844      3.2682    -21.5374     21.7840\n  3500   7200     0.0000   -14.7489    16.0316    38.994004      3.1794    -21.5507     21.7840\n  3500   7400     0.0000   -14.7489    16.0316    39.226914      3.0917    -21.5634     21.7840\n  3500   7600     0.0000   -14.7489    16.0316    39.456650      3.0052    -21.5756     21.7840\n  3500   7800     0.0000   -14.7489    16.0316    39.683287      2.9199    -21.5874     21.7840\n  3500   8000     0.0000   -14.7489    16.0316    39.906895      2.8356    -21.5986     21.7840\n  3500   8200     0.0000   -14.7489    16.0316    40.127542      2.7524    -21.6094     21.7840\n  3500   8400     0.0000   -14.7489    16.0316    40.345295      2.6703    -21.6197     21.7840\n  4000      0     0.0000   -14.7103    16.0133    27.489779      7.4152    -20.4410     21.7444\n  4000    200     0.0000   -14.7103    16.0133    27.932157      7.2572    -20.4976     21.7444\n  4000    400     0.0000   -14.7103    16.0133    28.363894      7.1025    -20.5517     21.7444\n  4000    600     0.0000   -14.7103    16.0133    28.785512      6.9511    -20.6034     21.7444\n  4000    800     0.0000   -14.7103    16.0133    29.197494      6.8028    -20.6529     21.7444\n  4000   1000     0.0000   -14.7103    16.0133    29.600282      6.6574    -20.7002     21.7444\n  4000   1200     0.0000   -14.7103    16.0133    29.994287      6.5149    -20.7455     21.7444\n  4000   1400     0.0000   -14.7103    16.0133    30.379890      6.3751    -20.7888     21.7444\n  4000   1600     0.0000   -14.7103    16.0133    30.757445      6.2380    -20.8304     21.7444\n  4000   1800     0.0000   -14.7103    16.0133    31.127281      6.1034    -20.8702     21.7444\n  4000   2000     0.0000   -14.7103    16.0133    31.489706      5.9713    -20.9084     21.7444\n  4000   2200     0.0000   -14.7103    16.0133    31.845009      5.8415    -20.9451     21.7444\n  4000   2400     0.0000   -14.7103    16.0133    32.193458      5.7140    -20.9802     21.7444\n  4000   2600     0.0000   -14.7103    16.0133    32.535308      5.5888    -21.0139     21.7444\n  4000   2800     0.0000   -14.7103    16.0133    32.870797      5.4656    -21.0463     21.7444\n  4000   3000     0.0000   -14.7103    16.0133    33.200149      5.3445    -21.0773     21.7444\n  4000   3200     0.0000   -14.7103    16.0133    33.523576      5.2255    -21.1072     21.7444\n  4000   3400     0.0000   -14.7103    16.0133    33.841277      5.1084    -21.1358     21.7444\n  4000   3600     0.0000   -14.7103    16.0133    34.153442      4.9931    -21.1633     21.7444\n  4000   3800     0.0000   -14.7103    16.0133    34.460249      4.8797    -21.1898     21.7444\n  4000   4000     0.0000   -14.7103    16.0133    34.761869      4.7681    -21.2152     21.7444\n  4000   4200     0.0000   -14.7103    16.0133    35.058463      4.6582    -21.2396     21.7444\n  4000   4400     0.0000   -14.7103    16.0133    35.350183      4.5500    -21.2630     21.7444\n  4000   4600     0.0000   -14.7103    16.0133    35.637176      4.4435    -21.2855     21.7444\n  4000   4800     0.0000   -14.7103    16.0133    35.919581      4.3385    -21.3072     21.7444\n  4000   5000     0.0000   -14.7103    16.0133    36.197528      4.2351    -21.3280     21.7444\n  4000   5200     0.0000   -14.7103    16.0133    36.471146      4.1332    -21.3480     21.7444\n  4000   5400     0.0000   -14.7103    16.0133    36.740554      4.0328    -21.3672     21.7444\n  4000   5600     0.0000   -14.7103    16.0133    37.005868      3.9338    -21.3856     21.7444\n  4000   5800     0.0000   -14.7103    16.0133    37.267197      3.8362    -21.4033     21.7444\n  4000   6000     0.0000   -14.7103    16.0133    37.524648      3.7400    -21.4204     21.7444\n  4000   6200     0.0000   -14.7103    16.0133    37.778321      3.6451    -21.4367     21.7444\n  4000   6400     0.0000   -14.7103    16.0133    38.028314      3.5515    -21.4524     21.7444\n  4000   6600     0.0000   -14.7103    16.0133    38.274719      3.4593    -21.4675     21.7444\n  4000   6800     0.0000   -14.7103    16.0133    38.517627      3.3682    -21.4819     21.7444\n  4000   7000     0.0000   -14.7103    16.0133    38.757122      3.2784    -21.4958     21.7444\n  4000   7200     0.0000   -14.7103    16.0133    38.993287      3.1898    -21.5092     21.7444\n  4000   7400     0.0000   -14.7103    16.0133    39.226203      3.1023    -21.5220     21.7444\n  4000   7600     0.0000   -14.7103    16.0133    39.455945      3.0160    -21.5342     21.7444\n  4000   7800     0.0000   -14.7103    16.0133    39.682586      2.9308    -21.5460     21.7444\n  4000   8000     0.0000   -14.7103    16.0133    39.906199      2.8467    -21.5573     21.7444\n  4000   8200     0.0000   -14.7103    16.0133    40.126852      2.7636    -21.5681     21.7444\n  4000   8400     0.0000   -14.7103    16.0133    40.344610      2.6816    -21.5784     21.7444\n  4500      0     0.0000   -14.6717    15.9951    27.488683      7.4173    -20.3982     21.7049\n  4500    200     0.0000   -14.6717    15.9951    27.931079      7.2596    -20.4548     21.7049\n  4500    400     0.0000   -14.6717    15.9951    28.362834      7.1052    -20.5090     21.7049\n  4500    600     0.0000   -14.6717    15.9951    28.784470      6.9541    -20.5607     21.7049\n  4500    800     0.0000   -14.6717    15.9951    29.196467      6.8061    -20.6102     21.7049\n  4500   1000     0.0000   -14.6717    15.9951    29.599271      6.6610    -20.6575     21.7049\n  4500   1200     0.0000   -14.6717    15.9951    29.993291      6.5188    -20.7028     21.7049\n  4500   1400     0.0000   -14.6717    15.9951    30.378908      6.3793    -20.7462     21.7049\n  4500   1600     0.0000   -14.6717    15.9951    30.756476      6.2425    -20.7878     21.7049\n  4500   1800     0.0000   -14.6717    15.9951    31.126325      6.1081    -20.8277     21.7049\n  4500   2000     0.0000   -14.6717    15.9951    31.488763      5.9763    -20.8659     21.7049\n  4500   2200     0.0000   -14.6717    15.9951    31.844077      5.8468    -20.9026     21.7049\n  4500   2400     0.0000   -14.6717    15.9951    32.192539      5.7195    -20.9377     21.7049\n  4500   2600     0.0000   -14.6717    15.9951    32.534400      5.5945    -20.9715     21.7049\n  4500   2800     0.0000   -14.6717    15.9951    32.869899      5.4716    -21.0039     21.7049\n  4500   3000     0.0000   -14.6717    15.9951    33.199262      5.3508    -21.0350     21.7049\n  4500   3200     0.0000   -14.6717    15.9951    33.522698      5.2319    -21.0649     21.7049\n  4500   3400     0.0000   -14.6717    15.9951    33.840409      5.1151    -21.0935     21.7049\n  4500   3600     0.0000   -14.6717    15.9951    34.152583      5.0000    -21.1211     21.7049\n  4500   3800     0.0000   -14.6717    15.9951    34.459400      4.8869    -21.1476     21.7049\n  4500   4000     0.0000   -14.6717    15.9951    34.761029      4.7755    -21.1730     21.7049\n  4500   4200     0.0000   -14.6717    15.9951    35.057631      4.6658    -21.1974     21.7049\n  4500   4400     0.0000   -14.6717    15.9951    35.349359      4.5578    -21.2209     21.7049\n  4500   4600     0.0000   -14.6717    15.9951    35.636360      4.4515    -21.2435     21.7049\n  4500   4800     0.0000   -14.6717    15.9951    35.918772      4.3467    -21.2652     21.7049\n  4500   5000     0.0000   -14.6717    15.9951    36.196728      4.2435    -21.2860     21.7049\n  4500   5200     0.0000   -14.6717    15.9951    36.470353      4.1418    -21.3060     21.7049\n  4500   5400     0.0000   -14.6717    15.9951    36.739768      4.0416    -21.3253     21.7049\n  4500   5600     0.0000   -14.6717    15.9951    37.005088      3.9428    -21.3438     21.7049\n  4500   5800     0.0000   -14.6717    15.9951    37.266425      3.8454    -21.3615     21.7049\n  4500   6000     0.0000   -14.6717    15.9951    37.523882      3.7493    -21.3786     21.7049\n  4500   6200     0.0000   -14.6717    15.9951    37.777562      3.6546    -21.3950     21.7049\n  4500   6400     0.0000   -14.6717    15.9951    38.027561      3.5613    -21.4107     21.7049\n  4500   6600     0.0000   -14.6717    15.9951    38.273972      3.4691    -21.4258     21.7049\n  4500   6800     0.0000   -14.6717    15.9951    38.516886      3.3783    -21.4404     21.7049\n  4500   7000     0.0000   -14.6717    15.9951    38.756387      3.2886    -21.4543     21.7049\n  4500   7200     0.0000   -14.6717    15.9951    38.992558      3.2002    -21.4677     21.7049\n  4500   7400     0.0000   -14.6717    15.9951    39.225479      3.1129    -21.4805     21.7049\n  4500   7600     0.0000   -14.6717    15.9951    39.455227      3.0267    -21.4928     21.7049\n  4500   7800     0.0000   -14.6717    15.9951    39.681874      2.9417    -21.5046     21.7049\n  4500   8000     0.0000   -14.6717    15.9951    39.905492      2.8577    -21.5159     21.7049\n  4500   8200     0.0000   -14.6717    15.9951    40.126149      2.7748    -21.5268     21.7049\n  4500   8400     0.0000   -14.6717    15.9951    40.343913      2.6930    -21.5372     21.7049\n\nuser time (s):         0.000\nsystem time (s):       0.010\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180705_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):          1.5504708       92.0622963        7.2392205   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0681920       -0.0045449   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000       91.8647619        7.3101981   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0687605       -0.0014463   m/s m/s m/s\nunwrap_phase_constant:           -0.00026     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180705_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):      1.550     92.062      7.239\norbit baseline rate   (TCN) (m/s):  0.000e+00 -6.819e-02 -4.545e-03\n\nbaseline vector (TCN)   (m):      0.000     91.865      7.310\nbaseline rate   (TCN) (m/s):  0.000e+00 -6.876e-02 -1.446e-03\n\nSLC-1 center baseline length (m):    92.1552\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0     0.0000    92.5066     7.3237    27.497816     49.2079     78.6743     92.7960\n     0    200     0.0000    92.5066     7.3237    27.940057     49.8137     78.2921     92.7960\n     0    400     0.0000    92.5066     7.3237    28.371664     50.4020     77.9147     92.7960\n     0    600     0.0000    92.5066     7.3237    28.793160     50.9738     77.5418     92.7960\n     0    800     0.0000    92.5066     7.3237    29.205025     51.5299     77.1734     92.7960\n     0   1000     0.0000    92.5066     7.3237    29.607701     52.0710     76.8093     92.7960\n     0   1200     0.0000    92.5066     7.3237    30.001600     52.5978     76.4496     92.7960\n     0   1400     0.0000    92.5066     7.3237    30.387101     53.1110     76.0939     92.7960\n     0   1600     0.0000    92.5066     7.3237    30.764557     53.6112     75.7424     92.7960\n     0   1800     0.0000    92.5066     7.3237    31.134300     54.0988     75.3949     92.7960\n     0   2000     0.0000    92.5066     7.3237    31.496635     54.5745     75.0512     92.7960\n     0   2200     0.0000    92.5066     7.3237    31.851850     55.0388     74.7115     92.7960\n     0   2400     0.0000    92.5066     7.3237    32.200217     55.4920     74.3754     92.7960\n     0   2600     0.0000    92.5066     7.3237    32.541986     55.9347     74.0431     92.7960\n     0   2800     0.0000    92.5066     7.3237    32.877397     56.3672     73.7144     92.7960\n     0   3000     0.0000    92.5066     7.3237    33.206674     56.7899     73.3893     92.7960\n     0   3200     0.0000    92.5066     7.3237    33.530029     57.2031     73.0676     92.7960\n     0   3400     0.0000    92.5066     7.3237    33.847660     57.6073     72.7494     92.7960\n     0   3600     0.0000    92.5066     7.3237    34.159757     58.0027     72.4345     92.7960\n     0   3800     0.0000    92.5066     7.3237    34.466499     58.3897     72.1229     92.7960\n     0   4000     0.0000    92.5066     7.3237    34.768056     58.7685     71.8146     92.7960\n     0   4200     0.0000    92.5066     7.3237    35.064588     59.1394     71.5095     92.7960\n     0   4400     0.0000    92.5066     7.3237    35.356248     59.5026     71.2075     92.7960\n     0   4600     0.0000    92.5066     7.3237    35.643183     59.8585     70.9087     92.7960\n     0   4800     0.0000    92.5066     7.3237    35.925531     60.2072     70.6128     92.7960\n     0   5000     0.0000    92.5066     7.3237    36.203424     60.5490     70.3200     92.7960\n     0   5200     0.0000    92.5066     7.3237    36.476989     60.8840     70.0301     92.7960\n     0   5400     0.0000    92.5066     7.3237    36.746345     61.2126     69.7431     92.7960\n     0   5600     0.0000    92.5066     7.3237    37.011608     61.5348     69.4590     92.7960\n     0   5800     0.0000    92.5066     7.3237    37.272888     61.8509     69.1776     92.7960\n     0   6000     0.0000    92.5066     7.3237    37.530291     62.1611     68.8991     92.7960\n     0   6200     0.0000    92.5066     7.3237    37.783917     62.4655     68.6232     92.7960\n     0   6400     0.0000    92.5066     7.3237    38.033864     62.7642     68.3501     92.7960\n     0   6600     0.0000    92.5066     7.3237    38.280225     63.0575     68.0796     92.7960\n     0   6800     0.0000    92.5066     7.3237    38.523089     63.3455     67.8117     92.7960\n     0   7000     0.0000    92.5066     7.3237    38.762542     63.6284     67.5464     92.7960\n     0   7200     0.0000    92.5066     7.3237    38.998666     63.9062     67.2836     92.7960\n     0   7400     0.0000    92.5066     7.3237    39.231541     64.1792     67.0233     92.7960\n     0   7600     0.0000    92.5066     7.3237    39.461243     64.4473     66.7654     92.7960\n     0   7800     0.0000    92.5066     7.3237    39.687846     64.7109     66.5100     92.7960\n     0   8000     0.0000    92.5066     7.3237    39.911421     64.9699     66.2570     92.7960\n     0   8200     0.0000    92.5066     7.3237    40.132036     65.2246     66.0064     92.7960\n     0   8400     0.0000    92.5066     7.3237    40.349758     65.4749     65.7580     92.7960\n   500      0     0.0000    92.3653     7.3207    27.496883     49.1387     78.5511     92.6549\n   500    200     0.0000    92.3653     7.3207    27.939140     49.7436     78.1695     92.6549\n   500    400     0.0000    92.3653     7.3207    28.370762     50.3310     77.7925     92.6549\n   500    600     0.0000    92.3653     7.3207    28.792272     50.9019     77.4202     92.6549\n   500    800     0.0000    92.3653     7.3207    29.204150     51.4572     77.0523     92.6549\n   500   1000     0.0000    92.3653     7.3207    29.606840     51.9974     76.6887     92.6549\n   500   1200     0.0000    92.3653     7.3207    30.000750     52.5235     76.3294     92.6549\n   500   1400     0.0000    92.3653     7.3207    30.386263     53.0358     75.9743     92.6549\n   500   1600     0.0000    92.3653     7.3207    30.763731     53.5352     75.6232     92.6549\n   500   1800     0.0000    92.3653     7.3207    31.133484     54.0221     75.2762     92.6549\n   500   2000     0.0000    92.3653     7.3207    31.495829     54.4971     74.9331     92.6549\n   500   2200     0.0000    92.3653     7.3207    31.851055     54.9606     74.5937     92.6549\n   500   2400     0.0000    92.3653     7.3207    32.199431     55.4132     74.2582     92.6549\n   500   2600     0.0000    92.3653     7.3207    32.541209     55.8551     73.9263     92.6549\n   500   2800     0.0000    92.3653     7.3207    32.876630     56.2870     73.5981     92.6549\n   500   3000     0.0000    92.3653     7.3207    33.205915     56.7090     73.2734     92.6549\n   500   3200     0.0000    92.3653     7.3207    33.529278     57.1216     72.9522     92.6549\n   500   3400     0.0000    92.3653     7.3207    33.846917     57.5252     72.6344     92.6549\n   500   3600     0.0000    92.3653     7.3207    34.159022     57.9200     72.3199     92.6549\n   500   3800     0.0000    92.3653     7.3207    34.465771     58.3064     72.0088     92.6549\n   500   4000     0.0000    92.3653     7.3207    34.767335     58.6845     71.7009     92.6549\n   500   4200     0.0000    92.3653     7.3207    35.063874     59.0549     71.3963     92.6549\n   500   4400     0.0000    92.3653     7.3207    35.355542     59.4175     71.0947     92.6549\n   500   4600     0.0000    92.3653     7.3207    35.642483     59.7728     70.7963     92.6549\n   500   4800     0.0000    92.3653     7.3207    35.924838     60.1210     70.5008     92.6549\n   500   5000     0.0000    92.3653     7.3207    36.202737     60.4622     70.2084     92.6549\n   500   5200     0.0000    92.3653     7.3207    36.476308     60.7968     69.9189     92.6549\n   500   5400     0.0000    92.3653     7.3207    36.745670     61.1248     69.6323     92.6549\n   500   5600     0.0000    92.3653     7.3207    37.010938     61.4465     69.3486     92.6549\n   500   5800     0.0000    92.3653     7.3207    37.272224     61.7622     69.0677     92.6549\n   500   6000     0.0000    92.3653     7.3207    37.529632     62.0718     68.7895     92.6549\n   500   6200     0.0000    92.3653     7.3207    37.783264     62.3757     68.5141     92.6549\n   500   6400     0.0000    92.3653     7.3207    38.033217     62.6740     68.2413     92.6549\n   500   6600     0.0000    92.3653     7.3207    38.279583     62.9669     67.9712     92.6549\n   500   6800     0.0000    92.3653     7.3207    38.522452     63.2544     67.7037     92.6549\n   500   7000     0.0000    92.3653     7.3207    38.761909     63.5368     67.4387     92.6549\n   500   7200     0.0000    92.3653     7.3207    38.998038     63.8142     67.1763     92.6549\n   500   7400     0.0000    92.3653     7.3207    39.230918     64.0867     66.9164     92.6549\n   500   7600     0.0000    92.3653     7.3207    39.460624     64.3545     66.6589     92.6549\n   500   7800     0.0000    92.3653     7.3207    39.687232     64.6176     66.4038     92.6549\n   500   8000     0.0000    92.3653     7.3207    39.910811     64.8763     66.1512     92.6549\n   500   8200     0.0000    92.3653     7.3207    40.131431     65.1305     65.9009     92.6549\n   500   8400     0.0000    92.3653     7.3207    40.349156     65.3805     65.6529     92.6549\n  1000      0     0.0000    92.2239     7.3178    27.495930     49.0695     78.4279     92.5138\n  1000    200     0.0000    92.2239     7.3178    27.938203     49.6734     78.0468     92.5138\n  1000    400     0.0000    92.2239     7.3178    28.369840     50.2600     77.6704     92.5138\n  1000    600     0.0000    92.2239     7.3178    28.791365     50.8300     77.2985     92.5138\n  1000    800     0.0000    92.2239     7.3178    29.203256     51.3844     76.9311     92.5138\n  1000   1000     0.0000    92.2239     7.3178    29.605959     51.9239     76.5681     92.5138\n  1000   1200     0.0000    92.2239     7.3178    29.999882     52.4490     76.2093     92.5138\n  1000   1400     0.0000    92.2239     7.3178    30.385407     52.9606     75.8547     92.5138\n  1000   1600     0.0000    92.2239     7.3178    30.762886     53.4592     75.5041     92.5138\n  1000   1800     0.0000    92.2239     7.3178    31.132650     53.9454     75.1575     92.5138\n  1000   2000     0.0000    92.2239     7.3178    31.495006     54.4196     74.8149     92.5138\n  1000   2200     0.0000    92.2239     7.3178    31.850242     54.8825     74.4760     92.5138\n  1000   2400     0.0000    92.2239     7.3178    32.198628     55.3343     74.1409     92.5138\n  1000   2600     0.0000    92.2239     7.3178    32.540416     55.7756     73.8095     92.5138\n  1000   2800     0.0000    92.2239     7.3178    32.875845     56.2067     73.4818     92.5138\n  1000   3000     0.0000    92.2239     7.3178    33.205140     56.6281     73.1575     92.5138\n  1000   3200     0.0000    92.2239     7.3178    33.528511     57.0401     72.8367     92.5138\n  1000   3400     0.0000    92.2239     7.3178    33.846158     57.4430     72.5194     92.5138\n  1000   3600     0.0000    92.2239     7.3178    34.158271     57.8372     72.2054     92.5138\n  1000   3800     0.0000    92.2239     7.3178    34.465028     58.2230     71.8947     92.5138\n  1000   4000     0.0000    92.2239     7.3178    34.766600     58.6006     71.5873     92.5138\n  1000   4200     0.0000    92.2239     7.3178    35.063146     58.9703     71.2830     92.5138\n  1000   4400     0.0000    92.2239     7.3178    35.354821     59.3324     70.9819     92.5138\n  1000   4600     0.0000    92.2239     7.3178    35.641769     59.6872     70.6839     92.5138\n  1000   4800     0.0000    92.2239     7.3178    35.924130     60.0348     70.3889     92.5138\n  1000   5000     0.0000    92.2239     7.3178    36.202036     60.3755     70.0969     92.5138\n  1000   5200     0.0000    92.2239     7.3178    36.475612     60.7095     69.8078     92.5138\n  1000   5400     0.0000    92.2239     7.3178    36.744981     61.0370     69.5216     92.5138\n  1000   5600     0.0000    92.2239     7.3178    37.010255     61.3583     69.2383     92.5138\n  1000   5800     0.0000    92.2239     7.3178    37.271547     61.6734     68.9577     92.5138\n  1000   6000     0.0000    92.2239     7.3178    37.528961     61.9826     68.6799     92.5138\n  1000   6200     0.0000    92.2239     7.3178    37.782598     62.2860     68.4049     92.5138\n  1000   6400     0.0000    92.2239     7.3178    38.032556     62.5838     68.1325     92.5138\n  1000   6600     0.0000    92.2239     7.3178    38.278927     62.8762     67.8628     92.5138\n  1000   6800     0.0000    92.2239     7.3178    38.521801     63.1633     67.5956     92.5138\n  1000   7000     0.0000    92.2239     7.3178    38.761264     63.4453     67.3311     92.5138\n  1000   7200     0.0000    92.2239     7.3178    38.997398     63.7222     67.0690     92.5138\n  1000   7400     0.0000    92.2239     7.3178    39.230282     63.9943     66.8095     92.5138\n  1000   7600     0.0000    92.2239     7.3178    39.459993     64.2616     66.5524     92.5138\n  1000   7800     0.0000    92.2239     7.3178    39.686605     64.5244     66.2977     92.5138\n  1000   8000     0.0000    92.2239     7.3178    39.910189     64.7826     66.0454     92.5138\n  1000   8200     0.0000    92.2239     7.3178    40.130813     65.0364     65.7954     92.5138\n  1000   8400     0.0000    92.2239     7.3178    40.348543     65.2860     65.5478     92.5138\n  1500      0     0.0000    92.0826     7.3148    27.494956     49.0003     78.3047     92.3726\n  1500    200     0.0000    92.0826     7.3148    27.937245     49.6033     77.9242     92.3726\n  1500    400     0.0000    92.0826     7.3148    28.368898     50.1889     77.5483     92.3726\n  1500    600     0.0000    92.0826     7.3148    28.790438     50.7581     77.1769     92.3726\n  1500    800     0.0000    92.0826     7.3148    29.202344     51.3116     76.8100     92.3726\n  1500   1000     0.0000    92.0826     7.3148    29.605059     51.8502     76.4475     92.3726\n  1500   1200     0.0000    92.0826     7.3148    29.998996     52.3746     76.0892     92.3726\n  1500   1400     0.0000    92.0826     7.3148    30.384532     52.8854     75.7350     92.3726\n  1500   1600     0.0000    92.0826     7.3148    30.762024     53.3833     75.3850     92.3726\n  1500   1800     0.0000    92.0826     7.3148    31.131799     53.8687     75.0389     92.3726\n  1500   2000     0.0000    92.0826     7.3148    31.494166     54.3422     74.6967     92.3726\n  1500   2200     0.0000    92.0826     7.3148    31.849412     54.8043     74.3583     92.3726\n  1500   2400     0.0000    92.0826     7.3148    32.197808     55.2554     74.0237     92.3726\n  1500   2600     0.0000    92.0826     7.3148    32.539606     55.6960     73.6928     92.3726\n  1500   2800     0.0000    92.0826     7.3148    32.875045     56.1265     73.3655     92.3726\n  1500   3000     0.0000    92.0826     7.3148    33.204348     56.5472     73.0417     92.3726\n  1500   3200     0.0000    92.0826     7.3148    33.527728     56.9586     72.7213     92.3726\n  1500   3400     0.0000    92.0826     7.3148    33.845384     57.3609     72.4044     92.3726\n  1500   3600     0.0000    92.0826     7.3148    34.157505     57.7544     72.0909     92.3726\n  1500   3800     0.0000    92.0826     7.3148    34.464270     58.1396     71.7807     92.3726\n  1500   4000     0.0000    92.0826     7.3148    34.765849     58.5166     71.4736     92.3726\n  1500   4200     0.0000    92.0826     7.3148    35.062403     58.8858     71.1698     92.3726\n  1500   4400     0.0000    92.0826     7.3148    35.354084     59.2473     70.8691     92.3726\n  1500   4600     0.0000    92.0826     7.3148    35.641040     59.6015     70.5715     92.3726\n  1500   4800     0.0000    92.0826     7.3148    35.923408     59.9486     70.2769     92.3726\n  1500   5000     0.0000    92.0826     7.3148    36.201320     60.2887     69.9853     92.3726\n  1500   5200     0.0000    92.0826     7.3148    36.474903     60.6222     69.6966     92.3726\n  1500   5400     0.0000    92.0826     7.3148    36.744278     60.9492     69.4109     92.3726\n  1500   5600     0.0000    92.0826     7.3148    37.009558     61.2700     69.1279     92.3726\n  1500   5800     0.0000    92.0826     7.3148    37.270856     61.5846     68.8478     92.3726\n  1500   6000     0.0000    92.0826     7.3148    37.528276     61.8933     68.5704     92.3726\n  1500   6200     0.0000    92.0826     7.3148    37.781919     62.1962     68.2957     92.3726\n  1500   6400     0.0000    92.0826     7.3148    38.031882     62.4936     68.0238     92.3726\n  1500   6600     0.0000    92.0826     7.3148    38.278259     62.7855     67.7544     92.3726\n  1500   6800     0.0000    92.0826     7.3148    38.521138     63.0722     67.4876     92.3726\n  1500   7000     0.0000    92.0826     7.3148    38.760605     63.3537     67.2234     92.3726\n  1500   7200     0.0000    92.0826     7.3148    38.996744     63.6302     66.9618     92.3726\n  1500   7400     0.0000    92.0826     7.3148    39.229633     63.9019     66.7026     92.3726\n  1500   7600     0.0000    92.0826     7.3148    39.459350     64.1688     66.4458     92.3726\n  1500   7800     0.0000    92.0826     7.3148    39.685966     64.4311     66.1915     92.3726\n  1500   8000     0.0000    92.0826     7.3148    39.909555     64.6889     65.9396     92.3726\n  1500   8200     0.0000    92.0826     7.3148    40.130183     64.9423     65.6900     92.3726\n  1500   8400     0.0000    92.0826     7.3148    40.347918     65.1915     65.4427     92.3726\n  2000      0     0.0000    91.9412     7.3118    27.493962     48.9310     78.1816     92.2315\n  2000    200     0.0000    91.9412     7.3118    27.936268     49.5331     77.8015     92.2315\n  2000    400     0.0000    91.9412     7.3118    28.367937     50.1179     77.4261     92.2315\n  2000    600     0.0000    91.9412     7.3118    28.789491     50.6862     77.0553     92.2315\n  2000    800     0.0000    91.9412     7.3118    29.201412     51.2388     76.6889     92.2315\n  2000   1000     0.0000    91.9412     7.3118    29.604141     51.7766     76.3269     92.2315\n  2000   1200     0.0000    91.9412     7.3118    29.998091     52.3002     75.9691     92.2315\n  2000   1400     0.0000    91.9412     7.3118    30.383640     52.8102     75.6154     92.2315\n  2000   1600     0.0000    91.9412     7.3118    30.761143     53.3073     75.2659     92.2315\n  2000   1800     0.0000    91.9412     7.3118    31.130930     53.7919     74.9203     92.2315\n  2000   2000     0.0000    91.9412     7.3118    31.493308     54.2647     74.5785     92.2315\n  2000   2200     0.0000    91.9412     7.3118    31.848565     54.7261     74.2407     92.2315\n  2000   2400     0.0000    91.9412     7.3118    32.196971     55.1765     73.9065     92.2315\n  2000   2600     0.0000    91.9412     7.3118    32.538779     55.6164     73.5760     92.2315\n  2000   2800     0.0000    91.9412     7.3118    32.874228     56.0462     73.2492     92.2315\n  2000   3000     0.0000    91.9412     7.3118    33.203540     56.4663     72.9258     92.2315\n  2000   3200     0.0000    91.9412     7.3118    33.526929     56.8770     72.6060     92.2315\n  2000   3400     0.0000    91.9412     7.3118    33.844594     57.2787     72.2895     92.2315\n  2000   3600     0.0000    91.9412     7.3118    34.156723     57.6716     71.9764     92.2315\n  2000   3800     0.0000    91.9412     7.3118    34.463496     58.0562     71.6666     92.2315\n  2000   4000     0.0000    91.9412     7.3118    34.765083     58.4326     71.3600     92.2315\n  2000   4200     0.0000    91.9412     7.3118    35.061644     58.8012     71.0566     92.2315\n  2000   4400     0.0000    91.9412     7.3118    35.353334     59.1622     70.7563     92.2315\n  2000   4600     0.0000    91.9412     7.3118    35.640296     59.5158     70.4591     92.2315\n  2000   4800     0.0000    91.9412     7.3118    35.922671     59.8623     70.1650     92.2315\n  2000   5000     0.0000    91.9412     7.3118    36.200590     60.2020     69.8738     92.2315\n  2000   5200     0.0000    91.9412     7.3118    36.474180     60.5349     69.5855     92.2315\n  2000   5400     0.0000    91.9412     7.3118    36.743561     60.8614     69.3001     92.2315\n  2000   5600     0.0000    91.9412     7.3118    37.008848     61.1817     69.0176     92.2315\n  2000   5800     0.0000    91.9412     7.3118    37.270151     61.4958     68.7379     92.2315\n  2000   6000     0.0000    91.9412     7.3118    37.527577     61.8040     68.4609     92.2315\n  2000   6200     0.0000    91.9412     7.3118    37.781226     62.1065     68.1866     92.2315\n  2000   6400     0.0000    91.9412     7.3118    38.031195     62.4034     67.9150     92.2315\n  2000   6600     0.0000    91.9412     7.3118    38.277577     62.6948     67.6460     92.2315\n  2000   6800     0.0000    91.9412     7.3118    38.520461     62.9810     67.3797     92.2315\n  2000   7000     0.0000    91.9412     7.3118    38.759934     63.2621     67.1158     92.2315\n  2000   7200     0.0000    91.9412     7.3118    38.996078     63.5382     66.8545     92.2315\n  2000   7400     0.0000    91.9412     7.3118    39.228972     63.8094     66.5957     92.2315\n  2000   7600     0.0000    91.9412     7.3118    39.458693     64.0759     66.3393     92.2315\n  2000   7800     0.0000    91.9412     7.3118    39.685315     64.3378     66.0854     92.2315\n  2000   8000     0.0000    91.9412     7.3118    39.908908     64.5952     65.8338     92.2315\n  2000   8200     0.0000    91.9412     7.3118    40.129541     64.8482     65.5846     92.2315\n  2000   8400     0.0000    91.9412     7.3118    40.347280     65.0970     65.3377     92.2315\n  2500      0     0.0000    91.7999     7.3088    27.492947     48.8618     78.0584     92.0904\n  2500    200     0.0000    91.7999     7.3088    27.935270     49.4629     77.6789     92.0904\n  2500    400     0.0000    91.7999     7.3088    28.366955     50.0468     77.3040     92.0904\n  2500    600     0.0000    91.7999     7.3088    28.788525     50.6142     76.9337     92.0904\n  2500    800     0.0000    91.7999     7.3088    29.200461     51.1660     76.5679     92.0904\n  2500   1000     0.0000    91.7999     7.3088    29.603204     51.7030     76.2063     92.0904\n  2500   1200     0.0000    91.7999     7.3088    29.997167     52.2257     75.8490     92.0904\n  2500   1400     0.0000    91.7999     7.3088    30.382730     52.7349     75.4959     92.0904\n  2500   1600     0.0000    91.7999     7.3088    30.760245     53.2312     75.1468     92.0904\n  2500   1800     0.0000    91.7999     7.3088    31.130044     53.7151     74.8016     92.0904\n  2500   2000     0.0000    91.7999     7.3088    31.492434     54.1872     74.4604     92.0904\n  2500   2200     0.0000    91.7999     7.3088    31.847702     54.6478     74.1230     92.0904\n  2500   2400     0.0000    91.7999     7.3088    32.196118     55.0976     73.7893     92.0904\n  2500   2600     0.0000    91.7999     7.3088    32.537936     55.5368     73.4593     92.0904\n  2500   2800     0.0000    91.7999     7.3088    32.873394     55.9659     73.1329     92.0904\n  2500   3000     0.0000    91.7999     7.3088    33.202716     56.3854     72.8100     92.0904\n  2500   3200     0.0000    91.7999     7.3088    33.526114     56.7954     72.4906     92.0904\n  2500   3400     0.0000    91.7999     7.3088    33.843788     57.1965     72.1746     92.0904\n  2500   3600     0.0000    91.7999     7.3088    34.155926     57.5888     71.8619     92.0904\n  2500   3800     0.0000    91.7999     7.3088    34.462707     57.9728     71.5525     92.0904\n  2500   4000     0.0000    91.7999     7.3088    34.764302     58.3486     71.2464     92.0904\n  2500   4200     0.0000    91.7999     7.3088    35.060871     58.7166     70.9434     92.0904\n  2500   4400     0.0000    91.7999     7.3088    35.352568     59.0770     70.6436     92.0904\n  2500   4600     0.0000    91.7999     7.3088    35.639538     59.4301     70.3468     92.0904\n  2500   4800     0.0000    91.7999     7.3088    35.921920     59.7761     70.0530     92.0904\n  2500   5000     0.0000    91.7999     7.3088    36.199846     60.1152     69.7623     92.0904\n  2500   5200     0.0000    91.7999     7.3088    36.473442     60.4476     69.4744     92.0904\n  2500   5400     0.0000    91.7999     7.3088    36.742830     60.7736     69.1894     92.0904\n  2500   5600     0.0000    91.7999     7.3088    37.008123     61.0933     68.9073     92.0904\n  2500   5800     0.0000    91.7999     7.3088    37.269433     61.4070     68.6280     92.0904\n  2500   6000     0.0000    91.7999     7.3088    37.526865     61.7147     68.3514     92.0904\n  2500   6200     0.0000    91.7999     7.3088    37.780519     62.0167     68.0775     92.0904\n  2500   6400     0.0000    91.7999     7.3088    38.030494     62.3131     67.8063     92.0904\n  2500   6600     0.0000    91.7999     7.3088    38.276882     62.6041     67.5377     92.0904\n  2500   6800     0.0000    91.7999     7.3088    38.519772     62.8899     67.2717     92.0904\n  2500   7000     0.0000    91.7999     7.3088    38.759250     63.1705     67.0082     92.0904\n  2500   7200     0.0000    91.7999     7.3088    38.995399     63.4461     66.7473     92.0904\n  2500   7400     0.0000    91.7999     7.3088    39.228299     63.7169     66.4889     92.0904\n  2500   7600     0.0000    91.7999     7.3088    39.458025     63.9830     66.2329     92.0904\n  2500   7800     0.0000    91.7999     7.3088    39.684651     64.2445     65.9793     92.0904\n  2500   8000     0.0000    91.7999     7.3088    39.908249     64.5015     65.7280     92.0904\n  2500   8200     0.0000    91.7999     7.3088    40.128887     64.7541     65.4792     92.0904\n  2500   8400     0.0000    91.7999     7.3088    40.346630     65.0025     65.2326     92.0904\n  3000      0     0.0000    91.6585     7.3059    27.491911     48.7925     77.9353     91.9492\n  3000    200     0.0000    91.6585     7.3059    27.934252     49.3927     77.5563     91.9492\n  3000    400     0.0000    91.6585     7.3059    28.365954     49.9756     77.1820     91.9492\n  3000    600     0.0000    91.6585     7.3059    28.787540     50.5422     76.8122     91.9492\n  3000    800     0.0000    91.6585     7.3059    29.199491     51.0932     76.4468     91.9492\n  3000   1000     0.0000    91.6585     7.3059    29.602249     51.6293     76.0858     91.9492\n  3000   1200     0.0000    91.6585     7.3059    29.996226     52.1512     75.7289     91.9492\n  3000   1400     0.0000    91.6585     7.3059    30.381801     52.6597     75.3763     91.9492\n  3000   1600     0.0000    91.6585     7.3059    30.759330     53.1552     75.0277     91.9492\n  3000   1800     0.0000    91.6585     7.3059    31.129141     53.6383     74.6830     91.9492\n  3000   2000     0.0000    91.6585     7.3059    31.491542     54.1097     74.3423     91.9492\n  3000   2200     0.0000    91.6585     7.3059    31.846821     54.5696     74.0053     91.9492\n  3000   2400     0.0000    91.6585     7.3059    32.195248     55.0186     73.6721     91.9492\n  3000   2600     0.0000    91.6585     7.3059    32.537077     55.4572     73.3426     91.9492\n  3000   2800     0.0000    91.6585     7.3059    32.872545     55.8856     73.0166     91.9492\n  3000   3000     0.0000    91.6585     7.3059    33.201877     56.3044     72.6942     91.9492\n  3000   3200     0.0000    91.6585     7.3059    33.525284     56.7138     72.3752     91.9492\n  3000   3400     0.0000    91.6585     7.3059    33.842966     57.1143     72.0597     91.9492\n  3000   3600     0.0000    91.6585     7.3059    34.155113     57.5060     71.7474     91.9492\n  3000   3800     0.0000    91.6585     7.3059    34.461903     57.8893     71.4385     91.9492\n  3000   4000     0.0000    91.6585     7.3059    34.763506     58.2646     71.1328     91.9492\n  3000   4200     0.0000    91.6585     7.3059    35.060083     58.6320     70.8302     91.9492\n  3000   4400     0.0000    91.6585     7.3059    35.351788     58.9919     70.5308     91.9492\n  3000   4600     0.0000    91.6585     7.3059    35.638765     59.3444     70.2345     91.9492\n  3000   4800     0.0000    91.6585     7.3059    35.921154     59.6898     69.9411     91.9492\n  3000   5000     0.0000    91.6585     7.3059    36.199088     60.0284     69.6508     91.9492\n  3000   5200     0.0000    91.6585     7.3059    36.472691     60.3603     69.3633     91.9492\n  3000   5400     0.0000    91.6585     7.3059    36.742085     60.6858     69.0787     91.9492\n  3000   5600     0.0000    91.6585     7.3059    37.007385     61.0050     68.7970     91.9492\n  3000   5800     0.0000    91.6585     7.3059    37.268701     61.3181     68.5181     91.9492\n  3000   6000     0.0000    91.6585     7.3059    37.526139     61.6254     68.2419     91.9492\n  3000   6200     0.0000    91.6585     7.3059    37.779800     61.9269     67.9684     91.9492\n  3000   6400     0.0000    91.6585     7.3059    38.029781     62.2228     67.6975     91.9492\n  3000   6600     0.0000    91.6585     7.3059    38.276174     62.5134     67.4293     91.9492\n  3000   6800     0.0000    91.6585     7.3059    38.519070     62.7987     67.1637     91.9492\n  3000   7000     0.0000    91.6585     7.3059    38.758554     63.0789     66.9006     91.9492\n  3000   7200     0.0000    91.6585     7.3059    38.994708     63.3541     66.6401     91.9492\n  3000   7400     0.0000    91.6585     7.3059    39.227613     63.6244     66.3820     91.9492\n  3000   7600     0.0000    91.6585     7.3059    39.457344     63.8901     66.1264     91.9492\n  3000   7800     0.0000    91.6585     7.3059    39.683975     64.1512     65.8731     91.9492\n  3000   8000     0.0000    91.6585     7.3059    39.907578     64.4077     65.6223     91.9492\n  3000   8200     0.0000    91.6585     7.3059    40.128221     64.6600     65.3738     91.9492\n  3000   8400     0.0000    91.6585     7.3059    40.345969     64.9080     65.1276     91.9492\n  3500      0     0.0000    91.5172     7.3029    27.490855     48.7231     77.8122     91.8081\n  3500    200     0.0000    91.5172     7.3029    27.933214     49.3225     77.4337     91.8081\n  3500    400     0.0000    91.5172     7.3029    28.364934     49.9045     77.0599     91.8081\n  3500    600     0.0000    91.5172     7.3029    28.786536     50.4702     76.6906     91.8081\n  3500    800     0.0000    91.5172     7.3029    29.198502     51.0203     76.3257     91.8081\n  3500   1000     0.0000    91.5172     7.3029    29.601275     51.5556     75.9652     91.8081\n  3500   1200     0.0000    91.5172     7.3029    29.995266     52.0767     75.6089     91.8081\n  3500   1400     0.0000    91.5172     7.3029    30.380855     52.5844     75.2567     91.8081\n  3500   1600     0.0000    91.5172     7.3029    30.758396     53.0791     74.9086     91.8081\n  3500   1800     0.0000    91.5172     7.3029    31.128219     53.5615     74.5644     91.8081\n  3500   2000     0.0000    91.5172     7.3029    31.490633     54.0321     74.2242     91.8081\n  3500   2200     0.0000    91.5172     7.3029    31.845923     54.4913     73.8877     91.8081\n  3500   2400     0.0000    91.5172     7.3029    32.194362     54.9397     73.5549     91.8081\n  3500   2600     0.0000    91.5172     7.3029    32.536201     55.3775     73.2259     91.8081\n  3500   2800     0.0000    91.5172     7.3029    32.871679     55.8053     72.9004     91.8081\n  3500   3000     0.0000    91.5172     7.3029    33.201021     56.2234     72.5784     91.8081\n  3500   3200     0.0000    91.5172     7.3029    33.524438     56.6322     72.2599     91.8081\n  3500   3400     0.0000    91.5172     7.3029    33.842129     57.0320     71.9448     91.8081\n  3500   3600     0.0000    91.5172     7.3029    34.154285     57.4231     71.6330     91.8081\n  3500   3800     0.0000    91.5172     7.3029    34.461084     57.8059     71.3245     91.8081\n  3500   4000     0.0000    91.5172     7.3029    34.762695     58.1805     71.0192     91.8081\n  3500   4200     0.0000    91.5172     7.3029    35.059281     58.5474     70.7171     91.8081\n  3500   4400     0.0000    91.5172     7.3029    35.350993     58.9067     70.4181     91.8081\n  3500   4600     0.0000    91.5172     7.3029    35.637978     59.2587     70.1221     91.8081\n  3500   4800     0.0000    91.5172     7.3029    35.920375     59.6035     69.8292     91.8081\n  3500   5000     0.0000    91.5172     7.3029    36.198315     59.9416     69.5393     91.8081\n  3500   5200     0.0000    91.5172     7.3029    36.471925     60.2730     69.2522     91.8081\n  3500   5400     0.0000    91.5172     7.3029    36.741326     60.5979     68.9681     91.8081\n  3500   5600     0.0000    91.5172     7.3029    37.006633     60.9166     68.6867     91.8081\n  3500   5800     0.0000    91.5172     7.3029    37.267956     61.2293     68.4082     91.8081\n  3500   6000     0.0000    91.5172     7.3029    37.525400     61.5360     68.1324     91.8081\n  3500   6200     0.0000    91.5172     7.3029    37.779067     61.8371     67.8593     91.8081\n  3500   6400     0.0000    91.5172     7.3029    38.029054     62.1326     67.5888     91.8081\n  3500   6600     0.0000    91.5172     7.3029    38.275453     62.4227     67.3210     91.8081\n  3500   6800     0.0000    91.5172     7.3029    38.518355     62.7075     67.0558     91.8081\n  3500   7000     0.0000    91.5172     7.3029    38.757844     62.9872     66.7931     91.8081\n  3500   7200     0.0000    91.5172     7.3029    38.994004     63.2620     66.5329     91.8081\n  3500   7400     0.0000    91.5172     7.3029    39.226914     63.5319     66.2752     91.8081\n  3500   7600     0.0000    91.5172     7.3029    39.456650     63.7972     66.0199     91.8081\n  3500   7800     0.0000    91.5172     7.3029    39.683287     64.0578     65.7670     91.8081\n  3500   8000     0.0000    91.5172     7.3029    39.906895     64.3140     65.5165     91.8081\n  3500   8200     0.0000    91.5172     7.3029    40.127542     64.5658     65.2684     91.8081\n  3500   8400     0.0000    91.5172     7.3029    40.345295     64.8134     65.0225     91.8081\n  4000      0     0.0000    91.3759     7.2999    27.489779     48.6538     77.6891     91.6670\n  4000    200     0.0000    91.3759     7.2999    27.932157     49.2522     77.3112     91.6670\n  4000    400     0.0000    91.3759     7.2999    28.363894     49.8333     76.9378     91.6670\n  4000    600     0.0000    91.3759     7.2999    28.785512     50.3981     76.5691     91.6670\n  4000    800     0.0000    91.3759     7.2999    29.197494     50.9474     76.2047     91.6670\n  4000   1000     0.0000    91.3759     7.2999    29.600282     51.4819     75.8447     91.6670\n  4000   1200     0.0000    91.3759     7.2999    29.994287     52.0022     75.4889     91.6670\n  4000   1400     0.0000    91.3759     7.2999    30.379890     52.5091     75.1372     91.6670\n  4000   1600     0.0000    91.3759     7.2999    30.757445     53.0031     74.7895     91.6670\n  4000   1800     0.0000    91.3759     7.2999    31.127281     53.4847     74.4459     91.6670\n  4000   2000     0.0000    91.3759     7.2999    31.489706     53.9545     74.1061     91.6670\n  4000   2200     0.0000    91.3759     7.2999    31.845009     54.4130     73.7701     91.6670\n  4000   2400     0.0000    91.3759     7.2999    32.193458     54.8607     73.4378     91.6670\n  4000   2600     0.0000    91.3759     7.2999    32.535308     55.2979     73.1092     91.6670\n  4000   2800     0.0000    91.3759     7.2999    32.870797     55.7250     72.7841     91.6670\n  4000   3000     0.0000    91.3759     7.2999    33.200149     56.1425     72.4626     91.6670\n  4000   3200     0.0000    91.3759     7.2999    33.523576     56.5506     72.1445     91.6670\n  4000   3400     0.0000    91.3759     7.2999    33.841277     56.9498     71.8299     91.6670\n  4000   3600     0.0000    91.3759     7.2999    34.153442     57.3403     71.5185     91.6670\n  4000   3800     0.0000    91.3759     7.2999    34.460249     57.7224     71.2104     91.6670\n  4000   4000     0.0000    91.3759     7.2999    34.761869     58.0965     70.9056     91.6670\n  4000   4200     0.0000    91.3759     7.2999    35.058463     58.4628     70.6039     91.6670\n  4000   4400     0.0000    91.3759     7.2999    35.350183     58.8215     70.3053     91.6670\n  4000   4600     0.0000    91.3759     7.2999    35.637176     59.1729     70.0098     91.6670\n  4000   4800     0.0000    91.3759     7.2999    35.919581     59.5173     69.7173     91.6670\n  4000   5000     0.0000    91.3759     7.2999    36.197528     59.8548     69.4278     91.6670\n  4000   5200     0.0000    91.3759     7.2999    36.471146     60.1856     69.1412     91.6670\n  4000   5400     0.0000    91.3759     7.2999    36.740554     60.5101     68.8574     91.6670\n  4000   5600     0.0000    91.3759     7.2999    37.005868     60.8283     68.5765     91.6670\n  4000   5800     0.0000    91.3759     7.2999    37.267197     61.1404     68.2983     91.6670\n  4000   6000     0.0000    91.3759     7.2999    37.524648     61.4467     68.0229     91.6670\n  4000   6200     0.0000    91.3759     7.2999    37.778321     61.7473     67.7502     91.6670\n  4000   6400     0.0000    91.3759     7.2999    38.028314     62.0423     67.4801     91.6670\n  4000   6600     0.0000    91.3759     7.2999    38.274719     62.3319     67.2127     91.6670\n  4000   6800     0.0000    91.3759     7.2999    38.517627     62.6163     66.9478     91.6670\n  4000   7000     0.0000    91.3759     7.2999    38.757122     62.8956     66.6855     91.6670\n  4000   7200     0.0000    91.3759     7.2999    38.993287     63.1699     66.4257     91.6670\n  4000   7400     0.0000    91.3759     7.2999    39.226203     63.4394     66.1684     91.6670\n  4000   7600     0.0000    91.3759     7.2999    39.455945     63.7042     65.9134     91.6670\n  4000   7800     0.0000    91.3759     7.2999    39.682586     63.9645     65.6609     91.6670\n  4000   8000     0.0000    91.3759     7.2999    39.906199     64.2203     65.4108     91.6670\n  4000   8200     0.0000    91.3759     7.2999    40.126852     64.4717     65.1630     91.6670\n  4000   8400     0.0000    91.3759     7.2999    40.344610     64.7189     64.9175     91.6670\n  4500      0     0.0000    91.2345     7.2969    27.488683     48.5844     77.5660     91.5259\n  4500    200     0.0000    91.2345     7.2969    27.931079     49.1819     77.1886     91.5259\n  4500    400     0.0000    91.2345     7.2969    28.362834     49.7622     76.8158     91.5259\n  4500    600     0.0000    91.2345     7.2969    28.784470     50.3261     76.4475     91.5259\n  4500    800     0.0000    91.2345     7.2969    29.196467     50.8745     76.0837     91.5259\n  4500   1000     0.0000    91.2345     7.2969    29.599271     51.4081     75.7242     91.5259\n  4500   1200     0.0000    91.2345     7.2969    29.993291     51.9277     75.3688     91.5259\n  4500   1400     0.0000    91.2345     7.2969    30.378908     52.4337     75.0176     91.5259\n  4500   1600     0.0000    91.2345     7.2969    30.756476     52.9270     74.6705     91.5259\n  4500   1800     0.0000    91.2345     7.2969    31.126325     53.4079     74.3273     91.5259\n  4500   2000     0.0000    91.2345     7.2969    31.488763     53.8770     73.9880     91.5259\n  4500   2200     0.0000    91.2345     7.2969    31.844077     54.3348     73.6524     91.5259\n  4500   2400     0.0000    91.2345     7.2969    32.192539     54.7817     73.3206     91.5259\n  4500   2600     0.0000    91.2345     7.2969    32.534400     55.2182     72.9925     91.5259\n  4500   2800     0.0000    91.2345     7.2969    32.869899     55.6446     72.6679     91.5259\n  4500   3000     0.0000    91.2345     7.2969    33.199262     56.0615     72.3468     91.5259\n  4500   3200     0.0000    91.2345     7.2969    33.522698     56.4690     72.0292     91.5259\n  4500   3400     0.0000    91.2345     7.2969    33.840409     56.8675     71.7150     91.5259\n  4500   3600     0.0000    91.2345     7.2969    34.152583     57.2574     71.4041     91.5259\n  4500   3800     0.0000    91.2345     7.2969    34.459400     57.6389     71.0964     91.5259\n  4500   4000     0.0000    91.2345     7.2969    34.761029     58.0124     70.7920     91.5259\n  4500   4200     0.0000    91.2345     7.2969    35.057631     58.3781     70.4908     91.5259\n  4500   4400     0.0000    91.2345     7.2969    35.349359     58.7363     70.1926     91.5259\n  4500   4600     0.0000    91.2345     7.2969    35.636360     59.0871     69.8975     91.5259\n  4500   4800     0.0000    91.2345     7.2969    35.918772     59.4309     69.6054     91.5259\n  4500   5000     0.0000    91.2345     7.2969    36.196728     59.7679     69.3163     91.5259\n  4500   5200     0.0000    91.2345     7.2969    36.470353     60.0983     69.0301     91.5259\n  4500   5400     0.0000    91.2345     7.2969    36.739768     60.4222     68.7467     91.5259\n  4500   5600     0.0000    91.2345     7.2969    37.005088     60.7399     68.4662     91.5259\n  4500   5800     0.0000    91.2345     7.2969    37.266425     61.0516     68.1885     91.5259\n  4500   6000     0.0000    91.2345     7.2969    37.523882     61.3573     67.9134     91.5259\n  4500   6200     0.0000    91.2345     7.2969    37.777562     61.6574     67.6411     91.5259\n  4500   6400     0.0000    91.2345     7.2969    38.027561     61.9520     67.3714     91.5259\n  4500   6600     0.0000    91.2345     7.2969    38.273972     62.2411     67.1044     91.5259\n  4500   6800     0.0000    91.2345     7.2969    38.516886     62.5251     66.8399     91.5259\n  4500   7000     0.0000    91.2345     7.2969    38.756387     62.8039     66.5780     91.5259\n  4500   7200     0.0000    91.2345     7.2969    38.992558     63.0778     66.3185     91.5259\n  4500   7400     0.0000    91.2345     7.2969    39.225479     63.3469     66.0615     91.5259\n  4500   7600     0.0000    91.2345     7.2969    39.455227     63.6113     65.8070     91.5259\n  4500   7800     0.0000    91.2345     7.2969    39.681874     63.8711     65.5549     91.5259\n  4500   8000     0.0000    91.2345     7.2969    39.905492     64.1265     65.3051     91.5259\n  4500   8200     0.0000    91.2345     7.2969    40.126149     64.3775     65.0576     91.5259\n  4500   8400     0.0000    91.2345     7.2969    40.343913     64.6243     64.8125     91.5259\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.000\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180717_VV_8rlks_base.par",
    "content": "initial_baseline(TCN):         -0.2418971       -4.6591305        9.7953949   m   m   m\ninitial_baseline_rate:          0.0000000        0.0146665       -0.0002475   m/s m/s m/s\nprecision_baseline(TCN):       -0.2418971       -4.6591305        9.7953949   m   m   m\nprecision_baseline_rate:        0.0000000        0.0146665       -0.0002475   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/cropA/geometry/20180506-20180717_VV_8rlks_bperp.par",
    "content": "*** Calculate baseline components perpendicular and parallel to look vector ***\n*** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw ***\n\ninterferogram lines: 4541   range samples/line: 8514\norbit baseline vector (TCN)   (m):     -0.242     -4.659      9.795\norbit baseline rate   (TCN) (m/s):  0.000e+00  1.467e-02 -2.475e-04\n\nbaseline vector (TCN)   (m):     -0.242     -4.659      9.795\nbaseline rate   (TCN) (m/s):  0.000e+00  1.467e-02 -2.475e-04\n\nSLC-1 center baseline length (m):    10.8497\nazimuth angle:      90.0000 (right looking)\n\n  line   range      B_t       B_c        B_n     look angle      bpara       bperp       blen\n************************************************************************************************\n     0      0    -0.2419    -4.7960     9.7977    27.497816      6.4767     -8.7774     10.9113\n     0    200    -0.2419    -4.7960     9.7977    27.940057      6.4088     -8.8272     10.9113\n     0    400    -0.2419    -4.7960     9.7977    28.371664      6.3421     -8.8752     10.9113\n     0    600    -0.2419    -4.7960     9.7977    28.793160      6.2766     -8.9216     10.9113\n     0    800    -0.2419    -4.7960     9.7977    29.205025      6.2123     -8.9665     10.9113\n     0   1000    -0.2419    -4.7960     9.7977    29.607701      6.1492     -9.0100     10.9113\n     0   1200    -0.2419    -4.7960     9.7977    30.001600      6.0871     -9.0520     10.9113\n     0   1400    -0.2419    -4.7960     9.7977    30.387101      6.0260     -9.0928     10.9113\n     0   1600    -0.2419    -4.7960     9.7977    30.764557      5.9660     -9.1323     10.9113\n     0   1800    -0.2419    -4.7960     9.7977    31.134300      5.9069     -9.1706     10.9113\n     0   2000    -0.2419    -4.7960     9.7977    31.496635      5.8488     -9.2078     10.9113\n     0   2200    -0.2419    -4.7960     9.7977    31.851850      5.7916     -9.2439     10.9113\n     0   2400    -0.2419    -4.7960     9.7977    32.200217      5.7353     -9.2789     10.9113\n     0   2600    -0.2419    -4.7960     9.7977    32.541986      5.6798     -9.3130     10.9113\n     0   2800    -0.2419    -4.7960     9.7977    32.877397      5.6252     -9.3461     10.9113\n     0   3000    -0.2419    -4.7960     9.7977    33.206674      5.5714     -9.3783     10.9113\n     0   3200    -0.2419    -4.7960     9.7977    33.530029      5.5184     -9.4095     10.9113\n     0   3400    -0.2419    -4.7960     9.7977    33.847660      5.4661     -9.4400     10.9113\n     0   3600    -0.2419    -4.7960     9.7977    34.159757      5.4146     -9.4696     10.9113\n     0   3800    -0.2419    -4.7960     9.7977    34.466499      5.3639     -9.4985     10.9113\n     0   4000    -0.2419    -4.7960     9.7977    34.768056      5.3138     -9.5266     10.9113\n     0   4200    -0.2419    -4.7960     9.7977    35.064588      5.2644     -9.5540     10.9113\n     0   4400    -0.2419    -4.7960     9.7977    35.356248      5.2157     -9.5807     10.9113\n     0   4600    -0.2419    -4.7960     9.7977    35.643183      5.1677     -9.6067     10.9113\n     0   4800    -0.2419    -4.7960     9.7977    35.925531      5.1203     -9.6320     10.9113\n     0   5000    -0.2419    -4.7960     9.7977    36.203424      5.0735     -9.6567     10.9113\n     0   5200    -0.2419    -4.7960     9.7977    36.476989      5.0273     -9.6808     10.9113\n     0   5400    -0.2419    -4.7960     9.7977    36.746345      4.9817     -9.7044     10.9113\n     0   5600    -0.2419    -4.7960     9.7977    37.011608      4.9368     -9.7273     10.9113\n     0   5800    -0.2419    -4.7960     9.7977    37.272888      4.8924     -9.7498     10.9113\n     0   6000    -0.2419    -4.7960     9.7977    37.530291      4.8485     -9.7716     10.9113\n     0   6200    -0.2419    -4.7960     9.7977    37.783917      4.8052     -9.7930     10.9113\n     0   6400    -0.2419    -4.7960     9.7977    38.033864      4.7624     -9.8139     10.9113\n     0   6600    -0.2419    -4.7960     9.7977    38.280225      4.7202     -9.8343     10.9113\n     0   6800    -0.2419    -4.7960     9.7977    38.523089      4.6785     -9.8542     10.9113\n     0   7000    -0.2419    -4.7960     9.7977    38.762542      4.6372     -9.8737     10.9113\n     0   7200    -0.2419    -4.7960     9.7977    38.998666      4.5965     -9.8927     10.9113\n     0   7400    -0.2419    -4.7960     9.7977    39.231541      4.5562     -9.9113     10.9113\n     0   7600    -0.2419    -4.7960     9.7977    39.461243      4.5165     -9.9295     10.9113\n     0   7800    -0.2419    -4.7960     9.7977    39.687846      4.4772     -9.9473     10.9113\n     0   8000    -0.2419    -4.7960     9.7977    39.911421      4.4383     -9.9647     10.9113\n     0   8200    -0.2419    -4.7960     9.7977    40.132036      4.3999     -9.9817     10.9113\n     0   8400    -0.2419    -4.7960     9.7977    40.349758      4.3620     -9.9983     10.9113\n   500      0    -0.2419    -4.7659     9.7972    27.496883      6.4903     -8.7504     10.8976\n   500    200    -0.2419    -4.7659     9.7972    27.939140      6.4226     -8.8002     10.8976\n   500    400    -0.2419    -4.7659     9.7972    28.370762      6.3561     -8.8483     10.8976\n   500    600    -0.2419    -4.7659     9.7972    28.792272      6.2908     -8.8949     10.8976\n   500    800    -0.2419    -4.7659     9.7972    29.204150      6.2267     -8.9399     10.8976\n   500   1000    -0.2419    -4.7659     9.7972    29.606840      6.1637     -8.9834     10.8976\n   500   1200    -0.2419    -4.7659     9.7972    30.000750      6.1018     -9.0256     10.8976\n   500   1400    -0.2419    -4.7659     9.7972    30.386263      6.0410     -9.0664     10.8976\n   500   1600    -0.2419    -4.7659     9.7972    30.763731      5.9811     -9.1061     10.8976\n   500   1800    -0.2419    -4.7659     9.7972    31.133484      5.9222     -9.1445     10.8976\n   500   2000    -0.2419    -4.7659     9.7972    31.495829      5.8643     -9.1817     10.8976\n   500   2200    -0.2419    -4.7659     9.7972    31.851055      5.8072     -9.2179     10.8976\n   500   2400    -0.2419    -4.7659     9.7972    32.199431      5.7511     -9.2531     10.8976\n   500   2600    -0.2419    -4.7659     9.7972    32.541209      5.6958     -9.2872     10.8976\n   500   2800    -0.2419    -4.7659     9.7972    32.876630      5.6413     -9.3204     10.8976\n   500   3000    -0.2419    -4.7659     9.7972    33.205915      5.5876     -9.3527     10.8976\n   500   3200    -0.2419    -4.7659     9.7972    33.529278      5.5347     -9.3841     10.8976\n   500   3400    -0.2419    -4.7659     9.7972    33.846917      5.4826     -9.4146     10.8976\n   500   3600    -0.2419    -4.7659     9.7972    34.159022      5.4313     -9.4443     10.8976\n   500   3800    -0.2419    -4.7659     9.7972    34.465771      5.3806     -9.4733     10.8976\n   500   4000    -0.2419    -4.7659     9.7972    34.767335      5.3307     -9.5015     10.8976\n   500   4200    -0.2419    -4.7659     9.7972    35.063874      5.2814     -9.5289     10.8976\n   500   4400    -0.2419    -4.7659     9.7972    35.355542      5.2329     -9.5557     10.8976\n   500   4600    -0.2419    -4.7659     9.7972    35.642483      5.1849     -9.5818     10.8976\n   500   4800    -0.2419    -4.7659     9.7972    35.924838      5.1377     -9.6072     10.8976\n   500   5000    -0.2419    -4.7659     9.7972    36.202737      5.0910     -9.6320     10.8976\n   500   5200    -0.2419    -4.7659     9.7972    36.476308      5.0449     -9.6562     10.8976\n   500   5400    -0.2419    -4.7659     9.7972    36.745670      4.9995     -9.6799     10.8976\n   500   5600    -0.2419    -4.7659     9.7972    37.010938      4.9546     -9.7029     10.8976\n   500   5800    -0.2419    -4.7659     9.7972    37.272224      4.9103     -9.7254     10.8976\n   500   6000    -0.2419    -4.7659     9.7972    37.529632      4.8666     -9.7474     10.8976\n   500   6200    -0.2419    -4.7659     9.7972    37.783264      4.8234     -9.7688     10.8976\n   500   6400    -0.2419    -4.7659     9.7972    38.033217      4.7807     -9.7898     10.8976\n   500   6600    -0.2419    -4.7659     9.7972    38.279583      4.7386     -9.8102     10.8976\n   500   6800    -0.2419    -4.7659     9.7972    38.522452      4.6969     -9.8302     10.8976\n   500   7000    -0.2419    -4.7659     9.7972    38.761909      4.6558     -9.8498     10.8976\n   500   7200    -0.2419    -4.7659     9.7972    38.998038      4.6152     -9.8689     10.8976\n   500   7400    -0.2419    -4.7659     9.7972    39.230918      4.5750     -9.8876     10.8976\n   500   7600    -0.2419    -4.7659     9.7972    39.460624      4.5354     -9.9058     10.8976\n   500   7800    -0.2419    -4.7659     9.7972    39.687232      4.4961     -9.9237     10.8976\n   500   8000    -0.2419    -4.7659     9.7972    39.910811      4.4574     -9.9412     10.8976\n   500   8200    -0.2419    -4.7659     9.7972    40.131431      4.4191     -9.9583     10.8976\n   500   8400    -0.2419    -4.7659     9.7972    40.349156      4.3812     -9.9750     10.8976\n  1000      0    -0.2419    -4.7357     9.7967    27.495930      6.5039     -8.7233     10.8840\n  1000    200    -0.2419    -4.7357     9.7967    27.938203      6.4364     -8.7732     10.8840\n  1000    400    -0.2419    -4.7357     9.7967    28.369840      6.3701     -8.8215     10.8840\n  1000    600    -0.2419    -4.7357     9.7967    28.791365      6.3050     -8.8681     10.8840\n  1000    800    -0.2419    -4.7357     9.7967    29.203256      6.2411     -8.9132     10.8840\n  1000   1000    -0.2419    -4.7357     9.7967    29.605959      6.1783     -8.9569     10.8840\n  1000   1200    -0.2419    -4.7357     9.7967    29.999882      6.1166     -8.9991     10.8840\n  1000   1400    -0.2419    -4.7357     9.7967    30.385407      6.0559     -9.0401     10.8840\n  1000   1600    -0.2419    -4.7357     9.7967    30.762886      5.9962     -9.0798     10.8840\n  1000   1800    -0.2419    -4.7357     9.7967    31.132650      5.9375     -9.1183     10.8840\n  1000   2000    -0.2419    -4.7357     9.7967    31.495006      5.8797     -9.1557     10.8840\n  1000   2200    -0.2419    -4.7357     9.7967    31.850242      5.8228     -9.1920     10.8840\n  1000   2400    -0.2419    -4.7357     9.7967    32.198628      5.7668     -9.2272     10.8840\n  1000   2600    -0.2419    -4.7357     9.7967    32.540416      5.7117     -9.2614     10.8840\n  1000   2800    -0.2419    -4.7357     9.7967    32.875845      5.6573     -9.2947     10.8840\n  1000   3000    -0.2419    -4.7357     9.7967    33.205140      5.6038     -9.3271     10.8840\n  1000   3200    -0.2419    -4.7357     9.7967    33.528511      5.5511     -9.3586     10.8840\n  1000   3400    -0.2419    -4.7357     9.7967    33.846158      5.4991     -9.3892     10.8840\n  1000   3600    -0.2419    -4.7357     9.7967    34.158271      5.4479     -9.4190     10.8840\n  1000   3800    -0.2419    -4.7357     9.7967    34.465028      5.3974     -9.4481     10.8840\n  1000   4000    -0.2419    -4.7357     9.7967    34.766600      5.3476     -9.4763     10.8840\n  1000   4200    -0.2419    -4.7357     9.7967    35.063146      5.2985     -9.5039     10.8840\n  1000   4400    -0.2419    -4.7357     9.7967    35.354821      5.2500     -9.5308     10.8840\n  1000   4600    -0.2419    -4.7357     9.7967    35.641769      5.2022     -9.5569     10.8840\n  1000   4800    -0.2419    -4.7357     9.7967    35.924130      5.1551     -9.5825     10.8840\n  1000   5000    -0.2419    -4.7357     9.7967    36.202036      5.1085     -9.6073     10.8840\n  1000   5200    -0.2419    -4.7357     9.7967    36.475612      5.0626     -9.6316     10.8840\n  1000   5400    -0.2419    -4.7357     9.7967    36.744981      5.0172     -9.6553     10.8840\n  1000   5600    -0.2419    -4.7357     9.7967    37.010255      4.9725     -9.6785     10.8840\n  1000   5800    -0.2419    -4.7357     9.7967    37.271547      4.9283     -9.7010     10.8840\n  1000   6000    -0.2419    -4.7357     9.7967    37.528961      4.8847     -9.7231     10.8840\n  1000   6200    -0.2419    -4.7357     9.7967    37.782598      4.8416     -9.7446     10.8840\n  1000   6400    -0.2419    -4.7357     9.7967    38.032556      4.7990     -9.7656     10.8840\n  1000   6600    -0.2419    -4.7357     9.7967    38.278927      4.7570     -9.7862     10.8840\n  1000   6800    -0.2419    -4.7357     9.7967    38.521801      4.7154     -9.8063     10.8840\n  1000   7000    -0.2419    -4.7357     9.7967    38.761264      4.6744     -9.8259     10.8840\n  1000   7200    -0.2419    -4.7357     9.7967    38.997398      4.6339     -9.8451     10.8840\n  1000   7400    -0.2419    -4.7357     9.7967    39.230282      4.5938     -9.8638     10.8840\n  1000   7600    -0.2419    -4.7357     9.7967    39.459993      4.5542     -9.8822     10.8840\n  1000   7800    -0.2419    -4.7357     9.7967    39.686605      4.5151     -9.9001     10.8840\n  1000   8000    -0.2419    -4.7357     9.7967    39.910189      4.4764     -9.9177     10.8840\n  1000   8200    -0.2419    -4.7357     9.7967    40.130813      4.4382     -9.9348     10.8840\n  1000   8400    -0.2419    -4.7357     9.7967    40.348543      4.4004     -9.9516     10.8840\n  1500      0    -0.2419    -4.7056     9.7962    27.494956      6.5176     -8.6962     10.8704\n  1500    200    -0.2419    -4.7056     9.7962    27.937245      6.4502     -8.7463     10.8704\n  1500    400    -0.2419    -4.7056     9.7962    28.368898      6.3841     -8.7946     10.8704\n  1500    600    -0.2419    -4.7056     9.7962    28.790438      6.3193     -8.8413     10.8704\n  1500    800    -0.2419    -4.7056     9.7962    29.202344      6.2555     -8.8865     10.8704\n  1500   1000    -0.2419    -4.7056     9.7962    29.605059      6.1929     -8.9303     10.8704\n  1500   1200    -0.2419    -4.7056     9.7962    29.998996      6.1314     -8.9727     10.8704\n  1500   1400    -0.2419    -4.7056     9.7962    30.384532      6.0709     -9.0137     10.8704\n  1500   1600    -0.2419    -4.7056     9.7962    30.762024      6.0113     -9.0535     10.8704\n  1500   1800    -0.2419    -4.7056     9.7962    31.131799      5.9528     -9.0922     10.8704\n  1500   2000    -0.2419    -4.7056     9.7962    31.494166      5.8952     -9.1296     10.8704\n  1500   2200    -0.2419    -4.7056     9.7962    31.849412      5.8384     -9.1660     10.8704\n  1500   2400    -0.2419    -4.7056     9.7962    32.197808      5.7826     -9.2013     10.8704\n  1500   2600    -0.2419    -4.7056     9.7962    32.539606      5.7276     -9.2357     10.8704\n  1500   2800    -0.2419    -4.7056     9.7962    32.875045      5.6734     -9.2691     10.8704\n  1500   3000    -0.2419    -4.7056     9.7962    33.204348      5.6200     -9.3015     10.8704\n  1500   3200    -0.2419    -4.7056     9.7962    33.527728      5.5675     -9.3331     10.8704\n  1500   3400    -0.2419    -4.7056     9.7962    33.845384      5.5156     -9.3638     10.8704\n  1500   3600    -0.2419    -4.7056     9.7962    34.157505      5.4645     -9.3937     10.8704\n  1500   3800    -0.2419    -4.7056     9.7962    34.464270      5.4142     -9.4229     10.8704\n  1500   4000    -0.2419    -4.7056     9.7962    34.765849      5.3645     -9.4512     10.8704\n  1500   4200    -0.2419    -4.7056     9.7962    35.062403      5.3155     -9.4789     10.8704\n  1500   4400    -0.2419    -4.7056     9.7962    35.354084      5.2672     -9.5058     10.8704\n  1500   4600    -0.2419    -4.7056     9.7962    35.641040      5.2195     -9.5321     10.8704\n  1500   4800    -0.2419    -4.7056     9.7962    35.923408      5.1724     -9.5577     10.8704\n  1500   5000    -0.2419    -4.7056     9.7962    36.201320      5.1260     -9.5827     10.8704\n  1500   5200    -0.2419    -4.7056     9.7962    36.474903      5.0802     -9.6070     10.8704\n  1500   5400    -0.2419    -4.7056     9.7962    36.744278      5.0350     -9.6308     10.8704\n  1500   5600    -0.2419    -4.7056     9.7962    37.009558      4.9903     -9.6540     10.8704\n  1500   5800    -0.2419    -4.7056     9.7962    37.270856      4.9463     -9.6767     10.8704\n  1500   6000    -0.2419    -4.7056     9.7962    37.528276      4.9027     -9.6988     10.8704\n  1500   6200    -0.2419    -4.7056     9.7962    37.781919      4.8597     -9.7204     10.8704\n  1500   6400    -0.2419    -4.7056     9.7962    38.031882      4.8173     -9.7415     10.8704\n  1500   6600    -0.2419    -4.7056     9.7962    38.278259      4.7754     -9.7622     10.8704\n  1500   6800    -0.2419    -4.7056     9.7962    38.521138      4.7339     -9.7823     10.8704\n  1500   7000    -0.2419    -4.7056     9.7962    38.760605      4.6930     -9.8020     10.8704\n  1500   7200    -0.2419    -4.7056     9.7962    38.996744      4.6526     -9.8213     10.8704\n  1500   7400    -0.2419    -4.7056     9.7962    39.229633      4.6126     -9.8401     10.8704\n  1500   7600    -0.2419    -4.7056     9.7962    39.459350      4.5731     -9.8585     10.8704\n  1500   7800    -0.2419    -4.7056     9.7962    39.685966      4.5341     -9.8765     10.8704\n  1500   8000    -0.2419    -4.7056     9.7962    39.909555      4.4955     -9.8942     10.8704\n  1500   8200    -0.2419    -4.7056     9.7962    40.130183      4.4574     -9.9114     10.8704\n  1500   8400    -0.2419    -4.7056     9.7962    40.347918      4.4197     -9.9283     10.8704\n  2000      0    -0.2419    -4.6754     9.7957    27.493962      6.5312     -8.6691     10.8570\n  2000    200    -0.2419    -4.6754     9.7957    27.936268      6.4640     -8.7193     10.8570\n  2000    400    -0.2419    -4.6754     9.7957    28.367937      6.3982     -8.7677     10.8570\n  2000    600    -0.2419    -4.6754     9.7957    28.789491      6.3335     -8.8146     10.8570\n  2000    800    -0.2419    -4.6754     9.7957    29.201412      6.2699     -8.8599     10.8570\n  2000   1000    -0.2419    -4.6754     9.7957    29.604141      6.2075     -8.9037     10.8570\n  2000   1200    -0.2419    -4.6754     9.7957    29.998091      6.1461     -8.9462     10.8570\n  2000   1400    -0.2419    -4.6754     9.7957    30.383640      6.0858     -8.9874     10.8570\n  2000   1600    -0.2419    -4.6754     9.7957    30.761143      6.0265     -9.0273     10.8570\n  2000   1800    -0.2419    -4.6754     9.7957    31.130930      5.9681     -9.0660     10.8570\n  2000   2000    -0.2419    -4.6754     9.7957    31.493308      5.9106     -9.1036     10.8570\n  2000   2200    -0.2419    -4.6754     9.7957    31.848565      5.8540     -9.1400     10.8570\n  2000   2400    -0.2419    -4.6754     9.7957    32.196971      5.7984     -9.1755     10.8570\n  2000   2600    -0.2419    -4.6754     9.7957    32.538779      5.7435     -9.2099     10.8570\n  2000   2800    -0.2419    -4.6754     9.7957    32.874228      5.6895     -9.2434     10.8570\n  2000   3000    -0.2419    -4.6754     9.7957    33.203540      5.6363     -9.2759     10.8570\n  2000   3200    -0.2419    -4.6754     9.7957    33.526929      5.5838     -9.3076     10.8570\n  2000   3400    -0.2419    -4.6754     9.7957    33.844594      5.5321     -9.3384     10.8570\n  2000   3600    -0.2419    -4.6754     9.7957    34.156723      5.4812     -9.3684     10.8570\n  2000   3800    -0.2419    -4.6754     9.7957    34.463496      5.4309     -9.3976     10.8570\n  2000   4000    -0.2419    -4.6754     9.7957    34.765083      5.3814     -9.4261     10.8570\n  2000   4200    -0.2419    -4.6754     9.7957    35.061644      5.3325     -9.4538     10.8570\n  2000   4400    -0.2419    -4.6754     9.7957    35.353334      5.2843     -9.4809     10.8570\n  2000   4600    -0.2419    -4.6754     9.7957    35.640296      5.2368     -9.5072     10.8570\n  2000   4800    -0.2419    -4.6754     9.7957    35.922671      5.1898     -9.5329     10.8570\n  2000   5000    -0.2419    -4.6754     9.7957    36.200590      5.1435     -9.5580     10.8570\n  2000   5200    -0.2419    -4.6754     9.7957    36.474180      5.0978     -9.5824     10.8570\n  2000   5400    -0.2419    -4.6754     9.7957    36.743561      5.0527     -9.6063     10.8570\n  2000   5600    -0.2419    -4.6754     9.7957    37.008848      5.0082     -9.6296     10.8570\n  2000   5800    -0.2419    -4.6754     9.7957    37.270151      4.9642     -9.6523     10.8570\n  2000   6000    -0.2419    -4.6754     9.7957    37.527577      4.9208     -9.6745     10.8570\n  2000   6200    -0.2419    -4.6754     9.7957    37.781226      4.8779     -9.6962     10.8570\n  2000   6400    -0.2419    -4.6754     9.7957    38.031195      4.8356     -9.7174     10.8570\n  2000   6600    -0.2419    -4.6754     9.7957    38.277577      4.7937     -9.7381     10.8570\n  2000   6800    -0.2419    -4.6754     9.7957    38.520461      4.7524     -9.7584     10.8570\n  2000   7000    -0.2419    -4.6754     9.7957    38.759934      4.7116     -9.7781     10.8570\n  2000   7200    -0.2419    -4.6754     9.7957    38.996078      4.6712     -9.7975     10.8570\n  2000   7400    -0.2419    -4.6754     9.7957    39.228972      4.6314     -9.8164     10.8570\n  2000   7600    -0.2419    -4.6754     9.7957    39.458693      4.5920     -9.8349     10.8570\n  2000   7800    -0.2419    -4.6754     9.7957    39.685315      4.5530     -9.8530     10.8570\n  2000   8000    -0.2419    -4.6754     9.7957    39.908908      4.5146     -9.8707     10.8570\n  2000   8200    -0.2419    -4.6754     9.7957    40.129541      4.4765     -9.8880     10.8570\n  2000   8400    -0.2419    -4.6754     9.7957    40.347280      4.4389     -9.9049     10.8570\n  2500      0    -0.2419    -4.6453     9.7952    27.492947      6.5448     -8.6420     10.8435\n  2500    200    -0.2419    -4.6453     9.7952    27.935270      6.4779     -8.6923     10.8435\n  2500    400    -0.2419    -4.6453     9.7952    28.366955      6.4122     -8.7408     10.8435\n  2500    600    -0.2419    -4.6453     9.7952    28.788525      6.3477     -8.7878     10.8435\n  2500    800    -0.2419    -4.6453     9.7952    29.200461      6.2844     -8.8332     10.8435\n  2500   1000    -0.2419    -4.6453     9.7952    29.603204      6.2221     -8.8772     10.8435\n  2500   1200    -0.2419    -4.6453     9.7952    29.997167      6.1609     -8.9198     10.8435\n  2500   1400    -0.2419    -4.6453     9.7952    30.382730      6.1008     -8.9610     10.8435\n  2500   1600    -0.2419    -4.6453     9.7952    30.760245      6.0416     -9.0010     10.8435\n  2500   1800    -0.2419    -4.6453     9.7952    31.130044      5.9834     -9.0398     10.8435\n  2500   2000    -0.2419    -4.6453     9.7952    31.492434      5.9261     -9.0775     10.8435\n  2500   2200    -0.2419    -4.6453     9.7952    31.847702      5.8697     -9.1141     10.8435\n  2500   2400    -0.2419    -4.6453     9.7952    32.196118      5.8141     -9.1496     10.8435\n  2500   2600    -0.2419    -4.6453     9.7952    32.537936      5.7594     -9.1841     10.8435\n  2500   2800    -0.2419    -4.6453     9.7952    32.873394      5.7056     -9.2177     10.8435\n  2500   3000    -0.2419    -4.6453     9.7952    33.202716      5.6525     -9.2503     10.8435\n  2500   3200    -0.2419    -4.6453     9.7952    33.526114      5.6002     -9.2821     10.8435\n  2500   3400    -0.2419    -4.6453     9.7952    33.843788      5.5486     -9.3130     10.8435\n  2500   3600    -0.2419    -4.6453     9.7952    34.155926      5.4978     -9.3431     10.8435\n  2500   3800    -0.2419    -4.6453     9.7952    34.462707      5.4477     -9.3724     10.8435\n  2500   4000    -0.2419    -4.6453     9.7952    34.764302      5.3983     -9.4010     10.8435\n  2500   4200    -0.2419    -4.6453     9.7952    35.060871      5.3495     -9.4288     10.8435\n  2500   4400    -0.2419    -4.6453     9.7952    35.352568      5.3015     -9.4559     10.8435\n  2500   4600    -0.2419    -4.6453     9.7952    35.639538      5.2540     -9.4823     10.8435\n  2500   4800    -0.2419    -4.6453     9.7952    35.921920      5.2072     -9.5081     10.8435\n  2500   5000    -0.2419    -4.6453     9.7952    36.199846      5.1611     -9.5333     10.8435\n  2500   5200    -0.2419    -4.6453     9.7952    36.473442      5.1155     -9.5578     10.8435\n  2500   5400    -0.2419    -4.6453     9.7952    36.742830      5.0705     -9.5818     10.8435\n  2500   5600    -0.2419    -4.6453     9.7952    37.008123      5.0261     -9.6051     10.8435\n  2500   5800    -0.2419    -4.6453     9.7952    37.269433      4.9822     -9.6280     10.8435\n  2500   6000    -0.2419    -4.6453     9.7952    37.526865      4.9389     -9.6502     10.8435\n  2500   6200    -0.2419    -4.6453     9.7952    37.780519      4.8961     -9.6720     10.8435\n  2500   6400    -0.2419    -4.6453     9.7952    38.030494      4.8539     -9.6933     10.8435\n  2500   6600    -0.2419    -4.6453     9.7952    38.276882      4.8121     -9.7141     10.8435\n  2500   6800    -0.2419    -4.6453     9.7952    38.519772      4.7709     -9.7344     10.8435\n  2500   7000    -0.2419    -4.6453     9.7952    38.759250      4.7302     -9.7543     10.8435\n  2500   7200    -0.2419    -4.6453     9.7952    38.995399      4.6899     -9.7737     10.8435\n  2500   7400    -0.2419    -4.6453     9.7952    39.228299      4.6502     -9.7927     10.8435\n  2500   7600    -0.2419    -4.6453     9.7952    39.458025      4.6109     -9.8112     10.8435\n  2500   7800    -0.2419    -4.6453     9.7952    39.684651      4.5720     -9.8294     10.8435\n  2500   8000    -0.2419    -4.6453     9.7952    39.908249      4.5336     -9.8472     10.8435\n  2500   8200    -0.2419    -4.6453     9.7952    40.128887      4.4957     -9.8645     10.8435\n  2500   8400    -0.2419    -4.6453     9.7952    40.346630      4.4581     -9.8816     10.8435\n  3000      0    -0.2419    -4.6151     9.7947    27.491911      6.5584     -8.6149     10.8302\n  3000    200    -0.2419    -4.6151     9.7947    27.934252      6.4917     -8.6653     10.8302\n  3000    400    -0.2419    -4.6151     9.7947    28.365954      6.4262     -8.7140     10.8302\n  3000    600    -0.2419    -4.6151     9.7947    28.787540      6.3619     -8.7610     10.8302\n  3000    800    -0.2419    -4.6151     9.7947    29.199491      6.2988     -8.8065     10.8302\n  3000   1000    -0.2419    -4.6151     9.7947    29.602249      6.2367     -8.8506     10.8302\n  3000   1200    -0.2419    -4.6151     9.7947    29.996226      6.1757     -8.8933     10.8302\n  3000   1400    -0.2419    -4.6151     9.7947    30.381801      6.1157     -8.9347     10.8302\n  3000   1600    -0.2419    -4.6151     9.7947    30.759330      6.0567     -8.9748     10.8302\n  3000   1800    -0.2419    -4.6151     9.7947    31.129141      5.9986     -9.0137     10.8302\n  3000   2000    -0.2419    -4.6151     9.7947    31.491542      5.9415     -9.0514     10.8302\n  3000   2200    -0.2419    -4.6151     9.7947    31.846821      5.8853     -9.0881     10.8302\n  3000   2400    -0.2419    -4.6151     9.7947    32.195248      5.8299     -9.1237     10.8302\n  3000   2600    -0.2419    -4.6151     9.7947    32.537077      5.7754     -9.1584     10.8302\n  3000   2800    -0.2419    -4.6151     9.7947    32.872545      5.7216     -9.1920     10.8302\n  3000   3000    -0.2419    -4.6151     9.7947    33.201877      5.6687     -9.2248     10.8302\n  3000   3200    -0.2419    -4.6151     9.7947    33.525284      5.6165     -9.2566     10.8302\n  3000   3400    -0.2419    -4.6151     9.7947    33.842966      5.5651     -9.2876     10.8302\n  3000   3600    -0.2419    -4.6151     9.7947    34.155113      5.5144     -9.3178     10.8302\n  3000   3800    -0.2419    -4.6151     9.7947    34.461903      5.4645     -9.3472     10.8302\n  3000   4000    -0.2419    -4.6151     9.7947    34.763506      5.4152     -9.3758     10.8302\n  3000   4200    -0.2419    -4.6151     9.7947    35.060083      5.3666     -9.4037     10.8302\n  3000   4400    -0.2419    -4.6151     9.7947    35.351788      5.3186     -9.4309     10.8302\n  3000   4600    -0.2419    -4.6151     9.7947    35.638765      5.2713     -9.4575     10.8302\n  3000   4800    -0.2419    -4.6151     9.7947    35.921154      5.2246     -9.4833     10.8302\n  3000   5000    -0.2419    -4.6151     9.7947    36.199088      5.1786     -9.5086     10.8302\n  3000   5200    -0.2419    -4.6151     9.7947    36.472691      5.1331     -9.5332     10.8302\n  3000   5400    -0.2419    -4.6151     9.7947    36.742085      5.0882     -9.5572     10.8302\n  3000   5600    -0.2419    -4.6151     9.7947    37.007385      5.0439     -9.5807     10.8302\n  3000   5800    -0.2419    -4.6151     9.7947    37.268701      5.0002     -9.6036     10.8302\n  3000   6000    -0.2419    -4.6151     9.7947    37.526139      4.9570     -9.6260     10.8302\n  3000   6200    -0.2419    -4.6151     9.7947    37.779800      4.9143     -9.6478     10.8302\n  3000   6400    -0.2419    -4.6151     9.7947    38.029781      4.8722     -9.6692     10.8302\n  3000   6600    -0.2419    -4.6151     9.7947    38.276174      4.8305     -9.6900     10.8302\n  3000   6800    -0.2419    -4.6151     9.7947    38.519070      4.7894     -9.7104     10.8302\n  3000   7000    -0.2419    -4.6151     9.7947    38.758554      4.7488     -9.7304     10.8302\n  3000   7200    -0.2419    -4.6151     9.7947    38.994708      4.7086     -9.7499     10.8302\n  3000   7400    -0.2419    -4.6151     9.7947    39.227613      4.6690     -9.7689     10.8302\n  3000   7600    -0.2419    -4.6151     9.7947    39.457344      4.6298     -9.7876     10.8302\n  3000   7800    -0.2419    -4.6151     9.7947    39.683975      4.5910     -9.8058     10.8302\n  3000   8000    -0.2419    -4.6151     9.7947    39.907578      4.5527     -9.8236     10.8302\n  3000   8200    -0.2419    -4.6151     9.7947    40.128221      4.5148     -9.8411     10.8302\n  3000   8400    -0.2419    -4.6151     9.7947    40.345969      4.4774     -9.8582     10.8302\n  3500      0    -0.2419    -4.5850     9.7941    27.490855      6.5720     -8.5878     10.8169\n  3500    200    -0.2419    -4.5850     9.7941    27.933214      6.5055     -8.6383     10.8169\n  3500    400    -0.2419    -4.5850     9.7941    28.364934      6.4403     -8.6871     10.8169\n  3500    600    -0.2419    -4.5850     9.7941    28.786536      6.3762     -8.7342     10.8169\n  3500    800    -0.2419    -4.5850     9.7941    29.198502      6.3132     -8.7799     10.8169\n  3500   1000    -0.2419    -4.5850     9.7941    29.601275      6.2513     -8.8240     10.8169\n  3500   1200    -0.2419    -4.5850     9.7941    29.995266      6.1905     -8.8668     10.8169\n  3500   1400    -0.2419    -4.5850     9.7941    30.380855      6.1307     -8.9083     10.8169\n  3500   1600    -0.2419    -4.5850     9.7941    30.758396      6.0718     -8.9485     10.8169\n  3500   1800    -0.2419    -4.5850     9.7941    31.128219      6.0139     -8.9875     10.8169\n  3500   2000    -0.2419    -4.5850     9.7941    31.490633      5.9570     -9.0254     10.8169\n  3500   2200    -0.2419    -4.5850     9.7941    31.845923      5.9009     -9.0621     10.8169\n  3500   2400    -0.2419    -4.5850     9.7941    32.194362      5.8457     -9.0979     10.8169\n  3500   2600    -0.2419    -4.5850     9.7941    32.536201      5.7913     -9.1326     10.8169\n  3500   2800    -0.2419    -4.5850     9.7941    32.871679      5.7377     -9.1663     10.8169\n  3500   3000    -0.2419    -4.5850     9.7941    33.201021      5.6849     -9.1992     10.8169\n  3500   3200    -0.2419    -4.5850     9.7941    33.524438      5.6329     -9.2311     10.8169\n  3500   3400    -0.2419    -4.5850     9.7941    33.842129      5.5816     -9.2622     10.8169\n  3500   3600    -0.2419    -4.5850     9.7941    34.154285      5.5311     -9.2925     10.8169\n  3500   3800    -0.2419    -4.5850     9.7941    34.461084      5.4812     -9.3220     10.8169\n  3500   4000    -0.2419    -4.5850     9.7941    34.762695      5.4321     -9.3507     10.8169\n  3500   4200    -0.2419    -4.5850     9.7941    35.059281      5.3836     -9.3787     10.8169\n  3500   4400    -0.2419    -4.5850     9.7941    35.350993      5.3358     -9.4060     10.8169\n  3500   4600    -0.2419    -4.5850     9.7941    35.637978      5.2886     -9.4326     10.8169\n  3500   4800    -0.2419    -4.5850     9.7941    35.920375      5.2421     -9.4586     10.8169\n  3500   5000    -0.2419    -4.5850     9.7941    36.198315      5.1961     -9.4839     10.8169\n  3500   5200    -0.2419    -4.5850     9.7941    36.471925      5.1508     -9.5086     10.8169\n  3500   5400    -0.2419    -4.5850     9.7941    36.741326      5.1060     -9.5327     10.8169\n  3500   5600    -0.2419    -4.5850     9.7941    37.006633      5.0618     -9.5562     10.8169\n  3500   5800    -0.2419    -4.5850     9.7941    37.267956      5.0181     -9.5792     10.8169\n  3500   6000    -0.2419    -4.5850     9.7941    37.525400      4.9751     -9.6017     10.8169\n  3500   6200    -0.2419    -4.5850     9.7941    37.779067      4.9325     -9.6236     10.8169\n  3500   6400    -0.2419    -4.5850     9.7941    38.029054      4.8905     -9.6450     10.8169\n  3500   6600    -0.2419    -4.5850     9.7941    38.275453      4.8489     -9.6660     10.8169\n  3500   6800    -0.2419    -4.5850     9.7941    38.518355      4.8079     -9.6865     10.8169\n  3500   7000    -0.2419    -4.5850     9.7941    38.757844      4.7674     -9.7065     10.8169\n  3500   7200    -0.2419    -4.5850     9.7941    38.994004      4.7273     -9.7260     10.8169\n  3500   7400    -0.2419    -4.5850     9.7941    39.226914      4.6877     -9.7452     10.8169\n  3500   7600    -0.2419    -4.5850     9.7941    39.456650      4.6486     -9.7639     10.8169\n  3500   7800    -0.2419    -4.5850     9.7941    39.683287      4.6100     -9.7822     10.8169\n  3500   8000    -0.2419    -4.5850     9.7941    39.906895      4.5718     -9.8001     10.8169\n  3500   8200    -0.2419    -4.5850     9.7941    40.127542      4.5340     -9.8177     10.8169\n  3500   8400    -0.2419    -4.5850     9.7941    40.345295      4.4966     -9.8348     10.8169\n  4000      0    -0.2419    -4.5548     9.7936    27.489779      6.5857     -8.5607     10.8037\n  4000    200    -0.2419    -4.5548     9.7936    27.932157      6.5194     -8.6113     10.8037\n  4000    400    -0.2419    -4.5548     9.7936    28.363894      6.4543     -8.6602     10.8037\n  4000    600    -0.2419    -4.5548     9.7936    28.785512      6.3904     -8.7075     10.8037\n  4000    800    -0.2419    -4.5548     9.7936    29.197494      6.3276     -8.7532     10.8037\n  4000   1000    -0.2419    -4.5548     9.7936    29.600282      6.2659     -8.7975     10.8037\n  4000   1200    -0.2419    -4.5548     9.7936    29.994287      6.2053     -8.8403     10.8037\n  4000   1400    -0.2419    -4.5548     9.7936    30.379890      6.1456     -8.8819     10.8037\n  4000   1600    -0.2419    -4.5548     9.7936    30.757445      6.0870     -8.9222     10.8037\n  4000   1800    -0.2419    -4.5548     9.7936    31.127281      6.0292     -8.9613     10.8037\n  4000   2000    -0.2419    -4.5548     9.7936    31.489706      5.9724     -8.9993     10.8037\n  4000   2200    -0.2419    -4.5548     9.7936    31.845009      5.9165     -9.0362     10.8037\n  4000   2400    -0.2419    -4.5548     9.7936    32.193458      5.8614     -9.0720     10.8037\n  4000   2600    -0.2419    -4.5548     9.7936    32.535308      5.8072     -9.1068     10.8037\n  4000   2800    -0.2419    -4.5548     9.7936    32.870797      5.7538     -9.1406     10.8037\n  4000   3000    -0.2419    -4.5548     9.7936    33.200149      5.7011     -9.1736     10.8037\n  4000   3200    -0.2419    -4.5548     9.7936    33.523576      5.6493     -9.2056     10.8037\n  4000   3400    -0.2419    -4.5548     9.7936    33.841277      5.5981     -9.2368     10.8037\n  4000   3600    -0.2419    -4.5548     9.7936    34.153442      5.5477     -9.2672     10.8037\n  4000   3800    -0.2419    -4.5548     9.7936    34.460249      5.4980     -9.2967     10.8037\n  4000   4000    -0.2419    -4.5548     9.7936    34.761869      5.4490     -9.3256     10.8037\n  4000   4200    -0.2419    -4.5548     9.7936    35.058463      5.4006     -9.3536     10.8037\n  4000   4400    -0.2419    -4.5548     9.7936    35.350183      5.3530     -9.3810     10.8037\n  4000   4600    -0.2419    -4.5548     9.7936    35.637176      5.3059     -9.4077     10.8037\n  4000   4800    -0.2419    -4.5548     9.7936    35.919581      5.2595     -9.4338     10.8037\n  4000   5000    -0.2419    -4.5548     9.7936    36.197528      5.2136     -9.4592     10.8037\n  4000   5200    -0.2419    -4.5548     9.7936    36.471146      5.1684     -9.4840     10.8037\n  4000   5400    -0.2419    -4.5548     9.7936    36.740554      5.1237     -9.5082     10.8037\n  4000   5600    -0.2419    -4.5548     9.7936    37.005868      5.0797     -9.5318     10.8037\n  4000   5800    -0.2419    -4.5548     9.7936    37.267197      5.0361     -9.5549     10.8037\n  4000   6000    -0.2419    -4.5548     9.7936    37.524648      4.9931     -9.5774     10.8037\n  4000   6200    -0.2419    -4.5548     9.7936    37.778321      4.9507     -9.5994     10.8037\n  4000   6400    -0.2419    -4.5548     9.7936    38.028314      4.9088     -9.6209     10.8037\n  4000   6600    -0.2419    -4.5548     9.7936    38.274719      4.8673     -9.6419     10.8037\n  4000   6800    -0.2419    -4.5548     9.7936    38.517627      4.8264     -9.6625     10.8037\n  4000   7000    -0.2419    -4.5548     9.7936    38.757122      4.7860     -9.6826     10.8037\n  4000   7200    -0.2419    -4.5548     9.7936    38.993287      4.7460     -9.7022     10.8037\n  4000   7400    -0.2419    -4.5548     9.7936    39.226203      4.7065     -9.7215     10.8037\n  4000   7600    -0.2419    -4.5548     9.7936    39.455945      4.6675     -9.7402     10.8037\n  4000   7800    -0.2419    -4.5548     9.7936    39.682586      4.6290     -9.7586     10.8037\n  4000   8000    -0.2419    -4.5548     9.7936    39.906199      4.5908     -9.7766     10.8037\n  4000   8200    -0.2419    -4.5548     9.7936    40.126852      4.5531     -9.7942     10.8037\n  4000   8400    -0.2419    -4.5548     9.7936    40.344610      4.5159     -9.8115     10.8037\n  4500      0    -0.2419    -4.5247     9.7931    27.488683      6.5993     -8.5336     10.7906\n  4500    200    -0.2419    -4.5247     9.7931    27.931079      6.5332     -8.5843     10.7906\n  4500    400    -0.2419    -4.5247     9.7931    28.362834      6.4683     -8.6333     10.7906\n  4500    600    -0.2419    -4.5247     9.7931    28.784470      6.4046     -8.6807     10.7906\n  4500    800    -0.2419    -4.5247     9.7931    29.196467      6.3420     -8.7265     10.7906\n  4500   1000    -0.2419    -4.5247     9.7931    29.599271      6.2805     -8.7709     10.7906\n  4500   1200    -0.2419    -4.5247     9.7931    29.993291      6.2200     -8.8139     10.7906\n  4500   1400    -0.2419    -4.5247     9.7931    30.378908      6.1606     -8.8555     10.7906\n  4500   1600    -0.2419    -4.5247     9.7931    30.756476      6.1021     -8.8960     10.7906\n  4500   1800    -0.2419    -4.5247     9.7931    31.126325      6.0445     -8.9352     10.7906\n  4500   2000    -0.2419    -4.5247     9.7931    31.488763      5.9879     -8.9732     10.7906\n  4500   2200    -0.2419    -4.5247     9.7931    31.844077      5.9321     -9.0102     10.7906\n  4500   2400    -0.2419    -4.5247     9.7931    32.192539      5.8772     -9.0461     10.7906\n  4500   2600    -0.2419    -4.5247     9.7931    32.534400      5.8231     -9.0810     10.7906\n  4500   2800    -0.2419    -4.5247     9.7931    32.869899      5.7699     -9.1150     10.7906\n  4500   3000    -0.2419    -4.5247     9.7931    33.199262      5.7174     -9.1480     10.7906\n  4500   3200    -0.2419    -4.5247     9.7931    33.522698      5.6656     -9.1801     10.7906\n  4500   3400    -0.2419    -4.5247     9.7931    33.840409      5.6146     -9.2114     10.7906\n  4500   3600    -0.2419    -4.5247     9.7931    34.152583      5.5644     -9.2418     10.7906\n  4500   3800    -0.2419    -4.5247     9.7931    34.459400      5.5148     -9.2715     10.7906\n  4500   4000    -0.2419    -4.5247     9.7931    34.761029      5.4659     -9.3004     10.7906\n  4500   4200    -0.2419    -4.5247     9.7931    35.057631      5.4177     -9.3286     10.7906\n  4500   4400    -0.2419    -4.5247     9.7931    35.349359      5.3701     -9.3561     10.7906\n  4500   4600    -0.2419    -4.5247     9.7931    35.636360      5.3232     -9.3828     10.7906\n  4500   4800    -0.2419    -4.5247     9.7931    35.918772      5.2769     -9.4090     10.7906\n  4500   5000    -0.2419    -4.5247     9.7931    36.196728      5.2312     -9.4345     10.7906\n  4500   5200    -0.2419    -4.5247     9.7931    36.470353      5.1860     -9.4593     10.7906\n  4500   5400    -0.2419    -4.5247     9.7931    36.739768      5.1415     -9.4836     10.7906\n  4500   5600    -0.2419    -4.5247     9.7931    37.005088      5.0975     -9.5073     10.7906\n  4500   5800    -0.2419    -4.5247     9.7931    37.266425      5.0541     -9.5305     10.7906\n  4500   6000    -0.2419    -4.5247     9.7931    37.523882      5.0112     -9.5531     10.7906\n  4500   6200    -0.2419    -4.5247     9.7931    37.777562      4.9689     -9.5752     10.7906\n  4500   6400    -0.2419    -4.5247     9.7931    38.027561      4.9271     -9.5968     10.7906\n  4500   6600    -0.2419    -4.5247     9.7931    38.273972      4.8857     -9.6179     10.7906\n  4500   6800    -0.2419    -4.5247     9.7931    38.516886      4.8449     -9.6385     10.7906\n  4500   7000    -0.2419    -4.5247     9.7931    38.756387      4.8046     -9.6587     10.7906\n  4500   7200    -0.2419    -4.5247     9.7931    38.992558      4.7647     -9.6784     10.7906\n  4500   7400    -0.2419    -4.5247     9.7931    39.225479      4.7253     -9.6977     10.7906\n  4500   7600    -0.2419    -4.5247     9.7931    39.455227      4.6864     -9.7166     10.7906\n  4500   7800    -0.2419    -4.5247     9.7931    39.681874      4.6479     -9.7351     10.7906\n  4500   8000    -0.2419    -4.5247     9.7931    39.905492      4.6099     -9.7531     10.7906\n  4500   8200    -0.2419    -4.5247     9.7931    40.126149      4.5723     -9.7708     10.7906\n  4500   8400    -0.2419    -4.5247     9.7931    40.343913      4.5351     -9.7881     10.7906\n\nuser time (s):         0.000\nsystem time (s):       0.000\nelapsed time (s):      0.010\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/cropA_20180106_VV_8rlks_eqa_dem.par",
    "content": "Gamma DIFF&GEO DEM/MAP parameter file\ntitle: dem\nDEM_projection:     EQA\ndata_format:        REAL*4\nDEM_hgt_offset:          0.00000\nDEM_scale:               1.00000\nwidth:                100\nnlines:               60\ncorner_lat:     19.4512926234517565  decimal degrees\ncorner_lon:    -99.1910697816367417  decimal degrees\npost_lat:   -0.001388888900000000105  decimal degrees\npost_lon:    0.001388888900000000105  decimal degrees\n\nellipsoid_name: WGS 84\nellipsoid_ra:        6378137.000   m\nellipsoid_reciprocal_flattening:  298.2572236\n\ndatum_name: WGS 84\ndatum_shift_dx:              0.000   m\ndatum_shift_dy:              0.000   m\ndatum_shift_dz:              0.000   m\ndatum_scale_m:         0.00000e+00\ndatum_rotation_alpha:  0.00000e+00   arc-sec\ndatum_rotation_beta:   0.00000e+00   arc-sec\ndatum_rotation_gamma:  0.00000e+00   arc-sec\ndatum_country_list: WGS 84\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180106_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180106t004006-20180106t004031-020027-0221ec-004.tiff S1A-IW-IW1-VV-20027 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 01 06\nstart_time:              2412.557627   s\ncenter_time:             2421.889852   s\nend_time:                2431.222078   s\nazimuth_line_time:     4.1111126e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000000e+00\nazimuth_scale_factor:   1.0000000e+00\ncenter_latitude:          19.5126101   degrees\ncenter_longitude:        -97.9182354   degrees\nheading:                 -12.2742586   degrees\nrange_pixel_spacing:       18.636496   m\nazimuth_pixel_spacing:     28.023300   m\nnear_range_slc:           798988.2904  m\ncenter_range_slc:         878314.5356  m\nfar_range_slc:            957640.7808  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7036   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         28.89379 -1.70466e-04  1.00457e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073899.1954   m\nearth_radius_below_sensor:       6375868.9414   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2399.144213   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442639.9545   -6604806.9075    2082951.4020   m   m   m\nstate_vector_velocity_1:    -1104.60340      2489.17836      7092.92324   m/s m/s m/s\nstate_vector_position_2:  -1453586.5506   -6579537.2326    2153761.6359   m   m   m\nstate_vector_velocity_2:    -1084.67698      2564.70460      7068.99018   m/s m/s m/s\nstate_vector_position_3:  -1464332.7222   -6553513.8710    2224328.5433   m   m   m\nstate_vector_velocity_3:    -1064.51896      2639.91423      7044.25842   m/s m/s m/s\nstate_vector_position_4:  -1474876.1671   -6526740.0329    2294644.1512   m   m   m\nstate_vector_velocity_4:    -1044.13208      2714.79840      7018.73075   m/s m/s m/s\nstate_vector_position_5:  -1485214.6104   -6499219.0172    2364700.5150   m   m   m\nstate_vector_velocity_5:    -1023.51913      2789.34829      6992.41010   m/s m/s m/s\nstate_vector_position_6:  -1495345.8056   -6470954.2105    2434489.7198   m   m   m\nstate_vector_velocity_6:    -1002.68294      2863.55516      6965.29946   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180106_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180106t004006-20180106t004031-020027-0221ec-004.tiff S1A-IW-IW1-VV-20027 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 01 06\nstart_time:              2412.556599   s\ncenter_time:             2421.890880   s\nend_time:                2431.225161   s\nazimuth_line_time:     2.0555563e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000000e+00\nazimuth_scale_factor:   1.0000000e+00\ncenter_latitude:          19.5126101   degrees\ncenter_longitude:        -97.9182354   degrees\nheading:                 -12.2742586   degrees\nrange_pixel_spacing:        2.329562   m\nazimuth_pixel_spacing:     14.011650   m\nnear_range_slc:           798980.1369  m\ncenter_range_slc:         878319.1947  m\nfar_range_slc:            957658.2525  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7036   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         28.89379 -1.70466e-04  1.00457e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073899.1954   m\nearth_radius_below_sensor:       6375868.9414   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2399.144213   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442639.9545   -6604806.9075    2082951.4020   m   m   m\nstate_vector_velocity_1:    -1104.60340      2489.17836      7092.92324   m/s m/s m/s\nstate_vector_position_2:  -1453586.5506   -6579537.2326    2153761.6359   m   m   m\nstate_vector_velocity_2:    -1084.67698      2564.70460      7068.99018   m/s m/s m/s\nstate_vector_position_3:  -1464332.7222   -6553513.8710    2224328.5433   m   m   m\nstate_vector_velocity_3:    -1064.51896      2639.91423      7044.25842   m/s m/s m/s\nstate_vector_position_4:  -1474876.1671   -6526740.0329    2294644.1512   m   m   m\nstate_vector_velocity_4:    -1044.13208      2714.79840      7018.73075   m/s m/s m/s\nstate_vector_position_5:  -1485214.6104   -6499219.0172    2364700.5150   m   m   m\nstate_vector_velocity_5:    -1023.51913      2789.34829      6992.41010   m/s m/s m/s\nstate_vector_position_6:  -1495345.8056   -6470954.2105    2434489.7198   m   m   m\nstate_vector_velocity_6:    -1002.68294      2863.55516      6965.29946   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180130_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180130t004005-20180130t004030-020377-022d0a-004.tiff S1A-IW-IW1-VV-20377 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 01 30\nstart_time:              2411.850365   s\ncenter_time:             2421.182580   s\nend_time:                2430.514796   s\nazimuth_line_time:     4.1111082e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9995776e-01\nazimuth_scale_factor:   9.9999894e-01\ncenter_latitude:          19.5126101   degrees\ncenter_longitude:        -97.9182314   degrees\nheading:                 -12.2736686   degrees\nrange_pixel_spacing:       18.635712   m\nazimuth_pixel_spacing:     28.023270   m\nnear_range_slc:           798965.1264  m\ncenter_range_slc:         878288.0346  m\nfar_range_slc:            957610.9427  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7016   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         71.13098 -1.27127e-04  5.17735e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073894.6696   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.431977   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442597.0798   -6604819.0700    2082928.3358   m   m   m\nstate_vector_velocity_1:    -1104.54558      2489.14851      7092.94042   m/s m/s m/s\nstate_vector_position_2:  -1453543.1000   -6579549.6926    2153738.7429   m   m   m\nstate_vector_velocity_2:    -1084.61965      2564.67494      7069.00759   m/s m/s m/s\nstate_vector_position_3:  -1464288.7007   -6553526.6264    2224305.8258   m   m   m\nstate_vector_velocity_3:    -1064.46211      2639.88477      7044.27604   m/s m/s m/s\nstate_vector_position_4:  -1474831.5793   -6526753.0818    2294621.6113   m   m   m\nstate_vector_velocity_4:    -1044.07572      2714.76915      7018.74859   m/s m/s m/s\nstate_vector_position_5:  -1485169.4614   -6499232.3574    2364678.1549   m   m   m\nstate_vector_velocity_5:    -1023.46326      2789.31927      6992.42815   m/s m/s m/s\nstate_vector_position_6:  -1495300.1003   -6470967.8397    2434467.5415   m   m   m\nstate_vector_velocity_6:    -1002.62756      2863.52635      6965.31772   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180130_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180130t004005-20180130t004030-020377-022d0a-004.tiff S1A-IW-IW1-VV-20377 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 01 30\nstart_time:              2411.849337   s\ncenter_time:             2421.183608   s\nend_time:                2430.517879   s\nazimuth_line_time:     2.0555541e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9995776e-01\nazimuth_scale_factor:   9.9999894e-01\ncenter_latitude:          19.5126101   degrees\ncenter_longitude:        -97.9182314   degrees\nheading:                 -12.2736686   degrees\nrange_pixel_spacing:        2.329464   m\nazimuth_pixel_spacing:     14.011635   m\nnear_range_slc:           798956.9733  m\ncenter_range_slc:         878292.6800  m\nfar_range_slc:            957628.3867  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7016   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         71.13098 -1.27127e-04  5.17735e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073894.6696   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.431977   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442597.0798   -6604819.0700    2082928.3358   m   m   m\nstate_vector_velocity_1:    -1104.54558      2489.14851      7092.94042   m/s m/s m/s\nstate_vector_position_2:  -1453543.1000   -6579549.6926    2153738.7429   m   m   m\nstate_vector_velocity_2:    -1084.61965      2564.67494      7069.00759   m/s m/s m/s\nstate_vector_position_3:  -1464288.7007   -6553526.6264    2224305.8258   m   m   m\nstate_vector_velocity_3:    -1064.46211      2639.88477      7044.27604   m/s m/s m/s\nstate_vector_position_4:  -1474831.5793   -6526753.0818    2294621.6113   m   m   m\nstate_vector_velocity_4:    -1044.07572      2714.76915      7018.74859   m/s m/s m/s\nstate_vector_position_5:  -1485169.4614   -6499232.3574    2364678.1549   m   m   m\nstate_vector_velocity_5:    -1023.46326      2789.31927      6992.42815   m/s m/s m/s\nstate_vector_position_6:  -1495300.1003   -6470967.8397    2434467.5415   m   m   m\nstate_vector_velocity_6:    -1002.62756      2863.52635      6965.31772   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180307_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180307t004004-20180307t004030-020902-023dc2-004.tiff S1A-IW-IW1-VV-20902 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 07\nstart_time:              2411.446007   s\ncenter_time:             2420.778229   s\nend_time:                2430.110452   s\nazimuth_line_time:     4.1111112e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999872e-01\nazimuth_scale_factor:   9.9999965e-01\ncenter_latitude:          19.5126084   degrees\ncenter_longitude:        -97.9182346   degrees\nheading:                 -12.2751820   degrees\nrange_pixel_spacing:       18.636472   m\nazimuth_pixel_spacing:     28.023290   m\nnear_range_slc:           798989.4063  m\ncenter_range_slc:         878315.5493  m\nfar_range_slc:            957641.6924  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7035   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:          7.57751 -1.73839e-04  9.26703e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073900.5085   m\nearth_radius_below_sensor:       6375868.9420   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.029794   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442634.0250   -6604817.8664    2082929.5800   m   m   m\nstate_vector_velocity_1:    -1104.71812      2489.22466      7092.89390   m/s m/s m/s\nstate_vector_position_2:  -1453581.7682   -6579547.7275    2153739.5218   m   m   m\nstate_vector_velocity_2:    -1084.79171      2564.75111      7068.96113   m/s m/s m/s\nstate_vector_position_3:  -1464329.0870   -6553523.8996    2224306.1400   m   m   m\nstate_vector_velocity_3:    -1064.63367      2639.96097      7044.22964   m/s m/s m/s\nstate_vector_position_4:  -1474873.6787   -6526749.5931    2294621.4614   m   m   m\nstate_vector_velocity_4:    -1044.24676      2714.84537      7018.70225   m/s m/s m/s\nstate_vector_position_5:  -1485213.2685   -6499228.1064    2364677.5414   m   m   m\nstate_vector_velocity_5:    -1023.63376      2789.39552      6992.38187   m/s m/s m/s\nstate_vector_position_6:  -1495345.6096   -6470962.8261    2434466.4652   m   m   m\nstate_vector_velocity_6:    -1002.79750      2863.60264      6965.27150   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180307_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180307t004004-20180307t004030-020902-023dc2-004.tiff S1A-IW-IW1-VV-20902 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 07\nstart_time:              2411.444979   s\ncenter_time:             2420.779257   s\nend_time:                2430.113535   s\nazimuth_line_time:     2.0555556e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999872e-01\nazimuth_scale_factor:   9.9999965e-01\ncenter_latitude:          19.5126084   degrees\ncenter_longitude:        -97.9182346   degrees\nheading:                 -12.2751820   degrees\nrange_pixel_spacing:        2.329559   m\nazimuth_pixel_spacing:     14.011645   m\nnear_range_slc:           798981.2528  m\ncenter_range_slc:         878320.2093  m\nfar_range_slc:            957659.1659  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7035   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:          7.57751 -1.73839e-04  9.26703e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073900.5085   m\nearth_radius_below_sensor:       6375868.9420   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.029794   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442634.0250   -6604817.8664    2082929.5800   m   m   m\nstate_vector_velocity_1:    -1104.71812      2489.22466      7092.89390   m/s m/s m/s\nstate_vector_position_2:  -1453581.7682   -6579547.7275    2153739.5218   m   m   m\nstate_vector_velocity_2:    -1084.79171      2564.75111      7068.96113   m/s m/s m/s\nstate_vector_position_3:  -1464329.0870   -6553523.8996    2224306.1400   m   m   m\nstate_vector_velocity_3:    -1064.63367      2639.96097      7044.22964   m/s m/s m/s\nstate_vector_position_4:  -1474873.6787   -6526749.5931    2294621.4614   m   m   m\nstate_vector_velocity_4:    -1044.24676      2714.84537      7018.70225   m/s m/s m/s\nstate_vector_position_5:  -1485213.2685   -6499228.1064    2364677.5414   m   m   m\nstate_vector_velocity_5:    -1023.63376      2789.39552      6992.38187   m/s m/s m/s\nstate_vector_position_6:  -1495345.6096   -6470962.8261    2434466.4652   m   m   m\nstate_vector_velocity_6:    -1002.79750      2863.60264      6965.27150   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180319_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180319t004005-20180319t004030-021077-024348-004.tiff S1A-IW-IW1-VV-21077 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 19\nstart_time:              2411.634001   s\ncenter_time:             2420.966234   s\nend_time:                2430.298467   s\nazimuth_line_time:     4.1111160e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999519e-01\nazimuth_scale_factor:   1.0000008e+00\ncenter_latitude:          19.5126105   degrees\ncenter_longitude:        -97.9182312   degrees\nheading:                 -12.2738302   degrees\nrange_pixel_spacing:       18.636408   m\nazimuth_pixel_spacing:     28.023282   m\nnear_range_slc:           798987.3020  m\ncenter_range_slc:         878313.1727  m\nfar_range_slc:            957639.0433  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7034   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         14.28596 -2.06716e-04  1.17660e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073899.8185   m\nearth_radius_below_sensor:       6375868.9415   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.217204   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442635.9813   -6604813.9430    2082939.7037   m   m   m\nstate_vector_velocity_1:    -1104.53733      2489.22215      7092.91118   m/s m/s m/s\nstate_vector_position_2:  -1453581.9166   -6579543.8306    2153749.8176   m   m   m\nstate_vector_velocity_2:    -1084.61091      2564.74831      7068.97828   m/s m/s m/s\nstate_vector_position_3:  -1464327.4273   -6553520.0321    2224316.6067   m   m   m\nstate_vector_velocity_3:    -1064.45288      2639.95789      7044.24666   m/s m/s m/s\nstate_vector_position_4:  -1474870.2112   -6526745.7577    2294632.0977   m   m   m\nstate_vector_velocity_4:    -1044.06599      2714.84202      7018.71914   m/s m/s m/s\nstate_vector_position_5:  -1485207.9936   -6499224.3060    2364688.3461   m   m   m\nstate_vector_velocity_5:    -1023.45304      2789.39190      6992.39863   m/s m/s m/s\nstate_vector_position_6:  -1495338.5279   -6470959.0632    2434477.4369   m   m   m\nstate_vector_velocity_6:    -1002.61684      2863.59875      6965.28813   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180319_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180319t004005-20180319t004030-021077-024348-004.tiff S1A-IW-IW1-VV-21077 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 19\nstart_time:              2411.632973   s\ncenter_time:             2420.967261   s\nend_time:                2430.301550   s\nazimuth_line_time:     2.0555580e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999519e-01\nazimuth_scale_factor:   1.0000008e+00\ncenter_latitude:          19.5126105   degrees\ncenter_longitude:        -97.9182312   degrees\nheading:                 -12.2738302   degrees\nrange_pixel_spacing:        2.329551   m\nazimuth_pixel_spacing:     14.011641   m\nnear_range_slc:           798979.1486  m\ncenter_range_slc:         878317.8248  m\nfar_range_slc:            957656.5011  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7034   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         14.28596 -2.06716e-04  1.17660e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073899.8185   m\nearth_radius_below_sensor:       6375868.9415   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.217204   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442635.9813   -6604813.9430    2082939.7037   m   m   m\nstate_vector_velocity_1:    -1104.53733      2489.22215      7092.91118   m/s m/s m/s\nstate_vector_position_2:  -1453581.9166   -6579543.8306    2153749.8176   m   m   m\nstate_vector_velocity_2:    -1084.61091      2564.74831      7068.97828   m/s m/s m/s\nstate_vector_position_3:  -1464327.4273   -6553520.0321    2224316.6067   m   m   m\nstate_vector_velocity_3:    -1064.45288      2639.95789      7044.24666   m/s m/s m/s\nstate_vector_position_4:  -1474870.2112   -6526745.7577    2294632.0977   m   m   m\nstate_vector_velocity_4:    -1044.06599      2714.84202      7018.71914   m/s m/s m/s\nstate_vector_position_5:  -1485207.9936   -6499224.3060    2364688.3461   m   m   m\nstate_vector_velocity_5:    -1023.45304      2789.39190      6992.39863   m/s m/s m/s\nstate_vector_position_6:  -1495338.5279   -6470959.0632    2434477.4369   m   m   m\nstate_vector_velocity_6:    -1002.61684      2863.59875      6965.28813   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180331_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180331t004005-20180331t004030-021252-0248d6-004.tiff S1A-IW-IW1-VV-21252 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 31\nstart_time:              2411.964291   s\ncenter_time:             2421.296517   s\nend_time:                2430.628743   s\nazimuth_line_time:     4.1111128e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000038e+00\nazimuth_scale_factor:   1.0000001e+00\ncenter_latitude:          19.5126095   degrees\ncenter_longitude:        -97.9182315   degrees\nheading:                 -12.2734015   degrees\nrange_pixel_spacing:       18.636568   m\nazimuth_pixel_spacing:     28.023282   m\nnear_range_slc:           798985.7770  m\ncenter_range_slc:         878312.3287  m\nfar_range_slc:            957638.8804  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7038   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         12.65333 -1.59964e-04  7.54734e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073895.5709   m\nearth_radius_below_sensor:       6375868.9417   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.548957   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442643.5536   -6604803.8015    2082953.8822   m   m   m\nstate_vector_velocity_1:    -1104.47169      2489.25258      7092.91187   m/s m/s m/s\nstate_vector_position_2:  -1453588.8317   -6579533.3853    2153764.0022   m   m   m\nstate_vector_velocity_2:    -1084.54511      2564.77865      7068.97877   m/s m/s m/s\nstate_vector_position_3:  -1464333.6837   -6553509.2838    2224330.7953   m   m   m\nstate_vector_velocity_3:    -1064.38692      2639.98814      7044.24695   m/s m/s m/s\nstate_vector_position_4:  -1474875.8073   -6526734.7073    2294646.2882   m   m   m\nstate_vector_velocity_4:    -1043.99989      2714.87219      7018.71922   m/s m/s m/s\nstate_vector_position_5:  -1485212.9279   -6499212.9542    2364702.5365   m   m   m\nstate_vector_velocity_5:    -1023.38680      2789.42198      6992.39850   m/s m/s m/s\nstate_vector_position_6:  -1495342.7990   -6470947.4109    2434491.6250   m   m   m\nstate_vector_velocity_6:    -1002.55046      2863.62876      6965.28778   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180331_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180331t004005-20180331t004030-021252-0248d6-004.tiff S1A-IW-IW1-VV-21252 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 31\nstart_time:              2411.963263   s\ncenter_time:             2421.297545   s\nend_time:                2430.631827   s\nazimuth_line_time:     2.0555564e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000038e+00\nazimuth_scale_factor:   1.0000001e+00\ncenter_latitude:          19.5126095   degrees\ncenter_longitude:        -97.9182315   degrees\nheading:                 -12.2734015   degrees\nrange_pixel_spacing:        2.329571   m\nazimuth_pixel_spacing:     14.011641   m\nnear_range_slc:           798977.6235  m\ncenter_range_slc:         878316.9858  m\nfar_range_slc:            957656.3481  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7038   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         12.65333 -1.59964e-04  7.54734e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073895.5709   m\nearth_radius_below_sensor:       6375868.9417   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.548957   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442643.5536   -6604803.8015    2082953.8822   m   m   m\nstate_vector_velocity_1:    -1104.47169      2489.25258      7092.91187   m/s m/s m/s\nstate_vector_position_2:  -1453588.8317   -6579533.3853    2153764.0022   m   m   m\nstate_vector_velocity_2:    -1084.54511      2564.77865      7068.97877   m/s m/s m/s\nstate_vector_position_3:  -1464333.6837   -6553509.2838    2224330.7953   m   m   m\nstate_vector_velocity_3:    -1064.38692      2639.98814      7044.24695   m/s m/s m/s\nstate_vector_position_4:  -1474875.8073   -6526734.7073    2294646.2882   m   m   m\nstate_vector_velocity_4:    -1043.99989      2714.87219      7018.71922   m/s m/s m/s\nstate_vector_position_5:  -1485212.9279   -6499212.9542    2364702.5365   m   m   m\nstate_vector_velocity_5:    -1023.38680      2789.42198      6992.39850   m/s m/s m/s\nstate_vector_position_6:  -1495342.7990   -6470947.4109    2434491.6250   m   m   m\nstate_vector_velocity_6:    -1002.55046      2863.62876      6965.28778   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180412_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180412t004005-20180412t004031-021427-024e4f-004.tiff S1A-IW-IW1-VV-21427 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 04 12\nstart_time:              2412.383690   s\ncenter_time:             2421.715820   s\nend_time:                2431.047950   s\nazimuth_line_time:     4.1110706e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0001041e+00\nazimuth_scale_factor:   9.9998976e-01\ncenter_latitude:          19.5126096   degrees\ncenter_longitude:        -97.9182343   degrees\nheading:                 -12.2746558   degrees\nrange_pixel_spacing:       18.638440   m\nazimuth_pixel_spacing:     28.023934   m\nnear_range_slc:           798987.6220  m\ncenter_range_slc:         878322.1419  m\nfar_range_slc:            957656.6617  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7085   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         35.67545 -1.88932e-04  9.36371e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073862.0888   m\nearth_radius_below_sensor:       6375868.9417   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.974817   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442701.9755   -6604751.0902    2082967.3376   m   m   m\nstate_vector_velocity_1:    -1104.60718      2489.33649      7092.91115   m/s m/s m/s\nstate_vector_position_2:  -1453648.6034   -6579479.8315    2153777.4481   m   m   m\nstate_vector_velocity_2:    -1084.67957      2564.86321      7068.97757   m/s m/s m/s\nstate_vector_position_3:  -1464394.7949   -6553454.8813    2224344.2268   m   m   m\nstate_vector_velocity_3:    -1064.52035      2640.07333      7044.24525   m/s m/s m/s\nstate_vector_position_4:  -1474938.2474   -6526679.4498    2294659.7003   m   m   m\nstate_vector_velocity_4:    -1044.13225      2714.95799      7018.71701   m/s m/s m/s\nstate_vector_position_5:  -1485276.6862   -6499156.8355    2364715.9239   m   m   m\nstate_vector_velocity_5:    -1023.51807      2789.50840      6992.39576   m/s m/s m/s\nstate_vector_position_6:  -1495407.8647   -6470890.4251    2434504.9823   m   m   m\nstate_vector_velocity_6:    -1002.68064      2863.71577      6965.28450   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180412_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180412t004005-20180412t004031-021427-024e4f-004.tiff S1A-IW-IW1-VV-21427 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 04 12\nstart_time:              2412.382662   s\ncenter_time:             2421.716847   s\nend_time:                2431.051033   s\nazimuth_line_time:     2.0555353e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0001041e+00\nazimuth_scale_factor:   9.9998976e-01\ncenter_latitude:          19.5126096   degrees\ncenter_longitude:        -97.9182343   degrees\nheading:                 -12.2746558   degrees\nrange_pixel_spacing:        2.329805   m\nazimuth_pixel_spacing:     14.011967   m\nnear_range_slc:           798979.4677  m\ncenter_range_slc:         878326.7880  m\nfar_range_slc:            957674.1083  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7085   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         35.67545 -1.88932e-04  9.36371e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073862.0888   m\nearth_radius_below_sensor:       6375868.9417   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.974817   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442701.9755   -6604751.0902    2082967.3376   m   m   m\nstate_vector_velocity_1:    -1104.60718      2489.33649      7092.91115   m/s m/s m/s\nstate_vector_position_2:  -1453648.6034   -6579479.8315    2153777.4481   m   m   m\nstate_vector_velocity_2:    -1084.67957      2564.86321      7068.97757   m/s m/s m/s\nstate_vector_position_3:  -1464394.7949   -6553454.8813    2224344.2268   m   m   m\nstate_vector_velocity_3:    -1064.52035      2640.07333      7044.24525   m/s m/s m/s\nstate_vector_position_4:  -1474938.2474   -6526679.4498    2294659.7003   m   m   m\nstate_vector_velocity_4:    -1044.13225      2714.95799      7018.71701   m/s m/s m/s\nstate_vector_position_5:  -1485276.6862   -6499156.8355    2364715.9239   m   m   m\nstate_vector_velocity_5:    -1023.51807      2789.50840      6992.39576   m/s m/s m/s\nstate_vector_position_6:  -1495407.8647   -6470890.4251    2434504.9823   m   m   m\nstate_vector_velocity_6:    -1002.68064      2863.71577      6965.28450   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180506_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180506t004006-20180506t004032-021777-02594a-004.tiff S1A-IW-IW1-VV-21777 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 06\nstart_time:              2413.404533   s\ncenter_time:             2422.736684   s\nend_time:                2432.068836   s\nazimuth_line_time:     4.1110800e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000227e+00\nazimuth_scale_factor:   9.9999209e-01\ncenter_latitude:          19.5126100   degrees\ncenter_longitude:        -97.9182327   degrees\nheading:                 -12.2742940   degrees\nrange_pixel_spacing:       18.636920   m\nazimuth_pixel_spacing:     28.023278   m\nnear_range_slc:           798966.0242  m\ncenter_range_slc:         878294.0741  m\nfar_range_slc:            957622.1241  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7047   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         11.52580 -2.34703e-04  1.11667e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073872.7088   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2399.995112   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442642.4939   -6604770.9821    2082981.5708   m   m   m\nstate_vector_velocity_1:    -1104.57944      2489.32644      7092.90469   m/s m/s m/s\nstate_vector_position_2:  -1453588.8482   -6579499.8248    2153791.6162   m   m   m\nstate_vector_velocity_2:    -1084.65260      2564.85301      7068.97105   m/s m/s m/s\nstate_vector_position_3:  -1464334.7737   -6553474.9773    2224358.3294   m   m   m\nstate_vector_velocity_3:    -1064.49415      2640.06298      7044.23869   m/s m/s m/s\nstate_vector_position_4:  -1474877.9681   -6526699.6500    2294673.7369   m   m   m\nstate_vector_velocity_4:    -1044.10683      2714.94751      7018.71041   m/s m/s m/s\nstate_vector_position_5:  -1485216.1566   -6499177.1414    2364729.8941   m   m   m\nstate_vector_velocity_5:    -1023.49343      2789.49777      6992.38912   m/s m/s m/s\nstate_vector_position_6:  -1495347.0925   -6470910.8381    2434518.8859   m   m   m\nstate_vector_velocity_6:    -1002.65679      2863.70501      6965.27783   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180506_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180506t004006-20180506t004032-021777-02594a-004.tiff S1A-IW-IW1-VV-21777 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 06\nstart_time:              2413.403505   s\ncenter_time:             2422.737713   s\nend_time:                2432.071920   s\nazimuth_line_time:     2.0555400e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000227e+00\nazimuth_scale_factor:   9.9999209e-01\ncenter_latitude:          19.5126100   degrees\ncenter_longitude:        -97.9182327   degrees\nheading:                 -12.2742940   degrees\nrange_pixel_spacing:        2.329615   m\nazimuth_pixel_spacing:     14.011639   m\nnear_range_slc:           798957.8705  m\ncenter_range_slc:         878298.7312  m\nfar_range_slc:            957639.5918  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7047   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         11.52580 -2.34703e-04  1.11667e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073872.7088   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2399.995112   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442642.4939   -6604770.9821    2082981.5708   m   m   m\nstate_vector_velocity_1:    -1104.57944      2489.32644      7092.90469   m/s m/s m/s\nstate_vector_position_2:  -1453588.8482   -6579499.8248    2153791.6162   m   m   m\nstate_vector_velocity_2:    -1084.65260      2564.85301      7068.97105   m/s m/s m/s\nstate_vector_position_3:  -1464334.7737   -6553474.9773    2224358.3294   m   m   m\nstate_vector_velocity_3:    -1064.49415      2640.06298      7044.23869   m/s m/s m/s\nstate_vector_position_4:  -1474877.9681   -6526699.6500    2294673.7369   m   m   m\nstate_vector_velocity_4:    -1044.10683      2714.94751      7018.71041   m/s m/s m/s\nstate_vector_position_5:  -1485216.1566   -6499177.1414    2364729.8941   m   m   m\nstate_vector_velocity_5:    -1023.49343      2789.49777      6992.38912   m/s m/s m/s\nstate_vector_position_6:  -1495347.0925   -6470910.8381    2434518.8859   m   m   m\nstate_vector_velocity_6:    -1002.65679      2863.70501      6965.27783   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180518_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180518t004007-20180518t004032-021952-025eda-004.tiff S1A-IW-IW1-VV-21952 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 18\nstart_time:              2414.049918   s\ncenter_time:             2423.382069   s\nend_time:                2432.714221   s\nazimuth_line_time:     4.1110800e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000406e+00\nazimuth_scale_factor:   9.9999208e-01\ncenter_latitude:          19.5126092   degrees\ncenter_longitude:        -97.9182347   degrees\nheading:                 -12.2739463   degrees\nrange_pixel_spacing:       18.637256   m\nazimuth_pixel_spacing:     28.023278   m\nnear_range_slc:           798971.3619  m\ncenter_range_slc:         878300.8421  m\nfar_range_slc:            957630.3222  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7055   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         30.50367 -1.56155e-04  5.89911e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073870.9747   m\nearth_radius_below_sensor:       6375868.9418   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2400.641508   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442658.8229   -6604763.3736    2082988.1616   m   m   m\nstate_vector_velocity_1:    -1104.52792      2489.32605      7092.91204   m/s m/s m/s\nstate_vector_position_2:  -1453604.6611   -6579492.2208    2153798.2799   m   m   m\nstate_vector_velocity_2:    -1084.60090      2564.85252      7068.97831   m/s m/s m/s\nstate_vector_position_3:  -1464350.0687   -6553467.3788    2224365.0649   m   m   m\nstate_vector_velocity_3:    -1064.44227      2640.06239      7044.24585   m/s m/s m/s\nstate_vector_position_4:  -1474892.7434   -6526692.0579    2294680.5434   m   m   m\nstate_vector_velocity_4:    -1044.05478      2714.94682      7018.71747   m/s m/s m/s\nstate_vector_position_5:  -1485230.4105   -6499169.5568    2364736.7706   m   m   m\nstate_vector_velocity_5:    -1023.44121      2789.49698      6992.39609   m/s m/s m/s\nstate_vector_position_6:  -1495360.8233   -6470903.2619    2434525.8315   m   m   m\nstate_vector_velocity_6:    -1002.60441      2863.70412      6965.28470   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180518_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180518t004007-20180518t004032-021952-025eda-004.tiff S1A-IW-IW1-VV-21952 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 18\nstart_time:              2414.048890   s\ncenter_time:             2423.383097   s\nend_time:                2432.717304   s\nazimuth_line_time:     2.0555400e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000406e+00\nazimuth_scale_factor:   9.9999208e-01\ncenter_latitude:          19.5126092   degrees\ncenter_longitude:        -97.9182347   degrees\nheading:                 -12.2739463   degrees\nrange_pixel_spacing:        2.329657   m\nazimuth_pixel_spacing:     14.011639   m\nnear_range_slc:           798963.2081  m\ncenter_range_slc:         878305.4861  m\nfar_range_slc:            957647.7641  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7055   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         30.50367 -1.56155e-04  5.89911e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073870.9747   m\nearth_radius_below_sensor:       6375868.9418   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2400.641508   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442658.8229   -6604763.3736    2082988.1616   m   m   m\nstate_vector_velocity_1:    -1104.52792      2489.32605      7092.91204   m/s m/s m/s\nstate_vector_position_2:  -1453604.6611   -6579492.2208    2153798.2799   m   m   m\nstate_vector_velocity_2:    -1084.60090      2564.85252      7068.97831   m/s m/s m/s\nstate_vector_position_3:  -1464350.0687   -6553467.3788    2224365.0649   m   m   m\nstate_vector_velocity_3:    -1064.44227      2640.06239      7044.24585   m/s m/s m/s\nstate_vector_position_4:  -1474892.7434   -6526692.0579    2294680.5434   m   m   m\nstate_vector_velocity_4:    -1044.05478      2714.94682      7018.71747   m/s m/s m/s\nstate_vector_position_5:  -1485230.4105   -6499169.5568    2364736.7706   m   m   m\nstate_vector_velocity_5:    -1023.44121      2789.49698      6992.39609   m/s m/s m/s\nstate_vector_position_6:  -1495360.8233   -6470903.2619    2434525.8315   m   m   m\nstate_vector_velocity_6:    -1002.60441      2863.70412      6965.28470   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180530_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180530t004008-20180530t004033-022127-02647c-004.tiff S1A-IW-IW1-VV-22127 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 30\nstart_time:              2414.670632   s\ncenter_time:             2424.002741   s\nend_time:                2433.334851   s\nazimuth_line_time:     4.1110614e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999479e-01\nazimuth_scale_factor:   9.9998757e-01\ncenter_latitude:          19.5126111   degrees\ncenter_longitude:        -97.9182280   degrees\nheading:                 -12.2738650   degrees\nrange_pixel_spacing:       18.636400   m\nazimuth_pixel_spacing:     28.023212   m\nnear_range_slc:           798935.5604  m\ncenter_range_slc:         878261.3970  m\nfar_range_slc:            957587.2336  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7034   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         15.22544 -1.80109e-04  7.24218e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073857.1356   m\nearth_radius_below_sensor:       6375868.9413   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2401.257760   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442602.6920   -6604769.2129    2082961.4008   m   m   m\nstate_vector_velocity_1:    -1104.54480      2489.29260      7092.93403   m/s m/s m/s\nstate_vector_position_2:  -1453548.7019   -6579498.3918    2153771.7399   m   m   m\nstate_vector_velocity_2:    -1084.61835      2564.81961      7069.00046   m/s m/s m/s\nstate_vector_position_3:  -1464294.2869   -6553473.8762    2224338.7472   m   m   m\nstate_vector_velocity_3:    -1064.46029      2640.03002      7044.26814   m/s m/s m/s\nstate_vector_position_4:  -1474837.1447   -6526698.8764    2294654.4494   m   m   m\nstate_vector_velocity_4:    -1044.07336      2714.91498      7018.73991   m/s m/s m/s\nstate_vector_position_5:  -1485175.0005   -6499176.6909    2364710.9018   m   m   m\nstate_vector_velocity_5:    -1023.46036      2789.46568      6992.41867   m/s m/s m/s\nstate_vector_position_6:  -1495305.6078   -6470910.7063    2434500.1891   m   m   m\nstate_vector_velocity_6:    -1002.62411      2863.67335      6965.30741   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180530_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180530t004008-20180530t004033-022127-02647c-004.tiff S1A-IW-IW1-VV-22127 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 30\nstart_time:              2414.669604   s\ncenter_time:             2424.003769   s\nend_time:                2433.337934   s\nazimuth_line_time:     2.0555307e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999479e-01\nazimuth_scale_factor:   9.9998757e-01\ncenter_latitude:          19.5126111   degrees\ncenter_longitude:        -97.9182280   degrees\nheading:                 -12.2738650   degrees\nrange_pixel_spacing:        2.329550   m\nazimuth_pixel_spacing:     14.011606   m\nnear_range_slc:           798927.4070  m\ncenter_range_slc:         878266.0511  m\nfar_range_slc:            957604.6952  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7034   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         15.22544 -1.80109e-04  7.24218e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073857.1356   m\nearth_radius_below_sensor:       6375868.9413   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2401.257760   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442602.6920   -6604769.2129    2082961.4008   m   m   m\nstate_vector_velocity_1:    -1104.54480      2489.29260      7092.93403   m/s m/s m/s\nstate_vector_position_2:  -1453548.7019   -6579498.3918    2153771.7399   m   m   m\nstate_vector_velocity_2:    -1084.61835      2564.81961      7069.00046   m/s m/s m/s\nstate_vector_position_3:  -1464294.2869   -6553473.8762    2224338.7472   m   m   m\nstate_vector_velocity_3:    -1064.46029      2640.03002      7044.26814   m/s m/s m/s\nstate_vector_position_4:  -1474837.1447   -6526698.8764    2294654.4494   m   m   m\nstate_vector_velocity_4:    -1044.07336      2714.91498      7018.73991   m/s m/s m/s\nstate_vector_position_5:  -1485175.0005   -6499176.6909    2364710.9018   m   m   m\nstate_vector_velocity_5:    -1023.46036      2789.46568      6992.41867   m/s m/s m/s\nstate_vector_position_6:  -1495305.6078   -6470910.7063    2434500.1891   m   m   m\nstate_vector_velocity_6:    -1002.62411      2863.67335      6965.30741   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180611_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180611t004009-20180611t004034-022302-0269f0-004.tiff S1A-IW-IW1-VV-22302 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 06 11\nstart_time:              2415.577608   s\ncenter_time:             2424.909765   s\nend_time:                2434.241922   s\nazimuth_line_time:     4.1110824e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000709e+00\nazimuth_scale_factor:   9.9999265e-01\ncenter_latitude:          19.5126100   degrees\ncenter_longitude:        -97.9182321   degrees\nheading:                 -12.2739122   degrees\nrange_pixel_spacing:       18.637816   m\nazimuth_pixel_spacing:     28.023274   m\nnear_range_slc:           798984.6715  m\ncenter_range_slc:         878316.5353  m\nfar_range_slc:            957648.3992  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7070   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         -6.44169 -1.70915e-04  8.43663e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073871.1809   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2402.170128   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442686.1339   -6604756.9510    2082990.0640   m   m   m\nstate_vector_velocity_1:    -1104.51358      2489.32919      7092.91276   m/s m/s m/s\nstate_vector_position_2:  -1453631.8271   -6579485.7671    2153800.1895   m   m   m\nstate_vector_velocity_2:    -1084.58623      2564.85556      7068.97900   m/s m/s m/s\nstate_vector_position_3:  -1464377.0865   -6553460.8951    2224366.9815   m   m   m\nstate_vector_velocity_3:    -1064.42727      2640.06533      7044.24651   m/s m/s m/s\nstate_vector_position_4:  -1474919.6097   -6526685.5453    2294682.4666   m   m   m\nstate_vector_velocity_4:    -1044.03946      2714.94965      7018.71811   m/s m/s m/s\nstate_vector_position_5:  -1485257.1220   -6499163.0163    2364738.7003   m   m   m\nstate_vector_velocity_5:    -1023.42557      2789.49971      6992.39670   m/s m/s m/s\nstate_vector_position_6:  -1495387.3769   -6470896.6945    2434527.7672   m   m   m\nstate_vector_velocity_6:    -1002.58844      2863.70674      6965.28529   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180611_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180611t004009-20180611t004034-022302-0269f0-004.tiff S1A-IW-IW1-VV-22302 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 06 11\nstart_time:              2415.576580   s\ncenter_time:             2424.910792   s\nend_time:                2434.245005   s\nazimuth_line_time:     2.0555412e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000709e+00\nazimuth_scale_factor:   9.9999265e-01\ncenter_latitude:          19.5126100   degrees\ncenter_longitude:        -97.9182321   degrees\nheading:                 -12.2739122   degrees\nrange_pixel_spacing:        2.329727   m\nazimuth_pixel_spacing:     14.011637   m\nnear_range_slc:           798976.5175  m\ncenter_range_slc:         878321.2042  m\nfar_range_slc:            957665.8909  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7070   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         -6.44169 -1.70915e-04  8.43663e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073871.1809   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2402.170128   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442686.1339   -6604756.9510    2082990.0640   m   m   m\nstate_vector_velocity_1:    -1104.51358      2489.32919      7092.91276   m/s m/s m/s\nstate_vector_position_2:  -1453631.8271   -6579485.7671    2153800.1895   m   m   m\nstate_vector_velocity_2:    -1084.58623      2564.85556      7068.97900   m/s m/s m/s\nstate_vector_position_3:  -1464377.0865   -6553460.8951    2224366.9815   m   m   m\nstate_vector_velocity_3:    -1064.42727      2640.06533      7044.24651   m/s m/s m/s\nstate_vector_position_4:  -1474919.6097   -6526685.5453    2294682.4666   m   m   m\nstate_vector_velocity_4:    -1044.03946      2714.94965      7018.71811   m/s m/s m/s\nstate_vector_position_5:  -1485257.1220   -6499163.0163    2364738.7003   m   m   m\nstate_vector_velocity_5:    -1023.42557      2789.49971      6992.39670   m/s m/s m/s\nstate_vector_position_6:  -1495387.3769   -6470896.6945    2434527.7672   m   m   m\nstate_vector_velocity_6:    -1002.58844      2863.70674      6965.28529   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180623_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180623t004009-20180623t004034-022477-026f32-004.tiff S1A-IW-IW1-VV-22477 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 06 23\nstart_time:              2416.281152   s\ncenter_time:             2425.613261   s\nend_time:                2434.945371   s\nazimuth_line_time:     4.1110614e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000527e+00\nazimuth_scale_factor:   9.9998753e-01\ncenter_latitude:          19.5126108   degrees\ncenter_longitude:        -97.9182325   degrees\nheading:                 -12.2742251   degrees\nrange_pixel_spacing:       18.637480   m\nazimuth_pixel_spacing:     28.023230   m\nnear_range_slc:           798959.0311  m\ncenter_range_slc:         878289.4647  m\nfar_range_slc:            957619.8983  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7061   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         14.54785 -1.25986e-04  5.07538e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073856.4793   m\nearth_radius_below_sensor:       6375868.9414   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2402.873946   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442656.4274   -6604747.9147    2082989.4255   m   m   m\nstate_vector_velocity_1:    -1104.56499      2489.34157      7092.91970   m/s m/s m/s\nstate_vector_position_2:  -1453602.6358   -6579476.6048    2153799.6198   m   m   m\nstate_vector_velocity_2:    -1084.63785      2564.86838      7068.98580   m/s m/s m/s\nstate_vector_position_3:  -1464348.4124   -6553451.6024    2224366.4792   m   m   m\nstate_vector_velocity_3:    -1064.47910      2640.07858      7044.25316   m/s m/s m/s\nstate_vector_position_4:  -1474891.4549   -6526676.1180    2294682.0302   m   m   m\nstate_vector_velocity_4:    -1044.09148      2714.96333      7018.72461   m/s m/s m/s\nstate_vector_position_5:  -1485229.4885   -6499153.4500    2364738.3280   m   m   m\nstate_vector_velocity_5:    -1023.47779      2789.51381      6992.40304   m/s m/s m/s\nstate_vector_position_6:  -1495360.2665   -6470886.9851    2434527.4576   m   m   m\nstate_vector_velocity_6:    -1002.64084      2863.72126      6965.29145   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180623_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180623t004009-20180623t004034-022477-026f32-004.tiff S1A-IW-IW1-VV-22477 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 06 23\nstart_time:              2416.280124   s\ncenter_time:             2425.614289   s\nend_time:                2434.948454   s\nazimuth_line_time:     2.0555307e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000527e+00\nazimuth_scale_factor:   9.9998753e-01\ncenter_latitude:          19.5126108   degrees\ncenter_longitude:        -97.9182325   degrees\nheading:                 -12.2742251   degrees\nrange_pixel_spacing:        2.329685   m\nazimuth_pixel_spacing:     14.011615   m\nnear_range_slc:           798950.8772  m\ncenter_range_slc:         878294.1123  m\nfar_range_slc:            957637.3475  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7061   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         14.54785 -1.25986e-04  5.07538e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073856.4793   m\nearth_radius_below_sensor:       6375868.9414   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2402.873946   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442656.4274   -6604747.9147    2082989.4255   m   m   m\nstate_vector_velocity_1:    -1104.56499      2489.34157      7092.91970   m/s m/s m/s\nstate_vector_position_2:  -1453602.6358   -6579476.6048    2153799.6198   m   m   m\nstate_vector_velocity_2:    -1084.63785      2564.86838      7068.98580   m/s m/s m/s\nstate_vector_position_3:  -1464348.4124   -6553451.6024    2224366.4792   m   m   m\nstate_vector_velocity_3:    -1064.47910      2640.07858      7044.25316   m/s m/s m/s\nstate_vector_position_4:  -1474891.4549   -6526676.1180    2294682.0302   m   m   m\nstate_vector_velocity_4:    -1044.09148      2714.96333      7018.72461   m/s m/s m/s\nstate_vector_position_5:  -1485229.4885   -6499153.4500    2364738.3280   m   m   m\nstate_vector_velocity_5:    -1023.47779      2789.51381      6992.40304   m/s m/s m/s\nstate_vector_position_6:  -1495360.2665   -6470886.9851    2434527.4576   m   m   m\nstate_vector_velocity_6:    -1002.64084      2863.72126      6965.29145   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180705_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180705t004010-20180705t004035-022652-027450-004.tiff S1A-IW-IW1-VV-22652 (software: Sentinel-1 IPF 002.91)\nsensor:    S1A IW IW1 VV\ndate:      2018 07 05\nstart_time:              2416.688145   s\ncenter_time:             2426.020257   s\nend_time:                2435.352370   s\nazimuth_line_time:     4.1110628e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9992375e-01\nazimuth_scale_factor:   9.9998786e-01\ncenter_latitude:          19.5126114   degrees\ncenter_longitude:        -97.9182295   degrees\nheading:                 -12.2745289   degrees\nrange_pixel_spacing:       18.635072   m\nazimuth_pixel_spacing:     28.023200   m\nnear_range_slc:           798915.1793  m\ncenter_range_slc:         878235.3633  m\nfar_range_slc:            957555.5473  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7001   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         -7.82027 -2.35094e-04  1.06023e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073865.2745   m\nearth_radius_below_sensor:       6375868.9413   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2403.279394   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442550.6790   -6604777.7915    2082997.8717   m   m   m\nstate_vector_velocity_1:    -1104.64152      2489.33373      7092.90430   m/s m/s m/s\nstate_vector_position_2:  -1453497.6590   -6579506.5592    2153807.9120   m   m   m\nstate_vector_velocity_2:    -1084.71565      2564.86071      7068.97040   m/s m/s m/s\nstate_vector_position_3:  -1464244.2199   -6553481.6328    2224374.6174   m   m   m\nstate_vector_velocity_3:    -1064.55816      2640.07108      7044.23776   m/s m/s m/s\nstate_vector_position_4:  -1474788.0592   -6526706.2224    2294690.0145   m   m   m\nstate_vector_velocity_4:    -1044.17179      2714.95601      7018.70921   m/s m/s m/s\nstate_vector_position_5:  -1485126.9019   -6499183.6268    2364746.1585   m   m   m\nstate_vector_velocity_5:    -1023.55933      2789.50667      6992.38766   m/s m/s m/s\nstate_vector_position_6:  -1495258.5015   -6470917.2323    2434535.1344   m   m   m\nstate_vector_velocity_6:    -1002.72362      2863.71431      6965.27609   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180705_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180705t004010-20180705t004035-022652-027450-004.tiff S1A-IW-IW1-VV-22652 (software: Sentinel-1 IPF 002.91)\nsensor:    S1A IW IW1 VV\ndate:      2018 07 05\nstart_time:              2416.687117   s\ncenter_time:             2426.021285   s\nend_time:                2435.355453   s\nazimuth_line_time:     2.0555314e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9992375e-01\nazimuth_scale_factor:   9.9998786e-01\ncenter_latitude:          19.5126114   degrees\ncenter_longitude:        -97.9182295   degrees\nheading:                 -12.2745289   degrees\nrange_pixel_spacing:        2.329384   m\nazimuth_pixel_spacing:     14.011600   m\nnear_range_slc:           798907.0265  m\ncenter_range_slc:         878240.0350  m\nfar_range_slc:            957573.0434  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7001   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         -7.82027 -2.35094e-04  1.06023e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073865.2745   m\nearth_radius_below_sensor:       6375868.9413   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2403.279394   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442550.6790   -6604777.7915    2082997.8717   m   m   m\nstate_vector_velocity_1:    -1104.64152      2489.33373      7092.90430   m/s m/s m/s\nstate_vector_position_2:  -1453497.6590   -6579506.5592    2153807.9120   m   m   m\nstate_vector_velocity_2:    -1084.71565      2564.86071      7068.97040   m/s m/s m/s\nstate_vector_position_3:  -1464244.2199   -6553481.6328    2224374.6174   m   m   m\nstate_vector_velocity_3:    -1064.55816      2640.07108      7044.23776   m/s m/s m/s\nstate_vector_position_4:  -1474788.0592   -6526706.2224    2294690.0145   m   m   m\nstate_vector_velocity_4:    -1044.17179      2714.95601      7018.70921   m/s m/s m/s\nstate_vector_position_5:  -1485126.9019   -6499183.6268    2364746.1585   m   m   m\nstate_vector_velocity_5:    -1023.55933      2789.50667      6992.38766   m/s m/s m/s\nstate_vector_position_6:  -1495258.5015   -6470917.2323    2434535.1344   m   m   m\nstate_vector_velocity_6:    -1002.72362      2863.71431      6965.27609   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180717_VV_8rlks_mli.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180717t004011-20180717t004036-022827-02799b-004.tiff S1A-IW-IW1-VV-22827 (software: Sentinel-1 IPF 002.91)\nsensor:    S1A IW IW1 VV\ndate:      2018 07 17\nstart_time:              2417.677696   s\ncenter_time:             2427.009819   s\nend_time:                2436.341942   s\nazimuth_line_time:     4.1110674e-03   s\nline_header_size:                  0\nrange_samples:                  8514\nazimuth_lines:                  4541\nrange_looks:                       8\nazimuth_looks:                     2\nimage_format:               FLOAT\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000365e+00\nazimuth_scale_factor:   9.9998902e-01\ncenter_latitude:          19.5126104   degrees\ncenter_longitude:        -97.9182335   degrees\nheading:                 -12.2742902   degrees\nrange_pixel_spacing:       18.637176   m\nazimuth_pixel_spacing:     28.023252   m\nnear_range_slc:           798959.5607  m\ncenter_range_slc:         878288.7003  m\nfar_range_slc:            957617.8400  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7053   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:          8.68743 -1.16261e-04  2.50691e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073862.6200   m\nearth_radius_below_sensor:       6375868.9415   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2404.269733   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442646.9928   -6604757.2056    2082987.8020   m   m   m\nstate_vector_velocity_1:    -1104.57610      2489.34060      7092.91227   m/s m/s m/s\nstate_vector_position_2:  -1453593.3130   -6579485.9059    2153797.9224   m   m   m\nstate_vector_velocity_2:    -1084.64913      2564.86733      7068.97846   m/s m/s m/s\nstate_vector_position_3:  -1464339.2032   -6553460.9144    2224364.7087   m   m   m\nstate_vector_velocity_3:    -1064.49055      2640.07746      7044.24590   m/s m/s m/s\nstate_vector_position_4:  -1474882.3608   -6526685.4415    2294680.1875   m   m   m\nstate_vector_velocity_4:    -1044.10309      2714.96214      7018.71744   m/s m/s m/s\nstate_vector_position_5:  -1485220.5112   -6499162.7858    2364736.4141   m   m   m\nstate_vector_velocity_5:    -1023.48956      2789.51256      6992.39596   m/s m/s m/s\nstate_vector_position_6:  -1495351.4077   -6470896.3337    2434525.4733   m   m   m\nstate_vector_velocity_6:    -1002.65278      2863.71995      6965.28447   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers/r20180717_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180717t004011-20180717t004036-022827-02799b-004.tiff S1A-IW-IW1-VV-22827 (software: Sentinel-1 IPF 002.91)\nsensor:    S1A IW IW1 VV\ndate:      2018 07 17\nstart_time:              2417.676668   s\ncenter_time:             2427.010846   s\nend_time:                2436.345025   s\nazimuth_line_time:     2.0555337e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000365e+00\nazimuth_scale_factor:   9.9998902e-01\ncenter_latitude:          19.5126104   degrees\ncenter_longitude:        -97.9182335   degrees\nheading:                 -12.2742902   degrees\nrange_pixel_spacing:        2.329647   m\nazimuth_pixel_spacing:     14.011626   m\nnear_range_slc:           798951.4069  m\ncenter_range_slc:         878293.3586  m\nfar_range_slc:            957635.3104  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7053   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:          8.68743 -1.16261e-04  2.50691e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073862.6200   m\nearth_radius_below_sensor:       6375868.9415   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2404.269733   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442646.9928   -6604757.2056    2082987.8020   m   m   m\nstate_vector_velocity_1:    -1104.57610      2489.34060      7092.91227   m/s m/s m/s\nstate_vector_position_2:  -1453593.3130   -6579485.9059    2153797.9224   m   m   m\nstate_vector_velocity_2:    -1084.64913      2564.86733      7068.97846   m/s m/s m/s\nstate_vector_position_3:  -1464339.2032   -6553460.9144    2224364.7087   m   m   m\nstate_vector_velocity_3:    -1064.49055      2640.07746      7044.24590   m/s m/s m/s\nstate_vector_position_4:  -1474882.3608   -6526685.4415    2294680.1875   m   m   m\nstate_vector_velocity_4:    -1044.10309      2714.96214      7018.71744   m/s m/s m/s\nstate_vector_position_5:  -1485220.5112   -6499162.7858    2364736.4141   m   m   m\nstate_vector_velocity_5:    -1023.48956      2789.51256      6992.39596   m/s m/s m/s\nstate_vector_position_6:  -1495351.4077   -6470896.3337    2434525.4733   m   m   m\nstate_vector_velocity_6:    -1002.65278      2863.71995      6965.28447   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/cropA/headers_13",
    "content": "tests/test_data/cropA/headers/r20180106_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180130_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180307_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180319_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180331_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180412_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180506_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180518_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180530_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180611_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180623_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180705_VV_8rlks_mli.par\ntests/test_data/cropA/headers/r20180717_VV_8rlks_mli.par\n"
  },
  {
    "path": "tests/test_data/cropA/ifg_30",
    "content": "tests/test_data/cropA/geotiffs/cropA_20180106-20180130_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180106-20180319_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180106-20180412_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180106-20180518_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180130-20180307_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180130-20180412_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180319_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180331_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180506_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180530_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180307-20180611_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180331_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180506_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180518_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180530_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180319-20180623_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180412_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180506_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180518_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180530_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180623_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180331-20180717_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180412-20180506_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180412-20180518_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180518_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180530_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180611_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180623_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180705_VV_8rlks_eqa_unw.tif\ntests/test_data/cropA/geotiffs/cropA_20180506-20180717_VV_8rlks_eqa_unw.tif\n"
  },
  {
    "path": "tests/test_data/cropA/pyrate_mexico_cropa.conf",
    "content": "# PyRate configuration file for GAMMA-format interferograms\n#\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Optional ON/OFF switches - ON = 1; OFF = 0\n\n# Coherence masking (PREPIFG)\ncohmask:       0\n\n# Orbital error correction (CORRECT)\norbfit:        1\n\n# APS correction using spatio-temporal filter (CORRECT)\napsest:        1\n\n# DEM error (residual topography) correction (CORRECT)\ndemerror:      1\n\n# Phase Closure correction (CORRECT)\nphase_closure:      1\n\n# Optional save of numpy array files for output products (MERGE)\nsavenpy:       1\n\n# Optional save of incremental time series products (TIMESERIES/MERGE)\nsavetsincr:    1\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Multi-threading parameters used by correct/stacking/timeseries\n# gamma prepifg runs in parallel on a single machine if parallel = 1\n# parallel: 1 = parallel, 0 = serial\nparallel:      0\n# number of processes\nprocesses:     4\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Input/Output file locations\n#\n# File containing the list of interferograms to use.\nifgfilelist:   tests/test_data/cropA/ifg_30\n\n# The DEM file used in the InSAR processing\ndemfile:       tests/test_data/cropA/geotiffs/cropA_T005A_dem.tif\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/cropA/headers/cropA_20180106_VV_8rlks_eqa_dem.par\n\n# File listing the pool of available header files (GAMMA: *mli.par, ROI_PAC: *.rsc)\nhdrfilelist:   tests/test_data/cropA/headers_13\n\n# File listing the pool of available coherence files.\ncohfilelist:   tests/test_data/cropA/coherence_30\n\n# File listing the pool of available baseline files (GAMMA).\nbasefilelist:  tests/test_data/cropA/baseline_30\n\n# Look-up table containing radar-coded row and column for lat/lon pixels (GAMMA)\nltfile:        tests/test_data/cropA/geometry/20180106_VV_8rlks_eqa_to_rdc.lt\n\n# Directory to write the outputs to\noutdir:        out/cropa/out\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# PREPIFG parameters\n#------------------------------------\n# Input data format: ROI_PAC = 0, GAMMA = 1\nprocessor:     1\n\n# Coherence threshold value for masking, between 0 and 1\ncohthresh:     0.05\n\n# Multi-look/subsampling factor in east (x) and north (y) dimension\nifglksx:       1\nifglksy:       1\n\n# Cropping options\n# ifgcropopt: 1 = minimum extent 2 = maximum extent 3 = crop 4 = no cropping\n# ifgxfirst,ifgyfirst: longitude (x) and latitude (y) of north-west corner\n# ifgxlast,ifgylast: longitude (x) and latitude (y) of south-east corner\nifgcropopt:    1\nifgxfirst:     150.92\nifgyfirst:     -34.18\nifgxlast:      150.94\nifgylast:      -34.22\n\n# No-data averaging threshold (0 = 0%; 1 = 100%)\nnoDataAveragingThreshold: 0.5\n\n# The No-data value used in the interferogram files\nnoDataValue:   0.0\n\n# Nan conversion flag. Set to 1 if missing No-data values are to be converted to NaN\nnan_conversion: 1\n\n# sign convention for phase data\nsignal_polarity: -1\n\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# CORRECT parameters\n#------------------------------------\n# Reference pixel search options\n\n# refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed\n# refnx/y: number of search grid points in x/y image dimensions\n# refchipsize: size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:          -1\nrefy:          -1\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.8\n\n#------------------------------------\n# Reference phase correction method\n\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        1\n\n#------------------------------------\n# Orbital error correction\n\n# orbfitmethod = 1: interferograms corrected independently; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for network orbital correction\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n# phase closure params - refer to input_parameters.conf for descriptions\nclosure_thr:         0.5\nifg_drop_thr:        0.07\nmin_loops_per_ifg:   2\nmax_loop_length:     4\nmax_loop_redundancy: 2\n\n\n#------------------------------------\n# APS filter parameters\n\n# tlpfcutoff: cutoff t0 for temporal high-pass Gaussian filter in days (int);\n# tlpfpthr: valid pixel threshold;\n# slpfcutoff: spatial low-pass Gaussian filter cutoff in km (greater than zero).\n#             slpfcutoff=0 triggers cutoff estimation from exponential covariance function\ntlpfcutoff:    30\ntlpfpthr:      1\nslpfcutoff:    1\n\n#------------------------------------\n# DEM error (residual topography) correction parameters\n\n# de_pthr: valid observations threshold;\nde_pthr:       20\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# TIMESERIES parameters\n#------------------------------------\n\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor)\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      2\nsmorder:       2\nsmfactor:      -0.25\nts_pthr:       10\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# STACK parameters\n#------------------------------------\n\n# pthr: threshold for minimum number of ifg observations for each pixel\n# nsig: threshold for iterative removal of observations\n# maxsig: maximum sigma (std dev) used as an output masking threshold applied in Merge step. 0 = OFF.\npthr:          5\nnsig:          3\nmaxsig:        100\n\n# LOS Projection of timeseries and stack products\n# converts slanted (native) LOS signals to either \"vertical\" or \"horizontal\", by multiplying by the sine or cosine of\n# the incidence angle for each pixel.\n# Three options, 0/1/2 - 0 = LOS (no conversion); 1 = pseudo-vertical; 2 = pseudo-horizontal.\n\nlos_projection: 0\n\n# optionally supply rows and cols for tiles used during correct and merge step\nrows:          4\ncols:          3\n\nlargetifs:     0\n\n[correct]\nsteps =\n    orbfit\n    refphase\n    demerror\n    phase_closure\n    mst\n    apscorrect\n    maxvar\n"
  },
  {
    "path": "tests/test_data/gamma/16x20_20090713-20090817_VV_4rlks_utm.unw",
    "content": "A\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA<A<A<A<A<A<A\u001dA\u001dA\u001dA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA\u0012kA<A<A<A<A<A<A\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u0012kA<A<A<A<A<A<A<A\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAA<A<A<A<A\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAAAA\u001dA\u001dA\u001dA\u001dA\u001dAAAAAAAAAAAAGAGAGA\u001dA\u001dAAAAAAAAAAAAGAGAGAGAGAAAAAAAAAAAAGAGAGAGAAAAAAAAAAAAAGAGAGAGAAAAAAAAAAAAAGAGAGAAAAAAAAAAAAAAGAGAGAAAAAAAAAAAAAAGAGAAAAAAAAAAAAAAAGAGAAAAAAAAAAAAAA"
  },
  {
    "path": "tests/test_data/gamma/20160114-20160126_base.par",
    "content": "initial_baseline(TCN):         -0.0000026     -103.7427072        2.8130731   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0173538       -0.0055098   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000     -103.8364725        2.8055662   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0182215       -0.0065402   m/s m/s m/s\nunwrap_phase_constant:           -0.00027     radians\n\n"
  },
  {
    "path": "tests/test_data/gamma/20160114-20160126_base_init.par",
    "content": "initial_baseline(TCN):          0.6529765     -103.9065694        2.9253896   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0231786       -0.0038703   m/s m/s m/s\nprecision_baseline(TCN):        0.0000000        0.0000000        0.0000000   m   m   m\nprecision_baseline_rate:        0.0000000        0.0000000        0.0000000   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/gamma/dem16x20raw.dem.par",
    "content": "Gamma DIFF&GEO DEM/MAP parameter file\ntitle:\nDEM_projection:     EQA\ndata_format:        REAL*4\nDEM_hgt_offset:          0.00000\nDEM_scale:               1.00000\nwidth:               16\nnlines:              20\ncorner_lat:    -33.3831945  decimal degrees\ncorner_lon:    150.3870833  decimal degrees\npost_lat:   -6.9444445e-05  decimal degrees\npost_lon:    6.9444445e-05  decimal degrees\n\nellipsoid_name: WGS 84\nellipsoid_ra:        6378137.000   m\nellipsoid_reciprocal_flattening:  298.2572236\n\ndatum_name: WGS 1984\ndatum_shift_dx:              0.000   m\ndatum_shift_dy:              0.000   m\ndatum_shift_dz:              0.000   m\ndatum_scale_m:         0.00000e+00\ndatum_rotation_alpha:  0.00000e+00   arc-sec\ndatum_rotation_beta:   0.00000e+00   arc-sec\ndatum_rotation_gamma:  0.00000e+00   arc-sec\ndatum_country_list Global Definition, WGS84, World\n\n"
  },
  {
    "path": "tests/test_data/gamma/r20090713_VV.slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     ASA_IM__0CNPDE20090713_231928_000000162080_00402_38532_3575.N1\nsensor:    IS2\ndate:      2009  7 13 8 28 59.6906\nstart_time:             83968.033110   s\ncenter_time:            83975.849243   s\nend_time:               83983.665375   s\nazimuth_line_time:     6.0517460e-04  s\nline_header_size:                  0\nrange_samples:                  5210\nazimuth_lines:                 25832\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000000e+00\nazimuth_scale_factor:   1.0000000e+00\ncenter_latitude:         -33.9600804   degrees\ncenter_longitude:        151.0588547   degrees\nheading:                -165.2026979   degrees\nrange_pixel_spacing:        7.803974   m\nazimuth_pixel_spacing:      4.076208   m\nnear_range_slc:           837140.0006   m\ncenter_range_slc:         857465.4509   m\nfar_range_slc:            877790.9012   m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             22.9671   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.3310040e+09   Hz\nadc_sampling_rate:      1.9207680e+07   Hz\nchirp_bandwidth:        1.6000000e+07   Hz\nprf:                     1652.415692   Hz\nazimuth_proc_bandwidth:   1321.93255   Hz\ndoppler_polynomial:       -991.91263 -2.12377e-03  0.00000e+00  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                0.0000   dB\ncalibration_gain:           -30.4000   dB\nsar_to_earth_center:             7168808.1329   m\nearth_radius_below_sensor:       6371502.5710   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                   11\ntime_of_first_state_vector:      83926.000000   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -5539202.8674    2580231.4462   -3748171.3323   m   m   m\nstate_vector_velocity_1:     4212.01160       -37.06400     -6259.19820   m/s m/s m/s\nstate_vector_position_2:  -5496785.8050    2579691.1187   -3810558.9902   m   m   m\nstate_vector_velocity_2:     4271.31410       -71.01470     -6218.21900   m/s m/s m/s\nstate_vector_position_3:  -5453778.2822    2578810.8997   -3872533.4977   m   m   m\nstate_vector_velocity_3:     4330.10260      -105.04160     -6176.56890   m/s m/s m/s\nstate_vector_position_4:  -5410185.4703    2577590.0489   -3934088.1691   m   m   m\nstate_vector_velocity_4:     4388.37090      -139.14040     -6134.25250   m/s m/s m/s\nstate_vector_position_5:  -5366012.6039    2576027.8701   -3995216.3654   m   m   m\nstate_vector_velocity_5:     4446.11250      -173.30650     -6091.27460   m/s m/s m/s\nstate_vector_position_6:  -5321264.9786    2574123.7106   -4055911.4933   m   m   m\nstate_vector_velocity_6:     4503.32130      -207.53560     -6047.63990   m/s m/s m/s\nstate_vector_position_7:  -5275947.9558    2571876.9643   -4116167.0110   m   m   m\nstate_vector_velocity_7:     4559.99100      -241.82320     -6003.35330   m/s m/s m/s\nstate_vector_position_8:  -5230066.9567    2569287.0688   -4175976.4239   m   m   m\nstate_vector_velocity_8:     4616.11560      -276.16480     -5958.41970   m/s m/s m/s\nstate_vector_position_9:  -5183627.4630    2566353.5066   -4235333.2867   m   m   m\nstate_vector_velocity_9:     4671.68900      -310.55580     -5912.84410   m/s m/s m/s\nstate_vector_position_10:  -5136635.0164    2563075.8049   -4294231.2043   m   m   m\nstate_vector_velocity_10:     4726.70520      -344.99190     -5866.63150   m/s m/s m/s\nstate_vector_position_11:  -5089095.2179    2559453.5363   -4352663.8323   m   m   m\nstate_vector_velocity_11:     4781.15840      -379.46850     -5819.78700   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/gamma/r20090817_VV.slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     ASA_IM__0CNPDE20090817_231927_000000162081_00402_39033_4349.N1\nsensor:    IS2\ndate:      2009  8 17 18 14 59.6906\nstart_time:             83967.454935   s\ncenter_time:            83975.271177   s\nend_time:               83983.087419   s\nazimuth_line_time:     6.0518306e-04  s\nline_header_size:                  0\nrange_samples:                  5210\nazimuth_lines:                 25832\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9957489e-01\nazimuth_scale_factor:   1.0000140e+00\ncenter_latitude:         -33.9620800   degrees\ncenter_longitude:        151.0553510   degrees\nheading:                -165.2008450   degrees\nrange_pixel_spacing:        7.800656   m\nazimuth_pixel_spacing:      4.076201   m\nnear_range_slc:           837149.0627   m\ncenter_range_slc:         857465.8723   m\nfar_range_slc:            877782.6820   m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             22.9564   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.3310040e+09   Hz\nadc_sampling_rate:      1.9207680e+07   Hz\nchirp_bandwidth:        1.6000000e+07   Hz\nprf:                     1652.415692   Hz\nazimuth_proc_bandwidth:   1321.93255   Hz\ndoppler_polynomial:      -1004.58744 -1.88790e-03  0.00000e+00  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                0.0000   dB\ncalibration_gain:           -30.4000   dB\nsar_to_earth_center:             7168862.8999   m\nearth_radius_below_sensor:       6371501.6815   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                   11\ntime_of_first_state_vector:      83925.000000   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -5540972.8403    2580396.5773   -3745532.7501   m   m   m\nstate_vector_velocity_1:     4209.44650       -35.49960     -6260.89760   m/s m/s m/s\nstate_vector_position_2:  -5498581.3264    2579871.9066   -3807937.5491   m   m   m\nstate_vector_velocity_2:     4268.76940       -69.44790     -6219.94770   m/s m/s m/s\nstate_vector_position_3:  -5455599.1467    2579007.3670   -3869929.4895   m   m   m\nstate_vector_velocity_3:     4327.57860      -103.47270     -6178.32670   m/s m/s m/s\nstate_vector_position_4:  -5412031.4699    2577802.2161   -3931501.8840   m   m   m\nstate_vector_velocity_4:     4385.86780      -137.56950     -6136.03920   m/s m/s m/s\nstate_vector_position_5:  -5367883.5281    2576255.7558   -3992648.0917   m   m   m\nstate_vector_velocity_5:     4443.63060      -171.73390     -6093.09010   m/s m/s m/s\nstate_vector_position_6:  -5323160.6148    2574367.3306   -4053361.5177   m   m   m\nstate_vector_velocity_6:     4500.86080      -205.96140     -6049.48400   m/s m/s m/s\nstate_vector_position_7:  -5277868.0883    2572136.3331   -4113635.6181   m   m   m\nstate_vector_velocity_7:     4557.55230      -240.24750     -6005.22580   m/s m/s m/s\nstate_vector_position_8:  -5232011.3673    2569562.1994   -4173463.8967   m   m   m\nstate_vector_velocity_8:     4613.69880      -274.58790     -5960.32040   m/s m/s m/s\nstate_vector_position_9:  -5185595.9309    2566644.4101   -4232839.9064   m   m   m\nstate_vector_velocity_9:     4669.29440      -308.97800     -5914.77280   m/s m/s m/s\nstate_vector_position_10:  -5138627.3183    2563382.4908   -4291757.2503   m   m   m\nstate_vector_velocity_10:     4724.33310      -343.41320     -5868.58800   m/s m/s m/s\nstate_vector_position_11:  -5091111.1281    2559776.0120   -4350209.5821   m   m   m\nstate_vector_velocity_11:     4778.80900      -377.88910     -5821.77120   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/cropA_20180106_VV_8rlks_eqa_dem.par",
    "content": "Gamma DIFF&GEO DEM/MAP parameter file\ntitle: dem\nDEM_projection:     EQA\ndata_format:        REAL*4\nDEM_hgt_offset:          0.00000\nDEM_scale:               1.00000\nwidth:                100\nnlines:               60\ncorner_lat:     19.4512926234517565  decimal degrees\ncorner_lon:    -99.1910697816367417  decimal degrees\npost_lat:   -0.001388888900000000105  decimal degrees\npost_lon:    0.001388888900000000105  decimal degrees\n\nellipsoid_name: WGS 84\nellipsoid_ra:        6378137.000   m\nellipsoid_reciprocal_flattening:  298.2572236\n\ndatum_name: WGS 84\ndatum_shift_dx:              0.000   m\ndatum_shift_dy:              0.000   m\ndatum_shift_dz:              0.000   m\ndatum_scale_m:         0.00000e+00\ndatum_rotation_alpha:  0.00000e+00   arc-sec\ndatum_rotation_beta:   0.00000e+00   arc-sec\ndatum_rotation_gamma:  0.00000e+00   arc-sec\ndatum_country_list: WGS 84\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180106_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180106t004006-20180106t004031-020027-0221ec-004.tiff S1A-IW-IW1-VV-20027 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 01 06\nstart_time:              2412.556599   s\ncenter_time:             2421.890880   s\nend_time:                2431.225161   s\nazimuth_line_time:     2.0555563e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000000e+00\nazimuth_scale_factor:   1.0000000e+00\ncenter_latitude:          19.5126101   degrees\ncenter_longitude:        -97.9182354   degrees\nheading:                 -12.2742586   degrees\nrange_pixel_spacing:        2.329562   m\nazimuth_pixel_spacing:     14.011650   m\nnear_range_slc:           798980.1369  m\ncenter_range_slc:         878319.1947  m\nfar_range_slc:            957658.2525  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7036   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         28.89379 -1.70466e-04  1.00457e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073899.1954   m\nearth_radius_below_sensor:       6375868.9414   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2399.144213   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442639.9545   -6604806.9075    2082951.4020   m   m   m\nstate_vector_velocity_1:    -1104.60340      2489.17836      7092.92324   m/s m/s m/s\nstate_vector_position_2:  -1453586.5506   -6579537.2326    2153761.6359   m   m   m\nstate_vector_velocity_2:    -1084.67698      2564.70460      7068.99018   m/s m/s m/s\nstate_vector_position_3:  -1464332.7222   -6553513.8710    2224328.5433   m   m   m\nstate_vector_velocity_3:    -1064.51896      2639.91423      7044.25842   m/s m/s m/s\nstate_vector_position_4:  -1474876.1671   -6526740.0329    2294644.1512   m   m   m\nstate_vector_velocity_4:    -1044.13208      2714.79840      7018.73075   m/s m/s m/s\nstate_vector_position_5:  -1485214.6104   -6499219.0172    2364700.5150   m   m   m\nstate_vector_velocity_5:    -1023.51913      2789.34829      6992.41010   m/s m/s m/s\nstate_vector_position_6:  -1495345.8056   -6470954.2105    2434489.7198   m   m   m\nstate_vector_velocity_6:    -1002.68294      2863.55516      6965.29946   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180130_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180130t004005-20180130t004030-020377-022d0a-004.tiff S1A-IW-IW1-VV-20377 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 01 30\nstart_time:              2411.849337   s\ncenter_time:             2421.183608   s\nend_time:                2430.517879   s\nazimuth_line_time:     2.0555541e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9995776e-01\nazimuth_scale_factor:   9.9999894e-01\ncenter_latitude:          19.5126101   degrees\ncenter_longitude:        -97.9182314   degrees\nheading:                 -12.2736686   degrees\nrange_pixel_spacing:        2.329464   m\nazimuth_pixel_spacing:     14.011635   m\nnear_range_slc:           798956.9733  m\ncenter_range_slc:         878292.6800  m\nfar_range_slc:            957628.3867  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7016   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         71.13098 -1.27127e-04  5.17735e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073894.6696   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.431977   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442597.0798   -6604819.0700    2082928.3358   m   m   m\nstate_vector_velocity_1:    -1104.54558      2489.14851      7092.94042   m/s m/s m/s\nstate_vector_position_2:  -1453543.1000   -6579549.6926    2153738.7429   m   m   m\nstate_vector_velocity_2:    -1084.61965      2564.67494      7069.00759   m/s m/s m/s\nstate_vector_position_3:  -1464288.7007   -6553526.6264    2224305.8258   m   m   m\nstate_vector_velocity_3:    -1064.46211      2639.88477      7044.27604   m/s m/s m/s\nstate_vector_position_4:  -1474831.5793   -6526753.0818    2294621.6113   m   m   m\nstate_vector_velocity_4:    -1044.07572      2714.76915      7018.74859   m/s m/s m/s\nstate_vector_position_5:  -1485169.4614   -6499232.3574    2364678.1549   m   m   m\nstate_vector_velocity_5:    -1023.46326      2789.31927      6992.42815   m/s m/s m/s\nstate_vector_position_6:  -1495300.1003   -6470967.8397    2434467.5415   m   m   m\nstate_vector_velocity_6:    -1002.62756      2863.52635      6965.31772   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180307_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180307t004004-20180307t004030-020902-023dc2-004.tiff S1A-IW-IW1-VV-20902 (software: Sentinel-1 IPF 002.84)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 07\nstart_time:              2411.444979   s\ncenter_time:             2420.779257   s\nend_time:                2430.113535   s\nazimuth_line_time:     2.0555556e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999872e-01\nazimuth_scale_factor:   9.9999965e-01\ncenter_latitude:          19.5126084   degrees\ncenter_longitude:        -97.9182346   degrees\nheading:                 -12.2751820   degrees\nrange_pixel_spacing:        2.329559   m\nazimuth_pixel_spacing:     14.011645   m\nnear_range_slc:           798981.2528  m\ncenter_range_slc:         878320.2093  m\nfar_range_slc:            957659.1659  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7035   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:          7.57751 -1.73839e-04  9.26703e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073900.5085   m\nearth_radius_below_sensor:       6375868.9420   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.029794   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442634.0250   -6604817.8664    2082929.5800   m   m   m\nstate_vector_velocity_1:    -1104.71812      2489.22466      7092.89390   m/s m/s m/s\nstate_vector_position_2:  -1453581.7682   -6579547.7275    2153739.5218   m   m   m\nstate_vector_velocity_2:    -1084.79171      2564.75111      7068.96113   m/s m/s m/s\nstate_vector_position_3:  -1464329.0870   -6553523.8996    2224306.1400   m   m   m\nstate_vector_velocity_3:    -1064.63367      2639.96097      7044.22964   m/s m/s m/s\nstate_vector_position_4:  -1474873.6787   -6526749.5931    2294621.4614   m   m   m\nstate_vector_velocity_4:    -1044.24676      2714.84537      7018.70225   m/s m/s m/s\nstate_vector_position_5:  -1485213.2685   -6499228.1064    2364677.5414   m   m   m\nstate_vector_velocity_5:    -1023.63376      2789.39552      6992.38187   m/s m/s m/s\nstate_vector_position_6:  -1495345.6096   -6470962.8261    2434466.4652   m   m   m\nstate_vector_velocity_6:    -1002.79750      2863.60264      6965.27150   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180319_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180319t004005-20180319t004030-021077-024348-004.tiff S1A-IW-IW1-VV-21077 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 19\nstart_time:              2411.632973   s\ncenter_time:             2420.967261   s\nend_time:                2430.301550   s\nazimuth_line_time:     2.0555580e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999519e-01\nazimuth_scale_factor:   1.0000008e+00\ncenter_latitude:          19.5126105   degrees\ncenter_longitude:        -97.9182312   degrees\nheading:                 -12.2738302   degrees\nrange_pixel_spacing:        2.329551   m\nazimuth_pixel_spacing:     14.011641   m\nnear_range_slc:           798979.1486  m\ncenter_range_slc:         878317.8248  m\nfar_range_slc:            957656.5011  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7034   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         14.28596 -2.06716e-04  1.17660e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073899.8185   m\nearth_radius_below_sensor:       6375868.9415   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.217204   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442635.9813   -6604813.9430    2082939.7037   m   m   m\nstate_vector_velocity_1:    -1104.53733      2489.22215      7092.91118   m/s m/s m/s\nstate_vector_position_2:  -1453581.9166   -6579543.8306    2153749.8176   m   m   m\nstate_vector_velocity_2:    -1084.61091      2564.74831      7068.97828   m/s m/s m/s\nstate_vector_position_3:  -1464327.4273   -6553520.0321    2224316.6067   m   m   m\nstate_vector_velocity_3:    -1064.45288      2639.95789      7044.24666   m/s m/s m/s\nstate_vector_position_4:  -1474870.2112   -6526745.7577    2294632.0977   m   m   m\nstate_vector_velocity_4:    -1044.06599      2714.84202      7018.71914   m/s m/s m/s\nstate_vector_position_5:  -1485207.9936   -6499224.3060    2364688.3461   m   m   m\nstate_vector_velocity_5:    -1023.45304      2789.39190      6992.39863   m/s m/s m/s\nstate_vector_position_6:  -1495338.5279   -6470959.0632    2434477.4369   m   m   m\nstate_vector_velocity_6:    -1002.61684      2863.59875      6965.28813   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180331_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180331t004005-20180331t004030-021252-0248d6-004.tiff S1A-IW-IW1-VV-21252 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 03 31\nstart_time:              2411.963263   s\ncenter_time:             2421.297545   s\nend_time:                2430.631827   s\nazimuth_line_time:     2.0555564e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000038e+00\nazimuth_scale_factor:   1.0000001e+00\ncenter_latitude:          19.5126095   degrees\ncenter_longitude:        -97.9182315   degrees\nheading:                 -12.2734015   degrees\nrange_pixel_spacing:        2.329571   m\nazimuth_pixel_spacing:     14.011641   m\nnear_range_slc:           798977.6235  m\ncenter_range_slc:         878316.9858  m\nfar_range_slc:            957656.3481  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7038   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         12.65333 -1.59964e-04  7.54734e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073895.5709   m\nearth_radius_below_sensor:       6375868.9417   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.548957   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442643.5536   -6604803.8015    2082953.8822   m   m   m\nstate_vector_velocity_1:    -1104.47169      2489.25258      7092.91187   m/s m/s m/s\nstate_vector_position_2:  -1453588.8317   -6579533.3853    2153764.0022   m   m   m\nstate_vector_velocity_2:    -1084.54511      2564.77865      7068.97877   m/s m/s m/s\nstate_vector_position_3:  -1464333.6837   -6553509.2838    2224330.7953   m   m   m\nstate_vector_velocity_3:    -1064.38692      2639.98814      7044.24695   m/s m/s m/s\nstate_vector_position_4:  -1474875.8073   -6526734.7073    2294646.2882   m   m   m\nstate_vector_velocity_4:    -1043.99989      2714.87219      7018.71922   m/s m/s m/s\nstate_vector_position_5:  -1485212.9279   -6499212.9542    2364702.5365   m   m   m\nstate_vector_velocity_5:    -1023.38680      2789.42198      6992.39850   m/s m/s m/s\nstate_vector_position_6:  -1495342.7990   -6470947.4109    2434491.6250   m   m   m\nstate_vector_velocity_6:    -1002.55046      2863.62876      6965.28778   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180412_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180412t004005-20180412t004031-021427-024e4f-004.tiff S1A-IW-IW1-VV-21427 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 04 12\nstart_time:              2412.382662   s\ncenter_time:             2421.716847   s\nend_time:                2431.051033   s\nazimuth_line_time:     2.0555353e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0001041e+00\nazimuth_scale_factor:   9.9998976e-01\ncenter_latitude:          19.5126096   degrees\ncenter_longitude:        -97.9182343   degrees\nheading:                 -12.2746558   degrees\nrange_pixel_spacing:        2.329805   m\nazimuth_pixel_spacing:     14.011967   m\nnear_range_slc:           798979.4677  m\ncenter_range_slc:         878326.7880  m\nfar_range_slc:            957674.1083  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7085   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         35.67545 -1.88932e-04  9.36371e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073862.0888   m\nearth_radius_below_sensor:       6375868.9417   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2398.974817   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442701.9755   -6604751.0902    2082967.3376   m   m   m\nstate_vector_velocity_1:    -1104.60718      2489.33649      7092.91115   m/s m/s m/s\nstate_vector_position_2:  -1453648.6034   -6579479.8315    2153777.4481   m   m   m\nstate_vector_velocity_2:    -1084.67957      2564.86321      7068.97757   m/s m/s m/s\nstate_vector_position_3:  -1464394.7949   -6553454.8813    2224344.2268   m   m   m\nstate_vector_velocity_3:    -1064.52035      2640.07333      7044.24525   m/s m/s m/s\nstate_vector_position_4:  -1474938.2474   -6526679.4498    2294659.7003   m   m   m\nstate_vector_velocity_4:    -1044.13225      2714.95799      7018.71701   m/s m/s m/s\nstate_vector_position_5:  -1485276.6862   -6499156.8355    2364715.9239   m   m   m\nstate_vector_velocity_5:    -1023.51807      2789.50840      6992.39576   m/s m/s m/s\nstate_vector_position_6:  -1495407.8647   -6470890.4251    2434504.9823   m   m   m\nstate_vector_velocity_6:    -1002.68064      2863.71577      6965.28450   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180506_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180506t004006-20180506t004032-021777-02594a-004.tiff S1A-IW-IW1-VV-21777 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 06\nstart_time:              2413.403505   s\ncenter_time:             2422.737713   s\nend_time:                2432.071920   s\nazimuth_line_time:     2.0555400e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000227e+00\nazimuth_scale_factor:   9.9999209e-01\ncenter_latitude:          19.5126100   degrees\ncenter_longitude:        -97.9182327   degrees\nheading:                 -12.2742940   degrees\nrange_pixel_spacing:        2.329615   m\nazimuth_pixel_spacing:     14.011639   m\nnear_range_slc:           798957.8705  m\ncenter_range_slc:         878298.7312  m\nfar_range_slc:            957639.5918  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7047   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         11.52580 -2.34703e-04  1.11667e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073872.7088   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2399.995112   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442642.4939   -6604770.9821    2082981.5708   m   m   m\nstate_vector_velocity_1:    -1104.57944      2489.32644      7092.90469   m/s m/s m/s\nstate_vector_position_2:  -1453588.8482   -6579499.8248    2153791.6162   m   m   m\nstate_vector_velocity_2:    -1084.65260      2564.85301      7068.97105   m/s m/s m/s\nstate_vector_position_3:  -1464334.7737   -6553474.9773    2224358.3294   m   m   m\nstate_vector_velocity_3:    -1064.49415      2640.06298      7044.23869   m/s m/s m/s\nstate_vector_position_4:  -1474877.9681   -6526699.6500    2294673.7369   m   m   m\nstate_vector_velocity_4:    -1044.10683      2714.94751      7018.71041   m/s m/s m/s\nstate_vector_position_5:  -1485216.1566   -6499177.1414    2364729.8941   m   m   m\nstate_vector_velocity_5:    -1023.49343      2789.49777      6992.38912   m/s m/s m/s\nstate_vector_position_6:  -1495347.0925   -6470910.8381    2434518.8859   m   m   m\nstate_vector_velocity_6:    -1002.65679      2863.70501      6965.27783   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180518_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180518t004007-20180518t004032-021952-025eda-004.tiff S1A-IW-IW1-VV-21952 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 18\nstart_time:              2414.048890   s\ncenter_time:             2423.383097   s\nend_time:                2432.717304   s\nazimuth_line_time:     2.0555400e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000406e+00\nazimuth_scale_factor:   9.9999208e-01\ncenter_latitude:          19.5126092   degrees\ncenter_longitude:        -97.9182347   degrees\nheading:                 -12.2739463   degrees\nrange_pixel_spacing:        2.329657   m\nazimuth_pixel_spacing:     14.011639   m\nnear_range_slc:           798963.2081  m\ncenter_range_slc:         878305.4861  m\nfar_range_slc:            957647.7641  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7055   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         30.50367 -1.56155e-04  5.89911e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073870.9747   m\nearth_radius_below_sensor:       6375868.9418   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2400.641508   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442658.8229   -6604763.3736    2082988.1616   m   m   m\nstate_vector_velocity_1:    -1104.52792      2489.32605      7092.91204   m/s m/s m/s\nstate_vector_position_2:  -1453604.6611   -6579492.2208    2153798.2799   m   m   m\nstate_vector_velocity_2:    -1084.60090      2564.85252      7068.97831   m/s m/s m/s\nstate_vector_position_3:  -1464350.0687   -6553467.3788    2224365.0649   m   m   m\nstate_vector_velocity_3:    -1064.44227      2640.06239      7044.24585   m/s m/s m/s\nstate_vector_position_4:  -1474892.7434   -6526692.0579    2294680.5434   m   m   m\nstate_vector_velocity_4:    -1044.05478      2714.94682      7018.71747   m/s m/s m/s\nstate_vector_position_5:  -1485230.4105   -6499169.5568    2364736.7706   m   m   m\nstate_vector_velocity_5:    -1023.44121      2789.49698      6992.39609   m/s m/s m/s\nstate_vector_position_6:  -1495360.8233   -6470903.2619    2434525.8315   m   m   m\nstate_vector_velocity_6:    -1002.60441      2863.70412      6965.28470   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180530_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180530t004008-20180530t004033-022127-02647c-004.tiff S1A-IW-IW1-VV-22127 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 05 30\nstart_time:              2414.669604   s\ncenter_time:             2424.003769   s\nend_time:                2433.337934   s\nazimuth_line_time:     2.0555307e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9999479e-01\nazimuth_scale_factor:   9.9998757e-01\ncenter_latitude:          19.5126111   degrees\ncenter_longitude:        -97.9182280   degrees\nheading:                 -12.2738650   degrees\nrange_pixel_spacing:        2.329550   m\nazimuth_pixel_spacing:     14.011606   m\nnear_range_slc:           798927.4070  m\ncenter_range_slc:         878266.0511  m\nfar_range_slc:            957604.6952  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7034   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         15.22544 -1.80109e-04  7.24218e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073857.1356   m\nearth_radius_below_sensor:       6375868.9413   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2401.257760   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442602.6920   -6604769.2129    2082961.4008   m   m   m\nstate_vector_velocity_1:    -1104.54480      2489.29260      7092.93403   m/s m/s m/s\nstate_vector_position_2:  -1453548.7019   -6579498.3918    2153771.7399   m   m   m\nstate_vector_velocity_2:    -1084.61835      2564.81961      7069.00046   m/s m/s m/s\nstate_vector_position_3:  -1464294.2869   -6553473.8762    2224338.7472   m   m   m\nstate_vector_velocity_3:    -1064.46029      2640.03002      7044.26814   m/s m/s m/s\nstate_vector_position_4:  -1474837.1447   -6526698.8764    2294654.4494   m   m   m\nstate_vector_velocity_4:    -1044.07336      2714.91498      7018.73991   m/s m/s m/s\nstate_vector_position_5:  -1485175.0005   -6499176.6909    2364710.9018   m   m   m\nstate_vector_velocity_5:    -1023.46036      2789.46568      6992.41867   m/s m/s m/s\nstate_vector_position_6:  -1495305.6078   -6470910.7063    2434500.1891   m   m   m\nstate_vector_velocity_6:    -1002.62411      2863.67335      6965.30741   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180611_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180611t004009-20180611t004034-022302-0269f0-004.tiff S1A-IW-IW1-VV-22302 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 06 11\nstart_time:              2415.576580   s\ncenter_time:             2424.910792   s\nend_time:                2434.245005   s\nazimuth_line_time:     2.0555412e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000709e+00\nazimuth_scale_factor:   9.9999265e-01\ncenter_latitude:          19.5126100   degrees\ncenter_longitude:        -97.9182321   degrees\nheading:                 -12.2739122   degrees\nrange_pixel_spacing:        2.329727   m\nazimuth_pixel_spacing:     14.011637   m\nnear_range_slc:           798976.5175  m\ncenter_range_slc:         878321.2042  m\nfar_range_slc:            957665.8909  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7070   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         -6.44169 -1.70915e-04  8.43663e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073871.1809   m\nearth_radius_below_sensor:       6375868.9416   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2402.170128   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442686.1339   -6604756.9510    2082990.0640   m   m   m\nstate_vector_velocity_1:    -1104.51358      2489.32919      7092.91276   m/s m/s m/s\nstate_vector_position_2:  -1453631.8271   -6579485.7671    2153800.1895   m   m   m\nstate_vector_velocity_2:    -1084.58623      2564.85556      7068.97900   m/s m/s m/s\nstate_vector_position_3:  -1464377.0865   -6553460.8951    2224366.9815   m   m   m\nstate_vector_velocity_3:    -1064.42727      2640.06533      7044.24651   m/s m/s m/s\nstate_vector_position_4:  -1474919.6097   -6526685.5453    2294682.4666   m   m   m\nstate_vector_velocity_4:    -1044.03946      2714.94965      7018.71811   m/s m/s m/s\nstate_vector_position_5:  -1485257.1220   -6499163.0163    2364738.7003   m   m   m\nstate_vector_velocity_5:    -1023.42557      2789.49971      6992.39670   m/s m/s m/s\nstate_vector_position_6:  -1495387.3769   -6470896.6945    2434527.7672   m   m   m\nstate_vector_velocity_6:    -1002.58844      2863.70674      6965.28529   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180623_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180623t004009-20180623t004034-022477-026f32-004.tiff S1A-IW-IW1-VV-22477 (software: Sentinel-1 IPF 002.90)\nsensor:    S1A IW IW1 VV\ndate:      2018 06 23\nstart_time:              2416.280124   s\ncenter_time:             2425.614289   s\nend_time:                2434.948454   s\nazimuth_line_time:     2.0555307e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000527e+00\nazimuth_scale_factor:   9.9998753e-01\ncenter_latitude:          19.5126108   degrees\ncenter_longitude:        -97.9182325   degrees\nheading:                 -12.2742251   degrees\nrange_pixel_spacing:        2.329685   m\nazimuth_pixel_spacing:     14.011615   m\nnear_range_slc:           798950.8772  m\ncenter_range_slc:         878294.1123  m\nfar_range_slc:            957637.3475  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7061   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         14.54785 -1.25986e-04  5.07538e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073856.4793   m\nearth_radius_below_sensor:       6375868.9414   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2402.873946   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442656.4274   -6604747.9147    2082989.4255   m   m   m\nstate_vector_velocity_1:    -1104.56499      2489.34157      7092.91970   m/s m/s m/s\nstate_vector_position_2:  -1453602.6358   -6579476.6048    2153799.6198   m   m   m\nstate_vector_velocity_2:    -1084.63785      2564.86838      7068.98580   m/s m/s m/s\nstate_vector_position_3:  -1464348.4124   -6553451.6024    2224366.4792   m   m   m\nstate_vector_velocity_3:    -1064.47910      2640.07858      7044.25316   m/s m/s m/s\nstate_vector_position_4:  -1474891.4549   -6526676.1180    2294682.0302   m   m   m\nstate_vector_velocity_4:    -1044.09148      2714.96333      7018.72461   m/s m/s m/s\nstate_vector_position_5:  -1485229.4885   -6499153.4500    2364738.3280   m   m   m\nstate_vector_velocity_5:    -1023.47779      2789.51381      6992.40304   m/s m/s m/s\nstate_vector_position_6:  -1495360.2665   -6470886.9851    2434527.4576   m   m   m\nstate_vector_velocity_6:    -1002.64084      2863.72126      6965.29145   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180705_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180705t004010-20180705t004035-022652-027450-004.tiff S1A-IW-IW1-VV-22652 (software: Sentinel-1 IPF 002.91)\nsensor:    S1A IW IW1 VV\ndate:      2018 07 05\nstart_time:              2416.687117   s\ncenter_time:             2426.021285   s\nend_time:                2435.355453   s\nazimuth_line_time:     2.0555314e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     9.9992375e-01\nazimuth_scale_factor:   9.9998786e-01\ncenter_latitude:          19.5126114   degrees\ncenter_longitude:        -97.9182295   degrees\nheading:                 -12.2745289   degrees\nrange_pixel_spacing:        2.329384   m\nazimuth_pixel_spacing:     14.011600   m\nnear_range_slc:           798907.0265  m\ncenter_range_slc:         878240.0350  m\nfar_range_slc:            957573.0434  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7001   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:         -7.82027 -2.35094e-04  1.06023e-09  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073865.2745   m\nearth_radius_below_sensor:       6375868.9413   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2403.279394   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442550.6790   -6604777.7915    2082997.8717   m   m   m\nstate_vector_velocity_1:    -1104.64152      2489.33373      7092.90430   m/s m/s m/s\nstate_vector_position_2:  -1453497.6590   -6579506.5592    2153807.9120   m   m   m\nstate_vector_velocity_2:    -1084.71565      2564.86071      7068.97040   m/s m/s m/s\nstate_vector_position_3:  -1464244.2199   -6553481.6328    2224374.6174   m   m   m\nstate_vector_velocity_3:    -1064.55816      2640.07108      7044.23776   m/s m/s m/s\nstate_vector_position_4:  -1474788.0592   -6526706.2224    2294690.0145   m   m   m\nstate_vector_velocity_4:    -1044.17179      2714.95601      7018.70921   m/s m/s m/s\nstate_vector_position_5:  -1485126.9019   -6499183.6268    2364746.1585   m   m   m\nstate_vector_velocity_5:    -1023.55933      2789.50667      6992.38766   m/s m/s m/s\nstate_vector_position_6:  -1495258.5015   -6470917.2323    2434535.1344   m   m   m\nstate_vector_velocity_6:    -1002.72362      2863.71431      6965.27609   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/geotiffs/r20180717_VV_slc.par",
    "content": "Gamma Interferometric SAR Processor (ISP) - Image Parameter File\n\ntitle:     s1a-iw1-slc-vv-20180717t004011-20180717t004036-022827-02799b-004.tiff S1A-IW-IW1-VV-22827 (software: Sentinel-1 IPF 002.91)\nsensor:    S1A IW IW1 VV\ndate:      2018 07 17\nstart_time:              2417.676668   s\ncenter_time:             2427.010846   s\nend_time:                2436.345025   s\nazimuth_line_time:     2.0555337e-03   s\nline_header_size:                  0\nrange_samples:                 68116\nazimuth_lines:                  9083\nrange_looks:                       1\nazimuth_looks:                     1\nimage_format:               FCOMPLEX\nimage_geometry:             SLANT_RANGE\nrange_scale_factor:     1.0000365e+00\nazimuth_scale_factor:   9.9998902e-01\ncenter_latitude:          19.5126104   degrees\ncenter_longitude:        -97.9182335   degrees\nheading:                 -12.2742902   degrees\nrange_pixel_spacing:        2.329647   m\nazimuth_pixel_spacing:     14.011626   m\nnear_range_slc:           798951.4069  m\ncenter_range_slc:         878293.3586  m\nfar_range_slc:            957635.3104  m\nfirst_slant_range_polynomial:        0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \ncenter_slant_range_polynomial:       0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nlast_slant_range_polynomial:         0.00000      0.00000  0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  s m 1 m^-1 m^-2 m^-3 \nincidence_angle:             39.7053   degrees\nazimuth_deskew:          ON\nazimuth_angle:               90.0000   degrees\nradar_frequency:        5.4050005e+09  Hz\nadc_sampling_rate:      6.4345241e+07  Hz\nchirp_bandwidth:        5.6500000e+07  Hz\nprf:                      486.4863103  Hz\nazimuth_proc_bandwidth:     327.00000  Hz\ndoppler_polynomial:          8.68743 -1.16261e-04  2.50691e-10  0.00000e+00  Hz     Hz/m     Hz/m^2     Hz/m^3\ndoppler_poly_dot:        0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s   Hz/s/m   Hz/s/m^2   Hz/s/m^3\ndoppler_poly_ddot:       0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00  Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3\nreceiver_gain:                 0.0000  dB\ncalibration_gain:              0.0000  dB\nsar_to_earth_center:             7073862.6200   m\nearth_radius_below_sensor:       6375868.9415   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\nnumber_of_state_vectors:                    6\ntime_of_first_state_vector:       2404.269733   s\nstate_vector_interval:              10.000000   s\nstate_vector_position_1:  -1442646.9928   -6604757.2056    2082987.8020   m   m   m\nstate_vector_velocity_1:    -1104.57610      2489.34060      7092.91227   m/s m/s m/s\nstate_vector_position_2:  -1453593.3130   -6579485.9059    2153797.9224   m   m   m\nstate_vector_velocity_2:    -1084.64913      2564.86733      7068.97846   m/s m/s m/s\nstate_vector_position_3:  -1464339.2032   -6553460.9144    2224364.7087   m   m   m\nstate_vector_velocity_3:    -1064.49055      2640.07746      7044.24590   m/s m/s m/s\nstate_vector_position_4:  -1474882.3608   -6526685.4415    2294680.1875   m   m   m\nstate_vector_velocity_4:    -1044.10309      2714.96214      7018.71744   m/s m/s m/s\nstate_vector_position_5:  -1485220.5112   -6499162.7858    2364736.4141   m   m   m\nstate_vector_velocity_5:    -1023.48956      2789.51256      6992.39596   m/s m/s m/s\nstate_vector_position_6:  -1495351.4077   -6470896.3337    2434525.4733   m   m   m\nstate_vector_velocity_6:    -1002.65278      2863.71995      6965.28447   m/s m/s m/s\n\n"
  },
  {
    "path": "tests/test_data/headers/geo_060619-060828.unw.rsc",
    "content": "WIDTH                                    5451                          \nFILE_LENGTH                              4366                          \nXMIN                                     0                             \nXMAX                                     5450                          \nYMIN                                     0                             \nYMAX                                     4365                          \nRLOOKS                                   1                             \nALOOKS                                   1                             \nX_FIRST                                  150.337777777783              \nX_STEP                                   0.00027777777778226           \nX_UNIT                                   degrees                       \nY_FIRST                                  -33.412222222245              \nY_STEP                                   -0.00027777777778226          \nY_UNIT                                   degrees                       \nTIME_SPAN_YEAR                           0.191307323750856             \nCOR_THRESHOLD                            0.15                          \nORBIT_NUMBER                             22500-23502                   \nVELOCITY                                 7542.96531592394              \nHEIGHT                                   0.7974412729E+06              \nEARTH_RADIUS                             6357663.19007647              \nWAVELENGTH                               0.0562356424                  \nDATE                                     060619                        \nDATE12                                   060619-060828                 \nHEADING_DEG                              -166.4283                     \nRGE_REF1                                 835.0346                      \nLOOK_REF1                                16.2403                       \nLAT_REF1                                 -33.6804                      \nLON_REF1                                 151.8513                      \nRGE_REF2                                 879.3612                      \nLOOK_REF2                                23.4052                       \nLAT_REF2                                 -33.4121                      \nLON_REF2                                 150.6452                      \nRGE_REF3                                 835.0346                      \nLOOK_REF3                                16.1529                       \nLAT_REF3                                 -34.6244                      \nLON_REF3                                 151.5606                      \nRGE_REF4                                 879.3612                      \nLOOK_REF4                                23.3470                       \nLAT_REF4                                 -34.3529                      \nLON_REF4                                 150.3376                      \n"
  },
  {
    "path": "tests/test_data/prepifg/obs/geo_060619-061002.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060619\nDATE12            060619-061002"
  },
  {
    "path": "tests/test_data/prepifg/obs/geo_070326-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.911666666\nX_STEP            0.000833333     \nY_FIRST           -34.172499999   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070326\nDATE12            070326-070917"
  },
  {
    "path": "tests/test_data/small_test/coherence/coherence_17",
    "content": "tests/test_data/small_test/coherence/20060619-20061002_utm.unw.cc\ntests/test_data/small_test/coherence/20060828-20061211_utm.unw.cc\ntests/test_data/small_test/coherence/20061002-20070219_utm.unw.cc\ntests/test_data/small_test/coherence/20061002-20070430_utm.unw.cc\ntests/test_data/small_test/coherence/20061106-20061211_utm.unw.cc\ntests/test_data/small_test/coherence/20061106-20070115_utm.unw.cc\ntests/test_data/small_test/coherence/20061106-20070326_utm.unw.cc\ntests/test_data/small_test/coherence/20061211-20070709_utm.unw.cc\ntests/test_data/small_test/coherence/20061211-20070813_utm.unw.cc\ntests/test_data/small_test/coherence/20070115-20070326_utm.unw.cc\ntests/test_data/small_test/coherence/20070115-20070917_utm.unw.cc\ntests/test_data/small_test/coherence/20070219-20070430_utm.unw.cc\ntests/test_data/small_test/coherence/20070219-20070604_utm.unw.cc\ntests/test_data/small_test/coherence/20070326-20070917_utm.unw.cc\ntests/test_data/small_test/coherence/20070430-20070604_utm.unw.cc\ntests/test_data/small_test/coherence/20070604-20070709_utm.unw.cc\ntests/test_data/small_test/coherence/20070709-20070813_utm.unw.cc\n"
  },
  {
    "path": "tests/test_data/small_test/conf/pyrate1.conf",
    "content": "#------------------------------------\n\n# File containing the list of (raw) interferograms.\nifgfilelist:  tests/test_data/small_test/roipac_obs/ifms_17\n\n# The DEM used by the processing software\ndemfile:      tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem.rsc\n\n# GAMMA only: The directory containing GAMMA slc.par files for all epochs\nslcFileDir:   tests/test_data/small_test/gamma_obs\n\n# Where to write the outputs\noutdir:       out/\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1\nprocessor:    0\n\n# No data averaging threshold for prepifg\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/gamma prepifg\n# gamma prepifg runs in parallel in single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done in parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  1\nprocesses: 8\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   1\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: coordinate of reference pixel. If <= 0 then search for pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:\nrefy:\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.8\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# APS Atmospheric Phase Screen correction\n# NOT CURRENTLY IMPLEMENTED\n# apsmethod 1: scene centre incidence angle used from GAMMA header files\n# apsmethod 2: uses GAMMA incidence angle map (lv_theta file)\n# incidencemap  or elevationmap is only used for method 2\n# one of incidencemap or elevationmap must be provided for method 2.\n# if both incidencemap and elevationmap is provided, only incidencemap is used\napscorrect:     1\napsmethod:      2\nincidencemap:   tests/test_data/small_test/gamma_obs/20060619_utm.inc\nelevationmap:   tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    1\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      1\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacked Rate calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativelLeast squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n"
  },
  {
    "path": "tests/test_data/small_test/conf/pyrate2.conf",
    "content": "#------------------------------------\n\n# File containing the list of (raw) interferograms.\nifgfilelist:  tests/test_data/small_test/roipac_obs/ifms_17\n\n# The DEM used by the processing software\ndemfile:      tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem.rsc\n\n# GAMMA only: The directory containing GAMMA slc.par files for all epochs\nslcFileDir:   tests/test_data/small_test/gamma_obs\n\n# Where to write the outputs\noutdir:       out/\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1\nprocessor:    0\n\n# No data averaging threshold for prepifg\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/gamma prepifg\n# gamma prepifg runs in parallel in single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done in parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  1\nprocesses: 8\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   1\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: coordinate of reference pixel. If <= 0 then search for pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:\nrefy:\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.8\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# APS Atmospheric Phase Screen correction\n# NOT CURRENTLY IMPLEMENTED\n# apsmethod 1: scene centre incidence angle used from GAMMA header files\n# apsmethod 2: uses GAMMA incidence angle map (lv_theta file)\n# incidencemap  or elevationmap is only used for method 2\n# one of incidencemap or elevationmap must be provided for method 2.\n# if both incidencemap and elevationmap is provided, only incidencemap is used\napscorrect:     1\napsmethod:      2\nincidencemap:   tests/test_data/small_test/gamma_obs/20060619_utm.inc\nelevationmap:   tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    1\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      1\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacked Rate calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativelLeast squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n"
  },
  {
    "path": "tests/test_data/small_test/conf/pyrate_gamma_test.conf",
    "content": "#------------------------------------\n\n# File containing the list of (raw) interferograms.\nifgfilelist:  tests/test_data/small_test/gamma_obs/ifms_17\n\n# The DEM used by the processing software\ndemfile:      tests/test_data/small_test/gamma_obs/20060619_utm.dem\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/small_test/gamma_obs/20060619_utm_dem.par\n\n# File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) \nhdrfilelist: tests/test_data/small_test/gamma_obs/headers\n\n# File listing the pool of available coherence files.\ncohfilelist: tests/test_data/small_test/coherence/coherence_17\n\n# File listing the pool of available baseline files.\nbasefilelist: tests/test_data/small_test/gamma_obs/baseline_17\n\n# Look-up table containing radar-coded row and column for lat/lon pixels\nltfile:       tests/test_data/small_test/gamma_obs/cropped_lookup_table.lt\n\n# Where to write the outputs\noutdir:       out/gamma/out\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1\nprocessor:    1\n\n# No data averaging threshold for prepifg\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/gamma prepifg\n# gamma prepifg runs in parallel in single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done in parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  0\nprocesses: 4\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   1\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n# sign convention for phase data\nsignal_polarity: -1\n\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: coordinate of reference pixel. If <= 0 then search for pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:          150.941666654\nrefy:          -34.218333314\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.8\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n\n#------------------------------------\n# APS correction using spatio-temporal filter\n# apsest: ON = 1, OFF = 0\n# Spatial low-pass filter parameters\n# slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters\n# slpnanfill: 1 for interpolation, 0 for zero fill\n# slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1\n# Temporal low-pass filter parameters\n# tlpfcutoff: cutoff t0 for gaussian filter in days;\n# tlpfpthr: valid pixel threshold;\napsest:         1\nslpfcutoff:     0.001\nslpnanfill:     1\nslpnanfill_method:  cubic\ntlpfcutoff:   12\ntlpfpthr:     1\n\n\nlargetifs: 0\n\n#------------------------------------\n# Coherence masking options: used by process\n# cohmask: 1 = ON, 0 = OFF\n# cohthresh: coherence threshold value, between 0 and 1\ncohmask:   0\ncohthresh:  0.05\n\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# APS Atmospheric Phase Screen correction\n# NOT CURRENTLY IMPLEMENTED\n# apsmethod 1: scene centre incidence angle used from GAMMA header files\n# apsmethod 2: uses GAMMA incidence angle map (lv_theta file)\n# incidencemap  or elevationmap is only used for method 2\n# one of incidencemap or elevationmap must be provided for method 2.\n# if both incidencemap and elevationmap is provided, only incidencemap is used\napscorrect:     1\napsmethod:      2\nincidencemap:   tests/test_data/small_test/gamma_obs/20060619_utm.inc\nelevationmap:   tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    0\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      1\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacked Rate calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativelLeast squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n# LOS Projection of timeseries and stack products\n# converts slanted (native) LOS signals to either \"vertical\" or \"horizontal\", by multiplying by the sine or cosine of\n# the incidence angle for each pixel.\n# Three options, 0/1/2 - 0 = LOS (no conversion); 1 = pseudo-vertical; 2 = pseudo-horizontal.\n\nlos_projection: 0\n\n# optionally supply rows and cols for tiles used during correct and merge step\nrows: 3\ncols: 2\n\n# optionally save stack numpy files during merge step\nsavenpy: 1\n# optionally save tsincr file outputs\nsavetsincr: 1\n\n[correct]\nsteps =\n    orbfit\n    refphase\n    demerror\n    phase_closure\n    mst\n    apscorrect\n    maxvar\n"
  },
  {
    "path": "tests/test_data/small_test/conf/pyrate_roipac_test.conf",
    "content": "#------------------------------------\n\n# File containing the list of (raw) interferograms.\nifgfilelist:  tests/test_data/small_test/roipac_obs/ifms_17\n\n# The DEM used by the processing software\ndemfile:      tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem.rsc\n\n# File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) \nhdrfilelist: tests/test_data/small_test/roipac_obs/headers\n\n# File listing the pool of available coherence files.\ncohfilelist:\n\n# Where to write the outputs\noutdir:       out/roipac/out\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1\nprocessor:    0\n\n# No data averaging threshold for prepifg\n\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n# number of sigma for rate uncertainty/error maps\nvelerror_nsig: 1\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/gamma prepifg\n# gamma prepifg runs in parallel in single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done in parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  1\nprocesses: 1\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   1\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: coordinate of reference pixel. If <= 0 then search for pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:\nrefy:\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.8\n\n#------------------------------------\n# Coherence masking options: used by process\n# cohmask: 1 = ON, 0 = OFF\n# cohthresh: coherence threshold value, between 0 and 1\ncohmask:   0\ncohthresh:  0.1\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n#------------------------------------\n# APS correction using spatio-temporal filter\n# apsest: ON = 1, OFF = 0\n# Spatial low-pass filter parameters\n# slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters\n# slpnanfill: 1 for interpolation, 0 for zero fill\n# slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1\n# Temporal low-pass filter parameters\n# tlpfcutoff: cutoff t0 for gaussian filter in days;\n# tlpfpthr: valid pixel threshold;\napsest:         1\nslpfcutoff:     0.001\nslpnanfill:     1\nslpnanfill_method:  cubic\ntlpfcutoff:   12\ntlpfpthr:     1\n\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# APS Atmospheric Phase Screen correction\n# NOT CURRENTLY IMPLEMENTED\n# apsmethod 1: scene centre incidence angle used from GAMMA header files\n# apsmethod 2: uses GAMMA incidence angle map (lv_theta file)\n# incidencemap  or elevationmap is only used for method 2\n# one of incidencemap or elevationmap must be provided for method 2.\n# if both incidencemap and elevationmap is provided, only incidencemap is used\napscorrect:     1\napsmethod:      2\nincidencemap:   tests/test_data/small_test/gamma_obs/20060619_utm.inc\nelevationmap:   tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    1\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      1\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacked Rate calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativelLeast squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n# LOS Projection of timeseries and stack products\n# converts slanted (native) LOS signals to either \"vertical\" or \"horizontal\", by multiplying by the sine or cosine of\n# the incidence angle for each pixel.\n# Three options, 0/1/2 - 0 = LOS (no conversion); 1 = pseudo-vertical; 2 = pseudo-horizontal.\nlos_projection: 1\n\n# optionally supply rows and cols for tiles used during correct and merge step\nrows: 3\ncols: 2\n\n# optionally save stack numpy files during merge step\nsavenpy: 1\n# optionally save tsincr file outputs\nsavetsincr: 1\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060619-20061002_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060619-20061002_utm_unw.tif.aux.xml",
    "content": "<PAMDataset>\r\n  <Metadata>\r\n    <MDI key=\"DATA_TYPE\">ORIGINAL_IFG</MDI>\r\n    <MDI key=\"DATA_UNITS\">RADIANS</MDI>\r\n    <MDI key=\"INCIDENCE_DEGREES\">22.9671</MDI>\r\n    <MDI key=\"INSAR_PROCESSOR\">GAMMA</MDI>\r\n    <MDI key=\"FIRST_DATE\">2006-06-19</MDI>\r\n    <MDI key=\"FIRST_TIME\">08:28:59</MDI>\r\n    <MDI key=\"SECOND_DATE\">2006-10-02</MDI>\r\n    <MDI key=\"SECOND_TIME\">13:05:19</MDI>\r\n    <MDI key=\"TIME_SPAN_YEAR\">0.2874743326488706</MDI>\r\n    <MDI key=\"WAVELENGTH_METRES\">0.05623564240081464</MDI>\r\n  </Metadata>\r\n  <PAMRasterBand band=\"1\">\r\n    <Metadata>\r\n      <MDI key=\"STATISTICS_MAXIMUM\">-0.30978414416313</MDI>\r\n      <MDI key=\"STATISTICS_MEAN\">-2.339052484656</MDI>\r\n      <MDI key=\"STATISTICS_MINIMUM\">-3.5677621364594</MDI>\r\n      <MDI key=\"STATISTICS_STDDEV\">0.37911647974349</MDI>\r\n      <MDI key=\"STATISTICS_VALID_PERCENT\">97.37</MDI>\r\n    </Metadata>\r\n  </PAMRasterBand>\r\n</PAMDataset>\r\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060619_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 06 19 8 28 59.6906\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060619_utm.inc",
    "content": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta",
    "content": "B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010B\u0010"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060619_utm_dem.par",
    "content": "Gamma DIFF&GEO DEM/MAP parameter file\ntitle: GAMMA DEM par file created from ROI_PAC rsc files MCG 28/01/2016\nDEM_projection:     EQA\ndata_format:        REAL*4\nDEM_hgt_offset:          0.00000\nDEM_scale:               1.00000\nwidth:                47\nnlines:               72\ncorner_lat:    -34.1700000  decimal degrees\ncorner_lon:     150.9100000  decimal degrees\npost_lat:   -8.33333e-04  decimal degrees\npost_lon:    8.33333e-04  decimal degrees\n\nellipsoid_name: WGS 84\nellipsoid_ra:        6378137.000   m\nellipsoid_reciprocal_flattening:  298.2572236\n\ndatum_name: WGS 1984\ndatum_shift_dx:              0.000   m\ndatum_shift_dy:              0.000   m\ndatum_shift_dz:              0.000   m\ndatum_scale_m:         0.00000e+00\ndatum_rotation_alpha:  0.00000e+00   arc-sec\ndatum_rotation_beta:   0.00000e+00   arc-sec\ndatum_rotation_gamma:  0.00000e+00   arc-sec\ndatum_country_list Global Definition, WGS84, World\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060828-20061211_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20060828_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 08 28 12 18 9.6906\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061002-20070219_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061002-20070430_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061002_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 10 02 13 5 19.0003\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061106-20061211_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061106-20070115_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061106-20070326_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061106_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 11 06 22 58 23.1246\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061211-20070709_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061211-20070813_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20061211_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 12 11 23 18 53.1241\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070115-20070326_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070115-20070917_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070115_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 01 15 21 48 23.124\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070219-20070430_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070219-20070604_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070219_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 02 19 1 18 3.124\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070326-20070917_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070326_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 03 26 2 32 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070430-20070604_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070430_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 04 30 5 32 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070604-20070709_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070604_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 06 04 9 31 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070709-20070813_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070709_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 07 09 19 01 43.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070813_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 08 13 16 23 43.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/20070917_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 09 17 12 41 25.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/bad_epochs_headers",
    "content": "60619_slc.par\n0060828_slc.par\n0061002_slc.par\n0061106_slc.par\n20061211_slc.par\n0070115_slc.par\n0070219_slc.par\n0070326_slc.par\n2070430_slc.par\n2070604_slc.par\n2070709_slc.par\n2070813_slc.par\n20007_slc.par\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/bad_epochs_ifms_17",
    "content": "60619-20061002_utm.unw\n60828-20061211_utm.unw\n61002-20070219_utm.unw\n61002-20070430_utm.unw\n61106-20061211_utm.unw\n61106-20070115_utm.unw\n61106-20070326_utm.unw\n61211-20070709_utm.unw\n61211-20070813_utm.unw\n70115-20070326_utm.unw\n70115-20070917_utm.unw\n0219-20070430_utm.unw\n0219-20070604_utm.unw\n26-20070917_utm.unw\n70430-20070604_utm.unw\n70604-20070709_utm.unw\n70709-20070813_utm.unw\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/baseline_17",
    "content": "tests/test_data/small_test/gamma_obs/20060619-20061002_base.par\ntests/test_data/small_test/gamma_obs/20060828-20061211_base.par\ntests/test_data/small_test/gamma_obs/20061002-20070219_base.par\ntests/test_data/small_test/gamma_obs/20061002-20070430_base.par\ntests/test_data/small_test/gamma_obs/20061106-20061211_base.par\ntests/test_data/small_test/gamma_obs/20061106-20070115_base.par\ntests/test_data/small_test/gamma_obs/20061106-20070326_base.par\ntests/test_data/small_test/gamma_obs/20061211-20070709_base.par\ntests/test_data/small_test/gamma_obs/20061211-20070813_base.par\ntests/test_data/small_test/gamma_obs/20070115-20070326_base.par\ntests/test_data/small_test/gamma_obs/20070115-20070917_base.par\ntests/test_data/small_test/gamma_obs/20070219-20070430_base.par\ntests/test_data/small_test/gamma_obs/20070219-20070604_base.par\ntests/test_data/small_test/gamma_obs/20070326-20070917_base.par\ntests/test_data/small_test/gamma_obs/20070430-20070604_base.par\ntests/test_data/small_test/gamma_obs/20070604-20070709_base.par\ntests/test_data/small_test/gamma_obs/20070709-20070813_base.par\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/headers",
    "content": "tests/test_data/small_test/gamma_obs/20060619_slc.par\ntests/test_data/small_test/gamma_obs/20060828_slc.par\ntests/test_data/small_test/gamma_obs/20061002_slc.par\ntests/test_data/small_test/gamma_obs/20061106_slc.par\ntests/test_data/small_test/gamma_obs/20061211_slc.par\ntests/test_data/small_test/gamma_obs/20070115_slc.par\ntests/test_data/small_test/gamma_obs/20070219_slc.par\ntests/test_data/small_test/gamma_obs/20070326_slc.par\ntests/test_data/small_test/gamma_obs/20070430_slc.par\ntests/test_data/small_test/gamma_obs/20070604_slc.par\ntests/test_data/small_test/gamma_obs/20070709_slc.par\ntests/test_data/small_test/gamma_obs/20070813_slc.par\ntests/test_data/small_test/gamma_obs/20070917_slc.par\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/ifms_17",
    "content": "tests/test_data/small_test/gamma_obs/20060619-20061002_utm.unw\ntests/test_data/small_test/gamma_obs/20060828-20061211_utm.unw\ntests/test_data/small_test/gamma_obs/20061002-20070219_utm.unw\ntests/test_data/small_test/gamma_obs/20061002-20070430_utm.unw\ntests/test_data/small_test/gamma_obs/20061106-20061211_utm.unw\ntests/test_data/small_test/gamma_obs/20061106-20070115_utm.unw\ntests/test_data/small_test/gamma_obs/20061106-20070326_utm.unw\ntests/test_data/small_test/gamma_obs/20061211-20070709_utm.unw\ntests/test_data/small_test/gamma_obs/20061211-20070813_utm.unw\ntests/test_data/small_test/gamma_obs/20070115-20070326_utm.unw\ntests/test_data/small_test/gamma_obs/20070115-20070917_utm.unw\ntests/test_data/small_test/gamma_obs/20070219-20070430_utm.unw\ntests/test_data/small_test/gamma_obs/20070219-20070604_utm.unw\ntests/test_data/small_test/gamma_obs/20070326-20070917_utm.unw\ntests/test_data/small_test/gamma_obs/20070430-20070604_utm.unw\ntests/test_data/small_test/gamma_obs/20070604-20070709_utm.unw\ntests/test_data/small_test/gamma_obs/20070709-20070813_utm.unw\n"
  },
  {
    "path": "tests/test_data/small_test/gamma_obs/tif_17",
    "content": "20060619-20061002_utm.tif\n20060828-20061211_utm.tif\n20061002-20070219_utm.tif\n20061002-20070430_utm.tif\n20061106-20061211_utm.tif\n20061106-20070115_utm.tif\n20061106-20070326_utm.tif\n20061211-20070709_utm.tif\n20061211-20070813_utm.tif\n20070115-20070326_utm.tif\n20070115-20070917_utm.tif\n20070219-20070430_utm.tif\n20070219-20070604_utm.tif\n20070326-20070917_utm.tif\n20070430-20070604_utm.tif\n20070604-20070709_utm.tif\n20070709-20070813_utm.tif\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_060619-061002.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_060828-061211.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061002-070219.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061002-070430.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061106-061211.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061106-070115.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061106-070326.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061211-070709.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_061211-070813.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070115-070326.csv",
    "content": "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070115-070917.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070219-070430.csv",
    "content": "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070219-070604.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070326-070917.csv",
    "content": "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070430-070604.csv",
    "content": "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070604-070709.csv",
    "content": "1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\n"
  },
  {
    "path": "tests/test_data/small_test/mst/mst_geo_070709-070813.csv",
    "content": "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_060619-061002.unw.csv",
    "content": "-18.587,-18.458,-18.36,-18.417,-18.135,-17.915,-18.26,-18.505,-18.566,-18.702,-19.249,-19.067,-19.233,-19.654,-19.695,-19.646,-19.611,-19.772,-19.795,-19.627,-18.728,-18.3,-18.394,-18.996,-19.377,-19.259,-19.378,-18.84,-18.538,-18.983,-19.532,-19.764,-19.694,-19.671,-19.568,-19.395,-19.472,-20.122,-20.465,-19.957,-19.164,-18.231,-17.607,-17.372,-17.065,-16.776,-16.659\n-18.457,-18.318,-18.289,-18.192,-18.063,-18.223,-18.656,-18.915,-18.71,-18.697,-18.827,-18.827,-19.241,-19.525,-19.439,-19.327,-19.144,-19.516,-19.567,-19.322,-19.018,-18.228,-18.454,-18.77,-19.22,-19.393,-19.301,-18.797,-18.651,-18.991,-19.037,-18.787,-19.136,-19.577,-19.723,-19.642,-19.755,-19.7,-19.254,-18.588,-18.145,-17.887,-17.661,-17.373,-16.961,-16.818,-16.867\n-18.468,-18.51,-18.348,-18.237,-18.216,-18.426,-18.945,-19.147,-18.57,-18.433,-18.727,-18.629,-19.132,-19.317,-19.279,-19.355,-19.097,-19.383,-19.524,-19.224,-18.979,-18.705,-19.419,-19.285,-19.053,-19.373,-19.087,-18.54,-18.67,-19.06,-18.689,-18.635,-18.895,-19.129,-19.426,-19.372,-18.783,-18.335,-17.855,-17.316,-17.421,-17.582,-17.413,-17.31,-17.087,-17.15,-17.223\n-18.487,-18.421,-18.444,-18.309,-18.208,-18.436,-19.028,-19.058,-18.793,-18.645,-18.834,-18.728,-18.9,-19.237,-19.335,-19.318,-18.995,-19.187,-19.397,-19.249,-19.046,-18.815,-18.79,-18.851,-18.973,-18.942,-18.683,-18.567,-18.455,-18.48,-18.652,-18.677,-18.582,-18.832,-19.208,-19.233,-18.893,-18.283,-17.718,-17.404,-17.382,-17.451,-17.454,-17.574,-17.641,-17.34,-17.002\n-18.642,-19.003,-18.887,-18.492,-18.341,-18.621,-18.911,-19.204,-19.38,-19.136,-18.842,-18.743,-18.712,-19.074,-19.18,-18.936,-18.652,-19.325,-19.237,-19.021,-18.948,-18.598,-18.523,-18.871,-18.913,-18.587,-18.212,-18.131,-18.237,-18.166,-18.584,-18.812,-18.554,-18.658,-19.154,-19.207,-18.705,-18.193,-17.767,-17.401,-17.35,-17.559,-17.602,-17.636,-17.591,-17.461,-17.263\n-18.237,-18.214,-18.431,-18.283,-18.363,-18.526,-18.904,-19.066,-19.134,-18.794,-18.441,-18.311,-18.592,-18.948,-19.091,-18.918,-19.036,-19.487,-19.415,-19.035,-18.698,-18.559,-18.409,-18.826,-19.167,-18.723,-18.187,-18.03,-18.194,-18.099,-18.222,-18.531,-18.508,-18.748,-19.121,-19.179,-18.521,-18.008,-17.726,-17.248,-17.563,-18.128,-18.141,-17.918,-17.659,-17.186,-16.979\n-17.818,-17.672,-17.793,-18.031,-18.479,-18.805,-18.897,-18.671,-18.498,-18.359,-18.313,-17.991,-18.294,-18.831,-18.972,-18.946,-19.175,-19.363,-19.46,-18.865,-18.208,-18.216,-18.205,-18.278,-18.385,-17.998,-17.723,-18.118,-18.261,-17.94,-18,-18.26,-18.194,-18.364,-18.555,-18.508,-18.139,-17.835,-18.009,-17.73,-17.744,-18.505,-18.423,-18.051,-17.69,-17.26,-17.105\n-17.548,-17.506,-17.933,-17.981,-18.434,-18.988,-19.043,-18.689,-18.519,-18.551,-18.56,-18.361,-18.391,-18.698,-18.77,-19.065,-19.585,-19.462,-18.933,-18.374,-17.571,-17.803,-18.123,-18.204,-18.139,-17.901,-17.462,-17.898,-18.491,-17.912,-17.94,-18.175,-18.133,-18.262,-18.211,-17.988,-17.708,-17.872,-18.147,-18.076,-17.98,-18.301,-18.142,-17.648,-17.526,-17.386,-17.202\n-17.844,-17.469,-17.489,-17.996,-18.441,-18.822,-18.722,-18.47,-18.288,-18.381,-18.712,-18.866,-18.827,-18.784,-18.917,-19.061,-19.367,-19.218,-18.557,-17.751,-17.445,-17.767,-17.967,-17.951,-18.239,-18.119,-18.122,-18.193,-18.441,-18.509,-18.394,-18.375,-18.304,-18.261,-18.245,-17.883,-17.74,-18.248,-18.746,-18.563,-18.345,-18.256,-18.272,-17.381,-17.372,-17.355,-17.234\n-17.791,-17.667,-17.655,-18.894,-18.841,-18.673,-18.682,-18.275,-17.972,-18.049,-18.666,-19.007,-18.927,-18.845,-19.159,-18.955,-18.687,-18.369,-18.202,-18.133,-18.037,-18.32,-18.207,-18.143,-18.288,-18.128,-18.099,-18.269,-18.415,-18.485,-18.511,-18.488,-18.459,-18.389,-18.524,-18.485,-18.281,-18.102,-18.608,-18.813,-18.686,-18.406,-18.032,-17.377,-17.067,-17.016,-17.006\n-17.816,-18.074,-18.455,-18.998,-18.437,-18.515,-18.48,-18.248,-18.066,-18.15,-18.505,-18.917,-18.793,-18.68,-18.687,-18.621,-18.702,-18.297,-17.728,-17.497,-17.77,-18.178,-18.286,-18.469,-18.29,-18.187,-18.258,-18.312,-18.332,-18.39,-18.58,-18.595,-18.447,-18.644,-18.896,-18.519,-18.128,-18.118,-18.581,-18.918,-18.697,-18.341,-18.028,-17.573,-16.989,-16.832,-16.954\n-17.707,-17.884,-18.391,-18.622,-18.314,-18.514,-18.616,-18.449,-18.331,-18.252,-18.686,-18.614,-18.415,-18.503,-18.763,-18.894,-18.864,-18.707,-18.641,-18.901,-18.777,-18.306,-18.038,-18.36,-18.254,-18.039,-18.129,-18.172,-18.16,-18.433,-18.624,-18.649,-18.564,-18.768,-18.929,-18.474,-17.795,-17.954,-18.599,-18.68,-18.496,-18.331,-18.063,-17.552,-17.129,-16.54,-16.63\n-17.453,-17.355,-18.056,-18.515,-18.575,-18.727,-18.806,-18.734,-18.558,-18.417,-18.561,-18.762,-18.374,-18.36,-18.787,-19.179,-19.404,-19.493,-19.109,-18.943,-18.754,-18.158,-17.808,-18.033,-17.88,-17.956,-18.172,-18.344,-18.193,-18.263,-18.545,-18.726,-18.826,-18.715,-18.643,-18.291,-17.749,-17.57,-18.658,-18.757,-18.375,-18.225,-17.932,-17.704,-17.047,-16.785,-16.469\n-17.781,-18.083,-18.264,-18.644,-18.82,-19.154,-18.748,-18.623,-18.542,-18.591,-18.428,-18.285,-18.303,-18.366,-18.807,-19.176,-19.154,-19.077,-18.655,-18.188,-18.294,-18.661,-18.774,-18.855,-19.075,-19.126,-18.767,-18.687,-18.478,-18.316,-18.468,-18.605,-18.763,-18.645,-18.255,-17.638,-17.306,-17.399,-18.054,-18.518,-17.876,-17.783,-17.615,-17.294,-16.797,-16.417,-16.21\n-17.772,-17.999,-18.623,-19.019,-18.964,-19.024,-18.377,-18.333,-18.805,-18.721,-18.459,-18.394,-18.291,-18.317,-18.516,-18.847,-18.777,-18.849,-18.605,-18.187,-18.613,-19.152,-19.268,-19.359,-19.252,-18.888,-18.52,-18.469,-18.399,-18.162,-17.877,-17.989,-18.4,-18.524,-18.066,-17.404,-17.345,-17.725,-18.228,-18.447,-18.195,-17.612,-17.361,-16.965,-16.78,-16.31,-16.111\n-17.681,-17.669,-18.162,-19.166,-20.031,-19.404,-18.686,-18.224,-18.703,-18.517,-18.321,-18.173,-18.138,-18.43,-18.685,-18.648,-18.822,-19.29,-19.462,-19.147,-19.292,-19.344,-19.278,-19.47,-19.055,-18.443,-18.13,-18.029,-17.77,-17.221,-17.225,-17.55,-17.933,-18.053,-17.68,-17.15,-17.387,-18.016,-18.325,-18.534,-18.33,-17.772,-17.469,-17.346,-17.276,-17.161,-16.939\n-17.573,-17.286,-17.991,-19.42,-20.429,-20.156,-19.186,-18.407,-18.096,-18.044,-17.965,-18.005,-18.229,-18.453,-18.338,-18.462,-18.986,-19.448,-19.496,-19.314,-18.764,-18.558,-18.652,-18.669,-17.853,-17.427,-17.708,-18.069,-17.675,-16.999,-17.066,-17.46,-17.603,-17.553,-17.292,-16.852,-16.425,-16.996,-17.775,-18.173,-18.553,-18.194,-17.602,-17.432,-17.36,-17.214,-17.252\n-17.806,-17.835,-18.552,-19.355,-20.64,-20.105,-19.287,-18.094,-18.14,-18.531,-18.466,-18.347,-18.501,-18.368,-18.368,-18.521,-18.748,-19.502,-19.702,-19.323,-18.649,-18.142,-18.062,-18.356,-17.89,-17.29,-17.449,-18.21,-18.389,-17.613,-17.277,-17.367,-17.451,-17.526,-17.16,-16.635,-15.92,-16.188,-16.365,-17.87,-18.449,-18.796,-18.003,-17.216,-17.355,-17.323,-17.301\n-18.223,-18.417,-18.77,-19.623,-19.757,-19.681,-19.029,-18.603,-18.785,-18.965,-18.541,-18.399,-18.732,-18.291,-18.018,-17.872,-18.035,-18.737,-19.329,-19.608,-19.406,-18.928,-18.156,-18.22,-18.271,-17.537,-17.077,-17.638,-18.576,-18.023,-17.399,-17.294,-16.789,-16.737,-16.523,-16.346,-15.987,-16.089,-16.426,-17.386,-17.805,-18.41,-18.191,-17.374,-17.485,-17.485,-17.288\n-19.047,-18.948,-18.574,-18.89,-19.302,-19.465,-18.841,-18.579,-18.523,-18.505,-18.259,-18.279,-18.425,-18.028,-17.601,-17.66,-17.675,-17.933,-18.318,-18.728,-18.581,-18.281,-17.845,-18,-18.29,-17.742,-17.295,-17.115,-17.413,-18.183,-17.663,-17.009,-16.148,-16.307,-15.877,-16.058,-15.962,-15.951,-16.356,-16.69,-17.275,-17.707,-17.824,-17.488,-17.487,-17.719,-17.436\n-19.009,-18.922,-18.406,-18.264,-18.415,-18.778,-18.5,-18.66,-18.547,-18.049,-17.984,-17.993,-18.055,-17.9,-17.596,-17.413,-17.201,-17.085,-17.671,-17.697,-17.506,-17.398,-17.547,-17.837,-17.934,-17.631,-17.294,-16.666,-16.75,-17.307,-16.917,-16.478,-15.747,-15.752,-15.309,-15.69,-16.129,-16.17,-16.474,-16.832,-17.184,-17.222,-17.225,-17.407,-17.537,-17.577,-17.375\n-19.014,-18.755,-18.769,-19.064,-19.665,-20.515,-19.061,-18.724,-18.212,-18.09,-17.998,-17.837,-17.642,-17.799,-17.933,-17.332,-17.361,-17.269,-17.109,-16.988,-16.834,-16.947,-17.341,-17.371,-17.307,-16.993,-16.635,-16.291,-16.202,-16.23,-16.324,-16.308,-15.759,-15.681,-15.403,-15.508,-15.843,-16.393,-16.643,-16.698,-16.992,-17.058,-17.17,-17.34,-17.48,-17.401,-17.405\n-18.626,-18.646,-18.308,-18.759,-19.894,-20.461,-18.944,-17.979,-17.926,-18.304,-17.997,-17.34,-17.396,-17.328,-17.772,-18.181,-17.924,-17.544,-17.173,-16.686,-16.462,-16.563,-16.691,-16.51,-16.563,-16.49,-16.175,-16.295,-16.357,-16.155,-16.063,-16.159,-16.098,-16.034,-15.977,-15.965,-16.156,-16.321,-16.414,-16.727,-16.659,-16.829,-16.979,-17.218,-17.219,-17.294,-17.479\n-18.312,-18.1,-18.091,-18.208,-18.406,-17.953,-17.577,-17.528,-17.713,-18.011,-17.87,-17.885,-17.586,-16.901,-16.633,-16.803,-17.985,-17.521,-16.919,-16.409,-16.194,-16.203,-16.268,-16.36,-16.529,-16.254,-15.862,-15.751,-15.837,-15.654,-15.527,-15.727,-15.766,-15.892,-16.096,-16.031,-16.092,-16.205,-16.128,-16.258,-16.429,-16.677,-16.826,-17.128,-17.156,-16.949,-17.024\n-18.893,-18.5,-17.991,-18.055,-18.285,-18.252,-18.086,-17.877,-17.669,-17.583,-17.714,-17.835,-17.601,-16.665,-16.706,-17.141,-17.443,-16.787,-16.043,-15.753,-15.855,-16.078,-15.976,-16,-16.422,-15.795,-15.493,-15.282,-14.968,-14.58,-15.137,-15.435,-15.442,-15.416,-15.177,-15.06,-15.696,-15.906,-15.955,-16.121,-16.387,-16.422,-16.616,-16.976,-17.17,-17.126,-16.91\n-18.416,-18.529,-18.31,-18.229,-18.303,-18.494,-18.219,-17.926,-17.806,-17.429,-17.372,-17.191,-17.074,-16.718,-16.58,-16.781,-16.768,-16.294,-15.591,-15.402,-15.462,-15.531,-15.705,-15.762,-15.338,-14.771,-14.328,-14.306,-13.548,-13.135,-13.195,-14.306,-14.949,-15.206,-15.019,-14.976,-15.477,-15.646,-15.961,-16.245,-16.395,-16.517,-16.622,-16.836,-17.048,-17.377,-17.441\n-18.354,-18.419,-18.787,-18.78,-18.718,-18.62,-18.52,-18.14,-17.935,-17.788,-17.585,-17.146,-16.394,-16.02,-16.135,-16.613,-16.665,-15.885,-15.034,-14.895,-15.026,-15.214,-15.286,-15.349,-14.683,-14.193,-14.213,-13.796,-13.065,-12.146,-12.552,-13.026,-13.846,-14.506,-14.665,-14.946,-15.362,-15.825,-16.142,-16.275,-16.461,-16.706,-16.651,-16.637,-16.85,-17.475,-17.846\n-18.112,-18.167,-18.65,-19.124,-18.846,-18.549,-18.456,-18.653,-18.118,-17.972,-17.719,-17.369,-16.971,-16.437,-16.139,-16.137,-15.912,-15.34,-14.967,-14.808,-14.842,-15.15,-15.308,-15.055,-14.524,-14.258,-14.095,-14.022,-13.071,-12.118,-11.698,-11.756,-12.172,-12.937,-13.649,-14.397,-15.378,-15.914,-16.173,-16.27,-16.721,-16.659,-16.74,-16.856,-17.03,-17.408,-17.606\n-17.847,-17.309,-18.057,-18.464,-18.398,-18.285,-18.475,-18.95,-18.499,-18.074,-17.986,-17.406,-17.054,-16.59,-16.208,-15.969,-15.887,-15.578,-14.947,-14.595,-14.523,-14.997,-14.762,-14.445,-14.216,-13.793,-12.717,NaN,NaN,NaN,-8.8676,-10.169,-10.422,-12.315,-13.804,-14.341,-15.211,-15.794,-15.83,-16.117,-16.993,-17.095,-16.668,-16.855,-16.999,-17.297,-17.572\n-17.626,-18.021,-18.109,-18.319,-18.273,-18.334,-18.225,-18.535,-18.328,-18.096,-18.017,-17.686,-17.273,-16.723,-16.229,-16.105,-16.252,-16.334,-15.951,-15.195,-14.612,-15.028,-14.669,-13.811,-12.778,-12.312,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.194,-13.751,-14.285,-14.552,-15.06,-15.461,-16.179,-17.211,-17.101,-16.483,-16.722,-17.357,-17.773,-17.854\n-18.31,-17.994,-17.934,-17.982,-18.146,-18.727,-19.31,-18.945,-18.264,-17.638,-17.493,-17.528,-17.379,-16.962,-16.148,-16.185,-16.429,-16.324,-16.111,-15.709,-15.159,-14.929,-14.516,-13.291,-11.457,-10.231,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.152,-14.859,-14.228,-13.451,-13.8,-14.807,-16.234,-16.932,-17.02,-16.682,-16.402,-17.236,-17.921,-17.992\n-18.41,-18.377,-18.045,-17.349,-17.494,-17.818,-18.299,-18.178,-18.097,-17.427,-17.018,-17.201,-17.137,-16.934,-16.291,-16.431,-16.648,-16.047,-15.462,-15.342,-15.116,-14.696,-14.223,-13.209,-13.055,-12.809,-12.874,NaN,NaN,NaN,NaN,NaN,-13.885,-14.969,-14.268,-13.605,-13.393,-13.722,-14.845,-16.261,-16.832,-16.997,-17.115,-16.408,-17.328,-18.218,-18.176\n-18.572,-18.271,-17.987,-17.585,-17.092,-17.42,-17.76,-17.606,-17.52,-17.159,-17.282,-17.446,-17.096,-16.395,-16.373,-16.523,-17.009,-16.654,-15.752,-15.332,-15.142,-14.595,-14.224,-13.807,-13.987,-14.139,-14.649,-15.865,-15.87,-15.357,-14.908,-14.763,-15.749,-16.621,-15.06,-14.3,-14.086,-13.681,-14.906,-16.181,-16.858,-17.112,-17.116,-17.173,-17.833,-18.183,-18.136\n-18.472,-18.193,-17.925,-17.425,-17.173,-17.731,-17.943,-17.883,-17.808,-17.375,-17.288,-17.333,-17.337,-16.858,-16.874,-16.747,-16.821,-16.573,-16.25,-15.978,-15.754,-15.243,-14.65,-14.319,-14.588,-15.384,-14.925,-15.612,-15.736,-15.49,-16.198,-16.814,-17.791,-17.055,NaN,-16.229,-14.272,-13.437,-13.963,-15.947,-17.106,-17.179,-16.69,-17.06,-18.08,-18.353,-18.346\n-18.148,-18.523,-18.398,-17.988,-18.111,-18.52,-19.048,-18.445,-18.052,-17.652,-17.417,-17.287,-17.362,-17.585,-17.554,-17.171,-16.832,-16.709,-16.516,-16.201,-16.013,-16.222,-15.715,-13.57,NaN,-15.76,-14.875,-15.99,-18.281,-17.381,-17.221,-18.118,-17.931,-18.85,NaN,NaN,NaN,NaN,NaN,-15.997,-16.804,-16.693,-16.421,-17.28,-18.053,-18.306,-18.431\n-18.719,-18.997,-19.483,-19.047,-18.333,-18.379,-18.402,-18.215,-18.026,-18.202,-17.489,-17.307,-17.353,-17.886,-18.832,-18.17,-16.962,-16.933,-16.85,-16.54,-16.195,-16.236,-14.284,NaN,NaN,NaN,NaN,-16.125,-16.382,-16.591,-17.691,-17.908,-19.453,NaN,NaN,NaN,NaN,NaN,-14.612,-16.205,-16.59,-16.534,-16.548,-17.204,-18.025,-18.123,-18.123\n-18.986,-18.944,-19.006,-19.412,-18.42,-18.069,-18.021,-18.038,-18.167,-18.418,-18.167,-18.006,-17.839,-18.954,-18.92,-18.024,-17.59,-17.362,-16.954,-16.684,-16.312,-15.141,-12.736,NaN,NaN,NaN,-17.965,-17.948,-15.632,-16.122,-17.592,-17.401,-16.954,-14.238,NaN,NaN,NaN,NaN,-15.65,-16.371,-16.481,-16.807,-17.317,-18.013,-18.31,-18.183,-18.189\n-19.041,-18.921,-18.976,-18.903,-18.462,-18.114,-17.897,-17.702,-17.736,-18.001,-17.991,-18.424,-18.748,-19.44,-18.79,-17.497,-17.373,-17.125,-16.574,-16.16,-15.793,-15.023,-13.844,NaN,NaN,NaN,NaN,-18.526,-16.063,-15.812,-16.73,-17.059,-16.803,-13.665,-13.465,-13.573,-15.478,-15.62,-15.823,-17.146,-17.44,-17.662,-18.127,-18.283,-18.19,-18.126,-18.094\n-18.559,-18.619,-18.961,-19.06,-18.848,-18.418,-17.842,-17.578,-17.561,-17.57,-17.569,-17.764,-17.953,-18.278,-17.926,-17.437,-17.69,-17.507,-16.893,-16.21,-15.902,-15.392,-15.361,-16.859,-18.913,-19.822,-19.203,-17.525,-16.874,-17.379,-17.64,-17.592,-17.601,-16.126,-14.496,-15.211,-15.74,-15.911,-16.574,-17.755,-18.2,-18.39,-17.858,-17.715,-17.594,-17.839,-18.014\n-18.657,-18.842,-18.958,-19.068,-18.614,-17.861,-17.84,-17.593,-17.394,-17.321,-17.331,-17.479,-17.811,-17.909,-17.349,-17.449,-17.595,-16.853,-16.913,-16.039,-15.468,-15.718,-16.681,-15.958,-14.515,-13.659,-14.412,-16.505,-17.08,-17.654,-18.526,-19.062,-18.241,-17.49,-15.656,-15.228,-15.319,-15.95,-17.068,-18.358,-18.637,-18.37,-17.866,-17.7,-17.535,-17.663,-17.773\n-18.561,-18.873,-18.606,-18.299,-17.968,-17.579,-17.337,-17.271,-17.21,-17.55,-17.356,-17.421,-17.551,-17.571,-17.164,-16.962,-17.029,-16.666,-16.652,-15.832,-15.412,-15.18,-16.337,-16.709,-16.951,NaN,NaN,-17.97,-18.574,-19.628,-21.162,-21.61,-19.259,-16.847,-16.103,-15.705,-15.431,-15.498,-16.616,-18.125,-18.839,-18.493,-17.892,-17.961,-18.033,-17.856,-17.774\n-17.874,-18.211,-18.449,-18.318,-17.871,-17.728,-17.144,-16.941,-17.262,-17.682,-17.644,-17.454,-17.388,-17.332,-17.308,-17.151,-16.701,-16.205,-15.931,-15.2,-15.564,-15.974,-16.4,-17.034,-17.9,NaN,NaN,-19.369,-18.37,-18.859,-19.581,-19.305,-18.397,-17.154,-16.538,-16.103,-16.126,-16.095,-16.776,-17.953,-18.55,-18.323,-17.85,-17.995,-17.971,-17.549,-17.621\n-17.195,-17.838,-18.17,-18.053,-18.091,-18.031,-17.618,-17.158,-17.456,-17.495,-17.378,-17.226,-17.237,-17.303,-17.381,-17.521,-17.245,-16.652,-16.179,-16.075,-16.327,-16.878,-17.065,-16.705,NaN,NaN,NaN,NaN,-16.175,-17.25,-18.61,-18.504,-18.236,-17.037,-16.719,-16.765,-16.528,-16.47,-16.858,-17.86,-17.955,-18.209,-18.089,-17.933,-17.799,-17.56,-17.962\n-17.521,-17.718,-17.862,-17.902,-18.236,-17.939,-17.567,-17.169,-17.361,-17.399,-17.222,-16.989,-16.969,-17.194,-17.2,-17.25,-17.231,-16.944,-16.67,-16.639,-16.531,-16.468,-14.835,-14.622,NaN,NaN,NaN,-15.47,-15.97,-17.189,-18.153,-18.581,-18.559,-17.986,-17.782,-17.695,-17.341,-16.94,-17.704,-18.049,-18.264,-18.344,-17.994,-18.039,-18.124,-18.015,-18.071\n-17.642,-17.745,-17.864,-17.676,-17.759,-17.644,-17.315,-17.167,-17.253,-17.406,-17.37,-17.097,-17.064,-17.141,-17.057,-17.131,-17.181,-16.817,-16.716,-16.354,-15.205,-14.932,-14.292,-13.265,-12.073,-12.722,-13.943,-16.312,-17.047,-17.811,-18.338,-18.531,-18.825,-18.424,-18.303,-17.94,-17.437,-17.663,-19.232,-18.934,-18.659,-18.634,-18.042,-17.713,-17.922,-18.125,-18.171\n-17.962,-18.045,-18.232,-18.164,-17.909,-17.644,-17.23,-17.353,-17.607,-17.677,-17.454,-17.16,-16.873,-16.727,-16.937,-16.906,-16.866,-16.844,-17.314,-17.799,-17.082,-16.502,-16.118,-16.142,-16.434,-16.222,-16.165,-17.095,-17.604,-17.911,-17.692,-17.656,-17.787,-18.015,-18.09,-18.399,-18.453,-19.099,-20.105,-19.902,-19.361,-19.05,-18.28,-17.603,-17.783,-18.096,-18.273\n-18.445,-18.093,-18.232,-18.399,-18.114,-17.79,-17.447,-17.677,-17.929,-17.529,-17.176,-17.123,-17.125,-17.106,-17.262,-17.488,-17.492,-17.451,-17.517,-18.154,-18.859,-17.806,-17.176,-17.245,-17.047,-17.164,-17.729,-17.739,-17.976,-17.084,-16.556,-17.159,-17.616,-17.998,-18.178,-19.371,-19.787,-20.013,-20.781,-20.419,-19.82,-19.053,-18.463,-17.989,-17.841,-17.944,-17.988\n-18.177,-18.164,-18.058,-17.95,-17.54,-17.471,-17.756,-18.024,-17.884,-17.628,-17.262,-17.076,-17.1,-17.24,-17.497,-17.772,-17.939,-17.905,-17.986,-18.257,-18.745,-19.017,-18.679,-17.89,-17.286,-17.546,-17.984,-18.126,-17.86,-17.782,-18.15,-18.315,-18.641,-18.088,-19.497,-20.467,-21.153,-20.651,-20.605,-20.588,-20.61,-19.883,-18.74,-17.945,-17.865,-18.05,-17.784\n-18.228,-18.323,-17.896,-17.745,-17.612,-17.634,-17.881,-18.208,-18.196,-18.008,-17.637,-17.514,-17.628,-17.484,-17.825,-18.128,-18.247,-18.152,-18.683,-18.858,-19.334,-19.15,-18.551,-17.686,-17.636,-18.181,-18.335,-18.432,-18.153,-18.765,-19.653,-19.662,-19.182,-19.719,-20.469,-21.651,-21.52,-20.857,-20.12,-20.24,-20.587,-20.582,-19.13,-18.634,-18.408,-18.227,-18.059\n-18.629,-18.442,-17.999,-18.014,-18.191,-18.057,-17.898,-17.931,-18.231,-18.417,-18.445,-18.101,-17.773,-17.615,-18.005,-18.37,-17.953,-17.914,-18.256,-18.867,-19.451,-18.956,-18.024,-17.906,-18.106,-18.616,-18.938,-18.858,-18.572,-19.646,-20.628,-20.227,-20.509,-21.006,-21.165,-21.53,-21.184,-19.808,-18.846,-19.057,-19.921,-20.192,-20.007,-19.132,-18.813,-18.522,-18.334\n-18.735,-18.51,-18.169,-18.068,-18.301,-18.378,-18.278,-18.297,-18.273,-18.387,-18.436,-18.146,-17.553,-17.676,-18.112,-18.32,-18.023,-17.738,-17.7,-17.672,-17.818,-18.183,-18.229,-18.372,-18.914,-19.065,-19.533,-19.346,-18.983,-19.72,-20.677,-20.31,-20.697,-21.135,-20.685,-20.103,-19.645,-19.138,-18.746,-18.963,-19.668,-20.758,-20.865,-20.281,-19.096,-18.535,-18.297\n-18.451,-18.266,-17.881,-18.024,-18.388,-18.425,-18.404,-18.495,-18.536,-18.119,-18.174,-18.011,-17.578,-17.579,-18.426,-18.39,-18.269,-17.778,-17.667,-17.538,-17.746,-18.327,-18.439,-18.647,-19.052,-19.447,-19.656,-19.515,-19.175,-19.307,-19.5,-19.845,-20.348,-20.581,-20.237,-19.654,-19.119,-19.061,-19.086,-19.342,-20.621,-20.899,-21.179,-20.045,-18.916,-18.363,-18.034\n-18.118,-17.702,-17.133,-17.827,-18.254,-18.402,-18.531,-18.544,-18.41,-17.885,-17.966,-18.026,-17.909,-17.822,-18.081,-18.112,-18.203,-17.748,-17.499,-17.382,-17.766,-18.297,-18.658,-18.857,-19.284,-19.316,-19.587,-19.576,-19.166,-18.813,-19.054,-19.701,-20.107,-20.308,-19.953,-20.189,-20.362,-20.464,-20.119,-20.235,-20.844,-21.044,-20.871,-19.594,-18.704,-18.169,-17.601\n-18.91,-18.132,-17.849,-17.714,-17.771,-18.09,-18.32,-18.263,-18.103,-18.097,-18.146,-18.682,-18.212,-18.061,-18.095,-18.239,-18.156,-17.814,-17.712,-17.746,-17.905,-18.048,-18.489,-18.87,-19.261,-19.511,-19.66,-19.58,-19.149,-18.665,-18.903,-19.69,-20.043,-20.484,-20.541,-21.159,-21.588,-21.056,-20.462,-20.426,-20.822,-21.049,-20.717,-19.293,-18.35,-18.075,-17.485\n-18.999,-18.608,-17.876,-17.908,-17.551,-17.866,-18.279,-18.134,-18.079,-18.375,-18.588,-18.878,-18.65,-18.141,-17.977,-17.987,-18.085,-18.099,-18.244,-17.998,-17.931,-18.01,-18.425,-19.042,-19.393,-19.577,-19.292,-19.151,-18.797,-18.643,-19.18,-19.847,-20.315,-21.392,-21.655,-21.858,-22.069,-21.443,-20.828,-20.484,-20.646,-20.772,-19.765,-18.704,-18.351,-18.089,-17.604\n-18.442,-18.027,-18.151,-18.414,-17.705,-18.002,-18.777,-18.819,-18.814,-18.73,-18.743,-18.766,-18.583,-18.353,-18.137,-18.083,-18.181,-18.32,-18.343,-18.012,-18.307,-18.673,-18.851,-19.197,-19.389,-19.482,-19.474,-19.395,-19.143,-18.859,-19.578,-20.156,-20.723,-21.284,-21.465,-21.53,-21.845,-21.582,-21.048,-20.77,-20.576,-20.416,-19.992,-19.439,-18.592,-17.723,-17.204\n-18.551,-18.279,-18.478,-18.478,-18.413,-18.635,-18.939,-19.396,-19.685,-19.754,-18.966,-18.82,-18.407,-18.078,-18.06,-18.095,-18.289,-18.382,-18.138,-17.82,-18.215,-18.757,-19.101,-19.322,-19.289,-19.296,-19.516,-19.645,-19.627,-19.597,-19.588,-19.793,-20.665,-21.053,-21.214,-21.264,-21.804,-22.082,-21.608,-20.767,-20.562,-20.487,-20.479,-20.061,-19.259,-17.964,-17.094\n-19.045,-18.797,-18.832,-18.58,-18.211,-17.874,-18.505,-19.069,-19.486,-19.577,-18.786,-18.849,-18.324,-18.145,-17.794,-17.851,-18.153,-18.227,-17.879,-17.961,-17.993,-18.626,-18.878,-19.015,-19.045,-19.229,-19.312,-19.572,-19.565,-19.642,-19.414,-19.565,-20.384,-20.998,-20.998,-20.843,-21.664,-22.331,-21.79,-21.446,-20.936,-20.297,-20.286,-20.246,-19.276,-17.962,-17.526\n-19.054,-18.752,-18.768,-18.886,-18.834,-18.605,-18.613,-18.512,-18.557,-18.747,-18.55,-18.633,-18.359,-18.259,-18.287,-18.449,-18.621,-18.706,-18.384,-18.144,-18.178,-18.194,-18.631,-18.808,-18.989,-19.209,-19.15,-19.065,-19.37,-20.111,-19.727,-19.722,-20.52,-21.78,-21.427,-21.011,-21.166,-21.651,-21.801,-21.898,-21.081,-20.068,-19.84,-19.86,-19.284,-18.733,-18.409\n-18.696,-18.284,-18.313,-18.908,-19.082,-19.299,-19.128,-18.189,-18.226,-18.804,-19.088,-19.773,-19.685,-17.503,-17.75,-18.063,-18.545,-18.721,-18.342,-18.148,-18.167,-17.94,-18.067,-18.503,-18.823,-19.078,-19.103,-18.91,-18.955,-19.57,-19.793,-19.976,-20.152,-21.851,-21.947,-21.03,-20.672,-21.189,-21.964,-21.891,-20.974,-19.852,-19.653,-19.56,-19.311,-18.748,-18.478\n-17.789,-17.785,-17.721,-18.279,-18.901,-19.005,-18.731,-18.443,-18.126,-18.667,-19.186,-20.577,-19.725,-19.021,-18.145,-18.04,-18.277,-18.44,-18.108,-17.773,-17.665,-17.62,-17.618,-18.512,-18.861,-18.929,-18.932,-18.835,-18.635,-18.687,-18.736,-19.652,-20.525,-21.35,-21.661,-20.714,-20.261,-20.947,-21.516,-21.237,-20.597,-20.165,-19.444,-18.789,-18.562,-18.525,-18.528\n-16.788,-17.491,-18.13,-18.259,-18.245,-18.311,-18.642,-18.603,-18.392,-18.545,-18.422,-20.077,-19.985,-19.487,-18.467,-18.788,-18.286,-18.145,-17.889,-17.725,-17.512,-17.17,-17.38,-18.187,-18.485,-18.718,-19.003,-19.348,-18.958,-18.299,-17.973,-18.928,-19.951,-21.492,-21.909,-20.74,-20.198,-20.936,-21.002,-20.456,-19.871,-19.533,-19.178,-18.614,-18.21,-18.403,-17.93\n-16.701,-17.532,-17.964,-17.798,-17.393,-17.965,-18.996,-19.004,-18.756,-18.987,-17.275,-18.303,-19.131,-20.364,-19.809,-20.187,-18.747,-17.051,-18.339,-18.503,-17.762,-17.077,-17.157,-18.033,-18.218,-18.536,-18.869,-19.063,-18.656,-17.989,-18.077,-18.889,-19.785,-20.784,-21.14,-19.623,-20.041,-20.666,-20.628,-19.947,-19.599,-19.388,-19.152,-18.456,-18.514,-19.115,-18.071\n-14.856,-17.267,-19.576,-18.175,-16.949,-16.784,-18.307,-18.187,-18.678,-18.838,-18.739,-18.123,-19.136,-21.325,-20.747,-20.047,-17.141,-16.438,-17.422,-18.297,-17.843,-16.884,-16.768,-17.794,-18.042,-18.096,-17.907,-17.861,-17.409,-17.719,-18.493,-19.154,-19.546,-20.372,-20.847,-19.822,-19.53,-19.64,-20.269,-19.873,-19.451,-19.489,-19.042,-18.381,-18.518,-18.864,-17.944\n-18.186,-20.258,-20.044,-18.538,-16.078,-17.265,-18.553,-17.192,-17.073,-17.124,-16.709,-14.949,NaN,NaN,NaN,NaN,-18.156,-16.322,-16.591,-18.28,-17.863,-17.049,-17.442,-18.356,-18.351,-18.139,-17.816,-17.071,-16.812,-17.167,-17.51,-18.408,-19.52,-19.883,-20.073,-19.552,-19.207,-19.297,-19.709,-19.15,-18.68,-18.848,-19.001,-18.806,-18.154,-17.261,-16.372\n-15.343,-17.7,-17.187,-15.952,-15.366,-16.968,-18.141,-17.561,-17.178,-16.985,-15.895,NaN,NaN,NaN,NaN,NaN,NaN,-17.097,-16.973,-17.838,-17.992,-18.177,-18.029,-17.766,-17.246,-17.555,-17.244,-16.711,-16.702,-16.433,-16.898,-17.507,-18.493,-18.986,-18.717,-18.311,-18.582,-18.834,-19.095,-18.868,-18.894,-18.863,-19.203,-19.222,-18.233,-17.098,-16.788\n-15.212,-15.395,-15.37,-14.12,-12.751,-16.679,-17.905,-16.462,-16.099,-15.992,-13.855,-14.166,-14.365,NaN,NaN,NaN,NaN,-17.794,-17.136,-17.906,-17.362,-17.208,-17.49,-17.195,-17.155,-17.593,-17.369,-17.025,-16.495,-16.357,-16.437,-17.468,-17.65,-17.767,-18.009,-17.879,-18.025,-18.628,-18.798,-18.339,-18.1,-18.327,-18.608,-18.37,-17.8,-17.129,-17.105\n-14.863,-17.331,-17.363,-14.835,-14.192,-14.884,-16.976,-15.893,-16.741,-16.556,-15.964,-16.294,-14.661,-13.973,-14.162,-16.043,-18.514,-19.643,-19.29,-17.183,-15.336,-16.094,-16.187,-16.354,-16.082,-16.143,-15.934,-16.165,-16.635,-16.361,-16.398,-16.853,-17.465,-17.265,-17.164,-18.02,-18.412,-18.526,-18.534,-18.154,-18.024,-17.979,-18.098,-17.927,-17.235,-16.72,-16.387\n-13.63,-13.303,-16.063,-15.879,-15.103,-14.349,-14.802,-15.476,-16.336,-17.766,-17.803,-16.807,-15.749,-15.121,-14.118,-14.428,-17.792,NaN,NaN,-17.384,-14.744,-15.905,-15.981,-15.526,-14.699,-14.328,-14.897,-15.585,-16.167,-15.809,-15.982,-16.492,-17.361,-17.738,-17.797,-18.579,-18.519,-18.68,-18.789,-18.891,-18.645,-18.078,-18.094,-18.115,-17.509,-17.27,-16.87\n-13.681,-12.125,-13.108,-16.852,-15.172,-13.451,-13.267,-15.302,-16.014,-16.594,-16.277,-16.205,-16.533,-16.479,-16.687,-15.755,NaN,NaN,NaN,NaN,-13.977,-16.086,-15.813,-14.845,-14.084,-13.811,-14.639,-15.586,-16.051,-16.022,-16.352,-16.646,-16.734,-17.033,-17.798,-19.064,-18.981,-19.153,-19.712,-19.634,-18.95,-18.434,-18.229,-18.279,-17.967,-17.944,-18.047\n-14.942,-15.285,-16,-17.677,-17.425,-16.53,-16.023,-15.752,-15.318,-14.671,-14.566,-15.659,-16.859,-18.165,-17.779,-17.242,NaN,NaN,NaN,NaN,-16.55,-16.887,-16.057,-15.01,-14.736,-14.702,-14.997,-15.745,-16.222,-16.473,-16.591,-16.842,-16.575,-16.823,-17.632,-18.523,-18.767,-18.386,-18.944,-18.895,-18.266,-17.958,-17.759,-18.038,-17.971,-17.771,-18.258\n-14.749,-15.623,-16.081,-17.144,-16.999,-15.467,-14.219,-13.683,-14.573,-14.444,-13.499,-13.827,-15.784,-17.787,-18.26,NaN,NaN,NaN,NaN,NaN,-18.541,-17.266,-15.634,-15.347,-15.555,-15.629,-15.902,-16.238,-16.289,-16.319,-16.65,-17.012,-17.085,-17.283,-17.797,-18.778,-18.452,-17.493,-17.926,-18.148,-17.849,-17.56,-17.863,-18.177,-18.088,-17.962,-18.047\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_060828-061211.unw.csv",
    "content": "27.25,26.299,26.409,26.588,26.557,27.161,26.792,27.13,26.662,26.369,27.231,26.26,27.525,28.302,27.519,27.222,27.116,26.711,27.049,27.481,28.118,27.739,27.496,27.626,26.657,27.114,26.746,27.431,27.627,27.559,27.018,26.994,26.356,26.839,27.251,27.221,28.211,28.411,28.897,28.639,28.472,28.713,28.287,27.926,28.29,27.927,27.833\n26.57,26.391,26.368,26.169,25.836,26.498,26.902,27.679,27.089,27.36,26.83,25.373,26.697,27.716,27.303,28.011,28.181,26.813,26.537,27.378,28.087,28.2,27.531,27.458,27.749,27.302,27.167,27.706,27.459,27.118,27.134,26.862,27.423,27.467,27.017,27.477,28.067,28.291,28.558,28.685,29.537,29.065,28.926,28.214,28.289,28.854,28.794\n25.817,25.499,26.585,26.517,26.29,26.431,27.375,27.994,26.764,27.391,27.143,26.112,27.447,27.641,28.064,28.414,28.189,27.37,27.412,27.237,27.085,26.835,26.506,26.646,27.033,26.972,27.505,28.093,27.164,27.047,27.307,27.331,28.286,27.87,27.401,26.816,27.923,27.941,27.791,28.682,29.799,28.771,28.478,27.841,28.839,29.356,28.974\n26.5,26.57,27.113,27.061,26.88,27.489,28.332,28.313,27.666,27.606,27.724,27.166,27.842,28.322,28.903,29.142,29.231,28.621,28.014,27.017,26.281,26.293,26.113,26.605,26.983,27.108,27.388,27.141,27.178,27.673,27.24,27.284,27.657,28.122,27.694,27.17,27.802,28.128,28.031,28.628,28.551,28.573,28.802,28.872,29.459,30.118,30.006\n27.438,28.099,28.78,27.359,27.616,28.652,28.596,28.62,27.513,27.806,27.669,27.211,27.38,27.622,28.409,28.846,27.723,27.44,27.171,26.756,27.875,26.759,26.568,26.841,26.706,26.97,26.968,27.376,27.544,27.353,26.259,27.357,27.282,28.165,27.721,27.847,27.609,27.722,28.006,28.331,28.216,27.523,28.957,29.243,29.144,29.172,29.273\n27.175,27.533,29.05,27.001,27.313,27.947,28.152,27.305,26.41,26.563,26.5,27.152,27.311,27.568,27.738,28.33,28.084,27.007,26.345,27.164,28.314,27.457,26.601,26.589,27.114,27.556,27.256,27.408,27.483,27.744,27.248,27.528,27.729,27.549,26.924,27.74,26.84,26.804,26.652,27.001,26.901,27.264,28.432,29.156,29.027,29.064,29.293\n26.576,26.346,26.924,27.046,28.135,28.881,28.952,27.933,26.761,26.555,27.133,27.88,28.347,28.14,28.515,28.601,27.666,27.256,27.347,28.15,27.031,26.655,26.266,25.738,26.87,27.101,27.198,27.478,27.754,28.815,27.723,27.39,27.371,27.225,27.081,27.312,27.518,27.406,26.61,26.305,27.381,27.518,28.15,28.989,29.454,29.318,30.87\n27.052,26.732,25.728,27.028,28.51,29.744,29.033,28.939,28.395,28.325,27.969,28.723,27.869,28.506,28.334,28.214,28.046,27.276,27.332,27.66,27.241,26.591,26.616,27.014,27.582,27.938,27.403,28.164,27.687,28.187,27.912,27.289,26.819,27.1,27.166,27.209,27.384,27.442,27.026,26.765,27.183,26.864,27.227,28.539,28.859,28.902,29.251\n27.665,26.572,26.564,27.312,27.87,28.566,28.843,28.75,29.023,29.234,28.816,29.147,29.216,28.493,27.689,28.266,29.5,29.752,27.425,27.555,27.023,26.167,26.819,27.64,28.133,27.984,27.902,28.059,27.731,27.803,28.576,27.998,27.7,27.698,27.167,26.873,27.993,27.485,26.377,26.253,26.366,26.57,27.498,27.823,27.95,27.612,27.304\n27.366,27.543,28.446,27.463,26.851,27.704,27.437,27.226,28.183,29.313,29.06,29.236,29.372,28.02,27.962,29.288,28.298,28.111,27.89,27.134,27.09,26.639,26.848,27.526,27.823,27.87,27.082,27.801,28.174,27.412,27.538,26.91,27.328,27.5,26.536,26.374,26.747,26.981,26.754,26.629,26.925,27.636,28.41,27.43,27.731,27.734,27.439\n26.527,26.747,27.53,26.917,26.271,27.319,27.244,26.913,27.931,28.67,28.364,28.531,28.508,27.904,27.975,28.701,28.001,28.297,27.856,27.268,27.071,26.974,27.001,27.48,27.616,27.356,27.641,28.15,27.752,27.04,27.082,27.177,27.26,27.23,26.617,26.704,27.043,27.548,27.721,27.093,27.75,28.148,28.34,27.966,28.338,29.275,29.066\n26.6,25.954,27.658,27.63,26.328,27.861,27.941,27.873,28.001,28.746,28.05,28.961,28.999,28.727,28.785,28.797,28.363,28.592,27.448,26.966,26.873,26.607,26.876,28.218,27.622,27.661,28.217,28.309,27.438,27.824,27.479,27.361,27.454,27.363,27.456,27.621,27.612,27.269,27.429,27.224,27.475,28.013,28.826,28.818,29.219,30.191,29.805\n26.81,26.586,28.259,31.21,25.156,27.739,27.426,26.089,28.087,28.851,27.605,28.253,27.673,29.09,29.382,28.783,28.57,28.588,27.569,27.278,27.309,27.266,27.824,28.058,28.091,27.543,27.614,28.06,27.197,27.398,26.736,27.185,26.973,27.238,27.56,27.393,26.913,26.732,27.971,27.817,28.001,28.179,28.659,28.809,28.466,NaN,NaN\n26.79,26.98,25.886,24.638,27.22,28.253,28.345,26.59,28.107,27.976,28.107,28.069,27.973,29.261,29.563,28.345,28.327,27.912,27.753,28.447,29.31,29.136,28.592,28.245,27.349,27.828,28.287,27.564,27.387,27.153,26.728,26.851,26.765,26.978,26.671,27.132,27.648,27.956,29.012,27.948,29.199,29.042,28.791,29.078,28.981,NaN,NaN\n26.38,26.023,23.306,25.291,26.232,26.647,26.873,27.422,28.537,28.539,28.352,28.007,28.184,28.629,28.967,28.873,29.79,28.48,27.788,28.632,29.407,29.16,28.249,27.662,26.074,28.156,28.302,27.759,28.073,27.847,27.877,27.558,27.086,27.01,26.451,27.315,27.48,27.512,27.604,26.746,27.849,30.248,28.642,28.404,28.314,NaN,NaN\n27.102,24.543,23.173,26.423,26.597,27.032,26.661,27.941,29.125,29.153,28.638,28.294,28.077,27.78,27.701,28.259,28.459,27.34,27.655,27.818,28.205,28.798,28.238,28.599,27.529,27.329,27.001,27.431,28.027,27.26,29.021,29.106,27.796,27.449,26.967,28.75,27.692,26.815,26.466,25.579,26.661,29.372,29.003,28.177,27.813,27.916,29.165\n27.297,26.52,26.683,27.695,27.865,27.361,26.575,28.558,28.344,28.901,30.116,30.382,28.397,26.878,28.039,28.418,27.155,28.042,28.315,27.208,26.933,29.325,27.397,27.951,28.123,27.593,27.604,28.001,27.631,26.927,28.576,29.112,27.728,27.589,28.089,28.747,28.1,27.384,26.778,24.441,27.606,26.681,27.831,27.933,28.258,26.451,27.696\n27.631,26.91,28.1,28.693,29.128,27.257,25.819,27.769,28.126,29.238,29.74,29.463,28.348,28.302,28.809,28.719,28.095,28.04,28.089,26.552,26.301,29.095,28.818,27.54,27.989,27.996,27.643,27.617,27.789,27.322,27.551,26.863,27.806,28.061,27.85,27.351,29.005,27.641,27.108,25.916,27.464,26.862,25.266,27.063,25.438,27.279,27.696\n24.968,26.323,27.15,29.824,31.161,25.052,26.291,28.033,28.18,28.948,29.041,28.138,27.183,27.798,28.451,28.219,28.137,28.184,27.9,27.388,27.078,27.495,27.009,26.854,27.763,27.803,27.836,27.427,28.117,27.695,27.766,25.918,27.882,28.225,27.996,26.497,27.356,27.417,27.147,26.387,26.803,28.152,25.09,26.662,26.437,27.071,27.68\n25.205,27.171,28.192,29.406,30.939,29.734,26.916,28.011,27.66,28.885,28.719,28.117,28.077,27.76,28.52,28.76,28.281,28.582,28.671,29.084,26.004,26.191,27.502,25.387,26.249,27.579,27.738,27.732,28.294,28.316,27.579,27.346,28.753,28.97,29.514,27.158,26.499,26.628,28.432,27.712,26.867,27.018,26.865,27.693,27.132,27.185,25.204\n27.121,27.397,28.299,29.401,31.124,29.235,25.785,27.52,27.619,28.207,28.141,27.875,27.855,28.348,27.637,27.872,28.659,28.625,28.055,27.274,25.521,26.083,26.623,26.721,26.934,27.357,27.951,27.971,29.486,28.224,26.595,27.158,27.484,27.812,29.535,27.743,25.927,26.006,28.297,27.177,26.633,26.635,27.194,26.883,26.993,26.814,26.572\n28.145,27.88,27.596,27.022,26.888,27.361,27.011,27.653,28.454,28.641,28.292,27.456,28.012,27.116,26.202,28.884,28.75,27.996,27.605,27.298,26.954,25.434,22.596,26.25,27.416,27.712,27.889,27.932,29.537,28.911,26.197,26.286,25.82,26.014,27.827,27.566,26.504,26.251,26.645,26.668,26.075,26.037,26.799,26.813,26.933,27.203,27.576\n28.735,28.404,28.518,28.585,28.094,27.85,28.145,28.328,29.286,29.578,27.439,27.773,29.282,28.17,26.401,27.311,27.264,26.45,26.715,26.774,26.526,26.213,24.751,27.175,28.044,27.951,28.747,28.623,28.401,27.578,26.486,26.313,26.032,25.885,26.737,27.067,26.7,26.023,24.729,25.622,25.073,26.12,27.017,26.414,26.161,26.658,27.16\n27.498,28.058,28.817,29.883,28.979,28.725,27.827,28.986,29.463,29.22,28.821,28.21,28.935,28.086,27.365,26.983,26.81,25.349,25.637,26.615,27.153,27.26,26.859,27.773,29.058,28.587,29.136,29.088,28.825,28.427,27.999,27.595,28.078,28.448,26.821,27.205,27.206,25.079,22.539,22.927,23.998,26.718,26.648,26.492,25.955,26.55,26.574\n27.229,26.649,27.154,29.355,29.835,28.577,27.917,28.492,28.816,28.235,29.004,29.248,28.612,27.272,27.187,27.329,27.638,26.976,26.941,28.16,27.429,27.286,27.555,27.617,29.819,29.76,29.484,29.045,28.818,29.36,28.846,28.863,28.793,27.515,26.785,27.294,25.138,25.443,25.208,25.067,26.565,27.554,27.25,26.462,25.968,26.217,26.532\n28.157,28.29,28.865,29.36,29.591,28.245,28.032,28.354,28.165,27.767,29.743,29.482,28.102,27.191,27.364,27.193,27.921,27.378,27.403,28.196,27.717,27.287,28.163,28.908,29.278,29.763,NaN,NaN,29.568,28.481,27.954,29.558,28.005,26.129,25.962,26.946,26.631,27.022,27.659,27.043,27.821,28.37,27.128,26.051,26.424,26.406,27.244\n27.636,29.04,29.661,28.567,27.408,27.676,28.171,28.222,27.574,27.755,28.361,28.465,27.754,27.139,26.432,26.25,26.581,27.134,26.874,26.256,26.825,27.33,28.224,28.502,28.511,30.467,NaN,NaN,NaN,24.279,26.4,27.072,28.076,27.765,27.635,27.213,27.421,28.965,28.237,27.501,28.111,27.587,26.876,26.924,27.56,27.651,27.374\n27.101,28.286,28.87,28.94,26.981,27.723,28.771,27.55,26.863,28.338,28.148,28.058,27.63,26.638,26.133,26.199,26.326,27.953,27.003,25.145,25.983,27.699,28.379,27.653,27.713,28.156,NaN,NaN,NaN,NaN,26.773,28.142,25.658,27.232,29.023,29.001,28.677,28.589,28.606,29.05,28.448,27.254,27.042,27.514,27.728,27.513,28.307\n27.817,28.574,29.467,27.69,27.277,28.075,28.485,27.762,27.827,28.408,28.609,27.16,27.151,27.136,26.518,26.202,26.048,27.802,26.566,25.366,25.038,27.098,27.758,27.749,26.637,29.573,30.712,30.193,29.263,30.733,29.743,29.783,27.559,27.124,29.285,29.117,28.676,28.472,28.547,28.923,28.107,26.773,26.637,26.689,26.407,27.164,28.203\n27.395,27.677,29.041,25.589,27.381,28.096,27.761,27.735,27.964,27.769,28.324,27.581,26.876,27.255,26.791,27.279,28.163,27.326,26.997,26.281,25.434,25.766,27.998,27.392,28.638,30.497,30.953,32.804,NaN,NaN,NaN,NaN,NaN,24.906,28.278,29.36,28.927,29.196,29.578,29.112,28.571,26.504,26.293,26.203,26.117,26.278,27.408\n29.244,28.805,28.58,28.15,28.78,29.242,27.986,28.601,28.465,26.897,27.331,27.33,26.705,26.972,26.76,24.985,24.624,26.968,25.689,25.614,28.085,28.437,28.467,29.391,27.498,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.797,30.615,31.052,30.105,29.419,29.917,29.244,27.602,26.124,26.649,25.919,25.594,26.874,27.705\n27.717,27.661,27.325,27.435,29.16,29.37,28.979,26.279,28.341,26.79,27.02,27.64,27.606,27.189,26.783,25.423,25.603,27.849,28.888,29.187,29.346,29.265,28.921,32.462,29.874,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.845,31.798,30.436,29.025,29.59,29.253,28.895,28.343,27.385,27.03,25.518,25.985,27.703,27.933\n27.087,28.29,31.196,31.858,30.742,27.817,26.383,24.596,26.674,26.446,NaN,NaN,NaN,NaN,28.58,27.443,26.992,27.804,28.907,28.607,29.052,28.827,28.522,29.835,30.447,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.913,29.478,30.272,27.917,27.903,28.514,27.709,27.917,27.54,27.261,27.829,27.245\n28.937,29.306,32.281,32.247,30.308,26.626,27.027,27.064,27.047,NaN,NaN,NaN,NaN,NaN,29.319,29.379,29.13,27.962,28.124,27.336,28.749,29.546,29.61,28.987,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.759,29.288,30.393,28.249,27.64,28.372,28.259,28.815,27.625,27.151,26.953,27.123\n28.419,28.009,27.839,28.817,29.465,29.002,28.406,28.323,28.012,NaN,NaN,NaN,NaN,NaN,28.245,29.161,29.355,28.521,28.651,28.968,30.011,30.055,31.15,33.569,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.883,28.058,28.374,28.567,28.233,28.658,26.745,27.186,27.15,26.878\n28.79,28.251,27.685,29.531,29.437,29.942,29.327,28.825,28.09,27.273,27.464,27.595,27.728,27.13,26.003,26.093,29.257,29.825,31.256,31.581,30.633,31.621,33.736,35.025,35.335,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.161,28.708,28.07,27.907,27.559,27.025,27.807,28.145,27.352\n28.661,28.324,28.44,28.241,27.52,27.839,28.071,27.957,28.124,25.75,26.855,27.641,27.693,27.256,27.749,29.069,30.209,32.819,33.074,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.87,27.937,28.299,28.281,28.053,27.247,27.743,27.927,27.243\n27.652,27.53,27.775,27.562,27.356,28.119,27.831,28.305,28.411,26.612,27.202,27.819,27.713,26.683,29.296,30.764,31.407,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,39.696,34.823,NaN,NaN,31.589,31.269,30.32,28.049,27.464,27.747,28.036,27.971,27.86,27.78,27.69\n27.815,27.628,27.494,27.762,27.579,28.301,28.542,28.687,28.282,27.662,27.804,29,29.129,29.363,30.517,31.541,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,39.391,37.006,35.38,32.107,31.441,31.055,29.727,27.881,26.875,26.606,27.831,28.241,28.363,27.546,27.841\n28.2,29.145,28.264,27.303,27.245,27.575,28.965,28.304,28.308,27.884,27.856,28.316,29.553,29.678,30.698,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.788,38.47,39.88,37.721,35.481,33.666,32,30.065,29.557,28.798,27.962,28.155,26.658,27.414,28.09,28.51,28.341,27.85\n27.826,28.233,28.246,27.663,27.485,27.028,27.293,28.331,28.141,27.776,28.261,28.082,28.595,28.785,30.643,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.939,31.386,33.883,35.674,35.472,31.917,32.364,32.525,30.699,29.887,29.815,26.324,26.864,26.666,27.007,27.386,28.119,27.865,27.935,27.844\n29.918,29.597,28.459,27.956,28.705,27.82,28.18,28.299,27.701,27.598,28.59,27.431,27.661,28.331,28.943,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,35.618,32.336,31.604,33.936,33.449,32.736,31.547,32.025,32.276,30.812,28.49,28.275,26.726,26.668,26.634,26.941,27.593,27.93,27.569,27.72,27.738\n30.01,28.951,27.7,27.961,28.246,28.27,28.335,27.394,26.692,26.302,26.804,27.64,27.443,28.983,29.755,29.513,27.852,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.221,32.313,32.496,32.879,32.173,32.243,29.212,32.441,32.86,32.351,28.671,27.712,28.3,28.07,27.983,27.96,27.876,27.858,27.684,27.852,27.259\n28.142,29.718,28.584,26.679,27.721,27.619,26.865,26.727,26.689,25.879,27.022,28.703,29.005,29.201,31.579,NaN,NaN,28.443,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.74,31.655,31.401,30.775,29.857,29.674,29.642,32.263,32.254,30.52,29.538,27.634,28.46,28.65,28.449,27.707,27.63,27.097,26.438,26.48,26.034\n28.377,28.576,27.939,26.841,27.01,26.698,25.205,24.499,25.624,26.714,27.659,29.557,29.343,28.693,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.207,31.182,30.678,31.242,31.141,30.92,30.357,30.56,30.998,29.561,28.464,28.133,28.397,28.688,28.483,28.016,27.072,26.327,26.056,25.832,25.848\n27.749,27.737,28.19,28.52,27.844,27.817,28.147,27.86,28.705,29.096,28.781,29.453,29.043,29.162,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.676,31.213,30.521,31.289,32.086,31.761,30.69,30.764,31.241,30.481,29.375,28.81,28.598,28.321,28.056,28.139,27.408,26.937,26.492,26.07,26.066,26.43\n28.134,27.713,28.682,28.185,28.613,28.737,28.95,29.037,28.654,28.746,29.648,30.112,30.233,30.622,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.679,29.293,30.689,30.476,30.451,30.286,31.152,31.095,31.032,29.928,30.805,30.128,28.999,28.265,27.013,27.082,26.687,26.495,26.871,26.66,26.972,27.414\n27.489,27.218,27.49,28.02,28.988,28.118,28.489,28.489,28.845,28.877,29.639,30.646,31.403,31.735,32.277,32.423,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.393,28.268,30.547,29.754,27.845,28.797,29.304,30.444,30.376,31.363,31.859,31.133,28.858,28.679,28.17,26.857,26.403,26.356,26.268,26.097,26.73,26.863,27.51\n27.008,26.871,27.349,28.578,28.84,28.009,28.812,28.19,28.91,29.079,29.528,30.18,31.017,31.213,33.005,33.743,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.961,29.769,29.758,30.912,28.864,27.853,28.222,29.163,30.078,30.025,29.692,29.34,28.296,27.796,28.278,28.049,27.179,26.596,26.093,26.543,27.132,27.368,26.995,27.255\n26.368,26.525,27.476,29.095,28.378,28.468,28.846,28.936,29.295,30.252,31.81,31.157,30.948,30.866,32.197,32.029,30.962,NaN,NaN,NaN,NaN,NaN,30.351,30.63,31.425,30.89,30.308,29.047,28.861,27.939,28.861,29.842,28.753,28.477,28.641,28.296,27.866,28.644,27.727,28.171,27.588,27.736,27.304,26.337,27.657,27.139,27.505\n25.974,26.56,27.674,28.876,28.989,28.223,28.509,28.953,28.861,29.899,31.168,30.777,31.274,31.135,30.837,30.22,29.419,NaN,NaN,NaN,NaN,NaN,29.286,30.251,29.974,30.179,30.018,29.523,27.354,27.81,28.85,29.396,28.772,29.471,29.269,28.416,28.06,27.881,27.651,27.982,28.235,28.379,28.09,27.495,27.615,27.711,28.177\n27.383,26.63,26.227,27.79,28.25,27.843,28.433,29.002,28.396,28.991,30.416,30.622,30.063,29.397,31.002,31.967,29.654,27.529,27.476,NaN,NaN,28.538,29.219,29.516,29.307,29.393,29.726,27.581,26.23,27.257,27.964,28.868,29.103,29.037,28.925,28.208,28.257,27.969,28.12,27.982,27.189,27.757,29.26,29.006,27.716,28.32,28.383\n26.908,26.675,25.773,28.161,26.902,26.828,28.184,27.93,27.489,27.704,28.703,28.891,28.753,28.693,29.586,29.611,30.306,27.542,29.34,29.695,27.433,28.691,28.59,28.479,28.55,28.835,28.606,27.488,27.344,27.735,27.928,28.226,28.713,28.347,28.341,28.863,29.053,29.313,28.809,27.776,27.091,27.785,28.194,28.377,27.523,27.5,28.706\n26.082,26.489,27.864,28.91,28.255,26.672,27.029,26.484,27.363,27.374,27.485,28.375,27.714,26.767,27.194,28.721,28.403,23.687,25.662,26.622,27.266,28.189,28.052,27.851,28.069,28.646,29.021,29.108,28.498,27.584,27.711,28.364,28.566,28.147,28.368,27.95,27.447,29.065,28.665,28.327,27.864,27.643,27.06,27.496,26.775,27.603,29.252\n25.464,26.121,26.889,26.27,26.322,26.672,26.522,26.1,27.649,27.585,27.656,29.067,28.728,26.86,26.394,28.143,28.102,26.89,28.738,26.936,27.962,28.583,28.568,27.905,28.059,28.357,28.58,28.437,27.017,27.741,27.958,27.388,27.263,27.121,27.655,27.924,27.834,28.574,28.667,28.467,28.275,27.306,27.159,27.676,27.612,28.135,28.178\n25.854,25.649,24.558,25.021,26.005,28.405,26.944,26.988,27.254,28.127,27.439,27.309,28.66,28.101,25.941,26.863,27.484,28.266,28.115,26.404,27.774,28.257,28.485,27.953,27.647,27.803,28.364,28.034,27.271,28.046,27.625,27.525,25.399,26.397,27.543,27.943,27.837,28.169,27.875,28.176,28.466,28.015,27.692,27.998,28.199,28.34,28.304\nNaN,23.673,24.449,24.935,26.484,28.975,28.235,26.601,26.564,27.51,28.223,26.911,26.978,27.607,25.997,25.332,25.819,27.599,27.674,26.233,26.333,27.516,27.852,28.148,27.61,27.714,27.903,27.079,26.736,26.999,27.627,27.992,26.615,26.749,27.619,27.814,27.696,27.622,27.171,27.454,28.23,27.967,27.739,28.306,28.463,29.226,29.172\nNaN,NaN,NaN,24.941,24.37,26.599,30.99,28.503,27.406,28.272,28.645,28.267,25.306,27.475,25.218,24.322,25.322,25.957,26.746,27.148,27.417,27.518,27.473,27.608,27.648,28.059,28.101,26.269,26.361,26.678,26.752,26.967,26.985,27.273,27.417,27.191,27.26,26.953,27.521,27.557,27.455,27.51,27.723,28.915,28.734,29.403,29.568\nNaN,NaN,NaN,24.656,24.033,24.121,30.693,26.898,27.985,28.089,28.335,26.059,NaN,NaN,NaN,NaN,NaN,25.801,26.746,28.013,28.482,27.262,26.935,26.781,27.021,27.766,29.097,29.318,27.329,27.258,26.322,25.995,26.16,26.27,26.712,26.837,27.153,27.15,27.428,27.188,27.032,27.463,28.234,29.002,29.488,29.83,29.578\nNaN,NaN,NaN,NaN,24.412,22.945,25.65,27.035,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,25.858,27.225,28.269,27.907,27.339,26.975,26.128,27.014,27.552,28.707,28.316,27.705,28.019,27.82,26.077,25.438,25.905,25.691,26.352,26.617,26.418,26.962,26.99,27.519,28.178,28.262,28.431,29.404,29.679,28.525\nNaN,NaN,NaN,NaN,NaN,21.415,23.082,25.77,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.183,27.075,27.52,27.822,27.388,27.757,28.659,27.181,27.602,27.442,27.132,27.228,27.399,28.977,27.946,27.328,25.833,25.143,25.553,26.035,26.324,25.75,26.351,26.995,27.852,28.206,28.289,28.488,28.675,28.218,27.834\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.096,26.941,26.681,27.148,27.108,26.92,27.774,28.466,27.819,27.228,27.333,27.576,28.322,28.606,27.822,27.679,27.133,26.313,26.513,26.265,26.693,25.549,25.652,26.424,27.318,27.852,28.131,28.722,28.895,27.84,27.163\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.774,26.309,26.301,25.296,25.713,25.677,27.143,28.348,26.859,26.677,26.342,27.175,27.645,26.603,26.393,26.887,27.095,26.866,26.111,25.572,26.495,26.082,26.013,26.587,27.149,27.369,27.753,28.988,28.97,28.318,26.928\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.225,24.603,25.309,25.184,27.921,27.868,27.508,26.567,25.92,26.697,27.158,27.627,26.946,25.855,25.961,26.401,26.685,26.724,25.805,26.313,26.62,26.681,26.789,26.287,27.042,27.833,28.105,28.644,28.552,27.823,27.227\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.497,27.181,27.839,27.349,26.012,26.111,27.166,27.496,27.991,26.727,26.325,26.573,26.295,26.591,26.593,26.047,26.133,26.182,26.559,26.86,27.293,27.867,27.975,27.838,27.98,28.568,28.813,28.641\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.278,25.641,28.193,29.621,26.3,27.166,27.272,27.197,27.7,26.468,26.272,26.958,27.189,26.745,26.128,22.896,25.495,26.445,27.326,27.648,28.033,28.139,27.846,27.871,28.072,29.297,29.388,28.791\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.267,28.841,NaN,NaN,NaN,26.711,29.086,29.958,28.823,27.419,26.458,26.787,27.716,26.67,25.872,22.409,25.996,27.24,27.545,27.846,28.248,27.524,27.786,28.094,28.608,29.488,29.18,28.158\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.551,NaN,NaN,NaN,26.173,28.527,29.182,27.845,25.553,25.551,26.254,27.506,26.715,26.357,25.27,25.459,26.326,26.703,27.092,28.001,28.369,28.465,28.408,28.532,28.568,28.21,27.971\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.284,NaN,NaN,21.168,25.594,26.184,26.612,26.71,25.123,23.207,24.614,25.805,25.722,25.757,25.796,26.503,26.807,26.926,27.28,27.838,28.281,27.677,27.98,28.283,27.807,27.192,27.375\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.528,NaN,NaN,25.156,26.628,25.949,26.636,26.544,25.818,24.948,26.242,26.826,27.175,26.194,25.829,26.575,27.226,27.359,27.232,27.915,28.056,27.976,28.242,27.858,26.766,26.549,26.965\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.215,29.92,26.258,25.263,25.719,26.202,25.856,24.906,26.001,25.532,25.447,25.73,26.854,27.248,26.451,26.997,28.114,28.022,26.829,26.473,27.038,27.406,28.112,27.929,27.458,26.752,26.732,27.816\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.037,29.83,27.338,24.314,24.361,26.065,25.896,25.011,25.557,24.711,24.424,25.427,26.861,26.845,26.491,26.857,27.843,27.433,27.18,26.78,26.371,26.7,27.348,28.044,27.219,27.163,27.333,27.351\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061002-070219.unw.csv",
    "content": "-13.99,-14.989,-15.356,-15.669,-16.648,-17.238,-17.656,-17.749,-16.912,-16.595,-17.364,-18.129,-18.09,-17.323,-16.187,-16.801,-17.424,-17.981,-18.234,-18.524,-19.273,-19.905,-20.01,-19.788,-20.087,-21,-20.319,-19.747,-20.416,-18.002,-14.804,-14.09,-13.471,-13.633,-12.499,-11.178,-10.446,-9.9362,-10.342,-10.176,-10.022,-11.025,-12.334,-12.494,-10.353,-10.496,-9.5575\n-13.841,-14.478,-15.261,-15.843,-16.826,-17.315,-17.417,-17.769,-17.359,-17.098,-17.656,-18.353,-17.942,-17.418,-17.183,-17.705,-18.22,-18.109,-17.414,-18.246,-19.351,-19.97,-19.55,-19.271,-20.385,-21.249,-20.674,-19.944,-20.09,-18.931,-15.979,-16.481,-15.259,-13.709,-12.604,-11.822,-10.8,-10.306,-10.359,-10.754,-11.015,-12.02,-12.983,-12.991,-12.087,-10.909,-9.3157\n-13.095,-13.44,-15.133,-15.564,-15.998,-17.453,-17.267,-17.161,-17.301,-17.144,-17.609,-18.477,-18.1,-17.599,-17.795,-18.178,-18.492,-18.148,-17.366,-17.852,-17.979,-18.235,-19.411,-21.071,-21.501,-21.511,-21.137,-21.296,-20.874,-18.737,-17.557,-17.285,-16.304,-14.973,-13.746,-11.919,-12.677,-11.182,-10.301,-10.784,-12.098,-12.758,-12.325,-11.8,-11.227,-11.156,-10.369\n-12.326,-12.644,NaN,NaN,-16.48,-17.861,-16.562,-16.284,-17.108,-17.93,-18.134,-17.906,-18.407,-17.847,-17.766,-17.529,-16.69,-17.118,-17.497,-17.524,-17.376,-18.452,-19.911,-21.51,-21.828,-21.77,-22.164,-22.105,-20.927,-19.581,-19.201,-18.873,-15.945,-14.905,-13.627,-12.239,-12.787,-12.031,-10.959,-11.782,-12.59,-13.073,-12.265,-11.229,-10.774,-10.97,-9.8168\nNaN,NaN,NaN,NaN,-16.55,-16.659,-15.989,-15.647,-16.233,-17.366,-18.202,-17.935,-18.654,-18.327,-18.037,-17.378,-16.619,-17.167,-17.869,-18.717,-18.868,-20.199,-21.328,-22.107,-22.069,-21.631,-23.44,NaN,NaN,NaN,-20.547,-20.087,-18.479,-16.094,-13.806,-12.321,-12.309,-12.138,-11.739,-12.497,-12.798,-13.219,-12.18,-11.25,-11.189,-10.854,-10.005\nNaN,NaN,NaN,-15.067,-15.337,-15.49,-15.472,-15.597,-15.897,-16.65,-17.643,-17.415,-17.915,-17.951,-18.052,-17.388,-17.33,-18.119,-19.423,-19.454,-20.418,-22.258,-24.084,-23.01,-20.815,-20.916,NaN,NaN,NaN,NaN,NaN,NaN,-20.364,-19.246,-17.383,-15.492,-12.177,-12.437,-12.637,-12.6,-12.702,-12.112,-11.125,-10.506,-10.682,-11.105,-10.44\nNaN,NaN,NaN,-14.951,-14.989,-15.569,-15.364,-15.637,-16.445,-17.471,-17.764,-17.274,-17.121,-17.53,-18.15,-17.683,-18.044,-18.62,-19.21,-19.808,-20.962,-23.224,-24.365,-23.142,-22.009,-22.037,NaN,NaN,NaN,NaN,NaN,NaN,-21.36,-20.278,-19.454,-18.056,-15.355,-12.959,-11.733,-10.358,-10.977,-11,-10.41,-10.674,-11.373,-12.277,-10.426\n-14.34,-14.445,-15.241,-15.329,-14.923,-15.891,-16.065,-15.984,-16.733,-17.08,-17.994,-18.209,-17.892,-17.247,-17.904,-18.696,-19.037,-18.529,-19.337,-20.575,-22.356,-23.348,-22.665,-23.264,-24.696,-25.089,-22.817,NaN,NaN,NaN,NaN,-21.472,-21.823,-20.101,-19.968,-19.386,-16.311,-13.275,-11.099,-9.7534,-9.4951,-9.972,-10.003,-10.667,-11.344,-12.101,-9.9061\n-14.692,-15,-15.557,-15.848,-15.861,-16.999,-16.862,-16.839,-16.876,-17.361,-18.063,-18.211,-18.13,-17.578,-17.736,-18.873,-19.017,-18.11,NaN,NaN,NaN,NaN,-21.732,-25.409,-26.646,-26.041,-26.675,-26.143,-24.055,-22.54,-22.469,-21.955,-21.477,-20.93,-20.816,-20.602,-17.078,-13.862,-11.633,-9.3355,-8.6141,-9.5904,-10.303,-10.871,NaN,NaN,NaN\n-15.128,-15.448,-15.747,-14.187,-15.241,-16.842,-16.646,-16.1,-16.807,-17.198,-17.715,-17.292,-18.252,-17.997,-18.131,-19.294,-19.522,NaN,NaN,NaN,NaN,NaN,NaN,-27.237,-27.48,-26.162,-27.936,-27.388,-25.966,-23.967,-23.002,-22.432,-22.134,-19.933,-19.445,-19.288,-17.253,-16.119,-14.602,-10.13,-8.2214,-8.4156,-9.4268,-9.9599,NaN,NaN,NaN\n-14.982,-15.796,-14.991,-13.815,-15.215,-16.268,-15.754,-15.601,-16.662,-17.561,-17.968,-18.015,-19.136,-19.404,-19.754,-19.784,-19.186,NaN,NaN,NaN,NaN,NaN,NaN,-27.315,-28.372,-28.08,-27.663,-27.012,-25.721,-24.079,-23.199,-22.133,-22.251,-21.193,-20.039,-17.35,-15.384,-14.644,-16.032,-12.352,-9.3107,-9.2302,-9.5641,NaN,NaN,NaN,NaN\n-15.283,-16.564,-16.277,-15.661,-15.906,-16.353,-15.426,-16.132,-17.226,-18.008,-18.92,-19.745,-20.317,-20.265,-19.803,-19.561,-18.756,NaN,NaN,NaN,NaN,NaN,NaN,-27.506,-27.356,-26.763,-27.295,-26.429,-24.863,-24.021,-22.551,-21.834,-21.693,-20.839,-19.25,-18.168,-18.815,-15.958,-14.216,-13.713,-12.109,-11.601,-12.038,NaN,NaN,NaN,NaN\n-14.141,-14.457,-14.651,-15.062,-15.805,-16.16,-15.71,-16.603,-17.254,-18.023,-18.63,-19.658,-20.895,-21.294,-22.083,-20.472,-20.75,-22.717,-25.385,-24.865,-23.714,-25.685,-24.6,-25.474,-26.41,-26.697,-26.724,-25.602,-24.094,-23.06,-22.171,-21.998,-21.56,-20.782,-19.412,-18.739,-18.479,-17.266,-16.221,-16.699,-15.688,-13.282,NaN,NaN,NaN,NaN,NaN\n-13.275,-12.957,-13.333,-15.105,-17.082,-17.161,-16.436,-16.522,-16.975,-17.758,-18.898,-19.79,-20.88,-21.313,-22.838,-21.365,-21.891,-22.636,-22.369,-22.153,-23.002,-24.395,-24.234,-24.757,-25.64,-24.971,-24.66,-23.968,-22.931,-22.483,-22.305,-22.191,-21.324,-19.606,-18.445,-17.648,-16.999,-17.014,-17.866,-17.135,-18.061,-14.203,NaN,NaN,NaN,NaN,NaN\n-13.436,-12.653,-13.394,-16.474,-17.763,-17.098,-16.229,-16.931,-18.43,-20.477,-21.552,-20.056,-20.188,-20.726,-20.596,-20.711,-21.144,-22.503,-21.927,-22.184,-23.153,-23.304,-23.786,-23.312,-23.621,-22.675,-22.098,-21.59,-20.32,-19.707,-19.461,-18.257,-17.529,-17.225,-17.003,-17.82,-17.011,-16.836,-16.994,-16.53,-16.492,-17.39,NaN,NaN,NaN,NaN,NaN\n-13.623,-14.256,-14.38,-15.702,-15.992,-16.728,-16.189,-17.109,-18.997,-19.52,-20.952,-19.133,-20.077,-21.89,-24.286,-24.928,NaN,NaN,NaN,-23.694,-23.497,-23.428,-23.015,-22.202,-23.18,-22.862,-21.672,-20.926,-19.526,-18.136,-17.624,-17.808,-17.818,-17.251,-16.972,-17.688,-18.003,-18.029,-17.484,-16.179,-16.092,-17.081,NaN,NaN,NaN,NaN,NaN\n-13.347,-13.648,-14.674,-15.421,-15.935,-16.101,-16.353,-17.826,-19.316,-20.12,-20.726,-21.08,-21.366,-22.112,-24.617,-24.992,NaN,NaN,NaN,NaN,NaN,NaN,-21.365,-21.028,-21.984,-21.697,-20.039,-18.993,-18.365,-18.092,-18.597,-18.834,-19.352,-18.904,-17.717,-16.843,-17.607,-17.808,-18.669,-18.915,-17.986,-16.925,NaN,NaN,NaN,NaN,NaN\n-12.137,-14.35,-14.854,-14.584,-14.814,-14.955,NaN,NaN,-20.457,-20.863,-21.552,-22.604,-24.221,-24.514,-23.832,-23.519,-24.595,NaN,NaN,NaN,NaN,NaN,NaN,-20.535,-21.05,-21.1,-20.115,-18.224,-18.829,-18.214,-18.718,-19.648,-18.86,-19.301,-19.021,-18.217,-17.579,-16.977,-16.83,-17.141,-17.608,-19.085,NaN,NaN,NaN,NaN,NaN\n-13.258,-13.984,-13.353,-11.582,NaN,NaN,NaN,NaN,-20.64,-21.137,-21.651,-22.68,-23.694,-24.41,-24.418,-23.794,-24.304,-24.319,NaN,NaN,NaN,NaN,NaN,-21.411,-22.099,-21.861,-22.053,-19.728,-20.011,-20.146,-18.404,-19.101,-17.624,-17.035,-16.642,-17.958,-18.469,-18.747,-17.064,-16.736,-17.042,-18.016,-19.655,NaN,NaN,NaN,NaN\n-13.043,-13.834,-14.893,-14.006,NaN,NaN,NaN,NaN,-21.965,-23.017,-22.524,-23.266,-24.075,-25.042,-25.52,-25.428,-25.442,-26.151,NaN,NaN,NaN,NaN,NaN,-23.944,-24.683,-23.574,-23.233,-23.379,-23.483,-22.35,-19.873,-19.058,-17.731,-17.51,-17.509,-18.261,-16.193,-16.616,-15.939,-16.176,-16.564,-17.222,-17.866,-16.413,-15.538,-15.998,-18.549\n-12.978,-14.934,-17.308,NaN,NaN,NaN,NaN,NaN,-24.158,-24.79,-25.783,-25.162,-24.478,-25.681,-26.017,-25.559,-25.131,-24.846,-25.74,-25.877,-25.359,-23.717,-22.915,-22.72,-23.382,-22.965,-22.464,-23.27,-23.517,-23.336,-21.585,-19.4,-17.767,-17.878,-18.012,-18.95,-17.726,-16.566,-15.739,-16.959,-17.06,-16.681,-15.987,-14.742,-15.768,-17.13,-20.072\n-15.211,-16.811,-15.991,-15.129,NaN,NaN,NaN,-24.08,-23.768,-25.459,-25.924,-26.021,-25.505,-26.185,-26.087,-25.747,-25.017,-25.618,-24.643,-23.675,-24.334,-24.104,-22.569,-22.781,-22.697,-22.324,-22.341,-22.838,-21.871,-21.385,-20.74,-19.961,-18.242,-18.073,-18.029,-17.72,-16.52,-17.366,-16.821,-15.516,-16.016,-15.802,-15.882,-16.18,-16.98,-17.218,-18.639\n-16.914,-17.787,-15.99,NaN,NaN,NaN,NaN,-21.877,-23.725,-24.869,-25.355,-26.462,-26.953,-25.68,-24.504,-24.091,-24.555,-25.85,-26.571,-25.123,-25.518,-25.661,-25.48,-23.422,-23.161,-22.73,-22.878,-22.544,-21.402,-20.068,-19.624,-19.425,-18.79,-18.314,-17.958,-17,-16.101,-15.618,-14.971,-14.53,-15.927,-16.691,-15.334,-15.868,-16.971,-17.462,-18.523\n-16.938,-17.24,-16.913,-16.028,-18.521,-23.964,-23.591,-24.056,-23.954,-23.756,-24.104,-25.205,-26.034,-25.708,-25.06,-21.872,-21.548,-23.473,-25.204,-25.024,-23.204,-23.528,-23.561,-20.641,-20.948,-22.555,-22.411,-20.795,-20.01,-18.434,-17.389,-17.67,-17.295,-17.352,-17.074,-15.986,NaN,NaN,NaN,-14.375,-15.222,-16.225,-15.047,-14.905,-16.501,-18.497,-20.016\n-17.132,-17.379,-17.668,-18.594,-18.932,-22.348,-22.617,-22.442,-21.809,-23.288,-23.931,-24.453,-23.522,-24.483,-25.167,-25.209,-24.437,-24.642,-24.308,-23.608,-22.616,-21.698,-20.223,-19.077,-19.532,-19.193,-19.022,-18.026,-17.043,-16.664,-16.077,-15.865,-16.013,-16.46,-15.684,-13.191,NaN,NaN,NaN,-15.363,-14.857,-15.761,-15.614,-15.994,-17.353,-19.204,-20.71\n-18.445,-18.099,-18.72,-19.519,-19.971,-21.235,-21.714,-21.245,-21.404,-22.682,-22.344,-23.772,-24.245,-24.34,-24.211,-24.339,-22.916,-21.818,-22.834,-22.66,-21.862,-19.429,-18.286,-17.957,-18.211,-17.784,-16.891,-16.058,-15.922,-15.751,-15.397,-14.147,-13.864,-15.566,-15.644,-14.628,NaN,NaN,NaN,-13.849,-14.424,-14.83,-15.082,-15.582,-17.593,-19.233,-19.802\n-18.409,-17.402,-18.665,-20.138,-20.334,-21.5,-22.024,-21.19,-21.945,-21.57,-21.672,-23.394,-23.902,-23.925,-23.964,-23.21,-22.204,-21.436,-21.365,-20.942,-19.815,-18.467,-17.354,-17.527,-18.509,-17.47,-16.277,-16.272,-16.283,-16.447,-14.962,-14.852,-15.883,-16.861,-16.622,-16.375,NaN,NaN,NaN,-13.302,-13.559,-14.331,-14.576,-16.523,-17.712,-18.588,-19.45\nNaN,NaN,-17.554,-20.543,-21.001,-21.877,-22.025,-21.598,-22.882,-21.627,-21.373,-22.111,-22.663,-23.063,-23.131,-22.687,-22.388,-21.807,-21.169,-20.301,-19.116,-17.888,-17.361,-17.42,-18.285,-18.447,-17.389,-16.94,-18.139,-17.055,-15.706,-14.657,-16.055,-17.192,-16.449,-13.562,-12.726,-13.348,-13.233,-12.754,-12.693,-13.891,-14.391,-16.571,-17.249,-18.421,-18.926\nNaN,NaN,NaN,-21.01,-22.664,-22.974,-22.7,-22.838,-23.06,-23.38,-22.949,-22.471,-22.097,-22.177,-22.475,-22.671,-22.984,-22.209,-20.622,-19.288,-18.664,-17.164,-16.742,-16.717,-18.075,-17.242,-16.857,-17.156,-16.566,-14.687,-14.514,-14.668,-16.519,-17.99,-15.157,-13.394,-13.807,-14.374,-14.332,-13.226,-11.796,-16.132,-16.774,-16.744,-18.522,-19.196,-18.703\nNaN,NaN,-19.31,-23.254,-24.073,-24.893,-24.4,-23.363,-23.55,-23.583,-22.895,-22.687,-21.865,-21.741,-22.104,-22.293,-22.209,-21.557,-20.077,-19.039,-18.199,-16.999,-15.705,-17.001,-16.858,-16.281,-16.483,-16.24,-15.668,-14.933,-14.486,-15.573,-17.216,-18.197,-15.378,-13.028,-14.203,-15.203,-16.753,-13.183,-11.311,-16.527,-19.015,-19.241,-20.185,-18.594,-18.866\nNaN,NaN,-22.663,-23.186,-23.693,-24.332,-24.248,-24.163,-24.356,-24.492,-23.933,-23.137,-22.511,-21.888,-22.006,-22.445,-22.471,-21.277,-19.749,-19.009,-18.188,-16.948,-14.718,-15.475,-17.094,-17.545,-17.284,-16.523,-15.54,-15.414,-15.324,-15.746,-16.386,-16.057,-11.774,-9.5279,-11.87,-12.933,-13.068,-12.57,-13.147,-16.622,-20.303,-20.795,-20.53,-18.27,-19.149\n-22.027,-23.228,-22.587,-23.731,-24.931,-24.061,-22.848,-22.538,-24.359,-24.425,-24.781,-24.071,-23.716,-23.135,-23.463,-24.231,-23.251,-21.263,-20.033,-19.776,-18.123,-16.747,-14.826,NaN,NaN,NaN,NaN,-16.62,-14.87,-13.089,-13.71,-15.586,NaN,NaN,-9.7949,-10.036,-11.894,-12.951,-12.709,-13.023,-15.869,-17.61,-18.078,-19.643,-19.612,-19.33,-20.494\n-19.836,-22.413,-22.7,-22.698,-21.738,-21.478,-21.282,-21.322,-22.781,-24.809,-24.497,-23.242,-22.768,-23.915,-24.532,-24.377,-24.354,-23.584,-21.683,-19.805,-19.052,-17.096,-15.81,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.4584,-4.4172,NaN,NaN,NaN,NaN,NaN,NaN,-13.052,-13.246,-15.357,-18.175,-18.721,-18.948,-19.597,-20.119,-20.646\n-20.058,-21.702,-22.631,-22.365,NaN,NaN,NaN,-20.035,-21.728,-24.242,-23.796,-23.387,-21.863,-23.496,-24.495,-23.927,-23.361,-20.094,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.594,10.959,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-12.966,-15.582,-16.84,-17.571,-18.559,-19.491,-20.242,-20.366\n-21.334,-22.099,-22.445,-22.853,NaN,NaN,NaN,NaN,-22.081,-22.298,-22.05,-21.721,-21.546,-22.848,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.882,10.673,7.2514,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.706,-14.696,-16.069,-17.823,-19.139,-20.307,-20.359\n-21.41,-21.118,-21.322,-22.102,NaN,NaN,NaN,NaN,-22.798,-22.899,-22.269,-21.784,-20.755,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.006,9.9355,2.8981,-0.88888,-3.201,NaN,NaN,NaN,NaN,NaN,-16.21,-14.639,-15.387,-17.324,-19.384,-20.862,-21.375\n-21.487,-21.185,-20.474,-19.786,NaN,NaN,NaN,NaN,-22.773,-22.673,-23.118,-19.328,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.411,9.0265,2.5438,-2.4144,-4.5134,-6.2166,NaN,NaN,NaN,NaN,-16.199,-16.273,-16.858,-18.133,-19.211,-20.536,-21.06\n-20.151,-20.244,-20.221,-19.247,-18.642,-20.76,NaN,-21.395,-20.307,-21.336,-22.06,-18.511,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.535,12.028,7.6866,4.327,-2.1112,-4.9695,-8.9458,-10.087,NaN,NaN,-15.864,-17.795,-17.81,-17.956,-18.774,-18.549,-19.365,-20.34\n-19.602,-19.104,-19.277,-19.246,-18.676,-19.101,-21.193,-20.358,-19.486,-19.383,-19.409,-16.903,-16.345,-14.665,-14.167,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.924,6.7057,4.6765,3.5564,0.80033,-6.6888,-9.8856,-10.358,-10.951,-12.772,-15.268,-18.206,-19.139,-19.617,-19.758,-20.04,-19.486,-19.593\n-18.943,-18.32,-18.188,-17.954,-17.795,-17.965,-18.884,-19.176,-18.683,-18.391,-17.799,-16.668,-15.698,-15.608,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.0612,4.0115,0.98624,-0.38253,-3.2196,-9.4561,-10.792,-11.828,-12.73,-13.92,-16.201,-18.503,-19.737,-20.07,-20.398,-20.142,-19.796,-20.244\n-17.838,-17.73,-18.053,-17.371,-17.623,-17.535,-17.123,-17.317,-17.652,-16.339,-16.227,-15.969,-14.619,-12.458,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.8832,-1.2523,-6.0222,-9.3967,-10.269,-12.327,-12.642,-12.857,-12.818,-16.592,-19.82,-19.344,-19.908,-19.982,-19.619,-20.433,-21.703\n-16.987,-16.694,-16.274,-16.339,-17.192,-17.972,-18,-17.758,-17.059,-15.743,-14.761,-15.399,-14.606,-11.552,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.98056,-2.7232,-6.6109,-10.062,-10.521,-12.124,-13.568,-14.661,-14.877,-17.726,-19.554,-18.796,-19.869,-20.091,-19.111,-19.743,-21.297\n-18.162,-17.198,-16.86,-17.168,-17.1,-17.204,-17.322,-18.22,-17.702,-16.154,-14.853,-14.693,-14.01,-10.692,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.7893,-1.9093,-4.776,-7.6781,-9.8798,-10.32,-10.836,-12.908,-14.267,-16.858,-18.719,-19.41,-19.404,-19.511,-19.764,-19.894,-20.603,-22.445\n-16.841,-15.576,-15.908,-17.995,-17.305,-17.149,-17.601,-18.266,-17.837,-16.054,-14.712,-13.357,-12.129,-9.2128,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.4494,-0.36916,-2.2437,-3.1601,-4.6436,-7.0877,-9.2144,-12.043,-13.745,-13.713,-15.656,-18.698,-19.393,-19.732,-19.805,-19.539,-19.572,-20.246,-21.25,-21.531\n-15.526,-14.859,-15.496,-18.146,-18.404,-16.754,-17.105,-17.711,-17.956,-14.613,-14.202,-13.455,-12.034,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.3152,-3.8348,-5.2827,-4.7873,-6.836,-9.0394,-10.647,-13.902,-17.775,-17.532,-17.204,-19.386,-20.244,-19.994,-20.305,-20.201,-20.639,-21.235,-21.705,-21.985\n-14.912,-15.165,-15.971,-16.907,-16.861,-15.459,-15.398,-15.752,-14.64,-13.855,-13.946,-12.849,-10.916,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.4949,-6.4054,-5.8146,-6.9324,-8.6667,-10.125,-11.793,-13.434,-15.545,-17.088,-17.8,-20.435,-20.451,-21.155,-21.803,-21.13,-21.178,-21.396,-21.723,-21.876\n-15.94,-16.144,-16.407,-16.391,-15.492,-14.54,-14.283,-14.679,-13.633,-14.276,-14.546,-13.641,-12.333,-11.09,-8.3628,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.866,-7.804,-8.881,-10.436,-11.915,-11.934,-11.69,-12.203,-14.321,-16.005,-16.858,-18.922,-21.859,-20.639,-21.644,-23.36,-23.281,-22.648,-21.828,-22.084,-22.309\n-16.206,-16.003,-15.814,-15.818,-15.127,-14.65,-14.651,-14.766,-14.331,-14.499,-14.544,-13.36,-12.659,-11.733,-9.779,-7.4239,-6.1828,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.9004,-10.669,-13.318,-13.481,-13.514,-12.982,-11.849,-12.159,-15.093,-16.813,-18.535,-18.847,-21.021,-21.148,-21.284,-21.859,-22.699,-23.7,-23.24,-21.974,-22.699\n-17.209,-16.71,-15.809,-15.119,-15.243,-15.993,-15.17,-14.526,-14.31,-14.166,-13.964,-13.342,-12.794,-12.45,-11.022,-9.8669,-9.207,-8.2075,-7.5208,NaN,NaN,NaN,NaN,-9.0141,-9.5351,-9.9125,NaN,NaN,-13.343,-13.525,-13.392,-12.783,-13.234,-15.282,-17.362,-18.985,-19.256,-20.189,-21.289,-21.031,-20.595,-20.529,-21.883,-23.244,-22.785,-22.325,-22.812\n-17.197,-17.204,-16.127,-14.912,-15.259,-15.809,-15.409,-15.094,-14.935,-15.009,-13.819,-13.606,-14.164,-14.294,-12.591,-11.759,-11.643,-10.15,-9.3504,-8.9878,-6.8011,NaN,-9.3909,-8.9232,-8.7214,-9.082,NaN,NaN,-11.354,-13.356,-13.364,-12.996,-14.141,-16.64,-17.403,-18.183,-18.868,-19.873,-20.388,-21.041,-20.705,-20.783,-22.048,-22.562,-22.375,-22.947,-22.839\n-16.835,-16.447,-16.126,-15.776,-15.2,-14.752,-15.976,-16.062,-15.613,-15.033,-14.409,-14.253,-14.949,-14.704,-13.813,-13.17,-13.235,-10.84,-9.4884,-8.8512,-9.5933,-11.049,-10.731,-10.928,-11.545,-11.852,NaN,NaN,-10.86,-13.176,-13.389,-13.068,-14.041,-15.19,-15.711,-15.843,-17.199,-19.245,-20.287,-20.819,-20.921,-20.341,-20.664,-22.283,-22.324,-22.86,-23.701\n-16.065,-16.432,-17.691,-16.775,-15.416,-14.917,-15.292,-15.419,-15.338,-15.451,-14.653,-14.095,-13.516,-13.493,-13.711,-14.151,-14.258,-13.54,-13.49,-11.431,-11.127,-11.663,-11.04,-10.791,-11.455,-12.51,-13.428,-11.798,-10.511,-13.938,-13.789,-14.683,-15.179,-16.037,-16.745,-16.582,-18.179,-19.557,-20.125,-20.669,-21.102,-21.21,-21.496,-22.103,-22.855,-23.672,-24.024\n-16.064,-16.837,-17.766,-16.668,-16.022,-15.226,-14.609,-14.771,-15.155,-15.477,-14.594,-14.001,-13.782,-13.393,-14.037,-14.204,-14.274,-13.918,-14.547,-14.885,-13.625,-12.148,-11.657,-11.857,-12.466,-12.528,-12.849,-12.364,-11.234,-12.921,-14.709,-15.446,-16.996,-17.456,-18.041,-18.41,-18.818,-18.644,-19.2,-20.35,-21.08,-20.826,-20.949,-21.869,-23.392,-23.58,-24.443\n-15.966,-16.531,-16.687,-16.454,-16.212,-16.384,-15.229,-15.1,-13.873,-13.705,-13.446,-13.287,-13.446,-12.615,-12.419,-12.865,-13.228,-13.576,-15.383,-15.194,-12.795,-11.637,-11.734,-12.839,-13.65,-14.188,-13.858,-13.241,-13.525,-14.786,-15.893,-16.788,-18.788,-19.064,-19.905,-21.01,-20.95,-18.754,-18.607,-20.11,-20.523,-20.621,-20.759,-21.646,-23.092,-23.57,-24.326\n-15.865,-15.264,-16.241,-17,-16.939,-16.643,-15.984,-15.203,-14.061,-13.891,-12.417,-11.815,-11.048,-11.889,-12.117,-11.921,-12.147,-12.594,-13.218,-13.724,-12.41,-11.266,-11.238,-12.004,-13.428,-14.262,-13.676,-13.678,-13.764,-16.251,-16.305,-18.501,-20.78,-20.285,-20.559,-21.022,-20.567,-19.753,-20.23,-21.402,-20.904,-20.048,-20.708,-21.706,-22.582,-23.475,-24.872\n-16.337,-16.541,-17.334,-17.5,-16.853,-15.646,-15.419,-15.943,-15.764,-15.079,-13.661,-13.183,-12.991,-12.633,-13.448,-12.551,-12.33,-12.251,-12.717,-13.355,-13.151,-12.519,-11.608,-12.102,-12.428,-13.045,-11.472,-11.631,-13.483,-16.786,-17.421,-18.783,-20.805,-20.587,-20.486,-20.174,-20.107,-20.254,-21.567,-22.637,-21.391,-20.022,-20.195,-21.152,-22.477,-24.079,-25.642\n-17.485,-17.667,-17.817,-17.293,-16.06,-14.129,-15.105,-15.713,-15.185,-14.334,-13.742,-14.506,-15.925,-17.02,NaN,NaN,NaN,-12.935,-13.301,-13.092,-12.399,-12.588,-12.338,-12.194,-12.356,-12.408,-11.892,-12.325,-15.763,-17.634,-17.92,-18.283,-18.87,-19.883,-20.466,-20.099,-20.18,-20.542,-21.033,-20.677,-20.851,-21.216,-20.753,-21.439,-23.214,-24.515,-24.67\n-17.425,-18.024,-18.956,-18.197,-17.608,-15.193,-14.913,-15.906,-15.964,-14.833,-13.281,-13.808,-15.386,NaN,NaN,NaN,NaN,NaN,-13.68,-11.633,-11.352,-12.266,-13.466,-12.658,-11.93,-12.824,-12.868,-13.896,-18.367,-18.254,-18.145,-18.607,-18.306,-18.929,-20.209,-20.776,-21.595,-21.248,-20.428,-20.826,-20.75,-21.179,-21.48,-22.265,-24.179,-24.868,-24.365\n-17.704,-16.912,-17.256,-18.393,-18.263,-16.966,-15.132,-15.752,-15.266,-14.444,-13.453,-13.452,NaN,NaN,NaN,NaN,NaN,NaN,-14.036,-12.362,-12.968,-13.566,-13.272,-13.668,-13.985,-13.941,-14.493,-15.721,-19.103,-19.028,-19.42,-19.844,-19.561,-19.873,-19.922,-20.395,-21.06,-21.016,-20.565,-21.005,-21.153,-21.507,-22.642,-23.051,-22.822,-23.01,-23.844\n-18.59,-16.1,-14.85,-17.438,-18.473,-19.381,-17.338,-16.328,-16.37,-15.577,-14.795,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.325,-14.971,-13.318,-13.07,-13.275,-14.818,-15.153,-15.383,-16.089,-16.902,-18.148,-19.097,-19.639,-20.455,-21.055,-20.243,-19.786,-20.161,-21.074,-20.613,-20.175,-20.618,-21.509,-22.208,-23.225,-23.395,-22.903,-23.512,-25.392\nNaN,NaN,NaN,NaN,NaN,-17.912,-17.862,-16.92,-16.408,-17.102,-18.268,NaN,NaN,NaN,NaN,NaN,NaN,-17.806,-14.701,-14.651,-13.723,-13.257,-14.284,-15.894,-15.055,-14.896,-15.894,-16.608,-18.318,-20.249,-21.287,-20.743,-22.117,-22.493,-21.833,-22.025,-21.935,-19.887,-20.548,-20.479,-20.992,-21.591,-22.062,-22.456,-23.55,-26.568,-28.609\nNaN,NaN,NaN,NaN,NaN,NaN,-17.795,-16.074,-15.778,-16.134,-18.249,NaN,NaN,NaN,NaN,NaN,NaN,-16.793,-15.785,-14.271,-14.73,-15.652,-16.916,-17.154,-16.576,-16.742,-17.076,-18.076,-18.943,-19.909,-20.763,-20.588,-21.553,-24.753,-23.87,-22.898,-22.845,-21.565,-21.695,-20.836,-20.444,-20.463,-19.788,-19.444,-21.571,-25.18,-26.367\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.827,-15.554,-15.422,-16.801,NaN,NaN,NaN,NaN,NaN,-14.978,-15.223,-16.624,-16.644,-16.27,-17.697,-18.601,-18.461,-17.588,-17.556,-19.545,-19.012,-18.796,-18.912,-20.594,-21.444,-21.532,-21.129,-21.478,-22.347,-22.12,-21.579,-21.813,-21.063,-20.64,-20.117,-19.778,-19.438,-20.56,-22.311,-24.507\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.556,-17.245,-16.434,-13.215,-14.422,-16.55,-16.971,-15.481,-14.838,-16.356,-18.639,-19.339,-18.989,-18.726,-18.114,-17.912,-19.597,-20.735,-21.291,-21.619,-22.225,-23.355,-24.777,-23.51,-22.132,-21.399,-21.137,-21.315,-21.166,-21.236,-21.518,-21.428,-22.354,-22.472\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.318,-16.005,-15.424,-16.245,-15.664,-15.116,-16.152,-18.427,-20.466,-20.252,-19.299,-19.01,-19.417,-20.182,-21.333,-22.187,-22.731,-22.562,-23.649,-24.421,-24.134,-23.268,-21.779,-21.498,-22.037,-22.353,-22.115,-20.978,-20.428,-20.607,-21.593,NaN\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.927,-14.554,-17.121,-17.663,-16.481,-15.766,-16.462,-19.104,-20.613,-19.129,-18.598,-19.409,-20.45,-21.715,-22.57,-22.489,-22.942,-23.511,-25.114,-26.191,-23.739,-22.035,-21.441,-21.549,-22.454,-22.97,-22.203,-20.589,-19.445,-18.479,-19.709,NaN\nNaN,NaN,-14.572,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.715,-14.193,-16.574,-17.918,-17.876,-17.089,-16.762,-17.464,-18.723,-18.409,-16.609,-18.132,-20.09,-21.646,-22.208,-21.999,-23.98,NaN,NaN,NaN,-23.893,-23.535,-23.227,-22.512,-21.917,-22.153,-22.116,-21.101,-19.74,-17.915,-19.728,NaN\n-12.462,-15.773,-16.014,-14.983,-13.9,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.913,-16.766,-17.695,-16.72,-16.129,-17.657,-19.783,-18.699,-20.144,-20.529,-18.789,-19.39,-21.137,-21.308,-22.077,-24.078,-25.36,NaN,NaN,NaN,-25.673,-26.294,-24.566,-22.867,-21.612,-21.823,-21.708,-20.622,-19.781,-20.203,NaN,NaN\n-13.417,-14.223,-14.304,-14.575,-13.792,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.305,NaN,NaN,NaN,-20.793,-21.984,-23.727,-23.023,-22.777,-22.352,-22.739,-23.506,-25.203,-27.476,-26.892,NaN,NaN,-25.464,-25.588,-22.115,-20.979,-20.819,-21.155,-21.092,-18.414,-17.229,-21.405,NaN,NaN\n-15.053,-13.902,-12.83,-13.14,-13.448,-13.83,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-22.939,-23.81,-22.971,-22.887,-22.382,-22.56,-23.377,-25.754,-27.933,-27.133,-25.103,-24.749,-23.811,-22.507,-18.834,-18.697,-20.249,-21.455,-21.279,-21.166,-21.283,-22.414,-23.708,NaN\n-13.646,-12.876,-13.477,-14.383,-12.739,-13.563,-15.143,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-19.789,-21.338,-22.733,-22.067,-23.2,NaN,NaN,NaN,-29.928,-26.81,-24.703,-23.585,-22.911,-23.353,-23.587,-22.287,-23.065,-22.322,-23.625,-23.664,-22.797,-22.81,NaN\n-13.174,-13.226,-14.412,-16.081,-14.775,-15.304,-16.515,-17.703,-18.05,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.373,-20.198,-22.097,-21.753,-23.577,NaN,NaN,NaN,-28.608,-26.155,-24.429,-22.774,-23.632,-24.552,-22.685,-22.948,-24.513,-24.113,-24.62,-24.353,-25.131,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061002-070430.unw.csv",
    "content": "-1.7512,-1.9685,-2.7351,-2.1641,-1.9008,-1.6864,-2.1276,-2.189,-1.8532,-2.9455,-2.8339,-2.2707,-1.5749,-0.97677,-0.50635,-1.3088,-2.0186,-1.5259,-1.3597,-1.6651,-2.3415,-2.754,-2.8598,-2.4286,-2.5251,-2.989,-2.6053,-2.3487,-2.3384,-1.8671,-1.585,-1.7805,-1.6163,-1.261,-1.4516,-1.5732,-1.9295,-2.4212,-2.0674,-1.7682,-1.623,-1.8452,-2.2154,-2.559,-2.3401,-2.928,-3.0514\n-2.129,-2.0092,-2.5203,-2.2409,-1.4821,-1.4585,-2.2539,-2.5214,-2.7967,-2.661,-2.7371,-2.7795,-2.0016,-1.4221,-1.4471,-1.1449,-1.8244,-1.4848,-1.3534,-1.7516,-1.8964,-2.8487,-3.1961,-3.3913,-3.6185,-3.2196,-2.3305,-2.0837,-2.2116,-2.3027,-2.183,-2.4118,-2.1096,-1.8436,-1.9001,-1.9228,-2.1323,-2.3705,-2.4591,-2.375,-2.0857,-1.9702,-2.6606,-2.9737,-3.2496,-3.3588,-3.2324\n-2.1399,-2.1902,-2.8489,-2.3554,-2.0855,-2.2623,-2.4154,-2.5068,-2.7082,-2.2908,-2.6951,-2.8646,-2.2061,-1.9136,-1.3699,-1.0506,-1.798,-1.4792,-1.105,-1.9465,-2.2939,-2.7385,-2.7638,-3.5353,-3.3702,-2.6523,-2.0343,-2.892,-3.1092,-2.6255,-3.1307,-2.6977,-2.4751,-2.3404,-2.375,-2.2588,-2.8949,-2.9192,-2.5545,-2.3716,-2.1171,-2.1256,-2.5733,-3.0127,-3.3931,-3.4402,-2.9682\n-2.352,-2.2668,-2.5618,-2.227,-2.1791,-2.0607,-1.5559,-1.5921,-2.5361,-2.7739,-2.6242,-2.4474,-2.4163,-1.6755,-1.5339,-1.5648,-1.6696,-1.2181,-0.96748,-1.8426,-1.9482,-2.3468,-2.9189,-3.2221,-3.1519,-2.991,-2.9907,-3.3789,-3.4737,-3.2185,-3.0915,-2.5412,-2.3058,-2.5126,-2.5332,-2.6257,-2.7704,-3.1127,-2.6063,-2.2266,-2.3159,-2.7268,-3.0477,-3.049,-2.9196,-2.9617,-2.945\n-2.8604,-3.1421,-3.2204,-2.159,-1.5346,-1.7488,-1.5045,-0.54093,-1.1201,-1.3395,-2.1429,-2.4633,-2.2273,-1.4192,-1.8248,-2.1183,-2.3237,-1.8039,-1.042,-0.89782,-1.3759,-2.1729,-3.3605,-3.1379,-3.2966,-3.6632,-4.21,-4.0635,-3.3684,-3.1076,-3.4195,-3.2071,-2.6919,-2.2442,-2.197,-2.2441,-2.8239,-2.9278,-2.6263,-2.4423,-2.6681,-2.6021,-2.757,-2.855,-2.7598,-2.8208,-2.8686\n-3.362,-3.6017,-2.6347,-1.9961,-1.7468,-1.8938,-1.9825,-1.9987,-2.2202,-2.2388,-2.0363,-2.5312,-1.684,-1.3214,-1.371,-1.7347,-1.7192,-1.8244,-1.6969,-2.071,-2.232,-2.8002,-3.8877,-3.2442,-2.5802,-2.9976,-3.8628,-4.1673,-3.4507,-3.0949,-3.4515,-3.6546,-3.394,-2.526,-2.3294,-2.1228,-2.1638,-2.4571,-2.6369,-2.917,-2.5571,-2.076,-2.1539,-2.3994,-2.4398,-2.4098,-2.5539\n-3.4321,-3.6351,-3.0492,-1.9169,-1.7121,-1.8926,-1.3872,-2.0016,-2.4921,-2.231,-2.4103,-2.4384,-2.0946,-1.7194,-1.7028,-1.8342,-2.2423,-2.4405,-2.5918,-2.6207,-3.3688,-3.7847,-3.667,-3.6079,-3.7663,-3.7545,-4.1897,-4.1693,-3.4299,-3.0474,-3.5768,-3.8639,-3.8649,-3.5765,-2.9741,-2.4265,-2.1679,-2.5631,-2.9427,-2.5462,-2.4421,-2.1182,-1.9422,-1.9704,-1.9726,-2.0444,-1.7342\n-2.7887,-3.0595,-3.5605,-2.0282,-1.7139,-1.8775,-2.0315,-1.9882,-1.8916,-2.4333,-3.0842,-2.3886,-2.3091,-2.051,-2.4391,-2.5283,-3.0465,-2.8448,-2.9608,-3.2452,-4.1424,-4.0953,-3.8123,-3.4703,-3.6932,-4.2816,-4.7944,-4.2308,-3.7183,-3.6977,-3.9222,-3.7741,-3.8325,-3.6311,-3.0743,-2.7763,-2.7466,-3.1442,-3.1529,-2.4391,-2.0554,-2.0863,-1.9755,-2.4244,-2.5837,-2.4101,-1.9211\n-2.4939,-2.6974,-3.2856,-2.0389,-1.8766,-1.7445,-1.865,-1.6011,-1.8637,-2.9326,-3.3925,-2.9408,-2.4507,-2.3806,-2.666,-3.2631,-3.4987,-3.6509,-3.5516,-4.3559,-4.9361,-4.8286,-3.8574,-3.3232,-3.4111,-3.6864,-3.9768,-3.7008,-4.0206,-4.3166,-4.2337,-4.0286,-3.9839,-3.6838,-3.3102,-3.3555,-3.4251,-3.5085,-3.0275,-2.6083,-2.1353,-1.7212,-1.6398,-2.6022,-3.0485,-2.9476,-2.1421\n-2.9168,-2.9458,-2.1634,-1.9417,-2.1827,-1.8253,-1.9189,-1.6729,-2.2071,-3.1224,-3.2531,-3.3176,-3.0884,-3.081,-2.9924,-3.1504,-3.909,-4.2745,-4.3722,-4.6047,-4.1852,-4.0248,-4.0305,-3.7617,-3.7495,-3.5849,-3.9917,-3.9698,-3.9922,-4.1082,-3.8631,-3.9536,-3.9722,-4.01,-4.1178,-3.8073,-3.7675,-3.9314,-3.5214,-2.8397,-2.1568,-1.8615,-1.9736,-2.7,-3.1869,-2.9726,-2.2989\n-2.6196,-2.2816,-1.6805,-1.1607,-1.1629,-1.6445,-2.0618,-2.2758,-2.5671,-3.342,-3.734,-3.7859,-3.366,-3.7357,-4.1306,-3.4542,-3.9217,-4.2268,-4.5647,-4.6665,-3.8955,-3.6602,-4.1062,-3.9314,-3.8899,-4.0014,-4.0775,-4.165,-4.0768,-3.8572,-3.4424,-3.302,-3.7158,-3.8469,-4.1909,-4.0105,-3.9583,-3.9997,-3.7301,-3.0239,-2.3699,-2.296,-2.466,-2.8064,-3.3045,-3.0927,-2.7828\n-2.4315,-2.0656,-1.2652,-1.1824,-1.9151,-1.3323,-2.0651,-2.5651,-2.4759,-3.5856,-4.2069,-4.2762,-3.7651,-3.852,-3.9921,-3.4122,-3.7241,-4.3448,-4.4091,-4.1471,-3.0874,-3.5063,-3.7962,-3.4076,-3.4401,-3.5783,-3.7804,-3.9212,-3.5869,-3.3667,-3.456,-3.6694,-3.6601,-3.6035,-3.8398,-3.8141,-3.5942,-3.6048,-3.5954,-3.0462,-2.3661,-2.3095,-2.6374,-3.0599,-3.3292,-3.5948,-3.2821\n-2.2845,-2.0255,-0.68458,-0.03184,-1.6147,-1.4572,-1.8287,-2.3752,-2.3335,-3.6435,-3.5005,-3.7062,-3.7273,-3.2926,-3.1718,-3.1302,-3.1267,-3.3956,-3.5246,-3.234,-3.7634,-3.3119,-3.1468,-3.4891,-3.7828,-3.4129,-3.353,-3.7415,-3.5176,-3.2677,-3.7749,-3.9894,-3.9196,-3.7942,-3.95,-3.9061,-3.2437,-3.1588,-3.1974,-2.6819,-2.1907,-2.1342,-2.537,-2.9604,-3.4698,-3.4797,-2.9279\n-1.2707,-0.82694,-0.12478,1.1035,-0.80287,-2.9657,-2.7147,-2.1393,-3.2915,-3.8785,-4.1115,-3.9776,-3.4489,-3.0194,-2.9452,-3.1055,-3.2055,-3.5767,-3.4892,-3.4334,-3.9788,-4.0126,-3.7402,-3.6843,-4.0355,-2.9231,-2.7393,-3.2828,-3.1963,-3.2943,-3.8187,-3.9809,-4.0263,-4.036,-4.0218,-4.1838,-3.9895,-3.5351,-2.798,-2.7812,-2.498,-2.489,-2.9563,-3.1918,-3.6396,-3.0685,-2.6864\n-0.77967,-1.4302,0.14338,1.2842,-1.0557,-2.5797,-2.9392,-3.8297,-3.6811,-3.701,-3.4888,-2.9265,-2.6589,-2.5397,-2.2552,-2.0932,-2.9347,-3.7155,-3.6384,-3.7096,-4.567,-4.2063,-3.527,-3.1833,-3.2478,-1.8297,-1.9838,-2.6252,-3.0432,-3.0011,-3.0361,-3.4318,-4.006,-4.2879,-4.3388,-4.745,-4.3858,-3.963,-3.2942,-3.1739,-3.3293,-3.3009,-3.5777,-3.7459,-3.8346,-3.1099,-3.0961\n-1.408,-1.5411,-1.8563,-1.055,-1.2181,-1.4103,-2.9068,-4.1607,-3.56,-3.783,-3.2452,-2.7647,-2.4434,-3.2038,-3.4277,-4.0824,-4.4673,-4.1796,-3.9957,-4.701,-4.7249,-3.9548,-3.4934,-2.4959,-2.474,-2.4561,-2.6768,-3.4287,-3.7389,-3.2664,-4.034,-4.2962,-4.6331,-4.7575,-4.804,-5.0728,-4.4595,-4.2192,-3.9789,-3.5353,-3.3324,-3.1177,-3.428,-3.5246,-3.5756,-3.9168,-3.5843\n-1.5411,-1.6881,-1.7077,-1.3801,-1.163,-1.4479,-2.515,-3.322,-3.9197,-4.3443,-4.1791,-3.144,-2.7833,-4.0819,-3.5323,-4.727,-4.3952,-3.9833,-4.7424,-5.2867,-5.2783,-4.4453,-4.0891,-3.5413,-3.6553,-3.0421,-3.3165,-3.9235,-3.9934,-3.5496,-3.6702,-4.4846,-4.8759,-4.878,-4.8822,-4.9944,-4.944,-4.9457,-4.4283,-3.9046,-3.1754,-3.1897,-3.3807,-3.3957,-3.3484,-3.8352,-3.6887\n-1.5441,-1.5205,-1.5451,-1.4563,-0.63583,-0.82553,-1.3985,-3.053,-2.9898,-2.4835,-2.8984,-3.5112,-3.9002,-4.3262,-3.9779,-4.5033,-5.2511,-4.6559,-4.5832,-4.2645,-4.081,-4.3575,-4.7318,-4.4558,-4.2172,-3.9995,-3.8395,-4.1468,-4.0409,-3.4476,-3.8577,-4.5998,-5.7689,-5.5765,-5.5076,-5.1702,-5.5535,-5.8391,-5.8589,-4.7861,-4.2386,-4.048,-4.2552,-3.7577,-3.4827,-3.8277,-3.8541\n-1.3422,-1.0454,-1.6579,-2.9878,-2.6906,-2.3465,-2.3332,-3.6202,-3.9594,-2.4084,-2.7266,-4.0442,-4.4397,-4.9595,-5.1167,-4.8472,-5.2569,-5.0733,-4.7315,-4.9553,-4.6228,-4.598,-4.9403,-5.4128,-5.487,-5.0708,-3.8986,-4.2397,-4.6949,-3.78,-3.8649,-4.3694,-5.5098,-5.9052,-5.8001,-5.5811,-5.9434,-5.9197,-5.3242,-4.9874,-4.5823,-3.6715,-4.1825,-3.5883,-3.1952,-3.2823,-3.8\n-0.18148,-0.54213,-0.89311,-1.7212,-3.0823,NaN,NaN,-4.0729,-4.045,-2.9892,-3.3641,-4.6927,-5.3016,-5.62,-5.8997,-5.7504,-5.7331,-6.073,-5.8462,-5.9378,-5.2438,-4.7496,-4.4735,-5.4235,-5.5366,-4.6987,-4.3274,-4.5349,-5.4189,-4.9021,-4.1098,-4.362,-5.4717,-5.6485,-6.4546,-6.0299,-5.1668,-5.2566,-5.041,-4.945,-4.3619,-3.7452,-3.5002,-3.1453,-2.8363,-2.8142,-2.8887\n0.1693,0.81685,-0.58824,-0.25498,-0.61511,NaN,NaN,-3.6232,-3.8647,-3.358,-4.9627,-5.9084,-6.1577,-5.8029,-5.9618,-5.666,-5.7559,-6.1864,-5.5439,-5.4497,-5.1408,-4.7945,-4.7483,-4.8885,-4.2415,-3.9968,-4.2815,-4.8285,-5.4545,-5.6186,-4.5867,-4.5382,-5.2649,-5.3092,-6.5474,-6.3763,-4.528,-4.5236,-4.3761,-4.5188,-3.9337,-3.6106,-3.5449,-3.0714,-2.8439,-2.6431,-2.7033\n-0.041032,-0.91186,-1.899,-2.8922,NaN,NaN,NaN,-3.6846,-4.1686,-4.5105,-5.2672,-5.9826,-6.4973,-6.5154,-6.4561,-5.8228,-5.4169,-5.6658,-5.7828,-5.3559,-5.0731,-4.9204,-4.9699,-4.1663,-3.9303,-4.1434,-4.4622,-4.6495,-5.023,-5.007,-4.4606,-4.2938,-4.7063,-4.8358,-5.8069,-5.6717,-4.4368,-3.9154,-3.7244,-4.14,-3.6022,-3.3547,-3.0897,-2.7964,-2.7233,-2.6704,-2.8651\n-1.1487,-1.8655,-1.5633,-2.8845,NaN,NaN,NaN,-4.9231,-4.8658,-5.058,-5.9305,-7.4154,-7.9169,-7.2391,-7.1355,-7.7056,-5.6587,-5.3548,-5.3602,-5.0253,-4.5644,-4.7201,-4.836,-4.3868,-4.5125,-4.2426,-4.2393,-4.3958,-4.6774,-4.8641,-4.375,-3.7867,-3.6455,-3.5249,-3.8804,-4.4463,-3.8252,-3.5012,-3.6053,-3.443,-3.2906,-3.1335,-2.5173,-2.0057,-2.5532,-2.8001,-2.9923\n-3.8394,-2.4029,-2.8725,-3.8263,-5.7831,-4.8962,-5.1267,-4.6959,-4.7506,-5.4488,-6.9903,-7.5811,-5.7932,-6.1258,-6.3268,-6.5696,-5.0581,-5.1504,-4.8767,-4.5133,-4.0535,-3.8426,-3.4407,-3.6022,-3.5855,-3.8659,-3.7461,-3.7314,-3.9206,-3.8566,-3.1986,-2.7618,-2.5266,-2.6529,-3.199,-3.5179,-3.5533,-3.6633,-3.8706,-3.3522,-3.1961,-2.9549,-2.6408,-2.47,-2.8551,-3.2223,-3.1043\n-1.8026,-2.0122,-2.3398,-3.1856,-3.9642,-4.82,-4.6502,-5.1151,-5.6915,-5.6424,-5.718,-6.0793,-5.4497,-6.1766,-6.1859,-5.79,-5.052,-5.0801,-4.7789,-4.2887,-3.7958,-3.1564,-2.4068,-3.16,-3.1916,-2.798,-2.8065,-2.8242,-2.5545,-2.7184,-2.6615,-2.3163,-1.9304,-2.5919,-3.1421,-3.1508,-3.0501,-3.3576,-3.4537,-3.1913,-3.3732,-2.8364,-2.6087,-2.7959,-2.8887,-3.004,-3.1765\n-2.9472,-3.2712,-3.0386,-3.4324,-4.253,-4.4191,-4.5571,-4.7219,-4.6197,-4.7406,-3.946,-4.7483,-5.7789,-5.7496,-5.1812,-5.0261,-5.0776,-4.737,-4.5026,-3.8896,-3.1226,-1.7728,-1.9272,-2.4372,-2.5952,-2.6139,-2.4663,-2.7577,-2.9107,-2.2798,-1.9641,-1.7037,-2.5295,-4.257,-3.6939,-3.394,-2.592,-2.7795,-2.6952,-2.3058,-2.9543,-2.4926,-2.401,-2.5848,-2.6542,-2.8676,-3.0052\n-3.7966,-4.5662,-4.0535,-4.2243,-4.1783,-4.1978,-4.5433,-4.5124,-4.4042,-4.5761,-4.6762,-4.8066,-4.6878,-4.991,-4.8765,-4.3077,-3.6264,-3.6325,-4.0567,-3.6123,-2.5752,-1.8166,-1.5969,-1.973,-2.5278,-2.6587,-2.4313,-2.6555,-3.1191,-2.0015,-1.8152,-2.0324,-2.382,-3.301,-3.4895,-3.2415,-2.351,-2.2773,-2.1587,-2.1684,-2.5788,-2.3422,-2.4791,-2.4104,-2.3893,-2.7235,-3.008\n-4.4068,-4.8039,-4.3295,-3.9548,-4.2893,-3.8633,-3.2535,-4.2531,-4.8727,-5.4683,-5.1032,-4.9119,-5.0347,-5.1965,-4.5644,-4.2673,-3.9961,-4.2563,-3.8119,-3.0449,-2.238,-1.2401,-0.85682,-0.78293,-1.7174,-2.2141,-2.178,-1.995,-2.0813,-2.0339,-1.6281,-1.4567,-2.7354,-3.3581,-3.127,-2.064,-2.0172,-1.9682,-2.5367,-2.3674,-2.0065,-2.1706,-2.4272,-2.2437,-2.2673,-2.6038,-2.8244\n-4.4492,-4.0797,-3.8044,-3.7362,-3.6814,-3.6189,-3.7472,-4.0739,-4.6673,-4.915,-4.5535,-4.478,-5.0416,-4.7211,-4.3502,-4.0547,-3.7962,-3.9498,-3.2076,-2.2854,-1.8976,-0.18116,0.75712,1.3487,-0.14055,-0.67856,-0.057338,-0.64586,-0.37755,0.075662,-0.47955,-0.98256,-2.1952,-2.7993,-2.5111,-1.9223,-1.6297,-1.347,-1.4216,-1.4971,-1.5923,-1.9842,-1.8861,-1.9304,-2.1488,-2.3132,-2.5139\n-4.3477,-4.0872,-4.3645,-4.7296,-3.6031,-2.782,-2.8878,-3.807,-4.5347,-4.7364,-4.612,-4.2229,-3.9218,-4.3958,-4.2994,-3.9697,-3.4606,-3.3251,-2.5048,-1.2354,-0.77002,1.0461,2.9493,1.5498,1.0476,0.93821,1.0812,1.4539,1.186,0.72693,0.22182,-1.8642,-3.2283,-3.1322,-2.2752,-1.421,-1.1383,-1.1321,-1.8395,-1.8037,-1.9368,-2.1279,-1.5289,-1.6462,-1.7144,-2.1599,-2.456\n-6.2712,-4.2843,-3.3692,-3.4557,-3.7226,-3.2439,-2.828,-3.5226,-4.0201,-3.8573,-4.1905,-4.2276,-3.8207,-4.3378,-4.3677,-4.2678,-3.2291,-2.4772,-2.4648,-0.81038,1.0283,2.3225,5.1964,4.911,2.3639,2.0045,2.2615,2.2699,0.93423,0.79124,0.56402,-0.88876,-1.843,-2.3654,-2.0198,-0.87195,-0.87703,-1.0601,-1.2466,-1.4585,-1.6501,-1.7497,-1.5954,-1.5538,-1.6755,-2.371,-2.5674\n-5.2818,-3.9751,-3.5146,-3.3201,-2.0663,-2.2967,-2.3483,-3.2409,-3.9401,-3.8844,-3.8639,-4.5589,-4.5831,-4.3962,-4.1476,-3.7224,-2.5765,-1.8352,-0.9949,-0.54837,1.9278,3.449,5.6569,6.2776,4.3911,1.6335,-0.85829,-0.6011,-1.4383,-1.8506,-2.329,-1.3461,-0.68379,-0.83321,0.015022,-0.22078,-0.88679,-0.78125,-0.90104,-1.1618,-1.47,-1.181,-1.6173,-1.4749,-1.7461,-2.124,-2.252\n-2.416,-1.4476,-1.6003,-1.5899,-2.2813,-2.4532,-3.1161,-4.2253,-5.5176,-4.6147,-4.4872,-4.407,-4.394,-4.159,-3.8993,-3.5157,-2.4822,-1.0931,-0.5199,-0.34128,0.95398,2.8727,3.9347,4.7212,3.5588,1.4329,-1.3041,-3.4501,-3.5435,-3.2873,-2.123,-1.2237,-1.2602,-1.4138,-1.355,-0.35091,-0.0052409,-0.039219,-0.93095,-2.1766,-2.0816,-1.3183,-1.665,-2.1076,-1.8521,-1.9674,-2.0286\n-3.4553,-2.8124,-2.6894,-2.2828,-1.6123,-1.9092,-2.4968,-3.7624,-4.9059,-5.0334,-4.7567,-4.6513,-4.5881,-4.1616,-3.8882,-2.9431,-1.5047,-0.26833,0.13411,-0.5675,-0.75884,0.63116,2.8488,2.7064,NaN,NaN,NaN,NaN,NaN,-2.424,-1.6493,-1.4377,-0.96305,NaN,NaN,0.27418,0.91976,0.53163,-0.50096,-1.5149,-1.6563,-1.5049,-2.1917,-2.3079,-2.0628,-1.9667,-2.0509\n-1.9868,-2.416,-3.9753,-3.6142,-1.9569,-1.4828,-2.1082,-4.2848,-5.2674,-5.4958,-5.2002,-4.6756,-4.7475,-3.7329,-3.1944,-2.7814,-1.3975,-0.49457,-0.0010581,-0.58784,-1.3578,-0.084327,-0.34967,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.5797,-1.1578,0.23036,NaN,NaN,NaN,2.8387,1.2392,-0.52002,-1.1347,-0.98474,-1.3949,-2.0188,-2.2448,-2.0152,-2.0816,-2.0329\n-3.9648,-3.2386,-3.457,-4.2614,-2.5654,-3.0147,-3.8963,-4.5882,-5.1539,-3.7284,-3.7666,-3.7333,-4.1045,-4.8088,-4.3606,-0.14566,-0.060261,-0.037923,0.40391,0.14094,-0.27992,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.74171,-2.7492,-2.1213,NaN,NaN,NaN,3.9601,2.6889,0.36598,-0.86008,-0.81671,-1.167,-1.8066,-2.1028,-2.1392,-2.1333,-2.0294,-2.0932\n-4.5424,-4.0111,-3.5975,-3.9098,-3.0586,-3.7812,-4.4519,-4.2875,-3.9469,-3.8125,-2.6355,-2.0534,-2.704,-3.9435,-3.3326,0.16858,0.42996,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0828,-2.224,NaN,NaN,NaN,NaN,3.4084,0.75217,-1.1279,-1.2259,-1.1041,-1.8544,-1.9584,-1.9129,-1.9342,-1.7644,-1.8,-2.4176\n-4.1361,-3.2169,-3.0598,-3.0673,-2.9731,-3.42,-4.2259,-4.3952,-4.3582,-3.723,-2.613,-1.758,-1.8741,-2.0941,-0.88496,0.064036,0.2951,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4363,-3.2341,NaN,NaN,NaN,NaN,1.2148,-0.67032,-1.2931,-2.2342,-1.3539,-1.79,-1.7181,-1.5276,-1.5021,-1.1789,-1.6831,-2.0092\n-3.9088,-3.2262,-2.532,-2.4771,-2.7771,-3.4551,-4.584,-4.6195,-4.877,-4.6986,-3.8747,-2.748,-1.9158,-1.4624,-0.58743,-0.34345,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.5515,-3.0933,-6.1725,NaN,NaN,NaN,NaN,-0.13258,-0.66989,-1.2127,-1.722,-1.0931,-1.7751,-1.6748,-1.8257,-1.7822,-1.8071,-1.7549,-1.6233\n-4.0914,-3.1105,-3.3707,-3.6614,-3.5861,-3.2052,-3.9716,-4.7529,-4.1279,-3.8319,-3.6605,-3.1698,-1.5813,-0.84456,0.4203,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4941,-2.3702,-4.5824,-10.227,NaN,NaN,NaN,0.29955,-0.59121,-0.89336,-0.7701,-0.097642,-1.1627,-2.0045,-1.8889,-1.7424,-1.8233,-2.0176,-1.9608,-1.719\n-3.9324,-3.2404,-2.9674,-3.5979,-3.3588,-2.9232,-2.644,-3.2405,-4.0984,-2.9018,-2.3651,-2.1765,-1.1672,-0.39043,0.11937,NaN,NaN,NaN,NaN,NaN,NaN,-1.1485,-1.9118,-1.9892,-1.775,-3.7567,-3.5766,-0.77638,NaN,NaN,NaN,NaN,NaN,NaN,-0.97115,-1.4495,-1.7514,-2.346,-3.3014,-2.6369,-2.8341,-2.4789,-1.8802,-1.7909,-0.97596,-1.0536,-1.269\n-3.8185,-3.4353,-2.9748,-3.2856,-3.5169,-3.1472,-3.1364,-3.4874,-3.4031,-2.4792,-1.6961,-1.6231,-1.1303,-0.5886,-1.1219,-2.4692,-3.1743,NaN,NaN,NaN,NaN,-1.6915,-1.2643,-0.77204,-0.41803,-1.8924,-0.41437,-0.97199,NaN,NaN,NaN,NaN,NaN,-0.55715,-1.2642,-2.0974,-3.5307,-4.0756,-4.0726,-3.0381,-2.7615,-2.7092,-2.2118,-1.8067,-0.9395,-0.82142,-1.4225\n-3.4419,-2.7079,-2.7941,-3.3515,-3.4219,-3.3339,-3.6511,-3.5291,-2.8527,-2.5333,-2.0828,-1.7629,-1.5861,-0.7581,-0.98276,-1.9443,-3.6264,-3.2952,-2.8804,-2.8001,-1.4948,-0.91906,0.45337,NaN,0.75221,0.040734,-0.02603,-1.8661,NaN,NaN,NaN,NaN,NaN,-0.78337,-1.7345,-2.2247,-3.3536,-3.2265,-2.8283,-2.3952,-1.8422,-1.6976,-1.5452,-1.4646,-1.3673,-1.2949,-1.8914\n-2.1686,-2.1042,-2.3621,-2.5339,-2.9103,-3.4674,-3.8043,-3.8331,-3.726,-3.0392,-2.4352,-1.7556,-1.2035,-0.462,-0.062866,-0.91736,-2.5681,-2.4986,-2.3145,-1.0464,-0.47804,0.45202,NaN,NaN,NaN,NaN,NaN,-1.8031,-0.16109,1.2578,1.1709,0.17034,-0.2736,-1.4633,-2.2512,-2.9228,-2.9749,-2.5202,-2.4417,-1.9193,-1.5454,-1.7529,-1.4641,-1.3949,-1.8305,-2.1632,-3.1599\n-2.4547,-2.1291,-2.5114,-2.6761,-2.6079,-3.7247,-4.5569,-4.9445,-4.7053,-2.7554,-2.0495,-1.5793,-1.0995,-0.25459,0.94763,1.7566,-0.55339,-1.9197,-1.6436,-1.4209,-0.5978,NaN,NaN,NaN,NaN,NaN,NaN,-1.449,-1.1979,0.19203,0.36603,-0.47705,-1.5202,-2.0855,-2.5707,-3.2708,-2.8937,-2.4059,-2.0688,-2.2001,-2.1317,-2.1909,-2.3165,-2.7315,-3.4127,-3.7242,-3.5576\n-2.4046,-2.3168,-2.6562,-2.8888,-2.6351,-3.1592,-3.7718,-3.5302,-2.2912,-1.143,-1.3311,-1.1473,-0.60733,0.39442,1.9405,3.4733,NaN,NaN,NaN,NaN,-0.39286,NaN,NaN,NaN,NaN,NaN,NaN,-1.6137,-1.047,-0.23585,-0.52804,-1.4359,-2.2567,-2.4981,-2.6081,-2.7688,-2.6591,-2.3984,-2.3603,-2.4602,-2.5026,-2.9122,-2.8673,-3.0643,-3.5465,-3.7469,-3.4968\n-2.6425,-2.6706,-2.8605,-2.7382,-2.4438,-2.5738,-2.823,-2.2543,-1.4052,-1.6233,-1.746,-1.2623,-0.72728,-0.38719,-0.13911,1.3634,NaN,NaN,NaN,NaN,-0.36589,-0.30129,0.45688,-0.60854,NaN,NaN,-2.1487,-1.3887,-1.2675,-1.4344,-1.5799,-2.2736,-2.1561,-2.1395,-2.3929,-2.7833,-2.0244,-1.9154,-2.3124,-2.4364,-2.6261,-3.921,-3.5316,-3.1084,-2.968,-2.8308,-2.8656\n-2.9765,-3.1953,-2.7429,-2.2113,-1.7594,-1.7111,-1.905,-1.9803,-1.2515,-1.3223,-1.4831,-0.90912,-0.61377,-0.11851,0.16913,-0.035416,1.2143,1.0494,NaN,NaN,NaN,-0.66934,-1.1128,-0.86688,-1.8518,-2.5453,-2.9752,-1.9163,-1.9642,-2.0104,-1.9641,-2.0151,-1.7073,-1.8411,-2.2609,-2.5677,-2.3983,-1.9245,-2.6154,-3.2966,-3.4117,-4.2088,-4.0594,-3.2621,-2.7345,-2.9849,-3.2234\n-2.7098,-3.2741,-2.8732,-2.1985,-1.8147,-1.9537,-2.1498,-2.1949,-1.9681,-1.5873,-1.042,-0.48673,-0.37185,-0.068438,0.01521,-0.13336,-0.25884,-0.83958,-1.4984,-1.1127,-0.24742,-0.2212,-0.95915,-1.6245,-2.1418,-2.7493,-2.6391,-1.6618,-1.8976,-2.1136,-2.0747,-2.0411,-1.7317,-2.0771,-2.4237,-2.6647,-3.4706,-3.6179,-3.5002,-3.5873,-3.8558,-4.074,-4.5923,-4.07,-3.3309,-2.8204,-2.8039\n-2.6134,-3.0337,-3.0414,-2.4314,-2.1007,-2.2083,-2.4535,-2.4587,-1.962,-1.0795,-1.2869,-0.66368,-0.29266,-0.27062,-0.12488,-0.81942,-1.0596,-1.7848,-2.5579,-3.1879,-1.8435,-0.41663,-1.3663,-2.0619,-1.9736,-1.9224,-1.6453,-1.3706,-1.5759,-2.3185,-1.9954,-2.0021,-1.3498,-2.0666,-2.3909,-2.4673,-3.0032,-3.196,-3.2013,-3.5513,-3.5143,-3.7675,-4.4571,-4.0571,-3.204,-2.8145,-2.8367\n-2.626,-2.9313,-2.9374,-2.5157,-2.1983,-1.9186,-2.4902,-2.0063,-1.505,-1.3257,-1.6543,-1.1639,-0.51912,-0.45107,-0.69431,-1.1649,-1.3994,-1.553,-2.8227,-3.4602,-3.137,-1.9281,-2.1486,-2.1593,-2.0488,-2.361,-2.9448,-2.0032,-1.4966,-1.9877,-2.1405,-1.939,-1.5344,-1.8971,-2.1353,-2.2431,-2.5903,-2.9499,-3.0076,-3.3807,-3.467,-3.4643,-3.852,-3.6945,-3.3703,-3.452,-3.1163\n-2.331,-2.5385,-2.7715,-2.5491,-2.2032,-2.0092,-2.1129,-1.931,-1.5538,-1.2892,-0.96854,-0.65201,-0.67506,-0.96961,-1.0319,-1.3724,-1.4427,-2.5995,-2.9233,-3.5909,-3.2622,-2.0705,-2.0821,-2.0036,-1.6306,-1.6837,-2.4129,-2.2687,-1.4579,-1.885,-2.1855,-1.7531,-1.7821,-2.0764,-2.3423,-2.6343,-2.96,-2.767,-2.9221,-3.4954,-2.9477,-2.8959,-2.8623,-3.153,-3.6264,-3.5408,-3.0686\n-2.5676,-2.7684,-3.0055,-2.474,-2.3294,-1.6697,-1.1083,-1.137,-1.1839,-0.95477,-0.90208,-0.80066,-1.0394,-1.1949,-1.0348,-1.5839,-2.0617,-3.4099,-4.0049,-4.3624,-3.3469,-2.1429,-1.833,-1.9532,-1.8218,-1.6424,-1.4336,-1.7332,-1.5326,-1.6934,-2.2118,-1.7632,-2.1665,-2.5845,-2.9405,-2.7256,-2.6874,-2.3526,-2.9131,-3.6073,-3.4946,-2.6478,-2.6237,-3.1419,-3.8423,-3.7485,-2.7485\n-3.2899,-2.5215,-2.6799,-2.5603,-1.9707,-1.5487,-0.96202,-0.84557,-0.95812,-0.86752,-0.57228,-0.7657,-1.3188,-1.4082,-1.4857,-2.3072,-2.4824,-3.8386,-3.8708,-3.4546,-2.4377,-2.0479,-1.8338,-1.7494,-1.8112,-1.6928,-1.3405,-1.1502,-1.2382,-1.2732,-1.8691,-2.0585,-2.5819,-3.2911,-2.9884,-2.0618,-2.6123,-2.7759,-3.3824,-4.1805,-3.3472,-2.6702,-2.2622,-2.6478,-3.8372,-3.3648,-2.5145\n-2.3505,-0.98651,-1.4204,-2.4402,-2.1512,-1.8995,-1.54,-0.97507,-0.90411,-0.51628,-0.6195,-0.11048,-0.011666,-1.0159,-1.6472,-1.6723,-2.1566,-3.1788,-3.6114,-3.2849,-2.3247,-2.0641,-2.0032,-1.8565,-1.9327,-1.9225,-1.3797,-1.2591,-1.1651,-1.2882,-2.036,-2.3655,-2.9619,-3.1787,-2.9739,-2.983,-3.4433,-3.7868,-4.1598,-4.1947,-2.9248,-2.0202,-1.9071,-2.5181,-3.0538,-2.9786,-2.7333\n-3.2399,-1.6514,-1.6769,-2.4338,-2.9252,-2.5113,-2.3476,-1.6161,-1.1842,-0.24662,-0.21064,-0.23742,-0.73754,-0.80556,-1.4602,-1.4248,-1.8874,-2.2782,-3.0534,-3.0926,-2.5173,-2.1946,-1.8893,-1.7043,-2.5006,-2.5369,-1.7998,-1.3276,-1.2005,-1.5936,-2.1453,-2.7761,-3.1344,-3.3715,-3.2316,-3.2561,-3.7623,-3.8631,-4.1536,-3.6541,-2.838,-2.3582,-2.1788,-1.9881,-2.6304,-2.888,-3.0211\n-3.9907,-2.7194,-2.1417,-2.104,-1.7995,-2.1731,-2.6165,-2.5473,-1.9048,-0.91349,-1.8525,-1.6558,-2.9519,-2.9289,-1.8816,-1.4615,-1.7181,-2.8197,-3.2082,-3.2322,-2.6582,-1.9977,-1.5204,-1.4803,-2.3213,-2.365,-2.6568,-1.7192,-1.8913,-2.205,-2.7989,-3.3863,-2.8249,-3.2695,-3.2996,-3.2825,-3.6221,-3.6573,-3.7336,-3.3023,-3.3442,-3.1471,-2.5311,-2.6612,-3.1434,-3.3299,-3.7737\n-3.5788,-2.9927,-3.0308,-3.1765,-3.1151,-2.9752,-3.208,-2.3873,-1.7644,-1.2413,-2.1532,-2.1103,-2.8873,-2.0771,-2.2575,-2.061,-2.108,-3.2321,-3.186,-3.1711,-3.6395,-3.2248,-2.0553,-1.9887,-2.0755,-2.9178,-2.8149,-2.1391,-3.1799,-2.7241,-3.1865,-3.6166,-3.0325,-2.9469,-2.7859,-2.8925,-3.4977,-3.5221,-3.601,-3.0952,-2.9789,-3.4494,-3.2902,-3.1559,-2.9387,-3.4238,-4.4179\n-3.5556,-3.0808,-2.9351,-3.6253,-3.9182,-3.296,-3.1473,-2.1988,-1.4139,-0.80329,-0.93644,-1.0713,-1.7316,-1.3425,-0.90378,-1.7438,-3.0999,-3.1756,-2.5992,-2.5766,-2.0675,-1.9448,-2.2373,-2.4994,-2.7158,-3.0449,-2.4861,-2.2327,-2.879,-3.2326,-3.3171,-3.4614,-3.1758,-2.617,-2.5373,-2.9217,-3.2199,-3.4747,-3.5298,-3.1415,-3.0982,-3.5598,-3.6519,-3.3099,-2.4785,-2.3316,-2.566\n-4.259,-4.0716,-3.2534,-3.5333,-4.1709,-3.708,-3.7332,-2.8682,-1.4014,-1.0139,-0.2901,-0.95045,-1.7371,-2.1678,-3.2394,-3.7441,-4.7884,-3.7213,-3.0851,-2.3309,-1.4856,-2.0044,-2.3973,-2.6911,-2.8308,-3.0469,-2.7112,-2.5856,-3.5242,-4.0892,-3.638,-3.4327,-3.6141,-2.8926,-3.2892,-3.3208,-3.2225,-3.421,-3.4491,-3.3699,-2.9389,-2.9512,-3.2743,-3.3533,-2.601,-2.7509,-3.5321\n-4.3326,-4.0898,-4.2524,-3.3943,-2.4797,-1.9075,-4.984,-4.7102,-3.0795,-4.496,-3.4919,-3.9684,-3.5051,-2.9063,-2.2055,-2.6388,-3.7026,-3.3031,-3.2787,-2.9192,-2.5696,-3.1358,-3.0741,-2.6,-2.1408,-2.2113,-2.654,-2.9744,-4.1078,-4.9946,-4.266,-3.71,-3.4002,-3.4641,-3.693,-3.5165,-3.5692,-3.5438,-3.5249,-3.7999,-3.8752,-3.6304,-3.5476,-3.6828,-3.0807,-3.088,-3.4285\n-4.2791,-3.4261,-2.8103,-1.8644,-1.3049,-0.85817,-2.8911,-3.8161,-2.9449,-4.279,-4.5425,-2.9441,-2.9503,-2.6612,-1.2497,-0.70679,-1.6455,-2.2261,-1.5827,-2.5553,-2.9522,-2.7828,-2.5548,-2.9221,-2.7001,-2.3133,-2.5049,-3.5029,-3.8226,-4.1382,-3.8888,-3.6995,-3.4094,-2.989,-2.9793,-3.2983,-3.9373,-3.675,-3.6452,-3.8683,-4.2639,-4.3861,-4.1448,-3.7957,-3.0293,-3.1116,-4.1565\n-4.7765,-3.885,-2.8228,-2.3873,-2.1836,-1.1788,-2.7867,-3.3053,-2.6861,-3.4797,-4.4814,-3.3028,-3.2025,-2.882,-3.0019,0.30889,1.0572,-0.91308,-2.2467,-1.6777,-1.473,-1.8847,-1.4401,-1.3378,-2.9529,-2.5444,-3.4096,-3.2397,-2.6966,-2.3905,-2.8915,-3.539,-3.8051,-2.904,-2.884,-4.6298,-4.2029,-3.956,-3.7977,-3.8562,-4.2017,-4.1737,-3.7734,-3.4297,-3.0926,-3.2743,-3.7271\n-7.217,-8.1312,-7.4615,-4.6941,-4.3054,-4.1882,-4.7135,-4.9803,-4.0611,-1.7916,-0.31207,-3.0235,-4.3655,-5.5763,-6.1866,-5.7677,-2.206,-1.2922,-2.7269,-2.2029,-1.649,-2.8451,-1.9729,-1.7179,-2.6455,-2.7266,-2.7237,-2.6224,-2.6906,-2.7498,-3.0443,-3.071,-2.9568,-2.911,-3.3555,-4.6201,-4.6669,-4.2888,-3.6424,-3.6681,-4.0823,-4.3176,-3.7867,-3.6249,-3.3105,-3.2277,-3.0581\n-6.4572,-6.7216,-5.7304,-4.9023,-2.0214,-2.6365,-4.2355,-5.0645,-4.5613,-2.7212,-1.7026,-2.9594,-2.9971,-4.3416,-5.0609,-6.8282,-4.9574,-3.0079,-3.0857,-1.9046,-2.6042,-3.9543,-2.2929,-1.5646,-1.6887,-2.1558,-3.1534,-3.7066,-4.1856,-4.0984,-4.0749,-3.3365,-2.7291,-3.1192,-3.5885,-4.1498,-4.155,-3.5604,-3.4255,-4.1244,-4.3516,-4.1798,-4.0183,-4.1253,-3.8957,-2.8752,NaN\nNaN,-2.7705,-3.0594,-3.0064,-3.0071,-3.1293,-2.6278,-2.9602,-2.6033,-2.4523,-1.9628,NaN,NaN,NaN,NaN,-3.5184,-2.2447,-2.8406,-3.4612,-1.7194,-3.5869,-4.6606,-2.8563,-0.71479,-2.178,-3.1263,-3.0329,-3.4492,-4.135,-4.2721,-3.6146,-3.225,-2.8477,-3.1985,-3.6388,-4.2417,-3.9376,-3.5425,-3.0566,-3.8843,-4.4811,-4.2815,-3.8016,-3.7189,-3.5863,-3.6658,NaN\nNaN,NaN,NaN,-4.1517,-4.5286,-3.0993,-1.4174,-2.8277,-3.7333,-2.4995,-1.2545,NaN,NaN,NaN,NaN,-2.5551,-0.95029,-1.3123,-2.8587,-1.1761,-2.736,-4.2085,-3.0466,-2.2706,-4.183,-4.2926,-3.9068,-3.4242,-3.8319,-3.8924,-3.282,-2.825,-3.0455,-3.2704,-3.9537,-4.6387,-4.6737,-3.7851,-3.2779,-3.6734,-4.3406,-4.0465,-3.1485,-2.8556,-2.6223,-4.4995,-5.1162\nNaN,NaN,NaN,NaN,-3.1458,-2.9249,-1.5393,-2.7211,-3.1999,-2.2522,NaN,NaN,NaN,NaN,NaN,-2.3703,-3.027,-4.7232,-2.3086,-2.4073,-1.9607,-1.0392,-3.6793,-4.228,-5.9993,-5.0438,-4.3868,-3.6938,-3.6287,-3.4222,-3.2615,-3.0075,-4.65,-4.7827,-4.0686,-4.4103,-5.4032,-4.2889,-3.6396,-3.8037,-3.804,-3.6311,-2.9332,-2.8145,-3.5938,-4.623,-4.959\nNaN,NaN,NaN,-4.0665,-4.7649,-2.3404,-3.5664,-3.4723,-3.3168,-3.2054,-3.6505,NaN,NaN,NaN,NaN,NaN,-1.8855,-3.761,-4.0659,-2.7349,-2.1062,-1.2154,-4.9616,-6.0983,-6.3525,-5.1339,-4.2828,-3.7395,-3.0668,-2.1938,-3.1327,-3.9716,-4.6189,-4.5367,-3.9327,-4.2073,-4.6277,-4.0768,-4.143,-4.06,-3.9381,-3.3977,-2.7559,-3.1535,-3.918,-3.7891,-3.3812\n-6.1743,-5.675,-5.5849,-6.4483,-4.6498,-1.6357,-4.58,-4.361,-2.9831,-3.0176,-3.1319,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.4556,-3.2559,-2.1756,-2.4179,-5.5045,-6.2954,-6.7541,-6.1174,-4.2515,-2.8828,-2.9763,-3.6078,-3.4125,-3.5353,-4.3333,-5.3824,-4.7277,-4.3044,-3.805,-3.2616,-3.9069,-4.2225,-4.1,-3.0697,-3.0409,-3.6498,-4.2799,-4.7345,-4.1932\n-6.1193,-4.869,-4.5869,-6.2034,-5.2548,-4.9569,-4.904,-5.0503,-5.7536,-4.5809,-3.9724,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.7604,-2.8647,-2.2628,-3.646,-4.3925,-4.4894,-3.8142,-3.5027,-3.4116,-4.3201,-4.0892,-3.7754,-3.2883,-3.1691,-5.0917,-5.1172,-4.3587,-3.9585,-3.2954,-2.9356,-2.8677,-3.0608,-3.1715,-3.7067,-3.9017,-4.1022,-5.6713,-3.8514\n-5.3384,-3.6717,-3.5738,-5.8343,-6.1187,-6.0245,-5.6118,-5.2073,-5.5283,-5.7099,-5.4779,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.0606,-2.4402,-1.9556,-1.4817,-3.1667,-3.8391,-3.4566,-3.443,-5.4648,-8.563,-9.6493,-6.3729,-3.7831,-3.8539,-4.9316,-5.4382,-4.3464,-3.5787,-3.257,-2.0337,-2.8136,-3.5459,-4.1091,-4.2828,-4.0129,-4.8018,-5.2525,-4.6179\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061106-061211.unw.csv",
    "content": "30.861,31.584,31.046,30.985,30.794,31.03,31.09,30.878,30.833,31.313,31.855,32.148,30.904,31.145,30.98,30.814,30.685,30.798,30.905,30.884,30.898,31.161,30.922,30.761,31.419,31.35,30.865,31.318,31.387,31.211,31.458,31.645,31.546,31.489,31.387,31.599,31.711,31.961,31.306,31.212,30.057,29.448,29.166,29.009,28.389,27.554,26.641\n31.766,32.108,31.575,31.05,31.109,31.292,31.352,30.654,30.824,31.025,31.233,31.185,31.008,31.094,30.983,30.701,30.517,30.443,30.66,30.582,30.464,30.769,30.497,30.69,30.937,30.735,30.864,31.216,30.976,31.219,31.552,31.361,31.779,31.474,31.37,31.604,31.896,31.761,31.162,30.768,29.556,29.491,28.742,28.057,27.351,27.172,26.801\n31.375,31.848,31.661,31.476,30.918,31.317,31.237,30.803,31.318,31.041,31.01,31.36,31.068,31.051,30.607,30.479,30.074,29.703,29.952,30.345,30.514,30.78,30.716,31.305,30.905,30.443,30.804,30.437,30.77,31.037,31.235,31.356,31.743,31.479,31.108,30.983,31.694,31.224,30.8,30.464,29.947,29.081,27.955,26.882,26.616,26.616,25.386\n32.353,31.452,32.135,31.856,31.237,31.266,30.762,30.384,30.554,30.967,31.426,31.509,31.143,30.763,30.083,29.68,29.627,29.757,29.818,30.34,30.691,30.92,30.936,30.783,30.673,30.743,30.703,30.539,30.639,30.98,31.4,31.118,31.384,31.324,31.037,31.54,31.797,30.796,30.005,29.727,29.051,27.884,26.924,26.437,26.363,26.116,24.599\n31.967,30.94,32.121,32.288,31.57,31.067,30.34,30.197,30.311,30.074,31.103,31.093,31.007,30.699,30.07,29.479,29.484,29.777,30.054,30.252,30.37,31.069,30.594,30.507,30.559,31.082,30.583,30.818,31.136,31.321,31.549,31.263,31.194,31.009,30.727,30.774,30.826,30.441,29.903,29.071,28.372,27.25,26.273,25.365,25.198,25.002,24.5\n32.03,32.27,32.499,31.85,31.818,31.579,30.802,30.667,30.563,30.67,30.97,30.832,30.991,30.933,30.776,30.037,29.691,29.753,30.367,30.152,30.429,30.802,30.768,30.972,30.855,31.022,31.058,31.095,31.333,31.635,31.279,31.091,31.088,30.708,30.504,30.481,30.46,30.259,29.687,29.112,28.327,27.641,26.537,25.094,25.266,23.169,22.931\n32.587,33.232,31.615,31.223,31.749,32.096,31.187,30.56,30.201,31.111,30.98,30.72,31.043,31,31.266,30.355,30.025,30.289,30.43,29.839,30.402,30.737,31.064,31.38,31.27,31.291,31.069,31.444,31.77,31.514,31.179,31.008,31.059,30.873,30.829,30.605,30.268,29.907,28.88,28.911,28.247,27.636,27.263,26.493,25.759,23.768,24.063\n31.519,31.639,31.009,31.355,31.666,32.101,30.934,30.479,30.379,31.128,31.035,30.952,31.481,31.143,30.995,30.502,30.295,30.404,30.395,30.445,30.984,30.832,30.885,31.623,31.049,30.946,31.074,31.431,31.415,31.305,31.207,30.75,30.904,30.731,30.777,30.359,30.3,30.05,29.447,28.755,28.626,28.226,28.338,27.246,25.935,24.254,24.497\n31.099,30.043,31.632,31.653,32.252,32.156,30.871,31.076,30.979,31.042,31.062,31.209,31.555,31.313,31.121,30.677,30.709,31.25,30.758,31.41,31.444,30.87,31.034,31.064,31.154,31.436,31.73,31.718,31.281,30.969,31.231,31.219,31.228,31.146,30.76,30.386,30.435,30.259,30.241,29.926,29.673,29.197,28.561,28.238,26.256,25.262,26.083\n31.284,31.032,33.148,31.687,32.133,32.02,31.107,31.253,30.888,31.373,30.724,31.301,31.296,31.082,30.91,30.805,30.66,30.818,30.833,31.25,31.164,31.006,30.991,31.195,31.357,31.953,31.949,31.967,31.514,31.599,31.415,31.381,31.873,31.402,31.179,30.878,30.835,30.81,30.733,30.641,30.562,30.167,29.557,29.152,27.267,26.631,26.809\n31.727,30.87,31.275,31.926,32.281,31.848,31.079,31.031,31.26,31.19,30.801,31.116,30.49,30.583,30.744,30.916,30.582,30.881,31.453,31.706,31.352,31.317,31.311,31.699,31.915,31.792,32.081,31.864,31.645,31.968,31.745,31.596,32.304,32.216,32.023,31.962,31.693,31.699,31.451,30.81,30.569,29.224,28.99,28.772,28.011,28.794,28.879\n31.711,30.818,31.707,32.82,32.355,31.521,31.521,31.189,31.425,30.989,30.707,30.569,30.518,30.379,30.467,30.872,30.821,31.234,31.751,31.866,31.761,31.87,31.49,32.23,31.987,31.83,32.108,31.691,31.918,32.268,32.462,32.9,32.839,32.906,32.874,32.009,31.635,31.403,31.021,30.128,29.941,28.181,28.087,28.29,28.477,29.194,NaN\n32.965,33.177,33.22,30.947,31.624,31.144,31.423,31.337,31.384,30.682,29.915,29.383,30.785,30.26,30.041,30.628,31.006,31.081,31.571,31.723,31.663,32.171,32.154,31.786,31.812,31.85,31.639,31.842,32.03,32.477,32.49,33.892,33.708,32.864,32.307,31.42,30.666,30.625,29.978,29.611,28.607,27.796,27.663,28.035,27.984,NaN,NaN\n32.872,33.661,32.301,30.439,32.199,32.107,32.113,31.52,31.159,30.785,29.777,29.943,30.687,30.236,29.874,30.53,30.399,30.728,31.131,31.556,31.649,32.036,32.246,31.729,31.931,31.903,31.836,32.068,32.033,31.88,32.127,32.785,32.557,32.048,31.744,31.154,30.872,31.133,30.174,28.752,27.126,27.467,26.678,NaN,NaN,NaN,NaN\n32.816,31.944,29.04,31.341,32.468,32.562,31.916,31.343,31.067,30.926,29.903,30.278,30.659,30.622,30.325,30.796,30.635,30.374,30.872,31.534,31.662,31.66,31.915,31.655,31.663,31.45,31.194,30.776,31.242,31.001,31.48,32.333,32.317,31.658,32.067,31.115,30.707,30.201,29.119,27.102,27.914,28.648,27.818,NaN,NaN,NaN,NaN\n31.695,31.81,30.113,31.745,33.135,31.933,31.667,31.964,31.251,30.695,30.206,31.337,30.844,30.229,30.128,30.409,30.427,30.357,30.873,31.15,31.514,31.422,31.123,30.672,30.814,30.635,30.023,29.559,30.601,29.934,31.619,31.828,31.183,30.751,30.944,30.663,31.65,31.06,30.174,27.429,28.87,28.695,28.635,NaN,NaN,NaN,NaN\n30.404,32.684,31.881,31.796,32.25,32.119,32.141,32.381,31.488,30.988,31.116,31.578,30.642,30.519,30.141,30.145,30.387,30.729,30.644,31.336,31.405,31.84,31.068,30.424,30.767,30.114,30.596,30.77,30.244,30.607,31.439,31.605,30.534,29.671,29.784,30.691,31.996,31.81,32.151,NaN,NaN,29.511,28.217,NaN,NaN,NaN,NaN\n31.558,32.859,32.311,32.44,32.028,33.031,32.611,31.776,31.742,31.75,31.639,31.314,30.753,30.791,30.235,30.226,30.817,30.851,30.014,31.939,31.642,31.96,32.352,31.909,31.506,31.836,31.911,31.766,30.712,30.72,31.422,31.186,31.484,30.017,30.35,31.767,32.053,31.073,30.849,NaN,NaN,NaN,29.411,28.711,NaN,NaN,NaN\n25.994,31.137,32.68,32.082,33.233,33.424,31.537,31.315,31.438,31.667,31.555,31.403,31.006,30.843,30.127,29.13,29.292,29.718,29.586,29.739,27.948,26.192,31.164,33.088,33.489,33.132,32.072,32.279,31.599,31.093,32.349,31.623,31.793,30.761,30.42,32.747,32.673,31.941,29.541,NaN,NaN,NaN,29.957,30.904,31.1,NaN,NaN\n28.749,31.428,31.165,31.374,32.983,34.052,31.59,32.073,31.406,30.809,31.123,30.756,30.568,30.696,30.44,29.97,29.141,29.857,30.106,29.967,29.594,29.175,29.672,33.257,33.892,33.173,32.64,31.964,31.753,32.535,32.931,32.177,31.724,31.929,33.321,32.712,32.659,32.676,28.814,28.592,29.813,31.437,30.619,31.365,30.821,30.058,30.728\n30.956,28.142,31.159,30.978,31.215,31.356,31.96,32.21,31.196,30.523,30.425,29.985,30.006,30.414,30.517,30.22,30.087,30.327,30.54,30.975,31.588,30.033,30.281,32.573,32.678,32.562,32.886,32.377,31.12,32.112,32.908,30.938,31.926,31.938,32.471,33.013,33.511,32.58,31.148,31.596,31.754,31.007,30.716,29.77,30.138,30.993,30.77\n30.397,29.535,30.445,30.507,30.144,29.94,32.548,31.818,31.633,30.743,30.131,29.957,30.285,30.501,29.75,30.669,30.572,30.879,30.881,32.48,32.93,32.635,33.085,33.332,33.222,33.151,32.651,32.289,31.252,31.318,31.708,30.661,31.629,31.843,32.258,32.747,32.123,32.898,31.898,31.153,32.978,31.235,30.766,30.418,30.044,31.324,31.276\n30.228,30.592,30.244,29.779,28.848,31.231,32.139,31.873,31.609,30.493,29.278,29.688,30.586,29.848,30.114,32.31,31.265,32.413,32.102,32.616,33.228,33.179,33.131,34.004,33.676,33.643,32.531,32.403,32.173,31.562,31.013,30.717,31.422,32.034,31.789,32.405,31.335,30.883,30.26,31.045,31.763,31.453,31.873,31.708,30.543,30.826,30.753\n29.64,29.294,29.899,29.996,31.483,31.899,32.415,32.009,31.168,30.297,29.964,30.358,30.61,30.454,31.184,32.743,32.727,33.26,33.261,32.858,33.533,33.679,33.548,33.404,33.275,33.316,32.776,32.559,32.119,31.504,30.226,30.698,31.024,30.945,31.654,31.577,31.64,31.31,30.635,33.125,32.549,31.527,31.17,31.137,30.03,31.05,30.698\n30.882,30.525,30.289,30.117,30.016,31.194,32.144,31.481,30.242,30.206,30.518,31.115,31.29,32.489,32.609,32.85,32.398,30.833,31.988,32.494,32.571,32.137,31.534,32.782,32.811,32.971,32.42,32.206,31.535,30.261,29.729,29.975,30.494,30.717,31.685,34.751,28.13,32.378,34.192,33.258,31.532,31.374,30.895,30.171,30.141,30.976,30.824\n31.547,31.515,30.673,30.191,30.053,30.222,30.885,30.85,30.681,30.953,30.668,30.561,31.261,31.584,31.714,32.396,30.817,30.704,31.595,32.08,31.726,30.927,31.965,32.238,32.225,32.457,31.164,30.175,30.008,29.766,28.957,28.705,32.861,28.278,30.892,36.262,30.392,31.228,33.085,32.567,31.937,32.033,31.59,31.266,30.959,31.589,31.867\n32.147,31.988,31.057,30.922,30.256,29.966,30.153,30.36,30.644,30.835,30.756,30.306,29.621,30.548,30.668,30.705,30.844,30.849,31.612,32.494,31.511,31.545,32.201,32.026,29.717,30.448,30.015,30.378,31.884,33.741,30.655,30.647,32.83,30.226,31.032,34.15,31.451,32.21,31.963,32.717,32.24,32.413,31.955,31.592,31.499,31.626,31.601\n32.574,32.62,31.715,30.8,31.252,30.918,31.616,29.801,31.009,31.34,31.152,30.472,30.062,30.156,29.839,29.082,29.313,30.327,31.221,31.475,30.485,31.364,31.973,32.381,29.55,30.489,30.7,30.747,31.931,32.834,NaN,NaN,NaN,34.421,33.415,32.663,32.171,31.077,31.935,32.491,32.346,32.146,31.769,31.505,31.595,31.398,31.027\n32.967,32.554,31.729,32.47,32.216,32.332,32.38,31.25,31.28,31.608,31.568,30.988,30.647,30.078,29.473,29.169,30.3,31.123,30.813,30.793,30.429,31.472,31.634,31.581,30.714,30.477,30.647,30.567,30.283,NaN,NaN,NaN,NaN,NaN,33.288,32.318,31.287,29.561,31.922,33.71,32.934,32.579,31.992,31.795,31.783,31.157,30.729\n32.519,32.273,32.666,34.093,32.8,32.33,31.591,31.176,31.472,31.824,31.73,31.165,30.039,29.58,29.802,30.347,31.215,31.303,31.424,30.855,31.252,32.15,31.833,31.518,30.589,30.477,31.01,30.828,NaN,NaN,NaN,NaN,NaN,34.353,32.992,31.774,31.351,30.568,33.836,33.385,32.42,32.629,32.36,32.344,32.048,31.234,30.947\n31.927,31.67,32.404,32.041,32.164,32.204,32.339,31.755,31.601,32.014,31.916,30.916,29.931,29.583,30.051,31.23,31.964,31.089,30.937,31.621,32.276,32.312,30.76,31.509,32.345,31.987,31.647,31.224,NaN,NaN,NaN,NaN,NaN,33.256,33.43,32.7,32.954,33.392,34.172,33.133,33.204,34.036,33.13,32.815,32.248,31.503,31.084\n31.228,30.877,31.545,31.078,31.099,31.572,32.298,32.503,31.725,31.742,32.317,31.509,30.993,30.42,31.994,31.889,31.554,31.724,31.602,31.572,33.67,32.299,30.711,35.297,34.361,33.456,32.826,31.936,NaN,NaN,NaN,NaN,33.097,33.429,34.286,34.11,34.23,33.313,33.688,33.768,33.498,33.644,32.667,32.998,32.489,31.369,30.753\n31.328,31.442,31.889,31.806,31.417,31.963,32.223,32.664,32.434,31.652,31.697,30.362,29.586,30.652,31.64,31.069,30.867,31.872,29.912,30.302,32.387,31.603,30.218,33.931,33.611,33.056,32.895,33.213,NaN,NaN,NaN,NaN,33.888,34.503,34.314,33.532,33.27,31.635,33.008,33.58,33.171,32.864,32.577,32.973,31.739,31.607,31.465\n32.5,32.688,32.298,32.289,32.221,31.832,33.099,33.119,32.427,32.054,31.478,31.015,29.557,31.337,32.323,31.71,30.693,31.824,31.121,29.152,31.918,33.082,32.136,31.563,32.289,32.188,33.303,NaN,NaN,NaN,NaN,33.96,34.488,34.328,33.658,33.303,33.015,31.452,32.572,33.561,32.877,32.429,32.449,32.887,32.213,31.726,31.749\n33.166,32.139,32.295,32.088,31.608,31.917,32.902,33.24,32.112,32.124,31.692,30.762,30.341,30.876,31.815,32.328,32.328,32.038,31.77,31.199,32.851,32.906,32.68,31.698,31.644,31.55,33.181,NaN,NaN,NaN,34.143,34.083,33.94,33.648,34.291,33.673,33.508,32.574,32.889,33.376,31.865,31.126,31.901,32.814,32.535,31.736,31.943\n32.639,33.185,32.92,32.314,32.246,32.215,32.404,32.867,32.328,31.994,31.786,31.625,31.68,30.795,29.594,32.846,32.354,32.517,32.528,32.687,33.562,33.765,32.89,31.375,31.362,31.435,32.128,32.756,35.573,34.935,34.908,35.211,34.537,34.593,35.139,35.272,34.665,33.983,33.436,32.932,31.986,31.725,32.028,32.664,32.186,31.877,32.646\n32.063,32.87,33.091,32.636,33.517,33.327,32.299,31.798,32,31.471,32.553,32.73,32.229,30.084,30.416,32.354,32.308,32.151,31.949,32.09,32.573,34.894,34.105,32.39,31.851,30.528,29.518,29.552,35.827,34.756,34.241,34.819,34.13,33.213,33.994,36.21,35.662,35.006,33.476,33.209,32.83,32.332,32.04,32.044,31.825,32.152,32.179\n32.141,32.686,32.89,32.512,32.572,32.653,31.737,31.565,31.851,31.594,31.45,33.006,32.577,30.978,31.495,31.961,32.441,31.412,31.914,32.193,34.258,NaN,NaN,NaN,NaN,NaN,31.58,31.935,34.92,33.21,33.751,34.162,34.749,33.559,34.603,37.112,35.495,34.827,34.249,33.406,32.568,32.115,31.354,31.315,30.899,30.427,30.616\n33.4,33.18,32.454,32.045,31.865,31.852,31.083,31.551,31.596,31.993,32.876,33.223,32.914,32.7,32.474,31.613,31.248,30.175,31.689,33.047,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.632,34.546,34.175,34.106,34.352,34.619,35.013,35.045,34.304,34.559,34.354,33.293,32.315,32.246,31.95,31.317,29.936,28.58,30.235\n34.022,32.452,32.709,31.968,31.897,32.013,31.412,31.909,32.78,32.562,32.572,32.472,32.728,32.889,32.054,31.923,32.036,32.138,32.045,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.267,34.895,34.132,35.613,35.419,35.446,34.891,34.168,33.688,33.634,32.795,33.834,34.088,32.37,31.899,30.939,29.764,29.391,30.535\n33.643,32.415,32.765,32.027,32.175,32.138,32.082,32.357,32.824,32.793,32.06,32.384,33.276,33.075,31.96,32.054,32.83,34.333,34.366,32.599,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.729,34.572,35.011,35.379,36.452,36.446,35.541,34.233,33.411,34.11,34.855,32.701,34.056,33.897,33.028,32.004,31.279,30.339,30.256,30.645\n32.967,32.516,31.784,31.576,31.634,32.005,32.291,31.985,32.169,32.315,31.453,34.421,35.617,34.039,33.45,32.215,30.5,33.031,34.809,33.404,NaN,NaN,NaN,NaN,NaN,NaN,33.924,35.623,35.551,35.561,35.083,35.531,35.908,35.067,34,33.361,33.289,33.709,33.462,33.888,34.02,32.951,31.592,30.942,29.809,30.276,30.766\n32.167,31.632,31.583,31.918,31.777,31.835,32.187,31.141,32.301,32.942,33.344,33.788,35.329,33.909,33.824,34.112,32.425,31.252,32.32,31.097,31.953,32.506,NaN,NaN,NaN,NaN,NaN,35.959,36.614,36.299,34.615,35.775,34.633,34.025,33.182,32.82,32.975,33.116,33.145,33.354,33.493,32.327,31.477,30.682,30.028,29.45,31.119\n31.383,31.217,31.345,31.793,31.335,31.994,32.099,31.261,32.456,33.177,32.961,32.337,32.627,32.276,30.914,30.844,32.509,32.309,32.01,32.153,31.373,32.79,32.81,NaN,NaN,NaN,NaN,35.743,35.596,35.651,36.04,35.842,34.613,33.36,31.616,32.859,33.338,33.893,33.072,32.865,32.444,32.329,31.05,31.371,32.648,32.658,31.142\n30.906,30.897,31.025,31.225,31.672,33.116,32.966,32.609,32.35,31.616,31.05,31.634,31.249,30.852,29.89,29.705,31.999,32.519,33.125,30.595,31.031,32.193,34.194,33.895,34.795,36.287,35.781,34.696,33.778,34.409,35.945,34.61,35.843,33.918,31.533,34.484,35.282,34.864,32.862,32.843,32.093,31.925,31.034,30.607,31.699,33.294,32.566\n30.807,30.652,30.832,31.119,31.394,31.85,31.854,31.789,30.98,30.081,30.109,30.447,30.137,29.71,29.577,30.288,31.854,33.592,34.714,31.928,29.451,30.234,32.725,33.749,32.518,33.696,33.764,34.272,33.486,34.127,34.169,34.465,34.729,33.449,33.602,34.57,35.347,33.804,33.111,32.717,31.589,31.068,30.685,29.623,30.316,32.513,34.344\n31.118,30.597,30.599,31.121,30.89,30.816,31.01,30.867,30.024,29.777,29.806,29.688,29.528,29.395,29.88,30.248,31.462,32.154,33.67,32.597,28.414,28.782,31.178,33.116,33.177,32.294,32.319,33.78,33.398,33.735,32.725,33.036,33.398,33.091,34.212,34.599,33.491,33.104,32.995,32.058,31.044,30.534,30.61,30.372,30.464,31.549,33.122\n30.737,30.46,30.492,30.868,30.858,30.496,30.638,30.19,29.573,28.941,28.995,29.169,29.258,29.448,29.809,30.555,31.35,31.667,31.294,30.818,29.697,29.026,31.01,32.294,32.496,32.229,31.456,32.501,32.169,32.75,33.177,33.288,33.996,34.137,34.466,34.639,33.492,32.707,32.606,30.911,30.272,29.961,30.819,31.08,31.551,31.13,31.771\n30.822,30.557,31.088,30.999,30.741,30.282,30.106,29.782,28.578,28.874,29.005,29.067,29.077,29.017,29.188,29.76,30.382,31.013,30.655,29.972,28.431,28.997,30.8,31.723,32.083,31.646,30.817,31.835,32.019,32.585,32.751,33.438,34.155,35.338,36.401,35.089,35.056,34.542,32.435,30.416,29.445,29.13,31.122,31.046,31.233,31.294,31.755\n30.774,30.662,30.776,30.342,30.159,29.889,29.709,29.478,28.767,29.469,29.465,29.156,28.903,28.651,28.995,30.037,29.988,29.622,29.991,30.517,29.296,34.001,30.937,30.158,28.861,28.54,28.113,30.2,31.344,32.389,32.28,34.117,33.886,34.642,34.437,34.1,35.041,33.935,32.295,31.132,30.173,30.312,31.011,30.848,31.826,32.135,32.477\n30.828,30.461,30.183,29.909,30.157,30.226,30.187,30.021,29.648,29.49,29.428,29.07,29.298,29.401,29.786,30.528,30.708,27.473,29.602,30.683,33.067,32.521,30.465,29.807,28.948,28.338,26.41,27.942,30.554,31.46,32.128,33.727,33.36,32.796,32.388,32.471,32.643,32.399,31.701,31.029,30.47,30.348,30.55,31.482,32.156,32.209,31.779\n30.869,30.312,30.583,30.177,30.128,30.233,30.272,29.964,29.536,28.295,29.167,29.711,29.562,29.793,29.661,31.442,32.957,30.234,29.488,29.102,33.46,31.517,30.798,30.192,29.761,28.62,26.815,27.106,29.868,31.051,31.564,31.831,32.525,31.985,31.984,32.366,31.984,31.599,31.413,30.982,31.024,30.972,30.708,33.139,32.266,32.134,31.692\n30.414,30.354,30.483,29.813,30.102,30.462,30.475,29.884,29.191,28.14,29.806,30.233,30.395,30.339,29.916,29.41,30.12,29.419,27.806,26.965,33.313,31.756,31.738,31.438,30.741,30.277,29.978,28.501,28.756,30.405,31.223,31.182,32.086,31.685,32.373,31.909,31.868,32.024,31.841,31.715,31.677,30.561,30.405,31.367,31.489,32.549,31.489\n30.302,29.723,29.364,29.166,29.37,29.845,30.391,29.347,28.346,28.376,29.063,30.756,32.568,32.275,31.285,29.43,29.472,29.523,30.85,31.746,31.333,31.155,30.599,31.195,30.981,31.789,30.797,27.862,27.268,28.749,31.206,31.533,32.87,33.113,32.902,33.593,32.716,32.046,31.906,32.453,31.723,31.43,30.858,31.179,32.45,33.084,31.83\n29.897,29.331,29.701,28.819,28.841,30.389,30.564,29.153,28.392,28.869,29.187,30.651,32.601,33.138,31.457,30.924,30.438,29.623,31.998,32.519,31.501,30.64,29.772,30.491,30.503,30.85,29.387,28.459,28.077,31.016,31.74,32.841,32.511,32.457,32.218,32.734,31.949,31.429,31.593,31.966,31.598,31.931,31.711,32.163,32.613,32.322,31.281\n30.047,29.832,30.158,29.4,28.23,31.781,31.209,29.039,28.716,28.917,29.316,29.415,32.157,32.766,31.521,32.438,31.341,30.917,31.081,31.446,31.508,30.852,30.083,30.092,29.525,29.552,28.674,28.669,28.776,32.085,32.044,32.918,32.497,32.198,32.214,32.015,31.298,31.152,31.071,30.88,30.855,31.287,31.421,31.937,31.938,31.304,31.633\n30.055,30.007,30.066,28.871,27.354,30.57,30.204,29.069,28.058,27.028,28.432,28.2,31.5,31.996,32.474,34.055,33.812,31.552,31.305,31.557,32.017,31.058,30.526,29.602,28.068,27.898,26.466,27.028,29.062,31.153,31.671,32.14,32.411,32.15,32.196,31.548,31.264,31.166,30.443,28.717,30.213,31.234,31.03,31.577,31.849,32.336,32.039\n31.681,31.867,31.508,29.642,28.768,27.009,29.009,28.596,27.823,27.877,29.665,28.959,30.878,31.676,32.399,32.275,31.987,31.432,31.604,31.537,30.705,32.306,32.107,30.376,28.738,28.668,27.594,28.217,31.368,31.597,31.138,31.226,31.484,31.92,32.082,31.831,31.235,30.847,30.264,30.162,30.888,30.966,30.657,31.232,31.942,32.144,31.028\n32.631,33.013,32.344,30.443,29.735,27.804,29.77,28.585,26.557,25.577,27.201,28.489,29.952,30.257,31.925,31.222,31.023,31.618,31.534,30.171,29.765,31.782,32.083,31.22,30.396,29.479,28.99,29.027,30.493,30.704,31.232,32.42,31.842,31.672,32.088,31.305,30.705,30.434,30.257,30.244,30.156,30.379,30.543,30.776,30.422,29.752,29.7\n32.093,32.473,31.261,32.202,32.249,31.819,31.394,30.261,26.677,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,33.782,31.477,30.255,30.135,31.404,32.542,32.276,31.062,30.058,29.258,29.327,29.913,30.801,32.178,33.882,33.08,31.704,31.563,31.266,31.079,30.788,30.23,30.268,29.838,29.99,30.201,30.992,30.551,29.727,29.849\n31.738,32.028,30.9,31.82,33.019,32.667,32.422,30.679,30.799,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,33.369,31.116,31.66,31.592,32.017,32.086,31.898,31.323,29.955,28.84,29.269,30.153,30.953,30.662,31.456,32.574,31.988,31.897,31.784,31.982,31.046,31.205,31.115,30.452,30.484,30.771,30.649,30.044,29.508,29.18\n31.908,30.919,29.604,29.419,30.717,30.975,31.054,29.174,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.451,31.107,32.309,30.588,30.03,30.226,31.007,32.07,31.554,30.798,31.697,30.829,30.327,29.945,31,31.7,31.939,32.024,31.698,32.23,31.964,31.812,31.387,30.693,30.819,30.627,29.735,29.869,29.51,29.901\n31.759,30.255,30.111,30.151,30.636,29.708,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.961,30.613,31.136,30.52,29.329,29.391,30.675,32.239,31.559,31.59,30.894,30.219,30.437,30.054,30.779,31.246,31.396,31.137,31.611,31.982,32.364,31.987,31.092,30.605,30.095,29.764,29.363,31.162,30.345,30.097\n29.575,29.47,28.317,29.315,29.271,30.397,30.983,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.887,31.384,32.951,30.853,30.56,30.688,31.29,30.269,28.67,28.432,28.693,29.124,28.775,29.848,30.933,31.379,30.986,31.23,32.102,31.443,31.285,31.327,30.512,29.953,29.43,29.647,30.605,31.312,30.25,29.193\n28.989,27.513,28.074,30.016,30.914,30.526,30.33,29.59,29.766,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.803,31.322,32.986,31.329,30.383,30.816,29.796,29.279,28.302,28.339,28.142,27.493,28.455,30.412,31.533,31.435,31.355,31.768,31.51,31.042,30.743,30.628,30.35,29.836,29.438,29.697,30.699,31.115,30.082,28.057\nNaN,28.343,29.411,29.518,30.22,30.606,30.235,29.995,30.825,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.995,31.528,31.228,29.703,29.425,30.437,30.252,30.156,29.737,28.607,28.123,28.48,28.917,29.755,30.098,30.726,30.751,32.459,31.156,30.576,30.391,30.733,30.707,30.45,30.17,30.256,30.675,30.118,29.253,27.27\nNaN,NaN,31.623,29.089,30.187,30.845,30.202,30.405,30.118,29.248,27.68,NaN,NaN,NaN,NaN,NaN,NaN,31.688,32.217,31.493,30.119,31.027,31.448,29.952,30.482,30.769,30.14,29.807,29.28,29.022,29.037,30.617,30.861,30.977,32.372,31.241,30.603,30.267,30.406,30.807,30.125,30.607,29.814,29.78,30.127,29.616,28.335\nNaN,NaN,NaN,NaN,NaN,NaN,30.837,32.25,29.965,29.006,29.261,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.159,31.605,31.457,31.364,30.601,30.315,30.256,30.176,29.734,29.026,29.346,29.562,29.755,30.094,30.435,30.993,31.825,32.183,31.04,30.552,30.2,30.203,29.983,30.144,29.987,30.18,29.86,29.143,28.14\nNaN,NaN,NaN,NaN,NaN,NaN,32.412,32.815,31.954,31.218,29.851,NaN,NaN,NaN,NaN,NaN,NaN,32.604,31.449,31.185,31.953,30.932,30.051,30.136,29.53,28.574,28.128,28.259,29.08,30.633,31.018,30.256,30.723,31.068,31.528,31.867,31.393,30.63,30.418,30.106,30.11,30.523,31.108,30.82,29.785,28.587,27.977\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,32.386,32.825,30.876,29.588,30.85,NaN,NaN,NaN,NaN,NaN,30.548,30.699,31.356,31.819,30.937,30.519,29.805,28.768,27.647,28.582,29.426,30.271,31.302,31.751,32.352,31.753,30.911,30.914,30.669,31.088,30.635,30.548,30.512,30.353,30.484,30.913,30.213,29.381,28.99,28.866\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,31.655,30.607,28.401,29.211,30.462,30.488,27.853,NaN,NaN,NaN,33.573,32.705,31.773,31.637,30.392,31.041,30.826,29.621,28.459,29.564,30.729,30.686,30.907,31.829,32.139,31.44,30.809,30.132,30.337,30.016,30.37,31.372,31.288,30.437,29.901,30.008,29.691,29.197,28.617,29.219\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,29.93,29.79,28.555,28.969,30.382,30.104,32.729,33.282,32.621,NaN,NaN,NaN,NaN,30.943,30.401,31.052,30.684,29.311,29.241,29.752,31.025,30.423,31.255,31.592,31.341,30.796,30.302,29.218,30.4,31.059,30.628,31.029,30.654,30.369,29.917,29.757,29.375,28.679,28.409,28.668\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061106-070115.unw.csv",
    "content": "22.167,21.82,21.613,21.786,22.373,21.254,21.292,21.72,22.049,21.396,21.274,21.395,20.532,20.654,20.816,20.791,20.798,19.801,19.612,19.438,19.248,20.046,20.841,21.053,19.957,20.344,19.474,19.142,19.174,19.683,19.739,20.661,20.069,19.928,19.959,20.411,21.767,22.378,24.108,22.972,22.079,21.759,22.309,22.272,21.487,22.396,22.489\n22.805,23.134,22.58,22.159,22.045,21.085,21.051,21.89,21.352,21.204,20.704,20.219,20.231,20.182,20.436,20.366,20.083,19.699,20.049,19.604,18.821,20.086,20.552,20.137,19.906,19.834,19.767,19.884,19.545,19.336,19.505,19.96,19.967,19.445,19.674,21.112,22.306,22.771,23.042,23.099,21.913,21.809,21.597,22.149,22.241,21.705,21.361\n22.999,23.726,22.979,22.061,21.948,21.164,21.322,21.019,21.355,20.804,20.777,20.77,20.323,20.063,20.735,20.226,19.966,19.467,19.975,19.644,20.268,21.177,21.386,21.215,20.061,19.847,19.594,19.111,19.166,18.975,19.33,19.786,19.331,19.432,19.959,20.533,21.329,21.686,21.105,21.158,22.143,22.116,21.911,22.578,22.428,21.122,20.749\n23.493,23.05,22.777,21.873,21.776,21.607,21.554,21.762,21.2,20.912,20.773,20.856,20.445,20.271,20.058,19.797,18.946,19.407,19.989,19.461,20.142,20.57,20.437,19.925,19.659,19.392,19.122,19.302,19.028,18.716,19.016,19.424,19.69,19.528,20.19,20.666,21.105,21.692,20.663,21.146,21.881,22.741,22.69,22.666,22.705,21.922,20.674\n22.359,22.81,23.026,22.583,21.918,21.569,21.591,22.437,22.557,22.31,21.643,21.31,20.957,20.457,20.188,19.832,20.134,19.774,19.798,20.287,21.194,20.559,20.239,19.869,19.632,18.758,19.134,19.187,19.687,19.505,19.208,19.417,19.618,19.441,20.38,20.952,21.411,21.499,21.223,21.357,21.831,22.766,22.089,22.207,21.727,21.581,21.19\n21.943,21.56,22.824,22.482,22.246,21.928,21.862,21.863,21.817,21.247,21.498,21.455,21.146,20.845,20.48,20.453,20.653,20.391,20.133,19.84,20.91,20.831,20.299,19.672,19.663,19.523,19.234,19.416,19.522,19.062,19.364,19.676,19.967,19.429,19.887,20.311,21.141,21.899,21.918,22.367,22.859,22.574,21.978,21.919,21.222,21.125,21.716\n21.778,21.448,23.075,22.982,22.412,21.946,22.248,21.737,21.369,22.643,21.935,21.429,20.697,20.596,21.18,21.067,21.293,20.826,20.358,20.219,19.974,19.783,18.976,19.6,20.05,19.591,19.162,19.18,18.874,19.034,19.412,20.307,20.288,19.642,20.194,20.556,20.695,21.178,21.762,22.088,21.493,21.707,21.746,21.835,21.863,22.035,23.728\n22.282,22.298,22.775,22.502,22.725,22.362,22.328,21.234,21.062,22.719,21.79,21.827,22.07,20.553,20.817,20.918,21.163,20.701,20.776,20.729,20.007,20.609,19.879,20.044,20.303,19.663,18.919,18.798,19.227,19.608,19.293,19.773,20.2,19.233,19.386,20.112,20.775,21.174,21.341,21.702,21.633,22.022,21.877,22.362,22.497,22.377,23.69\n22.657,22.5,22.703,22.723,22.668,22.619,22.258,21.347,21.51,22.75,23.501,22.817,21.565,20.844,20.688,20.788,20.749,20.668,21.683,21.328,20.851,20.386,19.857,20.048,19.809,19.7,20.359,20.061,19.429,18.986,18.84,18.896,18.972,19.458,19.834,20.317,21.125,22.126,21.879,22.278,22.089,21.585,21.6,21.739,22.507,22.647,22.305\n22.711,22.514,22.557,23.109,22.526,22.502,22.617,22.069,22.242,22.742,23.417,22.679,21.33,21.312,21.025,21.213,20.613,20.781,21.869,21.364,20.672,21.126,19.755,19.785,20.227,19.974,19.876,20.314,19.283,19.313,19.313,19.566,19.065,20.366,21.056,21.003,21.34,22.091,22.077,22.377,21.941,21.485,21.495,21.472,21.818,21.867,21.885\n22.514,22.298,22.099,23.645,23.036,22.455,22.938,22.474,22.47,22.934,23.039,21.796,21.237,21.769,21.244,20.137,21.528,21.583,21.53,21.635,21.473,20.685,19.689,19.817,20.036,19.501,19.642,19.666,19.399,19.608,19.495,19.148,19.093,20.332,20.69,21.289,21.463,21.542,21.585,21.59,22.06,21.634,21.394,21.742,22.238,21.955,21.907\n22.562,22.509,23.446,24.141,23.148,23.057,22.522,22.726,22.134,22.694,21.714,21.852,21.583,21.758,21.636,21.547,22.162,21.472,21.278,21.464,21.413,20.358,19.79,19.984,19.7,19.491,19.074,19.417,19.262,19.429,19.339,19.279,19.307,19.882,20.318,19.927,19.992,20.525,21.292,21.33,21.513,21.447,20.901,21.451,22.173,22.479,22.538\n23.175,23.299,23.586,23.562,23.545,23.072,22.248,23.273,22.277,22.406,21.994,22.096,22.014,22.589,22.441,22.209,21.946,21.857,21.514,21.082,20.523,20.28,19.412,19.751,19.555,19.815,19.61,19.802,19.417,19.632,19.261,19.433,19.633,19.487,19.596,19.533,20.224,20.336,21.145,21.33,21.398,21.233,20.683,20.684,22.013,22.469,23.278\n23.288,23.766,24.225,24.295,22.856,22.687,21.992,22.823,22.282,22.333,22.081,22.247,22.756,23.196,22.256,22.868,22.378,22.219,21.778,21.249,20.609,20.416,19.677,19.4,19.663,19.23,19.461,19.203,19.297,19.699,19.521,19.539,19.279,19.682,19.895,19.908,20.243,20.859,21.551,20.961,20.857,20.573,20.423,20.455,21.089,22.383,23.44\n23.056,23.941,25.475,23.284,22.284,22.359,22.668,22.422,22.588,21.824,21.656,21.447,21.443,21.961,22.162,22.273,21.929,21.842,21.508,21.216,20.54,21.14,19.511,19.023,19.197,19.022,19.274,19.743,20.203,19.843,19.473,19.693,19.912,19.987,19.961,19.64,20.179,20.219,20.364,20.201,20.471,20.158,20.032,20.463,20.492,21.261,21.762\n22.012,23.257,24.564,23.123,22.305,23.241,22.513,22.62,22.499,22.131,21.992,22.372,21.583,21.713,22.39,22.394,21.844,20.913,20.667,20.864,20.999,20.785,19.756,19.337,19.097,18.855,18.984,19.285,19.562,19.206,19.037,18.885,19.401,19.003,19.055,19.25,19.532,19.823,19.955,20.455,20.288,20.496,20.117,20.562,20.81,21.361,21.869\n21.391,22.467,23.27,22.912,22.232,22.699,22.605,22.857,22.654,22.781,22.642,22.307,21.901,21.686,23.207,21.96,21.131,20.673,20.333,20.962,21.671,20.606,19.308,19.436,18.991,18.81,18.597,18.597,19.031,19.461,19.002,18.382,20.159,19.968,19.302,19.079,19.52,19.722,20.107,20.203,20.376,21.093,21.021,21.129,21.566,21.98,22.016\n21.505,22.629,22.967,22.496,23.104,23.35,23.302,23.007,22.848,22.92,22.356,22.088,21.854,21.62,21.864,21.856,21.701,21.717,20.949,21.938,21.334,20.313,19.493,19.289,18.843,18.773,18.677,18.309,18.32,18.834,19.013,18.705,18.376,19.529,19.274,18.565,18.5,19.331,19.809,19.874,19.871,20.528,21.377,21.202,21.458,21.226,21.418\n22.212,22.922,23.681,23.477,23.53,25.952,24.111,22.754,22.745,22.961,22.619,22.271,22.063,21.542,22.053,21.568,21.532,21.131,20.4,19.205,19.097,19.242,18.988,18.89,18.838,19.014,18.916,18.72,18.118,18.914,18.874,19.482,18.24,17.494,18.146,18.946,18.705,19.105,20.687,19.808,19.623,19.631,21.119,21.53,21.97,21.907,21.549\n23.08,23.101,23.139,23.011,23.101,23.097,22.841,23.164,23.371,23.233,22.9,22.68,22.432,22.068,22.451,22.108,21.496,20.773,20.413,20.129,18.253,18.117,19.192,18.642,18.88,19.246,19.032,18.951,19.049,18.319,18.715,19.411,18.966,19.079,19.485,18.462,18.993,18.985,20.294,20.428,20.358,20.225,20.77,21.177,22.134,22.136,22.15\n22.809,24.095,23.46,22.93,22.629,22.785,23.432,22.916,23.65,22.838,23.241,22.523,22.313,22.299,21.445,20.89,20.69,20.728,20.544,20.538,20.399,19.923,19.648,19.342,19.456,18.945,18.755,17.898,18.26,18.19,18.11,19.089,19.34,19.352,19.523,19.262,19.345,19.09,19.998,20.018,19.992,20.311,20.873,21.795,22.062,21.975,21.612\n22.837,23.989,23.391,23.291,23.315,23.161,23.79,23.29,23.292,22.65,22.401,22.188,22.32,22.026,21.71,21.597,20.997,20.658,21.025,21.437,20.056,20.175,20.648,19.463,18.935,18.643,18.804,18.778,17.685,17.723,17.94,19.13,18.755,18.821,19.464,19.183,19.308,19.888,20.069,19.874,19.307,20.485,21.481,22.103,22.277,22.005,21.678\n22.633,23.252,22.75,22.992,23.091,22.736,22.632,23.05,23.428,23.146,22.147,21.184,21.746,22.616,22.633,22.463,21.542,21.199,20.689,21.007,19.664,19.528,19.832,20.714,19.698,19.224,19.326,19.407,18.559,18.12,18.103,18.099,18.216,18.236,19.184,19.188,19.486,19.609,19.615,19.831,19.966,20.81,21.713,22.342,21.555,21.474,21.525\n21.216,22.466,22.371,22.984,23.483,22.791,22.768,23.135,23.773,23.762,22.975,23.371,22.263,22.973,23.228,23.781,21.823,21.649,21.186,20.784,20.755,20.998,20.74,20.8,20.057,19.562,18.858,19.563,19.173,18.734,18.198,18.487,18.857,18.912,19.241,19.395,19.639,20.016,20.043,20.563,20.651,21.185,21.699,21.74,21.11,21.313,21.509\n21.366,21.799,22.321,23.09,23.304,22.752,23.335,23.998,24.404,24.227,23.777,22.942,22.093,23.591,23.224,22.098,21.233,21.475,21.932,21.888,22.053,22.098,21.673,21.46,20.265,19.931,20.12,20.516,19.974,19.56,19.794,20.341,19.823,18.646,19.403,20.072,18.834,19.688,20.21,20.226,20.631,21.151,21.231,20.9,20.925,21.491,21.789\n22.286,21.81,22.721,23.342,22.99,23.704,22.999,23.181,23.856,24.619,23.868,23.236,23.276,23.875,23.321,22.334,21.743,21.558,22.037,22.443,23.038,23.385,22.301,22.132,21.456,20.672,20.701,20.168,19.482,19.567,19.836,20.77,20.08,19.519,19.624,20.471,19.724,19.542,19.472,19.042,19.878,20.726,21.197,21.317,21.264,21.985,22.411\n22.2,22.182,22.962,23.896,23.005,22.642,22.624,22.931,23.498,24.136,24.343,23.712,24.411,23.758,23.277,22.133,21.476,22.193,23.244,23.782,23.652,23.474,22.527,21.797,20.519,20.508,20.981,20.758,20.381,19.671,19.329,19.224,19.313,19.405,19.492,19.755,19.821,19.768,19.401,18.969,19.944,20.615,21.163,21.686,22.042,22.339,22.409\n22.008,21.87,21.861,22.379,22.647,23.619,24.922,23.499,24.24,23.705,24.281,25.02,24.462,23.782,23.376,23.6,22.961,23.016,23.151,23.582,24.085,23.899,22.99,21.258,21.02,20.855,20.84,20.866,20.03,20.003,19.561,18.875,18.145,18.641,19.228,19.325,19.471,19.074,19.326,19.729,20.565,21.123,21.13,21.717,22.183,22.524,22.3\n22.452,22.388,22.348,23.521,23.155,24.278,25.794,26.219,25.096,23.778,23.508,23.848,23.59,23.233,23.185,23.274,22.296,22.066,22.173,22.588,23.396,23.724,23.532,23.333,22.132,21.381,21.548,20.978,20.596,20.788,20.179,19.393,19.01,18.788,19.484,19.413,19.029,18.544,18.485,19.224,20.814,21.185,20.821,21.739,22.309,22.314,22.008\n22.212,22.162,21.772,22.711,23,24.119,24.762,25.331,25.095,24.582,23.679,23.323,23.532,22.566,22.569,22.359,21.822,21.218,21.241,21.968,22.759,23.436,23.964,23.21,22.433,22.211,22.144,21.812,21.212,20.663,19.994,19.059,18.499,18.518,19.951,19.864,18.982,18.591,18.517,19.74,21.005,20.373,20.33,21.267,22.151,22.024,21.464\n20.127,22.446,21.999,22.194,22.925,23.136,23.8,23.925,24.149,23.764,22.47,22.455,23.186,22.367,22.433,21.888,21.438,22.002,22.399,22.404,22.363,22.31,23.238,22.822,22.054,21.713,21.623,21.289,20.428,20.35,19.925,19.457,19.31,19.468,20.2,20.164,19.572,19.476,19.747,20.242,20.056,19.63,20.133,21.38,22.334,21.929,22.056\n20.812,23.149,23.527,23.453,22.37,22.311,22.239,22.611,24.316,23.157,20.878,21.658,22.269,22.429,21.213,21.151,21.665,22.04,22.021,22.02,23.912,23.515,23.075,22.239,21.999,21.513,21.298,20.786,18.269,18.032,19.626,20.195,20.114,19.838,20.232,20.018,19.488,19.7,19.633,20.249,20.468,20.003,20.531,21.531,21.653,21.465,21.724\n21.862,22.868,23.5,23.493,22.787,21.07,22.467,23.683,23.636,23.382,21.723,20.233,19.76,20.031,19.699,20.829,21.925,21.798,21.509,21.897,22.902,23.504,23.723,21.941,22.126,21.795,NaN,NaN,17.05,17.072,19.501,21.061,21.632,21.841,20.745,20.17,19.978,20.063,19.667,20.329,20.657,20.844,21.146,21.298,21.288,21.59,21.512\n21.7,21.912,23.595,23.387,22.118,21.378,22.181,23.634,24.179,24.72,22.86,21.871,20.042,20.571,18.928,19.699,20.889,20.154,21.417,21.068,21.575,22.405,22.469,22.307,22.03,22.525,NaN,NaN,NaN,20.399,21.863,22.232,22.81,22.238,21.394,20.755,20.274,20.435,20.495,20.09,20.759,21.064,21.296,21.148,21.443,21.682,21.489\n21.041,22.273,22.38,24.037,24.329,22.93,20.934,22.971,23.71,23.554,23.26,21.884,21.603,21.031,18.715,19.265,19.905,20.684,21.282,21.624,22.953,23.821,23.729,23.628,NaN,NaN,NaN,NaN,NaN,23.02,22.463,22.444,22.902,22.081,21.563,20.656,20.384,20.696,20.404,20.34,20.438,20.398,20.789,20.869,21.372,21.76,21.53\n19.637,20.011,20.641,22.368,16.61,19.75,20.778,23.346,23.218,22.86,21.75,21.292,19.594,17.267,15.439,16.72,20.496,21.762,22.538,23.553,25.21,26.062,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.467,22.816,22.78,22.782,22.018,21.54,20.844,20.638,20.312,20.477,20.762,20.803,20.928,21.092,21.192,21.551,21.544,21.636\n20.141,20.875,21.206,20.997,19.672,21.811,21.435,20.987,20.875,21.238,21.277,20.349,19.707,17.096,17.35,18.949,21.754,23.617,25.469,25.465,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.082,22.842,22.712,22.33,22.53,22.271,21.419,20.831,20.416,20.733,20.769,20.611,20.975,20.885,21.311,21.493,21.343,21.609\n21.04,20.397,20.685,21.055,20.388,21.86,21.898,21.675,21.803,21.719,21.37,20.802,21.181,22.359,20.543,20.878,21.638,27.906,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.608,23.827,22.925,22.604,22.522,22.525,22.542,22.247,21.286,20.668,20.505,20.751,20.947,21.01,21.186,21.486,21.73,21.636,21.386\n21.683,21.165,20.932,21.175,21.081,21.383,21.675,21.533,22.097,22.311,21.637,21.278,20.341,19.59,19.96,20.285,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.68,23.767,23.674,22.968,22.771,22.8,22.234,21.49,20.979,20.745,20.712,21.121,21.302,20.9,21.405,21.562,21.405,21.651,20.879\n22.2,22.326,21.526,21.712,21.721,21.713,21.449,20.871,20.598,21.262,21.349,21.456,20.519,19.403,19.209,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.544,22.301,23.788,23.042,23.586,24.249,24.214,23.156,22.61,22.164,21.395,20.713,20.533,20.567,20.821,21.248,21.329,21.03,21.293,21.381,21.447,21.818,21.135\n22.635,21.691,20.594,20.405,21.292,21.918,21.327,20.087,20.699,19.868,20.742,21.122,21.128,20.415,19.563,NaN,NaN,NaN,NaN,NaN,NaN,20.199,19.252,20.277,20.446,20.929,22.304,23.812,23.832,23.807,24.217,24.22,22.373,21.544,21.44,20.381,20.631,21.023,21.706,21.084,21.166,20.897,20.999,21.212,21.267,21.655,21.58\n21.442,20.695,20.436,20.347,20.84,20.319,20.457,20.414,20.479,20.189,20.694,20.348,20.601,20.985,20.476,19.129,16.694,NaN,NaN,NaN,NaN,20.08,19.243,NaN,NaN,NaN,23.251,24.336,24.113,23.734,23.758,23.569,22.721,21.552,21.224,20.302,20.273,20.77,21.664,21.15,21.125,20.878,20.575,20.88,21.056,21.871,21.738\n19.967,19.436,20.311,20.213,20.109,19.908,19.672,19.747,19.908,19.674,19.714,20.616,20.431,20.753,21.476,22.085,20.113,18.16,18.57,17.513,19.25,19.974,NaN,NaN,NaN,NaN,NaN,23.566,23.408,22.963,23.07,23.306,22.979,21.377,20.955,21.053,20.915,21.419,21.365,20.901,20.856,20.867,20.786,20.772,21.049,21.958,21.697\n19.949,20.243,20.254,19.487,19.648,19.676,19.476,19.546,19.139,19.273,19.566,20.221,20.416,20.722,21.242,21.71,20.964,19.817,19.773,19.535,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.997,22.678,22.562,22.353,22.192,22.318,21.382,20.726,20.747,21.177,21.412,21.168,20.781,20.915,21.162,21.01,20.944,21.164,21.846,21.888\n20.07,20.293,20.195,19.913,19.901,20.103,19.171,18.553,18.436,19.523,20.032,20.21,19.974,20.095,21.003,21.082,20.701,20.147,19.272,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.082,21.893,21.893,21.883,21.266,21.683,21.388,20.652,20.61,20.959,21.019,20.804,20.965,21.203,21.27,20.688,20.813,21.125,21.829,21.824\n19.642,20.008,20.063,19.897,20.068,20.093,19.144,19.367,18.982,19.254,20.144,20.561,19.882,20.022,21.044,21.251,20.701,20.773,20.738,19.187,NaN,NaN,NaN,NaN,22.009,NaN,21.263,21.547,21.505,21.638,21.208,21.143,21.711,22.4,22.263,21.551,21.197,21.001,20.657,21.118,20.778,20.666,20.737,20.83,20.254,21.175,22.257\n20.365,19.872,19.521,19.747,19.791,19.925,19.589,20.021,19.669,19.578,19.396,19.76,19.77,19.788,20.579,20.231,20.212,20.759,21.083,18.941,19.497,19.908,20.811,20.492,20.572,20.651,20.376,21.068,20.726,20.402,20.376,21.057,21.34,21.685,22.091,21.558,20.702,20.57,20.436,20.968,21.022,20.887,20.943,21.007,20.889,21.175,21.438\n20.376,19.702,19.395,19.598,19.547,19.474,20.201,19.929,19.882,19.605,18.865,20.525,19.809,19.739,20.085,20.08,20.057,20.735,20.41,19.807,19.264,19.36,20.735,20.549,20.56,20.253,19.876,20.982,21.001,20.684,20.311,20.904,21.412,21.572,21.531,21.447,21.129,20.74,20.623,20.736,20.959,20.97,21.047,21.087,21.11,21.371,21.883\n20.089,19.461,19.43,19.584,19.601,19.122,19.578,19.494,19.614,19.396,19.43,20.071,19.419,19.672,20.02,20.643,20.82,20.625,20.459,19.618,18.358,19.451,20.438,19.948,19.359,18.894,20.037,20.763,20.401,20.474,20.546,21.454,21.635,21.034,20.704,21.741,21.765,21.491,20.995,20.797,21.036,21.34,21.292,21.523,21.404,21.754,22.599\n19.826,19.883,19.678,20.357,20.26,20.425,20.101,19.756,19.556,19.5,19.675,20.043,19.937,19.853,19.721,20.752,20.553,20.234,19.477,17.564,18.21,20.21,20.12,19.653,19.052,19.121,19.681,20.456,20.399,20.571,20.464,21.559,22.242,21.322,21.617,22.202,22.699,22.04,21.996,21.363,21.515,21.466,21.531,21.529,21.073,21.94,22.413\n20.177,19.717,19.931,20.466,19.406,19.488,19.24,19.468,19.64,19.502,19.483,19.888,20.158,20.503,20.38,20.328,20.256,20.195,19.295,18.87,19.678,20.382,19.772,19.484,19.312,19.173,19.152,20.381,20.15,20.357,20.956,21.461,22.105,21.759,21.573,21.937,22.224,22.186,22.084,21.874,21.765,21.47,21.624,21.807,21.658,21.91,22.2\n20.39,19.428,19.656,19.665,19.414,19.338,19.138,19.584,20.656,19.901,19.677,19.982,19.901,20.529,21.03,20.325,19.697,20.584,20.13,19.076,20.048,20.314,19.632,19.39,19.319,19.033,18.708,20.027,20.057,20.32,21.513,21.239,21.682,21.396,21.263,21.911,22.192,22.355,22.151,21.826,21.871,22.008,22.921,22.382,21.819,22.846,23.026\n19.354,19.404,19.506,19.447,19.827,19.048,19.524,19.902,20.141,19.768,20.489,20.211,20.292,20.346,20.547,20.652,20.487,20.498,19.571,19.175,20.274,20.167,19.144,19.506,19.338,19.35,19.569,20.067,20.148,20.897,21.405,21.409,21.617,21.171,21.313,21.875,22.076,22.108,22.093,22.162,22.008,21.633,22.073,21.797,22.062,22.364,22.63\n19.516,18.837,19.02,19.258,19.273,19.327,19.515,19.116,19.411,19.522,20.342,19.394,19.412,19.473,19.952,20.253,20.305,20.391,19.703,19.617,20.256,20.361,19.719,19.687,19.683,19.831,19.704,20,19.88,20.315,20.841,21.303,21.593,21.342,21.608,21.769,21.766,22.251,22.675,23.557,22.423,21.939,21.884,21.66,22.266,22.048,22.512\n19.519,19.258,19.076,19.477,19.348,19.123,19.247,19.394,19.492,19.975,19.534,18.897,18.631,18.915,19.476,19.725,20.201,20.498,19.886,18.972,20,20.076,20.163,20.065,19.858,19.884,20.661,20.724,20.143,20.251,20.969,21.113,21.472,21.302,21.59,21.882,21.825,22.358,22.827,23.325,22.764,22.411,21.955,22.673,22.472,21.841,22.219\n19.06,18.785,20.038,19.622,19.019,19.512,19.088,18.966,19.168,19.58,19.902,19.836,19.618,19.367,18.718,19.252,19.875,19.988,19.861,19.274,19.652,19.904,20.004,20.205,20.302,20.159,20.826,20.609,20.204,20.245,20.641,21.121,21.602,21.672,21.891,21.83,22.056,22.455,22.693,22.953,22.699,22.588,22.732,23.177,22.628,21.841,22.235\n20.106,20.235,20.108,19.949,19.535,18.628,18.906,18.482,18.474,18.814,18.169,18.948,20.197,19.789,18.89,19.675,20.346,19.998,20.104,20.102,20.387,20.472,20.558,20.626,20.238,20.232,19.923,19.986,20.116,20.449,20.424,20.937,21.305,21.889,22.206,21.913,21.913,22.107,22.559,22.238,22.618,22.492,22.564,22.863,22.529,22.828,22.977\n19.764,19.336,19.015,19.109,19.488,20.934,19.514,18.199,17.354,17.079,18.09,18.428,20.818,20.054,19.688,19.838,20.496,19.242,20.119,21.173,20.998,21.163,21.034,21.219,21.095,21.602,20.813,19.113,19.292,20.036,20.386,20.995,21.387,22.154,22.785,22.317,22.356,22.019,22.246,22.32,21.822,21.9,22.071,22.195,22.208,23.009,22.526\n18.363,18.92,19.225,18.531,18.303,19.964,19.32,18.526,18.225,17.397,17.962,16.266,17.776,18.404,19.947,20.304,19.309,18.47,20.417,21.335,21.116,20.699,21.199,21.45,21.483,21.582,21.364,20.854,19.922,20.575,20.686,20.734,21.164,21.715,22.349,22.094,22.032,22.158,22.442,22.516,22.425,22.233,22.16,22.471,22.444,22.478,21.86\n17.685,18.561,19.093,17.88,17.223,17.454,17.733,18.971,20.294,20.279,18.331,16.643,17.701,17.472,17.118,17.455,18.883,20.456,21.238,20.914,20.759,20.514,20.671,21.403,21.429,21.291,20.997,20.741,20.412,20.841,20.933,20.328,20.82,21.483,22.079,22.251,22.22,22.688,22.864,22.737,21.962,22.318,22.529,22.5,22.563,21.673,21.683\n18.184,18.54,19.142,18.859,17.867,15.072,20.216,18.571,19.05,18.797,18.905,18.267,19.808,20.055,18.696,18.672,17.797,19.334,20.943,20.931,20.98,20.99,20.365,20.645,21.033,21.58,20.482,20.106,19.756,20.145,20.398,20.842,20.785,21.34,21.943,22.176,22.193,23.213,22.47,22.357,22.085,22.408,22.506,21.963,21.563,20.852,20.253\n20.014,20.221,20.512,20.53,19.352,NaN,NaN,NaN,19.147,17.633,20.586,22.394,21.407,20.425,17.479,17.641,17.83,19.554,20.199,21.491,20.737,20.665,20.122,20.322,21.077,21.461,21.102,21.277,20.314,20.553,20.988,21.198,21.523,20.951,21.606,21.996,22.012,22.452,22.136,22.132,21.808,21.588,21.778,22.065,21.943,21.91,22.098\nNaN,19.221,20.145,20.667,20.933,NaN,NaN,NaN,19.546,16.898,17.357,20.826,21.808,21.255,18.537,18.613,19.311,21.053,19.4,21.192,21.827,21.54,19.793,21.193,21.291,22.241,22.647,21.922,21.21,21.307,21.358,22.04,21.827,21.682,22.018,21.953,22.487,22.344,21.725,21.403,21.526,21.551,22.242,22.661,22.419,22.561,22.071\nNaN,NaN,NaN,NaN,19.183,17.665,16.99,NaN,NaN,NaN,NaN,NaN,NaN,22.396,21.622,21.429,24.331,25.276,21.311,20.782,20.703,20.98,20.803,21.226,21.755,22.64,22.856,21.6,21.612,21.653,22.084,22.495,22.666,22.394,22.626,22.124,22.463,22.267,21.744,21.898,21.804,21.661,21.899,21.868,22.356,22.366,22.109\nNaN,NaN,NaN,NaN,17.997,17.576,18.525,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.536,25.199,22.747,21.436,20.704,20.282,21.03,21.166,21.665,22.118,22.187,21.961,22.312,22.159,22.362,22.585,22.778,22.911,22.441,22.117,22.149,21.855,21.733,21.983,22.015,22.321,22.353,22.457,22.422,21.767,21.492\nNaN,NaN,NaN,NaN,21.637,21.386,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.876,23.558,21.884,21.266,17.411,20.076,21.406,21.978,22.333,21.918,21.343,22.279,22.053,22.423,22.411,22.632,23.128,21.701,21.888,21.86,21.646,22.077,21.956,21.962,22.403,22.941,22.465,22.189,21.944,20.569\nNaN,NaN,NaN,21.99,23.05,21.547,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.01,21.18,19.982,18.907,21.385,22.955,21.294,22.066,22.662,22.703,23.349,22.65,22.25,21.758,22.362,22.773,21.176,21.715,21.437,21.65,22.181,22.309,21.855,22.372,22.707,22.716,22.445,23.314,22.416\nNaN,NaN,NaN,NaN,NaN,NaN,21.401,21.415,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.952,21.905,22.336,22.793,24.387,21.748,21.51,21.749,21.427,20.851,22.841,22.656,22.643,22.738,23.906,22.887,22.197,21.899,21.484,21.606,21.867,21.854,21.734,21.634,21.906,22.176,22.264,22.695,22.74\nNaN,NaN,NaN,NaN,NaN,NaN,20.049,21.29,21.797,21.478,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.31,20.582,21.36,22.064,21.536,21.06,22.214,21.442,21.209,20.908,21.864,22.082,22.656,23.014,23.073,22.389,22.278,21.893,21.944,21.493,21.693,22.38,21.958,21.524,22.149,22.127,22.636,23.725,23.694\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,20.947,19.31,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,20.683,20.095,21.283,20.913,21.613,22.562,22.787,21.969,21.574,21.503,22.219,22.786,23.001,22.437,22.208,21.991,22.11,22.147,22.264,20.829,21.593,22.511,21.491,22.199,22.564,21.994,21.816,22.043,22.198\n22.587,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.312,20.094,23.66,23.671,19.082,NaN,NaN,NaN,NaN,21.973,21.476,21.919,18.885,22.548,23.102,21.848,21.903,21.916,21.465,22.143,22.684,22.5,22.202,22.112,22.11,22.435,22.305,22.38,21.45,22.337,22.003,22.242,22.589,22.424,21.873,21.652,21.786,22.369\n22.174,22.127,22.981,22.99,NaN,NaN,NaN,NaN,NaN,19.644,20.295,21.707,22.679,22.368,21.705,20.939,21.173,19.718,19.387,23.619,20.274,20.278,21.773,22.367,20.837,20.753,21.002,20.66,20.48,22.137,22.865,22.181,21.821,21.804,22.089,22.291,21.206,20.885,21.47,22.032,22.598,23.57,23.232,22.436,21.915,21.679,21.7\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061106-070326.unw.csv",
    "content": "9.0427,8.806,8.8001,8.8779,9.0107,8.7977,8.5997,8.5607,8.7187,8.5752,8.3501,8.4665,8.4237,8.9902,9.3514,9.2213,9.0072,8.9586,9.2991,9.4186,9.8535,9.4901,9.4729,9.691,9.9765,9.1188,9.2216,9.7038,9.8745,9.6918,9.784,9.51,9.8464,9.7851,9.4759,9.4503,9.4461,8.9402,8.4962,8.4307,9.0796,9.3802,9.1624,8.7656,8.3814,8.3909,8.4256\n8.8827,9.0294,9.1456,8.9635,8.883,8.6376,8.5724,8.5363,8.2289,8.316,8.537,8.4955,8.7076,8.9814,8.8566,8.7731,8.8949,8.9202,9.0992,9.2684,9.3212,8.9782,9.6437,9.7308,9.2878,9.1775,9.4308,9.6957,9.6893,9.9066,9.8263,9.6057,9.7796,9.6833,9.2838,9.4321,9.3601,8.9936,8.5726,8.3918,8.9244,9.2309,8.8048,8.6228,8.1643,7.8608,7.8318\n8.9087,9.7083,9.3673,8.9354,8.8174,8.6329,8.5752,8.4463,8.0235,8.296,8.7429,9.02,9.0107,8.9946,8.7709,8.6633,8.8561,8.7957,8.8379,9.1361,9.2252,9.4332,10.184,9.0526,9.3039,9.3025,9.3626,9.3543,9.6016,10.131,9.7124,9.4626,9.6146,9.6879,9.515,9.4444,9.6733,9.2499,8.9589,8.9016,8.8906,9.1174,8.6055,8.419,7.7829,7.5982,7.4652\n8.9773,9.8811,9.0244,8.9424,8.7976,8.534,8.382,8.2954,8.1531,8.2601,8.6635,8.8212,8.9188,8.8587,8.6736,8.8232,8.9771,8.9258,8.8431,9.0317,8.9093,9.5691,9.6281,9.1895,9.3328,9.3269,9.3747,9.3078,9.5568,9.6237,9.4002,9.3718,9.6386,9.7931,9.6846,9.5852,9.4749,9.1401,9.0847,9.0649,8.887,8.7093,8.5669,8.2026,7.8011,7.6166,7.357\n8.6277,9.2269,8.3999,8.8385,8.7034,8.5037,8.3245,8.3112,8.0661,8.7245,8.911,8.494,8.8907,8.6178,8.3867,8.8339,9.1705,9.1404,8.8646,8.7585,8.8035,10.043,9.5125,9.4225,9.2306,9.2998,9.7208,9.4253,9.8704,9.3138,9.3593,9.4722,9.5761,9.8816,9.7783,9.4738,9.3532,9.2435,9.1653,8.9769,8.6511,8.5762,8.4835,8.2983,7.8566,7.7239,7.7738\n8.246,7.8716,8.6041,9.0342,8.698,8.574,8.3295,8.1532,8.4336,8.8396,8.4187,8.2322,8.5463,8.4206,8.2979,8.6661,8.8801,8.9733,8.6989,8.9525,9.4343,9.9066,9.715,9.1777,9.1369,9.8651,10.051,9.6027,9.6026,9.6919,9.9897,9.8298,9.5962,9.624,9.6682,9.4961,9.518,9.446,9.2735,8.9777,8.7518,8.5346,8.2839,8.1788,7.6706,7.7798,7.9272\n7.8031,7.4909,8.977,9.0228,8.8196,9.0264,8.3898,7.9433,8.0072,8.4163,8.1143,8.0469,8.3931,8.3916,8.6243,8.7648,8.9721,9.1343,9.1989,9.1295,9.1341,9.6348,9.4293,9.4615,9.6321,10.147,10.151,9.6534,9.6717,9.8944,10.147,9.7753,9.6016,9.5271,9.555,9.5451,9.4195,9.0685,8.7338,8.4698,7.985,8.2123,8.0321,7.8009,7.59,7.6032,8.0557\n7.899,7.9334,8.8686,8.8754,8.7975,9.2288,8.5922,8.1167,8.2146,8.591,8.1546,8.0893,8.1912,8.5102,8.6741,8.7282,8.9644,9.3027,9.1569,9.0385,9.0189,9.2568,9.2624,9.8386,9.8249,9.926,10.286,10.201,9.9484,9.9911,9.9379,9.6558,9.6317,9.5195,9.6065,9.5832,9.4223,9.1497,8.8307,8.5452,8.279,7.8775,8.0444,8.0797,7.8664,7.5656,7.9792\n8.2556,8.5062,9.1523,8.8045,8.9407,8.9975,8.4657,8.4449,8.6459,8.8443,8.6638,8.3112,8.5462,8.7701,8.7733,8.6882,8.7346,9.0793,9.0861,9.1824,9.205,9.4102,9.6797,10.055,10.183,10.366,10.417,10.107,9.9303,9.7851,9.8826,9.7733,9.7178,9.8926,9.7979,9.5675,9.2491,9.2138,9.0135,9.3035,9.1039,8.3971,8.2482,9.0573,8.6316,8.2393,7.902\n8.7356,8.798,9.7448,9.14,8.7024,8.7755,8.5665,8.638,8.7202,8.5911,8.5329,8.7151,8.6135,8.6868,8.8809,8.8719,8.6153,8.8174,9.2173,9.6017,9.4005,9.7729,9.8553,10.007,10.458,10.332,10.189,9.6063,10.089,10.417,10.202,9.8393,9.8843,9.9177,9.665,9.3772,9.2459,9.4653,9.3961,9.4648,9.4035,9.1423,8.963,9.2936,9.1023,8.6197,8.0959\n8.6514,9.5662,9.913,8.9389,8.9199,8.7932,8.7061,8.6324,8.5429,8.5149,8.583,8.5365,8.1091,8.368,8.5564,8.6116,8.7579,9.1722,9.607,9.6751,9.7942,9.8096,9.5395,9.9447,10.212,10.116,10.055,9.9396,10.366,10.899,10.708,10.437,10.116,9.7684,9.5367,9.4821,9.4827,9.5488,9.5825,9.5694,9.7068,9.2923,9.1438,9.1933,9.1236,8.6113,8.4378\n9.0986,10.059,9.7614,8.9881,8.7295,8.5967,8.6394,8.6767,8.6191,8.5922,8.4071,8.3154,8.0386,8.1667,8.5765,8.9625,9.2118,9.3871,9.5818,9.4066,9.5055,9.455,9.4197,10.132,10.17,10.628,10.003,10.048,10.688,10.936,10.938,10.447,10.114,9.7609,9.8783,9.5885,9.3718,9.3673,9.5372,9.6327,9.6424,9.2394,8.9794,9.0417,8.9711,8.6089,8.4601\n8.7343,8.9714,9.1313,9.307,8.589,8.852,8.6928,8.8684,8.7467,8.698,8.4003,7.9778,8.2501,8.7862,8.9429,9.2071,9.5101,9.6883,9.636,9.4669,9.4734,9.8627,9.8698,10.031,10.541,10.73,10.246,10.288,10.869,10.659,10.558,10.189,9.9293,9.6642,9.7065,9.3541,8.6227,8.5914,8.8913,8.8592,8.9909,8.9357,8.6921,8.9132,8.7744,8.6307,8.0972\n8.4918,8.4468,8.5762,8.7765,8.9878,9.3346,9.2111,8.9614,8.5715,8.5936,8.1345,7.9625,8.5658,9.242,9.0371,9.2098,9.474,9.93,9.9852,9.9688,10.004,9.9186,10.097,10.298,10.67,10.466,10.157,10.337,10.512,10.47,10.321,10.034,10.006,9.646,9.3525,8.7463,8.148,7.6857,7.9558,8.4359,8.5648,8.3647,7.9646,8.1357,8.2258,8.096,7.9583\n8.4062,8.4579,8.857,8.9863,9.0969,9.2714,9.2116,8.6715,8.5773,9.0689,8.7429,9.0621,9.3664,9.5631,9.2835,9.3707,9.3293,9.7258,9.8932,10.048,10.148,9.9005,9.9229,10.496,10.479,10.085,10.104,10.11,10.428,10.302,10.058,9.7791,9.7377,9.7519,9.3263,8.5543,8.0708,7.8525,7.8189,8.1347,8.3941,8.3726,7.4047,7.4132,7.3999,7.5445,7.4845\n7.7474,8.3368,8.9901,9.3195,9.122,9.305,9.225,8.8855,8.7977,9.1047,9.0874,9.5992,9.1578,9.4436,9.0473,8.8727,8.7057,8.8025,9.4212,9.6955,9.8112,9.7814,9.8988,10.445,10.537,10.344,10.266,10.19,10.619,9.8866,9.4881,9.4412,9.2109,9.3079,9.1233,8.6695,8.8992,8.4731,8.1705,7.6678,8.2231,8.2827,7.4722,7.1984,7.072,6.7105,6.5467\n7.4323,8.3473,8.8491,8.9095,8.515,8.6571,9.2617,9.1254,9.1471,8.9904,9.1144,8.9896,9.1856,9.462,9.076,8.6137,8.3048,8.9761,8.9723,9.3012,9.3446,9.6362,9.7602,10.069,10.28,10.243,10.131,10.09,10.089,9.7578,9.3239,9.0977,8.6783,8.9856,9.1321,8.7339,8.5832,8.6278,8.7216,7.7423,7.8451,7.8957,8.0214,7.5761,6.7927,6.3061,6.3827\n8.06,8.5332,8.8299,8.5091,7.9483,8.5972,9.2849,8.8689,8.9257,8.8172,8.8699,8.9557,9.3061,9.4124,8.9459,8.6094,8.7357,9.0517,8.6339,9.4288,9.439,9.3956,9.4998,9.6259,10.222,9.9908,9.6669,9.565,9.2803,9.0347,8.9066,8.629,8.9587,9.4726,9.4205,8.7555,8.1211,8.3065,8.4616,8.037,7.952,7.8948,8.0088,8.0324,6.939,6.6147,6.5826\n7.752,8.5431,9.09,8.8552,8.4962,9.1663,9.3432,8.8891,8.609,8.8546,8.9348,8.9565,8.9785,8.9108,8.8456,8.46,8.4534,8.477,8.5426,8.9669,9.0635,9.0533,9.3351,9.7066,9.7964,10.165,9.6768,9.4807,9.418,8.9783,8.9117,8.7191,8.7662,9.2708,9.5023,8.9548,8.318,8.3633,8.5983,8.3869,8.2533,7.8241,7.9168,7.8501,7.2823,7.0347,7.0066\n7.4151,8.57,8.8868,8.8923,8.7042,8.5726,8.8937,8.9688,8.8506,8.7915,8.8314,8.7818,8.6139,8.7399,8.6493,8.2872,8.3136,8.4378,8.8847,9.1365,8.9719,8.8752,8.9376,9.6536,9.8185,9.8512,9.7573,9.649,9.5984,9.2073,8.9279,8.7623,8.5156,8.6537,8.946,9.0365,8.692,8.6921,8.6202,8.582,8.2853,7.7824,7.7231,7.705,7.3709,7.3016,7.35\n8.2164,8.4674,9.0748,9.1068,9.0158,8.4935,8.9056,8.971,8.7987,8.6695,8.6317,8.7228,8.7337,8.5217,8.1027,8.2081,8.0896,7.8398,8.8049,8.7315,8.4353,8.6713,8.8351,9.1519,9.3674,9.6365,9.5632,9.9019,9.5647,9.1061,8.8621,8.4339,8.5352,8.5447,8.9406,9.0775,9.199,9.067,8.7652,8.4501,7.9338,7.7261,7.7417,7.7865,7.8381,7.889,7.9536\n8.3706,8.5486,9.1227,9.267,9.2847,8.9232,8.7337,8.8666,8.8436,8.6421,8.4937,8.5677,8.6078,7.8679,7.8785,7.822,7.9482,7.9955,7.9398,8.5226,8.3138,8.7175,8.6874,9.3446,9.5078,9.5945,9.5674,9.6114,9.2718,9.0708,8.9233,8.6982,8.6739,8.6776,9.2607,9.2418,9.2183,9.2723,9.2319,8.3975,7.5618,7.5983,7.7637,7.9102,7.9786,7.9709,8.0824\n8.5262,8.7862,9.1713,9.0981,9.2655,9.0061,8.8948,8.9595,9.0747,8.6234,8.4563,8.7483,8.5715,8.1066,7.9745,7.6111,8.0506,8.3107,8.3994,8.6623,8.5792,8.4826,8.6405,9.5504,9.8802,10.077,9.2868,9.2206,8.9928,8.867,8.8501,8.7245,8.7311,8.8056,9.2485,9.1409,9.0528,9.1138,8.6795,8.3268,7.6815,7.6261,7.8089,7.932,7.7338,7.8503,7.9591\n8.6178,8.7827,8.745,8.7682,8.9701,9.3259,9.039,8.8674,8.8279,8.5375,8.3467,8.6133,8.9582,8.3969,8.301,8.5995,8.6937,8.6222,8.6956,8.8755,9.214,9.5951,9.3847,9.5449,10.011,10.123,9.4784,9.1848,9.1965,9.154,9.1426,8.8372,8.5306,8.6793,9.0683,9.0776,8.9082,8.6836,8.3862,8.4853,8.122,7.9343,8.282,8.4347,8.2372,8.0524,8.1257\n8.938,8.7511,8.6655,8.9359,9.0634,9.2082,9.1756,8.7138,8.9111,8.9874,8.9591,8.9702,9.2351,8.6651,8.6258,8.7812,8.8191,8.3407,8.5218,8.9898,9.3791,9.5939,9.2893,9.3425,9.7502,9.4123,9.2275,9.2346,9.518,9.1798,9.2262,8.986,8.2769,8.2641,8.3345,8.2561,9.6067,8.9958,8.767,9.0769,8.5691,8.553,8.6218,8.9064,9.0446,8.6303,8.3004\n8.9343,9.2435,9.0567,9.0885,9.1682,9.2509,9.321,9.2034,9.233,8.7794,9.1278,9.0361,8.9473,8.634,8.1359,8.3914,8.2102,8.111,8.4954,8.9713,9.2004,9.1443,9.0983,8.6022,8.4338,8.6507,8.6541,9.019,8.8286,8.3716,8.2523,8.6313,7.5876,7.5465,8.0776,8.1674,9.8512,9.6996,9.4762,9.5885,9.2973,8.8927,8.7763,8.8333,8.9266,8.6609,8.5846\n8.976,9.4932,9.0237,9.1056,9.1405,9.2936,9.3051,9.3612,9.1617,9.1146,9.0775,8.8289,8.2954,8.0403,7.828,8.1196,8.1872,8.2084,8.8235,9.1215,9.0173,8.9624,8.7361,8.3058,7.6226,8.459,8.6854,8.807,8.9056,7.8959,8.2163,8.1985,7.7602,7.7336,8.1348,8.8834,9.6296,9.4226,9.4684,9.7396,9.2098,8.9527,9.0406,9.0675,8.979,8.8722,8.834\n8.7228,8.8297,8.9832,9.1152,9.2647,9.3444,9.3589,9.2347,8.9828,9.7189,8.956,8.4977,8.1454,8.1894,8.1296,8.4516,8.5949,8.5798,8.577,8.5838,8.7593,8.8357,8.6873,8.4173,7.7189,8.293,8.6162,8.6252,8.594,8.284,8.3209,8.3182,8.1129,7.9857,8.3737,9.1264,9.2356,9.2671,9.3142,9.5944,9.1259,9.3008,9.2977,9.5819,9.1427,8.9104,9.1255\n9.2077,8.8889,8.7444,9.1216,9.4388,9.285,8.8547,8.3084,8.4916,9.1013,8.9088,8.3508,8.1473,8.6729,8.6837,8.4826,8.7564,8.8096,8.5352,8.3885,8.4515,9.1526,9.2766,9.3747,9.0129,8.8927,9.2568,9.056,8.9838,8.6605,8.7532,8.7174,8.9989,7.9711,8.5907,9.0556,9.1804,9.3593,8.7064,8.6486,9.3765,9.3929,9.5592,9.4384,8.9173,8.9638,9.0728\n8.8761,8.7746,8.5563,8.5793,9.4876,9.1952,8.3451,8.0108,8.2995,8.6585,8.9137,8.6883,8.3979,8.7404,8.4077,8.3887,8.6732,8.7547,8.9914,8.6044,8.6275,9.5312,10.165,9.7829,9.6437,9.5392,9.5636,9.6218,9.0317,8.9009,9.4327,9.1387,8.8152,7.9444,9.2725,9.2836,8.8265,8.7401,8.3742,8.8769,9.4715,9.5294,9.4456,9.3752,8.8501,8.6246,8.3791\n9.3755,9.0548,8.693,9.0658,9.264,9.1335,8.8419,8.1012,8.0964,8.3245,8.4305,8.5473,8.5849,8.5499,8.0144,7.7982,8.054,8.8275,8.9,8.6291,8.8565,9.4545,10.178,10.403,9.8957,9.8573,9.9011,9.9095,9.5631,9.412,9.746,9.4535,9.3943,9.0282,10.019,9.4402,9.1794,9.0615,9.188,8.8141,8.9046,8.8749,9.2316,9.3921,8.8027,8.4855,8.3506\n9.3264,9.0515,8.5226,8.5327,9.024,9.3135,9.5548,8.6577,8.4301,7.8653,7.8171,8.3776,8.581,8.5753,8.3072,7.9964,8.1885,8.9974,9.2,9.1811,9.6009,9.7121,9.9251,10.66,10.382,10.126,10.2,10.758,13.166,13.279,11.088,9.6406,10.127,10.345,10.285,9.6615,9.6264,9.2894,9.5802,9.6287,9.3166,8.9588,9.4845,9.3404,8.6049,8.2,8.1846\n9.276,9.112,8.8084,8.1966,8.5645,9.5618,9.5881,9.0659,8.7431,7.8022,7.7873,8.026,8.5609,9.1877,9.4096,8.9928,8.8606,9.296,9.3825,9.2184,9.8019,10.157,10.192,10.881,10.997,11.208,NaN,NaN,NaN,NaN,NaN,10.202,9.9188,10.563,10.38,10.023,10.051,9.4088,9.8684,10.025,9.7463,9.296,9.5472,8.8712,8.3398,7.8487,7.7346\n9.7836,9.6852,9.1068,8.567,8.4135,9.6408,10.204,10.069,9.1579,8.3955,8.2728,8.374,8.9354,9.2177,9.7879,9.6891,9.6387,9.7473,9.5528,9.432,9.9761,10.377,10.577,11.651,12.62,13.635,NaN,NaN,NaN,NaN,10.607,10.285,9.2488,11.237,12.121,11.543,12.086,11.011,10.485,9.9375,9.7587,9.4232,9.0816,8.7877,8.3317,7.9067,7.5536\n9.9913,10.098,9.1098,9.0915,9.4662,9.662,9.8335,10.328,9.6918,9.2006,9.1234,9.6484,9.7648,9.6461,9.6204,9.6548,9.8499,10.17,9.9842,10.032,10.465,11.269,10.996,12.147,12.313,13.529,NaN,NaN,NaN,NaN,10.924,10.366,10.87,13.367,13.773,13.683,13.38,11.912,10.784,10.104,9.9729,9.4659,9.3309,8.7984,8.3603,7.9848,7.4939\n9.932,9.4983,9.157,9.0423,9.0732,9.2678,9.4442,9.9753,10.012,9.893,9.7455,9.8361,9.9738,9.8963,9.4769,10.023,10.467,10.691,10.804,11.079,11.791,11.21,10.965,11.225,10.668,11.831,11.907,10.91,9.5035,11.106,11.37,11.609,15.599,15.987,15.328,14.207,13.568,12.119,10.938,10.209,9.6487,9.3832,9.3816,8.8974,8.3607,8.0254,7.5454\n9.865,9.3494,9.5839,9.2067,8.774,9.1484,9.5045,9.8173,10.029,10.282,9.7832,10.058,9.8766,8.9435,9.2242,10.222,10.591,10.254,9.8606,9.8476,10.515,11.158,11.882,11.567,11.803,12.444,13.858,13.88,10.611,11.563,12.174,13.435,18.327,17.519,16.418,14.087,12.957,11.984,10.847,9.8669,9.1078,8.9261,8.8612,8.3968,8.2421,7.8278,7.4298\n10.272,10.297,10.057,9.5172,9.0983,9.164,9.4161,9.8189,10.249,10.292,9.9523,10.071,9.9311,9.2173,9.5841,10.275,10.989,11.673,9.3534,8.109,9.6653,11.953,14.101,11.734,11.463,11.581,13.607,13.957,11.671,11.175,13.621,15.699,17.057,17.199,16.32,14.235,12.442,11.333,10.665,9.2232,8.6582,8.7926,9.0066,8.6594,8.4007,7.781,7.6515\n9.5177,9.8558,10.183,9.7937,9.2911,9.1312,9.4019,10.107,10.621,10.703,10.322,10.236,10.44,10.004,10.523,11.047,10.609,9.5695,9.1565,9.3259,9.8795,12.556,12.99,10.851,10.675,10.289,12.964,12.115,10.274,11.235,15.531,17.199,16.875,15.818,14.476,13.087,11.987,11.087,10.407,9.2168,8.631,9.3159,9.4494,9.1559,8.7689,8.1695,8.1789\n9.2709,9.5347,9.6771,9.8671,9.7884,9.7369,9.6689,10.282,10.48,10.381,10.22,10.541,10.848,10.096,11.463,11.305,10.992,9.2669,9.4455,9.3306,10.811,11.137,11.871,11.591,11.458,12.175,14.295,10.484,9.8548,12.64,20.374,18.285,16.178,14.405,12.961,12.175,11.425,11.084,10.121,9.2224,8.9907,9.3061,9.3488,9.156,8.853,8.6952,8.463\n9.6222,9.6907,9.6155,9.5515,9.6889,9.7706,9.8513,10.046,9.9486,9.9875,10.368,11.016,11.397,11.196,11.416,11.665,11.466,10.94,10.841,11.007,10.909,10.667,11.389,11.958,12.066,12.718,11.748,8.9933,9.9278,13.384,17.296,17.799,14.339,13.053,11.81,11.37,11.399,11.423,10.134,9.678,9.6793,9.1479,9.3628,9.1689,8.9696,8.8962,8.7066\n9.1743,9.3277,9.8303,9.707,9.5192,9.3036,9.5215,9.5166,9.6611,10.053,10.33,10.964,11.719,11.557,11.502,11.232,11.558,11.135,11.351,11.85,11.022,10.514,10.499,10.947,11.783,12.035,10.146,8.5356,9.8388,12.591,14.773,14.928,13.943,11.93,10.831,10.874,10.577,10.531,10.144,10.036,9.874,9.1893,9.3626,9.2993,9.0476,9.4279,9.3735\n9.7962,9.8971,10.019,9.9588,9.7377,9.5645,9.3791,9.2682,9.5147,9.6421,9.8908,10.469,11.341,11.087,11.239,11.295,9.6305,10.754,10.722,10.705,10.344,10.202,9.8131,10.3,11.142,9.8627,8.6782,7.9005,10.079,11.401,13.467,12.833,12.029,10.787,10.299,10.178,10.237,10.038,9.8944,10.154,9.8572,9.3363,9.2808,9.118,9.0037,9.607,9.302\n10.008,9.8529,10.064,9.7861,9.8087,9.5524,9.2845,9.2276,9.446,9.4692,9.6441,9.9509,10.46,10.413,10.483,10.636,10.273,10.1,10.689,10.247,9.6702,9.2277,8.5938,9.7954,8.6079,6.8892,8.7668,8.9382,10.064,10.447,10.977,11.03,10.839,9.8515,9.5369,9.5718,9.7278,9.6061,9.937,9.8997,9.4472,9.2165,9.1422,8.7651,8.6134,8.9684,9.2023\n9.763,9.605,9.4347,9.7117,9.819,9.333,8.9686,8.8832,9.2054,9.247,9.1344,9.2992,9.4998,9.8156,10.273,10.875,10.988,11.712,10.645,9.2677,9.2067,8.7098,7.6429,7.8743,7.9544,5.5262,9.4567,9.4293,9.3015,9.6029,10.509,10.806,9.8403,9.5768,9.3769,9.2173,9.5168,9.6108,9.6699,9.4794,9.1948,9.1972,9.0904,8.5516,8.3012,8.3091,8.8399\n9.4145,9.1759,8.8682,8.8327,9.2935,9.2721,9.2895,9.3363,9.2908,8.8348,8.7008,8.5854,8.9214,8.9875,10.505,11.081,11.699,12.581,12.087,10.445,9.9718,9.7576,8.9648,8.7155,8.9595,7.9823,9.1221,8.7187,9.2977,9.7732,10.611,10.602,10.035,9.6464,9.1648,8.8912,9.3123,9.6739,9.3936,9.3147,9.5462,9.6241,9.3069,8.8223,8.2591,8.2046,8.4883\n9.9128,9.205,8.4865,8.9176,9.3265,9.4965,9.6625,9.6072,9.2354,8.6699,8.2241,8.5445,8.7202,8.6527,9.3209,9.8755,10.376,11.133,11.781,10.124,10.279,10.335,9.3658,8.9409,8.6171,8.051,7.6359,8.3506,8.776,9.0921,9.662,9.9396,9.5648,9.1973,8.7136,8.9782,8.9125,9.2331,8.9802,9.1355,9.701,9.7749,9.4848,9.1538,8.4818,8.5331,8.6963\n10.001,9.4653,8.7792,8.8866,9.7158,9.8995,9.8073,9.6008,9.0304,8.6012,8.1716,8.4408,8.3612,8.4655,8.9034,9.435,9.9169,10.173,9.9931,9.1137,9.1539,9.5945,8.2345,8.4808,8.2009,7.8253,7.6574,8.2052,8.4391,8.6299,8.8663,9.0145,8.4888,8.1733,8.6355,8.9184,9.0867,9.2128,9.2412,9.2067,9.525,9.6269,9.1232,9.12,8.7553,8.8586,8.9722\n9.8322,9.5121,9.1961,9.3871,9.731,9.9348,9.8383,9.522,8.7523,8.7266,8.6105,8.6899,8.6742,8.4424,8.8424,9.247,9.3917,9.0973,8.1359,7.8471,7.2826,7.6303,7.8517,8.0774,7.7445,7.2204,7.462,7.6429,7.8949,8.0936,8.3375,8.3215,7.9604,7.7423,7.7686,9.1716,9.5428,10.086,9.6853,9.2587,9.342,9.4379,8.9262,8.9432,8.965,9.1558,9.3907\n9.778,9.6147,9.4448,10.049,9.9771,9.8003,9.5414,9.3231,8.8435,9.1163,9.2103,9.111,8.7903,8.4751,8.7383,8.9171,8.675,8.3372,7.6357,6.0777,6.1761,7.2035,7.9048,7.6481,6.9322,6.7345,6.8541,6.9376,7.6501,7.5046,7.9114,8.1325,8.3697,8.2848,8.5915,9.189,9.739,9.5814,9.8812,9.496,9.4351,9.344,9.0607,9.3252,9.3871,9.3391,9.2315\n9.6869,9.4766,9.602,10.317,10.318,9.9628,9.6651,9.6885,9.696,9.5342,9.2395,9.2062,8.8524,8.8127,8.8702,8.7551,8.3931,7.8835,7.114,6.7398,6.8745,7.5289,7.9342,7.2306,6.3995,6.3099,5.8619,6.1132,7.3648,6.992,7.3758,8.207,8.5133,8.692,9.3464,9.4495,9.6416,9.8244,9.8587,9.6634,9.4977,9.5061,9.6952,10.005,9.7355,9.2791,9.5231\n10.027,10.019,10.156,10.406,10.403,10.165,10.059,9.9546,10.13,9.7947,9.34,9.0465,8.6315,8.9845,8.8578,8.5927,8.3348,8.0646,7.9908,7.3767,7.22,7.8489,7.8562,7.1543,6.7108,6.5341,5.9147,5.7008,6.6526,6.9588,7.0434,7.688,8.3729,8.5124,8.8709,9.3776,9.9093,10.008,9.7623,9.5347,9.7497,9.7948,10.184,10.66,9.8853,9.847,9.9894\n10.423,10.357,10.343,10.549,10.765,10.412,10.536,10.392,10.339,10.193,9.8979,9.1221,8.7616,8.7348,8.2704,8.2205,8.33,8.2104,8.1565,8.139,7.8262,7.9272,7.7012,7.4803,6.9703,6.9508,6.2821,5.9265,6.3668,6.6543,7.0054,7.7947,8.3373,8.5003,8.6036,9.2472,9.8961,9.8084,9.5699,9.4677,9.583,9.6146,9.9226,10.282,9.4383,9.3465,9.8146\n10.249,10.476,10.565,10.705,10.53,9.9178,10.493,10.022,10.242,10.252,10.063,9.8225,8.8858,8.731,8.7518,8.2671,8.2102,8.042,7.6102,7.7162,7.9194,7.8728,7.6469,7.686,7.1989,6.4526,6.1171,5.9754,6.1119,6.3017,7.2195,7.9619,8.3913,8.2265,8.6475,9.3461,9.9898,9.6145,9.6385,9.1648,9.1836,9.2909,9.5615,9.4584,9.5414,9.2667,9.2402\n11.068,10.997,10.965,10.86,10.453,10.146,9.9363,9.7026,10.104,9.8191,9.8555,9.5216,8.9775,8.712,8.7758,8.4182,8.1315,7.6786,7.0556,7.7158,7.7031,7.6951,7.8596,7.6656,7.1251,6.6242,6.3486,6.2844,6.5224,6.7476,7.8812,7.856,8.2443,8.4118,8.7509,9.0116,9.5153,9.6125,9.775,9.5873,9.5982,9.6875,9.4975,9.5523,9.3291,8.6347,8.9252\n11.307,10.717,10.765,10.851,10.743,10.304,9.5521,9.0106,8.8856,9.6072,9.186,9.0217,9.0026,8.8877,8.5588,8.2074,7.8305,7.5337,7.589,8.0928,7.7849,7.9665,7.9118,7.5267,7.328,7.1515,7.0441,7.0298,6.9853,7.3437,7.7692,7.8192,8.4554,8.6393,8.8831,8.8543,9.2122,9.6823,9.8992,9.8297,9.4513,9.5387,9.9369,9.7834,9.3573,8.7324,8.9823\n10.978,10.944,10.881,10.663,10.488,10.543,10.084,9.1621,8.6427,8.9015,8.6243,8.9592,8.7616,8.2089,8.0932,8.0462,8.4245,7.4364,7.8762,8.2926,8.1977,8.1296,8.0671,7.7484,7.3246,7.2994,7.0038,7.0738,7.2534,7.6876,7.6948,7.7915,8.4488,8.6132,8.8487,8.9138,9.0626,9.6416,9.9967,8.8848,8.5714,8.9256,9.2246,9.5918,9.429,9.0524,8.5828\n10.154,10.212,10.639,10.134,9.9873,10.728,10.459,9.7393,9.187,8.4944,8.5504,8.5896,8.4839,8.0927,7.764,7.6302,7.6101,7.465,8.1117,8.7328,8.5082,8.1917,8.4196,8.1404,7.5775,7.4432,7.1165,6.5338,6.9457,7.6363,7.6054,7.6484,8.2502,8.5238,8.5542,8.5612,8.9359,9.5448,9.9789,9.7636,9.5143,9.3877,9.0525,9.1542,9.1288,8.8457,8.273\n9.871,9.9801,10.539,9.8512,9.3666,10.286,10.146,9.1235,9.1634,8.9096,8.4896,7.8972,7.6084,7.577,7.1762,6.545,7.2435,8.2841,8.8324,8.6409,8.6374,8.5577,8.5058,8.1571,7.8984,7.7993,7.3471,6.7979,6.5777,6.9422,7.3432,7.5079,8.0752,8.5703,8.4711,8.5384,9.0967,9.6256,10.02,10.134,9.7617,9.4679,9.2625,9.4533,8.556,7.948,7.9326\n9.4189,9.8797,10.429,9.5895,9.1014,9.173,9.0788,8.9475,9.4212,8.5133,8.0094,8.1134,8.4284,6.404,6.1238,6.0422,8.5191,8.8819,8.8935,8.8577,9.4203,8.9821,8.4481,8.2249,8.221,7.9821,7.4934,7.1697,6.6034,6.7331,7.2487,7.3079,7.7362,8.6672,8.6696,8.7392,8.9911,9.3997,9.8564,9.8813,9.5587,9.5403,9.3043,9.4306,8.5947,7.2394,7.3105\n8.4081,10.205,9.9162,9.5531,9.0502,8.44,8.8592,8.7909,8.0113,8.965,8.0839,8.2119,7.1771,7.1153,8.0961,7.6724,9.1985,8.64,8.5164,9.0307,9.3367,9.1348,9.0667,8.6672,8.4917,8.1042,7.414,7.0484,6.5538,6.9542,7.742,8.0881,7.9593,8.4759,8.8318,8.7936,8.6703,9.1343,9.5849,9.7272,9.6243,9.8102,9.5583,8.7739,7.9659,7.5886,7.7227\n9.7437,10.169,9.3359,9.4519,9.2361,8.9339,8.9576,8.2384,7.1505,9.8977,8.2901,8.5513,8.1441,7.5409,9.3176,8.4979,9.0594,9.4084,9.1664,8.9494,9.2953,9.4161,9.2428,8.9782,8.9413,8.7704,8.3355,7.052,6.9516,7.384,8.2889,8.5086,8.3133,8.3223,8.579,8.7017,8.5687,8.7423,9.2883,9.825,9.4957,9.1972,9.1657,8.5808,8.3959,8.2032,8.1907\n9.0367,10.324,9.7211,9.3722,9.0901,8.6496,8.1062,7.7548,7.4827,9.5904,7.0859,8.0146,8.1514,8.1842,8.9695,8.529,9.7918,8.8342,9.5755,9.8891,10.064,9.929,9.2395,8.9193,8.8675,8.7168,8.0348,7.5169,7.5266,7.7713,8.314,8.6379,8.8983,8.7456,8.5619,8.2674,8.4481,8.4681,9.0183,9.28,9.0179,9.134,9.7385,9.2026,9.79,8.643,8.296\n9.5558,9.5993,10.031,9.8365,9.4867,8.8206,8.3407,8.8283,9.1484,8.5658,7.6232,7.4905,7.257,6.6161,8.6766,10.008,10.869,8.9871,9.0555,9.9359,9.84,9.409,9.0726,8.6439,8.6465,8.2861,8.1394,7.9204,7.8751,8.1474,8.3965,8.7106,8.7781,8.5601,8.4963,8.248,8.2731,8.4735,8.9372,8.952,8.8929,9.3355,9.8396,9.8096,9.5712,8.5203,8.2421\n9.305,9.7479,9.7059,9.6769,8.821,9.8232,8.6759,9.0135,8.8801,8.9322,9.5471,7.7232,7.5011,8.8514,10.154,9.7022,10.06,9.2428,9.3874,10.545,10.146,9.4181,8.8373,8.9227,9.025,8.5599,7.7613,7.9714,8.0616,8.4741,8.7032,8.5913,8.2706,8.6867,8.4756,8.3687,8.3348,8.8239,9.0655,9.1822,9.3731,9.4764,9.6526,9.0051,8.2502,7.9226,7.6353\n8.2558,9.1543,8.6692,9.7079,9.7751,9.6165,8.9517,8.9481,8.5146,8.8071,8.6272,8.3937,9.287,8.7354,8.9753,8.9767,9.8473,9.1362,9.2186,9.7335,9.6813,9.1607,8.8406,8.6962,8.5674,8.389,8.2656,8.5846,8.5565,8.7939,8.8523,8.5775,8.3842,8.9886,9.1917,8.7055,8.64,8.9357,9.0873,9.2443,9.5924,9.6337,9.4632,9.1312,8.6509,8.1004,8.0126\n9.3406,9.3857,9.3379,10.605,9.2779,9.1725,8.8826,8.9643,9.17,9.5187,9.8152,9.3342,9.6244,9.2823,8.6304,9.1901,9.9194,9.5618,8.9652,9.1453,9.1877,8.8848,8.5421,8.4905,8.6296,8.8296,9.0939,9.0868,9.0607,8.9004,8.7516,8.3468,8.8561,9.1499,9.3972,8.7906,8.8752,8.8626,8.9781,9.6982,10.502,10.089,9.4632,9.5065,9.8183,9.1374,8.3437\n9.2055,8.8488,8.7207,8.2688,7.5991,7.7585,8.0683,7.7601,8.3593,9.6196,10.026,10.256,9.5505,8.1118,8.253,9.4854,10.258,8.9601,8.7567,9.9702,9.8674,8.9329,9.0685,9.6516,9.1399,8.91,9.0678,9.2549,8.9422,8.7251,8.7048,8.8369,9.8656,9.6011,9.6261,9.4014,9.4238,9.1751,9.0624,9.5066,9.7627,9.7246,9.5143,9.6968,9.7907,9.511,8.9115\n8.8358,8.2917,8.62,8.0538,7.7178,7.7747,6.9161,6.3876,6.5882,8.0506,9.8042,9.8818,8.7386,7.9978,7.9643,8.3273,9.182,8.3431,8.7568,9.4273,10.12,9.1572,10.16,10.802,10.798,10.202,9.7564,9.6254,9.9833,9.7453,9.3792,9.0457,9.4447,9.5569,9.9345,9.2961,9.3973,9.4001,8.7553,8.6022,8.7958,8.5923,8.6753,9.8428,10.144,9.4659,8.9797\n8.7693,8.1666,7.699,7.4197,6.9025,7.6416,8.6379,7.2952,7.5908,8.4869,9.2998,9.5058,9.8103,10.106,10.246,8.551,7.597,8.8268,9.1233,9.6332,10.519,9.7473,10.08,10.582,10.911,10.606,9.9476,10.185,10.549,10.639,10.504,9.6766,9.8327,10.282,10.123,9.393,9.2569,9.3593,8.4731,8.4105,8.6029,8.9175,9.1821,8.9978,9.2916,8.5562,8.164\n8.8406,8.5696,7.7467,5.5594,5.6622,6.323,6.1613,6.9289,9.4182,8.8409,9.7946,9.4945,10.166,11.831,10.084,8.2873,8.5591,9.0412,8.9129,9.7262,9.6031,10.177,10.442,10.295,10.477,10.8,10.441,10.606,10.715,10.907,11.127,10.054,9.6078,10.049,10.168,9.9535,9.3261,9.0365,9.1463,8.6499,8.9067,9.559,9.1492,8.4408,8.6035,8.6229,8.4203\n8.055,7.9529,8.4056,8.6271,8.6061,9.2207,7.1899,6.8589,8.5238,10.185,11.567,9.6093,10.035,9.6147,9.2427,8.163,7.8823,8.8102,8.9844,9.2553,9.0757,9.5803,10.624,10.004,10.225,10.523,10.74,10.866,10.827,11.131,10.815,9.9134,9.6715,9.9266,10.216,10.224,9.4799,9.3149,9.4263,9.2508,9.3751,9.6257,8.8721,8.5657,8.5626,8.5339,8.4865\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061211-070709.unw.csv",
    "content": "4.9135,4.5414,3.8442,3.9889,3.8628,4.2843,4.4735,4.067,3.7247,3.3812,3.6284,2.9881,3.1528,3.0603,3.2698,3.136,2.7877,2.8459,2.6732,2.428,2.5872,2.8951,3.0519,2.8767,2.8325,3.6942,3.9228,3.7056,3.2983,3.1084,3.0922,3.1839,2.2321,1.4444,1.3234,1.6446,1.3596,0.92542,1.5486,1.5277,1.7046,2.3232,2.8445,3.2075,3.3449,3.193,3.0051\n4.9215,4.39,3.9672,4.3117,4.3079,4.3738,4.3154,3.6447,3.3163,3.362,3.8489,3.9904,3.5669,3.4238,3.6121,3.8091,3.0302,2.6128,2.3493,2.1709,1.9356,2.8708,3.3302,3.5812,3.4582,3.6092,3.7894,3.3863,3.1817,3.4514,3.727,3.262,2.342,1.5592,1.6846,1.8524,1.4933,1.3391,1.93,2.5822,2.4123,2.4952,3.2355,3.4719,3.1625,2.8638,2.8221\n5.208,4.6967,3.9304,4.0141,4.1979,3.7954,3.7988,3.2583,2.4377,2.9717,3.33,3.5872,3.3996,3.4464,3.433,3.4421,2.6349,2.483,2.8351,3.0911,3.0138,2.8028,3.1469,3.651,3.5209,3.831,4.1029,4.0038,3.5566,3.9024,3.8852,2.9987,2.6377,2.4718,2.4029,2.2107,1.5729,1.4393,1.5804,1.9876,2.2068,2.6571,3.2379,3.3695,2.9392,2.4325,2.5471\n4.9953,4.5802,3.6279,4.0168,4.4396,4.173,3.9558,3.3607,3.1098,3.1072,3.2839,3.3101,3.1826,3.6625,3.5064,3.3007,3.0223,2.674,2.6564,3.2859,3.4967,3.5928,3.8883,4.0255,3.9641,3.6685,3.1718,3.0901,3.967,4.0804,3.5447,3.3076,3.2041,3.0154,2.7375,2.3744,2.0253,1.8818,2.4386,2.6154,3.1243,3.5556,3.5662,3.3933,2.9944,2.6168,2.4472\n4.7435,4.4667,3.4938,3.7158,4.2898,4.0951,3.9598,3.7386,3.4885,3.2149,3.2739,3.208,3.1157,3.5755,3.5811,3.6455,3.8049,2.9694,2.4352,2.1735,2.7986,4.1017,4.5627,4.4045,4.2545,3.4372,2.4524,2.8216,4.5322,4.7819,3.6676,3.4477,3.3294,3.4879,3.1317,2.556,2.1574,2.2507,2.5983,3.1973,3.8714,4.0981,3.9998,4.02,3.6368,3.2672,2.9983\n4.4068,3.507,3.1373,3.8319,3.7155,3.5553,3.4532,3.449,3.5534,3.5096,3.363,3.1061,3.3987,3.6705,3.6798,3.7062,3.6564,3.4658,3.1498,2.9123,3.2672,4.4807,4.8641,4.4539,4.3446,3.4893,2.7895,3.0546,3.9635,4.0902,3.9296,4.1139,4.1574,3.6164,3.3872,2.9689,2.7298,2.8235,3.0862,3.7371,4.1238,3.786,3.5911,3.9388,3.7253,3.8554,3.7941\n4.3687,4.0142,3.423,3.7759,4.2361,3.982,3.3479,3.1063,3.493,3.7195,3.0681,3.1213,3.7083,3.6434,3.3756,3.6257,3.2344,2.9527,2.7359,2.8253,3.5365,4.2914,4.782,4.4683,4.0608,3.6681,3.5706,3.6495,3.777,4.0854,4.0302,4.2223,4.4977,4.5343,4.2152,3.8623,3.7282,3.7956,3.8141,4.147,3.6668,3.2152,3.3677,3.6616,3.9652,4.1981,4.343\n4.6949,4.5963,3.5861,3.7811,4.3492,3.8617,3.6625,3.3081,2.9711,3.1489,2.8575,2.995,3.6498,4.0797,4.0315,3.7575,3.1246,3.0398,3.4694,3.6475,3.9732,4.1633,4.3256,4.6844,4.4477,4.4498,4.4839,4.7369,4.2951,4.4634,4.3774,4.0352,4.3544,4.5875,4.4676,4.2003,3.9953,3.8818,3.9872,4.4488,4.6243,4.2169,4.3016,4.2413,3.968,3.8664,4.1068\n4.3111,3.5137,2.9797,3.8972,4.0549,3.901,4.1056,3.8691,3.8243,3.6378,3.4748,3.5257,3.6647,4.5656,4.7282,4.3411,4.0765,4.3425,4.7648,4.507,4.3946,4.4674,4.6284,4.8861,4.9815,5.1696,5.2181,5.342,4.8437,4.4522,4.5062,4.4443,4.588,4.7736,4.6996,4.3479,4.25,4.5646,4.4531,4.5669,4.7967,5.1481,5.2271,4.8211,4.2788,3.9565,4.0402\n4.1409,3.8697,3.3399,3.65,4.2925,4.391,4.368,4.4471,4.5755,4.6056,3.9615,4.3817,4.0347,4.8809,4.9862,4.7434,4.5683,4.894,5.232,4.4785,4.381,5.1645,5.509,5.7568,5.7635,5.815,5.4914,5.3416,5.4354,4.7977,4.4788,4.7752,5.1714,5.2635,5.2796,5.2403,5.3578,5.2306,4.949,4.9176,5.0448,5.1187,5.0958,4.768,4.4348,4.2562,4.2934\n4.674,4.5441,4.0692,4.0967,4.3238,4.3646,4.2513,4.9393,4.8559,4.1864,3.7842,4.3688,4.5272,5.2983,5.537,5.3867,4.805,4.8778,5.5663,5.397,4.7593,5.4993,6.2048,6.2761,6.08,6.3777,6.4382,5.9629,5.8448,5.7431,5.5715,5.677,5.6015,5.577,5.5064,5.6142,5.5923,5.4183,5.2709,5.4178,5.2406,5.2968,5.2043,5.0165,5.0068,4.7459,4.6315\n5.1419,5.1035,4.7538,4.4755,4.2624,4.7149,4.1699,4.7268,4.8136,4.4346,4.327,4.8522,4.6985,5.3693,5.8689,5.605,4.9168,4.7644,5.0661,4.5735,3.9943,5.2501,5.8299,5.8861,6.6893,7.5536,7.5592,7.0234,6.653,6.1863,6.0352,5.8869,5.7895,5.7478,5.6703,5.7437,5.9751,5.8949,5.708,5.9592,5.9144,5.8674,5.6985,5.5555,5.4363,5.1233,4.8664\n5.1107,4.8136,3.4987,2.9915,3.0469,4.3523,4.4959,4.7698,4.7405,4.7594,4.4702,4.1063,4.279,5.2962,5.7306,5.7411,5.5974,5.7452,5.1386,4.5721,4.7007,5.5674,6.239,7.2147,7.6745,7.7013,7.5792,7.5278,7.1584,6.7973,6.8532,6.2286,5.9236,5.9023,5.9787,6.1941,6.569,6.6233,6.1638,6.0462,6.1434,6.1001,5.9581,5.7648,5.7609,5.5585,4.8342\n5.2334,4.4629,4.1958,4.2441,3.9955,4.4554,4.7172,4.5796,4.6464,5.1184,4.7043,4.0567,4.7797,5.8349,6.0826,6.19,6.5563,6.53,6.1498,5.6299,5.4533,5.5742,6.2497,7.6378,7.917,7.1326,7.0942,7.4751,7.4437,7.2449,7.3587,7.1772,6.7636,6.725,6.8003,6.944,7.1174,7.3986,7.1569,6.6292,6.5244,6.8102,6.7225,6.3995,6.2718,5.3607,4.6152\n4.5111,4.522,4.7383,4.7362,4.1808,4.4337,4.3223,4.4774,5.577,5.786,5.2274,5.7603,6.3554,6.7693,6.8407,6.8601,6.9381,6.4034,6.0986,6.1818,5.7906,6.1971,7.1026,7.9208,8.4301,8.3132,7.6937,7.5657,7.9757,8.0045,7.8219,7.4441,7.2799,7.5412,7.37,7.201,7.3883,7.7581,7.9974,7.7128,7.096,7.1981,7.3531,6.921,6.6607,6.248,5.853\n5.5358,4.8666,4.4625,4.7766,4.8732,5.1581,5.2259,4.6997,5.4614,5.3382,4.9538,5.2776,5.5681,5.6001,6.0006,6.3025,6.4368,6.3152,6.3042,6.4354,6.677,7.102,7.5607,7.9269,8.7352,8.7554,8.1695,7.9346,8.3954,8.3683,7.9786,7.6461,7.7743,7.8975,8.0777,7.5575,7.6981,8.0728,8.272,8.3913,7.7581,7.5647,7.3857,7.1402,6.9678,7.1589,7.1724\n6.1423,5.8746,5.4106,5.038,4.9257,4.696,5.478,5.5336,5.9215,5.8244,5.6988,6.1973,6.0192,5.4524,5.5644,5.8389,6.117,6.8183,6.7423,6.011,5.3451,6.6089,7.9669,7.8141,8.7153,9.1464,9.1418,8.7692,8.3739,8.1958,8.4148,8.2927,8.4595,8.6037,8.3481,7.975,8.4499,8.2409,8.1684,8.6297,8.5395,8.1281,7.8909,7.7342,7.2605,7.2663,7.454\n7.5849,6.6887,5.4379,4.6698,2.9074,3.2557,4.8761,6.1627,6.7352,7.1373,7.4382,7.2586,7.0216,6.945,6.634,6.9759,7.1973,7.3533,7.4082,7.0542,6.5674,6.6841,7.237,8.2206,9.0893,9.9024,9.8615,9.6135,9.1482,8.8499,8.8245,8.8799,8.9033,8.794,8.6337,8.543,9.2085,8.5837,8.2003,8.0058,8.1071,8.3605,8.3005,8.0814,7.9467,7.7039,7.5607\n6.038,6.1518,5.7391,6.0761,5.9555,8.5116,8.3263,6.7729,7.0465,7.5823,8.0641,7.3818,6.7737,6.8283,7.4349,8.1617,8.1591,8.1195,8.3819,8.7977,7.7287,6.5815,7.1286,8.017,8.5774,9.675,10.083,10.032,9.8529,9.1989,8.9519,8.8588,8.8694,8.4738,8.4199,8.615,8.4566,8.123,7.4235,7.2831,7.3561,8.0035,8.5014,8.2848,8.0406,7.8577,7.5246\n6.702,6.5328,6.1113,6.0777,7.2763,7.7613,7.3663,6.7203,7.5216,7.8122,7.5601,7.4611,7.1961,7.1244,7.6058,8.2132,7.9898,7.7305,8.2163,8.7005,8.7316,8.6863,8.2075,8.8899,9.0862,9.7104,10,10.447,10.711,10.476,9.579,9.2744,9.1063,9.0883,9.5817,9.3147,8.8779,8.7619,7.7847,7.5234,7.7918,8.381,8.6523,8.6815,8.4496,7.7308,7.3198\n6.4834,6.6272,6.8203,7.3876,8.6541,9.7976,8.8558,6.8219,7.3902,8.2843,8.5586,8.3749,8.0148,8.2063,8.5644,8.2686,8.1331,8.1643,8.3614,8.8976,9.4574,9.1478,8.9261,8.9364,9.7836,10.677,11.014,11.651,11.378,11.17,10.965,10.767,10.028,9.7785,9.5647,10.065,9.8089,9.3501,8.7038,8.3442,8.4992,8.8045,8.6065,8.5193,8.4434,8.1644,7.6454\n7.1604,7.7819,7.094,6.5435,6.2575,6.8477,7.5061,6.6581,8.6172,8.9716,8.8401,8.834,9.2097,10.267,9.912,8.5808,9.2,9.4223,8.9131,9.7904,10.08,9.8798,9.5046,9.8674,10.506,11.066,11.641,12.035,11.695,11.421,11.106,10.869,10.801,10.666,10.445,10.403,10.308,9.6644,9.2178,8.9746,8.8554,8.8331,8.5829,8.5024,8.4149,8.4001,7.7621\n7.6022,8.5196,7.8777,6.5554,5.7918,5.5744,5.5482,7.5824,10.192,9.1861,8.7092,9.5404,10.23,10.667,10.551,10.412,10.76,10.521,10.174,10.124,10.501,10.319,9.9706,11.128,11.943,11.849,12.179,12.134,12,11.534,11.302,11.148,11.021,10.934,10.867,10.594,10.26,10.031,9.9675,9.8227,9.9451,9.4992,8.6851,8.8127,8.9377,8.5516,7.8233\n8.8024,8.3763,7.3168,6.4252,5.583,6.0152,7.0711,8.0798,8.6859,9.1835,9.4875,10.424,11.377,11.318,10.855,10.5,10.807,10.965,10.528,10.091,10.209,10.855,11.524,12.3,12.573,12.189,12.27,12.909,12.586,12.491,12.497,12.181,12.24,11.888,10.897,10.576,10.209,10.122,10.263,10.237,10.019,9.4562,9.0178,8.9743,9.0839,8.8236,8.7115\n7.8112,7.0481,6.9235,7.1526,6.985,7.1449,8.0352,8.3577,8.4692,9.1172,9.7149,10.495,11.238,11.279,10.484,9.6891,9.6692,11.064,11.565,11.469,11.64,12.14,13.603,13.276,13.332,14.313,14.339,14.564,14.343,13.948,13.583,13.303,12.631,11.556,10.826,11.143,11.29,10.734,10.548,10.559,9.5102,8.9673,9.0557,9.3215,9.2369,8.9454,8.9758\n8.6722,8.7964,7.9033,6.9782,6.3656,6.7492,7.8068,8.8367,9.2662,9.0327,9.6856,10.351,10.971,11.092,10.635,10.225,11.354,12.41,12.37,12.506,13.192,14.554,14.69,15.649,NaN,16.098,16.104,15.052,14.724,15.453,15.303,13.436,12.246,11.401,11.136,11.393,11.785,11.447,11.235,11.417,10.285,9.4939,9.5373,9.456,9.149,8.8018,8.4903\n8.4221,8.0738,7.4079,6.4447,6.4838,7.0235,8.108,8.7839,9.1863,9.5803,9.8298,10.197,10.155,10.259,10.861,11.521,11.862,12.836,13.983,14.81,15.201,15.861,16.895,18.171,NaN,NaN,NaN,NaN,15.92,16.974,17.429,17.096,15.973,13.743,12.966,12.083,11.787,11.362,11.164,11.24,10.62,9.9427,9.9948,9.7202,9.2502,8.7813,8.7747\n8.0062,6.9054,5.652,6.3751,6.8722,6.7798,7.4932,8.4694,9.0119,9.9909,9.8811,9.4862,9.862,10.757,12.072,12.901,13.025,13.749,15.113,16.184,16.719,18.108,19.539,21.014,NaN,NaN,NaN,NaN,NaN,NaN,NaN,19.796,18.603,16.238,14.332,12.663,11.78,11.982,11.517,10.223,10.484,10.577,10.665,9.9734,9.4393,9.4511,9.2844\n6.1542,5.6425,6.0098,6.5448,6.2608,6.7376,7.8555,8.5467,8.4255,9.0223,9.1265,9.3161,10.858,11.685,12.578,13.248,13.814,14.831,15.592,16.471,16.965,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.168,16.855,14.39,13.655,12.952,12.728,12.6,12.246,11.707,11.632,11.085,10.033,9.5251,9.4336,9.1727\n3.8785,4.9549,6.2558,5.3543,6.1496,7.0051,7.4846,7.9616,8.19,8.7047,9.1026,9.5932,10.842,11.931,12.57,12.998,14.495,15.712,16.596,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.844,14.153,14.297,13.973,13.611,12.61,12.107,12.364,11.18,10.267,9.7743,9.2624,8.9921\n7.3652,6.0606,6.7097,7.1107,6.8625,6.5148,6.8339,6.9398,7.325,8.3833,10.042,10.919,11.29,12.339,13.748,14.639,15.727,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.221,13.966,15.314,14.855,13.794,12.793,13.058,13.321,11.593,10.8,10.142,9.2085,9.1666\n7.6991,6.5474,6.1446,8.4443,9.2939,8.7788,8.3293,8.7109,8.8592,9.2803,10.538,11.737,11.945,12.308,13.82,15.661,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.717,15.471,13.72,12.599,12.788,13.338,12.208,11.355,10.094,9.2351,9.4049\n9.4145,8.5442,7.6341,8.2577,9.3899,9.2315,8.7116,8.3592,8.9959,10.369,11.532,12.002,11.917,12.575,15.28,15.822,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.838,13.573,13.174,12.801,12.828,12.151,10.469,9.6819,9.5817,9.7588\n10.61,11.218,8.5744,8.0119,8.9934,9.8641,9.6664,10.129,10.303,10.719,12.382,12.393,12.785,13.341,15.022,16.032,17.641,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.622,15.141,13.129,12.542,12.25,11.304,10.652,9.9155,9.7695,9.2884\n10.179,10.875,8.7589,7.9258,8.6064,9.281,9.263,9.7585,10.212,10.995,12.011,12.734,13.152,13.91,14.998,14.811,14.57,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.266,19.493,15.935,13.095,12.499,12.35,11.699,11.349,10.518,9.6761,8.9086\n7.7283,7.7177,7.7764,7.7121,8.3694,9.0989,9.5538,9.3257,10.073,11.09,11.987,12.385,13.448,15.449,17.703,14.522,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.219,22.659,21.733,18.614,14.905,12.999,12.509,12.325,12.207,11.731,10.679,9.6082,8.5399\n7.6603,7.0642,7.388,7.7894,8.1266,8.9609,10.036,10.58,11.058,11.443,12.022,13.088,13.614,16.534,15.979,13.018,13.058,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.964,22.136,18.559,15.775,13.546,12.169,11.531,11.292,11.085,10.888,9.9861,8.9093,8.3258\n8.1324,8.0757,7.8825,8.044,8.2395,8.8862,9.954,10.469,10.64,11.304,11.856,12.485,12.317,12.13,11.349,12.959,13.856,10.708,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.488,17.248,15.196,13.752,11.445,11.248,11.208,10.72,10.312,9.9551,9.0057,8.1485\n7.8234,8.1616,8.1999,8.0997,8.3057,9.0378,9.7318,10.044,10.271,10.872,11.973,12.776,12.242,12.16,12.469,13.814,NaN,NaN,12.435,9.5851,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.202,16.027,14.9,13.65,11.412,10.961,11.19,10.724,9.9578,9.8326,8.9704,8.2341\n7.1692,7.4512,7.6702,8.015,8.156,8.5923,9.0985,9.3444,9.5857,10.693,11.65,12.038,11.757,12.236,12.42,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.724,14.069,13.408,12.706,11.278,11.163,10.952,10.113,9.5104,9.1969,8.362,7.6035\n6.8027,6.7057,7.3847,7.1746,7.9122,8.5735,9.149,9.4499,9.5955,10.085,11.003,11.885,12.273,12.499,11.301,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.382,13.366,12.545,11.194,10.905,10.434,9.7742,9.5085,8.7218,8.6314,8.084,7.4689\n6.7728,6.6011,6.8015,7.1297,7.8415,8.1959,8.4738,8.9698,9.623,10.362,11.22,12.812,12.87,12.586,11.693,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.616,14.309,13.344,11.24,10.166,10.18,9.8018,9.0237,8.6242,8.7504,8.3144,8.2684\n7.8671,7.4322,7.1309,7.8559,8.255,8.425,8.5621,8.8317,9.4644,9.8034,10.597,12.107,12.599,12.222,11.727,10.948,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.654,13.903,12.563,10.249,8.9614,9.575,9.2471,8.518,8.4568,8.2499,7.9591,8.3359\n7.6865,8.0381,7.9677,8.3941,9.0385,9.0442,9.1091,8.9395,9.5277,9.707,9.9542,10.4,11.122,11.292,11.032,10.73,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.296,13.023,13.036,11.563,9.4444,8.9749,8.9522,8.6431,8.1754,7.3772,6.883,6.8499,7.8198\n7.7617,7.9099,8.0411,8.1959,8.7289,9.287,9.6125,9.815,9.9977,9.6643,9.6253,10.052,9.9209,9.7837,10.192,9.5827,10.033,10.404,10.646,11.969,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.859,13.549,13.193,12.454,11.524,11.392,9.9682,9.6265,8.8881,8.6548,8.7975,8.6503,7.6753,6.5946,5.7423,6.7054\n7.5807,7.481,7.4014,7.528,7.9444,8.3458,8.5058,8.8754,8.8419,8.5167,8.9192,9.3888,9.3294,9.319,9.4935,9.7959,10.058,9.9883,9.4508,10.513,12.626,13.482,13.824,NaN,NaN,NaN,NaN,NaN,10.083,NaN,11.66,12.062,11.8,10.749,10.281,10.006,8.4475,6.7438,8.2262,7.7458,7.8526,7.7075,8.1968,8.0155,7.0786,6.2788,6.4735\n7.4937,7.1657,7.3473,7.494,7.5039,7.733,7.9371,8.0284,7.7083,7.891,8.2802,8.6284,8.7403,8.6234,8.5297,8.9456,8.9849,8.8983,8.9808,9.0749,9.7308,11.367,11.109,10.029,NaN,NaN,NaN,7.991,8.8951,9.0385,9.525,9.8711,9.6509,9.7588,9.9274,8.8637,6.7516,6.2197,7.6293,6.9535,6.6761,6.6402,6.5354,6.7465,6.3622,6.3897,6.9253\n7.4014,7.3238,7.1674,7.1008,7.2264,7.9358,7.9964,7.6154,7.4294,7.577,8.0274,8.4121,8.4197,8.1423,8.0009,8.3658,8.3013,8.3297,8.6548,8.5443,8.4897,8.7895,8.0405,7.7926,9.0624,8.7565,7.2058,8.0351,8.6225,8.3957,8.3913,8.6729,8.4406,7.9615,8.2212,7.6839,6.5448,5.8741,7.3406,7.2156,6.8166,7.0351,6.2514,5.7005,5.2319,5.4333,6.4853\n7.6765,7.6128,7.2426,7.1058,7.281,8.0065,7.6944,7.1957,6.7643,6.887,7.5206,7.8227,7.673,7.4522,7.1114,7.0293,7.135,7.3531,7.5151,7.5623,7.4753,7.2835,7.0677,7.6742,7.8469,7.5162,6.8097,6.7648,6.9489,7.555,7.4908,7.3656,7.0803,7.97,8.8537,8.2372,6.9178,6.2844,6.7897,7.1974,7.3588,7.4928,6.6792,5.6206,5.4242,5.7602,6.292\n7.4911,7.613,7.3641,6.9756,7.1906,7.273,7.036,6.8265,6.4979,6.0437,6.3858,7.12,7.1625,6.9709,6.6624,6.4153,6.8858,7.0627,7.1714,6.8418,6.9988,6.6778,7.0979,7.4993,7.3937,6.7325,5.9315,5.406,5.7405,6.6654,6.651,6.5829,6.6094,8.1329,8.6436,8.5926,7.1174,6.8491,6.5951,6.7173,7.5796,7.4431,7.4345,6.1746,5.55,5.585,5.9721\n7.0876,7.2296,6.9671,6.3904,6.3249,6.4942,6.4399,6.4137,6.2939,6.0348,5.9776,6.5653,6.6988,6.6903,6.505,6.3775,6.8661,7.3844,7.1034,6.9258,6.6766,6.5252,7.1702,7.3127,6.86,6.5187,5.7767,5.3906,5.9713,6.4419,6.2517,6.3729,6.6629,7.4101,7.6022,6.9406,6.4715,6.261,6.2418,6.7193,6.8765,6.6613,6.4045,5.6824,5.4386,5.3678,5.7133\n6.3119,6.5239,6.3627,6.1835,6.2301,6.2532,6.2005,6.3054,6.1177,5.4695,5.3285,5.4009,5.937,6.5735,6.2794,6.0661,6.2538,6.5132,6.4804,6.7257,6.728,6.4943,6.9271,6.9663,6.4367,5.6584,5.495,4.8587,5.7758,6.3792,5.8923,6.1749,6.5882,7.3606,7.2098,6.5417,6.0217,5.7644,5.8957,6.2803,6.0728,5.9247,5.5834,5.4857,5.6578,5.5555,5.5841\n6.8034,6.6892,6.3325,5.9955,6.1078,6.0275,5.7342,5.6674,5.8131,5.5666,5.41,5.6969,6.0518,6.2723,6.1369,5.9306,5.786,5.7917,5.9814,6.0176,6.252,6.1094,6.2675,6.153,5.8948,5.7039,5.4439,5.2361,5.6824,5.9595,5.7895,5.7718,6.1623,6.9421,6.361,5.8667,5.6697,5.4666,5.2678,5.4804,5.4338,5.4501,5.4071,5.3238,5.6975,5.7123,5.8224\n6.8552,6.7321,6.5932,6.4982,6.5186,6.0965,5.6957,5.5814,5.6189,5.58,5.5557,5.8734,6.2367,6.3811,6.2681,6.0377,5.9339,5.9068,6.3715,6.6502,6.2991,5.9831,5.7866,5.6796,5.6247,5.5152,5.3242,5.0551,5.2283,5.4757,5.6126,5.6481,5.8892,6.0845,5.7432,5.7029,5.6904,5.6236,5.1889,5.0108,5.0783,5.045,5.322,5.4554,5.5186,5.6355,5.3992\n6.6331,6.0194,6.6284,7.5452,7.2036,6.0867,5.1719,5.2256,5.3177,5.5576,5.3549,5.6488,5.6833,6.0898,6.0995,5.9542,5.6371,5.4178,5.4571,6.2204,6.1422,5.7278,5.4396,5.5118,5.5213,5.4122,4.864,4.7357,4.7583,5.3313,5.5797,5.0764,4.4445,4.8492,4.8613,5.5037,6.0689,5.8732,5.3935,5.1023,5.1405,4.8512,4.8999,4.9148,4.7469,4.9972,4.9975\n7.1772,6.3637,5.613,6.6738,7.1575,6.1718,5.2257,4.612,5.1942,6.3147,5.9767,5.8644,5.5783,5.4068,5.6979,6.0043,5.4109,5.1714,5.2039,5.7052,5.7203,5.2624,5.431,5.7048,5.2829,5.1937,3.9429,3.7303,4.1084,5.2304,5.2181,4.6794,4.4664,4.3986,4.5461,5.2724,5.8529,5.6432,5.4715,5.3373,5.0888,4.8655,4.7574,4.6955,4.4542,4.6279,4.6666\n6.6647,5.8966,5.7149,5.9502,6.233,5.7405,5.4411,5.8864,6.6086,7.3324,6.5787,6.4621,5.9717,6.1396,6.22,6.4051,6.136,5.2392,5.332,5.5315,5.4859,5.2179,5.31,5.3211,5.2504,5.0747,4.1821,3.4673,3.8999,4.9419,5.1222,4.6388,4.5614,4.4114,4.4071,4.926,5.3921,5.3308,5.3445,5.4483,5.0691,4.5263,4.4168,4.1286,3.6928,3.9577,4.1405\n7.4132,7.4085,6.7266,6.8039,6.6582,5.7994,5.8027,6.0595,6.7428,7.3963,6.0964,6.3647,5.91,6.6079,6.3887,6.2401,6.0312,5.6771,5.3943,5.309,5.3277,5.1922,5.3896,5.3565,5.1761,4.9166,4.5777,3.7836,4.5803,5.4129,5.2017,4.7597,4.4504,4.1259,4.2324,4.509,4.8244,5.0612,4.8128,5.0729,4.8198,4.4369,4.3601,3.6753,2.9752,3.4663,3.6592\n8.1645,8.7584,8.239,7.0106,6.7454,6.8165,6.1135,5.9396,6.1377,6.4005,5.6691,4.7966,5.2652,5.7937,5.8691,5.6912,5.7854,5.7509,5.2358,5.1474,5.175,5.3145,5.4101,5.22,4.9325,4.6146,4.2321,3.9995,4.4545,4.6167,4.6538,4.3691,3.8045,4.0637,4.4186,4.5362,4.5667,4.7024,4.7467,4.8005,4.6399,4.5087,4.4547,4.1218,3.0948,2.9613,3.1864\n7.4843,8.0892,8.157,6.7652,6.1074,6.3804,6.5737,5.8641,6.2616,6.1216,5.8058,3.9119,2.5342,4.343,4.7831,5.0839,5.6364,5.636,5.2128,5.0022,4.7104,4.5319,5.0467,4.9078,4.53,4.259,3.8396,3.6353,3.6193,4.0566,4.2753,4.0441,3.4391,4.0523,4.0332,4.2251,4.3517,4.5917,4.8134,4.8536,4.536,4.1831,4.3487,4.5394,3.9815,3.8513,3.8804\n6.6855,8.0181,8.432,7.2267,5.8739,5.9461,6.9451,5.4096,4.9707,6.5245,7.2066,5.2062,3.6368,3.1472,5.2264,5.3731,5.9887,4.9994,4.5419,4.9302,4.7908,4.2869,4.0435,4.3525,4.4914,4.109,3.7266,3.5239,3.4977,3.9043,4.0918,4.0935,3.9424,3.6268,4.0581,4.493,4.5879,4.9434,5.0718,5.0794,4.5634,3.9907,3.9677,3.7999,3.588,3.5021,3.4344\n7.6308,7.5205,7.2582,6.5092,5.8984,5.5532,6.256,5.3855,4.1245,2.4271,5.3128,6.7246,6.3249,5.3508,4.9594,5.7358,6.7112,5.3354,4.9855,5.0496,5.1375,4.7056,4.2468,4.4677,4.725,4.4099,4.0363,3.3277,3.2417,3.642,3.7846,3.6774,3.5761,3.652,4.4473,4.6203,4.6139,4.812,4.9078,4.9917,4.6546,4.1482,4.0208,3.5649,3.0054,3.045,3.3951\n7.1028,7.2519,7.0326,6.5195,6.3232,5.6805,5.9909,5.7137,4.5273,1.2167,2.4001,5.3605,5.6998,5.4734,4.8348,4.7253,6.3401,6.739,4.8897,4.7276,4.6858,4.3381,4.3754,4.8108,4.8268,4.0806,3.5721,3.5522,3.7967,4.0927,3.8043,3.3383,3.3344,3.5089,4.3743,4.4956,4.5611,4.7028,4.6943,4.6516,4.4562,4.1412,4.4565,4.1219,3.3256,2.7309,3.3207\n8.5256,8.841,8.9886,8.4204,7.465,6.7689,6.291,5.917,5.1979,5.0675,4.5934,5.279,4.5092,4.4272,6.0703,7.0408,10.069,7.7005,5.5099,4.6899,4.2249,4.3034,4.565,4.4135,3.8551,3.5143,3.5382,3.9479,4.5566,4.4169,3.879,3.2612,2.8849,3.0601,3.8499,4.4102,4.6633,4.6496,4.666,4.8677,4.4064,3.9658,4.037,3.9519,3.4161,2.9228,3.0436\n7.9143,9.4909,10.044,9.2079,8.4343,7.2015,6.8616,7.0631,7.1361,7.1638,7.7584,5.8049,2.7513,4.0087,4.3375,6.2463,7.6394,6.0227,5.7129,5.0781,4.0661,3.8754,4.8547,4.5226,3.9353,3.3579,3.9808,4.5762,4.4996,4.1749,3.9174,3.3652,2.8215,2.8492,3.4295,4.2744,4.6279,4.6442,3.9973,4.5144,4.759,4.0645,3.7845,3.7399,3.3993,3.3191,3.4995\nNaN,7.9371,8.7704,9.1069,8.6515,7.9215,7.6913,7.3938,7.2396,7.0275,6.6706,3.8069,2.7955,5.0851,5.9055,7.7345,6.339,5.5247,5.4755,4.752,4.1652,3.6339,4.5527,5.4765,5.4102,4.3535,4.279,4.9409,4.8458,4.4877,4.3124,4.0468,3.4796,3.3726,3.9112,4.1783,4.3509,4.4491,4.5661,4.7421,4.7849,4.411,3.8851,3.492,3.6459,3.7295,4.0812\nNaN,NaN,NaN,7.771,7.4958,8.3394,9.1184,9.5943,9.9674,10.671,6.8545,4.8562,5.6595,6.573,7.6667,7.8911,5.7026,5.3621,5.2824,3.8527,3.5934,3.664,4.5062,5.5264,5.6345,4.3616,3.5929,3.4977,3.7345,3.9437,3.8343,4.1216,4.117,3.967,4.4589,4.1908,4.0854,4.3251,4.5159,5.0753,5.2081,4.8566,4.023,3.5503,3.54,3.8809,4.2208\nNaN,NaN,NaN,NaN,NaN,6.5848,8.8809,8.8549,8.2282,8.0469,7.1387,6.8733,6.1548,6.5929,NaN,NaN,9.204,7.0012,5.3904,4.3226,5.3324,4.8217,4.4094,5.2711,5.5022,4.5035,3.7763,3.3357,3.781,3.8405,3.8856,3.5879,4.3232,4.3004,4.1381,4.2936,4.8817,4.782,4.695,5.1362,5.02,4.5324,4.1134,3.9173,3.8509,4.0525,4.2635\nNaN,NaN,NaN,NaN,NaN,NaN,8.9991,8.4556,7.6975,6.8006,6.6332,8.1963,9.6876,10.24,NaN,NaN,NaN,NaN,5.965,5.5803,6.3762,5.486,4.7647,4.8844,4.6518,4.0897,3.9847,3.8241,3.8736,4.5769,4.3698,4.3889,4.5137,4.5458,3.892,4.4805,4.984,4.9395,5.2692,5.1804,4.6763,4.2826,4.0047,3.9243,4.2845,4.8293,4.9366\nNaN,NaN,NaN,NaN,NaN,NaN,7.6607,7.2238,7.3401,6.9403,7.3098,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.6201,5.9235,6.4603,5.9883,4.7966,4.692,4.4987,3.8957,4.5241,5.0696,5.0086,4.7762,4.7553,4.1535,4.0375,3.9111,3.8099,3.9641,4.382,4.6367,5.1803,4.8267,4.7823,4.9174,4.4503,4.2824,4.3243,4.303,4.883\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.8054,7.2113,8.3176,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.2317,4.3761,5.8177,5.6921,5.0494,5.0696,5.3445,5.5685,5.6741,5.8883,5.4969,4.9559,4.497,4.0759,4.0714,3.7146,3.5356,3.713,4.4631,4.6854,3.9731,4.6152,5.3194,5.271,4.7328,4.4785,4.1914,3.8768,4.4104\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.3541,7.6403,7.8875,6.2336,5.0616,NaN,NaN,NaN,NaN,4.8538,3.8965,4.6642,5.8877,5.7257,5.0015,5.0489,5.2618,5.7151,6.119,6.3063,6.0144,4.8872,3.9881,3.727,3.9912,4.3081,4.7158,4.16,4.8026,5.2163,4.6906,4.8839,5.1466,4.8242,4.6352,4.0661,3.5438,3.5735,3.9618\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_061211-070813.unw.csv",
    "content": "-4.7907,-4.6223,-5.0474,-4.6973,-4.9375,-5.7996,-5.8422,-5.7168,-5.0744,-4.9663,-5.3495,-5.7886,-4.5693,-4.2247,-4.3029,-4.1949,-4.1298,-4.2294,-4.1617,-4.3413,-5.3947,-5.1594,-4.2077,-2.7871,-2.2714,-2.7163,-3.4668,-4.2518,-4.674,-4.4766,-3.9928,-3.3632,-3.7393,-4.4852,-5.1909,-5.5842,-6.4621,-6.7418,-7.0473,-7.6192,-6.9566,-5.7014,-5.0384,-5.0185,-4.5158,-3.9249,-4.1319\n-5.2627,-4.7593,-4.8662,-4.6151,-4.5859,-5.5124,-5.6355,-5.4917,-5.2227,-4.7996,-4.5527,-4.7706,-4.2801,-3.9714,-3.8496,-4.1025,-4.3701,-4.4705,-4.205,-4.2105,-4.2739,-4.5027,-3.86,-2.8197,-2.8191,-3.2278,-3.1391,-3.7656,-3.841,-3.3991,-3.5872,-4.301,-5.099,-5.31,-4.9318,-4.9858,-5.6068,-5.8865,-5.6843,-5.394,-5.2016,-4.8455,-4.7031,-4.626,-4.8682,-5.0986,-5.0215\n-5.2275,-4.7649,-4.6715,-4.8717,-4.8939,-4.4166,-4.9256,-5.0339,-5.4118,-4.5321,-4.1011,-3.5631,-4.0993,-4.25,-3.8749,-4.1221,-4.6384,-4.9726,-5.0883,-4.4117,-4.3769,-4.7486,-4.7671,-3.3362,-3.3097,-3.8963,-3.1069,-2.9757,-3.8237,-3.2397,-3.2631,-4.6813,-5.8399,-6.167,-5.6107,-5.5782,-5.3085,-4.747,-4.9043,-4.766,-4.8011,-4.2352,-4.0338,-4.1712,-5.0307,-5.6821,-5.5018\n-5.406,-4.2866,-4.3905,-4.9647,-4.7535,-4.5643,-4.1908,-3.502,-3.3287,-3.6907,-3.8168,-3.9281,-4.2346,-4.5089,-4.1276,-4.4537,-5.2911,-5.6627,-5.6916,-4.7255,-3.4865,-3.363,-3.501,-3.3438,-3.1574,-3.9025,-3.7405,-3.0506,-3.6269,-4.1069,-3.8827,-4.4261,-4.8655,-5.2975,-5.3529,-5.4009,-5.4853,-4.8981,-5.2468,-4.773,-3.8396,-3.4291,-3.5171,-3.9316,-4.5321,-4.861,-4.2882\n-5.1822,-4.1604,-4.6735,-5.4836,-4.8924,-4.7843,-4.1917,-3.6912,-3.8831,-4.2854,-5.4075,-5.6313,-4.5598,-4.6375,-4.9529,-4.7667,-4.552,-5.3924,-6.1474,-6.157,-4.8884,-2.9535,-3.173,-3.0808,-2.87,-3.5928,-3.9268,-3.8168,-2.949,-3.374,-4.5221,-4.4164,-4.4744,-4.575,-4.9286,-5.0477,-5.2575,-5.5069,-5.5224,-4.3011,-3.0905,-2.4124,-2.3975,-2.8226,-3.6584,-4.3184,-4.138\n-6.193,-5.5753,-4.2312,-5.7979,-5.1389,-5.1631,-5.2686,-4.4707,-3.9486,-4.5857,-5.4149,-5.5742,-4.5172,-4.5391,-4.5401,-4.5495,-4.6684,-5.1441,-5.8264,-5.5677,-4.7539,-3.1297,-2.6333,-2.8425,-3.2872,-3.8528,-3.8558,-3.6917,-3.0618,-2.895,-3.9863,-4.9885,-4.8562,-4.0948,-4.2287,-4.8831,-5.4468,-5.2819,-4.3125,-3.1378,-2.6271,-2.8747,-3.0373,-2.7855,-3.1991,-3.1214,-3.0956\n-6.1769,-6.6257,-6.6607,-6.3881,-6.2463,-6.9239,-7.3805,-6.1376,-4.7324,-4.6199,-5.2731,-5.2178,-4.7345,-5.2292,-5.4797,-4.7378,-4.7571,-5.462,-6.0942,-5.6651,-4.338,-3.8596,-3.645,-3.2832,-2.8395,-3.574,-2.749,-2.7532,-3.6924,-3.7572,-4.1276,-5.0256,-4.57,-4.341,-4.3699,-4.6415,-4.9909,-4.6537,-3.7152,-2.9472,-3.257,-3.4766,-3.2968,-3.1405,-2.9326,-2.822,-2.7086\n-5.3655,-5.57,-4.5408,-5.7632,-6.4625,-7.384,-6.5553,-6.4367,-6.3641,-5.5428,-4.9652,-4.7824,-4.8373,-5.4626,-5.5517,-5.0537,-4.9368,-4.9032,-5.1375,-5.2659,-4.2293,-3.4728,-3.4841,-3.0478,-2.2899,-2.0816,-1.9718,-1.8282,-3.3868,-4.3018,-4.3405,-4.3767,-4.0758,-4.3247,-4.3968,-4.6731,-4.9474,-4.922,-4.2159,-3.2932,-3.3706,-3.0975,-2.3295,-2.1285,-2.4437,-3.1396,-3.2007\n-4.5938,-5.1855,-5.905,-6.0688,-6.6551,-7.0937,-5.9195,-5.2329,-4.0118,-4.2056,-5.0118,-5.1978,-5.1185,-4.7464,-4.4001,-5.0903,-5.6901,-5.6717,-4.9925,-4.9363,-4.5082,-3.4028,-1.9294,-2.4706,-2.4607,-2.0271,-2.3147,-2.5164,-1.8068,-2.4361,-3.6169,-3.5067,-3.5653,-3.8394,-3.9651,-4.1896,-4.2017,-4.2481,-3.9752,-3.7296,-3.5638,-3.0826,-2.4504,-2.4066,-2.5666,-2.6692,-2.5707\n-4.8761,-5.1561,-6.12,-6.6512,-7.4774,-6.0384,-4.9157,-4.3976,-3.0342,-2.9483,-4.7562,-4.6408,-4.7671,-4.2354,-4.5497,-5.1648,-5.3756,-5.455,-4.8136,-4.4121,-3.911,-3.3968,-2.5651,-1.9776,-2.0104,-2.3196,-2.2821,-2.4046,-3.2423,-3.6452,-3.0737,-2.8946,-3.7029,-3.9729,-3.8776,-3.7884,-3.7081,-3.4569,-3.3998,-3.4111,-3.4162,-3.1132,-2.5492,-2.3136,-2.8121,-2.6221,-2.0262\n-5.1692,-5.3838,-6.0304,-6.9018,-7.1492,-5.3482,-4.4248,-3.6619,-3.6656,-4.1137,-4.5365,-4.1285,-4.1141,-3.4098,-3.4775,-4.418,-4.7683,-5.2544,-6.2676,-5.8869,-3.4439,-2.6201,-2.1653,-1.5012,-1.7211,-1.8524,-1.9467,-2.442,-3.289,-3.8703,-3.7496,-4.1638,-4.4706,-4.3398,-4.5998,-4.6195,-4.2285,-3.7304,-3.353,-3.0708,-3.2479,-3.0212,-2.6965,-2.1782,-2.1992,-2.0023,-2.1345\n-5.4601,-5.6031,-6.2609,-6.2911,-7.3935,-6.8187,-5.3227,-4.0583,-4.1925,-4.1801,-3.5842,-3.8628,-3.8325,-3.2311,-3.4365,-4.017,-4.4099,-5.7468,-6.6921,-6.2389,-4.5955,-2.9634,-2.4035,-1.956,-1.6774,-1.9815,-1.6599,-2.0584,-3.1055,-3.4114,-4.222,-5.1165,-5.0064,-4.6879,-5.2686,-4.865,-3.5481,-3.4152,-3.2671,-2.9518,-2.9479,-2.9734,-2.7853,-2.0859,-1.8113,-1.4326,-1.766\n-6.228,-6.5156,-7.5447,-8.5559,-8.7924,-6.8959,-4.6849,-4.875,-4.972,-4.4761,-4.1507,-4.4846,-3.9907,-3.3789,-3.8299,-3.9343,-4.306,-4.8942,-5.6782,-4.597,-3.157,-3.1622,-2.7037,-1.4209,-1.3995,-2.0047,-2.0285,-2.1808,-3.2885,-3.741,-4.4549,-5.746,-5.4707,-4.5287,-4.1924,-3.2289,-2.5759,-2.9042,-3.2014,-3.3974,-3.0766,-2.7879,-2.4837,-2.217,-1.6982,-1.5458,-1.3005\n-6.3797,-6.7654,-6.2512,-4.9104,-5.4019,-5.1469,-4.8045,-4.8608,-4.6968,-4.2216,-5.1983,-5.4294,-4.5209,-3.5643,-4.1619,-4.5248,-4.2848,-4.4974,-4.2833,-4.0642,-3.8358,-3.7452,-3.5007,-1.8393,-0.9813,-1.7656,-2.4262,-2.3092,-2.5497,-3.3337,-3.8768,-4.2245,-4.0171,-3.3862,-2.5472,-2.4025,-2.7606,-2.9334,-3.0397,-3.4268,-3.3788,-2.6766,-2.2859,-2.3577,-2.13,-1.2869,-0.50471\n-4.7476,-5.3403,-3.9385,-4.16,-5.7611,-6.1185,-6.1118,-5.4992,-5.1203,-4.0278,-3.7929,-3.9505,-3.4969,-3.1149,-3.491,-4.3382,-4.416,-4.3626,-4.2109,-3.8964,-4.5904,-4.1605,-3.3118,-2.1238,-0.041844,-0.34886,-1.6982,-2.977,-3.0581,-2.24,-2.3516,-2.8326,-3.0173,-2.6652,-2.2144,-2.376,-2.7245,-2.8748,-2.9059,-2.7969,-2.7891,-2.862,-2.4082,-2.2757,-2.3814,-2.9407,-2.6972\n-4.6379,-4.4592,-4.402,-4.8729,-5.1247,-5.1882,-5.8659,-6.0068,-5.681,-4.4926,-3.6165,-3.853,-3.5243,-3.4907,-4.3373,-4.5646,-4.3354,-4.6194,-5.4524,-4.7316,-4.1038,-3.2271,-2.2875,-1.4939,-0.63056,-0.70712,-1.2848,-2.2157,-2.8017,-2.6865,-3.0497,-3.0806,-2.376,-2.4212,-2.3996,-2.9999,-3.4911,-3.2758,-3.0885,-2.5094,-2.6651,-3.1399,-3.1303,-3.1891,-3.2805,-3.9628,-3.7586\n-5.3696,-4.8728,-5.21,-6.2561,NaN,NaN,NaN,-4.9341,-4.831,-4.225,-4.5409,-4.3295,-3.7345,-3.8754,-4.9003,-4.559,-4.0242,-4.7289,-5.8757,-6.023,-4.2735,-2.8336,-1.8483,-0.34195,-0.0022511,-0.5594,-0.72477,-1.3794,-2.5286,-2.8653,-2.7851,-2.5215,-1.4071,-1.1874,-1.8733,-2.5371,-2.5547,-2.8999,-3.2421,-2.4616,-2.7757,-3.1553,-3.7182,-3.9912,-4.2129,-4.498,-4.0898\n-4.6644,-4.9246,-7.5932,-9.808,NaN,NaN,NaN,NaN,NaN,-3.3547,-2.5908,-3.382,-3.9951,-4.4451,-3.8615,-3.5477,-3.4645,-4.4176,-5.0422,-5.8079,-4.5943,-2.4273,-2.1534,-0.9913,0.37047,-0.7622,-1.7591,-2.3982,-2.6478,-2.8311,-2.2299,-1.7599,-2.1214,-2.0705,-2.4441,-2.5506,-2.2187,-2.462,-2.662,-2.1596,-2.6454,-3.5637,-4.0313,-4.1688,-3.7759,-3.8548,-3.9459\n-4.1033,-5.2051,-6.3292,NaN,NaN,NaN,NaN,NaN,NaN,-2.6278,-2.2314,-2.8566,-3.9078,-4.828,-4.2087,-3.1266,-2.7737,-2.7495,-3.93,-4.9638,-3.8982,-2.5053,-0.99769,-2.0077,-1.9716,-0.68033,-1.4857,-2.7574,-2.2069,-2.3116,-2.5434,-2.0619,-2.6533,-3.0476,-3.0251,-2.6195,-2.4464,-2.6776,-3.0577,-3.2528,-3.4326,-3.4169,-3.8619,-4.0064,-3.5685,-3.4682,-3.7819\n-5.9842,-4.9668,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.1184,-3.5877,-3.2222,-3.7217,-4.6238,-4.0476,-2.9448,-2.7474,-3.7641,-4.1554,-4.5935,-3.361,-2.4106,-1.3481,-2.231,-3.0286,-1.9184,-1.0987,-0.81152,-1.2465,-1.0057,-2.1189,-2.0133,-1.9902,-2.4031,-2.484,-2.287,-2.239,-2.3726,-2.6146,-2.9878,-3.0251,-3.1166,-3.1247,-2.9121,-2.7021,-2.5064,-3.4212\n-5.6872,-3.3641,-2.0365,NaN,NaN,NaN,NaN,NaN,-3.1724,-3.2651,-3.1934,-3.7301,-4.0011,-3.882,-2.8419,-1.9158,-2.2556,-3.4834,-4.5941,-4.1093,-3.5408,-2.7763,-2.4199,-2.1655,-1.4224,-0.73778,0.17387,0.82701,0.12342,-0.5313,-1.4045,-1.7836,-1.7918,-1.8979,-2.3444,-2.234,-1.9828,-1.8665,-1.5384,-1.8426,-2.5198,-3.1844,-3.0375,-2.805,-2.6943,-2.9619,-3.6014\n-5.183,-3.4971,NaN,NaN,NaN,NaN,NaN,NaN,-3.8052,-2.2015,-2.6652,-3.5858,-4.0659,-3.7122,-2.2163,-0.65928,-1.8359,-2.7478,-3.7588,-3.6914,-3.3357,-3.0901,-3.0603,-1.6098,0.16793,-0.71854,-0.55292,-0.43046,1.0791,0.57174,-0.89443,-1.4626,-1.5154,-1.6979,-1.8854,-1.4845,-1.051,-1.7275,-1.7218,-1.6825,-3.3526,-3.0918,-2.7056,-2.7478,-3.1547,-4.2123,-4.2878\n-3.8924,-3.4642,NaN,NaN,NaN,NaN,NaN,NaN,-4.0201,-2.0294,-2.2124,-3.3383,-4.2552,-4.3899,-1.864,0.78359,-1.3744,-2.5265,-2.7225,-2.1281,-0.54326,-1.2081,-0.89142,-0.19358,0.322,-1.4735,-2.477,-2.7338,-0.20991,0.30637,-0.15124,-0.79279,-0.93555,-1.006,-1.4614,-1.2722,-1.2294,-1.2916,-1.5225,-1.8387,-3.0066,-2.9624,-2.2315,-2.3717,-3.6382,-4.4259,-4.5191\n-4.3021,-2.7129,NaN,NaN,NaN,NaN,NaN,-2.2673,-2.8006,-3.0964,-2.6451,-2.7367,-5.3784,-4.6889,-3.2087,-1.2639,-0.53048,-1.568,-1.9972,-1.1977,0.44426,1.6439,1.3035,1.9853,2.8087,1.4964,1.1103,-0.010928,-0.26617,0.56153,2.2984,1.8301,1.2144,0.13957,-0.87217,-1.0911,-0.27794,-0.57077,-1.3256,-2.4222,-2.8928,-2.7586,-2.4385,-2.4601,-3.4403,-4.2922,-4.1483\n-3.0587,-1.6648,-1.8352,NaN,NaN,NaN,-2.9713,-3.0602,-2.8575,-3.1636,-3.4706,-3.4074,-2.9767,-0.89329,-0.97768,-0.87115,-0.91722,-1.0777,-1.1373,0.33513,1.6186,2.2895,1.2371,2.716,4.5237,4.1757,3.1197,1.8406,2.4184,4.1384,3.9819,2.8062,0.69704,-0.74643,-1.7077,-2.8407,-1.1026,-0.49279,-0.44063,-2.4669,-2.7913,-2.5364,-2.2226,-2.6532,-3.5011,-3.9091,-3.7719\n-4.642,-3.8776,-2.5846,NaN,NaN,NaN,-3.6458,-4.3484,-4.7357,-3.9451,-4.0622,-3.3305,-1.9153,-0.67887,0.0095181,0.27973,-0.088297,0.26503,0.60499,1.8023,2.7909,3.6757,3.8897,5.228,6.2577,6.4795,NaN,NaN,NaN,NaN,NaN,5.3727,0.45548,-1.4342,-1.7364,-1.9607,-1.1833,-0.99382,-1.0973,-2.2515,-2.7226,-2.6668,-2.7297,-3.3002,-4.0602,-3.931,-3.2165\n-7.066,-4.6964,-3.2954,-4.5359,NaN,NaN,-4.3943,-5.1148,-4.829,-3.4271,-3.0209,-2.9047,-2.1497,-1.1829,-0.080626,0.74626,0.98781,1.7141,3.4191,4.1825,4.4272,4.8188,5.6714,6.5759,8.7687,10.095,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.32189,-0.41008,-0.39424,-0.84589,-1.1183,-1.8059,-2.4301,-3.101,-3.3681,-3.1317,-3.5394,-3.8502,-3.4731,-3.0932\n-9.483,-6.9183,-5.7813,-5.5961,-4.7128,-4.2709,-5.4196,-5.5645,-3.7548,-3.0778,-3.1017,-2.8566,-2.1133,-0.68924,0.61878,1.706,2.0918,2.8325,3.9609,4.9853,5.103,5.4748,6.2036,7.6407,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5391,1.4861,-0.048709,-0.61052,-1.3446,-2.7654,-3.1623,-2.7923,-2.8458,-2.9349,-3.0019,-2.9838,-2.8388\n-11.198,-9.8213,-7.9754,-6.6735,-5.3873,-4.769,-4.8019,-5.4177,-4.2572,-3.9166,-3.2171,-2.5413,-1.9511,-0.51997,0.77645,1.5839,1.7114,2.7421,4.0492,4.5815,4.7718,6.4937,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9339,1.5882,0.5659,-0.25833,-0.67274,-1.493,-2.7073,-2.8991,-2.4349,-2.668,-2.7235,-2.5576,-2.4106\n-8.2792,-9.5138,-11.6,-12.564,-6.2793,-6.2407,-6.0151,-5.1242,-3.7192,-3.2671,-3.2193,-2.7867,-2.4676,-0.33612,0.85799,1.5896,2.1202,3.0027,4.0533,4.3738,4.4472,6.7939,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5791,1.6959,1.719,1.1546,0.19142,-1.3333,-2.7227,-2.5648,-2.3603,-3.4985,-3.5691,-2.8594,-2.6302\n-7.7453,-8.1877,-9.3873,-8.4892,-6.6042,-5.461,-5.5809,-5.1234,-4.3326,-3.0305,-1.7984,-1.6434,-1.9638,-0.37757,0.54745,1.0201,2.1025,3.2708,4.7887,6.2954,4.7694,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.4804,3.5323,2.4795,2.1118,0.50543,-1.7748,-2.2467,-1.7201,-2.1437,-3.567,-3.5934,-3.0742,-2.9362\n-7.9588,-9.5576,-9.8528,-5.6125,-4.5956,-4.5363,-4.7599,-5.1871,-5.3335,-3.7749,-0.82546,-0.15605,-0.27247,0.030019,0.25576,0.81691,2.1194,2.3561,3.5971,4.252,1.9706,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5366,1.5917,1.9226,1.4398,0.14669,-1.4911,-2.0374,-1.6916,-2.2344,-3.7179,-3.4436,-3.4056,-2.6256\n-5.278,-6.8008,-7.3613,-5.5982,-5.1343,-4.299,-4.7306,-5.0554,-5.3222,-2.7088,-0.57653,1.8167,2.6934,0.85526,-1.3414,-0.1981,1.4492,3.2711,3.5136,3.2031,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.86722,NaN,NaN,NaN,NaN,NaN,NaN,-0.96153,1.1253,1.91,1.914,-0.84647,-1.2925,-1.2946,-1.1789,-1.4818,-2.4795,-3.1545,-3.4168,-3.5038\n-3.9337,-4.5858,-6.5129,-5.6533,-5.0143,-4.4885,-3.5678,-3.173,-3.1237,-2.1977,-1.2585,-0.19736,0.99425,1.1266,-1.0212,-1.0717,-0.60835,1.4434,3.2628,4.531,NaN,NaN,NaN,NaN,NaN,NaN,0.47347,0.12269,NaN,NaN,NaN,-1.9233,-2.3978,-3.5118,-1.9217,0.62188,4.3018,4.0741,0.83307,-0.91961,-0.80056,-0.86262,-1.5377,-2.3774,-3.577,-3.7093,-4.2033\n-4.8955,-3.711,-4.9163,-3.6644,-3.8069,-4.9373,-6.0599,-4.2866,-2.7335,-1.9079,-1.5042,-0.89412,-0.81285,0.12692,-0.64942,-0.54211,0.19617,1.6469,3.1647,5.7887,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.839,-1.4458,-2.1204,-3.098,-4.7402,-3.6072,2.506,6.3957,6.6038,5.0481,0.91668,-1.5828,-1.3554,-1.3617,-1.4979,-2.5386,-3.81,-4.0356,-4.4423\n-6.2976,-6.6629,-6.2128,-4.6288,-4.8079,-5.1389,-5.3928,-5.3851,-3.034,-2.2808,-1.3217,-1.0058,-0.57661,-0.12139,0.023087,-0.056701,0.84721,1.906,3.869,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.483,-3.7305,-2.5748,NaN,NaN,NaN,6.9222,3.9559,-0.46637,-1.7578,-1.4781,-1.4188,-1.6739,-2.9127,-3.5739,-3.5166,-4.7736\n-6.7868,-7.4339,-7.2043,-6.5643,-6.0804,-5.3221,-4.4553,-3.5943,-3.1528,-2.9986,-2.1614,-0.69297,-0.33824,1.8412,1.6764,0.64498,-0.034627,1.4976,1.914,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.9261,NaN,NaN,NaN,NaN,NaN,NaN,0.60785,-1.5887,-1.2923,-0.86153,-1.7993,-2.8254,-3.7937,-3.731,-3.8755,-4.8128\n-5.9999,-5.9574,-6.4017,-6.7528,-6.6683,-5.3009,-3.6818,-2.8695,-2.6982,-2.5337,-1.9332,-0.37754,-0.24484,0.89912,0.9085,0.18319,-0.1437,2.0018,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.56075,NaN,NaN,NaN,NaN,NaN,2.0111,0.26958,-1.1479,-1.335,-1.5302,-2.4834,-3.2883,-3.4504,-3.2671,-3.5964,-4.6227\n-5.1564,-4.8678,-5.0849,-5.8197,-6.3246,-5.3009,-3.127,-2.7548,-2.8393,-2.9094,-2.7375,-2.0995,-0.84901,-0.26925,-0.19018,0.092149,1.4229,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4877,-2.0248,NaN,NaN,NaN,NaN,2.0905,1.3823,0.36355,-0.77556,-1.4558,-1.8414,-2.4738,-3.3479,-3.4304,-2.9951,-3.4898,-4.7766\n-4.6649,-4.9625,-4.9289,-5.2081,-5.2293,-4.8213,-4.0526,-3.1299,-2.8106,-2.7976,-2.8835,-3.0467,-0.96029,-0.14525,-0.16387,1.3795,2.4415,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.7011,-1.7494,-2.8843,-4.7388,-3.2878,-1.6832,0.077528,0.36198,0.27699,0.88372,0.071507,-2.1052,-2.3001,-2.6623,-3.44,-3.7929,-3.5601,-4.2105,-5.1196\n-4.0719,-4.9394,-4.8699,-5.6142,-5.0136,-4.4825,-3.9778,-3.518,-3.053,-2.6577,-2.6004,-2.5755,-1.5504,-1.0565,-0.47678,1.8961,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.0945,-1.5921,-2.9731,-3.098,-2.4923,-2.4461,-1.0022,-0.72169,-0.7238,-0.28955,-0.18019,-2.0172,-3.0557,-3.3978,-3.8099,-3.9739,-5.0133,-4.3791,-4.273,-4.7935\n-3.634,-4.2062,-4.7955,-4.5274,-3.6375,-3.9651,-3.9827,-3.2307,-2.4336,-2.1621,-1.8039,-1.8867,-1.8429,-2.0649,-1.644,-0.43523,1.4096,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.2093,-3.9463,-3.9258,-2.0005,0.50067,-0.97914,-1.5387,-1.576,-2.2432,-2.2922,-2.7359,-3.6084,-3.6698,-4.2263,-4.3408,-4.4845,-4.3718,-3.4563,-3.364\n-3.9829,-3.8533,-4.3103,-3.0967,-2.7968,-2.8433,-2.5925,-2.1595,-1.8161,-1.4684,-1.3869,-1.45,-1.936,-1.956,-1.7811,-1.1158,-0.083261,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.1754,-4.6248,-3.3284,-0.88177,-0.86825,-1.0818,-1.2009,-1.297,-2.5029,-3.0367,-3.6605,-3.9044,-3.9629,-4.0176,-4.1291,-4.2459,-4.4668,-3.826,-3.5222\n-3.9976,-2.7077,-2.6434,-3.2495,-2.2343,-2.3115,-2.2426,-2.0508,-1.6588,-1.3965,-1.441,-1.519,-1.4704,-1.5906,-0.81463,-0.066579,0.0022619,NaN,NaN,NaN,NaN,NaN,-0.5852,-0.99565,NaN,NaN,NaN,NaN,-4.2348,-2.5696,-1.8151,-1.9801,-1.7702,-2.3083,-2.3145,-2.9909,-2.9198,-3.3091,-3.6824,-3.9234,-3.7832,-3.5962,-3.8222,-4.399,-4.8539,-5.0331,-4.5344\n-3.539,-3.0448,-2.8185,-3.4357,-3.3071,-2.0183,-1.8482,-1.7926,-1.4901,-1.2008,-1.1519,-1.2997,-1.4772,-1.8425,-0.76319,0.095133,-0.33453,-1.7962,-2.1783,-2.7784,-4.0324,-2.6146,0.1907,0.994,1.3072,NaN,NaN,NaN,-3.4929,-2.1277,-1.6988,-1.7085,-3.4072,-3.0907,-3.5557,-4.3017,-4.263,-4.2179,-3.4134,-3.5445,-3.7574,-3.6488,-3.8326,-4.8343,-6.1509,-6.704,-5.4922\n-3.3929,-3.2879,-3.2245,-2.6562,-2.3665,-1.7439,-1.515,-1.6253,-1.7859,-2.0144,-1.666,-1.3554,-1.1687,-1.5247,-0.64877,-0.12901,-0.78946,-1.7884,-2.5354,-3.4135,-3.9885,-3.6421,-2.1347,-1.6699,-1.5505,-3.4799,-3.7174,-3.9431,-3.2314,-2.4711,-1.7964,-1.6266,-2.0118,-3.807,-4.2513,-4.3311,-4.3963,-3.5044,-3.0118,-3.5022,-3.9837,-4.5896,-4.5786,-4.0388,-6.064,-6.8415,-5.9308\n-3.3508,-2.8137,-2.8759,-2.0677,-1.8182,-1.8927,-2.0123,-2.1602,-1.5883,-1.5079,-1.7575,-1.486,-1.2045,-1.1865,-1.5242,-1.4978,-2.4763,-2.9535,-2.7944,-3.6589,-4.0849,-3.7749,-2.8929,-2.4211,-2.1964,-3.1434,-3.4211,-3.6208,-3.3213,-3.0682,-1.8824,-1.3229,-1.792,-2.8683,-3.521,-3.3433,-3.7985,-3.2999,-2.8952,NaN,NaN,NaN,NaN,NaN,-4.7917,-4.6284,-5.3753\n-2.6976,-2.4988,-2.2691,-1.9576,-1.7765,-2.3825,-2.5771,-2.3982,-1.3843,-0.99274,-1.1441,-1.5184,-1.2783,-1.6315,-2.2671,-2.4501,-3.1008,-3.3194,-3.5459,-3.8893,-4.3217,-4.7162,-3.6549,-3.0024,-2.7265,-2.8071,-2.2141,-3.7304,-4.0379,-3.6373,-2.5193,-2.0234,-2.6099,-2.8658,-3.8671,-4.3536,-4.6307,-3.3949,-3.0596,NaN,NaN,NaN,NaN,NaN,-5.3599,-4.1488,-4.5088\n-1.6777,-1.9299,-2.4245,-2.1836,-1.9063,-1.9835,-2.1926,-2.1575,-1.8811,-1.6752,-1.255,-1.2551,-1.4858,-1.8847,-2.8766,-3.3274,-3.1996,-3.0542,-3.8228,-3.838,-3.2732,-2.4148,-2.5687,-3.2719,-3.95,-4.2048,-2.7847,-4.6982,-4.041,-3.6508,-3.0679,-2.955,-3.3193,-4.0515,-4.7464,-4.731,-4.3934,-3.9571,-3.5422,-2.4436,-1.6206,NaN,NaN,NaN,-4.7105,-4.0451,-4.4523\n-1.3557,-1.6253,-1.7863,-1.5711,-1.4243,-1.2909,-1.2169,-1.2795,-1.664,-2.5213,-1.7757,-1.1554,-1.2997,-1.8157,-2.8219,-3.2019,-2.7959,-3.2738,-3.9504,-4.5008,-3.9249,-2.8474,-2.9998,-3.4317,-3.9705,-4.3142,-4.7604,-5.7251,-4.4762,-3.7685,-3.6807,-3.8971,-3.8095,-3.6686,-3.8252,-3.7599,-3.5256,-3.0577,-3.1251,-3.2819,-1.7702,-2.3524,-3.6739,-4.5686,-4.1638,-4.0663,-4.8649\n-1.4166,-1.0813,-1.1849,-1.2771,-1.7705,-2.1005,-2.012,-1.8401,-1.6296,-2.0586,-1.9171,-1.4573,-1.6616,-2.226,-2.8926,-3.0298,-2.7175,-3.3657,-3.8774,-4.1859,-4.1535,-3.5945,-3.3827,-3.8231,-4.3149,-4.6649,-5.2207,-5.2216,-4.5843,-4.2202,-4.5921,-4.1985,-4.0943,-3.7788,-3.2132,-2.9511,-2.6853,-2.5575,-2.8713,-3.1815,-2.8768,-2.9985,-3.3936,-4.3077,-4.1602,-4.4125,-4.5074\n-2.6481,-1.7601,-1.2009,-1.4417,-1.6416,-1.6102,-1.6063,-1.7665,-1.6043,-1.3674,-1.6826,-2.1063,-2.3348,-2.2256,-3.0751,-3.0859,-3.1041,-2.9851,-3.2848,-3.8045,-3.7279,-3.5359,-3.535,-3.8479,-4.6896,-5.8584,-6.0666,-5.0495,-4.043,-4.0901,-4.7783,-4.7665,-4.2876,-3.8228,-3.5985,-3.1214,-2.6265,-2.5686,-3.1928,-3.3116,-3.9842,-4.1409,-4.7926,-5.2479,-4.558,-4.4201,-4.2781\n-2.471,-1.9309,-1.1625,-1.5304,-1.3102,-0.83361,-1.4641,-1.8273,-1.6602,-1.1153,-0.85867,-1.642,-2.3688,-2.7722,-3.0102,-2.9818,-3.0284,-2.9285,-3.6223,-4.1151,-3.9215,-3.3728,-3.7664,-4.0569,-4.8507,-5.3754,-5.0515,-4.5115,-3.8926,-4.7903,-4.8711,-4.9811,-4.6349,-4.2568,-4.546,-4.5881,-3.8107,-4.0295,-4.4006,-4.4719,-4.806,-4.7216,-4.4654,-4.1856,-4.5332,-4.6175,-3.8908\n-2.999,-1.6244,-0.94256,-0.64755,-0.48011,-0.66952,-1.5608,-1.6759,-1.067,-0.78789,-0.29226,-0.68991,-2.0796,-3.127,-3.5926,-3.7877,-3.753,-3.8238,-4.3142,-4.45,-3.9737,-3.3424,-3.8891,-4.2517,-4.4634,-4.7103,-4.3705,-3.61,-4.1328,-5.1375,-5.0682,-4.8456,-5.045,-5.1123,-4.8511,-4.3251,-4.2306,-4.2976,-4.4427,-5.2124,-5.0211,-4.7616,-3.8086,-3.9879,-4.8505,-4.4619,-4.2953\n-4.9204,-5.464,-1.2094,0.091252,0.50737,-0.63868,-1.6932,-1.1436,-0.32824,-0.2827,-0.30175,-0.64782,-1.3898,-2.6599,-3.4451,-3.7506,-3.8127,-4.0284,-3.861,-3.6835,-3.4031,-3.3425,-3.8021,-4.067,-4.2971,-4.5478,-4.1473,-4.1105,-4.7302,-5.1547,-4.6628,-4.78,-5.2973,-4.9461,-4.3014,-3.8983,-3.8762,-3.8955,-3.9277,-4.5596,-4.6832,-4.4931,-4.2086,-4.5822,-4.839,-4.9059,-5.2645\n-2.9336,-4.1527,-4.2466,-2.2337,-0.52738,-0.82108,-1.5048,-2.2977,-1.8972,-0.66085,-0.35269,-0.42411,-1.8228,-2.6443,-3.7881,-3.8408,-3.5633,-3.3887,-3.1794,-3.2792,-3.1025,-3.5309,-3.87,-4.192,-4.3671,-4.4663,-4.3282,-4.656,-5.0042,-5.2592,-4.8744,-5.1398,-5.3454,-5.421,-5.1197,-4.3264,-4.3548,-4.2856,-3.8287,-3.9002,-4.2506,-4.5385,-4.9294,-4.9816,-4.8542,-5.1096,-5.2152\n-2.1977,-3.0746,-3.828,-3.117,-2.1695,-1.5492,-1.6159,-0.7792,0.50853,1.6345,-0.29516,-0.70806,-3.4195,-4.3718,-4.7643,-4.2145,-3.535,-3.1751,-3.3129,-3.5904,-3.7613,-3.834,-3.7741,-4.034,-4.3137,-4.4213,-4.5074,-4.7718,-4.8588,-4.5618,-4.7533,-4.9155,-5.2916,-5.27,-5.1179,-4.7887,-4.5835,-4.4128,-3.7669,-3.6381,-3.4711,-3.7325,-4.3924,-4.8633,-5.3614,-5.394,-4.7815\n-2.343,-1.9101,-1.8768,-1.0777,-0.98547,-0.92385,-0.3682,-0.58239,-0.16627,0.65541,-0.10492,-0.61715,-3.1278,-2.6849,-3.5884,-3.2781,-2.9605,-2.9333,-3.7352,-3.1158,-2.8159,-3.7934,-4.0984,-4.0354,-4.2706,-3.9987,-4.2806,-4.6548,-5.0617,-4.4929,-4.5138,-4.6564,-5.193,-5.3657,-4.7579,-4.4338,-4.4704,-4.1668,-3.7191,-3.8155,-3.7899,-3.8855,-4.2855,-4.8279,-5.971,-6.1086,-5.1379\n-2.6722,-3.3219,-3.4501,-1.5786,-1.1367,-0.32439,-0.53569,-0.39172,-0.13104,0.24634,-0.70725,-2.3524,-3.3327,-3.6871,-3.4964,-2.1611,-2.4148,-3.4759,-3.8127,-3.4186,-3.596,-4.0116,-4.1274,-4.1324,-4.2675,-4.478,-4.7208,-5.0456,-5.3451,-5.3532,-5.1977,-4.9299,-5.5371,-5.2849,-4.4044,-4.2066,-4.4041,-4.1658,-3.8916,-4.1218,-3.7525,-3.8264,-4.4958,-4.6596,-5.3692,-5.9352,-5.8296\n-3.693,-4.8212,-4.3963,-3.1076,-2.9556,-2.5923,-1.4512,-0.57823,-1.0398,-1.5494,-1.4261,-2.6624,-4.0968,-4.785,-5.0132,-4.4283,-3.0644,-4.6654,-4.7056,-4.4782,-4.8455,-5.0462,-4.5805,-4.4955,-4.5194,-4.6111,-4.6818,-4.6973,-4.7641,-5.0044,-5.4513,-5.7769,-5.7219,-4.8956,-4.1882,-4.3,-4.433,-4.2347,-3.8673,-3.8673,-3.8057,-3.9035,-4.7598,-4.9726,-5.2522,-5.1345,-4.8205\n-4.3294,-3.3778,-2.4051,-1.9612,-2.1061,-2.6209,-2.0005,-1.9639,-1.2579,-1.6903,-1.8466,-3.7267,-4.3616,-4.3449,-3.5881,-3.7924,-4.6956,-4.2345,-2.5834,-4.384,-5.0923,-5.8245,-5.6106,-4.7637,-4.7095,-4.4614,-4.4153,-4.6471,-4.6329,-5.0341,-5.3334,-5.8981,-6.2096,-5.7644,-4.6555,-4.3137,-4.2672,-4.165,-3.7567,-3.8809,-4.3751,-4.7967,-5.0675,-5.522,-5.9032,-5.6064,-5.2104\n-2.2413,-2.5149,-2.3866,-1.7716,-2.1244,-2.6717,-1.985,-1.7635,-0.94753,-0.2979,-1.1539,-1.9949,-3.0406,-4.2342,-3.8052,-3.4579,-3.5366,-5.1942,-4.3504,-4.2836,-4.1977,-4.6683,-5.4121,-4.6818,-4.2552,-4.5355,-4.8801,-5.6197,-5.7407,-5.2149,-4.7669,-5.236,-5.7981,-5.941,-4.8112,-4.3692,-4.535,-4.2823,-4.2215,-4.4838,-4.9941,-5.4878,-5.482,-5.4994,-5.8192,-5.7684,-5.457\nNaN,NaN,NaN,NaN,-0.66293,-2.327,-0.94342,1.3439,-0.000458,0.5223,-1.8225,-3.323,-4.112,-4.0668,-4.2253,-4.0876,-2.0716,-1.3191,-4.2867,-4.4607,-4.0231,-4.0223,-5.6459,-4.1107,-3.9141,-4.7786,-4.7115,-4.7975,-4.8174,-4.6724,-4.8903,-5.052,-4.9064,-4.6018,-4.6248,-4.3729,-4.0622,-4.3417,-4.4939,-4.8729,-5.1763,-5.1061,-4.7981,-5.4744,-5.6162,-5.7092,-5.39\nNaN,NaN,NaN,NaN,-0.61663,NaN,NaN,NaN,2.7647,-0.38895,-2.6362,-4.3377,-4.5629,-4.0054,-3.3216,-2.554,-1.3033,-1.9235,-4.5375,-4.867,-5.367,-5.6232,-4.5154,-2.7642,-3.8639,-4.3681,-4.1722,-3.5622,-3.2897,-3.8393,-4.1995,-4.4616,-4.6928,-4.5515,-5.1143,-5.1099,-4.5714,-4.6206,-4.6136,-4.3705,-4.0831,-4.4337,-4.8113,-5.1238,-5.3197,-5.458,-5.3235\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.3188,-0.94098,-0.71595,-1.4919,-2.7396,-5.7008,-5.2794,-4.3386,-5.3203,-5.5561,-4.1586,-2.7379,-3.1784,-3.6191,-3.7403,-3.7095,-3.0192,-3.268,-3.9458,-4.9017,-5.1594,-5.6516,-5.8838,-5.1095,-5.0276,-4.8795,-4.8449,-3.857,-3.8711,-4.4678,-4.8549,-5.1484,-4.9249,-4.5811,-3.5871\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.3057,-4.7949,-5.1337,-3.9357,-2.4096,-2.9055,-2.9727,-3.2455,-3.5138,-3.8253,-3.9059,-3.3437,-2.8111,-3.5329,-3.9778,-4.7867,-5.444,-5.8233,-5.2514,-5.1423,-5.318,-4.9223,-3.7872,-3.79,-4.5412,-5.2157,-5.628,-5.1636,-4.6849,-3.9344\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.9684,-5.1666,-3.0418,-2.384,-2.3289,-2.9846,-4.0378,-3.0211,-3.827,-4.01,-3.6847,-3.5713,-3.8287,-4.0351,-4.7267,-5.2242,-5.6075,-5.3048,-5.0142,-5.32,-5.3052,-4.8264,-4.4094,-4.675,-4.707,-5.2899,-5.223,-4.257,-3.1459\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.6719,-3.2117,NaN,NaN,-4.4491,-5.9702,-6.8041,-4.6963,-4.3878,-4.1668,-3.5356,-3.6607,-4.2908,-4.4051,-3.4003,-3.948,-5.0874,-5.241,-4.9693,-4.7048,-5.0344,-5.0061,-5.2096,-5.1353,-4.7875,-4.8086,-4.6189,-3.7539,-2.4591\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.849,-5.0504,-5.3502,-4.2243,-3.2618,-3.2138,-3.1228,-3.503,-5.1525,-5.5771,-5.1922,-4.965,-5.7872,-5.9982,-5.7104,-4.9093,-5.257,-5.3475,-5.143,-4.9413,-4.922,-4.8397,-4.2789,-3.351,-2.8286\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.7941,-4.7202,-4.4703,-4.0287,-3.1055,-2.8258,-3.3205,-3.8456,-4.4793,-5.3481,-5.4283,-4.9882,-5.4664,-5.9735,-5.9736,-4.6257,-5.5278,-5.449,-5.2502,-4.7691,-4.4225,-4.6654,-3.9893,-3.5485,-3.5138\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.8622,-4.1267,-3.6519,-3.1115,-2.2797,-1.2666,-2.205,-2.6731,-2.8621,-3.5025,-3.8999,-4.9638,-4.9853,-5.6656,-5.7288,-5.223,-4.5459,-4.7514,-4.852,-5.0519,-4.9147,-4.4289,-4.5337,-4.2913,-3.7659,-3.2703\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.9428,-4.888,-4.1051,-3.0939,-1.9814,-1.0898,-1.7548,-3.6669,-3.3197,-3.1608,-3.7708,-4.4011,-4.8722,-5.2222,-5.1289,-4.2578,-4.5945,-5.4302,-4.8503,-4.4955,-4.604,-4.7609,-4.5244,-3.4838,-3.1947,-3.468\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070115-070326.unw.csv",
    "content": "-11.678,-11.88,-11.768,-11.057,-11.471,-11.744,-12.223,-12.324,-12.209,-11.373,-11.81,-11.549,-10.8,-9.8645,-9.9604,-9.6935,-10.258,-9.293,-8.9929,-8.8254,-8.2774,-9.4651,-10.294,-10.202,-9.3209,-9.5992,-9.0783,-7.133,-7.4847,-8.4566,-8.5951,-7.8524,-7.7669,-7.7723,-7.9838,-8.3546,-9.4998,-10.105,-14.197,-13.849,-13.371,-13.677,-13.533,-11.99,-12.696,-11.949,-12.024\n-12.178,-12.268,-12.08,-11.376,-11.686,-11.961,-12.032,-12.197,-12.006,-11.749,-11.098,-10.602,-9.9766,-9.9568,-10.1,-10.036,-10.098,-10.107,-9.7964,-9.3381,-8.5454,-9.9916,-10.666,-10.769,-10.379,-9.3469,-7.9741,-7.3622,-8.3916,-8.7898,-7.5187,-7.4349,-7.4784,-7.7637,-7.2963,-7.8594,-9,-10.747,-13.593,-13.189,-12.942,-13.674,-11.824,-11.185,-11.284,-11.604,-11.694\n-12.239,-12.207,-11.957,-11.94,-11.863,-12.632,-12.205,-11.904,-12.426,-11.932,-10.855,-9.8592,-9.8993,-10.035,-9.881,-10.038,-9.9163,-10.189,-10.257,-10.473,-10.413,-9.7762,-9.6175,-10.1,-9.4589,-7.9671,-7.261,-7.2168,-8.5312,-8.8059,-7.4595,-7.576,-6.888,-7.1991,-7.4501,-7.5746,-8.1372,-9.7009,-10.827,-11.392,-13.029,-13.144,-11.549,-12.22,-11.705,-11.808,-12.345\n-13.135,-12.38,-11.562,-12.059,-12.051,-12.809,-12.319,-11.857,-11.717,-11.483,-10.669,-10.166,-10.455,-9.8744,-11.206,-10.685,-9.3767,-9.6122,-10.162,-10.292,-11.059,-10.127,-9.3424,-9.184,-8.3613,-7.1465,-6.9093,-7.0801,-7.3488,-7.7616,-7.2471,-7.0803,-6.3527,-7.1056,-7.764,-7.8142,-8.815,-9.8529,-10.491,-12.092,-12.804,-12.474,-12.305,-12.734,-12.936,-12.514,-13.3\n-12.919,-12.901,-11.945,-12.117,-12.421,-12.426,-12.164,-11.445,-11.952,-11.902,-11.738,-11.702,-11.324,-10.628,-10.683,-10.604,-9.8446,-9.2426,-9.8754,-10.621,-11.849,-9.8216,-9.1492,-8.5435,-7.8118,-6.6619,-7.0432,-7.4066,-6.1541,-6.5835,-6.1333,-7.2714,-5.561,-6.9642,-8.1071,-8.7486,-9.8352,-10.511,-11.17,-12.28,-12.271,-11.757,-12.27,-12.659,-12.519,-12.541,-12.87\n-12.895,-13.333,-12.499,-12.041,-12.465,-12.289,-12.564,-12.759,-12.511,-12.091,-12.184,-12.58,-12.227,-11.126,-10.568,-10.328,-10.328,-10.424,-10.035,-9.626,-9.4596,-9.367,-8.4233,-7.6646,-7.6572,-7.0285,-6.7475,-7.3057,-6.4227,-7.1514,-7.1194,-7.5642,-6.5535,-6.7835,-7.9645,-8.2843,-10.095,-11.102,-11.692,-12.663,-12.706,-12.352,-12.251,-12.54,-12.821,-12.862,-12.853\n-13.64,-13.891,-13.742,-12.578,-12.363,-12.386,-12.819,-13.264,-12.047,-11.571,-12.263,-12.517,-12.199,-10.898,-11.476,-10.698,-10.713,-10.574,-10.39,-10.629,-9.9771,-9.5911,-9.0318,-8.3447,-7.5806,-6.3628,-6.7831,-7.0222,-7.0706,-6.944,-7.5348,-6.8129,-6.4537,-6.8067,-7.6836,-8.583,-9.8157,-11.667,-12.487,-14.001,-13.492,-13.101,-12.738,-12.675,-13.266,-13.559,-14.1\n-14.363,-14.46,-14.596,-12.945,-12.596,-12.557,-12.711,-12.728,-11.73,-11.391,-12.295,-11.8,-11.561,-11.179,-12.08,-11.397,-11.217,-11.048,-11.199,-11.09,-9.9529,-9.558,-9.7209,-8.9558,-7.7387,-6.7677,-6.7062,-7.4476,-7.8876,-7.4061,-7.0878,-6.6327,-6.5297,-6.3689,-7.4271,-9.0309,-10.94,-12.381,-13.208,-13.745,-13.603,-13.741,-12.813,-13.308,-13.597,-14.254,-14.34\n-14.292,-14.558,-13.969,-12.918,-12.609,-12.616,-12.782,-12.288,-12.457,-11.998,-11.816,-12.251,-11.701,-11.582,-11.413,-11.27,-11.387,-11.631,-11.704,-10.895,-9.9496,-9.2096,-9.6973,-9.6628,-7.0138,-6.9337,-7.9314,-7.7568,-7.677,-7.1629,-7.2106,-6.6738,-6.7309,-7.0239,-8.0841,-10.227,-11.93,-13.003,-12.863,-12.682,-12.702,-12.41,-11.939,-13.695,-13.662,-13.834,-14.213\n-13.963,-13.87,-12.263,-13.005,-12.973,-12.918,-13.306,-12.85,-12.758,-12.358,-12.207,-12.96,-11.791,-12.965,-11.841,-11.002,-11.15,-11.419,-11.544,-10.788,-9.5347,-9.544,-10.032,-9.4686,-7.5787,-7.4175,-7.1591,-7.4999,-7.1885,-7.2821,-6.7235,-6.1993,-7.9759,-7.6357,-9.6796,-10.882,-11.057,-11.983,-12.002,-12.355,-12.374,-12.176,-12.024,-12.399,-12.474,-13.089,-13.772\n-13.777,-12.045,-11.118,-13.228,-13.978,-13.458,-14.245,-13.239,-12.696,-13.138,-13.244,-14.015,-12.984,-13.42,-12.733,-12.292,-11.155,-10.439,-10.132,-9.3052,-9.5109,-9.8045,-9.2904,-8.9089,-7.8184,-7.3194,-7.4438,-7.4597,-7.1658,-6.748,-6.0678,-6.2903,-7.8952,-8.5313,-9.7087,-9.6911,-10.31,-10.951,-10.983,-11.574,-12.076,-11.581,-11.563,-12.347,-12.178,-12.679,-13.036\n-13.041,-11.496,-12.463,-13.333,-13.792,-13.801,-14.355,-12.682,-12.92,-14.003,-14.837,-15.22,-14.024,-13.151,-12.756,-12.404,-11.287,-9.2792,-9.0707,-8.0944,-9.2292,-9.4897,-9.1578,-9.0054,-7.8571,-6.579,-7.7021,-8.1094,-8.0499,-6.3802,-6.2418,-7.0833,-7.8655,-8.9163,-9.9134,-10.049,-11.414,-11.342,-11.355,-11.639,-11.857,-11.336,-10.517,-11.562,-12.027,-12.582,-12.797\n-13.976,-13.827,-14.134,-13.398,-13.252,-13.327,-14.343,-12.784,-13.12,-14.342,-13.863,-14.601,-14.435,-12.855,-12.346,-12.301,-11.506,-9.8774,-8.8189,-8.0092,-8.3541,-8.4216,-8.4637,-8.8721,-7.9546,-6.9923,-8.047,-8.3404,-7.8996,-6.3513,-6.8243,-8.2435,-8.9448,-9.5163,-9.8768,-10.454,-12.171,-11.194,-11.987,-11.528,-11.581,-11.172,-10.964,-11.164,-12.222,-12.457,-12.589\n-14.898,-15.795,-15.705,-14.122,-12.798,-14.154,-14.238,-13.54,-13.831,-13.588,-13.521,-13.53,-13.074,-12.788,-12.307,-12.685,-11.902,-10.846,-10.21,-9.431,-8.747,-8.5525,-8.0469,-8.0168,-8.202,-7.2861,-7.7216,-7.8783,-7.4471,-7.1011,-7.5614,-8.6336,-9.639,-9.3572,-9.9284,-10.662,-11.408,-11.719,-12.132,-11.118,-11.779,-10.895,-11.761,-11.678,-11.935,-12.393,-12.367\n-15.168,-16.417,-16.086,-12.507,-13.33,-14.14,-13.694,-13.692,-13.215,-13.002,-13.631,-13.086,-13.178,-13.006,-12.521,-12.275,-12.089,-11.265,-10.405,-9.0842,-8.8921,-9.0315,-7.6069,-8.0042,-8.468,-7.386,-7.388,-7.6049,-7.9665,-7.6688,-7.7713,-8.1814,-8.4794,-8.6793,-10.042,-10.802,-11.132,-10.961,-10.696,-10.362,-11.314,-10.423,-11.635,-12.433,-12.715,-13.02,-12.895\n-14.218,-15.197,-15.644,-12.817,-12.502,-13.709,-13.014,-13.29,-13.26,-13.053,-13.603,-13.662,-13.423,-12.333,-11.733,-11.615,-11.596,-11.203,-10.151,-9.4416,-8.59,-7.7914,-7.938,-8.4711,-8.3145,-7.6363,-7.5724,-8.0916,-8.6267,-8,-8.327,-8.3596,-8.3726,-8.7458,-9.5893,-11.368,-10.312,-10.255,-10.185,-10.033,-11.034,-10.696,-11.792,-12.622,-13.051,-14.017,-13.567\n-13.522,-13.225,-13.28,-13.438,-14.079,-14.027,-13.134,-12.969,-12.737,-12.945,-12.542,-12.116,-12.561,-12.084,-11.531,-11.195,-11.756,-10.675,-10.701,-10.813,-9.6174,-8.0492,-8.6955,-8.3572,-7.3045,-7.6261,-8.2323,-8.8433,-8.6293,-7.6917,-8.5254,-9.0141,-8.917,-9.6686,-10.341,-10.831,-9.9175,-10.334,-10.345,-9.3618,-10.932,-10.744,-11.783,-12.274,-12.326,-13.58,-13.807\n-11.673,-12.257,-13.295,-14.187,-14.689,-14.478,-13.123,-13.191,-12.769,-12.509,-12.52,-12.544,-12.422,-11.867,-11.925,-12.174,-11.72,-10.436,-11.045,-11.347,-11.021,-8.8394,-7.9203,-8.6765,-7.575,-7.766,-8.3259,-8.693,-8.8974,-7.9776,-8.4141,-8.9827,-7.3958,-9.4272,-9.9705,-9.6011,-9.6688,-10.341,-10.706,-10.572,-11.342,-12.012,-11.959,-12.812,-12.972,-12.861,-13.088\n-13.448,-13.526,-13.865,-14.817,-15.141,-14.405,-10.8,-13.8,-12.906,-12.833,-13.426,-12.804,-12.69,-12.238,-12.567,-12.643,-11.141,-10.364,-10.606,-11.178,-9.5685,-8.32,-6.4495,-7.2542,-7.8494,-7.07,-8.0288,-8.292,-9.1056,-8.4639,-8.1448,-8.1967,-7.7695,-8.2482,-9.1227,-8.8091,-9.1044,-9.4417,-10.677,-11.092,-11.553,-12.089,-12.057,-12.668,-12.755,-12.839,-13.258\n-14.775,-13.495,-13.829,-14.439,-14.504,-13.178,-12.119,-13.617,-13.302,-12.77,-13.324,-13.091,-12.697,-12.513,-12.835,-12.878,-10.592,-9.2251,-9.239,-9.8094,-8.8287,-8.3015,-7.8405,-5.6307,-6.8349,-7.4977,-7.4675,-7.336,-8.01,-9.0342,-8.1652,-7.9639,-7.8906,-8.1619,-8.896,-8.9093,-8.9141,-8.9247,-10.772,-11.144,-11.461,-11.731,-12.399,-12.324,-12.795,-13.375,-12.951\n-13.331,-12.465,-13.492,-15.003,-15.933,-13.778,-12.936,-13.302,-12.655,-12.927,-13.15,-12.371,-12.775,-13.062,-12.657,-12.05,-11.25,-10.679,-9.2049,-9.7257,-10.547,-9.603,-8.721,-7.52,-7.5726,-7.4546,-7.2304,-6.7254,-7.6434,-8.7559,-7.78,-8.3122,-7.7609,-7.7956,-8.6002,-8.8268,-8.6871,-8.7889,-10.416,-10.635,-11.059,-12.568,-12.713,-13.354,-13.066,-12.849,-12.494\n-12.479,-11.812,-13.459,-14.184,-13.975,-13.376,-13.131,-12.441,-11.871,-13.662,-13.554,-12.872,-13.034,-13.86,-12.939,-12.114,-11.933,-11.511,-11.165,-10.533,-9.4395,-9.4441,-9.4458,-9.2668,-7.923,-7.0064,-6.8053,-6.795,-8.1798,-8.4397,-7.4747,-8.1087,-8.1027,-8.0768,-9.2446,-9.1866,-8.9358,-9.6338,-10.477,-10.771,-11.341,-12.773,-12.806,-12.722,-12.302,-12.097,-12.269\n-11.904,-12.48,-13.679,-12.648,-12.714,-13.337,-12.12,-11.689,-12.372,-14.156,-14.102,-13.536,-13.583,-13.186,-12.04,-11.046,-11.711,-11.47,-11.177,-10.491,-9.2368,-8.8754,-8.5302,-9.5702,-8.3499,-7.4197,-8.1954,-8.1383,-8.9888,-8.8673,-8.0229,-8.0933,-8.3646,-8.3754,-8.3308,-8.9737,-8.6502,-10.021,-10.595,-11.22,-11.834,-12.51,-12.657,-12.172,-11.987,-12.014,-12.96\n-12.947,-12.262,-11.411,-10.612,-11.127,-12.197,-12.817,-13.352,-13.804,-14.841,-15.693,-15.091,-13.355,-12.535,-11.677,-9.3725,-10.202,-11.479,-11.306,-10.6,-10.09,-9.3641,-8.959,-9.61,-9.6785,-8.9445,-9.7555,-9.8606,-9.3479,-9.1928,-9.1622,-9.5492,-9.121,-8.8429,-8.6425,-8.9833,-9.7054,-10.123,-10.273,-11.281,-11.54,-12.294,-11.894,-11.935,-11.881,-12.293,-13.34\n-13.034,-12.627,-11.941,-12.365,-11.941,-12.731,-14.426,-13.307,-13.979,-15.509,-15.505,-14.387,-14.614,-13.288,-12.456,-11.948,-12.197,-13.287,-12.796,-11.606,-11.065,-10.933,-11.47,-10.454,-10.801,-11.25,-10.905,-10.565,-10.341,-10.15,-9.6591,-9.6112,-9.5897,-9.3045,-8.9296,-10.417,-9.1673,-9.8331,-9.9837,-10.287,-11.138,-11.294,-11.204,-11.39,-11.481,-12.232,-13.398\n-12.562,-12.545,-12.743,-12.925,-12.703,-13.666,-13.457,-12.755,-12.764,-14.754,-14.968,-15.221,-15.325,-15.082,-13.392,-12.551,-13.297,-13.445,-12.915,-12.229,-12.307,-13.941,-12.876,-10.706,-10.31,-10.471,-10.029,-10.14,-10.378,-10.359,-10.181,-10.03,-11.055,-9.021,-9.078,-10.322,-9.0598,-9.47,-9.5218,-8.4565,-9.9669,-10.947,-10.922,-11.623,-11.276,-11.6,-11.772\n-11.119,-12.081,-12.002,-13.326,-13.326,-13.305,-12.785,-12.573,-13.454,-14.776,-15.034,-15.554,-16.186,-15.471,-14.209,-13.204,-13.041,-13.056,-13.471,-13.701,-14.022,-14.105,-12.858,-10.56,-10.521,-9.4151,-9.7591,-9.9012,-9.816,-9.1603,-9.7978,-10.124,-10.464,-9.1966,-9.094,-9.5093,-9.4092,-9.6993,-8.5114,-8.0032,-9.6625,-10.592,-11.217,-11.556,-11.446,-11.235,-10.956\n-10.283,-10.726,-11.678,-13.714,-12.929,-13.511,-14.472,-13.966,-13.978,-13.61,-14.627,-15.468,-15.79,-15.195,-14.016,-13.787,-12.762,-12.681,-12.958,-13.591,-14.447,-12.698,-11.737,-11.712,-12.247,-10.471,-9.6634,-9.7917,-9.8691,-9.486,-9.3636,-9.159,-9.34,-9.5553,-9.5501,-9.1145,-9.0563,-8.568,-8.8676,-8.9779,-10.196,-10.322,-11.024,-11.069,-10.745,-11.044,-11.28\n-9.9196,-10.682,-12.592,-12.207,-12.851,-14.278,-15.239,-15.343,-14.568,-13.05,-14.22,-14.159,-14.617,-14.064,-14.002,-13.569,-12.492,-12.096,-12.77,-13.838,-14.896,-13.228,-12.896,-12.835,-12.645,-10.948,-10.241,-10.187,-10.836,-10.444,-9.5773,-8.8558,-8.7468,-9.4835,-9.3613,-8.6826,-8.1811,-7.6536,-8.3998,-9.4583,-10.214,-10.112,-10.404,-10.132,-10.658,-11.152,-11.436\n-11.244,-10.189,-9.3429,-8.6804,-13.595,-14.779,-13.533,-13.883,-14.516,-13.706,-13.931,-13.394,-13.322,-13.061,-13.42,-13.114,-11.99,-11.608,-11.936,-12.292,-13.698,-13.344,-12.96,-12.634,-11.659,-10.729,-10.607,-11.651,-10.129,-9.3758,-8.2814,-8.4265,-8.4741,-8.8573,-8.5918,-8.8966,-8.3671,-8.0128,-8.9325,-9.6943,-9.9193,-10.006,-10.418,-10.424,-11.093,-11.217,-11.498\n-10.833,-9.6848,-8.5299,-11.88,-13.939,-14.065,-13.048,-14.094,-14.687,-14.388,-13.507,-12.929,-12.783,-12.712,-12.895,-12.739,-12.064,-11.479,-11.08,-10.275,-9.4713,-9.9562,-11.512,-12.011,-11.688,-10.591,-10.472,-11.171,-9.6978,-8.8358,-7.5249,-8.1871,-8.8315,-9.2027,-8.4053,-9.1201,-9.2217,-9.0771,-9.1282,-9.3982,-10.029,-10.581,-10.58,-10.856,-11.539,-11.27,-11.193\n-11.275,-10.65,-10.346,-12.888,-11.537,-12.207,-13.454,-13.927,-13.92,-13.062,-12.459,-12.902,-12.897,-12.314,-12.501,-12.261,-11.767,-11.627,-11.194,-10.958,-8.671,-9.5198,-10.969,-10.853,-11.233,-10.566,-8.845,-10.972,-12.446,-12.503,-9.5612,-8.686,-8.804,-8.686,-8.3526,-9.0192,-9.2705,-9.6573,-9.1471,-8.762,-10.109,-10.873,-10.843,-11.395,-11.359,-11.128,-11.321\n-12.23,-11.978,-12.178,-12.583,-13.084,-12.163,-12.654,-12.841,-13.638,-11.88,-11.408,-11.797,-11.98,-10.415,-9.7428,-10.999,-10.799,-10.496,-11.081,-11.496,-10.548,-11.067,-11.731,-9.5111,-9.8947,-9.4408,-8.3077,-14.646,-15.811,-14.559,-11.428,-7.8889,-7.2584,-7.4389,-8.4355,-9.3244,-9.5519,-10.194,-8.8299,-9.3054,-10.334,-10.923,-11.448,-11.674,-10.77,-11.451,-12.07\n-11.209,-11.388,-12.872,-13.477,-13.282,-11.325,-12.367,-12.254,-12.28,-12.057,-12.254,-12.005,-11.117,-10.087,-8.4829,-9.1664,-9.9306,-10.699,-10.284,-10.133,-11.751,-11.392,-10.683,-8.0861,-8.7676,-7.2347,-13.508,-13.806,-15.419,-10.948,-8.5362,-7.1782,-7.3186,-6.078,-7.2478,-7.4238,-6.3063,-8.4447,-7.7618,-9.3051,-10.476,-10.899,-11.2,-11.488,-10.695,-12.547,-13.049\n-10.978,-11.028,-12.969,-12.249,-11.925,-11.589,-11.479,-10.834,-11.518,-12.661,-12.438,-10.906,-10.389,-10.737,-9.2575,-9.486,-10.168,-9.7804,-9.4517,-8.6513,-11.726,-7.5384,NaN,NaN,NaN,NaN,NaN,NaN,-10.156,-9.117,-7.4541,-5.7112,-4.3876,-4.8936,-5.7904,-5.2067,-5.1042,-6.2399,-8.1258,-9.0006,-9.5035,-9.9494,-10.697,-10.949,-10.314,-12.691,-13.413\n-8.9973,-9.7135,-10.74,-10.658,-11.399,-11.821,-11.62,-10.334,-10.649,-11.948,-11.324,-10.859,-9.8881,-10.846,-13.112,-11.38,-8.8098,-8.0618,-8.2818,-8.9274,-7.4566,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.0589,-4.7893,-4.4571,-4.7731,-4.7897,-6.0262,-6.207,-7.3259,-8.6407,-8.6759,-9.7072,-10.372,-10.561,-11.214,-11.133,-12.212,-12.967\n-8.5872,-9.4082,-10.008,-10.79,-10.327,-11.224,-11.613,-11.271,-10.324,-10.299,-10.813,-9.7036,-9.9657,-12.317,-12.15,-10.45,-7.8365,-7.1118,-7.3042,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.0891,-4.0069,-3.1142,-3.8875,-4.6928,-6.4657,-7.8002,-8.5016,-9.1433,-9.1112,-10.089,-10.076,-10.41,-11.256,-11.49,-12.269,-12.827\n-9.9047,-9.1812,-9.4005,-10.775,-11.548,-11.73,-11.147,-11.145,-10.401,-8.9539,-8.8443,-8.5613,-9.3433,-10.645,-9.9202,-8.937,-7.1368,-8.4597,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.6024,-3.9999,-3.8582,-4.3734,-4.8342,-6.2758,-8.6687,-8.6911,-9.448,-9.956,-10.202,-10.165,-10.573,-11.274,-12.069,-12.669,-13.095\n-10.837,-10.289,-9.0375,-9.7512,-11.181,-11.382,-10.986,-11.076,-9.8844,-8.9943,-9.3616,-8.503,-8.6534,-9.1019,-8.0856,-6.5974,-5.0671,-8.9974,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.7944,-7.2666,-4.888,-3.6516,-4.0588,-5.0028,-6.1517,-7.6771,-8.6688,-8.7407,-9.3061,-10.241,-10.312,-10.287,-9.7026,-11.024,-11.619,-12.422,-13.15\n-10.616,-11.489,-10.671,-9.9843,-9.9243,-10.177,-9.8806,-10.33,-9.7417,-9.2388,-9.2211,-9.0569,-8.9108,-8.3071,-6.3263,-5.2664,-4.8963,-6.029,-6.5853,-8.484,-7.3377,-7.0391,NaN,-4.9103,-3.3767,-1.8722,-2.3591,-5.8721,-7.5819,-5.8634,-3.9381,-3.6716,-4.4902,-5.703,-7.2061,-7.5005,-7.8392,-8.4838,-9.4766,-10.094,-9.7142,-9.8428,-10.053,-10.775,-11.337,-11.606,-12.602\n-10.751,-10.701,-9.3572,-9.1531,-9.8426,-10.098,-10.328,-10.804,-10.189,-8.9077,-8.9352,-9.0848,-8.326,-7.3383,-6.0565,-6.3869,-6.7718,-7.4937,-7.4791,-7.4132,-7.3378,-6.5606,-4.2366,-6.3344,-7.3636,NaN,NaN,-6.6115,-6.5301,-5.4453,-4.9175,-5.64,-6.2466,-7.1643,-6.8585,-7.4883,-8.0146,-8.3828,-9.8931,-9.816,-9.657,-9.5695,-10.051,-10.506,-11.042,-11.712,-12.387\n-10.113,-9.0684,-8.7235,-9.4642,-10.36,-10.55,-10.412,-10.464,-9.8671,-9.081,-8.8837,-7.4494,-7.4233,-7.3186,-6.8048,-6.9871,-7.7557,-7.9379,-8.4974,-7.0557,-7.7133,-6.391,-5.5842,-6.2334,-8,NaN,NaN,-7.2335,-6.9836,-5.8908,-6.1432,-6.5862,-7.1054,-7.9453,-7.959,-7.8494,-7.7694,-8.3239,-9.5422,-9.2337,-9.5421,-9.5509,-10.337,-11.085,-11.626,-11.851,-11.876\n-9.9813,-7.8386,-8.8062,-10.174,-10.534,-10.562,-10.118,-9.4293,-9.2544,-8.646,-7.7163,-6.7921,-7.2772,-8.0424,-7.5405,-6.5459,-7.2493,-7.5311,-7.0557,-7.8069,-8.1558,-6.1568,-8.5684,NaN,NaN,NaN,NaN,-7.4712,-7.436,-6.7126,-6.9722,-7.952,-8.818,-8.5023,-8.5304,-8.5127,-8.0505,-8.7535,-9.2429,-9.2918,-9.3144,-10.145,-11.009,-11.546,-11.62,-11.498,-11.428\n-9.3538,-8.8425,-9.8032,-10.227,-10.166,-10.244,-9.8203,-9.1334,-8.678,-8.5511,-7.9619,-7.5323,-7.9641,-8.4271,-8.5034,-8.2774,-7.7281,-6.3027,-6.7059,-7.1626,-6.9654,-8.4853,NaN,NaN,NaN,NaN,NaN,-8.0964,-8.1134,-7.7034,-7.7426,-8.0637,-8.8475,-9.4041,-9.3904,-9.3325,-8.9325,-9.2475,-9.3609,-9.4722,-10.225,-10.2,-11.281,-11.548,-11.649,-11.822,-11.086\n-9.3902,-10.112,-10.476,-10.044,-10.508,-10.192,-9.6514,-9.1771,-8.6981,-8.5385,-8.7203,-8.4522,-8.5312,-9.0374,-9.1608,-9.1768,-7.7958,-7.5466,-7.0326,-6.033,-5.8799,-5.9063,-5.1328,NaN,NaN,NaN,-7.1103,-8.949,-9.0715,-8.7382,-8.7397,-9.1683,-9.324,-9.5406,-10.069,-9.7136,-9.4479,-9.0362,-9.0192,-10.06,-10.66,-10.29,-11.139,-11.358,-11.07,-11.271,-11.579\n-9.8147,-9.9892,-9.7184,-9.2284,-9.5247,-10.174,-10.062,-9.5504,-8.8751,-8.5425,-8.9432,-9.1068,-9.2343,-9.915,-10.431,-9.5744,-8.2822,-7.9083,-7.6991,-6.8376,-6.6414,-6.8542,-7.3825,-8.2634,-9.6896,-8.5066,-8.6728,-9.3229,-9.7984,-9.8584,-10.136,-9.9883,-9.7155,-10.526,-10.849,-10.225,-9.5724,-9.2691,-9.3353,-10.152,-10.385,-10.629,-11.078,-11.23,-10.551,-10.733,-11.083\n-9.4301,-8.9075,-9.4471,-9.0974,-9.0992,-9.7582,-9.8368,-10.133,-8.8564,-8.3566,-9.8465,-9.6922,-9.936,-10.714,-10.528,-9.1089,-8.2988,-8.5686,-8.1163,-7.3285,-7.2806,-7.254,-8.0062,-8.8114,-9.0489,-8.6495,-9.3086,-10.203,-10.58,-10.862,-10.175,-10.097,-10.448,-11.237,-11.385,-10.3,-10.276,-10.31,-10.253,-10.582,-10.524,-10.123,-10.67,-11.383,-11.081,-11.302,-11.444\n-8.6941,-9.4008,-9.694,-9.3158,-7.5153,-7.8748,-9.5864,-10.106,-9.8023,-9.1001,-9.7398,-10.188,-11.201,-11.253,-10.733,-10.299,-9.6666,-9.4007,-8.4302,-7.8669,-7.908,-7.7706,-8.8486,-8.9466,-8.8455,-9.0555,-9.7308,-11.002,-10.831,-10.917,-10.477,-10.616,-11.104,-11.1,-11.638,-11.586,-11.357,-10.517,-10.607,-10.725,-10.645,-10.687,-11.288,-11.027,-11.795,-11.605,-11.736\n-8.4106,-9.9227,-10.67,-8.9905,-7.4343,-7.4435,-8.975,-9.8175,-11.744,-10.323,-9.5824,-10.155,-9.1225,-10.081,-10.555,-10.696,-10.673,-10.318,-9.7023,-8.9949,-8.6595,-9.3934,-9.41,-9.5049,-10.035,-10.762,-10.891,-10.939,-11.312,-11.449,-11.355,-11.594,-11.713,-12.069,-12.313,-12.155,-11.637,-11.138,-11.179,-10.769,-10.678,-10.901,-11.759,-11.606,-12.313,-12.367,-11.95\n-8.7934,-8.9499,-9.4623,-9.1564,-8.1887,-8.2453,-8.9071,-9.7408,-11.289,-9.7622,-9.6051,-9.0547,-8.7531,-9.2501,-10.468,-10.721,-10.817,-10.214,-9.505,-8.9626,-8.7948,-10.071,-9.9085,-10.527,-11.556,-12.008,-12.151,-11.341,-11.488,-11.662,-11.707,-11.917,-12.455,-12.721,-12.477,-12.245,-11.546,-11.643,-11.403,-11.519,-11.346,-10.72,-10.253,-11.014,-13.114,-12.775,-12.624\n-9.3647,-9.1656,-9.221,-9.7336,-8.5321,-6.7142,-4.1136,-7.1801,-8.4248,-8.3039,-8.8241,-9.3047,-8.5351,-9.538,-10.325,-10.641,-10.874,-9.9472,-10.067,-10.168,-10.603,-10.083,-10.651,-11.352,-12.415,-12.576,-13.003,-12.234,-11.388,-11.612,-11.69,-11.836,-12.489,-12.992,-12.678,-13.029,-12.8,-12.128,-12.242,-12.18,-12.031,-10.895,-9.9336,-10.723,-11.996,-11.858,-12.688\n-8.569,-9.3325,-9.7292,-9.162,-8.1373,-5.9639,-5.0622,-7.7456,-8.4589,-8.15,-9.3603,-9.6727,-8.8412,-10.571,-9.7307,-10.784,-11.331,-11.278,-11.055,-10.628,-11.027,-9.8507,-10.724,-11.777,-12.324,-12.113,-11.838,-12.852,-12.185,-11.66,-11.453,-11.096,-11.964,-12.722,-13.082,-13.273,-12.536,-12.052,-12.456,-11.909,-11.638,-11.742,-12.117,-12.207,-11.937,-11.5,-11.894\n-7.8343,-8.0227,-8.5503,-8.2289,-7.914,-8.3631,-7.8652,-7.746,-8.3837,-8.4939,-10.02,-8.7894,-9.1906,-10.103,-9.8431,-10.317,-11.197,-11.189,-10.53,-10.223,-11.203,-10.521,-11.373,-11.966,-12.768,-12.64,-12.731,-12.868,-11.973,-11.73,-11.884,-11.649,-11.877,-13.152,-13.71,-13.208,-12.515,-12.602,-12.554,-11.905,-11.425,-11.054,-11.531,-11.636,-11.272,-11.333,-12.239\n-8.9868,-7.5553,-6.6346,-6.2855,-7.04,-8.0051,-8.4247,-7.6442,-8.3196,-8.8446,-9.493,-9.1717,-10.02,-10.65,-10.93,-11.47,-11.546,-11.773,-11.576,-11.984,-11.826,-10.876,-11.673,-11.854,-12.356,-12.613,-12.932,-13.367,-12.451,-12.013,-12.092,-12.044,-11.894,-12.527,-12.744,-12.377,-11.818,-12.582,-13.028,-12.63,-12.25,-11.934,-11.998,-11.765,-11.125,-11.63,-11.472\n-9.8836,-9.1373,-8.2773,-8.1699,-7.4825,-7.4295,-8.0552,-8.281,-8.1746,-8.566,-9.491,-9.7139,-10.222,-10.275,-10.03,-11.38,-11.765,-12.118,-12.169,-12.066,-11.579,-11.791,-11.365,-12.113,-12.715,-12.885,-13.984,-14.333,-13.47,-12.316,-12.06,-12.17,-12.475,-12.082,-12.523,-12.449,-11.993,-12.048,-12.272,-12.474,-12.261,-11.973,-11.939,-11.821,-11.726,-12.058,-11.222\n-10.146,-10.05,-9.8023,-8.8463,-7.861,-5.4391,-6.1131,-8.3534,-8.3994,-8.2415,-8.0943,-7.9298,-7.9601,-8.4368,-8.8057,-11.64,-12.658,-12.692,-12.312,-10.962,-11.042,-11.548,-11.172,-12.301,-13.347,-13.492,-14.28,-14.051,-13.397,-12.193,-11.849,-12.265,-12.112,-11.86,-12.424,-12.686,-12.5,-12.113,-11.715,-11.582,-11.55,-11.794,-11.921,-11.6,-11.92,-11.743,-11.844\n-9.1127,-8.9899,-8.5296,-8.3645,-8.949,-8.0284,-6.4209,-6.3944,-7.1155,-7.0525,-5.5154,-5.8163,-1.9604,NaN,NaN,NaN,NaN,-13.98,-12.348,-10.51,-11.259,-11.479,-10.986,-12.083,-12.704,-12.793,-13.498,-13.776,-13.115,-12.899,-11.523,-11.903,-12.167,-12.325,-12.642,-12.504,-12.361,-12.277,-11.499,-11.414,-11.054,-12.035,-12.16,-11.689,-12.187,-11.799,-12.369\nNaN,NaN,-6.1504,-5.9296,-6.443,-6.7587,-6.7025,-6.0408,-5.7264,-6.0715,-7.2142,-4.942,2.2802,NaN,NaN,NaN,NaN,NaN,-12.18,-10.745,-12.072,-11.818,-11.539,-12.192,-12.733,-12.837,-13.007,-12.447,-11.822,-13.145,-12.218,-12.299,-12.625,-12.921,-13.266,-12.969,-12.853,-12.914,-11.528,-11.546,-11.584,-12.212,-12.31,-11.775,-12.854,-11.941,-12.576\nNaN,NaN,-6.5113,-5.6014,-5.4017,-5.276,-7.4767,-5.9591,NaN,-6.8361,-8.061,-8.3866,-10.782,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.181,-11.36,-11.731,-12.062,-12.284,-12.708,-12.454,-12.112,-12.178,-13.137,-13.735,-13.409,-12.98,-12.673,-12.924,-12.825,-12.604,-12.444,-12.059,-11.892,-11.912,-12.326,-12.201,-12.07,-12.204,-12.288,-12.251\nNaN,NaN,-6.9736,-6.7229,-6.182,-6.1878,-7.109,-3.7702,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.228,-9.4296,-10.789,-10.992,-11.47,-12.054,-11.98,-12.059,-12.732,-12.736,-12.577,-13.339,-13.207,-12.907,-12.107,-12.213,-11.988,-11.981,-12.772,-12.255,-11.848,-12.559,-12.139,-11.863,-11.891,-12.099,-11.42\nNaN,-4.6706,-7.1316,-7.023,-6.8323,-7.2493,-6.6611,-3.8572,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.2548,-9.3267,-9.1308,-9.5133,-12.331,-12.859,-12.833,-13.121,-12.468,-12.107,-12.835,-12.675,-12.703,-12.008,-12.161,-12.269,-12.013,-12.39,-12.404,-11.886,-11.917,-11.891,-11.953,-12.319,-12.372,-11.995\nNaN,NaN,-8.6573,-8.4208,-8.1696,-8.5209,-7.7132,-6.2192,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.7387,-9.3458,-8.5668,-10.071,-12.359,-12.746,-12.067,-12.234,-11.787,-11.769,-12.397,-12.135,-12.19,-11.814,-12.093,-12.269,-12.192,-12.137,-11.89,-11.697,-11.686,-12.302,-12.65,-12.277,-12.082,-12.079\nNaN,NaN,NaN,NaN,NaN,-8.9572,-6.8182,-7.4352,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.9668,-9.2521,-9.3363,-11.398,-12.856,-13.026,-12.702,-12.364,-11.997,-12.474,-12.626,-12.847,-12.515,-12.582,-12.271,-12.163,-12.023,-11.936,-11.545,-11.544,-11.852,-12.294,-12.421,-12.084,-12.109,-12.282\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.4353,-8.912,-10.944,-11.864,-12.327,-13.022,-13.138,-12.488,-12.132,-12.237,-12.417,-12.7,-13.107,-13.776,-12.076,-11.929,-11.706,-11.725,-11.877,-11.826,-11.916,-11.964,-11.986,-12.186,-12.323,-12.837\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.3928,-9.2476,-11.36,-11.542,-12.036,-13.276,-13.485,-12.637,-12.381,-12.259,-13.411,-13.527,-13.107,-12.062,-11.88,-11.337,-11.169,-12.222,-11.908,-11.48,-11.684,-11.957,-12.37,-13.317,-14.063\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.106,-13.383,-12.576,-13.098,-13.737,-13.662,-12.889,-12.525,-11.783,-11.564,-11.599,-11.668,-11.701,-11.419,-11.41,-12.136,-12.469,-12.212,-13.355,-14.266\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.152,-12.381,-12.373,-13.017,-13.721,-14.495,-13.639,-13.01,-11.95,-11.906,-11.419,-11.947,-11.711,-11.384,-11.912,-11.622,-13.131,-11.983,-12.42,-12.924,-13.994\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.522,-12.423,-11.508,-12.413,-13.381,-14.189,-12.487,-12.598,-12.134,-11.348,-11.57,-11.917,-11.574,-12.017,-12.875,-12.382,-12.504,-11.95,-12.269,-12.873,-13.484\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.0801,-9.6225,-11.224,-11.248,-11.075,-9.5569,-9.8327,-11.109,-12.598,-11.76,-12.383,-12.573,-11.105,-11.225,-11.535,-12.08,-12.09,-12.629,-11.986,-11.582,-12.399,-12.697,-13.696,-14.174\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.8084,-12.324,-11.553,-11.133,-10.886,-11.226,-9.873,-10.274,-11.838,-12.381,-12.319,-11.3,-11.178,-11.352,-12.169,-11.171,-11.556,-11.733,-11.286,-12.567,-12.412,-12.908,-13.407\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-10.657,-11.645,-12.175,-11.475,-8.9071,-9.6589,-12.603,-11.498,-10.705,-10.25,-10.111,-11.663,-12.537,-11.775,-11.976,-12.143,-12.012,-12.248,-11.717,-11.853,-12.39\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.3103,-10.228,-11.942,-12.426,-10.095,-10.401,-11.838,-12.269,-12.342,-10.2,-9.7846,-12.124,-12.725,-12.772,-12.463,-11.755,-11.768,-12.285,-12.435,-12.297,-12.216\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070115-070917.unw.csv",
    "content": "-7.1758,-7.7222,-8.3793,-7.8659,-8.3455,-8.4756,-8.1427,-8.0805,-7.9623,-8.0381,-8.3524,-8.3032,-7.8225,-7.7168,-7.66,-8.0734,-7.4613,-7.1663,-6.7764,-6.6448,-7.1276,-6.9934,-6.5415,-6.5371,-7.2758,-7.1921,-7.322,-6.3018,-5.846,-6.4083,-5.8181,-6.4595,-7.3296,-8.1234,-8.7903,-9.6588,-10.817,-11.063,-12.381,-12.51,-12.066,-11.352,-11.721,-11.913,-11.685,-11.368,-10.584\n-7.7625,-7.9574,-8.5824,-7.8666,-7.9553,-8.0779,-8.1202,-8.1048,-8.2332,-8.3806,-8.5212,-8.5322,-8.1745,-7.9525,-7.5872,-7.581,-7.8747,-7.7224,-6.8574,-6.8444,-7.087,-6.6698,-6.0635,-6.4414,-6.5843,-6.5172,-6.6883,-6.7307,-6.4364,-5.8039,-5.8134,-6.8346,-7.1908,-7.7548,-8.1676,-10.441,-11.29,-11.12,-11.389,-11.279,-11.194,-10.684,-10.55,-11.088,-11.236,-11.681,-11.814\n-8.4307,-8.7497,-9.1753,-8.767,-8.5649,-8.1412,-8.0624,-7.7614,-8.0142,-9.3873,-8.7887,-8.3556,-8.3442,-8.1252,-7.4443,-7.8786,-8.0566,-7.7423,-7.4506,-8.0516,-8.0521,-7.8626,-7.3203,-7.8002,-6.7979,-6.9127,-6.5371,-7.2929,-6.7534,-5.556,-5.8399,-7.1206,-6.6589,-6.8802,-7.0776,-7.9937,-8.7118,-10.025,-10.502,-10.458,-10.335,-9.862,-10.439,-11.004,-10.564,-11.342,-11.358\n-8.6847,-8.6799,-9.2825,-9.1716,-9.011,-8.5561,-8.8605,-8.4778,-8.6036,-9.1544,-8.7403,-8.6336,-8.8168,-8.2536,-7.8076,-7.4454,-7.7106,-8.0743,-7.9502,-8.6998,-8.3074,-7.744,-7.5488,-7.4125,-6.9013,-6.8349,-6.5758,-6.8977,-6.5566,-5.2781,-5.1726,-5.6009,-5.7337,-6.529,-7.9357,-8.1948,-8.2921,-9.6616,-9.8455,-10.361,-9.6375,-9.1102,-9.6842,-10.575,-10.901,-10.995,-11.017\n-8.3696,-8.4207,-9.4246,-9.0104,-8.9103,-8.7526,-9.0542,-9.0926,-7.9347,-9.1059,-9.0199,-8.6644,-8.9969,-8.5355,-7.8691,-7.6869,-7.5033,-8.5294,-8.4199,-8.2307,-8.8259,-6.9083,-7.0275,-6.5165,-6.9085,-6.9004,-6.5895,-7.4188,-6.7536,-6.7114,-6.3676,-6.7131,-6.8534,-7.0435,-8.9227,-9.1924,-9.4445,-9.7398,-9.881,-9.6148,-8.9497,-9.2898,-9.8254,-9.8897,-10.035,-10.284,-10.478\n-9.0004,-9.3607,-9.3385,-9.1899,-9.5009,-9.041,-9.1486,-9.3455,-9.4196,-9.6438,-9.4418,-9.4412,-9.212,-8.628,-8.3861,-7.8067,-8.0006,-8.948,-8.1514,-8.0117,-7.9511,-7.3879,-6.8647,-6.4578,-7.2952,-6.8114,-6.3204,-7.1736,-7.046,-6.9642,-6.7863,-7.4652,-7.3877,-7.2521,-7.2963,-7.5492,-10.017,-9.7304,-10.117,-8.9681,-8.5976,-8.9833,-9.409,-9.5808,-10.1,-10.02,-9.5279\n-9.5672,-9.6806,-9.1374,-8.958,-9.4134,-9.2655,-9.7935,-9.3774,-9.1613,-9.6126,-9.3101,-9.8607,-9.3147,-8.3859,-8.6333,-8.1678,-8.856,-9.546,-9.8246,-8.9886,-8.1978,-6.84,-6.341,-6.6519,-6.703,-5.795,-6.1352,-6.2856,-6.2351,-5.9615,-5.9643,-6.6487,-6.5761,-6.8143,-6.8595,-7.3016,-8.7098,-9.7747,-10.513,-9.2646,-9.093,-9.2924,-9.2779,-9.7785,-10.495,-10.483,-10.331\n-10.372,-10.647,-9.7607,-8.8518,-9.6005,-9.4739,-9.2372,-9.0178,-8.9755,-9.3349,-9.091,-9.3923,-9.1363,-7.8501,-8.41,-8.9356,-9.4533,-9.5596,-9.3749,-9.2166,-8.3588,-6.2474,-5.5578,-6.5575,-6.0149,-5.5964,-5.2957,-5.4007,-4.9148,-5.227,-4.712,-5.5979,-5.8068,-6.6577,-6.9487,-7.5548,-8.9273,-9.402,-9.3161,-9.5934,-9.2912,-9.0434,-8.5567,-9.7413,-10.416,-10.707,-9.8251\n-9.4695,-9.8514,-9.5997,-9.1759,-10.076,-9.772,-8.8792,-9.0366,-9.2967,-9.0538,-9.1945,-9.9405,-9.324,-8.6332,-8.4393,-8.4287,-8.5755,-8.0555,-8.1904,-8.6984,-8.1601,-6.8248,-6.1017,-5.8431,-5.0578,-5.4816,-6.347,-5.2388,-4.1099,-4.0962,-4.5017,-4.6297,-5.1778,-5.8989,-7.2244,-8.17,-8.8327,-9.298,-8.9072,-8.9192,-9.0689,-8.8632,-8.3332,-8.9364,-9.6871,-9.9364,-9.4929\n-9.4122,-9.6441,-9.7597,-10.266,-10.363,-10.155,-9.2391,-9.1972,-9.2377,-8.5948,-9.2781,-9.4315,-8.6393,-8.962,-8.665,-8.2093,-7.6955,-7.3798,-7.3865,-7.0067,-5.6105,-6.4791,-5.7813,-6.4002,-5.2072,-5.3384,-6.2886,-5.6068,-5.7247,-4.5729,-4.81,-3.6864,-5.2112,-6.0611,-7.5909,-7.8335,-7.6569,-8.4339,-8.4259,-8.7505,-9.014,-8.7797,-8.5477,-8.207,-8.6114,-8.9809,-8.9045\n-9.4368,-9.6927,-10.272,-11.111,-10.576,-10.146,-9.7018,-8.9445,-8.9285,-9.3885,-9.505,-9.2923,-8.6762,-8.816,-8.7551,-8.6457,-7.4912,-7.7037,-7.5904,-6.8166,-6.7192,-6.2602,-5.4567,-6.0286,-5.5095,-5.2091,-5.2658,-5.2644,-4.9186,-4.1017,-4.4299,-4.7751,-5.895,-5.6953,-7.2824,-6.8382,-6.837,-7.24,-7.5167,-7.5808,-8.1168,-7.6001,-7.8605,-8.3166,-8.6397,-8.5938,-9.3131\n-9.5736,-9.9387,-10.267,-10.468,-10.184,-9.1822,-9.7979,-9.6971,-9.4128,-10.037,-9.7092,-9.6362,-9.3171,-8.6988,-8.362,-8.4425,-8.005,-7.8392,-7.3565,-6.3478,-8.2403,-6.1874,-5.5454,-4.9502,-4.9813,-4.6908,-4.9512,-5.1963,-4.3235,-3.9607,-4.6494,-5.7324,-6.3248,-6.7332,-7.5715,-7.2483,-6.9826,-7.0237,-6.9697,-7.2765,-7.6574,-7.1037,-7.6389,-7.8776,-7.964,-7.352,-7.8728\n-9.6711,-10.041,-10.473,-10.564,-10.337,-10.128,-9.8257,-10.102,-8.6648,-10.109,-9.8134,-10.124,-9.3011,-9.2161,-8.4475,-8.3357,-8.1424,-7.1166,-6.7269,-5.7092,-5.8387,-5.5355,-5.1064,-4.6826,-4.2681,-4.3596,-4.4286,-5.4662,-4.4518,-5.016,-5.0386,-6.6733,-6.9856,-6.929,-6.853,-6.8406,-6.6619,-6.8113,-7.4267,-8.0333,-7.4746,-7.179,-7.0767,-7.2462,-6.7191,-6.5712,-6.693\n-9.9112,-11.181,-11.177,-10.592,-10.166,-10.447,-9.755,-9.8538,-9.9743,-9.9924,-10.074,-9.7304,-8.6297,-9.713,-8.1179,-8.2929,-7.4236,-6.9403,-6.7726,-6.2229,-5.6693,-3.9688,-4.1902,-4.5455,-3.4933,-3.8169,-4.362,-5.3746,-5.5531,-5.4598,-5.2552,-6.1462,-6.9086,-6.3138,-5.4529,-5.0325,-6.0729,-6.6157,-6.9734,-7.3865,-7.5064,-5.9659,-6.2734,-5.8341,-5.4498,-6.0923,-6.5511\n-10.197,-11.385,-10.827,-10.215,-10.293,-10.383,-9.5412,-10.326,-10.109,-9.3769,-10.417,-9.7135,-9.685,-8.9078,-7.5219,-6.5941,-7.6059,-7.137,-6.8605,-6.2356,-6.2554,-1.7758,-4.6339,-3.9252,-2.7165,-3.9817,-2.7094,-4.2241,-4.4144,-4.5008,-4.5973,-4.3219,-4.7727,-5.1229,-4.4743,-4.3646,-6.6821,-6.9825,-6.6964,-6.1588,-6.1899,-5.4733,-6.3839,-5.8805,-5.7982,-5.9989,-5.8574\n-9.7196,-9.988,-10.323,-10.177,-10.217,-10.55,-9.1802,-10.291,-9.378,-9.1552,-10.227,-9.5309,-10.946,-9.3584,-6.9266,-6.8655,-7.8326,-7.409,-5.7618,-5.8012,-5.5268,-3.9814,-4.5135,-3.5075,-3.8409,-3.9412,-2.6908,-4.0957,-4.4774,-3.6371,-4.3848,-3.5682,-4.5823,-5.2276,-4.8987,-5.0215,-7.0246,-7.1962,-7.1696,-6.7502,-6.7905,-6.3988,-6.8397,-7.3792,-7.7056,-7.4013,-7.2991\n-9.243,-8.688,-9.7478,-9.9947,-9.805,-9.6947,-9.2114,-9.8014,-8.5513,-8.345,-9.0984,-8.9482,-9.6388,-9.2207,-7.6552,-7.6868,-8.1177,-6.459,-5.1636,-5.2806,-4.5704,-4.7213,-3.7791,-2.7646,-3.2603,-2.8103,-1.2859,-1.8856,-4.3044,-4.3213,-5.5491,-3.653,-5.0969,-5.6158,-5.619,-5.5874,-7.6889,-6.8682,-7.2942,-6.4652,-6.8203,-8.2343,-6.5678,-7.0102,-8.7126,-9.6827,-9.4451\n-8.2459,-7.8276,-9.2827,-8.9914,-9.8857,-10.853,-10.078,-8.7154,-8.2893,-8.2599,-9.0018,-8.8267,-8.5336,-8.0049,-7.8206,-7.6365,-7.3166,-5.7156,-5.7915,-4.8672,-4.663,-4.6079,-4.7365,-3.8597,-3.8773,-4.774,-2.8772,-0.85519,-3.1394,-4.4484,-5.3384,-3.9184,-4.6746,-4.8547,-4.8556,-5.4506,-7.3328,-6.2765,-5.9148,-5.6103,-6.3121,-7.5203,-7.6436,-6.8109,-7.8224,-9.6236,-10.026\n-7.3652,-8.0468,-8.8112,-8.5172,-8.7411,-11.139,-8.9289,-8.8083,-8.6032,-8.2916,-9.1974,-8.7314,-8.8164,-8.1423,-7.743,-6.8683,-6.8029,-6.7115,-6.3979,-6.0277,-5.4893,-5.8723,-6.2876,-5.2238,-5.0033,-4.3581,-4.643,-1.6066,-2.8519,-3.3585,-4.5354,-3.667,-3.9142,-3.835,-3.7433,-4.2497,-5.7483,-5.8082,-5.3028,-5.2653,-5.8097,-6.8239,-7.2167,-6.89,-7.2516,-8.0923,-9.2323\n-7.1703,-7.8166,-7.9232,-8.0822,-7.3389,-7.8041,-8.9513,-9.8895,-9.2801,-8.5979,-8.3925,-8.9148,-8.893,-7.8759,-7.1985,-7.026,-7.7514,-7.1282,-7.1001,-6.9447,-5.7646,-5.7265,-6.3215,-5.3435,-4.9224,-4.1098,-3.3219,-2.073,-1.6433,-2.8425,-2.0248,-3.5872,-3.1993,-3.6256,-3.5877,-4.3678,-4.9403,-5.1256,-4.4756,-4.8092,-5.2775,-5.7022,-6.4849,-6.0387,-6.562,-8.069,-8.4851\n-6.7036,-6.548,-7.8711,-7.1927,-5.9153,-8.1202,-9.4445,-9.52,-9.2632,-8.9102,-9.4218,-8.4272,-8.6994,-7.3453,-7.762,-8.0156,-7.6997,-7.1474,-7.1748,-6.8672,-6.4159,-5.876,-5.4327,-4.5616,-4.019,-2.895,-2.6697,-0.59036,-0.82798,-2.0066,-1.0257,-2.909,-2.3044,-2.5111,-2.8244,-5.0884,-4.1969,-4.43,-4.7024,-5.4186,-5.7921,-6.1466,-6.23,-7.1987,-7.8679,-8.6287,-8.8687\n-6.5308,-6.7085,-7.0349,-7.272,-7.444,-7.9971,-8.0226,-8.4116,-9.4027,-9.1612,-8.9823,-8.1508,-8.8534,-7.5327,-8.2907,-8.8504,-7.5465,-7.4568,-7.7003,-8.2955,-6.99,-5.6093,-6.1443,-3.658,-3.0872,-3.0852,-2.182,-0.79922,-0.20633,0.11941,0.64724,-1.5481,-2.042,-2.2717,-3.8462,-4.4355,-3.9958,-4.523,-5.2421,-4.8216,-5.9519,-6.5658,-6.8054,-6.8083,-6.9136,-7.9967,-8.4099\n-6.3977,-6.7156,-6.2076,-6.2557,-6.452,-6.8965,-8.0778,-8.6297,-9.7006,-9.6748,-8.7707,-8.9536,-8.3107,-7.7959,-8.3903,-9.0518,-6.5954,-6.6704,-6.9932,-6.8594,-5.625,-5.6043,-4.8257,-3.3693,-2.315,-2.5157,-1.3196,-1.0052,1.6514,1.1636,1.5129,0.64247,-1.055,-1.8826,-2.2939,-2.9299,-3.2178,-3.537,-3.5677,-3.9357,-5.2882,-6.1404,-6.9438,-5.8841,-7.582,-7.9627,-8.0057\n-6.7721,-6.0399,-5.5177,-5.7825,-4.0201,-6.8739,-8.0147,-8.7423,-9.2696,-9.3376,-8.6532,-9.3492,-7.6339,-7.7362,-8.0223,-9.1341,-6.5958,-6.7384,-6.162,-5.5803,-5.2292,-4.117,-3.0229,-0.79536,-0.55528,-0.86905,0.26361,2.7985,2.5468,2.3167,3.7161,3.2266,2.962,1.7614,-0.74887,-1.4948,-1.6184,-2.2474,-2.9138,-2.3785,-4.1736,-5.5714,-5.6441,-5.695,-6.7871,-6.8063,-7.6162\n-5.7801,-5.7725,-5.7895,-5.9918,-6.1372,-6.7186,-7.7514,-8.253,-7.8991,-9.0356,-8.9389,-8.5851,-8.1327,-7.4689,-7.6178,-7.3426,-6.8611,-6.902,-6.3121,-4.9491,-4.1832,-3.4579,-1.8004,-0.82943,0.034838,0.95248,2.8276,5.1282,5.097,5.5306,5.5508,5.0984,3.8134,2.8316,2.3784,-0.38096,-1.2068,-1.6102,-1.9766,-2.0965,-4.436,-4.1774,-4.974,-5.8201,-6.646,-6.9116,-7.5159\n-6.4633,-8.3863,-7.3423,-6.501,-6.6557,-6.3482,-7.0528,-7.5417,-8.4108,-9.2786,-8.399,-8.3293,-8.0306,-7.6691,-7.1246,-6.6332,-6.1172,-5.6191,-5.321,-3.9663,-2.8038,-2.825,-1.5666,0.75769,3.0206,4.1385,5.8145,8.6783,7.7325,7.6469,7.2177,6.1989,5.3801,5.5926,3.2385,0.26832,-1.3631,-1.2405,-1.0893,-1.4645,-3.5087,-3.8647,-5.0916,-5.5562,-6.6717,-7.145,-7.248\n-6.8614,-7.1947,-6.4607,-6.0509,-6.6626,-7.5383,-6.7129,-7.1801,-7.8259,-8.408,-8.0064,-8.0498,-8.3801,-7.6717,-6.633,-5.5122,-5.0164,-4.818,-4.4084,-3.0888,-3.3853,-3.2688,-1.5079,2.3608,5.5701,5.8963,NaN,NaN,11.103,9.1334,7.6356,6.7365,6.3214,4.7177,3.514,1.1704,-0.324,0.12547,-0.24762,-1.2953,-3.1854,-4.0383,-4.8284,-5.4341,-6.2412,-6.97,-7.1876\n-8.8037,-7.2495,-7.2608,-8.2881,-7.0204,-7.8049,-7.3608,-7.1622,-8.0942,-7.4755,-7.7086,-7.5256,-7.1085,-6.4221,-5.3146,-4.1754,-4.1393,-4.1838,-3.196,-2.3938,-3.4306,-3.3903,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.4525,5.3919,2.8031,1.9933,1.4667,1.3078,0.52231,-1.4481,-2.6186,-3.7129,-5.4229,-6.0436,-6.4579,-6.5824,-6.5069\n-7.7073,-5.6683,-10.974,-9.522,-7.9143,-8.1474,-9.3804,-10.136,-10.139,-7.4345,-7.3993,-6.9962,-6.6434,-5.7092,-4.6942,-3.7687,-3.1361,-2.6899,-2.0874,-1.7737,-2.253,-1.9441,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.5648,2.1969,1.2905,0.95382,1.2787,0.31579,-0.92558,-2.3603,-3.041,-4.9859,-5.8625,-5.9049,-6.3368,-6.4155\n-5.1151,-4.324,-6.3363,-8.9673,-7.8264,-8.2439,-9.5692,-10.114,-9.3334,-7.8182,-7.4883,-6.9975,-6.0905,-5.6398,-4.6246,-3.6553,-2.4014,-1.9675,-1.8831,-1.2387,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.708,1.7651,0.048086,-1.5111,-2.2352,-3.1459,-5.3699,-5.5591,-6.0387,-6.2013,-6.4698\n-5.1759,-4.7096,-5.1305,-4.5401,-5.2404,-6.1492,-9.8461,-9.6433,-8.8005,-7.6966,-7.0207,-6.4761,-6.1352,-5.7879,-4.7273,-4.0185,-3.4728,-3.0855,-2.0966,-0.0017118,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.8262,1.5349,-0.63392,-2.1448,-2.9508,-4.135,-5.1205,-5.513,-6.2968,-6.047,-6.4448\n-5.3935,-5.2571,-5.3463,-6.44,-5.8802,-7.0071,-9.7685,-10.061,-10.063,-8.1086,-7.4684,-6.9633,-6.4486,-5.4899,-5.1234,-4.6096,-4.0233,-3.443,-0.97616,0.070395,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.2253,-3.501,-3.7981,-5.4,-6.0097,-5.3493,-6.2323,-5.6791,-6.0531\n-6.1829,-7.3713,-7.0526,-7.0678,-6.2252,-8.1672,-9.2425,-9.6602,-10.888,-7.8901,-8.1281,-7.3155,-6.2636,-6.2463,-4.7216,-5.3817,-5.3022,-3.2781,-1.9608,-0.52146,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4438,-3.9575,-4.2782,-5.8322,-5.9639,-5.2984,-6.3248,-6.0945,-6.9223\n-6.3988,-6.1266,-7.6206,-7.2317,-6.9558,-8.1037,-8.5051,-9.6271,-9.2501,-7.6691,-7.8626,-7.9001,-6.8749,-8.2055,-5.1279,-5.2379,-5.9151,-4.9532,-2.5459,-3.5822,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.1112,-4.1184,-4.4727,-4.7906,-5.247,-6.1853,-6.5007,-7.2401\n-5.6928,-6.1873,-9.2985,-7.288,-7.5801,-7.8333,-7.4686,-7.5447,-8.706,-8.1949,-7.6378,-7.4476,-8.3094,-7.6522,-5.4582,-5.1264,-4.4932,-4.2075,-3.3028,-4.4402,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.6701,-3.6591,-4.2737,-5.3999,-5.9694,-6.9035,-7.5051\n-7.1135,-8.2708,-7.8443,-5.7938,-6.6188,-8.0855,-8.0452,-6.8099,-8.3232,-8.2747,-6.484,-6.1335,-5.9534,-6.5242,-7.2514,-5.442,-5.2852,-4.3199,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.4313,-4.4605,-4.3258,-5.1344,-6.6932,-7.0033,-7.8944\n-6.8034,-7.4664,-7.4166,-7.6127,-7.6941,-8.9097,-8.4379,-7.3387,-7.4074,-8.0671,-6.4353,-5.9611,-6.0401,-6.9808,-6.8024,-5.3963,-5.2444,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.6188,-4.5222,-5.1854,-5.8428,-6.5544,-6.2107,-6.4722\n-6.2833,-6.8909,-7.4064,-8.0664,-8.3493,-8.6671,-8.0034,-7.838,-7.539,-6.955,-6.5039,-6.0747,-6.1347,-5.6696,-5.5511,-5.0855,-5.0361,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.61467,NaN,NaN,-4.0365,-4.7408,-4.8296,-5.8554,-6.1785,-5.5019,-4.8666,-5.5788\n-7.338,-6.8284,-7.3747,-7.3708,-7.3219,-7.3438,-7.636,-7.6043,-7.1357,-6.6185,-6.4131,-5.335,-5.8884,-6.0263,-5.3992,-4.9433,-4.4219,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.6771,NaN,NaN,NaN,NaN,NaN,NaN,-1.1441,-1.7182,-3.5344,-5.1464,-5.5793,-5.2471,-5.491,-5.5126,-5.4453,-6.3368\n-7.3044,-4.7541,-6.1063,-6.7716,-6.5715,-6.1592,-6.308,-6.4228,-6.9857,-6.9367,-6.5614,-5.7975,-5.8472,-5.0028,-3.9151,-3.3893,-3.3869,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.5317,-9.0204,NaN,NaN,NaN,NaN,0.90115,-0.98702,-1.9082,-4.3947,-5.6217,-5.1703,-4.6683,-5.5969,-5.8988,-6.2381,-8.1168\n-5.5907,-5.1489,-6.446,-5.6437,-6.241,-6.2013,-5.821,-6.1485,-6.5685,-5.6104,-6.0362,-5.9326,-5.6491,-4.9817,-4.3989,-3.1737,-2.9593,-5.5416,-5.8321,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.7678,NaN,NaN,NaN,NaN,NaN,0.83717,-0.11493,-1.7533,-5.7818,-5.5893,-5.8327,-5.5324,-4.4564,-5.3096,-5.2509,-6.3212,-7.9939\n-5.243,-5.2072,-5.3584,-5.1088,-5.551,-6.0718,-6.1966,-6.3651,-5.8568,-5.2727,-5.6488,-5.6597,-6.1022,-5.8988,-6.3892,-6.021,-4.6896,-6.1314,-7.1008,NaN,NaN,NaN,-9.2139,NaN,NaN,NaN,NaN,NaN,-7.9779,-10.849,NaN,NaN,NaN,NaN,-1.144,-0.068026,-1.5058,-2.3119,-4.4911,-5.3476,-6.0431,-5.4779,-4.7857,-4.9836,-4.9688,-6.2383,-7.1858\n-4.9102,-4.5989,-4.8182,-5.5211,-6.0665,-6.4752,-6.6012,-6.4216,-5.9954,-6.1068,-6.1766,-5.9137,-6.3142,-6.1454,-6.23,-6.5268,-4.5937,-5.2482,-5.3565,-6.1648,-7.1221,-8.236,-8.8845,NaN,NaN,NaN,NaN,NaN,-8.2433,-8.1199,-4.5913,-2.363,-2.4116,-1.8144,-2.0618,-2.516,-2.4504,-4.0131,-5.2282,-5.4665,-4.7486,-5.1085,-5.4678,-5.318,-5.3807,-7.4539,-8.3441\n-5.0114,-4.7654,-4.7934,-5.7705,-6.2459,-6.4272,-6.5831,-6.5556,-6.6898,-6.6058,-6.514,-6.2362,-5.9969,-5.8451,-5.2766,-4.8907,-4.3923,-5.1446,-5.7546,-5.7639,-6.9648,-8.3661,NaN,NaN,NaN,NaN,NaN,NaN,-5.9168,-4.7053,-4.7155,-3.7883,-3.0784,-3.3334,-3.8882,-4.2446,-4.2921,-5.6983,-5.9172,-5.4285,-4.7172,-4.4658,-5.7332,-6.1735,-6.1139,-6.6062,-7.9888\n-4.9966,-5.0368,-5.0917,-5.5072,-5.9124,-6.2794,-6.1867,-6.4083,-6.8968,-5.9466,-6.6626,-6.431,-6.1663,-5.875,-5.0859,-5.0406,-5.8339,-5.2742,-5.1918,-3.8063,-6.3643,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.6531,-4.8801,-3.9188,-2.2073,-3.7193,-4.1473,-4.9265,-4.3728,-5.0911,-5.9281,-6.0987,-5.5897,-4.6743,-4.6613,-5.6536,-5.6818,-7.4439,-8.5799,-9.2578\n-4.5311,-4.3468,-4.0944,-4.5505,-5.6112,-5.4891,-5.7755,-5.4556,-5.265,-5.5975,-6.2147,-6.3748,-6.1807,-5.8377,-5.4807,-5.5233,-5.2491,-4.9376,-5.6485,-6.3962,-7.2967,-6.7668,-4.2726,-5.0737,-7.1553,NaN,NaN,NaN,-5.1031,-4.3683,-3.0898,-3.0446,-3.4269,-4.9815,-5.6622,-5.7249,-5.6196,-6.7361,-6.7961,-6.2721,-5.0044,-5.689,-6.1568,-5.722,-6.969,-8.9352,-10.192\n-4.1199,-3.6515,-3.7226,-5.425,-5.6895,-5.2308,-5.3591,-5.282,-5.9523,-5.7035,-5.9318,-6.0564,-5.8574,-5.7067,-6.78,-6.4143,-5.4418,-5.4961,-6.2141,-6.9147,-8.3403,-8.0428,-6.3628,-7.185,-7.7455,-7.977,-8.3702,-6.9597,-5.8482,-3.7872,-2.7208,-4.2397,-5.4348,-6.2537,-6.2011,-5.4441,-6.2162,-6.5286,-7.2047,-6.3843,-5.356,-6.7055,-8.1113,-7.5659,-6.9229,-7.5809,-8.8712\n-4.4587,-4.3688,-4.4134,-5.2704,-5.3994,-6.2889,-5.7552,-5.9899,-6.1237,-6.0623,-6.3172,-6.1625,-5.7949,-6.0246,-6.4932,-6.5549,-5.507,-5.839,-6.417,-6.9687,-7.8439,-8.6528,-8.9757,-8.188,-8.34,-8.3883,-8.3006,-7.4127,-6.3017,-5.7574,-5.5885,-6.4432,-7.4783,-7.2168,-6.6301,-6.5627,-7.8539,-7.3014,-7.3383,-6.3484,-6.1212,-7.3074,-9.41,-10.265,-9.3127,-8.8001,-8.1168\n-4.0876,-4.7521,-5.7139,-4.8938,-4.8396,-6.0143,-6.1636,-6.3875,-6.6846,-7.0731,-6.7834,-6.2557,-5.6194,-5.7356,-6.239,-6.4218,-6.3364,-6.1732,-6.2642,-7.2266,-8.7457,-9.296,-8.6938,-8.2669,-8.5592,-8.5266,-7.6856,-6.7922,-7.0822,-6.2147,-6.908,-7.4137,-7.2939,-7.2271,-7.5663,-7.7913,-8.2331,-7.1675,-6.5484,-7.0315,-7.3227,-7.7325,-9.5647,-8.4993,-8.1252,-8.4884,-6.4889\n-3.7934,-3.9531,-4.9991,-4.3054,-4.4465,-5.227,-6.4254,-6.8697,-6.3079,-6.7278,-6.6527,-6.1738,-5.6113,-5.5441,-6.402,-6.9044,-6.7424,-7.0356,-7.6874,-9.511,-8.8764,-8.556,-7.7917,-7.8313,-8.0266,-7.8236,-7.5473,-6.4422,-8.0185,-6.5255,-7.1247,-7.3007,-7.8653,-6.6129,-6.2826,-6.4293,-8.3151,-6.9214,-6.2116,-8.0852,-7.7062,-8.0292,-9.8155,-9.4882,-8.579,-9.6901,-8.5137\n-3.9166,-3.9158,-4.2527,-4.0261,-4.419,-4.1838,-5.9399,-5.1837,-5.5918,-5.7931,-6.0273,-6.4137,-5.6802,-6.4295,-7.2227,-7.2546,-7.2042,-8.1268,-8.9575,-9.4685,-8.7671,-7.7901,-7.2112,-7.1103,-7.6186,-7.7914,-8.6324,-6.7945,-7.6446,-6.8262,-7.2664,-7.7708,-8.1056,-7.5733,-5.918,-7.1151,-7.7076,-7.2846,-7.8015,-8.4146,-8.5946,-7.9273,-7.6547,-8.8849,-9.7264,-9.2687,-8.3369\n-4.6762,-3.8184,-3.6717,-4.2352,-4.6731,-5.2223,-6.3173,-5.7502,-6.3731,-5.7808,-6.8609,-7.4395,-6.4618,-5.9995,-7.4921,-7.2784,-7.6339,-7.977,-8.2709,-8.9892,-8.0864,-7.4575,-7.101,-7.122,-7.4602,-8.1277,-9.2794,-6.7422,-6.8024,-6.7873,-7.683,-8.2992,-8.2457,-7.4866,-6.5424,-7.4415,-7.9124,-8.1161,-8.8652,-8.7674,-8.5877,-8.5243,-8.6614,-7.6365,-9.1949,-8.6165,-8.6671\n-3.7271,-3.594,-3.7052,-4.9489,-4.4414,-5.2091,-5.6695,-5.9396,-6.148,-5.7273,-6.6118,-6.5175,-6.5121,-6.1918,-6.7947,-6.8478,-6.9736,-8.6086,-8.8058,-9.0966,-7.9321,-7.5303,-7.384,-7.5547,-7.6613,-7.7639,-6.9521,-6.616,-6.6325,-6.7108,-7.3778,-7.9146,-7.7944,-7.0592,-6.8176,-7.3578,-8.3927,-9.5856,-10.051,-9.1312,-8.5289,-8.475,-8.6907,-8.3712,-9.2415,-8.7622,-8.9449\n-3.9144,-3.9174,-4.1098,-3.8366,-3.2438,-4.9038,-4.2839,-4.6155,-4.9753,-4.8965,-4.7472,-5.9214,-6.4102,-6.1213,-6.3425,-7.2971,-7.5889,-9.7891,-10.233,-9.4435,-7.6191,-7.4269,-7.9696,-8.1497,-7.8916,-7.0728,-6.6194,-6.3741,-6.4355,-6.7271,-7.7723,-8.3804,-7.6253,-7.2646,-6.9239,-7.7798,-9.0619,-9.7057,-9.7883,-9.1433,-8.6496,-8.57,-8.9339,-8.6058,-9.6486,-8.9832,-8.7013\n-5.8991,-5.1102,-5.5545,-4.5859,-3.8095,-3.4926,-4.2565,-5.1831,-4.6573,-2.5207,-3.2137,-6.1283,-6.5604,-6.1373,-5.4415,-5.7175,-7.305,-9.5461,-8.0665,-7.3459,-7.277,-7.5814,-8.2134,-8.439,-8.062,-7.6025,-6.797,-6.7309,-6.4026,-6.7301,-8.1447,-8.8281,-9.1416,-8.1569,-8.0335,-7.8996,-8.7603,-9.2673,-9.4587,-9.0545,-8.3337,-8.5335,-8.4712,-8.5566,-9.569,-9.2847,-10.008\n-5.5113,-4.9378,-4.5934,-3.2512,-5.1008,-4.1055,-5.1807,-5.0157,-3.9351,-3.3442,-4.6175,-6.1216,-7.5016,-7.2896,-5.831,-6.1902,-8.2709,-7.8003,-6.8735,-7.1033,-7.9259,-7.7876,-8.2286,-7.5789,-7.8435,-8.0988,-7.2066,-7.3528,-7.1117,-7.1249,-8.0787,-8.8733,-9.9019,-9.3263,-8.1904,-8.2629,-8.9469,-9.023,-9.1124,-8.9601,-8.7752,-8.6499,-8.4108,-7.4817,-8.6443,-9.3002,-9.8584\n-3.6731,-4.9215,-1.2182,-1.6327,-5.2858,-4.3636,-4.5194,-4.601,-5.1635,-6.0578,-3.5363,-4.2777,-8.409,-7.7121,-8.5064,-8.5582,-7.3869,-8.3299,-7.2529,-7.4805,-8.509,-7.3603,-7.324,-7.1687,-7.5884,-7.6413,-7.3357,-8.0027,-7.607,-7.4935,-7.9259,-8.3664,-9.0371,-9.3223,-8.2986,-8.3609,-9.0081,-9.2748,-8.4947,-8.4575,-9.1239,-8.7488,-7.8195,-7.8541,-8.6685,-8.8832,-9.457\n-2.8359,-4.9549,-1.49,-1.7036,-2.8234,-2.9387,-4.475,-3.6013,-3.2703,-4.041,-4.4847,-4.0707,-6.6797,-7.9055,-10.317,-10.157,-7.1259,-8.8959,-8.1093,-7.3974,-8.7248,-8.1421,-7.1583,-7.3096,-7.3256,-7.5735,-7.567,-8.3219,-6.9839,-7.5722,-8.4638,-8.5713,-8.6847,-8.822,-8.6487,-8.3134,-8.8761,-9.6011,-8.5784,-7.6841,-8.1559,-8.3656,-8.062,-8.301,-8.9073,-8.7683,-9.7906\n-2.008,-5.1079,-5.1784,-3.0888,-3.1875,-4.6735,-5.1417,-2.4737,-2.2754,-2.2493,-4.7737,-4.5383,-5.5711,-6.8231,-8.6553,-7.0616,-5.7874,-6.3491,-6.8137,-6.8375,-7.5555,-8.2018,-7.2304,-7.5223,-7.93,-8.1473,-7.8603,-7.758,-7.3456,-8.0513,-9.7365,-8.7772,-8.9748,-9.3424,-8.8429,-8.6146,-8.8306,-9.1482,-8.4383,-7.891,-8.1391,-8.7751,-8.7384,-9.5513,-9.2781,-9.2198,-9.9134\nNaN,-3.9314,-6.334,-4.8433,-4.1355,-5.7333,-4.5645,-0.52127,NaN,NaN,-3.9701,-3.8716,-3.8939,-6.4996,-6.4223,-6.0686,-4.3624,-4.7264,-4.1229,-5.552,-7.2339,-7.8174,-8.0771,-8.1885,-8.1471,-8.0985,-7.7097,-7.8424,-7.8083,-8.8446,-9.2763,-9.0586,-9.165,-9.2766,-9.0022,-8.9961,-8.4793,-8.8149,-8.647,-8.4206,-8.2727,-9.4032,-9.1547,-10.039,-10.243,-10.606,-10.545\nNaN,NaN,-5.3203,-4.9426,-4.5677,-4.5353,-4.1813,-1.9345,NaN,NaN,NaN,-4.1063,-4.1596,-3.7937,-4.1217,-4.5277,-4.3258,-2.803,-2.6408,-5.6145,-7.3542,-7.9613,-8.2962,-8.2113,-8.1057,-8.9308,-8.1487,-8.095,-7.8917,-8.3257,-8.122,-9.3488,-10.023,-9.075,-8.8257,-9.3128,-8.8153,-8.7311,-8.3052,-8.8123,-8.8171,-9.5133,-9.3076,-9.6121,-10.12,-9.6616,-9.7179\nNaN,NaN,-6.7068,-4.8407,-5.0562,-5.3428,-3.9814,-2.529,NaN,NaN,NaN,NaN,NaN,NaN,-3.7272,NaN,NaN,-2.8257,-3.7753,-6.7421,-6.49,-7.6367,-7.7249,-7.6897,-8.1705,-8.9207,-8.9385,-8.7583,-7.9159,-7.7717,-7.4183,-8.4207,-9.4603,-9.1162,-8.9516,-9.12,-8.8728,-8.81,-8.5412,-8.6249,-8.8059,-9.3614,-8.8539,-9.5534,-9.592,-9.2728,-9.3943\nNaN,NaN,NaN,NaN,-5.5562,-4.0989,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.5057,-8.4268,-6.1789,-6.4374,-7.4296,-9.0232,-8.9794,-8.5708,-7.7059,-7.4998,-7.2612,-8.2245,-8.703,-8.6797,-9.8469,-9.7495,-9.1705,-8.8552,-8.2099,-8.2567,-8.8582,-9.6113,-9.0516,-9.4521,-9.4775,-9.1383,-9.7869\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.3456,-7.4035,-6.3588,-4.6669,-6.5605,-7.4974,-7.9342,-7.5488,-7.1036,-7.2643,-7.9376,-8.5824,-9.0151,-9.3673,-10.461,-10.094,-8.9874,-8.4295,-8.3281,-9.2677,-9.2686,-9.6909,-9.5236,-9.6091,-9.7124,-9.1347,-9.5417\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.2899,-4.8607,-6.9527,-7.3339,-7.8125,-7.3081,-7.3676,-7.2212,-7.7023,-7.9721,-8.9814,-9.829,-10.163,-9.5238,-8.9087,-8.7553,-8.8349,-9.6041,-9.5108,-9.6078,-9.9731,-9.7794,-9.6463,-9.677,-9.2861\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.4568,-5.0349,-6.709,-7.3895,-7.2801,-6.7509,-7.0219,-6.9159,-7.9177,-8.7917,-9.3119,-10.034,-10.293,-8.9022,-8.9199,-8.9911,-8.6518,-9.0475,-9.0296,-9.2468,-9.9654,-9.1173,-9.1754,-9.9064,-9.5098\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.9522,-6.6545,-6.4627,-6.2147,-6.2673,-6.7815,-6.6809,-7.2433,-7.7359,-9.5903,-9.545,-9.607,-10.028,-8.9273,-9.2757,-8.972,-8.443,-8.75,-8.0158,-9.5282,-10.429,-9.3817,-9.3408,-9.7052,-9.6795\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.2148,-6.9284,-6.1769,-5.4937,-5.5123,-6.6386,-6.704,-7.3095,-8.7145,-10.248,-7.9124,-8.0638,-8.3422,-8.5406,-9.1314,-8.7986,-8.486,-8.8698,-8.8088,-9.6253,-10.142,-9.6299,-9.2726,-9.672,-10.1\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.6833,-6.0033,-5.5152,-5.2879,-5.3621,-5.9324,-6.7676,-8.1297,-8.5326,-9.0032,-7.7418,-8.4627,-8.5954,-8.8313,-8.9105,-9.0269,-8.8749,-8.7154,-8.8837,-9.319,-10.157,-10.248,-9.7116,-10.213,-10.878\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.4048,-4.8057,-5.0958,-5.3167,-6.2765,-6.9253,-6.8154,-8.1655,-8.637,-8.9662,-8.8087,-8.3048,-7.8681,-9.2056,-9.2288,-8.5994,-8.9188,-9.2536,-10.203,-10.138,-9.7686,-10.966,-11.105\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.0029,-5.8672,-6.374,-6.382,-5.9367,-6.7928,-7.9131,-8.2219,-8.4038,-8.2232,-7.9606,-9.8066,-9.0631,-8.2312,-8.009,-8.3073,-8.8052,-8.9957,-9.0632,-10.575,-11.028\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.7127,-10.134,-6.3506,-5.8832,-5.5111,-6.3056,-7.0292,-7.1599,-7.4118,-6.8876,-10.081,-10.957,-7.6704,-7.0436,-7.8428,-8.3836,-9.3979,-9.2732,-8.5289,-9.2522,-10.5\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070219-070430.unw.csv",
    "content": "14.684,15.054,14.664,14.872,15.338,15.847,16.042,16.172,16.644,17.197,17.986,18.116,18.239,18.031,17.585,17.586,18.105,18.078,17.761,17.508,17.407,17.448,17.696,17.994,17.891,17.493,17.521,17.279,17.358,17.123,16.492,16.696,16.457,15.819,15.327,14.753,13.267,12.724,12.547,12.014,12.333,12.537,12.774,12.082,10.056,9.3135,9.463\n14.669,14.846,14.472,14.784,15.452,16.198,16.526,16.343,16.989,17.518,18.157,18.497,18.434,18.119,18.083,18.393,18.383,17.921,17.577,17.481,17.327,17.63,17.938,18.092,18.091,18.143,18.169,18.471,18.408,18.462,17.803,16.958,16.42,15.973,15.806,14.942,13.749,13.188,12.748,12.046,11.25,11.902,11.841,11.326,10.438,9.5925,9.3481\n14.128,13.974,14.358,14.896,15.301,16.518,17.058,17.318,17.558,17.993,18.047,18.062,18.165,17.996,18.212,18.599,18.393,17.976,17.823,17.821,17.946,17.996,18.319,18.582,18.415,18.472,18.579,18.118,18.481,19.044,18.249,16.71,16.487,16.578,16.943,15.815,13.788,13.192,12.579,11.491,11.063,12.175,12.416,11.493,10.205,9.8057,9.0941\n12.989,12.593,13.76,14.847,15.394,16.508,16.974,18.015,18.53,18.633,18.396,18.105,18.205,18.17,17.944,18.189,18.048,18.098,18.038,17.729,17.975,18.29,18.506,18.557,18.698,18.971,19.013,18.865,18.905,18.721,17.887,17.272,16.948,16.659,16.498,15.274,13.542,12.648,12.89,13.1,12.999,13.077,12.565,11.616,10.853,10.462,10.203\n12.632,11.469,12.375,14.785,15.455,16.29,16.804,18.017,18.969,18.677,18.408,18.607,18.537,18.294,18.162,17.995,17.342,17.378,18.093,18.47,18.459,18.588,18.395,18.912,19.32,19.379,19.329,18.882,19.02,18.946,18.056,17.318,17.129,16.662,15.559,14.175,13.219,12.861,13.14,13.604,13.285,12.704,12.198,11.704,11.246,10.937,10.353\n13.007,12.063,12.515,15.202,15.249,15.785,16.53,17.242,18.044,18.298,18.273,18.318,18.264,17.956,18.223,17.985,17.704,17.376,17.836,18.298,18.735,18.595,18.647,18.53,18.92,19.268,19.251,18.984,19.148,19.125,18.174,17.934,17.881,17.381,16.587,15.25,13.571,13.109,12.811,13.042,12.743,12.049,11.464,11.102,10.774,10.754,10.33\n12.741,12.223,12.826,14.792,15.175,16.288,17.057,17.133,17.272,17.914,17.923,17.856,17.747,17.696,17.968,17.729,17.659,17.872,18.125,18.604,19.223,19.365,19.323,19.052,19.116,19.417,19.4,19.277,19.592,19.042,18.382,18.38,17.939,17.232,16.511,15.881,15.044,13.538,12.1,11.453,11.002,11.02,10.349,10.503,10.955,11.325,10.941\n13.153,12.926,13.599,15.067,15.331,16.756,17.495,17.473,17.307,17.361,17.735,17.867,17.746,17.617,17.888,17.823,17.633,17.979,18.579,19.08,19.673,19.79,19.417,19.409,19.76,20.264,19.932,19.601,18.849,18.847,18.878,18.313,17.689,17.253,16.702,15.908,14.709,13.223,11.74,10.717,10.426,10.358,9.6861,9.7901,10.145,11.134,11.053\n14.371,14.418,14.553,15.673,16.316,17.09,17.304,17.452,17.379,17.197,17.143,17.224,17.67,17.915,18.099,17.95,17.949,18.094,18.687,19.17,19.512,19.467,19.448,19.778,20.092,21.305,21.666,20.756,19.513,19.893,19.344,18.025,17.287,17.196,16.826,15.735,14.268,12.52,11.448,11.03,10.905,10.712,10.259,9.9704,10.304,10.714,11.043\n15.008,15.213,15.217,16.011,16.846,17.308,17.46,17.563,17.409,16.923,16.435,16.961,17.642,17.954,18.132,17.678,17.803,18.279,18.779,18.728,19.091,19.564,20.122,20.785,20.89,21.995,22.363,20.862,19.966,19.954,18.774,18.201,17.429,17.499,17.291,15.877,14.059,12.455,11.789,11.672,11.451,11.052,10.362,10.143,10.669,10.753,10.968\n15.217,15.375,15.229,15.855,16.536,17.321,17.72,17.905,17.905,17.519,16.752,17.322,17.879,17.685,17.424,16.83,17.303,18.171,18.871,18.964,19.235,20.012,20.856,21.467,21.734,22.25,22.058,21.039,20.465,20.001,19.289,18.477,17.528,17.06,17.167,16.31,14.865,13.632,13.215,13.144,12.712,12.053,11.28,10.306,10.595,10.183,10.131\n15.503,15.957,16.341,16.685,16.656,17.19,17.811,17.974,18.06,17.766,17.404,17.944,18.059,17.763,17.677,17.654,17.503,18.101,18.492,18.733,18.91,19.687,20.812,21.897,22.184,22.153,21.559,21.024,20.533,19.514,18.738,17.883,17.36,16.938,16.638,15.364,14.408,13.931,13.893,14.118,13.656,12.921,12.03,11.308,10.765,9.8013,10.019\n15.976,16.424,16.916,17.872,18.288,17.833,17.979,17.995,18.079,17.727,17.638,17.876,18.054,17.984,18.14,18.231,18.191,18.276,18.662,19.186,19.327,19.843,20.526,21.697,22.488,21.978,21.293,20.655,20.195,19.104,17.746,17.438,17.06,16.58,16.267,15.531,14.897,14.47,14.45,14.999,14.576,13.459,12.674,12.124,10.755,10.526,10.811\n16.207,16.913,17.026,17.457,18.637,18.68,18.313,18.152,17.948,17.774,17.753,18.05,18.461,18.413,18.342,18.432,18.486,18.641,18.882,18.867,19.036,19.674,20.315,20.936,21.394,21.367,21.253,20.894,20.334,18.721,17.197,16.997,16.741,16.023,15.441,14.859,14.609,14.768,15.021,15.642,15.459,14.094,13.31,12.8,12.287,11.78,11.199\n16.396,16.529,16.833,17.5,18.109,17.72,17.811,17.974,17.949,18.105,17.919,18.467,19.054,19.236,19.054,19,18.738,18.446,18.731,18.76,19.121,20.098,20.528,20.756,20.935,21.171,21.153,20.192,18.773,17.463,17.065,16.341,15.15,14.388,14.552,14.561,14.436,14.675,15.094,15.455,15.022,14.903,14.79,14.098,14,13.205,13.359\n17.677,17.768,17.587,18.045,18.234,17.806,17.719,17.899,17.977,18.057,17.905,18.324,18.692,18.641,18.552,18.469,18.182,18.159,18.88,19.27,19.71,20.569,20.562,20.386,20.566,20.655,20.43,19.089,17.993,16.771,15.663,15.091,14.065,13.304,12.97,13.815,14.58,14.728,14.922,15.361,14.919,14.709,15.039,14.708,14.564,12.676,12.574\n18.215,18.467,18.32,18.118,17.922,17.749,17.901,17.988,17.711,18.006,18.045,18.182,18.216,17.969,18.352,18.036,17.371,18.618,19.64,19.873,20.565,20.799,20.383,19.904,20.006,20.566,20.337,19.441,18.192,16.473,14.527,14.754,14.039,13.335,13.036,13.286,14.032,13.93,14.297,14.665,15.295,14.629,14.176,14.853,15.62,15.142,14.643\n16.196,17.607,17.912,17.681,17.324,17.406,18.143,18.733,18.527,18.639,18.588,18.645,18.922,18.901,18.45,18.168,17.893,18.555,19.831,21.491,20.812,20.491,20.58,20.109,19.559,19.29,19.288,18.887,17.499,15.628,14.221,14.416,14.28,13.86,13.553,13.44,13.753,13.437,13.303,14.119,15.321,16.528,15.635,14.756,16.039,16.779,16.642\n16.588,17.08,18.034,17.983,17.485,17.161,17.772,19.119,18.993,18.774,18.974,18.954,19.198,19.345,18.92,18.73,19.051,19.768,20.599,21.265,21.77,21.489,20.542,20.542,20.111,19.175,19.242,18.879,17.921,16.57,15.183,14.754,13.63,13.376,13.351,13.853,13.261,13.079,12.782,14,14.744,16.005,15.791,14.994,15.541,16.257,16.974\n17.204,17.538,18.798,18.814,18.582,18.463,19.266,19.674,19.368,18.928,18.853,19.1,19.317,19.489,19.579,19.746,19.834,20.135,20.791,21.46,21.508,21.208,20.582,20.755,20.477,19.851,19.325,19.039,18.824,18.274,16.945,15.677,13.968,13.623,13.354,14.082,13.857,13.844,13,13.297,14.102,14.914,15.028,15.303,15.888,16.12,16.183\n17.437,18.399,19.127,18.986,18.939,20.068,20.809,20.222,19.283,19.276,19.572,19.837,19.541,19.568,20.43,20.318,20.498,20.742,20.705,20.954,21.134,20.381,20.147,20.598,20.809,19.945,19.678,19.343,19.064,19.183,18.855,17.113,14.752,14.716,13.271,13.883,14.297,14.187,13.496,13.907,14.466,14.639,14.996,15.707,16.03,16.204,16.405\n18.318,19.765,19.98,20.007,20.361,20.907,20.502,19.896,19.68,19.558,19.72,19.873,19.728,19.838,21.214,21.28,20.771,21.152,21.345,20.802,21.149,21.207,21.821,21.64,20.443,19.931,19.676,19.416,18.831,18.742,18.608,17.774,15.816,15.527,13.736,13.725,13.935,13.904,13.421,13.492,15.105,14.834,15.111,15.715,16.087,16.301,16.62\n19.169,20.126,20.103,19.873,20.46,20.661,19.802,19.567,19.267,19.104,19.289,19.595,19.756,20.187,21.095,21.786,20.989,20.792,21.064,20.839,21.419,21.687,22.216,21.155,20.142,19.879,19.442,19.312,18.787,18.526,18.165,17.444,16.64,16.206,15.226,14.483,14.223,13.874,13.286,12.671,14.032,14.849,15.219,15.916,16.157,16.474,16.957\n19.08,19.328,19.417,19.083,19.164,19.761,19.753,19.302,19.015,18.832,19.017,19.474,20.032,20.539,20.926,21.445,20.761,20.146,20.22,20.47,20.468,21.075,21.471,20.286,19.748,19.771,19.4,18.721,18.428,17.928,17.253,16.884,16.473,16.148,15.666,14.988,14.715,14.435,14.157,14.254,14.458,15.273,15.643,15.81,15.873,16.177,17.108\n18.455,18.755,19.18,19.574,19.884,19.794,19.227,18.893,19.144,19.345,19.303,19.376,19.892,20.22,20.599,20.577,20.03,19.672,19.802,19.9,19.885,20.009,19.66,19.204,19.095,18.696,18.423,17.702,17.089,16.48,16.633,16.651,16.262,15.747,15.326,15.246,15.09,14.915,14.898,14.608,14.768,15.372,15.751,15.78,16,16.636,17.305\n18.019,18.028,18.902,19.652,20.048,19.473,18.813,18.866,19.443,19.712,19.711,19.767,19.743,19.562,19.599,20.208,19.987,19.703,19.655,19.71,19.57,18.99,18.989,18.738,18.23,17.795,17.563,17.195,16.993,16.645,16.077,15.54,15.611,15.953,15.574,15.456,15.007,14.882,14.522,14.274,14.85,15.249,15.607,15.805,16.334,17.235,17.545\n18.858,18.313,19.133,19.351,19.509,19.327,18.9,18.959,19.317,19.645,19.635,19.597,19.178,19.01,19.382,19.847,19.874,19.447,19.379,19.353,19.023,18.827,18.982,18.954,18.658,18.396,17.686,17.245,17.238,17.291,16.231,15.546,15.097,15.56,15.672,15.573,14.797,14.437,14.142,14.203,14.578,15.089,15.786,16.113,16.737,17.442,17.779\n19.086,18.611,18.644,19.587,19.471,19.264,18.534,18.698,19.055,19.178,19.362,19.285,19.17,19.294,19.604,19.27,18.723,18.89,18.898,18.928,18.777,18.962,19.077,19.205,18.736,18.586,18.227,17.924,17.157,17.071,16.669,16.19,15.657,15.459,15.131,14.695,14.372,14.664,14.55,14.533,14.675,15.501,16.109,16.235,16.822,17.641,18.234\n18.36,18.639,19.325,19.227,19.061,18.877,18.787,18.965,19.059,19.043,19.134,19.082,18.935,18.773,18.882,18.857,18.428,18.636,19.027,19.358,19.33,19.264,19.061,18.837,18.378,18.045,17.827,17.786,17.337,16.358,16.015,15.863,15.211,15.472,15.053,14.663,14.751,15.231,15.019,14.52,14.377,15.3,16.387,17.085,17.615,18.548,18.903\n17.994,17.856,18.39,19.296,19.041,18.856,18.473,18.696,19.13,19.325,19.319,19.37,19.175,18.511,18.752,18.813,19.201,19.576,20.084,20.326,19.883,19.593,18.759,18.532,18.282,18.167,18.153,17.824,17.365,16.726,16.037,15.189,14.645,14.794,14.973,14.099,13.856,14.175,14.253,14.219,14.164,14.968,16.051,17.441,18.504,18.382,18.713\n17.325,17.723,17.533,17.881,18.636,19.08,19.214,19.203,19.301,19.377,19.539,19.57,19.506,18.746,19.327,20.169,20.807,19.992,20.031,20.712,20.43,20.102,20.49,20.501,19.722,19.479,19.081,18.392,17.768,17.365,16.734,15.899,15.079,14.913,14.77,13.866,13.078,13.16,13.459,13.887,14.326,15.094,16.036,17.773,19.075,18.488,18.902\n17.323,17.928,18.035,18.04,18.006,18.371,18.932,19.707,19.591,19.712,19.78,20.067,20.102,19.764,20.264,21.059,21.075,20.352,19.878,19.772,19.03,19.542,20.026,20.673,20.38,19.471,18.232,17.476,17.057,17.189,17.792,17.068,16.466,15.034,14.741,14.502,13.924,13.712,13.682,13.85,14.322,15.544,16.458,18.753,19.144,18.957,18.984\n18.038,18.529,17.821,17.125,17.194,18.196,19.097,19.847,19.535,19.36,19.676,19.899,20.28,20.774,21.067,20.905,20.817,19.781,19.546,19.647,19.569,19.511,19.44,20.145,19.697,18.792,17.433,16.31,15.437,15.288,15.87,17.236,17.425,16.802,15.428,14.669,14.359,13.697,13.824,14.159,14.283,15.656,16.718,17.1,18.389,19.027,19.295\n17.564,17.881,18.054,17.706,17.669,18.567,19.089,19.396,19.231,18.912,19.54,19.73,20.059,20.542,20.582,20.716,20.733,19.582,19.17,19.555,19.891,19.858,19.634,18.455,18,17.559,NaN,NaN,NaN,15.836,15.911,16.41,17.504,17.347,15.608,14.338,13.514,13.648,14.022,14.392,14.459,15.304,16.25,16.815,18.021,18.877,19.124\n17.24,18.025,18.232,18.212,18.418,18.434,18.38,18.828,20.028,20.383,19.941,20.033,19.733,20.194,20.13,19.892,19.367,19.177,19.024,19.181,19.643,19.623,18.213,17.181,17.548,NaN,NaN,NaN,NaN,NaN,16.332,16.874,17.653,17.033,15.326,13.841,13.468,13.544,13.909,14.249,14.529,14.888,15.379,16.678,18.119,18.917,19.241\n16.754,17.05,17.28,17.635,18.468,18.598,18.738,19.186,20.541,21.363,20.634,20.514,19.994,19.426,19.083,19.266,19.223,18.793,18.857,19.253,19.281,18.896,17.574,16.485,17.213,NaN,NaN,NaN,NaN,NaN,16.47,16.288,16.886,16.287,15.543,14.205,13.999,13.872,13.964,14.564,15.089,15.25,15.305,16.073,18.048,19.443,19.678\n15.899,15.529,15.416,16.244,18.223,19.017,19.357,19.538,20.06,20.641,20.108,19.732,19.659,18.763,18.617,18.895,19.27,19.725,19.885,17.806,16.466,15.177,16.867,18.159,17.043,NaN,NaN,NaN,NaN,NaN,15.593,15.714,15.839,14.867,14.625,14.264,14.412,14.427,14.148,14.893,15.675,16.16,16.691,17.221,18.45,19.675,20.108\n15.669,16.219,16.176,16.192,16.939,17.841,18.358,18.393,18.447,19.325,19.571,19.837,19.31,18.2,17.645,18.231,18.553,17.546,16.484,15.535,14.698,15.723,16.717,18.397,17.245,16.082,NaN,NaN,NaN,17.011,15.582,14.89,14.331,14.014,14.205,14,14.261,14.168,14.25,14.78,15.964,17.204,17.859,18.163,18.162,18.358,19.218\n15.821,16.096,16.357,16.317,16.135,16.863,17.504,17.523,17.613,17.96,18.421,18.494,18.157,17.518,16.939,17.231,16.783,17.144,16.362,15.496,15.091,16.576,17.603,18.57,16.207,15.224,13.27,14.572,16.029,15.592,15.161,14.699,14.294,14.117,14.243,14.032,13.943,13.978,14.048,14.598,16.016,17.601,18.372,18.517,18.316,18.432,19.767\n15.281,15.756,15.54,15.912,16.18,16.6,16.795,16.773,16.385,16.899,17.121,16.922,16.511,15.587,15.33,15.994,16.267,16.428,16.533,14.164,NaN,NaN,NaN,NaN,NaN,NaN,14.64,15.152,15.752,15.262,14.438,14.092,13.885,13.667,13.813,13.949,13.842,13.991,14.251,14.86,16.059,17.645,18.44,18.556,18.349,18.524,19.693\n14.442,15.127,15.222,15.753,16.199,16.499,16.38,16.122,15.476,15.163,15.102,15.569,15.496,14.94,14.568,14.901,15.361,15.614,15.946,13.395,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.478,14.477,13.799,13.419,13.42,13.689,13.526,13.409,13.398,13.605,13.599,14.424,15.999,17.922,18.112,18.583,19.086,19.142,19.215,19.758\n15.292,15.521,15.722,15.776,15.829,15.894,15.916,15.5,15.039,14.898,14.733,15.327,15.22,14.38,13.632,13.416,13.714,13.154,12.969,11.166,11.421,NaN,NaN,NaN,NaN,NaN,16.226,15.246,14.221,13.101,12.618,12.814,13.329,12.957,12.789,13.073,13.767,14.025,14.755,16.863,18.444,18.167,18.716,18.962,19.065,19.376,19.844\n16.57,16.15,15.945,15.924,15.682,15.525,15.413,15.281,15.192,14.969,14.883,14.883,14.299,13.242,13.242,13.253,13.655,13.431,13.223,11.829,11.591,12.749,14.237,NaN,NaN,NaN,14.164,13.943,13.259,12.624,12.201,12.076,12.137,12.2,12.474,13.044,14.044,15.101,16.487,18.299,19.191,18.882,18.951,18.934,19.215,20,19.947\n16.613,16.372,16.024,16.224,15.639,15.267,15.247,15.317,15.224,14.921,14.701,14.454,13.87,12.769,11.868,11.997,12.441,13.056,12.391,11.693,12.597,15.234,16.485,16.85,16.493,15.316,12.798,12.506,12.266,12.293,12.23,12.04,12.215,13.021,14.254,14.819,14.831,16.014,17.84,19.081,19.308,19.266,19.385,19.432,19.673,20.234,20.539\n16.08,15.978,15.933,16.132,16.004,15.566,15.737,15.677,15.309,14.444,14.17,13.84,13.606,12.709,11.211,10.968,11.79,11.661,10.945,10.79,12.012,12.467,13.109,13.999,14.85,13.322,12.434,11.475,11.323,11.666,12.208,12.374,12.863,13.71,14.674,15.357,15.593,16.561,18.224,19.472,19.511,19.52,19.784,19.338,19.457,19.708,20.014\n16.251,15.942,15.803,15.673,15.645,15.639,15.633,15.311,14.595,14.094,14.053,13.852,13.586,12.399,11.195,11.191,11.448,11.178,10.591,10.532,11.031,11.064,11.327,11.323,11.308,11.566,11.716,11.214,10.986,11.586,12.238,12.792,13.119,13.671,14.378,15.211,15.692,17.229,18.775,19.054,18.931,19.715,19.962,19.333,19.211,19.626,20.163\n15.957,15.502,15.593,15.555,15.372,15.281,15.212,14.912,14.63,14.317,14.125,13.989,13.914,13.818,13.032,11.829,11.261,10.767,10.583,10.648,10.906,10.916,11.478,11.621,11.503,11.427,11.131,10.943,11.119,11.871,12.516,12.958,13.394,14.231,14.993,15.392,15.861,17.005,18.917,19.529,19.524,20.844,20.692,20.457,19.634,19.821,20.452\n15.636,15.111,14.989,15.262,15.238,15.209,15.158,14.913,14.657,14.615,14.651,14.137,14.078,13.989,13.423,12.72,12.104,11.455,10.582,10.564,10.653,11.039,11.265,11.347,11.461,11.121,10.416,10.962,11.889,12.765,13.351,13.439,13.87,14.701,15.422,15.634,16.311,17.873,19.337,19.424,19.581,20.236,20.373,20.325,19.586,20.251,20.891\n16.066,15.4,15.076,15.148,15.25,15.264,15.248,14.784,14.396,14.298,14.561,14.745,14.749,14.415,13.525,12.819,12.593,12.187,11.506,10.832,9.8291,9.6632,10.472,10.79,10.651,10.221,9.9875,11.066,12.474,13.068,13.513,13.685,14.584,15.381,15.917,15.808,16.448,17.804,19.026,19.352,19.243,19.344,20.006,20.055,20.281,20.903,21.702\n16.151,15.963,15.325,15.39,15.577,15.621,15.269,14.969,14.796,14.824,14.807,14.924,15.119,15.066,14.18,13.013,13.173,12.583,11.979,11.333,10.723,10.191,10.94,10.697,9.9219,9.6488,9.2079,10.585,12.826,13.615,13.68,13.897,14.58,15.184,15.548,15.676,16.313,17.872,18.981,19.47,19.297,19.213,19.22,19.848,20.368,21.264,21.769\n16.072,15.698,15.601,15.678,15.615,15.664,15.596,15.603,15.363,15.308,15.323,15.197,14.918,14.55,14.101,13.655,13.508,12.863,11.223,10.424,10.443,11.056,11.329,11.37,11.189,11.012,10.242,10.309,12.809,14.093,14.261,14.225,14.825,15.44,16.55,16.799,17.197,18.138,18.742,19.126,19.246,19.008,19.077,19.331,20.139,20.592,21.692\n16.795,16.782,16.914,16.414,15.862,15.83,15.613,15.469,15.311,15.406,15.273,15.065,14.831,14.534,14.648,13.913,13.802,12.452,12.048,10.879,10.509,11.489,11.615,11.593,11.96,12.409,12.071,12.037,12.593,13.653,14.314,14.534,15.428,16.5,16.861,16.964,17.574,18.189,18.523,18.452,18.468,18.339,17.469,18.518,20.508,21.233,22.587\n17.178,17.369,17.376,16.637,16.319,16.087,15.925,15.615,15.412,15.296,15.108,14.796,14.225,13.855,13.519,13.28,13.104,12.161,11.912,11.845,11.965,12.076,12.241,12.299,12.636,12.703,12.84,12.459,12.405,12.784,13.93,14.818,15.58,16.286,16.646,17.321,17.865,18.132,18.278,18.363,18.472,17.926,17.732,18.752,20.335,21.229,22.568\n17.241,17.172,16.8,16.495,16.654,16.581,16.191,15.702,15.18,15.104,14.973,14.135,13.309,12.632,12.591,12.268,12.373,12.192,12.09,12.008,12.002,12.145,12.228,12.697,13.264,13.788,13.474,12.877,12.406,12.735,14.217,15.204,15.897,16.186,16.289,17.612,18.311,18.143,18.217,18.333,18.585,18.569,18.298,19.142,20.836,21.687,22.628\n17.203,17.176,17.044,17.28,17.421,16.736,15.995,15.605,15.282,15.499,15.053,14.038,13,12.218,12.106,12.083,11.783,11.572,11.111,11.796,12.432,12.369,12.277,12.692,13.284,13.74,13.39,13.225,12.529,13.278,15.264,16.063,16.548,16.962,17.442,18.133,18.524,18.55,18.507,18.472,18.775,18.868,18.764,20.08,21.76,22.456,22.49\n17.021,17.271,17.448,17.384,16.362,15.949,16.132,16.057,16.18,15.994,14.322,13.547,12.693,12.308,12.284,11.937,11.103,11.012,11.232,12.192,12.772,12.813,12.677,12.65,13.087,13.287,12.599,12.784,13.176,13.914,15.227,16.317,16.743,16.888,17.301,18.046,18.333,18.42,18.737,19.185,19.47,19.465,19.828,20.858,22.316,22.925,22.534\n17.06,17.204,17.551,17.226,16.447,16.295,16.637,16.922,16.386,15.06,12.966,13.031,12.355,12.868,12.716,12.291,12.182,11.69,11.746,12.473,13.726,13.408,13.081,13.198,13.27,13.282,13.038,13.292,14.193,15.213,15.423,15.64,16.393,16.976,17.366,17.794,18.204,18.537,19.157,19.753,19.681,19.783,20.302,21.182,22.095,23.068,23.239\n16.939,17.26,18.067,17.741,17.301,16.321,15.888,16.555,17.005,17.012,14.021,13.127,12.758,12.179,11.945,11.705,11.973,12.252,12.107,12.124,13.074,13.978,13.517,13.512,13.286,13.11,13.166,13.339,14.569,15.839,15.882,16.003,16.711,17.381,18.156,18.649,18.996,19.093,19.276,20.155,20.283,20.253,20.504,21.483,23.035,23.304,23.057\n16.283,16.683,17.481,17.845,17.716,17.088,16.081,16.443,16.758,16.78,14.794,13.635,12.165,11.664,10.86,11.286,12.231,12.647,12.106,11.878,11.992,12.957,13.406,13.914,13.997,13.732,14.052,14.497,15.654,16.563,17.298,17.417,17.735,18.45,18.765,19.076,19.256,19.016,19.194,19.869,20.385,20.675,21.393,22.2,22.704,22.436,22.065\n15.972,16.211,17.018,17.298,17.348,16.85,16.122,16.937,17.576,15.951,14.765,14.263,14.04,13.592,14.644,15.022,14.303,13.68,13.067,11.61,11.403,11.786,13.167,14.402,14.946,14.846,15.125,15.255,15.56,16.654,17.428,18.035,18.146,18.971,19.44,19.342,19.492,19.206,18.742,19.179,19.832,20.85,21.939,22.789,22.431,22.231,22.44\n18.301,18.365,18.174,17.733,17.364,17.251,16.589,16.704,17.199,16.706,15.868,16.361,18.098,19.248,20.434,19.849,16.023,14.625,14.303,12.852,12.578,12.057,13.364,14.699,15.045,15.452,15.484,15.53,15.537,16.781,18.149,18.579,18.877,19.97,20.429,20.015,20.068,19.461,18.91,18.677,18.998,20.252,21.475,22.388,22.353,22.065,22.212\n18.423,18.815,18.52,17.821,17.718,17.558,16.656,16.194,16.125,15.179,16.348,17.031,17.929,19.547,19.211,18.048,16.452,15.259,15.427,14.412,14.61,14.309,14.336,14.707,15.523,16.132,15.883,15.868,16.424,17.188,18.291,18.56,18.929,19.974,21.056,20.595,20.182,19.413,19.138,19.012,18.742,18.869,19.735,20.78,21.546,21.465,21.747\n16.333,16.902,18.213,18.117,18.336,17.1,16.142,15.431,15.703,14.168,16.211,17.171,17.482,18.587,18.932,17.938,16.794,15.327,15.292,15.762,15.738,15.311,15.696,16.849,16.793,17.741,17.535,17.008,17.321,17.548,17.914,18.685,19.275,20.253,21.91,22.474,20.59,19.728,19.328,19.129,18.95,19.057,19.41,20.326,20.619,20.235,20.815\n18.219,18.504,18.478,19.051,19.37,18.312,17.044,15.87,14.932,14.933,14.792,16.458,16.801,17.66,18.01,18.252,17.621,15.948,16.007,16.087,15.756,14.963,14.46,15.044,16.146,17.371,17.65,17.599,17.449,17.677,18.492,19.267,19.988,21.259,21.797,21.426,20.816,20.336,19.576,18.831,19.589,20.132,20.389,20.525,20.295,19.977,19.47\n13.957,15.778,16.65,17.918,19.115,18.717,17.605,16.415,16.261,16.614,18.513,18.916,17.688,NaN,NaN,NaN,NaN,NaN,17.058,17.007,16.557,15.54,14.514,16.062,16.671,17.405,17.916,17.428,17.602,18.064,18.95,20.149,20.931,21.233,20.923,20.846,20.476,20.117,19.946,19.735,19.894,20.106,19.812,19.769,19.546,19.747,20.006\n13.831,14.349,14.339,16.505,18.61,17.852,16.251,16.013,15.396,16.015,19.407,19.567,18.442,NaN,NaN,NaN,NaN,NaN,16.486,17.846,16.539,16.102,16.151,16.435,16.782,17.537,17.819,17.232,17.602,18.799,19.293,20.045,21.118,22.047,21.966,21.171,20.263,20.021,20.337,20.194,19.768,19.647,18.967,18.358,18.268,19.342,20.134\n13.743,13.247,13.305,14.903,15.928,16.148,16.344,15.363,14.999,15.991,14.962,15.254,NaN,NaN,NaN,NaN,NaN,NaN,16.378,17.96,16.109,14.763,15.758,16.565,14.779,13.139,15.814,18.648,19.509,19.346,19.126,20.409,21.716,22.514,22.756,21.781,21.2,20.385,20.861,20.762,20.445,20.12,19.196,18.249,17.551,18.073,17.065\n11.073,12.643,13.068,12.76,11.811,12.216,17.378,16.119,14.773,14.883,15.378,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.622,17.449,16.221,13.93,15.451,14.944,11.824,13.887,16.037,18.779,19.717,19.655,20.042,20.641,23.457,23.447,23.433,23.224,22.23,21.109,21.323,21.03,20.806,20.54,19.917,19.256,18.399,18.081,16.054\n10.823,11.056,10.686,10.054,10.2,12.146,13.197,12.799,13.001,14.841,14.673,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.491,17.456,16.58,17.086,17.872,17.572,18.64,19.528,19.51,20.136,21.718,21.76,21.516,22.813,23.598,23.171,22.21,21.196,21.161,21.103,20.318,20.259,20.351,20.17,20.243,19.634,20.486,20.135\n10.172,9.781,9.3413,8.9096,10.394,11.797,11.977,12.028,12.819,14.591,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.296,18.932,19.334,19.957,19.776,19.054,18.934,20.017,20.714,21.064,22.342,22.952,22.846,22.06,22.095,21.285,19.9,20.029,20.089,19.249,20.029,21.017,21.123,22.214,22.663,22.655,NaN\n9.7265,9.9982,10.8,9.756,8.8605,9.4995,10.863,12.461,14.062,15.021,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.381,17.804,18.552,19.324,19.316,18.182,19.745,21.523,22.636,22.838,23.112,22.773,22.549,21.724,20.675,20.818,22.405,22,20.07,20.652,22.187,22.963,23.302,23.156,21.164,NaN\n9.9832,11.202,12.273,12.336,10.687,10.875,12.762,14.144,14.223,14.614,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,20.887,21.178,19.563,19.096,19.053,19.524,21.239,21.788,22.606,22.619,22.779,23.273,23.085,22.442,20.986,23.031,21.052,21.073,21.488,22.473,23.251,23.815,23.726,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070219-070604.unw.csv",
    "content": "-16.599,-17.269,-16.785,-15.841,-15.721,-14.959,-13.633,-13.114,-13.697,-13.637,-13.652,-13.488,-13.113,-13.154,-12.882,-12.376,-12.117,-12.464,-12.237,-11.828,-12.465,-12.134,-11.617,-11.512,-12.052,-12.178,-12.64,-13.814,-13.912,-13.875,-14.192,-14.493,-14.12,-14.678,-15.774,-16.429,-17.447,-18.057,-18.247,-19.08,-19.036,-18.598,-17.939,-17.622,-17.615,-18.097,-18.039\n-16.951,-17.105,-16.679,-15.365,-15.041,-14.549,-13.673,-12.587,-12.523,-12.551,-12.983,-13.344,-13.543,-13.341,-12.678,-12.103,-11.852,-11.803,-11.429,-11.494,-11.565,-10.921,-10.732,-10.94,-11.359,-11.437,-11.821,-11.663,-12.17,-13.976,-15.461,-14.523,-14.079,-15.14,-16.051,-16.64,-17.471,-18.058,-18.402,-18.947,-18.174,-17.696,-17.814,-17.651,-17.572,-17.402,-16.902\n-17.274,-17.183,-16.783,-15.717,-15.186,-13.625,-13.269,-13.319,-12.647,-12.036,-12.162,-12.581,-13.159,-13.228,-12.384,-11.79,-11.588,-11.447,-11.502,-11.872,-11.681,-11.471,-11.533,-11.096,-10.899,-11.142,-11.276,-11.167,-11.857,-13.079,-13.899,-14.154,-14.273,-14.969,-16.305,-18.459,-19.13,-20.119,-20.158,-20.297,-19.829,-18.076,-17.167,-16.875,-17.522,-16.951,-16.454\n-17.401,-17.203,-16.903,-15.711,-15.077,-14.669,-13.85,-13.607,-12.807,-12.024,-11.963,-12.443,-12.944,-13.187,-12.326,-11.328,-10.463,-10.822,-11.369,-12.173,-11.427,-11.135,-10.922,-10.657,-10.446,-10.283,-10.305,-10.681,-11.063,-11.531,-12.43,-13.167,-14.137,-14.725,-16.127,-18.457,-19.34,-20.003,-20.129,-19.797,-19.571,-19.085,-18.241,-17.793,-17.57,-17.007,-16.15\n-17.833,-17.653,-18.751,-16.618,-15.436,-15.126,-14.925,-14.582,-14.129,-13.701,-13.037,-12.934,-13.024,-12.661,-11.843,-11.712,-11.206,-10.631,-10.748,-10.983,-11.267,-11.262,-10.948,-10.482,-9.9032,-9.3368,-9.4235,-10.284,-11.328,-11.357,-11.397,-12.626,-13.757,-14.904,-15.933,-18.044,-19.057,-19.466,-19.622,-19.485,-19.17,-18.376,-18.123,-18.381,-17.537,-16.793,-16.362\n-18.505,-19.012,-19.935,-17.856,-16.912,-16.28,-15.523,-14.68,-14.483,-14.097,-13.335,-13.062,-13.005,-12.473,-11.776,-11.333,-11.107,-10.594,-9.8357,-9.688,-10.385,-11.402,-10.839,-10.482,-9.7507,-9.44,-9.4876,-9.5715,-10.065,-10.362,-10.753,-11.349,-12.077,-13.452,-15.01,-17.201,-18.726,-18.926,-19.201,-18.784,-17.984,-17.966,-18.241,-18.28,-17.295,-16.186,-16.476\n-18.759,-19.201,-18.715,-18.053,-17.641,-16.769,-15.39,-14.424,-14.438,-13.523,-12.805,-12.657,-12.471,-11.923,-11.228,-10.517,-10.455,-10.477,-10.513,-9.8658,-9.3964,-8.9376,-9.284,-9.5934,-9.9258,-9.6049,-8.8056,-9.2507,-9.8867,-10.865,-11.332,-11.39,-12.364,-13.524,-14.959,-16.312,-17.606,-18.257,-19.69,-21.181,-20.943,-18.695,-18.778,-18.214,-17.229,-16.566,-15.626\n-18.055,-17.604,-16.497,-17.445,-17.18,-16.12,-14.692,-14.388,-14.744,-14.849,-13.279,-12.846,-12.114,-11.4,-10.777,-10.047,-9.5043,-9.7937,-9.5021,-9.4105,-9.0079,-8.7598,-9.0903,-8.9741,-8.3912,-8.2464,-8.4457,-9.1642,-10.979,-10.688,-11.129,-11.96,-12.656,-13.417,-14.334,-15.818,-17.202,-18.746,-20.021,-20.945,-21.081,-20.116,-20.359,-20.524,-19.791,-17.945,-18.201\n-18.694,-17.508,-16.487,-17.354,-16.676,-16.054,-14.498,-14.556,-14.376,-13.834,-13.604,-13.773,-13.193,-12.034,-10.992,-9.9489,-9.0768,-8.9254,-9.3962,-9.2023,-8.6129,-8.0458,-8.1686,-7.6446,-7.1437,-6.9807,-5.8796,-6.6909,-8.7712,-9.3619,-11.119,-12.27,-11.97,-11.198,-12.43,-14.988,-17.498,-19.937,-20.81,-21.241,-21.015,-20.281,-20.283,-20.422,-19.839,-18.722,-18.047\n-17.72,-17.532,-16.798,-17.223,-17.195,-16.646,-15.11,-14.567,-13.997,-13.382,-13.329,-12.997,-12.06,-11.459,-11.128,-9.9984,-9.0583,-8.9662,-9.3052,-9.1344,-7.5806,-6.9564,-7.0364,-6.8095,-5.9048,-5.8594,-5.8873,-6.5116,-7.363,-7.9047,-10.533,-11.18,-10.741,-10.197,-11.695,-14.408,-17.088,-18.89,-20.404,-20.789,-20.904,-20.701,-20.785,-21.053,-20.497,-19.077,-18.415\n-19.057,-17.804,-16.644,-16.414,-16.576,-16.469,-15.403,-14.202,-13.525,-13.174,-13.099,-12.436,-11.133,-10.651,-9.9609,-9.1584,-8.3529,-8.7639,-8.6886,-7.647,-7.1526,-7.2512,-7.1394,-6.5402,-5.2073,-5.3331,-5.4904,-5.2419,-5.8247,-7.8418,-9.2535,-10.091,-10.948,-12.246,-13.169,-15.696,-17.896,-19.36,-19.294,-18.734,-19.193,-19.444,-20.043,-20.81,-21.157,-20.659,-20.136\n-18.565,-17.88,-17.071,-16.287,-16.504,-16.123,-15.137,-14.088,-13.244,-12.882,-12.847,-11.463,-10.219,-9.6122,-9.1236,-8.9212,-8.5678,-8.5295,-7.6258,-7.4276,-7.4711,-7.493,-7.3062,-5.986,-5.0779,-5.3162,-5.6286,-5.1625,-6.2736,-7.7056,-9.0784,-10.293,-11.19,-12.227,-13.445,-16.062,-17.38,-18.41,-18.048,-17.079,-17.614,-18.232,-18.858,-19.651,-20,-20.268,NaN\n-18.421,-17.943,-16.982,-16.034,-15.398,-15.567,-14.945,-14.478,-14.195,-12.855,-11.918,-10.667,-9.2695,-9.3583,-9.1797,-8.9821,-8.9617,-8.3812,-6.8196,-6.232,-6.4849,-6.8835,-7.5248,-7.4776,-6.2395,-5.459,-6.0047,-6.0044,-6.8869,-8.3005,-8.7159,-9.1398,-10.945,-12.913,-13.932,-15.028,-15.394,-16.648,-16.746,-15.729,-16.442,-17.532,-17.815,-18.593,-19.437,-19.144,NaN\n-17.359,-16.258,-16.098,-16.227,-16.512,-17.148,-15.331,-14.13,-13.259,-12.806,-11.79,-10.111,-8.9059,-9.1704,-8.9057,-8.7296,-8.5343,-7.8702,-7.0703,-6.5247,-6.3762,-5.9032,-6.052,-6.2699,-6.3371,-6.3073,-6.8234,-7.517,-7.8552,-8.2993,-7.5534,-4.8782,-9.0541,-14.498,-16.442,-17.244,-17.117,-16.677,-16.229,-14.915,-14.529,-16.556,-16.709,-16.533,-17.248,-16.864,-16.452\n-16.527,-15.346,-15.815,-16.826,-16.976,-16.69,-14.631,-14.685,-13.416,-11.659,-11.223,-10.253,-9.5985,-9.3036,-9.157,-9.2846,-9.1113,-8.2369,-7.3252,-6.5279,-6.5668,-7.0161,-6.4396,-5.6926,-5.987,-5.703,-5.7089,-7.1242,-7.6822,-10.223,-11.491,-12.546,-13.957,-16.69,-17.902,-18.03,-18.015,-16.919,-15.7,-15.331,-16.476,-17.982,-17.206,-16.697,-16.47,-15.485,-15.319\n-17.274,-16.94,-16.256,-16.716,-16.794,-15.519,-14.963,-14.944,-12.708,-11.881,-11.038,-10.047,-9.5524,-9.6546,-9.773,-9.217,-9.0066,-8.3649,-7.9892,-7.5298,-6.3893,-5.8729,-5.8514,-5.3949,-5.5924,-5.833,-6.4683,-9.2365,-10.683,-10.374,-12.927,-15.407,-17.744,-19.243,-20.052,-19.976,-18.766,-17.335,-16.314,-15.942,-17.712,-18.247,-17.479,-17.777,-17.914,-19.22,-16.483\n-17.239,-16.628,-16.03,-15.745,-15.504,-14.958,-15.214,-14.668,-12.949,-11.686,-10.763,-10.12,-9.0719,-8.9903,-8.88,-8.6989,-8.8767,-9.233,-8.8162,-8.7841,-7.7807,-6.5494,-6.2632,-7.7592,-8.2637,-7.3614,-7.0325,-8.0961,-10.179,NaN,NaN,NaN,-18.266,-19.505,-20.529,-21.439,-20.484,-18.869,-18.027,-17.983,-17.92,-17.318,-16.684,-17.856,-19.5,-22.25,-19.714\n-16.091,-15.223,-15.223,-15.338,-14.177,-14.137,-14.406,-14.486,-13.306,-12.252,-10.843,-10.161,-9.7432,-8.7204,-8.3837,-8.1722,-7.858,-8.5174,-8.552,-8.825,-8.8644,-8.1714,-7.2145,-9.2248,-10.891,-10.978,-9.5239,-8.2022,NaN,NaN,NaN,NaN,NaN,NaN,-21.253,-21.017,-19.828,-19.024,-18.693,-19.195,-18.975,-19.018,-18.699,-18.662,-20.048,-21.753,-21.51\n-15.161,-15.091,-14.241,-13.042,-13.438,-13.58,-14.601,-13.59,-12.82,-11.371,-9.9271,-9.3333,-9.0552,-8.3217,-7.7932,-6.9492,-6.9227,-7.3686,-7.873,-8.5004,-8.786,-9.1497,-7.3915,-7.1973,-9.1198,-11.913,-10.409,-10.704,-10.877,NaN,NaN,NaN,NaN,NaN,-23.007,-21.044,-19.659,-19.731,-19.11,-18.902,-19.168,-19.355,-19.067,-18.683,-18.913,-19.603,-19.495\n-15.456,-15.516,-15.75,-15.496,-15.886,-14.615,-13.886,-11.998,-10.92,-9.6918,-8.5558,-8.0844,-8.1111,-7.8277,-7.4132,-6.4811,-6.2557,-6.8499,-7.0155,-7.191,-7.9073,-7.4736,-6.5905,-6.0031,-7.16,-9.0723,-9.9403,-9.7786,-11.357,-13.612,-15.855,NaN,-17.656,-18.789,-23.305,-21.643,-19.63,-19.732,-20.144,-19.934,-19.763,-18.977,-18.629,-18.175,-18.338,-19.5,-18.592\n-15.184,-15.344,-14.212,-15.897,-17.504,-15.96,-13.789,-12.296,-10.745,-9.0004,-8.2439,-7.4865,-7.2106,-7.1959,-6.5147,-6.3859,-7.0294,-8.0594,-7.9629,-7.8505,-8.1933,-7.2852,-6.8121,-6.3529,-6.6541,-6.1782,-6.3998,-8.9622,-10.968,-11.692,-11.752,-13.271,-17.178,-17.122,-24.083,-24.248,-21.61,-21.395,-21.539,-20.827,-20.027,-19.391,-18.552,-18.006,-18.218,-18.314,-17.241\n-14.879,-13.367,-13.921,-14.343,-13.974,NaN,-13.88,-12.39,-9.4302,-8.6448,-7.9882,-7.2497,-6.9371,-6.9076,-6.5036,-6.2078,-7.505,-8.1024,-8.8243,-8.4723,-8.2295,-7.337,-6.9138,-7.0696,-6.8688,-7.1149,-6.9943,-7.8716,-9.1862,-9.924,-10.786,-13.952,-16.935,-17.186,-20.076,-19.992,-19.034,-20.296,-19.861,-19.702,-20.244,-19.626,-18.71,-18.148,-17.936,-17.719,-16.656\n-12.88,-11.166,-11.808,-11.282,-12.38,NaN,NaN,-10.239,-9.9607,-8.8862,-7.9893,-7.0425,-7.5152,-8.0995,-7.3064,-6.556,-8.6623,-9.0114,-8.458,-8.0586,-7.7953,-7.8138,-7.4193,-7.5758,-7.4146,-7.917,-8.2851,-8.7649,-8.7531,-9.0325,-9.8801,-12.383,-14.323,-14.753,-16.927,-17.741,-17.934,-18.072,-18.906,-19.387,-20.233,-19.729,-18.411,-18.039,-18,-17.477,-16.294\n-12.697,-11.211,-8.8914,-6.7096,NaN,NaN,-9.6064,-8.4325,-7.9228,-8.1683,-8.1873,-7.1983,-6.9154,-7.8176,-8.2972,-8.5048,-8.844,-8.5786,-8.5075,-7.8715,-7.1454,-6.5781,-6.58,-6.4536,-6.8776,-7.0685,-6.7386,-7.5148,-8.3794,-8.7275,-8.705,-9.7707,-11.402,-11.945,-14.617,-15.994,-17.976,-19.098,-19.654,-19.237,-19.338,-19.335,-18.451,-18.009,-18.114,-17.931,-16.857\n-12.147,-10.509,-9.4741,-8.893,-7.4328,NaN,-9.1217,-8.0967,-7.9909,-7.9572,-7.5482,-7.1457,-7.0814,-6.8021,-7.6111,-8.4586,-8.2586,-7.7286,-7.4355,-7.4235,-7.2285,-6.4617,-6.7265,-6.6344,-6.7099,-7.1327,-7.3616,-7.9103,-8.2025,-8.0602,-8.388,-9.096,-10.474,NaN,NaN,NaN,-21.833,-20.683,-20.203,-19.375,-19.108,-18.68,-17.91,-17.505,-17.468,-17.301,-16.491\n-11.73,-11.837,-10.444,-8.8675,-7.5765,-8.5977,-8.7292,-7.9576,-7.7703,-7.9525,-7.1941,-6.5847,-6.5502,-6.5887,-7.0049,-7.1594,-6.6766,-6.6317,-7.0385,-7.2396,-7.3038,-7.0999,-7.5489,-6.8624,-7.1754,-7.2142,-7.03,-6.7132,-8.6807,-9.1672,-9.4433,-9.9486,-17.443,NaN,NaN,NaN,-20.354,-20.29,-19.49,-19.361,-19.571,-18.908,-17.932,-17.539,-17.535,-17.243,-15.484\n-12.743,-10.958,-8.9258,-9.1428,-9.4023,-9.1618,-8.3178,-7.8008,-7.5309,-6.7082,-6.322,-6.1592,-5.7891,-6.0112,-6.486,-6.6235,-6.2624,-6.3779,-7.4826,-7.8505,-7.1561,-7.1021,-7.0267,-6.932,-9.0843,-7.9586,-6.6685,-6.9066,-8.2424,-12.208,-13.536,-15.458,-18.382,-19.508,-19.365,-18.88,-19.383,-18.82,-18.71,-19.111,-19.334,-18.714,-17.752,-17.28,-16.835,-16.204,-15.377\nNaN,NaN,-10.72,-10.482,-10.467,-9.5471,-8.9608,-8.2153,-8.1523,-8.1536,-7.3454,-6.4904,-6.4716,-6.5656,-6.9681,-6.7235,-6.8123,-7.5815,-8.2028,-8.3316,-7.6253,-7.7415,-7.9114,-7.9965,-8.9216,-8.8442,-7.5514,-6.8924,-7.9334,-11.025,NaN,NaN,NaN,-17.922,-18.452,-18.518,-19.11,-20.186,-19.921,-19.984,-19.956,-19.317,-17.67,-16.998,-15.7,-14.763,-13.806\nNaN,NaN,NaN,NaN,-9.7383,-8.8598,-8.367,-8.5204,-8.448,-9.0307,-9.0078,-8.6808,-7.5335,-7.4381,-7.4262,-7.2531,-7.4683,-7.7864,-7.8021,-7.3197,-6.8466,-7.3619,-7.4356,-7.0864,-6.1695,-5.6932,-2.6694,-4.3155,-5.2022,NaN,NaN,NaN,NaN,NaN,-18.833,-19.363,-20.657,-21.705,-21.678,-21.116,-19.747,-18.353,-17.414,-16.712,-15.463,-13.352,-12.834\nNaN,NaN,NaN,NaN,-10.115,-11.151,-9.7355,-8.6981,-8.6924,-9.5928,-9.099,-8.4541,-7.7897,-8.1867,-8.1448,-7.9568,-7.8557,-7.7342,-6.6144,-6.0167,-6.2628,-6.2518,-6.0203,-4.9564,-3.4372,-1.7699,-0.091177,0.67142,NaN,NaN,NaN,NaN,NaN,-21.129,-19.941,-20.382,-21.403,-21.743,-21.687,-20.379,-18.946,-17.138,-16.261,-15.851,-14.694,-13.258,-12.457\nNaN,NaN,NaN,NaN,-11.599,-12.402,-11.924,-9.9818,-8.8273,-8.6136,-8.4595,-7.98,-8.1565,-9.1218,-9.4397,-9.3937,-8.8109,-7.7909,-6.997,-6.5337,-4.9786,-4.6972,-2.5077,-1.0334,0.39067,1.0454,1.5811,1.8886,NaN,NaN,NaN,NaN,NaN,-20.262,-20.668,-20.606,-21.395,-21.49,-20.99,-20.14,-19.212,-18.263,-16.193,-14.959,-13.575,-12.749,-12.22\nNaN,NaN,NaN,NaN,NaN,-11.088,-10.299,-9.1562,-8.5188,-9.1539,-10.561,-10.165,-9.9466,-10.206,-11.056,-11.194,-9.8924,-9.4668,-8.7852,-8.4706,-7.01,-5.6701,-2.9888,0.54095,1.3653,0.78247,0.29005,3.2372,NaN,NaN,NaN,NaN,-20.934,-19.474,-19.783,-20.055,-20.846,-20.701,-20.207,-19.269,-18.928,-17.506,-16.094,-14.212,-13.317,-12.634,-11.716\nNaN,NaN,NaN,NaN,-13.997,-11.399,-10.195,-9.4673,-8.5737,-9.192,-11.167,-11.712,-11.642,-11.841,-11.793,-10.936,-10.728,-10.521,-9.3265,-8.6195,-8.8394,-8.302,-7.6056,-5.3054,-2.2145,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.256,-18.053,-18.182,-18.289,-19.76,-19.094,-18.431,-18.467,-17.178,-16.388,-14.992,-13.947,-12.48,-10.931\n-14.624,-15.465,NaN,-14.753,-13.819,-11.393,-10.325,-10.685,-9.7893,-8.5006,-9.1694,-9.8092,-9.703,-12.296,-12.959,-12.44,-12.109,-10.758,-9.0917,-7.3271,-7.2691,-8.0128,-7.0948,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.702,-18.019,-18.727,-18.816,-18.493,-17.634,-16.914,-15.898,-14.744,-13.404,-11.909,-10.456\n-15.476,-15.226,-15.291,-14.55,-12.729,-11.665,-10.778,-9.953,-9.2479,-8.568,-8.7237,-8.0914,-6.9427,-9.9812,-12.026,-11.922,-10.849,-10.039,-9.1113,-7.7261,-6.4705,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-16.748,-16.718,-17.546,-17.464,-16.518,-15.859,-15.311,-14.123,-13.079,-11.318,-10.202\n-15.017,-14.174,-14.004,-16.033,-14.392,-12.41,-10.888,-9.9427,-9.0261,-7.8441,-9.1145,-10.14,-10.996,-11.032,-11,-10.87,-10.895,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.446,-16.244,-16.691,-16.856,-16.721,-15.757,-15.034,-14.717,-13.942,-12.181,-10.617,-9.6745\n-14.924,-15.111,-15.455,-15.47,-14.773,-13.282,-11.677,-10.361,-10.608,-9.1863,-8.9999,-12.206,-12.403,-13.574,-13.35,-11.268,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.849,-16.49,-16.868,-16.14,-15.383,-14.436,-14.088,-13.333,-12.514,-11.554,-10.02,-9.574\n-14.587,-14.957,-15.36,-15.04,-14.045,-12.959,-12.83,-13.365,-14.029,-12.065,-11.155,-13.212,-13.504,-14.088,-13.336,-11.085,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.498,-18.217,-18.974,-19.448,-17.29,-16.775,-16.453,-15.356,-14.304,-13.092,-12.19,-11.163,-10.19,-9.567,-9.5619\n-15.696,-15.25,-15.034,-15.247,-15.043,-13.434,-13.202,-14.08,-14.567,-14.605,-14.59,-14.774,-16.049,-16.865,-14.246,-11.788,-8.6344,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-12.324,-12.437,-13.305,-15.751,-16.985,-16.481,-16.599,-16.529,-15.611,-14.543,-12.941,-11.245,-10.654,-10.163,-9.6322,-9.7124\n-16.51,-16.039,-16.001,-15.537,-15.498,-15.608,-15.022,-15.31,-15.765,-15.299,-15.031,-14.481,-16.374,-16.368,-16.094,-13,-11.112,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.243,-13.936,-14.832,-15.709,-16.136,-15.794,-15.55,-15.678,-16.148,-15.637,-14.352,-12.244,-10.895,-10.514,-10.095,-9.6076,-9.0591\n-16.607,-15.851,-15.846,-15.786,-15.676,-15.8,-15.863,-15.862,-16.33,-16.699,-16.751,-16.083,-16.796,-17.349,-17.411,-15.303,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.975,-14.561,-15.473,-16.384,-16.953,-16.279,-15.951,-15.451,-15.135,-15.667,-15.466,-14.204,-12.771,-11.009,-10.082,-9.8375,-9.5588,-9.244\n-15.614,-15.373,-15.706,-16.014,-15.53,-15.421,-15.862,-16.373,-16.698,-17.099,-17.985,-20.036,-19.776,-19.318,-19.175,-18.323,-16.915,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.29,-15.888,-16.07,-16.096,-16.433,-16.689,-16.704,-16.503,-15.847,-15.455,-14.468,-13.715,-12.402,-10.595,-10.257,-9.8503,-9.3034,-9.2626\n-14.481,-14.875,-15.176,-15.902,-16.086,-16.239,-16.804,-17.096,-17.547,-18.071,-18.773,-19.547,-19.898,-19.484,-19.491,-18.657,-16.314,-17.843,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.845,-15.057,-15.624,-16.164,-16.818,-16.529,-17.181,-17.441,-16.94,-15.922,-14.365,-12.85,-12.228,-11.319,-10.507,-10.57,-10.65,-9.933,-9.6612\n-16.423,-15.965,-15.57,-16.44,-16.885,-16.901,-17.516,-17.783,-19.132,-19.169,-18.785,-18.362,-18.633,-18.573,-18.402,-18.24,-17.009,-15.541,-17.143,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.498,-17.006,-16.892,-16.689,-16.994,-17.145,-17.197,-17.482,-16.84,-16.573,-15.407,-13.215,-12.064,-11.23,-10.416,-10.342,-10.706,-10.949,-10.84,-10.379\n-16.34,-16.28,-16.143,-16.583,-16.626,-16.933,-18.165,-19.101,-19.884,-18.709,-17.569,-18.008,-18.234,-18.416,-18.353,-18.325,-19.029,-18.424,-19.759,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.219,-17.6,-17.467,-17.561,-17.861,-17.76,-17.36,-17.567,-17.069,-16.299,-14.995,-12.749,-11.508,-10.546,-9.8776,-9.8429,-10.528,-10.583,-10.851,-10.632\n-15.744,-15.778,-15.423,-15.371,-15.83,-16.259,-16.677,-17.388,-17.315,-17.034,-17.043,-17.742,-18.82,-19.196,-18.92,-18.666,-18.897,-19.988,-21.495,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.782,-17.612,-17.534,-17.891,-17.607,-17.093,-16.704,-16.333,-16.075,-15.297,-13.866,-12.459,-11.33,-9.8859,-9.5376,-9.5098,-10.126,-10.077,-10.384,-10.685\n-15.425,-15.554,-15.233,-14.935,-15.288,-15.852,-16.281,-16.483,-16.577,-16.556,-16.587,-17.113,-17.542,-17.474,-17.629,-17.403,-18.051,-19.081,-20.953,-21.268,-24.264,NaN,NaN,NaN,NaN,NaN,-18.02,-17.63,-17.277,-16.567,-16.818,-16.947,-16.268,-15.724,-15.298,-14.642,-14.383,-13.705,-12.579,-11.303,-10.223,-9.3018,-9.3429,-9.2871,-9.9978,-10.351,-10.019\n-15.135,-15.652,-15.639,-15.318,-15.743,-15.924,-16.137,-15.95,-15.973,-15.199,-14.873,-15.8,-15.747,-15.608,-15.941,-16.277,-16.898,-17.767,-18.762,-18.652,-21.363,-24.23,-22.538,-19.819,NaN,NaN,-19.016,-18.159,-18.439,-17.617,-16.487,-15.793,-14.736,-14.14,-14.29,-14.366,-14.7,-13.797,-12.213,-11.211,-10.679,-9.6999,-10.167,-9.6732,-9.9972,-9.8669,-9.5003\n-14.78,-15.351,-15.984,-15.967,-15.795,-15.89,-15.467,-15.424,-15.164,-14.978,-14.543,-14.398,-13.979,-14.574,-15.229,-15.819,-16.513,-17.715,-17.414,-18.331,-20.007,-20.098,-19.63,-19.111,-19.04,-18.588,-18.589,-18.131,-18.369,-17.296,-16.23,-15.313,-14.623,-14.472,-14.343,-14.167,-14.321,-13.815,-12.143,-10.965,-10.533,-9.9263,-10.75,-10.969,-10.662,-9.9874,-9.4276\n-14.455,-14.343,-15.142,-14.984,-14.599,-14.25,-14.568,-15.008,-14.981,-14.58,-14.123,-14.276,-14.283,-14.303,-15.248,-15.87,-16.256,-17.353,-17.986,-19.056,-18.922,-19.084,-18.477,-18.747,-18.464,-18.46,-18.474,-18.075,-17.455,-16.715,-16.027,-15.227,-14.318,-14.501,-14.369,-14.154,-13.635,-13.333,-11.943,-10.869,-10.041,-10.464,-13.529,-11.662,-10.71,-10.12,-9.122\n-14.51,-14.37,-14.139,-13.854,-14.088,-14.353,-14.305,-14.345,-14.587,-14.273,-13.758,-13.621,-14.408,-14.96,-15.481,-15.661,-15.992,-17.213,-17.11,-17.047,-17.184,-17.382,-17.834,-18.177,-18.088,-18.295,-18.881,-18.37,-16.882,-16.237,-15.993,-15.007,-14.046,-13.747,-13.898,-13.548,-13.166,-12.576,-11.605,-11.223,-11.014,-9.6462,-9.2801,-10.511,-9.9779,-9.7818,-8.5018\n-14.143,-14.256,-13.955,-13.841,-14.373,-14.473,-14.445,-14.351,-14.03,-13.684,-13.873,-13.764,-13.766,-14.483,-14.795,-16.265,-16.344,-18.359,-18.842,-18.659,-17.646,-17.554,-17.67,-17.42,-17.088,-17.161,-18.149,-17.665,-16.557,-16.028,-15.551,-14.728,-13.922,-13.285,-13.066,-12.947,-12.365,-11.675,-11.114,-10.89,-11.011,-11.03,-10.891,-9.1926,-8.9132,-8.4948,-7.0268\n-13.939,-13.996,-14.127,-13.455,-13.398,-13.611,-14.044,-14.469,-14.247,-13.683,-13.682,-14.124,-15.036,-15.553,-15.42,-16.313,-17.588,-18.72,-20.401,-20.98,-19.49,-18.498,-18.034,-17.04,-16.518,-16.532,-16.035,-15.902,-15.641,-15.657,-15.299,-14.93,-13.871,-13.446,-12.648,-12.088,-11.585,-11.055,-10.943,-10.907,-10.757,-10.558,-10.276,-9.0753,-8.9041,-7.5921,-5.6533\n-13.778,-13.61,-13.299,-13.089,-13.043,-13.772,-14.29,-14.852,-14.574,-14.013,-13.423,-13.188,NaN,NaN,NaN,-21.08,-20.422,-18.526,-20.347,-22.141,-21.512,-18.768,-17.25,-16.243,-15.606,-16.104,-15.489,-14.826,-15.152,-15.133,-14.719,-14.317,-13.394,-13.258,-12.766,-12.117,-11.378,-10.881,-10.934,-11.306,-11.214,-10.831,-9.8813,-9.2206,-7.9695,-6.7076,-5.7225\n-14.01,-13.52,-13.166,-12.61,-13.304,-14.462,-15.254,-14.908,-14.359,-13.712,-12.836,-12.518,NaN,NaN,NaN,NaN,NaN,-22.437,-21.636,-21.257,-19.498,-17.955,-16.863,-15.884,-15.555,-15.706,-14.962,-14.76,-14.252,-14.639,-13.916,-13.834,-13.541,-13.003,-12.127,-11.773,-11.312,-11.031,-10.809,-10.557,-10.451,-10.302,-9.7237,-8.8958,-7.7572,-7.2088,-7.098\n-14.727,-14.319,-13.366,-12.235,-10.824,-12.986,-15.051,-13.943,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-20.747,-20.663,-19.143,-17.61,-16.516,-15.68,-14.97,-14.994,-13.284,-13.048,-13.513,-14.451,-14.351,-14.029,-13.209,-12.785,-12.244,-11.697,-11.33,-10.873,-10.701,-10.1,-9.4191,-9.2471,-8.5721,-7.8983,-7.2281,-6.2724,-6.1825\n-15.485,-14.57,-12.838,NaN,-10.752,-12.144,-13.488,-14.362,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-20.384,-19.584,-17.839,-15.996,-14.91,-14.536,-14.378,-13.208,-13.274,-13.742,-14.158,-14.141,-14.009,-13.318,-12.53,-12.18,-12.225,-11.763,-11.157,-10.516,-9.7639,-9.3558,-9.2452,-8.2236,-7.012,-6.0013,-5.5091,-5.2969\n-14.771,-14.067,-13.307,NaN,NaN,NaN,NaN,NaN,NaN,-15.384,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.016,-17.788,-17.545,-16.888,-15.157,-14.291,-13.706,-13.423,-14.519,-14.45,-13.673,-13.58,-13.37,-12.818,-11.516,-11.35,-11.626,-10.928,-10.202,-9.7545,-9.3324,-9.0294,-8.7889,-7.9422,-6.2731,-5.7711,-5.7676,-5.8333\n-14.984,-14.747,-14.826,NaN,NaN,NaN,NaN,NaN,NaN,-15.043,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.113,-18.323,-18.167,-17.152,-16.453,-15.73,-14.917,-13.732,-13.05,-12.811,-13.107,-12.773,-12.701,-13.354,-12.449,-11.444,-11.395,-11.279,-10.402,-9.9205,-9.6858,-9.1186,-8.7413,-8.5117,-8.0643,-7.5064,-6.518,-5.2318,-5.0036\n-14.559,-14.392,-15.223,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-22.353,-24.078,-21.673,-21.74,-21.16,-16.856,-15.594,-16.418,-17.656,-17.571,-16.46,-15.652,-15.067,-13.759,-13.258,-12.511,-12.514,-11.942,-12.351,-12.298,-12.826,-12.411,-11.215,-11.585,-10.998,-9.998,-9.8838,-9.9382,-9.3616,-8.7777,-7.8284,-7.0251,-6.8181,-6.8381,-6.4054,-6.0071\n-12.617,-13.174,-13.308,-15.511,-17.918,-18.542,NaN,NaN,NaN,NaN,NaN,-24.432,-23.452,-22.551,-20.478,-20.084,-18.663,-17.134,-16.095,-16.34,-16.044,-15.247,-13.996,-13.776,-13.087,-12.043,-12.132,-12.167,-11.316,-10.915,-10.667,-10.901,-12.066,-12.153,-11.972,-10.913,-10.463,-10.559,-10.777,-10.9,-9.659,-8.0985,-7.3563,-7.2379,-6.9987,-6.6081,-6.8373\n-13.691,-14.286,-14.335,-13.368,-14.911,-16.993,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-21.681,-21.384,-19.627,-17.206,-16.523,-16.063,-15.334,-14.678,-14.625,-14.705,-14.514,-13.545,-12.617,-12.288,-12.06,-11.749,-11.495,-10.667,-10.298,-10.574,-11.597,-11.691,-11.312,-11.126,-11.415,-11.482,-11.43,-11.096,-9.9732,-9.6765,-8.9974,-7.5457,-6.9691,-8.2545\nNaN,NaN,NaN,NaN,NaN,-18.32,-19.142,-18.343,NaN,NaN,NaN,NaN,NaN,NaN,-22.7,-20.381,-18.381,-17.067,-15.424,-15.143,-14.731,-14.708,-15.008,-13.844,-13,-12.329,-11.902,-11.513,-10.901,-10.792,-10.625,-10.42,-10.505,-10.286,-10.445,-11.125,-11.017,-11.413,-11.542,-10.871,-10.668,-10.224,-9.7713,-9.2045,-8.6391,-8.6467,-9.6489\nNaN,NaN,NaN,NaN,NaN,-20.9,-19.236,-17.501,NaN,NaN,NaN,NaN,NaN,NaN,-21.693,-21.592,-20.6,-18.482,-16.661,-15.35,-15.179,-16.486,-14.914,-13.717,-12.605,-11.52,-11.275,-10.982,-10.621,-10.665,-10.729,-10.735,-10.538,-9.8262,-9.774,-10.267,-10.337,-10.747,-11.322,-10.784,-10.34,-9.6077,-8.9726,-8.7511,-8.9659,-9.3801,-9.8391\nNaN,NaN,NaN,NaN,-17.407,-18.426,-18.031,-17.606,-17.499,NaN,NaN,NaN,NaN,-20.895,-20.406,-19.449,-18.4,-16.402,-15.913,-14.98,-14.686,-15.53,-15.381,-13.454,-13.31,-11.984,-11.237,-10.666,-10.582,-10.349,-10.12,-9.7906,-9.3816,-9.7284,-10.337,-10.563,-10.454,-10.101,-10.473,-9.7223,-8.9517,-8.9238,-9.4461,-9.6196,-9.0327,-8.878,-8.7547\nNaN,NaN,NaN,NaN,-16.312,-16.348,-17.178,-18.335,-18.62,NaN,NaN,NaN,-18.656,-20.253,-20.691,-18.925,-19.306,-16.803,-15.781,-13.979,-14.142,-13.623,-14.301,-14.647,-13.351,-12.134,-11.038,-10.201,-10.041,-10.423,-10.176,-9.847,-9.6256,-10.271,-11.035,-10.468,-10.284,-9.8674,-9.3903,-8.7887,-8.9805,-9.4244,-10.411,-11.229,-10.033,-8.5071,NaN\nNaN,NaN,NaN,-13.997,-14.345,-15.359,-17.275,-18.984,-19.71,-17.514,-15.961,-16.944,-19.602,NaN,NaN,NaN,-21.976,-18.674,-16.022,-13.19,-13.074,-12.872,-12.524,-12.424,-11.731,-11.293,-11.02,-10.552,-10.313,-10.179,-10.108,-9.8774,-10.002,-10.134,-11.061,-9.9109,-9.599,-9.7247,-9.335,-9.2039,-8.6909,-8.9658,-9.9725,-9.6641,-9.3908,-10.41,NaN\n-18.851,-17.187,-18.619,-17.024,-16.365,NaN,-16.043,-17.747,-19.354,-19.682,-19.31,-18.518,NaN,NaN,NaN,NaN,NaN,NaN,-14.749,-12.371,-11.241,-11.903,-11.746,-11.287,-11.659,-11.33,-10.85,-10.255,-10.135,-9.8934,-9.7962,-9.8352,-9.5312,-9.5731,-9.8726,-9.9785,-9.9803,-9.9219,-9.4516,-9.1423,-8.4178,-8.4906,-9.1625,-9.3698,-9.6462,-10.145,NaN\n-19.184,-18.379,-18.009,-19.48,-18.015,NaN,NaN,NaN,-18.163,-16.919,-17.593,-18.281,-15.174,-13.61,NaN,NaN,NaN,NaN,NaN,NaN,-10.421,-10.976,-11.165,-11.128,-11.082,-10.66,-10.402,-10.073,-9.911,-9.5004,-9.3,-9.669,-9.6792,-9.1726,-9.5641,-9.9583,-9.7367,-9.4966,-9.1448,-9.0547,-9.3128,-9.3917,-8.6081,-8.1162,-9.0357,NaN,NaN\n-20.295,-19.063,-17.244,-17.19,NaN,NaN,NaN,-16.666,-18.985,-18.564,-18.545,-19.064,-17.485,-17.286,-15.454,NaN,NaN,NaN,NaN,NaN,-12.08,-9.921,-10.763,-11.572,-12.045,-11.198,-10.506,-9.976,-9.3931,-9.084,-8.3676,-8.5974,-8.7283,-8.6577,-8.9304,-9.3731,-8.9887,-8.4279,-9.504,-9.7662,-10.023,-9.6157,-7.8123,-6.6395,-5.5887,NaN,NaN\n-18.684,-18.352,-17,-16.469,NaN,NaN,NaN,-16.575,-15.026,-14.929,-15.974,-17.764,-16.334,-14.625,-15.203,NaN,NaN,NaN,NaN,NaN,-10.734,-9.587,-10.726,-10.511,-10.483,-10.728,-10.102,-9.9595,-10.045,-9.7897,-8.9935,-8.294,-8.6164,-8.5373,-8.6612,-9.2315,-9.3589,-8.5754,-8.5803,-9.7268,-8.4948,-8.9691,-8.0715,-4.6438,NaN,NaN,NaN\n-18.412,-17.446,-16.669,-16.937,-17.11,-16.909,-16.534,-15.996,-15.423,-14.981,-14.697,-13.958,-14.345,-15.854,-15.725,NaN,NaN,NaN,NaN,NaN,-10.176,-9.2858,-8.6221,-9.08,-9.8341,-10.05,-9.9789,-9.7264,-10.94,-10.94,-9.5565,-8.3668,-8.0374,-7.752,-7.224,-9.8087,-10.294,-9.2984,-8.5493,-8.2259,-7.7683,-6.309,-5.2622,-2.3516,NaN,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070326-070917.unw.csv",
    "content": "6.957,6.2325,5.4174,5.67,5.4465,5.2204,5.2106,5.172,5.3689,5.0138,5.5478,4.9845,4.7237,4.5305,3.8669,3.3138,3.3487,3.3946,3.0002,3.2271,3.4635,3.4172,3.3292,2.9052,2.2515,2.7813,2.7537,2.2851,2.5843,2.5301,2.495,2.7297,2.0493,1.7406,2.0719,2.068,1.8502,1.5604,2.3955,2.6036,2.3975,2.6203,2.3054,1.8725,2.5844,2.9398,2.8628\n6.1185,6.3053,5.2572,5.3808,5.5662,5.371,5.5867,5.2872,5.3098,5.3117,5.5455,5.2076,4.7497,4.2523,3.7345,3.6454,3.9384,3.7198,3.5706,3.7525,4.2324,3.0754,2.8779,3.0559,3.6407,3.5207,3.2482,2.7152,2.1547,2.1712,2.6286,2.5876,2.3009,1.9895,2.3723,1.796,1.5895,2.0067,2.6003,3.0564,3.1764,3.0363,2.3851,2.5269,3.0258,2.9368,2.8005\n5.9787,5.463,5.6265,5.5899,5.548,5.4629,5.618,5.5166,4.6619,5.3211,4.9328,5.6322,4.7879,4.1585,3.7431,3.8994,3.9808,3.511,3.3062,3.1058,3.4123,3.425,2.774,3.143,3.2123,3.0282,2.788,2.3477,2.3865,2.3111,2.6797,2.4586,2.2734,2.1584,2.1837,1.6996,1.0263,2.0586,2.6706,3.0445,3.2595,2.9868,3.0234,3.3225,3.3611,3.1009,2.9912\n6.2358,5.8479,5.9055,5.7766,5.9196,6.1313,5.5325,5.4207,5.0783,5.2315,4.9474,4.7896,4.2454,3.6884,4.4304,4.2875,4.4607,4.0739,3.47,1.8653,2.5882,3.0974,3.3387,3.277,2.7551,2.4607,2.7252,3.1243,2.8899,2.7377,2.7355,2.814,2.2812,2.2445,2.3595,2.0529,1.8016,1.921,2.4349,2.9313,3.2577,3.3174,3.2568,3.3889,3.4315,3.3452,3.1557\n5.9001,5.3191,6.147,5.876,5.691,5.6603,5.4883,5.2962,5.3087,4.7469,4.6487,4.3676,4.2,3.713,4.2049,4.2944,4.3419,3.9131,3.7626,3.4503,2.6906,3.8755,3.7451,3.1819,2.5037,1.9293,2.259,2.7056,3.305,2.6454,2.8771,3.1268,3.0307,2.3808,2.3627,2.3966,1.9258,1.8914,2.2674,3.0161,3.5744,3.8686,3.5693,3.8026,3.6752,3.3967,3.1675\n5.9369,5.4269,5.7358,5.6739,5.72,5.393,5.63,6.0412,5.951,5.6064,5.0834,4.4771,4.6163,4.4799,4.3085,4.109,3.9236,3.582,3.5659,3.481,3.7152,4.0187,3.4214,2.9436,3.0254,2.6234,2.1188,2.4192,2.7978,2.1029,2.5372,2.4455,2.3284,2.5968,2.6344,3.152,3.0644,2.5236,2.8101,3.6751,3.9514,3.737,3.7115,4.0619,3.8885,3.9207,3.6914\n6.1978,6.1994,6.7513,5.6083,5.3722,5.0604,5.6073,6.043,5.8452,5.4803,5.1104,4.7782,4.6259,4.5342,3.7517,3.9187,4.1058,3.9028,3.8293,3.198,3.7039,3.3254,3.1992,2.5452,2.4211,2.2509,2.4991,3.1627,2.4691,2.8029,2.9671,2.6854,2.7042,3.0072,3.0889,3.1917,3.2261,2.8309,3.1128,3.8966,4.1283,4.1748,4.4024,4.1932,4.1515,4.3856,4.5756\n6.6992,6.691,6.9526,5.8781,5.1891,4.7667,5.4115,5.8551,5.6706,5.1108,5.1455,5.4416,5.0006,3.7473,4.139,4.1198,4.3407,3.4118,3.3874,3.501,4.0364,3.5124,3.2248,2.8326,2.6281,2.6312,3.2262,3.908,3.095,2.4305,3.3825,3.4789,3.1972,3.2182,2.8546,2.8784,2.8546,2.8633,3.1021,3.7203,4.5152,4.9549,4.6559,4.7072,4.5692,4.1748,4.4156\n6.8413,6.3989,6.2833,5.7751,4.9965,5.0478,5.3349,5.4531,4.9417,4.8721,5.2543,5.7613,5.4017,4.2779,4.4489,4.3884,4.4991,4.2728,3.5353,3.792,3.6754,3.5573,3.8505,3.3466,2.9585,3.3382,3.4403,3.1191,2.5523,2.8978,3.5166,3.5312,3.5561,3.6188,3.2998,2.8681,3.2672,3.4951,3.7664,4.1382,4.3381,4.2868,4.0567,4.3563,4.557,4.6708,4.6249\n6.7032,6.6194,6.4274,5.4154,5.4174,5.0063,5.3242,5.0049,5.0386,5.5372,5.8053,5.8671,5.3514,4.8308,4.6915,4.4572,4.4866,4.2317,3.3623,3.4391,4.2674,4.3445,3.9035,2.7502,2.8082,3.0768,4.2349,3.8134,2.5848,2.6974,2.9855,3.0459,3.5572,3.3686,4.0468,4.0472,3.9918,4.0527,3.6346,3.8108,3.9767,4.1606,4.2342,3.7907,4.5116,4.7562,4.6686\n6.7812,6.1992,6.4034,5.5039,4.9266,5.1883,5.0332,4.9761,6.0956,6.4866,6.2222,5.6148,4.9881,4.876,5.0391,4.9102,5.0873,4.391,4.4976,4.6912,4.519,4.6099,3.9341,3.1326,2.9237,3.2572,3.5778,3.4576,2.9504,2.6634,2.6179,2.994,3.5791,3.6004,3.7477,3.3598,3.2695,3.2627,3.5803,4.0466,3.7356,3.8924,3.9017,3.6917,4.2746,4.9849,4.9671\n6.3419,5.3571,5.8418,5.2655,5.6485,5.9356,5.4181,5.6754,6.2351,6.3029,6.3202,6.0957,5.7997,5.5742,5.0188,4.526,4.7597,4.5044,4.286,3.7742,3.8626,3.7533,3.5316,3.8714,3.6583,3.9663,3.8444,3.2711,2.4807,2.6305,2.3458,2.8498,3.5744,3.8674,3.6026,3.2776,3.9185,3.8354,3.2872,3.3371,3.419,3.9516,4.5141,4.4893,4.6087,5.4107,NaN\n6.1442,5.7656,6.4627,5.6581,5.4373,5.4303,5.229,5.7749,5.2018,5.8771,6.1001,6.2167,6.0738,5.7753,5.365,4.5264,4.1655,3.8308,3.5956,3.6042,4.3977,3.897,3.046,3.7804,4.6289,4.2633,4.0055,3.145,1.9878,2.5108,3.1638,2.9305,3.2804,3.6613,4.1407,4.5744,5.2705,4.8197,4.5641,4.1373,4.1672,4.5598,5.2174,5.0877,5.2678,NaN,NaN\n5.818,5.6259,6.1165,6.5479,6.1245,5.4418,4.692,5.5843,5.6557,6.0161,6.0333,5.9329,6.0019,5.6641,5.334,4.6878,4.1958,3.4983,3.7857,4.6583,4.8329,4.2443,4.5913,3.6809,3.8179,3.8178,3.9546,3.7075,3.1431,3.0735,3.7909,3.9804,4.0099,4.3266,4.6044,4.6986,5.3926,5.8159,5.2745,4.8002,5.6139,5.7583,5.6964,5.9057,6.0992,NaN,NaN\n6.5428,6.7924,8.1257,5.9737,4.8969,4.8997,5.329,5.6623,5.6328,5.7359,6.3949,5.7263,5.0393,4.7811,5.1535,4.747,4.6922,3.38,4.8249,5.4077,5.124,5.8728,5.1916,3.4959,3.7306,4.301,4.1064,4.2037,4.7095,4.2383,3.7983,4.2014,4.8734,5.1988,4.9461,4.4945,5.4383,5.7607,5.957,6.2708,6.6401,5.1852,5.3016,5.8202,5.8443,6.4454,6.7177\n7.1115,6.9204,7.7963,6.4001,6.3279,5.4312,6.2424,5.9463,5.7441,5.8438,6.0028,5.1676,5.3811,5.3114,6.0918,5.1939,4.7515,5.2066,5.5854,5.8236,5.4486,5.6583,5.6581,4.9094,4.0727,4.0385,4.4159,4.8328,5.8801,5.4085,5.2216,5.177,5.2283,4.9484,4.4559,5.7202,5.6001,5.6494,5.8382,6.5729,6.0595,5.3502,5.2836,5.1093,4.9721,6.1202,6.2273\n7.9369,7.2641,6.7666,5.8877,5.0805,5.1114,5.8903,5.8454,5.9307,6.1391,6.2505,6.0315,5.273,5.2694,5.6311,5.537,4.3307,6.4406,6.3722,6.4585,6.9136,5.8539,5.6574,5.4582,5.7892,6.6119,7.6944,8.0913,7.1182,5.0998,5.7536,5.5296,5.108,5.1755,5.2467,5.6709,5.4637,5.4741,5.2213,5.2144,5.3418,5.3172,5.2761,5.3134,4.8798,6.6257,6.0162\n6.4558,7.0352,6.5866,6.8661,4.5191,3.4976,5.4068,6.2916,6.5089,5.7617,5.8993,6.0844,5.6477,5.8835,6.2162,6.5741,5.8528,5.8112,5.3175,5.6394,6.2871,5.4513,4.6458,5.3303,5.0879,5.6292,6.8146,7.3797,7.6338,6.0427,5.4079,5.7617,5.4114,5.4516,5.3699,5.0511,4.4053,5.7248,6.1447,6.2009,5.8105,4.8217,4.3479,4.5358,5.0247,5.2391,5.2231\n7.3485,6.7466,7.4202,6.4989,5.2253,1.8282,5.5409,6.2037,6.0898,5.5539,6.2809,6.3414,6.3527,6.817,7.1843,7.3211,7.0202,5.7483,5.381,5.2998,5.0031,3.4731,2.592,4.8162,4.8819,4.9214,6.2518,6.3938,6.8454,6.1987,5.2248,5.2595,5.7981,6.099,6.446,5.6178,6.4746,6.569,7.2949,6.4177,5.8815,5.5528,4.5977,4.9919,5.186,5.1586,4.6886\n8.6228,7.4349,6.9191,6.2453,4.6456,3.4191,5.8436,5.7023,6.1031,6.9048,7.6884,7.9283,8.1648,7.6702,8.0502,8.7251,7.7673,6.0074,5.0732,4.7209,5.5569,5.6053,4.4757,3.9238,3.7637,5.213,5.9922,6.2271,6.0561,6.2013,6.3552,5.8519,6.0889,6.328,7.4321,6.3103,6.0692,5.9308,7.8257,7.2521,6.3183,6.1796,5.9301,5.8627,6.1635,5.2973,4.7568\n8.4895,7.416,6.8272,6.1876,5.4479,6.5033,6.9882,6.915,7.2173,7.7705,7.6804,7.3343,7.9466,8.9582,8.2725,7.3898,7.1168,6.7607,5.5726,6.0606,6.9602,7.4965,7.2482,5.6213,5.0468,6.3202,6.8864,7.6793,7.588,7.2077,7.6583,7.2961,7.0583,6.789,6.9006,5.7913,5.6553,4.9829,6.0363,6.8345,6.8485,6.1136,6.1707,6.545,6.3709,6.0928,5.5104\n7.1293,6.8344,7.1013,7.7685,7.3825,5.6104,6.798,7.1647,7.2467,7.8853,8.0328,8.1286,8.9783,10.226,8.1086,7.3936,6.7946,6.4368,5.9497,4.5601,4.7145,5.4967,7.6119,6.9954,6.7375,7.5586,8.4525,8.9466,10.183,10.082,9.4647,8.5006,8.3885,8.1191,8.3622,7.3641,7.7299,6.8325,7.1381,7.3827,7.1864,6.8382,6.961,6.5852,6.0826,5.6479,5.9493\n7.5878,7.2613,6.9151,7.2665,6.6986,6.9532,7.1775,7.1333,7.83,7.2826,7.7484,7.6289,8.5431,8.6742,7.1607,6.7147,6.374,6.2056,6.2854,6.0161,6.6806,6.6136,8.4931,8.7414,8.6549,8.681,9.7222,9.6474,10.391,10.944,10.842,11.22,10.647,10.239,8.9608,8.4292,8.2729,8.34,8.1546,7.9558,8.1118,7.2965,6.9499,6.1996,6.0828,6.2469,6.4329\n8.491,8.2645,7.6307,8.0154,9.1754,7.2248,7.0987,7.2569,7.6231,7.8196,7.6942,8.8438,8.5929,8.2402,7.5585,6.1554,7.8763,8.0516,7.2002,7.024,6.9325,8.4246,9.2412,9.5324,9.2293,9.2494,9.9117,10.687,10.984,12.208,13.879,13.472,12.696,11.641,10.296,10.2,10.199,9.6785,9.2789,8.0149,8.0435,7.2768,6.8614,6.6029,6.3416,6.4801,6.5532\n8.1201,8.2127,7.9439,8.1615,7.044,7.9324,7.1872,7.7877,8.1409,8.1851,8.5315,9.4958,9.4553,9.1182,8.2132,7.5984,8.2096,9.0891,9.0433,9.2868,9.0388,9.7211,11.025,10.911,9.6989,10.201,10.765,12.038,13.763,15.666,15.45,14.434,12.472,12.074,11.999,11.269,9.9819,10.223,10.184,8.2082,8.1446,7.9345,7.0354,6.2708,5.9263,6.1509,6.3511\n7.8194,7.9165,8.18,8.0678,7.1703,7.8416,7.2812,7.6873,8.4408,8.3417,9.3591,9.5566,9.5213,9.5126,9.8571,10.164,9.3683,9.3809,10.07,11.046,11.971,12.737,12.384,13.471,13.66,12.948,13.946,15.641,16.775,18.101,17.342,15.278,13.221,10.691,11.457,10.862,9.2594,9.4438,9.4862,9.0326,8.1044,7.8401,7.4843,6.5666,6.2216,6.0335,6.0905\n7.9139,8.7157,7.4736,7.0537,7.4653,7.9295,7.0802,8.1203,8.8565,8.1014,9.0659,9.8834,9.8755,9.8052,10.123,10.669,10.548,10.578,11.066,12.061,12.915,13.393,14.077,14.987,16.922,15.152,14.458,15.388,16.552,12.587,15.258,15.294,15.269,11.67,10.903,10.294,9.6666,9.3297,9.1376,8.9081,7.9272,7.6921,7.6065,7.237,6.6551,6.1538,6.129\n7.661,7.6646,6.3073,5.4653,6.922,7.7555,8.5611,8.8707,9.0431,8.0179,8.4363,9.6136,10.309,10.51,10.715,11.155,10.85,11.319,12.308,13.227,13.691,14.324,NaN,NaN,NaN,NaN,13,12.642,12.393,12.921,14.116,15.862,15.815,14.307,11.932,10.264,10.394,9.7966,9.4676,8.6142,8.0729,8.0287,7.5418,7.1352,6.1626,6.3537,6.6119\n7.2177,7.521,6.0793,7.0978,6.9938,6.6688,6.8618,7.1213,7.5033,7.11,8.5373,8.7721,9.7154,9.9865,10.221,10.767,10.698,11.913,12.904,14.11,14.699,NaN,NaN,NaN,NaN,NaN,NaN,11.409,9.0077,11.702,14.848,16.668,17.506,14.716,10.778,10.349,10.205,9.3983,9.3906,9.2052,8.3046,7.9036,7.5314,6.8355,6.6083,6.7244,6.4266\n5.4233,5.6719,7.1701,9.1815,6.1013,5.3698,5.3741,6.4716,7.222,7.365,8.4097,8.3593,8.4223,9.3662,10.239,10.864,11.3,11.208,11.551,13.22,NaN,NaN,NaN,NaN,NaN,NaN,12.813,10.929,10.581,11.632,14.524,17.133,16.45,12.908,12.274,11.694,11.266,11.179,9.9515,9.208,8.0707,7.7091,7.2787,6.8167,6.7669,6.7132,6.4359\n4.8954,4.7464,3.8756,3.898,4.6669,5.0287,5.7326,6.323,7.2281,8.4255,8.2945,8.029,7.811,8.596,10.154,10.127,10.201,10.908,11.612,12.806,NaN,NaN,NaN,NaN,NaN,14.796,14.294,13.021,11.097,9.849,9.3953,12.043,10.661,11.807,13.605,12.667,12.343,11.501,10.206,8.6415,8.2141,7.7426,7.3313,6.9309,6.885,6.4788,6.2242\n5.0406,3.9478,5.4023,7.6839,3.6501,3.7757,5.0799,5.3281,6.6571,7.6044,7.0099,7.7478,8.1925,8.5846,8.1036,8.4808,9.6355,9.575,11.072,11.819,NaN,NaN,NaN,NaN,NaN,NaN,15.444,16.163,14.186,13.108,12.107,12.632,8.1738,12.401,13.801,13.598,12.574,11.85,10.087,8.7694,8.4462,8.1223,7.6734,7.2473,7.0047,6.7271,7.0774\n5.7875,5.2796,6.0885,6.7278,4.9957,3.8001,3.8773,4.5142,5.5016,6.6203,6.7068,6.6067,6.7158,6.6975,5.7282,6.8944,8.896,9.1651,11.2,13.492,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.36,14.852,13.791,12.798,12.213,11.718,10.689,12.233,11.634,11.226,11.599,9.1614,8.5902,8.3418,8.3032,7.3718,6.6073,6.8966,7.1675,7.9844\n6.2379,6.0913,6.3325,6.558,6.3426,4.642,3.8789,4.2195,5.1617,6.0748,7.1405,6.8489,5.9684,4.2717,4.1685,5.2963,6.3536,6.4704,7.3174,8.6965,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.48,12.188,12.4,10.168,8.4881,9.8929,9.8775,11.25,11.097,9.4958,8.3444,8.3978,7.8164,7.2021,6.7708,6.7127,7.5355,8.1305\n6.0832,5.3431,4.8365,5.188,5.5584,4.7499,3.3907,4.354,5.1388,5.7667,6.1181,5.6076,5.6417,5.6261,3.6235,4.1922,5.9535,6.5252,6.3721,8.5176,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.364,12.426,12.699,NaN,NaN,12.591,12.565,12.642,9.9845,8.2261,8.1883,7.453,6.9626,7.1106,6.5775,7.2239,7.6662\n5.9385,4.6071,4.2139,4.3791,5.4493,4.6596,3.8364,3.6803,5.0684,5.3429,4.936,4.9661,5.1251,4.6971,3.6989,4.1802,5.2732,6.686,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.775,12.212,NaN,NaN,NaN,NaN,15.695,15.141,12.313,8.6979,7.7453,7.885,7.6162,7.2358,7.3997,7.2453,7.0656,6.8771\n5.3666,4.9079,5.1969,5.2538,4.9364,4.5749,4.4752,4.5809,4.4001,4.5496,4.2766,5.0164,5.2143,5.9757,5.6379,4.7389,4.8852,5.531,4.4218,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.117,NaN,NaN,NaN,NaN,NaN,10.628,10.165,9.3465,8.3866,7.7888,7.7594,7.72,7.819,7.7784,7.6297,7.0751,6.9891\n4.8966,4.7046,4.8471,4.8336,4.2745,4.8357,5.0324,4.4984,4.1525,4.7288,4.7457,4.5956,4.5141,4.7889,4.9222,5.0756,6.1699,6.9991,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.938,NaN,NaN,NaN,15.358,NaN,10.201,9.6142,8.754,8.3557,8.2654,7.8104,7.3553,7.6216,7.9517,8.6202,8.7664,8.2997\n5.3952,5.3888,4.256,4.1295,4.5789,5.3023,5.387,4.6729,4.7745,5.0839,4.7787,4.2725,4.3148,3.9885,4.7081,4.6376,5.9551,5.9666,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.6193,10.887,NaN,NaN,NaN,13.049,12.005,10.025,9.308,8.9468,8.9699,8.6016,7.886,7.3527,7.5102,7.2161,7.6555,8.4502,7.813\n5.3555,5.9034,5.5705,4.7599,4.9656,5.6615,5.7347,5.5187,5.1418,4.9627,4.8422,4.8094,4.6894,5.0981,4.9169,5.169,5.5627,6.5282,6.3823,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.7224,11.227,15.683,15.575,14.061,11.686,9.2923,9.1359,8.1349,7.6959,8.2865,7.9166,7.3804,6.9432,6.8019,7.0914,7.1468,7.1248,6.753\n5.1691,5.5037,5.0565,5.2903,5.595,5.8042,5.6816,5.6741,5.5655,5.6062,5.2947,5.1013,4.5325,4.3964,4.3508,4.746,5.2967,6.7974,6.9346,3.0159,4.0278,5.6351,5.1802,NaN,NaN,NaN,NaN,4.4867,8.9752,12.518,14.744,14.27,12.677,9.3716,8.5907,8.5055,7.6862,6.9376,7.3981,7.0931,6.7506,6.7782,6.6517,7.4431,7.1434,7.0976,7.0233\n6.0125,5.8998,5.9415,5.4798,5.4151,5.6381,6.0776,6.1608,6.0233,5.701,5.2658,4.3151,3.5398,3.6625,3.9331,4.6538,5.0466,5.9461,6.2698,2.7656,3.7281,4.7513,4.3757,NaN,NaN,NaN,NaN,NaN,8.5375,11.419,10.846,10.649,10.771,8.7092,8.5795,8.6039,7.6181,7.0275,7.0501,6.432,6.5648,6.865,7.0516,7.5334,7.0077,6.897,6.4872\n6.7606,6.0898,6.2543,6.2893,5.7196,5.4971,5.646,5.4872,5.8802,5.443,5.0164,4.7103,3.862,4.0023,4.2646,4.5009,5.3445,5.373,5.6378,5.0687,4.8217,4.4536,3.8916,5.3786,NaN,NaN,NaN,NaN,9.6498,9.53,9.9111,10.329,9.0056,8.4056,8.417,8.1468,7.3131,5.974,6.3483,6.025,6.0594,6.6117,6.7153,6.8004,6.4054,6.311,5.5885\n6.105,5.8606,6.2016,5.9913,5.871,6.0286,5.8468,5.3165,5.8991,5.2246,5.182,5.15,4.7014,4.7968,5.2725,5.4846,5.5584,5.5915,5.2292,5.3507,5.33,5.3097,7.1543,8.5612,NaN,NaN,NaN,8.7145,7.9375,8.4629,8.7507,8.5274,8.5538,8.0325,7.9629,6.9627,6.5002,5.1689,6.1136,5.8183,6.1911,6.8345,6.7049,5.9034,4.8938,4.4488,4.9467\n5.9739,5.968,6.2984,6.1247,5.8361,5.236,5.1859,5.1624,5.2144,5.5382,5.2741,4.9604,5.08,5.4252,5.8926,6.0449,5.8536,4.9245,5.1095,6.2856,6.2697,6.5043,7.1311,8.3257,9.8514,9.4008,9.0877,7.8179,7.1798,7.3898,7.8043,7.7544,7.6062,7.7421,7.5445,6.3847,5.8792,5.1925,5.8529,5.759,6.1948,6.4002,6.5667,6.7375,6.2788,5.4116,4.5951\n6.7765,6.4993,6.5522,6.4756,5.989,5.518,5.5555,5.2016,5.6879,5.8743,5.4965,5.4217,5.4614,5.4951,6.0555,5.9361,5.0933,4.1347,3.1747,3.9161,5.1398,5.2594,6.2138,6.593,7.0149,6.5013,7.5239,7.45,7.3883,7.9083,8.0349,8.4342,8.8662,7.8944,6.9833,6.0741,5.6372,5.5197,5.5484,5.3539,5.6625,6.1091,6.3327,6.8849,6.2413,5.7124,5.1213\n6.335,6.7372,7.2359,6.2257,6.03,5.7159,5.6129,5.6751,5.7672,5.487,5.5215,5.6332,5.7846,5.6212,4.9217,4.938,4.9261,4.546,3.0071,2.7136,3.8425,4.4804,4.7006,5.1013,5.9532,6.1897,6.7771,6.2135,7.3722,7.2882,7.8049,7.9689,7.5025,6.8219,5.3403,5.2707,6.0278,6.0941,5.2984,5.0543,5.2513,4.5944,4.6254,5.7812,6.4748,6.18,5.5957\n5.9816,6.398,6.874,6.8559,6.7791,5.5042,5.5333,5.4998,5.4635,5.2699,5.2074,5.122,5.2689,5.6041,5.7456,5.1698,4.8119,4.5076,3.6721,3.1615,2.5835,2.5259,3.1419,4.6386,4.2159,4.7217,6.0874,6.372,6.8694,6.5861,6.4865,7.1544,6.8241,5.8023,4.6621,4.5347,5.182,6.1625,5.6231,5.2005,4.9977,4.4554,4.6283,3.7319,4.9945,4.4282,5.002\n6.6234,7.0307,6.5395,6.311,6.5078,5.7378,5.4983,5.1025,6.2165,5.832,5.4307,5.8076,5.6621,5.7305,5.7837,5.3574,5.0281,4.8421,4.5237,4.0404,3.525,3.9145,4.7401,5.24,4.2673,4.0722,6.1309,6.8997,6.4685,6.4877,6.3177,6.4059,6.4958,6.6952,6.7372,5.682,5.7213,5.5567,5.5942,5.3352,5.1816,4.9899,5.1132,4.8299,4.4302,4.4064,5.0559\n6.3517,6.5631,6.6586,6.4408,6.1625,5.862,5.9627,5.7958,5.1894,4.7024,5.3116,5.593,5.8924,5.885,5.7008,5.5011,5.0713,5.1068,4.9432,4.8226,3.9157,4.8879,5.5416,6.0411,6.2598,6.0575,6.1121,6.2932,5.7984,5.7648,5.8615,5.6789,6.2583,6.7399,6.6306,6.2471,5.6381,5.2658,5.1733,4.8788,4.7925,4.6825,4.4276,4.1303,4.3469,4.6915,4.8463\n6.173,6.4327,6.4427,6.1692,6.3898,6.0835,5.926,5.3867,5.1164,4.5758,4.9564,5.3237,5.5902,5.3949,5.2937,5.1733,4.8974,4.5667,4.4987,4.3752,5.3523,5.8002,5.8642,6.1379,5.8478,5.6166,4.9797,5.5176,6.0327,6.2468,5.768,5.5099,5.9903,5.9143,6.1991,6.8384,6.4202,5.5114,5.384,5.0028,4.6492,4.4836,4.6296,4.3827,4.4318,4.8153,5.2149\n6.2836,6.2448,6.5264,6.6405,5.7982,5.5544,5.6132,5.1885,5.2074,5.2899,4.959,4.7973,5.2894,4.5686,5.3305,5.0815,4.3695,5.7519,5.2606,4.1418,5.4447,5.1595,5.6882,6.0995,6.3666,6.5605,5.8189,7.8431,7.4719,6.5989,5.803,5.4031,5.5784,5.576,5.7833,6.5221,5.799,5.6338,5.6187,5.1538,4.463,4.2854,4.4546,4.7378,4.289,4.6897,4.6332\n6.04,6.1418,6.6036,7.6387,6.3202,5.9122,4.9693,4.7145,4.6061,5.0592,5.5036,5.4191,5.0539,4.8937,4.6645,4.431,4.3759,4.2722,4.0897,3.9511,4.5218,4.8748,5.6135,5.982,6.6065,6.6843,7.4661,7.6622,6.9153,6.3718,5.9643,5.3154,5.5076,5.3298,5.6126,5.2812,5.0995,4.9651,5.3552,4.8192,4.4878,4.582,4.484,4.445,4.4278,4.3356,3.9017\n5.7816,5.4351,5.1907,5.4435,6.6757,6.1637,5.1326,5.3096,5.4473,5.2712,5.2461,4.7285,5.0279,5.0857,4.7969,4.2447,4.0348,3.8721,2.4415,2.5901,4.5967,5.1678,6.0257,5.7308,6.0424,6.9597,7.7079,8.4375,7.5222,6.292,5.6718,5.1881,5.3263,5.4562,5.8644,5.4546,5.3305,4.7772,4.1747,3.7962,4.3991,4.5314,4.3905,4.7709,5.2403,4.9156,4.6795\n5.5338,5.6051,5.1955,5.9139,6.3598,5.9877,5.7206,5.6793,5.0557,5.1974,5.239,5.4346,5.2446,5.2341,5.0503,4.8318,5.0474,4.6203,4.9542,4.6385,5.3825,5.6739,5.9793,5.8513,5.9778,6.3427,7.9486,8.3979,8.1195,7.058,6.0085,5.138,4.9143,5.3466,5.2865,5.2078,5.3447,4.9862,4.3185,4.22,4.6344,4.4042,5.0037,4.9519,5.5377,5.679,4.3202\n5.1032,5.8748,6.1476,5.5355,6.1014,5.6502,6.0043,5.4209,4.6461,4.2755,4.2375,3.9191,4.5607,4.7235,4.7241,4.7825,5.1864,5.6078,5.69,4.9645,5.4017,5.8306,5.9754,6.2631,6.6215,6.3506,6.7819,6.8378,7.1981,6.7159,5.6622,4.9561,4.5789,4.9426,4.9227,5.1078,4.5755,4.0976,4.0108,4.2394,4.3249,4.199,4.4118,4.8087,4.9258,4.8383,4.32\n5.7183,5.5848,5.4235,5.4101,5.6313,5.7277,6.2783,5.7539,4.2807,3.1563,3.9873,3.6843,4.3174,4.7751,4.7282,4.9579,5.6178,5.7491,5.5525,5.265,5.3937,5.5211,5.6826,5.9362,6.8235,6.8382,7.5199,7.2962,6.7017,5.6721,5.1851,5.4014,5.3286,5.2957,5.0531,4.3856,4.2219,4.1029,4.091,4.5024,4.4547,4.3003,4.2505,4.1914,4.2434,4.6487,4.6552\n6.3987,5.9819,5.1598,4.9965,5.1901,6.2727,6.7515,6.2782,5.6374,4.7291,5.1667,4.119,3.9368,4.4818,4.051,4.1027,4.9979,5.1137,4.6892,5.2014,5.3179,4.9772,5.1617,5.9818,6.906,7.4966,7.6222,7.8184,7.03,6.3242,5.4154,5.8515,5.6776,5.8117,5.8518,5.6601,5.1806,4.5332,3.9954,4.5071,4.6159,4.5046,4.4497,4.38,4.5593,4.6307,4.4494\n6.535,6.7643,5.9447,4.8827,4.5618,3.9239,6.4348,6.0674,5.9635,5.2856,5.6081,5.2252,4.9377,4.9472,4.3623,3.5949,4.1503,4.4706,5.1376,5.8112,5.8393,5.5943,5.2736,5.751,6.2832,6.7957,6.9933,7.2096,7.0018,5.9125,5.1706,5.3416,5.4958,5.4185,5.7618,5.6066,5.0201,4.2178,3.9953,4.2294,4.3014,4.2284,4.2697,4.1015,4.4515,4.3585,4.3264\n6.1022,6.0193,5.0352,4.6823,4.5219,4.2862,5.3044,6.1319,7.7407,6.4276,4.9323,3.7775,2.477,3.8272,5.2138,5.364,4.7699,5.6125,4.617,5.7367,5.5747,6.3522,5.6381,5.9054,6.0318,6.0522,6.156,6.3511,6.6067,7.1311,6.1449,5.1016,5.0356,5.2713,5.5606,5.5041,5.0889,4.7695,4.2025,4.256,4.024,3.7838,3.9714,3.7063,3.5336,2.9202,3.0415\n6.4131,6.2241,5.5044,5.1606,4.8253,4.8415,4.6795,5.6702,6.8883,6.8145,5.9652,0.62702,2.4814,3.8245,2.5979,3.5223,4.0677,4.379,5.3491,5.2152,5.2042,6.0485,5.2181,5.7541,6.0165,5.7412,6.8758,6.8144,6.5979,6.7694,6.1085,5.349,4.4835,5.0789,5.4712,5.1622,5.1318,4.9302,4.3678,4.6005,4.3895,3.9457,3.9458,4.1653,3.9502,3.4239,3.0593\n6.2762,6.0405,5.8203,6.096,5.9658,5.2864,4.95,5.6395,6.1164,6.1515,3.7049,4.6719,2.9052,1.0018,0.21001,-0.36497,3.0927,3.9249,4.6261,4.9062,4.938,4.7024,4.4834,5.0402,4.9938,5.3634,5.9812,6.2341,6.474,6.0896,5.3673,5.3661,5.0305,4.9593,4.7393,4.6673,5.1884,5.0247,4.5641,4.4326,4.5401,4.4634,3.9815,4.1229,3.7867,3.1881,2.6956\n7.0235,8.2801,6.7802,6.318,6.5646,6.3365,5.6113,5.021,5.5096,5.8808,1.3248,3.5352,4.3459,3.9298,2.4986,0.84491,1.8407,3.3725,5.4331,4.8702,4.7841,4.319,4.1487,4.6799,4.3502,5.1169,5.8779,6.2644,6.37,5.7569,5.2923,5.5356,5.6704,5.2777,4.8754,4.6217,5.2362,5.2025,4.8289,4.5192,4.4471,4.4321,4.6507,4.3645,4.0949,4.1874,3.1397\n5.4934,6.8211,7.347,6.5343,5.4431,4.9482,5.8023,5.709,5.6603,6.0714,7.6552,5.6369,4.7256,5.0564,4.9936,4.6706,3.7707,3.5812,4.2814,5.0355,4.5425,4.0245,3.2494,3.4223,3.9136,5.441,6.1218,6.5879,6.6752,5.9083,5.3295,5.626,5.856,5.3471,4.726,4.681,5.0646,5.1908,5.2041,4.7873,4.2802,4.3281,4.4554,4.0131,4.2369,4.5173,4.4204\n5.0761,5.4031,5.9394,6.6327,6.1006,5.1573,6.2161,4.8156,4.6685,4.4511,4.5182,5.5163,4.2141,4.7165,5.6523,6.2634,5.3559,5.6336,4.7497,4.9558,4.0926,3.4598,3.5839,3.1801,3.1825,5.0771,5.8947,6.7498,6.5149,6.2255,5.96,6.0757,6.0515,5.1182,4.4462,4.7555,4.7401,4.568,5.232,4.6775,4.1062,3.737,3.7114,4.029,3.9044,3.7971,4.3614\n5.0316,4.9774,5.1008,6.1823,6.6644,6.2235,5.889,4.6689,4.8543,4.7968,5.051,5.022,4.1083,3.5687,3.3449,4.3333,6.2312,6.7231,5.991,5.1895,5.3518,4.5189,4.3142,4.1197,4.6146,4.6765,5.847,6.6078,6.2684,5.8527,5.8066,5.7494,5.7324,4.8124,4.5628,4.8953,4.6793,4.4757,4.5847,4.364,4.2057,3.9816,3.8834,3.671,3.7734,3.7806,3.8478\n4.3852,4.1355,4.6032,6.3837,6.0345,5.1686,7.0097,6.857,6.6249,4.796,5.9268,4.8923,5.8274,6.1003,6.4997,5.2386,5.8328,4.5398,5.9365,5.4016,6.3538,6.0384,4.9194,4.2458,4.23,3.9006,4.235,5.7637,6.8244,6.7018,6.4439,5.7416,5.1067,4.743,4.6209,4.9545,4.6753,4.7697,4.9925,5.014,4.9229,4.0682,4.0477,3.9656,3.8867,4.8782,5.2655\n3.0688,1.4909,4.1991,5.6831,6.383,6.9902,6.456,5.7698,7.3326,7.9646,7.6266,6.689,6.913,7.5743,7.1109,4.4722,2.2121,2.3393,5.0048,5.4639,5.3032,5.3055,4.9875,4.0608,4.9838,5.2898,5.4077,5.8855,6.4049,6.5705,6.4497,6.2208,4.422,4.013,4.1907,4.7722,4.2727,4.6576,5.0021,4.9939,4.5975,4.2024,4.2847,4.2051,4.2095,5.0075,5.5133\n4.7375,6.5564,5.947,6.3951,6.6613,5.2238,4.9317,5.0328,6.3536,7.6908,6.9678,6.564,5.5409,4.966,4.7591,4.4175,4.9328,4.0605,4.2254,4.9672,4.8304,4.2481,4.0913,4.0308,4.0156,5.0553,5.3601,5.4027,4.3543,4.6822,4.9538,5.5836,4.8356,4.2091,4.015,4.1292,4.0983,4.3723,4.7362,4.4921,4.1977,3.9714,3.6835,3.7848,4.1312,4.2894,4.7893\nNaN,4.0918,4.633,7.6147,5.3901,3.0093,6.406,5.6406,7.7137,7.3391,6.7028,7.7266,7.7119,6.8493,4.9648,4.2108,4.9128,5.255,4.7766,4.3483,4.0865,4.7639,4.625,4.3425,4.3828,5.4984,5.4992,4.9383,4.3074,3.8796,5.1423,4.7752,4.8716,4.7632,4.3493,3.8703,4.2138,4.274,4.6942,4.6108,4.3244,3.9769,3.6966,3.9353,3.8855,3.6822,3.6721\n8.392,3.3189,4.7518,5.9091,6.5906,6.5817,5.7143,4.8509,5.6888,6.2689,7.3878,7.693,8.2172,8.239,5.8647,4.328,NaN,7.7937,6.1549,4.1315,4.7,3.7787,2.5011,3.9522,4.7529,3.9168,4.5446,4.4958,3.9009,3.4938,4.2114,4.623,4.8488,5.396,4.2921,3.4989,3.0827,3.8961,4.467,4.9998,4.9547,4.9729,4.0545,4.0887,3.8079,3.442,3.134\n6.4281,3.3276,3.9318,4.5411,5.1861,5.8309,4.2804,5.4791,5.9796,5.4633,4.2786,4.9595,6.9851,8.6895,8.5994,NaN,NaN,NaN,NaN,8.145,6.2548,4.6549,3.9973,4.3775,4.5929,4.8417,4.4906,4.2759,4.9029,5.1087,4.6612,4.407,4.2935,4.2613,4.6049,3.8386,3.5045,4.1549,4.7767,4.5953,4.7022,4.7102,4.6879,4.3475,3.8234,3.3174,2.8575\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070430-070604.unw.csv",
    "content": "-36.65,-37.486,-37.569,-37.41,-37.452,-36.716,-36.836,-36.667,-36.55,-36.951,-37.187,-37.887,-37.118,-37.122,-36.888,-36.668,-36.326,-36.434,-36.113,-35.654,-35.789,-35.148,-35.062,-35.577,-35.898,-36.089,-36.1,-36.378,-36.398,-36.553,-36.69,-36.814,-36.413,-36.444,-36.477,-36.228,-35.84,-35.812,-35.624,-35.738,-35.873,-35.765,-35.665,-35.378,-35.066,-34.314,-33.799\n-36.751,-37.561,-37.216,-37.303,-37.174,-37.07,-36.98,-36.313,-36.329,-36.27,-36.698,-37.185,-36.967,-36.943,-37.06,-36.966,-36.554,-36.185,-35.509,-35.339,-35.341,-34.741,-35.02,-35.192,-35.572,-35.739,-36.028,-36.122,-36.168,-36.47,-36.703,-36.695,-36.507,-36.397,-36.398,-35.961,-35.731,-35.561,-35.373,-35.347,-35.656,-35.373,-35.335,-35.113,-34.756,-34.589,-34.465\n-36.811,-37.414,-37.033,-36.888,-36.675,-36.873,-36.793,-36.476,-36.121,-36.121,-36.193,-36.787,-36.75,-36.765,-36.468,-36.492,-36.048,-35.619,-35.191,-35.191,-35.3,-35.429,-35.415,-35.65,-35.725,-35.524,-35.686,-35.707,-36.135,-36.238,-36.421,-36.492,-36.417,-36.286,-36.266,-36.008,-35.933,-35.586,-35.445,-35.532,-35.476,-35.44,-35.335,-34.777,-34.475,-34.254,-33.709\n-36.37,-35.894,-37.657,-36.852,-36.886,-36.889,-36.694,-36.215,-36.25,-36.387,-36.323,-36.308,-36.671,-36.452,-35.965,-35.596,-35.251,-35.063,-35.105,-35.185,-35.152,-35.21,-35.477,-35.645,-35.424,-35.236,-35.227,-35.463,-35.794,-36.031,-36.045,-36.057,-36.08,-36.14,-36.1,-35.926,-35.883,-35.725,-35.627,-35.554,-35.606,-35.742,-35.394,-34.876,-34.488,-33.938,-33.032\n-35.984,-35.353,-40.341,-36.691,-36.733,-36.566,-36.419,-36.156,-36.007,-35.871,-35.847,-36.329,-36.556,-36.017,-35.586,-35.504,-35.441,-34.893,-35.198,-35.48,-34.737,-35.036,-35.438,-35.316,-35.209,-35.071,-35.151,-35.24,-35.474,-35.668,-35.84,-35.972,-35.979,-36.012,-36.008,-35.806,-35.895,-35.819,-35.717,-35.514,-35.392,-35.432,-35.59,-35.082,-34.631,-33.838,-32.887\n-35.71,-37.354,-39.25,-36.632,-36.583,-36.655,-36.543,-36.292,-36.264,-36.63,-36.602,-36.156,-36.1,-35.737,-35.614,-35.409,-35.27,-34.999,-35.174,-34.678,-34.627,-34.958,-35.211,-34.982,-34.784,-34.903,-34.81,-35.006,-35.481,-35.703,-35.693,-35.812,-35.912,-35.915,-35.736,-35.616,-35.66,-35.652,-35.704,-35.351,-35.295,-35.415,-35.42,-35.099,-34.551,-34.051,-32.825\n-36.055,-35.926,-40.983,-36.796,-36.684,-36.264,-36.102,-35.974,-35.868,-36.241,-36.088,-35.777,-35.483,-35.254,-35.256,-35.069,-35.034,-34.912,-34.816,-34.036,-35.267,-35.33,-35.411,-35.201,-34.807,-34.763,-34.723,-35.141,-35.517,-35.515,-35.364,-35.687,-35.865,-35.974,-35.898,-35.844,-35.888,-35.675,-35.914,-36.089,-36.071,-35.433,-35.365,-35.036,-34.38,-33.793,-32.707\n-37.041,-37.084,-37.065,-36.9,-36.607,-36.259,-35.885,-35.601,-35.358,-35.86,-35.63,-35.498,-34.893,-35.078,-34.977,-34.898,-34.818,-34.525,-34.275,-34.322,-35.344,-34.85,-35.133,-34.919,-34.379,-34.517,-34.705,-34.906,-35.054,-35.333,-35.316,-35.618,-35.801,-36.003,-35.762,-35.874,-35.94,-36.078,-36.311,-36.425,-36.375,-36.025,-35.71,-35.402,-34.809,-33.923,-32.862\n-36.998,-36.979,-36.728,-37.305,-36.744,-36.484,-36.017,-35.444,-35.197,-35.123,-34.968,-34.705,-34.916,-34.692,-34.621,-34.488,-34.365,-34.12,-34.199,-34.551,-34.588,-34.654,-34.925,-34.622,-34.416,-34.755,-34.636,-34.537,-35.058,-35.532,-35.414,-35.417,-35.621,-35.632,-35.671,-35.768,-35.874,-36.088,-36.459,-36.501,-36.364,-36.2,-36.264,-35.756,-35.02,-34.427,-34.348\n-36.564,-36.589,-35.696,-36.92,-37.23,-36.707,-35.611,-35.25,-35.179,-34.576,-34.427,-34.275,-34.697,-34.353,-34.604,-34.317,-33.857,-33.773,-33.987,-34.331,-34.027,-34.516,-34.871,-34.487,-34.42,-34.566,-34.44,-34.562,-34.783,-34.906,-35.19,-35.333,-35.494,-35.538,-35.446,-35.325,-35.434,-35.95,-36.301,-36.273,-36.054,-35.919,-35.906,-35.716,-35.49,-35.207,-35.052\n-36.838,-36.872,-36.1,-36.063,-37.472,-36.783,-35.874,-35.247,-35.154,-34.704,-34.186,-34.102,-33.98,-33.97,-33.763,-33.352,-33.69,-33.721,-33.864,-33.817,-34.222,-34.57,-34.513,-34.441,-34.498,-34.33,-34.357,-34.467,-34.564,-34.936,-35.335,-35.52,-35.655,-35.58,-35.775,-35.661,-35.693,-35.985,-36.025,-35.953,-35.883,-35.719,-35.863,-35.923,-35.626,-35.527,-35.576\n-36.958,-37.456,-37.517,-38.36,-38.015,-36.464,-36.253,-35.604,-35.268,-34.819,-34.316,-34.096,-33.799,-33.54,-33.391,-33.323,-33.406,-33.105,-33.261,-33.338,-34.139,-34.3,-34.243,-34.189,-34.301,-33.986,-34.204,-34.243,-34.467,-35.109,-35.372,-35.465,-35.655,-35.629,-35.499,-35.702,-35.744,-35.839,-35.895,-35.854,-35.807,-35.779,-36.203,-35.815,-35.531,-35.131,-34.935\n-36.738,-36.829,-37.176,-38.232,-38.252,-37.006,-36.306,-35.697,-35.109,-34.708,-34.352,-34.211,-33.764,-33.648,-33.589,-33.292,-33.071,-33.088,-33.147,-33.613,-34.189,-33.921,-34.097,-33.836,-33.915,-34.007,-34.047,-34.008,-34.331,-34.768,-34.728,-35.232,-35.437,-35.514,-35.408,-35.585,-35.847,-35.771,-35.684,-35.672,-35.624,-35.842,-35.902,-35.785,-35.326,-34.926,-33.516\n-36.815,-35.411,-35.618,-36.463,-37.905,-37.911,-37.599,-35.899,-34.898,-34.456,-34.027,-34.038,-34.096,-33.693,-33.515,-33.253,-33.269,-33.272,-33.397,-33.825,-33.945,-33.749,-34.024,-33.989,-34.114,-34.096,-34.019,-34.251,-34.285,-34.302,-34.47,-34.955,-35.282,-35.596,-35.929,-36.198,-36.297,-36.004,-35.696,-35.444,-34.893,-35.837,-35.842,-36.482,-36.836,-33.983,-33.051\n-35.907,-35.149,-36.124,-37.181,-38.756,-38.099,-37.536,-35.71,-34.69,-34.084,-33.578,-33.812,-33.93,-33.807,-33.413,-33.352,-33.434,-33.432,-33.411,-33.826,-33.677,-34.092,-33.82,-33.868,-34.122,-34.258,-33.838,-33.892,-34.172,-34.405,-34.59,-34.96,-35.369,-35.63,-36.065,-36.012,-36.44,-36.365,-36.026,-35.267,-35.486,-36.132,-35.249,-35.244,-35.127,-35.152,-33.697\n-35.182,-35.895,-36.716,-37.762,-38.388,-37.902,-37.782,-35.62,-34.67,-34.201,-33.538,-33.699,-33.658,-33.387,-33.184,-33.157,-33.209,-32.973,-33.281,-33.856,-34.051,-33.845,-33.741,-33.823,-34.063,-34.136,-33.874,-33.918,-34.149,-34.632,-34.759,-35.27,-35.381,-35.486,-35.602,-35.472,-35.871,-36.452,-36.586,-36.232,-35.929,-35.586,-35.285,-35.786,-36.055,-34.693,-34.691\n-35.39,-38.151,-37.381,-37.141,-37.67,-38.534,-38.027,-35.793,-34.747,-34.161,-33.707,-33.691,-33.573,-33.03,-33.197,-33.062,-32.904,-32.965,-33.589,-34.386,-34.379,-34.227,-34.139,-34.087,-33.05,-32.596,-33.672,-34.554,-34.429,-34.574,-35.161,-35.451,-35.38,-35.442,-35.58,-35.774,-36.101,-36.69,-36.831,-35.461,-35.475,-36.105,-36.827,-35.979,-35.307,-31.647,-33.496\n-35.883,-38.021,-37.124,-37.119,-37.838,-38.515,-36.957,-35.437,-34.707,-34.101,-33.686,-33.687,-33.619,-33.667,-33.179,-32.67,-32.328,-33.152,-33.905,-35.091,-34.442,-34.587,-34.888,-34.381,-33.726,-33.568,-34.057,-34.481,-34.573,-34.748,-35.171,-35.563,-35.932,-35.516,-35.825,-36.239,-36.225,-36.999,-37.353,-35.644,-35.387,-37.294,-38.187,-36.651,-35.162,-33.286,-33.536\n-37.426,-37.195,-36.679,-36.866,-37.804,-33.997,-36.732,-35.565,-34.611,-34.262,-33.785,-33.288,-33.38,-33.653,-33.041,-32.483,-32.711,-33.005,-33.901,-35.299,-36.297,-36.599,-36.073,-35.214,-34.484,-34.238,-34.033,-34.165,-34.768,-34.806,-35.209,-35.866,-37.429,-36.311,-35.584,-36.552,-37.467,-37.221,-36.328,-35.892,-36.382,-37.171,-38.907,-37.343,-36.883,-35.863,-35.615\n-37.532,-37.297,-36.486,-36.529,-35.647,-36.108,-36.132,-35.585,-34.609,-34.159,-33.662,-33.699,-33.658,-33.51,-33.41,-33.282,-33.265,-33.718,-34.43,-34.844,-35.845,-35.877,-35.288,-35.628,-34.927,-34.54,-34.781,-34.512,-34.061,-35.455,-35.341,-35.705,-36.488,-37.173,-37.71,-36.791,-37.281,-37.329,-37.347,-37.24,-37.221,-37.736,-38.179,-37.831,-37.244,-37.522,-37.136\n-36.675,-36.997,-36.183,-35.303,-33.814,-35.137,-35.095,-35.34,-34.956,-34.345,-33.259,-33.781,-33.487,-33.477,-33.584,-34.109,-34.395,-34.674,-35.465,-34.873,-34.458,-34.856,-35.161,-35.401,-34.928,-34.144,-34.332,-33.795,-33.323,-34.833,-35.362,-35.971,-35.381,-35.485,-39.7,-35.658,-36.465,-37.072,-37.484,-36.901,-36.629,-37.209,-37.741,-37.618,-37.853,-37.707,-37.417\n-36.743,-36.792,-35.944,-35.559,-34.821,-33.239,-34.547,-35.039,-34.796,-33.655,-33.026,-33.162,-32.797,-33.208,-34.345,-35.023,-35.71,-36.098,-35.972,-35.217,-35.981,-35.08,-35.841,-36.007,-35.003,-34.513,-34.335,-34.202,-34.116,-33.987,-34.421,-36.189,-35.853,-35.742,-37.33,-36.001,-35.351,-35.972,-35.794,-35.634,-37.015,-37.39,-37.288,-37.641,-38.264,-38.783,-38.186\n-35.929,-35.842,-35.129,-34.812,-33.821,-35.311,-34.76,-34.535,-33.924,-32.88,-32.877,-31.912,-32.766,-34.405,-35.364,-35.523,-36.047,-35.86,-36.243,-35.553,-35.658,-35.907,-35.395,-34.704,-34.508,-34.808,-33.879,-34.171,-35.052,-33.963,-33.76,-34.558,-35.2,-35.161,-35.405,-35.081,-35.092,-34.997,-34.712,-35.699,-36.852,-37.368,-37.427,-37.954,-38.393,-38.481,-38.124\n-35.354,-34.908,-34.316,-34.324,-35.554,-34.735,-34.071,-33.61,-33.344,-33.133,-33.151,-32.458,-33.424,-34.3,-34.954,-36.446,-36.489,-35.515,-35.039,-34.858,-33.94,-33.757,-34.004,-33.755,-33.749,-34.206,-33.646,-33.99,-34.058,-33.104,-33.11,-33.856,-33.389,-33.133,-34.413,-34.55,-34.636,-34.59,-34.313,-36.999,-37.213,-37.196,-36.998,-37.523,-38.093,-38.472,-39.115\n-35.592,-34.935,-34.156,-34.188,-34.342,-33.482,-33.846,-32.967,-33.048,-33.76,-33.546,-32.941,-33.232,-34.115,-34.414,-34.881,-34.769,-34.733,-33.927,-33.301,-33.126,-32.779,-32.504,-33.036,-33.058,-32.677,-32.755,-32.738,-32.275,-32.182,-32.073,-32.504,-32.691,-32.878,-34.552,-35.797,-35.191,-35.849,-36.348,-36.932,-37.078,-36.843,-36.882,-37.242,-37.596,-38.645,-38.882\n-35.66,-34.733,-34.19,-33.922,-33.454,-32.974,-33.144,-32.892,-32.768,-33.217,-32.755,-32.542,-32.949,-33.094,-32.956,-33.4,-33.122,-32.747,-33.02,-32.749,-32.498,-31.45,-32.189,-32.726,-32.55,-31.931,-31.558,-30.746,-30.518,-32.092,-32.104,-30.786,-30.097,-34.751,-35.271,-36.448,-36.458,-36.141,-36.43,-36.99,-37.243,-37.221,-37.31,-37.25,-37.329,-38.889,-38.563\n-36.65,-35.759,-34.29,-33.819,-33.413,-33.202,-32.6,-32.819,-32.791,-32.412,-31.947,-31.797,-31.771,-31.89,-32.602,-32.52,-32.22,-32.222,-33.217,-33.173,-32.251,-32.128,-32.963,-33.498,-34.072,-33.097,-32.629,-32.163,-32.187,-33.513,-32.968,-32.658,-32.939,-35.767,-36.096,-35.967,-36.029,-35.91,-36.877,-36.513,-37.106,-37.441,-37.352,-37.32,-37.706,-38.078,-38.024\n-36.79,-35.388,-34.722,-34.712,-34.102,-33.779,-32.829,-32.883,-33.071,-32.258,-31.703,-31.367,-31.775,-32.043,-32.46,-31.072,-32.206,-33.15,-33.254,-32.861,-32.702,-32.453,-32.749,-34.144,-35.105,-33.788,-33.12,-33.247,-33.436,-33.434,-33.614,-34.244,-35.247,-35.426,-35.265,-35.379,-35.68,-36.182,-36.028,-36.62,-37.604,-37.471,-37.454,-37.423,-37.606,-37.673,-37.718\n-36.15,-35.496,-34.914,-34.92,-34.6,-33.991,-33.137,-32.784,-33.199,-33.254,-32.882,-31.894,-32.589,-32.444,-32.025,-31.387,-32.634,-33.28,-32.734,-32.73,-32.986,-32.84,-32.173,-31.673,-32.607,-31.069,-30.415,-31.327,-31.955,-29.537,-31.339,-32.725,-31.299,-34.648,-35.251,-35.692,-36.611,-37.276,-37.368,-37.414,-37.37,-37.654,-37.692,-37.442,-37.609,-37.593,-37.536\n-36.18,-35.773,-35.832,-34.402,-34.654,-34.378,-33.716,-33.105,-33.585,-33.656,-32.829,-32.271,-33.305,-32.506,-31.892,-32.411,-33.518,-33.657,-33.063,-33.858,-33.3,-33.13,-30.571,-31.535,-30.181,-29.192,-29.783,-29.633,-28.712,-29.484,-32.381,-31.888,-32.208,-35.938,-35.755,-35.322,-37.25,-38.236,-37.453,-37.256,-36.954,-37.445,-37.44,-37.261,-37.697,-37.298,-37.212\n-36.571,-36.523,-34.771,-35.628,-36.551,-35.601,-35.464,-33.892,-33.822,-33.969,-33.497,-33.626,-33.768,-32.834,-31.059,-32.565,-34.407,-34.048,-33.882,-34.593,-33.02,-32.009,-29.82,-30.94,-29.491,-29.791,-29.937,-29.944,-29.282,-30.639,-34.591,-34.767,-34.719,-36.152,-35.457,-35.249,-36.622,-37.074,-37.627,-36.997,-37.263,-38.005,-37.614,-37.241,-37.651,-37.418,-37.185\n-36.682,-36.347,-36.051,-36.074,-36.916,-36.75,-36.134,-35.369,-33.737,-34.059,-33.809,-34.205,-34.121,-33.439,-33.166,-34.531,-34.761,-34.743,-34.401,-34.141,-32.658,-31.055,-29.11,-31.586,-30.767,-30.994,-32.098,-32.511,-33.422,-33.797,-34.264,-34.012,-33.942,-35.284,-35.582,-35.921,-37.173,-36.817,-37.001,-36.663,-37.57,-37.829,-37.975,-37.199,-37.259,-36.812,-36.628\n-37.375,-37.321,-37.202,-36.883,-36.885,-36.571,-36.263,-35.835,-34.099,-34.921,-34.766,-38.118,-38.913,-34.001,-34.616,-33.309,-32.422,-34.202,-34.478,-35.432,-32.822,-30.554,-29.911,-33.657,-32.965,-32.397,-33.485,-34.832,-35.064,-34.44,-34.353,-34.192,-33.494,-33.984,-35.2,-35.996,-36.379,-36.02,-36.607,-36.729,-37.178,-37.538,-37.784,-37.378,-36.976,-36.383,-36.011\n-37.699,-37.355,-37.688,-37.385,-36.753,-36.531,-37.011,-36.097,-34.899,-34.704,-33.845,-35.21,-38.211,-34.756,-35.031,-34.694,-33.932,-34.147,-34.16,-34.437,-32.866,-32.243,-33.285,-33.002,-32.911,-32.811,-34.046,-34.205,-33.577,-33.044,-33.981,-33.703,-31.943,-33.131,-34.938,-35.3,-35.802,-35.34,-35.744,-36.258,-36.404,-36.471,-36.776,-37.02,-36.802,-36.245,-35.93\n-37.458,-37.176,-37.328,-37.338,-37.186,-36.788,-36.415,-35.215,-34.819,-34.459,-34.371,-35.098,-35.497,-35.199,-33.245,-34.626,-34.518,-33.416,-33.473,-33.703,-33.29,-33.384,-32.748,-32.368,NaN,-32.159,-34.508,-33.008,-30.224,-31.374,-33.629,-32.247,-31.579,-32.75,-33.998,-34.426,-35.002,-35.488,-35.649,-35.914,-35.593,-35.685,-35.722,-36.392,-36.481,-36.022,-35.934\n-36.687,-37.051,-37.532,-37.389,-36.432,-36.699,-36.119,-35.068,-34.995,-35.249,-35.463,-35.566,-35.765,-34.928,-32.813,-34.89,-34.742,-33.794,-33.267,-33.26,-36.249,-34.613,-32.467,NaN,NaN,NaN,NaN,NaN,NaN,-33.473,-33.511,-32.773,-33.365,-33.847,-34.373,-36.551,-35.8,-36.032,-35.417,-35.671,-35.686,-35.142,-34.888,-35.841,-35.919,-35.878,-36.079\n-36.46,-36.898,-37.191,-37.415,-36.787,-36.74,-36.453,-35.725,-36.094,-36.244,-36.457,-36.921,-37.122,-37.7,-36.997,-35.831,-35.057,-33.426,-33.253,-32.719,-33.643,-32.791,-32.292,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-33.206,-32.899,-33.939,-39.444,-39.793,-38.787,-36.998,-35.732,-35.674,-35.838,-35.416,-34.766,-35.438,-35.156,-35.25,-35.715,-36.058\n-36.174,-36.552,-36.845,-36.658,-36.351,-35.886,-36.242,-36.561,-36.655,-36.366,-35.917,-37.398,-37.359,-37.768,-36.696,-35.847,-34.371,-33.749,-32.896,-32.015,-33.11,-34.452,-35.105,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-33.503,-33.033,-33.195,-36.805,-38.384,-37.303,-35.954,-35.564,-35.781,-35.797,-35.753,-35.467,-35.216,-34.905,-34.585,-34.629,-35.447\n-36.094,-36.31,-36.205,-36.048,-35.931,-35.536,-35.788,-36.727,-36.809,-36.867,-37.005,-37.826,-37.918,-37.466,-36.636,-35.678,-33.723,-26.942,-31.828,-34.671,-34.452,-35.86,-36.626,-37.417,-34.019,-32.007,-34.266,NaN,-32.852,-33.23,-33.665,-33.168,-33.189,-34.458,-35.228,-34.972,-34.934,-35.211,-35.455,-35.773,-36.017,-35.744,-35.217,-35.253,-34.507,-34.691,-35.384\n-36.138,-35.902,-35.615,-35.681,-35.7,-35.643,-35.768,-36.805,-36.372,-36.475,-36.64,-36.879,-37.554,-36.792,-36.545,-35.27,-34.487,-33.113,-32.403,-34.937,-33.32,-34.957,-35.675,-34.345,-32.955,-34.806,-36.635,-34.569,-33.513,-33.641,-33.387,-33.361,-33.629,-34.128,-34.43,-34.301,-34.517,-34.654,-35.212,-36.005,-36.082,-35.617,-35.549,-35.048,-34.591,-34.543,-35.293\n-35.582,-35.539,-35.509,-35.728,-35.56,-35.577,-35.855,-36.342,-36.178,-36.098,-36.443,-36.476,-37.461,-37.542,-36.578,-36.455,-35.833,-34.95,-35.05,-34.608,-32.483,-34.645,-34.61,-33.904,-34.004,-35.206,-35.057,-34.194,-33.829,-33.778,-34.225,-34.739,-34.07,-34.504,-34.058,-34.652,-34.601,-34.603,-35.033,-35.934,-36.162,-36.012,-35.673,-35.026,-35.069,-34.908,-35.053\n-35.467,-35.593,-35.579,-35.676,-35.384,-35.575,-35.911,-36.226,-36.14,-36.176,-36.892,-37.273,-39.089,-38.286,-37.268,-36.877,-37.263,-35.178,-34.343,-34.192,-33.384,-33.445,-33.977,-34.484,-35.202,-35.731,-34.272,-34.439,-33.952,-33.622,-34.035,-34.053,-34.08,-34.482,-34.508,-35.117,-35.084,-35.717,-35.508,-35.824,-36.333,-36.092,-35.72,-35.457,-35.347,-34.892,-35.119\n-36.023,-35.955,-35.822,-35.984,-35.842,-35.851,-36.278,-36.822,-36.986,-37.375,-37.66,-37.283,-38.914,-37.713,-36.936,-36.598,-35.145,-36.07,-34.921,-34.356,-33.67,-33.747,-33.236,-34.932,-34.636,-33.689,-34.797,-35.033,-34.396,-34.28,-34.244,-33.839,-34.419,-34.226,-34.249,-34.407,-35.006,-37.258,-36.411,-35.741,-35.997,-35.907,-35.505,-35.35,-35.469,-35.442,-35.846\n-35.835,-35.904,-35.511,-36.281,-36.166,-36.077,-36.628,-37.216,-37.704,-37.751,-37.607,-37.142,-36.959,-36.224,-34.995,-34.248,-34.017,-33.623,-33.481,-33.593,-33.68,-33.448,-36.661,-36.199,-33.971,-33.587,-34.891,-35.209,-35.181,-35.218,-35.181,-35.077,-34.663,-34.543,-36.223,-36.568,-36.126,-37.457,-35.962,-35.707,-35.664,-35.485,-35.391,-35.449,-35.744,-35.969,-36.259\n-35.791,-35.809,-35.727,-36.217,-36.283,-36.863,-37.429,-37.729,-37.758,-36.722,-36.832,-36.44,-36.069,-35.418,-34.269,-33.55,-34.111,-33.68,-33.647,-34.033,-33.561,-35.212,-38.566,-36.386,-35.16,-34.224,-34.762,-34.664,-34.788,-34.941,-34.775,-34.637,-35.299,-35.508,-37.508,-36.405,-36.583,-36.588,-35.832,-35.433,-35.292,-35.173,-35.576,-35.411,-36.042,-36.496,-36.614\n-35.957,-35.485,-35.418,-35.739,-36.13,-36.525,-36.708,-36.708,-36.158,-36.068,-36.326,-35.971,-35.852,-35.618,-34.167,-34.057,-34.121,-34.242,-34.924,-34.711,-34.245,-35.243,-38.104,-37.248,-35.584,-35.67,-34.995,-34.883,-34.778,-34.653,-34.293,-34.752,-35.606,-35.246,-35.297,-35.734,-36.287,-35.739,-35.88,-35.592,-35.604,-36.012,-35.811,-35.096,-35.757,-36.32,-36.376\n-35.66,-35.26,-35.22,-35.826,-35.736,-35.754,-35.879,-35.996,-35.919,-35.907,-35.805,-35.732,-35.525,-35.195,-34.811,-34.523,-34.3,-33.949,-34.67,-34.553,-33.912,-34.641,-37.207,-36.436,-35.895,-35.64,-35.26,-35.077,-34.734,-34.412,-34.686,-35.004,-35.206,-34.97,-35.274,-36.062,-35.344,-35.334,-36.033,-35.808,-36.058,-36.938,-36.553,-36.278,-35.552,-35.792,-35.854\n-35.399,-35.274,-35.353,-35.674,-35.741,-35.485,-35.418,-35.822,-36.223,-36.032,-35.611,-35.248,-34.607,-34.442,-34.466,-34.458,-34.299,-33.902,-33.665,-33.759,-33.896,-34.071,-35.378,-35.546,-35.535,-35.712,-35.261,-34.778,-35.066,-35.483,-35.098,-34.909,-35.323,-35.877,-36.087,-36.103,-35.703,-35.825,-36.144,-35.468,-35.797,-36.639,-36.203,-36.14,-35.826,-35.813,-35.615\n-35.729,-35.501,-35.797,-35.653,-35.776,-35.869,-35.371,-35.867,-35.574,-35.513,-35.192,-34.561,-34.375,-34.468,-34.249,-34.28,-34.404,-34.184,-33.879,-33.069,-32.939,-31.852,-33.259,-35.022,-34.824,-34.836,-34.991,-35.366,-35.366,-35.665,-35.076,-35.042,-35.446,-35.817,-36.046,-36.074,-35.806,-36.11,-35.951,-35.119,-35.155,-35.693,-36.371,-35.799,-35.69,-35.785,-35.847\n-35.69,-35.646,-35.663,-34.932,-35.775,-35.647,-35.073,-35.043,-35.392,-35.373,-35.25,-34.714,-34.54,-34.484,-34.643,-34.736,-34.607,-33.814,-34.524,-35.57,-31.213,-32.71,-34.813,-34.66,-34,-33.91,-34.629,-35.638,-35.193,-35.772,-35.533,-35.458,-35.828,-35.952,-36.003,-35.973,-35.402,-35.782,-35.82,-35.213,-35.271,-35.02,-35.196,-35.952,-35.714,-35.754,-35.739\n-35.41,-35.526,-35.482,-34.931,-35.241,-35.595,-35.33,-35.713,-35.7,-35.511,-35.215,-34.906,-34.587,-34.565,-34.709,-34.632,-34.277,-34.422,-36.025,-36.937,-37.054,-35.644,-34.88,-34.73,-34.277,-34.255,-38.266,-39.311,-35.126,-35.792,-35.911,-35.626,-35.966,-35.83,-35.993,-35.469,-35.316,-35.561,-35.111,-34.909,-35.005,-34.779,-35.043,-35.751,-35.523,-35.411,-35.504\n-35.669,-35.699,-35.419,-35.279,-35.421,-35.781,-35.644,-35.824,-35.37,-35.333,-35.062,-34.691,-34.447,-34.996,-34.452,-34.374,-33.368,-34.786,-34.942,-35.689,-36.484,-35.498,-35.239,-34.711,-33.807,-33.382,-34.902,-35.016,-35.122,-36.028,-35.655,-35.715,-35.739,-35.713,-35.74,-35.302,-35.155,-34.926,-34.698,-34.691,-34.841,-34.791,-33.828,-35.372,-35.648,-35.286,-34.878\n-35.865,-35.802,-35.803,-35.52,-35.834,-35.906,-35.9,-35.847,-35.644,-35.867,-35.063,-34.728,-34.833,-35.178,-34.464,-35.213,-35.614,-35.023,-34.993,-35.147,-35.885,-35.851,-35.448,-34.628,-33.475,-33.069,-33.203,-33.443,-33.7,-35.2,-35.845,-35.632,-35.633,-35.69,-35.855,-35.046,-34.853,-34.774,-34.84,-34.814,-34.824,-34.31,-34.041,-34.948,-35.526,-35.503,-34.981\n-35.729,-35.695,-35.642,-35.535,-35.385,-36.025,-35.873,-36.163,-35.939,-35.789,-34.911,-35.77,-35.676,-35.688,-35.62,-36.347,-36.376,-36.255,-36.614,-36.322,-35.615,-35.681,-34.898,-34.466,-34.037,-33.58,-33.183,-32.21,-32.627,-34.073,-35.617,-35.489,-35.511,-35.786,-35.744,-35.155,-35.461,-34.938,-35.119,-34.591,-34.693,-34.729,-34.545,-35.279,-35.257,-35.457,-35.253\n-35.74,-35.199,-35.834,-35.243,-35.692,-35.96,-35.942,-35.854,-35.5,-35.532,-35.236,-35.94,-36.76,-36.667,-36.382,-36.342,-36.116,-35.921,-34.997,-36.329,-35.424,-34.687,-34.484,-34.194,-33.755,-33.488,-32.366,-32.346,-34.462,-34.908,-35.433,-35.782,-35.664,-35.633,-35.329,-35.53,-35.295,-35.167,-35.165,-34.596,-34.61,-34.912,-35.143,-34.949,-35.097,-35.365,-35.488\n-36.092,-36.542,-35.83,-35.186,-35.405,-37.331,-37.229,-36.359,-36.237,-35.971,-35.816,-35.861,-37.337,-37.657,-36.964,-36.852,-36.211,-35.374,-35.166,-36.169,-35.446,-34.628,-34.456,-33.766,-33.31,-33.286,-32.16,-32.581,-34,-34.946,-35.102,-35.814,-36.502,-35.372,-35.285,-35.58,-35.269,-35.269,-34.984,-34.684,-34.85,-34.85,-34.532,-34.456,-35.275,-35.413,-35.414\n-35.718,-36.514,-35.773,-35.12,-34.531,-38.028,-37.588,-37.031,-37.146,-37.124,-36.921,-36.974,-37.228,-36.528,-36.986,-37.518,-37.362,-35.783,-35.664,-35.757,-35.446,-35.015,-34.729,-34.185,-33.332,-33.326,-32.991,-33.624,-34.181,-34.268,-34.854,-35.077,-35.63,-35.145,-35.2,-35.391,-35.203,-35.162,-34.98,-34.94,-35.069,-35.107,-34.501,-35.171,-35.506,-35.838,-35.655\n-35.795,-36.694,-36.941,-36.083,-35.764,-34.964,-36.626,-36.39,-36.715,-38.34,-37.174,-36.96,-36.868,-36.671,-36.708,-37.135,-37.614,-36.555,-35.498,-35.21,-35.09,-35.227,-34.483,-34.228,-33.407,-33.195,-33.036,-34.79,-34.231,-34.165,-34.672,-34.855,-35.32,-34.812,-34.983,-35.123,-35.018,-34.872,-34.599,-34.928,-34.95,-34.857,-34.532,-35.318,-35.324,-35.734,-35.649\n-36.349,-36.103,-36.469,-36.906,-36.961,-36.836,-36.93,-35.894,-36.519,-37.245,-36.475,-36.331,-36.4,-36.244,-35.483,-37.22,-37.922,-36.347,-35.428,-34.749,-34.813,-35.046,-34.627,-34.56,-34.102,-33.282,-32.871,-32.903,-34.19,-34.779,-35.646,-35.983,-35.325,-34.798,-35.096,-34.997,-34.948,-34.722,-34.624,-34.776,-34.959,-35.088,-35.252,-35.5,-35.744,-35.76,-35.337\n-36.587,-35.469,-35.797,-37.767,-38.717,-41.897,-37.287,-36.45,-36.119,-35.318,-34.957,-35.107,-35.177,-35.092,-35.337,-36.009,-37.477,-36.588,-35.287,-35.015,-35.216,-34.598,-34.874,-34.844,-34.165,-33.38,-32.935,-33.885,-33.591,-34.831,-35.384,-35.74,-35.466,-34.761,-35.021,-34.753,-34.95,-35.046,-34.717,-34.617,-35.119,-35.5,-35.635,-35.787,-35.451,-35.461,-35.626\n-35.549,-35.637,-36.042,-36.925,-37.443,-38.747,-37.651,-37.221,-36.477,-34.323,-34.164,-33.908,-33.695,-34.407,-36.37,-36.498,-37.881,-36.937,-35.768,-35.594,-35.267,-34.644,-34.419,-34.581,-34.608,-33.799,-33.628,-33.817,-32.999,-33.704,-34.2,-34.989,-36.227,-35.224,-34.964,-35.204,-35.104,-35.258,-35.132,-35.165,-35.305,-35.528,-35.712,-35.403,-35.081,-35.224,-35.448\n-34.953,-35.297,-35.416,-35.82,-35.535,-35.467,-37.064,-37.209,-35.993,-34.95,-35.03,-34.147,-34.034,-34.272,-36.004,-36.68,-37.123,-36.163,-35.195,-35.279,-34.548,-34.338,-34.34,-34.68,-35.012,-34.628,-34.141,-34.03,-34.037,-34.093,-34.175,-34.369,-35.15,-35.006,-35.011,-35.706,-35.343,-35.413,-35.498,-35.63,-35.497,-35.55,-35.671,-35.029,-34.672,-35.056,-35.572\n-34.86,-34.675,-34.944,-34.473,-33.979,-35.993,-37.275,-36.616,-35.778,-35.466,-34.612,-35.626,-35.362,-35.111,-35.514,-34.857,-36.546,-36.307,-35.098,-35.663,-35.513,-35.18,-34.432,-34.46,-34.958,-34.962,-34.304,-34.044,-34.203,-34.509,-34.247,-34.434,-34.568,-35.448,-35.49,-35.522,-35.777,-35.662,-35.681,-35.648,-35.356,-35.354,-35.237,-35.129,-34.947,-34.74,-35.46\n-35.852,-35.85,-36.243,-35.235,-36.285,-37.683,-37.783,-38.401,-38.304,-37.391,-37.329,-35.757,-35.005,-36.543,-36.056,-35.51,-34.149,-36.859,-35.603,-35.778,-35.426,-35.076,-34.923,-35.03,-34.755,-34.489,-33.969,-33.64,-33.567,-34.178,-34.888,-34.723,-34.586,-35.479,-34.945,-35.5,-36.134,-35.991,-35.793,-35.801,-35.535,-35.21,-35.163,-35.364,-35.12,-35.005,-34.954\n-36.155,-35.862,-35.641,-35.416,-37.012,-36.726,-37.289,-37.346,-36.93,-36.264,-36.18,-34.49,-35.103,-33.505,-31.637,-34.758,-37.294,-36.54,-35.542,-35.395,-35.018,-34.883,-34.651,-34.648,-34.522,-34.384,-33.976,-33.508,-32.822,-33.908,-35.252,-35.287,-35.017,-35.363,-35.192,-35.515,-36.001,-35.991,-35.658,-35.311,-35.311,-35.081,-35.053,-34.918,-34.824,-34.766,-34.136\n-36.084,-36.579,-36.076,-36.295,-36.683,-36.006,-36.421,-36.751,-36.409,-36.364,-35.562,-36.138,-37.129,-33.548,-32.871,-34.11,-37.441,-36.049,-35.438,-35.3,-34.722,-35.185,-34.804,-34.962,-34.853,-34.733,-34.169,-33.635,-33.277,-34.592,-35.515,-35.48,-35.305,-35.622,-35.749,-35.881,-35.501,-35.384,-35.284,-35.167,-35.014,-34.987,-35.169,-34.722,-34.676,-34.113,-33.268\n-35.436,-36.128,-36.177,-36.066,-36.141,-35.547,-35.594,-36.255,-36.795,-36.766,-37.675,-35.906,-35.392,-35.112,-34.379,-34.832,-37.197,-35.886,-35.413,-35.201,-35.522,-35.418,-35.113,-35.054,-34.691,-34.573,-34.904,-34.604,-34.557,-35.061,-35.282,-35.47,-35.661,-35.781,-36.25,-36.061,-35.789,-35.341,-35.25,-35.071,-34.896,-35.031,-34.887,-34.718,-34.493,-33.926,-34.329\n-35.713,-35.714,-35.022,-35.43,-34.469,-33.388,-34.54,-34.767,-36.001,-36.723,-36.505,-36.151,-34.92,-33.698,-34.12,-36.37,-36.427,-36.53,-35.33,-35.424,-35.219,-35.376,-35.363,-34.974,-34.768,-34.641,-34.702,-34.641,-34.937,-34.974,-35.145,-35.473,-36.884,-36.471,-36.165,-36.295,-36.294,-35.498,-35.258,-35.063,-35.068,-34.925,-34.903,-34.923,-34.629,-34.692,-35.101\n-36.477,-35.854,-34.611,-34.637,-32.936,-33.048,-32.364,-32.985,-34.699,-35.551,-37.083,-36.766,-36.707,-36.689,-36.663,-36.71,-35.844,-36.563,-35.534,-35.258,-35.06,-35.422,-35.204,-34.899,-35.063,-34.676,-34.529,-34.398,-34.361,-34.894,-35.441,-35.827,-36.383,-35.57,-35.448,-35.798,-36.013,-35.613,-35.482,-35.335,-35.217,-35.423,-35.468,-35.404,-35.504,-35.954,-35.959\n-34.957,-34.279,-33.431,-32.94,-32.217,-33.131,-33.155,-34.88,-35.376,-35.789,-36.221,-36.102,-35.563,-35.405,-36.28,-36.848,-36.96,-36.742,-35.76,-35.924,-35.49,-35.772,-35.065,-35.074,-35.07,-34.661,-34.608,-34.869,-34.72,-34.563,-34.999,-36.422,-36.14,-35.041,-34.826,-35.161,-35.519,-35.539,-35.6,-35.767,-35.382,-35.504,-35.529,-35.76,-35.8,-36.153,-37.458\n-33.734,-33.672,-32.462,-31.024,-31.521,-32.912,-34.834,-35.602,-34.881,-35.336,-34.933,-35.518,-35.609,-35.717,-36.316,-36.807,-35.484,-35.195,-35.468,-35.158,-35.731,-35.756,-35.627,-35.571,-35.445,-35.017,-34.668,-34.849,-34.904,-35.202,-35.899,-36.296,-36.085,-34.723,-34.43,-34.795,-35.175,-35.653,-35.753,-35.891,-35.726,-35.552,-35.564,-35.846,-35.91,-36.162,-37.685\n-33.715,-33.02,-32.722,-32.717,-32.919,-32.474,-34.744,-35.124,-35.282,-34.377,-33.351,-34.126,-34.119,-35.091,-35.68,-37.061,-36.885,-35.631,-35.608,-37.49,-36.76,-36.235,-35.885,-36.117,-36.152,-35.933,-35.472,-34.866,-35.146,-35.576,-35.913,-35.796,-35.548,-35.347,-35.292,-35.522,-35.9,-35.749,-35.962,-35.764,-35.536,-35.409,-35.533,-35.655,-35.78,-36.28,-37.084\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070604-070709.unw.csv",
    "content": "-13.592,-14.131,-14.546,-14.131,-14.555,-15.332,-14.995,-14.875,-15.202,-15.828,-15.943,-16.115,-16.034,-15.617,-15.99,-15.908,-16.174,-16.987,-17.153,-17.097,-16.911,-16.663,-16.692,-17.048,-17.425,-17.454,-17.608,-17.545,-16.8,-16.841,-17.357,-17.423,-17.603,-17.935,-18.138,-18.656,-17.972,-17.633,-18.05,-17.721,-17.573,-17.254,-17.688,-17.622,-16.938,-16.414,-15.916\n-13.584,-13.796,-14.573,-14.917,-14.996,-15.256,-15.175,-15.004,-15.149,-15.412,-15.988,-16.007,-15.642,-15.397,-15.538,-15.902,-16.285,-16.82,-16.935,-16.882,-16.996,-16.815,-16.963,-17.252,-17.485,-17.495,-17.29,-16.777,-16.289,-15.989,-16.171,-16.199,-17.03,-18.234,-18.663,-18.743,-17.992,-17.737,-18.07,-18.065,-17.362,-16.756,-16.389,-16.033,-15.868,-16.111,-16.28\n-13.218,-13.875,-13.98,-14.685,-14.808,-14.653,-14.881,-14.913,-14.853,-15.165,-15.775,-16.135,-15.727,-15.565,-15.511,-15.698,-16.012,-15.913,-15.956,-16.209,-16.354,-16.689,-16.863,-17.031,-17.444,-17.38,-17.1,-16.97,-16.176,-15.73,-15.723,-16.327,-16.839,-17.937,-18.474,-18.098,-18.04,-17.882,-17.694,-17.521,-17.168,-16.784,-16.602,-16.044,-15.139,-14.911,-14.937\n-13.753,-12.992,-13.334,-14.536,-14.441,-14.643,-14.544,-14.729,-15.033,-15.142,-15.503,-15.524,-15.434,-15.293,-15.204,-15.259,-15.648,-15.881,-15.911,-16.029,-16.253,-16.488,-16.816,-17.027,-17.017,-16.945,-16.652,-16.299,-16.162,-16.189,-16.278,-16.175,-15.824,-16.668,-17.885,-18.167,-17.632,-17.501,-16.924,-16.982,-17.212,-16.961,-16.615,-15.865,-15.056,-14.515,-14.588\n-13.815,-12.164,-12.563,-14.595,-14.662,-14.662,-14.34,-13.86,-14.288,-15.48,-15.609,-15.028,-14.964,-15.074,-15.363,-15.174,-15.444,-16.426,-16.327,-15.98,-15.516,-15.991,-16.393,-16.557,-16.566,-16.704,-16.486,-16.195,-15.937,-16.057,-16.284,-16.048,-17.159,-17.371,-16.607,-16.883,-17.211,-16.751,-16.289,-16.116,-16.717,-16.554,-16.082,-15.665,-15.266,-14.827,-13.762\n-13.34,-13.066,-13.891,-14.954,-15.073,-15.198,-15.22,-14.87,-14.39,-14.54,-14.518,-14.31,-14.659,-15.329,-15.591,-15.607,-15.682,-16.175,-16.668,-16.363,-15.875,-15.704,-15.723,-16.029,-15.971,-15.693,-15.842,-15.802,-15.444,-15.531,-15.748,-15.81,-16.08,-16.559,-16.269,-15.771,-16.147,-16.119,-16.048,-15.979,-16.174,-16.379,-16.079,-15.525,-14.801,-13.826,-13.114\n-12.465,-11.397,-11.536,-14.407,-14.398,-14.532,-14.6,-14.42,-14.638,-14.377,-14.466,-14.147,-14.544,-15.363,-15.28,-15.352,-15.478,-15.67,-15.998,-16.062,-15.475,-15.497,-15.22,-15.577,-15.479,-14.955,-15.162,-15.359,-15.269,-15.982,-16.276,-16.129,-15.97,-16.23,-16.443,-16.254,-15.891,-15.818,-15.554,-15.871,-16.426,-16.197,-15.519,-15.136,-15.214,-14.557,-12.49\n-13.523,-13.525,-13.946,-14.266,-14.283,-14.22,-13.858,-13.98,-14.177,-14.367,-14.456,-14.229,-14.275,-15.101,-15.257,-15.241,-15.189,-15.184,-15.314,-15.395,-14.683,-14.927,-15.165,-15.204,-15.151,-15.198,-15.001,-15.23,-15.768,-16.144,-15.941,-15.779,-15.897,-16.076,-16.191,-16.33,-16.098,-16.022,-15.874,-15.25,-14.759,-14.901,-14.605,-13.36,-12.449,-12.292,-11.323\n-13.367,-13.606,-13.849,-14.388,-14.692,-14.237,-13.631,-13.802,-13.811,-13.957,-13.879,-13.619,-14.102,-14.765,-14.836,-14.863,-14.75,-14.479,-14.616,-14.6,-14.374,-14.395,-14.492,-14.666,-14.899,-14.658,-14.92,-15.316,-15.535,-15.968,-15.951,-15.407,-15.513,-15.852,-16.459,-16.61,-16.102,-15.954,-15.847,-15.398,-14.576,-13.57,-14.006,-14.33,-13.195,-12.235,-11.557\n-12.96,-12.798,-12.849,-14.315,-14.398,-13.882,-13.669,-13.531,-13.341,-13.308,-13.368,-13.238,-13.519,-14.145,-14.553,-14.274,-13.984,-13.808,-13.47,-13.759,-14.334,-14.45,-14.664,-14.521,-14.493,-14.75,-14.871,-15.022,-15.047,-15.028,-15.215,-15.354,-15.404,-15.065,-14.741,-15.573,-15.39,-15.469,-15.256,-15.045,-14.878,-14.787,-14.609,-14.787,-13.594,-12.566,-11.717\n-13.598,-13.535,-14.648,-14.36,-14.376,-13.774,-13.844,-13.523,-13.498,-13.35,-13.077,-13.093,-13.058,-13.465,-14.126,-14.933,-14.372,-13.935,-14.231,-14.251,-14.224,-14.705,-14.845,-14.511,-14.378,-14.287,-14.662,-14.846,-14.747,-14.748,-14.864,-15.165,-15.55,-15.789,-15.708,-15.521,-14.966,-14.446,-14.444,-14.206,-14.1,-14.149,-14.423,-14.677,-14.354,-12.923,-11.41\n-13.68,-13.416,-13.606,-13.584,-13.739,-13.389,-13.707,-13.294,-13.018,-12.824,-12.479,-12.409,-12.562,-12.865,-13.333,-13.85,-14.352,-13.605,-13.282,-13.354,-13.863,-14.463,-14.904,-14.665,-14.465,-14.424,-14.921,-14.908,-14.882,-14.91,-14.984,-15.167,-15.328,-15.283,-14.93,-14.028,-14.084,-14.474,-13.885,-13.552,-13.408,-13.178,-13.123,-14.189,-14.083,-14.572,NaN\n-13.595,-14.064,-15.424,-14.77,-13.136,-13.368,-13.428,-13.665,-13.043,-12.447,-12.02,-11.925,-12.41,-13.03,-12.897,-12.979,-13.241,-13.377,-13.331,-13.537,-14.154,-14.228,-14.462,-14.407,-14.084,-14.2,-14.156,-14.157,-14.872,-15.112,-14.781,-15.351,-15.241,-14.725,-14.204,-14.743,-14.931,-14.523,-14.527,-13.636,-12.793,-12.594,-12.967,-14.081,-13.21,NaN,NaN\n-13.108,-12.857,-12.96,-12.676,-12.643,-12.412,-12.557,-13.201,-12.982,-12.757,-12.576,-12.247,-11.978,-12.828,-12.814,-12.515,-12.667,-12.666,-12.798,-13.304,-13.71,-13.807,-14.042,-13.981,-13.749,-13.537,-13.057,-12.448,-12.661,-14.052,-14.467,-14.764,-14.993,-14.623,-14.832,-14.523,-13.534,-12.735,-12.844,-12.113,-11.724,-11.812,-11.599,-11.111,-11.425,NaN,NaN\n-12.778,-12.361,-11.973,-12.567,-13.005,-13.331,-13.134,-13.092,-13.027,-12.91,-13.445,-13.123,-12.544,-12.416,-12.908,-13.078,-12.918,-12.318,-12.579,-13.435,-14.528,-14.627,-14.177,-13.786,-13.623,-13.87,-14.049,-13.442,-13.81,-14.053,-14.428,-14.774,-15.135,-15.192,-14.768,-14.663,-13.744,-13.331,-12.624,-11.06,-9.2734,-10.39,-10.162,-11.243,-11.338,NaN,NaN\n-13.738,-13.033,-12.571,-12.94,-12.763,-13.308,-13.496,-13.418,-13.217,-12.994,-13.5,-13.536,-12.434,-12.135,-12.297,-12.748,-12.618,-12.476,-12.851,-12.951,-13.279,-13.811,-13.494,-12.826,-12.356,-12.445,-13.084,-13.254,-13.467,-13.738,-13.818,-13.669,-13.297,-13.286,-14.07,-13.932,-14.21,-13.641,-13.463,-13.696,-12.525,-11.699,-10.111,-9.8166,-9.6979,NaN,NaN\n-14.053,-13.746,-13.293,-13.295,-13.602,-13.174,-13.105,-12.991,-12.464,-12.444,-12.934,-12.751,-11.616,-11.794,-12.154,-12.155,-12.213,-11.75,-12.864,-13.394,-13.593,-13.832,-12.687,-12.245,-12.72,-12.888,-13.167,-13.359,-13.5,-13.81,-14.148,-14.38,-14.11,-14.122,-13.913,-13.64,-15.628,-15.133,-12.717,-12.727,-11.82,-12.536,-12.614,-11.813,-9.6823,NaN,NaN\n-13.403,-12.437,-12.585,-12.676,-13.115,-12.492,-12.361,-12.481,-12.107,-12.184,-12.614,-11.794,-10.922,-10.734,-11.184,-11.309,-11.209,-11.495,-13.221,-14.631,-14.747,-14.662,-13.806,-13.378,-14.768,-14.292,-13.615,-13.477,-13.478,-14.065,-14.331,-14.313,-16.022,-16.015,-15.826,-14.613,-19.133,-19.468,-18.75,-12.023,-11.441,-11.242,-12.259,-13.515,-11.965,NaN,NaN\n-12.843,-12.51,-12.429,-10.959,-11.813,-13.229,-13.129,-13.101,-12.185,-12.183,-12.166,-11.433,-11.112,-11.587,-11.734,-11.288,-11.002,-11.63,-12.191,-12.421,-13.428,-14.491,-14.47,-14.021,-14.126,-13.686,-13.616,-13.71,-13.601,-13.294,-13.585,-13.019,-14.82,-16.574,-16.105,-14.3,-13.625,-13.764,-14.226,-12.608,-12.434,-12.145,-12.715,-13.784,-13.066,-12.915,-13.415\n-11.133,-11.995,-10.661,-9.9031,-9.2783,-9.4305,-11.674,-12.218,-11.731,-11.759,-11.444,-11.286,-11.437,-11.89,-12.195,-11.525,-10.984,-11.081,-11.525,-11.991,-11.819,-11.935,-12.911,-14.088,-13.643,-13.244,-13.17,-12.846,-12.62,-12.953,-13.099,-13.23,-10.652,-12.516,-12.802,-12.556,-11.895,-12.031,-15.301,-15.058,-14.07,-13.141,-13.236,-13.354,-13.571,-14.691,-14.561\n-11.206,-11.883,-12.085,-11.563,-11.175,-12.17,-12.047,-12.27,-11.993,-11.85,-11.596,-11.979,-11.734,-11.015,-10.4,-10.744,-10.852,-11.31,-11.834,-12.251,-12.781,-11.863,-11.996,-13.248,-13.146,-12.533,-12.745,-13.586,-13.656,-13.302,-13.493,-13.938,-11.357,-10.996,-9.26,-12.182,-11.547,-10.34,-12.605,-14.707,-14.384,-13.289,-12.963,-12.344,-13.011,-13.645,-13.569\n-11.77,-11.924,-10.879,-10.134,-10.529,-12.452,-13.148,-11.402,-11.776,-11.221,-11.063,-11.248,-11.568,-12.207,-13.801,-10.848,-11.353,-12.645,-13.604,-14.849,-14.067,-13.608,-12.951,-13.031,-12.597,-12.246,-12.205,-12.454,-13.336,-13.464,-12.843,-13.231,-12.842,-12.852,-11.206,-11.172,-10.992,-12.033,-11.839,-12.264,-14.847,-13.384,-13.128,-12.864,-12.311,-11.824,-12.711\n-10.85,-10.81,-10.852,-11.192,-10.521,-11.1,-11.011,-10.911,-10.922,-10.239,-10.596,-10.83,-11.505,-13.736,-15.87,-15.765,-11.806,-12.679,-13.361,-13.205,-13.258,-13.703,-14.042,-13.014,-12.46,-11.868,-11.623,-11.736,-11.993,-12.112,-11.968,-11.601,-11.671,-11.626,-11.322,-11.445,-11.555,-10.803,-10.793,-9.954,-13.637,-13.266,-11.971,-12.983,-12.797,-12.462,-12.333\n-10.598,-10.223,-11.447,-13.171,-14.085,-12.469,-11.603,-11.509,-11.405,-11.058,-10.967,-11.479,-12.009,-13.466,-14.187,-14.917,-14.974,-14.324,-12.727,-11.749,-10.923,-11.718,-12.628,-10.753,-10.52,-11.232,-11.113,-10.92,-10.498,-9.7255,-8.9652,-9.9796,-10.448,-10.613,-10.427,-10.999,-11.423,-11.836,-12.109,-11.422,-12.005,-12.767,-12.794,-12.673,-12.631,-12.019,-11.491\n-10.371,-10.209,-10.546,-10.85,-11.258,-11.274,-10.988,-11.015,-11.298,-11.676,-12.094,-11.577,-11.549,-12.018,-12.839,-12.921,-11.796,-11.236,-11.819,-11.314,-11.411,-11.105,-10.572,-9.719,-9.846,-9.2205,-9.0172,-9.7249,-9.483,-8.0949,-8.2115,-8.9052,-9.3979,-9.2516,-8.6647,-9.5696,-8.0681,-8.2007,-8.5445,-11.562,-11.657,-11.941,-11.875,-11.387,-11.627,-11.826,-11.413\n-10.924,-10.284,-10.362,-10.568,-10.68,-10.07,-10.339,-10.631,-10.671,-10.435,-10.892,-11.443,-11.011,-10.014,-10.306,-11.273,-10.682,-10.672,-10.933,-10.759,-10.466,-9.5928,-9.8805,-9.8027,-9.3651,-8.3524,-7.2325,-8.6228,-8.8114,-7.5036,-6.0311,-7.0535,-11.113,-12.536,-9.508,-9.9359,-11.312,-10.194,-10.522,-12.168,-11.911,-11.56,-11.493,-11.072,-11.022,-11.479,-10.929\n-10.235,-9.9357,-10.526,-10.443,-10.033,-10.169,-9.8969,-10.386,-10.266,-9.5184,-9.3938,-10.228,-10.105,-10.038,-10.153,-10.518,-10.129,-9.7823,-10.421,-10.861,-10.442,-9.4679,-9.2233,-10.188,-11.161,-11.254,-11.218,NaN,-11.034,NaN,NaN,NaN,-9.8806,-12.569,-12.491,-11.655,-10.769,-10.779,-10.939,-11.225,-11.303,-11.004,-11.298,-11.027,-10.953,-11.318,-11.577\n-11.714,-11.189,-11.52,-11.281,-10.694,-10.956,-10.677,-9.9308,-9.6698,-9.3937,-9.328,-9.612,-10.27,-10.998,-10.395,-9.4308,-10.074,-10.907,-10.933,-10.665,-10.76,-9.9216,-9.4759,-9.991,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-12.06,-10.99,-10.054,-9.9563,NaN,NaN,NaN,NaN,-10.724,-11.186,-10.549,-10.442,-11.648,-11.922\n-11.994,-11.451,-11.446,-11.381,-10.782,-10.662,-10.714,-10.411,-10.189,-10.181,-10.773,-10.569,-9.693,-9.9463,-9.8192,-9.3277,-9.6063,-10.099,-9.6267,-9.2394,-9.7582,-9.9415,-9.2036,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.7026,NaN,NaN,NaN,NaN,NaN,-10.815,-10.806,-11.105,-11.203,-11.697,-11.811\n-10.765,-11.508,-11.519,-11.389,-9.9445,-9.8827,-10.736,-10.75,-10.55,-10.88,-10.611,-10.262,-9.6111,-9.891,-9.834,-9.9116,-10.218,-11.197,-11.092,-11.119,-9.1475,-8.9603,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.0818,-9.9069,-10.833,NaN,NaN,NaN,-10.52,-11.286,-11.958,-11.43,-11.651,-11.689\n-12.787,-11.172,-11.437,-11.821,-11.2,-9.9922,-11.278,-11.173,-10.928,-10.786,-10.738,-10.503,-10.417,-10.047,-9.2201,-9.3369,-9.5933,-9.6638,-10.019,-11.439,-10.529,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.9363,-7.8093,-7.6003,-8.4733,-9.3709,-9.7852,-9.8969,-10.539,-11.006,-11.944,-11.456,-11.346,-11.101\n-12.876,-12.604,-11.932,-11.028,-11.761,-12.537,-13.656,-12.206,-11.944,-13.648,-12.775,NaN,NaN,NaN,NaN,-13.879,-11.36,-9.7339,-11.758,-13.043,-10.913,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.8099,-9.2009,-9.342,-9.4379,-9.1953,-8.9337,-8.7959,-8.869,-9.026,-10.689,-11.383,-11.371,-11.247,-11.909,-12.413\n-13.148,-12.646,-11.862,-12.059,-11.756,-12.185,-14.018,-13.459,-11.42,-13.949,-13.311,NaN,NaN,NaN,-13.414,-13.494,-12.371,-13.073,-14.135,-13.474,-11.181,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.9645,-6.1597,-6.619,-7.4353,-7.7736,-8.9218,-8.2654,-9.0306,-9.4543,-10.461,-11.33,-10.948,-10.471,-11.221,-11.215\n-11.702,-10.961,-10.808,-10.902,-11.317,-12.354,-11.64,-11.537,-11.825,-11.634,-11.124,NaN,NaN,NaN,-13.055,-13.005,-13.358,-13.01,-13.334,-13.459,-11.825,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.4408,-4.9739,-7.6632,-9.3487,-9.1109,-9.4918,-10.19,-10.359,-10.511,-10.738,-10.618,-10.607\n-10.731,-11.657,-11.719,-12.329,-12.941,-13.161,-13.21,-11.95,-11.415,-11.257,-12.106,-11.538,-10.995,NaN,-12.968,-12.103,-11.119,-11.767,-11.399,-9.6253,-8.9405,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.324,-8.7096,-9.1523,-8.1712,-8.0609,-9.0962,-9.7999,-10.686,-11.068,-10.531\n-12.218,-12.125,-11.786,-12.005,-13.368,-13.495,-13.142,-12.056,-11.271,-11.197,-11.178,-11.59,-12.265,-12.004,-10.864,-11.512,-12.629,-11.677,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.3766,NaN,NaN,NaN,NaN,NaN,NaN,-8.6231,-7.9728,-9.1303,-9.0962,-9.0517,-9.2267,-9.5901,-9.9484,-10.897,-11.404\n-11.76,-11.496,-10.849,-11.172,-12.091,-11.98,-12.01,-11.428,-11.007,-10.996,-10.909,-9.3195,-10.152,-8.9303,-8.8296,-10.715,-11.935,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.172,-4.3408,-4.7491,NaN,NaN,NaN,NaN,-10.252,-8.3636,-9.5026,-9.9605,-9.5502,-9.5501,-10.012,-9.7344,-10.146,-11.154\n-10.835,-10.675,-10.765,-10.978,-11.164,-11.722,-11.574,-11.503,-11.676,-12.111,-12.606,-10.17,-9.4079,-8.0466,-10.479,-11.607,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.0999,-3.7419,-3.9042,-8.8054,NaN,NaN,-8.8831,-9.0769,-9.3569,-10.07,-10.288,-9.8077,-9.5668,-9.586,-9.0717,-10.086,-11.814\n-11.391,-11.763,-11.468,-11.255,-10.928,-11.111,-11.716,-11.814,-11.921,-12.275,-12.582,-13.176,-12.448,-12.112,-12.6,-11.655,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.859,-4.7207,-5.8713,-5.4693,-5.1297,-6.4535,-9.1784,-9.0186,-8.3914,-8.7921,-9.2088,-10.234,-10.74,-10.745,-11.093,-10.966,-10.432,-9.7391,-10.635\n-11.437,-11.375,-10.927,-10.656,-10.916,-11.457,-11.666,-11.924,-12.411,-12.523,-12.504,-12.662,-12.69,-12.239,-13.298,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.7016,-5.3512,-7.3454,-5.598,-5.3045,-5.6757,-7.8882,-8.0158,-8.0917,-8.0509,-8.7527,-10.522,-10.675,-10.204,-11.713,-11.747,-11.313,-11.916,-11.548\n-11.291,-10.596,-10.666,-11.013,-11.464,-11.415,-10.739,-10.911,-11.978,-12.953,-12.828,-11.615,-12.226,-12.996,-14.313,-15.178,-14.959,-12.087,-11.736,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.4164,-7.4363,-7.7892,-7.3588,-7.5397,-8.0753,-8.3142,-9.4841,-9.6015,-8.8162,-8.7534,-9.5811,-11.101,-10.96,-10.606,-12.08,-12.483,-11.808,-11.511,-11.426\n-11.496,-11.692,-11.79,-11.615,-11.343,-11.849,-12.148,-12.228,-12.598,-13.056,-13.577,-9.7651,-11.306,-13.599,-13.848,-13.591,-14.477,-13.1,-12.257,-10.702,-11.348,-12.768,-11.686,-10.658,-8.8198,-8.5023,-10.299,-8.8577,-8.2171,-8.6859,-8.96,-9.2557,-9.6497,-10.305,-9.6454,-8.2574,-9.0398,-10.727,-10.559,-11.365,-11.141,-10.266,-10.569,-11.075,-11.334,-11.974,-12.544\n-11.383,-11.866,-12.504,-12.661,-12.246,-12.156,-12.531,-12.782,-13.349,-13.289,-12.878,-11.953,-12.873,-13.835,-14.841,-13.644,-13.222,-13.835,-13.734,-12.9,-11.97,-11.478,-11.284,-11.336,-10.58,-10.386,-11.111,-10.584,-10.721,-11.271,-10.482,-10.284,-10.779,-10.021,-9.73,-9.392,-8.3723,-10.158,-11.438,-11.531,-11.603,-12.15,-12.339,-11.915,-11.845,-12.616,-13.298\n-11.914,-12.027,-12.113,-12.073,-12.825,-12.06,-12.668,-13.39,-13.728,-12.638,-12.807,-13.273,-13.247,-12.789,-12.65,-12.57,-12.849,-12.71,-12.241,-12.333,-11.232,-9.7941,-10.708,-11.577,-12.477,-11.903,-11.406,-12.336,-11.612,-11.231,-11.232,-11.72,-11.917,-11.668,-12.5,-12.396,-10.763,-10.819,-11.799,-11.854,-11.76,-11.764,-12.729,-13.816,-14.502,-14.299,-14.314\n-11.601,-11.855,-11.907,-11.82,-12.193,-12.04,-13.442,-14.985,-15.548,-12.075,-12.488,-13.199,-13.335,-12.992,-12.473,-11.985,-12.071,-12.045,-12.74,-11.691,-6.3703,-7.4729,-9.2353,-11.79,-13.115,-12.352,-11.285,-12.515,-12.183,-11.935,-12.559,-13.152,-13.029,-13.23,-14.442,-15.446,-14.57,-13.777,-12.87,-12.426,-12.052,-11.35,-10.739,-12.777,-13.512,-14.464,-15.683\n-12.739,-12.784,-12.952,-12.795,-12.554,-12.868,-12.686,-12.463,-12.234,-11.971,-12.381,-13.406,-13.718,-12.947,-12.606,-12.703,-12.866,-13.126,-13.228,-12.761,-10.098,-10.63,-11.126,-11.047,-12.739,-12.125,-12.358,-12.797,-12.552,-12.509,-12.362,-11.799,-11.543,-12.457,-12.85,-13.464,-13.842,-13.694,-13.033,-12.731,-13.458,-12.38,-10.379,-11.298,-12.522,-13.704,-14.46\n-12.329,-12.887,-13.344,-13.072,-13.187,-13.386,-13,-12.614,-12.799,-12.798,-12.856,-13.339,-13.465,-13.056,-12.436,-12.554,-12.703,-12.348,-13.392,-14.074,-13.135,-12.166,-13.448,-14.051,-12.861,-12.268,-12.402,-12.559,-13.023,-13.013,-12.024,-12.022,-12.568,-12.918,-13.382,-13.28,-13.068,-12.87,-12.508,-12.497,-13.116,-12.504,-13.434,-10.898,-9.0561,-9.8654,-12.395\n-12.871,-13.196,-13.404,-13.451,-13.485,-12.638,-12.748,-13.059,-12.353,-12.535,-12.591,-12.443,-12.531,-12.621,-12.597,-12.696,-12.573,-12.065,-13.05,-14.775,-15.626,-15.578,-13.013,-14.261,-14.405,-13.995,-13.659,-13.321,-13.368,-13.404,-12.654,-12.035,-12.665,-13.667,-14.457,-14.055,-12.938,-13.137,-13.467,-12.485,-13.37,-15.43,-17.423,-16.475,-15.551,-13.807,-14.255\n-13.61,-13.405,-12.794,-13.452,-13.738,-12.977,-12.506,-13.188,-13.482,-13.227,-12.597,-12.347,-12.096,-12.248,-12.129,-12.134,-12.414,-12.915,-13.197,-13.818,-14.522,-13.439,-13.336,-14.005,-14.049,-13.403,-14.25,-14.091,-13.929,-13.72,-12.844,-12.747,-12.735,-12.775,-12.988,-13.511,-12.384,-12.217,-13.293,-12.595,-13.905,-16.547,-18.369,-16.233,-14.751,-14.136,-14.109\n-13.909,-13.58,-12.784,-13.664,-14.127,-13.424,-13.059,-13.077,-12.643,-12.631,-13.537,-12.906,-12.545,-12.39,-12.24,-12.648,-12.766,-13.806,-14.127,-14.82,-15.585,-15.453,-14.154,-14.012,-12.701,-13.599,-13.846,-12.295,-14.375,-14.432,-13.358,-13.901,-13.922,-13.601,-13.143,-12.968,-12.165,-12.526,-12.59,-12.825,-12.659,-12.784,-13.321,-16.016,-14.775,-14.381,-14.959\n-13.839,-14.016,-13.692,-13.713,-13.821,-13.529,-13.807,-13.969,-13.318,-12.948,-13.356,-13.482,-13.154,-13.501,-13.65,-13.128,NaN,-11.298,-10.559,-10.219,-11.419,-13.678,-13.996,-14.058,-13.767,-14.404,-15.244,-14.18,-13.735,-14.019,-13.857,-14.12,-13.832,-13.652,-13.745,-13.54,-13.07,-13.239,-13.323,-13.171,-12.979,-13.26,-13.875,-14.691,-14.638,-14.533,-15.065\n-13.961,-14.497,-15.22,-14.515,-13.491,-13.004,-13.22,-13.25,-12.814,-12.827,-12.688,-12.505,-12.972,-13.787,-12.95,-12.836,-12.286,NaN,NaN,NaN,NaN,-14.03,-14.167,-14.314,-14.363,-15.633,-16.519,-15.767,-14.075,-14.314,-14.075,-14.359,-13.935,-13.965,-13.793,-13.263,-12.931,-13.029,-13.293,-13.617,-13.865,-13.793,-12.956,-13.455,-14.68,-15.085,-14.783\n-14.604,-14.629,-14.805,-14.417,-14.123,-13.769,-14.028,-13.39,-12.819,-12.473,-12.654,-13.077,-13.465,-13.491,-12.885,-10.968,-10.118,NaN,NaN,NaN,-13.706,-14.881,-14.53,-14.659,-14.991,-15.122,-15.181,-14.331,-13.537,-14.398,-14.395,-14.481,-14.155,-14.307,-13.513,-13.026,-13.483,-14.056,-13.948,-13.805,-13.313,-12.982,-13.05,-13.888,-14.821,-14.299,-14.033\n-15.544,-14.287,-13.784,-13.546,-13.939,-14.53,-14.782,-13.733,-11.685,-11.618,-11.787,NaN,NaN,NaN,-14.129,-13.16,-12.36,NaN,NaN,NaN,-16.188,-14.87,-14.351,-14.571,-14.743,-15.72,-15.435,-13.851,-13.495,-14.34,-14.827,-14.233,-13.75,-14.497,-13.643,-13.759,-14.684,-14.822,-15.122,-14.737,-13.64,-13.373,-13.72,-14.37,-14.726,-14.558,-14.151\n-13.726,-13.46,-14.094,-14.004,-14.14,-14.893,-15.374,-14.567,-13.201,-12.923,NaN,NaN,NaN,NaN,NaN,-14.833,-14.183,-13.638,-14.963,-16.399,-15.419,-14.415,-14.57,-14.512,-15.944,-17.291,-15.204,-14.726,-13.562,-14.719,-15.666,-15.596,-14.926,-14.501,-14.973,-14.642,-13.833,-13.639,-14.122,-14.588,-13.99,-14.103,-14.381,-14.334,-14.755,-14.294,-13.725\n-14.533,-14.64,-14.85,-14.232,-14.122,-14.299,-14.46,-15.648,-15.226,-14.452,NaN,NaN,NaN,NaN,NaN,-15.939,-14.413,-14.293,-14.842,-15.524,-15.443,-15.378,-15.104,-14.942,-14.367,-15.695,-15.439,-14.581,-13.573,-14.726,-15.027,-15.946,-17.169,-15.052,-14.316,-14.801,-14.339,-13.889,-13.88,-13.982,-14.125,-14.225,-14.342,-13.856,-14.562,-13.817,-13.544\n-15.606,-16.203,-15.513,NaN,-12.163,-12.473,-13.121,-13.702,-14.385,-15.523,-16.231,-15.539,-15.293,NaN,NaN,-16.776,-16.89,-15.218,-15.185,-15.656,-16.086,-15.534,-14.616,-13.493,-12.157,-12.443,-14.363,-14.91,-15.019,-14.2,-14.842,-15.267,-16.068,-15.163,-14.226,-14.101,-14.412,-14.342,-14.423,-14.378,-14.15,-14.287,-14.087,-14.664,-15.31,-15.393,-14.936\n-15.219,-16.551,-17.295,NaN,NaN,NaN,NaN,-14.037,-14.271,-14.431,-14.93,-16.179,-16.443,-15.951,-15.951,-16.501,-16.879,-16.263,-16.038,-15.493,-15.786,-16.13,-15.338,-14.539,-13.829,-11.73,-13.104,-17.266,-16.858,-14.771,-15.322,-16.098,-15.509,-14.795,-14.805,-14.72,-14.82,-14.772,-14.263,-14.143,-13.678,-13.744,-13.944,-14.707,-15.345,-15.22,-14.573\n-16.082,-15.554,-15.513,NaN,NaN,NaN,NaN,-14.206,-13.957,-13.807,-14.137,-15.161,-15.401,-15.424,-15.288,-16.201,-16.377,-15.924,-15.5,-15.448,-15.316,-15.02,-14.921,-14.994,-14.374,-12.944,-12.472,-13.576,-16.558,-15.928,-15.071,-15.398,-15.017,-14.808,-14.899,-14.758,-14.649,-14.501,-14.317,-14.247,-13.884,-13.32,-13.342,-13.94,-14.377,-14.475,-14.879\n-14.992,-13.839,-12.598,-15.503,-17.173,-18.959,-17.747,-15.512,-14.792,-14.33,-14.56,NaN,NaN,-13.302,-14.369,-15.569,-15.931,-15.483,-15.355,-14.289,-14.649,-14.483,-14.444,-14.413,-14.488,-14.32,-13.797,-14.068,-14.023,-16.198,-16.315,-14.993,-14.529,-14.587,-15.046,-14.802,-14.516,-14.39,-14.326,-14.122,-13.912,-13.431,-13.63,-13.576,-13.517,-14.132,-15.15\n-15.13,-14.238,-14.777,-15.243,-15.232,-17.331,-17.682,-18.157,-17.115,-15.712,-14.356,NaN,NaN,NaN,NaN,NaN,-16.342,-15.888,-15.18,-14.454,-14.681,-14.369,-14.821,-15.159,-14.881,-15.304,-14.476,-14.38,-13.455,-13.859,-14.686,-14.88,-14.323,-13.547,-14.387,-14.81,-14.567,-14.298,-14.207,-14.222,-14.259,-13.712,-13.596,-13.499,-13.697,-14.419,-14.648\n-13.929,-14.059,-14.057,-14.628,-15.753,-15.957,-16.61,-15.96,-16.275,-17.732,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.133,-14.917,-14.576,-13.893,-13.674,-14.464,-15.285,-15.024,-15.074,-15.415,-15.815,-15.276,-15.126,-14.477,-14.361,-14.643,-15.317,-15.144,-14.822,-15.054,-15.209,-14.901,-14.425,-13.913,-14.027,-13.66,-13.474,-14.18,-14.881,-15.514\n-15.053,-14.653,-13.123,-12.702,-12.764,-16.791,-15.827,-15.347,-15.072,-16.558,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.587,-14.599,-14.261,-14.306,-13.975,-14.005,-14.091,-14.864,-14.939,-14.279,-14.518,-14.497,-14.825,-15.027,-15.093,-15.041,-14.197,-14.414,-14.631,-15.222,-15.023,-15.132,-14.728,-13.706,-14.054,-14.008,-13.482,-13.716,-13.675,-14.653\n-17.66,-15.925,-15.122,-14.628,-14.015,-14.993,-16.271,-16.784,-16.038,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.707,-15.09,-15.069,-14.361,-14.449,-14.378,-14.754,-15.053,-14.588,-13.794,-13.86,-14.4,-15.061,-15.365,-15.324,-15.084,-15.647,-15.965,-15.516,-15.008,-14.861,-14.837,-13.862,-13.66,-14.006,-13.708,-13.37,-13.342,-13.272\n-15.902,-14.708,-14.673,-15.857,-15.399,-15.023,-15.186,-16.038,-16.466,-16.569,-17.297,-15.413,NaN,NaN,NaN,NaN,NaN,NaN,-14.952,-14.665,-14.576,-14.023,-13.261,-14.043,-14.812,-14.817,-14.353,-13.922,-13.816,-14.353,-15.272,-15.467,-14.465,-14.366,-15.51,-15.988,-14.915,-14.423,-14.625,-14.422,-13.505,-13.177,-13.432,-13.533,-13.21,-13.791,-13.84\n-14.03,-14.418,-14.627,-14.635,-14.547,-14.018,-14.675,-16.333,-17.816,-17.852,-16.773,-15.009,NaN,NaN,NaN,NaN,NaN,-15.106,-13.976,-14.186,-14.118,-14.001,-14.083,-14.106,-14.108,-14.201,-13.971,-13.398,-13.404,-13.989,-14.999,-15.606,-15.649,-16.187,-16.703,-16.166,-14.429,-14.17,-14.134,-13.909,-13.052,-12.824,-13.205,-12.733,-12.47,-13.768,-14.08\n-15.294,-15.107,-15.092,-15.231,-16.339,-15.161,-13.794,-14.416,-15.256,-15.851,-16.63,-16.9,-17.39,-16.538,NaN,NaN,NaN,-14.69,-13.548,-14.035,-14.681,-14.724,-14.674,-14.252,-13.496,-13.091,-13.883,-13.736,-13.832,-13.918,-14.426,-15.269,-15.799,-16.15,-16.326,-15.65,-14.377,-14.331,-14.003,-13.767,-13.395,-13.115,-12.969,-12.501,-12.05,-12.339,-13.024\n-13.473,-13.863,-15.116,-13.223,-12.999,-13.455,-12.826,-12.982,-15.238,-16.646,-16.568,-15.926,-14.71,-12.851,-13.265,-14.387,-14.127,-15.358,-14.586,-14.559,-14.966,-14.81,-14.879,-14.227,-14.88,-14.37,-14.161,-13.494,-13.243,-13.269,-14.093,-14.706,-14.924,-14.599,-14.133,-14.434,-14.416,-13.947,-13.764,-13.496,-13.059,-12.78,-13.016,-13.014,-12.786,-12.727,-12.871\n-14.686,-14.491,NaN,NaN,NaN,-13.59,-12.512,-12.205,-12.954,-15.351,-14.462,-14.448,-13.293,-12.537,-13.829,-14.682,-17.288,-14.778,-15.098,-15.049,-15.009,-14.592,-14.413,-13.255,-12.813,-14.476,-14.356,-13.78,-13.138,-14.275,-14.974,-15.283,-14.955,-14.05,-14.324,-15.245,-14.762,-14.013,-13.746,-13.695,-13.475,-13.168,-13.096,-12.903,-11.936,-11.125,-11.211\n-16.327,-14.679,NaN,NaN,NaN,-14.742,-14.54,-13.448,-12.716,-13.83,-15.034,NaN,NaN,NaN,NaN,NaN,NaN,-14.413,-14.902,-14.756,-15.068,-15.076,-14.196,-13.292,-12.526,-13.179,-13.081,-13.022,-13.645,-14.496,-15.402,-14.982,-14.041,-14.04,-14.336,-14.857,-14.719,-14.862,-14.161,-13.54,-13.334,-13.418,-12.651,-12.366,-13.067,-13.538,-13.771\n-13.839,-13.875,NaN,NaN,NaN,-14.345,-15.941,-16.544,-16.679,-17.641,-15.447,NaN,NaN,NaN,NaN,NaN,NaN,-15.766,-15.736,-15.25,-14.311,-14.008,-13.772,-13.358,-13.039,-12.347,-12.173,-12.71,-13.833,-14.315,-15.028,-14.799,-13.67,-13.347,-13.514,-13.975,-14.464,-14.363,-13.843,-13.844,-13.039,-12.514,-12.639,-12.365,-12.639,-13.507,-14.19\n-13.759,-13.663,-14.918,-16.059,-14.578,-14.937,-16.392,-17.169,-17.334,-16.606,-16.071,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.327,-14.124,-13.788,-13.398,-12.903,-12.863,-13.081,-12.779,-12.684,-14.676,-15.216,-14.669,-14.284,-13.334,-12.71,-13.024,-14.722,-14.67,-13.668,-13.262,-12.947,-12.637,-12.623,-12.704,-12.165,-12.729,-13.565,-14.04\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method1_geo_070709-070813.unw.csv",
    "content": "-11.919,-12.299,-12.341,-12.176,-12.337,-11.715,-11.618,-11.152,-10.065,-10.397,-11.003,-10.516,-10.224,-10.137,-9.7734,-9.9073,-9.8373,-9.7156,-9.7362,-9.6031,-9.9377,-9.3947,-9.1481,-9.3824,-9.4807,-9.2971,-9.382,-9.3773,-8.9676,-9.2284,-9.234,-9.7048,-9.7042,-9.7819,-9.4546,-9.4546,-9.4539,-9.4339,-9.4555,-9.1554,-8.9648,-8.8916,-9.0072,-8.9777,-8.6846,-8.8099,-8.8095\n-11.969,-11.982,-12.505,-12.301,-12.171,-11.78,-11.705,-11.233,-10.797,-10.93,-10.888,-10.688,-10.481,-10.25,-10.183,-10.1,-9.8923,-9.869,-9.7248,-9.7125,-9.8061,-9.4956,-9.474,-9.6626,-9.5657,-9.3937,-9.4055,-8.9135,-8.9929,-9.4624,-9.6158,-9.7345,-9.6913,-10.403,-9.6105,-9.7908,-9.8855,-9.8211,-9.5828,-9.2546,-9.3191,-8.8748,-9.1486,-9.0392,-8.8989,-8.8512,-8.4542\n-12.225,-11.976,-12.172,-12.561,-12.339,-11.719,-11.303,-10.99,-10.776,-11.035,-10.585,-10.461,-10.455,-10.203,-10.194,-10.077,-9.8199,-10.014,-10.054,-10.044,-9.9406,-9.8782,-10.176,-9.8771,-9.6569,-9.4791,-9.3452,-9.1929,-9.409,-9.5073,-9.5034,-9.8743,-10.082,-10.759,-9.8636,-9.9613,-10.026,-10.012,-9.8751,-9.7199,-9.2163,-8.9413,-9.1885,-9.1711,-9.1159,-8.8157,-8.2232\n-13.156,-11.885,-12.057,-12.51,-12.067,-11.612,-11.244,-10.677,-10.765,-10.98,-10.719,-10.751,-10.643,-10.596,-10.256,-10.275,-10.042,-10.051,-10.075,-10.253,-10.09,-10.093,-10.163,-9.9434,-9.4695,-9.2219,-9.2283,-9.3355,-9.3231,-9.4716,-9.8963,-10.222,-10.389,-10.332,-10.159,-10.102,-10.032,-10.074,-9.8897,-9.6137,-9.5332,-9.6269,-9.4984,-9.4634,-9.5022,-9.282,-8.769\n-12.652,-11.951,-11.35,-12.43,-11.868,-11.646,-11.367,-11.244,-11.143,-11.449,-11.339,-10.685,-10.954,-11.176,-10.651,-10.629,-10.346,-10.309,-10.275,-10.333,-10.123,-9.8314,-10.315,-9.9384,-9.2412,-9.279,-9.2098,-9.3172,-9.4288,-9.5832,-10.21,-10.445,-10.714,-10.478,-10.395,-10.275,-10.002,-10.027,-9.9675,-9.7285,-9.7587,-8.9877,-9.4988,-9.3805,-9.5587,-9.3111,-8.7164\n-12.675,-12.534,-12.606,-12.777,-12.019,-11.686,-11.481,-11.431,-11.395,-11.51,-11.039,-10.602,-11.281,-11.332,-11.225,-10.958,-10.819,-10.664,-10.596,-10.782,-10.71,-10.149,-10.156,-9.8987,-9.5833,-9.7593,-9.5691,-9.4426,-9.8881,-10.247,-10.825,-11.238,-11.155,-10.846,-10.605,-9.9293,-9.7551,-9.7452,-9.7884,-9.7889,-9.8831,-9.981,-9.6601,-9.2068,-9.3937,-9.0056,-8.5247\n-12.78,-12.922,-12.793,-13.006,-12.309,-12.371,-11.738,-11.884,-11.509,-11.44,-11.08,-10.843,-11.33,-11.206,-11.435,-11.276,-11.043,-10.875,-10.745,-11.169,-10.927,-10.842,-10.447,-10.357,-10.214,-10.083,-9.7122,-9.9121,-10.422,-10.494,-11.081,-11.108,-10.699,-10.971,-10.442,-9.9628,-9.9945,-10.285,-9.8665,-10.067,-10.074,-9.5969,-9.6972,-9.3195,-8.9777,-8.9379,-8.3972\n-12.775,-12.645,-12.729,-12.998,-12.236,-12.6,-12.184,-11.837,-11.361,-11.171,-11.022,-10.9,-11.258,-11.359,-11.548,-11.537,-11.322,-11.102,-11.473,-11.535,-11.104,-11.431,-11.133,-10.593,-10.743,-10.609,-10.266,-10.67,-11.064,-10.603,-10.854,-10.829,-10.646,-11.325,-10.549,-10.001,-10.162,-10.296,-10.406,-10.321,-10.264,-9.5835,-10.121,-9.448,-9.1015,-9.2005,-8.3065\n-13.11,-12.889,-12.714,-12.968,-12.692,-12.66,-12.127,-11.889,-11.583,-11.144,-10.922,-10.946,-11.277,-11.356,-11.371,-11.664,-11.854,-11.868,-11.887,-11.724,-11.489,-11.369,-11.133,-11.028,-11.422,-11.327,-11.32,-11.225,-11.021,-11.008,-11.2,-11.155,-11.469,-11.472,-10.545,-10.027,-10.405,-10.232,-10.793,-11.116,-10.808,-10.114,-10.025,-10.582,-9.6762,-9.2289,-9.2516\n-13.582,-13.535,-13.148,-13.312,-13.545,-13.01,-12.571,-12.113,-11.477,-10.787,-11.134,-10.913,-11.318,-11.187,-11.579,-11.744,-11.893,-12.023,-12.06,-12.168,-11.717,-11.534,-11.362,-11.399,-11.473,-11.596,-11.896,-11.818,-11.317,-11.275,-11.666,-11.783,-12.087,-11.938,-11.172,-10.685,-10.886,-10.821,-11.16,-11.114,-10.727,-10.519,-10.45,-10.312,-9.8011,-9.6691,-9.869\n-13.947,-14.288,-13.857,-13.667,-13.52,-13.148,-12.691,-12.21,-11.636,-11.322,-11.366,-11.118,-11.563,-11.415,-11.548,-11.537,-11.677,-12.308,-12.272,-11.906,-11.745,-11.742,-11.806,-11.875,-11.602,-11.54,-11.942,-12.08,-11.986,-12.128,-12.433,-12.206,-12.553,-12.139,-11.887,-11.956,-11.712,-11.643,-11.663,-11.631,-11.195,-11.133,-10.808,-10.37,-10.077,-10.079,-10.113\n-14.485,-15.223,-14.575,-14.039,-13.417,-13.534,-13.135,-12.616,-11.925,-11.465,-11.121,-11.387,-11.589,-11.502,-11.662,-11.863,-11.627,-12.576,-12.453,-12.329,-12.207,-12.013,-12.004,-12.139,-11.77,-11.683,-12.043,-12.3,-12.214,-12.413,-12.783,-12.85,-12.786,-12.504,-12.517,-12.725,-12.261,-11.935,-11.889,-11.707,-11.429,-11.517,-11.324,-11.143,-10.697,-10.204,-10.112\n-14.581,-14.879,-13.847,-13.872,-13.375,-13.787,-13.294,-13.088,-12.321,-11.531,-11.419,-11.738,-11.742,-11.851,-11.993,-12.254,-12.444,-12.63,-12.666,-12.262,-12.381,-12.216,-12.152,-11.863,-11.822,-11.915,-12.202,-12.563,-12.389,-12.577,-13.173,-13.158,-12.898,-12.573,-12.998,-12.915,-12.309,-12.44,-12.115,-11.637,-11.55,-11.637,-11.938,-11.95,-11.271,-10.987,-10.24\n-14.602,-13.802,-13.392,-13.375,-14.431,-14.282,-13.399,-12.91,-12.51,-12.252,-11.862,-11.678,-11.703,-12.134,-12.08,-12.386,-12.526,-12.609,-12.354,-12.342,-12.441,-12.557,-12.335,-12.131,-12.261,-12.204,-12.421,-12.859,-12.991,-12.918,-13.068,-12.951,-12.699,-12.431,-12.779,-12.688,-12.54,-12.665,-12.623,-11.968,-11.484,-12.435,-12.589,-12.159,-11.74,-11.022,-10.416\n-14.227,-13.631,-12.628,-14.353,-14.644,-14.472,-13.398,-12.988,-12.459,-12.689,-12.388,-12.003,-12.188,-12.411,-12.465,-12.706,-12.672,-12.807,-12.169,-12.591,-12.602,-13.353,-12.733,-12.726,-12.506,-12.671,-12.763,-12.867,-12.843,-12.863,-12.944,-12.759,-12.636,-12.591,-12.813,-12.978,-12.83,-13.241,-13.343,-12.788,-11.745,-12.971,-13.769,-12.423,-11.958,-11.508,-11.467\n-14.309,-14.583,-14.032,-14.614,-14.728,-14.255,-13.665,-13.282,-12.768,-12.837,-12.461,-12.281,-12.775,-12.409,-12.476,-12.702,-12.869,-12.695,-12.634,-12.804,-12.748,-13.216,-13.136,-12.613,-12.648,-12.899,-12.775,-12.839,-13.074,-13.022,-13.463,-13.449,-13.016,-12.856,-12.776,-13.427,-13.235,-13.745,-13.946,-13.045,-12.576,-12.573,-14.194,-13.201,-12.715,-14.323,-13.41\n-14.281,-14.515,-14.364,-14.148,-13.918,-13.573,-13.748,-13.616,-13.07,-12.941,-12.786,-12.741,-12.939,-12.449,-12.682,-12.689,-12.98,-12.654,-12.993,-12.927,-12.966,-13.368,-13.089,-12.808,-13.342,-13.058,-12.952,-12.978,-13.368,-13.921,-13.863,-13.913,-13.223,-13.07,-13.137,-13.363,-13.731,-13.25,-13.729,-12.472,-13.336,-12.487,-13.083,-13.736,-13.95,-15.695,-14.604\n-14.976,-14.577,-14.442,-14.415,-12.941,-13.017,-13.933,-13.467,-13.35,-13.296,-13.33,-13.16,-13.344,-13.015,-12.772,-12.895,-13.002,-12.692,-13.04,-13.157,-13.737,-13.921,-13.413,-13.218,-13.061,-13.664,-13.861,-13.496,-13.243,-13.33,-13.71,-14.17,-14.289,-13.119,-12.883,-13.134,-14.409,-12.814,-12.147,-12.285,-12.87,-13.88,-13.791,-13.773,-14.585,-15.549,-15.517\n-14.317,-14.523,-14.546,-13.904,-13.075,-12.623,-14.248,-13.836,-13.713,-13.48,-13.469,-13.5,-13.592,-13.413,-13.157,-13.185,-13.292,-13.336,-13.333,-12.99,-13.672,-13.785,-13.607,-13.345,-13.137,-13.15,-14.077,-13.952,-13.488,-13.378,-13.539,-13.964,-15.065,-13.971,-12.726,-13.23,-13.144,-12.807,-11.506,-12.42,-13.208,-13.844,-13.899,-13.895,-14.123,-14.491,-14.93\n-14.275,-14.975,-14.635,-14.112,-13.816,-13.763,-14.482,-14.274,-13.913,-13.611,-13.567,-13.544,-13.465,-13.619,-13.583,-13.61,-13.904,-13.658,-13.62,-13.466,-13.784,-13.879,-13.566,-14.052,-13.714,-13.608,-13.777,-13.771,-13.268,-13.32,-13.796,-13.915,-14.765,-14.697,-13.11,-13.697,-13.43,-13.471,-11.543,-12.333,-13.498,-13.315,-13.802,-14.12,-14.044,-14.995,-14.483\n-14.594,-15.494,-14.718,-14.695,-14.691,-14.391,-14.404,-14.414,-14.026,-13.683,-13.764,-13.693,-13.637,-13.662,-14.194,-14.487,-14.468,-14.349,-14.094,-13.811,-13.569,-13.347,-13.494,-14.063,-14.121,-13.923,-13.895,-13.666,-13.079,-12.937,-13.802,-13.546,-14.204,-14.284,-13.877,-14.089,-14.051,-14.055,-13.01,-13.398,-13.673,-13.661,-13.969,-14.154,-14.477,-14.415,-13.654\n-14.909,-15.139,-14.357,-14.084,-14.355,-14.547,-14.266,-14.49,-14.294,-13.869,-13.749,-13.843,-13.874,-13.72,-14.288,-14.79,-14.612,-14.878,-14.721,-14.485,-14.445,-14.052,-14.158,-14.166,-14.161,-14.278,-14.195,-13.91,-13.027,-13.257,-13.902,-13.674,-14.283,-14.342,-14.439,-14.144,-13.777,-13.899,-13.903,-14.058,-14.299,-14.156,-14,-14.006,-14.048,-15.131,-14.035\n-14.888,-14.806,-14.203,-13.996,-14.33,-14.735,-14.739,-14.297,-14.037,-13.958,-13.904,-13.688,-13.978,-14.308,-14.75,-15.555,-14.883,-15.16,-15.112,-14.523,-14.842,-14.572,-14.029,-14.454,-14.558,-14.716,-14.058,-14.031,-13.417,-13.859,-13.849,-13.41,-13.78,-14.137,-13.945,-13.871,-13.759,-14.199,-14.339,-14.26,-14.765,-14.452,-14.412,-14.766,-15.173,-14.96,-14.158\n-14.768,-14.782,-14.243,-13.863,-16.008,-15.174,-14.192,-13.999,-14.072,-14.046,-14.245,-13.508,-14.215,-14.1,-14.145,-15.023,-14.371,-14.903,-15.06,-14.802,-14.524,-14.078,-13.824,-13.789,-14.207,-14.368,-13.566,-14.005,-13.776,-13.582,-12.745,-13.044,-13.222,-13.343,-13.664,-13.725,-14.052,-14.781,-15.507,-14.355,-14.443,-14.482,-14.416,-14.518,-14.683,-15.066,-14.111\n-15.224,-14.47,-13.893,-13.833,-14.401,-14.315,-13.819,-14.013,-14.25,-14.163,-14.251,-14.089,-13.927,-13.97,-13.976,-14.008,-14.153,-15.133,-14.779,-14.692,-14.597,-14.113,-13.943,-14.003,-14.011,-13.888,-13.716,-13.809,-13.331,-12.684,-12.148,-12.327,-12.98,-13.299,-13.893,-14.067,-14.43,-15.494,-16.132,-14.536,-14.577,-14.532,-14.482,-14.211,-14.324,-14.628,-14.119\n-15.526,-15.242,-14.26,-13.833,-14.235,-13.847,-14.037,-13.881,-14.132,-14.139,-14.248,-14.227,-13.778,-13.78,-14.06,-14.172,-14.291,-14.377,-14.492,-14.431,-14.308,-13.43,-13.553,-14.077,-13.701,-13.339,-13.042,-12.624,-12.467,-12.447,-12.305,-12.708,-13.137,-14.465,-14.419,-14.688,-14.342,-14.651,-15.137,-14.984,-14.568,-14.563,-14.731,-14.197,-14.436,-14.381,-14.034\n-15.552,-15.223,-14.82,-14.523,-14.251,-14.364,-14.369,-13.937,-14.015,-14.078,-14.103,-14.255,-13.582,-13.816,-14.014,-14.195,-14.047,-14.112,-14.483,-14.638,-14.031,-13.707,-13.894,-14.702,-14.155,-13.326,-13.337,-13.244,-13.155,-13.051,-12.965,-13.389,-14.346,-14.802,-14.752,-14.573,-14.492,-14.551,-15.014,-14.675,-14.72,-14.938,-14.598,-14.463,-14.447,-14.359,-14.242\n-15.454,-15.419,-15.194,-15.031,-15.037,-14.584,-14.182,-13.956,-14.318,-14.23,-13.958,-13.996,-14.018,-13.851,-14.135,-14.377,-14.477,-14.973,-15.12,-15.004,-14.686,-14.421,-14.53,-15.258,-14.867,-14.738,-14.145,-14.117,-14.686,-13.546,-13.105,-13.106,-14.396,-14.623,-14.59,-14.618,-14.604,-14.268,-14.362,-14.464,-15.053,-15.121,-14.52,-14.606,-14.377,-14.396,-14.039\n-15.858,-15.626,-15.143,-15.218,-15.119,-14.673,-14.294,-14.417,-14.736,-14.879,-14.722,-14.229,-14.546,-14.589,-14.44,-14.491,-14.665,-15.306,-15.028,-14.827,-14.746,-14.624,-14.381,-14.194,-14.021,-14.087,-13.431,-13.933,-14.416,-13.884,-13.168,-12.887,-13.796,-14.899,-14.736,-14.785,-14.716,-14.077,-15.158,-15.672,-15.392,-15.505,-14.794,-14.652,-14.548,-14.577,-14.439\n-15.965,-15.936,-15.665,-15.266,-14.897,-14.908,-14.648,-14.512,-14.93,-15.276,-14.79,-14.504,-15.166,-15.006,-14.553,-14.639,-15.284,-15.264,-15.183,-15.321,-14.966,-15.008,-14.034,-13.622,-14.054,-13.434,-12.993,-14.194,-14.416,-14.197,-13.925,-15.054,-15.384,-15.978,-14.97,-14.881,-15.828,-15.95,-15.401,-15.466,-15.218,-15.373,-14.889,-14.72,-14.706,-14.644,-14.307\n-16.726,-16.098,-15.587,-15.441,-15.274,-15.146,-15.483,-15.276,-15.283,-15.393,-15.228,-15.113,-15.25,-15.449,-14.832,-15.287,-15.67,-15.4,-15.323,-15.988,-15.617,-15.241,-14.57,-12.079,-15.642,-14.573,-12.728,-12.411,-13.827,-14.03,-13.705,-15.207,-15.366,-15.59,-15.136,-14.958,-15.824,-15.795,-15.319,-15.246,-15.56,-15.78,-15.231,-14.8,-14.686,-14.624,-14.226\n-16.657,-16.27,-17.062,-16.464,-15.889,-15.872,-16.004,-16.372,-15.662,-15.712,-15.615,-15.713,-15.822,-15.931,-16.366,-16.548,-15.815,-15.555,-16.356,-16.707,-15.247,-14.473,-13.746,-10.963,-12.966,-15.117,-15.529,-13.831,-14.946,-15.202,-14.867,-15.313,-14.639,-15.172,-15.133,-14.981,-15.416,-15.501,-15.323,-15.202,-15.626,-15.667,-15.59,-15.027,-14.943,-14.812,-14.575\n-16.427,-16.814,-16.732,-15.688,-15.55,-15.64,-16.502,-16.766,-16.198,-15.502,-15.342,-15.685,-16.094,-15.937,-16.033,-16.502,-16.521,-16.048,-16.417,-16.908,-15.814,-14.202,-13.338,-13.194,-13.522,-14.632,-14.95,-14.519,-16.452,-16.052,-15.448,-15.262,-14.898,-14.413,-14.605,-14.808,-15.06,-15.746,-15.167,-15.222,-15.372,-15.037,-15.006,-15.426,-15.254,-14.929,-14.736\n-16.744,-16.563,-16.378,-15.973,-15.752,-15.726,-16.768,-16.521,-15.843,-15.19,-15.506,-15.601,-16.103,-15.321,-16.901,-16.522,-16.669,-15.849,-15.584,-16.233,-15.774,-15.122,-14.389,-14.983,-14.968,-14.69,-14.401,-14.736,-15.768,-15.095,-14.72,-14.479,-13.912,-14.194,-14.837,-15.106,-15.936,-15.635,-14.844,-15.368,-15.326,-15.02,-15.071,-15.406,-15.372,-15.186,-15.06\n-16.833,-16.682,-16.505,-16.428,-16.491,-16.525,-16.787,-16.206,-15.653,-15.685,-15.603,-15.453,-15.684,-15.09,-16.888,-16.718,-15.924,-15.693,-15.638,-15.615,-15.459,-15.452,-15.146,-14.832,-14.511,-14.26,-15.243,-15.237,-15.221,-15.159,-14.58,-14.27,-13.915,-14.036,-15.195,-15.331,-15.601,-15.275,-15.41,-15.643,-15.525,-15.474,-15.369,-15.409,-15.579,-15.321,-15.186\n-16.562,-16.444,-16.549,-16.447,-16.058,-16.59,-16.696,-16.168,-15.571,-15.824,-15.291,-15.224,-15.594,-15.645,-15.394,-16.694,-16.283,-15.946,-15.678,-15.939,-15.875,-15.868,-15.49,-15.017,-14.777,-15.262,-15.577,-15.733,-13.991,-15.032,-14.785,-14.749,-14.468,-14.62,-15.21,-15.184,-15.461,-15.877,-15.232,-15.418,-15.403,-15.223,-15.249,-15.584,-15.527,-15.263,-15.006\n-16.402,-16.076,-16.204,-16.121,-16.417,-16.534,-16.571,-16.424,-16.303,-16.317,-16.194,-16.17,-15.978,-15.808,-15.774,-16.524,-16.45,-15.873,-15.381,-15.733,-15.161,-16.014,-18.851,-14.487,-13.803,-14.052,-15.608,-15.63,-13.654,-14.963,-15.125,-14.934,-15.109,-16.46,-16.504,-15.878,-15.888,-15.867,-15.373,-15.198,-15.168,-14.966,-15.132,-15.441,-15.49,-15.066,-15.028\n-16.004,-16.545,-16.632,-16.321,-16.449,-16.606,-16.28,-16.276,-16.321,-16.181,-16.125,-16.037,-16.058,-15.502,-15.931,-16.284,-16.029,-15.947,-14.946,-14.632,-13.849,-17.089,-22.063,-15.712,-14.74,-13.923,-15.427,-15.871,-15.095,-15.242,-15.1,-15.104,-14.887,-17.102,-16.787,-16.399,-15.892,-15.896,-15.871,-14.805,-14.973,-15.168,-15.191,-15.436,-15.26,-15.04,-15.534\n-15.712,-15.859,-16.385,-16.407,-16.214,-16.547,-16.122,-16.222,-16.132,-15.981,-16.016,-16.325,-16.095,-16.211,-16.217,-16.018,-16.117,-18.471,-15.691,-14.042,-13.918,-16.869,-18.609,-14.277,-13.825,-13.372,-15.866,-15.843,-15.444,-15.29,-15.437,-15.306,-15.074,-16.076,-16.119,-15.872,-15.689,-15.72,-15.496,-14.592,-14.867,-15.107,-15.38,-15.11,-15.153,-15.113,-15.318\n-15.491,-15.684,-15.924,-16.361,-16.444,-16.508,-16.19,-15.962,-16.274,-16.126,-16.081,-16.26,-16.27,-16.563,-16.192,-16.399,-16.72,-17.774,-17.934,-14.696,-15.034,-15.064,-13.414,-14.036,-14.976,-14.461,-14.134,-15.85,-15.511,-15.522,-15.185,-15.395,-15.483,-15.651,-15.991,-15.81,-15.474,-15.656,-14.971,-15.221,-15.355,-15.51,-15.331,-15.121,-15.109,-14.979,-14.584\n-15.557,-15.774,-15.599,-16.052,-16.459,-16.404,-16.072,-16.041,-15.752,-15.769,-15.901,-16.304,-16.901,-16.967,-16.263,-17.365,-17.664,-17.981,-18.298,-15.537,-15.551,-16.198,-15.613,-15.062,-14.913,-15.656,-15.815,-15.736,-15.8,-15.484,-15.254,-15.761,-15.375,-15.463,-15.311,-15.363,-16.031,-16.769,-16.238,-16.082,-15.557,-15.485,-15.63,-15.018,-15.073,-14.775,-14.565\n-15.157,-15.205,-15.417,-15.748,-15.714,-15.697,-15.511,-15.36,-15.27,-15.486,-15.658,-16.727,-17.643,-17.134,-16.708,-17.023,-18.053,-16.978,-16.558,-15.279,-15.515,-16.666,-16.48,-15.992,-15.333,-16.396,-15.731,-15.587,-15.82,-15.518,-15.644,-15.387,-15.007,-14.865,-15.275,-15.474,-15.627,-16.071,-16.075,-15.915,-15.783,-15.59,-15.703,-15.172,-15.591,-14.494,-14.549\n-14.98,-15.24,-15.516,-15.768,-15.476,-15.29,-15.262,-15.128,-14.88,-15.137,-15.733,-16.721,-17.351,-16.856,-16.649,-16.475,-16.4,-17.318,-16.862,-15.47,-15.553,-16.391,-15.897,-16.077,-16.07,-16.491,-16.046,-15.673,-15.379,-15.492,-15.74,-14.76,-14.929,-14.867,-14.977,-15.019,-15.397,-15.925,-15.328,-15.466,-15.693,-15.335,-15.049,-15.157,-15.347,-14.389,-15.049\n-14.549,-15.134,-14.519,-15.226,-15.145,-14.989,-15.059,-15.253,-14.906,-15.148,-15.449,-15.849,-15.947,-15.775,-15.567,-15.377,-15.646,-15.559,-15.592,-15.66,-15.779,-15.589,-16.415,-16.272,-16.153,-16.574,-16.327,-15.586,-15.179,-15.274,-15.247,-15.086,-15.124,-15.194,-15.909,-16.026,-15.86,-16.102,-15.19,-15.267,-15.533,-14.58,-14.79,-15.239,-15.259,-14.771,-14.602\n-14.254,-14.55,-14.46,-14.676,-15.069,-14.653,-14.715,-14.988,-15.115,-15.231,-14.909,-15.38,-15.387,-15.267,-15.078,-14.903,-15.758,-15.86,-16.128,-15.675,-15.649,-15.796,-16.258,-16.057,-15.885,-16.799,-16.768,-15.743,-15.198,-15.056,-14.939,-14.571,-15.53,-15.608,-16.29,-16.316,-16.327,-15.553,-14.999,-15.218,-15.127,-14.379,-14.534,-15.061,-15.043,-15.082,-14.476\n-14.259,-14.184,-14.101,-14.15,-14.368,-14.343,-14.593,-14.451,-14.559,-14.425,-14.451,-14.886,-14.91,-14.553,-14.252,-14.753,-15.58,-16.08,-16.566,-16.727,-16.669,-16.553,-16.149,-15.618,-15.583,-16.066,-16.319,-15.664,-15.178,-14.934,-14.566,-14.411,-14.64,-14.993,-15.086,-15.313,-15.229,-14.105,-14.744,-14.875,-14.245,-14.242,-14.417,-14.636,-14.634,-14.754,-14.705\n-13.67,-13.183,-13.707,-13.648,-13.353,-13.536,-14.039,-14.014,-13.849,-13.881,-13.863,-13.955,-13.973,-13.877,-13.909,-14.688,-15.36,-15.413,-16.306,-16.734,-17.078,-17.259,-15.902,-15.428,-15.652,-15.868,-15.14,-15.164,-14.973,-14.62,-14.395,-14.132,-14.494,-14.977,-14.835,-14.872,-13.967,-13.723,-14.323,-14.598,-14.38,-15.348,-15.121,-14.924,-14.375,-14.385,-14.32\n-13.21,-12.9,-13.045,-13.192,-12.995,-13.553,-13.662,-13.25,-13.394,-13.321,-13.249,-13.587,-13.618,-13.687,-13.935,-14.276,-14.824,-14.837,-15.59,-15.958,-15.911,-16.288,-15.26,-15.094,-15.871,-15.586,-14.904,-15.037,-14.773,-14.877,-14.659,-14.554,-15.006,-14.703,-14.771,-14.776,-14.079,-13.727,-14.145,-14.164,-14.284,-14.929,-14.902,-15.2,-15.013,-14.838,-14.451\n-12.755,-12.557,-12.786,-12.649,-12.798,-12.833,-12.736,-12.434,-12.095,-12.528,-12.968,-13.02,-13.082,-13.294,-13.671,-13.941,-14.06,-14.503,-15.303,-15.685,-15.501,-14.622,-14.749,-14.898,-14.899,-14.672,-14.929,-14.907,-14.67,-14.863,-14.654,-14.44,-14.485,-14.475,-14.49,-14.63,-14.024,-14.019,-14.023,-13.786,-14.203,-14.631,-14.519,-14.529,-14.279,-14.236,-14.238\n-12.499,-12.261,-12.559,-11.951,-12.33,-12.391,-12.085,-12.009,-11.757,-12.287,-12.584,-12.672,-12.711,-12.949,-13.341,-13.424,-14.447,-14.683,-15.204,-16.22,-16.497,-14.339,-14.399,-14.288,-14.343,-14.421,-14.644,-14.828,-14.082,-14.757,-14.402,-14.419,-14.181,-14.161,-14.041,-13.956,-13.366,-13.954,-13.815,-13.671,-13.475,-13.283,-13.271,-13.961,-13.862,-13.955,-14.042\n-12.154,-12.352,-12.107,-11.237,-11.203,-11.352,-11.773,-11.877,-11.676,-11.83,-11.85,-11.649,-12.427,-12.548,-12.663,-13.273,-14.663,-14.271,-14.993,-15.365,-14.804,-14.133,-13.859,-13.731,-14.024,-14.326,-15.418,-14.365,-13.828,-14.513,-14.481,-14.32,-14.038,-13.95,-13.302,-12.473,-12.563,-13.239,-13.466,-13.317,-13.144,-13.057,-12.786,-13.173,-13.577,-13.802,-13.642\n-12.023,-11.75,-10.562,-10.613,-11.222,-11.415,-11.621,-11.658,-10.998,-10.83,-11.118,-11.368,-11.972,-12.236,-12.178,-13.715,-15.087,-13.767,-14.12,-15.118,-14.303,-13.83,-13.453,-13.465,-13.644,-14.291,-15.287,-11.515,-13.975,-14.275,-14.184,-13.851,-13.566,-13.368,-13.12,-12.468,-12.757,-12.925,-13.054,-13.31,-13.213,-13.387,-13.813,-13.354,-12.841,-13.311,-12.954\n-11.88,-11.673,-10.934,-10.03,-10.663,-10.779,-10.823,-11.021,-10.6,-9.8484,-10.958,-11.405,-11.965,-12.467,-12.404,-12.921,-13.881,-13.543,-15.041,-15.747,-13.971,-13.363,-13.023,-12.749,-13.208,-13.468,-12.733,-11.568,-12.901,-13.468,-13.669,-13.462,-13.28,-13.227,-12.993,-12.857,-12.522,-12.324,-12.685,-13.082,-13.072,-13.309,-13.215,-12.584,-12.747,-13.177,-13.401\n-11.702,-11.673,-10.901,-10.189,-9.8909,-10.016,-10.499,-10.742,-10.332,-9.8753,-10.191,-11.879,-12.254,-12.444,-12.539,-13.467,-13.617,-13.622,-13.82,-13.545,-13.304,-12.983,-12.473,-12.45,-12.605,-12.877,-12.62,-11.847,-11.626,-12.402,-13.078,-13.225,-13.231,-12.877,-12.421,-12.95,-12.33,-12.246,-12.903,-12.425,-12.428,-12.54,-12.455,-12.47,-12.246,-12.791,-13.107\n-12.681,-11.92,-11.753,-9.896,-10.504,-10.525,-10.156,-9.7641,-9.6101,-9.7057,-10.06,-11.421,-11.363,-12.088,-12.552,-12.433,-12.801,-13.693,-12.507,-13.095,-12.853,-12.263,-12.142,-12.449,-12.773,-12.978,-11.794,-11.704,-11.994,-12.636,-12.886,-13.095,-12.687,-12.647,-12.442,-12.372,-12.287,-12.47,-12.51,-12.327,-12.385,-12.315,-12.567,-12.506,-11.969,-12.419,-12.51\n-12.822,-12.118,-10.879,-9.6203,-10.014,-9.908,-9.9665,-9.4035,-9.4549,-10.25,-10.499,-10.803,-12.006,-12.086,-12.588,-12.872,-13.074,-11.913,-11.276,-12.534,-12.431,-12.052,-11.999,-12.095,-12.225,-12.567,-10.901,-11.283,-12.016,-12.787,-12.883,-12.676,-12.977,-12.713,-12.564,-12.197,-12.342,-12.561,-12.391,-12.344,-12.195,-12.083,-12.169,-11.948,-11.903,-12.129,-12.629\n-12.217,-11.904,-10.84,-10.711,-11.136,-8.6555,-9.3618,-10.258,-10.683,-10.53,-8.6968,-9.0796,-14.304,-13.034,-12.613,-12.987,-12.957,-11.588,-11.666,-12.053,-12.163,-12.141,-11.989,-11.438,-11.146,-11.187,-11.431,-11.559,-11.822,-12.141,-12.436,-12.27,-12.312,-12.461,-12.435,-12.145,-12.244,-12.216,-11.753,-12.057,-12.627,-12.19,-11.946,-11.717,-11.82,-12.271,-12.773\n-13.532,-13.143,-12.801,-11.717,-11.261,-8.2653,-9.6413,-10.048,-10.065,-10.284,-9.6504,-9.2206,-12.217,-12.669,-13.41,-13.685,-13.273,-12.476,-11.887,-11.536,-11.26,-12.033,-12.434,-11.867,-11.581,-11.949,-11.673,-11.601,-12.055,-11.86,-12.179,-12.305,-12.32,-12.147,-12.177,-12.229,-12.112,-11.652,-11.299,-11.596,-11.718,-11.989,-11.888,-11.539,-11.82,-12.396,-12.295\n-15.306,-13.99,-13.666,-12.289,-11.551,-10.314,-10.957,-10.015,-10.108,-10.189,-11.043,-10.12,-11.601,-12.143,-12.337,-13.746,-13.309,-12.521,-11.213,-11.28,-11.765,-12.287,-12.462,-11.983,-11.888,-12.174,-11.708,-11.608,-12.181,-11.939,-12.144,-12.539,-12.212,-11.933,-12.151,-12.156,-11.907,-11.552,-11.468,-11.6,-11.747,-11.894,-12,-11.886,-11.671,-11.97,-11.949\n-15.449,-14.093,-13.127,-12.885,-12.469,-13.052,-12.156,-11.104,-11.97,-11.49,-10.366,-10.724,-12.233,-11.946,-13.846,-15.21,-13.622,-11.976,-11.661,-10.954,-11.389,-11.568,-12.355,-11.527,-11.46,-11.816,-11.567,-11.798,-11.621,-11.663,-12.154,-12.522,-12.245,-11.967,-11.716,-12.11,-12.029,-11.91,-11.552,-11.572,-11.616,-11.827,-12.043,-12.184,-12.301,-12.148,-11.436\n-13.811,-13.716,-12.394,-12.246,-12.646,-13.482,-12.362,-12.758,-12.101,-12.401,-11.292,-12.918,-12.454,-11.711,-11.36,-12.132,-12.154,-12.239,-11.563,-11.053,-10.917,-10.822,-11.437,-11.111,-11.36,-10.957,-11.547,-11.496,-11.037,-10.856,-11.41,-12.081,-11.994,-12.169,-12.041,-12.099,-12.078,-12.039,-11.909,-11.974,-11.924,-12.058,-12.195,-12.229,-12.185,-12.041,-11.964\n-12.145,-11.786,-11.329,-11.254,-11.06,-11.192,-11.927,-12.114,-10.082,-13.076,-10.735,-11.683,-12.443,-13.419,-10.252,-11.514,-12.453,-12.363,-11.376,-10.763,-11.528,-11.341,-10.949,-10.774,-11.085,-11.162,-11.256,-11.199,-11.288,-11.352,-11.414,-11.955,-11.43,-12.038,-12.357,-12.201,-12.425,-12.217,-12.256,-12.153,-11.9,-11.671,-11.666,-12.18,-12.221,-12.145,-11.845\n-10.811,-10.912,-10.14,-9.1592,-7.7287,-10.826,-10.934,-10.342,-9.9705,-13.187,-9.7921,-10.608,-11.638,-13.811,-13.193,-13.416,-13.435,-11.796,-11.503,-11.407,-11.218,-10.847,-11.518,-11.021,-11.063,-11.053,-11.214,-10.988,-11.174,-11.743,-12.097,-11.939,-11.563,-11.721,-11.998,-12.777,-12.704,-12.513,-12.4,-12.108,-11.924,-11.482,-11.717,-12.247,-12.265,-12.186,-11.845\n-9.7507,-10.358,-9.7372,-9.8628,-10.574,-11.593,-11.585,-11.729,-12.382,-10.795,-9.0559,-11.176,-12.188,-14.684,-14.163,-13.598,-15.075,-12.896,-11.76,-11.319,-11.511,-11.004,-11.055,-11.533,-10.999,-10.499,-10.52,-10.385,-11.267,-11.679,-12.157,-12.121,-12.049,-12.264,-11.907,-12.382,-12.394,-12.548,-12.501,-12.256,-11.81,-11.703,-11.91,-12.241,-12.291,-12.033,-11.496\n-9.4447,-8.5066,-8.6357,-9.3116,-10.561,-11.837,-11.306,-10.424,-10.442,-11.841,-12.514,-9.7338,-10.555,-15.33,-16.423,-15.611,-14.72,-12.188,-11.334,-11.779,-11.472,-10.427,-10.149,-11.491,-11.033,-10.625,-10.064,-10.64,-11.116,-11.098,-11.371,-11.414,-12.065,-12.069,-12.189,-12.426,-12.506,-12.714,-12.486,-12.115,-11.966,-11.978,-11.705,-11.872,-12.066,-11.787,-11.208\n-8.7989,-8.0195,-8.0142,-9.0954,-9.68,-9.9292,-10.36,-11.044,-10.419,-10.841,-11.893,-11.925,-12.658,-14.601,-14.013,-10.612,-11.817,-11.295,-11.541,-12.083,-11.323,-10.69,-10.809,-10.216,-10.955,-10.702,-10.344,-10.553,-10.821,-10.769,-11.292,-11.376,-11.611,-11.623,-12.383,-12.471,-12.593,-12.42,-12.315,-12.229,-12.338,-11.985,-11.484,-11.957,-12.178,-11.786,-11.489\n-8.1483,-8.3502,-8.272,-8.7546,-7.574,-8.9145,-9.5827,-9.9706,-10.303,-9.3112,-8.3582,-11.423,-9.2843,-8.9766,-10.957,-9.2993,-10.389,-11.374,-11.842,-11.667,-11.497,-11.479,-10.474,-10.257,-11.292,-11.245,-10.447,-10.879,-10.85,-11.049,-11.488,-11.494,-11.78,-11.957,-12.515,-12.571,-12.506,-12.272,-12.101,-12.249,-12.328,-12.243,-11.9,-12.155,-12.353,-11.302,-11.36\n-7.0719,-8.6379,-7.6229,-7.9264,-7.8839,-8.0989,-7.5969,-7.3795,-10.448,-9.5319,-10.151,-11.413,-9.463,-9.3166,-9.3264,-10.586,-11.164,-11.085,-12.879,-10.851,-11.565,-11.073,-10.739,-11.058,-10.729,-10.594,-10.592,-11.217,-11.242,-11.834,-11.791,-11.256,-11.495,-11.613,-12.405,-12.916,-13.084,-12.543,-12.346,-12.549,-12.606,-12.48,-12.082,-12.03,-11.954,-11.574,-11.751\n-8.2237,-7.8515,-7.4966,-8.5675,-6.2282,-7.5849,-5.897,-7.8343,-9.9941,-9.9422,-10.097,-10.859,-13.135,-14.441,-12.637,-10.556,-12.174,-12.954,-12.264,-11.759,-11.771,-10.861,-10.75,-10.289,-10.818,-11.377,-10.958,-10.891,-11.209,-11.846,-11.86,-11.923,-12.266,-12.116,-12.662,-12.81,-13.229,-12.82,-12.792,-12.721,-12.569,-12.498,-12.336,-12.292,-12.301,-12.389,-12.45\n-8.2332,-7.4586,-7.051,-6.9346,-5.9584,-7.242,-7.2911,-8.5701,-7.9401,-9.312,-10.439,-11.299,-11.747,-11.82,-10.777,-10.575,-10.141,-10.761,-12.168,-12.306,-11.216,-10.398,-10.77,-10.737,-11.238,-11.574,-10.601,-10.832,-11.302,-11.95,-11.441,-12.386,-12.532,-12.62,-12.707,-12.446,-13.068,-13.334,-13.02,-12.941,-12.633,-12.463,-12.702,-12.455,-12.452,-12.117,-12.232\n-8.3959,-8.4947,-7.5975,-7.1637,-7.9567,-8.0246,-7.6431,-8.3471,-8.7975,-9.0966,-8.9353,-10.441,-11.334,-11.961,-10.478,-9.396,-8.7053,-9.7278,-12.433,-12.105,-10.909,-10.534,-11.181,-11.056,-10.851,-10.641,-10.669,-11.37,-11.927,-12.056,-11.987,-12.377,-12.478,-12.579,-12.654,-12.913,-12.861,-12.91,-13.142,-12.573,-12.875,-12.888,-12.486,-12.374,-12.309,-11.839,-12.101\n-8.6548,-8.9699,-8.4866,-8.3084,-8.4265,-8.0903,-9.0107,-8.9518,-8.8303,-8.9325,-8.816,-8.4239,-9.8708,-11.385,-11.234,-10.585,-11.832,-12.762,-13.222,-13.68,-12.711,-11.052,-9.5082,-10.337,-11.202,-10.771,-10.814,-12.041,-12.938,-12.427,-12.216,-12.322,-12.49,-12.588,-12.582,-12.671,-12.675,-12.748,-12.785,-12.879,-12.976,-12.551,-12.393,-12.483,-12.234,-12.119,-12.423\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_060619-061002.unw.csv",
    "content": "-18.587,-18.458,-18.36,-18.417,-18.135,-17.915,-18.26,-18.505,-18.566,-18.702,-19.249,-19.067,-19.233,-19.654,-19.695,-19.646,-19.611,-19.772,-19.795,-19.627,-18.728,-18.3,-18.394,-18.996,-19.377,-19.259,-19.378,-18.84,-18.538,-18.983,-19.532,-19.764,-19.694,-19.671,-19.568,-19.395,-19.472,-20.122,-20.465,-19.957,-19.164,-18.231,-17.607,-17.372,-17.065,-16.776,-16.659\n-18.457,-18.318,-18.289,-18.192,-18.063,-18.223,-18.656,-18.915,-18.71,-18.697,-18.827,-18.827,-19.241,-19.525,-19.439,-19.327,-19.144,-19.516,-19.567,-19.322,-19.018,-18.228,-18.454,-18.77,-19.22,-19.393,-19.301,-18.797,-18.651,-18.991,-19.037,-18.787,-19.136,-19.577,-19.723,-19.642,-19.755,-19.7,-19.254,-18.588,-18.145,-17.887,-17.661,-17.373,-16.961,-16.818,-16.867\n-18.468,-18.51,-18.348,-18.237,-18.216,-18.426,-18.945,-19.147,-18.57,-18.433,-18.727,-18.629,-19.132,-19.317,-19.279,-19.355,-19.097,-19.383,-19.524,-19.224,-18.979,-18.705,-19.419,-19.285,-19.053,-19.373,-19.087,-18.54,-18.67,-19.06,-18.689,-18.635,-18.895,-19.129,-19.426,-19.372,-18.783,-18.335,-17.855,-17.316,-17.421,-17.582,-17.413,-17.31,-17.087,-17.15,-17.223\n-18.487,-18.421,-18.444,-18.309,-18.208,-18.436,-19.028,-19.058,-18.793,-18.645,-18.834,-18.728,-18.9,-19.237,-19.335,-19.318,-18.995,-19.187,-19.397,-19.249,-19.046,-18.815,-18.79,-18.851,-18.973,-18.942,-18.683,-18.567,-18.455,-18.48,-18.652,-18.677,-18.582,-18.832,-19.208,-19.233,-18.893,-18.283,-17.718,-17.404,-17.382,-17.451,-17.454,-17.574,-17.641,-17.34,-17.002\n-18.642,-19.003,-18.887,-18.492,-18.341,-18.621,-18.911,-19.204,-19.38,-19.136,-18.842,-18.743,-18.712,-19.074,-19.18,-18.936,-18.652,-19.325,-19.237,-19.021,-18.948,-18.598,-18.523,-18.871,-18.913,-18.587,-18.212,-18.131,-18.237,-18.166,-18.584,-18.812,-18.554,-18.658,-19.154,-19.207,-18.705,-18.193,-17.767,-17.401,-17.35,-17.559,-17.602,-17.636,-17.591,-17.461,-17.263\n-18.237,-18.214,-18.431,-18.283,-18.363,-18.526,-18.904,-19.066,-19.134,-18.794,-18.441,-18.311,-18.592,-18.948,-19.091,-18.918,-19.036,-19.487,-19.415,-19.035,-18.698,-18.559,-18.409,-18.826,-19.167,-18.723,-18.187,-18.03,-18.194,-18.099,-18.222,-18.531,-18.508,-18.748,-19.121,-19.179,-18.521,-18.008,-17.726,-17.248,-17.563,-18.128,-18.141,-17.918,-17.659,-17.186,-16.979\n-17.818,-17.672,-17.793,-18.031,-18.479,-18.805,-18.897,-18.671,-18.498,-18.359,-18.313,-17.991,-18.294,-18.831,-18.972,-18.946,-19.175,-19.363,-19.46,-18.865,-18.208,-18.216,-18.205,-18.278,-18.385,-17.998,-17.723,-18.118,-18.261,-17.94,-18,-18.26,-18.194,-18.364,-18.555,-18.508,-18.139,-17.835,-18.009,-17.73,-17.744,-18.505,-18.423,-18.051,-17.69,-17.26,-17.105\n-17.548,-17.506,-17.933,-17.981,-18.434,-18.988,-19.043,-18.689,-18.519,-18.551,-18.56,-18.361,-18.391,-18.698,-18.77,-19.065,-19.585,-19.462,-18.933,-18.374,-17.571,-17.803,-18.123,-18.204,-18.139,-17.901,-17.462,-17.898,-18.491,-17.912,-17.94,-18.175,-18.133,-18.262,-18.211,-17.988,-17.708,-17.872,-18.147,-18.076,-17.98,-18.301,-18.142,-17.648,-17.526,-17.386,-17.202\n-17.844,-17.469,-17.489,-17.996,-18.441,-18.822,-18.722,-18.47,-18.288,-18.381,-18.712,-18.866,-18.827,-18.784,-18.917,-19.061,-19.367,-19.218,-18.557,-17.751,-17.445,-17.767,-17.967,-17.951,-18.239,-18.119,-18.122,-18.193,-18.441,-18.509,-18.394,-18.375,-18.304,-18.261,-18.245,-17.883,-17.74,-18.248,-18.746,-18.563,-18.345,-18.256,-18.272,-17.381,-17.372,-17.355,-17.234\n-17.791,-17.667,-17.655,-18.894,-18.841,-18.673,-18.682,-18.275,-17.972,-18.049,-18.666,-19.007,-18.927,-18.845,-19.159,-18.955,-18.687,-18.369,-18.202,-18.133,-18.037,-18.32,-18.207,-18.143,-18.288,-18.128,-18.099,-18.269,-18.415,-18.485,-18.511,-18.488,-18.459,-18.389,-18.524,-18.485,-18.281,-18.102,-18.608,-18.813,-18.686,-18.406,-18.032,-17.377,-17.067,-17.016,-17.006\n-17.816,-18.074,-18.455,-18.998,-18.437,-18.515,-18.48,-18.248,-18.066,-18.15,-18.505,-18.917,-18.793,-18.68,-18.687,-18.621,-18.702,-18.297,-17.728,-17.497,-17.77,-18.178,-18.286,-18.469,-18.29,-18.187,-18.258,-18.312,-18.332,-18.39,-18.58,-18.595,-18.447,-18.644,-18.896,-18.519,-18.128,-18.118,-18.581,-18.918,-18.697,-18.341,-18.028,-17.573,-16.989,-16.832,-16.954\n-17.707,-17.884,-18.391,-18.622,-18.314,-18.514,-18.616,-18.449,-18.331,-18.252,-18.686,-18.614,-18.415,-18.503,-18.763,-18.894,-18.864,-18.707,-18.641,-18.901,-18.777,-18.306,-18.038,-18.36,-18.254,-18.039,-18.129,-18.172,-18.16,-18.433,-18.624,-18.649,-18.564,-18.768,-18.929,-18.474,-17.795,-17.954,-18.599,-18.68,-18.496,-18.331,-18.063,-17.552,-17.129,-16.54,-16.63\n-17.453,-17.355,-18.056,-18.515,-18.575,-18.727,-18.806,-18.734,-18.558,-18.417,-18.561,-18.762,-18.374,-18.36,-18.787,-19.179,-19.404,-19.493,-19.109,-18.943,-18.754,-18.158,-17.808,-18.033,-17.88,-17.956,-18.172,-18.344,-18.193,-18.263,-18.545,-18.726,-18.826,-18.715,-18.643,-18.291,-17.749,-17.57,-18.658,-18.757,-18.375,-18.225,-17.932,-17.704,-17.047,-16.785,-16.469\n-17.781,-18.083,-18.264,-18.644,-18.82,-19.154,-18.748,-18.623,-18.542,-18.591,-18.428,-18.285,-18.303,-18.366,-18.807,-19.176,-19.154,-19.077,-18.655,-18.188,-18.294,-18.661,-18.774,-18.855,-19.075,-19.126,-18.767,-18.687,-18.478,-18.316,-18.468,-18.605,-18.763,-18.645,-18.255,-17.638,-17.306,-17.399,-18.054,-18.518,-17.876,-17.783,-17.615,-17.294,-16.797,-16.417,-16.21\n-17.772,-17.999,-18.623,-19.019,-18.964,-19.024,-18.377,-18.333,-18.805,-18.721,-18.459,-18.394,-18.291,-18.317,-18.516,-18.847,-18.777,-18.849,-18.605,-18.187,-18.613,-19.152,-19.268,-19.359,-19.252,-18.888,-18.52,-18.469,-18.399,-18.162,-17.877,-17.989,-18.4,-18.524,-18.066,-17.404,-17.345,-17.725,-18.228,-18.447,-18.195,-17.612,-17.361,-16.965,-16.78,-16.31,-16.111\n-17.681,-17.669,-18.162,-19.166,-20.031,-19.404,-18.686,-18.224,-18.703,-18.517,-18.321,-18.173,-18.138,-18.43,-18.685,-18.648,-18.822,-19.29,-19.462,-19.147,-19.292,-19.344,-19.278,-19.47,-19.055,-18.443,-18.13,-18.029,-17.77,-17.221,-17.225,-17.55,-17.933,-18.053,-17.68,-17.15,-17.387,-18.016,-18.325,-18.534,-18.33,-17.772,-17.469,-17.346,-17.276,-17.161,-16.939\n-17.573,-17.286,-17.991,-19.42,-20.429,-20.156,-19.186,-18.407,-18.096,-18.044,-17.965,-18.005,-18.229,-18.453,-18.338,-18.462,-18.986,-19.448,-19.496,-19.314,-18.764,-18.558,-18.652,-18.669,-17.853,-17.427,-17.708,-18.069,-17.675,-16.999,-17.066,-17.46,-17.603,-17.553,-17.292,-16.852,-16.425,-16.996,-17.775,-18.173,-18.553,-18.194,-17.602,-17.432,-17.36,-17.214,-17.252\n-17.806,-17.835,-18.552,-19.355,-20.64,-20.105,-19.287,-18.094,-18.14,-18.531,-18.466,-18.347,-18.501,-18.368,-18.368,-18.521,-18.748,-19.502,-19.702,-19.323,-18.649,-18.142,-18.062,-18.356,-17.89,-17.29,-17.449,-18.21,-18.389,-17.613,-17.277,-17.367,-17.451,-17.526,-17.16,-16.635,-15.92,-16.188,-16.365,-17.87,-18.449,-18.796,-18.003,-17.216,-17.355,-17.323,-17.301\n-18.223,-18.417,-18.77,-19.623,-19.757,-19.681,-19.029,-18.603,-18.785,-18.965,-18.541,-18.399,-18.732,-18.291,-18.018,-17.872,-18.035,-18.737,-19.329,-19.608,-19.406,-18.928,-18.156,-18.22,-18.271,-17.537,-17.077,-17.638,-18.576,-18.023,-17.399,-17.294,-16.789,-16.737,-16.523,-16.346,-15.987,-16.089,-16.426,-17.386,-17.805,-18.41,-18.191,-17.374,-17.485,-17.485,-17.288\n-19.047,-18.948,-18.574,-18.89,-19.302,-19.465,-18.841,-18.579,-18.523,-18.505,-18.259,-18.279,-18.425,-18.028,-17.601,-17.66,-17.675,-17.933,-18.318,-18.728,-18.581,-18.281,-17.845,-18,-18.29,-17.742,-17.295,-17.115,-17.413,-18.183,-17.663,-17.009,-16.148,-16.307,-15.877,-16.058,-15.962,-15.951,-16.356,-16.69,-17.275,-17.707,-17.824,-17.488,-17.487,-17.719,-17.436\n-19.009,-18.922,-18.406,-18.264,-18.415,-18.778,-18.5,-18.66,-18.547,-18.049,-17.984,-17.993,-18.055,-17.9,-17.596,-17.413,-17.201,-17.085,-17.671,-17.697,-17.506,-17.398,-17.547,-17.837,-17.934,-17.631,-17.294,-16.666,-16.75,-17.307,-16.917,-16.478,-15.747,-15.752,-15.309,-15.69,-16.129,-16.17,-16.474,-16.832,-17.184,-17.222,-17.225,-17.407,-17.537,-17.577,-17.375\n-19.014,-18.755,-18.769,-19.064,-19.665,-20.515,-19.061,-18.724,-18.212,-18.09,-17.998,-17.837,-17.642,-17.799,-17.933,-17.332,-17.361,-17.269,-17.109,-16.988,-16.834,-16.947,-17.341,-17.371,-17.307,-16.993,-16.635,-16.291,-16.202,-16.23,-16.324,-16.308,-15.759,-15.681,-15.403,-15.508,-15.843,-16.393,-16.643,-16.698,-16.992,-17.058,-17.17,-17.34,-17.48,-17.401,-17.405\n-18.626,-18.646,-18.308,-18.759,-19.894,-20.461,-18.944,-17.979,-17.926,-18.304,-17.997,-17.34,-17.396,-17.328,-17.772,-18.181,-17.924,-17.544,-17.173,-16.686,-16.462,-16.563,-16.691,-16.51,-16.563,-16.49,-16.175,-16.295,-16.357,-16.155,-16.063,-16.159,-16.098,-16.034,-15.977,-15.965,-16.156,-16.321,-16.414,-16.727,-16.659,-16.829,-16.979,-17.218,-17.219,-17.294,-17.479\n-18.312,-18.1,-18.091,-18.208,-18.406,-17.953,-17.577,-17.528,-17.713,-18.011,-17.87,-17.885,-17.586,-16.901,-16.633,-16.803,-17.985,-17.521,-16.919,-16.409,-16.194,-16.203,-16.268,-16.36,-16.529,-16.254,-15.862,-15.751,-15.837,-15.654,-15.527,-15.727,-15.766,-15.892,-16.096,-16.031,-16.092,-16.205,-16.128,-16.258,-16.429,-16.677,-16.826,-17.128,-17.156,-16.949,-17.024\n-18.893,-18.5,-17.991,-18.055,-18.285,-18.252,-18.086,-17.877,-17.669,-17.583,-17.714,-17.835,-17.601,-16.665,-16.706,-17.141,-17.443,-16.787,-16.043,-15.753,-15.855,-16.078,-15.976,-16,-16.422,-15.795,-15.493,-15.282,-14.968,-14.58,-15.137,-15.435,-15.442,-15.416,-15.177,-15.06,-15.696,-15.906,-15.955,-16.121,-16.387,-16.422,-16.616,-16.976,-17.17,-17.126,-16.91\n-18.416,-18.529,-18.31,-18.229,-18.303,-18.494,-18.219,-17.926,-17.806,-17.429,-17.372,-17.191,-17.074,-16.718,-16.58,-16.781,-16.768,-16.294,-15.591,-15.402,-15.462,-15.531,-15.705,-15.762,-15.338,-14.771,-14.328,-14.306,-13.548,-13.135,-13.195,-14.306,-14.949,-15.206,-15.019,-14.976,-15.477,-15.646,-15.961,-16.245,-16.395,-16.517,-16.622,-16.836,-17.048,-17.377,-17.441\n-18.354,-18.419,-18.787,-18.78,-18.718,-18.62,-18.52,-18.14,-17.935,-17.788,-17.585,-17.146,-16.394,-16.02,-16.135,-16.613,-16.665,-15.885,-15.034,-14.895,-15.026,-15.214,-15.286,-15.349,-14.683,-14.193,-14.213,-13.796,-13.065,-12.146,-12.552,-13.026,-13.846,-14.506,-14.665,-14.946,-15.362,-15.825,-16.142,-16.275,-16.461,-16.706,-16.651,-16.637,-16.85,-17.475,-17.846\n-18.112,-18.167,-18.65,-19.124,-18.846,-18.549,-18.456,-18.653,-18.118,-17.972,-17.719,-17.369,-16.971,-16.437,-16.139,-16.137,-15.912,-15.34,-14.967,-14.808,-14.842,-15.15,-15.308,-15.055,-14.524,-14.258,-14.095,-14.022,-13.071,-12.118,-11.698,-11.756,-12.172,-12.937,-13.649,-14.397,-15.378,-15.914,-16.173,-16.27,-16.721,-16.659,-16.74,-16.856,-17.03,-17.408,-17.606\n-17.847,-17.309,-18.057,-18.464,-18.398,-18.285,-18.475,-18.95,-18.499,-18.074,-17.986,-17.406,-17.054,-16.59,-16.208,-15.969,-15.887,-15.578,-14.947,-14.595,-14.523,-14.997,-14.762,-14.445,-14.216,-13.793,-12.717,NaN,NaN,NaN,-8.8676,-10.169,-10.422,-12.315,-13.804,-14.341,-15.211,-15.794,-15.83,-16.117,-16.993,-17.095,-16.668,-16.855,-16.999,-17.297,-17.572\n-17.626,-18.021,-18.109,-18.319,-18.273,-18.334,-18.225,-18.535,-18.328,-18.096,-18.017,-17.686,-17.273,-16.723,-16.229,-16.105,-16.252,-16.334,-15.951,-15.195,-14.612,-15.028,-14.669,-13.811,-12.778,-12.312,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.194,-13.751,-14.285,-14.552,-15.06,-15.461,-16.179,-17.211,-17.101,-16.483,-16.722,-17.357,-17.773,-17.854\n-18.31,-17.994,-17.934,-17.982,-18.146,-18.727,-19.31,-18.945,-18.264,-17.638,-17.493,-17.528,-17.379,-16.962,-16.148,-16.185,-16.429,-16.324,-16.111,-15.709,-15.159,-14.929,-14.516,-13.291,-11.457,-10.231,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.152,-14.859,-14.228,-13.451,-13.8,-14.807,-16.234,-16.932,-17.02,-16.682,-16.402,-17.236,-17.921,-17.992\n-18.41,-18.377,-18.045,-17.349,-17.494,-17.818,-18.299,-18.178,-18.097,-17.427,-17.018,-17.201,-17.137,-16.934,-16.291,-16.431,-16.648,-16.047,-15.462,-15.342,-15.116,-14.696,-14.223,-13.209,-13.055,-12.809,-12.874,NaN,NaN,NaN,NaN,NaN,-13.885,-14.969,-14.268,-13.605,-13.393,-13.722,-14.845,-16.261,-16.832,-16.997,-17.115,-16.408,-17.328,-18.218,-18.176\n-18.572,-18.271,-17.987,-17.585,-17.092,-17.42,-17.76,-17.606,-17.52,-17.159,-17.282,-17.446,-17.096,-16.395,-16.373,-16.523,-17.009,-16.654,-15.752,-15.332,-15.142,-14.595,-14.224,-13.807,-13.987,-14.139,-14.649,-15.865,-15.87,-15.357,-14.908,-14.763,-15.749,-16.621,-15.06,-14.3,-14.086,-13.681,-14.906,-16.181,-16.858,-17.112,-17.116,-17.173,-17.833,-18.183,-18.136\n-18.472,-18.193,-17.925,-17.425,-17.173,-17.731,-17.943,-17.883,-17.808,-17.375,-17.288,-17.333,-17.337,-16.858,-16.874,-16.747,-16.821,-16.573,-16.25,-15.978,-15.754,-15.243,-14.65,-14.319,-14.588,-15.384,-14.925,-15.612,-15.736,-15.49,-16.198,-16.814,-17.791,-17.055,NaN,-16.229,-14.272,-13.437,-13.963,-15.947,-17.106,-17.179,-16.69,-17.06,-18.08,-18.353,-18.346\n-18.148,-18.523,-18.398,-17.988,-18.111,-18.52,-19.048,-18.445,-18.052,-17.652,-17.417,-17.287,-17.362,-17.585,-17.554,-17.171,-16.832,-16.709,-16.516,-16.201,-16.013,-16.222,-15.715,-13.57,NaN,-15.76,-14.875,-15.99,-18.281,-17.381,-17.221,-18.118,-17.931,-18.85,NaN,NaN,NaN,NaN,NaN,-15.997,-16.804,-16.693,-16.421,-17.28,-18.053,-18.306,-18.431\n-18.719,-18.997,-19.483,-19.047,-18.333,-18.379,-18.402,-18.215,-18.026,-18.202,-17.489,-17.307,-17.353,-17.886,-18.832,-18.17,-16.962,-16.933,-16.85,-16.54,-16.195,-16.236,-14.284,NaN,NaN,NaN,NaN,-16.125,-16.382,-16.591,-17.691,-17.908,-19.453,NaN,NaN,NaN,NaN,NaN,-14.612,-16.205,-16.59,-16.534,-16.548,-17.204,-18.025,-18.123,-18.123\n-18.986,-18.944,-19.006,-19.412,-18.42,-18.069,-18.021,-18.038,-18.167,-18.418,-18.167,-18.006,-17.839,-18.954,-18.92,-18.024,-17.59,-17.362,-16.954,-16.684,-16.312,-15.141,-12.736,NaN,NaN,NaN,-17.965,-17.948,-15.632,-16.122,-17.592,-17.401,-16.954,-14.238,NaN,NaN,NaN,NaN,-15.65,-16.371,-16.481,-16.807,-17.317,-18.013,-18.31,-18.183,-18.189\n-19.041,-18.921,-18.976,-18.903,-18.462,-18.114,-17.897,-17.702,-17.736,-18.001,-17.991,-18.424,-18.748,-19.44,-18.79,-17.497,-17.373,-17.125,-16.574,-16.16,-15.793,-15.023,-13.844,NaN,NaN,NaN,NaN,-18.526,-16.063,-15.812,-16.73,-17.059,-16.803,-13.665,-13.465,-13.573,-15.478,-15.62,-15.823,-17.146,-17.44,-17.662,-18.127,-18.283,-18.19,-18.126,-18.094\n-18.559,-18.619,-18.961,-19.06,-18.848,-18.418,-17.842,-17.578,-17.561,-17.57,-17.569,-17.764,-17.953,-18.278,-17.926,-17.437,-17.69,-17.507,-16.893,-16.21,-15.902,-15.392,-15.361,-16.859,-18.913,-19.822,-19.203,-17.525,-16.874,-17.379,-17.64,-17.592,-17.601,-16.126,-14.496,-15.211,-15.74,-15.911,-16.574,-17.755,-18.2,-18.39,-17.858,-17.715,-17.594,-17.839,-18.014\n-18.657,-18.842,-18.958,-19.068,-18.614,-17.861,-17.84,-17.593,-17.394,-17.321,-17.331,-17.479,-17.811,-17.909,-17.349,-17.449,-17.595,-16.853,-16.913,-16.039,-15.468,-15.718,-16.681,-15.958,-14.515,-13.659,-14.412,-16.505,-17.08,-17.654,-18.526,-19.062,-18.241,-17.49,-15.656,-15.228,-15.319,-15.95,-17.068,-18.358,-18.637,-18.37,-17.866,-17.7,-17.535,-17.663,-17.773\n-18.561,-18.873,-18.606,-18.299,-17.968,-17.579,-17.337,-17.271,-17.21,-17.55,-17.356,-17.421,-17.551,-17.571,-17.164,-16.962,-17.029,-16.666,-16.652,-15.832,-15.412,-15.18,-16.337,-16.709,-16.951,NaN,NaN,-17.97,-18.574,-19.628,-21.162,-21.61,-19.259,-16.847,-16.103,-15.705,-15.431,-15.498,-16.616,-18.125,-18.839,-18.493,-17.892,-17.961,-18.033,-17.856,-17.774\n-17.874,-18.211,-18.449,-18.318,-17.871,-17.728,-17.144,-16.941,-17.262,-17.682,-17.644,-17.454,-17.388,-17.332,-17.308,-17.151,-16.701,-16.205,-15.931,-15.2,-15.564,-15.974,-16.4,-17.034,-17.9,NaN,NaN,-19.369,-18.37,-18.859,-19.581,-19.305,-18.397,-17.154,-16.538,-16.103,-16.126,-16.095,-16.776,-17.953,-18.55,-18.323,-17.85,-17.995,-17.971,-17.549,-17.621\n-17.195,-17.838,-18.17,-18.053,-18.091,-18.031,-17.618,-17.158,-17.456,-17.495,-17.378,-17.226,-17.237,-17.303,-17.381,-17.521,-17.245,-16.652,-16.179,-16.075,-16.327,-16.878,-17.065,-16.705,NaN,NaN,NaN,NaN,-16.175,-17.25,-18.61,-18.504,-18.236,-17.037,-16.719,-16.765,-16.528,-16.47,-16.858,-17.86,-17.955,-18.209,-18.089,-17.933,-17.799,-17.56,-17.962\n-17.521,-17.718,-17.862,-17.902,-18.236,-17.939,-17.567,-17.169,-17.361,-17.399,-17.222,-16.989,-16.969,-17.194,-17.2,-17.25,-17.231,-16.944,-16.67,-16.639,-16.531,-16.468,-14.835,-14.622,NaN,NaN,NaN,-15.47,-15.97,-17.189,-18.153,-18.581,-18.559,-17.986,-17.782,-17.695,-17.341,-16.94,-17.704,-18.049,-18.264,-18.344,-17.994,-18.039,-18.124,-18.015,-18.071\n-17.642,-17.745,-17.864,-17.676,-17.759,-17.644,-17.315,-17.167,-17.253,-17.406,-17.37,-17.097,-17.064,-17.141,-17.057,-17.131,-17.181,-16.817,-16.716,-16.354,-15.205,-14.932,-14.292,-13.265,-12.073,-12.722,-13.943,-16.312,-17.047,-17.811,-18.338,-18.531,-18.825,-18.424,-18.303,-17.94,-17.437,-17.663,-19.232,-18.934,-18.659,-18.634,-18.042,-17.713,-17.922,-18.125,-18.171\n-17.962,-18.045,-18.232,-18.164,-17.909,-17.644,-17.23,-17.353,-17.607,-17.677,-17.454,-17.16,-16.873,-16.727,-16.937,-16.906,-16.866,-16.844,-17.314,-17.799,-17.082,-16.502,-16.118,-16.142,-16.434,-16.222,-16.165,-17.095,-17.604,-17.911,-17.692,-17.656,-17.787,-18.015,-18.09,-18.399,-18.453,-19.099,-20.105,-19.902,-19.361,-19.05,-18.28,-17.603,-17.783,-18.096,-18.273\n-18.445,-18.093,-18.232,-18.399,-18.114,-17.79,-17.447,-17.677,-17.929,-17.529,-17.176,-17.123,-17.125,-17.106,-17.262,-17.488,-17.492,-17.451,-17.517,-18.154,-18.859,-17.806,-17.176,-17.245,-17.047,-17.164,-17.729,-17.739,-17.976,-17.084,-16.556,-17.159,-17.616,-17.998,-18.178,-19.371,-19.787,-20.013,-20.781,-20.419,-19.82,-19.053,-18.463,-17.989,-17.841,-17.944,-17.988\n-18.177,-18.164,-18.058,-17.95,-17.54,-17.471,-17.756,-18.024,-17.884,-17.628,-17.262,-17.076,-17.1,-17.24,-17.497,-17.772,-17.939,-17.905,-17.986,-18.257,-18.745,-19.017,-18.679,-17.89,-17.286,-17.546,-17.984,-18.126,-17.86,-17.782,-18.15,-18.315,-18.641,-18.088,-19.497,-20.467,-21.153,-20.651,-20.605,-20.588,-20.61,-19.883,-18.74,-17.945,-17.865,-18.05,-17.784\n-18.228,-18.323,-17.896,-17.745,-17.612,-17.634,-17.881,-18.208,-18.196,-18.008,-17.637,-17.514,-17.628,-17.484,-17.825,-18.128,-18.247,-18.152,-18.683,-18.858,-19.334,-19.15,-18.551,-17.686,-17.636,-18.181,-18.335,-18.432,-18.153,-18.765,-19.653,-19.662,-19.182,-19.719,-20.469,-21.651,-21.52,-20.857,-20.12,-20.24,-20.587,-20.582,-19.13,-18.634,-18.408,-18.227,-18.059\n-18.629,-18.442,-17.999,-18.014,-18.191,-18.057,-17.898,-17.931,-18.231,-18.417,-18.445,-18.101,-17.773,-17.615,-18.005,-18.37,-17.953,-17.914,-18.256,-18.867,-19.451,-18.956,-18.024,-17.906,-18.106,-18.616,-18.938,-18.858,-18.572,-19.646,-20.628,-20.227,-20.509,-21.006,-21.165,-21.53,-21.184,-19.808,-18.846,-19.057,-19.921,-20.192,-20.007,-19.132,-18.813,-18.522,-18.334\n-18.735,-18.51,-18.169,-18.068,-18.301,-18.378,-18.278,-18.297,-18.273,-18.387,-18.436,-18.146,-17.553,-17.676,-18.112,-18.32,-18.023,-17.738,-17.7,-17.672,-17.818,-18.183,-18.229,-18.372,-18.914,-19.065,-19.533,-19.346,-18.983,-19.72,-20.677,-20.31,-20.697,-21.135,-20.685,-20.103,-19.645,-19.138,-18.746,-18.963,-19.668,-20.758,-20.865,-20.281,-19.096,-18.535,-18.297\n-18.451,-18.266,-17.881,-18.024,-18.388,-18.425,-18.404,-18.495,-18.536,-18.119,-18.174,-18.011,-17.578,-17.579,-18.426,-18.39,-18.269,-17.778,-17.667,-17.538,-17.746,-18.327,-18.439,-18.647,-19.052,-19.447,-19.656,-19.515,-19.175,-19.307,-19.5,-19.845,-20.348,-20.581,-20.237,-19.654,-19.119,-19.061,-19.086,-19.342,-20.621,-20.899,-21.179,-20.045,-18.916,-18.363,-18.034\n-18.118,-17.702,-17.133,-17.827,-18.254,-18.402,-18.531,-18.544,-18.41,-17.885,-17.966,-18.026,-17.909,-17.822,-18.081,-18.112,-18.203,-17.748,-17.499,-17.382,-17.766,-18.297,-18.658,-18.857,-19.284,-19.316,-19.587,-19.576,-19.166,-18.813,-19.054,-19.701,-20.107,-20.308,-19.953,-20.189,-20.362,-20.464,-20.119,-20.235,-20.844,-21.044,-20.871,-19.594,-18.704,-18.169,-17.601\n-18.91,-18.132,-17.849,-17.714,-17.771,-18.09,-18.32,-18.263,-18.103,-18.097,-18.146,-18.682,-18.212,-18.061,-18.095,-18.239,-18.156,-17.814,-17.712,-17.746,-17.905,-18.048,-18.489,-18.87,-19.261,-19.511,-19.66,-19.58,-19.149,-18.665,-18.903,-19.69,-20.043,-20.484,-20.541,-21.159,-21.588,-21.056,-20.462,-20.426,-20.822,-21.049,-20.717,-19.293,-18.35,-18.075,-17.485\n-18.999,-18.608,-17.876,-17.908,-17.551,-17.866,-18.279,-18.134,-18.079,-18.375,-18.588,-18.878,-18.65,-18.141,-17.977,-17.987,-18.085,-18.099,-18.244,-17.998,-17.931,-18.01,-18.425,-19.042,-19.393,-19.577,-19.292,-19.151,-18.797,-18.643,-19.18,-19.847,-20.315,-21.392,-21.655,-21.858,-22.069,-21.443,-20.828,-20.484,-20.646,-20.772,-19.765,-18.704,-18.351,-18.089,-17.604\n-18.442,-18.027,-18.151,-18.414,-17.705,-18.002,-18.777,-18.819,-18.814,-18.73,-18.743,-18.766,-18.583,-18.353,-18.137,-18.083,-18.181,-18.32,-18.343,-18.012,-18.307,-18.673,-18.851,-19.197,-19.389,-19.482,-19.474,-19.395,-19.143,-18.859,-19.578,-20.156,-20.723,-21.284,-21.465,-21.53,-21.845,-21.582,-21.048,-20.77,-20.576,-20.416,-19.992,-19.439,-18.592,-17.723,-17.204\n-18.551,-18.279,-18.478,-18.478,-18.413,-18.635,-18.939,-19.396,-19.685,-19.754,-18.966,-18.82,-18.407,-18.078,-18.06,-18.095,-18.289,-18.382,-18.138,-17.82,-18.215,-18.757,-19.101,-19.322,-19.289,-19.296,-19.516,-19.645,-19.627,-19.597,-19.588,-19.793,-20.665,-21.053,-21.214,-21.264,-21.804,-22.082,-21.608,-20.767,-20.562,-20.487,-20.479,-20.061,-19.259,-17.964,-17.094\n-19.045,-18.797,-18.832,-18.58,-18.211,-17.874,-18.505,-19.069,-19.486,-19.577,-18.786,-18.849,-18.324,-18.145,-17.794,-17.851,-18.153,-18.227,-17.879,-17.961,-17.993,-18.626,-18.878,-19.015,-19.045,-19.229,-19.312,-19.572,-19.565,-19.642,-19.414,-19.565,-20.384,-20.998,-20.998,-20.843,-21.664,-22.331,-21.79,-21.446,-20.936,-20.297,-20.286,-20.246,-19.276,-17.962,-17.526\n-19.054,-18.752,-18.768,-18.886,-18.834,-18.605,-18.613,-18.512,-18.557,-18.747,-18.55,-18.633,-18.359,-18.259,-18.287,-18.449,-18.621,-18.706,-18.384,-18.144,-18.178,-18.194,-18.631,-18.808,-18.989,-19.209,-19.15,-19.065,-19.37,-20.111,-19.727,-19.722,-20.52,-21.78,-21.427,-21.011,-21.166,-21.651,-21.801,-21.898,-21.081,-20.068,-19.84,-19.86,-19.284,-18.733,-18.409\n-18.696,-18.284,-18.313,-18.908,-19.082,-19.299,-19.128,-18.189,-18.226,-18.804,-19.088,-19.773,-19.685,-17.503,-17.75,-18.063,-18.545,-18.721,-18.342,-18.148,-18.167,-17.94,-18.067,-18.503,-18.823,-19.078,-19.103,-18.91,-18.955,-19.57,-19.793,-19.976,-20.152,-21.851,-21.947,-21.03,-20.672,-21.189,-21.964,-21.891,-20.974,-19.852,-19.653,-19.56,-19.311,-18.748,-18.478\n-17.789,-17.785,-17.721,-18.279,-18.901,-19.005,-18.731,-18.443,-18.126,-18.667,-19.186,-20.577,-19.725,-19.021,-18.145,-18.04,-18.277,-18.44,-18.108,-17.773,-17.665,-17.62,-17.618,-18.512,-18.861,-18.929,-18.932,-18.835,-18.635,-18.687,-18.736,-19.652,-20.525,-21.35,-21.661,-20.714,-20.261,-20.947,-21.516,-21.237,-20.597,-20.165,-19.444,-18.789,-18.562,-18.525,-18.528\n-16.788,-17.491,-18.13,-18.259,-18.245,-18.311,-18.642,-18.603,-18.392,-18.545,-18.422,-20.077,-19.985,-19.487,-18.467,-18.788,-18.286,-18.145,-17.889,-17.725,-17.512,-17.17,-17.38,-18.187,-18.485,-18.718,-19.003,-19.348,-18.958,-18.299,-17.973,-18.928,-19.951,-21.492,-21.909,-20.74,-20.198,-20.936,-21.002,-20.456,-19.871,-19.533,-19.178,-18.614,-18.21,-18.403,-17.93\n-16.701,-17.532,-17.964,-17.798,-17.393,-17.965,-18.996,-19.004,-18.756,-18.987,-17.275,-18.303,-19.131,-20.364,-19.809,-20.187,-18.747,-17.051,-18.339,-18.503,-17.762,-17.077,-17.157,-18.033,-18.218,-18.536,-18.869,-19.063,-18.656,-17.989,-18.077,-18.889,-19.785,-20.784,-21.14,-19.623,-20.041,-20.666,-20.628,-19.947,-19.599,-19.388,-19.152,-18.456,-18.514,-19.115,-18.071\n-14.856,-17.267,-19.576,-18.175,-16.949,-16.784,-18.307,-18.187,-18.678,-18.838,-18.739,-18.123,-19.136,-21.325,-20.747,-20.047,-17.141,-16.438,-17.422,-18.297,-17.843,-16.884,-16.768,-17.794,-18.042,-18.096,-17.907,-17.861,-17.409,-17.719,-18.493,-19.154,-19.546,-20.372,-20.847,-19.822,-19.53,-19.64,-20.269,-19.873,-19.451,-19.489,-19.042,-18.381,-18.518,-18.864,-17.944\n-18.186,-20.258,-20.044,-18.538,-16.078,-17.265,-18.553,-17.192,-17.073,-17.124,-16.709,-14.949,NaN,NaN,NaN,NaN,-18.156,-16.322,-16.591,-18.28,-17.863,-17.049,-17.442,-18.356,-18.351,-18.139,-17.816,-17.071,-16.812,-17.167,-17.51,-18.408,-19.52,-19.883,-20.073,-19.552,-19.207,-19.297,-19.709,-19.15,-18.68,-18.848,-19.001,-18.806,-18.154,-17.261,-16.372\n-15.343,-17.7,-17.187,-15.952,-15.366,-16.968,-18.141,-17.561,-17.178,-16.985,-15.895,NaN,NaN,NaN,NaN,NaN,NaN,-17.097,-16.973,-17.838,-17.992,-18.177,-18.029,-17.766,-17.246,-17.555,-17.244,-16.711,-16.702,-16.433,-16.898,-17.507,-18.493,-18.986,-18.717,-18.311,-18.582,-18.834,-19.095,-18.868,-18.894,-18.863,-19.203,-19.222,-18.233,-17.098,-16.788\n-15.212,-15.395,-15.37,-14.12,-12.751,-16.679,-17.905,-16.462,-16.099,-15.992,-13.855,-14.166,-14.365,NaN,NaN,NaN,NaN,-17.794,-17.136,-17.906,-17.362,-17.208,-17.49,-17.195,-17.155,-17.593,-17.369,-17.025,-16.495,-16.357,-16.437,-17.468,-17.65,-17.767,-18.009,-17.879,-18.025,-18.628,-18.798,-18.339,-18.1,-18.327,-18.608,-18.37,-17.8,-17.129,-17.105\n-14.863,-17.331,-17.363,-14.835,-14.192,-14.884,-16.976,-15.893,-16.741,-16.556,-15.964,-16.294,-14.661,-13.973,-14.162,-16.043,-18.514,-19.643,-19.29,-17.183,-15.336,-16.094,-16.187,-16.354,-16.082,-16.143,-15.934,-16.165,-16.635,-16.361,-16.398,-16.853,-17.465,-17.265,-17.164,-18.02,-18.412,-18.526,-18.534,-18.154,-18.024,-17.979,-18.098,-17.927,-17.235,-16.72,-16.387\n-13.63,-13.303,-16.063,-15.879,-15.103,-14.349,-14.802,-15.476,-16.336,-17.766,-17.803,-16.807,-15.749,-15.121,-14.118,-14.428,-17.792,NaN,NaN,-17.384,-14.744,-15.905,-15.981,-15.526,-14.699,-14.328,-14.897,-15.585,-16.167,-15.809,-15.982,-16.492,-17.361,-17.738,-17.797,-18.579,-18.519,-18.68,-18.789,-18.891,-18.645,-18.078,-18.094,-18.115,-17.509,-17.27,-16.87\n-13.681,-12.125,-13.108,-16.852,-15.172,-13.451,-13.267,-15.302,-16.014,-16.594,-16.277,-16.205,-16.533,-16.479,-16.687,-15.755,NaN,NaN,NaN,NaN,-13.977,-16.086,-15.813,-14.845,-14.084,-13.811,-14.639,-15.586,-16.051,-16.022,-16.352,-16.646,-16.734,-17.033,-17.798,-19.064,-18.981,-19.153,-19.712,-19.634,-18.95,-18.434,-18.229,-18.279,-17.967,-17.944,-18.047\n-14.942,-15.285,-16,-17.677,-17.425,-16.53,-16.023,-15.752,-15.318,-14.671,-14.566,-15.659,-16.859,-18.165,-17.779,-17.242,NaN,NaN,NaN,NaN,-16.55,-16.887,-16.057,-15.01,-14.736,-14.702,-14.997,-15.745,-16.222,-16.473,-16.591,-16.842,-16.575,-16.823,-17.632,-18.523,-18.767,-18.386,-18.944,-18.895,-18.266,-17.958,-17.759,-18.038,-17.971,-17.771,-18.258\n-14.749,-15.623,-16.081,-17.144,-16.999,-15.467,-14.219,-13.683,-14.573,-14.444,-13.499,-13.827,-15.784,-17.787,-18.26,NaN,NaN,NaN,NaN,NaN,-18.541,-17.266,-15.634,-15.347,-15.555,-15.629,-15.902,-16.238,-16.289,-16.319,-16.65,-17.012,-17.085,-17.283,-17.797,-18.778,-18.452,-17.493,-17.926,-18.148,-17.849,-17.56,-17.863,-18.177,-18.088,-17.962,-18.047\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_060828-061211.unw.csv",
    "content": "27.25,26.299,26.409,26.588,26.557,27.161,26.792,27.13,26.662,26.369,27.231,26.26,27.525,28.302,27.519,27.222,27.116,26.711,27.049,27.481,28.118,27.739,27.496,27.626,26.657,27.114,26.746,27.431,27.627,27.559,27.018,26.994,26.356,26.839,27.251,27.221,28.211,28.411,28.897,28.639,28.472,28.713,28.287,27.926,28.29,27.927,27.833\n26.57,26.391,26.368,26.169,25.836,26.498,26.902,27.679,27.089,27.36,26.83,25.373,26.697,27.716,27.303,28.011,28.181,26.813,26.537,27.378,28.087,28.2,27.531,27.458,27.749,27.302,27.167,27.706,27.459,27.118,27.134,26.862,27.423,27.467,27.017,27.477,28.067,28.291,28.558,28.685,29.537,29.065,28.926,28.214,28.289,28.854,28.794\n25.817,25.499,26.585,26.517,26.29,26.431,27.375,27.994,26.764,27.391,27.143,26.112,27.447,27.641,28.064,28.414,28.189,27.37,27.412,27.237,27.085,26.835,26.506,26.646,27.033,26.972,27.505,28.093,27.164,27.047,27.307,27.331,28.286,27.87,27.401,26.816,27.923,27.941,27.791,28.682,29.799,28.771,28.478,27.841,28.839,29.356,28.974\n26.5,26.57,27.113,27.061,26.88,27.489,28.332,28.313,27.666,27.606,27.724,27.166,27.842,28.322,28.903,29.142,29.231,28.621,28.014,27.017,26.281,26.293,26.113,26.605,26.983,27.108,27.388,27.141,27.178,27.673,27.24,27.284,27.657,28.122,27.694,27.17,27.802,28.128,28.031,28.628,28.551,28.573,28.802,28.872,29.459,30.118,30.006\n27.438,28.099,28.78,27.359,27.616,28.652,28.596,28.62,27.513,27.806,27.669,27.211,27.38,27.622,28.409,28.846,27.723,27.44,27.171,26.756,27.875,26.759,26.568,26.841,26.706,26.97,26.968,27.376,27.544,27.353,26.259,27.357,27.282,28.165,27.721,27.847,27.609,27.722,28.006,28.331,28.216,27.523,28.957,29.243,29.144,29.172,29.273\n27.175,27.533,29.05,27.001,27.313,27.947,28.152,27.305,26.41,26.563,26.5,27.152,27.311,27.568,27.738,28.33,28.084,27.007,26.345,27.164,28.314,27.457,26.601,26.589,27.114,27.556,27.256,27.408,27.483,27.744,27.248,27.528,27.729,27.549,26.924,27.74,26.84,26.804,26.652,27.001,26.901,27.264,28.432,29.156,29.027,29.064,29.293\n26.576,26.346,26.924,27.046,28.135,28.881,28.952,27.933,26.761,26.555,27.133,27.88,28.347,28.14,28.515,28.601,27.666,27.256,27.347,28.15,27.031,26.655,26.266,25.738,26.87,27.101,27.198,27.478,27.754,28.815,27.723,27.39,27.371,27.225,27.081,27.312,27.518,27.406,26.61,26.305,27.381,27.518,28.15,28.989,29.454,29.318,30.87\n27.052,26.732,25.728,27.028,28.51,29.744,29.033,28.939,28.395,28.325,27.969,28.723,27.869,28.506,28.334,28.214,28.046,27.276,27.332,27.66,27.241,26.591,26.616,27.014,27.582,27.938,27.403,28.164,27.687,28.187,27.912,27.289,26.819,27.1,27.166,27.209,27.384,27.442,27.026,26.765,27.183,26.864,27.227,28.539,28.859,28.902,29.251\n27.665,26.572,26.564,27.312,27.87,28.566,28.843,28.75,29.023,29.234,28.816,29.147,29.216,28.493,27.689,28.266,29.5,29.752,27.425,27.555,27.023,26.167,26.819,27.64,28.133,27.984,27.902,28.059,27.731,27.803,28.576,27.998,27.7,27.698,27.167,26.873,27.993,27.485,26.377,26.253,26.366,26.57,27.498,27.823,27.95,27.612,27.304\n27.366,27.543,28.446,27.463,26.851,27.704,27.437,27.226,28.183,29.313,29.06,29.236,29.372,28.02,27.962,29.288,28.298,28.111,27.89,27.134,27.09,26.639,26.848,27.526,27.823,27.87,27.082,27.801,28.174,27.412,27.538,26.91,27.328,27.5,26.536,26.374,26.747,26.981,26.754,26.629,26.925,27.636,28.41,27.43,27.731,27.734,27.439\n26.527,26.747,27.53,26.917,26.271,27.319,27.244,26.913,27.931,28.67,28.364,28.531,28.508,27.904,27.975,28.701,28.001,28.297,27.856,27.268,27.071,26.974,27.001,27.48,27.616,27.356,27.641,28.15,27.752,27.04,27.082,27.177,27.26,27.23,26.617,26.704,27.043,27.548,27.721,27.093,27.75,28.148,28.34,27.966,28.338,29.275,29.066\n26.6,25.954,27.658,27.63,26.328,27.861,27.941,27.873,28.001,28.746,28.05,28.961,28.999,28.727,28.785,28.797,28.363,28.592,27.448,26.966,26.873,26.607,26.876,28.218,27.622,27.661,28.217,28.309,27.438,27.824,27.479,27.361,27.454,27.363,27.456,27.621,27.612,27.269,27.429,27.224,27.475,28.013,28.826,28.818,29.219,30.191,29.805\n26.81,26.586,28.259,31.21,25.156,27.739,27.426,26.089,28.087,28.851,27.605,28.253,27.673,29.09,29.382,28.783,28.57,28.588,27.569,27.278,27.309,27.266,27.824,28.058,28.091,27.543,27.614,28.06,27.197,27.398,26.736,27.185,26.973,27.238,27.56,27.393,26.913,26.732,27.971,27.817,28.001,28.179,28.659,28.809,28.466,NaN,NaN\n26.79,26.98,25.886,24.638,27.22,28.253,28.345,26.59,28.107,27.976,28.107,28.069,27.973,29.261,29.563,28.345,28.327,27.912,27.753,28.447,29.31,29.136,28.592,28.245,27.349,27.828,28.287,27.564,27.387,27.153,26.728,26.851,26.765,26.978,26.671,27.132,27.648,27.956,29.012,27.948,29.199,29.042,28.791,29.078,28.981,NaN,NaN\n26.38,26.023,23.306,25.291,26.232,26.647,26.873,27.422,28.537,28.539,28.352,28.007,28.184,28.629,28.967,28.873,29.79,28.48,27.788,28.632,29.407,29.16,28.249,27.662,26.074,28.156,28.302,27.759,28.073,27.847,27.877,27.558,27.086,27.01,26.451,27.315,27.48,27.512,27.604,26.746,27.849,30.248,28.642,28.404,28.314,NaN,NaN\n27.102,24.543,23.173,26.423,26.597,27.032,26.661,27.941,29.125,29.153,28.638,28.294,28.077,27.78,27.701,28.259,28.459,27.34,27.655,27.818,28.205,28.798,28.238,28.599,27.529,27.329,27.001,27.431,28.027,27.26,29.021,29.106,27.796,27.449,26.967,28.75,27.692,26.815,26.466,25.579,26.661,29.372,29.003,28.177,27.813,27.916,29.165\n27.297,26.52,26.683,27.695,27.865,27.361,26.575,28.558,28.344,28.901,30.116,30.382,28.397,26.878,28.039,28.418,27.155,28.042,28.315,27.208,26.933,29.325,27.397,27.951,28.123,27.593,27.604,28.001,27.631,26.927,28.576,29.112,27.728,27.589,28.089,28.747,28.1,27.384,26.778,24.441,27.606,26.681,27.831,27.933,28.258,26.451,27.696\n27.631,26.91,28.1,28.693,29.128,27.257,25.819,27.769,28.126,29.238,29.74,29.463,28.348,28.302,28.809,28.719,28.095,28.04,28.089,26.552,26.301,29.095,28.818,27.54,27.989,27.996,27.643,27.617,27.789,27.322,27.551,26.863,27.806,28.061,27.85,27.351,29.005,27.641,27.108,25.916,27.464,26.862,25.266,27.063,25.438,27.279,27.696\n24.968,26.323,27.15,29.824,31.161,25.052,26.291,28.033,28.18,28.948,29.041,28.138,27.183,27.798,28.451,28.219,28.137,28.184,27.9,27.388,27.078,27.495,27.009,26.854,27.763,27.803,27.836,27.427,28.117,27.695,27.766,25.918,27.882,28.225,27.996,26.497,27.356,27.417,27.147,26.387,26.803,28.152,25.09,26.662,26.437,27.071,27.68\n25.205,27.171,28.192,29.406,30.939,29.734,26.916,28.011,27.66,28.885,28.719,28.117,28.077,27.76,28.52,28.76,28.281,28.582,28.671,29.084,26.004,26.191,27.502,25.387,26.249,27.579,27.738,27.732,28.294,28.316,27.579,27.346,28.753,28.97,29.514,27.158,26.499,26.628,28.432,27.712,26.867,27.018,26.865,27.693,27.132,27.185,25.204\n27.121,27.397,28.299,29.401,31.124,29.235,25.785,27.52,27.619,28.207,28.141,27.875,27.855,28.348,27.637,27.872,28.659,28.625,28.055,27.274,25.521,26.083,26.623,26.721,26.934,27.357,27.951,27.971,29.486,28.224,26.595,27.158,27.484,27.812,29.535,27.743,25.927,26.006,28.297,27.177,26.633,26.635,27.194,26.883,26.993,26.814,26.572\n28.145,27.88,27.596,27.022,26.888,27.361,27.011,27.653,28.454,28.641,28.292,27.456,28.012,27.116,26.202,28.884,28.75,27.996,27.605,27.298,26.954,25.434,22.596,26.25,27.416,27.712,27.889,27.932,29.537,28.911,26.197,26.286,25.82,26.014,27.827,27.566,26.504,26.251,26.645,26.668,26.075,26.037,26.799,26.813,26.933,27.203,27.576\n28.735,28.404,28.518,28.585,28.094,27.85,28.145,28.328,29.286,29.578,27.439,27.773,29.282,28.17,26.401,27.311,27.264,26.45,26.715,26.774,26.526,26.213,24.751,27.175,28.044,27.951,28.747,28.623,28.401,27.578,26.486,26.313,26.032,25.885,26.737,27.067,26.7,26.023,24.729,25.622,25.073,26.12,27.017,26.414,26.161,26.658,27.16\n27.498,28.058,28.817,29.883,28.979,28.725,27.827,28.986,29.463,29.22,28.821,28.21,28.935,28.086,27.365,26.983,26.81,25.349,25.637,26.615,27.153,27.26,26.859,27.773,29.058,28.587,29.136,29.088,28.825,28.427,27.999,27.595,28.078,28.448,26.821,27.205,27.206,25.079,22.539,22.927,23.998,26.718,26.648,26.492,25.955,26.55,26.574\n27.229,26.649,27.154,29.355,29.835,28.577,27.917,28.492,28.816,28.235,29.004,29.248,28.612,27.272,27.187,27.329,27.638,26.976,26.941,28.16,27.429,27.286,27.555,27.617,29.819,29.76,29.484,29.045,28.818,29.36,28.846,28.863,28.793,27.515,26.785,27.294,25.138,25.443,25.208,25.067,26.565,27.554,27.25,26.462,25.968,26.217,26.532\n28.157,28.29,28.865,29.36,29.591,28.245,28.032,28.354,28.165,27.767,29.743,29.482,28.102,27.191,27.364,27.193,27.921,27.378,27.403,28.196,27.717,27.287,28.163,28.908,29.278,29.763,NaN,NaN,29.568,28.481,27.954,29.558,28.005,26.129,25.962,26.946,26.631,27.022,27.659,27.043,27.821,28.37,27.128,26.051,26.424,26.406,27.244\n27.636,29.04,29.661,28.567,27.408,27.676,28.171,28.222,27.574,27.755,28.361,28.465,27.754,27.139,26.432,26.25,26.581,27.134,26.874,26.256,26.825,27.33,28.224,28.502,28.511,30.467,NaN,NaN,NaN,24.279,26.4,27.072,28.076,27.765,27.635,27.213,27.421,28.965,28.237,27.501,28.111,27.587,26.876,26.924,27.56,27.651,27.374\n27.101,28.286,28.87,28.94,26.981,27.723,28.771,27.55,26.863,28.338,28.148,28.058,27.63,26.638,26.133,26.199,26.326,27.953,27.003,25.145,25.983,27.699,28.379,27.653,27.713,28.156,NaN,NaN,NaN,NaN,26.773,28.142,25.658,27.232,29.023,29.001,28.677,28.589,28.606,29.05,28.448,27.254,27.042,27.514,27.728,27.513,28.307\n27.817,28.574,29.467,27.69,27.277,28.075,28.485,27.762,27.827,28.408,28.609,27.16,27.151,27.136,26.518,26.202,26.048,27.802,26.566,25.366,25.038,27.098,27.758,27.749,26.637,29.573,30.712,30.193,29.263,30.733,29.743,29.783,27.559,27.124,29.285,29.117,28.676,28.472,28.547,28.923,28.107,26.773,26.637,26.689,26.407,27.164,28.203\n27.395,27.677,29.041,25.589,27.381,28.096,27.761,27.735,27.964,27.769,28.324,27.581,26.876,27.255,26.791,27.279,28.163,27.326,26.997,26.281,25.434,25.766,27.998,27.392,28.638,30.497,30.953,32.804,NaN,NaN,NaN,NaN,NaN,24.906,28.278,29.36,28.927,29.196,29.578,29.112,28.571,26.504,26.293,26.203,26.117,26.278,27.408\n29.244,28.805,28.58,28.15,28.78,29.242,27.986,28.601,28.465,26.897,27.331,27.33,26.705,26.972,26.76,24.985,24.624,26.968,25.689,25.614,28.085,28.437,28.467,29.391,27.498,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.797,30.615,31.052,30.105,29.419,29.917,29.244,27.602,26.124,26.649,25.919,25.594,26.874,27.705\n27.717,27.661,27.325,27.435,29.16,29.37,28.979,26.279,28.341,26.79,27.02,27.64,27.606,27.189,26.783,25.423,25.603,27.849,28.888,29.187,29.346,29.265,28.921,32.462,29.874,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.845,31.798,30.436,29.025,29.59,29.253,28.895,28.343,27.385,27.03,25.518,25.985,27.703,27.933\n27.087,28.29,31.196,31.858,30.742,27.817,26.383,24.596,26.674,26.446,NaN,NaN,NaN,NaN,28.58,27.443,26.992,27.804,28.907,28.607,29.052,28.827,28.522,29.835,30.447,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.913,29.478,30.272,27.917,27.903,28.514,27.709,27.917,27.54,27.261,27.829,27.245\n28.937,29.306,32.281,32.247,30.308,26.626,27.027,27.064,27.047,NaN,NaN,NaN,NaN,NaN,29.319,29.379,29.13,27.962,28.124,27.336,28.749,29.546,29.61,28.987,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.759,29.288,30.393,28.249,27.64,28.372,28.259,28.815,27.625,27.151,26.953,27.123\n28.419,28.009,27.839,28.817,29.465,29.002,28.406,28.323,28.012,NaN,NaN,NaN,NaN,NaN,28.245,29.161,29.355,28.521,28.651,28.968,30.011,30.055,31.15,33.569,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.883,28.058,28.374,28.567,28.233,28.658,26.745,27.186,27.15,26.878\n28.79,28.251,27.685,29.531,29.437,29.942,29.327,28.825,28.09,27.273,27.464,27.595,27.728,27.13,26.003,26.093,29.257,29.825,31.256,31.581,30.633,31.621,33.736,35.025,35.335,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.161,28.708,28.07,27.907,27.559,27.025,27.807,28.145,27.352\n28.661,28.324,28.44,28.241,27.52,27.839,28.071,27.957,28.124,25.75,26.855,27.641,27.693,27.256,27.749,29.069,30.209,32.819,33.074,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.87,27.937,28.299,28.281,28.053,27.247,27.743,27.927,27.243\n27.652,27.53,27.775,27.562,27.356,28.119,27.831,28.305,28.411,26.612,27.202,27.819,27.713,26.683,29.296,30.764,31.407,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,39.696,34.823,NaN,NaN,31.589,31.269,30.32,28.049,27.464,27.747,28.036,27.971,27.86,27.78,27.69\n27.815,27.628,27.494,27.762,27.579,28.301,28.542,28.687,28.282,27.662,27.804,29,29.129,29.363,30.517,31.541,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,39.391,37.006,35.38,32.107,31.441,31.055,29.727,27.881,26.875,26.606,27.831,28.241,28.363,27.546,27.841\n28.2,29.145,28.264,27.303,27.245,27.575,28.965,28.304,28.308,27.884,27.856,28.316,29.553,29.678,30.698,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.788,38.47,39.88,37.721,35.481,33.666,32,30.065,29.557,28.798,27.962,28.155,26.658,27.414,28.09,28.51,28.341,27.85\n27.826,28.233,28.246,27.663,27.485,27.028,27.293,28.331,28.141,27.776,28.261,28.082,28.595,28.785,30.643,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.939,31.386,33.883,35.674,35.472,31.917,32.364,32.525,30.699,29.887,29.815,26.324,26.864,26.666,27.007,27.386,28.119,27.865,27.935,27.844\n29.918,29.597,28.459,27.956,28.705,27.82,28.18,28.299,27.701,27.598,28.59,27.431,27.661,28.331,28.943,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,35.618,32.336,31.604,33.936,33.449,32.736,31.547,32.025,32.276,30.812,28.49,28.275,26.726,26.668,26.634,26.941,27.593,27.93,27.569,27.72,27.738\n30.01,28.951,27.7,27.961,28.246,28.27,28.335,27.394,26.692,26.302,26.804,27.64,27.443,28.983,29.755,29.513,27.852,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.221,32.313,32.496,32.879,32.173,32.243,29.212,32.441,32.86,32.351,28.671,27.712,28.3,28.07,27.983,27.96,27.876,27.858,27.684,27.852,27.259\n28.142,29.718,28.584,26.679,27.721,27.619,26.865,26.727,26.689,25.879,27.022,28.703,29.005,29.201,31.579,NaN,NaN,28.443,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.74,31.655,31.401,30.775,29.857,29.674,29.642,32.263,32.254,30.52,29.538,27.634,28.46,28.65,28.449,27.707,27.63,27.097,26.438,26.48,26.034\n28.377,28.576,27.939,26.841,27.01,26.698,25.205,24.499,25.624,26.714,27.659,29.557,29.343,28.693,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.207,31.182,30.678,31.242,31.141,30.92,30.357,30.56,30.998,29.561,28.464,28.133,28.397,28.688,28.483,28.016,27.072,26.327,26.056,25.832,25.848\n27.749,27.737,28.19,28.52,27.844,27.817,28.147,27.86,28.705,29.096,28.781,29.453,29.043,29.162,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.676,31.213,30.521,31.289,32.086,31.761,30.69,30.764,31.241,30.481,29.375,28.81,28.598,28.321,28.056,28.139,27.408,26.937,26.492,26.07,26.066,26.43\n28.134,27.713,28.682,28.185,28.613,28.737,28.95,29.037,28.654,28.746,29.648,30.112,30.233,30.622,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.679,29.293,30.689,30.476,30.451,30.286,31.152,31.095,31.032,29.928,30.805,30.128,28.999,28.265,27.013,27.082,26.687,26.495,26.871,26.66,26.972,27.414\n27.489,27.218,27.49,28.02,28.988,28.118,28.489,28.489,28.845,28.877,29.639,30.646,31.403,31.735,32.277,32.423,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.393,28.268,30.547,29.754,27.845,28.797,29.304,30.444,30.376,31.363,31.859,31.133,28.858,28.679,28.17,26.857,26.403,26.356,26.268,26.097,26.73,26.863,27.51\n27.008,26.871,27.349,28.578,28.84,28.009,28.812,28.19,28.91,29.079,29.528,30.18,31.017,31.213,33.005,33.743,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.961,29.769,29.758,30.912,28.864,27.853,28.222,29.163,30.078,30.025,29.692,29.34,28.296,27.796,28.278,28.049,27.179,26.596,26.093,26.543,27.132,27.368,26.995,27.255\n26.368,26.525,27.476,29.095,28.378,28.468,28.846,28.936,29.295,30.252,31.81,31.157,30.948,30.866,32.197,32.029,30.962,NaN,NaN,NaN,NaN,NaN,30.351,30.63,31.425,30.89,30.308,29.047,28.861,27.939,28.861,29.842,28.753,28.477,28.641,28.296,27.866,28.644,27.727,28.171,27.588,27.736,27.304,26.337,27.657,27.139,27.505\n25.974,26.56,27.674,28.876,28.989,28.223,28.509,28.953,28.861,29.899,31.168,30.777,31.274,31.135,30.837,30.22,29.419,NaN,NaN,NaN,NaN,NaN,29.286,30.251,29.974,30.179,30.018,29.523,27.354,27.81,28.85,29.396,28.772,29.471,29.269,28.416,28.06,27.881,27.651,27.982,28.235,28.379,28.09,27.495,27.615,27.711,28.177\n27.383,26.63,26.227,27.79,28.25,27.843,28.433,29.002,28.396,28.991,30.416,30.622,30.063,29.397,31.002,31.967,29.654,27.529,27.476,NaN,NaN,28.538,29.219,29.516,29.307,29.393,29.726,27.581,26.23,27.257,27.964,28.868,29.103,29.037,28.925,28.208,28.257,27.969,28.12,27.982,27.189,27.757,29.26,29.006,27.716,28.32,28.383\n26.908,26.675,25.773,28.161,26.902,26.828,28.184,27.93,27.489,27.704,28.703,28.891,28.753,28.693,29.586,29.611,30.306,27.542,29.34,29.695,27.433,28.691,28.59,28.479,28.55,28.835,28.606,27.488,27.344,27.735,27.928,28.226,28.713,28.347,28.341,28.863,29.053,29.313,28.809,27.776,27.091,27.785,28.194,28.377,27.523,27.5,28.706\n26.082,26.489,27.864,28.91,28.255,26.672,27.029,26.484,27.363,27.374,27.485,28.375,27.714,26.767,27.194,28.721,28.403,23.687,25.662,26.622,27.266,28.189,28.052,27.851,28.069,28.646,29.021,29.108,28.498,27.584,27.711,28.364,28.566,28.147,28.368,27.95,27.447,29.065,28.665,28.327,27.864,27.643,27.06,27.496,26.775,27.603,29.252\n25.464,26.121,26.889,26.27,26.322,26.672,26.522,26.1,27.649,27.585,27.656,29.067,28.728,26.86,26.394,28.143,28.102,26.89,28.738,26.936,27.962,28.583,28.568,27.905,28.059,28.357,28.58,28.437,27.017,27.741,27.958,27.388,27.263,27.121,27.655,27.924,27.834,28.574,28.667,28.467,28.275,27.306,27.159,27.676,27.612,28.135,28.178\n25.854,25.649,24.558,25.021,26.005,28.405,26.944,26.988,27.254,28.127,27.439,27.309,28.66,28.101,25.941,26.863,27.484,28.266,28.115,26.404,27.774,28.257,28.485,27.953,27.647,27.803,28.364,28.034,27.271,28.046,27.625,27.525,25.399,26.397,27.543,27.943,27.837,28.169,27.875,28.176,28.466,28.015,27.692,27.998,28.199,28.34,28.304\nNaN,23.673,24.449,24.935,26.484,28.975,28.235,26.601,26.564,27.51,28.223,26.911,26.978,27.607,25.997,25.332,25.819,27.599,27.674,26.233,26.333,27.516,27.852,28.148,27.61,27.714,27.903,27.079,26.736,26.999,27.627,27.992,26.615,26.749,27.619,27.814,27.696,27.622,27.171,27.454,28.23,27.967,27.739,28.306,28.463,29.226,29.172\nNaN,NaN,NaN,24.941,24.37,26.599,30.99,28.503,27.406,28.272,28.645,28.267,25.306,27.475,25.218,24.322,25.322,25.957,26.746,27.148,27.417,27.518,27.473,27.608,27.648,28.059,28.101,26.269,26.361,26.678,26.752,26.967,26.985,27.273,27.417,27.191,27.26,26.953,27.521,27.557,27.455,27.51,27.723,28.915,28.734,29.403,29.568\nNaN,NaN,NaN,24.656,24.033,24.121,30.693,26.898,27.985,28.089,28.335,26.059,NaN,NaN,NaN,NaN,NaN,25.801,26.746,28.013,28.482,27.262,26.935,26.781,27.021,27.766,29.097,29.318,27.329,27.258,26.322,25.995,26.16,26.27,26.712,26.837,27.153,27.15,27.428,27.188,27.032,27.463,28.234,29.002,29.488,29.83,29.578\nNaN,NaN,NaN,NaN,24.412,22.945,25.65,27.035,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,25.858,27.225,28.269,27.907,27.339,26.975,26.128,27.014,27.552,28.707,28.316,27.705,28.019,27.82,26.077,25.438,25.905,25.691,26.352,26.617,26.418,26.962,26.99,27.519,28.178,28.262,28.431,29.404,29.679,28.525\nNaN,NaN,NaN,NaN,NaN,21.415,23.082,25.77,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.183,27.075,27.52,27.822,27.388,27.757,28.659,27.181,27.602,27.442,27.132,27.228,27.399,28.977,27.946,27.328,25.833,25.143,25.553,26.035,26.324,25.75,26.351,26.995,27.852,28.206,28.289,28.488,28.675,28.218,27.834\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.096,26.941,26.681,27.148,27.108,26.92,27.774,28.466,27.819,27.228,27.333,27.576,28.322,28.606,27.822,27.679,27.133,26.313,26.513,26.265,26.693,25.549,25.652,26.424,27.318,27.852,28.131,28.722,28.895,27.84,27.163\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.774,26.309,26.301,25.296,25.713,25.677,27.143,28.348,26.859,26.677,26.342,27.175,27.645,26.603,26.393,26.887,27.095,26.866,26.111,25.572,26.495,26.082,26.013,26.587,27.149,27.369,27.753,28.988,28.97,28.318,26.928\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.225,24.603,25.309,25.184,27.921,27.868,27.508,26.567,25.92,26.697,27.158,27.627,26.946,25.855,25.961,26.401,26.685,26.724,25.805,26.313,26.62,26.681,26.789,26.287,27.042,27.833,28.105,28.644,28.552,27.823,27.227\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.497,27.181,27.839,27.349,26.012,26.111,27.166,27.496,27.991,26.727,26.325,26.573,26.295,26.591,26.593,26.047,26.133,26.182,26.559,26.86,27.293,27.867,27.975,27.838,27.98,28.568,28.813,28.641\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,26.278,25.641,28.193,29.621,26.3,27.166,27.272,27.197,27.7,26.468,26.272,26.958,27.189,26.745,26.128,22.896,25.495,26.445,27.326,27.648,28.033,28.139,27.846,27.871,28.072,29.297,29.388,28.791\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.267,28.841,NaN,NaN,NaN,26.711,29.086,29.958,28.823,27.419,26.458,26.787,27.716,26.67,25.872,22.409,25.996,27.24,27.545,27.846,28.248,27.524,27.786,28.094,28.608,29.488,29.18,28.158\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.551,NaN,NaN,NaN,26.173,28.527,29.182,27.845,25.553,25.551,26.254,27.506,26.715,26.357,25.27,25.459,26.326,26.703,27.092,28.001,28.369,28.465,28.408,28.532,28.568,28.21,27.971\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.284,NaN,NaN,21.168,25.594,26.184,26.612,26.71,25.123,23.207,24.614,25.805,25.722,25.757,25.796,26.503,26.807,26.926,27.28,27.838,28.281,27.677,27.98,28.283,27.807,27.192,27.375\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.528,NaN,NaN,25.156,26.628,25.949,26.636,26.544,25.818,24.948,26.242,26.826,27.175,26.194,25.829,26.575,27.226,27.359,27.232,27.915,28.056,27.976,28.242,27.858,26.766,26.549,26.965\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.215,29.92,26.258,25.263,25.719,26.202,25.856,24.906,26.001,25.532,25.447,25.73,26.854,27.248,26.451,26.997,28.114,28.022,26.829,26.473,27.038,27.406,28.112,27.929,27.458,26.752,26.732,27.816\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,28.037,29.83,27.338,24.314,24.361,26.065,25.896,25.011,25.557,24.711,24.424,25.427,26.861,26.845,26.491,26.857,27.843,27.433,27.18,26.78,26.371,26.7,27.348,28.044,27.219,27.163,27.333,27.351\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061002-070219.unw.csv",
    "content": "-13.618,-14.588,-14.926,-15.211,-16.16,-16.722,-17.111,-17.174,-16.309,-15.963,-16.704,-17.439,-17.372,-16.575,-15.411,-15.996,-16.59,-17.118,-17.342,-17.604,-18.324,-18.927,-19.003,-18.752,-19.023,-19.906,-19.196,-18.596,-19.235,-16.793,-13.566,-12.823,-12.176,-12.309,-11.145,-9.7953,-9.0353,-8.4961,-8.8733,-8.6782,-8.4956,-9.4697,-10.75,-10.881,-8.7111,-8.8249,-7.8576\n-13.477,-14.085,-14.839,-15.393,-16.347,-16.807,-16.88,-17.203,-16.764,-16.475,-17.003,-17.672,-17.232,-16.679,-16.416,-16.908,-17.394,-17.255,-16.531,-17.334,-18.41,-19,-18.552,-18.243,-19.328,-20.164,-19.56,-18.801,-18.918,-17.73,-14.749,-15.222,-13.971,-12.393,-11.259,-10.448,-9.3971,-8.8747,-8.8982,-9.2642,-9.4972,-10.473,-11.407,-11.386,-10.454,-9.2462,-7.6242\n-12.74,-13.056,-14.72,-15.122,-15.527,-16.953,-16.738,-16.604,-16.715,-16.528,-16.965,-17.804,-17.398,-16.869,-17.035,-17.39,-17.675,-17.302,-16.492,-16.948,-17.046,-17.274,-18.42,-20.052,-20.453,-20.434,-20.032,-20.161,-19.71,-17.545,-16.336,-16.035,-15.025,-13.665,-12.409,-10.553,-11.282,-9.7584,-8.8487,-9.3025,-10.588,-11.219,-10.758,-10.203,-9.6015,-9.5017,-8.6855\n-11.979,-12.268,NaN,NaN,-16.017,-17.37,-16.042,-15.735,-16.531,-17.323,-17.499,-17.242,-17.714,-17.125,-17.015,-16.749,-15.882,-16.28,-16.63,-16.629,-16.452,-17.499,-18.929,-20.5,-20.789,-20.701,-21.067,-20.978,-19.772,-18.397,-17.988,-17.631,-14.674,-13.606,-12.299,-10.882,-11.401,-10.616,-9.5149,-10.31,-11.089,-11.543,-10.706,-9.6409,-9.1566,-9.324,-8.1421\nNaN,NaN,NaN,NaN,-16.096,-16.176,-15.477,-15.107,-15.663,-16.768,-17.574,-17.279,-17.969,-17.613,-17.295,-16.607,-15.819,-16.338,-17.011,-17.83,-17.953,-19.255,-20.354,-21.105,-21.038,-20.571,-22.351,NaN,NaN,NaN,-19.343,-18.853,-17.217,-14.803,-12.486,-10.972,-10.932,-10.731,-10.303,-11.033,-11.305,-11.697,-10.629,-9.6699,-9.58,-9.2169,-8.3391\nNaN,NaN,NaN,-14.65,-14.891,-15.015,-14.969,-15.065,-15.336,-16.06,-17.025,-16.767,-17.238,-17.245,-17.318,-16.625,-16.538,-17.298,-18.574,-18.576,-19.51,-21.322,-23.119,-22.016,-19.792,-19.864,NaN,NaN,NaN,NaN,NaN,NaN,-19.11,-17.963,-16.072,-14.151,-10.808,-11.039,-11.21,-11.144,-11.217,-10.599,-9.5821,-8.9342,-9.0818,-9.4757,-8.7826\nNaN,NaN,NaN,-14.543,-14.552,-15.103,-14.869,-15.113,-15.892,-16.89,-17.153,-16.635,-16.453,-16.833,-17.424,-16.928,-17.261,-17.808,-18.368,-18.937,-20.063,-22.296,-23.408,-22.156,-20.994,-20.994,NaN,NaN,NaN,NaN,NaN,NaN,-20.115,-19.003,-18.151,-16.724,-13.994,-11.569,-10.315,-8.9107,-9.5009,-9.4946,-8.8759,-9.1114,-9.7813,-10.656,-8.7764\n-14.026,-14.103,-14.87,-14.929,-14.494,-15.434,-15.578,-15.469,-16.189,-16.506,-17.392,-17.578,-17.232,-16.558,-17.186,-17.95,-18.262,-17.725,-18.504,-19.713,-21.465,-22.428,-21.717,-22.287,-23.69,-24.054,-21.753,NaN,NaN,NaN,NaN,-20.263,-20.586,-18.835,-18.673,-18.062,-14.959,-11.894,-9.6885,-8.3143,-8.0271,-8.4752,-8.4772,-9.1129,-9.7602,-10.489,-8.265\n-14.387,-14.666,-15.195,-15.457,-15.441,-16.549,-16.384,-16.332,-16.34,-16.796,-17.47,-17.588,-17.479,-16.897,-17.027,-18.135,-18.25,-17.315,NaN,NaN,NaN,NaN,-20.792,-24.44,-25.648,-25.014,-25.619,-25.059,-22.942,-21.398,-21.298,-20.755,-20.248,-19.672,-19.529,-19.287,-15.733,-12.489,-10.231,-7.9048,-7.1545,-8.102,-8.7857,-9.3247,NaN,NaN,NaN\n-14.832,-15.122,-15.393,-13.803,-14.829,-16.401,-16.177,-15.601,-16.279,-16.641,-17.13,-16.678,-17.609,-17.326,-17.431,-18.565,-18.763,NaN,NaN,NaN,NaN,NaN,NaN,-26.276,-26.491,-25.144,-26.889,-26.312,-24.862,-22.833,-21.84,-21.24,-20.913,-18.684,-18.167,-17.981,-15.918,-14.755,-13.209,-8.708,-6.7702,-6.9355,-7.9179,-8.4221,NaN,NaN,NaN\n-14.693,-15.479,-14.645,-13.44,-14.811,-15.836,-15.292,-15.111,-16.143,-17.013,-17.391,-17.409,-18.501,-18.74,-19.061,-19.062,-18.436,NaN,NaN,NaN,NaN,NaN,NaN,-26.362,-27.391,-27.07,-26.624,-25.944,-24.624,-22.954,-22.045,-20.95,-21.039,-19.953,-18.769,-16.052,-14.057,-13.288,-14.646,-10.938,-7.8678,-7.7585,-8.0635,NaN,NaN,NaN,NaN\n-15.003,-16.255,-15.939,-15.294,-15.511,-15.929,-14.973,-15.65,-16.715,-17.469,-18.352,-19.148,-19.691,-19.61,-19.119,-18.848,-18.014,NaN,NaN,NaN,NaN,NaN,NaN,-26.562,-26.384,-25.761,-26.265,-25.37,-23.775,-22.904,-21.406,-20.66,-20.489,-19.606,-17.988,-16.878,-17.496,-14.61,-12.84,-12.308,-10.675,-10.137,-10.546,NaN,NaN,NaN,NaN\n-13.87,-14.156,-14.321,-14.704,-15.418,-15.744,-15.265,-16.13,-16.752,-17.492,-18.07,-19.069,-20.277,-20.648,-21.407,-19.767,-20.017,-21.955,-24.594,-24.045,-22.866,-24.808,-23.694,-24.539,-25.445,-25.704,-25.702,-24.551,-23.015,-21.952,-21.034,-20.832,-20.365,-19.558,-18.159,-17.457,-17.168,-15.926,-14.852,-15.302,-14.262,-11.827,NaN,NaN,NaN,NaN,NaN\n-13.012,-12.665,-13.012,-14.755,-16.703,-16.753,-16,-16.057,-16.481,-17.235,-18.346,-19.209,-20.27,-20.675,-22.171,-20.669,-21.166,-21.882,-21.586,-21.341,-22.162,-23.525,-23.335,-23.83,-24.684,-23.987,-23.646,-22.926,-21.859,-21.383,-21.176,-21.033,-20.137,-18.391,-17.201,-16.374,-15.697,-15.683,-16.506,-15.746,-16.643,-12.757,NaN,NaN,NaN,NaN,NaN\n-13.181,-12.369,-13.082,-16.133,-17.393,-16.699,-15.802,-16.474,-17.944,-19.963,-21.008,-19.484,-19.587,-20.096,-19.937,-20.023,-20.428,-21.758,-21.152,-21.381,-22.321,-22.444,-22.896,-22.393,-22.673,-21.699,-21.093,-20.556,-19.257,-18.616,-18.341,-17.107,-16.35,-16.018,-15.767,-16.555,-15.717,-15.513,-15.643,-15.149,-15.083,-15.952,NaN,NaN,NaN,NaN,NaN\n-13.377,-13.981,-14.076,-15.369,-15.63,-16.337,-15.77,-16.661,-18.52,-19.014,-20.417,-18.569,-19.484,-21.268,-23.636,-24.249,NaN,NaN,NaN,-22.9,-22.674,-22.576,-22.134,-21.292,-22.24,-21.894,-20.675,-19.9,-18.472,-17.052,-16.512,-16.667,-16.648,-16.052,-15.744,-16.432,-16.717,-16.715,-16.141,-14.807,-14.691,-15.652,NaN,NaN,NaN,NaN,NaN\n-13.109,-13.381,-14.378,-15.096,-15.581,-15.719,-15.942,-17.386,-18.847,-19.622,-20.2,-20.525,-20.782,-21.499,-23.975,-24.321,NaN,NaN,NaN,NaN,NaN,NaN,-20.492,-20.126,-21.053,-20.738,-19.051,-17.975,-17.318,-17.017,-17.494,-17.701,-18.19,-17.713,-16.498,-15.594,-16.33,-16.502,-17.335,-17.551,-16.593,-15.504,NaN,NaN,NaN,NaN,NaN\n-11.908,-14.092,-14.567,-14.268,-14.469,-14.581,NaN,NaN,-19.996,-20.374,-21.034,-22.057,-23.645,-23.909,-23.199,-22.856,-23.903,NaN,NaN,NaN,NaN,NaN,NaN,-19.642,-20.128,-20.149,-19.135,-17.215,-17.791,-17.148,-17.622,-18.524,-17.707,-18.119,-17.81,-16.977,-16.311,-15.68,-15.504,-15.786,-16.224,-17.672,NaN,NaN,NaN,NaN,NaN\n-13.037,-13.734,-13.074,-11.274,NaN,NaN,NaN,NaN,-20.187,-20.656,-21.141,-22.141,-23.126,-23.813,-23.793,-23.14,-23.621,-23.608,NaN,NaN,NaN,NaN,NaN,-20.526,-21.185,-20.918,-21.082,-18.728,-18.981,-19.087,-17.317,-17.985,-16.48,-15.861,-15.439,-16.727,-17.209,-17.458,-15.746,-15.389,-15.666,-16.612,-18.222,NaN,NaN,NaN,NaN\n-12.83,-13.592,-14.622,-13.707,NaN,NaN,NaN,NaN,-21.521,-22.544,-22.022,-22.736,-23.516,-24.454,-24.903,-24.782,-24.767,-25.448,NaN,NaN,NaN,NaN,NaN,-23.068,-23.777,-22.639,-22.269,-22.387,-22.462,-21.3,-18.795,-17.95,-16.594,-16.345,-16.315,-17.037,-14.941,-15.335,-14.629,-14.838,-15.196,-15.826,-16.441,-14.959,-14.055,-14.486,-17.008\n-12.774,-14.7,-17.046,NaN,NaN,NaN,NaN,NaN,-23.723,-24.326,-25.29,-24.64,-23.927,-25.101,-25.408,-24.922,-24.464,-24.151,-25.016,-25.124,-24.577,-22.907,-22.075,-21.852,-22.485,-22.039,-21.509,-22.287,-22.504,-22.294,-20.515,-18.301,-16.639,-16.721,-16.827,-17.736,-16.482,-15.294,-14.437,-15.629,-15.701,-15.293,-14.571,-13.296,-14.293,-15.627,-18.539\n-15.015,-16.587,-15.737,-14.847,NaN,NaN,NaN,-23.682,-23.341,-25.003,-25.439,-25.507,-24.962,-25.614,-25.487,-25.118,-24.359,-24.931,-23.927,-22.931,-23.561,-23.302,-21.738,-21.921,-21.808,-21.406,-21.395,-21.863,-20.866,-20.352,-19.678,-18.87,-17.122,-16.924,-16.852,-16.514,-15.285,-16.102,-15.528,-14.194,-14.665,-14.423,-14.474,-14.742,-15.514,-15.723,-17.115\n-16.727,-17.571,-15.744,NaN,NaN,NaN,NaN,-21.487,-23.307,-24.421,-24.879,-25.957,-26.419,-25.117,-23.913,-23.471,-23.905,-25.172,-25.864,-24.387,-24.753,-24.868,-24.658,-22.571,-22.281,-21.821,-21.94,-21.577,-20.406,-19.044,-18.571,-18.342,-17.679,-17.173,-16.788,-15.802,-14.874,-14.363,-13.686,-13.217,-14.585,-15.319,-13.934,-14.439,-15.513,-15.975,-17.008\n-16.758,-17.032,-16.676,-15.762,-18.226,-23.64,-23.239,-23.675,-23.544,-23.317,-23.636,-24.709,-25.508,-25.154,-24.477,-21.259,-20.907,-22.803,-24.506,-24.296,-22.448,-22.742,-22.747,-19.798,-20.076,-21.655,-21.481,-19.836,-19.022,-17.418,-16.344,-16.596,-16.192,-16.221,-15.913,-14.796,NaN,NaN,NaN,-13.07,-13.888,-14.862,-13.656,-13.484,-15.052,-17.019,-18.509\n-16.961,-17.179,-17.439,-18.336,-18.646,-22.033,-22.273,-22.069,-21.407,-22.858,-23.472,-23.964,-23.005,-23.937,-24.592,-24.605,-23.804,-23.981,-23.618,-22.889,-21.868,-20.921,-19.417,-18.242,-18.668,-18.301,-18.101,-17.076,-16.063,-15.656,-15.04,-14.8,-14.918,-15.337,-14.532,-12.01,NaN,NaN,NaN,-14.066,-13.531,-14.407,-14.231,-14.582,-15.912,-17.734,-19.212\n-18.283,-17.908,-18.5,-19.27,-19.693,-20.929,-21.379,-20.88,-21.01,-22.259,-21.893,-23.292,-23.736,-23.802,-23.644,-23.743,-22.292,-21.165,-22.152,-21.949,-21.123,-18.661,-17.489,-17.13,-17.355,-16.9,-15.978,-15.116,-14.951,-14.752,-14.369,-13.089,-12.778,-14.451,-14.501,-13.455,NaN,NaN,NaN,-12.561,-13.107,-13.484,-13.708,-14.178,-16.161,-17.772,-18.312\n-18.254,-17.219,-18.453,-19.898,-20.064,-21.202,-21.697,-20.834,-21.56,-21.156,-21.23,-22.922,-23.402,-23.395,-23.406,-22.623,-21.589,-20.791,-20.692,-20.24,-19.084,-17.706,-16.565,-16.709,-17.662,-16.595,-15.372,-15.339,-15.321,-15.456,-13.942,-13.803,-14.805,-15.755,-15.486,-15.211,NaN,NaN,NaN,-12.023,-12.25,-12.993,-13.21,-15.128,-16.288,-17.135,-17.969\nNaN,NaN,-17.35,-20.311,-20.739,-21.587,-21.706,-21.25,-22.505,-21.222,-20.939,-21.648,-22.171,-22.542,-22.581,-22.108,-21.781,-21.171,-20.504,-19.606,-18.393,-17.136,-16.58,-16.611,-17.447,-17.58,-16.493,-16.015,-17.185,-16.073,-14.694,-13.616,-14.986,-16.094,-15.322,-12.406,-11.541,-12.135,-11.99,-11.482,-11.392,-12.562,-13.033,-15.184,-15.833,-16.976,-17.452\nNaN,NaN,NaN,-20.786,-22.411,-22.692,-22.389,-22.498,-22.691,-22.983,-22.523,-22.016,-21.614,-21.664,-21.933,-22.101,-22.385,-21.581,-19.965,-18.602,-17.949,-16.42,-15.97,-15.916,-17.245,-16.383,-15.969,-16.239,-15.62,-13.713,-13.511,-13.636,-15.458,-16.9,-14.039,-12.247,-12.631,-13.168,-13.098,-11.963,-10.504,-14.811,-15.425,-15.366,-17.115,-17.76,-17.238\nNaN,NaN,-19.123,-23.038,-23.829,-24.62,-24.098,-23.032,-23.19,-23.194,-22.478,-22.241,-21.39,-21.237,-21.571,-21.731,-21.618,-20.938,-19.428,-18.362,-17.493,-16.264,-14.941,-16.208,-16.037,-15.43,-15.603,-15.331,-14.73,-13.967,-13.491,-14.549,-16.163,-17.115,-14.267,-11.889,-13.034,-14.006,-15.527,-11.928,-10.027,-15.215,-17.673,-17.871,-18.786,-17.166,-17.41\nNaN,NaN,-22.484,-22.979,-23.457,-24.067,-23.955,-23.84,-24.004,-24.112,-23.524,-22.698,-22.044,-21.392,-21.482,-21.892,-21.889,-20.666,-19.109,-18.34,-17.491,-16.221,-13.962,-14.69,-16.28,-16.703,-16.413,-15.623,-14.611,-14.456,-14.338,-14.73,-15.342,-14.984,-10.672,-8.3971,-10.71,-11.744,-11.85,-11.324,-11.871,-15.318,-18.97,-19.433,-19.14,-16.851,-17.701\n-21.914,-23.087,-22.417,-23.532,-24.704,-23.804,-22.563,-22.224,-24.016,-24.053,-24.38,-23.641,-23.257,-22.648,-22.947,-23.685,-22.677,-20.66,-19.402,-19.115,-17.433,-16.029,-14.079,NaN,NaN,NaN,NaN,-15.728,-13.95,-12.139,-12.732,-14.579,NaN,NaN,-8.7013,-8.9139,-10.742,-11.771,-11.5,-11.785,-14.602,-16.315,-16.753,-18.29,-18.23,-17.919,-19.054\n-19.733,-22.28,-22.538,-22.507,-21.519,-21.23,-21.005,-21.016,-22.446,-24.445,-24.105,-22.821,-22.318,-23.436,-24.024,-23.84,-23.789,-22.989,-21.06,-19.152,-18.371,-16.386,-15.072,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.4282,-3.4186,NaN,NaN,NaN,NaN,NaN,NaN,-11.851,-12.016,-14.099,-16.887,-17.405,-17.603,-18.224,-18.717,-19.215\n-19.963,-21.578,-22.477,-22.183,NaN,NaN,NaN,-19.737,-21.402,-23.887,-23.412,-22.974,-21.421,-23.026,-23.995,-23.399,-22.804,-19.508,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.555,11.949,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.744,-14.332,-15.561,-16.263,-17.223,-18.125,-18.848,-18.943\n-21.247,-21.983,-22.301,-22.679,NaN,NaN,NaN,NaN,-21.763,-21.951,-21.674,-21.316,-21.112,-22.386,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.835,11.655,8.2621,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.464,-13.425,-14.77,-16.495,-17.782,-18.921,-18.944\n-21.331,-21.011,-21.186,-21.937,NaN,NaN,NaN,NaN,-22.488,-22.561,-21.902,-21.387,-20.33,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.951,10.909,3.9004,0.14232,-2.1409,NaN,NaN,NaN,NaN,NaN,-14.977,-13.377,-14.096,-16.004,-18.035,-19.484,-19.969\n-21.417,-21.086,-20.346,-19.63,NaN,NaN,NaN,NaN,-22.472,-22.343,-22.76,-18.941,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.347,9.9916,3.5377,-1.3916,-3.4617,-5.136,NaN,NaN,NaN,NaN,-14.974,-15.019,-15.576,-16.822,-17.87,-19.167,-19.662\n-20.089,-20.153,-20.102,-19.099,-18.464,-20.554,NaN,-21.131,-20.015,-21.015,-21.709,-18.132,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.434,12.955,8.6433,5.3125,-1.0968,-3.9262,-7.8736,-8.9859,NaN,NaN,-14.677,-16.578,-16.565,-16.682,-17.471,-17.217,-18.004,-18.951\n-19.548,-19.021,-19.166,-19.106,-18.507,-18.903,-20.966,-20.102,-19.202,-19.069,-19.067,-16.532,-15.945,-14.237,-13.71,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.815,7.6251,5.6249,4.5336,1.8064,-5.6539,-8.8218,-9.265,-9.8296,-11.622,-14.089,-16.998,-17.902,-18.351,-18.463,-18.716,-18.134,-18.212\n-18.898,-18.246,-18.085,-17.823,-17.635,-17.775,-18.666,-18.929,-18.407,-18.086,-17.465,-16.305,-15.306,-15.187,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.9434,4.9225,1.9262,0.58628,-2.2219,-8.4296,-9.737,-10.743,-11.617,-12.778,-15.03,-17.303,-18.508,-18.813,-19.111,-18.827,-18.452,-18.871\n-17.801,-17.664,-17.958,-17.247,-17.471,-17.354,-16.913,-17.078,-17.385,-16.042,-15.902,-15.615,-14.236,-12.046,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.7859,-0.32075,-5.0617,-8.4074,-9.2506,-11.28,-11.567,-11.752,-11.684,-15.43,-18.628,-18.124,-18.659,-18.704,-18.312,-19.097,-20.338\n-16.959,-16.637,-16.187,-16.224,-17.048,-17.799,-17.798,-17.527,-16.8,-15.454,-14.444,-15.053,-14.231,-11.149,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.8749,-1.8,-5.6588,-9.0815,-9.511,-11.085,-12.501,-13.564,-13.752,-16.572,-18.371,-17.585,-18.628,-18.822,-17.813,-18.416,-19.941\n-18.142,-17.149,-16.782,-17.061,-16.964,-17.04,-17.129,-17.998,-17.451,-15.874,-14.544,-14.355,-13.643,-10.296,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.6464,-1.0234,-3.8612,-6.7344,-8.9073,-9.3189,-9.8056,-11.849,-13.179,-15.741,-17.573,-18.235,-18.201,-18.279,-18.503,-18.604,-19.284,-21.097\n-16.83,-15.536,-15.838,-17.896,-17.178,-16.993,-17.416,-18.052,-17.594,-15.782,-14.412,-13.028,-11.771,-8.8259,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.2404,0.45066,-1.395,-2.2825,-3.7372,-6.1524,-8.2503,-11.05,-12.723,-12.662,-14.576,-17.59,-18.256,-18.566,-18.61,-18.315,-18.319,-18.965,-19.94,-20.192\n-15.523,-14.827,-15.435,-18.057,-18.285,-16.606,-16.929,-17.506,-17.721,-14.35,-13.91,-13.134,-11.684,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.5326,-3.0233,-4.4424,-3.9182,-5.9379,-8.1125,-9.6914,-12.917,-16.762,-16.49,-16.132,-18.286,-19.115,-18.836,-19.118,-18.985,-19.394,-19.961,-20.403,-20.654\n-14.917,-15.141,-15.918,-16.826,-16.751,-15.32,-15.23,-15.555,-14.414,-13.6,-13.663,-12.537,-10.574,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.7207,-5.6023,-4.9826,-6.0716,-7.777,-9.2064,-10.846,-12.458,-14.539,-16.054,-16.737,-19.343,-19.33,-20.006,-20.625,-19.923,-19.942,-20.132,-20.429,-20.554\n-15.954,-16.129,-16.363,-16.317,-15.39,-14.409,-14.123,-14.49,-13.416,-14.03,-14.271,-13.337,-12,-10.728,-7.9722,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.1291,-7.0382,-8.0863,-9.6126,-11.063,-11.052,-10.78,-11.264,-13.353,-15.009,-15.833,-17.868,-20.776,-19.527,-20.503,-22.19,-22.083,-21.42,-20.572,-20.798,-20.995\n-16.228,-15.996,-15.778,-15.753,-15.034,-14.527,-14.5,-14.586,-14.122,-14.261,-14.278,-13.064,-12.335,-11.38,-9.3968,-7.0128,-5.7429,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.1718,-9.9114,-12.532,-12.666,-12.67,-12.109,-10.947,-11.229,-14.134,-15.825,-17.518,-17.801,-19.946,-20.044,-20.152,-20.698,-21.509,-22.48,-21.992,-20.697,-21.393\n-17.24,-16.711,-15.782,-15.063,-15.158,-15.879,-15.027,-14.354,-14.109,-13.936,-13.706,-13.055,-12.478,-12.105,-10.648,-9.4642,-8.7754,-7.7471,-7.0315,NaN,NaN,NaN,NaN,-8.3806,-8.8726,-9.2212,NaN,NaN,-12.565,-12.718,-12.556,-11.918,-12.341,-14.359,-16.411,-18.005,-18.248,-19.152,-20.222,-19.936,-19.471,-19.376,-20.701,-22.034,-21.546,-21.056,-21.514\n-17.235,-17.213,-16.108,-14.864,-15.182,-15.703,-15.275,-14.931,-14.743,-14.788,-13.569,-13.328,-13.857,-13.957,-12.226,-11.364,-11.22,-9.6981,-8.8695,-8.478,-6.2625,NaN,-8.7945,-8.298,-8.0673,-8.3991,NaN,NaN,-10.584,-12.558,-12.537,-12.14,-13.256,-15.726,-16.46,-17.212,-17.867,-18.844,-19.33,-19.954,-19.589,-19.638,-20.875,-21.359,-21.144,-21.686,-21.55\n-16.882,-16.466,-16.115,-15.736,-15.131,-14.654,-15.85,-15.907,-15.429,-14.82,-14.167,-13.983,-14.649,-14.376,-13.456,-12.784,-12.821,-10.396,-9.0159,-8.3498,-9.063,-10.49,-10.143,-10.311,-10.899,-11.178,NaN,NaN,-10.099,-12.386,-12.57,-12.221,-13.164,-14.285,-14.777,-14.879,-16.207,-18.224,-19.238,-19.741,-19.813,-19.205,-19.499,-21.089,-21.101,-21.608,-22.42\n-16.121,-16.459,-17.688,-16.743,-15.356,-14.828,-15.174,-15.272,-15.163,-15.247,-14.42,-13.833,-13.225,-13.174,-13.363,-13.774,-13.851,-13.105,-13.026,-10.938,-10.605,-11.112,-10.46,-10.183,-10.818,-11.844,-12.733,-11.074,-9.7581,-13.157,-12.978,-13.844,-14.311,-15.14,-15.819,-15.627,-17.195,-18.544,-19.083,-19.599,-20.003,-20.082,-20.339,-20.917,-21.64,-22.428,-22.751\n-16.128,-16.872,-17.772,-16.645,-15.97,-15.145,-14.5,-14.633,-14.988,-15.281,-14.369,-13.747,-13.499,-13.082,-13.696,-13.835,-13.876,-13.491,-14.091,-14.401,-13.112,-11.606,-11.086,-11.257,-11.837,-11.87,-12.162,-11.649,-10.49,-12.148,-13.907,-14.615,-16.136,-16.568,-17.124,-17.464,-17.843,-17.64,-18.167,-19.288,-19.99,-19.707,-19.8,-20.692,-22.186,-22.345,-23.18\n-16.038,-16.575,-16.702,-16.439,-16.168,-16.312,-15.128,-14.97,-13.714,-13.517,-13.229,-13.042,-13.172,-12.312,-12.087,-12.504,-12.838,-13.158,-14.936,-14.718,-12.29,-11.103,-11.171,-12.248,-13.029,-13.539,-13.18,-12.534,-12.789,-14.022,-15.099,-15.965,-17.937,-18.184,-18.996,-20.072,-19.983,-17.758,-17.582,-19.056,-19.441,-19.51,-19.619,-20.477,-21.894,-22.343,-23.07\n-15.946,-15.316,-16.264,-16.994,-16.904,-16.579,-15.891,-15.081,-13.911,-13.711,-12.209,-11.578,-10.782,-11.594,-11.793,-11.569,-11.766,-12.184,-12.779,-13.256,-11.913,-10.74,-10.683,-11.421,-12.816,-13.621,-13.006,-12.979,-13.037,-15.494,-15.52,-17.687,-19.937,-19.414,-19.659,-20.092,-19.608,-18.766,-19.214,-20.357,-19.83,-18.945,-19.576,-20.545,-21.393,-22.257,-23.625\n-16.426,-16.602,-17.365,-17.502,-16.827,-15.59,-15.335,-15.83,-15.622,-14.908,-13.461,-12.955,-12.733,-12.347,-13.133,-12.207,-11.957,-11.85,-12.287,-12.896,-12.663,-12.002,-11.062,-11.527,-11.825,-12.412,-10.81,-10.941,-12.764,-16.038,-16.644,-17.978,-19.971,-19.723,-19.593,-19.253,-19.157,-19.275,-20.559,-21.6,-20.325,-18.928,-19.071,-20,-21.296,-22.869,-24.403\n-17.582,-17.735,-17.856,-17.304,-16.042,-14.082,-15.03,-15.608,-15.051,-14.171,-13.551,-14.286,-15.676,-16.742,NaN,NaN,NaN,-12.542,-12.878,-12.641,-11.919,-12.079,-11.8,-11.627,-11.761,-11.784,-11.239,-11.643,-15.052,-16.894,-17.151,-17.486,-18.044,-19.028,-19.582,-19.186,-19.238,-19.571,-20.033,-19.648,-19.794,-20.13,-19.638,-20.295,-22.042,-23.314,-23.439\n-17.531,-18.101,-19.004,-18.216,-17.598,-15.155,-14.846,-15.809,-15.839,-14.679,-13.098,-13.597,-15.146,NaN,NaN,NaN,NaN,NaN,-13.266,-11.19,-10.88,-11.765,-12.937,-12.099,-11.343,-12.208,-12.224,-13.222,-17.665,-17.523,-17.385,-17.818,-17.488,-18.083,-19.333,-19.871,-20.661,-20.286,-19.437,-19.806,-19.701,-20.101,-20.374,-21.13,-23.014,-23.674,-23.143\n-17.818,-16.997,-17.312,-18.421,-18.261,-16.936,-15.073,-15.664,-15.149,-14.298,-13.279,-13.249,NaN,NaN,NaN,NaN,NaN,NaN,-13.631,-11.928,-12.505,-13.074,-12.751,-13.118,-13.407,-13.334,-13.857,-15.055,-18.409,-18.305,-18.668,-19.063,-18.752,-19.035,-19.055,-19.498,-20.135,-20.062,-19.582,-19.994,-20.113,-20.438,-21.544,-21.924,-21.667,-21.825,-22.631\n-18.712,-16.193,-14.914,-17.474,-18.48,-19.359,-17.287,-16.248,-16.262,-15.44,-14.629,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.928,-14.545,-12.863,-12.587,-12.762,-14.277,-14.583,-14.784,-15.461,-16.246,-17.462,-18.383,-18.896,-19.683,-20.254,-19.413,-18.927,-19.274,-20.157,-19.667,-19.201,-19.615,-20.477,-21.147,-22.135,-22.276,-21.756,-22.336,-24.187\nNaN,NaN,NaN,NaN,NaN,-17.899,-17.819,-16.849,-16.307,-16.973,-18.111,NaN,NaN,NaN,NaN,NaN,NaN,-17.447,-14.312,-14.234,-13.277,-12.782,-13.78,-15.361,-14.493,-14.306,-15.275,-15.96,-17.641,-19.543,-20.552,-19.979,-21.324,-21.671,-20.982,-21.146,-21.027,-18.95,-19.582,-19.484,-19.969,-20.539,-20.981,-21.346,-22.411,-25.4,-27.412\nNaN,NaN,NaN,NaN,NaN,NaN,-17.761,-16.011,-15.686,-16.014,-18.099,NaN,NaN,NaN,NaN,NaN,NaN,-16.442,-15.405,-13.862,-14.292,-15.185,-16.42,-16.629,-16.022,-16.159,-16.465,-17.436,-18.274,-19.211,-20.036,-19.832,-20.768,-23.94,-23.028,-22.027,-21.945,-20.636,-20.738,-19.85,-19.429,-19.419,-18.715,-18.342,-20.44,-24.02,-25.178\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.772,-15.471,-15.309,-16.66,NaN,NaN,NaN,NaN,NaN,-14.664,-14.88,-16.252,-16.244,-15.84,-17.238,-18.114,-17.945,-17.043,-16.982,-18.942,-18.381,-18.136,-18.222,-19.875,-20.697,-20.756,-20.324,-20.644,-21.484,-21.228,-20.659,-20.863,-20.085,-19.634,-19.082,-18.714,-18.345,-19.438,-21.16,-23.327\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.337,-16.997,-16.157,-12.91,-14.087,-16.186,-16.579,-15.06,-14.388,-15.877,-18.131,-18.802,-18.423,-18.132,-17.491,-17.26,-18.916,-20.025,-20.552,-20.851,-21.428,-22.529,-23.923,-22.627,-21.22,-20.458,-20.168,-20.317,-20.139,-20.18,-20.433,-20.314,-21.211,-21.3\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.049,-15.708,-15.097,-15.89,-15.28,-14.703,-15.71,-17.956,-19.967,-19.724,-18.742,-18.424,-18.802,-19.538,-20.66,-21.486,-22,-21.802,-22.861,-23.604,-23.288,-22.393,-20.875,-20.566,-21.076,-21.363,-21.096,-19.93,-19.351,-19.501,-20.459,NaN\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.667,-14.265,-16.803,-17.317,-16.105,-15.362,-16.029,-18.641,-20.122,-18.609,-18.049,-18.831,-19.843,-21.08,-21.906,-21.796,-22.22,-22.76,-24.334,-25.383,-22.901,-21.169,-20.546,-20.625,-21.501,-21.988,-21.192,-19.549,-18.376,-17.382,-18.583,NaN\nNaN,NaN,-14.695,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.463,-13.913,-16.264,-17.58,-17.509,-16.693,-16.337,-17.01,-18.24,-17.898,-16.068,-17.563,-19.492,-21.019,-21.552,-21.314,-23.266,NaN,NaN,NaN,-23.064,-22.677,-22.34,-21.597,-20.972,-21.18,-21.114,-20.07,-18.68,-16.826,-18.61,NaN\n-12.652,-15.934,-16.146,-15.086,-13.974,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.669,-16.494,-17.394,-16.39,-15.77,-17.269,-19.366,-18.254,-19.67,-20.026,-18.257,-18.829,-20.548,-20.69,-21.43,-23.401,-24.654,NaN,NaN,NaN,-24.852,-25.445,-23.687,-21.96,-20.676,-20.858,-20.714,-19.599,-18.73,-19.123,NaN,NaN\n-13.615,-14.392,-14.444,-14.687,-13.875,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.983,NaN,NaN,NaN,-20.356,-21.518,-23.233,-22.5,-22.225,-21.771,-22.129,-22.867,-24.535,-26.779,-26.166,NaN,NaN,-24.651,-24.747,-21.245,-20.08,-19.891,-20.198,-20.106,-17.4,-16.186,-20.333,NaN,NaN\n-15.259,-14.079,-12.978,-13.259,-13.539,-13.892,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-22.482,-23.323,-22.455,-22.343,-21.809,-21.958,-22.746,-25.094,-27.244,-26.416,-24.357,-23.974,-23.007,-21.675,-17.972,-17.807,-19.33,-20.506,-20.302,-20.16,-20.249,-21.351,-22.615,NaN\n-13.861,-13.062,-13.634,-14.511,-12.838,-13.633,-15.184,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-19.311,-20.831,-22.197,-21.503,-22.607,NaN,NaN,NaN,-29.219,-26.073,-23.937,-22.789,-22.087,-22.5,-22.704,-21.376,-22.125,-21.353,-22.627,-22.638,-21.742,-21.726,NaN\n-13.397,-13.42,-14.578,-16.217,-14.883,-15.383,-16.565,-17.724,-18.042,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.903,-19.699,-21.57,-21.197,-22.992,NaN,NaN,NaN,-27.907,-25.426,-23.67,-21.986,-22.816,-23.707,-21.812,-22.045,-23.581,-23.153,-23.631,-23.335,-24.084,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061002-070430.unw.csv",
    "content": "-1.7512,-1.9685,-2.7351,-2.1641,-1.9008,-1.6864,-2.1276,-2.189,-1.8532,-2.9455,-2.8339,-2.2707,-1.5749,-0.97677,-0.50635,-1.3088,-2.0186,-1.5259,-1.3597,-1.6651,-2.3415,-2.754,-2.8598,-2.4286,-2.5251,-2.989,-2.6053,-2.3487,-2.3384,-1.8671,-1.585,-1.7805,-1.6163,-1.261,-1.4516,-1.5732,-1.9295,-2.4212,-2.0674,-1.7682,-1.623,-1.8452,-2.2154,-2.559,-2.3401,-2.928,-3.0514\n-2.129,-2.0092,-2.5203,-2.2409,-1.4821,-1.4585,-2.2539,-2.5214,-2.7967,-2.661,-2.7371,-2.7795,-2.0016,-1.4221,-1.4471,-1.1449,-1.8244,-1.4848,-1.3534,-1.7516,-1.8964,-2.8487,-3.1961,-3.3913,-3.6185,-3.2196,-2.3305,-2.0837,-2.2116,-2.3027,-2.183,-2.4118,-2.1096,-1.8436,-1.9001,-1.9228,-2.1323,-2.3705,-2.4591,-2.375,-2.0857,-1.9702,-2.6606,-2.9737,-3.2496,-3.3588,-3.2324\n-2.1399,-2.1902,-2.8489,-2.3554,-2.0855,-2.2623,-2.4154,-2.5068,-2.7082,-2.2908,-2.6951,-2.8646,-2.2061,-1.9136,-1.3699,-1.0506,-1.798,-1.4792,-1.105,-1.9465,-2.2939,-2.7385,-2.7638,-3.5353,-3.3702,-2.6523,-2.0343,-2.892,-3.1092,-2.6255,-3.1307,-2.6977,-2.4751,-2.3404,-2.375,-2.2588,-2.8949,-2.9192,-2.5545,-2.3716,-2.1171,-2.1256,-2.5733,-3.0127,-3.3931,-3.4402,-2.9682\n-2.352,-2.2668,-2.5618,-2.227,-2.1791,-2.0607,-1.5559,-1.5921,-2.5361,-2.7739,-2.6242,-2.4474,-2.4163,-1.6755,-1.5339,-1.5648,-1.6696,-1.2181,-0.96748,-1.8426,-1.9482,-2.3468,-2.9189,-3.2221,-3.1519,-2.991,-2.9907,-3.3789,-3.4737,-3.2185,-3.0915,-2.5412,-2.3058,-2.5126,-2.5332,-2.6257,-2.7704,-3.1127,-2.6063,-2.2266,-2.3159,-2.7268,-3.0477,-3.049,-2.9196,-2.9617,-2.945\n-2.8604,-3.1421,-3.2204,-2.159,-1.5346,-1.7488,-1.5045,-0.54093,-1.1201,-1.3395,-2.1429,-2.4633,-2.2273,-1.4192,-1.8248,-2.1183,-2.3237,-1.8039,-1.042,-0.89782,-1.3759,-2.1729,-3.3605,-3.1379,-3.2966,-3.6632,-4.21,-4.0635,-3.3684,-3.1076,-3.4195,-3.2071,-2.6919,-2.2442,-2.197,-2.2441,-2.8239,-2.9278,-2.6263,-2.4423,-2.6681,-2.6021,-2.757,-2.855,-2.7598,-2.8208,-2.8686\n-3.362,-3.6017,-2.6347,-1.9961,-1.7468,-1.8938,-1.9825,-1.9987,-2.2202,-2.2388,-2.0363,-2.5312,-1.684,-1.3214,-1.371,-1.7347,-1.7192,-1.8244,-1.6969,-2.071,-2.232,-2.8002,-3.8877,-3.2442,-2.5802,-2.9976,-3.8628,-4.1673,-3.4507,-3.0949,-3.4515,-3.6546,-3.394,-2.526,-2.3294,-2.1228,-2.1638,-2.4571,-2.6369,-2.917,-2.5571,-2.076,-2.1539,-2.3994,-2.4398,-2.4098,-2.5539\n-3.4321,-3.6351,-3.0492,-1.9169,-1.7121,-1.8926,-1.3872,-2.0016,-2.4921,-2.231,-2.4103,-2.4384,-2.0946,-1.7194,-1.7028,-1.8342,-2.2423,-2.4405,-2.5918,-2.6207,-3.3688,-3.7847,-3.667,-3.6079,-3.7663,-3.7545,-4.1897,-4.1693,-3.4299,-3.0474,-3.5768,-3.8639,-3.8649,-3.5765,-2.9741,-2.4265,-2.1679,-2.5631,-2.9427,-2.5462,-2.4421,-2.1182,-1.9422,-1.9704,-1.9726,-2.0444,-1.7342\n-2.7887,-3.0595,-3.5605,-2.0282,-1.7139,-1.8775,-2.0315,-1.9882,-1.8916,-2.4333,-3.0842,-2.3886,-2.3091,-2.051,-2.4391,-2.5283,-3.0465,-2.8448,-2.9608,-3.2452,-4.1424,-4.0953,-3.8123,-3.4703,-3.6932,-4.2816,-4.7944,-4.2308,-3.7183,-3.6977,-3.9222,-3.7741,-3.8325,-3.6311,-3.0743,-2.7763,-2.7466,-3.1442,-3.1529,-2.4391,-2.0554,-2.0863,-1.9755,-2.4244,-2.5837,-2.4101,-1.9211\n-2.4939,-2.6974,-3.2856,-2.0389,-1.8766,-1.7445,-1.865,-1.6011,-1.8637,-2.9326,-3.3925,-2.9408,-2.4507,-2.3806,-2.666,-3.2631,-3.4987,-3.6509,-3.5516,-4.3559,-4.9361,-4.8286,-3.8574,-3.3232,-3.4111,-3.6864,-3.9768,-3.7008,-4.0206,-4.3166,-4.2337,-4.0286,-3.9839,-3.6838,-3.3102,-3.3555,-3.4251,-3.5085,-3.0275,-2.6083,-2.1353,-1.7212,-1.6398,-2.6022,-3.0485,-2.9476,-2.1421\n-2.9168,-2.9458,-2.1634,-1.9417,-2.1827,-1.8253,-1.9189,-1.6729,-2.2071,-3.1224,-3.2531,-3.3176,-3.0884,-3.081,-2.9924,-3.1504,-3.909,-4.2745,-4.3722,-4.6047,-4.1852,-4.0248,-4.0305,-3.7617,-3.7495,-3.5849,-3.9917,-3.9698,-3.9922,-4.1082,-3.8631,-3.9536,-3.9722,-4.01,-4.1178,-3.8073,-3.7675,-3.9314,-3.5214,-2.8397,-2.1568,-1.8615,-1.9736,-2.7,-3.1869,-2.9726,-2.2989\n-2.6196,-2.2816,-1.6805,-1.1607,-1.1629,-1.6445,-2.0618,-2.2758,-2.5671,-3.342,-3.734,-3.7859,-3.366,-3.7357,-4.1306,-3.4542,-3.9217,-4.2268,-4.5647,-4.6665,-3.8955,-3.6602,-4.1062,-3.9314,-3.8899,-4.0014,-4.0775,-4.165,-4.0768,-3.8572,-3.4424,-3.302,-3.7158,-3.8469,-4.1909,-4.0105,-3.9583,-3.9997,-3.7301,-3.0239,-2.3699,-2.296,-2.466,-2.8064,-3.3045,-3.0927,-2.7828\n-2.4315,-2.0656,-1.2652,-1.1824,-1.9151,-1.3323,-2.0651,-2.5651,-2.4759,-3.5856,-4.2069,-4.2762,-3.7651,-3.852,-3.9921,-3.4122,-3.7241,-4.3448,-4.4091,-4.1471,-3.0874,-3.5063,-3.7962,-3.4076,-3.4401,-3.5783,-3.7804,-3.9212,-3.5869,-3.3667,-3.456,-3.6694,-3.6601,-3.6035,-3.8398,-3.8141,-3.5942,-3.6048,-3.5954,-3.0462,-2.3661,-2.3095,-2.6374,-3.0599,-3.3292,-3.5948,-3.2821\n-2.2845,-2.0255,-0.68458,-0.03184,-1.6147,-1.4572,-1.8287,-2.3752,-2.3335,-3.6435,-3.5005,-3.7062,-3.7273,-3.2926,-3.1718,-3.1302,-3.1267,-3.3956,-3.5246,-3.234,-3.7634,-3.3119,-3.1468,-3.4891,-3.7828,-3.4129,-3.353,-3.7415,-3.5176,-3.2677,-3.7749,-3.9894,-3.9196,-3.7942,-3.95,-3.9061,-3.2437,-3.1588,-3.1974,-2.6819,-2.1907,-2.1342,-2.537,-2.9604,-3.4698,-3.4797,-2.9279\n-1.2707,-0.82694,-0.12478,1.1035,-0.80287,-2.9657,-2.7147,-2.1393,-3.2915,-3.8785,-4.1115,-3.9776,-3.4489,-3.0194,-2.9452,-3.1055,-3.2055,-3.5767,-3.4892,-3.4334,-3.9788,-4.0126,-3.7402,-3.6843,-4.0355,-2.9231,-2.7393,-3.2828,-3.1963,-3.2943,-3.8187,-3.9809,-4.0263,-4.036,-4.0218,-4.1838,-3.9895,-3.5351,-2.798,-2.7812,-2.498,-2.489,-2.9563,-3.1918,-3.6396,-3.0685,-2.6864\n-0.77967,-1.4302,0.14338,1.2842,-1.0557,-2.5797,-2.9392,-3.8297,-3.6811,-3.701,-3.4888,-2.9265,-2.6589,-2.5397,-2.2552,-2.0932,-2.9347,-3.7155,-3.6384,-3.7096,-4.567,-4.2063,-3.527,-3.1833,-3.2478,-1.8297,-1.9838,-2.6252,-3.0432,-3.0011,-3.0361,-3.4318,-4.006,-4.2879,-4.3388,-4.745,-4.3858,-3.963,-3.2942,-3.1739,-3.3293,-3.3009,-3.5777,-3.7459,-3.8346,-3.1099,-3.0961\n-1.408,-1.5411,-1.8563,-1.055,-1.2181,-1.4103,-2.9068,-4.1607,-3.56,-3.783,-3.2452,-2.7647,-2.4434,-3.2038,-3.4277,-4.0824,-4.4673,-4.1796,-3.9957,-4.701,-4.7249,-3.9548,-3.4934,-2.4959,-2.474,-2.4561,-2.6768,-3.4287,-3.7389,-3.2664,-4.034,-4.2962,-4.6331,-4.7575,-4.804,-5.0728,-4.4595,-4.2192,-3.9789,-3.5353,-3.3324,-3.1177,-3.428,-3.5246,-3.5756,-3.9168,-3.5843\n-1.5411,-1.6881,-1.7077,-1.3801,-1.163,-1.4479,-2.515,-3.322,-3.9197,-4.3443,-4.1791,-3.144,-2.7833,-4.0819,-3.5323,-4.727,-4.3952,-3.9833,-4.7424,-5.2867,-5.2783,-4.4453,-4.0891,-3.5413,-3.6553,-3.0421,-3.3165,-3.9235,-3.9934,-3.5496,-3.6702,-4.4846,-4.8759,-4.878,-4.8822,-4.9944,-4.944,-4.9457,-4.4283,-3.9046,-3.1754,-3.1897,-3.3807,-3.3957,-3.3484,-3.8352,-3.6887\n-1.5441,-1.5205,-1.5451,-1.4563,-0.63583,-0.82553,-1.3985,-3.053,-2.9898,-2.4835,-2.8984,-3.5112,-3.9002,-4.3262,-3.9779,-4.5033,-5.2511,-4.6559,-4.5832,-4.2645,-4.081,-4.3575,-4.7318,-4.4558,-4.2172,-3.9995,-3.8395,-4.1468,-4.0409,-3.4476,-3.8577,-4.5998,-5.7689,-5.5765,-5.5076,-5.1702,-5.5535,-5.8391,-5.8589,-4.7861,-4.2386,-4.048,-4.2552,-3.7577,-3.4827,-3.8277,-3.8541\n-1.3422,-1.0454,-1.6579,-2.9878,-2.6906,-2.3465,-2.3332,-3.6202,-3.9594,-2.4084,-2.7266,-4.0442,-4.4397,-4.9595,-5.1167,-4.8472,-5.2569,-5.0733,-4.7315,-4.9553,-4.6228,-4.598,-4.9403,-5.4128,-5.487,-5.0708,-3.8986,-4.2397,-4.6949,-3.78,-3.8649,-4.3694,-5.5098,-5.9052,-5.8001,-5.5811,-5.9434,-5.9197,-5.3242,-4.9874,-4.5823,-3.6715,-4.1825,-3.5883,-3.1952,-3.2823,-3.8\n-0.18148,-0.54213,-0.89311,-1.7212,-3.0823,NaN,NaN,-4.0729,-4.045,-2.9892,-3.3641,-4.6927,-5.3016,-5.62,-5.8997,-5.7504,-5.7331,-6.073,-5.8462,-5.9378,-5.2438,-4.7496,-4.4735,-5.4235,-5.5366,-4.6987,-4.3274,-4.5349,-5.4189,-4.9021,-4.1098,-4.362,-5.4717,-5.6485,-6.4546,-6.0299,-5.1668,-5.2566,-5.041,-4.945,-4.3619,-3.7452,-3.5002,-3.1453,-2.8363,-2.8142,-2.8887\n0.1693,0.81685,-0.58824,-0.25498,-0.61511,NaN,NaN,-3.6232,-3.8647,-3.358,-4.9627,-5.9084,-6.1577,-5.8029,-5.9618,-5.666,-5.7559,-6.1864,-5.5439,-5.4497,-5.1408,-4.7945,-4.7483,-4.8885,-4.2415,-3.9968,-4.2815,-4.8285,-5.4545,-5.6186,-4.5867,-4.5382,-5.2649,-5.3092,-6.5474,-6.3763,-4.528,-4.5236,-4.3761,-4.5188,-3.9337,-3.6106,-3.5449,-3.0714,-2.8439,-2.6431,-2.7033\n-0.041032,-0.91186,-1.899,-2.8922,NaN,NaN,NaN,-3.6846,-4.1686,-4.5105,-5.2672,-5.9826,-6.4973,-6.5154,-6.4561,-5.8228,-5.4169,-5.6658,-5.7828,-5.3559,-5.0731,-4.9204,-4.9699,-4.1663,-3.9303,-4.1434,-4.4622,-4.6495,-5.023,-5.007,-4.4606,-4.2938,-4.7063,-4.8358,-5.8069,-5.6717,-4.4368,-3.9154,-3.7244,-4.14,-3.6022,-3.3547,-3.0897,-2.7964,-2.7233,-2.6704,-2.8651\n-1.1487,-1.8655,-1.5633,-2.8845,NaN,NaN,NaN,-4.9231,-4.8658,-5.058,-5.9305,-7.4154,-7.9169,-7.2391,-7.1355,-7.7056,-5.6587,-5.3548,-5.3602,-5.0253,-4.5644,-4.7201,-4.836,-4.3868,-4.5125,-4.2426,-4.2393,-4.3958,-4.6774,-4.8641,-4.375,-3.7867,-3.6455,-3.5249,-3.8804,-4.4463,-3.8252,-3.5012,-3.6053,-3.443,-3.2906,-3.1335,-2.5173,-2.0057,-2.5532,-2.8001,-2.9923\n-3.8394,-2.4029,-2.8725,-3.8263,-5.7831,-4.8962,-5.1267,-4.6959,-4.7506,-5.4488,-6.9903,-7.5811,-5.7932,-6.1258,-6.3268,-6.5696,-5.0581,-5.1504,-4.8767,-4.5133,-4.0535,-3.8426,-3.4407,-3.6022,-3.5855,-3.8659,-3.7461,-3.7314,-3.9206,-3.8566,-3.1986,-2.7618,-2.5266,-2.6529,-3.199,-3.5179,-3.5533,-3.6633,-3.8706,-3.3522,-3.1961,-2.9549,-2.6408,-2.47,-2.8551,-3.2223,-3.1043\n-1.8026,-2.0122,-2.3398,-3.1856,-3.9642,-4.82,-4.6502,-5.1151,-5.6915,-5.6424,-5.718,-6.0793,-5.4497,-6.1766,-6.1859,-5.79,-5.052,-5.0801,-4.7789,-4.2887,-3.7958,-3.1564,-2.4068,-3.16,-3.1916,-2.798,-2.8065,-2.8242,-2.5545,-2.7184,-2.6615,-2.3163,-1.9304,-2.5919,-3.1421,-3.1508,-3.0501,-3.3576,-3.4537,-3.1913,-3.3732,-2.8364,-2.6087,-2.7959,-2.8887,-3.004,-3.1765\n-2.9472,-3.2712,-3.0386,-3.4324,-4.253,-4.4191,-4.5571,-4.7219,-4.6197,-4.7406,-3.946,-4.7483,-5.7789,-5.7496,-5.1812,-5.0261,-5.0776,-4.737,-4.5026,-3.8896,-3.1226,-1.7728,-1.9272,-2.4372,-2.5952,-2.6139,-2.4663,-2.7577,-2.9107,-2.2798,-1.9641,-1.7037,-2.5295,-4.257,-3.6939,-3.394,-2.592,-2.7795,-2.6952,-2.3058,-2.9543,-2.4926,-2.401,-2.5848,-2.6542,-2.8676,-3.0052\n-3.7966,-4.5662,-4.0535,-4.2243,-4.1783,-4.1978,-4.5433,-4.5124,-4.4042,-4.5761,-4.6762,-4.8066,-4.6878,-4.991,-4.8765,-4.3077,-3.6264,-3.6325,-4.0567,-3.6123,-2.5752,-1.8166,-1.5969,-1.973,-2.5278,-2.6587,-2.4313,-2.6555,-3.1191,-2.0015,-1.8152,-2.0324,-2.382,-3.301,-3.4895,-3.2415,-2.351,-2.2773,-2.1587,-2.1684,-2.5788,-2.3422,-2.4791,-2.4104,-2.3893,-2.7235,-3.008\n-4.4068,-4.8039,-4.3295,-3.9548,-4.2893,-3.8633,-3.2535,-4.2531,-4.8727,-5.4683,-5.1032,-4.9119,-5.0347,-5.1965,-4.5644,-4.2673,-3.9961,-4.2563,-3.8119,-3.0449,-2.238,-1.2401,-0.85682,-0.78293,-1.7174,-2.2141,-2.178,-1.995,-2.0813,-2.0339,-1.6281,-1.4567,-2.7354,-3.3581,-3.127,-2.064,-2.0172,-1.9682,-2.5367,-2.3674,-2.0065,-2.1706,-2.4272,-2.2437,-2.2673,-2.6038,-2.8244\n-4.4492,-4.0797,-3.8044,-3.7362,-3.6814,-3.6189,-3.7472,-4.0739,-4.6673,-4.915,-4.5535,-4.478,-5.0416,-4.7211,-4.3502,-4.0547,-3.7962,-3.9498,-3.2076,-2.2854,-1.8976,-0.18116,0.75712,1.3487,-0.14055,-0.67856,-0.057338,-0.64586,-0.37755,0.075662,-0.47955,-0.98256,-2.1952,-2.7993,-2.5111,-1.9223,-1.6297,-1.347,-1.4216,-1.4971,-1.5923,-1.9842,-1.8861,-1.9304,-2.1488,-2.3132,-2.5139\n-4.3477,-4.0872,-4.3645,-4.7296,-3.6031,-2.782,-2.8878,-3.807,-4.5347,-4.7364,-4.612,-4.2229,-3.9218,-4.3958,-4.2994,-3.9697,-3.4606,-3.3251,-2.5048,-1.2354,-0.77002,1.0461,2.9493,1.5498,1.0476,0.93821,1.0812,1.4539,1.186,0.72693,0.22182,-1.8642,-3.2283,-3.1322,-2.2752,-1.421,-1.1383,-1.1321,-1.8395,-1.8037,-1.9368,-2.1279,-1.5289,-1.6462,-1.7144,-2.1599,-2.456\n-6.2712,-4.2843,-3.3692,-3.4557,-3.7226,-3.2439,-2.828,-3.5226,-4.0201,-3.8573,-4.1905,-4.2276,-3.8207,-4.3378,-4.3677,-4.2678,-3.2291,-2.4772,-2.4648,-0.81038,1.0283,2.3225,5.1964,4.911,2.3639,2.0045,2.2615,2.2699,0.93423,0.79124,0.56402,-0.88876,-1.843,-2.3654,-2.0198,-0.87195,-0.87703,-1.0601,-1.2466,-1.4585,-1.6501,-1.7497,-1.5954,-1.5538,-1.6755,-2.371,-2.5674\n-5.2818,-3.9751,-3.5146,-3.3201,-2.0663,-2.2967,-2.3483,-3.2409,-3.9401,-3.8844,-3.8639,-4.5589,-4.5831,-4.3962,-4.1476,-3.7224,-2.5765,-1.8352,-0.9949,-0.54837,1.9278,3.449,5.6569,6.2776,4.3911,1.6335,-0.85829,-0.6011,-1.4383,-1.8506,-2.329,-1.3461,-0.68379,-0.83321,0.015022,-0.22078,-0.88679,-0.78125,-0.90104,-1.1618,-1.47,-1.181,-1.6173,-1.4749,-1.7461,-2.124,-2.252\n-2.416,-1.4476,-1.6003,-1.5899,-2.2813,-2.4532,-3.1161,-4.2253,-5.5176,-4.6147,-4.4872,-4.407,-4.394,-4.159,-3.8993,-3.5157,-2.4822,-1.0931,-0.5199,-0.34128,0.95398,2.8727,3.9347,4.7212,3.5588,1.4329,-1.3041,-3.4501,-3.5435,-3.2873,-2.123,-1.2237,-1.2602,-1.4138,-1.355,-0.35091,-0.0052409,-0.039219,-0.93095,-2.1766,-2.0816,-1.3183,-1.665,-2.1076,-1.8521,-1.9674,-2.0286\n-3.4553,-2.8124,-2.6894,-2.2828,-1.6123,-1.9092,-2.4968,-3.7624,-4.9059,-5.0334,-4.7567,-4.6513,-4.5881,-4.1616,-3.8882,-2.9431,-1.5047,-0.26833,0.13411,-0.5675,-0.75884,0.63116,2.8488,2.7064,NaN,NaN,NaN,NaN,NaN,-2.424,-1.6493,-1.4377,-0.96305,NaN,NaN,0.27418,0.91976,0.53163,-0.50096,-1.5149,-1.6563,-1.5049,-2.1917,-2.3079,-2.0628,-1.9667,-2.0509\n-1.9868,-2.416,-3.9753,-3.6142,-1.9569,-1.4828,-2.1082,-4.2848,-5.2674,-5.4958,-5.2002,-4.6756,-4.7475,-3.7329,-3.1944,-2.7814,-1.3975,-0.49457,-0.0010581,-0.58784,-1.3578,-0.084327,-0.34967,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.5797,-1.1578,0.23036,NaN,NaN,NaN,2.8387,1.2392,-0.52002,-1.1347,-0.98474,-1.3949,-2.0188,-2.2448,-2.0152,-2.0816,-2.0329\n-3.9648,-3.2386,-3.457,-4.2614,-2.5654,-3.0147,-3.8963,-4.5882,-5.1539,-3.7284,-3.7666,-3.7333,-4.1045,-4.8088,-4.3606,-0.14566,-0.060261,-0.037923,0.40391,0.14094,-0.27992,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.74171,-2.7492,-2.1213,NaN,NaN,NaN,3.9601,2.6889,0.36598,-0.86008,-0.81671,-1.167,-1.8066,-2.1028,-2.1392,-2.1333,-2.0294,-2.0932\n-4.5424,-4.0111,-3.5975,-3.9098,-3.0586,-3.7812,-4.4519,-4.2875,-3.9469,-3.8125,-2.6355,-2.0534,-2.704,-3.9435,-3.3326,0.16858,0.42996,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0828,-2.224,NaN,NaN,NaN,NaN,3.4084,0.75217,-1.1279,-1.2259,-1.1041,-1.8544,-1.9584,-1.9129,-1.9342,-1.7644,-1.8,-2.4176\n-4.1361,-3.2169,-3.0598,-3.0673,-2.9731,-3.42,-4.2259,-4.3952,-4.3582,-3.723,-2.613,-1.758,-1.8741,-2.0941,-0.88496,0.064036,0.2951,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4363,-3.2341,NaN,NaN,NaN,NaN,1.2148,-0.67032,-1.2931,-2.2342,-1.3539,-1.79,-1.7181,-1.5276,-1.5021,-1.1789,-1.6831,-2.0092\n-3.9088,-3.2262,-2.532,-2.4771,-2.7771,-3.4551,-4.584,-4.6195,-4.877,-4.6986,-3.8747,-2.748,-1.9158,-1.4624,-0.58743,-0.34345,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.5515,-3.0933,-6.1725,NaN,NaN,NaN,NaN,-0.13258,-0.66989,-1.2127,-1.722,-1.0931,-1.7751,-1.6748,-1.8257,-1.7822,-1.8071,-1.7549,-1.6233\n-4.0914,-3.1105,-3.3707,-3.6614,-3.5861,-3.2052,-3.9716,-4.7529,-4.1279,-3.8319,-3.6605,-3.1698,-1.5813,-0.84456,0.4203,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4941,-2.3702,-4.5824,-10.227,NaN,NaN,NaN,0.29955,-0.59121,-0.89336,-0.7701,-0.097642,-1.1627,-2.0045,-1.8889,-1.7424,-1.8233,-2.0176,-1.9608,-1.719\n-3.9324,-3.2404,-2.9674,-3.5979,-3.3588,-2.9232,-2.644,-3.2405,-4.0984,-2.9018,-2.3651,-2.1765,-1.1672,-0.39043,0.11937,NaN,NaN,NaN,NaN,NaN,NaN,-1.1485,-1.9118,-1.9892,-1.775,-3.7567,-3.5766,-0.77638,NaN,NaN,NaN,NaN,NaN,NaN,-0.97115,-1.4495,-1.7514,-2.346,-3.3014,-2.6369,-2.8341,-2.4789,-1.8802,-1.7909,-0.97596,-1.0536,-1.269\n-3.8185,-3.4353,-2.9748,-3.2856,-3.5169,-3.1472,-3.1364,-3.4874,-3.4031,-2.4792,-1.6961,-1.6231,-1.1303,-0.5886,-1.1219,-2.4692,-3.1743,NaN,NaN,NaN,NaN,-1.6915,-1.2643,-0.77204,-0.41803,-1.8924,-0.41437,-0.97199,NaN,NaN,NaN,NaN,NaN,-0.55715,-1.2642,-2.0974,-3.5307,-4.0756,-4.0726,-3.0381,-2.7615,-2.7092,-2.2118,-1.8067,-0.9395,-0.82142,-1.4225\n-3.4419,-2.7079,-2.7941,-3.3515,-3.4219,-3.3339,-3.6511,-3.5291,-2.8527,-2.5333,-2.0828,-1.7629,-1.5861,-0.7581,-0.98276,-1.9443,-3.6264,-3.2952,-2.8804,-2.8001,-1.4948,-0.91906,0.45337,NaN,0.75221,0.040734,-0.02603,-1.8661,NaN,NaN,NaN,NaN,NaN,-0.78337,-1.7345,-2.2247,-3.3536,-3.2265,-2.8283,-2.3952,-1.8422,-1.6976,-1.5452,-1.4646,-1.3673,-1.2949,-1.8914\n-2.1686,-2.1042,-2.3621,-2.5339,-2.9103,-3.4674,-3.8043,-3.8331,-3.726,-3.0392,-2.4352,-1.7556,-1.2035,-0.462,-0.062866,-0.91736,-2.5681,-2.4986,-2.3145,-1.0464,-0.47804,0.45202,NaN,NaN,NaN,NaN,NaN,-1.8031,-0.16109,1.2578,1.1709,0.17034,-0.2736,-1.4633,-2.2512,-2.9228,-2.9749,-2.5202,-2.4417,-1.9193,-1.5454,-1.7529,-1.4641,-1.3949,-1.8305,-2.1632,-3.1599\n-2.4547,-2.1291,-2.5114,-2.6761,-2.6079,-3.7247,-4.5569,-4.9445,-4.7053,-2.7554,-2.0495,-1.5793,-1.0995,-0.25459,0.94763,1.7566,-0.55339,-1.9197,-1.6436,-1.4209,-0.5978,NaN,NaN,NaN,NaN,NaN,NaN,-1.449,-1.1979,0.19203,0.36603,-0.47705,-1.5202,-2.0855,-2.5707,-3.2708,-2.8937,-2.4059,-2.0688,-2.2001,-2.1317,-2.1909,-2.3165,-2.7315,-3.4127,-3.7242,-3.5576\n-2.4046,-2.3168,-2.6562,-2.8888,-2.6351,-3.1592,-3.7718,-3.5302,-2.2912,-1.143,-1.3311,-1.1473,-0.60733,0.39442,1.9405,3.4733,NaN,NaN,NaN,NaN,-0.39286,NaN,NaN,NaN,NaN,NaN,NaN,-1.6137,-1.047,-0.23585,-0.52804,-1.4359,-2.2567,-2.4981,-2.6081,-2.7688,-2.6591,-2.3984,-2.3603,-2.4602,-2.5026,-2.9122,-2.8673,-3.0643,-3.5465,-3.7469,-3.4968\n-2.6425,-2.6706,-2.8605,-2.7382,-2.4438,-2.5738,-2.823,-2.2543,-1.4052,-1.6233,-1.746,-1.2623,-0.72728,-0.38719,-0.13911,1.3634,NaN,NaN,NaN,NaN,-0.36589,-0.30129,0.45688,-0.60854,NaN,NaN,-2.1487,-1.3887,-1.2675,-1.4344,-1.5799,-2.2736,-2.1561,-2.1395,-2.3929,-2.7833,-2.0244,-1.9154,-2.3124,-2.4364,-2.6261,-3.921,-3.5316,-3.1084,-2.968,-2.8308,-2.8656\n-2.9765,-3.1953,-2.7429,-2.2113,-1.7594,-1.7111,-1.905,-1.9803,-1.2515,-1.3223,-1.4831,-0.90912,-0.61377,-0.11851,0.16913,-0.035416,1.2143,1.0494,NaN,NaN,NaN,-0.66934,-1.1128,-0.86688,-1.8518,-2.5453,-2.9752,-1.9163,-1.9642,-2.0104,-1.9641,-2.0151,-1.7073,-1.8411,-2.2609,-2.5677,-2.3983,-1.9245,-2.6154,-3.2966,-3.4117,-4.2088,-4.0594,-3.2621,-2.7345,-2.9849,-3.2234\n-2.7098,-3.2741,-2.8732,-2.1985,-1.8147,-1.9537,-2.1498,-2.1949,-1.9681,-1.5873,-1.042,-0.48673,-0.37185,-0.068438,0.01521,-0.13336,-0.25884,-0.83958,-1.4984,-1.1127,-0.24742,-0.2212,-0.95915,-1.6245,-2.1418,-2.7493,-2.6391,-1.6618,-1.8976,-2.1136,-2.0747,-2.0411,-1.7317,-2.0771,-2.4237,-2.6647,-3.4706,-3.6179,-3.5002,-3.5873,-3.8558,-4.074,-4.5923,-4.07,-3.3309,-2.8204,-2.8039\n-2.6134,-3.0337,-3.0414,-2.4314,-2.1007,-2.2083,-2.4535,-2.4587,-1.962,-1.0795,-1.2869,-0.66368,-0.29266,-0.27062,-0.12488,-0.81942,-1.0596,-1.7848,-2.5579,-3.1879,-1.8435,-0.41663,-1.3663,-2.0619,-1.9736,-1.9224,-1.6453,-1.3706,-1.5759,-2.3185,-1.9954,-2.0021,-1.3498,-2.0666,-2.3909,-2.4673,-3.0032,-3.196,-3.2013,-3.5513,-3.5143,-3.7675,-4.4571,-4.0571,-3.204,-2.8145,-2.8367\n-2.626,-2.9313,-2.9374,-2.5157,-2.1983,-1.9186,-2.4902,-2.0063,-1.505,-1.3257,-1.6543,-1.1639,-0.51912,-0.45107,-0.69431,-1.1649,-1.3994,-1.553,-2.8227,-3.4602,-3.137,-1.9281,-2.1486,-2.1593,-2.0488,-2.361,-2.9448,-2.0032,-1.4966,-1.9877,-2.1405,-1.939,-1.5344,-1.8971,-2.1353,-2.2431,-2.5903,-2.9499,-3.0076,-3.3807,-3.467,-3.4643,-3.852,-3.6945,-3.3703,-3.452,-3.1163\n-2.331,-2.5385,-2.7715,-2.5491,-2.2032,-2.0092,-2.1129,-1.931,-1.5538,-1.2892,-0.96854,-0.65201,-0.67506,-0.96961,-1.0319,-1.3724,-1.4427,-2.5995,-2.9233,-3.5909,-3.2622,-2.0705,-2.0821,-2.0036,-1.6306,-1.6837,-2.4129,-2.2687,-1.4579,-1.885,-2.1855,-1.7531,-1.7821,-2.0764,-2.3423,-2.6343,-2.96,-2.767,-2.9221,-3.4954,-2.9477,-2.8959,-2.8623,-3.153,-3.6264,-3.5408,-3.0686\n-2.5676,-2.7684,-3.0055,-2.474,-2.3294,-1.6697,-1.1083,-1.137,-1.1839,-0.95477,-0.90208,-0.80066,-1.0394,-1.1949,-1.0348,-1.5839,-2.0617,-3.4099,-4.0049,-4.3624,-3.3469,-2.1429,-1.833,-1.9532,-1.8218,-1.6424,-1.4336,-1.7332,-1.5326,-1.6934,-2.2118,-1.7632,-2.1665,-2.5845,-2.9405,-2.7256,-2.6874,-2.3526,-2.9131,-3.6073,-3.4946,-2.6478,-2.6237,-3.1419,-3.8423,-3.7485,-2.7485\n-3.2899,-2.5215,-2.6799,-2.5603,-1.9707,-1.5487,-0.96202,-0.84557,-0.95812,-0.86752,-0.57228,-0.7657,-1.3188,-1.4082,-1.4857,-2.3072,-2.4824,-3.8386,-3.8708,-3.4546,-2.4377,-2.0479,-1.8338,-1.7494,-1.8112,-1.6928,-1.3405,-1.1502,-1.2382,-1.2732,-1.8691,-2.0585,-2.5819,-3.2911,-2.9884,-2.0618,-2.6123,-2.7759,-3.3824,-4.1805,-3.3472,-2.6702,-2.2622,-2.6478,-3.8372,-3.3648,-2.5145\n-2.3505,-0.98651,-1.4204,-2.4402,-2.1512,-1.8995,-1.54,-0.97507,-0.90411,-0.51628,-0.6195,-0.11048,-0.011666,-1.0159,-1.6472,-1.6723,-2.1566,-3.1788,-3.6114,-3.2849,-2.3247,-2.0641,-2.0032,-1.8565,-1.9327,-1.9225,-1.3797,-1.2591,-1.1651,-1.2882,-2.036,-2.3655,-2.9619,-3.1787,-2.9739,-2.983,-3.4433,-3.7868,-4.1598,-4.1947,-2.9248,-2.0202,-1.9071,-2.5181,-3.0538,-2.9786,-2.7333\n-3.2399,-1.6514,-1.6769,-2.4338,-2.9252,-2.5113,-2.3476,-1.6161,-1.1842,-0.24662,-0.21064,-0.23742,-0.73754,-0.80556,-1.4602,-1.4248,-1.8874,-2.2782,-3.0534,-3.0926,-2.5173,-2.1946,-1.8893,-1.7043,-2.5006,-2.5369,-1.7998,-1.3276,-1.2005,-1.5936,-2.1453,-2.7761,-3.1344,-3.3715,-3.2316,-3.2561,-3.7623,-3.8631,-4.1536,-3.6541,-2.838,-2.3582,-2.1788,-1.9881,-2.6304,-2.888,-3.0211\n-3.9907,-2.7194,-2.1417,-2.104,-1.7995,-2.1731,-2.6165,-2.5473,-1.9048,-0.91349,-1.8525,-1.6558,-2.9519,-2.9289,-1.8816,-1.4615,-1.7181,-2.8197,-3.2082,-3.2322,-2.6582,-1.9977,-1.5204,-1.4803,-2.3213,-2.365,-2.6568,-1.7192,-1.8913,-2.205,-2.7989,-3.3863,-2.8249,-3.2695,-3.2996,-3.2825,-3.6221,-3.6573,-3.7336,-3.3023,-3.3442,-3.1471,-2.5311,-2.6612,-3.1434,-3.3299,-3.7737\n-3.5788,-2.9927,-3.0308,-3.1765,-3.1151,-2.9752,-3.208,-2.3873,-1.7644,-1.2413,-2.1532,-2.1103,-2.8873,-2.0771,-2.2575,-2.061,-2.108,-3.2321,-3.186,-3.1711,-3.6395,-3.2248,-2.0553,-1.9887,-2.0755,-2.9178,-2.8149,-2.1391,-3.1799,-2.7241,-3.1865,-3.6166,-3.0325,-2.9469,-2.7859,-2.8925,-3.4977,-3.5221,-3.601,-3.0952,-2.9789,-3.4494,-3.2902,-3.1559,-2.9387,-3.4238,-4.4179\n-3.5556,-3.0808,-2.9351,-3.6253,-3.9182,-3.296,-3.1473,-2.1988,-1.4139,-0.80329,-0.93644,-1.0713,-1.7316,-1.3425,-0.90378,-1.7438,-3.0999,-3.1756,-2.5992,-2.5766,-2.0675,-1.9448,-2.2373,-2.4994,-2.7158,-3.0449,-2.4861,-2.2327,-2.879,-3.2326,-3.3171,-3.4614,-3.1758,-2.617,-2.5373,-2.9217,-3.2199,-3.4747,-3.5298,-3.1415,-3.0982,-3.5598,-3.6519,-3.3099,-2.4785,-2.3316,-2.566\n-4.259,-4.0716,-3.2534,-3.5333,-4.1709,-3.708,-3.7332,-2.8682,-1.4014,-1.0139,-0.2901,-0.95045,-1.7371,-2.1678,-3.2394,-3.7441,-4.7884,-3.7213,-3.0851,-2.3309,-1.4856,-2.0044,-2.3973,-2.6911,-2.8308,-3.0469,-2.7112,-2.5856,-3.5242,-4.0892,-3.638,-3.4327,-3.6141,-2.8926,-3.2892,-3.3208,-3.2225,-3.421,-3.4491,-3.3699,-2.9389,-2.9512,-3.2743,-3.3533,-2.601,-2.7509,-3.5321\n-4.3326,-4.0898,-4.2524,-3.3943,-2.4797,-1.9075,-4.984,-4.7102,-3.0795,-4.496,-3.4919,-3.9684,-3.5051,-2.9063,-2.2055,-2.6388,-3.7026,-3.3031,-3.2787,-2.9192,-2.5696,-3.1358,-3.0741,-2.6,-2.1408,-2.2113,-2.654,-2.9744,-4.1078,-4.9946,-4.266,-3.71,-3.4002,-3.4641,-3.693,-3.5165,-3.5692,-3.5438,-3.5249,-3.7999,-3.8752,-3.6304,-3.5476,-3.6828,-3.0807,-3.088,-3.4285\n-4.2791,-3.4261,-2.8103,-1.8644,-1.3049,-0.85817,-2.8911,-3.8161,-2.9449,-4.279,-4.5425,-2.9441,-2.9503,-2.6612,-1.2497,-0.70679,-1.6455,-2.2261,-1.5827,-2.5553,-2.9522,-2.7828,-2.5548,-2.9221,-2.7001,-2.3133,-2.5049,-3.5029,-3.8226,-4.1382,-3.8888,-3.6995,-3.4094,-2.989,-2.9793,-3.2983,-3.9373,-3.675,-3.6452,-3.8683,-4.2639,-4.3861,-4.1448,-3.7957,-3.0293,-3.1116,-4.1565\n-4.7765,-3.885,-2.8228,-2.3873,-2.1836,-1.1788,-2.7867,-3.3053,-2.6861,-3.4797,-4.4814,-3.3028,-3.2025,-2.882,-3.0019,0.30889,1.0572,-0.91308,-2.2467,-1.6777,-1.473,-1.8847,-1.4401,-1.3378,-2.9529,-2.5444,-3.4096,-3.2397,-2.6966,-2.3905,-2.8915,-3.539,-3.8051,-2.904,-2.884,-4.6298,-4.2029,-3.956,-3.7977,-3.8562,-4.2017,-4.1737,-3.7734,-3.4297,-3.0926,-3.2743,-3.7271\n-7.217,-8.1312,-7.4615,-4.6941,-4.3054,-4.1882,-4.7135,-4.9803,-4.0611,-1.7916,-0.31207,-3.0235,-4.3655,-5.5763,-6.1866,-5.7677,-2.206,-1.2922,-2.7269,-2.2029,-1.649,-2.8451,-1.9729,-1.7179,-2.6455,-2.7266,-2.7237,-2.6224,-2.6906,-2.7498,-3.0443,-3.071,-2.9568,-2.911,-3.3555,-4.6201,-4.6669,-4.2888,-3.6424,-3.6681,-4.0823,-4.3176,-3.7867,-3.6249,-3.3105,-3.2277,-3.0581\n-6.4572,-6.7216,-5.7304,-4.9023,-2.0214,-2.6365,-4.2355,-5.0645,-4.5613,-2.7212,-1.7026,-2.9594,-2.9971,-4.3416,-5.0609,-6.8282,-4.9574,-3.0079,-3.0857,-1.9046,-2.6042,-3.9543,-2.2929,-1.5646,-1.6887,-2.1558,-3.1534,-3.7066,-4.1856,-4.0984,-4.0749,-3.3365,-2.7291,-3.1192,-3.5885,-4.1498,-4.155,-3.5604,-3.4255,-4.1244,-4.3516,-4.1798,-4.0183,-4.1253,-3.8957,-2.8752,NaN\nNaN,-2.7705,-3.0594,-3.0064,-3.0071,-3.1293,-2.6278,-2.9602,-2.6033,-2.4523,-1.9628,NaN,NaN,NaN,NaN,-3.5184,-2.2447,-2.8406,-3.4612,-1.7194,-3.5869,-4.6606,-2.8563,-0.71479,-2.178,-3.1263,-3.0329,-3.4492,-4.135,-4.2721,-3.6146,-3.225,-2.8477,-3.1985,-3.6388,-4.2417,-3.9376,-3.5425,-3.0566,-3.8843,-4.4811,-4.2815,-3.8016,-3.7189,-3.5863,-3.6658,NaN\nNaN,NaN,NaN,-4.1517,-4.5286,-3.0993,-1.4174,-2.8277,-3.7333,-2.4995,-1.2545,NaN,NaN,NaN,NaN,-2.5551,-0.95029,-1.3123,-2.8587,-1.1761,-2.736,-4.2085,-3.0466,-2.2706,-4.183,-4.2926,-3.9068,-3.4242,-3.8319,-3.8924,-3.282,-2.825,-3.0455,-3.2704,-3.9537,-4.6387,-4.6737,-3.7851,-3.2779,-3.6734,-4.3406,-4.0465,-3.1485,-2.8556,-2.6223,-4.4995,-5.1162\nNaN,NaN,NaN,NaN,-3.1458,-2.9249,-1.5393,-2.7211,-3.1999,-2.2522,NaN,NaN,NaN,NaN,NaN,-2.3703,-3.027,-4.7232,-2.3086,-2.4073,-1.9607,-1.0392,-3.6793,-4.228,-5.9993,-5.0438,-4.3868,-3.6938,-3.6287,-3.4222,-3.2615,-3.0075,-4.65,-4.7827,-4.0686,-4.4103,-5.4032,-4.2889,-3.6396,-3.8037,-3.804,-3.6311,-2.9332,-2.8145,-3.5938,-4.623,-4.959\nNaN,NaN,NaN,-4.0665,-4.7649,-2.3404,-3.5664,-3.4723,-3.3168,-3.2054,-3.6505,NaN,NaN,NaN,NaN,NaN,-1.8855,-3.761,-4.0659,-2.7349,-2.1062,-1.2154,-4.9616,-6.0983,-6.3525,-5.1339,-4.2828,-3.7395,-3.0668,-2.1938,-3.1327,-3.9716,-4.6189,-4.5367,-3.9327,-4.2073,-4.6277,-4.0768,-4.143,-4.06,-3.9381,-3.3977,-2.7559,-3.1535,-3.918,-3.7891,-3.3812\n-6.1743,-5.675,-5.5849,-6.4483,-4.6498,-1.6357,-4.58,-4.361,-2.9831,-3.0176,-3.1319,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.4556,-3.2559,-2.1756,-2.4179,-5.5045,-6.2954,-6.7541,-6.1174,-4.2515,-2.8828,-2.9763,-3.6078,-3.4125,-3.5353,-4.3333,-5.3824,-4.7277,-4.3044,-3.805,-3.2616,-3.9069,-4.2225,-4.1,-3.0697,-3.0409,-3.6498,-4.2799,-4.7345,-4.1932\n-6.1193,-4.869,-4.5869,-6.2034,-5.2548,-4.9569,-4.904,-5.0503,-5.7536,-4.5809,-3.9724,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.7604,-2.8647,-2.2628,-3.646,-4.3925,-4.4894,-3.8142,-3.5027,-3.4116,-4.3201,-4.0892,-3.7754,-3.2883,-3.1691,-5.0917,-5.1172,-4.3587,-3.9585,-3.2954,-2.9356,-2.8677,-3.0608,-3.1715,-3.7067,-3.9017,-4.1022,-5.6713,-3.8514\n-5.3384,-3.6717,-3.5738,-5.8343,-6.1187,-6.0245,-5.6118,-5.2073,-5.5283,-5.7099,-5.4779,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.0606,-2.4402,-1.9556,-1.4817,-3.1667,-3.8391,-3.4566,-3.443,-5.4648,-8.563,-9.6493,-6.3729,-3.7831,-3.8539,-4.9316,-5.4382,-4.3464,-3.5787,-3.257,-2.0337,-2.8136,-3.5459,-4.1091,-4.2828,-4.0129,-4.8018,-5.2525,-4.6179\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061106-061211.unw.csv",
    "content": "30.861,31.584,31.046,30.985,30.794,31.03,31.09,30.878,30.833,31.313,31.855,32.148,30.904,31.145,30.98,30.814,30.685,30.798,30.905,30.884,30.898,31.161,30.922,30.761,31.419,31.35,30.865,31.318,31.387,31.211,31.458,31.645,31.546,31.489,31.387,31.599,31.711,31.961,31.306,31.212,30.057,29.448,29.166,29.009,28.389,27.554,26.641\n31.766,32.108,31.575,31.05,31.109,31.292,31.352,30.654,30.824,31.025,31.233,31.185,31.008,31.094,30.983,30.701,30.517,30.443,30.66,30.582,30.464,30.769,30.497,30.69,30.937,30.735,30.864,31.216,30.976,31.219,31.552,31.361,31.779,31.474,31.37,31.604,31.896,31.761,31.162,30.768,29.556,29.491,28.742,28.057,27.351,27.172,26.801\n31.375,31.848,31.661,31.476,30.918,31.317,31.237,30.803,31.318,31.041,31.01,31.36,31.068,31.051,30.607,30.479,30.074,29.703,29.952,30.345,30.514,30.78,30.716,31.305,30.905,30.443,30.804,30.437,30.77,31.037,31.235,31.356,31.743,31.479,31.108,30.983,31.694,31.224,30.8,30.464,29.947,29.081,27.955,26.882,26.616,26.616,25.386\n32.353,31.452,32.135,31.856,31.237,31.266,30.762,30.384,30.554,30.967,31.426,31.509,31.143,30.763,30.083,29.68,29.627,29.757,29.818,30.34,30.691,30.92,30.936,30.783,30.673,30.743,30.703,30.539,30.639,30.98,31.4,31.118,31.384,31.324,31.037,31.54,31.797,30.796,30.005,29.727,29.051,27.884,26.924,26.437,26.363,26.116,24.599\n31.967,30.94,32.121,32.288,31.57,31.067,30.34,30.197,30.311,30.074,31.103,31.093,31.007,30.699,30.07,29.479,29.484,29.777,30.054,30.252,30.37,31.069,30.594,30.507,30.559,31.082,30.583,30.818,31.136,31.321,31.549,31.263,31.194,31.009,30.727,30.774,30.826,30.441,29.903,29.071,28.372,27.25,26.273,25.365,25.198,25.002,24.5\n32.03,32.27,32.499,31.85,31.818,31.579,30.802,30.667,30.563,30.67,30.97,30.832,30.991,30.933,30.776,30.037,29.691,29.753,30.367,30.152,30.429,30.802,30.768,30.972,30.855,31.022,31.058,31.095,31.333,31.635,31.279,31.091,31.088,30.708,30.504,30.481,30.46,30.259,29.687,29.112,28.327,27.641,26.537,25.094,25.266,23.169,22.931\n32.587,33.232,31.615,31.223,31.749,32.096,31.187,30.56,30.201,31.111,30.98,30.72,31.043,31,31.266,30.355,30.025,30.289,30.43,29.839,30.402,30.737,31.064,31.38,31.27,31.291,31.069,31.444,31.77,31.514,31.179,31.008,31.059,30.873,30.829,30.605,30.268,29.907,28.88,28.911,28.247,27.636,27.263,26.493,25.759,23.768,24.063\n31.519,31.639,31.009,31.355,31.666,32.101,30.934,30.479,30.379,31.128,31.035,30.952,31.481,31.143,30.995,30.502,30.295,30.404,30.395,30.445,30.984,30.832,30.885,31.623,31.049,30.946,31.074,31.431,31.415,31.305,31.207,30.75,30.904,30.731,30.777,30.359,30.3,30.05,29.447,28.755,28.626,28.226,28.338,27.246,25.935,24.254,24.497\n31.099,30.043,31.632,31.653,32.252,32.156,30.871,31.076,30.979,31.042,31.062,31.209,31.555,31.313,31.121,30.677,30.709,31.25,30.758,31.41,31.444,30.87,31.034,31.064,31.154,31.436,31.73,31.718,31.281,30.969,31.231,31.219,31.228,31.146,30.76,30.386,30.435,30.259,30.241,29.926,29.673,29.197,28.561,28.238,26.256,25.262,26.083\n31.284,31.032,33.148,31.687,32.133,32.02,31.107,31.253,30.888,31.373,30.724,31.301,31.296,31.082,30.91,30.805,30.66,30.818,30.833,31.25,31.164,31.006,30.991,31.195,31.357,31.953,31.949,31.967,31.514,31.599,31.415,31.381,31.873,31.402,31.179,30.878,30.835,30.81,30.733,30.641,30.562,30.167,29.557,29.152,27.267,26.631,26.809\n31.727,30.87,31.275,31.926,32.281,31.848,31.079,31.031,31.26,31.19,30.801,31.116,30.49,30.583,30.744,30.916,30.582,30.881,31.453,31.706,31.352,31.317,31.311,31.699,31.915,31.792,32.081,31.864,31.645,31.968,31.745,31.596,32.304,32.216,32.023,31.962,31.693,31.699,31.451,30.81,30.569,29.224,28.99,28.772,28.011,28.794,28.879\n31.711,30.818,31.707,32.82,32.355,31.521,31.521,31.189,31.425,30.989,30.707,30.569,30.518,30.379,30.467,30.872,30.821,31.234,31.751,31.866,31.761,31.87,31.49,32.23,31.987,31.83,32.108,31.691,31.918,32.268,32.462,32.9,32.839,32.906,32.874,32.009,31.635,31.403,31.021,30.128,29.941,28.181,28.087,28.29,28.477,29.194,NaN\n32.965,33.177,33.22,30.947,31.624,31.144,31.423,31.337,31.384,30.682,29.915,29.383,30.785,30.26,30.041,30.628,31.006,31.081,31.571,31.723,31.663,32.171,32.154,31.786,31.812,31.85,31.639,31.842,32.03,32.477,32.49,33.892,33.708,32.864,32.307,31.42,30.666,30.625,29.978,29.611,28.607,27.796,27.663,28.035,27.984,NaN,NaN\n32.872,33.661,32.301,30.439,32.199,32.107,32.113,31.52,31.159,30.785,29.777,29.943,30.687,30.236,29.874,30.53,30.399,30.728,31.131,31.556,31.649,32.036,32.246,31.729,31.931,31.903,31.836,32.068,32.033,31.88,32.127,32.785,32.557,32.048,31.744,31.154,30.872,31.133,30.174,28.752,27.126,27.467,26.678,NaN,NaN,NaN,NaN\n32.816,31.944,29.04,31.341,32.468,32.562,31.916,31.343,31.067,30.926,29.903,30.278,30.659,30.622,30.325,30.796,30.635,30.374,30.872,31.534,31.662,31.66,31.915,31.655,31.663,31.45,31.194,30.776,31.242,31.001,31.48,32.333,32.317,31.658,32.067,31.115,30.707,30.201,29.119,27.102,27.914,28.648,27.818,NaN,NaN,NaN,NaN\n31.695,31.81,30.113,31.745,33.135,31.933,31.667,31.964,31.251,30.695,30.206,31.337,30.844,30.229,30.128,30.409,30.427,30.357,30.873,31.15,31.514,31.422,31.123,30.672,30.814,30.635,30.023,29.559,30.601,29.934,31.619,31.828,31.183,30.751,30.944,30.663,31.65,31.06,30.174,27.429,28.87,28.695,28.635,NaN,NaN,NaN,NaN\n30.404,32.684,31.881,31.796,32.25,32.119,32.141,32.381,31.488,30.988,31.116,31.578,30.642,30.519,30.141,30.145,30.387,30.729,30.644,31.336,31.405,31.84,31.068,30.424,30.767,30.114,30.596,30.77,30.244,30.607,31.439,31.605,30.534,29.671,29.784,30.691,31.996,31.81,32.151,NaN,NaN,29.511,28.217,NaN,NaN,NaN,NaN\n31.558,32.859,32.311,32.44,32.028,33.031,32.611,31.776,31.742,31.75,31.639,31.314,30.753,30.791,30.235,30.226,30.817,30.851,30.014,31.939,31.642,31.96,32.352,31.909,31.506,31.836,31.911,31.766,30.712,30.72,31.422,31.186,31.484,30.017,30.35,31.767,32.053,31.073,30.849,NaN,NaN,NaN,29.411,28.711,NaN,NaN,NaN\n25.994,31.137,32.68,32.082,33.233,33.424,31.537,31.315,31.438,31.667,31.555,31.403,31.006,30.843,30.127,29.13,29.292,29.718,29.586,29.739,27.948,26.192,31.164,33.088,33.489,33.132,32.072,32.279,31.599,31.093,32.349,31.623,31.793,30.761,30.42,32.747,32.673,31.941,29.541,NaN,NaN,NaN,29.957,30.904,31.1,NaN,NaN\n28.749,31.428,31.165,31.374,32.983,34.052,31.59,32.073,31.406,30.809,31.123,30.756,30.568,30.696,30.44,29.97,29.141,29.857,30.106,29.967,29.594,29.175,29.672,33.257,33.892,33.173,32.64,31.964,31.753,32.535,32.931,32.177,31.724,31.929,33.321,32.712,32.659,32.676,28.814,28.592,29.813,31.437,30.619,31.365,30.821,30.058,30.728\n30.956,28.142,31.159,30.978,31.215,31.356,31.96,32.21,31.196,30.523,30.425,29.985,30.006,30.414,30.517,30.22,30.087,30.327,30.54,30.975,31.588,30.033,30.281,32.573,32.678,32.562,32.886,32.377,31.12,32.112,32.908,30.938,31.926,31.938,32.471,33.013,33.511,32.58,31.148,31.596,31.754,31.007,30.716,29.77,30.138,30.993,30.77\n30.397,29.535,30.445,30.507,30.144,29.94,32.548,31.818,31.633,30.743,30.131,29.957,30.285,30.501,29.75,30.669,30.572,30.879,30.881,32.48,32.93,32.635,33.085,33.332,33.222,33.151,32.651,32.289,31.252,31.318,31.708,30.661,31.629,31.843,32.258,32.747,32.123,32.898,31.898,31.153,32.978,31.235,30.766,30.418,30.044,31.324,31.276\n30.228,30.592,30.244,29.779,28.848,31.231,32.139,31.873,31.609,30.493,29.278,29.688,30.586,29.848,30.114,32.31,31.265,32.413,32.102,32.616,33.228,33.179,33.131,34.004,33.676,33.643,32.531,32.403,32.173,31.562,31.013,30.717,31.422,32.034,31.789,32.405,31.335,30.883,30.26,31.045,31.763,31.453,31.873,31.708,30.543,30.826,30.753\n29.64,29.294,29.899,29.996,31.483,31.899,32.415,32.009,31.168,30.297,29.964,30.358,30.61,30.454,31.184,32.743,32.727,33.26,33.261,32.858,33.533,33.679,33.548,33.404,33.275,33.316,32.776,32.559,32.119,31.504,30.226,30.698,31.024,30.945,31.654,31.577,31.64,31.31,30.635,33.125,32.549,31.527,31.17,31.137,30.03,31.05,30.698\n30.882,30.525,30.289,30.117,30.016,31.194,32.144,31.481,30.242,30.206,30.518,31.115,31.29,32.489,32.609,32.85,32.398,30.833,31.988,32.494,32.571,32.137,31.534,32.782,32.811,32.971,32.42,32.206,31.535,30.261,29.729,29.975,30.494,30.717,31.685,34.751,28.13,32.378,34.192,33.258,31.532,31.374,30.895,30.171,30.141,30.976,30.824\n31.547,31.515,30.673,30.191,30.053,30.222,30.885,30.85,30.681,30.953,30.668,30.561,31.261,31.584,31.714,32.396,30.817,30.704,31.595,32.08,31.726,30.927,31.965,32.238,32.225,32.457,31.164,30.175,30.008,29.766,28.957,28.705,32.861,28.278,30.892,36.262,30.392,31.228,33.085,32.567,31.937,32.033,31.59,31.266,30.959,31.589,31.867\n32.147,31.988,31.057,30.922,30.256,29.966,30.153,30.36,30.644,30.835,30.756,30.306,29.621,30.548,30.668,30.705,30.844,30.849,31.612,32.494,31.511,31.545,32.201,32.026,29.717,30.448,30.015,30.378,31.884,33.741,30.655,30.647,32.83,30.226,31.032,34.15,31.451,32.21,31.963,32.717,32.24,32.413,31.955,31.592,31.499,31.626,31.601\n32.574,32.62,31.715,30.8,31.252,30.918,31.616,29.801,31.009,31.34,31.152,30.472,30.062,30.156,29.839,29.082,29.313,30.327,31.221,31.475,30.485,31.364,31.973,32.381,29.55,30.489,30.7,30.747,31.931,32.834,NaN,NaN,NaN,34.421,33.415,32.663,32.171,31.077,31.935,32.491,32.346,32.146,31.769,31.505,31.595,31.398,31.027\n32.967,32.554,31.729,32.47,32.216,32.332,32.38,31.25,31.28,31.608,31.568,30.988,30.647,30.078,29.473,29.169,30.3,31.123,30.813,30.793,30.429,31.472,31.634,31.581,30.714,30.477,30.647,30.567,30.283,NaN,NaN,NaN,NaN,NaN,33.288,32.318,31.287,29.561,31.922,33.71,32.934,32.579,31.992,31.795,31.783,31.157,30.729\n32.519,32.273,32.666,34.093,32.8,32.33,31.591,31.176,31.472,31.824,31.73,31.165,30.039,29.58,29.802,30.347,31.215,31.303,31.424,30.855,31.252,32.15,31.833,31.518,30.589,30.477,31.01,30.828,NaN,NaN,NaN,NaN,NaN,34.353,32.992,31.774,31.351,30.568,33.836,33.385,32.42,32.629,32.36,32.344,32.048,31.234,30.947\n31.927,31.67,32.404,32.041,32.164,32.204,32.339,31.755,31.601,32.014,31.916,30.916,29.931,29.583,30.051,31.23,31.964,31.089,30.937,31.621,32.276,32.312,30.76,31.509,32.345,31.987,31.647,31.224,NaN,NaN,NaN,NaN,NaN,33.256,33.43,32.7,32.954,33.392,34.172,33.133,33.204,34.036,33.13,32.815,32.248,31.503,31.084\n31.228,30.877,31.545,31.078,31.099,31.572,32.298,32.503,31.725,31.742,32.317,31.509,30.993,30.42,31.994,31.889,31.554,31.724,31.602,31.572,33.67,32.299,30.711,35.297,34.361,33.456,32.826,31.936,NaN,NaN,NaN,NaN,33.097,33.429,34.286,34.11,34.23,33.313,33.688,33.768,33.498,33.644,32.667,32.998,32.489,31.369,30.753\n31.328,31.442,31.889,31.806,31.417,31.963,32.223,32.664,32.434,31.652,31.697,30.362,29.586,30.652,31.64,31.069,30.867,31.872,29.912,30.302,32.387,31.603,30.218,33.931,33.611,33.056,32.895,33.213,NaN,NaN,NaN,NaN,33.888,34.503,34.314,33.532,33.27,31.635,33.008,33.58,33.171,32.864,32.577,32.973,31.739,31.607,31.465\n32.5,32.688,32.298,32.289,32.221,31.832,33.099,33.119,32.427,32.054,31.478,31.015,29.557,31.337,32.323,31.71,30.693,31.824,31.121,29.152,31.918,33.082,32.136,31.563,32.289,32.188,33.303,NaN,NaN,NaN,NaN,33.96,34.488,34.328,33.658,33.303,33.015,31.452,32.572,33.561,32.877,32.429,32.449,32.887,32.213,31.726,31.749\n33.166,32.139,32.295,32.088,31.608,31.917,32.902,33.24,32.112,32.124,31.692,30.762,30.341,30.876,31.815,32.328,32.328,32.038,31.77,31.199,32.851,32.906,32.68,31.698,31.644,31.55,33.181,NaN,NaN,NaN,34.143,34.083,33.94,33.648,34.291,33.673,33.508,32.574,32.889,33.376,31.865,31.126,31.901,32.814,32.535,31.736,31.943\n32.639,33.185,32.92,32.314,32.246,32.215,32.404,32.867,32.328,31.994,31.786,31.625,31.68,30.795,29.594,32.846,32.354,32.517,32.528,32.687,33.562,33.765,32.89,31.375,31.362,31.435,32.128,32.756,35.573,34.935,34.908,35.211,34.537,34.593,35.139,35.272,34.665,33.983,33.436,32.932,31.986,31.725,32.028,32.664,32.186,31.877,32.646\n32.063,32.87,33.091,32.636,33.517,33.327,32.299,31.798,32,31.471,32.553,32.73,32.229,30.084,30.416,32.354,32.308,32.151,31.949,32.09,32.573,34.894,34.105,32.39,31.851,30.528,29.518,29.552,35.827,34.756,34.241,34.819,34.13,33.213,33.994,36.21,35.662,35.006,33.476,33.209,32.83,32.332,32.04,32.044,31.825,32.152,32.179\n32.141,32.686,32.89,32.512,32.572,32.653,31.737,31.565,31.851,31.594,31.45,33.006,32.577,30.978,31.495,31.961,32.441,31.412,31.914,32.193,34.258,NaN,NaN,NaN,NaN,NaN,31.58,31.935,34.92,33.21,33.751,34.162,34.749,33.559,34.603,37.112,35.495,34.827,34.249,33.406,32.568,32.115,31.354,31.315,30.899,30.427,30.616\n33.4,33.18,32.454,32.045,31.865,31.852,31.083,31.551,31.596,31.993,32.876,33.223,32.914,32.7,32.474,31.613,31.248,30.175,31.689,33.047,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.632,34.546,34.175,34.106,34.352,34.619,35.013,35.045,34.304,34.559,34.354,33.293,32.315,32.246,31.95,31.317,29.936,28.58,30.235\n34.022,32.452,32.709,31.968,31.897,32.013,31.412,31.909,32.78,32.562,32.572,32.472,32.728,32.889,32.054,31.923,32.036,32.138,32.045,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.267,34.895,34.132,35.613,35.419,35.446,34.891,34.168,33.688,33.634,32.795,33.834,34.088,32.37,31.899,30.939,29.764,29.391,30.535\n33.643,32.415,32.765,32.027,32.175,32.138,32.082,32.357,32.824,32.793,32.06,32.384,33.276,33.075,31.96,32.054,32.83,34.333,34.366,32.599,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.729,34.572,35.011,35.379,36.452,36.446,35.541,34.233,33.411,34.11,34.855,32.701,34.056,33.897,33.028,32.004,31.279,30.339,30.256,30.645\n32.967,32.516,31.784,31.576,31.634,32.005,32.291,31.985,32.169,32.315,31.453,34.421,35.617,34.039,33.45,32.215,30.5,33.031,34.809,33.404,NaN,NaN,NaN,NaN,NaN,NaN,33.924,35.623,35.551,35.561,35.083,35.531,35.908,35.067,34,33.361,33.289,33.709,33.462,33.888,34.02,32.951,31.592,30.942,29.809,30.276,30.766\n32.167,31.632,31.583,31.918,31.777,31.835,32.187,31.141,32.301,32.942,33.344,33.788,35.329,33.909,33.824,34.112,32.425,31.252,32.32,31.097,31.953,32.506,NaN,NaN,NaN,NaN,NaN,35.959,36.614,36.299,34.615,35.775,34.633,34.025,33.182,32.82,32.975,33.116,33.145,33.354,33.493,32.327,31.477,30.682,30.028,29.45,31.119\n31.383,31.217,31.345,31.793,31.335,31.994,32.099,31.261,32.456,33.177,32.961,32.337,32.627,32.276,30.914,30.844,32.509,32.309,32.01,32.153,31.373,32.79,32.81,NaN,NaN,NaN,NaN,35.743,35.596,35.651,36.04,35.842,34.613,33.36,31.616,32.859,33.338,33.893,33.072,32.865,32.444,32.329,31.05,31.371,32.648,32.658,31.142\n30.906,30.897,31.025,31.225,31.672,33.116,32.966,32.609,32.35,31.616,31.05,31.634,31.249,30.852,29.89,29.705,31.999,32.519,33.125,30.595,31.031,32.193,34.194,33.895,34.795,36.287,35.781,34.696,33.778,34.409,35.945,34.61,35.843,33.918,31.533,34.484,35.282,34.864,32.862,32.843,32.093,31.925,31.034,30.607,31.699,33.294,32.566\n30.807,30.652,30.832,31.119,31.394,31.85,31.854,31.789,30.98,30.081,30.109,30.447,30.137,29.71,29.577,30.288,31.854,33.592,34.714,31.928,29.451,30.234,32.725,33.749,32.518,33.696,33.764,34.272,33.486,34.127,34.169,34.465,34.729,33.449,33.602,34.57,35.347,33.804,33.111,32.717,31.589,31.068,30.685,29.623,30.316,32.513,34.344\n31.118,30.597,30.599,31.121,30.89,30.816,31.01,30.867,30.024,29.777,29.806,29.688,29.528,29.395,29.88,30.248,31.462,32.154,33.67,32.597,28.414,28.782,31.178,33.116,33.177,32.294,32.319,33.78,33.398,33.735,32.725,33.036,33.398,33.091,34.212,34.599,33.491,33.104,32.995,32.058,31.044,30.534,30.61,30.372,30.464,31.549,33.122\n30.737,30.46,30.492,30.868,30.858,30.496,30.638,30.19,29.573,28.941,28.995,29.169,29.258,29.448,29.809,30.555,31.35,31.667,31.294,30.818,29.697,29.026,31.01,32.294,32.496,32.229,31.456,32.501,32.169,32.75,33.177,33.288,33.996,34.137,34.466,34.639,33.492,32.707,32.606,30.911,30.272,29.961,30.819,31.08,31.551,31.13,31.771\n30.822,30.557,31.088,30.999,30.741,30.282,30.106,29.782,28.578,28.874,29.005,29.067,29.077,29.017,29.188,29.76,30.382,31.013,30.655,29.972,28.431,28.997,30.8,31.723,32.083,31.646,30.817,31.835,32.019,32.585,32.751,33.438,34.155,35.338,36.401,35.089,35.056,34.542,32.435,30.416,29.445,29.13,31.122,31.046,31.233,31.294,31.755\n30.774,30.662,30.776,30.342,30.159,29.889,29.709,29.478,28.767,29.469,29.465,29.156,28.903,28.651,28.995,30.037,29.988,29.622,29.991,30.517,29.296,34.001,30.937,30.158,28.861,28.54,28.113,30.2,31.344,32.389,32.28,34.117,33.886,34.642,34.437,34.1,35.041,33.935,32.295,31.132,30.173,30.312,31.011,30.848,31.826,32.135,32.477\n30.828,30.461,30.183,29.909,30.157,30.226,30.187,30.021,29.648,29.49,29.428,29.07,29.298,29.401,29.786,30.528,30.708,27.473,29.602,30.683,33.067,32.521,30.465,29.807,28.948,28.338,26.41,27.942,30.554,31.46,32.128,33.727,33.36,32.796,32.388,32.471,32.643,32.399,31.701,31.029,30.47,30.348,30.55,31.482,32.156,32.209,31.779\n30.869,30.312,30.583,30.177,30.128,30.233,30.272,29.964,29.536,28.295,29.167,29.711,29.562,29.793,29.661,31.442,32.957,30.234,29.488,29.102,33.46,31.517,30.798,30.192,29.761,28.62,26.815,27.106,29.868,31.051,31.564,31.831,32.525,31.985,31.984,32.366,31.984,31.599,31.413,30.982,31.024,30.972,30.708,33.139,32.266,32.134,31.692\n30.414,30.354,30.483,29.813,30.102,30.462,30.475,29.884,29.191,28.14,29.806,30.233,30.395,30.339,29.916,29.41,30.12,29.419,27.806,26.965,33.313,31.756,31.738,31.438,30.741,30.277,29.978,28.501,28.756,30.405,31.223,31.182,32.086,31.685,32.373,31.909,31.868,32.024,31.841,31.715,31.677,30.561,30.405,31.367,31.489,32.549,31.489\n30.302,29.723,29.364,29.166,29.37,29.845,30.391,29.347,28.346,28.376,29.063,30.756,32.568,32.275,31.285,29.43,29.472,29.523,30.85,31.746,31.333,31.155,30.599,31.195,30.981,31.789,30.797,27.862,27.268,28.749,31.206,31.533,32.87,33.113,32.902,33.593,32.716,32.046,31.906,32.453,31.723,31.43,30.858,31.179,32.45,33.084,31.83\n29.897,29.331,29.701,28.819,28.841,30.389,30.564,29.153,28.392,28.869,29.187,30.651,32.601,33.138,31.457,30.924,30.438,29.623,31.998,32.519,31.501,30.64,29.772,30.491,30.503,30.85,29.387,28.459,28.077,31.016,31.74,32.841,32.511,32.457,32.218,32.734,31.949,31.429,31.593,31.966,31.598,31.931,31.711,32.163,32.613,32.322,31.281\n30.047,29.832,30.158,29.4,28.23,31.781,31.209,29.039,28.716,28.917,29.316,29.415,32.157,32.766,31.521,32.438,31.341,30.917,31.081,31.446,31.508,30.852,30.083,30.092,29.525,29.552,28.674,28.669,28.776,32.085,32.044,32.918,32.497,32.198,32.214,32.015,31.298,31.152,31.071,30.88,30.855,31.287,31.421,31.937,31.938,31.304,31.633\n30.055,30.007,30.066,28.871,27.354,30.57,30.204,29.069,28.058,27.028,28.432,28.2,31.5,31.996,32.474,34.055,33.812,31.552,31.305,31.557,32.017,31.058,30.526,29.602,28.068,27.898,26.466,27.028,29.062,31.153,31.671,32.14,32.411,32.15,32.196,31.548,31.264,31.166,30.443,28.717,30.213,31.234,31.03,31.577,31.849,32.336,32.039\n31.681,31.867,31.508,29.642,28.768,27.009,29.009,28.596,27.823,27.877,29.665,28.959,30.878,31.676,32.399,32.275,31.987,31.432,31.604,31.537,30.705,32.306,32.107,30.376,28.738,28.668,27.594,28.217,31.368,31.597,31.138,31.226,31.484,31.92,32.082,31.831,31.235,30.847,30.264,30.162,30.888,30.966,30.657,31.232,31.942,32.144,31.028\n32.631,33.013,32.344,30.443,29.735,27.804,29.77,28.585,26.557,25.577,27.201,28.489,29.952,30.257,31.925,31.222,31.023,31.618,31.534,30.171,29.765,31.782,32.083,31.22,30.396,29.479,28.99,29.027,30.493,30.704,31.232,32.42,31.842,31.672,32.088,31.305,30.705,30.434,30.257,30.244,30.156,30.379,30.543,30.776,30.422,29.752,29.7\n32.093,32.473,31.261,32.202,32.249,31.819,31.394,30.261,26.677,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,33.782,31.477,30.255,30.135,31.404,32.542,32.276,31.062,30.058,29.258,29.327,29.913,30.801,32.178,33.882,33.08,31.704,31.563,31.266,31.079,30.788,30.23,30.268,29.838,29.99,30.201,30.992,30.551,29.727,29.849\n31.738,32.028,30.9,31.82,33.019,32.667,32.422,30.679,30.799,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,33.369,31.116,31.66,31.592,32.017,32.086,31.898,31.323,29.955,28.84,29.269,30.153,30.953,30.662,31.456,32.574,31.988,31.897,31.784,31.982,31.046,31.205,31.115,30.452,30.484,30.771,30.649,30.044,29.508,29.18\n31.908,30.919,29.604,29.419,30.717,30.975,31.054,29.174,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.451,31.107,32.309,30.588,30.03,30.226,31.007,32.07,31.554,30.798,31.697,30.829,30.327,29.945,31,31.7,31.939,32.024,31.698,32.23,31.964,31.812,31.387,30.693,30.819,30.627,29.735,29.869,29.51,29.901\n31.759,30.255,30.111,30.151,30.636,29.708,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.961,30.613,31.136,30.52,29.329,29.391,30.675,32.239,31.559,31.59,30.894,30.219,30.437,30.054,30.779,31.246,31.396,31.137,31.611,31.982,32.364,31.987,31.092,30.605,30.095,29.764,29.363,31.162,30.345,30.097\n29.575,29.47,28.317,29.315,29.271,30.397,30.983,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.887,31.384,32.951,30.853,30.56,30.688,31.29,30.269,28.67,28.432,28.693,29.124,28.775,29.848,30.933,31.379,30.986,31.23,32.102,31.443,31.285,31.327,30.512,29.953,29.43,29.647,30.605,31.312,30.25,29.193\n28.989,27.513,28.074,30.016,30.914,30.526,30.33,29.59,29.766,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.803,31.322,32.986,31.329,30.383,30.816,29.796,29.279,28.302,28.339,28.142,27.493,28.455,30.412,31.533,31.435,31.355,31.768,31.51,31.042,30.743,30.628,30.35,29.836,29.438,29.697,30.699,31.115,30.082,28.057\nNaN,28.343,29.411,29.518,30.22,30.606,30.235,29.995,30.825,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.995,31.528,31.228,29.703,29.425,30.437,30.252,30.156,29.737,28.607,28.123,28.48,28.917,29.755,30.098,30.726,30.751,32.459,31.156,30.576,30.391,30.733,30.707,30.45,30.17,30.256,30.675,30.118,29.253,27.27\nNaN,NaN,31.623,29.089,30.187,30.845,30.202,30.405,30.118,29.248,27.68,NaN,NaN,NaN,NaN,NaN,NaN,31.688,32.217,31.493,30.119,31.027,31.448,29.952,30.482,30.769,30.14,29.807,29.28,29.022,29.037,30.617,30.861,30.977,32.372,31.241,30.603,30.267,30.406,30.807,30.125,30.607,29.814,29.78,30.127,29.616,28.335\nNaN,NaN,NaN,NaN,NaN,NaN,30.837,32.25,29.965,29.006,29.261,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.159,31.605,31.457,31.364,30.601,30.315,30.256,30.176,29.734,29.026,29.346,29.562,29.755,30.094,30.435,30.993,31.825,32.183,31.04,30.552,30.2,30.203,29.983,30.144,29.987,30.18,29.86,29.143,28.14\nNaN,NaN,NaN,NaN,NaN,NaN,32.412,32.815,31.954,31.218,29.851,NaN,NaN,NaN,NaN,NaN,NaN,32.604,31.449,31.185,31.953,30.932,30.051,30.136,29.53,28.574,28.128,28.259,29.08,30.633,31.018,30.256,30.723,31.068,31.528,31.867,31.393,30.63,30.418,30.106,30.11,30.523,31.108,30.82,29.785,28.587,27.977\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,32.386,32.825,30.876,29.588,30.85,NaN,NaN,NaN,NaN,NaN,30.548,30.699,31.356,31.819,30.937,30.519,29.805,28.768,27.647,28.582,29.426,30.271,31.302,31.751,32.352,31.753,30.911,30.914,30.669,31.088,30.635,30.548,30.512,30.353,30.484,30.913,30.213,29.381,28.99,28.866\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,31.655,30.607,28.401,29.211,30.462,30.488,27.853,NaN,NaN,NaN,33.573,32.705,31.773,31.637,30.392,31.041,30.826,29.621,28.459,29.564,30.729,30.686,30.907,31.829,32.139,31.44,30.809,30.132,30.337,30.016,30.37,31.372,31.288,30.437,29.901,30.008,29.691,29.197,28.617,29.219\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,29.93,29.79,28.555,28.969,30.382,30.104,32.729,33.282,32.621,NaN,NaN,NaN,NaN,30.943,30.401,31.052,30.684,29.311,29.241,29.752,31.025,30.423,31.255,31.592,31.341,30.796,30.302,29.218,30.4,31.059,30.628,31.029,30.654,30.369,29.917,29.757,29.375,28.679,28.409,28.668\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061106-070115.unw.csv",
    "content": "22.167,21.82,21.613,21.786,22.373,21.254,21.292,21.72,22.049,21.396,21.274,21.395,20.532,20.654,20.816,20.791,20.798,19.801,19.612,19.438,19.248,20.046,20.841,21.053,19.957,20.344,19.474,19.142,19.174,19.683,19.739,20.661,20.069,19.928,19.959,20.411,21.767,22.378,24.108,22.972,22.079,21.759,22.309,22.272,21.487,22.396,22.489\n22.805,23.134,22.58,22.159,22.045,21.085,21.051,21.89,21.352,21.204,20.704,20.219,20.231,20.182,20.436,20.366,20.083,19.699,20.049,19.604,18.821,20.086,20.552,20.137,19.906,19.834,19.767,19.884,19.545,19.336,19.505,19.96,19.967,19.445,19.674,21.112,22.306,22.771,23.042,23.099,21.913,21.809,21.597,22.149,22.241,21.705,21.361\n22.999,23.726,22.979,22.061,21.948,21.164,21.322,21.019,21.355,20.804,20.777,20.77,20.323,20.063,20.735,20.226,19.966,19.467,19.975,19.644,20.268,21.177,21.386,21.215,20.061,19.847,19.594,19.111,19.166,18.975,19.33,19.786,19.331,19.432,19.959,20.533,21.329,21.686,21.105,21.158,22.143,22.116,21.911,22.578,22.428,21.122,20.749\n23.493,23.05,22.777,21.873,21.776,21.607,21.554,21.762,21.2,20.912,20.773,20.856,20.445,20.271,20.058,19.797,18.946,19.407,19.989,19.461,20.142,20.57,20.437,19.925,19.659,19.392,19.122,19.302,19.028,18.716,19.016,19.424,19.69,19.528,20.19,20.666,21.105,21.692,20.663,21.146,21.881,22.741,22.69,22.666,22.705,21.922,20.674\n22.359,22.81,23.026,22.583,21.918,21.569,21.591,22.437,22.557,22.31,21.643,21.31,20.957,20.457,20.188,19.832,20.134,19.774,19.798,20.287,21.194,20.559,20.239,19.869,19.632,18.758,19.134,19.187,19.687,19.505,19.208,19.417,19.618,19.441,20.38,20.952,21.411,21.499,21.223,21.357,21.831,22.766,22.089,22.207,21.727,21.581,21.19\n21.943,21.56,22.824,22.482,22.246,21.928,21.862,21.863,21.817,21.247,21.498,21.455,21.146,20.845,20.48,20.453,20.653,20.391,20.133,19.84,20.91,20.831,20.299,19.672,19.663,19.523,19.234,19.416,19.522,19.062,19.364,19.676,19.967,19.429,19.887,20.311,21.141,21.899,21.918,22.367,22.859,22.574,21.978,21.919,21.222,21.125,21.716\n21.778,21.448,23.075,22.982,22.412,21.946,22.248,21.737,21.369,22.643,21.935,21.429,20.697,20.596,21.18,21.067,21.293,20.826,20.358,20.219,19.974,19.783,18.976,19.6,20.05,19.591,19.162,19.18,18.874,19.034,19.412,20.307,20.288,19.642,20.194,20.556,20.695,21.178,21.762,22.088,21.493,21.707,21.746,21.835,21.863,22.035,23.728\n22.282,22.298,22.775,22.502,22.725,22.362,22.328,21.234,21.062,22.719,21.79,21.827,22.07,20.553,20.817,20.918,21.163,20.701,20.776,20.729,20.007,20.609,19.879,20.044,20.303,19.663,18.919,18.798,19.227,19.608,19.293,19.773,20.2,19.233,19.386,20.112,20.775,21.174,21.341,21.702,21.633,22.022,21.877,22.362,22.497,22.377,23.69\n22.657,22.5,22.703,22.723,22.668,22.619,22.258,21.347,21.51,22.75,23.501,22.817,21.565,20.844,20.688,20.788,20.749,20.668,21.683,21.328,20.851,20.386,19.857,20.048,19.809,19.7,20.359,20.061,19.429,18.986,18.84,18.896,18.972,19.458,19.834,20.317,21.125,22.126,21.879,22.278,22.089,21.585,21.6,21.739,22.507,22.647,22.305\n22.711,22.514,22.557,23.109,22.526,22.502,22.617,22.069,22.242,22.742,23.417,22.679,21.33,21.312,21.025,21.213,20.613,20.781,21.869,21.364,20.672,21.126,19.755,19.785,20.227,19.974,19.876,20.314,19.283,19.313,19.313,19.566,19.065,20.366,21.056,21.003,21.34,22.091,22.077,22.377,21.941,21.485,21.495,21.472,21.818,21.867,21.885\n22.514,22.298,22.099,23.645,23.036,22.455,22.938,22.474,22.47,22.934,23.039,21.796,21.237,21.769,21.244,20.137,21.528,21.583,21.53,21.635,21.473,20.685,19.689,19.817,20.036,19.501,19.642,19.666,19.399,19.608,19.495,19.148,19.093,20.332,20.69,21.289,21.463,21.542,21.585,21.59,22.06,21.634,21.394,21.742,22.238,21.955,21.907\n22.562,22.509,23.446,24.141,23.148,23.057,22.522,22.726,22.134,22.694,21.714,21.852,21.583,21.758,21.636,21.547,22.162,21.472,21.278,21.464,21.413,20.358,19.79,19.984,19.7,19.491,19.074,19.417,19.262,19.429,19.339,19.279,19.307,19.882,20.318,19.927,19.992,20.525,21.292,21.33,21.513,21.447,20.901,21.451,22.173,22.479,22.538\n23.175,23.299,23.586,23.562,23.545,23.072,22.248,23.273,22.277,22.406,21.994,22.096,22.014,22.589,22.441,22.209,21.946,21.857,21.514,21.082,20.523,20.28,19.412,19.751,19.555,19.815,19.61,19.802,19.417,19.632,19.261,19.433,19.633,19.487,19.596,19.533,20.224,20.336,21.145,21.33,21.398,21.233,20.683,20.684,22.013,22.469,23.278\n23.288,23.766,24.225,24.295,22.856,22.687,21.992,22.823,22.282,22.333,22.081,22.247,22.756,23.196,22.256,22.868,22.378,22.219,21.778,21.249,20.609,20.416,19.677,19.4,19.663,19.23,19.461,19.203,19.297,19.699,19.521,19.539,19.279,19.682,19.895,19.908,20.243,20.859,21.551,20.961,20.857,20.573,20.423,20.455,21.089,22.383,23.44\n23.056,23.941,25.475,23.284,22.284,22.359,22.668,22.422,22.588,21.824,21.656,21.447,21.443,21.961,22.162,22.273,21.929,21.842,21.508,21.216,20.54,21.14,19.511,19.023,19.197,19.022,19.274,19.743,20.203,19.843,19.473,19.693,19.912,19.987,19.961,19.64,20.179,20.219,20.364,20.201,20.471,20.158,20.032,20.463,20.492,21.261,21.762\n22.012,23.257,24.564,23.123,22.305,23.241,22.513,22.62,22.499,22.131,21.992,22.372,21.583,21.713,22.39,22.394,21.844,20.913,20.667,20.864,20.999,20.785,19.756,19.337,19.097,18.855,18.984,19.285,19.562,19.206,19.037,18.885,19.401,19.003,19.055,19.25,19.532,19.823,19.955,20.455,20.288,20.496,20.117,20.562,20.81,21.361,21.869\n21.391,22.467,23.27,22.912,22.232,22.699,22.605,22.857,22.654,22.781,22.642,22.307,21.901,21.686,23.207,21.96,21.131,20.673,20.333,20.962,21.671,20.606,19.308,19.436,18.991,18.81,18.597,18.597,19.031,19.461,19.002,18.382,20.159,19.968,19.302,19.079,19.52,19.722,20.107,20.203,20.376,21.093,21.021,21.129,21.566,21.98,22.016\n21.505,22.629,22.967,22.496,23.104,23.35,23.302,23.007,22.848,22.92,22.356,22.088,21.854,21.62,21.864,21.856,21.701,21.717,20.949,21.938,21.334,20.313,19.493,19.289,18.843,18.773,18.677,18.309,18.32,18.834,19.013,18.705,18.376,19.529,19.274,18.565,18.5,19.331,19.809,19.874,19.871,20.528,21.377,21.202,21.458,21.226,21.418\n22.212,22.922,23.681,23.477,23.53,25.952,24.111,22.754,22.745,22.961,22.619,22.271,22.063,21.542,22.053,21.568,21.532,21.131,20.4,19.205,19.097,19.242,18.988,18.89,18.838,19.014,18.916,18.72,18.118,18.914,18.874,19.482,18.24,17.494,18.146,18.946,18.705,19.105,20.687,19.808,19.623,19.631,21.119,21.53,21.97,21.907,21.549\n23.08,23.101,23.139,23.011,23.101,23.097,22.841,23.164,23.371,23.233,22.9,22.68,22.432,22.068,22.451,22.108,21.496,20.773,20.413,20.129,18.253,18.117,19.192,18.642,18.88,19.246,19.032,18.951,19.049,18.319,18.715,19.411,18.966,19.079,19.485,18.462,18.993,18.985,20.294,20.428,20.358,20.225,20.77,21.177,22.134,22.136,22.15\n22.809,24.095,23.46,22.93,22.629,22.785,23.432,22.916,23.65,22.838,23.241,22.523,22.313,22.299,21.445,20.89,20.69,20.728,20.544,20.538,20.399,19.923,19.648,19.342,19.456,18.945,18.755,17.898,18.26,18.19,18.11,19.089,19.34,19.352,19.523,19.262,19.345,19.09,19.998,20.018,19.992,20.311,20.873,21.795,22.062,21.975,21.612\n22.837,23.989,23.391,23.291,23.315,23.161,23.79,23.29,23.292,22.65,22.401,22.188,22.32,22.026,21.71,21.597,20.997,20.658,21.025,21.437,20.056,20.175,20.648,19.463,18.935,18.643,18.804,18.778,17.685,17.723,17.94,19.13,18.755,18.821,19.464,19.183,19.308,19.888,20.069,19.874,19.307,20.485,21.481,22.103,22.277,22.005,21.678\n22.633,23.252,22.75,22.992,23.091,22.736,22.632,23.05,23.428,23.146,22.147,21.184,21.746,22.616,22.633,22.463,21.542,21.199,20.689,21.007,19.664,19.528,19.832,20.714,19.698,19.224,19.326,19.407,18.559,18.12,18.103,18.099,18.216,18.236,19.184,19.188,19.486,19.609,19.615,19.831,19.966,20.81,21.713,22.342,21.555,21.474,21.525\n21.216,22.466,22.371,22.984,23.483,22.791,22.768,23.135,23.773,23.762,22.975,23.371,22.263,22.973,23.228,23.781,21.823,21.649,21.186,20.784,20.755,20.998,20.74,20.8,20.057,19.562,18.858,19.563,19.173,18.734,18.198,18.487,18.857,18.912,19.241,19.395,19.639,20.016,20.043,20.563,20.651,21.185,21.699,21.74,21.11,21.313,21.509\n21.366,21.799,22.321,23.09,23.304,22.752,23.335,23.998,24.404,24.227,23.777,22.942,22.093,23.591,23.224,22.098,21.233,21.475,21.932,21.888,22.053,22.098,21.673,21.46,20.265,19.931,20.12,20.516,19.974,19.56,19.794,20.341,19.823,18.646,19.403,20.072,18.834,19.688,20.21,20.226,20.631,21.151,21.231,20.9,20.925,21.491,21.789\n22.286,21.81,22.721,23.342,22.99,23.704,22.999,23.181,23.856,24.619,23.868,23.236,23.276,23.875,23.321,22.334,21.743,21.558,22.037,22.443,23.038,23.385,22.301,22.132,21.456,20.672,20.701,20.168,19.482,19.567,19.836,20.77,20.08,19.519,19.624,20.471,19.724,19.542,19.472,19.042,19.878,20.726,21.197,21.317,21.264,21.985,22.411\n22.2,22.182,22.962,23.896,23.005,22.642,22.624,22.931,23.498,24.136,24.343,23.712,24.411,23.758,23.277,22.133,21.476,22.193,23.244,23.782,23.652,23.474,22.527,21.797,20.519,20.508,20.981,20.758,20.381,19.671,19.329,19.224,19.313,19.405,19.492,19.755,19.821,19.768,19.401,18.969,19.944,20.615,21.163,21.686,22.042,22.339,22.409\n22.008,21.87,21.861,22.379,22.647,23.619,24.922,23.499,24.24,23.705,24.281,25.02,24.462,23.782,23.376,23.6,22.961,23.016,23.151,23.582,24.085,23.899,22.99,21.258,21.02,20.855,20.84,20.866,20.03,20.003,19.561,18.875,18.145,18.641,19.228,19.325,19.471,19.074,19.326,19.729,20.565,21.123,21.13,21.717,22.183,22.524,22.3\n22.452,22.388,22.348,23.521,23.155,24.278,25.794,26.219,25.096,23.778,23.508,23.848,23.59,23.233,23.185,23.274,22.296,22.066,22.173,22.588,23.396,23.724,23.532,23.333,22.132,21.381,21.548,20.978,20.596,20.788,20.179,19.393,19.01,18.788,19.484,19.413,19.029,18.544,18.485,19.224,20.814,21.185,20.821,21.739,22.309,22.314,22.008\n22.212,22.162,21.772,22.711,23,24.119,24.762,25.331,25.095,24.582,23.679,23.323,23.532,22.566,22.569,22.359,21.822,21.218,21.241,21.968,22.759,23.436,23.964,23.21,22.433,22.211,22.144,21.812,21.212,20.663,19.994,19.059,18.499,18.518,19.951,19.864,18.982,18.591,18.517,19.74,21.005,20.373,20.33,21.267,22.151,22.024,21.464\n20.127,22.446,21.999,22.194,22.925,23.136,23.8,23.925,24.149,23.764,22.47,22.455,23.186,22.367,22.433,21.888,21.438,22.002,22.399,22.404,22.363,22.31,23.238,22.822,22.054,21.713,21.623,21.289,20.428,20.35,19.925,19.457,19.31,19.468,20.2,20.164,19.572,19.476,19.747,20.242,20.056,19.63,20.133,21.38,22.334,21.929,22.056\n20.812,23.149,23.527,23.453,22.37,22.311,22.239,22.611,24.316,23.157,20.878,21.658,22.269,22.429,21.213,21.151,21.665,22.04,22.021,22.02,23.912,23.515,23.075,22.239,21.999,21.513,21.298,20.786,18.269,18.032,19.626,20.195,20.114,19.838,20.232,20.018,19.488,19.7,19.633,20.249,20.468,20.003,20.531,21.531,21.653,21.465,21.724\n21.862,22.868,23.5,23.493,22.787,21.07,22.467,23.683,23.636,23.382,21.723,20.233,19.76,20.031,19.699,20.829,21.925,21.798,21.509,21.897,22.902,23.504,23.723,21.941,22.126,21.795,NaN,NaN,17.05,17.072,19.501,21.061,21.632,21.841,20.745,20.17,19.978,20.063,19.667,20.329,20.657,20.844,21.146,21.298,21.288,21.59,21.512\n21.7,21.912,23.595,23.387,22.118,21.378,22.181,23.634,24.179,24.72,22.86,21.871,20.042,20.571,18.928,19.699,20.889,20.154,21.417,21.068,21.575,22.405,22.469,22.307,22.03,22.525,NaN,NaN,NaN,20.399,21.863,22.232,22.81,22.238,21.394,20.755,20.274,20.435,20.495,20.09,20.759,21.064,21.296,21.148,21.443,21.682,21.489\n21.041,22.273,22.38,24.037,24.329,22.93,20.934,22.971,23.71,23.554,23.26,21.884,21.603,21.031,18.715,19.265,19.905,20.684,21.282,21.624,22.953,23.821,23.729,23.628,NaN,NaN,NaN,NaN,NaN,23.02,22.463,22.444,22.902,22.081,21.563,20.656,20.384,20.696,20.404,20.34,20.438,20.398,20.789,20.869,21.372,21.76,21.53\n19.637,20.011,20.641,22.368,16.61,19.75,20.778,23.346,23.218,22.86,21.75,21.292,19.594,17.267,15.439,16.72,20.496,21.762,22.538,23.553,25.21,26.062,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.467,22.816,22.78,22.782,22.018,21.54,20.844,20.638,20.312,20.477,20.762,20.803,20.928,21.092,21.192,21.551,21.544,21.636\n20.141,20.875,21.206,20.997,19.672,21.811,21.435,20.987,20.875,21.238,21.277,20.349,19.707,17.096,17.35,18.949,21.754,23.617,25.469,25.465,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.082,22.842,22.712,22.33,22.53,22.271,21.419,20.831,20.416,20.733,20.769,20.611,20.975,20.885,21.311,21.493,21.343,21.609\n21.04,20.397,20.685,21.055,20.388,21.86,21.898,21.675,21.803,21.719,21.37,20.802,21.181,22.359,20.543,20.878,21.638,27.906,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.608,23.827,22.925,22.604,22.522,22.525,22.542,22.247,21.286,20.668,20.505,20.751,20.947,21.01,21.186,21.486,21.73,21.636,21.386\n21.683,21.165,20.932,21.175,21.081,21.383,21.675,21.533,22.097,22.311,21.637,21.278,20.341,19.59,19.96,20.285,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.68,23.767,23.674,22.968,22.771,22.8,22.234,21.49,20.979,20.745,20.712,21.121,21.302,20.9,21.405,21.562,21.405,21.651,20.879\n22.2,22.326,21.526,21.712,21.721,21.713,21.449,20.871,20.598,21.262,21.349,21.456,20.519,19.403,19.209,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.544,22.301,23.788,23.042,23.586,24.249,24.214,23.156,22.61,22.164,21.395,20.713,20.533,20.567,20.821,21.248,21.329,21.03,21.293,21.381,21.447,21.818,21.135\n22.635,21.691,20.594,20.405,21.292,21.918,21.327,20.087,20.699,19.868,20.742,21.122,21.128,20.415,19.563,NaN,NaN,NaN,NaN,NaN,NaN,20.199,19.252,20.277,20.446,20.929,22.304,23.812,23.832,23.807,24.217,24.22,22.373,21.544,21.44,20.381,20.631,21.023,21.706,21.084,21.166,20.897,20.999,21.212,21.267,21.655,21.58\n21.442,20.695,20.436,20.347,20.84,20.319,20.457,20.414,20.479,20.189,20.694,20.348,20.601,20.985,20.476,19.129,16.694,NaN,NaN,NaN,NaN,20.08,19.243,NaN,NaN,NaN,23.251,24.336,24.113,23.734,23.758,23.569,22.721,21.552,21.224,20.302,20.273,20.77,21.664,21.15,21.125,20.878,20.575,20.88,21.056,21.871,21.738\n19.967,19.436,20.311,20.213,20.109,19.908,19.672,19.747,19.908,19.674,19.714,20.616,20.431,20.753,21.476,22.085,20.113,18.16,18.57,17.513,19.25,19.974,NaN,NaN,NaN,NaN,NaN,23.566,23.408,22.963,23.07,23.306,22.979,21.377,20.955,21.053,20.915,21.419,21.365,20.901,20.856,20.867,20.786,20.772,21.049,21.958,21.697\n19.949,20.243,20.254,19.487,19.648,19.676,19.476,19.546,19.139,19.273,19.566,20.221,20.416,20.722,21.242,21.71,20.964,19.817,19.773,19.535,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.997,22.678,22.562,22.353,22.192,22.318,21.382,20.726,20.747,21.177,21.412,21.168,20.781,20.915,21.162,21.01,20.944,21.164,21.846,21.888\n20.07,20.293,20.195,19.913,19.901,20.103,19.171,18.553,18.436,19.523,20.032,20.21,19.974,20.095,21.003,21.082,20.701,20.147,19.272,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.082,21.893,21.893,21.883,21.266,21.683,21.388,20.652,20.61,20.959,21.019,20.804,20.965,21.203,21.27,20.688,20.813,21.125,21.829,21.824\n19.642,20.008,20.063,19.897,20.068,20.093,19.144,19.367,18.982,19.254,20.144,20.561,19.882,20.022,21.044,21.251,20.701,20.773,20.738,19.187,NaN,NaN,NaN,NaN,22.009,NaN,21.263,21.547,21.505,21.638,21.208,21.143,21.711,22.4,22.263,21.551,21.197,21.001,20.657,21.118,20.778,20.666,20.737,20.83,20.254,21.175,22.257\n20.365,19.872,19.521,19.747,19.791,19.925,19.589,20.021,19.669,19.578,19.396,19.76,19.77,19.788,20.579,20.231,20.212,20.759,21.083,18.941,19.497,19.908,20.811,20.492,20.572,20.651,20.376,21.068,20.726,20.402,20.376,21.057,21.34,21.685,22.091,21.558,20.702,20.57,20.436,20.968,21.022,20.887,20.943,21.007,20.889,21.175,21.438\n20.376,19.702,19.395,19.598,19.547,19.474,20.201,19.929,19.882,19.605,18.865,20.525,19.809,19.739,20.085,20.08,20.057,20.735,20.41,19.807,19.264,19.36,20.735,20.549,20.56,20.253,19.876,20.982,21.001,20.684,20.311,20.904,21.412,21.572,21.531,21.447,21.129,20.74,20.623,20.736,20.959,20.97,21.047,21.087,21.11,21.371,21.883\n20.089,19.461,19.43,19.584,19.601,19.122,19.578,19.494,19.614,19.396,19.43,20.071,19.419,19.672,20.02,20.643,20.82,20.625,20.459,19.618,18.358,19.451,20.438,19.948,19.359,18.894,20.037,20.763,20.401,20.474,20.546,21.454,21.635,21.034,20.704,21.741,21.765,21.491,20.995,20.797,21.036,21.34,21.292,21.523,21.404,21.754,22.599\n19.826,19.883,19.678,20.357,20.26,20.425,20.101,19.756,19.556,19.5,19.675,20.043,19.937,19.853,19.721,20.752,20.553,20.234,19.477,17.564,18.21,20.21,20.12,19.653,19.052,19.121,19.681,20.456,20.399,20.571,20.464,21.559,22.242,21.322,21.617,22.202,22.699,22.04,21.996,21.363,21.515,21.466,21.531,21.529,21.073,21.94,22.413\n20.177,19.717,19.931,20.466,19.406,19.488,19.24,19.468,19.64,19.502,19.483,19.888,20.158,20.503,20.38,20.328,20.256,20.195,19.295,18.87,19.678,20.382,19.772,19.484,19.312,19.173,19.152,20.381,20.15,20.357,20.956,21.461,22.105,21.759,21.573,21.937,22.224,22.186,22.084,21.874,21.765,21.47,21.624,21.807,21.658,21.91,22.2\n20.39,19.428,19.656,19.665,19.414,19.338,19.138,19.584,20.656,19.901,19.677,19.982,19.901,20.529,21.03,20.325,19.697,20.584,20.13,19.076,20.048,20.314,19.632,19.39,19.319,19.033,18.708,20.027,20.057,20.32,21.513,21.239,21.682,21.396,21.263,21.911,22.192,22.355,22.151,21.826,21.871,22.008,22.921,22.382,21.819,22.846,23.026\n19.354,19.404,19.506,19.447,19.827,19.048,19.524,19.902,20.141,19.768,20.489,20.211,20.292,20.346,20.547,20.652,20.487,20.498,19.571,19.175,20.274,20.167,19.144,19.506,19.338,19.35,19.569,20.067,20.148,20.897,21.405,21.409,21.617,21.171,21.313,21.875,22.076,22.108,22.093,22.162,22.008,21.633,22.073,21.797,22.062,22.364,22.63\n19.516,18.837,19.02,19.258,19.273,19.327,19.515,19.116,19.411,19.522,20.342,19.394,19.412,19.473,19.952,20.253,20.305,20.391,19.703,19.617,20.256,20.361,19.719,19.687,19.683,19.831,19.704,20,19.88,20.315,20.841,21.303,21.593,21.342,21.608,21.769,21.766,22.251,22.675,23.557,22.423,21.939,21.884,21.66,22.266,22.048,22.512\n19.519,19.258,19.076,19.477,19.348,19.123,19.247,19.394,19.492,19.975,19.534,18.897,18.631,18.915,19.476,19.725,20.201,20.498,19.886,18.972,20,20.076,20.163,20.065,19.858,19.884,20.661,20.724,20.143,20.251,20.969,21.113,21.472,21.302,21.59,21.882,21.825,22.358,22.827,23.325,22.764,22.411,21.955,22.673,22.472,21.841,22.219\n19.06,18.785,20.038,19.622,19.019,19.512,19.088,18.966,19.168,19.58,19.902,19.836,19.618,19.367,18.718,19.252,19.875,19.988,19.861,19.274,19.652,19.904,20.004,20.205,20.302,20.159,20.826,20.609,20.204,20.245,20.641,21.121,21.602,21.672,21.891,21.83,22.056,22.455,22.693,22.953,22.699,22.588,22.732,23.177,22.628,21.841,22.235\n20.106,20.235,20.108,19.949,19.535,18.628,18.906,18.482,18.474,18.814,18.169,18.948,20.197,19.789,18.89,19.675,20.346,19.998,20.104,20.102,20.387,20.472,20.558,20.626,20.238,20.232,19.923,19.986,20.116,20.449,20.424,20.937,21.305,21.889,22.206,21.913,21.913,22.107,22.559,22.238,22.618,22.492,22.564,22.863,22.529,22.828,22.977\n19.764,19.336,19.015,19.109,19.488,20.934,19.514,18.199,17.354,17.079,18.09,18.428,20.818,20.054,19.688,19.838,20.496,19.242,20.119,21.173,20.998,21.163,21.034,21.219,21.095,21.602,20.813,19.113,19.292,20.036,20.386,20.995,21.387,22.154,22.785,22.317,22.356,22.019,22.246,22.32,21.822,21.9,22.071,22.195,22.208,23.009,22.526\n18.363,18.92,19.225,18.531,18.303,19.964,19.32,18.526,18.225,17.397,17.962,16.266,17.776,18.404,19.947,20.304,19.309,18.47,20.417,21.335,21.116,20.699,21.199,21.45,21.483,21.582,21.364,20.854,19.922,20.575,20.686,20.734,21.164,21.715,22.349,22.094,22.032,22.158,22.442,22.516,22.425,22.233,22.16,22.471,22.444,22.478,21.86\n17.685,18.561,19.093,17.88,17.223,17.454,17.733,18.971,20.294,20.279,18.331,16.643,17.701,17.472,17.118,17.455,18.883,20.456,21.238,20.914,20.759,20.514,20.671,21.403,21.429,21.291,20.997,20.741,20.412,20.841,20.933,20.328,20.82,21.483,22.079,22.251,22.22,22.688,22.864,22.737,21.962,22.318,22.529,22.5,22.563,21.673,21.683\n18.184,18.54,19.142,18.859,17.867,15.072,20.216,18.571,19.05,18.797,18.905,18.267,19.808,20.055,18.696,18.672,17.797,19.334,20.943,20.931,20.98,20.99,20.365,20.645,21.033,21.58,20.482,20.106,19.756,20.145,20.398,20.842,20.785,21.34,21.943,22.176,22.193,23.213,22.47,22.357,22.085,22.408,22.506,21.963,21.563,20.852,20.253\n20.014,20.221,20.512,20.53,19.352,NaN,NaN,NaN,19.147,17.633,20.586,22.394,21.407,20.425,17.479,17.641,17.83,19.554,20.199,21.491,20.737,20.665,20.122,20.322,21.077,21.461,21.102,21.277,20.314,20.553,20.988,21.198,21.523,20.951,21.606,21.996,22.012,22.452,22.136,22.132,21.808,21.588,21.778,22.065,21.943,21.91,22.098\nNaN,19.221,20.145,20.667,20.933,NaN,NaN,NaN,19.546,16.898,17.357,20.826,21.808,21.255,18.537,18.613,19.311,21.053,19.4,21.192,21.827,21.54,19.793,21.193,21.291,22.241,22.647,21.922,21.21,21.307,21.358,22.04,21.827,21.682,22.018,21.953,22.487,22.344,21.725,21.403,21.526,21.551,22.242,22.661,22.419,22.561,22.071\nNaN,NaN,NaN,NaN,19.183,17.665,16.99,NaN,NaN,NaN,NaN,NaN,NaN,22.396,21.622,21.429,24.331,25.276,21.311,20.782,20.703,20.98,20.803,21.226,21.755,22.64,22.856,21.6,21.612,21.653,22.084,22.495,22.666,22.394,22.626,22.124,22.463,22.267,21.744,21.898,21.804,21.661,21.899,21.868,22.356,22.366,22.109\nNaN,NaN,NaN,NaN,17.997,17.576,18.525,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.536,25.199,22.747,21.436,20.704,20.282,21.03,21.166,21.665,22.118,22.187,21.961,22.312,22.159,22.362,22.585,22.778,22.911,22.441,22.117,22.149,21.855,21.733,21.983,22.015,22.321,22.353,22.457,22.422,21.767,21.492\nNaN,NaN,NaN,NaN,21.637,21.386,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.876,23.558,21.884,21.266,17.411,20.076,21.406,21.978,22.333,21.918,21.343,22.279,22.053,22.423,22.411,22.632,23.128,21.701,21.888,21.86,21.646,22.077,21.956,21.962,22.403,22.941,22.465,22.189,21.944,20.569\nNaN,NaN,NaN,21.99,23.05,21.547,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.01,21.18,19.982,18.907,21.385,22.955,21.294,22.066,22.662,22.703,23.349,22.65,22.25,21.758,22.362,22.773,21.176,21.715,21.437,21.65,22.181,22.309,21.855,22.372,22.707,22.716,22.445,23.314,22.416\nNaN,NaN,NaN,NaN,NaN,NaN,21.401,21.415,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.952,21.905,22.336,22.793,24.387,21.748,21.51,21.749,21.427,20.851,22.841,22.656,22.643,22.738,23.906,22.887,22.197,21.899,21.484,21.606,21.867,21.854,21.734,21.634,21.906,22.176,22.264,22.695,22.74\nNaN,NaN,NaN,NaN,NaN,NaN,20.049,21.29,21.797,21.478,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.31,20.582,21.36,22.064,21.536,21.06,22.214,21.442,21.209,20.908,21.864,22.082,22.656,23.014,23.073,22.389,22.278,21.893,21.944,21.493,21.693,22.38,21.958,21.524,22.149,22.127,22.636,23.725,23.694\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,20.947,19.31,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,20.683,20.095,21.283,20.913,21.613,22.562,22.787,21.969,21.574,21.503,22.219,22.786,23.001,22.437,22.208,21.991,22.11,22.147,22.264,20.829,21.593,22.511,21.491,22.199,22.564,21.994,21.816,22.043,22.198\n22.587,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.312,20.094,23.66,23.671,19.082,NaN,NaN,NaN,NaN,21.973,21.476,21.919,18.885,22.548,23.102,21.848,21.903,21.916,21.465,22.143,22.684,22.5,22.202,22.112,22.11,22.435,22.305,22.38,21.45,22.337,22.003,22.242,22.589,22.424,21.873,21.652,21.786,22.369\n22.174,22.127,22.981,22.99,NaN,NaN,NaN,NaN,NaN,19.644,20.295,21.707,22.679,22.368,21.705,20.939,21.173,19.718,19.387,23.619,20.274,20.278,21.773,22.367,20.837,20.753,21.002,20.66,20.48,22.137,22.865,22.181,21.821,21.804,22.089,22.291,21.206,20.885,21.47,22.032,22.598,23.57,23.232,22.436,21.915,21.679,21.7\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061106-070326.unw.csv",
    "content": "9.0427,8.806,8.8001,8.8779,9.0107,8.7977,8.5997,8.5607,8.7187,8.5752,8.3501,8.4665,8.4237,8.9902,9.3514,9.2213,9.0072,8.9586,9.2991,9.4186,9.8535,9.4901,9.4729,9.691,9.9765,9.1188,9.2216,9.7038,9.8745,9.6918,9.784,9.51,9.8464,9.7851,9.4759,9.4503,9.4461,8.9402,8.4962,8.4307,9.0796,9.3802,9.1624,8.7656,8.3814,8.3909,8.4256\n8.8827,9.0294,9.1456,8.9635,8.883,8.6376,8.5724,8.5363,8.2289,8.316,8.537,8.4955,8.7076,8.9814,8.8566,8.7731,8.8949,8.9202,9.0992,9.2684,9.3212,8.9782,9.6437,9.7308,9.2878,9.1775,9.4308,9.6957,9.6893,9.9066,9.8263,9.6057,9.7796,9.6833,9.2838,9.4321,9.3601,8.9936,8.5726,8.3918,8.9244,9.2309,8.8048,8.6228,8.1643,7.8608,7.8318\n8.9087,9.7083,9.3673,8.9354,8.8174,8.6329,8.5752,8.4463,8.0235,8.296,8.7429,9.02,9.0107,8.9946,8.7709,8.6633,8.8561,8.7957,8.8379,9.1361,9.2252,9.4332,10.184,9.0526,9.3039,9.3025,9.3626,9.3543,9.6016,10.131,9.7124,9.4626,9.6146,9.6879,9.515,9.4444,9.6733,9.2499,8.9589,8.9016,8.8906,9.1174,8.6055,8.419,7.7829,7.5982,7.4652\n8.9773,9.8811,9.0244,8.9424,8.7976,8.534,8.382,8.2954,8.1531,8.2601,8.6635,8.8212,8.9188,8.8587,8.6736,8.8232,8.9771,8.9258,8.8431,9.0317,8.9093,9.5691,9.6281,9.1895,9.3328,9.3269,9.3747,9.3078,9.5568,9.6237,9.4002,9.3718,9.6386,9.7931,9.6846,9.5852,9.4749,9.1401,9.0847,9.0649,8.887,8.7093,8.5669,8.2026,7.8011,7.6166,7.357\n8.6277,9.2269,8.3999,8.8385,8.7034,8.5037,8.3245,8.3112,8.0661,8.7245,8.911,8.494,8.8907,8.6178,8.3867,8.8339,9.1705,9.1404,8.8646,8.7585,8.8035,10.043,9.5125,9.4225,9.2306,9.2998,9.7208,9.4253,9.8704,9.3138,9.3593,9.4722,9.5761,9.8816,9.7783,9.4738,9.3532,9.2435,9.1653,8.9769,8.6511,8.5762,8.4835,8.2983,7.8566,7.7239,7.7738\n8.246,7.8716,8.6041,9.0342,8.698,8.574,8.3295,8.1532,8.4336,8.8396,8.4187,8.2322,8.5463,8.4206,8.2979,8.6661,8.8801,8.9733,8.6989,8.9525,9.4343,9.9066,9.715,9.1777,9.1369,9.8651,10.051,9.6027,9.6026,9.6919,9.9897,9.8298,9.5962,9.624,9.6682,9.4961,9.518,9.446,9.2735,8.9777,8.7518,8.5346,8.2839,8.1788,7.6706,7.7798,7.9272\n7.8031,7.4909,8.977,9.0228,8.8196,9.0264,8.3898,7.9433,8.0072,8.4163,8.1143,8.0469,8.3931,8.3916,8.6243,8.7648,8.9721,9.1343,9.1989,9.1295,9.1341,9.6348,9.4293,9.4615,9.6321,10.147,10.151,9.6534,9.6717,9.8944,10.147,9.7753,9.6016,9.5271,9.555,9.5451,9.4195,9.0685,8.7338,8.4698,7.985,8.2123,8.0321,7.8009,7.59,7.6032,8.0557\n7.899,7.9334,8.8686,8.8754,8.7975,9.2288,8.5922,8.1167,8.2146,8.591,8.1546,8.0893,8.1912,8.5102,8.6741,8.7282,8.9644,9.3027,9.1569,9.0385,9.0189,9.2568,9.2624,9.8386,9.8249,9.926,10.286,10.201,9.9484,9.9911,9.9379,9.6558,9.6317,9.5195,9.6065,9.5832,9.4223,9.1497,8.8307,8.5452,8.279,7.8775,8.0444,8.0797,7.8664,7.5656,7.9792\n8.2556,8.5062,9.1523,8.8045,8.9407,8.9975,8.4657,8.4449,8.6459,8.8443,8.6638,8.3112,8.5462,8.7701,8.7733,8.6882,8.7346,9.0793,9.0861,9.1824,9.205,9.4102,9.6797,10.055,10.183,10.366,10.417,10.107,9.9303,9.7851,9.8826,9.7733,9.7178,9.8926,9.7979,9.5675,9.2491,9.2138,9.0135,9.3035,9.1039,8.3971,8.2482,9.0573,8.6316,8.2393,7.902\n8.7356,8.798,9.7448,9.14,8.7024,8.7755,8.5665,8.638,8.7202,8.5911,8.5329,8.7151,8.6135,8.6868,8.8809,8.8719,8.6153,8.8174,9.2173,9.6017,9.4005,9.7729,9.8553,10.007,10.458,10.332,10.189,9.6063,10.089,10.417,10.202,9.8393,9.8843,9.9177,9.665,9.3772,9.2459,9.4653,9.3961,9.4648,9.4035,9.1423,8.963,9.2936,9.1023,8.6197,8.0959\n8.6514,9.5662,9.913,8.9389,8.9199,8.7932,8.7061,8.6324,8.5429,8.5149,8.583,8.5365,8.1091,8.368,8.5564,8.6116,8.7579,9.1722,9.607,9.6751,9.7942,9.8096,9.5395,9.9447,10.212,10.116,10.055,9.9396,10.366,10.899,10.708,10.437,10.116,9.7684,9.5367,9.4821,9.4827,9.5488,9.5825,9.5694,9.7068,9.2923,9.1438,9.1933,9.1236,8.6113,8.4378\n9.0986,10.059,9.7614,8.9881,8.7295,8.5967,8.6394,8.6767,8.6191,8.5922,8.4071,8.3154,8.0386,8.1667,8.5765,8.9625,9.2118,9.3871,9.5818,9.4066,9.5055,9.455,9.4197,10.132,10.17,10.628,10.003,10.048,10.688,10.936,10.938,10.447,10.114,9.7609,9.8783,9.5885,9.3718,9.3673,9.5372,9.6327,9.6424,9.2394,8.9794,9.0417,8.9711,8.6089,8.4601\n8.7343,8.9714,9.1313,9.307,8.589,8.852,8.6928,8.8684,8.7467,8.698,8.4003,7.9778,8.2501,8.7862,8.9429,9.2071,9.5101,9.6883,9.636,9.4669,9.4734,9.8627,9.8698,10.031,10.541,10.73,10.246,10.288,10.869,10.659,10.558,10.189,9.9293,9.6642,9.7065,9.3541,8.6227,8.5914,8.8913,8.8592,8.9909,8.9357,8.6921,8.9132,8.7744,8.6307,8.0972\n8.4918,8.4468,8.5762,8.7765,8.9878,9.3346,9.2111,8.9614,8.5715,8.5936,8.1345,7.9625,8.5658,9.242,9.0371,9.2098,9.474,9.93,9.9852,9.9688,10.004,9.9186,10.097,10.298,10.67,10.466,10.157,10.337,10.512,10.47,10.321,10.034,10.006,9.646,9.3525,8.7463,8.148,7.6857,7.9558,8.4359,8.5648,8.3647,7.9646,8.1357,8.2258,8.096,7.9583\n8.4062,8.4579,8.857,8.9863,9.0969,9.2714,9.2116,8.6715,8.5773,9.0689,8.7429,9.0621,9.3664,9.5631,9.2835,9.3707,9.3293,9.7258,9.8932,10.048,10.148,9.9005,9.9229,10.496,10.479,10.085,10.104,10.11,10.428,10.302,10.058,9.7791,9.7377,9.7519,9.3263,8.5543,8.0708,7.8525,7.8189,8.1347,8.3941,8.3726,7.4047,7.4132,7.3999,7.5445,7.4845\n7.7474,8.3368,8.9901,9.3195,9.122,9.305,9.225,8.8855,8.7977,9.1047,9.0874,9.5992,9.1578,9.4436,9.0473,8.8727,8.7057,8.8025,9.4212,9.6955,9.8112,9.7814,9.8988,10.445,10.537,10.344,10.266,10.19,10.619,9.8866,9.4881,9.4412,9.2109,9.3079,9.1233,8.6695,8.8992,8.4731,8.1705,7.6678,8.2231,8.2827,7.4722,7.1984,7.072,6.7105,6.5467\n7.4323,8.3473,8.8491,8.9095,8.515,8.6571,9.2617,9.1254,9.1471,8.9904,9.1144,8.9896,9.1856,9.462,9.076,8.6137,8.3048,8.9761,8.9723,9.3012,9.3446,9.6362,9.7602,10.069,10.28,10.243,10.131,10.09,10.089,9.7578,9.3239,9.0977,8.6783,8.9856,9.1321,8.7339,8.5832,8.6278,8.7216,7.7423,7.8451,7.8957,8.0214,7.5761,6.7927,6.3061,6.3827\n8.06,8.5332,8.8299,8.5091,7.9483,8.5972,9.2849,8.8689,8.9257,8.8172,8.8699,8.9557,9.3061,9.4124,8.9459,8.6094,8.7357,9.0517,8.6339,9.4288,9.439,9.3956,9.4998,9.6259,10.222,9.9908,9.6669,9.565,9.2803,9.0347,8.9066,8.629,8.9587,9.4726,9.4205,8.7555,8.1211,8.3065,8.4616,8.037,7.952,7.8948,8.0088,8.0324,6.939,6.6147,6.5826\n7.752,8.5431,9.09,8.8552,8.4962,9.1663,9.3432,8.8891,8.609,8.8546,8.9348,8.9565,8.9785,8.9108,8.8456,8.46,8.4534,8.477,8.5426,8.9669,9.0635,9.0533,9.3351,9.7066,9.7964,10.165,9.6768,9.4807,9.418,8.9783,8.9117,8.7191,8.7662,9.2708,9.5023,8.9548,8.318,8.3633,8.5983,8.3869,8.2533,7.8241,7.9168,7.8501,7.2823,7.0347,7.0066\n7.4151,8.57,8.8868,8.8923,8.7042,8.5726,8.8937,8.9688,8.8506,8.7915,8.8314,8.7818,8.6139,8.7399,8.6493,8.2872,8.3136,8.4378,8.8847,9.1365,8.9719,8.8752,8.9376,9.6536,9.8185,9.8512,9.7573,9.649,9.5984,9.2073,8.9279,8.7623,8.5156,8.6537,8.946,9.0365,8.692,8.6921,8.6202,8.582,8.2853,7.7824,7.7231,7.705,7.3709,7.3016,7.35\n8.2164,8.4674,9.0748,9.1068,9.0158,8.4935,8.9056,8.971,8.7987,8.6695,8.6317,8.7228,8.7337,8.5217,8.1027,8.2081,8.0896,7.8398,8.8049,8.7315,8.4353,8.6713,8.8351,9.1519,9.3674,9.6365,9.5632,9.9019,9.5647,9.1061,8.8621,8.4339,8.5352,8.5447,8.9406,9.0775,9.199,9.067,8.7652,8.4501,7.9338,7.7261,7.7417,7.7865,7.8381,7.889,7.9536\n8.3706,8.5486,9.1227,9.267,9.2847,8.9232,8.7337,8.8666,8.8436,8.6421,8.4937,8.5677,8.6078,7.8679,7.8785,7.822,7.9482,7.9955,7.9398,8.5226,8.3138,8.7175,8.6874,9.3446,9.5078,9.5945,9.5674,9.6114,9.2718,9.0708,8.9233,8.6982,8.6739,8.6776,9.2607,9.2418,9.2183,9.2723,9.2319,8.3975,7.5618,7.5983,7.7637,7.9102,7.9786,7.9709,8.0824\n8.5262,8.7862,9.1713,9.0981,9.2655,9.0061,8.8948,8.9595,9.0747,8.6234,8.4563,8.7483,8.5715,8.1066,7.9745,7.6111,8.0506,8.3107,8.3994,8.6623,8.5792,8.4826,8.6405,9.5504,9.8802,10.077,9.2868,9.2206,8.9928,8.867,8.8501,8.7245,8.7311,8.8056,9.2485,9.1409,9.0528,9.1138,8.6795,8.3268,7.6815,7.6261,7.8089,7.932,7.7338,7.8503,7.9591\n8.6178,8.7827,8.745,8.7682,8.9701,9.3259,9.039,8.8674,8.8279,8.5375,8.3467,8.6133,8.9582,8.3969,8.301,8.5995,8.6937,8.6222,8.6956,8.8755,9.214,9.5951,9.3847,9.5449,10.011,10.123,9.4784,9.1848,9.1965,9.154,9.1426,8.8372,8.5306,8.6793,9.0683,9.0776,8.9082,8.6836,8.3862,8.4853,8.122,7.9343,8.282,8.4347,8.2372,8.0524,8.1257\n8.938,8.7511,8.6655,8.9359,9.0634,9.2082,9.1756,8.7138,8.9111,8.9874,8.9591,8.9702,9.2351,8.6651,8.6258,8.7812,8.8191,8.3407,8.5218,8.9898,9.3791,9.5939,9.2893,9.3425,9.7502,9.4123,9.2275,9.2346,9.518,9.1798,9.2262,8.986,8.2769,8.2641,8.3345,8.2561,9.6067,8.9958,8.767,9.0769,8.5691,8.553,8.6218,8.9064,9.0446,8.6303,8.3004\n8.9343,9.2435,9.0567,9.0885,9.1682,9.2509,9.321,9.2034,9.233,8.7794,9.1278,9.0361,8.9473,8.634,8.1359,8.3914,8.2102,8.111,8.4954,8.9713,9.2004,9.1443,9.0983,8.6022,8.4338,8.6507,8.6541,9.019,8.8286,8.3716,8.2523,8.6313,7.5876,7.5465,8.0776,8.1674,9.8512,9.6996,9.4762,9.5885,9.2973,8.8927,8.7763,8.8333,8.9266,8.6609,8.5846\n8.976,9.4932,9.0237,9.1056,9.1405,9.2936,9.3051,9.3612,9.1617,9.1146,9.0775,8.8289,8.2954,8.0403,7.828,8.1196,8.1872,8.2084,8.8235,9.1215,9.0173,8.9624,8.7361,8.3058,7.6226,8.459,8.6854,8.807,8.9056,7.8959,8.2163,8.1985,7.7602,7.7336,8.1348,8.8834,9.6296,9.4226,9.4684,9.7396,9.2098,8.9527,9.0406,9.0675,8.979,8.8722,8.834\n8.7228,8.8297,8.9832,9.1152,9.2647,9.3444,9.3589,9.2347,8.9828,9.7189,8.956,8.4977,8.1454,8.1894,8.1296,8.4516,8.5949,8.5798,8.577,8.5838,8.7593,8.8357,8.6873,8.4173,7.7189,8.293,8.6162,8.6252,8.594,8.284,8.3209,8.3182,8.1129,7.9857,8.3737,9.1264,9.2356,9.2671,9.3142,9.5944,9.1259,9.3008,9.2977,9.5819,9.1427,8.9104,9.1255\n9.2077,8.8889,8.7444,9.1216,9.4388,9.285,8.8547,8.3084,8.4916,9.1013,8.9088,8.3508,8.1473,8.6729,8.6837,8.4826,8.7564,8.8096,8.5352,8.3885,8.4515,9.1526,9.2766,9.3747,9.0129,8.8927,9.2568,9.056,8.9838,8.6605,8.7532,8.7174,8.9989,7.9711,8.5907,9.0556,9.1804,9.3593,8.7064,8.6486,9.3765,9.3929,9.5592,9.4384,8.9173,8.9638,9.0728\n8.8761,8.7746,8.5563,8.5793,9.4876,9.1952,8.3451,8.0108,8.2995,8.6585,8.9137,8.6883,8.3979,8.7404,8.4077,8.3887,8.6732,8.7547,8.9914,8.6044,8.6275,9.5312,10.165,9.7829,9.6437,9.5392,9.5636,9.6218,9.0317,8.9009,9.4327,9.1387,8.8152,7.9444,9.2725,9.2836,8.8265,8.7401,8.3742,8.8769,9.4715,9.5294,9.4456,9.3752,8.8501,8.6246,8.3791\n9.3755,9.0548,8.693,9.0658,9.264,9.1335,8.8419,8.1012,8.0964,8.3245,8.4305,8.5473,8.5849,8.5499,8.0144,7.7982,8.054,8.8275,8.9,8.6291,8.8565,9.4545,10.178,10.403,9.8957,9.8573,9.9011,9.9095,9.5631,9.412,9.746,9.4535,9.3943,9.0282,10.019,9.4402,9.1794,9.0615,9.188,8.8141,8.9046,8.8749,9.2316,9.3921,8.8027,8.4855,8.3506\n9.3264,9.0515,8.5226,8.5327,9.024,9.3135,9.5548,8.6577,8.4301,7.8653,7.8171,8.3776,8.581,8.5753,8.3072,7.9964,8.1885,8.9974,9.2,9.1811,9.6009,9.7121,9.9251,10.66,10.382,10.126,10.2,10.758,13.166,13.279,11.088,9.6406,10.127,10.345,10.285,9.6615,9.6264,9.2894,9.5802,9.6287,9.3166,8.9588,9.4845,9.3404,8.6049,8.2,8.1846\n9.276,9.112,8.8084,8.1966,8.5645,9.5618,9.5881,9.0659,8.7431,7.8022,7.7873,8.026,8.5609,9.1877,9.4096,8.9928,8.8606,9.296,9.3825,9.2184,9.8019,10.157,10.192,10.881,10.997,11.208,NaN,NaN,NaN,NaN,NaN,10.202,9.9188,10.563,10.38,10.023,10.051,9.4088,9.8684,10.025,9.7463,9.296,9.5472,8.8712,8.3398,7.8487,7.7346\n9.7836,9.6852,9.1068,8.567,8.4135,9.6408,10.204,10.069,9.1579,8.3955,8.2728,8.374,8.9354,9.2177,9.7879,9.6891,9.6387,9.7473,9.5528,9.432,9.9761,10.377,10.577,11.651,12.62,13.635,NaN,NaN,NaN,NaN,10.607,10.285,9.2488,11.237,12.121,11.543,12.086,11.011,10.485,9.9375,9.7587,9.4232,9.0816,8.7877,8.3317,7.9067,7.5536\n9.9913,10.098,9.1098,9.0915,9.4662,9.662,9.8335,10.328,9.6918,9.2006,9.1234,9.6484,9.7648,9.6461,9.6204,9.6548,9.8499,10.17,9.9842,10.032,10.465,11.269,10.996,12.147,12.313,13.529,NaN,NaN,NaN,NaN,10.924,10.366,10.87,13.367,13.773,13.683,13.38,11.912,10.784,10.104,9.9729,9.4659,9.3309,8.7984,8.3603,7.9848,7.4939\n9.932,9.4983,9.157,9.0423,9.0732,9.2678,9.4442,9.9753,10.012,9.893,9.7455,9.8361,9.9738,9.8963,9.4769,10.023,10.467,10.691,10.804,11.079,11.791,11.21,10.965,11.225,10.668,11.831,11.907,10.91,9.5035,11.106,11.37,11.609,15.599,15.987,15.328,14.207,13.568,12.119,10.938,10.209,9.6487,9.3832,9.3816,8.8974,8.3607,8.0254,7.5454\n9.865,9.3494,9.5839,9.2067,8.774,9.1484,9.5045,9.8173,10.029,10.282,9.7832,10.058,9.8766,8.9435,9.2242,10.222,10.591,10.254,9.8606,9.8476,10.515,11.158,11.882,11.567,11.803,12.444,13.858,13.88,10.611,11.563,12.174,13.435,18.327,17.519,16.418,14.087,12.957,11.984,10.847,9.8669,9.1078,8.9261,8.8612,8.3968,8.2421,7.8278,7.4298\n10.272,10.297,10.057,9.5172,9.0983,9.164,9.4161,9.8189,10.249,10.292,9.9523,10.071,9.9311,9.2173,9.5841,10.275,10.989,11.673,9.3534,8.109,9.6653,11.953,14.101,11.734,11.463,11.581,13.607,13.957,11.671,11.175,13.621,15.699,17.057,17.199,16.32,14.235,12.442,11.333,10.665,9.2232,8.6582,8.7926,9.0066,8.6594,8.4007,7.781,7.6515\n9.5177,9.8558,10.183,9.7937,9.2911,9.1312,9.4019,10.107,10.621,10.703,10.322,10.236,10.44,10.004,10.523,11.047,10.609,9.5695,9.1565,9.3259,9.8795,12.556,12.99,10.851,10.675,10.289,12.964,12.115,10.274,11.235,15.531,17.199,16.875,15.818,14.476,13.087,11.987,11.087,10.407,9.2168,8.631,9.3159,9.4494,9.1559,8.7689,8.1695,8.1789\n9.2709,9.5347,9.6771,9.8671,9.7884,9.7369,9.6689,10.282,10.48,10.381,10.22,10.541,10.848,10.096,11.463,11.305,10.992,9.2669,9.4455,9.3306,10.811,11.137,11.871,11.591,11.458,12.175,14.295,10.484,9.8548,12.64,20.374,18.285,16.178,14.405,12.961,12.175,11.425,11.084,10.121,9.2224,8.9907,9.3061,9.3488,9.156,8.853,8.6952,8.463\n9.6222,9.6907,9.6155,9.5515,9.6889,9.7706,9.8513,10.046,9.9486,9.9875,10.368,11.016,11.397,11.196,11.416,11.665,11.466,10.94,10.841,11.007,10.909,10.667,11.389,11.958,12.066,12.718,11.748,8.9933,9.9278,13.384,17.296,17.799,14.339,13.053,11.81,11.37,11.399,11.423,10.134,9.678,9.6793,9.1479,9.3628,9.1689,8.9696,8.8962,8.7066\n9.1743,9.3277,9.8303,9.707,9.5192,9.3036,9.5215,9.5166,9.6611,10.053,10.33,10.964,11.719,11.557,11.502,11.232,11.558,11.135,11.351,11.85,11.022,10.514,10.499,10.947,11.783,12.035,10.146,8.5356,9.8388,12.591,14.773,14.928,13.943,11.93,10.831,10.874,10.577,10.531,10.144,10.036,9.874,9.1893,9.3626,9.2993,9.0476,9.4279,9.3735\n9.7962,9.8971,10.019,9.9588,9.7377,9.5645,9.3791,9.2682,9.5147,9.6421,9.8908,10.469,11.341,11.087,11.239,11.295,9.6305,10.754,10.722,10.705,10.344,10.202,9.8131,10.3,11.142,9.8627,8.6782,7.9005,10.079,11.401,13.467,12.833,12.029,10.787,10.299,10.178,10.237,10.038,9.8944,10.154,9.8572,9.3363,9.2808,9.118,9.0037,9.607,9.302\n10.008,9.8529,10.064,9.7861,9.8087,9.5524,9.2845,9.2276,9.446,9.4692,9.6441,9.9509,10.46,10.413,10.483,10.636,10.273,10.1,10.689,10.247,9.6702,9.2277,8.5938,9.7954,8.6079,6.8892,8.7668,8.9382,10.064,10.447,10.977,11.03,10.839,9.8515,9.5369,9.5718,9.7278,9.6061,9.937,9.8997,9.4472,9.2165,9.1422,8.7651,8.6134,8.9684,9.2023\n9.763,9.605,9.4347,9.7117,9.819,9.333,8.9686,8.8832,9.2054,9.247,9.1344,9.2992,9.4998,9.8156,10.273,10.875,10.988,11.712,10.645,9.2677,9.2067,8.7098,7.6429,7.8743,7.9544,5.5262,9.4567,9.4293,9.3015,9.6029,10.509,10.806,9.8403,9.5768,9.3769,9.2173,9.5168,9.6108,9.6699,9.4794,9.1948,9.1972,9.0904,8.5516,8.3012,8.3091,8.8399\n9.4145,9.1759,8.8682,8.8327,9.2935,9.2721,9.2895,9.3363,9.2908,8.8348,8.7008,8.5854,8.9214,8.9875,10.505,11.081,11.699,12.581,12.087,10.445,9.9718,9.7576,8.9648,8.7155,8.9595,7.9823,9.1221,8.7187,9.2977,9.7732,10.611,10.602,10.035,9.6464,9.1648,8.8912,9.3123,9.6739,9.3936,9.3147,9.5462,9.6241,9.3069,8.8223,8.2591,8.2046,8.4883\n9.9128,9.205,8.4865,8.9176,9.3265,9.4965,9.6625,9.6072,9.2354,8.6699,8.2241,8.5445,8.7202,8.6527,9.3209,9.8755,10.376,11.133,11.781,10.124,10.279,10.335,9.3658,8.9409,8.6171,8.051,7.6359,8.3506,8.776,9.0921,9.662,9.9396,9.5648,9.1973,8.7136,8.9782,8.9125,9.2331,8.9802,9.1355,9.701,9.7749,9.4848,9.1538,8.4818,8.5331,8.6963\n10.001,9.4653,8.7792,8.8866,9.7158,9.8995,9.8073,9.6008,9.0304,8.6012,8.1716,8.4408,8.3612,8.4655,8.9034,9.435,9.9169,10.173,9.9931,9.1137,9.1539,9.5945,8.2345,8.4808,8.2009,7.8253,7.6574,8.2052,8.4391,8.6299,8.8663,9.0145,8.4888,8.1733,8.6355,8.9184,9.0867,9.2128,9.2412,9.2067,9.525,9.6269,9.1232,9.12,8.7553,8.8586,8.9722\n9.8322,9.5121,9.1961,9.3871,9.731,9.9348,9.8383,9.522,8.7523,8.7266,8.6105,8.6899,8.6742,8.4424,8.8424,9.247,9.3917,9.0973,8.1359,7.8471,7.2826,7.6303,7.8517,8.0774,7.7445,7.2204,7.462,7.6429,7.8949,8.0936,8.3375,8.3215,7.9604,7.7423,7.7686,9.1716,9.5428,10.086,9.6853,9.2587,9.342,9.4379,8.9262,8.9432,8.965,9.1558,9.3907\n9.778,9.6147,9.4448,10.049,9.9771,9.8003,9.5414,9.3231,8.8435,9.1163,9.2103,9.111,8.7903,8.4751,8.7383,8.9171,8.675,8.3372,7.6357,6.0777,6.1761,7.2035,7.9048,7.6481,6.9322,6.7345,6.8541,6.9376,7.6501,7.5046,7.9114,8.1325,8.3697,8.2848,8.5915,9.189,9.739,9.5814,9.8812,9.496,9.4351,9.344,9.0607,9.3252,9.3871,9.3391,9.2315\n9.6869,9.4766,9.602,10.317,10.318,9.9628,9.6651,9.6885,9.696,9.5342,9.2395,9.2062,8.8524,8.8127,8.8702,8.7551,8.3931,7.8835,7.114,6.7398,6.8745,7.5289,7.9342,7.2306,6.3995,6.3099,5.8619,6.1132,7.3648,6.992,7.3758,8.207,8.5133,8.692,9.3464,9.4495,9.6416,9.8244,9.8587,9.6634,9.4977,9.5061,9.6952,10.005,9.7355,9.2791,9.5231\n10.027,10.019,10.156,10.406,10.403,10.165,10.059,9.9546,10.13,9.7947,9.34,9.0465,8.6315,8.9845,8.8578,8.5927,8.3348,8.0646,7.9908,7.3767,7.22,7.8489,7.8562,7.1543,6.7108,6.5341,5.9147,5.7008,6.6526,6.9588,7.0434,7.688,8.3729,8.5124,8.8709,9.3776,9.9093,10.008,9.7623,9.5347,9.7497,9.7948,10.184,10.66,9.8853,9.847,9.9894\n10.423,10.357,10.343,10.549,10.765,10.412,10.536,10.392,10.339,10.193,9.8979,9.1221,8.7616,8.7348,8.2704,8.2205,8.33,8.2104,8.1565,8.139,7.8262,7.9272,7.7012,7.4803,6.9703,6.9508,6.2821,5.9265,6.3668,6.6543,7.0054,7.7947,8.3373,8.5003,8.6036,9.2472,9.8961,9.8084,9.5699,9.4677,9.583,9.6146,9.9226,10.282,9.4383,9.3465,9.8146\n10.249,10.476,10.565,10.705,10.53,9.9178,10.493,10.022,10.242,10.252,10.063,9.8225,8.8858,8.731,8.7518,8.2671,8.2102,8.042,7.6102,7.7162,7.9194,7.8728,7.6469,7.686,7.1989,6.4526,6.1171,5.9754,6.1119,6.3017,7.2195,7.9619,8.3913,8.2265,8.6475,9.3461,9.9898,9.6145,9.6385,9.1648,9.1836,9.2909,9.5615,9.4584,9.5414,9.2667,9.2402\n11.068,10.997,10.965,10.86,10.453,10.146,9.9363,9.7026,10.104,9.8191,9.8555,9.5216,8.9775,8.712,8.7758,8.4182,8.1315,7.6786,7.0556,7.7158,7.7031,7.6951,7.8596,7.6656,7.1251,6.6242,6.3486,6.2844,6.5224,6.7476,7.8812,7.856,8.2443,8.4118,8.7509,9.0116,9.5153,9.6125,9.775,9.5873,9.5982,9.6875,9.4975,9.5523,9.3291,8.6347,8.9252\n11.307,10.717,10.765,10.851,10.743,10.304,9.5521,9.0106,8.8856,9.6072,9.186,9.0217,9.0026,8.8877,8.5588,8.2074,7.8305,7.5337,7.589,8.0928,7.7849,7.9665,7.9118,7.5267,7.328,7.1515,7.0441,7.0298,6.9853,7.3437,7.7692,7.8192,8.4554,8.6393,8.8831,8.8543,9.2122,9.6823,9.8992,9.8297,9.4513,9.5387,9.9369,9.7834,9.3573,8.7324,8.9823\n10.978,10.944,10.881,10.663,10.488,10.543,10.084,9.1621,8.6427,8.9015,8.6243,8.9592,8.7616,8.2089,8.0932,8.0462,8.4245,7.4364,7.8762,8.2926,8.1977,8.1296,8.0671,7.7484,7.3246,7.2994,7.0038,7.0738,7.2534,7.6876,7.6948,7.7915,8.4488,8.6132,8.8487,8.9138,9.0626,9.6416,9.9967,8.8848,8.5714,8.9256,9.2246,9.5918,9.429,9.0524,8.5828\n10.154,10.212,10.639,10.134,9.9873,10.728,10.459,9.7393,9.187,8.4944,8.5504,8.5896,8.4839,8.0927,7.764,7.6302,7.6101,7.465,8.1117,8.7328,8.5082,8.1917,8.4196,8.1404,7.5775,7.4432,7.1165,6.5338,6.9457,7.6363,7.6054,7.6484,8.2502,8.5238,8.5542,8.5612,8.9359,9.5448,9.9789,9.7636,9.5143,9.3877,9.0525,9.1542,9.1288,8.8457,8.273\n9.871,9.9801,10.539,9.8512,9.3666,10.286,10.146,9.1235,9.1634,8.9096,8.4896,7.8972,7.6084,7.577,7.1762,6.545,7.2435,8.2841,8.8324,8.6409,8.6374,8.5577,8.5058,8.1571,7.8984,7.7993,7.3471,6.7979,6.5777,6.9422,7.3432,7.5079,8.0752,8.5703,8.4711,8.5384,9.0967,9.6256,10.02,10.134,9.7617,9.4679,9.2625,9.4533,8.556,7.948,7.9326\n9.4189,9.8797,10.429,9.5895,9.1014,9.173,9.0788,8.9475,9.4212,8.5133,8.0094,8.1134,8.4284,6.404,6.1238,6.0422,8.5191,8.8819,8.8935,8.8577,9.4203,8.9821,8.4481,8.2249,8.221,7.9821,7.4934,7.1697,6.6034,6.7331,7.2487,7.3079,7.7362,8.6672,8.6696,8.7392,8.9911,9.3997,9.8564,9.8813,9.5587,9.5403,9.3043,9.4306,8.5947,7.2394,7.3105\n8.4081,10.205,9.9162,9.5531,9.0502,8.44,8.8592,8.7909,8.0113,8.965,8.0839,8.2119,7.1771,7.1153,8.0961,7.6724,9.1985,8.64,8.5164,9.0307,9.3367,9.1348,9.0667,8.6672,8.4917,8.1042,7.414,7.0484,6.5538,6.9542,7.742,8.0881,7.9593,8.4759,8.8318,8.7936,8.6703,9.1343,9.5849,9.7272,9.6243,9.8102,9.5583,8.7739,7.9659,7.5886,7.7227\n9.7437,10.169,9.3359,9.4519,9.2361,8.9339,8.9576,8.2384,7.1505,9.8977,8.2901,8.5513,8.1441,7.5409,9.3176,8.4979,9.0594,9.4084,9.1664,8.9494,9.2953,9.4161,9.2428,8.9782,8.9413,8.7704,8.3355,7.052,6.9516,7.384,8.2889,8.5086,8.3133,8.3223,8.579,8.7017,8.5687,8.7423,9.2883,9.825,9.4957,9.1972,9.1657,8.5808,8.3959,8.2032,8.1907\n9.0367,10.324,9.7211,9.3722,9.0901,8.6496,8.1062,7.7548,7.4827,9.5904,7.0859,8.0146,8.1514,8.1842,8.9695,8.529,9.7918,8.8342,9.5755,9.8891,10.064,9.929,9.2395,8.9193,8.8675,8.7168,8.0348,7.5169,7.5266,7.7713,8.314,8.6379,8.8983,8.7456,8.5619,8.2674,8.4481,8.4681,9.0183,9.28,9.0179,9.134,9.7385,9.2026,9.79,8.643,8.296\n9.5558,9.5993,10.031,9.8365,9.4867,8.8206,8.3407,8.8283,9.1484,8.5658,7.6232,7.4905,7.257,6.6161,8.6766,10.008,10.869,8.9871,9.0555,9.9359,9.84,9.409,9.0726,8.6439,8.6465,8.2861,8.1394,7.9204,7.8751,8.1474,8.3965,8.7106,8.7781,8.5601,8.4963,8.248,8.2731,8.4735,8.9372,8.952,8.8929,9.3355,9.8396,9.8096,9.5712,8.5203,8.2421\n9.305,9.7479,9.7059,9.6769,8.821,9.8232,8.6759,9.0135,8.8801,8.9322,9.5471,7.7232,7.5011,8.8514,10.154,9.7022,10.06,9.2428,9.3874,10.545,10.146,9.4181,8.8373,8.9227,9.025,8.5599,7.7613,7.9714,8.0616,8.4741,8.7032,8.5913,8.2706,8.6867,8.4756,8.3687,8.3348,8.8239,9.0655,9.1822,9.3731,9.4764,9.6526,9.0051,8.2502,7.9226,7.6353\n8.2558,9.1543,8.6692,9.7079,9.7751,9.6165,8.9517,8.9481,8.5146,8.8071,8.6272,8.3937,9.287,8.7354,8.9753,8.9767,9.8473,9.1362,9.2186,9.7335,9.6813,9.1607,8.8406,8.6962,8.5674,8.389,8.2656,8.5846,8.5565,8.7939,8.8523,8.5775,8.3842,8.9886,9.1917,8.7055,8.64,8.9357,9.0873,9.2443,9.5924,9.6337,9.4632,9.1312,8.6509,8.1004,8.0126\n9.3406,9.3857,9.3379,10.605,9.2779,9.1725,8.8826,8.9643,9.17,9.5187,9.8152,9.3342,9.6244,9.2823,8.6304,9.1901,9.9194,9.5618,8.9652,9.1453,9.1877,8.8848,8.5421,8.4905,8.6296,8.8296,9.0939,9.0868,9.0607,8.9004,8.7516,8.3468,8.8561,9.1499,9.3972,8.7906,8.8752,8.8626,8.9781,9.6982,10.502,10.089,9.4632,9.5065,9.8183,9.1374,8.3437\n9.2055,8.8488,8.7207,8.2688,7.5991,7.7585,8.0683,7.7601,8.3593,9.6196,10.026,10.256,9.5505,8.1118,8.253,9.4854,10.258,8.9601,8.7567,9.9702,9.8674,8.9329,9.0685,9.6516,9.1399,8.91,9.0678,9.2549,8.9422,8.7251,8.7048,8.8369,9.8656,9.6011,9.6261,9.4014,9.4238,9.1751,9.0624,9.5066,9.7627,9.7246,9.5143,9.6968,9.7907,9.511,8.9115\n8.8358,8.2917,8.62,8.0538,7.7178,7.7747,6.9161,6.3876,6.5882,8.0506,9.8042,9.8818,8.7386,7.9978,7.9643,8.3273,9.182,8.3431,8.7568,9.4273,10.12,9.1572,10.16,10.802,10.798,10.202,9.7564,9.6254,9.9833,9.7453,9.3792,9.0457,9.4447,9.5569,9.9345,9.2961,9.3973,9.4001,8.7553,8.6022,8.7958,8.5923,8.6753,9.8428,10.144,9.4659,8.9797\n8.7693,8.1666,7.699,7.4197,6.9025,7.6416,8.6379,7.2952,7.5908,8.4869,9.2998,9.5058,9.8103,10.106,10.246,8.551,7.597,8.8268,9.1233,9.6332,10.519,9.7473,10.08,10.582,10.911,10.606,9.9476,10.185,10.549,10.639,10.504,9.6766,9.8327,10.282,10.123,9.393,9.2569,9.3593,8.4731,8.4105,8.6029,8.9175,9.1821,8.9978,9.2916,8.5562,8.164\n8.8406,8.5696,7.7467,5.5594,5.6622,6.323,6.1613,6.9289,9.4182,8.8409,9.7946,9.4945,10.166,11.831,10.084,8.2873,8.5591,9.0412,8.9129,9.7262,9.6031,10.177,10.442,10.295,10.477,10.8,10.441,10.606,10.715,10.907,11.127,10.054,9.6078,10.049,10.168,9.9535,9.3261,9.0365,9.1463,8.6499,8.9067,9.559,9.1492,8.4408,8.6035,8.6229,8.4203\n8.055,7.9529,8.4056,8.6271,8.6061,9.2207,7.1899,6.8589,8.5238,10.185,11.567,9.6093,10.035,9.6147,9.2427,8.163,7.8823,8.8102,8.9844,9.2553,9.0757,9.5803,10.624,10.004,10.225,10.523,10.74,10.866,10.827,11.131,10.815,9.9134,9.6715,9.9266,10.216,10.224,9.4799,9.3149,9.4263,9.2508,9.3751,9.6257,8.8721,8.5657,8.5626,8.5339,8.4865\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061211-070709.unw.csv",
    "content": "4.9135,4.5414,3.8442,3.9889,3.8628,4.2843,4.4735,4.067,3.7247,3.3812,3.6284,2.9881,3.1528,3.0603,3.2698,3.136,2.7877,2.8459,2.6732,2.428,2.5872,2.8951,3.0519,2.8767,2.8325,3.6942,3.9228,3.7056,3.2983,3.1084,3.0922,3.1839,2.2321,1.4444,1.3234,1.6446,1.3596,0.92542,1.5486,1.5277,1.7046,2.3232,2.8445,3.2075,3.3449,3.193,3.0051\n4.9215,4.39,3.9672,4.3117,4.3079,4.3738,4.3154,3.6447,3.3163,3.362,3.8489,3.9904,3.5669,3.4238,3.6121,3.8091,3.0302,2.6128,2.3493,2.1709,1.9356,2.8708,3.3302,3.5812,3.4582,3.6092,3.7894,3.3863,3.1817,3.4514,3.727,3.262,2.342,1.5592,1.6846,1.8524,1.4933,1.3391,1.93,2.5822,2.4123,2.4952,3.2355,3.4719,3.1625,2.8638,2.8221\n5.208,4.6967,3.9304,4.0141,4.1979,3.7954,3.7988,3.2583,2.4377,2.9717,3.33,3.5872,3.3996,3.4464,3.433,3.4421,2.6349,2.483,2.8351,3.0911,3.0138,2.8028,3.1469,3.651,3.5209,3.831,4.1029,4.0038,3.5566,3.9024,3.8852,2.9987,2.6377,2.4718,2.4029,2.2107,1.5729,1.4393,1.5804,1.9876,2.2068,2.6571,3.2379,3.3695,2.9392,2.4325,2.5471\n4.9953,4.5802,3.6279,4.0168,4.4396,4.173,3.9558,3.3607,3.1098,3.1072,3.2839,3.3101,3.1826,3.6625,3.5064,3.3007,3.0223,2.674,2.6564,3.2859,3.4967,3.5928,3.8883,4.0255,3.9641,3.6685,3.1718,3.0901,3.967,4.0804,3.5447,3.3076,3.2041,3.0154,2.7375,2.3744,2.0253,1.8818,2.4386,2.6154,3.1243,3.5556,3.5662,3.3933,2.9944,2.6168,2.4472\n4.7435,4.4667,3.4938,3.7158,4.2898,4.0951,3.9598,3.7386,3.4885,3.2149,3.2739,3.208,3.1157,3.5755,3.5811,3.6455,3.8049,2.9694,2.4352,2.1735,2.7986,4.1017,4.5627,4.4045,4.2545,3.4372,2.4524,2.8216,4.5322,4.7819,3.6676,3.4477,3.3294,3.4879,3.1317,2.556,2.1574,2.2507,2.5983,3.1973,3.8714,4.0981,3.9998,4.02,3.6368,3.2672,2.9983\n4.4068,3.507,3.1373,3.8319,3.7155,3.5553,3.4532,3.449,3.5534,3.5096,3.363,3.1061,3.3987,3.6705,3.6798,3.7062,3.6564,3.4658,3.1498,2.9123,3.2672,4.4807,4.8641,4.4539,4.3446,3.4893,2.7895,3.0546,3.9635,4.0902,3.9296,4.1139,4.1574,3.6164,3.3872,2.9689,2.7298,2.8235,3.0862,3.7371,4.1238,3.786,3.5911,3.9388,3.7253,3.8554,3.7941\n4.3687,4.0142,3.423,3.7759,4.2361,3.982,3.3479,3.1063,3.493,3.7195,3.0681,3.1213,3.7083,3.6434,3.3756,3.6257,3.2344,2.9527,2.7359,2.8253,3.5365,4.2914,4.782,4.4683,4.0608,3.6681,3.5706,3.6495,3.777,4.0854,4.0302,4.2223,4.4977,4.5343,4.2152,3.8623,3.7282,3.7956,3.8141,4.147,3.6668,3.2152,3.3677,3.6616,3.9652,4.1981,4.343\n4.6949,4.5963,3.5861,3.7811,4.3492,3.8617,3.6625,3.3081,2.9711,3.1489,2.8575,2.995,3.6498,4.0797,4.0315,3.7575,3.1246,3.0398,3.4694,3.6475,3.9732,4.1633,4.3256,4.6844,4.4477,4.4498,4.4839,4.7369,4.2951,4.4634,4.3774,4.0352,4.3544,4.5875,4.4676,4.2003,3.9953,3.8818,3.9872,4.4488,4.6243,4.2169,4.3016,4.2413,3.968,3.8664,4.1068\n4.3111,3.5137,2.9797,3.8972,4.0549,3.901,4.1056,3.8691,3.8243,3.6378,3.4748,3.5257,3.6647,4.5656,4.7282,4.3411,4.0765,4.3425,4.7648,4.507,4.3946,4.4674,4.6284,4.8861,4.9815,5.1696,5.2181,5.342,4.8437,4.4522,4.5062,4.4443,4.588,4.7736,4.6996,4.3479,4.25,4.5646,4.4531,4.5669,4.7967,5.1481,5.2271,4.8211,4.2788,3.9565,4.0402\n4.1409,3.8697,3.3399,3.65,4.2925,4.391,4.368,4.4471,4.5755,4.6056,3.9615,4.3817,4.0347,4.8809,4.9862,4.7434,4.5683,4.894,5.232,4.4785,4.381,5.1645,5.509,5.7568,5.7635,5.815,5.4914,5.3416,5.4354,4.7977,4.4788,4.7752,5.1714,5.2635,5.2796,5.2403,5.3578,5.2306,4.949,4.9176,5.0448,5.1187,5.0958,4.768,4.4348,4.2562,4.2934\n4.674,4.5441,4.0692,4.0967,4.3238,4.3646,4.2513,4.9393,4.8559,4.1864,3.7842,4.3688,4.5272,5.2983,5.537,5.3867,4.805,4.8778,5.5663,5.397,4.7593,5.4993,6.2048,6.2761,6.08,6.3777,6.4382,5.9629,5.8448,5.7431,5.5715,5.677,5.6015,5.577,5.5064,5.6142,5.5923,5.4183,5.2709,5.4178,5.2406,5.2968,5.2043,5.0165,5.0068,4.7459,4.6315\n5.1419,5.1035,4.7538,4.4755,4.2624,4.7149,4.1699,4.7268,4.8136,4.4346,4.327,4.8522,4.6985,5.3693,5.8689,5.605,4.9168,4.7644,5.0661,4.5735,3.9943,5.2501,5.8299,5.8861,6.6893,7.5536,7.5592,7.0234,6.653,6.1863,6.0352,5.8869,5.7895,5.7478,5.6703,5.7437,5.9751,5.8949,5.708,5.9592,5.9144,5.8674,5.6985,5.5555,5.4363,5.1233,4.8664\n5.1107,4.8136,3.4987,2.9915,3.0469,4.3523,4.4959,4.7698,4.7405,4.7594,4.4702,4.1063,4.279,5.2962,5.7306,5.7411,5.5974,5.7452,5.1386,4.5721,4.7007,5.5674,6.239,7.2147,7.6745,7.7013,7.5792,7.5278,7.1584,6.7973,6.8532,6.2286,5.9236,5.9023,5.9787,6.1941,6.569,6.6233,6.1638,6.0462,6.1434,6.1001,5.9581,5.7648,5.7609,5.5585,4.8342\n5.2334,4.4629,4.1958,4.2441,3.9955,4.4554,4.7172,4.5796,4.6464,5.1184,4.7043,4.0567,4.7797,5.8349,6.0826,6.19,6.5563,6.53,6.1498,5.6299,5.4533,5.5742,6.2497,7.6378,7.917,7.1326,7.0942,7.4751,7.4437,7.2449,7.3587,7.1772,6.7636,6.725,6.8003,6.944,7.1174,7.3986,7.1569,6.6292,6.5244,6.8102,6.7225,6.3995,6.2718,5.3607,4.6152\n4.5111,4.522,4.7383,4.7362,4.1808,4.4337,4.3223,4.4774,5.577,5.786,5.2274,5.7603,6.3554,6.7693,6.8407,6.8601,6.9381,6.4034,6.0986,6.1818,5.7906,6.1971,7.1026,7.9208,8.4301,8.3132,7.6937,7.5657,7.9757,8.0045,7.8219,7.4441,7.2799,7.5412,7.37,7.201,7.3883,7.7581,7.9974,7.7128,7.096,7.1981,7.3531,6.921,6.6607,6.248,5.853\n5.5358,4.8666,4.4625,4.7766,4.8732,5.1581,5.2259,4.6997,5.4614,5.3382,4.9538,5.2776,5.5681,5.6001,6.0006,6.3025,6.4368,6.3152,6.3042,6.4354,6.677,7.102,7.5607,7.9269,8.7352,8.7554,8.1695,7.9346,8.3954,8.3683,7.9786,7.6461,7.7743,7.8975,8.0777,7.5575,7.6981,8.0728,8.272,8.3913,7.7581,7.5647,7.3857,7.1402,6.9678,7.1589,7.1724\n6.1423,5.8746,5.4106,5.038,4.9257,4.696,5.478,5.5336,5.9215,5.8244,5.6988,6.1973,6.0192,5.4524,5.5644,5.8389,6.117,6.8183,6.7423,6.011,5.3451,6.6089,7.9669,7.8141,8.7153,9.1464,9.1418,8.7692,8.3739,8.1958,8.4148,8.2927,8.4595,8.6037,8.3481,7.975,8.4499,8.2409,8.1684,8.6297,8.5395,8.1281,7.8909,7.7342,7.2605,7.2663,7.454\n7.5849,6.6887,5.4379,4.6698,2.9074,3.2557,4.8761,6.1627,6.7352,7.1373,7.4382,7.2586,7.0216,6.945,6.634,6.9759,7.1973,7.3533,7.4082,7.0542,6.5674,6.6841,7.237,8.2206,9.0893,9.9024,9.8615,9.6135,9.1482,8.8499,8.8245,8.8799,8.9033,8.794,8.6337,8.543,9.2085,8.5837,8.2003,8.0058,8.1071,8.3605,8.3005,8.0814,7.9467,7.7039,7.5607\n6.038,6.1518,5.7391,6.0761,5.9555,8.5116,8.3263,6.7729,7.0465,7.5823,8.0641,7.3818,6.7737,6.8283,7.4349,8.1617,8.1591,8.1195,8.3819,8.7977,7.7287,6.5815,7.1286,8.017,8.5774,9.675,10.083,10.032,9.8529,9.1989,8.9519,8.8588,8.8694,8.4738,8.4199,8.615,8.4566,8.123,7.4235,7.2831,7.3561,8.0035,8.5014,8.2848,8.0406,7.8577,7.5246\n6.702,6.5328,6.1113,6.0777,7.2763,7.7613,7.3663,6.7203,7.5216,7.8122,7.5601,7.4611,7.1961,7.1244,7.6058,8.2132,7.9898,7.7305,8.2163,8.7005,8.7316,8.6863,8.2075,8.8899,9.0862,9.7104,10,10.447,10.711,10.476,9.579,9.2744,9.1063,9.0883,9.5817,9.3147,8.8779,8.7619,7.7847,7.5234,7.7918,8.381,8.6523,8.6815,8.4496,7.7308,7.3198\n6.4834,6.6272,6.8203,7.3876,8.6541,9.7976,8.8558,6.8219,7.3902,8.2843,8.5586,8.3749,8.0148,8.2063,8.5644,8.2686,8.1331,8.1643,8.3614,8.8976,9.4574,9.1478,8.9261,8.9364,9.7836,10.677,11.014,11.651,11.378,11.17,10.965,10.767,10.028,9.7785,9.5647,10.065,9.8089,9.3501,8.7038,8.3442,8.4992,8.8045,8.6065,8.5193,8.4434,8.1644,7.6454\n7.1604,7.7819,7.094,6.5435,6.2575,6.8477,7.5061,6.6581,8.6172,8.9716,8.8401,8.834,9.2097,10.267,9.912,8.5808,9.2,9.4223,8.9131,9.7904,10.08,9.8798,9.5046,9.8674,10.506,11.066,11.641,12.035,11.695,11.421,11.106,10.869,10.801,10.666,10.445,10.403,10.308,9.6644,9.2178,8.9746,8.8554,8.8331,8.5829,8.5024,8.4149,8.4001,7.7621\n7.6022,8.5196,7.8777,6.5554,5.7918,5.5744,5.5482,7.5824,10.192,9.1861,8.7092,9.5404,10.23,10.667,10.551,10.412,10.76,10.521,10.174,10.124,10.501,10.319,9.9706,11.128,11.943,11.849,12.179,12.134,12,11.534,11.302,11.148,11.021,10.934,10.867,10.594,10.26,10.031,9.9675,9.8227,9.9451,9.4992,8.6851,8.8127,8.9377,8.5516,7.8233\n8.8024,8.3763,7.3168,6.4252,5.583,6.0152,7.0711,8.0798,8.6859,9.1835,9.4875,10.424,11.377,11.318,10.855,10.5,10.807,10.965,10.528,10.091,10.209,10.855,11.524,12.3,12.573,12.189,12.27,12.909,12.586,12.491,12.497,12.181,12.24,11.888,10.897,10.576,10.209,10.122,10.263,10.237,10.019,9.4562,9.0178,8.9743,9.0839,8.8236,8.7115\n7.8112,7.0481,6.9235,7.1526,6.985,7.1449,8.0352,8.3577,8.4692,9.1172,9.7149,10.495,11.238,11.279,10.484,9.6891,9.6692,11.064,11.565,11.469,11.64,12.14,13.603,13.276,13.332,14.313,14.339,14.564,14.343,13.948,13.583,13.303,12.631,11.556,10.826,11.143,11.29,10.734,10.548,10.559,9.5102,8.9673,9.0557,9.3215,9.2369,8.9454,8.9758\n8.6722,8.7964,7.9033,6.9782,6.3656,6.7492,7.8068,8.8367,9.2662,9.0327,9.6856,10.351,10.971,11.092,10.635,10.225,11.354,12.41,12.37,12.506,13.192,14.554,14.69,15.649,NaN,16.098,16.104,15.052,14.724,15.453,15.303,13.436,12.246,11.401,11.136,11.393,11.785,11.447,11.235,11.417,10.285,9.4939,9.5373,9.456,9.149,8.8018,8.4903\n8.4221,8.0738,7.4079,6.4447,6.4838,7.0235,8.108,8.7839,9.1863,9.5803,9.8298,10.197,10.155,10.259,10.861,11.521,11.862,12.836,13.983,14.81,15.201,15.861,16.895,18.171,NaN,NaN,NaN,NaN,15.92,16.974,17.429,17.096,15.973,13.743,12.966,12.083,11.787,11.362,11.164,11.24,10.62,9.9427,9.9948,9.7202,9.2502,8.7813,8.7747\n8.0062,6.9054,5.652,6.3751,6.8722,6.7798,7.4932,8.4694,9.0119,9.9909,9.8811,9.4862,9.862,10.757,12.072,12.901,13.025,13.749,15.113,16.184,16.719,18.108,19.539,21.014,NaN,NaN,NaN,NaN,NaN,NaN,NaN,19.796,18.603,16.238,14.332,12.663,11.78,11.982,11.517,10.223,10.484,10.577,10.665,9.9734,9.4393,9.4511,9.2844\n6.1542,5.6425,6.0098,6.5448,6.2608,6.7376,7.8555,8.5467,8.4255,9.0223,9.1265,9.3161,10.858,11.685,12.578,13.248,13.814,14.831,15.592,16.471,16.965,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.168,16.855,14.39,13.655,12.952,12.728,12.6,12.246,11.707,11.632,11.085,10.033,9.5251,9.4336,9.1727\n3.8785,4.9549,6.2558,5.3543,6.1496,7.0051,7.4846,7.9616,8.19,8.7047,9.1026,9.5932,10.842,11.931,12.57,12.998,14.495,15.712,16.596,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.844,14.153,14.297,13.973,13.611,12.61,12.107,12.364,11.18,10.267,9.7743,9.2624,8.9921\n7.3652,6.0606,6.7097,7.1107,6.8625,6.5148,6.8339,6.9398,7.325,8.3833,10.042,10.919,11.29,12.339,13.748,14.639,15.727,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.221,13.966,15.314,14.855,13.794,12.793,13.058,13.321,11.593,10.8,10.142,9.2085,9.1666\n7.6991,6.5474,6.1446,8.4443,9.2939,8.7788,8.3293,8.7109,8.8592,9.2803,10.538,11.737,11.945,12.308,13.82,15.661,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.717,15.471,13.72,12.599,12.788,13.338,12.208,11.355,10.094,9.2351,9.4049\n9.4145,8.5442,7.6341,8.2577,9.3899,9.2315,8.7116,8.3592,8.9959,10.369,11.532,12.002,11.917,12.575,15.28,15.822,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.838,13.573,13.174,12.801,12.828,12.151,10.469,9.6819,9.5817,9.7588\n10.61,11.218,8.5744,8.0119,8.9934,9.8641,9.6664,10.129,10.303,10.719,12.382,12.393,12.785,13.341,15.022,16.032,17.641,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.622,15.141,13.129,12.542,12.25,11.304,10.652,9.9155,9.7695,9.2884\n10.179,10.875,8.7589,7.9258,8.6064,9.281,9.263,9.7585,10.212,10.995,12.011,12.734,13.152,13.91,14.998,14.811,14.57,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.266,19.493,15.935,13.095,12.499,12.35,11.699,11.349,10.518,9.6761,8.9086\n7.7283,7.7177,7.7764,7.7121,8.3694,9.0989,9.5538,9.3257,10.073,11.09,11.987,12.385,13.448,15.449,17.703,14.522,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.219,22.659,21.733,18.614,14.905,12.999,12.509,12.325,12.207,11.731,10.679,9.6082,8.5399\n7.6603,7.0642,7.388,7.7894,8.1266,8.9609,10.036,10.58,11.058,11.443,12.022,13.088,13.614,16.534,15.979,13.018,13.058,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.964,22.136,18.559,15.775,13.546,12.169,11.531,11.292,11.085,10.888,9.9861,8.9093,8.3258\n8.1324,8.0757,7.8825,8.044,8.2395,8.8862,9.954,10.469,10.64,11.304,11.856,12.485,12.317,12.13,11.349,12.959,13.856,10.708,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.488,17.248,15.196,13.752,11.445,11.248,11.208,10.72,10.312,9.9551,9.0057,8.1485\n7.8234,8.1616,8.1999,8.0997,8.3057,9.0378,9.7318,10.044,10.271,10.872,11.973,12.776,12.242,12.16,12.469,13.814,NaN,NaN,12.435,9.5851,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.202,16.027,14.9,13.65,11.412,10.961,11.19,10.724,9.9578,9.8326,8.9704,8.2341\n7.1692,7.4512,7.6702,8.015,8.156,8.5923,9.0985,9.3444,9.5857,10.693,11.65,12.038,11.757,12.236,12.42,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.724,14.069,13.408,12.706,11.278,11.163,10.952,10.113,9.5104,9.1969,8.362,7.6035\n6.8027,6.7057,7.3847,7.1746,7.9122,8.5735,9.149,9.4499,9.5955,10.085,11.003,11.885,12.273,12.499,11.301,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.382,13.366,12.545,11.194,10.905,10.434,9.7742,9.5085,8.7218,8.6314,8.084,7.4689\n6.7728,6.6011,6.8015,7.1297,7.8415,8.1959,8.4738,8.9698,9.623,10.362,11.22,12.812,12.87,12.586,11.693,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.616,14.309,13.344,11.24,10.166,10.18,9.8018,9.0237,8.6242,8.7504,8.3144,8.2684\n7.8671,7.4322,7.1309,7.8559,8.255,8.425,8.5621,8.8317,9.4644,9.8034,10.597,12.107,12.599,12.222,11.727,10.948,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.654,13.903,12.563,10.249,8.9614,9.575,9.2471,8.518,8.4568,8.2499,7.9591,8.3359\n7.6865,8.0381,7.9677,8.3941,9.0385,9.0442,9.1091,8.9395,9.5277,9.707,9.9542,10.4,11.122,11.292,11.032,10.73,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.296,13.023,13.036,11.563,9.4444,8.9749,8.9522,8.6431,8.1754,7.3772,6.883,6.8499,7.8198\n7.7617,7.9099,8.0411,8.1959,8.7289,9.287,9.6125,9.815,9.9977,9.6643,9.6253,10.052,9.9209,9.7837,10.192,9.5827,10.033,10.404,10.646,11.969,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.859,13.549,13.193,12.454,11.524,11.392,9.9682,9.6265,8.8881,8.6548,8.7975,8.6503,7.6753,6.5946,5.7423,6.7054\n7.5807,7.481,7.4014,7.528,7.9444,8.3458,8.5058,8.8754,8.8419,8.5167,8.9192,9.3888,9.3294,9.319,9.4935,9.7959,10.058,9.9883,9.4508,10.513,12.626,13.482,13.824,NaN,NaN,NaN,NaN,NaN,10.083,NaN,11.66,12.062,11.8,10.749,10.281,10.006,8.4475,6.7438,8.2262,7.7458,7.8526,7.7075,8.1968,8.0155,7.0786,6.2788,6.4735\n7.4937,7.1657,7.3473,7.494,7.5039,7.733,7.9371,8.0284,7.7083,7.891,8.2802,8.6284,8.7403,8.6234,8.5297,8.9456,8.9849,8.8983,8.9808,9.0749,9.7308,11.367,11.109,10.029,NaN,NaN,NaN,7.991,8.8951,9.0385,9.525,9.8711,9.6509,9.7588,9.9274,8.8637,6.7516,6.2197,7.6293,6.9535,6.6761,6.6402,6.5354,6.7465,6.3622,6.3897,6.9253\n7.4014,7.3238,7.1674,7.1008,7.2264,7.9358,7.9964,7.6154,7.4294,7.577,8.0274,8.4121,8.4197,8.1423,8.0009,8.3658,8.3013,8.3297,8.6548,8.5443,8.4897,8.7895,8.0405,7.7926,9.0624,8.7565,7.2058,8.0351,8.6225,8.3957,8.3913,8.6729,8.4406,7.9615,8.2212,7.6839,6.5448,5.8741,7.3406,7.2156,6.8166,7.0351,6.2514,5.7005,5.2319,5.4333,6.4853\n7.6765,7.6128,7.2426,7.1058,7.281,8.0065,7.6944,7.1957,6.7643,6.887,7.5206,7.8227,7.673,7.4522,7.1114,7.0293,7.135,7.3531,7.5151,7.5623,7.4753,7.2835,7.0677,7.6742,7.8469,7.5162,6.8097,6.7648,6.9489,7.555,7.4908,7.3656,7.0803,7.97,8.8537,8.2372,6.9178,6.2844,6.7897,7.1974,7.3588,7.4928,6.6792,5.6206,5.4242,5.7602,6.292\n7.4911,7.613,7.3641,6.9756,7.1906,7.273,7.036,6.8265,6.4979,6.0437,6.3858,7.12,7.1625,6.9709,6.6624,6.4153,6.8858,7.0627,7.1714,6.8418,6.9988,6.6778,7.0979,7.4993,7.3937,6.7325,5.9315,5.406,5.7405,6.6654,6.651,6.5829,6.6094,8.1329,8.6436,8.5926,7.1174,6.8491,6.5951,6.7173,7.5796,7.4431,7.4345,6.1746,5.55,5.585,5.9721\n7.0876,7.2296,6.9671,6.3904,6.3249,6.4942,6.4399,6.4137,6.2939,6.0348,5.9776,6.5653,6.6988,6.6903,6.505,6.3775,6.8661,7.3844,7.1034,6.9258,6.6766,6.5252,7.1702,7.3127,6.86,6.5187,5.7767,5.3906,5.9713,6.4419,6.2517,6.3729,6.6629,7.4101,7.6022,6.9406,6.4715,6.261,6.2418,6.7193,6.8765,6.6613,6.4045,5.6824,5.4386,5.3678,5.7133\n6.3119,6.5239,6.3627,6.1835,6.2301,6.2532,6.2005,6.3054,6.1177,5.4695,5.3285,5.4009,5.937,6.5735,6.2794,6.0661,6.2538,6.5132,6.4804,6.7257,6.728,6.4943,6.9271,6.9663,6.4367,5.6584,5.495,4.8587,5.7758,6.3792,5.8923,6.1749,6.5882,7.3606,7.2098,6.5417,6.0217,5.7644,5.8957,6.2803,6.0728,5.9247,5.5834,5.4857,5.6578,5.5555,5.5841\n6.8034,6.6892,6.3325,5.9955,6.1078,6.0275,5.7342,5.6674,5.8131,5.5666,5.41,5.6969,6.0518,6.2723,6.1369,5.9306,5.786,5.7917,5.9814,6.0176,6.252,6.1094,6.2675,6.153,5.8948,5.7039,5.4439,5.2361,5.6824,5.9595,5.7895,5.7718,6.1623,6.9421,6.361,5.8667,5.6697,5.4666,5.2678,5.4804,5.4338,5.4501,5.4071,5.3238,5.6975,5.7123,5.8224\n6.8552,6.7321,6.5932,6.4982,6.5186,6.0965,5.6957,5.5814,5.6189,5.58,5.5557,5.8734,6.2367,6.3811,6.2681,6.0377,5.9339,5.9068,6.3715,6.6502,6.2991,5.9831,5.7866,5.6796,5.6247,5.5152,5.3242,5.0551,5.2283,5.4757,5.6126,5.6481,5.8892,6.0845,5.7432,5.7029,5.6904,5.6236,5.1889,5.0108,5.0783,5.045,5.322,5.4554,5.5186,5.6355,5.3992\n6.6331,6.0194,6.6284,7.5452,7.2036,6.0867,5.1719,5.2256,5.3177,5.5576,5.3549,5.6488,5.6833,6.0898,6.0995,5.9542,5.6371,5.4178,5.4571,6.2204,6.1422,5.7278,5.4396,5.5118,5.5213,5.4122,4.864,4.7357,4.7583,5.3313,5.5797,5.0764,4.4445,4.8492,4.8613,5.5037,6.0689,5.8732,5.3935,5.1023,5.1405,4.8512,4.8999,4.9148,4.7469,4.9972,4.9975\n7.1772,6.3637,5.613,6.6738,7.1575,6.1718,5.2257,4.612,5.1942,6.3147,5.9767,5.8644,5.5783,5.4068,5.6979,6.0043,5.4109,5.1714,5.2039,5.7052,5.7203,5.2624,5.431,5.7048,5.2829,5.1937,3.9429,3.7303,4.1084,5.2304,5.2181,4.6794,4.4664,4.3986,4.5461,5.2724,5.8529,5.6432,5.4715,5.3373,5.0888,4.8655,4.7574,4.6955,4.4542,4.6279,4.6666\n6.6647,5.8966,5.7149,5.9502,6.233,5.7405,5.4411,5.8864,6.6086,7.3324,6.5787,6.4621,5.9717,6.1396,6.22,6.4051,6.136,5.2392,5.332,5.5315,5.4859,5.2179,5.31,5.3211,5.2504,5.0747,4.1821,3.4673,3.8999,4.9419,5.1222,4.6388,4.5614,4.4114,4.4071,4.926,5.3921,5.3308,5.3445,5.4483,5.0691,4.5263,4.4168,4.1286,3.6928,3.9577,4.1405\n7.4132,7.4085,6.7266,6.8039,6.6582,5.7994,5.8027,6.0595,6.7428,7.3963,6.0964,6.3647,5.91,6.6079,6.3887,6.2401,6.0312,5.6771,5.3943,5.309,5.3277,5.1922,5.3896,5.3565,5.1761,4.9166,4.5777,3.7836,4.5803,5.4129,5.2017,4.7597,4.4504,4.1259,4.2324,4.509,4.8244,5.0612,4.8128,5.0729,4.8198,4.4369,4.3601,3.6753,2.9752,3.4663,3.6592\n8.1645,8.7584,8.239,7.0106,6.7454,6.8165,6.1135,5.9396,6.1377,6.4005,5.6691,4.7966,5.2652,5.7937,5.8691,5.6912,5.7854,5.7509,5.2358,5.1474,5.175,5.3145,5.4101,5.22,4.9325,4.6146,4.2321,3.9995,4.4545,4.6167,4.6538,4.3691,3.8045,4.0637,4.4186,4.5362,4.5667,4.7024,4.7467,4.8005,4.6399,4.5087,4.4547,4.1218,3.0948,2.9613,3.1864\n7.4843,8.0892,8.157,6.7652,6.1074,6.3804,6.5737,5.8641,6.2616,6.1216,5.8058,3.9119,2.5342,4.343,4.7831,5.0839,5.6364,5.636,5.2128,5.0022,4.7104,4.5319,5.0467,4.9078,4.53,4.259,3.8396,3.6353,3.6193,4.0566,4.2753,4.0441,3.4391,4.0523,4.0332,4.2251,4.3517,4.5917,4.8134,4.8536,4.536,4.1831,4.3487,4.5394,3.9815,3.8513,3.8804\n6.6855,8.0181,8.432,7.2267,5.8739,5.9461,6.9451,5.4096,4.9707,6.5245,7.2066,5.2062,3.6368,3.1472,5.2264,5.3731,5.9887,4.9994,4.5419,4.9302,4.7908,4.2869,4.0435,4.3525,4.4914,4.109,3.7266,3.5239,3.4977,3.9043,4.0918,4.0935,3.9424,3.6268,4.0581,4.493,4.5879,4.9434,5.0718,5.0794,4.5634,3.9907,3.9677,3.7999,3.588,3.5021,3.4344\n7.6308,7.5205,7.2582,6.5092,5.8984,5.5532,6.256,5.3855,4.1245,2.4271,5.3128,6.7246,6.3249,5.3508,4.9594,5.7358,6.7112,5.3354,4.9855,5.0496,5.1375,4.7056,4.2468,4.4677,4.725,4.4099,4.0363,3.3277,3.2417,3.642,3.7846,3.6774,3.5761,3.652,4.4473,4.6203,4.6139,4.812,4.9078,4.9917,4.6546,4.1482,4.0208,3.5649,3.0054,3.045,3.3951\n7.1028,7.2519,7.0326,6.5195,6.3232,5.6805,5.9909,5.7137,4.5273,1.2167,2.4001,5.3605,5.6998,5.4734,4.8348,4.7253,6.3401,6.739,4.8897,4.7276,4.6858,4.3381,4.3754,4.8108,4.8268,4.0806,3.5721,3.5522,3.7967,4.0927,3.8043,3.3383,3.3344,3.5089,4.3743,4.4956,4.5611,4.7028,4.6943,4.6516,4.4562,4.1412,4.4565,4.1219,3.3256,2.7309,3.3207\n8.5256,8.841,8.9886,8.4204,7.465,6.7689,6.291,5.917,5.1979,5.0675,4.5934,5.279,4.5092,4.4272,6.0703,7.0408,10.069,7.7005,5.5099,4.6899,4.2249,4.3034,4.565,4.4135,3.8551,3.5143,3.5382,3.9479,4.5566,4.4169,3.879,3.2612,2.8849,3.0601,3.8499,4.4102,4.6633,4.6496,4.666,4.8677,4.4064,3.9658,4.037,3.9519,3.4161,2.9228,3.0436\n7.9143,9.4909,10.044,9.2079,8.4343,7.2015,6.8616,7.0631,7.1361,7.1638,7.7584,5.8049,2.7513,4.0087,4.3375,6.2463,7.6394,6.0227,5.7129,5.0781,4.0661,3.8754,4.8547,4.5226,3.9353,3.3579,3.9808,4.5762,4.4996,4.1749,3.9174,3.3652,2.8215,2.8492,3.4295,4.2744,4.6279,4.6442,3.9973,4.5144,4.759,4.0645,3.7845,3.7399,3.3993,3.3191,3.4995\nNaN,7.9371,8.7704,9.1069,8.6515,7.9215,7.6913,7.3938,7.2396,7.0275,6.6706,3.8069,2.7955,5.0851,5.9055,7.7345,6.339,5.5247,5.4755,4.752,4.1652,3.6339,4.5527,5.4765,5.4102,4.3535,4.279,4.9409,4.8458,4.4877,4.3124,4.0468,3.4796,3.3726,3.9112,4.1783,4.3509,4.4491,4.5661,4.7421,4.7849,4.411,3.8851,3.492,3.6459,3.7295,4.0812\nNaN,NaN,NaN,7.771,7.4958,8.3394,9.1184,9.5943,9.9674,10.671,6.8545,4.8562,5.6595,6.573,7.6667,7.8911,5.7026,5.3621,5.2824,3.8527,3.5934,3.664,4.5062,5.5264,5.6345,4.3616,3.5929,3.4977,3.7345,3.9437,3.8343,4.1216,4.117,3.967,4.4589,4.1908,4.0854,4.3251,4.5159,5.0753,5.2081,4.8566,4.023,3.5503,3.54,3.8809,4.2208\nNaN,NaN,NaN,NaN,NaN,6.5848,8.8809,8.8549,8.2282,8.0469,7.1387,6.8733,6.1548,6.5929,NaN,NaN,9.204,7.0012,5.3904,4.3226,5.3324,4.8217,4.4094,5.2711,5.5022,4.5035,3.7763,3.3357,3.781,3.8405,3.8856,3.5879,4.3232,4.3004,4.1381,4.2936,4.8817,4.782,4.695,5.1362,5.02,4.5324,4.1134,3.9173,3.8509,4.0525,4.2635\nNaN,NaN,NaN,NaN,NaN,NaN,8.9991,8.4556,7.6975,6.8006,6.6332,8.1963,9.6876,10.24,NaN,NaN,NaN,NaN,5.965,5.5803,6.3762,5.486,4.7647,4.8844,4.6518,4.0897,3.9847,3.8241,3.8736,4.5769,4.3698,4.3889,4.5137,4.5458,3.892,4.4805,4.984,4.9395,5.2692,5.1804,4.6763,4.2826,4.0047,3.9243,4.2845,4.8293,4.9366\nNaN,NaN,NaN,NaN,NaN,NaN,7.6607,7.2238,7.3401,6.9403,7.3098,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.6201,5.9235,6.4603,5.9883,4.7966,4.692,4.4987,3.8957,4.5241,5.0696,5.0086,4.7762,4.7553,4.1535,4.0375,3.9111,3.8099,3.9641,4.382,4.6367,5.1803,4.8267,4.7823,4.9174,4.4503,4.2824,4.3243,4.303,4.883\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.8054,7.2113,8.3176,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.2317,4.3761,5.8177,5.6921,5.0494,5.0696,5.3445,5.5685,5.6741,5.8883,5.4969,4.9559,4.497,4.0759,4.0714,3.7146,3.5356,3.713,4.4631,4.6854,3.9731,4.6152,5.3194,5.271,4.7328,4.4785,4.1914,3.8768,4.4104\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.3541,7.6403,7.8875,6.2336,5.0616,NaN,NaN,NaN,NaN,4.8538,3.8965,4.6642,5.8877,5.7257,5.0015,5.0489,5.2618,5.7151,6.119,6.3063,6.0144,4.8872,3.9881,3.727,3.9912,4.3081,4.7158,4.16,4.8026,5.2163,4.6906,4.8839,5.1466,4.8242,4.6352,4.0661,3.5438,3.5735,3.9618\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_061211-070813.unw.csv",
    "content": "-5.3364,-5.1647,-5.5865,-5.2331,-5.4699,-6.3287,-6.3679,-6.2391,-5.5934,-5.4819,-5.8617,-6.2975,-5.0748,-4.7269,-4.8018,-4.6904,-4.6219,-4.7182,-4.6471,-4.8234,-5.8734,-5.6348,-4.6798,-3.2558,-2.7368,-3.1783,-3.9254,-4.7071,-5.126,-4.9252,-4.438,-3.8051,-4.1778,-4.9203,-5.6227,-6.0126,-6.8872,-7.1635,-7.4657,-8.0343,-7.3682,-6.1097,-5.4434,-5.4201,-4.9141,-4.3198,-4.5235\n-5.8265,-5.3197,-5.4233,-5.1688,-5.1363,-6.0594,-6.1791,-6.032,-5.7596,-5.3332,-5.0829,-5.2975,-4.8037,-4.4915,-4.3665,-4.6159,-4.8802,-4.9772,-4.7084,-4.7106,-4.7706,-4.996,-4.35,-3.3064,-3.3024,-3.7078,-3.6157,-4.2388,-4.3109,-3.8656,-4.0504,-4.7608,-5.5555,-5.7631,-5.3816,-5.4322,-6.0499,-6.3263,-6.1207,-5.827,-5.6313,-5.2718,-5.126,-5.0456,-5.2844,-5.5115,-5.4311\n-5.8093,-5.3433,-5.2465,-5.4434,-5.4622,-4.9815,-5.4873,-5.5921,-5.9668,-5.0837,-4.6493,-4.1079,-4.6408,-4.7881,-4.4097,-4.6536,-5.1665,-5.4973,-5.6097,-4.9298,-4.8916,-5.26,-5.2751,-3.8409,-3.811,-4.3942,-3.6015,-3.4669,-4.3116,-3.7242,-3.7442,-5.1591,-6.3144,-6.6381,-6.0784,-6.0426,-5.7695,-5.2047,-5.3587,-5.217,-5.2487,-4.6795,-4.4748,-4.6088,-5.465,-6.113,-5.9293\n-6.0058,-4.883,-4.9835,-5.5543,-5.3398,-5.1473,-4.7704,-4.0782,-3.9016,-4.2602,-4.383,-4.4909,-4.7941,-5.065,-4.6804,-5.0032,-5.8372,-6.2054,-6.231,-5.2616,-4.0192,-3.8923,-4.027,-3.8664,-3.6766,-4.4184,-4.253,-3.5598,-4.1327,-4.6093,-4.3818,-4.9219,-5.3579,-5.7866,-5.8386,-5.8833,-5.9643,-5.3737,-5.7191,-5.242,-4.3052,-3.8914,-3.976,-4.3871,-4.9843,-5.3099,-4.7337\n-5.7999,-4.7747,-5.2845,-6.0912,-5.4967,-5.3852,-4.7893,-4.2854,-4.474,-4.8729,-5.9916,-6.2121,-5.1373,-5.2116,-5.5236,-5.3341,-5.1161,-5.9531,-6.7047,-6.711,-5.4391,-3.5008,-3.7169,-3.6214,-3.4073,-4.1267,-4.4574,-4.3439,-3.4728,-3.8945,-5.0392,-4.9301,-4.9848,-5.082,-5.4323,-5.5481,-5.7545,-6.0005,-6.0127,-4.7881,-3.574,-2.8926,-2.8744,-3.2962,-4.1286,-4.7853,-4.6015\n-6.8287,-6.2076,-4.8602,-6.4235,-5.7611,-5.782,-5.8842,-5.0829,-4.5575,-5.1912,-6.0171,-6.173,-5.1127,-5.1312,-5.1288,-5.1349,-5.2504,-5.7228,-6.4018,-6.1397,-5.3225,-3.695,-3.1952,-3.4011,-3.8424,-4.4047,-4.4043,-4.2368,-3.6036,-3.4335,-4.5214,-5.5203,-5.3846,-4.6198,-4.7504,-5.4014,-5.9618,-5.7936,-4.8207,-3.6428,-3.1287,-3.3729,-3.5322,-3.277,-3.6872,-3.6062,-3.577\n-6.8305,-7.2759,-7.3076,-7.0316,-6.8865,-7.5608,-8.014,-6.7678,-5.3592,-5.2434,-5.8932,-5.8345,-5.3479,-5.8393,-6.0864,-5.3412,-5.3571,-6.0587,-6.6875,-6.2551,-4.9246,-4.4428,-4.2249,-3.8597,-3.4127,-4.1438,-3.3155,-3.3163,-4.2522,-4.3136,-4.6806,-5.5753,-5.1163,-4.884,-4.9095,-5.1778,-5.5238,-5.1833,-4.2415,-3.4701,-3.7765,-3.9928,-3.8096,-3.65,-3.4388,-3.3248,-3.208\n-6.0372,-6.2383,-5.2058,-6.4247,-7.1207,-8.0388,-7.2068,-7.0849,-7.0089,-6.1842,-5.6033,-5.4171,-5.4687,-6.0907,-6.1763,-5.675,-5.5548,-5.5178,-5.7487,-5.8738,-4.8338,-4.074,-4.0819,-3.6423,-2.881,-2.6694,-2.5562,-2.4093,-3.9645,-4.8762,-4.9116,-4.9444,-4.6402,-4.8856,-4.9544,-5.2274,-5.4983,-5.4696,-4.7601,-3.834,-3.9081,-3.6316,-2.8603,-2.656,-2.9678,-3.6603,-3.7181\n-5.2833,-5.8717,-6.5879,-6.7484,-7.3313,-7.7666,-6.589,-5.899,-4.6746,-4.8651,-5.6678,-5.8505,-5.7679,-5.3924,-5.0428,-5.7296,-6.3261,-6.3043,-5.6218,-5.5621,-5.1307,-4.0219,-2.5452,-3.0831,-3.0698,-2.6328,-2.9171,-3.1155,-2.4026,-3.0284,-4.2059,-4.0924,-4.1476,-4.4183,-4.5407,-4.7619,-4.7706,-4.8137,-4.5374,-4.2885,-4.1193,-3.6348,-2.9992,-2.952,-3.1087,-3.2079,-3.1061\n-5.5836,-5.8603,-6.8208,-7.3487,-8.1715,-6.7292,-5.6031,-5.0817,-3.715,-3.6257,-5.4303,-5.3115,-5.4344,-4.8994,-5.2103,-5.822,-6.0295,-6.1055,-5.4608,-5.0559,-4.5515,-4.0339,-3.1989,-2.608,-2.6375,-2.9434,-2.9025,-3.0216,-3.856,-4.2555,-3.6807,-3.4982,-4.3032,-4.5698,-4.4712,-4.3787,-4.295,-4.0404,-3.9799,-3.9879,-3.9897,-3.6834,-3.116,-2.877,-3.3721,-3.1788,-2.5796\n-5.8948,-6.106,-6.7493,-7.6173,-7.8613,-6.057,-5.1302,-4.364,-4.3643,-4.809,-5.2285,-4.8172,-4.7994,-4.0917,-4.1561,-5.0932,-5.4402,-5.923,-6.9328,-6.5487,-4.1024,-3.2753,-2.8171,-2.1496,-2.3662,-2.4942,-2.585,-3.077,-3.9207,-4.4986,-4.3745,-4.7854,-5.0888,-4.9547,-5.2113,-5.2277,-4.8334,-4.3319,-3.9512,-3.6656,-3.8394,-3.6092,-3.2812,-2.7596,-2.7773,-2.577,-2.7058\n-6.2036,-6.3432,-6.9977,-7.0245,-8.1236,-7.5455,-6.0461,-4.7783,-4.9092,-4.8935,-4.2942,-4.5695,-4.5357,-3.9311,-4.1331,-4.7102,-5.0998,-6.4333,-7.3752,-6.9187,-5.272,-3.6365,-3.0733,-2.6224,-2.3404,-2.6412,-2.3162,-2.7114,-3.7552,-4.0577,-4.8649,-5.7561,-5.6426,-5.3208,-5.8981,-5.4912,-4.171,-4.0347,-3.8832,-3.5645,-3.5573,-3.5795,-3.388,-2.6853,-2.4073,-2.0253,-2.3553\n-6.9895,-7.2737,-8.2995,-9.3073,-9.5405,-7.6406,-5.4263,-5.613,-5.7066,-5.2074,-4.8786,-5.2092,-4.712,-4.0968,-4.5445,-4.6455,-5.0138,-5.5987,-6.3793,-5.2948,-3.8514,-3.8533,-3.3915,-2.1052,-2.0806,-2.6823,-2.7028,-2.8518,-3.9561,-4.4052,-5.1158,-6.4035,-6.1249,-5.1795,-4.8399,-3.8731,-3.2167,-3.5417,-3.8355,-4.0282,-3.704,-3.412,-3.1044,-2.8343,-2.3122,-2.1564,-1.9078\n-7.1591,-7.5415,-7.0239,-5.6798,-6.1679,-5.9096,-5.5638,-5.6168,-5.4494,-4.9709,-5.9442,-6.172,-5.2602,-4.3002,-4.8944,-5.254,-5.0106,-5.2199,-5.0024,-4.78,-4.5483,-4.4543,-4.2064,-2.5417,-1.6803,-2.4613,-3.1185,-2.9982,-3.2353,-4.016,-4.5557,-4.9,-4.6893,-4.0551,-3.2127,-3.0646,-3.4193,-3.5889,-3.6917,-4.0755,-4.0242,-3.3186,-2.9245,-2.993,-2.7619,-1.9155,-1.13\n-5.545,-6.1343,-4.7293,-4.9473,-6.5451,-6.8992,-6.8891,-6.2731,-5.891,-4.795,-4.5568,-4.711,-4.2541,-3.8688,-4.2415,-5.0853,-5.1598,-5.103,-4.948,-4.6301,-5.3208,-4.8876,-4.0354,-2.8442,-0.75882,-1.0625,-2.4085,-3.6839,-3.7617,-2.9403,-3.0485,-3.5261,-3.7075,-3.352,-2.8978,-3.0561,-3.4012,-3.5482,-3.5759,-3.4636,-3.4524,-3.522,-3.0648,-2.9289,-3.0314,-3.5873,-3.3404\n-5.4533,-5.2713,-5.2107,-5.6783,-5.9267,-5.9868,-6.6612,-6.7987,-6.4696,-5.2779,-4.3984,-4.6315,-4.2995,-4.2625,-5.1057,-5.3297,-5.0971,-5.3779,-6.2075,-5.4833,-4.8522,-3.9721,-3.0292,-2.2322,-1.3655,-1.4387,-2.013,-2.9406,-3.5232,-3.4047,-3.7645,-3.7921,-3.0842,-3.126,-3.101,-3.698,-4.1858,-3.9672,-3.7765,-3.1941,-3.3464,-3.8179,-3.805,-3.8603,-3.9484,-4.6273,-4.4198\n-6.2029,-5.7029,-6.0366,-7.0794,NaN,NaN,NaN,-5.744,-5.6376,-5.0282,-5.3408,-5.126,-4.5276,-4.6652,-5.6868,-5.3421,-4.804,-5.5053,-6.6487,-6.7927,-5.0399,-3.5966,-2.608,-1.0982,-0.75518,-1.309,-1.471,-2.1222,-3.2681,-3.6015,-3.5179,-3.251,-2.1332,-1.9102,-2.5927,-3.2531,-3.2674,-3.6092,-3.9481,-3.1642,-3.475,-3.8512,-4.4108,-4.6804,-4.8987,-5.1805,-4.769\n-5.5157,-5.7726,-8.4379,-10.649,NaN,NaN,NaN,NaN,NaN,-4.1759,-3.4086,-4.1965,-4.8062,-5.2529,-4.6659,-4.3487,-4.2623,-5.212,-5.8332,-6.5955,-5.3786,-3.2082,-2.931,-1.7656,-0.40043,-1.5297,-2.5233,-3.159,-3.4052,-3.5853,-2.9806,-2.5073,-2.8654,-2.8112,-3.1815,-3.2846,-2.9493,-3.1894,-3.3859,-2.8802,-3.3627,-4.2777,-4.7419,-4.876,-4.4798,-4.5553,-4.643\n-4.9726,-6.0711,-7.1918,NaN,NaN,NaN,NaN,NaN,NaN,-3.4669,-3.0672,-3.6891,-4.737,-5.6537,-5.0311,-3.9456,-3.5894,-3.5619,-4.739,-5.7694,-4.7005,-3.3043,-1.7933,-2.8,-2.7605,-1.4659,-2.2679,-3.5362,-2.9824,-3.0837,-3.3121,-2.8273,-3.4154,-3.8063,-3.7804,-3.3715,-3.1951,-3.4229,-3.7997,-3.9914,-4.1679,-4.1487,-4.5905,-4.7316,-4.2903,-4.1866,-4.497\n-6.8716,-5.8508,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.9756,-4.4415,-4.0727,-4.5687,-5.4676,-4.888,-3.7818,-3.5811,-4.5944,-4.9824,-5.4171,-4.1813,-3.2275,-2.1616,-3.0412,-3.8354,-2.7219,-1.8988,-1.6083,-2.0399,-1.7958,-2.9056,-2.7967,-2.7703,-3.1798,-3.2574,-3.057,-3.0056,-3.1359,-3.3745,-3.7444,-3.7783,-3.8665,-3.8712,-3.6552,-3.4419,-3.2428,-4.1543\n-6.5925,-4.266,-2.9351,NaN,NaN,NaN,NaN,NaN,-4.0509,-4.1402,-4.0652,-4.5985,-4.8661,-4.7437,-3.7003,-2.7708,-3.1072,-4.3317,-5.4391,-4.9509,-4.3791,-3.6112,-3.2514,-2.9937,-2.2472,-1.5593,-0.64425,0.01224,-0.68799,-1.3394,-2.2092,-2.585,-2.5898,-2.6925,-3.1357,-3.022,-2.7674,-2.6477,-2.3163,-2.6171,-3.291,-3.9523,-3.802,-3.5662,-3.452,-3.7164,-4.3525\n-6.1063,-4.417,NaN,NaN,NaN,NaN,NaN,NaN,-4.7016,-3.0946,-3.555,-4.4722,-4.949,-4.5919,-3.0926,-1.5322,-2.7055,-3.6141,-4.6218,-4.551,-4.1919,-3.943,-3.9098,-2.456,-0.67487,-1.558,-1.389,-1.2632,0.24975,-0.2543,-1.7171,-2.2819,-2.3313,-2.5106,-2.6947,-2.2905,-1.8535,-2.5267,-2.5176,-2.475,-4.1417,-3.8777,-3.488,-3.5269,-3.9304,-4.9847,-5.0569\n-4.8336,-4.4021,NaN,NaN,NaN,NaN,NaN,NaN,-4.9345,-2.9405,-3.1201,-4.2427,-5.1562,-5.2876,-2.7583,-0.10736,-2.262,-3.4107,-3.6034,-3.0056,-1.4174,-2.0789,-1.7589,-1.0577,-0.53878,-2.3309,-3.3311,-3.5846,-1.0573,-0.53765,-0.9919,-1.6301,-1.7695,-1.8366,-2.2886,-2.0961,-2.05,-2.1087,-2.3363,-2.6492,-3.8137,-3.7662,-3.032,-3.1688,-4.4319,-5.2163,-5.3061\n-5.2613,-3.6688,NaN,NaN,NaN,NaN,NaN,-3.2031,-3.733,-4.0255,-3.5708,-3.659,-6.2974,-5.6046,-4.121,-2.1729,-1.436,-2.4703,-2.896,-2.0932,-0.4479,0.75505,0.41806,1.1032,1.9299,0.62104,0.23824,-0.87962,-1.1315,-0.30046,1.4398,0.97483,0.36245,-0.70901,-1.7174,-1.933,-1.1165,-1.4059,-2.1574,-3.2507,-3.7179,-3.5804,-3.2569,-3.2751,-4.252,-5.1005,-4.9533\n-4.0359,-2.6387,-2.8056,NaN,NaN,NaN,-3.9284,-4.0139,-3.8078,-4.1106,-4.4142,-4.3477,-3.9137,-1.8269,-1.9079,-1.798,-1.8408,-1.9979,-2.0541,-0.57836,0.70846,1.3827,0.3337,1.816,3.627,3.2823,2.2297,0.95397,1.535,3.2584,3.1053,1.9329,-0.17286,-1.613,-2.5709,-3.7005,-1.9591,-1.3459,-1.2904,-3.3134,-3.6344,-3.3761,-3.059,-3.4862,-4.3307,-4.7354,-4.5948\n-5.6371,-4.8694,-3.5731,NaN,NaN,NaN,-4.6208,-5.3201,-5.7041,-4.9101,-5.0238,-4.2888,-2.8702,-1.6304,-0.93871,-0.66514,-1.0298,-0.67314,-0.32982,0.87088,1.8628,2.7509,2.9683,4.3099,5.343,5.5682,NaN,NaN,NaN,NaN,NaN,4.4814,-0.4324,-2.3187,-2.6176,-2.8386,-2.0577,-1.8649,-1.965,-3.116,-3.5837,-3.5245,-3.5841,-4.1512,-4.9078,-4.7753,-4.0574\n-8.0791,-5.7062,-4.3019,-5.5389,NaN,NaN,-5.3873,-6.1044,-5.8153,-4.4101,-4.0005,-3.8809,-3.1226,-2.1524,-1.0468,-0.21659,0.028312,0.75792,2.4663,3.2331,3.4812,3.876,4.732,5.6399,7.836,9.1661,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.2244,-1.3092,-1.29,-1.7383,-2.0074,-2.6917,-3.3125,-3.9801,-4.2438,-4.004,-4.4084,-4.7158,-4.3354,-3.9522\n-10.514,-7.9461,-6.8057,-6.6172,-5.7305,-5.2852,-6.4306,-6.5721,-4.7591,-4.0787,-4.0993,-3.8508,-3.1042,-1.6768,-0.36539,0.72513,1.1143,1.8584,2.9901,4.0179,4.1389,4.5141,5.2463,6.6867,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.622,0.57229,-0.95913,-1.5176,-2.2483,-3.6658,-4.0594,-3.686,-3.7361,-3.8219,-3.8855,-3.8641,-3.7157\n-12.247,-10.867,-9.0178,-7.7125,-6.4229,-5.8013,-5.8309,-6.4434,-5.2795,-4.9355,-4.2326,-3.5535,-2.96,-1.5255,-0.2257,0.58506,0.71596,1.75,3.0604,3.5961,3.7898,5.515,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.99884,0.65641,-0.3625,-1.1834,-1.5944,-2.4114,-3.6223,-3.8107,-3.3432,-3.573,-3.6251,-3.4558,-3.3055\n-9.3463,-10.578,-12.66,-13.621,-7.333,-7.291,-7.0621,-6.1678,-4.7595,-4.304,-4.2528,-3.8169,-3.4944,-1.3596,-0.16213,0.57286,1.1068,1.9926,3.0466,3.3705,3.4472,5.7972,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.626,0.74613,0.77265,0.21162,-0.74825,-2.2696,-3.6557,-3.4944,-3.2866,-4.4214,-4.4887,-3.7756,-3.543\n-8.8303,-9.2694,-10.466,-9.5641,-7.6758,-6.5293,-6.6459,-6.185,-5.3908,-4.0853,-2.8499,-2.6915,-3.0086,-1.419,-0.49065,-0.014628,1.0711,2.2428,3.764,5.274,3.7514,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5094,2.5646,1.5151,1.1508,-0.45221,-2.7291,-3.1977,-2.6676,-3.0879,-4.5079,-4.5309,-4.0083,-3.867\n-9.0618,-10.657,-10.949,-6.7054,-5.6852,-5.6225,-5.8428,-6.2666,-6.4096,-4.8478,-1.8949,-1.2222,-1.3353,-1.0294,-0.80031,-0.23581,1.07,1.3101,2.5545,3.2127,0.93468,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5476,0.60599,0.94024,0.46086,-0.82893,-2.4634,-3.0063,-2.6571,-3.1966,-4.6768,-4.3991,-4.3578,-3.5744\n-6.399,-7.9184,-8.4756,-6.7091,-6.2419,-5.4032,-5.8314,-6.1529,-6.4163,-3.7996,-1.664,0.73257,1.6126,-0.22215,-2.4155,-1.2688,0.38191,2.2071,2.4529,2.1458,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.16324,NaN,NaN,NaN,NaN,NaN,NaN,-1.9685,0.12163,0.90969,0.91706,-1.8401,-2.2828,-2.2815,-2.1624,-2.462,-3.4563,-4.1279,-4.3869,-4.4706\n-5.0726,-5.7214,-7.6451,-6.7822,-6.1398,-5.6107,-4.6867,-4.2885,-4.2359,-3.3065,-2.3639,-1.2994,-0.10448,0.031204,-2.1132,-2.1603,-1.6937,0.36143,2.1841,3.4557,NaN,NaN,NaN,NaN,NaN,NaN,-0.57832,-0.92575,NaN,NaN,NaN,-2.9583,-3.4295,-4.5402,-2.9467,-0.39974,3.2835,3.0592,-0.1785,-1.9278,-1.8054,-1.8641,-2.5359,-3.3722,-4.5685,-4.6974,-5.188\n-6.0524,-4.8645,-6.0665,-4.8113,-4.9504,-6.0774,-7.1967,-5.42,-3.8636,-3.0347,-2.6276,-2.0142,-1.9296,-0.98643,-1.7594,-1.6488,-0.90712,0.54699,2.0681,4.6955,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.902,-2.5055,-3.1767,-4.151,-5.7898,-4.6535,1.463,5.3561,5.5676,4.0152,-0.11287,-2.609,-2.3782,-2.3811,-2.514,-3.5514,-4.8195,-5.0416,-5.4451\n-7.4725,-7.8344,-7.381,-5.7936,-5.9694,-6.297,-6.5476,-6.5366,-4.1821,-3.4255,-2.4631,-2.1438,-1.7113,-1.2527,-1.1049,-1.1813,-0.27406,0.78808,2.7545,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.5574,-4.8015,-3.6424,NaN,NaN,NaN,5.8679,2.905,-1.5139,-2.8019,-2.5189,-2.4563,-2.708,-3.9435,-4.6013,-4.5407,-5.7943\n-7.9797,-8.6234,-8.3905,-7.7472,-7.2599,-6.4982,-5.628,-4.7637,-4.3189,-4.1613,-3.3207,-1.849,-1.4909,0.69192,0.53048,-0.49761,-1.1739,0.36167,0.7815,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.0184,NaN,NaN,NaN,NaN,NaN,NaN,-0.46099,-2.6542,-2.3544,-1.9203,-2.8547,-3.8775,-4.8424,-4.7764,-4.9175,-5.8514\n-7.2107,-7.1649,-7.6059,-7.9536,-7.8657,-6.495,-4.8725,-4.0569,-3.8822,-3.7144,-3.1105,-1.5515,-1.4155,-0.26816,-0.25542,-0.97738,-1.3009,0.84793,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.671,NaN,NaN,NaN,NaN,NaN,0.92093,-0.81724,-2.2314,-2.4151,-2.607,-3.5569,-4.3583,-4.5171,-4.3305,-4.6564,-5.6794\n-6.3853,-6.0933,-6.3071,-7.0385,-7.54,-6.513,-4.3357,-3.9602,-4.0413,-4.1081,-3.9328,-3.2914,-2.0376,-1.4545,-1.3721,-1.0864,0.24774,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.6193,-3.153,NaN,NaN,NaN,NaN,0.979,0.27418,-0.74124,-1.877,-2.5539,-2.9361,-3.5652,-4.4359,-4.5151,-4.0765,-4.5678,-5.8512\n-5.9117,-6.206,-6.169,-6.4448,-6.4627,-6.0513,-5.2793,-4.3532,-4.0306,-4.0142,-4.0968,-4.2566,-2.1669,-1.3485,-1.3637,0.18301,1.2483,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.8541,-2.899,-4.0305,-5.8817,-4.4273,-2.8194,-1.0553,-0.7675,-0.84913,-0.23904,-1.0479,-3.2212,-3.4128,-3.7716,-4.546,-4.8955,-4.6594,-5.3064,-6.2122\n-5.3367,-6.2008,-6.128,-6.8689,-6.265,-5.7306,-5.2225,-4.7593,-4.2909,-3.8923,-3.8316,-3.8034,-2.775,-2.2777,-1.6946,0.6816,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.2687,-2.763,-4.1407,-4.2622,-3.6532,-3.6036,-2.1564,-1.8725,-1.8713,-1.4336,-1.3209,-3.1546,-4.1898,-4.5285,-4.9373,-5.0979,-6.1339,-5.4964,-5.3869,-5.904\n-4.9168,-5.4856,-6.0715,-5.8001,-4.9069,-5.2311,-5.2454,-4.49,-3.6895,-3.4147,-3.0531,-3.1326,-3.0855,-3.304,-2.8799,-1.6677,0.18052,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.3982,-5.1318,-5.108,-3.1793,-0.67481,-2.1513,-2.7074,-2.7414,-3.4053,-3.4509,-3.8913,-4.7604,-4.8185,-5.3716,-5.4827,-5.6231,-5.5071,-4.5882,-4.4926\n-5.2836,-5.1506,-5.6043,-4.3874,-4.0841,-4.1273,-3.8731,-3.4367,-3.09,-2.7389,-2.6541,-2.7138,-3.1965,-3.2132,-3.0349,-2.3663,-1.3304,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.3823,-5.8283,-4.5286,-2.0786,-2.0617,-2.2719,-2.3876,-2.4804,-3.6829,-4.2134,-4.8339,-5.0744,-5.1295,-5.1809,-5.289,-5.4024,-5.62,-4.9758,-4.6687\n-5.3163,-4.0231,-3.9554,-4.5581,-3.5396,-3.6134,-3.5412,-3.346,-2.9506,-2.685,-2.7262,-2.8008,-2.7489,-2.8657,-2.0864,-1.335,-1.2628,NaN,NaN,NaN,NaN,NaN,-1.8302,-2.2373,NaN,NaN,NaN,NaN,-5.4597,-3.7911,-3.0332,-3.1949,-2.9816,-3.5164,-3.5192,-4.1923,-4.1178,-4.5037,-4.8737,-5.1113,-4.9678,-4.7775,-5.0001,-5.5735,-6.0252,-6.201,-5.6989\n-4.8757,-4.3781,-4.1484,-4.7623,-4.6303,-3.3382,-3.1648,-3.1058,-2.8,-2.5073,-2.455,-2.5995,-2.7737,-3.1356,-2.0529,-1.1913,-1.6176,-3.0759,-3.4546,-4.0514,-5.3021,-3.8809,-1.0722,-0.26557,0.050945,NaN,NaN,NaN,-4.7358,-3.3671,-2.9349,-2.9412,-4.6366,-4.3168,-4.7784,-5.5211,-5.479,-5.4306,-4.6227,-4.7505,-4.96,-4.8481,-5.0285,-6.0268,-7.3401,-7.8898,-6.6747\n-4.7475,-4.6392,-4.5725,-4.0008,-3.7077,-3.0818,-2.8495,-2.9564,-3.1137,-3.3389,-2.9871,-2.6732,-2.4832,-2.8358,-1.9565,-1.4334,-2.0905,-3.086,-3.8297,-4.7045,-5.2761,-4.9263,-3.4156,-2.9474,-2.8247,-4.7507,-4.9849,-5.2072,-4.4922,-3.7285,-3.0505,-2.8773,-3.2592,-5.051,-5.492,-5.5684,-5.6303,-4.735,-4.239,-4.7261,-5.2043,-5.8068,-5.7924,-5.2493,-7.2711,-8.0453,-7.1312\n-4.7234,-4.1829,-4.2418,-3.4303,-3.1774,-3.2486,-3.3648,-3.5094,-2.9342,-2.8504,-3.0966,-2.8218,-2.5369,-2.5156,-2.8499,-2.8202,-3.7953,-4.2691,-4.1067,-4.9678,-5.3905,-5.0771,-4.1918,-3.7166,-3.4886,-4.4322,-4.7066,-4.9029,-4.6001,-4.3437,-3.1545,-2.5916,-3.0574,-4.1303,-4.7796,-4.5986,-5.0505,-4.5485,-4.1405,NaN,NaN,NaN,NaN,NaN,-6.0168,-5.8501,-6.5937\n-4.0882,-3.8861,-3.653,-3.3382,-3.1537,-3.7564,-3.9476,-3.7653,-2.7481,-2.3532,-2.5011,-2.8721,-2.6287,-2.9786,-3.6108,-3.7904,-4.4377,-4.6531,-4.8762,-5.2162,-5.6452,-6.0364,-4.9717,-4.3159,-4.0367,-4.1139,-3.5175,-5.0305,-5.3347,-4.9307,-3.8094,-3.31,-3.8933,-4.1457,-5.1437,-5.6268,-5.9006,-4.6615,-4.3228,NaN,NaN,NaN,NaN,NaN,-6.603,-5.3885,-5.7452\n-3.0863,-3.3351,-3.8264,-3.5821,-3.3015,-3.3753,-3.5811,-3.5426,-3.2628,-3.0536,-2.6301,-2.6268,-2.8542,-3.2497,-4.2382,-4.6857,-4.5545,-4.4058,-5.171,-5.1828,-4.6147,-3.753,-3.9035,-4.6034,-5.2781,-5.5295,-4.1061,-6.0162,-5.3557,-4.9622,-4.3759,-4.2597,-4.6206,-5.3494,-6.041,-6.0223,-5.6813,-5.2416,-4.8234,-3.7215,-2.8951,NaN,NaN,NaN,-5.9716,-5.3028,-5.7067\n-2.7822,-3.0485,-3.2061,-2.9876,-2.8375,-2.7007,-2.6233,-2.6826,-3.0637,-3.9177,-3.1687,-2.5451,-2.686,-3.1987,-4.2015,-4.5782,-4.1688,-4.6433,-5.3166,-5.8636,-5.2844,-4.2035,-4.3526,-4.7812,-5.3166,-5.657,-6.0998,-7.0612,-5.8089,-5.0978,-5.0067,-5.2197,-5.1287,-4.9845,-5.1378,-5.0692,-4.8314,-4.3603,-4.4243,-4.5777,-3.0627,-3.6415,-4.9597,-5.851,-5.4428,-5.342,-6.1373\n-2.8611,-2.5225,-2.6227,-2.7116,-3.2016,-3.5283,-3.4364,-3.2612,-3.0473,-3.473,-3.3281,-2.8649,-3.0659,-3.6269,-4.2902,-4.4241,-4.1084,-4.7532,-5.2616,-5.5668,-5.5309,-4.9686,-4.7535,-5.1906,-5.679,-6.0257,-6.5781,-6.5756,-5.9349,-5.5675,-5.936,-5.5391,-5.4315,-5.1127,-4.5438,-4.2783,-4.0091,-3.878,-4.1885,-4.4953,-4.1873,-4.3056,-4.6973,-5.6081,-5.4573,-5.7062,-5.7977\n-4.1106,-3.2193,-2.6567,-2.8941,-3.0907,-3.056,-3.0487,-3.2055,-3.04,-2.7998,-3.1116,-3.5319,-3.7571,-3.6445,-4.4907,-4.4981,-4.513,-4.3906,-4.687,-5.2033,-5.1234,-4.928,-4.9238,-5.2333,-6.0717,-7.2371,-7.442,-6.4215,-5.4116,-5.4554,-6.1402,-6.1251,-5.6428,-5.1747,-4.947,-4.4666,-3.9683,-3.907,-4.5279,-4.6433,-5.3126,-5.466,-6.1143,-6.5663,-5.873,-5.7317,-5.5864\n-3.9515,-3.4081,-2.6363,-3.0008,-2.7773,-2.2973,-2.9244,-3.2843,-3.1138,-2.5656,-2.3056,-3.0856,-3.809,-4.2091,-4.4438,-4.412,-4.4552,-4.352,-5.0424,-5.5318,-5.335,-4.7828,-5.1731,-5.4603,-6.2507,-6.7721,-6.4448,-5.9015,-5.2792,-6.1736,-6.251,-6.3576,-6.0081,-5.6267,-5.9125,-5.9512,-5.1705,-5.386,-5.7537,-5.8217,-6.1524,-6.0647,-5.8051,-5.5219,-5.8661,-5.9471,-5.2171\n-4.4974,-3.1195,-2.4343,-2.1359,-1.9652,-2.1512,-3.0392,-3.1509,-2.5386,-2.2562,-1.7572,-2.1515,-3.5378,-4.5819,-5.0442,-5.2359,-5.1978,-5.2652,-5.7523,-5.8847,-5.4051,-4.7705,-5.3138,-5.673,-5.8814,-6.125,-5.7818,-5.018,-5.5374,-6.5387,-6.4661,-6.2402,-6.4362,-6.5001,-6.2356,-5.7063,-5.6084,-5.6721,-5.8138,-6.5801,-6.3855,-6.1226,-5.1662,-5.3422,-6.2015,-5.8095,-5.6396\n-6.4368,-6.977,-2.7191,-1.4151,-0.99565,-2.1383,-3.1895,-2.6366,-1.8178,-1.769,-1.7847,-2.1274,-2.866,-4.1327,-4.9146,-5.2167,-5.2755,-5.4879,-5.317,-5.1362,-4.8524,-4.7886,-5.2447,-5.5064,-5.7331,-5.9804,-5.5766,-5.5364,-6.1528,-6.5739,-6.0787,-6.1925,-6.7064,-6.3519,-5.7038,-5.2974,-5.2719,-5.2879,-5.3167,-5.9453,-6.0655,-5.8721,-5.5842,-5.9545,-6.208,-6.2715,-6.6267\n-4.468,-5.6838,-5.7743,-3.758,-2.0484,-2.3387,-3.0191,-3.8086,-3.4048,-2.1651,-1.8536,-1.9216,-3.317,-4.1351,-5.2756,-5.3249,-5.044,-4.8661,-4.6534,-4.7499,-4.5699,-4.9949,-5.3307,-5.6493,-5.8211,-5.9169,-5.7755,-6.0999,-6.4448,-6.6964,-6.3083,-6.5703,-6.7725,-6.8447,-6.5401,-5.7435,-5.7686,-5.696,-5.2357,-5.3038,-5.6509,-5.9355,-6.323,-6.3719,-6.2411,-6.4931,-6.5954\n-3.75,-4.6237,-5.3736,-4.6593,-3.7084,-3.0848,-3.1481,-2.3081,-1.017,0.11228,-1.814,-2.2236,-4.9317,-5.8806,-6.2698,-5.7166,-5.0338,-4.6704,-4.805,-5.0791,-5.2466,-5.316,-5.2527,-5.5092,-5.7856,-5.8899,-5.9726,-6.2337,-6.3174,-6.017,-6.2051,-6.364,-6.7367,-6.7117,-6.5563,-6.2237,-6.0152,-5.8411,-5.1919,-5.0597,-4.8894,-5.1474,-5.804,-6.2715,-6.7663,-6.7956,-6.1797\n-3.9133,-3.4771,-3.4405,-2.638,-2.5424,-2.4774,-1.9184,-2.1293,-1.7098,-0.88477,-1.6418,-2.1506,-4.658,-4.2117,-5.1118,-4.7982,-4.4772,-4.4467,-5.2452,-4.6224,-4.3192,-5.2933,-5.595,-5.5287,-5.7605,-5.4852,-5.7638,-6.1346,-6.5382,-5.966,-5.9836,-6.1229,-6.6561,-6.8254,-6.2143,-5.8869,-5.92,-5.6132,-5.1621,-5.2551,-5.2262,-5.3184,-5.715,-6.2541,-7.3938,-7.5281,-6.554\n-4.2605,-4.9068,-5.0317,-3.1569,-2.7117,-1.896,-2.1039,-1.9566,-1.6925,-1.3118,-2.2621,-3.9038,-4.8808,-5.2318,-5.0378,-3.6991,-3.9494,-5.0073,-5.3407,-4.9433,-5.1173,-5.5295,-5.642,-5.6437,-5.7754,-5.9825,-6.2219,-6.5434,-6.8396,-6.8443,-6.6854,-6.4143,-7.0181,-6.7626,-5.8788,-5.6776,-5.8718,-5.6301,-5.3525,-5.5794,-5.2068,-5.2773,-5.9433,-6.1038,-6.81,-7.3727,-7.2637\n-5.2993,-6.4242,-5.9959,-4.7039,-4.5485,-4.1818,-3.0374,-2.1611,-2.6192,-3.1255,-2.9989,-4.2319,-5.6629,-6.3478,-6.5726,-5.9843,-4.6171,-6.2147,-6.2516,-6.0208,-6.3848,-6.5821,-6.1131,-6.0247,-6.0453,-6.1336,-6.2009,-6.2131,-6.2765,-6.5135,-6.957,-7.2792,-7.2209,-6.3912,-5.6806,-5.789,-5.9186,-5.7169,-5.3462,-5.3429,-5.2779,-5.3724,-6.2253,-6.4348,-6.711,-6.5899,-6.2726\n-5.9537,-4.9987,-4.0227,-3.5754,-3.717,-4.2285,-3.6047,-3.5647,-2.8553,-3.2844,-3.4374,-5.3141,-5.9456,-5.9256,-5.1655,-5.3664,-6.2663,-5.8018,-4.1473,-5.9445,-6.6496,-7.3784,-7.1611,-6.3108,-6.2533,-6.0019,-5.9524,-6.1809,-6.1633,-6.5611,-6.8571,-7.4184,-7.7266,-7.278,-6.1658,-5.8206,-5.7708,-5.6652,-5.2536,-5.3745,-5.8653,-6.2835,-6.551,-7.0021,-7.3799,-7.0798,-6.6805\n-3.8835,-4.1538,-4.0222,-3.4038,-3.7532,-4.2972,-3.6071,-3.3823,-2.563,-1.91,-2.7626,-3.6003,-4.6426,-5.8329,-5.4005,-5.0499,-5.1252,-6.7795,-5.9323,-5.8621,-5.7729,-6.2402,-6.9806,-6.247,-5.817,-6.0939,-6.4352,-7.1714,-7.2891,-6.7599,-6.3086,-6.7744,-7.333,-7.4726,-6.3395,-5.8941,-6.0566,-5.8005,-5.7364,-5.9953,-6.5023,-6.9927,-6.9834,-6.9975,-7.3139,-7.2598,-6.9451\nNaN,NaN,NaN,NaN,-2.3097,-3.9705,-2.5835,-0.29287,-1.6339,-1.1078,-3.4492,-4.9464,-5.732,-5.6834,-5.8386,-5.6975,-3.6782,-2.9223,-5.8866,-6.0572,-5.6162,-5.6121,-7.2324,-5.6938,-5.4939,-6.355,-6.2845,-6.3672,-6.3837,-6.2354,-6.45,-6.6083,-6.4594,-6.1514,-6.1711,-5.9158,-5.6018,-5.8779,-6.0268,-6.4024,-6.7024,-6.6289,-6.3175,-6.9904,-7.1289,-7.2186,-6.896\nNaN,NaN,NaN,NaN,-2.2814,NaN,NaN,NaN,1.1133,-2.037,-4.2809,-5.979,-6.2009,-5.64,-4.9528,-4.182,-2.9279,-3.5447,-6.1553,-6.4815,-6.9782,-7.231,-6.1198,-4.3653,-5.4617,-5.9625,-5.7632,-5.1499,-4.874,-5.4203,-5.7771,-6.0359,-6.2637,-6.1191,-6.6785,-6.6708,-6.1289,-6.1748,-6.1644,-5.918,-5.6272,-5.9745,-6.3487,-6.6579,-6.8504,-6.9854,-6.8475\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.9747,-2.5936,-2.3652,-3.1378,-4.3822,-7.34,-6.9152,-5.9711,-6.9495,-7.1818,-5.781,-4.357,-4.7941,-5.2315,-5.3493,-5.3152,-4.6215,-4.8669,-5.5414,-6.494,-6.7483,-7.2372,-7.466,-6.6883,-6.6031,-6.4517,-6.4137,-5.4225,-5.4332,-6.0266,-6.4102,-6.7004,-6.4735,-6.1264,-5.1291\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.9628,-6.4487,-6.7841,-5.5828,-4.0534,-4.5459,-4.6098,-4.8792,-5.1442,-5.4523,-5.5295,-4.964,-4.428,-5.1465,-5.588,-6.3936,-7.0475,-7.4235,-6.8482,-6.7358,-6.9081,-6.5091,-5.3706,-5.3701,-6.1179,-6.7891,-7.198,-6.7303,-6.2482,-5.4944\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.6402,-6.835,-4.7068,-4.0457,-3.9873,-4.6397,-5.6895,-4.6694,-5.4719,-5.6516,-5.323,-5.2062,-5.4602,-5.6633,-6.3516,-6.8457,-7.2256,-6.9196,-6.6256,-6.9281,-6.9099,-6.4278,-6.0075,-6.2696,-6.2984,-6.8779,-6.8076,-5.8383,-4.7238\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.3616,-4.8981,NaN,NaN,-6.1255,-7.6432,-8.4737,-6.3626,-6.0508,-5.8264,-5.1919,-5.3136,-5.9403,-6.0512,-5.0431,-5.5875,-6.7236,-6.8737,-6.5988,-6.3309,-6.6572,-6.6255,-6.8256,-6.7479,-6.3968,-6.4145,-6.2215,-5.3531,-4.055\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.5434,-6.7414,-7.0378,-5.9086,-4.9427,-4.8913,-4.797,-5.1738,-6.82,-7.2412,-6.853,-6.6224,-7.4413,-7.649,-7.3578,-6.5534,-6.8977,-6.9848,-6.7769,-6.572,-6.5492,-6.4636,-5.8995,-4.9682,-4.4424\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.5064,-6.4291,-6.1758,-5.731,-4.8044,-4.5213,-5.0127,-5.5344,-6.1648,-7.0302,-7.107,-6.6636,-7.1385,-7.6422,-7.6389,-6.2877,-7.1864,-7.1043,-6.9022,-6.4177,-6.0677,-6.3073,-5.6279,-5.1837,-5.1456\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.5958,-5.857,-5.3788,-4.8351,-3.9999,-2.9835,-3.9185,-4.3833,-4.5689,-5.2059,-5.6,-6.6605,-6.6787,-7.3556,-7.4155,-6.9064,-6.2259,-6.428,-6.5253,-6.7219,-6.5813,-6.0921,-6.1936,-5.9478,-5.4191,-4.9202\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.6944,-6.6363,-5.85,-4.8354,-3.7196,-2.8247,-3.4863,-5.3951,-5.0445,-4.8822,-5.4889,-6.1159,-6.5836,-6.9302,-6.8336,-5.9591,-6.2925,-7.1248,-6.5415,-6.1834,-6.2886,-6.4421,-6.2023,-5.1583,-4.8659,-5.1358\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070115-070326.unw.csv",
    "content": "-11.685,-11.889,-11.779,-11.07,-11.486,-11.762,-12.243,-12.345,-12.233,-11.399,-11.838,-11.579,-10.832,-9.8985,-9.9965,-9.7317,-10.298,-9.3354,-9.0373,-8.872,-8.326,-9.5159,-10.347,-10.257,-9.378,-9.6583,-9.1395,-7.1964,-7.5501,-8.5241,-8.6647,-7.9241,-7.8407,-7.8482,-8.0618,-8.4347,-9.582,-10.189,-14.284,-13.937,-13.462,-13.77,-13.628,-12.087,-12.795,-12.05,-12.128\n-12.183,-12.275,-12.09,-11.387,-11.699,-11.976,-12.049,-12.216,-12.027,-11.773,-11.124,-10.63,-10.007,-9.989,-10.134,-10.072,-10.137,-10.147,-9.8391,-9.3829,-8.5923,-10.041,-10.717,-10.822,-10.434,-9.4043,-8.0335,-7.4237,-8.4552,-8.8555,-7.5865,-7.5048,-7.5504,-7.8378,-7.3724,-7.9377,-9.0804,-10.83,-13.678,-13.275,-13.031,-13.765,-11.917,-11.28,-11.381,-11.703,-11.795\n-12.242,-12.212,-11.964,-11.949,-11.875,-12.645,-12.221,-11.922,-12.446,-11.954,-10.879,-9.8854,-9.9277,-10.066,-9.9135,-10.073,-9.953,-10.227,-10.297,-10.515,-10.458,-9.8233,-9.6668,-10.151,-9.5124,-8.0227,-7.3186,-7.2766,-8.593,-8.8699,-7.5255,-7.6441,-6.9582,-7.2714,-7.5245,-7.6511,-8.2158,-9.7816,-10.91,-11.477,-13.116,-13.234,-11.64,-12.313,-11.801,-11.906,-12.445\n-13.137,-12.383,-11.567,-12.066,-12.061,-12.821,-12.333,-11.873,-11.735,-11.503,-10.691,-10.19,-10.482,-9.903,-11.236,-10.718,-9.4116,-9.6492,-10.201,-10.333,-11.102,-10.172,-9.3899,-9.2336,-8.4129,-7.2003,-6.9651,-7.1381,-7.4088,-7.8237,-7.3113,-7.1466,-6.4211,-7.1761,-7.8365,-7.8889,-8.8918,-9.9318,-10.572,-12.175,-12.889,-12.561,-12.394,-12.826,-13.029,-12.61,-13.398\n-12.918,-12.903,-11.949,-12.123,-12.429,-12.436,-12.177,-11.459,-11.969,-11.921,-11.759,-11.725,-11.349,-10.655,-10.712,-10.635,-9.8777,-9.2778,-9.9127,-10.661,-11.89,-9.8652,-9.1949,-8.5912,-7.8616,-6.7138,-7.0972,-7.4627,-6.2123,-6.6438,-6.1958,-7.3359,-5.6276,-7.0329,-8.1779,-8.8214,-9.9102,-10.588,-11.249,-12.361,-12.355,-11.842,-12.358,-12.749,-12.611,-12.635,-12.966\n-12.893,-13.333,-12.501,-12.045,-12.471,-12.297,-12.574,-12.771,-12.525,-12.108,-12.203,-12.601,-12.25,-11.151,-10.595,-10.357,-10.359,-10.457,-10.071,-9.6636,-9.4993,-9.4088,-8.4671,-7.7105,-7.7052,-7.0787,-6.7998,-7.36,-6.4791,-7.2099,-7.18,-7.6269,-6.6183,-6.8504,-8.0335,-8.3554,-10.169,-11.177,-11.769,-12.742,-12.788,-12.435,-12.337,-12.627,-12.911,-12.954,-12.947\n-13.636,-13.889,-13.743,-12.58,-12.367,-12.393,-12.828,-13.275,-12.059,-11.586,-12.28,-12.536,-12.221,-10.921,-11.501,-10.726,-10.742,-10.606,-10.423,-10.665,-10.015,-9.631,-9.0738,-8.3889,-7.6269,-6.4112,-6.8336,-7.0747,-7.1252,-7.0007,-7.5937,-6.8738,-6.5167,-6.8718,-7.7508,-8.6523,-9.8871,-11.741,-12.563,-14.079,-13.572,-13.182,-12.822,-12.761,-13.354,-13.649,-14.192\n-14.357,-14.456,-14.594,-12.945,-12.598,-12.561,-12.718,-12.737,-11.741,-11.404,-12.311,-11.817,-11.58,-11.201,-12.103,-11.423,-11.245,-11.078,-11.231,-11.124,-9.989,-9.5961,-9.7611,-8.9981,-7.7831,-6.8142,-6.7548,-7.4983,-7.9405,-7.461,-7.1448,-6.6918,-6.5909,-6.4322,-7.4925,-9.0984,-11.009,-12.453,-13.282,-13.821,-13.681,-13.821,-12.895,-13.392,-13.683,-14.343,-14.431\n-14.285,-14.553,-13.965,-12.916,-12.609,-12.619,-12.787,-12.295,-12.466,-12.009,-11.829,-12.266,-11.718,-11.602,-11.435,-11.294,-11.413,-11.659,-11.734,-10.927,-9.9839,-9.2459,-9.7357,-9.7033,-7.0564,-6.9785,-7.9783,-7.8058,-7.728,-7.216,-7.2658,-6.7311,-6.7903,-7.0854,-8.1477,-10.292,-11.998,-13.072,-12.935,-12.756,-12.778,-12.488,-12.019,-13.778,-13.747,-13.921,-14.302\n-13.954,-13.863,-12.258,-13.002,-12.972,-12.919,-13.309,-12.856,-12.765,-12.368,-12.219,-12.974,-11.807,-12.982,-11.861,-11.024,-11.174,-11.446,-11.573,-10.818,-9.5672,-9.5786,-10.068,-9.5073,-7.6195,-7.4605,-7.2041,-7.547,-7.2378,-7.3334,-6.7769,-6.2548,-8.0335,-7.6954,-9.7414,-10.946,-11.123,-12.051,-12.072,-12.428,-12.448,-12.252,-12.102,-12.479,-12.557,-13.174,-13.859\n-13.766,-12.036,-11.111,-13.223,-13.975,-13.458,-14.246,-13.242,-12.702,-13.146,-13.254,-14.026,-12.998,-13.436,-12.751,-12.312,-11.177,-10.463,-10.158,-9.3338,-9.5416,-9.8373,-9.3253,-8.9458,-7.8574,-7.3605,-7.487,-7.505,-7.2132,-6.7976,-6.1194,-6.344,-7.951,-8.5892,-9.7687,-9.7532,-10.375,-11.018,-11.052,-11.645,-12.149,-11.655,-11.639,-12.426,-12.259,-12.762,-13.121\n-13.028,-11.485,-12.455,-13.327,-13.787,-13.799,-14.355,-12.683,-12.924,-14.009,-14.845,-15.23,-14.036,-13.166,-12.772,-12.423,-11.308,-9.3017,-9.0954,-8.1212,-9.2581,-9.5206,-9.1909,-9.0406,-7.8944,-6.6184,-7.7435,-8.153,-8.0955,-6.4279,-6.2917,-7.1352,-7.9195,-8.9724,-9.9716,-10.11,-11.476,-11.407,-11.421,-11.707,-11.928,-11.409,-10.592,-11.639,-12.107,-12.663,-12.88\n-13.961,-13.814,-14.123,-13.389,-13.245,-13.322,-14.341,-12.784,-13.122,-14.346,-13.869,-14.609,-14.446,-12.867,-12.36,-12.317,-11.525,-9.8982,-8.8418,-8.0342,-8.3812,-8.4508,-8.495,-8.9054,-7.99,-7.0298,-8.0866,-8.3821,-7.9434,-6.3972,-6.8723,-8.2936,-8.997,-9.5706,-9.9332,-10.512,-12.231,-11.257,-12.052,-11.594,-11.65,-11.243,-11.037,-11.239,-12.3,-12.536,-12.67\n-14.881,-15.781,-15.692,-14.112,-12.79,-14.147,-14.234,-13.538,-13.831,-13.59,-13.525,-13.537,-13.082,-12.799,-12.32,-12.7,-11.919,-10.865,-10.231,-9.4542,-8.7723,-8.5799,-8.0764,-8.0484,-8.2356,-7.3219,-7.7594,-7.9183,-7.4891,-7.1452,-7.6076,-8.6819,-9.6894,-9.4097,-9.983,-10.718,-11.466,-11.78,-12.194,-11.183,-11.846,-10.964,-11.832,-11.751,-12.01,-12.47,-12.447\n-15.15,-16.401,-16.072,-12.495,-13.32,-14.132,-13.688,-13.688,-13.214,-13.003,-13.633,-13.091,-13.185,-13.015,-12.532,-12.288,-12.104,-11.283,-10.424,-9.1055,-8.9155,-9.0571,-7.6346,-8.034,-8.4999,-7.42,-7.4241,-7.643,-8.0067,-7.7112,-7.8158,-8.2279,-8.528,-8.73,-10.095,-10.857,-11.189,-11.02,-10.757,-10.425,-11.379,-10.49,-11.705,-12.505,-12.789,-13.096,-12.973\n-14.197,-15.179,-15.628,-12.804,-12.49,-13.699,-13.007,-13.285,-13.256,-13.052,-13.603,-13.665,-13.428,-12.34,-11.742,-11.626,-11.609,-11.219,-10.168,-9.4612,-8.6117,-7.8152,-7.9639,-8.499,-8.3446,-7.6684,-7.6067,-8.128,-8.6651,-8.0405,-8.3696,-8.4043,-8.4194,-8.7947,-9.6403,-11.421,-10.367,-10.313,-10.244,-10.094,-11.098,-10.762,-11.859,-12.692,-13.123,-14.091,-13.643\n-13.5,-13.206,-13.262,-13.422,-14.066,-14.016,-13.125,-12.962,-12.732,-12.942,-12.541,-12.117,-12.564,-12.089,-11.539,-11.204,-11.768,-10.689,-10.717,-10.831,-9.6372,-8.0711,-8.7196,-8.3834,-7.3327,-7.6565,-8.2647,-8.8778,-8.6659,-7.7304,-8.5663,-9.057,-8.9621,-9.7157,-10.39,-10.883,-9.9709,-10.39,-10.402,-9.4215,-10.993,-10.808,-11.849,-12.342,-12.396,-13.653,-13.881\n-11.649,-12.235,-13.275,-14.17,-14.674,-14.465,-13.112,-13.182,-12.762,-12.504,-12.517,-12.543,-12.423,-11.871,-11.93,-12.181,-11.729,-10.448,-11.059,-11.363,-11.039,-8.8596,-7.9425,-8.7009,-7.6014,-7.7946,-8.3565,-8.7258,-8.9323,-8.0145,-8.4532,-9.0238,-7.439,-9.4725,-10.018,-9.6506,-9.7204,-10.395,-10.762,-10.63,-11.402,-12.074,-12.023,-12.878,-13.041,-12.932,-13.16\n-13.422,-13.502,-13.843,-14.797,-15.124,-14.39,-10.787,-13.789,-12.897,-12.827,-13.421,-12.802,-12.689,-12.24,-12.571,-12.649,-11.149,-10.374,-10.618,-11.192,-9.5848,-8.3384,-6.47,-7.2768,-7.874,-7.0967,-8.0577,-8.3229,-9.1386,-8.499,-8.1821,-8.236,-7.8109,-8.2917,-9.1683,-8.8568,-9.1542,-9.4936,-10.731,-11.148,-11.611,-12.15,-12.119,-12.732,-12.822,-12.907,-13.328\n-14.748,-13.47,-13.806,-14.418,-14.485,-13.161,-12.104,-13.605,-13.292,-12.762,-13.317,-13.087,-12.695,-12.513,-12.836,-12.882,-10.598,-9.2333,-9.2493,-9.8218,-8.8432,-8.318,-7.8591,-5.6515,-6.8578,-7.5227,-7.4946,-7.3651,-8.0413,-9.0675,-8.2006,-8.0014,-7.9303,-8.2036,-8.9398,-8.9552,-8.9621,-8.9748,-10.824,-11.198,-11.517,-11.79,-12.459,-12.387,-12.86,-13.441,-13.02\n-13.301,-12.438,-13.467,-14.98,-15.913,-13.76,-12.919,-13.287,-12.643,-12.917,-13.142,-12.365,-12.771,-13.06,-12.658,-12.052,-11.254,-10.685,-9.2134,-9.7363,-10.559,-9.6178,-8.7379,-7.539,-7.5937,-7.4778,-7.2556,-6.7527,-7.6728,-8.7874,-7.8136,-8.3479,-7.7987,-7.8355,-8.6422,-8.8709,-8.7333,-8.8372,-10.467,-10.687,-11.113,-12.625,-12.772,-13.415,-13.129,-12.914,-12.561\n-12.448,-11.783,-13.432,-14.159,-13.952,-13.356,-13.113,-12.425,-11.857,-13.65,-13.544,-12.864,-13.028,-13.856,-12.938,-12.115,-11.936,-11.515,-11.171,-10.542,-9.4503,-9.4571,-9.4609,-9.284,-7.9423,-7.0278,-6.8288,-6.8206,-8.2074,-8.4695,-7.5065,-8.1426,-8.1387,-8.1149,-9.2848,-9.2289,-8.9802,-9.6803,-10.525,-10.821,-11.394,-12.828,-12.863,-12.781,-12.363,-12.161,-12.334\n-11.871,-12.45,-13.651,-12.622,-12.69,-13.314,-12.1,-11.671,-12.356,-14.142,-14.09,-13.526,-13.575,-13.181,-12.037,-11.045,-11.712,-11.473,-11.182,-10.498,-9.2458,-8.8865,-8.5434,-9.5856,-8.3674,-7.4392,-8.2171,-8.1621,-9.0146,-8.8952,-8.053,-8.1254,-8.3989,-8.4117,-8.3692,-9.0142,-8.6928,-10.066,-10.642,-11.269,-11.885,-12.563,-12.712,-12.229,-12.046,-12.075,-13.024\n-12.912,-12.23,-11.381,-10.584,-11.1,-12.173,-12.795,-13.332,-13.786,-14.825,-15.679,-15.08,-13.346,-12.527,-11.672,-9.3693,-10.201,-11.48,-11.309,-10.605,-10.098,-9.3734,-8.9704,-9.6236,-9.6942,-8.9623,-9.7754,-9.8825,-9.372,-9.219,-9.1904,-9.5795,-9.1534,-8.8774,-8.6791,-9.022,-9.7461,-10.166,-10.318,-11.328,-11.59,-12.345,-11.947,-11.991,-11.938,-12.352,-13.402\n-12.998,-12.593,-11.909,-12.335,-11.913,-12.705,-14.402,-13.285,-13.959,-15.491,-15.49,-14.374,-14.603,-13.279,-12.449,-11.943,-12.194,-13.287,-12.797,-11.61,-11.071,-10.941,-11.479,-10.466,-10.815,-11.266,-10.923,-10.586,-10.363,-10.174,-9.6855,-9.6397,-9.6203,-9.3372,-8.9644,-10.454,-9.2063,-9.8742,-10.027,-10.333,-11.186,-11.343,-11.256,-11.443,-11.537,-12.29,-13.458\n-12.523,-12.509,-12.709,-12.894,-12.673,-13.639,-13.432,-12.731,-12.743,-14.735,-14.951,-15.206,-15.312,-15.071,-13.383,-12.544,-13.293,-13.442,-12.914,-12.23,-12.311,-13.947,-12.884,-10.716,-10.322,-10.485,-10.045,-10.159,-10.399,-10.381,-10.206,-10.056,-11.084,-9.0519,-9.111,-10.357,-9.097,-9.5093,-9.5632,-8.5,-10.012,-10.995,-10.972,-11.675,-11.33,-11.656,-11.83\n-11.079,-12.043,-11.966,-13.293,-13.294,-13.275,-12.758,-12.547,-13.431,-14.755,-15.015,-15.537,-16.171,-15.458,-14.198,-13.196,-13.035,-13.051,-13.468,-13.701,-14.024,-14.109,-12.864,-10.569,-10.532,-9.4274,-9.7736,-9.9178,-9.8347,-9.181,-9.8206,-10.149,-10.491,-9.2258,-9.1252,-9.5426,-9.4446,-9.7368,-8.551,-8.0449,-9.7063,-10.638,-11.265,-11.606,-11.498,-11.289,-11.012\n-10.241,-10.686,-11.641,-13.679,-12.896,-13.48,-14.443,-13.939,-13.953,-13.587,-14.606,-15.45,-15.774,-15.181,-14.004,-13.777,-12.754,-12.675,-12.954,-13.589,-14.447,-12.7,-11.741,-11.718,-12.256,-10.481,-9.676,-9.8064,-9.8859,-9.505,-9.3846,-9.1821,-9.3653,-9.5826,-9.5795,-9.146,-9.0899,-8.6037,-8.9054,-9.0178,-10.238,-10.366,-11.07,-11.118,-10.795,-11.096,-11.335\n-9.876,-10.641,-12.552,-12.17,-12.816,-14.245,-15.208,-15.314,-14.541,-13.026,-14.197,-14.138,-14.599,-14.048,-13.987,-13.557,-12.482,-12.088,-12.764,-13.834,-14.895,-13.229,-12.899,-12.84,-12.652,-10.957,-10.252,-10.2,-10.851,-10.461,-9.5965,-8.8772,-8.7702,-9.509,-9.3889,-8.7123,-8.2129,-7.6875,-8.4358,-9.4964,-10.255,-10.154,-10.449,-10.179,-10.707,-11.203,-11.488\n-11.199,-10.146,-9.3017,-8.6413,-13.558,-14.744,-13.5,-13.852,-14.488,-13.679,-13.906,-13.372,-13.302,-13.043,-13.403,-13.1,-11.978,-11.598,-11.929,-12.286,-13.694,-13.343,-12.961,-12.637,-11.664,-10.736,-10.616,-11.662,-10.142,-9.3911,-8.2989,-8.446,-8.4958,-8.881,-8.6176,-8.9245,-8.3971,-8.0449,-8.9667,-9.7306,-9.9577,-10.046,-10.46,-10.469,-11.14,-11.266,-11.549\n-10.786,-9.6397,-8.4869,-11.839,-13.901,-14.029,-13.014,-14.062,-14.656,-14.36,-13.481,-12.905,-12.761,-12.692,-12.878,-12.723,-12.05,-11.467,-11.07,-10.268,-9.466,-9.9529,-11.511,-12.012,-11.691,-10.597,-10.479,-11.18,-9.7092,-8.8493,-7.5406,-8.2049,-8.8513,-9.2246,-8.4293,-9.1462,-9.2499,-9.1074,-9.1606,-9.4327,-10.066,-10.62,-10.62,-10.899,-11.584,-11.317,-11.242\n-11.226,-10.604,-10.301,-12.845,-11.496,-12.169,-13.417,-13.892,-13.887,-13.031,-12.431,-12.876,-12.873,-12.292,-12.481,-12.243,-11.751,-11.614,-11.182,-10.949,-8.6639,-9.5148,-10.967,-10.852,-11.234,-10.569,-8.8505,-10.979,-12.455,-12.515,-9.575,-8.7019,-8.822,-8.7061,-8.3748,-9.0435,-9.2969,-9.6858,-9.1777,-8.7947,-10.144,-10.909,-10.881,-11.436,-11.402,-11.173,-11.369\n-12.179,-11.929,-12.132,-12.538,-13.042,-12.122,-12.616,-12.805,-13.604,-11.848,-11.378,-11.769,-11.954,-10.392,-9.7213,-10.98,-10.782,-10.48,-11.068,-11.485,-10.539,-11.06,-11.726,-9.5085,-9.8941,-9.4424,-8.3113,-14.651,-15.819,-14.569,-11.44,-7.9031,-7.2746,-7.4572,-8.4559,-9.3469,-9.5765,-10.221,-8.8587,-9.3362,-10.367,-10.958,-11.485,-11.714,-10.811,-11.495,-12.116\n-11.156,-11.338,-12.824,-13.43,-13.238,-11.283,-12.327,-12.216,-12.244,-12.023,-12.222,-11.975,-11.089,-10.062,-8.4596,-9.1453,-9.9115,-10.682,-10.269,-10.12,-11.74,-11.383,-10.676,-8.0816,-8.7653,-7.2345,-13.51,-13.81,-15.425,-10.956,-8.5465,-7.1905,-7.333,-6.0945,-7.2664,-7.4445,-6.3291,-8.4696,-7.7888,-9.3342,-10.507,-10.932,-11.235,-11.525,-10.735,-12.589,-13.093\n-10.923,-10.975,-12.918,-12.201,-11.879,-11.545,-11.437,-10.794,-11.48,-12.626,-12.404,-10.875,-10.359,-10.709,-9.2324,-9.463,-10.147,-9.7616,-9.435,-8.6367,-11.714,-7.528,NaN,NaN,NaN,NaN,NaN,NaN,-10.16,-9.1234,-7.4625,-5.7218,-4.4003,-4.9083,-5.8072,-5.2256,-5.1252,-6.263,-8.151,-9.0279,-9.5329,-9.9809,-10.731,-10.984,-10.352,-12.73,-13.455\n-8.9411,-9.6594,-10.688,-10.609,-11.351,-11.776,-11.577,-10.292,-10.609,-11.911,-11.289,-10.825,-9.857,-10.817,-13.085,-11.355,-8.7871,-8.0412,-8.2633,-8.9109,-7.4423,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.0655,-4.798,-4.468,-4.786,-4.8048,-6.0433,-6.2262,-7.3472,-8.6641,-8.7014,-9.7348,-10.402,-10.593,-11.248,-11.169,-12.25,-13.007\n-8.5292,-9.3523,-9.9542,-10.738,-10.277,-11.176,-11.567,-11.228,-10.282,-10.26,-10.776,-9.6687,-9.9328,-12.286,-12.121,-10.423,-7.8121,-7.0894,-7.2839,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.0939,-4.0138,-3.1232,-3.8986,-4.706,-6.481,-7.8176,-8.5211,-9.1649,-9.1349,-10.115,-10.104,-10.44,-11.288,-11.524,-12.305,-12.865\n-9.8449,-9.1235,-9.3449,-10.721,-11.497,-11.681,-11.1,-11.099,-10.358,-8.913,-8.8055,-8.5245,-9.3086,-10.612,-9.8897,-8.9086,-7.1105,-8.4355,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.6054,-4.005,-3.8654,-4.3827,-4.8456,-6.2893,-8.6843,-8.7088,-9.4678,-9.9779,-10.226,-10.191,-10.601,-11.305,-12.101,-12.704,-13.132\n-10.775,-10.23,-8.9801,-9.6959,-11.128,-11.33,-10.937,-11.029,-9.8396,-8.9516,-9.321,-8.4645,-8.617,-9.0675,-8.0534,-6.5672,-5.039,-8.9715,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.7914,-7.2657,-4.8892,-3.6549,-4.0642,-5.0103,-6.1614,-7.6888,-8.6826,-8.7566,-9.3241,-10.261,-10.334,-10.312,-9.729,-11.052,-11.65,-12.455,-13.184\n-10.552,-11.428,-10.612,-9.9272,-9.8693,-10.124,-9.8298,-10.281,-9.695,-9.1943,-9.1786,-9.0166,-8.8725,-8.2709,-6.2923,-5.2345,-4.8664,-6.0013,-6.5596,-8.4604,-7.3162,-7.0197,NaN,-4.8951,-3.3636,-1.8612,-2.3502,-5.8652,-7.5771,-5.8607,-3.9376,-3.6732,-4.4938,-5.7088,-7.2139,-7.5104,-7.8512,-8.4979,-9.4928,-10.112,-9.7346,-9.8653,-10.077,-10.801,-11.366,-11.637,-12.635\n-10.686,-10.637,-9.2962,-9.0942,-9.7858,-10.043,-10.275,-10.754,-10.14,-8.8614,-8.8909,-9.0426,-8.2859,-7.3003,-6.0206,-6.3531,-6.7402,-7.4641,-7.4516,-7.3878,-7.3145,-6.5394,-4.2175,-6.3174,-7.3486,NaN,NaN,-6.6028,-6.5235,-5.4409,-4.9151,-5.6398,-6.2484,-7.1682,-6.8645,-7.4964,-8.0248,-8.3951,-9.9075,-9.8325,-9.6756,-9.5902,-10.074,-10.531,-11.069,-11.741,-12.418\n-10.046,-9.0035,-8.6607,-9.4035,-10.301,-10.494,-10.358,-10.412,-9.8169,-9.0329,-8.8377,-7.4055,-7.3814,-7.2789,-6.7671,-6.9515,-7.7222,-7.9065,-8.4681,-7.0285,-7.6882,-6.368,-5.5633,-6.2146,-7.9833,NaN,NaN,-7.223,-6.9753,-5.8846,-6.139,-6.5841,-7.1055,-7.9474,-7.9632,-7.8557,-7.7778,-8.3344,-9.5548,-9.2484,-9.5589,-9.5698,-10.358,-11.108,-11.652,-11.879,-11.905\n-9.9125,-7.7719,-8.7416,-10.111,-10.474,-10.504,-10.062,-9.3751,-9.2023,-8.5961,-7.6685,-6.7464,-7.2335,-8.0008,-7.501,-6.5085,-7.214,-7.4979,-7.0246,-7.7779,-8.1289,-6.1319,-8.5457,NaN,NaN,NaN,NaN,-7.4589,-7.4258,-6.7046,-6.9663,-7.9481,-8.8162,-8.5026,-8.5328,-8.5173,-8.0571,-8.7622,-9.2537,-9.3046,-9.3293,-10.162,-11.028,-11.567,-11.643,-11.524,-11.455\n-9.2832,-8.774,-9.7368,-10.162,-10.103,-10.184,-9.7623,-9.0775,-8.6242,-8.4993,-7.9122,-7.4847,-7.9186,-8.3837,-8.4621,-8.2383,-7.691,-6.2677,-6.6731,-7.1318,-6.9367,-8.4586,NaN,NaN,NaN,NaN,NaN,-8.0824,-8.1014,-7.6935,-7.7348,-8.0581,-8.8439,-9.4026,-9.391,-9.3352,-8.9373,-9.2544,-9.3699,-9.4833,-10.238,-10.216,-11.298,-11.568,-11.671,-11.846,-11.111\n-9.3178,-10.042,-10.408,-9.9774,-10.444,-10.13,-9.5916,-9.1194,-8.6425,-8.4849,-8.6688,-8.4028,-8.484,-8.9922,-9.1178,-9.1359,-7.757,-7.5099,-6.9979,-6.0004,-5.8494,-5.8779,-5.1065,NaN,NaN,NaN,-7.0924,-8.9332,-9.0577,-8.7266,-8.7302,-9.1608,-9.3186,-9.5373,-10.068,-9.7146,-9.4509,-9.0413,-9.0264,-10.07,-10.671,-10.303,-11.154,-11.376,-11.089,-11.292,-11.603\n-9.7405,-9.9171,-9.6484,-9.1605,-9.4589,-10.11,-10,-9.4909,-8.8176,-8.4872,-8.8899,-9.0557,-9.1853,-9.8681,-10.386,-9.5316,-8.2415,-7.8697,-7.6626,-6.8032,-6.6091,-6.824,-7.3543,-8.2374,-9.6656,-8.4847,-8.653,-9.3053,-9.7829,-9.845,-10.125,-9.9791,-9.7083,-10.521,-10.846,-10.225,-9.5736,-9.2725,-9.3407,-10.16,-10.395,-10.641,-11.092,-11.246,-10.569,-10.753,-11.105\n-9.3541,-8.8336,-9.3753,-9.0277,-9.0316,-9.6927,-9.7734,-10.072,-8.7971,-8.2994,-9.7915,-9.6392,-9.8852,-10.665,-10.481,-9.0644,-8.2563,-8.5282,-8.078,-7.2923,-7.2465,-7.222,-7.9763,-8.7836,-9.0232,-8.6259,-9.287,-10.184,-10.563,-10.847,-10.162,-10.086,-10.439,-11.23,-11.38,-10.297,-10.275,-10.312,-10.257,-10.588,-10.532,-10.133,-10.682,-11.397,-11.097,-11.32,-11.464\n-8.6163,-9.3251,-9.6204,-9.2443,-7.4459,-7.8075,-9.5212,-10.043,-9.7413,-9.0412,-9.6829,-10.133,-11.149,-11.202,-10.684,-10.253,-9.6223,-9.3585,-8.3901,-7.8289,-7.8721,-7.7368,-8.8169,-8.917,-8.8179,-9.0301,-9.7074,-10.98,-10.812,-10.9,-10.462,-10.604,-11.093,-11.091,-11.631,-11.582,-11.355,-10.517,-10.609,-10.729,-10.651,-10.695,-11.298,-11.039,-11.809,-11.621,-11.754\n-8.3311,-9.8452,-10.594,-8.9172,-7.3631,-7.3744,-8.908,-9.7525,-11.681,-10.262,-9.5237,-10.099,-9.068,-10.029,-10.505,-10.648,-10.626,-10.274,-9.6605,-8.9551,-8.6218,-9.3578,-9.3765,-9.4735,-10.006,-10.735,-10.866,-10.916,-11.291,-11.43,-11.339,-11.58,-11.701,-12.059,-12.305,-12.149,-11.632,-11.136,-11.179,-10.771,-10.682,-10.908,-11.768,-11.617,-12.326,-12.382,-11.967\n-8.712,-8.8706,-9.3851,-9.0813,-8.1157,-8.1743,-8.8383,-9.6741,-11.224,-9.6996,-9.5446,-8.9964,-8.6968,-9.196,-10.416,-10.671,-10.77,-10.168,-9.4613,-8.921,-8.7553,-10.034,-9.8732,-10.494,-11.525,-11.979,-12.125,-11.317,-11.465,-11.642,-11.689,-11.901,-12.441,-12.709,-12.467,-12.237,-11.54,-11.639,-11.401,-11.519,-11.349,-10.724,-10.26,-11.023,-13.125,-12.788,-12.639\n-9.2815,-9.0845,-9.142,-9.6567,-8.4573,-6.6415,-4.043,-7.1116,-8.3584,-8.2396,-8.7619,-9.2445,-8.477,-9.482,-10.271,-10.589,-10.824,-9.8996,-10.022,-10.124,-10.561,-10.044,-10.614,-11.317,-12.382,-12.545,-12.974,-12.208,-11.363,-11.59,-11.669,-11.818,-12.472,-12.977,-12.666,-13.019,-12.792,-12.122,-12.238,-12.178,-12.031,-10.898,-9.9384,-10.729,-12.005,-11.869,-12.701\n-8.484,-9.2497,-9.6484,-9.0833,-8.0607,-5.8894,-4.9898,-7.6753,-8.3907,-8.0838,-9.2962,-9.6108,-8.7813,-10.513,-9.675,-10.73,-11.279,-11.229,-11.008,-10.583,-10.984,-9.8097,-10.685,-11.74,-12.289,-12.08,-11.808,-12.824,-12.158,-11.636,-11.431,-11.076,-11.946,-12.706,-13.068,-13.261,-12.526,-12.044,-12.451,-11.905,-11.637,-11.742,-12.12,-12.213,-11.944,-11.509,-11.905\n-7.7475,-7.938,-8.4677,-8.1484,-7.8356,-8.2868,-7.7909,-7.6739,-8.3136,-8.426,-9.9537,-8.7256,-9.1289,-10.043,-9.7857,-10.261,-11.144,-11.138,-10.481,-10.176,-11.158,-10.478,-11.333,-11.928,-12.732,-12.605,-12.699,-12.838,-11.945,-11.704,-11.86,-11.627,-11.857,-13.134,-13.694,-13.195,-12.503,-12.593,-12.547,-11.9,-11.422,-11.053,-11.532,-11.639,-11.277,-11.341,-12.248\n-8.8983,-7.4688,-6.5502,-6.2032,-6.9598,-7.9269,-8.3487,-7.5703,-8.2477,-8.7749,-9.4254,-9.1061,-9.9569,-10.588,-10.87,-11.413,-11.491,-11.72,-11.526,-11.935,-11.779,-10.832,-11.631,-11.813,-12.318,-12.576,-12.897,-13.335,-12.421,-11.985,-12.066,-12.02,-11.872,-12.507,-12.727,-12.362,-11.805,-12.571,-13.019,-12.623,-12.245,-11.931,-11.997,-11.766,-11.128,-11.636,-11.48\n-9.7932,-9.049,-8.1911,-8.0858,-7.4005,-7.3496,-7.9774,-8.2052,-8.1009,-8.4944,-9.4216,-9.6465,-10.157,-10.212,-9.9688,-11.321,-11.708,-12.064,-12.117,-12.016,-11.531,-11.745,-11.321,-12.071,-12.675,-12.847,-13.948,-14.3,-13.438,-12.287,-12.033,-12.144,-12.452,-12.061,-12.504,-12.431,-11.978,-12.035,-12.261,-12.466,-12.254,-11.969,-11.937,-11.82,-11.728,-12.062,-11.228\n-10.054,-9.9597,-9.7143,-8.7604,-7.7772,-5.3574,-6.0334,-8.2759,-8.324,-8.1682,-8.0231,-7.8606,-7.893,-8.3718,-8.7428,-11.579,-12.599,-12.635,-12.258,-10.909,-10.992,-11.5,-11.126,-12.257,-13.305,-13.452,-14.243,-14.016,-13.364,-12.161,-11.819,-12.237,-12.087,-11.837,-12.403,-12.667,-12.483,-12.099,-11.702,-11.571,-11.542,-11.788,-11.917,-11.598,-11.92,-11.745,-11.848\n-9.0187,-8.898,-8.4398,-8.2768,-8.8634,-7.9449,-6.3395,-6.3151,-7.0383,-6.9774,-5.4424,-5.7454,-1.8915,NaN,NaN,NaN,NaN,-13.921,-12.292,-10.456,-11.206,-11.429,-10.939,-12.037,-12.66,-12.751,-13.459,-13.739,-13.079,-12.865,-11.492,-11.874,-12.14,-12.3,-12.619,-12.483,-12.342,-12.26,-11.485,-11.402,-11.044,-12.027,-12.154,-11.686,-12.185,-11.8,-12.371\nNaN,NaN,-6.0588,-5.8401,-6.3556,-6.6734,-6.6193,-5.9597,-5.6473,-5.9946,-7.1393,-4.8692,2.3508,NaN,NaN,NaN,NaN,NaN,-12.122,-10.689,-12.018,-11.767,-11.489,-12.144,-12.688,-12.794,-12.966,-12.408,-11.785,-13.11,-12.185,-12.268,-12.596,-12.894,-13.242,-12.947,-12.832,-12.896,-11.512,-11.532,-11.572,-12.202,-12.302,-11.769,-12.85,-11.94,-12.576\nNaN,NaN,-6.4179,-5.5101,-5.3125,-5.1889,-7.3917,-5.8762,NaN,-6.7573,-7.9844,-8.3121,-10.709,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.126,-11.306,-11.679,-12.013,-12.237,-12.663,-12.41,-12.071,-12.14,-13.1,-13.701,-13.376,-12.949,-12.644,-12.897,-12.8,-12.582,-12.424,-12.041,-11.876,-11.898,-12.314,-12.191,-12.063,-12.198,-12.285,-12.249\nNaN,NaN,-6.8784,-6.6298,-6.091,-6.0989,-7.0222,-3.6855,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.1705,-9.3742,-10.736,-10.94,-11.421,-12.007,-11.935,-12.016,-12.691,-12.697,-12.54,-13.305,-13.174,-12.877,-12.079,-12.187,-11.964,-11.959,-12.752,-12.238,-11.832,-12.546,-12.128,-11.853,-11.884,-12.094,-11.417\nNaN,-4.5715,-7.0346,-6.9281,-6.7395,-7.1586,-6.5725,-3.7707,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.1976,-9.2716,-9.0778,-9.4624,-12.282,-12.812,-12.788,-13.078,-12.428,-12.069,-12.799,-12.641,-12.671,-11.978,-12.133,-12.243,-11.989,-12.369,-12.385,-11.868,-11.902,-11.878,-11.941,-12.31,-12.365,-11.99\nNaN,NaN,-8.5585,-8.3241,-8.075,-8.4284,-7.6228,-6.1309,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.6797,-9.2889,-8.512,-10.018,-12.308,-12.698,-12.021,-12.189,-11.745,-11.728,-12.359,-12.099,-12.156,-11.783,-12.063,-12.241,-12.166,-12.113,-11.868,-11.678,-11.669,-12.287,-12.638,-12.266,-12.073,-12.072\nNaN,NaN,NaN,NaN,NaN,-8.8628,-6.726,-7.3451,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.906,-9.1934,-9.2797,-11.343,-12.803,-12.976,-12.654,-12.318,-11.953,-12.432,-12.586,-12.809,-12.479,-12.548,-12.239,-12.133,-11.996,-11.911,-11.522,-11.523,-11.833,-12.278,-12.406,-12.071,-12.099,-12.273\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.3727,-8.8515,-10.886,-11.807,-12.273,-12.97,-13.088,-12.44,-12.087,-12.193,-12.376,-12.661,-13.069,-13.741,-12.043,-11.898,-11.677,-11.698,-11.852,-11.803,-11.895,-11.945,-11.969,-12.171,-12.311,-12.827\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.3305,-9.1874,-11.302,-11.486,-11.982,-13.224,-13.436,-12.59,-12.335,-12.216,-13.369,-13.488,-13.07,-12.027,-11.847,-11.306,-11.14,-12.195,-11.883,-11.458,-11.664,-11.938,-12.354,-13.302,-14.051\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.052,-13.332,-12.526,-13.051,-13.692,-13.618,-12.848,-12.486,-11.746,-11.529,-11.566,-11.638,-11.672,-11.392,-11.386,-12.114,-12.449,-12.194,-13.339,-14.252\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.095,-12.326,-12.319,-12.965,-13.672,-14.448,-13.594,-12.967,-11.909,-11.868,-11.383,-11.913,-11.679,-11.354,-11.884,-11.596,-13.107,-11.962,-12.4,-12.907,-13.978\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.463,-12.366,-11.453,-12.36,-13.33,-14.14,-12.44,-12.553,-12.091,-11.308,-11.531,-11.881,-11.54,-11.985,-12.845,-12.354,-12.478,-11.927,-12.248,-12.854,-13.466\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.0127,-9.5571,-11.161,-11.187,-11.016,-9.5,-9.7779,-11.056,-12.547,-11.711,-12.336,-12.529,-11.063,-11.185,-11.497,-12.044,-12.056,-12.597,-11.956,-11.554,-12.373,-12.673,-13.674,-14.155\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.7413,-12.258,-11.49,-11.073,-10.827,-11.169,-9.8185,-10.221,-11.788,-12.333,-12.273,-11.256,-11.136,-11.312,-12.131,-11.135,-11.522,-11.701,-11.257,-12.54,-12.387,-12.884,-13.386\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-10.592,-11.582,-12.115,-11.417,-8.8508,-9.6047,-12.55,-11.448,-10.657,-10.204,-10.067,-11.622,-12.498,-11.737,-11.94,-12.11,-11.981,-12.219,-11.69,-11.828,-12.367\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.2438,-10.163,-11.88,-12.366,-10.037,-10.345,-11.784,-12.217,-12.292,-10.153,-9.739,-12.081,-12.684,-12.733,-12.425,-11.72,-11.735,-12.254,-12.407,-12.27,-12.191\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070115-070917.unw.csv",
    "content": "-7.7179,-8.2799,-8.9526,-8.4549,-8.9501,-9.0957,-8.7784,-8.7319,-8.6293,-8.7207,-9.0506,-9.017,-8.5519,-8.4618,-8.4207,-8.8496,-8.2532,-7.9737,-7.5995,-7.4835,-7.9819,-7.8633,-7.427,-7.4382,-8.1925,-8.1245,-8.27,-7.2653,-6.8252,-7.4031,-6.8285,-7.4855,-8.3713,-9.1806,-9.8631,-10.747,-11.921,-12.183,-13.516,-13.661,-13.232,-12.534,-12.918,-13.126,-12.914,-12.612,-11.844\n-8.3083,-8.5188,-9.1594,-8.4592,-8.5636,-8.7018,-8.7598,-8.76,-8.9039,-9.0669,-9.2231,-9.2498,-8.9076,-8.7013,-8.3516,-8.361,-8.6703,-8.5336,-7.6842,-7.6868,-7.945,-7.5435,-6.9528,-7.3463,-7.5048,-7.4533,-7.64,-7.698,-7.4194,-6.8024,-6.8276,-7.8643,-8.2361,-8.8158,-9.2442,-11.533,-12.397,-12.244,-12.528,-12.434,-12.364,-11.87,-11.751,-12.305,-12.469,-12.929,-13.078\n-8.9803,-9.3149,-9.7561,-9.3634,-9.1769,-8.7688,-8.7057,-8.4203,-8.6887,-10.077,-9.4944,-9.0769,-9.0812,-8.8777,-8.2124,-8.6624,-8.856,-8.5573,-8.2812,-8.8978,-8.9139,-8.74,-8.2134,-8.7088,-7.7222,-7.8526,-7.4925,-8.264,-7.7401,-6.5584,-6.8578,-8.1541,-7.708,-7.945,-8.1579,-9.0897,-9.8234,-11.152,-11.645,-11.616,-11.509,-11.052,-11.645,-12.225,-11.801,-12.594,-12.625\n-9.238,-9.2489,-9.8671,-9.7718,-9.6268,-9.1875,-9.5075,-9.1404,-9.2819,-9.8482,-9.4497,-9.3587,-9.5575,-9.0099,-8.5795,-8.2329,-8.5137,-8.8931,-8.7846,-9.5497,-9.173,-8.6251,-8.4455,-8.3249,-7.8293,-7.7785,-7.535,-7.8725,-7.547,-6.2842,-6.1943,-6.6382,-6.7866,-7.5975,-9.0198,-9.2946,-9.4074,-10.793,-10.992,-11.523,-10.815,-10.304,-10.893,-11.799,-12.142,-12.251,-12.289\n-8.9267,-8.9934,-10.013,-9.6143,-9.5299,-9.3877,-9.705,-9.759,-8.6167,-9.8035,-9.7331,-9.3932,-9.7414,-9.2956,-8.6447,-8.4781,-8.3102,-9.3519,-9.258,-9.0844,-9.6952,-7.7932,-7.9281,-7.4327,-7.8402,-7.8478,-7.5525,-8.3974,-7.7478,-7.7213,-7.3931,-7.7542,-7.9101,-8.1157,-10.011,-10.296,-10.564,-10.875,-11.031,-10.781,-10.131,-10.487,-11.038,-11.118,-11.279,-11.543,-11.753\n-9.5613,-9.9371,-9.9306,-9.7976,-10.124,-9.6799,-9.8031,-10.016,-10.105,-10.345,-10.159,-10.174,-9.9602,-9.3918,-9.1656,-8.6017,-8.8112,-9.7743,-8.9932,-8.8691,-8.8242,-8.2766,-7.769,-7.3777,-8.2308,-7.7625,-7.2872,-8.1559,-8.044,-7.9778,-7.8155,-8.51,-8.4481,-8.3281,-8.3879,-8.6564,-11.14,-10.869,-11.271,-10.138,-9.7829,-10.184,-10.626,-10.813,-11.347,-11.283,-10.807\n-10.132,-10.261,-9.7333,-9.5695,-10.041,-9.9082,-10.452,-10.051,-9.8508,-10.318,-10.031,-10.597,-10.067,-9.1534,-9.4165,-8.9666,-9.6704,-10.376,-10.67,-9.8498,-9.0746,-7.7325,-7.2491,-7.5756,-7.6423,-6.7499,-7.1057,-7.2717,-7.2369,-6.9788,-6.9972,-7.6973,-7.6403,-7.894,-7.9549,-8.4126,-9.8364,-10.917,-11.671,-10.438,-10.282,-10.497,-10.498,-11.014,-11.746,-11.75,-11.614\n-10.94,-11.231,-10.36,-9.467,-10.231,-10.12,-9.8993,-9.6955,-9.6688,-10.044,-9.8155,-10.132,-9.892,-8.6214,-9.197,-9.7382,-10.271,-10.393,-10.224,-10.082,-9.2394,-7.1436,-6.4697,-7.4849,-6.958,-6.5551,-6.27,-6.3906,-5.9203,-6.2481,-5.7487,-6.6502,-6.8747,-7.7413,-8.0479,-8.6696,-10.058,-10.548,-10.478,-10.771,-10.484,-10.252,-9.7807,-10.981,-11.671,-11.978,-11.112\n-10.042,-10.439,-10.203,-9.7949,-10.711,-10.422,-9.545,-9.718,-9.9937,-9.7664,-9.9227,-10.684,-10.083,-9.4083,-9.23,-9.235,-9.3974,-8.8931,-9.0435,-9.5672,-9.0445,-7.7247,-7.0173,-6.7742,-6.0046,-6.444,-7.325,-6.2325,-5.1192,-5.1211,-5.5422,-5.6858,-6.2495,-6.9862,-8.3273,-9.2885,-9.9668,-10.448,-10.073,-10.1,-10.265,-10.075,-9.561,-10.18,-10.946,-11.211,-10.783\n-9.9881,-10.236,-10.367,-10.888,-11.002,-10.809,-9.9087,-9.8824,-9.9385,-9.3112,-10.01,-10.179,-9.4025,-9.7408,-9.4594,-9.0194,-8.5211,-8.2211,-8.2434,-7.8792,-6.4986,-7.3829,-6.7006,-7.3352,-6.1577,-6.3046,-7.2703,-6.6042,-6.7377,-5.6015,-5.8542,-4.7462,-6.2867,-7.1522,-8.6976,-8.9558,-8.7948,-9.5874,-9.595,-9.9352,-10.214,-9.9956,-9.7793,-9.4542,-9.8742,-10.259,-10.198\n-10.017,-10.288,-10.883,-11.737,-11.218,-10.804,-10.375,-9.6335,-9.6331,-10.109,-10.241,-10.044,-9.4432,-9.5986,-9.5533,-9.4595,-8.3207,-8.5488,-8.451,-7.6929,-7.611,-7.1677,-6.3798,-6.9673,-6.4638,-6.179,-6.2514,-6.2655,-5.9353,-5.134,-5.4779,-5.8387,-6.9742,-6.7901,-8.3929,-7.9642,-7.9786,-8.3973,-8.6895,-8.7693,-9.3209,-8.8197,-9.0958,-9.5675,-9.9062,-9.8759,-10.611\n-10.157,-10.538,-10.881,-11.098,-10.83,-9.8437,-10.475,-10.39,-10.121,-10.761,-10.449,-10.391,-10.088,-9.4852,-9.164,-9.2601,-8.8382,-8.6881,-8.2209,-7.2278,-9.1359,-7.0987,-6.4723,-5.8926,-5.9393,-5.6644,-5.9405,-6.2012,-5.344,-4.9968,-5.7012,-6.7998,-7.4077,-7.8318,-8.6857,-8.3781,-8.128,-8.1847,-8.1464,-8.4687,-8.8653,-8.3271,-8.8779,-9.1323,-9.2343,-8.6379,-9.1743\n-10.258,-10.644,-11.091,-11.198,-10.987,-10.793,-10.507,-10.799,-9.3769,-10.836,-10.557,-10.883,-10.076,-10.006,-9.2532,-9.157,-8.9793,-7.9692,-7.5951,-6.593,-6.7381,-6.4505,-6.0371,-5.6289,-5.23,-5.337,-5.4216,-6.4748,-5.4761,-6.0559,-6.0941,-7.7444,-8.0723,-8.0313,-7.971,-7.9741,-7.811,-7.9761,-8.6071,-9.2293,-8.6862,-8.4062,-8.3195,-8.5047,-7.9931,-7.8608,-7.9982\n-10.502,-11.787,-11.8,-11.229,-10.819,-11.116,-10.44,-10.554,-10.69,-10.724,-10.821,-10.493,-9.4079,-10.507,-8.9274,-9.118,-8.2643,-7.7966,-7.6446,-7.1105,-6.5725,-4.8876,-5.1246,-5.4955,-4.4588,-4.7981,-5.3589,-6.387,-6.5812,-6.5034,-6.3144,-7.2211,-7.9991,-7.4198,-6.5746,-6.1698,-7.2258,-7.7842,-8.1576,-8.5863,-8.7217,-7.1969,-7.52,-7.0963,-6.7276,-7.3857,-7.8601\n-10.792,-11.995,-11.453,-10.856,-10.95,-11.055,-10.23,-11.03,-10.829,-10.112,-11.168,-10.48,-10.467,-9.7054,-8.3351,-7.423,-8.4504,-7.9971,-7.7362,-7.1269,-7.1623,-2.6983,-5.572,-4.879,-3.6858,-4.9667,-3.71,-5.2402,-5.4462,-5.5482,-5.6603,-5.4005,-5.867,-6.2327,-5.5998,-5.5057,-7.8387,-8.1548,-7.8842,-7.3623,-7.4091,-6.708,-7.6342,-7.1464,-7.0798,-7.296,-7.1702\n-10.318,-10.602,-10.952,-10.823,-10.878,-11.227,-9.8723,-10.998,-10.101,-9.8942,-10.981,-10.301,-11.732,-10.16,-7.7436,-7.6981,-8.6808,-8.2729,-6.6413,-6.6963,-6.4375,-4.9077,-5.4554,-4.465,-4.814,-4.9299,-3.6951,-5.1156,-5.5129,-4.6883,-5.4516,-4.6506,-5.6803,-6.3412,-6.0279,-6.1663,-8.1851,-8.3722,-8.3612,-7.9575,-8.0134,-7.6373,-8.0938,-8.6489,-8.9909,-8.7022,-8.6156\n-9.8452,-9.3058,-10.381,-10.644,-10.47,-10.375,-9.9073,-10.513,-9.2784,-9.0878,-9.8567,-9.7222,-10.428,-10.026,-8.4759,-8.5232,-8.9697,-7.3266,-6.0468,-6.1794,-5.4848,-5.6513,-4.7248,-3.7259,-4.2371,-3.8028,-2.294,-2.9093,-5.3437,-5.3762,-6.6196,-4.7391,-6.1987,-6.7331,-6.752,-6.736,-8.8531,-8.048,-8.4896,-7.6763,-8.0469,-9.4765,-7.8256,-8.2837,-10.002,-10.987,-10.765\n-8.8519,-8.4492,-9.9199,-9.6442,-10.554,-11.537,-10.778,-9.4307,-9.0202,-9.0064,-9.7639,-9.6044,-9.3269,-8.8139,-8.6451,-8.4766,-8.1723,-6.587,-6.6785,-5.7698,-5.5811,-5.5417,-5.6859,-4.8247,-4.8579,-5.7703,-3.8891,-1.8826,-4.1824,-5.5071,-6.4127,-5.0083,-5.7801,-5.9759,-5.9924,-6.6029,-8.5008,-7.4601,-7.1139,-6.8251,-7.5425,-8.7663,-8.9052,-8.0881,-9.1152,-10.932,-11.35\n-7.975,-8.6721,-9.4521,-9.1738,-9.4133,-11.826,-9.6323,-9.5273,-9.3378,-9.0418,-9.9633,-9.5128,-9.6134,-8.9549,-8.5713,-7.7122,-7.6624,-7.5866,-7.2886,-6.934,-6.4112,-6.8098,-7.2408,-6.1926,-5.9877,-5.3581,-5.6586,-2.6378,-3.8987,-4.4209,-5.6135,-4.7607,-5.0235,-4.9599,-4.8838,-5.4058,-6.92,-6.9956,-6.5058,-6.4839,-7.0438,-8.0736,-8.482,-8.171,-8.5482,-9.4045,-10.56\n-7.7838,-8.4457,-8.5679,-8.7425,-8.0149,-8.4957,-9.6585,-10.612,-10.018,-9.3519,-9.1621,-9.7,-9.6938,-8.6923,-8.0305,-7.8737,-8.6146,-8.0071,-7.9946,-7.8548,-6.6903,-6.6678,-7.2785,-6.3161,-5.9105,-5.1135,-4.3412,-3.108,-2.6939,-3.9087,-3.1066,-4.6846,-4.3123,-4.7542,-4.7319,-5.5276,-6.1158,-6.3167,-5.6823,-6.0315,-6.5154,-6.9557,-7.7541,-7.3235,-7.8624,-9.385,-9.8167\n-7.3208,-7.1809,-8.5195,-7.8568,-6.595,-8.8155,-10.155,-10.247,-10.005,-9.6679,-10.195,-9.2162,-9.504,-8.1655,-8.5978,-8.867,-8.5667,-8.0301,-8.073,-7.7811,-7.3454,-6.8211,-6.3933,-5.5379,-5.0109,-3.9025,-3.6928,-1.6291,-1.8823,-3.0766,-2.1112,-4.0102,-3.4212,-3.6435,-3.9724,-6.252,-5.3761,-5.6248,-5.9129,-6.6447,-7.0337,-7.4039,-7.5029,-8.4872,-9.172,-9.9485,-10.204\n-7.1519,-7.3451,-7.6871,-7.9398,-8.1274,-8.6962,-8.7372,-9.1419,-10.149,-9.9227,-9.7594,-8.9436,-9.6618,-8.3567,-9.1302,-9.7055,-8.4173,-8.3431,-8.6023,-9.2131,-7.9232,-6.5582,-7.1088,-4.6381,-4.0829,-4.0965,-3.2089,-1.8417,-1.2644,-0.95431,-0.44209,-2.653,-3.1625,-3.4079,-4.998,-5.6029,-5.1787,-5.7216,-6.4563,-6.0514,-7.1974,-7.8268,-8.0821,-8.1006,-8.2214,-9.3202,-9.749\n-7.0225,-7.356,-6.8636,-6.9273,-7.1392,-7.5993,-8.7962,-9.3638,-10.45,-10.44,-9.5516,-9.7501,-9.1228,-8.6236,-9.2336,-9.9108,-7.47,-7.5606,-7.899,-7.7807,-6.562,-6.5569,-5.7939,-4.3531,-3.3145,-3.5308,-2.3503,-2.0515,0.58952,0.086138,0.41983,-0.46622,-2.1793,-3.0225,-3.4494,-4.101,-4.4045,-4.7394,-4.7857,-5.1693,-6.5374,-7.4052,-8.2243,-7.1802,-8.8937,-9.29,-9.3485\n-7.4006,-6.6841,-6.1774,-6.4579,-4.7111,-7.5805,-8.7369,-9.4801,-10.023,-10.107,-9.4379,-10.149,-8.4498,-8.5676,-8.8694,-9.9967,-7.4741,-7.6323,-7.0716,-6.5055,-6.17,-5.0734,-3.9949,-1.7829,-1.5585,-1.8878,-0.77079,1.7484,1.4812,1.2355,2.6192,2.1141,1.8339,0.61768,-1.9082,-2.6697,-2.8089,-3.4535,-4.1355,-3.6158,-5.4265,-6.8399,-6.9283,-6.9947,-8.1025,-8.1372,-8.9629\n-6.4124,-6.4204,-6.453,-6.6709,-6.8319,-7.429,-8.4774,-8.9946,-8.6563,-9.8084,-9.7273,-9.3891,-8.9523,-8.3041,-8.4686,-8.209,-7.7432,-7.7996,-7.2253,-5.878,-5.1277,-4.418,-2.7761,-1.8208,-0.9721,-0.070065,1.7895,4.0745,4.0276,4.4456,4.4502,3.9822,2.6816,1.6842,1.2154,-1.5596,-2.401,-2.8201,-3.2021,-3.3375,-5.6927,-5.4497,-6.2619,-7.1236,-7.9652,-8.2464,-8.8662\n-7.0993,-9.038,-8.0095,-7.1838,-7.3542,-7.0623,-7.7826,-8.287,-9.1718,-10.055,-9.1912,-9.1371,-8.8539,-8.5081,-7.9792,-7.5034,-7.003,-6.5205,-6.238,-4.899,-3.752,-3.7889,-2.5461,-0.2374,2.0099,3.1122,4.7725,7.6207,6.6593,6.5582,6.1133,5.079,4.2446,4.4415,2.0717,-0.91409,-2.5611,-2.4541,-2.3185,-2.7093,-4.7692,-5.1408,-6.3833,-6.8635,-7.9946,-8.4835,-8.6021\n-7.5012,-7.8501,-7.1317,-6.7376,-7.3649,-8.2561,-7.4464,-7.9292,-8.5906,-9.1883,-8.8023,-8.8613,-9.2072,-8.5145,-7.4913,-6.3861,-5.906,-5.7232,-5.3292,-4.0252,-4.3373,-4.2364,-2.4911,1.362,4.5556,4.8662,NaN,NaN,10.026,8.0409,6.5275,5.6128,5.182,3.5627,2.3434,-0.015747,-1.5258,-1.0919,-1.4806,-2.5439,-4.4496,-5.3182,-6.1239,-6.7452,-7.5678,-8.3122,-8.5455\n-9.4472,-7.9087,-7.9356,-8.9785,-7.7264,-8.5265,-8.098,-7.915,-8.8626,-8.2596,-8.5082,-8.3409,-7.9394,-7.2686,-6.1767,-5.0531,-5.0326,-5.0928,-4.1206,-3.334,-4.3863,-4.3617,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.3094,4.2332,1.6288,0.80336,0.26114,0.0867,-0.71445,-2.7005,-3.8866,-4.9964,-6.7221,-7.3584,-7.7883,-7.9284,-7.8685\n-8.3546,-6.3312,-11.653,-10.216,-8.6241,-8.8728,-10.121,-10.893,-10.911,-8.2223,-8.2027,-7.8153,-7.478,-6.5595,-5.56,-4.6501,-4.0332,-3.6026,-3.0157,-2.7176,-3.2125,-2.9193,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.4024,1.0188,0.096795,-0.25547,0.053765,-0.92472,-2.1817,-3.632,-4.3284,-6.2888,-7.1811,-7.2391,-7.6866,-7.7809\n-5.7662,-4.9907,-7.0186,-9.6652,-8.5399,-8.973,-10.314,-10.874,-10.109,-8.6098,-8.2955,-7.8203,-6.9289,-6.4938,-5.4942,-4.5406,-3.3023,-2.8839,-2.8152,-2.1864,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.49497,0.53644,-1.1962,-2.771,-3.5107,-4.437,-6.6767,-6.8814,-7.3766,-7.5548,-7.839\n-5.8307,-5.3801,-5.8165,-5.2417,-5.9577,-6.8821,-10.595,-10.407,-9.5802,-8.492,-7.8316,-7.3026,-6.9773,-6.6457,-5.6007,-4.9075,-4.3774,-4.0057,-3.0325,-0.95315,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.60942,0.30245,-1.882,-3.4084,-4.2301,-5.4299,-6.4309,-6.8391,-7.6385,-7.4043,-7.8177\n-6.0521,-5.9313,-6.0361,-7.1454,-6.6012,-7.7437,-10.521,-10.829,-10.846,-8.9077,-8.2831,-7.7937,-7.2945,-6.3514,-6.0006,-5.5023,-4.9317,-4.367,-1.9157,-0.8848,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4771,-4.7684,-5.0812,-6.6986,-7.3239,-6.6791,-7.5778,-7.0402,-7.4298\n-6.8453,-8.0493,-7.7462,-7.777,-6.95,-8.9076,-9.9986,-10.432,-11.675,-8.693,-8.9466,-8.1496,-7.1133,-7.1116,-5.6025,-6.2782,-6.2143,-4.2059,-2.9042,-1.4804,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.6994,-5.2286,-5.5649,-7.1345,-7.2818,-6.632,-7.674,-7.4593,-8.3027\n-7.0649,-6.8083,-8.318,-7.9446,-7.6844,-8.8479,-9.2649,-10.402,-10.041,-8.4757,-8.6848,-8.738,-7.7283,-9.0745,-6.0126,-6.1381,-6.831,-5.8847,-3.493,-4.5449,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.3862,-5.4089,-5.7788,-6.1124,-6.5844,-7.5382,-7.8693,-8.6243\n-6.3627,-6.8728,-9.9996,-8.0047,-8.3124,-8.5812,-8.2321,-8.3239,-9.5008,-9.0052,-8.4638,-8.2892,-9.1666,-8.525,-6.3466,-6.0304,-5.4128,-5.1427,-4.2536,-5.4067,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.9644,-4.969,-5.5993,-6.741,-7.3261,-8.2759,-8.893\n-7.7871,-8.96,-8.5491,-6.5143,-7.3549,-8.8372,-8.8125,-7.5928,-9.1217,-9.0889,-7.3137,-6.9789,-6.8143,-7.4008,-8.1436,-6.3497,-6.2086,-5.2589,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.7293,-5.7742,-5.6551,-6.4792,-8.0536,-8.3794,-9.2861\n-7.4808,-8.1594,-8.1252,-8.337,-8.4339,-9.6652,-9.2089,-8.1254,-8.2096,-8.885,-7.2688,-6.8102,-6.9048,-7.8612,-7.6983,-6.3078,-6.1715,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.9206,-5.8396,-6.5184,-7.1914,-7.9186,-7.5906,-7.8677\n-6.9644,-7.5877,-8.1188,-8.7944,-9.0929,-9.4263,-8.7782,-8.6284,-8.345,-7.7766,-7.3412,-6.9275,-7.0032,-6.5537,-6.4508,-6.0008,-5.967,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.8578,NaN,NaN,-5.3264,-6.0464,-6.1507,-7.1922,-7.5309,-6.8699,-6.2502,-6.978\n-8.0229,-7.5289,-8.0908,-8.1025,-8.0693,-8.1068,-8.4146,-8.3985,-7.9455,-7.4439,-7.2541,-6.1916,-6.7607,-6.9141,-6.3026,-5.8624,-5.3566,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.8303,NaN,NaN,NaN,NaN,NaN,NaN,-2.4066,-2.9963,-4.8281,-6.4557,-6.9042,-6.5876,-6.8472,-6.8844,-6.8326,-7.7398\n-7.9931,-5.4584,-6.8262,-7.5071,-7.3226,-6.9259,-7.0903,-7.2207,-7.7993,-7.7658,-7.4062,-6.6579,-6.7232,-5.8944,-4.8223,-4.3121,-4.3253,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.6887,-10.193,NaN,NaN,NaN,NaN,-0.34948,-2.2533,-3.1901,-5.6922,-6.9348,-6.499,-6.0126,-6.9568,-7.2744,-7.6292,-9.5235\n-6.2832,-5.8569,-7.1696,-6.3829,-6.9958,-6.9718,-6.6071,-6.9502,-7.3858,-6.4433,-6.8847,-6.7967,-6.5288,-5.8771,-5.3098,-4.1002,-3.9015,-6.4994,-6.8055,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.9129,NaN,NaN,NaN,NaN,NaN,-0.40161,-1.3693,-3.0233,-7.0674,-6.8905,-7.1496,-6.8648,-5.8044,-6.6733,-6.6302,-7.7161,-9.4044\n-5.9392,-5.919,-6.0858,-5.8518,-6.3096,-6.846,-6.9864,-7.1706,-6.6779,-6.1094,-6.5011,-6.5276,-6.9857,-6.7979,-7.3039,-6.9513,-5.6356,-7.0929,-8.078,NaN,NaN,NaN,-10.254,NaN,NaN,NaN,NaN,NaN,-9.1112,-11.998,NaN,NaN,NaN,NaN,-2.3709,-1.3106,-2.7639,-3.5857,-5.7805,-6.6526,-7.3637,-6.8141,-6.1375,-6.3511,-6.3518,-7.637,-8.6001\n-5.6102,-5.3145,-5.5494,-6.2679,-6.8289,-7.2531,-7.3948,-7.2308,-6.8202,-6.9473,-7.0326,-6.7854,-7.2015,-7.0483,-7.1485,-7.4609,-5.5434,-6.2135,-6.3374,-7.1613,-8.1343,-9.2638,-9.9278,NaN,NaN,NaN,NaN,NaN,-9.3803,-9.2725,-5.7595,-3.5469,-3.6111,-3.0294,-3.2925,-3.7623,-3.7124,-5.2906,-6.5213,-6.7752,-6.073,-6.4485,-6.8234,-6.6892,-6.7675,-8.8563,-9.7621\n-5.7151,-5.4847,-5.5283,-6.521,-7.012,-7.2089,-7.3804,-7.3686,-7.5183,-7.45,-7.3738,-7.1116,-6.888,-6.7518,-6.1989,-5.8286,-5.3458,-6.1137,-6.7393,-6.7642,-7.9807,-9.3977,NaN,NaN,NaN,NaN,NaN,NaN,-7.0576,-5.8617,-5.8875,-4.9759,-4.2817,-4.5522,-5.1226,-5.4947,-5.5578,-6.9795,-7.214,-6.741,-6.0453,-5.8095,-7.0926,-7.5484,-7.5044,-8.0123,-9.4106\n-5.704,-5.7598,-5.8304,-6.2615,-6.6823,-7.0649,-6.9878,-7.2251,-7.7291,-6.7946,-7.5262,-7.3102,-7.0611,-6.7854,-6.0119,-5.9822,-6.7911,-6.2471,-6.1803,-4.8104,-7.384,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.7976,-6.0403,-5.0946,-3.3987,-4.9262,-5.3699,-6.1648,-5.6266,-6.3605,-7.2131,-7.3993,-6.9059,-6.0062,-6.0088,-7.0167,-7.0605,-8.8382,-9.9898,-10.683\n-5.2423,-5.0736,-4.8368,-5.3085,-6.3849,-6.2784,-6.5803,-6.2761,-6.1011,-6.4492,-7.082,-7.2577,-7.0792,-6.7519,-6.4104,-6.4686,-6.2101,-5.9142,-6.6407,-7.404,-8.3201,-7.8058,-5.3272,-6.1439,-8.2411,NaN,NaN,NaN,-6.2514,-5.5322,-4.2693,-4.2398,-4.6376,-6.2079,-6.9042,-6.9824,-6.8928,-8.0249,-8.1006,-7.5922,-6.3401,-7.0403,-7.5236,-7.1044,-8.3671,-10.349,-11.622\n-4.8348,-4.3821,-4.4688,-6.1868,-6.467,-6.0238,-6.1677,-6.1062,-6.7922,-6.559,-6.8029,-6.943,-6.7597,-6.6246,-7.7136,-7.3634,-6.4066,-6.4764,-7.2101,-7.9263,-9.3675,-9.0856,-7.4212,-8.259,-8.8351,-9.0822,-9.491,-8.0962,-7.0003,-4.9549,-3.9041,-5.4386,-6.6493,-7.4839,-7.4469,-6.7054,-7.4932,-7.8211,-8.5128,-7.7081,-6.6954,-8.0605,-9.4819,-8.9522,-8.3247,-8.9983,-10.304\n-5.1774,-5.1032,-5.1634,-6.036,-6.1805,-7.0857,-6.5676,-6.8179,-6.9673,-6.9215,-7.192,-7.0529,-6.701,-6.9462,-7.4305,-7.5078,-6.4755,-6.8231,-7.4167,-7.984,-8.8749,-9.6994,-10.038,-9.2658,-9.4334,-9.4973,-9.4252,-8.5529,-7.4576,-6.9288,-6.7755,-7.6458,-8.6965,-8.4507,-7.8796,-7.8278,-9.1346,-8.5978,-8.6502,-7.676,-7.4644,-8.6661,-10.784,-11.655,-10.718,-10.221,-9.5536\n-4.81,-5.4902,-6.4676,-5.6631,-5.6245,-6.8148,-6.9797,-7.2193,-7.532,-7.9361,-7.662,-7.1499,-6.5292,-6.661,-7.18,-7.3784,-7.3086,-7.1611,-7.2677,-8.2457,-9.7803,-10.346,-9.7597,-9.3484,-9.6563,-9.6394,-8.814,-7.9362,-8.2418,-7.3899,-8.0988,-8.6202,-8.5159,-8.4648,-8.8195,-9.0601,-9.5175,-8.4676,-7.864,-8.3628,-8.6696,-9.095,-10.943,-9.893,-9.5346,-9.9134,-7.9295\n-4.5197,-4.6949,-5.7565,-5.0785,-5.2352,-6.0313,-7.2453,-7.7052,-7.159,-7.5945,-7.5351,-7.0717,-6.5249,-6.4733,-7.3468,-7.8648,-7.7184,-8.0272,-8.6946,-10.534,-9.9149,-9.6101,-8.8614,-8.9166,-9.1275,-8.9401,-8.6794,-7.5899,-9.1819,-7.7044,-8.3192,-8.5109,-9.091,-7.8543,-7.5396,-7.7019,-9.6034,-8.2252,-7.5311,-9.4202,-9.0569,-9.3955,-11.197,-10.886,-9.9921,-11.119,-9.958\n-4.6466,-4.6614,-5.0139,-4.8029,-5.2115,-4.9919,-6.7635,-6.023,-6.4467,-6.6636,-6.9134,-7.3154,-6.5975,-7.3624,-8.1713,-8.2187,-8.184,-9.1222,-9.9685,-10.495,-9.8093,-8.848,-8.2846,-8.1994,-8.7233,-8.9117,-9.7682,-7.946,-8.8117,-8.0089,-8.4647,-8.9848,-9.3351,-8.8184,-7.1787,-8.3914,-8.9996,-8.5922,-9.1247,-9.7534,-9.9491,-9.2974,-9.0404,-10.286,-11.143,-10.701,-9.7849\n-5.41,-4.5678,-4.4367,-5.0158,-5.4693,-6.0341,-7.1447,-6.5933,-7.2318,-6.655,-7.7507,-8.345,-7.3828,-6.9362,-8.4445,-8.2463,-8.6174,-8.9761,-9.2856,-10.02,-9.1324,-8.5191,-8.1782,-8.2148,-8.5686,-9.2517,-10.419,-7.8974,-7.9732,-7.9737,-8.885,-9.5169,-9.479,-8.7356,-7.8069,-8.7216,-9.2081,-9.4275,-10.192,-10.11,-9.9458,-9.8981,-10.051,-9.0415,-10.616,-10.053,-10.119\n-4.4646,-4.3472,-4.474,-5.7333,-5.2414,-6.0247,-6.5007,-6.7864,-7.0104,-6.6053,-7.5054,-7.4267,-7.437,-7.1322,-7.7508,-7.8194,-7.9609,-9.6115,-9.8243,-10.131,-8.9818,-8.5956,-8.4649,-8.6512,-8.7734,-8.8917,-8.0955,-7.775,-7.8071,-7.9011,-8.5836,-9.136,-9.0315,-8.3119,-8.0859,-8.6417,-9.6922,-10.901,-11.382,-10.478,-9.8908,-9.8525,-10.084,-9.78,-10.666,-10.202,-10.401\n-4.6557,-4.6743,-4.8824,-4.6248,-4.0475,-5.7232,-5.1188,-5.466,-5.8415,-5.7782,-5.6446,-6.8344,-7.3388,-7.0655,-7.3023,-8.2725,-8.58,-10.796,-11.256,-10.481,-8.6726,-8.496,-9.0543,-9.25,-9.0075,-8.2043,-7.7665,-7.5369,-7.6139,-7.9211,-8.9819,-9.6056,-8.8661,-8.521,-8.1959,-9.0675,-10.365,-11.025,-11.123,-10.493,-10.015,-9.9513,-10.331,-10.018,-11.077,-10.427,-10.161\n-6.6442,-5.8709,-6.3307,-5.3778,-4.617,-4.3157,-5.0952,-6.0374,-5.5272,-3.4062,-4.1149,-7.045,-7.4928,-7.0852,-6.4051,-6.6967,-8.2998,-10.557,-9.0926,-8.3875,-8.3342,-8.6543,-9.3019,-9.5431,-9.1817,-8.7378,-7.9479,-7.8974,-7.5847,-7.9279,-9.3581,-10.057,-10.386,-9.4171,-9.3093,-9.191,-10.067,-10.59,-10.797,-10.408,-9.7032,-9.9186,-9.8719,-9.9729,-11.001,-10.732,-11.471\n-6.2601,-5.7022,-5.3734,-4.0468,-5.9121,-4.9324,-6.0232,-5.8738,-4.8087,-4.2335,-5.5224,-7.0421,-8.4377,-8.2413,-6.7983,-7.1731,-9.2694,-8.8145,-7.9033,-8.1487,-8.987,-8.8642,-9.3208,-8.6868,-8.967,-9.2379,-8.3613,-8.5231,-8.2976,-8.3264,-9.2958,-10.106,-11.15,-10.59,-9.4699,-9.558,-10.258,-10.349,-10.454,-10.318,-10.148,-10.039,-9.8152,-8.9017,-10.08,-10.752,-11.325\n-4.4257,-5.6897,-2.002,-2.4321,-6.1008,-5.1942,-5.3657,-5.4629,-6.0409,-6.9508,-4.445,-5.2019,-9.3489,-8.6676,-9.4775,-9.5449,-8.3892,-9.3479,-8.2865,-8.5296,-9.5738,-8.4407,-8.42,-8.2803,-8.7156,-8.7841,-8.4942,-9.1767,-8.7966,-8.6988,-9.1468,-9.6029,-10.289,-10.59,-9.5819,-9.6598,-10.323,-10.605,-9.8404,-9.8189,-10.501,-10.141,-9.2277,-9.2779,-10.108,-10.338,-10.928\n-3.5923,-5.7268,-2.2775,-2.5067,-3.6421,-3.7731,-5.325,-4.4669,-4.1515,-4.9378,-5.3971,-4.9987,-7.6233,-8.8647,-11.292,-11.148,-8.132,-9.9176,-9.1466,-8.4503,-9.7934,-9.2263,-8.2581,-8.425,-8.4565,-8.72,-8.7292,-9.4997,-8.1773,-8.7812,-9.6884,-9.8115,-9.9405,-10.093,-9.9358,-9.6161,-10.194,-10.935,-9.928,-9.0492,-9.5366,-9.7619,-9.474,-9.7285,-10.351,-10.227,-11.265\n-2.768,-5.8836,-5.9697,-3.8957,-4.01,-5.5117,-5.9954,-3.343,-3.1603,-3.1499,-5.6899,-5.4701,-6.5185,-7.7862,-9.634,-8.0558,-6.7972,-7.3745,-7.8547,-7.8941,-8.6278,-9.2897,-8.3339,-8.6414,-9.0647,-9.2976,-9.0262,-8.9396,-8.5427,-9.2641,-10.965,-10.021,-10.234,-10.618,-10.134,-9.921,-10.153,-10.486,-9.7916,-9.2599,-9.5236,-10.175,-10.154,-10.983,-10.725,-10.682,-11.392\nNaN,-4.7109,-7.1291,-5.6539,-4.9617,-6.5751,-5.422,-1.3944,NaN,NaN,-4.89,-4.8071,-4.8451,-7.4664,-7.4047,-7.0666,-5.376,-5.7556,-5.1677,-6.6124,-8.31,-8.909,-9.1844,-9.3114,-9.2856,-9.2526,-8.8794,-9.0277,-9.0092,-10.061,-10.508,-10.306,-10.428,-10.556,-10.297,-10.306,-9.8052,-10.156,-10.004,-9.7932,-9.6609,-10.807,-10.574,-11.474,-11.694,-12.072,-12.027\nNaN,NaN,-6.1191,-5.757,-5.3977,-5.3809,-5.0425,-2.8113,NaN,NaN,NaN,-5.0457,-5.1146,-4.7642,-5.1079,-5.5294,-5.3432,-3.836,-3.6894,-6.6787,-8.434,-9.0567,-9.4072,-9.3379,-9.2479,-10.089,-9.3221,-9.284,-9.0963,-9.546,-9.3579,-10.6,-11.291,-10.358,-10.124,-10.627,-10.145,-10.076,-9.666,-10.189,-10.209,-10.921,-10.731,-11.051,-11.574,-11.132,-11.204\nNaN,NaN,-7.5094,-5.6589,-5.8899,-6.1922,-4.8464,-3.4096,NaN,NaN,NaN,NaN,NaN,NaN,-4.7171,NaN,NaN,-3.8625,-4.8276,-7.81,-7.5736,-8.7358,-8.8397,-8.8201,-9.3164,-10.082,-10.116,-9.9511,-9.1243,-8.9957,-8.658,-9.676,-10.731,-10.403,-10.254,-10.438,-10.206,-10.159,-9.9057,-10.005,-10.202,-10.773,-10.281,-10.996,-11.05,-10.747,-10.884\nNaN,NaN,NaN,NaN,-6.3938,-4.952,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.593,-9.5297,-7.2974,-7.5715,-8.5794,-10.189,-10.16,-9.7674,-8.9181,-8.7276,-8.5046,-9.4835,-9.9776,-9.97,-11.153,-11.071,-10.508,-10.208,-9.5782,-9.6406,-10.258,-11.026,-10.482,-10.898,-10.939,-10.616,-11.28\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.4367,-8.5102,-7.4811,-5.8048,-7.714,-8.6665,-9.1189,-8.7492,-8.3195,-8.4959,-9.1848,-9.8452,-10.294,-10.661,-11.771,-11.419,-10.328,-9.7859,-9.7002,-10.655,-10.672,-11.11,-10.958,-11.059,-11.178,-10.616,-11.039\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.4159,-6.0024,-8.1099,-8.5068,-9.001,-8.5122,-8.5873,-8.4565,-8.9533,-9.2386,-10.264,-11.127,-11.477,-10.853,-10.253,-10.116,-10.211,-10.996,-10.918,-11.03,-11.411,-11.233,-11.116,-11.162,-10.787\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.5866,-6.1803,-7.87,-8.5661,-8.4723,-7.9588,-8.2454,-8.155,-9.1724,-10.062,-10.598,-11.335,-11.61,-10.235,-10.268,-10.355,-10.031,-10.443,-10.44,-10.673,-11.407,-10.575,-10.649,-11.395,-11.014\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.0858,-7.8037,-7.6275,-7.3951,-7.4633,-7.9932,-7.9081,-8.4862,-8.9944,-10.864,-10.835,-10.912,-11.349,-10.264,-10.628,-10.34,-9.8263,-10.149,-9.4304,-10.958,-11.875,-10.843,-10.818,-11.198,-11.188\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.3522,-8.0813,-7.3455,-6.6778,-6.7121,-7.854,-7.935,-8.5561,-9.9767,-11.526,-9.2059,-9.3728,-9.6668,-9.8809,-10.487,-10.17,-9.8731,-10.272,-10.227,-11.059,-11.591,-11.095,-10.753,-11.168,-11.612\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.8243,-7.16,-6.6875,-6.4758,-6.5656,-7.1515,-8.0024,-9.38,-9.7986,-10.285,-9.0389,-9.7755,-9.9238,-10.175,-10.27,-10.402,-10.266,-10.122,-10.306,-10.757,-11.611,-11.717,-11.196,-11.713,-12.394\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.5809,-5.9974,-6.3031,-6.5396,-7.515,-8.1794,-8.0852,-9.4508,-9.9379,-10.283,-10.141,-9.6526,-9.2315,-10.585,-10.623,-10.01,-10.345,-10.695,-11.66,-11.611,-11.257,-12.47,-12.624\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.214,-7.0938,-7.6163,-7.6398,-7.2102,-8.0819,-9.2178,-9.5422,-9.7397,-9.5747,-9.3277,-11.189,-10.461,-9.6452,-9.4386,-9.7525,-10.266,-10.472,-10.555,-12.082,-12.551\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.9275,-11.365,-7.5966,-7.1448,-6.7883,-7.5984,-8.3376,-8.484,-8.7515,-8.2429,-11.452,-12.344,-9.0725,-8.4614,-9.2761,-9.8325,-10.862,-10.753,-10.025,-10.764,-12.027\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070219-070430.unw.csv",
    "content": "14.684,15.054,14.664,14.872,15.338,15.847,16.042,16.172,16.644,17.197,17.986,18.116,18.239,18.031,17.585,17.586,18.105,18.078,17.761,17.508,17.407,17.448,17.696,17.994,17.891,17.493,17.521,17.279,17.358,17.123,16.492,16.696,16.457,15.819,15.327,14.753,13.267,12.724,12.547,12.014,12.333,12.537,12.774,12.082,10.056,9.3135,9.463\n14.669,14.846,14.472,14.784,15.452,16.198,16.526,16.343,16.989,17.518,18.157,18.497,18.434,18.119,18.083,18.393,18.383,17.921,17.577,17.481,17.327,17.63,17.938,18.092,18.091,18.143,18.169,18.471,18.408,18.462,17.803,16.958,16.42,15.973,15.806,14.942,13.749,13.188,12.748,12.046,11.25,11.902,11.841,11.326,10.438,9.5925,9.3481\n14.128,13.974,14.358,14.896,15.301,16.518,17.058,17.318,17.558,17.993,18.047,18.062,18.165,17.996,18.212,18.599,18.393,17.976,17.823,17.821,17.946,17.996,18.319,18.582,18.415,18.472,18.579,18.118,18.481,19.044,18.249,16.71,16.487,16.578,16.943,15.815,13.788,13.192,12.579,11.491,11.063,12.175,12.416,11.493,10.205,9.8057,9.0941\n12.989,12.593,13.76,14.847,15.394,16.508,16.974,18.015,18.53,18.633,18.396,18.105,18.205,18.17,17.944,18.189,18.048,18.098,18.038,17.729,17.975,18.29,18.506,18.557,18.698,18.971,19.013,18.865,18.905,18.721,17.887,17.272,16.948,16.659,16.498,15.274,13.542,12.648,12.89,13.1,12.999,13.077,12.565,11.616,10.853,10.462,10.203\n12.632,11.469,12.375,14.785,15.455,16.29,16.804,18.017,18.969,18.677,18.408,18.607,18.537,18.294,18.162,17.995,17.342,17.378,18.093,18.47,18.459,18.588,18.395,18.912,19.32,19.379,19.329,18.882,19.02,18.946,18.056,17.318,17.129,16.662,15.559,14.175,13.219,12.861,13.14,13.604,13.285,12.704,12.198,11.704,11.246,10.937,10.353\n13.007,12.063,12.515,15.202,15.249,15.785,16.53,17.242,18.044,18.298,18.273,18.318,18.264,17.956,18.223,17.985,17.704,17.376,17.836,18.298,18.735,18.595,18.647,18.53,18.92,19.268,19.251,18.984,19.148,19.125,18.174,17.934,17.881,17.381,16.587,15.25,13.571,13.109,12.811,13.042,12.743,12.049,11.464,11.102,10.774,10.754,10.33\n12.741,12.223,12.826,14.792,15.175,16.288,17.057,17.133,17.272,17.914,17.923,17.856,17.747,17.696,17.968,17.729,17.659,17.872,18.125,18.604,19.223,19.365,19.323,19.052,19.116,19.417,19.4,19.277,19.592,19.042,18.382,18.38,17.939,17.232,16.511,15.881,15.044,13.538,12.1,11.453,11.002,11.02,10.349,10.503,10.955,11.325,10.941\n13.153,12.926,13.599,15.067,15.331,16.756,17.495,17.473,17.307,17.361,17.735,17.867,17.746,17.617,17.888,17.823,17.633,17.979,18.579,19.08,19.673,19.79,19.417,19.409,19.76,20.264,19.932,19.601,18.849,18.847,18.878,18.313,17.689,17.253,16.702,15.908,14.709,13.223,11.74,10.717,10.426,10.358,9.6861,9.7901,10.145,11.134,11.053\n14.371,14.418,14.553,15.673,16.316,17.09,17.304,17.452,17.379,17.197,17.143,17.224,17.67,17.915,18.099,17.95,17.949,18.094,18.687,19.17,19.512,19.467,19.448,19.778,20.092,21.305,21.666,20.756,19.513,19.893,19.344,18.025,17.287,17.196,16.826,15.735,14.268,12.52,11.448,11.03,10.905,10.712,10.259,9.9704,10.304,10.714,11.043\n15.008,15.213,15.217,16.011,16.846,17.308,17.46,17.563,17.409,16.923,16.435,16.961,17.642,17.954,18.132,17.678,17.803,18.279,18.779,18.728,19.091,19.564,20.122,20.785,20.89,21.995,22.363,20.862,19.966,19.954,18.774,18.201,17.429,17.499,17.291,15.877,14.059,12.455,11.789,11.672,11.451,11.052,10.362,10.143,10.669,10.753,10.968\n15.217,15.375,15.229,15.855,16.536,17.321,17.72,17.905,17.905,17.519,16.752,17.322,17.879,17.685,17.424,16.83,17.303,18.171,18.871,18.964,19.235,20.012,20.856,21.467,21.734,22.25,22.058,21.039,20.465,20.001,19.289,18.477,17.528,17.06,17.167,16.31,14.865,13.632,13.215,13.144,12.712,12.053,11.28,10.306,10.595,10.183,10.131\n15.503,15.957,16.341,16.685,16.656,17.19,17.811,17.974,18.06,17.766,17.404,17.944,18.059,17.763,17.677,17.654,17.503,18.101,18.492,18.733,18.91,19.687,20.812,21.897,22.184,22.153,21.559,21.024,20.533,19.514,18.738,17.883,17.36,16.938,16.638,15.364,14.408,13.931,13.893,14.118,13.656,12.921,12.03,11.308,10.765,9.8013,10.019\n15.976,16.424,16.916,17.872,18.288,17.833,17.979,17.995,18.079,17.727,17.638,17.876,18.054,17.984,18.14,18.231,18.191,18.276,18.662,19.186,19.327,19.843,20.526,21.697,22.488,21.978,21.293,20.655,20.195,19.104,17.746,17.438,17.06,16.58,16.267,15.531,14.897,14.47,14.45,14.999,14.576,13.459,12.674,12.124,10.755,10.526,10.811\n16.207,16.913,17.026,17.457,18.637,18.68,18.313,18.152,17.948,17.774,17.753,18.05,18.461,18.413,18.342,18.432,18.486,18.641,18.882,18.867,19.036,19.674,20.315,20.936,21.394,21.367,21.253,20.894,20.334,18.721,17.197,16.997,16.741,16.023,15.441,14.859,14.609,14.768,15.021,15.642,15.459,14.094,13.31,12.8,12.287,11.78,11.199\n16.396,16.529,16.833,17.5,18.109,17.72,17.811,17.974,17.949,18.105,17.919,18.467,19.054,19.236,19.054,19,18.738,18.446,18.731,18.76,19.121,20.098,20.528,20.756,20.935,21.171,21.153,20.192,18.773,17.463,17.065,16.341,15.15,14.388,14.552,14.561,14.436,14.675,15.094,15.455,15.022,14.903,14.79,14.098,14,13.205,13.359\n17.677,17.768,17.587,18.045,18.234,17.806,17.719,17.899,17.977,18.057,17.905,18.324,18.692,18.641,18.552,18.469,18.182,18.159,18.88,19.27,19.71,20.569,20.562,20.386,20.566,20.655,20.43,19.089,17.993,16.771,15.663,15.091,14.065,13.304,12.97,13.815,14.58,14.728,14.922,15.361,14.919,14.709,15.039,14.708,14.564,12.676,12.574\n18.215,18.467,18.32,18.118,17.922,17.749,17.901,17.988,17.711,18.006,18.045,18.182,18.216,17.969,18.352,18.036,17.371,18.618,19.64,19.873,20.565,20.799,20.383,19.904,20.006,20.566,20.337,19.441,18.192,16.473,14.527,14.754,14.039,13.335,13.036,13.286,14.032,13.93,14.297,14.665,15.295,14.629,14.176,14.853,15.62,15.142,14.643\n16.196,17.607,17.912,17.681,17.324,17.406,18.143,18.733,18.527,18.639,18.588,18.645,18.922,18.901,18.45,18.168,17.893,18.555,19.831,21.491,20.812,20.491,20.58,20.109,19.559,19.29,19.288,18.887,17.499,15.628,14.221,14.416,14.28,13.86,13.553,13.44,13.753,13.437,13.303,14.119,15.321,16.528,15.635,14.756,16.039,16.779,16.642\n16.588,17.08,18.034,17.983,17.485,17.161,17.772,19.119,18.993,18.774,18.974,18.954,19.198,19.345,18.92,18.73,19.051,19.768,20.599,21.265,21.77,21.489,20.542,20.542,20.111,19.175,19.242,18.879,17.921,16.57,15.183,14.754,13.63,13.376,13.351,13.853,13.261,13.079,12.782,14,14.744,16.005,15.791,14.994,15.541,16.257,16.974\n17.204,17.538,18.798,18.814,18.582,18.463,19.266,19.674,19.368,18.928,18.853,19.1,19.317,19.489,19.579,19.746,19.834,20.135,20.791,21.46,21.508,21.208,20.582,20.755,20.477,19.851,19.325,19.039,18.824,18.274,16.945,15.677,13.968,13.623,13.354,14.082,13.857,13.844,13,13.297,14.102,14.914,15.028,15.303,15.888,16.12,16.183\n17.437,18.399,19.127,18.986,18.939,20.068,20.809,20.222,19.283,19.276,19.572,19.837,19.541,19.568,20.43,20.318,20.498,20.742,20.705,20.954,21.134,20.381,20.147,20.598,20.809,19.945,19.678,19.343,19.064,19.183,18.855,17.113,14.752,14.716,13.271,13.883,14.297,14.187,13.496,13.907,14.466,14.639,14.996,15.707,16.03,16.204,16.405\n18.318,19.765,19.98,20.007,20.361,20.907,20.502,19.896,19.68,19.558,19.72,19.873,19.728,19.838,21.214,21.28,20.771,21.152,21.345,20.802,21.149,21.207,21.821,21.64,20.443,19.931,19.676,19.416,18.831,18.742,18.608,17.774,15.816,15.527,13.736,13.725,13.935,13.904,13.421,13.492,15.105,14.834,15.111,15.715,16.087,16.301,16.62\n19.169,20.126,20.103,19.873,20.46,20.661,19.802,19.567,19.267,19.104,19.289,19.595,19.756,20.187,21.095,21.786,20.989,20.792,21.064,20.839,21.419,21.687,22.216,21.155,20.142,19.879,19.442,19.312,18.787,18.526,18.165,17.444,16.64,16.206,15.226,14.483,14.223,13.874,13.286,12.671,14.032,14.849,15.219,15.916,16.157,16.474,16.957\n19.08,19.328,19.417,19.083,19.164,19.761,19.753,19.302,19.015,18.832,19.017,19.474,20.032,20.539,20.926,21.445,20.761,20.146,20.22,20.47,20.468,21.075,21.471,20.286,19.748,19.771,19.4,18.721,18.428,17.928,17.253,16.884,16.473,16.148,15.666,14.988,14.715,14.435,14.157,14.254,14.458,15.273,15.643,15.81,15.873,16.177,17.108\n18.455,18.755,19.18,19.574,19.884,19.794,19.227,18.893,19.144,19.345,19.303,19.376,19.892,20.22,20.599,20.577,20.03,19.672,19.802,19.9,19.885,20.009,19.66,19.204,19.095,18.696,18.423,17.702,17.089,16.48,16.633,16.651,16.262,15.747,15.326,15.246,15.09,14.915,14.898,14.608,14.768,15.372,15.751,15.78,16,16.636,17.305\n18.019,18.028,18.902,19.652,20.048,19.473,18.813,18.866,19.443,19.712,19.711,19.767,19.743,19.562,19.599,20.208,19.987,19.703,19.655,19.71,19.57,18.99,18.989,18.738,18.23,17.795,17.563,17.195,16.993,16.645,16.077,15.54,15.611,15.953,15.574,15.456,15.007,14.882,14.522,14.274,14.85,15.249,15.607,15.805,16.334,17.235,17.545\n18.858,18.313,19.133,19.351,19.509,19.327,18.9,18.959,19.317,19.645,19.635,19.597,19.178,19.01,19.382,19.847,19.874,19.447,19.379,19.353,19.023,18.827,18.982,18.954,18.658,18.396,17.686,17.245,17.238,17.291,16.231,15.546,15.097,15.56,15.672,15.573,14.797,14.437,14.142,14.203,14.578,15.089,15.786,16.113,16.737,17.442,17.779\n19.086,18.611,18.644,19.587,19.471,19.264,18.534,18.698,19.055,19.178,19.362,19.285,19.17,19.294,19.604,19.27,18.723,18.89,18.898,18.928,18.777,18.962,19.077,19.205,18.736,18.586,18.227,17.924,17.157,17.071,16.669,16.19,15.657,15.459,15.131,14.695,14.372,14.664,14.55,14.533,14.675,15.501,16.109,16.235,16.822,17.641,18.234\n18.36,18.639,19.325,19.227,19.061,18.877,18.787,18.965,19.059,19.043,19.134,19.082,18.935,18.773,18.882,18.857,18.428,18.636,19.027,19.358,19.33,19.264,19.061,18.837,18.378,18.045,17.827,17.786,17.337,16.358,16.015,15.863,15.211,15.472,15.053,14.663,14.751,15.231,15.019,14.52,14.377,15.3,16.387,17.085,17.615,18.548,18.903\n17.994,17.856,18.39,19.296,19.041,18.856,18.473,18.696,19.13,19.325,19.319,19.37,19.175,18.511,18.752,18.813,19.201,19.576,20.084,20.326,19.883,19.593,18.759,18.532,18.282,18.167,18.153,17.824,17.365,16.726,16.037,15.189,14.645,14.794,14.973,14.099,13.856,14.175,14.253,14.219,14.164,14.968,16.051,17.441,18.504,18.382,18.713\n17.325,17.723,17.533,17.881,18.636,19.08,19.214,19.203,19.301,19.377,19.539,19.57,19.506,18.746,19.327,20.169,20.807,19.992,20.031,20.712,20.43,20.102,20.49,20.501,19.722,19.479,19.081,18.392,17.768,17.365,16.734,15.899,15.079,14.913,14.77,13.866,13.078,13.16,13.459,13.887,14.326,15.094,16.036,17.773,19.075,18.488,18.902\n17.323,17.928,18.035,18.04,18.006,18.371,18.932,19.707,19.591,19.712,19.78,20.067,20.102,19.764,20.264,21.059,21.075,20.352,19.878,19.772,19.03,19.542,20.026,20.673,20.38,19.471,18.232,17.476,17.057,17.189,17.792,17.068,16.466,15.034,14.741,14.502,13.924,13.712,13.682,13.85,14.322,15.544,16.458,18.753,19.144,18.957,18.984\n18.038,18.529,17.821,17.125,17.194,18.196,19.097,19.847,19.535,19.36,19.676,19.899,20.28,20.774,21.067,20.905,20.817,19.781,19.546,19.647,19.569,19.511,19.44,20.145,19.697,18.792,17.433,16.31,15.437,15.288,15.87,17.236,17.425,16.802,15.428,14.669,14.359,13.697,13.824,14.159,14.283,15.656,16.718,17.1,18.389,19.027,19.295\n17.564,17.881,18.054,17.706,17.669,18.567,19.089,19.396,19.231,18.912,19.54,19.73,20.059,20.542,20.582,20.716,20.733,19.582,19.17,19.555,19.891,19.858,19.634,18.455,18,17.559,NaN,NaN,NaN,15.836,15.911,16.41,17.504,17.347,15.608,14.338,13.514,13.648,14.022,14.392,14.459,15.304,16.25,16.815,18.021,18.877,19.124\n17.24,18.025,18.232,18.212,18.418,18.434,18.38,18.828,20.028,20.383,19.941,20.033,19.733,20.194,20.13,19.892,19.367,19.177,19.024,19.181,19.643,19.623,18.213,17.181,17.548,NaN,NaN,NaN,NaN,NaN,16.332,16.874,17.653,17.033,15.326,13.841,13.468,13.544,13.909,14.249,14.529,14.888,15.379,16.678,18.119,18.917,19.241\n16.754,17.05,17.28,17.635,18.468,18.598,18.738,19.186,20.541,21.363,20.634,20.514,19.994,19.426,19.083,19.266,19.223,18.793,18.857,19.253,19.281,18.896,17.574,16.485,17.213,NaN,NaN,NaN,NaN,NaN,16.47,16.288,16.886,16.287,15.543,14.205,13.999,13.872,13.964,14.564,15.089,15.25,15.305,16.073,18.048,19.443,19.678\n15.899,15.529,15.416,16.244,18.223,19.017,19.357,19.538,20.06,20.641,20.108,19.732,19.659,18.763,18.617,18.895,19.27,19.725,19.885,17.806,16.466,15.177,16.867,18.159,17.043,NaN,NaN,NaN,NaN,NaN,15.593,15.714,15.839,14.867,14.625,14.264,14.412,14.427,14.148,14.893,15.675,16.16,16.691,17.221,18.45,19.675,20.108\n15.669,16.219,16.176,16.192,16.939,17.841,18.358,18.393,18.447,19.325,19.571,19.837,19.31,18.2,17.645,18.231,18.553,17.546,16.484,15.535,14.698,15.723,16.717,18.397,17.245,16.082,NaN,NaN,NaN,17.011,15.582,14.89,14.331,14.014,14.205,14,14.261,14.168,14.25,14.78,15.964,17.204,17.859,18.163,18.162,18.358,19.218\n15.821,16.096,16.357,16.317,16.135,16.863,17.504,17.523,17.613,17.96,18.421,18.494,18.157,17.518,16.939,17.231,16.783,17.144,16.362,15.496,15.091,16.576,17.603,18.57,16.207,15.224,13.27,14.572,16.029,15.592,15.161,14.699,14.294,14.117,14.243,14.032,13.943,13.978,14.048,14.598,16.016,17.601,18.372,18.517,18.316,18.432,19.767\n15.281,15.756,15.54,15.912,16.18,16.6,16.795,16.773,16.385,16.899,17.121,16.922,16.511,15.587,15.33,15.994,16.267,16.428,16.533,14.164,NaN,NaN,NaN,NaN,NaN,NaN,14.64,15.152,15.752,15.262,14.438,14.092,13.885,13.667,13.813,13.949,13.842,13.991,14.251,14.86,16.059,17.645,18.44,18.556,18.349,18.524,19.693\n14.442,15.127,15.222,15.753,16.199,16.499,16.38,16.122,15.476,15.163,15.102,15.569,15.496,14.94,14.568,14.901,15.361,15.614,15.946,13.395,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.478,14.477,13.799,13.419,13.42,13.689,13.526,13.409,13.398,13.605,13.599,14.424,15.999,17.922,18.112,18.583,19.086,19.142,19.215,19.758\n15.292,15.521,15.722,15.776,15.829,15.894,15.916,15.5,15.039,14.898,14.733,15.327,15.22,14.38,13.632,13.416,13.714,13.154,12.969,11.166,11.421,NaN,NaN,NaN,NaN,NaN,16.226,15.246,14.221,13.101,12.618,12.814,13.329,12.957,12.789,13.073,13.767,14.025,14.755,16.863,18.444,18.167,18.716,18.962,19.065,19.376,19.844\n16.57,16.15,15.945,15.924,15.682,15.525,15.413,15.281,15.192,14.969,14.883,14.883,14.299,13.242,13.242,13.253,13.655,13.431,13.223,11.829,11.591,12.749,14.237,NaN,NaN,NaN,14.164,13.943,13.259,12.624,12.201,12.076,12.137,12.2,12.474,13.044,14.044,15.101,16.487,18.299,19.191,18.882,18.951,18.934,19.215,20,19.947\n16.613,16.372,16.024,16.224,15.639,15.267,15.247,15.317,15.224,14.921,14.701,14.454,13.87,12.769,11.868,11.997,12.441,13.056,12.391,11.693,12.597,15.234,16.485,16.85,16.493,15.316,12.798,12.506,12.266,12.293,12.23,12.04,12.215,13.021,14.254,14.819,14.831,16.014,17.84,19.081,19.308,19.266,19.385,19.432,19.673,20.234,20.539\n16.08,15.978,15.933,16.132,16.004,15.566,15.737,15.677,15.309,14.444,14.17,13.84,13.606,12.709,11.211,10.968,11.79,11.661,10.945,10.79,12.012,12.467,13.109,13.999,14.85,13.322,12.434,11.475,11.323,11.666,12.208,12.374,12.863,13.71,14.674,15.357,15.593,16.561,18.224,19.472,19.511,19.52,19.784,19.338,19.457,19.708,20.014\n16.251,15.942,15.803,15.673,15.645,15.639,15.633,15.311,14.595,14.094,14.053,13.852,13.586,12.399,11.195,11.191,11.448,11.178,10.591,10.532,11.031,11.064,11.327,11.323,11.308,11.566,11.716,11.214,10.986,11.586,12.238,12.792,13.119,13.671,14.378,15.211,15.692,17.229,18.775,19.054,18.931,19.715,19.962,19.333,19.211,19.626,20.163\n15.957,15.502,15.593,15.555,15.372,15.281,15.212,14.912,14.63,14.317,14.125,13.989,13.914,13.818,13.032,11.829,11.261,10.767,10.583,10.648,10.906,10.916,11.478,11.621,11.503,11.427,11.131,10.943,11.119,11.871,12.516,12.958,13.394,14.231,14.993,15.392,15.861,17.005,18.917,19.529,19.524,20.844,20.692,20.457,19.634,19.821,20.452\n15.636,15.111,14.989,15.262,15.238,15.209,15.158,14.913,14.657,14.615,14.651,14.137,14.078,13.989,13.423,12.72,12.104,11.455,10.582,10.564,10.653,11.039,11.265,11.347,11.461,11.121,10.416,10.962,11.889,12.765,13.351,13.439,13.87,14.701,15.422,15.634,16.311,17.873,19.337,19.424,19.581,20.236,20.373,20.325,19.586,20.251,20.891\n16.066,15.4,15.076,15.148,15.25,15.264,15.248,14.784,14.396,14.298,14.561,14.745,14.749,14.415,13.525,12.819,12.593,12.187,11.506,10.832,9.8291,9.6632,10.472,10.79,10.651,10.221,9.9875,11.066,12.474,13.068,13.513,13.685,14.584,15.381,15.917,15.808,16.448,17.804,19.026,19.352,19.243,19.344,20.006,20.055,20.281,20.903,21.702\n16.151,15.963,15.325,15.39,15.577,15.621,15.269,14.969,14.796,14.824,14.807,14.924,15.119,15.066,14.18,13.013,13.173,12.583,11.979,11.333,10.723,10.191,10.94,10.697,9.9219,9.6488,9.2079,10.585,12.826,13.615,13.68,13.897,14.58,15.184,15.548,15.676,16.313,17.872,18.981,19.47,19.297,19.213,19.22,19.848,20.368,21.264,21.769\n16.072,15.698,15.601,15.678,15.615,15.664,15.596,15.603,15.363,15.308,15.323,15.197,14.918,14.55,14.101,13.655,13.508,12.863,11.223,10.424,10.443,11.056,11.329,11.37,11.189,11.012,10.242,10.309,12.809,14.093,14.261,14.225,14.825,15.44,16.55,16.799,17.197,18.138,18.742,19.126,19.246,19.008,19.077,19.331,20.139,20.592,21.692\n16.795,16.782,16.914,16.414,15.862,15.83,15.613,15.469,15.311,15.406,15.273,15.065,14.831,14.534,14.648,13.913,13.802,12.452,12.048,10.879,10.509,11.489,11.615,11.593,11.96,12.409,12.071,12.037,12.593,13.653,14.314,14.534,15.428,16.5,16.861,16.964,17.574,18.189,18.523,18.452,18.468,18.339,17.469,18.518,20.508,21.233,22.587\n17.178,17.369,17.376,16.637,16.319,16.087,15.925,15.615,15.412,15.296,15.108,14.796,14.225,13.855,13.519,13.28,13.104,12.161,11.912,11.845,11.965,12.076,12.241,12.299,12.636,12.703,12.84,12.459,12.405,12.784,13.93,14.818,15.58,16.286,16.646,17.321,17.865,18.132,18.278,18.363,18.472,17.926,17.732,18.752,20.335,21.229,22.568\n17.241,17.172,16.8,16.495,16.654,16.581,16.191,15.702,15.18,15.104,14.973,14.135,13.309,12.632,12.591,12.268,12.373,12.192,12.09,12.008,12.002,12.145,12.228,12.697,13.264,13.788,13.474,12.877,12.406,12.735,14.217,15.204,15.897,16.186,16.289,17.612,18.311,18.143,18.217,18.333,18.585,18.569,18.298,19.142,20.836,21.687,22.628\n17.203,17.176,17.044,17.28,17.421,16.736,15.995,15.605,15.282,15.499,15.053,14.038,13,12.218,12.106,12.083,11.783,11.572,11.111,11.796,12.432,12.369,12.277,12.692,13.284,13.74,13.39,13.225,12.529,13.278,15.264,16.063,16.548,16.962,17.442,18.133,18.524,18.55,18.507,18.472,18.775,18.868,18.764,20.08,21.76,22.456,22.49\n17.021,17.271,17.448,17.384,16.362,15.949,16.132,16.057,16.18,15.994,14.322,13.547,12.693,12.308,12.284,11.937,11.103,11.012,11.232,12.192,12.772,12.813,12.677,12.65,13.087,13.287,12.599,12.784,13.176,13.914,15.227,16.317,16.743,16.888,17.301,18.046,18.333,18.42,18.737,19.185,19.47,19.465,19.828,20.858,22.316,22.925,22.534\n17.06,17.204,17.551,17.226,16.447,16.295,16.637,16.922,16.386,15.06,12.966,13.031,12.355,12.868,12.716,12.291,12.182,11.69,11.746,12.473,13.726,13.408,13.081,13.198,13.27,13.282,13.038,13.292,14.193,15.213,15.423,15.64,16.393,16.976,17.366,17.794,18.204,18.537,19.157,19.753,19.681,19.783,20.302,21.182,22.095,23.068,23.239\n16.939,17.26,18.067,17.741,17.301,16.321,15.888,16.555,17.005,17.012,14.021,13.127,12.758,12.179,11.945,11.705,11.973,12.252,12.107,12.124,13.074,13.978,13.517,13.512,13.286,13.11,13.166,13.339,14.569,15.839,15.882,16.003,16.711,17.381,18.156,18.649,18.996,19.093,19.276,20.155,20.283,20.253,20.504,21.483,23.035,23.304,23.057\n16.283,16.683,17.481,17.845,17.716,17.088,16.081,16.443,16.758,16.78,14.794,13.635,12.165,11.664,10.86,11.286,12.231,12.647,12.106,11.878,11.992,12.957,13.406,13.914,13.997,13.732,14.052,14.497,15.654,16.563,17.298,17.417,17.735,18.45,18.765,19.076,19.256,19.016,19.194,19.869,20.385,20.675,21.393,22.2,22.704,22.436,22.065\n15.972,16.211,17.018,17.298,17.348,16.85,16.122,16.937,17.576,15.951,14.765,14.263,14.04,13.592,14.644,15.022,14.303,13.68,13.067,11.61,11.403,11.786,13.167,14.402,14.946,14.846,15.125,15.255,15.56,16.654,17.428,18.035,18.146,18.971,19.44,19.342,19.492,19.206,18.742,19.179,19.832,20.85,21.939,22.789,22.431,22.231,22.44\n18.301,18.365,18.174,17.733,17.364,17.251,16.589,16.704,17.199,16.706,15.868,16.361,18.098,19.248,20.434,19.849,16.023,14.625,14.303,12.852,12.578,12.057,13.364,14.699,15.045,15.452,15.484,15.53,15.537,16.781,18.149,18.579,18.877,19.97,20.429,20.015,20.068,19.461,18.91,18.677,18.998,20.252,21.475,22.388,22.353,22.065,22.212\n18.423,18.815,18.52,17.821,17.718,17.558,16.656,16.194,16.125,15.179,16.348,17.031,17.929,19.547,19.211,18.048,16.452,15.259,15.427,14.412,14.61,14.309,14.336,14.707,15.523,16.132,15.883,15.868,16.424,17.188,18.291,18.56,18.929,19.974,21.056,20.595,20.182,19.413,19.138,19.012,18.742,18.869,19.735,20.78,21.546,21.465,21.747\n16.333,16.902,18.213,18.117,18.336,17.1,16.142,15.431,15.703,14.168,16.211,17.171,17.482,18.587,18.932,17.938,16.794,15.327,15.292,15.762,15.738,15.311,15.696,16.849,16.793,17.741,17.535,17.008,17.321,17.548,17.914,18.685,19.275,20.253,21.91,22.474,20.59,19.728,19.328,19.129,18.95,19.057,19.41,20.326,20.619,20.235,20.815\n18.219,18.504,18.478,19.051,19.37,18.312,17.044,15.87,14.932,14.933,14.792,16.458,16.801,17.66,18.01,18.252,17.621,15.948,16.007,16.087,15.756,14.963,14.46,15.044,16.146,17.371,17.65,17.599,17.449,17.677,18.492,19.267,19.988,21.259,21.797,21.426,20.816,20.336,19.576,18.831,19.589,20.132,20.389,20.525,20.295,19.977,19.47\n13.957,15.778,16.65,17.918,19.115,18.717,17.605,16.415,16.261,16.614,18.513,18.916,17.688,NaN,NaN,NaN,NaN,NaN,17.058,17.007,16.557,15.54,14.514,16.062,16.671,17.405,17.916,17.428,17.602,18.064,18.95,20.149,20.931,21.233,20.923,20.846,20.476,20.117,19.946,19.735,19.894,20.106,19.812,19.769,19.546,19.747,20.006\n13.831,14.349,14.339,16.505,18.61,17.852,16.251,16.013,15.396,16.015,19.407,19.567,18.442,NaN,NaN,NaN,NaN,NaN,16.486,17.846,16.539,16.102,16.151,16.435,16.782,17.537,17.819,17.232,17.602,18.799,19.293,20.045,21.118,22.047,21.966,21.171,20.263,20.021,20.337,20.194,19.768,19.647,18.967,18.358,18.268,19.342,20.134\n13.743,13.247,13.305,14.903,15.928,16.148,16.344,15.363,14.999,15.991,14.962,15.254,NaN,NaN,NaN,NaN,NaN,NaN,16.378,17.96,16.109,14.763,15.758,16.565,14.779,13.139,15.814,18.648,19.509,19.346,19.126,20.409,21.716,22.514,22.756,21.781,21.2,20.385,20.861,20.762,20.445,20.12,19.196,18.249,17.551,18.073,17.065\n11.073,12.643,13.068,12.76,11.811,12.216,17.378,16.119,14.773,14.883,15.378,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.622,17.449,16.221,13.93,15.451,14.944,11.824,13.887,16.037,18.779,19.717,19.655,20.042,20.641,23.457,23.447,23.433,23.224,22.23,21.109,21.323,21.03,20.806,20.54,19.917,19.256,18.399,18.081,16.054\n10.823,11.056,10.686,10.054,10.2,12.146,13.197,12.799,13.001,14.841,14.673,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.491,17.456,16.58,17.086,17.872,17.572,18.64,19.528,19.51,20.136,21.718,21.76,21.516,22.813,23.598,23.171,22.21,21.196,21.161,21.103,20.318,20.259,20.351,20.17,20.243,19.634,20.486,20.135\n10.172,9.781,9.3413,8.9096,10.394,11.797,11.977,12.028,12.819,14.591,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.296,18.932,19.334,19.957,19.776,19.054,18.934,20.017,20.714,21.064,22.342,22.952,22.846,22.06,22.095,21.285,19.9,20.029,20.089,19.249,20.029,21.017,21.123,22.214,22.663,22.655,NaN\n9.7265,9.9982,10.8,9.756,8.8605,9.4995,10.863,12.461,14.062,15.021,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.381,17.804,18.552,19.324,19.316,18.182,19.745,21.523,22.636,22.838,23.112,22.773,22.549,21.724,20.675,20.818,22.405,22,20.07,20.652,22.187,22.963,23.302,23.156,21.164,NaN\n9.9832,11.202,12.273,12.336,10.687,10.875,12.762,14.144,14.223,14.614,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,20.887,21.178,19.563,19.096,19.053,19.524,21.239,21.788,22.606,22.619,22.779,23.273,23.085,22.442,20.986,23.031,21.052,21.073,21.488,22.473,23.251,23.815,23.726,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070219-070604.unw.csv",
    "content": "-16.631,-17.319,-16.852,-15.925,-15.823,-15.078,-13.77,-13.268,-13.868,-13.827,-13.858,-13.712,-13.355,-13.413,-13.159,-12.671,-12.429,-12.793,-12.583,-12.192,-12.847,-12.533,-12.034,-11.946,-12.504,-12.647,-13.126,-14.318,-14.434,-14.415,-14.748,-15.067,-14.712,-15.287,-16.401,-17.073,-18.109,-18.736,-18.944,-19.794,-19.768,-19.347,-18.705,-18.406,-18.416,-18.916,-18.875\n-16.974,-17.145,-16.737,-15.44,-15.134,-14.659,-13.801,-12.733,-12.686,-12.731,-13.181,-13.56,-13.776,-13.591,-12.946,-12.389,-12.154,-12.124,-11.767,-11.849,-11.938,-11.311,-11.14,-11.365,-11.802,-11.897,-12.299,-12.158,-12.683,-14.506,-16.009,-15.089,-14.662,-15.74,-16.669,-17.275,-18.123,-18.728,-19.09,-19.652,-18.897,-18.436,-18.572,-18.426,-18.364,-18.212,-17.73\n-17.288,-17.215,-16.832,-15.784,-15.27,-13.727,-13.389,-13.456,-12.801,-12.208,-12.352,-12.788,-13.383,-13.47,-12.644,-12.067,-11.882,-11.759,-11.831,-12.219,-12.045,-11.852,-11.932,-11.513,-11.333,-11.593,-11.745,-11.654,-12.361,-13.601,-14.438,-14.71,-14.847,-15.561,-16.914,-19.086,-19.774,-20.78,-20.837,-20.993,-20.543,-18.807,-17.915,-17.641,-18.306,-17.752,-17.273\n-17.407,-17.226,-16.944,-15.769,-15.152,-14.762,-13.961,-13.735,-12.953,-12.187,-12.143,-12.641,-13.16,-13.419,-12.576,-11.596,-10.748,-11.125,-11.69,-12.511,-11.782,-11.508,-11.312,-11.065,-10.871,-10.726,-10.765,-11.158,-11.558,-12.044,-12.96,-13.714,-14.702,-15.308,-16.727,-19.074,-19.975,-20.656,-20.799,-20.485,-20.276,-19.808,-18.981,-18.55,-18.345,-17.8,-16.96\n-17.83,-17.667,-18.782,-16.668,-15.502,-15.21,-15.027,-14.701,-14.266,-13.855,-13.209,-13.123,-13.23,-12.886,-12.084,-11.971,-11.483,-10.925,-11.059,-11.312,-11.614,-11.626,-11.33,-10.881,-10.32,-9.7707,-9.875,-10.753,-11.815,-11.861,-11.918,-13.165,-14.314,-15.478,-16.524,-18.653,-19.683,-20.11,-20.283,-20.164,-19.866,-19.09,-18.854,-19.13,-18.303,-17.576,-17.163\n-18.493,-19.017,-19.958,-17.897,-16.97,-16.356,-15.616,-14.79,-14.611,-14.242,-13.498,-13.243,-13.202,-12.689,-12.009,-11.583,-11.374,-10.88,-10.139,-10.008,-10.723,-11.757,-11.212,-10.872,-10.158,-9.8652,-9.9303,-10.032,-10.543,-10.857,-11.265,-11.879,-12.625,-14.017,-15.593,-17.801,-19.343,-19.561,-19.853,-19.454,-18.671,-18.671,-18.963,-19.02,-18.052,-16.961,-17.269\n-18.738,-19.198,-18.729,-18.085,-17.691,-16.836,-15.474,-14.526,-14.557,-13.66,-12.959,-12.829,-12.66,-12.129,-11.452,-10.759,-10.714,-10.754,-10.807,-10.177,-9.7253,-9.2841,-9.648,-9.9748,-10.325,-10.021,-9.2395,-9.702,-10.356,-11.351,-11.835,-11.912,-12.902,-14.08,-15.533,-16.904,-18.215,-18.883,-20.333,-21.842,-21.622,-19.391,-19.492,-18.945,-17.978,-17.332,-16.409\n-18.025,-17.592,-16.502,-17.468,-17.221,-16.178,-14.767,-14.481,-14.855,-14.977,-13.424,-13.009,-12.294,-11.598,-10.992,-10.28,-9.7545,-10.061,-9.7873,-9.7131,-9.3281,-9.0974,-9.4454,-9.3467,-8.7813,-8.654,-8.8708,-9.6067,-11.439,-11.165,-11.624,-12.473,-13.186,-13.964,-14.899,-16.4,-17.802,-19.364,-20.656,-21.597,-21.751,-20.803,-21.064,-21.246,-20.531,-18.702,-18.976\n-18.655,-17.487,-16.483,-17.368,-16.708,-16.103,-14.564,-14.64,-14.478,-13.953,-13.74,-13.927,-13.364,-12.223,-11.198,-10.173,-9.3182,-9.1843,-9.6726,-9.4962,-8.9242,-8.3746,-8.5149,-8.0084,-7.525,-7.3795,-6.2959,-7.1246,-9.2224,-9.8306,-11.605,-12.774,-12.491,-11.737,-12.986,-15.561,-18.089,-20.545,-21.436,-21.885,-21.676,-20.96,-20.979,-21.135,-20.57,-19.47,-18.813\n-17.672,-17.502,-16.786,-17.229,-17.217,-16.686,-15.167,-14.642,-14.09,-13.492,-13.457,-13.142,-12.223,-11.639,-11.326,-10.214,-9.2909,-9.2163,-9.5728,-9.4195,-7.8832,-7.2764,-7.374,-7.1645,-6.2773,-6.2494,-6.2948,-6.9366,-7.8054,-8.3646,-11.01,-11.675,-11.253,-10.727,-12.243,-14.973,-17.67,-19.49,-21.021,-21.423,-21.556,-21.371,-21.473,-21.758,-21.219,-19.817,-19.172\n-19.001,-17.765,-16.623,-16.41,-16.59,-16.5,-15.452,-14.268,-13.609,-13.276,-13.218,-12.572,-11.287,-10.822,-10.15,-9.3648,-8.5767,-9.0052,-8.9474,-7.9232,-7.4464,-7.5624,-7.4681,-6.8864,-5.571,-5.7143,-5.8891,-5.658,-6.2583,-8.2929,-9.7221,-10.577,-11.452,-12.767,-13.707,-16.252,-18.469,-19.951,-19.902,-19.36,-19.836,-20.105,-20.721,-21.506,-21.87,-21.389,-20.884\n-18.5,-17.833,-17.041,-16.274,-16.509,-16.145,-15.177,-14.146,-13.319,-12.975,-12.957,-11.591,-10.364,-9.7748,-9.3037,-9.1187,-8.7828,-8.762,-7.8758,-7.6951,-7.7561,-7.7954,-7.6261,-6.3234,-5.4328,-5.6885,-6.0185,-5.5698,-6.6984,-8.1479,-9.5382,-10.77,-11.685,-12.739,-13.974,-16.609,-17.945,-18.992,-18.648,-17.696,-18.249,-18.884,-19.528,-20.338,-20.705,-20.99,NaN\n-18.347,-17.886,-16.944,-16.013,-15.395,-15.581,-14.977,-14.527,-14.261,-12.938,-12.019,-10.786,-9.4058,-9.512,-9.3509,-9.1708,-9.1679,-8.6049,-7.0608,-6.4907,-6.7611,-7.1771,-7.8359,-7.8062,-6.5856,-5.8225,-6.3857,-6.403,-7.303,-8.7341,-9.1669,-9.6083,-11.431,-13.417,-14.453,-15.567,-15.95,-17.221,-17.337,-16.337,-17.068,-18.175,-18.476,-19.271,-20.133,-19.858,NaN\n-17.277,-16.193,-16.05,-16.197,-16.5,-17.153,-15.353,-14.17,-13.317,-12.881,-11.882,-10.22,-9.0333,-9.3153,-9.0681,-8.9095,-8.7317,-8.0851,-7.3027,-6.7746,-6.6435,-6.188,-6.3544,-6.5898,-6.6744,-6.6621,-7.1956,-7.9068,-8.2624,-8.724,-7.9956,-5.3379,-9.5313,-14.993,-16.954,-17.774,-17.665,-17.242,-16.811,-15.514,-15.146,-17.191,-17.361,-17.202,-17.935,-17.569,-17.174\n-16.435,-15.272,-15.759,-16.787,-16.955,-16.686,-14.645,-14.716,-13.464,-11.725,-11.307,-10.354,-9.7172,-9.4398,-9.3106,-9.4557,-9.2999,-8.443,-7.5487,-6.769,-6.8254,-7.2921,-6.7331,-6.0036,-6.3155,-6.049,-6.0724,-7.5051,-8.0807,-10.639,-11.924,-12.997,-14.426,-17.176,-18.405,-18.551,-18.554,-17.475,-16.273,-15.922,-17.085,-18.608,-17.849,-17.357,-17.148,-16.181,-16.033\n-17.174,-16.858,-16.191,-16.669,-16.764,-15.506,-14.968,-14.966,-12.748,-11.938,-11.113,-10.139,-9.6623,-9.782,-9.9178,-9.3794,-9.1864,-8.5622,-8.204,-7.7621,-6.6391,-6.1402,-6.1361,-5.6971,-5.9122,-6.1702,-6.8229,-9.6086,-11.072,-10.781,-13.351,-15.849,-18.204,-19.72,-20.547,-20.488,-19.296,-17.882,-16.878,-16.524,-18.311,-18.864,-18.114,-18.428,-18.584,-19.906,-17.187\n-17.131,-16.537,-15.956,-15.689,-15.465,-14.936,-15.21,-14.682,-12.98,-11.735,-10.829,-10.204,-9.1729,-9.1089,-9.016,-8.8524,-9.0477,-9.4215,-9.0222,-9.0076,-8.0217,-6.8078,-6.5392,-8.0526,-8.5746,-7.6898,-7.3784,-8.4595,-10.559,NaN,NaN,NaN,-18.716,-19.974,-21.014,-21.942,-21.005,-19.407,-18.583,-18.556,-18.511,-17.926,-17.31,-18.5,-20.161,-22.928,-20.41\n-15.974,-15.123,-15.141,-15.273,-14.129,-14.107,-14.394,-14.491,-13.329,-12.292,-10.9,-10.236,-9.8355,-8.8301,-8.5109,-8.317,-8.0203,-8.6971,-8.7492,-9.0396,-9.0966,-8.4211,-7.4816,-9.5094,-11.193,-11.297,-9.861,-8.5568,NaN,NaN,NaN,NaN,NaN,NaN,-21.73,-21.511,-20.34,-19.554,-19.24,-19.759,-19.557,-19.617,-19.316,-19.296,-20.7,-22.422,-22.197\n-15.034,-14.982,-14.149,-12.968,-13.382,-13.541,-14.58,-13.586,-12.833,-11.402,-9.9756,-9.3992,-9.1387,-8.4227,-7.9116,-7.0851,-7.0761,-7.5396,-8.0614,-8.7063,-9.0094,-9.3905,-7.6498,-7.4731,-9.4132,-12.224,-10.738,-11.05,-11.24,NaN,NaN,NaN,NaN,NaN,-23.475,-21.53,-20.162,-20.251,-19.648,-19.457,-19.741,-19.946,-19.675,-19.308,-19.556,-20.263,-20.173\n-15.321,-15.398,-15.65,-15.414,-15.821,-14.567,-13.856,-11.986,-10.925,-9.714,-8.5955,-8.1416,-8.1858,-7.9199,-7.5229,-6.6083,-6.4003,-7.012,-7.1951,-7.3881,-8.1219,-7.7057,-6.84,-6.2701,-7.4446,-9.3742,-10.26,-10.116,-11.712,-13.984,-16.244,NaN,-18.08,-19.23,-23.764,-22.12,-20.125,-20.244,-20.673,-20.481,-20.328,-19.559,-19.229,-18.792,-18.972,-20.152,-19.261\n-15.04,-15.217,-14.103,-15.805,-17.43,-15.904,-13.75,-12.274,-10.741,-9.0138,-8.2748,-7.5349,-7.2764,-7.2792,-6.6155,-6.5042,-7.1652,-8.2127,-8.1337,-8.0388,-8.399,-7.5084,-7.0528,-6.6111,-6.9298,-6.4714,-6.7105,-9.2904,-11.314,-12.056,-12.133,-13.669,-17.594,-17.555,-24.533,-24.716,-22.096,-21.898,-22.059,-21.365,-20.582,-19.964,-19.143,-18.614,-18.843,-18.956,-17.902\n-14.726,-13.232,-13.803,-14.243,-13.891,NaN,-13.832,-12.36,-9.4173,-8.6494,-8.0103,-7.2893,-6.9942,-6.9822,-6.5956,-6.3174,-7.6321,-8.2469,-8.9863,-8.6518,-8.4264,-7.5515,-7.1458,-7.319,-7.1357,-7.3993,-7.2962,-8.191,-9.523,-10.278,-11.158,-14.342,-17.342,-17.611,-20.518,-20.451,-19.511,-20.791,-20.373,-20.231,-20.791,-20.191,-19.292,-18.747,-18.553,-18.353,-17.308\n-12.718,-11.022,-11.682,-11.173,-12.288,NaN,NaN,-10.199,-9.939,-8.8821,-8.0026,-7.0733,-7.5635,-8.1653,-7.3896,-6.6567,-8.7805,-9.1471,-8.6112,-8.2292,-7.9835,-8.0195,-7.6425,-7.8164,-7.6727,-8.1926,-8.5782,-9.0755,-9.0812,-9.3781,-10.243,-12.763,-14.721,-15.169,-17.36,-18.192,-18.402,-18.558,-19.409,-19.908,-20.771,-20.284,-18.984,-18.629,-18.608,-18.102,-16.937\n-12.527,-11.058,-8.756,-6.5917,NaN,NaN,-9.541,-8.3846,-7.8924,-8.1553,-8.1918,-7.2203,-6.9549,-7.8746,-8.3717,-8.5967,-8.9535,-8.7055,-8.6519,-8.0334,-7.3247,-6.775,-6.7943,-6.6854,-7.1269,-7.3353,-7.0229,-7.8166,-8.6987,-9.0642,-9.0592,-10.142,-11.791,-12.352,-15.041,-16.435,-18.435,-19.575,-20.148,-19.749,-19.867,-19.881,-19.015,-18.591,-18.713,-18.547,-17.491\n-11.967,-10.348,-9.3299,-8.7664,-7.3235,NaN,-9.0474,-8.0399,-7.9517,-7.9355,-7.5439,-7.1589,-7.1121,-6.8502,-7.6768,-8.5418,-8.3592,-7.8467,-7.5711,-7.5766,-7.3991,-6.6497,-6.932,-6.8574,-6.9504,-7.3907,-7.6371,-8.2033,-8.5129,-8.3881,-8.7335,-9.4589,-10.855,NaN,NaN,NaN,-22.283,-21.151,-20.688,-19.878,-19.628,-19.217,-18.465,-18.078,-18.059,-17.909,-17.116\n-11.542,-11.667,-10.291,-8.732,-7.4585,-8.4972,-8.6462,-7.892,-7.7223,-7.9219,-7.1811,-6.5891,-6.5721,-6.6281,-7.0618,-7.2338,-6.7685,-6.741,-7.1653,-7.3839,-7.4655,-7.2792,-7.7457,-7.0766,-7.4072,-7.4634,-7.2967,-6.9973,-8.9824,-9.4864,-9.78,-10.303,-17.814,NaN,NaN,NaN,-20.796,-20.749,-19.966,-19.855,-20.082,-19.437,-18.478,-18.103,-18.117,-17.842,-16.1\n-12.546,-10.779,-8.7641,-8.9985,-9.2755,-9.0525,-8.226,-7.7264,-7.474,-6.6688,-6.3001,-6.1548,-5.8022,-6.0418,-6.5341,-6.6891,-6.3454,-6.4784,-7.6006,-7.986,-7.3091,-7.2726,-7.2146,-7.1374,-9.3073,-8.199,-6.9263,-7.182,-8.5352,-12.519,-13.864,-15.803,-18.745,-19.889,-19.763,-19.295,-19.816,-19.27,-19.177,-19.596,-19.836,-19.235,-18.29,-17.835,-17.408,-16.794,-15.985\nNaN,NaN,-10.549,-10.329,-10.332,-9.429,-8.8601,-8.1322,-8.0866,-8.1055,-7.3147,-6.4772,-6.4759,-6.5874,-7.0073,-6.7802,-6.8865,-7.6732,-8.312,-8.4583,-7.7695,-7.9032,-8.0906,-8.1931,-9.1357,-9.0758,-7.8005,-7.159,-8.2175,-11.327,NaN,NaN,NaN,-18.293,-18.841,-18.924,-19.534,-20.628,-20.38,-20.461,-20.45,-19.828,-18.199,-17.545,-16.264,-15.345,-14.405\nNaN,NaN,NaN,NaN,-9.5939,-8.7328,-8.2576,-8.4284,-8.3735,-8.9738,-8.9684,-8.6588,-7.529,-7.4511,-7.4566,-7.3011,-7.5337,-7.8693,-7.9025,-7.4376,-6.982,-7.5147,-7.606,-7.2743,-6.3748,-5.916,-2.9097,-4.5733,-5.4775,NaN,NaN,NaN,NaN,NaN,-19.213,-19.761,-21.072,-22.138,-22.128,-21.584,-20.232,-18.856,-17.934,-17.249,-16.018,-13.924,-13.424\nNaN,NaN,NaN,NaN,-9.9617,-11.015,-9.6173,-8.5974,-8.6092,-9.5271,-9.0507,-8.4233,-7.7764,-8.1909,-8.1665,-7.996,-7.9123,-7.8083,-6.706,-6.1258,-6.3894,-6.3958,-6.1819,-5.1354,-3.6337,-1.9839,-0.32268,0.42243,NaN,NaN,NaN,NaN,NaN,-21.483,-20.312,-20.771,-21.809,-22.167,-22.129,-20.838,-19.423,-17.632,-16.772,-16.379,-15.24,-13.822,-13.038\nNaN,NaN,NaN,NaN,-11.437,-12.257,-11.797,-9.8723,-8.7352,-8.539,-8.4024,-7.9404,-8.1344,-9.1172,-9.4526,-9.4241,-8.8587,-7.8563,-7.0798,-6.634,-5.0964,-4.8325,-2.6604,-1.2036,0.20294,0.84015,1.3584,1.6484,NaN,NaN,NaN,NaN,NaN,-20.608,-21.031,-20.986,-21.793,-21.905,-21.423,-20.59,-19.679,-18.748,-16.696,-15.478,-14.113,-13.303,-12.792\nNaN,NaN,NaN,NaN,NaN,-10.934,-10.163,-9.0379,-8.4179,-9.0705,-10.496,-10.116,-9.9157,-10.192,-11.06,-11.216,-9.9315,-9.5233,-8.8592,-8.5621,-7.119,-5.7965,-3.1327,0.3795,1.1864,0.58605,0.076149,3.0058,NaN,NaN,NaN,NaN,-21.253,-19.81,-20.137,-20.427,-21.235,-21.107,-20.63,-19.71,-19.387,-17.982,-16.588,-14.723,-13.846,-13.18,-12.28\nNaN,NaN,NaN,NaN,-13.817,-11.237,-10.05,-9.3401,-8.4641,-9.0999,-11.092,-11.655,-11.602,-11.819,-11.788,-10.949,-10.758,-10.568,-9.3917,-8.7022,-8.9396,-8.4196,-7.7408,-5.4581,-2.3847,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.583,-18.398,-18.545,-18.669,-20.157,-19.509,-18.864,-18.917,-17.645,-16.873,-15.494,-14.467,-13.018,-11.486\n-14.366,-15.224,NaN,-14.547,-13.63,-11.222,-10.172,-10.549,-9.6709,-8.3996,-9.0859,-9.7432,-9.6545,-12.265,-12.946,-12.444,-12.131,-10.797,-9.1482,-7.401,-7.3605,-8.1216,-7.2211,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.056,-18.39,-19.116,-19.222,-18.916,-18.075,-17.373,-16.374,-15.238,-13.915,-12.438,-11.002\n-15.209,-14.976,-15.059,-14.335,-12.532,-11.486,-10.615,-9.8083,-9.1206,-8.4583,-8.6314,-8.0167,-6.8854,-9.9414,-12.004,-11.917,-10.862,-10.07,-9.1589,-7.7912,-6.5531,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.11,-17.098,-17.943,-17.879,-16.951,-16.309,-15.778,-14.608,-13.581,-11.838,-10.739\n-14.741,-13.916,-13.763,-15.81,-14.186,-12.222,-10.717,-9.7892,-8.8901,-7.7256,-9.0134,-10.057,-10.929,-10.984,-10.969,-10.856,-10.899,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.782,-16.598,-17.062,-17.245,-17.127,-16.181,-15.475,-15.176,-14.418,-12.674,-11.128,-10.203\n-14.639,-14.844,-15.205,-15.238,-14.558,-13.085,-11.497,-10.199,-10.463,-9.0589,-8.8901,-12.114,-12.328,-13.516,-13.31,-11.246,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-16.176,-16.834,-17.231,-16.519,-15.781,-14.851,-14.52,-13.783,-12.981,-12.039,-10.522,-10.094\n-14.293,-14.681,-15.102,-14.798,-13.822,-12.753,-12.642,-13.193,-13.875,-11.929,-11.036,-13.111,-13.421,-14.022,-13.287,-11.054,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-13.764,-18.501,-19.275,-19.767,-17.626,-17.128,-16.823,-15.744,-14.71,-13.516,-12.631,-11.622,-10.666,-10.06,-10.073\n-15.393,-14.965,-14.766,-14.997,-14.81,-13.22,-13.005,-13.9,-14.405,-14.46,-14.463,-14.664,-15.957,-16.79,-14.188,-11.748,-8.6119,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-12.564,-12.694,-13.579,-16.043,-17.295,-16.808,-16.943,-16.891,-15.991,-14.94,-13.355,-11.677,-11.103,-10.63,-10.117,-10.214\n-16.199,-15.745,-15.725,-15.279,-15.257,-15.384,-14.816,-15.121,-15.593,-15.146,-14.894,-14.363,-16.273,-16.285,-16.027,-12.951,-11.081,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.457,-14.167,-15.081,-15.975,-16.42,-16.094,-15.868,-16.014,-16.502,-16.008,-14.74,-12.65,-11.319,-10.955,-10.554,-10.083,-9.5523\n-16.287,-15.549,-15.561,-15.518,-15.426,-15.567,-15.648,-15.665,-16.15,-16.536,-16.606,-15.956,-16.686,-17.257,-17.336,-15.245,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.162,-14.765,-15.695,-16.624,-17.21,-16.554,-16.243,-15.76,-15.462,-16.012,-15.828,-14.584,-13.168,-11.424,-10.514,-10.287,-10.026,-9.7285\n-15.285,-15.062,-15.412,-15.737,-15.272,-15.18,-15.639,-16.167,-16.509,-16.928,-17.831,-19.9,-19.657,-19.217,-19.091,-18.257,-16.866,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.468,-16.084,-16.283,-16.327,-16.681,-16.954,-16.987,-16.804,-16.165,-15.791,-14.821,-14.086,-12.79,-11.001,-10.681,-10.291,-9.7615,-9.7383\n-14.143,-14.555,-14.873,-15.617,-15.818,-15.989,-16.572,-16.881,-17.35,-17.891,-18.61,-19.401,-19.77,-19.374,-19.399,-18.582,-16.257,-17.803,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.997,-15.227,-15.811,-16.369,-17.04,-16.768,-17.438,-17.716,-17.232,-16.232,-14.692,-13.194,-12.59,-11.699,-10.904,-10.984,-11.082,-10.382,-10.128\n-16.076,-15.636,-15.259,-16.147,-16.608,-16.642,-17.275,-17.559,-18.925,-18.98,-18.613,-18.208,-18.497,-18.454,-18.301,-18.156,-16.942,-15.491,-17.111,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.624,-17.15,-17.053,-16.867,-17.19,-17.359,-17.427,-17.73,-17.106,-16.856,-15.707,-13.534,-12.4,-11.583,-10.787,-10.73,-11.111,-11.372,-11.281,-10.837\n-15.985,-15.943,-15.822,-16.281,-16.341,-16.665,-17.914,-18.869,-19.669,-18.511,-17.389,-17.845,-18.089,-18.288,-18.243,-18.232,-18.954,-18.366,-19.719,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-18.336,-17.734,-17.619,-17.73,-18.048,-17.964,-17.582,-17.806,-17.326,-16.573,-15.287,-13.059,-11.835,-10.89,-10.239,-10.222,-10.925,-10.998,-11.282,-11.081\n-15.38,-15.432,-15.094,-15.06,-15.536,-15.983,-16.418,-17.147,-17.091,-16.827,-16.853,-17.571,-18.666,-19.059,-18.801,-18.565,-18.813,-19.921,-21.446,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.89,-17.738,-17.677,-18.052,-17.786,-17.288,-16.918,-16.564,-16.323,-15.563,-14.149,-12.76,-11.648,-10.221,-9.8906,-9.8803,-10.514,-10.483,-10.807,-11.125\n-15.053,-15.199,-14.895,-14.614,-14.985,-15.567,-16.013,-16.233,-16.344,-16.341,-16.389,-16.933,-17.379,-17.329,-17.501,-17.293,-17.958,-19.006,-20.895,-21.228,-24.241,NaN,NaN,NaN,NaN,NaN,-18.102,-17.729,-17.394,-16.701,-16.97,-17.116,-16.455,-15.929,-15.52,-14.882,-14.64,-13.98,-12.871,-11.612,-10.55,-9.646,-9.7046,-9.6663,-10.395,-10.765,-10.451\n-14.753,-15.288,-15.292,-14.989,-15.432,-15.63,-15.86,-15.69,-15.731,-14.975,-14.666,-15.611,-15.575,-15.454,-15.805,-16.158,-16.796,-17.683,-18.696,-18.603,-21.331,-24.215,-22.541,-19.839,NaN,NaN,-19.089,-18.249,-18.547,-17.743,-16.63,-15.953,-14.914,-14.336,-14.503,-14.596,-14.948,-14.063,-12.496,-11.512,-10.997,-10.035,-10.52,-10.044,-10.385,-10.272,-9.9232\n-14.39,-14.978,-15.629,-15.629,-15.475,-15.587,-15.182,-15.156,-14.914,-14.745,-14.328,-14.2,-13.799,-14.411,-15.084,-15.69,-16.402,-17.622,-17.338,-18.273,-19.966,-20.075,-19.624,-19.123,-19.069,-18.635,-18.654,-18.213,-18.468,-17.413,-16.364,-15.465,-14.792,-14.659,-14.547,-14.388,-14.56,-14.072,-12.417,-11.257,-10.842,-10.253,-11.094,-11.33,-11.041,-10.384,-9.8417\n-14.055,-13.961,-14.777,-14.637,-14.269,-13.938,-14.273,-14.731,-14.722,-14.338,-13.898,-14.069,-14.094,-14.131,-15.094,-15.733,-16.137,-17.251,-17.902,-18.989,-18.873,-19.052,-18.462,-18.75,-18.485,-18.498,-18.529,-18.148,-17.545,-16.823,-16.152,-15.37,-14.478,-14.679,-14.565,-14.367,-13.865,-13.581,-12.209,-11.152,-10.342,-10.782,-13.864,-12.015,-11.081,-10.508,-9.5272\n-14.102,-13.98,-13.766,-13.499,-13.75,-14.032,-14.002,-14.059,-14.319,-14.023,-13.525,-13.405,-14.21,-14.78,-15.318,-15.515,-15.863,-17.103,-17.017,-16.971,-17.126,-17.342,-17.811,-18.171,-18.1,-18.324,-18.928,-18.434,-16.963,-16.336,-16.109,-15.141,-14.197,-13.916,-14.084,-13.752,-13.387,-12.815,-11.862,-11.497,-11.306,-9.9552,-9.6066,-10.855,-10.339,-10.161,-8.8983\n-13.726,-13.857,-13.573,-13.476,-14.026,-14.144,-14.133,-14.057,-13.753,-13.424,-13.631,-13.54,-13.559,-14.293,-14.623,-16.111,-16.207,-18.24,-18.74,-18.574,-17.579,-17.504,-17.638,-17.405,-17.091,-17.182,-18.187,-17.721,-16.63,-16.118,-15.659,-14.853,-14.065,-13.446,-13.244,-13.142,-12.578,-11.905,-11.361,-11.155,-11.294,-11.33,-11.209,-9.5278,-9.2659,-8.865,-7.4144\n-13.513,-13.588,-13.737,-13.082,-13.043,-13.273,-13.724,-14.166,-13.961,-13.415,-13.431,-13.891,-14.821,-15.355,-15.24,-16.15,-17.442,-18.592,-20.29,-20.887,-19.414,-18.439,-17.993,-17.017,-16.512,-16.544,-16.064,-15.948,-15.705,-15.738,-15.398,-15.046,-14.005,-13.598,-12.817,-12.274,-11.789,-11.277,-11.182,-11.164,-11.031,-10.849,-10.585,-9.4017,-9.248,-7.9535,-6.0322\n-13.344,-13.193,-12.9,-12.707,-12.678,-13.425,-13.961,-14.54,-14.279,-13.736,-13.163,-12.946,NaN,NaN,NaN,-20.908,-20.267,-18.389,-20.227,-22.039,-21.427,-18.701,-17.2,-16.21,-15.592,-16.107,-15.51,-14.864,-15.207,-15.206,-14.809,-14.424,-13.519,-13.4,-12.926,-12.294,-11.573,-11.094,-11.164,-11.554,-11.479,-11.114,-10.181,-9.5382,-8.3046,-7.0602,-6.0926\n-13.567,-13.094,-12.758,-12.22,-12.931,-14.106,-14.916,-14.587,-14.055,-13.426,-12.568,-12.267,NaN,NaN,NaN,NaN,NaN,-22.291,-21.507,-21.146,-19.405,-17.879,-16.804,-15.843,-15.531,-15.7,-14.973,-14.789,-14.299,-14.703,-13.998,-13.933,-13.658,-13.137,-12.278,-11.942,-11.499,-11.235,-11.03,-10.796,-10.707,-10.575,-10.015,-9.2046,-8.0835,-7.5526,-7.4593\n-14.276,-13.885,-12.949,-11.835,-10.442,-12.621,-14.704,-13.613,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-20.61,-20.544,-19.04,-17.525,-16.449,-15.631,-14.937,-14.979,-13.286,-13.069,-13.551,-14.506,-14.424,-14.119,-13.317,-12.911,-12.387,-11.857,-11.508,-11.068,-10.914,-10.33,-9.6666,-9.5122,-8.8546,-8.1983,-7.5456,-6.6074,-6.5349\n-15.024,-14.127,-12.412,NaN,-10.362,-11.771,-13.132,-14.023,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-20.256,-19.473,-17.745,-15.92,-14.852,-14.495,-14.354,-13.202,-13.285,-13.771,-14.205,-14.204,-14.09,-13.417,-12.646,-12.314,-12.377,-11.932,-11.344,-10.72,-9.9852,-9.5946,-9.5015,-8.4973,-7.3032,-6.31,-5.8353,-5.6406\n-14.301,-13.615,-12.872,NaN,NaN,NaN,NaN,NaN,NaN,-15.072,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.879,-17.669,-17.443,-16.803,-15.09,-14.241,-13.674,-13.408,-14.521,-14.47,-13.711,-13.636,-13.443,-12.908,-11.623,-11.475,-11.769,-11.088,-10.38,-9.9495,-9.5449,-9.2593,-9.0363,-8.2072,-6.5556,-6.071,-6.085,-6.1682\n-14.506,-14.286,-14.383,NaN,NaN,NaN,NaN,NaN,NaN,-14.722,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-17.949,-18.177,-18.039,-17.041,-16.36,-15.654,-14.859,-13.691,-13.026,-12.805,-13.119,-12.801,-12.747,-13.418,-12.53,-11.543,-11.511,-11.413,-10.553,-10.089,-9.872,-9.3222,-8.9625,-8.7504,-8.3205,-7.78,-6.8091,-5.5403,-5.3297\n-14.072,-13.922,-14.77,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-22.058,-23.801,-21.413,-21.498,-20.935,-16.649,-15.404,-16.246,-17.501,-17.433,-16.341,-15.549,-14.983,-13.692,-13.208,-12.479,-12.499,-11.945,-12.371,-12.335,-12.881,-12.484,-11.305,-11.693,-11.122,-10.14,-10.044,-10.116,-9.5565,-8.9901,-8.0582,-7.2724,-7.0829,-7.1204,-6.7052,-6.3244\n-12.121,-12.696,-12.847,-15.068,-17.492,-18.133,NaN,NaN,NaN,NaN,NaN,-24.129,-23.166,-22.282,-20.227,-19.851,-18.447,-16.935,-15.914,-16.177,-15.898,-15.118,-13.885,-13.683,-13.01,-11.984,-12.09,-12.143,-11.31,-10.926,-10.695,-10.947,-12.13,-12.235,-12.071,-11.029,-10.597,-10.711,-10.945,-11.086,-9.8626,-8.3195,-7.5949,-7.4939,-7.2722,-6.8991,-7.1458\n-13.186,-13.799,-13.865,-12.915,-14.476,-16.575,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-21.404,-21.124,-19.384,-16.981,-16.315,-15.873,-15.162,-14.523,-14.487,-14.585,-14.412,-13.46,-12.549,-12.238,-12.027,-11.734,-11.497,-10.687,-10.335,-10.629,-11.67,-11.781,-11.419,-11.251,-11.558,-11.642,-11.607,-11.291,-10.185,-9.9063,-9.2446,-7.8104,-7.2513,-8.5542\nNaN,NaN,NaN,NaN,NaN,-17.894,-18.734,-17.952,NaN,NaN,NaN,NaN,NaN,NaN,-22.431,-20.13,-18.148,-16.851,-15.225,-14.962,-14.567,-14.562,-14.879,-13.733,-12.906,-12.252,-11.843,-11.472,-10.877,-10.785,-10.636,-10.448,-10.551,-10.349,-10.526,-11.224,-11.133,-11.547,-11.693,-11.04,-10.854,-10.427,-9.9922,-9.4429,-8.8951,-8.9201,-9.9397\nNaN,NaN,NaN,NaN,NaN,-20.465,-18.818,-17.101,NaN,NaN,NaN,NaN,NaN,NaN,-21.416,-21.332,-20.357,-18.257,-16.453,-15.16,-15.006,-16.331,-14.776,-13.597,-12.503,-11.435,-11.208,-10.932,-10.589,-10.65,-10.731,-10.754,-10.575,-9.881,-9.8463,-10.356,-10.444,-10.872,-11.464,-10.944,-10.517,-9.8024,-9.1848,-8.9807,-9.213,-9.6447,-10.121\nNaN,NaN,NaN,NaN,-16.946,-17.982,-17.605,-17.197,-17.108,NaN,NaN,NaN,NaN,-20.592,-20.12,-19.181,-18.149,-16.168,-15.697,-14.781,-14.505,-15.366,-15.235,-13.325,-13.198,-11.89,-11.16,-10.607,-10.54,-10.325,-10.114,-9.8016,-9.4101,-9.7743,-10.4,-10.644,-10.553,-10.217,-10.606,-9.8732,-9.12,-9.1097,-9.6495,-9.8404,-9.271,-9.1338,-9.028\nNaN,NaN,NaN,NaN,-15.842,-15.895,-16.743,-17.917,-18.22,NaN,NaN,NaN,-18.326,-19.941,-20.396,-18.647,-19.046,-16.561,-15.555,-13.771,-13.952,-13.451,-14.146,-14.509,-13.23,-12.032,-10.953,-10.133,-9.9911,-10.39,-10.161,-9.8492,-9.6453,-10.308,-11.09,-10.54,-10.374,-9.9745,-9.5149,-8.9308,-9.1401,-9.6015,-10.605,-11.441,-10.263,-8.7541,NaN\nNaN,NaN,NaN,-13.501,-13.866,-14.898,-16.832,-18.558,-19.301,-17.123,-15.587,-16.587,-19.263,NaN,NaN,NaN,-21.707,-18.423,-15.788,-12.974,-12.875,-12.691,-12.36,-12.278,-11.602,-11.181,-10.926,-10.476,-10.254,-10.137,-10.084,-9.8708,-10.013,-10.162,-11.106,-9.9743,-9.6798,-9.823,-9.4508,-9.3372,-8.8417,-9.1341,-10.158,-9.8673,-9.6116,-10.648,NaN\n-18.294,-16.647,-18.096,-16.519,-15.877,NaN,-15.59,-17.312,-18.936,-19.282,-18.928,-18.153,NaN,NaN,NaN,NaN,NaN,NaN,-14.506,-12.146,-11.034,-11.713,-11.573,-11.132,-11.522,-11.21,-10.747,-10.17,-10.067,-9.843,-9.7633,-9.8198,-9.5333,-9.5927,-9.9097,-10.033,-10.052,-10.011,-9.5587,-9.2668,-8.5598,-8.6501,-9.3394,-9.5643,-9.8581,-10.375,NaN\n-18.618,-17.83,-17.478,-18.967,-17.519,NaN,NaN,NaN,-17.736,-16.511,-17.201,-17.907,-14.818,-13.271,NaN,NaN,NaN,NaN,NaN,NaN,-10.204,-10.777,-10.984,-10.964,-10.936,-10.53,-10.29,-9.9786,-9.8343,-9.4412,-9.2583,-9.6448,-9.6725,-9.1834,-9.5924,-10.004,-9.7999,-9.5773,-9.243,-9.1703,-9.446,-9.5424,-8.7763,-8.3019,-9.2388,NaN,NaN\n-19.72,-18.505,-16.704,-16.667,NaN,NaN,NaN,-16.213,-18.55,-18.146,-18.145,-18.681,-17.12,-16.938,-15.123,NaN,NaN,NaN,NaN,NaN,-11.854,-9.7131,-10.573,-11.399,-11.89,-11.06,-10.385,-9.873,-9.3076,-9.016,-8.3171,-8.5644,-8.7128,-8.6597,-8.9498,-9.4101,-9.0432,-8.4999,-9.5934,-9.8731,-10.147,-9.7575,-7.9716,-6.8164,-5.783,NaN,NaN\n-18.1,-17.785,-16.451,-15.938,NaN,NaN,NaN,-16.113,-14.582,-14.503,-15.565,-17.372,-15.96,-14.268,-14.863,NaN,NaN,NaN,NaN,NaN,-10.5,-9.3703,-10.527,-10.329,-10.319,-10.582,-9.9729,-9.8478,-9.951,-9.7129,-8.9342,-8.2522,-8.5921,-8.5305,-8.6719,-9.2596,-9.4045,-8.6385,-8.6609,-9.8249,-8.6104,-9.1021,-8.222,-4.8119,NaN,NaN,NaN\n-17.82,-16.871,-16.111,-16.397,-16.587,-16.403,-16.047,-15.525,-14.97,-14.546,-14.279,-13.558,-13.962,-15.488,-15.378,NaN,NaN,NaN,NaN,NaN,-9.9333,-9.0603,-8.4141,-8.8895,-9.6611,-9.895,-9.8409,-9.6059,-10.837,-10.855,-9.4884,-8.3162,-8.0043,-7.7364,-7.2259,-9.8281,-10.331,-9.3528,-8.6211,-8.3152,-7.875,-6.4333,-5.404,-2.5108,NaN,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070326-070917.unw.csv",
    "content": "6.957,6.2325,5.4174,5.67,5.4465,5.2204,5.2106,5.172,5.3689,5.0138,5.5478,4.9845,4.7237,4.5305,3.8669,3.3138,3.3487,3.3946,3.0002,3.2271,3.4635,3.4172,3.3292,2.9052,2.2515,2.7813,2.7537,2.2851,2.5843,2.5301,2.495,2.7297,2.0493,1.7406,2.0719,2.068,1.8502,1.5604,2.3955,2.6036,2.3975,2.6203,2.3054,1.8725,2.5844,2.9398,2.8628\n6.1185,6.3053,5.2572,5.3808,5.5662,5.371,5.5867,5.2872,5.3098,5.3117,5.5455,5.2076,4.7497,4.2523,3.7345,3.6454,3.9384,3.7198,3.5706,3.7525,4.2324,3.0754,2.8779,3.0559,3.6407,3.5207,3.2482,2.7152,2.1547,2.1712,2.6286,2.5876,2.3009,1.9895,2.3723,1.796,1.5895,2.0067,2.6003,3.0564,3.1764,3.0363,2.3851,2.5269,3.0258,2.9368,2.8005\n5.9787,5.463,5.6265,5.5899,5.548,5.4629,5.618,5.5166,4.6619,5.3211,4.9328,5.6322,4.7879,4.1585,3.7431,3.8994,3.9808,3.511,3.3062,3.1058,3.4123,3.425,2.774,3.143,3.2123,3.0282,2.788,2.3477,2.3865,2.3111,2.6797,2.4586,2.2734,2.1584,2.1837,1.6996,1.0263,2.0586,2.6706,3.0445,3.2595,2.9868,3.0234,3.3225,3.3611,3.1009,2.9912\n6.2358,5.8479,5.9055,5.7766,5.9196,6.1313,5.5325,5.4207,5.0783,5.2315,4.9474,4.7896,4.2454,3.6884,4.4304,4.2875,4.4607,4.0739,3.47,1.8653,2.5882,3.0974,3.3387,3.277,2.7551,2.4607,2.7252,3.1243,2.8899,2.7377,2.7355,2.814,2.2812,2.2445,2.3595,2.0529,1.8016,1.921,2.4349,2.9313,3.2577,3.3174,3.2568,3.3889,3.4315,3.3452,3.1557\n5.9001,5.3191,6.147,5.876,5.691,5.6603,5.4883,5.2962,5.3087,4.7469,4.6487,4.3676,4.2,3.713,4.2049,4.2944,4.3419,3.9131,3.7626,3.4503,2.6906,3.8755,3.7451,3.1819,2.5037,1.9293,2.259,2.7056,3.305,2.6454,2.8771,3.1268,3.0307,2.3808,2.3627,2.3966,1.9258,1.8914,2.2674,3.0161,3.5744,3.8686,3.5693,3.8026,3.6752,3.3967,3.1675\n5.9369,5.4269,5.7358,5.6739,5.72,5.393,5.63,6.0412,5.951,5.6064,5.0834,4.4771,4.6163,4.4799,4.3085,4.109,3.9236,3.582,3.5659,3.481,3.7152,4.0187,3.4214,2.9436,3.0254,2.6234,2.1188,2.4192,2.7978,2.1029,2.5372,2.4455,2.3284,2.5968,2.6344,3.152,3.0644,2.5236,2.8101,3.6751,3.9514,3.737,3.7115,4.0619,3.8885,3.9207,3.6914\n6.1978,6.1994,6.7513,5.6083,5.3722,5.0604,5.6073,6.043,5.8452,5.4803,5.1104,4.7782,4.6259,4.5342,3.7517,3.9187,4.1058,3.9028,3.8293,3.198,3.7039,3.3254,3.1992,2.5452,2.4211,2.2509,2.4991,3.1627,2.4691,2.8029,2.9671,2.6854,2.7042,3.0072,3.0889,3.1917,3.2261,2.8309,3.1128,3.8966,4.1283,4.1748,4.4024,4.1932,4.1515,4.3856,4.5756\n6.6992,6.691,6.9526,5.8781,5.1891,4.7667,5.4115,5.8551,5.6706,5.1108,5.1455,5.4416,5.0006,3.7473,4.139,4.1198,4.3407,3.4118,3.3874,3.501,4.0364,3.5124,3.2248,2.8326,2.6281,2.6312,3.2262,3.908,3.095,2.4305,3.3825,3.4789,3.1972,3.2182,2.8546,2.8784,2.8546,2.8633,3.1021,3.7203,4.5152,4.9549,4.6559,4.7072,4.5692,4.1748,4.4156\n6.8413,6.3989,6.2833,5.7751,4.9965,5.0478,5.3349,5.4531,4.9417,4.8721,5.2543,5.7613,5.4017,4.2779,4.4489,4.3884,4.4991,4.2728,3.5353,3.792,3.6754,3.5573,3.8505,3.3466,2.9585,3.3382,3.4403,3.1191,2.5523,2.8978,3.5166,3.5312,3.5561,3.6188,3.2998,2.8681,3.2672,3.4951,3.7664,4.1382,4.3381,4.2868,4.0567,4.3563,4.557,4.6708,4.6249\n6.7032,6.6194,6.4274,5.4154,5.4174,5.0063,5.3242,5.0049,5.0386,5.5372,5.8053,5.8671,5.3514,4.8308,4.6915,4.4572,4.4866,4.2317,3.3623,3.4391,4.2674,4.3445,3.9035,2.7502,2.8082,3.0768,4.2349,3.8134,2.5848,2.6974,2.9855,3.0459,3.5572,3.3686,4.0468,4.0472,3.9918,4.0527,3.6346,3.8108,3.9767,4.1606,4.2342,3.7907,4.5116,4.7562,4.6686\n6.7812,6.1992,6.4034,5.5039,4.9266,5.1883,5.0332,4.9761,6.0956,6.4866,6.2222,5.6148,4.9881,4.876,5.0391,4.9102,5.0873,4.391,4.4976,4.6912,4.519,4.6099,3.9341,3.1326,2.9237,3.2572,3.5778,3.4576,2.9504,2.6634,2.6179,2.994,3.5791,3.6004,3.7477,3.3598,3.2695,3.2627,3.5803,4.0466,3.7356,3.8924,3.9017,3.6917,4.2746,4.9849,4.9671\n6.3419,5.3571,5.8418,5.2655,5.6485,5.9356,5.4181,5.6754,6.2351,6.3029,6.3202,6.0957,5.7997,5.5742,5.0188,4.526,4.7597,4.5044,4.286,3.7742,3.8626,3.7533,3.5316,3.8714,3.6583,3.9663,3.8444,3.2711,2.4807,2.6305,2.3458,2.8498,3.5744,3.8674,3.6026,3.2776,3.9185,3.8354,3.2872,3.3371,3.419,3.9516,4.5141,4.4893,4.6087,5.4107,NaN\n6.1442,5.7656,6.4627,5.6581,5.4373,5.4303,5.229,5.7749,5.2018,5.8771,6.1001,6.2167,6.0738,5.7753,5.365,4.5264,4.1655,3.8308,3.5956,3.6042,4.3977,3.897,3.046,3.7804,4.6289,4.2633,4.0055,3.145,1.9878,2.5108,3.1638,2.9305,3.2804,3.6613,4.1407,4.5744,5.2705,4.8197,4.5641,4.1373,4.1672,4.5598,5.2174,5.0877,5.2678,NaN,NaN\n5.818,5.6259,6.1165,6.5479,6.1245,5.4418,4.692,5.5843,5.6557,6.0161,6.0333,5.9329,6.0019,5.6641,5.334,4.6878,4.1958,3.4983,3.7857,4.6583,4.8329,4.2443,4.5913,3.6809,3.8179,3.8178,3.9546,3.7075,3.1431,3.0735,3.7909,3.9804,4.0099,4.3266,4.6044,4.6986,5.3926,5.8159,5.2745,4.8002,5.6139,5.7583,5.6964,5.9057,6.0992,NaN,NaN\n6.5428,6.7924,8.1257,5.9737,4.8969,4.8997,5.329,5.6623,5.6328,5.7359,6.3949,5.7263,5.0393,4.7811,5.1535,4.747,4.6922,3.38,4.8249,5.4077,5.124,5.8728,5.1916,3.4959,3.7306,4.301,4.1064,4.2037,4.7095,4.2383,3.7983,4.2014,4.8734,5.1988,4.9461,4.4945,5.4383,5.7607,5.957,6.2708,6.6401,5.1852,5.3016,5.8202,5.8443,6.4454,6.7177\n7.1115,6.9204,7.7963,6.4001,6.3279,5.4312,6.2424,5.9463,5.7441,5.8438,6.0028,5.1676,5.3811,5.3114,6.0918,5.1939,4.7515,5.2066,5.5854,5.8236,5.4486,5.6583,5.6581,4.9094,4.0727,4.0385,4.4159,4.8328,5.8801,5.4085,5.2216,5.177,5.2283,4.9484,4.4559,5.7202,5.6001,5.6494,5.8382,6.5729,6.0595,5.3502,5.2836,5.1093,4.9721,6.1202,6.2273\n7.9369,7.2641,6.7666,5.8877,5.0805,5.1114,5.8903,5.8454,5.9307,6.1391,6.2505,6.0315,5.273,5.2694,5.6311,5.537,4.3307,6.4406,6.3722,6.4585,6.9136,5.8539,5.6574,5.4582,5.7892,6.6119,7.6944,8.0913,7.1182,5.0998,5.7536,5.5296,5.108,5.1755,5.2467,5.6709,5.4637,5.4741,5.2213,5.2144,5.3418,5.3172,5.2761,5.3134,4.8798,6.6257,6.0162\n6.4558,7.0352,6.5866,6.8661,4.5191,3.4976,5.4068,6.2916,6.5089,5.7617,5.8993,6.0844,5.6477,5.8835,6.2162,6.5741,5.8528,5.8112,5.3175,5.6394,6.2871,5.4513,4.6458,5.3303,5.0879,5.6292,6.8146,7.3797,7.6338,6.0427,5.4079,5.7617,5.4114,5.4516,5.3699,5.0511,4.4053,5.7248,6.1447,6.2009,5.8105,4.8217,4.3479,4.5358,5.0247,5.2391,5.2231\n7.3485,6.7466,7.4202,6.4989,5.2253,1.8282,5.5409,6.2037,6.0898,5.5539,6.2809,6.3414,6.3527,6.817,7.1843,7.3211,7.0202,5.7483,5.381,5.2998,5.0031,3.4731,2.592,4.8162,4.8819,4.9214,6.2518,6.3938,6.8454,6.1987,5.2248,5.2595,5.7981,6.099,6.446,5.6178,6.4746,6.569,7.2949,6.4177,5.8815,5.5528,4.5977,4.9919,5.186,5.1586,4.6886\n8.6228,7.4349,6.9191,6.2453,4.6456,3.4191,5.8436,5.7023,6.1031,6.9048,7.6884,7.9283,8.1648,7.6702,8.0502,8.7251,7.7673,6.0074,5.0732,4.7209,5.5569,5.6053,4.4757,3.9238,3.7637,5.213,5.9922,6.2271,6.0561,6.2013,6.3552,5.8519,6.0889,6.328,7.4321,6.3103,6.0692,5.9308,7.8257,7.2521,6.3183,6.1796,5.9301,5.8627,6.1635,5.2973,4.7568\n8.4895,7.416,6.8272,6.1876,5.4479,6.5033,6.9882,6.915,7.2173,7.7705,7.6804,7.3343,7.9466,8.9582,8.2725,7.3898,7.1168,6.7607,5.5726,6.0606,6.9602,7.4965,7.2482,5.6213,5.0468,6.3202,6.8864,7.6793,7.588,7.2077,7.6583,7.2961,7.0583,6.789,6.9006,5.7913,5.6553,4.9829,6.0363,6.8345,6.8485,6.1136,6.1707,6.545,6.3709,6.0928,5.5104\n7.1293,6.8344,7.1013,7.7685,7.3825,5.6104,6.798,7.1647,7.2467,7.8853,8.0328,8.1286,8.9783,10.226,8.1086,7.3936,6.7946,6.4368,5.9497,4.5601,4.7145,5.4967,7.6119,6.9954,6.7375,7.5586,8.4525,8.9466,10.183,10.082,9.4647,8.5006,8.3885,8.1191,8.3622,7.3641,7.7299,6.8325,7.1381,7.3827,7.1864,6.8382,6.961,6.5852,6.0826,5.6479,5.9493\n7.5878,7.2613,6.9151,7.2665,6.6986,6.9532,7.1775,7.1333,7.83,7.2826,7.7484,7.6289,8.5431,8.6742,7.1607,6.7147,6.374,6.2056,6.2854,6.0161,6.6806,6.6136,8.4931,8.7414,8.6549,8.681,9.7222,9.6474,10.391,10.944,10.842,11.22,10.647,10.239,8.9608,8.4292,8.2729,8.34,8.1546,7.9558,8.1118,7.2965,6.9499,6.1996,6.0828,6.2469,6.4329\n8.491,8.2645,7.6307,8.0154,9.1754,7.2248,7.0987,7.2569,7.6231,7.8196,7.6942,8.8438,8.5929,8.2402,7.5585,6.1554,7.8763,8.0516,7.2002,7.024,6.9325,8.4246,9.2412,9.5324,9.2293,9.2494,9.9117,10.687,10.984,12.208,13.879,13.472,12.696,11.641,10.296,10.2,10.199,9.6785,9.2789,8.0149,8.0435,7.2768,6.8614,6.6029,6.3416,6.4801,6.5532\n8.1201,8.2127,7.9439,8.1615,7.044,7.9324,7.1872,7.7877,8.1409,8.1851,8.5315,9.4958,9.4553,9.1182,8.2132,7.5984,8.2096,9.0891,9.0433,9.2868,9.0388,9.7211,11.025,10.911,9.6989,10.201,10.765,12.038,13.763,15.666,15.45,14.434,12.472,12.074,11.999,11.269,9.9819,10.223,10.184,8.2082,8.1446,7.9345,7.0354,6.2708,5.9263,6.1509,6.3511\n7.8194,7.9165,8.18,8.0678,7.1703,7.8416,7.2812,7.6873,8.4408,8.3417,9.3591,9.5566,9.5213,9.5126,9.8571,10.164,9.3683,9.3809,10.07,11.046,11.971,12.737,12.384,13.471,13.66,12.948,13.946,15.641,16.775,18.101,17.342,15.278,13.221,10.691,11.457,10.862,9.2594,9.4438,9.4862,9.0326,8.1044,7.8401,7.4843,6.5666,6.2216,6.0335,6.0905\n7.9139,8.7157,7.4736,7.0537,7.4653,7.9295,7.0802,8.1203,8.8565,8.1014,9.0659,9.8834,9.8755,9.8052,10.123,10.669,10.548,10.578,11.066,12.061,12.915,13.393,14.077,14.987,16.922,15.152,14.458,15.388,16.552,12.587,15.258,15.294,15.269,11.67,10.903,10.294,9.6666,9.3297,9.1376,8.9081,7.9272,7.6921,7.6065,7.237,6.6551,6.1538,6.129\n7.661,7.6646,6.3073,5.4653,6.922,7.7555,8.5611,8.8707,9.0431,8.0179,8.4363,9.6136,10.309,10.51,10.715,11.155,10.85,11.319,12.308,13.227,13.691,14.324,NaN,NaN,NaN,NaN,13,12.642,12.393,12.921,14.116,15.862,15.815,14.307,11.932,10.264,10.394,9.7966,9.4676,8.6142,8.0729,8.0287,7.5418,7.1352,6.1626,6.3537,6.6119\n7.2177,7.521,6.0793,7.0978,6.9938,6.6688,6.8618,7.1213,7.5033,7.11,8.5373,8.7721,9.7154,9.9865,10.221,10.767,10.698,11.913,12.904,14.11,14.699,NaN,NaN,NaN,NaN,NaN,NaN,11.409,9.0077,11.702,14.848,16.668,17.506,14.716,10.778,10.349,10.205,9.3983,9.3906,9.2052,8.3046,7.9036,7.5314,6.8355,6.6083,6.7244,6.4266\n5.4233,5.6719,7.1701,9.1815,6.1013,5.3698,5.3741,6.4716,7.222,7.365,8.4097,8.3593,8.4223,9.3662,10.239,10.864,11.3,11.208,11.551,13.22,NaN,NaN,NaN,NaN,NaN,NaN,12.813,10.929,10.581,11.632,14.524,17.133,16.45,12.908,12.274,11.694,11.266,11.179,9.9515,9.208,8.0707,7.7091,7.2787,6.8167,6.7669,6.7132,6.4359\n4.8954,4.7464,3.8756,3.898,4.6669,5.0287,5.7326,6.323,7.2281,8.4255,8.2945,8.029,7.811,8.596,10.154,10.127,10.201,10.908,11.612,12.806,NaN,NaN,NaN,NaN,NaN,14.796,14.294,13.021,11.097,9.849,9.3953,12.043,10.661,11.807,13.605,12.667,12.343,11.501,10.206,8.6415,8.2141,7.7426,7.3313,6.9309,6.885,6.4788,6.2242\n5.0406,3.9478,5.4023,7.6839,3.6501,3.7757,5.0799,5.3281,6.6571,7.6044,7.0099,7.7478,8.1925,8.5846,8.1036,8.4808,9.6355,9.575,11.072,11.819,NaN,NaN,NaN,NaN,NaN,NaN,15.444,16.163,14.186,13.108,12.107,12.632,8.1738,12.401,13.801,13.598,12.574,11.85,10.087,8.7694,8.4462,8.1223,7.6734,7.2473,7.0047,6.7271,7.0774\n5.7875,5.2796,6.0885,6.7278,4.9957,3.8001,3.8773,4.5142,5.5016,6.6203,6.7068,6.6067,6.7158,6.6975,5.7282,6.8944,8.896,9.1651,11.2,13.492,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.36,14.852,13.791,12.798,12.213,11.718,10.689,12.233,11.634,11.226,11.599,9.1614,8.5902,8.3418,8.3032,7.3718,6.6073,6.8966,7.1675,7.9844\n6.2379,6.0913,6.3325,6.558,6.3426,4.642,3.8789,4.2195,5.1617,6.0748,7.1405,6.8489,5.9684,4.2717,4.1685,5.2963,6.3536,6.4704,7.3174,8.6965,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.48,12.188,12.4,10.168,8.4881,9.8929,9.8775,11.25,11.097,9.4958,8.3444,8.3978,7.8164,7.2021,6.7708,6.7127,7.5355,8.1305\n6.0832,5.3431,4.8365,5.188,5.5584,4.7499,3.3907,4.354,5.1388,5.7667,6.1181,5.6076,5.6417,5.6261,3.6235,4.1922,5.9535,6.5252,6.3721,8.5176,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.364,12.426,12.699,NaN,NaN,12.591,12.565,12.642,9.9845,8.2261,8.1883,7.453,6.9626,7.1106,6.5775,7.2239,7.6662\n5.9385,4.6071,4.2139,4.3791,5.4493,4.6596,3.8364,3.6803,5.0684,5.3429,4.936,4.9661,5.1251,4.6971,3.6989,4.1802,5.2732,6.686,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.775,12.212,NaN,NaN,NaN,NaN,15.695,15.141,12.313,8.6979,7.7453,7.885,7.6162,7.2358,7.3997,7.2453,7.0656,6.8771\n5.3666,4.9079,5.1969,5.2538,4.9364,4.5749,4.4752,4.5809,4.4001,4.5496,4.2766,5.0164,5.2143,5.9757,5.6379,4.7389,4.8852,5.531,4.4218,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.117,NaN,NaN,NaN,NaN,NaN,10.628,10.165,9.3465,8.3866,7.7888,7.7594,7.72,7.819,7.7784,7.6297,7.0751,6.9891\n4.8966,4.7046,4.8471,4.8336,4.2745,4.8357,5.0324,4.4984,4.1525,4.7288,4.7457,4.5956,4.5141,4.7889,4.9222,5.0756,6.1699,6.9991,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.938,NaN,NaN,NaN,15.358,NaN,10.201,9.6142,8.754,8.3557,8.2654,7.8104,7.3553,7.6216,7.9517,8.6202,8.7664,8.2997\n5.3952,5.3888,4.256,4.1295,4.5789,5.3023,5.387,4.6729,4.7745,5.0839,4.7787,4.2725,4.3148,3.9885,4.7081,4.6376,5.9551,5.9666,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.6193,10.887,NaN,NaN,NaN,13.049,12.005,10.025,9.308,8.9468,8.9699,8.6016,7.886,7.3527,7.5102,7.2161,7.6555,8.4502,7.813\n5.3555,5.9034,5.5705,4.7599,4.9656,5.6615,5.7347,5.5187,5.1418,4.9627,4.8422,4.8094,4.6894,5.0981,4.9169,5.169,5.5627,6.5282,6.3823,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.7224,11.227,15.683,15.575,14.061,11.686,9.2923,9.1359,8.1349,7.6959,8.2865,7.9166,7.3804,6.9432,6.8019,7.0914,7.1468,7.1248,6.753\n5.1691,5.5037,5.0565,5.2903,5.595,5.8042,5.6816,5.6741,5.5655,5.6062,5.2947,5.1013,4.5325,4.3964,4.3508,4.746,5.2967,6.7974,6.9346,3.0159,4.0278,5.6351,5.1802,NaN,NaN,NaN,NaN,4.4867,8.9752,12.518,14.744,14.27,12.677,9.3716,8.5907,8.5055,7.6862,6.9376,7.3981,7.0931,6.7506,6.7782,6.6517,7.4431,7.1434,7.0976,7.0233\n6.0125,5.8998,5.9415,5.4798,5.4151,5.6381,6.0776,6.1608,6.0233,5.701,5.2658,4.3151,3.5398,3.6625,3.9331,4.6538,5.0466,5.9461,6.2698,2.7656,3.7281,4.7513,4.3757,NaN,NaN,NaN,NaN,NaN,8.5375,11.419,10.846,10.649,10.771,8.7092,8.5795,8.6039,7.6181,7.0275,7.0501,6.432,6.5648,6.865,7.0516,7.5334,7.0077,6.897,6.4872\n6.7606,6.0898,6.2543,6.2893,5.7196,5.4971,5.646,5.4872,5.8802,5.443,5.0164,4.7103,3.862,4.0023,4.2646,4.5009,5.3445,5.373,5.6378,5.0687,4.8217,4.4536,3.8916,5.3786,NaN,NaN,NaN,NaN,9.6498,9.53,9.9111,10.329,9.0056,8.4056,8.417,8.1468,7.3131,5.974,6.3483,6.025,6.0594,6.6117,6.7153,6.8004,6.4054,6.311,5.5885\n6.105,5.8606,6.2016,5.9913,5.871,6.0286,5.8468,5.3165,5.8991,5.2246,5.182,5.15,4.7014,4.7968,5.2725,5.4846,5.5584,5.5915,5.2292,5.3507,5.33,5.3097,7.1543,8.5612,NaN,NaN,NaN,8.7145,7.9375,8.4629,8.7507,8.5274,8.5538,8.0325,7.9629,6.9627,6.5002,5.1689,6.1136,5.8183,6.1911,6.8345,6.7049,5.9034,4.8938,4.4488,4.9467\n5.9739,5.968,6.2984,6.1247,5.8361,5.236,5.1859,5.1624,5.2144,5.5382,5.2741,4.9604,5.08,5.4252,5.8926,6.0449,5.8536,4.9245,5.1095,6.2856,6.2697,6.5043,7.1311,8.3257,9.8514,9.4008,9.0877,7.8179,7.1798,7.3898,7.8043,7.7544,7.6062,7.7421,7.5445,6.3847,5.8792,5.1925,5.8529,5.759,6.1948,6.4002,6.5667,6.7375,6.2788,5.4116,4.5951\n6.7765,6.4993,6.5522,6.4756,5.989,5.518,5.5555,5.2016,5.6879,5.8743,5.4965,5.4217,5.4614,5.4951,6.0555,5.9361,5.0933,4.1347,3.1747,3.9161,5.1398,5.2594,6.2138,6.593,7.0149,6.5013,7.5239,7.45,7.3883,7.9083,8.0349,8.4342,8.8662,7.8944,6.9833,6.0741,5.6372,5.5197,5.5484,5.3539,5.6625,6.1091,6.3327,6.8849,6.2413,5.7124,5.1213\n6.335,6.7372,7.2359,6.2257,6.03,5.7159,5.6129,5.6751,5.7672,5.487,5.5215,5.6332,5.7846,5.6212,4.9217,4.938,4.9261,4.546,3.0071,2.7136,3.8425,4.4804,4.7006,5.1013,5.9532,6.1897,6.7771,6.2135,7.3722,7.2882,7.8049,7.9689,7.5025,6.8219,5.3403,5.2707,6.0278,6.0941,5.2984,5.0543,5.2513,4.5944,4.6254,5.7812,6.4748,6.18,5.5957\n5.9816,6.398,6.874,6.8559,6.7791,5.5042,5.5333,5.4998,5.4635,5.2699,5.2074,5.122,5.2689,5.6041,5.7456,5.1698,4.8119,4.5076,3.6721,3.1615,2.5835,2.5259,3.1419,4.6386,4.2159,4.7217,6.0874,6.372,6.8694,6.5861,6.4865,7.1544,6.8241,5.8023,4.6621,4.5347,5.182,6.1625,5.6231,5.2005,4.9977,4.4554,4.6283,3.7319,4.9945,4.4282,5.002\n6.6234,7.0307,6.5395,6.311,6.5078,5.7378,5.4983,5.1025,6.2165,5.832,5.4307,5.8076,5.6621,5.7305,5.7837,5.3574,5.0281,4.8421,4.5237,4.0404,3.525,3.9145,4.7401,5.24,4.2673,4.0722,6.1309,6.8997,6.4685,6.4877,6.3177,6.4059,6.4958,6.6952,6.7372,5.682,5.7213,5.5567,5.5942,5.3352,5.1816,4.9899,5.1132,4.8299,4.4302,4.4064,5.0559\n6.3517,6.5631,6.6586,6.4408,6.1625,5.862,5.9627,5.7958,5.1894,4.7024,5.3116,5.593,5.8924,5.885,5.7008,5.5011,5.0713,5.1068,4.9432,4.8226,3.9157,4.8879,5.5416,6.0411,6.2598,6.0575,6.1121,6.2932,5.7984,5.7648,5.8615,5.6789,6.2583,6.7399,6.6306,6.2471,5.6381,5.2658,5.1733,4.8788,4.7925,4.6825,4.4276,4.1303,4.3469,4.6915,4.8463\n6.173,6.4327,6.4427,6.1692,6.3898,6.0835,5.926,5.3867,5.1164,4.5758,4.9564,5.3237,5.5902,5.3949,5.2937,5.1733,4.8974,4.5667,4.4987,4.3752,5.3523,5.8002,5.8642,6.1379,5.8478,5.6166,4.9797,5.5176,6.0327,6.2468,5.768,5.5099,5.9903,5.9143,6.1991,6.8384,6.4202,5.5114,5.384,5.0028,4.6492,4.4836,4.6296,4.3827,4.4318,4.8153,5.2149\n6.2836,6.2448,6.5264,6.6405,5.7982,5.5544,5.6132,5.1885,5.2074,5.2899,4.959,4.7973,5.2894,4.5686,5.3305,5.0815,4.3695,5.7519,5.2606,4.1418,5.4447,5.1595,5.6882,6.0995,6.3666,6.5605,5.8189,7.8431,7.4719,6.5989,5.803,5.4031,5.5784,5.576,5.7833,6.5221,5.799,5.6338,5.6187,5.1538,4.463,4.2854,4.4546,4.7378,4.289,4.6897,4.6332\n6.04,6.1418,6.6036,7.6387,6.3202,5.9122,4.9693,4.7145,4.6061,5.0592,5.5036,5.4191,5.0539,4.8937,4.6645,4.431,4.3759,4.2722,4.0897,3.9511,4.5218,4.8748,5.6135,5.982,6.6065,6.6843,7.4661,7.6622,6.9153,6.3718,5.9643,5.3154,5.5076,5.3298,5.6126,5.2812,5.0995,4.9651,5.3552,4.8192,4.4878,4.582,4.484,4.445,4.4278,4.3356,3.9017\n5.7816,5.4351,5.1907,5.4435,6.6757,6.1637,5.1326,5.3096,5.4473,5.2712,5.2461,4.7285,5.0279,5.0857,4.7969,4.2447,4.0348,3.8721,2.4415,2.5901,4.5967,5.1678,6.0257,5.7308,6.0424,6.9597,7.7079,8.4375,7.5222,6.292,5.6718,5.1881,5.3263,5.4562,5.8644,5.4546,5.3305,4.7772,4.1747,3.7962,4.3991,4.5314,4.3905,4.7709,5.2403,4.9156,4.6795\n5.5338,5.6051,5.1955,5.9139,6.3598,5.9877,5.7206,5.6793,5.0557,5.1974,5.239,5.4346,5.2446,5.2341,5.0503,4.8318,5.0474,4.6203,4.9542,4.6385,5.3825,5.6739,5.9793,5.8513,5.9778,6.3427,7.9486,8.3979,8.1195,7.058,6.0085,5.138,4.9143,5.3466,5.2865,5.2078,5.3447,4.9862,4.3185,4.22,4.6344,4.4042,5.0037,4.9519,5.5377,5.679,4.3202\n5.1032,5.8748,6.1476,5.5355,6.1014,5.6502,6.0043,5.4209,4.6461,4.2755,4.2375,3.9191,4.5607,4.7235,4.7241,4.7825,5.1864,5.6078,5.69,4.9645,5.4017,5.8306,5.9754,6.2631,6.6215,6.3506,6.7819,6.8378,7.1981,6.7159,5.6622,4.9561,4.5789,4.9426,4.9227,5.1078,4.5755,4.0976,4.0108,4.2394,4.3249,4.199,4.4118,4.8087,4.9258,4.8383,4.32\n5.7183,5.5848,5.4235,5.4101,5.6313,5.7277,6.2783,5.7539,4.2807,3.1563,3.9873,3.6843,4.3174,4.7751,4.7282,4.9579,5.6178,5.7491,5.5525,5.265,5.3937,5.5211,5.6826,5.9362,6.8235,6.8382,7.5199,7.2962,6.7017,5.6721,5.1851,5.4014,5.3286,5.2957,5.0531,4.3856,4.2219,4.1029,4.091,4.5024,4.4547,4.3003,4.2505,4.1914,4.2434,4.6487,4.6552\n6.3987,5.9819,5.1598,4.9965,5.1901,6.2727,6.7515,6.2782,5.6374,4.7291,5.1667,4.119,3.9368,4.4818,4.051,4.1027,4.9979,5.1137,4.6892,5.2014,5.3179,4.9772,5.1617,5.9818,6.906,7.4966,7.6222,7.8184,7.03,6.3242,5.4154,5.8515,5.6776,5.8117,5.8518,5.6601,5.1806,4.5332,3.9954,4.5071,4.6159,4.5046,4.4497,4.38,4.5593,4.6307,4.4494\n6.535,6.7643,5.9447,4.8827,4.5618,3.9239,6.4348,6.0674,5.9635,5.2856,5.6081,5.2252,4.9377,4.9472,4.3623,3.5949,4.1503,4.4706,5.1376,5.8112,5.8393,5.5943,5.2736,5.751,6.2832,6.7957,6.9933,7.2096,7.0018,5.9125,5.1706,5.3416,5.4958,5.4185,5.7618,5.6066,5.0201,4.2178,3.9953,4.2294,4.3014,4.2284,4.2697,4.1015,4.4515,4.3585,4.3264\n6.1022,6.0193,5.0352,4.6823,4.5219,4.2862,5.3044,6.1319,7.7407,6.4276,4.9323,3.7775,2.477,3.8272,5.2138,5.364,4.7699,5.6125,4.617,5.7367,5.5747,6.3522,5.6381,5.9054,6.0318,6.0522,6.156,6.3511,6.6067,7.1311,6.1449,5.1016,5.0356,5.2713,5.5606,5.5041,5.0889,4.7695,4.2025,4.256,4.024,3.7838,3.9714,3.7063,3.5336,2.9202,3.0415\n6.4131,6.2241,5.5044,5.1606,4.8253,4.8415,4.6795,5.6702,6.8883,6.8145,5.9652,0.62702,2.4814,3.8245,2.5979,3.5223,4.0677,4.379,5.3491,5.2152,5.2042,6.0485,5.2181,5.7541,6.0165,5.7412,6.8758,6.8144,6.5979,6.7694,6.1085,5.349,4.4835,5.0789,5.4712,5.1622,5.1318,4.9302,4.3678,4.6005,4.3895,3.9457,3.9458,4.1653,3.9502,3.4239,3.0593\n6.2762,6.0405,5.8203,6.096,5.9658,5.2864,4.95,5.6395,6.1164,6.1515,3.7049,4.6719,2.9052,1.0018,0.21001,-0.36497,3.0927,3.9249,4.6261,4.9062,4.938,4.7024,4.4834,5.0402,4.9938,5.3634,5.9812,6.2341,6.474,6.0896,5.3673,5.3661,5.0305,4.9593,4.7393,4.6673,5.1884,5.0247,4.5641,4.4326,4.5401,4.4634,3.9815,4.1229,3.7867,3.1881,2.6956\n7.0235,8.2801,6.7802,6.318,6.5646,6.3365,5.6113,5.021,5.5096,5.8808,1.3248,3.5352,4.3459,3.9298,2.4986,0.84491,1.8407,3.3725,5.4331,4.8702,4.7841,4.319,4.1487,4.6799,4.3502,5.1169,5.8779,6.2644,6.37,5.7569,5.2923,5.5356,5.6704,5.2777,4.8754,4.6217,5.2362,5.2025,4.8289,4.5192,4.4471,4.4321,4.6507,4.3645,4.0949,4.1874,3.1397\n5.4934,6.8211,7.347,6.5343,5.4431,4.9482,5.8023,5.709,5.6603,6.0714,7.6552,5.6369,4.7256,5.0564,4.9936,4.6706,3.7707,3.5812,4.2814,5.0355,4.5425,4.0245,3.2494,3.4223,3.9136,5.441,6.1218,6.5879,6.6752,5.9083,5.3295,5.626,5.856,5.3471,4.726,4.681,5.0646,5.1908,5.2041,4.7873,4.2802,4.3281,4.4554,4.0131,4.2369,4.5173,4.4204\n5.0761,5.4031,5.9394,6.6327,6.1006,5.1573,6.2161,4.8156,4.6685,4.4511,4.5182,5.5163,4.2141,4.7165,5.6523,6.2634,5.3559,5.6336,4.7497,4.9558,4.0926,3.4598,3.5839,3.1801,3.1825,5.0771,5.8947,6.7498,6.5149,6.2255,5.96,6.0757,6.0515,5.1182,4.4462,4.7555,4.7401,4.568,5.232,4.6775,4.1062,3.737,3.7114,4.029,3.9044,3.7971,4.3614\n5.0316,4.9774,5.1008,6.1823,6.6644,6.2235,5.889,4.6689,4.8543,4.7968,5.051,5.022,4.1083,3.5687,3.3449,4.3333,6.2312,6.7231,5.991,5.1895,5.3518,4.5189,4.3142,4.1197,4.6146,4.6765,5.847,6.6078,6.2684,5.8527,5.8066,5.7494,5.7324,4.8124,4.5628,4.8953,4.6793,4.4757,4.5847,4.364,4.2057,3.9816,3.8834,3.671,3.7734,3.7806,3.8478\n4.3852,4.1355,4.6032,6.3837,6.0345,5.1686,7.0097,6.857,6.6249,4.796,5.9268,4.8923,5.8274,6.1003,6.4997,5.2386,5.8328,4.5398,5.9365,5.4016,6.3538,6.0384,4.9194,4.2458,4.23,3.9006,4.235,5.7637,6.8244,6.7018,6.4439,5.7416,5.1067,4.743,4.6209,4.9545,4.6753,4.7697,4.9925,5.014,4.9229,4.0682,4.0477,3.9656,3.8867,4.8782,5.2655\n3.0688,1.4909,4.1991,5.6831,6.383,6.9902,6.456,5.7698,7.3326,7.9646,7.6266,6.689,6.913,7.5743,7.1109,4.4722,2.2121,2.3393,5.0048,5.4639,5.3032,5.3055,4.9875,4.0608,4.9838,5.2898,5.4077,5.8855,6.4049,6.5705,6.4497,6.2208,4.422,4.013,4.1907,4.7722,4.2727,4.6576,5.0021,4.9939,4.5975,4.2024,4.2847,4.2051,4.2095,5.0075,5.5133\n4.7375,6.5564,5.947,6.3951,6.6613,5.2238,4.9317,5.0328,6.3536,7.6908,6.9678,6.564,5.5409,4.966,4.7591,4.4175,4.9328,4.0605,4.2254,4.9672,4.8304,4.2481,4.0913,4.0308,4.0156,5.0553,5.3601,5.4027,4.3543,4.6822,4.9538,5.5836,4.8356,4.2091,4.015,4.1292,4.0983,4.3723,4.7362,4.4921,4.1977,3.9714,3.6835,3.7848,4.1312,4.2894,4.7893\nNaN,4.0918,4.633,7.6147,5.3901,3.0093,6.406,5.6406,7.7137,7.3391,6.7028,7.7266,7.7119,6.8493,4.9648,4.2108,4.9128,5.255,4.7766,4.3483,4.0865,4.7639,4.625,4.3425,4.3828,5.4984,5.4992,4.9383,4.3074,3.8796,5.1423,4.7752,4.8716,4.7632,4.3493,3.8703,4.2138,4.274,4.6942,4.6108,4.3244,3.9769,3.6966,3.9353,3.8855,3.6822,3.6721\n8.392,3.3189,4.7518,5.9091,6.5906,6.5817,5.7143,4.8509,5.6888,6.2689,7.3878,7.693,8.2172,8.239,5.8647,4.328,NaN,7.7937,6.1549,4.1315,4.7,3.7787,2.5011,3.9522,4.7529,3.9168,4.5446,4.4958,3.9009,3.4938,4.2114,4.623,4.8488,5.396,4.2921,3.4989,3.0827,3.8961,4.467,4.9998,4.9547,4.9729,4.0545,4.0887,3.8079,3.442,3.134\n6.4281,3.3276,3.9318,4.5411,5.1861,5.8309,4.2804,5.4791,5.9796,5.4633,4.2786,4.9595,6.9851,8.6895,8.5994,NaN,NaN,NaN,NaN,8.145,6.2548,4.6549,3.9973,4.3775,4.5929,4.8417,4.4906,4.2759,4.9029,5.1087,4.6612,4.407,4.2935,4.2613,4.6049,3.8386,3.5045,4.1549,4.7767,4.5953,4.7022,4.7102,4.6879,4.3475,3.8234,3.3174,2.8575\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070430-070604.unw.csv",
    "content": "-36.65,-37.486,-37.569,-37.41,-37.452,-36.716,-36.836,-36.667,-36.55,-36.951,-37.187,-37.887,-37.118,-37.122,-36.888,-36.668,-36.326,-36.434,-36.113,-35.654,-35.789,-35.148,-35.062,-35.577,-35.898,-36.089,-36.1,-36.378,-36.398,-36.553,-36.69,-36.814,-36.413,-36.444,-36.477,-36.228,-35.84,-35.812,-35.624,-35.738,-35.873,-35.765,-35.665,-35.378,-35.066,-34.314,-33.799\n-36.751,-37.561,-37.216,-37.303,-37.174,-37.07,-36.98,-36.313,-36.329,-36.27,-36.698,-37.185,-36.967,-36.943,-37.06,-36.966,-36.554,-36.185,-35.509,-35.339,-35.341,-34.741,-35.02,-35.192,-35.572,-35.739,-36.028,-36.122,-36.168,-36.47,-36.703,-36.695,-36.507,-36.397,-36.398,-35.961,-35.731,-35.561,-35.373,-35.347,-35.656,-35.373,-35.335,-35.113,-34.756,-34.589,-34.465\n-36.811,-37.414,-37.033,-36.888,-36.675,-36.873,-36.793,-36.476,-36.121,-36.121,-36.193,-36.787,-36.75,-36.765,-36.468,-36.492,-36.048,-35.619,-35.191,-35.191,-35.3,-35.429,-35.415,-35.65,-35.725,-35.524,-35.686,-35.707,-36.135,-36.238,-36.421,-36.492,-36.417,-36.286,-36.266,-36.008,-35.933,-35.586,-35.445,-35.532,-35.476,-35.44,-35.335,-34.777,-34.475,-34.254,-33.709\n-36.37,-35.894,-37.657,-36.852,-36.886,-36.889,-36.694,-36.215,-36.25,-36.387,-36.323,-36.308,-36.671,-36.452,-35.965,-35.596,-35.251,-35.063,-35.105,-35.185,-35.152,-35.21,-35.477,-35.645,-35.424,-35.236,-35.227,-35.463,-35.794,-36.031,-36.045,-36.057,-36.08,-36.14,-36.1,-35.926,-35.883,-35.725,-35.627,-35.554,-35.606,-35.742,-35.394,-34.876,-34.488,-33.938,-33.032\n-35.984,-35.353,-40.341,-36.691,-36.733,-36.566,-36.419,-36.156,-36.007,-35.871,-35.847,-36.329,-36.556,-36.017,-35.586,-35.504,-35.441,-34.893,-35.198,-35.48,-34.737,-35.036,-35.438,-35.316,-35.209,-35.071,-35.151,-35.24,-35.474,-35.668,-35.84,-35.972,-35.979,-36.012,-36.008,-35.806,-35.895,-35.819,-35.717,-35.514,-35.392,-35.432,-35.59,-35.082,-34.631,-33.838,-32.887\n-35.71,-37.354,-39.25,-36.632,-36.583,-36.655,-36.543,-36.292,-36.264,-36.63,-36.602,-36.156,-36.1,-35.737,-35.614,-35.409,-35.27,-34.999,-35.174,-34.678,-34.627,-34.958,-35.211,-34.982,-34.784,-34.903,-34.81,-35.006,-35.481,-35.703,-35.693,-35.812,-35.912,-35.915,-35.736,-35.616,-35.66,-35.652,-35.704,-35.351,-35.295,-35.415,-35.42,-35.099,-34.551,-34.051,-32.825\n-36.055,-35.926,-40.983,-36.796,-36.684,-36.264,-36.102,-35.974,-35.868,-36.241,-36.088,-35.777,-35.483,-35.254,-35.256,-35.069,-35.034,-34.912,-34.816,-34.036,-35.267,-35.33,-35.411,-35.201,-34.807,-34.763,-34.723,-35.141,-35.517,-35.515,-35.364,-35.687,-35.865,-35.974,-35.898,-35.844,-35.888,-35.675,-35.914,-36.089,-36.071,-35.433,-35.365,-35.036,-34.38,-33.793,-32.707\n-37.041,-37.084,-37.065,-36.9,-36.607,-36.259,-35.885,-35.601,-35.358,-35.86,-35.63,-35.498,-34.893,-35.078,-34.977,-34.898,-34.818,-34.525,-34.275,-34.322,-35.344,-34.85,-35.133,-34.919,-34.379,-34.517,-34.705,-34.906,-35.054,-35.333,-35.316,-35.618,-35.801,-36.003,-35.762,-35.874,-35.94,-36.078,-36.311,-36.425,-36.375,-36.025,-35.71,-35.402,-34.809,-33.923,-32.862\n-36.998,-36.979,-36.728,-37.305,-36.744,-36.484,-36.017,-35.444,-35.197,-35.123,-34.968,-34.705,-34.916,-34.692,-34.621,-34.488,-34.365,-34.12,-34.199,-34.551,-34.588,-34.654,-34.925,-34.622,-34.416,-34.755,-34.636,-34.537,-35.058,-35.532,-35.414,-35.417,-35.621,-35.632,-35.671,-35.768,-35.874,-36.088,-36.459,-36.501,-36.364,-36.2,-36.264,-35.756,-35.02,-34.427,-34.348\n-36.564,-36.589,-35.696,-36.92,-37.23,-36.707,-35.611,-35.25,-35.179,-34.576,-34.427,-34.275,-34.697,-34.353,-34.604,-34.317,-33.857,-33.773,-33.987,-34.331,-34.027,-34.516,-34.871,-34.487,-34.42,-34.566,-34.44,-34.562,-34.783,-34.906,-35.19,-35.333,-35.494,-35.538,-35.446,-35.325,-35.434,-35.95,-36.301,-36.273,-36.054,-35.919,-35.906,-35.716,-35.49,-35.207,-35.052\n-36.838,-36.872,-36.1,-36.063,-37.472,-36.783,-35.874,-35.247,-35.154,-34.704,-34.186,-34.102,-33.98,-33.97,-33.763,-33.352,-33.69,-33.721,-33.864,-33.817,-34.222,-34.57,-34.513,-34.441,-34.498,-34.33,-34.357,-34.467,-34.564,-34.936,-35.335,-35.52,-35.655,-35.58,-35.775,-35.661,-35.693,-35.985,-36.025,-35.953,-35.883,-35.719,-35.863,-35.923,-35.626,-35.527,-35.576\n-36.958,-37.456,-37.517,-38.36,-38.015,-36.464,-36.253,-35.604,-35.268,-34.819,-34.316,-34.096,-33.799,-33.54,-33.391,-33.323,-33.406,-33.105,-33.261,-33.338,-34.139,-34.3,-34.243,-34.189,-34.301,-33.986,-34.204,-34.243,-34.467,-35.109,-35.372,-35.465,-35.655,-35.629,-35.499,-35.702,-35.744,-35.839,-35.895,-35.854,-35.807,-35.779,-36.203,-35.815,-35.531,-35.131,-34.935\n-36.738,-36.829,-37.176,-38.232,-38.252,-37.006,-36.306,-35.697,-35.109,-34.708,-34.352,-34.211,-33.764,-33.648,-33.589,-33.292,-33.071,-33.088,-33.147,-33.613,-34.189,-33.921,-34.097,-33.836,-33.915,-34.007,-34.047,-34.008,-34.331,-34.768,-34.728,-35.232,-35.437,-35.514,-35.408,-35.585,-35.847,-35.771,-35.684,-35.672,-35.624,-35.842,-35.902,-35.785,-35.326,-34.926,-33.516\n-36.815,-35.411,-35.618,-36.463,-37.905,-37.911,-37.599,-35.899,-34.898,-34.456,-34.027,-34.038,-34.096,-33.693,-33.515,-33.253,-33.269,-33.272,-33.397,-33.825,-33.945,-33.749,-34.024,-33.989,-34.114,-34.096,-34.019,-34.251,-34.285,-34.302,-34.47,-34.955,-35.282,-35.596,-35.929,-36.198,-36.297,-36.004,-35.696,-35.444,-34.893,-35.837,-35.842,-36.482,-36.836,-33.983,-33.051\n-35.907,-35.149,-36.124,-37.181,-38.756,-38.099,-37.536,-35.71,-34.69,-34.084,-33.578,-33.812,-33.93,-33.807,-33.413,-33.352,-33.434,-33.432,-33.411,-33.826,-33.677,-34.092,-33.82,-33.868,-34.122,-34.258,-33.838,-33.892,-34.172,-34.405,-34.59,-34.96,-35.369,-35.63,-36.065,-36.012,-36.44,-36.365,-36.026,-35.267,-35.486,-36.132,-35.249,-35.244,-35.127,-35.152,-33.697\n-35.182,-35.895,-36.716,-37.762,-38.388,-37.902,-37.782,-35.62,-34.67,-34.201,-33.538,-33.699,-33.658,-33.387,-33.184,-33.157,-33.209,-32.973,-33.281,-33.856,-34.051,-33.845,-33.741,-33.823,-34.063,-34.136,-33.874,-33.918,-34.149,-34.632,-34.759,-35.27,-35.381,-35.486,-35.602,-35.472,-35.871,-36.452,-36.586,-36.232,-35.929,-35.586,-35.285,-35.786,-36.055,-34.693,-34.691\n-35.39,-38.151,-37.381,-37.141,-37.67,-38.534,-38.027,-35.793,-34.747,-34.161,-33.707,-33.691,-33.573,-33.03,-33.197,-33.062,-32.904,-32.965,-33.589,-34.386,-34.379,-34.227,-34.139,-34.087,-33.05,-32.596,-33.672,-34.554,-34.429,-34.574,-35.161,-35.451,-35.38,-35.442,-35.58,-35.774,-36.101,-36.69,-36.831,-35.461,-35.475,-36.105,-36.827,-35.979,-35.307,-31.647,-33.496\n-35.883,-38.021,-37.124,-37.119,-37.838,-38.515,-36.957,-35.437,-34.707,-34.101,-33.686,-33.687,-33.619,-33.667,-33.179,-32.67,-32.328,-33.152,-33.905,-35.091,-34.442,-34.587,-34.888,-34.381,-33.726,-33.568,-34.057,-34.481,-34.573,-34.748,-35.171,-35.563,-35.932,-35.516,-35.825,-36.239,-36.225,-36.999,-37.353,-35.644,-35.387,-37.294,-38.187,-36.651,-35.162,-33.286,-33.536\n-37.426,-37.195,-36.679,-36.866,-37.804,-33.997,-36.732,-35.565,-34.611,-34.262,-33.785,-33.288,-33.38,-33.653,-33.041,-32.483,-32.711,-33.005,-33.901,-35.299,-36.297,-36.599,-36.073,-35.214,-34.484,-34.238,-34.033,-34.165,-34.768,-34.806,-35.209,-35.866,-37.429,-36.311,-35.584,-36.552,-37.467,-37.221,-36.328,-35.892,-36.382,-37.171,-38.907,-37.343,-36.883,-35.863,-35.615\n-37.532,-37.297,-36.486,-36.529,-35.647,-36.108,-36.132,-35.585,-34.609,-34.159,-33.662,-33.699,-33.658,-33.51,-33.41,-33.282,-33.265,-33.718,-34.43,-34.844,-35.845,-35.877,-35.288,-35.628,-34.927,-34.54,-34.781,-34.512,-34.061,-35.455,-35.341,-35.705,-36.488,-37.173,-37.71,-36.791,-37.281,-37.329,-37.347,-37.24,-37.221,-37.736,-38.179,-37.831,-37.244,-37.522,-37.136\n-36.675,-36.997,-36.183,-35.303,-33.814,-35.137,-35.095,-35.34,-34.956,-34.345,-33.259,-33.781,-33.487,-33.477,-33.584,-34.109,-34.395,-34.674,-35.465,-34.873,-34.458,-34.856,-35.161,-35.401,-34.928,-34.144,-34.332,-33.795,-33.323,-34.833,-35.362,-35.971,-35.381,-35.485,-39.7,-35.658,-36.465,-37.072,-37.484,-36.901,-36.629,-37.209,-37.741,-37.618,-37.853,-37.707,-37.417\n-36.743,-36.792,-35.944,-35.559,-34.821,-33.239,-34.547,-35.039,-34.796,-33.655,-33.026,-33.162,-32.797,-33.208,-34.345,-35.023,-35.71,-36.098,-35.972,-35.217,-35.981,-35.08,-35.841,-36.007,-35.003,-34.513,-34.335,-34.202,-34.116,-33.987,-34.421,-36.189,-35.853,-35.742,-37.33,-36.001,-35.351,-35.972,-35.794,-35.634,-37.015,-37.39,-37.288,-37.641,-38.264,-38.783,-38.186\n-35.929,-35.842,-35.129,-34.812,-33.821,-35.311,-34.76,-34.535,-33.924,-32.88,-32.877,-31.912,-32.766,-34.405,-35.364,-35.523,-36.047,-35.86,-36.243,-35.553,-35.658,-35.907,-35.395,-34.704,-34.508,-34.808,-33.879,-34.171,-35.052,-33.963,-33.76,-34.558,-35.2,-35.161,-35.405,-35.081,-35.092,-34.997,-34.712,-35.699,-36.852,-37.368,-37.427,-37.954,-38.393,-38.481,-38.124\n-35.354,-34.908,-34.316,-34.324,-35.554,-34.735,-34.071,-33.61,-33.344,-33.133,-33.151,-32.458,-33.424,-34.3,-34.954,-36.446,-36.489,-35.515,-35.039,-34.858,-33.94,-33.757,-34.004,-33.755,-33.749,-34.206,-33.646,-33.99,-34.058,-33.104,-33.11,-33.856,-33.389,-33.133,-34.413,-34.55,-34.636,-34.59,-34.313,-36.999,-37.213,-37.196,-36.998,-37.523,-38.093,-38.472,-39.115\n-35.592,-34.935,-34.156,-34.188,-34.342,-33.482,-33.846,-32.967,-33.048,-33.76,-33.546,-32.941,-33.232,-34.115,-34.414,-34.881,-34.769,-34.733,-33.927,-33.301,-33.126,-32.779,-32.504,-33.036,-33.058,-32.677,-32.755,-32.738,-32.275,-32.182,-32.073,-32.504,-32.691,-32.878,-34.552,-35.797,-35.191,-35.849,-36.348,-36.932,-37.078,-36.843,-36.882,-37.242,-37.596,-38.645,-38.882\n-35.66,-34.733,-34.19,-33.922,-33.454,-32.974,-33.144,-32.892,-32.768,-33.217,-32.755,-32.542,-32.949,-33.094,-32.956,-33.4,-33.122,-32.747,-33.02,-32.749,-32.498,-31.45,-32.189,-32.726,-32.55,-31.931,-31.558,-30.746,-30.518,-32.092,-32.104,-30.786,-30.097,-34.751,-35.271,-36.448,-36.458,-36.141,-36.43,-36.99,-37.243,-37.221,-37.31,-37.25,-37.329,-38.889,-38.563\n-36.65,-35.759,-34.29,-33.819,-33.413,-33.202,-32.6,-32.819,-32.791,-32.412,-31.947,-31.797,-31.771,-31.89,-32.602,-32.52,-32.22,-32.222,-33.217,-33.173,-32.251,-32.128,-32.963,-33.498,-34.072,-33.097,-32.629,-32.163,-32.187,-33.513,-32.968,-32.658,-32.939,-35.767,-36.096,-35.967,-36.029,-35.91,-36.877,-36.513,-37.106,-37.441,-37.352,-37.32,-37.706,-38.078,-38.024\n-36.79,-35.388,-34.722,-34.712,-34.102,-33.779,-32.829,-32.883,-33.071,-32.258,-31.703,-31.367,-31.775,-32.043,-32.46,-31.072,-32.206,-33.15,-33.254,-32.861,-32.702,-32.453,-32.749,-34.144,-35.105,-33.788,-33.12,-33.247,-33.436,-33.434,-33.614,-34.244,-35.247,-35.426,-35.265,-35.379,-35.68,-36.182,-36.028,-36.62,-37.604,-37.471,-37.454,-37.423,-37.606,-37.673,-37.718\n-36.15,-35.496,-34.914,-34.92,-34.6,-33.991,-33.137,-32.784,-33.199,-33.254,-32.882,-31.894,-32.589,-32.444,-32.025,-31.387,-32.634,-33.28,-32.734,-32.73,-32.986,-32.84,-32.173,-31.673,-32.607,-31.069,-30.415,-31.327,-31.955,-29.537,-31.339,-32.725,-31.299,-34.648,-35.251,-35.692,-36.611,-37.276,-37.368,-37.414,-37.37,-37.654,-37.692,-37.442,-37.609,-37.593,-37.536\n-36.18,-35.773,-35.832,-34.402,-34.654,-34.378,-33.716,-33.105,-33.585,-33.656,-32.829,-32.271,-33.305,-32.506,-31.892,-32.411,-33.518,-33.657,-33.063,-33.858,-33.3,-33.13,-30.571,-31.535,-30.181,-29.192,-29.783,-29.633,-28.712,-29.484,-32.381,-31.888,-32.208,-35.938,-35.755,-35.322,-37.25,-38.236,-37.453,-37.256,-36.954,-37.445,-37.44,-37.261,-37.697,-37.298,-37.212\n-36.571,-36.523,-34.771,-35.628,-36.551,-35.601,-35.464,-33.892,-33.822,-33.969,-33.497,-33.626,-33.768,-32.834,-31.059,-32.565,-34.407,-34.048,-33.882,-34.593,-33.02,-32.009,-29.82,-30.94,-29.491,-29.791,-29.937,-29.944,-29.282,-30.639,-34.591,-34.767,-34.719,-36.152,-35.457,-35.249,-36.622,-37.074,-37.627,-36.997,-37.263,-38.005,-37.614,-37.241,-37.651,-37.418,-37.185\n-36.682,-36.347,-36.051,-36.074,-36.916,-36.75,-36.134,-35.369,-33.737,-34.059,-33.809,-34.205,-34.121,-33.439,-33.166,-34.531,-34.761,-34.743,-34.401,-34.141,-32.658,-31.055,-29.11,-31.586,-30.767,-30.994,-32.098,-32.511,-33.422,-33.797,-34.264,-34.012,-33.942,-35.284,-35.582,-35.921,-37.173,-36.817,-37.001,-36.663,-37.57,-37.829,-37.975,-37.199,-37.259,-36.812,-36.628\n-37.375,-37.321,-37.202,-36.883,-36.885,-36.571,-36.263,-35.835,-34.099,-34.921,-34.766,-38.118,-38.913,-34.001,-34.616,-33.309,-32.422,-34.202,-34.478,-35.432,-32.822,-30.554,-29.911,-33.657,-32.965,-32.397,-33.485,-34.832,-35.064,-34.44,-34.353,-34.192,-33.494,-33.984,-35.2,-35.996,-36.379,-36.02,-36.607,-36.729,-37.178,-37.538,-37.784,-37.378,-36.976,-36.383,-36.011\n-37.699,-37.355,-37.688,-37.385,-36.753,-36.531,-37.011,-36.097,-34.899,-34.704,-33.845,-35.21,-38.211,-34.756,-35.031,-34.694,-33.932,-34.147,-34.16,-34.437,-32.866,-32.243,-33.285,-33.002,-32.911,-32.811,-34.046,-34.205,-33.577,-33.044,-33.981,-33.703,-31.943,-33.131,-34.938,-35.3,-35.802,-35.34,-35.744,-36.258,-36.404,-36.471,-36.776,-37.02,-36.802,-36.245,-35.93\n-37.458,-37.176,-37.328,-37.338,-37.186,-36.788,-36.415,-35.215,-34.819,-34.459,-34.371,-35.098,-35.497,-35.199,-33.245,-34.626,-34.518,-33.416,-33.473,-33.703,-33.29,-33.384,-32.748,-32.368,NaN,-32.159,-34.508,-33.008,-30.224,-31.374,-33.629,-32.247,-31.579,-32.75,-33.998,-34.426,-35.002,-35.488,-35.649,-35.914,-35.593,-35.685,-35.722,-36.392,-36.481,-36.022,-35.934\n-36.687,-37.051,-37.532,-37.389,-36.432,-36.699,-36.119,-35.068,-34.995,-35.249,-35.463,-35.566,-35.765,-34.928,-32.813,-34.89,-34.742,-33.794,-33.267,-33.26,-36.249,-34.613,-32.467,NaN,NaN,NaN,NaN,NaN,NaN,-33.473,-33.511,-32.773,-33.365,-33.847,-34.373,-36.551,-35.8,-36.032,-35.417,-35.671,-35.686,-35.142,-34.888,-35.841,-35.919,-35.878,-36.079\n-36.46,-36.898,-37.191,-37.415,-36.787,-36.74,-36.453,-35.725,-36.094,-36.244,-36.457,-36.921,-37.122,-37.7,-36.997,-35.831,-35.057,-33.426,-33.253,-32.719,-33.643,-32.791,-32.292,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-33.206,-32.899,-33.939,-39.444,-39.793,-38.787,-36.998,-35.732,-35.674,-35.838,-35.416,-34.766,-35.438,-35.156,-35.25,-35.715,-36.058\n-36.174,-36.552,-36.845,-36.658,-36.351,-35.886,-36.242,-36.561,-36.655,-36.366,-35.917,-37.398,-37.359,-37.768,-36.696,-35.847,-34.371,-33.749,-32.896,-32.015,-33.11,-34.452,-35.105,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-33.503,-33.033,-33.195,-36.805,-38.384,-37.303,-35.954,-35.564,-35.781,-35.797,-35.753,-35.467,-35.216,-34.905,-34.585,-34.629,-35.447\n-36.094,-36.31,-36.205,-36.048,-35.931,-35.536,-35.788,-36.727,-36.809,-36.867,-37.005,-37.826,-37.918,-37.466,-36.636,-35.678,-33.723,-26.942,-31.828,-34.671,-34.452,-35.86,-36.626,-37.417,-34.019,-32.007,-34.266,NaN,-32.852,-33.23,-33.665,-33.168,-33.189,-34.458,-35.228,-34.972,-34.934,-35.211,-35.455,-35.773,-36.017,-35.744,-35.217,-35.253,-34.507,-34.691,-35.384\n-36.138,-35.902,-35.615,-35.681,-35.7,-35.643,-35.768,-36.805,-36.372,-36.475,-36.64,-36.879,-37.554,-36.792,-36.545,-35.27,-34.487,-33.113,-32.403,-34.937,-33.32,-34.957,-35.675,-34.345,-32.955,-34.806,-36.635,-34.569,-33.513,-33.641,-33.387,-33.361,-33.629,-34.128,-34.43,-34.301,-34.517,-34.654,-35.212,-36.005,-36.082,-35.617,-35.549,-35.048,-34.591,-34.543,-35.293\n-35.582,-35.539,-35.509,-35.728,-35.56,-35.577,-35.855,-36.342,-36.178,-36.098,-36.443,-36.476,-37.461,-37.542,-36.578,-36.455,-35.833,-34.95,-35.05,-34.608,-32.483,-34.645,-34.61,-33.904,-34.004,-35.206,-35.057,-34.194,-33.829,-33.778,-34.225,-34.739,-34.07,-34.504,-34.058,-34.652,-34.601,-34.603,-35.033,-35.934,-36.162,-36.012,-35.673,-35.026,-35.069,-34.908,-35.053\n-35.467,-35.593,-35.579,-35.676,-35.384,-35.575,-35.911,-36.226,-36.14,-36.176,-36.892,-37.273,-39.089,-38.286,-37.268,-36.877,-37.263,-35.178,-34.343,-34.192,-33.384,-33.445,-33.977,-34.484,-35.202,-35.731,-34.272,-34.439,-33.952,-33.622,-34.035,-34.053,-34.08,-34.482,-34.508,-35.117,-35.084,-35.717,-35.508,-35.824,-36.333,-36.092,-35.72,-35.457,-35.347,-34.892,-35.119\n-36.023,-35.955,-35.822,-35.984,-35.842,-35.851,-36.278,-36.822,-36.986,-37.375,-37.66,-37.283,-38.914,-37.713,-36.936,-36.598,-35.145,-36.07,-34.921,-34.356,-33.67,-33.747,-33.236,-34.932,-34.636,-33.689,-34.797,-35.033,-34.396,-34.28,-34.244,-33.839,-34.419,-34.226,-34.249,-34.407,-35.006,-37.258,-36.411,-35.741,-35.997,-35.907,-35.505,-35.35,-35.469,-35.442,-35.846\n-35.835,-35.904,-35.511,-36.281,-36.166,-36.077,-36.628,-37.216,-37.704,-37.751,-37.607,-37.142,-36.959,-36.224,-34.995,-34.248,-34.017,-33.623,-33.481,-33.593,-33.68,-33.448,-36.661,-36.199,-33.971,-33.587,-34.891,-35.209,-35.181,-35.218,-35.181,-35.077,-34.663,-34.543,-36.223,-36.568,-36.126,-37.457,-35.962,-35.707,-35.664,-35.485,-35.391,-35.449,-35.744,-35.969,-36.259\n-35.791,-35.809,-35.727,-36.217,-36.283,-36.863,-37.429,-37.729,-37.758,-36.722,-36.832,-36.44,-36.069,-35.418,-34.269,-33.55,-34.111,-33.68,-33.647,-34.033,-33.561,-35.212,-38.566,-36.386,-35.16,-34.224,-34.762,-34.664,-34.788,-34.941,-34.775,-34.637,-35.299,-35.508,-37.508,-36.405,-36.583,-36.588,-35.832,-35.433,-35.292,-35.173,-35.576,-35.411,-36.042,-36.496,-36.614\n-35.957,-35.485,-35.418,-35.739,-36.13,-36.525,-36.708,-36.708,-36.158,-36.068,-36.326,-35.971,-35.852,-35.618,-34.167,-34.057,-34.121,-34.242,-34.924,-34.711,-34.245,-35.243,-38.104,-37.248,-35.584,-35.67,-34.995,-34.883,-34.778,-34.653,-34.293,-34.752,-35.606,-35.246,-35.297,-35.734,-36.287,-35.739,-35.88,-35.592,-35.604,-36.012,-35.811,-35.096,-35.757,-36.32,-36.376\n-35.66,-35.26,-35.22,-35.826,-35.736,-35.754,-35.879,-35.996,-35.919,-35.907,-35.805,-35.732,-35.525,-35.195,-34.811,-34.523,-34.3,-33.949,-34.67,-34.553,-33.912,-34.641,-37.207,-36.436,-35.895,-35.64,-35.26,-35.077,-34.734,-34.412,-34.686,-35.004,-35.206,-34.97,-35.274,-36.062,-35.344,-35.334,-36.033,-35.808,-36.058,-36.938,-36.553,-36.278,-35.552,-35.792,-35.854\n-35.399,-35.274,-35.353,-35.674,-35.741,-35.485,-35.418,-35.822,-36.223,-36.032,-35.611,-35.248,-34.607,-34.442,-34.466,-34.458,-34.299,-33.902,-33.665,-33.759,-33.896,-34.071,-35.378,-35.546,-35.535,-35.712,-35.261,-34.778,-35.066,-35.483,-35.098,-34.909,-35.323,-35.877,-36.087,-36.103,-35.703,-35.825,-36.144,-35.468,-35.797,-36.639,-36.203,-36.14,-35.826,-35.813,-35.615\n-35.729,-35.501,-35.797,-35.653,-35.776,-35.869,-35.371,-35.867,-35.574,-35.513,-35.192,-34.561,-34.375,-34.468,-34.249,-34.28,-34.404,-34.184,-33.879,-33.069,-32.939,-31.852,-33.259,-35.022,-34.824,-34.836,-34.991,-35.366,-35.366,-35.665,-35.076,-35.042,-35.446,-35.817,-36.046,-36.074,-35.806,-36.11,-35.951,-35.119,-35.155,-35.693,-36.371,-35.799,-35.69,-35.785,-35.847\n-35.69,-35.646,-35.663,-34.932,-35.775,-35.647,-35.073,-35.043,-35.392,-35.373,-35.25,-34.714,-34.54,-34.484,-34.643,-34.736,-34.607,-33.814,-34.524,-35.57,-31.213,-32.71,-34.813,-34.66,-34,-33.91,-34.629,-35.638,-35.193,-35.772,-35.533,-35.458,-35.828,-35.952,-36.003,-35.973,-35.402,-35.782,-35.82,-35.213,-35.271,-35.02,-35.196,-35.952,-35.714,-35.754,-35.739\n-35.41,-35.526,-35.482,-34.931,-35.241,-35.595,-35.33,-35.713,-35.7,-35.511,-35.215,-34.906,-34.587,-34.565,-34.709,-34.632,-34.277,-34.422,-36.025,-36.937,-37.054,-35.644,-34.88,-34.73,-34.277,-34.255,-38.266,-39.311,-35.126,-35.792,-35.911,-35.626,-35.966,-35.83,-35.993,-35.469,-35.316,-35.561,-35.111,-34.909,-35.005,-34.779,-35.043,-35.751,-35.523,-35.411,-35.504\n-35.669,-35.699,-35.419,-35.279,-35.421,-35.781,-35.644,-35.824,-35.37,-35.333,-35.062,-34.691,-34.447,-34.996,-34.452,-34.374,-33.368,-34.786,-34.942,-35.689,-36.484,-35.498,-35.239,-34.711,-33.807,-33.382,-34.902,-35.016,-35.122,-36.028,-35.655,-35.715,-35.739,-35.713,-35.74,-35.302,-35.155,-34.926,-34.698,-34.691,-34.841,-34.791,-33.828,-35.372,-35.648,-35.286,-34.878\n-35.865,-35.802,-35.803,-35.52,-35.834,-35.906,-35.9,-35.847,-35.644,-35.867,-35.063,-34.728,-34.833,-35.178,-34.464,-35.213,-35.614,-35.023,-34.993,-35.147,-35.885,-35.851,-35.448,-34.628,-33.475,-33.069,-33.203,-33.443,-33.7,-35.2,-35.845,-35.632,-35.633,-35.69,-35.855,-35.046,-34.853,-34.774,-34.84,-34.814,-34.824,-34.31,-34.041,-34.948,-35.526,-35.503,-34.981\n-35.729,-35.695,-35.642,-35.535,-35.385,-36.025,-35.873,-36.163,-35.939,-35.789,-34.911,-35.77,-35.676,-35.688,-35.62,-36.347,-36.376,-36.255,-36.614,-36.322,-35.615,-35.681,-34.898,-34.466,-34.037,-33.58,-33.183,-32.21,-32.627,-34.073,-35.617,-35.489,-35.511,-35.786,-35.744,-35.155,-35.461,-34.938,-35.119,-34.591,-34.693,-34.729,-34.545,-35.279,-35.257,-35.457,-35.253\n-35.74,-35.199,-35.834,-35.243,-35.692,-35.96,-35.942,-35.854,-35.5,-35.532,-35.236,-35.94,-36.76,-36.667,-36.382,-36.342,-36.116,-35.921,-34.997,-36.329,-35.424,-34.687,-34.484,-34.194,-33.755,-33.488,-32.366,-32.346,-34.462,-34.908,-35.433,-35.782,-35.664,-35.633,-35.329,-35.53,-35.295,-35.167,-35.165,-34.596,-34.61,-34.912,-35.143,-34.949,-35.097,-35.365,-35.488\n-36.092,-36.542,-35.83,-35.186,-35.405,-37.331,-37.229,-36.359,-36.237,-35.971,-35.816,-35.861,-37.337,-37.657,-36.964,-36.852,-36.211,-35.374,-35.166,-36.169,-35.446,-34.628,-34.456,-33.766,-33.31,-33.286,-32.16,-32.581,-34,-34.946,-35.102,-35.814,-36.502,-35.372,-35.285,-35.58,-35.269,-35.269,-34.984,-34.684,-34.85,-34.85,-34.532,-34.456,-35.275,-35.413,-35.414\n-35.718,-36.514,-35.773,-35.12,-34.531,-38.028,-37.588,-37.031,-37.146,-37.124,-36.921,-36.974,-37.228,-36.528,-36.986,-37.518,-37.362,-35.783,-35.664,-35.757,-35.446,-35.015,-34.729,-34.185,-33.332,-33.326,-32.991,-33.624,-34.181,-34.268,-34.854,-35.077,-35.63,-35.145,-35.2,-35.391,-35.203,-35.162,-34.98,-34.94,-35.069,-35.107,-34.501,-35.171,-35.506,-35.838,-35.655\n-35.795,-36.694,-36.941,-36.083,-35.764,-34.964,-36.626,-36.39,-36.715,-38.34,-37.174,-36.96,-36.868,-36.671,-36.708,-37.135,-37.614,-36.555,-35.498,-35.21,-35.09,-35.227,-34.483,-34.228,-33.407,-33.195,-33.036,-34.79,-34.231,-34.165,-34.672,-34.855,-35.32,-34.812,-34.983,-35.123,-35.018,-34.872,-34.599,-34.928,-34.95,-34.857,-34.532,-35.318,-35.324,-35.734,-35.649\n-36.349,-36.103,-36.469,-36.906,-36.961,-36.836,-36.93,-35.894,-36.519,-37.245,-36.475,-36.331,-36.4,-36.244,-35.483,-37.22,-37.922,-36.347,-35.428,-34.749,-34.813,-35.046,-34.627,-34.56,-34.102,-33.282,-32.871,-32.903,-34.19,-34.779,-35.646,-35.983,-35.325,-34.798,-35.096,-34.997,-34.948,-34.722,-34.624,-34.776,-34.959,-35.088,-35.252,-35.5,-35.744,-35.76,-35.337\n-36.587,-35.469,-35.797,-37.767,-38.717,-41.897,-37.287,-36.45,-36.119,-35.318,-34.957,-35.107,-35.177,-35.092,-35.337,-36.009,-37.477,-36.588,-35.287,-35.015,-35.216,-34.598,-34.874,-34.844,-34.165,-33.38,-32.935,-33.885,-33.591,-34.831,-35.384,-35.74,-35.466,-34.761,-35.021,-34.753,-34.95,-35.046,-34.717,-34.617,-35.119,-35.5,-35.635,-35.787,-35.451,-35.461,-35.626\n-35.549,-35.637,-36.042,-36.925,-37.443,-38.747,-37.651,-37.221,-36.477,-34.323,-34.164,-33.908,-33.695,-34.407,-36.37,-36.498,-37.881,-36.937,-35.768,-35.594,-35.267,-34.644,-34.419,-34.581,-34.608,-33.799,-33.628,-33.817,-32.999,-33.704,-34.2,-34.989,-36.227,-35.224,-34.964,-35.204,-35.104,-35.258,-35.132,-35.165,-35.305,-35.528,-35.712,-35.403,-35.081,-35.224,-35.448\n-34.953,-35.297,-35.416,-35.82,-35.535,-35.467,-37.064,-37.209,-35.993,-34.95,-35.03,-34.147,-34.034,-34.272,-36.004,-36.68,-37.123,-36.163,-35.195,-35.279,-34.548,-34.338,-34.34,-34.68,-35.012,-34.628,-34.141,-34.03,-34.037,-34.093,-34.175,-34.369,-35.15,-35.006,-35.011,-35.706,-35.343,-35.413,-35.498,-35.63,-35.497,-35.55,-35.671,-35.029,-34.672,-35.056,-35.572\n-34.86,-34.675,-34.944,-34.473,-33.979,-35.993,-37.275,-36.616,-35.778,-35.466,-34.612,-35.626,-35.362,-35.111,-35.514,-34.857,-36.546,-36.307,-35.098,-35.663,-35.513,-35.18,-34.432,-34.46,-34.958,-34.962,-34.304,-34.044,-34.203,-34.509,-34.247,-34.434,-34.568,-35.448,-35.49,-35.522,-35.777,-35.662,-35.681,-35.648,-35.356,-35.354,-35.237,-35.129,-34.947,-34.74,-35.46\n-35.852,-35.85,-36.243,-35.235,-36.285,-37.683,-37.783,-38.401,-38.304,-37.391,-37.329,-35.757,-35.005,-36.543,-36.056,-35.51,-34.149,-36.859,-35.603,-35.778,-35.426,-35.076,-34.923,-35.03,-34.755,-34.489,-33.969,-33.64,-33.567,-34.178,-34.888,-34.723,-34.586,-35.479,-34.945,-35.5,-36.134,-35.991,-35.793,-35.801,-35.535,-35.21,-35.163,-35.364,-35.12,-35.005,-34.954\n-36.155,-35.862,-35.641,-35.416,-37.012,-36.726,-37.289,-37.346,-36.93,-36.264,-36.18,-34.49,-35.103,-33.505,-31.637,-34.758,-37.294,-36.54,-35.542,-35.395,-35.018,-34.883,-34.651,-34.648,-34.522,-34.384,-33.976,-33.508,-32.822,-33.908,-35.252,-35.287,-35.017,-35.363,-35.192,-35.515,-36.001,-35.991,-35.658,-35.311,-35.311,-35.081,-35.053,-34.918,-34.824,-34.766,-34.136\n-36.084,-36.579,-36.076,-36.295,-36.683,-36.006,-36.421,-36.751,-36.409,-36.364,-35.562,-36.138,-37.129,-33.548,-32.871,-34.11,-37.441,-36.049,-35.438,-35.3,-34.722,-35.185,-34.804,-34.962,-34.853,-34.733,-34.169,-33.635,-33.277,-34.592,-35.515,-35.48,-35.305,-35.622,-35.749,-35.881,-35.501,-35.384,-35.284,-35.167,-35.014,-34.987,-35.169,-34.722,-34.676,-34.113,-33.268\n-35.436,-36.128,-36.177,-36.066,-36.141,-35.547,-35.594,-36.255,-36.795,-36.766,-37.675,-35.906,-35.392,-35.112,-34.379,-34.832,-37.197,-35.886,-35.413,-35.201,-35.522,-35.418,-35.113,-35.054,-34.691,-34.573,-34.904,-34.604,-34.557,-35.061,-35.282,-35.47,-35.661,-35.781,-36.25,-36.061,-35.789,-35.341,-35.25,-35.071,-34.896,-35.031,-34.887,-34.718,-34.493,-33.926,-34.329\n-35.713,-35.714,-35.022,-35.43,-34.469,-33.388,-34.54,-34.767,-36.001,-36.723,-36.505,-36.151,-34.92,-33.698,-34.12,-36.37,-36.427,-36.53,-35.33,-35.424,-35.219,-35.376,-35.363,-34.974,-34.768,-34.641,-34.702,-34.641,-34.937,-34.974,-35.145,-35.473,-36.884,-36.471,-36.165,-36.295,-36.294,-35.498,-35.258,-35.063,-35.068,-34.925,-34.903,-34.923,-34.629,-34.692,-35.101\n-36.477,-35.854,-34.611,-34.637,-32.936,-33.048,-32.364,-32.985,-34.699,-35.551,-37.083,-36.766,-36.707,-36.689,-36.663,-36.71,-35.844,-36.563,-35.534,-35.258,-35.06,-35.422,-35.204,-34.899,-35.063,-34.676,-34.529,-34.398,-34.361,-34.894,-35.441,-35.827,-36.383,-35.57,-35.448,-35.798,-36.013,-35.613,-35.482,-35.335,-35.217,-35.423,-35.468,-35.404,-35.504,-35.954,-35.959\n-34.957,-34.279,-33.431,-32.94,-32.217,-33.131,-33.155,-34.88,-35.376,-35.789,-36.221,-36.102,-35.563,-35.405,-36.28,-36.848,-36.96,-36.742,-35.76,-35.924,-35.49,-35.772,-35.065,-35.074,-35.07,-34.661,-34.608,-34.869,-34.72,-34.563,-34.999,-36.422,-36.14,-35.041,-34.826,-35.161,-35.519,-35.539,-35.6,-35.767,-35.382,-35.504,-35.529,-35.76,-35.8,-36.153,-37.458\n-33.734,-33.672,-32.462,-31.024,-31.521,-32.912,-34.834,-35.602,-34.881,-35.336,-34.933,-35.518,-35.609,-35.717,-36.316,-36.807,-35.484,-35.195,-35.468,-35.158,-35.731,-35.756,-35.627,-35.571,-35.445,-35.017,-34.668,-34.849,-34.904,-35.202,-35.899,-36.296,-36.085,-34.723,-34.43,-34.795,-35.175,-35.653,-35.753,-35.891,-35.726,-35.552,-35.564,-35.846,-35.91,-36.162,-37.685\n-33.715,-33.02,-32.722,-32.717,-32.919,-32.474,-34.744,-35.124,-35.282,-34.377,-33.351,-34.126,-34.119,-35.091,-35.68,-37.061,-36.885,-35.631,-35.608,-37.49,-36.76,-36.235,-35.885,-36.117,-36.152,-35.933,-35.472,-34.866,-35.146,-35.576,-35.913,-35.796,-35.548,-35.347,-35.292,-35.522,-35.9,-35.749,-35.962,-35.764,-35.536,-35.409,-35.533,-35.655,-35.78,-36.28,-37.084\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070604-070709.unw.csv",
    "content": "-13.592,-14.131,-14.546,-14.131,-14.555,-15.332,-14.995,-14.875,-15.202,-15.828,-15.943,-16.115,-16.034,-15.617,-15.99,-15.908,-16.174,-16.987,-17.153,-17.097,-16.911,-16.663,-16.692,-17.048,-17.425,-17.454,-17.608,-17.545,-16.8,-16.841,-17.357,-17.423,-17.603,-17.935,-18.138,-18.656,-17.972,-17.633,-18.05,-17.721,-17.573,-17.254,-17.688,-17.622,-16.938,-16.414,-15.916\n-13.584,-13.796,-14.573,-14.917,-14.996,-15.256,-15.175,-15.004,-15.149,-15.412,-15.988,-16.007,-15.642,-15.397,-15.538,-15.902,-16.285,-16.82,-16.935,-16.882,-16.996,-16.815,-16.963,-17.252,-17.485,-17.495,-17.29,-16.777,-16.289,-15.989,-16.171,-16.199,-17.03,-18.234,-18.663,-18.743,-17.992,-17.737,-18.07,-18.065,-17.362,-16.756,-16.389,-16.033,-15.868,-16.111,-16.28\n-13.218,-13.875,-13.98,-14.685,-14.808,-14.653,-14.881,-14.913,-14.853,-15.165,-15.775,-16.135,-15.727,-15.565,-15.511,-15.698,-16.012,-15.913,-15.956,-16.209,-16.354,-16.689,-16.863,-17.031,-17.444,-17.38,-17.1,-16.97,-16.176,-15.73,-15.723,-16.327,-16.839,-17.937,-18.474,-18.098,-18.04,-17.882,-17.694,-17.521,-17.168,-16.784,-16.602,-16.044,-15.139,-14.911,-14.937\n-13.753,-12.992,-13.334,-14.536,-14.441,-14.643,-14.544,-14.729,-15.033,-15.142,-15.503,-15.524,-15.434,-15.293,-15.204,-15.259,-15.648,-15.881,-15.911,-16.029,-16.253,-16.488,-16.816,-17.027,-17.017,-16.945,-16.652,-16.299,-16.162,-16.189,-16.278,-16.175,-15.824,-16.668,-17.885,-18.167,-17.632,-17.501,-16.924,-16.982,-17.212,-16.961,-16.615,-15.865,-15.056,-14.515,-14.588\n-13.815,-12.164,-12.563,-14.595,-14.662,-14.662,-14.34,-13.86,-14.288,-15.48,-15.609,-15.028,-14.964,-15.074,-15.363,-15.174,-15.444,-16.426,-16.327,-15.98,-15.516,-15.991,-16.393,-16.557,-16.566,-16.704,-16.486,-16.195,-15.937,-16.057,-16.284,-16.048,-17.159,-17.371,-16.607,-16.883,-17.211,-16.751,-16.289,-16.116,-16.717,-16.554,-16.082,-15.665,-15.266,-14.827,-13.762\n-13.34,-13.066,-13.891,-14.954,-15.073,-15.198,-15.22,-14.87,-14.39,-14.54,-14.518,-14.31,-14.659,-15.329,-15.591,-15.607,-15.682,-16.175,-16.668,-16.363,-15.875,-15.704,-15.723,-16.029,-15.971,-15.693,-15.842,-15.802,-15.444,-15.531,-15.748,-15.81,-16.08,-16.559,-16.269,-15.771,-16.147,-16.119,-16.048,-15.979,-16.174,-16.379,-16.079,-15.525,-14.801,-13.826,-13.114\n-12.465,-11.397,-11.536,-14.407,-14.398,-14.532,-14.6,-14.42,-14.638,-14.377,-14.466,-14.147,-14.544,-15.363,-15.28,-15.352,-15.478,-15.67,-15.998,-16.062,-15.475,-15.497,-15.22,-15.577,-15.479,-14.955,-15.162,-15.359,-15.269,-15.982,-16.276,-16.129,-15.97,-16.23,-16.443,-16.254,-15.891,-15.818,-15.554,-15.871,-16.426,-16.197,-15.519,-15.136,-15.214,-14.557,-12.49\n-13.523,-13.525,-13.946,-14.266,-14.283,-14.22,-13.858,-13.98,-14.177,-14.367,-14.456,-14.229,-14.275,-15.101,-15.257,-15.241,-15.189,-15.184,-15.314,-15.395,-14.683,-14.927,-15.165,-15.204,-15.151,-15.198,-15.001,-15.23,-15.768,-16.144,-15.941,-15.779,-15.897,-16.076,-16.191,-16.33,-16.098,-16.022,-15.874,-15.25,-14.759,-14.901,-14.605,-13.36,-12.449,-12.292,-11.323\n-13.367,-13.606,-13.849,-14.388,-14.692,-14.237,-13.631,-13.802,-13.811,-13.957,-13.879,-13.619,-14.102,-14.765,-14.836,-14.863,-14.75,-14.479,-14.616,-14.6,-14.374,-14.395,-14.492,-14.666,-14.899,-14.658,-14.92,-15.316,-15.535,-15.968,-15.951,-15.407,-15.513,-15.852,-16.459,-16.61,-16.102,-15.954,-15.847,-15.398,-14.576,-13.57,-14.006,-14.33,-13.195,-12.235,-11.557\n-12.96,-12.798,-12.849,-14.315,-14.398,-13.882,-13.669,-13.531,-13.341,-13.308,-13.368,-13.238,-13.519,-14.145,-14.553,-14.274,-13.984,-13.808,-13.47,-13.759,-14.334,-14.45,-14.664,-14.521,-14.493,-14.75,-14.871,-15.022,-15.047,-15.028,-15.215,-15.354,-15.404,-15.065,-14.741,-15.573,-15.39,-15.469,-15.256,-15.045,-14.878,-14.787,-14.609,-14.787,-13.594,-12.566,-11.717\n-13.598,-13.535,-14.648,-14.36,-14.376,-13.774,-13.844,-13.523,-13.498,-13.35,-13.077,-13.093,-13.058,-13.465,-14.126,-14.933,-14.372,-13.935,-14.231,-14.251,-14.224,-14.705,-14.845,-14.511,-14.378,-14.287,-14.662,-14.846,-14.747,-14.748,-14.864,-15.165,-15.55,-15.789,-15.708,-15.521,-14.966,-14.446,-14.444,-14.206,-14.1,-14.149,-14.423,-14.677,-14.354,-12.923,-11.41\n-13.68,-13.416,-13.606,-13.584,-13.739,-13.389,-13.707,-13.294,-13.018,-12.824,-12.479,-12.409,-12.562,-12.865,-13.333,-13.85,-14.352,-13.605,-13.282,-13.354,-13.863,-14.463,-14.904,-14.665,-14.465,-14.424,-14.921,-14.908,-14.882,-14.91,-14.984,-15.167,-15.328,-15.283,-14.93,-14.028,-14.084,-14.474,-13.885,-13.552,-13.408,-13.178,-13.123,-14.189,-14.083,-14.572,NaN\n-13.595,-14.064,-15.424,-14.77,-13.136,-13.368,-13.428,-13.665,-13.043,-12.447,-12.02,-11.925,-12.41,-13.03,-12.897,-12.979,-13.241,-13.377,-13.331,-13.537,-14.154,-14.228,-14.462,-14.407,-14.084,-14.2,-14.156,-14.157,-14.872,-15.112,-14.781,-15.351,-15.241,-14.725,-14.204,-14.743,-14.931,-14.523,-14.527,-13.636,-12.793,-12.594,-12.967,-14.081,-13.21,NaN,NaN\n-13.108,-12.857,-12.96,-12.676,-12.643,-12.412,-12.557,-13.201,-12.982,-12.757,-12.576,-12.247,-11.978,-12.828,-12.814,-12.515,-12.667,-12.666,-12.798,-13.304,-13.71,-13.807,-14.042,-13.981,-13.749,-13.537,-13.057,-12.448,-12.661,-14.052,-14.467,-14.764,-14.993,-14.623,-14.832,-14.523,-13.534,-12.735,-12.844,-12.113,-11.724,-11.812,-11.599,-11.111,-11.425,NaN,NaN\n-12.778,-12.361,-11.973,-12.567,-13.005,-13.331,-13.134,-13.092,-13.027,-12.91,-13.445,-13.123,-12.544,-12.416,-12.908,-13.078,-12.918,-12.318,-12.579,-13.435,-14.528,-14.627,-14.177,-13.786,-13.623,-13.87,-14.049,-13.442,-13.81,-14.053,-14.428,-14.774,-15.135,-15.192,-14.768,-14.663,-13.744,-13.331,-12.624,-11.06,-9.2734,-10.39,-10.162,-11.243,-11.338,NaN,NaN\n-13.738,-13.033,-12.571,-12.94,-12.763,-13.308,-13.496,-13.418,-13.217,-12.994,-13.5,-13.536,-12.434,-12.135,-12.297,-12.748,-12.618,-12.476,-12.851,-12.951,-13.279,-13.811,-13.494,-12.826,-12.356,-12.445,-13.084,-13.254,-13.467,-13.738,-13.818,-13.669,-13.297,-13.286,-14.07,-13.932,-14.21,-13.641,-13.463,-13.696,-12.525,-11.699,-10.111,-9.8166,-9.6979,NaN,NaN\n-14.053,-13.746,-13.293,-13.295,-13.602,-13.174,-13.105,-12.991,-12.464,-12.444,-12.934,-12.751,-11.616,-11.794,-12.154,-12.155,-12.213,-11.75,-12.864,-13.394,-13.593,-13.832,-12.687,-12.245,-12.72,-12.888,-13.167,-13.359,-13.5,-13.81,-14.148,-14.38,-14.11,-14.122,-13.913,-13.64,-15.628,-15.133,-12.717,-12.727,-11.82,-12.536,-12.614,-11.813,-9.6823,NaN,NaN\n-13.403,-12.437,-12.585,-12.676,-13.115,-12.492,-12.361,-12.481,-12.107,-12.184,-12.614,-11.794,-10.922,-10.734,-11.184,-11.309,-11.209,-11.495,-13.221,-14.631,-14.747,-14.662,-13.806,-13.378,-14.768,-14.292,-13.615,-13.477,-13.478,-14.065,-14.331,-14.313,-16.022,-16.015,-15.826,-14.613,-19.133,-19.468,-18.75,-12.023,-11.441,-11.242,-12.259,-13.515,-11.965,NaN,NaN\n-12.843,-12.51,-12.429,-10.959,-11.813,-13.229,-13.129,-13.101,-12.185,-12.183,-12.166,-11.433,-11.112,-11.587,-11.734,-11.288,-11.002,-11.63,-12.191,-12.421,-13.428,-14.491,-14.47,-14.021,-14.126,-13.686,-13.616,-13.71,-13.601,-13.294,-13.585,-13.019,-14.82,-16.574,-16.105,-14.3,-13.625,-13.764,-14.226,-12.608,-12.434,-12.145,-12.715,-13.784,-13.066,-12.915,-13.415\n-11.133,-11.995,-10.661,-9.9031,-9.2783,-9.4305,-11.674,-12.218,-11.731,-11.759,-11.444,-11.286,-11.437,-11.89,-12.195,-11.525,-10.984,-11.081,-11.525,-11.991,-11.819,-11.935,-12.911,-14.088,-13.643,-13.244,-13.17,-12.846,-12.62,-12.953,-13.099,-13.23,-10.652,-12.516,-12.802,-12.556,-11.895,-12.031,-15.301,-15.058,-14.07,-13.141,-13.236,-13.354,-13.571,-14.691,-14.561\n-11.206,-11.883,-12.085,-11.563,-11.175,-12.17,-12.047,-12.27,-11.993,-11.85,-11.596,-11.979,-11.734,-11.015,-10.4,-10.744,-10.852,-11.31,-11.834,-12.251,-12.781,-11.863,-11.996,-13.248,-13.146,-12.533,-12.745,-13.586,-13.656,-13.302,-13.493,-13.938,-11.357,-10.996,-9.26,-12.182,-11.547,-10.34,-12.605,-14.707,-14.384,-13.289,-12.963,-12.344,-13.011,-13.645,-13.569\n-11.77,-11.924,-10.879,-10.134,-10.529,-12.452,-13.148,-11.402,-11.776,-11.221,-11.063,-11.248,-11.568,-12.207,-13.801,-10.848,-11.353,-12.645,-13.604,-14.849,-14.067,-13.608,-12.951,-13.031,-12.597,-12.246,-12.205,-12.454,-13.336,-13.464,-12.843,-13.231,-12.842,-12.852,-11.206,-11.172,-10.992,-12.033,-11.839,-12.264,-14.847,-13.384,-13.128,-12.864,-12.311,-11.824,-12.711\n-10.85,-10.81,-10.852,-11.192,-10.521,-11.1,-11.011,-10.911,-10.922,-10.239,-10.596,-10.83,-11.505,-13.736,-15.87,-15.765,-11.806,-12.679,-13.361,-13.205,-13.258,-13.703,-14.042,-13.014,-12.46,-11.868,-11.623,-11.736,-11.993,-12.112,-11.968,-11.601,-11.671,-11.626,-11.322,-11.445,-11.555,-10.803,-10.793,-9.954,-13.637,-13.266,-11.971,-12.983,-12.797,-12.462,-12.333\n-10.598,-10.223,-11.447,-13.171,-14.085,-12.469,-11.603,-11.509,-11.405,-11.058,-10.967,-11.479,-12.009,-13.466,-14.187,-14.917,-14.974,-14.324,-12.727,-11.749,-10.923,-11.718,-12.628,-10.753,-10.52,-11.232,-11.113,-10.92,-10.498,-9.7255,-8.9652,-9.9796,-10.448,-10.613,-10.427,-10.999,-11.423,-11.836,-12.109,-11.422,-12.005,-12.767,-12.794,-12.673,-12.631,-12.019,-11.491\n-10.371,-10.209,-10.546,-10.85,-11.258,-11.274,-10.988,-11.015,-11.298,-11.676,-12.094,-11.577,-11.549,-12.018,-12.839,-12.921,-11.796,-11.236,-11.819,-11.314,-11.411,-11.105,-10.572,-9.719,-9.846,-9.2205,-9.0172,-9.7249,-9.483,-8.0949,-8.2115,-8.9052,-9.3979,-9.2516,-8.6647,-9.5696,-8.0681,-8.2007,-8.5445,-11.562,-11.657,-11.941,-11.875,-11.387,-11.627,-11.826,-11.413\n-10.924,-10.284,-10.362,-10.568,-10.68,-10.07,-10.339,-10.631,-10.671,-10.435,-10.892,-11.443,-11.011,-10.014,-10.306,-11.273,-10.682,-10.672,-10.933,-10.759,-10.466,-9.5928,-9.8805,-9.8027,-9.3651,-8.3524,-7.2325,-8.6228,-8.8114,-7.5036,-6.0311,-7.0535,-11.113,-12.536,-9.508,-9.9359,-11.312,-10.194,-10.522,-12.168,-11.911,-11.56,-11.493,-11.072,-11.022,-11.479,-10.929\n-10.235,-9.9357,-10.526,-10.443,-10.033,-10.169,-9.8969,-10.386,-10.266,-9.5184,-9.3938,-10.228,-10.105,-10.038,-10.153,-10.518,-10.129,-9.7823,-10.421,-10.861,-10.442,-9.4679,-9.2233,-10.188,-11.161,-11.254,-11.218,NaN,-11.034,NaN,NaN,NaN,-9.8806,-12.569,-12.491,-11.655,-10.769,-10.779,-10.939,-11.225,-11.303,-11.004,-11.298,-11.027,-10.953,-11.318,-11.577\n-11.714,-11.189,-11.52,-11.281,-10.694,-10.956,-10.677,-9.9308,-9.6698,-9.3937,-9.328,-9.612,-10.27,-10.998,-10.395,-9.4308,-10.074,-10.907,-10.933,-10.665,-10.76,-9.9216,-9.4759,-9.991,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-12.06,-10.99,-10.054,-9.9563,NaN,NaN,NaN,NaN,-10.724,-11.186,-10.549,-10.442,-11.648,-11.922\n-11.994,-11.451,-11.446,-11.381,-10.782,-10.662,-10.714,-10.411,-10.189,-10.181,-10.773,-10.569,-9.693,-9.9463,-9.8192,-9.3277,-9.6063,-10.099,-9.6267,-9.2394,-9.7582,-9.9415,-9.2036,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.7026,NaN,NaN,NaN,NaN,NaN,-10.815,-10.806,-11.105,-11.203,-11.697,-11.811\n-10.765,-11.508,-11.519,-11.389,-9.9445,-9.8827,-10.736,-10.75,-10.55,-10.88,-10.611,-10.262,-9.6111,-9.891,-9.834,-9.9116,-10.218,-11.197,-11.092,-11.119,-9.1475,-8.9603,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.0818,-9.9069,-10.833,NaN,NaN,NaN,-10.52,-11.286,-11.958,-11.43,-11.651,-11.689\n-12.787,-11.172,-11.437,-11.821,-11.2,-9.9922,-11.278,-11.173,-10.928,-10.786,-10.738,-10.503,-10.417,-10.047,-9.2201,-9.3369,-9.5933,-9.6638,-10.019,-11.439,-10.529,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.9363,-7.8093,-7.6003,-8.4733,-9.3709,-9.7852,-9.8969,-10.539,-11.006,-11.944,-11.456,-11.346,-11.101\n-12.876,-12.604,-11.932,-11.028,-11.761,-12.537,-13.656,-12.206,-11.944,-13.648,-12.775,NaN,NaN,NaN,NaN,-13.879,-11.36,-9.7339,-11.758,-13.043,-10.913,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.8099,-9.2009,-9.342,-9.4379,-9.1953,-8.9337,-8.7959,-8.869,-9.026,-10.689,-11.383,-11.371,-11.247,-11.909,-12.413\n-13.148,-12.646,-11.862,-12.059,-11.756,-12.185,-14.018,-13.459,-11.42,-13.949,-13.311,NaN,NaN,NaN,-13.414,-13.494,-12.371,-13.073,-14.135,-13.474,-11.181,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.9645,-6.1597,-6.619,-7.4353,-7.7736,-8.9218,-8.2654,-9.0306,-9.4543,-10.461,-11.33,-10.948,-10.471,-11.221,-11.215\n-11.702,-10.961,-10.808,-10.902,-11.317,-12.354,-11.64,-11.537,-11.825,-11.634,-11.124,NaN,NaN,NaN,-13.055,-13.005,-13.358,-13.01,-13.334,-13.459,-11.825,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.4408,-4.9739,-7.6632,-9.3487,-9.1109,-9.4918,-10.19,-10.359,-10.511,-10.738,-10.618,-10.607\n-10.731,-11.657,-11.719,-12.329,-12.941,-13.161,-13.21,-11.95,-11.415,-11.257,-12.106,-11.538,-10.995,NaN,-12.968,-12.103,-11.119,-11.767,-11.399,-9.6253,-8.9405,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.324,-8.7096,-9.1523,-8.1712,-8.0609,-9.0962,-9.7999,-10.686,-11.068,-10.531\n-12.218,-12.125,-11.786,-12.005,-13.368,-13.495,-13.142,-12.056,-11.271,-11.197,-11.178,-11.59,-12.265,-12.004,-10.864,-11.512,-12.629,-11.677,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.3766,NaN,NaN,NaN,NaN,NaN,NaN,-8.6231,-7.9728,-9.1303,-9.0962,-9.0517,-9.2267,-9.5901,-9.9484,-10.897,-11.404\n-11.76,-11.496,-10.849,-11.172,-12.091,-11.98,-12.01,-11.428,-11.007,-10.996,-10.909,-9.3195,-10.152,-8.9303,-8.8296,-10.715,-11.935,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.172,-4.3408,-4.7491,NaN,NaN,NaN,NaN,-10.252,-8.3636,-9.5026,-9.9605,-9.5502,-9.5501,-10.012,-9.7344,-10.146,-11.154\n-10.835,-10.675,-10.765,-10.978,-11.164,-11.722,-11.574,-11.503,-11.676,-12.111,-12.606,-10.17,-9.4079,-8.0466,-10.479,-11.607,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.0999,-3.7419,-3.9042,-8.8054,NaN,NaN,-8.8831,-9.0769,-9.3569,-10.07,-10.288,-9.8077,-9.5668,-9.586,-9.0717,-10.086,-11.814\n-11.391,-11.763,-11.468,-11.255,-10.928,-11.111,-11.716,-11.814,-11.921,-12.275,-12.582,-13.176,-12.448,-12.112,-12.6,-11.655,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.859,-4.7207,-5.8713,-5.4693,-5.1297,-6.4535,-9.1784,-9.0186,-8.3914,-8.7921,-9.2088,-10.234,-10.74,-10.745,-11.093,-10.966,-10.432,-9.7391,-10.635\n-11.437,-11.375,-10.927,-10.656,-10.916,-11.457,-11.666,-11.924,-12.411,-12.523,-12.504,-12.662,-12.69,-12.239,-13.298,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.7016,-5.3512,-7.3454,-5.598,-5.3045,-5.6757,-7.8882,-8.0158,-8.0917,-8.0509,-8.7527,-10.522,-10.675,-10.204,-11.713,-11.747,-11.313,-11.916,-11.548\n-11.291,-10.596,-10.666,-11.013,-11.464,-11.415,-10.739,-10.911,-11.978,-12.953,-12.828,-11.615,-12.226,-12.996,-14.313,-15.178,-14.959,-12.087,-11.736,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.4164,-7.4363,-7.7892,-7.3588,-7.5397,-8.0753,-8.3142,-9.4841,-9.6015,-8.8162,-8.7534,-9.5811,-11.101,-10.96,-10.606,-12.08,-12.483,-11.808,-11.511,-11.426\n-11.496,-11.692,-11.79,-11.615,-11.343,-11.849,-12.148,-12.228,-12.598,-13.056,-13.577,-9.7651,-11.306,-13.599,-13.848,-13.591,-14.477,-13.1,-12.257,-10.702,-11.348,-12.768,-11.686,-10.658,-8.8198,-8.5023,-10.299,-8.8577,-8.2171,-8.6859,-8.96,-9.2557,-9.6497,-10.305,-9.6454,-8.2574,-9.0398,-10.727,-10.559,-11.365,-11.141,-10.266,-10.569,-11.075,-11.334,-11.974,-12.544\n-11.383,-11.866,-12.504,-12.661,-12.246,-12.156,-12.531,-12.782,-13.349,-13.289,-12.878,-11.953,-12.873,-13.835,-14.841,-13.644,-13.222,-13.835,-13.734,-12.9,-11.97,-11.478,-11.284,-11.336,-10.58,-10.386,-11.111,-10.584,-10.721,-11.271,-10.482,-10.284,-10.779,-10.021,-9.73,-9.392,-8.3723,-10.158,-11.438,-11.531,-11.603,-12.15,-12.339,-11.915,-11.845,-12.616,-13.298\n-11.914,-12.027,-12.113,-12.073,-12.825,-12.06,-12.668,-13.39,-13.728,-12.638,-12.807,-13.273,-13.247,-12.789,-12.65,-12.57,-12.849,-12.71,-12.241,-12.333,-11.232,-9.7941,-10.708,-11.577,-12.477,-11.903,-11.406,-12.336,-11.612,-11.231,-11.232,-11.72,-11.917,-11.668,-12.5,-12.396,-10.763,-10.819,-11.799,-11.854,-11.76,-11.764,-12.729,-13.816,-14.502,-14.299,-14.314\n-11.601,-11.855,-11.907,-11.82,-12.193,-12.04,-13.442,-14.985,-15.548,-12.075,-12.488,-13.199,-13.335,-12.992,-12.473,-11.985,-12.071,-12.045,-12.74,-11.691,-6.3703,-7.4729,-9.2353,-11.79,-13.115,-12.352,-11.285,-12.515,-12.183,-11.935,-12.559,-13.152,-13.029,-13.23,-14.442,-15.446,-14.57,-13.777,-12.87,-12.426,-12.052,-11.35,-10.739,-12.777,-13.512,-14.464,-15.683\n-12.739,-12.784,-12.952,-12.795,-12.554,-12.868,-12.686,-12.463,-12.234,-11.971,-12.381,-13.406,-13.718,-12.947,-12.606,-12.703,-12.866,-13.126,-13.228,-12.761,-10.098,-10.63,-11.126,-11.047,-12.739,-12.125,-12.358,-12.797,-12.552,-12.509,-12.362,-11.799,-11.543,-12.457,-12.85,-13.464,-13.842,-13.694,-13.033,-12.731,-13.458,-12.38,-10.379,-11.298,-12.522,-13.704,-14.46\n-12.329,-12.887,-13.344,-13.072,-13.187,-13.386,-13,-12.614,-12.799,-12.798,-12.856,-13.339,-13.465,-13.056,-12.436,-12.554,-12.703,-12.348,-13.392,-14.074,-13.135,-12.166,-13.448,-14.051,-12.861,-12.268,-12.402,-12.559,-13.023,-13.013,-12.024,-12.022,-12.568,-12.918,-13.382,-13.28,-13.068,-12.87,-12.508,-12.497,-13.116,-12.504,-13.434,-10.898,-9.0561,-9.8654,-12.395\n-12.871,-13.196,-13.404,-13.451,-13.485,-12.638,-12.748,-13.059,-12.353,-12.535,-12.591,-12.443,-12.531,-12.621,-12.597,-12.696,-12.573,-12.065,-13.05,-14.775,-15.626,-15.578,-13.013,-14.261,-14.405,-13.995,-13.659,-13.321,-13.368,-13.404,-12.654,-12.035,-12.665,-13.667,-14.457,-14.055,-12.938,-13.137,-13.467,-12.485,-13.37,-15.43,-17.423,-16.475,-15.551,-13.807,-14.255\n-13.61,-13.405,-12.794,-13.452,-13.738,-12.977,-12.506,-13.188,-13.482,-13.227,-12.597,-12.347,-12.096,-12.248,-12.129,-12.134,-12.414,-12.915,-13.197,-13.818,-14.522,-13.439,-13.336,-14.005,-14.049,-13.403,-14.25,-14.091,-13.929,-13.72,-12.844,-12.747,-12.735,-12.775,-12.988,-13.511,-12.384,-12.217,-13.293,-12.595,-13.905,-16.547,-18.369,-16.233,-14.751,-14.136,-14.109\n-13.909,-13.58,-12.784,-13.664,-14.127,-13.424,-13.059,-13.077,-12.643,-12.631,-13.537,-12.906,-12.545,-12.39,-12.24,-12.648,-12.766,-13.806,-14.127,-14.82,-15.585,-15.453,-14.154,-14.012,-12.701,-13.599,-13.846,-12.295,-14.375,-14.432,-13.358,-13.901,-13.922,-13.601,-13.143,-12.968,-12.165,-12.526,-12.59,-12.825,-12.659,-12.784,-13.321,-16.016,-14.775,-14.381,-14.959\n-13.839,-14.016,-13.692,-13.713,-13.821,-13.529,-13.807,-13.969,-13.318,-12.948,-13.356,-13.482,-13.154,-13.501,-13.65,-13.128,NaN,-11.298,-10.559,-10.219,-11.419,-13.678,-13.996,-14.058,-13.767,-14.404,-15.244,-14.18,-13.735,-14.019,-13.857,-14.12,-13.832,-13.652,-13.745,-13.54,-13.07,-13.239,-13.323,-13.171,-12.979,-13.26,-13.875,-14.691,-14.638,-14.533,-15.065\n-13.961,-14.497,-15.22,-14.515,-13.491,-13.004,-13.22,-13.25,-12.814,-12.827,-12.688,-12.505,-12.972,-13.787,-12.95,-12.836,-12.286,NaN,NaN,NaN,NaN,-14.03,-14.167,-14.314,-14.363,-15.633,-16.519,-15.767,-14.075,-14.314,-14.075,-14.359,-13.935,-13.965,-13.793,-13.263,-12.931,-13.029,-13.293,-13.617,-13.865,-13.793,-12.956,-13.455,-14.68,-15.085,-14.783\n-14.604,-14.629,-14.805,-14.417,-14.123,-13.769,-14.028,-13.39,-12.819,-12.473,-12.654,-13.077,-13.465,-13.491,-12.885,-10.968,-10.118,NaN,NaN,NaN,-13.706,-14.881,-14.53,-14.659,-14.991,-15.122,-15.181,-14.331,-13.537,-14.398,-14.395,-14.481,-14.155,-14.307,-13.513,-13.026,-13.483,-14.056,-13.948,-13.805,-13.313,-12.982,-13.05,-13.888,-14.821,-14.299,-14.033\n-15.544,-14.287,-13.784,-13.546,-13.939,-14.53,-14.782,-13.733,-11.685,-11.618,-11.787,NaN,NaN,NaN,-14.129,-13.16,-12.36,NaN,NaN,NaN,-16.188,-14.87,-14.351,-14.571,-14.743,-15.72,-15.435,-13.851,-13.495,-14.34,-14.827,-14.233,-13.75,-14.497,-13.643,-13.759,-14.684,-14.822,-15.122,-14.737,-13.64,-13.373,-13.72,-14.37,-14.726,-14.558,-14.151\n-13.726,-13.46,-14.094,-14.004,-14.14,-14.893,-15.374,-14.567,-13.201,-12.923,NaN,NaN,NaN,NaN,NaN,-14.833,-14.183,-13.638,-14.963,-16.399,-15.419,-14.415,-14.57,-14.512,-15.944,-17.291,-15.204,-14.726,-13.562,-14.719,-15.666,-15.596,-14.926,-14.501,-14.973,-14.642,-13.833,-13.639,-14.122,-14.588,-13.99,-14.103,-14.381,-14.334,-14.755,-14.294,-13.725\n-14.533,-14.64,-14.85,-14.232,-14.122,-14.299,-14.46,-15.648,-15.226,-14.452,NaN,NaN,NaN,NaN,NaN,-15.939,-14.413,-14.293,-14.842,-15.524,-15.443,-15.378,-15.104,-14.942,-14.367,-15.695,-15.439,-14.581,-13.573,-14.726,-15.027,-15.946,-17.169,-15.052,-14.316,-14.801,-14.339,-13.889,-13.88,-13.982,-14.125,-14.225,-14.342,-13.856,-14.562,-13.817,-13.544\n-15.606,-16.203,-15.513,NaN,-12.163,-12.473,-13.121,-13.702,-14.385,-15.523,-16.231,-15.539,-15.293,NaN,NaN,-16.776,-16.89,-15.218,-15.185,-15.656,-16.086,-15.534,-14.616,-13.493,-12.157,-12.443,-14.363,-14.91,-15.019,-14.2,-14.842,-15.267,-16.068,-15.163,-14.226,-14.101,-14.412,-14.342,-14.423,-14.378,-14.15,-14.287,-14.087,-14.664,-15.31,-15.393,-14.936\n-15.219,-16.551,-17.295,NaN,NaN,NaN,NaN,-14.037,-14.271,-14.431,-14.93,-16.179,-16.443,-15.951,-15.951,-16.501,-16.879,-16.263,-16.038,-15.493,-15.786,-16.13,-15.338,-14.539,-13.829,-11.73,-13.104,-17.266,-16.858,-14.771,-15.322,-16.098,-15.509,-14.795,-14.805,-14.72,-14.82,-14.772,-14.263,-14.143,-13.678,-13.744,-13.944,-14.707,-15.345,-15.22,-14.573\n-16.082,-15.554,-15.513,NaN,NaN,NaN,NaN,-14.206,-13.957,-13.807,-14.137,-15.161,-15.401,-15.424,-15.288,-16.201,-16.377,-15.924,-15.5,-15.448,-15.316,-15.02,-14.921,-14.994,-14.374,-12.944,-12.472,-13.576,-16.558,-15.928,-15.071,-15.398,-15.017,-14.808,-14.899,-14.758,-14.649,-14.501,-14.317,-14.247,-13.884,-13.32,-13.342,-13.94,-14.377,-14.475,-14.879\n-14.992,-13.839,-12.598,-15.503,-17.173,-18.959,-17.747,-15.512,-14.792,-14.33,-14.56,NaN,NaN,-13.302,-14.369,-15.569,-15.931,-15.483,-15.355,-14.289,-14.649,-14.483,-14.444,-14.413,-14.488,-14.32,-13.797,-14.068,-14.023,-16.198,-16.315,-14.993,-14.529,-14.587,-15.046,-14.802,-14.516,-14.39,-14.326,-14.122,-13.912,-13.431,-13.63,-13.576,-13.517,-14.132,-15.15\n-15.13,-14.238,-14.777,-15.243,-15.232,-17.331,-17.682,-18.157,-17.115,-15.712,-14.356,NaN,NaN,NaN,NaN,NaN,-16.342,-15.888,-15.18,-14.454,-14.681,-14.369,-14.821,-15.159,-14.881,-15.304,-14.476,-14.38,-13.455,-13.859,-14.686,-14.88,-14.323,-13.547,-14.387,-14.81,-14.567,-14.298,-14.207,-14.222,-14.259,-13.712,-13.596,-13.499,-13.697,-14.419,-14.648\n-13.929,-14.059,-14.057,-14.628,-15.753,-15.957,-16.61,-15.96,-16.275,-17.732,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.133,-14.917,-14.576,-13.893,-13.674,-14.464,-15.285,-15.024,-15.074,-15.415,-15.815,-15.276,-15.126,-14.477,-14.361,-14.643,-15.317,-15.144,-14.822,-15.054,-15.209,-14.901,-14.425,-13.913,-14.027,-13.66,-13.474,-14.18,-14.881,-15.514\n-15.053,-14.653,-13.123,-12.702,-12.764,-16.791,-15.827,-15.347,-15.072,-16.558,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-15.587,-14.599,-14.261,-14.306,-13.975,-14.005,-14.091,-14.864,-14.939,-14.279,-14.518,-14.497,-14.825,-15.027,-15.093,-15.041,-14.197,-14.414,-14.631,-15.222,-15.023,-15.132,-14.728,-13.706,-14.054,-14.008,-13.482,-13.716,-13.675,-14.653\n-17.66,-15.925,-15.122,-14.628,-14.015,-14.993,-16.271,-16.784,-16.038,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.707,-15.09,-15.069,-14.361,-14.449,-14.378,-14.754,-15.053,-14.588,-13.794,-13.86,-14.4,-15.061,-15.365,-15.324,-15.084,-15.647,-15.965,-15.516,-15.008,-14.861,-14.837,-13.862,-13.66,-14.006,-13.708,-13.37,-13.342,-13.272\n-15.902,-14.708,-14.673,-15.857,-15.399,-15.023,-15.186,-16.038,-16.466,-16.569,-17.297,-15.413,NaN,NaN,NaN,NaN,NaN,NaN,-14.952,-14.665,-14.576,-14.023,-13.261,-14.043,-14.812,-14.817,-14.353,-13.922,-13.816,-14.353,-15.272,-15.467,-14.465,-14.366,-15.51,-15.988,-14.915,-14.423,-14.625,-14.422,-13.505,-13.177,-13.432,-13.533,-13.21,-13.791,-13.84\n-14.03,-14.418,-14.627,-14.635,-14.547,-14.018,-14.675,-16.333,-17.816,-17.852,-16.773,-15.009,NaN,NaN,NaN,NaN,NaN,-15.106,-13.976,-14.186,-14.118,-14.001,-14.083,-14.106,-14.108,-14.201,-13.971,-13.398,-13.404,-13.989,-14.999,-15.606,-15.649,-16.187,-16.703,-16.166,-14.429,-14.17,-14.134,-13.909,-13.052,-12.824,-13.205,-12.733,-12.47,-13.768,-14.08\n-15.294,-15.107,-15.092,-15.231,-16.339,-15.161,-13.794,-14.416,-15.256,-15.851,-16.63,-16.9,-17.39,-16.538,NaN,NaN,NaN,-14.69,-13.548,-14.035,-14.681,-14.724,-14.674,-14.252,-13.496,-13.091,-13.883,-13.736,-13.832,-13.918,-14.426,-15.269,-15.799,-16.15,-16.326,-15.65,-14.377,-14.331,-14.003,-13.767,-13.395,-13.115,-12.969,-12.501,-12.05,-12.339,-13.024\n-13.473,-13.863,-15.116,-13.223,-12.999,-13.455,-12.826,-12.982,-15.238,-16.646,-16.568,-15.926,-14.71,-12.851,-13.265,-14.387,-14.127,-15.358,-14.586,-14.559,-14.966,-14.81,-14.879,-14.227,-14.88,-14.37,-14.161,-13.494,-13.243,-13.269,-14.093,-14.706,-14.924,-14.599,-14.133,-14.434,-14.416,-13.947,-13.764,-13.496,-13.059,-12.78,-13.016,-13.014,-12.786,-12.727,-12.871\n-14.686,-14.491,NaN,NaN,NaN,-13.59,-12.512,-12.205,-12.954,-15.351,-14.462,-14.448,-13.293,-12.537,-13.829,-14.682,-17.288,-14.778,-15.098,-15.049,-15.009,-14.592,-14.413,-13.255,-12.813,-14.476,-14.356,-13.78,-13.138,-14.275,-14.974,-15.283,-14.955,-14.05,-14.324,-15.245,-14.762,-14.013,-13.746,-13.695,-13.475,-13.168,-13.096,-12.903,-11.936,-11.125,-11.211\n-16.327,-14.679,NaN,NaN,NaN,-14.742,-14.54,-13.448,-12.716,-13.83,-15.034,NaN,NaN,NaN,NaN,NaN,NaN,-14.413,-14.902,-14.756,-15.068,-15.076,-14.196,-13.292,-12.526,-13.179,-13.081,-13.022,-13.645,-14.496,-15.402,-14.982,-14.041,-14.04,-14.336,-14.857,-14.719,-14.862,-14.161,-13.54,-13.334,-13.418,-12.651,-12.366,-13.067,-13.538,-13.771\n-13.839,-13.875,NaN,NaN,NaN,-14.345,-15.941,-16.544,-16.679,-17.641,-15.447,NaN,NaN,NaN,NaN,NaN,NaN,-15.766,-15.736,-15.25,-14.311,-14.008,-13.772,-13.358,-13.039,-12.347,-12.173,-12.71,-13.833,-14.315,-15.028,-14.799,-13.67,-13.347,-13.514,-13.975,-14.464,-14.363,-13.843,-13.844,-13.039,-12.514,-12.639,-12.365,-12.639,-13.507,-14.19\n-13.759,-13.663,-14.918,-16.059,-14.578,-14.937,-16.392,-17.169,-17.334,-16.606,-16.071,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-14.327,-14.124,-13.788,-13.398,-12.903,-12.863,-13.081,-12.779,-12.684,-14.676,-15.216,-14.669,-14.284,-13.334,-12.71,-13.024,-14.722,-14.67,-13.668,-13.262,-12.947,-12.637,-12.623,-12.704,-12.165,-12.729,-13.565,-14.04\n"
  },
  {
    "path": "tests/test_data/small_test/orbital_error_correction/ifg_orb_planar_1lks_method2_geo_070709-070813.unw.csv",
    "content": "-11.919,-12.299,-12.341,-12.176,-12.337,-11.715,-11.618,-11.152,-10.065,-10.397,-11.003,-10.516,-10.224,-10.137,-9.7734,-9.9073,-9.8373,-9.7156,-9.7362,-9.6031,-9.9377,-9.3947,-9.1481,-9.3824,-9.4807,-9.2971,-9.382,-9.3773,-8.9676,-9.2284,-9.234,-9.7048,-9.7042,-9.7819,-9.4546,-9.4546,-9.4539,-9.4339,-9.4555,-9.1554,-8.9648,-8.8916,-9.0072,-8.9777,-8.6846,-8.8099,-8.8095\n-11.969,-11.982,-12.505,-12.301,-12.171,-11.78,-11.705,-11.233,-10.797,-10.93,-10.888,-10.688,-10.481,-10.25,-10.183,-10.1,-9.8923,-9.869,-9.7248,-9.7125,-9.8061,-9.4956,-9.474,-9.6626,-9.5657,-9.3937,-9.4055,-8.9135,-8.9929,-9.4624,-9.6158,-9.7345,-9.6913,-10.403,-9.6105,-9.7908,-9.8855,-9.8211,-9.5828,-9.2546,-9.3191,-8.8748,-9.1486,-9.0392,-8.8989,-8.8512,-8.4542\n-12.225,-11.976,-12.172,-12.561,-12.339,-11.719,-11.303,-10.99,-10.776,-11.035,-10.585,-10.461,-10.455,-10.203,-10.194,-10.077,-9.8199,-10.014,-10.054,-10.044,-9.9406,-9.8782,-10.176,-9.8771,-9.6569,-9.4791,-9.3452,-9.1929,-9.409,-9.5073,-9.5034,-9.8743,-10.082,-10.759,-9.8636,-9.9613,-10.026,-10.012,-9.8751,-9.7199,-9.2163,-8.9413,-9.1885,-9.1711,-9.1159,-8.8157,-8.2232\n-13.156,-11.885,-12.057,-12.51,-12.067,-11.612,-11.244,-10.677,-10.765,-10.98,-10.719,-10.751,-10.643,-10.596,-10.256,-10.275,-10.042,-10.051,-10.075,-10.253,-10.09,-10.093,-10.163,-9.9434,-9.4695,-9.2219,-9.2283,-9.3355,-9.3231,-9.4716,-9.8963,-10.222,-10.389,-10.332,-10.159,-10.102,-10.032,-10.074,-9.8897,-9.6137,-9.5332,-9.6269,-9.4984,-9.4634,-9.5022,-9.282,-8.769\n-12.652,-11.951,-11.35,-12.43,-11.868,-11.646,-11.367,-11.244,-11.143,-11.449,-11.339,-10.685,-10.954,-11.176,-10.651,-10.629,-10.346,-10.309,-10.275,-10.333,-10.123,-9.8314,-10.315,-9.9384,-9.2412,-9.279,-9.2098,-9.3172,-9.4288,-9.5832,-10.21,-10.445,-10.714,-10.478,-10.395,-10.275,-10.002,-10.027,-9.9675,-9.7285,-9.7587,-8.9877,-9.4988,-9.3805,-9.5587,-9.3111,-8.7164\n-12.675,-12.534,-12.606,-12.777,-12.019,-11.686,-11.481,-11.431,-11.395,-11.51,-11.039,-10.602,-11.281,-11.332,-11.225,-10.958,-10.819,-10.664,-10.596,-10.782,-10.71,-10.149,-10.156,-9.8987,-9.5833,-9.7593,-9.5691,-9.4426,-9.8881,-10.247,-10.825,-11.238,-11.155,-10.846,-10.605,-9.9293,-9.7551,-9.7452,-9.7884,-9.7889,-9.8831,-9.981,-9.6601,-9.2068,-9.3937,-9.0056,-8.5247\n-12.78,-12.922,-12.793,-13.006,-12.309,-12.371,-11.738,-11.884,-11.509,-11.44,-11.08,-10.843,-11.33,-11.206,-11.435,-11.276,-11.043,-10.875,-10.745,-11.169,-10.927,-10.842,-10.447,-10.357,-10.214,-10.083,-9.7122,-9.9121,-10.422,-10.494,-11.081,-11.108,-10.699,-10.971,-10.442,-9.9628,-9.9945,-10.285,-9.8665,-10.067,-10.074,-9.5969,-9.6972,-9.3195,-8.9777,-8.9379,-8.3972\n-12.775,-12.645,-12.729,-12.998,-12.236,-12.6,-12.184,-11.837,-11.361,-11.171,-11.022,-10.9,-11.258,-11.359,-11.548,-11.537,-11.322,-11.102,-11.473,-11.535,-11.104,-11.431,-11.133,-10.593,-10.743,-10.609,-10.266,-10.67,-11.064,-10.603,-10.854,-10.829,-10.646,-11.325,-10.549,-10.001,-10.162,-10.296,-10.406,-10.321,-10.264,-9.5835,-10.121,-9.448,-9.1015,-9.2005,-8.3065\n-13.11,-12.889,-12.714,-12.968,-12.692,-12.66,-12.127,-11.889,-11.583,-11.144,-10.922,-10.946,-11.277,-11.356,-11.371,-11.664,-11.854,-11.868,-11.887,-11.724,-11.489,-11.369,-11.133,-11.028,-11.422,-11.327,-11.32,-11.225,-11.021,-11.008,-11.2,-11.155,-11.469,-11.472,-10.545,-10.027,-10.405,-10.232,-10.793,-11.116,-10.808,-10.114,-10.025,-10.582,-9.6762,-9.2289,-9.2516\n-13.582,-13.535,-13.148,-13.312,-13.545,-13.01,-12.571,-12.113,-11.477,-10.787,-11.134,-10.913,-11.318,-11.187,-11.579,-11.744,-11.893,-12.023,-12.06,-12.168,-11.717,-11.534,-11.362,-11.399,-11.473,-11.596,-11.896,-11.818,-11.317,-11.275,-11.666,-11.783,-12.087,-11.938,-11.172,-10.685,-10.886,-10.821,-11.16,-11.114,-10.727,-10.519,-10.45,-10.312,-9.8011,-9.6691,-9.869\n-13.947,-14.288,-13.857,-13.667,-13.52,-13.148,-12.691,-12.21,-11.636,-11.322,-11.366,-11.118,-11.563,-11.415,-11.548,-11.537,-11.677,-12.308,-12.272,-11.906,-11.745,-11.742,-11.806,-11.875,-11.602,-11.54,-11.942,-12.08,-11.986,-12.128,-12.433,-12.206,-12.553,-12.139,-11.887,-11.956,-11.712,-11.643,-11.663,-11.631,-11.195,-11.133,-10.808,-10.37,-10.077,-10.079,-10.113\n-14.485,-15.223,-14.575,-14.039,-13.417,-13.534,-13.135,-12.616,-11.925,-11.465,-11.121,-11.387,-11.589,-11.502,-11.662,-11.863,-11.627,-12.576,-12.453,-12.329,-12.207,-12.013,-12.004,-12.139,-11.77,-11.683,-12.043,-12.3,-12.214,-12.413,-12.783,-12.85,-12.786,-12.504,-12.517,-12.725,-12.261,-11.935,-11.889,-11.707,-11.429,-11.517,-11.324,-11.143,-10.697,-10.204,-10.112\n-14.581,-14.879,-13.847,-13.872,-13.375,-13.787,-13.294,-13.088,-12.321,-11.531,-11.419,-11.738,-11.742,-11.851,-11.993,-12.254,-12.444,-12.63,-12.666,-12.262,-12.381,-12.216,-12.152,-11.863,-11.822,-11.915,-12.202,-12.563,-12.389,-12.577,-13.173,-13.158,-12.898,-12.573,-12.998,-12.915,-12.309,-12.44,-12.115,-11.637,-11.55,-11.637,-11.938,-11.95,-11.271,-10.987,-10.24\n-14.602,-13.802,-13.392,-13.375,-14.431,-14.282,-13.399,-12.91,-12.51,-12.252,-11.862,-11.678,-11.703,-12.134,-12.08,-12.386,-12.526,-12.609,-12.354,-12.342,-12.441,-12.557,-12.335,-12.131,-12.261,-12.204,-12.421,-12.859,-12.991,-12.918,-13.068,-12.951,-12.699,-12.431,-12.779,-12.688,-12.54,-12.665,-12.623,-11.968,-11.484,-12.435,-12.589,-12.159,-11.74,-11.022,-10.416\n-14.227,-13.631,-12.628,-14.353,-14.644,-14.472,-13.398,-12.988,-12.459,-12.689,-12.388,-12.003,-12.188,-12.411,-12.465,-12.706,-12.672,-12.807,-12.169,-12.591,-12.602,-13.353,-12.733,-12.726,-12.506,-12.671,-12.763,-12.867,-12.843,-12.863,-12.944,-12.759,-12.636,-12.591,-12.813,-12.978,-12.83,-13.241,-13.343,-12.788,-11.745,-12.971,-13.769,-12.423,-11.958,-11.508,-11.467\n-14.309,-14.583,-14.032,-14.614,-14.728,-14.255,-13.665,-13.282,-12.768,-12.837,-12.461,-12.281,-12.775,-12.409,-12.476,-12.702,-12.869,-12.695,-12.634,-12.804,-12.748,-13.216,-13.136,-12.613,-12.648,-12.899,-12.775,-12.839,-13.074,-13.022,-13.463,-13.449,-13.016,-12.856,-12.776,-13.427,-13.235,-13.745,-13.946,-13.045,-12.576,-12.573,-14.194,-13.201,-12.715,-14.323,-13.41\n-14.281,-14.515,-14.364,-14.148,-13.918,-13.573,-13.748,-13.616,-13.07,-12.941,-12.786,-12.741,-12.939,-12.449,-12.682,-12.689,-12.98,-12.654,-12.993,-12.927,-12.966,-13.368,-13.089,-12.808,-13.342,-13.058,-12.952,-12.978,-13.368,-13.921,-13.863,-13.913,-13.223,-13.07,-13.137,-13.363,-13.731,-13.25,-13.729,-12.472,-13.336,-12.487,-13.083,-13.736,-13.95,-15.695,-14.604\n-14.976,-14.577,-14.442,-14.415,-12.941,-13.017,-13.933,-13.467,-13.35,-13.296,-13.33,-13.16,-13.344,-13.015,-12.772,-12.895,-13.002,-12.692,-13.04,-13.157,-13.737,-13.921,-13.413,-13.218,-13.061,-13.664,-13.861,-13.496,-13.243,-13.33,-13.71,-14.17,-14.289,-13.119,-12.883,-13.134,-14.409,-12.814,-12.147,-12.285,-12.87,-13.88,-13.791,-13.773,-14.585,-15.549,-15.517\n-14.317,-14.523,-14.546,-13.904,-13.075,-12.623,-14.248,-13.836,-13.713,-13.48,-13.469,-13.5,-13.592,-13.413,-13.157,-13.185,-13.292,-13.336,-13.333,-12.99,-13.672,-13.785,-13.607,-13.345,-13.137,-13.15,-14.077,-13.952,-13.488,-13.378,-13.539,-13.964,-15.065,-13.971,-12.726,-13.23,-13.144,-12.807,-11.506,-12.42,-13.208,-13.844,-13.899,-13.895,-14.123,-14.491,-14.93\n-14.275,-14.975,-14.635,-14.112,-13.816,-13.763,-14.482,-14.274,-13.913,-13.611,-13.567,-13.544,-13.465,-13.619,-13.583,-13.61,-13.904,-13.658,-13.62,-13.466,-13.784,-13.879,-13.566,-14.052,-13.714,-13.608,-13.777,-13.771,-13.268,-13.32,-13.796,-13.915,-14.765,-14.697,-13.11,-13.697,-13.43,-13.471,-11.543,-12.333,-13.498,-13.315,-13.802,-14.12,-14.044,-14.995,-14.483\n-14.594,-15.494,-14.718,-14.695,-14.691,-14.391,-14.404,-14.414,-14.026,-13.683,-13.764,-13.693,-13.637,-13.662,-14.194,-14.487,-14.468,-14.349,-14.094,-13.811,-13.569,-13.347,-13.494,-14.063,-14.121,-13.923,-13.895,-13.666,-13.079,-12.937,-13.802,-13.546,-14.204,-14.284,-13.877,-14.089,-14.051,-14.055,-13.01,-13.398,-13.673,-13.661,-13.969,-14.154,-14.477,-14.415,-13.654\n-14.909,-15.139,-14.357,-14.084,-14.355,-14.547,-14.266,-14.49,-14.294,-13.869,-13.749,-13.843,-13.874,-13.72,-14.288,-14.79,-14.612,-14.878,-14.721,-14.485,-14.445,-14.052,-14.158,-14.166,-14.161,-14.278,-14.195,-13.91,-13.027,-13.257,-13.902,-13.674,-14.283,-14.342,-14.439,-14.144,-13.777,-13.899,-13.903,-14.058,-14.299,-14.156,-14,-14.006,-14.048,-15.131,-14.035\n-14.888,-14.806,-14.203,-13.996,-14.33,-14.735,-14.739,-14.297,-14.037,-13.958,-13.904,-13.688,-13.978,-14.308,-14.75,-15.555,-14.883,-15.16,-15.112,-14.523,-14.842,-14.572,-14.029,-14.454,-14.558,-14.716,-14.058,-14.031,-13.417,-13.859,-13.849,-13.41,-13.78,-14.137,-13.945,-13.871,-13.759,-14.199,-14.339,-14.26,-14.765,-14.452,-14.412,-14.766,-15.173,-14.96,-14.158\n-14.768,-14.782,-14.243,-13.863,-16.008,-15.174,-14.192,-13.999,-14.072,-14.046,-14.245,-13.508,-14.215,-14.1,-14.145,-15.023,-14.371,-14.903,-15.06,-14.802,-14.524,-14.078,-13.824,-13.789,-14.207,-14.368,-13.566,-14.005,-13.776,-13.582,-12.745,-13.044,-13.222,-13.343,-13.664,-13.725,-14.052,-14.781,-15.507,-14.355,-14.443,-14.482,-14.416,-14.518,-14.683,-15.066,-14.111\n-15.224,-14.47,-13.893,-13.833,-14.401,-14.315,-13.819,-14.013,-14.25,-14.163,-14.251,-14.089,-13.927,-13.97,-13.976,-14.008,-14.153,-15.133,-14.779,-14.692,-14.597,-14.113,-13.943,-14.003,-14.011,-13.888,-13.716,-13.809,-13.331,-12.684,-12.148,-12.327,-12.98,-13.299,-13.893,-14.067,-14.43,-15.494,-16.132,-14.536,-14.577,-14.532,-14.482,-14.211,-14.324,-14.628,-14.119\n-15.526,-15.242,-14.26,-13.833,-14.235,-13.847,-14.037,-13.881,-14.132,-14.139,-14.248,-14.227,-13.778,-13.78,-14.06,-14.172,-14.291,-14.377,-14.492,-14.431,-14.308,-13.43,-13.553,-14.077,-13.701,-13.339,-13.042,-12.624,-12.467,-12.447,-12.305,-12.708,-13.137,-14.465,-14.419,-14.688,-14.342,-14.651,-15.137,-14.984,-14.568,-14.563,-14.731,-14.197,-14.436,-14.381,-14.034\n-15.552,-15.223,-14.82,-14.523,-14.251,-14.364,-14.369,-13.937,-14.015,-14.078,-14.103,-14.255,-13.582,-13.816,-14.014,-14.195,-14.047,-14.112,-14.483,-14.638,-14.031,-13.707,-13.894,-14.702,-14.155,-13.326,-13.337,-13.244,-13.155,-13.051,-12.965,-13.389,-14.346,-14.802,-14.752,-14.573,-14.492,-14.551,-15.014,-14.675,-14.72,-14.938,-14.598,-14.463,-14.447,-14.359,-14.242\n-15.454,-15.419,-15.194,-15.031,-15.037,-14.584,-14.182,-13.956,-14.318,-14.23,-13.958,-13.996,-14.018,-13.851,-14.135,-14.377,-14.477,-14.973,-15.12,-15.004,-14.686,-14.421,-14.53,-15.258,-14.867,-14.738,-14.145,-14.117,-14.686,-13.546,-13.105,-13.106,-14.396,-14.623,-14.59,-14.618,-14.604,-14.268,-14.362,-14.464,-15.053,-15.121,-14.52,-14.606,-14.377,-14.396,-14.039\n-15.858,-15.626,-15.143,-15.218,-15.119,-14.673,-14.294,-14.417,-14.736,-14.879,-14.722,-14.229,-14.546,-14.589,-14.44,-14.491,-14.665,-15.306,-15.028,-14.827,-14.746,-14.624,-14.381,-14.194,-14.021,-14.087,-13.431,-13.933,-14.416,-13.884,-13.168,-12.887,-13.796,-14.899,-14.736,-14.785,-14.716,-14.077,-15.158,-15.672,-15.392,-15.505,-14.794,-14.652,-14.548,-14.577,-14.439\n-15.965,-15.936,-15.665,-15.266,-14.897,-14.908,-14.648,-14.512,-14.93,-15.276,-14.79,-14.504,-15.166,-15.006,-14.553,-14.639,-15.284,-15.264,-15.183,-15.321,-14.966,-15.008,-14.034,-13.622,-14.054,-13.434,-12.993,-14.194,-14.416,-14.197,-13.925,-15.054,-15.384,-15.978,-14.97,-14.881,-15.828,-15.95,-15.401,-15.466,-15.218,-15.373,-14.889,-14.72,-14.706,-14.644,-14.307\n-16.726,-16.098,-15.587,-15.441,-15.274,-15.146,-15.483,-15.276,-15.283,-15.393,-15.228,-15.113,-15.25,-15.449,-14.832,-15.287,-15.67,-15.4,-15.323,-15.988,-15.617,-15.241,-14.57,-12.079,-15.642,-14.573,-12.728,-12.411,-13.827,-14.03,-13.705,-15.207,-15.366,-15.59,-15.136,-14.958,-15.824,-15.795,-15.319,-15.246,-15.56,-15.78,-15.231,-14.8,-14.686,-14.624,-14.226\n-16.657,-16.27,-17.062,-16.464,-15.889,-15.872,-16.004,-16.372,-15.662,-15.712,-15.615,-15.713,-15.822,-15.931,-16.366,-16.548,-15.815,-15.555,-16.356,-16.707,-15.247,-14.473,-13.746,-10.963,-12.966,-15.117,-15.529,-13.831,-14.946,-15.202,-14.867,-15.313,-14.639,-15.172,-15.133,-14.981,-15.416,-15.501,-15.323,-15.202,-15.626,-15.667,-15.59,-15.027,-14.943,-14.812,-14.575\n-16.427,-16.814,-16.732,-15.688,-15.55,-15.64,-16.502,-16.766,-16.198,-15.502,-15.342,-15.685,-16.094,-15.937,-16.033,-16.502,-16.521,-16.048,-16.417,-16.908,-15.814,-14.202,-13.338,-13.194,-13.522,-14.632,-14.95,-14.519,-16.452,-16.052,-15.448,-15.262,-14.898,-14.413,-14.605,-14.808,-15.06,-15.746,-15.167,-15.222,-15.372,-15.037,-15.006,-15.426,-15.254,-14.929,-14.736\n-16.744,-16.563,-16.378,-15.973,-15.752,-15.726,-16.768,-16.521,-15.843,-15.19,-15.506,-15.601,-16.103,-15.321,-16.901,-16.522,-16.669,-15.849,-15.584,-16.233,-15.774,-15.122,-14.389,-14.983,-14.968,-14.69,-14.401,-14.736,-15.768,-15.095,-14.72,-14.479,-13.912,-14.194,-14.837,-15.106,-15.936,-15.635,-14.844,-15.368,-15.326,-15.02,-15.071,-15.406,-15.372,-15.186,-15.06\n-16.833,-16.682,-16.505,-16.428,-16.491,-16.525,-16.787,-16.206,-15.653,-15.685,-15.603,-15.453,-15.684,-15.09,-16.888,-16.718,-15.924,-15.693,-15.638,-15.615,-15.459,-15.452,-15.146,-14.832,-14.511,-14.26,-15.243,-15.237,-15.221,-15.159,-14.58,-14.27,-13.915,-14.036,-15.195,-15.331,-15.601,-15.275,-15.41,-15.643,-15.525,-15.474,-15.369,-15.409,-15.579,-15.321,-15.186\n-16.562,-16.444,-16.549,-16.447,-16.058,-16.59,-16.696,-16.168,-15.571,-15.824,-15.291,-15.224,-15.594,-15.645,-15.394,-16.694,-16.283,-15.946,-15.678,-15.939,-15.875,-15.868,-15.49,-15.017,-14.777,-15.262,-15.577,-15.733,-13.991,-15.032,-14.785,-14.749,-14.468,-14.62,-15.21,-15.184,-15.461,-15.877,-15.232,-15.418,-15.403,-15.223,-15.249,-15.584,-15.527,-15.263,-15.006\n-16.402,-16.076,-16.204,-16.121,-16.417,-16.534,-16.571,-16.424,-16.303,-16.317,-16.194,-16.17,-15.978,-15.808,-15.774,-16.524,-16.45,-15.873,-15.381,-15.733,-15.161,-16.014,-18.851,-14.487,-13.803,-14.052,-15.608,-15.63,-13.654,-14.963,-15.125,-14.934,-15.109,-16.46,-16.504,-15.878,-15.888,-15.867,-15.373,-15.198,-15.168,-14.966,-15.132,-15.441,-15.49,-15.066,-15.028\n-16.004,-16.545,-16.632,-16.321,-16.449,-16.606,-16.28,-16.276,-16.321,-16.181,-16.125,-16.037,-16.058,-15.502,-15.931,-16.284,-16.029,-15.947,-14.946,-14.632,-13.849,-17.089,-22.063,-15.712,-14.74,-13.923,-15.427,-15.871,-15.095,-15.242,-15.1,-15.104,-14.887,-17.102,-16.787,-16.399,-15.892,-15.896,-15.871,-14.805,-14.973,-15.168,-15.191,-15.436,-15.26,-15.04,-15.534\n-15.712,-15.859,-16.385,-16.407,-16.214,-16.547,-16.122,-16.222,-16.132,-15.981,-16.016,-16.325,-16.095,-16.211,-16.217,-16.018,-16.117,-18.471,-15.691,-14.042,-13.918,-16.869,-18.609,-14.277,-13.825,-13.372,-15.866,-15.843,-15.444,-15.29,-15.437,-15.306,-15.074,-16.076,-16.119,-15.872,-15.689,-15.72,-15.496,-14.592,-14.867,-15.107,-15.38,-15.11,-15.153,-15.113,-15.318\n-15.491,-15.684,-15.924,-16.361,-16.444,-16.508,-16.19,-15.962,-16.274,-16.126,-16.081,-16.26,-16.27,-16.563,-16.192,-16.399,-16.72,-17.774,-17.934,-14.696,-15.034,-15.064,-13.414,-14.036,-14.976,-14.461,-14.134,-15.85,-15.511,-15.522,-15.185,-15.395,-15.483,-15.651,-15.991,-15.81,-15.474,-15.656,-14.971,-15.221,-15.355,-15.51,-15.331,-15.121,-15.109,-14.979,-14.584\n-15.557,-15.774,-15.599,-16.052,-16.459,-16.404,-16.072,-16.041,-15.752,-15.769,-15.901,-16.304,-16.901,-16.967,-16.263,-17.365,-17.664,-17.981,-18.298,-15.537,-15.551,-16.198,-15.613,-15.062,-14.913,-15.656,-15.815,-15.736,-15.8,-15.484,-15.254,-15.761,-15.375,-15.463,-15.311,-15.363,-16.031,-16.769,-16.238,-16.082,-15.557,-15.485,-15.63,-15.018,-15.073,-14.775,-14.565\n-15.157,-15.205,-15.417,-15.748,-15.714,-15.697,-15.511,-15.36,-15.27,-15.486,-15.658,-16.727,-17.643,-17.134,-16.708,-17.023,-18.053,-16.978,-16.558,-15.279,-15.515,-16.666,-16.48,-15.992,-15.333,-16.396,-15.731,-15.587,-15.82,-15.518,-15.644,-15.387,-15.007,-14.865,-15.275,-15.474,-15.627,-16.071,-16.075,-15.915,-15.783,-15.59,-15.703,-15.172,-15.591,-14.494,-14.549\n-14.98,-15.24,-15.516,-15.768,-15.476,-15.29,-15.262,-15.128,-14.88,-15.137,-15.733,-16.721,-17.351,-16.856,-16.649,-16.475,-16.4,-17.318,-16.862,-15.47,-15.553,-16.391,-15.897,-16.077,-16.07,-16.491,-16.046,-15.673,-15.379,-15.492,-15.74,-14.76,-14.929,-14.867,-14.977,-15.019,-15.397,-15.925,-15.328,-15.466,-15.693,-15.335,-15.049,-15.157,-15.347,-14.389,-15.049\n-14.549,-15.134,-14.519,-15.226,-15.145,-14.989,-15.059,-15.253,-14.906,-15.148,-15.449,-15.849,-15.947,-15.775,-15.567,-15.377,-15.646,-15.559,-15.592,-15.66,-15.779,-15.589,-16.415,-16.272,-16.153,-16.574,-16.327,-15.586,-15.179,-15.274,-15.247,-15.086,-15.124,-15.194,-15.909,-16.026,-15.86,-16.102,-15.19,-15.267,-15.533,-14.58,-14.79,-15.239,-15.259,-14.771,-14.602\n-14.254,-14.55,-14.46,-14.676,-15.069,-14.653,-14.715,-14.988,-15.115,-15.231,-14.909,-15.38,-15.387,-15.267,-15.078,-14.903,-15.758,-15.86,-16.128,-15.675,-15.649,-15.796,-16.258,-16.057,-15.885,-16.799,-16.768,-15.743,-15.198,-15.056,-14.939,-14.571,-15.53,-15.608,-16.29,-16.316,-16.327,-15.553,-14.999,-15.218,-15.127,-14.379,-14.534,-15.061,-15.043,-15.082,-14.476\n-14.259,-14.184,-14.101,-14.15,-14.368,-14.343,-14.593,-14.451,-14.559,-14.425,-14.451,-14.886,-14.91,-14.553,-14.252,-14.753,-15.58,-16.08,-16.566,-16.727,-16.669,-16.553,-16.149,-15.618,-15.583,-16.066,-16.319,-15.664,-15.178,-14.934,-14.566,-14.411,-14.64,-14.993,-15.086,-15.313,-15.229,-14.105,-14.744,-14.875,-14.245,-14.242,-14.417,-14.636,-14.634,-14.754,-14.705\n-13.67,-13.183,-13.707,-13.648,-13.353,-13.536,-14.039,-14.014,-13.849,-13.881,-13.863,-13.955,-13.973,-13.877,-13.909,-14.688,-15.36,-15.413,-16.306,-16.734,-17.078,-17.259,-15.902,-15.428,-15.652,-15.868,-15.14,-15.164,-14.973,-14.62,-14.395,-14.132,-14.494,-14.977,-14.835,-14.872,-13.967,-13.723,-14.323,-14.598,-14.38,-15.348,-15.121,-14.924,-14.375,-14.385,-14.32\n-13.21,-12.9,-13.045,-13.192,-12.995,-13.553,-13.662,-13.25,-13.394,-13.321,-13.249,-13.587,-13.618,-13.687,-13.935,-14.276,-14.824,-14.837,-15.59,-15.958,-15.911,-16.288,-15.26,-15.094,-15.871,-15.586,-14.904,-15.037,-14.773,-14.877,-14.659,-14.554,-15.006,-14.703,-14.771,-14.776,-14.079,-13.727,-14.145,-14.164,-14.284,-14.929,-14.902,-15.2,-15.013,-14.838,-14.451\n-12.755,-12.557,-12.786,-12.649,-12.798,-12.833,-12.736,-12.434,-12.095,-12.528,-12.968,-13.02,-13.082,-13.294,-13.671,-13.941,-14.06,-14.503,-15.303,-15.685,-15.501,-14.622,-14.749,-14.898,-14.899,-14.672,-14.929,-14.907,-14.67,-14.863,-14.654,-14.44,-14.485,-14.475,-14.49,-14.63,-14.024,-14.019,-14.023,-13.786,-14.203,-14.631,-14.519,-14.529,-14.279,-14.236,-14.238\n-12.499,-12.261,-12.559,-11.951,-12.33,-12.391,-12.085,-12.009,-11.757,-12.287,-12.584,-12.672,-12.711,-12.949,-13.341,-13.424,-14.447,-14.683,-15.204,-16.22,-16.497,-14.339,-14.399,-14.288,-14.343,-14.421,-14.644,-14.828,-14.082,-14.757,-14.402,-14.419,-14.181,-14.161,-14.041,-13.956,-13.366,-13.954,-13.815,-13.671,-13.475,-13.283,-13.271,-13.961,-13.862,-13.955,-14.042\n-12.154,-12.352,-12.107,-11.237,-11.203,-11.352,-11.773,-11.877,-11.676,-11.83,-11.85,-11.649,-12.427,-12.548,-12.663,-13.273,-14.663,-14.271,-14.993,-15.365,-14.804,-14.133,-13.859,-13.731,-14.024,-14.326,-15.418,-14.365,-13.828,-14.513,-14.481,-14.32,-14.038,-13.95,-13.302,-12.473,-12.563,-13.239,-13.466,-13.317,-13.144,-13.057,-12.786,-13.173,-13.577,-13.802,-13.642\n-12.023,-11.75,-10.562,-10.613,-11.222,-11.415,-11.621,-11.658,-10.998,-10.83,-11.118,-11.368,-11.972,-12.236,-12.178,-13.715,-15.087,-13.767,-14.12,-15.118,-14.303,-13.83,-13.453,-13.465,-13.644,-14.291,-15.287,-11.515,-13.975,-14.275,-14.184,-13.851,-13.566,-13.368,-13.12,-12.468,-12.757,-12.925,-13.054,-13.31,-13.213,-13.387,-13.813,-13.354,-12.841,-13.311,-12.954\n-11.88,-11.673,-10.934,-10.03,-10.663,-10.779,-10.823,-11.021,-10.6,-9.8484,-10.958,-11.405,-11.965,-12.467,-12.404,-12.921,-13.881,-13.543,-15.041,-15.747,-13.971,-13.363,-13.023,-12.749,-13.208,-13.468,-12.733,-11.568,-12.901,-13.468,-13.669,-13.462,-13.28,-13.227,-12.993,-12.857,-12.522,-12.324,-12.685,-13.082,-13.072,-13.309,-13.215,-12.584,-12.747,-13.177,-13.401\n-11.702,-11.673,-10.901,-10.189,-9.8909,-10.016,-10.499,-10.742,-10.332,-9.8753,-10.191,-11.879,-12.254,-12.444,-12.539,-13.467,-13.617,-13.622,-13.82,-13.545,-13.304,-12.983,-12.473,-12.45,-12.605,-12.877,-12.62,-11.847,-11.626,-12.402,-13.078,-13.225,-13.231,-12.877,-12.421,-12.95,-12.33,-12.246,-12.903,-12.425,-12.428,-12.54,-12.455,-12.47,-12.246,-12.791,-13.107\n-12.681,-11.92,-11.753,-9.896,-10.504,-10.525,-10.156,-9.7641,-9.6101,-9.7057,-10.06,-11.421,-11.363,-12.088,-12.552,-12.433,-12.801,-13.693,-12.507,-13.095,-12.853,-12.263,-12.142,-12.449,-12.773,-12.978,-11.794,-11.704,-11.994,-12.636,-12.886,-13.095,-12.687,-12.647,-12.442,-12.372,-12.287,-12.47,-12.51,-12.327,-12.385,-12.315,-12.567,-12.506,-11.969,-12.419,-12.51\n-12.822,-12.118,-10.879,-9.6203,-10.014,-9.908,-9.9665,-9.4035,-9.4549,-10.25,-10.499,-10.803,-12.006,-12.086,-12.588,-12.872,-13.074,-11.913,-11.276,-12.534,-12.431,-12.052,-11.999,-12.095,-12.225,-12.567,-10.901,-11.283,-12.016,-12.787,-12.883,-12.676,-12.977,-12.713,-12.564,-12.197,-12.342,-12.561,-12.391,-12.344,-12.195,-12.083,-12.169,-11.948,-11.903,-12.129,-12.629\n-12.217,-11.904,-10.84,-10.711,-11.136,-8.6555,-9.3618,-10.258,-10.683,-10.53,-8.6968,-9.0796,-14.304,-13.034,-12.613,-12.987,-12.957,-11.588,-11.666,-12.053,-12.163,-12.141,-11.989,-11.438,-11.146,-11.187,-11.431,-11.559,-11.822,-12.141,-12.436,-12.27,-12.312,-12.461,-12.435,-12.145,-12.244,-12.216,-11.753,-12.057,-12.627,-12.19,-11.946,-11.717,-11.82,-12.271,-12.773\n-13.532,-13.143,-12.801,-11.717,-11.261,-8.2653,-9.6413,-10.048,-10.065,-10.284,-9.6504,-9.2206,-12.217,-12.669,-13.41,-13.685,-13.273,-12.476,-11.887,-11.536,-11.26,-12.033,-12.434,-11.867,-11.581,-11.949,-11.673,-11.601,-12.055,-11.86,-12.179,-12.305,-12.32,-12.147,-12.177,-12.229,-12.112,-11.652,-11.299,-11.596,-11.718,-11.989,-11.888,-11.539,-11.82,-12.396,-12.295\n-15.306,-13.99,-13.666,-12.289,-11.551,-10.314,-10.957,-10.015,-10.108,-10.189,-11.043,-10.12,-11.601,-12.143,-12.337,-13.746,-13.309,-12.521,-11.213,-11.28,-11.765,-12.287,-12.462,-11.983,-11.888,-12.174,-11.708,-11.608,-12.181,-11.939,-12.144,-12.539,-12.212,-11.933,-12.151,-12.156,-11.907,-11.552,-11.468,-11.6,-11.747,-11.894,-12,-11.886,-11.671,-11.97,-11.949\n-15.449,-14.093,-13.127,-12.885,-12.469,-13.052,-12.156,-11.104,-11.97,-11.49,-10.366,-10.724,-12.233,-11.946,-13.846,-15.21,-13.622,-11.976,-11.661,-10.954,-11.389,-11.568,-12.355,-11.527,-11.46,-11.816,-11.567,-11.798,-11.621,-11.663,-12.154,-12.522,-12.245,-11.967,-11.716,-12.11,-12.029,-11.91,-11.552,-11.572,-11.616,-11.827,-12.043,-12.184,-12.301,-12.148,-11.436\n-13.811,-13.716,-12.394,-12.246,-12.646,-13.482,-12.362,-12.758,-12.101,-12.401,-11.292,-12.918,-12.454,-11.711,-11.36,-12.132,-12.154,-12.239,-11.563,-11.053,-10.917,-10.822,-11.437,-11.111,-11.36,-10.957,-11.547,-11.496,-11.037,-10.856,-11.41,-12.081,-11.994,-12.169,-12.041,-12.099,-12.078,-12.039,-11.909,-11.974,-11.924,-12.058,-12.195,-12.229,-12.185,-12.041,-11.964\n-12.145,-11.786,-11.329,-11.254,-11.06,-11.192,-11.927,-12.114,-10.082,-13.076,-10.735,-11.683,-12.443,-13.419,-10.252,-11.514,-12.453,-12.363,-11.376,-10.763,-11.528,-11.341,-10.949,-10.774,-11.085,-11.162,-11.256,-11.199,-11.288,-11.352,-11.414,-11.955,-11.43,-12.038,-12.357,-12.201,-12.425,-12.217,-12.256,-12.153,-11.9,-11.671,-11.666,-12.18,-12.221,-12.145,-11.845\n-10.811,-10.912,-10.14,-9.1592,-7.7287,-10.826,-10.934,-10.342,-9.9705,-13.187,-9.7921,-10.608,-11.638,-13.811,-13.193,-13.416,-13.435,-11.796,-11.503,-11.407,-11.218,-10.847,-11.518,-11.021,-11.063,-11.053,-11.214,-10.988,-11.174,-11.743,-12.097,-11.939,-11.563,-11.721,-11.998,-12.777,-12.704,-12.513,-12.4,-12.108,-11.924,-11.482,-11.717,-12.247,-12.265,-12.186,-11.845\n-9.7507,-10.358,-9.7372,-9.8628,-10.574,-11.593,-11.585,-11.729,-12.382,-10.795,-9.0559,-11.176,-12.188,-14.684,-14.163,-13.598,-15.075,-12.896,-11.76,-11.319,-11.511,-11.004,-11.055,-11.533,-10.999,-10.499,-10.52,-10.385,-11.267,-11.679,-12.157,-12.121,-12.049,-12.264,-11.907,-12.382,-12.394,-12.548,-12.501,-12.256,-11.81,-11.703,-11.91,-12.241,-12.291,-12.033,-11.496\n-9.4447,-8.5066,-8.6357,-9.3116,-10.561,-11.837,-11.306,-10.424,-10.442,-11.841,-12.514,-9.7338,-10.555,-15.33,-16.423,-15.611,-14.72,-12.188,-11.334,-11.779,-11.472,-10.427,-10.149,-11.491,-11.033,-10.625,-10.064,-10.64,-11.116,-11.098,-11.371,-11.414,-12.065,-12.069,-12.189,-12.426,-12.506,-12.714,-12.486,-12.115,-11.966,-11.978,-11.705,-11.872,-12.066,-11.787,-11.208\n-8.7989,-8.0195,-8.0142,-9.0954,-9.68,-9.9292,-10.36,-11.044,-10.419,-10.841,-11.893,-11.925,-12.658,-14.601,-14.013,-10.612,-11.817,-11.295,-11.541,-12.083,-11.323,-10.69,-10.809,-10.216,-10.955,-10.702,-10.344,-10.553,-10.821,-10.769,-11.292,-11.376,-11.611,-11.623,-12.383,-12.471,-12.593,-12.42,-12.315,-12.229,-12.338,-11.985,-11.484,-11.957,-12.178,-11.786,-11.489\n-8.1483,-8.3502,-8.272,-8.7546,-7.574,-8.9145,-9.5827,-9.9706,-10.303,-9.3112,-8.3582,-11.423,-9.2843,-8.9766,-10.957,-9.2993,-10.389,-11.374,-11.842,-11.667,-11.497,-11.479,-10.474,-10.257,-11.292,-11.245,-10.447,-10.879,-10.85,-11.049,-11.488,-11.494,-11.78,-11.957,-12.515,-12.571,-12.506,-12.272,-12.101,-12.249,-12.328,-12.243,-11.9,-12.155,-12.353,-11.302,-11.36\n-7.0719,-8.6379,-7.6229,-7.9264,-7.8839,-8.0989,-7.5969,-7.3795,-10.448,-9.5319,-10.151,-11.413,-9.463,-9.3166,-9.3264,-10.586,-11.164,-11.085,-12.879,-10.851,-11.565,-11.073,-10.739,-11.058,-10.729,-10.594,-10.592,-11.217,-11.242,-11.834,-11.791,-11.256,-11.495,-11.613,-12.405,-12.916,-13.084,-12.543,-12.346,-12.549,-12.606,-12.48,-12.082,-12.03,-11.954,-11.574,-11.751\n-8.2237,-7.8515,-7.4966,-8.5675,-6.2282,-7.5849,-5.897,-7.8343,-9.9941,-9.9422,-10.097,-10.859,-13.135,-14.441,-12.637,-10.556,-12.174,-12.954,-12.264,-11.759,-11.771,-10.861,-10.75,-10.289,-10.818,-11.377,-10.958,-10.891,-11.209,-11.846,-11.86,-11.923,-12.266,-12.116,-12.662,-12.81,-13.229,-12.82,-12.792,-12.721,-12.569,-12.498,-12.336,-12.292,-12.301,-12.389,-12.45\n-8.2332,-7.4586,-7.051,-6.9346,-5.9584,-7.242,-7.2911,-8.5701,-7.9401,-9.312,-10.439,-11.299,-11.747,-11.82,-10.777,-10.575,-10.141,-10.761,-12.168,-12.306,-11.216,-10.398,-10.77,-10.737,-11.238,-11.574,-10.601,-10.832,-11.302,-11.95,-11.441,-12.386,-12.532,-12.62,-12.707,-12.446,-13.068,-13.334,-13.02,-12.941,-12.633,-12.463,-12.702,-12.455,-12.452,-12.117,-12.232\n-8.3959,-8.4947,-7.5975,-7.1637,-7.9567,-8.0246,-7.6431,-8.3471,-8.7975,-9.0966,-8.9353,-10.441,-11.334,-11.961,-10.478,-9.396,-8.7053,-9.7278,-12.433,-12.105,-10.909,-10.534,-11.181,-11.056,-10.851,-10.641,-10.669,-11.37,-11.927,-12.056,-11.987,-12.377,-12.478,-12.579,-12.654,-12.913,-12.861,-12.91,-13.142,-12.573,-12.875,-12.888,-12.486,-12.374,-12.309,-11.839,-12.101\n-8.6548,-8.9699,-8.4866,-8.3084,-8.4265,-8.0903,-9.0107,-8.9518,-8.8303,-8.9325,-8.816,-8.4239,-9.8708,-11.385,-11.234,-10.585,-11.832,-12.762,-13.222,-13.68,-12.711,-11.052,-9.5082,-10.337,-11.202,-10.771,-10.814,-12.041,-12.938,-12.427,-12.216,-12.322,-12.49,-12.588,-12.582,-12.671,-12.675,-12.748,-12.785,-12.879,-12.976,-12.551,-12.393,-12.483,-12.234,-12.119,-12.423\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_060619-061002.unw.csv",
    "content": "2.8591,2.9882,3.0856,3.0293,3.3106,3.5314,3.1864,2.9408,2.8797,2.744,2.197,2.3794,2.2133,1.7922,1.7506,1.7997,1.8353,1.6739,1.6506,1.8189,2.7181,3.1456,3.0522,2.4501,2.0694,2.1867,2.0684,2.6056,2.9083,2.4632,1.9144,1.6824,1.7515,1.7755,1.8777,2.0512,1.9742,1.3242,0.98122,1.4889,2.2815,3.2152,3.839,4.0744,4.3811,4.6702,4.787\n2.9887,3.1277,3.1572,3.2543,3.3827,3.2234,2.7903,2.5307,2.7358,2.7487,2.6185,2.6186,2.2048,1.9207,2.0073,2.1191,2.302,1.9296,1.8792,2.1235,2.4277,3.2178,2.9924,2.6762,2.2257,2.0535,2.1451,2.6489,2.7951,2.4553,2.4085,2.659,2.3098,1.8689,1.7234,1.8035,1.6909,1.7461,2.1923,2.8577,3.3014,3.5585,3.7847,4.0734,4.4852,4.6275,4.5793\n2.9777,2.9356,3.0983,3.2094,3.2302,3.0204,2.5008,2.2992,2.8758,3.0132,2.7189,2.8169,2.3144,2.1292,2.1669,2.0909,2.3485,2.0632,1.922,2.2216,2.4668,2.7407,2.0274,2.1605,2.3928,2.0726,2.3592,2.9055,2.7759,2.3861,2.7572,2.8112,2.5515,2.3166,2.0197,2.0742,2.6633,3.1107,3.5913,4.1304,4.0249,3.8635,4.033,4.136,4.3588,4.2964,4.223\n2.9589,3.0246,3.0024,3.1369,3.2382,3.0102,2.4176,2.3879,2.6532,2.8009,2.6123,2.7184,2.5455,2.2093,2.1106,2.1277,2.4511,2.2591,2.0486,2.1968,2.3998,2.631,2.6559,2.5949,2.4732,2.5041,2.7626,2.8787,2.991,2.9655,2.7939,2.7693,2.8642,2.6142,2.2381,2.2134,2.5526,3.1634,3.7283,4.0415,4.0635,3.9949,3.992,3.8721,3.8054,4.106,4.4439\n2.8035,2.4434,2.5592,2.9538,3.1048,2.8249,2.535,2.2416,2.0656,2.3102,2.6044,2.703,2.7335,2.3721,2.2663,2.5101,2.7943,2.1207,2.2085,2.4254,2.4975,2.8476,2.9225,2.5754,2.5333,2.8586,3.2338,3.3145,3.2093,3.2799,2.8621,2.6339,2.8917,2.7878,2.2915,2.2391,2.7406,3.2532,3.679,4.0454,4.0962,3.8873,3.8441,3.8096,3.8547,3.985,4.1828\n3.2091,3.2323,3.0152,3.1625,3.0827,2.9199,2.5425,2.3799,2.3124,2.6522,3.0046,3.1345,2.8541,2.4981,2.3549,2.5278,2.4097,1.9587,2.0315,2.4113,2.7484,2.8872,3.0371,2.6205,2.2785,2.7228,3.2591,3.4157,3.2523,3.3474,3.2239,2.9145,2.9378,2.6983,2.3246,2.267,2.9245,3.4379,3.7196,4.1981,3.8832,3.3179,3.3045,3.5283,3.7873,4.2595,4.4673\n3.6276,3.7735,3.6526,3.4148,2.9672,2.6406,2.5486,2.7754,2.948,3.0865,3.1333,3.4549,3.1518,2.6152,2.4743,2.5,2.2706,2.0833,1.9856,2.5813,3.2382,3.2304,3.2411,3.168,3.0606,3.4477,3.7231,3.3276,3.1853,3.5061,3.4457,3.186,3.252,3.0816,2.8908,2.9376,3.3065,3.6112,3.437,3.7162,3.7021,2.941,3.0228,3.3948,3.7556,4.1855,4.3408\n3.8982,3.9395,3.5133,3.4648,3.0122,2.4577,2.4032,2.7573,2.9273,2.8951,2.8855,3.0852,3.055,2.7477,2.6765,2.3813,1.8607,1.9841,2.5126,3.0724,3.8749,3.6426,3.3228,3.2424,3.3069,3.5449,3.9835,3.5483,2.9547,3.5339,3.5056,3.2707,3.3131,3.184,3.2352,3.4581,3.7382,3.5735,3.299,3.3702,3.4658,3.1447,3.3041,3.798,3.9202,4.0595,4.2441\n3.6017,3.9774,3.9574,3.4499,3.0048,2.6243,2.7244,2.976,3.1583,3.0648,2.734,2.5804,2.6186,2.6616,2.5288,2.3851,2.079,2.2278,2.8887,3.695,4.0008,3.6785,3.4791,3.495,3.2065,3.3266,3.3236,3.2531,3.0046,2.9366,3.0523,3.0707,3.1415,3.1854,3.2014,3.5629,3.7058,3.1975,2.6996,2.8828,3.1014,3.1904,3.1742,4.0652,4.0743,4.0906,4.2119\n3.6553,3.7787,3.7913,2.552,2.6048,2.7731,2.7639,3.1709,3.4737,3.3972,2.7795,2.4393,2.519,2.6006,2.2869,2.4906,2.7585,3.0768,3.2442,3.3131,3.4085,3.1261,3.2394,3.3032,3.1576,3.3183,3.3469,3.1771,3.0311,2.9614,2.9351,2.9581,2.9873,3.057,2.9224,2.9613,3.1645,3.3443,2.8383,2.6327,2.7595,3.0402,3.4141,4.0691,4.3785,4.43,4.4401\n3.6295,3.3722,2.9907,2.4484,3.0086,2.9308,2.9662,3.1977,3.3801,3.296,2.9413,2.5294,2.6532,2.7655,2.7594,2.8246,2.7444,3.1493,3.7181,3.9491,3.676,3.2676,3.1603,2.9766,3.1557,3.2585,3.1878,3.134,3.1136,3.0564,2.8664,2.8509,2.9994,2.8018,2.5499,2.9265,3.3184,3.3277,2.8647,2.5278,2.7488,3.1051,3.4183,3.8726,4.4568,4.6142,4.4923\n3.7388,3.5623,3.0553,2.8239,3.1322,2.9319,2.83,2.9965,3.1146,3.1939,2.76,2.8321,3.0307,2.9429,2.6826,2.5523,2.582,2.7386,2.8053,2.5451,2.6686,3.1396,3.408,3.0855,3.1918,3.4068,3.3166,3.2742,3.2856,3.0132,2.8223,2.7974,2.8819,2.6784,2.5169,2.9719,3.651,3.4917,2.8469,2.7662,2.9495,3.1149,3.3831,3.8943,4.3169,4.9055,4.8162\n3.9928,4.0911,3.3902,2.9311,2.8707,2.7189,2.6399,2.7117,2.8875,3.0292,2.885,2.6838,3.0722,3.0861,2.6591,2.2669,2.0424,1.9531,2.3367,2.5033,2.6917,3.2883,3.6384,3.4133,3.5657,3.4904,3.2739,3.1025,3.2533,3.1835,2.9005,2.7195,2.6198,2.7311,2.803,3.1548,3.6973,3.8759,2.7876,2.689,3.0706,3.221,3.514,3.7419,4.3991,4.6605,4.9771\n3.6652,3.3633,3.1823,2.8017,2.6257,2.2924,2.6977,2.8227,2.9043,2.8551,3.0183,3.1607,3.1429,3.0802,2.6387,2.2702,2.2919,2.3685,2.7913,3.2585,3.1524,2.7853,2.6718,2.5911,2.3709,2.3195,2.6786,2.7589,2.9682,3.1298,2.978,2.8406,2.6828,2.8011,3.1905,3.8082,4.1396,4.0469,3.3919,2.9277,3.5702,3.6626,3.8314,4.1522,4.6488,5.0292,5.2363\n3.6744,3.447,2.8231,2.427,2.4821,2.4215,3.069,3.1126,2.6406,2.7245,2.9874,3.0517,3.1555,3.1294,2.9296,2.599,2.6686,2.5971,2.8412,3.2592,2.833,2.2936,2.1775,2.0867,2.1943,2.5582,2.9257,2.9765,3.0465,3.2838,3.5688,3.4565,3.0455,2.9224,3.3799,4.0415,4.1009,3.7205,3.2183,2.9993,3.2514,3.8341,4.0845,4.4812,4.6655,5.1356,5.3346\n3.765,3.7772,3.2836,2.2803,1.415,2.0415,2.7596,3.222,2.7428,2.9287,3.1246,3.2729,3.3077,3.0163,2.7605,2.7976,2.6237,2.1556,1.9843,2.2994,2.1537,2.1022,2.1682,1.9756,2.391,3.0032,3.3157,3.4166,3.6765,4.2249,4.2212,3.8957,3.5126,3.3925,3.7656,4.2958,4.0589,3.4299,3.1212,2.9118,3.1159,3.6745,3.9768,4.0999,4.1703,4.2854,4.5069\n3.8733,4.1597,3.4548,2.0255,1.0171,1.2896,2.2595,3.0392,3.3503,3.4022,3.4807,3.4414,3.2173,2.993,3.1081,2.9841,2.4602,1.9982,1.9496,2.1316,2.6821,2.8875,2.7943,2.7767,3.5927,4.0193,3.7381,3.3767,3.7713,4.4467,4.3804,3.9858,3.8426,3.8934,4.1537,4.5945,5.0212,4.4503,3.6707,3.2735,2.8927,3.2515,3.8443,4.0136,4.0861,4.2324,4.1942\n3.6402,3.6112,2.894,2.0907,0.80548,1.3408,2.1586,3.3521,3.3058,2.9147,2.9797,3.0985,2.9448,3.0779,3.0775,2.925,2.6976,1.9442,1.7439,2.1229,2.7967,3.3038,3.3837,3.0896,3.556,4.1559,3.9965,3.2363,3.0568,3.8328,4.1691,4.0788,3.9948,3.9204,4.2863,4.811,5.5258,5.2584,5.0811,3.5762,2.9966,2.6501,3.4428,4.2296,4.091,4.1234,4.145\n3.2231,3.0286,2.676,1.8225,1.6889,1.765,2.4171,2.8426,2.6606,2.4809,2.9048,3.0469,2.714,3.1546,3.4277,3.5738,3.4114,2.7091,2.1166,1.8381,2.0404,2.518,3.2902,3.2262,3.1753,3.9085,4.3693,3.8076,2.8696,3.4228,4.0475,4.1521,4.6565,4.7089,4.9228,5.0996,5.4588,5.3565,5.0202,4.0595,3.641,3.0355,3.2554,4.0717,3.961,3.9606,4.1575\n2.3988,2.4975,2.8716,2.5564,2.1437,1.9812,2.6052,2.8666,2.9231,2.9408,3.1869,3.1672,3.0207,3.4182,3.8453,3.7863,3.7707,3.5127,3.1278,2.7178,2.8647,3.1647,3.6014,3.4458,3.1557,3.7041,4.1508,4.3313,4.0326,3.2632,3.783,4.4369,5.2977,5.1392,5.5685,5.3882,5.4838,5.4947,5.0904,4.7558,4.1706,3.7389,3.6219,3.9575,3.9591,3.7266,4.0103\n2.4373,2.5239,3.0404,3.1819,3.0312,2.6684,2.9458,2.7858,2.8986,3.3973,3.462,3.453,3.3913,3.5455,3.8498,4.0334,4.2454,4.3605,3.7746,3.7495,3.9395,4.0481,3.899,3.609,3.5116,3.815,4.1523,4.7802,4.6964,4.1392,4.5293,4.9676,5.6986,5.6939,6.1367,5.7558,5.3169,5.2763,4.9716,4.614,4.2621,4.2239,4.2209,4.0393,3.9086,3.8685,4.0707\n2.4319,2.6905,2.6771,2.3819,1.7807,0.93101,2.3846,2.7223,3.2344,3.356,3.4479,3.6088,3.8041,3.6469,3.5132,4.1137,4.0845,4.1766,4.3365,4.4581,4.6116,4.4989,4.1053,4.0748,4.139,4.4534,4.8113,5.1546,5.2438,5.2155,5.1219,5.1379,5.6865,5.7646,6.0435,5.9378,5.6031,5.0532,4.8026,4.7481,4.4538,4.3882,4.2758,4.1057,3.9661,4.0446,4.0413\n2.8199,2.8001,3.138,2.6865,1.5524,0.98518,2.5024,3.4673,3.5195,3.1416,3.4493,4.1057,4.05,4.1182,3.6738,3.2648,3.5218,3.9019,4.2728,4.7595,4.9839,4.8825,4.7549,4.9359,4.8833,4.956,5.2714,5.1509,5.0891,5.2908,5.3834,5.2868,5.3476,5.4117,5.4685,5.4808,5.2895,5.1251,5.0321,4.7186,4.7866,4.6166,4.467,4.2278,4.2265,4.1519,3.9667\n3.1343,3.3464,3.3549,3.2381,3.0397,3.4931,3.8689,3.9176,3.7333,3.435,3.5758,3.5612,3.8595,4.5448,4.8128,4.6427,3.4605,3.9252,4.5272,5.0366,5.2524,5.243,5.1782,5.086,4.917,5.1922,5.5843,5.6953,5.6092,5.7917,5.9194,5.7192,5.6801,5.554,5.35,5.4152,5.3544,5.241,5.3179,5.1876,5.0171,4.7691,4.6196,4.3184,4.2898,4.4967,4.4224\n2.5526,2.946,3.4553,3.3912,3.1609,3.194,3.3599,3.569,3.7772,3.8632,3.7318,3.6108,3.8449,4.7809,4.7397,4.3054,4.0034,4.6588,5.4033,5.693,5.5907,5.368,5.4703,5.4461,5.0244,5.651,5.9534,6.1638,6.4784,6.8658,6.3092,6.0114,6.0037,6.0299,6.2687,6.3858,5.7496,5.5399,5.4908,5.3251,5.0591,5.0238,4.83,4.47,4.2763,4.3204,4.5359\n3.0303,2.9172,3.1357,3.2172,3.1427,2.9518,3.2274,3.5199,3.6401,4.0168,4.074,4.2552,4.3723,4.7284,4.866,4.665,4.678,5.1521,5.8546,6.0444,5.984,5.915,5.7411,5.6835,6.1078,6.6747,7.1183,7.14,7.8985,8.3114,8.2511,7.1399,6.4974,6.2398,6.4272,6.4697,5.9691,5.8001,5.4853,5.2014,5.051,4.9293,4.8237,4.6102,4.398,4.0689,4.0052\n3.0922,3.0269,2.6587,2.6656,2.7281,2.8255,2.9263,3.3062,3.5109,3.6579,3.8611,4.2998,5.0519,5.4255,5.3107,4.8333,4.7807,5.5606,6.4124,6.5514,6.4202,6.232,6.16,6.0966,6.7633,7.2528,7.2327,7.6495,8.3807,9.3002,8.8936,8.4198,7.6001,6.9402,6.7813,6.5002,6.0841,5.6211,5.3037,5.1712,4.9847,4.7403,4.795,4.8093,4.5958,3.9709,3.6002\n3.3338,3.2788,2.7955,2.3217,2.6001,2.8966,2.9899,2.7933,3.3276,3.4736,3.7268,4.0773,4.4754,5.0094,5.3067,5.3095,5.5341,6.1056,6.479,6.6384,6.6036,6.2963,6.1378,6.3912,6.9223,7.1879,7.3512,7.4235,8.3751,9.3276,9.748,9.6897,9.2742,8.5091,7.7971,7.049,6.0676,5.5322,5.2733,5.1761,4.7248,4.7873,4.7064,4.5901,4.4164,4.0381,3.8399\n3.5985,4.1369,3.3893,2.9816,3.0481,3.1607,2.9709,2.4956,2.9469,3.3717,3.4605,4.0396,4.3915,4.8559,5.2382,5.4767,5.5586,5.868,6.4994,6.851,6.9233,6.4491,6.6839,7.001,7.2304,7.6526,8.7286,NaN,NaN,NaN,12.578,11.277,11.024,9.1307,7.6423,7.1052,6.235,5.6518,5.6155,5.3294,4.4534,4.3506,4.778,4.5911,4.4466,4.149,3.8741\n3.8201,3.4253,3.3369,3.1265,3.1731,3.1118,3.2208,2.9112,3.1182,3.35,3.4295,3.7597,4.1725,4.7232,5.2174,5.3409,5.1936,5.112,5.4949,6.2512,6.8337,6.4181,6.7774,7.6353,8.6677,9.1338,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.252,7.6953,7.161,6.8941,6.3862,5.9848,5.2671,4.2353,4.345,4.9634,4.7245,4.0892,3.673,3.5919\n3.1364,3.4516,3.5118,3.4642,3.2997,2.7192,2.1355,2.501,3.1815,3.8075,3.9535,3.9177,4.0668,4.4835,5.2983,5.2608,5.0171,5.122,5.3349,5.7368,6.2872,6.5169,6.9302,8.1554,9.9893,11.215,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.2935,6.5866,7.2182,7.9951,7.6461,6.6392,5.2122,4.5135,4.4262,4.7635,5.0439,4.21,3.525,3.4542\n3.0356,3.069,3.4012,4.0972,3.9524,3.6282,3.1467,3.2676,3.3486,4.0189,4.4284,4.2449,4.309,4.5118,5.1548,5.0146,4.7981,5.3986,5.984,6.1037,6.3296,6.7503,7.2231,8.2369,8.3905,8.637,8.5723,NaN,NaN,NaN,NaN,NaN,7.5611,6.477,7.1781,7.8406,8.0528,7.7237,6.6006,5.1853,4.6143,4.4488,4.3314,5.0378,4.1182,3.2285,3.2701\n2.8744,3.1746,3.4588,3.8606,4.354,4.0263,3.6861,3.8403,3.926,4.2866,4.1642,4.0003,4.3502,5.0505,5.0731,4.9232,4.4366,4.7923,5.6944,6.1135,6.3041,6.851,7.2215,7.6393,7.4591,7.3066,6.7965,5.5805,5.5757,6.0892,6.5377,6.6831,5.6971,4.8248,6.3863,7.1455,7.3598,7.7649,6.5396,5.265,4.5884,4.3341,4.3303,4.2731,3.6134,3.2626,3.3096\n2.9735,3.2535,3.521,4.0209,4.2726,3.7146,3.5033,3.5627,3.6376,4.0714,4.1584,4.1127,4.109,4.588,4.572,4.6988,4.6254,4.8732,5.196,5.4683,5.6924,6.2032,6.796,7.127,6.8581,6.0616,6.5205,5.8336,5.7099,5.9564,5.2483,4.6316,3.6553,4.3913,NaN,5.2174,7.1744,8.0085,7.4834,5.4989,4.34,4.267,4.7557,4.3863,3.366,3.0927,3.0998\n3.2982,2.9232,3.0484,3.4583,3.3349,2.9256,2.3979,3.0005,3.3944,3.7937,4.029,4.1594,4.0842,3.8609,3.8921,4.2754,4.6142,4.7373,4.9299,5.245,5.4332,5.2243,5.7309,7.8757,NaN,5.6856,6.5709,5.4559,3.1645,4.0645,4.2245,3.328,3.515,2.5961,NaN,NaN,NaN,NaN,NaN,5.4492,4.6416,4.7534,5.0251,4.1659,3.3929,3.14,3.0154\n2.7267,2.4488,1.9631,2.3989,3.1127,3.0666,3.044,3.2314,3.4197,3.2439,3.9572,4.1392,4.0926,3.5601,2.6137,3.2761,4.4835,4.5125,4.5961,4.9061,5.2508,5.2102,7.1623,NaN,NaN,NaN,NaN,5.3207,5.0636,4.8551,3.7545,3.5384,1.9933,NaN,NaN,NaN,NaN,NaN,6.8344,5.2414,4.8563,4.9118,4.8977,4.2417,3.4211,3.3227,3.323\n2.4598,2.502,2.4399,2.0345,3.0263,3.3772,3.4254,3.4079,3.2786,3.0275,3.2793,3.44,3.6071,2.4921,2.5256,3.422,3.8557,4.0835,4.4918,4.7623,5.1343,6.3048,8.7101,NaN,NaN,NaN,3.4806,3.4983,5.8143,5.3244,3.854,4.0455,4.4918,7.2083,NaN,NaN,NaN,NaN,5.7955,5.0746,4.9646,4.6392,4.1287,3.4325,3.1361,3.2632,3.2573\n2.405,2.5252,2.4697,2.5432,2.9841,3.332,3.5492,3.7438,3.7103,3.4447,3.4553,3.0216,2.6979,2.0061,2.6559,3.9489,4.0727,4.3212,4.8717,5.2855,5.6534,6.4232,7.6021,NaN,NaN,NaN,NaN,2.9199,5.3834,5.634,4.716,4.3872,4.6429,7.781,7.9812,7.8727,5.9681,5.8264,5.6228,4.2999,4.0056,3.7843,3.3194,3.1634,3.2555,3.3204,3.3518\n2.8867,2.8273,2.4845,2.3862,2.5977,3.0281,3.6037,3.868,3.8853,3.8755,3.8766,3.682,3.4932,3.1678,3.5202,4.0088,3.7556,3.939,4.5534,5.2362,5.5442,6.0535,6.0845,4.5869,2.5335,1.6244,2.2426,3.9213,4.5719,4.0667,3.8059,3.8536,3.8445,5.3202,6.9502,6.2348,5.7058,5.5354,4.8721,3.6905,3.2462,3.0563,3.5884,3.7314,3.852,3.6074,3.4316\n2.7885,2.6041,2.4875,2.3783,2.8317,3.5848,3.606,3.8527,4.0517,4.1246,4.1149,3.9668,3.6352,3.5365,4.097,3.9965,3.8506,4.5931,4.5329,5.407,5.9779,5.7284,4.7648,5.4881,6.9314,7.7874,7.0337,4.9413,4.3661,3.7923,2.9196,2.3841,3.2047,3.9557,5.7899,6.2182,6.1269,5.4962,4.3784,3.0877,2.8085,3.0764,3.5803,3.7457,3.9107,3.783,3.6729\n2.8845,2.5733,2.8395,3.1473,3.4778,3.8671,4.1093,4.1755,4.2363,3.8958,4.0901,4.0254,3.8951,3.8754,4.282,4.4837,4.4165,4.78,4.7938,5.6143,6.0338,6.2664,5.1085,4.7372,4.4953,NaN,NaN,3.4758,2.8715,1.8177,0.28365,-0.16408,2.1867,4.5988,5.343,5.7414,6.015,5.9483,4.8302,3.3209,2.6073,2.9531,3.5539,3.4854,3.4127,3.5898,3.6722\n3.5718,3.2352,2.9966,3.1282,3.5754,3.7178,4.3022,4.5053,4.1844,3.7637,3.8015,3.9916,4.0581,4.1142,4.1375,4.2945,4.7448,5.2411,5.5152,6.2463,5.8815,5.472,5.0459,4.4123,3.5463,NaN,NaN,2.0766,3.0756,2.5869,1.8647,2.1409,3.0492,4.292,4.9078,5.3426,5.3201,5.3505,4.6697,3.4927,2.8962,3.1227,3.5957,3.4514,3.4754,3.8969,3.8252\n4.2505,3.6082,3.2759,3.3928,3.3548,3.4152,3.8283,4.2879,3.9899,3.9507,4.0675,4.2198,4.2086,4.1427,4.0653,3.9246,4.2012,4.7944,5.2672,5.3714,5.1194,4.5678,4.3807,4.7414,NaN,NaN,NaN,NaN,5.2711,4.1957,2.8356,2.9419,3.2097,4.4092,4.7274,4.6811,4.9178,4.9763,4.5877,3.5857,3.4909,3.2372,3.3566,3.513,3.6472,3.8857,3.4841\n3.9254,3.7279,3.5837,3.5437,3.2099,3.5065,3.8792,4.2768,4.0848,4.0467,4.2241,4.4572,4.4772,4.2524,4.2458,4.196,4.2148,4.502,4.7763,4.8065,4.9153,4.9779,6.6106,6.8245,NaN,NaN,NaN,5.9764,5.476,4.2571,3.2934,2.8645,2.8872,3.4598,3.6644,3.7511,4.1049,4.5063,3.7422,3.3968,3.1816,3.1017,3.4516,3.4067,3.3215,3.4314,3.3746\n3.8038,3.7012,3.5818,3.7698,3.6874,3.8022,4.1313,4.2793,4.1931,4.0398,4.076,4.3487,4.3817,4.3048,4.3894,4.3153,4.2652,4.6286,4.7296,5.0925,6.2409,6.5136,7.1544,8.1811,9.3725,8.7244,7.5029,5.1344,4.3992,3.6349,3.1081,2.9153,2.6212,3.0217,3.1434,3.5061,4.0088,3.7833,2.2144,2.5115,2.787,2.8123,3.4037,3.7332,3.5242,3.321,3.2745\n3.4837,3.4007,3.2143,3.2815,3.5367,3.8021,4.216,4.093,3.8391,3.7687,3.992,4.2858,4.5728,4.7194,4.5088,4.5399,4.5804,4.6015,4.1322,3.6467,4.3635,4.944,5.328,5.3041,5.0115,5.2241,5.2808,4.3508,3.8423,3.5348,3.7538,3.7898,3.6587,3.4311,3.3556,3.0468,2.9928,2.3471,1.3411,1.5444,2.0853,2.3964,3.1656,3.8427,3.6631,3.3503,3.1728\n3.0006,3.3534,3.214,3.0465,3.3321,3.6556,3.9994,3.7687,3.5165,3.9165,4.2696,4.3227,4.3208,4.3397,4.1838,3.9579,3.9542,3.9949,3.9294,3.2919,2.5871,3.6398,4.2703,4.2012,4.3986,4.2817,3.7173,3.7074,3.4698,4.3624,4.89,4.2872,3.8297,3.4475,3.2675,2.0754,1.6591,1.4333,0.66447,1.0271,1.6263,2.393,2.983,3.4573,3.6047,3.5018,3.4583\n3.2692,3.2823,3.3875,3.4958,3.9059,3.9749,3.6901,3.4224,3.5621,3.8184,4.1839,4.3703,4.3456,4.206,3.9486,3.6735,3.5073,3.5405,3.4598,3.1894,2.7006,2.4294,2.7669,3.5555,4.1599,3.9,3.462,3.32,3.5863,3.6637,3.296,3.1314,2.8048,3.3582,1.9487,0.97941,0.29325,0.79456,0.84134,0.85795,0.83594,1.5634,2.7064,3.5008,3.5812,3.3959,3.662\n3.2183,3.1229,3.5503,3.7013,3.8344,3.812,3.5653,3.2384,3.25,3.4384,3.8095,3.9324,3.8184,3.9618,3.621,3.3181,3.1988,3.2944,2.7631,2.5876,2.1117,2.2963,2.8945,3.7597,3.8099,3.2648,3.1108,3.0142,3.2927,2.681,1.7928,1.7839,2.2644,1.727,0.97683,-0.20547,-0.07395,0.58928,1.3258,1.2064,0.85937,0.86349,2.3159,2.8115,3.0383,3.2193,3.3872\n2.8169,3.0036,3.4473,3.432,3.2545,3.3889,3.5475,3.5145,3.2145,3.0293,3.0009,3.3454,3.673,3.831,3.4408,3.076,3.4926,3.5322,3.1899,2.5794,1.9947,2.4904,3.4219,3.54,3.3401,2.8301,2.5084,2.588,2.8745,1.8002,0.81791,1.2188,0.9371,0.43968,0.28064,-0.084248,0.26216,1.6375,2.6003,2.3887,1.5246,1.2539,1.439,2.3144,2.6328,2.924,3.1121\n2.7114,2.9365,3.2766,3.378,3.1445,3.0682,3.1679,3.1493,3.1728,3.0586,3.0096,3.3001,3.8926,3.7702,3.3339,3.1256,3.423,3.7077,3.7458,3.7737,3.6283,3.2628,3.2169,3.0735,2.5318,2.3812,1.913,2.0995,2.4631,1.7263,0.76864,1.1364,0.74854,0.31093,0.761,1.3432,1.8014,2.3082,2.7001,2.4828,1.7781,0.68788,0.58067,1.1647,2.35,2.9114,3.1486\n2.9955,3.1798,3.5646,3.4223,3.0581,3.0212,3.0422,2.9509,2.9098,3.3268,3.2719,3.4354,3.868,3.867,3.02,3.0559,3.1769,3.6678,3.7788,3.9078,3.6998,3.119,3.0069,2.7992,2.3935,1.9994,1.7895,1.9314,2.2707,2.1389,1.9462,1.6014,1.0976,0.8649,1.2093,1.7921,2.3269,2.385,2.3603,2.1037,0.82482,0.54661,0.26677,1.4008,2.5303,3.0825,3.4117\n3.3278,3.7435,4.3133,3.619,3.1916,3.0435,2.9149,2.9025,3.0356,3.5608,3.4796,3.4203,3.5374,3.6238,3.3646,3.3337,3.2426,3.698,3.9465,4.0635,3.6801,3.1488,2.7884,2.5889,2.1618,2.13,1.8588,1.8699,2.28,2.6327,2.3915,1.7447,1.339,1.1375,1.4933,1.2573,1.0844,0.98159,1.3269,1.2108,0.60148,0.40189,0.57476,1.8515,2.7416,3.2773,3.845\n2.5357,3.3142,3.5973,3.7322,3.6746,3.3558,3.1256,3.1828,3.3429,3.3489,3.3,2.764,3.2338,3.3846,3.3512,3.2066,3.2904,3.6322,3.7341,3.6998,3.5413,3.398,2.957,2.5757,2.1848,1.9346,1.7855,1.8663,2.2971,2.7813,2.5426,1.7558,1.4033,0.96233,0.90503,0.28668,-0.14203,0.38985,0.9842,1.0201,0.62349,0.39668,0.72907,2.1529,3.0964,3.371,3.9605\n2.447,2.8378,3.5695,3.5375,3.895,3.5799,3.1674,3.3121,3.3671,3.0714,2.8577,2.5677,2.7956,3.3052,3.4685,3.4589,3.3612,3.3468,3.2016,3.448,3.5153,3.4357,3.0209,2.4042,2.0525,1.8691,2.1543,2.2947,2.6486,2.8032,2.2658,1.5994,1.1306,0.053762,-0.20939,-0.41181,-0.62275,0.0027046,0.61761,0.96164,0.7995,0.67417,1.6809,2.7424,3.0949,3.3574,3.8416\n3.0037,3.4187,3.2953,3.0319,3.7412,3.4438,2.6694,2.6272,2.6319,2.7162,2.7032,2.6797,2.863,3.0934,3.3093,3.3631,3.2645,3.1263,3.1032,3.4343,3.1392,2.7734,2.5949,2.2487,2.0573,1.9641,1.9715,2.0506,2.3027,2.5865,1.868,1.2901,0.72305,0.16195,-0.018827,-0.083836,-0.39862,-0.13597,0.39821,0.67594,0.87004,1.0299,1.4539,2.0073,2.8543,3.7227,4.2421\n2.8953,3.1667,2.9684,2.9678,3.033,2.8114,2.5069,2.0498,1.7613,1.6916,2.4801,2.6257,3.0391,3.3683,3.3858,3.3508,3.1574,3.0638,3.3083,3.6255,3.2314,2.6887,2.3451,2.1236,2.157,2.1502,1.9304,1.801,1.819,1.8493,1.8577,1.6525,0.78088,0.39275,0.23233,0.18222,-0.35759,-0.6364,-0.16222,0.67939,0.8843,0.95889,0.96679,1.3854,2.1866,3.4823,4.3519\n2.4013,2.6488,2.614,2.8657,3.2347,3.5718,2.9408,2.3765,1.9598,1.8688,2.6595,2.5968,3.1217,3.3008,3.6515,3.5946,3.2925,3.2189,3.567,3.4845,3.4531,2.8202,2.5681,2.4315,2.4012,2.2166,2.134,1.8736,1.8809,1.804,2.0318,1.8812,1.0624,0.44785,0.44777,0.60321,-0.21787,-0.88499,-0.34377,0,0.51,1.1491,1.1596,1.2,2.1695,3.4837,3.9198\n2.3921,2.6941,2.6778,2.5598,2.6115,2.8406,2.8327,2.9337,2.8887,2.6986,2.8961,2.8125,3.0865,3.1872,3.159,2.9974,2.8245,2.7403,3.0624,3.3019,3.2679,3.2521,2.8145,2.638,2.4571,2.2372,2.2962,2.381,2.0761,1.3354,1.7191,1.724,0.92631,-0.33361,0.019241,0.43447,0.28016,-0.20505,-0.35478,-0.45239,0.365,1.3779,1.6064,1.5863,2.1616,2.7133,3.0366\n2.7503,3.1618,3.1328,2.5384,2.3642,2.1467,2.3181,3.2572,3.22,2.642,2.358,1.6734,1.761,3.943,3.6959,3.3827,2.9008,2.7253,3.1043,3.2982,3.2785,3.5058,3.3787,2.943,2.6232,2.3684,2.3434,2.536,2.4914,1.8763,1.6533,1.4697,1.2944,-0.4054,-0.50064,0.41632,0.77426,0.25712,-0.51756,-0.44515,0.47234,1.5941,1.7927,1.886,2.1346,2.698,2.9677\n3.6568,3.6614,3.725,3.1673,2.5454,2.4412,2.7154,3.0031,3.32,2.7787,2.2599,0.86901,1.721,2.4245,3.3011,3.4064,3.1688,3.0057,3.3384,3.6727,3.7814,3.826,3.8282,2.9342,2.5846,2.517,2.5135,2.6113,2.8115,2.759,2.71,1.7939,0.92067,0.096413,-0.2148,0.73177,1.1848,0.49889,-0.070477,0.20856,0.84942,1.2805,2.002,2.6571,2.8839,2.9207,2.9182\n4.6577,3.9548,3.3158,3.1874,3.201,3.1347,2.804,2.8435,3.0543,2.9014,3.0242,1.3691,1.4611,1.9585,2.9789,2.6581,3.1602,3.3009,3.5571,3.7212,3.9339,4.2756,4.0658,3.2588,2.9606,2.7277,2.4434,2.0982,2.4876,3.1473,3.4727,2.5183,1.4948,-0.046148,-0.46256,0.70577,1.2477,0.50992,0.44359,0.99031,1.5754,1.9127,2.2675,2.8321,3.2356,3.0432,3.5162\n4.7454,3.9136,3.4818,3.6477,4.0525,3.4805,2.4502,2.4418,2.6902,2.4587,4.1707,3.1433,2.3149,1.0817,1.6368,1.2592,2.6992,4.3952,3.1066,2.9432,3.6841,4.3685,4.289,3.4133,3.2278,2.9101,2.5769,2.3829,2.7898,3.4571,3.3691,2.5571,1.6605,0.66176,0.3061,1.8232,1.4045,0.77996,0.81823,1.4992,1.8472,2.0579,2.2939,2.99,2.932,2.3309,3.3752\n6.5901,4.1793,1.8703,3.2707,4.4971,4.6622,3.1395,3.2589,2.7677,2.6076,2.7072,3.3231,2.3101,0.12067,0.69894,1.3987,4.3052,5.0083,4.0243,3.1493,3.6031,4.5615,4.6775,3.6523,3.4035,3.3502,3.5393,3.585,4.0367,3.7271,2.9531,2.2924,1.9001,1.0741,0.5987,1.6236,1.9164,1.8063,1.1766,1.5729,1.9949,1.9565,2.4038,3.0652,2.9277,2.5819,3.5019\n3.2598,1.1881,1.4023,2.9081,5.3676,4.1812,2.893,4.2544,4.3731,4.3222,4.7373,6.497,NaN,NaN,NaN,NaN,3.2903,5.1241,4.8549,3.1664,3.5827,4.3965,4.004,3.0904,3.0947,3.3072,3.6297,4.3751,4.6335,4.2791,3.9362,3.0377,1.9261,1.563,1.3726,1.8938,2.2389,2.1493,1.7365,2.2964,2.7659,2.5983,2.4448,2.6402,3.2922,4.185,5.0742\n6.1026,3.746,4.259,5.4938,6.0802,4.4779,3.3045,3.8846,4.2679,4.4611,5.5509,NaN,NaN,NaN,NaN,NaN,NaN,4.3493,4.4733,3.6085,3.4536,3.2689,3.4172,3.6801,4.2004,3.8907,4.2019,4.7347,4.7436,5.0125,4.548,3.9389,2.9533,2.4601,2.7289,3.1353,2.8636,2.6118,2.3508,2.5782,2.5522,2.5827,2.2432,2.2245,3.2126,4.3481,4.6575\n6.2341,6.0509,6.0757,7.3264,8.6948,4.7674,3.5409,4.9835,5.3466,5.4544,7.591,7.2802,7.0805,NaN,NaN,NaN,NaN,3.6517,4.3098,3.5397,4.0839,4.2379,3.9562,4.2507,4.2914,3.8532,4.0769,4.4206,4.9512,5.0885,5.009,3.9778,3.7956,3.6792,3.4369,3.5669,3.4207,2.8183,2.6482,3.1066,3.346,3.1193,2.8383,3.0763,3.6462,4.3172,4.3411\n6.5828,4.1146,4.0832,6.6114,7.2539,6.562,4.4705,5.5533,4.7048,4.8897,5.4817,5.1521,6.7846,7.4727,7.2843,5.4034,2.9316,1.8025,2.1559,4.263,6.1097,5.3522,5.259,5.0921,5.3635,5.3029,5.5119,5.2813,4.811,5.0854,5.0483,4.5933,3.9814,4.1812,4.2819,3.4257,3.0336,2.9204,2.9118,3.2918,3.4224,3.467,3.348,3.5188,4.211,4.7262,5.0587\n7.8162,8.1426,5.3834,5.5671,6.343,7.0971,6.6441,5.9703,5.1099,3.6796,3.6433,4.6389,5.6968,6.3252,7.3283,7.0181,3.6535,NaN,NaN,4.0622,6.7016,5.5406,5.4648,5.9196,6.7467,7.1179,6.5492,5.861,5.2787,5.6372,5.4638,4.9541,4.0854,3.7082,3.6495,2.8674,2.927,2.766,2.6566,2.5545,2.8011,3.3683,3.352,3.3307,3.9367,4.1758,4.5757\n7.7651,9.3211,8.3376,4.5944,6.2738,7.995,8.1786,6.1439,5.4324,4.8519,5.169,5.2409,4.9133,4.9672,4.7592,5.6911,NaN,NaN,NaN,NaN,7.469,5.3604,5.633,6.6012,7.3617,7.6346,6.8069,5.8596,5.3948,5.4242,5.0937,4.8002,4.7116,4.4127,3.6484,2.3823,2.4654,2.2933,1.7344,1.8116,2.4958,3.0118,3.2168,3.1669,3.4794,3.5022,3.3994\n6.5038,6.1612,5.4462,3.7688,4.0208,4.916,5.4231,5.6944,6.1281,6.7753,6.8796,5.7874,4.5872,3.2813,3.6674,4.2041,NaN,NaN,NaN,NaN,4.8963,4.5585,5.3887,6.436,6.7096,6.744,6.449,5.7014,5.2238,4.9734,4.8547,4.6041,4.8706,4.6225,3.8139,2.9234,2.6786,3.0596,2.5021,2.551,3.1798,3.4878,3.6869,3.4076,3.475,3.6746,3.1878\n6.6968,5.8233,5.3649,4.302,4.4469,5.9792,7.2267,7.7632,6.8726,7.0017,7.9467,7.6191,5.6616,3.6594,3.1859,NaN,NaN,NaN,NaN,NaN,2.9049,4.1799,5.8119,6.099,5.8907,5.8168,5.5438,5.2084,5.1572,5.1271,4.7963,4.434,4.3607,4.1627,3.6487,2.6677,2.9943,3.9531,3.5202,3.2976,3.5973,3.8864,3.5832,3.2693,3.3582,3.4836,3.3994\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_060828-061211.unw.csv",
    "content": "0.078358,-0.87264,-0.76242,-0.58321,-0.6142,-0.010204,-0.37939,-0.041515,-0.5097,-0.80233,0.059532,-0.91181,0.35305,1.1303,0.3475,0.050159,-0.055758,-0.46088,-0.12278,0.30972,0.9467,0.56754,0.32431,0.45455,-0.51396,-0.057419,-0.42555,0.25983,0.45571,0.38733,-0.15328,-0.17775,-0.81536,-0.33241,0.079697,0.049978,1.0392,1.2399,1.7259,1.4671,1.3009,1.5413,1.1156,0.75497,1.1187,0.75508,0.66129\n-0.60159,-0.78095,-0.80392,-1.0025,-1.335,-0.67384,-0.26946,0.50729,-0.081991,0.1887,-0.34174,-1.7981,-0.47469,0.54416,0.132,0.83933,1.0094,-0.35857,-0.63438,0.20617,0.91532,1.0288,0.35921,0.28695,0.5777,0.13017,-0.004528,0.53415,0.2874,-0.053131,-0.037565,-0.30939,0.25173,0.29546,-0.15417,0.30579,0.89579,1.1193,1.3864,1.5134,2.3654,1.8934,1.7545,1.043,1.1178,1.6823,1.623\n-1.3548,-1.6722,-0.58666,-0.65407,-0.88118,-0.74011,0.20321,0.82273,-0.40705,0.21912,-0.028091,-1.0591,0.27553,0.46939,0.89251,1.2425,1.0172,0.19831,0.24086,0.065208,-0.086206,-0.33613,-0.66574,-0.52587,-0.13889,-0.19924,0.33368,0.92194,-0.0072212,-0.12444,0.13512,0.1592,1.1147,0.69822,0.22924,-0.35529,0.75148,0.76928,0.6197,1.5108,2.6279,1.6,1.3069,0.66917,1.6674,2.1842,1.8026\n-0.67192,-0.60139,-0.058929,-0.1109,-0.29122,0.31763,1.1605,1.142,0.49496,0.43492,0.55259,-0.0049648,0.67057,1.1508,1.7312,1.9709,2.0598,1.4498,0.84207,-0.15479,-0.89061,-0.87877,-1.0581,-0.56677,-0.18844,-0.063816,0.21616,-0.030041,0.0061684,0.5012,0.068306,0.11251,0.48582,0.95083,0.52247,-0.0016594,0.63057,0.95683,0.85974,1.4563,1.3796,1.4013,1.6302,1.7002,2.2871,2.9467,2.835\n0.26674,0.92727,1.609,0.18712,0.44439,1.4807,1.4243,1.4481,0.34129,0.63428,0.49732,0.039135,0.20874,0.45031,1.2376,1.6746,0.55172,0.26836,-0.00080872,-0.41525,0.70344,-0.41199,-0.60306,-0.33047,-0.46513,-0.20156,-0.20362,0.20468,0.37262,0.18156,-0.91285,0.18539,0.11098,0.99364,0.5495,0.67516,0.43794,0.55066,0.83475,1.1598,1.045,0.35203,1.786,2.0711,1.9728,2.0005,2.1019\n0.0035858,0.36203,1.8784,-0.17,0.14174,0.77575,0.98071,0.13311,-0.76096,-0.60892,-0.67144,-0.019878,0.13962,0.39702,0.56615,1.1581,0.91228,-0.16417,-0.82661,-0.0073586,1.1421,0.2859,-0.57048,-0.58198,-0.057774,0.38428,0.08465,0.23674,0.31113,0.57272,0.076284,0.35657,0.55802,0.37759,-0.24741,0.56883,-0.33189,-0.3675,-0.51953,-0.17052,-0.27062,0.092308,1.2606,1.9842,1.8555,1.8921,2.1218\n-0.5952,-0.82578,-0.24708,-0.12509,0.96344,1.7095,1.7809,0.76199,-0.4104,-0.61673,-0.038654,0.70894,1.1751,0.96893,1.344,1.4293,0.49412,0.0849,0.17583,0.97876,-0.14081,-0.5165,-0.9054,-1.4334,-0.30106,-0.070042,0.026476,0.30639,0.58278,1.6436,0.55176,0.21805,0.19976,0.053431,-0.090942,0.14083,0.34686,0.23451,-0.56104,-0.86629,0.20973,0.34669,0.979,1.8179,2.2828,2.147,3.699\n-0.11963,-0.43988,-1.4433,-0.14322,1.3383,2.5723,1.8616,1.7679,1.2235,1.1531,0.79782,1.5515,0.6974,1.3344,1.1624,1.0427,0.87497,0.10487,0.16089,0.48866,0.069653,-0.5806,-0.55544,-0.15751,0.41086,0.76639,0.23192,0.99251,0.51545,1.0152,0.74006,0.11766,-0.3524,-0.071701,-0.0057869,0.037226,0.21233,0.27066,-0.14585,-0.40599,0.011049,-0.30742,0.055649,1.3676,1.6876,1.7307,2.0797\n0.49324,-0.59939,-0.60774,0.14089,0.69865,1.3947,1.6712,1.5784,1.8511,2.0629,1.645,1.9755,2.0442,1.3211,0.51713,1.0947,2.3286,2.5807,0.25401,0.38368,-0.14859,-1.0048,-0.35196,0.4682,0.96157,0.81231,0.73025,0.88733,0.55969,0.6316,1.4049,0.82638,0.5284,0.52656,-0.0042686,-0.29863,0.82159,0.31399,-0.79463,-0.91849,-0.80556,-0.6011,0.32697,0.65145,0.77856,0.44044,0.1323\n0.19492,0.3716,1.2741,0.29156,-0.32005,0.53288,0.26527,0.054543,1.0116,2.1419,1.8886,2.0646,2.2005,0.84859,0.79037,2.1166,1.1268,0.93987,0.71817,-0.037584,-0.081779,-0.53273,-0.3238,0.35496,0.65157,0.69834,-0.089951,0.62978,1.0021,0.24082,0.36619,-0.26179,0.15657,0.32812,-0.63569,-0.79714,-0.42483,-0.19081,-0.4176,-0.54267,-0.24694,0.46407,1.2381,0.25861,0.55954,0.56298,0.26786\n-0.64412,-0.42413,0.3587,-0.25431,-0.90031,0.14798,0.072186,-0.25846,0.75918,1.4986,1.1929,1.3594,1.3365,0.73249,0.80386,1.5291,0.82963,1.1257,0.68451,0.096779,-0.10005,-0.19711,-0.17065,0.309,0.44475,0.18436,0.46976,0.9783,0.58103,-0.13102,-0.089041,0.0058327,0.0886,0.058727,-0.55433,-0.46743,-0.12807,0.37666,0.54909,-0.078156,0.57824,0.97627,1.169,0.79495,1.1669,2.1032,1.8941\n-0.57178,-1.2172,0.48678,0.45807,-0.84322,0.6893,0.77001,0.70184,0.82957,1.5746,0.87865,1.7895,1.8278,1.556,1.6139,1.626,1.192,1.4206,0.27616,-0.20516,-0.29808,-0.5647,-0.29579,1.0462,0.45082,0.48938,1.0458,1.1376,0.26684,0.6527,0.30746,0.18967,0.28277,0.19197,0.28459,0.44958,0.44101,0.097597,0.25764,0.052092,0.30356,0.84152,1.6542,1.6463,2.0475,3.0195,2.6331\n-0.36132,-0.58567,1.0873,4.0385,-2.0154,0.56758,0.25503,-1.0829,0.91572,1.6795,0.4334,1.0819,0.50129,1.9183,2.2106,1.6113,1.399,1.4164,0.3975,0.10676,0.13739,0.094904,0.65237,0.88643,0.91949,0.37185,0.44229,0.88902,0.025839,0.22656,-0.43534,0.013523,-0.19875,0.066965,0.38896,0.22149,-0.25883,-0.43905,0.79985,0.6455,0.82943,1.0073,1.4879,1.6371,1.2943,NaN,NaN\n-0.38115,-0.19161,-1.2854,-2.5333,0.049025,1.0811,1.1731,-0.58144,0.93569,0.80483,0.93597,0.89709,0.80106,2.0891,2.3915,1.1737,1.1559,0.74063,0.5816,1.2753,2.1388,1.9644,1.4203,1.0731,0.17804,0.6566,1.1156,0.39229,0.21538,-0.017963,-0.44372,-0.3208,-0.40598,-0.19387,-0.50055,-0.039043,0.47607,0.78407,1.8402,0.77607,2.0274,1.8703,1.6199,1.9062,1.8098,NaN,NaN\n-0.79127,-1.1482,-3.8653,-1.8803,-0.93969,-0.52482,-0.29865,0.25102,1.3659,1.3673,1.1802,0.83564,1.0121,1.4578,1.7954,1.7019,2.6181,1.3089,0.61636,1.4607,2.2355,1.989,1.0776,0.49031,-1.0973,0.9842,1.1307,0.58738,0.90152,0.67569,0.70521,0.38681,-0.085039,-0.16154,-0.72044,0.14333,0.30864,0.34016,0.43254,-0.42508,0.67768,3.0765,1.4705,1.2328,1.1428,NaN,NaN\n-0.069122,-2.6284,-3.9986,-0.74809,-0.57471,-0.13961,-0.51057,0.76917,1.9533,1.9819,1.4665,1.1223,0.90537,0.60887,0.53004,1.0874,1.2876,0.1688,0.4832,0.64662,1.0332,1.6269,1.0662,1.4273,0.35709,0.15765,-0.17088,0.26,0.85604,0.088234,1.8497,1.9348,0.6241,0.27781,-0.20456,1.579,0.52016,-0.35645,-0.70585,-1.5924,-0.51006,2.2001,1.832,1.0059,0.64143,0.74451,1.9938\n0.12576,-0.65099,-0.48829,0.52391,0.69365,0.18919,-0.59667,1.3866,1.1728,1.73,2.9447,3.2106,1.2254,-0.29372,0.86797,1.2462,-0.016426,0.87027,1.1434,0.036512,-0.23842,2.1536,0.22508,0.77977,0.95106,0.42162,0.43255,0.83001,0.45956,-0.24429,1.4048,1.9402,0.55669,0.41787,0.9172,1.5758,0.92878,0.21291,-0.39361,-2.7306,0.4348,-0.49028,0.65962,0.76128,1.0868,-0.72067,0.52432\n0.4595,-0.26182,0.92828,1.5216,1.9563,0.085739,-1.3525,0.59782,0.95434,2.067,2.569,2.2914,1.1761,1.131,1.6379,1.5476,0.92328,0.86836,0.91767,-0.6193,-0.87047,1.9233,1.6464,0.36893,0.81711,0.82457,0.47141,0.44535,0.61756,0.1505,0.37971,-0.30811,0.63445,0.8895,0.6785,0.17949,1.8338,0.46965,-0.063528,-1.2558,0.29218,-0.30936,-1.905,-0.10846,-1.7339,0.10733,0.52426\n-2.2037,-0.84855,-0.021301,2.653,3.9893,-2.1196,-0.88079,0.86161,1.0083,1.7761,1.8693,0.96645,0.011372,0.62669,1.2791,1.0472,0.96532,1.0129,0.72874,0.217,-0.093769,0.32311,-0.16233,-0.3172,0.59159,0.63202,0.66417,0.25571,0.94601,0.52347,0.5947,-1.2538,0.71045,1.0537,0.8245,-0.67428,0.18467,0.2456,-0.024113,-0.78486,-0.36836,0.98024,-2.0818,-0.50981,-0.73458,-0.10035,0.50845\n-1.9666,-0.00080109,1.021,2.235,3.7676,2.5627,-0.25511,0.83985,0.489,1.7138,1.5474,0.94567,0.9056,0.58817,1.3487,1.5882,1.1094,1.4104,1.4995,1.9121,-1.1677,-0.97998,0.33079,-1.784,-0.92196,0.40758,0.56606,0.56075,1.1224,1.1443,0.40756,0.17494,1.5812,1.7983,2.3426,-0.013706,-0.67214,-0.54394,1.261,0.54048,-0.30428,-0.15343,-0.30648,0.52131,-0.039299,0.013714,-1.9677\n-0.050449,0.22536,1.1277,2.2294,3.9527,2.0634,-1.3865,0.34896,0.44796,1.0358,0.96989,0.70308,0.6838,1.1769,0.46551,0.70006,1.4878,1.4531,0.88404,0.10293,-1.6503,-1.088,-0.54839,-0.45048,-0.2372,0.18524,0.77952,0.79956,2.315,1.0522,-0.57635,-0.013706,0.31282,0.64069,2.3634,0.5718,-1.2445,-1.1658,1.1254,0.0056171,-0.53835,-0.53674,0.022835,-0.28834,-0.17805,-0.35716,-0.59928\n0.97375,0.70857,0.42446,-0.14959,-0.28392,0.18921,-0.15998,0.48157,1.2826,1.4695,1.1201,0.28435,0.84043,-0.05563,-0.9698,1.7121,1.5783,0.82466,0.43369,0.12669,-0.21751,-1.7377,-4.5751,-0.92143,0.24482,0.54026,0.71715,0.76078,2.3651,1.74,-0.97433,-0.88525,-1.3519,-1.1576,0.65598,0.39434,-0.66715,-0.9206,-0.52663,-0.50307,-1.096,-1.1347,-0.37227,-0.3582,-0.23879,0.03117,0.40429\n1.5636,1.2324,1.3463,1.4133,0.92243,0.67868,0.97361,1.1566,2.1141,2.4061,0.26722,0.60148,2.1103,0.99808,-0.77075,0.13978,0.092161,-0.7213,-0.45663,-0.39793,-0.64589,-0.95853,-2.4209,0.0032692,0.87213,0.77947,1.5754,1.4513,1.2296,0.40618,-0.68499,-0.85856,-1.1399,-1.286,-0.43425,-0.10461,-0.47101,-1.1489,-2.4422,-1.5497,-2.0981,-1.0519,-0.15445,-0.75716,-1.0105,-0.51333,-0.011854\n0.32656,0.8867,1.6453,2.7114,1.8073,1.5536,0.65554,1.8141,2.2919,2.0484,1.6492,1.0389,1.7637,0.91494,0.19358,-0.18832,-0.36162,-1.8222,-1.5344,-0.55658,-0.018398,0.088219,-0.31221,0.6015,1.8868,1.4156,1.9646,1.9168,1.6533,1.2558,0.82792,0.4237,0.90652,1.2766,-0.35061,0.033615,0.03462,-2.0928,-4.6328,-4.2446,-3.1733,-0.45296,-0.52369,-0.67938,-1.2162,-0.62156,-0.59737\n0.057251,-0.52264,-0.017431,2.1839,2.6632,1.4058,0.74585,1.3208,1.6448,1.0631,1.8327,2.0764,1.4407,0.10088,0.015045,0.15748,0.46633,-0.19564,-0.23038,0.98895,0.25784,0.11418,0.38382,0.44539,2.6471,2.5889,2.3121,1.8733,1.6467,2.1883,1.6746,1.6911,1.6215,0.34324,-0.38646,0.12278,-2.0332,-1.7281,-1.9639,-2.1044,-0.60606,0.38272,0.078613,-0.70959,-1.2036,-0.95399,-0.63929\n0.98507,1.1186,1.6932,2.1887,2.4193,1.0733,0.86092,1.1821,0.99306,0.59545,2.5715,2.3101,0.93024,0.019325,0.19242,0.021183,0.7492,0.20628,0.23148,1.0248,0.54514,0.1158,0.99157,1.737,2.106,2.5919,NaN,NaN,2.3964,1.3098,0.783,2.387,0.83369,-1.0424,-1.2093,-0.2259,-0.54093,-0.14985,0.48711,-0.12862,0.64923,1.1986,-0.043182,-1.121,-0.74721,-0.76558,0.072647\n0.46492,1.8688,2.4895,1.3953,0.23618,0.50482,0.99992,1.0501,0.40245,0.58387,1.1894,1.2932,0.58271,-0.03208,-0.73995,-0.92102,-0.59058,-0.037844,-0.29772,-0.91581,-0.34636,0.15836,1.0528,1.3307,1.3399,3.2954,NaN,NaN,NaN,-2.8922,-0.77137,-0.099342,0.90499,0.59334,0.46395,0.041681,0.2492,1.7934,1.0651,0.32944,0.93937,0.41507,-0.29517,-0.24781,0.38846,0.47959,0.20252\n-0.070549,1.1142,1.6985,1.7686,-0.1905,0.55132,1.5994,0.37861,-0.30846,1.1666,0.97605,0.88702,0.45843,-0.53369,-1.0386,-0.97201,-0.84542,0.78107,-0.16798,-2.0266,-1.1887,0.5274,1.2079,0.48155,0.54193,0.98489,NaN,NaN,NaN,NaN,-0.39881,0.97028,-1.5137,0.060947,1.8519,1.8292,1.5056,1.4178,1.4343,1.8788,1.2765,0.082321,-0.12988,0.3423,0.55655,0.34183,1.1354\n0.64549,1.4028,2.2957,0.51871,0.10594,0.90355,1.3139,0.59073,0.65587,1.2366,1.4372,-0.011528,-0.020199,-0.034958,-0.65323,-0.96936,-1.1231,0.63066,-0.60553,-1.8056,-2.133,-0.073128,0.5862,0.57782,-0.53442,2.4017,3.5402,3.0216,2.0914,3.5615,2.5717,2.6113,0.38713,-0.047791,2.1132,1.9452,1.5045,1.3007,1.3753,1.7515,0.93536,-0.39829,-0.53427,-0.48239,-0.7642,-0.0075989,1.0317\n0.22387,0.50598,1.8693,-1.5825,0.20925,0.92419,0.58918,0.56391,0.79297,0.59749,1.1526,0.40971,-0.29507,0.083815,-0.38007,0.10755,0.99137,0.15501,-0.17466,-0.89093,-1.7373,-1.405,0.82653,0.22011,1.4669,3.3254,3.7818,5.6324,NaN,NaN,NaN,NaN,NaN,-2.2659,1.1069,2.1885,1.7553,2.025,2.4067,1.9402,1.3995,-0.66728,-0.87827,-0.9683,-1.0549,-0.89372,0.23629\n2.0724,1.6334,1.4088,0.97881,1.6084,2.071,0.81424,1.4294,1.294,-0.27435,0.1596,0.15903,-0.46602,-0.19909,-0.4118,-2.1863,-2.5471,-0.20372,-1.4828,-1.5575,0.91328,1.2654,1.2957,2.2198,0.32625,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.6257,3.4434,3.8808,2.9339,2.2478,2.7459,2.0724,0.43081,-1.0471,-0.52271,-1.2521,-1.5774,-0.29784,0.53327\n0.54529,0.48964,0.15312,0.2632,1.9885,2.1986,1.8075,-0.89212,1.1694,-0.38182,-0.15176,0.46887,0.43483,0.017921,-0.3887,-1.7485,-1.5687,0.67773,1.7169,2.0156,2.175,2.0937,1.7497,5.2905,2.7022,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.6738,4.6268,3.2642,1.8533,2.4189,2.0813,1.7239,1.1719,0.21379,-0.14105,-1.653,-1.1863,0.53163,0.76144\n-0.084862,1.1189,4.025,4.6867,3.5704,0.64588,-0.7884,-2.5752,-0.49749,-0.72581,NaN,NaN,NaN,NaN,1.4082,0.27187,-0.17944,0.63239,1.7357,1.4359,1.8807,1.656,1.3504,2.6636,3.276,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.7416,2.3067,3.1009,0.74566,0.73155,1.3421,0.53713,0.74584,0.369,0.089626,0.65721,0.073542\n1.7655,2.1347,5.1091,5.0759,3.1369,-0.54557,-0.14485,-0.10753,-0.12431,NaN,NaN,NaN,NaN,NaN,2.1474,2.2076,1.9583,0.79037,0.95258,0.16421,1.5776,2.3742,2.4388,1.8151,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.58713,2.1163,3.2215,1.0773,0.46811,1.2003,1.0873,1.6434,0.45322,-0.020508,-0.21806,-0.048622\n1.2479,0.83764,0.66783,1.6457,2.2932,1.8301,1.235,1.1517,0.84089,NaN,NaN,NaN,NaN,NaN,1.074,1.9899,2.1839,1.3494,1.4792,1.7966,2.84,2.8834,3.9789,6.3974,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.7117,0.8869,1.203,1.3951,1.0617,1.4861,-0.42676,0.014786,-0.021076,-0.29318\n1.6184,1.0794,0.51309,2.3598,2.2655,2.7706,2.1557,1.6538,0.91879,0.10166,0.29253,0.42378,0.55641,-0.04192,-1.1684,-1.0787,2.0858,2.6532,4.0841,4.41,3.462,4.4491,6.565,7.8534,8.1633,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.99,1.5366,0.8989,0.73584,0.38789,-0.14635,0.63543,0.97356,0.18018\n1.4896,1.1529,1.2688,1.0695,0.34818,0.66728,0.89922,0.78602,0.95228,-1.4219,-0.31632,0.46923,0.52185,0.084347,0.57751,1.8974,3.0371,5.6475,5.9025,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.699,0.76507,1.1271,1.1098,0.88132,0.075367,0.57195,0.75571,0.071602\n0.48061,0.3588,0.60322,0.39017,0.18426,0.94746,0.65999,1.1333,1.2399,-0.55959,0.030556,0.64751,0.54121,-0.48844,2.1241,3.5927,4.2355,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.525,7.6515,NaN,NaN,4.4173,4.0977,3.1485,0.87755,0.29211,0.57565,0.86472,0.79996,0.68821,0.60869,0.5189\n0.64307,0.4563,0.32266,0.59044,0.40708,1.1297,1.3706,1.5158,1.111,0.49092,0.6322,1.8289,1.9577,2.192,3.3458,4.3691,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.219,9.8344,8.2083,4.9359,4.2695,3.8833,2.5552,0.70953,-0.29602,-0.56565,0.65944,1.0698,1.1913,0.37491,0.66984\n1.0283,1.9731,1.0921,0.13148,0.07394,0.40316,1.7938,1.1323,1.1368,0.71281,0.68465,1.1447,2.3816,2.5062,3.5267,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.6169,11.299,12.708,10.55,8.31,6.4944,4.8285,2.8935,2.3859,1.6266,0.79022,0.98358,-0.51391,0.24265,0.91876,1.3382,1.1699,0.67811\n0.65463,1.0613,1.075,0.49182,0.31332,-0.14364,0.12126,1.1592,0.96945,0.60463,1.0896,0.9104,1.4232,1.614,3.4718,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.7674,4.2148,6.7117,8.5023,8.3007,4.7455,5.1927,5.3535,3.5275,2.7154,2.6435,-0.84734,-0.30723,-0.50586,-0.16461,0.21424,0.94741,0.6931,0.76306,0.67274\n2.7462,2.4256,1.2876,0.78454,1.5332,0.64835,1.0084,1.1272,0.52983,0.42609,1.4188,0.25963,0.48999,1.1594,1.7714,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.4462,5.1641,4.4323,6.7643,6.2776,5.5648,4.3759,4.8536,5.1045,3.6406,1.3184,1.1036,-0.44496,-0.50385,-0.53773,-0.23058,0.42169,0.75809,0.39711,0.5484,0.56666\n2.8385,1.7792,0.52818,0.78936,1.0745,1.0983,1.1638,0.22282,-0.47993,-0.86925,-0.36768,0.46845,0.27105,1.8115,2.5835,2.3418,0.68035,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.0496,5.1412,5.3242,5.7077,5.0012,5.0716,2.0405,5.2696,5.6884,5.1796,1.4998,0.5405,1.1282,0.89846,0.81194,0.78847,0.70436,0.68607,0.5124,0.68081,0.087217\n0.97005,2.5466,1.4129,-0.49238,0.54995,0.44727,-0.30694,-0.44433,-0.48221,-1.2922,-0.14949,1.5316,1.8337,2.03,4.408,NaN,NaN,1.2711,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.5683,4.484,4.2291,3.6032,2.6853,2.503,2.4709,5.092,5.0823,3.349,2.3663,0.46263,1.2887,1.4782,1.2777,0.53571,0.45896,-0.074675,-0.73304,-0.69106,-1.1373\n1.2057,1.4044,0.76775,-0.33,-0.16158,-0.4739,-1.9662,-2.6725,-1.5479,-0.45763,0.48769,2.3857,2.1717,1.522,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.036,4.0105,3.5067,4.0704,3.9695,3.7488,3.1855,3.3882,3.8269,2.3894,1.2929,0.96137,1.2257,1.5166,1.3112,0.84462,-0.099457,-0.8444,-1.1154,-1.3392,-1.3233\n0.57728,0.56505,1.0187,1.3481,0.67255,0.64516,0.97593,0.68888,1.5335,1.9244,1.6098,2.2811,1.8712,1.9902,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5047,4.0412,3.3493,4.1174,4.9143,4.5896,3.519,3.5921,4.0693,3.3092,2.2034,1.6383,1.427,1.1493,0.88494,0.96776,0.23684,-0.23417,-0.67984,-1.1019,-1.1055,-0.74154\n0.96283,0.54146,1.511,1.0133,1.4413,1.5659,1.7789,1.8654,1.4821,1.5743,2.4761,2.941,3.0615,3.4504,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5073,2.1216,3.5178,3.3048,3.2797,3.1143,3.9806,3.924,3.8601,2.7563,3.6338,2.9561,1.8277,1.0932,-0.15851,-0.089012,-0.48404,-0.67687,-0.30025,-0.51189,-0.19901,0.24208\n0.31784,0.046669,0.3185,0.84856,1.817,0.94624,1.3179,1.3177,1.6739,1.7053,2.4678,3.4745,4.2318,4.5633,5.1058,5.2516,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.2215,1.0961,3.3756,2.5829,0.67401,1.6253,2.1328,3.2721,3.2048,4.1915,4.6879,3.9615,1.6862,1.508,0.9985,-0.31403,-0.76861,-0.81523,-0.90384,-1.0743,-0.44109,-0.30817,0.33858\n-0.16337,-0.30072,0.17742,1.4069,1.6687,0.8378,1.6403,1.0186,1.7383,1.908,2.3568,3.0089,3.8458,4.0418,5.8339,6.572,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.7896,2.5972,2.5866,3.7402,1.6925,0.68176,1.0502,1.9913,2.9062,2.8531,2.5208,2.1681,1.1242,0.62424,1.1062,0.87718,0.0072708,-0.57534,-1.0783,-0.62849,-0.038994,0.19609,-0.17616,0.084002\n-0.80375,-0.64644,0.30464,1.9237,1.2066,1.2963,1.6743,1.7646,2.1237,3.0802,4.6385,3.986,3.7761,3.6946,5.0252,4.8573,3.7908,NaN,NaN,NaN,NaN,NaN,3.1793,3.4585,4.2534,3.7188,3.1365,1.8758,1.6899,0.76724,1.69,2.6703,1.5813,1.306,1.4693,1.1245,0.69476,1.4722,0.55535,0.99931,0.41665,0.56432,0.13216,-0.83465,0.48528,-0.032465,0.33318\n-1.197,-0.61181,0.50207,1.7044,1.8179,1.0518,1.3371,1.7819,1.6899,2.7275,3.9964,3.6056,4.1022,3.964,3.6655,3.049,2.2476,NaN,NaN,NaN,NaN,NaN,2.1146,3.0795,2.8023,3.0071,2.8468,2.3514,0.18282,0.63844,1.6786,2.2248,1.6007,2.3,2.0971,1.2447,0.88873,0.7094,0.47937,0.81046,1.064,1.2077,0.91843,0.32306,0.44394,0.54002,1.006\n0.21181,-0.54177,-0.94431,0.61814,1.0788,0.67194,1.2617,1.8309,1.2241,1.8197,3.2446,3.4505,2.892,2.2253,3.831,4.7951,2.4821,0.35791,0.30414,NaN,NaN,1.367,2.0475,2.3446,2.1359,2.2213,2.5541,0.40949,-0.94109,0.086008,0.79212,1.6962,1.9313,1.8657,1.7537,1.0367,1.0857,0.79733,0.94835,0.81012,0.017584,0.5852,2.0882,1.8343,0.54467,1.1481,1.2116\n-0.26331,-0.496,-1.3989,0.98962,-0.26932,-0.34339,1.0121,0.75892,0.31721,0.53237,1.5314,1.7195,1.5819,1.5219,2.415,2.4394,3.1348,0.37077,2.1686,2.524,0.262,1.5199,1.4188,1.3077,1.3782,1.6634,1.4349,0.31635,0.17231,0.5639,0.75625,1.0543,1.5417,1.1758,1.1698,1.6915,1.8813,2.142,1.6371,0.60435,-0.080482,0.61333,1.0227,1.2059,0.35118,0.32894,1.535\n-1.0891,-0.68246,0.69287,1.7387,1.0836,-0.49901,-0.14243,-0.68794,0.19128,0.20277,0.3136,1.2037,0.54215,-0.40423,0.022766,1.5492,1.2318,-3.4841,-1.5092,-0.54904,0.095013,1.0179,0.88074,0.67909,0.89748,1.4747,1.8491,1.9361,1.3269,0.41253,0.5397,1.1922,1.3947,0.9759,1.1965,0.77896,0.27551,1.8937,1.4933,1.1558,0.69227,0.47131,-0.11123,0.32426,-0.39614,0.43169,2.0802\n-1.7073,-1.0504,-0.28233,-0.90195,-0.84977,-0.49923,-0.64897,-1.0712,0.47781,0.41392,0.48442,1.8954,1.557,-0.31139,-0.77715,0.97132,0.93103,-0.28108,1.5662,-0.23553,0.7909,1.4112,1.3963,0.73349,0.88761,1.186,1.4089,1.2655,-0.15423,0.56952,0.78677,0.21611,0.091557,-0.050285,0.48353,0.753,0.66213,1.4022,1.4957,1.2958,1.1038,0.13467,-0.012642,0.50476,0.44104,0.96381,1.0061\n-1.3172,-1.5225,-2.6138,-2.1505,-1.1668,1.2337,-0.22742,-0.18304,0.082212,0.95515,0.26706,0.13712,1.4882,0.92971,-1.2304,-0.30824,0.31279,1.0947,0.94388,-0.76696,0.60228,1.0853,1.3137,0.78141,0.47506,0.63128,1.1927,0.86301,0.099247,0.87413,0.45306,0.35396,-1.7727,-0.77409,0.37198,0.77165,0.66601,0.99716,0.70353,1.0042,1.295,0.84341,0.52017,0.82668,1.0278,1.1682,1.1324\nNaN,-3.4982,-2.7226,-2.2369,-0.68727,1.8038,1.0633,-0.57027,-0.60725,0.3381,1.0514,-0.26,-0.19343,0.43585,-1.174,-1.8396,-1.3523,0.42744,0.5023,-0.93868,-0.83848,0.34428,0.68029,0.97661,0.43893,0.54245,0.73132,-0.092819,-0.43533,-0.17223,0.45535,0.82029,-0.55638,-0.42222,0.4478,0.64294,0.52462,0.45085,0,0.28212,1.059,0.79532,0.56798,1.135,1.2915,2.0545,2.0008\nNaN,NaN,NaN,-2.231,-2.8019,-0.57227,3.8186,1.3319,0.23423,1.1009,1.474,1.0953,-1.8655,0.30346,-1.9533,-2.8494,-1.8498,-1.2143,-0.42554,-0.02393,0.24519,0.34635,0.30169,0.43643,0.47654,0.88755,0.92906,-0.90268,-0.8101,-0.49374,-0.41975,-0.20454,-0.18619,0.10188,0.24529,0.019724,0.088669,-0.2189,0.3495,0.3855,0.28327,0.33826,0.55159,1.7433,1.5629,2.2319,2.3963\nNaN,NaN,NaN,-2.5154,-3.1381,-3.0505,3.5217,-0.27331,0.81394,0.91715,1.1632,-1.112,NaN,NaN,NaN,NaN,NaN,-1.3709,-0.42579,0.84146,1.3109,0.090477,-0.23607,-0.39089,-0.15088,0.59499,1.9256,2.1462,0.15799,0.086582,-0.84939,-1.1764,-1.0114,-0.90194,-0.45947,-0.33442,-0.018635,-0.020996,0.2564,0.016397,-0.13912,0.29159,1.0622,1.8303,2.3169,2.658,2.4069\nNaN,NaN,NaN,NaN,-2.7591,-4.2263,-1.5213,-0.13686,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.3131,0.053185,1.0976,0.73508,0.16715,-0.1964,-1.0434,-0.15706,0.38068,1.5355,1.1449,0.53333,0.84798,0.64829,-1.0949,-1.7336,-1.2666,-1.4803,-0.81993,-0.55408,-0.75381,-0.20949,-0.18139,0.34747,1.0061,1.0901,1.2598,2.2326,2.5075,1.3532\nNaN,NaN,NaN,NaN,NaN,-5.7563,-4.0897,-1.401,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.011833,-0.096687,0.34813,0.65101,0.21609,0.58582,1.4873,0.0093975,0.43054,0.27027,-0.039055,0.056585,0.2277,1.8058,0.77462,0.15625,-1.338,-2.0287,-1.6189,-1.1364,-0.84772,-1.4217,-0.82011,-0.17669,0.68014,1.0342,1.1174,1.3168,1.5033,1.0463,0.66248\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0758,-0.23018,-0.49022,-0.023308,-0.063625,-0.25101,0.60303,1.2946,0.6471,0.056343,0.16181,0.40428,1.151,1.4341,0.65033,0.5077,-0.038294,-0.85876,-0.65825,-0.90596,-0.47856,-1.6226,-1.5193,-0.74763,0.14614,0.68076,0.9594,1.5509,1.7237,0.66897,-0.0087357\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.39768,-0.8622,-0.87009,-1.875,-1.4589,-1.4948,-0.028416,1.1762,-0.3126,-0.49492,-0.82968,0.0037498,0.47343,-0.56848,-0.77806,-0.284,-0.076954,-0.30512,-1.0609,-1.5996,-0.6762,-1.0899,-1.1584,-0.58435,-0.022718,0.19725,0.5817,1.8162,1.799,1.1469,-0.24302\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.94596,-2.5688,-1.863,-1.9871,0.75003,0.69624,0.33619,-0.60469,-1.2517,-0.47446,-0.013866,0.45588,-0.22525,-1.316,-1.2104,-0.77029,-0.48673,-0.44785,-1.3664,-0.85797,-0.55123,-0.49046,-0.38295,-0.88436,-0.12907,0.66161,0.93314,1.4722,1.3806,0.65168,0.055565\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.6744,0.0096283,0.66734,0.17774,-1.1599,-1.0602,-0.0054913,0.32425,0.8199,-0.44478,-0.84659,-0.59855,-0.87694,-0.58082,-0.57881,-1.1247,-1.0385,-0.98962,-0.61263,-0.31179,0.12184,0.69542,0.80369,0.66619,0.80806,1.3967,1.6415,1.4694\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.89394,-1.5305,1.022,2.4491,-0.87144,-0.0052071,0.10041,0.025713,0.52841,-0.70388,-0.899,-0.21349,0.017334,-0.42644,-1.0439,-4.2752,-1.6763,-0.72628,0.15481,0.47634,0.86151,0.96748,0.67457,0.70001,0.90037,2.126,2.2164,1.6193\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.0953,1.6696,NaN,NaN,NaN,-0.46042,1.9147,2.7866,1.6512,0.24729,-0.71394,-0.38483,0.54427,-0.50123,-1.2992,-4.7627,-1.1753,0.068336,0.37339,0.6745,1.0768,0.35227,0.61477,0.92244,1.4369,2.317,2.0085,0.98682\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3793,NaN,NaN,NaN,-0.99891,1.3557,2.0105,0.67358,-1.6188,-1.6206,-0.91716,0.33494,-0.45676,-0.8143,-1.901,-1.7122,-0.84592,-0.46819,-0.079041,0.82927,1.1972,1.2936,1.2368,1.3605,1.3963,1.0386,0.79937\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1124,NaN,NaN,-6.0033,-1.5778,-0.98788,-0.55964,-0.46122,-2.0482,-3.9649,-2.5573,-1.3663,-1.4499,-1.4148,-1.3751,-0.6689,-0.36461,-0.24527,0.10876,0.66695,1.1098,0.50586,0.80838,1.1112,0.6354,0.02076,0.20346\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3561,NaN,NaN,-2.0157,-0.54356,-1.2224,-0.53552,-0.62788,-1.3532,-2.2238,-0.92934,-0.34547,0.0035419,-0.97745,-1.3425,-0.59678,0.054234,0.18776,0.060425,0.74393,0.88412,0.80416,1.0703,0.68655,-0.4052,-0.62237,-0.20667\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.0432,2.7489,-0.91338,-1.9087,-1.4529,-0.96955,-1.3156,-2.2659,-1.1708,-1.6397,-1.7244,-1.4411,-0.31698,0.076855,-0.72074,-0.17412,0.94226,0.85057,-0.34199,-0.69893,-0.13394,0.23458,0.94013,0.75785,0.28693,-0.41945,-0.4397,0.64411\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.86518,2.6583,0.16643,-2.857,-2.8102,-1.1064,-1.2753,-2.1603,-1.6146,-2.4608,-2.7475,-1.7447,-0.3104,-0.32691,-0.68034,-0.31433,0.67145,0.26181,0.0084915,-0.39106,-0.8,-0.47155,0.1764,0.87299,0.047932,-0.0084343,0.16132,0.17929\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061002-070219.unw.csv",
    "content": "6.8367,5.8378,5.4709,5.157,4.1785,3.5883,3.1702,3.0779,3.9145,4.2313,3.4621,2.6978,2.7365,3.5038,4.6392,4.025,3.4025,2.8452,2.5928,2.3024,1.5532,0.92155,0.81683,1.0381,0.73895,-0.17353,0.50764,1.0791,0.41091,2.8241,6.0221,6.7364,7.3551,7.1933,8.3275,9.6488,10.38,10.89,10.484,10.65,10.804,9.8012,8.4922,8.3322,10.473,10.331,11.269\n6.9853,6.3484,5.5658,4.9831,4.0006,3.5119,3.4095,3.0573,3.4679,3.7282,3.1708,2.4736,2.8845,3.4089,3.6431,3.1215,2.6068,2.7174,3.4121,2.5802,1.4758,0.85655,1.2762,1.5555,0.44168,-0.42283,0.15231,0.88231,0.73674,1.8958,4.8475,4.3458,5.5676,7.1173,8.222,9.0048,10.027,10.52,10.468,10.073,9.811,8.8062,7.8437,7.8352,8.7392,9.9177,11.511\n7.7316,7.3868,5.6937,5.2621,4.8282,3.3733,3.5598,3.6651,3.525,3.6829,3.2172,2.3494,2.7269,3.2272,3.0318,2.6485,2.3346,2.678,3.46,2.9748,2.8474,2.5911,1.4159,-0.2444,-0.67424,-0.6848,-0.31096,-0.4697,-0.047363,2.0895,3.2694,3.5417,4.5228,5.8534,7.0803,8.9078,8.1498,9.6448,10.526,10.043,8.7286,8.0682,8.5011,9.0267,9.5996,9.6705,10.458\n8.5008,8.1826,NaN,NaN,4.3468,2.9652,4.2641,4.5421,3.718,2.8966,2.692,2.92,2.419,2.9793,3.06,3.2971,4.136,3.7085,3.3296,3.3026,3.4505,2.374,0.91554,-0.68403,-1.0019,-0.94351,-1.3379,-1.2782,-0.10077,1.245,1.6259,1.9537,4.8815,5.9211,7.1996,8.5875,8.0392,8.7953,9.8678,9.0441,8.2364,7.753,8.5616,9.5975,10.053,9.8566,11.01\nNaN,NaN,NaN,NaN,4.2762,4.1674,4.8378,5.179,4.5936,3.4602,2.6248,2.8918,2.1721,2.4998,2.7891,3.4482,4.2074,3.6593,2.9575,2.1098,1.958,0.62715,-0.50154,-1.2808,-1.2424,-0.80436,-2.6135,NaN,NaN,NaN,0.27942,0.73984,2.3473,4.7324,7.0207,8.5055,8.5171,8.6889,9.0879,8.3296,8.0282,7.6079,8.6463,9.5768,9.6378,9.9721,10.821\nNaN,NaN,NaN,5.7594,5.4894,5.3366,5.3543,5.2295,4.929,4.1761,3.183,3.4116,2.9117,2.8758,2.774,3.4387,3.4967,2.7079,1.403,1.372,0.40881,-1.4316,-3.2575,-2.1839,0.011862,-0.089823,NaN,NaN,NaN,NaN,NaN,NaN,0.46283,1.5804,3.4432,5.3346,8.6496,8.389,8.1897,8.2262,8.1246,8.7142,9.7018,10.321,10.144,9.7217,10.386\nNaN,NaN,NaN,5.875,5.8375,5.2574,5.4622,5.1899,4.3819,3.3552,3.0626,3.5523,3.705,3.2968,2.6769,3.1433,2.7821,2.2064,1.6166,1.0188,-0.13547,-2.3974,-3.5386,-2.3153,-1.1824,-1.211,NaN,NaN,NaN,NaN,NaN,NaN,-0.53356,0.54889,1.3725,2.7705,5.4713,7.8675,9.0933,10.468,9.8492,9.8267,10.416,10.152,9.4533,8.5498,10.401\n6.4867,6.3812,5.585,5.4979,5.9034,4.935,4.7614,4.8421,4.0932,3.7468,2.8329,2.617,2.9348,3.5798,2.9227,2.13,1.7894,2.2972,1.4895,0.25152,-1.5296,-2.5213,-1.8386,-2.4377,-3.8694,-4.2626,-1.9903,NaN,NaN,NaN,NaN,-0.64518,-0.99679,0.7256,0.85855,1.4409,4.5151,7.551,9.7277,11.073,11.331,10.854,10.824,10.159,9.4828,8.7251,10.92\n6.1348,5.8264,5.2691,4.9782,4.9653,3.8279,3.9644,3.9876,3.9503,3.4659,2.7632,2.6157,2.6965,3.2489,3.0902,1.9534,1.8097,2.716,NaN,NaN,NaN,NaN,-0.90516,-4.5822,-5.8197,-5.2145,-5.8482,-5.3169,-3.2288,-1.7134,-1.6421,-1.1287,-0.6503,-0.10348,0.010607,0.22399,3.7489,6.9649,9.1935,11.491,12.212,11.236,10.523,9.9556,NaN,NaN,NaN\n5.6981,5.3786,5.0795,6.6398,5.5857,3.9846,4.1801,4.7264,4.0199,3.6289,3.1111,3.5344,2.5742,2.8291,2.6951,1.532,1.3047,NaN,NaN,NaN,NaN,NaN,NaN,-6.4105,-6.6536,-5.336,-7.1101,-6.5619,-5.14,-3.1407,-2.1757,-1.6053,-1.3071,0.8931,1.3809,1.5384,3.5731,4.707,6.2244,10.696,12.605,12.411,11.4,10.867,NaN,NaN,NaN\n5.8449,5.0306,5.835,7.0117,5.6114,4.5583,5.0726,5.225,4.1641,3.2657,2.8585,2.8113,1.6906,1.4226,1.0729,1.0428,1.6403,NaN,NaN,NaN,NaN,NaN,NaN,-6.4881,-7.546,-7.2539,-6.8366,-6.1856,-4.8943,-3.2528,-2.3729,-1.3067,-1.4248,-0.36684,0.78742,3.4763,5.4425,6.1825,4.7949,8.4743,11.516,11.596,11.262,NaN,NaN,NaN,NaN\n5.5439,4.2627,4.5499,5.1657,4.9203,4.4732,5.4006,4.6944,3.6007,2.8181,1.9061,1.0813,0.50964,0.56184,1.0232,1.2655,2.0709,NaN,NaN,NaN,NaN,NaN,NaN,-6.6797,-6.5299,-5.9361,-6.4689,-5.6026,-4.0368,-3.1947,-1.7249,-1.008,-0.86646,-0.012436,1.5768,2.6581,2.0114,4.8687,6.6101,7.1131,8.7172,9.2257,8.7883,NaN,NaN,NaN,NaN\n6.6852,6.3698,6.1758,5.7644,5.021,4.6665,5.1163,4.2234,3.5721,2.8035,2.1968,1.1684,-0.068283,-0.4678,-1.2566,0.35483,0.076427,-1.8905,-4.559,-4.0383,-2.888,-4.8589,-3.7739,-4.6478,-5.5832,-5.8702,-5.8979,-4.7753,-3.2678,-2.2338,-1.3448,-1.1718,-0.73357,0.044035,1.4143,2.0873,2.3476,3.5609,4.6056,4.1271,5.1382,7.5444,NaN,NaN,NaN,NaN,NaN\n7.551,7.8698,7.4934,5.7217,3.7446,3.6659,4.3903,4.3046,3.8513,3.0689,1.9285,1.0368,-0.053373,-0.48676,-2.0118,-0.53882,-1.0646,-1.8095,-1.5426,-1.3265,-2.176,-3.5683,-3.4071,-3.9303,-4.8134,-4.1449,-3.8334,-3.1418,-2.1042,-1.6563,-1.4785,-1.3641,-0.49737,1.2201,2.381,3.1788,3.827,3.8121,2.9609,3.6917,2.7658,6.6234,NaN,NaN,NaN,NaN,NaN\n7.3902,8.1738,7.4322,4.3522,3.063,3.7288,4.597,3.8955,2.3964,0.34898,-0.72532,0.7703,0.63812,0.10047,0.23033,0.1157,-0.31766,-1.6768,-1.1002,-1.358,-2.3262,-2.4779,-2.9594,-2.4855,-2.7944,-1.8488,-1.272,-0.76364,0.50661,1.1191,1.365,2.5695,3.2979,3.6012,3.8237,3.0067,3.8159,3.9909,3.8321,4.2969,4.3344,3.4365,NaN,NaN,NaN,NaN,NaN\n7.2036,6.5705,6.4465,5.1241,4.8344,4.0989,4.6371,3.717,1.8295,1.3063,-0.12506,1.6937,0.7493,-1.0632,-3.4599,-4.1015,NaN,NaN,NaN,-2.8678,-2.6709,-2.6018,-2.1889,-1.376,-2.3531,-2.0358,-0.84516,-0.099066,1.3003,2.6907,3.2021,3.0181,3.0087,3.5756,3.8542,3.1382,2.8236,2.7976,3.3421,4.6479,4.7341,3.745,NaN,NaN,NaN,NaN,NaN\n7.4799,7.1787,6.1524,5.4055,4.8918,4.725,4.4734,3.0008,1.5104,0.70662,0.10011,-0.25402,-0.53975,-1.2858,-3.791,-4.1659,NaN,NaN,NaN,NaN,NaN,NaN,-0.5387,-0.20173,-1.1576,-0.87073,0.78729,1.8339,2.4618,2.734,2.229,1.9926,1.4745,1.9226,3.1091,3.9838,3.2193,3.0181,2.1571,1.9118,2.8406,3.9011,NaN,NaN,NaN,NaN,NaN\n8.6893,6.476,5.9723,6.242,6.0121,5.8715,NaN,NaN,0.3697,-0.036673,-0.72539,-1.7776,-3.3948,-3.6873,-3.0059,-2.6925,-3.7684,NaN,NaN,NaN,NaN,NaN,NaN,0.29148,-0.22338,-0.27378,0.71148,2.6024,1.9972,2.612,2.1088,1.178,1.9665,1.5256,1.8051,2.6093,3.247,3.849,3.9962,3.685,3.2186,1.7418,NaN,NaN,NaN,NaN,NaN\n7.5685,6.8429,7.4736,9.2448,NaN,NaN,NaN,NaN,0.18688,-0.31069,-0.82481,-1.8534,-2.8674,-3.5833,-3.5919,-2.968,-3.4779,-3.493,NaN,NaN,NaN,NaN,NaN,-0.58455,-1.2724,-1.0345,-1.2269,1.0979,0.81575,0.68091,2.4221,1.7258,3.2021,3.7913,4.1846,2.8685,2.3576,2.0791,3.7626,4.0904,3.7843,2.8101,1.1712,NaN,NaN,NaN,NaN\n7.7839,6.9929,5.9337,6.8204,NaN,NaN,NaN,NaN,-1.1385,-2.1904,-1.6972,-2.4399,-3.2485,-4.2153,-4.6934,-4.6013,-4.6156,-5.3248,NaN,NaN,NaN,NaN,NaN,-3.1179,-3.8563,-2.7474,-2.4061,-2.5527,-2.6563,-1.5233,0.95316,1.7686,3.0958,3.316,3.3177,2.5659,4.6337,4.2102,4.8873,4.6501,4.2629,3.604,2.9607,4.4134,5.2884,4.8289,2.2778\n7.848,5.8928,3.5186,NaN,NaN,NaN,NaN,NaN,-3.3318,-3.9637,-4.957,-4.3352,-3.6514,-4.8547,-5.1904,-4.7327,-4.3041,-4.0193,-4.9134,-5.0506,-4.5324,-2.8909,-2.0883,-1.8936,-2.5557,-2.1388,-1.6377,-2.444,-2.6904,-2.5093,-0.75875,1.4263,3.0596,2.9487,2.8141,1.8762,3.1008,4.2604,5.0879,3.8674,3.7661,4.1453,4.8391,6.0846,5.0589,3.6965,0.75484\n5.6154,4.015,4.8352,5.6972,NaN,NaN,NaN,-3.2539,-2.9417,-4.6325,-5.0976,-5.1944,-4.6783,-5.3585,-5.2608,-4.9206,-4.1905,-4.7913,-3.8165,-2.8486,-3.5077,-3.2774,-1.7422,-1.9549,-1.8706,-1.4973,-1.5148,-2.012,-1.0441,-0.55846,0.086039,0.86559,2.5844,2.7539,2.7974,3.1061,4.3066,3.4607,4.0056,5.3105,4.8108,5.0244,4.9443,4.6469,3.8466,3.6089,2.1878\n3.912,3.0392,4.8368,NaN,NaN,NaN,NaN,-1.0506,-2.8989,-4.0422,-4.5284,-5.6355,-6.1262,-4.8539,-3.678,-3.265,-3.7281,-5.0234,-5.7446,-4.2969,-4.6917,-4.835,-4.654,-2.5957,-2.3345,-1.9037,-2.0513,-1.7174,-0.57525,0.75805,1.2022,1.4017,2.0363,2.5129,2.8689,3.8268,4.7255,5.2081,5.8558,6.2964,4.8993,4.1359,5.4924,4.9587,3.8554,3.3648,2.3034\n3.8887,3.586,3.9132,4.7989,2.3053,-3.1373,-2.7646,-3.23,-3.1278,-2.93,-3.2779,-4.379,-5.2072,-4.8817,-4.2338,-1.0453,-0.72126,-2.6464,-4.378,-4.1973,-2.3778,-2.7014,-2.7346,0.18527,-0.12144,-1.729,-1.5844,0.031479,0.81676,2.3922,3.4376,3.1564,3.5313,3.4741,3.7524,4.8408,NaN,NaN,NaN,6.4517,5.6043,4.6016,5.7793,5.9218,4.3251,2.3295,0.81084\n3.6942,3.4478,3.1586,2.2328,1.8943,-1.5219,-1.7906,-1.6153,-0.98252,-2.4618,-3.1046,-3.6262,-2.6954,-3.6569,-4.3408,-4.3824,-3.6106,-3.8157,-3.4817,-2.7818,-1.7895,-0.87116,0.6032,1.7495,1.2949,1.6331,1.8041,2.8006,3.7839,4.1626,4.7494,4.9612,4.8134,4.3665,5.1424,7.6354,NaN,NaN,NaN,5.4634,5.9695,5.0655,5.2123,4.8329,3.4739,1.6227,0.11599\n2.381,2.7271,2.1067,1.3074,0.85586,-0.40897,-0.88777,-0.41836,-0.57722,-1.8551,-1.5178,-2.9456,-3.4182,-3.5135,-3.3841,-3.5121,-2.0894,-0.9916,-2.008,-1.8333,-1.0359,1.3973,2.5404,2.8697,2.6157,3.0423,3.9357,4.7685,4.9048,5.075,5.4292,6.6798,6.9626,5.26,5.182,6.1984,NaN,NaN,NaN,6.9774,6.4028,5.9968,5.744,5.2443,3.2331,1.5935,1.0244\n2.4178,3.4247,2.1613,0.68801,0.49263,-0.67374,-1.1974,-0.36355,-1.1188,-0.74328,-0.84583,-2.5674,-3.0758,-3.0984,-3.138,-2.3833,-1.378,-0.60948,-0.5388,-0.11576,1.0111,2.3598,3.4724,3.2991,2.3176,3.356,4.5497,4.554,4.5435,4.3797,5.8645,5.9747,4.9433,3.9651,4.2047,4.4514,NaN,NaN,NaN,7.524,7.2673,6.4959,6.2503,4.3035,3.1145,2.2386,1.376\nNaN,NaN,3.2727,0.28336,-0.17421,-1.0504,-1.199,-0.77156,-2.0556,-0.8006,-0.54686,-1.2845,-1.8362,-2.2363,-2.3042,-1.8601,-1.562,-0.98051,-0.34269,0.52587,1.7101,2.9387,3.4653,3.4063,2.5414,2.3792,3.4373,3.8863,2.6877,3.771,5.1206,6.1699,4.7715,3.6343,4.377,7.2646,8.1005,7.4782,7.5936,8.0726,8.1339,6.935,6.4351,4.2558,3.5773,2.4056,1.9008\nNaN,NaN,NaN,-0.18386,-1.8373,-2.1477,-1.8736,-2.0111,-2.2333,-2.5532,-2.1229,-1.6446,-1.271,-1.3501,-1.6483,-1.8446,-2.1575,-1.3827,0.20472,1.5384,2.1626,3.6628,4.0842,4.1096,2.7513,3.5844,3.9693,3.6703,4.2609,6.1393,6.312,6.1582,4.307,2.8361,5.6692,7.4321,7.0191,6.4527,6.494,7.6001,9.0307,4.6947,4.0522,4.0824,2.304,1.6304,2.1231\nNaN,NaN,1.5163,-2.4273,-3.2465,-4.0668,-3.5735,-2.5368,-2.7238,-2.7564,-2.069,-1.8607,-1.0389,-0.91434,-1.2774,-1.4661,-1.3826,-0.73076,0.74962,1.787,2.627,3.8277,5.121,3.8257,3.968,4.5456,4.3435,4.5868,5.1588,5.8938,6.3406,5.2534,3.6104,2.6299,5.4486,7.7986,6.6239,5.6234,4.0733,7.6439,9.5156,4.2994,1.8118,1.5856,0.64132,2.2324,1.96\nNaN,NaN,-1.8363,-2.3596,-2.8663,-3.5054,-3.422,-3.3361,-3.5295,-3.666,-3.107,-2.3101,-1.6847,-1.0619,-1.1799,-1.6188,-1.6446,-0.45095,1.0774,1.8171,2.638,3.8786,6.1087,5.3517,3.7327,3.2811,3.5422,4.3038,5.2867,5.4129,5.5023,5.0809,4.4402,4.7697,9.0528,11.299,8.9567,7.8939,7.7589,8.2566,7.6799,4.2042,0.52343,0.031782,0.2963,2.5564,1.6775\n-1.2002,-2.4018,-1.7601,-2.9045,-4.1049,-3.2345,-2.0218,-1.7117,-3.5324,-3.5987,-3.9545,-3.2442,-2.8892,-2.3085,-2.6365,-3.4041,-2.4249,-0.43686,0.79303,1.0503,2.7037,4.0791,6.0001,NaN,NaN,NaN,NaN,4.2064,5.9565,7.7378,7.1162,5.2404,NaN,NaN,11.032,10.79,8.9326,7.875,8.1173,7.8031,4.9572,3.2161,2.7488,1.183,1.2141,1.4967,0.3326\n0.99002,-1.5862,-1.8733,-1.8712,-0.91201,-0.65192,-0.45567,-0.49528,-1.9544,-3.9823,-3.6706,-2.416,-1.942,-3.0888,-3.7059,-3.5501,-3.5278,-2.7574,-0.85655,1.0219,1.7744,3.7308,5.016,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.285,16.409,NaN,NaN,NaN,NaN,NaN,NaN,7.7743,7.5809,5.4693,2.6519,2.1051,1.8783,1.2291,0.70706,0.18036\n0.76842,-0.87553,-1.8042,-1.5384,NaN,NaN,NaN,0.79155,-0.90142,-3.4155,-2.9696,-2.5602,-1.0369,-2.6699,-3.6682,-3.1005,-2.5344,0.73207,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.42,31.786,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.8609,5.2447,3.9866,3.2557,2.2671,1.3356,0.58415,0.46046\n-0.50751,-1.2721,-1.6189,-2.0264,NaN,NaN,NaN,NaN,-1.2548,-1.4713,-1.2236,-0.89407,-0.71906,-2.0217,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.709,31.499,28.078,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.1205,6.1307,4.7573,3.0033,1.6874,0.51955,0.46721\n-0.58364,-0.29202,-0.49602,-1.276,NaN,NaN,NaN,NaN,-1.9711,-2.0726,-1.4424,-0.95714,0.07143,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,31.833,30.762,23.725,19.938,17.625,NaN,NaN,NaN,NaN,NaN,4.616,6.1874,5.4397,3.5027,1.4428,-0.035219,-0.54897\n-0.66094,-0.35876,0.35249,1.04,NaN,NaN,NaN,NaN,-1.9465,-1.8465,-2.292,1.498,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.238,29.853,23.37,18.412,16.313,14.61,NaN,NaN,NaN,NaN,4.6271,4.5534,3.9681,2.6932,1.6159,0.29028,-0.23335\n0.67521,0.58289,0.60528,1.5791,2.1847,0.066017,NaN,-0.56832,0.51905,-0.50988,-1.2334,2.315,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,36.362,32.854,28.513,25.153,18.715,15.857,11.881,10.74,NaN,NaN,4.9621,3.0316,3.016,2.8703,2.0526,2.277,1.4612,0.48613\n1.2248,1.7227,1.5495,1.5804,2.1502,1.7251,-0.36649,0.46893,1.3402,1.4439,1.4177,3.9233,4.4815,6.161,6.6593,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,32.751,27.532,25.503,24.383,21.627,14.138,10.941,10.469,9.8754,8.0542,5.558,2.6205,1.6878,1.2092,1.0683,0.78686,1.34,1.2332\n1.8831,2.5069,2.6389,2.872,3.031,2.8616,1.9426,1.6507,2.1433,2.4351,3.0273,4.1587,5.1287,5.2186,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.888,24.838,21.813,20.444,17.607,11.37,10.034,8.9988,8.0964,6.9063,4.6253,2.3239,1.0899,0.75623,0.42872,0.68451,1.0305,0.58291\n2.9889,3.0968,2.7737,3.4558,3.2036,3.2916,3.7039,3.5092,3.1742,4.4878,4.5994,4.8573,6.2071,8.368,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.71,19.574,14.804,11.43,10.558,8.4991,8.184,7.9696,8.0088,4.2342,1.0068,1.4825,0.91865,0.84464,1.2078,0.39358,-0.87657\n3.8395,4.1323,4.5529,4.4877,3.6346,2.8545,2.8265,3.0686,3.7673,5.0838,6.0656,5.4274,6.2208,9.274,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.807,18.103,14.216,10.764,10.306,8.7025,7.2581,6.1656,5.9494,3.1003,1.2721,2.03,0.95737,0.73499,1.7154,1.0832,-0.47101\n2.6647,3.6282,3.9666,3.6586,3.7268,3.6221,3.5045,2.606,3.124,4.6722,5.9739,6.1339,6.8168,10.135,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.616,18.917,16.05,13.148,10.947,10.506,9.9906,7.9181,6.5593,3.9682,2.1073,1.4166,1.422,1.3155,1.0627,0.932,0.22394,-1.6185\n3.9852,5.25,4.9187,2.8318,3.5219,3.6778,3.2258,2.5607,2.9894,4.7727,6.114,7.4694,8.6978,11.614,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.276,20.457,18.583,17.666,16.183,13.739,11.612,8.7839,7.0813,7.1135,5.1705,2.1281,1.4334,1.0946,1.0217,1.2872,1.2545,0.58012,-0.42402,-0.70446\n5.3002,5.9676,5.3305,2.68,2.4228,4.0727,3.7211,3.1151,2.8708,6.2134,6.6243,7.3715,8.7928,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.511,16.992,15.544,16.039,13.99,11.787,10.179,6.9248,3.0514,3.2943,3.6227,1.4407,0.58245,0.83225,0.52175,0.62556,0.18778,-0.40818,-0.87819,-1.1585\n5.9145,5.6617,4.8556,3.9194,3.965,5.3673,5.4281,5.0741,6.1865,6.9714,6.8803,7.9773,9.9108,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.332,14.421,15.012,13.894,12.16,10.702,9.0333,7.3925,5.2819,3.7384,3.0264,0.39189,0.37582,-0.32878,-0.97661,-0.30405,-0.35203,-0.56999,-0.8962,-1.0497\n4.8862,4.6825,4.4193,4.4359,5.3342,6.2864,6.5438,6.1478,7.193,6.5505,6.2806,7.1852,8.4933,9.7364,12.464,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.96,13.022,11.945,10.39,8.911,8.8927,9.136,8.6237,6.5057,4.8211,3.9683,1.904,-1.0326,0.18698,-0.81755,-2.5339,-2.4548,-1.8213,-1.0016,-1.2573,-1.4829\n4.6201,4.8232,5.0128,5.0084,5.6991,6.1765,6.175,6.0606,6.495,6.3279,6.2821,7.4666,8.1673,9.0931,11.047,13.403,14.644,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.926,10.158,7.5084,7.3455,7.3128,7.8442,8.9774,8.6672,5.7334,4.0131,2.2912,1.9795,-0.19427,-0.32175,-0.45774,-1.0326,-1.8728,-2.8731,-2.414,-1.148,-1.8728\n3.617,4.1166,5.0174,5.7075,5.5835,4.8337,5.6561,6.3007,6.5165,6.6609,6.8621,7.4843,8.032,8.3761,9.8044,10.96,11.619,12.619,13.306,NaN,NaN,NaN,NaN,11.812,11.291,10.914,NaN,NaN,7.4833,7.3019,7.4349,8.0439,7.5922,5.5448,3.4641,1.8415,1.5701,0.63707,-0.46214,-0.20465,0.23126,0.29735,-1.0566,-2.418,-1.9589,-1.4986,-1.9855\n3.6296,3.6228,4.6999,5.9147,5.5675,5.0173,5.4174,5.7321,5.891,5.8177,7.0074,7.2201,6.6622,6.5324,8.2353,9.0678,9.1832,10.676,11.476,11.839,14.025,NaN,11.436,11.903,12.105,11.744,NaN,NaN,9.4729,7.4704,7.4621,7.8303,6.6851,4.1866,3.4233,2.6431,1.9588,0.95356,0.43803,-0.21474,0.12192,0.043602,-1.2217,-1.7354,-1.5486,-2.1201,-2.013\n3.9916,4.3791,4.7006,5.0508,5.6269,6.0749,4.8502,4.7641,5.2139,5.7934,6.4176,6.5733,5.8779,6.1221,7.0132,7.6566,7.591,9.9869,11.338,11.975,11.233,9.7778,10.095,9.8988,9.2819,8.9742,NaN,NaN,9.9663,7.6506,7.4374,7.758,6.7856,5.6362,5.1153,4.9838,3.6272,1.5815,0.53902,0.0072231,-0.09421,0.48526,0.16209,-1.4562,-1.4978,-2.0333,-2.8746\n4.7612,4.3945,3.1358,4.0519,5.4104,5.9091,5.5349,5.4075,5.488,5.3756,6.1735,6.7319,7.3104,7.333,7.1153,6.675,6.5687,7.2861,7.3361,9.3954,9.6995,9.1634,9.7865,10.035,9.3714,8.3159,7.3988,9.0285,10.316,6.8883,7.0379,6.1435,5.6476,4.7893,4.0819,4.2446,2.6475,1.2696,0.70187,0.15724,-0.27604,-0.38398,-0.66905,-1.2762,-2.0284,-2.8454,-3.1973\n4.7624,3.989,3.0601,4.1582,4.8045,5.6007,6.2174,6.0553,5.6711,5.3491,6.2329,6.8254,7.0444,7.433,6.7898,6.6223,6.5528,6.9083,6.2793,5.941,7.2011,8.6782,9.169,8.9692,8.3601,8.2986,7.9775,8.4622,9.592,7.9056,6.1174,5.3807,3.8305,3.3701,2.7851,2.416,2.0084,2.1827,1.6261,0.47683,-0.25386,0.00013161,-0.12252,-1.0429,-2.5659,-2.7537,-3.617\n4.8602,4.295,4.1394,4.3727,4.6147,4.4426,5.597,5.7267,6.9539,7.1215,7.3807,7.5394,7.38,8.2118,8.4077,7.9615,7.5984,7.25,5.4432,5.6324,8.0316,9.1893,9.0928,7.9872,7.1768,6.6383,6.9687,7.585,7.3012,6.0401,4.9336,4.0387,2.0385,1.7621,0.92117,-0.18325,-0.12331,2.0728,2.2194,0.71678,0.30335,0.20558,0.067286,-0.81905,-2.2657,-2.7433,-3.4995\n4.9612,5.5622,4.5853,3.8264,3.8879,4.1834,4.8426,5.6239,6.7656,6.9359,8.4094,9.0112,9.7784,8.9378,8.7097,8.9054,8.679,8.2326,7.608,7.1022,8.4168,9.5606,9.5889,8.8225,7.3982,6.5642,7.1507,7.1487,7.0621,4.5759,4.5214,2.3252,0.046764,0.54097,0.26701,-0.19561,0.25979,1.073,0.59624,-0.57573,-0.077152,0.77829,0.11863,-0.87937,-1.7556,-2.6489,-4.046\n4.4892,4.285,3.4928,3.3269,3.9734,5.1807,5.4073,4.8835,5.0622,5.7473,7.1654,7.6434,7.8356,8.1932,7.3787,8.2755,8.4967,8.575,8.109,7.4714,7.6753,8.3073,9.2186,8.7246,8.398,7.7819,9.3549,9.1951,7.3432,4.04,3.4057,2.043,0.021128,0.23966,0.34091,0.65213,0.71902,0.57204,-0.7406,-1.8101,-0.56457,0.80441,0.63178,-0.32575,-1.6506,-3.2522,-4.8154\n3.3418,3.1598,3.0096,3.5331,4.7664,6.6971,5.721,5.1136,5.6417,6.4929,7.0845,6.3205,4.9016,3.8068,NaN,NaN,NaN,7.8912,7.5258,7.7341,8.4273,8.2387,8.4887,8.6328,8.4702,8.418,8.9344,8.5015,5.0634,3.1926,2.9065,2.5433,1.9566,0.94309,0.36027,0.72765,0.64665,0.28474,-0.20647,0.14985,-0.02482,-0.38935,0.073835,-0.61218,-2.3877,-3.689,-3.8433\n3.4012,2.8027,1.8702,2.6297,3.2184,5.6331,5.9135,4.9209,4.8626,5.9935,7.5455,7.0181,5.4402,NaN,NaN,NaN,NaN,NaN,7.1468,9.1935,9.4748,8.5606,7.3604,8.1689,8.8962,8.0024,7.9582,6.9304,2.4591,2.5726,2.6815,2.2196,2.5208,1.897,0.61779,0.050844,-0.76826,-0.42156,0.39831,0,0.076845,-0.35252,-0.6539,-1.4389,-3.3523,-4.0411,-3.5383\n3.1222,3.9147,3.5705,2.4334,2.5638,3.8605,5.6944,5.0748,5.5606,6.3826,7.3732,7.3746,NaN,NaN,NaN,NaN,NaN,NaN,6.7902,8.4643,7.8587,7.2607,7.5542,7.1583,6.8413,6.8852,6.3332,5.1058,1.7235,1.7987,1.4064,0.98266,1.2652,0.95321,0.90465,0.43189,-0.23401,-0.1898,0.26169,-0.17901,-0.32652,-0.68089,-1.8158,-2.2249,-1.996,-2.1837,-3.018\n2.2367,4.7267,5.9768,3.3885,2.3536,1.4457,3.4886,4.4987,4.4563,5.2493,6.0312,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.5019,5.8559,7.5085,7.7561,7.5515,6.008,5.6732,5.4438,4.7379,3.9241,2.6789,1.7292,1.187,0.37165,-0.22869,0.58393,1.0403,0.66511,-0.24749,0.21373,0.65131,0.20817,-0.6828,-1.3815,-2.3984,-2.5682,-2.0768,-2.6856,-4.566\nNaN,NaN,NaN,NaN,NaN,2.9141,2.9647,3.906,4.4189,3.7243,2.558,NaN,NaN,NaN,NaN,NaN,NaN,3.02,6.1257,6.1752,7.1032,7.569,6.5421,4.932,5.7716,5.9301,4.9321,4.2183,2.5084,0.5774,-0.46036,0.083628,-1.2903,-1.6661,-1.0063,-1.1986,-1.1084,0.93923,0.27806,0.34758,-0.16583,-0.76501,-1.2355,-1.6296,-2.7232,-5.7411,-7.7822\nNaN,NaN,NaN,NaN,NaN,NaN,3.0314,4.7526,5.0484,4.6922,2.5777,NaN,NaN,NaN,NaN,NaN,NaN,4.0333,5.0414,6.5553,6.0968,5.1748,3.9104,3.6725,4.2507,4.0847,3.7501,2.7501,1.8831,0.91778,0.063847,0.2385,-0.72642,-3.9265,-3.0436,-2.0711,-2.0182,-0.73866,-0.86872,-0.010031,0.38262,0.36321,1.038,1.3829,-0.74422,-4.3532,-5.5402\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,4.9998,5.272,5.4049,4.0251,NaN,NaN,NaN,NaN,NaN,5.8488,5.6033,4.2022,4.182,4.5565,3.1295,2.2255,2.3653,3.2383,3.2703,1.2813,1.8141,2.0301,1.9147,0.23274,-0.61782,-0.70595,-0.30283,-0.65119,-1.5202,-1.2935,-0.75256,-0.98611,-0.23611,0.18601,0.70913,1.0482,1.3884,0.26653,-1.4849,-3.6805\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.2703,3.5814,4.3929,7.611,6.4049,4.2768,3.8555,5.3452,5.9881,4.4706,2.1879,1.4873,1.8375,2.1003,2.712,2.914,1.2293,0.091425,-0.46445,-0.79261,-1.3984,-2.5283,-3.9504,-2.6839,-1.3057,-0.57234,-0.31098,-0.48865,-0.33993,-0.40989,-0.69203,-0.60202,-1.5276,-1.6451\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5087,4.8211,5.4028,4.5818,5.1622,5.7104,4.6748,2.3995,0.36027,0.57448,1.5273,1.8167,1.4093,0.64472,-0.50655,-1.361,-1.9044,-1.7351,-2.8229,-3.5947,-3.3073,-2.4414,-0.95256,-0.67151,-1.2108,-1.5267,-1.2888,-0.15161,0.39886,0.21974,-0.76685,NaN\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.8991,6.2726,3.7051,3.163,4.3455,5.0604,4.3644,1.7227,0.21326,1.6975,2.2284,1.4179,0.37688,-0.88888,-1.7438,-1.6626,-2.1156,-2.6841,-4.2877,-5.3648,-2.9124,-1.2084,-0.61454,-0.72242,-1.6279,-2.1434,-1.3766,0.23788,1.3818,2.3474,1.1176,NaN\nNaN,NaN,6.2549,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.1119,6.633,4.2526,2.9084,2.9501,3.7377,4.0645,3.3626,2.1039,2.4172,4.2176,2.6946,0.73597,-0.81963,-1.3814,-1.1727,-3.1535,NaN,NaN,NaN,-3.0663,-2.7084,-2.4006,-1.6859,-1.0903,-1.3265,-1.2899,-0.27475,1.0868,2.9114,1.0988,NaN\n8.3641,5.053,4.8127,5.8431,6.9268,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.9138,4.0605,3.131,4.1066,4.6978,3.1698,1.0434,2.1273,0.68206,0.29764,2.0374,1.4365,-0.31096,-0.48201,-1.2506,-3.2513,-4.5332,NaN,NaN,NaN,-4.8462,-5.4679,-3.7393,-2.0405,-0.78594,-0.99673,-0.88138,0.20481,1.0451,0.62304,NaN,NaN\n7.409,6.6038,6.5228,6.251,7.0344,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5214,NaN,NaN,NaN,0.03381,-1.1571,-2.901,-2.1968,-1.9509,-1.5258,-1.9127,-2.6792,-4.3768,-6.6494,-6.0654,NaN,NaN,-4.6371,-4.7619,-1.289,-0.1529,0.0073509,-0.32815,-0.2655,2.4121,3.5971,-0.57832,NaN,NaN\n5.7738,6.9248,7.9968,7.6867,7.3781,6.9966,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.1126,-2.9834,-2.1441,-2.0605,-1.5554,-1.7333,-2.5508,-4.9275,-7.1065,-6.3068,-4.2763,-3.9222,-2.9848,-1.681,1.9927,2.1294,0.5771,-0.62822,-0.4526,-0.33986,-0.45696,-1.588,-2.8813,NaN\n7.1801,7.9505,7.3494,6.4431,8.0877,7.2638,5.6839,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.0372,-0.51191,-1.9064,-1.2409,-2.3739,NaN,NaN,NaN,-9.1018,-5.9839,-3.8769,-2.7584,-2.0849,-2.5267,-2.7602,-1.4602,-2.2383,-1.4957,-2.7984,-2.8376,-1.9707,-1.9839,NaN\n7.6522,7.6003,6.4143,4.7458,6.051,5.5225,4.3115,3.1232,2.7769,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.4538,0.62865,-1.2706,-0.92639,-2.7504,NaN,NaN,NaN,-7.7816,-5.3286,-3.6021,-1.9471,-2.8058,-3.7258,-1.859,-2.1214,-3.6863,-3.2869,-3.7938,-3.5264,-4.3048,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061002-070430.unw.csv",
    "content": "1.7235,1.5062,0.73959,1.3106,1.5739,1.7883,1.3471,1.2857,1.6215,0.52917,0.64081,1.204,1.8998,2.4979,2.9683,2.1659,1.4561,1.9488,2.115,1.8096,1.1331,0.72066,0.61487,1.0461,0.94962,0.48567,0.86934,1.126,1.1363,1.6076,1.8897,1.6941,1.8584,2.2137,2.023,1.9015,1.5452,1.0535,1.4073,1.7065,1.8517,1.6295,1.2593,0.91564,1.1346,0.54672,0.42324\n1.3457,1.4655,0.95435,1.2338,1.9926,2.0162,1.2208,0.95323,0.678,0.81373,0.73762,0.69516,1.4731,2.0526,2.0276,2.3298,1.6503,1.9899,2.1213,1.723,1.5783,0.62601,0.27862,0.083338,-0.14382,0.25505,1.1442,1.3909,1.263,1.172,1.2917,1.0629,1.3651,1.6311,1.5746,1.5519,1.3423,1.1042,1.0156,1.0996,1.389,1.5045,0.81412,0.50101,0.2251,0.11585,0.24232\n1.3348,1.2844,0.62581,1.1193,1.3892,1.2123,1.0593,0.96787,0.76646,1.1839,0.77954,0.61011,1.2686,1.5611,2.1048,2.4241,1.6767,1.9955,2.3697,1.5282,1.1807,0.73614,0.71091,-0.060639,0.10447,0.82238,1.4404,0.58273,0.36552,0.84921,0.34403,0.77697,0.99957,1.1343,1.0997,1.2159,0.57977,0.5555,0.92023,1.1031,1.3575,1.349,0.90141,0.46202,0.081537,0.034509,0.50644\n1.1226,1.2078,0.91285,1.2477,1.2956,1.414,1.9188,1.8826,0.93856,0.7008,0.85044,1.0273,1.0584,1.7991,1.9408,1.9099,1.8051,2.2566,2.5072,1.6321,1.5265,1.1279,0.55578,0.25261,0.32277,0.48373,0.484,0.095783,0.00095844,0.2562,0.38318,0.9335,1.1688,0.96212,0.94146,0.84899,0.70428,0.36195,0.86834,1.2481,1.1588,0.74787,0.42697,0.42572,0.55507,0.51295,0.52965\n0.61427,0.33262,0.25427,1.3157,1.94,1.7258,1.9702,2.9338,2.3546,2.1352,1.3317,1.0114,1.2474,2.0555,1.6498,1.3564,1.151,1.6707,2.4327,2.5769,2.0988,1.3017,0.11418,0.3368,0.17803,-0.18857,-0.73529,-0.58886,0.10631,0.36706,0.055215,0.26758,0.78278,1.2305,1.2777,1.2306,0.65083,0.54689,0.84841,1.0324,0.80662,0.87256,0.71772,0.61968,0.71491,0.65385,0.60607\n0.11267,-0.12699,0.83996,1.4786,1.7279,1.5808,1.4921,1.476,1.2545,1.2359,1.4384,0.94353,1.7907,2.1533,2.1037,1.74,1.7554,1.6503,1.7778,1.4037,1.2427,0.67448,-0.41297,0.23047,0.89449,0.47706,-0.38815,-0.69259,0.023953,0.37979,0.023197,-0.17991,0.080638,0.94868,1.1452,1.3519,1.3109,1.0176,0.83776,0.55772,0.91757,1.3987,1.3208,1.0753,1.0349,1.0649,0.92075\n0.042538,-0.1604,0.42553,1.5578,1.7626,1.5821,2.0875,1.473,0.98263,1.2437,1.0643,1.0363,1.3801,1.7553,1.7718,1.6405,1.2324,1.0342,0.88288,0.85396,0.10586,-0.31,-0.1923,-0.13323,-0.29157,-0.27983,-0.71505,-0.69462,0.044815,0.42726,-0.10216,-0.38918,-0.39019,-0.10179,0.5006,1.0482,1.3068,0.91158,0.53197,0.92849,1.0326,1.3565,1.5325,1.5043,1.5021,1.4303,1.7405\n0.68601,0.4152,-0.085819,1.4465,1.7608,1.5972,1.4432,1.4865,1.5831,1.0414,0.39047,1.0861,1.1656,1.4237,1.0356,0.94636,0.4282,0.62984,0.51393,0.22952,-0.6677,-0.62058,-0.33758,0.0043733,-0.21853,-0.80693,-1.3197,-0.75615,-0.24364,-0.22299,-0.4475,-0.29945,-0.3578,-0.15646,0.40038,0.69838,0.72813,0.33048,0.32182,1.0356,1.4192,1.3884,1.4992,1.0503,0.89094,1.0646,1.5536\n0.98076,0.77731,0.18907,1.4358,1.5981,1.7302,1.6097,1.8736,1.611,0.54206,0.08215,0.5339,1.024,1.0941,0.80865,0.21162,-0.024065,-0.17619,-0.07688,-0.88119,-1.4614,-1.3539,-0.38273,0.15148,0.063563,-0.21171,-0.50215,-0.22613,-0.54589,-0.84191,-0.75897,-0.55396,-0.5092,-0.20911,0.16446,0.11918,0.049579,-0.033858,0.44715,0.86633,1.3394,1.7535,1.8349,0.87246,0.42615,0.5271,1.3326\n0.55785,0.52883,1.3113,1.533,1.292,1.6494,1.5558,1.8018,1.2676,0.3523,0.22155,0.15713,0.38632,0.39364,0.48226,0.32428,-0.43427,-0.79978,-0.89756,-1.1301,-0.71051,-0.55012,-0.5558,-0.28701,-0.27479,-0.1102,-0.51705,-0.49511,-0.51752,-0.63347,-0.38837,-0.47889,-0.4975,-0.53536,-0.64311,-0.33266,-0.29281,-0.45668,-0.046752,0.63496,1.3179,1.6131,1.5011,0.77468,0.28774,0.50211,1.1758\n0.8551,1.1931,1.7942,2.314,2.3117,1.8302,1.4129,1.1989,0.90755,0.13265,-0.2593,-0.31126,0.10872,-0.26099,-0.65589,0.020434,-0.44701,-0.75209,-1.09,-1.1918,-0.42087,-0.1855,-0.63156,-0.45667,-0.41519,-0.52675,-0.60287,-0.69036,-0.60215,-0.38255,0.03228,0.17265,-0.24109,-0.37223,-0.71626,-0.53578,-0.48362,-0.52497,-0.25545,0.45073,1.1048,1.1787,1.0087,0.6683,0.17016,0.38196,0.69188\n1.0431,1.4091,2.2095,2.2923,1.5595,2.1424,1.4096,0.90962,0.99878,-0.11088,-0.7322,-0.80147,-0.29045,-0.37731,-0.51744,0.062521,-0.24942,-0.8701,-0.93441,-0.67244,0.38725,-0.031598,-0.32152,0.067121,0.034584,-0.10364,-0.30571,-0.44648,-0.11218,0.10801,0.018658,-0.19469,-0.18544,-0.12877,-0.36508,-0.33942,-0.11955,-0.13015,-0.12072,0.42844,1.1086,1.1652,0.83723,0.41474,0.14545,-0.12015,0.19261\n1.1902,1.4492,2.7901,3.4428,1.86,2.0175,1.646,1.0995,1.1412,-0.16883,-0.025781,-0.23157,-0.25265,0.18213,0.30291,0.34453,0.34799,0.079121,-0.049919,0.24073,-0.28876,0.16277,0.32786,-0.014389,-0.3081,0.06177,0.12167,-0.26677,-0.042904,0.20702,-0.3002,-0.51471,-0.44489,-0.31951,-0.47531,-0.43143,0.23097,0.31584,0.27727,0.79278,1.284,1.3405,0.93769,0.51428,0.0048726,-0.0049944,0.54677\n2.204,2.6477,3.3499,4.5782,2.6718,0.50894,0.75999,1.3354,0.18322,-0.40384,-0.63679,-0.50294,0.025746,0.45528,0.52951,0.36914,0.26918,-0.10206,-0.014482,0.041283,-0.50416,-0.53796,-0.26554,-0.20963,-0.56086,0.55156,0.73536,0.19189,0.2784,0.1804,-0.34405,-0.50621,-0.55165,-0.5613,-0.54714,-0.70911,-0.51487,-0.060424,0.67666,0.69353,0.97667,0.9857,0.51836,0.28292,-0.16492,0.40622,0.78824\n2.695,2.0445,3.6181,4.7588,2.419,0.89499,0.53549,-0.35501,-0.20645,-0.22631,-0.014151,0.54817,0.81578,0.93499,1.2194,1.3815,0.53995,-0.24085,-0.16371,-0.23489,-1.0924,-0.73157,-0.052282,0.29143,0.22691,1.645,1.4909,0.84948,0.43145,0.47361,0.43858,0.042922,-0.5313,-0.81325,-0.86407,-1.2703,-0.91116,-0.48829,0.18044,0.3008,0.14534,0.1738,-0.10299,-0.2712,-0.35987,0.36482,0.37855\n2.0667,1.9335,1.6184,2.4197,2.2565,2.0644,0.56788,-0.68598,-0.085319,-0.30834,0.22952,0.71002,1.0313,0.27092,0.047017,-0.60767,-0.99261,-0.70493,-0.52104,-1.2264,-1.2502,-0.48007,-0.018767,0.97878,1.0007,1.0186,0.79791,0.045937,-0.26425,0.20824,-0.55927,-0.82151,-1.1585,-1.2828,-1.3293,-1.5981,-0.98486,-0.74456,-0.50423,-0.060659,0.14225,0.35698,0.046684,-0.049937,-0.10095,-0.44208,-0.10958\n1.9336,1.7866,1.767,2.0946,2.3117,2.0268,0.9597,0.15268,-0.44499,-0.86967,-0.70438,0.33072,0.69141,-0.60719,-0.057582,-1.2523,-0.92055,-0.50862,-1.2678,-1.8121,-1.8037,-0.97064,-0.61445,-0.066587,-0.18059,0.43258,0.15814,-0.44885,-0.51868,-0.074917,-0.19549,-1.0099,-1.4013,-1.4033,-1.4075,-1.5198,-1.4693,-1.471,-0.9536,-0.42994,0.29927,0.28499,0.093975,0.078941,0.12629,-0.36051,-0.21399\n1.9306,1.9542,1.9296,2.0184,2.8389,2.6492,2.0762,0.42171,0.48487,0.99116,0.57629,-0.03652,-0.42552,-0.85148,-0.50317,-1.0286,-1.7764,-1.1812,-1.1085,-0.78978,-0.6063,-0.88281,-1.2572,-0.98117,-0.74248,-0.52477,-0.36485,-0.6721,-0.56621,0.027048,-0.38302,-1.1251,-2.2942,-2.1018,-2.0329,-1.6955,-2.0788,-2.3645,-2.3842,-1.3114,-0.76394,-0.57334,-0.78054,-0.28302,-0.0079968,-0.35305,-0.37943\n2.1325,2.4293,1.8168,0.48689,0.78412,1.1281,1.1414,-0.14554,-0.48475,1.0663,0.74809,-0.56949,-0.96498,-1.4848,-1.642,-1.3725,-1.7822,-1.5986,-1.2568,-1.4807,-1.1481,-1.1233,-1.4656,-1.9382,-2.0123,-1.5961,-0.42389,-0.76505,-1.2202,-0.30534,-0.39022,-0.89477,-2.0352,-2.4305,-2.3254,-2.1064,-2.4687,-2.445,-1.8495,-1.5127,-1.1077,-0.19682,-0.70787,-0.11363,0.27953,0.19239,-0.32534\n3.2932,2.9325,2.5816,1.7535,0.39238,NaN,NaN,-0.59825,-0.57029,0.48545,0.11063,-1.2181,-1.827,-2.1454,-2.425,-2.2757,-2.2585,-2.5983,-2.3715,-2.4631,-1.7691,-1.2749,-0.99887,-1.9488,-2.0619,-1.224,-0.85269,-1.0602,-1.9443,-1.4274,-0.63517,-0.88732,-1.997,-2.1738,-2.9799,-2.5553,-1.6922,-1.7819,-1.5663,-1.4704,-0.88724,-0.2705,-0.02552,0.32937,0.63842,0.66047,0.58602\n3.644,4.2915,2.8864,3.2197,2.8596,NaN,NaN,-0.14856,-0.39005,0.11667,-1.488,-2.4337,-2.6831,-2.3282,-2.4872,-2.1913,-2.2812,-2.7117,-2.0692,-1.9751,-1.6661,-1.3198,-1.2736,-1.4138,-0.76678,-0.52217,-0.80679,-1.3539,-1.9798,-2.1439,-1.112,-1.0635,-1.7902,-1.8345,-3.0727,-2.9016,-1.0533,-1.0489,-0.90147,-1.0442,-0.45904,-0.1359,-0.070209,0.4033,0.6308,0.83161,0.77136\n3.4337,2.5628,1.5756,0.58245,NaN,NaN,NaN,-0.20993,-0.69389,-1.0358,-1.7925,-2.5079,-3.0226,-3.0408,-2.9814,-2.3481,-1.9422,-2.1911,-2.3081,-1.8812,-1.5984,-1.4457,-1.4952,-0.69158,-0.45559,-0.66876,-0.98749,-1.1748,-1.5483,-1.5323,-0.98592,-0.81913,-1.2316,-1.3611,-2.3322,-2.197,-0.96216,-0.44073,-0.24975,-0.66531,-0.12753,0.11993,0.38496,0.6783,0.75143,0.80429,0.6096\n2.326,1.6092,1.9113,0.59019,NaN,NaN,NaN,-1.4484,-1.3911,-1.5834,-2.4559,-3.9407,-4.4422,-3.7645,-3.6608,-4.231,-2.184,-1.8801,-1.8855,-1.5506,-1.0897,-1.2455,-1.3613,-0.91213,-1.0378,-0.76792,-0.7646,-0.92109,-1.2027,-1.3895,-0.90036,-0.31203,-0.17081,-0.05023,-0.40574,-0.97157,-0.35055,-0.026523,-0.13057,0.031647,0.18409,0.34116,0.95737,1.469,0.92149,0.67459,0.48242\n-0.36472,1.0718,0.60217,-0.35161,-2.3084,-1.4215,-1.652,-1.2212,-1.2759,-1.9742,-3.5156,-4.1064,-2.3185,-2.6511,-2.8521,-3.0949,-1.5834,-1.6757,-1.402,-1.0386,-0.5788,-0.36796,0.033992,-0.12755,-0.11085,-0.39119,-0.27141,-0.25676,-0.44589,-0.38192,0.27605,0.71292,0.94808,0.82177,0.27566,-0.043202,-0.078635,-0.1886,-0.39596,0.12252,0.27854,0.51981,0.8339,1.0046,0.61955,0.25234,0.3704\n1.6721,1.4625,1.1349,0.2891,-0.48947,-1.3454,-1.1755,-1.6404,-2.2168,-2.1678,-2.2433,-2.6046,-1.9751,-2.7019,-2.7112,-2.3153,-1.5773,-1.6054,-1.3042,-0.81402,-0.32112,0.3183,1.0679,0.31467,0.28308,0.67666,0.66818,0.65045,0.92016,0.75631,0.8132,1.1584,1.5443,0.88282,0.33261,0.32388,0.42455,0.11707,0.021015,0.2834,0.10146,0.63826,0.86601,0.67883,0.58603,0.47067,0.29818\n0.5275,0.20346,0.43604,0.04225,-0.77828,-0.9444,-1.0824,-1.2473,-1.145,-1.2659,-0.47129,-1.2736,-2.3042,-2.2749,-1.7065,-1.5515,-1.6029,-1.2623,-1.0279,-0.41496,0.35211,1.7019,1.5475,1.0375,0.87951,0.86077,1.0084,0.71699,0.564,1.1949,1.5106,1.771,0.94519,-0.78233,-0.21927,0.08064,0.8827,0.69516,0.77952,1.1689,0.52034,0.98205,1.0737,0.88988,0.82043,0.6071,0.46945\n-0.32192,-1.0915,-0.57884,-0.74964,-0.70364,-0.72308,-1.0686,-1.0378,-0.92954,-1.1014,-1.2015,-1.3319,-1.2131,-1.5163,-1.4018,-0.83305,-0.15176,-0.1578,-0.58204,-0.13762,0.89948,1.6581,1.8778,1.5017,0.94687,0.816,1.0433,0.81915,0.3556,1.4732,1.6595,1.4423,1.0926,0.17367,-0.01481,0.23319,1.1236,1.1974,1.316,1.3063,0.89588,1.1325,0.99554,1.0643,1.0854,0.75123,0.46663\n-0.93213,-1.3292,-0.85483,-0.48016,-0.81458,-0.38865,0.22115,-0.7784,-1.398,-1.9936,-1.6286,-1.4373,-1.56,-1.7218,-1.0898,-0.79266,-0.52146,-0.78159,-0.33719,0.42981,1.2367,2.2345,2.6179,2.6918,1.7573,1.2606,1.2967,1.4797,1.3933,1.4408,1.8466,2.018,0.73929,0.11661,0.34768,1.4106,1.4574,1.5065,0.938,1.1073,1.4682,1.3041,1.0475,1.231,1.2074,0.87085,0.65026\n-0.9745,-0.60505,-0.32975,-0.26152,-0.20669,-0.14418,-0.27254,-0.59922,-1.1927,-1.4404,-1.0788,-1.0033,-1.5669,-1.2464,-0.87553,-0.58006,-0.32149,-0.47508,0.26708,1.1893,1.5771,3.2935,4.2318,4.8234,3.3341,2.7961,3.4173,2.8288,3.0971,3.5503,2.9951,2.4921,1.2795,0.67539,0.96361,1.5524,1.845,2.1277,2.053,1.9776,1.8823,1.4904,1.5886,1.5443,1.3259,1.1615,0.9608\n-0.87302,-0.6125,-0.88978,-1.2549,-0.12837,0.69265,0.58692,-0.33235,-1.06,-1.2617,-1.1374,-0.74823,-0.44712,-0.92109,-0.82473,-0.49501,0.014091,0.14958,0.96987,2.2393,2.7047,4.5208,6.424,5.0244,4.5223,4.4129,4.5558,4.9286,4.6607,4.2016,3.6965,1.6105,0.24642,0.34249,1.1995,2.0537,2.3363,2.3426,1.6352,1.671,1.5379,1.3467,1.9458,1.8285,1.7602,1.3148,1.0187\n-2.7966,-0.80963,0.10544,0.019015,-0.24794,0.23076,0.64672,-0.047928,-0.54543,-0.38261,-0.71581,-0.75295,-0.34603,-0.86314,-0.89305,-0.79314,0.24557,0.99747,1.0099,2.6643,4.503,5.7972,8.6711,8.3857,5.8386,5.4792,5.7362,5.7445,4.4089,4.2659,4.0387,2.5859,1.6317,1.1093,1.4549,2.6027,2.5977,2.4146,2.2281,2.0162,1.8245,1.725,1.8793,1.9209,1.7992,1.1037,0.90726\n-1.8071,-0.50038,-0.039923,0.15455,1.4084,1.178,1.1264,0.23383,-0.46545,-0.40969,-0.38924,-1.0843,-1.1084,-0.92148,-0.67293,-0.24773,0.89819,1.6395,2.4798,2.9263,5.4025,6.9237,9.1316,9.7523,7.8658,5.1082,2.6164,2.8736,2.0364,1.6241,1.1457,2.1286,2.7909,2.6415,3.4897,3.2539,2.5879,2.6934,2.5736,2.3128,2.0047,2.2937,1.8574,1.9997,1.7286,1.3507,1.2226\n1.0587,2.027,1.8743,1.8848,1.1934,1.0215,0.35854,-0.75062,-2.043,-1.14,-1.0125,-0.93232,-0.91935,-0.68436,-0.42466,-0.040996,0.99246,2.3816,2.9548,3.1334,4.4287,6.3474,7.4094,8.1959,7.0334,4.9076,2.1706,0.024609,-0.068788,0.18735,1.3517,2.251,2.2145,2.0609,2.1197,3.1238,3.4694,3.4355,2.5437,1.2981,1.3931,2.1564,1.8097,1.3671,1.6225,1.5073,1.4461\n0.019354,0.66224,0.78525,1.1919,1.8624,1.5654,0.97784,-0.28773,-1.4312,-1.5587,-1.282,-1.1766,-1.1134,-0.68691,-0.41352,0.53155,1.97,3.2064,3.6088,2.9072,2.7158,4.1058,6.3235,6.1811,NaN,NaN,NaN,NaN,NaN,1.0506,1.8254,2.0369,2.5116,NaN,NaN,3.7489,4.3944,4.0063,2.9737,1.9598,1.8184,1.9698,1.283,1.1668,1.4119,1.5079,1.4238\n1.4879,1.0587,-0.50058,-0.13951,1.5178,1.9919,1.3665,-0.81016,-1.7927,-2.0211,-1.7255,-1.2009,-1.2728,-0.25824,0.28025,0.69332,2.0772,2.9801,3.4736,2.8868,2.1168,3.3904,3.125,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.89501,2.3169,3.705,NaN,NaN,NaN,6.3133,4.7139,2.9547,2.34,2.4899,2.0798,1.4559,1.2299,1.4595,1.3931,1.4418\n-0.49012,0.23603,0.017682,-0.78672,0.90926,0.46001,-0.4216,-1.1135,-1.6793,-0.25375,-0.29193,-0.25865,-0.62978,-1.3342,-0.88597,3.329,3.4144,3.4368,3.8786,3.6156,3.1948,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.733,0.72545,1.3534,NaN,NaN,NaN,7.4348,6.1636,3.8407,2.6146,2.658,2.3077,1.6681,1.3719,1.3355,1.3414,1.4453,1.3815\n-1.0677,-0.53642,-0.12281,-0.43509,0.41606,-0.30648,-0.97725,-0.81278,-0.47217,-0.3378,0.83923,1.4213,0.7707,-0.4688,0.14211,3.6433,3.9046,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.3919,1.2507,NaN,NaN,NaN,NaN,6.8831,4.2269,2.3468,2.2488,2.3705,1.6203,1.5162,1.5618,1.5405,1.7103,1.6746,1.0571\n-0.66144,0.25782,0.41484,0.40743,0.5016,0.054683,-0.7512,-0.92055,-0.88351,-0.2483,0.86171,1.7167,1.6006,1.3806,2.5897,3.5387,3.7698,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.0384,0.24061,NaN,NaN,NaN,NaN,4.6894,2.8044,2.1816,1.2405,2.1208,1.6847,1.7565,1.9471,1.9726,2.2958,1.7916,1.4655\n-0.43407,0.24851,0.94272,0.99754,0.69753,0.019614,-1.1094,-1.1448,-1.4023,-1.2239,-0.40002,0.72668,1.5589,2.0123,2.8873,3.1312,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.076846,0.38137,-2.6978,NaN,NaN,NaN,NaN,3.3421,2.8048,2.262,1.7527,2.3816,1.6996,1.7999,1.649,1.6925,1.6676,1.7198,1.8514\n-0.61673,0.36417,0.10396,-0.18674,-0.11144,0.26946,-0.49689,-1.2782,-0.65326,-0.35725,-0.18587,0.3049,1.8933,2.6301,3.895,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.98058,1.1045,-1.1077,-6.7519,NaN,NaN,NaN,3.7742,2.8835,2.5813,2.7046,3.377,2.3119,1.4702,1.5858,1.7323,1.6514,1.4571,1.5139,1.7557\n-0.45773,0.23426,0.50726,-0.12322,0.11586,0.55148,0.8307,0.2342,-0.62371,0.57289,1.1096,1.2982,2.3074,3.0843,3.594,NaN,NaN,NaN,NaN,NaN,NaN,2.3262,1.5628,1.4854,1.6996,-0.282,-0.10195,2.6983,NaN,NaN,NaN,NaN,NaN,NaN,2.5035,2.0252,1.7233,1.1287,0.17326,0.83773,0.64057,0.99574,1.5944,1.6838,2.4987,2.4211,2.2057\n-0.34378,0.039342,0.49986,0.18912,-0.042173,0.32746,0.33827,-0.012751,0.071568,0.99549,1.7786,1.8516,2.3444,2.8861,2.3528,1.0055,0.3004,NaN,NaN,NaN,NaN,1.7832,2.2104,2.7026,3.0566,1.5823,3.0603,2.5027,NaN,NaN,NaN,NaN,NaN,2.9175,2.2105,1.3773,-0.056019,-0.60089,-0.59795,0.43659,0.71315,0.7655,1.2629,1.6679,2.5352,2.6533,2.0521\n0.0328,0.76677,0.68063,0.12316,0.052795,0.14075,-0.17643,-0.054382,0.62201,0.94136,1.3919,1.7118,1.8886,2.7166,2.4919,1.5304,-0.15175,0.17943,0.59426,0.67462,1.9799,2.5556,3.928,NaN,4.2269,3.5154,3.4487,1.6086,NaN,NaN,NaN,NaN,NaN,2.6913,1.7402,1.25,0.12107,0.24815,0.6464,1.0795,1.6324,1.7771,1.9295,2.0101,2.1073,2.1798,1.5833\n1.306,1.3705,1.1126,0.94075,0.56437,0.0072973,-0.32965,-0.35843,-0.25133,0.43552,1.0395,1.7191,2.2711,3.0127,3.4118,2.5573,0.90654,0.97603,1.1602,2.4283,2.9966,3.9267,NaN,NaN,NaN,NaN,NaN,1.6716,3.3136,4.7325,4.6456,3.645,3.2011,2.0114,1.2235,0.55184,0.49976,0.95444,1.033,1.5554,1.9293,1.7218,2.0106,2.0798,1.6442,1.3115,0.31483\n1.02,1.3456,0.96325,0.79859,0.86678,-0.24997,-1.0822,-1.4698,-1.2306,0.71925,1.4252,1.8954,2.3752,3.2201,4.4223,5.2312,2.9213,1.555,1.8311,2.0538,2.8769,NaN,NaN,NaN,NaN,NaN,NaN,2.0257,2.2768,3.6667,3.8407,2.9976,1.9545,1.3892,0.90398,0.20383,0.58094,1.0688,1.4059,1.2746,1.343,1.2838,1.1582,0.74323,0.061981,-0.24948,-0.082908\n1.0701,1.1579,0.8185,0.58592,0.83956,0.31545,-0.29713,-0.055539,1.1835,2.3316,2.1436,2.3274,2.8673,3.8691,5.4152,6.948,NaN,NaN,NaN,NaN,3.0818,NaN,NaN,NaN,NaN,NaN,NaN,1.8609,2.4277,3.2388,2.9466,2.0388,1.218,0.97661,0.86656,0.70586,0.81556,1.0763,1.1144,1.0145,0.97204,0.56248,0.60735,0.41036,-0.07185,-0.27219,-0.022109\n0.83218,0.80408,0.61418,0.73644,1.0309,0.90084,0.65172,1.2204,2.0694,1.8514,1.7286,2.2124,2.7474,3.0875,3.3356,4.8381,NaN,NaN,NaN,NaN,3.1088,3.1734,3.9316,2.8661,NaN,NaN,1.3259,2.086,2.2071,2.0403,1.8948,1.2011,1.3186,1.3352,1.0817,0.69141,1.4502,1.5593,1.1623,1.0383,0.84856,-0.44632,-0.056935,0.36632,0.50671,0.64387,0.60911\n0.49823,0.27936,0.73176,1.2634,1.7153,1.7636,1.5697,1.4944,2.2232,2.1524,1.9916,2.5656,2.8609,3.3562,3.6438,3.4393,4.689,4.524,NaN,NaN,NaN,2.8053,2.3619,2.6078,1.6228,0.92938,0.49948,1.5584,1.5105,1.4642,1.5106,1.4596,1.7673,1.6336,1.2138,0.90694,1.0764,1.5501,0.85932,0.1781,0.063024,-0.73408,-0.58471,0.21255,0.74021,0.48973,0.25132\n0.76487,0.20062,0.60152,1.2762,1.6599,1.521,1.3249,1.2798,1.5066,1.8874,2.4327,2.988,3.1028,3.4062,3.4899,3.3413,3.2158,2.6351,1.9763,2.3619,3.2273,3.2535,2.5155,1.8501,1.3329,0.72536,0.83557,1.8129,1.577,1.3611,1.4,1.4336,1.743,1.3975,1.051,0.80998,0.0041034,-0.14326,-0.025549,-0.11259,-0.38113,-0.5993,-1.1176,-0.59528,0.14377,0.65426,0.67076\n0.86126,0.44095,0.43325,1.0433,1.374,1.2664,1.0211,1.016,1.5127,2.3952,2.1878,2.811,3.182,3.2041,3.3498,2.6553,2.4151,1.6899,0.91683,0.28673,1.6311,3.058,2.1084,1.4128,1.5011,1.5522,1.8294,2.1041,1.8988,1.1562,1.4793,1.4725,2.1249,1.4081,1.0838,1.0074,0.47146,0.27867,0.27334,-0.076568,-0.039582,-0.29277,-0.98238,-0.58245,0.27067,0.66017,0.63797\n0.84871,0.54337,0.53726,0.95894,1.2764,1.5561,0.98452,1.4684,1.9697,2.149,1.8204,2.3108,2.9556,3.0236,2.7804,2.3098,2.0752,1.9217,0.652,0.014461,0.33766,1.5466,1.3261,1.3154,1.4259,1.1137,0.52988,1.4715,1.978,1.487,1.3342,1.5356,1.9403,1.5776,1.3394,1.2316,0.88435,0.52483,0.46704,0.093939,0.0076668,0.0104,-0.37734,-0.21977,0.1044,0.022659,0.3584\n1.1436,0.93619,0.70314,0.92553,1.2715,1.4655,1.3618,1.5437,1.9209,2.1855,2.5061,2.8227,2.7996,2.5051,2.4428,2.1023,2.032,0.87518,0.55142,-0.11626,0.21245,1.4042,1.3926,1.4711,1.8441,1.791,1.0618,1.206,2.0168,1.5897,1.2892,1.7215,1.6926,1.3983,1.1324,0.84038,0.51471,0.70765,0.55258,-0.020727,0.52698,0.57874,0.61238,0.32165,-0.1517,-0.066088,0.4061\n0.90706,0.70627,0.46921,1.0007,1.1453,1.805,2.3664,2.3377,2.2907,2.5199,2.5726,2.674,2.4353,2.2798,2.4399,1.8908,1.413,0.064828,-0.53027,-0.8877,0.12783,1.3318,1.6417,1.5215,1.6529,1.8323,2.0411,1.7415,1.9421,1.7812,1.2628,1.7114,1.3082,0.89017,0.53417,0.74911,0.7873,1.1221,0.56161,-0.13262,-0.019947,0.82693,0.85095,0.33274,-0.36757,-0.27383,0.72623\n0.18477,0.95323,0.79473,0.91441,1.504,1.9259,2.5127,2.6291,2.5166,2.6072,2.9024,2.709,2.1558,2.0664,1.989,1.1675,0.99232,-0.36393,-0.39611,0.020069,1.0369,1.4267,1.6409,1.7253,1.6634,1.7819,2.1341,2.3245,2.2364,2.2015,1.6055,1.4161,0.89278,0.18361,0.48628,1.4129,0.86237,0.6988,0.092314,-0.70585,0.12746,0.80449,1.2125,0.82688,-0.36253,0.10984,0.96023\n1.1242,2.4882,2.0543,1.0345,1.3235,1.5752,1.9347,2.4996,2.5706,2.9584,2.8552,3.3642,3.463,2.4588,1.8275,1.8024,1.3181,0.29593,-0.13669,0.18976,1.15,1.4106,1.4715,1.6182,1.542,1.5521,2.095,2.2155,2.3096,2.1864,1.4387,1.1092,0.51274,0.29598,0.50077,0.49172,0.031348,-0.31215,-0.68514,-0.72006,0.5499,1.4544,1.5676,0.95661,0.42091,0.49612,0.74135\n0.23475,1.8233,1.7978,1.0409,0.54952,0.96334,1.1271,1.8585,2.2905,3.2281,3.264,3.2373,2.7371,2.6691,2.0145,2.0499,1.5873,1.1965,0.42132,0.38208,0.95734,1.2801,1.5854,1.7704,0.97404,0.93781,1.6748,2.147,2.2742,1.8811,1.3294,0.69857,0.34026,0.10323,0.24307,0.2186,-0.28762,-0.38842,-0.67891,-0.17938,0.63671,1.1165,1.2959,1.4866,0.84423,0.58664,0.45359\n-0.51605,0.75527,1.333,1.3706,1.6752,1.3016,0.85818,0.92736,1.5699,2.5612,1.6222,1.8189,0.52277,0.54581,1.5931,2.0132,1.7566,0.65501,0.26644,0.24244,0.81647,1.477,1.9543,1.9944,1.1533,1.1097,0.81787,1.7555,1.5834,1.2697,0.67575,0.088367,0.64982,0.20515,0.17509,0.19223,-0.14745,-0.18257,-0.25887,0.1724,0.13051,0.32755,0.9436,0.81346,0.33128,0.14483,-0.29897\n-0.10412,0.48196,0.44392,0.29823,0.35956,0.49944,0.26667,1.0874,1.7103,2.2334,1.3215,1.3644,0.58734,1.3976,1.2171,1.4137,1.3667,0.24257,0.28865,0.30359,-0.1648,0.24992,1.4194,1.486,1.3992,0.55691,0.65978,1.3356,0.29474,0.75061,0.28818,-0.14196,0.44221,0.52781,0.68875,0.5822,-0.023011,-0.047374,-0.12635,0.37951,0.49583,0.025267,0.18446,0.31873,0.53596,0.050903,-0.94323\n-0.080925,0.39393,0.53955,-0.15065,-0.44348,0.17871,0.32742,1.2759,2.0608,2.6714,2.5382,2.4034,1.7431,2.1322,2.5709,1.7309,0.37476,0.29908,0.87544,0.8981,1.4072,1.5299,1.2374,0.97531,0.75892,0.42982,0.98854,1.242,0.59573,0.24212,0.15763,0.013266,0.29886,0.85769,0.93741,0.55296,0.25482,0,-0.055156,0.33314,0.3765,-0.085128,-0.17718,0.16481,0.99622,1.1431,0.9087\n-0.78429,-0.59687,0.22124,-0.058645,-0.6962,-0.23332,-0.2585,0.60648,2.0733,2.4608,3.1846,2.5242,1.7375,1.3068,0.23528,-0.26945,-1.3137,-0.24667,0.38962,1.1438,1.9891,1.4703,1.0774,0.78359,0.64388,0.42777,0.76344,0.88913,-0.049537,-0.61452,-0.1633,0.041972,-0.13945,0.58211,0.18544,0.15388,0.25217,0.053667,0.025539,0.10475,0.53583,0.52351,0.20035,0.12135,0.87369,0.72375,-0.057407\n-0.8579,-0.61511,-0.7777,0.080405,0.99499,1.5672,-1.5093,-1.2356,0.39517,-1.0213,-0.017227,-0.49369,-0.030446,0.56842,1.2692,0.83591,-0.22791,0.17154,0.19595,0.55552,0.90507,0.33885,0.40062,0.87469,1.3339,1.2634,0.82071,0.50029,-0.63315,-1.52,-0.79127,-0.23527,0.074478,0.010537,-0.2183,-0.041777,-0.094529,-0.069102,-0.050178,-0.3252,-0.40054,-0.15568,-0.072903,-0.20817,0.39397,0.38667,0.04623\n-0.80437,0.04861,0.66439,1.6103,2.1698,2.6165,0.58357,-0.34139,0.52976,-0.80428,-1.0679,0.53057,0.52436,0.81348,2.225,2.7679,1.8292,1.2486,1.892,0.91935,0.52245,0.69184,0.91991,0.55257,0.77461,1.1614,0.96977,-0.028196,-0.3479,-0.66357,-0.41412,-0.22485,0.065281,0.48573,0.49541,0.1764,-0.46265,-0.20034,-0.17054,-0.39359,-0.78918,-0.91138,-0.67013,-0.32099,0.4454,0.36309,-0.6818\n-1.3018,-0.41035,0.65192,1.0874,1.2911,2.2958,0.68799,0.16934,0.7886,-0.0050125,-1.0067,0.17184,0.27214,0.59265,0.47283,3.7836,4.5319,2.5616,1.228,1.7969,2.0017,1.59,2.0346,2.1369,0.52176,0.93029,0.065076,0.235,0.7781,1.0842,0.58319,-0.064274,-0.33045,0.57065,0.59066,-1.1551,-0.7282,-0.48131,-0.32307,-0.3815,-0.72699,-0.69899,-0.29876,0.045002,0.38206,0.20035,-0.25242\n-3.7423,-4.6565,-3.9869,-1.2195,-0.83072,-0.7135,-1.2388,-1.5057,-0.58646,1.683,3.1626,0.45122,-0.89085,-2.1016,-2.7119,-2.293,1.2686,2.1825,0.74779,1.2718,1.8257,0.62955,1.5018,1.7568,0.82913,0.74813,0.75102,0.85226,0.78409,0.72485,0.4304,0.40365,0.51791,0.56365,0.11918,-1.1454,-1.1923,-0.81408,-0.16775,-0.19344,-0.60759,-0.84289,-0.31203,-0.15025,0.16423,0.24696,0.41654\n-2.9826,-3.2469,-2.2558,-1.4276,1.4533,0.83819,-0.76085,-1.5898,-1.0866,0.75353,1.7721,0.5153,0.47756,-0.86695,-1.5862,-3.3535,-1.4827,0.46679,0.38894,1.5701,0.87044,-0.47957,1.1818,1.9101,1.786,1.3188,0.32125,-0.23196,-0.71087,-0.62368,-0.60019,0.13822,0.74559,0.35549,-0.1138,-0.67516,-0.68029,-0.0857,0.04917,-0.64971,-0.87692,-0.70507,-0.54359,-0.65063,-0.42101,0.59952,NaN\nNaN,0.70414,0.41526,0.46832,0.46755,0.34541,0.8469,0.51453,0.87141,1.0224,1.5118,NaN,NaN,NaN,NaN,-0.043729,1.23,0.63411,0.013438,1.7553,-0.11217,-1.1859,0.61838,2.7599,1.2967,0.34834,0.44179,0.02548,-0.66032,-0.7974,-0.13988,0.24971,0.62702,0.27614,-0.16413,-0.76704,-0.46289,-0.067814,0.41809,-0.40958,-1.0064,-0.80679,-0.32692,-0.24424,-0.11162,-0.19111,NaN\nNaN,NaN,NaN,-0.67699,-1.0539,0.37538,2.0573,0.64697,-0.25858,0.97523,2.2201,NaN,NaN,NaN,NaN,0.91958,2.5244,2.1624,0.61597,2.2986,0.73865,-0.73384,0.42808,1.2041,-0.7083,-0.81791,-0.43217,0.050441,-0.35726,-0.4177,0.19273,0.64965,0.42917,0.20426,-0.47902,-1.164,-1.199,-0.31044,0.19683,-0.19869,-0.86596,-0.57178,0.32623,0.61904,0.85238,-1.0249,-1.6415\nNaN,NaN,NaN,NaN,0.32885,0.54979,1.9353,0.7536,0.27477,1.2225,NaN,NaN,NaN,NaN,NaN,1.1044,0.44764,-1.2485,1.166,1.0674,1.514,2.4355,-0.20461,-0.75328,-2.5246,-1.5691,-0.91215,-0.21916,-0.15403,0.052442,0.21316,0.4672,-1.1753,-1.3081,-0.59395,-0.93563,-1.9285,-0.81423,-0.1649,-0.32901,-0.32931,-0.15641,0.54153,0.66023,-0.11912,-1.1483,-1.4843\nNaN,NaN,NaN,-0.59184,-1.2902,1.1343,-0.091747,0.0023723,0.1579,0.26925,-0.1758,NaN,NaN,NaN,NaN,NaN,1.5892,-0.2863,-0.59126,0.73982,1.3685,2.2593,-1.4869,-2.6237,-2.8778,-1.6592,-0.80813,-0.26483,0.40787,1.2808,0.34195,-0.49688,-1.1442,-1.062,-0.45803,-0.73258,-1.1531,-0.60213,-0.66832,-0.58535,-0.46337,0.076964,0.71875,0.32114,-0.44329,-0.31445,0.093446\n-2.6996,-2.2003,-2.1102,-2.9736,-1.1752,1.839,-1.1053,-0.88635,0.4916,0.45708,0.34273,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.98091,0.21874,1.2991,1.0568,-2.0298,-2.8208,-3.2794,-2.6427,-0.77683,0.59187,0.49838,-0.13312,0.062206,-0.060631,-0.85862,-1.9077,-1.253,-0.82975,-0.33029,0.2131,-0.43223,-0.74778,-0.62535,0.40497,0.4338,-0.17515,-0.80521,-1.2599,-0.71856\n-2.6446,-1.3943,-1.1122,-2.7287,-1.7801,-1.4823,-1.4293,-1.5756,-2.2789,-1.1062,-0.49771,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.2857,0.61,1.2119,-0.17128,-0.91778,-1.0147,-0.33947,-0.027976,0.063086,-0.84545,-0.61449,-0.30074,0.18635,0.30559,-1.617,-1.6425,-0.88399,-0.4838,0.1793,0.53908,0.60702,0.41387,0.30318,-0.23206,-0.427,-0.6275,-2.1966,-0.37674\n-1.8637,-0.19698,-0.099089,-2.3596,-2.644,-2.5499,-2.1372,-1.7326,-2.0536,-2.2352,-2.0032,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.41413,1.0345,1.5191,1.9929,0.30798,-0.36439,0.018034,0.031726,-1.9901,-5.0883,-6.1746,-2.8982,-0.30841,-0.37925,-1.4569,-1.9635,-0.87173,-0.10402,0.21765,1.441,0.66109,-0.071197,-0.63439,-0.80816,-0.53817,-1.3271,-1.7778,-1.1432\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061106-061211.unw.csv",
    "content": "0.4091,1.1317,0.5941,0.53288,0.34221,0.5784,0.63791,0.42553,0.38148,0.86083,1.4029,1.696,0.45153,0.69271,0.52762,0.3625,0.23262,0.34642,0.45328,0.43193,0.4463,0.70862,0.46974,0.3089,0.96688,0.89842,0.41272,0.86584,0.93469,0.75876,1.0061,1.193,1.0942,1.0369,0.93495,1.1468,1.259,1.5093,0.85419,0.76011,-0.39493,-1.0044,-1.2856,-1.4426,-2.0626,-2.8982,-3.8108\n1.3136,1.6558,1.1225,0.59806,0.65678,0.8405,0.90019,0.20198,0.37216,0.57264,0.78146,0.73266,0.55608,0.64193,0.53053,0.24895,0.065165,-0.0088825,0.20835,0.13015,0.012243,0.31715,0.044828,0.23847,0.48506,0.28252,0.41199,0.76403,0.52391,0.76698,1.1002,0.90868,1.3275,1.0221,0.91754,1.1524,1.444,1.3094,0.70967,0.316,-0.89616,-0.96061,-1.7102,-2.3949,-3.1007,-3.2799,-3.6507\n0.92347,1.3965,1.2094,1.0239,0.46565,0.86538,0.78546,0.35115,0.86645,0.58899,0.55779,0.90754,0.6156,0.59907,0.15549,0.02746,-0.37799,-0.74897,-0.50007,-0.10723,0.062098,0.32828,0.26438,0.85291,0.45272,-0.0089664,0.35191,-0.014956,0.31836,0.58513,0.78335,0.90444,1.2911,1.0275,0.65589,0.53135,1.2419,0.77197,0.34792,0.011808,-0.50534,-1.3706,-2.4972,-3.5698,-3.8359,-3.836,-5.0656\n1.9011,0.99965,1.6834,1.4036,0.78477,0.81396,0.31027,-0.068388,0.10196,0.51537,0.97417,1.057,0.69108,0.3107,-0.36861,-0.772,-0.8254,-0.69503,-0.63417,-0.1123,0.23892,0.46758,0.48391,0.33114,0.22062,0.29127,0.25125,0.086557,0.18659,0.52801,0.94821,0.66584,0.93205,0.87236,0.58503,1.0877,1.3451,0.3444,-0.44674,-0.72481,-1.4006,-2.5683,-3.5283,-4.0149,-4.0885,-4.336,-5.8525\n1.515,0.48758,1.6693,1.836,1.1182,0.61525,-0.11176,-0.25473,-0.14113,-0.37752,0.65082,0.64086,0.55544,0.24654,-0.38217,-0.9733,-0.96821,-0.67488,-0.39831,-0.19966,-0.081942,0.61712,0.14246,0.055071,0.10708,0.63024,0.13123,0.36604,0.68398,0.86936,1.0965,0.81059,0.74196,0.55747,0.27482,0.32168,0.37408,-0.011286,-0.54928,-1.3813,-2.0802,-3.2017,-4.1792,-5.0867,-5.2538,-5.4503,-5.9524\n1.5783,1.8183,2.0466,1.3982,1.3657,1.1273,0.35044,0.21504,0.11055,0.21835,0.51764,0.38022,0.53856,0.48137,0.32416,-0.4149,-0.76068,-0.69944,-0.085409,-0.29965,-0.023468,0.34973,0.31621,0.5202,0.40344,0.5704,0.6065,0.64341,0.88075,1.1831,0.82669,0.63918,0.63627,0.25634,0.052023,0.029181,0.0083256,-0.19312,-0.76452,-1.3396,-2.125,-2.8114,-3.9148,-5.3584,-5.186,-7.2829,-7.521\n2.1354,2.7805,1.1626,0.77088,1.2975,1.6436,0.73514,0.10829,-0.25107,0.65932,0.52778,0.26785,0.59118,0.54761,0.81423,-0.097073,-0.42672,-0.16274,-0.022352,-0.6125,-0.0501,0.28545,0.61187,0.92819,0.81808,0.83922,0.61686,0.9919,1.3184,1.0624,0.7266,0.55646,0.60684,0.42113,0.37659,0.15253,-0.18367,-0.54497,-1.5715,-1.541,-2.2054,-2.8164,-3.1885,-3.9592,-4.6931,-6.6837,-6.3889\n1.0669,1.1871,0.55712,0.90325,1.2144,1.649,0.48204,0.027025,-0.073328,0.67618,0.58337,0.50044,1.0289,0.69082,0.54292,0.050432,-0.15677,-0.048218,-0.057276,-0.0073338,0.53245,0.38021,0.43308,1.1708,0.59717,0.49378,0.62179,0.97878,0.96276,0.8529,0.75539,0.29841,0.45187,0.27884,0.32462,-0.092886,-0.15191,-0.40169,-1.0048,-1.6966,-1.8265,-2.2255,-2.1141,-3.2063,-4.5165,-6.1982,-5.9545\n0.64725,-0.40914,1.1798,1.2012,1.7996,1.7037,0.41883,0.62363,0.52709,0.59047,0.61039,0.75669,1.1034,0.86134,0.66853,0.22466,0.25747,0.79757,0.30593,0.95782,0.99207,0.418,0.58213,0.61161,0.70157,0.98425,1.2776,1.2655,0.82908,0.51694,0.77949,0.7672,0.77636,0.69395,0.30802,-0.065498,-0.017286,-0.19275,-0.21127,-0.5258,-0.77869,-1.2554,-1.8908,-2.2142,-4.1956,-5.1896,-4.3692\n0.83177,0.57974,2.696,1.2347,1.6809,1.5682,0.65539,0.80144,0.4359,0.92062,0.27162,0.84854,0.84373,0.63022,0.45835,0.35341,0.20818,0.36607,0.38116,0.79814,0.71219,0.55435,0.53858,0.74291,0.90543,1.501,1.4968,1.5154,1.0624,1.1465,0.9627,0.92887,1.4207,0.95046,0.72686,0.42621,0.38338,0.35792,0.28145,0.18887,0.1096,-0.28509,-0.8951,-1.3002,-3.1852,-3.8214,-3.643\n1.2751,0.41841,0.82287,1.4736,1.8292,1.3961,0.62726,0.57872,0.80773,0.73834,0.34878,0.66385,0.038292,0.13091,0.29184,0.4642,0.13002,0.42852,1.0012,1.2539,0.89982,0.86466,0.85923,1.2471,1.4629,1.3401,1.6293,1.4124,1.1928,1.5158,1.2931,1.1441,1.852,1.7636,1.5712,1.5101,1.2411,1.2472,0.99857,0.35775,0.11709,-1.2279,-1.4617,-1.6799,-2.4413,-1.6577,-1.5725\n1.2594,0.36596,1.255,2.3681,1.9031,1.069,1.0686,0.73691,0.97313,0.53666,0.25485,0.11674,0.066347,-0.073345,0.014853,0.41957,0.36891,0.78218,1.2987,1.4138,1.309,1.4185,1.0378,1.7782,1.5352,1.378,1.6564,1.2392,1.4661,1.8163,2.0102,2.4483,2.3866,2.4543,2.4221,1.5568,1.1826,0.9509,0.56929,-0.32437,-0.51135,-2.2715,-2.365,-2.162,-1.9755,-1.258,NaN\n2.5131,2.7254,2.7683,0.49545,1.1722,0.69217,0.97144,0.88503,0.93251,0.2296,-0.53734,-1.0685,0.33345,-0.19191,-0.41124,0.1756,0.55449,0.62948,1.1188,1.2712,1.2111,1.7187,1.7022,1.3345,1.3602,1.3976,1.1872,1.3903,1.5779,2.0254,2.0377,3.4404,3.2562,2.4116,1.8546,0.96784,0.21432,0.17319,-0.47425,-0.84057,-1.8453,-2.6556,-2.7893,-2.4172,-2.4678,NaN,NaN\n2.4202,3.209,1.8487,-0.013048,1.7469,1.655,1.6611,1.0678,0.70749,0.33326,-0.6752,-0.50883,0.23467,-0.21587,-0.57825,0.078411,-0.05249,0.27609,0.67899,1.1041,1.197,1.5843,1.7945,1.2775,1.4787,1.4512,1.3837,1.6165,1.5809,1.4276,1.6748,2.333,2.1054,1.5959,1.2925,0.70172,0.42009,0.68129,-0.27794,-1.7004,-3.3257,-2.9855,-3.7744,NaN,NaN,NaN,NaN\n2.3637,1.4918,-1.4122,0.8891,2.0158,2.1104,1.464,0.89115,0.61535,0.47357,-0.54853,-0.17418,0.20685,0.1705,-0.12731,0.34418,0.18252,-0.077795,0.42018,1.0818,1.2098,1.2079,1.4627,1.2026,1.2111,0.9976,0.74202,0.32415,0.78983,0.54921,1.0277,1.8813,1.865,1.2065,1.6155,0.66324,0.25464,-0.25067,-1.3328,-3.3496,-2.5377,-1.8042,-2.6343,NaN,NaN,NaN,NaN\n1.2434,1.3578,-0.33888,1.293,2.6832,1.4808,1.215,1.5124,0.79863,0.24308,-0.2455,0.8849,0.392,-0.22325,-0.32393,-0.042805,-0.024527,-0.095148,0.42062,0.69817,1.0622,0.9698,0.67141,0.22009,0.36227,0.18338,-0.42916,-0.89257,0.14947,-0.51801,1.167,1.3757,0.73126,0.29861,0.49205,0.21113,1.1983,0.60826,-0.27811,-3.0233,-1.5822,-1.7572,-1.8171,NaN,NaN,NaN,NaN\n-0.048113,2.232,1.4293,1.3442,1.7979,1.6673,1.6895,1.9294,1.0359,0.5365,0.66421,1.1259,0.18972,0.066702,-0.31053,-0.30694,-0.065332,0.27664,0.1924,0.88416,0.9529,1.3876,0.61621,-0.027803,0.31545,-0.33838,0.14409,0.31767,-0.20816,0.15458,0.9868,1.1526,0.082033,-0.78058,-0.66824,0.23944,1.5442,1.3578,1.6991,NaN,NaN,-0.94095,-2.2354,NaN,NaN,NaN,NaN\n1.1065,2.4071,1.8588,1.9876,1.5763,2.5785,2.1587,1.3238,1.2899,1.2985,1.1866,0.86165,0.30145,0.33857,-0.21671,-0.22606,0.36479,0.39948,-0.43797,1.4868,1.1904,1.5078,1.9,1.4572,1.0538,1.3843,1.4593,1.3142,0.25958,0.268,0.97002,0.73393,1.0324,-0.43505,-0.10171,1.3147,1.6008,0.62146,0.39742,NaN,NaN,NaN,-1.0414,-1.7405,NaN,NaN,NaN\n-4.4579,0.68507,2.228,1.6305,2.7806,2.9718,1.0846,0.86323,0.98565,1.2154,1.1028,0.95119,0.55407,0.39056,-0.32538,-1.3216,-1.1597,-0.73401,-0.86638,-0.71253,-2.5041,-4.2598,0.71234,2.6363,3.0366,2.6795,1.6202,1.8265,1.1469,0.64083,1.8967,1.1712,1.3411,0.30861,-0.032154,2.295,2.2206,1.4889,-0.91122,NaN,NaN,NaN,-0.49477,0.452,0.6476,NaN,NaN\n-1.7026,0.9765,0.71319,0.92187,2.5309,3.5998,1.1377,1.6212,0.95415,0.3567,0.67109,0.30447,0.11583,0.24441,-0.011858,-0.48179,-1.3113,-0.59489,-0.34635,-0.48547,-0.85828,-1.2769,-0.77952,2.8047,3.4403,2.7208,2.1878,1.5124,1.3014,2.0833,2.4791,1.7255,1.2722,1.4771,2.8689,2.2602,2.2074,2.2244,-1.6376,-1.8596,-0.63851,0.98522,0.1671,0.91297,0.36902,-0.39392,0.27608\n0.50407,-2.3101,0.70717,0.52633,0.76339,0.90398,1.5077,1.7583,0.74413,0.070925,-0.026869,-0.46725,-0.44585,-0.038347,0.064762,-0.23156,-0.36546,-0.12533,0.087925,0.52259,1.1364,-0.41948,-0.17108,2.1214,2.2259,2.1098,2.434,1.9252,0.66773,1.6599,2.4558,0.4856,1.4742,1.486,2.0185,2.5614,3.0586,2.128,0.6958,1.1441,1.3016,0.55545,0.26415,-0.68203,-0.31375,0.54065,0.31771\n-0.055235,-0.91711,-0.0067997,0.05497,-0.30821,-0.51189,2.0964,1.3661,1.1807,0.29098,-0.32121,-0.49541,-0.16724,0.048754,-0.70189,0.21685,0.12034,0.42744,0.42949,2.0283,2.4783,2.1832,2.6329,2.8796,2.7702,2.6988,2.1994,1.8366,0.80027,0.86627,1.2556,0.20898,1.177,1.3909,1.8064,2.2952,1.6709,2.446,1.4461,0.70106,2.5262,0.7827,0.31417,-0.034071,-0.40762,0.87219,0.8245\n-0.22389,0.13988,-0.20847,-0.67251,-1.6036,0.77895,1.687,1.421,1.1575,0.040655,-1.1738,-0.76366,0.1343,-0.60402,-0.33788,1.8582,0.81303,1.9611,1.65,2.1636,2.7765,2.7267,2.6794,3.5522,3.2244,3.1906,2.0793,1.951,1.7211,1.1098,0.5615,0.26502,0.96968,1.5816,1.3371,1.953,0.8826,0.43064,-0.19183,0.59321,1.3114,1.0005,1.4206,1.2564,0.090851,0.37356,0.30118\n-0.81238,-1.158,-0.55284,-0.45614,1.0308,1.4472,1.9628,1.5566,0.71621,-0.15458,-0.48837,-0.094164,0.15793,0.0023174,0.73187,2.2912,2.2751,2.8079,2.8094,2.4058,3.0811,3.2266,3.0956,2.9521,2.8234,2.8638,2.3241,2.1066,1.6673,1.0522,-0.22619,0.24587,0.57158,0.49313,1.2016,1.1252,1.1881,0.85837,0.18274,2.6731,2.0971,1.0745,0.71802,0.68465,-0.42175,0.59768,0.24633\n0.43019,0.073267,-0.16258,-0.33536,-0.436,0.7419,1.6916,1.0291,-0.20955,-0.24559,0.066332,0.66339,0.83797,2.0372,2.1574,2.3978,1.9461,0.38146,1.5364,2.0422,2.1193,1.6848,1.0822,2.3296,2.3587,2.5189,1.9675,1.7541,1.0829,-0.19126,-0.72293,-0.47677,0.04191,0.2646,1.2329,4.2992,-2.3216,1.9265,3.74,2.8057,1.08,0.92196,0.44288,-0.28137,-0.31134,0.52354,0.37168\n1.0948,1.0627,0.22093,-0.26145,-0.39947,-0.22953,0.4334,0.39799,0.22856,0.50138,0.21629,0.10918,0.80862,1.132,1.262,1.944,0.36536,0.25233,1.1433,1.6284,1.2735,0.47533,1.5126,1.7861,1.7728,2.0053,0.71188,-0.27693,-0.44444,-0.68613,-1.4952,-1.7475,2.4091,-2.1745,0.44004,5.8103,-0.059671,0.77581,2.6326,2.1146,1.4845,1.5805,1.1377,0.8137,0.50696,1.1373,1.4151\n1.6952,1.5363,0.60513,0.46959,-0.19597,-0.48592,-0.29883,-0.092115,0.19153,0.3826,0.30357,-0.14564,-0.83085,0.09586,0.21646,0.25287,0.39175,0.39736,1.1597,2.0424,1.0594,1.093,1.7492,1.5739,-0.73453,-0.0037327,-0.43693,-0.07427,1.4321,3.2895,0.20296,0.1946,2.3776,-0.22608,0.57985,3.6983,0.99923,1.7577,1.5115,2.2646,1.7875,1.9607,1.5026,1.1404,1.0475,1.1743,1.1492\n2.1221,2.1683,1.2631,0.34785,0.79992,0.4659,1.1639,-0.65078,0.55718,0.88778,0.69995,0.019831,-0.39016,-0.29591,-0.61279,-1.3698,-1.1388,-0.12501,0.76924,1.0233,0.032839,0.91159,1.5212,1.9286,-0.90179,0.037313,0.24832,0.29491,1.479,2.3819,NaN,NaN,NaN,3.969,2.9626,2.211,1.7194,0.62474,1.4828,2.0386,1.8941,1.6936,1.317,1.0534,1.1425,0.94582,0.57549\n2.5153,2.1017,1.2767,2.0176,1.7642,1.8804,1.9277,0.79758,0.82789,1.1564,1.1156,0.53592,0.19527,-0.37402,-0.9793,-1.2829,-0.15169,0.67084,0.36107,0.34102,-0.022783,1.0201,1.1825,1.1294,0.2624,0.024813,0.1955,0.11519,-0.16932,NaN,NaN,NaN,NaN,NaN,2.8362,1.8657,0.83476,-0.89101,1.4696,3.2578,2.482,2.1266,1.5396,1.3425,1.3314,0.70535,0.27743\n2.0672,1.821,2.2137,3.6405,2.348,1.8776,1.1391,0.72373,1.0197,1.3717,1.2782,0.71332,-0.41297,-0.87211,-0.64956,-0.10483,0.76308,0.85097,0.97246,0.4028,0.79986,1.6982,1.3807,1.0665,0.13655,0.025368,0.55804,0.37576,NaN,NaN,NaN,NaN,NaN,3.9013,2.5398,1.3218,0.89916,0.11554,3.3841,2.9334,1.9676,2.1767,1.9079,1.8919,1.5962,0.78178,0.49475\n1.4749,1.2184,1.9519,1.5891,1.7116,1.7518,1.8867,1.3031,1.1486,1.5621,1.4644,0.46443,-0.52125,-0.8688,-0.40135,0.77782,1.5121,0.63659,0.48516,1.1687,1.8241,1.8602,0.30807,1.0571,1.8926,1.5352,1.1948,0.77212,NaN,NaN,NaN,NaN,NaN,2.8043,2.978,2.2482,2.5017,2.9397,3.72,2.6809,2.7524,3.5839,2.6783,2.3631,1.7959,1.0507,0.63248\n0.77617,0.42507,1.0928,0.62574,0.64654,1.1205,1.8459,2.0513,1.2731,1.2898,1.8653,1.0571,0.54091,-0.032173,1.5422,1.4371,1.102,1.2722,1.1504,1.1202,3.2184,1.8465,0.25867,4.8452,3.9092,3.0039,2.374,1.4843,NaN,NaN,NaN,NaN,2.6446,2.9767,3.8342,3.6583,3.7783,2.8607,3.2362,3.3156,3.0459,3.1925,2.215,2.5458,2.0368,0.91687,0.30085\n0.87563,0.98959,1.4366,1.3545,0.96493,1.5113,1.7711,2.2117,1.9824,1.1998,1.2449,-0.089693,-0.86604,0.19961,1.1877,0.61706,0.41476,1.4195,-0.53978,-0.15021,1.9348,1.1512,-0.2338,3.4793,3.1595,2.6043,2.4433,2.7614,NaN,NaN,NaN,NaN,3.4365,4.051,3.8618,3.0802,2.8183,1.183,2.5557,3.1282,2.7186,2.4123,2.1251,2.5205,1.2868,1.155,1.0133\n2.048,2.2364,1.8465,1.8373,1.7689,1.3801,2.6475,2.6672,1.9746,1.6019,1.0257,0.56285,-0.89449,0.8846,1.8707,1.2578,0.2406,1.3719,0.66902,-1.2998,1.4663,2.6303,1.6842,1.1112,1.8367,1.736,2.8505,NaN,NaN,NaN,NaN,3.5078,4.0359,3.8756,3.2062,2.8513,2.5627,0.99969,2.1196,3.1094,2.4248,1.9769,1.9969,2.4348,1.7613,1.2741,1.2972\n2.714,1.687,1.843,1.636,1.1562,1.4654,2.4504,2.7877,1.6598,1.6724,1.2404,0.31004,-0.11069,0.42411,1.3632,1.8757,1.8764,1.5858,1.3183,0.74669,2.3994,2.4541,2.2277,1.2463,1.1917,1.0983,2.7292,NaN,NaN,NaN,3.691,3.6313,3.4882,3.196,3.8386,3.2214,3.0564,2.1224,2.4375,2.9241,1.4132,0.67413,1.4491,2.3625,2.0831,1.2837,1.4915\n2.1874,2.7335,2.468,1.8617,1.7937,1.7634,1.9517,2.4152,1.8758,1.5419,1.3335,1.1728,1.2281,0.34327,-0.85758,2.3939,1.9024,2.0646,2.0764,2.2353,3.11,3.3131,2.4379,0.92322,0.90967,0.98307,1.6757,2.3038,5.1212,4.4833,4.4558,4.7592,4.0849,4.1415,4.6869,4.82,4.2134,3.5305,2.9841,2.4804,1.5342,1.2732,1.5756,2.2119,1.7339,1.4247,2.1938\n1.6107,2.4181,2.6385,2.1839,3.0654,2.8748,1.8469,1.3456,1.5477,1.0194,2.1015,2.2782,1.7772,-0.36813,-0.035643,1.9018,1.8564,1.6994,1.4968,1.6383,2.1208,4.4418,3.6534,1.9376,1.3991,0.076433,-0.93406,-0.90041,5.3749,4.3042,3.7889,4.3666,3.6784,2.7606,3.5425,5.7584,5.21,4.5537,3.0235,2.7571,2.3782,1.8802,1.5878,1.5917,1.3725,1.6998,1.7268\n1.6895,2.2338,2.4377,2.0601,2.1203,2.2012,1.2852,1.1132,1.3988,1.1419,0.9985,2.5541,2.1247,0.52645,1.0433,1.5094,1.9889,0.95972,1.4621,1.741,3.8057,NaN,NaN,NaN,NaN,NaN,1.1283,1.4827,4.4684,2.7581,3.2995,3.7101,4.2967,3.1068,4.1515,6.6596,5.0434,4.3754,3.7967,2.9541,2.1158,1.6633,0.90189,0.86349,0.44661,-0.025152,0.16442\n2.9483,2.7278,2.0017,1.5931,1.4134,1.3998,0.63055,1.0989,1.1444,1.5413,2.4239,2.7708,2.4623,2.2478,2.0224,1.161,0.79634,-0.27729,1.2373,2.5953,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.1802,4.0939,3.7229,3.6537,3.8999,4.1665,4.5613,4.5929,3.8521,4.1072,3.9023,2.8411,1.8632,1.7935,1.4979,0.86454,-0.51601,-1.872,-0.21678\n3.5703,1.9996,2.2574,1.5164,1.4454,1.5609,0.95966,1.4569,2.3285,2.1099,2.1199,2.0205,2.2755,2.4369,1.6017,1.4706,1.5841,1.6859,1.5934,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.8147,4.4426,3.6798,5.1611,4.9671,4.994,4.4389,3.7158,3.2365,3.1821,2.3431,3.3815,3.6356,1.9181,1.4467,0.48662,-0.6884,-1.0608,0.083197\n3.1913,1.9629,2.3129,1.5748,1.7232,1.686,1.6303,1.9049,2.3723,2.3409,1.6078,1.9318,2.8237,2.6233,1.508,1.602,2.3781,3.8807,3.9137,2.1469,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.2773,4.1202,4.5595,4.9269,5.9998,5.9939,5.0888,3.7812,2.9585,3.6579,4.4028,2.2489,3.6041,3.4454,2.5763,1.5516,0.82678,-0.1132,-0.19608,0.1933\n2.5145,2.0635,1.3319,1.1245,1.1824,1.5527,1.8395,1.5331,1.7169,1.863,1.001,3.9685,5.1648,3.5874,2.9979,1.7631,0.048506,2.5789,4.3573,2.9524,NaN,NaN,NaN,NaN,NaN,NaN,3.4722,5.1714,5.0994,5.1091,4.6314,5.0795,5.4563,4.6146,3.5483,2.909,2.8367,3.2567,3.01,3.4356,3.5676,2.4995,1.1396,0.49018,-0.64283,-0.17567,0.31435\n1.7146,1.1797,1.1313,1.4659,1.3246,1.3834,1.7346,0.68879,1.8488,2.4905,2.892,3.3359,4.8772,3.4572,3.3715,3.6601,1.9726,0.80003,1.8684,0.64463,1.501,2.0545,NaN,NaN,NaN,NaN,NaN,5.5072,6.1623,5.847,4.1634,5.3233,4.1806,3.5732,2.7297,2.3678,2.5227,2.6641,2.693,2.9023,3.041,1.875,1.0253,0.22998,-0.42388,-1.0019,0.66727\n0.93089,0.76518,0.89286,1.3412,0.8827,1.5419,1.6465,0.80905,2.0042,2.7251,2.5094,1.8855,2.1748,1.8238,0.46171,0.39232,2.0565,1.8566,1.5584,1.7014,0.92129,2.3381,2.3579,NaN,NaN,NaN,NaN,5.2907,5.1443,5.1989,5.588,5.3903,4.1612,2.9083,1.1638,2.4071,2.8859,3.4412,2.6203,2.4131,1.9917,1.8768,0.59775,0.91858,2.1959,2.2064,0.68973\n0.45408,0.4451,0.57306,0.77329,1.2202,2.6639,2.5139,2.1569,1.8981,1.1641,0.59839,1.1824,0.79737,0.39954,-0.56204,-0.74744,1.5471,2.0671,2.6728,0.14262,0.57857,1.7413,3.7424,3.4426,4.3433,5.8346,5.3291,4.2439,3.3264,3.9572,5.4933,4.1584,5.3906,3.4664,1.0813,4.0317,4.8298,4.4122,2.4102,2.3906,1.6412,1.4733,0.58169,0.15487,1.2473,2.8416,2.1137\n0.35491,0.19954,0.3802,0.66704,0.94155,1.3985,1.4022,1.3371,0.52778,-0.37081,-0.34262,-0.0045567,-0.31503,-0.74151,-0.87467,-0.16353,1.4016,3.1398,4.2616,1.4762,-1.001,-0.21824,2.273,3.2966,2.0659,3.2435,3.3122,3.8198,3.0336,3.6748,3.7167,4.013,4.2769,2.9971,3.1499,4.1181,4.8954,3.3518,2.6587,2.2654,1.1375,0.61558,0.23285,-0.82936,-0.13608,2.0608,3.8919\n0.66556,0.14459,0.14724,0.66908,0.43824,0.36443,0.55849,0.41484,-0.42749,-0.67461,-0.64635,-0.7644,-0.92404,-1.0567,-0.57166,-0.20375,1.0103,1.7018,3.2182,2.1455,-2.0376,-1.6697,0.7265,2.6643,2.7249,1.8419,1.867,3.3277,2.9455,3.2827,2.2728,2.5837,2.9457,2.6388,3.7605,4.1474,3.0393,2.6525,2.5429,1.6064,0.59179,0.082111,0.15835,-0.080374,0.011637,1.0971,2.6697\n0.28452,0.0078945,0.040026,0.4159,0.40555,0.043558,0.18561,-0.26206,-0.87908,-1.5114,-1.4572,-1.2834,-1.1941,-1.0041,-0.64339,0.10257,0.89814,1.2151,0.84212,0.36595,-0.75461,-1.4264,0.55799,1.842,2.0435,1.777,1.0038,2.0487,1.7173,2.2978,2.7246,2.8356,3.5441,3.6848,4.014,4.187,3.04,2.2548,2.1542,0.45872,-0.18001,-0.49071,0.36705,0.62809,1.0993,0.67822,1.3194\n0.37034,0.10549,0.63601,0.54689,0.28908,-0.17018,-0.34632,-0.66976,-1.8738,-1.5783,-1.4468,-1.3846,-1.3755,-1.4345,-1.2636,-0.69158,-0.069544,0.56088,0.20253,-0.47971,-2.0212,-1.4552,0.34827,1.2711,1.6311,1.1935,0.36465,1.3831,1.5669,2.1329,2.2986,2.9857,3.7029,4.8864,5.9492,4.6366,4.6038,4.0898,1.9831,-0.036125,-1.0073,-1.322,0.66957,0.59352,0.78144,0.84245,1.3032\n0.32231,0.20995,0.32387,-0.10963,-0.29343,-0.56255,-0.74345,-0.97368,-1.6846,-0.98295,-0.98658,-1.2956,-1.5493,-1.801,-1.4575,-0.41459,-0.46432,-0.83044,-0.46131,0.065144,-1.1555,3.5486,0.48469,-0.2938,-1.5913,-1.9118,-2.3391,-0.25154,0.89164,1.9368,1.8277,3.6655,3.4338,4.1899,3.9849,3.6479,4.5885,3.4831,1.8428,0.68002,-0.27854,-0.14032,0.55894,0.39645,1.3742,1.6831,2.0251\n0.37569,0.0090237,-0.26907,-0.54343,-0.29508,-0.22604,-0.26527,-0.43122,-0.80404,-0.96195,-1.0239,-1.3816,-1.1543,-1.0513,-0.66559,0.07649,0.25606,-2.9786,-0.8501,0.231,2.6148,2.0695,0.012535,-0.64525,-1.5043,-2.1145,-4.0421,-2.51,0.10219,1.0085,1.6758,3.2747,2.9081,2.3443,1.9363,2.0189,2.1913,1.9467,1.2488,0.57697,0.017649,-0.10443,0.098227,1.0297,1.7043,1.7569,1.3274\n0.41665,-0.14046,0.13136,-0.27488,-0.32357,-0.21865,-0.17989,-0.48823,-0.91629,-2.157,-1.2853,-0.74064,-0.88997,-0.65862,-0.7907,0.98971,2.5046,-0.21766,-0.96447,-1.3504,3.008,1.0653,0.34596,-0.25981,-0.69145,-1.8316,-3.6373,-3.3464,-0.58364,0.59933,1.1118,1.3795,2.0727,1.5334,1.5322,1.9138,1.5317,1.1473,0.96086,0.5298,0.57223,0.51999,0.25641,2.687,1.8138,1.6824,1.2396\n-0.038328,-0.09767,0.030886,-0.63862,-0.34965,0.0098972,0.022947,-0.56783,-1.2605,-2.3121,-0.64569,-0.21902,-0.056562,-0.11253,-0.53554,-1.0415,-0.33165,-1.0335,-2.6458,-3.4874,2.8613,1.3042,1.2858,0.98589,0.28919,-0.17477,-0.47421,-1.9505,-1.6956,-0.046835,0.7713,0.72967,1.6336,1.2328,1.9211,1.4568,1.4156,1.5722,1.3887,1.2635,1.225,0.10926,-0.046827,0.91482,1.0367,2.0968,1.0372\n-0.15016,-0.72923,-1.0877,-1.2859,-1.0818,-0.60678,-0.061373,-1.1046,-2.1057,-2.0761,-1.3894,0.30398,2.116,1.8232,0.83313,-1.0224,-0.98,-0.92938,0.39757,1.2942,0.88063,0.70265,0.1475,0.74253,0.52866,1.3366,0.34503,-2.5901,-3.1842,-1.7033,0.75373,1.0813,2.4177,2.6607,2.4496,3.1408,2.264,1.5938,1.4541,2.0013,1.2709,0.97837,0.40599,0.7272,1.9979,2.6321,1.3779\n-0.55548,-1.1214,-0.75114,-1.6335,-1.611,-0.06251,0.112,-1.2985,-2.0598,-1.5829,-1.2647,0.19939,2.1494,2.6865,1.0053,0.47238,-0.01404,-0.82863,1.5455,2.0667,1.0492,0.18841,-0.67995,0.039301,0.051125,0.39764,-1.0651,-1.9933,-2.3754,0.56373,1.2885,2.3886,2.0589,2.005,1.7657,2.2822,1.4968,0.97662,1.1407,1.5138,1.1463,1.4793,1.2586,1.7112,2.161,1.87,0.82881\n-0.40521,-0.61976,-0.29356,-1.0519,-2.2222,1.3291,0.7566,-1.4133,-1.736,-1.5346,-1.1364,-1.0369,1.7046,2.3137,1.0693,1.9861,0.88866,0.46479,0.62892,0.99393,1.0558,0.40036,-0.36897,-0.36009,-0.92724,-0.89963,-1.7776,-1.7831,-1.6759,1.6331,1.5916,2.466,2.0448,1.7464,1.7622,1.563,0.84594,0.7002,0.61878,0.42818,0.40339,0.8353,0.96905,1.4849,1.4856,0.85193,1.1814\n-0.39668,-0.44502,-0.38599,-1.5808,-3.0979,0.11801,-0.24828,-1.383,-2.3939,-3.4244,-2.0199,-2.2519,1.0483,1.5442,2.022,3.6031,3.3599,1.1004,0.85317,1.1051,1.5647,0.60589,0.073772,-0.85016,-2.3841,-2.5544,-3.986,-3.4238,-1.39,0.70125,1.2189,1.6876,1.959,1.6978,1.7437,1.0964,0.81229,0.71449,-0.0093479,-1.7352,-0.23909,0.78243,0.57836,1.1247,1.397,1.8845,1.5871\n1.2293,1.4154,1.0561,-0.81048,-1.6842,-3.443,-1.4427,-1.8564,-2.6288,-2.5746,-0.78712,-1.4935,0.42577,1.2239,1.9471,1.823,1.5347,0.97994,1.1522,1.0847,0.25301,1.8545,1.6547,-0.075541,-1.7144,-1.7844,-2.8584,-2.2352,0.9157,1.1448,0.68627,0.77423,1.0321,1.4683,1.6299,1.3788,0.78322,0.39478,-0.18793,-0.28964,0.43577,0.51446,0.2055,0.77972,1.4902,1.6921,0.57591\n2.179,2.5614,1.8918,-0.0094624,-0.71747,-2.6484,-0.68195,-1.8669,-3.8946,-4.875,-3.2507,-1.9632,-0.49969,-0.195,1.4732,0.76973,0.57096,1.1658,1.0825,-0.28114,-0.68697,1.33,1.631,0.76806,-0.056074,-0.97312,-1.4615,-1.4249,0.041229,0.25164,0.77986,1.9677,1.3904,1.2201,1.6361,0.85334,0.2529,-0.017817,-0.19461,-0.20785,-0.2957,-0.072857,0.090834,0.32417,-0.029623,-0.70005,-0.75189\n1.641,2.0211,0.80888,1.7496,1.7974,1.3668,0.94187,-0.19099,-3.7752,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3304,1.0247,-0.19692,-0.31693,0.95168,2.0899,1.8245,0.61018,-0.39405,-1.1939,-1.1251,-0.53918,0.34921,1.7264,3.4299,2.6277,1.2524,1.1114,0.81358,0.62709,0.33636,-0.22165,-0.18445,-0.6137,-0.46205,-0.25134,0.53959,0.099512,-0.72487,-0.60338\n1.2859,1.5761,0.44838,1.3683,2.5675,2.2148,1.9703,0.2272,0.3475,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.9166,0.66411,1.2083,1.1405,1.5649,1.6344,1.4464,0.87116,-0.49742,-1.6119,-1.1826,-0.2992,0.50055,0.21,1.0041,2.1222,1.5365,1.4451,1.3321,1.5303,0.59379,0.75328,0.66302,0,0.032015,0.31917,0.19744,-0.40803,-0.94399,-1.2721\n1.4558,0.4669,-0.84826,-1.0325,0.26514,0.52327,0.60192,-1.2782,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.999,0.65536,1.8568,0.13568,-0.42149,-0.22565,0.5555,1.6182,1.1025,0.34559,1.2451,0.37733,-0.12502,-0.50669,0.54838,1.2477,1.4867,1.5719,1.2459,1.7779,1.5123,1.3599,0.93538,0.24076,0.36711,0.17494,-0.71679,-0.58334,-0.94197,-0.55061\n1.3071,-0.19679,-0.34068,-0.30114,0.18361,-0.7437,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.50871,0.16135,0.68396,0.068197,-1.1233,-1.0607,0.22318,1.7866,1.1066,1.1383,0.44188,-0.23259,-0.015099,-0.39777,0.32703,0.79397,0.94433,0.68503,1.1593,1.53,1.9122,1.5351,0.64008,0.1528,-0.35652,-0.68778,-1.0891,0.71024,-0.10652,-0.35466\n-0.87701,-0.98178,-2.1346,-1.1367,-1.1808,-0.054527,0.53127,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4351,0.93211,2.499,0.40149,0.10841,0.23645,0.83754,-0.1832,-1.7822,-2.0198,-1.7587,-1.3278,-1.6767,-0.60427,0.481,0.92658,0.53435,0.77822,1.65,0.99099,0.83291,0.8748,0.059752,-0.49924,-1.0221,-0.80479,0.15263,0.86043,-0.20204,-1.2588\n-1.4632,-2.9391,-2.3775,-0.43597,0.46224,0.073938,-0.12232,-0.86238,-0.68598,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.3512,0.87044,2.5344,0.87662,-0.068554,0.36371,-0.65627,-1.1732,-2.1501,-2.1134,-2.3095,-2.9586,-1.9966,-0.040335,1.0811,0.98291,0.90272,1.3161,1.0584,0.58955,0.29071,0.1764,-0.10174,-0.616,-1.0145,-0.75465,0.24734,0.66307,-0.37019,-2.3949\nNaN,-2.1093,-1.0406,-0.93401,-0.23241,0.15358,-0.21703,-0.45733,0.37281,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5426,1.0763,0.77643,-0.7494,-1.0269,-0.014585,-0.20013,-0.29549,-0.71511,-1.8448,-2.3293,-1.9721,-1.5353,-0.697,-0.35349,0.27409,0.29915,2.0075,0.70354,0.12406,-0.06098,0.2813,0.25518,-0.0020084,-0.28152,-0.19559,0.2232,-0.33405,-1.1991,-3.1824\nNaN,NaN,1.1709,-1.3626,-0.26548,0.39293,-0.25039,-0.047398,-0.33434,-1.204,-2.7719,NaN,NaN,NaN,NaN,NaN,NaN,1.2364,1.7654,1.0409,-0.33289,0.57475,0.99648,-0.49956,0.029585,0.31716,-0.31227,-0.64494,-1.1723,-1.4297,-1.4149,0.16452,0.4094,0.52488,1.9197,0.78921,0.15055,-0.18457,-0.046022,0.3551,-0.32651,0.15493,-0.63782,-0.67163,-0.32524,-0.83645,-2.1172\nNaN,NaN,NaN,NaN,NaN,NaN,0.38481,1.7979,-0.48744,-1.4461,-1.1907,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.7066,1.153,1.0049,0.91157,0.14889,-0.13743,-0.19632,-0.27618,-0.71771,-1.426,-1.1058,-0.89029,-0.69699,-0.35816,-0.016542,0.54107,1.3734,1.7311,0.58848,0.099596,-0.25195,-0.24869,-0.46912,-0.30804,-0.46449,-0.27175,-0.59216,-1.3095,-2.312\nNaN,NaN,NaN,NaN,NaN,NaN,1.9599,2.363,1.5023,0.76618,-0.60076,NaN,NaN,NaN,NaN,NaN,NaN,2.152,0.99671,0.73292,1.5015,0.47977,-0.40094,-0.31625,-0.9224,-1.8777,-2.3237,-2.1926,-1.3722,0.18135,0.56603,-0.19618,0.27147,0.61558,1.0757,1.4152,0.94058,0.17841,-0.033567,-0.34602,-0.34207,0.070932,0.65632,0.36819,-0.66692,-1.8652,-2.475\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9338,2.3728,0.42439,-0.86404,0.39763,NaN,NaN,NaN,NaN,NaN,0.095905,0.24746,0.90375,1.3674,0.4851,0.067438,-0.64703,-1.6845,-2.8045,-1.8695,-1.0263,-0.18137,0.84955,1.2986,1.9005,1.3007,0.45867,0.46171,0.21676,0.636,0.18336,0.09564,0.060175,-0.099134,0.032078,0.46086,-0.23891,-1.0706,-1.4618,-1.5863\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,1.2031,0.1548,-2.0514,-1.2411,0.010389,0.036201,-2.5994,NaN,NaN,NaN,3.1208,2.2526,1.3205,1.1845,-0.060314,0.58858,0.37378,-0.83133,-1.9935,-0.8878,0.277,0.23399,0.45508,1.3775,1.6871,0.98838,0.35716,-0.32019,-0.1154,-0.43582,-0.082354,0.92015,0.83645,-0.015471,-0.55088,-0.44435,-0.76114,-1.2547,-1.8354,-1.2329\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.52205,-0.66171,-1.8972,-1.4834,-0.069834,-0.34811,2.2766,2.83,2.1694,NaN,NaN,NaN,NaN,0.49104,-0.050657,0.5997,0.23245,-1.1413,-1.2106,-0.70025,0.57272,-0.028669,0.80337,1.1402,0.88951,0.34411,-0.15001,-1.2337,-0.052107,0.60679,0.1761,0.57699,0.20218,-0.083044,-0.53536,-0.69538,-1.0768,-1.7731,-2.0427,-1.7843\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061106-070115.unw.csv",
    "content": "-0.15297,-0.5,-0.70719,-0.53454,0.052721,-1.0665,-1.0276,-0.60048,-0.27139,-0.9245,-1.0462,-0.92529,-1.788,-1.6662,-1.5037,-1.5296,-1.522,-2.519,-2.708,-2.882,-3.0726,-2.2739,-1.4788,-1.267,-2.3627,-1.9761,-2.8465,-3.1784,-3.1461,-2.6375,-2.5808,-1.6589,-2.2512,-2.3918,-2.3611,-1.9089,-0.55314,0.05806,1.7882,0.65168,-0.24109,-0.56133,-0.011415,-0.048441,-0.83336,0.07583,0.16871\n0.48534,0.81343,0.26018,-0.16163,-0.27558,-1.2351,-1.2692,-0.42982,-0.96766,-1.1166,-1.6165,-2.1016,-2.0893,-2.1381,-1.8838,-1.9538,-2.2374,-2.6216,-2.2708,-2.7165,-3.4991,-2.2342,-1.7681,-2.1831,-2.4137,-2.4857,-2.5532,-2.4363,-2.775,-2.9839,-2.8155,-2.3598,-2.3531,-2.8749,-2.6457,-1.2084,-0.014408,0.45089,0.72146,0.77883,-0.4071,-0.51085,-0.72282,-0.17095,-0.079329,-0.61526,-0.95911\n0.6792,1.406,0.65911,-0.25908,-0.37241,-1.156,-0.9977,-1.3012,-0.96522,-1.5158,-1.5433,-1.5502,-1.997,-2.2573,-1.5847,-2.0937,-2.3542,-2.8527,-2.345,-2.6759,-2.0522,-1.1433,-0.93394,-1.1051,-2.2591,-2.4734,-2.7257,-3.2093,-3.154,-3.3452,-2.9899,-2.5339,-2.9893,-2.8881,-2.3608,-1.787,-0.99088,-0.63386,-1.2147,-1.162,-0.17707,-0.2042,-0.40877,0.25821,0.1074,-1.1979,-1.5709\n1.1729,0.72951,0.45667,-0.44667,-0.54436,-0.71306,-0.76649,-0.55818,-1.1201,-1.4083,-1.5467,-1.4639,-1.8748,-2.0489,-2.2623,-2.5227,-3.3737,-2.9132,-2.3315,-2.8589,-2.1784,-1.7502,-1.8832,-2.3956,-2.6615,-2.928,-3.198,-3.0181,-3.2918,-3.6045,-3.3044,-2.8964,-2.6298,-2.7917,-2.1297,-1.6545,-1.2153,-0.62861,-1.6571,-1.1746,-0.43942,0.42133,0.36965,0.34571,0.38449,-0.3982,-1.6461\n0.039049,0.48948,0.70562,0.26325,-0.40183,-0.75108,-0.72926,0.1166,0.23654,-0.010633,-0.67732,-1.0105,-1.3634,-1.8631,-2.1325,-2.4885,-2.1865,-2.5461,-2.5217,-2.0333,-1.1257,-1.7611,-2.0808,-2.4513,-2.6885,-3.562,-3.1865,-3.1332,-2.6336,-2.815,-3.1124,-2.9027,-2.7026,-2.8789,-1.9406,-1.3683,-0.90939,-0.82113,-1.097,-0.96285,-0.48934,0.44546,-0.23157,-0.11364,-0.59275,-0.7394,-1.1306\n-0.37736,-0.76024,0.50412,0.1623,-0.074057,-0.39209,-0.45772,-0.45716,-0.50332,-1.073,-0.82235,-0.86472,-1.174,-1.4752,-1.8402,-1.8667,-1.6672,-1.9294,-2.1867,-2.4798,-1.4105,-1.4896,-2.0213,-2.6477,-2.6567,-2.7973,-3.0864,-2.904,-2.7982,-3.2578,-2.9558,-2.644,-2.3533,-2.891,-2.4336,-2.0095,-1.1787,-0.42066,-0.40209,0.04653,0.5393,0.2539,-0.34203,-0.40102,-1.0984,-1.195,-0.60393\n-0.54251,-0.87182,0.75454,0.66191,0.092257,-0.37445,-0.072216,-0.58312,-0.9509,0.32332,-0.38476,-0.89066,-1.6232,-1.7243,-1.14,-1.2529,-1.0272,-1.4942,-1.9622,-2.1007,-2.3464,-2.5376,-3.3444,-2.7201,-2.2705,-2.7288,-3.1582,-3.1406,-3.4463,-3.2864,-2.908,-2.0128,-2.0318,-2.678,-2.1257,-1.7637,-1.6248,-1.1421,-0.55795,-0.23252,-0.82701,-0.61334,-0.57442,-0.4852,-0.45734,-0.28477,1.4075\n-0.038225,-0.022467,0.4547,0.18175,0.40489,0.042185,0.0078335,-1.0863,-1.2585,0.39905,-0.52982,-0.4935,-0.25007,-1.7669,-1.5031,-1.4022,-1.1571,-1.6192,-1.5437,-1.5913,-2.3132,-1.7107,-2.4412,-2.2757,-2.0174,-2.6567,-3.4011,-3.5222,-3.0933,-2.7117,-3.0271,-2.547,-2.1205,-3.0872,-2.934,-2.208,-1.5456,-1.146,-0.97869,-0.61791,-0.68668,-0.29782,-0.44328,0.041773,0.17729,0.056738,1.3694\n0.337,0.18017,0.38305,0.40292,0.34814,0.29935,-0.061646,-0.97267,-0.81042,0.42966,1.1808,0.49669,-0.75534,-1.476,-1.6322,-1.5321,-1.5714,-1.6518,-0.63721,-0.99172,-1.469,-1.9344,-2.463,-2.2717,-2.5112,-2.6198,-1.9612,-2.259,-2.8915,-3.3346,-3.4804,-3.4241,-3.3482,-2.8623,-2.4857,-2.0032,-1.1947,-0.19371,-0.44133,-0.041643,-0.23129,-0.73564,-0.72024,-0.58109,0.18645,0.32701,-0.015566\n0.39126,0.19341,0.23685,0.78883,0.20559,0.18214,0.29716,-0.25152,-0.078104,0.42143,1.0966,0.35932,-0.99016,-1.0085,-1.2955,-1.1076,-1.7071,-1.5393,-0.4514,-0.95594,-1.648,-1.1944,-2.5656,-2.5355,-2.0936,-2.3465,-2.444,-2.006,-3.0372,-3.0075,-3.0069,-2.7538,-3.2547,-1.9541,-1.2639,-1.3174,-0.98028,-0.22881,-0.24333,0.057192,-0.37914,-0.83473,-0.82537,-0.84808,-0.50237,-0.45334,-0.43547\n0.19383,-0.022217,-0.22112,1.3249,0.71601,0.13494,0.61757,0.15365,0.14985,0.61355,0.71874,-0.5246,-1.0831,-0.55069,-1.0757,-2.1827,-0.79173,-0.73684,-0.78991,-0.68499,-0.84721,-1.6354,-2.6312,-2.503,-2.2842,-2.8189,-2.6778,-2.6546,-2.9211,-2.7124,-2.8248,-3.1723,-3.2269,-1.9878,-1.6297,-1.0314,-0.85665,-0.7786,-0.73483,-0.72966,-0.26054,-0.68591,-0.92612,-0.57772,-0.081856,-0.36484,-0.41361\n0.24142,0.18867,1.1262,1.8213,0.8277,0.73701,0.20202,0.40566,-0.18582,0.37349,-0.60634,-0.46773,-0.737,-0.56252,-0.6843,-0.77282,-0.15784,-0.84779,-1.042,-0.85656,-0.90707,-1.9622,-2.5303,-2.3365,-2.6197,-2.8293,-3.2464,-2.9031,-3.0583,-2.8914,-2.9808,-3.0412,-3.0133,-2.4385,-2.0024,-2.3931,-2.3282,-1.7948,-1.0278,-0.99018,-0.80724,-0.87267,-1.419,-0.86934,-0.14692,0.15926,0.218\n0.85444,0.97869,1.2655,1.2415,1.2245,0.75215,-0.071798,0.95251,-0.042902,0.08588,-0.32595,-0.22367,-0.30635,0.26857,0.12062,-0.11159,-0.37426,-0.46321,-0.8066,-1.2384,-1.7973,-2.0403,-2.9077,-2.5687,-2.7649,-2.5054,-2.7101,-2.518,-2.903,-2.6877,-3.0587,-2.8871,-2.687,-2.8328,-2.7243,-2.7876,-2.0959,-1.984,-1.1756,-0.99031,-0.9219,-1.0867,-1.6371,-1.6362,-0.30672,0.14917,0.9577\n0.96789,1.4454,1.905,1.9751,0.53574,0.36697,-0.32856,0.50263,-0.037756,0.013107,-0.23897,-0.072937,0.43582,0.87633,-0.064482,0.54807,0.057638,-0.10109,-0.5418,-1.0709,-1.7114,-1.9037,-2.643,-2.9202,-2.6573,-3.0901,-2.8589,-3.1168,-3.023,-2.6215,-2.799,-2.7814,-3.0409,-2.6385,-2.4247,-2.4126,-2.077,-1.4613,-0.76937,-1.3593,-1.4631,-1.7474,-1.8976,-1.8655,-1.2309,0.062366,1.1197\n0.73564,1.6213,3.1552,0.96431,-0.036386,0.039221,0.34759,0.10211,0.26782,-0.49613,-0.66413,-0.87317,-0.8771,-0.35921,-0.15827,-0.047262,-0.39066,-0.47844,-0.8124,-1.104,-1.7803,-1.1803,-2.8095,-3.2975,-3.1233,-3.2978,-3.0461,-2.5769,-2.1172,-2.4771,-2.8468,-2.6271,-2.4079,-2.3331,-2.3595,-2.6802,-2.1409,-2.1007,-1.9557,-2.119,-1.8492,-2.1624,-2.2883,-1.8568,-1.8281,-1.0589,-0.55839\n-0.30767,0.93653,2.2442,0.80236,-0.014751,0.92078,0.19247,0.30021,0.17932,-0.18963,-0.32774,0.051394,-0.73681,-0.60736,0.07008,0.073862,-0.47659,-1.4073,-1.6534,-1.4561,-1.3207,-1.5348,-2.5644,-2.9836,-3.2231,-3.4649,-3.3357,-3.0355,-2.7577,-3.1143,-3.2832,-3.4351,-2.9188,-3.3168,-3.2648,-3.0699,-2.788,-2.4973,-2.3649,-1.8647,-2.0319,-1.8245,-2.2027,-1.7577,-1.5102,-0.95934,-0.45065\n-0.92894,0.14642,0.94952,0.59155,-0.087978,0.3791,0.28473,0.5368,0.33405,0.46095,0.3214,-0.013615,-0.41946,-0.63368,0.88687,-0.35971,-1.1888,-1.6475,-1.9868,-1.3578,-0.64894,-1.7144,-3.012,-2.8837,-3.3292,-3.5106,-3.7234,-3.7234,-3.2891,-2.8594,-3.3179,-3.938,-2.1612,-2.3525,-3.0186,-3.241,-2.8006,-2.5978,-2.2136,-2.1168,-1.9441,-1.2268,-1.2996,-1.1907,-0.75449,-0.34054,-0.30428\n-0.81484,0.30924,0.64642,0.17618,0.78336,1.0294,0.9815,0.68717,0.52793,0.59979,0.035425,-0.23253,-0.46641,-0.69992,-0.45592,-0.46383,-0.61874,-0.60343,-1.3714,-0.38208,-0.98593,-2.007,-2.8267,-3.0308,-3.4768,-3.5474,-3.6428,-4.0115,-4.0002,-3.4858,-3.307,-3.615,-3.9445,-2.7915,-3.0463,-3.7549,-3.8201,-2.9894,-2.5107,-2.4457,-2.4496,-1.792,-0.94274,-1.1179,-0.86166,-1.0943,-0.90169\n-0.1082,0.60166,1.3604,1.1564,1.2098,3.6314,1.7909,0.43413,0.42512,0.64062,0.29935,-0.049417,-0.25754,-0.77776,-0.26684,-0.75242,-0.78859,-1.1889,-1.9205,-3.1151,-3.2229,-3.0781,-3.3317,-3.4299,-3.4823,-3.3057,-3.4045,-3.6002,-4.2018,-3.4064,-3.446,-2.8383,-4.0801,-4.8263,-4.1737,-3.3746,-3.6149,-3.2148,-1.6333,-2.5124,-2.6974,-2.6893,-1.201,-0.79018,-0.3506,-0.41315,-0.77081\n0.75982,0.78053,0.8184,0.69082,0.78052,0.7767,0.5212,0.84408,1.0513,0.91276,0.58,0.35994,0.11188,-0.25236,0.13071,-0.21171,-0.82463,-1.5467,-1.9072,-2.1911,-4.0674,-4.2027,-3.1284,-3.6786,-3.4402,-3.0737,-3.2881,-3.3689,-3.2716,-4.0015,-3.6053,-2.9089,-3.3543,-3.2416,-2.8356,-3.8579,-3.327,-3.3354,-2.0266,-1.8925,-1.9618,-2.0948,-1.5502,-1.1429,-0.18604,-0.18381,-0.16965\n0.4886,1.775,1.1403,0.60961,0.30927,0.46461,1.112,0.59592,1.3302,0.51827,0.92114,0.20256,-0.0074043,-0.02162,-0.87477,-1.4299,-1.63,-1.5922,-1.7758,-1.7826,-1.9213,-2.3973,-2.6717,-2.978,-2.8636,-3.3752,-3.5648,-4.4217,-4.0605,-4.1301,-4.21,-3.231,-2.9802,-2.9677,-2.7975,-3.0584,-2.9749,-3.2298,-2.3221,-2.3026,-2.3286,-2.0091,-1.4469,-0.52552,-0.25776,-0.3448,-0.70813\n0.51664,1.669,1.0704,0.9712,0.99459,0.84067,1.4696,0.97028,0.97141,0.32974,0.080362,-0.13181,-0.00053978,-0.29452,-0.61003,-0.72357,-1.3233,-1.6621,-1.2955,-0.88276,-2.2644,-2.1448,-1.6717,-2.8568,-3.3855,-3.677,-3.516,-3.5419,-4.6348,-4.597,-4.3805,-3.1902,-3.5648,-3.4988,-2.8563,-3.1373,-3.0118,-2.4318,-2.2508,-2.4465,-3.0134,-1.8348,-0.83925,-0.21745,-0.042751,-0.31471,-0.64242\n0.31267,0.93215,0.43013,0.67138,0.77047,0.41567,0.31206,0.7294,1.1075,0.82549,-0.17327,-1.1366,-0.57439,0.29563,0.31289,0.14244,-0.77849,-1.1209,-1.6307,-1.3126,-2.6557,-2.7923,-2.4877,-1.6061,-2.6222,-3.0959,-2.9945,-2.9135,-3.7614,-4.2002,-4.2168,-4.2211,-4.1038,-4.0844,-3.1361,-3.132,-2.8345,-2.7114,-2.7051,-2.4891,-2.3546,-1.5105,-0.60705,0.021816,-0.76545,-0.84641,-0.79529\n-1.1039,0.14587,0.05084,0.66397,1.1632,0.47129,0.44815,0.81512,1.453,1.4417,0.65452,1.0511,-0.057127,0.65315,0.90798,1.4609,-0.49694,-0.67125,-1.1344,-1.5358,-1.5649,-1.3225,-1.58,-1.5198,-2.2632,-2.7586,-3.462,-2.7575,-3.1471,-3.5858,-4.1226,-3.8333,-3.463,-3.4079,-3.0791,-2.925,-2.6812,-2.3038,-2.2768,-1.7573,-1.669,-1.1351,-0.62092,-0.57997,-1.2105,-1.0068,-0.81101\n-0.95383,-0.52128,0.00058556,0.76979,0.98429,0.43163,1.0148,1.6781,2.0836,1.9067,1.457,0.62199,-0.22759,1.2706,0.90412,-0.22236,-1.0873,-0.84548,-0.38788,-0.43165,-0.26715,-0.22241,-0.64762,-0.86035,-2.0552,-2.3889,-2.1998,-1.8043,-2.3459,-2.7605,-2.5259,-1.9789,-2.4969,-3.6739,-2.917,-2.2479,-3.4859,-2.6324,-2.1106,-2.0938,-1.6888,-1.1689,-1.0887,-1.4206,-1.3956,-0.82894,-0.53116\n-0.033854,-0.51023,0.4012,1.022,0.66957,1.3842,0.67909,0.86045,1.5356,2.2991,1.5482,0.91583,0.95628,1.5549,1.0011,0.01396,-0.57715,-0.76237,-0.28279,0.12316,0.71774,1.0647,-0.018806,-0.18785,-0.86451,-1.6483,-1.619,-2.1524,-2.8379,-2.7529,-2.4845,-1.55,-2.2397,-2.8013,-2.6961,-1.849,-2.5963,-2.7784,-2.8479,-3.2782,-2.4422,-1.5944,-1.1231,-1.0028,-1.0565,-0.33467,0.090582\n-0.11989,-0.13839,0.64211,1.5757,0.68452,0.32178,0.30374,0.61134,1.1777,1.8161,2.0233,1.392,2.0906,1.438,0.95693,-0.18759,-0.84421,-0.12667,0.92407,1.4618,1.3317,1.154,0.20729,-0.52311,-1.8015,-1.8122,-1.3387,-1.5623,-1.9388,-2.6493,-2.9907,-3.0965,-3.0073,-2.9153,-2.828,-2.5655,-2.4987,-2.5519,-2.9187,-3.3514,-2.3762,-1.7052,-1.1575,-0.63453,-0.27804,0.018511,0.088661\n-0.31196,-0.44994,-0.45925,0.059069,0.32657,1.2991,2.6016,1.1784,1.92,1.3852,1.9607,2.6999,2.1418,1.4615,1.0562,1.2796,0.64041,0.69593,0.83076,1.2616,1.7644,1.5789,0.66984,-1.0619,-1.3005,-1.4651,-1.4805,-1.4544,-2.2897,-2.317,-2.759,-3.4456,-4.1753,-3.679,-3.0925,-2.9956,-2.8488,-3.2458,-2.9937,-2.5907,-1.7553,-1.1971,-1.1903,-0.603,-0.13762,0.20368,-0.020304\n0.13222,0.067465,0.027739,1.2009,0.83511,1.9579,3.4737,3.8992,2.7758,1.4583,1.1883,1.5283,1.2703,0.91252,0.86505,0.95371,-0.024052,-0.25445,-0.14672,0.2675,1.0754,1.4036,1.2123,1.013,-0.18798,-0.93955,-0.772,-1.3419,-1.7246,-1.5326,-2.1411,-2.9267,-3.3102,-3.5325,-2.8363,-2.907,-3.2909,-3.7761,-3.8351,-3.0962,-1.5059,-1.135,-1.4996,-0.58099,-0.010675,-0.0059929,-0.31207\n-0.10802,-0.15864,-0.54803,0.39123,0.68029,1.799,2.4414,3.0112,2.7747,2.2621,1.3592,1.0031,1.2114,0.24609,0.24878,0.039312,-0.49849,-1.102,-1.0794,-0.3519,0.43915,1.1155,1.6439,0.89002,0.11333,-0.10884,-0.17587,-0.50809,-1.1079,-1.6575,-2.3257,-3.2613,-3.8207,-3.8024,-2.3688,-2.4564,-3.3377,-3.7288,-3.8027,-2.5802,-1.3148,-1.9471,-1.9904,-1.0534,-0.16925,-0.29571,-0.85589\n-2.1936,0.12614,-0.32126,-0.12567,0.60474,0.81573,1.4798,1.6052,1.8289,1.4437,0.14976,0.13445,0.86627,0.047079,0.11315,-0.43241,-0.88222,-0.3181,0.078856,0.084274,0.042568,-0.0097103,0.91775,0.50183,-0.26579,-0.60695,-0.6968,-1.0315,-1.8921,-1.9704,-2.3947,-2.8636,-3.0102,-2.8519,-2.12,-2.1563,-2.7481,-2.8437,-2.5731,-2.0786,-2.2639,-2.6898,-2.1872,-0.9401,0.013536,-0.39138,-0.26407\n-1.5083,0.82861,1.2068,1.1327,0.050039,-0.0087738,-0.081533,0.29086,1.9954,0.83681,-1.442,-0.66225,-0.050838,0.10903,-1.1071,-1.1687,-0.65534,-0.28001,-0.29882,-0.29969,1.5917,1.1949,0.75484,-0.081089,-0.3216,-0.80686,-1.022,-1.5343,-4.0515,-4.2879,-2.6943,-2.1251,-2.2064,-2.4826,-2.088,-2.3026,-2.832,-2.6197,-2.6873,-2.0712,-1.8518,-2.3175,-1.7894,-0.78886,-0.66742,-0.85504,-0.59583\n-0.45768,0.54791,1.1798,1.1726,0.46663,-1.25,0.14682,1.3624,1.3162,1.0614,-0.59673,-2.0873,-2.5601,-2.2895,-2.6216,-1.4915,-0.39522,-0.52185,-0.81074,-0.42312,0.58208,1.1839,1.4032,-0.37875,-0.19397,-0.52563,NaN,NaN,-5.2704,-5.248,-2.819,-1.2593,-0.68824,-0.47949,-1.5753,-2.1503,-2.3426,-2.2569,-2.6529,-1.9912,-1.6628,-1.4762,-1.1745,-1.0218,-1.0321,-0.73009,-0.80834\n-0.62039,-0.40791,1.275,1.0664,-0.20206,-0.94205,-0.13881,1.3138,1.8586,2.3998,0.53955,-0.44935,-2.2785,-1.7495,-3.3921,-2.6211,-1.4309,-2.1662,-0.90362,-1.2517,-0.74485,0.084661,0.14891,-0.013634,-0.29062,0.20517,NaN,NaN,NaN,-1.9208,-0.45733,-0.088526,0.48947,-0.081821,-0.9259,-1.5648,-2.0462,-1.8853,-1.8248,-2.2299,-1.5615,-1.2565,-1.0239,-1.1718,-0.87715,-0.63783,-0.83099\n-1.2789,-0.047426,0.059727,1.7164,2.0093,0.61018,-1.3862,0.65067,1.3895,1.2336,0.93991,-0.43574,-0.71762,-1.2893,-3.6053,-3.0549,-2.4147,-1.6358,-1.0377,-0.69603,0.63318,1.5006,1.409,1.3078,NaN,NaN,NaN,NaN,NaN,0.70032,0.1424,0.12436,0.58228,-0.23871,-0.75688,-1.6643,-1.9362,-1.6242,-1.916,-1.9805,-1.8817,-1.9223,-1.5316,-1.4513,-0.94863,-0.56013,-0.78993\n-2.683,-2.3088,-1.6793,0.048018,-5.7102,-2.5697,-1.5419,1.0263,0.89797,0.54004,-0.57057,-1.0282,-2.7262,-5.0527,-6.8813,-5.6005,-1.8237,-0.55847,0.21782,1.2331,2.8898,3.7419,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.1472,0.49573,0.45956,0.46164,-0.3026,-0.78035,-1.4761,-1.6822,-2.0083,-1.8429,-1.5581,-1.5171,-1.3924,-1.2277,-1.1279,-0.76914,-0.77652,-0.68448\n-2.1795,-1.4447,-1.1137,-1.3228,-2.6484,-0.50961,-0.88477,-1.3327,-1.4456,-1.0817,-1.043,-1.9708,-2.6135,-5.2242,-4.9701,-3.3708,-0.56644,1.2966,3.1489,3.1444,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.76184,0.52213,0.39141,0.0095844,0.20971,-0.049549,-0.90092,-1.4894,-1.9039,-1.5875,-1.5514,-1.7089,-1.3455,-1.4351,-1.0091,-0.82733,-0.97737,-0.71164\n-1.2802,-1.9233,-1.6352,-1.2651,-1.9318,-0.46007,-0.42237,-0.64553,-0.51763,-0.60095,-0.94987,-1.5182,-1.1395,0.038532,-1.7772,-1.4426,-0.68254,5.5857,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2875,1.5069,0.60525,0.2837,0.20189,0.20534,0.22223,-0.073149,-1.0339,-1.6517,-1.8149,-1.5694,-1.3727,-1.3104,-1.1344,-0.8343,-0.59006,-0.68463,-0.93398\n-0.6372,-1.1551,-1.3877,-1.1453,-1.2393,-0.93673,-0.64486,-0.78681,-0.22296,-0.0091286,-0.68325,-1.0421,-1.9795,-2.7298,-2.3596,-2.0349,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3598,1.4464,1.3535,0.64741,0.45082,0.47999,-0.085691,-0.83002,-1.3408,-1.5747,-1.6086,-1.1994,-1.0184,-1.4205,-0.91474,-0.75796,-0.91562,-0.66934,-1.441\n-0.11989,0.0057888,-0.79372,-0.60789,-0.59916,-0.60675,-0.87094,-1.4488,-1.7221,-1.0586,-0.97151,-0.86429,-1.8011,-2.9171,-3.1108,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.77601,-0.019262,1.4682,0.72138,1.2656,1.9284,1.8938,0.83547,0.29014,-0.15617,-0.9252,-1.6067,-1.787,-1.7535,-1.4996,-1.0723,-0.99065,-1.29,-1.027,-0.93921,-0.87341,-0.50213,-1.1854\n0.31439,-0.62892,-1.7258,-1.9152,-1.028,-0.40222,-0.9929,-2.2334,-1.6214,-2.4526,-1.578,-1.1977,-1.1924,-1.9048,-2.7567,NaN,NaN,NaN,NaN,NaN,NaN,-2.1209,-3.0678,-2.0427,-1.8737,-1.3908,-0.015877,1.4916,1.5122,1.4868,1.8964,1.8995,0.053148,-0.77658,-0.87966,-1.9387,-1.689,-1.2971,-0.61455,-1.2362,-1.1537,-1.423,-1.3213,-1.1081,-1.0529,-0.66544,-0.74015\n-0.87847,-1.6247,-1.8839,-1.9736,-1.4802,-2.0009,-1.8632,-1.9058,-1.8415,-2.1308,-1.6257,-1.9723,-1.7187,-1.3347,-1.8441,-3.1912,-5.6258,NaN,NaN,NaN,NaN,-2.2405,-3.0771,NaN,NaN,NaN,0.93109,2.0157,1.7931,1.414,1.4377,1.2485,0.40095,-0.76786,-1.0963,-2.0179,-2.0474,-1.5503,-0.65605,-1.1698,-1.1949,-1.4424,-1.745,-1.44,-1.2644,-0.44953,-0.58172\n-2.353,-2.884,-2.009,-2.1069,-2.211,-2.4123,-2.6483,-2.5728,-2.4126,-2.6457,-2.6061,-1.7042,-1.8894,-1.5668,-0.84373,-0.23517,-2.2072,-4.1597,-3.7505,-4.8072,-3.0703,-2.3457,NaN,NaN,NaN,NaN,NaN,1.2462,1.0879,0.64327,0.75007,0.98578,0.65899,-0.94334,-1.3656,-1.2673,-1.4054,-0.90074,-0.95537,-1.4188,-1.4637,-1.4531,-1.5343,-1.5486,-1.2707,-0.36224,-0.62265\n-2.3711,-2.0767,-2.0662,-2.8329,-2.6725,-2.6442,-2.8439,-2.7739,-3.1816,-3.0467,-2.7538,-2.0994,-1.9045,-1.598,-1.0781,-0.61,-1.3563,-2.5035,-2.5476,-2.7848,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.67707,0.35764,0.24214,0.032499,-0.12808,-0.001749,-0.9384,-1.5936,-1.5729,-1.143,-0.90798,-1.1525,-1.539,-1.4049,-1.1581,-1.31,-1.3761,-1.156,-0.47463,-0.43166\n-2.2504,-2.0275,-2.1252,-2.4075,-2.4196,-2.2174,-3.1491,-3.7667,-3.8839,-2.7969,-2.2886,-2.1102,-2.3465,-2.2248,-1.3169,-1.2385,-1.6191,-2.1736,-3.0478,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.23798,-0.42731,-0.42692,-0.43713,-1.0543,-0.63678,-0.93193,-1.668,-1.7097,-1.3613,-1.3007,-1.5162,-1.3551,-1.1172,-1.0498,-1.6319,-1.5072,-1.1954,-0.49103,-0.49606\n-2.6783,-2.312,-2.2574,-2.4233,-2.2518,-2.2276,-3.1764,-2.9534,-3.3382,-3.0657,-2.1764,-1.7589,-2.4379,-2.2982,-1.2764,-1.0692,-1.6189,-1.5474,-1.5826,-3.1329,NaN,NaN,NaN,NaN,-0.31159,NaN,-1.0574,-0.77299,-0.815,-0.68258,-1.1118,-1.1767,-0.60866,0.080158,-0.057602,-0.76864,-1.1233,-1.3189,-1.6627,-1.2023,-1.5417,-1.6546,-1.5835,-1.4906,-2.0658,-1.1449,-0.063017\n-1.9553,-2.4479,-2.7994,-2.5727,-2.5289,-2.3948,-2.7312,-2.2996,-2.6515,-2.7418,-2.9246,-2.5599,-2.5503,-2.5317,-1.7412,-2.0891,-2.1083,-1.5612,-1.2372,-3.3796,-2.8236,-2.4124,-1.5095,-1.8286,-1.7485,-1.6687,-1.944,-1.252,-1.5939,-1.9184,-1.9442,-1.2631,-0.98051,-0.63559,-0.2293,-0.76167,-1.6182,-1.7505,-1.8838,-1.3523,-1.2981,-1.4329,-1.3773,-1.3131,-1.4307,-1.1448,-0.88254\n-1.9442,-2.6182,-2.9255,-2.7224,-2.7734,-2.8465,-2.1194,-2.3915,-2.4377,-2.7151,-3.455,-1.7948,-2.5114,-2.5814,-2.2356,-2.2401,-2.2632,-1.5853,-1.9104,-2.5136,-3.0557,-2.9599,-1.5854,-1.7708,-1.7597,-2.0676,-2.4443,-1.3383,-1.319,-1.636,-2.009,-1.4157,-0.90795,-0.74842,-0.78944,-0.87313,-1.191,-1.5797,-1.6975,-1.5846,-1.3613,-1.3504,-1.2734,-1.2332,-1.2103,-0.94872,-0.43706\n-2.231,-2.8592,-2.8899,-2.736,-2.7188,-3.1978,-2.7425,-2.8262,-2.7061,-2.9241,-2.8905,-2.2491,-2.9008,-2.6485,-2.3005,-1.6769,-1.5002,-1.6953,-1.8611,-2.7019,-3.9621,-2.8694,-1.8824,-2.3722,-2.9616,-3.426,-2.2835,-1.5576,-1.9192,-1.8461,-1.7739,-0.86625,-0.68477,-1.2863,-1.6161,-0.57887,-0.55504,-0.82894,-1.3255,-1.5235,-1.2837,-0.98056,-1.0284,-0.7972,-0.91599,-0.56662,0.2789\n-2.4942,-2.4372,-2.6417,-1.9629,-2.0601,-1.8949,-2.2192,-2.5643,-2.7637,-2.8198,-2.6452,-2.2774,-2.3831,-2.4674,-2.599,-1.568,-1.7672,-2.0858,-2.8428,-4.7561,-4.1098,-2.1103,-2.2006,-2.6676,-3.2682,-3.1996,-2.6391,-1.8644,-1.9209,-1.7488,-1.8561,-0.7615,-0.078022,-0.99851,-0.70291,-0.11801,0.37933,-0.28003,-0.32439,-0.95695,-0.80501,-0.85434,-0.78901,-0.79164,-1.2475,-0.38,0.093067\n-2.1436,-2.6028,-2.3894,-1.8545,-2.9146,-2.8319,-3.08,-2.8518,-2.68,-2.8178,-2.8372,-2.4323,-2.1616,-1.817,-1.9405,-1.9923,-2.0638,-2.1255,-3.0252,-3.4501,-2.6418,-1.938,-2.5484,-2.8362,-3.0082,-3.1475,-3.168,-1.9391,-2.1705,-1.9627,-1.3645,-0.85897,-0.21535,-0.56091,-0.74676,-0.38346,-0.095802,-0.13397,-0.2358,-0.44658,-0.55526,-0.85023,-0.69646,-0.51294,-0.6617,-0.4105,-0.12024\n-1.9301,-2.8921,-2.6637,-2.6546,-2.9065,-2.9825,-3.1823,-2.7364,-1.6642,-2.4196,-2.6432,-2.3378,-2.419,-1.7915,-1.2902,-1.9952,-2.6229,-1.7363,-2.1899,-3.2438,-2.2719,-2.0059,-2.6883,-2.9299,-3.0016,-3.2876,-3.6118,-2.2935,-2.2628,-2.0005,-0.80685,-1.081,-0.6378,-0.92406,-1.0575,-0.40907,-0.12804,0.035299,-0.16881,-0.49448,-0.44874,-0.31179,0.6005,0.062162,-0.50065,0.52553,0.7063\n-2.9657,-2.9157,-2.8141,-2.8728,-2.4927,-3.2719,-2.7958,-2.4186,-2.1794,-2.552,-1.8313,-2.1092,-2.0283,-1.9741,-1.7728,-1.6684,-1.8332,-1.822,-2.7488,-3.1449,-2.0462,-2.1535,-3.1764,-2.8144,-2.9824,-2.9704,-2.7511,-2.2531,-2.1725,-1.423,-0.91538,-0.91079,-0.70283,-1.1494,-1.0073,-0.44551,-0.24462,-0.21202,-0.22701,-0.15858,-0.3125,-0.68669,-0.24672,-0.52317,-0.25826,0.043846,0.30948\n-2.8043,-3.4827,-3.3006,-3.0625,-3.0469,-2.9929,-2.8051,-3.2046,-2.9095,-2.7979,-1.9777,-2.9261,-2.9086,-2.8469,-2.3684,-2.0669,-2.0154,-1.9294,-2.6168,-2.7031,-2.0644,-1.9588,-2.601,-2.6328,-2.6371,-2.4894,-2.6159,-2.3206,-2.4404,-2.0055,-1.4791,-1.0175,-0.72743,-0.97789,-0.71185,-0.55126,-0.55384,-0.068769,0.3553,1.2366,0.10263,-0.38158,-0.43574,-0.6602,-0.053919,-0.27221,0.19178\n-2.801,-3.0625,-3.2444,-2.8434,-2.9717,-3.1969,-3.0727,-2.9261,-2.8279,-2.3449,-2.7857,-3.4231,-3.6893,-3.405,-2.8437,-2.5954,-2.119,-1.822,-2.4344,-3.3484,-2.3198,-2.2438,-2.1572,-2.2554,-2.4624,-2.4363,-1.659,-1.5963,-2.1767,-2.0694,-1.3508,-1.2073,-0.84845,-1.0183,-0.73036,-0.43842,-0.49484,0.037882,0.50669,1.005,0.44406,0.090607,-0.36522,0.35243,0.15211,-0.47937,-0.10159\n-3.2605,-3.5349,-2.2819,-2.6978,-3.3008,-2.8085,-3.2326,-3.3543,-3.1521,-2.7399,-2.4181,-2.4844,-2.7024,-2.9533,-3.6024,-3.0679,-2.4452,-2.332,-2.4591,-3.0458,-2.6679,-2.4165,-2.3159,-2.1153,-2.0182,-2.1609,-1.4945,-1.7111,-2.1161,-2.0748,-1.6792,-1.1988,-0.7184,-0.64795,-0.42902,-0.49017,-0.26426,0.13517,0.37331,0.63293,0.37899,0.26739,0.41198,0.8564,0.3082,-0.47917,-0.085623\n-2.2143,-2.0847,-2.2126,-2.3715,-2.7849,-3.6925,-3.4143,-3.8377,-3.8466,-3.506,-4.1511,-3.372,-2.1229,-2.5308,-3.4306,-2.645,-1.9741,-2.3225,-2.2163,-2.2179,-1.9328,-1.848,-1.7618,-1.694,-2.0823,-2.0882,-2.3974,-2.3345,-2.2042,-1.8714,-1.896,-1.3829,-1.0149,-0.43122,-0.11437,-0.40683,-0.4072,-0.213,0.23871,-0.081758,0.29746,0.17155,0.24347,0.54247,0.20879,0.50813,0.65664\n-2.5562,-2.9846,-3.3052,-3.2112,-2.8324,-1.3859,-2.8063,-4.1212,-4.9666,-5.2413,-4.2298,-3.8923,-1.5019,-2.2657,-2.6321,-2.4826,-1.8245,-3.0779,-2.2016,-1.1469,-1.3216,-1.1568,-1.2858,-1.1011,-1.2253,-0.71792,-1.507,-3.207,-3.0278,-2.2843,-1.9338,-1.325,-0.93356,-0.16644,0.46502,-0.0029106,0.035954,-0.30143,-0.074228,0,-0.49824,-0.41988,-0.24957,-0.12554,-0.11166,0.68888,0.20614\n-3.9567,-3.4005,-3.0954,-3.7891,-4.0174,-2.3565,-2.9999,-3.7946,-4.0954,-4.9231,-4.3579,-6.0538,-4.5442,-3.9161,-2.3734,-2.016,-3.0109,-3.8505,-1.9027,-0.98503,-1.2042,-1.6207,-1.121,-0.87002,-0.83726,-0.73783,-0.95653,-1.4664,-2.3981,-1.7448,-1.6342,-1.5866,-1.156,-0.60501,0.029165,-0.22567,-0.28803,-0.16238,0.12221,0.1958,0.10526,-0.087036,-0.16023,0.15037,0.12422,0.15776,-0.46048\n-4.6352,-3.7588,-3.2276,-4.4402,-5.097,-4.8665,-4.5873,-3.3491,-2.0262,-2.0414,-3.989,-5.677,-4.6193,-4.8482,-5.2021,-4.8652,-3.4376,-1.8641,-1.0818,-1.4061,-1.5616,-1.8064,-1.6491,-0.9173,-0.89068,-1.0294,-1.3233,-1.5792,-1.9084,-1.4794,-1.3868,-1.9925,-1.5002,-0.83673,-0.24121,-0.069363,-0.10024,0.36789,0.54381,0.41651,-0.35855,-0.0026379,0.2091,0.17943,0.2431,-0.64741,-0.63744\n-4.1359,-3.7798,-3.1777,-3.4609,-4.4532,-7.2481,-2.1044,-3.7492,-3.27,-3.5233,-3.4155,-4.0535,-2.5126,-2.2652,-3.6246,-3.6478,-4.5228,-2.9865,-1.377,-1.3894,-1.34,-1.3306,-1.9554,-1.6752,-1.2873,-0.74056,-1.8378,-2.2138,-2.5638,-2.1753,-1.9221,-1.4785,-1.5349,-0.97997,-0.37753,-0.14432,-0.12756,0.89302,0.15029,0.036951,-0.23547,0.087717,0.18561,-0.35671,-0.75702,-1.4685,-2.0667\n-2.3058,-2.0989,-1.8084,-1.7906,-2.9685,NaN,NaN,NaN,-3.1728,-4.6868,-1.7338,0.073547,-0.91288,-1.8954,-4.8411,-4.6788,-4.4903,-2.7665,-2.1209,-0.82868,-1.5829,-1.6547,-2.1983,-1.9985,-1.243,-0.85946,-1.2185,-1.0429,-2.0064,-1.7675,-1.3319,-1.122,-0.79698,-1.3687,-0.71401,-0.32409,-0.30842,0.13167,-0.18365,-0.18844,-0.51218,-0.73187,-0.54262,-0.25509,-0.37673,-0.41057,-0.22194\nNaN,-3.0996,-2.1755,-1.653,-1.3868,NaN,NaN,NaN,-2.7741,-5.4223,-4.9629,-1.4946,-0.51228,-1.0652,-3.7835,-3.7068,-3.0094,-1.2674,-2.9203,-1.128,-0.49298,-0.78005,-2.527,-1.1269,-1.0287,-0.078959,0.32704,-0.39772,-1.1101,-1.0128,-0.96236,-0.28026,-0.49329,-0.63796,-0.30199,-0.36674,0.16672,0.024296,-0.59505,-0.91695,-0.7942,-0.76954,-0.07826,0.34083,0.098501,0.24126,-0.24921\nNaN,NaN,NaN,NaN,-3.1374,-4.655,-5.3304,NaN,NaN,NaN,NaN,NaN,NaN,0.075785,-0.69778,-0.89125,2.0104,2.9554,-1.0093,-1.5386,-1.6169,-1.3398,-1.5176,-1.094,-0.56511,0.31993,0.53613,-0.72058,-0.70828,-0.66685,-0.23592,0.17533,0.34587,0.07419,0.30586,-0.19581,0.14334,-0.053354,-0.57566,-0.42189,-0.51569,-0.65921,-0.42071,-0.45212,0.03578,0.045706,-0.21145\nNaN,NaN,NaN,NaN,-4.3229,-4.7446,-3.7947,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2156,2.879,0.42711,-0.88399,-1.6165,-2.038,-1.2906,-1.1538,-0.65527,-0.2019,-0.13357,-0.35919,-0.0083637,-0.16067,0.041752,0.26455,0.4575,0.59045,0.12126,-0.20326,-0.17142,-0.46533,-0.58708,-0.33741,-0.30537,0.0013161,0.033323,0.13675,0.10154,-0.5527,-0.82854\nNaN,NaN,NaN,NaN,-0.68312,-0.93371,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.55614,1.2375,-0.43662,-1.0537,-4.9095,-2.2437,-0.91447,-0.34224,0.013163,-0.40217,-0.97719,-0.040964,-0.26744,0.10299,0.090921,0.31189,0.80829,-0.6195,-0.43223,-0.4605,-0.6737,-0.24355,-0.36443,-0.35847,0.082674,0.6212,0.14499,-0.13101,-0.37605,-1.7514\nNaN,NaN,NaN,-0.33051,0.73012,-0.77338,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.68994,-1.1399,-2.3384,-3.4132,-0.93517,0.63504,-1.0262,-0.25428,0.3418,0.38251,1.029,0.32973,-0.070423,-0.56199,0.041658,0.45309,-1.1441,-0.60478,-0.88363,-0.67047,-0.13932,-0.010691,-0.46511,0.052036,0.38733,0.39588,0.12498,0.99422,0.09623\nNaN,NaN,NaN,NaN,NaN,NaN,-0.91876,-0.90476,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.36822,-0.41558,0.015917,0.47267,2.0664,-0.57213,-0.81042,-0.5708,-0.89318,-1.4696,0.52127,0.33572,0.32249,0.41764,1.5863,0.56734,-0.12303,-0.42067,-0.83585,-0.7139,-0.45316,-0.46568,-0.58581,-0.68597,-0.4137,-0.14385,-0.055862,0.37476,0.42021\nNaN,NaN,NaN,NaN,NaN,NaN,-2.2713,-1.0303,-0.52315,-0.84198,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.98943,-1.7384,-0.95994,-0.25634,-0.78439,-1.2605,-0.10599,-0.87806,-1.1111,-1.4121,-0.45619,-0.23807,0.33591,0.6935,0.75331,0.069225,-0.041786,-0.42693,-0.37612,-0.8271,-0.62702,0.060331,-0.36195,-0.79661,-0.17088,-0.19364,0.31576,1.4044,1.3741\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.3733,-3.0097,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.6375,-2.2248,-1.0371,-1.4076,-0.70752,0.24142,0.4664,-0.35132,-0.74582,-0.81692,-0.10096,0.46586,0.68062,0.11717,-0.11207,-0.32875,-0.21052,-0.17286,-0.055849,-1.4908,-0.72752,0.19113,-0.82918,-0.12159,0.2441,-0.3259,-0.50393,-0.27696,-0.12185\n0.26717,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.0083,-2.2263,1.3401,1.3505,-3.2386,NaN,NaN,NaN,NaN,-0.34735,-0.84394,-0.40116,-3.4352,0.22828,0.78176,-0.47224,-0.41727,-0.4045,-0.85522,-0.17697,0.36391,0.17965,-0.1177,-0.20797,-0.20996,0.11452,-0.015141,0.059618,-0.87024,0.01663,-0.31676,-0.077692,0.26857,0.10399,-0.44748,-0.66795,-0.53422,0.048433\n-0.14659,-0.19303,0.66073,0.6698,NaN,NaN,NaN,NaN,NaN,-2.676,-2.0256,-0.61333,0.35837,0.047609,-0.61469,-1.3816,-1.1469,-2.6018,-2.9334,1.2992,-2.0464,-2.0423,-0.54703,0.046747,-1.4828,-1.5671,-1.3181,-1.6597,-1.8405,-0.1833,0.5444,-0.13894,-0.49919,-0.5157,-0.23129,-0.029081,-1.1144,-1.4353,-0.85006,-0.28834,0.27793,1.2495,0.91165,0.11599,-0.40468,-0.64073,-0.61986\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061106-070326.unw.csv",
    "content": "-0.54213,-0.77885,-0.7848,-0.70694,-0.57421,-0.78716,-0.98517,-1.0241,-0.86616,-1.0096,-1.2348,-1.1183,-1.1612,-0.59472,-0.23351,-0.36359,-0.57765,-0.6263,-0.28578,-0.16624,0.26864,-0.094798,-0.11197,0.10613,0.39167,-0.46607,-0.36327,0.11893,0.28965,0.10694,0.19914,-0.074922,0.26156,0.20024,-0.10897,-0.13457,-0.13879,-0.64467,-1.0886,-1.1542,-0.50526,-0.20471,-0.42245,-0.81927,-1.2035,-1.194,-1.1592\n-0.7022,-0.55552,-0.43924,-0.62133,-0.70185,-0.94726,-1.0125,-1.0486,-1.3559,-1.2688,-1.0479,-1.0893,-0.87728,-0.60352,-0.72824,-0.81177,-0.68995,-0.6647,-0.48563,-0.3165,-0.26367,-0.60664,0.058845,0.14596,-0.29709,-0.40736,-0.15408,0.11085,0.10441,0.32173,0.24142,0.02085,0.19476,0.09846,-0.30102,-0.15273,-0.2248,-0.59126,-1.0122,-1.1931,-0.66047,-0.35394,-0.78012,-0.9621,-1.4206,-1.7241,-1.7531\n-0.67618,0.12342,-0.21752,-0.6495,-0.76747,-0.95197,-1.0096,-1.1385,-1.5614,-1.2889,-0.84201,-0.56489,-0.57419,-0.59032,-0.81402,-0.92161,-0.72873,-0.78921,-0.74694,-0.4488,-0.35966,-0.15162,0.5989,-0.5323,-0.28094,-0.28236,-0.22227,-0.23061,0.016733,0.54611,0.12755,-0.12224,0.029727,0.10301,-0.069894,-0.1405,0.08845,-0.33497,-0.62596,-0.68326,-0.69431,-0.46752,-0.97936,-1.1659,-1.8019,-1.9867,-2.1197\n-0.60762,0.29623,-0.56044,-0.64248,-0.78726,-1.0509,-1.2029,-1.2895,-1.4318,-1.3248,-0.92133,-0.76367,-0.66602,-0.72615,-0.91128,-0.76167,-0.60779,-0.65907,-0.74182,-0.55313,-0.67552,-0.015752,0.043189,-0.39533,-0.25205,-0.25792,-0.21019,-0.27708,-0.028028,0.038827,-0.18463,-0.21305,0.053698,0.20826,0.099698,0.00036907,-0.11001,-0.44479,-0.50021,-0.51999,-0.69786,-0.87561,-1.018,-1.3823,-1.7838,-1.9683,-2.2279\n-0.95722,-0.358,-1.1849,-0.74634,-0.88151,-1.0812,-1.2604,-1.2737,-1.5188,-0.86032,-0.67384,-1.0909,-0.69413,-0.96708,-1.1982,-0.75093,-0.41436,-0.4445,-0.72029,-0.82636,-0.78142,0.45798,-0.072379,-0.16233,-0.35424,-0.28506,0.13596,-0.1596,0.28553,-0.27108,-0.22557,-0.11264,-0.0087662,0.29678,0.19344,-0.11112,-0.23164,-0.34136,-0.41961,-0.60802,-0.93375,-1.0087,-1.1014,-1.2866,-1.7283,-1.8609,-1.8111\n-1.3388,-1.7133,-0.98082,-0.55069,-0.88689,-1.0109,-1.2554,-1.4317,-1.1513,-0.74524,-1.1661,-1.3527,-1.0386,-1.1643,-1.287,-0.91877,-0.70477,-0.61157,-0.88599,-0.63236,-0.15054,0.32174,0.13013,-0.40713,-0.448,0.28027,0.46622,0.017832,0.017699,0.10708,0.40481,0.24489,0.011295,0.039109,0.083362,-0.088757,-0.066918,-0.13884,-0.31138,-0.60719,-0.83305,-1.0502,-1.3009,-1.4061,-1.9142,-1.8051,-1.6577\n-1.7818,-2.094,-0.60788,-0.5621,-0.76526,-0.55848,-1.1951,-1.6416,-1.5777,-1.1685,-1.4706,-1.538,-1.1918,-1.1933,-0.96061,-0.82008,-0.61282,-0.45055,-0.386,-0.45534,-0.45077,0.049959,-0.15562,-0.1234,0.047229,0.56238,0.56563,0.068561,0.086797,0.30951,0.56226,0.19043,0.016695,-0.057732,-0.02984,-0.039771,-0.1654,-0.51636,-0.85108,-1.115,-1.5999,-1.3725,-1.5528,-1.784,-1.9949,-1.9817,-1.5292\n-1.6859,-1.6515,-0.71628,-0.70944,-0.78737,-0.35612,-0.99263,-1.4682,-1.3703,-0.99385,-1.4303,-1.4956,-1.3937,-1.0747,-0.91074,-0.85665,-0.62043,-0.28214,-0.42802,-0.5464,-0.56601,-0.32808,-0.32249,0.25375,0.24,0.34112,0.70137,0.61581,0.3635,0.40621,0.35308,0.070925,0.046823,-0.065337,0.021619,-0.0017166,-0.16256,-0.43517,-0.75421,-1.0397,-1.3059,-1.7074,-1.5404,-1.5052,-1.7185,-2.0193,-1.6057\n-1.3292,-1.0787,-0.43256,-0.78034,-0.64417,-0.58734,-1.1192,-1.14,-0.93893,-0.74054,-0.92105,-1.2737,-1.0387,-0.81477,-0.81152,-0.89672,-0.85027,-0.50555,-0.49874,-0.40246,-0.37983,-0.17465,0.094851,0.47058,0.59799,0.78117,0.8322,0.52211,0.34544,0.20018,0.29771,0.18847,0.13292,0.30775,0.21298,-0.017364,-0.33578,-0.37107,-0.57134,-0.28136,-0.481,-1.1878,-1.3366,-0.52754,-0.95327,-1.3456,-1.6829\n-0.84926,-0.78683,0.15992,-0.44483,-0.88246,-0.80933,-1.0184,-0.94683,-0.86464,-0.99373,-1.052,-0.86979,-0.97133,-0.89806,-0.70395,-0.713,-0.96956,-0.76746,-0.36753,0.01685,-0.18441,0.18798,0.27041,0.42175,0.87298,0.74723,0.60384,0.021451,0.50377,0.83168,0.61707,0.25447,0.29944,0.33278,0.080129,-0.20766,-0.33896,-0.11958,-0.18881,-0.12005,-0.18136,-0.44255,-0.62184,-0.29125,-0.48255,-0.96521,-1.489\n-0.93348,-0.018712,0.32813,-0.64593,-0.665,-0.79166,-0.87879,-0.95251,-1.0419,-1.07,-1.0019,-1.0483,-1.4758,-1.2168,-1.0285,-0.97325,-0.827,-0.4127,0.022172,0.090196,0.20932,0.22468,-0.045328,0.3598,0.62715,0.53161,0.46963,0.35472,0.7812,1.3137,1.1234,0.85208,0.53084,0.18357,-0.048182,-0.10275,-0.10218,-0.036064,-0.002409,-0.015505,0.12191,-0.29258,-0.44106,-0.39159,-0.46125,-0.97353,-1.1471\n-0.48632,0.4745,0.17655,-0.59682,-0.85535,-0.98821,-0.94552,-0.90818,-0.96578,-0.99265,-1.1778,-1.2695,-1.5463,-1.4182,-1.0084,-0.62235,-0.3731,-0.19779,-0.0030928,-0.17829,-0.079325,-0.12984,-0.1652,0.54758,0.58524,1.0433,0.4178,0.46272,1.1035,1.3516,1.3531,0.86165,0.52943,0.17603,0.29346,0.0036325,-0.21304,-0.21757,-0.04767,0.047789,0.057533,-0.34543,-0.60548,-0.54313,-0.61376,-0.97601,-1.1248\n-0.85062,-0.61347,-0.45358,-0.27785,-0.9959,-0.73283,-0.89207,-0.71648,-0.83821,-0.88684,-1.1845,-1.607,-1.3348,-0.79865,-0.64197,-0.3778,-0.074786,0.10346,0.051161,-0.11798,-0.11147,0.27786,0.28493,0.44621,0.95566,1.1455,0.66158,0.70269,1.2844,1.0742,0.97361,0.60438,0.34442,0.079301,0.12159,-0.23072,-0.96222,-0.99348,-0.69356,-0.72569,-0.59397,-0.64918,-0.8928,-0.6717,-0.81042,-0.95413,-1.4876\n-1.0931,-1.1381,-1.0087,-0.80838,-0.59705,-0.25025,-0.37382,-0.62347,-1.0134,-0.9913,-1.4504,-1.6224,-1.019,-0.34283,-0.54776,-0.37504,-0.11091,0.34516,0.40034,0.38392,0.41889,0.33374,0.51244,0.71311,1.0852,0.8816,0.57205,0.75198,0.9271,0.88479,0.73597,0.44866,0.42145,0.061085,-0.23235,-0.83862,-1.4368,-1.8992,-1.6291,-1.149,-1.0201,-1.2202,-1.6203,-1.4492,-1.3591,-1.4888,-1.6266\n-1.1786,-1.1269,-0.72784,-0.59855,-0.48795,-0.31351,-0.3733,-0.91336,-1.0076,-0.51602,-0.84194,-0.52274,-0.21851,-0.021771,-0.30137,-0.21417,-0.25559,0.14088,0.30837,0.46272,0.5632,0.31566,0.33805,0.91078,0.89412,0.50001,0.51886,0.52551,0.84269,0.71676,0.4729,0.19422,0.15279,0.16701,-0.25858,-1.0305,-1.514,-1.7324,-1.766,-1.4501,-1.1908,-1.2123,-2.1802,-2.1716,-2.185,-2.0403,-2.1004\n-1.8375,-1.2481,-0.59476,-0.26539,-0.46291,-0.27983,-0.35986,-0.69939,-0.78713,-0.48022,-0.49746,0.0143,-0.42707,-0.14128,-0.53762,-0.71213,-0.87918,-0.78234,-0.1637,0.11065,0.22629,0.1965,0.31393,0.86043,0.9518,0.75895,0.6811,0.6052,1.0342,0.30175,-0.096744,-0.14362,-0.374,-0.27692,-0.46155,-0.91541,-0.68566,-1.1118,-1.4144,-1.9171,-1.3618,-1.3022,-2.1126,-2.3865,-2.5129,-2.8744,-3.0382\n-2.1526,-1.2376,-0.73582,-0.67534,-1.0699,-0.92773,-0.32322,-0.45948,-0.43778,-0.59445,-0.47045,-0.59531,-0.39923,-0.12289,-0.50887,-0.97115,-1.2801,-0.60877,-0.61259,-0.28369,-0.24026,0.051355,0.17538,0.48371,0.69482,0.65778,0.54647,0.50515,0.50446,0.17289,-0.26098,-0.48715,-0.90661,-0.59927,-0.45277,-0.851,-1.0016,-0.95712,-0.86325,-1.8425,-1.7398,-1.6891,-1.5635,-2.0088,-2.7921,-3.2787,-3.2021\n-1.5249,-1.0517,-0.75494,-1.0758,-1.6366,-0.98771,-0.30002,-0.71593,-0.65916,-0.76769,-0.71502,-0.62913,-0.2788,-0.1725,-0.63901,-0.97548,-0.84918,-0.53315,-0.95093,-0.15602,-0.14583,-0.18928,-0.085104,0.041022,0.6367,0.40594,0.081994,-0.019839,-0.30457,-0.55015,-0.6783,-0.95589,-0.62618,-0.11224,-0.16437,-0.82939,-1.4638,-1.2784,-1.1233,-1.5479,-1.6328,-1.6901,-1.5761,-1.5525,-2.6458,-2.9701,-3.0023\n-1.8329,-1.0418,-0.49486,-0.72971,-1.0886,-0.41855,-0.24172,-0.69579,-0.97586,-0.73031,-0.65005,-0.6284,-0.60633,-0.67412,-0.73931,-1.1248,-1.1314,-1.1079,-1.0423,-0.61793,-0.52141,-0.53162,-0.24979,0.12176,0.21152,0.58018,0.091977,-0.10417,-0.16688,-0.60658,-0.67313,-0.86574,-0.81863,-0.31404,-0.082534,-0.63008,-1.2669,-1.2215,-0.98655,-1.198,-1.3315,-1.7607,-1.6681,-1.7347,-2.3026,-2.5501,-2.5783\n-2.1698,-1.0149,-0.6981,-0.69256,-0.88071,-1.0123,-0.69121,-0.61612,-0.73426,-0.79338,-0.75348,-0.8031,-0.97097,-0.84501,-0.93557,-1.2977,-1.2712,-1.1471,-0.70013,-0.44834,-0.61301,-0.70963,-0.64724,0.06868,0.23365,0.26636,0.17246,0.06416,0.013554,-0.37758,-0.65695,-0.82259,-1.0693,-0.93119,-0.63885,-0.54834,-0.89286,-0.89273,-0.96467,-1.0029,-1.2996,-1.8025,-1.8618,-1.8799,-2.214,-2.2832,-2.2348\n-1.3685,-1.1174,-0.51005,-0.4781,-0.56903,-1.0914,-0.67928,-0.61387,-0.78621,-0.91539,-0.95321,-0.86206,-0.85115,-1.0631,-1.4821,-1.3768,-1.4953,-1.7451,-0.77993,-0.85333,-1.1496,-0.91359,-0.74976,-0.43299,-0.2175,0.051643,-0.021669,0.31705,-0.020169,-0.47882,-0.72279,-1.151,-1.0497,-1.0402,-0.64425,-0.50734,-0.38586,-0.51792,-0.81962,-1.1348,-1.6511,-1.8588,-1.8432,-1.7984,-1.7468,-1.6959,-1.6312\n-1.2143,-1.0362,-0.46222,-0.31789,-0.30015,-0.66165,-0.85121,-0.71828,-0.7413,-0.94277,-1.0912,-1.0172,-0.9771,-1.717,-1.7064,-1.7629,-1.6367,-1.5893,-1.6451,-1.0622,-1.2711,-0.86741,-0.89751,-0.2403,-0.077073,0.0096664,-0.017504,0.026517,-0.31305,-0.51403,-0.66153,-0.8867,-0.91094,-0.90729,-0.32422,-0.34303,-0.36657,-0.31255,-0.35301,-1.1873,-2.0231,-1.9866,-1.8212,-1.6747,-1.6063,-1.6139,-1.5025\n-1.0587,-0.7987,-0.41355,-0.48681,-0.31936,-0.57877,-0.69011,-0.62535,-0.5102,-0.96152,-1.1286,-0.83653,-1.0133,-1.4783,-1.6104,-1.9738,-1.5343,-1.2742,-1.1855,-0.92259,-1.0056,-1.1023,-0.94435,-0.034472,0.29528,0.49214,-0.29804,-0.36429,-0.59205,-0.71792,-0.7348,-0.86037,-0.85374,-0.77923,-0.33638,-0.44395,-0.53203,-0.47112,-0.90536,-1.258,-1.9034,-1.9587,-1.776,-1.6529,-1.8511,-1.7346,-1.6257\n-0.96706,-0.8022,-0.83988,-0.81665,-0.61474,-0.25896,-0.54592,-0.71752,-0.75693,-1.0473,-1.2381,-0.9716,-0.62671,-1.188,-1.2839,-0.9854,-0.89116,-0.9627,-0.8893,-0.7094,-0.37086,0.010197,-0.20014,-0.039941,0.42642,0.53783,-0.10643,-0.40007,-0.38837,-0.43084,-0.44228,-0.74771,-1.0543,-0.90555,-0.5166,-0.50723,-0.67672,-0.90123,-1.1986,-1.0996,-1.4628,-1.6505,-1.3029,-1.1501,-1.3476,-1.5325,-1.4592\n-0.64684,-0.83381,-0.91935,-0.64901,-0.52143,-0.37663,-0.4093,-0.87104,-0.67375,-0.59745,-0.62576,-0.6147,-0.34979,-0.91979,-0.95909,-0.8037,-0.76573,-1.2441,-1.063,-0.59505,-0.20575,0.0090742,-0.29558,-0.24242,0.16529,-0.17261,-0.35738,-0.35028,-0.066898,-0.40503,-0.35868,-0.59883,-1.308,-1.3208,-1.2504,-1.3288,0.021831,-0.58911,-0.81791,-0.50802,-1.0158,-1.0319,-0.96304,-0.6785,-0.54025,-0.95462,-1.2845\n-0.65053,-0.34136,-0.52816,-0.49637,-0.41665,-0.33392,-0.26389,-0.38151,-0.35183,-0.80544,-0.45705,-0.54875,-0.63758,-0.95086,-1.449,-1.1935,-1.3747,-1.4739,-1.0895,-0.61357,-0.38444,-0.4406,-0.48661,-0.98265,-1.1511,-0.93416,-0.93079,-0.56588,-0.7563,-1.2133,-1.3326,-0.95356,-1.9973,-2.0384,-1.5073,-1.4175,0.2663,0.11471,-0.10863,0.0036364,-0.28757,-0.69217,-0.80856,-0.75156,-0.65831,-0.92399,-1.0003\n-0.60892,-0.091701,-0.56116,-0.47926,-0.44435,-0.29128,-0.2798,-0.2237,-0.42315,-0.47025,-0.50735,-0.75602,-1.2894,-1.5445,-1.7569,-1.4653,-1.3977,-1.3765,-0.76136,-0.46334,-0.56761,-0.6225,-0.84881,-1.2791,-1.9623,-1.1259,-0.89952,-0.77791,-0.67931,-1.689,-1.3686,-1.3864,-1.8247,-1.8513,-1.4501,-0.70145,0.044691,-0.16228,-0.11652,0.15471,-0.37509,-0.63213,-0.54426,-0.51739,-0.60592,-0.71266,-0.75089\n-0.86209,-0.7552,-0.60168,-0.46967,-0.32015,-0.2405,-0.22593,-0.35018,-0.60204,0.13398,-0.62886,-1.0872,-1.4394,-1.3955,-1.4553,-1.1333,-0.98995,-1.0051,-1.0079,-1.001,-0.82557,-0.74917,-0.89757,-1.1675,-1.866,-1.2919,-0.96869,-0.95968,-0.99089,-1.3009,-1.264,-1.2667,-1.4719,-1.5992,-1.2112,-0.45845,-0.34923,-0.3178,-0.27066,0.0095482,-0.45894,-0.2841,-0.28722,-0.0030098,-0.44212,-0.67448,-0.45941\n-0.37719,-0.69596,-0.84048,-0.46324,-0.14606,-0.29982,-0.73014,-1.2765,-1.0933,-0.48358,-0.67603,-1.2341,-1.4376,-0.91198,-0.90119,-1.1022,-0.82847,-0.7753,-1.0497,-1.1964,-1.1334,-0.43232,-0.30825,-0.21022,-0.57202,-0.69218,-0.32805,-0.52892,-0.60108,-0.92438,-0.83172,-0.86747,-0.58597,-1.6138,-0.99415,-0.52928,-0.40448,-0.22554,-0.87851,-0.93632,-0.20842,-0.19197,-0.025682,-0.14645,-0.66757,-0.62103,-0.5121\n-0.70879,-0.81028,-1.0286,-1.0056,-0.097272,-0.38963,-1.2397,-1.574,-1.2854,-0.92642,-0.67122,-0.89659,-1.187,-0.84452,-1.1772,-1.1962,-0.91162,-0.83022,-0.5935,-0.98045,-0.95734,-0.053717,0.57975,0.19807,0.058805,-0.045664,-0.021317,0.036913,-0.55314,-0.68397,-0.15213,-0.44617,-0.7697,-1.6405,-0.31236,-0.30123,-0.75838,-0.84477,-1.2107,-0.70799,-0.11341,-0.055484,-0.13926,-0.20969,-0.73478,-0.96024,-1.2058\n-0.20934,-0.53008,-0.89185,-0.5191,-0.32085,-0.45132,-0.74292,-1.4837,-1.4884,-1.2604,-1.1544,-1.0375,-0.99999,-1.035,-1.5705,-1.7866,-1.5309,-0.75733,-0.68491,-0.95578,-0.72838,-0.13034,0.59289,0.81812,0.31086,0.27239,0.31624,0.32464,-0.021741,-0.17291,0.16108,-0.13141,-0.19054,-0.55665,0.43455,-0.14462,-0.40551,-0.52334,-0.39686,-0.77081,-0.68031,-0.70998,-0.35331,-0.19275,-0.78218,-1.0994,-1.2343\n-0.25848,-0.53339,-1.0622,-1.0521,-0.56088,-0.27132,-0.030094,-0.92719,-1.1547,-1.7196,-1.7678,-1.2073,-1.0038,-1.0096,-1.2777,-1.5884,-1.3964,-0.58747,-0.3849,-0.40374,0.016047,0.12725,0.3402,1.0747,0.79672,0.54065,0.61523,1.1735,3.5816,3.6937,1.5033,0.055751,0.54231,0.7605,0.70015,0.076579,0.041575,-0.2955,-0.0047159,0.043802,-0.26828,-0.62608,-0.10035,-0.2445,-0.97999,-1.3849,-1.4002\n-0.30889,-0.47291,-0.77646,-1.3882,-1.0203,-0.023055,0.0032406,-0.51898,-0.84173,-1.7826,-1.7975,-1.5589,-1.024,-0.39715,-0.17529,-0.59204,-0.72427,-0.28887,-0.20234,-0.36647,0.21708,0.57236,0.60676,1.2957,1.4119,1.6234,NaN,NaN,NaN,NaN,NaN,0.61728,0.33395,0.97822,0.79533,0.43801,0.46569,-0.17605,0.28348,0.44031,0.16142,-0.28883,-0.037703,-0.7137,-1.245,-1.7362,-1.8503\n0.19875,0.10036,-0.47806,-1.0179,-1.1714,0.055918,0.6187,0.48427,-0.427,-1.1894,-1.312,-1.2109,-0.64946,-0.36715,0.20306,0.10424,0.053874,0.16245,-0.032111,-0.15284,0.39126,0.79254,0.99234,2.0662,3.035,4.0503,NaN,NaN,NaN,NaN,1.0218,0.69965,-0.33603,1.6519,2.5357,1.9585,2.5009,1.4261,0.89995,0.35268,0.17384,-0.16172,-0.50328,-0.79714,-1.2531,-1.6782,-2.0313\n0.40643,0.51349,-0.47505,-0.49336,-0.11867,0.077117,0.24867,0.7436,0.10697,-0.38428,-0.4615,0.06354,0.17997,0.061213,0.035547,0.069953,0.265,0.58494,0.39933,0.44739,0.88057,1.6838,1.4112,2.5623,2.728,3.9446,NaN,NaN,NaN,NaN,1.3387,0.78099,1.2848,3.7823,4.1881,4.0983,3.7952,2.3275,1.1995,0.51956,0.38801,-0.11897,-0.25401,-0.78647,-1.2245,-1.6,-2.091\n0.34713,-0.086575,-0.4279,-0.54257,-0.51163,-0.31712,-0.14072,0.39046,0.42681,0.30816,0.16061,0.25125,0.38888,0.31147,-0.108,0.43772,0.88208,1.106,1.2189,1.4939,2.2065,1.6253,1.3798,1.6405,1.0826,2.2466,2.322,1.3253,-0.081421,1.5206,1.7851,2.0243,6.0138,6.4021,5.7433,4.6217,3.9828,2.5342,1.3529,0.62391,0.063782,-0.20168,-0.20326,-0.68746,-1.2242,-1.5594,-2.0395\n0.28009,-0.23542,-0.0009594,-0.37821,-0.81085,-0.43647,-0.080358,0.23248,0.44375,0.69752,0.19832,0.47299,0.29168,-0.64135,-0.36064,0.63717,1.0057,0.66907,0.2757,0.26272,0.93041,1.5735,2.2974,1.9821,2.2185,2.8588,4.2736,4.2947,1.0265,1.9781,2.5886,3.8503,8.7423,7.934,6.8328,4.502,3.3718,2.3989,1.262,0.28203,-0.47703,-0.65878,-0.72366,-1.1881,-1.3428,-1.7571,-2.1551\n0.68746,0.7121,0.47208,-0.0677,-0.48662,-0.42091,-0.16876,0.23404,0.6641,0.70729,0.36747,0.48642,0.34624,-0.36755,-0.00072384,0.68999,1.4043,2.088,-0.23148,-1.4758,0.080453,2.368,4.5164,2.1489,1.8783,1.9958,4.0224,4.3717,2.0863,1.5899,4.0358,6.1142,7.4717,7.6141,6.7352,4.6505,2.8569,1.7477,1.0805,-0.3617,-0.92663,-0.7923,-0.57829,-0.92551,-1.1842,-1.8039,-1.9333\n-0.067206,0.27089,0.59813,0.20885,-0.29376,-0.45367,-0.18293,0.52249,1.0361,1.1177,0.73686,0.65124,0.85486,0.41936,0.93817,1.4617,1.0242,-0.015351,-0.42835,-0.25893,0.29463,2.971,3.4051,1.2657,1.0904,0.7044,3.3789,2.5304,0.68865,1.6506,5.9463,7.6141,7.2904,6.233,4.891,3.5022,2.402,1.5019,0.82189,-0.36808,-0.95385,-0.269,-0.13547,-0.42902,-0.81599,-1.4153,-1.406\n-0.31394,-0.050203,0.092216,0.28226,0.20353,0.15207,0.08402,0.69697,0.89539,0.79643,0.6349,0.95613,1.2636,0.51091,1.8785,1.7203,1.4068,-0.31795,-0.13941,-0.25431,1.2264,1.5518,2.2863,2.0064,1.8732,2.5898,4.7104,0.89863,0.26997,3.0547,10.789,8.7001,6.5929,4.8205,3.3757,2.5901,1.8405,1.4996,0.53572,-0.36251,-0.59419,-0.27877,-0.23611,-0.42889,-0.73188,-0.88965,-1.1219\n0.037284,0.10581,0.030599,-0.033355,0.10403,0.18573,0.26644,0.46153,0.36371,0.40264,0.78285,1.4306,1.8118,1.6108,1.8316,2.0797,1.881,1.355,1.2559,1.4222,1.3239,1.0817,1.8042,2.3734,2.4812,3.1328,2.163,-0.59154,0.34293,3.7987,7.7112,8.2144,4.7538,3.4686,2.2256,1.7851,1.8145,1.8377,0.5495,0.093112,0.094472,-0.43698,-0.22202,-0.41595,-0.61523,-0.68868,-0.87829\n-0.41055,-0.25714,0.24539,0.12208,-0.065639,-0.28131,-0.063371,-0.068234,0.076195,0.46771,0.74524,1.3795,2.1343,1.9722,1.9175,1.6474,1.9729,1.5506,1.7666,2.2656,1.4373,0.92914,0.91401,1.3622,2.1977,2.4496,0.56156,-1.0493,0.25395,3.0057,5.1885,5.3432,4.3585,2.3453,1.2457,1.2887,0.99168,0.94563,0.55949,0.45073,0.28917,-0.39553,-0.22229,-0.28554,-0.53725,-0.15695,-0.21134\n0.21136,0.31223,0.43426,0.37394,0.15284,-0.02042,-0.20573,-0.31666,-0.070131,0.0572,0.3059,0.88377,1.7561,1.5019,1.6546,1.7097,0.045621,1.1692,1.1376,1.1205,0.7596,0.6171,0.2282,0.71475,1.5574,0.27778,-0.90665,-1.6843,0.49427,1.8159,3.8819,3.2483,2.4437,1.2024,0.71462,0.59301,0.65242,0.45295,0.30949,0.56952,0.27235,-0.24856,-0.30409,-0.4669,-0.58119,0.022132,-0.2829\n0.42332,0.268,0.47912,0.20121,0.22387,-0.032441,-0.3004,-0.35729,-0.13892,-0.11572,0.059197,0.36607,0.87528,0.8283,0.8984,1.0506,0.68769,0.5151,1.1045,0.66185,0.085318,-0.35713,-0.99104,0.21051,-0.97695,-2.6957,-0.8181,-0.64671,0.47949,0.86232,1.392,1.4456,1.2546,0.26661,-0.047958,-0.013028,0.14292,0.021188,0.35209,0.31484,-0.13768,-0.36834,-0.44264,-0.81978,-0.97149,-0.61652,-0.38259\n0.17811,0.020173,-0.15014,0.12682,0.23414,-0.25182,-0.61631,-0.70168,-0.37948,-0.3379,-0.45051,-0.28572,-0.085056,0.23075,0.68832,1.2902,1.4035,2.1272,1.0606,-0.31719,-0.37815,-0.87511,-1.942,-1.7105,-1.6304,-4.0586,-0.12818,-0.15557,-0.28339,0.018041,0.92441,1.2216,0.25546,-0.0080805,-0.20802,-0.36754,-0.068032,0.025882,0.084991,-0.1055,-0.39011,-0.38767,-0.49448,-1.0333,-1.2837,-1.2758,-0.74494\n-0.17042,-0.40901,-0.71663,-0.75217,-0.29139,-0.3128,-0.29532,-0.24861,-0.29407,-0.75011,-0.88406,-0.99946,-0.66352,-0.59737,0.92,1.4965,2.1139,2.9957,2.5019,0.85975,0.38695,0.17275,-0.6201,-0.8694,-0.62534,-1.6025,-0.46279,-0.86621,-0.28722,0.18837,1.0259,1.0175,0.44994,0.061547,-0.42009,-0.69364,-0.2726,0.089055,-0.19127,-0.2702,-0.038711,0.039231,-0.27798,-0.76254,-1.3258,-1.3802,-1.0966\n0.32788,-0.37987,-1.0983,-0.66728,-0.25836,-0.088382,0.077677,0.022355,-0.34949,-0.91497,-1.3608,-1.0403,-0.86468,-0.93219,-0.264,0.29066,0.79118,1.5483,2.1957,0.53898,0.694,0.75043,-0.21906,-0.64401,-0.96779,-1.5338,-1.949,-1.2342,-0.80891,-0.49273,0.077113,0.35474,-0.020099,-0.38755,-0.87126,-0.60666,-0.67232,-0.35182,-0.60466,-0.44941,0.11614,0.19004,-0.10011,-0.43102,-1.1031,-1.0517,-0.88855\n0.41613,-0.11954,-0.80566,-0.69829,0.13096,0.31467,0.2224,0.015965,-0.55447,-0.98364,-1.4133,-1.144,-1.2237,-1.1194,-0.68143,-0.14987,0.33205,0.58823,0.40821,-0.47118,-0.43098,0.0096436,-1.3503,-1.1041,-1.384,-1.7596,-1.9274,-1.3797,-1.1458,-0.95495,-0.71854,-0.57038,-1.0961,-1.4116,-0.94938,-0.66643,-0.49818,-0.3721,-0.34371,-0.37817,-0.05991,0.042032,-0.46167,-0.46489,-0.82954,-0.72632,-0.61269\n0.2473,-0.072792,-0.38876,-0.19776,0.14613,0.34994,0.25346,-0.062868,-0.83257,-0.85825,-0.97434,-0.89496,-0.91067,-1.1425,-0.74246,-0.33785,-0.19314,-0.48754,-1.449,-1.7378,-2.3023,-1.9546,-1.7332,-1.5074,-1.8404,-2.3645,-2.1229,-1.9419,-1.69,-1.4913,-1.2473,-1.2634,-1.6245,-1.8426,-1.8163,-0.41328,-0.042096,0.50126,0.10039,-0.32617,-0.24284,-0.14694,-0.65869,-0.64171,-0.61982,-0.42905,-0.1942\n0.19313,0.029864,-0.14011,0.46373,0.39225,0.21544,-0.043485,-0.26179,-0.74134,-0.4686,-0.37454,-0.47388,-0.79458,-1.1098,-0.8466,-0.66778,-0.90992,-1.2477,-1.9492,-3.5071,-3.4088,-2.3814,-1.68,-1.9368,-2.6527,-2.8504,-2.7307,-2.6473,-1.9348,-2.0802,-1.6735,-1.4524,-1.2152,-1.3,-0.99335,-0.39587,0.15418,-0.0035,0.29631,-0.088841,-0.14972,-0.24085,-0.52417,-0.25963,-0.19775,-0.24574,-0.35338\n0.10203,-0.10831,0.017104,0.73239,0.73356,0.37793,0.080192,0.10363,0.11112,-0.050642,-0.34539,-0.37872,-0.73243,-0.77216,-0.71465,-0.82982,-1.1918,-1.7014,-2.4708,-2.845,-2.7103,-2.056,-1.6507,-2.3543,-3.1854,-3.2749,-3.723,-3.4716,-2.2201,-2.5929,-2.2091,-1.3779,-1.0716,-0.89287,-0.23847,-0.13538,0.05668,0.23955,0.27387,0.078547,-0.087173,-0.078787,0.11035,0.41998,0.15067,-0.30573,-0.061774\n0.44201,0.43414,0.57152,0.82083,0.8185,0.57984,0.47369,0.36973,0.54547,0.20979,-0.2449,-0.53836,-0.95338,-0.60039,-0.72711,-0.99219,-1.25,-1.5203,-1.5941,-2.2082,-2.3648,-1.7359,-1.7286,-2.4306,-2.874,-3.0508,-3.6702,-3.8841,-2.9323,-2.6261,-2.5415,-1.8968,-1.212,-1.0725,-0.71394,-0.20729,0.32444,0.42272,0.17747,-0.050128,0.16482,0.20996,0.59918,1.075,0.30039,0.26209,0.40454\n0.83793,0.77213,0.75834,0.96382,1.1799,0.82761,0.95157,0.80671,0.75381,0.60834,0.31307,-0.46274,-0.82328,-0.85008,-1.3145,-1.3644,-1.2549,-1.3744,-1.4283,-1.4458,-1.7587,-1.6577,-1.8836,-2.1046,-2.6146,-2.6341,-3.3027,-3.6584,-3.218,-2.9305,-2.5794,-1.7902,-1.2475,-1.0846,-0.98126,-0.33764,0.31124,0.22348,-0.015004,-0.11715,-0.001895,0.029744,0.3377,0.69669,-0.14662,-0.2384,0.22976\n0.66383,0.8908,0.97996,1.1204,0.94493,0.33292,0.9083,0.43667,0.65667,0.66677,0.4779,0.23759,-0.69907,-0.85386,-0.83311,-1.3177,-1.3747,-1.5428,-1.9746,-1.8686,-1.6655,-1.7121,-1.9379,-1.8989,-2.386,-3.1323,-3.4678,-3.6095,-3.4729,-3.2832,-2.3654,-1.623,-1.1936,-1.3583,-0.93735,-0.23876,0.40491,0.029664,0.053609,-0.42004,-0.40127,-0.29402,-0.023408,-0.12648,-0.043441,-0.31818,-0.34466\n1.4828,1.4122,1.3803,1.275,0.86835,0.56108,0.35143,0.11773,0.5196,0.23419,0.27064,-0.063237,-0.60736,-0.87283,-0.80911,-1.1667,-1.4534,-1.9063,-2.5293,-1.8691,-1.8818,-1.8898,-1.7252,-1.9193,-2.4597,-2.9606,-3.2363,-3.3004,-3.0625,-2.8373,-1.7037,-1.7289,-1.3406,-1.1731,-0.83398,-0.57329,-0.069601,0.027653,0.19017,0.0024395,0.013329,0.10266,-0.087339,-0.032549,-0.25578,-0.95017,-0.65968\n1.7223,1.1323,1.1803,1.2662,1.1578,0.71905,-0.032785,-0.57426,-0.69929,0.022342,-0.39888,-0.56319,-0.58225,-0.69716,-1.0261,-1.3775,-1.7544,-2.0511,-1.9959,-1.4921,-1.8,-1.6183,-1.6731,-2.0581,-2.2568,-2.4334,-2.5408,-2.555,-2.5996,-2.2412,-1.8156,-1.7657,-1.1294,-0.94556,-0.70179,-0.73057,-0.37267,0.097404,0.31436,0.24479,-0.13357,-0.046127,0.35199,0.19854,-0.22762,-0.85245,-0.6026\n1.393,1.3594,1.296,1.0784,0.90324,0.95816,0.49894,-0.42274,-0.9422,-0.68342,-0.96056,-0.62571,-0.82322,-1.376,-1.4917,-1.5387,-1.1604,-2.1485,-1.7087,-1.2922,-1.3872,-1.4553,-1.5178,-1.8365,-2.2603,-2.2854,-2.581,-2.5111,-2.3315,-1.8973,-1.89,-1.7934,-1.1361,-0.97166,-0.7362,-0.67105,-0.52231,0.056738,0.41185,-0.70009,-1.0135,-0.65929,-0.36025,0.006918,-0.15583,-0.53244,-1.0021\n0.56886,0.62674,1.0544,0.54936,0.4024,1.1428,0.87452,0.15439,-0.39788,-1.0904,-1.0345,-0.99532,-1.1009,-1.4922,-1.8209,-1.9547,-1.9747,-2.1198,-1.4731,-0.85211,-1.0766,-1.3932,-1.1653,-1.4445,-2.0074,-2.1417,-2.4684,-3.0511,-2.6391,-1.9486,-1.9795,-1.9364,-1.3347,-1.0611,-1.0307,-1.0237,-0.64897,-0.040024,0.39408,0.1787,-0.070605,-0.19721,-0.53239,-0.43068,-0.45609,-0.73919,-1.3119\n0.28611,0.39524,0.95379,0.26631,-0.21823,0.70092,0.56085,-0.46141,-0.42147,-0.67523,-1.0952,-1.6877,-1.9765,-2.0078,-2.4087,-3.0398,-2.3413,-1.3008,-0.75244,-0.94394,-0.94747,-1.0272,-1.0791,-1.4278,-1.6865,-1.7856,-2.2377,-2.787,-3.0072,-2.6427,-2.2416,-2.077,-1.5097,-1.0145,-1.1138,-1.0465,-0.48814,0.040771,0.43501,0.54914,0.17685,-0.11701,-0.32238,-0.13158,-1.0289,-1.6368,-1.6522\n-0.16601,0.29486,0.84373,0.0045815,-0.48348,-0.41185,-0.50608,-0.63739,-0.16364,-1.0716,-1.5754,-1.4715,-1.1565,-3.1809,-3.4611,-3.5427,-1.0658,-0.70294,-0.6914,-0.72718,-0.16456,-0.60282,-1.1368,-1.3599,-1.3639,-1.6028,-2.0915,-2.4152,-2.9815,-2.8518,-2.3362,-2.2769,-1.8486,-0.91768,-0.9153,-0.84572,-0.59377,-0.18516,0.27152,0.29645,-0.026158,-0.044611,-0.28058,-0.15423,-0.9902,-2.3455,-2.2743\n-1.1767,0.62048,0.33131,-0.031773,-0.53467,-1.1448,-0.72568,-0.79398,-1.5736,-0.6199,-1.501,-1.3729,-2.4078,-2.4696,-1.4888,-1.9125,-0.38641,-0.94492,-1.0684,-0.55413,-0.24822,-0.45009,-0.51817,-0.91768,-1.0932,-1.4807,-2.1709,-2.5365,-3.0311,-2.6306,-1.8429,-1.4967,-1.6256,-1.109,-0.75305,-0.79122,-0.9146,-0.45056,0,0.14233,0.039444,0.22532,-0.026584,-0.81102,-1.619,-1.9963,-1.8622\n0.1588,0.58459,-0.24896,-0.13295,-0.34879,-0.65102,-0.62725,-1.3464,-2.4344,0.31284,-1.2947,-1.0335,-1.4407,-2.0439,-0.26732,-1.087,-0.52548,-0.17646,-0.41846,-0.63544,-0.28956,-0.16873,-0.34209,-0.60665,-0.64357,-0.81443,-1.2494,-2.5329,-2.6333,-2.2008,-1.296,-1.0763,-1.2716,-1.2626,-1.0059,-0.88313,-1.0162,-0.8426,-0.29657,0.24017,-0.089192,-0.38771,-0.41921,-1.0041,-1.1889,-1.3817,-1.3941\n-0.54813,0.73896,0.13619,-0.21268,-0.49478,-0.93524,-1.4787,-1.8301,-2.1022,0.0055695,-2.499,-1.5703,-1.4334,-1.4007,-0.61538,-1.0558,0.20693,-0.75072,-0.0094128,0.30419,0.47867,0.34412,-0.34533,-0.66559,-0.71737,-0.86812,-1.5501,-2.0679,-2.0583,-1.8135,-1.2709,-0.94698,-0.68653,-0.83932,-1.023,-1.3175,-1.1368,-1.1168,-0.56652,-0.30492,-0.56702,-0.45085,0.15359,-0.38229,0.20511,-0.94192,-1.2889\n-0.029037,0.01438,0.44654,0.2516,-0.098134,-0.76431,-1.2441,-0.7566,-0.43647,-1.019,-1.9617,-2.0944,-2.3278,-2.9687,-0.90827,0.42346,1.2846,-0.59772,-0.52937,0.35099,0.25517,-0.17591,-0.51223,-0.94098,-0.93833,-1.2987,-1.4455,-1.6644,-1.7098,-1.4375,-1.1883,-0.87428,-0.80677,-1.0248,-1.0885,-1.3368,-1.3118,-1.1113,-0.64771,-0.63285,-0.69195,-0.24933,0.25477,0.22469,-0.013717,-1.0645,-1.3428\n-0.27987,0.16302,0.12105,0.092047,-0.76389,0.23832,-0.90895,-0.57135,-0.70473,-0.65268,-0.037807,-1.8617,-2.0838,-0.73349,0.56921,0.11737,0.47524,-0.34203,-0.19744,0.96061,0.56069,-0.16674,-0.74754,-0.66217,-0.55989,-1.025,-1.8236,-1.6135,-1.5233,-1.1108,-0.88171,-0.99353,-1.3143,-0.89815,-1.1092,-1.2162,-1.2501,-0.76092,-0.51938,-0.40267,-0.21175,-0.10843,0.067725,-0.5798,-1.3347,-1.6622,-1.9496\n-1.3291,-0.4306,-0.9157,0.12303,0.19027,0.031664,-0.63319,-0.63681,-1.0703,-0.77777,-0.9577,-1.1911,-0.29791,-0.8495,-0.60958,-0.6082,0.26245,-0.44872,-0.36632,0.14866,0.09644,-0.42413,-0.74424,-0.88865,-1.0175,-1.1959,-1.3192,-1.0003,-1.0284,-0.79096,-0.73255,-1.0074,-1.2007,-0.59622,-0.3932,-0.87941,-0.94491,-0.64922,-0.4976,-0.34059,0.0075388,0.048842,-0.12171,-0.45372,-0.934,-1.4845,-1.5723\n-0.24427,-0.19918,-0.24692,1.0202,-0.30696,-0.4124,-0.70229,-0.62053,-0.41487,-0.066169,0.23036,-0.25069,0.0395,-0.30261,-0.95451,-0.39475,0.33452,-0.023063,-0.61967,-0.43953,-0.39716,-0.70003,-1.0427,-1.0944,-0.95529,-0.75523,-0.49101,-0.4981,-0.52414,-0.6845,-0.83323,-1.238,-0.72882,-0.43497,-0.18766,-0.79424,-0.7097,-0.72224,-0.60678,0.1133,0.91698,0.50421,-0.12165,-0.078354,0.23345,-0.4475,-1.2412\n-0.37941,-0.73606,-0.86414,-1.3161,-1.9858,-1.8264,-1.5165,-1.8248,-1.2255,0.034734,0.4414,0.67151,-0.034371,-1.4731,-1.3319,-0.099499,0.67283,-0.62474,-0.82819,0.38531,0.28257,-0.65195,-0.51633,0.066747,-0.44501,-0.67491,-0.51702,-0.32996,-0.64268,-0.85975,-0.88008,-0.74797,0.28068,0.016234,0.041216,-0.18347,-0.16105,-0.40974,-0.52252,-0.07832,0.1778,0.13973,-0.07061,0.11188,0.20584,-0.073858,-0.67335\n-0.74907,-1.2932,-0.96486,-1.531,-1.867,-1.8101,-2.6687,-3.1972,-2.9967,-1.5343,0.21931,0.29693,-0.84631,-1.5871,-1.6206,-1.2576,-0.40292,-1.2418,-0.82812,-0.15753,0.53524,-0.42767,0.57514,1.2174,1.213,0.61715,0.17154,0.040497,0.39841,0.1604,-0.20569,-0.53914,-0.14017,-0.027987,0.34965,-0.28873,-0.18759,-0.18478,-0.82955,-0.98262,-0.78906,-0.99259,-0.90959,0.25791,0.5592,-0.119,-0.60515\n-0.81557,-1.4183,-1.8859,-2.1652,-2.6824,-1.9433,-0.94697,-2.2896,-1.9941,-1.098,-0.28511,-0.079033,0.22542,0.52085,0.66147,-1.0339,-1.9879,-0.75806,-0.46161,0.048358,0.93428,0.16243,0.49534,0.99734,1.326,1.0214,0.36272,0.60017,0.96416,1.0546,0.91937,0.091709,0.2478,0.69728,0.53822,-0.19191,-0.32801,-0.22559,-1.1117,-1.1743,-0.98197,-0.66738,-0.40275,-0.58709,-0.29328,-1.0287,-1.4209\n-0.74426,-1.0153,-1.8381,-4.0255,-3.9226,-3.2619,-3.4236,-2.656,-0.1667,-0.74393,0.20976,-0.090343,0.58068,2.2464,0.49914,-1.2976,-1.0258,-0.54369,-0.67202,0.14129,0.018179,0.59224,0.85726,0.71044,0.8922,1.2155,0.8557,1.0211,1.1297,1.3223,1.5425,0.46958,0.022896,0.46374,0.58324,0.36867,-0.25873,-0.54834,-0.43862,-0.93498,-0.67815,-0.025866,-0.4357,-1.144,-0.98137,-0.96197,-1.1645\n-1.5299,-1.6319,-1.1793,-0.95773,-0.97872,-0.36421,-2.395,-2.726,-1.0611,0.60053,1.9823,0.024438,0.4506,0.02984,-0.34218,-1.4219,-1.7026,-0.77472,-0.60052,-0.32954,-0.50918,-0.0046072,1.039,0.41954,0.64001,0.93853,1.1547,1.2816,1.2424,1.5462,1.2302,0.32852,0.086582,0.34175,0.63106,0.63913,-0.10498,-0.26993,-0.15862,-0.33411,-0.20975,0.040875,-0.71274,-1.0191,-1.0223,-1.051,-1.0983\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061211-070709.unw.csv",
    "content": "0.093719,-0.27835,-0.97562,-0.83092,-0.95695,-0.53548,-0.3463,-0.75282,-1.0951,-1.4386,-1.1914,-1.8317,-1.667,-1.7595,-1.55,-1.6837,-2.0321,-1.9739,-2.1465,-2.3918,-2.2326,-1.9247,-1.7679,-1.9431,-1.9873,-1.1256,-0.89702,-1.1141,-1.5215,-1.7114,-1.7276,-1.6359,-2.5876,-3.3754,-3.4964,-3.1752,-3.4602,-3.8944,-3.2712,-3.2921,-3.1152,-2.4966,-1.9753,-1.6123,-1.4749,-1.6267,-1.8146\n0.10173,-0.42978,-0.85262,-0.50806,-0.51187,-0.44598,-0.50441,-1.1751,-1.5035,-1.4578,-0.9709,-0.82939,-1.2529,-1.396,-1.2077,-1.0107,-1.7896,-2.2069,-2.4705,-2.6489,-2.8842,-1.949,-1.4896,-1.2386,-1.3616,-1.2106,-1.0304,-1.4335,-1.6381,-1.3684,-1.0927,-1.5578,-2.4778,-3.2606,-3.1352,-2.9674,-3.3265,-3.4807,-2.8898,-2.2376,-2.4075,-2.3246,-1.5843,-1.3479,-1.6573,-1.9559,-1.9977\n0.38826,-0.12308,-0.8894,-0.80573,-0.62185,-1.0244,-1.021,-1.5615,-2.3821,-1.8481,-1.4898,-1.2326,-1.4202,-1.3734,-1.3868,-1.3777,-2.1849,-2.3367,-1.9847,-1.7287,-1.806,-2.017,-1.6729,-1.1688,-1.2989,-0.98884,-0.71688,-0.81602,-1.2632,-0.91738,-0.93457,-1.8211,-2.182,-2.348,-2.4168,-2.609,-3.2469,-3.3805,-3.2394,-2.8322,-2.613,-2.1627,-1.5818,-1.4503,-1.8806,-2.3873,-2.2727\n0.1755,-0.23959,-1.1919,-0.80301,-0.3802,-0.64676,-0.86399,-1.4591,-1.71,-1.7126,-1.5359,-1.5097,-1.6372,-1.1573,-1.3134,-1.5191,-1.7975,-2.1458,-2.1634,-1.5339,-1.3231,-1.227,-0.93148,-0.79429,-0.85564,-1.1513,-1.648,-1.7297,-0.85283,-0.73941,-1.2751,-1.5121,-1.6157,-1.8044,-2.0823,-2.4454,-2.7945,-2.938,-2.3812,-2.2044,-1.6955,-1.2642,-1.2536,-1.4265,-1.8254,-2.203,-2.3726\n-0.076254,-0.35307,-1.326,-1.104,-0.53002,-0.72468,-0.86001,-1.0812,-1.3313,-1.6048,-1.5459,-1.6118,-1.7041,-1.2443,-1.2387,-1.1743,-1.0149,-1.8504,-2.3846,-2.6463,-2.0212,-0.71805,-0.25707,-0.41532,-0.56534,-1.3826,-2.3674,-1.9982,-0.28761,-0.037898,-1.1522,-1.3721,-1.4904,-1.3319,-1.6881,-2.2638,-2.6624,-2.5691,-2.2215,-1.6225,-0.94842,-0.72167,-0.81997,-0.7998,-1.183,-1.5526,-1.8215\n-0.41299,-1.3128,-1.6825,-0.98791,-1.1043,-1.2645,-1.3666,-1.3708,-1.2664,-1.3102,-1.4568,-1.7136,-1.4211,-1.1493,-1.14,-1.1136,-1.1634,-1.354,-1.67,-1.9075,-1.5526,-0.33909,0.044322,-0.36585,-0.47516,-1.3305,-2.0303,-1.7652,-0.8563,-0.72957,-0.89022,-0.70592,-0.66237,-1.2034,-1.4326,-1.8509,-2.09,-1.9963,-1.7336,-1.0827,-0.69603,-1.0338,-1.2287,-0.88097,-1.0945,-0.96437,-1.0257\n-0.45108,-0.80563,-1.3967,-1.0439,-0.58372,-0.83776,-1.4719,-1.7135,-1.3268,-1.1003,-1.7517,-1.6985,-1.1115,-1.1763,-1.4442,-1.1941,-1.5854,-1.8671,-2.0839,-1.9945,-1.2833,-0.52844,-0.037762,-0.35145,-0.75899,-1.1517,-1.2492,-1.1703,-1.0428,-0.7344,-0.78955,-0.59753,-0.32213,-0.28546,-0.60461,-0.95751,-1.0916,-1.0241,-1.0057,-0.67274,-1.153,-1.6046,-1.4521,-1.1582,-0.85456,-0.62168,-0.47682\n-0.12487,-0.22352,-1.2337,-1.0387,-0.47059,-0.95808,-1.1573,-1.5117,-1.8487,-1.6709,-1.9622,-1.8248,-1.17,-0.74005,-0.78834,-1.0623,-1.6951,-1.78,-1.3504,-1.1723,-0.84656,-0.65646,-0.49422,-0.13536,-0.37211,-0.37003,-0.33584,-0.082921,-0.52473,-0.35637,-0.44241,-0.78458,-0.46536,-0.23225,-0.35219,-0.6195,-0.82445,-0.93802,-0.83264,-0.37104,-0.19553,-0.6029,-0.5182,-0.57853,-0.85177,-0.95343,-0.71295\n-0.50865,-1.3061,-1.8401,-0.9226,-0.76489,-0.9188,-0.71422,-0.95071,-0.99546,-1.182,-1.3449,-1.2941,-1.1551,-0.25417,-0.091582,-0.47866,-0.74328,-0.47729,-0.054965,-0.31279,-0.42519,-0.35235,-0.19141,0.066349,0.16174,0.34982,0.39835,0.52216,0.023873,-0.36762,-0.31356,-0.37546,-0.23184,-0.046218,-0.12018,-0.47187,-0.56979,-0.25523,-0.36673,-0.25285,-0.023067,0.32829,0.40735,0.0012836,-0.541,-0.86327,-0.77956\n-0.67887,-0.95007,-1.4798,-1.1698,-0.52724,-0.42881,-0.45176,-0.3727,-0.2443,-0.21421,-0.85829,-0.43807,-0.78504,0.061138,0.16639,-0.076393,-0.25153,0.074229,0.41224,-0.3413,-0.43878,0.34468,0.68917,0.93701,0.9437,0.9952,0.67158,0.52179,0.61561,-0.022067,-0.34095,-0.044567,0.35163,0.44372,0.45983,0.42053,0.53797,0.41085,0.12918,0.097763,0.22497,0.29894,0.27598,-0.051747,-0.38499,-0.5636,-0.52642\n-0.14581,-0.2757,-0.75063,-0.7231,-0.49602,-0.45516,-0.56848,0.11949,0.036146,-0.63339,-1.0356,-0.45098,-0.2926,0.4785,0.71721,0.56691,-0.01476,0.057992,0.74646,0.57721,-0.060527,0.67948,1.385,1.4563,1.2602,1.5579,1.6184,1.1431,1.025,0.92329,0.75168,0.85722,0.78167,0.75721,0.68663,0.79436,0.77251,0.59852,0.45108,0.59804,0.42085,0.47701,0.38456,0.19672,0.18702,-0.073894,-0.18831\n0.32213,0.28376,-0.066025,-0.34433,-0.55736,-0.10491,-0.64984,-0.093027,-0.0062304,-0.38515,-0.49276,0.032428,-0.12129,0.54952,1.0492,0.78518,0.097043,-0.05541,0.24628,-0.24631,-0.82552,0.43034,1.0101,1.0663,1.8695,2.7338,2.7394,2.2036,1.8332,1.3665,1.2154,1.0671,0.96971,0.92803,0.85049,0.92393,1.1553,1.0751,0.88822,1.1394,1.0947,1.0476,0.87867,0.73575,0.61648,0.30351,0.046638\n0.29091,-0.0062051,-1.3211,-1.8283,-1.7729,-0.46754,-0.32386,-0.04996,-0.079328,-0.060379,-0.34957,-0.71353,-0.54079,0.47641,0.91081,0.92131,0.7776,0.92544,0.31876,-0.24773,-0.11908,0.74763,1.4192,2.3949,2.8547,2.8815,2.7594,2.708,2.3386,1.9775,2.0334,1.4088,1.1038,1.0825,1.1589,1.3743,1.7492,1.8035,1.3441,1.2264,1.3237,1.2803,1.1383,0.94499,0.94112,0.7387,0.014445\n0.4136,-0.35692,-0.62402,-0.57574,-0.82427,-0.36435,-0.10255,-0.24023,-0.17344,0.29858,-0.11547,-0.76314,-0.040133,1.0151,1.2629,1.3703,1.7365,1.7102,1.33,0.81008,0.6335,0.75444,1.4299,2.818,3.0972,2.3129,2.2744,2.6553,2.6239,2.4251,2.5389,2.3574,1.9438,1.9052,1.9805,2.1242,2.2976,2.5788,2.3371,1.8094,1.7046,1.9904,1.9027,1.5797,1.452,0.5409,-0.20461\n-0.30871,-0.2978,-0.081497,-0.083576,-0.639,-0.38608,-0.49747,-0.34242,0.75719,0.96617,0.40756,0.94046,1.5357,1.9495,2.0209,2.0403,2.1183,1.5836,1.2788,1.362,0.97078,1.3773,2.2828,3.1011,3.6103,3.4934,2.874,2.7459,3.1559,3.1847,3.0021,2.6243,2.4601,2.7214,2.5502,2.3812,2.5685,2.9383,3.1777,2.893,2.2762,2.3783,2.5333,2.1013,1.8409,1.4282,1.0333\n0.716,0.046839,-0.35732,-0.043217,0.053451,0.33828,0.40611,-0.1201,0.64163,0.51839,0.13402,0.45785,0.74831,0.78028,1.1808,1.4827,1.617,1.4954,1.4844,1.6156,1.8572,2.2822,2.7409,3.1071,3.9155,3.9356,3.3497,3.1148,3.5756,3.5485,3.1588,2.8263,2.9545,3.0777,3.2579,2.7377,2.8783,3.2531,3.4522,3.5715,2.9383,2.7449,2.5659,2.3204,2.148,2.3391,2.3526\n1.3225,1.0548,0.59082,0.21825,0.1059,-0.12381,0.65819,0.71381,1.1017,1.0046,0.87896,1.3775,1.1994,0.63265,0.74465,1.0191,1.2972,1.9985,1.9225,1.1912,0.52534,1.7891,3.1471,2.9943,3.8955,4.3266,4.322,3.9495,3.5541,3.376,3.595,3.4729,3.6397,3.7839,3.5283,3.1552,3.6301,3.4211,3.3486,3.81,3.7198,3.3083,3.0711,2.9144,2.4407,2.4465,2.6342\n2.7652,1.8689,0.61813,-0.15004,-1.9124,-1.5641,0.056351,1.3429,1.9154,2.3175,2.6184,2.4388,2.2018,2.1252,1.8142,2.1561,2.3775,2.5335,2.5884,2.2344,1.7476,1.8643,2.4172,3.4008,4.2695,5.0826,5.0417,4.7937,4.3284,4.0301,4.0047,4.0601,4.0835,3.9742,3.8139,3.7232,4.3887,3.7639,3.3805,3.186,3.2873,3.5407,3.4807,3.2616,3.1269,2.8841,2.7409\n1.2182,1.332,0.9193,1.2563,1.1357,3.6918,3.5065,1.9531,2.2267,2.7625,3.2443,2.562,1.9539,2.0085,2.6151,3.3419,3.3393,3.2997,3.5621,3.9779,2.9089,1.7617,2.3088,3.1972,3.7576,4.8552,5.2631,5.2118,5.0331,4.3791,4.1321,4.039,4.0496,3.654,3.6001,3.7952,3.6368,3.3032,2.6038,2.4633,2.5363,3.1837,3.6816,3.465,3.2208,3.0379,2.7048\n1.8823,1.713,1.2915,1.2579,2.4565,2.9415,2.5465,1.9005,2.7019,2.9924,2.7403,2.6413,2.3764,2.3046,2.786,3.3934,3.17,2.9107,3.3965,3.8807,3.9118,3.8665,3.3877,4.0701,4.2664,4.8906,5.1804,5.6269,5.891,5.6558,4.7592,4.4546,4.2866,4.2685,4.7619,4.4949,4.0581,3.9421,2.9649,2.7036,2.972,3.5612,3.8325,3.8617,3.6298,2.911,2.5\n1.6636,1.8075,2.0005,2.5678,3.8343,4.9778,4.036,2.0021,2.5704,3.4645,3.7388,3.5551,3.195,3.3865,3.7446,3.4488,3.3133,3.3445,3.5416,4.0779,4.6376,4.328,4.1063,4.1166,4.9638,5.8571,6.1942,6.8316,6.5581,6.35,6.145,5.9471,5.2079,4.9587,4.7449,5.2451,4.9891,4.5303,3.884,3.5245,3.6794,3.9847,3.7867,3.6995,3.6236,3.3446,2.8256\n2.3406,2.9621,2.2742,1.7237,1.4377,2.0279,2.6863,1.8383,3.7974,4.1519,4.0203,4.0142,4.3899,5.4471,5.0922,3.761,4.3802,4.6025,4.0933,4.9706,5.2601,5.06,4.6848,5.0476,5.6864,6.2463,6.8208,7.2154,6.8748,6.6014,6.2866,6.0496,5.9809,5.8462,5.6253,5.5833,5.4881,4.8446,4.398,4.1548,4.0356,4.0133,3.7631,3.6826,3.5951,3.5803,2.9423\n2.7824,3.6998,3.0579,1.7357,0.97206,0.7546,0.72838,2.7626,5.3724,4.3663,3.8894,4.7206,5.4103,5.8474,5.7307,5.5919,5.9406,5.7011,5.3538,5.3038,5.6812,5.4991,5.1508,6.3086,7.1235,7.029,7.3596,7.3141,7.18,6.7147,6.4825,6.3287,6.2012,6.1141,6.0476,5.7738,5.4406,5.2111,5.1477,5.0029,5.1253,4.6794,3.8653,3.993,4.1179,3.7318,3.0035\n3.9826,3.5566,2.497,1.6054,0.76322,1.1954,2.2513,3.26,3.8661,4.3637,4.6677,5.6041,6.5571,6.4979,6.0351,5.6801,5.987,6.1457,5.7084,5.2708,5.3889,6.0355,6.7045,7.4805,7.7536,7.3692,7.45,8.089,7.7664,7.6711,7.6773,7.3617,7.4206,7.0685,6.0774,5.7565,5.3895,5.3018,5.4436,5.4173,5.1991,4.6364,4.198,4.1545,4.2641,4.0038,3.8917\n2.9914,2.2283,2.1037,2.3328,2.1652,2.3251,3.2154,3.5379,3.6495,4.2974,4.8951,5.6757,6.4178,6.4593,5.6642,4.8694,4.8494,6.2445,6.745,6.6487,6.8206,7.3201,8.7828,8.4562,8.5122,9.4931,9.5196,9.7444,9.5236,9.1284,8.7637,8.4835,7.8115,6.7366,6.0064,6.323,6.47,5.9144,5.7285,5.7391,4.6904,4.1475,4.236,4.5017,4.4171,4.1256,4.156\n3.8524,3.9766,3.0835,2.1584,1.5458,1.9294,2.987,4.0169,4.4464,4.2129,4.8658,5.5308,6.1508,6.272,5.8152,5.4049,6.5342,7.5905,7.5504,7.6864,8.3724,9.7346,9.8698,10.829,NaN,11.278,11.285,10.233,9.9043,10.633,10.483,8.6162,7.4267,6.5811,6.3163,6.5734,6.9652,6.6276,6.4154,6.5974,5.4652,4.6741,4.7175,4.6362,4.3292,3.982,3.6705\n3.6023,3.2541,2.5881,1.6249,1.664,2.2038,3.2882,3.9641,4.3665,4.7605,5.01,5.3773,5.3348,5.4391,6.0408,6.7013,7.0422,8.0166,9.1629,9.99,10.382,11.041,12.075,13.351,NaN,NaN,NaN,NaN,11.1,12.154,12.609,12.276,11.153,8.9231,8.146,7.2627,6.9673,6.542,6.3443,6.4204,5.8003,5.1229,5.175,4.9004,4.4304,3.9615,3.9549\n3.1864,2.0856,0.83224,1.5553,2.0524,1.96,2.6734,3.6496,4.1921,5.1711,5.0614,4.6664,5.0422,5.9374,7.2521,8.0812,8.2054,8.9293,10.293,11.364,11.9,13.288,14.719,16.194,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.976,13.784,11.419,9.5119,7.843,6.9605,7.1618,6.6968,5.4036,5.6644,5.7577,5.8453,5.1536,4.6195,4.6313,4.4647\n1.3344,0.82274,1.19,1.725,1.441,1.9178,3.0357,3.7269,3.6057,4.2025,4.3067,4.4964,6.0382,6.865,7.7582,8.4286,8.9946,10.011,10.772,11.652,12.146,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.349,12.035,9.57,8.8349,8.1318,7.908,7.7804,7.4258,6.8875,6.8121,6.2649,5.2136,4.7053,4.6138,4.3529\n-0.94126,0.13516,1.436,0.53446,1.3298,2.1853,2.6649,3.1418,3.3702,3.8849,4.2828,4.7734,6.0219,7.1116,7.7501,8.1784,9.6756,10.892,11.776,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.025,9.3335,9.4775,9.1534,8.7907,7.79,7.2873,7.5445,6.3602,5.4476,4.9546,4.4426,4.1723\n2.5454,1.2408,1.8899,2.2909,2.0427,1.695,2.0142,2.12,2.5052,3.5635,5.2221,6.0994,6.4701,7.5197,8.9277,9.8189,10.907,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.4016,9.1462,10.494,10.035,8.9742,7.9734,8.2377,8.5012,6.7731,5.9805,5.3226,4.3887,4.3468\n2.8793,1.7276,1.3249,3.6245,4.4741,3.959,3.5095,3.8911,4.0394,4.4605,5.7179,6.9168,7.1255,7.4886,9.0002,10.842,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.898,10.652,8.9,7.7797,7.9681,8.5186,7.3879,6.5349,5.2741,4.4154,4.5851\n4.5947,3.7245,2.8143,3.4379,4.5701,4.4117,3.8918,3.5394,4.1761,5.5496,6.712,7.1819,7.0976,7.7548,10.461,11.003,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.018,8.7531,8.3541,7.9812,8.0078,7.3307,5.6491,4.8621,4.7619,4.939\n5.7898,6.3986,3.7546,3.1921,4.1736,5.0443,4.8466,5.309,5.4833,5.8994,7.562,7.5736,7.9653,8.5208,10.203,11.212,12.821,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.802,10.321,8.3095,7.7221,7.43,6.4845,5.8321,5.0957,4.9497,4.4686\n5.3593,6.0554,3.9391,3.106,3.7866,4.4612,4.4432,4.9387,5.3922,6.1748,7.1916,7.914,8.3326,9.0901,10.178,9.9911,9.7499,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.447,14.674,11.116,8.2757,7.6788,7.5305,6.8794,6.5296,5.6984,4.8563,4.0888\n2.9085,2.8979,2.9566,2.8923,3.5497,4.2791,4.734,4.5059,5.2532,6.2701,7.1676,7.5647,8.6279,10.629,12.883,9.7023,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,25.399,17.839,16.913,13.795,10.085,8.1796,7.6889,7.5052,7.3873,6.9113,5.8596,4.7884,3.7201\n2.8405,2.2444,2.5683,2.9696,3.3068,4.1411,5.2164,5.7606,6.2381,6.6228,7.2022,8.2683,8.7945,11.714,11.16,8.1984,8.2383,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,23.145,17.316,13.739,10.956,8.7261,7.3489,6.7117,6.4725,6.265,6.0687,5.1663,4.0895,3.506\n3.3126,3.2559,3.0627,3.2243,3.4197,4.0664,5.1342,5.6492,5.8202,6.4839,7.0362,7.6656,7.4974,7.3107,6.5294,8.1396,9.0358,5.8885,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.668,12.428,10.376,8.9318,6.6255,6.4285,6.3879,5.9,5.4925,5.1353,4.1859,3.3287\n3.0036,3.3419,3.3801,3.28,3.486,4.2181,4.912,5.2239,5.4508,6.0523,7.153,7.9566,7.4224,7.3406,7.6496,8.994,NaN,NaN,7.615,4.7653,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.382,11.208,10.08,8.8299,6.5923,6.1416,6.3698,5.9038,5.138,5.0128,4.1506,3.4143\n2.3494,2.6314,2.8504,3.1952,3.3362,3.7725,4.2787,4.5246,4.7659,5.8727,6.8302,7.2183,6.9372,7.4158,7.6006,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.9041,9.2497,8.5882,7.8865,6.4583,6.3436,6.1326,5.2931,4.6906,4.3771,3.5422,2.7837\n1.9829,1.8859,2.5649,2.3548,3.0924,3.7537,4.3292,4.6301,4.7757,5.2653,6.1837,7.0649,7.4532,7.6794,6.4811,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.5623,8.5458,7.7257,6.3739,6.0847,5.6144,4.9544,4.6888,3.902,3.8116,3.2642,2.6491\n1.953,1.7814,1.9817,2.3099,3.0217,3.3761,3.654,4.1501,4.8032,5.5417,6.4007,7.9927,8.0498,7.766,6.8736,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.7958,9.4895,8.5244,6.4198,5.3459,5.3599,4.982,4.2039,3.8044,3.9306,3.4946,3.4486\n3.0473,2.6124,2.3111,3.0361,3.4352,3.6052,3.7423,4.0119,4.6446,4.9836,5.7773,7.2876,7.7796,7.4023,6.9077,6.1283,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.8337,9.0833,7.7436,5.4295,4.1416,4.7552,4.4273,3.6982,3.637,3.4301,3.1393,3.5161\n2.8667,3.2183,3.1479,3.5744,4.2187,4.2244,4.2893,4.1197,4.7079,4.8872,5.1344,5.5807,6.3019,6.4723,6.212,5.9101,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.4764,8.203,8.2158,6.7433,4.6246,4.1551,4.1324,3.8233,3.3556,2.5574,2.0632,2.0301,3\n2.9419,3.0901,3.2213,3.3762,3.9091,4.4672,4.7927,4.9952,5.178,4.8445,4.8055,5.2321,5.1011,4.9639,5.3726,4.7629,5.2128,5.5843,5.8261,7.149,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.0388,8.7288,8.3729,7.6344,6.7042,6.572,5.1484,4.8067,4.0684,3.835,3.9777,3.8305,2.8556,1.7748,0.9225,1.8856\n2.7609,2.6612,2.5816,2.7082,3.1246,3.526,3.686,4.0556,4.0221,3.6969,4.0994,4.569,4.5096,4.4993,4.6737,4.9761,5.2381,5.1685,4.631,5.6935,7.8064,8.6625,9.004,NaN,NaN,NaN,NaN,NaN,5.2629,NaN,6.8398,7.242,6.9807,5.9288,5.4614,5.1857,3.6277,1.924,3.4064,2.926,3.0328,2.8877,3.377,3.1958,2.2588,1.4591,1.6537\n2.6739,2.3459,2.5275,2.6743,2.6841,2.9132,3.1173,3.2086,2.8885,3.0712,3.4604,3.8086,3.9205,3.8036,3.71,4.1258,4.1651,4.0785,4.1611,4.2552,4.911,6.5476,6.2888,5.2096,NaN,NaN,NaN,3.1712,4.0753,4.2187,4.7053,5.0513,4.8312,4.939,5.1076,4.0439,1.9318,1.3999,2.8095,2.1337,1.8563,1.8205,1.7156,1.9267,1.5425,1.5699,2.1055\n2.5817,2.5041,2.3476,2.2811,2.4066,3.1161,3.1766,2.7956,2.6096,2.7572,3.2076,3.5923,3.5999,3.3225,3.1811,3.546,3.4815,3.5099,3.835,3.7245,3.6699,3.9697,3.2207,2.9728,4.2427,3.9367,2.386,3.2153,3.8027,3.5759,3.5715,3.8531,3.6208,3.1418,3.4014,2.8641,1.725,1.0543,2.5208,2.3958,1.9968,2.2154,1.4316,0.88074,0.41211,0.61355,1.6655\n2.8567,2.793,2.4228,2.286,2.4612,3.1867,2.8746,2.3759,1.9445,2.0672,2.7008,3.0029,2.8533,2.6324,2.2916,2.2095,2.3152,2.5333,2.6953,2.7425,2.6555,2.4637,2.2479,2.8544,3.0271,2.6965,1.9899,1.945,2.1291,2.7352,2.671,2.5458,2.2606,3.1502,4.0339,3.4174,2.098,1.4647,1.9699,2.3776,2.5391,2.673,1.8594,0.80077,0.60436,0.94036,1.4722\n2.6714,2.7932,2.5443,2.1558,2.3708,2.4532,2.2162,2.0067,1.6782,1.2239,1.566,2.3002,2.3427,2.1511,1.8426,1.5955,2.066,2.2429,2.3516,2.022,2.179,1.858,2.2781,2.6795,2.5739,1.9127,1.1117,0.58623,0.92066,1.8456,1.8312,1.7631,1.7896,3.3131,3.8238,3.7728,2.2976,2.0293,1.7753,1.8975,2.7598,2.6233,2.6147,1.3548,0.73018,0.76518,1.1523\n2.2678,2.4098,2.1473,1.5706,1.5051,1.6744,1.6201,1.5939,1.4741,1.215,1.1578,1.7455,1.879,1.8705,1.6852,1.5577,2.0463,2.5646,2.2837,2.1061,1.8568,1.7055,2.3504,2.4929,2.0402,1.6989,0.95687,0.57086,1.1515,1.6221,1.4319,1.5531,1.8432,2.5903,2.7824,2.1208,1.6517,1.4412,1.422,1.8996,2.0567,1.8416,1.5847,0.86265,0.61884,0.54797,0.89352\n1.4921,1.7041,1.5429,1.3637,1.4103,1.4334,1.3807,1.4856,1.2979,0.64972,0.50869,0.58113,1.1172,1.7537,1.4596,1.2463,1.434,1.6934,1.6606,1.9059,1.9082,1.6745,2.1073,2.1466,1.6169,0.83856,0.67524,0.038918,0.95598,1.5594,1.0725,1.3551,1.7684,2.5408,2.39,1.7219,1.2019,0.94463,1.0759,1.4605,1.253,1.1049,0.76365,0.66588,0.838,0.73572,0.76436\n1.9836,1.8694,1.5127,1.1757,1.288,1.2077,0.91437,0.84765,0.99327,0.74685,0.59018,0.87714,1.232,1.4525,1.3171,1.1108,0.96616,0.97187,1.1616,1.1978,1.4322,1.2896,1.4477,1.3332,1.075,0.88412,0.62406,0.41631,0.86263,1.1397,0.96967,0.952,1.3425,2.1223,1.5412,1.0469,0.84994,0.64678,0.44805,0.66059,0.61402,0.63032,0.58732,0.50396,0.87769,0.89249,1.0026\n2.0355,1.9123,1.7734,1.6784,1.6988,1.2767,0.87594,0.76166,0.7991,0.76022,0.7359,1.0536,1.4169,1.5613,1.4483,1.2179,1.1141,1.0871,1.5517,1.8304,1.4793,1.1633,0.96677,0.85983,0.80488,0.69539,0.50443,0.23534,0.40848,0.65596,0.7928,0.82834,1.0694,1.2647,0.92336,0.88309,0.87062,0.8038,0.36908,0.19106,0.2585,0.22525,0.50219,0.63557,0.69881,0.81569,0.57938\n1.8133,1.1997,1.8086,2.7254,2.3838,1.2669,0.35207,0.40579,0.49791,0.73779,0.53514,0.82904,0.86348,1.2701,1.2797,1.1344,0.81736,0.59805,0.63733,1.4006,1.3224,0.90797,0.61985,0.69206,0.70147,0.59239,0.044251,-0.084105,-0.061461,0.51146,0.75988,0.25666,-0.3753,0.029457,0.041461,0.68388,1.2491,1.0534,0.57375,0.2825,0.32073,0.031404,0.080121,0.095027,-0.072875,0.17744,0.17771\n2.3574,1.5439,0.79317,1.854,2.3377,1.352,0.40591,-0.20781,0.37436,1.4949,1.1569,1.0446,0.75848,0.587,0.87807,1.1845,0.59109,0.35162,0.38411,0.88536,0.90047,0.4426,0.61122,0.885,0.46308,0.37387,-0.87685,-1.0895,-0.71144,0.41058,0.39827,-0.14036,-0.35339,-0.42114,-0.27374,0.45258,1.0331,0.82346,0.6517,0.51751,0.26898,0.045723,-0.062397,-0.12433,-0.3656,-0.19191,-0.15315\n1.8449,1.0768,0.89512,1.1304,1.4133,0.92068,0.62136,1.0666,1.7888,2.5126,1.7589,1.6423,1.1519,1.3199,1.4002,1.5853,1.3162,0.41939,0.51221,0.71168,0.6661,0.39814,0.49016,0.50126,0.43056,0.25491,-0.63773,-1.3525,-0.9199,0.12211,0.30242,-0.18097,-0.25842,-0.40839,-0.4127,0.10617,0.57231,0.51105,0.52474,0.62856,0.24926,-0.29352,-0.40299,-0.69116,-1.127,-0.86213,-0.6793\n2.5934,2.5887,1.9068,1.9841,1.8384,0.97963,0.98291,1.2397,1.923,2.5765,1.2766,1.5449,1.0902,1.7881,1.5689,1.4203,1.2114,0.85735,0.57452,0.48921,0.50786,0.37238,0.56977,0.53666,0.35635,0.096853,-0.24214,-1.0362,-0.23945,0.59306,0.38196,-0.060049,-0.36938,-0.69394,-0.5874,-0.31077,0.0045662,0.2414,-0.0069556,0.25306,0,-0.38292,-0.45965,-1.1445,-1.8446,-1.3535,-1.1606\n3.3447,3.9386,3.4192,2.1908,1.9256,1.9967,1.2937,1.1198,1.3179,1.5807,0.84928,-0.023221,0.44539,0.97391,1.0493,0.87143,0.96561,0.93112,0.41596,0.32766,0.35523,0.49468,0.59036,0.40022,0.11267,-0.20519,-0.58766,-0.82025,-0.3653,-0.20306,-0.16604,-0.45066,-1.0153,-0.75613,-0.4012,-0.28364,-0.25312,-0.11736,-0.07307,-0.019283,-0.1799,-0.3111,-0.36509,-0.69799,-1.725,-1.8585,-1.6334\n2.6645,3.2694,3.3372,1.9454,1.2876,1.5606,1.7539,1.0444,1.4418,1.3018,0.98599,-0.90786,-2.2856,-0.47681,-0.036674,0.26414,0.81658,0.81623,0.393,0.18236,-0.10938,-0.28788,0.22688,0.088,-0.28983,-0.56082,-0.98024,-1.1845,-1.2005,-0.76321,-0.54453,-0.77573,-1.3807,-0.76746,-0.78663,-0.59472,-0.46806,-0.22807,-0.006422,0.03381,-0.28383,-0.63674,-0.47107,-0.28036,-0.83825,-0.96846,-0.93938\n1.8657,3.1984,3.6123,2.4069,1.0541,1.1263,2.1253,0.58977,0.15094,1.7047,2.3868,0.38636,-1.183,-1.6726,0.4066,0.55328,1.1689,0.17958,-0.27792,0.11041,-0.028955,-0.53285,-0.77624,-0.46732,-0.3284,-0.71079,-1.0932,-1.2958,-1.3221,-0.91551,-0.72804,-0.72631,-0.87743,-1.193,-0.76174,-0.32682,-0.23192,0.12364,0.25206,0.25966,-0.2564,-0.82912,-0.85207,-1.0199,-1.2318,-1.3177,-1.3854\n2.811,2.7008,2.4384,1.6894,1.0786,0.73343,1.4362,0.5657,-0.69534,-2.3927,0.49296,1.9048,1.5052,0.53106,0.1396,0.91604,1.8915,0.51558,0.16576,0.22978,0.31766,-0.11414,-0.57303,-0.35207,-0.094758,-0.40992,-0.78347,-1.4921,-1.5781,-1.1777,-1.0352,-1.1424,-1.2437,-1.1678,-0.37251,-0.19946,-0.20584,-0.0078301,0.087981,0.17188,-0.16523,-0.67157,-0.79896,-1.2549,-1.8144,-1.7748,-1.4247\n2.283,2.4321,2.2128,1.6997,1.5034,0.86067,1.1711,0.89393,-0.29248,-3.6031,-2.4197,0.54075,0.87999,0.65364,0.01503,-0.094517,1.5203,1.9192,0.069929,-0.092232,-0.13401,-0.48171,-0.4444,-0.0089955,0.0069818,-0.73924,-1.2477,-1.2676,-1.0231,-0.72706,-1.0155,-1.4815,-1.4854,-1.3109,-0.44547,-0.32421,-0.25865,-0.11702,-0.12549,-0.1682,-0.36355,-0.6786,-0.36331,-0.69787,-1.4942,-2.0889,-1.4991\n3.7058,4.0213,4.1688,3.6006,2.6452,1.9491,1.4713,1.0972,0.37809,0.24775,-0.22643,0.45919,-0.31058,-0.39263,1.2505,2.2211,5.2496,2.8807,0.69006,-0.12994,-0.59491,-0.51639,-0.2548,-0.40631,-0.96473,-1.3055,-1.2816,-0.87184,-0.26324,-0.40285,-0.94081,-1.5586,-1.9349,-1.7597,-0.96992,-0.40963,-0.1565,-0.17022,-0.15378,0.047924,-0.41339,-0.85404,-0.78275,-0.86794,-1.4037,-1.897,-1.7762\n3.0945,4.6711,5.2239,4.3881,3.6145,2.3817,2.0418,2.2433,2.3164,2.344,2.9386,0.98509,-2.0685,-0.81107,-0.48232,1.4266,2.8196,1.2029,0.89307,0.25829,-0.75364,-0.94443,0.03487,-0.29717,-0.88451,-1.4619,-0.83894,-0.2436,-0.32017,-0.64493,-0.90242,-1.4546,-1.9983,-1.9706,-1.3902,-0.54535,-0.19187,-0.17559,-0.82248,-0.30543,-0.060822,-0.75534,-1.0352,-1.0799,-1.4205,-1.5007,-1.3203\nNaN,3.1173,3.9506,4.2871,3.8317,3.1017,2.8715,2.574,2.4198,2.2077,1.8508,-1.0129,-2.0243,0.2653,1.0857,2.9147,1.5192,0.70492,0.65572,-0.067831,-0.65463,-1.1859,-0.26709,0.65669,0.59037,-0.46631,-0.54077,0.12108,0.026044,-0.33209,-0.50739,-0.77303,-1.3402,-1.4472,-0.90855,-0.64152,-0.46894,-0.37066,-0.25369,-0.077647,-0.03488,-0.40878,-0.93466,-1.3278,-1.1739,-1.0903,-0.73863\nNaN,NaN,NaN,2.9512,2.676,3.5196,4.2986,4.7745,5.1477,5.8515,2.0347,0.036384,0.83966,1.7532,2.8469,3.0713,0.88277,0.54228,0.46258,-0.96707,-1.2264,-1.1558,-0.31355,0.70665,0.81474,-0.45822,-1.2269,-1.3221,-1.0853,-0.87613,-0.98548,-0.69824,-0.70276,-0.85277,-0.36093,-0.62899,-0.7344,-0.49473,-0.30392,0.25553,0.38831,0.03676,-0.7968,-1.2695,-1.2798,-0.93887,-0.59896\nNaN,NaN,NaN,NaN,NaN,1.7651,4.0611,4.0351,3.4084,3.2271,2.3189,2.0535,1.335,1.7731,NaN,NaN,4.3842,2.1814,0.57056,-0.49715,0.51264,0.0019388,-0.41038,0.45129,0.68239,-0.31634,-1.0435,-1.4841,-1.0387,-0.9793,-0.93417,-1.2319,-0.49657,-0.51938,-0.68167,-0.52622,0.061912,-0.037781,-0.12481,0.31644,0.2002,-0.28739,-0.70644,-0.90246,-0.96885,-0.76728,-0.55629\nNaN,NaN,NaN,NaN,NaN,NaN,4.1793,3.6358,2.8777,1.9808,1.8134,3.3765,4.8678,5.4198,NaN,NaN,NaN,NaN,1.1453,0.76053,1.5564,0.66623,-0.055045,0.064571,-0.16803,-0.73008,-0.8351,-0.99573,-0.94624,-0.24292,-0.44994,-0.43085,-0.30611,-0.27401,-0.92781,-0.33932,0.16416,0.11971,0.44941,0.36058,-0.14354,-0.53723,-0.81514,-0.89551,-0.53534,0.0095558,0.11677\nNaN,NaN,NaN,NaN,NaN,NaN,2.8409,2.404,2.5203,2.1205,2.49,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.8003,1.1037,1.6405,1.1685,-0.023171,-0.12781,-0.32104,-0.92408,-0.29565,0.24979,0.18884,-0.043598,-0.064501,-0.66627,-0.78225,-0.9087,-1.0099,-0.85564,-0.43778,-0.18312,0.3605,0.0069232,-0.037456,0.097648,-0.36945,-0.53744,-0.49545,-0.51683,0.063251\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.98559,2.3915,3.4979,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.5881,-0.44365,0.99788,0.87232,0.22956,0.24981,0.52474,0.74868,0.85433,1.0685,0.67709,0.13611,-0.32278,-0.74385,-0.74838,-1.1052,-1.2842,-1.1068,-0.35668,-0.1344,-0.84673,-0.20458,0.4996,0.45123,-0.086946,-0.34126,-0.62837,-0.94298,-0.40936\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5344,2.8205,3.0677,1.4139,0.24184,NaN,NaN,NaN,NaN,0.034014,-0.92332,-0.15562,1.0679,0.90594,0.18175,0.22914,0.442,0.89533,1.2992,1.4865,1.1946,0.067402,-0.8317,-1.0928,-0.82864,-0.51166,-0.10403,-0.65983,-0.0172,0.39651,-0.12923,0.064068,0.3268,0.0044336,-0.18459,-0.7537,-1.276,-1.2463,-0.85801\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_061211-070813.unw.csv",
    "content": "-0.89904,-0.7307,-1.1558,-0.80574,-1.0459,-1.908,-1.9506,-1.8251,-1.1828,-1.0747,-1.4579,-1.897,-0.67766,-0.33306,-0.41131,-0.30331,-0.23819,-0.33775,-0.27006,-0.44968,-1.5031,-1.2678,-0.31613,1.1045,1.6202,1.1753,0.42484,-0.36023,-0.78243,-0.58503,-0.10117,0.52837,0.15229,-0.59357,-1.2993,-1.6926,-2.5705,-2.8501,-3.1557,-3.7276,-3.0649,-1.8098,-1.1468,-1.1269,-0.62418,-0.033267,-0.24031\n-1.3711,-0.86772,-0.97461,-0.7235,-0.6943,-1.6208,-1.7438,-1.6001,-1.3311,-0.90803,-0.66108,-0.87904,-0.38852,-0.07976,0.04197,-0.21086,-0.47851,-0.57886,-0.31339,-0.31892,-0.38225,-0.61107,0.031646,1.0719,1.0725,0.66379,0.75249,0.12602,0.050638,0.49253,0.30436,-0.40935,-1.2074,-1.4183,-1.0402,-1.0941,-1.7152,-1.9949,-1.7927,-1.5024,-1.31,-0.95386,-0.81145,-0.7344,-0.97658,-1.207,-1.1299\n-1.3359,-0.87328,-0.77991,-0.98012,-1.0023,-0.52496,-1.034,-1.1423,-1.5202,-0.6405,-0.20951,0.32854,-0.20771,-0.35838,0.016718,-0.2305,-0.74684,-1.081,-1.1967,-0.52013,-0.48532,-0.85704,-0.87546,0.55536,0.58189,-0.004714,0.78471,0.91593,0.067919,0.65192,0.62856,-0.78966,-1.9483,-2.2754,-1.7191,-1.6866,-1.4168,-0.85539,-1.0127,-0.87441,-0.90944,-0.34363,-0.14223,-0.27957,-1.1391,-1.7905,-1.6102\n-1.5144,-0.39504,-0.49889,-1.0731,-0.86187,-0.67271,-0.2992,0.38961,0.56289,0.20092,0.074764,-0.036464,-0.34298,-0.61729,-0.23604,-0.56213,-1.3995,-1.7711,-1.8,-0.83394,0.40507,0.52862,0.39056,0.54777,0.73421,-0.010844,0.15113,0.84103,0.26475,-0.21524,0.0089457,-0.53451,-0.97385,-1.4059,-1.4613,-1.5093,-1.5937,-1.0064,-1.3552,-0.88138,0.052041,0.46252,0.37448,-0.039953,-0.64044,-0.9694,-0.39658\n-1.2906,-0.26876,-0.78192,-1.592,-1.0008,-0.8927,-0.30014,0.20043,0.0084865,-0.39378,-1.5159,-1.7397,-0.66821,-0.74592,-1.0612,-0.87512,-0.6604,-1.5008,-2.2558,-2.2654,-0.99681,0.9381,0.71864,0.81076,1.0216,0.2988,-0.035239,0.074856,0.94262,0.51757,-0.63045,-0.52474,-0.58274,-0.68337,-1.037,-1.1561,-1.3659,-1.6153,-1.6308,-0.40953,0.80115,1.4792,1.4941,1.069,0.23323,-0.42683,-0.24641\n-2.3014,-1.6837,-0.33962,-1.9063,-1.2473,-1.2715,-1.377,-0.57907,-0.057019,-0.69412,-1.5233,-1.6826,-0.62562,-0.64753,-0.64849,-0.6579,-0.77679,-1.2525,-1.9348,-1.6761,-0.86228,0.76187,1.2583,1.0491,0.60443,0.038761,0.035848,0.19991,0.82978,0.99657,-0.094678,-1.0969,-0.96463,-0.2032,-0.33707,-0.99149,-1.5552,-1.3903,-0.42084,0.75376,1.2645,1.0169,0.85427,1.1061,0.69252,0.77018,0.79605\n-2.2853,-2.734,-2.7691,-2.4964,-2.3547,-3.0323,-3.4889,-2.246,-0.84082,-0.72829,-1.3815,-1.3262,-0.84288,-1.3376,-1.588,-0.8462,-0.86549,-1.5704,-2.2026,-1.7735,-0.44636,0.032056,0.24658,0.60844,1.0521,0.31763,1.1426,1.1384,0.19923,0.13443,-0.23598,-1.134,-0.67836,-0.44942,-0.47827,-0.74985,-1.0993,-0.76209,0.17637,0.9444,0.63465,0.41496,0.59483,0.7511,0.95899,1.0696,1.1831\n-1.4739,-1.6784,-0.64924,-1.8715,-2.5709,-3.4924,-2.6637,-2.5451,-2.4725,-1.6512,-1.0736,-0.89076,-0.94573,-1.571,-1.6601,-1.162,-1.0452,-1.0116,-1.2459,-1.3743,-0.33768,0.41886,0.40752,0.84384,1.6017,1.81,1.9199,2.0635,0.50482,-0.41016,-0.44891,-0.48513,-0.18423,-0.43305,-0.50517,-0.78154,-1.0558,-1.0304,-0.32426,0.59845,0.52099,0.79415,1.5621,1.7631,1.4479,0.75202,0.69089\n-0.70214,-1.2939,-2.0134,-2.1772,-2.7635,-3.2021,-2.0279,-1.3413,-0.12023,-0.31403,-1.1202,-1.3062,-1.2269,-0.85475,-0.5085,-1.1987,-1.7985,-1.7801,-1.1009,-1.0447,-0.6166,0.48886,1.9623,1.421,1.4309,1.8646,1.5769,1.3752,2.0848,1.4555,0.27469,0.38488,0.32632,0.052238,-0.073531,-0.29803,-0.31011,-0.35651,-0.083603,0.16197,0.32783,0.80899,1.4412,1.485,1.325,1.2224,1.3209\n-0.98446,-1.2645,-2.2284,-2.7596,-3.5858,-2.1467,-1.0241,-0.50596,0.8574,0.94332,-0.86463,-0.7492,-0.87544,-0.34381,-0.6581,-1.2732,-1.484,-1.5634,-0.92199,-0.52047,-0.019362,0.49484,1.3265,1.914,1.8812,1.572,1.6095,1.487,0.6493,0.24643,0.81787,0.99705,0.18868,-0.081265,0.013963,0.10318,0.1835,0.43472,0.49185,0.48051,0.47538,0.77836,1.3424,1.5781,1.0795,1.2695,1.8654\n-1.2776,-1.4922,-2.1388,-3.0102,-3.2576,-1.4566,-0.53319,0.22968,0.22604,-0.22207,-0.64492,-0.2369,-0.22244,0.48181,0.41408,-0.52635,-0.87672,-1.3628,-2.376,-1.9953,0.4477,1.2715,1.7263,2.3904,2.1705,2.0392,1.9449,1.4496,0.6026,0.021329,0.14203,-0.27214,-0.57895,-0.44814,-0.70817,-0.72788,-0.33693,0.16122,0.53859,0.8208,0.6437,0.87045,1.1951,1.7134,1.6924,1.8893,1.7571\n-1.5685,-1.7115,-2.3693,-2.3994,-3.5018,-2.9271,-1.4311,-0.16669,-0.30089,-0.28853,0.30738,0.028759,0.059154,0.66047,0.45512,-0.12536,-0.5183,-1.8552,-2.8004,-2.3473,-0.70393,0.92821,1.4881,1.9357,2.2143,1.9101,2.2317,1.8332,0.78608,0.48019,-0.33039,-1.2249,-1.1148,-0.79628,-1.377,-0.97343,0.34347,0.47638,0.62455,0.93985,0.94372,0.91816,1.1063,1.8057,2.0803,2.459,2.1257\n-2.3364,-2.624,-3.6531,-4.6643,-4.9008,-3.0043,-0.79328,-0.98337,-1.0804,-0.58448,-0.25908,-0.59299,-0.099115,0.51268,0.061673,-0.042661,-0.41436,-1.0026,-1.7866,-0.70543,0.73463,0.72938,1.1879,2.4707,2.4921,1.8869,1.8631,1.7108,0.60312,0.15063,-0.56325,-1.8544,-1.5791,-0.63708,-0.30081,0.66269,1.3157,0.98738,0.69023,0.49416,0.81503,1.1037,1.4079,1.6746,2.1934,2.3458,2.5911\n-2.4881,-2.8738,-2.3596,-1.0188,-1.5103,-1.2553,-0.91288,-0.96921,-0.80515,-0.33004,-1.3067,-1.5378,-0.62933,0.3273,-0.27024,-0.63318,-0.39316,-0.60578,-0.39165,-0.17263,0.055759,0.14638,0.39092,2.0523,2.9103,2.126,1.4654,1.5824,1.3419,0.55787,0.014814,-0.33284,-0.12547,0.50539,1.3444,1.4892,1.131,0.95818,0.85195,0.46485,0.51279,1.215,1.6058,1.5339,1.7616,2.6047,3.3869\n-0.85596,-1.4487,-0.046919,-0.26835,-1.8694,-2.2269,-2.2202,-1.6076,-1.2287,-0.13618,0.098706,-0.058852,0.39467,0.77669,0.40063,-0.44657,-0.52437,-0.47097,-0.31929,-0.0047803,-0.69884,-0.26891,0.57986,1.7678,3.8498,3.5427,2.1934,0.91464,0.8335,1.6516,1.54,1.059,0.87427,1.2264,1.6773,1.5156,1.1671,1.0168,0.98575,1.0947,1.1025,1.0296,1.4835,1.6159,1.5102,0.95092,1.1944\n-0.74633,-0.56762,-0.51037,-0.9813,-1.2331,-1.2966,-1.9743,-2.1152,-1.7894,-0.60101,0.27512,0.038655,0.36729,0.40093,-0.44566,-0.67297,-0.44377,-0.72783,-1.5608,-0.84002,-0.21219,0.66453,1.6041,2.3977,3.2611,3.1845,2.6069,1.6759,1.0899,1.2051,0.84193,0.81099,1.5156,1.4704,1.492,0.89167,0.40054,0.61577,0.80316,1.3822,1.2265,0.75169,0.76126,0.70255,0.6111,-0.071172,0.13301\n-1.4779,-0.98124,-1.3184,-2.3645,NaN,NaN,NaN,-1.0425,-0.9394,-0.33338,-0.64931,-0.43793,0.15712,0.016217,-1.0087,-0.66741,-0.13264,-0.8373,-1.9841,-2.1314,-0.38194,1.058,2.0433,3.5497,3.8894,3.3322,3.1668,2.5122,1.363,1.0263,1.1065,1.3701,2.4845,2.7042,2.0183,1.3545,1.3369,0.99173,0.6495,1.43,1.1159,0.73634,0.17336,-0.099581,-0.32124,-0.60638,-0.19819\n-0.77276,-1.033,-3.7016,-5.9163,NaN,NaN,NaN,NaN,NaN,0.53687,1.3008,0.50964,-0.1035,-0.55349,0.030132,0.34394,0.42707,-0.52603,-1.1506,-1.9163,-0.70267,1.4644,1.7382,2.9003,4.2621,3.1294,2.1325,1.4934,1.2439,1.0605,1.6618,2.1317,1.7703,1.8211,1.4475,1.341,1.6729,1.4296,1.2296,1.732,1.2462,0.32787,-0.1397,-0.27715,0.11568,0.036829,-0.054259\n-0.21171,-1.3135,-2.4376,NaN,NaN,NaN,NaN,NaN,NaN,1.2638,1.6602,1.035,-0.01624,-0.93637,-0.31714,0.76502,1.1179,1.1421,-0.038431,-1.0721,-0.0066154,1.3863,2.8939,1.8839,1.92,3.2113,2.4059,1.1342,1.6847,1.58,1.3482,1.8297,1.2383,0.84398,0.86654,1.2721,1.4452,1.214,0.83386,0.63883,0.45897,0.47475,0.029668,-0.11476,0.32311,0.42345,0.10971\n-2.0926,-1.0752,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.77318,0.30387,0.66938,0.16995,-0.73223,-0.15599,0.94685,1.1442,0.12749,-0.26378,-0.70186,0.53061,1.481,2.5435,1.6606,0.86304,1.9732,2.7929,3.0801,2.6451,2.8859,1.7727,1.8783,1.9014,1.4885,1.4076,1.6046,1.6526,1.519,1.277,0.9038,0.86652,0.77497,0.76692,0.97955,1.1896,1.3852,0.47043\n-1.7956,0.5275,1.8551,NaN,NaN,NaN,NaN,NaN,0.71922,0.62651,0.69822,0.16147,-0.10948,0.0096195,1.0497,1.9758,1.636,0.4082,-0.70252,-0.2177,0.35077,1.1153,1.4717,1.7261,2.4692,3.1538,4.0655,4.7186,4.015,3.3603,2.4871,2.108,2.0998,1.9938,1.5472,1.6576,1.9088,2.0252,2.3532,2.049,1.3718,0.70716,0.8541,1.0866,1.1973,0.92968,0.29017\n-1.2914,0.39452,NaN,NaN,NaN,NaN,NaN,NaN,0.086405,1.6901,1.2264,0.30581,-0.17434,0.17937,1.6753,3.2323,2.0557,1.1438,0.13276,0.20017,0.55596,0.8015,0.83129,2.2818,4.0595,3.1731,3.3387,3.4612,4.9707,4.4633,2.9972,2.4291,2.3763,2.1937,2.0062,2.4071,2.8406,2.1641,2.1698,2.2092,0.53903,0.79977,1.186,1.1438,0.73692,-0.32071,-0.39621\n-0.00077796,0.42736,NaN,NaN,NaN,NaN,NaN,NaN,-0.12844,1.8622,1.6792,0.55329,-0.3636,-0.49832,2.0276,4.6752,2.5172,1.3652,1.1691,1.7635,3.3483,2.6835,3.0002,3.698,4.2136,2.4181,1.4146,1.1578,3.6817,4.198,3.7404,3.0988,2.9561,2.8856,2.4302,2.6194,2.6622,2.6001,2.3691,2.0529,0.88503,0.92923,1.6601,1.5199,0.25342,-0.53431,-0.62747\n-0.41052,1.1787,NaN,NaN,NaN,NaN,NaN,1.6243,1.091,0.79517,1.2465,1.1549,-1.4868,-0.79732,0.6829,2.6277,3.3611,2.3236,1.8944,2.694,4.3359,5.5355,5.1951,5.8769,6.7003,5.3881,5.0019,3.8807,3.6254,4.4531,6.19,5.7217,5.106,4.0312,3.0194,2.8005,3.6137,3.3208,2.566,1.4694,0.99881,1.133,1.4531,1.4315,0.45129,-0.40056,-0.25673\n0.83292,2.2268,2.0564,NaN,NaN,NaN,0.92028,0.83139,1.0341,0.72805,0.42102,0.4842,0.9149,2.9983,2.9139,3.0205,2.9744,2.8139,2.7543,4.2267,5.5102,6.1811,5.1287,6.6076,8.4153,8.0673,7.0114,5.7322,6.31,8.03,7.8735,6.6978,4.5887,3.1452,2.1839,1.0509,2.789,3.3988,3.451,1.4247,1.1003,1.3552,1.669,1.2384,0.39056,-0.017467,0.11973\n-0.75038,0.014024,1.307,NaN,NaN,NaN,0.24583,-0.45681,-0.84412,-0.053512,-0.17059,0.56111,1.9763,3.2127,3.9011,4.1713,3.8033,4.1566,4.4966,5.6939,6.6825,7.5673,7.7813,9.1196,10.149,10.371,NaN,NaN,NaN,NaN,NaN,9.2643,4.3471,2.4574,2.1552,1.9309,2.7083,2.8978,2.7943,1.6401,1.169,1.2248,1.1619,0.59138,-0.16857,-0.039436,0.67514\n-3.1744,-0.80484,0.59617,-0.64424,NaN,NaN,-0.50271,-1.2232,-0.93738,0.46446,0.87071,0.98695,1.7419,2.7088,3.811,4.6379,4.8794,5.6057,7.3107,8.0742,8.3188,8.7104,9.563,10.468,12.66,13.987,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.5697,3.4815,3.4974,3.0457,2.7733,2.0857,1.4615,0.79059,0.52352,0.75992,0.35221,0.041412,0.41846,0.79836\n-5.5914,-3.0267,-1.8897,-1.7045,-0.82122,-0.37926,-1.528,-1.6729,0.13677,0.81382,0.78989,1.035,1.7783,3.2024,4.5104,5.5976,5.9834,6.7241,7.8525,8.8769,8.9946,9.3664,10.095,11.532,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.4307,5.3777,3.8429,3.2811,2.547,1.1262,0.72926,1.0993,1.0458,0.95671,0.88974,0.9078,1.0528\n-7.3061,-5.9297,-4.0838,-2.7819,-1.4957,-0.87736,-0.91031,-1.5261,-0.36561,-0.024943,0.67452,1.3503,1.9405,3.3716,4.6681,5.4755,5.603,6.6337,7.9408,8.4731,8.6634,10.385,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.8255,5.4798,4.4575,3.6333,3.2189,2.3986,1.1843,0.99255,1.4567,1.2236,1.1681,1.334,1.481\n-4.3876,-5.6222,-7.7079,-8.672,-2.3877,-2.3491,-2.1235,-1.2326,0.17239,0.62451,0.67235,1.1049,1.424,3.5555,4.7496,5.4812,6.0118,6.8943,7.9449,8.2654,8.3388,10.685,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.4707,5.5875,5.6106,5.0463,4.083,2.5583,1.1689,1.3268,1.5313,0.3931,0.32248,1.0322,1.2614\n-3.8537,-4.2961,-5.4957,-4.5976,-2.7126,-1.5694,-1.6893,-1.2318,-0.44097,0.86116,2.0932,2.2482,1.9278,3.514,4.4391,4.9117,5.9941,7.1624,8.6803,10.187,8.661,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.372,7.4239,6.3711,6.0034,4.397,2.1168,1.6449,2.1716,1.7479,0.32461,0.29821,0.81746,0.95544\n-4.0672,-5.6659,-5.9612,-1.7209,-0.70396,-0.64468,-0.86826,-1.2955,-1.4419,0.11666,3.0661,3.7356,3.6191,3.9216,4.1474,4.7085,6.011,6.2477,7.4887,8.1436,5.8623,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.4283,5.4833,5.8142,5.3314,4.0383,2.4005,1.8543,2.2001,1.6572,0.17369,0.44798,0.48598,1.266\n-1.3864,-2.9092,-3.4697,-1.7066,-1.2427,-0.40739,-0.83895,-1.1638,-1.4306,1.1828,3.3151,5.7083,6.585,4.7469,2.5502,3.6935,5.3409,7.1627,7.4052,7.0947,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.7588,NaN,NaN,NaN,NaN,NaN,NaN,2.9301,5.0169,5.8016,5.8056,3.0451,2.5991,2.597,2.7128,2.4098,1.4121,0.73714,0.47481,0.38776\n-0.042051,-0.69419,-2.6213,-1.7617,-1.1227,-0.59686,0.32377,0.7186,0.76786,1.6939,2.6331,3.6942,4.8859,5.0182,2.8704,2.8199,3.2833,5.335,7.1544,8.4226,NaN,NaN,NaN,NaN,NaN,NaN,4.3651,4.0143,NaN,NaN,NaN,1.9683,1.4938,0.37978,1.9699,4.5135,8.1934,7.9657,4.7247,2.972,3.091,3.029,2.3539,1.5142,0.3146,0.1823,-0.31165\n-1.0039,0.18065,-1.0247,0.22722,0.084727,-1.0456,-2.1683,-0.39497,1.1581,1.9837,2.3874,2.9975,3.0788,4.0185,3.2422,3.3495,4.0878,5.5385,7.0563,9.6803,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.0526,2.4458,1.7713,0.79363,-0.84855,0.28442,6.3976,10.287,10.495,8.9397,4.8083,2.3088,2.5362,2.5299,2.3937,1.353,0.081563,-0.14395,-0.55072\n-2.406,-2.7713,-2.3212,-0.73718,-0.9163,-1.2473,-1.5012,-1.4935,0.8576,1.6108,2.5699,2.8858,3.315,3.7702,3.9147,3.8349,4.7388,5.7976,7.7606,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.40856,0.16107,1.3168,NaN,NaN,NaN,10.814,7.8475,3.4252,2.1339,2.4135,2.4728,2.2177,0.97889,0.31766,0.375,-0.88204\n-2.8952,-3.5423,-3.3127,-2.6727,-2.1888,-1.4305,-0.56367,0.29733,0.73878,0.89305,1.7302,3.1986,3.5534,5.7328,5.568,4.5366,3.857,5.3892,5.8056,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.96555,NaN,NaN,NaN,NaN,NaN,NaN,4.4995,2.3029,2.5993,3.0301,2.0924,1.0662,0.097918,0.16057,0.016107,-0.92115\n-2.1083,-2.0658,-2.5101,-2.8612,-2.7767,-1.4093,0.20981,1.0221,1.1934,1.3579,1.9585,3.5141,3.6468,4.7907,4.8001,4.0748,3.7479,5.8934,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3309,NaN,NaN,NaN,NaN,NaN,5.9027,4.1612,2.7437,2.5566,2.3614,1.4082,0.60334,0.44119,0.62449,0.29524,-0.73109\n-1.2648,-0.97623,-1.1933,-1.9281,-2.433,-1.4093,0.76462,1.1368,1.0523,0.9822,1.1541,1.7921,3.0426,3.6224,3.7014,3.9838,5.3145,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4039,1.8668,NaN,NaN,NaN,NaN,5.9821,5.2739,4.2552,3.1161,2.4358,2.0502,1.4178,0.54371,0.46122,0.89649,0.40176,-0.88496\n-0.7733,-1.0709,-1.0373,-1.3165,-1.3377,-0.92968,-0.16097,0.76175,1.081,1.094,1.0081,0.84493,2.9313,3.7464,3.7277,5.2711,6.3331,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.19048,2.1422,1.0073,-0.8472,0.60384,2.2084,3.9691,4.2536,4.1686,4.7753,3.9631,1.7865,1.5915,1.2293,0.45161,0.098714,0.33151,-0.31884,-1.228\n-0.18027,-1.0477,-0.97832,-1.7226,-1.122,-0.59093,-0.086221,0.37363,0.83865,1.234,1.2912,1.3161,2.3412,2.8351,3.4148,5.7877,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.20287,2.2995,0.91852,0.7936,1.3993,1.4455,2.8894,3.1699,3.1678,3.6021,3.7114,1.8744,0.83587,0.49379,0.081673,-0.08229,-1.1217,-0.48749,-0.38136,-0.90186\n0.25757,-0.31463,-0.90388,-0.63575,0.25409,-0.073541,-0.091104,0.66089,1.458,1.7295,2.0877,2.0049,2.0487,1.8267,2.2476,3.4564,5.3012,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.6823,-0.054672,-0.034168,1.8911,4.3923,2.9125,2.353,2.3156,1.6484,1.5994,1.1557,0.28317,0.22178,-0.33464,-0.44917,-0.59291,-0.48023,0.43535,0.52758\n-0.091261,0.038345,-0.41866,0.79489,1.0948,1.0483,1.2991,1.7321,2.0755,2.4232,2.5047,2.4416,1.9556,1.9356,2.1105,2.7758,3.8083,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.2838,-0.7332,0.56317,3.0098,3.0234,2.8098,2.6907,2.5946,1.3887,0.85489,0.23107,-0.012807,-0.071284,-0.12602,-0.2375,-0.35424,-0.57515,0.065642,0.36939\n-0.10594,1.1839,1.2482,0.64216,1.6573,1.5801,1.649,1.8408,2.2328,2.4951,2.4506,2.3726,2.4212,2.301,3.077,3.825,3.8939,NaN,NaN,NaN,NaN,NaN,3.3064,2.896,NaN,NaN,NaN,NaN,-0.34321,1.322,2.0765,1.9115,2.1214,1.5833,1.5771,0.90071,0.97184,0.58254,0.20922,-0.031746,0.10842,0.2954,0.06942,-0.50734,-0.96234,-1.1415,-0.64279\n0.35261,0.8468,1.0731,0.45595,0.58456,1.8733,2.0434,2.099,2.4015,2.6908,2.7398,2.5919,2.4144,2.0491,3.1284,3.9867,3.5571,2.0954,1.7133,1.1132,-0.14084,1.277,4.0823,4.8856,5.1988,NaN,NaN,NaN,0.39867,1.7639,2.1928,2.1831,0.48442,0.80089,0.33591,-0.4101,-0.37142,-0.32631,0.47823,0.34707,0.1342,0.24277,0.058979,-0.94265,-2.2593,-2.8124,-1.6006\n0.49872,0.60367,0.66709,1.2354,1.5251,2.1477,2.3766,2.2664,2.1057,1.8772,2.2257,2.5362,2.7229,2.3669,3.2428,3.7626,3.1022,2.1033,1.3562,0.47808,-0.096925,0.24955,1.7569,2.2217,2.3411,0.41173,0.17418,-0.05147,0.66017,1.4206,2.0952,2.265,1.8798,0.084625,-0.35973,-0.43944,-0.50469,0.38722,0.87983,0.38946,-0.092096,-0.69797,-0.68694,-0.14721,-2.1723,-2.9499,-2.0392\n0.54083,1.0779,1.0157,1.8239,2.0734,1.9989,1.8793,1.7314,2.3033,2.3837,2.1341,2.4056,2.6871,2.7051,2.3674,2.3938,1.4153,0.93813,1.0972,0.23273,-0.19328,0.11671,0.9987,1.4705,1.6952,0.74822,0.47052,0.27085,0.5703,0.82336,2.0092,2.5687,2.0996,1.0233,0.37065,0.54828,0.093064,0.59169,0.99637,NaN,NaN,NaN,NaN,NaN,-0.9001,-0.73675,-1.4836\n1.194,1.3928,1.6225,1.934,2.1151,1.5091,1.3145,1.4934,2.5073,2.8989,2.7475,2.3732,2.6133,2.2601,1.6245,1.4415,0.79083,0.57216,0.3457,0.0022745,-0.43008,-0.82458,0.23673,0.88919,1.1651,1.0845,1.6775,0.16123,-0.14634,0.25431,1.3723,1.8683,1.2817,1.0258,0.024529,-0.46195,-0.73906,0.49671,0.83199,NaN,NaN,NaN,NaN,NaN,-1.4683,-0.25718,-0.61716\n2.2139,1.9617,1.4671,1.708,1.9853,1.9081,1.699,1.7341,2.0106,2.2164,2.6366,2.6366,2.4058,2.0069,1.015,0.5642,0.69206,0.83736,0.068799,0.053646,0.61841,1.4768,1.3229,0.6197,-0.058346,-0.31315,1.1069,-0.80657,-0.14938,0.24079,0.82369,0.93658,0.57229,-0.15985,-0.85482,-0.83941,-0.50175,-0.065444,0.34943,1.448,2.271,NaN,NaN,NaN,-0.81891,-0.15347,-0.5607\n2.5359,2.2663,2.1053,2.3205,2.4673,2.6007,2.6747,2.6121,2.2276,1.3703,2.1159,2.7362,2.5919,2.0759,1.0697,0.68972,1.0957,0.61784,-0.058786,-0.60916,-0.033262,1.0443,0.89185,0.45988,-0.078936,-0.42262,-0.86879,-1.8335,-0.58456,0.12315,0.21089,-0.0055072,0.082151,0.22301,0.06637,0.13167,0.36606,0.83387,0.76651,0.6097,2.1214,1.5392,0.21767,-0.677,-0.27217,-0.1747,-0.9733\n2.4751,2.8103,2.7067,2.6145,2.1211,1.7911,1.8796,2.0515,2.262,1.833,1.9745,2.4343,2.23,1.6656,0.99905,0.8618,1.1741,0.52594,0.014244,-0.29432,-0.26184,0.29712,0.50892,0.06848,-0.42333,-0.77333,-1.3291,-1.33,-0.69265,-0.32857,-0.70045,-0.30687,-0.20267,0.11283,0.67836,0.94054,1.2063,1.3341,1.0203,0.71008,1.0148,0.89311,0.49799,-0.41608,-0.26863,-0.52093,-0.61577\n1.2435,2.1315,2.6907,2.4499,2.25,2.2814,2.2853,2.1251,2.2873,2.5242,2.209,1.7853,1.5568,1.666,0.81647,0.80575,0.7875,0.90653,0.6068,0.087115,0.16369,0.35568,0.35657,0.043728,-0.798,-1.9668,-2.175,-1.1579,-0.15135,-0.19854,-0.8867,-0.8749,-0.39601,0.0688,0.2931,0.77023,1.2651,1.323,0.69885,0.58005,-0.092594,-0.2493,-0.901,-1.3563,-0.66642,-0.52846,-0.38647\n1.4206,1.9607,2.7291,2.3612,2.5814,3.058,2.4275,2.0643,2.2314,2.7763,3.0329,2.2496,1.5229,1.1194,0.8814,0.90984,0.8632,0.96311,0.26934,-0.22345,-0.029928,0.51885,0.12521,-0.16528,-0.9591,-1.4838,-1.1599,-0.61989,-0.0009644,-0.89869,-0.9795,-1.0895,-0.74324,-0.36521,-0.65437,-0.69648,0.080907,-0.13794,-0.50898,-0.58031,-0.91439,-0.83001,-0.57383,-0.29399,-0.64155,-0.7259,0.00079465\n0.89262,2.2672,2.9491,3.2441,3.4115,3.2221,2.3308,2.2157,2.8246,3.1037,3.5994,3.2017,1.812,0.76461,0.29897,0.10386,0.13862,0.067852,-0.42263,-0.55837,-0.082106,0.5492,0.0024927,-0.36009,-0.57183,-0.81872,-0.47891,0.28159,-0.24119,-1.2459,-1.1766,-0.95404,-1.1534,-1.2207,-0.95949,-0.43352,-0.339,-0.40604,-0.55111,-1.3208,-1.1295,-0.86994,0.083045,-0.096262,-0.95893,-0.57026,-0.40372\n-1.0288,-1.5724,2.6823,3.9829,4.399,3.2529,2.1984,2.748,3.5634,3.6089,3.5899,3.2438,2.5018,1.2317,0.44655,0.14102,0.078937,-0.13683,0.03065,0.20811,0.48854,0.54906,0.089557,-0.17544,-0.4055,-0.65615,-0.25573,-0.21891,-0.83859,-1.2631,-0.77119,-0.88841,-1.4057,-1.0545,-0.40977,-0.0066793,0.015417,-0.0038934,-0.03606,-0.66799,-0.7916,-0.60147,-0.31696,-0.69064,-0.94743,-1.0143,-1.3729\n0.95802,-0.2611,-0.355,1.6579,3.3642,3.0705,2.3868,1.5939,1.9944,3.2308,3.5389,3.4675,2.0688,1.2473,0.10348,0.050822,0.32835,0.50293,0.71225,0.61243,0.78909,0.36071,0.021608,-0.30035,-0.47551,-0.57474,-0.43661,-0.76437,-1.1126,-1.3676,-0.98284,-1.2482,-1.4537,-1.5294,-1.2281,-0.43478,-0.46323,-0.39398,0.062926,-0.0085559,-0.35898,-0.64693,-1.0378,-1.09,-0.96257,-1.218,-1.3236\n1.6939,0.81696,0.063636,0.77464,1.7221,2.3424,2.2757,3.1124,4.4001,5.5261,3.5965,3.1836,0.47207,-0.48022,-0.87273,-0.32289,0.35658,0.71655,0.57867,0.30124,0.13035,0.0576,0.11751,-0.14236,-0.42211,-0.5297,-0.61582,-0.88023,-0.96724,-0.67018,-0.86173,-1.0239,-1.4,-1.3784,-1.2263,-0.89706,-0.69187,-0.52117,0.12469,0.25354,0.42053,0.15915,-0.50077,-0.97169,-1.4698,-1.5024,-0.88987\n1.5486,1.9815,2.0148,2.8139,2.9061,2.9678,3.5234,3.3092,3.7253,4.547,3.7867,3.2745,0.76378,1.2067,0.30319,0.61346,0.93112,0.95827,0.15645,0.77583,1.0757,0.09824,-0.20675,-0.14383,-0.37897,-0.10706,-0.389,-0.76319,-1.1701,-0.60126,-0.62217,-0.76484,-1.3014,-1.4741,-0.86631,-0.54223,-0.57877,-0.27523,0.17248,0.076086,0.10169,0.0061054,-0.39388,-0.93632,-2.0794,-2.217,-1.2463\n1.2194,0.56975,0.44151,2.313,2.7549,3.5672,3.3559,3.4999,3.7606,4.1379,3.1844,1.5392,0.55895,0.20451,0.39518,1.7305,1.4769,0.41568,0.078928,0.47299,0.29558,-0.11998,-0.2358,-0.24082,-0.37589,-0.58635,-0.82914,-1.154,-1.4535,-1.4616,-1.306,-1.0383,-1.6455,-1.3933,-0.51281,-0.31499,-0.5125,-0.27419,0,-0.23017,0.13907,0.065188,-0.6042,-0.76798,-1.4775,-2.0436,-1.938\n0.19861,-0.92963,-0.5047,0.78399,0.93597,1.2993,2.4404,3.3134,2.8518,2.3422,2.4655,1.2292,-0.20518,-0.89344,-1.1216,-0.53669,0.82718,-0.77378,-0.81401,-0.58659,-0.95391,-1.1546,-0.68891,-0.60394,-0.62783,-0.71945,-0.79015,-0.8057,-0.87246,-1.1128,-1.5597,-1.8853,-1.8302,-1.004,-0.29663,-0.40844,-0.54141,-0.34306,0.0243,0.024288,0.085923,-0.011935,-0.86822,-1.081,-1.3606,-1.2429,-0.92891\n-0.4378,0.5138,1.4865,1.9304,1.7855,1.2707,1.8911,1.9278,2.6337,2.2013,2.045,0.16493,-0.46996,-0.45325,0.3035,0.099172,-0.80401,-0.34292,1.3082,-0.49235,-1.2007,-1.9329,-1.719,-0.87204,-0.81785,-0.56983,-0.52371,-0.7555,-0.74131,-1.1425,-1.4418,-2.0065,-2.318,-1.8728,-0.76384,-0.42205,-0.37555,-0.27335,0.13489,0.010682,-0.48354,-0.90511,-1.1759,-1.6304,-2.0116,-1.7148,-1.3188\n1.6504,1.3767,1.505,2.12,1.7673,1.2199,1.9067,2.1281,2.9441,3.5937,2.7377,1.8967,0.85102,-0.34258,0.086395,0.43372,0.35498,-1.3026,-0.45881,-0.39196,-0.30611,-0.77671,-1.5205,-0.7902,-0.36356,-0.64386,-0.98851,-1.7281,-1.8491,-1.3233,-0.87531,-1.3444,-1.9065,-2.0493,-0.91963,-0.47756,-0.64341,-0.3907,-0.3299,-0.5922,-1.1025,-1.5962,-1.5903,-1.6078,-1.9276,-1.8768,-1.5654\nNaN,NaN,NaN,NaN,3.2287,1.5646,2.9482,5.2355,3.8912,4.4139,2.0691,0.56861,-0.22036,-0.17517,-0.3337,-0.19596,1.82,2.5725,-0.39513,-0.5691,-0.13144,-0.13066,-1.7543,-0.21904,-0.022528,-0.88698,-0.81986,-0.90589,-0.92574,-0.78082,-0.99869,-1.1604,-1.0148,-0.71024,-0.7332,-0.48131,-0.17061,-0.45008,-0.60232,-0.98133,-1.2847,-1.2145,-0.90647,-1.5828,-1.7246,-1.8176,-1.4984\nNaN,NaN,NaN,NaN,3.275,NaN,NaN,NaN,6.6563,3.5027,1.2554,-0.44607,-0.67127,-0.11376,0.57004,1.3376,2.5883,1.9681,-0.64586,-0.97539,-1.4754,-1.7316,-0.62379,1.1274,0.027689,-0.47653,-0.28058,0.32941,0.60191,0.052336,-0.30788,-0.57003,-0.80115,-0.65988,-1.2227,-1.2183,-0.67979,-0.72901,-0.72196,-0.47891,-0.19147,-0.54209,-0.91968,-1.2322,-1.4281,-1.5664,-1.4319\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5728,2.9506,3.1757,2.3997,1.152,-1.8092,-1.3878,-0.44697,-1.4287,-1.6645,-0.26697,1.1537,0.71322,0.27251,0.15133,0.18208,0.87243,0.62365,-0.054145,-1.0101,-1.2678,-1.76,-1.9922,-1.2179,-1.136,-0.98791,-0.9533,0.034586,0.020533,-0.57623,-0.96326,-1.2568,-1.0333,-0.68946,0.30448\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4141,-0.9033,-1.2421,-0.044061,1.482,0.9861,0.91889,0.64609,0.37777,0.066334,-0.014248,0.54789,1.0805,0.35868,-0.086181,-0.89512,-1.5523,-1.9317,-1.3598,-1.2507,-1.4264,-1.0307,0.1044,0.10159,-0.64956,-1.3241,-1.7364,-1.272,-0.79333,-0.042798\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.076787,-1.275,0.84986,1.5076,1.5627,0.90698,-0.14619,0.87051,0.064635,-0.11836,0.20686,0.32029,0.062929,-0.14347,-0.83513,-1.3326,-1.7159,-1.4132,-1.1226,-1.4284,-1.4136,-0.93483,-0.51782,-0.78335,-0.81543,-1.3983,-1.3313,-0.36544,0.74573\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.21973,0.67991,NaN,NaN,-0.55753,-2.0786,-2.9125,-0.80466,-0.4962,-0.27516,0.35599,0.23091,-0.39915,-0.51345,0.49129,-0.056383,-1.1958,-1.3494,-1.0777,-0.81323,-1.1428,-1.1145,-1.318,-1.2437,-0.89585,-0.91694,-0.7273,0.13773,1.4325\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.95743,-1.1588,-1.4586,-0.33269,0.62979,0.67782,0.76881,0.38862,-1.2609,-1.6855,-1.3006,-1.0734,-1.8956,-2.1066,-1.8188,-1.0177,-1.3654,-1.4558,-1.2513,-1.0497,-1.0304,-0.94808,-0.38732,0.54059,1.063\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.90246,-0.82856,-0.57864,-0.13711,0.78612,1.0658,0.57107,0.046013,-0.58771,-1.4565,-1.5366,-1.0966,-1.5748,-2.0819,-2.082,-0.73408,-1.6362,-1.5574,-1.3586,-0.8775,-0.53088,-0.77381,-0.097701,0.34309,0.37783\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.0294,-0.23507,0.2397,0.78008,1.6119,2.625,1.6866,1.2185,1.0295,0.38913,-0.0082691,-1.0722,-1.0937,-1.774,-1.8372,-1.3314,-0.65431,-0.85978,-0.96037,-1.1603,-1.0231,-0.53729,-0.64212,-0.39967,0.12571,0.62127\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0511,-0.99644,-0.21353,0.79772,1.9102,2.8018,2.1368,0.22467,0.57191,0.73081,0.12079,-0.50953,-0.98062,-1.3306,-1.2373,-0.36621,-0.70289,-1.5386,-0.95867,-0.60393,-0.7124,-0.86928,-0.63281,0.40782,0.69689,0.42362\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070115-070326.unw.csv",
    "content": "0.33527,0.133,0.24558,0.95617,0.54222,0.26864,-0.21003,-0.31068,-0.19587,0.63977,0.20282,0.46395,1.2129,2.1487,2.0527,2.3196,1.7554,2.7201,3.0203,3.1877,3.7358,2.548,1.7187,1.8114,2.6922,2.414,2.9348,4.8801,4.5285,3.5566,3.418,4.1607,4.2462,4.2408,4.0293,3.6585,2.5133,1.9083,-2.1842,-1.8356,-1.3579,-1.6642,-1.5198,0.02319,-0.68333,0.064549,-0.011289\n-0.16486,-0.25476,-0.067273,0.63723,0.32703,0.052527,-0.018607,-0.18343,0.0075378,0.26402,0.91507,1.4112,2.0366,2.0563,1.9135,1.9771,1.9147,1.9065,2.2167,2.675,3.4677,2.0215,1.3469,1.244,1.6345,2.6662,4.0391,4.651,3.6216,3.2233,4.4944,4.5782,4.5347,4.2494,4.7169,4.1537,3.0131,1.2658,-1.5799,-1.1756,-0.92905,-1.6606,0.18937,0.82828,0.72921,0.40928,0.31956\n-0.22577,-0.19377,0.056258,0.073478,0.15002,-0.61853,-0.19235,0.10942,-0.41289,0.080911,1.1586,2.154,2.1138,1.9777,2.1321,1.9752,2.0968,1.8246,1.7566,1.5406,1.6003,2.237,2.3956,1.9131,2.5542,4.046,4.7521,4.7963,3.482,3.2072,4.5536,4.4371,5.1252,4.814,4.563,4.4385,3.8759,2.3122,1.1862,0.62129,-1.016,-1.1313,0.46423,-0.20654,0.30779,0.20492,-0.33209\n-1.1222,-0.36661,0.45156,-0.045701,-0.038065,-0.79565,-0.30603,0.15638,0.29655,0.53036,1.3443,1.8476,1.558,2.1387,0.80754,1.3283,2.6365,2.4009,1.8508,1.7213,0.95448,1.8865,2.6707,2.8291,3.6519,4.8666,5.1038,4.933,4.6643,4.2515,4.766,4.9328,5.6604,4.9075,4.2492,4.1989,3.1981,2.1602,1.5223,-0.079097,-0.79091,-0.46106,-0.29154,-0.7213,-0.92242,-0.50121,-1.2869\n-0.90551,-0.88836,0.067945,-0.10353,-0.40765,-0.41294,-0.15127,0.56804,0.060794,0.11068,0.2751,0.31122,0.68876,1.3847,1.3299,1.4088,2.1685,2.7706,2.1377,1.3917,0.1644,2.1915,2.8639,3.4696,4.2013,5.3513,4.97,4.6066,5.8591,5.4297,5.8798,4.7418,6.4522,5.049,3.906,3.2646,2.1779,1.5018,0.84354,-0.26697,-0.25803,0.25663,-0.25704,-0.64609,-0.5057,-0.52777,-0.85719\n-0.88183,-1.32,-0.48613,-0.027719,-0.45215,-0.27549,-0.55078,-0.74581,-0.49744,-0.078229,-0.1709,-0.56672,-0.21372,0.88746,1.4448,1.6854,1.6855,1.589,1.9777,2.3871,2.5535,2.6461,3.5899,4.3486,4.356,4.9846,5.2656,4.7075,5.5904,4.8617,4.8937,4.4489,5.4596,5.2296,4.0487,3.7288,1.9177,0.91098,0.32146,-0.6497,-0.69314,-0.33862,-0.23809,-0.52639,-0.80797,-0.84862,-0.83942\n-1.6266,-1.8774,-1.7292,-0.56462,-0.34969,-0.37307,-0.80624,-1.2508,-0.033429,0.44172,-0.24964,-0.50389,-0.18636,1.1152,0.53755,1.3149,1.3003,1.4388,1.6236,1.3839,2.036,2.4221,2.9814,3.6684,4.4325,5.6503,5.23,4.991,4.9426,5.0691,4.4783,5.2003,5.5594,5.2064,4.3295,3.4302,2.1974,0.34571,-0.47394,-1.9881,-1.4793,-1.0875,-0.72484,-0.66222,-1.2529,-1.5456,-2.0864\n-2.3495,-2.4467,-2.5827,-0.93154,-0.5824,-0.54347,-0.69822,-0.71462,0.28263,0.62261,-0.28227,0.21335,0.45256,0.83399,-0.06652,0.61565,0.79606,0.96523,0.81405,0.92314,2.0602,2.4552,2.2923,3.0574,4.2745,5.2455,5.307,4.5655,4.1255,4.607,4.9254,5.3804,5.4835,5.6442,4.586,2.9822,1.0735,-0.36783,-1.1947,-1.7319,-1.5904,-1.7281,-0.79947,-1.2947,-1.5839,-2.2413,-2.3272\n-2.2791,-2.5453,-1.9554,-0.90455,-0.59542,-0.60308,-0.76911,-0.27517,-0.44342,0.015125,0.1976,-0.23777,0.31255,0.43092,0.60016,0.74306,0.62565,0.38187,0.30906,1.1186,2.0636,2.8036,2.3159,2.3504,4.9994,5.0794,4.0817,4.2563,4.3361,4.8503,4.8026,5.3394,5.2822,4.9892,3.929,1.7866,0.082815,-0.98937,-0.84986,-0.66857,-0.6891,-0.39637,0.074113,-1.6822,-1.6492,-1.8208,-2.2001\n-1.9502,-1.8572,-0.24965,-0.9916,-0.95996,-0.90513,-1.2928,-0.83718,-0.74472,-0.34518,-0.19435,-0.94712,0.22192,-0.95154,0.17221,1.0114,0.86316,0.59374,0.46868,1.2251,2.4784,2.4691,1.9816,2.5446,4.4345,4.5956,4.854,4.5133,4.8246,4.731,5.2897,5.8139,4.0373,4.3775,2.3335,1.131,0.95565,0.030508,0.011241,-0.34233,-0.36061,-0.16278,-0.010755,-0.38568,-0.46134,-1.0761,-1.7589\n-1.7636,-0.031715,0.89524,-1.2152,-1.9649,-1.4454,-2.232,-1.2255,-0.68328,-1.1248,-1.2311,-2.0014,-0.97045,-1.4071,-0.71941,-0.27894,0.85808,1.5742,1.8812,2.7079,2.5023,2.2086,2.7227,3.1043,4.1948,4.6937,4.5693,4.5534,4.8473,5.2651,5.9453,5.7228,4.118,3.4819,2.3045,2.322,1.7027,1.0618,1.03,0.43883,-0.063221,0.43263,0.45059,-0.33363,-0.16518,-0.6659,-1.0224\n-1.0283,0.51737,-0.45027,-1.3202,-1.7787,-1.7883,-2.3419,-0.66872,-0.90701,-1.9901,-2.8237,-3.2068,-2.011,-1.1382,-0.74279,-0.39111,0.7257,2.734,2.9424,3.9187,2.7839,2.5235,2.8553,3.0077,4.156,5.4341,4.3111,3.9037,3.9632,5.6329,5.7713,4.9298,4.1477,3.0968,2.0997,1.9637,0.59949,0.67093,0.65839,0.37437,0.15608,0.67687,1.4958,0.45111,-0.014319,-0.56861,-0.7836\n-1.9631,-1.8139,-2.1205,-1.3847,-1.2385,-1.3136,-2.3304,-0.77094,-1.1065,-2.3289,-1.8497,-2.5875,-2.4221,-0.84172,-0.3325,-0.28765,0.507,2.1357,3.1943,4.0039,3.659,3.5915,3.5494,3.1411,4.0585,5.0209,3.9661,3.6728,4.1136,5.6618,5.1888,3.7696,3.0684,2.4968,2.1363,1.5593,-0.15767,0.81915,0.026254,0.48553,0.43253,0.84125,1.0492,0.84934,-0.20903,-0.44378,-0.57579\n-2.8845,-3.7821,-3.6917,-2.1091,-0.78499,-2.1404,-2.2248,-1.5268,-1.8174,-1.5744,-1.508,-1.5172,-1.0606,-0.77518,-0.29385,-0.672,0.11138,1.1668,1.8034,2.5822,3.2661,3.4606,3.9662,3.9963,3.8112,4.727,4.2915,4.1348,4.566,4.912,4.4517,3.3795,2.3742,2.6559,2.0847,1.3515,0.60543,0.2942,-0.11838,0.8953,0.23404,1.1179,0.2524,0.3351,0.078321,-0.37965,-0.35414\n-3.155,-4.4039,-4.0732,-0.4936,-1.3166,-2.1265,-1.6808,-1.6788,-1.2024,-0.98916,-1.6175,-1.0728,-1.1647,-0.99268,-0.50835,-0.26164,-0.075994,0.74766,1.6084,2.929,3.1211,2.9816,4.4062,4.0089,3.5451,4.6271,4.6251,4.4083,4.0466,4.3443,4.2418,3.8317,3.5337,3.3338,1.9709,1.2108,0.88138,1.0518,1.3176,1.651,0.69923,1.5901,0.37813,-0.42005,-0.70201,-1.0072,-0.88207\n-2.2044,-3.1836,-3.6311,-0.80437,-0.48866,-1.6957,-1.0012,-1.2773,-1.2468,-1.0402,-1.5895,-1.6488,-1.4096,-0.31946,0.28011,0.3983,0.41733,0.80996,1.8621,2.5715,3.4231,4.2217,4.0751,3.5421,3.6986,4.3769,4.4407,3.9215,3.3864,4.0131,3.6862,3.6535,3.6405,3.2673,2.4238,0.64489,1.7012,1.7578,1.8282,1.9806,0.97893,1.317,0.22156,-0.6089,-1.0375,-2.0036,-1.5539\n-1.5091,-1.2123,-1.2672,-1.4249,-2.0661,-2.0141,-1.121,-0.95587,-0.72404,-0.93154,-0.52877,-0.10305,-0.548,-0.07068,0.4818,0.81827,0.25695,1.338,1.3118,1.2002,2.3958,3.964,3.3176,3.6559,4.7087,4.387,3.7809,3.1698,3.3839,4.3214,3.4877,2.999,3.0961,2.3445,1.6722,1.1817,2.0956,1.6788,1.6685,2.6513,1.0814,1.2694,0.2304,-0.26084,-0.31254,-1.5672,-1.7937\n0.3406,-0.24369,-1.2815,-2.174,-2.6761,-2.4651,-1.1097,-1.1782,-0.75607,-0.4962,-0.50685,-0.53058,-0.40838,0.1458,0.088151,-0.1604,0.29352,1.5771,0.96793,0.66636,0.99229,3.1737,4.0929,3.3366,4.4382,4.2471,3.6872,3.3201,3.1157,4.0355,3.599,3.0304,4.6173,2.5859,2.0426,2.4121,2.3443,1.6719,1.3067,1.4408,0.67085,0.0010338,0.054284,-0.79843,-0.95906,-0.84802,-1.0746\n-1.4346,-1.5128,-1.8516,-2.8036,-3.128,-2.3916,1.2128,-1.7867,-0.8932,-0.82013,-1.4128,-0.79125,-0.67659,-0.22504,-0.55408,-0.62983,0.8721,1.6493,1.4072,0.83541,2.4446,3.6931,5.5636,4.7589,4.1637,4.9432,3.9843,3.7211,2.9075,3.5493,3.8683,3.8164,4.2436,3.7649,2.8905,3.204,2.9087,2.5714,1.3359,0.92093,0.46045,-0.076198,-0.043897,-0.65486,-0.74216,-0.82555,-1.2445\n-2.7622,-1.4823,-1.8161,-2.426,-2.4913,-1.165,-0.10548,-1.6044,-1.2891,-0.75711,-1.3108,-1.0782,-0.68419,-0.49985,-0.82139,-0.86443,1.4212,2.7881,2.7742,2.2037,3.1845,3.7117,4.1727,6.3824,5.1782,4.5154,4.5456,4.6772,4.0031,2.9789,3.848,4.0493,4.1225,3.8513,3.1172,3.1038,3.099,3.0884,1.2416,0.86933,0.55214,0.28193,-0.38559,-0.31096,-0.78214,-1.3614,-0.93808\n-1.3174,-0.45197,-1.4785,-2.9898,-3.9203,-1.7653,-0.92244,-1.2887,-0.64225,-0.9141,-1.1367,-0.35768,-0.76158,-1.0487,-0.64432,-0.03703,0.76302,1.3346,2.8083,2.2874,1.4666,2.4101,3.2921,4.4931,4.4405,4.5585,4.7828,5.2878,4.3698,3.2572,4.2331,3.7009,4.2522,4.2175,3.4129,3.1863,3.3261,3.2242,1.5968,1.3786,0.95442,-0.55505,-0.69974,-1.3411,-1.0532,-0.8355,-0.48074\n-0.4662,0.20112,-1.4456,-2.1704,-1.9619,-1.3633,-1.1179,-0.42818,0.14166,-1.6486,-1.5409,-0.85915,-1.0207,-1.847,-0.92633,-0.10117,0.079814,0.5024,0.84844,1.4799,2.5737,2.569,2.5673,2.7463,4.0901,5.0067,5.2078,5.2181,3.8333,3.5734,4.5384,3.9044,3.9105,3.9363,2.7686,2.8265,3.0773,2.3793,1.5366,1.2424,0.67168,-0.76037,-0.79266,-0.70855,-0.28891,-0.084308,-0.25551\n0.10902,-0.46733,-1.666,-0.63519,-0.70093,-1.3235,-0.10711,0.32443,-0.35854,-2.1425,-2.0886,-1.5231,-1.5699,-1.1733,-0.027165,0.96699,0.30232,0.54283,0.83603,1.5218,2.7764,3.1378,3.483,2.4429,3.6632,4.5935,3.8177,3.8748,3.0243,3.1458,3.9902,3.9198,3.6485,3.6377,3.6824,3.0395,3.3629,1.9919,1.4181,0.79271,0.1788,-0.49713,-0.64417,-0.15898,0.026454,-0.00069618,-0.94736\n-0.93376,-0.24919,0.60199,1.4007,0.8866,-0.18363,-0.80365,-1.3389,-1.7912,-2.8281,-3.6795,-3.078,-1.342,-0.52173,0.33626,2.6406,1.8109,0.53403,0.70757,1.4135,1.9228,2.6491,3.0542,2.4031,2.3346,3.0686,2.2576,2.1526,2.6652,2.8203,2.851,2.4639,2.8921,3.1703,3.3706,3.0298,2.3078,1.8901,1.74,0.73175,0.47268,-0.28108,0.11952,0.077643,0.1326,-0.27952,-1.3271\n-1.0211,-0.61385,0.071837,-0.35227,0.072202,-0.71798,-2.4126,-1.2939,-1.9659,-3.4955,-3.4921,-2.3741,-2.6009,-1.275,-0.44328,0.064765,-0.18339,-1.2742,-0.78273,0.4068,0.94767,1.0801,0.54355,1.5593,1.2125,0.76275,1.1086,1.4476,1.6725,1.8636,2.354,2.402,2.4235,2.7086,3.0836,1.5957,2.8458,2.18,2.0294,1.7257,0.87481,0.71948,0.80873,0.62346,0.53222,-0.21935,-1.3845\n-0.54848,-0.53172,-0.72946,-0.9123,-0.68964,-1.6533,-1.4441,-0.74153,-0.75115,-2.7409,-2.9546,-3.2079,-3.3123,-3.0685,-1.3789,-0.53796,-1.2842,-1.4314,-0.90149,-0.21537,-0.2941,-1.9283,-0.86316,1.3069,1.7032,1.5425,1.9845,1.8729,1.6348,1.6545,1.8322,1.9836,0.95769,2.9921,2.9352,1.6916,2.9533,2.5432,2.4913,3.5566,2.0463,1.0661,1.0909,0.38968,0.7368,0.41272,0.24125\n0.89396,-0.067513,0.011067,-1.3133,-1.3127,-1.2916,-0.77221,-0.55954,-1.441,-2.763,-3.0213,-3.5413,-4.1729,-3.4579,-2.1958,-1.1913,-1.0282,-1.0427,-1.4574,-1.6882,-2.0093,-2.0918,-0.84473,1.4527,1.4918,2.5981,2.254,2.1119,2.1971,2.8528,2.2153,1.8895,1.5496,2.8165,2.9192,2.5038,2.6039,2.3138,3.5017,4.0099,2.3507,1.4213,0.79624,0.457,0.56717,0.77809,1.0573\n1.7303,1.2873,0.33471,-1.7012,-0.91601,-1.4979,-2.4593,-1.9527,-1.965,-1.5968,-2.6137,-3.4553,-3.7773,-3.1823,-2.0033,-1.7739,-0.74924,-0.6682,-0.9447,-1.5778,-2.4339,-0.68514,0.27637,0.30123,-0.23411,1.5424,2.3498,2.2214,2.1441,2.5271,2.6495,2.8542,2.6731,2.4578,2.4631,2.8986,2.9568,3.4451,3.1455,3.0352,1.8172,1.6909,0.98915,0.94385,1.2685,0.96925,0.73279\n2.0935,1.3309,-0.57871,-0.19436,-0.83786,-2.2651,-3.2258,-3.3295,-2.5546,-1.0374,-2.2069,-2.1454,-2.6042,-2.051,-1.9884,-1.5557,-0.47849,-0.082635,-0.75716,-1.8251,-2.8834,-1.2151,-0.88332,-0.82216,-0.63217,1.0652,1.772,1.8261,1.1772,1.5692,2.4358,3.1573,3.2663,2.5297,2.6518,3.3305,3.8321,4.3595,3.6133,2.5548,1.7987,1.9015,1.6088,1.8808,1.3551,0.86107,0.57746\n0.76903,1.8242,2.6703,3.3328,-1.5824,-2.7656,-1.5196,-1.8695,-2.5033,-1.6924,-1.9174,-1.3811,-1.3089,-1.0478,-1.4064,-1.1009,0.023066,0.40542,0.076685,-0.27887,-1.6844,-1.3309,-0.94684,-0.62062,0.35388,1.2842,1.4058,0.36204,1.884,2.6374,3.7317,3.5866,3.539,3.1558,3.4213,3.1165,3.6461,4.0003,3.0806,2.3188,2.0938,2.0075,1.5956,1.5887,0.91978,0.79564,0.5148\n1.1801,2.3283,3.4833,0.13323,-1.9263,-2.0521,-1.035,-2.081,-2.6738,-2.3751,-1.4941,-0.91605,-0.7699,-0.69866,-0.88232,-0.72573,-0.050893,0.53445,0.93355,1.7378,2.5418,2.057,0.50084,0.0025663,0.3255,1.4217,1.5416,0.84201,2.3154,3.1773,4.4882,3.826,3.1816,2.8105,3.6078,2.893,2.7915,2.936,2.8849,2.6149,1.9839,1.4318,1.4335,1.1569,0.47369,0.74328,0.81993\n0.73832,1.3626,1.6674,-0.87486,0.47655,-0.19429,-1.4407,-1.9134,-1.9064,-1.0484,-0.44627,-0.88863,-0.88369,-0.30062,-0.48762,-0.24784,0.24642,0.38617,0.8194,1.0554,3.3422,2.4934,1.0437,1.1601,0.78,1.4476,3.1681,1.0415,-0.43266,-0.48967,2.452,3.3271,3.2091,3.3272,3.6605,2.994,2.7426,2.3558,2.866,3.2511,1.9042,1.1406,1.1706,0.61847,0.65407,0.88531,0.69188\n-0.21674,0.035083,-0.165,-0.56968,-1.0713,-0.14964,-0.64105,-0.8282,-1.6246,0.1332,0.60526,0.21628,0.03307,1.5979,2.2703,1.0138,1.214,1.5176,0.9324,0.51701,1.4656,0.94617,0.28224,2.502,2.1185,2.5723,3.7055,-2.6326,-3.7977,-2.5461,0.58549,4.1242,4.7548,4.5743,3.5777,2.6887,2.4613,1.8192,3.1833,2.7078,1.6796,1.0903,0.56549,0.3388,1.2431,0.5619,-0.057063\n0.8041,0.6247,-0.85893,-1.4634,-1.269,0.68822,-0.35427,-0.24083,-0.26673,-0.04355,-0.24039,0.008131,0.89631,1.9262,3.5303,2.8467,2.0826,1.3143,1.7295,1.8804,0.26256,0.62144,1.3304,3.9271,3.2455,4.7784,-1.4949,-1.7927,-3.4057,1.0655,3.4769,4.8349,4.6945,5.9351,4.7653,4.5894,5.7068,3.5684,4.2513,2.708,1.537,1.1141,0.81308,0.52536,1.3182,-0.53385,-1.0361\n1.0356,0.98559,-0.95544,-0.23597,0.088087,0.42397,0.53403,1.1792,0.49519,-0.64814,-0.42466,1.1068,1.6246,1.2766,2.7556,2.5271,1.8451,2.2327,2.5614,3.3618,0.2869,4.4747,NaN,NaN,NaN,NaN,NaN,NaN,1.8575,2.8961,4.559,6.3019,7.6255,7.1196,6.2227,6.8065,6.9089,5.7733,3.8873,3.0125,2.5096,2.0637,1.316,1.0646,1.699,-0.67751,-1.3996\n3.0158,2.2996,1.2728,1.3547,0.61419,0.19164,0.39276,1.6793,1.3646,0.065093,0.68911,1.1546,2.1251,1.1672,-1.0987,0.63341,3.2034,3.9513,3.7313,3.0858,4.5565,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.9542,7.2239,7.556,7.24,7.2234,5.9869,5.8062,4.6873,3.3724,3.3372,2.3059,1.6412,1.4518,0.79878,0.88052,-0.19883,-0.95387\n3.4259,2.6049,2.0051,1.2232,1.6862,0.78926,0.40022,0.74187,1.6895,1.7136,1.2001,2.3095,2.0475,-0.30384,-0.13665,1.5631,4.1766,4.9013,4.7089,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.9241,8.0063,8.8989,8.1256,7.3203,5.5474,4.2129,3.5116,2.8699,2.9019,1.9244,1.9375,1.6035,0.75712,0.52291,-0.2554,-0.81353\n2.1084,2.832,2.6126,1.2383,0.46509,0.28267,0.8664,0.86853,1.6117,3.0592,3.1688,3.4518,2.6698,1.3686,2.0929,3.0762,4.8764,3.5535,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.4108,8.0133,8.1549,7.6397,7.1789,5.7373,3.3444,3.322,2.5651,2.0571,1.8116,1.8477,1.4404,0.73866,-0.055697,-0.65597,-1.0822\n1.1762,1.7241,2.9756,2.2619,0.83227,0.63162,1.0271,0.93701,2.1287,3.0188,2.6515,3.5101,3.3597,2.9113,3.9275,5.4158,6.946,3.0157,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2188,4.7465,7.1252,8.3615,7.9544,7.0103,5.8614,4.336,3.3444,3.2725,2.7071,1.7722,1.7013,1.7258,2.3105,0.98938,0.39394,-0.40878,-1.1366\n1.3974,0.52391,1.3422,2.0289,2.0888,1.8359,2.1325,1.6829,2.2715,2.7743,2.792,2.9562,3.1023,3.7061,5.6868,6.7467,7.1168,5.9841,5.4278,3.5291,4.6755,4.974,NaN,7.1028,8.6364,10.141,9.654,6.1411,4.4313,6.1498,8.075,8.3415,7.5229,6.3101,4.807,4.5126,4.1739,3.5294,2.5366,1.9193,2.2989,2.1703,1.9604,1.2385,0.67581,0.40704,-0.58856\n1.2617,1.3126,2.6559,2.86,2.1706,1.9152,1.6853,1.2087,1.8246,3.1054,3.0779,2.9284,3.6872,4.6749,5.9567,5.6262,5.2413,4.5195,4.5341,4.5999,4.6753,5.4525,7.7765,5.6787,4.6496,NaN,NaN,5.4016,5.4831,6.5678,7.0957,6.3731,5.7665,4.8488,5.1546,4.5248,3.9985,3.6303,2.1201,2.1971,2.3561,2.4436,1.9619,1.5071,0.97097,0.30142,-0.37398\n1.9001,2.9447,3.2896,2.5489,1.6532,1.4628,1.601,1.5491,2.146,2.9321,3.1294,4.5637,4.5898,4.6945,5.2083,5.026,4.2575,4.0752,3.5157,4.9574,4.2998,5.6221,6.429,5.7797,4.0131,NaN,NaN,4.7796,5.0295,6.1223,5.8699,5.427,4.9077,4.0679,4.0541,4.1637,4.2437,3.6892,2.471,2.7794,2.471,2.4623,1.6764,0.92781,0.38678,0.16171,0.13734\n2.0318,4.1745,3.2069,1.8395,1.4789,1.4512,1.8952,2.5839,2.7588,3.3671,4.2968,5.221,4.736,3.9707,4.4727,5.4672,4.7639,4.482,4.9574,4.2063,3.8573,5.8564,3.4447,NaN,NaN,NaN,NaN,4.542,4.5772,5.3005,5.0409,4.0612,3.1951,3.5109,3.4828,3.5004,3.9626,3.2597,2.7702,2.7214,2.6988,1.8685,1.0045,0.46732,0.39339,0.51503,0.58557\n2.6594,3.1706,2.2099,1.7865,1.8476,1.7687,2.1928,2.8797,3.3351,3.4621,4.0513,4.4809,4.049,3.5861,3.5097,3.7357,4.285,5.7104,5.3072,4.8506,5.0477,3.5279,NaN,NaN,NaN,NaN,NaN,3.9167,3.8998,4.3098,4.2706,3.9494,3.1656,2.6091,2.6228,2.6806,3.0807,2.7656,2.6522,2.5409,1.7881,1.8128,0.73212,0.46478,0.3638,0.19066,0.92745\n2.6229,1.9009,1.5373,1.9696,1.5048,1.8208,2.3618,2.836,3.315,3.4747,3.2929,3.5609,3.4819,2.9757,2.8523,2.8363,4.2173,4.4665,4.9805,5.9801,6.1332,6.1069,6.8803,NaN,NaN,NaN,4.9028,3.0641,2.9417,3.2749,3.2734,2.8448,2.6891,2.4725,1.944,2.2995,2.5652,2.9769,2.9939,1.9529,1.3533,1.7232,0.87448,0.65489,0.94361,0.74257,0.43429\n2.1984,2.024,2.2947,2.7848,2.4884,1.8392,1.9514,2.4627,3.1381,3.4706,3.07,2.9063,2.7788,2.0981,1.5818,2.4388,3.7309,4.1049,4.314,5.1755,5.3717,5.1589,4.6307,3.7497,2.3236,3.5066,3.3404,2.6902,2.2147,2.1547,1.8771,2.0248,2.2976,1.4871,1.1643,1.7877,2.4407,2.744,2.6778,1.861,1.6279,1.3841,0.9349,0.78275,1.4623,1.2798,0.93006\n2.583,3.1057,2.566,2.9158,2.9139,2.2549,2.1763,1.8797,3.1568,3.6566,2.1666,2.321,2.0771,1.299,1.4853,2.9042,3.7144,3.4445,3.8968,4.6847,4.7325,4.7592,4.0069,3.2018,2.9642,3.3637,2.7046,1.8098,1.4327,1.1514,1.8384,1.9157,1.5649,0.77576,0.62816,1.7135,1.7376,1.7029,1.7599,1.4307,1.4894,1.8905,1.3434,0.62986,0.93199,0.71119,0.56935\n3.3191,2.6124,2.3191,2.6973,4.4979,4.1384,2.4267,1.9073,2.2108,2.913,2.2734,1.8252,0.81192,0.76038,1.2805,1.7141,2.3466,2.6125,3.583,4.1462,4.1052,4.2426,3.1645,3.0665,3.1677,2.9576,2.2824,1.0116,1.1823,1.096,1.536,1.3967,0.90898,0.9136,0.3755,0.42681,0.65578,1.496,1.406,1.2881,1.3684,1.3266,0.7256,0.98646,0.21819,0.40846,0.27753\n3.6025,2.0904,1.3434,3.0226,4.5788,4.5696,3.0381,2.1957,0.26908,1.6905,2.4308,1.858,2.8907,1.9317,1.4577,1.3173,1.3406,1.6953,2.3108,3.0183,3.3536,2.6197,2.6032,2.5083,1.9779,1.2509,1.1224,1.0742,0.70147,0.56401,0.65769,0.41893,0.29971,-0.056242,-0.30023,-0.14224,0.37662,0.87529,0.83371,1.2446,1.3353,1.1117,0.25385,0.40688,-0.29991,-0.3539,0.062837\n3.2197,3.0633,2.5509,2.8568,3.8244,3.7679,3.106,2.2723,0.72405,2.251,2.4081,2.9584,3.2601,2.763,1.5447,1.2925,1.1958,1.799,2.5082,3.0505,3.2183,1.9421,2.1046,1.4857,0.45686,0.0052614,-0.13836,0.67164,0.52531,0.3509,0.30565,0.095721,-0.44197,-0.7081,-0.46436,-0.23224,0.467,0.37039,0.61053,0.49427,0.66692,1.2936,1.7599,0.9993,-1.1008,-0.76225,-0.61089\n2.6484,2.8476,2.7921,2.2796,3.481,5.2989,7.8995,4.833,3.5883,3.7092,3.189,2.7084,3.478,2.4751,1.6884,1.3723,1.1395,2.0659,1.9458,1.8453,1.4105,1.9303,1.3622,0.66158,-0.40171,-0.56243,-0.99001,-0.2212,0.62543,0.40082,0.32352,0.17735,-0.47539,-0.97843,-0.66449,-1.0161,-0.78693,-0.1144,-0.22867,-0.16654,-0.017487,1.1179,2.0795,1.2906,0.016739,0.15511,-0.67447\n3.4442,2.6806,2.284,2.8511,3.8758,6.0493,6.951,4.2675,3.5542,3.8632,2.6529,2.3404,3.172,1.442,2.2825,1.2294,0.68243,0.73475,0.95813,1.3848,0.98614,2.1625,1.2895,0.23663,-0.31059,-0.099502,0.1749,-0.83914,-0.17156,0.35302,0.55998,0.91748,0.049459,-0.70914,-1.0688,-1.2594,-0.5224,-0.038606,-0.4432,0.1045,0.37521,0.27161,-0.10419,-0.19436,0.075946,0.51321,0.11943\n4.1788,3.9904,3.4628,3.7842,4.0992,3.65,4.148,4.2671,3.6295,3.5192,1.9936,3.2238,2.8226,1.9101,2.17,1.6963,0.81614,0.82386,1.4828,1.7901,0.81032,1.4925,0.63987,0.046902,-0.75518,-0.62659,-0.71827,-0.85483,0.039781,0.28296,0.12947,0.36433,0.13612,-1.1385,-1.6965,-1.1952,-0.50151,-0.58918,-0.54084,0.10763,0.5882,0.95887,0.48185,0.37693,0.74157,0.67974,-0.22538\n3.0263,4.4578,5.3785,5.7276,4.9731,4.0081,3.5884,4.369,3.6936,3.1685,2.5201,2.8415,1.9928,1.3636,1.0836,0.54346,0.46673,0.24053,0.43671,0.029099,0.18757,1.1368,0.3397,0.15928,-0.34334,-0.59958,-0.91842,-1.3541,-0.4379,0.00022602,-0.078553,-0.030898,0.1194,-0.51347,-0.73135,-0.36397,0.19495,-0.56926,-1.0148,-0.61656,-0.23695,0.079256,0.01507,0.24838,0.88856,0.38274,0.54132\n2.1295,2.8758,3.7358,3.8433,4.5306,4.5836,3.9579,3.7322,3.8386,3.4472,2.5221,2.2993,1.7907,1.7377,1.9833,0.63287,0.2479,-0.10531,-0.15621,-0.053159,0.43394,0.22194,0.64823,-0.10012,-0.70197,-0.87187,-1.9708,-2.3203,-1.4569,-0.30316,-0.047337,-0.15638,-0.46199,-0.069079,-0.51,-0.43543,0.020307,-0.035004,-0.25845,-0.46111,-0.24737,0.039777,0.074187,0.19246,0.2868,-0.044773,0.79129\n1.8672,1.9634,2.2108,3.1668,4.1522,6.574,5.9001,3.6597,3.6137,3.7716,3.9188,4.0834,4.0531,3.5764,3.2075,0.37347,-0.64453,-0.67874,-0.29913,1.0513,0.97074,0.46509,0.84126,-0.28794,-1.3341,-1.4788,-2.2673,-2.0383,-1.3841,-0.17965,0.16448,-0.25142,-0.09855,0.15291,-0.41119,-0.67255,-0.48653,-0.10021,0.29853,0.43148,0.46279,0.21892,0.092008,0.41332,0.093342,0.26981,0.16924\n2.9004,3.0232,3.4836,3.6486,3.0642,3.9848,5.5923,5.6187,4.8976,4.9606,6.4977,6.1968,10.053,NaN,NaN,NaN,NaN,-1.9666,-0.33534,1.5032,0.75457,0.53368,1.0267,-0.069557,-0.69077,-0.77993,-1.4849,-1.7633,-1.1015,-0.88553,0.49028,0.10968,-0.15412,-0.31217,-0.62902,-0.49088,-0.34781,-0.26349,0.51365,0.59901,0.95909,-0.021735,-0.14735,0.32369,-0.17415,0.21384,-0.35563\nNaN,NaN,5.8627,6.0835,5.5701,5.2544,5.3106,5.9723,6.2868,5.9416,4.799,7.0712,14.293,NaN,NaN,NaN,NaN,NaN,-0.1665,1.268,-0.058569,0.19474,0.47445,-0.17839,-0.72021,-0.82382,-0.99402,-0.43393,0.19141,-1.1318,-0.20483,-0.28588,-0.6114,-0.90751,-1.2533,-0.956,-0.83974,-0.90097,0.48489,0.46688,0.42926,-0.19854,-0.29689,0.2381,-0.84042,0.071791,-0.5628\nNaN,NaN,5.5019,6.4117,6.6114,6.7371,4.5365,6.054,NaN,5.1771,3.9521,3.6265,1.2312,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.83189,0.65316,0.28217,-0.04905,-0.27137,-0.69496,-0.44046,-0.098844,-0.16533,-1.1239,-1.7224,-1.396,-0.96638,-0.65976,-0.9106,-0.81138,-0.59073,-0.43049,-0.045581,0.12149,0.10153,-0.31265,-0.18762,-0.057346,-0.19073,-0.27536,-0.23757\nNaN,NaN,5.0396,5.2902,5.8312,5.8254,4.9042,8.243,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.7851,2.5835,1.224,1.0215,0.54343,-0.04065,0.033443,-0.045643,-0.71894,-0.7226,-0.56352,-1.326,-1.1934,-0.89435,-0.094089,-0.20035,0.025084,0.032036,-0.75865,-0.24209,0.16556,-0.5459,-0.12625,0.1505,0.1223,-0.086146,0.59354\nNaN,7.3425,4.8815,4.9901,5.1808,4.7638,5.3521,8.1559,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.7583,2.6864,2.8824,2.4998,-0.31737,-0.8458,-0.81958,-1.1078,-0.45505,-0.093905,-0.82235,-0.66169,-0.68962,0.0047245,-0.14781,-0.25568,0,-0.37729,-0.3911,0.12748,0.095856,0.12236,0.060612,-0.30608,-0.35904,0.01835\nNaN,NaN,3.3559,3.5923,3.8435,3.4922,4.2999,5.7939,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2745,2.6673,3.4463,1.9422,-0.3459,-0.73292,-0.053929,-0.22066,0.22596,0.24463,-0.38433,-0.12181,-0.17694,0.19883,-0.079392,-0.2555,-0.17878,-0.12369,0.12335,0.3158,0.32737,-0.28922,-0.6373,-0.26359,-0.068627,-0.065553\nNaN,NaN,NaN,NaN,NaN,3.056,5.1949,4.5779,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.0464,2.761,2.6768,0.61528,-0.84257,-1.0133,-0.68871,-0.35061,0.016116,-0.4606,-0.61252,-0.83343,-0.50179,-0.5684,-0.25758,-0.14938,-0.0096798,0.077354,0.46851,0.46897,0.16136,-0.28129,-0.40798,-0.070832,-0.096231,-0.26871\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5778,3.1012,1.069,0.14939,-0.31375,-1.0093,-1.1249,-0.47528,-0.11934,-0.22338,-0.40418,-0.68715,-1.0936,-1.7633,-0.063323,0.083826,0.30744,0.28825,0.13663,0.18722,0.097343,0.049038,0.027598,-0.17272,-0.31024,-0.82403\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.6204,2.7655,0.65292,0.47071,-0.022662,-1.2624,-1.4722,-0.62424,-0.36754,-0.24636,-1.3975,-1.514,-1.0938,-0.048459,0.13314,0.67644,0.84463,-0.20851,0.10547,0.53267,0.32902,0.056428,-0.35664,-1.3034,-2.0495\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0925,-1.3703,-0.56275,-1.0851,-1.7238,-1.6485,-0.876,-0.51163,0.22998,0.44917,0.41402,0.34474,0.31214,0.59431,0.60304,-0.12333,-0.45557,-0.19879,-1.342,-2.2529\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.86107,-0.36792,-0.35952,-1.0034,-1.7076,-2.4823,-1.6255,-0.99682,0.062957,0.10672,0.59389,0.065794,0.30221,0.62928,0.10109,0.39114,-1.1182,0.029712,-0.4067,-0.91111,-1.9807\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.49085,-0.41022,0.50501,-0.39954,-1.368,-2.1756,-0.47337,-0.58474,-0.12064,0.66502,0.44339,0.095634,0.43877,-0.0042858,-0.86199,-0.36871,-0.49085,0.062764,-0.25612,-0.85996,-1.4706\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.933,2.3907,0.7892,0.76504,0.93786,2.4562,2.1804,0.90437,-0.58441,0.25338,-0.36951,-0.56035,0.90803,0.7884,0.47788,-0.066894,-0.07702,-0.61608,0.027287,0.43137,-0.38544,-0.68366,-1.6825,-2.1607\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.2047,-0.31037,0.46008,0.87976,1.1269,0.78734,2.1401,1.7396,0.17511,-0.36807,-0.30615,0.71317,0.83504,0.66144,-0.15548,0.84248,0.45757,0.28051,0.72694,-0.55368,-0.39875,-0.89447,-1.3941\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3561,0.36814,-0.16195,0.53786,3.106,2.3542,-0.58951,0.51474,1.3078,1.763,1.9021,0.34971,-0.52427,0.23839,0.037417,-0.12982,0.0011463,-0.23467,0.2964,0.16016,-0.37683\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.7029,1.7856,0.070836,-0.41273,1.9178,1.6122,0.17536,-0.25604,-0.32882,1.8126,2.2285,-0.11107,-0.71186,-0.75909,-0.44949,0.25773,0.24532,-0.27188,-0.42231,-0.28398,-0.20264\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070115-070917.unw.csv",
    "content": "1.4712,0.92484,0.26774,0.78109,0.30149,0.17144,0.50438,0.5665,0.68473,0.60888,0.2946,0.34381,0.82454,0.93021,0.98701,0.57368,1.1857,1.4808,1.8706,2.0022,1.5194,1.6536,2.1055,2.1099,1.3713,1.4549,1.325,2.3452,2.801,2.2387,2.8289,2.1875,1.3174,0.52365,-0.14325,-1.0118,-2.17,-2.4158,-3.7338,-3.8631,-3.4188,-2.7051,-3.0736,-3.2661,-3.0376,-2.7208,-1.9373\n0.88454,0.68966,0.064657,0.78047,0.69176,0.5691,0.52678,0.54219,0.41388,0.26646,0.12587,0.1148,0.47258,0.69452,1.0598,1.066,0.77231,0.92464,1.7897,1.8026,1.5601,1.9772,2.5835,2.2056,2.0627,2.1299,1.9587,1.9163,2.2106,2.8432,2.8336,1.8125,1.4563,0.89224,0.4794,-1.7939,-2.6426,-2.4731,-2.7417,-2.6322,-2.5465,-2.0368,-1.9025,-2.441,-2.5888,-3.0337,-3.1668\n0.21636,-0.10263,-0.52828,-0.11997,0.082148,0.50586,0.58459,0.88563,0.63286,-0.74028,-0.14166,0.29148,0.3028,0.52188,1.2028,0.76842,0.5904,0.90474,1.1965,0.59543,0.59492,0.78441,1.3267,0.84683,1.8491,1.7343,2.11,1.3541,1.8937,3.091,2.8071,1.5264,1.9881,1.7668,1.5695,0.65333,-0.06477,-1.3775,-1.8548,-1.8106,-1.6884,-1.215,-1.7923,-2.3569,-1.9174,-2.6951,-2.7108\n-0.037652,-0.03285,-0.63548,-0.52454,-0.36398,0.09093,-0.21344,0.16924,0.043383,-0.50736,-0.09324,0.013391,-0.16973,0.39339,0.83947,1.2016,0.93643,0.57271,0.69682,-0.052748,0.33964,0.90307,1.0983,1.2345,1.7457,1.8122,2.0712,1.7493,2.0905,3.3689,3.4744,3.0461,2.9133,2.118,0.71138,0.4522,0.35497,-1.0146,-1.1985,-1.7142,-0.9905,-0.46319,-1.0372,-1.9277,-2.2543,-2.3481,-2.3703\n0.27741,0.22636,-0.77759,-0.36332,-0.2633,-0.10554,-0.40715,-0.44558,0.71234,-0.45883,-0.37285,-0.017357,-0.3499,0.1115,0.77796,0.96017,1.1437,0.11758,0.22712,0.41634,-0.17883,1.7388,1.6195,2.1305,1.7386,1.7466,2.0575,1.2282,1.8935,1.9356,2.2794,1.9339,1.7936,1.6036,-0.27569,-0.5454,-0.79745,-1.0928,-1.2339,-0.96774,-0.30269,-0.64281,-1.1784,-1.2426,-1.3882,-1.6367,-1.8307\n-0.35336,-0.71363,-0.69143,-0.54284,-0.85386,-0.39392,-0.50153,-0.69842,-0.77262,-0.9968,-0.7948,-0.79412,-0.56496,0.019065,0.2609,0.84032,0.64644,-0.30097,0.49568,0.63536,0.69592,1.2591,1.7824,2.1892,1.3518,1.8356,2.3266,1.4734,1.601,1.6828,1.8607,1.1818,1.2594,1.3949,1.3508,1.0978,-1.3701,-1.0833,-1.4698,-0.32108,0.049414,-0.33632,-0.76196,-0.93375,-1.4525,-1.3731,-0.88091\n-0.92015,-1.0336,-0.49036,-0.311,-0.76641,-0.61844,-1.1465,-0.73034,-0.51426,-0.96552,-0.66307,-1.2136,-0.66772,0.26116,0.013733,0.47923,-0.20894,-0.899,-1.1776,-0.34158,0.44928,1.807,2.306,1.9951,1.944,2.852,2.5119,2.3614,2.4119,2.6855,2.6828,1.9983,2.0709,1.8328,1.7875,1.3454,-0.062769,-1.1276,-1.8662,-0.61761,-0.446,-0.64538,-0.63089,-1.1314,-1.8479,-1.8362,-1.6842\n-1.7245,-1.9996,-1.1137,-0.20478,-0.95347,-0.82692,-0.59019,-0.3708,-0.32848,-0.68782,-0.44392,-0.74527,-0.48927,0.79695,0.23699,-0.28858,-0.80626,-0.91257,-0.72783,-0.56955,0.28823,2.3996,3.0892,2.0896,2.6321,3.0506,3.3513,3.2463,3.7322,3.42,3.9351,3.0491,2.8402,1.9893,1.6983,1.0922,-0.28027,-0.75495,-0.66907,-0.94638,-0.64416,-0.39638,0.090353,-1.0942,-1.7686,-2.0598,-1.178\n-0.82245,-1.2044,-0.95263,-0.52886,-1.4293,-1.1249,-0.23218,-0.38959,-0.64963,-0.40674,-0.54745,-1.2934,-0.67696,0.013824,0.20777,0.2183,0.071556,0.59149,0.45667,-0.05138,0.48694,1.8223,2.5453,2.804,3.5893,3.1654,2.3,3.4082,4.5371,4.5508,4.1453,4.0173,3.4692,2.7481,1.4226,0.47703,-0.18566,-0.65101,-0.2602,-0.27215,-0.42183,-0.21618,0.31387,-0.28933,-1.04,-1.2893,-0.84588\n-0.76516,-0.99706,-1.1127,-1.6185,-1.7164,-1.5078,-0.59205,-0.55015,-0.59066,0.052215,-0.63102,-0.78452,0.0077448,-0.31493,-0.017953,0.4377,0.95157,1.2672,1.2605,1.6404,3.0365,2.1679,2.8657,2.2468,3.4399,3.3086,2.3585,3.0402,2.9223,4.0741,3.837,4.9606,3.4358,2.5859,1.0561,0.8135,0.9901,0.21317,0.22114,-0.10345,-0.36697,-0.13265,0.09929,0.44002,0.035617,-0.33387,-0.25743\n-0.7898,-1.0457,-1.625,-2.4638,-1.9289,-1.4993,-1.0548,-0.29748,-0.2815,-0.74147,-0.858,-0.64523,-0.029147,-0.16898,-0.1081,0.0013504,1.1558,0.94329,1.0566,1.8304,1.9279,2.3868,3.1903,2.6184,3.1376,3.438,3.3812,3.3827,3.7284,4.5454,4.2171,3.8719,2.7521,2.9517,1.3646,1.8088,1.8101,1.407,1.1304,1.0662,0.53022,1.047,0.78657,0.33045,0.00737,0.053266,-0.66607\n-0.92658,-1.2916,-1.6197,-1.821,-1.5372,-0.53521,-1.1508,-1.0501,-0.76574,-1.3904,-1.0621,-0.98913,-0.67012,-0.051775,0.28504,0.20454,0.64205,0.80779,1.2905,2.2992,0.40673,2.4596,3.1016,3.6969,3.6658,3.9563,3.6958,3.4507,4.3235,4.6863,3.9976,2.9146,2.3223,1.9138,1.0755,1.3987,1.6644,1.6233,1.6773,1.3706,0.98961,1.5434,1.0082,0.76944,0.68306,1.295,0.77419\n-1.0241,-1.3941,-1.826,-1.9173,-1.6905,-1.4806,-1.1787,-1.4552,-0.017782,-1.4617,-1.1664,-1.4769,-0.65405,-0.56904,0.19956,0.31134,0.50465,1.5304,1.9201,2.9378,2.8083,3.1115,3.5406,3.9644,4.3789,4.2875,4.2184,3.1809,4.1952,3.631,3.6084,1.9738,1.6615,1.7181,1.794,1.8064,1.9852,1.8357,1.2203,0.6137,1.1724,1.4681,1.5704,1.4008,1.928,2.0759,1.9541\n-1.2641,-2.5338,-2.5305,-1.9447,-1.5188,-1.8004,-1.1079,-1.2067,-1.3273,-1.3454,-1.427,-1.0834,0.017355,-1.0659,0.52913,0.35416,1.2234,1.7067,1.8744,2.4241,2.9777,4.6782,4.4568,4.1015,5.1538,4.8302,4.285,3.2725,3.0939,3.1873,3.3918,2.5008,1.7384,2.3333,3.1941,3.6146,2.5741,2.0313,1.6736,1.2605,1.1407,2.6811,2.3736,2.813,3.1972,2.5548,2.096\n-1.55,-2.7381,-2.1797,-1.5676,-1.6455,-1.7355,-0.8942,-1.6793,-1.462,-0.72992,-1.7701,-1.0665,-1.038,-0.26078,1.1251,2.0529,1.0411,1.51,1.7866,2.4114,2.3916,6.8712,4.0132,4.7218,5.9306,4.6653,5.9376,4.423,4.2326,4.1462,4.0498,4.3251,3.8743,3.5242,4.1727,4.2824,1.965,1.6646,1.9507,2.4882,2.4571,3.1737,2.2632,2.7666,2.8488,2.6482,2.7896\n-1.0726,-1.341,-1.6757,-1.5303,-1.5699,-1.9031,-0.53319,-1.6435,-0.73093,-0.5082,-1.5795,-0.88389,-2.2995,-0.71137,1.7204,1.7815,0.81445,1.238,2.8852,2.8458,3.1202,4.6656,4.1335,5.1395,4.8062,4.7058,5.9562,4.5514,4.1697,5.0099,4.2622,5.0788,4.0647,3.4195,3.7483,3.6255,1.6224,1.4509,1.4775,1.8968,1.8565,2.2483,1.8073,1.2678,0.9414,1.2457,1.3479\n-0.59599,-0.040941,-1.1007,-1.3477,-1.158,-1.0477,-0.56437,-1.1544,0.095711,0.30199,-0.45139,-0.30122,-0.99174,-0.57365,0.99186,0.96022,0.52931,2.1881,3.4834,3.3665,4.0766,3.9258,4.8679,5.8824,5.3868,5.8367,7.3611,6.7614,4.3427,4.3257,3.098,4.9941,3.5501,3.0312,3.028,3.0596,0.95817,1.7788,1.3529,2.1818,1.8267,0.41275,2.0793,1.6368,-0.065551,-1.0356,-0.79809\n0.40112,0.81943,-0.63566,-0.34438,-1.2387,-2.2061,-1.4313,-0.068415,0.3577,0.38715,-0.35476,-0.17967,0.11348,0.64209,0.82647,1.0106,1.3304,2.9314,2.8555,3.7798,3.9841,4.0391,3.9105,4.7873,4.7697,3.873,5.7698,7.7918,5.5077,4.1986,3.3086,4.7287,3.9725,3.7923,3.7914,3.1964,1.3142,2.3705,2.7323,3.0367,2.3349,1.1267,1.0034,1.8362,0.82464,-0.97658,-1.379\n1.2818,0.60028,-0.16415,0.12982,-0.09405,-2.4916,-0.28182,-0.16129,0.043862,0.35544,-0.5504,-0.084328,-0.16932,0.50478,0.90404,1.7788,1.8442,1.9355,2.2492,2.6194,3.1578,2.7747,2.3594,3.4232,3.6437,4.2889,4.0041,7.0405,5.7951,5.2886,4.1116,4.98,4.7328,4.812,4.9037,4.3974,2.8988,2.8388,3.3442,3.3817,2.8373,1.8232,1.4304,1.757,1.3954,0.5547,-0.58524\n1.4768,0.83043,0.72385,0.56483,1.3081,0.84292,-0.30426,-1.2425,-0.63303,0.049167,0.25455,-0.26778,-0.24596,0.77115,1.4485,1.621,0.89564,1.5188,1.5469,1.7023,2.8825,2.9205,2.3255,3.3035,3.7247,4.5373,5.3252,6.574,7.0037,5.8046,6.6222,5.0598,5.4477,5.0215,5.0593,4.2793,3.7067,3.5214,4.1714,3.8379,3.3695,2.9449,2.1621,2.6083,2.085,0.57803,0.16193\n1.9435,2.099,0.77597,1.4543,2.7317,0.52683,-0.79749,-0.87294,-0.61613,-0.26314,-0.77472,0.21983,-0.0524,1.3017,0.88507,0.63143,0.94737,1.4996,1.4722,1.7798,2.2311,2.771,3.2144,4.0854,4.628,5.7521,5.9773,8.0567,7.8191,6.6404,7.6214,5.738,6.3426,6.1359,5.8227,3.5587,4.4502,4.2171,3.9446,3.2284,2.855,2.5004,2.417,1.4484,0.77917,0.018297,-0.22165\n2.1162,1.9385,1.6122,1.3751,1.2031,0.64992,0.62447,0.23538,-0.75569,-0.51414,-0.3353,0.49619,-0.20639,1.1143,0.35637,-0.20334,1.1005,1.1903,0.94678,0.35156,1.657,3.0377,2.5027,4.989,5.5598,5.5618,6.4651,7.8478,8.4407,8.7664,9.2943,7.099,6.6051,6.3753,4.8008,4.2115,4.6513,4.124,3.405,3.8254,2.6951,2.0813,1.8416,1.8387,1.7335,0.6503,0.23713\n2.2493,1.9314,2.4395,2.3914,2.1951,1.7505,0.56924,0.017288,-1.0535,-1.0278,-0.12365,-0.30655,0.33634,0.85113,0.25677,-0.40481,2.0516,1.9766,1.6538,1.7877,3.022,3.0427,3.8213,5.2777,6.332,6.1313,7.3274,7.6418,10.298,9.8106,10.16,9.2895,7.592,6.7645,6.3531,5.7171,5.4292,5.11,5.0793,4.7113,3.3588,2.5066,1.7032,2.7629,1.065,0.68428,0.64138\n1.875,2.6071,3.1294,2.8645,4.6269,1.7732,0.63233,-0.095286,-0.62256,-0.69052,-0.0062199,-0.7022,1.0131,0.91085,0.62473,-0.48703,2.0512,1.9086,2.485,3.0667,3.4178,4.53,5.6241,7.8517,8.0918,7.778,8.9106,11.445,11.194,10.964,12.363,11.874,11.609,10.408,7.8982,7.1522,7.0287,6.3996,5.7333,6.2686,4.4735,3.0756,3.0029,2.9521,1.8599,1.8408,1.0308\n2.8669,2.8745,2.8575,2.6553,2.5099,1.9284,0.89562,0.39404,0.7479,-0.38858,-0.29186,0.061978,0.5143,1.1782,1.0292,1.3044,1.7859,1.7451,2.335,3.698,4.4638,5.1891,6.8466,7.8176,8.6819,9.5995,11.475,13.775,13.744,14.178,14.198,13.745,12.46,11.479,11.025,8.2661,7.4403,7.0368,6.6704,6.5506,4.2111,4.4696,3.6731,2.827,2.001,1.7354,1.1312\n2.1837,0.26073,1.3047,2.1461,1.9913,2.2988,1.5942,1.1053,0.23621,-0.63153,0.24799,0.31771,0.61647,0.97796,1.5225,2.0139,2.5298,3.0279,3.3261,4.6807,5.8433,5.822,7.0804,9.4047,11.668,12.786,14.461,17.325,16.379,16.294,15.865,14.846,14.027,14.24,11.886,8.9153,7.2839,7.4066,7.5577,7.1826,5.1383,4.7823,3.5554,3.0908,1.9753,1.5021,1.399\n1.7856,1.4524,2.1864,2.5961,1.9844,1.1088,1.9341,1.4669,0.82108,0.23898,0.64065,0.59721,0.26695,0.97532,2.0141,3.1348,3.6307,3.829,4.2386,5.5582,5.2617,5.3782,7.1392,11.008,14.217,14.543,NaN,NaN,19.75,17.78,16.283,15.384,14.968,13.365,12.161,9.8174,8.323,8.7725,8.3994,7.3518,5.4616,4.6087,3.8186,3.2129,2.4058,1.6771,1.4594\n-0.15663,1.3975,1.3862,0.35893,1.6266,0.84213,1.2862,1.4848,0.55286,1.1715,0.93845,1.1214,1.5385,2.2249,3.3325,4.4717,4.5077,4.4632,5.451,6.2532,5.2165,5.2568,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.1,14.039,11.45,10.64,10.114,9.9549,9.1693,7.1989,6.0284,4.9342,3.2242,2.6034,2.1892,2.0647,2.1401\n0.9397,2.9788,-2.3274,-0.87495,0.73269,0.49961,-0.73335,-1.4889,-1.4921,1.2125,1.2477,1.6508,2.0036,2.9378,3.9528,4.8784,5.5109,5.9572,6.5597,6.8734,6.394,6.7029,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.212,10.844,9.9375,9.6009,9.9257,8.9628,7.7214,6.2867,5.606,3.6612,2.7845,2.7421,2.3102,2.2315\n3.5319,4.323,2.3108,-0.32027,0.8206,0.40318,-0.92212,-1.4667,-0.6864,0.8288,1.1587,1.6495,2.5565,3.0072,4.0225,4.9917,6.2456,6.6795,6.7639,7.4083,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.355,10.412,8.6951,7.136,6.4118,5.5011,3.2771,3.0879,2.6083,2.4458,2.1772\n3.4712,3.9374,3.5166,4.107,3.4066,2.4978,-1.1991,-0.99626,-0.1535,0.95039,1.6263,2.1709,2.5119,2.8591,3.9198,4.6285,5.1742,5.5616,6.5504,8.6453,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.473,10.182,8.0131,6.5022,5.6962,4.512,3.5266,3.134,2.3502,2.6,2.2022\n3.2535,3.39,3.3007,2.207,2.7668,1.6399,-1.1214,-1.414,-1.4158,0.53841,1.1786,1.6837,2.1984,3.1571,3.5236,4.0375,4.6237,5.204,7.6709,8.7174,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.4218,5.146,4.8489,3.247,2.6374,3.2977,2.4147,2.9679,2.5939\n2.4641,1.2757,1.5944,1.5792,2.4218,0.47983,-0.59552,-1.0132,-2.2408,0.75691,0.5189,1.3315,2.3834,2.4008,3.9254,3.2653,3.3448,5.3689,6.6862,8.1256,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.2032,4.6896,4.3689,2.8149,2.6832,3.3486,2.3222,2.5526,1.7248\n2.2482,2.5205,1.0264,1.4154,1.6912,0.54333,0.14193,-0.98003,-0.60308,0.97797,0.78441,0.74688,1.7722,0.44156,3.5191,3.4092,2.732,3.6938,6.1011,5.0648,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.5358,4.5287,4.1743,3.8564,3.4,2.4618,2.1463,1.407\n2.9542,2.4597,-0.65151,1.359,1.067,0.81371,1.1785,1.1023,-0.059,0.45217,1.0092,1.1994,0.33764,0.99485,3.1889,3.5207,4.1539,4.4396,5.3442,4.2068,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.977,4.988,4.3733,3.2472,2.6777,1.7435,1.1419\n1.5335,0.37627,0.80275,2.8532,2.0282,0.56149,0.60187,1.8372,0.32385,0.37229,2.1631,2.5135,2.6936,2.1228,1.3956,3.2051,3.3618,4.3271,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.2158,4.1865,4.3212,3.5127,1.9539,1.6437,0.75263\n1.8437,1.1806,1.2304,1.0343,0.95294,-0.2627,0.20918,1.3083,1.2397,0.57988,2.2118,2.6859,2.6069,1.6662,1.8447,3.2507,3.4026,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.0282,4.1249,3.4616,2.8043,2.0927,2.4363,2.1748\n2.3637,1.7561,1.2406,0.58061,0.2977,-0.020052,0.64366,0.809,1.108,1.692,2.1431,2.5724,2.5123,2.9774,3.0959,3.5615,3.6109,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.0324,NaN,NaN,4.6106,3.9062,3.8175,2.7917,2.4685,3.1451,3.7804,3.0682\n1.309,1.8186,1.2724,1.2763,1.3251,1.3032,1.011,1.0427,1.5114,2.0285,2.2339,3.312,2.7586,2.6208,3.2479,3.7037,4.2251,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.9699,NaN,NaN,NaN,NaN,NaN,NaN,7.5029,6.9288,5.1126,3.5006,3.0678,3.3999,3.156,3.1344,3.2018,2.3102\n1.3426,3.8929,2.5407,1.8754,2.0756,2.4879,2.339,2.2243,1.6613,1.7104,2.0856,2.8495,2.7999,3.6442,4.7319,5.2578,5.2601,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1153,-0.37332,NaN,NaN,NaN,NaN,9.5482,7.66,6.7388,4.2523,3.0253,3.4767,3.9788,3.0501,2.7482,2.4089,0.53026\n3.0563,3.4982,2.2011,3.0034,2.4061,2.4457,2.8261,2.4985,2.0786,3.0366,2.6109,2.7144,2.998,3.6653,4.2482,5.4734,5.6877,3.1055,2.815,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.12075,NaN,NaN,NaN,NaN,NaN,9.4842,8.5321,6.8937,2.8652,3.0577,2.8143,3.1147,4.1907,3.3374,3.3961,2.3259,0.65309\n3.404,3.4398,3.2886,3.5383,3.0961,2.5752,2.4504,2.2819,2.7902,3.3743,2.9982,2.9873,2.5449,2.7482,2.2578,2.6261,3.9574,2.5157,1.5462,NaN,NaN,NaN,-0.56688,NaN,NaN,NaN,NaN,NaN,0.66913,-2.2022,NaN,NaN,NaN,NaN,7.5031,8.579,7.1413,6.3351,4.1559,3.2994,2.6039,3.1691,3.8613,3.6634,3.6783,2.4087,1.4612\n3.7368,4.0481,3.8288,3.1259,2.5805,2.1719,2.0458,2.2255,2.6516,2.5402,2.4705,2.7333,2.3328,2.5016,2.417,2.1203,4.0533,3.3989,3.2906,2.4822,1.5249,0.41103,-0.23745,NaN,NaN,NaN,NaN,NaN,0.40371,0.52713,4.0558,6.284,6.2354,6.8327,6.5852,6.131,6.1966,4.634,3.4188,3.1805,3.8984,3.5385,3.1792,3.329,3.2663,1.1932,0.30296\n3.6356,3.8816,3.8537,2.8765,2.4012,2.2198,2.0639,2.0914,1.9573,2.0412,2.1331,2.4108,2.6501,2.8019,3.3704,3.7563,4.2547,3.5024,2.8924,2.8831,1.6822,0.28089,NaN,NaN,NaN,NaN,NaN,NaN,2.7302,3.9417,3.9315,4.8588,5.5686,5.3136,4.7588,4.4024,4.3549,2.9488,2.7299,3.2185,3.9298,4.1813,2.9138,2.4735,2.5332,2.0409,0.65819\n3.6505,3.6103,3.5553,3.1398,2.7347,2.3677,2.4604,2.2387,1.7502,2.7004,1.9844,2.216,2.4807,2.772,3.5611,3.6064,2.8132,3.3728,3.4552,4.8407,2.2827,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.9939,3.7669,4.7282,6.4397,4.9278,4.4998,3.7205,4.2742,3.556,2.7189,2.5483,3.0574,3.9727,3.9857,2.9934,2.9652,1.2032,0.067167,-0.61076\n4.1159,4.3002,4.5526,4.0965,3.0358,3.1579,2.8716,3.1914,3.3821,3.0495,2.4324,2.2722,2.4663,2.8093,3.1663,3.1238,3.3979,3.7094,2.9985,2.2509,1.3503,1.8803,4.3744,3.5733,1.4917,NaN,NaN,NaN,3.5439,4.2787,5.5573,5.6024,5.2202,3.6655,2.9848,2.9222,3.0275,1.9109,1.8509,2.3749,3.6426,2.958,2.4903,2.9251,1.678,-0.28812,-1.5452\n4.5272,4.9955,4.9244,3.2221,2.9575,3.4162,3.288,3.3651,2.6947,2.9435,2.7153,2.5907,2.7896,2.9403,1.867,2.2328,3.2052,3.151,2.4329,1.7323,0.30673,0.60418,2.2842,1.4621,0.9015,0.67003,0.27687,1.6873,2.7988,4.8598,5.9262,4.4073,3.2122,2.3933,2.4459,3.2029,2.4308,2.1185,1.4424,2.2627,3.291,1.9416,0.53575,1.0811,1.7241,1.0661,-0.22418\n4.1884,4.2782,4.2336,3.3766,3.2477,2.3581,2.8918,2.6571,2.5234,2.5848,2.3298,2.4845,2.8521,2.6225,2.1538,2.0921,3.1401,2.808,2.23,1.6783,0.80312,-0.0057764,-0.32865,0.45899,0.30704,0.25876,0.34642,1.2344,2.3453,2.8896,3.0586,2.2039,1.1688,1.4302,2.0169,2.0843,0.79311,1.3456,1.3088,2.2986,2.5258,1.3396,-0.76293,-1.6176,-0.66564,-0.15306,0.53022\n4.5595,3.895,2.9332,3.7532,3.8075,2.6327,2.4834,2.2595,1.9624,1.5739,1.8637,2.3914,3.0276,2.9115,2.408,2.2253,2.3107,2.4738,2.3828,1.4204,-0.098622,-0.64899,-0.046756,0.38018,0.087876,0.12039,0.96143,1.8548,1.5648,2.4323,1.7391,1.2333,1.3532,1.4199,1.0808,0.85574,0.41396,1.4795,2.0987,1.6155,1.3244,0.91455,-0.91771,0.14773,0.5218,0.15859,2.1581\n4.8536,4.6939,3.648,4.3416,4.2005,3.4201,2.2217,1.7773,2.3392,1.9192,1.9943,2.4732,3.0357,3.103,2.245,1.7426,1.9047,1.6115,0.95968,-0.86395,-0.22939,0.091013,0.85534,0.81575,0.62044,0.82344,1.0997,2.2048,0.62848,2.1216,1.5224,1.3463,0.78177,2.0341,2.3645,2.2177,0.33188,1.7256,2.4354,0.56185,0.94082,0.61781,-1.1685,-0.84117,0.068,-1.043,0.13336\n4.7304,4.7312,4.3944,4.621,4.228,4.4632,2.7072,3.4633,3.0552,2.8539,2.6198,2.2333,2.9669,2.2175,1.4243,1.3925,1.4428,0.52021,-0.31045,-0.8215,-0.12009,0.8569,1.4358,1.5367,1.0284,0.85563,0.014658,1.8525,1.0024,1.8209,1.3806,0.87619,0.54143,1.0738,2.729,1.532,0.9394,1.3624,0.84556,0.23241,0.052383,0.71968,0.9923,-0.23783,-1.0794,-0.62166,0.31017\n3.9708,4.8286,4.9753,4.4118,3.9739,3.4247,2.3298,2.8968,2.2739,2.8662,1.7862,1.2075,2.1853,2.6475,1.1549,1.3687,1.0131,0.67004,0.37616,-0.34215,0.56059,1.1895,1.5461,1.525,1.1868,0.51933,-0.63239,1.9048,1.8447,1.8598,0.96406,0.34778,0.40136,1.1604,2.1047,1.2055,0.73468,0.53089,-0.21815,-0.12034,0.059374,0.12272,-0.014334,1.0105,-0.54785,0.030563,-0.020094\n4.9199,5.053,4.9418,3.6981,4.2056,3.4379,2.9775,2.7074,2.499,2.9197,2.0353,2.1296,2.1349,2.4552,1.8523,1.7993,1.6734,0.038417,-0.1588,-0.4496,0.71492,1.1167,1.2631,1.0924,0.98578,0.88309,1.6949,2.031,2.0145,1.9362,1.2692,0.73247,0.8526,1.5878,1.8294,1.2892,0.25431,-0.93854,-1.4044,-0.4842,0.11818,0.17203,-0.043676,0.27582,-0.59449,-0.11513,-0.29789\n4.7326,4.7296,4.5372,4.8104,5.4032,3.7432,4.3632,4.0315,3.6717,3.7506,3.8998,2.7257,2.2368,2.5257,2.3046,1.35,1.0581,-1.1421,-1.5864,-0.79648,1.0279,1.2201,0.67746,0.4973,0.75547,1.5743,2.0276,2.2729,2.2115,1.9199,0.87473,0.26667,1.0218,1.3824,1.7232,0.86719,-0.41488,-1.0587,-1.1413,-0.49631,-0.0025415,0.077016,-0.28688,0.041234,-1.0015,-0.33615,-0.054298\n2.7479,3.5368,3.0926,4.0611,4.8376,5.1545,4.3906,3.4639,3.9898,6.1263,5.4333,2.5188,2.0866,2.5098,3.2055,2.9296,1.3421,-0.89906,0.58049,1.3012,1.37,1.0656,0.43361,0.20804,0.58504,1.0445,1.8501,1.9162,2.2444,1.9169,0.50228,-0.1811,-0.49461,0.49009,0.61349,0.74739,-0.11327,-0.62028,-0.81165,-0.40746,0.3133,0.11352,0.17585,0.090408,-0.92202,-0.6377,-1.3607\n3.1357,3.7093,4.0536,5.3959,3.5462,4.5415,3.4663,3.6313,4.712,5.3028,4.0295,2.5255,1.1454,1.3575,2.8161,2.4569,0.37618,0.84669,1.7735,1.5437,0.72108,0.85941,0.41843,1.0681,0.8035,0.5482,1.4404,1.2942,1.5353,1.5221,0.56836,-0.22626,-1.2549,-0.67928,0.45666,0.38414,-0.2999,-0.37601,-0.46542,-0.31306,-0.12819,-0.0028877,0.23623,1.1653,0.0027456,-0.65322,-1.2114\n4.9739,3.7255,7.4288,7.0144,3.3612,4.2834,4.1276,4.046,3.4836,2.5892,5.1107,4.3694,0.23805,0.93493,0.14058,0.088838,1.2601,0.31711,1.3941,1.1666,0.13804,1.2867,1.323,1.4784,1.0587,1.0058,1.3113,0.64437,1.0401,1.1535,0.72112,0.28061,-0.39011,-0.67522,0.34848,0.28616,-0.36102,-0.62781,0.15233,0.18951,-0.47688,-0.10177,0.82754,0.79292,-0.021468,-0.23622,-0.81\n5.8111,3.6921,7.157,6.9435,5.8236,5.7083,4.172,5.0457,5.3767,4.606,4.1623,4.5764,1.9674,0.74157,-1.6704,-1.5102,1.5211,-0.24885,0.53775,1.2496,-0.077808,0.5049,1.4887,1.3374,1.3215,1.0736,1.08,0.32514,1.6631,1.0748,0.18323,0.075744,-0.037634,-0.17499,-0.0017166,0.33364,-0.22902,-0.9541,0.068582,0.96294,0.49115,0.28145,0.58498,0.34607,-0.26032,-0.12126,-1.1436\n6.6391,3.5391,3.4686,5.5582,5.4595,3.9735,3.5053,6.1733,6.3717,6.3977,3.8734,4.1087,3.0759,1.8239,-0.0083179,1.5854,2.8597,2.2979,1.8334,1.8096,1.0915,0.44519,1.4166,1.1247,0.71705,0.49976,0.78675,0.88899,1.3015,0.59573,-1.0894,-0.13019,-0.32775,-0.69533,-0.19592,0.032477,-0.18355,-0.50113,0.20871,0.75602,0.50795,-0.12805,-0.091385,-0.90429,-0.63108,-0.5728,-1.2664\nNaN,4.7156,2.313,3.8038,4.5116,2.9138,4.0825,8.1258,NaN,NaN,4.6769,4.7755,4.7531,2.1474,2.2247,2.5784,4.2846,3.9207,4.5242,3.095,1.4131,0.82963,0.56991,0.45852,0.49993,0.54853,0.93734,0.80463,0.83875,-0.19761,-0.62924,-0.41156,-0.51794,-0.62956,-0.35516,-0.3491,0.16768,-0.16787,0,0.22646,0.37436,-0.75617,-0.50764,-1.3915,-1.5958,-1.9588,-1.8981\nNaN,NaN,3.3267,3.7044,4.0793,4.1118,4.4658,6.7126,NaN,NaN,NaN,4.5407,4.4874,4.8533,4.5253,4.1193,4.3212,5.844,6.0062,3.0325,1.2929,0.68576,0.35083,0.43575,0.54132,-0.28379,0.49834,0.55207,0.75537,0.3213,0.52501,-0.70175,-1.3764,-0.42799,-0.17864,-0.66574,-0.16823,-0.084112,0.34185,-0.16527,-0.17012,-0.86629,-0.66058,-0.96505,-1.4728,-1.0145,-1.0709\nNaN,NaN,1.9402,3.8063,3.5909,3.3042,4.6656,6.1181,NaN,NaN,NaN,NaN,NaN,NaN,4.9198,NaN,NaN,5.8213,4.8717,1.9049,2.157,1.0104,0.92211,0.9573,0.47658,-0.27369,-0.29144,-0.11123,0.73115,0.87535,1.2287,0.2263,-0.81326,-0.46917,-0.30457,-0.473,-0.22577,-0.16301,0.10587,0.022131,-0.15884,-0.71437,-0.20691,-0.90642,-0.94501,-0.62577,-0.74731\nNaN,NaN,NaN,NaN,3.0908,4.5482,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1414,0.22024,2.4681,2.2097,1.2174,-0.37614,-0.33236,0.07625,0.94115,1.1472,1.3858,0.42255,-0.055971,-0.032678,-1.1999,-1.1025,-0.52349,-0.20817,0.43716,0.39038,-0.21113,-0.9643,-0.40457,-0.80509,-0.83051,-0.49122,-1.1398\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3014,1.2435,2.2882,3.9802,2.0865,1.1496,0.71287,1.0982,1.5435,1.3827,0.70942,0.064614,-0.36811,-0.72024,-1.8142,-1.4468,-0.34035,0.21753,0.31892,-0.62062,-0.62161,-1.0439,-0.87658,-0.96204,-1.0654,-0.48768,-0.89463\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3571,3.7863,1.6944,1.3131,0.83448,1.3389,1.2795,1.4258,0.9447,0.67494,-0.33432,-1.182,-1.5164,-0.87678,-0.26169,-0.10828,-0.18783,-0.9571,-0.86379,-0.96076,-1.3261,-1.1324,-0.9993,-1.0299,-0.63903\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1902,3.6122,1.938,1.2576,1.367,1.8961,1.6251,1.7311,0.72936,-0.14463,-0.66491,-1.3866,-1.6459,-0.25513,-0.27284,-0.34402,-0.004734,-0.40043,-0.38259,-0.59982,-1.3183,-0.47023,-0.52836,-1.2594,-0.86274\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.69484,1.9925,2.1843,2.4323,2.3797,1.8655,1.9661,1.4037,0.91113,-0.94332,-0.89794,-0.96002,-1.3808,-0.28023,-0.62871,-0.325,0.20405,-0.10298,0.6312,-0.88122,-1.7823,-0.73465,-0.69374,-1.0581,-1.0325\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4322,1.7187,2.4701,3.1533,3.1347,2.0084,1.943,1.3375,-0.067446,-1.6015,0.7346,0.58323,0.30484,0.10644,-0.48435,-0.15153,0.161,-0.22276,-0.16178,-0.97826,-1.4949,-0.98284,-0.62559,-1.025,-1.4528\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9638,2.6437,3.1318,3.3591,3.285,2.7147,1.8794,0.51735,0.11442,-0.3562,0.90528,0.1843,0.051662,-0.18431,-0.2635,-0.37992,-0.22791,-0.068382,-0.23667,-0.672,-1.5105,-1.601,-1.0645,-1.5657,-2.231\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2422,3.8413,3.5512,3.3303,2.3705,1.7217,1.8316,0.48154,0.010075,-0.31917,-0.16166,0.34223,0.77892,-0.55858,-0.58178,0.047634,-0.27178,-0.60653,-1.5559,-1.4913,-1.1216,-2.3189,-2.4579\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.6441,2.7799,2.273,2.2651,2.7104,1.8543,0.7339,0.42512,0.24326,0.42382,0.68643,-1.1595,-0.4161,0.4158,0.63805,0.33972,-0.15818,-0.34867,-0.41613,-1.9278,-2.381\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9343,-1.4873,2.2964,2.7639,3.136,2.3415,1.6179,1.4871,1.2352,1.7594,-1.4336,-2.3102,0.97664,1.6034,0.80427,0.26344,-0.75084,-0.62616,0.1181,-0.60512,-1.8525\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070219-070430.unw.csv",
    "content": "-4.5222,-4.1525,-4.5423,-4.3342,-3.8685,-3.359,-3.1641,-3.0346,-2.5624,-2.0094,-1.2199,-1.0897,-0.96698,-1.175,-1.621,-1.6198,-1.1008,-1.1281,-1.4454,-1.6979,-1.7992,-1.7581,-1.5099,-1.2118,-1.3149,-1.7135,-1.685,-1.9271,-1.8484,-2.0833,-2.714,-2.5097,-2.7493,-3.387,-3.8795,-4.4534,-5.9387,-6.482,-6.6588,-7.1917,-6.8735,-6.6695,-6.4319,-7.124,-9.1502,-9.8926,-9.7431\n-4.5369,-4.3601,-4.7343,-4.4221,-3.7545,-3.0084,-2.6797,-2.8635,-2.2168,-1.6876,-1.0489,-0.70942,-0.77225,-1.0873,-1.1234,-0.81343,-0.8229,-1.2847,-1.6289,-1.7249,-1.8788,-1.5756,-1.2682,-1.114,-1.1151,-1.063,-1.0367,-0.73541,-0.79848,-0.74388,-1.4033,-2.2478,-2.7864,-3.2332,-3.4001,-4.2642,-5.4576,-6.0185,-6.4576,-7.1597,-7.9561,-7.3041,-7.3649,-7.8804,-8.7681,-9.6136,-9.8579\n-5.0785,-5.232,-4.8476,-4.3105,-3.9047,-2.688,-2.1479,-1.8879,-1.648,-1.2134,-1.1587,-1.1442,-1.0411,-1.2101,-0.99415,-0.60666,-0.81328,-1.2306,-1.3829,-1.3849,-1.2605,-1.2104,-0.88727,-0.62424,-0.7906,-0.73412,-0.62734,-1.0877,-0.72518,-0.16228,-0.95668,-2.4958,-2.7194,-2.6279,-2.2631,-3.3914,-5.4181,-6.014,-6.6266,-7.7148,-8.143,-7.0312,-6.7896,-7.7128,-9.0015,-9.4004,-10.112\n-6.2169,-6.6132,-5.4456,-4.359,-3.8118,-2.6979,-2.2321,-1.1913,-0.67623,-0.57328,-0.81028,-1.1008,-1.0007,-1.0359,-1.262,-1.0166,-1.1579,-1.1085,-1.1685,-1.477,-1.2309,-0.91643,-0.70054,-0.6493,-0.50849,-0.23523,-0.19315,-0.34107,-0.30103,-0.48537,-1.319,-1.9342,-2.258,-2.5474,-2.708,-3.9326,-5.6637,-6.5585,-6.3158,-6.1062,-6.2069,-6.1292,-6.641,-7.5896,-8.3533,-8.7439,-9.0033\n-6.5741,-7.7367,-6.8316,-4.4209,-3.7513,-2.9158,-2.4018,-1.1893,-0.23683,-0.52947,-0.79818,-0.59937,-0.66952,-0.9124,-1.0441,-1.2116,-1.8641,-1.8277,-1.1129,-0.73582,-0.74756,-0.61838,-0.81128,-0.29374,0.11349,0.17268,0.12266,-0.32445,-0.18569,-0.26037,-1.1502,-1.8878,-2.0769,-2.5437,-3.6473,-5.0308,-5.987,-6.3451,-6.0661,-5.6017,-5.9211,-6.5017,-7.0085,-7.5023,-7.96,-8.269,-8.8533\n-6.1996,-7.1435,-6.6911,-4.0038,-3.9567,-3.4209,-2.6757,-1.9636,-1.1621,-0.90857,-0.93338,-0.88777,-0.94184,-1.2502,-0.98349,-1.2209,-1.5021,-1.83,-1.3703,-0.90772,-0.47094,-0.61081,-0.559,-0.67628,-0.28569,0.062403,0.044836,-0.22239,-0.058081,-0.080788,-1.0325,-1.2721,-1.3253,-1.8251,-2.6192,-3.9556,-5.6356,-6.0976,-6.3952,-6.1641,-6.4632,-7.1571,-7.7418,-8.104,-8.4317,-8.4525,-8.8765\n-6.4648,-6.9831,-6.3803,-4.4142,-4.0309,-2.9186,-2.1492,-2.0735,-1.9343,-1.2918,-1.283,-1.3502,-1.4587,-1.5103,-1.2382,-1.477,-1.5466,-1.3342,-1.0811,-0.60198,0.017139,0.15848,0.1174,-0.15369,-0.0902,0.21073,0.19414,0.071356,0.38605,-0.16446,-0.82435,-0.82632,-1.2673,-1.974,-2.6953,-3.3249,-4.1625,-5.6678,-7.1056,-7.753,-8.2044,-8.1857,-8.8567,-8.7033,-8.2513,-7.8811,-8.2651\n-6.0532,-6.2796,-5.6071,-4.1388,-3.8754,-2.4496,-1.7111,-1.7335,-1.899,-1.845,-1.4713,-1.3393,-1.4601,-1.5891,-1.3183,-1.3831,-1.5732,-1.2274,-0.62659,-0.12634,0.46668,0.5843,0.21124,0.20329,0.55379,1.058,0.72546,0.39506,-0.35681,-0.35859,-0.32854,-0.89336,-1.5174,-1.953,-2.5046,-3.2979,-4.4966,-5.9831,-7.4658,-8.4891,-8.7798,-8.8479,-9.5199,-9.416,-9.0616,-8.0719,-8.1527\n-4.8346,-4.7885,-4.6532,-3.533,-2.89,-2.1161,-1.9022,-1.7542,-1.8269,-2.0086,-2.0629,-1.982,-1.5359,-1.2909,-1.1073,-1.2559,-1.2571,-1.1121,-0.51907,-0.035948,0.30586,0.26126,0.24233,0.57207,0.88629,2.0991,2.4595,1.5503,0.30716,0.68658,0.13762,-1.1816,-1.9195,-2.0096,-2.3797,-3.471,-4.9382,-6.6857,-7.758,-8.1759,-8.3015,-8.4938,-8.9474,-9.2357,-8.9021,-8.4919,-8.1633\n-4.1978,-3.9934,-3.9893,-3.1955,-2.3602,-1.898,-1.7463,-1.6427,-1.797,-2.2829,-2.771,-2.2456,-1.5639,-1.252,-1.0745,-1.5278,-1.4029,-0.92754,-0.42695,-0.47858,-0.11481,0.35822,0.91569,1.5784,1.6836,2.7889,3.1571,1.6558,0.76028,0.74792,-0.43168,-1.0051,-1.7766,-1.7074,-1.9154,-3.3295,-5.1472,-6.7516,-7.4173,-7.5337,-7.7555,-8.1542,-8.8437,-9.0628,-8.5367,-8.453,-8.238\n-3.9886,-3.8306,-3.9772,-3.3509,-2.6698,-1.8847,-1.4865,-1.3012,-1.3013,-1.6874,-2.4542,-1.8846,-1.3266,-1.5206,-1.7824,-2.3764,-1.9033,-1.0346,-0.33502,-0.24245,0.028519,0.80625,1.6495,2.261,2.5279,3.0439,2.8521,1.8326,1.2593,0.79484,0.083055,-0.72901,-1.6784,-2.1459,-2.0394,-2.8963,-4.3414,-5.5743,-5.9913,-6.0619,-6.4943,-7.1527,-7.9263,-8.9,-8.6107,-9.0228,-9.0753\n-3.7031,-3.2491,-2.8646,-2.5207,-2.5503,-2.0161,-1.3946,-1.2323,-1.1457,-1.4404,-1.802,-1.262,-1.1472,-1.4432,-1.5292,-1.5516,-1.7029,-1.1047,-0.71381,-0.47321,-0.29639,0.48117,1.6061,2.6904,2.9779,2.9467,2.3525,1.8184,1.3269,0.30758,-0.46841,-1.3234,-1.8459,-2.2679,-2.5681,-3.8423,-4.7978,-5.2749,-5.3135,-5.0885,-5.55,-6.2849,-7.1763,-7.8978,-8.4409,-9.4048,-9.1869\n-3.2302,-2.782,-2.2902,-1.3342,-0.91768,-1.3728,-1.2268,-1.2114,-1.1269,-1.4789,-1.5685,-1.3302,-1.1517,-1.2222,-1.0664,-0.97471,-1.0149,-0.93029,-0.54428,-0.020542,0.12055,0.63707,1.3201,2.4905,3.2818,2.772,2.0868,1.4485,0.98904,-0.10215,-1.4606,-1.7683,-2.1459,-2.6258,-2.9393,-3.6753,-4.3088,-4.736,-4.756,-4.2069,-4.6301,-5.7473,-6.5325,-7.0821,-8.4507,-8.6802,-8.3955\n-2.9991,-2.2929,-2.1802,-1.7493,-0.56893,-0.52611,-0.89302,-1.0537,-1.2579,-1.4323,-1.453,-1.1562,-0.7455,-0.7927,-0.8645,-0.77427,-0.72037,-0.5652,-0.32393,-0.3391,-0.17027,0.46818,1.1089,1.7299,2.1879,2.161,2.0471,1.688,1.1284,-0.4851,-2.0087,-2.2086,-2.4649,-3.183,-3.7654,-4.3473,-4.5971,-4.4381,-4.1854,-3.5643,-3.7468,-5.1121,-5.8962,-6.406,-6.9189,-7.4265,-8.0072\n-2.8102,-2.6775,-2.3733,-1.7061,-1.0967,-1.4863,-1.3955,-1.2318,-1.2574,-1.1008,-1.2875,-0.73905,-0.15209,0.030058,-0.15185,-0.20567,-0.46787,-0.76011,-0.47549,-0.44581,-0.085024,0.89167,1.3215,1.5495,1.7293,1.9652,1.9467,0.98604,-0.43306,-1.743,-2.1412,-2.8656,-4.0557,-4.8178,-4.6537,-4.6452,-4.7699,-4.5307,-4.1125,-3.7509,-4.1842,-4.3028,-4.4165,-5.1078,-5.2066,-6.0009,-5.8467\n-1.5286,-1.4384,-1.6186,-1.1609,-0.97167,-1.4001,-1.487,-1.3072,-1.2295,-1.1489,-1.3013,-0.88159,-0.51409,-0.56529,-0.65413,-0.73663,-1.0246,-1.0468,-0.3264,0.063932,0.50389,1.3628,1.3559,1.1803,1.3601,1.449,1.2239,-0.11678,-1.2135,-2.4351,-3.5427,-4.1152,-5.1414,-5.9018,-6.2362,-5.3913,-4.6265,-4.4781,-4.2836,-3.8447,-4.2875,-4.4969,-4.1666,-4.4977,-4.642,-6.5299,-6.6321\n-0.99101,-0.73944,-0.88625,-1.0879,-1.2845,-1.4568,-1.3052,-1.2177,-1.4952,-1.2,-1.1613,-1.0237,-0.99013,-1.2371,-0.85397,-1.1701,-1.8355,-0.58773,0.43439,0.66669,1.3586,1.5925,1.177,0.69785,0.80021,1.3603,1.1313,0.23468,-1.0136,-2.7332,-4.6787,-4.4524,-5.167,-5.8714,-6.1702,-5.9201,-5.1736,-5.2757,-4.9091,-4.5414,-3.9111,-4.5769,-5.0297,-4.3526,-3.5862,-4.0636,-4.5635\n-3.0102,-1.5986,-1.2942,-1.5247,-1.8824,-1.8005,-1.0627,-0.47296,-0.67897,-0.56664,-0.61829,-0.5608,-0.28383,-0.30535,-0.75649,-1.0384,-1.3135,-0.65079,0.62473,2.2847,1.6059,1.2853,1.3736,0.90243,0.35306,0.084017,0.082085,-0.31869,-1.707,-3.5783,-4.9849,-4.7904,-4.9258,-5.3463,-5.6526,-5.7663,-5.4532,-5.7694,-5.903,-5.0873,-3.8849,-2.6784,-3.5716,-4.4501,-3.1672,-2.427,-2.5637\n-2.6182,-2.1265,-1.1726,-1.2234,-1.7216,-2.0451,-1.434,-0.08674,-0.21333,-0.43199,-0.2322,-0.25206,-0.0084133,0.13927,-0.28591,-0.47628,-0.15467,0.56201,1.3925,2.0593,2.5639,2.283,1.3354,1.3358,0.90511,-0.031153,0.036188,-0.32683,-1.2847,-2.6364,-4.0231,-4.4519,-5.5763,-5.8297,-5.8555,-5.3529,-5.945,-6.1271,-6.4238,-5.2061,-4.4626,-3.2012,-3.4151,-4.2118,-3.6655,-2.9495,-2.2324\n-2.0024,-1.6684,-0.40843,-0.39244,-0.62435,-0.74277,0.060228,0.46778,0.16175,-0.27817,-0.35318,-0.1063,0.11047,0.28326,0.37338,0.53983,0.62788,0.9288,1.5852,2.2543,2.3016,2.0014,1.3757,1.5493,1.2707,0.64448,0.11904,-0.167,-0.3816,-0.93235,-2.2612,-3.5292,-5.2385,-5.5832,-5.8525,-5.1242,-5.3495,-5.3621,-6.2064,-5.9091,-5.104,-4.2921,-4.1778,-3.9033,-3.3179,-3.0862,-3.023\n-1.7692,-0.80671,-0.079329,-0.22022,-0.26681,0.862,1.6024,1.0154,0.077147,0.069544,0.36629,0.63096,0.33532,0.36229,1.2239,1.112,1.292,1.5355,1.4991,1.748,1.9279,1.1745,0.94086,1.3918,1.6025,0.73869,0.47145,0.13649,-0.14213,-0.022799,-0.35147,-2.0934,-4.4544,-4.4903,-5.9348,-5.3231,-4.9087,-5.0189,-5.71,-5.2995,-4.7397,-4.5673,-4.2099,-3.4993,-3.1757,-3.0017,-2.8011\n-0.88781,0.55877,0.77401,0.80061,1.1548,1.7008,1.2964,0.68998,0.47431,0.35229,0.51385,0.66698,0.52204,0.63162,2.0081,2.0735,1.5645,1.9458,2.1386,1.5954,1.943,2.0006,2.6154,2.4335,1.2366,0.7252,0.46992,0.20955,-0.37493,-0.46431,-0.5982,-1.4323,-3.3905,-3.6795,-5.4697,-5.4814,-5.2706,-5.3024,-5.7851,-5.7137,-4.1014,-4.3719,-4.0953,-3.4911,-3.1188,-2.9053,-2.5856\n-0.036638,0.91991,0.89691,0.66686,1.2541,1.4554,0.59602,0.36106,0.060759,-0.10205,0.082644,0.3889,0.5501,0.98048,1.8888,2.5795,1.783,1.586,1.858,1.6333,2.2133,2.4805,3.01,1.9489,0.93632,0.67275,0.23583,0.1064,-0.41921,-0.68004,-1.0411,-1.7624,-2.5665,-2.9999,-3.9803,-4.7236,-4.9827,-5.3323,-5.9199,-6.5347,-5.1742,-4.3571,-3.9867,-3.2903,-3.0491,-2.7325,-2.2493\n-0.12569,0.12231,0.21075,-0.1228,-0.042055,0.55491,0.54732,0.095472,-0.19114,-0.37387,-0.18866,0.26759,0.82546,1.3332,1.7201,2.2394,1.5545,0.94026,1.0134,1.2642,1.2616,1.8692,2.2651,1.0804,0.54147,0.56476,0.19403,-0.48495,-0.77857,-1.2778,-1.9528,-2.3223,-2.7335,-3.0586,-3.5398,-4.218,-4.491,-4.7712,-5.0491,-4.9519,-4.7481,-3.9332,-3.5636,-3.3957,-3.333,-3.0295,-2.0985\n-0.75073,-0.45117,-0.026304,0.36827,0.67773,0.5876,0.020502,-0.31307,-0.062254,0.13861,0.097246,0.17018,0.68546,1.0142,1.3928,1.371,0.82379,0.46597,0.59564,0.69411,0.67856,0.80298,0.45416,-0.0023727,-0.11146,-0.51008,-0.78319,-1.5039,-2.1169,-2.7261,-2.5728,-2.5548,-2.9438,-3.4587,-3.8805,-3.96,-4.1157,-4.2915,-4.3077,-4.5982,-4.4386,-3.8343,-3.4554,-3.4258,-3.2063,-2.5704,-1.9006\n-1.1866,-1.1785,-0.30384,0.44612,0.84236,0.26645,-0.39293,-0.33989,0.2369,0.50553,0.50488,0.5612,0.53712,0.35546,0.39283,1.0019,0.78135,0.49688,0.44866,0.5037,0.36424,-0.21623,-0.21666,-0.46799,-0.97581,-1.4115,-1.6429,-2.0107,-2.2134,-2.5612,-3.1286,-3.6657,-3.5954,-3.2531,-3.6318,-3.7503,-4.1987,-4.3246,-4.6838,-4.9319,-4.3565,-3.9569,-3.599,-3.401,-2.8722,-1.9714,-1.6614\n-0.34833,-0.89314,-0.072744,0.1453,0.30289,0.12057,-0.30615,-0.247,0.11044,0.43891,0.42875,0.39116,-0.028067,-0.19605,0.17597,0.64074,0.6676,0.24096,0.17274,0.14643,-0.18337,-0.37896,-0.2237,-0.25193,-0.54816,-0.80977,-1.5199,-1.9615,-1.9678,-1.9153,-2.9751,-3.6597,-4.1089,-3.646,-3.5342,-3.6328,-4.4088,-4.7696,-5.0641,-5.0035,-4.6286,-4.1169,-3.4201,-3.0932,-2.4688,-1.7641,-1.4271\n-0.12046,-0.5953,-0.56196,0.38074,0.26482,0.058043,-0.67193,-0.50776,-0.15097,-0.028282,0.15603,0.078485,-0.035723,0.08769,0.39835,0.0644,-0.48287,-0.31574,-0.30817,-0.27847,-0.42898,-0.24394,-0.12941,-0.00091362,-0.47022,-0.61992,-0.97885,-1.2823,-2.0493,-2.1349,-2.5367,-3.0159,-3.5488,-3.7474,-4.0754,-4.5109,-4.8338,-4.5423,-4.6565,-4.6729,-4.5313,-3.7055,-3.0975,-2.9708,-2.3843,-1.5647,-0.97235\n-0.84606,-0.56672,0.11851,0.020464,-0.14526,-0.32896,-0.41952,-0.24109,-0.14758,-0.16312,-0.072435,-0.12456,-0.2713,-0.43303,-0.32399,-0.34906,-0.77775,-0.57021,-0.17869,0.15155,0.12374,0.058161,-0.14554,-0.36958,-0.82817,-1.1612,-1.3795,-1.4203,-1.8687,-2.8477,-3.1909,-3.343,-3.9951,-3.7341,-4.1531,-4.5433,-4.4551,-3.9748,-4.1875,-4.6858,-4.8291,-3.9058,-2.8187,-2.1206,-1.591,-0.65837,-0.30331\n-1.2124,-1.3498,-0.81655,0.089947,-0.16534,-0.34992,-0.73265,-0.51058,-0.076044,0.11879,0.1133,0.16422,-0.030958,-0.69489,-0.4541,-0.39293,-0.0051365,0.36981,0.8776,1.1203,0.6771,0.38682,-0.44707,-0.67441,-0.92369,-1.0393,-1.053,-1.3819,-1.8406,-2.4803,-3.1687,-4.0171,-4.5612,-4.4125,-4.2327,-5.107,-5.3497,-5.0312,-4.9527,-4.9871,-5.0424,-4.2386,-3.155,-1.7651,-0.70183,-0.82436,-0.49347\n-1.8813,-1.4833,-1.6727,-1.3248,-0.57012,-0.12568,0.0079975,-0.0035229,0.095217,0.17107,0.33316,0.36366,0.29979,-0.46047,0.12093,0.96262,1.6013,0.78617,0.82534,1.506,1.2236,0.89609,1.2844,1.2946,0.51561,0.27287,-0.12502,-0.814,-1.4379,-1.8412,-2.4719,-3.3071,-4.1266,-4.2934,-4.436,-5.3398,-6.1282,-6.0465,-5.7476,-5.3193,-4.8801,-4.1124,-3.17,-1.433,-0.13065,-0.71816,-0.30435\n-1.8829,-1.2786,-1.1715,-1.1664,-1.2004,-0.83486,-0.27388,0.50085,0.38527,0.50566,0.57435,0.86139,0.89557,0.55837,1.0583,1.8534,1.8685,1.1459,0.6724,0.56545,-0.17564,0.33623,0.82036,1.467,1.1735,0.26494,-0.97385,-1.7304,-2.1492,-2.017,-1.4143,-2.1379,-2.7396,-4.1724,-4.4652,-4.704,-5.2822,-5.4942,-5.5246,-5.3556,-4.8842,-3.6617,-2.7479,-0.45303,-0.0618,-0.24958,-0.22229\n-1.1679,-0.67662,-1.3854,-2.0812,-2.0119,-1.0102,-0.10886,0.64126,0.32872,0.1537,0.46988,0.69246,1.0743,1.5677,1.8612,1.6986,1.6111,0.57464,0.34038,0.44121,0.36302,0.30485,0.23365,0.93866,0.49102,-0.41458,-1.7732,-2.8964,-3.7692,-3.9177,-3.3365,-1.9696,-1.7813,-2.4039,-3.7781,-4.5369,-4.8471,-5.5093,-5.3817,-5.0474,-4.923,-3.5506,-2.4878,-2.1064,-0.81687,-0.17948,0.089132\n-1.6423,-1.3254,-1.152,-1.5005,-1.5372,-0.63935,-0.11693,0.18997,0.025114,-0.29383,0.33348,0.52346,0.8528,1.3363,1.3759,1.5094,1.5265,0.37583,-0.035868,0.34846,0.68443,0.65191,0.4281,-0.75068,-1.2062,-1.6471,NaN,NaN,NaN,-3.3698,-3.2954,-2.7961,-1.7025,-1.8588,-3.5983,-4.8678,-5.6926,-5.5579,-5.1838,-4.8145,-4.7475,-3.9024,-2.9563,-2.3915,-1.1847,-0.32894,-0.081905\n-1.9658,-1.181,-0.97458,-0.99382,-0.78782,-0.7717,-0.82622,-0.3785,0.8215,1.177,0.73535,0.82681,0.52735,0.98834,0.92388,0.68604,0.16126,-0.028791,-0.18169,-0.025583,0.43683,0.41725,-0.99322,-2.0249,-1.6581,NaN,NaN,NaN,NaN,NaN,-2.8744,-2.3318,-1.5534,-2.1735,-3.8801,-5.365,-5.7381,-5.6621,-5.2974,-4.9567,-4.677,-4.3184,-3.827,-2.5285,-1.0873,-0.28933,0.034657\n-2.4521,-2.1563,-1.9263,-1.5706,-0.73845,-0.60817,-0.46827,-0.019756,1.3349,2.1566,1.4276,1.3081,0.78799,0.21997,-0.1235,0.059752,0.017113,-0.41294,-0.34863,0.046734,0.075209,-0.30965,-1.6317,-2.721,-1.993,NaN,NaN,NaN,NaN,NaN,-2.7364,-2.9182,-2.3198,-2.919,-3.6631,-5.0008,-5.2069,-5.3343,-5.2417,-4.6416,-4.1173,-3.956,-3.9014,-3.1328,-1.1582,0.23652,0.47187\n-3.3067,-3.6772,-3.7902,-2.9625,-0.98297,-0.18865,0.15064,0.33231,0.85412,1.4354,0.90146,0.52616,0.45286,-0.4428,-0.58899,-0.31147,0.063578,0.51867,0.67892,-1.4002,-2.7399,-4.0286,-2.3392,-1.0467,-2.1626,NaN,NaN,NaN,NaN,NaN,-3.6131,-3.4921,-3.3669,-4.3393,-4.5809,-4.9424,-4.7936,-4.7787,-5.0585,-4.313,-3.5307,-3.0463,-2.5149,-1.9848,-0.75637,0.46934,0.90227\n-3.5369,-2.9873,-3.0299,-3.0145,-2.2667,-1.3647,-0.84844,-0.81309,-0.75947,0.11913,0.36527,0.63097,0.1038,-1.0064,-1.5608,-0.9755,-0.6526,-1.6605,-2.7224,-3.6714,-4.508,-3.4827,-2.4895,-0.80924,-1.9614,-3.1237,NaN,NaN,NaN,-2.1953,-3.6237,-4.3163,-4.8755,-5.192,-5.0014,-5.2056,-4.9448,-5.0379,-4.9556,-4.4258,-3.2422,-2.002,-1.3466,-1.043,-1.0439,-0.84804,0.011992\n-3.3854,-3.1099,-2.8489,-2.8892,-3.0709,-2.3428,-1.7023,-1.683,-1.5927,-1.246,-0.78543,-0.71225,-1.0491,-1.688,-2.2667,-1.9747,-2.4232,-2.0618,-2.8444,-3.7098,-4.1155,-2.6304,-1.6033,-0.63597,-2.9992,-3.9817,-5.9365,-4.634,-3.1769,-3.6141,-4.0451,-4.5071,-4.9122,-5.0893,-4.9629,-5.1745,-5.2635,-5.2282,-5.1577,-4.6084,-3.1898,-1.605,-0.83419,-0.68937,-0.88989,-0.774,0.56138\n-3.9253,-3.4505,-3.666,-3.2937,-3.0258,-2.6057,-2.4106,-2.4334,-2.8207,-2.3067,-2.085,-2.2845,-2.6949,-3.6193,-3.8763,-3.2119,-2.9392,-2.7778,-2.6726,-5.0426,NaN,NaN,NaN,NaN,NaN,NaN,-4.5665,-4.0537,-3.4536,-3.9436,-4.7683,-5.1138,-5.3208,-5.5388,-5.3928,-5.257,-5.364,-5.2149,-4.9554,-4.3465,-3.147,-1.561,-0.76646,-0.64961,-0.85736,-0.68171,0.48723\n-4.764,-4.0794,-3.9839,-3.453,-3.0067,-2.7075,-2.8256,-3.0845,-3.7304,-4.0433,-4.1043,-3.6369,-3.7099,-4.2665,-4.6381,-4.3052,-3.845,-3.5925,-3.2601,-5.8112,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.7276,-4.7288,-5.4069,-5.7871,-5.7861,-5.5166,-5.6801,-5.797,-5.808,-5.6009,-5.6067,-4.7825,-3.2076,-1.2836,-1.0937,-0.62351,-0.12058,-0.064219,0.008585,0.55235\n-3.9139,-3.6851,-3.4836,-3.4304,-3.3766,-3.3125,-3.2904,-3.7066,-4.1675,-4.3078,-4.4732,-3.8787,-3.9858,-4.8262,-5.5745,-5.7896,-5.4923,-6.0517,-6.2368,-8.0403,-7.7848,NaN,NaN,NaN,NaN,NaN,-2.9798,-3.9597,-4.9849,-6.105,-6.5877,-6.3916,-5.8767,-6.2495,-6.4174,-6.1331,-5.439,-5.1808,-4.4516,-2.343,-0.7621,-1.0393,-0.49033,-0.24427,-0.14071,0.16956,0.63792\n-2.6359,-3.0565,-3.2612,-3.2823,-3.5238,-3.6812,-3.7935,-3.9246,-4.0137,-4.2366,-4.3234,-4.3229,-4.9066,-5.9639,-5.964,-5.9529,-5.5508,-5.7755,-5.9834,-7.3774,-7.6147,-6.4566,-4.9687,NaN,NaN,NaN,-5.0421,-5.2636,-5.9467,-6.582,-7.0053,-7.1304,-7.0691,-7.0064,-6.7319,-6.1626,-5.1624,-4.1048,-2.7192,-0.90664,-0.014591,-0.32433,-0.25466,-0.27192,0.0094013,0.79375,0.74065\n-2.5935,-2.834,-3.1825,-2.9817,-3.5672,-3.9393,-3.9595,-3.8894,-3.982,-4.2849,-4.5049,-4.7522,-5.3359,-6.4366,-7.3376,-7.2088,-6.7653,-6.1498,-6.815,-7.5133,-6.6095,-3.9716,-2.7213,-2.3557,-2.713,-3.8898,-6.408,-6.7,-6.94,-6.9131,-6.9762,-7.1661,-6.9907,-6.1853,-4.952,-4.3874,-4.3751,-3.1925,-1.3665,-0.12477,0.10142,0.060364,0.17893,0.22627,0.46648,1.028,1.3327\n-3.1259,-3.2284,-3.2732,-3.074,-3.2024,-3.6396,-3.4695,-3.529,-3.8973,-4.7621,-5.0361,-5.3666,-5.5999,-6.4971,-7.9949,-8.2385,-7.4162,-7.545,-8.2611,-8.4162,-7.1943,-6.7386,-6.0973,-5.2073,-4.3559,-5.8839,-6.7724,-7.7313,-7.8836,-7.5405,-6.9984,-6.8326,-6.3428,-5.496,-4.5316,-3.8489,-3.6136,-2.6446,-0.98194,0.2662,0.30515,0.31364,0.57753,0.13223,0.25062,0.5018,0.80808\n-2.9553,-3.2643,-3.4032,-3.5329,-3.5608,-3.5672,-3.5727,-3.8951,-4.6111,-5.1118,-5.1532,-5.3538,-5.6199,-6.8067,-8.0114,-8.0151,-7.7576,-8.0277,-8.6155,-8.6746,-8.1754,-8.1421,-7.8789,-7.8829,-7.8978,-7.6398,-7.49,-7.9918,-8.2199,-7.62,-6.9682,-6.4138,-6.0871,-5.5349,-4.8281,-3.9954,-3.5137,-1.9771,-0.43078,-0.15165,-0.27541,0.50868,0.75557,0.12677,0.0046406,0.41948,0.95671\n-3.2488,-3.7045,-3.6127,-3.6514,-3.8346,-3.9251,-3.9942,-4.2936,-4.5757,-4.8891,-5.0812,-5.2168,-5.2921,-5.3881,-6.1738,-7.3768,-7.9446,-8.4394,-8.6227,-8.5584,-8.3004,-8.2898,-7.7285,-7.5853,-7.7031,-7.7793,-8.0748,-8.2627,-8.0872,-7.3348,-6.6901,-6.2482,-5.8119,-4.9751,-4.2131,-3.8142,-3.3454,-2.2014,-0.28914,0.32308,0.31792,1.638,1.4858,1.2512,0.42821,0.61471,1.2461\n-3.5704,-4.0947,-4.2167,-3.9442,-3.9676,-3.9968,-4.0481,-4.2926,-4.5488,-4.5915,-4.5555,-5.0688,-5.1277,-5.2172,-5.7827,-6.4865,-7.1017,-7.751,-8.6246,-8.6418,-8.5531,-8.1675,-7.941,-7.859,-7.7447,-8.0848,-8.7905,-8.244,-7.3169,-6.4407,-5.8555,-5.7674,-5.3357,-4.5054,-3.7837,-3.5718,-2.8953,-1.3335,0.1311,0.21768,0.37487,1.0301,1.1668,1.1186,0.38038,1.0447,1.6846\n-3.1396,-3.806,-4.1303,-4.0585,-3.9558,-3.9425,-3.9585,-4.4219,-4.8105,-4.9081,-4.6446,-4.4613,-4.4576,-4.7907,-5.6808,-6.3866,-6.6134,-7.0194,-7.7003,-8.374,-9.377,-9.5428,-8.7341,-8.4164,-8.5548,-8.9856,-9.2185,-8.1404,-6.732,-6.1378,-5.6932,-5.5209,-4.622,-3.8251,-3.2886,-3.398,-2.7578,-1.4026,-0.18056,0.14548,0.037132,0.13799,0.79947,0.84934,1.0745,1.6966,2.4956\n-3.0555,-3.2434,-3.8815,-3.8156,-3.6288,-3.5846,-3.9371,-4.2368,-4.4102,-4.3817,-4.3994,-4.2824,-4.087,-4.1404,-5.0264,-6.1933,-6.0326,-6.6233,-7.2267,-7.8731,-8.483,-9.015,-8.2658,-8.509,-9.2842,-9.5572,-9.9982,-8.6209,-6.3804,-5.5913,-5.5258,-5.3091,-4.6258,-4.0216,-3.6579,-3.5304,-2.8932,-1.334,-0.22555,0.26411,0.090645,0.0070705,0.014292,0.64221,1.1616,2.0575,2.5629\n-3.1339,-3.5077,-3.6055,-3.5279,-3.5908,-3.542,-3.6105,-3.6026,-3.8431,-3.8978,-3.8826,-4.0093,-4.2881,-4.656,-5.1054,-5.551,-5.698,-6.3428,-7.983,-8.7818,-8.7629,-8.1502,-7.8772,-7.8364,-8.0174,-8.1937,-8.964,-8.8974,-6.3972,-5.1134,-4.9455,-4.981,-4.3811,-3.7664,-2.6556,-2.4066,-2.0091,-1.0678,-0.46414,-0.079752,0.03956,-0.19827,-0.12892,0.12532,0.93283,1.3857,2.4854\n-2.4115,-2.4243,-2.2919,-2.7922,-3.3437,-3.3763,-3.5927,-3.7374,-3.8947,-3.7999,-3.9327,-4.1415,-4.3753,-4.6725,-4.5579,-5.2927,-5.404,-6.7537,-7.1578,-8.3276,-8.697,-7.7171,-7.5906,-7.6129,-7.2465,-6.7968,-7.1348,-7.1691,-6.6135,-5.5532,-4.8919,-4.6723,-3.7779,-2.7064,-2.3449,-2.2417,-1.6323,-1.017,-0.68333,-0.754,-0.73798,-0.86659,-1.7372,-0.68761,1.3023,2.0267,3.3808\n-2.028,-1.8368,-1.8298,-2.5695,-2.8869,-3.1193,-3.2815,-3.5909,-3.7938,-3.9104,-4.0978,-4.4104,-4.9815,-5.3516,-5.6874,-5.9265,-6.1023,-7.0456,-7.2941,-7.3613,-7.2408,-7.1302,-6.9655,-6.9073,-6.57,-6.5032,-6.3664,-6.7475,-6.801,-6.4225,-5.2765,-4.388,-3.6262,-2.9201,-2.5601,-1.8853,-1.3407,-1.0741,-0.92806,-0.84314,-0.73403,-1.2798,-1.4745,-0.45395,1.1294,2.0228,3.3623\n-1.9655,-2.0345,-2.4059,-2.7113,-2.5517,-2.6251,-3.0147,-3.5037,-4.0265,-4.1023,-4.2326,-5.0714,-5.897,-6.5738,-6.615,-6.9378,-6.8335,-7.0141,-7.1158,-7.1983,-7.2045,-7.0615,-6.9782,-6.5092,-5.9416,-5.4176,-5.7323,-6.3288,-6.8001,-6.4715,-4.9888,-4.0017,-3.3094,-3.0204,-2.9173,-1.5937,-0.89554,-1.0631,-0.98923,-0.87285,-0.6215,-0.63666,-0.90813,-0.063698,1.6298,2.4812,3.422\n-2.0033,-2.03,-2.1622,-1.9259,-1.7851,-2.4697,-3.2114,-3.6012,-3.9241,-3.7076,-4.1534,-5.1685,-6.2059,-6.988,-7.1003,-7.1229,-7.4234,-7.6342,-8.0949,-7.4098,-6.7745,-6.8368,-6.9294,-6.5141,-5.9223,-5.466,-5.8163,-5.9808,-6.6771,-5.9281,-3.942,-3.1432,-2.6578,-2.2437,-1.7637,-1.0731,-0.682,-0.65634,-0.69868,-0.73384,-0.43103,-0.33841,-0.44193,0.87407,2.5544,3.2496,3.2836\n-2.1852,-1.935,-1.7584,-1.8225,-2.8441,-3.2573,-3.0743,-3.1494,-3.026,-3.2118,-4.8842,-5.6587,-6.513,-6.8985,-6.9223,-7.2694,-8.1032,-8.1945,-7.9741,-7.0143,-6.4338,-6.3929,-6.529,-6.5564,-6.1194,-5.919,-6.6075,-6.4217,-6.0298,-5.2922,-3.9791,-2.8895,-2.4635,-2.3183,-1.9047,-1.1598,-0.87268,-0.78633,-0.46888,-0.020685,0.26354,0.25939,0.62151,1.6518,3.11,3.7186,3.3282\n-2.1464,-2.0025,-1.6549,-1.9798,-2.759,-2.9111,-2.5694,-2.2844,-2.8204,-4.1458,-6.2402,-6.1756,-6.8508,-6.3377,-6.4899,-6.9152,-7.0239,-7.5159,-7.4601,-6.7326,-5.4804,-5.7976,-6.1255,-6.0079,-5.9357,-5.9244,-6.1686,-5.9137,-5.013,-3.9935,-3.7828,-3.5656,-2.8133,-2.2306,-1.8398,-1.4121,-1.002,-0.66884,-0.049458,0.54681,0.4752,0.57652,1.0964,1.9756,2.889,3.8615,4.0334\n-2.2666,-1.9464,-1.1391,-1.4655,-1.9051,-2.8855,-3.3184,-2.6509,-2.201,-2.1938,-5.1846,-6.0793,-6.4476,-7.0266,-7.2612,-7.5007,-7.233,-6.9538,-7.0987,-7.0825,-6.1318,-5.228,-5.6893,-5.6937,-5.9204,-6.0963,-6.04,-5.8667,-4.6366,-3.3667,-3.324,-3.2034,-2.4948,-1.825,-1.0504,-0.5568,-0.20999,-0.11344,0.069496,0.94866,1.0766,1.0464,1.2977,2.277,3.8289,4.0981,3.8507\n-2.9231,-2.5235,-1.7251,-1.3608,-1.4905,-2.1184,-3.1252,-2.7631,-2.4486,-2.4265,-4.412,-5.5707,-7.0415,-7.5421,-8.3459,-7.9204,-6.9756,-6.5595,-7.1002,-7.3278,-7.2145,-6.2486,-5.8001,-5.2925,-5.2094,-5.4745,-5.1538,-4.7093,-3.5519,-2.6429,-1.9077,-1.7886,-1.4714,-0.75604,-0.44076,-0.1305,0.049528,-0.18999,-0.012352,0.66312,1.1794,1.4691,2.1866,2.9942,3.4981,3.2298,2.8591\n-3.2337,-2.9948,-2.1878,-1.9076,-1.8579,-2.3562,-3.0838,-2.2693,-1.6299,-3.2547,-4.4407,-4.9428,-5.1657,-5.6145,-4.5619,-4.1845,-4.9035,-5.5262,-6.1393,-7.5958,-7.8033,-7.42,-6.0393,-4.8043,-4.2596,-4.3603,-4.0809,-3.9511,-3.6462,-2.5517,-1.7776,-1.1716,-1.0603,-0.23508,0.23351,0.13637,0.28591,0,-0.46395,-0.027111,0.62607,1.6442,2.7331,3.5832,3.2248,3.0245,3.2341\n-0.90512,-0.84067,-1.0317,-1.4731,-1.8426,-1.9547,-2.617,-2.5017,-2.0069,-2.5005,-3.3381,-2.8452,-1.108,0.042013,1.2282,0.64276,-3.183,-4.5809,-4.9034,-6.3544,-6.628,-7.149,-5.8419,-4.5067,-4.1606,-3.7539,-3.7219,-3.6762,-3.6692,-2.4252,-1.0571,-0.62686,-0.32907,0.76424,1.2226,0.80882,0.8622,0.25533,-0.29567,-0.52907,-0.20841,1.0457,2.2686,3.1818,3.1472,2.8586,3.0063\n-0.78265,-0.39098,-0.68594,-1.3852,-1.4881,-1.6478,-2.55,-3.0126,-3.081,-4.0272,-2.8581,-2.1749,-1.2769,0.3413,0.0046768,-1.1576,-2.7539,-3.9468,-3.7795,-4.7943,-4.5961,-4.8968,-4.87,-4.4988,-3.683,-3.0746,-3.3229,-3.3376,-2.7822,-2.0179,-0.91466,-0.64592,-0.27737,0.76764,1.8497,1.3893,0.97614,0.20741,-0.067739,-0.1939,-0.46363,-0.33739,0.52898,1.5742,2.3399,2.2588,2.5414\n-2.8731,-2.3044,-0.99349,-1.0887,-0.87018,-2.1064,-3.0639,-3.7755,-3.5026,-5.0376,-2.9951,-2.0348,-1.7241,-0.6188,-0.27421,-1.2676,-2.4122,-3.8787,-3.9143,-3.4442,-3.4685,-3.8949,-3.5104,-2.3574,-2.4134,-1.4652,-1.6713,-2.1976,-1.8852,-1.6583,-1.2923,-0.52108,0.068712,1.047,2.7042,3.268,1.3843,0.5224,0.12184,-0.077473,-0.25626,-0.14876,0.20349,1.1203,1.4134,1.0284,1.6087\n-0.98679,-0.70216,-0.72784,-0.15509,0.1636,-0.89398,-2.1622,-3.3365,-4.2738,-4.273,-4.4138,-2.7485,-2.4051,-1.5458,-1.1959,-0.95435,-1.5856,-3.2582,-3.1988,-3.119,-3.4496,-4.2428,-4.746,-4.1625,-3.06,-1.8347,-1.5561,-1.6075,-1.7573,-1.5296,-0.71452,0.061033,0.78155,2.0533,2.5912,2.2197,1.6098,1.1297,0.37033,-0.37482,0.38284,0.92578,1.1828,1.3193,1.0888,0.77075,0.26411\n-5.2488,-3.4284,-2.5562,-1.2879,-0.09115,-0.48891,-1.6013,-2.7913,-2.945,-2.5919,-0.69281,-0.28986,-1.5183,NaN,NaN,NaN,NaN,NaN,-2.1478,-2.199,-2.6489,-3.6664,-4.6921,-3.1436,-2.5347,-1.8015,-1.2899,-1.7778,-1.6039,-1.1421,-0.25561,0.94263,1.7247,2.0266,1.7173,1.6403,1.2695,0.91141,0.73941,0.52914,0.68776,0.90028,0.60589,0.56266,0.33975,0.54064,0.80013\n-5.3752,-4.8566,-4.8667,-2.7011,-0.5962,-1.3543,-2.955,-3.1929,-3.8099,-3.1913,0.20132,0.36124,-0.7644,NaN,NaN,NaN,NaN,NaN,-2.7204,-1.3604,-2.6675,-3.1042,-3.0552,-2.771,-2.4239,-1.6686,-1.3869,-1.974,-1.6038,-0.40667,0.086664,0.83908,1.9124,2.8409,2.7599,1.9653,1.057,0.81515,1.1311,0.98792,0.56152,0.44071,-0.2386,-0.84791,-0.93828,0.13605,0.92818\n-5.4632,-5.9589,-5.9014,-4.3032,-3.2784,-3.0579,-2.8619,-3.8435,-4.2071,-3.2154,-4.2443,-3.9521,NaN,NaN,NaN,NaN,NaN,NaN,-2.8285,-1.2465,-3.0976,-4.4434,-3.4482,-2.6406,-4.4275,-6.0673,-3.3921,-0.55845,0.30263,0.13975,-0.079638,1.2027,2.5102,3.3078,3.5503,2.5749,1.9943,1.1785,1.6554,1.556,1.2391,0.91407,-0.010279,-0.95742,-1.6555,-1.1326,-2.1406\n-8.133,-6.5626,-6.1377,-6.446,-7.3948,-6.9898,-1.8277,-3.0872,-4.4335,-4.3232,-3.8282,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.5846,-1.7569,-2.9846,-5.2758,-3.7553,-4.2624,-7.3825,-5.319,-3.1687,-0.42702,0.51043,0.44938,0.83618,1.435,4.2513,4.2404,4.2269,4.0182,3.0244,1.9034,2.1173,1.8237,1.5999,1.3341,0.7111,0.050287,-0.80685,-1.1251,-3.1525\n-8.3832,-8.1501,-8.5201,-9.1519,-9.0061,-7.0603,-6.0086,-6.4074,-6.2052,-4.3655,-4.5329,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.7148,-1.75,-2.6259,-2.1204,-1.3345,-1.6342,-0.56605,0.32193,0.30426,0.92993,2.5121,2.5538,2.3101,3.6068,4.3916,3.9652,3.0039,1.9897,1.9547,1.897,1.1116,1.0526,1.1447,0.96437,1.0369,0.42779,1.2799,0.92894\n-9.034,-9.4251,-9.8647,-10.297,-8.8124,-7.4089,-7.2288,-7.178,-6.387,-4.615,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.9102,-0.27361,0.12829,0.75105,0.56944,-0.15184,-0.27188,0.81063,1.5084,1.8577,3.1357,3.746,3.6396,2.8535,2.8885,2.0791,0.69373,0.82263,0.88302,0.043379,0.82326,1.8113,1.9173,3.0079,3.4572,3.4493,NaN\n-9.4796,-9.2079,-8.4064,-9.4501,-10.346,-9.7065,-8.3433,-6.7454,-5.144,-4.1851,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.8248,-1.4025,-0.6538,0.11812,0.10955,-1.0239,0.53899,2.3167,3.4299,3.6315,3.9057,3.5665,3.3434,2.5181,1.4685,1.6123,3.1991,2.7935,0.8639,1.4463,2.9804,3.7564,4.0957,3.9498,1.9581,NaN\n-9.2229,-8.0039,-6.9334,-6.8698,-8.5195,-8.3313,-6.444,-5.0618,-4.9833,-4.5919,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.6814,1.9722,0.35676,-0.11018,-0.15339,0.31755,2.0333,2.5814,3.3994,3.4126,3.573,4.0666,3.8787,3.236,1.7799,3.8245,1.846,1.8673,2.2814,3.2669,4.0447,4.6089,4.5199,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070219-070604.unw.csv",
    "content": "-6.6782,-7.3486,-6.8649,-5.9202,-5.8004,-5.0383,-3.7121,-3.1932,-3.7761,-3.7167,-3.7311,-3.567,-3.1923,-3.2332,-2.9617,-2.4559,-2.1966,-2.5433,-2.3161,-1.9071,-2.545,-2.2132,-1.6964,-1.5912,-2.1315,-2.2573,-2.7193,-3.8935,-3.9919,-3.955,-4.271,-4.5728,-4.1995,-4.7575,-5.8538,-6.5086,-7.5265,-8.1365,-8.3267,-9.1595,-9.1156,-8.6773,-8.0184,-7.7019,-7.6944,-8.1767,-8.1183\n-7.0305,-7.1841,-6.7584,-5.4441,-5.1201,-4.6281,-3.7523,-2.6665,-2.6024,-2.6303,-3.0628,-3.4235,-3.6226,-3.4201,-2.7573,-2.1826,-1.931,-1.8827,-1.509,-1.5732,-1.6443,-1.0001,-0.81111,-1.0195,-1.4388,-1.5166,-1.9002,-1.7426,-2.2492,-4.0556,-5.5403,-4.6028,-4.1584,-5.2196,-6.1309,-6.7195,-7.5502,-8.1375,-8.4815,-9.0264,-8.2534,-7.7753,-7.8937,-7.7302,-7.6513,-7.4812,-6.9818\n-7.3535,-7.2625,-6.8622,-5.7967,-5.2651,-3.7049,-3.3488,-3.3985,-2.7263,-2.1156,-2.242,-2.6609,-3.2386,-3.3073,-2.4639,-1.8696,-1.6676,-1.5269,-1.5813,-1.9519,-1.7601,-1.55,-1.6121,-1.1758,-0.9783,-1.2214,-1.3551,-1.2468,-1.9364,-3.1587,-3.9782,-4.2332,-4.3527,-5.0488,-6.3843,-8.5388,-9.2095,-10.199,-10.238,-10.376,-9.9086,-8.1554,-7.2462,-6.9546,-7.6019,-7.0304,-6.5333\n-7.481,-7.2828,-6.9828,-5.7906,-5.1564,-4.7487,-3.9299,-3.6862,-2.8868,-2.1032,-2.0425,-2.5222,-3.0237,-3.2661,-2.4052,-1.4073,-0.54221,-0.90174,-1.4488,-2.253,-1.5062,-1.2149,-1.0016,-0.73669,-0.52555,-0.36299,-0.38466,-0.76022,-1.1426,-1.6103,-2.5093,-3.2461,-4.2167,-4.8044,-6.2068,-8.5362,-9.4192,-10.082,-10.208,-9.8767,-9.6506,-9.1648,-8.3207,-7.8724,-7.6498,-7.0866,-6.2297\n-7.9129,-7.7322,-8.8302,-6.6978,-5.5152,-5.2052,-5.0044,-4.6616,-4.2088,-3.78,-3.1166,-3.0138,-3.1033,-2.7409,-1.922,-1.7916,-1.2859,-0.71034,-0.82721,-1.0629,-1.3469,-1.3418,-1.0278,-0.56106,0.017303,0.58373,0.49697,-0.36342,-1.4077,-1.4361,-1.4766,-2.7054,-3.837,-4.9837,-6.0121,-8.1239,-9.1364,-9.5456,-9.701,-9.5646,-9.2492,-8.4553,-8.2025,-8.4604,-7.6167,-6.8721,-6.4418\n-8.5848,-9.0914,-10.015,-7.9358,-6.9913,-6.3598,-5.6028,-4.7593,-4.5627,-4.1763,-3.4148,-3.1417,-3.084,-2.553,-1.8556,-1.4124,-1.186,-0.67386,0.084758,0.23255,-0.46446,-1.4811,-0.91895,-0.56158,0.16978,0.48048,0.43287,0.34903,-0.14483,-0.44163,-0.83209,-1.4283,-2.1567,-3.5318,-5.0898,-7.2806,-8.8054,-9.0054,-9.2805,-8.8639,-8.0635,-8.0452,-8.3204,-8.3591,-7.3745,-6.2653,-6.5559\n-8.838,-9.2807,-8.7943,-8.1326,-7.7209,-6.8487,-5.4698,-4.5034,-4.5178,-3.6028,-2.8842,-2.7366,-2.5506,-2.0021,-1.307,-0.5967,-0.53422,-0.55685,-0.59234,0.05472,0.52414,0.98286,0.63645,0.32707,-0.0052671,0.31556,1.1149,0.66984,0.033818,-0.94401,-1.4111,-1.4699,-2.4432,-3.6033,-5.0383,-6.3918,-7.6855,-8.336,-9.769,-11.26,-11.023,-8.7741,-8.8575,-8.2935,-7.309,-6.6458,-5.7054\n-8.1343,-7.6833,-6.576,-7.5242,-7.2599,-6.2,-4.7716,-4.468,-4.8237,-4.9289,-3.3584,-2.9258,-2.1933,-1.4799,-0.85604,-0.12662,0.41622,0.12677,0.41836,0.51003,0.91257,1.1607,0.83022,0.94639,1.5292,1.6741,1.4748,0.75633,-1.0585,-0.76732,-1.2084,-2.0396,-2.7351,-3.496,-4.4138,-5.8971,-7.2814,-8.8256,-10.101,-11.024,-11.16,-10.196,-10.438,-10.603,-9.8705,-8.0245,-8.2805\n-8.7732,-7.5878,-6.5662,-7.4338,-6.7556,-6.1334,-4.5773,-4.6354,-4.4557,-3.9137,-3.6833,-3.8525,-3.2724,-2.1135,-1.0712,-0.028446,0.84366,0.99512,0.52425,0.71817,1.3076,1.8747,1.7519,2.2759,2.7768,2.9398,4.0409,3.2296,1.1493,0.55862,-1.1986,-2.3497,-2.0492,-1.2777,-2.5094,-5.067,-7.5771,-10.016,-10.889,-11.321,-11.095,-10.361,-10.362,-10.501,-9.9185,-8.8012,-8.1269\n-7.7991,-7.6112,-6.8776,-7.3028,-7.2741,-6.7254,-5.1891,-4.6461,-4.0769,-3.4614,-3.4085,-3.0767,-2.1399,-1.5382,-1.2074,-0.077906,0.86219,0.95428,0.61532,0.78611,2.3399,2.9641,2.884,3.111,4.0157,4.0611,4.0332,3.4089,2.5575,2.0158,-0.61231,-1.2592,-0.82007,-0.27661,-1.7747,-4.4874,-7.1671,-8.9697,-10.483,-10.868,-10.984,-10.781,-10.865,-11.133,-10.576,-9.1569,-8.4942\n-9.1365,-7.8832,-6.7238,-6.4935,-6.6558,-6.5481,-5.4829,-4.2816,-3.6045,-3.254,-3.1786,-2.5155,-1.2124,-0.73059,-0.040417,0.76206,1.5676,1.1566,1.2319,2.2735,2.7678,2.6693,2.7811,3.3803,4.7132,4.5874,4.4301,4.6786,4.0958,2.0787,0.66702,-0.17046,-1.0279,-2.3254,-3.2483,-5.7759,-7.9754,-9.4398,-9.3732,-8.8134,-9.2722,-9.5232,-10.122,-10.89,-11.236,-10.738,-10.215\n-8.6443,-7.9596,-7.1506,-6.3661,-6.5832,-6.202,-5.216,-4.168,-3.3236,-2.9615,-2.9263,-1.543,-0.29829,0.30827,0.79687,0.99934,1.3527,1.391,2.2947,2.4929,2.4494,2.4275,2.6143,3.9345,4.8426,4.6043,4.2919,4.758,3.6469,2.2149,0.84209,-0.37261,-1.2694,-2.3066,-3.524,-6.1413,-7.46,-8.4892,-8.1278,-7.1583,-7.6935,-8.3112,-8.9374,-9.7305,-10.08,-10.348,NaN\n-8.5002,-8.0221,-7.0617,-6.1133,-5.4779,-5.6466,-5.0249,-4.5579,-4.2744,-2.9342,-1.9973,-0.74683,0.65099,0.56221,0.74083,0.93837,0.95884,1.5393,3.1009,3.6885,3.4356,3.037,2.3957,2.4429,3.681,4.4615,3.9158,3.9161,3.0336,1.62,1.2046,0.78068,-1.0244,-2.9929,-4.0119,-5.1077,-5.4732,-6.7275,-6.8253,-5.808,-6.5212,-7.6112,-7.8946,-8.6726,-9.517,-9.2239,NaN\n-7.4386,-6.3378,-6.177,-6.3063,-6.5917,-7.2274,-5.4102,-4.2093,-3.339,-2.8852,-1.8695,-0.19001,1.0146,0.75012,1.0148,1.1909,1.3862,2.0503,2.8502,3.3958,3.5443,4.0173,3.8685,3.6506,3.5834,3.6132,3.0971,2.4035,2.0653,1.6212,2.3671,5.0423,0.86638,-4.5775,-6.5218,-7.3234,-7.1969,-6.7565,-6.3085,-4.9941,-4.6084,-6.6355,-6.7885,-6.6125,-7.3279,-6.9435,-6.5314\n-6.606,-5.4254,-5.8947,-6.9055,-7.0558,-6.7693,-4.7103,-4.7642,-3.4951,-1.7382,-1.3029,-0.33258,0.32199,0.61689,0.76351,0.63592,0.80916,1.6836,2.5953,3.3926,3.3537,2.9044,3.4809,4.2279,3.9335,4.2175,4.2116,2.7963,2.2383,-0.30286,-1.5706,-2.6255,-4.0368,-6.7693,-7.9813,-8.1097,-8.095,-6.999,-5.7792,-5.4104,-6.5558,-8.062,-7.2856,-6.7762,-6.5494,-5.5647,-5.399\n-7.3535,-7.0198,-6.3359,-6.7955,-6.8736,-5.5981,-5.0423,-5.0231,-2.7876,-1.9601,-1.1178,-0.12653,0.3681,0.26588,0.14751,0.70346,0.91395,1.5556,1.9313,2.3907,3.5312,4.0476,4.0691,4.5256,4.328,4.0875,3.4522,0.68403,-0.76227,-0.45366,-3.0063,-5.4868,-7.8239,-9.322,-10.132,-10.055,-8.8456,-7.4142,-6.3931,-6.021,-7.7911,-8.3269,-7.5589,-7.856,-7.9936,-9.2991,-6.5625\n-7.3188,-6.7073,-6.1093,-5.8248,-5.5833,-5.0372,-5.2933,-4.748,-3.0289,-1.7658,-0.84212,-0.19943,0.84863,0.93019,1.0405,1.2216,1.0438,0.68755,1.1043,1.1364,2.1398,3.3711,3.6573,2.1613,1.6568,2.5591,2.888,1.8244,-0.25811,NaN,NaN,NaN,-8.3452,-9.5848,-10.608,-11.519,-10.564,-8.9482,-8.1068,-8.062,-7.9995,-7.3977,-6.7637,-7.9359,-9.5799,-12.329,-9.7936\n-6.171,-5.3028,-5.3026,-5.4178,-4.2565,-4.2165,-4.4857,-4.5653,-3.3857,-2.3314,-0.92226,-0.24028,0.17728,1.2001,1.5368,1.7483,2.0625,1.4031,1.3685,1.0955,1.0561,1.749,2.706,0.69568,-0.97082,-1.0571,0.39658,1.7183,NaN,NaN,NaN,NaN,NaN,NaN,-11.332,-11.096,-9.9074,-9.1039,-8.7726,-9.2741,-9.0543,-9.0972,-8.7784,-8.7414,-10.128,-11.832,-11.59\n-5.2403,-5.1708,-4.3204,-3.1211,-3.5178,-3.6599,-4.6808,-3.6692,-2.8994,-1.4502,-0.006588,0.58725,0.86529,1.5988,2.1273,2.9713,2.9978,2.5519,2.0475,1.4201,1.1345,0.77081,2.529,2.7232,0.80066,-1.9922,-0.48897,-0.7838,-0.95635,NaN,NaN,NaN,NaN,NaN,-13.087,-11.124,-9.7383,-9.8101,-9.1897,-8.9811,-9.2479,-9.4347,-9.1462,-8.7623,-8.9921,-9.6825,-9.5747\n-5.5359,-5.5952,-5.8295,-5.576,-5.9652,-4.6946,-3.9653,-2.0778,-0.99989,0.2287,1.3647,1.8361,1.8094,2.0928,2.5073,3.4394,3.6648,3.0706,2.905,2.7295,2.0132,2.4469,3.33,3.9174,2.7604,0.84825,-0.019779,0.14187,-1.437,-3.6914,-5.9346,NaN,-7.7354,-8.868,-13.385,-11.723,-9.7098,-9.8114,-10.223,-10.014,-9.8429,-9.0568,-8.7088,-8.2544,-8.4176,-9.5793,-8.6714\n-5.2639,-5.4234,-4.2913,-5.9762,-7.583,-6.0396,-3.8681,-2.3754,-0.82459,0.92015,1.6766,2.434,2.7099,2.7246,3.4058,3.5346,2.8911,1.8611,1.9576,2.07,1.7272,2.6353,3.1084,3.5676,3.2664,3.7423,3.5207,0.9583,-1.048,-1.7719,-1.8319,-3.3508,-7.2579,-7.2017,-14.162,-14.327,-11.69,-11.475,-11.618,-10.907,-10.106,-9.4708,-8.6317,-8.0852,-8.2975,-8.393,-7.3208\n-4.9586,-3.4464,-4.0007,-4.4228,-4.0532,NaN,-3.9597,-2.4695,0.49034,1.2757,1.9323,2.6708,2.9834,3.0129,3.4169,3.7127,2.4155,1.8181,1.0961,1.4482,1.691,2.5835,3.0067,2.8509,3.0517,2.8056,2.9262,2.0489,0.73434,-0.0035458,-0.86535,-4.0319,-7.0148,-7.2659,-10.155,-10.071,-9.1136,-10.376,-9.9408,-9.781,-10.324,-9.706,-8.79,-8.2272,-8.0156,-7.7986,-6.7355\n-2.9595,-1.2458,-1.888,-1.3617,-2.4593,NaN,NaN,-0.31807,-0.040194,1.0343,1.9312,2.878,2.4053,1.821,2.6141,3.3645,1.2582,0.90912,1.4625,1.8619,2.1252,2.1067,2.5012,2.3447,2.5059,2.0035,1.6354,1.1556,1.1674,0.88795,0.04044,-2.4623,-4.403,-4.8328,-7.0069,-7.8206,-8.0131,-8.1516,-8.9859,-9.4666,-10.312,-9.808,-8.4905,-8.118,-8.0796,-7.5561,-6.3735\n-2.7767,-1.2902,1.0291,3.2109,NaN,NaN,0.31408,1.488,1.9977,1.7522,1.7332,2.7222,3.0051,2.1029,1.6233,1.4157,1.0765,1.3419,1.413,2.049,2.7751,3.3424,3.3405,3.4669,3.0429,2.852,3.1819,2.4057,1.5411,1.193,1.2155,0.14979,-1.481,-2.0243,-4.6967,-6.0733,-8.0553,-9.1774,-9.7337,-9.3167,-9.4173,-9.4142,-8.5308,-8.0887,-8.1936,-8.0102,-6.936\n-2.2261,-0.5887,0.44645,1.0275,2.4877,NaN,0.79885,1.8238,1.9296,1.9633,2.3723,2.7748,2.8391,3.1184,2.3094,1.4619,1.6619,2.1919,2.485,2.497,2.692,3.4588,3.194,3.2861,3.2106,2.7878,2.5589,2.0102,1.718,1.8603,1.5325,0.82451,-0.55371,NaN,NaN,NaN,-11.912,-10.762,-10.282,-9.4548,-9.1871,-8.7592,-7.9891,-7.5848,-7.5478,-7.3804,-6.5702\n-1.8092,-1.917,-0.52388,1.053,2.344,1.3228,1.1913,1.9629,2.1502,1.968,2.7264,3.3358,3.3703,3.3318,2.9156,2.7611,3.2439,3.2888,2.882,2.6809,2.6167,2.8206,2.3716,3.0581,2.7451,2.7063,2.8905,3.2073,1.2398,0.7533,0.47718,-0.028134,-7.522,NaN,NaN,NaN,-10.434,-10.37,-9.5693,-9.4405,-9.65,-8.9876,-8.0113,-7.6183,-7.6146,-7.3227,-5.5631\n-2.8227,-1.0374,0.99465,0.77766,0.51818,0.75871,1.6027,2.1197,2.3896,3.2123,3.5985,3.7613,4.1314,3.9093,3.4345,3.297,3.6581,3.5426,2.4379,2.07,2.7644,2.8184,2.8938,2.9885,0.83617,1.9619,3.252,3.0139,1.6781,-2.288,-3.6157,-5.537,-8.4614,-9.5878,-9.4447,-8.959,-9.4627,-8.8996,-8.7892,-9.1903,-9.4132,-8.7938,-7.832,-7.359,-6.9144,-6.2833,-5.4568\nNaN,NaN,-0.79903,-0.56115,-0.54694,0.37341,0.95973,1.7052,1.7682,1.7669,2.5751,3.4301,3.4489,3.3549,2.9524,3.197,3.1082,2.339,1.7177,1.5889,2.2952,2.179,2.0091,1.924,0.99893,1.0763,2.3691,3.0281,1.9871,-1.1047,NaN,NaN,NaN,-8.0012,-8.5312,-8.5974,-9.1895,-10.266,-10.001,-10.064,-10.036,-9.3964,-7.7495,-7.0778,-5.7793,-4.8428,-3.8855\nNaN,NaN,NaN,NaN,0.18224,1.0607,1.5535,1.4001,1.4725,0.88978,0.91265,1.2397,2.387,2.4824,2.4943,2.6674,2.4522,2.1341,2.1184,2.6008,3.0739,2.5586,2.4849,2.8341,3.751,4.2273,7.2511,5.605,4.7183,NaN,NaN,NaN,NaN,NaN,-8.9124,-9.4429,-10.736,-11.785,-11.757,-11.196,-9.8261,-8.433,-7.4931,-6.791,-5.5422,-3.431,-2.9137\nNaN,NaN,NaN,NaN,-0.1944,-1.2302,0.18501,1.2224,1.2281,0.32766,0.82153,1.4664,2.1308,1.7338,1.7757,1.9637,2.0648,2.1863,3.3061,3.9038,3.6577,3.6687,3.9002,4.9641,6.4833,8.1506,9.8293,10.592,NaN,NaN,NaN,NaN,NaN,-11.208,-10.02,-10.461,-11.482,-11.822,-11.767,-10.458,-9.0257,-7.2179,-6.3402,-5.9302,-4.7737,-3.3379,-2.5364\nNaN,NaN,NaN,NaN,-1.6783,-2.4811,-2.0039,-0.061319,1.0932,1.3069,1.461,1.9405,1.764,0.7987,0.4808,0.52678,1.1096,2.1296,2.9235,3.3868,4.9419,5.2232,7.4128,8.8871,10.311,10.966,11.502,11.809,NaN,NaN,NaN,NaN,NaN,-10.342,-10.748,-10.686,-11.475,-11.57,-11.07,-10.22,-9.2913,-8.3428,-6.2729,-5.038,-3.6547,-2.8281,-2.2995\nNaN,NaN,NaN,NaN,NaN,-1.1671,-0.3781,0.76431,1.4017,0.76664,-0.641,-0.24422,-0.026152,-0.28536,-1.1356,-1.2735,0.028069,0.45368,1.1353,1.4499,2.9104,4.2504,6.9317,10.461,11.286,10.703,10.211,13.158,NaN,NaN,NaN,NaN,-11.014,-9.5535,-9.863,-10.135,-10.926,-10.78,-10.286,-9.3487,-9.0074,-7.5851,-6.1736,-4.2917,-3.3967,-2.7131,-1.7957\nNaN,NaN,NaN,NaN,-4.0763,-1.4784,-0.27423,0.45324,1.3468,0.72848,-1.2464,-1.7913,-1.7211,-1.921,-1.8727,-1.016,-0.80761,-0.60013,0.59405,1.301,1.0811,1.6185,2.3149,4.6151,7.706,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.3351,-8.1328,-8.2619,-8.3688,-9.8393,-9.1739,-8.5109,-8.5469,-7.2575,-6.4675,-5.0714,-4.027,-2.5598,-1.0108\n-4.7036,-5.5443,NaN,-4.8324,-3.8981,-1.4729,-0.40495,-0.7642,0.13116,1.4199,0.75111,0.1113,0.21751,-2.3755,-3.0388,-2.5191,-2.1888,-0.83729,0.82876,2.5934,2.6514,1.9077,2.8257,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.7817,-8.0985,-8.8068,-8.8958,-8.572,-7.7131,-6.9938,-5.9776,-4.8239,-3.4832,-1.9889,-0.53502\n-5.556,-5.3052,-5.3706,-4.6296,-2.8089,-1.7448,-0.85715,-0.032482,0.67262,1.3525,1.1968,1.8291,2.9778,-0.060674,-2.1056,-2.0016,-0.92888,-0.11887,0.80921,2.1944,3.45,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-6.8276,-6.7973,-7.625,-7.544,-6.5979,-5.9383,-5.3904,-4.2029,-3.1581,-1.3977,-0.28145\n-5.0965,-4.2538,-4.0838,-6.1129,-4.4718,-2.49,-0.96725,-0.022221,0.89437,2.0764,0.80602,-0.21974,-1.075,-1.1118,-1.0799,-0.94943,-0.97495,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.5254,-6.3238,-6.7704,-6.9359,-6.8002,-5.8369,-5.1136,-4.7968,-4.0212,-2.26,-0.69664,0.24602\n-5.0035,-5.1905,-5.5344,-5.5496,-4.8527,-3.3619,-1.7568,-0.44084,-0.68745,0.73421,0.92055,-2.2856,-2.4827,-3.653,-3.4294,-1.3479,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.9286,-6.5691,-6.9478,-6.2191,-5.4629,-4.5157,-4.1671,-3.4128,-2.5932,-1.6334,-0.099483,0.34652\n-4.6664,-5.0364,-5.4397,-5.119,-4.1247,-3.0386,-2.9099,-3.4441,-4.1081,-2.1449,-1.2345,-3.2913,-3.5838,-4.1674,-3.4151,-1.1644,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.5772,-8.2969,-9.0532,-9.5276,-7.3698,-6.8543,-6.532,-5.4351,-4.3832,-3.172,-2.2696,-1.2429,-0.26993,0.35345,0.35863\n-5.7751,-5.3293,-5.1131,-5.3262,-5.1222,-3.514,-3.2817,-4.1599,-4.6467,-4.684,-4.6698,-4.8531,-6.1289,-6.945,-4.3252,-1.8676,1.2861,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4033,-2.5163,-3.3841,-5.8303,-7.0646,-6.5606,-6.6781,-6.6081,-5.6909,-4.6225,-3.0202,-1.3249,-0.73316,-0.24239,0.28832,0.20806\n-6.5893,-6.1183,-6.081,-5.6169,-5.5773,-5.6873,-5.102,-5.3893,-5.8442,-5.3788,-5.1102,-4.5608,-6.4539,-6.4479,-6.1731,-3.0792,-1.1914,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.3226,-4.0152,-4.9117,-5.7889,-6.2157,-5.873,-5.6295,-5.7579,-6.228,-5.7164,-4.4316,-2.3233,-0.97484,-0.59359,-0.17497,0.31293,0.86144\n-6.686,-5.9308,-5.9257,-5.8654,-5.7558,-5.8793,-5.9423,-5.9415,-6.4097,-6.7785,-6.8309,-6.1629,-6.8753,-7.4287,-7.4903,-5.3825,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.0545,-4.6401,-5.5528,-6.4638,-7.0324,-6.3587,-6.0306,-5.5303,-5.2144,-5.7465,-5.5452,-4.2837,-2.8508,-1.0886,-0.16105,0.08296,0.36167,0.67649\n-5.6932,-5.4524,-5.7855,-6.0931,-5.6099,-5.5007,-5.9418,-6.4526,-6.7773,-7.1788,-8.0643,-10.116,-9.8556,-9.3976,-9.2548,-8.4028,-6.9943,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.3691,-5.9673,-6.1495,-6.1755,-6.5122,-6.7681,-6.7832,-6.5827,-5.9262,-5.5345,-4.5472,-3.7946,-2.4813,-0.67458,-0.33695,0.070177,0.61713,0.65789\n-4.5602,-4.9549,-5.2554,-5.9818,-6.1655,-6.3183,-6.8838,-7.1757,-7.6269,-8.1505,-8.8522,-9.6261,-9.9774,-9.5637,-9.5709,-8.7364,-6.3937,-7.923,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.9245,-5.1364,-5.7035,-6.2438,-6.8971,-6.6084,-7.2606,-7.5209,-7.0199,-6.0019,-4.4445,-2.9295,-2.3078,-1.3987,-0.58657,-0.64949,-0.72926,-0.012503,0.25927\n-6.5023,-6.0446,-5.6496,-6.5199,-6.9642,-6.9807,-7.5954,-7.8623,-9.2112,-9.2482,-8.8643,-8.4416,-8.7126,-8.653,-8.4816,-8.3195,-7.0881,-5.62,-7.2224,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.5778,-7.0859,-6.9716,-6.7687,-7.0735,-7.2248,-7.2761,-7.5613,-6.9196,-6.6522,-5.4861,-3.295,-2.1437,-1.3092,-0.49539,-0.42111,-0.78535,-1.0283,-0.91952,-0.45815\n-6.4197,-6.3599,-6.222,-6.6629,-6.7053,-7.0122,-8.2442,-9.1808,-9.9639,-8.7885,-7.6486,-8.0875,-8.3136,-8.4954,-8.4328,-8.4044,-9.1085,-8.503,-9.8388,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.2983,-7.6793,-7.546,-7.6401,-7.9403,-7.8393,-7.4396,-7.6465,-7.1482,-6.3782,-5.0743,-2.8288,-1.5878,-0.62527,0.042934,0.077627,-0.60759,-0.66286,-0.93003,-0.71122\n-5.8237,-5.8576,-5.5029,-5.4509,-5.9093,-6.3389,-6.7564,-7.4677,-7.3947,-7.1132,-7.122,-7.8217,-8.8998,-9.2753,-8.9998,-8.7456,-8.9769,-10.067,-11.575,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.8612,-7.6913,-7.6134,-7.9706,-7.687,-7.1723,-6.784,-6.4129,-6.1542,-5.3766,-3.9459,-2.5389,-1.4096,0.034601,0.3829,0.41067,-0.20563,-0.15673,-0.46334,-0.76405\n-5.5048,-5.6339,-5.3127,-5.014,-5.3672,-5.9315,-6.3603,-6.5627,-6.6568,-6.6359,-6.6666,-7.1926,-7.6214,-7.5535,-7.7082,-7.4827,-8.1301,-9.1607,-11.033,-11.348,-14.343,NaN,NaN,NaN,NaN,NaN,-8.0994,-7.7092,-7.3567,-6.6465,-6.8977,-7.0261,-6.3478,-5.8038,-5.3773,-4.7218,-4.4628,-3.7849,-2.6585,-1.3826,-0.30279,0.61869,0.57761,0.63343,-0.07733,-0.43074,-0.098425\n-5.2142,-5.7316,-5.7183,-5.3979,-5.8228,-6.0036,-6.2161,-6.0291,-6.052,-5.2786,-4.952,-5.8799,-5.8261,-5.6873,-6.0208,-6.3563,-6.9773,-7.847,-8.8418,-8.7313,-11.442,-14.309,-12.618,-9.8983,NaN,NaN,-9.0957,-8.2382,-8.5189,-7.6965,-6.5669,-5.8721,-4.8158,-4.2198,-4.369,-4.4453,-4.7792,-3.8769,-2.2927,-1.291,-0.75833,0.22063,-0.24642,0.24735,-0.076704,0.053584,0.42018\n-4.8596,-5.4303,-6.0639,-6.0461,-5.8745,-5.9692,-5.5468,-5.5033,-5.2438,-5.0573,-4.623,-4.4778,-4.0585,-4.6537,-5.3085,-5.898,-6.5923,-7.7942,-7.4932,-8.4108,-10.086,-10.177,-9.7091,-9.1901,-9.1194,-8.6674,-8.669,-8.211,-8.4482,-7.3757,-6.3097,-5.3926,-4.7027,-4.5514,-4.4225,-4.2461,-4.4004,-3.8944,-2.222,-1.0448,-0.61206,-0.0057888,-0.82969,-1.0482,-0.74156,-0.066952,0.49288\n-4.534,-4.4224,-5.2211,-5.0635,-4.678,-4.3292,-4.647,-5.0872,-5.0605,-4.659,-4.2022,-4.3551,-4.3626,-4.3821,-5.3277,-5.9494,-6.3359,-7.4328,-8.0657,-9.1354,-9.0017,-9.1632,-8.5562,-8.8267,-8.5437,-8.5398,-8.5534,-8.154,-7.5342,-6.7948,-6.1061,-5.3069,-4.3975,-4.5807,-4.4489,-4.2332,-3.7141,-3.4123,-2.0227,-0.94849,-0.12079,-0.54319,-3.6084,-1.7416,-0.78999,-0.19979,0.79854\n-4.5897,-4.4497,-4.2186,-3.9339,-4.1677,-4.4323,-4.3845,-4.4242,-4.6666,-4.3526,-3.8371,-3.7003,-4.4872,-5.0396,-5.5607,-5.7405,-6.0711,-7.2927,-7.1893,-7.1266,-7.264,-7.4618,-7.9139,-8.2562,-8.168,-8.3747,-8.9605,-8.4494,-6.9612,-6.3161,-6.0721,-5.0861,-4.1252,-3.8268,-3.977,-3.6273,-3.2452,-2.6551,-1.6847,-1.3024,-1.0936,0.27433,0.6404,-0.59005,-0.057438,0.13871,1.4187\n-4.2225,-4.3355,-4.0345,-3.9202,-4.4525,-4.5525,-4.5242,-4.431,-4.1092,-3.7632,-3.9521,-3.8435,-3.8452,-4.5623,-4.8744,-6.3449,-6.4233,-8.4387,-8.9215,-8.7382,-7.7251,-7.6333,-7.7494,-7.4994,-7.1677,-7.2409,-8.2288,-7.7449,-6.6362,-6.1071,-5.6308,-4.8073,-4.0014,-3.3648,-3.1456,-3.0263,-2.4449,-1.7544,-1.1932,-0.9692,-1.0907,-1.109,-0.97035,0.72791,1.0073,1.4257,2.8937\n-4.0184,-4.0757,-4.2069,-3.5346,-3.4779,-3.6903,-4.124,-4.5485,-4.3262,-3.7621,-3.7614,-4.2037,-5.1159,-5.6322,-5.4999,-6.3924,-7.6671,-8.7996,-10.48,-11.059,-9.5691,-8.577,-8.1132,-7.12,-6.5975,-6.6115,-6.1147,-5.9811,-5.7203,-5.7364,-5.3783,-5.0094,-3.9501,-3.5259,-2.7273,-2.1674,-1.6642,-1.135,-1.0224,-0.98683,-0.83631,-0.63733,-0.35595,0.84521,1.0164,2.3284,4.2672\n-3.8579,-3.6897,-3.3784,-3.1683,-3.1222,-3.8519,-4.3696,-4.9313,-4.6531,-4.0922,-3.502,-3.2677,NaN,NaN,NaN,-11.16,-10.501,-8.6059,-10.426,-12.221,-11.591,-8.8475,-7.3292,-6.3221,-5.6859,-6.1833,-5.569,-4.9058,-5.2314,-5.2126,-4.7986,-4.3962,-3.4737,-3.3371,-2.8452,-2.196,-1.4574,-0.96094,-1.0132,-1.3855,-1.2931,-0.91091,0.039151,0.6999,1.951,3.2129,4.198\n-4.0895,-3.599,-3.246,-2.6897,-3.3834,-4.5413,-5.3339,-4.9876,-4.438,-3.7915,-2.9153,-2.5975,NaN,NaN,NaN,NaN,NaN,-12.517,-11.715,-11.336,-9.5776,-8.0347,-6.9423,-5.9637,-5.634,-5.7859,-5.0411,-4.839,-4.3317,-4.7187,-3.9958,-3.9137,-3.6208,-3.083,-2.2062,-1.8525,-1.3919,-1.111,-0.88842,-0.63693,-0.53034,-0.38106,0.19683,1.0247,2.1633,2.7117,2.8225\n-4.8069,-4.3986,-3.4453,-2.3144,-0.90389,-3.0654,-5.1309,-4.0223,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-10.826,-10.743,-9.2222,-7.6895,-6.5957,-5.7599,-5.0491,-5.0731,-3.3631,-3.128,-3.5924,-4.53,-4.4306,-4.1083,-3.2884,-2.8649,-2.3239,-1.7762,-1.4099,-0.95202,-0.78045,-0.17904,0.5014,0.67337,1.3484,2.0222,2.6924,3.6481,3.738\n-5.5642,-4.6493,-2.9176,NaN,-0.83193,-2.2233,-3.567,-4.4412,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-10.464,-9.6632,-7.9184,-6.0757,-4.9898,-4.6155,-4.4573,-3.2879,-3.3535,-3.8211,-4.238,-4.22,-4.0883,-3.3979,-2.6096,-2.2599,-2.3048,-1.8429,-1.2367,-0.59586,0.15655,0.56468,0.67529,1.6969,2.9085,3.9192,4.4114,4.6236\n-4.8503,-4.1462,-3.386,NaN,NaN,NaN,NaN,NaN,NaN,-5.4636,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.0955,-7.868,-7.625,-6.9672,-5.2365,-4.3708,-3.7859,-3.5022,-4.5983,-4.5294,-3.7527,-3.6599,-3.4499,-2.8978,-1.5952,-1.4293,-1.7057,-1.0074,-0.28152,0.16595,0.58807,0.89113,1.1316,1.9783,3.6474,4.1494,4.1529,4.0872\n-5.0639,-4.8265,-4.9058,NaN,NaN,NaN,NaN,NaN,NaN,-5.1227,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.1921,-8.4027,-8.2469,-7.2316,-6.5329,-5.8098,-4.9968,-3.8112,-3.1292,-2.8907,-3.1869,-2.8521,-2.7803,-3.4333,-2.5284,-1.5239,-1.4741,-1.3589,-0.48113,0,0.23465,0.80194,1.1792,1.4088,1.8562,2.4141,3.4025,4.6887,4.9169\n-4.6383,-4.4715,-5.302,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-12.432,-14.158,-11.753,-11.819,-11.239,-6.9359,-5.6732,-6.4974,-7.7353,-7.6504,-6.54,-5.7312,-5.1469,-3.8388,-3.3376,-2.5905,-2.593,-2.0219,-2.4306,-2.3773,-2.9051,-2.4906,-1.2944,-1.6648,-1.077,-0.077491,0.03667,-0.017725,0.55887,1.1428,2.0921,2.8954,3.1024,3.0824,3.5151,3.9134\n-2.6965,-3.2538,-3.3874,-5.5905,-7.9976,-8.6211,NaN,NaN,NaN,NaN,NaN,-14.512,-13.532,-12.63,-10.557,-10.164,-8.7427,-7.2133,-6.1748,-6.4198,-6.124,-5.3265,-4.0756,-3.8558,-3.1661,-2.1225,-2.2112,-2.2464,-1.396,-0.99444,-0.74614,-0.98025,-2.1454,-2.233,-2.0517,-0.99212,-0.54283,-0.63895,-0.85613,-0.97914,0.26147,1.822,2.5642,2.6826,2.9218,3.3124,3.0832\n-3.7707,-4.3654,-4.4145,-3.4471,-4.9907,-7.0721,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-11.761,-11.464,-9.7061,-7.2852,-6.6023,-6.1425,-5.414,-4.7572,-4.7043,-4.7848,-4.5936,-3.6248,-2.6961,-2.3678,-2.1392,-1.829,-1.574,-0.74665,-0.37745,-0.65359,-1.6768,-1.7705,-1.3912,-1.2059,-1.4949,-1.5617,-1.509,-1.1753,-0.052701,0.24396,0.92315,2.3748,2.9514,1.666\nNaN,NaN,NaN,NaN,NaN,-8.3993,-9.2216,-8.4224,NaN,NaN,NaN,NaN,NaN,NaN,-12.779,-10.46,-8.4607,-7.1466,-5.5031,-5.2223,-4.8101,-4.7879,-5.0871,-3.9235,-3.0796,-2.4081,-1.9817,-1.5926,-0.98071,-0.87134,-0.7041,-0.49903,-0.584,-0.36508,-0.52414,-1.2048,-1.0966,-1.4927,-1.6219,-0.95062,-0.74735,-0.3031,0.14924,0.71603,1.2814,1.2738,0.27164\nNaN,NaN,NaN,NaN,NaN,-10.98,-9.315,-7.5803,NaN,NaN,NaN,NaN,NaN,NaN,-11.773,-11.672,-10.679,-8.5612,-6.7401,-5.4295,-5.2583,-6.5651,-4.9931,-3.7962,-2.6847,-1.5999,-1.3549,-1.0616,-0.70092,-0.74431,-0.80808,-0.81408,-0.61705,0.094252,0.14648,-0.3462,-0.41646,-0.82698,-1.4016,-0.8637,-0.41936,0.31279,0.94786,1.1694,0.95457,0.54039,0.08135\nNaN,NaN,NaN,NaN,-7.4862,-8.5054,-8.1105,-7.6853,-7.5787,NaN,NaN,NaN,NaN,-10.975,-10.486,-9.5289,-8.4794,-6.4816,-5.9925,-5.0596,-4.766,-5.6092,-5.4609,-3.5338,-3.3893,-2.0639,-1.3164,-0.74557,-0.66143,-0.42823,-0.19988,0.12993,0.53891,0.19214,-0.41633,-0.64256,-0.53382,-0.18063,-0.55215,0.19817,0.96882,0.99665,0.47437,0.30091,0.88778,1.0425,1.1658\nNaN,NaN,NaN,NaN,-6.3913,-6.4273,-7.2571,-8.4142,-8.6991,NaN,NaN,NaN,-8.7356,-10.333,-10.771,-9.0044,-9.3851,-6.8827,-5.86,-4.0584,-4.2215,-3.7027,-4.3804,-4.7262,-3.4301,-2.2139,-1.1175,-0.28017,-0.12083,-0.5021,-0.25599,0.073521,0.29486,-0.35015,-1.115,-0.54763,-0.36355,0.053062,0.53021,1.1318,0.94002,0.49607,-0.49013,-1.3089,-0.11252,1.4134,NaN\nNaN,NaN,NaN,-4.0765,-4.424,-5.4384,-7.3548,-9.064,-9.7896,-7.5938,-6.0401,-7.0232,-9.6814,NaN,NaN,NaN,-12.055,-8.7539,-6.1015,-3.2695,-3.1531,-2.9516,-2.6036,-2.5035,-1.811,-1.3723,-1.1,-0.63168,-0.39262,-0.25843,-0.18784,0.043079,-0.081382,-0.21323,-1.14,0.0095882,0.32153,0.1958,0.58549,0.7166,1.2296,0.95468,-0.051979,0.25641,0.52965,-0.4897,NaN\n-8.9305,-7.2664,-8.6983,-7.1031,-6.4445,NaN,-6.1222,-7.8269,-9.4332,-9.7617,-9.3896,-8.5976,NaN,NaN,NaN,NaN,NaN,NaN,-4.8282,-2.4507,-1.321,-1.9824,-1.8255,-1.3668,-1.7388,-1.4099,-0.92934,-0.3347,-0.21426,0.027099,0.12434,0.085286,0.38927,0.34737,0.047873,-0.058024,-0.05983,-0.0013628,0.46885,0.77819,1.5027,1.4299,0.758,0.55066,0.27432,-0.22475,NaN\n-9.264,-8.4582,-8.0885,-9.56,-8.0945,NaN,NaN,NaN,-8.2422,-6.9989,-7.6722,-8.36,-5.2535,-3.6891,NaN,NaN,NaN,NaN,NaN,NaN,-0.50009,-1.0557,-1.2446,-1.2078,-1.1618,-0.73904,-0.48133,-0.1523,0.0094938,0.42014,0.62055,0.25154,0.24129,0.7479,0.35637,-0.037785,0.1838,0.42392,0.77565,0.86585,0.60772,0.52879,1.3124,1.8043,0.88478,NaN,NaN\n-10.375,-9.1426,-7.3236,-7.2691,NaN,NaN,NaN,-6.7452,-9.0646,-8.6436,-8.6249,-9.1431,-7.565,-7.3655,-5.5332,NaN,NaN,NaN,NaN,NaN,-2.1593,-0.00049019,-0.8426,-1.6515,-2.125,-1.2779,-0.58517,-0.055492,0.52741,0.83653,1.5529,1.3231,1.1922,1.2628,0.99011,0.54735,0.93178,1.4926,0.4165,0.15432,-0.10232,0.30483,2.1082,3.281,4.3318,NaN,NaN\n-8.763,-8.4313,-7.0793,-6.549,NaN,NaN,NaN,-6.654,-5.1059,-5.0088,-6.0538,-7.8431,-6.4133,-4.7043,-5.2821,NaN,NaN,NaN,NaN,NaN,-0.81353,0.33352,-0.80552,-0.59062,-0.56274,-0.80793,-0.18165,-0.039047,-0.12478,0.13083,0.92702,1.6265,1.3041,1.3832,1.2593,0.68904,0.56161,1.3451,1.3402,0.19373,1.4257,0.95143,1.849,5.2767,NaN,NaN,NaN\n-8.492,-7.5256,-6.7485,-7.0167,-7.1896,-6.988,-6.6139,-6.0751,-5.5027,-5.0606,-4.7762,-4.0376,-4.4247,-5.9331,-5.8049,NaN,NaN,NaN,NaN,NaN,-0.25578,0.6347,1.2984,0.84049,0.08639,-0.12999,-0.058388,0.19411,-1.0197,-1.02,0.36401,1.5537,1.8831,2.1685,2.6965,0.11175,-0.37385,0.62207,1.3712,1.6946,2.1522,3.6114,4.6583,7.5689,NaN,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070326-070917.unw.csv",
    "content": "2.5675,1.843,1.0279,1.2805,1.057,0.83088,0.8211,0.78245,0.97935,0.62425,1.1583,0.59502,0.33419,0.14097,-0.52267,-1.0757,-1.0408,-0.99497,-1.3893,-1.1624,-0.92599,-0.97234,-1.0603,-1.4844,-2.1381,-1.6082,-1.6359,-2.1044,-1.8052,-1.8595,-1.8945,-1.6598,-2.3403,-2.6489,-2.3176,-2.3216,-2.5394,-2.8291,-1.994,-1.7859,-1.992,-1.7692,-2.0841,-2.5171,-1.8051,-1.4497,-1.5268\n1.729,1.9158,0.86767,0.99132,1.1766,0.98148,1.1972,0.89772,0.9203,0.92219,1.156,0.81805,0.36019,-0.13724,-0.65503,-0.74412,-0.45111,-0.6697,-0.81893,-0.63704,-0.15713,-1.3141,-1.5116,-1.3336,-0.74882,-0.86883,-1.1413,-1.6743,-2.2348,-2.2183,-1.761,-1.802,-2.0886,-2.4,-2.0173,-2.5936,-2.8,-2.3828,-1.7892,-1.3331,-1.2131,-1.3532,-2.0044,-1.8626,-1.3638,-1.4528,-1.589\n1.5891,1.0735,1.237,1.2004,1.1585,1.0734,1.2285,1.127,0.27238,0.93157,0.54329,1.2427,0.39834,-0.23103,-0.64638,-0.49008,-0.40873,-0.87851,-1.0833,-1.2837,-0.97722,-0.96454,-1.6155,-1.2466,-1.1772,-1.3613,-1.6015,-2.0418,-2.003,-2.0784,-1.7098,-1.931,-2.1161,-2.2311,-2.2059,-2.69,-3.3633,-2.3309,-1.7189,-1.3451,-1.13,-1.4027,-1.3661,-1.067,-1.0284,-1.2886,-1.3983\n1.8462,1.4583,1.516,1.3871,1.5301,1.7418,1.143,1.0311,0.68881,0.84196,0.55789,0.40004,-0.14416,-0.70115,0.040912,-0.10207,0.071215,-0.31559,-0.91951,-2.5243,-1.8014,-1.2921,-1.0508,-1.1125,-1.6345,-1.9288,-1.6643,-1.2653,-1.4996,-1.6518,-1.654,-1.5755,-2.1083,-2.145,-2.03,-2.3366,-2.5879,-2.4685,-1.9546,-1.4582,-1.1319,-1.0721,-1.1327,-1.0007,-0.95804,-1.0443,-1.2338\n1.5106,0.92957,1.7575,1.4865,1.3015,1.2708,1.0988,0.9067,0.91914,0.35736,0.25918,-0.021928,-0.18955,-0.6765,-0.18461,-0.095097,-0.047628,-0.47645,-0.62692,-0.93921,-1.699,-0.51402,-0.6444,-1.2077,-1.8858,-2.4602,-2.1305,-1.6839,-1.0846,-1.7441,-1.5124,-1.2627,-1.3588,-2.0087,-2.0268,-1.993,-2.4637,-2.4982,-2.1221,-1.3734,-0.81512,-0.52098,-0.82019,-0.58693,-0.71434,-0.99287,-1.222\n1.5474,1.0374,1.3462,1.2844,1.3304,1.0034,1.2405,1.6517,1.5614,1.2169,0.69383,0.087539,0.22675,0.090345,-0.081029,-0.28057,-0.4659,-0.80756,-0.82366,-0.9085,-0.67429,-0.3708,-0.9681,-1.4459,-1.3642,-1.7661,-2.2707,-1.9703,-1.5917,-2.2867,-1.8523,-1.944,-2.0611,-1.7927,-1.7551,-1.2375,-1.3252,-1.8659,-1.5794,-0.71441,-0.4381,-0.65256,-0.67803,-0.32762,-0.50107,-0.46881,-0.69817\n1.8083,1.8099,2.3618,1.2187,0.98263,0.6709,1.2177,1.6535,1.4557,1.0908,0.72083,0.38866,0.23636,0.14472,-0.63787,-0.47088,-0.28373,-0.48674,-0.56021,-1.1915,-0.68566,-1.0641,-1.1903,-1.8443,-1.9684,-2.1386,-1.8904,-1.2268,-1.9205,-1.5867,-1.4225,-1.7041,-1.6853,-1.3823,-1.3006,-1.1979,-1.1635,-1.5586,-1.2767,-0.49294,-0.26121,-0.2147,0.012884,-0.19632,-0.23798,-0.0038967,0.18611\n2.3097,2.3015,2.5631,1.4886,0.79957,0.3772,1.022,1.4656,1.2811,0.72126,0.75595,1.0521,0.61103,-0.64226,-0.25054,-0.26975,-0.048818,-0.97772,-1.0022,-0.88852,-0.35313,-0.87715,-1.1648,-1.5569,-1.7615,-1.7583,-1.1633,-0.48154,-1.2945,-1.959,-1.007,-0.91066,-1.1924,-1.1713,-1.5349,-1.5112,-1.535,-1.5263,-1.2874,-0.66921,0.12563,0.56538,0.26634,0.31767,0.17967,-0.21473,0.026028\n2.4518,2.0094,1.8937,1.3856,0.60699,0.65825,0.94539,1.0635,0.55214,0.48261,0.86474,1.3718,1.0122,-0.11158,0.05936,-0.0010839,0.10962,-0.11675,-0.85421,-0.59751,-0.71416,-0.83224,-0.53899,-1.0429,-1.4311,-1.0513,-0.94927,-1.2705,-1.8372,-1.4917,-0.87297,-0.85828,-0.83343,-0.77076,-1.0897,-1.5215,-1.1223,-0.89443,-0.62312,-0.25133,-0.051391,-0.10272,-0.33278,-0.03323,0.1675,0.28129,0.23541\n2.3137,2.2299,2.0378,1.0258,1.0278,0.61679,0.93471,0.61541,0.64905,1.1476,1.4158,1.4776,0.96189,0.44123,0.30197,0.067666,0.097111,-0.15782,-1.0272,-0.95039,-0.12215,-0.045026,-0.48599,-1.6393,-1.5813,-1.3128,-0.15466,-0.5761,-1.8047,-1.6921,-1.4041,-1.3436,-0.83236,-1.021,-0.3427,-0.34229,-0.39769,-0.33685,-0.75492,-0.57876,-0.4128,-0.22895,-0.15529,-0.59886,0.12206,0.36666,0.27911\n2.3916,1.8096,2.0139,1.1144,0.53708,0.79876,0.64364,0.58658,1.7061,2.0971,1.8327,1.2252,0.59861,0.48651,0.6496,0.52063,0.69777,0.0014653,0.10809,0.30163,0.12949,0.22034,-0.45538,-1.2569,-1.4658,-1.1323,-0.81169,-0.93192,-1.4391,-1.7261,-1.7717,-1.3955,-0.81045,-0.78912,-0.64178,-1.0297,-1.12,-1.1268,-0.80922,-0.34297,-0.65392,-0.49716,-0.48785,-0.69787,-0.11491,0.59538,0.57762\n1.9524,0.96761,1.4523,0.876,1.259,1.5461,1.0286,1.2859,1.8455,1.9134,1.9307,1.7061,1.4102,1.1846,0.62923,0.1365,0.37021,0.11489,-0.10353,-0.61536,-0.52694,-0.63619,-0.85795,-0.51813,-0.73125,-0.42327,-0.54511,-1.1184,-1.9089,-1.759,-2.0437,-1.5398,-0.81515,-0.52217,-0.78691,-1.1119,-0.47104,-0.55413,-1.1024,-1.0525,-0.97054,-0.43792,0.12461,0.099759,0.2192,1.0212,NaN\n1.7547,1.3761,2.0732,1.2685,1.0478,1.0408,0.83948,1.3854,0.81223,1.4876,1.7105,1.8272,1.6843,1.3858,0.97547,0.13687,-0.22402,-0.55874,-0.79389,-0.78534,0.0081563,-0.49257,-1.3435,-0.60912,0.2394,-0.12624,-0.384,-1.2445,-2.4017,-1.8787,-1.2257,-1.459,-1.1091,-0.72824,-0.24883,0.1849,0.88099,0.43021,0.1746,-0.25227,-0.22236,0.17027,0.82785,0.6982,0.87823,NaN,NaN\n1.4284,1.2363,1.727,2.1584,1.7349,1.0523,0.30244,1.1948,1.2662,1.6266,1.6438,1.5434,1.6124,1.2746,0.94448,0.29828,-0.19368,-0.89125,-0.60385,0.26873,0.44334,-0.14524,0.20173,-0.70863,-0.57161,-0.57173,-0.43496,-0.68205,-1.2465,-1.316,-0.5986,-0.40908,-0.37958,-0.062976,0.21484,0.30909,1.0031,1.4264,0.88502,0.4107,1.2244,1.3688,1.3069,1.5162,1.7097,NaN,NaN\n2.1533,2.4028,3.7362,1.5842,0.50733,0.5102,0.93947,1.2728,1.2433,1.3463,2.0054,1.3367,0.64982,0.39154,0.76402,0.35748,0.30271,-1.0096,0.43537,1.0182,0.73446,1.4833,0.80204,-0.89361,-0.65894,-0.0885,-0.28314,-0.18581,0.32,-0.15126,-0.59124,-0.18808,0.48387,0.80925,0.55661,0.10495,1.0488,1.3712,1.5675,1.8813,2.2506,0.7957,0.9121,1.4307,1.4547,2.0559,2.3282\n2.722,2.5309,3.4068,2.0105,1.9383,1.0417,1.8528,1.5568,1.3546,1.4543,1.6132,0.77812,0.99157,0.9219,1.7023,0.80441,0.36194,0.81704,1.1959,1.4341,1.0591,1.2688,1.2686,0.51983,-0.31687,-0.35107,0.026356,0.44328,1.4905,1.019,0.83205,0.78746,0.83872,0.55885,0.066347,1.3307,1.2106,1.2598,1.4486,2.1834,1.67,0.96068,0.89407,0.71974,0.58255,1.7306,1.8378\n3.5474,2.8745,2.3771,1.4982,0.69093,0.72191,1.5008,1.4559,1.5412,1.7496,1.8609,1.6419,0.88348,0.87989,1.2416,1.1475,-0.058834,2.0511,1.9827,2.069,2.524,1.4644,1.2679,1.0687,1.3997,2.2224,3.3049,3.7018,2.7287,0.71023,1.3641,1.1401,0.71851,0.78595,0.85719,1.2813,1.0741,1.0845,0.83177,0.82487,0.95226,0.92768,0.88654,0.92387,0.49031,2.2362,1.6267\n2.0663,2.6457,2.1971,2.4765,0.12961,-0.89192,1.0173,1.9021,2.1194,1.3722,1.5098,1.6948,1.2582,1.494,1.8267,2.1845,1.4633,1.4217,0.92799,1.2499,1.8976,1.0618,0.2563,0.94074,0.69833,1.2397,2.4251,2.9901,3.2443,1.6532,1.0183,1.3721,1.0219,1.062,0.98035,0.66153,0.015819,1.3353,1.7551,1.8113,1.421,0.4322,-0.041648,0.14625,0.6352,0.84958,0.83362\n2.959,2.3571,3.0306,2.1093,0.83577,-2.5613,1.1514,1.8141,1.7003,1.1644,1.8914,1.9519,1.9631,2.4275,2.7947,2.9315,2.6306,1.3588,0.99144,0.91024,0.61356,-0.91648,-1.7976,0.42666,0.4924,0.53186,1.8623,2.0043,2.4559,1.8092,0.83528,0.86993,1.4085,1.7095,2.0565,1.2282,2.0851,2.1795,2.9054,2.0282,1.492,1.1633,0.20816,0.60233,0.79652,0.76911,0.29912\n4.2333,3.0454,2.5296,1.8558,0.25606,-0.97039,1.4541,1.3128,1.7135,2.5153,3.2989,3.5388,3.7753,3.2807,3.6606,4.3355,3.3778,1.6178,0.6837,0.33138,1.1674,1.2157,0.086208,-0.46568,-0.62583,0.82345,1.6027,1.8376,1.6666,1.8118,1.9657,1.4624,1.6994,1.9385,3.0426,1.9208,1.6797,1.5413,3.4362,2.8626,1.9288,1.7901,1.5405,1.4731,1.774,0.90779,0.3673\n4.1,3.0264,2.4377,1.7981,1.0584,2.1137,2.5987,2.5254,2.8277,3.3809,3.2909,2.9447,3.5571,4.5686,3.883,3.0003,2.7273,2.3712,1.1831,1.671,2.5707,3.107,2.8586,1.2318,0.65724,1.9307,2.4969,3.2898,3.1985,2.8182,3.2688,2.9065,2.6688,2.3995,2.5111,1.4018,1.2658,0.59332,1.6467,2.445,2.459,1.7241,1.7812,2.1555,1.9814,1.7033,1.1209\n2.7397,2.4448,2.7117,3.379,2.993,1.2209,2.4084,2.7752,2.8572,3.4958,3.6433,3.7391,4.5888,5.8365,3.719,3.0041,2.405,2.0473,1.5602,0.1706,0.32497,1.1072,3.2224,2.6059,2.348,3.1691,4.063,4.5571,5.7933,5.6928,5.0751,4.1111,3.999,3.7296,3.9726,2.9746,3.3404,2.4429,2.7485,2.9932,2.7969,2.4486,2.5714,2.1957,1.6931,1.2584,1.5598\n3.1982,2.8718,2.5255,2.8769,2.309,2.5637,2.7879,2.7438,3.4405,2.8931,3.3588,3.2394,4.1536,4.2846,2.7712,2.3252,1.9845,1.8161,1.8959,1.6266,2.291,2.2241,4.1036,4.3519,4.2653,4.2915,5.3327,5.2579,6.0019,6.5545,6.4526,6.8303,6.2573,5.8493,4.5713,4.0397,3.8834,3.9505,3.7651,3.5663,3.7223,2.907,2.5604,1.8101,1.6933,1.8574,2.0434\n4.1015,3.875,3.2412,3.6259,4.7859,2.8352,2.7091,2.8674,3.2335,3.4301,3.3046,4.4542,4.2033,3.8506,3.169,1.7659,3.4868,3.662,2.8106,2.6344,2.5429,4.0351,4.8516,5.1429,4.8398,4.8599,5.5221,6.2978,6.5949,7.8185,9.4893,9.0821,8.306,7.2512,5.9065,5.8108,5.8095,5.289,4.8894,3.6254,3.654,2.8873,2.4719,2.2134,1.9521,2.0905,2.1637\n3.7306,3.8232,3.5544,3.7719,2.6544,3.5428,2.7976,3.3981,3.7514,3.7956,4.1419,5.1063,5.0657,4.7287,3.8236,3.2088,3.8201,4.6996,4.6537,4.8972,4.6492,5.3316,6.6356,6.5215,5.3093,5.8118,6.3756,7.648,9.3732,11.276,11.06,10.045,8.0823,7.6849,7.6092,6.8798,5.5924,5.834,5.7945,3.8186,3.7551,3.545,2.6459,1.8813,1.5368,1.7614,1.9616\n3.4299,3.527,3.7905,3.6783,2.7808,3.4521,2.8917,3.2977,4.0513,3.9522,4.9696,5.1671,5.1318,5.1231,5.4675,5.7746,4.9787,4.9914,5.68,6.6561,7.5815,8.3475,7.9946,9.0812,9.2701,8.5589,9.557,11.251,12.385,13.711,12.952,10.888,8.8317,6.3013,7.0676,6.4727,4.8699,5.0542,5.0967,4.6431,3.7148,3.4506,3.0948,2.1771,1.8321,1.6439,1.701\n3.5244,4.3262,3.0841,2.6642,3.0758,3.54,2.6907,3.7307,4.467,3.7119,4.6763,5.4939,5.4859,5.4157,5.7338,6.2794,6.1582,6.1888,6.676,7.6714,8.5258,9.0034,9.6878,10.597,12.533,10.762,10.069,10.999,12.162,8.1973,10.868,10.904,10.879,7.2808,6.5132,5.9046,5.2771,4.9402,4.7481,4.5185,3.5376,3.3026,3.217,2.8475,2.2656,1.7642,1.7395\n3.2715,3.275,1.9178,1.0757,2.5325,3.366,4.1716,4.4811,4.6535,3.6283,4.0468,5.224,5.919,6.1209,6.3257,6.7655,6.4605,6.9299,7.9189,8.8377,9.3015,9.9347,NaN,NaN,NaN,NaN,8.6104,8.2529,8.0032,8.5312,9.7269,11.473,11.426,9.9171,7.5426,5.8742,6.0042,5.4071,5.0781,4.2247,3.6833,3.6392,3.1523,2.7457,1.773,1.9642,2.2224\n2.8282,3.1315,1.6898,2.7083,2.6043,2.2793,2.4723,2.7318,3.1138,2.7205,4.1477,4.3826,5.3259,5.597,5.831,6.3771,6.3089,7.5236,8.5141,9.7209,10.31,NaN,NaN,NaN,NaN,NaN,NaN,7.0195,4.6182,7.3125,10.458,12.279,13.117,10.327,6.3887,5.9597,5.8157,5.0088,5.0011,4.8156,3.9151,3.5141,3.1418,2.446,2.2188,2.3349,2.0371\n1.0338,1.2824,2.7805,4.792,1.7118,0.98032,0.9846,2.082,2.8325,2.9754,4.0202,3.9698,4.0328,4.9767,5.8496,6.4744,6.9109,6.8189,7.1618,8.8302,NaN,NaN,NaN,NaN,NaN,NaN,8.423,6.5395,6.1916,7.2422,10.135,12.743,12.06,8.5184,7.8848,7.3047,6.8766,6.7895,5.562,4.8185,3.6812,3.3196,2.8891,2.4272,2.3774,2.3237,2.0464\n0.50588,0.35691,-0.51392,-0.49157,0.27737,0.63922,1.3431,1.9335,2.8386,4.036,3.905,3.6394,3.4214,4.2065,5.7646,5.7375,5.8111,6.5188,7.2224,8.4169,NaN,NaN,NaN,NaN,NaN,10.406,9.9049,8.6313,6.708,5.4595,5.0058,7.6534,6.2711,7.4178,9.215,8.2774,7.9533,7.1116,5.816,4.2519,3.8245,3.353,2.9418,2.5413,2.4955,2.0893,1.8347\n0.65103,-0.44168,1.0127,3.2943,-0.73944,-0.6138,0.69039,0.93853,2.2676,3.2148,2.6204,3.3582,3.803,4.1951,3.7141,4.0913,5.246,5.1855,6.682,7.4295,NaN,NaN,NaN,NaN,NaN,NaN,11.054,11.773,9.7966,8.718,7.7175,8.2421,3.7843,8.0115,9.411,9.2087,8.1844,7.4601,5.697,4.3798,4.0567,3.7328,3.2839,2.8578,2.6152,2.3376,2.6878\n1.398,0.89004,1.699,2.3383,0.60617,-0.58944,-0.51226,0.12465,1.1121,2.2308,2.3173,2.2172,2.3263,2.308,1.3386,2.5048,4.5065,4.7755,6.81,9.1023,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.971,10.463,9.4016,8.4087,7.8232,7.3289,6.2994,7.843,7.245,6.8363,7.2096,4.7718,4.2007,3.9523,3.9137,2.9823,2.2178,2.5071,2.778,3.5949\n1.8484,1.7017,1.943,2.1685,1.9531,0.25248,-0.51064,-0.17001,0.77218,1.6853,2.751,2.4594,1.5789,-0.11782,-0.22098,0.90677,1.964,2.0809,2.9279,4.307,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.0907,7.7983,8.0109,5.7789,4.0985,5.5033,5.4879,6.8608,6.7075,5.1063,3.9549,4.0083,3.4269,2.8126,2.3813,2.3231,3.146,3.7409\n1.6937,0.95355,0.44699,0.79846,1.1689,0.36034,-0.99885,-0.035485,0.74924,1.3772,1.7285,1.2181,1.2522,1.2365,-0.76601,-0.19729,1.564,2.1356,1.9826,4.128,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.974,8.0361,8.3095,NaN,NaN,8.2013,8.1758,8.2529,5.595,3.8366,3.7988,3.0635,2.5731,2.721,2.1879,2.8344,3.2766\n1.549,0.21754,-0.17565,-0.010377,1.0598,0.27007,-0.55316,-0.70918,0.67887,0.95342,0.54652,0.57653,0.73555,0.30758,-0.69058,-0.20929,0.88366,2.2965,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.3854,7.8229,NaN,NaN,NaN,NaN,11.306,10.751,7.9233,4.3084,3.3558,3.4954,3.2267,2.8463,3.0102,2.8557,2.6761,2.4876\n0.97708,0.51834,0.80742,0.8643,0.54683,0.18538,0.085657,0.19134,0.010621,0.1601,-0.11292,0.62688,0.82475,1.5862,1.2484,0.34937,0.49569,1.1415,0.032263,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.7271,NaN,NaN,NaN,NaN,NaN,6.2386,5.7758,4.957,3.9971,3.3993,3.3698,3.3305,3.4295,3.3889,3.2402,2.6856,2.5996\n0.50706,0.31503,0.45755,0.44411,-0.11501,0.44618,0.64291,0.10883,-0.23707,0.33923,0.35617,0.20604,0.12455,0.3994,0.53271,0.68603,1.7804,2.6095,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.5489,NaN,NaN,NaN,10.968,NaN,5.8119,5.2247,4.3645,3.9662,3.8759,3.4209,2.9658,3.2321,3.5622,4.2307,4.3769,3.9101\n1.0056,0.99931,-0.13354,-0.26003,0.18937,0.91276,0.99749,0.28342,0.38496,0.69436,0.38915,-0.11705,-0.07477,-0.40102,0.3186,0.2481,1.5656,1.577,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.2297,6.4974,NaN,NaN,NaN,8.6594,7.6154,5.6351,4.9185,4.5573,4.5804,4.2121,3.4964,2.9632,3.1207,2.8266,3.2659,4.0607,3.4234\n0.96599,1.5138,1.1809,0.37036,0.57606,1.2719,1.3452,1.1292,0.75226,0.5732,0.45264,0.41988,0.29992,0.70854,0.52736,0.77947,1.1732,2.1386,1.9927,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3329,6.837,11.294,11.186,9.6719,7.2966,4.9027,4.7464,3.7454,3.3064,3.897,3.5271,2.9909,2.5536,2.4124,2.7019,2.7573,2.7352,2.3635\n0.77954,1.1141,0.66696,0.90075,1.2055,1.4147,1.2921,1.2846,1.176,1.2167,0.90521,0.71182,0.14299,0.0068841,-0.038741,0.35651,0.90714,2.4078,2.5451,-1.3736,-0.36168,1.2456,0.79063,NaN,NaN,NaN,NaN,0.097202,4.5857,8.1283,10.355,9.8801,8.2872,4.9821,4.2011,4.1159,3.2967,2.548,3.0086,2.7036,2.3611,2.3886,2.2622,3.0536,2.7538,2.7081,2.6337\n1.623,1.5102,1.552,1.0903,1.0256,1.2486,1.688,1.7713,1.6337,1.3115,0.87632,-0.074378,-0.84969,-0.72704,-0.4564,0.26424,0.6571,1.5566,1.8803,-1.6239,-0.66141,0.36182,-0.013855,NaN,NaN,NaN,NaN,NaN,4.148,7.0297,6.456,6.2596,6.3812,4.3196,4.19,4.2144,3.2286,2.638,2.6605,2.0425,2.1753,2.4755,2.662,3.1438,2.6182,2.5075,2.0976\n2.371,1.7002,1.8648,1.8998,1.3301,1.1075,1.2565,1.0977,1.4907,1.0535,0.62691,0.32082,-0.52751,-0.38722,-0.12488,0.11141,0.95496,0.98346,1.2483,0.67918,0.43221,0.064074,-0.49796,0.98907,NaN,NaN,NaN,NaN,5.2603,5.1405,5.5216,5.94,4.6161,4.0161,4.0275,3.7573,2.9236,1.5844,1.9588,1.6355,1.6699,2.2222,2.3258,2.4108,2.0159,1.9215,1.199\n1.7155,1.4711,1.8121,1.6017,1.4815,1.639,1.4573,0.92699,1.5095,0.83507,0.79245,0.76048,0.31183,0.40722,0.88294,1.0951,1.1689,1.202,0.83966,0.96113,0.9405,0.92019,2.7648,4.1717,NaN,NaN,NaN,4.325,3.548,4.0734,4.3612,4.1379,4.1643,3.643,3.5733,2.5732,2.1107,0.77942,1.7241,1.4288,1.8016,2.4449,2.3154,1.5139,0.50428,0.059271,0.55717\n1.5844,1.5785,1.9089,1.7352,1.4465,0.84648,0.79636,0.77292,0.82486,1.1487,0.88461,0.57084,0.69047,1.0356,1.503,1.6553,1.4641,0.53497,0.72001,1.8961,1.8802,2.1148,2.7416,3.9362,5.4619,5.0113,4.6981,3.4284,2.7903,3.0002,3.4147,3.3649,3.2167,3.3526,3.155,1.9951,1.4896,0.80297,1.4634,1.3695,1.8053,2.0107,2.1772,2.348,1.8893,1.0221,0.20553\n2.387,2.1098,2.1627,2.0861,1.5995,1.1285,1.1659,0.81203,1.2983,1.4847,1.107,1.0321,1.0719,1.1056,1.666,1.5466,0.70373,-0.25483,-1.2149,-0.47342,0.75024,0.86985,1.8243,2.2035,2.6254,2.1117,3.1344,3.0605,2.9988,3.5188,3.6454,4.0447,4.4767,3.5049,2.5938,1.6845,1.2477,1.1301,1.1588,0.96436,1.273,1.7195,1.9432,2.4953,1.8518,1.3229,0.73175\n1.9455,2.3477,2.8464,1.8362,1.6405,1.3264,1.2234,1.2856,1.3777,1.0975,1.132,1.2437,1.3951,1.2317,0.53219,0.54847,0.53657,0.15644,-1.3824,-1.676,-0.54698,0.090887,0.31107,0.71173,1.5637,1.8002,2.3875,1.824,2.9826,2.8987,3.4154,3.5794,3.1129,2.4324,0.95079,0.88121,1.6383,1.7046,0.90891,0.66478,0.86176,0.20491,0.2359,1.3916,2.0853,1.7904,1.2062\n1.5921,2.0084,2.4845,2.4664,2.3896,1.1147,1.1438,1.1103,1.074,0.88038,0.81784,0.73249,0.87937,1.2146,1.3561,0.78032,0.42239,0.11808,-0.71739,-1.228,-1.806,-1.8636,-1.2476,0.2491,-0.17362,0.33218,1.6979,1.9825,2.4799,2.1966,2.097,2.7649,2.4346,1.4127,0.27253,0.14514,0.79243,1.7729,1.2336,0.81098,0.60813,0.065914,0.23875,-0.65761,0.60501,0.038691,0.61251\n2.2339,2.6412,2.1499,1.9215,2.1183,1.3482,1.1088,0.71296,1.8269,1.4425,1.0412,1.4181,1.2726,1.341,1.3942,0.96788,0.63853,0.45261,0.13422,-0.34917,-0.86448,-0.47499,0.3506,0.85045,-0.12223,-0.31731,1.7414,2.5102,2.0789,2.0981,1.9281,2.0164,2.1063,2.3057,2.3477,1.2925,1.3318,1.1672,1.2047,0.94565,0.79203,0.60042,0.72371,0.44036,0.040721,0.016846,0.66639\n1.9622,2.1736,2.269,2.0512,1.7729,1.4725,1.5732,1.4063,0.79989,0.31292,0.92208,1.2035,1.5029,1.4955,1.3113,1.1115,0.68176,0.71731,0.55364,0.43305,-0.47385,0.49836,1.1521,1.6516,1.8703,1.668,1.7226,1.9037,1.4089,1.3753,1.472,1.2894,1.8688,2.3503,2.2411,1.8576,1.2486,0.87626,0.7838,0.48928,0.40294,0.29296,0.038077,-0.25921,-0.042656,0.30202,0.45679\n1.7835,2.0432,2.0531,1.7797,2.0003,1.694,1.5364,0.99715,0.72692,0.18626,0.56684,0.93421,1.2006,1.0053,0.90414,0.78373,0.5079,0.17721,0.10919,-0.014339,0.96281,1.4107,1.4747,1.7483,1.4583,1.2271,0.59015,1.128,1.6432,1.8573,1.3785,1.1204,1.6008,1.5248,1.8096,2.4489,2.0307,1.1219,0.99452,0.61332,0.25968,0.094031,0.2401,-0.0067883,0.042309,0.42573,0.8254\n1.8941,1.8552,2.1369,2.251,1.4087,1.1648,1.2236,0.79898,0.81788,0.90035,0.56943,0.40778,0.89983,0.17905,0.94099,0.692,-0.019994,1.3624,0.87103,-0.24773,1.0552,0.76998,1.2987,1.7099,1.977,2.171,1.4293,3.4536,3.0824,2.2094,1.4135,1.0136,1.1888,1.1865,1.3938,2.1325,1.4095,1.2443,1.2292,0.7643,0.073494,-0.10412,0.065083,0.34823,-0.10055,0.30016,0.24367\n1.6505,1.7523,2.2141,3.2491,1.9307,1.5226,0.57979,0.32497,0.21662,0.6697,1.1141,1.0295,0.66437,0.50418,0.27501,0.041429,-0.01362,-0.11737,-0.29979,-0.43839,0.13225,0.48531,1.2239,1.5925,2.217,2.2948,3.0766,3.2726,2.5257,1.9822,1.5748,0.92592,1.118,0.94025,1.2231,0.8917,0.70998,0.57559,0.96569,0.42969,0.098277,0.19245,0.09445,0.055473,0.038298,-0.053931,-0.48786\n1.392,1.0455,0.80117,1.054,2.2862,1.7742,0.74303,0.92005,1.0578,0.88163,0.85656,0.33898,0.63841,0.69622,0.40738,-0.14486,-0.35475,-0.51745,-1.948,-1.7994,0.20721,0.77832,1.6362,1.3412,1.6529,2.5702,3.3184,4.0479,3.1326,1.9024,1.2823,0.79861,0.93681,1.0667,1.4748,1.065,0.941,0.38764,-0.21481,-0.59333,0.0096221,0.14183,0.00095701,0.38142,0.85079,0.52606,0.28993\n1.1442,1.2156,0.80597,1.5244,1.9703,1.5981,1.3311,1.2897,0.66613,0.80788,0.84948,1.0451,0.85508,0.84454,0.66076,0.44231,0.65784,0.23079,0.56471,0.24893,0.99294,1.2844,1.5898,1.4617,1.5882,1.9532,3.559,4.0084,3.73,2.6684,1.619,0.7485,0.52475,0.95704,0.897,0.81824,0.95515,0.59672,-0.071052,-0.16955,0.24486,0.014639,0.61416,0.56238,1.1482,1.2895,-0.069311\n0.71369,1.4853,1.7581,1.1459,1.7119,1.2606,1.6148,1.0314,0.25659,-0.11399,-0.15206,-0.47044,0.1712,0.33395,0.33458,0.39297,0.79688,1.2183,1.3004,0.57493,1.0121,1.4411,1.5859,1.8736,2.232,1.9611,2.3924,2.4483,2.8085,2.3264,1.2727,0.56662,0.1894,0.55309,0.53315,0.71827,0.18598,-0.29196,-0.37874,-0.1501,-0.06464,-0.19056,0.022307,0.41915,0.53623,0.4488,-0.069488\n1.3288,1.1952,1.034,1.0206,1.2417,1.3381,1.8888,1.3643,-0.10884,-1.2333,-0.40222,-0.70521,-0.072116,0.38559,0.3387,0.56837,1.2282,1.3595,1.163,0.87543,1.0042,1.1316,1.2931,1.5467,2.434,2.4487,3.1304,2.9066,2.3122,1.2826,0.79556,1.0119,0.93906,0.90621,0.66354,-0.0039678,-0.16766,-0.28666,-0.29849,0.11282,0.065156,-0.089185,-0.13904,-0.19817,-0.14617,0.25914,0.2657\n2.0091,1.5924,0.77031,0.60696,0.80054,1.8832,2.362,1.8887,1.2479,0.33961,0.77714,-0.27048,-0.45271,0.092244,-0.33852,-0.28679,0.6084,0.72417,0.29963,0.81186,0.92837,0.58772,0.77221,1.5923,2.5165,3.107,3.2327,3.4288,2.6405,1.9347,1.0259,1.462,1.288,1.4222,1.4623,1.2705,0.79111,0.14369,-0.39411,0.11753,0.22641,0.11507,0.060173,-0.0094962,0.16974,0.24118,0.059826\n2.1455,2.3748,1.5552,0.4932,0.17231,-0.46565,2.0452,1.6778,1.574,0.89604,1.2186,0.83569,0.54814,0.55766,-0.027239,-0.79461,-0.23919,0.081072,0.74812,1.4217,1.4498,1.2048,0.88405,1.3615,1.8936,2.4061,2.6038,2.8201,2.6123,1.523,0.78112,0.95207,1.1062,1.029,1.3723,1.2171,0.63057,-0.17174,-0.3942,-0.16012,-0.088149,-0.16113,-0.11983,-0.28799,0.061996,-0.031076,-0.06311\n1.7126,1.6298,0.64564,0.29276,0.13239,-0.10329,0.91485,1.7424,3.3512,2.038,0.54275,-0.61199,-1.9126,-0.56233,0.82428,0.9745,0.38038,1.223,0.22744,1.3472,1.1852,1.9627,1.2486,1.5159,1.6423,1.6627,1.7665,1.9616,2.2172,2.7416,1.7554,0.71211,0.64606,0.88182,1.1711,1.1146,0.69936,0.37999,-0.18706,-0.13348,-0.36553,-0.60576,-0.41817,-0.68323,-0.8559,-1.4693,-1.348\n2.0236,1.8346,1.1149,0.7711,0.43581,0.45196,0.28995,1.2807,2.4988,2.425,1.5757,-3.7625,-1.9081,-0.56499,-1.7916,-0.86722,-0.32186,-0.010562,0.9596,0.82566,0.81472,1.659,0.82852,1.3646,1.6269,1.3517,2.4863,2.4249,2.2084,2.3798,1.719,0.9595,0.094009,0.6894,1.0817,0.77263,0.74224,0.54071,-0.021683,0.21102,0,-0.44379,-0.44368,-0.22421,-0.43935,-0.96567,-1.3302\n1.8867,1.651,1.4308,1.7065,1.5763,0.89691,0.5605,1.2499,1.7269,1.762,-0.68467,0.2824,-1.4843,-3.3877,-4.1795,-4.7545,-1.2969,-0.46458,0.23659,0.51672,0.54844,0.31286,0.093827,0.65069,0.60428,0.97384,1.5917,1.8446,2.0845,1.7001,0.97781,0.97659,0.64099,0.56973,0.34974,0.27778,0.79888,0.63516,0.17462,0.043055,0.15053,0.073899,-0.40802,-0.26661,-0.60287,-1.2014,-1.6939\n2.634,3.8906,2.3906,1.9284,2.175,1.9469,1.2218,0.63147,1.1201,1.4912,-3.0647,-0.85436,-0.043645,-0.45972,-1.8909,-3.5446,-2.5488,-1.017,1.0436,0.48072,0.39461,-0.070553,-0.24085,0.29038,-0.039362,0.72737,1.4884,1.8749,1.9805,1.3674,0.90279,1.1461,1.2809,0.88813,0.48588,0.23213,0.84666,0.81296,0.43936,0.12963,0.05756,0.042608,0.26119,-0.025038,-0.29462,-0.20208,-1.2499\n1.1039,2.4316,2.9574,2.1448,1.0536,0.55863,1.4128,1.3195,1.2708,1.6819,3.2657,1.2474,0.33607,0.66689,0.60404,0.28107,-0.61879,-0.80837,-0.10817,0.64594,0.15295,-0.36499,-1.1402,-0.96725,-0.47592,1.0515,1.7322,2.1983,2.2857,1.5187,0.94,1.2365,1.4664,0.95756,0.33652,0.29148,0.67509,0.80125,0.8146,0.39777,-0.10928,-0.061457,0.065892,-0.37645,-0.1526,0.12773,0.030853\n0.68662,1.0136,1.5499,2.2432,1.711,0.76772,1.8265,0.42612,0.27893,0.061618,0.12864,1.1268,-0.17546,0.32693,1.2628,1.8738,0.96636,1.2441,0.36019,0.56625,-0.29689,-0.92972,-0.80561,-1.2094,-1.207,0.68758,1.5052,2.3602,2.1254,1.836,1.5704,1.6861,1.662,0.72869,0.056689,0.36601,0.35054,0.17852,0.84245,0.28794,-0.28332,-0.6525,-0.67813,-0.36049,-0.48517,-0.59243,-0.028134\n0.64211,0.58788,0.71128,1.7927,2.2748,1.834,1.4995,0.2794,0.46474,0.40726,0.66142,0.63246,-0.28118,-0.8208,-1.0446,-0.056226,1.8417,2.3336,1.6015,0.79995,0.96231,0.12936,-0.075295,-0.26978,0.22509,0.28695,1.4575,2.2182,1.8789,1.4631,1.417,1.3598,1.3429,0.42284,0.17325,0.50573,0.28979,0.086218,0.19522,-0.025536,-0.18387,-0.40795,-0.50615,-0.71856,-0.61616,-0.60896,-0.54175\n-0.0043139,-0.25405,0.21372,1.9941,1.645,0.77911,2.6202,2.4675,2.2354,0.4065,1.5373,0.50278,1.4379,1.7108,2.1102,0.84908,1.4433,0.15029,1.547,1.0121,1.9643,1.6489,0.52983,-0.14371,-0.15948,-0.48895,-0.15449,1.3741,2.4349,2.3122,2.0544,1.3521,0.71721,0.35346,0.23142,0.56492,0.28577,0.38017,0.60296,0.62442,0.53336,-0.32129,-0.34184,-0.42395,-0.50283,0.4887,0.87597\n-1.3207,-2.8986,-0.19044,1.2936,1.9934,2.6007,2.0665,1.3803,2.943,3.575,3.2371,2.2995,2.5235,3.1848,2.7214,0.082684,-2.1775,-2.0502,0.61522,1.0744,0.91363,0.91599,0.59793,-0.32878,0.59427,0.90029,1.0182,1.4959,2.0154,2.181,2.0602,1.8312,0.03246,-0.37654,-0.19882,0.38265,-0.11687,0.26803,0.61255,0.60433,0.20798,-0.18717,-0.10484,-0.18442,-0.17999,0.61801,1.1237\n0.348,2.1668,1.5575,2.0056,2.2718,0.8343,0.54212,0.64328,1.964,3.3012,2.5782,2.1745,1.1513,0.5765,0.3696,0.027995,0.54332,-0.32901,-0.16416,0.57765,0.4409,-0.14148,-0.29825,-0.35873,-0.37389,0.6658,0.97061,1.0132,-0.035269,0.29262,0.56429,1.194,0.44605,-0.18041,-0.37451,-0.2603,-0.29118,-0.017251,0.34667,0.10254,-0.19187,-0.41816,-0.70601,-0.60472,-0.25832,-0.10008,0.39976\nNaN,-0.29771,0.24344,3.2251,1.0006,-1.3802,2.0164,1.251,3.3242,2.9496,2.3132,3.3371,3.3223,2.4598,0.57527,-0.17873,0.52329,0.86549,0.38707,-0.041198,-0.30299,0.37434,0.23547,-0.047042,-0.0066772,1.1088,1.1097,0.54874,-0.082134,-0.50991,0.75281,0.38564,0.48203,0.37365,-0.040251,-0.51921,-0.17576,-0.11553,0.30464,0.22131,-0.065129,-0.41261,-0.69296,-0.45423,-0.50401,-0.70731,-0.7174\n4.0025,-1.0707,0.36229,1.5195,2.201,2.1921,1.3248,0.46137,1.2992,1.8794,2.9983,3.3035,3.8276,3.8495,1.4752,-0.061508,NaN,3.4041,1.7653,-0.25806,0.31051,-0.61083,-1.8884,-0.43729,0.36339,-0.47271,0.15511,0.10625,-0.48865,-0.89577,-0.17815,0.23348,0.45927,1.0064,-0.097425,-0.89061,-1.3068,-0.49342,0.077451,0.61026,0.56515,0.58333,-0.33498,-0.30084,-0.58163,-0.9475,-1.2556\n2.0386,-1.0619,-0.45772,0.15152,0.79661,1.4414,-0.1091,1.0895,1.5901,1.0738,-0.11089,0.56999,2.5956,4.3,4.2099,NaN,NaN,NaN,NaN,3.7555,1.8653,0.26539,-0.39223,-0.012021,0.20338,0.4522,0.10104,-0.11367,0.51335,0.71913,0.27166,0.017434,-0.096035,-0.12819,0.21535,-0.55095,-0.88499,-0.23464,0.38719,0.20573,0.31264,0.32069,0.29834,-0.042074,-0.56616,-1.0721,-1.5321\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070430-070604.unw.csv",
    "content": "-1.6907,-2.5268,-2.6101,-2.4508,-2.4926,-1.7568,-1.8768,-1.7083,-1.5908,-1.9917,-2.2278,-2.9278,-2.1593,-2.1629,-1.9291,-1.7092,-1.3665,-1.4745,-1.1542,-0.69511,-0.83033,-0.18874,-0.103,-0.61795,-0.9392,-1.1304,-1.1407,-1.4192,-1.4389,-1.5937,-1.7315,-1.8554,-1.4537,-1.4851,-1.5182,-1.2686,-0.88144,-0.85319,-0.66512,-0.77855,-0.91441,-0.80607,-0.70621,-0.41874,-0.10685,0.64508,1.1602\n-1.7918,-2.6024,-2.257,-2.3437,-2.2151,-2.1107,-2.0208,-1.3543,-1.3699,-1.3108,-1.7385,-2.2256,-2.0081,-1.9844,-2.1014,-2.0068,-1.5948,-1.2258,-0.5497,-0.38045,-0.38228,0.21835,-0.061092,-0.23311,-0.61345,-0.77974,-1.0686,-1.1628,-1.209,-1.5111,-1.7443,-1.7356,-1.5483,-1.438,-1.4394,-1.0018,-0.77169,-0.6023,-0.41418,-0.38784,-0.6973,-0.41397,-0.37574,-0.15425,0.20343,0.37011,0.49382\n-1.8521,-2.4553,-2.0743,-1.9294,-1.7159,-1.9141,-1.8338,-1.5172,-1.1619,-1.1615,-1.2345,-1.8279,-1.791,-1.8055,-1.5094,-1.5327,-1.0895,-0.6597,-0.2317,-0.23216,-0.34074,-0.47046,-0.45555,-0.69062,-0.76614,-0.56542,-0.72718,-0.74792,-1.1756,-1.2787,-1.4618,-1.5332,-1.458,-1.3268,-1.3066,-1.0488,-0.97436,-0.62702,-0.48557,-0.57275,-0.51717,-0.48081,-0.37636,0.18206,0.48414,0.70516,1.2499\n-1.411,-0.93481,-2.6979,-1.893,-1.9273,-1.9303,-1.7349,-1.2557,-1.2905,-1.4276,-1.3637,-1.3491,-1.712,-1.4925,-1.0057,-0.63685,-0.292,-0.10357,-0.14554,-0.22568,-0.19269,-0.25129,-0.51786,-0.68611,-0.46536,-0.27742,-0.26782,-0.50409,-0.83541,-1.0716,-1.0859,-1.0984,-1.1207,-1.1807,-1.1411,-0.96701,-0.9244,-0.76563,-0.66787,-0.59545,-0.64722,-0.78257,-0.43527,0.083488,0.47132,1.0209,1.9271\n-1.0249,-0.39405,-5.3819,-1.7322,-1.7739,-1.6069,-1.4603,-1.1966,-1.0481,-0.91168,-0.88783,-1.3703,-1.5968,-1.0584,-0.62685,-0.54522,-0.48187,0.066254,-0.23889,-0.52108,0.22213,-0.076771,-0.47893,-0.35741,-0.25037,-0.1119,-0.19182,-0.2805,-0.51457,-0.70876,-0.88145,-1.0128,-1.0195,-1.0526,-1.0489,-0.84693,-0.93597,-0.8596,-0.75757,-0.55522,-0.43288,-0.47262,-0.63069,-0.12274,0.32779,1.1207,2.072\n-0.75129,-2.3947,-4.2912,-1.6729,-1.6236,-1.6962,-1.5835,-1.3328,-1.3046,-1.671,-1.6434,-1.1972,-1.1413,-0.77824,-0.65541,-0.44956,-0.31107,-0.03949,-0.21483,0.28061,0.33249,0.0015259,-0.2524,-0.02293,0.1751,0.055882,0.14946,-0.046528,-0.52227,-0.74429,-0.73401,-0.85258,-0.953,-0.95603,-0.77736,-0.65669,-0.701,-0.69294,-0.74546,-0.39172,-0.33549,-0.45628,-0.461,-0.14009,0.4082,0.90825,2.1344\n-1.0961,-0.9672,-6.0241,-1.8369,-1.7252,-1.3053,-1.1427,-1.0152,-0.9093,-1.2817,-1.1289,-0.81839,-0.52365,-0.29499,-0.29691,-0.10973,-0.074844,0.047279,0.14257,0.92286,-0.3075,-0.3711,-0.45188,-0.24202,0.15188,0.19606,0.23613,-0.18233,-0.55831,-0.5558,-0.40528,-0.72773,-0.90564,-1.0154,-0.93856,-0.88456,-0.92918,-0.71572,-0.955,-1.1301,-1.1117,-0.47379,-0.40571,-0.076931,0.57924,1.1659,2.2517\n-2.0818,-2.1254,-2.1058,-1.941,-1.6482,-1.3003,-0.92635,-0.64176,-0.39896,-0.90106,-0.67114,-0.53876,0.066273,-0.11903,-0.017643,0.06115,0.14133,0.43451,0.68365,0.63681,-0.38472,0.10911,-0.17363,0.039959,0.57996,0.44214,0.25432,0.052799,-0.095402,-0.37437,-0.35668,-0.65898,-0.84244,-1.0435,-0.80334,-0.91468,-0.98056,-1.1193,-1.3517,-1.4659,-1.416,-1.0661,-0.7509,-0.44261,0.15032,1.0359,2.0973\n-2.0393,-2.0195,-1.7688,-2.3458,-1.7851,-1.5254,-1.058,-0.48521,-0.2377,-0.16402,-0.0086098,0.25438,0.043125,0.26656,0.33829,0.47102,0.59384,0.83944,0.76012,0.40755,0.371,0.30474,0.033768,0.33663,0.54263,0.20402,0.3232,0.42155,-0.098621,-0.57281,-0.45485,-0.4582,-0.66232,-0.67347,-0.71147,-0.80864,-0.91515,-1.1287,-1.5,-1.5424,-1.4049,-1.2415,-1.305,-0.79659,-0.061451,0.53164,0.61141\n-1.6052,-1.6297,-0.73673,-1.9613,-2.2708,-1.7478,-0.65195,-0.29141,-0.22025,0.38302,0.53217,0.68399,0.2618,0.60556,0.35463,0.64228,1.1018,1.1858,0.97199,0.62762,0.9321,0.44289,0.088432,0.47158,0.53938,0.39294,0.51907,0.39655,0.17612,0.052769,-0.23131,-0.37381,-0.53517,-0.57906,-0.48655,-0.36605,-0.47545,-0.99054,-1.3424,-1.3139,-1.0951,-0.96043,-0.94687,-0.75675,-0.53086,-0.24753,-0.093235\n-1.8788,-1.9125,-1.1412,-1.1041,-2.513,-1.8241,-0.91504,-0.28811,-0.19522,0.25534,0.77317,0.8567,0.97909,0.98932,1.1956,1.6069,1.2691,1.2377,1.0954,1.1423,0.73675,0.38939,0.4456,0.51786,0.46146,0.62873,0.60163,0.49207,0.39506,0.023411,-0.37592,-0.56131,-0.69553,-0.62057,-0.81547,-0.70242,-0.73348,-1.0262,-1.0659,-0.99411,-0.9242,-0.75995,-0.90378,-0.96393,-0.66668,-0.5675,-0.61648\n-1.9986,-2.4968,-2.558,-3.4006,-3.0559,-1.5052,-1.2942,-0.64524,-0.30882,0.13985,0.64309,0.86322,1.1604,1.4186,1.5676,1.6362,1.5534,1.8543,1.6983,1.6212,0.82006,0.6587,0.7164,0.76994,0.65805,0.97272,0.75528,0.71591,0.49158,-0.14968,-0.41269,-0.50553,-0.69564,-0.67,-0.53998,-0.743,-0.78466,-0.87982,-0.93616,-0.89508,-0.84778,-0.82002,-1.2435,-0.85576,-0.57148,-0.17158,0.024433\n-1.7786,-1.8703,-2.2166,-3.2727,-3.2925,-2.047,-1.347,-0.7378,-0.15005,0.25093,0.60736,0.74763,1.1954,1.3114,1.3704,1.6673,1.8881,1.8709,1.8119,1.3458,0.76957,1.0379,0.86176,1.1227,1.044,0.95229,0.9122,0.95062,0.62767,0.19091,0.23114,-0.27273,-0.47799,-0.55449,-0.44879,-0.62642,-0.88808,-0.81194,-0.72473,-0.71295,-0.66468,-0.8833,-0.9431,-0.82562,-0.36652,0.033241,1.4431\n-1.8563,-0.45245,-0.65875,-1.5038,-2.9463,-2.9518,-2.6403,-0.93966,0.061039,0.50341,0.93221,0.92096,0.86268,1.2663,1.4437,1.7061,1.6899,1.6872,1.5617,1.1339,1.0141,1.2103,0.9355,0.96994,0.84545,0.86303,0.93959,0.70801,0.67433,0.6572,0.48922,0.004303,-0.32307,-0.63705,-0.96991,-1.239,-1.3384,-1.0445,-0.73719,-0.4845,0.066441,-0.87819,-0.88307,-1.523,-1.877,0.97567,1.9079\n-0.94753,-0.19007,-1.1652,-2.2218,-3.7974,-3.1398,-2.5767,-0.75123,0.26881,0.87494,1.3812,1.1468,1.0286,1.1517,1.5465,1.6071,1.5252,1.5267,1.5485,1.1331,1.2824,0.86689,1.1395,1.0908,0.83695,0.7008,1.1213,1.0673,0.78734,0.55403,0.36872,-0.00060272,-0.41026,-0.6713,-1.106,-1.0532,-1.4807,-1.4059,-1.0674,-0.30844,-0.52664,-1.1732,-0.28988,-0.28547,-0.1684,-0.19276,1.2618\n-0.22278,-0.93579,-1.7571,-2.8029,-3.4294,-2.9434,-2.823,-0.6606,0.28933,0.75833,1.4212,1.2596,1.3013,1.5718,1.7751,1.8024,1.7503,1.9863,1.6785,1.1034,0.90764,1.1139,1.2184,1.1364,0.89573,0.82328,1.0854,1.0406,0.8096,0.32708,0.1996,-0.31136,-0.42196,-0.5266,-0.64295,-0.513,-0.91246,-1.4929,-1.627,-1.2731,-0.96983,-0.62685,-0.32645,-0.82649,-1.0955,0.26588,0.26772\n-0.4306,-3.1924,-2.4224,-2.1822,-2.7111,-3.5745,-3.0678,-0.83361,0.21213,0.79799,1.2521,1.268,1.3865,1.9288,1.7616,1.8968,2.0555,1.9944,1.3695,0.57256,0.58023,0.73234,0.82038,0.87243,1.9086,2.363,1.2874,0.40549,0.53033,0.38509,-0.20166,-0.49201,-0.42057,-0.48315,-0.62136,-0.81518,-1.1419,-1.731,-1.8719,-0.50147,-0.51638,-1.146,-1.868,-1.0196,-0.34775,3.3123,1.4628\n-0.92373,-3.0621,-2.1647,-2.16,-2.8788,-3.5558,-1.9977,-0.47762,0.25249,0.85788,1.273,1.2724,1.3403,1.2924,1.7802,2.2886,2.6307,1.8067,1.0545,-0.1319,0.51714,0.37212,0.070721,0.57813,1.2331,1.3913,0.90163,0.47774,0.38566,0.21062,-0.21207,-0.60447,-0.97267,-0.5569,-0.86644,-1.2801,-1.2655,-2.0399,-2.3944,-0.68525,-0.42826,-2.3346,-3.2279,-1.6918,-0.2034,1.6729,1.4233\n-2.4673,-2.2363,-1.7204,-1.9073,-2.8447,0.96231,-1.7733,-0.60547,0.3485,0.69667,1.1744,1.6711,1.579,1.3065,1.9182,2.4757,2.2477,1.9541,1.0581,-0.33989,-1.3382,-1.6404,-1.1144,-0.25527,0.47488,0.72074,0.92647,0.79451,0.19099,0.15313,-0.25039,-0.90685,-2.4695,-1.3521,-0.62463,-1.5933,-2.5077,-2.2621,-1.3689,-0.93272,-1.4228,-2.2122,-3.9483,-2.3844,-1.9242,-0.90425,-0.65585\n-2.5727,-2.338,-1.5269,-1.5702,-0.68778,-1.1488,-1.1729,-0.62607,0.35025,0.80034,1.2973,1.2599,1.3015,1.4491,1.5491,1.6769,1.6941,1.2408,0.52935,0.11464,-0.88552,-0.91802,-0.32877,-0.66938,0.031986,0.41944,0.1784,0.44654,0.8981,-0.49555,-0.38195,-0.74608,-1.5289,-2.2143,-2.7514,-1.8322,-2.3217,-2.3696,-2.3884,-2.2812,-2.2617,-2.777,-3.2203,-2.8715,-2.2847,-2.5629,-2.1768\n-1.7162,-2.0377,-1.2243,-0.34398,1.1455,-0.17753,-0.13645,-0.38105,0.0033684,0.61415,1.6999,1.1784,1.4717,1.4822,1.3755,0.85044,0.56354,0.28474,-0.5055,0.086475,0.50065,0.10303,-0.20235,-0.44196,0.031425,0.81454,0.62661,1.164,1.6355,0.1262,-0.40284,-1.0118,-0.42157,-0.52608,-4.7407,-0.69893,-1.506,-2.1131,-2.5246,-1.9418,-1.6699,-2.2503,-2.7819,-2.6593,-2.8936,-2.7477,-2.4577\n-1.7842,-1.8328,-0.98539,-0.59975,0.13817,1.72,0.41165,-0.079662,0.16279,1.3041,1.933,1.7968,2.162,1.751,0.61446,-0.064293,-0.75141,-1.1387,-1.0129,-0.25783,-1.0216,-0.12076,-0.88213,-1.0483,-0.044388,0.44571,0.62442,0.75686,0.84338,0.97242,0.53798,-1.2301,-0.89375,-0.78323,-2.3706,-1.0416,-0.39227,-1.0129,-0.83544,-0.67495,-2.0564,-2.4308,-2.3291,-2.6822,-3.3054,-3.824,-3.2271\n-0.97035,-0.88334,-0.17033,0.14687,1.138,-0.35154,0.19863,0.4236,1.0354,2.0786,2.0818,3.0471,2.1929,0.554,-0.40487,-0.5644,-1.088,-0.90123,-1.2844,-0.59417,-0.69912,-0.94779,-0.43583,0.25532,0.45118,0.15085,1.0796,0.78843,-0.092571,0.99601,1.1995,0.40139,-0.24052,-0.20176,-0.44577,-0.12156,-0.13336,-0.037525,0.24672,-0.73993,-1.893,-2.409,-2.4676,-2.9952,-3.4339,-3.5218,-3.1646\n-0.39537,0.050579,0.64332,0.63514,-0.59535,0.22441,0.88829,1.3492,1.6146,1.8265,1.8076,2.5007,1.5354,0.65933,0.0051079,-1.4868,-1.5296,-0.55627,-0.080143,0.10072,1.0195,1.2016,0.95552,1.2042,1.2101,0.75294,1.3127,0.96895,0.90134,1.8554,1.849,1.1028,1.5704,1.8257,0.54614,0.40859,0.32307,0.3689,0.64629,-2.04,-2.2537,-2.2372,-2.0389,-2.5641,-3.1343,-3.5126,-4.1563\n-0.63285,0.024345,0.80352,0.77111,0.61719,1.4773,1.1128,1.9918,1.9115,1.1988,1.4131,2.0177,1.7265,0.84427,0.54553,0.07851,0.1904,0.22556,1.0324,1.6585,1.8331,2.1802,2.4546,1.9226,1.901,2.2821,2.2038,2.2207,2.6842,2.7774,2.8861,2.4555,2.2682,2.0808,0.40681,-0.8379,-0.23232,-0.88956,-1.3888,-1.9729,-2.1187,-1.884,-1.9231,-2.2833,-2.6373,-3.6859,-3.9232\n-0.70132,0.22596,0.76929,1.037,1.505,1.9851,1.8152,2.0671,2.1909,1.7424,2.2039,2.4166,2.0104,1.8655,2.0032,1.5592,1.8375,2.2118,1.9392,2.21,2.4608,3.509,2.7696,2.2333,2.4088,3.0284,3.4008,4.2128,4.4411,2.8668,2.8551,4.1727,4.8618,0.20793,-0.31228,-1.4893,-1.4986,-1.1816,-1.4713,-2.0311,-2.2841,-2.2622,-2.3513,-2.2913,-2.3703,-3.9302,-3.6044\n-1.6911,-0.80008,0.66864,1.1401,1.5462,1.7566,2.3593,2.1398,2.1683,2.5475,3.0122,3.1617,3.1881,3.0688,2.3572,2.4386,2.7392,2.7371,1.7418,1.7862,2.7084,2.8307,1.9957,1.4614,0.88709,1.8623,2.3299,2.7965,2.7724,1.446,1.9907,2.3013,2.0203,-0.80832,-1.1369,-1.0076,-1.0703,-0.95067,-1.9181,-1.5543,-2.1471,-2.4815,-2.3927,-2.3608,-2.7472,-3.1188,-3.0648\n-1.8312,-0.4292,0.23747,0.24705,0.85749,1.1797,2.1302,2.0764,1.8878,2.7009,3.2562,3.5922,3.1843,2.9164,2.4992,3.8867,2.7529,1.8094,1.7053,2.0977,2.2569,2.506,2.2102,0.81522,-0.14639,1.1707,1.8387,1.712,1.5227,1.5251,1.3447,0.71475,-0.28756,-0.46672,-0.30632,-0.42017,-0.72052,-1.2231,-1.0686,-1.6612,-2.6447,-2.5125,-2.4952,-2.4641,-2.6468,-2.714,-2.759\n-1.1912,-0.5371,0.044758,0.038769,0.3595,0.96786,1.8221,2.1753,1.7602,1.7055,2.077,3.0654,2.3703,2.5151,2.9343,3.5717,2.3249,1.6788,2.2254,2.229,1.9727,2.1192,2.7864,3.2861,2.3523,3.8904,4.5438,3.6317,3.0043,5.4216,3.6195,2.2344,3.6598,0.31114,-0.29168,-0.73256,-1.6523,-2.3171,-2.4092,-2.455,-2.4106,-2.6947,-2.7332,-2.4832,-2.6498,-2.6337,-2.5771\n-1.2209,-0.81424,-0.87263,0.55695,0.305,0.58096,1.243,1.8537,1.3741,1.3035,2.13,2.6877,1.6544,2.453,3.0666,2.5476,1.4407,1.3024,1.8964,1.1005,1.6589,1.8295,4.3883,3.4242,4.7779,5.7666,5.1764,5.3264,6.247,5.4753,2.5783,3.0705,2.7508,-0.97855,-0.79575,-0.36251,-2.2913,-3.277,-2.4944,-2.2974,-1.9947,-2.486,-2.4812,-2.3018,-2.738,-2.3387,-2.2528\n-1.6121,-1.564,0.18842,-0.66928,-1.5918,-0.64217,-0.50523,1.0672,1.1374,0.99005,1.4618,1.3329,1.1912,2.1245,3.9002,2.3938,0.55187,0.9113,1.0767,0.3658,1.9388,2.9495,5.1391,4.0186,5.4681,5.1685,5.0222,5.0154,5.6769,4.3202,0.36777,0.19161,0.24012,-1.1933,-0.49796,-0.29012,-1.6632,-2.1146,-2.6681,-2.0378,-2.3036,-3.0458,-2.6545,-2.2819,-2.6915,-2.4586,-2.2257\n-1.7234,-1.3875,-1.0923,-1.1149,-1.9567,-1.7911,-1.175,-0.40976,1.2222,0.90043,1.1497,0.7542,0.83845,1.5201,1.7926,0.42762,0.19833,0.21616,0.55841,0.81797,2.3012,3.9037,5.8495,3.3733,4.1922,3.9654,2.8608,2.4484,1.5371,1.1624,0.69524,0.94745,1.0166,-0.32539,-0.6232,-0.96167,-2.2139,-1.858,-2.0421,-1.7039,-2.6111,-2.8702,-3.0159,-2.2397,-2.3005,-1.8534,-1.6693\n-2.4162,-2.3624,-2.2434,-1.9235,-1.9259,-1.6115,-1.3043,-0.87573,0.85984,0.038258,0.19294,-3.1585,-3.9536,0.95754,0.34302,1.6495,2.5372,0.75723,0.48061,-0.47306,2.1369,4.4048,5.0482,1.3024,1.9937,2.5618,1.4744,0.12696,-0.1052,0.51921,0.60555,0.76684,1.4648,0.97485,-0.2408,-1.0367,-1.4195,-1.0613,-1.6475,-1.7697,-2.2187,-2.5787,-2.8253,-2.4193,-2.0174,-1.4236,-1.0515\n-2.7399,-2.3962,-2.7294,-2.4264,-1.7941,-1.5716,-2.0516,-1.1382,0.060486,0.25465,1.1137,-0.25071,-3.2521,0.20345,-0.071785,0.26514,1.0268,0.81208,0.79949,0.52183,2.093,2.7159,1.6744,1.9575,2.0477,2.148,0.91265,0.7539,1.3817,1.9151,0.9781,1.2557,3.0158,1.8276,0.021297,-0.34089,-0.84264,-0.3808,-0.78472,-1.2988,-1.4454,-1.5118,-1.8168,-2.0606,-1.8431,-1.286,-0.97114\n-2.4991,-2.217,-2.3691,-2.3786,-2.2266,-1.8287,-1.4557,-0.25624,0.13961,0.49994,0.58761,-0.13921,-0.53827,-0.2398,1.7141,0.3331,0.44143,1.5435,1.4864,1.2562,1.6691,1.5747,2.2113,2.5909,NaN,2.7999,0.45066,1.9506,4.7355,3.5852,1.3299,2.7116,3.3802,2.2087,0.96141,0.53288,-0.042801,-0.52868,-0.68972,-0.95465,-0.63422,-0.72578,-0.76295,-1.4331,-1.5215,-1.0629,-0.97502\n-1.7279,-2.0924,-2.5729,-2.4298,-1.4732,-1.7401,-1.1602,-0.10933,-0.036453,-0.2899,-0.50381,-0.60684,-0.80606,0.030796,2.1461,0.069279,0.21722,1.1646,1.6917,1.699,-1.2903,0.34578,2.4919,NaN,NaN,NaN,NaN,NaN,NaN,1.4857,1.4482,2.186,1.5943,1.1121,0.58643,-1.5923,-0.84141,-1.0726,-0.45771,-0.7118,-0.72666,-0.18303,0.071091,-0.88191,-0.95956,-0.91936,-1.1198\n-1.501,-1.9392,-2.2323,-2.4564,-1.8276,-1.781,-1.4937,-0.76574,-1.1346,-1.2852,-1.4975,-1.9616,-2.1635,-2.7413,-2.038,-0.87178,-0.097988,1.5329,1.7056,2.24,1.3162,2.1683,2.6667,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.753,2.0598,1.0199,-4.4848,-4.834,-3.8277,-2.0385,-0.77343,-0.71489,-0.87861,-0.45732,0.19257,-0.4789,-0.19725,-0.29066,-0.75554,-1.0993\n-1.2148,-1.5926,-1.886,-1.6986,-1.3915,-0.9267,-1.2828,-1.6022,-1.6961,-1.4072,-0.95808,-2.439,-2.3999,-2.8091,-1.7368,-0.88833,0.58805,1.2104,2.0628,2.944,1.8493,0.50662,-0.14584,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4563,1.9262,1.764,-1.8459,-3.4251,-2.3436,-0.99506,-0.60469,-0.82177,-0.83808,-0.79416,-0.50772,-0.25651,0.05418,0.37451,0.32968,-0.48781\n-1.1352,-1.3513,-1.2462,-1.0891,-0.97188,-0.57738,-0.82946,-1.7683,-1.8495,-1.9081,-2.0462,-2.8668,-2.9587,-2.5069,-1.6774,-0.71936,1.2357,8.017,3.1311,0.28799,0.50663,-0.90098,-1.6666,-2.4582,0.94019,2.9519,0.69337,NaN,2.1073,1.7293,1.2945,1.7915,1.7696,0.50087,-0.26856,-0.012669,0.024609,-0.25214,-0.49622,-0.81407,-1.0584,-0.78462,-0.25779,-0.29428,0.45159,0.26836,-0.42492\n-1.1789,-0.94323,-0.65557,-0.72183,-0.7412,-0.68381,-0.80883,-1.8458,-1.4134,-1.5156,-1.6809,-1.9199,-2.5953,-1.8325,-1.586,-0.31111,0.47207,1.8462,2.5565,0.022106,1.6389,0.0022621,-0.71564,0.61403,2.004,0.15286,-1.676,0.38968,1.4463,1.3179,1.572,1.5985,1.3303,0.83113,0.52883,0.65837,0.44246,0.30494,-0.25252,-1.0464,-1.1225,-0.65788,-0.59045,-0.089069,0.36762,0.41602,-0.33352\n-0.62315,-0.58001,-0.54974,-0.76921,-0.60077,-0.61793,-0.8964,-1.383,-1.219,-1.1393,-1.4841,-1.5168,-2.5024,-2.5825,-1.6188,-1.4959,-0.87374,0.0091896,-0.090488,0.35072,2.4762,0.31363,0.34932,1.0553,0.95501,-0.247,-0.0975,0.76511,1.1298,1.1812,0.73426,0.21972,0.88903,0.45528,0.90069,0.3075,0.35797,0.35563,-0.073921,-0.97458,-1.2032,-1.0534,-0.71379,-0.066898,-0.11029,0.050617,-0.094112\n-0.50814,-0.6344,-0.62034,-0.71654,-0.42514,-0.61611,-0.95188,-1.2665,-1.1808,-1.2167,-1.9327,-2.3137,-4.1298,-3.3271,-2.3091,-1.9184,-2.3039,-0.2187,0.61652,0.76747,1.5747,1.5143,0.98164,0.47505,-0.24255,-0.77163,0.68693,0.52044,1.007,1.337,0.92373,0.90578,0.87931,0.47678,0.45058,-0.15749,-0.1249,-0.75811,-0.54895,-0.86523,-1.3741,-1.133,-0.76069,-0.49778,-0.38762,0.067112,-0.16042\n-1.0635,-0.99569,-0.86263,-1.0245,-0.88282,-0.89215,-1.319,-1.8626,-2.0269,-2.4158,-2.7012,-2.3237,-3.9548,-2.754,-1.9765,-1.6391,-0.18551,-1.1107,0.038132,0.6029,1.2892,1.2117,1.7229,0.026714,0.32266,1.2704,0.1622,-0.073536,0.56345,0.67931,0.71471,1.1202,0.54039,0.73278,0.70965,0.55159,-0.047306,-2.2991,-1.452,-0.78207,-1.0377,-0.94822,-0.54619,-0.39145,-0.51004,-0.48318,-0.88663\n-0.87584,-0.94471,-0.55149,-1.3221,-1.2068,-1.1177,-1.6693,-2.2573,-2.7446,-2.7923,-2.6476,-2.1832,-1.9998,-1.2653,-0.036091,0.71063,0.94252,1.3359,1.4781,1.3657,1.2786,1.5111,-1.7015,-1.2399,0.98834,1.3718,0.068356,-0.24972,-0.22152,-0.25911,-0.22196,-0.11805,0.29626,0.41599,-1.2642,-1.6092,-1.1674,-2.4978,-1.0034,-0.74791,-0.70539,-0.5261,-0.43155,-0.49003,-0.78529,-1.0095,-1.2999\n-0.83226,-0.85023,-0.76831,-1.2581,-1.3242,-1.9038,-2.4703,-2.7696,-2.7989,-1.7632,-1.8726,-1.4813,-1.1102,-0.45861,0.69023,1.4091,0.84834,1.2791,1.3124,0.92581,1.3981,-0.25315,-3.6072,-1.4268,-0.20074,0.73485,0.19662,0.29533,0.17139,0.018177,0.18442,0.32212,-0.34024,-0.54847,-2.5486,-1.4459,-1.6241,-1.6291,-0.87323,-0.47424,-0.33345,-0.21443,-0.61733,-0.45217,-1.0829,-1.537,-1.6546\n-0.99836,-0.52591,-0.45909,-0.78023,-1.1708,-1.5662,-1.7489,-1.7487,-1.1993,-1.1088,-1.3666,-1.0115,-0.8928,-0.65883,0.79187,0.90195,0.83754,0.71701,0.035015,0.24831,0.71406,-0.28399,-3.1451,-2.2887,-0.62458,-0.71139,-0.036442,0.076191,0.1815,0.30561,0.66588,0.2072,-0.64685,-0.28714,-0.33818,-0.77466,-1.3279,-0.78011,-0.92063,-0.63338,-0.64466,-1.053,-0.85245,-0.13678,-0.79782,-1.3605,-1.4173\n-0.7008,-0.30055,-0.26104,-0.86743,-0.77718,-0.79539,-0.9203,-1.0365,-0.9604,-0.94827,-0.84607,-0.77266,-0.56548,-0.23596,0.14819,0.43611,0.65869,1.0099,0.28936,0.406,1.0471,0.31844,-2.2476,-1.4772,-0.93639,-0.68128,-0.30063,-0.11829,0.22534,0.54712,0.27299,-0.045361,-0.24728,-0.011112,-0.31468,-1.1025,-0.38483,-0.37473,-1.0738,-0.84875,-1.0989,-1.979,-1.5943,-1.3192,-0.59259,-0.83286,-0.89449\n-0.44011,-0.31457,-0.3938,-0.71493,-0.78186,-0.52639,-0.45913,-0.86271,-1.2641,-1.073,-0.65169,-0.289,0.35223,0.51722,0.49311,0.50064,0.65992,1.057,1.2944,1.2003,1.0634,0.88785,-0.41848,-0.58653,-0.57588,-0.75303,-0.30206,0.18105,-0.10711,-0.52374,-0.13848,0.049732,-0.36369,-0.9176,-1.1278,-1.1441,-0.7436,-0.86645,-1.1845,-0.50913,-0.83773,-1.6801,-1.2438,-1.1806,-0.86743,-0.85349,-0.65589\n-0.76995,-0.54161,-0.83789,-0.69439,-0.81721,-0.90994,-0.41172,-0.90819,-0.61503,-0.55396,-0.2332,0.39809,0.58359,0.49131,0.71017,0.67952,0.55528,0.77526,1.0805,1.8905,2.0204,3.1075,1.6996,-0.06287,0.13468,0.12352,-0.032169,-0.40732,-0.40736,-0.70562,-0.11657,-0.083389,-0.48732,-0.8584,-1.0868,-1.1155,-0.84655,-1.1509,-0.9917,-0.16027,-0.19615,-0.73375,-1.4115,-0.83972,-0.73124,-0.82636,-0.8877\n-0.73089,-0.68702,-0.70443,0.0275,-0.81606,-0.68806,-0.11435,-0.084301,-0.43252,-0.41426,-0.29096,0.2447,0.41856,0.47505,0.31569,0.22349,0.35161,1.1447,0.43528,-0.61083,3.7458,2.2488,0.14579,0.29926,0.95894,1.0491,0.33011,-0.67943,-0.23429,-0.81258,-0.57436,-0.49851,-0.86863,-0.99305,-1.0445,-1.0141,-0.44339,-0.82338,-0.86119,-0.25401,-0.31195,-0.060776,-0.2374,-0.99342,-0.75528,-0.79447,-0.78032\n-0.45134,-0.56715,-0.52296,0.028156,-0.28244,-0.63621,-0.37134,-0.75444,-0.74086,-0.5522,-0.25572,0.052818,0.37249,0.39361,0.24994,0.32676,0.68201,0.53701,-1.066,-1.9781,-2.0949,-0.68467,0.079098,0.229,0.68224,0.70366,-3.3074,-4.3516,-0.16692,-0.83331,-0.95152,-0.66697,-1.0066,-0.87085,-1.0336,-0.50987,-0.35693,-0.60186,-0.15234,0.050182,-0.04612,0.18042,-0.084316,-0.79219,-0.56367,-0.45226,-0.54455\n-0.7095,-0.73968,-0.46009,-0.32028,-0.46187,-0.82245,-0.68532,-0.86542,-0.41053,-0.37397,-0.10271,0.26802,0.51237,-0.037434,0.50677,0.58458,1.5908,0.1733,0.017509,-0.73005,-1.5249,-0.53926,-0.28031,0.24799,1.1518,1.5769,0.057201,-0.057335,-0.16282,-1.0687,-0.69616,-0.75623,-0.78007,-0.75368,-0.78075,-0.3429,-0.1958,0.033436,0.26088,0.26833,0.11813,0.1676,1.1309,-0.41343,-0.68848,-0.32669,0.081024\n-0.90611,-0.84275,-0.84396,-0.56131,-0.87536,-0.94672,-0.94104,-0.88837,-0.68472,-0.90835,-0.10376,0.2307,0.12642,-0.21895,0.49527,-0.25409,-0.65493,-0.063797,-0.033741,-0.18803,-0.92568,-0.89184,-0.48938,0.33124,1.484,1.8903,1.7556,1.5158,1.2589,-0.24055,-0.88606,-0.67276,-0.67352,-0.73109,-0.8956,-0.086826,0.10577,0.18517,0.1186,0.14476,0.1355,0.64907,0.91843,0.011005,-0.56658,-0.54433,-0.021511\n-0.77029,-0.7362,-0.6829,-0.57613,-0.42554,-1.0662,-0.91408,-1.2037,-0.97976,-0.83029,0.047543,-0.8105,-0.71734,-0.72862,-0.66078,-1.3882,-1.4169,-1.2955,-1.6549,-1.3627,-0.65636,-0.72162,0.060787,0.49297,0.92228,1.3791,1.7763,2.7488,2.3315,0.88599,-0.65786,-0.53003,-0.55159,-0.82692,-0.78477,-0.19629,-0.50203,0.021015,-0.15966,0.36802,0.2662,0.22969,0.41431,-0.3195,-0.29832,-0.49776,-0.29397\n-0.78109,-0.24027,-0.87457,-0.2843,-0.73274,-1.0012,-0.9825,-0.89467,-0.54077,-0.57318,-0.27694,-0.98122,-1.8013,-1.7083,-1.4228,-1.3829,-1.1566,-0.96193,-0.037823,-1.3701,-0.46457,0.27209,0.47458,0.76475,1.204,1.4706,2.5927,2.6128,0.49694,0.051338,-0.47391,-0.82276,-0.7047,-0.67415,-0.37039,-0.57081,-0.33601,-0.20797,-0.20615,0.36293,0.34901,0.047306,-0.18372,0.01049,-0.13801,-0.40577,-0.52869\n-1.1328,-1.5832,-0.87136,-0.22691,-0.44561,-2.3723,-2.2697,-1.4002,-1.2775,-1.0123,-0.85742,-0.902,-2.3777,-2.6984,-2.0049,-1.893,-1.2516,-0.41467,-0.20741,-1.2098,-0.48715,0.33084,0.5029,1.193,1.6493,1.6726,2.7991,2.3783,0.9591,0.012867,-0.14307,-0.85468,-1.5433,-0.41336,-0.32635,-0.62049,-0.3097,-0.31025,-0.024719,0.27518,0.10952,0.10854,0.42739,0.50347,-0.31549,-0.45444,-0.4549\n-0.75902,-1.5548,-0.81378,-0.16135,0.42761,-3.069,-2.6285,-2.0716,-2.1866,-2.1646,-1.9616,-2.0147,-2.2689,-1.5694,-2.0268,-2.5588,-2.4034,-0.82398,-0.70523,-0.79789,-0.48668,-0.055489,0.23032,0.77391,1.6269,1.6334,1.9684,1.3352,0.77799,0.69139,0.10472,-0.11791,-0.67144,-0.18629,-0.24074,-0.43193,-0.24413,-0.20255,-0.020855,0.018864,-0.11042,-0.14766,0.45829,-0.2119,-0.54666,-0.8794,-0.69573\n-0.83638,-1.7345,-1.9821,-1.1243,-0.80494,-0.0050354,-1.6674,-1.4305,-1.7564,-3.3814,-2.2147,-2.0009,-1.9089,-1.712,-1.7491,-2.176,-2.6547,-1.5962,-0.53895,-0.2513,-0.1308,-0.26765,0.47649,0.73113,1.5525,1.7637,1.9226,0.16951,0.72792,0.79377,0.28659,0.10411,-0.36062,0.1474,-0.02422,-0.16422,-0.058918,0.087151,0.35997,0.031452,0.0085564,0.10215,0.42657,-0.35905,-0.36469,-0.77462,-0.69007\n-1.3904,-1.1444,-1.5098,-1.9473,-2.0019,-1.8771,-1.9708,-0.93481,-1.5603,-2.2859,-1.5159,-1.3719,-1.441,-1.2848,-0.52445,-2.2606,-2.9632,-1.3876,-0.46912,0.21051,0.1463,-0.086826,0.33186,0.39888,0.8568,1.6769,2.0879,2.0562,0.76899,0.18039,-0.68743,-1.0243,-0.36641,0.16061,-0.13663,-0.037731,0.010872,0.23655,0.33503,0.18338,0,-0.12941,-0.2933,-0.54105,-0.78511,-0.801,-0.37819\n-1.6281,-0.50949,-0.83835,-2.8079,-3.7578,-6.9376,-2.3281,-1.4907,-1.1597,-0.35901,0.0020256,-0.14803,-0.21777,-0.13261,-0.37838,-1.0501,-2.5182,-1.629,-0.32798,-0.056004,-0.257,0.36054,0.085087,0.11518,0.79406,1.5787,2.0244,1.0741,1.3679,0.12769,-0.42468,-0.78082,-0.50682,0.19783,-0.061855,0.20595,0.0085487,-0.087433,0.24185,0.34187,-0.15959,-0.54139,-0.67571,-0.82837,-0.49231,-0.50219,-0.6674\n-0.59006,-0.67837,-1.0826,-1.966,-2.4844,-3.7881,-2.6923,-2.2616,-1.5182,0.63649,0.79525,1.0513,1.2639,0.55204,-1.4112,-1.5386,-2.9219,-1.9776,-0.8092,-0.63447,-0.30817,0.31474,0.53962,0.37766,0.35133,1.1597,1.3306,1.1421,1.9603,1.2546,0.75917,-0.030025,-1.2684,-0.26484,-0.0045509,-0.24498,-0.14453,-0.29888,-0.17339,-0.20572,-0.34581,-0.56847,-0.75272,-0.4436,-0.12223,-0.26526,-0.48859\n0.006073,-0.33792,-0.45714,-0.86081,-0.57565,-0.50823,-2.105,-2.2501,-1.034,0.0094948,-0.071239,0.81195,0.92461,0.6866,-1.045,-1.7207,-2.1642,-1.2037,-0.23553,-0.32016,0.4106,0.62126,0.61931,0.27855,-0.053455,0.33107,0.81833,0.92889,0.92159,0.86567,0.78405,0.58963,-0.19124,-0.046528,-0.05167,-0.74726,-0.38427,-0.45401,-0.53891,-0.67138,-0.53778,-0.59105,-0.71232,-0.069996,0.28723,-0.096493,-0.61255\n0.099041,0.28418,0.014809,0.48564,0.98033,-1.0342,-2.3163,-1.6571,-0.81945,-0.50705,0.3469,-0.66703,-0.40262,-0.15162,-0.55485,0.10252,-1.5871,-1.3483,-0.13869,-0.70386,-0.55391,-0.22062,0.52742,0.49874,0.00096512,-0.0032387,0.65546,0.91516,0.75602,0.45013,0.71176,0.52497,0.39134,-0.4887,-0.53096,-0.56321,-0.81771,-0.70326,-0.72191,-0.68917,-0.39685,-0.39526,-0.27832,-0.17029,0.011597,0.21899,-0.50122\n-0.8925,-0.8906,-1.2843,-0.27548,-1.3259,-2.7239,-2.8242,-3.4424,-3.3452,-2.432,-2.3697,-0.79816,-0.045589,-1.584,-1.0974,-0.55105,0.80981,-1.8998,-0.64407,-0.81916,-0.46697,-0.11667,0.035851,-0.071083,0.20405,0.47051,0.98987,1.3187,1.3922,0.78144,0.071056,0.23592,0.3729,-0.51954,0.014431,-0.54136,-1.1748,-1.0318,-0.834,-0.84152,-0.57621,-0.25082,-0.2043,-0.40461,-0.16117,-0.045856,0.0052681\n-1.1959,-0.90321,-0.68156,-0.45647,-2.0527,-1.7667,-2.33,-2.3872,-1.9713,-1.3046,-1.2205,0.4692,-0.14386,1.454,3.3219,0.20131,-2.3345,-1.5812,-0.58301,-0.43575,-0.058643,0.076283,0.30833,0.31061,0.43718,0.57522,0.98351,1.4514,2.1374,1.0511,-0.2932,-0.32828,-0.057964,-0.40348,-0.23319,-0.55633,-1.042,-1.0324,-0.69892,-0.35231,-0.35167,-0.12228,-0.093624,0.041397,0.13528,0.19292,0.82296\n-1.1247,-1.6203,-1.1168,-1.3357,-1.7243,-1.0466,-1.4616,-1.7923,-1.4499,-1.4048,-0.60263,-1.1788,-2.1698,1.4106,2.0877,0.84886,-2.482,-1.0904,-0.47901,-0.34145,0.23696,-0.22618,0.15477,-0.0028038,0.10598,0.22609,0.79031,1.3243,1.6816,0.36721,-0.55548,-0.52105,-0.34549,-0.6628,-0.78986,-0.92221,-0.54242,-0.42471,-0.32509,-0.20831,-0.054543,-0.027573,-0.21022,0.23683,0.28345,0.84582,1.6908\n-0.47649,-1.1687,-1.2183,-1.1074,-1.1821,-0.58749,-0.635,-1.2957,-1.8363,-1.8068,-2.7156,-0.94712,-0.4325,-0.15313,0.57969,0.12682,-2.238,-0.92693,-0.45406,-0.2415,-0.56324,-0.45913,-0.15413,-0.095119,0.2676,0.38556,0.055195,0.35501,0.40246,-0.10235,-0.32327,-0.51103,-0.70211,-0.82202,-1.2905,-1.1022,-0.82991,-0.38211,-0.29111,-0.11179,0.063503,-0.071484,0.071728,0.24094,0.46569,1.0331,0.62976\n-0.75434,-0.7547,-0.063282,-0.47077,0.49036,1.5707,0.41945,0.19199,-1.0421,-1.7641,-1.5455,-1.1916,0.039242,1.2607,0.83892,-1.411,-1.4677,-1.5705,-0.37078,-0.46459,-0.26025,-0.41679,-0.40377,-0.014545,0.19089,0.31778,0.25729,0.31836,0.021744,-0.014606,-0.18572,-0.51374,-1.9254,-1.5121,-1.206,-1.336,-1.3349,-0.53897,-0.29947,-0.10363,-0.10883,0.033741,0.056416,0.035892,0.32985,0.2675,-0.142\n-1.5175,-0.8947,0.34802,0.3218,2.0226,1.9112,2.5951,1.9736,0.25956,-0.592,-2.1235,-1.8069,-1.7476,-1.7302,-1.7043,-1.7512,-0.88523,-1.6041,-0.57534,-0.29936,-0.1012,-0.46297,-0.24449,0.059757,-0.10356,0.2832,0.4302,0.56117,0.59774,0.064632,-0.48179,-0.86798,-1.4236,-0.61051,-0.48942,-0.83908,-1.0542,-0.65369,-0.52313,-0.3758,-0.25768,-0.46424,-0.50913,-0.44477,-0.54538,-0.9949,-1.0004\n0.0022011,0.68038,1.5276,2.0189,2.7424,1.8284,1.804,0.079494,-0.41653,-0.82995,-1.262,-1.1426,-0.60403,-0.44636,-1.3214,-1.8891,-2.0014,-1.7831,-0.80143,-0.9654,-0.53129,-0.8133,-0.10565,-0.11512,-0.11119,0.29816,0.35094,0.090405,0.2394,0.39609,-0.039768,-1.4633,-1.1812,-0.081997,0.13345,-0.20189,-0.56022,-0.57955,-0.64055,-0.80811,-0.42261,-0.54485,-0.5695,-0.80102,-0.84142,-1.1939,-2.4994\n1.2247,1.2866,2.4969,3.9353,3.4384,2.0471,0.12547,-0.64277,0.078358,-0.37654,0.026245,-0.55862,-0.64952,-0.75812,-1.3571,-1.8476,-0.5254,-0.23575,-0.50856,-0.19925,-0.77186,-0.79663,-0.66826,-0.61229,-0.48566,-0.057766,0.29107,0.10965,0.054646,-0.24328,-0.94002,-1.3367,-1.1256,0.23625,0.52887,0.16358,-0.21582,-0.69378,-0.79391,-0.93245,-0.76701,-0.59299,-0.60543,-0.88685,-0.95099,-1.2026,-2.7261\n1.2444,1.9386,2.237,2.2416,2.0405,2.4852,0.21502,-0.16541,-0.32285,0.58179,1.6085,0.83323,0.84019,-0.13181,-0.72077,-2.1016,-1.9262,-0.67198,-0.64918,-2.531,-1.801,-1.2762,-0.92603,-1.1578,-1.1929,-0.97362,-0.51273,0.092937,-0.18729,-0.61663,-0.95446,-0.83681,-0.58847,-0.38784,-0.33342,-0.56308,-0.94058,-0.78967,-1.0029,-0.80455,-0.57742,-0.4498,-0.57418,-0.69588,-0.82117,-1.3206,-2.1249\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070604-070709.unw.csv",
    "content": "0.72481,0.18628,-0.22943,0.18535,-0.23826,-1.0149,-0.67847,-0.55859,-0.88567,-1.511,-1.6266,-1.7985,-1.7174,-1.3005,-1.6729,-1.5908,-1.8571,-2.6697,-2.8359,-2.7803,-2.5944,-2.3465,-2.375,-2.7315,-3.1085,-3.1377,-3.2911,-3.2281,-2.4831,-2.5242,-3.04,-3.1057,-3.2864,-3.6185,-3.8211,-4.3395,-3.6549,-3.3158,-3.7329,-3.4047,-3.2563,-2.9374,-3.3708,-3.3051,-2.6217,-2.0968,-1.5996\n0.733,0.5207,-0.25652,-0.60046,-0.67935,-0.93881,-0.85803,-0.68768,-0.83272,-1.0948,-1.6714,-1.69,-1.3255,-1.0798,-1.2212,-1.5856,-1.9685,-2.5034,-2.6181,-2.5652,-2.6797,-2.4981,-2.6465,-2.9351,-3.1682,-3.1782,-2.9737,-2.46,-1.9719,-1.6717,-1.8543,-1.8819,-2.7127,-3.9175,-4.3458,-4.4265,-3.6751,-3.42,-3.7531,-3.7478,-3.0455,-2.4391,-2.0723,-1.7158,-1.5508,-1.7941,-1.9631\n1.099,0.4413,0.33651,-0.36806,-0.49125,-0.33611,-0.56408,-0.59627,-0.53577,-0.84853,-1.4579,-1.818,-1.4101,-1.2486,-1.194,-1.3809,-1.6947,-1.5965,-1.6391,-1.8925,-2.0371,-2.3719,-2.5464,-2.7139,-3.1276,-3.0631,-2.7832,-2.6535,-1.8587,-1.4132,-1.406,-2.0098,-2.5221,-3.6203,-4.1571,-3.7811,-3.7233,-3.5649,-3.377,-3.2046,-2.8515,-2.4669,-2.2857,-1.7275,-0.82241,-0.59437,-0.62062\n0.56382,1.3248,0.98265,-0.21929,-0.12394,-0.32641,-0.22752,-0.41198,-0.71597,-0.82482,-1.1864,-1.2076,-1.1169,-0.97581,-0.88732,-0.94261,-1.3316,-1.5646,-1.5942,-1.7123,-1.9363,-2.1712,-2.4991,-2.7105,-2.7005,-2.6283,-2.3354,-1.9825,-1.845,-1.8721,-1.9613,-1.8585,-1.5068,-2.3511,-3.5681,-3.85,-3.3157,-3.1845,-2.6071,-2.6652,-2.8951,-2.6437,-2.2983,-1.5482,-0.73888,-0.19846,-0.27115\n0.5019,2.153,1.7536,-0.27855,-0.3456,-0.34516,-0.023035,0.45666,0.028631,-1.1628,-1.2924,-0.71099,-0.64768,-0.7572,-1.046,-0.85721,-1.1268,-2.109,-2.01,-1.6628,-1.1997,-1.674,-2.0761,-2.2402,-2.2496,-2.3874,-2.1697,-1.8783,-1.6203,-1.7406,-1.9674,-1.7313,-2.8422,-3.0542,-2.2905,-2.5661,-2.8945,-2.4347,-1.9722,-1.7993,-2.3998,-2.2369,-1.7654,-1.3477,-0.94959,-0.50988,0.55503\n0.97672,1.2509,0.42596,-0.63732,-0.75622,-0.88081,-0.90349,-0.5533,-0.073421,-0.22355,-0.20158,0.0071516,-0.34215,-1.0119,-1.2744,-1.2906,-1.3653,-1.8579,-2.351,-2.0458,-1.5587,-1.3872,-1.4063,-1.7121,-1.6546,-1.376,-1.525,-1.4851,-1.127,-1.2146,-1.4316,-1.493,-1.7628,-2.2423,-1.9526,-1.4546,-1.83,-1.8019,-1.7316,-1.6618,-1.8569,-2.062,-1.7623,-1.2086,-0.48468,0.4908,1.2024\n1.8515,2.9198,2.7813,-0.089751,-0.08126,-0.21541,-0.28324,-0.1037,-0.32151,-0.060039,-0.14918,0.16949,-0.22684,-1.0458,-0.96323,-1.0349,-1.1608,-1.3529,-1.6814,-1.7453,-1.1586,-1.1801,-0.9032,-1.2602,-1.162,-0.63822,-0.84512,-1.0423,-0.95264,-1.665,-1.9591,-1.8125,-1.6528,-1.9136,-2.1258,-1.9376,-1.5746,-1.5015,-1.2375,-1.554,-2.1092,-1.8805,-1.2022,-0.81916,-0.89706,-0.23995,1.8268\n0.79356,0.79166,0.37096,0.051135,0.03348,0.096702,0.4592,0.337,0.14014,-0.049779,-0.1392,0.087364,0.0416,-0.7846,-0.93984,-0.92411,-0.87209,-0.8671,-0.99722,-1.0781,-0.36589,-0.61064,-0.84782,-0.88743,-0.83436,-0.88122,-0.68413,-0.9129,-1.4511,-1.8272,-1.6245,-1.462,-1.5802,-1.7592,-1.8746,-2.0137,-1.7814,-1.705,-1.557,-0.93294,-0.44263,-0.58424,-0.28823,0.957,1.8677,2.025,2.9934\n0.94967,0.71095,0.46785,-0.0716,-0.37554,0.079962,0.68595,0.51518,0.50571,0.35946,0.43818,0.6978,0.21489,-0.4484,-0.51945,-0.54648,-0.43338,-0.16255,-0.29952,-0.28366,-0.057645,-0.078362,-0.1754,-0.34945,-0.58205,-0.34115,-0.60297,-0.99882,-1.2183,-1.6514,-1.6345,-1.0904,-1.1958,-1.5354,-2.1423,-2.2934,-1.7853,-1.6377,-1.5301,-1.081,-0.25956,0.74722,0.31055,-0.01274,1.1221,2.082,2.7596\n1.3569,1.5192,1.468,0.0016489,-0.081367,0.43517,0.64826,0.78609,0.97605,1.0088,0.9491,1.0789,0.79815,0.17223,-0.23633,0.042419,0.33314,0.5091,0.84639,0.55764,-0.016728,-0.13355,-0.34698,-0.20383,-0.17589,-0.4336,-0.55396,-0.70528,-0.73048,-0.71155,-0.89869,-1.0373,-1.0877,-0.74827,-0.42452,-1.2563,-1.0737,-1.1524,-0.93961,-0.72784,-0.56092,-0.47054,-0.29174,-0.46973,0.72235,1.7505,2.5998\n0.71864,0.78149,-0.33148,-0.042798,-0.059325,0.54245,0.47283,0.79332,0.81917,0.96652,1.2403,1.2239,1.2584,0.8517,0.19118,-0.61597,-0.055055,0.38155,0.085479,0.065996,0.093156,-0.38835,-0.5286,-0.1939,-0.061159,0.030206,-0.34558,-0.52966,-0.43012,-0.43151,-0.54761,-0.84791,-1.2329,-1.4722,-1.391,-1.2041,-0.64971,-0.12943,-0.12715,0.11063,0.21675,0.16802,-0.10573,-0.36006,-0.037305,1.3937,2.9072\n0.6369,0.90092,0.71038,0.73324,0.57821,0.92772,0.61027,1.0224,1.2992,1.4923,1.838,1.9079,1.7544,1.4515,0.98337,0.46707,-0.035693,0.71164,1.0348,0.96235,0.45384,-0.14651,-0.58732,-0.34839,-0.14831,-0.10708,-0.60428,-0.59109,-0.56508,-0.59296,-0.66702,-0.84992,-1.0114,-0.96609,-0.61295,0.28898,0.23305,-0.1571,0.43132,0.76464,0.90837,1.1388,1.1937,0.12807,0.2341,-0.25544,NaN\n0.72136,0.25251,-1.107,-0.45316,1.1808,0.94911,0.88835,0.65171,1.2739,1.8694,2.2969,2.3922,1.9071,1.2868,1.4193,1.3374,1.0758,0.93929,0.98623,0.77956,0.16326,0.088853,-0.1453,-0.090178,0.23284,0.11645,0.16081,0.15996,-0.55546,-0.79529,-0.46404,-1.0344,-0.92393,-0.40806,0.11249,-0.42602,-0.61376,-0.20581,-0.21043,0.68042,1.5234,1.7232,1.3497,0.2357,1.1067,NaN,NaN\n1.2087,1.4597,1.3568,1.6412,1.6735,1.9048,1.7601,1.1153,1.335,1.5593,1.7404,2.07,2.3387,1.4883,1.5027,1.8013,1.65,1.6506,1.5188,1.0129,0.60716,0.5102,0.27437,0.33569,0.56796,0.78005,1.2599,1.8691,1.6559,0.26462,-0.1504,-0.44687,-0.6766,-0.30586,-0.51491,-0.20649,0.78266,1.5814,1.4728,2.2035,2.593,2.5052,2.7175,3.2055,2.8922,NaN,NaN\n1.5385,1.9559,2.3435,1.7495,1.3118,0.98596,1.1823,1.2243,1.2902,1.4072,0.87218,1.1937,1.7725,1.9009,1.4085,1.239,1.3984,1.9989,1.7383,0.88204,-0.21116,-0.31039,0.13931,0.5303,0.6933,0.44634,0.26772,0.87464,0.50689,0.26348,-0.11082,-0.45738,-0.81784,-0.87486,-0.45079,-0.34655,0.57255,0.98592,1.6925,3.2572,5.0434,3.9264,4.1549,3.0741,2.9785,NaN,NaN\n0.57883,1.2837,1.7459,1.3767,1.5535,1.0091,0.82083,0.89867,1.1003,1.3232,0.81651,0.78105,1.8833,2.182,2.0195,1.5692,1.6986,1.8408,1.4662,1.3656,1.0375,0.50623,0.82291,1.491,1.961,1.8715,1.233,1.0625,0.84948,0.57891,0.49836,0.64748,1.0198,1.031,0.24667,0.38525,0.10633,0.67616,0.85388,0.62065,1.7922,2.6175,4.2061,4.5002,4.6189,NaN,NaN\n0.26346,0.57088,1.0237,1.0214,0.71476,1.1431,1.2122,1.3259,1.8526,1.8727,1.3824,1.5659,2.7004,2.5232,2.1627,2.1617,2.104,2.5671,1.4528,0.92254,0.72378,0.48448,1.63,2.0714,1.5969,1.4286,1.1497,0.95813,0.81709,0.50719,0.16897,-0.06331,0.20703,0.19442,0.4039,0.67677,-1.3111,-0.81646,1.5994,1.5901,2.4972,1.7812,1.703,2.5042,4.6345,NaN,NaN\n0.91372,1.88,1.7323,1.6405,1.202,1.8245,1.9554,1.8355,2.2096,2.133,1.7028,2.5231,3.3946,3.5823,3.1324,3.0082,3.1074,2.8216,1.0962,-0.31399,-0.42986,-0.34485,0.51053,0.93829,-0.45123,0.025266,0.70173,0.83997,0.83829,0.25173,-0.013946,0.003314,-1.7054,-1.6978,-1.509,-0.29667,-4.8162,-5.1514,-4.4335,2.2939,2.8753,3.0752,2.058,0.802,2.3519,NaN,NaN\n1.4735,1.8066,1.8876,3.3577,2.5034,1.0875,1.1879,1.2163,2.1315,2.1335,2.1507,2.884,3.205,2.7298,2.5826,3.0286,3.3152,2.6872,2.1258,1.8955,0.88846,-0.17468,-0.15314,0.2961,0.19116,0.63052,0.70047,0.60694,0.71534,1.0231,0.73144,1.2973,-0.50348,-2.2569,-1.7879,0.016772,0.69186,0.55302,0.090667,1.7087,1.8825,2.1721,1.6016,0.53313,1.2505,1.4021,0.90182\n3.1834,2.3217,3.6556,4.4137,5.0385,4.8863,2.643,2.0988,2.586,2.5579,2.8729,3.0308,2.8794,2.4265,2.1218,2.7913,3.333,3.2355,2.7917,2.3256,2.4974,2.3815,1.4062,0.22914,0.67411,1.0726,1.1471,1.4711,1.6967,1.3636,1.2178,1.087,3.6644,1.8007,1.5149,1.7612,2.4217,2.2857,-0.98446,-0.74141,0.247,1.1755,1.081,0.96299,0.74563,-0.37419,-0.2446\n3.1107,2.4339,2.2318,2.7535,3.142,2.1469,2.2701,2.0463,2.3241,2.4668,2.721,2.3382,2.583,3.302,3.9166,3.5727,3.4645,3.0072,2.4832,2.066,1.5361,2.4542,2.3208,1.0688,1.1703,1.784,1.5719,0.73086,0.66061,1.015,0.82418,0.37923,2.9595,3.321,5.0568,2.1346,2.7699,3.9772,1.7122,-0.3899,-0.067024,1.0276,1.3536,1.9724,1.3059,0.67214,0.74798\n2.5468,2.3927,3.4377,4.1827,3.7882,1.8644,1.1685,2.9144,2.5412,3.096,3.2541,3.0685,2.7492,2.11,0.51557,3.4687,2.9642,1.6723,0.71244,-0.53259,0.24967,0.70908,1.3654,1.2858,1.7203,2.0709,2.1119,1.8625,0.98065,0.8525,1.4734,1.0854,1.4752,1.4643,3.1105,3.1445,3.3247,2.2835,2.4773,2.0527,-0.53065,0.9325,1.1889,1.453,2.0057,2.4931,1.6061\n3.4666,3.5066,3.4649,3.1251,3.7957,3.2163,3.3057,3.4061,3.3949,4.0774,3.7209,3.4871,2.8115,0.58049,-1.5532,-1.4481,2.5103,1.638,0.95593,1.1114,1.0592,0.61379,0.27445,1.3023,1.8569,2.4483,2.6936,2.5808,2.3236,2.2043,2.3492,2.7162,2.6455,2.691,2.9945,2.8716,2.7618,3.5136,3.5239,4.3628,0.68009,1.0509,2.3456,1.3334,1.5201,1.8552,1.9835\n3.7191,4.0939,2.8701,1.1462,0.23134,1.8482,2.7135,2.8074,2.9119,3.2586,3.3493,2.8374,2.3082,0.85032,0.12971,-0.59977,-0.65744,-0.0067835,1.5897,2.568,3.3935,2.5988,1.6892,3.5636,3.7964,3.0849,3.2041,3.3965,3.8188,4.5913,5.3516,4.3372,3.8692,3.7038,3.8899,3.3176,2.894,2.4807,2.2076,2.8948,2.3113,1.5497,1.5228,1.6441,1.6858,2.2978,2.8257\n3.9461,4.1074,3.7708,3.4668,3.0583,3.043,3.329,3.3023,3.019,2.6411,2.2232,2.7401,2.7678,2.2986,1.4774,1.3956,2.5211,3.0805,2.498,3.0033,2.9053,3.212,3.745,4.5978,4.4707,5.0963,5.2995,4.5919,4.8338,6.2219,6.1053,5.4115,4.9189,5.0651,5.6521,4.7471,6.2487,6.1161,5.7723,2.7552,2.6593,2.3756,2.442,2.9297,2.6895,2.4907,2.9034\n3.3926,4.0332,3.9549,3.7487,3.637,4.2472,3.9776,3.6859,3.646,3.8814,3.425,2.8734,3.3055,4.3027,4.0105,3.0442,3.635,3.6445,3.3842,3.5576,3.8508,4.724,4.4363,4.5141,4.9517,5.9644,7.0843,5.694,5.5054,6.8132,8.2857,7.2633,3.2042,1.7809,4.8088,4.3808,3.005,4.1225,3.7946,2.1486,2.4054,2.7568,2.8238,3.245,3.2944,2.8382,3.3881\n4.0821,4.3811,3.7907,3.8734,4.2839,4.1475,4.4198,3.9311,4.051,4.7984,4.923,4.0891,4.212,4.2786,4.1641,3.7987,4.1877,4.5344,3.8963,3.4558,3.8751,4.8488,5.0935,4.129,3.1559,3.0628,3.0988,NaN,3.2824,NaN,NaN,NaN,4.4362,1.7476,1.8261,2.6622,3.5475,3.5375,3.3774,3.0922,3.014,3.3123,3.0187,3.2901,3.3634,2.9986,2.7395\n2.6031,3.128,2.7969,3.0354,3.6226,3.3608,3.6399,4.386,4.647,4.923,4.9888,4.7048,4.047,3.3189,3.9222,4.8859,4.2431,3.4094,3.3841,3.6516,3.5567,4.3951,4.8409,4.3258,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2566,3.3264,4.2632,4.3605,NaN,NaN,NaN,NaN,3.5927,3.1305,3.7677,3.8745,2.669,2.3944\n2.3226,2.8662,2.871,2.9355,3.5344,3.6553,3.6024,3.9062,4.1273,4.1362,3.5434,3.7476,4.6238,4.3704,4.4976,4.9891,4.7105,4.2182,4.6901,5.0774,4.5586,4.3753,5.1132,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.6142,NaN,NaN,NaN,NaN,NaN,3.5015,3.511,3.2116,3.1137,2.6197,2.5057\n3.5516,2.809,2.7977,2.9281,4.3722,4.434,3.5805,3.5667,3.7672,3.4363,3.7061,4.0552,4.7057,4.4258,4.4828,4.4052,4.0993,3.1199,3.2251,3.198,5.1693,5.3565,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.235,4.4099,3.4836,NaN,NaN,NaN,3.7967,3.0304,2.359,2.8867,2.6658,2.6279\n1.5295,3.1447,2.8801,2.4955,3.1173,4.3245,3.0388,3.1438,3.3892,3.5308,3.5787,3.8141,3.8999,4.2702,5.0967,4.9798,4.7234,4.653,4.2979,2.8777,3.7873,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.3805,6.5075,6.7165,5.8435,4.9459,4.5316,4.4199,3.7776,3.311,2.3726,2.8603,2.9712,3.2161\n1.4412,1.7126,2.3846,3.2889,2.5557,1.7798,0.66092,2.1112,2.3732,0.6691,1.5414,NaN,NaN,NaN,NaN,0.43786,2.9565,4.5829,2.5586,1.2733,3.4033,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.5069,5.1159,4.9748,4.8788,5.1215,5.3831,5.5209,5.4478,5.2908,3.6283,2.9333,2.9457,3.0702,2.4083,1.904\n1.1689,1.6712,2.4552,2.2573,2.5609,2.132,0.29888,0.85732,2.8972,0.36801,1.0054,NaN,NaN,NaN,0.90308,0.82232,1.9463,1.2441,0.18185,0.84229,3.1354,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.3523,8.157,7.6978,6.8815,6.5431,5.395,6.0514,5.2862,4.8625,3.8561,2.9865,3.3684,3.8462,3.0961,3.1017\n2.615,3.3561,3.5087,3.415,3.0001,1.9623,2.6766,2.7798,2.4917,2.6828,3.1928,NaN,NaN,NaN,1.2617,1.312,0.95841,1.3063,0.9825,0.85768,2.4921,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.876,9.3429,6.6536,4.9681,5.2059,4.825,4.1272,3.9579,3.806,3.5791,3.6992,3.7099\n3.5862,2.6596,2.598,1.9875,1.3757,1.1561,1.1066,2.3671,2.9022,3.0597,2.2103,2.7787,3.3222,NaN,1.3487,2.2136,3.1979,2.5503,2.9174,4.6915,5.3763,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.9928,5.6071,5.1645,6.1455,6.2559,5.2205,4.5169,3.6305,3.2492,3.7859\n2.0988,2.1915,2.5304,2.312,0.94909,0.82157,1.1753,2.2611,3.0456,3.1201,3.1392,2.727,2.0518,2.3132,3.4531,2.8052,1.688,2.6398,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.9402,NaN,NaN,NaN,NaN,NaN,NaN,5.6937,6.3439,5.1865,5.2206,5.265,5.0901,4.7267,4.3684,3.4203,2.9125\n2.5569,2.8211,3.4675,3.145,2.2261,2.3371,2.3071,2.8891,3.3094,3.3207,3.4075,4.9973,4.1647,5.3864,5.4872,3.6014,2.3814,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.145,9.976,9.5676,NaN,NaN,NaN,NaN,4.065,5.9531,4.8142,4.3563,4.7666,4.7667,4.3046,4.5823,4.1704,3.1623\n3.4818,3.6418,3.5519,3.3391,3.1524,2.5949,2.743,2.8136,2.6409,2.2057,1.7111,4.1464,4.9088,6.2701,3.8378,2.7103,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.217,10.575,10.413,5.5114,NaN,NaN,5.4337,5.2399,4.9598,4.2464,4.0285,4.5091,4.75,4.7308,5.245,4.2311,2.5031\n2.9262,2.5543,2.8484,3.0616,3.3891,3.206,2.6003,2.5029,2.3957,2.0418,1.7349,1.1412,1.8693,2.2047,1.7173,2.6616,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.4578,9.5961,8.4455,8.8475,9.1871,7.8633,5.1384,5.2981,5.9254,5.5247,5.108,4.0827,3.5773,3.5721,3.2237,3.3504,3.8847,4.5777,3.6818\n2.8794,2.9414,3.3902,3.6612,3.4004,2.8601,2.651,2.3925,1.9053,1.7933,1.8129,1.655,1.6271,2.0777,1.0187,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.6152,8.9656,6.9714,8.7187,9.0123,8.6411,6.4286,6.301,6.2251,6.2659,5.5641,3.7949,3.6421,4.1123,2.6035,2.5696,3.0034,2.4004,2.7692\n3.0256,3.7206,3.6503,3.3037,2.8524,2.9021,3.5782,3.4059,2.3393,1.3634,1.4892,2.7016,2.0907,1.3209,0.003953,-0.86166,-0.64242,2.2297,2.5809,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.9004,6.8805,6.5276,6.9579,6.7771,6.2415,6.0026,4.8327,4.7153,5.5006,5.5633,4.7357,3.216,3.3571,3.7112,2.2371,1.8337,2.5086,2.8057,2.8905\n2.8212,2.6247,2.5265,2.7018,2.9737,2.4676,2.1684,2.0884,1.7186,1.2609,0.73958,4.5517,3.0104,0.7174,0.46871,0.72595,-0.15991,1.2164,2.0594,3.6151,2.9691,1.5485,2.6306,3.659,5.4969,5.8145,4.0179,5.4591,6.0997,5.6309,5.3568,5.0611,4.6671,4.0119,4.6714,6.0594,5.277,3.5893,3.7573,2.9519,3.1756,4.0508,3.7476,3.2422,2.9826,2.3427,1.7733\n2.9339,2.4511,1.813,1.6555,2.0709,2.161,1.7853,1.5346,0.96816,1.0276,1.4391,2.3634,1.4436,0.4821,-0.52409,0.67291,1.0947,0.48166,0.5823,1.4166,2.3467,2.8389,3.0323,2.9805,3.737,3.9313,3.2057,3.7328,3.5956,3.0454,3.8352,4.0325,3.5373,4.2958,4.5868,4.9248,5.9445,4.159,2.8789,2.7856,2.7134,2.1668,1.9779,2.4017,2.4718,1.7012,1.019\n2.4032,2.2898,2.2035,2.2437,1.4915,2.2566,1.6492,0.92637,0.58887,1.6786,1.5102,1.0439,1.0693,1.5279,1.6664,1.7469,1.4677,1.6068,2.076,1.9841,3.0846,4.5227,3.6092,2.7397,1.8395,2.414,2.9103,1.9806,2.7052,3.0858,3.0851,2.5972,2.4,2.6487,1.8163,1.9207,3.5537,3.4977,2.5177,2.4632,2.5564,2.5531,1.5881,0.50122,-0.18561,0.017804,0.0025501\n2.7162,2.4619,2.4098,2.4963,2.1236,2.2765,0.8744,-0.66789,-1.231,2.2414,1.8284,1.1178,0.98176,1.3244,1.8443,2.3314,2.2458,2.2714,1.5772,2.6254,7.9465,6.8439,5.0815,2.5263,1.2013,1.9649,3.0315,1.8021,2.134,2.3819,1.7577,1.1652,1.2877,1.0867,-0.12556,-1.1292,-0.25318,0.54013,1.4466,1.8909,2.2651,2.967,3.5777,1.5399,0.80504,-0.14727,-1.3665\n1.5779,1.5327,1.3649,1.5217,1.7626,1.4486,1.6312,1.8542,2.0825,2.3458,1.9353,0.91091,0.59878,1.3694,1.7108,1.6138,1.4506,1.1904,1.0887,1.5561,4.2183,3.6871,3.1906,3.2697,1.5777,2.1915,1.959,1.5197,1.7648,1.808,1.9549,2.5182,2.7735,1.8602,1.4667,0.85281,0.47429,0.62234,1.2836,1.5858,0.85845,1.9372,3.9379,3.0185,1.795,0.61279,-0.14371\n1.9873,1.4295,0.97251,1.2452,1.1299,0.93123,1.3165,1.7028,1.5176,1.5187,1.4608,0.97739,0.85228,1.2604,1.8807,1.7624,1.6135,1.9685,0.92434,0.24311,1.1817,2.1506,0.86863,0.2655,1.4563,2.0489,1.9147,1.7579,1.2934,1.3038,2.2931,2.2944,1.749,1.3991,0.93492,1.037,1.249,1.4471,1.8083,1.8198,1.2013,1.8132,0.88269,3.4188,5.2607,4.4513,1.9215\n1.4457,1.1205,0.91263,0.86589,0.83203,1.679,1.5686,1.2578,1.9638,1.7822,1.7255,1.8734,1.7859,1.696,1.72,1.6206,1.7433,2.2513,1.2672,-0.45791,-1.3094,-1.2615,1.3034,0.055972,-0.088666,0.32175,0.65806,0.99587,0.94853,0.9128,1.6624,2.2821,1.6522,0.64971,-0.14016,0.26225,1.3787,1.1802,0.84952,1.832,0.94634,-1.1132,-3.1058,-2.1583,-1.2339,0.50953,0.061406\n0.70647,0.91145,1.5232,0.86455,0.57928,1.3403,1.8105,1.1288,0.83494,1.0896,1.7194,1.9696,2.2212,2.0689,2.1876,2.1833,1.9028,1.4021,1.1196,0.49873,-0.20483,0.87751,0.98122,0.31147,0.26793,0.9137,0.06705,0.2255,0.38773,0.59721,1.4728,1.5693,1.5817,1.5423,1.329,0.80626,1.9326,2.1002,1.0234,1.7213,0.41183,-2.2299,-4.0519,-1.9159,-0.43404,0.18102,0.20735\n0.40774,0.73634,1.5327,0.65252,0.18954,0.89304,1.2581,1.2397,1.6736,1.6856,0.77984,1.4106,1.7715,1.9264,2.0767,1.6686,1.5509,0.51088,0.18965,-0.50333,-1.2678,-1.1366,0.16315,0.30515,1.6155,0.71792,0.47032,2.0222,-0.057942,-0.11509,0.95901,0.41603,0.39473,0.71609,1.174,1.3492,2.1522,1.7906,1.7267,1.4922,1.658,1.5332,0.99564,-1.6988,-0.45843,-0.064285,-0.6425\n0.47775,0.30115,0.62433,0.60376,0.49568,0.78779,0.5102,0.34747,0.99833,1.3685,0.96067,0.83523,1.1625,0.81542,0.66673,1.189,NaN,3.0186,3.7573,4.0982,2.8973,0.63915,0.32116,0.25908,0.54977,-0.087255,-0.92746,0.13687,0.58179,0.29827,0.45949,0.19647,0.48499,0.66493,0.57142,0.77651,1.2469,1.0777,0.99344,1.1462,1.3378,1.0568,0.44132,-0.374,-0.3212,-0.21599,-0.74845\n0.35537,-0.18031,-0.90278,-0.1986,0.82552,1.3131,1.0967,1.067,1.5023,1.4895,1.6288,1.8118,1.3444,0.52964,1.3663,1.4805,2.0303,NaN,NaN,NaN,NaN,0.28652,0.14976,0.0029678,-0.045896,-1.3159,-2.2017,-1.4506,0.24187,0.0028658,0.24202,-0.041854,0.38221,0.35139,0.524,1.0535,1.3858,1.2875,1.0239,0.69974,0.45187,0.52417,1.3603,0.8616,-0.36308,-0.76847,-0.46666\n-0.28759,-0.31174,-0.48825,-0.099793,0.19351,0.54813,0.2891,0.92665,1.4979,1.8435,1.663,1.2393,0.8515,0.82597,1.4316,3.3483,4.1991,NaN,NaN,NaN,0.61033,-0.56459,-0.21321,-0.3421,-0.67461,-0.80522,-0.8644,-0.013835,0.77961,-0.08145,-0.077737,-0.1644,0.16148,0.009798,0.80332,1.2911,0.83331,0.26056,0.36858,0.51216,1.004,1.335,1.2664,0.42851,-0.50383,0.017422,0.28353\n-1.2271,0.0301,0.53247,0.77046,0.37764,-0.21344,-0.46509,0.58425,2.6314,2.6989,2.5301,NaN,NaN,NaN,0.1874,1.1572,1.9568,NaN,NaN,NaN,-1.8707,-0.55272,-0.034452,-0.25418,-0.42659,-1.4028,-1.1181,0.46543,0.8217,-0.023257,-0.51025,0.084137,0.56689,-0.18032,0.67348,0.55791,-0.36721,-0.50565,-0.80508,-0.42022,0.67636,0.94409,0.5967,-0.052955,-0.40894,-0.24156,0.1654\n0.5907,0.85684,0.22233,0.31251,0.17685,-0.57651,-1.0573,-0.24995,1.1162,1.3938,NaN,NaN,NaN,NaN,NaN,-0.5162,0.13379,0.67832,-0.6458,-2.0823,-1.102,-0.09772,-0.25361,-0.19483,-1.6271,-2.9738,-0.887,-0.40942,0.75454,-0.40191,-1.3497,-1.2796,-0.60956,-0.18403,-0.6566,-0.32482,0.48402,0.67804,0.19467,-0.27114,0.32695,0.2134,-0.06434,-0.016807,-0.4386,0.022696,0.59148\n-0.21671,-0.32346,-0.53325,0.084996,0.19487,0.018222,-0.14287,-1.331,-0.90889,-0.13507,NaN,NaN,NaN,NaN,NaN,-1.6223,-0.096424,0.024023,-0.52485,-1.2071,-1.126,-1.0608,-0.78684,-0.62486,-0.050434,-1.3784,-1.1221,-0.26425,0.74407,-0.4093,-0.71023,-1.6288,-2.8523,-0.73558,0.00051403,-0.48438,-0.022259,0.42757,0.43665,0.33457,0.192,0.092109,-0.025132,0.46103,-0.24512,0.49997,0.77281\n-1.2896,-1.8862,-1.1965,NaN,2.154,1.8434,1.1956,0.61498,-0.068206,-1.2059,-1.9142,-1.2223,-0.97585,NaN,NaN,-2.4592,-2.5728,-0.90101,-0.86775,-1.3393,-1.7694,-1.2175,-0.29885,0.82413,2.1595,1.8737,-0.046028,-0.59342,-0.70266,0.11687,-0.52519,-0.95029,-1.751,-0.84629,0.090423,0.21604,-0.095372,-0.025531,-0.10635,-0.061176,0.16716,0.029384,0.22943,-0.34686,-0.99352,-1.0762,-0.61897\n-0.90225,-2.2344,-2.978,NaN,NaN,NaN,NaN,0.28015,0.045495,-0.11403,-0.61372,-1.8622,-2.1264,-1.6341,-1.6341,-2.1841,-2.5622,-1.9461,-1.7213,-1.1761,-1.4695,-1.8136,-1.0214,-0.2223,0.48751,2.5866,1.2126,-2.9492,-2.5411,-0.45417,-1.0053,-1.7815,-1.1926,-0.47833,-0.48777,-0.40283,-0.50329,-0.45555,0.053435,0.17411,0.63912,0.57282,0.37309,-0.39071,-1.0285,-0.90365,-0.25639\n-1.7649,-1.2377,-1.1963,NaN,NaN,NaN,NaN,0.11076,0.36008,0.51023,0.1794,-0.84388,-1.0846,-1.1071,-0.97115,-1.8842,-2.0604,-1.6071,-1.1828,-1.1312,-0.99935,-0.70322,-0.60393,-0.67723,-0.056996,1.3732,1.8448,0.74036,-2.2411,-1.6109,-0.7539,-1.0814,-0.69978,-0.49138,-0.58251,-0.44095,-0.3318,-0.18454,0,0.069987,0.4326,0.99721,0.97495,0.37633,-0.060386,-0.15838,-0.56228\n-0.67522,0.47743,1.7191,-1.1861,-2.8564,-4.6424,-3.4302,-1.1954,-0.47565,-0.013597,-0.24337,NaN,NaN,1.0144,-0.052431,-1.2519,-1.6142,-1.166,-1.0377,0.027298,-0.33206,-0.1659,-0.1277,-0.095952,-0.17108,-0.0031767,0.51974,0.24887,0.29333,-1.8817,-1.9987,-0.67578,-0.21252,-0.27026,-0.72909,-0.48509,-0.19971,-0.072886,-0.0087709,0.19513,0.40439,0.88579,0.68658,0.7409,0.79978,0.18525,-0.83299\n-0.81339,0.079175,-0.46041,-0.92669,-0.91482,-3.0141,-3.3656,-3.8399,-2.7986,-1.3953,-0.039629,NaN,NaN,NaN,NaN,NaN,-2.0252,-1.5716,-0.86312,-0.13717,-0.36438,-0.052541,-0.5044,-0.84204,-0.56383,-0.98752,-0.15899,-0.063384,0.8615,0.45801,-0.36916,-0.56328,-0.0064268,0.7695,-0.070083,-0.49369,-0.25032,0.018644,0.10986,0.095148,0.058085,0.60503,0.72096,0.8174,0.61968,-0.1027,-0.3316\n0.38806,0.25777,0.26016,-0.31139,-1.4358,-1.6403,-2.2932,-1.6429,-1.9582,-3.4157,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.81594,-0.59972,-0.25878,0.42389,0.64326,-0.14743,-0.96865,-0.70681,-0.75675,-1.0985,-1.4986,-0.95959,-0.80942,-0.16061,-0.044038,-0.32596,-0.99986,-0.82747,-0.50488,-0.73696,-0.89241,-0.58381,-0.10851,0.40386,0.29018,0.65635,0.84291,0.13666,-0.5639,-1.1975\n-0.73644,-0.3361,1.1934,1.6148,1.5524,-2.4745,-1.5105,-1.0299,-0.75565,-2.2413,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.2699,-0.28186,0.05613,0.010755,0.34139,0.31131,0.22575,-0.547,-0.62271,0.038247,-0.20172,-0.17987,-0.50848,-0.71061,-0.77585,-0.72427,0.1193,-0.097148,-0.31429,-0.90491,-0.70637,-0.81536,-0.41155,0.6104,0.26234,0.30861,0.8346,0.60062,0.64143,-0.33668\n-3.3435,-1.6078,-0.80551,-0.3114,0.30143,-0.67626,-1.9541,-2.4673,-1.7214,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.38981,-0.77309,-0.7523,-0.043775,-0.13175,-0.061131,-0.43766,-0.73643,-0.27154,0.52307,0.45727,-0.083573,-0.74415,-1.048,-1.0077,-0.76682,-1.33,-1.6478,-1.1991,-0.69118,-0.5441,-0.52023,0.45485,0.65684,0.31054,0.6083,0.94715,0.9752,1.0446\n-1.5857,-0.39139,-0.35583,-1.5401,-1.0823,-0.70579,-0.86883,-1.7213,-2.1496,-2.2518,-2.9802,-1.096,NaN,NaN,NaN,NaN,NaN,NaN,-0.63531,-0.34803,-0.25925,0.29372,1.0558,0.27413,-0.49511,-0.50026,-0.036485,0.3945,0.50124,-0.036052,-0.95514,-1.1507,-0.14795,-0.048781,-1.1936,-1.6716,-0.59802,-0.10596,-0.30784,-0.10533,0.81223,1.1396,0.88527,0.7835,1.1068,0.52549,0.47648\n0.28688,-0.10102,-0.31047,-0.31808,-0.23025,0.29875,-0.35836,-2.0167,-3.499,-3.5356,-2.4564,-0.69186,NaN,NaN,NaN,NaN,NaN,-0.78918,0.34104,0.13124,0.19861,0.31595,0.23422,0.21086,0.20924,0.11596,0.34612,0.91851,0.91279,0.32791,-0.68219,-1.2889,-1.3319,-1.8699,-2.3866,-1.8488,-0.11179,0.14658,0.18246,0.40811,1.2646,1.493,1.1123,1.5842,1.8463,0.54896,0.23663\n-0.97706,-0.79001,-0.77519,-0.91436,-2.0223,-0.8438,0.52254,-0.098765,-0.93934,-1.5337,-2.3131,-2.5835,-3.0736,-2.2209,NaN,NaN,NaN,-0.37328,0.76881,0.28189,-0.36448,-0.40771,-0.35698,0.065174,0.82097,1.226,0.43416,0.58061,0.48517,0.39926,-0.1097,-0.95234,-1.4827,-1.8337,-2.0095,-1.3331,-0.060322,-0.013852,0.31424,0.55012,0.92204,1.2023,1.3479,1.8154,2.2664,1.9782,1.2924\n0.84419,0.45396,-0.79955,1.0933,1.3176,0.8617,1.4911,1.3346,-0.92087,-2.3292,-2.2514,-1.609,-0.39367,1.4653,1.052,-0.070511,0.19016,-1.0414,-0.2697,-0.24196,-0.64883,-0.49301,-0.56208,0.089766,-0.56276,-0.052811,0.15571,0.82317,1.0742,1.0475,0.22369,-0.38968,-0.60745,-0.28263,0.18381,-0.11722,-0.09886,0.36991,0.55292,0.8209,1.258,1.5372,1.3011,1.3024,1.5305,1.5899,1.4459\n-0.36887,-0.17428,NaN,NaN,NaN,0.72635,1.8044,2.1114,1.3627,-1.0338,-0.14505,-0.13081,1.0236,1.7793,0.4875,-0.36565,-2.9707,-0.46092,-0.78082,-0.73217,-0.69263,-0.27564,-0.096709,1.0621,1.5039,-0.15955,-0.038998,0.53702,1.1786,0.041532,-0.65754,-0.966,-0.6382,0.26629,-0.0075254,-0.92789,-0.44482,0.30394,0.57119,0.6217,0.84224,1.149,1.2209,1.4139,2.3803,3.1922,3.1054\n-2.0103,-0.36223,NaN,NaN,NaN,-0.42475,-0.22303,0.86867,1.6007,0.48653,-0.7172,NaN,NaN,NaN,NaN,NaN,NaN,-0.096672,-0.58542,-0.43879,-0.75117,-0.75924,0.12059,1.0247,1.7907,1.1379,1.2355,1.2951,0.6716,-0.17914,-1.0854,-0.6655,0.27582,0.27679,-0.019168,-0.54013,-0.40216,-0.54551,0.1554,0.77703,0.9825,0.89902,1.6656,1.9506,1.2494,0.77906,0.54598\n0.4781,0.44143,NaN,NaN,NaN,-0.027921,-1.6246,-2.2274,-2.3619,-3.3246,-1.1307,NaN,NaN,NaN,NaN,NaN,NaN,-1.4493,-1.4194,-0.93272,0.0057411,0.30842,0.54472,0.95897,1.278,1.9701,2.1438,1.6071,0.48349,0.001935,-0.71117,-0.48212,0.64708,0.96935,0.80287,0.34218,-0.14716,-0.046529,0.47358,0.47279,1.2778,1.8031,1.6783,1.9517,1.6781,0.81028,0.1269\n0.55827,0.65335,-0.601,-1.7424,-0.26154,-0.62035,-2.0755,-2.8522,-3.0168,-2.2889,-1.7538,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.0099726,0.19327,0.52907,0.91857,1.4139,1.4538,1.2362,1.5375,1.6328,-0.35905,-0.89954,-0.352,0.032758,0.98254,1.6063,1.2932,-0.4053,-0.35302,0.64849,1.055,1.3697,1.6801,1.6935,1.6124,2.1514,1.5873,0.75196,0.27724\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_corrected_method2geo_070709-070813.unw.csv",
    "content": "-0.012601,-0.39192,-0.4342,-0.26968,-0.43011,0.19134,0.28836,0.75444,1.8419,1.5097,0.9039,1.3904,1.6822,1.7696,2.1332,1.9993,2.0693,2.191,2.1704,2.3035,1.9689,2.5119,2.7585,2.5242,2.426,2.6096,2.5246,2.5294,2.939,2.6782,2.6726,2.2018,2.2024,2.1247,2.452,2.452,2.4527,2.4728,2.4511,2.7513,2.9419,3.015,2.8994,2.9289,3.222,3.0968,3.0971\n-0.062094,-0.075418,-0.59837,-0.39434,-0.26471,0.12708,0.20139,0.6737,1.1099,0.97613,1.0187,1.2187,1.4255,1.657,1.7239,1.8071,2.0143,2.0377,2.1818,2.1942,2.1005,2.411,2.4326,2.244,2.3409,2.513,2.5012,2.9932,2.9137,2.4442,2.2909,2.1721,2.2153,1.5041,2.2961,2.1158,2.0211,2.0856,2.3238,2.652,2.5876,3.0318,2.758,2.8674,3.0077,3.0554,3.4525\n-0.31885,-0.069877,-0.26504,-0.65469,-0.43257,0.18798,0.60397,0.91619,1.1311,0.872,1.3214,1.446,1.4515,1.7037,1.7125,1.8296,2.0868,1.8922,1.8523,1.8629,1.966,2.0284,1.7305,2.0295,2.2497,2.4275,2.5614,2.7138,2.4977,2.3993,2.4032,2.0323,1.8244,1.1472,2.043,1.9453,1.8802,1.8944,2.0315,2.1867,2.6904,2.9653,2.7181,2.7355,2.7907,3.0909,3.6834\n-1.2498,0.021461,-0.14994,-0.60324,-0.16007,0.29504,0.66282,1.2298,1.1416,0.92683,1.1876,1.156,1.2636,1.311,1.6501,1.6315,1.8642,1.8556,1.8321,1.654,1.8169,1.8138,1.7436,1.9632,2.4371,2.6847,2.6783,2.5711,2.5835,2.435,2.0103,1.6849,1.5172,1.5746,1.7481,1.8044,1.875,1.8325,2.017,2.2929,2.3735,2.2797,2.4082,2.4432,2.4045,2.6246,3.1376\n-0.74557,-0.044833,0.55636,-0.52361,0.039061,0.26036,0.53988,0.66284,0.76323,0.4581,0.56739,1.2212,0.95243,0.73072,1.256,1.2779,1.5607,1.5973,1.6313,1.5739,1.7837,2.0752,1.5911,1.9682,2.6654,2.6276,2.6968,2.5894,2.4779,2.3235,1.6966,1.4612,1.1923,1.4288,1.5117,1.6319,1.9049,1.8795,1.9392,2.1781,2.1479,2.9189,2.4079,2.5261,2.348,2.5955,3.1903\n-0.76839,-0.62759,-0.69959,-0.86996,-0.11239,0.22073,0.42582,0.4757,0.5114,0.39673,0.86776,1.3051,0.6254,0.57468,0.68114,0.94912,1.0873,1.2426,1.3104,1.1244,1.1967,1.7578,1.7507,2.0079,2.3234,2.1474,2.3375,2.464,2.0185,1.6592,1.0819,0.66896,0.75196,1.0606,1.3018,1.9774,2.1515,2.1614,2.1182,2.1177,2.0235,1.9256,2.2465,2.6998,2.5129,2.9011,3.382\n-0.87383,-1.015,-0.88644,-1.0995,-0.40192,-0.46465,0.16844,0.022159,0.39755,0.46697,0.82656,1.0634,0.57694,0.7009,0.47176,0.63035,0.8636,1.0317,1.162,0.73798,0.97925,1.0649,1.4593,1.5496,1.6928,1.8236,2.1944,1.9945,1.4842,1.4124,0.82599,0.79869,1.2081,0.93548,1.4643,1.9438,1.9121,1.6213,2.0401,1.8393,1.8329,2.3097,2.2095,2.5871,2.9289,2.9687,3.5094\n-0.86872,-0.73853,-0.82245,-1.0912,-0.3292,-0.69342,-0.27707,0.069646,0.54562,0.73571,0.8847,1.0071,0.64871,0.54762,0.3584,0.36938,0.58426,0.80417,0.43402,0.37134,0.80292,0.47569,0.77342,1.3135,1.1638,1.2973,1.6401,1.237,0.84308,1.304,1.053,1.0778,1.2607,0.58212,1.3578,1.9053,1.7448,1.6109,1.5007,1.5855,1.6428,2.3232,1.7858,2.4586,2.8051,2.7061,3.6002\n-1.2031,-0.98218,-0.8074,-1.0614,-0.7857,-0.75316,-0.21993,0.01715,0.32363,0.76257,0.98412,0.96043,0.62956,0.55071,0.53527,0.24263,0.052579,0.038123,0.019899,0.1822,0.41724,0.53776,0.77336,0.87851,0.48428,0.57912,0.58678,0.68184,0.8854,0.89896,0.70666,0.75176,0.43804,0.43459,1.3618,1.8799,1.5012,1.6745,1.1138,0.79014,1.0986,1.7922,1.8813,1.3247,2.2305,2.6777,2.6551\n-1.6757,-1.6286,-1.2415,-1.4056,-1.638,-1.1037,-0.66416,-0.20645,0.42948,1.1197,0.77244,0.99324,0.58897,0.71993,0.32804,0.16293,0.013352,-0.1159,-0.15378,-0.26175,0.18997,0.37259,0.54487,0.50784,0.43344,0.3105,0.010339,0.088484,0.58999,0.63186,0.24037,0.12326,-0.18004,-0.031102,0.7347,1.2219,1.0204,1.0856,0.74689,0.79252,1.1795,1.3876,1.4565,1.5942,2.1055,2.2375,2.0376\n-2.0403,-2.3812,-1.9499,-1.7607,-1.6134,-1.2418,-0.78457,-0.30342,0.27062,0.58423,0.54111,0.78861,0.34344,0.49166,0.35844,0.36972,0.23,-0.40168,-0.36578,0.00020313,0.16142,0.16447,0.10013,0.031634,0.30477,0.36675,-0.035132,-0.17292,-0.078901,-0.2218,-0.52679,-0.29941,-0.64662,-0.23279,0.019538,-0.049188,0.19464,0.26404,0.24332,0.2754,0.71125,0.77313,1.0982,1.5365,1.8294,1.8278,1.7937\n-2.5781,-3.316,-2.6685,-2.1326,-1.5109,-1.6269,-1.2286,-0.70972,-0.018109,0.44122,0.78531,0.51975,0.31801,0.40427,0.24505,0.043658,0.27972,-0.66913,-0.54687,-0.42221,-0.3005,-0.10647,-0.097152,-0.23235,0.13643,0.22316,-0.13618,-0.39324,-0.30774,-0.50593,-0.87624,-0.9432,-0.8794,-0.59692,-0.61012,-0.81833,-0.35398,-0.028102,0.017787,0.19954,0.47734,0.38958,0.58231,0.76401,1.2094,1.7022,1.7947\n-2.6742,-2.9722,-1.9401,-1.965,-1.4683,-1.8805,-1.3875,-1.1818,-0.41464,0.37579,0.48723,0.16892,0.1649,0.055921,-0.086286,-0.34702,-0.53727,-0.72369,-0.7598,-0.35554,-0.47426,-0.30899,-0.24534,0.043521,0.084623,-0.0083895,-0.2956,-0.65656,-0.48238,-0.67033,-1.2668,-1.2515,-0.99106,-0.66666,-1.0915,-1.0087,-0.4025,-0.53345,-0.20835,0.27002,0.35615,0.26973,-0.031035,-0.042922,0.63601,0.92,1.667\n-2.6953,-1.8954,-1.4852,-1.4682,-2.5243,-2.3752,-1.4928,-1.0035,-0.6032,-0.34489,0.044642,0.22825,0.20397,-0.22739,-0.17315,-0.47964,-0.61985,-0.70232,-0.44783,-0.43574,-0.53436,-0.65072,-0.42866,-0.22416,-0.35402,-0.29769,-0.51418,-0.9523,-1.0844,-1.0118,-1.1613,-1.0444,-0.79198,-0.52392,-0.87189,-0.78119,-0.63354,-0.75822,-0.71664,-0.061816,0.4225,-0.52798,-0.6823,-0.25244,0.16676,0.88441,1.4903\n-2.3201,-1.7241,-0.72124,-2.4459,-2.7377,-2.5649,-1.4909,-1.0813,-0.55285,-0.7824,-0.48144,-0.096408,-0.28176,-0.50454,-0.55876,-0.79947,-0.76543,-0.90068,-0.26259,-0.68412,-0.69552,-1.4468,-0.82681,-0.81962,-0.59925,-0.76388,-0.85682,-0.96079,-0.93598,-0.95601,-1.0371,-0.85283,-0.7296,-0.68388,-0.90638,-1.0717,-0.92323,-1.3347,-1.4364,-0.88144,0.16174,-1.0649,-1.8628,-0.51609,-0.051751,0.3988,0.4401\n-2.4019,-2.676,-2.1253,-2.7075,-2.8216,-2.3486,-1.758,-1.3749,-0.8617,-0.93022,-0.55439,-0.3748,-0.86872,-0.50254,-0.5689,-0.79567,-0.96284,-0.78847,-0.72732,-0.89754,-0.84168,-1.3094,-1.2295,-0.70605,-0.74129,-0.99272,-0.868,-0.93222,-1.1669,-1.1152,-1.5561,-1.5424,-1.1091,-0.94939,-0.86923,-1.5203,-1.3282,-1.8387,-2.0394,-1.1383,-0.6698,-0.6659,-2.2876,-1.294,-0.80804,-2.416,-1.5036\n-2.3742,-2.6084,-2.4576,-2.2414,-2.011,-1.6666,-1.8416,-1.7093,-1.1634,-1.0347,-0.8791,-0.83449,-1.0325,-0.54254,-0.77573,-0.78221,-1.0729,-0.74771,-1.0867,-1.0202,-1.0595,-1.4611,-1.1828,-0.90101,-1.4352,-1.1518,-1.0459,-1.0714,-1.4616,-2.0145,-1.9559,-2.0065,-1.3167,-1.1633,-1.2304,-1.4563,-1.8244,-1.3431,-1.8221,-0.56546,-1.4297,-0.58087,-1.176,-1.8294,-2.0433,-3.7883,-2.6974\n-3.0691,-2.6705,-2.5349,-2.5088,-1.0345,-1.1101,-2.0262,-1.5603,-1.4432,-1.3898,-1.4231,-1.2531,-1.4369,-1.1082,-0.86549,-0.988,-1.0951,-0.78572,-1.1336,-1.25,-1.8299,-2.014,-1.5067,-1.311,-1.1543,-1.7578,-1.9548,-1.5895,-1.3367,-1.4238,-1.8033,-2.2633,-2.382,-1.2123,-0.9767,-1.2272,-2.5027,-0.90754,-0.23989,-0.3788,-0.96327,-1.973,-1.8848,-1.8661,-2.6788,-3.6424,-3.6102\n-2.4101,-2.6161,-2.6397,-1.9977,-1.1682,-0.71687,-2.3412,-1.9293,-1.8069,-1.5732,-1.5623,-1.5938,-1.6851,-1.5066,-1.2507,-1.2782,-1.3851,-1.4291,-1.4262,-1.0835,-1.7654,-1.8786,-1.7003,-1.438,-1.2302,-1.2437,-2.1708,-2.045,-1.5818,-1.4716,-1.6329,-2.0575,-3.158,-2.0643,-0.81969,-1.3233,-1.2377,-0.90009,0.40084,-0.51367,-1.3016,-1.9378,-1.9922,-1.9879,-2.2168,-2.5849,-3.0229\n-2.368,-3.0687,-2.7285,-2.2052,-1.9094,-1.856,-2.5753,-2.3671,-2.0068,-1.7045,-1.6603,-1.6375,-1.5588,-1.7127,-1.6765,-1.7032,-1.9975,-1.7513,-1.7137,-1.5596,-1.8771,-1.9721,-1.6591,-2.1453,-1.8069,-1.7012,-1.87,-1.8639,-1.361,-1.4132,-1.889,-2.0086,-2.8588,-2.7904,-1.2037,-1.7903,-1.5233,-1.5641,0.36361,-0.42605,-1.5918,-1.4089,-1.8951,-2.2133,-2.1374,-3.0888,-2.5767\n-2.6878,-3.5879,-2.811,-2.7885,-2.7847,-2.4844,-2.4977,-2.5071,-2.1194,-1.7765,-1.8572,-1.7864,-1.7305,-1.7558,-2.2878,-2.5805,-2.5616,-2.4421,-2.1878,-1.9047,-1.6621,-1.4407,-1.5875,-2.1562,-2.2145,-2.0166,-1.9881,-1.7596,-1.1722,-1.0307,-1.895,-1.639,-2.2979,-2.3774,-1.9703,-2.1826,-2.1445,-2.1481,-1.1036,-1.4912,-1.7668,-1.754,-2.0622,-2.2474,-2.5705,-2.5081,-1.7476\n-3.0025,-3.2322,-2.4508,-2.1776,-2.4481,-2.6399,-2.3591,-2.5833,-2.387,-1.9624,-1.8423,-1.9364,-1.9674,-1.8134,-2.3812,-2.8829,-2.7055,-2.9713,-2.8148,-2.5785,-2.5383,-2.1449,-2.2514,-2.2591,-2.2543,-2.3716,-2.2885,-2.0038,-1.1201,-1.3504,-1.9959,-1.7669,-2.3764,-2.4355,-2.5319,-2.2376,-1.8706,-1.9923,-1.9962,-2.1512,-2.3919,-2.2496,-2.0937,-2.0993,-2.1409,-3.2244,-2.1284\n-2.9812,-2.899,-2.2967,-2.0896,-2.4232,-2.8281,-2.8325,-2.39,-2.1309,-2.0516,-1.9976,-1.7817,-2.0712,-2.4015,-2.8429,-3.6481,-2.9766,-3.2537,-3.2057,-2.6159,-2.9355,-2.6657,-2.1223,-2.547,-2.651,-2.8098,-2.1514,-2.1242,-1.5104,-1.9519,-1.9428,-1.5036,-1.8735,-2.23,-2.0386,-1.9649,-1.8528,-2.2924,-2.4321,-2.353,-2.8586,-2.5458,-2.505,-2.8595,-3.2664,-3.0531,-2.2514\n-2.861,-2.8756,-2.3359,-1.9559,-4.1019,-3.2678,-2.2853,-2.0922,-2.1651,-2.1395,-2.3384,-1.6011,-2.3087,-2.1934,-2.2385,-3.1164,-2.4642,-2.9968,-3.153,-2.8949,-2.6172,-2.1711,-1.9169,-1.8826,-2.3002,-2.4613,-1.659,-2.0988,-1.8697,-1.6751,-0.83795,-1.1374,-1.3153,-1.4364,-1.7577,-1.8187,-2.145,-2.8741,-3.5999,-2.4482,-2.5362,-2.5752,-2.5091,-2.6116,-2.7763,-3.1592,-2.2041\n-3.3173,-2.5633,-1.9865,-1.9267,-2.4943,-2.4087,-1.9126,-2.1062,-2.3435,-2.2564,-2.3447,-2.1828,-2.0205,-2.0639,-2.0698,-2.1015,-2.2465,-3.2261,-2.8729,-2.7858,-2.69,-2.206,-2.0364,-2.0968,-2.1043,-1.9817,-1.8094,-1.9025,-1.4241,-0.77765,-0.24181,-0.42048,-1.0738,-1.392,-1.9865,-2.1608,-2.5234,-3.587,-4.2252,-2.6296,-2.6701,-2.6256,-2.5755,-2.3047,-2.4179,-2.7216,-2.2123\n-3.619,-3.3349,-2.3535,-1.9268,-2.3285,-1.9402,-2.1305,-1.9743,-2.2255,-2.2328,-2.3413,-2.3202,-1.8716,-1.8729,-2.1533,-2.2656,-2.3841,-2.4704,-2.5849,-2.5247,-2.4019,-1.5231,-1.6467,-2.1705,-1.7948,-1.4326,-1.1351,-0.71701,-0.56026,-0.54017,-0.39837,-0.80152,-1.2304,-2.5585,-2.5123,-2.7817,-2.4352,-2.7443,-3.2306,-3.077,-2.6613,-2.6561,-2.8243,-2.2908,-2.5292,-2.4748,-2.1278\n-3.6449,-3.3164,-2.9137,-2.6166,-2.3444,-2.4579,-2.4626,-2.0306,-2.1086,-2.1716,-2.1966,-2.3488,-1.6754,-1.9095,-2.1075,-2.2879,-2.1404,-2.2057,-2.5761,-2.7315,-2.1247,-1.8007,-1.9875,-2.795,-2.2484,-1.4192,-1.43,-1.337,-1.2487,-1.1439,-1.058,-1.4827,-2.4397,-2.8953,-2.8449,-2.6659,-2.5855,-2.6447,-3.1069,-2.7687,-2.8134,-3.0315,-2.6916,-2.5566,-2.5405,-2.4528,-2.3358\n-3.5477,-3.5119,-3.2874,-3.1241,-3.1305,-2.6774,-2.2751,-2.0493,-2.4116,-2.323,-2.0513,-2.0897,-2.1113,-1.9445,-2.228,-2.4707,-2.5702,-3.066,-3.2134,-3.0974,-2.7795,-2.5146,-2.6237,-3.3513,-2.9606,-2.8311,-2.2385,-2.2108,-2.7795,-1.6389,-1.1981,-1.1995,-2.4894,-2.7163,-2.6837,-2.711,-2.6973,-2.3614,-2.4553,-2.5574,-3.1464,-3.2146,-2.6136,-2.6997,-2.4708,-2.489,-2.1324\n-3.9515,-3.7192,-3.2362,-3.3113,-3.2128,-2.7661,-2.3875,-2.5102,-2.8296,-2.9723,-2.8158,-2.3224,-2.6397,-2.6819,-2.5338,-2.5848,-2.7583,-3.3994,-3.121,-2.9199,-2.8395,-2.7177,-2.4744,-2.2873,-2.1148,-2.1801,-1.5247,-2.0264,-2.5097,-1.9777,-1.2611,-0.97991,-1.8898,-2.9928,-2.8298,-2.8781,-2.809,-2.17,-3.2515,-3.765,-3.4855,-3.5981,-2.8876,-2.7457,-2.6411,-2.6699,-2.532\n-4.0587,-4.0295,-3.7584,-3.3596,-2.9901,-3.0014,-2.7416,-2.6054,-3.0237,-3.369,-2.883,-2.5976,-3.2593,-3.0993,-2.6465,-2.732,-3.377,-3.3574,-3.2767,-3.4146,-3.0598,-3.1011,-2.127,-1.7152,-2.1469,-1.5269,-1.0861,-2.2869,-2.5095,-2.29,-2.0188,-3.1477,-3.477,-4.0712,-3.063,-2.9747,-3.9211,-4.043,-3.4939,-3.5594,-3.311,-3.4666,-2.9824,-2.8129,-2.7991,-2.7369,-2.3999\n-4.819,-4.1909,-3.6807,-3.5342,-3.3669,-3.2399,-3.5765,-3.3692,-3.3768,-3.4866,-3.3211,-3.2068,-3.3433,-3.5423,-2.9256,-3.3799,-3.7639,-3.4934,-3.4168,-4.0814,-3.7104,-3.3345,-2.6634,-0.1722,-3.7358,-2.666,-0.82103,-0.50463,-1.9203,-2.1238,-1.7985,-3.3006,-3.4597,-3.6832,-3.2295,-3.0516,-3.9173,-3.8885,-3.4127,-3.3398,-3.6531,-3.8734,-3.3246,-2.8933,-2.7796,-2.7177,-2.3194\n-4.7504,-4.3634,-5.1555,-4.5577,-3.9821,-3.965,-4.0969,-4.4656,-3.7552,-3.8057,-3.7086,-3.8064,-3.9149,-4.0245,-4.459,-4.641,-3.9086,-3.648,-4.449,-4.8004,-3.3405,-2.5661,-1.8389,0.94368,-1.0592,-3.2103,-3.6219,-1.9247,-3.0392,-3.2952,-2.9605,-3.4063,-2.7326,-3.265,-3.226,-3.0742,-3.5097,-3.5941,-3.4164,-3.2952,-3.7193,-3.7605,-3.6836,-3.1207,-3.0359,-2.9053,-2.6686\n-4.5204,-4.907,-4.8259,-3.7817,-3.6434,-3.7337,-4.5953,-4.8594,-4.2909,-3.5952,-3.4357,-3.7787,-4.1875,-4.0308,-4.1261,-4.5952,-4.6146,-4.1411,-4.5103,-5.001,-3.9074,-2.2953,-1.4313,-1.2876,-1.6154,-2.7252,-3.0438,-2.6121,-4.5451,-4.1457,-3.5414,-3.3553,-2.9913,-2.506,-2.698,-2.9018,-3.1538,-3.839,-3.2609,-3.315,-3.4658,-3.13,-3.0991,-3.5198,-3.3472,-3.0224,-2.829\n-4.8376,-4.6567,-4.4715,-4.0666,-3.8457,-3.8197,-4.8609,-4.6144,-3.9362,-3.2833,-3.5999,-3.6941,-4.1962,-3.4148,-4.9944,-4.6152,-4.762,-3.9423,-3.6769,-4.3265,-3.8674,-3.2153,-2.4827,-3.0765,-3.0616,-2.7835,-2.4948,-2.8294,-3.861,-3.1883,-2.8129,-2.5722,-2.0054,-2.2873,-2.9305,-3.1989,-4.0294,-3.7288,-2.9371,-3.4616,-3.419,-3.1136,-3.164,-3.4993,-3.4654,-3.2791,-3.1536\n-4.9263,-4.7756,-4.5981,-4.521,-4.584,-4.6185,-4.8808,-4.2998,-3.7465,-3.7783,-3.6963,-3.5467,-3.7773,-3.183,-4.9811,-4.8115,-4.0173,-3.7863,-3.7318,-3.7087,-3.5527,-3.5454,-3.2392,-2.9253,-2.6048,-2.3533,-3.3366,-3.3302,-3.3141,-3.2524,-2.6733,-2.3638,-2.0084,-2.1293,-3.2883,-3.4243,-3.6949,-3.3687,-3.5034,-3.7361,-3.6187,-3.567,-3.4623,-3.5024,-3.6722,-3.4143,-3.2791\n-4.6558,-4.5371,-4.6421,-4.5404,-4.1509,-4.683,-4.7892,-4.2612,-3.6642,-3.9174,-3.384,-3.3178,-3.687,-3.7383,-3.4874,-4.7877,-4.3767,-4.039,-3.7717,-4.0326,-3.9683,-3.9618,-3.5836,-3.1104,-2.8707,-3.3553,-3.6708,-3.8262,-2.0848,-3.1254,-2.8782,-2.8428,-2.5618,-2.7135,-3.3034,-3.2778,-3.5547,-3.9702,-3.3256,-3.5118,-3.4962,-3.3164,-3.342,-3.677,-3.6208,-3.3566,-3.099\n-4.4955,-4.1698,-4.2975,-4.2145,-4.51,-4.6278,-4.6645,-4.517,-4.3965,-4.4104,-4.287,-4.2634,-4.0712,-3.9017,-3.8674,-4.6176,-4.5439,-3.9667,-3.4741,-3.8268,-3.2539,-4.1076,-6.944,-2.5804,-1.8966,-2.1457,-3.7016,-3.7232,-1.7479,-3.0561,-3.2184,-3.027,-3.2023,-4.5533,-4.5973,-3.9715,-3.9815,-3.9607,-3.4668,-3.2909,-3.2618,-3.0597,-3.2249,-3.5339,-3.583,-3.1596,-3.1217\n-4.0973,-4.6381,-4.7255,-4.4142,-4.5425,-4.6991,-4.3735,-4.3695,-4.4145,-4.2741,-4.2187,-4.13,-4.151,-3.5952,-4.0245,-4.3778,-4.1226,-4.0399,-3.0398,-2.7251,-1.9422,-5.1827,-10.157,-3.8058,-2.8331,-2.0161,-3.5202,-3.9649,-3.1889,-3.335,-3.1937,-3.1975,-2.9807,-5.1956,-4.8802,-4.4924,-3.9852,-3.9892,-3.9642,-2.898,-3.0667,-3.2612,-3.284,-3.5293,-3.3533,-3.1331,-3.6271\n-3.8055,-3.9522,-4.4783,-4.5,-4.3077,-4.6403,-4.2158,-4.3156,-4.2258,-4.0745,-4.1091,-4.4187,-4.1885,-4.3043,-4.3106,-4.1111,-4.2105,-6.564,-3.7842,-2.135,-2.0118,-4.962,-6.7027,-2.3704,-1.9186,-1.465,-3.959,-3.9367,-3.5377,-3.3839,-3.5305,-3.3991,-3.1672,-4.1698,-4.2125,-3.9655,-3.7828,-3.8132,-3.5895,-2.6857,-2.9607,-3.2008,-3.4737,-3.2038,-3.2469,-3.2061,-3.411\n-3.5847,-3.7771,-4.0169,-4.4547,-4.5375,-4.6013,-4.283,-4.0554,-4.3674,-4.219,-4.1743,-4.3534,-4.3635,-4.6568,-4.2858,-4.492,-4.8131,-5.8678,-6.0269,-2.7889,-3.1272,-3.1579,-1.5078,-2.1289,-3.0696,-2.5547,-2.2271,-3.9436,-3.6043,-3.6155,-3.2787,-3.488,-3.5761,-3.7445,-4.0839,-3.9035,-3.5678,-3.7499,-3.0645,-3.3139,-3.4487,-3.6029,-3.424,-3.2143,-3.202,-3.0724,-2.6775\n-3.65,-3.8677,-3.6928,-4.1452,-4.5521,-4.497,-4.1649,-4.1349,-3.8456,-3.8627,-3.9941,-4.3975,-4.9944,-5.0601,-4.356,-5.4581,-5.7578,-6.0743,-6.3911,-3.6305,-3.644,-4.291,-3.7068,-3.1553,-3.0061,-3.7495,-3.9082,-3.8295,-3.8937,-3.5777,-3.3469,-3.8539,-3.4685,-3.5565,-3.4047,-3.4567,-4.1246,-4.8624,-4.331,-4.1751,-3.65,-3.5785,-3.7233,-3.1116,-3.1662,-2.8687,-2.6581\n-3.2501,-3.2988,-3.5103,-3.8415,-3.8078,-3.7905,-3.6045,-3.4535,-3.3631,-3.579,-3.7517,-4.8201,-5.7365,-5.2276,-4.8013,-5.1161,-6.1465,-5.0718,-4.6517,-3.3728,-3.6086,-4.7592,-4.5738,-4.085,-3.4267,-4.4894,-3.8239,-3.6803,-3.9136,-3.611,-3.7371,-3.4801,-3.1006,-2.9583,-3.3681,-3.5674,-3.7202,-4.1648,-4.1682,-4.0088,-3.8766,-3.6829,-3.7959,-3.2659,-3.6843,-2.587,-2.6422\n-3.0738,-3.3337,-3.6096,-3.8618,-3.5693,-3.383,-3.3553,-3.2212,-2.9731,-3.2304,-3.8261,-4.8148,-5.4442,-4.9498,-4.7419,-4.5682,-4.4934,-5.4115,-4.9551,-3.5636,-3.6462,-4.4841,-3.9901,-4.1709,-4.163,-4.5847,-4.1392,-3.7667,-3.4726,-3.5855,-3.8333,-2.8537,-3.0223,-2.9606,-3.0707,-3.1123,-3.4908,-4.0189,-3.421,-3.5597,-3.786,-3.4281,-3.1427,-3.2502,-3.4404,-2.4822,-3.1423\n-2.6425,-3.2278,-2.6128,-3.319,-3.2386,-3.0824,-3.1523,-3.3459,-2.9994,-3.2412,-3.5422,-3.942,-4.0408,-3.8689,-3.6604,-3.4702,-3.7392,-3.6521,-3.6857,-3.7535,-3.8723,-3.6821,-4.5088,-4.365,-4.2459,-4.6678,-4.4206,-3.6791,-3.2724,-3.3675,-3.3403,-3.179,-3.2178,-3.2873,-4.0023,-4.1194,-3.9537,-4.1956,-3.2839,-3.3608,-3.6264,-2.6735,-2.8831,-3.3325,-3.3526,-2.8643,-2.6956\n-2.3478,-2.6433,-2.5535,-2.7698,-3.1629,-2.7465,-2.8086,-3.0817,-3.2088,-3.3241,-3.0019,-3.4736,-3.48,-3.3607,-3.1711,-2.9961,-3.8514,-3.9536,-4.2217,-3.7679,-3.7429,-3.8896,-4.3517,-4.1506,-3.9789,-4.8928,-4.8613,-3.8368,-3.2917,-3.1492,-3.0321,-2.6641,-3.6234,-3.7015,-4.3838,-4.4095,-4.4208,-3.6461,-3.0919,-3.3115,-3.2202,-2.4725,-2.6277,-3.1543,-3.1367,-3.1759,-2.5698\n-2.3521,-2.2776,-2.194,-2.2435,-2.4617,-2.4362,-2.6868,-2.5448,-2.6526,-2.5184,-2.5447,-2.9792,-3.003,-2.6461,-2.3454,-2.8465,-3.6734,-4.1738,-4.6598,-4.82,-4.7628,-4.6462,-4.2419,-3.7118,-3.6768,-4.1595,-4.4122,-3.7575,-3.2716,-3.0276,-2.6591,-2.5042,-2.7333,-3.0869,-3.1791,-3.4067,-3.3224,-2.1979,-2.8375,-2.9687,-2.3381,-2.3355,-2.5104,-2.7293,-2.7273,-2.8471,-2.7979\n-1.7636,-1.276,-1.8006,-1.7414,-1.4468,-1.6292,-2.1321,-2.1076,-1.9421,-1.974,-1.9567,-2.0486,-2.0665,-1.9707,-2.0021,-2.781,-3.4532,-3.506,-4.3996,-4.8276,-5.1712,-5.3519,-3.9958,-3.5211,-3.7457,-3.9616,-3.2337,-3.257,-3.0664,-2.7135,-2.4881,-2.2252,-2.5878,-3.0705,-2.9284,-2.965,-2.0604,-1.816,-2.4168,-2.6917,-2.4737,-3.4415,-3.2144,-3.0171,-2.4683,-2.478,-2.4133\n-1.3034,-0.99309,-1.1381,-1.2853,-1.0881,-1.646,-1.7552,-1.3435,-1.4872,-1.4144,-1.3425,-1.6803,-1.7115,-1.7804,-2.0279,-2.3699,-2.9171,-2.93,-3.6834,-4.0516,-4.004,-4.3811,-3.3537,-3.1875,-3.9645,-3.6793,-2.9979,-3.1302,-2.8662,-2.9706,-2.7526,-2.6479,-3.0998,-2.7967,-2.8644,-2.8695,-2.1728,-1.8201,-2.2387,-2.2578,-2.3774,-3.022,-2.9955,-3.2938,-3.1062,-2.9313,-2.544\n-0.848,-0.65069,-0.87901,-0.74226,-0.89102,-0.9267,-0.82929,-0.52785,-0.18849,-0.62137,-1.0612,-1.1139,-1.1749,-1.3873,-1.7647,-2.0348,-2.1537,-2.5968,-3.3961,-3.7787,-3.5941,-2.7156,-2.8425,-2.9917,-2.9924,-2.7657,-3.0224,-3.0001,-2.7633,-2.9561,-2.747,-2.5331,-2.5781,-2.5686,-2.5836,-2.7236,-2.1175,-2.1121,-2.1168,-1.8791,-2.2964,-2.7244,-2.6121,-2.6221,-2.3727,-2.3293,-2.3311\n-0.59212,-0.35437,-0.65197,-0.044322,-0.42334,-0.48464,-0.17811,-0.10228,0.14926,-0.38072,-0.67735,-0.76553,-0.8039,-1.0424,-1.4345,-1.5175,-2.5403,-2.7759,-3.2974,-4.3136,-4.5905,-2.4325,-2.4927,-2.3813,-2.4359,-2.5141,-2.7377,-2.9214,-2.175,-2.8507,-2.4953,-2.5119,-2.274,-2.2544,-2.1349,-2.0495,-1.4597,-2.047,-1.9084,-1.7643,-1.5688,-1.3768,-1.3643,-2.0546,-1.9555,-2.0487,-2.1353\n-0.24693,-0.44553,-0.20006,0.66917,0.70313,0.55486,0.13368,0.029654,0.23024,0.077081,0.056918,0.25768,-0.52038,-0.64095,-0.7564,-1.3664,-2.7561,-2.3647,-3.0867,-3.4586,-2.8971,-2.2265,-1.9521,-1.8244,-2.1169,-2.419,-3.5112,-2.4583,-1.9217,-2.6059,-2.5741,-2.4133,-2.1313,-2.0431,-1.3957,-0.56684,-0.65635,-1.3322,-1.5595,-1.4106,-1.2378,-1.1509,-0.87899,-1.2668,-1.6702,-1.8952,-1.7357\n-0.11619,0.15703,1.3443,1.2932,0.68442,0.49191,0.28556,0.24907,0.90823,1.0766,0.78875,0.53864,-0.064882,-0.3296,-0.27108,-1.8081,-3.1802,-1.8602,-2.213,-3.211,-2.3963,-1.9233,-1.5468,-1.5587,-1.7371,-2.3844,-3.3805,0.39194,-2.0683,-2.3679,-2.2774,-1.9442,-1.6591,-1.4617,-1.2134,-0.56093,-0.85038,-1.0188,-1.1471,-1.403,-1.3062,-1.4801,-1.9063,-1.4478,-0.93478,-1.4047,-1.0471\n0.026525,0.23332,0.97254,1.8766,1.2433,1.1272,1.0833,0.88517,1.3062,2.0582,0.94869,0.50136,-0.05824,-0.56069,-0.49715,-1.0147,-1.9747,-1.6368,-3.1342,-3.8407,-2.0644,-1.4561,-1.1161,-0.84228,-1.3009,-1.5616,-0.82672,0.33824,-0.99466,-1.5612,-1.7626,-1.5551,-1.3732,-1.32,-1.0867,-0.95063,-0.61503,-0.41741,-0.77869,-1.1758,-1.1658,-1.4021,-1.3087,-0.67731,-0.84013,-1.2704,-1.4939\n0.20434,0.23372,1.006,1.718,2.0157,1.8909,1.408,1.1643,1.5748,2.0313,1.7152,0.027931,-0.34699,-0.53787,-0.6328,-1.5605,-1.7105,-1.7156,-1.9134,-1.6384,-1.3971,-1.0767,-0.56619,-0.54378,-0.69828,-0.97072,-0.71343,0.059493,0.28079,-0.49524,-1.1717,-1.3183,-1.3241,-0.97022,-0.51389,-1.0434,-0.42299,-0.33982,-0.99668,-0.51849,-0.52152,-0.63344,-0.54837,-0.56318,-0.33916,-0.88434,-1.2008\n-0.77479,-0.0129,0.15379,2.0106,1.403,1.3816,1.7509,2.1425,2.2965,2.2009,1.8462,0.48556,0.54397,-0.18114,-0.64585,-0.52672,-0.89478,-1.7862,-0.60006,-1.1884,-0.9467,-0.35668,-0.23542,-0.54276,-0.86643,-1.0718,0.11285,0.20238,-0.087375,-0.7295,-0.97984,-1.1882,-0.78034,-0.73999,-0.5357,-0.46576,-0.38038,-0.56301,-0.6036,-0.42019,-0.47836,-0.40846,-0.66078,-0.59957,-0.062824,-0.51201,-0.60356\n-0.9156,-0.21129,1.028,2.2864,1.893,1.9987,1.9401,2.5031,2.4518,1.6564,1.4075,1.1038,-0.099395,-0.1797,-0.6813,-0.96514,-1.1669,-0.0060463,0.63028,-0.62754,-0.52398,-0.14583,-0.092757,-0.18883,-0.3181,-0.65998,1.0061,0.62344,-0.10948,-0.88033,-0.97594,-0.76902,-1.0708,-0.8065,-0.65753,-0.29006,-0.43508,-0.65456,-0.48444,-0.43696,-0.28823,-0.17682,-0.2626,-0.041358,0.0033369,-0.22201,-0.72189\n-0.31085,0.0027447,1.0669,1.1952,0.77102,3.2511,2.5448,1.6487,1.2237,1.3763,3.2098,2.827,-2.397,-1.1276,-0.70681,-1.0799,-1.0502,0.31898,0.24058,-0.14618,-0.25651,-0.23396,-0.082694,0.46872,0.76022,0.71977,0.47529,0.34791,0.084718,-0.23421,-0.52919,-0.36364,-0.40494,-0.55469,-0.528,-0.23826,-0.33784,-0.30915,0.15401,-0.15081,-0.71995,-0.28357,-0.03942,0.19009,0.086705,-0.36442,-0.86636\n-1.6249,-1.2363,-0.89437,0.18966,0.64514,3.6413,2.2653,1.8589,1.8417,1.6226,2.2563,2.686,-0.31055,-0.76191,-1.5032,-1.7783,-1.3668,-0.56976,0.019547,0.37054,0.64701,-0.12658,-0.52787,0.039414,0.32547,-0.042568,0.23386,0.30612,-0.14828,0.046187,-0.27271,-0.39844,-0.41349,-0.24054,-0.27027,-0.32247,-0.20538,0.25494,0.60785,0.31024,0.18871,-0.081903,0.018786,0.36751,0.086782,-0.48987,-0.38846\n-3.3992,-2.0839,-1.7593,-0.38204,0.35526,1.5929,0.94937,1.8917,1.7991,1.7181,0.86395,1.7864,0.30528,-0.23659,-0.4301,-1.8392,-1.4021,-0.61445,0.69319,0.62693,0.142,-0.38038,-0.55585,-0.076662,0.018313,-0.26739,0.1983,0.29847,-0.2746,-0.032217,-0.23771,-0.63276,-0.30544,-0.025884,-0.24404,-0.24984,0,0.35459,0.43891,0.30622,0.15929,0.012505,-0.093565,0.020424,0.23599,-0.063346,-0.042088\n-3.542,-2.1867,-1.2201,-0.97855,-0.5625,-1.1454,-0.24901,0.80308,-0.062946,0.41704,1.5407,1.1823,-0.32663,-0.039718,-1.9398,-3.3038,-1.7156,-0.068895,0.24515,0.95214,0.5175,0.33826,-0.44814,0.37939,0.44648,0.090384,0.3393,0.10847,0.28524,0.24367,-0.2476,-0.61489,-0.33837,-0.060735,0.19061,-0.20309,-0.12241,-0.0029726,0.35493,0.33477,0.29112,0.080083,-0.13611,-0.27773,-0.39411,-0.24106,0.47082\n-1.9043,-1.8091,-0.48779,-0.33983,-0.73955,-1.575,-0.45539,-0.85106,-0.19467,-0.4947,0.61478,-1.0118,-0.54776,0.19557,0.54652,-0.22506,-0.24769,-0.3319,0.34357,0.85382,0.98991,1.0843,0.46972,0.79608,0.54711,0.94974,0.35983,0.41064,0.86927,1.0503,0.49678,-0.17457,-0.087771,-0.26233,-0.13487,-0.19243,-0.17183,-0.13205,-0.0020704,-0.067628,-0.017005,-0.15111,-0.28813,-0.32278,-0.27844,-0.13457,-0.057475\n-0.23865,0.12016,0.57778,0.65213,0.8469,0.71431,-0.020505,-0.20763,1.8242,-1.1697,1.1717,0.22389,-0.53657,-1.512,1.6548,0.39285,-0.54594,-0.45636,0.53051,1.1436,0.37876,0.56567,0.95789,1.133,0.82125,0.74436,0.65031,0.70796,0.6183,0.5551,0.49219,-0.04878,0.47701,-0.13186,-0.44989,-0.29407,-0.51842,-0.30989,-0.34893,-0.24664,0.0070162,0.23562,0.2406,-0.27353,-0.31425,-0.23809,0.061811\n1.0959,0.99417,1.7669,2.7475,4.1779,1.0804,0.97305,1.5643,1.9362,-1.2808,2.1145,1.2984,0.26853,-1.9049,-1.2865,-1.5098,-1.528,0.11049,0.40393,0.49923,0.6884,1.0598,0.3889,0.88571,0.84389,0.85349,0.69221,0.91889,0.73308,0.16365,-0.18995,-0.032855,0.34391,0.18528,-0.091058,-0.87032,-0.79749,-0.60607,-0.49331,-0.20099,-0.017817,0.42475,0.18978,-0.33999,-0.35802,-0.27947,0.061816\n2.1559,1.5491,2.1694,2.0438,1.3326,0.31337,0.32165,0.17769,-0.47514,1.1118,2.8507,0.73063,-0.28173,-2.777,-2.2559,-1.6909,-3.168,-0.98965,0.14698,0.58725,0.39515,0.90252,0.85164,0.3735,0.90779,1.4079,1.387,1.5216,0.63999,0.22727,-0.25077,-0.21392,-0.14245,-0.35725,0.00011349,-0.47546,-0.48749,-0.64105,-0.59471,-0.34939,0.096866,0.20375,-0.0038452,-0.33474,-0.38414,-0.12669,0.41066\n2.4619,3.4,3.2709,2.595,1.3455,0.069933,0.60071,1.4824,1.465,0.065136,-0.60772,2.1728,1.3516,-3.4237,-4.5169,-3.7048,-2.8131,-0.28088,0.57281,0.12773,0.43429,1.4792,1.7578,0.41576,0.87409,1.2821,1.8426,1.2662,0.79045,0.80912,0.53599,0.49279,-0.15836,-0.16228,-0.28241,-0.51974,-0.59951,-0.80728,-0.57904,-0.20882,-0.059016,-0.071497,0.20196,0.034522,-0.15967,0.11947,0.69882\n3.1077,3.8871,3.8924,2.8112,2.2266,1.9775,1.5469,0.86225,1.4881,1.0653,0.013855,-0.018682,-0.75134,-2.6939,-2.1063,1.2944,0.0892,0.61129,0.36528,-0.17661,0.58363,1.2164,1.0977,1.6903,0.95182,1.2042,1.5621,1.3534,1.0856,1.1381,0.61452,0.53075,0.29518,0.28372,-0.47673,-0.56414,-0.68629,-0.51288,-0.40863,-0.322,-0.43172,-0.078301,0.42239,-0.050151,-0.27118,0.12021,0.41779\n3.7583,3.5564,3.6346,3.152,4.3327,2.9922,2.3239,1.936,1.6033,2.5954,3.5484,0.48347,2.6223,2.93,0.9499,2.6073,1.5173,0.53297,0.064939,0.23969,0.40957,0.42784,1.4322,1.65,0.61472,0.66132,1.4595,1.0278,1.0563,0.8581,0.41901,0.41216,0.12656,-0.050528,-0.60862,-0.66483,-0.59932,-0.36557,-0.19485,-0.34193,-0.42107,-0.336,0.0068703,-0.24864,-0.44636,0.60434,0.54703\n4.8347,3.2687,4.2837,3.9802,4.0228,3.8078,4.3097,4.5271,1.4589,2.3747,1.7552,0.49334,2.4436,2.59,2.5803,1.3206,0.74213,0.82144,-0.9727,1.0554,0.342,0.83331,1.1672,0.84834,1.1774,1.3128,1.3142,0.69006,0.66496,0.072939,0.1158,0.65032,0.41207,0.29385,-0.49858,-1.0093,-1.1771,-0.63613,-0.43942,-0.64229,-0.69929,-0.57354,-0.17557,-0.12304,-0.047185,0.33277,0.15546\n3.6829,4.0551,4.4101,3.3391,5.6785,4.3217,6.0096,4.0724,1.9125,1.9645,1.8099,1.0476,-1.2281,-2.5343,-0.73078,1.3505,-0.26786,-1.0475,-0.35784,0.1472,0.13528,1.0454,1.1565,1.6179,1.0883,0.52998,0.9484,1.0154,0.69736,0.060475,0.046253,-0.016684,-0.35907,-0.20968,-0.75542,-0.90323,-1.3222,-0.91374,-0.88564,-0.81404,-0.66259,-0.59135,-0.42938,-0.38498,-0.39397,-0.48216,-0.54303\n3.6734,4.448,4.8556,4.972,5.9482,4.6646,4.6155,3.3365,3.9666,2.5946,1.468,0.60809,0.15935,0.086762,1.13,1.3316,1.766,1.1453,-0.26131,-0.39982,0.69086,1.5088,1.1366,1.1693,0.66851,0.33223,1.3054,1.0751,0.60444,-0.043571,0.46532,-0.47952,-0.62545,-0.71302,-0.80075,-0.53922,-1.1618,-1.4273,-1.113,-1.0342,-0.72674,-0.55666,-0.79532,-0.54817,-0.54536,-0.21039,-0.32556\n3.5108,3.4119,4.3091,4.7429,3.9499,3.882,4.2635,3.5596,3.1091,2.81,2.9713,1.4654,0.57266,-0.054497,1.4283,2.5106,3.2013,2.1788,-0.52664,-0.198,0.99784,1.3724,0.72594,0.85045,1.0556,1.2654,1.2375,0.53645,-0.020366,-0.14889,-0.080027,-0.47038,-0.57171,-0.67278,-0.74752,-1.0065,-0.9546,-1.0035,-1.2354,-0.66654,-0.96816,-0.98112,-0.57981,-0.46707,-0.40227,0.067204,-0.19467\n3.2519,2.9367,3.4201,3.5982,3.4801,3.8163,2.8959,2.9548,3.0764,2.9741,3.0907,3.4828,2.0358,0.52137,0.67275,1.3215,0.074231,-0.85549,-1.3149,-1.7737,-0.80388,0.85503,2.3984,1.5693,0.70433,1.1354,1.0921,-0.13413,-1.0317,-0.52017,-0.30947,-0.41554,-0.58297,-0.68089,-0.67546,-0.76445,-0.76809,-0.8414,-0.87879,-0.97189,-1.0697,-0.64395,-0.48621,-0.57598,-0.32765,-0.21212,-0.51673\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_060619-061002.unw.csv",
    "content": "-0.36766,-0.23864,-0.14121,-0.19751,0.083782,0.30457,-0.04038,-0.28601,-0.34706,-0.48281,-1.0298,-0.84745,-1.0135,-1.4346,-1.4762,-1.4271,-1.3915,-1.5529,-1.5762,-1.4079,-0.50874,-0.081203,-0.17455,-0.77666,-1.1574,-1.0401,-1.1584,-0.62115,-0.31847,-0.76365,-1.3124,-1.5444,-1.4753,-1.4513,-1.3491,-1.1756,-1.2526,-1.9026,-2.2456,-1.7379,-0.94529,-0.011604,0.6122,0.84764,1.1543,1.4434,1.5602\n-0.23811,-0.099125,-0.069628,0.02746,0.15586,-0.0033646,-0.43647,-0.69615,-0.49101,-0.47812,-0.6083,-0.60815,-1.0219,-1.3061,-1.2195,-1.1077,-0.92484,-1.2972,-1.3476,-1.1033,-0.79914,-0.0089493,-0.23435,-0.55065,-1.001,-1.1733,-1.0817,-0.57795,-0.43166,-0.77147,-0.8183,-0.56776,-0.91699,-1.3579,-1.5034,-1.4233,-1.5359,-1.4807,-1.0345,-0.36905,0.074604,0.33173,0.55785,0.84664,1.2584,1.4007,1.3526\n-0.24913,-0.29116,-0.12854,-0.01741,0.0034294,-0.20642,-0.726,-0.9276,-0.35098,-0.21359,-0.50793,-0.40986,-0.9124,-1.0976,-1.0599,-1.1359,-0.87832,-1.1636,-1.3048,-1.0052,-0.76002,-0.48615,-1.1994,-1.0663,-0.83401,-1.1542,-0.86758,-0.32127,-0.45093,-0.84071,-0.46963,-0.41564,-0.67535,-0.91018,-1.2071,-1.1526,-0.56348,-0.11612,0.36453,0.90359,0.79811,0.63671,0.80624,0.90924,1.132,1.0696,0.99623\n-0.26788,-0.20221,-0.22437,-0.089947,0.011364,-0.21659,-0.80924,-0.83891,-0.57362,-0.42585,-0.61446,-0.50837,-0.68133,-1.0175,-1.1162,-1.0991,-0.7757,-0.96772,-1.1782,-1.03,-0.82698,-0.59584,-0.57093,-0.63191,-0.75356,-0.72274,-0.46418,-0.34813,-0.23582,-0.26132,-0.43294,-0.45754,-0.36262,-0.61261,-0.9887,-1.0134,-0.67418,-0.06341,0.5015,0.81471,0.83672,0.76808,0.76518,0.64534,0.57858,0.8792,1.2171\n-0.4233,-0.78336,-0.66757,-0.27303,-0.12197,-0.40192,-0.69181,-0.98517,-1.1612,-0.91658,-0.62236,-0.52382,-0.49328,-0.85472,-0.96054,-0.7167,-0.43253,-1.1061,-1.0183,-0.8014,-0.72925,-0.37919,-0.30429,-0.65135,-0.69354,-0.36818,0.0069847,0.08773,-0.017498,0.053093,-0.36469,-0.59286,-0.33508,-0.43896,-0.93526,-0.98773,-0.48615,0.026443,0.45225,0.81862,0.86938,0.66052,0.61734,0.58283,0.62793,0.75816,0.95599\n-0.017704,0.005455,-0.21157,-0.064295,-0.14408,-0.3069,-0.68434,-0.84686,-0.91439,-0.57461,-0.22223,-0.092327,-0.37275,-0.72871,-0.8719,-0.69895,-0.81708,-1.2681,-1.1953,-0.81553,-0.47838,-0.33961,-0.18972,-0.60634,-0.94832,-0.50404,0.032322,0.18887,0.025505,0.12062,-0.0028763,-0.3123,-0.28904,-0.52846,-0.90221,-0.9598,-0.3023,0.21114,0.49281,0.97132,0.65637,0.091133,0.077717,0.30147,0.5605,1.0327,1.2405\n0.40078,0.54668,0.42578,0.188,-0.25962,-0.58623,-0.67819,-0.45142,-0.27882,-0.14027,-0.093487,0.22814,-0.074989,-0.6116,-0.75246,-0.72676,-0.95616,-1.1435,-1.2412,-0.6455,0.011435,0.0035934,0.014294,-0.058779,-0.16618,0.22086,0.49628,0.10079,-0.041473,0.27933,0.21888,-0.040756,0.025181,-0.14518,-0.33598,-0.28925,0.079733,0.38437,0.21025,0.48936,0.47527,-0.2858,-0.20397,0.16804,0.52879,0.95872,1.114\n0.67144,0.71267,0.28651,0.23799,-0.21455,-0.76912,-0.8236,-0.46947,-0.29947,-0.33174,-0.34127,-0.14164,-0.17184,-0.47913,-0.55034,-0.84552,-1.3661,-1.2427,-0.71422,-0.15438,0.64812,0.41581,0.096024,0.015553,0.080084,0.31813,0.75673,0.32151,-0.27211,0.30705,0.27876,0.043911,0.086325,-0.042814,0.0083847,0.23131,0.51139,0.34671,0.072178,0.14336,0.239,-0.082058,0.077316,0.57122,0.69344,0.83273,1.0173\n0.37492,0.75057,0.73061,0.22307,-0.22198,-0.60249,-0.50236,-0.25077,-0.068512,-0.16202,-0.49278,-0.64638,-0.60822,-0.5652,-0.69802,-0.84167,-1.1478,-0.99902,-0.33808,0.46817,0.77395,0.4517,0.25229,0.26822,-0.020327,0.099808,0.096781,0.026333,-0.22224,-0.29022,-0.17445,-0.15613,-0.085278,-0.041363,-0.025425,0.33614,0.47903,-0.029276,-0.5272,-0.34404,-0.12536,-0.036423,-0.052599,0.8384,0.84752,0.86381,0.98508\n0.42853,0.55192,0.56446,-0.67477,-0.62199,-0.4537,-0.46286,-0.055946,0.24695,0.17036,-0.44732,-0.78751,-0.70783,-0.62619,-0.93992,-0.73619,-0.46827,-0.15003,0.017403,0.086292,0.18173,-0.10067,0.012596,0.076363,-0.069223,0.091507,0.12007,-0.049688,-0.19568,-0.26536,-0.29175,-0.26868,-0.23948,-0.16977,-0.30441,-0.26546,-0.062332,0.11748,-0.38852,-0.59407,-0.46729,-0.18657,0.18725,0.84229,1.1517,1.2032,1.2133\n0.40273,0.14541,-0.23605,-0.77836,-0.21823,-0.29596,-0.26062,-0.029144,0.15333,0.069197,-0.28546,-0.6974,-0.5736,-0.46131,-0.46738,-0.40223,-0.48241,-0.077469,0.49132,0.72226,0.44922,0.040783,-0.066536,-0.25017,-0.071075,0.031727,-0.039013,-0.092819,-0.11324,-0.17039,-0.36039,-0.37586,-0.22744,-0.42498,-0.6769,-0.30032,0.091604,0.10092,-0.36214,-0.699,-0.47799,-0.12167,0.19147,0.64576,1.23,1.3874,1.2655\n0.51203,0.33551,-0.17152,-0.40286,-0.094627,-0.29486,-0.3968,-0.23032,-0.11221,-0.032888,-0.46682,-0.39466,-0.19606,-0.28388,-0.54418,-0.67445,-0.64484,-0.48817,-0.4215,-0.68171,-0.55819,-0.087227,0.18121,-0.14133,-0.034979,0.18004,0.089767,0.047441,0.058834,-0.2136,-0.40446,-0.42939,-0.34487,-0.54843,-0.70993,-0.25485,0.42419,0.2649,-0.37986,-0.46056,-0.27733,-0.11188,0.15629,0.66745,1.0901,1.6787,1.5894\n0.766,0.86431,0.1634,-0.2957,-0.35613,-0.50794,-0.58694,-0.51507,-0.33928,-0.19756,-0.34175,-0.543,-0.15459,-0.1407,-0.56767,-0.95993,-1.1844,-1.2737,-0.89014,-0.72345,-0.53506,0.061491,0.4116,0.18648,0.33891,0.26357,0.047142,-0.12435,0.026485,-0.043343,-0.32627,-0.50731,-0.60703,-0.49573,-0.42378,-0.071962,0.47046,0.64907,-0.43921,-0.5378,-0.15616,-0.0057545,0.28718,0.51507,1.1723,1.4337,1.7503\n0.43842,0.13647,-0.044518,-0.42506,-0.60114,-0.93436,-0.52905,-0.40405,-0.32255,-0.37166,-0.2085,-0.066097,-0.083864,-0.14659,-0.5881,-0.95664,-0.93488,-0.85828,-0.43546,0.031658,-0.074444,-0.44146,-0.55504,-0.63572,-0.85586,-0.90725,-0.54825,-0.46793,-0.25856,-0.097012,-0.24877,-0.38617,-0.54402,-0.42573,-0.036255,0.58144,0.9128,0.82011,0.16512,-0.29906,0.34344,0.43581,0.60461,0.92541,1.422,1.8024,2.0095\n0.44764,0.22024,-0.40365,-0.79977,-0.74468,-0.80526,-0.15782,-0.11424,-0.58617,-0.50233,-0.23937,-0.17507,-0.071335,-0.097368,-0.29717,-0.62777,-0.55822,-0.62973,-0.38556,0.032404,-0.39383,-0.93322,-1.0493,-1.1401,-1.0325,-0.66856,-0.30105,-0.25031,-0.1803,0.057001,0.342,0.22971,-0.18127,-0.30441,0.15312,0.81474,0.87408,0.49373,-0.0085468,-0.22753,0.024605,0.60728,0.85771,1.2544,1.4387,1.9088,2.1078\n0.53815,0.55036,0.056797,-0.94654,-1.8118,-1.1853,-0.4672,-0.0048103,-0.48401,-0.29812,-0.10218,0.046101,0.080914,-0.21052,-0.46632,-0.42916,-0.6031,-1.0712,-1.2425,-0.92739,-1.0731,-1.1246,-1.0586,-1.2512,-0.83577,-0.22359,0.088892,0.18978,0.44967,0.99811,0.99438,0.66892,0.28584,0.16569,0.53876,1.069,0.83214,0.20312,-0.10562,-0.31495,-0.11095,0.44766,0.75002,0.87308,0.94348,1.0586,1.2801\n0.64653,0.93292,0.22795,-1.2013,-2.2097,-1.9372,-0.96726,-0.1876,0.1235,0.17542,0.25391,0.21458,-0.0094891,-0.23377,-0.11869,-0.24267,-0.76656,-1.2286,-1.2772,-1.0952,-0.54472,-0.33926,-0.43251,-0.4501,0.36592,0.79253,0.51131,0.14986,0.54449,1.2199,1.1536,0.75902,0.61581,0.6666,0.92691,1.3677,1.7944,1.2235,0.4439,0.046654,-0.33408,0.024729,0.61752,0.78678,0.85926,1.0056,0.96736\n0.41342,0.3844,-0.33275,-1.1361,-2.4213,-1.886,-1.0682,0.12525,0.079006,-0.3121,-0.24714,-0.12829,-0.28197,-0.14894,-0.14931,-0.30178,-0.52922,-1.2826,-1.4829,-1.1039,-0.43008,0.076969,0.15691,-0.13721,0.32923,0.92908,0.76973,0.0094757,-0.17004,0.60599,0.94228,0.85199,0.76801,0.69362,1.0595,1.5842,2.299,2.0316,1.8543,0.34939,-0.23022,-0.57667,0.21603,1.0028,0.86423,0.89661,0.91818\n-0.0037041,-0.19824,-0.55081,-1.4043,-1.5379,-1.4618,-0.80969,-0.38424,-0.56619,-0.74593,-0.32203,-0.17985,-0.51281,-0.07222,0.20092,0.34696,0.18456,-0.51774,-1.1102,-1.3887,-1.1864,-0.70882,0.063431,-0.00061417,-0.051533,0.68169,1.1425,0.58083,-0.35725,0.19597,0.82066,0.92533,1.4297,1.4821,1.696,1.8728,2.232,2.1297,1.7934,0.8327,0.4142,-0.19127,0.028629,0.8449,0.73423,0.73381,0.93073\n-0.82804,-0.72931,-0.3552,-0.67038,-1.0831,-1.2456,-0.62155,-0.36023,-0.30371,-0.28603,-0.039875,-0.059563,-0.20611,0.19141,0.61855,0.55946,0.54386,0.28585,-0.098953,-0.50905,-0.36213,-0.06205,0.37457,0.21903,-0.071089,0.47728,0.92404,1.1045,0.80585,0.036432,0.55619,1.2101,2.0709,1.9124,2.3417,2.1614,2.257,2.2679,1.8636,1.529,0.94377,0.51208,0.39508,0.73074,0.73232,0.49975,0.7835\n-0.78954,-0.70291,-0.18643,-0.044941,-0.1956,-0.55839,-0.28102,-0.44097,-0.32817,0.17046,0.23516,0.22618,0.16451,0.3187,0.623,0.80662,1.0186,1.1337,0.54784,0.52266,0.71272,0.82125,0.67221,0.38219,0.28484,0.58823,0.92546,1.5534,1.4696,0.91238,1.3025,1.7408,2.4718,2.4671,2.9099,2.529,2.0901,2.0495,1.7448,1.3872,1.0353,0.99714,0.99413,0.81249,0.68177,0.64169,0.84386\n-0.79493,-0.5363,-0.54969,-0.84487,-1.4461,-2.2958,-0.84222,-0.50449,0.0076141,0.12923,0.22107,0.38205,0.57731,0.42005,0.28635,0.88693,0.8577,0.94985,1.1097,1.2313,1.3848,1.2721,0.87852,0.848,0.91222,1.2266,1.5845,1.9278,2.017,1.9887,1.8951,1.9111,2.4597,2.5378,2.8167,2.711,2.3763,1.8264,1.5758,1.5213,1.227,1.1614,1.049,0.87885,0.73934,0.81779,0.81447\n-0.40689,-0.42668,-0.088835,-0.54029,-1.6744,-2.2416,-0.7244,0.24046,0.29271,-0.085224,0.22251,0.87888,0.8232,0.8914,0.44701,0.037994,0.29498,0.6751,1.046,1.5327,1.7571,1.6557,1.5281,1.7091,1.6565,1.7292,2.0446,1.9241,1.8623,2.064,2.1566,2.06,2.1208,2.1849,2.2417,2.254,2.0627,1.8983,1.8053,1.4918,1.5598,1.3898,1.2402,1.001,0.99968,0.92512,0.73992\n-0.092537,0.11959,0.12808,0.011267,-0.1871,0.26631,0.64209,0.69085,0.50655,0.2082,0.34898,0.33437,0.63272,1.318,1.586,1.4159,0.2337,0.6984,1.3004,1.8098,2.0256,2.0162,1.9514,1.8592,1.6902,1.9654,2.3575,2.4685,2.3824,2.5649,2.6926,2.4924,2.4533,2.3273,2.1232,2.1884,2.1276,2.0142,2.0911,1.9608,1.7903,1.5423,1.3928,1.0916,1.063,1.2699,1.1956\n-0.67417,-0.2808,0.22854,0.16436,-0.065868,-0.032837,0.1331,0.34219,0.5504,0.63637,0.50502,0.38404,0.61811,1.5541,1.5129,1.0786,0.77665,1.432,2.1765,2.4662,2.3639,2.1412,2.2435,2.2193,1.7976,2.4242,2.7266,2.937,3.2516,3.639,3.0824,2.7846,2.7769,2.8031,3.0419,3.159,2.5228,2.3131,2.264,2.0983,1.8323,1.797,1.6032,1.2432,1.0495,1.0936,1.3091\n-0.19653,-0.30964,-0.091114,-0.0096054,-0.084116,-0.27498,0.00061417,0.29308,0.41332,0.79002,0.84715,1.0284,1.1455,1.5016,1.6392,1.4382,1.4512,1.9253,2.6278,2.8176,2.7572,2.6882,2.5143,2.4567,2.881,3.4479,3.8915,3.9132,4.6717,5.0846,5.0243,3.9131,3.2706,3.013,3.2004,3.2429,2.7423,2.5733,2.2585,1.9746,1.8242,1.7025,1.5969,1.3834,1.1712,0.84209,0.77839\n-0.13465,-0.19993,-0.56813,-0.56121,-0.49871,-0.4013,-0.30054,0.079414,0.28414,0.4311,0.63433,1.073,1.8251,2.1987,2.0839,1.6065,1.5539,2.3338,3.1856,3.3246,3.1934,3.0052,2.9332,2.8698,3.5365,4.026,4.0059,4.4227,5.1539,6.0734,5.6668,5.193,4.3733,3.7134,3.5546,3.2734,2.8573,2.3943,2.0769,1.9444,1.7579,1.5135,1.5682,1.5825,1.369,0.74413,0.3734\n0.10699,0.051956,-0.43131,-0.9051,-0.62671,-0.33025,-0.23687,-0.43348,0.10078,0.24681,0.49999,0.8505,1.2486,1.7826,2.0799,2.0827,2.3073,2.8788,3.2522,3.4116,3.3768,3.0695,2.911,3.1644,3.6955,3.9611,4.1244,4.1967,5.1483,6.1008,6.5212,6.4629,6.0474,5.2823,4.5703,3.8222,2.8408,2.3054,2.0465,1.9493,1.498,1.5605,1.4796,1.3633,1.1896,0.81126,0.61308\n0.37172,0.91009,0.16245,-0.2452,-0.17866,-0.066082,-0.25593,-0.73121,-0.27993,0.14487,0.23366,0.81285,1.1647,1.6291,2.0114,2.2499,2.3318,2.6412,3.2726,3.6242,3.6965,3.2223,3.4571,3.7742,4.0036,4.4258,5.5018,NaN,NaN,NaN,9.3515,8.0506,7.7975,5.9039,4.4155,3.8784,3.0082,2.425,2.3887,2.1026,1.2266,1.1238,1.5512,1.3643,1.2198,0.92217,0.64734\n0.59325,0.1985,0.11006,-0.10025,-0.053669,-0.11495,-0.0060234,-0.31559,-0.1086,0.1232,0.20266,0.53289,0.94568,1.4964,1.9906,2.1141,1.9668,1.8852,2.2681,3.0244,3.6069,3.1913,3.5506,4.4085,5.4409,5.907,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.0254,4.4685,3.9342,3.6673,3.1594,2.758,2.0403,1.0085,1.1182,1.7366,1.4977,0.86242,0.44617,0.36506\n-0.090401,0.22481,0.28501,0.23737,0.072889,-0.50762,-1.0913,-0.72584,-0.045303,0.58072,0.72665,0.6909,0.84005,1.2567,2.0715,2.034,1.7903,1.8952,2.1081,2.51,3.0604,3.2901,3.7034,4.9286,6.7625,7.988,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.0667,3.3598,3.9914,4.7683,4.4193,3.4124,1.9854,1.2867,1.1994,1.5367,1.8171,0.98325,0.29816,0.2274\n-0.1912,-0.15781,0.17442,0.87036,0.72562,0.40144,-0.08012,0.040831,0.12181,0.79207,1.2016,1.0181,1.0822,1.285,1.928,1.7878,1.5713,2.1718,2.7572,2.8769,3.1028,3.5235,3.9963,5.0101,5.1637,5.4102,5.3455,NaN,NaN,NaN,NaN,NaN,4.3343,3.2502,3.9513,4.6138,4.826,4.4969,3.3738,1.9585,1.3875,1.222,1.1046,1.811,0.89139,0.0016518,0.043331\n-0.35244,-0.052191,0.23196,0.63378,1.1272,0.79945,0.4593,0.61354,0.69916,1.0598,0.93735,0.77354,1.1234,1.8237,1.8463,1.6964,1.2098,1.5655,2.4676,2.8867,3.0773,3.6242,3.9948,4.4125,4.2323,4.0798,3.5697,2.3537,2.3489,2.8624,3.3109,3.4563,2.4703,1.598,3.1595,3.9187,4.133,4.5381,3.3128,2.0382,1.3616,1.1073,1.1035,1.0463,0.38655,0.035782,0.082806\n-0.25331,0.026651,0.29423,0.79411,1.0458,0.48779,0.27651,0.3359,0.41084,0.84459,0.93157,0.88589,0.88223,1.3612,1.3452,1.472,1.3986,1.6464,1.9692,2.2415,2.4656,2.9764,3.5692,3.9002,3.6313,2.8348,3.2937,2.6068,2.4831,2.7296,2.0215,1.4048,0.42849,1.1645,NaN,1.9906,3.9476,4.7817,4.2566,2.2721,1.1132,1.0402,1.5289,1.1595,0.13924,-0.1341,-0.12698\n0.071426,-0.30363,-0.17837,0.2315,0.10806,-0.30116,-0.82891,-0.2263,0.16762,0.56692,0.80219,0.93264,0.85741,0.63408,0.66528,1.0486,1.3874,1.5105,1.7031,2.0182,2.2064,1.9975,2.5041,4.6489,NaN,2.4588,3.3441,2.2292,-0.062315,0.83772,0.99771,0.10122,0.28825,-0.63073,NaN,NaN,NaN,NaN,NaN,2.2224,1.4148,1.5266,1.7983,0.93909,0.16607,-0.086842,-0.21141\n-0.50014,-0.77804,-1.2637,-0.82789,-0.11406,-0.16024,-0.18281,0.0045891,0.19294,0.017145,0.73037,0.91242,0.86585,0.33334,-0.61307,0.049259,1.2567,1.2857,1.3693,1.6793,2.024,1.9834,3.9355,NaN,NaN,NaN,NaN,2.0939,1.8368,1.6283,0.52767,0.31163,-1.2335,NaN,NaN,NaN,NaN,NaN,3.6076,2.0146,1.6295,1.685,1.6709,1.0149,0.19427,0.095856,0.096203\n-0.76699,-0.72482,-0.78688,-1.1923,-0.20055,0.15042,0.19857,0.18114,0.051788,-0.19929,0.052475,0.21325,0.3803,-0.7347,-0.70115,0.19523,0.62892,0.85668,1.265,1.5355,1.9075,3.078,5.4833,NaN,NaN,NaN,0.25377,0.27152,2.5875,2.0976,0.62719,0.81865,1.265,3.9815,NaN,NaN,NaN,NaN,2.5687,1.8478,1.7378,1.4124,0.90192,0.20567,-0.090675,0.036362,0.03051\n-0.82183,-0.70163,-0.75711,-0.68361,-0.24266,0.10524,0.32245,0.51701,0.48352,0.21792,0.22846,-0.20522,-0.52888,-1.2207,-0.57092,0.72208,0.84591,1.0944,1.6449,2.0587,2.4266,3.1964,4.3753,NaN,NaN,NaN,NaN,-0.30691,2.1566,2.4072,1.4892,1.1604,1.4161,4.5542,4.7544,4.6459,2.7413,2.5996,2.396,1.0731,0.77878,0.55749,0.092575,-0.063438,0.028725,0.093641,0.12503\n-0.34008,-0.39953,-0.74231,-0.84063,-0.62909,-0.19868,0.37688,0.64118,0.65853,0.64875,0.64979,0.45518,0.26637,-0.058966,0.29336,0.78195,0.52877,0.71217,1.3267,2.0094,2.3174,2.8267,2.8577,1.3601,-0.69334,-1.6024,-0.9842,0.69447,1.3451,0.8399,0.57906,0.62683,0.6177,2.0934,3.7234,3.008,2.479,2.3086,1.6453,0.46367,0.019447,-0.17049,0.36158,0.50459,0.62522,0.38056,0.2048\n-0.43828,-0.62265,-0.73933,-0.84848,-0.39512,0.35798,0.37917,0.62585,0.82489,0.8978,0.88815,0.73996,0.4084,0.30968,0.87022,0.76969,0.6238,1.3663,1.3061,2.1802,2.7511,2.5016,1.538,2.2613,3.7046,4.5606,3.8069,1.7145,1.1393,0.56547,-0.30722,-0.84274,-0.0221,0.72891,2.5631,2.9914,2.9001,2.2694,1.1516,-0.13911,-0.41829,-0.15038,0.35354,0.51888,0.68394,0.55623,0.44614\n-0.34233,-0.65352,-0.3873,-0.079485,0.25097,0.64032,0.8825,0.94866,1.0095,0.66899,0.86333,0.79863,0.66828,0.64856,1.0552,1.2569,1.1897,1.5532,1.567,2.3875,2.807,3.0396,1.8817,1.5104,1.2685,NaN,NaN,0.24899,-0.35529,-1.4091,-2.9432,-3.3909,-1.0401,1.372,2.1162,2.5146,2.7882,2.7215,1.6034,0.094137,-0.61948,-0.27374,0.32713,0.25862,0.18592,0.36303,0.44535\n0.34503,0.0084076,-0.23019,-0.098629,0.34856,0.49105,1.0754,1.2785,0.95763,0.53692,0.57468,0.76476,0.83129,0.88737,0.91071,1.0677,1.518,2.0143,2.2884,3.0195,2.6547,2.2452,1.8191,1.1855,0.31953,NaN,NaN,-1.1502,-0.15118,-0.6399,-1.3621,-1.0859,-0.1776,1.0652,1.681,2.1158,2.0933,2.1237,1.4429,0.26595,-0.33057,-0.10412,0.36893,0.22457,0.24858,0.67014,0.59839\n1.0237,0.38142,0.049065,0.16603,0.12801,0.18843,0.60149,1.0611,0.76313,0.72388,0.84073,0.99301,0.98181,0.91595,0.83849,0.69782,0.97438,1.5676,2.0404,2.1446,1.8926,1.341,1.1539,1.5146,NaN,NaN,NaN,NaN,2.0443,0.96885,-0.39124,-0.28491,-0.017061,1.1824,1.5006,1.4543,1.691,1.7495,1.3609,0.35892,0.26413,0.010412,0.12979,0.28615,0.42036,0.65888,0.25726\n0.69858,0.50108,0.3569,0.31687,-0.016878,0.27972,0.65242,1.05,0.85802,0.81992,0.99726,1.2304,1.2504,1.0256,1.019,0.96918,0.98803,1.2752,1.5495,1.5797,1.6885,1.7511,3.3838,3.5977,NaN,NaN,NaN,2.7496,2.2492,1.0303,0.066637,-0.36226,-0.33955,0.23295,0.4376,0.52434,0.87812,1.2795,0.51538,0.17,-0.045176,-0.1251,0.2248,0.17989,0.09474,0.2046,0.14775\n0.57702,0.47437,0.35497,0.54298,0.46061,0.57535,0.90449,1.0525,0.96629,0.81304,0.84921,1.1219,1.1549,1.078,1.1626,1.0885,1.0384,1.4018,1.5028,1.8657,3.0141,3.2868,3.9276,4.9543,6.1457,5.4976,4.2761,1.9076,1.1724,0.40808,-0.11865,-0.31146,-0.60558,-0.20506,-0.08342,0.27935,0.78205,0.55648,-1.0124,-0.71532,-0.43979,-0.41449,0.17692,0.50639,0.29735,0.0942,0.047722\n0.25689,0.17388,-0.012503,0.054678,0.30989,0.57528,0.98923,0.86621,0.61225,0.54187,0.76517,1.059,1.346,1.4926,1.282,1.3131,1.3536,1.3747,0.90535,0.41995,1.1367,1.7172,2.1012,2.0773,1.7847,1.9973,2.054,1.124,0.61546,0.30803,0.527,0.56305,0.43189,0.20428,0.12884,-0.18003,-0.23402,-0.87967,-1.8857,-1.6824,-1.1415,-0.8304,-0.061247,0.61594,0.43628,0.12354,-0.053974\n-0.22619,0.12659,-0.012808,-0.1803,0.10535,0.42881,0.77263,0.54189,0.28969,0.68974,1.0428,1.0959,1.094,1.1129,0.95704,0.73112,0.72738,0.76808,0.70261,0.06514,-0.63974,0.41295,1.0435,0.97439,1.1718,1.0549,0.49052,0.48056,0.243,1.1356,1.6632,1.0604,0.60291,0.22075,0.040731,-1.1514,-1.5677,-1.7935,-2.5623,-2.1997,-1.6005,-0.83377,-0.24383,0.23048,0.3779,0.27502,0.23146\n0.0424,0.055477,0.1607,0.26904,0.67913,0.74815,0.46326,0.19562,0.33526,0.59161,0.95714,1.1435,1.1188,0.97918,0.72177,0.4467,0.2805,0.31374,0.23297,-0.037422,-0.52616,-0.79737,-0.45993,0.32867,0.93312,0.67319,0.23521,0.093197,0.35949,0.43689,0.069176,-0.095375,-0.42205,0.13145,-1.2781,-2.2474,-2.9335,-2.4322,-2.3855,-2.3689,-2.3909,-1.6634,-0.52041,0.27404,0.35444,0.16909,0.43518\n-0.0085144,-0.10389,0.32348,0.47449,0.60757,0.5852,0.33846,0.011591,0.023182,0.21161,0.58266,0.70558,0.59159,0.73499,0.39423,0.091331,-0.027973,0.067646,-0.46373,-0.63925,-1.1151,-0.9305,-0.33226,0.53288,0.5831,0.03804,-0.11597,-0.21255,0.065853,-0.54584,-1.434,-1.4429,-0.96245,-1.4998,-2.25,-3.4323,-3.3007,-2.6375,-1.901,-2.0204,-2.3674,-2.3633,-0.91085,-0.41529,-0.18852,-0.0075016,0.16035\n-0.40995,-0.22321,0.22049,0.20524,0.027679,0.16212,0.32068,0.28768,-0.01226,-0.19749,-0.22592,0.11856,0.44616,0.60424,0.21401,-0.15078,0.26582,0.30543,-0.036943,-0.64736,-1.2321,-0.73641,0.19512,0.31315,0.1133,-0.39665,-0.71842,-0.63882,-0.35234,-1.4266,-2.4089,-2.008,-2.2897,-2.7871,-2.9462,-3.311,-2.9646,-1.5893,-0.62651,-0.83808,-1.7022,-1.9729,-1.7878,-0.91239,-0.59402,-0.30281,-0.11472\n-0.51541,-0.29033,0.049837,0.15118,-0.082333,-0.15863,-0.058897,-0.077459,-0.054008,-0.16821,-0.21716,0.073254,0.66582,0.54335,0.10707,-0.10118,0.19617,0.48088,0.51898,0.54692,0.40152,0.035969,-0.0099449,-0.15331,-0.69504,-0.84563,-1.3138,-1.1273,-0.76369,-1.5005,-2.4582,-2.0904,-2.4783,-2.9159,-2.4658,-1.8836,-1.4254,-0.91862,-0.5267,-0.74396,-1.4487,-2.5389,-2.6461,-2.0621,-0.8768,-0.31542,-0.078222\n-0.23134,-0.046965,0.33783,0.1955,-0.16868,-0.20557,-0.18464,-0.27588,-0.31702,0.099966,0.045137,0.20861,0.64121,0.64022,-0.20677,-0.17091,-0.04986,0.441,0.55203,0.68104,0.47301,-0.10781,-0.21992,-0.42764,-0.83331,-1.2274,-1.4373,-1.2954,-0.9561,-1.0879,-1.2806,-1.6254,-2.1292,-2.3619,-2.0175,-1.4347,-0.89993,-0.84177,-0.86647,-1.1231,-2.402,-2.6802,-2.96,-1.826,-0.69648,-0.14427,0.18489\n0.101,0.51671,1.0865,0.39221,-0.035236,-0.18329,-0.31194,-0.32434,-0.19116,0.33401,0.25279,0.19348,0.3106,0.39704,0.13779,0.10691,0.015795,0.47117,0.71973,0.83672,0.45331,-0.078003,-0.43838,-0.63792,-1.065,-1.0968,-1.368,-1.3569,-0.9468,-0.59412,-0.83526,-1.4821,-1.8878,-2.0893,-1.7335,-1.9695,-2.1424,-2.2452,-1.8999,-2.016,-2.6253,-2.8249,-2.652,-1.3753,-0.48522,0.050457,0.61825\n-0.69108,0.087395,0.37048,0.5054,0.44779,0.12899,-0.10118,-0.043983,0.11607,0.12215,0.073217,-0.46278,0.0070419,0.15785,0.1244,-0.020199,0.063599,0.4054,0.50726,0.47303,0.31447,0.17125,-0.26978,-0.65112,-1.042,-1.2922,-1.4413,-1.3605,-0.92975,-0.44552,-0.68422,-1.471,-1.8235,-2.2645,-2.3218,-2.9401,-3.3688,-2.8369,-2.2426,-2.2067,-2.6033,-2.8301,-2.4977,-1.0739,-0.13041,0.14418,0.73368\n-0.77976,-0.38895,0.34268,0.31069,0.66823,0.35311,-0.059349,0.085258,0.1403,-0.15536,-0.36913,-0.65907,-0.43116,0.078426,0.24168,0.23205,0.13439,0.11995,-0.025164,0.22117,0.2885,0.20887,-0.20588,-0.82262,-1.1743,-1.3577,-1.0725,-0.93206,-0.57815,-0.42357,-0.96098,-1.6274,-2.0962,-3.173,-3.4362,-3.6386,-3.8495,-3.2241,-2.6092,-2.2652,-2.4273,-2.5526,-1.5459,-0.48441,-0.13189,0.1306,0.61483\n-0.22313,0.19186,0.06851,-0.19493,0.51444,0.217,-0.55735,-0.59961,-0.59486,-0.51059,-0.52359,-0.54706,-0.36375,-0.13344,0.082546,0.1363,0.037682,-0.10047,-0.12356,0.20748,-0.08762,-0.45337,-0.63194,-0.97806,-1.1695,-1.2627,-1.2553,-1.1762,-0.92413,-0.64032,-1.3588,-1.9367,-2.5037,-3.0649,-3.2456,-3.3106,-3.6254,-3.3628,-2.8286,-2.5509,-2.3568,-2.1969,-1.7729,-1.2195,-0.37252,0.49588,1.0153\n-0.33154,-0.060122,-0.25835,-0.25897,-0.19375,-0.41537,-0.71991,-1.177,-1.4655,-1.5352,-0.74665,-0.60106,-0.18768,0.14148,0.15905,0.12402,-0.069366,-0.16299,0.081459,0.39871,0.0045567,-0.53814,-0.88173,-1.1032,-1.0698,-1.0766,-1.2964,-1.4258,-1.4078,-1.3775,-1.3691,-1.5743,-2.4459,-2.8341,-2.9945,-3.0446,-3.5844,-3.8632,-3.389,-2.5474,-2.3425,-2.2679,-2.26,-1.8414,-1.0402,0.25554,1.1251\n-0.82546,-0.57803,-0.61278,-0.36107,0.0079308,0.34502,-0.28605,-0.85033,-1.267,-1.358,-0.56727,-0.63003,-0.10508,0.074051,0.42474,0.3678,0.065683,-0.0078754,0.34021,0.25774,0.22629,-0.40656,-0.6587,-0.79534,-0.82564,-1.0102,-1.0928,-1.3532,-1.3459,-1.4228,-1.195,-1.3456,-2.1644,-2.7789,-2.779,-2.6236,-3.4447,-4.1118,-3.5706,-3.2268,-2.7168,-2.0777,-2.0672,-2.0268,-1.0573,0.25688,0.69305\n-0.83468,-0.53274,-0.54899,-0.66697,-0.61525,-0.38622,-0.39412,-0.29307,-0.33811,-0.5282,-0.33071,-0.41427,-0.14029,-0.039631,-0.067804,-0.22944,-0.40226,-0.48649,-0.16444,0.075146,0.041073,0.025311,-0.41232,-0.58884,-0.76974,-0.98963,-0.93059,-0.84585,-1.1507,-1.8914,-1.5077,-1.5028,-2.3005,-3.5604,-3.2076,-2.7923,-2.9466,-3.4318,-3.5816,-3.6792,-2.8618,-1.8489,-1.6204,-1.6405,-1.0652,-0.51354,-0.19019\n-0.47649,-0.064972,-0.094009,-0.68838,-0.86255,-1.0801,-0.90874,0.030407,-0.0068474,-0.58482,-0.86876,-1.5534,-1.4658,0.71616,0.46913,0.15593,-0.32601,-0.50148,-0.12249,0.071383,0.051716,0.27905,0.15193,-0.2838,-0.6036,-0.85842,-0.88338,-0.69081,-0.73539,-1.3505,-1.5735,-1.7571,-1.9324,-3.6322,-3.7274,-2.8105,-2.4525,-2.9697,-3.7444,-3.6719,-2.7545,-1.6327,-1.4341,-1.3408,-1.0922,-0.52877,-0.25905\n0.42998,0.43462,0.49824,-0.059456,-0.68135,-0.7856,-0.51143,-0.22367,0.093197,-0.44805,-0.96693,-2.3578,-1.5058,-0.80226,0.074268,0.17964,-0.057955,-0.22105,0.11163,0.44593,0.55462,0.5992,0.60137,-0.2926,-0.64223,-0.70976,-0.71326,-0.61549,-0.41533,-0.46784,-0.51682,-1.4328,-2.3061,-3.1304,-3.4416,-2.495,-2.042,-2.7279,-3.2973,-3.0182,-2.3774,-1.9463,-1.2248,-0.56972,-0.34286,-0.30612,-0.3086\n1.4309,0.72797,0.089016,-0.039375,-0.025848,-0.092106,-0.42282,-0.38334,-0.17248,-0.3254,-0.20258,-1.8577,-1.7657,-1.2683,-0.24786,-0.56868,-0.066582,0.074057,0.33029,0.49436,0.70705,1.0488,0.83902,0.032003,-0.26624,-0.49908,-0.78339,-1.1286,-0.73915,-0.07951,0.24587,-0.70846,-1.732,-3.2729,-3.6894,-2.521,-1.9791,-2.7169,-2.7832,-2.2365,-1.6514,-1.3141,-0.95933,-0.39472,0.0087967,-0.18355,0.28941\n1.5186,0.68685,0.25496,0.42092,0.82572,0.25369,-0.77658,-0.78502,-0.5366,-0.76805,0.94388,-0.083485,-0.91191,-2.1451,-1.59,-1.9676,-0.5276,1.1684,-0.12022,-0.28363,0.45731,1.1417,1.0622,0.18646,0.00097847,-0.31673,-0.64994,-0.84394,-0.43698,0.23026,0.14232,-0.66972,-1.5663,-2.565,-2.9207,-1.4036,-1.8223,-2.4468,-2.4086,-1.7276,-1.3796,-1.1689,-0.93287,-0.23678,-0.29476,-0.89595,0.14842\n3.3633,0.95252,-1.3565,0.043861,1.2703,1.4354,-0.087343,0.032066,-0.45914,-0.61917,-0.5196,0.09634,-0.91669,-3.1061,-2.5279,-1.8281,1.0784,1.7815,0.79748,-0.077488,0.37632,1.3347,1.4507,0.42551,0.17674,0.1234,0.31251,0.35823,0.80987,0.50031,-0.27366,-0.93437,-1.3267,-2.1527,-2.6281,-1.6032,-1.3104,-1.4205,-2.0502,-1.6539,-1.2319,-1.2703,-0.82298,-0.16162,-0.2991,-0.64494,0.27509\n0.033009,-2.0387,-1.8245,-0.3187,2.1408,0.95444,-0.33381,1.0276,1.1463,1.0954,1.5105,3.2702,NaN,NaN,NaN,NaN,0.063541,1.8973,1.6281,-0.06035,0.35593,1.1697,0.77716,-0.13639,-0.13207,0.080362,0.40289,1.1483,1.4067,1.0523,0.70936,-0.18911,-1.3007,-1.6638,-1.8542,-1.333,-0.98793,-1.0775,-1.4903,-0.93035,-0.46087,-0.62846,-0.782,-0.58656,0.065392,0.95818,1.8474\n2.8758,0.5192,1.0322,2.267,2.8534,1.2511,0.077728,0.65779,1.0411,1.2343,2.3241,NaN,NaN,NaN,NaN,NaN,NaN,1.1225,1.2465,0.38166,0.22682,0.042084,0.19044,0.45327,0.97356,0.66389,0.9751,1.5079,1.5168,1.7857,1.3212,0.71207,-0.27348,-0.76668,-0.49792,-0.09145,-0.36316,-0.61499,-0.87598,-0.6486,-0.67464,-0.64412,-0.98359,-1.0023,-0.014214,1.1213,1.4307\n3.0073,2.8241,2.8489,4.0996,5.468,1.5406,0.31408,1.7567,2.1198,2.2276,4.3642,4.0534,3.8537,NaN,NaN,NaN,NaN,0.42491,1.083,0.31286,0.85707,1.0111,0.72936,1.0239,1.0646,0.62643,0.85006,1.1938,1.7244,1.8617,1.7822,0.75097,0.56878,0.4524,0.21015,0.3401,0.19395,-0.4085,-0.57862,-0.12018,0.11918,-0.10746,-0.38853,-0.15051,0.41943,1.0904,1.1143\n3.356,0.88785,0.85638,3.3846,4.0271,3.3352,1.2437,2.3265,1.478,1.6629,2.2549,1.9253,3.5578,4.2459,4.0575,2.1766,-0.29524,-1.4243,-1.0709,1.0362,2.8829,2.1254,2.0322,1.8653,2.1367,2.0761,2.2851,2.0545,1.5842,1.8586,1.8215,1.3665,0.75464,0.95443,1.0551,0.19887,-0.1932,-0.30642,-0.31498,0.065012,0.19562,0.24016,0.12123,0.29202,0.98416,1.4994,1.8319\n4.5894,4.9158,2.1566,2.3403,3.1162,3.8703,3.4173,2.7435,1.8831,0.45281,0.41649,1.4121,2.47,3.0984,4.1015,3.7913,0.42669,NaN,NaN,0.83541,3.4748,2.3138,2.238,2.6928,3.5199,3.8911,3.3224,2.6342,2.0519,2.4104,2.237,1.7273,0.85858,0.4814,0.42266,-0.35944,-0.29979,-0.46082,-0.57018,-0.67226,-0.4257,0.14145,0.12521,0.10385,0.7099,0.94904,1.3489\n4.5383,6.0943,5.1108,1.3676,3.047,4.7682,4.9518,2.9171,2.2056,1.6251,1.9422,2.0141,1.6866,1.7404,1.5324,2.4643,NaN,NaN,NaN,NaN,4.2422,2.1336,2.4062,3.3744,4.1349,4.4078,3.5801,2.6328,2.168,2.1974,1.8669,1.5734,1.4848,1.1859,0.42159,-0.84452,-0.76144,-0.93351,-1.4924,-1.4152,-0.73102,-0.21495,-0.0099869,-0.059912,0.25261,0.27536,0.17262\n3.277,2.9344,2.2194,0.54202,0.79397,1.6892,2.1963,2.4676,2.9013,3.5485,3.6528,2.5606,1.3604,0.054478,0.44065,0.97726,NaN,NaN,NaN,NaN,1.6695,1.3317,2.1619,3.2092,3.4828,3.5172,3.2222,2.4746,1.997,1.7466,1.6279,1.3773,1.6438,1.3957,0.58708,-0.30337,-0.54821,-0.16724,-0.7247,-0.67583,-0.04697,0.26101,0.46008,0.18082,0.24816,0.44779,-0.038963\n3.47,2.5965,2.1381,1.0752,1.2201,2.7524,3.9999,4.5364,3.6458,3.7749,4.7199,4.3923,2.4348,0.43261,-0.040897,NaN,NaN,NaN,NaN,NaN,-0.32188,0.95315,2.5851,2.8722,2.6639,2.59,2.317,1.9816,1.9304,1.9003,1.5695,1.2072,1.1339,0.93595,0.42187,-0.55907,-0.23252,0.72628,0.29341,0.070835,0.37053,0.6596,0.35637,0.0425,0.13143,0.25677,0.17256\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_060828-061211.unw.csv",
    "content": "-0.46213,-1.4131,-1.3029,-1.1237,-1.1547,-0.55069,-0.91988,-0.582,-1.0502,-1.3428,-0.48096,-1.4523,-0.18744,0.5898,-0.19299,-0.49033,-0.59625,-1.0014,-0.66327,-0.23077,0.40622,0.02705,-0.21618,-0.085938,-1.0545,-0.59791,-0.96604,-0.28066,-0.084784,-0.15316,-0.69377,-0.71824,-1.3558,-0.8729,-0.46079,-0.49051,0.49874,0.69937,1.1854,0.92662,0.76046,1.0008,0.57509,0.21448,0.57825,0.21459,0.1208\n-1.1421,-1.3214,-1.3444,-1.543,-1.8755,-1.2143,-0.80995,-0.033201,-0.62248,-0.35179,-0.88223,-2.3386,-1.0152,0.0036659,-0.40849,0.29884,0.46889,-0.89906,-1.1749,-0.33431,0.37483,0.4883,-0.18127,-0.25354,0.037209,-0.41032,-0.54502,-0.00634,-0.25309,-0.59362,-0.57805,-0.84988,-0.28876,-0.24503,-0.69466,-0.2347,0.3553,0.57878,0.84591,0.9729,1.825,1.3529,1.214,0.50251,0.57731,1.1418,1.0825\n-1.8953,-2.2127,-1.1271,-1.1946,-1.4217,-1.2806,-0.33728,0.28224,-0.94754,-0.32137,-0.56858,-1.5996,-0.26496,-0.071104,0.35202,0.70198,0.47671,-0.34218,-0.29963,-0.47528,-0.6267,-0.87662,-1.2062,-1.0664,-0.67938,-0.73973,-0.20681,0.38145,-0.54771,-0.66493,-0.40536,-0.38129,0.57421,0.15773,-0.31125,-0.89578,0.21099,0.22879,0.07921,0.97027,2.0874,1.0595,0.76639,0.12868,1.1269,1.6437,1.2621\n-1.2124,-1.1419,-0.59942,-0.65139,-0.83171,-0.22286,0.61997,0.60151,-0.045532,-0.10557,0.012096,-0.54545,0.13008,0.61034,1.1907,1.4304,1.5193,0.90932,0.30158,-0.69528,-1.4311,-1.4193,-1.5986,-1.1073,-0.72893,-0.60431,-0.32433,-0.57053,-0.53432,-0.039286,-0.47218,-0.42798,-0.054668,0.41034,-0.018017,-0.54215,0.090076,0.41634,0.31925,0.91582,0.83908,0.86078,1.0897,1.1597,1.7466,2.4062,2.2945\n-0.27374,0.38678,1.0685,-0.35337,-0.096104,0.94016,0.8838,0.90764,-0.1992,0.093792,-0.043169,-0.50135,-0.33175,-0.090179,0.69713,1.1341,0.011227,-0.27213,-0.5413,-0.95574,0.16295,-0.95248,-1.1436,-0.87096,-1.0056,-0.74205,-0.74411,-0.33581,-0.16787,-0.35893,-1.4533,-0.3551,-0.42951,0.45315,0.0090065,0.13467,-0.10255,0.010174,0.29426,0.61932,0.50446,-0.18846,1.2455,1.5306,1.4323,1.46,1.5614\n-0.5369,-0.17846,1.3379,-0.71049,-0.39875,0.23526,0.44022,-0.40738,-1.3015,-1.1494,-1.2119,-0.56037,-0.40087,-0.14347,0.025658,0.61756,0.37179,-0.70466,-1.3671,-0.54785,0.60166,-0.25459,-1.111,-1.1225,-0.59826,-0.1562,-0.45584,-0.30375,-0.22936,0.032227,-0.4642,-0.18392,0.017527,-0.1629,-0.7879,0.028339,-0.87238,-0.90799,-1.06,-0.71101,-0.81111,-0.44818,0.7201,1.4437,1.3151,1.3516,1.5813\n-1.1357,-1.3663,-0.78757,-0.66558,0.42295,1.169,1.2404,0.2215,-0.95089,-1.1572,-0.57914,0.16845,0.63462,0.42844,0.80355,0.88878,-0.046366,-0.45559,-0.36466,0.43827,-0.6813,-1.057,-1.4459,-1.9739,-0.84155,-0.61053,-0.51401,-0.23409,0.042294,1.1031,0.011267,-0.32244,-0.34072,-0.48706,-0.63143,-0.39966,-0.19363,-0.30598,-1.1015,-1.4068,-0.33076,-0.1938,0.43851,1.2774,1.7423,1.6065,3.1585\n-0.66012,-0.98037,-1.9838,-0.68371,0.79777,2.0318,1.3211,1.2274,0.68305,0.61259,0.25733,1.011,0.15692,0.79391,0.6219,0.50225,0.33448,-0.43562,-0.3796,-0.051834,-0.47084,-1.1211,-1.0959,-0.69799,-0.12963,0.2259,-0.30857,0.45202,-0.025043,0.4747,0.19957,-0.42283,-0.89289,-0.61219,-0.54628,-0.50326,-0.32816,-0.26983,-0.68634,-0.94648,-0.52944,-0.84791,-0.48484,0.82713,1.1471,1.1902,1.5392\n-0.047253,-1.1399,-1.1482,-0.3996,0.15816,0.8542,1.1307,1.0379,1.3107,1.5224,1.1045,1.435,1.5037,0.78066,-0.023363,0.55416,1.7881,2.0402,-0.28648,-0.15681,-0.68908,-1.5453,-0.89244,-0.072292,0.42108,0.27182,0.18976,0.34684,0.019199,0.09111,0.86445,0.28589,-0.012091,-0.013931,-0.54476,-0.83912,0.28111,-0.2265,-1.3351,-1.459,-1.3461,-1.1416,-0.21352,0.11097,0.23807,-0.10005,-0.40819\n-0.34557,-0.16888,0.7336,-0.24893,-0.86053,-0.0076084,-0.27522,-0.48595,0.47113,1.6015,1.3481,1.5242,1.66,0.3081,0.24989,1.5761,0.58635,0.39938,0.17768,-0.57807,-0.62227,-1.0732,-0.86428,-0.18553,0.11108,0.15785,-0.63044,0.089287,0.4616,-0.29967,-0.1743,-0.80228,-0.38392,-0.21237,-1.1762,-1.3376,-0.96532,-0.73129,-0.95809,-1.0832,-0.78743,-0.07642,0.69761,-0.28188,0.019053,0.022488,-0.27263\n-1.1846,-0.96462,-0.18179,-0.7948,-1.4408,-0.39251,-0.4683,-0.79895,0.21869,0.95808,0.65241,0.81887,0.79604,0.19201,0.26337,0.98857,0.28914,0.58524,0.14402,-0.44371,-0.64054,-0.7376,-0.71114,-0.23149,-0.095737,-0.35613,-0.070724,0.43781,0.040539,-0.67151,-0.62953,-0.53466,-0.45189,-0.48176,-1.0948,-1.0079,-0.66856,-0.16383,0.0085983,-0.61864,0.03775,0.43578,0.6285,0.25447,0.62638,1.5627,1.3537\n-1.1123,-1.7577,-0.053711,-0.082422,-1.3837,0.14881,0.22952,0.16135,0.28908,1.0341,0.33816,1.249,1.2873,1.0156,1.0734,1.0855,0.65151,0.88015,-0.26433,-0.74565,-0.83857,-1.1052,-0.83628,0.50575,-0.089664,-0.051113,0.50531,0.5971,-0.27365,0.11221,-0.23303,-0.35081,-0.25772,-0.34852,-0.2559,-0.090904,-0.099483,-0.44289,-0.28285,-0.4884,-0.23693,0.30103,1.1137,1.1058,1.507,2.479,2.0926\n-0.90181,-1.1262,0.54685,3.498,-2.5559,0.027092,-0.28546,-1.6234,0.37523,1.139,-0.10708,0.54145,-0.039204,1.3778,1.6701,1.0709,0.85854,0.87587,-0.14299,-0.43373,-0.4031,-0.44559,0.11189,0.34594,0.37901,-0.16864,-0.098202,0.34853,-0.51465,-0.31393,-0.97583,-0.52697,-0.73923,-0.47352,-0.15153,-0.319,-0.79932,-0.97954,0.25936,0.10501,0.28894,0.46685,0.94744,1.0966,0.75377,NaN,NaN\n-0.92164,-0.7321,-1.8259,-3.0737,-0.49146,0.54062,0.63264,-1.1219,0.3952,0.26434,0.39548,0.3566,0.26057,1.5486,1.851,0.63324,0.61536,0.20014,0.041109,0.73478,1.5983,1.4239,0.87981,0.53263,-0.36245,0.11611,0.57509,-0.1482,-0.32511,-0.55845,-0.98421,-0.86129,-0.94647,-0.73436,-1.041,-0.57953,-0.064423,0.24358,1.2997,0.23558,1.4869,1.3298,1.0794,1.3657,1.2693,NaN,NaN\n-1.3318,-1.6887,-4.4058,-2.4207,-1.4802,-1.0653,-0.83914,-0.28947,0.82537,0.82681,0.63974,0.29515,0.47166,0.91731,1.2549,1.1614,2.0776,0.76843,0.075867,0.92025,1.695,1.4485,0.53713,-0.050175,-1.6378,0.44371,0.59019,0.046888,0.36103,0.1352,0.16472,-0.15368,-0.62553,-0.70203,-1.2609,-0.39716,-0.23185,-0.20033,-0.10795,-0.96557,0.13719,2.536,0.92998,0.69232,0.60227,NaN,NaN\n-0.60961,-3.1689,-4.5391,-1.2886,-1.1152,-0.6801,-1.0511,0.22868,1.4128,1.4414,0.92603,0.58181,0.36488,0.068378,-0.010452,0.54687,0.74707,-0.37169,-0.057293,0.10613,0.4927,1.0864,0.52574,0.88681,-0.1834,-0.38284,-0.71137,-0.28049,0.31555,-0.45226,1.3092,1.3943,0.083607,-0.26268,-0.74505,1.0385,-0.020325,-0.89693,-1.2463,-2.1329,-1.0506,1.6596,1.2915,0.46539,0.10094,0.20403,1.4533\n-0.41473,-1.1915,-1.0288,-0.016575,0.15316,-0.3513,-1.1372,0.84611,0.63226,1.1896,2.4042,2.6701,0.68489,-0.83421,0.32748,0.70566,-0.55692,0.32978,0.6029,-0.50398,-0.77891,1.6131,-0.31541,0.23928,0.41057,-0.11887,-0.10794,0.28952,-0.080929,-0.78477,0.86435,1.3997,0.016203,-0.12262,0.37671,1.0353,0.38829,-0.32758,-0.9341,-3.2711,-0.10569,-1.0308,0.11913,0.22079,0.5463,-1.2612,-0.016171\n-0.080986,-0.80231,0.38779,0.98114,1.4158,-0.45475,-1.893,0.057331,0.41385,1.5265,2.0285,1.7509,0.63563,0.59048,1.0974,1.0071,0.38279,0.32787,0.37718,-1.1598,-1.411,1.3828,1.1059,-0.17156,0.27662,0.28408,-0.069078,-0.095139,0.077074,-0.38999,-0.16078,-0.8486,0.093962,0.34901,0.13801,-0.361,1.2933,-0.070839,-0.60402,-1.7963,-0.24831,-0.84985,-2.4455,-0.64895,-2.2744,-0.43316,-0.016228\n-2.7442,-1.389,-0.56179,2.1125,3.4488,-2.6601,-1.4213,0.32113,0.46784,1.2356,1.3288,0.42596,-0.52912,0.086201,0.73864,0.50671,0.42484,0.47238,0.18825,-0.32349,-0.63426,-0.21738,-0.70282,-0.85769,0.051102,0.091532,0.12368,-0.28477,0.40552,-0.017017,0.054211,-1.7943,0.16996,0.51318,0.28402,-1.2148,-0.35582,-0.29489,-0.5646,-1.3254,-0.90885,0.43975,-2.6223,-1.0503,-1.2751,-0.64084,-0.032036\n-2.5071,-0.54129,0.48054,1.6945,3.2271,2.0222,-0.7956,0.29936,-0.051491,1.1733,1.0069,0.40518,0.36511,0.04768,0.80825,1.0478,0.56889,0.86993,0.95899,1.3716,-1.7082,-1.5205,-0.2097,-2.3245,-1.4624,-0.13291,0.025574,0.020256,0.58189,0.60383,-0.13293,-0.36555,1.0407,1.2578,1.8021,-0.5542,-1.2126,-1.0844,0.72055,-7.6294e-06,-0.84477,-0.69392,-0.84697,-0.019184,-0.57979,-0.52678,-2.5082\n-0.59094,-0.31513,0.5872,1.6889,3.4122,1.5229,-1.927,-0.19153,-0.092529,0.49533,0.42941,0.16259,0.14331,0.6364,-0.074978,0.15957,0.94733,0.91257,0.34355,-0.43756,-2.1908,-1.6285,-1.0889,-0.99097,-0.77769,-0.35525,0.23903,0.25907,1.7745,0.51168,-1.1168,-0.5542,-0.22767,0.1002,1.8229,0.031311,-1.785,-1.7063,0.58487,-0.53487,-1.0788,-1.0772,-0.51765,-0.82883,-0.71854,-0.89765,-1.1398\n0.43326,0.16808,-0.11603,-0.69008,-0.82441,-0.35128,-0.70047,-0.058918,0.74208,0.92896,0.57961,-0.25614,0.29994,-0.59612,-1.5103,1.1716,1.0379,0.28417,-0.1068,-0.4138,-0.758,-2.2781,-5.1156,-1.4619,-0.29567,-0.00022888,0.17666,0.22029,1.8246,1.1995,-1.5148,-1.4257,-1.8924,-1.6981,0.11549,-0.14615,-1.2076,-1.4611,-1.0671,-1.0436,-1.6365,-1.6752,-0.91276,-0.89869,-0.77928,-0.50932,-0.1362\n1.0231,0.69187,0.8058,0.87285,0.38194,0.13819,0.43312,0.61608,1.5736,1.8657,-0.27327,0.060993,1.5698,0.45759,-1.3112,-0.40071,-0.44833,-1.2618,-0.99712,-0.93842,-1.1864,-1.499,-2.9614,-0.53722,0.33164,0.23898,1.0349,0.91082,0.68914,-0.13431,-1.2255,-1.3991,-1.6804,-1.8265,-0.97474,-0.6451,-1.0115,-1.6894,-2.9827,-2.0902,-2.6386,-1.5923,-0.69494,-1.2976,-1.551,-1.0538,-0.55234\n-0.21392,0.34621,1.1048,2.1709,1.2668,1.0131,0.11505,1.2736,1.7514,1.5079,1.1087,0.49841,1.2232,0.37445,-0.34691,-0.72881,-0.90211,-2.3627,-2.0749,-1.0971,-0.55889,-0.45227,-0.8527,0.061008,1.3463,0.87511,1.4241,1.3763,1.1128,0.71535,0.28743,-0.11679,0.36604,0.73609,-0.89109,-0.50687,-0.50587,-2.6333,-5.1733,-4.7851,-3.7138,-0.99345,-1.0642,-1.2199,-1.7567,-1.162,-1.1379\n-0.48324,-1.0631,-0.55792,1.6434,2.1227,0.86531,0.20536,0.78031,1.1043,0.52262,1.2922,1.5359,0.90022,-0.43961,-0.52544,-0.38301,-0.074162,-0.73613,-0.77087,0.44846,-0.28264,-0.42631,-0.15667,-0.0951,2.1066,2.0484,1.7716,1.3328,1.1062,1.6479,1.1341,1.1506,1.081,-0.19725,-0.92695,-0.41771,-2.5737,-2.2686,-2.5044,-2.6448,-1.1465,-0.15777,-0.46188,-1.2501,-1.7441,-1.4945,-1.1798\n0.44458,0.57808,1.1527,1.6482,1.8788,0.53283,0.32043,0.64156,0.45257,0.054958,2.031,1.7696,0.38975,-0.52116,-0.34807,-0.51931,0.20871,-0.33421,-0.30901,0.48435,0.0046501,-0.42469,0.45108,1.1965,1.5656,2.0514,NaN,NaN,1.8559,0.76934,0.24251,1.8465,0.2932,-1.5829,-1.7498,-0.76639,-1.0814,-0.69033,-0.053383,-0.66911,0.10874,0.65816,-0.58367,-1.6614,-1.2877,-1.3061,-0.46784\n-0.075573,1.3283,1.949,0.85479,-0.30431,-0.035667,0.45943,0.50962,-0.13804,0.043385,0.64888,0.75267,0.042219,-0.57257,-1.2804,-1.4615,-1.1311,-0.57833,-0.83821,-1.4563,-0.88684,-0.38213,0.51235,0.79022,0.79941,2.7549,NaN,NaN,NaN,-3.4327,-1.3119,-0.63983,0.3645,0.052855,-0.076534,-0.49881,-0.29129,1.2529,0.52461,-0.21105,0.39888,-0.12542,-0.83566,-0.7883,-0.15203,-0.060902,-0.33797\n-0.61104,0.57372,1.158,1.2281,-0.73099,0.010832,1.0589,-0.16188,-0.84895,0.62608,0.43556,0.34653,-0.08206,-1.0742,-1.579,-1.5125,-1.3859,0.24058,-0.70847,-2.5671,-1.7292,-0.013092,0.66743,-0.058941,0.0014381,0.4444,NaN,NaN,NaN,NaN,-0.93929,0.42979,-2.0541,-0.47954,1.3114,1.2887,0.96516,0.87733,0.89378,1.3383,0.73605,-0.45817,-0.67037,-0.19819,0.016064,-0.19866,0.59494\n0.105,0.86231,1.7552,-0.021774,-0.43455,0.36306,0.7734,0.05024,0.11538,0.69607,0.89668,-0.55202,-0.56069,-0.57545,-1.1937,-1.5098,-1.6636,0.09017,-1.146,-2.3461,-2.6735,-0.61362,0.045712,0.037327,-1.0749,1.8612,2.9997,2.4811,1.5509,3.021,2.0312,2.0708,-0.15336,-0.58828,1.5727,1.4047,0.96401,0.76019,0.83479,1.211,0.39487,-0.93878,-1.0748,-1.0229,-1.3047,-0.54809,0.49124\n-0.31662,-0.034508,1.3288,-2.123,-0.33124,0.3837,0.048687,0.023422,0.25248,0.056997,0.61209,-0.13078,-0.83556,-0.45667,-0.92056,-0.43294,0.45089,-0.38548,-0.71515,-1.4314,-2.2778,-1.9455,0.28604,-0.32038,0.92646,2.7849,3.2413,5.0919,NaN,NaN,NaN,NaN,NaN,-2.8063,0.56639,1.6481,1.2148,1.4845,1.8662,1.3997,0.85906,-1.2078,-1.4188,-1.5088,-1.5954,-1.4342,-0.3042\n1.5319,1.0929,0.86831,0.43832,1.0679,1.5305,0.27375,0.88893,0.75354,-0.81484,-0.38089,-0.38146,-1.0065,-0.73958,-0.95229,-2.7268,-3.0876,-0.74421,-2.0233,-2.098,0.37279,0.72494,0.75524,1.6793,-0.21424,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.0852,2.903,3.3403,2.3934,1.7073,2.2054,1.5319,-0.10968,-1.5876,-1.0632,-1.7925,-2.1179,-0.83833,-0.0072174\n0.0047989,-0.050854,-0.38737,-0.27729,1.448,1.6581,1.267,-1.4326,0.62892,-0.92231,-0.69225,-0.071617,-0.10566,-0.52257,-0.92919,-2.289,-2.1092,0.13724,1.1764,1.4751,1.6345,1.5532,1.2092,4.75,2.1617,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.1333,4.0863,2.7238,1.3128,1.8784,1.5408,1.1834,0.63143,-0.3267,-0.68154,-2.1935,-1.7267,-0.0088634,0.22095\n-0.62535,0.57838,3.4845,4.1462,3.0299,0.10539,-1.3289,-3.1157,-1.038,-1.2663,NaN,NaN,NaN,NaN,0.86773,-0.26862,-0.71993,0.091898,1.1952,0.89544,1.3402,1.1156,0.80993,2.1231,2.7355,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2011,1.7662,2.5605,0.20517,0.19106,0.80158,-0.0033627,0.20535,-0.17149,-0.45086,0.11672,-0.46695\n1.225,1.5942,4.5687,4.5354,2.5964,-1.0861,-0.68534,-0.64802,-0.6648,NaN,NaN,NaN,NaN,NaN,1.6069,1.6671,1.4178,0.24988,0.41209,-0.37628,1.0372,1.8337,1.8983,1.2746,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.046642,1.5758,2.681,0.53685,-0.072376,0.65981,0.54684,1.1029,-0.087273,-0.561,-0.75854,-0.58911\n0.70745,0.29715,0.12734,1.1052,1.7527,1.2896,0.69454,0.61123,0.3004,NaN,NaN,NaN,NaN,NaN,0.53351,1.4494,1.6434,0.80889,0.93869,1.2561,2.2995,2.343,3.4384,5.857,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.1712,0.34641,0.66249,0.85463,0.52124,0.94559,-0.96725,-0.5257,-0.56157,-0.83367\n1.0779,0.53893,-0.027397,1.8193,1.725,2.2302,1.6152,1.1133,0.3783,-0.43883,-0.24796,-0.11671,0.015923,-0.58241,-1.7089,-1.6192,1.5453,2.1128,3.5437,3.8695,2.9215,3.9086,6.0246,7.3129,7.6228,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4495,0.99606,0.35841,0.19535,-0.1526,-0.68684,0.094936,0.43307,-0.36031\n0.94914,0.61239,0.72832,0.52897,-0.19231,0.12679,0.35873,0.24553,0.41179,-1.9624,-0.85681,-0.07126,-0.018642,-0.45614,0.037022,1.3569,2.4966,5.107,5.362,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1585,0.22458,0.58663,0.56928,0.34084,-0.46512,0.03146,0.21523,-0.46889\n-0.059879,-0.18169,0.062731,-0.15032,-0.35623,0.40697,0.1195,0.59281,0.69936,-1.1001,-0.50993,0.10702,0.00071716,-1.0289,1.5836,3.0522,3.695,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.984,7.111,NaN,NaN,3.8768,3.5572,2.608,0.33706,-0.24838,0.035164,0.32423,0.25947,0.14772,0.068203,-0.021593\n0.10258,-0.084187,-0.21783,0.049955,-0.13341,0.58922,0.83016,0.97531,0.57052,-0.049568,0.091713,1.2884,1.4172,1.6515,2.8053,3.8287,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11.679,9.294,7.6678,4.3954,3.729,3.3428,2.0147,0.16904,-0.83651,-1.1061,0.11895,0.52927,0.65083,-0.16558,0.12935\n0.48777,1.4326,0.55164,-0.40901,-0.46655,-0.13733,1.2533,0.59185,0.59629,0.17233,0.14417,0.60423,1.8412,1.9658,2.9862,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.0764,10.758,12.168,10.009,7.7695,5.9539,4.288,2.353,1.8454,1.0861,0.24973,0.44309,-1.0544,-0.29784,0.37827,0.79768,0.62941,0.13762\n0.11414,0.52078,0.53448,-0.048668,-0.22717,-0.68413,-0.41923,0.61873,0.42896,0.064138,0.54913,0.36991,0.88275,1.0735,2.9313,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.2269,3.6743,6.1712,7.9618,7.7602,4.205,4.6522,4.813,2.987,2.1749,2.103,-1.3878,-0.84772,-1.0463,-0.7051,-0.32624,0.40692,0.15261,0.22257,0.13225\n2.2057,1.8851,0.7471,0.24405,0.99267,0.10786,0.4679,0.58668,-0.010658,-0.1144,0.87831,-0.28086,-0.050503,0.61893,1.2309,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.9058,4.6236,3.8918,6.2238,5.7371,5.0243,3.8354,4.3131,4.564,3.1001,0.77792,0.56311,-0.98545,-1.0443,-1.0782,-0.77107,-0.1188,0.2176,-0.14338,0.0079079,0.026173\n2.298,1.2387,-0.012306,0.24887,0.53405,0.55778,0.62331,-0.31767,-1.0204,-1.4097,-0.90817,-0.072037,-0.26944,1.271,2.043,1.8013,0.13986,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.5091,4.6007,4.7837,5.1672,4.4607,4.5311,1.5,4.7291,5.1479,4.6391,0.95935,7.6294e-06,0.58769,0.35797,0.27146,0.24798,0.16387,0.14558,-0.02809,0.14032,-0.45327\n0.42956,2.0061,0.87244,-1.0329,0.0094566,-0.093224,-0.84743,-0.98482,-1.0227,-1.8327,-0.68998,0.99109,1.2932,1.4895,3.8675,NaN,NaN,0.73057,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.0278,3.9435,3.6886,3.0627,2.1448,1.9625,1.9304,4.5515,4.5418,2.8085,1.8259,-0.077858,0.74822,0.93774,0.73719,-0.0047798,-0.081528,-0.61516,-1.2735,-1.2316,-1.6778\n0.66519,0.86396,0.22726,-0.87049,-0.70207,-1.0144,-2.5067,-3.213,-2.0883,-0.99812,-0.052795,1.8452,1.6312,0.98151,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.4955,3.47,2.9662,3.5299,3.429,3.2084,2.6451,2.8477,3.2864,1.8489,0.75238,0.42088,0.68517,0.97614,0.77068,0.30413,-0.63995,-1.3849,-1.6559,-1.8796,-1.8637\n0.036793,0.024557,0.47824,0.80766,0.13206,0.10468,0.43544,0.1484,0.99301,1.3839,1.0694,1.7407,1.3307,1.4497,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9642,3.5007,2.8088,3.5769,4.3738,4.0491,2.9785,3.0516,3.5288,2.7687,1.6629,1.0978,0.88655,0.60883,0.34445,0.42727,-0.30365,-0.77465,-1.2203,-1.6423,-1.6459,-1.282\n0.42234,0.00097275,0.97049,0.47276,0.90079,1.0254,1.2384,1.3249,0.94161,1.0338,1.9356,2.4005,2.5211,2.9099,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.96685,1.5811,2.9773,2.7643,2.7392,2.5738,3.4401,3.3835,3.3196,2.2158,3.0933,2.4156,1.2872,0.55269,-0.699,-0.6295,-1.0245,-1.2174,-0.84074,-1.0524,-0.7395,-0.29841\n-0.22264,-0.49382,-0.22199,0.30807,1.2765,0.40575,0.77738,0.7772,1.1334,1.1648,1.9273,2.934,3.6913,4.0228,4.5653,4.7111,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.68097,0.55559,2.8351,2.0424,0.13352,1.0848,1.5923,2.7316,2.6643,3.651,4.1474,3.421,1.1458,0.96746,0.45801,-0.85452,-1.3091,-1.3557,-1.4443,-1.6148,-0.98158,-0.84866,-0.20191\n-0.70386,-0.84121,-0.36307,0.86642,1.1282,0.29731,1.0998,0.47815,1.1978,1.3675,1.8163,2.4684,3.3053,3.5014,5.2934,6.0315,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2492,2.0567,2.0461,3.1997,1.1521,0.14127,0.50976,1.4508,2.3657,2.3126,1.9803,1.6276,0.58374,0.083748,0.56573,0.33669,-0.53322,-1.1158,-1.6188,-1.169,-0.57948,-0.3444,-0.71664,-0.45649\n-1.3442,-1.1869,-0.23585,1.3832,0.66616,0.75581,1.1338,1.2241,1.5832,2.5397,4.098,3.4455,3.2357,3.1541,4.4847,4.3168,3.2503,NaN,NaN,NaN,NaN,NaN,2.6388,2.918,3.7129,3.1783,2.596,1.3354,1.1494,0.22675,1.1495,2.1298,1.0408,0.76551,0.92886,0.58402,0.15427,0.93172,0.014858,0.45882,-0.12384,0.02383,-0.40833,-1.3751,-0.05521,-0.57295,-0.20731\n-1.7375,-1.1523,-0.038416,1.1639,1.2774,0.51133,0.79663,1.2414,1.1494,2.187,3.4559,3.0651,3.5618,3.4235,3.125,2.5086,1.7072,NaN,NaN,NaN,NaN,NaN,1.5741,2.539,2.2618,2.4666,2.3063,1.8109,-0.35767,0.097954,1.1381,1.6843,1.0602,1.7595,1.5566,0.70419,0.34824,0.16891,-0.061123,0.26997,0.52348,0.66724,0.37794,-0.21743,-0.09655,-0.00046539,0.46552\n-0.32867,-1.0823,-1.4848,0.077656,0.53831,0.13145,0.72118,1.2905,0.68366,1.2792,2.7041,2.9101,2.3515,1.6849,3.2905,4.2546,1.9416,-0.18258,-0.23635,NaN,NaN,0.82651,1.507,1.8041,1.5954,1.6808,2.0137,-0.131,-1.4816,-0.45448,0.25163,1.1558,1.3909,1.3252,1.2132,0.49619,0.54519,0.25684,0.40786,0.26963,-0.52291,0.044708,1.5477,1.2938,0.004179,0.60758,0.67109\n-0.8038,-1.0365,-1.9394,0.44913,-0.80981,-0.88388,0.47163,0.21843,-0.22327,-0.0081215,0.99088,1.179,1.0414,0.98144,1.8745,1.8989,2.5943,-0.16972,1.6281,1.9835,-0.27849,0.97946,0.87828,0.76718,0.83775,1.1229,0.89444,-0.22414,-0.36818,0.023407,0.21576,0.5138,1.0012,0.63535,0.62932,1.151,1.3409,1.6015,1.0966,0.063864,-0.62097,0.072842,0.48223,0.66537,-0.18931,-0.21155,0.99449\n-1.6296,-1.223,0.15238,1.1982,0.54314,-1.0395,-0.68292,-1.2284,-0.34921,-0.33772,-0.22689,0.66316,0.0016575,-0.94472,-0.51772,1.0087,0.69129,-4.0246,-2.0497,-1.0895,-0.44548,0.47741,0.34026,0.1386,0.35699,0.93422,1.3087,1.3956,0.78643,-0.12796,-0.00079155,0.65174,0.85425,0.43541,0.65602,0.23847,-0.26498,1.3532,0.95278,0.61527,0.15178,-0.069183,-0.65172,-0.21622,-0.93663,-0.1088,1.5397\n-2.2478,-1.5909,-0.82282,-1.4424,-1.3903,-1.0397,-1.1895,-1.6117,-0.062683,-0.12657,-0.056068,1.3549,1.0165,-0.85188,-1.3176,0.43083,0.39054,-0.82157,1.0257,-0.77602,0.25042,0.87075,0.85578,0.193,0.34712,0.64547,0.86841,0.72498,-0.69472,0.029026,0.24628,-0.32438,-0.44893,-0.59077,-0.056957,0.21251,0.12164,0.86174,0.95521,0.75531,0.56326,-0.40582,-0.55313,-0.035732,-0.099449,0.42332,0.46557\n-1.8577,-2.0629,-3.1543,-2.691,-1.7073,0.69318,-0.76791,-0.72353,-0.45828,0.41466,-0.27343,-0.40337,0.94772,0.38922,-1.7709,-0.84873,-0.2277,0.55426,0.40339,-1.3074,0.061794,0.54485,0.77321,0.24092,-0.065426,0.09079,0.65225,0.32252,-0.44124,0.33364,-0.087433,-0.18653,-2.3132,-1.3146,-0.16851,0.23116,0.12552,0.45667,0.16304,0.46376,0.75453,0.30292,-0.020319,0.28619,0.48732,0.62772,0.59193\nNaN,-4.0387,-3.2631,-2.7774,-1.2278,1.2633,0.52279,-1.1108,-1.1477,-0.20239,0.51093,-0.80049,-0.73392,-0.10464,-1.7145,-2.38,-1.8928,-0.11304,-0.038193,-1.4792,-1.379,-0.19621,0.1398,0.43612,-0.10156,0.0019646,0.19083,-0.63331,-0.97582,-0.71272,-0.085136,0.2798,-1.0969,-0.96271,-0.09269,0.10245,-0.015873,-0.089636,-0.54049,-0.25837,0.51851,0.25483,0.027491,0.59448,0.75099,1.514,1.4603\nNaN,NaN,NaN,-2.7714,-3.3424,-1.1128,3.2781,0.79137,-0.30626,0.56042,0.93352,0.55484,-2.406,-0.23703,-2.4938,-3.3899,-2.3902,-1.7548,-0.96603,-0.56442,-0.2953,-0.19414,-0.23879,-0.10406,-0.063953,0.34706,0.38857,-1.4432,-1.3506,-1.0342,-0.96024,-0.74503,-0.72668,-0.43861,-0.2952,-0.52077,-0.45182,-0.75939,-0.19099,-0.15499,-0.25722,-0.20223,0.011105,1.2028,1.0224,1.6914,1.8558\nNaN,NaN,NaN,-3.0559,-3.6786,-3.591,2.9812,-0.8138,0.27345,0.37666,0.62267,-1.6525,NaN,NaN,NaN,NaN,NaN,-1.9114,-0.96628,0.30097,0.77045,-0.45001,-0.77656,-0.93138,-0.69137,0.054497,1.3851,1.6057,-0.3825,-0.45391,-1.3899,-1.7169,-1.5519,-1.4424,-0.99996,-0.87491,-0.55912,-0.56149,-0.28409,-0.52409,-0.67961,-0.2489,0.52173,1.2898,1.7764,2.1176,1.8664\nNaN,NaN,NaN,NaN,-3.2996,-4.7668,-2.0618,-0.67735,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.8536,-0.4873,0.55711,0.19459,-0.37334,-0.73689,-1.5839,-0.69755,-0.15981,0.99504,0.60437,-0.0071564,0.3075,0.1078,-1.6354,-2.2741,-1.8071,-2.0208,-1.3604,-1.0946,-1.2943,-0.74998,-0.72188,-0.19302,0.46559,0.54963,0.71935,1.6921,1.967,0.81275\nNaN,NaN,NaN,NaN,NaN,-6.2968,-4.6301,-1.9415,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.52866,-0.63718,-0.19236,0.11052,-0.32439,0.045334,0.94679,-0.53109,-0.10995,-0.27022,-0.57954,-0.4839,-0.31279,1.2653,0.23413,-0.38424,-1.8784,-2.5692,-2.1594,-1.6769,-1.3882,-1.9622,-1.3606,-0.71718,0.13965,0.49369,0.57693,0.77631,0.96281,0.50578,0.12199\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.6163,-0.77067,-1.0307,-0.5638,-0.60411,-0.7915,0.062546,0.75412,0.10661,-0.48415,-0.37868,-0.13621,0.61049,0.89362,0.10984,-0.032791,-0.57878,-1.3993,-1.1987,-1.4464,-1.019,-2.1631,-2.0598,-1.2881,-0.39435,0.14027,0.41891,1.0104,1.1832,0.12848,-0.54922\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.93817,-1.4027,-1.4106,-2.4155,-1.9994,-2.0353,-0.5689,0.63573,-0.85309,-1.0354,-1.3702,-0.53674,-0.067062,-1.109,-1.3185,-0.82449,-0.61744,-0.84561,-1.6014,-2.1401,-1.2167,-1.6304,-1.6989,-1.1248,-0.56321,-0.34324,0.04121,1.2757,1.2585,0.60646,-0.7835\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.4865,-3.1093,-2.4034,-2.5276,0.20954,0.15575,-0.20429,-1.1452,-1.7922,-1.0149,-0.55436,-0.084604,-0.76574,-1.8565,-1.7509,-1.3108,-1.0272,-0.98833,-1.9069,-1.3985,-1.0917,-1.0309,-0.92344,-1.4249,-0.66956,0.12112,0.39265,0.93169,0.84008,0.11119,-0.48492\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.2149,-0.53086,0.12685,-0.36275,-1.7004,-1.6007,-0.54598,-0.21624,0.27941,-0.98527,-1.3871,-1.139,-1.4174,-1.1213,-1.1193,-1.6652,-1.579,-1.5301,-1.1531,-0.85228,-0.41865,0.15493,0.2632,0.1257,0.26757,0.85621,1.101,0.92886\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.4344,-2.071,0.48149,1.9086,-1.4119,-0.5457,-0.44008,-0.51478,-0.012079,-1.2444,-1.4395,-0.75398,-0.52316,-0.96693,-1.5844,-4.8157,-2.2167,-1.2668,-0.38568,-0.064146,0.32102,0.42699,0.13408,0.15952,0.35988,1.5855,1.6759,1.0788\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.55478,1.1291,NaN,NaN,NaN,-1.0009,1.3742,2.2462,1.1108,-0.2932,-1.2544,-0.92532,0.0037823,-1.0417,-1.8397,-5.3032,-1.7158,-0.47215,-0.1671,0.13401,0.53635,-0.18822,0.07428,0.38195,0.8964,1.7765,1.468,0.44633\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.83885,NaN,NaN,NaN,-1.5394,0.81516,1.4701,0.13309,-2.1593,-2.1611,-1.4576,-0.20555,-0.99725,-1.3548,-2.4415,-2.2527,-1.3864,-1.0087,-0.61953,0.28878,0.65667,0.75308,0.69633,0.82003,0.85578,0.49815,0.25888\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5719,NaN,NaN,-6.5438,-2.1182,-1.5284,-1.1001,-1.0017,-2.5887,-4.5054,-3.0978,-1.9068,-1.9903,-1.9553,-1.9156,-1.2094,-0.9051,-0.78576,-0.43173,0.12646,0.56934,-0.034626,0.26789,0.5707,0.094906,-0.51973,-0.33703\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.8156,NaN,NaN,-2.5562,-1.084,-1.7629,-1.076,-1.1684,-1.8936,-2.7643,-1.4698,-0.88596,-0.53695,-1.5179,-1.883,-1.1373,-0.48626,-0.35273,-0.48006,0.20344,0.34363,0.26368,0.52985,0.14606,-0.94569,-1.1629,-0.74716\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5027,2.2084,-1.4539,-2.4492,-1.9934,-1.51,-1.8561,-2.8064,-1.7113,-2.1801,-2.2649,-1.9816,-0.85747,-0.46363,-1.2612,-0.71461,0.40178,0.31009,-0.88248,-1.2394,-0.67443,-0.30591,0.39964,0.21737,-0.25356,-0.95994,-0.98019,0.10362\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.32469,2.1178,-0.37406,-3.3975,-3.3507,-1.6468,-1.8158,-2.7008,-2.1551,-3.0013,-3.2879,-2.2851,-0.85089,-0.8674,-1.2208,-0.85482,0.13096,-0.27868,-0.532,-0.93155,-1.3405,-1.012,-0.36409,0.3325,-0.49256,-0.54892,-0.37917,-0.3612\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061002-070219.unw.csv",
    "content": "4.5047,3.5058,3.1389,2.825,1.8465,1.2562,0.83817,0.74591,1.5825,1.8993,1.1301,0.3658,0.40443,1.1718,2.3072,1.693,1.0705,0.51319,0.26075,-0.029644,-0.77878,-1.4105,-1.5152,-1.294,-1.5931,-2.5056,-1.8244,-1.253,-1.9211,0.49211,3.6901,4.4044,5.0231,4.8613,5.9955,7.3168,8.0479,8.5582,8.1522,8.3184,8.4722,7.4692,6.1602,6.0002,8.1412,7.9986,8.937\n4.6532,4.0164,3.2337,2.6511,1.6686,1.1799,1.0774,0.72523,1.1358,1.3962,0.83874,0.14154,0.55245,1.0769,1.3111,0.78946,0.27477,0.38537,1.0801,0.24819,-0.85619,-1.4755,-1.0558,-0.77653,-1.8903,-2.7549,-2.1797,-1.4497,-1.5953,-0.43623,2.5155,2.0138,3.2356,4.7853,5.89,6.6728,7.6946,8.1881,8.1357,7.7408,7.479,6.4741,5.5117,5.5031,6.4071,7.5856,9.1787\n5.3996,5.0547,3.3617,2.93,2.4962,1.0413,1.2278,1.333,1.193,1.3509,0.88516,0.017405,0.39492,0.89519,0.69978,0.31647,0.0025463,0.34602,1.128,0.64272,0.51533,0.25912,-0.91609,-2.5764,-3.0063,-3.0168,-2.643,-2.8017,-2.3794,-0.24249,0.93742,1.2096,2.1908,3.5213,4.7483,6.5757,5.8178,7.3128,8.1936,7.7109,6.3965,5.7362,6.1691,6.6947,7.2675,7.3385,8.1258\n6.1688,5.8505,NaN,NaN,2.0147,0.63319,1.9321,2.2101,1.3859,0.56459,0.35998,0.58798,0.086971,0.64725,0.72797,0.96503,1.804,1.3765,0.99755,0.97054,1.1184,0.041941,-1.4165,-3.0161,-3.3339,-3.2755,-3.6699,-3.6102,-2.4328,-1.087,-0.70615,-0.37836,2.5495,3.5891,4.8676,6.2555,5.7072,6.4633,7.5357,6.712,5.9044,5.421,6.2296,7.2654,7.7208,7.5246,8.6776\nNaN,NaN,NaN,NaN,1.9442,1.8354,2.5058,2.8469,2.2616,1.1281,0.29278,0.55981,-0.1599,0.16774,0.45703,1.1161,1.8754,1.3273,0.62552,-0.22226,-0.37405,-1.7049,-2.8336,-3.6128,-3.5744,-3.1364,-4.9455,NaN,NaN,NaN,-2.0526,-1.5922,0.015278,2.4004,4.6887,6.1734,6.185,6.3569,6.7559,5.9975,5.6962,5.2759,6.3142,7.2448,7.3058,7.64,8.489\nNaN,NaN,NaN,3.4273,3.1573,3.0046,3.0222,2.8975,2.597,1.8441,0.85098,1.0796,0.57968,0.54374,0.44198,1.1066,1.1647,0.37583,-0.92899,-0.96003,-1.9232,-3.7636,-5.5895,-4.5159,-2.3202,-2.4218,NaN,NaN,NaN,NaN,NaN,NaN,-1.8692,-0.75167,1.1112,3.0026,6.3176,6.0569,5.8576,5.8941,5.7926,6.3821,7.3698,7.9889,7.8125,7.3897,8.0539\nNaN,NaN,NaN,3.5429,3.5055,2.9254,3.1302,2.8579,2.0499,1.0232,0.73061,1.2203,1.373,0.96474,0.34491,0.81128,0.45004,-0.12568,-0.7154,-1.3132,-2.4675,-4.7294,-5.8706,-4.6474,-3.5144,-3.5431,NaN,NaN,NaN,NaN,NaN,NaN,-2.8656,-1.7831,-0.9595,0.43847,3.1393,5.5354,6.7612,8.1362,7.5172,7.4946,8.0844,7.8201,7.1213,6.2178,8.0685\n4.1546,4.0492,3.2529,3.1658,3.5714,2.603,2.4294,2.51,1.7612,1.4148,0.50087,0.28501,0.60275,1.2478,0.59066,-0.20204,-0.54263,-0.034866,-0.8425,-2.0805,-3.8616,-4.8533,-4.1706,-4.7697,-6.2014,-6.5946,-4.3223,NaN,NaN,NaN,NaN,-2.9772,-3.3288,-1.6064,-1.4735,-0.89116,2.1831,5.219,7.3957,8.741,8.9993,8.5224,8.4916,7.827,7.1508,6.3931,8.5883\n3.8028,3.4944,2.9371,2.6462,2.6333,1.4959,1.6324,1.6556,1.6183,1.1339,0.43113,0.28365,0.36446,0.91685,0.75814,-0.37859,-0.52232,0.38398,NaN,NaN,NaN,NaN,-3.2372,-6.9142,-8.1518,-7.5465,-8.1802,-7.6489,-5.5608,-4.0454,-3.9742,-3.4608,-2.9823,-2.4355,-2.3214,-2.108,1.4168,4.6328,6.8615,9.1589,9.8804,8.904,8.1914,7.6236,NaN,NaN,NaN\n3.3661,3.0466,2.7475,4.3078,3.2537,1.6525,1.8481,2.3944,1.6878,1.2969,0.7791,1.2024,0.2422,0.49704,0.36303,-0.80003,-1.0273,NaN,NaN,NaN,NaN,NaN,NaN,-8.7426,-8.9856,-7.668,-9.4421,-8.8939,-7.472,-5.4728,-4.5077,-3.9373,-3.6392,-1.4389,-0.95108,-0.79362,1.2411,2.375,3.8924,8.3641,10.273,10.079,9.0676,8.5345,NaN,NaN,NaN\n3.5129,2.6986,3.503,4.6797,3.2794,2.2263,2.7406,2.8929,1.8321,0.9337,0.52651,0.47932,-0.64143,-0.90947,-1.2591,-1.2892,-0.69168,NaN,NaN,NaN,NaN,NaN,NaN,-8.8202,-9.878,-9.5859,-9.1687,-8.5176,-7.2263,-5.5848,-4.7049,-3.6387,-3.7568,-2.6989,-1.5446,1.1443,3.1105,3.8505,2.4629,6.1422,9.1838,9.2642,8.9304,NaN,NaN,NaN,NaN\n3.2118,1.9307,2.2178,2.8337,2.5883,2.1412,3.0686,2.3624,1.2687,0.48604,-0.42596,-1.2507,-1.8224,-1.7702,-1.3088,-1.0666,-0.26116,NaN,NaN,NaN,NaN,NaN,NaN,-9.0117,-8.8619,-8.2681,-8.801,-7.9346,-6.3688,-5.5267,-4.0569,-3.34,-3.1985,-2.3445,-0.75524,0.32604,-0.32061,2.5366,4.2781,4.781,6.3852,6.8937,6.4563,NaN,NaN,NaN,NaN\n4.3532,4.0377,3.8438,3.4324,2.689,2.3345,2.7843,1.8913,1.2401,0.4715,-0.13525,-1.1637,-2.4003,-2.7998,-3.5887,-1.9772,-2.2556,-4.2225,-6.8911,-6.3704,-5.22,-7.1909,-6.1059,-6.9798,-7.9152,-8.2022,-8.23,-7.1074,-5.5998,-4.5658,-3.6769,-3.5038,-3.0656,-2.288,-0.91774,-0.24471,0.015537,1.2288,2.2736,1.7951,2.8061,5.2124,NaN,NaN,NaN,NaN,NaN\n5.219,5.5378,5.1614,3.3897,1.4125,1.3338,2.0583,1.9726,1.5193,0.73685,-0.40351,-1.2953,-2.3854,-2.8188,-4.3438,-2.8708,-3.3966,-4.1415,-3.8746,-3.6585,-4.508,-5.9003,-5.7391,-6.2624,-7.1454,-6.477,-6.1655,-5.4739,-4.4362,-3.9884,-3.8105,-3.6961,-2.8294,-1.1119,0.048943,0.84674,1.4949,1.4801,0.62891,1.3597,0.43377,4.2913,NaN,NaN,NaN,NaN,NaN\n5.0582,5.8418,5.1002,2.0202,0.73096,1.3967,2.2649,1.5635,0.064362,-1.983,-3.0573,-1.5617,-1.6939,-2.2316,-2.1017,-2.2163,-2.6497,-4.0089,-3.4322,-3.69,-4.6582,-4.8099,-5.2914,-4.8175,-5.1264,-4.1809,-3.6041,-3.0957,-1.8254,-1.2129,-0.96704,0.23746,0.96586,1.2691,1.4917,0.67463,1.4839,1.6589,1.5001,1.9649,2.0024,1.1045,NaN,NaN,NaN,NaN,NaN\n4.8715,4.2385,4.1145,2.792,2.5024,1.7668,2.305,1.385,-0.50255,-1.0257,-2.4571,-0.63833,-1.5827,-3.3952,-5.7919,-6.4335,NaN,NaN,NaN,-5.1998,-5.0029,-4.9339,-4.521,-3.708,-4.6852,-4.3679,-3.1772,-2.4311,-1.0318,0.35869,0.87005,0.68607,0.67666,1.2435,1.5222,0.80613,0.49158,0.46553,1.0101,2.3159,2.4021,1.413,NaN,NaN,NaN,NaN,NaN\n5.1479,4.8466,3.8203,3.0735,2.5598,2.3929,2.1413,0.66881,-0.82159,-1.6254,-2.2319,-2.586,-2.8718,-3.6178,-6.1231,-6.4979,NaN,NaN,NaN,NaN,NaN,NaN,-2.8707,-2.5338,-3.4896,-3.2028,-1.5447,-0.49812,0.12981,0.40196,-0.10307,-0.33941,-0.85751,-0.40941,0.77703,1.6518,0.88728,0.68603,-0.17492,-0.42024,0.50857,1.5691,NaN,NaN,NaN,NaN,NaN\n6.3573,4.144,3.6403,3.91,3.6801,3.5394,NaN,NaN,-1.9623,-2.3687,-3.0574,-4.1096,-5.7268,-6.0193,-5.338,-5.0246,-6.1004,NaN,NaN,NaN,NaN,NaN,NaN,-2.0406,-2.5554,-2.6058,-1.6205,0.27038,-0.33479,0.27993,-0.22319,-1.1541,-0.36553,-0.80645,-0.5269,0.27724,0.91502,1.517,1.6641,1.353,0.88655,-0.5902,NaN,NaN,NaN,NaN,NaN\n5.2365,4.5108,5.1415,6.9127,NaN,NaN,NaN,NaN,-2.1451,-2.6427,-3.1568,-4.1854,-5.1994,-5.9154,-5.924,-5.3001,-5.8099,-5.825,NaN,NaN,NaN,NaN,NaN,-2.9166,-3.6044,-3.3665,-3.559,-1.2341,-1.5163,-1.6511,0.090101,-0.60624,0.87006,1.4593,1.8525,0.53646,0.025574,-0.25295,1.4306,1.7584,1.4522,0.47808,-1.1608,NaN,NaN,NaN,NaN\n5.4518,4.6609,3.6017,4.4883,NaN,NaN,NaN,NaN,-3.4705,-4.5224,-4.0293,-4.7719,-5.5805,-6.5473,-7.0254,-6.9333,-6.9476,-7.6568,NaN,NaN,NaN,NaN,NaN,-5.45,-6.1883,-5.0794,-4.7382,-4.8847,-4.9883,-3.8553,-1.3789,-0.56342,0.76378,0.98402,0.98569,0.2339,2.3017,1.8782,2.5553,2.3181,1.9309,1.2719,0.62868,2.0814,2.9563,2.4969,-0.054232\n5.516,3.5608,1.1866,NaN,NaN,NaN,NaN,NaN,-5.6638,-6.2957,-7.289,-6.6673,-5.9834,-7.1867,-7.5224,-7.0647,-6.6361,-6.3513,-7.2455,-7.3826,-6.8644,-5.2229,-4.4204,-4.2257,-4.8877,-4.4709,-3.9698,-4.776,-5.0224,-4.8413,-3.0908,-0.90572,0.72762,0.6167,0.48207,-0.45584,0.76878,1.9284,2.7558,1.5354,1.4341,1.8133,2.5071,3.7525,2.7269,1.3645,-1.5772\n3.2834,1.683,2.5032,3.3651,NaN,NaN,NaN,-5.586,-5.2737,-6.9646,-7.4296,-7.5264,-7.0103,-7.6905,-7.5928,-7.2527,-6.5225,-7.1234,-6.1485,-5.1806,-5.8397,-5.6094,-4.0743,-4.287,-4.2026,-3.8293,-3.8469,-4.344,-3.3762,-2.8905,-2.246,-1.4664,0.2524,0.42188,0.46533,0.77404,1.9746,1.1287,1.6736,2.9785,2.4788,2.6924,2.6122,2.3148,1.5146,1.2769,-0.14427\n1.58,0.70718,2.5048,NaN,NaN,NaN,NaN,-3.3826,-5.2309,-6.3742,-6.8604,-7.9676,-8.4582,-7.1859,-6.01,-5.597,-6.0601,-7.3554,-8.0766,-6.6289,-7.0237,-7.1671,-6.986,-4.9278,-4.6666,-4.2357,-4.3833,-4.0494,-2.9073,-1.574,-1.1298,-0.9303,-0.29574,0.18084,0.5369,1.4947,2.3935,2.8761,3.5238,3.9644,2.5673,1.8039,3.1604,2.6267,1.5234,1.0328,-0.028603\n1.5567,1.254,1.5812,2.4669,-0.026745,-5.4693,-5.0966,-5.562,-5.4598,-5.262,-5.61,-6.711,-7.5392,-7.2137,-6.5658,-3.3773,-3.0533,-4.9784,-6.71,-6.5293,-4.7099,-5.0335,-5.0666,-2.1468,-2.4535,-4.061,-3.9164,-2.3005,-1.5153,0.060223,1.1056,0.82436,1.1993,1.1421,1.4203,2.5087,NaN,NaN,NaN,4.1197,3.2723,2.2696,3.4473,3.5897,1.9931,-0.0025482,-1.5212\n1.3622,1.1158,0.82654,-0.099266,-0.43773,-3.8539,-4.1226,-3.9473,-3.3146,-4.7938,-5.4366,-5.9582,-5.0275,-5.9889,-6.6728,-6.7144,-5.9426,-6.1477,-5.8137,-5.1138,-4.1215,-3.2032,-1.7288,-0.58248,-1.0371,-0.69892,-0.52791,0.46856,1.4519,1.8306,2.4174,2.6292,2.4814,2.0345,2.8104,5.3033,NaN,NaN,NaN,3.1314,3.6375,2.7335,2.8803,2.5008,1.1419,-0.70933,-2.216\n0.048927,0.3951,-0.22532,-1.0246,-1.4762,-2.741,-3.2198,-2.7504,-2.9092,-4.1871,-3.8498,-5.2777,-5.7503,-5.8455,-5.7161,-5.8441,-4.4214,-3.3236,-4.34,-4.1653,-3.3679,-0.93475,0.20833,0.53763,0.2837,0.71025,1.6037,2.4365,2.5728,2.743,3.0972,4.3478,4.6306,2.928,2.8499,3.8663,NaN,NaN,NaN,4.6454,4.0708,3.6647,3.412,2.9123,0.90109,-0.73848,-1.3076\n0.085796,1.0927,-0.17076,-1.644,-1.8394,-3.0058,-3.5295,-2.6956,-3.4508,-3.0753,-3.1779,-4.8994,-5.4078,-5.4304,-5.47,-4.7153,-3.7101,-2.9415,-2.8708,-2.4478,-1.321,0.02779,1.1404,0.96705,-0.014431,1.024,2.2177,2.2219,2.2115,2.0477,3.5325,3.6427,2.6113,1.6331,1.8726,2.1194,NaN,NaN,NaN,5.192,4.9353,4.1638,3.9182,1.9715,0.7825,-0.093475,-0.95603\nNaN,NaN,0.9407,-2.0487,-2.5062,-3.3825,-3.531,-3.1036,-4.3876,-3.1326,-2.8789,-3.6165,-4.1682,-4.5684,-4.6362,-4.1921,-3.894,-3.3125,-2.6747,-1.8062,-0.62197,0.60672,1.1332,1.0742,0.20938,0.047142,1.1053,1.5543,0.35569,1.439,2.7886,3.8379,2.4395,1.3022,2.045,4.9326,5.7685,5.1462,5.2615,5.7405,5.8019,4.6029,4.1031,1.9238,1.2453,0.07362,-0.4312\nNaN,NaN,NaN,-2.5159,-4.1693,-4.4797,-4.2056,-4.3431,-4.5653,-4.8852,-4.4549,-3.9766,-3.603,-3.6821,-3.9804,-4.1766,-4.4896,-3.7148,-2.1273,-0.79361,-0.16942,1.3308,1.7522,1.7775,0.41923,1.2524,1.6373,1.3383,1.9289,3.8073,3.98,3.8262,1.975,0.5041,3.3372,5.1001,4.6871,4.1206,4.162,5.268,6.6986,2.3627,1.7201,1.7503,-0.028069,-0.7016,-0.2089\nNaN,NaN,-0.81568,-4.7593,-5.5786,-6.3988,-5.9056,-4.8688,-5.0558,-5.0884,-4.401,-4.1927,-3.3709,-3.2464,-3.6094,-3.7981,-3.7146,-3.0628,-1.5824,-0.54505,0.29498,1.4957,2.789,1.4937,1.636,2.2136,2.0115,2.2548,2.8268,3.5618,4.0086,2.9213,1.2784,0.29786,3.1166,5.4666,4.2919,3.2914,1.7413,5.3119,7.1836,1.9673,-0.52024,-0.74641,-1.6907,-0.099602,-0.37201\nNaN,NaN,-4.1683,-4.6916,-5.1983,-5.8374,-5.7541,-5.6681,-5.8616,-5.9981,-5.439,-4.6421,-4.0168,-3.3939,-3.5119,-3.9508,-3.9766,-2.783,-1.2546,-0.5149,0.30602,1.5466,3.7766,3.0197,1.4006,0.94905,1.2102,1.9718,2.9547,3.0809,3.1703,2.7488,2.1081,2.4376,6.7207,8.9665,6.6246,5.5619,5.4269,5.9245,5.3479,1.8722,-1.8086,-2.3002,-2.0357,0.22436,-0.6545\n-3.5322,-4.7338,-4.0922,-5.2365,-6.4369,-5.5666,-4.3538,-4.0438,-5.8644,-5.9307,-6.2865,-5.5762,-5.2212,-4.6406,-4.9686,-5.7361,-4.7569,-2.7689,-1.539,-1.2817,0.37171,1.747,3.668,NaN,NaN,NaN,NaN,1.8744,3.6245,5.4058,4.7841,2.9084,NaN,NaN,8.6995,8.4581,6.6006,5.543,5.7853,5.4711,2.6252,0.88411,0.41676,-1.1491,-1.1179,-0.83535,-1.9994\n-1.342,-3.9183,-4.2053,-4.2032,-3.244,-2.9839,-2.7877,-2.8273,-4.2864,-6.3143,-6.0026,-4.7481,-4.274,-5.4208,-6.0379,-5.8821,-5.8598,-5.0895,-3.1886,-1.3102,-0.5576,1.3988,2.6839,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.953,14.077,NaN,NaN,NaN,NaN,NaN,NaN,5.4423,5.2489,3.1373,0.3199,-0.22688,-0.45372,-1.103,-1.625,-2.1517\n-1.5636,-3.2076,-4.1362,-3.8704,NaN,NaN,NaN,-1.5405,-3.2334,-5.7476,-5.3016,-4.8922,-3.3689,-5.002,-6.0002,-5.4325,-4.8664,-1.6,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.088,29.454,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.5289,2.9127,1.6546,0.9237,-0.064926,-0.9964,-1.7479,-1.8716\n-2.8395,-3.6041,-3.9509,-4.3585,NaN,NaN,NaN,NaN,-3.5869,-3.8033,-3.5557,-3.2261,-3.0511,-4.3537,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.377,29.167,25.746,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.7884,3.7987,2.4253,0.67128,-0.64464,-1.8125,-1.8648\n-2.9157,-2.624,-2.828,-3.6081,NaN,NaN,NaN,NaN,-4.3031,-4.4046,-3.7744,-3.2892,-2.2606,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.501,28.43,21.392,17.606,15.293,NaN,NaN,NaN,NaN,NaN,2.284,3.8554,3.1077,1.1707,-0.88927,-2.3672,-2.881\n-2.993,-2.6908,-1.9795,-1.292,NaN,NaN,NaN,NaN,-4.2786,-4.1785,-4.6241,-0.83399,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29.906,27.521,21.038,16.08,13.981,12.278,NaN,NaN,NaN,NaN,2.2951,2.2214,1.636,0.36122,-0.71609,-2.0417,-2.5654\n-1.6568,-1.7491,-1.7267,-0.75294,-0.1473,-2.266,NaN,-2.9003,-1.813,-2.8419,-3.5654,-0.017,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34.029,30.522,26.181,22.821,16.383,13.525,9.5487,8.4075,NaN,NaN,2.6301,0.69962,0.68395,0.53823,-0.27941,-0.054983,-0.8708,-1.8459\n-1.1073,-0.60934,-0.78257,-0.75162,-0.18181,-0.60688,-2.6985,-1.8631,-0.99183,-0.88808,-0.91434,1.5913,2.1495,3.829,4.3273,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,30.419,25.2,23.171,22.051,19.295,11.806,8.6088,8.1367,7.5434,5.7222,3.2259,0.2885,-0.64425,-1.1228,-1.2638,-1.5452,-0.99206,-1.0989\n-0.44897,0.17488,0.30691,0.53997,0.69893,0.52955,-0.38945,-0.68135,-0.18871,0.10306,0.69526,1.8267,2.7967,2.8866,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,27.556,22.506,19.481,18.112,15.275,9.0383,7.7021,6.6668,5.7644,4.5743,2.2933,-0.0081139,-1.2421,-1.5758,-1.9033,-1.6475,-1.3015,-1.7491\n0.65687,0.76475,0.44172,1.1238,0.87157,0.95954,1.3719,1.1772,0.84217,2.1558,2.2674,2.5253,3.875,6.036,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,22.378,17.242,12.472,9.0977,8.2257,6.1671,5.852,5.6375,5.6768,1.9022,-1.3252,-0.84952,-1.4134,-1.4874,-1.1242,-1.9384,-3.2086\n1.5075,1.8003,2.2209,2.1557,1.3026,0.52243,0.4945,0.73659,1.4353,2.7518,3.7336,3.0953,3.8887,6.9419,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,19.475,15.771,11.884,8.432,7.9736,6.3705,4.9261,3.8336,3.6174,0.76828,-1.0599,-0.30202,-1.3747,-1.597,-0.61664,-1.2489,-2.803\n0.33269,1.2962,1.6346,1.3266,1.3948,1.2901,1.1725,0.27399,0.79202,2.3402,3.6418,3.8019,4.4848,7.8028,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,20.284,16.585,13.718,10.816,8.6146,8.1741,7.6586,5.5861,4.2273,1.6362,-0.22472,-0.91542,-0.91001,-1.0166,-1.2694,-1.4,-2.1081,-3.9505\n1.6532,2.9179,2.5867,0.4998,1.1898,1.3458,0.89378,0.22869,0.65741,2.4407,3.782,5.1374,6.3658,9.2816,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,18.944,18.125,16.251,15.334,13.851,11.407,9.28,6.4519,4.7493,4.7815,2.8385,-0.20394,-0.89859,-1.2375,-1.3103,-1.0448,-1.0776,-1.7519,-2.756,-3.0365\n2.9681,3.6355,2.9985,0.34794,0.090755,1.7407,1.3891,0.78307,0.53881,3.8814,4.2923,5.0395,6.4608,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.179,14.66,13.212,13.707,11.658,9.455,7.8473,4.5928,0.71939,0.96226,1.2907,-0.89134,-1.7496,-1.4998,-1.8103,-1.7065,-2.1442,-2.7402,-3.2102,-3.4905\n3.5825,3.3297,2.5236,1.5874,1.633,3.0353,3.0961,2.742,3.8545,4.6394,4.5483,5.6453,7.5787,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13,12.089,12.68,11.562,9.8278,8.3695,6.7013,5.0605,2.9499,1.4064,0.69438,-1.9401,-1.9562,-2.6608,-3.3086,-2.6361,-2.6841,-2.902,-3.2282,-3.3817\n2.5541,2.3505,2.0873,2.1039,3.0022,3.9544,4.2118,3.8158,4.861,4.2185,3.9486,4.8532,6.1613,7.4044,10.132,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.6284,10.69,9.6134,8.0583,6.5789,6.5607,6.8039,6.2917,4.1737,2.4891,1.6363,-0.42798,-3.3647,-2.145,-3.1496,-4.8659,-4.7869,-4.1533,-3.3336,-3.5894,-3.8149\n2.2881,2.4911,2.6808,2.6764,3.3671,3.8445,3.843,3.7286,4.1629,3.9959,3.9501,5.1346,5.8352,6.761,8.7154,11.071,12.312,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.5941,7.8256,5.1764,5.0134,4.9807,5.5122,6.6454,6.3351,3.4014,1.6811,-0.040783,-0.35252,-2.5263,-2.6538,-2.7898,-3.3647,-4.2048,-5.2051,-4.7461,-3.48,-4.2049\n1.285,1.7846,2.6853,3.3754,3.2515,2.5017,3.3241,3.9687,4.1844,4.3289,4.5301,5.1523,5.6999,6.0441,7.4724,8.6275,9.2874,10.287,10.974,NaN,NaN,NaN,NaN,9.4803,8.9593,8.5819,NaN,NaN,5.1513,4.9699,5.1028,5.7118,5.2602,3.2127,1.1321,-0.49049,-0.76191,-1.695,-2.7942,-2.5367,-2.1008,-2.0347,-3.3887,-4.7501,-4.2909,-3.8306,-4.3175\n1.2976,1.2907,2.3679,3.5827,3.2354,2.6853,3.0854,3.4,3.559,3.4857,4.6754,4.8881,4.3302,4.2004,5.9033,6.7358,6.8511,8.3443,9.144,9.5066,11.693,NaN,9.1035,9.5712,9.7731,9.4124,NaN,NaN,7.1409,5.1384,5.13,5.4983,4.3531,1.8546,1.0913,0.31108,-0.37321,-1.3785,-1.894,-2.5468,-2.2101,-2.2884,-3.5537,-4.0674,-3.8806,-4.4522,-4.345\n1.6596,2.047,2.3686,2.7188,3.2949,3.7428,2.5182,2.4321,2.8819,3.4614,4.0855,4.2413,3.5458,3.7901,4.6811,5.3245,5.259,7.6549,9.006,9.6433,8.9012,7.4458,7.7633,7.5668,6.9499,6.6422,NaN,NaN,7.6343,5.3186,5.1054,5.426,4.4536,3.3041,2.7833,2.6517,1.2952,-0.75056,-1.793,-2.3248,-2.4262,-1.8468,-2.1699,-3.7882,-3.8298,-4.3653,-5.2066\n2.4291,2.0624,0.80379,1.7198,3.0784,3.5771,3.2029,3.0755,3.156,3.0436,3.8414,4.3998,4.9784,5.001,4.7832,4.343,4.2366,4.9541,5.0041,7.0634,7.3675,6.8314,7.4545,7.7034,7.0394,5.9839,5.0668,6.6965,7.9836,4.5562,4.7059,3.8115,3.3156,2.4572,1.7499,1.9125,0.31552,-1.0624,-1.6302,-2.1748,-2.6081,-2.716,-3.0011,-3.6082,-4.3605,-5.1774,-5.5293\n2.4304,1.657,0.72812,1.8262,2.4725,3.2686,3.8854,3.7233,3.3391,3.0171,3.9009,4.4934,4.7124,5.101,4.4577,4.2903,4.2207,4.5763,3.9473,3.609,4.869,6.3462,6.837,6.6372,6.028,5.9666,5.6455,6.1301,7.26,5.5736,3.7854,3.0487,1.4985,1.0381,0.45303,0.08396,-0.32367,-0.14936,-0.70589,-1.8552,-2.5859,-2.3319,-2.4545,-3.375,-4.8979,-5.0857,-5.9491\n2.5282,1.963,1.8074,2.0406,2.2827,2.1106,3.265,3.3947,4.6218,4.7894,5.0487,5.2074,5.048,5.8798,6.0756,5.6295,5.2664,4.9179,3.1112,3.3004,5.6996,6.8573,6.7608,5.6552,4.8447,4.3063,4.6366,5.253,4.9692,3.708,2.6016,1.7067,-0.29355,-0.56997,-1.4109,-2.5153,-2.4553,-0.25926,-0.11266,-1.6152,-2.0287,-2.1264,-2.2647,-3.1511,-4.5977,-5.0754,-5.8315\n2.6292,3.2302,2.2533,1.4944,1.5559,1.8514,2.5106,3.2919,4.4336,4.6039,6.0773,6.6792,7.4464,6.6058,6.3777,6.5734,6.347,5.9006,5.276,4.7702,6.0847,7.2286,7.2569,6.4904,5.0662,4.2322,4.8187,4.8167,4.7301,2.2439,2.1894,-0.0067787,-2.2853,-1.7911,-2.065,-2.5276,-2.0722,-1.259,-1.7358,-2.9078,-2.4092,-1.5537,-2.2134,-3.2114,-4.0877,-4.9809,-6.378\n2.1572,1.9529,1.1608,0.99487,1.6414,2.8487,3.0753,2.5515,2.7302,3.4153,4.8333,5.3114,5.5036,5.8611,5.0467,5.9435,6.1647,6.243,5.7769,5.1393,5.3433,5.9753,6.8866,6.3925,6.066,5.4499,7.0229,6.8631,5.0112,1.708,1.0737,-0.28903,-2.3109,-2.0924,-1.9911,-1.6799,-1.613,-1.76,-3.0726,-4.1421,-2.8966,-1.5276,-1.7002,-2.6578,-3.9826,-5.5842,-7.1474\n1.0098,0.82774,0.67759,1.2011,2.4344,4.365,3.3889,2.7816,3.3097,4.1609,4.7524,3.9885,2.5696,1.4747,NaN,NaN,NaN,5.5592,5.1938,5.402,6.0953,5.9066,6.1567,6.3008,6.1382,6.086,6.6024,6.1695,2.7313,0.86055,0.57444,0.21128,-0.37542,-1.3889,-1.9718,-1.6044,-1.6854,-2.0473,-2.5385,-2.1822,-2.3568,-2.7214,-2.2582,-2.9442,-4.7198,-6.021,-6.1753\n1.0692,0.4707,-0.46183,0.29769,0.88638,3.3011,3.5815,2.5889,2.5305,3.6615,5.2135,4.6861,3.1082,NaN,NaN,NaN,NaN,NaN,4.8148,6.8615,7.1428,6.2286,5.0284,5.8369,6.5642,5.6703,5.6261,4.5983,0.12712,0.24059,0.34948,-0.11244,0.18878,-0.43502,-1.7142,-2.2812,-3.1003,-2.7536,-1.9337,-2.332,-2.2552,-2.6845,-2.9859,-3.771,-5.6843,-6.3731,-5.8703\n0.79018,1.5827,1.2385,0.10137,0.2318,1.5285,3.3624,2.7427,3.2285,4.0506,5.0412,5.0425,NaN,NaN,NaN,NaN,NaN,NaN,4.4582,6.1323,5.5267,4.9286,5.2222,4.8262,4.5093,4.5531,4.0012,2.7738,-0.60856,-0.53337,-0.92564,-1.3494,-1.0669,-1.3788,-1.4274,-1.9001,-2.566,-2.5218,-2.0703,-2.511,-2.6586,-3.0129,-4.1479,-4.557,-4.328,-4.5157,-5.35\n-0.095329,2.3947,3.6447,1.0564,0.02158,-0.8863,1.1566,2.1667,2.1243,2.9173,3.6992,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.1699,3.5239,5.1765,5.424,5.2194,3.676,3.3412,3.1118,2.4059,1.5921,0.34689,-0.60278,-1.145,-1.9604,-2.5607,-1.7481,-1.2918,-1.6669,-2.5795,-2.1183,-1.6807,-2.1239,-3.0148,-3.7135,-4.7304,-4.9002,-4.4088,-5.0176,-6.898\nNaN,NaN,NaN,NaN,NaN,0.5821,0.63268,1.574,2.0869,1.3923,0.22593,NaN,NaN,NaN,NaN,NaN,NaN,0.68796,3.7937,3.8432,4.7712,5.2369,4.2101,2.6,3.4396,3.598,2.6,1.8863,0.17636,-1.7546,-2.7924,-2.2484,-3.6224,-3.9981,-3.3384,-3.5306,-3.4405,-1.3928,-2.054,-1.9844,-2.4979,-3.097,-3.5675,-3.9617,-5.0552,-8.0731,-10.114\nNaN,NaN,NaN,NaN,NaN,NaN,0.6994,2.4206,2.7164,2.3602,0.24566,NaN,NaN,NaN,NaN,NaN,NaN,1.7012,2.7093,4.2232,3.7648,2.8427,1.5784,1.3405,1.9187,1.7527,1.4181,0.41812,-0.44891,-1.4142,-2.2682,-2.0935,-3.0584,-6.2585,-5.3757,-4.4031,-4.3502,-3.0707,-3.2007,-2.3421,-1.9494,-1.9688,-1.294,-0.94915,-3.0763,-6.6852,-7.8723\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,2.6678,2.9399,3.0729,1.6931,NaN,NaN,NaN,NaN,NaN,3.5167,3.2712,1.8702,1.8499,2.2244,0.79743,-0.10656,0.033264,0.90627,0.93831,-1.0508,-0.51791,-0.3019,-0.4173,-2.0993,-2.9499,-3.038,-2.6349,-2.9832,-3.8522,-3.6255,-3.0846,-3.3181,-2.5681,-2.146,-1.6229,-1.2838,-0.9436,-2.0655,-3.8169,-6.0125\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.93827,1.2494,2.0609,5.279,4.0729,1.9447,1.5235,3.0131,3.6561,2.1386,-0.14409,-0.84474,-0.49449,-0.23174,0.37997,0.58202,-1.1027,-2.2406,-2.7965,-3.1246,-3.7305,-4.8603,-6.2825,-5.0159,-3.6377,-2.9044,-2.643,-2.8207,-2.672,-2.7419,-3.0241,-2.934,-3.8597,-3.9772\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.17669,2.4891,3.0707,2.2498,2.8301,3.3784,2.3428,0.06749,-1.9718,-1.7575,-0.80472,-0.5153,-0.92273,-1.6873,-2.8386,-3.6931,-4.2365,-4.0671,-5.1549,-5.9267,-5.6393,-4.7735,-3.2846,-3.0035,-3.5429,-3.8588,-3.6208,-2.4836,-1.9332,-2.1123,-3.0989,NaN\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5671,3.9406,1.3731,0.83093,2.0135,2.7284,2.0324,-0.60929,-2.1188,-0.63453,-0.10361,-0.91413,-1.9551,-3.2209,-4.0759,-3.9946,-4.4476,-5.0161,-6.6197,-7.6968,-5.2444,-3.5405,-2.9466,-3.0544,-3.9599,-4.4755,-3.7086,-2.0941,-0.95018,0.015417,-1.2145,NaN\nNaN,NaN,3.9229,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.7799,4.301,1.9206,0.57634,0.61812,1.4057,1.7325,1.0305,-0.22817,0.085163,1.8856,0.36255,-1.5961,-3.1517,-3.7135,-3.5047,-5.4855,NaN,NaN,NaN,-5.3984,-5.0404,-4.7326,-4.0179,-3.4223,-3.6586,-3.6219,-2.6068,-1.2452,0.57939,-1.2333,NaN\n6.0321,2.721,2.4807,3.5111,4.5947,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.58182,1.7285,0.799,1.7746,2.3658,0.83776,-1.2886,-0.20468,-1.65,-2.0344,-0.29461,-0.89555,-2.643,-2.814,-3.5827,-5.5833,-6.8652,NaN,NaN,NaN,-7.1782,-7.8,-6.0713,-4.3725,-3.118,-3.3288,-3.2134,-2.1272,-1.2869,-1.709,NaN,NaN\n5.077,4.2718,4.1908,3.919,4.7024,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.18939,NaN,NaN,NaN,-2.2982,-3.4891,-5.233,-4.5289,-4.283,-3.8578,-4.2447,-5.0113,-6.7089,-8.9814,-8.3975,NaN,NaN,-6.9691,-7.0939,-3.621,-2.4849,-2.3247,-2.6602,-2.5975,0.080091,1.265,-2.9103,NaN,NaN\n3.4417,4.5928,5.6648,5.3547,5.0461,4.6645,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.4446,-5.3154,-4.4761,-4.3925,-3.8874,-4.0653,-4.8828,-7.2595,-9.4385,-8.6388,-6.6083,-6.2542,-5.3168,-4.0131,-0.33936,-0.20267,-1.7549,-2.9602,-2.7846,-2.6719,-2.789,-3.92,-5.2133,NaN\n4.8481,5.6185,5.0174,4.1111,5.7557,4.9318,3.3518,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.2949,-2.8439,-4.2385,-3.5729,-4.706,NaN,NaN,NaN,-11.434,-8.316,-6.2089,-5.0904,-4.4169,-4.8587,-5.0922,-3.7922,-4.5703,-3.8278,-5.1304,-5.1696,-4.3028,-4.3159,NaN\n5.3202,5.2683,4.0822,2.4138,3.719,3.1905,1.9795,0.79117,0.44488,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.12179,-1.7034,-3.6026,-3.2584,-5.0825,NaN,NaN,NaN,-10.114,-7.6606,-5.9341,-4.2791,-5.1378,-6.0579,-4.1911,-4.4534,-6.0183,-5.6189,-6.1259,-5.8585,-6.6368,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061002-070430.unw.csv",
    "content": "1.1709,0.95363,0.18701,0.75798,1.0213,1.2357,0.79452,0.73309,1.0689,-0.023409,0.088232,0.65139,1.3472,1.9453,2.4158,1.6133,0.90353,1.3962,1.5624,1.257,0.58056,0.16808,0.062289,0.49354,0.39704,-0.066903,0.31677,0.57339,0.58372,1.055,1.3371,1.1416,1.3058,1.6611,1.4705,1.3489,0.99265,0.50088,0.85475,1.1539,1.2991,1.0769,0.70673,0.36306,0.58203,-0.0058579,-0.12934\n0.79308,0.91291,0.40177,0.68123,1.44,1.4636,0.6682,0.40066,0.12543,0.26115,0.18504,0.14258,0.92051,1.5,1.475,1.7772,1.0977,1.4373,1.5687,1.1705,1.0257,0.073434,-0.27396,-0.46924,-0.6964,-0.29752,0.59158,0.83837,0.71047,0.61944,0.73913,0.51032,0.81255,1.0785,1.022,0.99932,0.78977,0.55165,0.463,0.54707,0.8364,0.9519,0.26154,-0.051565,-0.32748,-0.43673,-0.31025\n0.78224,0.73186,0.073229,0.5667,0.8366,0.65977,0.50673,0.41529,0.21388,0.63134,0.22696,0.05753,0.71605,1.0085,1.5522,1.8715,1.1241,1.4429,1.8171,0.97561,0.62817,0.18356,0.15833,-0.61322,-0.44811,0.26981,0.88783,0.030152,-0.18706,0.29663,-0.20855,0.2244,0.447,0.58171,0.54711,0.66334,0.027196,0.0029221,0.36765,0.5505,0.80497,0.79647,0.34883,-0.090553,-0.47104,-0.51807,-0.046141\n0.57007,0.65526,0.36028,0.69509,0.74305,0.86144,1.3662,1.33,0.38599,0.14822,0.29786,0.47469,0.5058,1.2466,1.3882,1.3573,1.2525,1.704,1.9546,1.0795,0.97394,0.57531,0.0031989,-0.29997,-0.22981,-0.06885,-0.068573,-0.45679,-0.55162,-0.29638,-0.16939,0.38092,0.61627,0.40954,0.38889,0.29641,0.1517,-0.19063,0.31576,0.6955,0.60621,0.19529,-0.12561,-0.12686,0.0024946,-0.039628,-0.022932\n0.061691,-0.21996,-0.29831,0.76312,1.3875,1.1733,1.4176,2.3812,1.802,1.5826,0.77916,0.45878,0.69483,1.5029,1.0973,0.80384,0.59839,1.1182,1.8801,2.0243,1.5462,0.74916,-0.4384,-0.21578,-0.37454,-0.74114,-1.2879,-1.1414,-0.44626,-0.18552,-0.49736,-0.285,0.2302,0.67793,0.72513,0.67803,0.098248,-0.0056911,0.29583,0.47978,0.25404,0.31998,0.16514,0.067099,0.16233,0.10127,0.053494\n-0.43991,-0.67957,0.28738,0.92602,1.1753,1.0283,0.93956,0.9234,0.70194,0.68332,0.8858,0.39095,1.2381,1.6007,1.5511,1.1874,1.2029,1.0977,1.2252,0.85113,0.69014,0.1219,-0.96555,-0.32211,0.34191,-0.075521,-0.94073,-1.2452,-0.52862,-0.17279,-0.52938,-0.73249,-0.47194,0.39611,0.59266,0.7993,0.75829,0.46498,0.28518,0.0051477,0.36499,0.84612,0.76818,0.5227,0.48233,0.51235,0.36818\n-0.51004,-0.71297,-0.12705,1.0052,1.21,1.0295,1.5349,0.92047,0.43005,0.69115,0.51175,0.4837,0.82755,1.2027,1.2193,1.0879,0.67984,0.48162,0.33031,0.30138,-0.44672,-0.86257,-0.74488,-0.68581,-0.84415,-0.83241,-1.2676,-1.2472,-0.50776,-0.12531,-0.65474,-0.94175,-0.94277,-0.65437,-0.051978,0.49558,0.75425,0.359,-0.02061,0.37591,0.47999,0.80393,0.97995,0.95173,0.94947,0.87775,1.1879\n0.13344,-0.13738,-0.6384,0.89388,1.2083,1.0446,0.89065,0.93388,1.0305,0.48885,-0.16211,0.53351,0.61298,0.87108,0.48304,0.39379,-0.12437,0.077263,-0.038646,-0.32306,-1.2203,-1.1732,-0.89016,-0.5482,-0.77111,-1.3595,-1.8723,-1.3087,-0.79622,-0.77556,-1.0001,-0.85203,-0.91038,-0.70904,-0.1522,0.14581,0.17555,-0.2221,-0.23076,0.48302,0.86666,0.83579,0.94665,0.49768,0.33837,0.51205,1.001\n0.42818,0.22473,-0.36351,0.88321,1.0455,1.1776,1.0571,1.321,1.0584,-0.01052,-0.47043,-0.018677,0.47144,0.54148,0.25608,-0.34095,-0.57664,-0.72877,-0.62946,-1.4338,-2.014,-1.9065,-0.9353,-0.40109,-0.48901,-0.76428,-1.0547,-0.77871,-1.0985,-1.3945,-1.3116,-1.1065,-1.0618,-0.76168,-0.38812,-0.43339,-0.503,-0.58644,-0.10543,0.31376,0.78679,1.2009,1.2824,0.31988,-0.12643,-0.025474,0.78003\n0.0052745,-0.023744,0.75868,0.98043,0.73945,1.0968,1.0032,1.2492,0.71503,-0.20028,-0.33102,-0.39545,-0.16626,-0.15894,-0.070318,-0.2283,-0.98685,-1.3524,-1.4501,-1.6826,-1.2631,-1.1027,-1.1084,-0.83959,-0.82737,-0.66277,-1.0696,-1.0477,-1.0701,-1.186,-0.94095,-1.0315,-1.0501,-1.0879,-1.1957,-0.88523,-0.84539,-1.0093,-0.59933,0.082382,0.76531,1.0606,0.94849,0.2221,-0.26484,-0.050464,0.62321\n0.30252,0.64052,1.2416,1.7614,1.7592,1.2776,0.86032,0.64633,0.35497,-0.41992,-0.81187,-0.86384,-0.44386,-0.81357,-1.2085,-0.53214,-0.99959,-1.3047,-1.6426,-1.7444,-0.97344,-0.73808,-1.1841,-1.0093,-0.96776,-1.0793,-1.1554,-1.2429,-1.1547,-0.93513,-0.5203,-0.37993,-0.79367,-0.92481,-1.2688,-1.0884,-1.0362,-1.0775,-0.80803,-0.10184,0.55223,0.62612,0.45612,0.11572,-0.38241,-0.17062,0.1393\n0.49056,0.85651,1.6569,1.7397,1.007,1.5898,0.85705,0.35704,0.4462,-0.66346,-1.2848,-1.354,-0.84302,-0.92988,-1.07,-0.49006,-0.802,-1.4227,-1.487,-1.225,-0.16533,-0.58418,-0.8741,-0.48546,-0.51799,-0.65622,-0.85829,-0.99906,-0.66476,-0.44456,-0.53392,-0.74726,-0.73802,-0.68135,-0.91766,-0.89199,-0.67213,-0.68273,-0.6733,-0.12413,0.55604,0.61259,0.28466,-0.13784,-0.40713,-0.67273,-0.35997\n0.63762,0.89659,2.2375,2.8903,1.3074,1.4649,1.0934,0.5469,0.58864,-0.72141,-0.57836,-0.78414,-0.80523,-0.37045,-0.24967,-0.20805,-0.20459,-0.47346,-0.6025,-0.31185,-0.84134,-0.38981,-0.22472,-0.56697,-0.86067,-0.49081,-0.4309,-0.81935,-0.59548,-0.34555,-0.85278,-1.0673,-0.99747,-0.87208,-1.0279,-0.98401,-0.3216,-0.23674,-0.27531,0.2402,0.73144,0.78787,0.38511,-0.038297,-0.5477,-0.55757,-0.0058119\n1.6514,2.0952,2.7973,4.0256,2.1192,-0.043639,0.20741,0.78282,-0.36936,-0.95642,-1.1894,-1.0555,-0.52683,-0.0973,-0.023069,-0.18343,-0.2834,-0.65464,-0.56706,-0.51129,-1.0567,-1.0905,-0.81812,-0.76221,-1.1134,-0.0010216,0.18278,-0.36069,-0.27418,-0.37218,-0.89663,-1.0588,-1.1042,-1.1139,-1.0997,-1.2617,-1.0674,-0.613,0.12408,0.14095,0.42409,0.43312,-0.034221,-0.26966,-0.71749,-0.14636,0.23567\n2.1424,1.4919,3.0655,4.2063,1.8664,0.34241,-0.01709,-0.90759,-0.75903,-0.77889,-0.56673,-0.004406,0.2632,0.38241,0.66686,0.82889,-0.012625,-0.79342,-0.71629,-0.78747,-1.6449,-1.2841,-0.60486,-0.26115,-0.32566,1.0924,0.93833,0.29691,-0.12112,-0.078966,-0.114,-0.50966,-1.0839,-1.3658,-1.4167,-1.8229,-1.4637,-1.0409,-0.37214,-0.25178,-0.40724,-0.37878,-0.65556,-0.82378,-0.91245,-0.18775,-0.17403\n1.5141,1.381,1.0658,1.8671,1.704,1.5118,0.015304,-1.2386,-0.6379,-0.86092,-0.32306,0.15745,0.47871,-0.28166,-0.50556,-1.1603,-1.5452,-1.2575,-1.0736,-1.7789,-1.8028,-1.0327,-0.57134,0.4262,0.44815,0.466,0.24533,-0.50664,-0.81683,-0.34434,-1.1118,-1.3741,-1.711,-1.8354,-1.8819,-2.1507,-1.5374,-1.2971,-1.0568,-0.61324,-0.41032,-0.1956,-0.50589,-0.60251,-0.65352,-0.99465,-0.66215\n1.381,1.234,1.2144,1.542,1.7591,1.4742,0.40712,-0.3999,-0.99757,-1.4222,-1.257,-0.22186,0.13884,-1.1598,-0.61016,-1.8049,-1.4731,-1.0612,-1.8203,-2.3646,-2.3562,-1.5232,-1.167,-0.61916,-0.73317,-0.12,-0.39444,-1.0014,-1.0713,-0.62749,-0.74807,-1.5625,-1.9538,-1.9559,-1.9601,-2.0723,-2.0219,-2.0236,-1.5062,-0.98251,-0.25331,-0.26759,-0.4586,-0.47364,-0.42628,-0.91309,-0.76657\n1.378,1.4016,1.377,1.4658,2.2863,2.0966,1.5236,-0.13086,-0.067711,0.43859,0.023708,-0.5891,-0.9781,-1.4041,-1.0558,-1.5812,-2.329,-1.7338,-1.6611,-1.3424,-1.1589,-1.4354,-1.8097,-1.5337,-1.2951,-1.0773,-0.91743,-1.2247,-1.1188,-0.52553,-0.9356,-1.6777,-2.8468,-2.6544,-2.5855,-2.2481,-2.6314,-2.917,-2.9368,-1.864,-1.3165,-1.1259,-1.3331,-0.83559,-0.56057,-0.90563,-0.93201\n1.5799,1.8767,1.2642,-0.065692,0.23154,0.57556,0.58886,-0.69811,-1.0373,0.51369,0.19551,-1.1221,-1.5176,-2.0374,-2.1946,-1.9251,-2.3348,-2.1512,-1.8094,-2.0332,-1.7007,-1.6758,-2.0182,-2.4907,-2.5649,-2.1486,-0.97646,-1.3176,-1.7728,-0.85792,-0.94279,-1.4473,-2.5877,-2.9831,-2.878,-2.659,-3.0213,-2.9976,-2.4021,-2.0653,-1.6602,-0.74939,-1.2604,-0.6662,-0.27305,-0.36018,-0.87792\n2.7406,2.38,2.029,1.2009,-0.1602,NaN,NaN,-1.1508,-1.1229,-0.067126,-0.44195,-1.7706,-2.3795,-2.6979,-2.9776,-2.8283,-2.811,-3.1509,-2.9241,-3.0157,-2.3217,-1.8275,-1.5514,-2.5014,-2.6145,-1.7765,-1.4053,-1.6128,-2.4968,-1.98,-1.1877,-1.4399,-2.5496,-2.7264,-3.5325,-3.1078,-2.2447,-2.3345,-2.1189,-2.0229,-1.4398,-0.82308,-0.5781,-0.22321,0.085846,0.10789,0.033441\n3.0914,3.739,2.3339,2.6671,2.307,NaN,NaN,-0.70114,-0.94263,-0.4359,-2.0406,-2.9862,-3.2356,-2.8808,-3.0397,-2.7439,-2.8338,-3.2642,-2.6218,-2.5276,-2.2187,-1.8724,-1.8262,-1.9664,-1.3194,-1.0747,-1.3594,-1.9064,-2.5324,-2.6965,-1.6646,-1.6161,-2.3428,-2.3871,-3.6252,-3.4542,-1.6059,-1.6015,-1.454,-1.5967,-1.0116,-0.68848,-0.62279,-0.14927,0.07822,0.27903,0.21879\n2.8811,2.0102,1.0231,0.029872,NaN,NaN,NaN,-0.76251,-1.2465,-1.5884,-2.3451,-3.0605,-3.5752,-3.5933,-3.534,-2.9007,-2.4948,-2.7437,-2.8607,-2.4338,-2.151,-1.9983,-2.0478,-1.2442,-1.0082,-1.2213,-1.5401,-1.7274,-2.1009,-2.0848,-1.5385,-1.3717,-1.7842,-1.9137,-2.8848,-2.7496,-1.5147,-0.9933,-0.80233,-1.2179,-0.68011,-0.43264,-0.16761,0.12572,0.19885,0.25171,0.05702\n1.7734,1.0566,1.3588,0.03761,NaN,NaN,NaN,-2.001,-1.9437,-2.1359,-3.0084,-4.4933,-4.9948,-4.317,-4.2134,-4.7835,-2.7366,-2.4327,-2.4381,-2.1032,-1.6423,-1.798,-1.9138,-1.4647,-1.5904,-1.3205,-1.3172,-1.4737,-1.7553,-1.942,-1.4529,-0.86461,-0.72338,-0.60281,-0.95831,-1.5242,-0.90312,-0.5791,-0.68315,-0.52093,-0.36849,-0.21141,0.40479,0.91638,0.36891,0.12201,-0.070161\n-0.91729,0.51922,0.049598,-0.90419,-2.861,-1.9741,-2.2046,-1.7738,-1.8285,-2.5267,-4.0682,-4.659,-2.8711,-3.2037,-3.4047,-3.6475,-2.136,-2.2283,-1.9546,-1.5912,-1.1314,-0.92054,-0.51859,-0.68013,-0.66343,-0.94376,-0.82399,-0.80934,-0.99846,-0.9345,-0.27652,0.16034,0.3955,0.2692,-0.27692,-0.59578,-0.63121,-0.74118,-0.94854,-0.43006,-0.27404,-0.032763,0.28132,0.45207,0.066971,-0.30023,-0.18218\n1.1195,0.90987,0.58229,-0.26348,-1.042,-1.8979,-1.7281,-2.193,-2.7694,-2.7203,-2.7959,-3.1572,-2.5276,-3.2544,-3.2638,-2.8679,-2.1299,-2.158,-1.8568,-1.3666,-0.8737,-0.23428,0.51529,-0.23791,-0.2695,0.12408,0.1156,0.097869,0.36758,0.20373,0.26062,0.60585,0.99173,0.33024,-0.21997,-0.2287,-0.12803,-0.4355,-0.53156,-0.26917,-0.45112,0.085688,0.31343,0.12625,0.033452,-0.081907,-0.2544\n-0.025078,-0.34912,-0.11654,-0.51033,-1.3309,-1.497,-1.635,-1.7998,-1.6976,-1.8185,-1.0239,-1.8262,-2.8568,-2.8275,-2.259,-2.104,-2.1554,-1.8149,-1.5805,-0.96754,-0.20047,1.1493,0.99488,0.48492,0.32693,0.30819,0.4558,0.16441,0.011422,0.64228,0.958,1.2184,0.39261,-1.3349,-0.77185,-0.47194,0.33012,0.14258,0.22695,0.61631,-0.032238,0.42947,0.52115,0.3373,0.26786,0.054524,-0.083127\n-0.8745,-1.6441,-1.1314,-1.3022,-1.2562,-1.2757,-1.6212,-1.5903,-1.4821,-1.654,-1.7541,-1.8845,-1.7657,-2.0689,-1.9544,-1.3856,-0.70433,-0.71038,-1.1346,-0.6902,0.3469,1.1056,1.3252,0.94913,0.39429,0.26342,0.49077,0.26657,-0.19697,0.92059,1.1069,0.88969,0.54007,-0.3789,-0.56739,-0.31939,0.57107,0.64482,0.76341,0.75375,0.3433,0.57993,0.44296,0.51168,0.53283,0.19865,-0.085945\n-1.4847,-1.8818,-1.4074,-1.0327,-1.3672,-0.94123,-0.33143,-1.331,-1.9506,-2.5462,-2.1811,-1.9898,-2.1126,-2.2744,-1.6423,-1.3452,-1.074,-1.3342,-0.88976,-0.12277,0.68411,1.682,2.0653,2.1392,1.2047,0.70803,0.74409,0.92715,0.84076,0.88819,1.294,1.4654,0.18671,-0.43597,-0.2049,0.85806,0.90487,0.95391,0.38542,0.55468,0.91563,0.75148,0.49492,0.67844,0.65481,0.31827,0.097683\n-1.5271,-1.1576,-0.88233,-0.8141,-0.75926,-0.69676,-0.82512,-1.1518,-1.7452,-1.9929,-1.6314,-1.5559,-2.1195,-1.799,-1.4281,-1.1326,-0.87407,-1.0277,-0.2855,0.63675,1.0245,2.7409,3.6792,4.2708,2.7815,2.2435,2.8648,2.2762,2.5446,2.9978,2.4426,1.9395,0.72689,0.12281,0.41103,0.9998,1.2924,1.5751,1.5005,1.425,1.3298,0.93787,1.036,0.99175,0.77335,0.60893,0.40823\n-1.4256,-1.1651,-1.4424,-1.8075,-0.68095,0.14007,0.03434,-0.88493,-1.6126,-1.8143,-1.6899,-1.3008,-0.99969,-1.4737,-1.3773,-1.0476,-0.53849,-0.403,0.41729,1.6867,2.1521,3.9682,5.8714,4.4719,3.9697,3.8603,4.0033,4.376,4.1081,3.649,3.1439,1.0579,-0.30616,-0.21008,0.64694,1.5011,1.7838,1.79,1.0826,1.1184,0.98528,0.79416,1.3932,1.2759,1.2077,0.76221,0.46608\n-3.3491,-1.3622,-0.44714,-0.53356,-0.80051,-0.32182,0.094146,-0.60051,-1.098,-0.93519,-1.2684,-1.3055,-0.8986,-1.4157,-1.4456,-1.3457,-0.30701,0.44489,0.45733,2.1117,3.9504,5.2446,8.1185,7.8331,5.286,4.9266,5.1836,5.192,3.8563,3.7133,3.4861,2.0333,1.0791,0.55672,0.90229,2.0502,2.0451,1.862,1.6755,1.4636,1.272,1.1724,1.3267,1.3683,1.2466,0.55115,0.35468\n-2.3597,-1.053,-0.5925,-0.39802,0.85577,0.62545,0.57381,-0.31875,-1.018,-0.96227,-0.94181,-1.6368,-1.661,-1.4741,-1.2255,-0.80031,0.34561,1.0869,1.9272,2.3737,4.8499,6.3711,8.579,9.1998,7.3132,4.5556,2.0638,2.321,1.4838,1.0715,0.59308,1.576,2.2383,2.0889,2.9371,2.7013,2.0353,2.1409,2.0211,1.7603,1.4522,1.7411,1.3048,1.4472,1.176,0.79809,0.67007\n0.5061,1.4745,1.3218,1.3322,0.64084,0.46893,-0.19403,-1.3032,-2.5955,-1.6926,-1.5651,-1.4849,-1.4719,-1.2369,-0.97723,-0.59357,0.43988,1.829,2.4022,2.5808,3.8761,5.7948,6.8568,7.6433,6.4809,4.355,1.618,-0.52797,-0.62136,-0.36523,0.79914,1.6984,1.6619,1.5083,1.5671,2.5712,2.9169,2.8829,1.9912,0.74552,0.84051,1.6038,1.2571,0.81455,1.07,0.95475,0.89353\n-0.53322,0.10967,0.23268,0.63928,1.3098,1.0129,0.42526,-0.84031,-1.9838,-2.1113,-1.8346,-1.7292,-1.666,-1.2395,-0.96609,-0.021027,1.4174,2.6538,3.0562,2.3546,2.1633,3.5533,5.7709,5.6285,NaN,NaN,NaN,NaN,NaN,0.49807,1.2728,1.4844,1.9591,NaN,NaN,3.1963,3.8419,3.4537,2.4211,1.4072,1.2658,1.4172,0.73039,0.61422,0.85932,0.95536,0.87125\n0.93531,0.50608,-1.0532,-0.69209,0.9652,1.4393,0.8139,-1.3627,-2.3453,-2.5737,-2.2781,-1.7535,-1.8254,-0.81082,-0.27233,0.14075,1.5246,2.4275,2.921,2.3343,1.5643,2.8378,2.5724,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.34243,1.7644,3.1525,NaN,NaN,NaN,5.7608,4.1613,2.4021,1.7874,1.9374,1.5272,0.90333,0.67731,0.90691,0.84049,0.88922\n-1.0427,-0.31654,-0.53489,-1.3393,0.35668,-0.092569,-0.97417,-1.6661,-2.2318,-0.80633,-0.84451,-0.81122,-1.1824,-1.8867,-1.4385,2.7764,2.8618,2.8842,3.326,3.063,2.6422,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1804,0.17288,0.80085,NaN,NaN,NaN,6.8822,5.611,3.2881,2.062,2.1054,1.7551,1.1155,0.81932,0.78288,0.78883,0.89268,0.82894\n-1.6203,-1.089,-0.67539,-0.98767,-0.13651,-0.85906,-1.5298,-1.3654,-1.0247,-0.89038,0.28665,0.86868,0.21812,-1.0214,-0.41047,3.0907,3.3521,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.8394,0.69808,NaN,NaN,NaN,NaN,6.3305,3.6743,1.7942,1.6962,1.818,1.0677,0.96366,1.0092,0.9879,1.1577,1.1221,0.5045\n-1.214,-0.29476,-0.13774,-0.14515,-0.050975,-0.49789,-1.3038,-1.4731,-1.4361,-0.80088,0.30913,1.1641,1.048,0.82802,2.0371,2.9861,3.2172,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.48578,-0.31196,NaN,NaN,NaN,NaN,4.1369,2.2518,1.629,0.68791,1.5682,1.1321,1.204,1.3945,1.42,1.7432,1.239,0.91293\n-0.98665,-0.30406,0.39014,0.44496,0.14495,-0.53296,-1.6619,-1.6974,-1.9549,-1.7765,-0.9526,0.1741,1.0063,1.4597,2.3347,2.5787,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.62942,-0.1712,-3.2504,NaN,NaN,NaN,NaN,2.7895,2.2522,1.7094,1.2001,1.829,1.147,1.2473,1.0964,1.1399,1.1151,1.1672,1.2988\n-1.1693,-0.18841,-0.44862,-0.73932,-0.66402,-0.28312,-1.0495,-1.8308,-1.2058,-0.90983,-0.73844,-0.24768,1.3408,2.0775,3.3424,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.42801,0.55188,-1.6603,-7.3045,NaN,NaN,NaN,3.2217,2.3309,2.0287,2.152,2.8245,1.7594,0.91762,1.0332,1.1797,1.0988,0.90448,0.96129,1.2031\n-1.0103,-0.31832,-0.045322,-0.67579,-0.43672,-0.001097,0.27812,-0.31838,-1.1763,0.020308,0.55698,0.74562,1.7549,2.5317,3.0415,NaN,NaN,NaN,NaN,NaN,NaN,1.7736,1.0103,0.93286,1.1471,-0.83458,-0.65453,2.1457,NaN,NaN,NaN,NaN,NaN,NaN,1.951,1.4726,1.1707,0.57611,-0.37931,0.28516,0.087989,0.44316,1.0419,1.1312,1.9461,1.8685,1.6531\n-0.89636,-0.51324,-0.052717,-0.36346,-0.59475,-0.22512,-0.21431,-0.56533,-0.48101,0.44292,1.226,1.299,1.7918,2.3335,1.8002,0.45292,-0.25218,NaN,NaN,NaN,NaN,1.2306,1.6578,2.1501,2.5041,1.0297,2.5077,1.9501,NaN,NaN,NaN,NaN,NaN,2.365,1.6579,0.8247,-0.6086,-1.1535,-1.1505,-0.11599,0.16058,0.21292,0.71035,1.1154,1.9826,2.1007,1.4996\n-0.51978,0.21419,0.12805,-0.42941,-0.49978,-0.41183,-0.72901,-0.60696,0.069434,0.38878,0.83935,1.1592,1.336,2.164,1.9393,0.9778,-0.70433,-0.37314,0.041682,0.12204,1.4273,2.003,3.3755,NaN,3.6743,2.9628,2.8961,1.056,NaN,NaN,NaN,NaN,NaN,2.1387,1.1876,0.69741,-0.43151,-0.30442,0.093824,0.52695,1.0799,1.2245,1.3769,1.4575,1.5548,1.6272,1.0307\n0.75347,0.81789,0.56001,0.38817,0.011794,-0.54528,-0.88222,-0.91101,-0.80391,-0.11705,0.48693,1.1665,1.7186,2.4601,2.8592,2.0047,0.35397,0.42346,0.60759,1.8757,2.4441,3.3741,NaN,NaN,NaN,NaN,NaN,1.119,2.761,4.1799,4.093,3.0924,2.6485,1.4588,0.67087,-0.00073981,-0.052814,0.40186,0.48042,1.0028,1.3767,1.1692,1.458,1.5272,1.0916,0.75892,-0.23775\n0.46742,0.79298,0.41067,0.24601,0.3142,-0.80255,-1.6348,-2.0224,-1.7832,0.16667,0.87265,1.3428,1.8226,2.6675,3.8697,4.6787,2.3687,1.0024,1.2785,1.5012,2.3243,NaN,NaN,NaN,NaN,NaN,NaN,1.4731,1.7242,3.1141,3.2881,2.4451,1.4019,0.8366,0.3514,-0.34874,0.028365,0.51618,0.85327,0.72203,0.79038,0.73124,0.6056,0.19065,-0.4906,-0.80206,-0.63549\n0.51753,0.6053,0.26592,0.03334,0.28698,-0.23713,-0.84971,-0.60812,0.63088,1.7791,1.591,1.7748,2.3148,3.3165,4.8626,6.3954,NaN,NaN,NaN,NaN,2.5292,NaN,NaN,NaN,NaN,NaN,NaN,1.3084,1.8752,2.6863,2.3941,1.4862,0.66543,0.42403,0.31399,0.15328,0.26299,0.52371,0.56181,0.46193,0.41946,0.0098987,0.05477,-0.14222,-0.62443,-0.82477,-0.57469\n0.27961,0.2515,0.0616,0.18386,0.47831,0.34826,0.099147,0.66781,1.5169,1.2988,1.1761,1.6598,2.1948,2.5349,2.783,4.2855,NaN,NaN,NaN,NaN,2.5562,2.6208,3.379,2.3136,NaN,NaN,0.77336,1.5334,1.6546,1.4877,1.3422,0.6485,0.76597,0.78257,0.52916,0.13883,0.89767,1.0067,0.60969,0.48572,0.29598,-0.9989,-0.60951,-0.18625,-0.045862,0.091297,0.05653\n-0.054352,-0.27322,0.17918,0.7108,1.1627,1.211,1.0171,0.94179,1.6706,1.5998,1.439,2.013,2.3083,2.8036,3.0912,2.8867,4.1364,3.9715,NaN,NaN,NaN,2.2528,1.8093,2.0552,1.0703,0.3768,-0.053102,1.0058,0.95794,0.91167,0.95802,0.907,1.2148,1.081,0.66119,0.35437,0.52379,0.99757,0.30675,-0.37448,-0.48955,-1.2867,-1.1373,-0.34003,0.18763,-0.062843,-0.30126\n0.2123,-0.35196,0.048945,0.72358,1.1074,0.96839,0.77228,0.72721,0.95405,1.3348,1.8801,2.4354,2.5503,2.8537,2.9373,2.7887,2.6633,2.0825,1.4237,1.8094,2.6747,2.7009,1.963,1.2976,0.78035,0.17278,0.28299,1.2603,1.0245,0.80853,0.84739,0.88101,1.1904,0.84497,0.49842,0.2574,-0.54847,-0.69584,-0.57813,-0.66517,-0.93371,-1.1519,-1.6702,-1.1479,-0.4088,0.10169,0.11819\n0.30868,-0.11162,-0.11933,0.49074,0.82137,0.71382,0.46857,0.46342,0.96014,1.8426,1.6352,2.2584,2.6294,2.6515,2.7972,2.1027,1.8625,1.1373,0.36425,-0.26584,1.0786,2.5055,1.5558,0.86022,0.94852,0.99967,1.2768,1.5515,1.3462,0.60365,0.92667,0.91996,1.5723,0.85549,0.53125,0.45482,-0.08112,-0.2739,-0.27923,-0.62915,-0.59216,-0.84535,-1.535,-1.135,-0.2819,0.10759,0.085392\n0.29613,-0.0092089,-0.015314,0.40636,0.72385,1.0036,0.43194,0.9158,1.4171,1.5964,1.2678,1.7582,2.403,2.471,2.2278,1.7572,1.5227,1.3691,0.099419,-0.53812,-0.21491,0.99403,0.77348,0.76285,0.87331,0.56108,-0.022694,0.91891,1.4255,0.93438,0.78159,0.98306,1.3878,1.025,0.78678,0.67899,0.33177,-0.027749,-0.085537,-0.45864,-0.54491,-0.54218,-0.92991,-0.77235,-0.44818,-0.52992,-0.19417\n0.59106,0.38361,0.15057,0.37296,0.71895,0.91294,0.80921,0.99111,1.3683,1.6329,1.9536,2.2701,2.247,1.9525,1.8903,1.5497,1.4794,0.3226,-0.0011566,-0.66884,-0.34012,0.85161,0.84003,0.91855,1.2915,1.2384,0.50921,0.65343,1.4642,1.0371,0.73664,1.169,1.14,0.8457,0.57979,0.2878,-0.037862,0.15508,5.9605e-06,-0.5733,-0.025598,0.026163,0.059805,-0.23093,-0.70428,-0.61867,-0.14648\n0.35448,0.15369,-0.083366,0.44815,0.59274,1.2524,1.8138,1.7851,1.7382,1.9673,2.02,2.1214,1.8827,1.7272,1.8873,1.3382,0.86038,-0.48775,-1.0828,-1.4403,-0.42475,0.77918,1.0891,0.96892,1.1003,1.2797,1.4885,1.1889,1.3895,1.2287,0.71026,1.1589,0.75558,0.3376,-0.018403,0.19654,0.23472,0.56952,0.0090306,-0.6852,-0.57252,0.27435,0.29837,-0.21983,-0.92015,-0.82641,0.17365\n-0.3678,0.40065,0.24215,0.36183,0.95138,1.3734,1.9601,2.0765,1.964,2.0546,2.3498,2.1564,1.6033,1.5139,1.4364,0.6149,0.43974,-0.91651,-0.94869,-0.53251,0.48436,0.87416,1.0883,1.1727,1.1109,1.2293,1.5816,1.7719,1.6839,1.6489,1.053,0.86356,0.34021,-0.36896,-0.066298,0.86033,0.30979,0.14622,-0.46026,-1.2584,-0.42511,0.25192,0.65993,0.2743,-0.91511,-0.44274,0.40765\n0.57163,1.9356,1.5017,0.48195,0.77093,1.0226,1.3821,1.947,2.018,2.4058,2.3026,2.8116,2.9104,1.9062,1.2749,1.2498,0.76548,-0.25665,-0.68927,-0.36281,0.59741,0.85804,0.91891,1.0656,0.9894,0.99956,1.5424,1.663,1.757,1.6339,0.88614,0.55661,-0.039839,-0.2566,-0.051803,-0.06086,-0.52123,-0.86472,-1.2377,-1.2726,-0.0026753,0.90186,1.015,0.40404,-0.13167,-0.056453,0.18878\n-0.31783,1.2707,1.2452,0.48832,-0.003056,0.41076,0.57455,1.306,1.7379,2.6755,2.7115,2.6847,2.1846,2.1165,1.4619,1.4973,1.0347,0.64388,-0.13126,-0.1705,0.40476,0.72748,1.0328,1.2178,0.42147,0.38524,1.1223,1.5945,1.7216,1.3285,0.77677,0.14599,-0.21231,-0.44935,-0.3095,-0.33398,-0.8402,-0.941,-1.2315,-0.73196,0.084128,0.56395,0.74333,0.934,0.29166,0.03406,-0.098985\n-1.0686,0.2027,0.7804,0.81807,1.1226,0.74903,0.30561,0.37478,1.0173,2.0086,1.0696,1.2663,-0.029812,-0.0067713,1.0405,1.4606,1.204,0.10243,-0.28614,-0.31014,0.2639,0.92439,1.4017,1.4418,0.60077,0.55707,0.26529,1.2029,1.0308,0.71715,0.12317,-0.46421,0.097247,-0.34743,-0.37749,-0.36035,-0.70002,-0.73515,-0.81145,-0.38018,-0.42207,-0.22503,0.39102,0.26088,-0.22129,-0.40775,-0.85155\n-0.6567,-0.070619,-0.10865,-0.25435,-0.19301,-0.053133,-0.28591,0.53481,1.1577,1.6808,0.76891,0.81179,0.034761,0.84502,0.66456,0.86114,0.81408,-0.31001,-0.26393,-0.24899,-0.71738,-0.30266,0.86682,0.93341,0.84663,0.0043318,0.1072,0.78303,-0.25783,0.19803,-0.2644,-0.69454,-0.11036,-0.024766,0.13617,0.029627,-0.57559,-0.59995,-0.67893,-0.17306,-0.05675,-0.52731,-0.36811,-0.23384,-0.016615,-0.50167,-1.4958\n-0.6335,-0.15865,-0.013024,-0.70323,-0.99606,-0.37387,-0.22516,0.72329,1.5082,2.1188,1.9857,1.8509,1.1905,1.5796,2.0183,1.1783,-0.17782,-0.2535,0.32286,0.34552,0.85461,0.9773,0.68484,0.42273,0.20634,-0.12275,0.43596,0.68944,0.043149,-0.31046,-0.39495,-0.53931,-0.25371,0.30511,0.38484,0.00038433,-0.29776,-0.55258,-0.60773,-0.21944,-0.17607,-0.63771,-0.72976,-0.38777,0.44364,0.59047,0.35612\n-1.3369,-1.1494,-0.33134,-0.61122,-1.2488,-0.78589,-0.81108,0.053899,1.5207,1.9082,2.632,1.9717,1.185,0.75427,-0.3173,-0.82203,-1.8662,-0.79925,-0.16296,0.59119,1.4365,0.91774,0.52485,0.23101,0.091299,-0.12481,0.21086,0.33655,-0.60211,-1.1671,-0.71588,-0.5106,-0.69203,0.029538,-0.36714,-0.3987,-0.30041,-0.49891,-0.52704,-0.44783,-0.016747,-0.029069,-0.35222,-0.43123,0.32111,0.17118,-0.60998\n-1.4105,-1.1677,-1.3303,-0.47217,0.44241,1.0146,-2.0619,-1.7881,-0.15741,-1.5739,-0.5698,-1.0463,-0.58302,0.015845,0.71663,0.28334,-0.78049,-0.38104,-0.35663,0.0029418,0.35249,-0.21373,-0.15196,0.32211,0.78132,0.71082,0.26813,-0.052286,-1.1857,-2.0725,-1.3439,-0.78785,-0.4781,-0.54204,-0.77088,-0.59435,-0.64711,-0.62168,-0.60276,-0.87777,-0.95312,-0.70825,-0.62548,-0.76074,-0.15861,-0.16591,-0.50635\n-1.3569,-0.50397,0.11182,1.0577,1.6172,2.0639,0.030992,-0.89397,-0.022813,-1.3569,-1.6204,-0.022004,-0.028214,0.2609,1.6724,2.2153,1.2766,0.69604,1.3394,0.36677,-0.030128,0.13927,0.36733,-5.9605e-06,0.22203,0.60884,0.41719,-0.58077,-0.90048,-1.2161,-0.9667,-0.77742,-0.4873,-0.066851,-0.057166,-0.37617,-1.0152,-0.75292,-0.72311,-0.94617,-1.3418,-1.464,-1.2227,-0.87357,-0.10718,-0.18949,-1.2344\n-1.8544,-0.96293,0.099339,0.53478,0.73853,1.7433,0.13541,-0.38324,0.23602,-0.55759,-1.5593,-0.38074,-0.28044,0.040071,-0.079748,3.231,3.9793,2.009,0.67544,1.2444,1.4491,1.0374,1.482,1.5843,-0.030817,0.37771,-0.4875,-0.31758,0.22552,0.53162,0.030613,-0.61685,-0.88302,0.018069,0.038078,-1.7077,-1.2808,-1.0339,-0.87564,-0.93408,-1.2796,-1.2516,-0.85134,-0.50758,-0.17052,-0.35223,-0.805\n-4.2949,-5.2091,-4.5394,-1.772,-1.3833,-1.2661,-1.7914,-2.0582,-1.139,1.1305,2.61,-0.10136,-1.4434,-2.6542,-3.2645,-2.8456,0.71606,1.6299,0.19521,0.71922,1.2731,0.076976,0.94919,1.2042,0.27656,0.19555,0.19844,0.29968,0.23151,0.17227,-0.12217,-0.14892,-0.034669,0.011073,-0.4334,-1.6979,-1.7448,-1.3667,-0.72032,-0.74602,-1.1602,-1.3955,-0.8646,-0.70283,-0.38835,-0.30562,-0.13604\n-3.5351,-3.7995,-2.8083,-1.9802,0.90072,0.28561,-1.3134,-2.1424,-1.6392,0.20095,1.2195,-0.037275,-0.075018,-1.4195,-2.1388,-3.9061,-2.0353,-0.085784,-0.16364,1.0175,0.31786,-1.0321,0.6292,1.3575,1.2334,0.76627,-0.23132,-0.78454,-1.2634,-1.1763,-1.1528,-0.41436,0.19301,-0.19709,-0.66638,-1.2277,-1.2329,-0.63828,-0.50341,-1.2023,-1.4295,-1.2576,-1.0962,-1.2032,-0.97359,0.046938,NaN\nNaN,0.15156,-0.13731,-0.084258,-0.085024,-0.20717,0.29432,-0.038045,0.31884,0.46985,0.95926,NaN,NaN,NaN,NaN,-0.59631,0.67742,0.081537,-0.53914,1.2027,-0.66475,-1.7385,0.065798,2.2073,0.74413,-0.20424,-0.11078,-0.5271,-1.2129,-1.35,-0.69246,-0.30286,0.074444,-0.27644,-0.71671,-1.3196,-1.0155,-0.62039,-0.13449,-0.96215,-1.559,-1.3594,-0.87949,-0.79682,-0.6642,-0.74369,NaN\nNaN,NaN,NaN,-1.2296,-1.6065,-0.1772,1.5047,0.094391,-0.81116,0.42265,1.6676,NaN,NaN,NaN,NaN,0.367,1.9718,1.6098,0.063389,1.746,0.18608,-1.2864,-0.1245,0.65149,-1.2609,-1.3705,-0.98474,-0.50214,-0.90983,-0.97028,-0.35985,0.097072,-0.12341,-0.34832,-1.0316,-1.7165,-1.7516,-0.86302,-0.35575,-0.75126,-1.4185,-1.1244,-0.22635,0.066465,0.29981,-1.5774,-2.1941\nNaN,NaN,NaN,NaN,-0.22373,-0.0027831,1.3828,0.20102,-0.2778,0.66992,NaN,NaN,NaN,NaN,NaN,0.55183,-0.10494,-1.8011,0.61347,0.5148,0.96144,1.8829,-0.75719,-1.3059,-3.0772,-2.1217,-1.4647,-0.77174,-0.70661,-0.50013,-0.33942,-0.085374,-1.7279,-1.8606,-1.1465,-1.4882,-2.4811,-1.3668,-0.71747,-0.88159,-0.88189,-0.70899,-0.011052,0.10765,-0.6717,-1.7008,-2.0369\nNaN,NaN,NaN,-1.1444,-1.8428,0.58171,-0.64432,-0.5502,-0.39468,-0.28333,-0.72838,NaN,NaN,NaN,NaN,NaN,1.0366,-0.83888,-1.1438,0.18725,0.81589,1.7067,-2.0395,-3.1762,-3.4304,-2.2118,-1.3607,-0.81741,-0.14471,0.72827,-0.21062,-1.0495,-1.6968,-1.6146,-1.0106,-1.2852,-1.7056,-1.1547,-1.2209,-1.1379,-1.016,-0.47561,0.16617,-0.23144,-0.99587,-0.86702,-0.45913\n-3.2522,-2.7529,-2.6628,-3.5262,-1.7277,1.2864,-1.6579,-1.4389,-0.060974,-0.095496,-0.20984,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.5335,-0.33384,0.7465,0.50421,-2.5824,-3.3733,-3.832,-3.1953,-1.3294,0.039293,-0.054199,-0.6857,-0.49037,-0.61321,-1.4112,-2.4603,-1.8056,-1.3823,-0.88286,-0.33948,-0.98481,-1.3004,-1.1779,-0.14761,-0.11877,-0.72773,-1.3578,-1.8124,-1.2711\n-3.1972,-1.9469,-1.6648,-3.2813,-2.3327,-2.0348,-1.9819,-2.1282,-2.8315,-1.6587,-1.0503,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.8383,0.05742,0.65935,-0.72386,-1.4704,-1.5673,-0.89205,-0.58055,-0.48949,-1.398,-1.1671,-0.85331,-0.36623,-0.24699,-2.1696,-2.1951,-1.4366,-1.0364,-0.37328,-0.013495,0.05444,-0.1387,-0.2494,-0.78463,-0.97958,-1.1801,-2.7492,-0.92932\n-2.4163,-0.74956,-0.65167,-2.9122,-3.1966,-3.1024,-2.6897,-2.2852,-2.6062,-2.7878,-2.5558,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.13845,0.48192,0.96651,1.4404,-0.2446,-0.91696,-0.53454,-0.52085,-2.5427,-5.6409,-6.7272,-3.4508,-0.86098,-0.93183,-2.0095,-2.5161,-1.4243,-0.65659,-0.33493,0.88843,0.10852,-0.62377,-1.187,-1.3607,-1.0907,-1.8797,-2.3304,-1.6958\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061106-061211.unw.csv",
    "content": "-0.25578,0.46677,-0.070782,-0.132,-0.32268,-0.086481,-0.026978,-0.23935,-0.2834,0.19594,0.73799,1.0312,-0.21336,0.027824,-0.13727,-0.30238,-0.43226,-0.31846,-0.21161,-0.23296,-0.21859,0.043732,-0.19515,-0.35598,0.30199,0.23354,-0.25216,0.20096,0.26981,0.09388,0.34119,0.5281,0.42934,0.37202,0.27007,0.48192,0.59408,0.84446,0.18931,0.095226,-1.0598,-1.6693,-1.9504,-2.1075,-2.7275,-3.5631,-4.4757\n0.64875,0.99094,0.45763,-0.066826,-0.0081024,0.17561,0.23531,-0.46291,-0.29272,-0.092243,0.11657,0.067772,-0.10881,-0.022953,-0.13436,-0.41594,-0.59972,-0.67377,-0.45654,-0.53474,-0.65264,-0.34773,-0.62006,-0.42641,-0.17982,-0.38236,-0.25289,0.099144,-0.14098,0.1021,0.43527,0.24379,0.66258,0.35717,0.25265,0.48753,0.77914,0.6445,0.044781,-0.34888,-1.561,-1.6255,-2.3751,-3.0598,-3.7656,-3.9448,-4.3155\n0.25858,0.73163,0.54456,0.35898,-0.19923,0.20049,0.12058,-0.31373,0.20156,-0.075891,-0.1071,0.24266,-0.04928,-0.065819,-0.5094,-0.63742,-1.0429,-1.4139,-1.165,-0.77211,-0.60279,-0.33661,-0.40051,0.18802,-0.21217,-0.67385,-0.31297,-0.67984,-0.34653,-0.079758,0.11847,0.23956,0.62622,0.36257,-0.0089951,-0.13354,0.57698,0.10709,-0.31697,-0.65308,-1.1702,-2.0355,-3.1621,-4.2347,-4.5008,-4.5008,-5.7304\n1.2362,0.33477,1.0185,0.73874,0.11989,0.14908,-0.35462,-0.73327,-0.56292,-0.14952,0.30929,0.39209,0.026196,-0.35419,-1.0335,-1.4369,-1.4903,-1.3599,-1.2991,-0.77718,-0.42596,-0.19731,-0.18097,-0.33374,-0.44426,-0.37361,-0.41363,-0.57833,-0.4783,-0.13688,0.28333,0.00095367,0.26716,0.20748,-0.079857,0.42283,0.68024,-0.32048,-1.1116,-1.3897,-2.0655,-3.2332,-4.1932,-4.6798,-4.7534,-5.0009,-6.5174\n0.85012,-0.17731,1.0044,1.1711,0.45332,-0.049639,-0.77665,-0.91962,-0.80601,-1.0424,-0.014067,-0.024029,-0.10944,-0.41834,-1.0471,-1.6382,-1.6331,-1.3398,-1.0632,-0.86454,-0.74683,-0.047768,-0.52243,-0.60981,-0.5578,-0.034641,-0.53366,-0.29884,0.019094,0.20448,0.43166,0.1457,0.077072,-0.10741,-0.39007,-0.34321,-0.2908,-0.67617,-1.2142,-2.0462,-2.7451,-3.8666,-4.8441,-5.7516,-5.9187,-6.1152,-6.6173\n0.91346,1.1534,1.3817,0.73333,0.70079,0.46237,-0.31444,-0.44985,-0.55433,-0.44653,-0.14725,-0.28466,-0.12633,-0.18351,-0.34073,-1.0798,-1.4256,-1.3643,-0.75029,-0.96453,-0.68835,-0.31516,-0.34868,-0.14469,-0.26144,-0.094482,-0.058388,-0.021471,0.21587,0.5182,0.16181,-0.025709,-0.028618,-0.40854,-0.61286,-0.6357,-0.65656,-0.858,-1.4294,-2.0045,-2.7899,-3.4763,-4.5796,-6.0233,-5.8509,-7.9478,-8.1859\n1.4705,2.1156,0.49774,0.106,0.63258,0.97876,0.070259,-0.5566,-0.91595,-0.0055656,-0.1371,-0.39703,-0.0737,-0.11727,0.14935,-0.76196,-1.0916,-0.82763,-0.68724,-1.2774,-0.71498,-0.37944,-0.053017,0.2633,0.15319,0.17434,-0.048023,0.32702,0.65353,0.39748,0.061712,-0.10842,-0.058048,-0.24376,-0.28829,-0.51236,-0.84855,-1.2099,-2.2364,-2.2059,-2.8703,-3.4812,-3.8534,-4.6241,-5.3579,-7.3486,-7.0538\n0.40202,0.5222,-0.10777,0.23836,0.54951,0.98408,-0.18285,-0.63786,-0.73821,0.011295,-0.081516,-0.16444,0.364,0.025932,-0.12196,-0.61445,-0.82166,-0.7131,-0.72216,-0.67222,-0.13243,-0.28467,-0.2318,0.50596,-0.067715,-0.17111,-0.043098,0.31389,0.29788,0.18801,0.090508,-0.36648,-0.21302,-0.38604,-0.34027,-0.75777,-0.8168,-1.0666,-1.6696,-2.3615,-2.4914,-2.8904,-2.779,-3.8712,-5.1814,-6.8631,-6.6194\n-0.01763,-1.074,0.51493,0.53632,1.1347,1.0388,-0.24606,-0.041256,-0.1378,-0.074413,-0.054493,0.091808,0.43847,0.19645,0.003643,-0.44023,-0.40742,0.13269,-0.35896,0.29294,0.32718,-0.24688,-0.08275,-0.053276,0.036684,0.31936,0.61276,0.60066,0.1642,-0.14795,0.1146,0.10232,0.11148,0.029064,-0.35686,-0.73038,-0.68217,-0.85764,-0.87615,-1.1907,-1.4436,-1.9203,-2.5557,-2.879,-4.8605,-5.8545,-5.0341\n0.16688,-0.085144,2.0311,0.5698,1.016,0.9033,-0.0094929,0.13655,-0.22898,0.25573,-0.39326,0.18365,0.17885,-0.034668,-0.20654,-0.31147,-0.4567,-0.29881,-0.28372,0.13326,0.047304,-0.11053,-0.1263,0.078022,0.24054,0.83608,0.83192,0.85054,0.39747,0.48165,0.29782,0.26399,0.75578,0.28558,0.061977,-0.23868,-0.2815,-0.30696,-0.38344,-0.47602,-0.55529,-0.94998,-1.56,-1.9651,-3.8501,-4.4862,-4.3079\n0.61018,-0.24648,0.15799,0.80869,1.1643,0.73121,-0.037628,-0.086164,0.14284,0.07346,-0.3161,-0.0010319,-0.62659,-0.53398,-0.37304,-0.20069,-0.53487,-0.23637,0.33632,0.58906,0.23493,0.19978,0.19435,0.58219,0.79802,0.67523,0.96437,0.74756,0.52794,0.85088,0.62823,0.47921,1.1871,1.0987,0.9063,0.84524,0.57618,0.58232,0.33368,-0.30714,-0.54779,-1.8928,-2.1266,-2.3448,-3.1062,-2.3226,-2.2374\n0.59455,-0.29893,0.59013,1.7033,1.2382,0.40412,0.40369,0.072021,0.30825,-0.12822,-0.41003,-0.54814,-0.59854,-0.73823,-0.65003,-0.24531,-0.29598,0.11729,0.63383,0.74896,0.64407,0.75361,0.37288,1.1133,0.87036,0.71315,0.9915,0.57436,0.80122,1.1514,1.3453,1.7835,1.7217,1.7894,1.7572,0.89194,0.51768,0.28602,-0.095591,-0.98925,-1.1762,-2.9363,-3.0299,-2.8269,-2.6404,-1.9229,NaN\n1.8482,2.0606,2.1034,-0.16944,0.5073,0.027287,0.30656,0.22015,0.26762,-0.43528,-1.2022,-1.7334,-0.33143,-0.85679,-1.0761,-0.48928,-0.1104,-0.035404,0.45395,0.60631,0.54625,1.0538,1.0373,0.66958,0.69532,0.73269,0.52232,0.72544,0.91302,1.3605,1.3728,2.7755,2.5913,1.7467,1.1897,0.30296,-0.45056,-0.49169,-1.1391,-1.5055,-2.5102,-3.3205,-3.4542,-3.082,-3.1327,NaN,NaN\n1.7553,2.5441,1.1838,-0.67793,1.082,0.99013,0.99625,0.40294,0.042601,-0.33163,-1.3401,-1.1737,-0.43022,-0.88075,-1.2431,-0.58647,-0.71737,-0.38879,0.014107,0.43918,0.53211,0.9194,1.1296,0.61259,0.81384,0.78627,0.71879,0.9516,0.91606,0.76268,1.0099,1.6681,1.4405,0.93101,0.62759,0.036833,-0.24479,0.016407,-0.94282,-2.3652,-3.9906,-3.6504,-4.4393,NaN,NaN,NaN,NaN\n1.6988,0.82696,-2.0771,0.22422,1.351,1.4455,0.79907,0.22627,-0.04953,-0.19131,-1.2134,-0.83907,-0.45803,-0.49439,-0.79219,-0.32071,-0.48236,-0.74268,-0.24471,0.41688,0.54491,0.54302,0.79786,0.53768,0.5462,0.33272,0.077133,-0.34073,0.12494,-0.11567,0.36281,1.2164,1.2001,0.54159,0.95063,-0.0016403,-0.41025,-0.91556,-1.9977,-4.0145,-3.2026,-2.4691,-3.2991,NaN,NaN,NaN,NaN\n0.57851,0.69294,-1.0038,0.6281,2.0183,0.8159,0.55014,0.84752,0.13375,-0.4218,-0.91039,0.22002,-0.27288,-0.88813,-0.98882,-0.70769,-0.68941,-0.76003,-0.24426,0.033287,0.3973,0.30491,0.0065269,-0.44479,-0.30261,-0.4815,-1.094,-1.5575,-0.51541,-1.1829,0.50215,0.7108,0.066378,-0.36627,-0.17284,-0.45375,0.53338,-0.056627,-0.943,-3.6882,-2.247,-2.4221,-2.482,NaN,NaN,NaN,NaN\n-0.713,1.5671,0.7644,0.67929,1.1331,1.0025,1.0246,1.2645,0.37098,-0.12838,-0.00067711,0.46098,-0.47517,-0.59818,-0.97542,-0.97183,-0.73022,-0.38825,-0.47248,0.21928,0.28801,0.72273,-0.04867,-0.69269,-0.34943,-1.0033,-0.52079,-0.34722,-0.87304,-0.51031,0.32191,0.48775,-0.58285,-1.4455,-1.3331,-0.42545,0.87933,0.69292,1.0342,NaN,NaN,-1.6058,-2.9003,NaN,NaN,NaN,NaN\n0.44163,1.7422,1.1939,1.3227,0.91143,1.9137,1.4938,0.65891,0.62506,0.63358,0.52171,0.19676,-0.36344,-0.32631,-0.8816,-0.89095,-0.3001,-0.26541,-1.1029,0.8219,0.52552,0.84291,1.2351,0.79236,0.3889,0.71939,0.79443,0.64929,-0.4053,-0.39688,0.30514,0.069042,0.36757,-1.0999,-0.7666,0.64983,0.93587,-0.043427,-0.26746,NaN,NaN,NaN,-1.7063,-2.4054,NaN,NaN,NaN\n-5.1228,0.020182,1.5631,0.96558,2.1157,2.3069,0.41976,0.19834,0.32076,0.55048,0.43789,0.2863,-0.11081,-0.27433,-0.99027,-1.9865,-1.8246,-1.3989,-1.5313,-1.3774,-3.169,-4.9247,0.047459,1.9714,2.3717,2.0146,0.95531,1.1617,0.48206,-0.024057,1.2318,0.50629,0.67626,-0.35627,-0.69704,1.6301,1.5557,0.82399,-1.5761,NaN,NaN,NaN,-1.1597,-0.21288,-0.017281,NaN,NaN\n-2.3675,0.31161,0.048304,0.25698,1.8661,2.9349,0.47279,0.95628,0.28926,-0.30819,0.0062065,-0.36042,-0.54906,-0.42048,-0.67674,-1.1467,-1.9762,-1.2598,-1.0112,-1.1504,-1.5232,-1.9418,-1.4444,2.1398,2.7754,2.0559,1.5229,0.84753,0.63655,1.4184,1.8142,1.0606,0.60733,0.81227,2.204,1.5954,1.5425,1.5595,-2.3025,-2.5245,-1.3034,0.32034,-0.49778,0.24809,-0.29587,-1.0588,-0.38881\n-0.16081,-2.975,0.042286,-0.13855,0.098503,0.2391,0.84282,1.0934,0.079241,-0.59396,-0.69175,-1.1321,-1.1107,-0.70323,-0.60012,-0.89644,-1.0303,-0.79021,-0.57696,-0.1423,0.47147,-1.0844,-0.83596,1.4565,1.561,1.445,1.7691,1.2603,0.0028496,0.99502,1.7909,-0.17928,0.80936,0.82116,1.3536,1.8965,2.3937,1.4631,0.03091,0.4792,0.63673,-0.10944,-0.40073,-1.3469,-0.97863,-0.12423,-0.34717\n-0.72012,-1.582,-0.67168,-0.60991,-0.9731,-1.1768,1.4315,0.70121,0.51585,-0.37391,-0.9861,-1.1603,-0.83212,-0.61613,-1.3668,-0.44803,-0.54455,-0.23744,-0.23539,1.3634,1.8134,1.5183,1.968,2.2148,2.1054,2.034,1.5345,1.1717,0.13539,0.20139,0.59068,-0.4559,0.5121,0.72604,1.1415,1.6303,1.006,1.7811,0.78122,0.036171,1.8613,0.11782,-0.35072,-0.69896,-1.0725,0.20731,0.15961\n-0.88877,-0.52501,-0.87335,-1.3374,-2.2685,0.11407,1.0221,0.75615,0.49262,-0.62423,-1.8387,-1.4285,-0.53058,-1.2689,-1.0028,1.1933,0.14815,1.2962,0.98515,1.4987,2.1116,2.0618,2.0146,2.8873,2.5595,2.5257,1.4144,1.2862,1.0563,0.44495,-0.10338,-0.39986,0.30479,0.91668,0.67221,1.2881,0.21771,-0.23425,-0.85672,-0.071671,0.64649,0.33564,0.75571,0.59147,-0.57403,-0.29132,-0.3637\n-1.4773,-1.8229,-1.2177,-1.121,0.36596,0.78236,1.2979,0.89169,0.051327,-0.81946,-1.1533,-0.75905,-0.50696,-0.66257,0.066986,1.6263,1.6102,2.143,2.1445,1.7409,2.4162,2.5617,2.4307,2.2872,2.1586,2.1989,1.6593,1.4417,1.0024,0.38729,-0.89107,-0.41901,-0.093302,-0.17176,0.53676,0.46035,0.52319,0.19349,-0.48215,2.0082,1.4322,0.40965,0.053135,0.019768,-1.0866,-0.067207,-0.41856\n-0.23469,-0.59162,-0.82747,-1.0002,-1.1009,0.077017,1.0267,0.36426,-0.87443,-0.91047,-0.59855,-0.0014896,0.17309,1.3723,1.4925,1.7329,1.2812,-0.28342,0.87151,1.3773,1.4544,1.0199,0.41734,1.6648,1.6938,1.854,1.3026,1.0892,0.418,-0.85615,-1.3878,-1.1417,-0.62297,-0.40028,0.56804,3.6343,-2.9865,1.2616,3.0751,2.1408,0.41515,0.25707,-0.222,-0.94625,-0.97622,-0.14135,-0.29321\n0.42992,0.39783,-0.44395,-0.92634,-1.0644,-0.89441,-0.23148,-0.26689,-0.43633,-0.16351,-0.44859,-0.55571,0.14374,0.46708,0.59714,1.2791,-0.29953,-0.41255,0.47837,0.96347,0.60864,-0.18955,0.84771,1.1212,1.108,1.3404,0.046997,-0.94181,-1.1093,-1.351,-2.1601,-2.4123,1.7442,-2.8393,-0.22484,5.1454,-0.72456,0.11093,1.9677,1.4497,0.81966,0.91565,0.47277,0.14881,-0.15792,0.47239,0.75023\n1.0303,0.87137,-0.059757,-0.19529,-0.86086,-1.1508,-0.96371,-0.757,-0.47335,-0.28228,-0.36131,-0.81052,-1.4957,-0.56903,-0.44843,-0.41201,-0.27314,-0.26753,0.4948,1.3776,0.39447,0.42815,1.0843,0.90901,-1.3994,-0.66862,-1.1018,-0.73915,0.76722,2.6246,-0.46193,-0.47028,1.7127,-0.89097,-0.085032,3.0334,0.33435,1.0928,0.8466,1.5997,1.1226,1.2959,0.83775,0.47552,0.3826,0.50939,0.48431\n1.4572,1.5035,0.59822,-0.31704,0.13504,-0.19898,0.49902,-1.3157,-0.1077,0.2229,0.03507,-0.64505,-1.055,-0.9608,-1.2777,-2.0346,-1.8037,-0.78989,0.10435,0.35837,-0.63205,0.24671,0.85634,1.2637,-1.5667,-0.62757,-0.41656,-0.36997,0.81412,1.7171,NaN,NaN,NaN,3.3041,2.2977,1.5462,1.0545,-0.040144,0.81793,1.3737,1.2292,1.0287,0.65211,0.38847,0.47764,0.28093,-0.08939\n1.8504,1.4369,0.61178,1.3528,1.0993,1.2155,1.2628,0.13269,0.16301,0.49152,0.45072,-0.12897,-0.46961,-1.0389,-1.6442,-1.9478,-0.81658,0.0059509,-0.30382,-0.32386,-0.68767,0.35521,0.5176,0.46453,-0.40248,-0.64007,-0.46939,-0.5497,-0.8342,NaN,NaN,NaN,NaN,NaN,2.1713,1.2008,0.16988,-1.5559,0.80474,2.5929,1.8171,1.4617,0.87471,0.67765,0.66652,0.04047,-0.38745\n1.4023,1.1561,1.5488,2.9756,1.6831,1.2127,0.4742,0.058846,0.35483,0.70684,0.6133,0.048439,-1.0779,-1.537,-1.3144,-0.76971,0.0982,0.18608,0.30758,-0.26208,0.13498,1.0333,0.71578,0.40162,-0.52834,-0.63952,-0.10684,-0.28913,NaN,NaN,NaN,NaN,NaN,3.2364,1.8749,0.65688,0.23427,-0.54935,2.7192,2.2685,1.3027,1.5118,1.2431,1.227,0.93129,0.11689,-0.17014\n0.81003,0.55352,1.287,0.92424,1.0467,1.0869,1.2218,0.63822,0.48375,0.89719,0.79955,-0.20046,-1.1861,-1.5337,-1.0662,0.11293,0.84723,-0.028297,-0.17973,0.50382,1.1592,1.1953,-0.35682,0.39218,1.2278,0.87032,0.52993,0.10724,NaN,NaN,NaN,NaN,NaN,2.1394,2.3131,1.5833,1.8368,2.2748,3.0551,2.016,2.0875,2.919,2.0134,1.6982,1.131,0.38581,-0.032406\n0.11128,-0.23981,0.42787,-0.039145,-0.018349,0.45557,1.181,1.3864,0.60822,0.62488,1.2004,0.39224,-0.12397,-0.69706,0.87734,0.77222,0.43714,0.60732,0.48549,0.45527,2.5535,1.1817,-0.40622,4.1804,3.2443,2.339,1.7091,0.8194,NaN,NaN,NaN,NaN,1.9797,2.3118,3.1693,2.9935,3.1135,2.1958,2.5713,2.6507,2.381,2.5276,1.5501,1.8809,1.3719,0.25199,-0.36403\n0.21075,0.32471,0.77168,0.68957,0.30004,0.84646,1.1062,1.5469,1.3175,0.53491,0.58,-0.75458,-1.5309,-0.46528,0.52277,-0.047829,-0.25013,0.75465,-1.2047,-0.81509,1.2699,0.48635,-0.89869,2.8144,2.4946,1.9394,1.7784,2.0965,NaN,NaN,NaN,NaN,2.7716,3.3861,3.1969,2.4153,2.1534,0.51811,1.8909,2.4634,2.0538,1.7474,1.4602,1.8557,0.62189,0.49009,0.34838\n1.3831,1.5715,1.1816,1.1724,1.104,0.71518,1.9826,2.0023,1.3097,0.93702,0.36086,-0.10203,-1.5594,0.21971,1.2058,0.5929,-0.42429,0.70703,0.0041389,-1.9647,0.80145,1.9655,1.0193,0.4463,1.1718,1.0711,2.1856,NaN,NaN,NaN,NaN,2.8429,3.3711,3.2107,2.5413,2.1864,1.8978,0.3348,1.4547,2.4445,1.7599,1.312,1.332,1.7699,1.0965,0.60923,0.63228\n2.0491,1.0221,1.1781,0.97108,0.49135,0.80056,1.7855,2.1228,0.99496,1.0075,0.57549,-0.35484,-0.77558,-0.24078,0.69834,1.2108,1.2115,0.92094,0.65341,0.081804,1.7346,1.7892,1.5628,0.58146,0.5268,0.43343,2.0643,NaN,NaN,NaN,3.0261,2.9664,2.8233,2.5312,3.1737,2.5565,2.3915,1.4575,1.7726,2.2592,0.74829,0.0092468,0.78417,1.6976,1.4182,0.6188,0.82659\n1.5225,2.0686,1.8031,1.1968,1.1289,1.0985,1.2868,1.7504,1.2109,0.87706,0.66865,0.50787,0.56317,-0.32162,-1.5225,1.729,1.2375,1.3997,1.4115,1.5704,2.4451,2.6482,1.773,0.25834,0.24478,0.31818,1.0108,1.6389,4.4563,3.8184,3.7909,4.0943,3.42,3.4766,4.022,4.1551,3.5485,2.8656,2.3192,1.8155,0.86933,0.60834,0.9107,1.547,1.069,0.75983,1.5289\n0.94584,1.7532,1.9737,1.519,2.4005,2.2099,1.182,0.6807,0.88283,0.35455,1.4366,1.6133,1.1124,-1.033,-0.70053,1.237,1.1915,1.0345,0.83188,0.97346,1.4559,3.7769,2.9885,1.2727,0.7342,-0.58845,-1.5989,-1.5653,4.71,3.6393,3.124,3.7017,3.0135,2.0957,2.8776,5.0935,4.5452,3.8889,2.3586,2.0922,1.7134,1.2154,0.92295,0.92683,0.70766,1.0349,1.0619\n1.0246,1.5689,1.7728,1.3952,1.4554,1.5363,0.62033,0.44833,0.73392,0.47706,0.33361,1.8893,1.4598,-0.13844,0.37839,0.84454,1.3241,0.29484,0.79726,1.0761,3.1409,NaN,NaN,NaN,NaN,NaN,0.46338,0.81784,3.8035,2.0932,2.6346,3.0453,3.6318,2.4419,3.4866,5.9947,4.3786,3.7105,3.1318,2.2892,1.4509,0.99846,0.237,0.1986,-0.21827,-0.69004,-0.50046\n2.2834,2.0629,1.3368,0.92821,0.74855,0.73492,-0.034332,0.43398,0.47956,0.87641,1.7591,2.1059,1.7974,1.583,1.3576,0.49614,0.13145,-0.94218,0.57246,1.9304,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.5154,3.4291,3.058,2.9888,3.235,3.5016,3.8964,3.9281,3.1872,3.4423,3.2374,2.1762,1.1983,1.1287,0.83297,0.19966,-1.1809,-2.5369,-0.88166\n2.9054,1.3347,1.5925,0.8515,0.78049,0.896,0.29478,0.79199,1.6636,1.445,1.455,1.3556,1.6107,1.772,0.93681,0.80571,0.91919,1.021,0.9285,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.1498,3.7778,3.015,4.4962,4.3022,4.3291,3.774,3.0509,2.5716,2.5172,1.6782,2.7167,2.9708,1.2532,0.78184,-0.17826,-1.3533,-1.7257,-0.58169\n2.5264,1.298,1.648,0.90989,1.0583,1.0212,0.9654,1.24,1.7074,1.6761,0.94291,1.2669,2.1588,1.9584,0.84308,0.93715,1.7132,3.2159,3.2488,1.482,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.6124,3.4553,3.8946,4.262,5.3349,5.329,4.4239,3.1163,2.2936,2.993,3.7379,1.584,2.9393,2.7805,1.9114,0.88676,0.1619,-0.77809,-0.86096,-0.47158\n1.8496,1.3986,0.66706,0.45957,0.51756,0.88777,1.1746,0.86821,1.052,1.1981,0.33609,3.3037,4.4999,2.9225,2.333,1.0982,-0.61638,1.914,3.6924,2.2875,NaN,NaN,NaN,NaN,NaN,NaN,2.8073,4.5065,4.4345,4.4442,3.9665,4.4146,4.7914,3.9497,2.8834,2.2441,2.1718,2.5919,2.3451,2.7707,2.9027,1.8346,0.47476,-0.17471,-1.3077,-0.84055,-0.35053\n1.0497,0.51478,0.46644,0.80101,0.6597,0.71849,1.0697,0.023903,1.1839,1.8256,2.2271,2.671,4.2123,2.7923,2.7067,2.9952,1.3078,0.13515,1.2035,-0.020252,0.8361,1.3896,NaN,NaN,NaN,NaN,NaN,4.8423,5.4974,5.1821,3.4985,4.6584,3.5157,2.9083,2.0648,1.7029,1.8578,1.9992,2.0281,2.2374,2.3762,1.2101,0.36045,-0.43491,-1.0888,-1.6668,0.002388\n0.26601,0.1003,0.22797,0.67627,0.21782,0.87698,0.98163,0.14416,1.3393,2.0602,1.8445,1.2206,1.5099,1.1589,-0.20317,-0.27257,1.3917,1.1917,0.89355,1.0365,0.25641,1.6732,1.693,NaN,NaN,NaN,NaN,4.6258,4.4794,4.534,4.9231,4.7254,3.4963,2.2434,0.49891,1.7422,2.221,2.7764,1.9554,1.7482,1.3269,1.2119,-0.067139,0.25369,1.531,1.5415,0.024841\n-0.21081,-0.21979,-0.091824,0.10841,0.55534,1.999,1.849,1.4921,1.2332,0.49923,-0.066498,0.51747,0.13248,-0.26534,-1.2269,-1.4123,0.8822,1.4023,2.0079,-0.52227,-0.086311,1.0764,3.0775,2.7777,3.6784,5.1697,4.6642,3.579,2.6615,3.2923,4.8285,3.4935,4.7257,2.8015,0.41638,3.3668,4.1649,3.7473,1.7453,1.7257,0.97632,0.80844,-0.083197,-0.51001,0.58238,2.1768,1.4488\n-0.30998,-0.46535,-0.28469,0.0021591,0.27666,0.73361,0.73734,0.6722,-0.1371,-1.0357,-1.0075,-0.66944,-0.97992,-1.4064,-1.5396,-0.82842,0.73672,2.4749,3.5967,0.81132,-1.6659,-0.88313,1.6081,2.6317,1.4011,2.5787,2.6474,3.1549,2.3687,3.01,3.0518,3.3481,3.612,2.3322,2.485,3.4532,4.2305,2.6869,1.9939,1.6005,0.4726,-0.049309,-0.43204,-1.4942,-0.80096,1.3959,3.227\n0.00067902,-0.5203,-0.51765,0.0041981,-0.22664,-0.30046,-0.10639,-0.25004,-1.0924,-1.3395,-1.3112,-1.4293,-1.5889,-1.7215,-1.2365,-0.86864,0.34543,1.0369,2.5533,1.4806,-2.7025,-2.3346,0.061619,1.9994,2.06,1.1771,1.2021,2.6628,2.2806,2.6178,1.6079,1.9188,2.2808,1.9739,3.0956,3.4825,2.3744,1.9876,1.878,0.9415,-0.073097,-0.58277,-0.50654,-0.74526,-0.65325,0.43223,2.0048\n-0.38037,-0.65699,-0.62486,-0.24898,-0.25934,-0.62133,-0.47927,-0.92694,-1.544,-2.1763,-2.1221,-1.9483,-1.859,-1.669,-1.3083,-0.56232,0.23325,0.55021,0.17724,-0.29894,-1.4195,-2.0913,-0.1069,1.1772,1.3787,1.1121,0.33893,1.3839,1.0524,1.6329,2.0597,2.1707,2.8792,3.0199,3.3491,3.5222,2.3751,1.5899,1.4893,-0.20616,-0.84489,-1.1556,-0.29783,-0.036797,0.43445,0.013336,0.65451\n-0.29455,-0.55939,-0.028877,-0.118,-0.3758,-0.83506,-1.0112,-1.3346,-2.5387,-2.2431,-2.1117,-2.0494,-2.0404,-2.0994,-1.9285,-1.3565,-0.73443,-0.104,-0.46236,-1.1446,-2.6861,-2.1201,-0.31662,0.60618,0.96625,0.52864,-0.30024,0.71824,0.90201,1.468,1.6337,2.3208,3.038,4.2215,5.2843,3.9717,3.939,3.4249,1.3182,-0.70101,-1.6721,-1.9869,0.0046844,-0.071365,0.11655,0.17757,0.63835\n-0.34257,-0.45493,-0.34101,-0.77451,-0.95831,-1.2274,-1.4083,-1.6386,-2.3495,-1.6478,-1.6515,-1.9604,-2.2142,-2.4658,-2.1223,-1.0795,-1.1292,-1.4953,-1.1262,-0.59974,-1.8204,2.8837,-0.18019,-0.95868,-2.2562,-2.5767,-3.004,-0.91643,0.22676,1.2719,1.1628,3.0006,2.7689,3.525,3.3201,2.983,3.9237,2.8182,1.1779,0.015133,-0.94342,-0.8052,-0.10595,-0.26844,0.70934,1.0182,1.3602\n-0.2892,-0.65586,-0.93396,-1.2083,-0.95996,-0.89093,-0.93016,-1.0961,-1.4689,-1.6268,-1.6887,-2.0465,-1.8192,-1.7162,-1.3305,-0.58839,-0.40882,-3.6435,-1.515,-0.43388,1.95,1.4046,-0.65235,-1.3101,-2.1692,-2.7794,-4.707,-3.1749,-0.56269,0.34363,1.011,2.6099,2.2432,1.6794,1.2715,1.354,1.5264,1.2818,0.58393,-0.087912,-0.64724,-0.76931,-0.56666,0.36486,1.0394,1.092,0.66253\n-0.24824,-0.80535,-0.53353,-0.93976,-0.98846,-0.88353,-0.84478,-1.1531,-1.5812,-2.8219,-1.9501,-1.4055,-1.5549,-1.3235,-1.4556,0.32482,1.8397,-0.88255,-1.6294,-2.0152,2.3431,0.40042,-0.31892,-0.92469,-1.3563,-2.4965,-4.3022,-4.0113,-1.2485,-0.065559,0.44694,0.71458,1.4078,0.8685,0.86728,1.2489,0.86679,0.48238,0.29597,-0.13509,-0.092655,-0.1449,-0.40848,2.0222,1.1489,1.0175,0.57469\n-0.70321,-0.76255,-0.634,-1.3035,-1.0145,-0.65499,-0.64194,-1.2327,-1.9254,-2.977,-1.3106,-0.8839,-0.72145,-0.77742,-1.2004,-1.7064,-0.99653,-1.6983,-3.3107,-4.1522,2.1964,0.63932,0.62087,0.32101,-0.3757,-0.83966,-1.1391,-2.6154,-2.3605,-0.71172,0.10641,0.064787,0.96872,0.56791,1.2563,0.79192,0.75076,0.90733,0.7238,0.59859,0.56009,-0.55562,-0.71171,0.24994,0.37181,1.4319,0.3723\n-0.81504,-1.3941,-1.7526,-1.9508,-1.7467,-1.2717,-0.72626,-1.7695,-2.7705,-2.741,-2.0543,-0.36091,1.4511,1.1583,0.16825,-1.6873,-1.6449,-1.5943,-0.26732,0.62936,0.21575,0.037767,-0.51738,0.077641,-0.13622,0.67176,-0.31986,-3.255,-3.8491,-2.3682,0.08885,0.41641,1.7528,1.9958,1.7848,2.4759,1.5991,0.92888,0.78923,1.3364,0.60602,0.31349,-0.25889,0.062317,1.333,1.9672,0.71299\n-1.2204,-1.7863,-1.416,-2.2984,-2.2759,-0.72739,-0.55289,-1.9634,-2.7247,-2.2478,-1.9296,-0.46549,1.4845,2.0216,0.34041,-0.1925,-0.67892,-1.4935,0.88063,1.4018,0.38427,-0.47647,-1.3448,-0.62558,-0.61376,-0.26724,-1.73,-2.6582,-3.0403,-0.10116,0.62358,1.7238,1.394,1.3401,1.1009,1.6173,0.83195,0.31173,0.47578,0.84889,0.48146,0.81442,0.59375,1.0463,1.4961,1.2051,0.16393\n-1.0701,-1.2846,-0.95845,-1.7168,-2.8871,0.66418,0.091711,-2.0782,-2.4009,-2.1995,-1.8012,-1.7017,1.0397,1.6489,0.40442,1.3213,0.22377,-0.20009,-0.035969,0.32905,0.39088,-0.26452,-1.0339,-1.025,-1.5921,-1.5645,-2.4425,-2.448,-2.3408,0.9682,0.92669,1.8011,1.3799,1.0815,1.0974,0.89808,0.18105,0.035311,-0.046101,-0.2367,-0.26149,0.17042,0.30416,0.82006,0.82068,0.18704,0.51655\n-1.0616,-1.1099,-1.0509,-2.2457,-3.7628,-0.54688,-0.91317,-2.0479,-3.0588,-4.0892,-2.6848,-2.9168,0.38343,0.87933,1.3571,2.9382,2.695,0.43549,0.18829,0.44018,0.89979,-0.058998,-0.59111,-1.515,-3.0489,-3.2193,-4.6509,-4.0886,-2.0549,0.036362,0.55401,1.0227,1.2942,1.0329,1.0788,0.43152,0.1474,0.04961,-0.67423,-2.4001,-0.90397,0.11755,-0.086525,0.4598,0.73212,1.2196,0.92223\n0.56443,0.7505,0.39121,-1.4754,-2.3491,-4.1079,-2.1076,-2.5213,-3.2937,-3.2395,-1.452,-2.1584,-0.23911,0.55906,1.2822,1.1581,0.8698,0.31505,0.48733,0.41979,-0.41187,1.1896,0.98985,-0.74043,-2.3793,-2.4493,-3.5233,-2.9001,0.25082,0.47996,0.021389,0.10934,0.36717,0.80343,0.96505,0.71388,0.11834,-0.27011,-0.85281,-0.95453,-0.22911,-0.15043,-0.45938,0.11484,0.82533,1.0272,-0.088974\n1.5141,1.8965,1.2269,-0.67435,-1.3824,-3.3133,-1.3468,-2.5318,-4.5595,-5.5399,-3.9156,-2.6281,-1.1646,-0.85988,0.80831,0.10485,-0.093924,0.50096,0.41762,-0.94602,-1.3519,0.6651,0.96612,0.10317,-0.72096,-1.638,-2.1264,-2.0898,-0.62366,-0.41325,0.11498,1.3028,0.72547,0.5552,0.97121,0.18846,-0.41199,-0.6827,-0.8595,-0.87273,-0.96059,-0.73774,-0.57405,-0.34072,-0.69451,-1.3649,-1.4168\n0.97607,1.3562,0.14399,1.0847,1.1325,0.70193,0.27699,-0.85587,-4.4401,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.6655,0.35984,-0.86181,-0.98182,0.28679,1.425,1.1596,-0.054703,-1.0589,-1.8588,-1.79,-1.2041,-0.31568,1.0615,2.765,1.9628,0.58754,0.44653,0.1487,-0.037794,-0.32853,-0.88654,-0.84933,-1.2786,-1.1269,-0.91623,-0.12529,-0.56537,-1.3898,-1.2683\n0.621,0.9112,-0.21651,0.70345,1.9026,1.5499,1.3054,-0.43769,-0.31738,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2517,-0.00077438,0.54345,0.4756,0.90006,0.96954,0.7815,0.20627,-1.1623,-2.2768,-1.8474,-0.96408,-0.16433,-0.45488,0.33917,1.4574,0.87157,0.78025,0.66722,0.86539,-0.071098,0.088398,-0.0018616,-0.66488,-0.63287,-0.34571,-0.46745,-1.0729,-1.6089,-1.9369\n0.79087,-0.19798,-1.5131,-1.6974,-0.39975,-0.14161,-0.062962,-1.9431,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3341,-0.0095215,1.1919,-0.52921,-1.0864,-0.89053,-0.10938,0.9533,0.43757,-0.31929,0.58024,-0.28755,-0.7899,-1.1716,-0.1165,0.58286,0.82185,0.90705,0.58101,1.113,0.84737,0.695,0.2705,-0.42413,-0.29777,-0.48994,-1.3817,-1.2482,-1.6068,-1.2155\n0.64218,-0.86167,-1.0056,-0.96602,-0.48128,-1.4086,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.15618,-0.50354,0.019073,-0.59669,-1.7881,-1.7256,-0.44171,1.1217,0.44176,0.47345,-0.223,-0.89748,-0.67998,-1.0627,-0.33785,0.12908,0.27945,0.020149,0.49442,0.86512,1.2473,0.87024,-0.024801,-0.51208,-1.0214,-1.3527,-1.754,0.045359,-0.77141,-1.0195\n-1.5419,-1.6467,-2.7995,-1.8015,-1.8457,-0.71941,-0.13361,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.7702,0.26723,1.8341,-0.26339,-0.55648,-0.42843,0.17266,-0.84808,-2.4471,-2.6847,-2.4236,-1.9927,-2.3416,-1.2692,-0.18388,0.2617,-0.13054,0.11334,0.98509,0.32611,0.16803,0.20992,-0.60513,-1.1641,-1.687,-1.4697,-0.51225,0.19555,-0.86693,-1.9237\n-2.1281,-3.604,-3.0424,-1.1009,-0.20264,-0.59095,-0.7872,-1.5273,-1.3509,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.6863,0.20555,1.8695,0.21173,-0.73344,-0.30117,-1.3212,-1.838,-2.815,-2.7783,-2.9744,-3.6235,-2.6615,-0.70522,0.41623,0.31802,0.23784,0.65118,0.39354,-0.075335,-0.37417,-0.48849,-0.76663,-1.2809,-1.6793,-1.4195,-0.41755,-0.0018139,-1.0351,-3.0598\nNaN,-2.7742,-1.7055,-1.5989,-0.89729,-0.5113,-0.88192,-1.1222,-0.29208,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.87769,0.41141,0.11155,-1.4143,-1.6917,-0.67947,-0.86502,-0.96038,-1.38,-2.5097,-2.9941,-2.6369,-2.2002,-1.3619,-1.0184,-0.39079,-0.36574,1.3426,0.038652,-0.54083,-0.72586,-0.38358,-0.4097,-0.66689,-0.94641,-0.86047,-0.44168,-0.99893,-1.864,-3.8473\nNaN,NaN,0.50606,-2.0275,-0.93036,-0.27196,-0.91527,-0.71228,-0.99922,-1.8689,-3.4368,NaN,NaN,NaN,NaN,NaN,NaN,0.57152,1.1005,0.37606,-0.99778,-0.09013,0.33159,-1.1644,-0.6353,-0.34773,-0.97716,-1.3098,-1.8372,-2.0946,-2.0798,-0.50036,-0.25549,-0.14001,1.2548,0.12433,-0.51434,-0.84946,-0.71091,-0.30978,-0.99139,-0.50996,-1.3027,-1.3365,-0.99012,-1.5013,-2.7821\nNaN,NaN,NaN,NaN,NaN,NaN,-0.28007,1.133,-1.1523,-2.111,-1.8556,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.0417,0.48815,0.34002,0.24669,-0.516,-0.80231,-0.8612,-0.94106,-1.3826,-2.0909,-1.7707,-1.5552,-1.3619,-1.023,-0.68143,-0.12382,0.70848,1.0662,-0.076405,-0.56529,-0.91683,-0.91358,-1.134,-0.97293,-1.1294,-0.93663,-1.257,-1.9743,-2.9769\nNaN,NaN,NaN,NaN,NaN,NaN,1.295,1.6981,0.83747,0.1013,-1.2656,NaN,NaN,NaN,NaN,NaN,NaN,1.4871,0.33182,0.068039,0.83659,-0.18511,-1.0658,-0.98113,-1.5873,-2.5426,-2.9886,-2.8575,-2.0371,-0.48354,-0.098858,-0.86106,-0.39341,-0.049305,0.41085,0.75034,0.2757,-0.48647,-0.69845,-1.0109,-1.007,-0.59395,-0.0085678,-0.2967,-1.3318,-2.5301,-3.1398\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,1.2689,1.7079,-0.2405,-1.5289,-0.26726,NaN,NaN,NaN,NaN,NaN,-0.56898,-0.41742,0.23887,0.7025,-0.17979,-0.59745,-1.3119,-2.3493,-3.4694,-2.5344,-1.6912,-0.84626,0.18466,0.63369,1.2356,0.63578,-0.20621,-0.20317,-0.44812,-0.028889,-0.48153,-0.56924,-0.60471,-0.76402,-0.63281,-0.20402,-0.9038,-1.7354,-2.1267,-2.2512\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,0.53825,-0.51009,-2.7163,-1.906,-0.6545,-0.62868,-3.2642,NaN,NaN,NaN,2.4559,1.5878,0.65564,0.51966,-0.7252,-0.076307,-0.29111,-1.4962,-2.6583,-1.5527,-0.38789,-0.43089,-0.2098,0.71258,1.0222,0.3235,-0.30773,-0.98507,-0.78029,-1.1007,-0.74724,0.25526,0.17157,-0.68036,-1.2158,-1.1092,-1.426,-1.9196,-2.5003,-1.8978\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.1869,-1.3266,-2.5621,-2.1483,-0.73472,-1.013,1.6117,2.1651,1.5046,NaN,NaN,NaN,NaN,-0.17384,-0.71554,-0.065189,-0.43243,-1.8062,-1.8754,-1.3651,-0.092161,-0.69355,0.13848,0.47533,0.22462,-0.32077,-0.81489,-1.8986,-0.71699,-0.058098,-0.48879,-0.087898,-0.4627,-0.74793,-1.2002,-1.3603,-1.7417,-2.438,-2.7076,-2.4492\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061106-070115.unw.csv",
    "content": "0.95487,0.60784,0.40065,0.57331,1.1606,0.041323,0.080198,0.50736,0.83645,0.18334,0.061632,0.18255,-0.68012,-0.55839,-0.3959,-0.42176,-0.41411,-1.4111,-1.6002,-1.7742,-1.9648,-1.1661,-0.37095,-0.15918,-1.2549,-0.86822,-1.7386,-2.0706,-2.0383,-1.5296,-1.4729,-0.55102,-1.1434,-1.284,-1.2533,-0.80108,0.5547,1.1659,2.8961,1.7595,0.86675,0.54651,1.0964,1.0594,0.27448,1.1837,1.2766\n1.5932,1.9213,1.368,0.94621,0.83226,-0.12726,-0.16131,0.67802,0.14018,-0.0087872,-0.50861,-0.99378,-0.98151,-1.0302,-0.77596,-0.84601,-1.1295,-1.5137,-1.163,-1.6087,-2.3913,-1.1264,-0.66027,-1.0752,-1.3058,-1.3778,-1.4453,-1.3285,-1.6671,-1.8761,-1.7077,-1.252,-1.2453,-1.7671,-1.5378,-0.10058,1.0934,1.5587,1.8293,1.8867,0.70074,0.59699,0.38502,0.9369,1.0285,0.49258,0.14873\n1.787,2.5139,1.7669,0.84876,0.73543,-0.048113,0.11014,-0.19335,0.14263,-0.40793,-0.43548,-0.44235,-0.88917,-1.1494,-0.47684,-0.98583,-1.2463,-1.7449,-1.2372,-1.568,-0.9444,-0.035486,0.17391,0.0027637,-1.1513,-1.3656,-1.6178,-2.1015,-2.0462,-2.2374,-1.8821,-1.426,-1.8814,-1.7803,-1.2529,-0.67918,0.11696,0.47398,-0.1069,-0.054132,0.93077,0.90364,0.69907,1.366,1.2152,-0.090075,-0.46309\n2.2808,1.8374,1.5645,0.66117,0.56348,0.39478,0.34135,0.54966,-0.012224,-0.30046,-0.43887,-0.35601,-0.76692,-0.94111,-1.1545,-1.4148,-2.2659,-1.8054,-1.2237,-1.751,-1.0705,-0.64235,-0.77538,-1.2877,-1.5537,-1.8201,-2.0902,-1.9103,-2.184,-2.4966,-2.1966,-1.7885,-1.522,-1.6838,-1.0219,-0.54669,-0.10742,0.47923,-0.5493,-0.066761,0.66842,1.5292,1.4775,1.4536,1.4923,0.70964,-0.53827\n1.1469,1.5973,1.8135,1.3711,0.70601,0.35676,0.37858,1.2244,1.3444,1.0972,0.43052,0.097361,-0.25557,-0.75529,-1.0247,-1.3806,-1.0787,-1.4383,-1.4139,-0.92549,-0.017866,-0.65325,-0.97291,-1.3434,-1.5807,-2.4541,-2.0787,-2.0253,-1.5258,-1.7072,-2.0046,-1.7948,-1.5948,-1.7711,-0.83271,-0.26045,0.19845,0.28671,0.010801,0.14499,0.6185,1.5533,0.87627,0.9942,0.51509,0.36844,-0.022741\n0.73048,0.3476,1.612,1.2701,1.0338,0.71575,0.65012,0.65069,0.60452,0.03488,0.28549,0.24312,-0.06613,-0.36732,-0.73239,-0.75883,-0.55935,-0.8216,-1.0788,-1.372,-0.30269,-0.38176,-0.91346,-1.5399,-1.5489,-1.6894,-1.9785,-1.7961,-1.6904,-2.1499,-1.848,-1.5362,-1.2455,-1.7832,-1.3257,-0.90164,-0.070879,0.68718,0.70576,1.1544,1.6471,1.3617,0.76581,0.70682,0.0094013,-0.087112,0.50391\n0.56533,0.23602,1.8624,1.7698,1.2001,0.73339,1.0356,0.52472,0.15694,1.4312,0.72308,0.21718,-0.51533,-0.61642,-0.032167,-0.14508,0.080669,-0.38634,-0.85436,-0.99284,-1.2386,-1.4297,-2.2366,-1.6123,-1.1627,-1.621,-2.0503,-2.0328,-2.3384,-2.1785,-1.8001,-0.90492,-0.92401,-1.5701,-1.0179,-0.65588,-0.517,-0.034243,0.54989,0.87532,0.28083,0.4945,0.53342,0.62264,0.6505,0.82307,2.5154\n1.0696,1.0854,1.5625,1.2896,1.5127,1.15,1.1157,0.021578,-0.15067,1.5069,0.57802,0.61434,0.85777,-0.65909,-0.39522,-0.29439,-0.049295,-0.51139,-0.43588,-0.48346,-1.2054,-0.60285,-1.3334,-1.1679,-0.90958,-1.5488,-2.2932,-2.4144,-1.9855,-1.6039,-1.9193,-1.4392,-1.0126,-1.9794,-1.8262,-1.1002,-0.43778,-0.038111,0.12915,0.48993,0.42116,0.81003,0.66456,1.1496,1.2851,1.1646,2.4773\n1.4448,1.288,1.4909,1.5108,1.456,1.4072,1.0462,0.13517,0.29742,1.5375,2.2886,1.6045,0.3525,-0.36818,-0.52439,-0.42422,-0.46354,-0.54399,0.47063,0.11612,-0.36118,-0.8266,-1.3551,-1.1639,-1.4033,-1.5119,-0.85334,-1.1512,-1.7837,-2.2268,-2.3726,-2.3162,-2.2404,-1.7545,-1.3778,-0.8954,-0.086882,0.91413,0.66651,1.0662,0.87655,0.37221,0.3876,0.52676,1.2943,1.4349,1.0923\n1.4991,1.3013,1.3447,1.8967,1.3134,1.29,1.405,0.85633,1.0297,1.5293,2.2045,1.4672,0.11768,0.099298,-0.18768,0.00024986,-0.59924,-0.43145,0.65644,0.1519,-0.5402,-0.086576,-1.4578,-1.4276,-0.98577,-1.2386,-1.3362,-0.89814,-1.9293,-1.8997,-1.8991,-1.6459,-2.1468,-0.84627,-0.15609,-0.20955,0.12757,0.87903,0.86451,1.165,0.7287,0.27312,0.28248,0.25976,0.60547,0.6545,0.67237\n1.3017,1.0856,0.88672,2.4327,1.8238,1.2428,1.7254,1.2615,1.2577,1.7214,1.8266,0.58324,0.024782,0.55715,0.032124,-1.0749,0.31611,0.371,0.31794,0.42286,0.26063,-0.52761,-1.5234,-1.3952,-1.1763,-1.711,-1.5699,-1.5467,-1.8133,-1.6046,-1.7169,-2.0644,-2.1191,-0.87996,-0.5219,0.076427,0.25119,0.32924,0.37301,0.37819,0.8473,0.42193,0.18172,0.53012,1.026,0.74301,0.69423\n1.3493,1.2965,2.2341,2.9292,1.9355,1.8448,1.3099,1.5135,0.92202,1.4813,0.5015,0.64011,0.37084,0.54532,0.42354,0.33502,0.95,0.26005,0.065861,0.25128,0.20077,-0.85436,-1.4224,-1.2287,-1.5119,-1.7215,-2.1386,-1.7953,-1.9505,-1.7835,-1.8729,-1.9334,-1.9055,-1.3306,-0.89452,-1.2852,-1.2203,-0.68694,0.080061,0.11767,0.30061,0.23517,-0.3112,0.23851,0.96092,1.2671,1.3258\n1.9623,2.0865,2.3733,2.3493,2.3323,1.86,1.036,2.0604,1.0649,1.1937,0.78189,0.88417,0.80149,1.3764,1.2285,0.99626,0.73358,0.64464,0.30124,-0.13057,-0.68945,-0.93243,-1.7999,-1.4608,-1.6571,-1.3976,-1.6023,-1.4101,-1.7951,-1.5799,-1.9509,-1.7792,-1.5792,-1.725,-1.6164,-1.6797,-0.98802,-0.87619,-0.067743,0.11753,0.18594,0.021128,-0.52923,-0.52836,0.80112,1.257,2.0655\n2.0757,2.5533,3.0129,3.083,1.6436,1.4748,0.77928,1.6105,1.0701,1.1209,0.86887,1.0349,1.5437,1.9842,1.0434,1.6559,1.1655,1.0067,0.56604,0.036974,-0.60354,-0.79582,-1.5352,-1.8123,-1.5494,-1.9823,-1.751,-2.009,-1.9152,-1.5136,-1.6912,-1.6736,-1.9331,-1.5306,-1.3169,-1.3047,-0.96919,-0.35345,0.33847,-0.25141,-0.35529,-0.63957,-0.78974,-0.75767,-0.12308,1.1702,2.2276\n1.8435,2.7291,4.263,2.0722,1.0715,1.1471,1.4554,1.21,1.3757,0.61172,0.44371,0.23467,0.23074,0.74863,0.94957,1.0606,0.71718,0.62941,0.29544,0.0038185,-0.67248,-0.072409,-1.7016,-2.1897,-2.0155,-2.19,-1.9382,-1.469,-1.0093,-1.3692,-1.739,-1.5193,-1.3001,-1.2252,-1.2517,-1.5723,-1.033,-0.99283,-0.84784,-1.0112,-0.74137,-1.0546,-1.1805,-0.749,-0.72025,0.048925,0.54945\n0.80017,2.0444,3.352,1.9102,1.0931,2.0286,1.3003,1.408,1.2872,0.91821,0.78011,1.1592,0.37103,0.50048,1.1779,1.1817,0.63126,-0.29945,-0.54554,-0.34827,-0.21285,-0.42696,-1.4565,-1.8757,-2.1153,-2.357,-2.2278,-1.9276,-1.6499,-2.0064,-2.1753,-2.3273,-1.811,-2.209,-2.1569,-1.9621,-1.6801,-1.3895,-1.2571,-0.75687,-0.92405,-0.71662,-1.0948,-0.64984,-0.40236,0.1485,0.65719\n0.1789,1.2543,2.0574,1.6994,1.0199,1.4869,1.3926,1.6446,1.4419,1.5688,1.4292,1.0942,0.68838,0.47416,1.9947,0.74813,-0.080957,-0.53963,-0.87895,-0.24996,0.4589,-0.60656,-1.9042,-1.7759,-2.2214,-2.4027,-2.6155,-2.6155,-2.1812,-1.7516,-2.21,-2.8301,-1.0534,-1.2446,-1.9107,-2.1332,-1.6927,-1.49,-1.1057,-1.0089,-0.83624,-0.11892,-0.19171,-0.082825,0.35336,0.7673,0.80356\n0.293,1.4171,1.7543,1.284,1.8912,2.1372,2.0893,1.795,1.6358,1.7076,1.1433,0.87531,0.64143,0.40792,0.65192,0.64402,0.4891,0.50442,-0.26357,0.72576,0.12191,-0.89913,-1.7188,-1.9229,-2.3689,-2.4396,-2.535,-2.9037,-2.8924,-2.3779,-2.1991,-2.5072,-2.8367,-1.6837,-1.9385,-2.6471,-2.7123,-1.8815,-1.4028,-1.3379,-1.3418,-0.68413,0.16511,-0.010075,0.24618,0.013498,0.20616\n0.99964,1.7095,2.4683,2.2642,2.3177,4.7392,2.8988,1.542,1.533,1.7485,1.4072,1.0584,0.85031,0.33009,0.841,0.35542,0.31926,-0.081074,-0.81263,-2.0072,-2.115,-1.9702,-2.2239,-2.3221,-2.3745,-2.1979,-2.2967,-2.4924,-3.0939,-2.2985,-2.3382,-1.7304,-2.9723,-3.7184,-3.0659,-2.2667,-2.507,-2.107,-0.52547,-1.4045,-1.5895,-1.5815,-0.093128,0.31766,0.75724,0.6947,0.33703\n1.8677,1.8884,1.9262,1.7987,1.8884,1.8845,1.629,1.9519,2.1591,2.0206,1.6878,1.4678,1.2197,0.85548,1.2386,0.89613,0.28321,-0.43888,-0.7994,-1.0833,-2.9595,-3.0949,-2.0206,-2.5708,-2.3324,-1.9659,-2.1803,-2.2611,-2.1638,-2.8936,-2.4975,-1.8011,-2.2465,-2.1337,-1.7278,-2.75,-2.2192,-2.2276,-0.91879,-0.78465,-0.85396,-0.98696,-0.44241,-0.035025,0.9218,0.92404,0.93819\n1.5964,2.8828,2.2482,1.7174,1.4171,1.5724,2.2198,1.7038,2.4381,1.6261,2.029,1.3104,1.1004,1.0862,0.23308,-0.32208,-0.52213,-0.48432,-0.66797,-0.67473,-0.81346,-1.2895,-1.5639,-1.8702,-1.7558,-2.2674,-2.4569,-3.3138,-2.9527,-3.0222,-3.1022,-2.1231,-1.8724,-1.8599,-1.6896,-1.9505,-1.867,-2.122,-1.2142,-1.1948,-1.2207,-0.90125,-0.33907,0.58232,0.85009,0.76304,0.39971\n1.6245,2.7769,2.1782,2.079,2.1024,1.9485,2.5774,2.0781,2.0792,1.4376,1.1882,0.97604,1.1073,0.81332,0.49782,0.38428,-0.21542,-0.5543,-0.1877,0.22509,-1.1566,-1.0369,-0.56389,-1.749,-2.2777,-2.5691,-2.4081,-2.434,-3.527,-3.4892,-3.2727,-2.0823,-2.4569,-2.391,-1.7485,-2.0294,-1.9039,-1.3239,-1.143,-1.3386,-1.9055,-0.72693,0.26859,0.89039,1.0651,0.79313,0.46542\n1.4205,2.04,1.538,1.7792,1.8783,1.5235,1.4199,1.8372,2.2153,1.9333,0.93457,-0.028717,0.53345,1.4035,1.4207,1.2503,0.32935,-0.01301,-0.52283,-0.2048,-1.5478,-1.6845,-1.3799,-0.49828,-1.5144,-1.988,-1.8867,-1.8057,-2.6536,-3.0923,-3.109,-3.1132,-2.996,-2.9766,-2.0283,-2.0241,-1.7266,-1.6035,-1.5973,-1.3813,-1.2467,-0.4027,0.50079,1.1297,0.3424,0.26143,0.31255\n0.0039177,1.2537,1.1587,1.7718,2.2711,1.5791,1.556,1.923,2.5608,2.5495,1.7624,2.159,1.0507,1.761,2.0158,2.5687,0.6109,0.43659,-0.026562,-0.42797,-0.45706,-0.21467,-0.47212,-0.41193,-1.1554,-1.6508,-2.3542,-1.6497,-2.0392,-2.478,-3.0148,-2.7255,-2.3552,-2.3001,-1.9713,-1.8172,-1.5734,-1.196,-1.169,-0.6495,-0.56117,-0.027231,0.48692,0.52788,-0.1027,0.10108,0.29683\n0.15401,0.58656,1.1084,1.8776,2.0921,1.5395,2.1227,2.7859,3.1915,3.0145,2.5648,1.7298,0.88025,2.3784,2.012,0.88548,0.020536,0.26236,0.71996,0.6762,0.84069,0.88544,0.46022,0.24749,-0.9474,-1.281,-1.0919,-0.69645,-1.238,-1.6526,-1.418,-0.87109,-1.389,-2.5661,-1.8092,-1.14,-2.378,-1.5246,-1.0027,-0.98599,-0.58092,-0.061083,0.019127,-0.31275,-0.28778,0.27891,0.57668\n1.074,0.59761,1.509,2.1298,1.7774,2.492,1.7869,1.9683,2.6435,3.407,2.6561,2.0237,2.0641,2.6628,2.1089,1.1218,0.53069,0.34547,0.82505,1.231,1.8256,2.1725,1.089,0.92,0.24333,-0.54043,-0.51114,-1.0446,-1.73,-1.6451,-1.3766,-0.44212,-1.1318,-1.6934,-1.5883,-0.74113,-1.4884,-1.6705,-1.7401,-2.1704,-1.3343,-0.48656,-0.015253,0.10501,0.051365,0.77317,1.1984\n0.98795,0.96945,1.75,2.6835,1.7924,1.4296,1.4116,1.7192,2.2856,2.9239,3.1311,2.4998,3.1985,2.5459,2.0648,0.92025,0.26364,0.98118,2.0319,2.5697,2.4395,2.2619,1.3151,0.58473,-0.69363,-0.70436,-0.23087,-0.45444,-0.83094,-1.5415,-1.8829,-1.9887,-1.8995,-1.8075,-1.7202,-1.4577,-1.3908,-1.4441,-1.8108,-2.2436,-1.2684,-0.59737,-0.049625,0.47331,0.8298,1.1264,1.1965\n0.79588,0.6579,0.6486,1.1669,1.4344,2.407,3.7094,2.2862,3.0278,2.493,3.0685,3.8078,3.2497,2.5693,2.164,2.3874,1.7483,1.8038,1.9386,2.3695,2.8722,2.6867,1.7777,0.045954,-0.19263,-0.3573,-0.37263,-0.34661,-1.1819,-1.2091,-1.6511,-2.3378,-3.0675,-2.5712,-1.9846,-1.8878,-1.7409,-2.138,-1.8858,-1.4829,-0.64744,-0.089209,-0.082468,0.50484,0.97022,1.3115,1.0875\n1.2401,1.1753,1.1356,2.3087,1.943,3.0657,4.5815,5.007,3.8837,2.5662,2.2961,2.6362,2.3781,2.0204,1.9729,2.0616,1.0838,0.85339,0.96112,1.3753,2.1832,2.5114,2.3201,2.1208,0.91986,0.16829,0.33584,-0.23407,-0.61673,-0.42478,-1.0332,-1.8188,-2.2024,-2.4246,-1.7285,-1.7991,-2.1831,-2.6682,-2.7273,-1.9883,-0.39806,-0.027109,-0.39174,0.52685,1.0972,1.1018,0.79577\n0.99982,0.94921,0.55981,1.4991,1.7881,2.9069,3.5493,4.119,3.8826,3.3699,2.467,2.111,2.3192,1.3539,1.3566,1.1472,0.60935,0.0057964,0.028486,0.75594,1.547,2.2233,2.7517,1.9979,1.2212,0.999,0.93197,0.59975,-4.7684e-05,-0.54968,-1.2179,-2.1535,-2.7128,-2.6945,-1.261,-1.3486,-2.2299,-2.621,-2.6948,-1.4723,-0.20692,-0.83925,-0.88258,0.054428,0.93859,0.81214,0.25195\n-1.0857,1.234,0.78658,0.98217,1.7126,1.9236,2.5877,2.713,2.9367,2.5515,1.2576,1.2423,1.9741,1.1549,1.221,0.67543,0.22562,0.78974,1.1867,1.1921,1.1504,1.0981,2.0256,1.6097,0.84205,0.50089,0.41104,0.076384,-0.78428,-0.86255,-1.2869,-1.7557,-1.9024,-1.744,-1.0121,-1.0485,-1.6402,-1.7359,-1.4653,-0.97077,-1.1561,-1.5819,-1.0793,0.16775,1.1214,0.71646,0.84377\n-0.4005,1.9365,2.3146,2.2405,1.1579,1.0991,1.0263,1.3987,3.1032,1.9447,-0.33415,0.44559,1.057,1.2169,0.00071335,-0.060904,0.45251,0.82783,0.80902,0.80815,2.6996,2.3027,1.8627,1.0268,0.78624,0.30099,0.085823,-0.4265,-2.9437,-3.18,-1.5864,-1.0172,-1.0986,-1.3747,-0.98014,-1.1947,-1.7242,-1.5119,-1.5795,-0.96334,-0.74398,-1.2096,-0.68152,0.31898,0.44043,0.2528,0.51201\n0.65016,1.6557,2.2877,2.2805,1.5745,-0.14217,1.2547,2.4702,2.4241,2.1692,0.51111,-0.97944,-1.4523,-1.1817,-1.5137,-0.38363,0.71262,0.58599,0.2971,0.68472,1.6899,2.2918,2.5111,0.72909,0.91387,0.58222,NaN,NaN,-4.1625,-4.1402,-1.7112,-0.15146,0.4196,0.62835,-0.46746,-1.0424,-1.2348,-1.1491,-1.5451,-0.88336,-0.55496,-0.36835,-0.066618,0.086046,0.075766,0.37775,0.2995\n0.48746,0.69993,2.3828,2.1743,0.90578,0.16579,0.96903,2.4216,2.9664,3.5076,1.6474,0.65849,-1.1706,-0.64167,-2.2842,-1.5132,-0.32303,-1.0584,0.20422,-0.14384,0.363,1.1925,1.2568,1.0942,0.81722,1.313,NaN,NaN,NaN,-0.81296,0.65051,1.0193,1.5973,1.026,0.18194,-0.45696,-0.93835,-0.77751,-0.71697,-1.122,-0.45362,-0.14861,0.083897,-0.063917,0.2307,0.47002,0.27685\n-0.17102,1.0604,1.1676,2.8243,3.1171,1.718,-0.27834,1.7585,2.4974,2.3414,2.0478,0.6721,0.39022,-0.18146,-2.4974,-1.947,-1.3069,-0.52795,0.070097,0.41181,1.741,2.6084,2.5168,2.4157,NaN,NaN,NaN,NaN,NaN,1.8082,1.2502,1.2322,1.6901,0.86913,0.35096,-0.55645,-0.82832,-0.51639,-0.80814,-0.87262,-0.77383,-0.81447,-0.42373,-0.34349,0.15921,0.54771,0.31791\n-1.5752,-1.2009,-0.57142,1.1559,-4.6024,-1.4619,-0.4341,2.1341,2.0058,1.6479,0.53727,0.079647,-1.6184,-3.9448,-5.7735,-4.4927,-0.71583,0.54937,1.3257,2.3409,3.9976,4.8497,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.255,1.6036,1.5674,1.5695,0.80524,0.32749,-0.3683,-0.57433,-0.90042,-0.73502,-0.45026,-0.40926,-0.28458,-0.11985,-0.020025,0.3387,0.33132,0.42336\n-1.0717,-0.33682,-0.0058231,-0.21491,-1.5405,0.59823,0.22307,-0.22485,-0.33773,0.026186,0.064842,-0.86291,-1.5057,-4.1164,-3.8623,-2.2629,0.5414,2.4045,4.2567,4.2522,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.8697,1.63,1.4993,1.1174,1.3176,1.0583,0.20692,-0.38159,-0.79606,-0.47967,-0.44353,-0.60101,-0.23765,-0.32728,0.098753,0.28051,0.13047,0.3962\n-0.1724,-0.81548,-0.52737,-0.15723,-0.82393,0.64777,0.68547,0.46231,0.59021,0.50689,0.15798,-0.41038,-0.031673,1.1464,-0.66932,-0.33477,0.4253,6.6935,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3953,2.6147,1.7131,1.3915,1.3097,1.3132,1.3301,1.0347,0.073959,-0.54387,-0.70701,-0.46153,-0.26484,-0.2026,-0.026567,0.27354,0.51778,0.42321,0.17386\n0.47064,-0.047232,-0.27983,-0.037451,-0.13148,0.17112,0.46298,0.32103,0.88488,1.0987,0.42459,0.06579,-0.87164,-1.6219,-1.2518,-0.92707,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.4676,2.5542,2.4614,1.7552,1.5587,1.5878,1.0222,0.27782,-0.23293,-0.4669,-0.50076,-0.091553,0.089474,-0.31267,0.1931,0.34988,0.19223,0.43851,-0.33319\n0.98796,1.1136,0.31412,0.49996,0.50868,0.50109,0.2369,-0.34093,-0.61424,0.049257,0.13633,0.24355,-0.69326,-1.8092,-2.003,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.33183,1.0886,2.576,1.8292,2.3734,3.0363,3.0016,1.9433,1.398,0.95167,0.18264,-0.4989,-0.67915,-0.64563,-0.39173,0.035555,0.1172,-0.18215,0.080805,0.16863,0.23443,0.60571,-0.077524\n1.4222,0.47892,-0.61797,-0.80732,0.07984,0.70562,0.11495,-1.1256,-0.51353,-1.3448,-0.47014,-0.089882,-0.084524,-0.79691,-1.6488,NaN,NaN,NaN,NaN,NaN,NaN,-1.0131,-1.9599,-0.93482,-0.76584,-0.28292,1.092,2.5995,2.6201,2.5947,3.0042,3.0073,1.161,0.33127,0.22819,-0.83085,-0.58112,-0.18923,0.4933,-0.12832,-0.045839,-0.31512,-0.21347,-0.00024986,0.054987,0.4424,0.36769\n0.22937,-0.51682,-0.77601,-0.86572,-0.37238,-0.89304,-0.7554,-0.79801,-0.73364,-1.0229,-0.51789,-0.86447,-0.61089,-0.22686,-0.73623,-2.0834,-4.518,NaN,NaN,NaN,NaN,-1.1327,-1.9692,NaN,NaN,NaN,2.0389,3.1235,2.901,2.5219,2.5455,2.3563,1.5088,0.33998,0.011541,-0.91004,-0.93951,-0.44242,0.45179,-0.061922,-0.087084,-0.3346,-0.6372,-0.33213,-0.15654,0.65832,0.52612\n-1.2452,-1.7762,-0.90116,-0.99902,-1.1032,-1.3045,-1.5404,-1.4649,-1.3047,-1.5378,-1.4982,-0.59632,-0.7816,-0.45891,0.26411,0.87267,-1.0994,-3.0518,-2.6427,-3.6993,-1.9624,-1.2379,NaN,NaN,NaN,NaN,NaN,2.354,2.1958,1.7511,1.8579,2.0936,1.7668,0.1645,-0.25773,-0.15942,-0.29756,0.2071,0.15247,-0.31096,-0.35584,-0.34525,-0.42644,-0.44072,-0.16281,0.7456,0.48519\n-1.2632,-0.96887,-0.95832,-1.725,-1.5647,-1.5364,-1.736,-1.6661,-2.0737,-1.9388,-1.6459,-0.99154,-0.79662,-0.49014,0.029711,0.49784,-0.24842,-1.3957,-1.4397,-1.6769,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.7849,1.4655,1.35,1.1403,0.97976,1.1061,0.16945,-0.48581,-0.46509,-0.035154,0.19986,-0.044682,-0.43119,-0.29701,-0.05028,-0.20211,-0.26827,-0.048117,0.63321,0.67618\n-1.1426,-0.91966,-1.0174,-1.2996,-1.3118,-1.1095,-2.0413,-2.6589,-2.7761,-1.689,-1.1808,-1.0024,-1.2386,-1.1169,-0.20905,-0.13063,-0.51122,-1.0658,-1.94,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.86986,0.68054,0.68092,0.67071,0.053587,0.47107,0.17591,-0.56018,-0.60181,-0.25342,-0.19285,-0.40837,-0.24723,-0.0093174,0.058008,-0.52411,-0.39936,-0.087591,0.61681,0.61178\n-1.5704,-1.2041,-1.1496,-1.3155,-1.144,-1.1198,-2.0685,-1.8455,-2.2304,-1.9579,-1.0685,-0.65106,-1.3301,-1.1903,-0.16854,0.038637,-0.51101,-0.43952,-0.47472,-2.0251,NaN,NaN,NaN,NaN,0.79625,NaN,0.050476,0.33485,0.29284,0.42526,-0.003973,-0.068846,0.49918,1.188,1.0502,0.3392,-0.015444,-0.2111,-0.55486,-0.094488,-0.43383,-0.54674,-0.4757,-0.38271,-0.95792,-0.037016,1.0448\n-0.8475,-1.3401,-1.6915,-1.4648,-1.4211,-1.287,-1.6233,-1.1918,-1.5437,-1.634,-1.8167,-1.4521,-1.4424,-1.4239,-0.63338,-0.98128,-1.0005,-0.45339,-0.12938,-2.2718,-1.7158,-1.3045,-0.40171,-0.72079,-0.64063,-0.56091,-0.83618,-0.14414,-0.48602,-0.81059,-0.83635,-0.15527,0.12733,0.47225,0.87854,0.34617,-0.51039,-0.64261,-0.77596,-0.24441,-0.19022,-0.32509,-0.26941,-0.20526,-0.3229,-0.036983,0.2253\n-0.83634,-1.5103,-1.8177,-1.6146,-1.6656,-1.7386,-1.0116,-1.2836,-1.3298,-1.6073,-2.3472,-0.68695,-1.4035,-1.4735,-1.1278,-1.1323,-1.1553,-0.47746,-0.80256,-1.4057,-1.9478,-1.852,-0.4776,-0.66294,-0.65184,-0.95977,-1.3364,-0.23046,-0.21118,-0.52812,-0.90114,-0.30786,0.19989,0.35942,0.3184,0.23471,-0.083191,-0.4719,-0.58968,-0.47678,-0.25344,-0.24259,-0.16559,-0.12532,-0.10247,0.15912,0.67078\n-1.1232,-1.7513,-1.782,-1.6282,-1.611,-2.09,-1.6347,-1.7184,-1.5983,-1.8162,-1.7826,-1.1413,-1.7929,-1.5406,-1.1927,-0.56907,-0.39239,-0.58743,-0.7533,-1.594,-2.8543,-1.7615,-0.7746,-1.2643,-1.8538,-2.3182,-1.1757,-0.44977,-0.8114,-0.73825,-0.66608,0.24159,0.42307,-0.17847,-0.50826,0.52897,0.5528,0.2789,-0.21766,-0.41567,-0.17587,0.12728,0.079489,0.31064,0.19185,0.54122,1.3867\n-1.3864,-1.3293,-1.5339,-0.85508,-0.95228,-0.78711,-1.1114,-1.4565,-1.6559,-1.7119,-1.5373,-1.1696,-1.2752,-1.3595,-1.4912,-0.46017,-0.65936,-0.97791,-1.735,-3.6483,-3.0019,-1.0024,-1.0928,-1.5597,-2.1604,-2.0918,-1.5313,-0.75658,-0.81307,-0.64094,-0.74824,0.34635,1.0298,0.10933,0.40494,0.98983,1.4872,0.82781,0.78345,0.1509,0.30283,0.2535,0.31883,0.31621,-0.1397,0.72784,1.2009\n-1.0358,-1.4949,-1.2816,-0.74662,-1.8067,-1.724,-1.9722,-1.744,-1.5722,-1.7099,-1.7293,-1.3245,-1.0538,-0.70917,-0.8327,-0.88448,-0.95599,-1.0177,-1.9174,-2.3422,-1.534,-0.83016,-1.4406,-1.7283,-1.9003,-2.0396,-2.0601,-0.83125,-1.0626,-0.85484,-0.25665,0.24887,0.89249,0.54693,0.36109,0.72438,1.012,0.97388,0.87205,0.66126,0.55258,0.25761,0.41138,0.5949,0.44615,0.69734,0.9876\n-0.82226,-1.7843,-1.5559,-1.5468,-1.7987,-1.8746,-2.0744,-1.6286,-0.55636,-1.3118,-1.5354,-1.2299,-1.3111,-0.68366,-0.18236,-0.88738,-1.515,-0.62849,-1.0821,-2.1359,-1.164,-0.89807,-1.5804,-1.8221,-1.8938,-2.1797,-2.504,-1.1857,-1.1549,-0.89262,0.30099,0.026884,0.47004,0.18379,0.050352,0.69877,0.9798,1.1431,0.93903,0.61336,0.65911,0.79605,1.7083,1.17,0.60719,1.6334,1.8141\n-1.8579,-1.8079,-1.7062,-1.7649,-1.3848,-2.1641,-1.688,-1.3107,-1.0716,-1.4441,-0.72341,-1.0013,-0.9205,-0.86623,-0.66492,-0.56053,-0.72535,-0.71414,-1.641,-2.0371,-0.93839,-1.0457,-2.0685,-1.7066,-1.8746,-1.8626,-1.6432,-1.1452,-1.0646,-0.31519,0.19246,0.19705,0.40501,-0.041576,0.10055,0.66234,0.86322,0.89582,0.88083,0.94926,0.79534,0.42115,0.86113,0.58467,0.84959,1.1517,1.4173\n-1.6965,-2.3749,-2.1928,-1.9546,-1.9391,-1.8851,-1.6973,-2.0967,-1.8017,-1.69,-0.86984,-1.8183,-1.8008,-1.739,-1.2606,-0.95903,-0.90757,-0.82153,-1.5089,-1.5952,-0.95654,-0.85098,-1.4931,-1.5249,-1.5292,-1.3816,-1.5081,-1.2128,-1.3326,-0.89769,-0.37126,0.090364,0.38041,0.12995,0.39599,0.55658,0.554,1.0391,1.4631,2.3444,1.2105,0.72626,0.6721,0.44764,1.0539,0.83564,1.2996\n-1.6932,-1.9546,-2.1366,-1.7356,-1.8639,-2.089,-1.9649,-1.8183,-1.72,-1.2371,-1.6779,-2.3153,-2.5814,-2.2972,-1.7359,-1.4875,-1.0112,-0.7142,-1.3266,-2.2405,-1.212,-1.136,-1.0494,-1.1476,-1.3546,-1.3285,-0.55115,-0.48849,-1.0688,-0.96152,-0.24301,-0.09944,0.25939,0.089588,0.37749,0.66942,0.613,1.1457,1.6145,2.1128,1.5519,1.1984,0.74262,1.4603,1.26,0.62847,1.0062\n-2.1527,-2.4271,-1.1741,-1.59,-2.193,-1.7006,-2.1247,-2.2464,-2.0442,-1.6321,-1.3103,-1.3765,-1.5946,-1.8455,-2.4946,-1.9601,-1.3374,-1.2242,-1.3512,-1.9379,-1.56,-1.3086,-1.208,-1.0075,-0.91039,-1.053,-0.38667,-0.6033,-1.0083,-0.96694,-0.57135,-0.090935,0.38944,0.45989,0.67882,0.61767,0.84358,1.243,1.4812,1.7408,1.4868,1.3752,1.5198,1.9642,1.416,0.62867,1.0222\n-1.1065,-0.97683,-1.1047,-1.2636,-1.6771,-2.5846,-2.3065,-2.7299,-2.7388,-2.3981,-3.0432,-2.2642,-1.0151,-1.4229,-2.3228,-1.5371,-0.86623,-1.2147,-1.1084,-1.11,-0.82496,-0.74017,-0.65394,-0.58619,-0.97448,-0.98037,-1.2896,-1.2267,-1.0964,-0.76353,-0.78814,-0.27503,0.092968,0.67662,0.99347,0.70101,0.70065,0.89484,1.3465,1.0261,1.4053,1.2794,1.3513,1.6503,1.3166,1.616,1.7645\n-1.4483,-1.8767,-2.1974,-2.1033,-1.7246,-0.27805,-1.6984,-3.0134,-3.8587,-4.1334,-3.122,-2.7844,-0.39406,-1.1578,-1.5243,-1.3748,-0.71664,-1.97,-1.0938,-0.039072,-0.21381,-0.048946,-0.17792,0.0067158,-0.11743,0.38993,-0.39921,-2.0992,-1.92,-1.1765,-0.82597,-0.21714,0.17428,0.9414,1.5729,1.1049,1.1438,0.80641,1.0336,1.1078,0.6096,0.68796,0.85827,0.98231,0.99618,1.7967,1.314\n-2.8489,-2.2927,-1.9875,-2.6812,-2.9096,-1.2487,-1.8921,-2.6868,-2.9875,-3.8153,-3.25,-4.9459,-3.4364,-2.8083,-1.2656,-0.90812,-1.9031,-2.7427,-0.7949,0.12281,-0.096384,-0.5129,-0.013161,0.23782,0.27058,0.37002,0.15132,-0.35861,-1.2902,-0.63696,-0.52638,-0.47875,-0.04817,0.50284,1.137,0.88217,0.81981,0.94546,1.23,1.3036,1.2131,1.0208,0.94761,1.2582,1.2321,1.2656,0.64736\n-3.5273,-2.651,-2.1197,-3.3323,-3.9892,-3.7587,-3.4795,-2.2413,-0.9184,-0.93354,-2.8812,-4.5691,-3.5115,-3.7404,-4.0942,-3.7573,-2.3297,-0.75622,0.026051,-0.29831,-0.45376,-0.69859,-0.54123,0.19055,0.21716,0.078403,-0.21541,-0.47139,-0.8006,-0.37154,-0.27898,-0.8847,-0.39238,0.27111,0.86663,1.0385,1.0076,1.4757,1.6517,1.5243,0.7493,1.1052,1.3169,1.2873,1.3509,0.46044,0.47041\n-3.0281,-2.6719,-2.0699,-2.3531,-3.3454,-6.1402,-0.99655,-2.6414,-2.1621,-2.4154,-2.3076,-2.9456,-1.4048,-1.1573,-2.5168,-2.54,-3.415,-1.8787,-0.26921,-0.28157,-0.2322,-0.22277,-0.84755,-0.56739,-0.17949,0.36728,-0.72997,-1.106,-1.456,-1.0675,-0.81427,-0.37066,-0.4271,0.12787,0.73031,0.96352,0.98028,2.0009,1.2581,1.1448,0.87237,1.1956,1.2935,0.75113,0.35082,-0.36062,-0.9589\n-1.1979,-0.99102,-0.70054,-0.68279,-1.8607,NaN,NaN,NaN,-2.065,-3.5789,-0.62596,1.1814,0.19496,-0.78754,-3.7332,-3.571,-3.3825,-1.6586,-1.013,0.27916,-0.47508,-0.54688,-1.0904,-0.8907,-0.13514,0.24838,-0.11062,0.064941,-0.89854,-0.65967,-0.22408,-0.014126,0.31086,-0.26082,0.39383,0.78375,0.79942,1.2395,0.92419,0.9194,0.59566,0.37597,0.56522,0.85275,0.73111,0.69727,0.88591\nNaN,-1.9918,-1.0676,-0.54517,-0.27897,NaN,NaN,NaN,-1.6663,-4.3144,-3.8551,-0.38677,0.59556,0.04262,-2.6756,-2.599,-1.9015,-0.15953,-1.8125,-0.020168,0.61486,0.32779,-1.4191,-0.019085,0.079107,1.0289,1.4349,0.71012,-0.0022907,0.095037,0.14548,0.82758,0.61455,0.46988,0.80585,0.7411,1.2746,1.1321,0.51279,0.19089,0.31364,0.3383,1.0296,1.4487,1.2063,1.3491,0.85863\nNaN,NaN,NaN,NaN,-2.0295,-3.5472,-4.2226,NaN,NaN,NaN,NaN,NaN,NaN,1.1836,0.41006,0.21659,3.1182,4.0632,0.098505,-0.43078,-0.50904,-0.23194,-0.40974,0.013794,0.54273,1.4278,1.644,0.38726,0.39956,0.441,0.87192,1.2832,1.4537,1.182,1.4137,0.91203,1.2512,1.0545,0.53218,0.68595,0.59215,0.44863,0.68713,0.65572,1.1436,1.1535,0.89639\nNaN,NaN,NaN,NaN,-3.2151,-3.6367,-2.6868,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3234,3.9869,1.535,0.22385,-0.50861,-0.93014,-0.18277,-0.04594,0.45257,0.90594,0.97427,0.74865,1.0995,0.94717,1.1496,1.3724,1.5653,1.6983,1.2291,0.90458,0.93642,0.64252,0.52076,0.77044,0.80247,1.1092,1.1412,1.2446,1.2094,0.55514,0.2793\nNaN,NaN,NaN,NaN,0.42472,0.17413,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.664,2.3454,0.67122,0.05419,-3.8017,-1.1359,0.19337,0.7656,1.121,0.70567,0.13066,1.0669,0.8404,1.2108,1.1988,1.4197,1.9161,0.48834,0.67561,0.64734,0.43414,0.86429,0.74342,0.74937,1.1905,1.729,1.2528,0.97683,0.73179,-0.64357\nNaN,NaN,NaN,0.77733,1.838,0.33446,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.7978,-0.032036,-1.2305,-2.3054,0.17267,1.7429,0.081671,0.85356,1.4496,1.4904,2.1368,1.4376,1.0374,0.54585,1.1495,1.5609,-0.036221,0.50306,0.22421,0.43737,0.96852,1.0972,0.64273,1.1599,1.4952,1.5037,1.2328,2.1021,1.2041\nNaN,NaN,NaN,NaN,NaN,NaN,0.18908,0.20308,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.73962,0.69226,1.1238,1.5805,3.1742,0.53571,0.29742,0.53704,0.21467,-0.36176,1.6291,1.4436,1.4303,1.5255,2.6941,1.6752,0.98481,0.68718,0.27199,0.39394,0.65469,0.64216,0.52203,0.42187,0.69414,0.96399,1.052,1.4826,1.5281\nNaN,NaN,NaN,NaN,NaN,NaN,-1.1635,0.077551,0.58469,0.26586,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.0973,-0.63055,0.1479,0.8515,0.32346,-0.15264,1.0019,0.22978,-0.0032997,-0.3043,0.65165,0.86977,1.4437,1.8013,1.8611,1.1771,1.0661,0.68091,0.73173,0.28074,0.48082,1.1682,0.7459,0.31123,0.93696,0.9142,1.4236,2.5122,2.4819\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.26542,-1.9018,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.52971,-1.1169,0.070696,-0.29976,0.40033,1.3493,1.5742,0.75652,0.36203,0.29093,1.0069,1.5737,1.7885,1.225,0.99577,0.77909,0.89732,0.93498,1.052,-0.38295,0.38032,1.299,0.27866,0.98625,1.3519,0.78195,0.60391,0.83088,0.98599\n1.375,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.9005,-1.1184,2.448,2.4584,-2.1308,NaN,NaN,NaN,NaN,0.76049,0.2639,0.70668,-2.3273,1.3361,1.8896,0.6356,0.69057,0.70334,0.25262,0.93088,1.4718,1.2875,0.99014,0.89987,0.89788,1.2224,1.0927,1.1675,0.2376,1.1245,0.79108,1.0301,1.3764,1.2118,0.66036,0.43989,0.57362,1.1563\n0.96125,0.91481,1.7686,1.7776,NaN,NaN,NaN,NaN,NaN,-1.5681,-0.91777,0.49451,1.4662,1.1555,0.49315,-0.27376,-0.039021,-1.494,-1.8255,2.4071,-0.9386,-0.93442,0.56081,1.1546,-0.37501,-0.45925,-0.21026,-0.55182,-0.73265,0.92455,1.6522,0.9689,0.60866,0.59214,0.87655,1.0788,-0.0065136,-0.3275,0.25778,0.8195,1.3858,2.3574,2.0195,1.2238,0.70316,0.46711,0.48798\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061106-070326.unw.csv",
    "content": "0.024639,-0.21208,-0.21803,-0.14017,-0.0074348,-0.22039,-0.4184,-0.45738,-0.29939,-0.44286,-0.66803,-0.55156,-0.59441,-0.027947,0.33327,0.20319,-0.010876,-0.059525,0.281,0.40053,0.83541,0.47197,0.4548,0.6729,0.95844,0.1007,0.2035,0.6857,0.85642,0.67371,0.76591,0.49185,0.82833,0.76701,0.4578,0.4322,0.42798,-0.077897,-0.52186,-0.58745,0.061516,0.36207,0.14432,-0.25249,-0.63671,-0.6272,-0.59247\n-0.13543,0.011251,0.12753,-0.054558,-0.13507,-0.38049,-0.44575,-0.48179,-0.78918,-0.70206,-0.48114,-0.52256,-0.31051,-0.036745,-0.16147,-0.245,-0.12318,-0.097925,0.081145,0.25027,0.3031,-0.039873,0.62562,0.71273,0.26968,0.15941,0.41269,0.67762,0.67118,0.8885,0.80819,0.58762,0.76153,0.66523,0.26575,0.41404,0.34197,-0.024487,-0.44545,-0.62631,-0.093697,0.21283,-0.21334,-0.39533,-0.85384,-1.1573,-1.1863\n-0.10941,0.69019,0.34925,-0.082731,-0.2007,-0.3852,-0.44287,-0.57175,-0.99463,-0.72209,-0.27524,0.0018768,-0.0074167,-0.023546,-0.24725,-0.35484,-0.16196,-0.22243,-0.18017,0.11797,0.20712,0.41515,1.1657,0.034471,0.28584,0.28441,0.34451,0.33616,0.5835,1.1129,0.69432,0.44453,0.5965,0.66978,0.49688,0.42627,0.65522,0.2318,-0.059187,-0.11649,-0.12754,0.099252,-0.41259,-0.5991,-1.2352,-1.4199,-1.5529\n-0.040848,0.863,0.0063362,-0.075708,-0.22049,-0.48411,-0.63609,-0.72273,-0.86502,-0.75805,-0.35456,-0.1969,-0.099253,-0.15938,-0.34451,-0.1949,-0.041023,-0.0923,-0.17505,0.01364,-0.10875,0.55102,0.60996,0.17144,0.31472,0.30885,0.35658,0.28969,0.53874,0.6056,0.38214,0.35372,0.62047,0.77503,0.66647,0.56714,0.45676,0.12199,0.066566,0.046781,-0.13109,-0.30884,-0.45118,-0.81552,-1.217,-1.4015,-1.6611\n-0.39045,0.20877,-0.61816,-0.17956,-0.31474,-0.51442,-0.69359,-0.70689,-0.95205,-0.29355,-0.10707,-0.52408,-0.12736,-0.40031,-0.63142,-0.18416,0.15241,0.12227,-0.15351,-0.25958,-0.21464,1.0248,0.49439,0.40445,0.21254,0.28171,0.70273,0.40717,0.8523,0.29569,0.3412,0.45413,0.55801,0.86355,0.76021,0.45565,0.33513,0.22541,0.14716,-0.041245,-0.36698,-0.44191,-0.53462,-0.71983,-1.1615,-1.2942,-1.2443\n-0.77208,-1.1465,-0.41405,0.016085,-0.32012,-0.4441,-0.68863,-0.86489,-0.58455,-0.17847,-0.59935,-0.7859,-0.47181,-0.59748,-0.72021,-0.35199,-0.13799,-0.044803,-0.31922,-0.065588,0.41623,0.88851,0.6969,0.15964,0.11877,0.84705,1.033,0.5846,0.58447,0.67385,0.97158,0.81167,0.57807,0.60588,0.65013,0.47801,0.49985,0.42793,0.25539,-0.040414,-0.26628,-0.48347,-0.73416,-0.83933,-1.3475,-1.2383,-1.0909\n-1.215,-1.5272,-0.041109,0.0046682,-0.19849,0.0082932,-0.62835,-1.0748,-1.0109,-0.60176,-0.90379,-0.97123,-0.62503,-0.62648,-0.39384,-0.25331,-0.046048,0.11622,0.18077,0.11143,0.116,0.61673,0.41115,0.44337,0.614,1.1292,1.1324,0.63533,0.65357,0.87628,1.129,0.7572,0.58347,0.50904,0.53693,0.527,0.40137,0.05041,-0.28431,-0.54827,-1.0331,-0.80576,-0.98601,-1.2172,-1.4281,-1.4149,-0.9624\n-1.1191,-1.0847,-0.14951,-0.14267,-0.2206,0.21066,-0.42586,-0.90138,-0.80354,-0.42708,-0.86353,-0.9288,-0.82688,-0.5079,-0.34397,-0.28988,-0.053658,0.28463,0.13875,0.020374,0.00075722,0.2387,0.24428,0.82052,0.80677,0.9079,1.2681,1.1826,0.93027,0.97298,0.91985,0.6377,0.61359,0.50143,0.58839,0.56505,0.40421,0.1316,-0.18744,-0.47294,-0.7391,-1.1406,-0.97368,-0.9384,-1.1517,-1.4525,-1.0389\n-0.76246,-0.51194,0.13421,-0.21357,-0.077401,-0.020571,-0.55245,-0.57323,-0.37216,-0.17377,-0.35427,-0.70694,-0.4719,-0.248,-0.24475,-0.32994,-0.2835,0.061223,0.068027,0.16431,0.18694,0.39212,0.66162,1.0373,1.1648,1.3479,1.399,1.0889,0.91221,0.76695,0.86448,0.75524,0.69969,0.87452,0.77975,0.54941,0.23099,0.1957,-0.0045643,0.28541,0.085773,-0.621,-0.76985,0.039232,-0.3865,-0.77883,-1.1161\n-0.28249,-0.22006,0.72669,0.12195,-0.31569,-0.24256,-0.45158,-0.38005,-0.29787,-0.42696,-0.48521,-0.30302,-0.40456,-0.33129,-0.13717,-0.14623,-0.40278,-0.20069,0.19924,0.58362,0.38236,0.75475,0.83718,0.98853,1.4397,1.314,1.1706,0.58822,1.0705,1.3985,1.1838,0.82125,0.86622,0.89955,0.6469,0.35911,0.22781,0.44719,0.37796,0.44672,0.38541,0.12423,-0.055067,0.27552,0.084222,-0.39844,-0.92219\n-0.36671,0.54806,0.89491,-0.079154,-0.098228,-0.22489,-0.31202,-0.38574,-0.47518,-0.50323,-0.4351,-0.48156,-0.909,-0.65005,-0.46168,-0.40648,-0.26023,0.15407,0.58894,0.65697,0.77609,0.79145,0.52144,0.92657,1.1939,1.0984,1.0364,0.92149,1.348,1.8805,1.6901,1.4188,1.0976,0.75035,0.51859,0.46403,0.46459,0.53071,0.56436,0.55127,0.68868,0.27419,0.12571,0.17518,0.10552,-0.40676,-0.58034\n0.080454,1.0413,0.74333,-0.030045,-0.28858,-0.42144,-0.37875,-0.34141,-0.39901,-0.42588,-0.611,-0.7027,-0.97949,-0.85138,-0.44158,-0.055574,0.19367,0.36898,0.56368,0.38848,0.48745,0.43693,0.40157,1.1144,1.152,1.6101,0.98457,1.0295,1.6703,1.9183,1.9199,1.4284,1.0962,0.74281,0.86023,0.5704,0.35373,0.34921,0.5191,0.61456,0.6243,0.22134,-0.03871,0.023639,-0.046991,-0.40923,-0.55804\n-0.28385,-0.046701,0.11319,0.28892,-0.42913,-0.16606,-0.3253,-0.1497,-0.27144,-0.32007,-0.61777,-1.0403,-0.76805,-0.23188,-0.075194,0.18897,0.49199,0.67023,0.61793,0.44879,0.4553,0.84464,0.8517,1.013,1.5224,1.7123,1.2283,1.2695,1.8512,1.6409,1.5404,1.1711,0.91119,0.64607,0.68836,0.33605,-0.39545,-0.42671,-0.12679,-0.15892,-0.027198,-0.082411,-0.32603,-0.10493,-0.24365,-0.38736,-0.92087\n-0.52635,-0.57128,-0.44193,-0.24161,-0.030283,0.31652,0.19295,-0.056698,-0.44665,-0.42453,-0.88365,-1.0556,-0.45228,0.22394,0.019007,0.19174,0.45586,0.91193,0.96712,0.95069,0.98566,0.90051,1.0792,1.2799,1.652,1.4484,1.1388,1.3187,1.4939,1.4516,1.3027,1.0154,0.98822,0.62786,0.33442,-0.27185,-0.87007,-1.3324,-1.0623,-0.58221,-0.45335,-0.65344,-1.0535,-0.8824,-0.79228,-0.92208,-1.0598\n-0.61185,-0.56017,-0.16107,-0.031783,0.078818,0.25326,0.19347,-0.34659,-0.44082,0.050753,-0.27517,0.044034,0.34826,0.545,0.2654,0.3526,0.31118,0.70765,0.87514,1.0295,1.13,0.88243,0.90482,1.4776,1.4609,1.0668,1.0856,1.0923,1.4095,1.2835,1.0397,0.76099,0.71956,0.73378,0.30819,-0.46376,-0.94728,-1.1656,-1.1992,-0.88335,-0.62401,-0.64549,-1.6134,-1.6049,-1.6182,-1.4736,-1.5336\n-1.2707,-0.6813,-0.027988,0.30138,0.10386,0.28695,0.20691,-0.13262,-0.22036,0.086552,0.069312,0.58107,0.1397,0.42549,0.029153,-0.14536,-0.31241,-0.21557,0.40307,0.67742,0.79306,0.76327,0.8807,1.4272,1.5186,1.3257,1.2479,1.172,1.601,0.86852,0.47003,0.42315,0.19277,0.28985,0.10522,-0.34864,-0.11889,-0.54505,-0.84761,-1.3503,-0.79503,-0.73539,-1.5459,-1.8197,-1.9461,-2.3076,-2.4714\n-1.5858,-0.6708,-0.16905,-0.10857,-0.50315,-0.36096,0.24355,0.10729,0.12899,-0.027675,0.096321,-0.02854,0.16754,0.44388,0.057899,-0.40438,-0.7133,-0.041999,-0.045819,0.28308,0.32651,0.61813,0.74215,1.0505,1.2616,1.2246,1.1132,1.0719,1.0712,0.73966,0.30579,0.079625,-0.33984,-0.032501,0.114,-0.28423,-0.43487,-0.39035,-0.29648,-1.2758,-1.173,-1.1224,-0.99673,-1.442,-2.2254,-2.712,-2.6354\n-0.95809,-0.48494,-0.18817,-0.50898,-1.0698,-0.42094,0.26675,-0.14916,-0.092385,-0.20092,-0.14824,-0.062359,0.28797,0.39427,-0.072242,-0.40871,-0.28241,0.033625,-0.38416,0.41075,0.42094,0.37749,0.48167,0.60779,1.2035,0.97271,0.64877,0.54693,0.2622,0.016626,-0.11153,-0.38912,-0.059404,0.45453,0.4024,-0.26262,-0.89704,-0.71161,-0.55652,-0.9811,-1.0661,-1.1233,-1.0093,-0.98572,-2.0791,-2.4034,-2.4355\n-1.2661,-0.47501,0.071909,-0.16294,-0.52187,0.14822,0.32505,-0.12902,-0.40909,-0.16354,-0.083282,-0.061632,-0.039557,-0.10735,-0.17254,-0.55807,-0.56467,-0.54108,-0.47549,-0.05116,0.045357,0.035151,0.31698,0.68853,0.77829,1.147,0.65875,0.4626,0.39989,-0.039808,-0.10636,-0.29897,-0.25186,0.25273,0.48424,-0.063313,-0.70011,-0.65477,-0.41978,-0.63121,-0.76476,-1.194,-1.1013,-1.168,-1.7358,-1.9834,-2.0115\n-1.603,-0.44815,-0.13133,-0.12578,-0.31394,-0.44548,-0.12444,-0.049349,-0.16748,-0.22661,-0.18671,-0.23633,-0.4042,-0.27824,-0.3688,-0.73094,-0.70446,-0.58028,-0.13336,0.11843,-0.046236,-0.14286,-0.080468,0.63545,0.80042,0.83313,0.73923,0.63093,0.58033,0.18919,-0.090178,-0.25582,-0.50253,-0.36442,-0.072081,0.018429,-0.32608,-0.32596,-0.3979,-0.43609,-0.7328,-1.2357,-1.295,-1.3131,-1.6473,-1.7165,-1.6681\n-0.80169,-0.55066,0.056725,0.088675,-0.0022593,-0.52461,-0.11251,-0.047097,-0.21944,-0.34862,-0.38644,-0.29528,-0.28438,-0.49636,-0.91537,-0.80999,-0.92853,-1.1783,-0.21316,-0.28656,-0.5828,-0.34682,-0.18299,0.13378,0.34927,0.61841,0.5451,0.88382,0.5466,0.087955,-0.15602,-0.58421,-0.48289,-0.47343,-0.077475,0.059435,0.18091,0.048851,-0.25285,-0.56799,-1.0843,-1.292,-1.2764,-1.2316,-1.18,-1.1291,-1.0645\n-0.64753,-0.46946,0.10455,0.24888,0.26663,-0.09488,-0.28444,-0.15151,-0.17453,-0.37599,-0.52442,-0.45039,-0.41033,-1.1502,-1.1396,-1.1961,-1.0699,-1.0226,-1.0783,-0.49546,-0.70428,-0.30063,-0.33074,0.32648,0.4897,0.57644,0.54927,0.59329,0.25373,0.052738,-0.094761,-0.31993,-0.34417,-0.34052,0.24255,0.22374,0.2002,0.25422,0.21376,-0.62055,-1.4563,-1.4198,-1.2544,-1.1079,-1.0395,-1.0472,-0.93569\n-0.49193,-0.23192,0.15322,0.079961,0.24741,-0.012,-0.12334,-0.05858,0.056568,-0.39475,-0.56179,-0.26976,-0.44657,-0.91151,-1.0436,-1.407,-0.96754,-0.70741,-0.61873,-0.35582,-0.43886,-0.53554,-0.37758,0.5323,0.86205,1.0589,0.26873,0.20248,-0.025279,-0.15114,-0.16802,-0.2936,-0.28697,-0.21246,0.23039,0.12282,0.034739,0.09565,-0.33859,-0.69126,-1.3366,-1.392,-1.2092,-1.0861,-1.2843,-1.1678,-1.059\n-0.40029,-0.23543,-0.27311,-0.24987,-0.047969,0.30781,0.020853,-0.15075,-0.19016,-0.48057,-0.67135,-0.40483,-0.059934,-0.6212,-0.71709,-0.41863,-0.32439,-0.39593,-0.32253,-0.14263,0.19591,0.57697,0.36664,0.52683,0.99319,1.1046,0.46034,0.1667,0.17841,0.13593,0.12449,-0.18094,-0.48749,-0.33878,0.050171,0.059541,-0.10995,-0.33446,-0.63186,-0.53281,-0.89606,-1.0838,-0.73615,-0.58336,-0.78085,-0.9657,-0.89238\n-0.08007,-0.26704,-0.35258,-0.082242,0.045339,0.19015,0.15747,-0.30426,-0.10697,-0.030678,-0.058987,-0.047929,0.21699,-0.35302,-0.39232,-0.23693,-0.19896,-0.67737,-0.49627,-0.02828,0.36102,0.57585,0.2712,0.32435,0.73206,0.39416,0.20939,0.21649,0.49987,0.16175,0.2081,-0.032056,-0.7412,-0.75405,-0.68364,-0.76199,0.5886,-0.022341,-0.25114,0.058753,-0.44902,-0.46514,-0.39627,-0.11173,0.026519,-0.38785,-0.71768\n-0.083754,0.22541,0.038608,0.070397,0.15012,0.23285,0.30288,0.18526,0.21494,-0.23867,0.10972,0.018022,-0.07081,-0.38408,-0.88222,-0.62669,-0.80792,-0.90709,-0.52274,-0.046801,0.18233,0.12617,0.080157,-0.41588,-0.58428,-0.36739,-0.36402,0.00089645,-0.18953,-0.6465,-0.76581,-0.38679,-1.4305,-1.4716,-0.9405,-0.85068,0.83308,0.68148,0.45814,0.57041,0.2792,-0.1254,-0.24179,-0.18479,-0.091536,-0.35722,-0.43351\n-0.042144,0.47507,0.0056076,0.08751,0.12242,0.2755,0.28697,0.34307,0.14362,0.096522,0.05942,-0.18925,-0.72266,-0.97777,-1.1901,-0.89848,-0.83095,-0.80973,-0.19458,0.10343,-0.00083733,-0.055729,-0.28204,-0.71233,-1.3955,-0.55911,-0.33275,-0.21113,-0.11254,-1.1222,-0.80182,-0.81962,-1.2579,-1.2845,-0.8833,-0.13467,0.61146,0.40449,0.45025,0.72148,0.19168,-0.065355,0.02251,0.049381,-0.03915,-0.14589,-0.18412\n-0.29532,-0.18843,-0.034909,0.097104,0.24662,0.32628,0.34084,0.2166,-0.035271,0.70075,-0.062089,-0.52045,-0.87267,-0.8287,-0.88849,-0.56649,-0.42318,-0.4383,-0.44115,-0.43427,-0.2588,-0.1824,-0.3308,-0.60076,-1.2992,-0.72512,-0.40192,-0.39291,-0.42412,-0.73412,-0.69723,-0.69992,-0.90516,-1.0324,-0.64439,0.10832,0.21754,0.24897,0.29611,0.57632,0.10783,0.28267,0.27955,0.56376,0.12465,-0.10771,0.10736\n0.18958,-0.12918,-0.2737,0.10353,0.42071,0.26695,-0.16337,-0.70973,-0.52653,0.083191,-0.10926,-0.66728,-0.87085,-0.34521,-0.33442,-0.53545,-0.26169,-0.20852,-0.48292,-0.62961,-0.56661,0.13445,0.25852,0.35655,-0.0052462,-0.12541,0.23872,0.037851,-0.034312,-0.3576,-0.26494,-0.3007,-0.019197,-1.047,-0.42737,0.037491,0.16229,0.34123,-0.31174,-0.36955,0.35835,0.3748,0.54109,0.42033,-0.1008,-0.054255,0.054668\n-0.14201,-0.24351,-0.46178,-0.43879,0.4695,0.17714,-0.67296,-1.0073,-0.7186,-0.35964,-0.10444,-0.32982,-0.62019,-0.27774,-0.61044,-0.62944,-0.34485,-0.26345,-0.02673,-0.41368,-0.39057,0.51305,1.1465,0.76484,0.62558,0.52111,0.54545,0.60368,0.013632,-0.1172,0.41465,0.1206,-0.20292,-1.0737,0.25441,0.26554,-0.19161,-0.278,-0.64393,-0.14121,0.45336,0.51129,0.42751,0.35708,-0.168,-0.39347,-0.63898\n0.35743,0.036689,-0.32507,0.04767,0.24592,0.11545,-0.17615,-0.9169,-0.92167,-0.69359,-0.58759,-0.47078,-0.43322,-0.46818,-1.0037,-1.2199,-0.96413,-0.19056,-0.11814,-0.389,-0.1616,0.43643,1.1597,1.3849,0.87763,0.83916,0.88301,0.89141,0.54503,0.39386,0.72785,0.43536,0.37623,0.010122,1.0013,0.42215,0.16126,0.04343,0.16991,-0.20404,-0.11354,-0.1432,0.21346,0.37402,-0.2154,-0.53261,-0.66752\n0.30829,0.033385,-0.49546,-0.48537,0.0058956,0.29545,0.53668,-0.36042,-0.58797,-1.1528,-1.201,-0.64051,-0.43706,-0.44284,-0.71092,-1.0217,-0.82958,-0.020698,0.18187,0.16303,0.58282,0.69402,0.90697,1.6415,1.3635,1.1074,1.182,1.7402,4.1484,4.2604,2.0701,0.62252,1.1091,1.3273,1.2669,0.64335,0.60835,0.27127,0.56206,0.61057,0.29849,-0.059313,0.46642,0.32227,-0.41322,-0.8181,-0.83347\n0.25788,0.093858,-0.20969,-0.82146,-0.45356,0.54372,0.57001,0.047796,-0.27496,-1.2159,-1.2308,-0.99208,-0.45723,0.16962,0.39148,-0.025273,-0.1575,0.2779,0.36443,0.2003,0.78385,1.1391,1.1735,1.8625,1.9787,2.1901,NaN,NaN,NaN,NaN,NaN,1.1841,0.90072,1.545,1.3621,1.0048,1.0325,0.39072,0.85025,1.0071,0.72819,0.27794,0.52907,-0.14692,-0.67826,-1.1694,-1.2835\n0.76552,0.66713,0.088713,-0.45112,-0.60463,0.62269,1.1855,1.051,0.13977,-0.62259,-0.74526,-0.64414,-0.082692,0.19962,0.76983,0.67101,0.62065,0.72922,0.53466,0.41393,0.95803,1.3593,1.5591,2.6329,3.6018,4.6171,NaN,NaN,NaN,NaN,1.5886,1.2664,0.23074,2.2187,3.1025,2.5253,3.0677,1.9929,1.4667,0.91945,0.74061,0.40506,0.063493,-0.23037,-0.68635,-1.1114,-1.4645\n0.9732,1.0803,0.09172,0.073412,0.4481,0.64389,0.81544,1.3104,0.67375,0.18249,0.10527,0.63031,0.74674,0.62799,0.60232,0.63672,0.83177,1.1517,0.9661,1.0142,1.4473,2.2505,1.978,3.1291,3.2948,4.5114,NaN,NaN,NaN,NaN,1.9055,1.3478,1.8516,4.3491,4.7548,4.665,4.362,2.8943,1.7662,1.0863,0.95479,0.4478,0.31276,-0.2197,-0.65776,-1.0333,-1.5242\n0.9139,0.4802,0.13888,0.0242,0.055144,0.24966,0.42605,0.95723,0.99358,0.87493,0.72739,0.81802,0.95565,0.87824,0.45877,1.0045,1.4488,1.6727,1.7857,2.0607,2.7733,2.1921,1.9466,2.2073,1.6494,2.8134,2.8888,1.8921,0.48535,2.0874,2.3518,2.591,6.5806,6.9688,6.31,5.1885,4.5496,3.101,1.9197,1.1907,0.63055,0.36509,0.36351,-0.12069,-0.65738,-0.99267,-1.4727\n0.84686,0.33135,0.56581,0.18856,-0.24408,0.1303,0.48641,0.79925,1.0105,1.2643,0.76509,1.0398,0.85845,-0.074582,0.20613,1.2039,1.5725,1.2358,0.84247,0.82949,1.4972,2.1402,2.8641,2.5489,2.7853,3.4255,4.8403,4.8614,1.5933,2.5449,3.1554,4.417,9.3091,8.5008,7.3996,5.0688,3.9385,2.9657,1.8288,0.8488,0.089737,-0.092009,-0.15689,-0.6213,-0.77602,-1.1903,-1.5883\n1.2542,1.2789,1.0388,0.49907,0.080155,0.14586,0.39802,0.80081,1.2309,1.2741,0.93424,1.0532,0.91301,0.19922,0.56605,1.2568,1.971,2.6548,0.33529,-0.90906,0.64722,2.9348,5.0832,2.7157,2.4451,2.5626,4.5892,4.9385,2.6531,2.1566,4.6025,6.681,8.0384,8.1809,7.3019,5.2173,3.4236,2.3144,1.6472,0.20507,-0.35985,-0.22553,-0.011523,-0.35874,-0.61741,-1.2371,-1.3666\n0.49957,0.83767,1.1649,0.77562,0.27301,0.1131,0.38384,1.0893,1.6029,1.6845,1.3036,1.218,1.4216,0.98613,1.5049,2.0284,1.5909,0.55142,0.13843,0.30784,0.8614,3.5377,3.9719,1.8324,1.6572,1.2712,3.9457,3.0972,1.2554,2.2173,6.5131,8.1808,7.8571,6.7998,5.4577,4.069,2.9688,2.0686,1.3887,0.1987,-0.38708,0.29777,0.4313,0.13775,-0.24921,-0.84855,-0.83923\n0.25283,0.51657,0.65899,0.84903,0.77031,0.71884,0.65079,1.2637,1.4622,1.3632,1.2017,1.5229,1.8304,1.0777,2.4452,2.287,1.9736,0.24882,0.42736,0.31246,1.7932,2.1185,2.8531,2.5732,2.4399,3.1566,5.2771,1.4654,0.83674,3.6215,11.356,9.2669,7.1597,5.3872,3.9425,3.1569,2.4073,2.0664,1.1025,0.20426,-0.027414,0.288,0.33066,0.13788,-0.1651,-0.32288,-0.55512\n0.60406,0.67259,0.59737,0.53342,0.6708,0.75251,0.83321,1.0283,0.93048,0.96941,1.3496,1.9974,2.3786,2.1776,2.3984,2.6465,2.4477,1.9218,1.8227,1.989,1.8906,1.6485,2.3709,2.9402,3.048,3.6996,2.7298,-0.024768,0.9097,4.3654,8.2779,8.7811,5.3206,4.0353,2.7923,2.3519,2.3812,2.4044,1.1163,0.65988,0.66124,0.12979,0.34475,0.15082,-0.048458,-0.12191,-0.31152\n0.15622,0.30963,0.81216,0.68886,0.50113,0.28546,0.5034,0.49854,0.64297,1.0345,1.312,1.9463,2.7011,2.539,2.4842,2.2141,2.5396,2.1174,2.3333,2.8323,2.0041,1.4959,1.4808,1.929,2.7645,3.0164,1.1283,-0.48252,0.82072,3.5725,5.7553,5.91,4.9253,2.9121,1.8125,1.8554,1.5584,1.5124,1.1263,1.0175,0.85594,0.17124,0.34448,0.28123,0.029522,0.40982,0.35543\n0.77813,0.879,1.001,0.94071,0.71961,0.54635,0.36104,0.25011,0.49664,0.62397,0.87267,1.4505,2.3229,2.0687,2.2214,2.2764,0.61239,1.736,1.7044,1.6873,1.3264,1.1839,0.79497,1.2815,2.1241,0.84455,-0.33988,-1.1176,1.061,2.3827,4.4487,3.8151,3.0105,1.7692,1.2814,1.1598,1.2192,1.0197,0.87626,1.1363,0.83912,0.31821,0.26268,0.099867,-0.014414,0.5889,0.28388\n0.99009,0.83477,1.0459,0.76798,0.79064,0.53433,0.26637,0.20948,0.42785,0.45106,0.62597,0.93284,1.4421,1.3951,1.4652,1.6174,1.2545,1.0819,1.6712,1.2286,0.65209,0.20964,-0.42427,0.77728,-0.41018,-2.1289,-0.25133,-0.079936,1.0463,1.4291,1.9587,2.0124,1.8214,0.83338,0.51881,0.55374,0.70969,0.58796,0.91886,0.88161,0.42909,0.19843,0.12413,-0.25301,-0.40472,-0.049746,0.18418\n0.74488,0.58694,0.41663,0.6936,0.80091,0.31495,-0.049541,-0.1349,0.18729,0.22888,0.11626,0.28106,0.48172,0.79752,1.2551,1.8569,1.9703,2.6939,1.6274,0.24958,0.18862,-0.30834,-1.3752,-1.1438,-1.0637,-3.4919,0.43859,0.4112,0.28338,0.58481,1.4912,1.7883,0.82223,0.55869,0.35875,0.19923,0.49874,0.59265,0.65176,0.46127,0.17666,0.1791,0.072288,-0.46649,-0.71691,-0.70904,-0.17817\n0.39635,0.15776,-0.14986,-0.1854,0.27538,0.25397,0.27145,0.31816,0.2727,-0.18334,-0.31729,-0.43269,-0.096744,-0.030603,1.4868,2.0633,2.6807,3.5624,3.0687,1.4265,0.95372,0.73952,-0.053329,-0.30263,-0.058568,-1.0358,0.10398,-0.29944,0.27955,0.75514,1.5927,1.5843,1.0167,0.62832,0.14668,-0.12687,0.29417,0.65583,0.3755,0.29657,0.52806,0.606,0.28879,-0.19576,-0.75902,-0.81346,-0.52984\n0.89465,0.1869,-0.53156,-0.10051,0.30841,0.47839,0.64445,0.58913,0.21728,-0.3482,-0.794,-0.47357,-0.29791,-0.36542,0.30277,0.85743,1.358,2.1151,2.7625,1.1058,1.2608,1.3172,0.34771,-0.077239,-0.40102,-0.96706,-1.3822,-0.66747,-0.24214,0.074043,0.64388,0.92151,0.54667,0.17922,-0.30449,-0.039884,-0.10555,0.21495,-0.037893,0.11736,0.68291,0.75682,0.46667,0.13575,-0.53633,-0.48496,-0.32178\n0.9829,0.44723,-0.23889,-0.13151,0.69773,0.88144,0.78917,0.58274,0.012299,-0.41687,-0.84652,-0.57726,-0.65689,-0.55261,-0.11466,0.4169,0.89882,1.155,0.97499,0.095592,0.13579,0.57642,-0.78355,-0.53733,-0.81723,-1.1928,-1.3607,-0.8129,-0.57903,-0.38818,-0.15177,-0.0036068,-0.52934,-0.84484,-0.38261,-0.099663,0.06859,0.19468,0.22306,0.1886,0.50686,0.6088,0.1051,0.10188,-0.26277,-0.15955,-0.04592\n0.81407,0.49398,0.17801,0.36901,0.7129,0.91671,0.82023,0.5039,-0.26579,-0.29148,-0.40757,-0.32819,-0.3439,-0.57571,-0.17569,0.22893,0.37363,0.079232,-0.88219,-1.171,-1.7355,-1.3878,-1.1664,-0.94067,-1.2736,-1.7977,-1.5561,-1.3752,-1.1232,-0.9245,-0.68056,-0.69663,-1.0577,-1.2758,-1.2495,0.15349,0.52468,1.068,0.66717,0.2406,0.32393,0.41983,-0.091916,-0.074936,-0.053053,0.13772,0.37257\n0.7599,0.59664,0.42666,1.0305,0.95902,0.78221,0.52329,0.30498,-0.17457,0.098174,0.19223,0.092887,-0.22781,-0.54302,-0.27983,-0.10101,-0.34315,-0.68092,-1.3824,-2.9404,-2.842,-1.8146,-1.1133,-1.37,-2.0859,-2.2836,-2.164,-2.0805,-1.368,-1.5135,-1.1067,-0.88565,-0.64845,-0.73327,-0.42658,0.1709,0.72095,0.56327,0.86308,0.47793,0.41705,0.32593,0.042599,0.30714,0.36903,0.32103,0.21339\n0.6688,0.45846,0.58388,1.2992,1.3003,0.9447,0.64696,0.6704,0.67789,0.51613,0.22138,0.18806,-0.16566,-0.20539,-0.14788,-0.26305,-0.62499,-1.1346,-1.9041,-2.2783,-2.1436,-1.4892,-1.0839,-1.7875,-2.6186,-2.7082,-3.1562,-2.9049,-1.6533,-2.0261,-1.6423,-0.81112,-0.50484,-0.3261,0.3283,0.43139,0.62345,0.80632,0.84064,0.64532,0.4796,0.48798,0.67712,0.98675,0.71745,0.26104,0.505\n1.0088,1.0009,1.1383,1.3876,1.3853,1.1466,1.0405,0.9365,1.1122,0.77656,0.32187,0.028416,-0.38661,-0.033616,-0.16033,-0.42542,-0.68326,-0.95349,-1.0273,-1.6414,-1.7981,-1.1692,-1.1619,-1.8638,-2.3073,-2.484,-3.1034,-3.3173,-2.3655,-2.0593,-1.9747,-1.3301,-0.64521,-0.50575,-0.14717,0.35949,0.89121,0.98949,0.74424,0.51664,0.7316,0.77673,1.1659,1.6418,0.86716,0.82886,0.97131\n1.4047,1.3389,1.3251,1.5306,1.7466,1.3944,1.5183,1.3735,1.3206,1.1751,0.87984,0.10403,-0.25651,-0.2833,-0.7477,-0.79764,-0.68812,-0.80766,-0.86157,-0.87907,-1.1919,-1.0909,-1.3169,-1.5378,-2.0478,-2.0673,-2.736,-3.0916,-2.6513,-2.3638,-2.0127,-1.2234,-0.68076,-0.51782,-0.41449,0.22913,0.87801,0.79025,0.55177,0.44962,0.56488,0.59652,0.90447,1.2635,0.42016,0.32837,0.79653\n1.2306,1.4576,1.5467,1.6872,1.5117,0.89969,1.4751,1.0034,1.2234,1.2335,1.0447,0.80436,-0.1323,-0.28709,-0.26634,-0.75097,-0.80791,-0.97608,-1.4079,-1.3019,-1.0987,-1.1453,-1.3712,-1.3321,-1.8192,-2.5655,-2.901,-3.0427,-2.9062,-2.7164,-1.7986,-1.0563,-0.62683,-0.79155,-0.37058,0.32801,0.97168,0.59644,0.62038,0.14673,0.16551,0.27275,0.54336,0.44029,0.52333,0.24859,0.22211\n2.0496,1.979,1.9471,1.8417,1.4351,1.1279,0.9182,0.6845,1.0864,0.80096,0.83741,0.50353,-0.040586,-0.30606,-0.24234,-0.59988,-0.88664,-1.3395,-1.9625,-1.3023,-1.315,-1.323,-1.1585,-1.3525,-1.893,-2.3939,-2.6695,-2.7337,-2.4957,-2.2705,-1.1369,-1.1621,-0.77378,-0.60629,-0.26721,-0.0065184,0.49717,0.59442,0.75695,0.56921,0.5801,0.66943,0.47943,0.53422,0.31099,-0.3834,-0.09291\n2.2891,1.699,1.747,1.833,1.7246,1.2858,0.53399,-0.0074844,-0.13252,0.58911,0.16789,0.003583,-0.015477,-0.13039,-0.45928,-0.81069,-1.1876,-1.4844,-1.4291,-0.92534,-1.2332,-1.0516,-1.1063,-1.4914,-1.6901,-1.8666,-1.974,-1.9883,-2.0328,-1.6744,-1.2489,-1.1989,-0.56267,-0.37879,-0.13502,-0.1638,0.1941,0.66418,0.88113,0.81156,0.4332,0.52064,0.91876,0.76531,0.33916,-0.28568,-0.035832\n1.9597,1.9262,1.8627,1.6452,1.47,1.5249,1.0657,0.14403,-0.37543,-0.11665,-0.39379,-0.058939,-0.25645,-0.80919,-0.9249,-0.97193,-0.59365,-1.5817,-1.1419,-0.72547,-0.82045,-0.88853,-0.95101,-1.2698,-1.6935,-1.7187,-2.0143,-1.9443,-1.7647,-1.3305,-1.3233,-1.2266,-0.56928,-0.40489,-0.16943,-0.10428,0.044459,0.62351,0.97862,-0.13332,-0.44672,-0.092514,0.20652,0.57369,0.41094,0.034331,-0.43534\n1.1356,1.1935,1.6212,1.1161,0.96917,1.7095,1.4413,0.72116,0.1689,-0.52366,-0.46773,-0.42855,-0.53417,-0.92545,-1.2541,-1.3879,-1.408,-1.5531,-0.90636,-0.28534,-0.50986,-0.82639,-0.59851,-0.87771,-1.4406,-1.5749,-1.9016,-2.4843,-2.0724,-1.3818,-1.4127,-1.3697,-0.76788,-0.49429,-0.46393,-0.45693,-0.082203,0.52675,0.96085,0.74547,0.49617,0.36957,0.034382,0.13609,0.11068,-0.17242,-0.74514\n0.85289,0.96201,1.5206,0.83308,0.34854,1.2677,1.1276,0.10536,0.1453,-0.10846,-0.52846,-1.1209,-1.4097,-1.4411,-1.8419,-2.4731,-1.7746,-0.73399,-0.18567,-0.37717,-0.3807,-0.4604,-0.51228,-0.86104,-1.1197,-1.2188,-1.671,-2.2202,-2.4404,-2.0759,-1.6749,-1.5102,-0.94291,-0.44777,-0.54702,-0.47972,0.07863,0.60754,1.0018,1.1159,0.74362,0.44976,0.24439,0.43519,-0.46214,-1.0701,-1.0855\n0.40076,0.86163,1.4105,0.57135,0.083296,0.15492,0.060691,-0.070614,0.40313,-0.50482,-1.0087,-0.90469,-0.58974,-2.6141,-2.8943,-2.9759,-0.49903,-0.13617,-0.12463,-0.16041,0.40221,-0.03605,-0.57004,-0.79317,-0.79709,-1.036,-1.5247,-1.8484,-2.4147,-2.285,-1.7694,-1.7102,-1.2819,-0.35091,-0.34853,-0.27895,-0.027003,0.38161,0.83829,0.86322,0.54061,0.52216,0.28619,0.41255,-0.42343,-1.7787,-1.7076\n-0.60996,1.1872,0.89808,0.535,0.0321,-0.57807,-0.15891,-0.22721,-1.0068,-0.053128,-0.93421,-0.80617,-1.841,-1.9028,-0.92202,-1.3457,0.18036,-0.37815,-0.50166,0.012639,0.31855,0.11669,0.0486,-0.35091,-0.52642,-0.9139,-1.6041,-1.9697,-2.4643,-2.0639,-1.2761,-0.92996,-1.0588,-0.54221,-0.18628,-0.22445,-0.34783,0.11621,0.56677,0.7091,0.60622,0.7921,0.54019,-0.24425,-1.0522,-1.4295,-1.2954\n0.72557,1.1514,0.31781,0.43382,0.21798,-0.084249,-0.060475,-0.77968,-1.8676,0.87961,-0.72796,-0.46675,-0.87397,-1.4772,0.29945,-0.52023,0.041288,0.39031,0.14831,-0.068668,0.27722,0.39804,0.22468,-0.03988,-0.076799,-0.24765,-0.68259,-1.9661,-2.0665,-1.6341,-0.72919,-0.50952,-0.70482,-0.6958,-0.43909,-0.31636,-0.44945,-0.27583,0.2702,0.80695,0.47758,0.17906,0.14757,-0.43731,-0.62217,-0.81489,-0.82737\n0.018646,1.3057,0.70296,0.35409,0.071989,-0.36847,-0.91189,-1.2633,-1.5354,0.57234,-1.9322,-1.0035,-0.86666,-0.83389,-0.04861,-0.48907,0.7737,-0.18395,0.55736,0.87097,1.0454,0.91089,0.22144,-0.09882,-0.1506,-0.30135,-0.98334,-1.5012,-1.4915,-1.2468,-0.70414,-0.38021,-0.11976,-0.27255,-0.45622,-0.75075,-0.57003,-0.55003,0.00024891,0.26185,-0.00024796,0.11593,0.72036,0.18448,0.77188,-0.37514,-0.72208\n0.53773,0.58115,1.0133,0.81837,0.46864,-0.19754,-0.67736,-0.18983,0.1303,-0.45227,-1.3949,-1.5276,-1.7611,-2.402,-0.3415,0.99024,1.8514,-0.030952,0.037403,0.91776,0.82194,0.39086,0.054539,-0.37421,-0.37156,-0.73197,-0.8787,-1.0977,-1.143,-0.87071,-0.62156,-0.30751,-0.24,-0.45801,-0.52178,-0.77006,-0.74499,-0.54457,-0.080935,-0.066082,-0.12518,0.31744,0.82154,0.79147,0.55305,-0.49776,-0.77604\n0.2869,0.7298,0.68783,0.65882,-0.19712,0.80509,-0.34218,-0.0045795,-0.13796,-0.085907,0.52896,-1.2949,-1.517,-0.16672,1.136,0.68414,1.042,0.22474,0.36933,1.5274,1.1275,0.40004,-0.18077,-0.095401,0.0068817,-0.45824,-1.2568,-1.0467,-0.95652,-0.54405,-0.31494,-0.42676,-0.74754,-0.33138,-0.54247,-0.64941,-0.68332,-0.19415,0.04739,0.1641,0.35502,0.45834,0.6345,-0.013031,-0.76795,-1.0955,-1.3828\n-0.76233,0.13617,-0.34893,0.6898,0.75705,0.59844,-0.066414,-0.070043,-0.50351,-0.211,-0.39092,-0.62437,0.26886,-0.28273,-0.04281,-0.041426,0.82922,0.11806,0.20045,0.71543,0.66321,0.14265,-0.17747,-0.32188,-0.4507,-0.62909,-0.75247,-0.43348,-0.46159,-0.22419,-0.16578,-0.44059,-0.63395,-0.029452,0.17357,-0.31264,-0.37814,-0.082448,0.069168,0.22618,0.57431,0.61561,0.44506,0.11305,-0.36722,-0.91768,-1.0055\n0.3225,0.36759,0.31985,1.587,0.25981,0.15437,-0.13552,-0.053757,0.1519,0.5006,0.79713,0.31608,0.60627,0.26416,-0.38774,0.17202,0.90129,0.54371,-0.052902,0.12724,0.16961,-0.13326,-0.47596,-0.52758,-0.38852,-0.18846,0.075762,0.06867,0.042636,-0.11773,-0.26645,-0.67126,-0.16205,0.13181,0.37911,-0.22746,-0.14293,-0.15547,-0.040009,0.68007,1.4838,1.071,0.44512,0.48842,0.80022,0.11927,-0.67445\n0.18736,-0.16929,-0.29737,-0.74931,-1.419,-1.2596,-0.94977,-1.258,-0.65875,0.60151,1.0082,1.2383,0.5324,-0.90631,-0.76512,0.46727,1.2396,-0.057972,-0.26142,0.95208,0.84934,-0.085174,0.05044,0.63352,0.12176,-0.10814,0.049747,0.23681,-0.075907,-0.29298,-0.31331,-0.1812,0.84745,0.58301,0.60799,0.3833,0.40573,0.15703,0.044252,0.48845,0.74457,0.70651,0.49616,0.67865,0.77262,0.49291,-0.10658\n-0.1823,-0.72638,-0.39809,-0.96426,-1.3003,-1.2434,-2.102,-2.6305,-2.4299,-0.96749,0.78608,0.8637,-0.27953,-1.0203,-1.0538,-0.69078,0.16386,-0.675,-0.26135,0.40925,1.102,0.1391,1.1419,1.7842,1.7798,1.1839,0.73831,0.60727,0.96518,0.72717,0.36108,0.027627,0.4266,0.53878,0.91642,0.27804,0.37918,0.38199,-0.26278,-0.41585,-0.22229,-0.42582,-0.34282,0.82468,1.126,0.44777,-0.038383\n-0.24879,-0.85152,-1.3191,-1.5984,-2.1156,-1.3765,-0.38019,-1.7229,-1.4273,-0.53118,0.28166,0.48774,0.7922,1.0876,1.2282,-0.46715,-1.4211,-0.19128,0.10516,0.61513,1.5011,0.7292,1.0621,1.5641,1.8928,1.5882,0.92949,1.1669,1.5309,1.6213,1.4861,0.65848,0.81458,1.2641,1.105,0.37486,0.23876,0.34118,-0.54496,-0.60757,-0.4152,-0.10061,0.16402,-0.020317,0.27349,-0.4619,-0.85415\n-0.17749,-0.44849,-1.2714,-3.4587,-3.3559,-2.6951,-2.8568,-2.0892,0.40007,-0.17716,0.77653,0.47643,1.1475,2.8131,1.0659,-0.73083,-0.45901,0.023077,-0.10525,0.70806,0.58495,1.159,1.424,1.2772,1.459,1.7823,1.4225,1.5878,1.6965,1.889,2.1093,1.0363,0.58967,1.0305,1.15,0.93544,0.30805,0.018434,0.12816,-0.36821,-0.11138,0.54091,0.13107,-0.57726,-0.4146,-0.3952,-0.59778\n-0.96314,-1.0652,-0.61249,-0.39095,-0.41195,0.20256,-1.8282,-2.1592,-0.49432,1.1673,2.5491,0.59121,1.0174,0.59661,0.2246,-0.85515,-1.1358,-0.20794,-0.033749,0.23723,0.057589,0.56216,1.6057,0.98631,1.2068,1.5053,1.7214,1.8483,1.8091,2.113,1.797,0.89529,0.65335,0.90852,1.1978,1.2059,0.46179,0.29685,0.40815,0.23266,0.35702,0.60765,-0.14597,-0.45237,-0.4555,-0.48423,-0.53156\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061211-070709.unw.csv",
    "content": "-1.1678,-1.5399,-2.2371,-2.0924,-2.2185,-1.797,-1.6078,-2.0143,-2.3566,-2.7001,-2.4529,-3.0932,-2.9285,-3.021,-2.8115,-2.9453,-3.2936,-3.2354,-3.4081,-3.6533,-3.4941,-3.1862,-3.0294,-3.2046,-3.2488,-2.3871,-2.1585,-2.3757,-2.783,-2.9729,-2.9891,-2.8974,-3.8492,-4.6369,-4.7579,-4.4367,-4.7217,-5.1559,-4.5327,-4.5536,-4.3767,-3.7581,-3.2368,-2.8738,-2.7364,-2.8883,-3.0762\n-1.1598,-1.6913,-2.1141,-1.7696,-1.7734,-1.7075,-1.7659,-2.4366,-2.765,-2.7193,-2.2324,-2.0909,-2.5144,-2.6576,-2.4692,-2.2722,-3.0511,-3.4685,-3.732,-3.9104,-4.1457,-3.2105,-2.7511,-2.5001,-2.6231,-2.4721,-2.2919,-2.695,-2.8996,-2.6299,-2.3543,-2.8193,-3.7393,-4.5221,-4.3967,-4.2289,-4.588,-4.7422,-4.1513,-3.4991,-3.669,-3.5861,-2.8458,-2.6094,-2.9188,-3.2175,-3.2592\n-0.87326,-1.3846,-2.1509,-2.0672,-1.8834,-2.2859,-2.2825,-2.823,-3.6436,-3.1097,-2.7513,-2.4941,-2.6817,-2.6349,-2.6483,-2.6392,-3.4464,-3.5983,-3.2462,-2.9902,-3.0675,-3.2786,-2.9344,-2.4303,-2.5604,-2.2504,-1.9784,-2.0775,-2.5248,-2.1789,-2.1961,-3.0826,-3.4436,-3.6095,-3.6784,-3.8706,-4.5084,-4.642,-4.5009,-4.0937,-3.8745,-3.4242,-2.8434,-2.7118,-3.1421,-3.6488,-3.5342\n-1.086,-1.5011,-2.4534,-2.0645,-1.6417,-1.9083,-2.1255,-2.7206,-2.9715,-2.9741,-2.7974,-2.7712,-2.8987,-2.4188,-2.5749,-2.7806,-3.059,-3.4073,-3.4249,-2.7954,-2.5846,-2.4885,-2.193,-2.0558,-2.1172,-2.4129,-2.9095,-2.9912,-2.1143,-2.0009,-2.5366,-2.7737,-2.8772,-3.0659,-3.3438,-3.7069,-4.056,-4.1995,-3.6427,-3.4659,-2.957,-2.5257,-2.5151,-2.688,-3.0869,-3.4645,-3.6341\n-1.3378,-1.6146,-2.5875,-2.3655,-1.7915,-1.9862,-2.1215,-2.3427,-2.5928,-2.8664,-2.8074,-2.8733,-2.9656,-2.5058,-2.5002,-2.4358,-2.2764,-3.1119,-3.6461,-3.9078,-3.2827,-1.9796,-1.5186,-1.6768,-1.8269,-2.6441,-3.6289,-3.2597,-1.5491,-1.2994,-2.4137,-2.6336,-2.7519,-2.5934,-2.9496,-3.5253,-3.9239,-3.8306,-3.483,-2.884,-2.2099,-1.9832,-2.0815,-2.0613,-2.4445,-2.8141,-3.083\n-1.6745,-2.5743,-2.944,-2.2494,-2.3658,-2.526,-2.6281,-2.6323,-2.5279,-2.5717,-2.7183,-2.9752,-2.6826,-2.4108,-2.4015,-2.3751,-2.4249,-2.6155,-2.9315,-3.1691,-2.8141,-1.6006,-1.2172,-1.6274,-1.7367,-2.592,-3.2918,-3.0268,-2.1178,-1.9911,-2.1517,-1.9674,-1.9239,-2.4649,-2.6941,-3.1124,-3.3515,-3.2578,-2.9951,-2.3442,-1.9575,-2.2953,-2.4902,-2.1425,-2.356,-2.2259,-2.2872\n-1.7126,-2.0671,-2.6583,-2.3054,-1.8452,-2.0993,-2.7334,-2.975,-2.5884,-2.3618,-3.0132,-2.96,-2.373,-2.4379,-2.7057,-2.4557,-2.8469,-3.1286,-3.3454,-3.256,-2.5449,-1.79,-1.2993,-1.613,-2.0205,-2.4132,-2.5107,-2.4318,-2.3043,-1.9959,-2.0511,-1.859,-1.5836,-1.547,-1.8661,-2.219,-2.3531,-2.2857,-2.2673,-1.9343,-2.4145,-2.8661,-2.7136,-2.4197,-2.1161,-1.8832,-1.7383\n-1.3864,-1.485,-2.4952,-2.3002,-1.7321,-2.2196,-2.4188,-2.7733,-3.1102,-2.9324,-3.2238,-3.0863,-2.4315,-2.0016,-2.0499,-2.3238,-2.9567,-3.0415,-2.6119,-2.4338,-2.1081,-1.918,-1.7557,-1.3969,-1.6336,-1.6315,-1.5974,-1.3444,-1.7862,-1.6179,-1.7039,-2.0461,-1.7269,-1.4938,-1.6137,-1.881,-2.086,-2.1995,-2.0942,-1.6326,-1.457,-1.8644,-1.7797,-1.84,-2.1133,-2.2149,-1.9745\n-1.7702,-2.5676,-3.1016,-2.1841,-2.0264,-2.1803,-1.9757,-2.2122,-2.257,-2.4435,-2.6065,-2.5556,-2.4166,-1.5157,-1.3531,-1.7402,-2.0048,-1.7388,-1.3165,-1.5743,-1.6867,-1.6139,-1.4529,-1.1952,-1.0998,-0.9117,-0.86316,-0.73936,-1.2376,-1.6291,-1.5751,-1.637,-1.4934,-1.3077,-1.3817,-1.7334,-1.8313,-1.5167,-1.6282,-1.5144,-1.2846,-0.93323,-0.85417,-1.2602,-1.8025,-2.1248,-2.0411\n-1.9404,-2.2116,-2.7414,-2.4313,-1.7888,-1.6903,-1.7133,-1.6342,-1.5058,-1.4757,-2.1198,-1.6996,-2.0466,-1.2004,-1.0951,-1.3379,-1.513,-1.1873,-0.84928,-1.6028,-1.7003,-0.91683,-0.57235,-0.3245,-0.31782,-0.26632,-0.58994,-0.73973,-0.64591,-1.2836,-1.6025,-1.3061,-0.90989,-0.8178,-0.80169,-0.84099,-0.72354,-0.85067,-1.1323,-1.1638,-1.0365,-0.96258,-0.98554,-1.3133,-1.6465,-1.8251,-1.7879\n-1.4073,-1.5372,-2.0121,-1.9846,-1.7575,-1.7167,-1.83,-1.142,-1.2254,-1.8949,-2.2971,-1.7125,-1.5541,-0.78302,-0.54431,-0.69461,-1.2763,-1.2035,-0.51506,-0.6843,-1.322,-0.58203,0.12353,0.19477,-0.0013366,0.29638,0.35685,-0.11842,-0.23647,-0.33823,-0.50984,-0.4043,-0.47985,-0.5043,-0.57489,-0.46716,-0.489,-0.66299,-0.81044,-0.66348,-0.84066,-0.7845,-0.87696,-1.0648,-1.0745,-1.3354,-1.4498\n-0.93939,-0.97776,-1.3275,-1.6058,-1.8189,-1.3664,-1.9114,-1.3545,-1.2677,-1.6467,-1.7543,-1.2291,-1.3828,-0.712,-0.21236,-0.47633,-1.1645,-1.3169,-1.0152,-1.5078,-2.087,-0.83117,-0.25138,-0.19525,0.608,1.4723,1.4779,0.94205,0.57169,0.10501,-0.046083,-0.19444,-0.29181,-0.33349,-0.41102,-0.33758,-0.10618,-0.18641,-0.3733,-0.12213,-0.16686,-0.21389,-0.38284,-0.52577,-0.64504,-0.95801,-1.2149\n-0.9706,-1.2677,-2.5826,-3.0898,-3.0344,-1.7291,-1.5854,-1.3115,-1.3408,-1.3219,-1.6111,-1.975,-1.8023,-0.78511,-0.35071,-0.34021,-0.48391,-0.33608,-0.94275,-1.5092,-1.3806,-0.51389,0.15771,1.1334,1.5932,1.62,1.4979,1.4465,1.0771,0.71599,0.7719,0.14726,-0.15772,-0.17905,-0.10263,0.11276,0.48771,0.54203,0.082542,-0.035105,0.062142,0.018793,-0.12324,-0.31653,-0.3204,-0.52281,-1.2471\n-0.84791,-1.6184,-1.8855,-1.8373,-2.0858,-1.6259,-1.3641,-1.5017,-1.435,-0.96293,-1.377,-2.0247,-1.3016,-0.24638,0.0013361,0.10874,0.47498,0.44869,0.068481,-0.45144,-0.62802,-0.50708,0.16842,1.5565,1.8357,1.0513,1.0129,1.3938,1.3624,1.1636,1.2774,1.0959,0.68226,0.64371,0.71894,0.86267,1.0361,1.3173,1.0756,0.54787,0.44312,0.72891,0.64122,0.31817,0.19049,-0.72062,-1.4661\n-1.5702,-1.5593,-1.343,-1.3451,-1.9005,-1.6476,-1.759,-1.6039,-0.50433,-0.29534,-0.85395,-0.32106,0.27414,0.68801,0.75935,0.77875,0.85682,0.32206,0.017314,0.10047,-0.29074,0.11578,1.0212,1.8395,2.3487,2.2319,1.6124,1.4844,1.8944,1.9232,1.7406,1.3628,1.1986,1.4599,1.2887,1.1197,1.307,1.6768,1.9161,1.6315,1.0147,1.1168,1.2718,0.83974,0.5794,0.16672,-0.22826\n-0.54551,-1.2147,-1.6188,-1.3047,-1.2081,-0.92323,-0.85541,-1.3816,-0.61989,-0.74312,-1.1275,-0.80367,-0.5132,-0.48124,-0.080701,0.22118,0.35547,0.23391,0.22286,0.35407,0.59569,1.0207,1.4794,1.8456,2.6539,2.6741,2.0881,1.8533,2.3141,2.287,1.8973,1.5648,1.693,1.8162,1.9964,1.4762,1.6168,1.9915,2.1907,2.31,1.6768,1.4834,1.3044,1.0589,0.88648,1.0776,1.0911\n0.060997,-0.2067,-0.67069,-1.0433,-1.1556,-1.3853,-0.60332,-0.54771,-0.15978,-0.25689,-0.38255,0.11599,-0.062105,-0.62887,-0.51687,-0.24239,0.035685,0.73695,0.66097,-0.070274,-0.73618,0.52758,1.8856,1.7327,2.634,3.0651,3.0605,2.6879,2.2926,2.1145,2.3335,2.2114,2.3781,2.5224,2.2668,1.8937,2.3686,2.1596,2.0871,2.5484,2.4582,2.0468,1.8096,1.6529,1.1792,1.185,1.3727\n1.5036,0.60743,-0.64338,-1.4116,-3.1739,-2.8256,-1.2052,0.081398,0.65386,1.056,1.3568,1.1773,0.94027,0.86371,0.55268,0.89459,1.116,1.272,1.3269,0.97285,0.48605,0.60279,1.1557,2.1393,3.008,3.8211,3.7802,3.5322,3.0669,2.7686,2.7432,2.7986,2.822,2.7127,2.5524,2.4617,3.1272,2.5024,2.119,1.9245,2.0258,2.2792,2.2192,2.0001,1.8654,1.6226,1.4794\n-0.043316,0.070484,-0.34221,-0.0051918,-0.12579,2.4303,2.245,0.69159,0.96518,1.501,1.9828,1.3005,0.6924,0.74702,1.3536,2.0804,2.0778,2.0382,2.3006,2.7164,1.6474,0.50016,1.0473,1.9357,2.4961,3.5937,4.0016,3.9503,3.7716,3.1176,2.8706,2.7775,2.7881,2.3925,2.3386,2.5337,2.3753,2.0417,1.3422,1.2018,1.2748,1.9221,2.4201,2.2035,1.9593,1.7764,1.4433\n0.62074,0.45151,0.029949,-0.0035925,1.195,1.68,1.285,0.63896,1.4403,1.7309,1.4788,1.3798,1.1148,1.043,1.5245,2.1319,1.9085,1.6492,2.135,2.6192,2.6503,2.605,2.1262,2.8086,3.0049,3.6291,3.9188,4.3654,4.6295,4.3943,3.4977,3.1931,3.025,3.007,3.5004,3.2334,2.7966,2.6806,1.7034,1.4421,1.7105,2.2997,2.571,2.6002,2.3683,1.6494,1.2385\n0.40208,0.54594,0.73903,1.3062,2.5728,3.7163,2.7745,0.74062,1.3089,2.203,2.4773,2.2936,1.9335,2.125,2.4831,2.1873,2.0518,2.083,2.2801,2.8163,3.376,3.0665,2.8448,2.8551,3.7023,4.5956,4.9327,5.5701,5.2966,5.0885,4.8834,4.6856,3.9464,3.6972,3.4834,3.9836,3.7276,3.2688,2.6225,2.2629,2.4179,2.7232,2.5252,2.438,2.3621,2.0831,1.5641\n1.0791,1.7006,1.0127,0.4622,0.17617,0.76635,1.4248,0.57683,2.5359,2.8903,2.7588,2.7527,3.1284,4.1856,3.8307,2.4995,3.1187,3.341,2.8318,3.7091,3.9985,3.7985,3.4233,3.7861,4.4249,4.9847,5.5593,5.9539,5.6133,5.3399,5.0251,4.7881,4.7193,4.5847,4.3638,4.3217,4.2266,3.5831,3.1365,2.8933,2.7741,2.7518,2.5015,2.4211,2.3336,2.3187,1.6807\n1.5209,2.4383,1.7964,0.47414,-0.28946,-0.50692,-0.53314,1.5011,4.1109,3.1048,2.6279,3.4591,4.1488,4.5859,4.4692,4.3304,4.6791,4.4396,4.0922,4.0422,4.4197,4.2376,3.8893,5.0471,5.862,5.7675,6.0981,6.0526,5.9185,5.4531,5.2209,5.0672,4.9397,4.8526,4.7861,4.5122,4.179,3.9496,3.8862,3.7413,3.8637,3.4179,2.6038,2.7314,2.8564,2.4703,1.7419\n2.7211,2.295,1.2355,0.34392,-0.49829,-0.066142,0.98982,1.9985,2.6046,3.1022,3.4062,4.3425,5.2956,5.2364,4.7736,4.4186,4.7255,4.8841,4.4469,4.0093,4.1273,4.774,5.443,6.219,6.492,6.1077,6.1885,6.8275,6.5049,6.4096,6.4157,6.1001,6.1591,5.807,4.8159,4.495,4.128,4.0403,4.182,4.1558,3.9376,3.3749,2.9365,2.893,3.0026,2.7423,2.6301\n1.7299,0.96681,0.84219,1.0713,0.90367,1.0636,1.9539,2.2764,2.3879,3.0359,3.6336,4.4142,5.1563,5.1978,4.4027,3.6078,3.5878,4.983,5.4835,5.3872,5.5591,6.0585,7.5213,7.1947,7.2506,8.2316,8.2581,8.4829,8.262,7.8669,7.5022,7.222,6.55,5.4751,4.7449,5.0615,5.2084,4.6529,4.4669,4.4776,3.4289,2.886,2.9744,3.2402,3.1556,2.8641,2.8944\n2.5909,2.7151,1.822,0.89685,0.28427,0.66789,1.7255,2.7554,3.1849,2.9514,3.6042,4.2693,4.8893,5.0105,4.5537,4.1433,5.2727,6.329,6.2889,6.4249,7.1109,8.4731,8.6083,9.5675,NaN,10.016,10.023,8.9711,8.6428,9.3715,9.2218,7.3547,6.1652,5.3196,5.0548,5.3119,5.7037,5.3661,5.1539,5.3359,4.2037,3.4126,3.4559,3.3747,3.0677,2.7205,2.409\n2.3408,1.9925,1.3266,0.36338,0.40247,0.94224,2.0267,2.7026,3.105,3.499,3.7485,4.1158,4.0733,4.1776,4.7793,5.4398,5.7807,6.7551,7.9014,8.7285,9.1201,9.7792,10.814,12.09,NaN,NaN,NaN,NaN,9.8386,10.892,11.347,11.014,9.8915,7.6616,6.8845,6.0012,5.7058,5.2805,5.0828,5.1588,4.5388,3.8614,3.9135,3.6389,3.1689,2.7,2.6933\n1.9249,0.8241,-0.42928,0.29381,0.79085,0.6985,1.4119,2.3881,2.9306,3.9096,3.7998,3.4049,3.7807,4.6759,5.9906,6.8197,6.9439,7.6678,9.0316,10.103,10.638,12.027,13.457,14.932,NaN,NaN,NaN,NaN,NaN,NaN,NaN,13.715,12.522,10.157,8.2503,6.5815,5.699,5.9003,5.4353,4.142,4.4028,4.4961,4.5838,3.8921,3.358,3.3698,3.2031\n0.072927,-0.43878,-0.071499,0.46348,0.17945,0.65627,1.7742,2.4654,2.3442,2.941,3.0452,3.2348,4.7767,5.6035,6.4967,7.1671,7.7331,8.7499,9.5103,10.39,10.884,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,15.087,10.774,8.3085,7.5734,6.8703,6.6465,6.5189,6.1643,5.626,5.5506,5.0034,3.9521,3.4438,3.3523,3.0914\n-2.2028,-1.1264,0.17449,-0.72706,0.06833,0.92375,1.4033,1.8803,2.1087,2.6234,3.0213,3.5119,4.7604,5.85,6.4886,6.9169,8.4141,9.6305,10.515,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.7631,8.072,8.216,7.8919,7.5292,6.5285,6.0258,6.2829,5.0987,4.1861,3.693,3.1811,2.9108\n1.2839,-0.020712,0.62842,1.0294,0.78119,0.43346,0.75264,0.85851,1.2437,2.302,3.9605,4.8379,5.2086,6.2582,7.6662,8.5574,9.6456,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.1401,7.8847,9.2324,8.7735,7.7127,6.7119,6.9762,7.2397,5.5116,4.719,4.0611,3.1272,3.0853\n1.6178,0.46609,0.063339,2.363,3.2126,2.6974,2.248,2.6296,2.7779,3.199,4.4564,5.6553,5.864,6.2271,7.7387,9.5801,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.636,9.39,7.6385,6.5182,6.7065,7.2571,6.1264,5.2734,4.0126,3.1538,3.3235\n3.3332,2.4629,1.5528,2.1764,3.3086,3.1502,2.6303,2.2779,2.9146,4.2881,5.4505,5.9204,5.8361,6.4933,9.1992,9.7411,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.757,7.4916,7.0926,6.7197,6.7463,6.0692,4.3875,3.6006,3.5004,3.6775\n4.5282,5.1371,2.4931,1.9306,2.9121,3.7828,3.5851,4.0475,4.2218,4.6379,6.3005,6.312,6.7038,7.2593,8.9412,9.9503,11.56,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.541,9.0595,7.048,6.4606,6.1685,5.223,4.5706,3.8342,3.6882,3.2071\n4.0978,4.7939,2.6776,1.8445,2.525,3.1997,3.1817,3.6772,4.1307,4.9133,5.9301,6.6525,7.0711,7.8285,8.9163,8.7296,8.4884,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,17.185,13.412,9.8542,7.0142,6.4173,6.2689,5.6179,5.2681,4.4369,3.5948,2.8273\n1.647,1.6364,1.6951,1.6307,2.2881,3.0176,3.4725,3.2444,3.9917,5.0086,5.9061,6.3032,7.3664,9.3673,11.622,8.4408,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,24.137,16.577,15.651,12.533,8.8239,6.9181,6.4274,6.2437,6.1258,5.6498,4.5981,3.5269,2.4586\n1.579,0.98287,1.3067,1.7081,2.0453,2.8796,3.9549,4.4991,4.9766,5.3613,5.9407,7.0068,7.5329,10.453,9.8981,6.9369,6.9768,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,21.883,16.054,12.478,9.6941,7.4646,6.0874,5.4502,5.211,5.0035,4.8071,3.9048,2.828,2.2444\n2.0511,1.9944,1.8012,1.9627,2.1581,2.8048,3.8727,4.3877,4.5587,5.2224,5.7747,6.4041,6.2359,6.0492,5.2679,6.8781,7.7743,4.627,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,16.407,11.166,9.115,7.6703,5.364,5.1669,5.1264,4.6385,4.231,3.8738,2.9244,2.0672\n1.7421,2.0803,2.1186,2.0184,2.2244,2.9565,3.6505,3.9623,4.1893,4.7908,5.8915,6.6951,6.1609,6.0791,6.388,7.7325,NaN,NaN,6.3535,3.5038,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.121,9.9461,8.8186,7.5684,5.3308,4.8801,5.1083,4.6423,3.8765,3.7512,2.8891,2.1528\n1.0878,1.3699,1.5889,1.9337,2.0747,2.511,3.0172,3.2631,3.5044,4.6112,5.5687,5.9568,5.6757,6.1543,6.3391,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.6426,7.9882,7.3267,6.6249,5.1968,5.0821,4.8711,4.0316,3.4291,3.1156,2.2807,1.5222\n0.72137,0.62441,1.3034,1.0933,1.8309,2.4922,3.0676,3.3686,3.5142,4.0037,4.9221,5.8033,6.1917,6.4179,5.2196,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.3008,7.2843,6.4642,5.1124,4.8232,4.3529,3.6929,3.4272,2.6405,2.5501,2.0026,1.3876\n0.69148,0.51983,0.7202,1.0484,1.7602,2.1146,2.3925,2.8885,3.5417,4.2802,5.1392,6.7312,6.7883,6.5045,5.6121,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.5343,8.228,7.2629,5.1583,4.0844,4.0984,3.7205,2.9424,2.5429,2.6691,2.2331,2.1871\n1.7858,1.3509,1.0496,1.7745,2.1737,2.3437,2.4808,2.7504,3.3831,3.7221,4.5158,6.0261,6.5181,6.1407,5.6461,4.8668,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.5722,7.8217,6.4821,4.1679,2.8801,3.4937,3.1658,2.4367,2.3755,2.1686,1.8778,2.2546\n1.6052,1.9568,1.8863,2.3128,2.9572,2.9629,3.0278,2.8582,3.4464,3.6257,3.8729,4.3192,5.0404,5.2108,4.9505,4.6486,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.2149,6.9415,6.9542,5.4818,3.3631,2.8936,2.8709,2.5618,2.0941,1.2959,0.80169,0.76862,1.7385\n1.6804,1.8286,1.9598,2.1146,2.6476,3.2057,3.5312,3.7337,3.9164,3.583,3.544,3.9706,3.8396,3.7024,4.111,3.5014,3.9512,4.3228,4.5646,5.8875,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.7773,7.4673,7.1114,6.3729,5.4427,5.3105,3.8869,3.5452,2.8068,2.5735,2.7162,2.569,1.594,0.51332,-0.33901,0.62406\n1.4994,1.3997,1.3201,1.4467,1.8631,2.2645,2.4245,2.7941,2.7606,2.4354,2.8379,3.3075,3.2481,3.2377,3.4122,3.7146,3.9766,3.907,3.3695,4.432,6.5449,7.401,7.7425,NaN,NaN,NaN,NaN,NaN,4.0013,NaN,5.5783,5.9804,5.7192,4.6673,4.1999,3.9242,2.3662,0.66253,2.1448,1.6645,1.7713,1.6262,2.1155,1.9342,0.99733,0.19754,0.39223\n1.4124,1.0844,1.266,1.4127,1.4226,1.6517,1.8558,1.9471,1.627,1.8097,2.1989,2.5471,2.6589,2.5421,2.4484,2.8643,2.9036,2.817,2.8995,2.9936,3.6494,5.2861,5.0273,3.948,NaN,NaN,NaN,1.9097,2.8138,2.9572,3.4437,3.7898,3.5696,3.6774,3.8461,2.7824,0.67027,0.13837,1.548,0.87223,0.59477,0.55894,0.45407,0.66516,0.28094,0.30841,0.84402\n1.3201,1.2425,1.0861,1.0195,1.1451,1.8545,1.9151,1.5341,1.3481,1.4957,1.9461,2.3308,2.3384,2.061,1.9196,2.2845,2.22,2.2484,2.5735,2.463,2.4084,2.7082,1.9592,1.7113,2.9811,2.6752,1.1245,1.9538,2.5412,2.3143,2.31,2.5916,2.3593,1.8802,2.1399,1.6026,0.46351,-0.2072,1.2593,1.1343,0.73528,0.95384,0.17013,-0.38078,-0.8494,-0.64796,0.40398\n1.5952,1.5315,1.1613,1.0245,1.1997,1.9252,1.6131,1.1144,0.68301,0.8057,1.4392,1.7414,1.5917,1.3709,1.0301,0.94797,1.0537,1.2718,1.4338,1.481,1.394,1.2022,0.98639,1.5929,1.7656,1.4349,0.7284,0.68353,0.86759,1.4737,1.4095,1.2843,0.99904,1.8887,2.7724,2.1559,0.83647,0.20314,0.7084,1.1161,1.2775,1.4115,0.5979,-0.46074,-0.65715,-0.32116,0.21072\n1.4098,1.5317,1.2828,0.89427,1.1093,1.1917,0.95471,0.74515,0.41664,-0.037599,0.3045,1.0387,1.0812,0.8896,0.58111,0.33396,0.80445,0.98143,1.0901,0.76049,0.91749,0.59648,1.0166,1.418,1.3124,0.65115,-0.1498,-0.67529,-0.34085,0.58411,0.56968,0.50154,0.52811,2.0515,2.5623,2.5113,1.0361,0.76777,0.51377,0.63595,1.4982,1.3618,1.3532,0.093312,-0.53133,-0.49634,-0.10924\n1.0062,1.1483,0.88578,0.30909,0.24359,0.41288,0.35859,0.33238,0.21258,-0.046522,-0.10369,0.48398,0.61749,0.60899,0.42368,0.29615,0.78478,1.3031,1.0221,0.84454,0.59525,0.44394,1.0889,1.2314,0.77873,0.43743,-0.30464,-0.69066,-0.11004,0.36062,0.17041,0.29163,0.58163,1.3288,1.5209,0.85928,0.39015,0.1797,0.16049,0.63804,0.79519,0.58004,0.32316,-0.39887,-0.64267,-0.71354,-0.36799\n0.2306,0.44256,0.28142,0.1022,0.14877,0.17187,0.1192,0.22411,0.036347,-0.6118,-0.75282,-0.68039,-0.14429,0.49215,0.19812,-0.015182,0.17246,0.4319,0.39911,0.64436,0.64669,0.41301,0.84579,0.88504,0.35537,-0.42295,-0.58627,-1.2226,-0.30553,0.29785,-0.18901,0.093617,0.50686,1.2793,1.1284,0.46041,-0.059655,-0.31689,-0.18561,0.19902,-0.0084724,-0.15657,-0.49786,-0.59563,-0.42352,-0.5258,-0.49716\n0.72205,0.6079,0.25119,-0.085849,0.026466,-0.053832,-0.34714,-0.41387,-0.26824,-0.51466,-0.67134,-0.38437,-0.02952,0.19101,0.05559,-0.15075,-0.29536,-0.28964,-0.099921,-0.063729,0.17065,0.028119,0.1862,0.071699,-0.18652,-0.3774,-0.63745,-0.8452,-0.39888,-0.12178,-0.29185,-0.30951,0.080957,0.86083,0.27971,-0.21463,-0.41157,-0.61474,-0.81347,-0.60093,-0.6475,-0.6312,-0.6742,-0.75755,-0.38382,-0.36903,-0.25889\n0.77394,0.65076,0.51191,0.41693,0.43732,0.015198,-0.38558,-0.49986,-0.46242,-0.50129,-0.52562,-0.2079,0.15538,0.29977,0.18682,-0.043613,-0.14739,-0.17446,0.29023,0.56888,0.21781,-0.098217,-0.29475,-0.40168,-0.45663,-0.56612,-0.75708,-1.0262,-0.85304,-0.60556,-0.46872,-0.43318,-0.19214,0.0031657,-0.33815,-0.37843,-0.3909,-0.45772,-0.89243,-1.0705,-1.003,-1.0363,-0.75932,-0.62594,-0.56271,-0.44583,-0.68214\n0.55176,-0.061861,0.54713,1.4639,1.1223,0.0054145,-0.90945,-0.85573,-0.76361,-0.52373,-0.72638,-0.43248,-0.39804,0.0085354,0.018195,-0.12715,-0.44416,-0.66347,-0.62419,0.13909,0.060908,-0.35355,-0.64167,-0.56946,-0.56005,-0.66913,-1.2173,-1.3456,-1.323,-0.75005,-0.50164,-1.0049,-1.6368,-1.2321,-1.2201,-0.57763,-0.012414,-0.2081,-0.68776,-0.97902,-0.94079,-1.2301,-1.1814,-1.1665,-1.3344,-1.0841,-1.0838\n1.0959,0.2824,-0.46835,0.59247,1.0762,0.09053,-0.85561,-1.4693,-0.88716,0.23338,-0.10465,-0.21692,-0.50303,-0.67451,-0.38344,-0.076992,-0.67043,-0.9099,-0.8774,-0.37615,-0.36105,-0.81892,-0.65029,-0.37652,-0.79844,-0.88764,-2.1384,-2.351,-1.973,-0.85093,-0.86325,-1.4019,-1.6149,-1.6827,-1.5353,-0.80893,-0.2284,-0.43806,-0.60981,-0.74401,-0.99253,-1.2158,-1.3239,-1.3858,-1.6271,-1.4534,-1.4147\n0.58336,-0.18473,-0.36639,-0.13109,0.15174,-0.34083,-0.64016,-0.19492,0.52728,1.2511,0.49743,0.38078,-0.10963,0.058339,0.13872,0.32378,0.054696,-0.84213,-0.74931,-0.54984,-0.59542,-0.86338,-0.77135,-0.76025,-0.83095,-1.0066,-1.8992,-2.614,-2.1814,-1.1394,-0.95909,-1.4425,-1.5199,-1.6699,-1.6742,-1.1553,-0.68921,-0.75047,-0.73678,-0.63296,-1.0123,-1.555,-1.6645,-1.9527,-2.3885,-2.1236,-1.9408\n1.3319,1.3272,0.64532,0.72258,0.57685,-0.28188,-0.27861,-0.021847,0.66152,1.315,0.015121,0.28337,-0.17127,0.52661,0.30739,0.15882,-0.050135,-0.40416,-0.687,-0.77231,-0.75365,-0.88913,-0.69174,-0.72485,-0.90517,-1.1647,-1.5037,-2.2977,-1.501,-0.66845,-0.87956,-1.3216,-1.6309,-1.9555,-1.8489,-1.5723,-1.2569,-1.0201,-1.2685,-1.0085,-1.2615,-1.6444,-1.7212,-2.4061,-3.1061,-2.615,-2.4222\n2.0831,2.6771,2.1577,0.92929,0.66406,0.73517,0.032201,-0.14168,0.056363,0.31919,-0.41224,-1.2847,-0.81612,-0.2876,-0.21217,-0.39008,-0.29591,-0.3304,-0.84556,-0.93386,-0.90629,-0.76683,-0.67116,-0.8613,-1.1488,-1.4667,-1.8492,-2.0818,-1.6268,-1.4646,-1.4276,-1.7122,-2.2768,-2.0177,-1.6627,-1.5452,-1.5146,-1.3789,-1.3346,-1.2808,-1.4414,-1.5726,-1.6266,-1.9595,-2.9865,-3.12,-2.8949\n1.403,2.0079,2.0757,0.68385,0.026101,0.29913,0.49235,-0.21716,0.18032,0.04029,-0.27552,-2.1694,-3.5471,-1.7383,-1.2982,-0.99737,-0.44494,-0.44529,-0.86852,-1.0792,-1.3709,-1.5494,-1.0346,-1.1735,-1.5514,-1.8223,-2.2418,-2.4461,-2.462,-2.0247,-1.806,-2.0372,-2.6422,-2.029,-2.0481,-1.8562,-1.7296,-1.4896,-1.2679,-1.2277,-1.5453,-1.8983,-1.7326,-1.5419,-2.0998,-2.23,-2.2009\n0.60419,1.9368,2.3507,1.1454,-0.20742,-0.1352,0.86378,-0.67175,-1.1106,0.44323,1.1253,-0.87515,-2.4445,-2.9341,-0.85491,-0.70823,-0.092634,-1.0819,-1.5394,-1.1511,-1.2905,-1.7944,-2.0378,-1.7288,-1.5899,-1.9723,-2.3548,-2.5574,-2.5836,-2.177,-1.9896,-1.9878,-2.1389,-2.4545,-2.0233,-1.5883,-1.4934,-1.1379,-1.0095,-1.0019,-1.5179,-2.0906,-2.1136,-2.2814,-2.4933,-2.5792,-2.647\n1.5495,1.4392,1.1769,0.42789,-0.18291,-0.52809,0.17468,-0.69582,-1.9569,-3.6542,-0.76855,0.64324,0.24364,-0.73046,-1.1219,-0.34548,0.62994,-0.74594,-1.0958,-1.0317,-0.94385,-1.3757,-1.8345,-1.6136,-1.3563,-1.6714,-2.045,-2.7536,-2.8396,-2.4393,-2.2967,-2.4039,-2.5052,-2.4293,-1.634,-1.461,-1.4674,-1.2693,-1.1735,-1.0896,-1.4267,-1.9331,-2.0605,-2.5164,-3.0759,-3.0363,-2.6862\n1.0215,1.1706,0.95126,0.43823,0.24188,-0.40085,-0.090401,-0.36759,-1.554,-4.8646,-3.6812,-0.72076,-0.38152,-0.60787,-1.2465,-1.356,0.25876,0.65771,-1.1916,-1.3537,-1.3955,-1.7432,-1.7059,-1.2705,-1.2545,-2.0008,-2.5092,-2.5292,-2.2847,-1.9886,-2.277,-2.743,-2.7469,-2.5724,-1.707,-1.5857,-1.5202,-1.3785,-1.387,-1.4297,-1.6251,-1.9401,-1.6248,-1.9594,-2.7557,-3.3504,-2.7606\n2.4443,2.7597,2.9073,2.3391,1.3837,0.6876,0.20974,-0.16431,-0.88343,-1.0138,-1.488,-0.80232,-1.5721,-1.6542,-0.011012,0.95954,3.9881,1.6192,-0.57145,-1.3915,-1.8564,-1.7779,-1.5163,-1.6678,-2.2262,-2.567,-2.5431,-2.1334,-1.5248,-1.6644,-2.2023,-2.8201,-3.1964,-3.0212,-2.2314,-1.6711,-1.418,-1.4317,-1.4153,-1.2136,-1.6749,-2.1156,-2.0443,-2.1295,-2.6653,-3.1585,-3.0377\n1.833,3.4096,3.9624,3.1266,2.353,1.1201,0.78025,0.98181,1.0548,1.0825,1.6771,-0.27642,-3.33,-2.0726,-1.7438,0.16504,1.5581,-0.058567,-0.36844,-1.0032,-2.0152,-2.2059,-1.2266,-1.5587,-2.146,-2.7234,-2.1005,-1.5051,-1.5817,-1.9064,-2.1639,-2.7161,-3.2598,-3.2321,-2.6518,-1.8069,-1.4534,-1.4371,-2.084,-1.5669,-1.3223,-2.0169,-2.2968,-2.3414,-2.682,-2.7622,-2.5818\nNaN,1.8558,2.6891,3.0256,2.5702,1.8402,1.61,1.3125,1.1583,0.94619,0.58932,-2.2744,-3.2859,-0.99621,-0.17583,1.6532,0.25767,-0.55659,-0.60579,-1.3293,-1.9162,-2.4474,-1.5286,-0.60482,-0.67115,-1.7278,-1.8023,-1.1404,-1.2355,-1.5936,-1.7689,-2.0346,-2.6017,-2.7087,-2.1701,-1.903,-1.7305,-1.6322,-1.5152,-1.3392,-1.2964,-1.6703,-2.1962,-2.5893,-2.4354,-2.3518,-2.0001\nNaN,NaN,NaN,1.6897,1.4145,2.2581,3.0371,3.513,3.8861,4.59,0.77321,-1.2251,-0.42185,0.49172,1.5854,1.8098,-0.37875,-0.71923,-0.79894,-2.2286,-2.4879,-2.4173,-1.5751,-0.55486,-0.44677,-1.7197,-2.4884,-2.5836,-2.3468,-2.1377,-2.247,-1.9598,-1.9643,-2.1143,-1.6224,-1.8905,-1.9959,-1.7562,-1.5654,-1.006,-0.87321,-1.2248,-2.0583,-2.5311,-2.5413,-2.2004,-1.8605\nNaN,NaN,NaN,NaN,NaN,0.50354,2.7996,2.7736,2.1469,1.9656,1.0574,0.79203,0.073476,0.51161,NaN,NaN,3.1227,0.91986,-0.69095,-1.7587,-0.74887,-1.2596,-1.6719,-0.81023,-0.57913,-1.5779,-2.305,-2.7456,-2.3003,-2.2408,-2.1957,-2.4934,-1.7581,-1.7809,-1.9432,-1.7877,-1.1996,-1.2993,-1.3863,-0.94507,-1.0613,-1.5489,-1.968,-2.164,-2.2304,-2.0288,-1.8178\nNaN,NaN,NaN,NaN,NaN,NaN,2.9178,2.3743,1.6162,0.71926,0.55192,2.115,3.6063,4.1583,NaN,NaN,NaN,NaN,-0.11626,-0.50098,0.29488,-0.59529,-1.3166,-1.1969,-1.4295,-1.9916,-2.0966,-2.2572,-2.2078,-1.5044,-1.7115,-1.6924,-1.5676,-1.5355,-2.1893,-1.6008,-1.0974,-1.1418,-0.81211,-0.90094,-1.4051,-1.7987,-2.0767,-2.157,-1.7969,-1.252,-1.1447\nNaN,NaN,NaN,NaN,NaN,NaN,1.5794,1.1425,1.2588,0.85897,1.2284,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.53878,-0.15785,0.379,-0.093004,-1.2847,-1.3893,-1.5826,-2.1856,-1.5572,-1.0117,-1.0727,-1.3051,-1.326,-1.9278,-2.0438,-2.1702,-2.2714,-2.1172,-1.6993,-1.4446,-0.90102,-1.2546,-1.299,-1.1639,-1.631,-1.799,-1.757,-1.7783,-1.1983\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.27592,1.13,2.2363,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.8496,-1.7052,-0.26364,-0.38919,-1.032,-1.0117,-0.73677,-0.51283,-0.40719,-0.19298,-0.58442,-1.1254,-1.5843,-2.0054,-2.0099,-2.3667,-2.5457,-2.3684,-1.6182,-1.3959,-2.1082,-1.4661,-0.76191,-0.81028,-1.3485,-1.6028,-1.8899,-2.2045,-1.6709\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.2728,1.559,1.8062,0.15234,-1.0197,NaN,NaN,NaN,NaN,-1.2275,-2.1848,-1.4171,-0.19364,-0.35558,-1.0798,-1.0324,-0.81952,-0.36619,0.037733,0.22503,-0.066895,-1.1941,-2.0932,-2.3543,-2.0902,-1.7732,-1.3655,-1.9213,-1.2787,-0.865,-1.3907,-1.1974,-0.93472,-1.2571,-1.4461,-2.0152,-2.5375,-2.5078,-2.1195\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_061211-070813.unw.csv",
    "content": "-0.99752,-0.82917,-1.2543,-0.90421,-1.1444,-2.0065,-2.0491,-1.9236,-1.2813,-1.1732,-1.5563,-1.9954,-0.77613,-0.43153,-0.50979,-0.40178,-0.33667,-0.43622,-0.36854,-0.54816,-1.6016,-1.3663,-0.41461,1.006,1.5217,1.0769,0.32636,-0.4587,-0.8809,-0.6835,-0.19964,0.4299,0.053814,-0.69205,-1.3978,-1.791,-2.669,-2.9486,-3.2542,-3.8261,-3.1634,-1.9083,-1.2453,-1.2254,-0.72265,-0.13174,-0.33879\n-1.4696,-0.96619,-1.0731,-0.82197,-0.79277,-1.7193,-1.8423,-1.6986,-1.4296,-1.0065,-0.75955,-0.97751,-0.48699,-0.17823,-0.056503,-0.30933,-0.57698,-0.67733,-0.41186,-0.41739,-0.48072,-0.70955,-0.066827,0.97342,0.97401,0.56532,0.65402,0.027545,-0.047835,0.39406,0.20589,-0.50782,-1.3059,-1.5168,-1.1387,-1.1926,-1.8137,-2.0934,-1.8912,-1.6008,-1.4085,-1.0523,-0.90992,-0.83287,-1.075,-1.3055,-1.2284\n-1.4344,-0.97175,-0.87838,-1.0786,-1.1007,-0.62344,-1.1325,-1.2407,-1.6187,-0.73897,-0.30798,0.23006,-0.30619,-0.45685,-0.081755,-0.32897,-0.84531,-1.1794,-1.2952,-0.61861,-0.5838,-0.95551,-0.97394,0.45689,0.48341,-0.10319,0.68624,0.81746,-0.030554,0.55345,0.53008,-0.88813,-2.0468,-2.3739,-1.8175,-1.785,-1.5153,-0.95386,-1.1112,-0.97289,-1.0079,-0.4421,-0.24071,-0.37805,-1.2376,-1.889,-1.7087\n-1.6129,-0.49351,-0.59737,-1.1715,-0.96035,-0.77119,-0.39767,0.29114,0.46442,0.10245,-0.023709,-0.13494,-0.44145,-0.71576,-0.33451,-0.6606,-1.498,-1.8696,-1.8985,-0.93241,0.30659,0.43015,0.29209,0.44929,0.63574,-0.10932,0.052653,0.74255,0.16628,-0.31372,-0.089528,-0.63298,-1.0723,-1.5043,-1.5597,-1.6077,-1.6922,-1.1049,-1.4537,-0.97985,-0.046432,0.36405,0.27601,-0.13843,-0.73892,-1.0679,-0.49505\n-1.3891,-0.36724,-0.88039,-1.6905,-1.0992,-0.99117,-0.39861,0.10195,-0.089987,-0.49226,-1.6144,-1.8382,-0.76668,-0.84439,-1.1597,-0.97359,-0.75887,-1.5993,-2.3543,-2.3638,-1.0953,0.83963,0.62017,0.71229,0.9231,0.20033,-0.13371,-0.023618,0.84415,0.4191,-0.72892,-0.62322,-0.68122,-0.78184,-1.1354,-1.2546,-1.4643,-1.7138,-1.7293,-0.508,0.70268,1.3808,1.3956,0.9705,0.13475,-0.5253,-0.34489\n-2.3999,-1.7821,-0.43809,-2.0048,-1.3457,-1.37,-1.4755,-0.67754,-0.15549,-0.7926,-1.6218,-1.781,-0.7241,-0.74601,-0.74696,-0.75637,-0.87526,-1.351,-2.0333,-1.7746,-0.96075,0.66339,1.1599,0.95059,0.50596,-0.059713,-0.062625,0.10144,0.7313,0.89809,-0.19315,-1.1954,-1.0631,-0.30167,-0.43554,-1.09,-1.6537,-1.4888,-0.51932,0.65529,1.166,0.91845,0.7558,1.0077,0.59405,0.67171,0.69757\n-2.3838,-2.8325,-2.8676,-2.5949,-2.4532,-3.1308,-3.5874,-2.3445,-0.9393,-0.82676,-1.48,-1.4246,-0.94135,-1.4361,-1.6865,-0.94467,-0.96396,-1.6689,-2.3011,-1.872,-0.54484,-0.066417,0.14811,0.50997,0.95361,0.21915,1.0441,1.04,0.10075,0.035955,-0.33445,-1.2325,-0.77684,-0.5479,-0.57674,-0.84832,-1.1977,-0.86056,0.077899,0.84593,0.53617,0.31649,0.49636,0.65262,0.86051,0.97109,1.0846\n-1.5724,-1.7769,-0.74771,-1.97,-2.6694,-3.5909,-2.7622,-2.6436,-2.571,-1.7497,-1.1721,-0.98923,-1.0442,-1.6695,-1.7585,-1.2605,-1.1437,-1.11,-1.3443,-1.4728,-0.43615,0.32038,0.30904,0.74537,1.5033,1.7115,1.8214,1.965,0.40635,-0.50864,-0.54739,-0.5836,-0.28271,-0.53153,-0.60364,-0.88001,-1.1543,-1.1289,-0.42274,0.49997,0.42252,0.69568,1.4637,1.6646,1.3494,0.65355,0.59242\n-0.80062,-1.3923,-2.1119,-2.2757,-2.862,-3.3006,-2.1263,-1.4397,-0.2187,-0.41251,-1.2186,-1.4047,-1.3254,-0.95323,-0.60697,-1.2971,-1.897,-1.8785,-1.1994,-1.1431,-0.71507,0.39038,1.8638,1.3225,1.3324,1.7661,1.4785,1.2767,1.9863,1.3571,0.17621,0.28641,0.22784,-0.046236,-0.172,-0.39651,-0.40859,-0.45498,-0.18208,0.063493,0.22936,0.71051,1.3427,1.3865,1.2265,1.124,1.2224\n-1.0829,-1.363,-2.3268,-2.8581,-3.6842,-2.2452,-1.1225,-0.60443,0.75892,0.84485,-0.96311,-0.84767,-0.97392,-0.44228,-0.75657,-1.3716,-1.5824,-1.6618,-1.0205,-0.61895,-0.11784,0.39636,1.2281,1.8155,1.7827,1.4735,1.511,1.3885,0.55082,0.14795,0.71939,0.89857,0.090202,-0.17974,-0.08451,0.0047054,0.085025,0.33625,0.39338,0.38204,0.3769,0.67989,1.2439,1.4796,0.98106,1.171,1.7669\n-1.3761,-1.5907,-2.2373,-3.1087,-3.3561,-1.5551,-0.63166,0.1312,0.12757,-0.32054,-0.74339,-0.33538,-0.32092,0.38334,0.31561,-0.62482,-0.97519,-1.4613,-2.4745,-2.0937,0.34922,1.173,1.6278,2.2919,2.072,1.9407,1.8465,1.3512,0.50413,-0.077144,0.043553,-0.37062,-0.67742,-0.54662,-0.80665,-0.82636,-0.4354,0.062744,0.44011,0.72233,0.54522,0.77198,1.0967,1.6149,1.5939,1.7908,1.6586\n-1.6669,-1.81,-2.4677,-2.4979,-3.6003,-3.0256,-1.5296,-0.26516,-0.39937,-0.38701,0.20891,-0.069714,-0.03932,0.562,0.35665,-0.22383,-0.61678,-1.9537,-2.8989,-2.4458,-0.80241,0.82974,1.3896,1.8372,2.1158,1.8116,2.1332,1.7347,0.68761,0.38172,-0.42886,-1.3234,-1.2133,-0.89475,-1.4754,-1.0719,0.24499,0.3779,0.52607,0.84137,0.84525,0.81969,1.0079,1.7072,1.9818,2.3605,2.0272\n-2.4349,-2.7225,-3.7516,-4.7628,-4.9993,-3.1028,-0.89175,-1.0818,-1.1788,-0.68296,-0.35755,-0.69146,-0.19759,0.41421,-0.0368,-0.14113,-0.51283,-1.1011,-1.8851,-0.8039,0.63615,0.63091,1.0894,2.3723,2.3936,1.7885,1.7647,1.6123,0.50465,0.052156,-0.66172,-1.9528,-1.6776,-0.73555,-0.39928,0.56422,1.2172,0.88891,0.59175,0.39569,0.71655,1.0052,1.3095,1.5761,2.0949,2.2474,2.4927\n-2.5865,-2.9723,-2.4581,-1.1173,-1.6087,-1.3537,-1.0114,-1.0677,-0.90362,-0.42851,-1.4051,-1.6363,-0.7278,0.22883,-0.36872,-0.73166,-0.49164,-0.70426,-0.49012,-0.2711,-0.042715,0.047905,0.29245,1.9538,2.8118,2.0275,1.3669,1.4839,1.2434,0.45939,-0.08366,-0.43132,-0.22394,0.40691,1.2459,1.3907,1.0326,0.85971,0.75348,0.36637,0.41432,1.1166,1.5073,1.4354,1.6632,2.5062,3.2884\n-0.95443,-1.5471,-0.14539,-0.36683,-1.9679,-2.3254,-2.3187,-1.706,-1.3272,-0.23465,0.00023293,-0.15733,0.2962,0.67822,0.30216,-0.54504,-0.62284,-0.56945,-0.41777,-0.10325,-0.79731,-0.36738,0.48138,1.6693,3.7513,3.4443,2.0949,0.81617,0.73503,1.5531,1.4415,0.96051,0.77579,1.128,1.5788,1.4172,1.0687,0.91829,0.88728,0.99621,1.004,0.93112,1.385,1.5175,1.4117,0.85245,1.0959\n-0.8448,-0.66609,-0.60884,-1.0798,-1.3315,-1.3951,-2.0728,-2.2136,-1.8879,-0.69948,0.17665,-0.059819,0.26881,0.30245,-0.54413,-0.77144,-0.54224,-0.8263,-1.6593,-0.9385,-0.31066,0.56605,1.5056,2.2992,3.1626,3.086,2.5084,1.5774,0.99147,1.1066,0.74346,0.71252,1.4171,1.3719,1.3935,0.7932,0.30207,0.5173,0.70468,1.2837,1.1281,0.65322,0.66279,0.60407,0.51263,-0.16964,0.034541\n-1.5764,-1.0797,-1.4168,-2.463,NaN,NaN,NaN,-1.141,-1.0379,-0.43186,-0.74779,-0.53641,0.05865,-0.082257,-1.1072,-0.76589,-0.23111,-0.93577,-2.0825,-2.2298,-0.48041,0.95949,1.9448,3.4512,3.7909,3.2337,3.0684,2.4138,1.2646,0.92782,1.008,1.2716,2.386,2.6057,1.9199,1.2561,1.2385,0.89325,0.55103,1.3316,1.0174,0.63786,0.07489,-0.19805,-0.41972,-0.70486,-0.29666\n-0.87123,-1.1315,-3.8001,-6.0148,NaN,NaN,NaN,NaN,NaN,0.4384,1.2023,0.41116,-0.20198,-0.65196,-0.068341,0.24547,0.3286,-0.62451,-1.2491,-2.0147,-0.80114,1.3659,1.6397,2.8018,4.1636,3.0309,2.034,1.395,1.1454,0.96199,1.5633,2.0332,1.6718,1.7226,1.3491,1.2425,1.5745,1.3311,1.1312,1.6335,1.1477,0.22939,-0.23817,-0.37562,0.017208,-0.061645,-0.15273\n-0.31018,-1.412,-2.536,NaN,NaN,NaN,NaN,NaN,NaN,1.1654,1.5617,0.93652,-0.11471,-1.0348,-0.41561,0.66655,1.0194,1.0436,-0.1369,-1.1706,-0.10509,1.2878,2.7954,1.7854,1.8215,3.1128,2.3074,1.0357,1.5862,1.4815,1.2498,1.7312,1.1398,0.7455,0.76807,1.1736,1.3467,1.1155,0.73539,0.54035,0.36049,0.37627,-0.068805,-0.21324,0.22464,0.32498,0.011239\n-2.1911,-1.1737,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.67471,0.20539,0.57091,0.071476,-0.8307,-0.25446,0.84837,1.0457,0.029019,-0.36226,-0.80033,0.43213,1.3826,2.4451,1.5621,0.76457,1.8747,2.6944,2.9816,2.5466,2.7874,1.6742,1.7798,1.8029,1.39,1.3091,1.5061,1.5541,1.4205,1.1786,0.80532,0.76805,0.6765,0.66845,0.88107,1.0911,1.2868,0.37196\n-1.894,0.42902,1.7566,NaN,NaN,NaN,NaN,NaN,0.62074,0.52803,0.59974,0.062996,-0.20795,-0.088854,0.9512,1.8773,1.5376,0.30973,-0.801,-0.31617,0.25229,1.0168,1.3733,1.6276,2.3708,3.0554,3.967,4.6201,3.9166,3.2618,2.3886,2.0095,2.0014,1.8953,1.4488,1.5591,1.8104,1.9267,2.2548,1.9506,1.2733,0.60869,0.75563,0.98809,1.0989,0.83121,0.19169\n-1.3899,0.29605,NaN,NaN,NaN,NaN,NaN,NaN,-0.012069,1.5917,1.1279,0.20734,-0.27281,0.080899,1.5768,3.1339,1.9572,1.0453,0.034287,0.1017,0.45748,0.70303,0.73282,2.1833,3.9611,3.0746,3.2402,3.3627,4.8723,4.3649,2.8987,2.3306,2.2778,2.0952,1.9077,2.3086,2.7422,2.0656,2.0714,2.1107,0.44056,0.70129,1.0876,1.0453,0.63845,-0.41919,-0.49469\n-0.099251,0.32889,NaN,NaN,NaN,NaN,NaN,NaN,-0.22692,1.7637,1.5808,0.45482,-0.46208,-0.59679,1.9292,4.5767,2.4187,1.2667,1.0707,1.665,3.2499,2.585,2.9017,3.5996,4.1151,2.3196,1.3162,1.0593,3.5832,4.0995,3.6419,3.0003,2.8576,2.7871,2.3318,2.5209,2.5637,2.5016,2.2706,1.9544,0.78656,0.83075,1.5616,1.4214,0.15494,-0.63278,-0.72594\n-0.50899,1.0802,NaN,NaN,NaN,NaN,NaN,1.5258,0.99257,0.69669,1.148,1.0564,-1.5853,-0.89579,0.58443,2.5292,3.2627,2.2251,1.796,2.5955,4.2374,5.437,5.0966,5.7784,6.6018,5.2896,4.9034,3.7822,3.527,4.3547,6.0916,5.6232,5.0075,3.9327,2.921,2.702,3.5152,3.2224,2.4675,1.3709,0.90033,1.0345,1.3547,1.333,0.35281,-0.49903,-0.3552\n0.73445,2.1283,1.958,NaN,NaN,NaN,0.8218,0.73291,0.93568,0.62957,0.32255,0.38572,0.81643,2.8998,2.8155,2.922,2.8759,2.7155,2.6558,4.1283,5.4117,6.0826,5.0303,6.5092,8.3168,7.9688,6.9129,5.6338,6.2115,7.9315,7.775,6.5993,4.4902,3.0467,2.0854,0.95244,2.6906,3.3003,3.3525,1.3262,1.0019,1.2567,1.5705,1.14,0.29208,-0.11594,0.021261\n-0.84886,-0.08445,1.2085,NaN,NaN,NaN,0.14735,-0.55528,-0.94259,-0.15199,-0.26907,0.46264,1.8778,3.1143,3.8027,4.0729,3.7048,4.0582,4.3981,5.5955,6.584,7.4688,7.6829,9.0211,10.051,10.273,NaN,NaN,NaN,NaN,NaN,9.1658,4.2486,2.3589,2.0568,1.8324,2.6099,2.7993,2.6959,1.5416,1.0705,1.1264,1.0634,0.49291,-0.26704,-0.13791,0.57666\n-3.2729,-0.90331,0.49769,-0.74272,NaN,NaN,-0.60118,-1.3216,-1.0359,0.36599,0.77224,0.88848,1.6434,2.6103,3.7125,4.5394,4.7809,5.5072,7.2122,7.9757,8.2204,8.6119,9.4645,10.369,12.562,13.889,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.4712,3.3831,3.3989,2.9472,2.6748,1.9872,1.363,0.69211,0.42504,0.66144,0.25374,-0.057061,0.31999,0.69989\n-5.6899,-3.1252,-1.9882,-1.803,-0.9197,-0.47774,-1.6265,-1.7714,0.038294,0.71535,0.69142,0.93657,1.6798,3.1039,4.4119,5.4991,5.8849,6.6257,7.754,8.7784,8.8961,9.2679,9.9968,11.434,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.3322,5.2792,3.7444,3.1826,2.4485,1.0277,0.63079,1.0008,0.94736,0.85824,0.79127,0.80933,0.95429\n-7.4046,-6.0282,-4.1823,-2.8803,-1.5941,-0.97583,-1.0088,-1.6246,-0.46409,-0.12342,0.57605,1.2518,1.842,3.2732,4.5696,5.377,5.5045,6.5352,7.8423,8.3746,8.5649,10.287,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.7271,5.3813,4.359,3.5348,3.1204,2.3001,1.0858,0.89408,1.3582,1.1251,1.0696,1.2355,1.3825\n-4.4861,-5.7207,-7.8064,-8.7705,-2.4862,-2.4476,-2.222,-1.3311,0.073915,0.52604,0.57388,1.0064,1.3256,3.457,4.6511,5.3828,5.9134,6.7958,7.8465,8.167,8.2404,10.587,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.3722,5.489,5.5122,4.9478,3.9846,2.4598,1.0704,1.2283,1.4328,0.29463,0.224,0.93374,1.163\n-3.9521,-4.3946,-5.5942,-4.696,-2.8111,-1.6679,-1.7878,-1.3303,-0.53945,0.76268,1.9947,2.1498,1.8293,3.4156,4.3406,4.8133,5.8956,7.0639,8.5818,10.088,8.5626,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.2735,7.3254,6.2726,5.9049,4.2986,2.0183,1.5464,2.0731,1.6494,0.22614,0.19973,0.71898,0.85697\n-4.1656,-5.7644,-6.0597,-1.8193,-0.80243,-0.74315,-0.96674,-1.394,-1.5403,0.01819,2.9677,3.6371,3.5207,3.8232,4.0489,4.61,5.9125,6.1493,7.3903,8.0451,5.7638,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.3298,5.3848,5.7157,5.233,3.9398,2.302,1.7558,2.1016,1.5588,0.075217,0.34951,0.38751,1.1675\n-1.4849,-3.0076,-3.5681,-1.805,-1.3412,-0.50587,-0.93742,-1.2623,-1.529,1.0844,3.2166,5.6098,6.4865,4.6484,2.4517,3.595,5.2424,7.0643,7.3067,6.9963,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.6604,NaN,NaN,NaN,NaN,NaN,NaN,2.8316,4.9184,5.7031,5.7071,2.9467,2.5006,2.4985,2.6143,2.3113,1.3136,0.63867,0.37634,0.28929\n-0.14052,-0.79266,-2.7197,-1.8602,-1.2211,-0.69534,0.2253,0.62013,0.66939,1.5955,2.5346,3.5958,4.7874,4.9197,2.7719,2.7215,3.1848,5.2365,7.0559,8.3241,NaN,NaN,NaN,NaN,NaN,NaN,4.2666,3.9158,NaN,NaN,NaN,1.8698,1.3953,0.28131,1.8714,4.415,8.0949,7.8672,4.6262,2.8735,2.9926,2.9305,2.2554,1.4158,0.21612,0.083827,-0.41012\n-1.1024,0.082174,-1.1231,0.12874,-0.013746,-1.1441,-2.2668,-0.49345,1.0597,1.8852,2.2889,2.899,2.9803,3.9201,3.1437,3.251,3.9893,5.4401,6.9579,9.5818,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9542,2.3473,1.6728,0.69515,-0.94702,0.18595,6.2991,10.189,10.397,8.8413,4.7098,2.2103,2.4378,2.4315,2.2952,1.2545,-0.016911,-0.24242,-0.64919\n-2.5045,-2.8697,-2.4197,-0.83565,-1.0148,-1.3457,-1.5997,-1.592,0.75912,1.5123,2.4714,2.7873,3.2165,3.6717,3.8162,3.7364,4.6403,5.6991,7.6622,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.31009,0.062597,1.2183,NaN,NaN,NaN,10.715,7.749,3.3268,2.0354,2.315,2.3743,2.1192,0.88042,0.21919,0.27653,-0.98051\n-2.9937,-3.6407,-3.4112,-2.7712,-2.2873,-1.529,-0.66215,0.19885,0.64031,0.79458,1.6318,3.1002,3.4549,5.6344,5.4696,4.4381,3.7585,5.2907,5.7072,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.86707,NaN,NaN,NaN,NaN,NaN,NaN,4.401,2.2044,2.5008,2.9316,1.9939,0.96775,-0.00055552,0.0621,-0.082366,-1.0196\n-2.2067,-2.1643,-2.6086,-2.9597,-2.8752,-1.5078,0.11134,0.92366,1.095,1.2594,1.86,3.4156,3.5483,4.6923,4.7016,3.9763,3.6494,5.7949,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.2324,NaN,NaN,NaN,NaN,NaN,5.8042,4.0627,2.6452,2.4582,2.2629,1.3097,0.50487,0.34271,0.52602,0.19677,-0.82957\n-1.3633,-1.0747,-1.2918,-2.0266,-2.5315,-1.5078,0.66615,1.0383,0.95381,0.88373,1.0556,1.6937,2.9441,3.5239,3.603,3.8853,5.2161,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3054,1.7683,NaN,NaN,NaN,NaN,5.8836,5.1755,4.1567,3.0176,2.3373,1.9517,1.3193,0.44524,0.36275,0.79801,0.30329,-0.98343\n-0.87177,-1.1694,-1.1358,-1.4149,-1.4362,-1.0282,-0.25944,0.66327,0.98256,0.99556,0.90961,0.74646,2.8328,3.6479,3.6293,5.1727,6.2346,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.09201,2.0437,0.90887,-0.94567,0.50536,2.1099,3.8707,4.1551,4.0701,4.6769,3.8646,1.688,1.4931,1.1308,0.35314,0.00024033,0.23303,-0.41732,-1.3265\n-0.27875,-1.1462,-1.0768,-1.8211,-1.2205,-0.6894,-0.18469,0.27516,0.74018,1.1355,1.1928,1.2176,2.2427,2.7366,3.3164,5.6892,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.30135,2.201,0.82005,0.69512,1.3008,1.347,2.7909,3.0714,3.0693,3.5036,3.6129,1.7759,0.7374,0.39531,-0.0168,-0.18076,-1.2202,-0.58596,-0.47983,-1.0003\n0.1591,-0.41311,-1.0023,-0.73423,0.15561,-0.17201,-0.18958,0.56242,1.3596,1.631,1.9892,1.9065,1.9502,1.7283,2.1491,3.3579,5.2028,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5838,-0.15315,-0.13264,1.7926,4.2938,2.814,2.2545,2.2171,1.5499,1.501,1.0572,0.1847,0.1233,-0.43312,-0.54764,-0.69138,-0.5787,0.33688,0.42911\n-0.18973,-0.060128,-0.51713,0.69641,0.99635,0.94984,1.2007,1.6337,1.977,2.3248,2.4063,2.3431,1.8572,1.8371,2.012,2.6773,3.7099,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.3823,-0.83168,0.4647,2.9114,2.9249,2.7114,2.5923,2.4961,1.2902,0.75641,0.13259,-0.11128,-0.16976,-0.22449,-0.33597,-0.45272,-0.67362,-0.032832,0.27092\n-0.20442,1.0854,1.1498,0.54368,1.5588,1.4817,1.5505,1.7423,2.1344,2.3966,2.3521,2.2741,2.3228,2.2026,2.9785,3.7266,3.7954,NaN,NaN,NaN,NaN,NaN,3.2079,2.7975,NaN,NaN,NaN,NaN,-0.44169,1.2235,1.978,1.813,2.0229,1.4848,1.4786,0.80224,0.87337,0.48406,0.11075,-0.13022,0.0099511,0.19693,-0.029053,-0.60582,-1.0608,-1.24,-0.74127\n0.25414,0.74832,0.97467,0.35748,0.48608,1.7748,1.9449,2.0005,2.303,2.5924,2.6413,2.4934,2.3159,1.9506,3.0299,3.8883,3.4586,1.9969,1.6148,1.0147,-0.23931,1.1785,3.9838,4.7871,5.1003,NaN,NaN,NaN,0.3002,1.6655,2.0943,2.0846,0.38594,0.70242,0.23744,-0.50857,-0.46989,-0.42478,0.37976,0.24859,0.035723,0.1443,-0.039495,-1.0411,-2.3578,-2.9108,-1.6991\n0.40024,0.50519,0.56862,1.1369,1.4266,2.0492,2.2781,2.1679,2.0072,1.7788,2.1272,2.4377,2.6244,2.2684,3.1444,3.6641,3.0037,2.0048,1.2577,0.3796,-0.1954,0.15108,1.6584,2.1232,2.2426,0.31326,0.07571,-0.14994,0.5617,1.3221,1.9967,2.1665,1.7813,-0.013849,-0.4582,-0.53792,-0.60316,0.28874,0.78136,0.29098,-0.19057,-0.79645,-0.78542,-0.24568,-2.2708,-3.0484,-2.1377\n0.44235,0.97948,0.91727,1.7254,1.975,1.9004,1.7808,1.6329,2.2048,2.2852,2.0357,2.3071,2.5886,2.6066,2.2689,2.2953,1.3168,0.83966,0.99877,0.13426,-0.29176,0.018232,0.90022,1.3721,1.5967,0.64975,0.37204,0.17238,0.47183,0.72489,1.9107,2.4702,2.0011,0.92484,0.27218,0.44981,-0.0054097,0.49321,0.8979,NaN,NaN,NaN,NaN,NaN,-0.99857,-0.83522,-1.5821\n1.0955,1.2943,1.524,1.8355,2.0166,1.4106,1.216,1.395,2.4088,2.8004,2.6491,2.2747,2.5148,2.1616,1.526,1.3431,0.69235,0.47369,0.24723,-0.096199,-0.52855,-0.92305,0.13826,0.79072,1.0666,0.98607,1.5791,0.062759,-0.24481,0.15584,1.2738,1.7698,1.1832,0.92737,-0.073945,-0.56043,-0.83753,0.39824,0.73351,NaN,NaN,NaN,NaN,NaN,-1.5668,-0.35565,-0.71564\n2.1154,1.8632,1.3687,1.6095,1.8869,1.8097,1.6005,1.6356,1.9121,2.118,2.5381,2.5381,2.3073,1.9084,0.91656,0.46572,0.59358,0.73889,-0.029675,-0.044827,0.51994,1.3783,1.2244,0.52122,-0.15682,-0.41163,1.0084,-0.90505,-0.24785,0.14232,0.72522,0.83811,0.47381,-0.25832,-0.95329,-0.93788,-0.60022,-0.16392,0.25096,1.3495,2.1725,NaN,NaN,NaN,-0.91739,-0.25195,-0.65918\n2.4375,2.1679,2.0069,2.222,2.3688,2.5023,2.5763,2.5136,2.1292,1.2719,2.0175,2.6377,2.4934,1.9774,0.97121,0.59124,0.99724,0.51936,-0.15726,-0.70763,-0.13174,0.94578,0.79338,0.36141,-0.17741,-0.5211,-0.96726,-1.932,-0.68303,0.024678,0.11242,-0.10398,-0.016322,0.12454,-0.032104,0.033193,0.26758,0.73539,0.66804,0.51122,2.0229,1.4408,0.1192,-0.77547,-0.37064,-0.27317,-1.0718\n2.3766,2.7119,2.6082,2.5161,2.0226,1.6926,1.7812,1.953,2.1635,1.7345,1.876,2.3358,2.1315,1.5671,0.90057,0.76332,1.0756,0.42747,-0.084229,-0.3928,-0.36032,0.19865,0.41045,-0.029994,-0.5218,-0.8718,-1.4276,-1.4285,-0.79112,-0.42704,-0.79892,-0.40534,-0.30115,0.014358,0.57989,0.84207,1.1078,1.2356,0.9218,0.61161,0.91629,0.79464,0.39952,-0.51456,-0.3671,-0.6194,-0.71425\n1.1451,2.033,2.5922,2.3514,2.1516,2.1829,2.1869,2.0266,2.1888,2.4257,2.1105,1.6868,1.4584,1.5676,0.718,0.70728,0.68903,0.80805,0.50833,-0.011358,0.065212,0.2572,0.2581,-0.054745,-0.89647,-2.0653,-2.2735,-1.2564,-0.24983,-0.29701,-0.98517,-0.97337,-0.49448,-0.029674,0.19463,0.67175,1.1666,1.2246,0.60038,0.48158,-0.19107,-0.34777,-0.99947,-1.4548,-0.76489,-0.62693,-0.48494\n1.3221,1.8622,2.6306,2.2627,2.4829,2.9595,2.3291,1.9658,2.133,2.6778,2.9345,2.1511,1.4244,1.0209,0.78292,0.81137,0.76473,0.86463,0.17087,-0.32192,-0.1284,0.42037,0.026733,-0.26375,-1.0576,-1.5823,-1.2583,-0.71836,-0.099438,-0.99716,-1.078,-1.1879,-0.84172,-0.46368,-0.75284,-0.79495,-0.017566,-0.23641,-0.60746,-0.67878,-1.0129,-0.92848,-0.67231,-0.39246,-0.74002,-0.82437,-0.097679\n0.79415,2.1687,2.8506,3.1456,3.313,3.1236,2.2323,2.1172,2.7261,3.0052,3.5009,3.1032,1.7136,0.66614,0.2005,0.0053864,0.040144,-0.030622,-0.5211,-0.65685,-0.18058,0.45073,-0.095981,-0.45856,-0.6703,-0.91719,-0.57739,0.18311,-0.33966,-1.3443,-1.2751,-1.0525,-1.2519,-1.3192,-1.058,-0.532,-0.43748,-0.50451,-0.64958,-1.4192,-1.228,-0.96842,-0.015428,-0.19473,-1.0574,-0.66873,-0.50219\n-1.1273,-1.6708,2.5838,3.8844,4.3005,3.1545,2.0999,2.6495,3.4649,3.5104,3.4914,3.1453,2.4033,1.1332,0.34807,0.042543,-0.019536,-0.2353,-0.067823,0.10964,0.39007,0.45059,-0.0089159,-0.27391,-0.50398,-0.75462,-0.35421,-0.31738,-0.93706,-1.3615,-0.86966,-0.98689,-1.5041,-1.153,-0.50824,-0.10515,-0.083056,-0.10237,-0.13453,-0.76646,-0.89007,-0.69994,-0.41543,-0.78911,-1.0459,-1.1128,-1.4714\n0.85954,-0.35957,-0.45347,1.5595,3.2658,2.9721,2.2884,1.4955,1.896,3.1323,3.4404,3.369,1.9703,1.1488,0.005007,-0.047652,0.22987,0.40445,0.61378,0.51396,0.69061,0.26223,-0.076866,-0.39883,-0.57399,-0.67321,-0.53509,-0.86284,-1.2111,-1.4661,-1.0813,-1.3467,-1.5522,-1.6278,-1.3266,-0.53325,-0.5617,-0.49245,-0.035547,-0.10703,-0.45746,-0.74541,-1.1363,-1.1885,-1.061,-1.3164,-1.4221\n1.5955,0.71849,-0.034837,0.67617,1.6237,2.2439,2.1773,3.0139,4.3017,5.4276,3.498,3.0851,0.3736,-0.57869,-0.9712,-0.42136,0.25811,0.61808,0.4802,0.20276,0.031882,-0.040874,0.019038,-0.24083,-0.52058,-0.62818,-0.71429,-0.9787,-1.0657,-0.76866,-0.9602,-1.1224,-1.4985,-1.4769,-1.3247,-0.99554,-0.79035,-0.61965,0.026221,0.15506,0.32206,0.060673,-0.59924,-1.0702,-1.5683,-1.6009,-0.98835\n1.4502,1.883,1.9163,2.7154,2.8077,2.8693,3.4249,3.2107,3.6269,4.4485,3.6882,3.176,0.66531,1.1082,0.20472,0.51499,0.83264,0.8598,0.057972,0.67736,0.97724,-0.00023317,-0.30522,-0.2423,-0.47744,-0.20553,-0.48748,-0.86166,-1.2686,-0.69974,-0.72065,-0.86331,-1.3999,-1.5725,-0.96478,-0.6407,-0.67724,-0.3737,0.074003,-0.022388,0.0032177,-0.092368,-0.49235,-1.0348,-2.1779,-2.3155,-1.3447\n1.1209,0.47127,0.34303,2.2146,2.6564,3.4687,3.2574,3.4014,3.6621,4.0395,3.0859,1.4407,0.46048,0.10604,0.29671,1.6321,1.3784,0.31721,-0.019546,0.37452,0.19711,-0.21845,-0.33428,-0.33929,-0.47436,-0.68483,-0.92762,-1.2525,-1.552,-1.5601,-1.4045,-1.1368,-1.744,-1.4918,-0.61129,-0.41346,-0.61097,-0.37266,-0.098473,-0.32864,0.040593,-0.033285,-0.70267,-0.86645,-1.576,-2.1421,-2.0365\n0.10014,-1.0281,-0.60317,0.68551,0.8375,1.2008,2.342,3.2149,2.7534,2.2438,2.367,1.1307,-0.30365,-0.99191,-1.2201,-0.63516,0.72871,-0.87226,-0.91248,-0.68506,-1.0524,-1.2531,-0.78738,-0.70241,-0.7263,-0.81793,-0.88862,-0.90418,-0.97093,-1.2113,-1.6582,-1.9837,-1.9287,-1.1024,-0.3951,-0.50691,-0.63989,-0.44153,-0.074173,-0.074185,-0.01255,-0.11041,-0.96669,-1.1795,-1.4591,-1.3414,-1.0274\n-0.53628,0.41533,1.388,1.8319,1.687,1.1722,1.7926,1.8293,2.5353,2.1028,1.9465,0.066455,-0.56844,-0.55172,0.20503,0.0006988,-0.90248,-0.44139,1.2097,-0.59082,-1.2992,-2.0314,-1.8174,-0.97052,-0.91632,-0.6683,-0.62218,-0.85398,-0.83979,-1.241,-1.5403,-2.105,-2.4165,-1.9712,-0.86232,-0.52052,-0.47402,-0.37183,0.036421,-0.087791,-0.58201,-1.0036,-1.2744,-1.7288,-2.11,-1.8133,-1.4173\n1.5519,1.2783,1.4065,2.0215,1.6688,1.1214,1.8082,2.0296,2.8456,3.4952,2.6392,1.7982,0.75255,-0.44105,-0.012078,0.33524,0.25651,-1.4011,-0.55728,-0.49043,-0.40458,-0.87519,-1.619,-0.88867,-0.46204,-0.74234,-1.087,-1.8265,-1.9476,-1.4217,-0.97379,-1.4429,-2.0049,-2.1478,-1.0181,-0.57604,-0.74189,-0.48917,-0.42837,-0.69068,-1.201,-1.6947,-1.6888,-1.7062,-2.0261,-1.9753,-1.6639\nNaN,NaN,NaN,NaN,3.1302,1.4661,2.8497,5.137,3.7927,4.3154,1.9706,0.47013,-0.31883,-0.27365,-0.43217,-0.29444,1.7216,2.4741,-0.49361,-0.66757,-0.22992,-0.22913,-1.8528,-0.31751,-0.121,-0.98545,-0.91833,-1.0044,-1.0242,-0.87929,-1.0972,-1.2589,-1.1133,-0.80871,-0.83167,-0.57978,-0.26908,-0.54855,-0.70079,-1.0798,-1.3832,-1.313,-1.0049,-1.6812,-1.8231,-1.9161,-1.5968\nNaN,NaN,NaN,NaN,3.1765,NaN,NaN,NaN,6.5578,3.4042,1.1569,-0.54454,-0.76975,-0.21223,0.47157,1.2391,2.4898,1.8696,-0.74433,-1.0739,-1.5739,-1.8301,-0.72226,1.029,-0.070784,-0.57501,-0.37905,0.23094,0.50344,-0.046138,-0.40635,-0.6685,-0.89962,-0.75835,-1.3211,-1.3168,-0.77826,-0.82748,-0.82043,-0.57739,-0.28994,-0.64056,-1.0182,-1.3307,-1.5266,-1.6649,-1.5304\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.4744,2.8522,3.0772,2.3012,1.0535,-1.9076,-1.4863,-0.54544,-1.5272,-1.7629,-0.36544,1.0552,0.61475,0.17403,0.052859,0.083605,0.77396,0.52518,-0.15262,-1.1086,-1.3663,-1.8585,-2.0907,-1.3164,-1.2345,-1.0864,-1.0518,-0.063887,-0.07794,-0.67471,-1.0617,-1.3553,-1.1317,-0.78793,0.206\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.5126,-1.0018,-1.3405,-0.14253,1.3835,0.88763,0.82041,0.54762,0.27929,-0.03214,-0.11272,0.44942,0.98205,0.26021,-0.18465,-0.9936,-1.6508,-2.0302,-1.4582,-1.3492,-1.5249,-1.1292,0.0059311,0.0031204,-0.74803,-1.4226,-1.8348,-1.3705,-0.89181,-0.14127\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.17526,-1.3734,0.75138,1.4091,1.4642,0.80851,-0.24467,0.77204,-0.033839,-0.21684,0.10839,0.22181,-0.035544,-0.24194,-0.9336,-1.4311,-1.8143,-1.5116,-1.2211,-1.5269,-1.512,-1.0333,-0.61629,-0.88183,-0.9139,-1.4968,-1.4298,-0.46391,0.64726\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.12125,0.58144,NaN,NaN,-0.656,-2.1771,-3.011,-0.90313,-0.59468,-0.37363,0.25752,0.13244,-0.49762,-0.61193,0.39281,-0.15486,-1.2943,-1.4478,-1.1762,-0.9117,-1.2413,-1.213,-1.4165,-1.3421,-0.99433,-1.0154,-0.82578,0.039253,1.334\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0559,-1.2573,-1.5571,-0.43116,0.53131,0.57935,0.67033,0.29015,-1.3594,-1.7839,-1.399,-1.1718,-1.9941,-2.2051,-1.9172,-1.1162,-1.4639,-1.5543,-1.3498,-1.1482,-1.1288,-1.0465,-0.4858,0.44211,0.96456\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.0009,-0.92704,-0.67712,-0.23558,0.68765,0.96735,0.4726,-0.05246,-0.68619,-1.5549,-1.6351,-1.195,-1.6733,-2.1803,-2.1804,-0.83255,-1.7346,-1.6559,-1.4571,-0.97597,-0.62935,-0.87228,-0.19617,0.24461,0.27935\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.93096,-0.33355,0.14123,0.6816,1.5134,2.5265,1.5881,1.12,0.93103,0.29066,-0.10674,-1.1707,-1.1922,-1.8724,-1.9357,-1.4299,-0.75278,-0.95825,-1.0588,-1.2588,-1.1215,-0.63576,-0.7406,-0.49814,0.02724,0.52279\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.1496,-1.0949,-0.31201,0.69925,1.8117,2.7033,2.0384,0.1262,0.47344,0.63233,0.02232,-0.608,-1.0791,-1.4291,-1.3358,-0.46468,-0.80137,-1.6371,-1.0571,-0.7024,-0.81087,-0.96775,-0.73129,0.30935,0.59842,0.32514\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070115-070326.unw.csv",
    "content": "-0.29518,-0.49745,-0.38487,0.32572,-0.088229,-0.36181,-0.84048,-0.94113,-0.82632,0.0093184,-0.42763,-0.1665,0.58242,1.5182,1.4222,1.6892,1.1249,2.0897,2.3898,2.5573,3.1053,1.9175,1.0883,1.1809,2.0618,1.7835,2.3044,4.2496,3.898,2.9261,2.7876,3.5303,3.6158,3.6103,3.3989,3.028,1.8829,1.2778,-2.8146,-2.466,-1.9883,-2.2946,-2.1503,-0.60726,-1.3138,-0.5659,-0.64174\n-0.79531,-0.88521,-0.69772,0.0067778,-0.30342,-0.57792,-0.64906,-0.81388,-0.62291,-0.36643,0.28462,0.78073,1.4061,1.4259,1.2831,1.3467,1.2842,1.2761,1.5863,2.0446,2.8373,1.3911,0.71645,0.61352,1.004,2.0358,3.4086,4.0205,2.9911,2.5929,3.864,3.9478,3.9042,3.6189,4.0864,3.5233,2.3827,0.63538,-2.2103,-1.8061,-1.5595,-2.2911,-0.44108,0.19783,0.098756,-0.22117,-0.31089\n-0.85622,-0.82422,-0.57419,-0.55697,-0.48043,-1.249,-0.8228,-0.52103,-1.0433,-0.54954,0.52811,1.5235,1.4833,1.3473,1.5017,1.3447,1.4664,1.1942,1.1261,0.91016,0.96984,1.6065,1.7652,1.2826,1.9238,3.4156,4.1217,4.1658,2.8515,2.5767,3.9232,3.8067,4.4947,4.1836,3.9325,3.8081,3.2455,1.6817,0.55572,-0.0091543,-1.6465,-1.7618,-0.16622,-0.83699,-0.32266,-0.42553,-0.96254\n-1.7526,-0.99706,-0.17889,-0.67615,-0.66851,-1.4261,-0.93647,-0.47407,-0.3339,-0.10008,0.71384,1.2172,0.92758,1.5083,0.17709,0.69786,2.006,1.7705,1.2204,1.0909,0.32403,1.256,2.0402,2.1986,3.0214,4.2361,4.4734,4.3026,4.0339,3.6211,4.1356,4.3024,5.0299,4.2771,3.6187,3.5684,2.5677,1.5297,0.89183,-0.70955,-1.4214,-1.0915,-0.92199,-1.3518,-1.5529,-1.1317,-1.9173\n-1.536,-1.5188,-0.5625,-0.73398,-1.0381,-1.0434,-0.78172,-0.062405,-0.56966,-0.51977,-0.35535,-0.31923,0.058312,0.75422,0.6994,0.77836,1.5381,2.1401,1.5072,0.76121,-0.46605,1.561,2.2335,2.8392,3.5709,4.7208,4.3395,3.9761,5.2286,4.7992,5.2493,4.1113,5.8217,4.4185,3.2755,2.6341,1.5475,0.87135,0.21309,-0.89742,-0.88848,-0.37382,-0.88749,-1.2765,-1.1361,-1.1582,-1.4876\n-1.5123,-1.9504,-1.1166,-0.65817,-1.0826,-0.90594,-1.1812,-1.3763,-1.1279,-0.70868,-0.80135,-1.1972,-0.84417,0.25701,0.81437,1.0549,1.055,0.95858,1.3472,1.7567,1.923,2.0157,2.9594,3.7181,3.7255,4.3542,4.6352,4.077,4.96,4.2313,4.2633,3.8185,4.8292,4.5991,3.4182,3.0984,1.2872,0.28053,-0.30899,-1.2802,-1.3236,-0.96907,-0.86854,-1.1568,-1.4384,-1.4791,-1.4699\n-2.2571,-2.5079,-2.3597,-1.1951,-0.98014,-1.0035,-1.4367,-1.8813,-0.66388,-0.18873,-0.88009,-1.1343,-0.81681,0.48473,-0.092904,0.6844,0.6699,0.80831,0.99313,0.75349,1.4056,1.7916,2.3509,3.038,3.8021,5.0199,4.5996,4.3605,4.3121,4.4387,3.8478,4.5698,4.929,4.5759,3.6991,2.7997,1.567,-0.28474,-1.1044,-2.6185,-2.1098,-1.7179,-1.3553,-1.2927,-1.8833,-2.1761,-2.7169\n-2.9799,-3.0772,-3.2131,-1.562,-1.2128,-1.1739,-1.3287,-1.3451,-0.34782,-0.0078411,-0.91272,-0.4171,-0.17789,0.20354,-0.69697,-0.014802,0.16561,0.33478,0.1836,0.2927,1.4298,1.8247,1.6618,2.4269,3.644,4.615,4.6765,3.9351,3.495,3.9766,4.2949,4.75,4.853,5.0138,3.9555,2.3518,0.44306,-0.99828,-1.8252,-2.3624,-2.2208,-2.3585,-1.4299,-1.9251,-2.2144,-2.8717,-2.9576\n-2.9096,-3.1757,-2.5859,-1.535,-1.2259,-1.2335,-1.3996,-0.90562,-1.0739,-0.61532,-0.43285,-0.86822,-0.31789,-0.19953,-0.030288,0.11261,-0.0048027,-0.24858,-0.32139,0.48813,1.4331,2.1731,1.6854,1.7199,4.3689,4.449,3.4512,3.6259,3.7057,4.2198,4.1721,4.7089,4.6518,4.3588,3.2985,1.1562,-0.54763,-1.6198,-1.4803,-1.299,-1.3196,-1.0268,-0.55634,-2.3126,-2.2797,-2.4513,-2.8306\n-2.5806,-2.4876,-0.8801,-1.622,-1.5904,-1.5356,-1.9232,-1.4676,-1.3752,-0.97563,-0.8248,-1.5776,-0.40853,-1.582,-0.45824,0.38095,0.23271,-0.036706,-0.16177,0.59462,1.848,1.8387,1.3512,1.9141,3.804,3.9652,4.2236,3.8828,4.1941,4.1006,4.6592,5.1834,3.4068,3.747,1.7031,0.50052,0.3252,-0.59994,-0.61921,-0.97278,-0.99106,-0.79323,-0.6412,-1.0161,-1.0918,-1.7065,-2.3894\n-2.394,-0.66216,0.26479,-1.8456,-2.5954,-2.0758,-2.8624,-1.8559,-1.3137,-1.7553,-1.8616,-2.6318,-1.6009,-2.0375,-1.3499,-0.90939,0.22763,0.94374,1.2507,2.0775,1.8718,1.5782,2.0922,2.4738,3.5643,4.0633,3.9389,3.923,4.2169,4.6346,5.3149,5.0924,3.4875,2.8514,1.674,1.6916,1.0722,0.43135,0.39951,-0.19162,-0.69367,-0.19782,-0.17986,-0.96408,-0.79562,-1.2963,-1.6528\n-1.6587,-0.11308,-1.0807,-1.9507,-2.4092,-2.4187,-2.9724,-1.2992,-1.5375,-2.6205,-3.4542,-3.8373,-2.6414,-1.7686,-1.3732,-1.0216,0.095251,2.1035,2.312,3.2883,2.1535,1.893,2.2248,2.3773,3.5256,4.8036,3.6806,3.2733,3.3328,5.0025,5.1408,4.2994,3.5172,2.4664,1.4693,1.3332,-0.030959,0.040477,0.027944,-0.25608,-0.47437,0.046425,0.86536,-0.17934,-0.64477,-1.1991,-1.4141\n-2.5936,-2.4443,-2.751,-2.0152,-1.869,-1.944,-2.9608,-1.4014,-1.7369,-2.9593,-2.4801,-3.2179,-3.0526,-1.4722,-0.96294,-0.9181,-0.12345,1.5053,2.5638,3.3735,3.0286,2.9611,2.919,2.5106,3.4281,4.3904,3.3357,3.0423,3.4831,5.0314,4.5584,3.1392,2.4379,1.8664,1.5059,0.92887,-0.78812,0.1887,-0.6042,-0.14492,-0.19792,0.2108,0.41876,0.21889,-0.83948,-1.0742,-1.2062\n-3.5149,-4.4125,-4.3222,-2.7395,-1.4154,-2.7708,-2.8552,-2.1572,-2.4479,-2.2048,-2.1384,-2.1477,-1.6911,-1.4056,-0.9243,-1.3025,-0.51907,0.53636,1.173,1.9517,2.6356,2.8302,3.3358,3.3659,3.1807,4.0965,3.6611,3.5043,3.9356,4.2816,3.8213,2.7491,1.7437,2.0255,1.4543,0.72104,-0.025022,-0.33625,-0.74883,0.26485,-0.39641,0.48748,-0.37805,-0.29535,-0.55213,-1.0101,-0.98459\n-3.7855,-5.0344,-4.7037,-1.124,-1.947,-2.757,-2.3112,-2.3093,-1.8328,-1.6196,-2.248,-1.7032,-1.7952,-1.6231,-1.1388,-0.89209,-0.70644,0.11721,0.97795,2.2985,2.4906,2.3512,3.7758,3.3785,2.9146,3.9967,3.9947,3.7778,3.4162,3.7138,3.6114,3.2013,2.9033,2.7034,1.3405,0.58032,0.25093,0.42135,0.68714,1.0206,0.068781,0.95967,-0.25232,-1.0505,-1.3325,-1.6377,-1.5125\n-2.8348,-3.814,-4.2616,-1.4348,-1.1191,-2.3261,-1.6316,-1.9077,-1.8772,-1.6707,-2.2199,-2.2792,-2.04,-0.94991,-0.35034,-0.23215,-0.21312,0.17951,1.2317,1.9411,2.7927,3.5913,3.4447,2.9116,3.0681,3.7464,3.8103,3.291,2.756,3.3827,3.0557,3.0231,3.01,2.6369,1.7934,0.014441,1.0708,1.1274,1.1978,1.3501,0.34848,0.68659,-0.40889,-1.2394,-1.668,-2.6341,-2.1844\n-2.1395,-1.8428,-1.8976,-2.0554,-2.6965,-2.6446,-1.7515,-1.5863,-1.3545,-1.562,-1.1592,-0.7335,-1.1785,-0.70113,-0.14865,0.18783,-0.3735,0.70752,0.68132,0.56975,1.7653,3.3335,2.6872,3.0255,4.0782,3.7566,3.1504,2.5394,2.7534,3.691,2.8573,2.3686,2.4656,1.714,1.0418,0.55128,1.4652,1.0484,1.038,2.0209,0.451,0.63892,-0.40005,-0.89128,-0.94299,-2.1976,-2.4242\n-0.28985,-0.87414,-1.912,-2.8045,-3.3066,-3.0956,-1.7402,-1.8086,-1.3865,-1.1266,-1.1373,-1.161,-1.0388,-0.48465,-0.5423,-0.79084,-0.33692,0.94661,0.33748,0.03591,0.36184,2.5433,3.4624,2.7062,3.8077,3.6167,3.0568,2.6896,2.4852,3.4051,2.9686,2.4,3.9869,1.9555,1.4122,1.7816,1.7139,1.0415,0.6762,0.81035,0.040397,-0.62942,-0.57617,-1.4289,-1.5895,-1.4785,-1.705\n-2.0651,-2.1433,-2.4821,-3.434,-3.7584,-3.022,0.58238,-2.4171,-1.5237,-1.4506,-2.0433,-1.4217,-1.307,-0.85549,-1.1845,-1.2603,0.24165,1.0189,0.77675,0.20496,1.8141,3.0627,4.9332,4.1285,3.5333,4.3127,3.3539,3.0907,2.2771,2.9188,3.2379,3.186,3.6132,3.1345,2.26,2.5736,2.2783,1.9409,0.70545,0.29048,-0.17,-0.70665,-0.67435,-1.2853,-1.3726,-1.456,-1.875\n-3.3927,-2.1128,-2.4465,-3.0564,-3.1218,-1.7954,-0.73592,-2.2348,-1.9195,-1.3876,-1.9412,-1.7086,-1.3146,-1.1303,-1.4518,-1.4949,0.79075,2.1576,2.1437,1.5733,2.554,3.0812,3.5422,5.752,4.5477,3.885,3.9152,4.0467,3.3726,2.3485,3.2175,3.4188,3.492,3.2208,2.4867,2.4734,2.4686,2.458,0.61116,0.23888,-0.078305,-0.34852,-1.016,-0.94141,-1.4126,-1.9918,-1.5685\n-1.9479,-1.0824,-2.1089,-3.6203,-4.5507,-2.3957,-1.5529,-1.9191,-1.2727,-1.5445,-1.7671,-0.98813,-1.392,-1.6791,-1.2748,-0.66748,0.13257,0.70416,2.1778,1.657,0.83618,1.7797,2.6617,3.8627,3.81,3.9281,4.1523,4.6573,3.7393,2.6268,3.6027,3.0705,3.6218,3.587,2.7825,2.5559,2.6956,2.5938,0.96637,0.7481,0.32397,-1.1855,-1.3302,-1.9715,-1.6837,-1.466,-1.1112\n-1.0966,-0.42933,-2.0761,-2.8008,-2.5923,-1.9938,-1.7483,-1.0586,-0.48879,-2.279,-2.1714,-1.4896,-1.6512,-2.4774,-1.5568,-0.73162,-0.55064,-0.12805,0.21799,0.84941,1.9432,1.9386,1.9369,2.1158,3.4597,4.3763,4.5773,4.5877,3.2029,2.943,3.908,3.274,3.28,3.3059,2.1381,2.1961,2.4469,1.7489,0.90616,0.61198,0.041234,-1.3908,-1.4231,-1.339,-0.91935,-0.71476,-0.88596\n-0.52143,-1.0978,-2.2965,-1.2656,-1.3314,-1.9539,-0.73756,-0.30602,-0.98899,-2.773,-2.719,-2.1535,-2.2004,-1.8038,-0.65761,0.33654,-0.32813,-0.08762,0.20558,0.89132,2.1459,2.5073,2.8525,1.8124,3.0328,3.963,3.1872,3.2444,2.3939,2.5154,3.3597,3.2894,3.018,3.0072,3.0519,2.409,2.7325,1.3615,0.78765,0.16226,-0.45165,-1.1276,-1.2746,-0.78943,-0.604,-0.63115,-1.5778\n-1.5642,-0.87964,-0.02846,0.77023,0.25616,-0.81408,-1.4341,-1.9693,-2.4217,-3.4585,-4.31,-3.7084,-1.9725,-1.1522,-0.29419,2.0102,1.1805,-0.096417,0.077123,0.78307,1.2924,2.0186,2.4237,1.7727,1.7042,2.4382,1.6272,1.5221,2.0347,2.1899,2.2205,1.8335,2.2617,2.5398,2.7402,2.3994,1.6773,1.2597,1.1095,0.1013,-0.15777,-0.91153,-0.51093,-0.55281,-0.49785,-0.90997,-1.9576\n-1.6515,-1.2443,-0.55861,-0.98272,-0.55825,-1.3484,-3.0431,-1.9244,-2.5963,-4.126,-4.1225,-3.0046,-3.2313,-1.9055,-1.0737,-0.56568,-0.81384,-1.9046,-1.4132,-0.22365,0.31722,0.44966,-0.0869,0.92887,0.58202,0.1323,0.47817,0.8172,1.0421,1.2332,1.7236,1.7715,1.793,2.0782,2.4531,0.96526,2.2154,1.5496,1.399,1.0952,0.24436,0.089034,0.17828,-0.0069895,-0.098229,-0.8498,-2.015\n-1.1789,-1.1622,-1.3599,-1.5427,-1.3201,-2.2838,-2.0746,-1.372,-1.3816,-3.3714,-3.5851,-3.8384,-3.9428,-3.6989,-2.0093,-1.1684,-1.9146,-2.0619,-1.5319,-0.84582,-0.92455,-2.5588,-1.4936,0.67643,1.0728,0.91208,1.3541,1.2424,1.0043,1.0241,1.2018,1.3532,0.32724,2.3616,2.3047,1.0611,2.3229,1.9127,1.8609,2.9262,1.4158,0.43562,0.46049,-0.24077,0.10635,-0.21773,-0.3892\n0.26351,-0.69796,-0.61938,-1.9437,-1.9431,-1.9221,-1.4027,-1.19,-2.0715,-3.3935,-3.6518,-4.1717,-4.8033,-4.0884,-2.8262,-1.8217,-1.6587,-1.6732,-2.0878,-2.3187,-2.6397,-2.7222,-1.4752,0.82224,0.86133,1.9676,1.6236,1.4814,1.5666,2.2224,1.5849,1.2591,0.91917,2.186,2.2887,1.8734,1.9734,1.6834,2.8713,3.3795,1.7202,0.79086,0.16579,-0.17345,-0.063275,0.14764,0.42683\n1.0999,0.65682,-0.29573,-2.3317,-1.5465,-2.1284,-3.0898,-2.5831,-2.5954,-2.2272,-3.2442,-4.0858,-4.4077,-3.8128,-2.6337,-2.4044,-1.3797,-1.2987,-1.5751,-2.2083,-3.0643,-1.3156,-0.35408,-0.32922,-0.86456,0.91196,1.7193,1.591,1.5136,1.8966,2.0191,2.2237,2.0426,1.8274,1.8326,2.2682,2.3264,2.8147,2.5151,2.4048,1.1868,1.0604,0.3587,0.3134,0.63808,0.3388,0.10234\n1.4631,0.70047,-1.2092,-0.82481,-1.4683,-2.8955,-3.8563,-3.9599,-3.1851,-1.6678,-2.8373,-2.7759,-3.2346,-2.6815,-2.6189,-2.1862,-1.1089,-0.71308,-1.3876,-2.4556,-3.5138,-1.8455,-1.5138,-1.4526,-1.2626,0.43478,1.1415,1.1956,0.54677,0.93874,1.8054,2.5268,2.6359,1.8992,2.0214,2.7,3.2016,3.7291,2.9829,1.9244,1.1682,1.271,0.97836,1.2504,0.72462,0.23062,-0.052985\n0.13858,1.1937,2.0398,2.7023,-2.2128,-3.396,-2.15,-2.5,-3.1338,-2.3229,-2.5479,-2.0115,-1.9393,-1.6783,-2.0369,-1.7313,-0.60738,-0.22503,-0.55376,-0.90932,-2.3149,-1.9614,-1.5773,-1.2511,-0.27657,0.65375,0.77537,-0.26841,1.2536,2.0069,3.1013,2.9562,2.9085,2.5254,2.7909,2.4861,3.0156,3.3699,2.4501,1.6883,1.4633,1.3771,0.96512,0.95828,0.28933,0.16519,-0.11565\n0.54962,1.6978,2.8528,-0.49722,-2.5568,-2.6826,-1.6655,-2.7114,-3.3042,-3.0056,-2.1245,-1.5465,-1.4003,-1.3291,-1.5128,-1.3562,-0.68134,-0.095999,0.3031,1.1073,1.9113,1.4265,-0.12961,-0.62788,-0.30495,0.79125,0.91114,0.21156,1.6849,2.5469,3.8578,3.1955,2.5512,2.18,2.9774,2.2626,2.161,2.3056,2.2545,1.9845,1.3535,0.80132,0.80305,0.52645,-0.15675,0.11283,0.18948\n0.10787,0.73219,1.037,-1.5053,-0.1539,-0.82474,-2.0711,-2.5439,-2.5368,-1.6788,-1.0767,-1.5191,-1.5141,-0.93107,-1.1181,-0.87829,-0.38403,-0.24428,0.18895,0.4249,2.7117,1.8629,0.41321,0.52966,0.14955,0.81714,2.5377,0.41109,-1.0631,-1.1201,1.8215,2.6967,2.5787,2.6967,3.0301,2.3635,2.1122,1.7253,2.2356,2.6207,1.2737,0.51013,0.54016,-0.011983,0.023625,0.25486,0.061432\n-0.84719,-0.59537,-0.79545,-1.2001,-1.7018,-0.78009,-1.2715,-1.4587,-2.2551,-0.49724,-0.025193,-0.41417,-0.59738,0.96747,1.6399,0.38334,0.58353,0.88715,0.30195,-0.11344,0.83515,0.31572,-0.34821,1.8716,1.488,1.9418,3.075,-3.263,-4.4282,-3.1765,-0.044961,3.4937,4.1243,3.9438,2.9472,2.0583,1.8308,1.1887,2.5528,2.0773,1.0491,0.45983,-0.064958,-0.29165,0.61269,-0.068553,-0.68751\n0.17365,-0.0057535,-1.4894,-2.0939,-1.8995,0.057766,-0.98472,-0.87128,-0.89718,-0.674,-0.87084,-0.62232,0.26587,1.2958,2.8998,2.2162,1.4521,0.68387,1.099,1.25,-0.36789,-0.0090075,0.69995,3.2966,2.6151,4.148,-2.1253,-2.4231,-4.0362,0.43502,2.8464,4.2045,4.0641,5.3047,4.1349,3.9589,5.0764,2.938,3.6209,2.0776,0.90656,0.4837,0.18263,-0.10509,0.68771,-1.1643,-1.6665\n0.40518,0.35514,-1.5859,-0.86642,-0.54236,-0.20648,-0.096424,0.54874,-0.13526,-1.2786,-1.0551,0.47636,0.9941,0.64618,2.1252,1.8967,1.2147,1.6023,1.931,2.7314,-0.34355,3.8443,NaN,NaN,NaN,NaN,NaN,NaN,1.2271,2.2657,3.9286,5.6715,6.995,6.4891,5.5923,6.176,6.2785,5.1428,3.2568,2.3821,1.8792,1.4333,0.68555,0.43411,1.0686,-1.308,-2.03\n2.3854,1.6692,0.64233,0.72422,-0.016257,-0.43881,-0.23769,1.0489,0.73411,-0.56536,0.058664,0.52411,1.4946,0.53673,-1.7291,0.0029612,2.5729,3.3208,3.1009,2.4553,3.9261,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.3238,6.5934,6.9255,6.6096,6.5929,5.3564,5.1757,4.0568,2.742,2.7067,1.6755,1.0108,0.82139,0.16833,0.25007,-0.82928,-1.5843\n2.7955,1.9745,1.3746,0.59278,1.0558,0.15881,-0.23023,0.11142,1.0591,1.0832,0.56966,1.6791,1.417,-0.93429,-0.7671,0.93269,3.5461,4.2708,4.0785,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.2936,7.3758,8.2685,7.4952,6.6899,4.917,3.5825,2.8811,2.2394,2.2715,1.2939,1.307,0.97302,0.12667,-0.10753,-0.88585,-1.444\n1.478,2.2015,1.9822,0.60783,-0.16536,-0.34778,0.23595,0.23808,0.9813,2.4287,2.5384,2.8214,2.0394,0.73814,1.4625,2.4457,4.2459,2.923,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.7803,7.3828,7.5245,7.0093,6.5485,5.1069,2.7139,2.6916,1.9347,1.4267,1.1811,1.2173,0.80991,0.10821,-0.68615,-1.2864,-1.7126\n0.54575,1.0936,2.3451,1.6315,0.20183,0.001174,0.39667,0.30656,1.4983,2.3883,2.0211,2.8796,2.7292,2.2808,3.297,4.7853,6.3156,2.3852,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.5883,4.1161,6.4947,7.7311,7.3239,6.3799,5.2309,3.7056,2.7139,2.642,2.0766,1.1417,1.0708,1.0953,1.6801,0.35893,-0.23651,-1.0392,-1.767\n0.7669,-0.10654,0.7118,1.3984,1.4584,1.2054,1.5021,1.0525,1.641,2.1439,2.1616,2.3258,2.4719,3.0756,5.0564,6.1162,6.4864,5.3536,4.7974,2.8987,4.045,4.3436,NaN,6.4724,8.006,9.5104,9.0236,5.5106,3.8008,5.5193,7.4446,7.7111,6.8925,5.6796,4.1766,3.8822,3.5435,2.8989,1.9061,1.2888,1.6685,1.5399,1.3299,0.60809,0.045362,-0.22341,-1.219\n0.63124,0.68216,2.0255,2.2296,1.5401,1.2847,1.0549,0.57826,1.1941,2.4749,2.4475,2.2979,3.0567,4.0444,5.3262,4.9958,4.6108,3.889,3.9036,3.9695,4.0449,4.8221,7.1461,5.0483,4.0191,NaN,NaN,4.7712,4.8526,5.9374,6.4652,5.7427,5.1361,4.2184,4.5242,3.8944,3.368,2.9999,1.4896,1.5667,1.7257,1.8132,1.3314,0.87667,0.34052,-0.32903,-1.0044\n1.2697,2.3143,2.6592,1.9185,1.0227,0.83237,0.97054,0.9187,1.5156,2.3017,2.499,3.9332,3.9594,4.064,4.5779,4.3956,3.627,3.4447,2.8853,4.327,3.6694,4.9917,5.7985,5.1493,3.3827,NaN,NaN,4.1492,4.3991,5.4919,5.2395,4.7965,4.2772,3.4374,3.4237,3.5333,3.6133,3.0588,1.8405,2.149,1.8405,1.8318,1.046,0.29736,-0.24366,-0.46874,-0.4931\n1.4014,3.5441,2.5764,1.209,0.84846,0.82071,1.2647,1.9534,2.1283,2.7367,3.6663,4.5905,4.1055,3.3403,3.8422,4.8368,4.1334,3.8516,4.327,3.5758,3.2268,5.2259,2.8142,NaN,NaN,NaN,NaN,3.9115,3.9467,4.67,4.4104,3.4307,2.5647,2.8804,2.8523,2.8699,3.3322,2.6292,2.1398,2.0909,2.0683,1.2381,0.37409,-0.16313,-0.23706,-0.11542,-0.044877\n2.0289,2.5402,1.5795,1.156,1.2171,1.1383,1.5624,2.2492,2.7047,2.8316,3.4208,3.8504,3.4186,2.9556,2.8793,3.1052,3.6546,5.08,4.6767,4.2201,4.4172,2.8974,NaN,NaN,NaN,NaN,NaN,3.2862,3.2693,3.6793,3.6401,3.319,2.5352,1.9786,1.9923,2.0502,2.4502,2.1352,2.0217,1.9105,1.1577,1.1824,0.10167,-0.16567,-0.26665,-0.43979,0.297\n1.9925,1.2705,0.90689,1.3391,0.8743,1.1903,1.7313,2.2056,2.6846,2.8442,2.6624,2.9305,2.8514,2.3453,2.2218,2.2059,3.5869,3.836,4.3501,5.3497,5.5027,5.4764,6.2499,NaN,NaN,NaN,4.2724,2.4337,2.3112,2.6445,2.643,2.2144,2.0587,1.8421,1.3135,1.669,1.9347,2.3465,2.3635,1.3224,0.72288,1.0927,0.24403,0.024442,0.31316,0.11212,-0.19616\n1.568,1.3935,1.6642,2.1543,1.8579,1.2087,1.3209,1.8323,2.5076,2.8402,2.4395,2.2758,2.1483,1.4677,0.95137,1.8083,3.1005,3.4744,3.6836,4.5451,4.7413,4.5285,4.0002,3.1192,1.6931,2.8761,2.7099,2.0598,1.5843,1.5243,1.2466,1.3943,1.6672,0.85668,0.53381,1.1572,1.8103,2.1135,2.0473,1.2306,0.99745,0.75365,0.30445,0.1523,0.83186,0.64935,0.29961\n1.9526,2.4752,1.9356,2.2853,2.2835,1.6244,1.5459,1.2492,2.5263,3.0261,1.5362,1.6905,1.4467,0.6686,0.85487,2.2737,3.0839,2.8141,3.2664,4.0542,4.1021,4.1287,3.3765,2.5713,2.3338,2.7332,2.0741,1.1794,0.80226,0.5209,1.2079,1.2853,0.93446,0.14531,-0.0022879,1.083,1.1071,1.0725,1.1294,0.80029,0.85892,1.2601,0.71293,-0.00058842,0.30154,0.080736,-0.061102\n2.6886,1.9819,1.6887,2.0669,3.8674,3.5079,1.7963,1.2768,1.5803,2.2826,1.6429,1.1948,0.18147,0.12993,0.65006,1.0837,1.7161,1.982,2.9525,3.5158,3.4747,3.6121,2.5341,2.4361,2.5372,2.3272,1.6519,0.38113,0.55181,0.46558,0.90556,0.76622,0.27853,0.28315,-0.25495,-0.20364,0.025331,0.8656,0.77551,0.65762,0.73791,0.69613,0.095151,0.35602,-0.41226,-0.22199,-0.35292\n2.972,1.46,0.71299,2.3922,3.9484,3.9392,2.4077,1.5652,-0.36137,1.06,1.8003,1.2276,2.2602,1.3012,0.82729,0.68682,0.71014,1.0648,1.6803,2.3878,2.7232,1.9893,1.9727,1.8778,1.3475,0.62042,0.49196,0.44378,0.071018,-0.066436,0.027239,-0.21152,-0.33074,-0.68669,-0.93068,-0.77269,-0.25383,0.24484,0.20326,0.61416,0.70487,0.48124,-0.3766,-0.22357,-0.93036,-0.98435,-0.56761\n2.5893,2.4328,1.9204,2.2263,3.1939,3.1374,2.4756,1.6418,0.093603,1.6205,1.7776,2.328,2.6296,2.1326,0.91423,0.66207,0.5653,1.1685,1.8777,2.4201,2.5879,1.3117,1.4742,0.85529,-0.17359,-0.62519,-0.76881,0.04119,-0.10514,-0.27955,-0.3248,-0.53473,-1.0724,-1.3386,-1.0948,-0.86269,-0.16345,-0.26006,-0.019918,-0.13618,0.036468,0.66318,1.1295,0.36885,-1.7313,-1.3927,-1.2413\n2.018,2.2171,2.1616,1.6491,2.8505,4.6684,7.2691,4.2025,2.9579,3.0788,2.5586,2.078,2.8476,1.8447,1.058,0.74189,0.50901,1.4355,1.3153,1.2149,0.78008,1.2998,0.73173,0.031129,-1.0322,-1.1929,-1.6205,-0.85165,-0.0050173,-0.22963,-0.30693,-0.4531,-1.1058,-1.6089,-1.2949,-1.6465,-1.4174,-0.74485,-0.85911,-0.79699,-0.64794,0.48743,1.4491,0.66015,-0.61371,-0.47534,-1.3049\n2.8137,2.0501,1.6535,2.2207,3.2454,5.4188,6.3205,3.637,2.9237,3.2327,2.0224,1.7099,2.5415,0.81153,1.652,0.59896,0.051982,0.1043,0.32768,0.7543,0.35569,1.532,0.65901,-0.39382,-0.94104,-0.72995,-0.45555,-1.4696,-0.80201,-0.27743,-0.070472,0.28703,-0.58099,-1.3396,-1.6993,-1.8899,-1.1528,-0.66905,-1.0736,-0.52595,-0.25524,-0.35884,-0.73464,-0.82481,-0.5545,-0.11724,-0.51102\n3.5484,3.36,2.8324,3.1538,3.4687,3.0196,3.5175,3.6366,2.999,2.8888,1.3631,2.5933,2.1921,1.2797,1.5396,1.0658,0.18569,0.19341,0.85231,1.1597,0.17987,0.86204,0.0094213,-0.58355,-1.3856,-1.257,-1.3487,-1.4853,-0.59067,-0.34748,-0.50098,-0.26612,-0.49433,-1.7689,-2.327,-1.8256,-1.132,-1.2196,-1.1713,-0.52282,-0.042253,0.32842,-0.1486,-0.25352,0.11112,0.049293,-0.85583\n2.3958,3.8274,4.748,5.0972,4.3427,3.3776,2.958,3.7385,3.0631,2.5381,1.8897,2.211,1.3623,0.73315,0.45317,-0.086987,-0.16372,-0.38992,-0.19374,-0.60135,-0.44288,0.50636,-0.29075,-0.47117,-0.97379,-1.23,-1.5489,-1.9846,-1.0684,-0.63022,-0.709,-0.66135,-0.51105,-1.1439,-1.3618,-0.99442,-0.4355,-1.1997,-1.6452,-1.247,-0.86739,-0.55119,-0.61538,-0.38207,0.25811,-0.24771,-0.08913\n1.4991,2.2453,3.1054,3.2128,3.9002,3.9532,3.3274,3.1017,3.2081,2.8167,1.8917,1.6688,1.1603,1.1073,1.3528,0.0024195,-0.38255,-0.73576,-0.78666,-0.68361,-0.19651,-0.4085,0.017781,-0.73057,-1.3324,-1.5023,-2.6013,-2.9508,-2.0873,-0.93361,-0.67779,-0.78683,-1.0924,-0.69953,-1.1405,-1.0659,-0.61014,-0.66545,-0.8889,-1.0916,-0.87782,-0.59067,-0.55626,-0.43799,-0.34365,-0.67522,0.16084\n1.2368,1.3329,1.5804,2.5363,3.5217,5.9436,5.2696,3.0293,2.9832,3.1412,3.2884,3.4529,3.4226,2.9459,2.577,-0.25698,-1.275,-1.3092,-0.92958,0.42089,0.34029,-0.16536,0.21082,-0.91839,-1.9646,-2.1092,-2.8978,-2.6688,-2.0145,-0.8101,-0.46597,-0.88187,-0.729,-0.47754,-1.0416,-1.303,-1.117,-0.73066,-0.33192,-0.19897,-0.16766,-0.41153,-0.53844,-0.21713,-0.53711,-0.36064,-0.46121\n2.27,2.3928,2.8531,3.0182,2.4337,3.3543,4.9618,4.9883,4.2671,4.3302,5.8672,5.5664,9.4223,NaN,NaN,NaN,NaN,-2.5971,-0.96579,0.8727,0.12412,-0.096769,0.39627,-0.70001,-1.3212,-1.4104,-2.1154,-2.3937,-1.732,-1.516,-0.14017,-0.52077,-0.78457,-0.94262,-1.2595,-1.1213,-0.97826,-0.89394,-0.1168,-0.03144,0.32864,-0.65218,-0.7778,-0.30676,-0.80459,-0.4166,-0.98608\nNaN,NaN,5.2322,5.4531,4.9397,4.624,4.6802,5.3419,5.6563,5.3112,4.1685,6.4407,13.663,NaN,NaN,NaN,NaN,NaN,-0.79695,0.63753,-0.68902,-0.43571,-0.156,-0.80884,-1.3507,-1.4543,-1.6245,-1.0644,-0.43904,-1.7623,-0.83528,-0.91633,-1.2419,-1.538,-1.8837,-1.5865,-1.4702,-1.5314,-0.14556,-0.16357,-0.20119,-0.82899,-0.92734,-0.39235,-1.4709,-0.55866,-1.1932\nNaN,NaN,4.8714,5.7813,5.981,6.1067,3.906,5.4236,NaN,4.5466,3.3217,2.996,0.60077,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.20144,0.022708,-0.34828,-0.6795,-0.90182,-1.3254,-1.0709,-0.72929,-0.79578,-1.7543,-2.3528,-2.0264,-1.5968,-1.2902,-1.5411,-1.4418,-1.2212,-1.0609,-0.67603,-0.50896,-0.52892,-0.9431,-0.81807,-0.6878,-0.82118,-0.9058,-0.86801\nNaN,NaN,4.4091,4.6598,5.2007,5.1949,4.2737,7.6125,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1547,1.9531,0.59353,0.39105,-0.087019,-0.6711,-0.59701,-0.67609,-1.3494,-1.353,-1.194,-1.9565,-1.8239,-1.5248,-0.72454,-0.8308,-0.60537,-0.59841,-1.3891,-0.87254,-0.46489,-1.1763,-0.7567,-0.47995,-0.50815,-0.7166,-0.036911\nNaN,6.7121,4.2511,4.3597,4.5504,4.1333,4.7216,7.5255,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1279,2.056,2.2519,1.8694,-0.94782,-1.4763,-1.45,-1.7383,-1.0855,-0.72435,-1.4528,-1.2921,-1.3201,-0.62572,-0.77826,-0.88613,-0.63045,-1.0077,-1.0216,-0.50297,-0.53459,-0.50809,-0.56984,-0.93653,-0.98949,-0.6121\nNaN,NaN,2.7254,2.9619,3.2131,2.8618,3.6695,5.1635,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.644,2.0368,2.8158,1.3118,-0.97635,-1.3634,-0.68438,-0.85111,-0.40449,-0.38582,-1.0148,-0.75226,-0.80739,-0.43162,-0.70984,-0.88595,-0.80923,-0.75414,-0.5071,-0.31465,-0.30308,-0.91967,-1.2678,-0.89404,-0.69908,-0.696\nNaN,NaN,NaN,NaN,NaN,2.4255,4.5645,3.9475,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4159,2.1306,2.0464,-0.015164,-1.473,-1.6438,-1.3192,-0.98106,-0.61433,-1.0911,-1.243,-1.4639,-1.1322,-1.1988,-0.88803,-0.77983,-0.64013,-0.55309,-0.16194,-0.16148,-0.46909,-0.91174,-1.0384,-0.70128,-0.72668,-0.89916\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9474,2.4707,0.43851,-0.48106,-0.9442,-1.6398,-1.7554,-1.1057,-0.74979,-0.85382,-1.0346,-1.3176,-1.7241,-2.3937,-0.69377,-0.54662,-0.323,-0.3422,-0.49382,-0.44323,-0.53311,-0.58141,-0.60285,-0.80317,-0.94069,-1.4545\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.9899,2.1351,0.022468,-0.15974,-0.65311,-1.8928,-2.1027,-1.2547,-0.99799,-0.87681,-2.028,-2.1445,-1.7243,-0.67891,-0.49731,0.045992,0.21418,-0.83896,-0.52498,-0.097781,-0.30143,-0.57402,-0.98709,-1.9338,-2.6799\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.7229,-2.0008,-1.1932,-1.7156,-2.3543,-2.2789,-1.5064,-1.1421,-0.40047,-0.18128,-0.21643,-0.28571,-0.31831,-0.036135,-0.02741,-0.75378,-1.086,-0.82924,-1.9725,-2.8834\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.23062,-0.99837,-0.98997,-1.6339,-2.3381,-3.1127,-2.2559,-1.6273,-0.56749,-0.52373,-0.036556,-0.56466,-0.32824,-0.001174,-0.52936,-0.23931,-1.7487,-0.60074,-1.0372,-1.5416,-2.6112\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.1396,-1.0407,-0.12544,-1.03,-1.9984,-2.806,-1.1038,-1.2152,-0.75109,0.034575,-0.18706,-0.53481,-0.19168,-0.63474,-1.4924,-0.99916,-1.1213,-0.56769,-0.88657,-1.4904,-2.101\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.3026,1.7602,0.15875,0.13459,0.30741,1.8258,1.55,0.27392,-1.2149,-0.37707,-0.99996,-1.1908,0.27758,0.15795,-0.15257,-0.69734,-0.70747,-1.2465,-0.60316,-0.19908,-1.0159,-1.3141,-2.3129,-2.7912\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.5742,-0.94082,-0.17037,0.24931,0.49648,0.15689,1.5097,1.1092,-0.45534,-0.99852,-0.9366,0.08272,0.20459,0.030986,-0.78593,0.21204,-0.17288,-0.34994,0.09649,-1.1841,-1.0292,-1.5249,-2.0246\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.72565,-0.26231,-0.7924,-0.09259,2.4756,1.7238,-1.22,-0.11571,0.67731,1.1325,1.2717,-0.28074,-1.1547,-0.39206,-0.59303,-0.76027,-0.6293,-0.86512,-0.33405,-0.47028,-1.0073\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.0724,1.1551,-0.55961,-1.0432,1.2874,0.98179,-0.45509,-0.88649,-0.95927,1.1822,1.5981,-0.74152,-1.3423,-1.3895,-1.0799,-0.37272,-0.38513,-0.90232,-1.0528,-0.91443,-0.83309\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070115-070917.unw.csv",
    "content": "0.10769,-0.43867,-1.0958,-0.58241,-1.062,-1.1921,-0.85913,-0.797,-0.67877,-0.75462,-1.0689,-1.0197,-0.53896,-0.43329,-0.37649,-0.78982,-0.17782,0.11726,0.50711,0.63872,0.15588,0.29012,0.74203,0.74644,0.0077643,0.091405,-0.038471,0.98175,1.4375,0.8752,1.4654,0.82401,-0.04612,-0.83986,-1.5068,-2.3753,-3.5335,-3.7793,-5.0973,-5.2266,-4.7823,-4.0686,-4.4371,-4.6296,-4.4011,-4.0843,-3.3008\n-0.47896,-0.67385,-1.2988,-0.58303,-0.67174,-0.7944,-0.83672,-0.82131,-0.94963,-1.097,-1.2376,-1.2487,-0.89093,-0.66898,-0.30366,-0.29751,-0.5912,-0.43886,0.42618,0.43913,0.19655,0.61371,1.22,0.84215,0.69919,0.76635,0.59525,0.55282,0.8471,1.4797,1.4701,0.44896,0.092766,-0.47127,-0.88411,-3.1574,-4.0061,-3.8366,-4.1052,-3.9957,-3.91,-3.4003,-3.266,-3.8045,-3.9523,-4.3972,-4.5303\n-1.1471,-1.4661,-1.8918,-1.4835,-1.2814,-0.85764,-0.77892,-0.47788,-0.73064,-2.1038,-1.5052,-1.072,-1.0607,-0.84163,-0.16075,-0.59508,-0.7731,-0.45876,-0.16705,-0.76807,-0.76858,-0.5791,-0.036808,-0.51668,0.48562,0.3708,0.74647,-0.0093532,0.53016,1.7275,1.4436,0.16291,0.62461,0.40328,0.20597,-0.71017,-1.4283,-2.741,-3.2183,-3.1741,-3.0519,-2.5785,-3.1559,-3.7204,-3.2809,-4.0586,-4.0743\n-1.4012,-1.3964,-1.999,-1.888,-1.7275,-1.2726,-1.5769,-1.1943,-1.3201,-1.8709,-1.4567,-1.3501,-1.5332,-0.97011,-0.52403,-0.16186,-0.42707,-0.79079,-0.66668,-1.4163,-1.0239,-0.46043,-0.26523,-0.12897,0.38221,0.44866,0.70772,0.38584,0.72697,2.0054,2.1109,1.6826,1.5498,0.75454,-0.65213,-0.9113,-1.0085,-2.3781,-2.562,-3.0777,-2.354,-1.8267,-2.4007,-3.2912,-3.6178,-3.7116,-3.7338\n-1.0861,-1.1371,-2.1411,-1.7268,-1.6268,-1.469,-1.7706,-1.8091,-0.65116,-1.8223,-1.7363,-1.3809,-1.7134,-1.252,-0.58555,-0.40333,-0.2198,-1.2459,-1.1364,-0.94716,-1.5423,0.37526,0.256,0.76703,0.37506,0.38309,0.69399,-0.13526,0.52997,0.57209,0.91591,0.57039,0.43009,0.24007,-1.6392,-1.9089,-2.1609,-2.4563,-2.5974,-2.3312,-1.6662,-2.0063,-2.5419,-2.6061,-2.7517,-3.0002,-3.1942\n-1.7169,-2.0771,-2.0549,-1.9063,-2.2174,-1.7574,-1.865,-2.0619,-2.1361,-2.3603,-2.1583,-2.1576,-1.9285,-1.3444,-1.1026,-0.52318,-0.71706,-1.6645,-0.86783,-0.72814,-0.66758,-0.10436,0.41886,0.82572,-0.011713,0.47213,0.9631,0.10995,0.23753,0.31929,0.49719,-0.18166,-0.10412,0.031412,-0.012728,-0.26566,-2.7336,-2.4468,-2.8333,-1.6846,-1.3141,-1.6998,-2.1255,-2.2972,-2.816,-2.7366,-2.2444\n-2.2837,-2.3971,-1.8539,-1.6745,-2.1299,-1.9819,-2.51,-2.0938,-1.8778,-2.329,-2.0266,-2.5771,-2.0312,-1.1023,-1.3498,-0.88427,-1.5724,-2.2625,-2.5411,-1.7051,-0.91423,0.44351,0.94252,0.63158,0.58053,1.4885,1.1484,0.99793,1.0484,1.322,1.3193,0.63481,0.70738,0.46926,0.42398,-0.018086,-1.4263,-2.4911,-3.2297,-1.9811,-1.8095,-2.0089,-1.9944,-2.4949,-3.2114,-3.1997,-3.0477\n-3.088,-3.3631,-2.4772,-1.5683,-2.317,-2.1904,-1.9537,-1.7343,-1.692,-2.0513,-1.8074,-2.1088,-1.8528,-0.56655,-1.1265,-1.6521,-2.1698,-2.2761,-2.0913,-1.933,-1.0753,1.0361,1.7257,0.72608,1.2686,1.6871,1.9878,1.8828,2.3687,2.0565,2.5716,1.6856,1.4767,0.6258,0.33481,-0.27128,-1.6438,-2.1185,-2.0326,-2.3099,-2.0077,-1.7599,-1.2731,-2.4578,-3.1321,-3.4233,-2.5415\n-2.186,-2.5679,-2.3161,-1.8924,-2.7928,-2.4884,-1.5957,-1.7531,-2.0131,-1.7702,-1.911,-2.6569,-2.0405,-1.3497,-1.1557,-1.1452,-1.2919,-0.77201,-0.90684,-1.4149,-0.87657,0.45876,1.1818,1.4405,2.2258,1.8019,0.93654,2.0447,3.1736,3.1873,2.7818,2.6538,2.1057,1.3846,0.059122,-0.88648,-1.5492,-2.0145,-1.6237,-1.6357,-1.7853,-1.5797,-1.0496,-1.6528,-2.4035,-2.6528,-2.2094\n-2.1287,-2.3606,-2.4762,-2.982,-3.0799,-2.8713,-1.9556,-1.9136,-1.9542,-1.3113,-1.9945,-2.148,-1.3558,-1.6784,-1.3815,-0.9258,-0.41193,-0.096305,-0.10296,0.27687,1.673,0.80438,1.5022,0.8833,2.0764,1.9451,0.99497,1.6767,1.5588,2.7106,2.4735,3.5971,2.0723,1.2224,-0.30739,-0.55,-0.3734,-1.1503,-1.1424,-1.467,-1.7305,-1.4962,-1.2642,-0.92348,-1.3279,-1.6974,-1.6209\n-2.1533,-2.4092,-2.9885,-3.8273,-3.2924,-2.8629,-2.4183,-1.661,-1.645,-2.105,-2.2215,-2.0087,-1.3926,-1.5325,-1.4716,-1.3622,-0.20771,-0.42021,-0.30685,0.46688,0.56436,1.0233,1.8268,1.2549,1.7741,2.0745,2.0177,2.0192,2.3649,3.1819,2.8536,2.5084,1.3886,1.5882,0.0010872,0.44534,0.44657,0.04351,-0.23313,-0.29729,-0.83328,-0.31652,-0.57693,-1.033,-1.3561,-1.3102,-2.0296\n-2.2901,-2.6551,-2.9832,-3.1845,-2.9007,-1.8987,-2.5143,-2.4136,-2.1292,-2.7539,-2.4256,-2.3526,-2.0336,-1.4153,-1.0785,-1.159,-0.72145,-0.55572,-0.072999,0.93573,-0.95678,1.0961,1.7381,2.3334,2.3023,2.5928,2.3323,2.0872,2.96,3.3228,2.6341,1.5511,0.95877,0.5503,-0.28796,0.035178,0.30095,0.2598,0.3138,0.0070701,-0.37389,0.17986,-0.35533,-0.59407,-0.68044,-0.068489,-0.58931\n-2.3876,-2.7576,-3.1895,-3.2808,-3.054,-2.8441,-2.5422,-2.8187,-1.3813,-2.8252,-2.5299,-2.8404,-2.0176,-1.9325,-1.1639,-1.0522,-0.85885,0.16691,0.55661,1.5743,1.4448,1.748,2.1771,2.6009,3.0154,2.9239,2.8549,1.8174,2.8317,2.2675,2.2449,0.61025,0.29797,0.35455,0.4305,0.44295,0.62167,0.47219,-0.14319,-0.7498,-0.1911,0.10455,0.20688,0.037298,0.56447,0.71236,0.59055\n-2.6276,-3.8973,-3.894,-3.3082,-2.8823,-3.1639,-2.4714,-2.5702,-2.6908,-2.7089,-2.7905,-2.4469,-1.3461,-2.4294,-0.83437,-1.0093,-0.14008,0.34323,0.51089,1.0606,1.6142,3.3147,3.0933,2.738,3.7903,3.4667,2.9215,1.9089,1.7304,1.8238,2.0283,1.1373,0.37494,0.96977,1.8306,2.2511,1.2106,0.66784,0.31008,-0.10298,-0.22282,1.3176,1.0101,1.4495,1.8337,1.1912,0.73246\n-2.9135,-4.1016,-3.5432,-2.9311,-3.009,-3.099,-2.2577,-3.0428,-2.8255,-2.0934,-3.1336,-2.43,-2.4015,-1.6243,-0.23836,0.68942,-0.32236,0.14649,0.42307,1.0479,1.0281,5.5077,2.6497,3.3583,4.5671,3.3018,4.5741,3.0595,2.8691,2.7827,2.6863,2.9616,2.5108,2.1607,2.8092,2.9189,0.60146,0.30105,0.58718,1.1247,1.0936,1.8102,0.89966,1.4031,1.4853,1.2847,1.4261\n-2.4361,-2.7045,-3.0392,-2.8938,-2.9334,-3.2666,-1.8967,-3.007,-2.0944,-1.8717,-2.943,-2.2474,-3.663,-2.0749,0.35692,0.41802,-0.54905,-0.1255,1.5217,1.4823,1.7567,3.3021,2.77,3.776,3.4427,3.3423,4.5927,3.1879,2.8062,3.6464,2.8987,3.7153,2.7012,2.056,2.3848,2.262,0.25888,0.087363,0.11396,0.5333,0.49303,0.88476,0.4438,-0.095695,-0.4221,-0.11776,-0.015583\n-1.9595,-1.4044,-2.4642,-2.7112,-2.5215,-2.4112,-1.9279,-2.5179,-1.2678,-1.0615,-1.8149,-1.6647,-2.3552,-1.9371,-0.37164,-0.40328,-0.83419,0.82455,2.1199,2.003,2.7131,2.5623,3.5044,4.5189,4.0233,4.4732,5.9976,5.3979,2.9792,2.9622,1.7345,3.6306,2.1866,1.6677,1.6645,1.6961,-0.40533,0.41533,-0.010651,0.8183,0.46323,-0.95075,0.71578,0.27332,-1.4291,-2.3991,-2.1616\n-0.96238,-0.54407,-1.9992,-1.7079,-2.6022,-3.5696,-2.7948,-1.4319,-1.0058,-0.97635,-1.7183,-1.5432,-1.25,-0.72141,-0.53703,-0.35293,-0.03307,1.5679,1.492,2.4163,2.6206,2.6756,2.547,3.4238,3.4062,2.5095,4.4063,6.4283,4.1442,2.8351,1.9451,3.3652,2.6089,2.4288,2.4279,1.8329,-0.049294,1.007,1.3688,1.6732,0.97144,-0.23678,-0.36007,0.47266,-0.53886,-2.3401,-2.7425\n-0.081703,-0.76322,-1.5277,-1.2337,-1.4576,-3.8551,-1.6453,-1.5248,-1.3196,-1.0081,-1.9139,-1.4478,-1.5328,-0.85872,-0.45946,0.41527,0.48065,0.57204,0.88565,1.2559,1.7943,1.4112,0.9959,2.0597,2.2802,2.9254,2.6406,5.677,4.4316,3.9251,2.7481,3.6165,3.3693,3.4485,3.5402,3.0339,1.5353,1.4753,1.9807,2.0182,1.4738,0.45965,0.066851,0.39353,0.031898,-0.8088,-1.9487\n0.11327,-0.53308,-0.63965,-0.79867,-0.055393,-0.52059,-1.6678,-2.606,-1.9965,-1.3143,-1.1089,-1.6313,-1.6095,-0.59236,0.085029,0.25748,-0.46786,0.15531,0.1834,0.33884,1.519,1.557,0.96198,1.94,2.3611,3.1738,3.9617,5.2105,5.6402,4.441,5.2587,3.6963,4.0842,3.658,3.6958,2.9158,2.3432,2.1579,2.8079,2.4744,2.006,1.5814,0.79859,1.2448,0.72149,-0.78547,-1.2016\n0.57996,0.73551,-0.58753,0.090804,1.3682,-0.83667,-2.161,-2.2364,-1.9796,-1.6266,-2.1382,-1.1437,-1.4159,-0.061788,-0.47844,-0.73207,-0.41613,0.13609,0.10874,0.41632,0.86763,1.4075,1.8509,2.7219,3.2645,4.3886,4.6138,6.6932,6.4556,5.2769,6.2579,4.3745,4.9791,4.7724,4.4592,2.1952,3.0867,2.8536,2.5811,1.8649,1.4915,1.1369,1.0535,0.084854,-0.58433,-1.3452,-1.5851\n0.75268,0.57503,0.24865,0.011557,-0.16045,-0.71358,-0.73903,-1.1281,-2.1192,-1.8776,-1.6988,-0.86731,-1.5699,-0.24919,-1.0071,-1.5668,-0.263,-0.17322,-0.41673,-1.0119,0.29353,1.6742,1.1392,3.6255,4.1963,4.1983,5.1016,6.4843,7.0772,7.4029,7.9308,5.7355,5.2416,5.0118,3.4373,2.848,3.2878,2.7605,2.0415,2.4619,1.3316,0.71777,0.47809,0.47518,0.36998,-0.7132,-1.1264\n0.8858,0.56793,1.076,1.0279,0.83155,0.38702,-0.79426,-1.3462,-2.417,-2.3913,-1.4872,-1.6701,-1.0272,-0.51238,-1.1067,-1.7683,0.6881,0.61309,0.29033,0.42417,1.6585,1.6792,2.4578,3.9142,4.9685,4.7678,5.9639,6.2783,8.9349,8.4471,8.7964,7.926,6.2285,5.401,4.9896,4.3536,4.0657,3.7465,3.7158,3.3478,1.9953,1.1431,0.33968,1.3994,-0.29852,-0.67922,-0.72213\n0.51147,1.2436,1.7659,1.501,3.2634,0.40966,-0.73117,-1.4588,-1.9861,-2.054,-1.3697,-2.0657,-0.3504,-0.45265,-0.73877,-1.8505,0.6877,0.54513,1.1215,1.7032,2.0543,3.1665,4.2606,6.4882,6.7282,6.4145,7.5471,10.082,9.8304,9.6002,11,10.51,10.245,9.0449,6.5347,5.7887,5.6652,5.0361,4.3698,4.9051,3.11,1.7121,1.6394,1.5886,0.49644,0.47728,-0.33272\n1.5034,1.511,1.494,1.2918,1.1464,0.5649,-0.46788,-0.96947,-0.6156,-1.7521,-1.6554,-1.3015,-0.84921,-0.18533,-0.33426,-0.059068,0.42242,0.38156,0.97147,2.3345,3.1003,3.8256,5.4831,6.4541,7.3184,8.236,10.111,12.412,12.381,12.814,12.834,12.382,11.097,10.115,9.662,6.9026,6.0768,5.6733,5.3069,5.1871,2.8476,3.1061,2.3095,1.4635,0.63751,0.37188,-0.23234\n0.82025,-1.1028,-0.058754,0.78256,0.62778,0.93532,0.23068,-0.25819,-1.1273,-1.995,-1.1155,-1.0458,-0.74703,-0.38554,0.15896,0.65035,1.1663,1.6644,1.9626,3.3172,4.4798,4.4585,5.7169,8.0412,10.304,11.422,13.098,15.962,15.016,14.93,14.501,13.482,12.664,12.876,10.522,7.5518,5.9204,6.0431,6.1942,5.8191,3.7748,3.4188,2.1919,1.7273,0.6118,0.13855,0.035515\n0.4221,0.08885,0.82286,1.2326,0.62089,-0.25473,0.57064,0.10342,-0.54242,-1.1245,-0.72286,-0.7663,-1.0966,-0.38818,0.65058,1.7713,2.2671,2.4655,2.8751,4.1947,3.8982,4.0147,5.7756,9.6443,12.854,13.18,NaN,NaN,18.386,16.417,14.919,14.02,13.605,12.001,10.797,8.4539,6.9595,7.409,7.0359,5.9883,4.0981,3.2452,2.4551,1.8494,1.0423,0.31355,0.095899\n-1.5201,0.034029,0.022695,-1.0046,0.26313,-0.52138,-0.077266,0.12134,-0.81065,-0.192,-0.42505,-0.2421,0.17504,0.86138,1.969,3.1082,3.1442,3.0997,4.0875,4.8897,3.853,3.8933,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,14.736,12.675,10.087,9.2768,8.7502,8.5914,7.8058,5.8354,4.6649,3.5707,1.8607,1.2399,0.82566,0.70115,0.77663\n-0.4238,1.6153,-3.6909,-2.2385,-0.63081,-0.8639,-2.0969,-2.8524,-2.8556,-0.15098,-0.11579,0.28729,0.64014,1.5743,2.5893,3.5149,4.1474,4.5936,5.1961,5.5099,5.0305,5.3394,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,12.848,9.4804,8.574,8.2373,8.5622,7.5993,6.3579,4.9232,4.2425,2.2977,1.421,1.3786,0.94669,0.86803\n2.1684,2.9595,0.94726,-1.6838,-0.5429,-0.96033,-2.2856,-2.8302,-2.0499,-0.5347,-0.20475,0.28603,1.193,1.6437,2.659,3.6282,4.8821,5.316,5.4004,6.0448,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.9915,9.0486,7.3316,5.7725,5.0483,4.1376,1.9136,1.7244,1.2448,1.0823,0.81372\n2.1077,2.5739,2.1531,2.7435,2.0431,1.1343,-2.5626,-2.3598,-1.517,-0.41312,0.26283,0.80744,1.1484,1.4956,2.5563,3.265,3.8107,4.1981,5.1869,7.2818,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.1098,8.8184,6.6496,5.1387,4.3327,3.1485,2.1631,1.7705,0.98668,1.2365,0.8387\n1.89,2.0265,1.9372,0.84351,1.4033,0.27644,-2.485,-2.7775,-2.7793,-0.82509,-0.18486,0.32018,0.8349,1.7936,2.1601,2.674,3.2602,3.8405,6.3074,7.3539,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.0583,3.7825,3.4854,1.8835,1.2739,1.9342,1.0512,1.6044,1.2304\n1.1006,-0.087808,0.23094,0.2157,1.0583,-0.88368,-1.959,-2.3767,-3.6043,-0.6066,-0.8446,-0.031983,1.0199,1.0373,2.5619,1.9018,1.9813,4.0054,5.3227,6.7621,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.8397,3.3261,3.0054,1.4514,1.3197,1.9851,0.95874,1.1891,0.36126\n0.88471,1.157,-0.3371,0.051858,0.32769,-0.82017,-1.2216,-2.3435,-1.9666,-0.38553,-0.57909,-0.61662,0.40866,-0.92194,2.1556,2.0456,1.3684,2.3303,4.7376,3.7013,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.1723,3.1652,2.8108,2.4929,2.0365,1.0983,0.78282,0.043454\n1.5907,1.0962,-2.015,-0.0044818,-0.29654,-0.54979,-0.18505,-0.26118,-1.4225,-0.91134,-0.35431,-0.16412,-1.0259,-0.36866,1.8254,2.1572,2.7904,3.0761,3.9807,2.8433,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.6135,3.6245,3.0098,1.8837,1.3142,0.38001,-0.22157\n0.17003,-0.98723,-0.56075,1.4897,0.66474,-0.80201,-0.76163,0.47365,-1.0396,-0.99121,0.79957,1.15,1.3301,0.7593,0.032094,1.8416,1.9983,2.9636,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.8523,2.823,2.9577,2.1492,0.59037,0.28018,-0.61087\n0.48016,-0.18289,-0.13306,-0.32922,-0.41056,-1.6262,-1.1543,-0.055198,-0.12383,-0.78362,0.84826,1.3224,1.2434,0.30268,0.48117,1.8872,2.0391,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.6647,2.7614,2.0981,1.4408,0.72916,1.0728,0.81131\n1.0002,0.39262,-0.12289,-0.78289,-1.0658,-1.3836,-0.71984,-0.5545,-0.25547,0.32852,0.77958,1.2088,1.1488,1.6139,1.7324,2.198,2.2474,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.6689,NaN,NaN,3.2471,2.5427,2.454,1.4281,1.105,1.7816,2.4169,1.7047\n-0.054473,0.45513,-0.091149,-0.087234,-0.03842,-0.060318,-0.35246,-0.32082,0.14785,0.66503,0.87044,1.9485,1.3951,1.2573,1.8844,2.3402,2.8616,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.6064,NaN,NaN,NaN,NaN,NaN,NaN,6.1394,5.5653,3.7491,2.1371,1.7043,2.0364,1.7925,1.7709,1.8383,0.94672\n-0.020919,2.5294,1.1772,0.51193,0.71207,1.1244,0.97555,0.86077,0.29779,0.34685,0.7221,1.486,1.4364,2.2807,3.3684,3.8942,3.8966,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.75179,-1.7368,NaN,NaN,NaN,NaN,8.1847,6.2965,5.3753,2.8888,1.6618,2.1132,2.6153,1.6866,1.3847,1.0454,-0.83324\n1.6928,2.1347,0.83755,1.6399,1.0426,1.0822,1.4625,1.135,0.71507,1.6731,1.2474,1.3509,1.6345,2.3018,2.8847,4.1099,4.3242,1.742,1.4515,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.4843,NaN,NaN,NaN,NaN,NaN,8.1207,7.1686,5.5302,1.5017,1.6942,1.4508,1.7512,2.8272,1.9739,2.0326,0.96236,-0.71042\n2.0405,2.0763,1.9251,2.1748,1.7326,1.2117,1.0869,0.91838,1.4267,2.0108,1.6347,1.6238,1.1814,1.3847,0.89435,1.2625,2.5939,1.1522,0.18271,NaN,NaN,NaN,-1.9304,NaN,NaN,NaN,NaN,NaN,-0.69437,-3.5657,NaN,NaN,NaN,NaN,6.1396,7.2155,5.7778,4.9716,2.7924,1.9359,1.2404,1.8056,2.4978,2.2999,2.3148,1.0452,0.097719\n2.3733,2.6846,2.4653,1.7624,1.217,0.80838,0.68232,0.86195,1.2881,1.1767,1.107,1.3698,0.96928,1.1381,1.0535,0.75675,2.6898,2.0353,1.9271,1.1187,0.16142,-0.95248,-1.601,NaN,NaN,NaN,NaN,NaN,-0.95979,-0.83638,2.6923,4.9205,4.8719,5.4692,5.2217,4.7675,4.8331,3.2704,2.0553,1.817,2.5349,2.175,1.8157,1.9655,1.9028,-0.17034,-1.0605\n2.2721,2.5181,2.4902,1.513,1.0377,0.85634,0.70044,0.7279,0.59376,0.67772,0.76955,1.0473,1.2866,1.4384,2.0069,2.3928,2.8912,2.1389,1.5289,1.5196,0.31874,-1.0826,NaN,NaN,NaN,NaN,NaN,NaN,1.3667,2.5782,2.568,3.4953,4.2051,3.9501,3.3953,3.0389,2.9914,1.5853,1.3664,1.855,2.5663,2.8177,1.5503,1.11,1.1697,0.67736,-0.70531\n2.2869,2.2468,2.1918,1.7763,1.3712,1.0042,1.0969,0.87519,0.38672,1.3369,0.62088,0.8525,1.1172,1.4085,2.1976,2.2429,1.4497,2.0093,2.0917,3.4772,0.91921,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.6304,2.4034,3.3647,5.0762,3.5643,3.1363,2.357,2.9107,2.1925,1.3554,1.1848,1.6939,2.6092,2.6222,1.6299,1.6017,-0.16035,-1.2963,-1.9743\n2.7524,2.9367,3.1891,2.733,1.6723,1.7944,1.5081,1.8279,2.0186,1.686,1.0689,0.90872,1.1028,1.4458,1.8028,1.7603,2.0344,2.3459,1.635,0.88735,-0.013174,0.51678,3.0109,2.2098,0.12824,NaN,NaN,NaN,2.1804,2.9152,4.1938,4.2389,3.8567,2.302,1.6213,1.5587,1.664,0.54742,0.48738,1.0114,2.2791,1.5945,1.1268,1.5616,0.31453,-1.6516,-2.9087\n3.1637,3.632,3.5609,1.8586,1.594,2.0527,1.9245,2.0016,1.3312,1.58,1.3518,1.2272,1.4261,1.5768,0.50348,0.86927,1.8417,1.7875,1.0694,0.3688,-1.0568,-0.75932,0.92068,0.098565,-0.462,-0.69347,-1.0866,0.32379,1.4353,3.4963,4.5627,3.0438,1.8487,1.0298,1.0824,1.8394,1.0673,0.75495,0.078856,0.89923,1.9275,0.57806,-0.82776,-0.28242,0.36064,-0.29737,-1.5877\n2.8248,2.9147,2.8701,2.0131,1.8842,0.99463,1.5283,1.2936,1.1599,1.2212,0.96632,1.121,1.4886,1.259,0.79028,0.7286,1.7766,1.4445,0.86654,0.31482,-0.56039,-1.3693,-1.6922,-0.90452,-1.0565,-1.1047,-1.0171,-0.12914,0.98178,1.5261,1.6951,0.84036,-0.19473,0.066705,0.65343,0.72084,-0.5704,-0.017919,-0.054741,0.93508,1.1623,-0.023859,-2.1264,-2.9811,-2.0291,-1.5166,-0.83329\n3.196,2.5315,1.5697,2.3897,2.444,1.2692,1.1199,0.89603,0.59891,0.21042,0.50015,1.0279,1.6641,1.548,1.0445,0.86178,0.94718,1.1103,1.0193,0.05694,-1.4621,-2.0125,-1.4103,-0.98332,-1.2756,-1.2431,-0.40208,0.49128,0.20128,1.0688,0.37555,-0.13022,-0.010344,0.056377,-0.28273,-0.50777,-0.94954,0.11599,0.73516,0.25202,-0.039144,-0.44895,-2.2812,-1.2158,-0.8417,-1.2049,0.79462\n3.4901,3.3304,2.2845,2.9781,2.837,2.0566,0.85816,0.41382,0.97567,0.55574,0.63078,1.1097,1.6722,1.7395,0.88154,0.37913,0.54116,0.24798,-0.40383,-2.2274,-1.5929,-1.2725,-0.50817,-0.54775,-0.74306,-0.54007,-0.2638,0.84134,-0.73502,0.75805,0.15888,-0.017185,-0.58173,0.67064,1.001,0.85419,-1.0316,0.36213,1.0719,-0.80165,-0.42268,-0.74569,-2.532,-2.2047,-1.2955,-2.4065,-1.2301\n3.3669,3.3677,3.0309,3.2575,2.8645,3.0997,1.3437,2.0998,1.6917,1.4904,1.2563,0.86981,1.6034,0.85404,0.060794,0.028965,0.079308,-0.84329,-1.674,-2.185,-1.4836,-0.5066,0.072346,0.1732,-0.33507,-0.50788,-1.3488,0.48902,-0.36109,0.45736,0.01711,-0.48731,-0.82207,-0.28975,1.3655,0.16846,-0.42411,-0.0010872,-0.51795,-1.1311,-1.3111,-0.64382,-0.37121,-1.6013,-2.4429,-1.9852,-1.0533\n2.6073,3.4651,3.6118,3.0483,2.6104,2.0612,0.96626,1.5333,0.91041,1.5027,0.42265,-0.15597,0.82177,1.284,-0.20862,0.005156,-0.35036,-0.69346,-0.98734,-1.7056,-0.80291,-0.17399,0.18256,0.16153,-0.17668,-0.84417,-1.9959,0.54134,0.48116,0.49627,-0.39944,-1.0157,-0.96214,-0.20312,0.74117,-0.15799,-0.62882,-0.83261,-1.5817,-1.4838,-1.3041,-1.2408,-1.3778,-0.35298,-1.9114,-1.3329,-1.3836\n3.5564,3.6895,3.5783,2.3346,2.8421,2.0744,1.614,1.3439,1.1355,1.5562,0.67175,0.76607,0.77139,1.0917,0.48879,0.43576,0.30992,-1.3251,-1.5223,-1.8131,-0.64858,-0.24676,-0.10044,-0.27113,-0.37772,-0.48042,0.33142,0.66753,0.65105,0.57269,-0.094283,-0.63103,-0.5109,0.22433,0.46592,-0.074263,-1.1092,-2.302,-2.7679,-1.8477,-1.2453,-1.1915,-1.4072,-1.0877,-1.958,-1.4786,-1.6614\n3.3691,3.3661,3.1737,3.4469,4.0397,2.3797,2.9997,2.668,2.3082,2.3871,2.5363,1.3622,0.87332,1.1622,0.94105,-0.013534,-0.30539,-2.5056,-2.9499,-2.16,-0.33559,-0.14336,-0.68604,-0.8662,-0.60803,0.21075,0.66414,0.90943,0.84799,0.55643,-0.48877,-1.0968,-0.34174,0.018943,0.35968,-0.49631,-1.7784,-2.4222,-2.5048,-1.8598,-1.366,-1.2865,-1.6504,-1.3223,-2.365,-1.6997,-1.4178\n1.3844,2.1733,1.7291,2.6976,3.4741,3.791,3.0271,2.1004,2.6263,4.7628,4.0698,1.1553,0.7231,1.1463,1.842,1.5661,-0.021438,-2.2626,-0.78302,-0.062341,0.006547,-0.29788,-0.92989,-1.1555,-0.77846,-0.319,0.48656,0.55266,0.88091,0.55339,-0.86122,-1.5446,-1.8581,-0.87341,-0.75001,-0.61612,-1.4768,-1.9838,-2.1752,-1.771,-1.0502,-1.25,-1.1876,-1.2731,-2.2855,-2.0012,-2.7242\n1.7722,2.3458,2.6901,4.0324,2.1827,3.178,2.1028,2.2678,3.3485,3.9393,2.666,1.162,-0.21807,-0.0060301,1.4526,1.0934,-0.98732,-0.51681,0.41001,0.18019,-0.64242,-0.50409,-0.94507,-0.2954,-0.56,-0.8153,0.076899,-0.069259,0.17178,0.15864,-0.79514,-1.5898,-2.6184,-2.0428,-0.90684,-0.97936,-1.6634,-1.7395,-1.8289,-1.6766,-1.4917,-1.3664,-1.1273,-0.19816,-1.3608,-2.0167,-2.5749\n3.6104,2.362,6.0653,5.6509,1.9977,2.9199,2.7641,2.6825,2.1201,1.2257,3.7472,3.0059,-1.1255,-0.42857,-1.2229,-1.2747,-0.10338,-1.0464,0.030585,-0.19695,-1.2255,-0.076807,-0.040518,0.11486,-0.30483,-0.35773,-0.052215,-0.71913,-0.32345,-0.21002,-0.64238,-1.0829,-1.7536,-2.0387,-1.015,-1.0773,-1.7245,-1.9913,-1.2112,-1.174,-1.8404,-1.4653,-0.53596,-0.57058,-1.385,-1.5997,-2.1735\n4.4476,2.3286,5.7935,5.5799,4.4601,4.3448,2.8085,3.6822,4.0132,3.2425,2.7988,3.2129,0.60387,-0.62194,-3.0339,-2.8737,0.15764,-1.6124,-0.82575,-0.11391,-1.4413,-0.8586,0.12518,-0.026073,-0.042051,-0.28995,-0.28352,-1.0384,0.29963,-0.28867,-1.1803,-1.2878,-1.4011,-1.5385,-1.3652,-1.0299,-1.5925,-2.3176,-1.2949,-0.40056,-0.87235,-1.0821,-0.77852,-1.0174,-1.6238,-1.4848,-2.5071\n5.2756,2.1756,2.1051,4.1947,4.096,2.61,2.1418,4.8098,5.0082,5.0342,2.5099,2.7452,1.7124,0.46038,-1.3718,0.22193,1.4962,0.93444,0.46986,0.44606,-0.27196,-0.91831,0.053132,-0.23878,-0.64645,-0.86374,-0.57675,-0.47451,-0.062046,-0.76778,-2.4529,-1.4937,-1.6913,-2.0588,-1.5594,-1.331,-1.547,-1.8646,-1.1548,-0.60749,-0.85556,-1.4916,-1.4549,-2.2678,-1.9946,-1.9363,-2.6299\nNaN,3.3521,0.94952,2.4403,3.1481,1.5503,2.719,6.7623,NaN,NaN,3.3134,3.412,3.3896,0.7839,0.8612,1.2149,2.9211,2.5572,3.1607,1.7315,0.049604,-0.53387,-0.79359,-0.90498,-0.86357,-0.81497,-0.42617,-0.55888,-0.52475,-1.5611,-1.9927,-1.7751,-1.8814,-1.9931,-1.7187,-1.7126,-1.1958,-1.5314,-1.3635,-1.137,-0.98914,-2.1197,-1.8711,-2.7551,-2.9593,-3.3223,-3.2616\nNaN,NaN,1.9632,2.3409,2.7158,2.7483,3.1023,5.3491,NaN,NaN,NaN,3.1772,3.1239,3.4898,3.1618,2.7558,2.9577,4.4805,4.6427,1.669,-0.070647,-0.67774,-1.0127,-0.92776,-0.82218,-1.6473,-0.86516,-0.81143,-0.60814,-1.0422,-0.83849,-2.0653,-2.7399,-1.7915,-1.5421,-2.0292,-1.5317,-1.4476,-1.0216,-1.5288,-1.5336,-2.2298,-2.0241,-2.3286,-2.8363,-2.378,-2.4344\nNaN,NaN,0.57671,2.4428,2.2274,1.9407,3.3021,4.7546,NaN,NaN,NaN,NaN,NaN,NaN,3.5563,NaN,NaN,4.4578,3.5082,0.54144,0.79353,-0.35313,-0.44139,-0.40621,-0.88693,-1.6372,-1.6549,-1.4747,-0.63236,-0.48815,-0.13479,-1.1372,-2.1768,-1.8327,-1.6681,-1.8365,-1.5893,-1.5265,-1.2576,-1.3414,-1.5223,-2.0779,-1.5704,-2.2699,-2.3085,-1.9893,-2.1108\nNaN,NaN,NaN,NaN,1.7273,3.1847,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.77785,-1.1433,1.1046,0.84615,-0.14611,-1.7396,-1.6959,-1.2873,-0.42235,-0.2163,0.022318,-0.94095,-1.4195,-1.3962,-2.5634,-2.466,-1.887,-1.5717,-0.92634,-0.97312,-1.5746,-2.3278,-1.7681,-2.1686,-2.194,-1.8547,-2.5033\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.062085,-0.12,0.92471,2.6166,0.72302,-0.21386,-0.65063,-0.2653,0.17995,0.019215,-0.65408,-1.2989,-1.7316,-2.0837,-3.1777,-2.8103,-1.7039,-1.146,-1.0446,-1.9841,-1.9851,-2.4074,-2.2401,-2.3255,-2.4289,-1.8512,-2.2581\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9936,2.4228,0.33086,-0.050379,-0.52902,-0.024601,-0.084034,0.062309,-0.41881,-0.68857,-1.6978,-2.5455,-2.8799,-2.2403,-1.6252,-1.4718,-1.5513,-2.3206,-2.2273,-2.3243,-2.6896,-2.4959,-2.3628,-2.3934,-2.0025\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.82669,2.2487,0.57452,-0.10595,0.0034623,0.53263,0.26162,0.36763,-0.63414,-1.5081,-2.0284,-2.7501,-3.0094,-1.6186,-1.6363,-1.7075,-1.3682,-1.7639,-1.7461,-1.9633,-2.6818,-1.8337,-1.8919,-2.6229,-2.2262\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.66866,0.62898,0.82078,1.0688,1.0162,0.50198,0.60261,0.040201,-0.45238,-2.3068,-2.2614,-2.3235,-2.7443,-1.6437,-1.9922,-1.6885,-1.1594,-1.4665,-0.7323,-2.2447,-3.1458,-2.0982,-2.0572,-2.4216,-2.396\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.068701,0.35517,1.1066,1.7898,1.7712,0.6449,0.5795,-0.025993,-1.4309,-2.965,-0.6289,-0.78027,-1.0587,-1.2571,-1.8479,-1.515,-1.2025,-1.5863,-1.5253,-2.3418,-2.8584,-2.3463,-1.9891,-2.3885,-2.8163\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.60026,1.2802,1.7683,1.9956,1.9215,1.3512,0.5159,-0.84615,-1.2491,-1.7197,-0.45822,-1.1792,-1.3118,-1.5478,-1.627,-1.7434,-1.5914,-1.4319,-1.6002,-2.0355,-2.874,-2.9645,-2.428,-2.9292,-3.5945\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.87869,2.4778,2.1877,1.9668,1.007,0.35823,0.46808,-0.88196,-1.3534,-1.6827,-1.5252,-1.0213,-0.58458,-1.9221,-1.9453,-1.3159,-1.6353,-1.97,-2.9194,-2.8548,-2.4851,-3.6824,-3.8214\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2806,1.4163,0.9095,0.90156,1.3469,0.49076,-0.62961,-0.93838,-1.1202,-0.93968,-0.67708,-2.5231,-1.7796,-0.9477,-0.72545,-1.0238,-1.5217,-1.7122,-1.7796,-3.2913,-3.7445\nNaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.57085,-2.8508,0.93293,1.4004,1.7725,0.97796,0.25436,0.12363,-0.12827,0.3959,-2.7971,-3.6737,-0.38686,0.2399,-0.55924,-1.1001,-2.1143,-1.9897,-1.2454,-1.9686,-3.216\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070219-070430.unw.csv",
    "content": "-2.9527,-2.583,-2.9728,-2.7646,-2.2989,-1.7894,-1.5946,-1.465,-0.99282,-0.4398,0.34968,0.47981,0.60257,0.39451,-0.051462,-0.050287,0.46877,0.44144,0.12414,-0.12838,-0.22962,-0.18852,0.059698,0.35776,0.25467,-0.1439,-0.11548,-0.35751,-0.27881,-0.51377,-1.1444,-0.94016,-1.1797,-1.8175,-2.31,-2.8838,-4.3692,-4.9125,-5.0892,-5.6222,-5.304,-5.0999,-4.8624,-5.5545,-7.5807,-8.3231,-8.1736\n-2.9674,-2.7905,-3.1648,-2.8525,-2.1849,-1.4389,-1.1101,-1.2939,-0.64726,-0.11808,0.52063,0.86013,0.7973,0.48222,0.44613,0.75612,0.74664,0.28481,-0.059317,-0.15536,-0.30926,-0.0060558,0.30139,0.45553,0.45444,0.50657,0.53287,0.83414,0.77107,0.82567,0.16627,-0.67825,-1.2169,-1.6636,-1.8305,-2.6946,-3.888,-4.4489,-4.888,-5.5902,-6.3866,-5.7346,-5.7953,-6.3108,-7.1985,-8.044,-8.2884\n-3.5089,-3.6624,-3.2781,-2.741,-2.3351,-1.1184,-0.5784,-0.31839,-0.078413,0.3561,0.4108,0.42534,0.52849,0.35942,0.5754,0.96288,0.75627,0.33898,0.18664,0.18469,0.30908,0.35912,0.68228,0.94531,0.77895,0.83543,0.94221,0.48185,0.84437,1.4073,0.61287,-0.92623,-1.1499,-1.0583,-0.69357,-1.8219,-3.8486,-4.4444,-5.057,-6.1453,-6.5734,-5.4616,-5.2201,-6.1433,-7.432,-7.8309,-8.5425\n-4.6474,-5.0436,-3.876,-2.7895,-2.2423,-1.1283,-0.6625,0.37824,0.89332,0.99627,0.75927,0.46875,0.56882,0.5337,0.30752,0.55292,0.41165,0.46107,0.40104,0.092535,0.33862,0.65312,0.86901,0.92025,1.0611,1.3343,1.3764,1.2285,1.2685,1.0842,0.25058,-0.36463,-0.68844,-0.97787,-1.1385,-2.363,-4.0941,-4.9889,-4.7462,-4.5366,-4.6374,-4.5597,-5.0715,-6.02,-6.7838,-7.1743,-7.4337\n-5.0045,-6.1672,-5.262,-2.8513,-2.1818,-1.3462,-0.83229,0.38028,1.3327,1.0401,0.77137,0.97018,0.90003,0.65715,0.52546,0.35798,-0.29452,-0.25815,0.45662,0.83373,0.82199,0.95117,0.75827,1.2758,1.683,1.7422,1.6922,1.2451,1.3839,1.3092,0.41938,-0.31821,-0.50732,-0.97412,-2.0777,-3.4613,-4.4175,-4.7756,-4.4965,-4.0322,-4.3515,-4.9321,-5.4389,-5.9328,-6.3905,-6.6994,-7.2837\n-4.63,-5.574,-5.1215,-2.4342,-2.3871,-1.8513,-1.1061,-0.39408,0.4074,0.66098,0.63617,0.68178,0.62771,0.31933,0.58606,0.34862,0.067492,-0.2605,0.19921,0.66183,1.0986,0.95874,1.0106,0.89327,1.2839,1.632,1.6144,1.3472,1.5115,1.4888,0.53705,0.29746,0.24427,-0.25551,-1.0496,-2.386,-4.066,-4.528,-4.8257,-4.5946,-4.8937,-5.5876,-6.1723,-6.5345,-6.8621,-6.883,-7.307\n-4.8953,-5.4135,-4.8108,-2.8447,-2.4614,-1.349,-0.57969,-0.50395,-0.36471,0.2777,0.2865,0.21939,0.1109,0.059286,0.33134,0.092596,0.02293,0.23537,0.48844,0.96757,1.5867,1.728,1.687,1.4159,1.4793,1.7803,1.7637,1.6409,1.9556,1.4051,0.7452,0.74323,0.30229,-0.40444,-1.1257,-1.7554,-2.593,-4.0982,-5.5361,-6.1835,-6.6348,-6.6162,-7.2872,-7.1338,-6.6817,-6.3116,-6.6956\n-4.4837,-4.7101,-4.0375,-2.5692,-2.3059,-0.88006,-0.1415,-0.16397,-0.32944,-0.27541,0.098272,0.23026,0.10945,-0.019587,0.2513,0.18642,-0.003643,0.34214,0.94296,1.4432,2.0362,2.1538,1.7808,1.7728,2.1233,2.6275,2.295,1.9646,1.2127,1.211,1.241,0.67619,0.052155,-0.38346,-0.935,-1.7284,-2.9271,-4.4136,-5.8963,-6.9195,-7.2103,-7.2783,-7.9504,-7.8464,-7.492,-6.5023,-6.5832\n-3.2651,-3.2189,-3.0837,-1.9634,-1.3205,-0.54655,-0.33263,-0.18467,-0.25736,-0.43909,-0.49334,-0.41243,0.033644,0.27866,0.46224,0.31366,0.31242,0.45745,1.0505,1.5336,1.8754,1.8308,1.8119,2.1416,2.4558,3.6687,4.029,3.1198,1.8767,2.2561,1.7072,0.38799,-0.34999,-0.44007,-0.81018,-1.9015,-3.3687,-5.1161,-6.1885,-6.6064,-6.732,-6.9243,-7.3779,-7.6661,-7.3325,-6.9224,-6.5938\n-2.6282,-2.4238,-2.4198,-1.626,-0.79063,-0.32848,-0.17674,-0.073133,-0.22749,-0.71338,-1.2014,-0.676,0.0056973,0.31758,0.49502,0.041763,0.16667,0.64201,1.1426,1.091,1.4547,1.9278,2.4852,3.148,3.2531,4.3584,4.7266,3.2253,2.3298,2.3175,1.1379,0.56444,-0.20709,-0.13785,-0.34587,-1.76,-3.5777,-5.182,-5.8477,-5.9642,-6.1859,-6.5847,-7.2741,-7.4933,-6.9671,-6.8834,-6.6685\n-2.4191,-2.2611,-2.4077,-1.7814,-1.1002,-0.31517,0.083067,0.26838,0.26823,-0.11782,-0.88468,-0.31503,0.24291,0.04895,-0.21282,-0.80684,-0.33378,0.53495,1.2345,1.3271,1.5981,2.3758,3.219,3.8305,4.0974,4.6134,4.4217,3.4022,2.8288,2.3644,1.6526,0.84054,-0.1088,-0.57638,-0.46989,-1.3267,-2.7719,-4.0048,-4.4217,-4.4924,-4.9247,-5.5831,-6.3567,-7.3304,-7.0412,-7.4533,-7.5058\n-2.1335,-1.6796,-1.295,-0.95112,-0.98072,-0.44658,0.17495,0.3373,0.42387,0.12915,-0.23241,0.30758,0.42238,0.12635,0.0403,0.017942,-0.13331,0.46482,0.85574,1.0963,1.2732,2.0507,3.1757,4.26,4.5474,4.5163,3.9221,3.3879,2.8965,1.8771,1.1011,0.24615,-0.27637,-0.69832,-0.99858,-2.2727,-3.2282,-3.7054,-3.744,-3.519,-3.9804,-4.7153,-5.6067,-6.3282,-6.8714,-7.8352,-7.6174\n-1.6606,-1.2125,-0.72066,0.23535,0.65187,0.19672,0.34278,0.35817,0.44263,0.090601,0.0010471,0.23932,0.41786,0.34735,0.50317,0.59484,0.55468,0.63926,1.0253,1.549,1.6901,2.2066,2.8897,4.06,4.8513,4.3416,3.6563,3.018,2.5586,1.4674,0.10897,-0.19878,-0.57639,-1.0562,-1.3697,-2.1057,-2.7392,-3.1665,-3.1865,-2.6374,-3.0605,-4.1778,-4.963,-5.5125,-6.8811,-7.1106,-6.826\n-1.4295,-0.72337,-0.61067,-0.17979,1.0006,1.0434,0.67653,0.51586,0.31161,0.13727,0.11657,0.41331,0.82405,0.77685,0.70505,0.79528,0.84918,1.0044,1.2456,1.2304,1.3993,2.0377,2.6785,3.2995,3.7575,3.7306,3.6166,3.2576,2.6979,1.0845,-0.43913,-0.63909,-0.8953,-1.6134,-2.1958,-2.7777,-3.0276,-2.8685,-2.6159,-1.9948,-2.1772,-3.5425,-4.3267,-4.8365,-5.3494,-5.857,-6.4376\n-1.2406,-1.108,-0.80376,-0.13651,0.47286,0.083229,0.17408,0.33776,0.31217,0.46876,0.282,0.8305,1.4175,1.5996,1.4177,1.3639,1.1017,0.80943,1.0941,1.1237,1.4845,2.4612,2.8911,3.119,3.2989,3.5348,3.5162,2.5556,1.1365,-0.17341,-0.5716,-1.296,-2.4862,-3.2482,-3.0842,-3.0756,-3.2004,-2.9611,-2.5429,-2.1814,-2.6147,-2.7332,-2.8469,-3.5383,-3.637,-4.4314,-4.2772\n0.040915,0.13112,-0.049049,0.40867,0.59788,0.16949,0.082533,0.26239,0.34001,0.42064,0.26824,0.68796,1.0555,1.0043,0.91542,0.83292,0.54497,0.52273,1.2431,1.6335,2.0734,2.9323,2.9255,2.7499,2.9296,3.0185,2.7934,1.4528,0.35604,-0.86551,-1.9732,-2.5456,-3.5718,-4.3323,-4.6667,-3.8217,-3.0569,-2.9085,-2.714,-2.2751,-2.718,-2.9274,-2.5971,-2.9282,-3.0725,-4.9604,-5.0626\n0.57854,0.83011,0.6833,0.48168,0.28507,0.11275,0.26438,0.35182,0.074312,0.36954,0.40829,0.54584,0.57942,0.33245,0.71558,0.39948,-0.26598,0.98182,2.0039,2.2362,2.9281,3.162,2.7466,2.2674,2.3698,2.9298,2.7008,1.8042,0.55595,-1.1636,-3.1092,-2.8828,-3.5975,-4.3019,-4.6007,-4.3505,-3.6041,-3.7061,-3.3395,-2.9718,-2.3415,-3.0073,-3.4601,-2.7831,-2.0166,-2.4941,-2.9939\n-1.4406,-0.029055,0.2754,0.044813,-0.31289,-0.23098,0.50689,1.0966,0.89058,1.0029,0.95126,1.0087,1.2857,1.2642,0.81306,0.53113,0.25607,0.91876,2.1943,3.8543,3.1755,2.8549,2.9431,2.472,1.9226,1.6536,1.6516,1.2509,-0.13742,-2.0088,-3.4153,-3.2209,-3.3563,-3.7767,-4.083,-4.1967,-3.8836,-4.1999,-4.3334,-3.5178,-2.3153,-1.1088,-2.002,-2.8805,-1.5977,-0.85742,-0.99416\n-1.0486,-0.55692,0.39698,0.34617,-0.152,-0.47556,0.13555,1.4828,1.3562,1.1376,1.3373,1.3175,1.5611,1.7088,1.2836,1.0933,1.4149,2.1316,2.962,3.6288,4.1334,3.8525,2.905,2.9053,2.4747,1.5384,1.6057,1.2427,0.28488,-1.0668,-2.4536,-2.8823,-4.0067,-4.2601,-4.286,-3.7834,-4.3754,-4.5576,-4.8543,-3.6366,-2.893,-1.6317,-1.8456,-2.6423,-2.0959,-1.3799,-0.66281\n-0.43287,-0.098888,1.1611,1.1771,0.9452,0.82678,1.6298,2.0373,1.7313,1.2914,1.2164,1.4633,1.68,1.8528,1.9429,2.1094,2.1974,2.4984,3.1548,3.8239,3.8712,3.571,2.9452,3.1189,2.8403,2.214,1.6886,1.4026,1.188,0.6372,-0.69169,-1.9597,-3.669,-4.0137,-4.2829,-3.5546,-3.7799,-3.7926,-4.6369,-4.3396,-3.5344,-2.7226,-2.6083,-2.3337,-1.7484,-1.5166,-1.4534\n-0.19964,0.76284,1.4902,1.3493,1.3027,2.4315,3.172,2.585,1.6467,1.6391,1.9358,2.2005,1.9049,1.9318,2.7934,2.6816,2.8616,3.105,3.0686,3.3175,3.4974,2.744,2.5104,2.9613,3.172,2.3082,2.041,1.706,1.4274,1.5468,1.2181,-0.52382,-2.8849,-2.9208,-4.3652,-3.7536,-3.3392,-3.4494,-4.1404,-3.73,-3.1702,-2.9978,-2.6403,-1.9297,-1.6062,-1.4322,-1.2315\n0.68174,2.1283,2.3436,2.3702,2.7244,3.2703,2.8659,2.2595,2.0439,1.9218,2.0834,2.2365,2.0916,2.2012,3.5777,3.6431,3.134,3.5153,3.7082,3.165,3.5126,3.5701,4.1849,4.0031,2.8061,2.2948,2.0395,1.7791,1.1946,1.1052,0.97135,0.13728,-1.821,-2.1099,-3.9001,-3.9119,-3.7011,-3.7328,-4.2155,-4.1441,-2.5318,-2.8024,-2.5257,-1.9216,-1.5492,-1.3358,-1.0161\n1.5329,2.4895,2.4665,2.2364,2.8237,3.0249,2.1656,1.9306,1.6303,1.4675,1.6522,1.9585,2.1196,2.55,3.4584,4.149,3.3526,3.1556,3.4275,3.2028,3.7828,4.0501,4.5795,3.5184,2.5059,2.2423,1.8054,1.6759,1.1503,0.88951,0.52841,-0.19287,-0.99695,-1.4303,-2.4108,-3.154,-3.4131,-3.7627,-4.3504,-4.9651,-3.6046,-2.7875,-2.4171,-1.7208,-1.4796,-1.1629,-0.67971\n1.4439,1.6919,1.7803,1.4467,1.5275,2.1245,2.1169,1.665,1.3784,1.1957,1.3809,1.8371,2.395,2.9027,3.2897,3.809,3.1241,2.5098,2.583,2.8337,2.8312,3.4387,3.8346,2.65,2.111,2.1343,1.7636,1.0846,0.79098,0.29177,-0.38324,-0.75273,-1.1639,-1.489,-1.9702,-2.6485,-2.9214,-3.2016,-3.4796,-3.3823,-3.1785,-2.3636,-1.994,-1.8261,-1.7635,-1.4599,-0.529\n0.81882,1.1184,1.5432,1.9378,2.2473,2.1572,1.5901,1.2565,1.5073,1.7082,1.6668,1.7397,2.255,2.5838,2.9623,2.9406,2.3933,2.0355,2.1652,2.2637,2.2481,2.3725,2.0237,1.5672,1.4581,1.0595,0.78636,0.065626,-0.54735,-1.1566,-1.0032,-0.9853,-1.3742,-1.8891,-2.3109,-2.3904,-2.5462,-2.722,-2.7382,-3.0287,-2.869,-2.2647,-1.8858,-1.8562,-1.6367,-1.0009,-0.33107\n0.38296,0.39109,1.2657,2.0157,2.4119,1.836,1.1766,1.2297,1.8065,2.0751,2.0744,2.1308,2.1067,1.925,1.9624,2.5715,2.3509,2.0664,2.0182,2.0733,1.9338,1.3533,1.3529,1.1016,0.59374,0.15806,-0.073347,-0.44119,-0.64383,-0.99163,-1.5591,-2.0961,-2.0259,-1.6835,-2.0622,-2.1807,-2.6292,-2.755,-3.1142,-3.3624,-2.787,-2.3874,-2.0294,-1.8314,-1.3027,-0.40187,-0.091883\n1.2212,0.6764,1.4968,1.7148,1.8724,1.6901,1.2634,1.3226,1.68,2.0085,1.9983,1.9607,1.5415,1.3735,1.7455,2.2103,2.2372,1.8105,1.7423,1.716,1.3862,1.1906,1.3458,1.3176,1.0214,0.75978,0.049643,-0.39191,-0.39827,-0.34572,-1.4055,-2.0901,-2.5394,-2.0765,-1.9646,-2.0633,-2.8393,-3.2,-3.4946,-3.434,-3.059,-2.5473,-1.8505,-1.5237,-0.89926,-0.1946,0.14248\n1.4491,0.97425,1.0076,1.9503,1.8344,1.6276,0.89762,1.0618,1.4186,1.5413,1.7256,1.648,1.5338,1.6572,1.9679,1.6339,1.0867,1.2538,1.2614,1.2911,1.1406,1.3256,1.4401,1.5686,1.0993,0.94963,0.5907,0.28724,-0.47977,-0.56532,-0.96716,-1.4463,-1.9793,-2.1779,-2.5058,-2.9413,-3.2642,-2.9728,-3.087,-3.1034,-2.9617,-2.1359,-1.5279,-1.4013,-0.81479,0.0048161,0.5972\n0.72349,1.0028,1.6881,1.59,1.4243,1.2406,1.15,1.3285,1.422,1.4064,1.4971,1.445,1.2982,1.1365,1.2456,1.2205,0.7918,0.99934,1.3909,1.7211,1.6933,1.6277,1.424,1.2,0.74138,0.40838,0.1901,0.14928,-0.29919,-1.2781,-1.6214,-1.7734,-2.4255,-2.1645,-2.5836,-2.9737,-2.8855,-2.4053,-2.6179,-3.1163,-3.2596,-2.3363,-1.2491,-0.55107,-0.021496,0.91118,1.2662\n0.35716,0.21977,0.753,1.6595,1.4042,1.2196,0.8369,1.059,1.4935,1.6883,1.6828,1.7338,1.5386,0.87466,1.1154,1.1766,1.5644,1.9394,2.4471,2.6899,2.2466,1.9564,1.1225,0.89514,0.64586,0.53025,0.51656,0.18766,-0.27104,-0.91072,-1.5992,-2.4475,-2.9917,-2.843,-2.6632,-3.5375,-3.7801,-3.4616,-3.3832,-3.4176,-3.4729,-2.669,-1.5854,-0.19558,0.86772,0.74519,1.0761\n-0.31172,0.08621,-0.1031,0.24477,0.99943,1.4439,1.5775,1.566,1.6648,1.7406,1.9027,1.9332,1.8693,1.1091,1.6905,2.5322,3.1709,2.3557,2.3949,3.0755,2.7932,2.4656,2.8539,2.8641,2.0852,1.8424,1.4445,0.75554,0.13161,-0.27162,-0.90236,-1.7375,-2.557,-2.7239,-2.8665,-3.7702,-4.5587,-4.4769,-4.178,-3.7498,-3.3105,-2.5428,-1.6005,0.13654,1.4389,0.85139,1.2652\n-0.31334,0.29099,0.39805,0.40316,0.36915,0.73469,1.2957,2.0704,1.9548,2.0752,2.1439,2.4309,2.4651,2.1279,2.6278,3.423,3.438,2.7154,2.2419,2.135,1.3939,1.9058,2.3899,3.0366,2.743,1.8345,0.5957,-0.16082,-0.57964,-0.44742,0.15526,-0.56837,-1.1701,-2.6029,-2.8956,-3.1345,-3.7127,-3.9247,-3.955,-3.7861,-3.3147,-2.0921,-1.1784,1.1165,1.5077,1.32,1.3473\n0.40162,0.89293,0.18415,-0.51164,-0.44237,0.55935,1.4607,2.2108,1.8983,1.7232,2.0394,2.262,2.6438,3.1372,3.4308,3.2682,3.1806,2.1442,1.9099,2.0108,1.9326,1.8744,1.8032,2.5082,2.0606,1.155,-0.20367,-1.3269,-2.1996,-2.3481,-1.7669,-0.40005,-0.2117,-0.83435,-2.2086,-2.9674,-3.2775,-3.9397,-3.8121,-3.4778,-3.3534,-1.981,-0.91822,-0.53684,0.75268,1.3901,1.6587\n-0.07271,0.24413,0.41757,0.069006,0.032305,0.9302,1.4526,1.7595,1.5947,1.2757,1.903,2.093,2.4223,2.9059,2.9455,3.079,3.096,1.9454,1.5337,1.918,2.254,2.2215,1.9976,0.81887,0.36335,-0.077589,NaN,NaN,NaN,-1.8002,-1.7258,-1.2266,-0.13297,-0.28927,-2.0288,-3.2983,-4.123,-3.9883,-3.6143,-3.2449,-3.1779,-2.3328,-1.3868,-0.82191,0.38489,1.2406,1.4876\n-0.39622,0.38855,0.59497,0.57573,0.78173,0.79785,0.74333,1.191,2.3911,2.7465,2.3049,2.3964,2.0969,2.5579,2.4934,2.2556,1.7308,1.5408,1.3879,1.544,2.0064,1.9868,0.57633,-0.45534,-0.08853,NaN,NaN,NaN,NaN,NaN,-1.3049,-0.76228,0.01618,-0.60394,-2.3105,-3.7955,-4.1685,-4.0926,-3.7278,-3.3872,-3.1075,-2.7489,-2.2574,-0.95896,0.48225,1.2802,1.6042\n-0.8825,-0.58672,-0.35679,-0.0010471,0.8311,0.96138,1.1013,1.5498,2.9045,3.7262,2.9972,2.8776,2.3575,1.7895,1.4461,1.6293,1.5867,1.1566,1.2209,1.6163,1.6448,1.2599,-0.06218,-1.1514,-0.42343,NaN,NaN,NaN,NaN,NaN,-1.1668,-1.3486,-0.75027,-1.3494,-2.0935,-3.4313,-3.6374,-3.7647,-3.6722,-3.0721,-2.5477,-2.3864,-2.3318,-1.5633,0.4113,1.8061,2.0414\n-1.7372,-2.1076,-2.2206,-1.3929,0.58658,1.3809,1.7202,1.9019,2.4237,3.005,2.471,2.0957,2.0224,1.1268,0.98056,1.2581,1.6331,2.0882,2.2485,0.16934,-1.1703,-2.4591,-0.76961,0.52285,-0.59304,NaN,NaN,NaN,NaN,NaN,-2.0435,-1.9226,-1.7974,-2.7698,-3.0114,-3.3729,-3.2241,-3.2091,-3.4889,-2.7435,-1.9611,-1.4768,-0.94537,-0.41524,0.81318,2.0389,2.4718\n-1.9674,-1.4177,-1.4604,-1.445,-0.69718,0.20487,0.72111,0.75646,0.81008,1.6887,1.9348,2.2005,1.6733,0.56317,0.0087242,0.59405,0.91695,-0.090971,-1.1528,-2.1019,-2.9385,-1.9131,-0.91995,0.76031,-0.3919,-1.5541,NaN,NaN,NaN,-0.6258,-2.0541,-2.7467,-3.3059,-3.6225,-3.4318,-3.6361,-3.3753,-3.4683,-3.3861,-2.8562,-1.6727,-0.43245,0.22293,0.52653,0.52567,0.72151,1.5815\n-1.8159,-1.5403,-1.2794,-1.3197,-1.5013,-0.77324,-0.13276,-0.11349,-0.023157,0.32351,0.78412,0.8573,0.52041,-0.11847,-0.69717,-0.40516,-0.85365,-0.4923,-1.2748,-2.1402,-2.5459,-1.0609,-0.033791,0.93358,-1.4297,-2.4122,-4.367,-3.0645,-1.6074,-2.0446,-2.4756,-2.9376,-3.3427,-3.5197,-3.3934,-3.605,-3.6939,-3.6586,-3.5881,-3.0389,-1.6202,-0.035425,0.73536,0.88018,0.67966,0.79555,2.1309\n-2.3558,-1.881,-2.0965,-1.7242,-1.4562,-1.0361,-0.84105,-0.86385,-1.2512,-0.73714,-0.51546,-0.71498,-1.1254,-2.0497,-2.3068,-1.6424,-1.3697,-1.2082,-1.1031,-3.473,NaN,NaN,NaN,NaN,NaN,NaN,-2.9969,-2.4841,-1.8841,-2.3741,-3.1987,-3.5443,-3.7513,-3.9692,-3.8232,-3.6874,-3.7944,-3.6453,-3.3858,-2.777,-1.5774,0.0085964,0.80309,0.91994,0.71219,0.88784,2.0568\n-3.1945,-2.5098,-2.4143,-1.8834,-1.4371,-1.1379,-1.2561,-1.515,-2.1609,-2.4738,-2.5348,-2.0673,-2.1403,-2.697,-3.0686,-2.7357,-2.2755,-2.0229,-1.6906,-4.2417,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.1581,-3.1593,-3.8373,-4.2175,-4.2166,-3.9471,-4.1106,-4.2274,-4.2384,-4.0314,-4.0371,-3.213,-1.638,0.28591,0.47586,0.94604,1.449,1.5053,1.5781,2.1219\n-2.3444,-2.1155,-1.9141,-1.8609,-1.807,-1.7429,-1.7208,-2.137,-2.598,-2.7383,-2.9037,-2.3092,-2.4163,-3.2567,-4.005,-4.2201,-3.9228,-4.4821,-4.6673,-6.4708,-6.2152,NaN,NaN,NaN,NaN,NaN,-1.4102,-2.3902,-3.4154,-4.5355,-5.0181,-4.8221,-4.3071,-4.68,-4.8479,-4.5635,-3.8694,-3.6112,-2.882,-0.77346,0.80745,0.53022,1.0792,1.3253,1.4288,1.7391,2.2075\n-1.0664,-1.4869,-1.6916,-1.7128,-1.9542,-2.1117,-2.2239,-2.3551,-2.4442,-2.667,-2.7539,-2.7534,-3.337,-4.3943,-4.3945,-4.3833,-3.9812,-4.2059,-4.4139,-5.8079,-6.0451,-4.8871,-3.3991,NaN,NaN,NaN,-3.4726,-3.694,-4.3772,-5.0124,-5.4357,-5.5609,-5.4995,-5.4368,-5.1623,-4.593,-3.5929,-2.5352,-1.1497,0.66291,1.555,1.2452,1.3149,1.2976,1.579,2.3633,2.3102\n-1.024,-1.2644,-1.6129,-1.4121,-1.9977,-2.3697,-2.3899,-2.3198,-2.4124,-2.7153,-2.9354,-3.1827,-3.7663,-4.867,-5.7681,-5.6393,-5.1958,-4.5802,-5.2455,-5.9437,-5.04,-2.4021,-1.1518,-0.78617,-1.1435,-2.3202,-4.8385,-5.1305,-5.3704,-5.3436,-5.4067,-5.5966,-5.4212,-4.6158,-3.3825,-2.8179,-2.8055,-1.6229,0.20306,1.4448,1.671,1.6299,1.7485,1.7958,2.036,2.5975,2.9022\n-1.5563,-1.6588,-1.7037,-1.5045,-1.6328,-2.0701,-1.8999,-1.9595,-2.3278,-3.1926,-3.4666,-3.797,-4.0304,-4.9275,-6.4254,-6.669,-5.8466,-5.9754,-6.6915,-6.8467,-5.6248,-5.1691,-4.5278,-3.6377,-2.7864,-4.3144,-5.2028,-6.1618,-6.314,-5.971,-5.4289,-5.263,-4.7733,-3.9265,-2.9621,-2.2793,-2.044,-1.0751,0.58761,1.8358,1.8747,1.8832,2.1471,1.7018,1.8202,2.0713,2.3776\n-1.3857,-1.6947,-1.8336,-1.9634,-1.9913,-1.9976,-2.0032,-2.3256,-3.0415,-3.5423,-3.5836,-3.7842,-4.0503,-5.2372,-6.4418,-6.4456,-6.1881,-6.4581,-7.0459,-7.105,-6.6059,-6.5725,-6.3094,-6.3134,-6.3282,-6.0702,-5.9204,-6.4222,-6.6504,-6.0505,-5.3987,-4.8443,-4.5175,-3.9654,-3.2586,-2.4258,-1.9442,-0.40755,1.1388,1.4179,1.2941,2.0782,2.3251,1.6963,1.5742,1.989,2.5263\n-1.6792,-2.1349,-2.0431,-2.0818,-2.265,-2.3555,-2.4247,-2.724,-3.0062,-3.3196,-3.5117,-3.6472,-3.7225,-3.8186,-4.6043,-5.8072,-6.375,-6.8698,-7.0532,-6.9889,-6.7308,-6.7202,-6.159,-6.0158,-6.1335,-6.2097,-6.5053,-6.6931,-6.5177,-5.7652,-5.1205,-4.6786,-4.2424,-3.4056,-2.6436,-2.2446,-1.7759,-0.6319,1.2804,1.8926,1.8875,3.2076,3.0553,2.8208,1.9978,2.1843,2.8157\n-2.0009,-2.5252,-2.6472,-2.3746,-2.3981,-2.4272,-2.4785,-2.7231,-2.9792,-3.022,-2.986,-3.4992,-3.5582,-3.6477,-4.2131,-4.9169,-5.5322,-6.1814,-7.055,-7.0723,-6.9836,-6.5979,-6.3715,-6.2895,-6.1752,-6.5152,-7.2209,-6.6744,-5.7473,-4.8711,-4.286,-4.1978,-3.7662,-2.9358,-2.2141,-2.0022,-1.3258,0.23602,1.7007,1.7872,1.9444,2.5996,2.7363,2.6881,1.9499,2.6143,3.2541\n-1.5701,-2.2364,-2.5608,-2.489,-2.3862,-2.373,-2.3889,-2.8523,-3.241,-3.3385,-3.0751,-2.8917,-2.888,-3.2211,-4.1113,-4.8171,-5.0439,-5.4499,-6.1307,-6.8045,-7.8074,-7.9733,-7.1645,-6.8468,-6.9852,-7.416,-7.649,-6.5709,-5.1625,-4.5682,-4.1237,-3.9514,-3.0525,-2.2555,-1.7191,-1.8284,-1.1883,0.16698,1.389,1.715,1.6067,1.7075,2.369,2.4189,2.644,3.2662,4.0652\n-1.4859,-1.6739,-2.312,-2.2461,-2.0592,-2.0151,-2.3676,-2.6673,-2.8406,-2.8121,-2.8298,-2.7129,-2.5174,-2.5708,-3.4569,-4.6237,-4.4631,-5.0538,-5.6572,-6.3036,-6.9135,-7.4454,-6.6962,-6.9394,-7.7146,-7.9877,-8.4286,-7.0513,-4.8109,-4.0217,-3.9563,-3.7395,-3.0563,-2.452,-2.0883,-1.9609,-1.3236,0.2355,1.344,1.8337,1.6602,1.5766,1.5838,2.2118,2.7311,3.627,4.1324\n-1.5644,-1.9382,-2.036,-1.9583,-2.0213,-1.9725,-2.0409,-2.033,-2.2735,-2.3283,-2.3131,-2.4398,-2.7185,-3.0864,-3.5358,-3.9814,-4.1284,-4.7733,-6.4134,-7.2123,-7.1934,-6.5807,-6.3076,-6.2668,-6.4479,-6.6241,-7.3944,-7.3278,-4.8277,-3.5438,-3.3759,-3.4115,-2.8116,-2.1969,-1.0861,-0.83706,-0.43954,0.50177,1.1054,1.4898,1.6091,1.3713,1.4406,1.6949,2.5024,2.9553,4.055\n-0.84199,-0.85476,-0.72232,-1.2226,-1.7741,-1.8068,-2.0231,-2.1678,-2.3251,-2.2304,-2.3632,-2.5719,-2.8058,-3.103,-2.9883,-3.7231,-3.8345,-5.1841,-5.5882,-6.758,-7.1275,-6.1475,-6.0211,-6.0433,-5.677,-5.2272,-5.5652,-5.5996,-5.0439,-3.9836,-3.3223,-3.1028,-2.2083,-1.1369,-0.77539,-0.67218,-0.062702,0.5525,0.88622,0.81555,0.83157,0.70296,-0.16768,0.88194,2.8719,3.5963,4.9503\n-0.45849,-0.26725,-0.26028,-0.99998,-1.3173,-1.5497,-1.7119,-2.0214,-2.2242,-2.3408,-2.5282,-2.8409,-3.4119,-3.782,-4.1178,-4.357,-4.5327,-5.476,-5.7246,-5.7918,-5.6712,-5.5607,-5.3959,-5.3378,-5.0005,-4.9337,-4.7969,-5.1779,-5.2315,-4.8529,-3.707,-2.8185,-2.0567,-1.3506,-0.99051,-0.3158,0.22888,0.49549,0.64149,0.72641,0.83552,0.28979,0.095072,1.1156,2.699,3.5923,4.9319\n-0.39594,-0.46494,-0.83637,-1.1417,-0.98216,-1.0555,-1.4452,-1.9341,-2.457,-2.5328,-2.663,-3.5019,-4.3275,-5.0042,-5.0455,-5.3682,-5.264,-5.4445,-5.5463,-5.6287,-5.635,-5.492,-5.4087,-4.9397,-4.3721,-3.8481,-4.1627,-4.7593,-5.2305,-4.9019,-3.4193,-2.4321,-1.7399,-1.4508,-1.3477,-0.024107,0.67401,0.50647,0.58032,0.6967,0.94805,0.93289,0.66142,1.5059,3.1994,4.0508,4.9915\n-0.43373,-0.46045,-0.5927,-0.35636,-0.21552,-0.90013,-1.6419,-2.0316,-2.3545,-2.138,-2.5839,-3.599,-4.6363,-5.4185,-5.5308,-5.5534,-5.8538,-6.0646,-6.5254,-5.8403,-5.2049,-5.2673,-5.3599,-4.9446,-4.3527,-3.8964,-4.2467,-4.4113,-5.1075,-4.3586,-2.3724,-1.5737,-1.0883,-0.67414,-0.19418,0.49648,0.88755,0.91321,0.87087,0.83571,1.1385,1.2311,1.1276,2.4436,4.124,4.8192,4.8532\n-0.61568,-0.36548,-0.18881,-0.25297,-1.2745,-1.6877,-1.5047,-1.5798,-1.4565,-1.6422,-3.3146,-4.0892,-4.9434,-5.3289,-5.3527,-5.6998,-6.5336,-6.625,-6.4046,-5.4448,-4.8643,-4.8234,-4.9594,-4.9869,-4.5499,-4.3494,-5.038,-4.8522,-4.4603,-3.7226,-2.4096,-1.3199,-0.89398,-0.74873,-0.33519,0.40976,0.69687,0.78322,1.1007,1.5489,1.8331,1.8289,2.1911,3.2214,4.6796,5.2882,4.8978\n-0.57686,-0.43291,-0.08531,-0.41024,-1.1894,-1.3416,-0.99985,-0.71485,-1.2509,-2.5763,-4.6706,-4.606,-5.2812,-4.7682,-4.9203,-5.3456,-5.4544,-5.9464,-5.8906,-5.1631,-3.9109,-4.2281,-4.556,-4.4383,-4.3662,-4.3548,-4.599,-4.3441,-3.4434,-2.424,-2.2132,-1.9961,-1.2437,-0.66101,-0.27025,0.15746,0.56753,0.90071,1.5201,2.1164,2.0447,2.1461,2.6659,3.5451,4.4586,5.4311,5.6029\n-0.69706,-0.37689,0.43049,0.10402,-0.33558,-1.316,-1.7489,-1.0814,-0.63148,-0.62427,-3.6151,-4.5098,-4.8781,-5.4571,-5.6917,-5.9311,-5.6635,-5.3843,-5.5292,-5.513,-4.5623,-3.6585,-4.1197,-4.1242,-4.3509,-4.5267,-4.4705,-4.2972,-3.067,-1.7971,-1.7545,-1.6339,-0.92526,-0.25549,0.51911,1.0127,1.3596,1.4561,1.639,2.5182,2.6462,2.616,2.8672,3.8466,5.3985,5.6676,5.4203\n-1.3535,-0.95391,-0.1556,0.20877,0.079065,-0.54886,-1.5556,-1.1936,-0.87903,-0.85692,-2.8425,-4.0011,-5.472,-5.9725,-6.7763,-6.3508,-5.406,-4.99,-5.5306,-5.7583,-5.645,-4.6791,-4.2305,-3.723,-3.6398,-3.9049,-3.5842,-3.1398,-1.9824,-1.0733,-0.33817,-0.21908,0.098166,0.81351,1.1288,1.4391,1.6191,1.3796,1.5572,2.2327,2.7489,3.0386,3.7561,4.5637,5.0677,4.7994,4.4286\n-1.6642,-1.4253,-0.61828,-0.33808,-0.28834,-0.78667,-1.5143,-0.69971,-0.060305,-1.6851,-2.8711,-3.3732,-3.5962,-4.045,-2.9924,-2.6149,-3.334,-3.9566,-4.5698,-6.0262,-6.2338,-5.8504,-4.4698,-3.2348,-2.6901,-2.7908,-2.5114,-2.3815,-2.0767,-0.98213,-0.20809,0.39799,0.50929,1.3345,1.8031,1.7059,1.8555,1.5695,1.1056,1.5424,2.1956,3.2137,4.3026,5.1527,4.7943,4.5941,4.8036\n0.66443,0.72888,0.53783,0.096485,-0.27301,-0.38512,-1.0475,-0.93217,-0.43734,-0.93091,-1.7686,-1.2757,0.46159,1.6116,2.7977,2.2123,-1.6134,-3.0114,-3.3339,-4.7848,-5.0585,-5.5795,-4.2723,-2.9371,-2.5911,-2.1843,-2.1523,-2.1066,-2.0997,-0.85565,0.51243,0.94269,1.2405,2.3338,2.7922,2.3784,2.4318,1.8249,1.2739,1.0405,1.3611,2.6152,3.8382,4.7514,4.7168,4.4281,4.5758\n0.7869,1.1786,0.88361,0.18437,0.081495,-0.07827,-0.98049,-1.443,-1.5114,-2.4576,-1.2886,-0.60536,0.29268,1.9108,1.5742,0.41195,-1.1843,-2.3773,-2.21,-3.2247,-3.0265,-3.3272,-3.3004,-2.9292,-2.1134,-1.505,-1.7534,-1.7681,-1.2127,-0.44838,0.65489,0.92363,1.2922,2.3372,3.4192,2.9589,2.5457,1.777,1.5018,1.3756,1.1059,1.2322,2.0985,3.1437,3.9095,3.8284,4.1109\n-1.3036,-0.7348,0.57606,0.48083,0.69937,-0.53689,-1.4943,-2.2059,-1.9331,-3.4681,-1.4255,-0.46528,-0.15456,0.95075,1.2953,0.30195,-0.84269,-2.3092,-2.3447,-1.8747,-1.8989,-2.3253,-1.9408,-0.78782,-0.84381,0.10435,-0.10173,-0.62809,-0.31565,-0.088797,0.27721,1.0485,1.6383,2.6166,4.2738,4.8375,2.9539,2.092,1.6914,1.4921,1.3133,1.4208,1.773,2.6898,2.9829,2.598,3.1782\n0.58276,0.86739,0.84171,1.4145,1.7331,0.67557,-0.59268,-1.7669,-2.7042,-2.7034,-2.8443,-1.179,-0.83553,0.023796,0.37366,0.6152,-0.016026,-1.6887,-1.6293,-1.5495,-1.8801,-2.6732,-3.1765,-2.593,-1.4904,-0.26512,0.013475,-0.03793,-0.18774,0.039982,0.85503,1.6306,2.3511,3.6229,4.1607,3.7893,3.1794,2.6992,1.9399,1.1947,1.9524,2.4953,2.7524,2.8889,2.6584,2.3403,1.8337\n-3.6793,-1.8588,-0.98667,0.28162,1.4784,1.0806,-0.03179,-1.2217,-1.3754,-1.0224,0.87674,1.2797,0.051279,NaN,NaN,NaN,NaN,NaN,-0.57824,-0.62941,-1.0794,-2.0969,-3.1225,-1.5741,-0.96517,-0.23191,0.27963,-0.20824,-0.034388,0.42746,1.3139,2.5122,3.2942,3.5961,3.2869,3.2099,2.839,2.481,2.309,2.0987,2.2573,2.4698,2.1754,2.1322,1.9093,2.1102,2.3697\n-3.8057,-3.2871,-3.2972,-1.1316,0.97335,0.21524,-1.3854,-1.6233,-2.2403,-1.6218,1.7709,1.9308,0.80515,NaN,NaN,NaN,NaN,NaN,-1.1509,0.20916,-1.098,-1.5347,-1.4857,-1.2015,-0.85432,-0.099054,0.18264,-0.4045,-0.034246,1.1629,1.6562,2.4086,3.4819,4.4105,4.3295,3.5348,2.6266,2.3847,2.7007,2.5575,2.1311,2.0103,1.3309,0.72164,0.63127,1.7056,2.4977\n-3.8936,-4.3893,-4.3319,-2.7336,-1.7088,-1.4883,-1.2924,-2.2739,-2.6375,-1.6458,-2.6747,-2.3826,NaN,NaN,NaN,NaN,NaN,NaN,-1.259,0.32306,-1.528,-2.8739,-1.8787,-1.0711,-2.8579,-4.4977,-1.8225,1.0111,1.8722,1.7093,1.4899,2.7722,4.0798,4.8774,5.1198,4.1444,3.5638,2.748,3.225,3.1255,2.8087,2.4836,1.5593,0.61213,-0.085976,0.43694,-0.57105\n-6.5635,-4.9931,-4.5681,-4.8765,-5.8252,-5.4202,-0.25811,-1.5176,-2.864,-2.7536,-2.2587,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.015034,-0.18735,-1.4151,-3.7063,-2.1857,-2.6929,-5.8129,-3.7494,-1.5991,1.1425,2.08,2.0189,2.4057,3.0045,5.8208,5.81,5.7965,5.5878,4.5939,3.473,3.6868,3.3933,3.1694,2.9036,2.2807,1.6198,0.7627,0.4445,-1.583\n-6.8137,-6.5806,-6.9505,-7.5823,-7.4365,-5.4908,-4.4391,-4.8378,-4.6357,-2.7959,-2.9634,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.14524,-0.18049,-1.0564,-0.55088,0.23504,-0.064623,1.0035,1.8915,1.8738,2.4995,4.0816,4.1234,3.8797,5.1763,5.9611,5.5347,4.5735,3.5592,3.5243,3.4666,2.6812,2.6221,2.7142,2.5339,2.6065,1.9973,2.8495,2.4985\n-7.4645,-7.8555,-8.2952,-8.727,-7.2428,-5.8393,-5.6593,-5.6084,-4.8175,-3.0454,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.34068,1.2959,1.6978,2.3206,2.139,1.4177,1.2977,2.3802,3.0779,3.4273,4.7052,5.3156,5.2091,4.423,4.4581,3.6487,2.2633,2.3922,2.4526,1.6129,2.3928,3.3809,3.4868,4.5775,5.0268,5.0189,NaN\n-7.9101,-7.6383,-6.8369,-7.8805,-8.776,-8.137,-6.7738,-5.1759,-3.5745,-2.6155,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.74475,0.16705,0.91575,1.6877,1.6791,0.54562,2.1085,3.8862,4.9995,5.2011,5.4752,5.1361,4.9129,4.0877,3.038,3.1818,4.7687,4.3631,2.4334,3.0159,4.55,5.326,5.6652,5.5194,3.5277,NaN\n-7.6533,-6.4343,-5.3638,-5.3003,-6.95,-6.7618,-4.8745,-3.4923,-3.4138,-3.0223,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.251,3.5417,1.9263,1.4594,1.4162,1.8871,3.6028,4.151,4.969,4.9822,5.1425,5.6362,5.4482,4.8055,3.3494,5.394,3.4155,3.4368,3.851,4.8365,5.6143,6.1784,6.0895,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070219-070604.unw.csv",
    "content": "-3.7313,-4.4017,-3.918,-2.9733,-2.8536,-2.0915,-0.76528,-0.24635,-0.82925,-0.76986,-0.78427,-0.62016,-0.24545,-0.28634,-0.014816,0.49099,0.75023,0.40352,0.63073,1.0398,0.40188,0.73366,1.2504,1.3556,0.81531,0.68959,0.22759,-0.94666,-1.0451,-1.0081,-1.3242,-1.626,-1.2526,-1.8107,-2.9069,-3.5617,-4.5797,-5.1897,-5.3799,-6.2126,-6.1688,-5.7304,-5.0715,-4.755,-4.7476,-5.2298,-5.1714\n-4.0837,-4.2372,-3.8116,-2.4973,-2.1733,-1.6812,-0.80541,0.28035,0.34448,0.31652,-0.11595,-0.47669,-0.67572,-0.47328,0.18955,0.76422,1.0158,1.0641,1.4379,1.3736,1.3025,1.9468,2.1357,1.9273,1.508,1.4303,1.0467,1.2042,0.69765,-1.1087,-2.5935,-1.656,-1.2116,-2.2727,-3.1841,-3.7727,-4.6033,-5.1906,-5.5347,-6.0796,-5.3066,-4.8285,-4.9469,-4.7833,-4.7045,-4.5344,-4.0349\n-4.4066,-4.3157,-3.9153,-2.8499,-2.3182,-0.75806,-0.40195,-0.45168,0.22052,0.83125,0.70486,0.28596,-0.29174,-0.36045,0.483,1.0773,1.2792,1.42,1.3655,0.99492,1.1868,1.3968,1.3347,1.7711,1.9685,1.7254,1.5918,1.7,1.0104,-0.21183,-1.0314,-1.2863,-1.4059,-2.102,-3.4375,-5.5919,-6.2627,-7.2517,-7.2911,-7.4294,-6.9618,-5.2086,-4.2993,-4.0077,-4.655,-4.0836,-3.5865\n-4.5341,-4.336,-4.0359,-2.8438,-2.2095,-1.8018,-0.9831,-0.73935,0.060052,0.84362,0.90432,0.42461,-0.076867,-0.3192,0.54166,1.5395,2.4046,2.0451,1.4981,0.69385,1.4406,1.732,1.9453,2.2102,2.4213,2.5839,2.5622,2.1866,1.8043,1.3365,0.43757,-0.29928,-1.2698,-1.8576,-3.26,-5.5893,-6.4724,-7.1356,-7.2612,-6.9298,-6.7037,-6.2179,-5.3738,-4.9256,-4.703,-4.1398,-3.2829\n-4.9661,-4.7854,-5.8833,-3.7509,-2.5684,-2.2583,-2.0575,-1.7148,-1.262,-0.83317,-0.16971,-0.066916,-0.15648,0.20597,1.0248,1.1552,1.661,2.2365,2.1196,1.884,1.5999,1.6051,1.9191,2.3858,2.9641,3.5306,3.4438,2.5834,1.5391,1.5108,1.4703,0.24141,-0.89012,-2.0369,-3.0653,-5.1771,-6.1895,-6.5988,-6.7542,-6.6178,-6.3023,-5.5084,-5.2557,-5.5136,-4.6699,-3.9252,-3.4949\n-5.638,-6.1445,-7.0677,-4.9889,-4.0445,-3.413,-2.656,-1.8125,-1.6158,-1.2294,-0.468,-0.19483,-0.13718,0.39386,1.0912,1.5345,1.7608,2.273,3.0316,3.1794,2.4824,1.4657,2.0279,2.3853,3.1166,3.4273,3.3797,3.2959,2.802,2.5052,2.1148,1.5186,0.79016,-0.585,-2.1429,-4.3338,-5.8586,-6.0585,-6.3336,-5.917,-5.1167,-5.0983,-5.3736,-5.4123,-4.4277,-3.3184,-3.6091\n-5.8912,-6.3339,-5.8475,-5.1857,-4.7741,-3.9018,-2.523,-1.5565,-1.5709,-0.65593,0.062695,0.21022,0.39628,0.94474,1.6398,2.3501,2.4126,2.39,2.3545,3.0016,3.471,3.9297,3.5833,3.2739,2.9416,3.2624,4.0617,3.6167,2.9807,2.0028,1.5357,1.4769,0.50364,-0.65645,-2.0914,-3.445,-4.7387,-5.3892,-6.8222,-8.3136,-8.0762,-5.8272,-5.9107,-5.3466,-4.3621,-3.6989,-2.7585\n-5.1875,-4.7364,-3.6292,-4.5773,-4.3131,-3.2531,-1.8247,-1.5211,-1.8769,-1.982,-0.41157,0.021006,0.75356,1.4669,2.0908,2.8202,3.3631,3.0736,3.3652,3.4569,3.8594,4.1076,3.7771,3.8932,4.4761,4.6209,4.4216,3.7032,1.8883,2.1795,1.7384,0.90721,0.21177,-0.54916,-1.4669,-2.9503,-4.3346,-5.8788,-7.1541,-8.0773,-8.2135,-7.2487,-7.4915,-7.6563,-6.9237,-5.0776,-5.3337\n-5.8264,-4.641,-3.6193,-4.487,-3.8087,-3.1865,-1.6304,-1.6885,-1.5089,-0.96681,-0.73641,-0.90568,-0.32554,0.83334,1.8756,2.9184,3.7905,3.942,3.4711,3.665,4.2545,4.8216,4.6988,5.2227,5.7237,5.8867,6.9877,6.1765,4.0961,3.5055,1.7483,0.5971,0.89766,1.6691,0.43743,-2.1202,-4.6303,-7.0693,-7.9425,-8.3738,-8.1479,-7.414,-7.4156,-7.5542,-6.9717,-5.8544,-5.1801\n-4.8522,-4.6644,-3.9308,-4.3559,-4.3273,-3.7785,-2.2422,-1.6993,-1.13,-0.5146,-0.46169,-0.12982,0.80696,1.4087,1.7394,2.8689,3.809,3.9011,3.5622,3.733,5.2867,5.9109,5.8309,6.0579,6.9625,7.0079,6.98,6.3557,5.5044,4.9626,2.3345,1.6877,2.1268,2.6702,1.1721,-1.5406,-4.2202,-6.0229,-7.5365,-7.9213,-8.0367,-7.8341,-7.9181,-8.186,-7.6297,-6.2101,-5.5473\n-6.1896,-4.9364,-3.7769,-3.5467,-3.709,-3.6012,-2.536,-1.3347,-0.65767,-0.30713,-0.23174,0.43134,1.7344,2.2163,2.9064,3.7089,4.5145,4.1034,4.1787,5.2204,5.7147,5.6161,5.7279,6.3272,7.6601,7.5342,7.3769,7.6255,7.0427,5.0255,3.6139,2.7764,1.9189,0.62141,-0.30143,-2.8291,-5.0286,-6.4929,-6.4264,-5.8665,-6.3254,-6.5763,-7.1756,-7.9431,-8.2892,-7.7912,-7.2685\n-5.6975,-5.0127,-4.2038,-3.4192,-3.6363,-3.2552,-2.2692,-1.2211,-0.37675,-0.014679,0.020506,1.4039,2.6486,3.2551,3.7437,3.9462,4.2995,4.3378,5.2415,5.4397,5.3962,5.3744,5.5611,6.8813,7.7895,7.5512,7.2387,7.7049,6.5937,5.1617,3.7889,2.5742,1.6775,0.64024,-0.57717,-3.1944,-4.5131,-5.5424,-5.1809,-4.2115,-4.7467,-5.3644,-5.9906,-6.7836,-7.133,-7.4008,NaN\n-5.5534,-5.0752,-4.1149,-3.1665,-2.5311,-2.6998,-2.078,-1.611,-1.3276,0.012693,0.94956,2.2,3.5978,3.5091,3.6877,3.8852,3.9057,4.4862,6.0477,6.6353,6.3824,5.9839,5.3426,5.3897,6.6278,7.4084,6.8627,6.8629,5.9804,4.5668,4.1515,3.7275,1.9224,-0.046079,-1.0651,-2.1608,-2.5263,-3.7807,-3.8785,-2.8612,-3.5743,-4.6644,-4.9477,-5.7257,-6.5701,-6.277,NaN\n-4.4917,-3.3909,-3.2302,-3.3595,-3.6449,-4.2805,-2.4634,-1.2624,-0.39215,0.061654,1.0774,2.7568,3.9615,3.697,3.9617,4.1377,4.333,4.9971,5.797,6.3426,6.4912,6.9641,6.8153,6.5974,6.5303,6.5601,6.044,5.3503,5.0121,4.5681,5.3139,7.9892,3.8132,-1.6307,-3.575,-4.3765,-4.2501,-3.8097,-3.3617,-2.0472,-1.6615,-3.6887,-3.8416,-3.6656,-4.3811,-3.9967,-3.5845\n-3.6592,-2.4786,-2.9479,-3.9586,-4.109,-3.8224,-1.7635,-1.8173,-0.54826,1.2087,1.6439,2.6143,3.2688,3.5637,3.7104,3.5828,3.756,4.6305,5.5422,6.3394,6.3005,5.8513,6.4278,7.1748,6.8803,7.1643,7.1585,5.7432,5.1851,2.644,1.3763,0.32136,-1.09,-3.8224,-5.0345,-5.1628,-5.1481,-4.0521,-2.8323,-2.4636,-3.6089,-5.1151,-4.3388,-3.8293,-3.6026,-2.6179,-2.4521\n-4.4066,-4.0729,-3.389,-3.8487,-3.9268,-2.6513,-2.0955,-2.0763,0.1592,0.98675,1.829,2.8203,3.3149,3.2127,3.0944,3.6503,3.8608,4.5025,4.8782,5.3376,6.478,6.9944,7.016,7.4724,7.2749,7.0343,6.3991,3.6309,2.1846,2.4932,-0.059418,-2.5399,-4.8771,-6.3752,-7.1847,-7.1085,-5.8988,-4.4673,-3.4463,-3.0742,-4.8443,-5.38,-4.6121,-4.9092,-5.0467,-6.3522,-3.6157\n-4.3719,-3.7605,-3.1624,-2.878,-2.6365,-2.0904,-2.3465,-1.8011,-0.082012,1.181,2.1047,2.7474,3.7955,3.877,3.9874,4.1685,3.9907,3.6344,4.0512,4.0832,5.0866,6.318,6.6041,5.1082,4.6036,5.506,5.8348,4.7712,2.6887,NaN,NaN,NaN,-5.3983,-6.638,-7.6612,-8.5718,-7.6168,-6.0014,-5.1599,-5.1152,-5.0527,-4.4509,-3.8169,-4.9891,-6.633,-9.3823,-6.8467\n-3.2241,-2.356,-2.3558,-2.4709,-1.3096,-1.2697,-1.5389,-1.6185,-0.43885,0.61542,2.0246,2.7066,3.1241,4.147,4.4837,4.6951,5.0093,4.3499,4.3153,4.0424,4.0029,4.6959,5.6528,3.6425,1.976,1.8897,3.3434,4.6651,NaN,NaN,NaN,NaN,NaN,NaN,-8.3856,-8.1496,-6.9606,-6.1571,-5.8257,-6.3273,-6.1075,-6.1503,-5.8315,-5.7946,-7.1808,-8.8853,-8.643\n-2.2934,-2.224,-1.3735,-0.17426,-0.57094,-0.71307,-1.734,-0.72231,0.047422,1.4966,2.9403,3.5341,3.8121,4.5456,5.0742,5.9182,5.9447,5.4987,4.9944,4.3669,4.0813,3.7177,5.4759,5.67,3.7475,0.95462,2.4579,2.1631,1.9905,NaN,NaN,NaN,NaN,NaN,-10.14,-8.1768,-6.7915,-6.8632,-6.2429,-6.0343,-6.3011,-6.4878,-6.1993,-5.8155,-6.0453,-6.7356,-6.6278\n-2.5891,-2.6484,-2.8826,-2.6291,-3.0184,-1.7477,-1.0185,0.869,1.947,3.1755,4.3115,4.783,4.7562,5.0396,5.4541,6.3862,6.6116,6.0174,5.8518,5.6763,4.96,5.3937,6.2769,6.8642,5.7073,3.7951,2.9271,3.0887,1.5099,-0.74458,-2.9877,NaN,-4.7886,-5.9212,-10.438,-8.7759,-6.763,-6.8646,-7.2764,-7.0669,-6.8961,-6.11,-5.7619,-5.3076,-5.4708,-6.6325,-5.7246\n-2.317,-2.4766,-1.3445,-3.0293,-4.6362,-3.0928,-0.92123,0.57145,2.1223,3.867,4.6234,5.3809,5.6568,5.6715,6.3527,6.4815,5.838,4.808,4.9044,5.0168,4.6741,5.5822,6.0552,6.5145,6.2133,6.6891,6.4675,3.9051,1.8989,1.1749,1.1149,-0.40398,-4.311,-4.2548,-11.215,-11.381,-8.7428,-8.5281,-8.6716,-7.9598,-7.1594,-6.5239,-5.6849,-5.1383,-5.3507,-5.4462,-4.3739\n-2.0117,-0.49951,-1.0539,-1.4759,-1.1063,NaN,-1.0129,0.47738,3.4372,4.2225,4.8792,5.6176,5.9302,5.9597,6.3638,6.6595,5.3623,4.765,4.043,4.395,4.6379,5.5303,5.9535,5.7977,5.9986,5.7524,5.873,4.9957,3.6812,2.9433,2.0815,-1.085,-4.0679,-4.319,-7.2084,-7.1246,-6.1668,-7.429,-6.994,-6.8342,-7.3768,-6.7591,-5.8432,-5.2804,-5.0687,-4.8517,-3.7886\n-0.012693,1.701,1.0589,1.5852,0.48753,NaN,NaN,2.6288,2.9067,3.9811,4.8781,5.8248,5.3521,4.7678,5.561,6.3113,4.2051,3.856,4.4093,4.8088,5.0721,5.0536,5.448,5.2915,5.4528,4.9503,4.5823,4.1024,4.1142,3.8348,2.9873,0.48455,-1.4561,-1.886,-4.0601,-4.8737,-5.0663,-5.2048,-6.0391,-6.5198,-7.3655,-6.8612,-5.5437,-5.1712,-5.1328,-4.6092,-3.4266\n0.17013,1.6566,3.9759,6.1577,NaN,NaN,3.2609,4.4348,4.9445,4.6991,4.68,5.6691,5.9519,5.0497,4.5701,4.3626,4.0233,4.2887,4.3598,4.9959,5.722,6.2892,6.2873,6.4138,5.9897,5.7989,6.1288,5.3525,4.4879,4.1398,4.1623,3.0966,1.4658,0.92252,-1.7498,-3.1265,-5.1085,-6.2306,-6.7869,-6.3698,-6.4704,-6.4674,-5.584,-5.1418,-5.2467,-5.0634,-3.9892\n0.72071,2.3581,3.3933,3.9743,5.4346,NaN,3.7457,4.7707,4.8764,4.9101,5.3192,5.7216,5.786,6.0653,5.2562,4.4087,4.6087,5.1387,5.4319,5.4439,5.6388,6.4057,6.1408,6.233,6.1574,5.7347,5.5057,4.957,4.6649,4.8072,4.4793,3.7714,2.3931,NaN,NaN,NaN,-8.9654,-7.8155,-7.3355,-6.5079,-6.2403,-5.8123,-5.0423,-4.6379,-4.6009,-4.4335,-3.6233\n1.1376,1.0299,2.423,3.9999,5.2908,4.2696,4.1381,4.9098,5.097,4.9148,5.6732,6.2827,6.3171,6.2786,5.8624,5.7079,6.1907,6.2356,5.8288,5.6277,5.5636,5.7674,5.3184,6.005,5.6919,5.6531,5.8373,6.1542,4.1866,3.7001,3.424,2.9187,-4.5752,NaN,NaN,NaN,-7.4871,-7.423,-6.6225,-6.4936,-6.7032,-6.0408,-5.0645,-4.6715,-4.6678,-4.3759,-2.6162\n0.12412,1.9094,3.9415,3.7245,3.465,3.7056,4.5495,5.0666,5.3365,6.1591,6.5454,6.7081,7.0783,6.8561,6.3814,6.2438,6.605,6.4894,5.3847,5.0168,5.7112,5.7652,5.8407,5.9353,3.783,4.9087,6.1989,5.9607,4.625,0.65885,-0.66887,-2.5902,-5.5146,-6.6409,-6.4979,-6.0122,-6.5158,-5.9527,-5.8424,-6.2435,-6.4664,-5.847,-4.8851,-4.4122,-3.9676,-3.3365,-2.51\nNaN,NaN,2.1478,2.3857,2.3999,3.3203,3.9066,4.652,4.7151,4.7137,5.5219,6.3769,6.3957,6.3017,5.8993,6.1439,6.0551,5.2859,4.6645,4.5358,5.242,5.1258,4.9559,4.8708,3.9458,4.0231,5.3159,5.9749,4.9339,1.8422,NaN,NaN,NaN,-5.0544,-5.5843,-5.6506,-6.2426,-7.3191,-7.054,-7.1171,-7.0887,-6.4495,-4.8027,-4.131,-2.8325,-1.896,-0.93861\nNaN,NaN,NaN,NaN,3.1291,4.0076,4.5003,4.347,4.4194,3.8366,3.8595,4.1865,5.3338,5.4292,5.4412,5.6142,5.399,5.081,5.0652,5.5476,6.0207,5.5055,5.4317,5.7809,6.6979,7.1741,10.198,8.5518,7.6651,NaN,NaN,NaN,NaN,NaN,-5.9655,-6.4961,-7.7892,-8.8378,-8.8104,-8.2489,-6.8792,-5.4861,-4.5463,-3.8442,-2.5954,-0.48419,0.033161\nNaN,NaN,NaN,NaN,2.7524,1.7167,3.1319,4.1693,4.1749,3.2745,3.7684,4.4132,5.0776,4.6806,4.7225,4.9105,5.0117,5.1332,6.253,6.8506,6.6046,6.6156,6.847,7.911,9.4302,11.097,12.776,13.539,NaN,NaN,NaN,NaN,NaN,-8.2614,-7.0734,-7.5143,-8.5354,-8.8756,-8.8199,-7.5115,-6.0788,-4.271,-3.3934,-2.9834,-1.8268,-0.39106,0.41046\nNaN,NaN,NaN,NaN,1.2686,0.4657,0.94291,2.8855,4.0401,4.2538,4.4078,4.8873,4.7108,3.7455,3.4276,3.4736,4.0565,5.0764,5.8703,6.3336,7.8888,8.1701,10.36,11.834,13.258,13.913,14.448,14.756,NaN,NaN,NaN,NaN,NaN,-7.3951,-7.8009,-7.739,-8.5281,-8.6228,-8.123,-7.2727,-6.3445,-5.3959,-3.326,-2.0912,-0.70786,0.11879,0.64732\nNaN,NaN,NaN,NaN,NaN,1.7798,2.5687,3.7112,4.3486,3.7135,2.3059,2.7026,2.9207,2.6615,1.8113,1.6734,2.9749,3.4005,4.0821,4.3967,5.8573,7.1973,9.8786,13.408,14.233,13.65,13.157,16.105,NaN,NaN,NaN,NaN,-8.0667,-6.6067,-6.9161,-7.1881,-7.9789,-7.8333,-7.3392,-6.4018,-6.0605,-4.6382,-3.2268,-1.3448,-0.44986,0.23377,1.1511\nNaN,NaN,NaN,NaN,-1.1295,1.4684,2.6726,3.4001,4.2936,3.6753,1.7004,1.1555,1.2258,1.0259,1.0742,1.9309,2.1392,2.3467,3.5409,4.2478,4.0279,4.5654,5.2617,7.5619,10.653,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.3882,-5.1859,-5.315,-5.4219,-6.8925,-6.227,-5.564,-5.6,-4.3107,-3.5206,-2.1246,-1.0801,0.38705,1.9361\n-1.7567,-2.5975,NaN,-1.8856,-0.95125,1.474,2.5419,2.1827,3.078,4.3667,3.698,3.0581,3.1644,0.57136,-0.091972,0.42775,0.75802,2.1096,3.7756,5.5402,5.5983,4.8546,5.7726,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.8348,-5.1517,-5.8599,-5.949,-5.6252,-4.7662,-4.047,-3.0308,-1.8771,-0.53633,0.95792,2.4118\n-2.6091,-2.3583,-2.4238,-1.6827,0.13791,1.202,2.0897,2.9144,3.6195,4.2993,4.1436,4.7759,5.9247,2.8862,0.84129,0.94522,2.018,2.828,3.7561,5.1412,6.3968,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-3.8808,-3.8504,-4.6782,-4.5971,-3.651,-2.9915,-2.4436,-1.256,-0.21126,1.5492,2.6654\n-2.1496,-1.3069,-1.137,-3.166,-1.5249,0.45686,1.9796,2.9246,3.8412,5.0232,3.7529,2.7271,1.8718,1.835,1.8669,1.9974,1.9719,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.5786,-3.377,-3.8236,-3.9891,-3.8533,-2.8901,-2.1668,-1.85,-1.0743,0.68682,2.2502,3.1929\n-2.0567,-2.2437,-2.5876,-2.6028,-1.9059,-0.4151,1.19,2.506,2.2594,3.6811,3.8674,0.66124,0.46418,-0.70618,-0.48253,1.599,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.9817,-3.6223,-4.001,-3.2723,-2.516,-1.5689,-1.2203,-0.46595,0.3536,1.3134,2.8474,3.2934\n-1.7196,-2.0895,-2.4929,-2.1722,-1.1778,-0.091803,0.036914,-0.49721,-1.1613,0.80196,1.7123,-0.34447,-0.63691,-1.2205,-0.46822,1.7824,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.63031,-5.3501,-6.1063,-6.5808,-4.423,-3.9075,-3.5852,-2.4882,-1.4364,-0.22511,0.67726,1.7039,2.6769,3.3003,3.3055\n-2.8282,-2.3825,-2.1662,-2.3794,-2.1754,-0.56712,-0.33485,-1.213,-1.6998,-1.7372,-1.723,-1.9063,-3.1821,-3.9981,-1.3783,1.0792,4.2329,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0.54354,0.43059,-0.43724,-2.8835,-4.1177,-3.6138,-3.7313,-3.6613,-2.7441,-1.6757,-0.073355,1.622,2.2137,2.7045,3.2352,3.1549\n-3.6425,-3.1714,-3.1341,-2.67,-2.6305,-2.7404,-2.1551,-2.4425,-2.8973,-2.4319,-2.1634,-1.614,-3.5071,-3.5011,-3.2262,-0.13239,1.7554,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.6243,-1.0683,-1.9649,-2.842,-3.2688,-2.9262,-2.6827,-2.8111,-3.2811,-2.7695,-1.4848,0.62351,1.972,2.3533,2.7719,3.2598,3.8083\n-3.7392,-2.984,-2.9789,-2.9186,-2.8089,-2.9325,-2.9954,-2.9947,-3.4628,-3.8316,-3.8841,-3.216,-3.9285,-4.4818,-4.5435,-2.4356,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.1077,-1.6933,-2.6059,-3.5169,-4.0855,-3.4118,-3.0838,-2.5835,-2.2676,-2.7997,-2.5984,-1.3368,0.096043,1.8582,2.7858,3.0298,3.3085,3.6233\n-2.7464,-2.5056,-2.8387,-3.1462,-2.6631,-2.5538,-2.995,-3.5058,-3.8305,-4.2319,-5.1174,-7.169,-6.9088,-6.4507,-6.3079,-5.456,-4.0474,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.4222,-3.0205,-3.2027,-3.2286,-3.5654,-3.8212,-3.8363,-3.6359,-2.9793,-2.5876,-1.6004,-0.84773,0.46551,2.2723,2.6099,3.017,3.564,3.6047\n-1.6133,-2.008,-2.3086,-3.035,-3.2186,-3.3715,-3.9369,-4.2289,-4.6801,-5.2036,-5.9053,-6.6793,-7.0306,-6.6169,-6.624,-5.7895,-3.4469,-4.9761,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.9776,-2.1896,-2.7566,-3.2969,-3.9502,-3.6616,-4.3138,-4.574,-4.073,-3.0551,-1.4977,0.017382,0.63908,1.5482,2.3603,2.2974,2.2176,2.9343,3.2061\n-3.5554,-3.0977,-2.7028,-3.5731,-4.0174,-4.0339,-4.6486,-4.9154,-6.2644,-6.3014,-5.9175,-5.4948,-5.7658,-5.7061,-5.5347,-5.3726,-4.1412,-2.6732,-4.2755,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.631,-4.1391,-4.0248,-3.8218,-4.1267,-4.2779,-4.3292,-4.6145,-3.9727,-3.7054,-2.5392,-0.34813,0.80318,1.6376,2.4515,2.5257,2.1615,1.9186,2.0273,2.4887\n-3.4728,-3.413,-3.2752,-3.7161,-3.7584,-4.0654,-5.2973,-6.2339,-7.017,-5.8417,-4.7017,-5.1407,-5.3668,-5.5485,-5.4859,-5.4575,-6.1616,-5.5562,-6.8919,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.3515,-4.7324,-4.5992,-4.6932,-4.9935,-4.8925,-4.4928,-4.6996,-4.2013,-3.4314,-2.1275,0.11808,1.359,2.3216,2.9898,3.0245,2.3393,2.284,2.0168,2.2356\n-2.8769,-2.9107,-2.556,-2.5041,-2.9625,-3.3921,-3.8096,-4.5208,-4.4479,-4.1663,-4.1752,-4.8749,-5.953,-6.3285,-6.0529,-5.7988,-6.0301,-7.1202,-8.6278,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-4.9144,-4.7445,-4.6666,-5.0238,-4.7401,-4.2254,-3.8371,-3.466,-3.2074,-2.4297,-0.99907,0.40791,1.5373,2.9814,3.3297,3.3575,2.7412,2.7901,2.4835,2.1828\n-2.558,-2.687,-2.3658,-2.0672,-2.4203,-2.9846,-3.4135,-3.6158,-3.7099,-3.6891,-3.7197,-4.2458,-4.6746,-4.6067,-4.7614,-4.5359,-5.1833,-6.2139,-8.0857,-8.401,-11.396,NaN,NaN,NaN,NaN,NaN,-5.1525,-4.7623,-4.4098,-3.6996,-3.9509,-4.0792,-3.401,-2.857,-2.4305,-1.775,-1.516,-0.83802,0.28837,1.5642,2.6441,3.5655,3.5245,3.5803,2.8695,2.5161,2.8484\n-2.2673,-2.7847,-2.7715,-2.451,-2.876,-3.0567,-3.2692,-3.0822,-3.1052,-2.3318,-2.0052,-2.9331,-2.8793,-2.7404,-3.074,-3.4094,-4.0304,-4.9001,-5.895,-5.7845,-8.4952,-11.362,-9.6707,-6.9514,NaN,NaN,-6.1489,-5.2914,-5.572,-4.7497,-3.62,-2.9252,-1.869,-1.273,-1.4222,-1.4984,-1.8323,-0.93003,0.65414,1.6559,2.1885,3.1675,2.7004,3.1942,2.8701,3.0004,3.367\n-1.9128,-2.4834,-3.117,-3.0992,-2.9276,-3.0223,-2.6,-2.5565,-2.2969,-2.1104,-1.6761,-1.5309,-1.1117,-1.7069,-2.3617,-2.9512,-3.6454,-4.8473,-4.5464,-5.464,-7.1396,-7.2306,-6.7622,-6.2433,-6.1725,-5.7206,-5.7221,-5.2641,-5.5014,-4.4288,-3.3628,-2.4457,-1.7559,-1.6045,-1.4757,-1.2993,-1.4536,-0.9475,0.72481,1.902,2.3348,2.9411,2.1172,1.8987,2.2053,2.8799,3.4397\n-1.5872,-1.4756,-2.2742,-2.1167,-1.7312,-1.3824,-1.7002,-2.1404,-2.1137,-1.7122,-1.2554,-1.4082,-1.4157,-1.4352,-2.3808,-3.0025,-3.3891,-4.486,-5.1189,-6.1885,-6.0549,-6.2164,-5.6093,-5.8798,-5.5969,-5.5929,-5.6065,-5.2072,-4.5874,-3.8479,-3.1592,-2.36,-1.4507,-1.6339,-1.502,-1.2864,-0.76722,-0.46541,0.92414,1.9984,2.8261,2.4037,-0.66156,1.2052,2.1569,2.7471,3.7454\n-1.6429,-1.5029,-1.2717,-0.98703,-1.2208,-1.4855,-1.4377,-1.4774,-1.7198,-1.4058,-0.89021,-0.75341,-1.5404,-2.0928,-2.6138,-2.7936,-3.1243,-4.3459,-4.2424,-4.1798,-4.3171,-4.5149,-4.967,-5.3093,-5.2211,-5.4279,-6.0136,-5.5025,-4.0144,-3.3693,-3.1253,-2.1393,-1.1783,-0.87997,-1.0302,-0.68048,-0.29834,0.2917,1.2621,1.6444,1.8532,3.2212,3.5872,2.3568,2.8894,3.0856,4.3655\n-1.2757,-1.3887,-1.0877,-0.97339,-1.5056,-1.6057,-1.5773,-1.4841,-1.1623,-0.81634,-1.0052,-0.89664,-0.89835,-1.6154,-1.9275,-3.3981,-3.4764,-5.4919,-5.9746,-5.7913,-4.7782,-4.6864,-4.8026,-4.5525,-4.2209,-4.294,-5.282,-4.798,-3.6894,-3.1602,-2.6839,-1.8604,-1.0545,-0.41799,-0.19872,-0.079428,0.50199,1.1925,1.7536,1.9776,1.8562,1.8378,1.9765,3.6748,3.9542,4.3726,5.8406\n-1.0715,-1.1289,-1.26,-0.58775,-0.53107,-0.7434,-1.1771,-1.6017,-1.3794,-0.8153,-0.81455,-1.2568,-2.169,-2.6854,-2.5531,-3.4455,-4.7202,-5.8527,-7.5333,-8.1124,-6.6222,-5.6302,-5.1663,-4.1731,-3.6506,-3.6647,-3.1678,-3.0342,-2.7735,-2.7895,-2.4314,-2.0626,-1.0032,-0.57903,0.21951,0.77947,1.2826,1.8119,1.9244,1.96,2.1105,2.3095,2.5909,3.7921,3.9632,5.2753,7.214\n-0.91106,-0.7429,-0.43157,-0.22142,-0.17539,-0.90501,-1.4228,-1.9845,-1.7062,-1.1454,-0.55519,-0.3209,NaN,NaN,NaN,-8.2127,-7.5546,-5.6591,-7.4796,-9.2738,-8.6442,-5.9006,-4.3824,-3.3752,-2.739,-3.2364,-2.6221,-1.959,-2.2845,-2.2657,-1.8518,-1.4494,-0.52685,-0.3903,0.10161,0.75084,1.4894,1.9859,1.9337,1.5613,1.6537,2.0359,2.986,3.6467,4.8978,6.1598,7.1448\n-1.1427,-0.65219,-0.29916,0.25714,-0.43659,-1.5944,-2.3871,-2.0408,-1.4912,-0.84461,0.031569,0.34938,NaN,NaN,NaN,NaN,NaN,-9.5697,-8.7683,-8.3892,-6.6308,-5.0879,-3.9955,-3.0169,-2.6872,-2.839,-2.0942,-1.8922,-1.3848,-1.7718,-1.0489,-0.96687,-0.67395,-0.13611,0.74065,1.0944,1.5549,1.8358,2.0584,2.3099,2.4165,2.5658,3.1437,3.9716,5.1101,5.6586,5.7693\n-1.8601,-1.4517,-0.49846,0.6325,2.043,-0.11856,-2.1841,-1.0754,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.8793,-7.796,-6.2753,-4.7427,-3.6489,-2.813,-2.1023,-2.1263,-0.41625,-0.18111,-0.6456,-1.5832,-1.4838,-1.1615,-0.34152,0.081916,0.62292,1.1707,1.5369,1.9948,2.1664,2.7678,3.4482,3.6202,4.2952,4.969,5.6393,6.5949,6.6849\n-2.6174,-1.7025,0.029263,NaN,2.1149,0.72357,-0.62016,-1.4944,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-7.517,-6.7163,-4.9716,-3.1288,-2.043,-1.6686,-1.5104,-0.34109,-0.40668,-0.87425,-1.2911,-1.2732,-1.1415,-0.4511,0.33727,0.68699,0.64209,1.104,1.7101,2.351,3.1034,3.5115,3.6221,4.6438,5.8553,6.8661,7.3582,7.5704\n-1.9035,-1.1993,-0.43917,NaN,NaN,NaN,NaN,NaN,NaN,-2.5168,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.1486,-4.9211,-4.6781,-4.0203,-2.2897,-1.4239,-0.83904,-0.55533,-1.6514,-1.5826,-0.80585,-0.71309,-0.50301,0.049083,1.3517,1.5175,1.2411,1.9394,2.6653,3.1128,3.5349,3.838,4.0785,4.9251,6.5942,7.0962,7.0997,7.034\n-2.1171,-1.8797,-1.959,NaN,NaN,NaN,NaN,NaN,NaN,-2.1759,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-5.2453,-5.4559,-5.3,-4.2848,-3.586,-2.8629,-2.0499,-0.86436,-0.18231,0.056098,-0.24008,0.094762,0.16657,-0.48642,0.41842,1.4229,1.4728,1.5879,2.4657,2.9468,3.1815,3.7488,4.1261,4.3556,4.803,5.3609,6.3494,7.6356,7.8637\n-1.6915,-1.5247,-2.3552,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-9.4852,-11.211,-8.8058,-8.8726,-8.2922,-3.9891,-2.7264,-3.5506,-4.7884,-4.7035,-3.5931,-2.7844,-2.2001,-0.89196,-0.39074,0.35636,0.35382,0.92494,0.51622,0.56957,0.041708,0.45626,1.6524,1.282,1.8698,2.8694,2.9835,2.9291,3.5057,4.0896,5.039,5.8423,6.0493,6.0293,6.462,6.8602\n0.25039,-0.30691,-0.44053,-2.6437,-5.0508,-5.6742,NaN,NaN,NaN,NaN,NaN,-11.565,-10.585,-9.6834,-7.6103,-7.217,-5.7959,-4.2665,-3.228,-3.4729,-3.1771,-2.3796,-1.1287,-0.90898,-0.21927,0.82439,0.73568,0.70047,1.5509,1.9524,2.2007,1.9666,0.80145,0.71385,0.89518,1.9547,2.404,2.3079,2.0907,1.9677,3.2083,4.7689,5.511,5.6294,5.8687,6.2593,6.03\n-0.82383,-1.4185,-1.4676,-0.50023,-2.0439,-4.1253,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-8.8137,-8.517,-6.7593,-4.3384,-3.6554,-3.1956,-2.4671,-1.8104,-1.7575,-1.8379,-1.6468,-0.67798,0.25075,0.579,0.80767,1.1179,1.3728,2.2002,2.5694,2.2933,1.2701,1.1764,1.5556,1.7409,1.452,1.3851,1.4378,1.7715,2.8941,3.1908,3.87,5.3217,5.8983,4.6129\nNaN,NaN,NaN,NaN,NaN,-5.4525,-6.2747,-5.4755,NaN,NaN,NaN,NaN,NaN,NaN,-9.8326,-7.5135,-5.5139,-4.1998,-2.5562,-2.2754,-1.8633,-1.841,-2.1403,-0.97665,-0.13273,0.53879,0.96514,1.3543,1.9661,2.0755,2.2427,2.4478,2.3628,2.5818,2.4227,1.7421,1.8503,1.4541,1.3249,1.9962,2.1995,2.6437,3.0961,3.6629,4.2282,4.2206,3.2185\nNaN,NaN,NaN,NaN,NaN,-8.033,-6.3682,-4.6335,NaN,NaN,NaN,NaN,NaN,NaN,-8.8261,-8.725,-7.7326,-5.6144,-3.7933,-2.4827,-2.3114,-3.6182,-2.0463,-0.8494,0.2621,1.3469,1.5919,1.8853,2.2459,2.2025,2.1388,2.1328,2.3298,3.0411,3.0933,2.6007,2.5304,2.1199,1.5452,2.0831,2.5275,3.2596,3.8947,4.1162,3.9014,3.4872,3.0282\nNaN,NaN,NaN,NaN,-4.5394,-5.5585,-5.1636,-4.7384,-4.6319,NaN,NaN,NaN,NaN,-8.028,-7.539,-6.5821,-5.5326,-3.5347,-3.0457,-2.1127,-1.8191,-2.6623,-2.5141,-0.58694,-0.44247,0.88298,1.6305,2.2013,2.2854,2.5186,2.747,3.0768,3.4858,3.139,2.5305,2.3043,2.413,2.7662,2.3947,3.145,3.9157,3.9435,3.4212,3.2478,3.8346,3.9893,4.1127\nNaN,NaN,NaN,NaN,-3.4444,-3.4804,-4.3102,-5.4674,-5.7522,NaN,NaN,NaN,-5.7888,-7.386,-7.8237,-6.0576,-6.4383,-3.9359,-2.9132,-1.1115,-1.2746,-0.75585,-1.4335,-1.7794,-0.48326,0.73297,1.8293,2.6667,2.826,2.4447,2.6909,3.0204,3.2417,2.5967,1.8319,2.3992,2.5833,2.9999,3.4771,4.0786,3.8869,3.4429,2.4567,1.6379,2.8343,4.3602,NaN\nNaN,NaN,NaN,-1.1297,-1.4772,-2.4916,-4.4079,-6.1171,-6.8427,-4.6469,-3.0933,-4.0763,-6.7346,NaN,NaN,NaN,-9.1086,-5.807,-3.1547,-0.32262,-0.20623,-0.0047703,0.34324,0.44334,1.1359,1.5746,1.8469,2.3152,2.5542,2.6884,2.759,2.9899,2.8655,2.7336,1.8068,2.9564,3.2684,3.1426,3.5323,3.6634,4.1764,3.9015,2.8949,3.2033,3.4765,2.4571,NaN\n-5.9836,-4.3195,-5.7514,-4.1563,-3.4977,NaN,-3.1754,-4.8801,-6.4864,-6.8149,-6.4428,-5.6508,NaN,NaN,NaN,NaN,NaN,NaN,-1.8813,0.49613,1.6259,0.96442,1.1213,1.5801,1.208,1.5369,2.0175,2.6121,2.7326,2.9739,3.0712,3.0321,3.3361,3.2942,2.9947,2.8888,2.887,2.9455,3.4157,3.725,4.4495,4.3767,3.7048,3.4975,3.2212,2.7221,NaN\n-6.3171,-5.5114,-5.1417,-6.6132,-5.1477,NaN,NaN,NaN,-5.2953,-4.0521,-4.7254,-5.4132,-2.3066,-0.74226,NaN,NaN,NaN,NaN,NaN,NaN,2.4468,1.8912,1.7022,1.739,1.7851,2.2078,2.4655,2.7945,2.9563,3.367,3.5674,3.1984,3.1881,3.6947,3.3032,2.9091,3.1306,3.3708,3.7225,3.8127,3.5546,3.4756,4.2592,4.7511,3.8316,NaN,NaN\n-7.4278,-6.1957,-4.3767,-4.3223,NaN,NaN,NaN,-3.7984,-6.1177,-5.6967,-5.6781,-6.1962,-4.6181,-4.4186,-2.5864,NaN,NaN,NaN,NaN,NaN,0.78755,2.9464,2.1042,1.2953,0.82187,1.669,2.3617,2.8914,3.4743,3.7834,4.4998,4.2699,4.139,4.2096,3.937,3.4942,3.8786,4.4394,3.3633,3.1012,2.8445,3.2517,5.0551,6.2278,7.2786,NaN,NaN\n-5.8162,-5.4844,-4.1325,-3.6021,NaN,NaN,NaN,-3.7072,-2.1591,-2.062,-3.107,-4.8963,-3.4665,-1.7575,-2.3352,NaN,NaN,NaN,NaN,NaN,2.1333,3.2804,2.1413,2.3562,2.3841,2.1389,2.7652,2.9078,2.8221,3.0777,3.8739,4.5734,4.251,4.3301,4.2061,3.6359,3.5085,4.292,4.287,3.1406,4.3725,3.8983,4.7959,8.2235,NaN,NaN,NaN\n-5.5452,-4.5787,-3.8016,-4.0698,-4.2428,-4.0412,-3.6671,-3.1283,-2.5559,-2.1137,-1.8294,-1.0908,-1.4778,-2.9862,-2.858,NaN,NaN,NaN,NaN,NaN,2.6911,3.5815,4.2452,3.7873,3.0332,2.8169,2.8885,3.141,1.9271,1.9269,3.3109,4.5006,4.8299,5.1153,5.6433,3.0586,2.573,3.5689,4.3181,4.6415,5.0991,6.5583,7.6051,10.516,NaN,NaN,NaN\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070326-070917.unw.csv",
    "content": "1.4937,0.76927,-0.045834,0.20678,-0.016782,-0.24285,-0.25263,-0.29128,-0.094385,-0.44948,0.084566,-0.47872,-0.73954,-0.93276,-1.5964,-2.1494,-2.1145,-2.0687,-2.463,-2.2361,-1.9997,-2.0461,-2.1341,-2.5581,-3.2118,-2.6819,-2.7096,-3.1781,-2.879,-2.9332,-2.9682,-2.7336,-3.414,-3.7227,-3.3914,-3.3953,-3.6131,-3.9028,-3.0677,-2.8596,-3.0657,-2.8429,-3.1579,-3.5908,-2.8788,-2.5234,-2.6005\n0.65523,0.84207,-0.20606,-0.082409,0.10289,-0.092257,0.12345,-0.17602,-0.15343,-0.15154,0.082232,-0.25568,-0.71354,-1.211,-1.7288,-1.8179,-1.5248,-1.7434,-1.8927,-1.7108,-1.2309,-2.3879,-2.5853,-2.4074,-1.8226,-1.9426,-2.2151,-2.748,-3.3085,-3.292,-2.8347,-2.8757,-3.1624,-3.4737,-3.091,-3.6673,-3.8738,-3.4566,-2.863,-2.4068,-2.2869,-2.427,-3.0781,-2.9364,-2.4375,-2.5265,-2.6628\n0.5154,-0.00023317,0.16325,0.12663,0.084787,-0.00031042,0.15476,0.053302,-0.80135,-0.14216,-0.53044,0.16894,-0.67539,-1.3048,-1.7201,-1.5638,-1.4825,-1.9522,-2.1571,-2.3575,-2.051,-2.0383,-2.6893,-2.3203,-2.2509,-2.4351,-2.6752,-3.1155,-3.0768,-3.1521,-2.7835,-3.0047,-3.1898,-3.3049,-3.2796,-3.7637,-4.437,-3.4047,-2.7927,-2.4188,-2.2038,-2.4764,-2.4398,-2.1407,-2.1022,-2.3623,-2.472\n0.77251,0.38461,0.44225,0.31335,0.45638,0.66802,0.069283,-0.042589,-0.38492,-0.23177,-0.51585,-0.67369,-1.2179,-1.7749,-1.0328,-1.1758,-1.0025,-1.3893,-1.9932,-3.598,-2.8751,-2.3658,-2.1245,-2.1862,-2.7082,-3.0026,-2.7381,-2.339,-2.5733,-2.7255,-2.7278,-2.6492,-3.182,-3.2187,-3.1037,-3.4103,-3.6617,-3.5423,-3.0284,-2.532,-2.2056,-2.1458,-2.2065,-2.0744,-2.0318,-2.1181,-2.3076\n0.43683,-0.14416,0.68378,0.41273,0.22775,0.19703,0.025039,-0.16703,-0.15459,-0.71637,-0.81455,-1.0957,-1.2633,-1.7502,-1.2583,-1.1688,-1.1214,-1.5502,-1.7007,-2.0129,-2.7727,-1.5878,-1.7181,-2.2814,-2.9596,-3.5339,-3.2042,-2.7577,-2.1583,-2.8178,-2.5862,-2.3365,-2.4326,-3.0825,-3.1005,-3.0667,-3.5374,-3.5719,-3.1958,-2.4472,-1.8889,-1.5947,-1.8939,-1.6607,-1.7881,-2.0666,-2.2958\n0.47362,-0.036321,0.2725,0.21066,0.2567,-0.070287,0.16675,0.57793,0.4877,0.14316,-0.3799,-0.98619,-0.84698,-0.98339,-1.1548,-1.3543,-1.5396,-1.8813,-1.8974,-1.9822,-1.748,-1.4445,-2.0418,-2.5196,-2.4379,-2.8399,-3.3444,-3.044,-2.6654,-3.3604,-2.926,-3.0178,-3.1348,-2.8665,-2.8289,-2.3112,-2.3989,-2.9396,-2.6532,-1.7881,-1.5118,-1.7263,-1.7518,-1.4014,-1.5748,-1.5425,-1.7719\n0.73456,0.73613,1.288,0.14499,-0.091098,-0.40283,0.144,0.57976,0.38196,0.017048,-0.3529,-0.68507,-0.83737,-0.92901,-1.7116,-1.5446,-1.3575,-1.5605,-1.6339,-2.2652,-1.7594,-2.1378,-2.2641,-2.9181,-3.0421,-3.2123,-2.9641,-2.3005,-2.9942,-2.6604,-2.4962,-2.7778,-2.759,-2.456,-2.3743,-2.2716,-2.2372,-2.6323,-2.3505,-1.5667,-1.3349,-1.2884,-1.0608,-1.2701,-1.3117,-1.0776,-0.88762\n1.236,1.2278,1.4894,0.41483,-0.27416,-0.69653,-0.051758,0.39184,0.20735,-0.35248,-0.31778,-0.02162,-0.46271,-1.716,-1.3243,-1.3435,-1.1226,-2.0515,-2.0759,-1.9623,-1.4269,-1.9509,-2.2385,-2.6307,-2.8352,-2.8321,-2.237,-1.5553,-2.3682,-3.0327,-2.0807,-1.9844,-2.2661,-2.2451,-2.6086,-2.5849,-2.6087,-2.6,-2.3611,-1.7429,-0.9481,-0.50836,-0.8074,-0.75606,-0.89406,-1.2885,-1.0477\n1.378,0.93563,0.82,0.31186,-0.46674,-0.41549,-0.12834,-0.010195,-0.5216,-0.59113,-0.20899,0.29808,-0.061572,-1.1853,-1.0144,-1.0748,-0.96411,-1.1905,-1.9279,-1.6712,-1.7879,-1.906,-1.6127,-2.1166,-2.5048,-2.1251,-2.023,-2.3442,-2.9109,-2.5654,-1.9467,-1.932,-1.9072,-1.8445,-2.1634,-2.5952,-2.196,-1.9682,-1.6969,-1.3251,-1.1251,-1.1765,-1.4065,-1.107,-0.90623,-0.79245,-0.83833\n1.24,1.1562,0.96411,-0.047892,-0.045907,-0.45694,-0.13903,-0.45832,-0.42468,0.073897,0.34209,0.40385,-0.11184,-0.6325,-0.77176,-1.0061,-0.97662,-1.2316,-2.101,-2.0241,-1.1959,-1.1188,-1.5597,-2.7131,-2.655,-2.3865,-1.2284,-1.6498,-2.8784,-2.7659,-2.4778,-2.4174,-1.9061,-2.0947,-1.4164,-1.416,-1.4714,-1.4106,-1.8286,-1.6525,-1.4865,-1.3027,-1.229,-1.6726,-0.95168,-0.70707,-0.79462\n1.3179,0.7359,0.94018,0.040648,-0.53665,-0.27497,-0.43009,-0.48715,0.63236,1.0233,0.75896,0.1515,-0.47512,-0.58723,-0.42413,-0.5531,-0.37596,-1.0723,-0.96564,-0.7721,-0.94425,-0.85339,-1.5291,-2.3306,-2.5396,-2.2061,-1.8854,-2.0057,-2.5128,-2.7998,-2.8454,-2.4693,-1.8842,-1.8629,-1.7155,-2.1034,-2.1937,-2.2006,-1.883,-1.4167,-1.7276,-1.5709,-1.5616,-1.7716,-1.1886,-0.47836,-0.49611\n0.87865,-0.10612,0.37853,-0.19773,0.18525,0.47236,-0.045135,0.21218,0.7718,0.83968,0.85693,0.63242,0.33642,0.1109,-0.4445,-0.93724,-0.70352,-0.95884,-1.1773,-1.6891,-1.6007,-1.7099,-1.9317,-1.5919,-1.805,-1.497,-1.6188,-2.1921,-2.9826,-2.8328,-3.1175,-2.6135,-1.8889,-1.5959,-1.8606,-2.1857,-1.5448,-1.6279,-2.1761,-2.1262,-2.0443,-1.5117,-0.94912,-0.97397,-0.85454,-0.052533,NaN\n0.68099,0.30237,0.99942,0.19481,-0.025954,-0.032939,-0.23425,0.31162,-0.2615,0.41386,0.6368,0.75349,0.61057,0.31208,-0.09826,-0.93686,-1.2978,-1.6325,-1.8676,-1.8591,-1.0656,-1.5663,-2.4172,-1.6829,-0.83433,-1.2,-1.4577,-2.3182,-3.4754,-2.9524,-2.2994,-2.5327,-2.1828,-1.802,-1.3226,-0.88883,-0.19275,-0.64352,-0.89913,-1.326,-1.2961,-0.90346,-0.24589,-0.37553,-0.1955,NaN,NaN\n0.3547,0.1626,0.65328,1.0847,0.66121,-0.021456,-0.77129,0.12103,0.19249,0.55282,0.57002,0.46969,0.53864,0.20082,-0.12925,-0.77545,-1.2674,-1.965,-1.6776,-0.805,-0.63039,-1.219,-0.872,-1.7824,-1.6453,-1.6455,-1.5087,-1.7558,-2.3202,-2.3898,-1.6723,-1.4828,-1.4533,-1.1367,-0.85889,-0.76464,-0.070621,0.35264,-0.18872,-0.66303,0.15062,0.29503,0.23317,0.44247,0.63598,NaN,NaN\n1.0796,1.3291,2.6624,0.51047,-0.56641,-0.56353,-0.13427,0.19908,0.16958,0.27261,0.93162,0.26302,-0.42391,-0.68219,-0.30971,-0.71625,-0.77102,-2.0833,-0.63836,-0.055558,-0.33927,0.40953,-0.27169,-1.9673,-1.7327,-1.1622,-1.3569,-1.2595,-0.75373,-1.225,-1.665,-1.2618,-0.58986,-0.26448,-0.51713,-0.96878,-0.024978,0.29747,0.49378,0.80759,1.1769,-0.27803,-0.16164,0.35694,0.381,0.98218,1.2544\n1.6483,1.4572,2.3331,0.9368,0.86459,-0.032059,0.77911,0.48309,0.28084,0.38054,0.53949,-0.29561,-0.082162,-0.15183,0.62857,-0.26932,-0.7118,-0.25669,0.12218,0.36036,-0.01465,0.19503,0.19488,-0.5539,-1.3906,-1.4248,-1.0474,-0.63045,0.4168,-0.054724,-0.24168,-0.28627,-0.23501,-0.51489,-1.0074,0.25695,0.13682,0.18611,0.3749,1.1096,0.59624,-0.11305,-0.17966,-0.35399,-0.49118,0.65689,0.76408\n2.4737,1.8008,1.3034,0.42448,-0.38281,-0.35182,0.42705,0.38217,0.46745,0.67585,0.7872,0.5682,-0.19025,-0.19384,0.16785,0.073742,-1.1326,0.97736,0.90896,0.99522,1.4503,0.39064,0.19412,-0.005013,0.32598,1.1486,2.2311,2.6281,1.655,-0.3635,0.29037,0.066389,-0.35522,-0.28778,-0.21654,0.2076,0.00040674,0.010805,-0.24196,-0.24887,-0.12147,-0.14605,-0.18719,-0.14986,-0.58342,1.1625,0.55298\n0.99252,1.5719,1.1234,1.4028,-0.94412,-1.9657,-0.056449,0.82837,1.0457,0.29846,0.43605,0.62111,0.18448,0.42024,0.75297,1.1108,0.38954,0.34796,-0.14574,0.17613,0.82388,-0.011976,-0.81743,-0.13299,-0.3754,0.16593,1.3514,1.9164,2.1706,0.57947,-0.055385,0.29841,-0.051865,-0.011684,-0.09338,-0.4122,-1.0579,0.26154,0.68141,0.73761,0.34728,-0.64153,-1.1154,-0.92748,-0.43853,-0.22415,-0.24012\n1.8852,1.2833,1.9569,1.0356,-0.23796,-3.635,0.077679,0.74041,0.62655,0.090621,0.81765,0.87812,0.88941,1.3538,1.721,1.8578,1.5569,0.28509,-0.082291,-0.16349,-0.46017,-1.9902,-2.8713,-0.64707,-0.58133,-0.54187,0.78856,0.93056,1.3821,0.73542,-0.23845,-0.2038,0.33481,0.63572,0.98272,0.1545,1.0114,1.1057,1.8316,0.95443,0.41826,0.089547,-0.86557,-0.4714,-0.27722,-0.30462,-0.77461\n3.1596,1.9717,1.4558,0.78204,-0.81767,-2.0441,0.38037,0.23906,0.63979,1.4416,2.2252,2.465,2.7015,2.207,2.5869,3.2618,2.304,0.5441,-0.39003,-0.74235,0.093647,0.14199,-0.98752,-1.5394,-1.6996,-0.25028,0.52892,0.76382,0.59285,0.73805,0.89197,0.38862,0.62564,0.86477,1.9689,0.84704,0.60597,0.46753,2.3624,1.7889,0.85504,0.71637,0.4668,0.39941,0.70028,-0.16595,-0.70643\n3.0263,1.9527,1.3639,0.72436,-0.015381,1.04,1.525,1.4517,1.754,2.3072,2.2172,1.871,2.4834,3.4949,2.8093,1.9266,1.6536,1.2974,0.10938,0.59731,1.4969,2.0333,1.7849,0.15802,-0.41649,0.85697,1.4231,2.2161,2.1247,1.7445,2.195,1.8328,1.5951,1.3258,1.4374,0.32808,0.19205,-0.48041,0.57299,1.3712,1.3853,0.65032,0.70747,1.0817,0.90764,0.62958,0.047142\n1.666,1.3711,1.638,2.3053,1.9192,0.14719,1.3347,1.7014,1.7834,2.4221,2.5696,2.6653,3.5151,4.7627,2.6453,1.9304,1.3313,0.97353,0.48644,-0.90314,-0.74877,0.033434,2.1486,1.5321,1.2743,2.0954,2.9893,3.4834,4.7196,4.619,4.0014,3.0373,2.9253,2.6559,2.8989,1.9009,2.2667,1.3692,1.6748,1.9195,1.7232,1.3749,1.4977,1.1219,0.61934,0.18465,0.48602\n2.1245,1.798,1.4518,1.8032,1.2353,1.49,1.7142,1.67,2.3668,1.8194,2.2851,2.1656,3.0799,3.2109,1.6974,1.2515,0.91077,0.74233,0.82213,0.55284,1.2173,1.1503,3.0299,3.2782,3.1916,3.2177,4.259,4.1842,4.9281,5.4807,5.3789,5.7565,5.1836,4.7755,3.4976,2.966,2.8097,2.8767,2.6914,2.4925,2.6485,1.8333,1.4867,0.73637,0.61958,0.78363,0.96968\n3.0277,2.8013,2.1675,2.5522,3.7121,1.7615,1.6354,1.7936,2.1598,2.3563,2.2309,3.3805,3.1296,2.7769,2.0952,0.69215,2.4131,2.5883,1.7369,1.5607,1.4692,2.9614,3.7779,4.0691,3.7661,3.7862,4.4484,5.2241,5.5212,6.7448,8.4156,8.0084,7.2323,6.1774,4.8328,4.7371,4.7357,4.2152,3.8156,2.5516,2.5803,1.8136,1.3982,1.1397,0.87836,1.0168,1.0899\n2.6569,2.7494,2.4806,2.6982,1.5807,2.4691,1.7239,2.3244,2.6776,2.7219,3.0682,4.0326,3.992,3.6549,2.7499,2.1351,2.7463,3.6258,3.58,3.8235,3.5755,4.2579,5.5618,5.4477,4.2356,4.738,5.3018,6.5743,8.2995,10.202,9.9863,8.9708,7.0085,6.6112,6.5355,5.806,4.5187,4.7602,4.7208,2.7449,2.6814,2.4713,1.5721,0.80753,0.46306,0.68765,0.88785\n2.3561,2.4533,2.7168,2.6046,1.707,2.3784,1.818,2.224,2.9776,2.8785,3.8958,4.0934,4.058,4.0493,4.3938,4.7009,3.905,3.9176,4.6063,5.5824,6.5077,7.2738,6.9209,8.0074,8.1963,7.4852,8.4832,10.177,11.312,12.638,11.879,9.8147,7.758,5.2276,5.9938,5.399,3.7961,3.9805,4.023,3.5694,2.6411,2.3769,2.0211,1.1034,0.75833,0.57021,0.62727\n2.4507,3.2524,2.0104,1.5904,2.0021,2.4663,1.6169,2.657,3.3933,2.6381,3.6026,4.4201,4.4122,4.342,4.66,5.2057,5.0845,5.115,5.6023,6.5977,7.4521,7.9297,8.614,9.5235,11.459,9.6883,8.9948,9.9252,11.089,7.1236,9.7945,9.8305,9.8053,6.207,5.4395,4.8309,4.2034,3.8665,3.6744,3.4448,2.4639,2.2288,2.1432,1.7737,1.1918,0.6905,0.66574\n2.1977,2.2013,0.84403,0.0019913,1.4588,2.2922,3.0979,3.4074,3.5798,2.5546,2.9731,4.1503,4.8453,5.0472,5.252,5.6918,5.3868,5.8561,6.8451,7.764,8.2278,8.8609,NaN,NaN,NaN,NaN,7.5367,7.1792,6.9295,7.4574,8.6532,10.399,10.352,8.8434,6.4689,4.8005,4.9305,4.3333,4.0043,3.151,2.6096,2.5655,2.0786,1.6719,0.69929,0.89046,1.1486\n1.7544,2.0578,0.61605,1.6345,1.5305,1.2056,1.3985,1.658,2.04,1.6467,3.074,3.3089,4.2521,4.5233,4.7573,5.3034,5.2351,6.4499,7.4404,8.6472,9.236,NaN,NaN,NaN,NaN,NaN,NaN,5.9458,3.5445,6.2388,9.3847,11.205,12.043,9.2532,5.3149,4.886,4.7419,3.935,3.9274,3.7419,2.8413,2.4403,2.0681,1.3723,1.145,1.2611,0.96334\n-0.039946,0.20866,1.7068,3.7183,0.63807,-0.09341,-0.089135,1.0083,1.7588,1.9017,2.9464,2.8961,2.959,3.9029,4.7759,5.4007,5.8372,5.7452,6.0881,7.7565,NaN,NaN,NaN,NaN,NaN,NaN,7.3492,5.4658,5.1179,6.1684,9.0611,11.67,10.987,7.4447,6.8111,6.231,5.8028,5.7157,4.4883,3.7448,2.6074,2.2459,1.8154,1.3534,1.3036,1.2499,0.97266\n-0.56785,-0.71682,-1.5876,-1.5653,-0.79636,-0.43452,0.26933,0.85977,1.7649,2.9623,2.8313,2.5657,2.3477,3.1328,4.6909,4.6638,4.7374,5.445,6.1487,7.3431,NaN,NaN,NaN,NaN,NaN,9.3327,8.8312,7.5576,5.6342,4.3858,3.932,6.5797,5.1974,6.344,8.1413,7.2037,6.8796,6.0378,4.7423,3.1782,2.7508,2.2793,1.868,1.4676,1.4218,1.0156,0.76094\n-0.42271,-1.5154,-0.060989,2.2206,-1.8132,-1.6875,-0.38334,-0.1352,1.1938,2.1411,1.5467,2.2845,2.7293,3.1214,2.6404,3.0175,4.1722,4.1118,5.6083,6.3558,NaN,NaN,NaN,NaN,NaN,NaN,9.9806,10.7,8.7228,7.6443,6.6438,7.1684,2.7105,6.9378,8.3373,8.135,7.1107,6.3863,4.6233,3.3061,2.983,2.659,2.2101,1.784,1.5414,1.2638,1.6141\n0.32422,-0.18369,0.62523,1.2646,-0.46756,-1.6632,-1.586,-0.94908,0.038348,1.157,1.2435,1.1435,1.2525,1.2342,0.26492,1.4311,3.4328,3.7018,5.7362,8.0286,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.897,9.389,8.3279,7.335,6.7495,6.2552,5.2256,6.7693,6.1712,5.7626,6.1358,3.6981,3.1269,2.8786,2.8399,1.9085,1.144,1.4334,1.7043,2.5211\n0.77469,0.628,0.86923,1.0948,0.87935,-0.82125,-1.5844,-1.2437,-0.30155,0.61158,1.6773,1.3856,0.50513,-1.1916,-1.2947,-0.16696,0.8903,1.0072,1.8542,3.2333,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.0169,6.7246,6.9372,4.7052,3.0248,4.4296,4.4142,5.787,5.6338,4.0325,2.8811,2.9346,2.3532,1.7388,1.3076,1.2494,2.0723,2.6672\n0.61993,-0.12018,-0.62675,-0.27528,0.095151,-0.71339,-2.0726,-1.1092,-0.32449,0.30348,0.6548,0.14437,0.17844,0.1628,-1.8397,-1.271,0.49023,1.0619,0.90882,3.0543,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.9003,6.9624,7.2358,NaN,NaN,7.1276,7.102,7.1792,4.5213,2.7629,2.725,1.9898,1.4994,1.6473,1.1142,1.7607,2.2029\n0.47527,-0.85619,-1.2494,-1.0841,-0.013974,-0.80366,-1.6269,-1.7829,-0.39486,-0.12031,-0.52721,-0.4972,-0.33818,-0.76615,-1.7643,-1.283,-0.19007,1.2228,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.3116,6.7491,NaN,NaN,NaN,NaN,10.232,9.6774,6.8495,3.2346,2.282,2.4217,2.1529,1.7725,1.9365,1.782,1.6024,1.4139\n-0.096653,-0.55539,-0.26631,-0.20943,-0.52691,-0.88835,-0.98808,-0.88239,-1.0631,-0.91364,-1.1866,-0.44685,-0.24898,0.51247,0.17467,-0.72436,-0.57805,0.067764,-1.0415,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.6534,NaN,NaN,NaN,NaN,NaN,5.1649,4.702,3.8833,2.9233,2.3256,2.2961,2.2568,2.3558,2.3152,2.1664,1.6119,1.5259\n-0.56667,-0.7587,-0.61619,-0.62962,-1.1887,-0.62755,-0.43082,-0.96491,-1.3108,-0.7345,-0.71756,-0.8677,-0.94918,-0.67433,-0.54102,-0.3877,0.70667,1.5358,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.4752,NaN,NaN,NaN,9.8946,NaN,4.7381,4.151,3.2908,2.8925,2.8021,2.3472,1.8921,2.1583,2.4885,3.157,3.3032,2.8364\n-0.068087,-0.074419,-1.2073,-1.3338,-0.88436,-0.16097,-0.076247,-0.79031,-0.68877,-0.37938,-0.68458,-1.1908,-1.1485,-1.4748,-0.75513,-0.82563,0.49185,0.5033,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.156,5.4236,NaN,NaN,NaN,7.5857,6.5416,4.5614,3.8448,3.4836,3.5066,3.1384,2.4227,1.8894,2.0469,1.7528,2.1922,2.9869,2.3497\n-0.10774,0.44009,0.1072,-0.70337,-0.49767,0.1982,0.27144,0.055436,-0.32147,-0.50053,-0.62109,-0.65385,-0.77382,-0.36519,-0.54638,-0.29426,0.09944,1.0649,0.91902,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2591,5.7632,10.22,10.112,8.5982,6.2228,3.829,3.6727,2.6716,2.2326,2.8232,2.4534,1.9171,1.4799,1.3386,1.6281,1.6836,1.6615,1.2897\n-0.29419,0.040398,-0.40677,-0.17298,0.13174,0.34097,0.21837,0.21082,0.10224,0.14294,-0.16853,-0.36191,-0.93074,-1.0668,-1.1125,-0.71722,-0.16659,1.3341,1.4713,-2.4473,-1.4354,0.17185,-0.2831,NaN,NaN,NaN,NaN,-0.97653,3.512,7.0546,9.281,8.8063,7.2135,3.9083,3.1274,3.0422,2.223,1.4743,1.9349,1.6298,1.2874,1.3149,1.1884,1.9799,1.6801,1.6343,1.56\n0.54924,0.43652,0.47822,0.016576,-0.048141,0.17484,0.61431,0.69753,0.55999,0.23774,-0.19741,-1.1481,-1.9234,-1.8008,-1.5301,-0.80949,-0.41664,0.48285,0.80656,-2.6977,-1.7351,-0.71191,-1.0876,NaN,NaN,NaN,NaN,NaN,3.0743,5.956,5.3823,5.1859,5.3074,3.2459,3.1163,3.1407,2.1549,1.5643,1.5868,0.96876,1.1016,1.4018,1.5883,2.0701,1.5444,1.4338,1.0239\n1.2973,0.62651,0.79107,0.82605,0.25639,0.033812,0.18276,0.023964,0.41697,-0.020258,-0.44682,-0.75291,-1.6012,-1.461,-1.1986,-0.96232,-0.11877,-0.090272,0.17453,-0.39455,-0.64152,-1.0097,-1.5717,-0.084666,NaN,NaN,NaN,NaN,4.1865,4.0667,4.4478,4.8662,3.5423,2.9424,2.9538,2.6835,1.8498,0.51069,0.88507,0.56178,0.59613,1.1485,1.252,1.3371,0.94214,0.84775,0.12526\n0.64177,0.39738,0.73833,0.528,0.40777,0.56531,0.38358,-0.14674,0.4358,-0.23866,-0.28128,-0.31325,-0.7619,-0.66651,-0.19079,0.021367,0.095178,0.12823,-0.23407,-0.1126,-0.13323,-0.15354,1.6911,3.0979,NaN,NaN,NaN,3.2512,2.4742,2.9997,3.2875,3.0642,3.0906,2.5693,2.4996,1.4994,1.037,-0.29431,0.65032,0.35509,0.72784,1.3712,1.2417,0.44019,-0.56946,-1.0145,-0.51656\n0.51066,0.50477,0.83518,0.66142,0.3728,-0.22725,-0.27737,-0.30082,-0.24887,0.074988,-0.18912,-0.50289,-0.38326,-0.038084,0.4293,0.58162,0.39034,-0.53876,-0.35372,0.82235,0.80648,1.0411,1.6679,2.8625,4.3882,3.9376,3.6244,2.3546,1.7165,1.9265,2.341,2.2912,2.1429,2.2788,2.0813,0.92139,0.4159,-0.27076,0.38969,0.29575,0.73153,0.93693,1.1034,1.2743,0.81557,-0.051674,-0.8682\n1.3132,1.036,1.089,1.0123,0.52572,0.054782,0.092209,-0.2617,0.22459,0.41102,0.033281,-0.041605,-0.0018182,0.031864,0.59227,0.47283,-0.37001,-1.3286,-2.2886,-1.5472,-0.32349,-0.20388,0.75059,1.1297,1.5516,1.038,2.0606,1.9868,1.9251,2.4451,2.5716,2.971,3.403,2.4311,1.52,0.6108,0.17398,0.056397,0.085095,-0.10937,0.19922,0.64581,0.86944,1.4216,0.77805,0.24913,-0.34198\n0.87177,1.274,1.7727,0.76243,0.56676,0.25266,0.14969,0.21187,0.30395,0.023759,0.058245,0.16992,0.32135,0.15796,-0.54154,-0.52527,-0.53716,-0.91729,-2.4562,-2.7497,-1.6207,-0.98285,-0.76266,-0.362,0.48997,0.72645,1.3138,0.75029,1.9089,1.825,2.3417,2.5056,2.0392,1.3587,-0.12294,-0.19252,0.56454,0.63088,-0.16483,-0.40895,-0.21197,-0.86882,-0.83783,0.31791,1.0115,0.71671,0.13245\n0.51836,0.9347,1.4108,1.3927,1.3159,0.040921,0.070078,0.036562,0.00023317,-0.19336,-0.25589,-0.34125,-0.19436,0.14084,0.28234,-0.29342,-0.65135,-0.95566,-1.7911,-2.3017,-2.8797,-2.9374,-2.3213,-0.82463,-1.2474,-0.74155,0.62416,0.90875,1.4061,1.1228,1.0232,1.6912,1.3609,0.33901,-0.80121,-0.92859,-0.2813,0.6992,0.15982,-0.26275,-0.4656,-1.0078,-0.83498,-1.7313,-0.46872,-1.035,-0.46122\n1.1602,1.5675,1.0762,0.84778,1.0446,0.27451,0.035075,-0.36077,0.7532,0.36875,-0.032537,0.34435,0.19886,0.26725,0.32048,-0.10585,-0.4352,-0.62112,-0.93951,-1.4229,-1.9382,-1.5487,-0.72313,-0.22328,-1.196,-1.391,0.66767,1.4364,1.0052,1.0244,0.8544,0.94265,1.0326,1.232,1.274,0.21877,0.25808,0.093459,0.13092,-0.12808,-0.2817,-0.47331,-0.35002,-0.63337,-1.033,-1.0569,-0.40734\n0.88842,1.0999,1.1953,0.97751,0.69922,0.39873,0.49943,0.33252,-0.27385,-0.76082,-0.15165,0.12974,0.42912,0.42178,0.23758,0.037816,-0.39197,-0.35643,-0.52009,-0.64068,-1.5476,-0.57537,0.078374,0.57786,0.79657,0.59428,0.64886,0.82995,0.33514,0.30155,0.39822,0.21569,0.79504,1.2766,1.1674,0.78389,0.17482,-0.19747,-0.28993,-0.58445,-0.67079,-0.78077,-1.0357,-1.3329,-1.1164,-0.77171,-0.61695\n0.70978,0.96946,0.9794,0.70596,0.92654,0.62028,0.4627,-0.07658,-0.34681,-0.88747,-0.5069,-0.13952,0.1269,-0.068393,-0.16959,-0.29001,-0.56583,-0.89653,-0.96454,-1.0881,-0.11092,0.33693,0.40099,0.67461,0.38454,0.15335,-0.48358,0.054306,0.56947,0.78358,0.30474,0.046682,0.52709,0.45102,0.73589,1.3751,0.95693,0.048122,-0.079216,-0.46041,-0.81405,-0.9797,-0.83363,-1.0805,-1.0314,-0.64801,-0.24834\n0.82037,0.7815,1.0632,1.1773,0.33495,0.091109,0.14991,-0.27475,-0.25586,-0.17338,-0.50431,-0.66596,-0.1739,-0.89468,-0.13274,-0.38174,-1.0937,0.28868,-0.2027,-1.3215,-0.018577,-0.30375,0.22497,0.6362,0.90329,1.0972,0.3556,2.3798,2.0087,1.1357,0.33973,-0.060178,0.11509,0.11277,0.32003,1.0588,0.33577,0.17057,0.15544,-0.30943,-1.0002,-1.1778,-1.0086,-0.7255,-1.1743,-0.77357,-0.83006\n0.57676,0.67856,1.1404,2.1754,0.85695,0.4489,-0.49394,-0.74876,-0.85712,-0.40404,0.040342,-0.044206,-0.40937,-0.56955,-0.79872,-1.0323,-1.0874,-1.1911,-1.3735,-1.5121,-0.94148,-0.58843,0.15019,0.51872,1.1433,1.2211,2.0028,2.1989,1.452,0.90851,0.50102,-0.14781,0.044309,-0.13348,0.14933,-0.18203,-0.36375,-0.49814,-0.10805,-0.64404,-0.97546,-0.88128,-0.97928,-1.0183,-1.0354,-1.1277,-1.5616\n0.31829,-0.028195,-0.27256,-0.019742,1.2125,0.70044,-0.3307,-0.15369,-0.015915,-0.19211,-0.21718,-0.73475,-0.43532,-0.37751,-0.66636,-1.2186,-1.4285,-1.5912,-3.0217,-2.8731,-0.86652,-0.29541,0.56242,0.26752,0.57912,1.4965,2.2447,2.9742,2.0589,0.82871,0.20854,-0.27512,-0.13693,-0.0070653,0.4011,-0.0087056,-0.13273,-0.68609,-1.2885,-1.6671,-1.0641,-0.9319,-1.0728,-0.69231,-0.22295,-0.54767,-0.78381\n0.070505,0.14189,-0.26777,0.45068,0.89652,0.52439,0.25733,0.21599,-0.4076,-0.26585,-0.22425,-0.028635,-0.21865,-0.2292,-0.41297,-0.63143,-0.4159,-0.84295,-0.50902,-0.8248,-0.080792,0.21065,0.51609,0.38801,0.51451,0.87944,2.4853,2.9347,2.6562,1.5947,0.54528,-0.32523,-0.54898,-0.11669,-0.17673,-0.25549,-0.11858,-0.47701,-1.1448,-1.2433,-0.82887,-1.0591,-0.45957,-0.51135,0.074436,0.21574,-1.143\n-0.36004,0.41154,0.68437,0.072196,0.63816,0.1869,0.54105,-0.04232,-0.81715,-1.1877,-1.2258,-1.5442,-0.90253,-0.73978,-0.73916,-0.68076,-0.27686,0.14454,0.22672,-0.4988,-0.061597,0.36737,0.51219,0.79984,1.1582,0.88735,1.3186,1.3746,1.7348,1.2526,0.19898,-0.50711,-0.88434,-0.52065,-0.54058,-0.35547,-0.88775,-1.3657,-1.4525,-1.2238,-1.1384,-1.2643,-1.0514,-0.65458,-0.5375,-0.62493,-1.1432\n0.25507,0.12149,-0.039748,-0.053177,0.16801,0.26441,0.81507,0.29061,-1.1826,-2.307,-1.476,-1.7789,-1.1458,-0.68814,-0.73504,-0.50536,0.1545,0.2858,0.089288,-0.1983,-0.069553,0.057858,0.21932,0.47298,1.3603,1.3749,2.0567,1.8329,1.2385,0.20888,-0.27817,-0.061857,-0.13467,-0.16752,-0.4102,-1.0777,-1.2414,-1.3604,-1.3722,-0.96091,-1.0086,-1.1629,-1.2128,-1.2719,-1.2199,-0.81459,-0.80803\n0.9354,0.51868,-0.30343,-0.46677,-0.27319,0.80947,1.2883,0.81493,0.17416,-0.73412,-0.29659,-1.3442,-1.5264,-0.98149,-1.4122,-1.3605,-0.46533,-0.34956,-0.7741,-0.26187,-0.14537,-0.48602,-0.30153,0.51856,1.4427,2.0333,2.159,2.3551,1.5668,0.86098,-0.047869,0.38824,0.21431,0.34846,0.38856,0.19679,-0.28262,-0.93005,-1.4678,-0.95621,-0.84732,-0.95866,-1.0136,-1.0832,-0.90399,-0.83256,-1.0139\n1.0718,1.3011,0.48145,-0.58053,-0.90142,-1.5394,0.9715,0.6041,0.50024,-0.1777,0.14483,-0.23804,-0.52559,-0.51607,-1.101,-1.8683,-1.3129,-0.99266,-0.32562,0.34794,0.37606,0.13105,-0.18968,0.28778,0.81991,1.3324,1.53,1.7463,1.5385,0.44927,-0.29262,-0.12166,0.032504,-0.044754,0.29855,0.14332,-0.44317,-1.2455,-1.4679,-1.2339,-1.1619,-1.2349,-1.1936,-1.3617,-1.0117,-1.1048,-1.1368\n0.6389,0.55608,-0.4281,-0.78097,-0.94134,-1.177,-0.15888,0.66865,2.2775,0.96429,-0.53098,-1.6857,-2.9863,-1.6361,-0.24946,-0.099234,-0.69336,0.14929,-0.84629,0.27344,0.11142,0.88898,0.17482,0.44215,0.56855,0.58894,0.69276,0.88783,1.1435,1.6678,0.68163,-0.36162,-0.42767,-0.19191,0.097369,0.040852,-0.37437,-0.69374,-1.2608,-1.2072,-1.4393,-1.6795,-1.4919,-1.757,-1.9296,-2.543,-2.4218\n0.94983,0.76086,0.041186,-0.30264,-0.63793,-0.62177,-0.78378,0.20693,1.425,1.3513,0.50197,-4.8362,-2.9818,-1.6387,-2.8654,-1.941,-1.3956,-1.0843,-0.11414,-0.24808,-0.25902,0.58529,-0.24521,0.29082,0.55321,0.27795,1.4125,1.3512,1.1346,1.3061,0.64526,-0.11423,-0.97972,-0.38434,0.0079665,-0.3011,-0.3315,-0.53302,-1.0954,-0.86271,-1.0737,-1.5175,-1.5174,-1.2979,-1.5131,-2.0394,-2.4039\n0.81296,0.57728,0.35708,0.63278,0.50258,-0.17683,-0.51324,0.17621,0.65313,0.68826,-1.7584,-0.79133,-2.5581,-4.4615,-5.2532,-5.8282,-2.3706,-1.5383,-0.83714,-0.55701,-0.5253,-0.76087,-0.97991,-0.42304,-0.46945,-0.099896,0.51799,0.77085,1.0108,0.62639,-0.095925,-0.097137,-0.43274,-0.504,-0.72399,-0.79595,-0.27485,-0.43857,-0.89911,-1.0307,-0.9232,-0.99983,-1.4818,-1.3403,-1.6766,-2.2752,-2.7677\n1.5602,2.8169,1.3169,0.85471,1.1013,0.8732,0.14809,-0.44226,0.046344,0.4175,-4.1384,-1.9281,-1.1174,-1.5335,-2.9646,-4.6183,-3.6225,-2.0907,-0.030168,-0.59302,-0.67913,-1.1443,-1.3146,-0.78335,-1.1131,-0.34637,0.41467,0.80113,0.90675,0.29369,-0.17094,0.072371,0.20714,-0.18561,-0.58785,-0.8416,-0.22708,-0.26077,-0.63437,-0.94411,-1.0162,-1.0311,-0.81254,-1.0988,-1.3684,-1.2758,-2.3236\n0.030122,1.3578,1.8837,1.0711,-0.02017,-0.51511,0.33905,0.24575,0.19704,0.60812,2.192,0.17363,-0.73767,-0.40684,-0.46969,-0.79267,-1.6925,-1.8821,-1.1819,-0.42779,-0.92078,-1.4387,-2.2139,-2.041,-1.5497,-0.022264,0.6585,1.1246,1.212,0.44501,-0.13373,0.16276,0.3927,-0.11618,-0.73721,-0.78226,-0.39864,-0.27249,-0.25913,-0.67596,-1.183,-1.1352,-1.0078,-1.4502,-1.2263,-0.946,-1.0429\n-0.38711,-0.060167,0.47616,1.1695,0.6373,-0.30601,0.7528,-0.64762,-0.7948,-1.0121,-0.94509,0.053087,-1.2492,-0.7468,0.18902,0.8001,-0.10737,0.17037,-0.71355,-0.50748,-1.3706,-2.0035,-1.8793,-2.2831,-2.2807,-0.38615,0.43147,1.2865,1.0516,0.76227,0.4967,0.6124,0.58824,-0.34504,-1.017,-0.70773,-0.72319,-0.89521,-0.23128,-0.78579,-1.357,-1.7262,-1.7519,-1.4342,-1.5589,-1.6662,-1.1019\n-0.43162,-0.48585,-0.36245,0.71901,1.2011,0.76026,0.42576,-0.79433,-0.60899,-0.66648,-0.41231,-0.44127,-1.3549,-1.8945,-2.1183,-1.13,0.76793,1.2599,0.52772,-0.27378,-0.11142,-0.94437,-1.149,-1.3435,-0.84864,-0.78679,0.38374,1.1445,0.80518,0.3894,0.3433,0.2861,0.26915,-0.65089,-0.90048,-0.56801,-0.78394,-0.98751,-0.87851,-1.0993,-1.2576,-1.4817,-1.5799,-1.7923,-1.6899,-1.6827,-1.6155\n-1.078,-1.3278,-0.86001,0.9204,0.57124,-0.29462,1.5465,1.3938,1.1616,-0.66723,0.46356,-0.57095,0.36413,0.63703,1.0364,-0.22466,0.36954,-0.92344,0.47327,-0.061634,0.89055,0.57513,-0.5439,-1.2174,-1.2332,-1.5627,-1.2282,0.30039,1.3612,1.2385,0.98065,0.27838,-0.35652,-0.72028,-0.84232,-0.50881,-0.78796,-0.69356,-0.47077,-0.44931,-0.54038,-1.395,-1.4156,-1.4977,-1.5766,-0.58503,-0.19776\n-2.3944,-3.9724,-1.2642,0.21985,0.91971,1.5269,0.99278,0.30656,1.8693,2.5013,2.1634,1.2258,1.4498,2.111,1.6477,-0.99105,-3.2512,-3.124,-0.45851,0.00065851,-0.16011,-0.15774,-0.4758,-1.4025,-0.47946,-0.17344,-0.055571,0.42221,0.94167,1.1072,0.98643,0.75751,-1.0413,-1.4503,-1.2726,-0.69109,-1.1906,-0.8057,-0.46119,-0.46941,-0.86575,-1.2609,-1.1786,-1.2582,-1.2537,-0.45572,0.050007\n-0.72573,1.0931,0.48376,0.93183,1.1981,-0.23944,-0.53161,-0.43046,0.8903,2.2275,1.5045,1.1008,0.077595,-0.49723,-0.70413,-1.0457,-0.53041,-1.4027,-1.2379,-0.49608,-0.63283,-1.2152,-1.372,-1.4325,-1.4476,-0.40793,-0.10312,-0.060563,-1.109,-0.78111,-0.50945,0.1203,-0.62768,-1.2541,-1.4482,-1.334,-1.3649,-1.091,-0.72706,-0.97119,-1.2656,-1.4919,-1.7797,-1.6784,-1.3321,-1.1738,-0.67397\nNaN,-1.3714,-0.8303,2.1514,-0.073169,-2.4539,0.94272,0.1773,2.2505,1.8759,1.2395,2.2634,2.2486,1.386,-0.49846,-1.2525,-0.55045,-0.20824,-0.68666,-1.1149,-1.3767,-0.69939,-0.83826,-1.1208,-1.0804,0.035115,0.035941,-0.52499,-1.1559,-1.5836,-0.32092,-0.68809,-0.5917,-0.70008,-1.114,-1.5929,-1.2495,-1.1893,-0.76909,-0.85243,-1.1389,-1.4863,-1.7667,-1.528,-1.5777,-1.781,-1.7911\n2.9288,-2.1444,-0.71144,0.44581,1.1273,1.1184,0.25108,-0.61236,0.22551,0.80566,1.9246,2.2298,2.7539,2.7757,0.40145,-1.1352,NaN,2.3304,0.69161,-1.3318,-0.76322,-1.6846,-2.9622,-1.511,-0.71035,-1.5464,-0.91862,-0.96749,-1.5624,-1.9695,-1.2519,-0.84025,-0.61446,-0.067292,-1.1712,-1.9643,-2.3805,-1.5672,-0.99628,-0.46347,-0.50858,-0.49041,-1.4087,-1.3746,-1.6554,-2.0212,-2.3293\n0.96484,-2.1357,-1.5314,-0.92221,-0.27712,0.36767,-1.1828,0.015802,0.51635,2.1935e-05,-1.1846,-0.50375,1.5219,3.2262,3.1362,NaN,NaN,NaN,NaN,2.6817,0.79158,-0.80834,-1.466,-1.0858,-0.87036,-0.62153,-0.97269,-1.1874,-0.56038,-0.3546,-0.80207,-1.0563,-1.1698,-1.2019,-0.85838,-1.6247,-1.9587,-1.3084,-0.68654,-0.86801,-0.76109,-0.75305,-0.77539,-1.1158,-1.6399,-2.1458,-2.6058\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070430-070604.unw.csv",
    "content": "-1.2348,-2.0708,-2.1542,-1.9949,-2.0367,-1.3009,-1.4209,-1.2523,-1.1349,-1.5358,-1.7719,-2.4719,-1.7034,-1.707,-1.4732,-1.2533,-0.91056,-1.0186,-0.69833,-0.2392,-0.37441,0.26717,0.35292,-0.16204,-0.48328,-0.67447,-0.68478,-0.96325,-0.98299,-1.1378,-1.2756,-1.3994,-0.99779,-1.0292,-1.0623,-0.81271,-0.42552,-0.39727,-0.20921,-0.32263,-0.4585,-0.35016,-0.2503,0.037178,0.34906,1.101,1.6161\n-1.3359,-2.1465,-1.8011,-1.8878,-1.7592,-1.6548,-1.5649,-0.89835,-0.91401,-0.85492,-1.2826,-1.7697,-1.5522,-1.5285,-1.6455,-1.5509,-1.1389,-0.76992,-0.093784,0.075462,0.073631,0.67426,0.39482,0.22281,-0.15754,-0.32383,-0.61272,-0.70685,-0.75305,-1.0552,-1.2884,-1.2797,-1.0924,-0.98204,-0.98353,-0.54593,-0.31577,-0.14639,0.041737,0.068077,-0.24139,0.041946,0.08017,0.30167,0.65935,0.82602,0.94973\n-1.3962,-1.9994,-1.6184,-1.4735,-1.26,-1.4582,-1.3779,-1.0613,-0.70601,-0.70561,-0.77854,-1.372,-1.335,-1.3496,-1.0534,-1.0768,-0.63354,-0.20379,0.22421,0.22375,0.11517,-0.014542,0.00036621,-0.23471,-0.31023,-0.1095,-0.27127,-0.29201,-0.71969,-0.82276,-1.0059,-1.0773,-1.002,-0.87084,-0.85067,-0.59291,-0.51845,-0.1711,-0.029652,-0.11684,-0.06126,-0.024895,0.079552,0.63798,0.94006,1.1611,1.7058\n-0.95506,-0.47889,-2.242,-1.437,-1.4714,-1.4744,-1.279,-0.79982,-0.83457,-0.97172,-0.90782,-0.89322,-1.2561,-1.0366,-0.54981,-0.18094,0.16391,0.35234,0.31037,0.23023,0.26322,0.20463,-0.061943,-0.23019,-0.009449,0.17849,0.1881,-0.04818,-0.3795,-0.61565,-0.62994,-0.64246,-0.66475,-0.7248,-0.68521,-0.5111,-0.46849,-0.30972,-0.21195,-0.13954,-0.1913,-0.32666,0.020645,0.5394,0.92723,1.4768,2.3831\n-0.56902,0.061859,-4.926,-1.2763,-1.318,-1.151,-1.0044,-0.74068,-0.59219,-0.45577,-0.43192,-0.91437,-1.1409,-0.60247,-0.17094,-0.089302,-0.025955,0.52217,0.21703,-0.065163,0.67804,0.37914,-0.023018,0.098503,0.20554,0.34401,0.2641,0.17542,-0.058655,-0.25285,-0.42554,-0.55689,-0.56358,-0.59673,-0.59295,-0.39101,-0.48006,-0.40369,-0.30166,-0.099304,0.023029,-0.016708,-0.17477,0.33318,0.78371,1.5766,2.5279\n-0.29538,-1.9388,-3.8353,-1.217,-1.1677,-1.2403,-1.1276,-0.87692,-0.84868,-1.2151,-1.1875,-0.74129,-0.68535,-0.32232,-0.1995,0.0063553,0.14484,0.41642,0.24108,0.73653,0.7884,0.45744,0.20351,0.43298,0.63101,0.5118,0.60537,0.40939,-0.06636,-0.28838,-0.2781,-0.39666,-0.49709,-0.50011,-0.32145,-0.20078,-0.24508,-0.23703,-0.28955,0.06419,0.12042,-0.00036621,-0.0050888,0.31583,0.86411,1.3642,2.5903\n-0.64014,-0.51128,-5.5682,-1.381,-1.2693,-0.84935,-0.68677,-0.55931,-0.45338,-0.82574,-0.67299,-0.36248,-0.067734,0.16092,0.15901,0.34618,0.38107,0.50319,0.59849,1.3788,0.14841,0.084816,0.0040321,0.21389,0.6078,0.65198,0.69205,0.27358,-0.10239,-0.099884,0.050636,-0.27182,-0.44973,-0.55952,-0.48264,-0.42865,-0.47327,-0.2598,-0.49909,-0.6742,-0.65583,-0.017876,0.050201,0.37898,1.0352,1.6218,2.7076\n-1.6258,-1.6695,-1.6498,-1.4851,-1.1923,-0.84443,-0.47043,-0.18584,0.056953,-0.44515,-0.21523,-0.082851,0.52219,0.33688,0.43827,0.51706,0.59724,0.89042,1.1396,1.0927,0.07119,0.56503,0.28229,0.49587,1.0359,0.89806,0.71024,0.50871,0.36051,0.081543,0.099232,-0.20306,-0.38652,-0.58761,-0.34743,-0.45877,-0.52464,-0.66343,-0.8958,-1.01,-0.9601,-0.61015,-0.29498,0.013306,0.60623,1.4918,2.5532\n-1.5834,-1.5636,-1.3129,-1.8899,-1.3292,-1.0695,-0.60211,-0.029293,0.21821,0.2919,0.4473,0.71029,0.49904,0.72247,0.79421,0.92693,1.0498,1.2953,1.216,0.86346,0.82692,0.76065,0.48968,0.79254,0.99854,0.65993,0.77911,0.87746,0.35729,-0.1169,0.0010605,-0.0022888,-0.20641,-0.21755,-0.25555,-0.35272,-0.45924,-0.67282,-1.0441,-1.0864,-0.949,-0.78555,-0.84913,-0.34068,0.39446,0.98756,1.0673\n-1.1493,-1.1738,-0.28082,-1.5053,-1.8149,-1.2919,-0.19604,0.16451,0.23567,0.83893,0.98809,1.1399,0.71771,1.0615,0.81054,1.0982,1.5578,1.6417,1.4279,1.0835,1.388,0.8988,0.54435,0.92749,0.99529,0.84885,0.97498,0.85246,0.63203,0.50868,0.2246,0.0821,-0.079254,-0.12314,-0.030636,0.089867,-0.019535,-0.53462,-0.88652,-0.858,-0.63921,-0.50452,-0.49095,-0.30083,-0.074944,0.20838,0.36268\n-1.4229,-1.4566,-0.6853,-0.64815,-2.0571,-1.3681,-0.45913,0.16781,0.2607,0.71125,1.2291,1.3126,1.435,1.4452,1.6515,2.0628,1.725,1.6936,1.5514,1.5982,1.1927,0.8453,0.90151,0.97377,0.91737,1.0846,1.0575,0.94798,0.85098,0.47932,0.079994,-0.1054,-0.23962,-0.16465,-0.35956,-0.24651,-0.27757,-0.57032,-0.60998,-0.53819,-0.46829,-0.30404,-0.44787,-0.50802,-0.21077,-0.11159,-0.16056\n-1.5427,-2.0409,-2.1021,-2.9447,-2.5999,-1.0493,-0.83825,-0.18932,0.14709,0.59576,1.099,1.3191,1.6163,1.8745,2.0235,2.0921,2.0093,2.3102,2.1542,2.0771,1.276,1.1146,1.1723,1.2258,1.114,1.4286,1.2112,1.1718,0.94749,0.30623,0.043228,-0.049614,-0.23972,-0.21409,-0.084068,-0.28708,-0.32875,-0.42391,-0.48025,-0.43917,-0.39187,-0.36411,-0.78762,-0.39985,-0.11557,0.28433,0.48035\n-1.3227,-1.4144,-1.7607,-2.8168,-2.8366,-1.5911,-0.89107,-0.28188,0.30586,0.70684,1.0633,1.2035,1.6513,1.7673,1.8263,2.1232,2.344,2.3268,2.2679,1.8017,1.2255,1.4938,1.3177,1.5786,1.4999,1.4082,1.3681,1.4065,1.0836,0.64682,0.68706,0.18319,-0.022079,-0.098572,0.0071259,-0.17051,-0.43217,-0.35603,-0.26881,-0.25703,-0.20877,-0.42739,-0.48719,-0.36971,0.089394,0.48915,1.899\n-1.4004,0.0034637,-0.20284,-1.0479,-2.4904,-2.4959,-2.1844,-0.48375,0.51695,0.95932,1.3881,1.3769,1.3186,1.7222,1.8996,2.162,2.1458,2.1431,2.0176,1.5898,1.47,1.6662,1.3914,1.4259,1.3014,1.3189,1.3955,1.1639,1.1302,1.1131,0.94513,0.46022,0.13284,-0.18114,-0.51399,-0.78313,-0.88248,-0.58862,-0.28128,-0.028587,0.52235,-0.42227,-0.42715,-1.0671,-1.4211,1.4316,2.3638\n-0.49162,0.26584,-0.70925,-1.7659,-3.3415,-2.6839,-2.1207,-0.29532,0.72472,1.3308,1.8372,1.6027,1.4845,1.6076,2.0024,2.063,1.9811,1.9827,2.0044,1.589,1.7383,1.3228,1.5954,1.5467,1.2929,1.1567,1.5772,1.5232,1.2433,1.0099,0.82463,0.45531,0.045654,-0.21539,-0.65007,-0.59725,-1.0248,-0.95,-0.61144,0.14747,-0.070728,-0.71725,0.16604,0.17045,0.28751,0.26315,1.7178\n0.23313,-0.47988,-1.3011,-2.3469,-2.9735,-2.4875,-2.3671,-0.20469,0.74524,1.2142,1.8771,1.7155,1.7572,2.0277,2.231,2.2584,2.2062,2.4422,2.1344,1.5593,1.3636,1.5698,1.6743,1.5923,1.3516,1.2792,1.5413,1.4965,1.2655,0.78299,0.65552,0.14455,0.033951,-0.070686,-0.18704,-0.057091,-0.45655,-1.037,-1.1711,-0.81718,-0.51392,-0.17094,0.12946,-0.37058,-0.6396,0.72179,0.72363\n0.025314,-2.7365,-1.9664,-1.7263,-2.2552,-3.1186,-2.6119,-0.3777,0.66805,1.2539,1.7081,1.7239,1.8424,2.3848,2.2176,2.3528,2.5114,2.4503,1.8255,1.0285,1.0361,1.1883,1.2763,1.3283,2.3645,2.8189,1.7433,0.8614,0.98624,0.841,0.25425,-0.036095,0.035339,-0.027241,-0.16545,-0.35926,-0.68603,-1.2751,-1.416,-0.045555,-0.060463,-0.69004,-1.4121,-0.56364,0.10816,3.7682,1.9188\n-0.46782,-2.6061,-1.7088,-1.7041,-2.4229,-3.0999,-1.5417,-0.021709,0.70841,1.3138,1.7289,1.7283,1.7962,1.7483,2.2361,2.7446,3.0866,2.2626,1.5104,0.32401,0.97306,0.82803,0.52663,1.034,1.6891,1.8472,1.3575,0.93365,0.84158,0.66653,0.24384,-0.14855,-0.51675,-0.10099,-0.41052,-0.82422,-0.80959,-1.5839,-1.9385,-0.22933,0.027649,-1.8787,-2.772,-1.2359,0.25251,2.1288,1.8792\n-2.0114,-1.7804,-1.2645,-1.4514,-2.3887,1.4182,-1.3174,-0.14956,0.80441,1.1526,1.6303,2.127,2.0349,1.7624,2.3741,2.9316,2.7036,2.4101,1.5141,0.11603,-0.88231,-1.1845,-0.65853,0.20065,0.93079,1.1767,1.3824,1.2504,0.6469,0.60905,0.20552,-0.45094,-2.0136,-0.89614,-0.16872,-1.1374,-2.0518,-1.8062,-0.91302,-0.47681,-0.96687,-1.7563,-3.4924,-1.9285,-1.4683,-0.44834,-0.19994\n-2.1168,-1.8821,-1.071,-1.1143,-0.23186,-0.69287,-0.717,-0.17015,0.80616,1.2563,1.7532,1.7158,1.7574,1.905,2.005,2.1328,2.15,1.6967,0.98526,0.57056,-0.4296,-0.46211,0.12714,-0.21347,0.4879,0.87535,0.63431,0.90245,1.354,-0.039639,0.073967,-0.29017,-1.073,-1.7584,-2.2955,-1.3763,-1.8658,-1.9137,-1.9325,-1.8252,-1.8058,-2.321,-2.7643,-2.4156,-1.8288,-2.107,-1.7209\n-1.2603,-1.5818,-0.76844,0.11194,1.6014,0.27838,0.31946,0.074867,0.45928,1.0701,2.1558,1.6343,1.9276,1.9381,1.8314,1.3064,1.0195,0.74065,-0.049591,0.54239,0.95657,0.55894,0.25356,0.01395,0.48734,1.2705,1.0825,1.6199,2.0915,0.58212,0.05307,-0.55586,0.03434,-0.070168,-4.2848,-0.24302,-1.0501,-1.6572,-2.0686,-1.4858,-1.214,-1.7944,-2.326,-2.2034,-2.4377,-2.2918,-2.0017\n-1.3283,-1.3769,-0.52948,-0.14384,0.59409,2.176,0.86757,0.37625,0.61871,1.76,2.389,2.2527,2.6179,2.2069,1.0704,0.39162,-0.29549,-0.6828,-0.55695,0.19808,-0.56567,0.33515,-0.42621,-0.59239,0.41153,0.90162,1.0803,1.2128,1.2993,1.4283,0.9939,-0.77422,-0.43784,-0.32732,-1.9147,-0.58573,0.063641,-0.55701,-0.37952,-0.21904,-1.6005,-1.9749,-1.8732,-2.2263,-2.8495,-3.3681,-2.7711\n-0.51443,-0.42743,0.28558,0.60278,1.5939,0.10437,0.65454,0.87952,1.4913,2.5345,2.5377,3.503,2.6488,1.0099,0.051048,-0.10849,-0.63207,-0.44532,-0.82851,-0.13825,-0.24321,-0.49187,0.020084,0.71124,0.9071,0.60677,1.5356,1.2443,0.36334,1.4519,1.6554,0.8573,0.21539,0.25415,0.010139,0.33435,0.32255,0.41839,0.70264,-0.28401,-1.4371,-1.9531,-2.0117,-2.5393,-2.978,-3.0659,-2.7087\n0.060547,0.50649,1.0992,1.0911,-0.13943,0.68032,1.3442,1.8051,2.0705,2.2824,2.2635,2.9566,1.9913,1.1152,0.46102,-1.0308,-1.0737,-0.10036,0.37577,0.55663,1.4754,1.6575,1.4114,1.6601,1.666,1.2089,1.7686,1.4249,1.3573,2.3113,2.3049,1.5587,2.0263,2.2816,1.0021,0.86451,0.77899,0.82481,1.1022,-1.584,-1.7978,-1.7813,-1.583,-2.1082,-2.6784,-3.0566,-3.7004\n-0.17693,0.48026,1.2594,1.227,1.0731,1.9332,1.5687,2.4477,2.3674,1.6547,1.869,2.4736,2.1825,1.3002,1.0014,0.53442,0.64631,0.68148,1.4883,2.1144,2.289,2.6361,2.9105,2.3785,2.3569,2.738,2.6597,2.6766,3.1401,3.2334,3.342,2.9114,2.7241,2.5367,0.86272,-0.38198,0.22359,-0.43365,-0.93286,-1.517,-1.6628,-1.4281,-1.4672,-1.8274,-2.1814,-3.23,-3.4672\n-0.24541,0.68187,1.2252,1.4929,1.9609,2.441,2.2711,2.523,2.6468,2.1983,2.6598,2.8725,2.4663,2.3214,2.4592,2.0151,2.2934,2.6677,2.3951,2.6659,2.9167,3.9649,3.2256,2.6892,2.8647,3.4843,3.8568,4.6687,4.897,3.3227,3.311,4.6286,5.3177,0.66384,0.14363,-1.0334,-1.0427,-0.72564,-1.0154,-1.5752,-1.8282,-1.8063,-1.8954,-1.8354,-1.9144,-3.4743,-3.1485\n-1.2352,-0.34417,1.1246,1.596,2.0021,2.2125,2.8152,2.5957,2.6243,3.0034,3.4681,3.6176,3.644,3.5247,2.8131,2.8945,3.1951,3.1931,2.1977,2.2421,3.1644,3.2866,2.4516,1.9173,1.343,2.3182,2.7859,3.2524,3.2283,1.9019,2.4466,2.7572,2.4762,-0.3524,-0.68097,-0.55173,-0.61443,-0.49476,-1.4621,-1.0984,-1.6912,-2.0256,-1.9368,-1.9049,-2.2913,-2.6629,-2.6089\n-1.3753,0.026711,0.69339,0.70296,1.3134,1.6356,2.5861,2.5323,2.3437,3.1568,3.7121,4.0481,3.6402,3.3724,2.9552,4.3426,3.2088,2.2654,2.1612,2.5536,2.7128,2.9619,2.6661,1.2711,0.30953,1.6266,2.2946,2.168,1.9786,1.981,1.8006,1.1707,0.16836,-0.010803,0.14959,0.035744,-0.26461,-0.76719,-0.61272,-1.2053,-2.1887,-2.0565,-2.0393,-2.0082,-2.1909,-2.2581,-2.3031\n-0.73525,-0.081184,0.50067,0.49468,0.81541,1.4238,2.278,2.6312,2.2161,2.1614,2.5329,3.5213,2.8262,2.9711,3.3902,4.0276,2.7809,2.1347,2.6814,2.6849,2.4286,2.5751,3.2423,3.742,2.8082,4.3463,4.9997,4.0876,3.4602,5.8775,4.0755,2.6903,4.1157,0.76706,0.16423,-0.27665,-1.1964,-1.8612,-1.9533,-1.9991,-1.9547,-2.2388,-2.2773,-2.0272,-2.1939,-2.1778,-2.1212\n-0.76499,-0.35832,-0.41671,1.0129,0.76091,1.0369,1.6989,2.3096,1.8301,1.7594,2.5859,3.1436,2.1104,2.9089,3.5225,3.0035,1.8966,1.7583,2.3523,1.5564,2.1148,2.2854,4.8442,3.8801,5.2338,6.2226,5.6323,5.7824,6.703,5.9312,3.0342,3.5265,3.2067,-0.52264,-0.33984,0.093407,-1.8353,-2.8211,-2.0384,-1.8415,-1.5388,-2.0301,-2.0253,-1.8459,-2.2821,-1.8828,-1.7968\n-1.1562,-1.108,0.64434,-0.21337,-1.1358,-0.18626,-0.049316,1.5231,1.5933,1.446,1.9177,1.7888,1.6471,2.5805,4.3561,2.8497,1.0078,1.3672,1.5326,0.82171,2.3947,3.4055,5.595,4.4745,5.924,5.6244,5.4781,5.4713,6.1328,4.7761,0.82368,0.64752,0.69604,-0.73743,-0.042046,0.16579,-1.2073,-1.6587,-2.2122,-1.5819,-1.8477,-2.5899,-2.1986,-1.826,-2.2356,-2.0027,-1.7698\n-1.2675,-0.93159,-0.63642,-0.65903,-1.5008,-1.3352,-0.71911,0.046158,1.6781,1.3563,1.6056,1.2101,1.2944,1.976,2.2486,0.88353,0.65424,0.67207,1.0143,1.2739,2.7571,4.3596,6.3054,3.8292,4.6481,4.4213,3.3167,2.9043,1.993,1.6183,1.1512,1.4034,1.4725,0.13052,-0.16728,-0.50575,-1.7579,-1.4021,-1.5862,-1.248,-2.1552,-2.4143,-2.56,-1.7838,-1.8446,-1.3975,-1.2134\n-1.9603,-1.9065,-1.7875,-1.4676,-1.47,-1.1556,-0.84838,-0.41982,1.3158,0.49417,0.64885,-2.7026,-3.4977,1.4135,0.79893,2.1055,2.9931,1.2131,0.93652,-0.017143,2.5928,4.8607,5.5041,1.7583,2.4496,3.0177,1.9303,0.58287,0.35072,0.97512,1.0615,1.2228,1.9207,1.4308,0.21512,-0.58077,-0.96362,-0.60535,-1.1916,-1.3137,-1.7628,-2.1228,-2.3694,-1.9634,-1.5615,-0.96769,-0.59559\n-2.284,-1.9403,-2.2735,-1.9705,-1.3382,-1.1157,-1.5957,-0.68232,0.5164,0.71056,1.5696,0.2052,-2.7962,0.65936,0.38413,0.72106,1.4828,1.268,1.2554,0.97775,2.5489,3.1718,2.1303,2.4134,2.5036,2.6039,1.3686,1.2098,1.8376,2.3711,1.434,1.7116,3.4717,2.2835,0.47721,0.11503,-0.38673,0.075115,-0.3288,-0.84292,-0.98944,-1.0559,-1.3609,-1.6047,-1.3872,-0.83012,-0.51523\n-2.0432,-1.7611,-1.9132,-1.9227,-1.7707,-1.3728,-0.99983,0.19968,0.59553,0.95585,1.0435,0.31671,-0.082352,0.21611,2.17,0.78901,0.89734,1.9994,1.9423,1.7121,2.125,2.0306,2.6672,3.0468,NaN,3.2558,0.90658,2.4066,5.1914,4.0411,1.7859,3.1675,3.8361,2.6646,1.4173,0.98879,0.41311,-0.072765,-0.23381,-0.49873,-0.17831,-0.26987,-0.30704,-0.97718,-1.0656,-0.60698,-0.5191\n-1.272,-1.6365,-2.117,-1.9739,-1.0173,-1.2841,-0.70432,0.34659,0.41946,0.16602,-0.047897,-0.15092,-0.35014,0.48671,2.602,0.52519,0.67313,1.6206,2.1476,2.1549,-0.83435,0.8017,2.9478,NaN,NaN,NaN,NaN,NaN,NaN,1.9416,1.9041,2.6419,2.0502,1.568,1.0423,-1.1363,-0.3855,-0.61669,-0.0018005,-0.25589,-0.27074,0.27288,0.527,-0.42599,-0.50365,-0.46345,-0.66393\n-1.0451,-1.4833,-1.7763,-2.0005,-1.3717,-1.3251,-1.0378,-0.30982,-0.67864,-0.82925,-1.0416,-1.5057,-1.7076,-2.2854,-1.5821,-0.41586,0.35793,1.9888,2.1615,2.6959,1.7721,2.6242,3.1226,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.2089,2.5157,1.4758,-4.0289,-4.378,-3.3718,-1.5826,-0.31751,-0.25897,-0.4227,-0.0014038,0.64848,-0.022991,0.25866,0.16525,-0.29962,-0.64339\n-0.75893,-1.1367,-1.4301,-1.2427,-0.93563,-0.47079,-0.82692,-1.1462,-1.2402,-0.95125,-0.50216,-1.9831,-1.944,-2.3532,-1.2809,-0.43242,1.044,1.6663,2.5187,3.3999,2.3052,0.96253,0.31007,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.9123,2.3822,2.2199,-1.3899,-2.9692,-1.8876,-0.53915,-0.14878,-0.36586,-0.38216,-0.33824,-0.051811,0.19941,0.51009,0.83042,0.78559,-0.031898\n-0.67933,-0.89536,-0.79031,-0.63314,-0.51597,-0.12147,-0.37355,-1.3123,-1.3936,-1.4522,-1.5903,-2.4109,-2.5028,-2.051,-1.2215,-0.26344,1.6916,8.4729,3.587,0.7439,0.96254,-0.44507,-1.2107,-2.0023,1.3961,3.4078,1.1493,NaN,2.5632,2.1852,1.7504,2.2474,2.2255,0.95678,0.18736,0.44324,0.48052,0.20377,-0.040306,-0.35815,-0.60251,-0.3287,0.19812,0.16163,0.9075,0.72427,0.030998\n-0.72298,-0.48732,-0.19965,-0.26591,-0.28529,-0.2279,-0.35291,-1.3899,-0.95749,-1.0596,-1.225,-1.464,-2.1394,-1.3766,-1.1301,0.14481,0.92799,2.3021,3.0124,0.47802,2.0949,0.45818,-0.25972,1.0699,2.4599,0.60878,-1.2201,0.84559,1.9022,1.7738,2.0279,2.0544,1.7862,1.287,0.98474,1.1143,0.89837,0.76086,0.2034,-0.59045,-0.66656,-0.20196,-0.13454,0.36684,0.82353,0.87193,0.12239\n-0.16724,-0.12409,-0.093826,-0.31329,-0.14485,-0.16201,-0.44048,-0.92705,-0.76313,-0.68338,-1.0282,-1.0609,-2.0465,-2.1266,-1.1629,-1.0399,-0.41783,0.4651,0.36543,0.80663,2.9321,0.76954,0.80523,1.5112,1.4109,0.20892,0.35841,1.221,1.5857,1.6371,1.1902,0.67563,1.3449,0.91119,1.3566,0.76342,0.81388,0.81155,0.38199,-0.51867,-0.74725,-0.59751,-0.25787,0.38902,0.34562,0.50653,0.3618\n-0.052231,-0.17849,-0.16443,-0.26062,0.030777,-0.16019,-0.49597,-0.81057,-0.72484,-0.7608,-1.4768,-1.8578,-3.6738,-2.8712,-1.8531,-1.4625,-1.848,0.23722,1.0724,1.2234,2.0306,1.9702,1.4376,0.93096,0.21336,-0.31571,1.1428,0.97635,1.4629,1.7929,1.3796,1.3617,1.3352,0.93269,0.90649,0.29843,0.33102,-0.30219,-0.093033,-0.40932,-0.91818,-0.67712,-0.30478,-0.04187,0.068298,0.52303,0.29549\n-0.60763,-0.53977,-0.40672,-0.56863,-0.42691,-0.43624,-0.86309,-1.4067,-1.571,-1.9598,-2.2453,-1.8677,-3.4988,-2.2981,-1.5206,-1.1832,0.2704,-0.65475,0.49405,1.0588,1.7451,1.6676,2.1788,0.48263,0.77857,1.7263,0.61811,0.38238,1.0194,1.1352,1.1706,1.5761,0.9963,1.1887,1.1656,1.0075,0.40861,-1.8432,-0.99606,-0.32615,-0.58182,-0.49231,-0.090279,0.064468,-0.054123,-0.027267,-0.43072\n-0.41992,-0.4888,-0.095581,-0.86623,-0.75085,-0.66179,-1.2134,-1.8014,-2.2887,-2.3364,-2.1917,-1.7272,-1.5439,-0.80938,0.41982,1.1665,1.3984,1.7918,1.934,1.8216,1.7346,1.9671,-1.2456,-0.78396,1.4443,1.8277,0.52427,0.2062,0.23439,0.1968,0.23396,0.33786,0.75217,0.8719,-0.80832,-1.1533,-0.71146,-2.0419,-0.54753,-0.292,-0.24947,-0.070187,0.024368,-0.034119,-0.32937,-0.55363,-0.844\n-0.37634,-0.39432,-0.3124,-0.80217,-0.8683,-1.4479,-2.0144,-2.3137,-2.3429,-1.3073,-1.4167,-1.0253,-0.6543,-0.0026932,1.1461,1.865,1.3043,1.735,1.7683,1.3817,1.854,0.20277,-3.1513,-0.97089,0.25517,1.1908,0.65254,0.75124,0.6273,0.47409,0.64034,0.77803,0.11567,-0.092556,-2.0927,-0.98998,-1.1681,-1.1732,-0.41732,-0.018326,0.12247,0.24149,-0.16142,0.003746,-0.62696,-1.0811,-1.1987\n-0.54244,-0.07,-0.0031738,-0.32431,-0.71491,-1.1103,-1.293,-1.2928,-0.74336,-0.65285,-0.91068,-0.55562,-0.43688,-0.20292,1.2478,1.3579,1.2934,1.1729,0.49093,0.70422,1.17,0.17192,-2.6892,-1.8328,-0.16866,-0.25548,0.41947,0.5321,0.63741,0.76152,1.1218,0.66312,-0.19094,0.16877,0.11773,-0.31875,-0.87203,-0.3242,-0.46471,-0.17747,-0.18875,-0.59713,-0.39654,0.31914,-0.3419,-0.90459,-0.9614\n-0.24489,0.15536,0.19488,-0.41151,-0.32127,-0.33947,-0.46438,-0.58056,-0.50448,-0.49236,-0.39016,-0.31675,-0.10957,0.21996,0.6041,0.89202,1.1146,1.4659,0.74528,0.86192,1.5031,0.77436,-1.7916,-1.0213,-0.48048,-0.22536,0.15528,0.33762,0.68125,1.003,0.7289,0.41055,0.20863,0.4448,0.14124,-0.64657,0.071079,0.081184,-0.61785,-0.39284,-0.64301,-1.5231,-1.1384,-0.86333,-0.13667,-0.37695,-0.43858\n0.0158,0.14135,0.062111,-0.25902,-0.32594,-0.07048,-0.0032196,-0.40679,-0.8082,-0.61711,-0.19578,0.16691,0.80815,0.97313,0.94902,0.95655,1.1158,1.5129,1.7503,1.6563,1.5193,1.3438,0.037437,-0.13062,-0.11997,-0.29712,0.15385,0.63696,0.3488,-0.067825,0.31744,0.50565,0.092224,-0.46169,-0.67193,-0.68822,-0.28769,-0.41054,-0.72855,-0.053215,-0.38182,-1.2242,-0.78784,-0.7247,-0.41151,-0.39758,-0.19998\n-0.31404,-0.085693,-0.38197,-0.23847,-0.3613,-0.45403,0.044197,-0.45228,-0.15911,-0.098045,0.22271,0.854,1.0395,0.94722,1.1661,1.1354,1.0112,1.2312,1.5364,2.3464,2.4764,3.5634,2.1555,0.39304,0.59059,0.57943,0.42374,0.048592,0.048553,-0.24971,0.33935,0.37252,-0.031403,-0.40249,-0.63087,-0.65955,-0.39064,-0.69498,-0.53578,0.29565,0.25977,-0.27783,-0.95556,-0.3838,-0.27533,-0.37045,-0.43179\n-0.27497,-0.2311,-0.24851,0.48341,-0.36015,-0.23215,0.34156,0.37161,0.023392,0.041656,0.16496,0.70061,0.87447,0.93096,0.7716,0.67941,0.80753,1.6006,0.8912,-0.15491,4.2017,2.7047,0.6017,0.75517,1.4149,1.5051,0.78603,-0.22352,0.22162,-0.35667,-0.11845,-0.042599,-0.41271,-0.53714,-0.58855,-0.55821,0.01252,-0.36746,-0.40527,0.20191,0.14396,0.39514,0.21851,-0.53751,-0.29937,-0.33855,-0.32441\n0.00457,-0.11123,-0.067047,0.48407,0.17347,-0.1803,0.084572,-0.29853,-0.28494,-0.096291,0.2002,0.50873,0.82841,0.84953,0.70585,0.78268,1.1379,0.99292,-0.61007,-1.5222,-1.6389,-0.22875,0.53501,0.68492,1.1382,1.1596,-2.8515,-3.8957,0.289,-0.3774,-0.49561,-0.21106,-0.55065,-0.41493,-0.57766,-0.053959,0.09898,-0.14594,0.30358,0.5061,0.40979,0.63633,0.3716,-0.33628,-0.10776,0.0036545,-0.088638\n-0.25359,-0.28377,-0.0041733,0.13564,-0.0059586,-0.36654,-0.22941,-0.4095,0.045387,0.081947,0.3532,0.72394,0.96829,0.41848,0.96268,1.0405,2.0467,0.62921,0.47342,-0.27414,-1.0689,-0.083351,0.17561,0.7039,1.6077,2.0328,0.51311,0.39858,0.29309,-0.61279,-0.24024,-0.30032,-0.32415,-0.29777,-0.32484,0.11301,0.26011,0.48935,0.7168,0.72424,0.57404,0.62351,1.5868,0.042488,-0.23257,0.12922,0.53694\n-0.4502,-0.38684,-0.38805,-0.10539,-0.41945,-0.49081,-0.48512,-0.43246,-0.22881,-0.45244,0.35216,0.68661,0.58233,0.23697,0.95119,0.20183,-0.19902,0.39212,0.42217,0.26788,-0.46976,-0.43592,-0.03347,0.78716,1.9399,2.3462,2.2115,1.9717,1.7148,0.21536,-0.43015,-0.21684,-0.21761,-0.27518,-0.43969,0.36909,0.56169,0.64108,0.57452,0.60067,0.59142,1.105,1.3743,0.46692,-0.11067,-0.088413,0.4344\n-0.31437,-0.28029,-0.22698,-0.12022,0.030373,-0.61029,-0.45816,-0.7478,-0.52385,-0.37438,0.50346,-0.35458,-0.26143,-0.27271,-0.20486,-0.93228,-0.96101,-0.8396,-1.199,-0.90675,-0.20045,-0.26571,0.5167,0.94888,1.3782,1.8351,2.2322,3.2047,2.7875,1.3419,-0.20195,-0.074112,-0.09568,-0.37101,-0.32886,0.25963,-0.04612,0.47693,0.29626,0.82393,0.72211,0.6856,0.87022,0.13641,0.15759,-0.041847,0.16194\n-0.32518,0.21564,-0.41866,0.17162,-0.27683,-0.54526,-0.52658,-0.43876,-0.084854,-0.11726,0.17897,-0.52531,-1.3454,-1.2524,-0.96692,-0.92699,-0.70064,-0.50601,0.41809,-0.9142,-0.0086594,0.72801,0.9305,1.2207,1.6599,1.9265,3.0486,3.0687,0.95286,0.50725,-0.017998,-0.36685,-0.24879,-0.21824,0.085522,-0.1149,0.1199,0.24794,0.24976,0.81884,0.80492,0.50322,0.27219,0.4664,0.3179,0.050148,-0.072773\n-0.6769,-1.1273,-0.41545,0.22901,0.0103,-1.9164,-1.8138,-0.94424,-0.82156,-0.5564,-0.40151,-0.44608,-1.9218,-2.2425,-1.549,-1.4371,-0.79566,0.041245,0.2485,-0.75391,-0.031235,0.78675,0.95881,1.6489,2.1052,2.1285,3.255,2.8342,1.415,0.46878,0.31284,-0.39877,-1.0874,0.042553,0.12956,-0.16457,0.14621,0.14566,0.43119,0.73109,0.56543,0.56446,0.88331,0.95938,0.14043,0.0014687,0.0010147\n-0.30311,-1.0989,-0.35786,0.29456,0.88352,-2.6131,-2.1726,-1.6157,-1.7307,-1.7087,-1.5057,-1.5588,-1.813,-1.1135,-1.5709,-2.1029,-1.9475,-0.36806,-0.24931,-0.34198,-0.030769,0.40042,0.68624,1.2298,2.0829,2.0893,2.4244,1.7912,1.2339,1.1473,0.56063,0.338,-0.21553,0.26963,0.21518,0.023979,0.21178,0.25336,0.43506,0.47478,0.3455,0.30825,0.91421,0.24401,-0.090744,-0.42348,-0.23981\n-0.38047,-1.2786,-1.5262,-0.66837,-0.34903,0.45088,-1.2115,-0.9746,-1.3005,-2.9254,-1.7588,-1.545,-1.453,-1.2561,-1.2932,-1.72,-2.1988,-1.1403,-0.083038,0.20462,0.32511,0.18826,0.9324,1.187,2.0084,2.2196,2.3785,0.62543,1.1838,1.2497,0.7425,0.56003,0.095291,0.60332,0.43169,0.29169,0.397,0.54306,0.81588,0.48737,0.46447,0.55807,0.88248,0.096863,0.091225,-0.31871,-0.23415\n-0.93446,-0.68849,-1.0539,-1.4914,-1.546,-1.4211,-1.5149,-0.4789,-1.1044,-1.83,-1.06,-0.91602,-0.98511,-0.8289,-0.068535,-1.8047,-2.5073,-0.9317,-0.013206,0.66642,0.60221,0.36909,0.78778,0.85479,1.3127,2.1328,2.5439,2.5121,1.2249,0.6363,-0.23151,-0.56836,0.089508,0.61652,0.31928,0.41818,0.46679,0.69247,0.79094,0.6393,0.45591,0.32651,0.16261,-0.085133,-0.3292,-0.34509,0.077728\n-1.1722,-0.053577,-0.38244,-2.352,-3.3019,-6.4817,-1.8722,-1.0348,-0.70376,0.096901,0.45794,0.30788,0.23815,0.3233,0.07753,-0.59422,-2.0623,-1.1731,0.12793,0.39991,0.19891,0.81646,0.541,0.57109,1.25,2.0346,2.4804,1.53,1.8238,0.58361,0.031231,-0.32491,-0.050903,0.65374,0.39406,0.66187,0.46446,0.36848,0.69776,0.79779,0.29633,-0.08548,-0.2198,-0.37246,-0.036392,-0.046276,-0.21149\n-0.13414,-0.22245,-0.62668,-1.5101,-2.0284,-3.3321,-2.2364,-1.8056,-1.0623,1.0924,1.2512,1.5072,1.7198,1.008,-0.95531,-1.0827,-2.466,-1.5216,-0.35329,-0.17856,0.14774,0.77065,0.99554,0.83357,0.80724,1.6156,1.7865,1.598,2.4162,1.7105,1.2151,0.42589,-0.81244,0.19108,0.45136,0.21093,0.31139,0.15704,0.28252,0.25019,0.1101,-0.11256,-0.2968,0.012314,0.33368,0.19065,-0.032673\n0.46199,0.118,-0.0012283,-0.4049,-0.11974,-0.052319,-1.6491,-1.7942,-0.57811,0.46541,0.38467,1.2679,1.3805,1.1425,-0.58907,-1.2647,-1.7083,-0.74776,0.22039,0.13575,0.86652,1.0772,1.0752,0.73447,0.40246,0.78698,1.2742,1.3848,1.3775,1.3216,1.24,1.0455,0.26468,0.40939,0.40424,-0.29134,0.071648,0.0018997,-0.082993,-0.21546,-0.081863,-0.13513,-0.25641,0.38592,0.74314,0.35942,-0.15663\n0.55495,0.74009,0.47072,0.94155,1.4362,-0.57825,-1.8604,-1.2012,-0.36353,-0.051132,0.80281,-0.21111,0.053295,0.3043,-0.098938,0.55843,-1.1312,-0.8924,0.31723,-0.24794,-0.098,0.23529,0.98334,0.95465,0.45688,0.45267,1.1114,1.3711,1.2119,0.90604,1.1677,0.98088,0.84725,-0.032784,-0.07505,-0.1073,-0.36179,-0.24734,-0.266,-0.23326,0.059067,0.060658,0.17759,0.28562,0.46751,0.67491,-0.045311\n-0.43658,-0.43468,-0.82838,0.18044,-0.86995,-2.268,-2.3683,-2.9865,-2.8893,-1.9761,-1.9138,-0.34224,0.41032,-1.128,-0.64145,-0.095139,1.2657,-1.4439,-0.18816,-0.36324,-0.011055,0.33924,0.49176,0.38483,0.65997,0.92642,1.4458,1.7746,1.8481,1.2374,0.52697,0.69183,0.82881,-0.063629,0.47034,-0.085449,-0.71889,-0.57589,-0.37809,-0.38561,-0.1203,0.20509,0.25161,0.051304,0.29474,0.41006,0.46118\n-0.73994,-0.4473,-0.22565,-0.00056076,-1.5967,-1.3108,-1.8741,-1.9312,-1.5154,-0.84869,-0.76462,0.92512,0.31205,1.9099,3.7778,0.65723,-1.8786,-1.1253,-0.1271,0.020164,0.39727,0.5322,0.76424,0.76652,0.8931,1.0311,1.4394,1.9073,2.5933,1.507,0.16271,0.12763,0.39795,0.052429,0.22272,-0.10042,-0.58611,-0.5765,-0.24301,0.1036,0.10425,0.33363,0.36229,0.49731,0.59119,0.64883,1.2789\n-0.66883,-1.1643,-0.66092,-0.87983,-1.2683,-0.59071,-1.0057,-1.3364,-0.99403,-0.94892,-0.14672,-0.72288,-1.7139,1.8665,2.5436,1.3048,-2.0261,-0.63449,-0.023098,0.11446,0.69287,0.22973,0.61068,0.45311,0.56189,0.682,1.2462,1.7802,2.1375,0.82313,-0.099564,-0.065136,0.11042,-0.20689,-0.33395,-0.46629,-0.086502,0.031208,0.13083,0.2476,0.40137,0.42834,0.2457,0.69274,0.73936,1.3017,2.1467\n-0.020576,-0.71283,-0.76239,-0.6515,-0.72623,-0.13157,-0.17908,-0.83982,-1.3804,-1.3509,-2.2597,-0.4912,0.023411,0.30278,1.0356,0.58273,-1.7821,-0.47102,0.0018539,0.21441,-0.10732,-0.003212,0.30178,0.36079,0.72351,0.84147,0.51111,0.81092,0.85838,0.35356,0.13264,-0.055115,-0.2462,-0.3661,-0.83457,-0.6463,-0.374,0.073799,0.1648,0.34412,0.51942,0.38443,0.52764,0.69685,0.9216,1.489,1.0857\n-0.29842,-0.29879,0.39263,-0.014854,0.94627,2.0266,0.87536,0.64791,-0.58624,-1.3082,-1.0896,-0.73572,0.49516,1.7166,1.2948,-0.9551,-1.0118,-1.1146,0.085136,-0.0086746,0.19566,0.039124,0.052147,0.44137,0.6468,0.77369,0.7132,0.77427,0.47766,0.44131,0.2702,-0.057823,-1.4695,-1.0562,-0.75007,-0.88004,-0.87903,-0.083061,0.15645,0.35228,0.34708,0.48965,0.51233,0.49181,0.78577,0.72341,0.31392\n-1.0616,-0.43879,0.80394,0.77771,2.4785,2.3671,3.051,2.4295,0.71548,-0.13608,-1.6676,-1.351,-1.2917,-1.2743,-1.2484,-1.2953,-0.42932,-1.1482,-0.11943,0.15655,0.35471,-0.0070572,0.21143,0.51567,0.35236,0.73912,0.88612,1.0171,1.0537,0.52055,-0.025879,-0.41207,-0.96769,-0.15459,-0.033504,-0.38317,-0.59824,-0.19778,-0.067215,0.080116,0.19823,-0.0083313,-0.053215,0.011147,-0.089466,-0.53899,-0.54448\n0.45811,1.1363,1.9835,2.4748,3.1983,2.2843,2.2599,0.53541,0.039383,-0.37403,-0.80605,-0.68665,-0.14812,0.0095558,-0.86546,-1.4331,-1.5455,-1.3272,-0.34551,-0.50948,-0.075375,-0.35739,0.35026,0.3408,0.34472,0.75407,0.80685,0.54632,0.69531,0.85201,0.41615,-1.0073,-0.72532,0.37392,0.58936,0.25403,-0.10431,-0.12364,-0.18464,-0.35219,0.033306,-0.088932,-0.11359,-0.34511,-0.38551,-0.73801,-2.0435\n1.6806,1.7425,2.9528,4.3912,3.8944,2.503,0.58139,-0.18686,0.53427,0.079376,0.48216,-0.10271,-0.19361,-0.3022,-0.90119,-1.3917,-0.069489,0.22016,-0.052643,0.25666,-0.31595,-0.34072,-0.21235,-0.15637,-0.029743,0.39815,0.74698,0.56557,0.51056,0.21263,-0.48411,-0.88078,-0.66968,0.69216,0.98478,0.6195,0.24009,-0.23787,-0.338,-0.47653,-0.3111,-0.13708,-0.14952,-0.43093,-0.49508,-0.74672,-2.2701\n1.7003,2.3945,2.6929,2.6975,2.4964,2.9411,0.67093,0.2905,0.13306,1.0377,2.0644,1.2891,1.2961,0.3241,-0.26485,-1.6456,-1.4703,-0.21606,-0.19327,-2.0751,-1.3451,-0.8203,-0.47012,-0.70185,-0.73703,-0.51771,-0.056816,0.54885,0.26862,-0.16071,-0.49854,-0.3809,-0.13256,0.068069,0.12249,-0.10716,-0.48466,-0.33376,-0.54695,-0.34864,-0.12151,0.006115,-0.11826,-0.23996,-0.36526,-0.86468,-1.669\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070604-070709.unw.csv",
    "content": "-0.054777,-0.59331,-1.009,-0.59424,-1.0178,-1.7945,-1.4581,-1.3382,-1.6653,-2.2906,-2.4062,-2.5781,-2.497,-2.0801,-2.4525,-2.3704,-2.6367,-3.4493,-3.6155,-3.5598,-3.3739,-3.126,-3.1546,-3.5111,-3.888,-3.9173,-4.0707,-4.0077,-3.2627,-3.3038,-3.8196,-3.8853,-4.0659,-4.3981,-4.6007,-5.1191,-4.4345,-4.0954,-4.5125,-4.1843,-4.0359,-3.717,-4.1503,-4.0846,-3.4013,-2.8764,-2.3792\n-0.046587,-0.25889,-1.0361,-1.38,-1.4589,-1.7184,-1.6376,-1.4673,-1.6123,-1.8743,-2.4509,-2.4696,-2.1051,-1.8594,-2.0008,-2.3652,-2.7481,-3.283,-3.3977,-3.3448,-3.4593,-3.2777,-3.4261,-3.7147,-3.9478,-3.9578,-3.7532,-3.2396,-2.7515,-2.4513,-2.6338,-2.6615,-3.4923,-4.6971,-5.1253,-5.2061,-4.4547,-4.1995,-4.5327,-4.5274,-3.8251,-3.2187,-2.8518,-2.4953,-2.3303,-2.5737,-2.7427\n0.31938,-0.33829,-0.44308,-1.1476,-1.2708,-1.1157,-1.3437,-1.3759,-1.3154,-1.6281,-2.2375,-2.5976,-2.1897,-2.0282,-1.9736,-2.1605,-2.4743,-2.3761,-2.4187,-2.6721,-2.8167,-3.1515,-3.326,-3.4935,-3.9072,-3.8427,-3.5628,-3.4331,-2.6383,-2.1928,-2.1856,-2.7893,-3.3017,-4.3999,-4.9367,-4.5607,-4.5028,-4.3445,-4.1566,-3.9842,-3.631,-3.2465,-3.0653,-2.5071,-1.602,-1.374,-1.4002\n-0.21576,0.54522,0.20307,-0.99887,-0.90352,-1.106,-1.0071,-1.1916,-1.4956,-1.6044,-1.966,-1.9872,-1.8965,-1.7554,-1.6669,-1.7222,-2.1112,-2.3442,-2.3738,-2.4919,-2.7159,-2.9508,-3.2786,-3.4901,-3.4801,-3.4079,-3.115,-2.7621,-2.6246,-2.6516,-2.7409,-2.638,-2.2864,-3.1307,-4.3477,-4.6295,-4.0953,-3.964,-3.3867,-3.4448,-3.6746,-3.4233,-3.0778,-2.3278,-1.5185,-0.97804,-1.0507\n-0.27768,1.3734,0.97401,-1.0581,-1.1252,-1.1247,-0.80262,-0.32293,-0.75095,-1.9424,-2.072,-1.4906,-1.4273,-1.5368,-1.8256,-1.6368,-1.9064,-2.8886,-2.7896,-2.4424,-1.9792,-2.4535,-2.8557,-3.0198,-3.0292,-3.167,-2.9493,-2.6579,-2.3999,-2.5202,-2.747,-2.5109,-3.6218,-3.8338,-3.0701,-3.3457,-3.6741,-3.2142,-2.7518,-2.5789,-3.1793,-3.0165,-2.545,-2.1273,-1.7292,-1.2895,-0.22455\n0.19714,0.47132,-0.35363,-1.4169,-1.5358,-1.6604,-1.6831,-1.3329,-0.85301,-1.0031,-0.98117,-0.77243,-1.1217,-1.7915,-2.054,-2.0702,-2.1449,-2.6375,-3.1306,-2.8254,-2.3382,-2.1668,-2.1859,-2.4916,-2.4342,-2.1556,-2.3046,-2.2646,-1.9066,-1.9942,-2.2111,-2.2726,-2.5424,-3.0219,-2.7322,-2.2342,-2.6095,-2.5815,-2.5112,-2.4414,-2.6365,-2.8416,-2.5419,-1.9882,-1.2643,-0.28878,0.42282\n1.0719,2.1402,2.0017,-0.86934,-0.86084,-0.99499,-1.0628,-0.88328,-1.1011,-0.83962,-0.92876,-0.61009,-1.0064,-1.8254,-1.7428,-1.8145,-1.9404,-2.1325,-2.4609,-2.5249,-1.9381,-1.9597,-1.6828,-2.0398,-1.9416,-1.4178,-1.6247,-1.8219,-1.7322,-2.4446,-2.7387,-2.5921,-2.4324,-2.6932,-2.9054,-2.7172,-2.3542,-2.281,-2.0171,-2.3336,-2.8887,-2.66,-1.9818,-1.5987,-1.6766,-1.0195,1.0472\n0.013972,0.012076,-0.40862,-0.72845,-0.74611,-0.68288,-0.32039,-0.44258,-0.63944,-0.82936,-0.91879,-0.69222,-0.73798,-1.5642,-1.7194,-1.7037,-1.6517,-1.6467,-1.7768,-1.8577,-1.1455,-1.3902,-1.6274,-1.667,-1.6139,-1.6608,-1.4637,-1.6925,-2.2307,-2.6068,-2.4041,-2.2415,-2.3598,-2.5388,-2.6541,-2.7933,-2.561,-2.4846,-2.3366,-1.7125,-1.2222,-1.3638,-1.0678,0.17742,1.0881,1.2455,2.2138\n0.17008,-0.068635,-0.31174,-0.85118,-1.1551,-0.69962,-0.093633,-0.26441,-0.27387,-0.42013,-0.34141,-0.081781,-0.56469,-1.228,-1.299,-1.3261,-1.213,-0.94213,-1.0791,-1.0632,-0.83723,-0.85795,-0.95499,-1.129,-1.3616,-1.1207,-1.3826,-1.7784,-1.9979,-2.431,-2.4141,-1.87,-1.9754,-2.315,-2.9219,-3.073,-2.5649,-2.4173,-2.3096,-1.8606,-1.0391,-0.03237,-0.46904,-0.79233,0.34249,1.3024,1.98\n0.57735,0.73957,0.68837,-0.77794,-0.86095,-0.34442,-0.13133,0.0065069,0.19647,0.22917,0.16952,0.29932,0.018562,-0.60736,-1.0159,-0.73717,-0.44645,-0.27049,0.066809,-0.22194,-0.79631,-0.91313,-1.1266,-0.98342,-0.95547,-1.2132,-1.3335,-1.4849,-1.5101,-1.4911,-1.6783,-1.8168,-1.8673,-1.5279,-1.2041,-2.0359,-1.8533,-1.932,-1.7192,-1.5074,-1.3405,-1.2501,-1.0713,-1.2493,-0.057236,0.97094,1.8202\n-0.060947,0.0019016,-1.1111,-0.82238,-0.83891,-0.23714,-0.30675,0.013737,0.03959,0.18693,0.46068,0.44427,0.47883,0.072114,-0.5884,-1.3956,-0.83464,-0.39804,-0.69411,-0.71359,-0.68643,-1.1679,-1.3082,-0.97349,-0.84074,-0.74938,-1.1252,-1.3092,-1.2097,-1.2111,-1.3272,-1.6275,-2.0124,-2.2518,-2.1706,-1.9837,-1.4293,-0.90902,-0.90673,-0.66896,-0.56283,-0.61156,-0.88531,-1.1396,-0.81689,0.61416,2.1276\n-0.14269,0.12133,-0.069205,-0.04635,-0.20138,0.14814,-0.16931,0.24284,0.51964,0.71272,1.0584,1.1283,0.97485,0.6719,0.20378,-0.31252,-0.81528,-0.067943,0.25517,0.18277,-0.32574,-0.92609,-1.3669,-1.128,-0.9279,-0.88666,-1.3839,-1.3707,-1.3447,-1.3725,-1.4466,-1.6295,-1.7909,-1.7457,-1.3925,-0.4906,-0.54654,-0.93668,-0.34827,-0.014944,0.12879,0.35917,0.41408,-0.65151,-0.54549,-1.035,NaN\n-0.058224,-0.52707,-1.8866,-1.2327,0.40118,0.16953,0.10876,-0.12788,0.4943,1.0898,1.5173,1.6126,1.1275,0.50719,0.63971,0.55786,0.29625,0.15971,0.20664,-2.3842e-05,-0.61633,-0.69073,-0.92488,-0.86976,-0.54674,-0.66314,-0.61877,-0.61963,-1.335,-1.5749,-1.2436,-1.814,-1.7035,-1.1876,-0.66709,-1.2056,-1.3933,-0.98539,-0.99002,-0.099166,0.74381,0.94358,0.57009,-0.54388,0.32713,NaN,NaN\n0.42909,0.68007,0.57726,0.86163,0.89394,1.1252,0.98055,0.3357,0.55546,0.77972,0.96079,1.2904,1.5591,0.70871,0.72309,1.0217,0.87042,0.87102,0.73919,0.23333,-0.17243,-0.26939,-0.50521,-0.44389,-0.21162,0.00046444,0.48036,1.0895,0.87627,-0.51497,-0.92998,-1.2265,-1.4562,-1.0854,-1.2945,-0.98608,0.0030785,0.80178,0.69317,1.424,1.8134,1.7256,1.938,2.4259,2.1126,NaN,NaN\n0.75888,1.1763,1.5639,0.96996,0.53218,0.20637,0.40276,0.4447,0.51062,0.62761,0.092598,0.41414,0.99292,1.1213,0.62894,0.45946,0.6188,1.2193,0.95869,0.10245,-0.99074,-1.09,-0.64027,-0.24929,-0.086282,-0.33325,-0.51186,0.095052,-0.2727,-0.5161,-0.89041,-1.237,-1.5974,-1.6544,-1.2304,-1.1261,-0.20704,0.20633,0.91296,2.4776,4.2638,3.1468,3.3753,2.2945,2.1989,NaN,NaN\n-0.20075,0.50412,0.9663,0.59711,0.77392,0.22948,0.041243,0.11908,0.32067,0.54357,0.036925,0.001462,1.1037,1.4024,1.2399,0.78963,0.91906,1.0612,0.68666,0.58598,0.25791,-0.27336,0.043321,0.71137,1.1814,1.092,0.45344,0.28292,0.069892,-0.20068,-0.28122,-0.13211,0.24025,0.2514,-0.53291,-0.39434,-0.67325,-0.10342,0.07429,-0.15893,1.0126,1.8379,3.4265,3.7206,3.8393,NaN,NaN\n-0.51613,-0.2087,0.24409,0.24178,-0.064824,0.36351,0.43263,0.54634,1.073,1.0931,0.6028,0.78633,1.9208,1.7437,1.3831,1.3822,1.3244,1.7875,0.67321,0.14295,-0.055809,-0.29511,0.85045,1.2918,0.81734,0.64899,0.37013,0.17854,0.037503,-0.2724,-0.61061,-0.84289,-0.57255,-0.58517,-0.37568,-0.10282,-2.0907,-1.596,0.81978,0.81051,1.7176,1.0016,0.92338,1.7247,3.8549,NaN,NaN\n0.13413,1.1004,0.95268,0.86092,0.42239,1.0449,1.1758,1.0559,1.43,1.3534,0.92323,1.7435,2.615,2.8027,2.3528,2.2286,2.3279,2.042,0.31657,-1.0936,-1.2094,-1.1244,-0.26905,0.1587,-1.2308,-0.75432,-0.077858,0.060385,0.058708,-0.52785,-0.79353,-0.77627,-2.485,-2.4774,-2.2886,-1.0763,-5.5958,-5.931,-5.2131,1.5143,2.0957,2.2956,1.2784,0.02241,1.5723,NaN,NaN\n0.69396,1.027,1.108,2.5781,1.7238,0.30792,0.40834,0.43669,1.3519,1.3539,1.3711,2.1044,2.4254,1.9502,1.803,2.249,2.5357,1.9077,1.3462,1.1159,0.10888,-0.95426,-0.93272,-0.48349,-0.58842,-0.14906,-0.079117,-0.17265,-0.06424,0.24348,-0.048146,0.51771,-1.2831,-3.0364,-2.5675,-0.76281,-0.087728,-0.22656,-0.68892,0.92908,1.1029,1.3926,0.82199,-0.24645,0.47087,0.62249,0.12224\n2.4039,1.5421,2.876,3.6341,4.2589,4.1067,1.8634,1.3192,1.8065,1.7784,2.0933,2.2512,2.0998,1.647,1.3422,2.0117,2.5534,2.4559,2.0121,1.546,1.7178,1.6019,0.62658,-0.55044,-0.10548,0.293,0.36755,0.6915,0.91716,0.58405,0.43817,0.30744,2.8848,1.0211,0.73536,0.98161,1.6421,1.5061,-1.764,-1.521,-0.53258,0.39595,0.30143,0.18341,-0.033952,-1.1538,-1.0242\n2.3311,1.6543,1.4522,1.9739,2.3624,1.3673,1.4905,1.2667,1.5445,1.6872,1.9414,1.5586,1.8035,2.5225,3.137,2.7931,2.6849,2.2276,1.7036,1.2865,0.75647,1.6747,1.5412,0.28924,0.39071,1.0045,0.79235,-0.048729,-0.11898,0.23537,0.044595,-0.40036,2.1799,2.5414,4.2772,1.355,1.9903,3.1976,0.93264,-1.1695,-0.84661,0.24797,0.574,1.1928,0.52633,-0.10744,-0.031604\n1.7672,1.6131,2.6581,3.4031,3.0086,1.0849,0.38893,2.1348,1.7616,2.3164,2.4745,2.289,1.9696,1.3304,-0.26401,2.6891,2.1846,0.89267,-0.067142,-1.3122,-0.52992,-0.0705,0.58583,0.50626,0.94068,1.2913,1.3323,1.0829,0.20106,0.072915,0.69386,0.30581,0.6956,0.6847,2.3309,2.3649,2.5451,1.5039,1.6978,1.2732,-1.3102,0.15292,0.40931,0.67343,1.2262,1.7135,0.82654\n2.687,2.7271,2.6853,2.3455,3.0161,2.4367,2.5262,2.6265,2.6154,3.2978,2.9413,2.7075,2.0319,-0.1991,-2.3328,-2.2277,1.7307,0.85846,0.17635,0.33182,0.27964,-0.16579,-0.50513,0.52276,1.0773,1.6687,1.9141,1.8013,1.544,1.4247,1.5696,1.9366,1.8659,1.9114,2.2149,2.0921,1.9822,2.7341,2.7443,3.5832,-0.099496,0.27131,1.566,0.55385,0.74054,1.0756,1.2039\n2.9395,3.3143,2.0905,0.36663,-0.54824,1.0687,1.9339,2.0278,2.1323,2.479,2.5697,2.0578,1.5286,0.070732,-0.64987,-1.3794,-1.437,-0.78637,0.81015,1.7884,2.6139,1.8192,0.90957,2.7841,3.0168,2.3053,2.4245,2.6169,3.0392,3.8117,4.572,3.5576,3.0896,2.9242,3.1103,2.538,2.1144,1.7011,1.428,2.1152,1.5317,0.77009,0.74326,0.8645,0.90626,1.5182,2.0461\n3.1665,3.3278,2.9912,2.6872,2.2787,2.2634,2.5494,2.5227,2.2394,1.8615,1.4436,1.9606,1.9882,1.519,0.69781,0.61603,1.7415,2.301,1.7184,2.2237,2.1257,2.4324,2.9654,3.8182,3.6912,4.3167,4.5199,3.8123,4.0542,5.4423,5.3257,4.632,4.1393,4.2856,4.8725,3.9676,5.4691,5.3365,4.9927,1.9757,1.8798,1.596,1.6624,2.1502,1.9099,1.7111,2.1238\n2.613,3.2537,3.1753,2.9691,2.8574,3.4676,3.1981,2.9063,2.8664,3.1018,2.6454,2.0938,2.5259,3.5231,3.2309,2.2646,2.8554,2.8649,2.6047,2.7781,3.0712,3.9444,3.6567,3.7345,4.1721,5.1848,6.3047,4.9144,4.7258,6.0336,7.5061,6.4837,2.4246,1.0013,4.0292,3.6012,2.2254,3.3429,3.015,1.369,1.6258,1.9772,2.0442,2.4654,2.5148,2.0586,2.6085\n3.3026,3.6015,3.0111,3.0938,3.5043,3.3679,3.6403,3.1515,3.2714,4.0188,4.1434,3.3095,3.4324,3.499,3.3845,3.0191,3.4081,3.7549,3.1167,2.6762,3.0955,4.0693,4.3139,3.3494,2.3763,2.2832,2.3193,NaN,2.5029,NaN,NaN,NaN,3.6566,0.96804,1.0465,1.8826,2.7679,2.7579,2.5978,2.3126,2.2345,2.5327,2.2391,2.5105,2.5838,2.219,1.9599\n1.8235,2.3485,2.0173,2.2558,2.843,2.5812,2.8603,3.6064,3.8674,4.1435,4.2092,3.9252,3.2674,2.5393,3.1427,4.1064,3.4636,2.6298,2.6045,2.872,2.7771,3.6156,4.0613,3.5462,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4771,2.5468,3.4837,3.5809,NaN,NaN,NaN,NaN,2.8131,2.3509,2.9881,3.0949,1.8894,1.6149\n1.543,2.0866,2.0914,2.1559,2.7548,2.8757,2.8228,3.1266,3.3477,3.3567,2.7638,2.968,3.8442,3.5909,3.718,4.2095,3.9309,3.4386,3.9105,4.2978,3.779,3.5957,4.3336,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.8346,NaN,NaN,NaN,NaN,NaN,2.7219,2.7315,2.432,2.3341,1.8401,1.7261\n2.772,2.0294,2.0181,2.1485,3.5927,3.6544,2.801,2.7871,2.9876,2.6567,2.9265,3.2757,3.9261,3.6462,3.7032,3.6256,3.3197,2.3403,2.4455,2.4184,4.3897,4.5769,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.4554,3.6303,2.704,NaN,NaN,NaN,3.0171,2.2508,1.5794,2.1071,1.8862,1.8483\n0.7499,2.3651,2.1005,1.7159,2.3377,3.5449,2.2592,2.3642,2.6096,2.7513,2.7991,3.0345,3.1204,3.4906,4.3171,4.2002,3.9439,3.8734,3.5183,2.0981,3.0077,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.6009,5.7279,5.9369,5.0639,4.1663,3.752,3.6403,2.9981,2.5314,1.593,2.0807,2.1916,2.4366\n0.66159,0.93305,1.605,2.5094,1.7761,1.0003,-0.11867,1.3316,1.5936,-0.11049,0.76186,NaN,NaN,NaN,NaN,-0.34173,2.177,3.8033,1.779,0.49373,2.6238,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.7273,4.3363,4.1952,4.0993,4.3419,4.6035,4.7413,4.6682,4.5112,2.8487,2.1537,2.1662,2.2906,1.6287,1.1244\n0.38929,0.89162,1.6757,1.4777,1.7814,1.3524,-0.4807,0.077737,2.1176,-0.41157,0.2258,NaN,NaN,NaN,0.12349,0.042739,1.1667,0.46447,-0.59774,0.062703,2.3558,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.5727,7.3775,6.9182,6.1019,5.7635,4.6154,5.2718,4.5066,4.0829,3.0765,2.2069,2.5889,3.0666,2.3165,2.3222\n1.8354,2.5765,2.7291,2.6354,2.2205,1.1827,1.897,2.0003,1.7121,1.9033,2.4132,NaN,NaN,NaN,0.48215,0.53244,0.17882,0.52676,0.20292,0.078098,1.7125,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.0964,8.5633,5.874,4.1885,4.4263,4.0454,3.3476,3.1783,3.0264,2.7996,2.9196,2.9304\n2.8066,1.88,1.8184,1.2079,0.59608,0.37653,0.32701,1.5875,2.1227,2.2801,1.4307,1.9992,2.5426,NaN,0.56909,1.434,2.4183,1.7707,2.1378,3.9119,4.5967,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.2132,4.8276,4.3849,5.366,5.4763,4.441,3.7373,2.851,2.4696,3.0064\n1.3192,1.4119,1.7508,1.5324,0.16951,0.041986,0.39569,1.4815,2.2661,2.3406,2.3596,1.9475,1.2722,1.5336,2.6735,2.0257,0.90841,1.8602,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.1606,NaN,NaN,NaN,NaN,NaN,NaN,4.9141,5.5644,4.4069,4.441,4.4855,4.3105,3.9471,3.5888,2.6407,2.1329\n1.7773,2.0415,2.6879,2.3654,1.4465,1.5575,1.5275,2.1095,2.5299,2.5412,2.6279,4.2177,3.3851,4.6069,4.7076,2.8218,1.6018,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.3652,9.1964,8.7881,NaN,NaN,NaN,NaN,3.2854,5.1736,4.0346,3.5767,3.987,3.9871,3.525,3.8027,3.3908,2.3827\n2.7022,2.8622,2.7724,2.5595,2.3728,1.8153,1.9634,2.034,1.8613,1.4261,0.93147,3.3668,4.1293,5.4906,3.0582,1.9307,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,9.4373,9.7953,9.633,4.7318,NaN,NaN,4.6541,4.4603,4.1802,3.4668,3.2489,3.7295,3.9704,3.9512,4.4655,3.4515,1.7235\n2.1466,1.7747,2.0688,2.282,2.6096,2.4264,1.8207,1.7233,1.6162,1.2623,0.95534,0.36157,1.0897,1.4251,0.93768,1.8821,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.6782,8.8165,7.6659,8.0679,8.4075,7.0837,4.3588,4.5186,5.1458,4.7451,4.3284,3.3032,2.7977,2.7926,2.4441,2.5708,3.1051,3.7981,2.9022\n2.0998,2.1618,2.6107,2.8816,2.6208,2.0805,1.8714,1.6129,1.1257,1.0137,1.0333,0.87545,0.84756,1.2981,0.23916,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.8356,8.186,6.1918,7.9392,8.2327,7.8615,5.649,5.5214,5.4455,5.4863,4.7845,3.0153,2.8626,3.3327,1.8239,1.79,2.2238,1.6208,1.9896\n2.246,2.941,2.8707,2.5241,2.0728,2.1225,2.7986,2.6263,1.5597,0.58383,0.70963,1.922,1.3111,0.54134,-0.77563,-1.6412,-1.422,1.4501,1.8013,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.1208,6.1009,5.748,6.1784,5.9975,5.4619,5.223,4.0531,3.9357,4.721,4.7838,3.9561,2.4364,2.5775,2.9316,1.4575,1.0542,1.729,2.0262,2.1109\n2.0416,1.8451,1.7469,1.9222,2.1941,1.6881,1.3889,1.3088,0.93902,0.48129,-0.040004,3.7721,2.2308,-0.062184,-0.31088,-0.053635,-0.9395,0.43686,1.2798,2.8355,2.1895,0.76892,1.851,2.8794,4.7174,5.0349,3.2383,4.6795,5.3201,4.8513,4.5772,4.2815,3.8875,3.2323,3.8918,5.2798,4.4974,2.8097,2.9777,2.1723,2.3961,3.2712,2.968,2.4626,2.203,1.5631,0.99368\n2.1543,1.6715,1.0334,0.87596,1.2913,1.3814,1.0057,0.75499,0.18858,0.24806,0.6595,1.5838,0.66397,-0.29749,-1.3037,-0.10667,0.31513,-0.29793,-0.19728,0.63705,1.5672,2.0593,2.2528,2.2009,2.9574,3.1517,2.4261,2.9532,2.8161,2.2658,3.0556,3.253,2.7577,3.5162,3.8072,4.1452,5.1649,3.3794,2.0994,2.006,1.9338,1.3872,1.1983,1.6221,1.6922,0.92165,0.23946\n1.6236,1.5103,1.4239,1.4641,0.71188,1.477,0.86963,0.14678,-0.19072,0.89898,0.73064,0.26435,0.28973,0.74831,0.88682,0.96732,0.68811,0.82722,1.2964,1.2045,2.305,3.7431,2.8296,1.9601,1.0599,1.6344,2.1307,1.201,1.9256,2.3062,2.3055,1.8176,1.6204,1.8691,1.0367,1.1412,2.7741,2.7181,1.7381,1.6836,1.7768,1.7735,0.80854,-0.27836,-0.9652,-0.76178,-0.77703\n1.9367,1.6824,1.6302,1.7167,1.344,1.497,0.094811,-1.4475,-2.0106,1.4618,1.0488,0.33817,0.20218,0.54486,1.0647,1.5518,1.4662,1.4918,0.7976,1.8458,7.1669,6.0643,4.3019,1.7467,0.42172,1.1854,2.2519,1.0225,1.3544,1.6023,0.97813,0.38566,0.50808,0.30708,-0.90514,-1.9088,-1.0328,-0.23946,0.66703,1.1113,1.4856,2.1874,2.7982,0.7603,0.025453,-0.92686,-2.146\n0.79835,0.75311,0.58532,0.7421,0.98304,0.66903,0.85161,1.0746,1.3029,1.5662,1.1557,0.13133,-0.1808,0.58979,0.93126,0.8342,0.67102,0.41084,0.30913,0.77656,3.4387,2.9076,2.411,2.4901,0.79807,1.4119,1.1795,0.7401,0.98526,1.0284,1.1753,1.7386,1.9939,1.0806,0.68711,0.073228,-0.30529,-0.15725,0.50404,0.80618,0.07887,1.1576,3.1583,2.239,1.0154,-0.16679,-0.9233\n1.2077,0.64994,0.19293,0.46565,0.35027,0.15165,0.53694,0.9232,0.73804,0.73913,0.68117,0.1978,0.072692,0.4808,1.1011,0.98278,0.83392,1.1889,0.14476,-0.53647,0.40207,1.371,0.089048,-0.51409,0.67669,1.2693,1.1351,0.97828,0.51377,0.52421,1.5135,1.5148,0.96937,0.6195,0.15534,0.25744,0.46939,0.66756,1.0287,1.0402,0.42168,1.0336,0.10311,2.6392,4.4811,3.6718,1.1419\n0.66611,0.3409,0.13305,0.086301,0.052445,0.89941,0.78903,0.47824,1.1842,1.0026,0.94587,1.0939,1.0063,0.91646,0.94042,0.84101,0.96371,1.4717,0.48762,-1.2375,-2.089,-2.0411,0.52385,-0.72361,-0.86825,-0.45784,-0.12153,0.21628,0.16894,0.13322,0.88277,1.5025,0.87258,-0.12988,-0.91974,-0.51734,0.59915,0.40061,0.069939,1.0524,0.16675,-1.8928,-3.8854,-2.9379,-2.0135,-0.27005,-0.71818\n-0.073111,0.13187,0.74366,0.08497,-0.20031,0.56068,1.031,0.34923,0.055359,0.31001,0.93981,1.1901,1.4416,1.2893,1.408,1.4037,1.1232,0.62256,0.34,-0.28085,-0.98441,0.097925,0.20163,-0.46811,-0.51166,0.13412,-0.71253,-0.55409,-0.39185,-0.18237,0.69318,0.78975,0.80207,0.76268,0.5494,0.026677,1.153,1.3207,0.24384,0.94176,-0.36776,-3.0095,-4.8315,-2.6955,-1.2136,-0.59857,-0.57224\n-0.37185,-0.043248,0.7531,-0.12707,-0.59004,0.11346,0.47848,0.46015,0.89397,0.90599,0.0002594,0.63105,0.99192,1.1468,1.2971,0.88903,0.77132,-0.26871,-0.58993,-1.2829,-2.0474,-1.9162,-0.61644,-0.47444,0.83587,-0.061664,-0.30926,1.2426,-0.83753,-0.89468,0.17943,-0.36355,-0.38485,-0.063495,0.39437,0.56961,1.3726,1.011,0.94715,0.71264,0.87837,0.75365,0.21605,-2.4784,-1.238,-0.84387,-1.4221\n-0.30183,-0.47843,-0.15525,-0.17582,-0.2839,0.0082026,-0.26939,-0.43211,0.21874,0.5889,0.18109,0.055642,0.38293,0.035832,-0.11285,0.40937,NaN,2.239,2.9777,3.3186,2.1177,-0.14044,-0.45843,-0.5205,-0.22982,-0.86684,-1.707,-0.64272,-0.19779,-0.48132,-0.3201,-0.58311,-0.29459,-0.11466,-0.20816,-0.0030718,0.46731,0.29813,0.21386,0.36658,0.55823,0.27719,-0.33826,-1.1536,-1.1008,-0.99558,-1.528\n-0.42421,-0.9599,-1.6824,-0.97819,0.045939,0.53351,0.31713,0.28739,0.7227,0.70992,0.84922,1.0322,0.56478,-0.24995,0.58672,0.70089,1.2507,NaN,NaN,NaN,NaN,-0.49306,-0.62983,-0.77662,-0.82548,-2.0954,-2.9813,-2.2302,-0.53772,-0.77672,-0.53756,-0.82144,-0.39738,-0.4282,-0.25559,0.27393,0.60617,0.50787,0.24428,-0.079844,-0.32771,-0.25542,0.58076,0.082017,-1.1427,-1.5481,-1.2462\n-1.0672,-1.0913,-1.2678,-0.87938,-0.58608,-0.23145,-0.49048,0.14706,0.71832,1.0639,0.88343,0.4597,0.071914,0.046384,0.65198,2.5687,3.4196,NaN,NaN,NaN,-0.16925,-1.3442,-0.99279,-1.1217,-1.4542,-1.5848,-1.644,-0.79342,2.3842e-05,-0.86104,-0.85732,-0.94398,-0.61811,-0.76979,0.023736,0.51151,0.053725,-0.51903,-0.411,-0.26743,0.22443,0.55537,0.48685,-0.35107,-1.2834,-0.76216,-0.49606\n-2.0067,-0.74949,-0.24711,-0.0091286,-0.40195,-0.99302,-1.2447,-0.19533,1.8518,1.9194,1.7505,NaN,NaN,NaN,-0.59219,0.37759,1.1772,NaN,NaN,NaN,-2.6503,-1.3323,-0.81404,-1.0338,-1.2062,-2.1823,-1.8977,-0.31415,0.042114,-0.80284,-1.2898,-0.69545,-0.21269,-0.9599,-0.1061,-0.22168,-1.1468,-1.2852,-1.5847,-1.1998,-0.10322,0.1645,-0.18288,-0.83254,-1.1885,-1.0211,-0.61418\n-0.18888,0.07725,-0.55725,-0.46707,-0.60273,-1.3561,-1.8369,-1.0295,0.33657,0.61419,NaN,NaN,NaN,NaN,NaN,-1.2958,-0.64579,-0.10127,-1.4254,-2.8619,-1.8816,-0.87731,-1.0332,-0.97442,-2.4066,-3.7534,-1.6666,-1.189,-0.025041,-1.1815,-2.1293,-2.0592,-1.3891,-0.96362,-1.4362,-1.1044,-0.29556,-0.10154,-0.58491,-1.0507,-0.45263,-0.56618,-0.84392,-0.79639,-1.2182,-0.75689,-0.18811\n-0.99629,-1.103,-1.3128,-0.69459,-0.58472,-0.76136,-0.92245,-2.1106,-1.6885,-0.91466,NaN,NaN,NaN,NaN,NaN,-2.4019,-0.87601,-0.75556,-1.3044,-1.9867,-1.9056,-1.8404,-1.5664,-1.4044,-0.83002,-2.158,-1.9017,-1.0438,-0.035514,-1.1889,-1.4898,-2.4083,-3.6319,-1.5152,-0.77907,-1.264,-0.80184,-0.35202,-0.34293,-0.44502,-0.58758,-0.68748,-0.80472,-0.31856,-1.0247,-0.27961,-0.0067787\n-2.0691,-2.6658,-1.9761,NaN,1.3745,1.0638,0.41604,-0.16461,-0.84779,-1.9855,-2.6938,-2.0019,-1.7554,NaN,NaN,-3.2388,-3.3523,-1.6806,-1.6473,-2.1189,-2.549,-1.9971,-1.0784,0.044543,1.3799,1.0941,-0.82561,-1.373,-1.4822,-0.66272,-1.3048,-1.7299,-2.5306,-1.6259,-0.68916,-0.56354,-0.87496,-0.80512,-0.88594,-0.84076,-0.61243,-0.7502,-0.55015,-1.1264,-1.7731,-1.8558,-1.3986\n-1.6818,-3.014,-3.7576,NaN,NaN,NaN,NaN,-0.49944,-0.73409,-0.89362,-1.3933,-2.6418,-2.906,-2.4137,-2.4137,-2.9637,-3.3418,-2.7257,-2.5009,-1.9557,-2.2491,-2.5931,-1.801,-1.0019,-0.29208,1.807,0.43301,-3.7287,-3.3207,-1.2338,-1.7848,-2.5611,-1.9721,-1.2579,-1.2674,-1.1824,-1.2829,-1.2351,-0.72615,-0.60548,-0.14046,-0.20676,-0.4065,-1.1703,-1.8081,-1.6832,-1.036\n-2.5444,-2.0173,-1.9759,NaN,NaN,NaN,NaN,-0.66883,-0.4195,-0.26935,-0.60018,-1.6235,-1.8642,-1.8867,-1.7507,-2.6638,-2.84,-2.3866,-1.9624,-1.9108,-1.7789,-1.4828,-1.3835,-1.4568,-0.83658,0.59366,1.0652,-0.039227,-3.0207,-2.3905,-1.5335,-1.861,-1.4794,-1.271,-1.3621,-1.2205,-1.1114,-0.96412,-0.77958,-0.7096,-0.34698,0.21763,0.19537,-0.40326,-0.83997,-0.93796,-1.3419\n-1.4548,-0.30215,0.93951,-1.9657,-3.636,-5.422,-4.2098,-1.975,-1.2552,-0.79318,-1.023,NaN,NaN,0.23481,-0.83202,-2.0314,-2.3938,-1.9455,-1.8173,-0.75229,-1.1116,-0.94549,-0.90729,-0.87554,-0.95066,-0.78276,-0.25985,-0.53072,-0.48625,-2.6612,-2.7783,-1.4554,-0.9921,-1.0498,-1.5087,-1.2647,-0.97929,-0.85247,-0.78836,-0.58446,-0.3752,0.10621,-0.093002,-0.03868,0.020194,-0.59433,-1.6126\n-1.593,-0.70041,-1.24,-1.7063,-1.6944,-3.7936,-4.1452,-4.6194,-3.5781,-2.1749,-0.81921,NaN,NaN,NaN,NaN,NaN,-2.8048,-2.3512,-1.6427,-0.91675,-1.144,-0.83213,-1.284,-1.6216,-1.3434,-1.7671,-0.93857,-0.84297,0.081912,-0.32158,-1.1487,-1.3429,-0.78601,-0.010087,-0.84967,-1.2733,-1.0299,-0.76094,-0.66973,-0.68444,-0.7215,-0.17456,-0.058621,0.03782,-0.15991,-0.88228,-1.1112\n-0.39153,-0.52182,-0.51942,-1.091,-2.2153,-2.4199,-3.0728,-2.4225,-2.7378,-4.1953,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.5955,-1.3793,-1.0384,-0.35569,-0.13632,-0.92702,-1.7482,-1.4864,-1.5363,-1.8781,-2.2782,-1.7392,-1.589,-0.9402,-0.82362,-1.1055,-1.7794,-1.6071,-1.2845,-1.5165,-1.672,-1.3634,-0.88809,-0.37573,-0.48941,-0.12323,0.063321,-0.64293,-1.3435,-1.9771\n-1.516,-1.1157,0.41379,0.83523,0.77278,-3.2541,-2.29,-1.8095,-1.5352,-3.0209,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-2.0495,-1.0614,-0.72345,-0.76883,-0.43819,-0.46828,-0.55383,-1.3266,-1.4023,-0.74134,-0.9813,-0.95946,-1.2881,-1.4902,-1.5554,-1.5038,-0.66029,-0.87673,-1.0939,-1.6845,-1.486,-1.5949,-1.1911,-0.16919,-0.51724,-0.47098,0.055019,-0.17896,-0.13815,-1.1163\n-4.1231,-2.3874,-1.5851,-1.091,-0.47815,-1.4558,-2.7336,-3.2469,-2.5009,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-1.1694,-1.5527,-1.5319,-0.82336,-0.91133,-0.84072,-1.2172,-1.516,-1.0511,-0.25651,-0.32231,-0.86316,-1.5237,-1.8276,-1.7873,-1.5464,-2.1096,-2.4273,-1.9787,-1.4708,-1.3237,-1.2998,-0.32474,-0.12274,-0.46904,-0.17129,0.16756,0.19561,0.26497\n-2.3653,-1.171,-1.1354,-2.3197,-1.8619,-1.4854,-1.6484,-2.5008,-2.9292,-3.0314,-3.7598,-1.8756,NaN,NaN,NaN,NaN,NaN,NaN,-1.4149,-1.1276,-1.0388,-0.48586,0.27619,-0.50546,-1.2747,-1.2798,-0.81607,-0.38509,-0.27834,-0.81564,-1.7347,-1.9302,-0.92754,-0.82837,-1.9732,-2.4512,-1.3776,-0.88555,-1.0874,-0.88491,0.032643,0.36003,0.10569,0.0039129,0.3272,-0.2541,-0.3031\n-0.49271,-0.88061,-1.0901,-1.0977,-1.0098,-0.48083,-1.1379,-2.7962,-4.2786,-4.3152,-3.236,-1.4714,NaN,NaN,NaN,NaN,NaN,-1.5688,-0.43854,-0.64834,-0.58098,-0.46363,-0.54536,-0.56872,-0.57035,-0.66362,-0.43346,0.13892,0.13321,-0.45167,-1.4618,-2.0685,-2.1115,-2.6495,-3.1661,-2.6284,-0.89138,-0.633,-0.59712,-0.37147,0.48504,0.71339,0.33267,0.80462,1.0667,-0.23062,-0.54295\n-1.7566,-1.5696,-1.5548,-1.6939,-2.8019,-1.6234,-0.25704,-0.87835,-1.7189,-2.3133,-3.0927,-3.3631,-3.8531,-3.0005,NaN,NaN,NaN,-1.1529,-0.010778,-0.49769,-1.1441,-1.1873,-1.1366,-0.71441,0.041381,0.44646,-0.34543,-0.19898,-0.29442,-0.38033,-0.88929,-1.7319,-2.2623,-2.6133,-2.7891,-2.1127,-0.83991,-0.79344,-0.46535,-0.22946,0.14245,0.42268,0.56835,1.0358,1.4868,1.1986,0.51278\n0.064608,-0.32563,-1.5791,0.31376,0.53806,0.082114,0.71156,0.55497,-1.7005,-3.1087,-3.031,-2.3886,-1.1733,0.6857,0.27238,-0.8501,-0.58943,-1.821,-1.0493,-1.0215,-1.4284,-1.2726,-1.3417,-0.68982,-1.3423,-0.8324,-0.62388,0.043586,0.29465,0.26796,-0.5559,-1.1693,-1.387,-1.0622,-0.59578,-0.89681,-0.87844,-0.40968,-0.22666,0.041317,0.47843,0.75766,0.5215,0.52277,0.75093,0.81029,0.66628\n-1.1485,-0.95386,NaN,NaN,NaN,-0.053238,1.0248,1.3319,0.58315,-1.8134,-0.92463,-0.91039,0.24406,0.9997,-0.29209,-1.1452,-3.7503,-1.2405,-1.5604,-1.5118,-1.4722,-1.0552,-0.87629,0.28248,0.72431,-0.93913,-0.81858,-0.24257,0.39903,-0.73805,-1.4371,-1.7456,-1.4178,-0.5133,-0.78711,-1.7075,-1.2244,-0.47565,-0.2084,-0.15788,0.062652,0.36942,0.44133,0.63436,1.6007,2.4126,2.3259\n-2.7899,-1.1418,NaN,NaN,NaN,-1.2043,-1.0026,0.089087,0.82113,-0.29305,-1.4968,NaN,NaN,NaN,NaN,NaN,NaN,-0.87626,-1.365,-1.2184,-1.5308,-1.5388,-0.65899,0.24507,1.0111,0.35827,0.45588,0.51548,-0.10798,-0.95873,-1.865,-1.4451,-0.50377,-0.50279,-0.79875,-1.3197,-1.1817,-1.3251,-0.62419,-0.0025549,0.20291,0.11943,0.886,1.171,0.46985,-0.00052261,-0.23361\n-0.30149,-0.33815,NaN,NaN,NaN,-0.80751,-2.4042,-3.007,-3.1415,-4.1042,-1.9103,NaN,NaN,NaN,NaN,NaN,NaN,-2.2289,-2.199,-1.7123,-0.77384,-0.47117,-0.23487,0.17938,0.49845,1.1905,1.3642,0.82747,-0.2961,-0.77765,-1.4908,-1.2617,-0.13251,0.18976,0.023286,-0.43741,-0.92675,-0.82611,-0.30601,-0.30679,0.49818,1.0235,0.89869,1.1721,0.89854,0.030692,-0.65269\n-0.22132,-0.12623,-1.3806,-2.522,-1.0411,-1.3999,-2.8551,-3.6317,-3.7964,-3.0685,-2.5334,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.78956,-0.58631,-0.25052,0.13898,0.63428,0.67423,0.45661,0.7579,0.85317,-1.1386,-1.6791,-1.1316,-0.74683,0.20296,0.8267,0.51359,-1.1849,-1.1326,-0.13109,0.27539,0.59013,0.90049,0.91396,0.83284,1.3718,0.8077,-0.027625,-0.50235\n"
  },
  {
    "path": "tests/test_data/small_test/ref_phase_est/ifg_orb_and_ref_phase_correctedgeo_070709-070813.unw.csv",
    "content": "0.86726,0.48794,0.44567,0.61019,0.44975,1.0712,1.1682,1.6343,2.7218,2.3895,1.7838,2.2703,2.5621,2.6495,3.0131,2.8792,2.9492,3.0709,3.0502,3.1834,2.8488,3.3917,3.6384,3.404,3.3058,3.4894,3.4045,3.4092,3.8189,3.5581,3.5525,3.0816,3.0823,3.0045,3.3319,3.3319,3.3326,3.3526,3.331,3.6311,3.8217,3.8949,3.7793,3.8087,4.1019,3.9766,3.977\n0.81777,0.80445,0.28149,0.48552,0.61516,1.0069,1.0812,1.5536,1.9897,1.856,1.8985,2.0986,2.3054,2.5368,2.6038,2.687,2.8941,2.9175,3.0617,3.074,2.9804,3.2909,3.3125,3.1239,3.2208,3.3928,3.381,3.873,3.7936,3.3241,3.1707,3.0519,3.0952,2.384,3.176,2.9957,2.9009,2.9654,3.2036,3.5319,3.4674,3.9117,3.6379,3.7473,3.8875,3.9353,4.3323\n0.56101,0.80999,0.61482,0.22517,0.4473,1.0678,1.4838,1.796,2.011,1.7519,2.2012,2.3259,2.3314,2.5835,2.5924,2.7095,2.9666,2.7721,2.7322,2.7428,2.8459,2.9083,2.6103,2.9094,3.1296,3.3074,3.4413,3.5936,3.3775,3.2792,3.283,2.9122,2.7043,2.027,2.9229,2.8252,2.7601,2.7743,2.9114,3.0666,3.5702,3.8452,3.598,3.6153,3.6706,3.9708,4.5633\n-0.36991,0.90132,0.72993,0.27662,0.7198,1.1749,1.5427,2.1096,2.0215,1.8067,2.0674,2.0359,2.1434,2.1908,2.53,2.5114,2.744,2.7355,2.7119,2.5339,2.6967,2.6937,2.6234,2.843,3.317,3.5645,3.5581,3.451,3.4633,3.3149,2.8902,2.5648,2.397,2.4544,2.628,2.6842,2.7548,2.7124,2.8968,3.1728,3.2533,3.1595,3.2881,3.3231,3.2843,3.5045,4.0175\n0.13429,0.83503,1.4362,0.35625,0.91892,1.1402,1.4197,1.5427,1.6431,1.338,1.4473,2.1011,1.8323,1.6106,2.1359,2.1577,2.4405,2.4771,2.5112,2.4538,2.6636,2.9551,2.471,2.8481,3.5452,3.5075,3.5767,3.4693,3.3577,3.2033,2.5765,2.3411,2.0722,2.3087,2.3915,2.5118,2.7847,2.7594,2.819,3.058,3.0278,3.7988,3.2877,3.406,3.2278,3.4754,4.0701\n0.11147,0.25227,0.18027,0.009903,0.76748,1.1006,1.3057,1.3556,1.3913,1.2766,1.7476,2.185,1.5053,1.4545,1.561,1.829,1.9672,2.1225,2.1902,2.0043,2.0766,2.6376,2.6306,2.8878,3.2032,3.0272,3.2174,3.3438,2.8984,2.539,1.9617,1.5488,1.6318,1.9405,2.1817,2.8572,3.0313,3.0413,2.9981,2.9976,2.9033,2.8055,3.1264,3.5796,3.3928,3.7809,4.2618\n0.006031,-0.13511,-0.0065784,-0.21968,0.47795,0.41521,1.0483,0.90202,1.2774,1.3468,1.7064,1.9433,1.4568,1.5808,1.3516,1.5102,1.7435,1.9116,2.0419,1.6178,1.8591,1.9448,2.3392,2.4295,2.5727,2.7034,3.0743,2.8743,2.364,2.2922,1.7059,1.6785,2.0879,1.8153,2.3442,2.8237,2.7919,2.5012,2.92,2.7192,2.7127,3.1896,3.0893,3.467,3.8088,3.8486,4.3893\n0.011141,0.14134,0.057412,-0.21138,0.55066,0.18644,0.6028,0.94951,1.4255,1.6156,1.7646,1.887,1.5286,1.4275,1.2383,1.2492,1.4641,1.684,1.3139,1.2512,1.6828,1.3556,1.6533,2.1934,2.0436,2.1772,2.52,2.1168,1.7229,2.1838,1.9328,1.9577,2.1406,1.462,2.2377,2.7852,2.6247,2.4907,2.3805,2.4654,2.5227,3.203,2.6656,3.3385,3.6849,3.586,4.48\n-0.32329,-0.10231,0.072464,-0.18149,0.094158,0.1267,0.65993,0.89701,1.2035,1.6424,1.864,1.8403,1.5094,1.4306,1.4151,1.1225,0.93244,0.91799,0.89976,1.0621,1.2971,1.4176,1.6532,1.7584,1.3641,1.459,1.4666,1.5617,1.7653,1.7788,1.5865,1.6316,1.3179,1.3145,2.2417,2.7598,2.3811,2.5543,1.9936,1.67,1.9784,2.6721,2.7612,2.2046,3.1103,3.5576,3.5349\n-0.79588,-0.74875,-0.3616,-0.52569,-0.75811,-0.22387,0.2157,0.67342,1.3093,1.9995,1.6523,1.8731,1.4688,1.5998,1.2079,1.0428,0.89322,0.76397,0.72608,0.61811,1.0698,1.2524,1.4247,1.3877,1.3133,1.1904,0.8902,0.96835,1.4699,1.5117,1.1202,1.0031,0.69982,0.84876,1.6146,2.1018,1.9002,1.9654,1.6268,1.6724,2.0594,2.2675,2.3363,2.4741,2.9854,3.1173,2.9175\n-1.1604,-1.5013,-1.0701,-0.88079,-0.73351,-0.36193,0.095289,0.57644,1.1505,1.4641,1.421,1.6685,1.2233,1.3715,1.2383,1.2496,1.1099,0.47818,0.51408,0.88007,1.0413,1.0443,0.97999,0.9115,1.1846,1.2466,0.84473,0.70694,0.80096,0.65806,0.35307,0.58045,0.23325,0.64707,0.8994,0.83068,1.0745,1.1439,1.1232,1.1553,1.5911,1.653,1.9781,2.4163,2.7092,2.7076,2.6736\n-1.6983,-2.4361,-1.7886,-1.2527,-0.631,-0.74703,-0.34871,0.17014,0.86175,1.3211,1.6652,1.3996,1.1979,1.2841,1.1249,0.92352,1.1596,0.21073,0.33299,0.45765,0.57937,0.77339,0.78271,0.64752,1.0163,1.103,0.74368,0.48663,0.57212,0.37393,0.0036192,-0.063337,0.00046158,0.28295,0.26974,0.061537,0.52588,0.85176,0.89765,1.0794,1.3572,1.2694,1.4622,1.6439,2.0893,2.582,2.6746\n-1.7943,-2.0923,-1.0602,-1.0851,-0.58848,-1.0006,-0.50762,-0.30195,0.46523,1.2557,1.3671,1.0488,1.0448,0.93578,0.79358,0.53284,0.34259,0.15617,0.12006,0.52433,0.4056,0.57087,0.63452,0.92338,0.96449,0.87147,0.58426,0.2233,0.39749,0.20954,-0.38696,-0.37169,-0.1112,0.21321,-0.21159,-0.12888,0.47736,0.34642,0.67152,1.1499,1.236,1.1496,0.84883,0.83694,1.5159,1.7999,2.5468\n-1.8155,-1.0155,-0.60537,-0.58831,-1.6445,-1.4953,-0.61295,-0.12368,0.27666,0.53498,0.92451,1.1081,1.0838,0.65248,0.70671,0.40022,0.26002,0.17754,0.43203,0.44412,0.34551,0.22914,0.4512,0.6557,0.52584,0.58218,0.36568,-0.072438,-0.20453,-0.13196,-0.28147,-0.16449,0.087883,0.35594,0.0079699,0.098671,0.24632,0.12164,0.16323,0.81805,1.3024,0.35188,0.19757,0.62742,1.0466,1.7643,2.3701\n-1.4402,-0.84421,0.15862,-1.566,-1.8578,-1.685,-0.61109,-0.20142,0.32701,0.097467,0.39843,0.78345,0.5981,0.37532,0.3211,0.080397,0.11443,-0.020821,0.61728,0.19574,0.18435,-0.56698,0.053057,0.060247,0.28061,0.11598,0.023046,-0.080925,-0.056119,-0.076145,-0.15723,0.027031,0.15026,0.19598,-0.026513,-0.19188,-0.043366,-0.45482,-0.55656,-0.0015745,1.0416,-0.18501,-0.98295,0.36377,0.82811,1.2787,1.32\n-1.522,-1.7961,-1.2454,-1.8276,-1.9417,-1.4688,-0.87817,-0.49508,0.018167,-0.050359,0.32547,0.50507,0.011143,0.37733,0.31096,0.084196,-0.082977,0.091393,0.15254,-0.017679,0.038179,-0.4295,-0.34965,0.17381,0.13857,-0.11286,0.011862,-0.052358,-0.28704,-0.23533,-0.67626,-0.6625,-0.22925,-0.069523,0.01063,-0.64043,-0.44834,-0.95883,-1.1596,-0.25846,0.21006,0.21396,-1.4078,-0.41416,0.071819,-1.5362,-0.62376\n-1.4943,-1.7285,-1.5778,-1.3615,-1.1312,-0.78679,-0.96169,-0.82947,-0.28357,-0.15486,0.00076199,0.045373,-0.15261,0.33732,0.10414,0.097655,-0.19308,0.13215,-0.20687,-0.1403,-0.1796,-0.58121,-0.30292,-0.021143,-0.55535,-0.27197,-0.166,-0.19152,-0.58176,-1.1346,-1.076,-1.1266,-0.43683,-0.28342,-0.3505,-0.57643,-0.9445,-0.4632,-0.94222,0.31441,-0.54981,0.29899,-0.29612,-0.94954,-1.1635,-2.9084,-1.8175\n-2.1893,-1.7906,-1.6551,-1.629,-0.15468,-0.23023,-1.1463,-0.68047,-0.56332,-0.50989,-0.54319,-0.37321,-0.55702,-0.22834,0.014374,-0.10814,-0.21525,0.094145,-0.25374,-0.37018,-0.95002,-1.1341,-0.62685,-0.43117,-0.27444,-0.87797,-1.075,-0.70966,-0.45684,-0.54389,-0.92342,-1.3834,-1.5022,-0.3324,-0.09684,-0.34731,-1.6228,-0.027674,0.63997,0.50107,-0.08341,-1.0932,-1.0049,-0.98621,-1.7989,-2.7626,-2.7303\n-1.5302,-1.7362,-1.7598,-1.1179,-0.28833,0.16299,-1.4613,-1.0494,-0.927,-0.69331,-0.68241,-0.71389,-0.80527,-0.62675,-0.37086,-0.3983,-0.50528,-0.54926,-0.54636,-0.20364,-0.8855,-0.99869,-0.82041,-0.55813,-0.35033,-0.36386,-1.2909,-1.1652,-0.70198,-0.59173,-0.75299,-1.1777,-2.2782,-1.1845,0.060177,-0.44339,-0.35786,-0.020232,1.2807,0.36619,-0.42177,-1.0579,-1.1123,-1.108,-1.3369,-1.705,-2.1431\n-1.4881,-2.1889,-1.8486,-1.3254,-1.0296,-0.97612,-1.6954,-1.4873,-1.127,-0.82464,-0.78041,-0.75765,-0.67896,-0.83281,-0.79664,-0.82337,-1.1176,-0.87139,-0.83388,-0.67969,-0.99724,-1.0922,-0.77923,-1.2655,-0.92709,-0.82131,-0.99016,-0.98406,-0.48112,-0.53335,-1.0091,-1.1288,-1.979,-1.9106,-0.32384,-0.91043,-0.64339,-0.68421,1.2435,0.45381,-0.7119,-0.52899,-1.0152,-1.3335,-1.2575,-2.2089,-1.6969\n-1.808,-2.708,-1.9311,-1.9086,-1.9048,-1.6045,-1.6178,-1.6273,-1.2396,-0.89665,-0.97738,-0.90652,-0.85061,-0.87591,-1.4079,-1.7007,-1.6817,-1.5623,-1.3079,-1.0248,-0.78228,-0.56085,-0.70763,-1.2764,-1.3346,-1.1368,-1.1082,-0.87971,-0.29236,-0.15086,-1.0151,-0.75912,-1.418,-1.4976,-1.0904,-1.3027,-1.2646,-1.2683,-0.22369,-0.61134,-0.88698,-0.87417,-1.1824,-1.3675,-1.6906,-1.6282,-0.86773\n-2.1226,-2.3523,-1.5709,-1.2977,-1.5683,-1.7601,-1.4793,-1.7034,-1.5071,-1.0826,-0.96241,-1.0565,-1.0875,-0.93353,-1.5014,-2.0031,-1.8257,-2.0914,-1.9349,-1.6986,-1.6584,-1.2651,-1.3716,-1.3792,-1.3744,-1.4918,-1.4086,-1.1239,-0.24028,-0.47051,-1.116,-0.88704,-1.4965,-1.5556,-1.652,-1.3578,-0.99076,-1.1124,-1.1163,-1.2713,-1.512,-1.3698,-1.2139,-1.2195,-1.2611,-2.3445,-1.2485\n-2.1013,-2.0192,-1.4168,-1.2097,-1.5433,-1.9482,-1.9526,-1.5101,-1.251,-1.1717,-1.1177,-0.90183,-1.1914,-1.5216,-1.963,-2.7683,-2.0968,-2.3738,-2.3258,-1.7361,-2.0556,-1.7858,-1.2424,-1.6671,-1.7711,-1.9299,-1.2715,-1.2443,-0.63057,-1.0721,-1.0629,-0.62376,-0.99361,-1.3502,-1.1588,-1.085,-0.97291,-1.4126,-1.5522,-1.4731,-1.9787,-1.6659,-1.6251,-1.9796,-2.3865,-2.1733,-1.3715\n-1.9811,-1.9957,-1.4561,-1.0761,-3.222,-2.3879,-1.4055,-1.2123,-1.2852,-1.2597,-1.4585,-0.72128,-1.4288,-1.3135,-1.3586,-2.2365,-1.5843,-2.117,-2.2731,-2.015,-1.7373,-1.2912,-1.037,-1.0027,-1.4203,-1.5814,-0.77915,-1.2189,-0.98983,-0.79525,0.041914,-0.25755,-0.43539,-0.55652,-0.87785,-0.93879,-1.2651,-1.9942,-2.72,-1.5684,-1.6563,-1.6953,-1.6292,-1.7318,-1.8964,-2.2793,-1.3242\n-2.4374,-1.6835,-1.1066,-1.0468,-1.6144,-1.5289,-1.0327,-1.2263,-1.4637,-1.3765,-1.4648,-1.303,-1.1406,-1.184,-1.19,-1.2217,-1.3666,-2.3462,-1.993,-1.9059,-1.8102,-1.3261,-1.1565,-1.217,-1.2244,-1.1018,-0.92951,-1.0227,-0.5442,0.10221,0.63805,0.45938,-0.1939,-0.51212,-1.1067,-1.2809,-1.6435,-2.7071,-3.3454,-1.7498,-1.7903,-1.7457,-1.6956,-1.4248,-1.538,-1.8417,-1.3325\n-2.7391,-2.4551,-1.4736,-1.0469,-1.4487,-1.0604,-1.2507,-1.0945,-1.3457,-1.3529,-1.4615,-1.4403,-0.99175,-0.99302,-1.2734,-1.3858,-1.5042,-1.5906,-1.705,-1.6449,-1.522,-0.6432,-0.76685,-1.2906,-0.9149,-0.55273,-0.25528,0.16285,0.3196,0.33969,0.48149,0.078342,-0.35052,-1.6786,-1.6324,-1.9019,-1.5553,-1.8645,-2.3508,-2.1972,-1.7814,-1.7763,-1.9444,-1.4109,-1.6493,-1.5949,-1.2479\n-2.7651,-2.4365,-2.0338,-1.7368,-1.4645,-1.578,-1.5827,-1.1507,-1.2288,-1.2917,-1.3167,-1.4689,-0.79556,-1.0296,-1.2276,-1.408,-1.2605,-1.3258,-1.6962,-1.8517,-1.2449,-0.92086,-1.1076,-1.9152,-1.3685,-0.53937,-0.55012,-0.45715,-0.36879,-0.26409,-0.17809,-0.60281,-1.5599,-2.0154,-1.9651,-1.786,-1.7057,-1.7649,-2.227,-1.8888,-1.9335,-2.1517,-1.8117,-1.6767,-1.6606,-1.5729,-1.456\n-2.6678,-2.6321,-2.4075,-2.2442,-2.2506,-1.7975,-1.3952,-1.1694,-1.5317,-1.4431,-1.1714,-1.2098,-1.2315,-1.0647,-1.3482,-1.5909,-1.6903,-2.1861,-2.3336,-2.2175,-1.8996,-1.6348,-1.7438,-2.4714,-2.0807,-1.9513,-1.3586,-1.331,-1.8997,-0.75904,-0.31822,-0.3196,-1.6096,-1.8365,-1.8039,-1.8312,-1.8175,-1.4815,-1.5754,-1.6775,-2.2665,-2.3347,-1.7338,-1.8199,-1.591,-1.6092,-1.2526\n-3.0717,-2.8394,-2.3563,-2.4315,-2.3329,-1.8862,-1.5076,-1.6303,-1.9498,-2.0925,-1.936,-1.4425,-1.7599,-1.8021,-1.654,-1.705,-1.8784,-2.5196,-2.2411,-2.04,-1.9596,-1.8379,-1.5945,-1.4075,-1.235,-1.3002,-0.64479,-1.1466,-1.6299,-1.0978,-0.38128,-0.10005,-1.0099,-2.1129,-1.9499,-1.9982,-1.9291,-1.2901,-2.3716,-2.8851,-2.6057,-2.7182,-2.0077,-1.8658,-1.7612,-1.7901,-1.6521\n-3.1788,-3.1496,-2.8785,-2.4797,-2.1103,-2.1216,-1.8618,-1.7256,-2.1439,-2.4891,-2.0032,-1.7178,-2.3795,-2.2194,-1.7666,-1.8521,-2.4972,-2.4776,-2.3968,-2.5348,-2.1799,-2.2213,-1.2471,-0.83534,-1.267,-0.64707,-0.20626,-1.407,-1.6297,-1.4101,-1.1389,-2.2679,-2.5971,-3.1913,-2.1831,-2.0948,-3.0412,-3.1631,-2.6141,-2.6795,-2.4311,-2.5867,-2.1025,-1.9331,-1.9193,-1.857,-1.52\n-3.9391,-3.311,-2.8008,-2.6544,-2.487,-2.36,-2.6967,-2.4894,-2.4969,-2.6068,-2.4412,-2.327,-2.4635,-2.6625,-2.0457,-2.5001,-2.884,-2.6136,-2.537,-3.2016,-2.8305,-2.4546,-1.7836,0.70766,-2.8559,-1.7861,0.058828,0.37524,-1.0404,-1.2439,-0.91862,-2.4208,-2.5798,-2.8033,-2.3496,-2.1717,-3.0374,-3.0086,-2.5328,-2.4599,-2.7733,-2.9936,-2.4447,-2.0134,-1.8998,-1.8378,-1.4395\n-3.8705,-3.4836,-4.2757,-3.6778,-3.1023,-3.0851,-3.217,-3.5858,-2.8753,-2.9258,-2.8287,-2.9265,-3.0351,-3.1447,-3.5792,-3.7611,-3.0288,-2.7681,-3.5692,-3.9205,-2.4607,-1.6862,-0.95907,1.8235,-0.17937,-2.3304,-2.742,-1.0448,-2.1593,-2.4153,-2.0806,-2.5265,-1.8527,-2.3852,-2.3461,-2.1943,-2.6298,-2.7143,-2.5366,-2.4154,-2.8394,-2.8806,-2.8038,-2.2408,-2.1561,-2.0255,-1.7887\n-3.6405,-4.0272,-3.946,-2.9018,-2.7636,-2.8539,-3.7154,-3.9796,-3.4111,-2.7153,-2.5559,-2.8988,-3.3077,-3.1509,-3.2463,-3.7153,-3.7348,-3.2613,-3.6305,-4.1212,-3.0276,-1.4154,-0.55148,-0.40777,-0.73558,-1.8453,-2.1639,-1.7322,-3.6653,-3.2659,-2.6615,-2.4754,-2.1114,-1.6261,-1.8181,-2.022,-2.2739,-2.9592,-2.381,-2.4351,-2.5859,-2.2502,-2.2192,-2.64,-2.4673,-2.1426,-1.9491\n-3.9577,-3.7768,-3.5917,-3.1868,-2.9658,-2.9399,-3.981,-3.7345,-3.0563,-2.4034,-2.72,-2.8142,-3.3163,-2.535,-4.1145,-3.7353,-3.8821,-3.0625,-2.7971,-3.4466,-2.9875,-2.3354,-1.6029,-2.1966,-2.1818,-1.9037,-1.6149,-1.9495,-2.9811,-2.3084,-1.9331,-1.6923,-1.1256,-1.4074,-2.0506,-2.319,-3.1495,-2.8489,-2.0572,-2.5818,-2.5391,-2.2337,-2.2842,-2.6194,-2.5855,-2.3992,-2.2738\n-4.0465,-3.8958,-3.7182,-3.6411,-3.7042,-3.7387,-4.001,-3.42,-2.8667,-2.8984,-2.8164,-2.6668,-2.8974,-2.3032,-4.1013,-3.9317,-3.1375,-2.9065,-2.8519,-2.8288,-2.6728,-2.6656,-2.3593,-2.0454,-1.7249,-1.4735,-2.4568,-2.4503,-2.4343,-2.3725,-1.7934,-1.484,-1.1285,-1.2494,-2.4085,-2.5444,-2.815,-2.4889,-2.6235,-2.8563,-2.7389,-2.6871,-2.5825,-2.6225,-2.7924,-2.5345,-2.3993\n-3.776,-3.6573,-3.7623,-3.6605,-3.2711,-3.8031,-3.9093,-3.3813,-2.7843,-3.0375,-2.5041,-2.438,-2.8071,-2.8585,-2.6075,-3.9078,-3.4969,-3.1591,-2.8919,-3.1527,-3.0884,-3.0819,-2.7037,-2.2305,-1.9908,-2.4755,-2.7909,-2.9463,-1.2049,-2.2456,-1.9983,-1.963,-1.682,-1.8337,-2.4236,-2.3979,-2.6748,-3.0904,-2.4457,-2.632,-2.6163,-2.4366,-2.4621,-2.7971,-2.741,-2.4767,-2.2191\n-3.6157,-3.29,-3.4176,-3.3346,-3.6301,-3.7479,-3.7846,-3.6372,-3.5166,-3.5305,-3.4071,-3.3835,-3.1914,-3.0219,-2.9876,-3.7377,-3.664,-3.0869,-2.5942,-2.947,-2.374,-3.2277,-6.0642,-1.7005,-1.0168,-1.2658,-2.8217,-2.8434,-0.868,-2.1762,-2.3386,-2.1472,-2.3224,-3.6734,-3.7175,-3.0917,-3.1016,-3.0809,-2.5869,-2.411,-2.382,-2.1798,-2.3451,-2.654,-2.7031,-2.2797,-2.2418\n-3.2174,-3.7582,-3.8456,-3.5343,-3.6626,-3.8192,-3.4936,-3.4897,-3.5346,-3.3943,-3.3388,-3.2501,-3.2711,-2.7153,-3.1446,-3.4979,-3.2428,-3.1601,-2.1599,-1.8453,-1.0624,-4.3029,-9.277,-2.9259,-1.9533,-1.1363,-2.6403,-3.085,-2.309,-2.4552,-2.3138,-2.3177,-2.1008,-4.3157,-4.0003,-3.6125,-3.1053,-3.1094,-3.0843,-2.0182,-2.1868,-2.3814,-2.4041,-2.6494,-2.4734,-2.2533,-2.7472\n-2.9256,-3.0724,-3.5985,-3.6201,-3.4278,-3.7604,-3.3359,-3.4358,-3.3459,-3.1947,-3.2292,-3.5389,-3.3087,-3.4245,-3.4307,-3.2313,-3.3306,-5.6841,-2.9043,-1.2551,-1.132,-4.0822,-5.8228,-1.4905,-1.0388,-0.58514,-3.0791,-3.0568,-2.6578,-2.504,-2.6507,-2.5192,-2.2874,-3.29,-3.3326,-3.0857,-2.9029,-2.9333,-2.7097,-1.8059,-2.0809,-2.321,-2.5939,-2.3239,-2.367,-2.3263,-2.5312\n-2.7049,-2.8972,-3.1371,-3.5748,-3.6577,-3.7214,-3.4031,-3.1755,-3.4875,-3.3392,-3.2945,-3.4736,-3.4836,-3.7769,-3.406,-3.6122,-3.9333,-4.9879,-5.147,-1.9091,-2.2474,-2.278,-0.62797,-1.2491,-2.1897,-1.6749,-1.3472,-3.0637,-2.7244,-2.7357,-2.3988,-2.6081,-2.6963,-2.8646,-3.204,-3.0236,-2.6879,-2.87,-2.1847,-2.434,-2.5688,-2.7231,-2.5441,-2.3344,-2.3221,-2.1925,-1.7976\n-2.7701,-2.9878,-2.8129,-3.2653,-3.6722,-3.6172,-3.2851,-3.255,-2.9658,-2.9829,-3.1142,-3.5176,-4.1146,-4.1802,-3.4761,-4.5783,-4.8779,-5.1944,-5.5112,-2.7506,-2.7642,-3.4112,-2.827,-2.2755,-2.1263,-2.8696,-3.0283,-2.9497,-3.0139,-2.6978,-2.4671,-2.9741,-2.5886,-2.6766,-2.5249,-2.5769,-3.2448,-3.9825,-3.4511,-3.2952,-2.7701,-2.6986,-2.8434,-2.2317,-2.2863,-1.9889,-1.7783\n-2.3702,-2.4189,-2.6304,-2.9616,-2.9279,-2.9107,-2.7246,-2.5737,-2.4833,-2.6991,-2.8719,-3.9402,-4.8566,-4.3477,-3.9215,-4.2362,-5.2666,-4.1919,-3.7718,-2.4929,-2.7288,-3.8793,-3.6939,-3.2052,-2.5468,-3.6096,-2.9441,-2.8005,-3.0337,-2.7311,-2.8572,-2.6003,-2.2208,-2.0784,-2.4882,-2.6875,-2.8404,-3.285,-3.2883,-3.129,-2.9968,-2.8031,-2.9161,-2.386,-2.8044,-1.7071,-1.7624\n-2.194,-2.4538,-2.7297,-2.982,-2.6894,-2.5031,-2.4754,-2.3413,-2.0933,-2.3505,-2.9463,-3.9349,-4.5644,-4.0699,-3.862,-3.6883,-3.6135,-4.5317,-4.0752,-2.6837,-2.7663,-3.6042,-3.1103,-3.291,-3.2831,-3.7048,-3.2593,-2.8869,-2.5928,-2.7057,-2.9534,-1.9738,-2.1424,-2.0807,-2.1908,-2.2324,-2.611,-3.139,-2.5411,-2.6799,-2.9061,-2.5482,-2.2628,-2.3704,-2.5606,-1.6023,-2.2624\n-1.7626,-2.3479,-1.733,-2.4391,-2.3588,-2.2025,-2.2725,-2.466,-2.1196,-2.3613,-2.6623,-3.0621,-3.161,-2.989,-2.7805,-2.5903,-2.8594,-2.7722,-2.8058,-2.8736,-2.9925,-2.8022,-3.6289,-3.4852,-3.366,-3.788,-3.5408,-2.7993,-2.3925,-2.4876,-2.4604,-2.2991,-2.3379,-2.4075,-3.1224,-3.2396,-3.0739,-3.3158,-2.404,-2.481,-2.7465,-1.7936,-2.0032,-2.4526,-2.4727,-1.9844,-1.8157\n-1.4679,-1.7634,-1.6736,-1.8899,-2.283,-1.8666,-1.9288,-2.2019,-2.3289,-2.4443,-2.1221,-2.5938,-2.6002,-2.4809,-2.2913,-2.1162,-2.9715,-3.0737,-3.3418,-2.8881,-2.863,-3.0097,-3.4719,-3.2707,-3.099,-4.0129,-3.9815,-2.957,-2.4118,-2.2694,-2.1523,-1.7843,-2.7435,-2.8216,-3.5039,-3.5296,-3.5409,-2.7662,-2.212,-2.4316,-2.3404,-1.5927,-1.7478,-2.2745,-2.2568,-2.296,-1.6899\n-1.4723,-1.3977,-1.3142,-1.3636,-1.5819,-1.5563,-1.807,-1.6649,-1.7727,-1.6385,-1.6648,-2.0994,-2.1231,-1.7662,-1.4655,-1.9666,-2.7935,-3.2939,-3.78,-3.9402,-3.883,-3.7663,-3.362,-2.8319,-2.797,-3.2796,-3.5324,-2.8777,-2.3917,-2.1477,-1.7792,-1.6243,-1.8535,-2.207,-2.2992,-2.5268,-2.4426,-1.318,-1.9576,-2.0889,-1.4582,-1.4556,-1.6305,-1.8494,-1.8475,-1.9672,-1.9181\n-0.88373,-0.39612,-0.92069,-0.86156,-0.56692,-0.7493,-1.2523,-1.2277,-1.0623,-1.0941,-1.0769,-1.1688,-1.1866,-1.0909,-1.1223,-1.9011,-2.5734,-2.6261,-3.5198,-3.9478,-4.2913,-4.472,-3.116,-2.6412,-2.8659,-3.0818,-2.3538,-2.3771,-2.1865,-1.8337,-1.6083,-1.3454,-1.7079,-2.1906,-2.0485,-2.0851,-1.1806,-0.93612,-1.5369,-1.8118,-1.5939,-2.5616,-2.3346,-2.1372,-1.5884,-1.5981,-1.5335\n-0.42355,-0.11323,-0.2582,-0.40547,-0.20823,-0.76615,-0.87537,-0.46368,-0.60732,-0.53457,-0.46264,-0.80045,-0.83164,-0.90051,-1.1481,-1.49,-2.0372,-2.0501,-2.8036,-3.1717,-3.1242,-3.5012,-2.4738,-2.3077,-3.0846,-2.7995,-2.118,-2.2503,-1.9863,-2.0908,-1.8727,-1.768,-2.2199,-1.9169,-1.9845,-1.9897,-1.293,-0.94027,-1.3588,-1.378,-1.4975,-2.1421,-2.1156,-2.414,-2.2263,-2.0514,-1.6641\n0.031865,0.22918,0.00084877,0.13761,-0.011152,-0.046833,0.050577,0.35201,0.69137,0.25849,-0.18138,-0.23401,-0.29504,-0.50745,-0.8848,-1.155,-1.2738,-1.717,-2.5162,-2.8988,-2.7142,-1.8357,-1.9626,-2.1118,-2.1125,-1.8858,-2.1426,-2.1202,-1.8834,-2.0763,-1.8672,-1.6532,-1.6982,-1.6888,-1.7038,-1.8438,-1.2377,-1.2322,-1.237,-0.99926,-1.4165,-1.8445,-1.7322,-1.7423,-1.4928,-1.4495,-1.4512\n0.28774,0.52549,0.2279,0.83554,0.45652,0.39522,0.70175,0.77759,1.0291,0.49914,0.20251,0.11433,0.075962,-0.16249,-0.55467,-0.63764,-1.6604,-1.8961,-2.4175,-3.4338,-3.7107,-1.5526,-1.6128,-1.5015,-1.556,-1.6342,-1.8579,-2.0416,-1.2952,-1.9708,-1.6155,-1.632,-1.3941,-1.3746,-1.255,-1.1696,-0.57981,-1.1671,-1.0285,-0.88448,-0.68891,-0.49693,-0.48446,-1.1747,-1.0756,-1.1688,-1.2555\n0.63294,0.43434,0.67981,1.549,1.583,1.4347,1.0135,0.90952,1.1101,0.95694,0.93678,1.1375,0.35948,0.23891,0.12346,-0.48655,-1.8762,-1.4848,-2.2069,-2.5787,-2.0173,-1.3466,-1.0722,-0.94449,-1.2371,-1.5392,-2.6314,-1.5785,-1.0418,-1.726,-1.6942,-1.5335,-1.2515,-1.1632,-0.51584,0.31303,0.22351,-0.45233,-0.67967,-0.53075,-0.3579,-0.27099,0.00087738,-0.38691,-0.7903,-1.0154,-0.85586\n0.76368,1.0369,2.2241,2.1731,1.5643,1.3718,1.1654,1.1289,1.7881,1.9565,1.6686,1.4185,0.81498,0.55026,0.60879,-0.92821,-2.3003,-0.9803,-1.3332,-2.3311,-1.5164,-1.0434,-0.66697,-0.67879,-0.85721,-1.5045,-2.5007,1.2718,-1.1884,-1.488,-1.3975,-1.0643,-0.77919,-0.58184,-0.33353,0.31893,0.029483,-0.1389,-0.26721,-0.52309,-0.42633,-0.60019,-1.0264,-0.56794,-0.054921,-0.52487,-0.16726\n0.90639,1.1132,1.8524,2.7564,2.1232,2.0071,1.9632,1.765,2.186,2.9381,1.8286,1.3812,0.82162,0.31917,0.38271,-0.13485,-1.0949,-0.75696,-2.2544,-2.9609,-1.1845,-0.57621,-0.23629,0.037584,-0.42107,-0.68174,0.053144,1.2181,-0.1148,-0.68134,-0.88269,-0.67528,-0.49338,-0.4401,-0.20681,-0.070767,0.26483,0.46245,0.10118,-0.2959,-0.28589,-0.52225,-0.42886,0.20255,0.039738,-0.39057,-0.61408\n1.0842,1.1136,1.8859,2.5978,2.8956,2.7707,2.2879,2.0441,2.4546,2.9111,2.595,0.90779,0.53287,0.342,0.24706,-0.68066,-0.83063,-0.83569,-1.0335,-0.75852,-0.51726,-0.19681,0.31367,0.33608,0.18158,-0.090858,0.16643,0.93936,1.1607,0.38462,-0.29187,-0.43848,-0.44424,-0.090361,0.36597,-0.16351,0.45687,0.54004,-0.11682,0.36138,0.35834,0.24643,0.33149,0.31668,0.54071,-0.0044775,-0.32098\n0.10508,0.86696,1.0337,2.8905,2.2829,2.2614,2.6307,3.0224,3.1763,3.0808,2.726,1.3654,1.4238,0.69872,0.23401,0.35314,-0.014915,-0.90636,0.2798,-0.30858,-0.066833,0.52318,0.64445,0.33711,0.013437,-0.19191,0.99271,1.0822,0.79249,0.15036,-0.099979,-0.30832,0.09952,0.13988,0.34416,0.4141,0.49948,0.31685,0.27627,0.45967,0.4015,0.47141,0.21909,0.28029,0.81704,0.36785,0.2763\n-0.035736,0.66857,1.9079,3.1662,2.7728,2.8785,2.82,3.383,3.3316,2.5363,2.2874,1.9836,0.78047,0.70017,0.19856,-0.085277,-0.28702,0.87382,1.5101,0.25233,0.35588,0.73403,0.78711,0.69103,0.56176,0.21988,1.886,1.5033,0.77038,-0.00046253,-0.096077,0.11084,-0.1909,0.073364,0.22234,0.5898,0.44478,0.2253,0.39543,0.44291,0.59163,0.70304,0.61726,0.8385,0.8832,0.65785,0.15797\n0.56901,0.88261,1.9468,2.0751,1.6509,4.131,3.4247,2.5286,2.1035,2.2562,4.0897,3.7069,-1.5172,-0.24773,0.17306,-0.20007,-0.17031,1.1988,1.1204,0.73368,0.62336,0.64591,0.79717,1.3486,1.6401,1.5996,1.3552,1.2278,0.96458,0.64565,0.35068,0.51623,0.47492,0.32518,0.35187,0.6416,0.54202,0.57071,1.0339,0.72905,0.15991,0.5963,0.84044,1.07,0.96657,0.51545,0.013502\n-0.74508,-0.35643,-0.014508,1.0695,1.525,4.5212,3.1452,2.7388,2.7216,2.5025,3.1361,3.5659,0.56931,0.11795,-0.62335,-0.89844,-0.48698,0.3101,0.89941,1.2504,1.5269,0.75328,0.352,0.91928,1.2053,0.83729,1.1137,1.186,0.73158,0.92605,0.60715,0.48142,0.46638,0.63932,0.60959,0.55739,0.67449,1.1348,1.4877,1.1901,1.0686,0.79796,0.89865,1.2474,0.96664,0.38999,0.4914\n-2.5194,-1.204,-0.87946,0.49783,1.2351,2.4728,1.8292,2.7716,2.6789,2.598,1.7438,2.6663,1.1851,0.64328,0.44976,-0.95937,-0.52227,0.26542,1.5731,1.5068,1.0219,0.49948,0.32402,0.8032,0.89818,0.61247,1.0782,1.1783,0.60526,0.84765,0.64215,0.24711,0.57442,0.85398,0.63582,0.63002,0.87986,1.2345,1.3188,1.1861,1.0392,0.89237,0.7863,0.90029,1.1159,0.81652,0.83778\n-2.6621,-1.3068,-0.34022,-0.098685,0.31736,-0.26555,0.63085,1.6829,0.81692,1.2969,2.4205,2.0621,0.55324,0.84015,-1.0599,-2.424,-0.83569,0.81097,1.125,1.832,1.3974,1.2181,0.43172,1.2592,1.3263,0.97025,1.2192,0.98834,1.1651,1.1235,0.63227,0.26497,0.54149,0.81913,1.0705,0.67677,0.75745,0.87689,1.2348,1.2146,1.171,0.95995,0.74375,0.60213,0.48575,0.6388,1.3507\n-1.0245,-0.92927,0.39207,0.54004,0.14032,-0.69515,0.42448,0.028799,0.6852,0.38517,1.4946,-0.13197,0.3321,1.0754,1.4264,0.6548,0.63217,0.54796,1.2234,1.7337,1.8698,1.9642,1.3496,1.6759,1.427,1.8296,1.2397,1.2905,1.7491,1.9302,1.3766,0.70529,0.79209,0.61753,0.74499,0.68744,0.70803,0.74781,0.87779,0.81223,0.86286,0.72875,0.59173,0.55708,0.60142,0.74529,0.82239\n0.64121,1,1.4576,1.532,1.7268,1.5942,0.85936,0.67223,2.704,-0.28982,2.0516,1.1037,0.3433,-0.63214,2.5347,1.2727,0.33392,0.4235,1.4104,2.0234,1.2586,1.4455,1.8378,2.0128,1.7011,1.6242,1.5302,1.5878,1.4982,1.435,1.3721,0.83108,1.3569,0.748,0.42997,0.58579,0.36144,0.56997,0.53093,0.63322,0.88688,1.1155,1.1205,0.60633,0.56561,0.64177,0.94167\n1.9757,1.874,2.6467,3.6273,5.0578,1.9603,1.8529,2.4441,2.816,-0.40093,2.9944,2.1783,1.1484,-1.025,-0.40662,-0.62994,-0.64818,0.99035,1.2838,1.3791,1.5683,1.9397,1.2688,1.7656,1.7238,1.7334,1.5721,1.7987,1.6129,1.0435,0.68991,0.84701,1.2238,1.0651,0.78881,0.0095387,0.082369,0.27379,0.38655,0.67887,0.86205,1.3046,1.0696,0.53987,0.52184,0.60039,0.94168\n3.0358,2.429,3.0493,2.9237,2.2125,1.1932,1.2015,1.0576,0.40472,1.9917,3.7306,1.6105,0.59813,-1.8971,-1.376,-0.81104,-2.2882,-0.10978,1.0268,1.4671,1.275,1.7824,1.7315,1.2534,1.7877,2.2878,2.2668,2.4014,1.5199,1.1071,0.62909,0.66594,0.73741,0.52262,0.87998,0.40441,0.39237,0.23881,0.28516,0.53048,0.97673,1.0836,0.87602,0.54513,0.49572,0.75318,1.2905\n3.3418,4.2799,4.1507,3.4749,2.2254,0.9498,1.4806,2.3622,2.3449,0.945,0.27214,3.0527,2.2315,-2.5439,-3.637,-2.825,-1.9332,0.59898,1.4527,1.0076,1.3142,2.3591,2.6376,1.2956,1.754,2.1619,2.7225,2.1461,1.6703,1.689,1.4159,1.3727,0.72151,0.71758,0.59745,0.36012,0.28035,0.072583,0.30082,0.67104,0.82085,0.80837,1.0818,0.91438,0.72019,0.99933,1.5787\n3.9876,4.7669,4.7723,3.6911,3.1064,2.8573,2.4268,1.7421,2.3679,1.9451,0.89372,0.86118,0.12853,-1.814,-1.2265,2.1743,0.96906,1.4911,1.2451,0.70326,1.4635,2.0963,1.9776,2.5701,1.8317,2.0841,2.442,2.2333,1.9655,2.018,1.4944,1.4106,1.175,1.1636,0.40314,0.31572,0.19357,0.36699,0.47123,0.55787,0.44814,0.80156,1.3023,0.82971,0.60868,1.0001,1.2977\n4.6382,4.4363,4.5145,4.0318,5.2125,3.872,3.2038,2.8159,2.4832,3.4753,4.4283,1.3633,3.5022,3.8099,1.8298,3.4871,2.3972,1.4128,0.9448,1.1196,1.2894,1.3077,2.3121,2.5298,1.4946,1.5412,2.3393,1.9076,1.9361,1.738,1.2989,1.292,1.0064,0.82934,0.27125,0.21503,0.28054,0.51429,0.68501,0.53794,0.45879,0.54386,0.88673,0.63122,0.4335,1.4842,1.4269\n5.7145,4.1486,5.1636,4.8601,4.9026,4.6876,5.1896,5.407,2.3388,3.2546,2.635,1.3732,3.3235,3.4699,3.4601,2.2005,1.622,1.7013,-0.092842,1.9353,1.2219,1.7132,2.0471,1.7282,2.0573,2.1926,2.194,1.5699,1.5448,0.9528,0.99566,1.5302,1.2919,1.1737,0.38128,-0.12949,-0.29727,0.24373,0.44044,0.23757,0.18057,0.30632,0.70429,0.75682,0.83268,1.2126,1.0353\n4.5628,4.9349,5.2899,4.219,6.5583,5.2015,6.8895,4.9522,2.7924,2.8443,2.6897,1.9274,-0.34822,-1.6545,0.14908,2.2304,0.612,-0.16768,0.52203,1.0271,1.0151,1.9252,2.0364,2.4977,1.9682,1.4098,1.8283,1.8952,1.5772,0.94034,0.92612,0.86318,0.52079,0.67018,0.12444,-0.023365,-0.44232,-0.03388,-0.0057812,0.065818,0.21727,0.28851,0.45048,0.49488,0.48589,0.39771,0.33684\n4.5533,5.3279,5.7354,5.8519,6.8281,5.5445,5.4954,4.2164,4.8464,3.4745,2.3479,1.488,1.0392,0.96663,2.0098,2.2114,2.6459,2.0252,0.61856,0.48005,1.5707,2.3886,2.0165,2.0491,1.5484,1.2121,2.1852,1.9549,1.4843,0.83629,1.3452,0.40034,0.25441,0.16685,0.079114,0.34064,-0.28196,-0.54748,-0.23312,-0.15429,0.15313,0.3232,0.084545,0.3317,0.33451,0.66947,0.5543\n4.3906,4.2918,5.1889,5.6228,4.8298,4.7619,5.1434,4.4394,3.989,3.6899,3.8512,2.3453,1.4525,0.82537,2.3082,3.3905,4.0812,3.0587,0.35322,0.68186,1.8777,2.2522,1.6058,1.7303,1.9355,2.1452,2.1174,1.4163,0.8595,0.73097,0.79984,0.40948,0.30815,0.20708,0.13234,-0.12669,-0.074733,-0.12368,-0.35554,0.21333,-0.088296,-0.10126,0.30005,0.41279,0.47759,0.94707,0.6852\n4.1317,3.8165,4.2999,4.4781,4.36,4.6962,3.7757,3.8347,3.9562,3.8539,3.9705,4.3626,2.9156,1.4012,1.5526,2.2013,0.95409,0.024374,-0.43504,-0.89385,0.075981,1.7349,3.2782,2.4491,1.5842,2.0152,1.972,0.74573,-0.15183,0.35969,0.57039,0.46432,0.29689,0.19897,0.2044,0.11541,0.11177,0.038467,0.0010719,-0.092026,-0.18989,0.23591,0.39365,0.30389,0.55222,0.66774,0.36313\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/bad_epochs_ifms_17",
    "content": "geo_019-061002.unw\ngeo_06880111.unw\ngeo_061002-070219.unw\ngeo_061002070430.unw\ngeo_06110061211.unw\ngeo_06116-070115.unw\ngeo_06106-070326.unw\ngeo_061211-070709.unw\ngeo_06121-070813.unw\ngeo_07011-070326.unw\ngeo_070115070917.unw\ngeo_070219-70430.unw\ngeo_070219-00604.unw\ngeo_070326-0917.unw\ngeo_070430070604.unw\ngeo_0706070709.unw\ngeo_07070070813.unw\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_060619-061002.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060619\nDATE12            060619-061002\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_060828-061211.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060828\nDATE12            060828-061211\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061002-070219.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061002\nDATE12            061002-070219\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061002-070430.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061002\nDATE12            061002-070430\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061106-061211.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-061211\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061106-070115.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-070115\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061106-070326.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-070326\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061211-070709.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061211\nDATE12            061211-070709\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_061211-070813.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061211\nDATE12            061211-070813\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070115-070326.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070115\nDATE12            070115-070326\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070115-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070115\nDATE12            070115-070917\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070219-070430.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070219\nDATE12            070219-070430\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070219-070604.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070219\nDATE12            070219-070604\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070326-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070326\nDATE12            070326-070917\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070430-070604.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070430\nDATE12            070430-070604\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070604-070709.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070604\nDATE12            070604-070709\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/geo_070709-070813.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070709\nDATE12            070709-070813\n"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/headers",
    "content": "tests/test_data/small_test/roipac_obs/geo_060619-061002.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_060828-061211.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061002-070219.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061002-070430.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061106-061211.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061106-070115.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061106-070326.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061211-070709.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_061211-070813.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070115-070326.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070115-070917.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070219-070430.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070219-070604.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070326-070917.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070430-070604.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070604-070709.unw.rsc\ntests/test_data/small_test/roipac_obs/geo_070709-070813.unw.rsc"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/ifms_17",
    "content": "tests/test_data/small_test/roipac_obs/geo_060619-061002.unw\ntests/test_data/small_test/roipac_obs/geo_060828-061211.unw\ntests/test_data/small_test/roipac_obs/geo_061002-070219.unw\ntests/test_data/small_test/roipac_obs/geo_061002-070430.unw\ntests/test_data/small_test/roipac_obs/geo_061106-061211.unw\ntests/test_data/small_test/roipac_obs/geo_061106-070115.unw\ntests/test_data/small_test/roipac_obs/geo_061106-070326.unw\ntests/test_data/small_test/roipac_obs/geo_061211-070709.unw\ntests/test_data/small_test/roipac_obs/geo_061211-070813.unw\ntests/test_data/small_test/roipac_obs/geo_070115-070326.unw\ntests/test_data/small_test/roipac_obs/geo_070115-070917.unw\ntests/test_data/small_test/roipac_obs/geo_070219-070430.unw\ntests/test_data/small_test/roipac_obs/geo_070219-070604.unw\ntests/test_data/small_test/roipac_obs/geo_070326-070917.unw\ntests/test_data/small_test/roipac_obs/geo_070430-070604.unw\ntests/test_data/small_test/roipac_obs/geo_070604-070709.unw\ntests/test_data/small_test/roipac_obs/geo_070709-070813.unw"
  },
  {
    "path": "tests/test_data/small_test/roipac_obs/roipac_test_trimmed.dem.rsc",
    "content": "WIDTH\t\t47\nFILE_LENGTH\t72\nX_FIRST\t\t150.91\nY_FIRST\t\t-34.17\nX_STEP\t\t0.000833333\nY_STEP\t\t-0.000833333\nX_UNIT\t\tdegrees\nY_UNIT\t\tdegrees\nZ_OFFSET\t0\nZ_SCALE\t\t1\nPROJECTION\tLATLON\nDATUM\t\tWGS84\n"
  },
  {
    "path": "tests/test_data/small_test/stackrate/coh_sta.csv",
    "content": "12,12,12,12,12,12,12,12,12,11,11,11,11,12,12,12,12,11,11,12,12,12,12,12,11,11,11,11,11,11,11,11,11,11,11,10,10,10,10,10,11,11,11,11,11,11,12\n12,12,12,12,12,11,11,12,12,12,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,11,11,11,12,11,11,11,11,11,11,11,11,11,10,10,11,11,12,12,11,11,11\n12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,11,11,12,12,11,11,11,11,11,11,10,12,12,11,11,11,12,12,12,10,10\n12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,10,11,12,12,12,11,12,12,12,10,10\n12,12,10,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,12,11,11,12,11,11,12,12,12,12,12,12,12,12,11,11,11,11,10\n12,10,10,12,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,11,11,10,10,11,10\n11,11,10,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,12,12,12,12,12,12,12,12,12,11,11,11,11,11,10,10,10\n10,10,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,10,10\n12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,11,11\n12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,12,12,12,11,11,11,11,11,11,11,12,10\n12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,12,12,12,12,11,11,11,11,12\n12,12,12,10,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,11,11,11,11,11,11,11,11,11,12,12,12,12,11,11,11,11,9\n11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,12,12,12,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,11,12,9,9\n11,11,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,11,12,12,12,12,12,11,11,9,9\n12,12,11,12,10,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,12,12,12,12,12,12,11,11,9,9\n12,12,11,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,12,11,11,11,11,12,12,12,12,12,11,11,10,10\n12,11,12,12,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,12,12,11,10,11,11,11,11,12,12,11,11,10,10\n11,11,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,11,11,11,11,12,11,11,11,10,11,10,11,8,8,8,11,11,11,12,12,11,10,10\n11,12,12,12,10,10,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,11,11,11,8,8,9,11,10,10,12,11,11,11,11,11,11,11,11\n12,12,12,12,12,10,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,11,11,11,10,9,11,11,11,10,11,12,11,10,11,11,11,11\n12,12,12,12,12,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,10,11,11,11,11,11,11,9,11,11,11,10,10,11,11,11,12,12,11,11\n12,12,12,12,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,11,11,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,11,11\n12,12,12,12,10,11,11,12,12,12,12,11,12,12,12,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,12,11,11,11,11,11,12,12,12,10,11,11,11,10,11,11\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,10,11,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,11,11,12,11,11,11,11,11,11,12,11,11\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,12,11,12,12,12,12,11,11,11,11,11,12,11,11,12,12,12,12,11,12,11,11,11,11,11,12,12,12,11,11\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,11,11,10,9,10,10,10,9,11,10,12,12,11,12,12,11,10,11,11,11,11,12,11,11\n12,12,12,12,12,12,12,12,12,12,11,11,11,11,12,12,12,12,12,11,12,12,11,11,9,9,10,9,9,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11\n11,12,12,12,12,12,12,12,12,11,11,11,11,12,12,11,12,12,12,12,12,11,9,10,9,9,9,9,9,9,9,10,9,11,11,11,11,11,10,10,10,11,11,12,11,11,12\n12,12,12,12,12,12,12,11,12,12,12,11,12,12,11,11,12,12,12,12,12,10,9,9,9,9,9,9,9,7,9,9,10,10,10,11,10,11,10,10,10,11,11,11,11,12,12\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,11,11,11,10,10,9,9,9,8,8,8,6,6,7,6,6,8,10,11,10,10,10,10,10,11,11,11,11,12,12\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,11,11,10,10,9,9,8,8,7,9,8,8,6,7,7,7,7,10,11,11,11,10,10,11,11,10,11,11,11,11,12\n12,12,12,12,12,11,11,11,12,12,11,11,11,11,10,11,10,11,10,10,9,8,8,7,7,7,9,8,6,6,7,7,10,11,10,11,10,10,10,11,11,11,11,11,11,11,12\n11,12,11,10,10,11,11,11,12,11,10,10,10,10,10,11,11,10,10,10,10,9,8,8,9,8,7,9,6,7,8,8,10,9,10,11,11,11,11,11,11,11,11,11,11,11,11\n10,11,11,11,11,11,11,11,12,11,11,10,10,9,10,10,11,11,10,10,10,9,9,9,7,7,6,5,4,8,8,9,9,9,8,11,11,11,11,11,11,11,11,11,11,11,11\n11,11,11,12,12,11,11,11,12,11,11,11,11,10,11,11,11,11,11,10,10,9,9,8,4,5,4,3,5,6,10,10,10,8,6,7,7,10,10,11,12,12,11,11,11,11,11\n11,11,11,10,10,10,10,12,12,12,11,11,11,10,10,10,11,11,9,9,9,8,7,5,5,3,3,4,4,8,11,8,9,7,6,7,7,9,11,11,11,11,11,11,11,11,11\n12,11,11,11,11,11,11,12,12,12,11,11,11,10,11,11,11,8,8,7,6,6,5,4,4,3,4,4,4,7,8,7,9,6,5,6,7,9,11,11,11,11,11,11,12,11,11\n12,12,11,11,11,11,12,12,12,12,11,11,11,12,11,11,10,9,6,6,6,5,5,3,3,3,3,4,5,8,9,9,9,8,6,8,10,11,11,11,11,11,12,12,12,12,12\n11,11,12,12,12,12,12,12,12,12,11,9,10,10,10,10,8,7,7,6,5,5,5,5,5,5,5,4,8,9,8,9,10,11,10,10,11,11,11,11,11,11,12,12,12,12,12\n11,12,12,12,12,12,12,12,11,11,11,11,10,11,9,8,8,7,8,6,5,5,4,5,5,5,6,7,11,12,7,11,10,10,11,11,11,11,11,11,11,11,11,12,12,12,12\n11,12,12,12,12,12,12,11,11,11,11,11,10,10,9,7,9,9,9,8,6,7,7,6,6,5,5,11,10,11,10,10,12,11,11,11,11,11,12,11,11,11,11,12,12,12,12\n11,12,12,12,12,12,11,11,11,11,10,11,9,9,10,7,7,9,9,9,8,8,8,7,7,5,9,9,10,11,11,12,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12\n12,12,12,12,12,11,11,11,11,10,10,10,9,9,9,10,10,8,10,9,10,10,8,6,5,5,7,8,10,11,12,11,11,11,11,10,11,11,11,11,11,11,12,12,12,12,12\n12,12,12,11,11,11,11,11,10,10,10,10,10,10,11,11,10,11,10,10,10,8,7,8,5,5,6,10,10,10,10,10,11,10,10,10,11,11,11,11,11,11,12,12,11,11,12\n12,12,12,12,11,11,11,9,10,11,10,10,10,10,10,11,11,10,10,10,9,9,6,7,7,7,9,9,10,10,10,10,10,10,9,9,10,11,11,11,12,12,12,12,12,11,11\n12,12,12,12,12,11,11,11,11,12,12,11,10,11,10,10,10,8,8,8,11,10,8,8,8,9,9,10,10,10,11,11,10,10,10,11,11,11,11,11,12,12,12,12,12,11,11\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,10,10,8,8,10,10,9,9,8,9,10,10,10,11,11,11,10,10,11,10,11,11,11,12,12,12,12,12,12,12,11\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,10,11,10,10,10,10,9,9,10,10,10,10,11,11,11,11,11,10,10,10,11,11,11,12,12,12,12,12,12,12,12\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,9,10,11,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,12,12,12,12,11,12,12,12,12\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,9,10,8,10,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,11\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,10,11,9,9,9,10,11,12,12,9,10,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,10,8,10,10,11,12,12,11,12,12,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12\n12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11,11,10,10,10,11,11,12,12,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12\n12,12,12,12,12,12,12,12,12,12,12,11,10,10,11,11,12,11,11,10,10,10,11,11,11,10,10,10,10,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,11,12\n12,12,12,12,12,12,12,12,12,12,11,11,10,9,11,10,11,11,11,10,10,11,12,12,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12\n11,12,12,12,12,11,11,12,12,12,11,11,9,9,11,10,10,10,11,10,11,11,12,12,12,12,11,11,11,11,11,11,11,11,12,11,12,12,12,12,12,12,12,12,12,12,12\n11,12,12,11,11,12,12,12,12,11,11,11,10,10,10,9,10,10,10,10,11,11,12,12,11,11,10,10,12,12,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n10,10,9,11,11,11,11,12,12,12,10,10,10,10,10,10,9,10,10,11,12,11,11,12,12,12,10,12,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n9,10,10,11,11,11,11,12,12,12,12,12,9,9,9,9,9,10,10,12,12,11,11,12,12,12,10,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n9,10,11,9,10,10,9,12,11,10,10,9,9,9,10,10,9,10,11,12,11,12,11,11,12,12,12,12,12,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n10,9,10,10,10,8,10,11,10,10,10,9,9,9,9,9,9,10,12,11,11,12,11,11,12,12,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n10,11,11,11,10,10,10,10,10,9,9,9,9,9,9,9,10,11,12,11,12,12,12,12,11,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n10,11,11,11,11,11,10,10,10,9,9,9,9,9,9,9,10,11,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12\n9,10,10,10,11,10,9,6,7,8,8,8,8,9,9,9,10,11,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n9,10,10,10,10,10,10,9,9,9,9,9,7,7,6,7,9,10,11,11,12,12,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11\n7,10,10,10,11,11,10,10,8,9,9,7,6,6,5,7,7,10,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,11\n7,7,9,11,11,10,10,9,9,9,9,8,8,6,5,7,7,10,11,12,12,11,11,11,12,12,12,12,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12\n8,8,8,8,8,9,10,10,10,8,9,8,7,7,6,8,9,9,10,11,12,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12\n7,8,7,7,7,7,9,9,10,10,10,8,8,8,6,6,7,7,10,11,11,11,11,11,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11\n7,8,7,7,7,7,9,10,10,10,10,7,6,6,6,5,4,6,9,9,11,11,11,12,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11\n9,8,7,7,6,7,8,9,10,10,11,8,8,8,6,5,3,6,8,10,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11\n9,9,9,9,8,8,8,9,10,11,11,9,9,8,8,5,4,5,5,9,12,12,11,12,12,12,12,12,12,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,11,11\n"
  },
  {
    "path": "tests/test_data/small_test/stackrate/errormap.csv",
    "content": "0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0396,1.0396,1.0396,1.0396,1.0396,0.92698,0.92698,0.92698,0.92698,0.92698,0.92698,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0396,1.0396,0.92698,0.92698,0.92628,0.92628,0.92698,0.92698,0.92698\n0.92628,0.92698,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0396,0.92628,0.92628,0.92698,0.92698,0.92698,0.92628,0.92628,0.92628,1.1902,1.1902\n0.92628,0.92628,0.92698,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0396,0.92698,0.92628,0.92628,0.92628,0.92698,0.92628,0.92628,0.92628,1.1902,1.1902\n0.92628,0.92628,1.2025,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,1.0381,1.0381,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,1.0381,1.0381,1.0381,1.1902\n0.92628,1.0396,1.2025,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,1.0396,1.1902,1.0381,1.1902\n1.0381,1.0381,1.2025,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,0.92698,0.92698,1.1902,1.1902,1.1902\n1.0396,1.0396,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,0.92698,0.92698,0.92698,1.0381,1.1902,1.1902\n0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,0.92698,0.92698,0.92698,1.0381,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,0.92698,0.92698,0.92698,0.92698,0.92628,1.1902\n0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,0.92698,0.92628\n0.92628,0.92628,0.92628,1.0396,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,0.92698,1.2215\n1.0381,1.0381,1.0381,1.1478,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92628,1.3161,1.3161\n1.0381,1.0381,0.92628,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.3161,1.3161\n0.92628,0.92628,0.95902,0.92628,1.1891,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.1933,1.1933\n0.92628,0.92628,0.95902,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.1029,1.1029\n0.92628,1.0381,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0587,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0381,1.1029,1.1029\n1.0381,1.0381,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381,1.0381,1.0587,0.95902,1.0587,1.0381,1.0606,1.0606,1.0606,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.1029,1.1029\n1.0381,0.92628,0.92628,0.92628,1.0587,1.0588,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0813,1.0813,1.0808,1.0381,1.0396,1.0396,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,1.508,1.3977,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0587,1.0593,1.0381,1.0381,1.0381,0.96091,0.92698,0.92628,1.0381,1.0396,1.0381,1.0381,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,1.3977,1.3977,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0588,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0593,1.0381,1.0381,1.0381,1.0396,1.0396,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,1.3977,1.3977,1.3977,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0588,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,1.508,1.3977,1.3977,0.92628,0.92628,0.92628,0.92628,1.1478,0.92628,0.92628,0.92628,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,1.0396,1.0381,1.0381,1.0381,1.0396,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,0.92628,0.95902,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,1.1478,1.1478,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1478,0.92628,1.0381,1.0006,1.1891,1.3128,1.3009,1.3595,1.3595,1.3652,1.1478,1.1891,0.92628,0.92628,1.0381,0.92628,0.92628,1.0381,1.0396,1.0381,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1478,1.1478,1.1478,1.1478,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,1.0381,1.0381,1.154,1.0598,1.0353,1.1895,1.1755,1.1029,1.1029,1.1029,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381\n1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1478,1.1478,1.1478,1.1478,0.92628,0.92628,1.1478,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.2151,1.1474,1.3022,1.3022,1.1895,1.1895,1.1895,1.1895,1.2012,1.1029,1.2368,1.0381,1.0381,1.0381,1.0381,1.0654,1.1029,1.1029,1.1029,1.0381,1.0381,0.92628,1.0381,1.0381,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92692,0.92628,0.92628,0.92628,1.1478,0.92628,0.92628,1.1478,1.1478,0.92628,0.92628,0.92628,0.92628,0.92628,1.0954,1.211,1.3022,1.3022,1.3022,1.3022,1.2245,1.2245,1.506,1.2012,1.2012,1.1029,1.1029,1.1029,1.0381,1.1029,1.0654,1.1029,1.1029,1.1029,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1478,0.92628,0.92628,1.0381,1.0381,1.0006,1.0974,1.0974,1.3022,1.3022,1.3022,1.4623,1.2853,1.2853,1.558,1.558,1.4532,1.8103,1.8103,1.2271,1.1029,1.0381,1.0396,1.0396,1.1029,1.1029,1.1029,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1478,0.92628,1.0381,1.0006,1.043,1.043,1.1562,1.3022,1.4814,1.4814,1.5553,1.1895,1.3919,1.3919,1.558,1.4532,1.4532,1.4532,1.4532,1.0799,1.0381,1.0381,1.0381,1.0396,1.0396,1.0381,1.0381,1.0396,1.0381,1.0381,1.0381,1.0381,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0654,1.0654,1.0654,1.1029,1.0381,1.043,1.0006,1.043,1.043,1.1562,1.4814,1.4814,1.5036,1.5036,1.4203,1.1895,1.3919,1.5118,1.5118,1.4532,1.4532,1.0353,0.9606,1.054,1.0006,1.0396,1.0396,1.0396,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628\n1.0381,0.92628,0.95902,1.0587,1.0587,1.0381,1.0381,1.0381,0.92628,1.0381,1.0587,1.1859,1.1859,1.1859,1.0587,1.0381,1.0006,1.043,1.043,1.043,1.1071,1.3022,1.4814,1.3806,1.3022,1.4068,1.5589,1.2391,1.5644,1.4661,1.3749,1.2271,1.0353,1.0598,1.054,1.0006,1.0006,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381\n1.0587,1.0381,0.95902,0.95902,1.0381,1.0381,1.0381,1.0381,0.92628,0.95902,0.95902,1.1859,1.1859,1.1933,1.0587,1.0587,1.0381,1.0006,1.043,1.043,1.1071,1.3022,1.3022,1.3022,NaN,NaN,NaN,NaN,NaN,1.3749,1.2271,1.1429,1.1429,1.6126,1.6239,1.0006,1.0006,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381\n1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,0.95902,0.95902,0.95902,0.95902,1.1859,1.0381,1.0381,1.0381,1.0006,1.0006,1.043,1.1071,1.3022,1.3022,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.1418,1.1418,1.1418,NaN,NaN,1.627,1.4025,1.2303,1.2303,1.0381,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381\n1.0381,1.0381,1.0381,1.0587,1.0588,1.0587,1.0587,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0588,1.0588,1.0588,1.0006,1.0006,1.1286,1.3022,1.3022,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.2128,1.0253,1.294,1.8044,NaN,NaN,1.4025,1.4025,1.2964,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381\n0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0588,1.0381,1.0381,1.0381,1.6156,1.6156,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3904,1.4068,NaN,1.8893,NaN,NaN,1.5283,1.4025,1.2964,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381\n0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381,1.1029,1.7446,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3077,1.26,1.8893,1.8271,1.4507,NaN,1.2169,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0598,1.0587,1.0587,1.0587,1.0587,1.694,1.837,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.3077,1.1429,1.2908,1.8893,1.6562,1.2575,1.4182,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0587,1.0381,1.0593,1.694,1.694,1.805,1.8031,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1.4203,1.0253,0.9605,1.5925,1.2575,1.4619,1.4619,1.0006,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628\n1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0396,1.0396,1.0593,1.9791,1.7071,1.7071,1.7071,1.8031,NaN,1.2528,1.2528,1.4606,1.4606,1.82,1.82,1.0006,1.3099,1.2995,1.4182,1.4182,1.2325,1.259,1.0006,1.0381,1.0381,1.0381,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628\n1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0396,1.0381,1.2503,1.2503,1.0396,1.0827,1.0827,1.7071,1.7071,1.7071,1.8811,1.0606,1.0606,1.1742,1.1742,1.5446,1.3877,1.154,1.3099,1.2995,1.2575,1.2325,1.259,1.0006,1.0006,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0396,1.0396,1.0396,1.2503,1.2503,1.0593,1.0396,1.043,1.0458,1.0353,1.0451,1.0353,1.0353,1.2026,1.9844,1.5446,1.5446,1.4888,1.4836,1.3099,1.259,1.2325,1.259,1.259,1.0006,1.0006,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0396,1.0396,1.0396,1.0396,1.0396,1.0396,0.92698,0.95902,1.054,0.9606,1.0353,1.0353,1.0447,1.2026,1.8857,1.6908,NaN,NaN,NaN,1.043,1.0046,1.0046,1.0046,1.0046,1.0006,1.054,1.0587,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0381,0.92628\n0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0437,1.0396,1.0381,1.0396,1.0396,1.0396,1.0396,0.96091,0.95902,0.95902,1.0587,1.0587,1.0799,1.2001,1.6876,1.9844,1.8857,1.8857,1.8857,1.7917,1.0437,1.0046,1.0046,1.0046,1.0587,1.0587,1.0587,1.0593,1.0598,1.0587,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,1.0381,1.0396,0.92698,0.96091,0.96091,1.6532,1.7852,1.7852,1.7852,0.94429,1.6646,1.8926,1.7171,1.6357,1.352,1.3112,1.0046,1.0396,1.0046,1.0381,1.0381,1.0587,1.0587,1.0587,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.95902,0.95902,1.6532,1.6532,1.7852,1.7852,0.96091,0.96091,1.0593,1.0593,1.6357,1.3112,1.0046,1.0396,1.0396,1.0381,1.0381,1.0381,1.0587,1.0587,1.0381,1.0587,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0587,0.95902,1.6532,1.6532,1.6532,0.96091,1.0593,1.0593,1.0396,1.0396,1.0396,1.0396,1.0381,1.0381,1.0381,1.0381,1.0381,1.0587,1.0587,1.0587,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1857,0.95902,0.95902,0.95902,0.95902,0.95902,1.2203,1.2198,0.95902,1.0396,1.0396,1.0396,1.0396,1.0396,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.95902,0.95902,1.0593,1.2198,1.2224,1.0396,1.0396,1.1871,1.1871,0.92698,0.92698,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0654,1.2198,0.95902,1.0593,1.0593,1.0593,1.0396,1.0381,0.92628,0.92628,1.3823,1.2025,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0654,1.0654,1.1859,1.2169,1.0396,1.0396,1.0381,0.92628,0.92628,0.92698,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0654,1.0654,1.0654,1.0396,1.0396,1.0396,1.0381,1.0381,0.92628,0.92628,1.1857,1.1857,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0654,1.1029,1.1029,1.0381,0.92698,0.92628,1.0654,1.0654,1.1029,1.0396,1.0396,1.0381,1.0381,1.0381,1.1902,1.1902,1.1902,1.1902,1.1857,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628\n0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0654,1.0654,1.1029,1.1214,1.0654,1.0396,0.92698,0.92698,1.0381,1.0396,1.0396,1.0381,0.92628,0.92628,1.0381,1.0381,1.1857,1.1857,1.1857,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.92692,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,1.0654,1.0654,1.1214,1.1214,1.0654,1.0396,1.0396,1.0396,1.0381,1.0396,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,1.1857,1.1857,1.1857,1.0381,1.0381,1.0381,1.0381,1.0381,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.95902,0.92628,0.92628,1.0654,1.0381,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698,0.92698,1.0396,1.1029,1.1029,1.2503,1.0396,1.0396,1.0396,1.0396,1.0381,1.0381,0.92628,0.92628,1.1857,1.1857,1.1902,1.1902,0.92628,0.92628,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0587,1.0587,1.0808,1.0654,1.0654,1.0654,1.0654,0.92628,0.92628,0.92628,1.0396,1.0396,1.0396,1.0396,1.0396,1.0396,1.2503,1.0396,1.0396,1.0381,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,1.1902,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0808,1.0587,1.0587,1.0654,1.0654,1.0654,1.0654,0.92628,0.92628,0.92628,0.92628,0.92628,1.0593,1.0593,1.0593,1.0593,1.0593,1.0396,1.0396,0.92628,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,1.1902,1.1857,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0808,1.0587,0.95902,1.0808,1.1891,1.1891,1.064,0.92628,0.95902,1.0587,1.0587,1.1933,1.1933,1.2216,1.0587,1.0587,1.0593,1.0396,1.0381,0.92628,0.92698,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0587,1.0808,1.0587,1.0587,1.0587,1.3555,1.043,1.0381,1.0587,1.0587,1.0587,1.1933,1.1933,1.1933,1.1933,1.1933,1.0605,1.0396,0.92628,1.0381,1.0381,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0587,0.95902,0.95902,0.95902,1.0587,1.0799,1.0799,0.95915,1.0587,1.0808,1.1933,1.1933,1.1933,1.1933,1.1933,1.1933,1.1029,1.0381,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n0.95915,0.95902,0.95902,0.95902,0.95902,0.94429,1.0799,1.0799,1.0587,1.0808,1.1933,1.1933,1.1933,1.1933,1.1933,1.1933,1.1029,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0451,0.95915,0.95915,0.95915,0.95902,1.0587,1.0808,1.5016,1.4483,1.2251,1.2251,1.2251,1.2251,1.1933,1.1933,1.1933,1.1029,1.0654,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.0451,0.95915,0.95915,0.95915,1.0587,0.95915,1.0587,1.0451,1.0451,1.0808,1.0808,1.0808,1.45,1.4276,1.455,1.4276,1.1661,1.1592,0.95902,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.3977\n1.8857,0.95915,0.95915,0.95915,0.95902,0.95902,0.95915,0.95915,1.0819,1.0808,1.0808,1.853,1.8952,1.9259,1.9278,1.4276,1.4276,1.0516,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.3977\n1.8857,1.8857,1.4676,0.95902,0.95902,1.0587,0.95915,1.0808,1.0451,1.0451,0.96097,1.8362,1.8872,1.9158,1.9278,1.3744,1.4276,0.96075,0.95902,0.92628,0.92628,0.95902,0.95902,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.5752,1.5752,1.5752,1.5752,1.0819,1.0808,1.0587,1.0587,0.95915,1.0458,1.6756,1.8872,1.8973,1.8973,1.9844,1.0361,1.0318,1.0318,1.0587,0.95902,0.92628,0.95902,0.95902,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628\n1.9323,1.5752,1.8094,1.2617,1.2617,1.2327,1.2216,1.2216,1.0587,1.0587,0.95915,1.8872,1.8872,1.8872,1.9844,1.9844,1.0827,1.2742,1.0657,0.95902,1.0381,0.95902,0.95902,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.0381,1.0381\n1.1795,1.0819,1.2617,1.2617,1.2617,1.2327,1.0808,1.0587,1.0587,0.95915,0.96075,1.8315,NaN,NaN,NaN,NaN,NaN,1.7966,1.2132,1.2132,1.0381,0.95902,0.95902,0.92628,1.0381,1.0381,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698\n1.0598,1.0819,1.2617,1.2617,1.5216,1.2327,1.0819,1.0451,0.95915,0.95915,0.96071,1.7802,1.7802,1.7802,NaN,NaN,NaN,1.7966,1.7472,1.1212,0.92617,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698\n1.0598,1.0598,1.0598,1.0598,1.0819,1.0819,1.0819,1.0451,0.95915,0.95902,0.96071,1.7685,1.7685,1.7802,1.7802,NaN,NaN,NaN,NaN,1.2305,0.92617,0.92617,0.95902,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,1.1857,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92628,0.92698,0.92698\n"
  },
  {
    "path": "tests/test_data/small_test/stackrate/stackmap.csv",
    "content": "0.58295,-1.5141,-2.1624,-1.1823,-0.98591,-0.74504,-1.3893,-0.99804,-0.47879,-1.1536,-1.3781,-1.5966,-0.14748,-0.43762,0.049633,-0.54348,-0.96594,0.11127,0.61899,-0.20874,-0.1573,-0.2082,-0.16841,-0.36374,-0.001528,-0.7831,-0.44462,0.011616,0.54063,0.46734,0.29479,-0.35639,0.02927,0.16332,-0.036069,-0.33575,-0.19584,-0.98854,-0.97528,-0.43999,-0.0067295,0.81059,0.24521,-0.1751,0.90117,1.8868,2.9199\n-0.73538,-1.4056,-1.7456,-1.6561,-1.0508,0.19099,-0.3744,-0.77795,-1.3983,-1.3667,-0.7229,-1.3421,-0.23573,0.55976,0.17937,0.4867,-0.70067,-0.62186,0.028835,0.34094,0.59003,-0.18461,-0.35169,-0.93239,-1.9263,-0.51615,0.16561,0.75132,-0.66273,0.39855,0.27474,-0.13358,0.040326,-0.33122,-0.61576,-0.47493,-0.41969,-0.53352,-0.7206,-0.40901,0.47562,1.3431,0.63155,0.9089,1.4698,1.417,1.719\n-0.64734,-1.068,-1.2486,-1.3984,-0.89215,-1.4028,-1.3771,-0.86811,-1.4841,-0.74575,-1.2303,-2.0714,-1.1815,-1.0215,-0.22871,-0.0076801,0.20581,0.70669,1.1471,0.31307,-0.018053,-0.55218,-0.41359,-1.0849,-2.04,-1.1526,0.73336,0.26042,-1.3743,-0.75428,-0.072975,-0.27316,-0.03452,-0.42554,-0.71977,-0.61418,-1.0814,-1.6945,-1.0213,-0.27881,0.70821,1.2361,0.98762,1.7952,2.1125,2.7774,3.8647\n-1.0497,1.1936,-1.4769,-1.2595,-0.97754,-0.98143,-0.20925,0.38876,-0.88451,-1.3672,-1.364,-1.2719,-1.2523,-0.41843,0.72386,1.3929,1.7575,1.8813,1.6402,-0.0031303,-0.38611,-0.2812,-1.1192,-1.8289,-1.4013,-1.0806,-0.63366,-0.87819,-1.2086,0.14692,-0.15183,0.20507,0.54841,0.20872,-0.39584,-0.67659,-0.76251,-1.5781,-0.44903,0.33562,0.34562,0.57969,1.1302,1.9611,2.667,3.8253,4.7889\n-1.0705,1.1734,-0.2615,0.059044,0.70091,-0.28589,0.40438,1.6944,0.72418,0.74363,-0.35929,-1.1618,-0.80084,-0.037395,0.35083,1.1091,1.0043,1.1059,1.1302,0.89572,1.3698,0.82906,-1.0415,-0.93578,-1.245,-0.1439,-1.0853,-1.1616,-0.62201,0.26583,-0.50265,-1.553,-0.11323,0.42467,-0.61752,-0.75685,-1.5538,-1.1426,-0.19109,0.77008,0.54507,1.1684,1.8242,0.88545,1.1603,1.7982,5.2142\n-1.2714,-1.7491,-0.014254,-1.0141,0.36218,0.18175,-0.73491,-0.50573,-0.34634,-0.49383,-0.79951,-0.67303,-0.069413,0.043221,-0.054331,0.66924,1.0516,0.68949,-0.22769,0.61371,1.3337,0.65583,-0.93984,-1.0383,-0.15597,0.2372,-0.2553,-0.94263,-0.88204,0.47924,-1.1049,-1.5296,-1.7094,-0.92797,-0.43515,0.35868,0.11855,-0.087789,-0.033494,0.43512,0.84829,1.282,2.0229,1.0902,3.387,2.6117,6.1418\n-0.27079,-0.18723,-0.10336,-0.47481,-0.59165,-0.39461,0.41747,0.063329,-0.34956,-0.60508,-0.66773,-0.043144,0.23911,0.23867,0.22836,0.91108,0.83243,0.5786,0.38877,1.349,-0.43411,-0.87932,-1.2315,-1.5124,-0.77105,0.088294,-0.016575,-0.98475,0.41621,0.9214,-0.78502,-1.6368,-1.8805,-1.8844,-1.3132,-0.43162,0.11198,-0.18287,-0.35684,-0.34419,-0.218,1.2562,1.982,2.8349,4.1709,5.0609,7.9813\n-0.78133,-1.0392,-1.6796,-0.72248,-0.40508,0.0066428,0.60731,0.91857,1.2429,0.054678,-0.62349,0.39343,0.34915,0.091981,0.061247,0.35189,0.40774,0.73837,0.68585,0.41325,-1.007,-0.70916,-1.0094,-0.51388,0.22651,-0.12627,-0.24722,-0.22325,-0.77555,-1.0133,-0.92347,-1.0577,-1.6065,-1.8241,-1.1297,-0.67717,-0.61031,-1.1253,-1.1735,-0.21716,0.47769,0.71716,1.2421,2.8114,2.2242,5.2621,7.7289\n-0.85088,-0.41494,-1.0311,-1.3498,0.12186,-0.45273,0.59283,1.2058,1.4455,0.84361,0.51818,0.99333,0.81204,0.50024,0.24595,0.15131,0.37727,0.57029,0.24418,-0.69198,-1.1134,-0.87058,-0.10668,0.82124,0.76654,0.43356,0.087802,-0.13114,-1.0307,-1.702,-1.3113,-1.0809,-1.4379,-1.1786,-1.1177,-1.2754,-1.1668,-1.448,-1.6538,-0.68812,0.26984,0.94815,1.1001,1.7613,1.6492,2.2751,3.0226\n-0.40829,-0.10173,1.1773,-0.83901,-0.85406,-0.92793,0.4967,1.0461,1.3528,1.2186,1.5065,1.4223,0.90921,0.61528,0.35128,0.921,0.59829,0.44989,0.41802,-0.14328,0.33412,0.15723,-0.29575,0.17275,0.58839,0.11859,-0.11683,-0.74196,-0.55821,-0.54966,-0.69903,-1.4126,-0.10466,-1.3289,-1.2054,-1.2985,-1.327,-1.6419,-1.6389,-0.86649,0.077232,0.70827,1.1452,1.0283,2.6629,3.5499,4.1484\n-1.3172,0.01493,0.97107,0.021505,-0.22964,-0.70834,0.13977,0.55317,0.79777,1.0467,1.4689,1.0083,1.2387,0.70556,0.31057,0.89255,0.80261,0.80618,0.25751,0.17666,0.72565,0.20499,-0.69735,-0.35312,-0.12263,-0.028185,-0.44166,-0.56572,-0.24532,-0.35938,-0.53721,-0.78079,-0.016848,-0.44368,-1.0942,-0.89416,-0.53379,-0.48415,-1.4852,-0.49535,0.56263,1.3004,1.1781,0.94341,1.6753,2.1616,2.7268\n-1.1431,-0.35519,-0.086059,-0.82086,-0.98967,0.21881,-0.28358,0.37095,0.934,1.0684,1.0169,1.2205,1.5838,1.5659,1.3839,1.6326,1.4368,1.3986,0.944,0.67982,0.834,-0.21971,-0.47277,0.45596,0.35583,0.99973,1.5251,-0.20936,-0.021402,1.2199,0.7243,0.2241,0.039715,-0.08484,-0.13188,-0.057115,0.32383,0.023867,-1.0229,0.12348,1.0254,2.0877,1.7869,1.3753,1.5692,1.3924,2.1925\n-0.15981,-0.21911,0.67744,3.8602,-1.8865,-0.082278,-0.16822,-0.083229,1.0011,1.3799,2.0289,2.1217,1.6228,2.55,2.8055,2.5366,2.4806,2.1529,1.6127,1.2152,0.20591,0.67137,1.9319,0.80808,1.2401,1.31,0.94122,0.39968,1.5063,1.2519,0.82714,-0.15684,-0.32688,-0.035359,0.1628,-0.12447,-0.014816,0.10804,-0.48266,0.4763,2.0761,2.5543,2.1399,1.5297,1.9467,2.12,3.3845\n0.48825,1.9439,1.5272,3.7813,-0.45973,-1.8139,-1.556,0.23476,0.54487,0.8143,1.4421,1.5897,2.0681,3.1176,3.2152,2.7529,2.6733,2.333,2.2491,1.9189,1.3557,0.90401,0.65959,0.71836,0.40915,1.4597,1.8525,1.2153,1.169,0.45934,1.1028,0.40378,0.07216,-0.12243,-0.59659,-0.92893,-0.59189,0.0058168,0.014369,1.5426,4.0716,2.6706,2.4486,1.0612,0.50682,2.8712,3.8539\n0.50625,1.4371,4.8589,1.7942,0.86023,-2.8746,-1.8342,-0.80158,0.35028,1.3809,2.3299,2.5253,2.7265,3.1163,3.4961,3.2055,2.7515,2.3774,2.5768,1.8081,0.80889,0.37588,0.7868,1.1451,0.64617,1.9216,2.3895,2.2928,1.6656,1.2553,1.8785,1.0257,0.10076,-0.28184,-0.86001,-1.3787,-1.1955,-2.1654,-0.49317,2.3878,3.2629,1.7936,1.765,0.95702,1.0432,1.0693,1.6543\n0.37799,-0.03539,1.5885,-0.44613,-0.49111,-1.3195,-2.1144,-1.1835,0.59314,1.57,2.6802,2.4452,3.0619,3.3368,3.0594,1.8721,1.2756,1.7296,1.783,0.84766,0.3277,0.93119,1.6175,3.204,2.7563,2.6253,2.8469,2.5431,2.0716,1.4682,1.3628,0.74943,0.15227,-1.3687,-0.54326,-0.45436,-0.44686,-1.1245,-2.623,-0.89229,0.037199,1.5411,1.4749,0.63542,0.35306,-0.11784,0.53325\n0.53812,-0.68997,-0.97341,-0.59762,-0.43221,-1.1158,-2.3042,-0.60725,0.64845,1.1968,1.8448,2.3192,3.3217,2.8003,3.1287,1.8654,1.1183,2.5042,0.85749,-0.66535,-0.46532,0.18739,0.96518,2.3798,3.0151,4.2966,2.8404,1.5002,1.5556,0.60557,1.1935,0.1366,-0.6808,-1.442,-1.237,-0.37528,-2.1679,-1.8846,-1.2159,-0.50238,0.66685,-0.82032,-0.56259,0.0097823,0.61417,0.032887,-0.040501\n1.1394,-0.009117,-0.42488,-0.58062,-1.2541,-0.75369,-0.5299,0.22966,1.2061,1.9294,1.947,2.1583,2.5925,2.4873,3.1625,2.8355,1.9858,1.6924,0.16972,-1.7784,-0.7237,-0.88471,0.2182,-0.50618,1.8619,2.3204,2.2261,1.726,0.35955,1.4298,0.5252,-0.64691,-2.892,-3.4259,-1.9869,-1.3192,-4.5445,-5.0544,-4.8862,-0.43398,0.26388,-1.63,-3.8879,-1.7568,-0.53879,-0.35011,-0.39307\n-0.20931,-0.065726,-0.094708,-0.31925,-1.3018,1.4989,-1.2065,-0.56531,0.094911,1.6467,2.1449,1.9602,1.7985,1.2602,2.1572,3.1916,2.5783,1.5258,0.55555,-0.93128,-0.97669,-0.8045,-1.8136,-0.71921,0.014467,1.0794,2.1289,1.5229,0.90567,1.3098,0.74276,-0.53031,-4.3038,-3.8336,-2.2812,-1.7316,-2.6391,-2.4337,-1.8725,-0.61162,-0.93117,-1.0084,-3.6601,-1.941,-1.5184,-0.91943,-1.363\n1.423,0.47616,2.2362,1.8752,0.52273,-0.1955,-1.4641,-1.0214,0.33215,2.2517,2.6463,1.7502,1.2692,0.84416,0.88405,1.4845,1.913,0.54774,-0.23742,-0.75406,-1.4064,-0.85852,-0.60496,-1.5327,-0.76608,0.96571,1.339,1.5235,1.2828,0.32192,0.69969,-0.028144,-0.52334,-2.4077,-3.0756,-1.406,-1.2087,-1.377,-3.0698,-2.196,-2.9794,-1.6784,-2.3403,-1.5634,-1.2917,-2.3075,-2.297\n2.219,3.6035,2.1456,3.5547,4.4997,0.26835,-1.3312,-0.39557,0.37951,1.873,1.6547,0.46174,0.82891,1.3926,0.67976,0.39408,0.13557,-1.071,-1.3411,-1.3226,-1.6942,0.027469,-0.24817,-0.46139,0.48975,2.1517,1.9901,1.6821,2.2415,0.48324,0.46806,-0.26742,0.41573,0.39019,-3.6495,-0.79592,-0.030178,-0.28658,-0.9491,-1.6667,-1.1777,-1.2953,-1.5813,-1.9878,-2.5558,-1.3679,-1.0736\n1.8938,1.5522,2.2034,2.3193,1.4953,0.83515,-1.453,0.46568,0.17499,1.7029,1.9728,1.2293,1.131,-0.34942,-2.368,-1.0145,-1.6652,-3.2422,-3.7779,-1.8395,-1.8477,-0.74279,-1.4033,0.1188,1.5604,2.22,2.3827,2.5274,1.9949,2.4254,1.9136,0.22073,0.20146,0.14828,-0.68295,0.26071,1.6931,0.92107,1.2869,0.48497,-1.571,-1.3764,-0.90021,-0.95738,-2.3344,-1.7149,-1.2884\n2.6359,1.9238,3.4582,2.9865,0.77294,0.39564,0.11288,0.33628,1.1421,2.5124,2.0001,-0.78607,-0.13975,-2.3691,-5.2261,-5.7875,-1.9183,-1.7659,-2.0333,-0.91343,-0.42906,-0.95578,-0.53929,1.5142,2.1959,2.5228,3.3865,2.9073,2.1538,2.6607,1.5345,1.6288,2.5309,2.5522,2.2551,1.9019,2.1712,1.3747,1.2102,0.22826,-0.98143,-1.0561,-0.19236,-0.85971,-1.9401,-1.7059,-1.2971\n1.3581,3.5951,3.0375,1.8961,-1.892,-0.029589,0.085823,1.1008,1.8279,1.7358,0.39381,0.41588,0.73518,-1.467,-3.2121,-4.5505,-2.0936,-1.4682,-0.34806,0.79308,2.4793,3.1363,3.0749,4.0424,4.5438,3.8074,4.5613,4.2321,4.2525,4.2117,6.2053,4.6909,4.5122,4.5642,2.4558,3.5064,3.0629,0.61559,0.94918,-0.077873,-0.39132,-0.39818,-0.0059071,-0.29614,-2.5017,-1.6871,-1.7067\n2.3039,3.0558,3.626,3.5606,2.5227,2.0244,1.0661,1.6829,2.016,1.1371,0.96346,1.276,1.446,-1.4217,-0.60245,-0.63271,0.63292,-0.34295,1.4897,1.6167,2.1668,3.626,4.9519,5.3901,5.7401,6.6814,6.7147,6.6625,6.7087,6.6217,6.5062,7.7668,6.3219,5.328,3.1382,3.1286,4.8876,2.8374,1.8853,0.81395,0.30661,0.84287,-0.47518,-0.49511,-1.0784,-1.3366,-1.5254\n0.73503,1.9209,3.3748,3.8063,3.5268,3.9541,2.9108,2.858,3.2383,2.3518,4.1216,3.3057,1.5733,1.3825,1.4989,0.63966,1.5881,1.9718,1.9155,3.2707,4.6644,4.4696,5.9181,6.5339,7.8276,6.2229,5.8741,6.2427,5.3483,5.3374,5.5919,7.2403,4.6496,0.85826,1.5123,1.9856,1.6303,1.8122,2.8396,1.9634,1.1863,1.3258,0.89994,0.65535,-0.72035,-1.3517,-0.96441\n-0.81379,0.57406,2.0822,2.4588,3.3442,3.8072,3.8383,3.644,3.5226,3.7672,1.154,1.4271,1.1026,0.18314,2.3806,2.8115,3.7585,4.0317,2.6022,4.2747,5.1475,6.3631,7.4407,6.3844,3.9865,6.4024,6.8004,6.1973,6.4471,5.3125,7.2153,7.2385,6.9003,2.106,1.7187,2.4251,3.6506,3.7599,2.8479,3.117,1.7181,1.2618,1.0407,1.1005,0.67699,-0.30367,-0.71119\n-1.1192,-0.96985,0.40382,1.0928,1.4742,2.7131,4.177,4.2306,2.7147,0.79847,0.40265,0.59964,0.51358,2.934,3.2257,1.6686,4.6255,2.9585,2.5954,3.2904,4.9485,7.7056,9.3511,6.3611,2.6241,3.0435,5.1025,5.1335,4.9734,4.9316,6.3053,8.2878,4.185,3.5833,3.9901,4.9435,4.5867,3.7574,4.0001,3.7315,2.7698,1.5973,1.2288,0.30811,1.0047,0.038486,-1.0723\n-1.6951,-0.16543,0.61285,0.35701,1.3307,1.8905,2.1394,2.1204,1.7726,1.8035,2.3961,-0.0094849,2.5715,3.58,1.5919,1.8001,3.9569,3.0505,4.3273,5.1527,5.2761,10.212,11.798,7.9407,5.675,7.1172,8.9694,5.7904,4.5577,5.5215,9.7594,9.0516,9.8513,5.6109,5.0939,4.9425,4.906,4.2877,4.0925,3.7988,3.3423,1.4182,1.7059,1.381,0.55156,-0.87683,-0.57707\n-1.2108,-1.0722,-1.3212,-1.0275,1.3318,2.1136,1.8391,1.7619,1.0997,0.72484,2.3004,3.2509,2.9267,3.8793,1.4182,3.922,2.8649,3.8216,5.2346,6.4665,7.2539,9.8578,10.608,8.5461,9.6795,8.1386,6.895,7.4322,5.5078,5.7312,8.0744,3.9632,2.0343,3.9778,5.5057,6.5238,4.0092,2.837,4.2182,3.8604,3.4384,1.7165,1.962,1.4451,0.63325,-1.0409,-1.0496\n-3.9173,-1.3679,-0.19968,-0.83461,-1.1152,0.71543,-0.01445,0.93786,1.0604,0.98927,1.1137,1.5316,2.1372,2.9931,0.92344,1.8739,2.6638,5.2107,4.5225,4.8513,10.789,8.4521,17.234,16.639,15.627,11.961,9.8014,9.5306,6.446,7.3351,5.7497,5.4061,4.2273,2.9053,6.0237,7.6125,6.4117,5.0396,3.8972,3.2985,2.4421,1.0519,1.7479,1.5724,0.61217,0.10211,-0.95207\n-3.2368,-1.7729,-1.3085,-0.085709,-0.55227,0.32542,0.57364,-0.13521,0.50631,-1.0822,0.99403,0.1379,0.54778,1.0916,1.8029,0.44436,2.2956,5.1609,5.115,5.3626,11.589,14.189,18.615,19.135,16.556,11.241,8.4575,8.7133,8.9172,8.134,6.3171,5.6039,6.762,6.5074,8.1088,8.0688,5.5625,5.6117,4.9527,4.4957,2.9656,2.1008,1.4223,1.7506,0.89928,0.55911,-0.65698\n-0.59601,-0.85325,-0.80236,0.40346,-0.024735,0.64502,-0.91691,-2.0163,-1.5504,-1.323,-0.81196,-1.7172,-1.393,1.3934,0.80484,2.2675,4.733,4.1357,4.7215,4.5826,8.1927,10.843,16.016,13.142,10.7,9.9259,7.1048,6.0818,1.793,4.2528,5.2731,6.6709,6.8692,8.1474,7.5001,7.783,7.8229,7.0729,5.3529,3.6082,2.893,2.5256,1.6959,0.96051,1.1822,1.1089,1.3981\n-1.2186,0.53829,-1.7258,-1.234,1.6015,0.98824,0.21652,-0.23351,-1.846,-1.9043,-0.41334,-0.57318,-1.1774,0.71938,0.33972,1.6533,4.0231,5.8159,5.1915,4.386,6.1514,7.2651,9.6483,10.456,NaN,NaN,NaN,NaN,NaN,5.8971,6.3685,6.1066,5.4668,5.4433,7.656,8.2561,10.823,9.4106,6.6813,4.3785,3.7359,3.2576,2.1811,1.2555,0.92341,1.2739,1.4116\n1.2726,0.50766,-1.6431,-2.5055,-0.68831,0.7665,0.36039,0.27838,-1.0754,-1.4339,-1.5042,-0.79732,-0.78428,1.0596,2.5776,2.4683,4.6209,6.6382,7.2301,6.876,6.7465,6.6106,7.2516,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.6817,6.5303,7.7284,NaN,NaN,14.707,12.848,9.9534,6.2178,5.0251,4.0969,3.5428,3.6578,1.9533,1.21,1.1861,1.2177\n-0.21826,-0.52903,-1.5176,-2.7005,0.16131,-0.94637,-1.3332,-1.4082,-0.89887,-0.19159,1.0106,1.0406,0.70166,0.19263,2.0207,3.851,5.9573,7.2882,10.273,7.3598,5.3147,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.7637,6.7564,4.7561,13.229,NaN,NaN,14.678,13.267,8.1732,7.0909,5.4366,4.6495,4.3338,4.058,2.6356,1.9887,1.6032,0.79982\n-1.7724,-0.89657,-0.43234,-1.2188,-0.47816,-1.031,-1.0689,-1.2076,-1.0965,-1.1321,0.84038,2.1055,1.2877,-0.58517,1.2765,4.7032,5.2555,4.5122,3.0423,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.7034,4.8628,NaN,23.716,NaN,NaN,13.638,8.6709,5.683,6.1539,4.6221,3.7627,4.0878,3.4323,2.81,1.5527,2.0232,0.53214\n-0.78354,-0.7106,0.37685,0.14292,0.15423,0.075535,-1.7565,-1.8038,-1.8391,-1.444,1.0119,1.5453,1.4915,-0.30109,2.6977,4.6019,7.1784,3.718,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.4778,6.1482,24.499,26.808,21.951,NaN,10.196,6.9337,6.5057,4.7984,3.9265,2.884,3.2213,2.735,2.8433,3.8872,2.9214,0.85364\n-0.003983,0.47714,-0.30944,-0.098378,-0.29727,-0.44638,-1.0825,-1.8329,-1.8213,-2.1684,-0.42401,-1.0152,0.07555,0.71376,2.5001,4.2933,5.6873,0.75995,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.4466,4.9582,1.1025,22.978,24.238,18.114,14.204,8.6971,8.2232,6.7493,5.2955,4.0254,2.2821,2.7499,1.7783,2.1428,3.6452,4.2441,2.3598\n-0.34476,-0.021638,-0.13756,-0.23059,-0.24898,0.031715,-0.26968,-1.8688,0.17588,0.1499,0.092219,0.45988,0.90621,2.7599,3.926,5.6965,6.2996,-0.012112,0.20015,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.5503,5.7319,7.1298,-5.0467,23.673,20.813,16.314,10.491,9.0844,7.7777,7.2375,6.4735,3.3158,2.1948,2.6494,2.5145,2.1408,3.1741,3.1509,1.9888\n0.2927,0.53081,0.31461,-0.21409,-0.1006,0.29192,0.6805,1.3981,0.38659,1.171,1.5652,2.2534,1.9998,2.2951,3.0703,1.9498,-0.28025,0.49348,0.42776,0.09374,NaN,4.8815,4.6934,5.0853,5.241,2.1391,1.4502,5.2095,5.6147,10.298,20.304,19.47,10.769,10.15,8.2547,6.8856,6.4734,5.6006,1.3288,1.7376,1.2572,1.6159,2.1096,1.8444,3.0025,3.3811,2.8587\n1.0096,-0.0085088,0.82852,0.31259,0.44306,-0.21839,1.2257,0.81693,0.78297,1.4668,1.3756,2.4621,4.3742,4.9515,1.804,0.065957,-1.2022,0.39393,0.96195,1.2793,2.2613,4.5021,4.8207,5.3948,6.2536,3.8619,6.4174,5.5205,5.2103,8.6573,12.627,11.486,11.501,7.8902,6.9873,6.1107,3.9201,2.099,1.489,1.4557,1.1921,1.5142,1.4616,2.3773,3.1985,3.759,2.3877\n0.61588,0.94731,0.59438,-0.37319,-0.33183,0.98915,0.35257,-0.15219,0.21385,-0.080997,0.25547,1.267,3.9854,4.8802,1.0361,1.5701,0.68993,-0.020213,1.9485,2.7441,4.601,4.5875,8.3739,2.0639,6.3362,5.939,4.3223,2.1172,4.8608,8.0254,9.2588,9.9995,7.1329,7.4227,6.7122,5.0849,3.9755,1.0799,1.6524,2.2719,2.2923,2.1361,1.4345,2.1835,2.37,3.1523,0.31131\n1.655,1.6388,1.77,1.3957,1.069,0.87646,-0.22091,-1.0458,-1.2812,-0.71385,0.044025,1.1245,1.9592,3.2702,4.3613,3.8814,3.4418,2.7686,4.1315,4.8203,5.0627,9.3,-1.0425,3.6675,NaN,NaN,NaN,3.3628,5.3381,6.6888,6.7875,5.916,6.0234,4.3194,1.4776,0.42271,2.0696,0.71138,2.0281,2.508,2.5425,2.5187,1.6018,0.36612,0.30651,0.070543,-1.9034\n1.6885,1.6458,1.1012,0.41681,1.3635,-0.26068,-2.0742,-3.6745,-3.0636,0.68357,0.911,1.6376,2.4062,3.847,6.2565,8.5621,4.5248,5.2012,4.7088,4.4398,10.435,1.4809,-1.8827,-1.9747,-0.36812,-3.5819,3.4491,3.9146,4.1036,5.5247,6.1475,4.9754,2.5819,1.787,-1.1791,-1.2097,-0.69583,0.56714,1.6904,1.8684,0.7629,1.286,1.4768,0.15869,-2.1207,-2.0749,-2.1015\n0.83617,1.142,0.58758,0.027485,-0.067,0.42175,0.065288,0.24703,1.9446,1.9087,1.048,2.1856,2.6797,3.4927,7.8424,9.2495,2.6314,5.8215,4.0588,2.5749,4.8295,-1.5803,0.81455,1.9026,2.2529,2.8653,4.7187,3.258,4.0925,5.6134,6.2921,5.3322,3.0227,2.1327,1.3545,0.8355,0.43146,1.2781,0.97123,0.99448,-0.010864,0.21788,1.3397,1.734,-0.80545,-1.502,-1.6361\n1.3341,1.2773,0.5272,-0.098489,0.74722,0.66423,0.54347,1.093,1.8646,1.3528,1.1115,1.7268,2.5488,3.3945,3.999,5.5799,0.47228,1.4582,3.3415,1.0851,4.8198,4.5352,1.9425,1.4093,1.4026,2.1601,1.9199,2.6764,3.398,4.1462,4.6815,4.0584,2.657,2.1528,1.4347,-0.20188,1.958,2.1066,0.73127,-0.71956,-0.12024,-1.4968,-1.4502,0.62093,1.9209,0.84758,0.5386\n1.2344,0.80341,0.58847,0.70003,1.9197,2.2971,2.018,1.5702,2.0847,2.0891,2.0415,3.1574,3.9625,4.5946,5.0632,4.6214,6.2338,5.6636,-0.73327,-3.1691,-3.1241,2.0935,1.2624,1.4946,0.55518,-0.10629,0.57868,2.0007,1.9696,1.8228,2.589,3.1976,2.3364,0.61151,-0.53296,-0.7074,0.79742,1.4235,0.31933,-0.42075,-0.69549,-2.9231,-3.893,-2.952,-1.8461,-1.0221,-1.299\n0.67181,0.47186,0.33905,1.1488,1.7109,1.8212,2.4804,1.4269,2.2713,2.4599,3.216,4.5736,5.0398,5.0565,7.4278,4.998,4.2462,2.9548,1.5724,2.2838,5.3545,7.8093,2.5234,1.8018,1.21,0.57846,0.71608,1.092,1.2034,0.89282,1.745,1.8973,1.689,0.98321,0.35567,-0.027302,0.068921,0.23012,-1.349,0.13098,-0.41771,-2.2866,-3.4702,-2.8571,-1.5004,-0.84058,-0.74861\n0.41793,0.33002,0.76029,2.6321,1.6417,1.9615,2.5289,2.4913,2.7212,3.3245,3.2725,4.5195,5.0934,5.1223,5.3257,3.9661,3.2227,2.2431,-0.36692,-2.2037,4.6666,5.534,2.1462,1.8645,4.4874,3.9712,1.8981,0.78013,1.3389,-0.29972,0.77373,0.82328,1.0427,0.54849,0.53466,0.62135,1.0192,0.61042,-0.27409,0.16845,0.30182,0.11203,-1.5766,-2.8876,-1.4225,-1.1206,-0.18343\n0.56449,0.314,1.1146,3.0759,2.8826,2.3159,1.7278,1.7519,2.6504,3.1603,3.0942,3.8716,4.5581,4.2708,3.5607,2.7549,2.5195,4.9896,-0.33596,-1.2593,-1.307,0.55597,1.5479,1.6882,0.76677,0.46656,-2.2402,-0.19477,1.3151,-0.083327,-0.22181,0.62152,0.85846,0.91764,1.1928,1.8019,0.4185,-0.079415,0.62637,0.66804,0.60875,0.58271,-0.17077,-1.3186,-1.1838,-1.2998,-0.71828\n1.2727,0.98004,0.90261,2.2906,2.6782,2.3125,2.2476,2.2795,3.5911,4.5501,4.422,4.5414,4.1791,2.8047,4.0191,2.624,3.6323,1.0645,0.86089,-0.35948,-1.1066,0.70746,1.235,1.5791,1.1411,1.394,-0.60716,-0.020578,-0.12909,-0.16609,-0.26429,0.4396,0.89242,0.7764,0.93412,0.27892,0.95797,1.511,1.349,0.5094,0.34584,0.55357,2.6052,0.10081,-1.374,-0.83021,0.53417\n1.0086,0.97397,0.80727,2.9454,2.2133,2.1891,2.6757,3.0895,3.7893,4.7594,4.4616,3.6543,2.5738,2.0024,3.0076,2.9652,2.3366,0.42861,1.1114,1.1506,-0.76584,0.21851,1.0466,1.6135,2.1463,1.4336,1.2943,4.4509,4.4106,0.63029,-0.31217,0.45766,0.58185,0.19928,0.20481,0.49194,1.0846,1.0498,0.54771,-0.35473,-0.2361,1.7677,2.45,0.94498,-1.4028,-1.6876,0.88186\n-0.31741,1.5698,2.3771,3.2977,3.6484,1.9258,2.3699,2.8069,4.9051,5.1602,5.5554,3.2037,2.7051,2.3178,1.834,0.14843,0.06346,-1.9733,-2.4245,-0.67473,-0.46385,0.4777,1.724,1.902,1.8186,3.4452,4.4957,6.6356,6.254,4.2242,0.24884,0.54162,0.43447,-0.69436,0.2341,1.367,0.69006,-0.18166,-0.98445,-1.6942,-0.2354,0.57461,1.3121,0.1875,-1.3422,0.11225,0.51642\n1.9087,4.254,2.905,3.2068,2.7795,1.3561,1.1784,2.6461,4.5858,4.4299,3.9229,3.835,3.7677,1.6571,0.8854,0.72905,-0.42382,-1.1575,-0.40712,-1.7436,0.28709,1.7988,1.3328,0.86459,1.4109,0.94081,5.7867,6.3248,4.4331,1.5211,0.41101,-0.59917,-0.72926,-0.76199,-0.30322,-0.22848,-0.95249,-0.45872,-0.88749,-0.90823,0.59283,0.65921,0.61697,0.44176,-0.45005,-0.74655,0.1918\n0.68688,1.4386,1.9551,2.5845,2.9045,1.4519,0.87448,0.78429,1.2849,2.9465,3.0961,2.7869,1.9099,1.5455,0.34613,-0.20409,0.21148,0.82026,0.53504,-0.92627,0.36521,1.477,0.9931,1.3642,1.6165,0.73388,5.567,5.8541,4.6561,1.4662,0.53541,-1.0976,-2.5959,-1.0266,-1.5287,-0.54733,-1.4277,-0.80898,-0.47768,0.38763,0.68473,0.80914,1.5172,1.8707,0.20454,0.15612,0.10494\n-0.22305,-0.39472,1.2076,2.6024,4.2257,1.1817,0.64472,0.072198,-0.30789,0.66887,-0.42049,0.10026,-1.7873,0.56851,0.80433,0.46232,-1.2285,-0.3144,-0.28836,-0.59613,-0.0053622,1.0929,0.96143,2.4082,5.0838,4.9386,3.7668,3.6352,1.284,0.17949,0.21805,-0.50766,-0.81271,-1.9858,-1.3438,-1.2118,-1.4218,-0.91473,-0.088807,0.58802,-0.36211,-0.50961,0.97943,0.26734,-0.76158,-1.431,-1.5645\n0.045078,-0.6019,-1.6387,0.76782,0.99893,3.7986,3.862,1.8906,1.3811,-0.54661,0.54817,0.15019,-1.9386,-0.67472,-1.525,-2.1781,-0.5077,-2.0014,-0.59704,0.42564,-1.2093,-0.38707,1.4351,1.2879,3.0121,3.6567,4.1858,-0.46284,-0.30299,1.0143,-0.17575,-0.92311,-1.8016,-0.8724,-0.92524,-1.0682,-1.1415,-0.61,0.66481,0.83176,0.63117,0.29029,0.56622,-0.2387,-0.50853,-1.2931,-1.5239\n-1.7952,0.12717,0.13936,-0.47865,-1.1034,0.64186,3.0207,1.6677,3.1295,3.1161,2.4608,0.44543,0.10481,0.37883,0.89402,-2.6664,-3.8687,-1.3638,0.60829,0.73073,1.3839,1.545,1.3124,-0.24224,0.7335,2.5558,5.1471,5.2532,-0.086911,-0.82184,-1.3369,-1.8401,-2.13,-0.81782,-1.2101,-0.83217,-0.25916,0.14182,0.68488,0.96998,0.89799,0.5673,0.27861,0.26177,0.42273,0.46772,0.43201\n-2.4463,0.40207,0.39397,-2.7179,0.019564,-0.1799,-3.3142,-1.0583,3.2782,2.6085,3.3514,2.5714,1.3758,2.6838,-0.862,-2.7969,-3.9243,-1.4403,0.34281,1.4529,2.3076,1.461,1.3759,0.9208,0.52857,1.5546,2.8428,1.6608,0.36375,-1.3149,-1.4854,-1.6725,-1.4199,-0.95475,-1.5755,-0.82086,-0.46554,-0.37269,0.45462,0.72915,0.93576,1.0372,0.31616,-0.24067,0.49746,-0.57112,-1.892\n-0.98515,-0.196,-0.35767,-0.83989,-0.86954,1.325,-4.7964,-3.3583,-1.3859,0.15519,1.1603,-0.88575,-1.0172,-0.49142,0.18319,-0.30763,-2.8643,-1.569,-1.351,1.0874,1.6888,0.57694,1.8451,1.5024,0.68493,1.6054,2.0189,1.1531,0.60032,-0.71818,-0.60542,-1.6458,-1.6669,-1.692,-1.6109,-1.7466,-1.6764,-1.0816,-0.7751,-0.64666,-0.25332,0.27769,0.016003,-0.12271,0.2661,-0.42711,-0.86826\n1.0913,0.65205,0.99207,1.4827,2.2349,2.087,-1.8017,-3.3888,-0.79124,-1.2459,-0.78928,0.72105,0.034157,-0.77859,1.7756,1.1339,1.1851,0.65235,0.82883,1.341,1.3878,2.2784,2.0125,0.49208,1.2062,0.40058,0.73313,-0.11646,-0.88967,-0.45897,0.66436,0.057777,-1.3691,-1.8896,-1.8641,-2.1665,-2.5896,-2.6248,-2.1268,-1.3041,-0.7672,-1.0324,-0.65678,0.57087,1.0518,-0.033592,-2.0284\n-1.4518,1.689,2.5162,3.4739,4.0044,-0.28163,-1.671,-1.502,0.071494,-0.7543,-1.4005,-0.31271,-0.23307,-0.38763,0.11482,2.4227,3.2273,1.1507,1.1708,0.97863,1.975,2.7434,3.2471,2.5309,0.77508,-0.091618,-0.32736,0.10507,0.896,0.45012,0.69415,-0.018801,-0.4051,-0.64182,-1.2261,-3.2514,-3.2395,-1.7841,-2.6337,-1.5572,-0.82261,-0.40429,0.83436,1.5362,1.396,0.84931,-1.163\n-3.0896,-4.7266,-3.4114,-0.12001,-0.72317,-1.98,-4.1273,-0.88707,0.10189,1.1794,2.482,0.09942,-1.5046,-4.6601,-3.0473,-1.2614,2.5155,-0.30794,-0.94951,1.1798,1.5452,1.1357,1.2786,0.39217,0.41996,1.6944,2.6811,3.2385,2.7061,1.7989,0.022221,-0.17179,-0.0062994,-0.92631,-1.7379,-3.9721,-3.8174,-2.8991,-1.8858,-1.5738,-0.70401,0.39768,1.1284,0.75658,0.89843,0.76686,1.3915\n-2.7012,-2.5871,-1.7325,-1.8683,0.90337,-0.84135,-2.0042,-2.9018,-2.4015,-0.76992,0.17291,1.6321,-1.5399,-0.088466,0.72093,-1.3742,-1.3733,0.22183,-0.3718,1.8776,1.1672,0.65532,1.5698,1.8044,2.014,2.7763,2.183,2.9067,1.7022,1.8344,-0.7311,-1.0633,-0.19768,-0.66543,-2.2579,-3.1578,-2.8582,-1.5973,-0.75392,-0.63435,0.10502,0.93182,0.98273,0.0076347,-0.20002,0.89584,0.37622\n-2.0759,0.17048,-0.69854,0.29473,-0.083095,0.16986,-0.88411,-2.5495,-0.98536,-0.88423,0.6864,-2.3068,-1.922,-2.2923,-1.3965,1.0612,0.9105,0.62719,-0.032521,1.4676,1.3802,0.21392,1.2787,2.126,1.3258,0.92044,2.501,3.6744,2.9211,1.2966,0.11318,-0.28984,-0.54408,-1.2628,-1.9387,-3.0272,-1.3243,-0.3618,0.11681,-0.14289,0.31318,0.80112,0.71889,0.87436,1.4325,1.5391,1.6521\n-0.75146,-1.4482,-0.91997,0.8608,-1.4598,1.0856,1.6034,0.98258,-0.37829,0.70973,1.4736,-1.369,1.0531,1.3042,0.70839,0.7725,2.2207,0.93567,0.14964,2.0283,1.4332,-0.82686,-0.31551,1.1407,-0.23807,0.49154,1.2487,2.3554,2.3685,1.5686,1.4189,-0.21158,-0.91188,-1.4238,-2.2211,-3.0567,-1.6535,-0.39272,0.35708,0.75115,1.4341,0.9534,1.9581,2.7745,3.6581,2.6574,1.3307\n0.27407,-2.0856,-0.64664,0.64089,3.2105,4.1813,4.1177,2.7555,-1.1631,1.154,0.61337,2.0128,2.6502,1.7031,1.3234,0.7506,-0.72512,-3.7212,0.56508,1.3497,1.8344,1.6922,-0.071056,0.33028,-1.5329,0.16143,1.4308,2.73,1.7837,1.604,1.1644,0.93105,-1.8505,-2.1244,-1.7367,-2.7261,-3.0158,-0.91664,0.20353,0.88438,1.4274,1.7252,2.2004,2.3934,2.5452,2.0998,1.5069\n0.80741,-0.38589,-0.15873,1.2364,1.4824,7.3274,7.1889,6.1544,1.0961,0.3736,-0.86461,1.919,-0.22072,-1.6076,-2.5504,-2.0269,0.079708,-3.6501,-3.68,0.14582,3.0927,1.7296,0.17773,0.76341,0.80569,1.8033,2.6712,3.2458,3.286,2.5715,-0.22058,-0.95078,-2.1265,-1.2021,-0.70462,-2.5023,-2.5348,-0.85399,-0.9898,-0.6617,-0.047818,-0.061523,0.25166,1.3113,1.9416,0.388,0.62501\n-1.122,0.78496,0.46492,-0.37414,1.097,7.6428,3.1997,0.71126,2.5939,0.1041,0.13615,1.7789,NaN,NaN,NaN,NaN,NaN,-1.3533,-2.5289,-1.5055,3.2589,0.58826,-0.14355,0.29154,-0.13108,0.50411,3.1829,3.3695,2.5726,1.0248,0.57729,-1.6663,-1.0685,-0.35151,-0.21121,-1.0946,-1.234,-0.65224,-1.6245,-1.5565,-0.65821,0.22034,0.64717,0.35738,0.017946,-1.3137,-2.4625\n1.3801,1.5398,1.1549,-1.0577,3.0732,3.3828,-1.0228,-2.5182,-1.9148,-1.3017,1.4941,1.7802,3.3128,9.012,NaN,NaN,NaN,-0.69974,-1.835,-1.2104,0.66958,1.342,0.093072,0.40309,1.7007,3.5885,3.2024,2.3055,0.83053,0.49539,-0.26044,-0.89453,0.10171,0.64096,0.99018,0.54291,-0.32923,-0.63083,-0.57755,-0.83561,0.26152,1.6464,0.67523,-0.10094,-0.18517,-1.7019,-2.4689\n1.3123,2.4925,2.6176,0.23981,0.75177,1.8875,-1.4949,-1.8216,-2.9648,0.065452,2.5491,1.6439,3.0271,1.8623,0.91221,NaN,NaN,NaN,NaN,-0.89356,-0.14527,1.1282,3.2209,0.82614,1.3655,2.0326,2.1297,0.33,-3.0953,-7.3955,-2.1905,-0.29499,0.311,0.24973,0.58571,-0.30241,-1.2381,-0.049111,0.85855,0.43866,0.57747,1.0283,0.32978,0.41982,-0.22516,-1.4242,-2.2362\n"
  },
  {
    "path": "tests/test_data/small_test/tif/ifms_17",
    "content": "geo_060619-061002_unw.tif\ngeo_060828-061211_unw.tif\ngeo_061002-070219_unw.tif\ngeo_061002-070430_unw.tif\ngeo_061106-061211_unw.tif\ngeo_061106-070115_unw.tif\ngeo_061106-070326_unw.tif\ngeo_061211-070709_unw.tif\ngeo_061211-070813_unw.tif\ngeo_070115-070326_unw.tif\ngeo_070115-070917_unw.tif\ngeo_070219-070430_unw.tif\ngeo_070219-070604_unw.tif\ngeo_070326-070917_unw.tif\ngeo_070430-070604_unw.tif\ngeo_070604-070709_unw.tif\ngeo_070709-070813_unw.tif\n"
  },
  {
    "path": "tests/test_data/small_test/time_series/ts_cum_interp0_method1.csv",
    "content": "0.10794\n-0.14015\n-0.12985\n-0.20832\n-0.19689\n-0.21865\n-0.32379\n-0.34774\n-0.15873\n-0.076641\n-0.26196\n-0.24276\n-0.33314\n-0.19914\n0.12157\n0.11318\n0.14037\n-0.051849\n0.24641\n0.28141\n0.44836\n0.37849\n0.54384\n0.30076\n0.47134\n0.17309\n-0.13072\n-0.48569\n-0.31384\n-0.21342\n-0.76913\n-0.64933\n-0.41751\n-0.4844\n-0.16789\n-0.37402\n-0.36123\n-0.16726\n-0.3327\n-0.43268\n-0.21723\n-0.030492\n0.13567\n0.31751\n0.33175\n0.15787\n0.25732\n0.23372\n0.12103\n0.068302\n0.10349\n0.23651\n0.16687\n-0.096049\n0.31609\n0.045421\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30957\n-0.28435\n-0.26568\n0.22012\n0.24349\n-0.59394\n-0.33312\n-0.41072\n-0.092056\n-0.018136\n-0.033772\n-0.13108\n-0.36742\n0.11245\n0.31245\n0.0042441\n-0.51284\n-0.36721\n-0.018175\n0.090556\n0.67904\n0.30548\n0.40916\n0.73893\n0.63335\n0.41562\n0.14624\n-0.14411\n0.0056973\n-0.1851\n-0.25364\n-0.35238\n-0.1842\n-0.27009\n-0.22646\n-0.52306\n-0.49068\n-0.15848\n-0.26433\n0.0063023\n0.11254\n0.019646\n0.17882\n0.31666\n0.3227\n0.22694\n0.26041\n0.15892\n0.091339\n0.061028\n0.055111\n0.1707\n0.16445\n0.27369\n0.78406\n0.22091\n-0.15643\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.44432\n-0.36406\n-0.26123\n-0.32404\n-0.84814\n-0.71875\n-0.75737\n-0.34695\n-0.21235\n0.23895\n0.15028\n-0.057794\n-0.19699\n0.31836\n0.71453\n0.10934\n-0.19793\n-0.076971\n-0.013245\n0.45508\n0.42809\n0.44919\n0.70832\n0.63978\n0.76177\n0.71378\n0.46262\n0.11789\n0.16926\n-0.21523\n0.0098651\n-0.24413\n0.0051661\n-0.10096\n-0.60719\n-0.69284\n-0.40339\n-0.23295\n-0.080396\n-0.016514\n0.077426\n0.15738\n0.095024\n0.34151\n0.22077\n0.12922\n0.12806\n0.1291\n0.077337\n0.15619\n0.2158\n0.16165\n0.13543\n0.44249\n0.52103\n0.33463\n0.18191\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24908\n-0.34837\n-0.28865\n-0.25561\n-0.25362\n-0.21227\n-0.11105\n-0.15452\n-0.2753\n-0.18263\n-0.0041224\n-0.42369\n0.10868\n0.54489\n0.32367\n-0.11956\n-0.12467\n-0.10255\n-0.030068\n0.40145\n0.72036\n0.47857\n0.61548\n0.4105\n0.75147\n0.80175\n0.53712\n0.24819\n0.10901\n-0.14002\n-0.13739\n0.0080518\n0.05414\n-0.011147\n-0.48882\n-0.64822\n-0.55912\n-0.25653\n-0.02914\n-0.0481\n-0.03807\n0.055994\n-0.090484\n-0.036125\n0.072669\n0.021953\n-0.0066221\n0.14774\n0.23163\n0.51512\n0.5958\n0.43334\n0.5584\n0.62914\n0.60038\n0.47683\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21874\n-0.22728\n-0.18816\n-0.19651\n-0.11157\n-0.18187\n-0.11403\n-0.072714\n-0.24001\n-0.43896\n-0.34162\n-0.48041\n-0.386\n-0.1057\n-0.47243\n-0.44876\n-0.38363\n-0.23138\n-0.39068\n0.18355\n0.93971\nNaN\nNaN\n-0.3618\n0.54004\n0.74813\n0.70883\n0.32845\n0.2956\n0.30735\n-0.19688\n-0.09884\n-0.071118\n0.043021\n-0.14993\n-0.30079\n-0.5762\n-0.29487\n-0.051547\n-0.051323\n-0.019376\n0.099454\n-0.069264\n-0.091756\n-0.034689\n-0.010956\n0.15088\n0.37045\n0.32735\n0.30259\n0.55322\n0.50891\n0.40029\n0.70001\n0.51561\n0.53295\n1.083\nNaN\nNaN\n-1.4537\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15324\n-0.2614\n-0.27503\n-0.18561\n-0.044509\n-0.20255\n-0.070248\n0.0080566\n-0.082294\n-0.17449\n-0.13621\n0.052584\n-0.023314\n-0.36646\n-0.58816\n-0.28626\n-0.50226\n-0.44977\n-0.15157\nNaN\nNaN\nNaN\nNaN\n0.026169\n0.45556\n0.83853\n0.79543\n0.57352\n0.42639\n0.47075\n0.18421\n-0.16927\n-0.19102\n-0.1339\n-0.13008\n-0.43852\n-0.59757\n-0.27135\n-0.067051\n0.006018\n0.056751\n-0.03104\n-0.08624\n-0.16671\n-0.54894\n-0.26826\n0.12936\n0.44106\n0.34365\n0.37522\n0.4455\n0.43991\n0.40419\n0.36021\n0.24366\n-0.096098\n0.2149\nNaN\nNaN\n-2.2115\n-1.4105\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27233\n-0.35679\n-0.263\n-0.028101\n0.094522\n-0.13562\n0.10182\n0.14018\n0.14097\n0.11882\n0.041273\n-0.041531\n-0.022936\n-0.30304\n-0.36848\n-0.43378\n-0.47008\n-0.10972\n-0.244\nNaN\nNaN\nNaN\nNaN\n0.082519\n0.27782\n0.63392\n0.81709\n0.88441\n0.50351\n0.43544\n0.03266\n-0.17708\n-0.51004\n-0.33662\n-0.36221\n-0.49768\n-0.52343\n-0.33209\n-0.19903\n-0.047682\n0.13636\n-0.064696\n-0.29241\n-0.46043\n-1.0256\n-0.36216\n0.10341\n0.39076\n0.48607\n0.50347\n0.33771\n0.43023\n0.49753\n0.43161\n0.20837\n-0.16439\n0.11034\nNaN\nNaN\n-1.0166\n-1.5226\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18971\n-0.14419\n-0.15692\n0.09768\n0.35336\n-0.084044\n0.03733\n0.21234\n0.26864\n0.23431\n0.13861\n0.1008\n-0.0065666\n0.058524\n-0.12855\n-0.20354\n-0.09267\n0.081769\n-0.083476\n-0.16246\n-0.040618\n0.13594\n0.12926\n0.29448\n0.41709\n0.63328\n0.77922\n0.88872\n0.53098\n0.43605\n0.25799\n-0.28126\n-0.68516\n-0.34515\n-0.22654\n-0.25115\n-0.22404\n-0.35122\n-0.36882\n-0.38289\n-0.034104\n-0.15049\n-0.34423\n-0.58711\n-1.315\n-0.32939\n0.21073\n0.29852\n0.27139\n0.49919\n0.33502\n0.43602\n0.57974\n0.51854\n0.49644\n0.13972\n0.0009082\n0.35187\n0.32287\n-0.21223\n-1.0922\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.087472\n-0.25519\n-0.25966\n-0.14855\n0.16842\n-0.057221\n-0.046849\n0.2745\n0.31755\n0.30264\n0.20035\n0.22551\n0.23379\n0.15031\n0.11837\n0.16023\n0.16713\n0.28479\n0.073487\n0.11728\n0.11752\n0.089996\n0.30019\n0.43673\n0.4671\n0.70666\n0.76292\n0.60857\n0.43242\n0.29893\n0.28704\n0.16704\n-0.24143\n-0.31663\n-0.17089\n-0.14223\n-0.20427\n-0.36789\n-0.37928\n-0.34564\n-0.3069\n-0.21864\n-0.44317\n-0.7759\n-1.2365\n0.09905\n0.35829\n0.40629\n0.4489\n0.54943\n0.5119\n0.69501\n0.72116\n0.93948\n0.88109\n0.24458\n-0.079656\n0.24923\n0.58836\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.49447\n-0.24986\n-0.12541\n-0.24555\n0.14595\n-0.09942\n-0.098796\n0.036952\n0.20246\n0.30153\n0.26008\n0.26404\n0.32428\n0.20969\n0.31323\n0.34656\n0.28593\n0.43933\n0.37819\n0.4974\n0.42148\n0.40858\n0.58556\n0.42474\n0.28259\n0.5439\n0.82738\n0.75744\n0.42924\n0.21545\n0.26642\n-0.15085\n-0.52795\nNaN\nNaN\n-0.011604\n-0.23104\n-0.29871\n-0.44866\n-0.36696\n-0.24681\n-0.15712\n-0.56589\n-0.71905\n-0.2553\n0.37908\n0.27191\n0.4152\n0.48835\n0.66249\n0.62063\n0.88307\n0.91201\n0.99488\n0.86159\n0.5602\n0.063505\n-0.12827\n0.57481\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.60189\n-0.41849\n-0.23261\n-0.25159\n-0.064361\n-0.14192\n-0.10002\n-0.082979\n0.146\n0.34781\n0.34175\n0.26284\n0.45332\n0.34514\n0.49979\n0.56254\n0.41745\n0.44517\n0.48179\n0.5839\n0.39881\n0.47011\n0.47064\n0.16686\n0.25397\n0.88601\n0.98462\n0.87392\n0.54981\n0.53319\n0.30088\n-0.12602\nNaN\nNaN\nNaN\n-0.057879\n-0.23196\n-0.11753\n-0.56335\n-0.42496\n-0.10418\n-0.10546\n-0.55149\n-0.53524\n-0.12745\n0.2101\n0.24188\n0.42682\n0.64892\n0.64941\n0.61983\n0.88178\n0.87659\n1.0886\nNaN\nNaN\n-0.12738\n-0.15558\n0.4653\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.70136\n-0.59359\n-0.41258\n-0.23972\n-0.20776\n-0.099567\n0.033094\n0.12301\n0.25229\n0.3373\n0.25972\n0.31158\n0.47947\n0.38251\n0.53616\n0.51307\n0.51833\n0.48941\n0.45324\n0.40909\n0.1528\n0.31719\n0.41252\n0.17991\n0.33377\n0.72918\n0.95861\n1.0199\n0.73181\n0.72938\n0.37532\nNaN\nNaN\nNaN\nNaN\n-0.083911\n-0.014391\n-0.17289\n-0.66705\n-0.36188\n0.044679\n-0.15454\n-0.34752\n-0.23326\n-0.043054\n0.16123\n0.35143\n0.64673\n0.92453\n0.90244\n0.77331\n0.92084\n0.73834\nNaN\nNaN\nNaN\n-0.035249\n-0.22187\n0.082325\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.29112\n-0.31418\n-0.23545\n-0.24195\n-0.15226\n0.0025283\n0.079678\n0.11827\n0.20488\n0.22936\n0.30922\n0.3889\n0.38794\n0.4698\n0.5763\n0.64437\n0.70085\n0.56358\n0.41378\n0.31809\n0.22826\n0.30542\n0.072123\n0.21716\n0.35896\n0.39962\n1.0121\n0.86512\n0.60967\n0.6524\n0.48146\nNaN\nNaN\nNaN\nNaN\n-0.27599\n-0.20556\n-0.099611\n-0.40946\n-0.19828\n-0.12401\n-0.64246\n-0.83722\n-0.10429\n0.15066\n0.30298\n0.50693\n0.81441\n1.0213\n1.0242\n0.91973\n0.85317\n0.5291\nNaN\nNaN\nNaN\n-0.71168\n-0.73695\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.092201\n-0.14931\n-0.20161\n-0.076391\n0.0029427\n0.024661\n0.073058\n0.04955\n0.13374\n0.16703\n0.19876\n0.38279\n0.55531\n0.6567\n0.65153\n0.69454\n0.59471\n0.54484\n0.30836\n0.23122\n0.35333\n0.025295\n-0.39927\n-0.21919\n-0.18996\n0.37619\n0.79304\n0.67816\n0.79058\n0.83891\n0.66195\nNaN\nNaN\nNaN\nNaN\n-0.26377\n-0.41809\n-0.04343\n-0.28731\n-0.032083\n-0.12043\n-0.44772\n-0.38574\n0.22138\n0.50879\n0.6471\n0.68066\n0.93595\n1.0251\n1.0349\n0.86057\n0.56356\n0.40983\nNaN\nNaN\nNaN\nNaN\n-0.58069\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.0085996\n-0.23502\n-0.039111\n0.15476\n0.089222\n0.0084089\n0.068403\n0.037583\n0.080442\n0.099466\n0.11536\n0.3361\n0.60137\n0.67985\n0.73026\n0.65015\n0.66461\n0.68708\n0.49202\n0.24483\n0.22547\n-0.40529\n-0.98814\n-0.56631\n-0.37138\n0.40709\n0.58094\n0.73233\n0.97864\n0.94251\n1.0274\nNaN\n-0.18046\n-0.32312\n0.054517\n0.17776\n-0.15393\n0.23041\n0.13035\n0.4312\n0.2873\n-0.24093\n-0.28009\n0.80443\nNaN\nNaN\nNaN\n1.0168\n1.1883\n1.0679\n0.71635\n0.81911\n0.62889\n0.039007\nNaN\nNaN\nNaN\n-0.85321\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.11938\n-0.16388\n0.0055579\n0.28338\n0.22692\n0.14273\n0.19461\n0.088413\n0.061733\n0.21912\n0.22643\n0.37161\n0.54489\n0.58955\n0.67119\n0.425\n0.43351\n0.63577\n0.70481\n0.37493\n0.1536\n-0.11415\n-1.3808\n-1.1685\n-0.43248\n0.22277\n0.65281\n1.2446\n1.1951\n0.86867\n0.48634\n-0.42987\n0.089233\n-0.016646\n-0.0082428\n0.22784\n0.47169\n0.53252\n0.62794\nNaN\nNaN\nNaN\n-0.23702\nNaN\nNaN\nNaN\nNaN\n0.92004\n1.1517\n0.79917\n0.56449\n0.55881\n0.61997\n0.0040117\n-0.15124\n-0.57813\n-1.043\n-1.0061\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1933\n-0.13877\n0.043258\n0.35303\n0.19541\n0.2098\n0.16858\n0.09209\n0.10947\n0.16574\n0.201\n0.32161\n0.52676\n0.56534\n0.58793\n0.31237\n0.28791\n0.47435\n0.58627\n0.45121\n0.10493\n-0.25699\n-0.60956\n-0.91324\n-0.11777\n0.41195\n0.84456\n0.99278\n0.86231\n0.64738\n0.17262\n0.29112\n0.71043\n0.39467\n0.48392\n0.76844\n0.67825\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.033955\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.65153\nNaN\n0.42723\n0.49709\n0.019766\n-0.10493\n-0.26664\n-0.93268\n-1.0192\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26285\n-0.13375\n0.13748\n0.37575\n0.21111\n0.13143\n0.1163\n0.15223\n0.15078\n0.13788\n0.19799\n0.31844\n0.45755\n0.48997\n0.50218\n0.39526\n0.55412\n0.39826\n0.37468\n0.18493\n-0.12629\n-0.5806\n-0.72679\n-0.80355\n-0.0030444\n0.4924\n0.90612\n0.66506\n0.68432\n0.46763\n0.79363\n0.75033\n0.66476\n0.76314\n0.99901\n1.0991\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24232\n-0.065757\n-0.40646\n-0.83672\n-0.72106\n-0.84692\n-0.86851\n-0.31099\n-0.13821\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16146\n-0.0037118\n0.22873\n0.32738\n0.21996\n-0.044018\n0.078549\n0.14923\n0.077686\n0.12542\n0.085389\n0.22814\n0.35339\n0.47443\n0.54075\n0.39028\n0.21846\n0.082135\n0.16629\n0.013025\n-0.2165\n-0.68919\n-0.78578\n-0.50129\n0.069183\n0.47455\n0.60477\n0.59789\n0.9475\n0.73065\n0.78155\n0.91741\n0.92475\n0.87636\n1.0879\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.47447\n-0.19289\n-0.35577\n-0.48005\n-0.18801\n-0.21343\n-0.24934\n0.16747\n0.21993\n-0.17816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.054106\n0.058809\n0.062723\n0.0030016\n0.1768\n0.12473\n0.27518\n0.10285\n-0.10004\n-0.0018909\n0.071661\n0.17777\n0.27527\n0.40655\n0.38189\n0.19903\n-0.09729\n-0.32951\n-0.14931\n-0.098875\n-0.20346\n-0.77816\n-0.57588\n-0.17698\n0.40427\n0.73877\n0.58882\n0.7368\n1.1132\n0.98692\n0.76725\n0.96624\n0.88298\n0.83398\n1.2573\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.90188\n-0.5667\n-0.46747\n-0.21995\n0.15464\n0.2982\n-0.055539\n-0.053423\n0.17259\n-0.17318\n0.018002\n0.2836\n0.4183\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.050485\n0.10567\n-0.0027749\n-0.066788\n0.2917\n0.26939\n-0.074628\n-0.16876\n-0.179\n0.096771\n0.1654\n0.18865\n0.066862\n0.29487\n0.18209\n0.096384\n-0.059433\n-0.12709\n-0.20189\n-0.25354\n-0.26719\n-0.88744\n-0.60032\n0.15847\n0.50127\n1.006\n1.1084\n1.0544\n1.1262\n1.3616\n1.7587\n1.9497\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.61768\n-0.45701\n-0.28967\n-0.31093\n-0.42345\n-0.21902\n0.2761\n0.41229\n0.077558\n0.28848\n0.36489\n0.28875\n0.22139\n0.26546\n0.28391\n0.36798\n0.35108\n0.38315\n0.1699\n-0.011849\n-0.033428\n-0.013776\n-0.1121\n-0.060101\n0.15772\n0.1312\n-0.16943\n-0.11782\n-0.14594\n0.047215\n0.056058\n-0.014421\n0.16025\n0.21276\n0.09191\n0.21199\n0.072461\n-0.14527\n-0.19351\n-0.15007\n0.053273\n-0.57679\n-0.72922\n0.25512\n0.78599\n1.5693\n1.3588\n1.3167\n1.6646\n1.8241\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15206\n-0.30524\n-0.186\n0.1405\n0.040866\n-0.058663\n-0.47022\n-0.012092\n0.29913\n0.13992\n0.45888\n0.51928\n0.23494\n0.15102\n0.055028\nNaN\nNaN\nNaN\nNaN\n0.25576\n0.23544\n-0.023572\n-0.068817\n-0.10828\n-0.22941\n-0.20704\n-0.17836\n-0.22782\n-0.18156\n-0.0049485\n-0.045424\n-0.1104\n-0.062191\n0.10966\n0.1582\n0.18158\n0.34204\n0.21965\n-0.2471\n-0.65047\n-0.091272\n-0.00797\n-0.7316\n-0.60624\n0.1705\n1.057\n1.2683\n1.1895\n1.3258\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20132\n0.11459\n-0.0020187\n-0.1596\n0.10481\n0.29121\n0.22237\n0.2137\n-0.046494\n-0.053859\n-0.080524\n0.11923\n0.41042\n0.64191\n0.26927\n0.35092\n0.28812\nNaN\nNaN\nNaN\nNaN\n0.012418\n0.46604\n-0.080455\n-0.18875\n-0.47822\n-0.35794\n-0.18148\n-0.18513\n-0.28329\n-0.084783\n0.17431\n0.054471\n-0.043918\n0.11507\n0.19424\n0.16675\n0.2379\n0.64211\n0.48895\n-0.064282\n-0.58208\n-0.83377\n-0.48495\n-0.47858\n-0.19526\n0.43832\n0.81211\n1.0348\n0.87759\n0.84446\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.031718\n0.20772\n0.14908\n0.077906\n-0.019005\n0.09175\n0.2079\n0.30977\n0.51631\n0.28399\n-0.017074\n-0.1196\n0.029779\n0.11961\n0.52929\n0.10067\n0.36384\n0.4451\nNaN\nNaN\n-0.087699\n0.064676\n0.087831\n0.17485\n-0.31281\n-0.37628\n-0.40283\n-0.27123\n-0.22961\n-0.0076368\n-0.13291\n0.064802\n0.16113\n0.12776\n0.00027543\n0.096396\n0.27193\n0.09691\n0.13694\n0.55388\n0.62034\n0.076537\n-0.46845\n-0.69349\n-0.31461\n-0.16636\n-0.051529\n0.54744\n0.85413\n1.26\n1.0766\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.36052\n-0.15637\n0.53664\n0.20753\n0.27988\n0.19119\n0.10741\n0.048573\n0.36813\n0.80581\n0.63713\n0.18408\n0.14543\n0.16432\n-0.042749\n-0.13613\n0.10307\n0.39872\n0.29626\n-0.0053173\n-0.28112\n0.15715\n0.30355\n0.33659\n0.25989\n-0.50465\n-0.37234\n-0.23113\n-0.20319\n-0.30145\n0.055194\n0.029583\n-0.0040722\n0.095982\n0.041757\n0.024016\n0.21731\n0.27466\n0.30549\n0.39447\n0.52921\n0.87201\n0.1259\n-0.23306\n-0.25008\n0.040164\n-0.015087\n0.017326\n0.35424\n1.0016\n1.3335\n1.3372\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.46035\n-0.23265\n0.4406\n0.13926\n0.31299\n0.33676\n-0.016304\n-0.11797\n0.18862\n0.7937\n0.78323\n0.55462\n0.35215\n0.3522\n0.10642\n0.012554\n0.35781\n0.5613\n0.22213\n0.14139\n0.0701\n0.34666\n0.51322\n0.69835\n0.38459\n-0.4362\n-0.26886\n-0.14943\n-0.11479\n-0.2078\n-0.042751\n0.010541\n-0.034707\n0.030979\n-0.0052833\n-0.058539\n-0.020902\n0.20779\n0.39416\n0.48716\n0.56693\n0.57858\n0.10302\n0.067966\n-0.14895\n-0.049806\n0.067696\n0.32064\n0.53944\n1.0661\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15705\n-0.15172\n-0.059385\n0.36611\n-0.72328\n-0.13478\n0.32416\n0.24866\n0.64251\n0.74289\n0.76691\n0.77117\n0.84313\n0.62316\n0.44172\n0.17606\n-0.0073558\n0.56442\n0.48471\n0.53575\n0.28229\n0.3161\n0.52851\n0.63976\n0.63725\n0.40673\n-0.3909\n-0.14937\n-0.20782\n-0.16345\n-0.21234\n-0.16504\n-0.17224\n-0.03294\n-0.0077076\n-0.11617\n-0.086923\n-0.017948\n0.10157\n0.26445\n0.46535\n0.50355\n0.30586\n0.0022164\n-0.094852\n-0.0426\n0.023405\n0.11481\n0.22358\n0.48426\n1.0552\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.93634\nNaN\nNaN\nNaN\nNaN\n0.36076\n0.097926\n0.060484\n-0.083225\n0.15841\n-0.7228\n0.03784\n0.4246\n0.96341\n0.82268\n0.80673\n0.65878\n-0.075135\n0.71681\n0.38394\n0.28452\n-0.23026\n0.087063\n0.69256\n0.62877\n0.76611\n0.49825\n0.55757\n0.64741\n0.67021\n0.46126\n0.068079\n-0.24257\n-0.13462\n-0.27344\n-0.23308\n-0.11372\n-0.15724\n-0.19686\n-0.15057\n-0.19437\n-0.0954\n-0.039452\n-0.0026013\n-0.0068214\n0.24669\n0.3332\n0.40512\n0.30701\n0.093821\n-0.1812\n-0.044221\n0.17092\n0.19767\n0.020436\n0.54321\n1.4107\n2.0463\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.80677\n0.5931\n0.39214\n0.24472\n0.0096203\n-0.13073\n-0.009992\n0.050881\n0.0053582\n0.44246\n0.93392\n0.55501\n0.60553\n0.28261\n-0.40867\n-0.2335\n0.15414\n0.21948\n-0.099598\n0.23515\n0.58661\n0.70838\n0.6233\n0.49712\n0.37546\n0.64944\n0.50447\n0.1602\n-0.62264\n-0.2347\n-0.21894\n-0.16249\n-0.26401\n-0.26681\n-0.20654\n-0.094994\n-0.19649\n-0.33301\n-0.1065\n-0.081996\n-0.076556\n-0.11791\n0.093738\n0.24617\n0.28697\n0.11948\n-0.0057919\n-0.033977\n-0.41457\n-0.34291\n0.14579\n0.22158\n0.91512\n1.7749\n1.951\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4556\nNaN\nNaN\n1.4214\n1.0117\n0.79968\n0.85045\n0.32819\n-0.075291\n-0.25474\n-0.48526\n-0.34577\n-0.35163\n-0.088827\n0.34804\n0.028679\n-0.060393\n0.083464\n-0.074425\n-0.37467\n-0.44548\n-0.038848\n-0.022659\n0.13244\n0.38141\n0.39352\n0.28686\n0.33105\n0.34211\n0.298\n0.19797\n0.084197\n-0.90615\n-0.32872\n-0.30215\n-0.30976\n-0.35391\n-0.42997\n-0.226\n-0.16686\n-0.18034\n-0.25559\n-0.14374\n-0.12548\n-0.21223\n-0.20687\n-0.1114\n0.07762\n-0.029565\n-0.10595\n-0.28576\n-0.30927\n-0.36246\n-0.47243\n-0.0043054\n0.3597\n1.3006\n1.8288\n2.1579\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8869\n3.3528\n2.3024\n1.7923\n0.9728\n0.81585\n0.71761\n0.56673\n0.079429\n-0.024988\n-0.19965\n-0.39298\n-0.33074\n-0.33401\n-0.24619\n-0.25569\n-0.22825\n-0.24156\n-0.28538\n-0.52261\n-0.63785\n-0.056004\n0.17652\n0.16348\n0.020076\n-0.1207\n0.039179\n0.2942\n0.25374\n-0.032745\n0.11528\n-0.075831\n-0.44291\n-0.45497\n-0.34488\n-0.36249\n-0.23741\n-0.30931\n-0.31073\n-0.32831\n-0.21078\n-0.20966\n-0.28079\n-0.17264\n-0.37692\n-0.56278\n-0.34506\n-0.19448\n-0.18165\n-0.35886\n-0.51771\n-0.48646\n-0.4348\n-0.45164\n-0.29791\n0.37081\n0.9929\n1.6218\n2.3917\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.1215\n3.1295\n2.2066\n1.7136\n0.80319\n0.45091\n0.49053\n0.43402\n0.24934\n-0.016532\n-0.33776\n-0.35158\n-0.23466\n-0.15222\n-0.14491\n-0.55357\n-0.65827\n-0.40817\n-0.42965\n-0.74522\n-0.83468\n-0.28188\n0.05204\n0.022895\n-0.016664\n-0.18033\n-0.035771\n-0.0075234\n0.20711\n-0.17609\n-0.31764\n-0.17585\n-0.052653\n-0.34899\n-0.34298\n-0.36226\n-0.16793\n-0.32532\n-0.34224\n-0.36929\n-0.31945\n-0.28598\n-0.34748\n-0.39214\n-0.424\n-0.57688\n-0.40816\n-0.39893\n-0.24227\n-0.38747\n-0.90796\n-1.0659\n-0.41035\n-0.2563\n-0.39144\n0.14886\n0.96975\n1.3469\n1.2611\n0.88945\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4709\n2.067\n1.9383\n1.1035\n0.8405\n-0.1625\n0.15033\n0.22899\n-0.02051\n-0.13248\n-0.26905\n-0.25688\n-0.17296\n-0.18336\n-0.25497\n-0.46601\n-0.96056\n-0.52997\n-0.33507\n-0.39016\n-0.63191\n-0.66609\n-0.22841\n-0.053608\n0.020398\n-0.001761\n-0.080222\n-0.16302\n-0.36817\n-0.41576\n-0.19789\n0.035146\n0.076637\n-0.31365\n-0.42014\n-0.43847\n-0.25524\n-0.22178\n-0.20139\n-0.37932\n-0.37079\n-0.24289\n-0.26481\n-0.45035\n-0.4481\n-0.4319\n-0.39349\n-0.43098\n-0.25943\n-0.2769\n-0.52851\n-0.85056\n-0.73857\n-0.2404\n-0.41284\n0.095025\n0.977\n1.1433\n0.21431\n0.056404\n-0.13413\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7159\n1.7366\n1.247\n1.2179\n0.81441\n-0.11176\n0.1555\n0.15956\n-0.25928\n-0.45666\n-0.51039\n-0.19671\n-0.16714\n-0.26167\n-0.55917\n-0.45831\n-0.4691\n-0.37214\n-0.14369\n-0.13926\n-0.1661\n-0.30102\n-0.34331\n-0.10352\n-0.16635\n-0.1057\n-0.2428\n-0.27528\n-0.40573\n-0.21726\n-0.067567\n0.14162\n0.063596\n-0.3538\n-0.50138\n-0.47511\n-0.36994\n-0.14822\n-0.10782\n-0.27011\n-0.23116\n-0.23298\n-0.23103\n-0.55931\n-0.41647\n-0.30048\n-0.48469\n-0.59562\n-0.44454\n-0.23937\n-0.58676\n-0.52638\n-0.96228\n-0.93333\n-0.55716\n0.098627\n0.5374\n0.699\n0.36326\n-0.072414\n0.18883\nNaN\nNaN\n0.79811\n1.3893\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8054\n1.3916\n1.1413\n1.128\n0.11836\n-0.45655\n-0.055094\n-0.27632\n-0.50289\n-0.74261\n-0.47241\n-0.10078\n-0.11454\n-0.26806\n-0.28799\n-0.32778\n-0.28191\n-0.24116\n-0.15293\n-0.21144\n-0.29168\n-0.29308\n-0.34029\n-0.21685\n-0.31659\n-0.41668\n-0.71198\n-0.7883\n-0.33007\n-0.13465\n-0.041877\n0.20152\n0.11371\n-0.40694\n-0.4452\n-0.413\n-0.4715\n-0.17025\n0.059856\n-0.096139\n-0.14538\n-0.26203\n-0.2505\n-0.49937\n-0.32137\n-0.32247\n-0.51032\n-0.61043\n-0.32957\n-0.34343\n-0.74912\n-0.90831\n-0.79748\n-0.64167\n-0.38834\n-0.019304\n0.43347\n0.038056\n-0.37422\n-0.15831\n0.54237\n0.54275\n0.99348\n1.168\n1.182\n1.2422\n1.422\nNaN\nNaN\nNaN\nNaN\n1.2205\n1.3909\n0.91803\n0.78606\n0.80736\n-0.26475\n-0.84476\n-0.45594\n-0.45809\n-0.54724\n-0.63079\n-0.38241\n0.072159\n0.078498\n0.12253\n-0.089188\n-0.36031\n-0.35694\n-0.21805\n-0.18298\n-0.13961\n-0.14051\n-0.32112\n-0.4062\n-0.6008\n-0.74427\n-0.59635\n-0.5859\n-0.58651\n-0.52466\n-0.48008\n-0.20777\n0.10921\n-0.070917\n-0.2948\n-0.37838\n-0.48106\n-0.43874\n-0.32436\n0.0051592\n0.011417\n-0.13045\n-0.23145\n-0.25647\n-0.38742\n-0.23521\n-0.2731\n-0.38782\n-0.5146\n-0.45684\n-0.78731\n-1.3681\n-1.0548\n-0.74494\n-0.56709\n-0.025644\n0.14069\n0.3379\n0.96794\n0.31289\n0.39856\n0.54797\nNaN\n0.38356\n0.77194\n0.38505\n1.1852\n1.8051\nNaN\nNaN\nNaN\n0.81142\n1.1159\n1.1396\n0.84452\n0.32601\n0.42156\n-0.093399\n-0.81391\n-0.59755\n-0.0073073\n-0.23068\n-0.48862\n-0.25988\n0.1022\n0.20084\n0.22494\n-0.16644\n-0.17495\n-0.2682\n-0.26482\n-0.2084\n-0.040243\n-0.075404\n-0.29665\n-0.47706\n-0.60984\n-0.72257\n-0.53768\n-0.23936\n-0.30357\n-0.58827\n-0.49345\n-0.23734\n-0.055748\n-0.23978\n-0.44059\n-0.35736\n-0.34669\n-0.35024\n-0.23853\n-0.03219\n-0.040948\n-0.23125\n-0.29289\n-0.33119\n-0.36487\n-0.28211\n-0.23376\n-0.2522\n-0.39497\n-0.55383\n-0.85198\n-1.4007\n-0.91527\n-0.78158\n-0.50163\n-0.29155\n0.29752\n0.15368\n0.27915\n0.36839\n0.36636\nNaN\nNaN\n0.1663\n0.44554\n0.55038\n0.99922\n1.4946\nNaN\nNaN\nNaN\n0.68097\n0.78323\n1.0444\n0.65139\n-0.1102\n-0.26989\n-0.43333\n-0.58904\n-0.18385\n0.089584\n-0.012814\n-0.36355\n-0.24758\n-0.006654\n0.30377\n0.21978\n-0.02397\n-0.077425\n-0.15546\n-0.17948\n-0.119\n0.029214\n-0.069985\n-0.20244\n-0.4988\n-0.61183\n-0.54756\n-0.30473\n-0.054098\n-0.056433\n-0.1639\n-0.16003\n-0.13498\n-0.11229\n0.0080884\n-0.35345\n-0.32228\n-0.2095\n-0.099428\n-0.047258\n-0.021847\n-0.078191\n-0.27444\n-0.37757\n-0.36876\n-0.2955\n-0.20244\n-0.091258\n0.047109\n-0.068915\n-0.50561\n-0.653\n-1.3291\n-0.35372\n-0.46603\n-0.49676\n-0.083138\n0.26684\n0.023537\n-0.10121\n0.083076\n0.15494\nNaN\nNaN\nNaN\n0.13472\n0.4324\n0.57224\n0.85375\nNaN\n0.94542\n0.79899\n0.41148\n0.50916\n0.92098\n0.27767\n-0.10172\n-0.082937\n0.033467\n-0.081109\n-0.21336\n-0.23671\n-0.29033\n-0.26078\n-0.04567\n0.12959\n0.27812\n0.12338\n-0.19015\n-0.16488\n-0.090029\n-0.02546\n0.12913\n0.12925\n0.08304\n-0.14987\n-0.4152\n-0.50901\n-0.36223\n-0.14563\n0.041241\n0.094481\n0.060683\n-0.16707\n-0.29581\n-0.10202\n0.17417\n-0.27663\n-0.2498\n-0.092198\n0.05804\n0.14812\n0.082497\n-0.10512\n-0.087884\n-0.19947\n-0.22096\n-0.10941\n0.015232\n0.10375\n0.33064\n0.49065\n-0.17155\nNaN\nNaN\nNaN\n-0.47428\n-0.73242\n-0.18826\n0.085705\n-0.57122\n-0.3922\n-0.12201\n0.11506\nNaN\nNaN\nNaN\n0.1128\n0.35877\n0.18727\n0.34071\n0.51642\n0.66878\n0.47807\n0.31211\n0.34453\n0.14333\n-0.17187\n-0.17371\n0.090484\n0.16704\n0.034416\n-0.11454\n-0.13001\n-0.073854\n0.037749\n0.050417\n0.14555\n0.11284\n-0.054526\n-0.29845\n-0.15674\n0.083863\n0.12217\n0.16328\n0.17924\n0.14001\n-0.12669\n-0.26281\n-0.30265\n-0.30759\n-0.11226\n-0.0053312\n0.16514\n0.19115\n-0.098734\n-0.27626\n-0.13986\n0.098304\n-0.049195\n0.051479\n0.10801\n0.058758\n0.10449\n0.15917\n-0.079288\n0.057928\n0.0021936\n-0.030149\n0.097752\n0.19217\n0.41257\n0.82655\n0.68349\n0.025749\nNaN\nNaN\nNaN\n-0.58667\n-0.69291\n-0.96606\n-0.6628\n-0.59426\n-0.27301\n-0.18185\n-0.094284\nNaN\nNaN\nNaN\n-0.10284\n0.03572\n0.074485\n0.30865\n0.82909\n0.61621\n0.36148\n0.18865\n0.081835\n-0.093573\n-0.22523\n-0.24609\n0.038355\n0.21577\n0.17757\n0.00068488\n-0.038678\n-0.15344\n-0.094727\n0.069359\n0.13506\n0.064799\n-0.035389\n-0.018608\n0.13267\n0.14842\n-0.045222\n0.13814\n0.17545\n0.18415\n-0.049253\n-0.14367\n-0.14079\n-0.12058\n0.037181\n0.081699\n0.27677\n0.2932\n0.021307\n-0.094938\n0.074377\n0.13055\n0.11075\n0.22966\n0.20937\n0.079122\n0.22231\n0.21321\n0.21301\n0.11894\n0.16467\n0.10097\n0.24381\n0.39704\n0.49697\n0.53803\n0.38906\n0.33826\n-0.12858\nNaN\nNaN\n-0.75153\n-0.6371\n-0.68152\n-0.64334\n-0.46443\n-0.10924\n-0.10142\n-0.16613\n-0.05878\n-0.18985\n-0.17301\n-0.50806\n-0.27491\n-0.036478\n0.23734\n0.72853\n0.60976\n0.53588\n0.349\n0.19732\n0.20815\n-0.044665\n-0.043285\n0.10091\n0.23317\n0.28953\n0.042969\n-0.31903\n-0.61712\n-0.49433\n0.03895\n0.12594\n0.10442\n0.35129\n0.13145\n0.13515\n0.17106\n-0.075025\n0.076264\n0.12752\n0.21123\n0.054969\n-0.18463\n-0.064112\n0.08951\n0.19688\n0.17639\n0.19684\n0.3567\n0.024277\n0.071641\n0.33859\n0.22122\n0.00446\n0.12577\n0.19208\n0.21363\n0.32588\n0.36258\n0.36226\n0.22009\n0.18989\n0.18986\n0.19452\n0.32039\n0.4156\n0.49702\n0.40135\n0.34762\n-0.10008\n-0.76205\n-1.0116\n-0.77493\n-0.6356\n-0.47444\n-0.39927\n-0.30692\n-0.077938\n-0.14329\n-0.15765\n-0.11878\n-0.064543\n-0.093017\n-0.22811\n-0.26551\n-0.16631\n0.054906\n0.45869\n0.53545\n0.41626\n0.57898\n0.37551\n0.22367\n0.13223\n0.30472\n0.29325\n0.3259\n0.31911\n0.28487\n-0.30352\n-0.79606\n-1.0428\n-0.2836\n-0.030684\n0.51054\n0.48122\n0.26246\n0.12429\n0.30315\n0.21611\n0.13998\n0.0799\n0.077901\n0.015188\n-0.1068\n0.1638\n0.22009\n0.20239\n0.15951\n0.40148\n0.45028\n0.088491\n0.15682\n0.16399\n0.10422\n-0.065814\n0.18376\n0.35421\n0.38993\n0.53558\n0.62726\n0.54698\n0.55047\n0.3042\n0.15927\n0.14305\n0.23702\n0.26572\nNaN\nNaN\nNaN\nNaN\n-0.33802\n-0.72096\n-0.7078\n-0.37484\n-0.42867\n-0.56499\n-0.37313\n-0.095627\n-0.15173\n-0.071703\n0.062443\n-0.044917\n-0.14303\n-0.17873\n-0.14649\n-0.26684\n-0.18087\n-0.025781\n0.15312\n0.29389\n0.6088\n0.45217\n0.44375\n0.38192\n0.48279\n0.44131\n0.080154\n0.049626\n0.36362\n0.13194\n-0.60567\n-0.56935\n-0.58516\n-0.27415\n0.015646\n0.18216\n0.043234\n0.10618\n0.39112\n0.067631\n-0.022132\n0.070915\n-0.026341\n0.014438\n0.14973\n0.31981\n0.15719\n0.036111\n0.20406\n0.56172\n0.4842\n0.26949\n0.10192\n0.024112\n0.1227\n0.15384\n0.2837\n0.44141\n0.54615\n0.62232\n0.70028\n0.74096\n0.86297\n0.69219\n0.49696\n0.2999\n0.2887\n0.38902\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.6003\n-0.55665\n-0.49397\n-0.45099\n-0.6583\n-0.49798\n-0.22165\n-0.13337\n-0.12537\n-0.048108\n-0.19356\n-0.22957\n-0.26197\n-0.22192\n-0.043622\n-0.15239\n-0.13584\n0.11552\n0.36508\n0.82255\n0.75614\n0.65371\n0.60653\n0.63398\n0.4732\n-0.31974\n-0.39537\n-0.13382\n0.42513\n-0.36752\n-0.28916\n-0.27691\n-0.23157\n-0.26425\n-0.26381\n-0.24057\n-0.05454\n0.068832\n-0.13173\n-0.071998\n0.11679\n0.13861\n0.10683\n0.2483\n0.29022\n0.19809\n0.021491\n0.32839\n0.73663\n0.51691\n0.38478\n0.015649\n-0.0055507\n-0.012015\n0.35654\n0.27747\n0.49489\n0.74238\n0.81616\n1.1495\n1.125\n1.1899\n0.97822\n0.70637\n0.42732\n0.27351\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.70595\n-0.58951\n-0.65977\n-0.64629\n-0.65315\n-0.56787\n-0.60413\n-0.3312\n-0.25503\n-0.16458\n-0.19092\n-0.25024\n-0.15472\n-0.03951\n0.0034203\n-0.013175\n0.076411\n0.1784\n0.6379\n0.87407\n0.64466\n0.68825\n0.74911\n0.61215\n-0.3354\n-0.80962\n-0.62475\n0.21541\n-0.1945\n-0.16011\n-0.21185\n-0.23922\n-0.15459\n-0.30417\n-0.2781\n-0.096879\n0.076789\n-0.2471\n-0.21467\n0.13353\n-0.045369\n-0.032483\n0.024822\n0.20186\n0.18674\n0.23465\n0.36223\n0.57416\n0.43611\n0.41092\n-0.23409\n-0.31328\nNaN\n0.58356\n0.33588\n0.75634\n1.0346\n1.1734\n1.4974\n1.7158\n1.6538\n1.0418\n0.81107\n0.57283\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76022\n-0.49686\n-0.56487\n-0.50211\n-0.61631\n-0.57648\n-0.48811\n-0.41405\n-0.20295\n-0.10736\n-0.18094\n-0.15217\n-0.099777\n0.036646\n0.052292\n0.0074516\n-0.1655\n-0.14963\n0.21294\n0.50383\n0.42707\n0.59469\n0.48166\n0.061866\n-0.37817\n-0.82909\n-0.83117\n-0.2096\n-0.23616\n-0.13965\n-0.29909\n-0.13358\n0.11837\n0.19231\n0.13604\n0.075695\n0.063197\n-0.25787\n-0.24401\n0.12832\n-0.30929\n-0.12896\n-0.36629\n-0.19573\n0.31145\nNaN\nNaN\n0.30883\n0.31594\n0.49597\nNaN\nNaN\nNaN\n0.2456\n-0.13109\n-0.13193\n-0.21727\n-0.19307\n-0.26543\n-0.44876\n-0.39852\n-0.11188\n-0.034468\n-0.3112\n-0.26768\n-0.40329\n-0.2041\n0.2012\n0.19431\n0.24994\n0.016347\n0.43214\n0.44531\n0.7043\n0.66111\n0.85795\n0.40447\n0.65789\n0.27953\n-0.18639\n-0.7002\n-0.43949\n-0.32561\n-1.0223\n-0.87057\n-0.49216\n-0.55742\n-0.14051\n-0.46624\n-0.45201\n-0.21327\n-0.44511\n-0.57514\n-0.27865\n0.043356\n0.28356\n0.50594\n0.53539\n0.27725\n0.41994\n0.36941\n0.20345\n0.10519\n0.13382\n0.37668\n0.26724\n-0.13328\n0.42412\n0.066114\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.36619\n-0.32118\n-0.30618\n0.37376\n0.43446\n-0.7536\n-0.50205\n-0.50201\n-0.046155\n0.050297\n0.035298\n-0.10359\n-0.44877\n0.19273\n0.43938\n-0.033634\n-0.65292\n-0.44307\n0.044175\n0.22242\n1.1231\n0.5581\n0.64282\n1.0669\n0.84498\n0.58261\n0.21706\n-0.22748\n0.018453\n-0.26344\n-0.28565\n-0.41978\n-0.10656\n-0.25027\n-0.20991\n-0.67846\n-0.63307\n-0.20104\n-0.33705\n0.10353\n0.19506\n0.10765\n0.32785\n0.57829\n0.54452\n0.37435\n0.40693\n0.24784\n0.13653\n0.090696\n0.090679\n0.25883\n0.25051\n0.39894\n1.1109\n0.32957\n-0.25212\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.54659\n-0.42895\n-0.29334\n-0.36174\n-0.99232\n-0.79617\n-0.91639\n-0.44787\n-0.24854\n0.4086\n0.3186\n0.064041\n-0.11767\n0.47764\n0.99299\n0.10326\n-0.20926\n-0.0094077\n0.032243\n0.72075\n0.71323\n0.66986\n1.0538\n0.9372\n1.0317\n1.0199\n0.68048\n0.16861\n0.27857\n-0.27802\n0.046415\n-0.29986\n0.24725\n0.1109\n-0.78639\n-0.92649\n-0.49909\n-0.28724\n-0.066997\n0.014207\n0.1374\n0.27581\n0.18167\n0.55738\n0.36604\n0.24552\n0.25147\n0.22097\n0.13624\n0.24997\n0.35749\n0.22507\n0.17082\n0.68351\n0.78744\n0.43532\n0.2217\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26651\n-0.40331\n-0.33137\n-0.27488\n-0.26281\n-0.20536\n-0.043837\n-0.11917\n-0.27407\n-0.14668\n0.085944\n-0.44496\n0.46416\n0.80504\n0.51837\n-0.096425\n-0.076641\n-0.036932\n0.069454\n0.65961\n1.1449\n0.64351\n0.90176\n0.66505\n1.0966\n1.1537\n0.73872\n0.37179\n0.13326\n-0.37008\n-0.14585\n0.055062\n0.33885\n0.23294\n-0.54981\n-0.78356\n-0.72622\n-0.32129\n0.016346\n-0.055567\n-0.042305\n0.11648\n-0.073266\n-0.040144\n0.12166\n0.10441\n0.057952\n0.27914\n0.41871\n0.83131\n0.93634\n0.66208\n0.84941\n0.98411\n0.85224\n0.62266\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20476\n-0.24403\n-0.19469\n-0.1967\n-0.045131\n-0.15524\n-0.028522\n0.044422\n-0.22312\n-0.54265\n-0.39243\n-0.60007\n-0.51588\n-0.063246\n-0.59196\n-0.57828\n-0.43079\n-0.16119\n-0.36013\n0.34815\n1.4719\nNaN\nNaN\n-0.4583\n0.8212\n1.0608\n0.91721\n0.38139\n0.36538\n0.36924\n-0.20512\n0.026162\n0.11937\n0.23319\n-0.0064712\n-0.35196\n-0.80194\n-0.39635\n-0.03382\n-0.055967\n-0.0044088\n0.20027\n-0.038347\n-0.06247\n-0.014074\n0.049987\n0.30689\n0.64067\n0.58106\n0.54105\n0.87563\n0.78622\n0.59927\n1.0603\n0.74808\n0.75456\n1.5298\nNaN\nNaN\n-2.0781\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.11371\n-0.2899\n-0.34525\n-0.17373\n0.069824\n-0.16809\n0.027839\n0.16669\n0.0034765\n-0.15616\n-0.11113\n0.17833\n0.077943\n-0.42837\n-0.77064\n-0.3008\n-0.59905\n-0.5323\n-0.24245\nNaN\nNaN\nNaN\nNaN\n0.040271\n0.59843\n1.125\n1.0485\n0.77005\n0.57971\n0.65998\n0.30575\n-0.075643\n-0.18971\n-0.14127\n-0.009129\n-0.47743\n-0.80361\n-0.3331\n-0.046494\n0.04656\n0.10272\n-0.0084068\n-0.062041\n-0.20012\n-0.76538\n-0.30787\n0.29154\n0.70813\n0.57043\n0.63859\n0.70614\n0.67781\n0.59697\n0.54877\n0.37665\n-0.033753\n0.40893\nNaN\nNaN\n-3.0711\n-2.1164\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.3078\n-0.43402\n-0.30254\n0.07893\n0.26123\n-0.072787\n0.27571\n0.31051\n0.31236\n0.22483\n0.11992\n0.021904\n0.029162\n-0.33957\n-0.46822\n-0.55382\n-0.60775\n-0.14588\n-0.27845\nNaN\nNaN\nNaN\nNaN\n0.050419\n0.3381\n0.83426\n1.0847\n1.2412\n0.70243\n0.61548\n0.072165\n-0.12422\n-0.67471\n-0.42521\n-0.40723\n-0.59938\n-0.69483\n-0.42343\n-0.21411\n0.018197\n0.22259\n-0.030879\n-0.34464\n-0.62507\n-1.4772\n-0.44485\n0.25533\n0.65458\n0.78378\n0.80841\n0.55374\n0.68303\n0.80039\n0.67636\n0.33307\n-0.19102\n0.22932\nNaN\nNaN\n-1.4093\n-2.2059\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18438\n-0.113\n-0.14479\n0.23887\n0.62743\n-0.050505\n0.13107\n0.39193\n0.46492\n0.36543\n0.21769\n0.19594\n-0.0025418\n0.098705\n-0.16896\n-0.26123\n-0.069401\n0.14012\n-0.077432\n-0.21539\n-0.074508\n0.16818\n0.15095\n0.40281\n0.54707\n0.84679\n1.0451\n1.1829\n0.71494\n0.59142\n0.36814\n-0.4269\n-1.0237\n-0.46425\n-0.28515\n-0.30098\n-0.29902\n-0.4234\n-0.42477\n-0.47163\n0.0095686\n-0.13465\n-0.41341\n-0.76829\n-1.8852\n-0.39929\n0.42441\n0.53701\n0.48383\n0.80533\n0.5931\n0.73241\n0.91248\n0.78369\n0.7411\n0.27219\n0.02528\n0.58702\n0.48935\n-0.25276\n-1.5104\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.056854\n-0.31106\n-0.3502\n-0.15323\n0.30895\n-0.061979\n-0.035459\n0.45763\n0.54007\n0.49054\n0.32281\n0.3489\n0.37065\n0.23468\n0.20331\n0.28522\n0.25922\n0.41868\n0.10641\n0.15096\n0.16597\n0.14023\n0.4333\n0.63419\n0.64671\n0.9483\n0.9974\n0.74963\n0.56122\n0.393\n0.39541\n0.24882\n-0.41615\n-0.45765\n-0.24206\n-0.20897\n-0.26398\n-0.43653\n-0.44726\n-0.41691\n-0.37478\n-0.24173\n-0.56766\n-1.0392\n-1.7139\n0.24707\n0.63751\n0.72073\n0.77088\n0.90439\n0.84585\n1.081\n1.0954\n1.3902\n1.3287\n0.43938\n-0.037268\n0.423\n0.94743\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65829\n-0.29743\n-0.13259\n-0.29985\n0.30794\n-0.1118\n-0.10487\n0.14071\n0.38305\n0.49864\n0.41143\n0.41027\n0.4939\n0.29698\n0.45553\n0.53348\n0.42892\n0.67462\n0.58561\n0.7434\n0.60778\n0.55655\n0.81479\n0.59958\n0.38887\n0.71314\n1.0807\n0.97946\n0.5631\n0.26448\n0.30637\n-0.23642\n-0.74876\nNaN\nNaN\n-0.030754\n-0.37448\n-0.40069\n-0.56233\n-0.44086\n-0.27712\n-0.13496\n-0.74531\n-0.99264\n-0.31138\n0.683\n0.5234\n0.7384\n0.83286\n1.1035\n1.0309\n1.3733\n1.3808\n1.466\n1.2982\n0.92981\n0.27065\n-0.026413\n0.96227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.7875\n-0.5503\n-0.28674\n-0.30194\n-0.02194\n-0.17194\n-0.10192\n-0.072875\n0.28036\n0.56577\n0.50463\n0.34665\n0.61956\n0.48189\n0.71745\n0.82269\n0.6523\n0.68748\n0.71465\n0.82498\n0.52467\n0.60299\n0.56539\n0.19248\n0.36809\n1.269\n1.3107\n1.1319\n0.73526\n0.69591\n0.34618\n-0.23851\nNaN\nNaN\nNaN\n-0.082253\n-0.31881\n-0.11258\n-0.71584\n-0.51351\n-0.014071\n0.038296\n-0.69343\n-0.67112\n-0.061156\n0.45917\n0.51142\n0.76312\n1.0715\n1.1514\n1.0668\n1.407\n1.3415\n1.592\nNaN\nNaN\n-0.0018216\n-0.076059\n0.80474\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.93964\n-0.8455\n-0.56694\n-0.29775\n-0.25172\n-0.10977\n0.099596\n0.24853\n0.42385\n0.52849\n0.37012\n0.44428\n0.67471\n0.52406\n0.75841\n0.74551\n0.80855\n0.71586\n0.59291\n0.53211\n0.16648\n0.3513\n0.44011\n0.1631\n0.44012\n1.0254\n1.2833\n1.3458\n0.92596\n0.93832\n0.46798\nNaN\nNaN\nNaN\nNaN\n-0.10476\n0.0038742\n-0.18639\n-0.77552\n-0.38465\n0.17896\n-0.15031\n-0.37259\n-0.15322\n0.14044\n0.43352\n0.70606\n1.1364\n1.4796\n1.4839\n1.2842\n1.4639\n1.1494\nNaN\nNaN\nNaN\n0.091085\n-0.14144\n0.15781\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32022\n-0.40196\n-0.26816\n-0.26942\n-0.15934\n0.055297\n0.17964\n0.20368\n0.3599\n0.37848\n0.44378\n0.55618\n0.50453\n0.64684\n0.81731\n0.90457\n0.99001\n0.76285\n0.48999\n0.38745\n0.25188\n0.34042\n0.030493\n0.28982\n0.4563\n0.49119\n1.3533\n1.1291\n0.75571\n0.85321\n0.63087\nNaN\nNaN\nNaN\nNaN\n-0.37215\n-0.25593\n-0.088335\n-0.41295\n-0.081278\n0.0052048\n-0.75621\n-1.0197\n0.055356\n0.41045\n0.62896\n0.93832\n1.3803\n1.6308\n1.644\n1.5138\n1.357\n0.86456\nNaN\nNaN\nNaN\n-0.8795\n-0.96065\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.0091416\n-0.12705\n-0.2112\n-0.008006\n0.073482\n0.1036\n0.17309\n0.14577\n0.24312\n0.25057\n0.27907\n0.54607\n0.82848\n0.98941\n0.94643\n0.95\n0.75115\n0.72081\n0.36221\n0.24924\n0.43039\n-0.078762\n-0.57077\n-0.30854\n-0.34362\n0.42049\n1.0038\n0.83958\n1.0268\n1.1114\n0.86459\nNaN\nNaN\nNaN\nNaN\n-0.41354\n-0.58943\n-0.024533\n-0.24248\n0.13617\n0.043721\n-0.40335\n-0.30145\n0.52607\n0.89728\n1.1341\n1.191\n1.5631\n1.6593\n1.6556\n1.4388\n0.96259\n0.70989\nNaN\nNaN\nNaN\nNaN\n-0.67401\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10025\n-0.24966\n0.047699\n0.32935\n0.21141\n0.081562\n0.18383\n0.11807\n0.13237\n0.16594\n0.15846\n0.48794\n0.90524\n1.0234\n1.0777\n0.88589\n0.92954\n0.94963\n0.65098\n0.30319\n0.20301\n-0.66218\n-1.4119\n-0.80797\n-0.59234\n0.46812\n0.69002\n0.91268\n1.2746\n1.2201\n1.3052\nNaN\n-0.20944\n-0.3847\n0.084019\n0.11412\n-0.20817\n0.46346\n0.40344\n0.87333\n0.69142\n-0.11807\n-0.1256\n1.4367\nNaN\nNaN\nNaN\n1.7058\n1.9575\n1.7556\n1.2225\n1.3506\n1.0361\n0.1434\nNaN\nNaN\nNaN\n-1.1476\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.081199\n-0.11453\n0.12138\n0.51078\n0.41846\n0.29494\n0.37239\n0.18767\n0.12185\n0.37815\n0.34418\n0.55016\n0.79191\n0.84132\n0.99256\n0.58858\n0.58275\n0.85748\n0.93851\n0.48306\n0.13578\n-0.18829\n-1.9746\n-1.637\n-0.67457\n0.19945\n0.77824\n1.614\n1.5573\n1.1417\n0.49372\n-0.66395\n0.12735\n0.070336\n0.053577\n0.27664\n0.76955\n0.96391\n1.1306\nNaN\nNaN\nNaN\n-0.1181\nNaN\nNaN\nNaN\nNaN\n1.5759\n1.9403\n1.3776\n0.96437\n0.99074\n1.0159\n0.17306\n-0.061706\n-0.69895\n-1.3989\n-1.368\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21006\n-0.09394\n0.14896\n0.59816\n0.3446\n0.38856\n0.30011\n0.17634\n0.22168\n0.23355\n0.28932\n0.47376\n0.75142\n0.80847\n0.89517\n0.42816\n0.33308\n0.5836\n0.75852\n0.57218\n0.10123\n-0.35992\n-0.89408\n-1.2911\n-0.23211\n0.501\n1.0492\n1.3027\n1.1024\n0.887\n0.087463\n0.21162\n0.88062\n0.68299\n0.75946\n1.0761\n1.141\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14731\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1337\nNaN\n0.67319\n0.85031\n0.16992\n-0.0078712\n-0.2592\n-1.224\n-1.3336\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.31482\n-0.13319\n0.24937\n0.61749\n0.35295\n0.2363\n0.19567\n0.22379\n0.27115\n0.17817\n0.28303\n0.43517\n0.65321\n0.67896\n0.71582\n0.48539\n0.70962\n0.49795\n0.46738\n0.19854\n-0.22267\n-0.82493\n-1.0887\n-1.2223\n-0.067876\n0.60099\n1.1722\n0.9222\n0.9228\n0.61851\n0.98208\n0.96213\n0.83702\n1.0148\n1.3639\n1.5622\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.25369\n0.022002\n-0.48366\n-1.1239\n-0.98817\n-1.1939\n-1.1725\n-0.38781\n-0.086648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16102\n0.031951\n0.38311\n0.54198\n0.36095\n-0.038753\n0.13439\n0.21747\n0.11311\n0.16776\n0.10285\n0.25444\n0.45393\n0.64208\n0.72639\n0.48732\n0.2548\n0.091552\n0.18392\n-0.021031\n-0.33267\n-0.97934\n-1.1483\n-0.81853\n0.022019\n0.58672\n0.77934\n0.79778\n1.2426\n0.97349\n0.91739\n1.2347\n1.3061\n1.2083\n1.5019\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56634\n-0.18017\n-0.41092\n-0.60928\n-0.20055\n-0.22549\n-0.27815\n0.27621\n0.31137\n-0.25879\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.012202\n0.13501\n0.12856\n0.041681\n0.29001\n0.20327\n0.43216\n0.15866\n-0.15447\n-0.04378\n0.054852\n0.16968\n0.33528\n0.5729\n0.54379\n0.22509\n-0.21774\n-0.51655\n-0.26908\n-0.15422\n-0.35763\n-1.1055\n-0.86304\n-0.34212\n0.51534\n0.98393\n0.74436\n0.92869\n1.4429\n1.2499\n0.88017\n1.3111\n1.2359\n1.1383\n1.7197\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1883\n-0.74124\n-0.6143\n-0.2328\n0.34017\n0.53849\n0.0056268\n-0.0306\n0.23858\n-0.27794\n-0.038822\n0.41202\n0.65419\nNaN\nNaN\nNaN\nNaN\nNaN\n0.010035\n0.21742\n0.033541\n-0.082983\n0.47639\n0.44578\n-0.10632\n-0.26163\n-0.306\n0.084238\n0.19708\n0.22103\n0.047888\n0.43428\n0.27398\n0.098856\n-0.17258\n-0.24809\n-0.29224\n-0.47235\n-0.52094\n-1.2876\n-0.9254\n0.11516\n0.63445\n1.3572\n1.4937\n1.4369\n1.4846\n1.8137\n2.4314\n2.7932\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.81276\n-0.52377\n-0.29471\n-0.34707\n-0.55689\n-0.23022\n0.53924\n0.71412\n0.18307\n0.4326\n0.54448\n0.50385\n0.3381\n0.33586\n0.49439\n0.64882\n0.62744\n0.73553\n0.3438\n0.068719\n0.0017378\n0.042366\n-0.12042\n-0.074379\n0.23495\n0.21581\n-0.25973\n-0.20702\n-0.28501\n0.019836\n0.043522\n-0.082131\n0.17529\n0.29734\n0.15218\n0.29206\n0.10632\n-0.18206\n-0.2094\n-0.30166\n-0.031361\n-0.93167\n-1.1141\n0.25667\n1.0292\n2.1379\n1.8563\n1.8508\n2.2379\n2.4762\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.077308\n-0.2803\n-0.12605\n0.31109\n0.15479\n0.0031871\n-0.5921\n0.055354\n0.48159\n0.24766\n0.66957\n0.75902\n0.40684\n0.24546\n0.078184\nNaN\nNaN\nNaN\nNaN\n0.34965\n0.33918\n0.014168\n-0.045424\n-0.11951\n-0.32014\n-0.28066\n-0.26361\n-0.36971\n-0.2881\n-0.050362\n-0.11017\n-0.2106\n-0.14648\n0.12625\n0.19784\n0.21998\n0.44872\n0.23465\n-0.35884\n-0.93782\n-0.1492\n-0.092407\n-1.2644\n-1.0163\n0.14146\n1.4238\n1.7393\n1.6489\n1.9096\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.49837\n0.31822\n0.15135\n-0.10962\n0.24394\n0.52986\n0.42691\n0.39403\n-0.0011418\n-0.03231\n-0.084827\n0.23796\n0.63365\n0.9499\n0.45998\n0.53205\n0.50493\nNaN\nNaN\nNaN\nNaN\n-0.0036576\n0.58387\n-0.034842\n-0.22083\n-0.65505\n-0.48753\n-0.24163\n-0.27763\n-0.45793\n-0.14164\n0.23899\n0.047311\n-0.10828\n0.13446\n0.23886\n0.20493\n0.2876\n0.8978\n0.64882\n-0.15378\n-0.9154\n-1.3063\n-0.76619\n-0.7691\n-0.35591\n0.53026\n1.0525\n1.4373\n1.2386\n1.2216\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24159\n0.50617\n0.39971\n0.26643\n0.068188\n0.19941\n0.3678\n0.51579\n0.80783\n0.46897\n0.011866\n-0.18348\n0.061198\n0.24546\n0.82036\n0.18083\n0.52898\n0.66563\nNaN\nNaN\n-0.37237\n-0.0074273\n0.098831\n0.15784\n-0.40423\n-0.47907\n-0.53225\n-0.35935\n-0.32794\n-0.010133\n-0.21442\n0.080769\n0.23155\n0.16235\n-0.043579\n0.086814\n0.33123\n0.065761\n0.09212\n0.73791\n0.82003\n0.084213\n-0.73963\n-1.1062\n-0.51524\n-0.30255\n-0.13072\n0.72918\n1.1836\n1.6277\n1.3605\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34245\n-0.048167\n0.97647\n0.46554\n0.52415\n0.34544\n0.21421\n0.14918\n0.5546\n1.15\n0.95011\n0.28451\n0.21158\n0.27716\n-0.0098978\n-0.19044\n0.14645\n0.59118\n0.46911\n-0.0060138\n-0.38665\n0.15474\n0.38631\n0.44807\n0.34587\n-0.65163\n-0.47839\n-0.28373\n-0.26993\n-0.44465\n0.081042\n0.0061726\n-0.019688\n0.11727\n0.03131\n-0.036777\n0.25271\n0.3354\n0.38084\n0.55551\n0.70163\n1.1571\n0.15018\n-0.38685\n-0.41766\n-0.021616\n-0.096054\n-0.03703\n0.45057\n1.3784\n1.8386\n1.7625\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.50558\n-0.17708\n0.83742\n0.38896\n0.58569\n0.5614\n0.04631\n-0.072005\n0.32435\n1.1414\n1.125\n0.79838\n0.50852\n0.54008\n0.17829\n0.0227\n0.53817\n0.84463\n0.3461\n0.28981\n0.17327\n0.46648\n0.67265\n0.95676\n0.52358\n-0.56313\n-0.31913\n-0.14575\n-0.14596\n-0.31546\n-0.084307\n-0.026359\n-0.098265\n0.020286\n-0.081747\n-0.13773\n-0.056343\n0.25139\n0.52181\n0.68852\n0.75088\n0.75477\n0.099804\n0.06388\n-0.24963\n-0.1279\n0.027776\n0.40639\n0.71349\n1.4676\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24651\n-0.020012\n0.14161\n0.74898\n-0.70083\n0.03237\n0.53672\n0.43193\n0.97412\n1.1167\n1.1364\n1.1275\n1.2392\n0.91755\n0.62507\n0.27186\n-0.059024\n0.82701\n0.70756\n0.7702\n0.52864\n0.53036\n0.73304\n0.87956\n0.82228\n0.50795\n-0.46813\n-0.13804\n-0.21552\n-0.22517\n-0.30573\n-0.24877\n-0.26928\n-0.056889\n-0.014641\n-0.18835\n-0.14442\n-0.034306\n0.12151\n0.30356\n0.64009\n0.68865\n0.39934\n-0.04881\n-0.18745\n-0.10232\n-0.031195\n0.10038\n0.27452\n0.65876\n1.4577\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4605\nNaN\nNaN\nNaN\nNaN\n0.56086\n0.32756\n0.25003\n0.037829\n0.38209\n-0.70841\n0.17444\n0.6469\n1.4372\n1.2286\n1.1952\n0.98126\n-0.053544\n1.0762\n0.5887\n0.39258\n-0.3236\n0.10396\n0.98593\n0.91162\n1.0933\n0.7641\n0.79557\n0.89046\n0.9267\n0.59328\n-0.016488\n-0.268\n-0.13237\n-0.35569\n-0.31624\n-0.14048\n-0.2174\n-0.28018\n-0.20676\n-0.27163\n-0.13432\n-0.082254\n-0.030771\n-0.037712\n0.2893\n0.48445\n0.57573\n0.41655\n0.11109\n-0.27859\n-0.10815\n0.22999\n0.27327\n-0.010351\n0.72343\n1.9381\n2.8059\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2517\n0.92842\n0.77883\n0.52481\n0.11364\n-0.087991\n0.12366\n0.1488\n0.049902\n0.67713\n1.3943\n0.84286\n0.89823\n0.42426\n-0.59579\n-0.3058\n0.20199\n0.23796\n-0.1317\n0.34188\n0.81512\n0.9689\n0.83478\n0.71124\n0.46987\n0.83683\n0.66698\n0.15054\n-1.0431\n-0.23864\n-0.26633\n-0.20955\n-0.33671\n-0.35221\n-0.2684\n-0.068661\n-0.22968\n-0.46083\n-0.18069\n-0.15013\n-0.097975\n-0.16643\n0.1079\n0.37804\n0.42332\n0.17339\n0.010578\n-0.045297\n-0.60374\n-0.53065\n0.17698\n0.21941\n1.2004\n2.4479\n2.6645\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1648\nNaN\nNaN\n2.16\n1.5233\n1.2269\n1.324\n0.61995\n0.019278\n-0.25979\n-0.59274\n-0.4028\n-0.41141\n-0.018195\n0.57904\n0.12172\n-0.014533\n0.11765\n-0.13758\n-0.50935\n-0.59707\n-0.076992\n-0.0083545\n0.18409\n0.5159\n0.52611\n0.37329\n0.45393\n0.42571\n0.29031\n0.19802\n0.051085\n-1.4555\n-0.37355\n-0.36943\n-0.40346\n-0.47116\n-0.62291\n-0.29881\n-0.19902\n-0.23123\n-0.32703\n-0.20356\n-0.1845\n-0.2905\n-0.32344\n-0.18883\n0.14637\n0.01861\n-0.070221\n-0.35579\n-0.41485\n-0.53366\n-0.74628\n-0.12006\n0.38575\n1.7318\n2.5085\n2.9408\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8911\n5.1264\n3.5156\n2.7412\n1.4138\n1.2005\n1.2285\n0.94846\n0.21799\n0.061435\n-0.1807\n-0.43925\n-0.37141\n-0.3633\n-0.25108\n-0.26727\n-0.27006\n-0.30786\n-0.40305\n-0.75057\n-0.8691\n-0.099465\n0.24846\n0.215\n0.015745\n-0.17479\n0.085332\n0.43543\n0.33982\n-0.11877\n0.12935\n-0.15457\n-0.72407\n-0.5445\n-0.42754\n-0.44235\n-0.28358\n-0.39945\n-0.39746\n-0.42372\n-0.27623\n-0.2756\n-0.40506\n-0.2342\n-0.52789\n-0.80886\n-0.5106\n-0.24833\n-0.18959\n-0.42373\n-0.7132\n-0.70955\n-0.6025\n-0.61945\n-0.44165\n0.42269\n1.3249\n2.2497\n3.3355\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.3442\n4.7954\n3.3377\n2.5678\n1.1526\n0.84161\n0.84732\n0.78692\n0.48582\n0.10304\n-0.34299\n-0.37014\n-0.19811\n-0.099668\n-0.10659\n-0.72624\n-0.87709\n-0.53384\n-0.58819\n-1.0815\n-1.2487\n-0.41496\n0.057974\n0.014176\n-0.043401\n-0.28418\n-0.013184\n0.028448\n0.32666\n-0.2666\n-0.45598\n-0.2584\n-0.099346\n-0.42632\n-0.39647\n-0.4076\n-0.16659\n-0.3913\n-0.41976\n-0.47787\n-0.42934\n-0.37661\n-0.48416\n-0.54081\n-0.58253\n-0.82924\n-0.59416\n-0.53979\n-0.30972\n-0.48425\n-1.2167\n-1.4116\n-0.53522\n-0.38144\n-0.6041\n0.12228\n1.318\n1.8759\n1.6572\n1.2014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2896\n3.0311\n2.8631\n1.6102\n1.2265\n-0.056376\n0.36977\n0.5056\n0.12029\n-0.064083\n-0.26521\n-0.24515\n-0.12196\n-0.15435\n-0.29021\n-0.63095\n-1.3411\n-0.72881\n-0.45141\n-0.5896\n-0.99047\n-1.0041\n-0.34456\n-0.10849\n0.0013458\n-0.04351\n-0.1116\n-0.22197\n-0.50467\n-0.6244\n-0.30307\n0.043135\n0.063426\n-0.34488\n-0.4831\n-0.50794\n-0.26206\n-0.19472\n-0.21016\n-0.48614\n-0.48341\n-0.29628\n-0.34323\n-0.60665\n-0.61457\n-0.6095\n-0.54921\n-0.56937\n-0.33717\n-0.31551\n-0.64702\n-1.1016\n-0.94772\n-0.35038\n-0.62971\n0.041499\n1.3447\n1.52\n0.28182\n0.14649\n-0.21512\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0992\n2.5808\n1.8434\n1.8308\n1.2553\n0.034074\n0.41554\n0.40968\n-0.15899\n-0.55916\n-0.65305\n-0.14755\n-0.12522\n-0.28364\n-0.73987\n-0.64615\n-0.67804\n-0.5301\n-0.20077\n-0.24061\n-0.30773\n-0.553\n-0.54593\n-0.17965\n-0.25271\n-0.18547\n-0.35211\n-0.40587\n-0.6046\n-0.39472\n-0.18122\n0.10336\n0.00448\n-0.38347\n-0.60861\n-0.56736\n-0.4125\n-0.09685\n-0.094416\n-0.3173\n-0.27469\n-0.27496\n-0.33354\n-0.78226\n-0.56966\n-0.41377\n-0.66353\n-0.82337\n-0.59551\n-0.25172\n-0.73592\n-0.66892\n-1.2575\n-1.2047\n-0.75152\n0.10349\n0.6879\n0.89056\n0.42695\n-0.046901\n0.31075\nNaN\nNaN\n1.2291\n1.9735\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6957\n2.0706\n1.709\n1.7081\n0.44233\n-0.36465\n0.087748\n-0.24492\n-0.48361\n-0.99378\n-0.59909\n-0.025405\n-0.066981\n-0.32127\n-0.35931\n-0.43834\n-0.38588\n-0.32751\n-0.20284\n-0.31475\n-0.48361\n-0.52021\n-0.53457\n-0.36408\n-0.51031\n-0.63978\n-1.1555\n-1.2857\n-0.55716\n-0.26857\n-0.149\n0.22503\n0.1021\n-0.44834\n-0.49372\n-0.48928\n-0.56554\n-0.10955\n0.17355\n-0.050239\n-0.12364\n-0.30211\n-0.33804\n-0.67645\n-0.41571\n-0.42034\n-0.66948\n-0.81082\n-0.38556\n-0.3953\n-1.0098\n-1.281\n-1.1338\n-0.91296\n-0.55952\n-0.065855\n0.57109\n-0.0041293\n-0.5909\n-0.23381\n0.82652\n0.84687\n1.4801\n1.7784\n1.6504\n1.7495\n1.99\nNaN\nNaN\nNaN\nNaN\n1.9423\n2.1263\n1.4388\n1.2375\n1.329\n-0.18062\n-1.0279\n-0.52701\n-0.48441\n-0.59415\n-0.83652\n-0.47761\n0.16419\n0.15215\n0.23046\n-0.1079\n-0.48168\n-0.46768\n-0.27974\n-0.2569\n-0.21201\n-0.24296\n-0.5107\n-0.61027\n-0.94853\n-1.1017\n-0.87766\n-0.87166\n-0.87129\n-0.83306\n-0.71411\n-0.32765\n0.16976\n-0.083686\n-0.25126\n-0.37733\n-0.5347\n-0.48934\n-0.32199\n0.098597\n0.123\n-0.076707\n-0.20278\n-0.30829\n-0.49315\n-0.27604\n-0.32739\n-0.48125\n-0.66043\n-0.59618\n-1.0164\n-1.752\n-1.4534\n-1.0616\n-0.84659\n-0.082735\n0.17605\n0.44824\n1.2752\n0.48268\n0.605\n0.8309\nNaN\n0.6848\n1.2274\n0.66072\n1.6631\n2.5576\nNaN\nNaN\nNaN\n1.3433\n1.7424\n1.7072\n1.2832\n0.49195\n0.62903\n-0.0082946\n-1.0306\n-0.75098\n0.096722\n-0.26414\n-0.70436\n-0.37121\n0.17765\n0.32139\n0.38183\n-0.21981\n-0.24111\n-0.35356\n-0.35342\n-0.2951\n-0.060238\n-0.13466\n-0.4734\n-0.71216\n-0.89116\n-1.0334\n-0.78475\n-0.36339\n-0.4443\n-0.88392\n-0.70948\n-0.31937\n-0.03623\n-0.32001\n-0.45878\n-0.34229\n-0.34427\n-0.33418\n-0.19512\n0.061396\n0.062125\n-0.19992\n-0.27741\n-0.36938\n-0.43358\n-0.33424\n-0.27529\n-0.29001\n-0.48739\n-0.74557\n-1.1271\n-1.8197\n-1.2454\n-1.1067\n-0.74543\n-0.43074\n0.37466\n0.13226\n0.28305\n0.5326\n0.60335\nNaN\nNaN\n0.42504\n0.75617\n0.91525\n1.5967\n2.3016\nNaN\nNaN\nNaN\n1.1243\n1.2503\n1.5549\n0.99598\n-0.099595\n-0.30059\n-0.53003\n-0.74131\n-0.19886\n0.17933\n0.033181\n-0.51626\n-0.3139\n0.023737\n0.45741\n0.3901\n0.046398\n-0.070506\n-0.18057\n-0.23757\n-0.18403\n0.035481\n-0.1237\n-0.32976\n-0.76135\n-0.91215\n-0.78572\n-0.43517\n-0.06897\n-0.070567\n-0.26818\n-0.24725\n-0.17078\n-0.17157\n-0.0083005\n-0.28221\n-0.26841\n-0.1542\n0.0095456\n0.082149\n0.079392\n0.0062543\n-0.25081\n-0.4167\n-0.41218\n-0.32104\n-0.20982\n-0.020643\n0.19368\n-0.022506\n-0.6756\n-0.89784\n-1.7384\n-0.42797\n-0.49279\n-0.58799\n-0.11216\n0.29021\n-0.13008\n-0.26158\n0.14362\n0.30848\nNaN\nNaN\nNaN\n0.3448\n0.72612\n0.86898\n1.3061\nNaN\n1.4249\n1.2414\n0.70165\n0.8099\n1.383\n0.3612\n-0.15803\n-0.042674\n0.10657\n-0.049451\n-0.26147\n-0.31073\n-0.37597\n-0.34997\n-0.046186\n0.20302\n0.42035\n0.22771\n-0.1897\n-0.18036\n-0.10749\n-0.030474\n0.18259\n0.1858\n0.11609\n-0.24314\n-0.63757\n-0.76134\n-0.51937\n-0.21021\n0.07839\n0.15264\n0.066569\n-0.2492\n-0.41691\n-0.17588\n0.2429\n-0.1894\n-0.14633\n0.062544\n0.26438\n0.37233\n0.23671\n-0.018111\n0.028917\n-0.1546\n-0.19747\n-0.069643\n0.10228\n0.24342\n0.53907\n0.71024\n-0.20537\nNaN\nNaN\nNaN\n-0.52966\n-0.95945\n-0.26058\n0.075393\n-0.92038\n-0.59402\n-0.11735\n0.20573\nNaN\nNaN\nNaN\n0.2888\n0.59761\n0.31423\n0.51928\n0.80062\n1.0347\n0.72962\n0.48885\n0.53684\n0.25992\n-0.24701\n-0.2662\n0.1519\n0.28854\n0.094495\n-0.1365\n-0.19833\n-0.13538\n0.029521\n0.0912\n0.22911\n0.18427\n-0.067281\n-0.38478\n-0.18168\n0.15141\n0.20699\n0.24103\n0.25052\n0.18732\n-0.18803\n-0.39182\n-0.4446\n-0.43734\n-0.15457\n0.01042\n0.25711\n0.28028\n-0.11599\n-0.35715\n-0.19244\n0.11218\n0.13552\n0.3203\n0.40247\n0.28546\n0.32812\n0.36704\n0.067338\n0.24665\n0.12653\n0.081194\n0.26606\n0.37615\n0.69886\n1.2865\n0.9851\n0.071843\nNaN\nNaN\nNaN\n-0.7414\n-0.93395\n-1.3451\n-0.93196\n-0.89366\n-0.3514\n-0.17323\n-0.043934\nNaN\nNaN\nNaN\n-0.074443\n0.14447\n0.20037\n0.51499\n1.2387\n0.93356\n0.55361\n0.29099\n0.13422\n-0.088943\n-0.35079\n-0.37794\n0.083289\n0.35574\n0.2901\n0.050617\n-0.039858\n-0.23424\n-0.1421\n0.11241\n0.22431\n0.10065\n-0.074141\n-0.020086\n0.22294\n0.26384\n-0.015357\n0.18048\n0.23645\n0.27665\n-0.042409\n-0.20324\n-0.21547\n-0.17578\n0.070444\n0.12021\n0.37875\n0.43696\n0.05971\n-0.12168\n0.12073\n0.1576\n0.36733\n0.5346\n0.50427\n0.33484\n0.49582\n0.47847\n0.47358\n0.31561\n0.34917\n0.29393\n0.50264\n0.70735\n0.845\n0.90785\n0.69282\n0.60406\n-0.12879\nNaN\nNaN\n-0.99196\n-0.83677\n-0.91442\n-0.85175\n-0.58591\n-0.076986\n-0.037505\n-0.15871\n-0.010721\n-0.20095\n-0.20195\n-0.69193\n-0.30741\n0.041282\n0.42588\n1.0868\n0.89773\n0.77813\n0.51134\n0.26914\n0.27384\n-0.084846\n-0.091008\n0.18915\n0.3411\n0.41585\n0.066953\n-0.45465\n-0.84167\n-0.66206\n0.062721\n0.21012\n0.17875\n0.50681\n0.18763\n0.20412\n0.2816\n-0.079518\n0.089361\n0.16751\n0.33807\n0.11187\n-0.24881\n-0.090062\n0.1319\n0.29652\n0.25032\n0.28231\n0.52307\n0.039983\n0.13338\n0.50397\n0.30365\n0.21643\n0.38183\n0.4705\n0.53021\n0.6991\n0.73451\n0.70898\n0.46955\n0.4411\n0.4572\n0.45144\n0.63109\n0.74872\n0.84677\n0.63708\n0.56661\n-0.0071348\n-1.0362\n-1.3818\n-0.99387\n-0.79847\n-0.58407\n-0.48518\n-0.35837\n-0.032522\n-0.12809\n-0.16594\n-0.10112\n-0.035976\n-0.086289\n-0.27575\n-0.2919\n-0.12122\n0.18122\n0.71847\n0.77155\n0.6122\n0.83982\n0.55866\n0.34702\n0.21603\n0.43367\n0.45739\n0.50082\n0.42512\n0.36445\n-0.42664\n-1.0864\n-1.4158\n-0.41281\n-0.016061\n0.79208\n0.71056\n0.37228\n0.1958\n0.45169\n0.31302\n0.18732\n0.12146\n0.14519\n0.051766\n-0.12814\n0.26172\n0.34006\n0.30211\n0.25085\n0.60514\n0.67302\n0.15272\n0.25172\n0.23423\n0.15081\n0.10347\n0.44414\n0.6778\n0.76922\n1.0007\n1.1338\n0.99705\n0.96662\n0.60545\n0.37665\n0.38225\n0.52629\n0.55233\nNaN\nNaN\nNaN\nNaN\n-0.36602\n-0.92574\n-0.87411\n-0.43198\n-0.50244\n-0.69527\n-0.44422\n-0.079762\n-0.18467\n-0.038763\n0.17215\n-0.0045069\n-0.16114\n-0.22466\n-0.20984\n-0.31055\n-0.20249\n-0.040615\n0.22074\n0.40774\n0.87018\n0.68613\n0.67627\n0.58249\n0.70709\n0.66697\n0.15013\n0.048105\n0.47424\n0.16323\n-0.82361\n-0.76405\n-0.8074\n-0.3583\n0.078561\n0.2996\n0.090282\n0.16962\n0.56824\n0.13557\n0.014374\n0.14904\n-0.0092504\n0.032392\n0.24812\n0.52654\n0.26537\n0.056412\n0.29885\n0.85264\n0.73509\n0.41222\n0.13973\n0.0084624\n0.13264\n0.42521\n0.58722\n0.81986\n0.99606\n1.1033\n1.1999\n1.28\n1.4012\n1.1384\n0.84919\n0.60848\n0.61124\n0.71049\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.78472\n-0.6768\n-0.57945\n-0.51705\n-0.83141\n-0.62475\n-0.26459\n-0.14658\n-0.084295\n0.024662\n-0.22364\n-0.28682\n-0.35531\n-0.31141\n-0.025567\n-0.18535\n-0.16945\n0.18538\n0.51453\n1.1594\n1.1077\n0.98297\n0.90634\n0.94331\n0.72052\n-0.44072\n-0.58975\n-0.23405\n0.52146\n-0.48778\n-0.37984\n-0.36379\n-0.30821\n-0.35438\n-0.35089\n-0.37284\n-0.087152\n0.12141\n-0.14466\n-0.07152\n0.24879\n0.26883\n0.17647\n0.39784\n0.45788\n0.30789\n0.050462\n0.52431\n1.1253\n0.77583\n0.56237\n-0.01203\n-0.050652\n-0.057403\n0.69161\n0.59899\n0.88808\n1.2697\n1.3533\n1.8356\n1.8179\n1.8561\n1.5079\n1.1297\n0.78494\n0.61617\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.84933\n-0.72527\n-0.81101\n-0.81536\n-0.84205\n-0.72954\n-0.78129\n-0.37003\n-0.26359\n-0.15371\n-0.23443\n-0.30792\n-0.15595\n-0.010175\n0.0070341\n-0.006744\n0.14149\n0.25183\n0.90044\n1.2476\n0.97548\n1.0163\n1.1095\n0.94366\n-0.46012\n-1.1762\n-0.92126\n0.24642\n-0.26557\n-0.20987\n-0.29116\n-0.32264\n-0.16909\n-0.42949\n-0.40029\n-0.13632\n0.12275\n-0.27907\n-0.23343\n0.30287\n0.029348\n-0.020171\n0.07029\n0.30835\n0.27177\n0.38907\n0.58161\n0.85292\n0.64827\n0.60053\n-0.35757\n-0.4633\nNaN\n0.99821\n0.68208\n1.2539\n1.6629\n1.826\n2.3066\n2.6737\n2.502\n1.5691\n1.2567\n0.96243\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0186\n-0.61808\n-0.68338\n-0.61961\n-0.78777\n-0.73757\n-0.61133\n-0.50269\n-0.16771\n-0.041658\n-0.19497\n-0.15055\n-0.059478\n0.060634\n0.061121\n-0.014363\n-0.23436\n-0.22582\n0.3255\n0.72406\n0.62719\n0.86485\n0.72695\n0.13884\n-0.52546\n-1.1648\n-1.1939\n-0.31418\n-0.30828\n-0.17251\n-0.39374\n-0.14165\n0.20702\n0.32499\n0.26232\n0.13467\n0.10844\n-0.30433\n-0.28369\n0.27824\n-0.38237\n-0.16969\n-0.50011\n-0.27448\n0.44422\nNaN\nNaN\n0.46608\n0.50929\n0.74483\nNaN\nNaN\nNaN\n0.43828\n-0.068646\n-0.085662\n-0.15926\n-0.12701\n-0.2774\n-0.55488\n-0.37515\n0.011793\n0.05979\n-0.30304\n-0.22288\n-0.40895\n-0.14552\n0.29858\n0.28476\n0.37481\n0.13574\n0.6726\n0.63319\n0.982\n0.99848\n1.187\n0.46667\n0.81166\n0.38774\n-0.24464\n-0.9047\n-0.55032\n-0.44087\n-1.206\n-1.0295\n-0.47994\n-0.53343\n-0.045947\n-0.50272\n-0.49028\n-0.23303\n-0.52806\n-0.67896\n-0.31248\n0.15522\n0.46551\n0.71136\n0.75758\n0.41959\n0.60052\n0.51655\n0.30018\n0.14786\n0.15602\n0.53176\n0.38328\n-0.15624\n0.52135\n0.10341\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35343\n-0.28405\n-0.27156\n0.56556\n0.66608\n-0.83224\n-0.678\n-0.52334\n0.064457\n0.16871\n0.1718\n0.0051641\n-0.45762\n0.2881\n0.55338\n-0.081933\n-0.71695\n-0.45046\n0.1589\n0.41254\n1.6409\n0.8698\n0.88218\n1.3574\n0.98907\n0.71139\n0.27104\n-0.33225\n0.026127\n-0.33616\n-0.26665\n-0.41966\n0.076011\n-0.14712\n-0.11333\n-0.76995\n-0.71481\n-0.21468\n-0.37216\n0.25135\n0.2928\n0.23154\n0.50892\n0.89463\n0.79828\n0.53909\n0.55783\n0.33997\n0.17967\n0.12028\n0.13226\n0.35323\n0.34384\n0.52463\n1.4146\n0.4558\n-0.32815\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56539\n-0.40998\n-0.25476\n-0.31624\n-0.96665\n-0.6998\n-0.92519\n-0.49098\n-0.23197\n0.61019\n0.55711\n0.29194\n0.077257\n0.64688\n1.2505\n0.088347\n-0.15948\n0.1226\n0.11392\n1.0072\n1.0396\n0.88467\n1.3825\n1.2035\n1.2239\n1.2767\n0.86797\n0.20078\n0.38452\n-0.32946\n0.088955\n-0.31428\n0.62062\n0.45908\n-0.89358\n-1.0903\n-0.53083\n-0.29827\n-0.013953\n0.065646\n0.20671\n0.41746\n0.29713\n0.79717\n0.52839\n0.38509\n0.39681\n0.32293\n0.20283\n0.34847\n0.51583\n0.28167\n0.19395\n0.93582\n1.0687\n0.51677\n0.25251\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20905\n-0.3733\n-0.30256\n-0.22163\n-0.19597\n-0.12108\n0.10819\n-0.0067889\n-0.18179\n-0.024375\n0.24215\n-0.34063\n1.0028\n1.081\n0.75664\n-0.0059477\n0.043039\n0.096792\n0.22382\n0.94076\n1.5982\n0.7605\n1.163\n0.92798\n1.4019\n1.4546\n0.88334\n0.48142\n0.13116\n-0.70142\n-0.12517\n0.12231\n0.75478\n0.6025\n-0.50466\n-0.81393\n-0.82765\n-0.34518\n0.10205\n-0.049437\n-0.037375\n0.19727\n-0.016544\n-0.029987\n0.18179\n0.22002\n0.15548\n0.43863\n0.64105\n1.1743\n1.2963\n0.89665\n1.1446\n1.3572\n1.0796\n0.72398\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10353\n-0.1882\n-0.13841\n-0.13249\n0.096684\n-0.053423\n0.14371\n0.25229\n-0.121\n-0.56857\n-0.35621\n-0.63506\n-0.58858\n0.048156\n-0.62178\n-0.63577\n-0.38714\n0.01862\n-0.21508\n0.52673\n2.0155\nNaN\nNaN\n-0.51382\n1.0874\n1.3137\n1.0248\n0.35967\n0.38324\n0.36904\n-0.16728\n0.2476\n0.42555\n0.50554\n0.2663\n-0.35523\n-0.9945\n-0.47096\n0.0099362\n-0.044641\n0.027071\n0.3247\n0.029169\n0.012732\n0.033161\n0.14783\n0.50399\n0.95616\n0.88376\n0.83046\n1.2198\n1.0758\n0.80477\n1.4225\n0.9706\n0.95617\n1.927\nNaN\nNaN\n-2.6244\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.6976e-05\n-0.24967\n-0.3694\n-0.099341\n0.26071\n-0.052091\n0.20265\n0.4124\n0.16437\n-0.073001\n-0.025835\n0.36462\n0.25249\n-0.41378\n-0.87216\n-0.22477\n-0.59478\n-0.51952\n-0.34229\nNaN\nNaN\nNaN\nNaN\n0.042028\n0.66946\n1.3125\n1.2004\n0.90157\n0.68415\n0.80986\n0.4312\n0.11771\n-0.13234\n-0.1086\n0.22338\n-0.41947\n-0.96005\n-0.3573\n-0.0015741\n0.11048\n0.16285\n0.033116\n-0.0021062\n-0.20707\n-0.94884\n-0.2948\n0.50556\n1.0018\n0.82865\n0.9435\n0.9864\n0.92661\n0.79059\n0.7459\n0.52114\n0.09185\n0.64983\nNaN\nNaN\n-3.7454\n-2.79\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27934\n-0.44756\n-0.28744\n0.25372\n0.49317\n0.065609\n0.51645\n0.53288\n0.53577\n0.35681\n0.23414\n0.13425\n0.12062\n-0.30767\n-0.5093\n-0.60617\n-0.67644\n-0.1568\n-0.25348\nNaN\nNaN\nNaN\nNaN\n-0.044701\n0.33095\n0.94518\n1.2467\n1.5273\n0.84738\n0.75647\n0.11844\n0.0090313\n-0.77743\n-0.45908\n-0.3697\n-0.62407\n-0.82016\n-0.47695\n-0.18925\n0.12946\n0.32083\n0.0378\n-0.34672\n-0.75084\n-1.8887\n-0.47526\n0.46055\n0.95593\n1.1071\n1.1355\n0.79141\n0.95551\n1.139\n0.94701\n0.47711\n-0.18216\n0.38359\nNaN\nNaN\n-1.7236\n-2.8249\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.11792\n-0.021563\n-0.083087\n0.42818\n0.95357\n0.026759\n0.26431\n0.60394\n0.68819\n0.50166\n0.29909\n0.31157\n0.01158\n0.14907\n-0.20066\n-0.30003\n-0.012819\n0.19988\n-0.052776\n-0.26261\n-0.12595\n0.16789\n0.12636\n0.46038\n0.602\n0.97357\n1.2142\n1.369\n0.82651\n0.68681\n0.44642\n-0.5789\n-1.3503\n-0.55158\n-0.31811\n-0.31708\n-0.36083\n-0.44079\n-0.40915\n-0.50071\n0.086172\n-0.07003\n-0.42106\n-0.87835\n-2.3945\n-0.41763\n0.69705\n0.82596\n0.7393\n1.1344\n0.89969\n1.0711\n1.2755\n1.0627\n0.99209\n0.44363\n0.065014\n0.85198\n0.65622\n-0.25627\n-1.8427\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.020918\n-0.32683\n-0.42016\n-0.12306\n0.47887\n-0.050008\n-0.0053794\n0.65915\n0.78897\n0.69029\n0.44703\n0.4676\n0.51023\n0.31265\n0.28709\n0.42016\n0.34357\n0.53365\n0.11963\n0.15495\n0.19585\n0.17337\n0.52555\n0.78902\n0.76681\n1.0987\n1.1217\n0.77357\n0.61472\n0.43133\n0.46029\n0.30861\n-0.63722\n-0.60024\n-0.32\n-0.28724\n-0.30754\n-0.44102\n-0.44334\n-0.4279\n-0.39607\n-0.21581\n-0.62977\n-1.224\n-2.0998\n0.4472\n0.97333\n1.0969\n1.1418\n1.2944\n1.2219\n1.4882\n1.4827\n1.8346\n1.7825\n0.67654\n0.056529\n0.6238\n1.3426\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76917\n-0.30244\n-0.11042\n-0.31926\n0.52392\n-0.096906\n-0.084216\n0.2888\n0.59755\n0.70222\n0.55857\n0.54727\n0.64961\n0.35914\n0.57373\n0.71217\n0.55323\n0.89831\n0.78518\n0.96742\n0.76011\n0.64303\n0.97047\n0.71927\n0.45091\n0.7962\n1.2115\n1.0795\n0.62438\n0.25805\n0.27437\n-0.34423\n-0.95944\nNaN\nNaN\n-0.067374\n-0.53078\n-0.47162\n-0.60861\n-0.44972\n-0.25185\n-0.050448\n-0.85483\n-1.2069\n-0.32218\n1.0475\n0.8364\n1.1227\n1.2277\n1.5975\n1.4912\n1.8879\n1.8602\n1.9249\n1.7387\n1.3527\n0.58656\n0.16886\n1.4084\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.90568\n-0.63607\n-0.30505\n-0.31196\n0.064357\n-0.17589\n-0.079382\n-0.042004\n0.43759\n0.79313\n0.6476\n0.38323\n0.73519\n0.57803\n0.90016\n1.0562\n0.87833\n0.91661\n0.92051\n1.0135\n0.58064\n0.64465\n0.54953\n0.14818\n0.44578\n1.5841\n1.5057\n1.2495\n0.84018\n0.77033\n0.30484\n-0.40445\nNaN\nNaN\nNaN\n-0.11771\n-0.39226\n-0.071621\n-0.79437\n-0.53063\n0.16284\n0.30318\n-0.7473\n-0.72275\n0.082826\n0.78766\n0.85385\n1.1555\n1.5391\n1.7406\n1.5775\n1.9681\n1.8184\n2.072\nNaN\nNaN\n0.23035\n0.087566\n1.205\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1043\n-1.0643\n-0.68471\n-0.31944\n-0.26299\n-0.1019\n0.1857\n0.39825\n0.60424\n0.7131\n0.44944\n0.53814\n0.82433\n0.61592\n0.93947\n0.95313\n1.0907\n0.90612\n0.6593\n0.5843\n0.11872\n0.28453\n0.3256\n0.0447\n0.47442\n1.2461\n1.4819\n1.5272\n0.98974\n1.0274\n0.48012\nNaN\nNaN\nNaN\nNaN\n-0.12696\n0.02938\n-0.16128\n-0.75493\n-0.31367\n0.38467\n-0.092192\n-0.30289\n0.045929\n0.43933\n0.81053\n1.1528\n1.7063\n2.0725\n2.1248\n1.8517\n2.0387\n1.5761\nNaN\nNaN\nNaN\n0.30664\n0.046967\n0.25638\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.2803\n-0.44553\n-0.24935\n-0.24294\n-0.12471\n0.13746\n0.30535\n0.29332\n0.53041\n0.52912\n0.54552\n0.68115\n0.55712\n0.77205\n1.0159\n1.1153\n1.2275\n0.89431\n0.47221\n0.37938\n0.19669\n0.2736\n-0.10048\n0.30717\n0.47217\n0.48475\n1.5617\n1.2588\n0.77573\n0.9572\n0.70737\nNaN\nNaN\nNaN\nNaN\n-0.45776\n-0.28527\n-0.045294\n-0.29919\n0.16562\n0.25854\n-0.72829\n-1.0434\n0.34478\n0.77646\n1.0548\n1.4625\n2.0164\n2.2773\n2.3083\n2.1676\n1.8897\n1.2328\nNaN\nNaN\nNaN\n-0.92404\n-1.085\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15134\n-0.046249\n-0.16662\n0.11988\n0.18351\n0.21961\n0.30315\n0.27386\n0.3654\n0.31972\n0.32984\n0.66724\n1.0786\n1.3065\n1.2075\n1.1388\n0.81359\n0.81962\n0.33606\n0.18671\n0.41835\n-0.28559\n-0.75681\n-0.4163\n-0.56207\n0.34674\n1.0691\n0.86432\n1.1396\n1.2696\n0.96577\nNaN\nNaN\nNaN\nNaN\n-0.59272\n-0.75756\n0.016301\n-0.091335\n0.40889\n0.35253\n-0.18146\n-0.040798\n0.94866\n1.3678\n1.7154\n1.7866\n2.261\n2.3441\n2.3176\n2.0867\n1.4216\n1.0594\nNaN\nNaN\nNaN\nNaN\n-0.64993\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.28312\n-0.20082\n0.19521\n0.55313\n0.37084\n0.19106\n0.34048\n0.22622\n0.18168\n0.23177\n0.17471\n0.60661\n1.1901\n1.3475\n1.3967\n1.0536\n1.1396\n1.14\n0.7322\n0.29556\n0.069291\n-0.97324\n-1.8115\n-1.0506\n-0.86126\n0.40995\n0.65914\n0.953\n1.4273\n1.3508\n1.4031\nNaN\n-0.22915\n-0.41165\n0.091586\n-0.06616\n-0.26872\n0.75814\n0.79247\n1.4515\n1.2558\n0.16246\n0.2125\n2.1978\nNaN\nNaN\nNaN\n2.4804\n2.8055\n2.5131\n1.8011\n1.9352\n1.4831\n0.29994\nNaN\nNaN\nNaN\n-1.3536\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.021025\n0.011552\n0.29971\n0.77986\n0.64765\n0.48921\n0.59112\n0.31249\n0.18744\n0.54988\n0.44469\n0.70545\n1.0068\n1.0516\n1.2892\n0.70367\n0.66904\n0.99407\n1.0725\n0.51095\n0.034437\n-0.30549\n-2.5367\n-2.0528\n-0.95546\n0.06584\n0.75438\n1.8012\n1.7487\n1.2854\n0.32945\n-0.92955\n0.1358\n0.18699\n0.13046\n0.28377\n1.0955\n1.4796\n1.7306\nNaN\nNaN\nNaN\n0.1486\nNaN\nNaN\nNaN\nNaN\n2.3293\n2.8307\n2.0435\n1.4204\n1.4894\n1.4486\n0.43818\n0.12496\n-0.71378\n-1.644\n-1.6293\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17629\n0.016262\n0.30105\n0.87411\n0.52227\n0.60806\n0.45851\n0.27857\n0.35097\n0.27574\n0.35602\n0.60675\n0.93902\n1.0136\n1.1881\n0.49817\n0.30158\n0.58961\n0.82944\n0.60147\n0.033251\n-0.47692\n-1.1898\n-1.6358\n-0.40315\n0.48448\n1.092\n1.4745\n1.2029\n1.0426\n-0.12294\n-0.038654\n0.90366\n0.99947\n1.031\n1.3109\n1.6661\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34568\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.694\nNaN\n0.92097\n1.2501\n0.40047\n0.17575\n-0.16775\n-1.3985\n-1.5185\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.31365\n-0.087008\n0.38764\n0.88526\n0.51737\n0.36687\n0.28828\n0.29199\n0.39946\n0.18468\n0.34567\n0.51086\n0.81596\n0.82387\n0.89335\n0.49488\n0.77126\n0.51604\n0.47561\n0.13468\n-0.37146\n-1.0659\n-1.467\n-1.6617\n-0.19304\n0.59147\n1.2906\n1.1012\n1.0661\n0.68925\n1.0052\n1.0364\n0.87403\n1.1606\n1.6185\n1.9417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19842\n0.17119\n-0.49495\n-1.331\n-1.1934\n-1.4907\n-1.3983\n-0.41258\n0.042617\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10566\n0.096727\n0.56136\n0.78438\n0.52287\n-0.013186\n0.19998\n0.27858\n0.13844\n0.18429\n0.092852\n0.21711\n0.49998\n0.75807\n0.84981\n0.51182\n0.22655\n0.0616\n0.13891\n-0.10618\n-0.4742\n-1.2585\n-1.5085\n-1.1891\n-0.099756\n0.59108\n0.84714\n0.90737\n1.3986\n1.1126\n0.86119\n1.4209\n1.5997\n1.443\n1.8027\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.5904\n-0.11543\n-0.40062\n-0.6741\n-0.16235\n-0.18155\n-0.25351\n0.40257\n0.39788\n-0.32794\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.074313\n0.2418\n0.21501\n0.10332\n0.42248\n0.29113\n0.59508\n0.21003\n-0.22217\n-0.11608\n-0.0040899\n0.094933\n0.34123\n0.70536\n0.67541\n0.19394\n-0.39286\n-0.7226\n-0.43353\n-0.24043\n-0.56406\n-1.4156\n-1.1677\n-0.57112\n0.54109\n1.1202\n0.79041\n0.99017\n1.6078\n1.3353\n0.78659\n1.5235\n1.4982\n1.3406\n2.0351\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3789\n-0.84887\n-0.70759\n-0.18897\n0.58585\n0.82813\n0.11586\n0.01729\n0.30743\n-0.38049\n-0.11607\n0.54002\n0.90107\nNaN\nNaN\nNaN\nNaN\nNaN\n0.12375\n0.36422\n0.09312\n-0.086906\n0.67896\n0.64201\n-0.13773\n-0.36854\n-0.46592\n0.027178\n0.19284\n0.21415\n-0.0093957\n0.55303\n0.34992\n0.059222\n-0.34511\n-0.40652\n-0.38215\n-0.75625\n-0.86391\n-1.6817\n-1.2836\n-0.024357\n0.67251\n1.5862\n1.7464\n1.7061\n1.6952\n2.1069\n2.9472\n3.534\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.94679\n-0.49488\n-0.22476\n-0.31995\n-0.64336\n-0.18842\n0.8729\n1.0778\n0.32862\n0.57408\n0.73674\n0.76495\n0.46284\n0.37611\n0.73961\n0.98313\n0.95309\n1.1691\n0.54889\n0.18307\n0.064298\n0.12671\n-0.096988\n-0.074268\n0.31387\n0.30909\n-0.35434\n-0.31863\n-0.4681\n-0.041735\n0.0015631\n-0.19154\n0.14368\n0.35201\n0.20682\n0.33998\n0.1188\n-0.21461\n-0.17794\n-0.50853\n-0.19811\n-1.3396\n-1.5281\n0.15946\n1.1583\n2.5431\n2.2088\n2.2721\n2.6169\n2.9513\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.07953\n-0.15709\n0.018953\n0.52783\n0.31465\n0.11553\n-0.653\n0.16568\n0.68389\n0.37255\n0.86868\n0.99952\n0.60776\n0.34357\n0.088176\nNaN\nNaN\nNaN\nNaN\n0.42649\n0.42785\n0.078094\n0.010123\n-0.094954\n-0.39109\n-0.33589\n-0.34875\n-0.53132\n-0.40774\n-0.12585\n-0.20535\n-0.34786\n-0.272\n0.10812\n0.19915\n0.21265\n0.50264\n0.17947\n-0.48291\n-1.2086\n-0.22913\n-0.24023\n-1.896\n-1.5019\n0.0214\n1.6636\n2.0746\n1.9849\n2.4098\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.89756\n0.59627\n0.3839\n0.0065049\n0.42491\n0.81279\n0.67646\n0.61201\n0.080896\n0.016581\n-0.070617\n0.38241\n0.86306\n1.2509\n0.68157\n0.70785\n0.74709\nNaN\nNaN\nNaN\nNaN\n-0.022668\n0.6449\n0.063443\n-0.21713\n-0.79582\n-0.58795\n-0.28552\n-0.37467\n-0.65197\n-0.21168\n0.28377\n0.010207\n-0.20888\n0.11863\n0.23561\n0.20111\n0.28675\n1.1019\n0.74526\n-0.29285\n-1.2857\n-1.8164\n-1.084\n-1.1014\n-0.57446\n0.51691\n1.1624\n1.7262\n1.5082\n1.5367\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.55416\n0.90066\n0.73338\n0.52504\n0.19865\n0.33366\n0.55085\n0.74001\n1.1066\n0.67221\n0.05932\n-0.25312\n0.097116\n0.40088\n1.1171\n0.27964\n0.68893\n0.88318\nNaN\nNaN\n-0.77877\n-0.1396\n0.093928\n0.085421\n-0.44853\n-0.53578\n-0.61943\n-0.42188\n-0.42162\n-0.019125\n-0.30955\n0.07894\n0.28801\n0.17146\n-0.12436\n0.030671\n0.32841\n-0.022697\n-0.023615\n0.85757\n0.93726\n0.058842\n-1.052\n-1.5699\n-0.75657\n-0.48887\n-0.26577\n0.82443\n1.4154\n1.7711\n1.4184\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21107\n0.1547\n1.4897\n0.79139\n0.80975\n0.51648\n0.33764\n0.28169\n0.72836\n1.4415\n1.247\n0.38221\n0.26591\n0.40163\n0.048027\n-0.24241\n0.18684\n0.785\n0.64868\n-0.014295\n-0.47451\n0.10773\n0.42625\n0.52605\n0.4093\n-0.73674\n-0.53929\n-0.29887\n-0.32012\n-0.5899\n0.10141\n-0.043682\n-0.054336\n0.1143\n-0.0077131\n-0.15334\n0.23194\n0.33983\n0.39971\n0.68315\n0.81041\n1.3361\n0.13395\n-0.58125\n-0.62834\n-0.15156\n-0.24292\n-0.15005\n0.47216\n1.6364\n2.191\n1.9575\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.44815\n-0.039463\n1.3153\n0.7229\n0.90968\n0.80087\n0.12839\n0.01925\n0.47111\n1.4424\n1.4187\n1.0085\n0.6413\n0.72603\n0.25649\n0.031835\n0.71514\n1.1262\n0.46747\n0.46938\n0.30191\n0.55665\n0.77793\n1.1649\n0.63608\n-0.63335\n-0.31792\n-0.0979\n-0.16651\n-0.42819\n-0.14118\n-0.094122\n-0.1978\n-0.014752\n-0.20961\n-0.25952\n-0.12377\n0.24722\n0.59392\n0.85272\n0.86901\n0.84882\n0.04734\n0.019432\n-0.3831\n-0.26124\n-0.081081\n0.41429\n0.79398\n1.7462\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31609\n0.21641\n0.46113\n1.2366\n-0.45577\n0.32539\n0.75735\n0.62416\n1.2873\n1.4731\n1.4824\n1.4482\n1.5963\n1.1809\n0.77471\n0.365\n-0.14779\n1.0685\n0.90179\n0.97202\n0.82015\n0.7614\n0.89946\n1.0671\n0.9329\n0.55655\n-0.47187\n-0.076319\n-0.17275\n-0.27905\n-0.39819\n-0.34072\n-0.3813\n-0.095247\n-0.033632\n-0.28069\n-0.22319\n-0.069463\n0.11036\n0.27673\n0.77123\n0.82651\n0.44377\n-0.14731\n-0.32401\n-0.20421\n-0.15012\n0.016759\n0.25701\n0.75668\n1.7447\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9801\nNaN\nNaN\nNaN\nNaN\n0.74707\n0.647\n0.52048\n0.24815\n0.68306\n-0.47263\n0.36683\n0.8549\n1.8773\n1.6077\n1.5593\n1.2939\n0.00071306\n1.4093\n0.78375\n0.46539\n-0.42175\n0.089842\n1.231\n1.1553\n1.3708\n1.0258\n1.0004\n1.0819\n1.1351\n0.66769\n-0.17003\n-0.23465\n-0.0884\n-0.40603\n-0.38361\n-0.15545\n-0.27302\n-0.36136\n-0.25545\n-0.34357\n-0.1781\n-0.14612\n-0.079363\n-0.087482\n0.27681\n0.61867\n0.7195\n0.49147\n0.095198\n-0.39877\n-0.22345\n0.23804\n0.29862\n-0.096946\n0.81418\n2.3199\n3.3521\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7039\n1.2656\n1.2607\n0.88247\n0.27122\n0.014134\n0.3309\n0.28524\n0.11761\n0.90538\n1.8259\n1.1274\n1.1759\n0.56103\n-0.78235\n-0.36731\n0.21005\n0.17523\n-0.17631\n0.42622\n0.99133\n1.1586\n0.9741\n0.89526\n0.50823\n0.94892\n0.78088\n0.092286\n-1.5244\n-0.17074\n-0.27278\n-0.2334\n-0.37902\n-0.4168\n-0.31112\n-0.0067667\n-0.23019\n-0.5717\n-0.27078\n-0.23282\n-0.10949\n-0.20967\n0.10011\n0.50997\n0.55073\n0.22142\n0.028638\n-0.068205\n-0.80552\n-0.75789\n0.14954\n0.12089\n1.3464\n2.9474\n3.1707\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8231\nNaN\nNaN\n2.8925\n2.0242\n1.6461\n1.8\n0.9691\n0.18387\n-0.19877\n-0.62633\n-0.39961\n-0.40719\n0.11029\n0.82918\n0.24904\n0.064678\n0.13799\n-0.22801\n-0.6267\n-0.72662\n-0.16045\n-0.0020863\n0.21948\n0.61406\n0.61286\n0.41831\n0.54684\n0.45685\n0.20434\n0.14879\n-0.018099\n-2.0549\n-0.33746\n-0.38205\n-0.46163\n-0.55405\n-0.80061\n-0.34611\n-0.20058\n-0.264\n-0.3737\n-0.25702\n-0.23683\n-0.3481\n-0.44508\n-0.27927\n0.22708\n0.088886\n0.0076523\n-0.39171\n-0.50505\n-0.71561\n-1.061\n-0.32198\n0.29763\n1.9974\n3.0052\n3.4983\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9236\n6.9698\n4.7668\n3.7228\n1.818\n1.5492\n1.8103\n1.3723\n0.4023\n0.19407\n-0.10096\n-0.40709\n-0.34734\n-0.32001\n-0.19397\n-0.2168\n-0.27661\n-0.35296\n-0.51508\n-0.96572\n-1.063\n-0.17254\n0.29588\n0.24551\n0.0042126\n-0.22929\n0.14278\n0.57\n0.39888\n-0.23915\n0.11997\n-0.24621\n-1.0331\n-0.54246\n-0.45014\n-0.45834\n-0.2847\n-0.45056\n-0.4424\n-0.47814\n-0.31902\n-0.32406\n-0.51898\n-0.27747\n-0.65419\n-1.0338\n-0.67347\n-0.28171\n-0.16243\n-0.43551\n-0.87814\n-0.92417\n-0.75344\n-0.76989\n-0.5941\n0.37569\n1.5297\n2.7281\n4.08\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.6603\n6.5466\n4.4909\n3.4117\n1.4611\n1.3159\n1.2523\n1.2002\n0.76932\n0.2807\n-0.26535\n-0.306\n-0.080043\n0.023137\n-0.010812\n-0.84145\n-1.0364\n-0.62509\n-0.72129\n-1.3997\n-1.6688\n-0.55398\n0.040698\n-0.01085\n-0.080451\n-0.39879\n0.027499\n0.077322\n0.44873\n-0.35744\n-0.58211\n-0.33539\n-0.15757\n-0.43005\n-0.37163\n-0.37025\n-0.11174\n-0.39455\n-0.44199\n-0.54301\n-0.50848\n-0.4386\n-0.59927\n-0.65908\n-0.70813\n-1.0595\n-0.76895\n-0.64718\n-0.35466\n-0.53492\n-1.4474\n-1.6508\n-0.63222\n-0.52506\n-0.83684\n0.020962\n1.5521\n2.279\n1.865\n1.3887\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.1427\n3.9553\n3.7622\n2.0904\n1.5811\n0.14239\n0.65425\n0.85855\n0.33188\n0.064724\n-0.18542\n-0.15566\n0.00072354\n-0.06262\n-0.28124\n-0.75868\n-1.6499\n-0.88656\n-0.53966\n-0.79283\n-1.3769\n-1.3471\n-0.47026\n-0.18562\n-0.036071\n-0.11359\n-0.14215\n-0.26988\n-0.61149\n-0.83011\n-0.41472\n0.042652\n0.01701\n-0.28466\n-0.4453\n-0.48026\n-0.19394\n-0.081414\n-0.16034\n-0.54274\n-0.55084\n-0.30948\n-0.39035\n-0.72161\n-0.74738\n-0.7662\n-0.68204\n-0.66149\n-0.39056\n-0.30255\n-0.68604\n-1.2552\n-1.0776\n-0.47551\n-0.86375\n-0.084752\n1.6077\n1.7478\n0.30765\n0.2477\n-0.33971\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.4772\n3.4007\n2.4022\n2.4165\n1.6851\n0.28013\n0.76277\n0.73994\n0.054014\n-0.60983\n-0.74046\n-0.021098\n-0.020578\n-0.24957\n-0.87291\n-0.81272\n-0.86913\n-0.67162\n-0.25354\n-0.36074\n-0.48327\n-0.86368\n-0.76953\n-0.27306\n-0.34131\n-0.28239\n-0.45115\n-0.5287\n-0.80252\n-0.6142\n-0.34053\n-0.0021071\n-0.1116\n-0.31275\n-0.61908\n-0.56515\n-0.36345\n0.036637\n-0.033042\n-0.30881\n-0.27383\n-0.27065\n-0.42835\n-0.97002\n-0.69074\n-0.50752\n-0.80373\n-1.0086\n-0.70891\n-0.209\n-0.80582\n-0.74838\n-1.4651\n-1.3791\n-0.90867\n0.069402\n0.75024\n0.96446\n0.40259\n-0.0063497\n0.4127\nNaN\nNaN\n1.6536\n2.4466\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5518\n2.7124\n2.246\n2.2613\n0.89887\n-0.094486\n0.31189\n-0.13316\n-0.32635\n-1.1976\n-0.6733\n0.1163\n0.032023\n-0.33665\n-0.39787\n-0.52111\n-0.47076\n-0.3976\n-0.24188\n-0.41893\n-0.70109\n-0.79444\n-0.7465\n-0.53624\n-0.72598\n-0.87248\n-1.6337\n-1.8253\n-0.81662\n-0.43732\n-0.30285\n0.20094\n0.04748\n-0.38095\n-0.42803\n-0.47652\n-0.56539\n0.045047\n0.34023\n0.05498\n-0.042846\n-0.28486\n-0.40233\n-0.80875\n-0.47194\n-0.48223\n-0.77608\n-0.95648\n-0.3921\n-0.39077\n-1.2124\n-1.6115\n-1.445\n-1.1738\n-0.73355\n-0.15046\n0.6442\n-0.108\n-0.84825\n-0.3253\n1.0934\n1.1443\n1.9332\n2.3703\n2.0064\n2.1575\n2.4667\nNaN\nNaN\nNaN\nNaN\n2.7097\n2.8627\n1.9769\n1.6996\n1.8868\n0.013868\n-1.0813\n-0.52074\n-0.41\n-0.52404\n-0.98261\n-0.52277\n0.28202\n0.23811\n0.35595\n-0.11541\n-0.57391\n-0.5445\n-0.31884\n-0.32505\n-0.28956\n-0.36578\n-0.71809\n-0.81573\n-1.329\n-1.4569\n-1.1515\n-1.1501\n-1.1516\n-1.1653\n-0.94519\n-0.45691\n0.23069\n-0.083473\n-0.086197\n-0.25509\n-0.46797\n-0.4323\n-0.21372\n0.25584\n0.30087\n0.051127\n-0.089636\n-0.31385\n-0.54589\n-0.27392\n-0.33533\n-0.52638\n-0.75059\n-0.6913\n-1.1593\n-1.9726\n-1.7881\n-1.3533\n-1.1289\n-0.17769\n0.1792\n0.50784\n1.4759\n0.65881\n0.80687\n1.1003\nNaN\n1.0339\n1.6959\n0.96275\n2.0417\n3.1975\nNaN\nNaN\nNaN\n1.9307\n2.3879\n2.2561\n1.7171\n0.64299\n0.8151\n0.13786\n-1.1405\n-0.82541\n0.24885\n-0.25636\n-0.90771\n-0.47733\n0.2616\n0.44883\n0.55741\n-0.25327\n-0.30005\n-0.41572\n-0.42113\n-0.37526\n-0.08258\n-0.20776\n-0.6712\n-0.95084\n-1.1621\n-1.3199\n-1.0215\n-0.49259\n-0.58524\n-1.1808\n-0.90762\n-0.37975\n0.0062819\n-0.3786\n-0.35252\n-0.20746\n-0.23091\n-0.19959\n-0.048674\n0.22977\n0.24167\n-0.077533\n-0.1628\n-0.32737\n-0.43434\n-0.33344\n-0.27376\n-0.29237\n-0.52841\n-0.89089\n-1.3195\n-2.0781\n-1.5121\n-1.4012\n-0.98916\n-0.57059\n0.40333\n0.048153\n0.20032\n0.67845\n0.8557\nNaN\nNaN\n0.77487\n1.0958\n1.3088\n2.2267\n3.1206\nNaN\nNaN\nNaN\n1.6062\n1.7388\n2.0442\n1.3395\n-0.060517\n-0.2737\n-0.55859\n-0.813\n-0.17279\n0.28663\n0.10491\n-0.65496\n-0.35473\n0.071618\n0.60959\n0.58753\n0.15743\n-0.044588\n-0.17835\n-0.27823\n-0.25243\n0.036289\n-0.18585\n-0.46905\n-1.0292\n-1.2109\n-1.0067\n-0.55219\n-0.08133\n-0.083474\n-0.3894\n-0.34086\n-0.18658\n-0.23503\n-0.042848\n-0.06267\n-0.087034\n0.0014652\n0.21603\n0.30587\n0.25666\n0.17038\n-0.12484\n-0.35533\n-0.36265\n-0.27219\n-0.15994\n0.11523\n0.39598\n0.060801\n-0.79895\n-1.0982\n-1.9996\n-0.44951\n-0.4064\n-0.59648\n-0.13797\n0.24642\n-0.37458\n-0.49171\n0.206\n0.49965\nNaN\nNaN\nNaN\n0.61775\n1.0481\n1.161\n1.7611\nNaN\n1.8921\n1.6932\n1.0222\n1.119\n1.8338\n0.41174\n-0.22001\n0.03948\n0.2062\n0.018332\n-0.28162\n-0.36736\n-0.43218\n-0.41866\n-0.035611\n0.27956\n0.559\n0.34944\n-0.13653\n-0.16289\n-0.11179\n-0.029648\n0.22779\n0.23764\n0.14665\n-0.34423\n-0.86294\n-1.0096\n-0.66265\n-0.26883\n0.11986\n0.20868\n0.052029\n-0.33929\n-0.52887\n-0.26114\n0.30285\n0.033027\n0.089666\n0.33788\n0.57609\n0.68455\n0.4614\n0.15995\n0.24923\n-0.0080068\n-0.082782\n0.032337\n0.24006\n0.43083\n0.76239\n0.90649\n-0.20925\nNaN\nNaN\nNaN\n-0.48094\n-1.1098\n-0.32862\n0.027619\n-1.2988\n-0.79748\n-0.074976\n0.31076\nNaN\nNaN\nNaN\n0.51693\n0.85745\n0.451\n0.69344\n1.0898\n1.408\n0.97953\n0.665\n0.72994\n0.39417\n-0.3179\n-0.36159\n0.21894\n0.42492\n0.16931\n-0.14345\n-0.26925\n-0.21379\n-0.00012102\n0.13269\n0.31208\n0.2583\n-0.07595\n-0.44391\n-0.1834\n0.22919\n0.3009\n0.31429\n0.31348\n0.22264\n-0.24732\n-0.51458\n-0.57981\n-0.55019\n-0.1929\n0.028085\n0.34587\n0.35687\n-0.12512\n-0.4134\n-0.23847\n0.10631\n0.45036\n0.73223\n0.83572\n0.62906\n0.65102\n0.65557\n0.32752\n0.53448\n0.33654\n0.27675\n0.5103\n0.61583\n1.029\n1.7574\n1.2472\n0.1318\nNaN\nNaN\nNaN\n-0.81517\n-1.1193\n-1.6685\n-1.1645\n-1.1901\n-0.39894\n-0.11115\n0.056165\nNaN\nNaN\nNaN\n-0.0087797\n0.29681\n0.36854\n0.74196\n1.6343\n1.2457\n0.73571\n0.38462\n0.185\n-0.06705\n-0.48701\n-0.51561\n0.13835\n0.50685\n0.40534\n0.12506\n-0.025761\n-0.31201\n-0.18788\n0.15413\n0.31743\n0.14002\n-0.12612\n-0.024304\n0.32151\n0.39681\n0.03568\n0.20251\n0.28232\n0.36803\n-0.019526\n-0.25807\n-0.29802\n-0.23265\n0.10458\n0.14833\n0.45713\n0.57048\n0.10269\n-0.14837\n0.16647\n0.15975\n0.74725\n0.95176\n0.91205\n0.71802\n0.86613\n0.84367\n0.82877\n0.59594\n0.59855\n0.5756\n0.84472\n1.0854\n1.2501\n1.326\n1.04\n0.90844\n-0.099065\nNaN\nNaN\n-1.1599\n-0.97268\n-1.0864\n-0.99683\n-0.64742\n6.8523e-05\n0.085637\n-0.10228\n0.081741\n-0.16099\n-0.19637\n-0.8368\n-0.2849\n0.1686\n0.64515\n1.4288\n1.1612\n0.9851\n0.65132\n0.32009\n0.31145\n-0.14216\n-0.16072\n0.29373\n0.4353\n0.51858\n0.090014\n-0.56872\n-1.0059\n-0.77229\n0.08029\n0.29814\n0.26645\n0.64889\n0.23408\n0.2755\n0.40316\n-0.071933\n0.082002\n0.18839\n0.47289\n0.18353\n-0.30336\n-0.11769\n0.16623\n0.39033\n0.30888\n0.35569\n0.6733\n0.047977\n0.20121\n0.65954\n0.3636\n0.55633\n0.74822\n0.85442\n0.96784\n1.193\n1.2192\n1.1535\n0.80399\n0.78603\n0.82702\n0.80385\n1.028\n1.1518\n1.2448\n0.87284\n0.79065\n0.16362\n-1.2419\n-1.6644\n-1.1163\n-0.87594\n-0.62271\n-0.50961\n-0.35137\n0.058709\n-0.063955\n-0.13431\n-0.04008\n0.030223\n-0.045367\n-0.28507\n-0.25455\n-0.0049477\n0.35132\n0.98062\n0.97496\n0.7844\n1.0694\n0.72905\n0.4714\n0.30676\n0.54289\n0.62668\n0.67823\n0.49123\n0.40069\n-0.5262\n-1.3042\n-1.6952\n-0.54415\n0.010456\n1.0849\n0.9315\n0.47039\n0.2751\n0.59774\n0.39718\n0.21227\n0.15371\n0.22166\n0.099746\n-0.13917\n0.36648\n0.46218\n0.39658\n0.3437\n0.80489\n0.88664\n0.21935\n0.34764\n0.28635\n0.17997\n0.3939\n0.80134\n1.0864\n1.2513\n1.5756\n1.7485\n1.5438\n1.4591\n0.9992\n0.68131\n0.72399\n0.91393\n0.92718\nNaN\nNaN\nNaN\nNaN\n-0.3137\n-1.0409\n-0.93706\n-0.4153\n-0.49499\n-0.73315\n-0.44506\n-0.025119\n-0.19129\n0.033264\n0.3273\n0.075102\n-0.1444\n-0.24202\n-0.26538\n-0.30745\n-0.19277\n-0.065735\n0.27228\n0.48721\n1.0904\n0.91242\n0.90496\n0.77969\n0.9142\n0.88984\n0.23733\n0.028173\n0.53669\n0.17265\n-0.98037\n-0.90398\n-0.98193\n-0.40823\n0.17231\n0.43033\n0.15041\n0.23519\n0.72852\n0.21796\n0.067528\n0.24144\n0.016493\n0.042653\n0.34592\n0.75044\n0.38458\n0.067755\n0.38115\n1.1438\n0.98511\n0.55521\n0.15973\n-0.036882\n0.10088\n0.80892\n0.98023\n1.2808\n1.539\n1.6753\n1.7773\n1.9044\n1.9903\n1.6415\n1.2637\n1.0095\n1.0323\n1.1016\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.90679\n-0.71069\n-0.57036\n-0.48898\n-0.90835\n-0.67397\n-0.26156\n-0.12713\n0.014938\n0.15056\n-0.21349\n-0.30819\n-0.42411\n-0.39045\n0.0078535\n-0.20107\n-0.19311\n0.24645\n0.62767\n1.4349\n1.4273\n1.2996\n1.1973\n1.2451\n0.97046\n-0.53672\n-0.78299\n-0.35809\n0.53984\n-0.56547\n-0.43982\n-0.42175\n-0.3622\n-0.42265\n-0.41611\n-0.5168\n-0.132\n0.17522\n-0.1338\n-0.060256\n0.40925\n0.41881\n0.23701\n0.54717\n0.63138\n0.41673\n0.072775\n0.72237\n1.5179\n1.0275\n0.72943\n-0.06404\n-0.12875\n-0.13655\n1.1171\n1.0189\n1.3521\n1.8715\n1.9539\n2.5756\n2.573\n2.5489\n2.0557\n1.5912\n1.2167\n1.0652\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.86951\n-0.7669\n-0.85607\n-0.89218\n-0.94863\n-0.81376\n-0.88088\n-0.33685\n-0.20548\n-0.089577\n-0.24711\n-0.32992\n-0.12244\n0.032552\n-0.00036313\n-0.0048851\n0.20445\n0.29668\n1.1108\n1.5701\n1.2995\n1.3267\n1.457\n1.2888\n-0.55712\n-1.5179\n-1.2105\n0.22257\n-0.31963\n-0.241\n-0.35489\n-0.3892\n-0.15178\n-0.54604\n-0.52198\n-0.18607\n0.15749\n-0.27111\n-0.21554\n0.51479\n0.13666\n-0.0081559\n0.12601\n0.40819\n0.34311\n0.54698\n0.80095\n1.1073\n0.84707\n0.77826\n-0.49117\n-0.61603\nNaN\n1.4827\n1.1238\n1.8073\n2.3356\n2.4999\n3.1344\n3.6653\n3.3424\n2.0922\n1.7205\n1.401\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1997\n-0.66478\n-0.70862\n-0.6613\n-0.87845\n-0.8239\n-0.66634\n-0.52398\n-0.058314\n0.08873\n-0.17166\n-0.11388\n0.022495\n0.071821\n0.043517\n-0.065993\n-0.31117\n-0.31968\n0.42813\n0.9125\n0.80792\n1.1092\n0.96953\n0.24267\n-0.64402\n-1.4485\n-1.5282\n-0.42905\n-0.35872\n-0.18448\n-0.45901\n-0.12026\n0.30562\n0.46983\n0.40824\n0.19384\n0.1484\n-0.32024\n-0.29724\n0.46012\n-0.42877\n-0.2108\n-0.61305\n-0.34827\n0.55519\nNaN\nNaN\n0.60777\n0.71505\n0.98952\nNaN\nNaN\nNaN\n0.63552\n-0.0029846\n-0.02943\n-0.092332\n-0.065678\n-0.30292\n-0.68011\n-0.35688\n0.13383\n0.15731\n-0.28445\n-0.1592\n-0.41056\n-0.088662\n0.38596\n0.34967\n0.47445\n0.23702\n0.93967\n0.82233\n1.2475\n1.3229\n1.49\n0.50661\n0.95051\n0.476\n-0.31578\n-1.1169\n-0.66458\n-0.56068\n-1.3847\n-1.1726\n-0.44975\n-0.50215\n0.053595\n-0.5283\n-0.51848\n-0.24042\n-0.61592\n-0.79412\n-0.35181\n0.241\n0.63173\n0.91638\n0.97379\n0.56217\n0.77949\n0.66688\n0.40278\n0.19979\n0.1849\n0.69251\n0.51721\n-0.15543\n0.65726\n0.18598\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32574\n-0.23287\n-0.20656\n0.77078\n0.88907\n-0.91761\n-0.88244\n-0.54733\n0.18883\n0.29068\n0.33636\n0.16049\n-0.45454\n0.36028\n0.65322\n-0.12589\n-0.77309\n-0.45665\n0.28045\n0.60388\n2.1667\n1.175\n1.0953\n1.62\n1.1111\n0.81229\n0.30468\n-0.46114\n0.013371\n-0.41799\n-0.25387\n-0.41214\n0.26555\n-0.051091\n-0.0053599\n-0.85557\n-0.79335\n-0.21347\n-0.40248\n0.38962\n0.38547\n0.33767\n0.68848\n1.203\n1.0449\n0.69737\n0.69849\n0.42987\n0.22375\n0.15337\n0.1783\n0.4604\n0.45382\n0.67006\n1.7445\n0.62136\n-0.34261\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56311\n-0.36884\n-0.19713\n-0.25819\n-0.92779\n-0.58841\n-0.89221\n-0.50927\n-0.19837\n0.80737\n0.82029\n0.54702\n0.28292\n0.80752\n1.5291\n0.10793\n-0.099246\n0.25083\n0.19619\n1.2787\n1.3545\n1.0864\n1.6833\n1.434\n1.3814\n1.4937\n1.0152\n0.20804\n0.4561\n-0.40635\n0.098902\n-0.3305\n0.976\n0.78209\n-0.99826\n-1.2459\n-0.55749\n-0.29645\n0.051647\n0.11063\n0.26479\n0.55934\n0.42528\n1.0338\n0.68378\n0.51071\n0.51936\n0.41301\n0.26176\n0.44099\n0.67235\n0.3493\n0.23605\n1.1965\n1.3749\n0.63403\n0.32397\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.13493\n-0.32106\n-0.25817\n-0.15841\n-0.12468\n-0.024198\n0.27539\n0.11808\n-0.078359\n0.1123\n0.4053\n-0.22077\n1.5519\n1.3714\n1.0144\n0.11012\n0.1675\n0.22133\n0.35446\n1.1941\n2.0228\n0.86488\n1.3997\n1.1582\n1.6653\n1.713\n0.99187\n0.56604\n0.10855\n-1.055\n-0.12032\n0.17755\n1.132\n0.93556\n-0.45848\n-0.84735\n-0.92536\n-0.36457\n0.19244\n-0.038198\n-0.032752\n0.28078\n0.051013\n-0.0090157\n0.24901\n0.32032\n0.24071\n0.58617\n0.85194\n1.5074\n1.6511\n1.1357\n1.445\n1.7305\n1.3215\n0.84622\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.019175\n-0.11271\n-0.068594\n-0.06131\n0.23675\n0.0505\n0.31351\n0.45327\n-0.017658\n-0.58353\n-0.30218\n-0.65346\n-0.63744\n0.17258\n-0.62222\n-0.67129\n-0.3417\n0.1845\n-0.10405\n0.64538\n2.5\nNaN\nNaN\n-0.56963\n1.3172\n1.5237\n1.0995\n0.31733\n0.38381\n0.34456\n-0.14152\n0.45614\n0.70071\n0.7427\n0.54283\n-0.36927\n-1.1954\n-0.55053\n0.049083\n-0.028379\n0.060562\n0.43819\n0.09761\n0.09657\n0.089303\n0.24206\n0.69124\n1.2653\n1.1819\n1.124\n1.5606\n1.3675\n1.0295\n1.7844\n1.2086\n1.1794\n2.3338\nNaN\nNaN\n-3.1242\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11886\n-0.20128\n-0.38795\n-0.027914\n0.43967\n0.060385\n0.36889\n0.64687\n0.31884\n0.0064453\n0.059693\n0.54338\n0.4314\n-0.38782\n-0.94835\n-0.12736\n-0.5784\n-0.50212\n-0.46751\nNaN\nNaN\nNaN\nNaN\n0.025996\n0.70244\n1.4579\n1.3199\n1.0071\n0.75448\n0.9232\n0.52646\n0.30268\n-0.077286\n-0.07001\n0.4598\n-0.36856\n-1.1238\n-0.39371\n0.028657\n0.17578\n0.22609\n0.066644\n0.05399\n-0.21545\n-1.1328\n-0.28391\n0.71286\n1.2928\n1.0878\n1.2459\n1.2637\n1.1776\n0.99663\n0.9504\n0.67753\n0.2237\n0.89508\nNaN\nNaN\n-4.3238\n-3.4063\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24775\n-0.45711\n-0.27461\n0.41598\n0.70917\n0.19647\n0.73809\n0.73609\n0.73795\n0.47456\n0.3407\n0.23742\n0.20613\n-0.27474\n-0.53603\n-0.63762\n-0.7249\n-0.15599\n-0.21861\nNaN\nNaN\nNaN\nNaN\n-0.17599\n0.28281\n1.0152\n1.3641\n1.7668\n0.93797\n0.84873\n0.14304\n0.13908\n-0.86409\n-0.47954\n-0.3274\n-0.6573\n-0.94948\n-0.53551\n-0.17311\n0.23461\n0.41809\n0.10068\n-0.35248\n-0.87089\n-2.2866\n-0.50801\n0.66077\n1.2514\n1.42\n1.4477\n1.0214\n1.2262\n1.4852\n1.2325\n0.63348\n-0.16454\n0.54479\nNaN\nNaN\n-2.0062\n-3.4019\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.05328\n0.064812\n-0.030673\n0.59882\n1.2572\n0.094854\n0.37983\n0.78873\n0.88408\n0.61812\n0.36252\n0.40912\n0.025594\n0.19564\n-0.24516\n-0.35186\n0.02791\n0.23794\n-0.044651\n-0.33017\n-0.19696\n0.14244\n0.064613\n0.46655\n0.60173\n1.0516\n1.3388\n1.5157\n0.87947\n0.72466\n0.47533\n-0.7451\n-1.6639\n-0.63779\n-0.35837\n-0.34858\n-0.4303\n-0.45851\n-0.38664\n-0.51596\n0.16233\n-0.008954\n-0.42054\n-0.97094\n-2.8756\n-0.43792\n0.96331\n1.1121\n0.99224\n1.4459\n1.2008\n1.4057\n1.646\n1.3567\n1.2535\n0.61909\n0.11241\n1.1189\n0.82076\n-0.25682\n-2.1396\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.097297\n-0.35131\n-0.50473\n-0.10865\n0.62854\n-0.040912\n0.013348\n0.83556\n1.0085\n0.86262\n0.54213\n0.5579\n0.62335\n0.36253\n0.33728\n0.52294\n0.40275\n0.61644\n0.097814\n0.12794\n0.20084\n0.17093\n0.56595\n0.88951\n0.84059\n1.2014\n1.1995\n0.75398\n0.61636\n0.41901\n0.47457\n0.32397\n-0.88787\n-0.7642\n-0.41941\n-0.38349\n-0.35864\n-0.43987\n-0.42203\n-0.42653\n-0.41712\n-0.18851\n-0.67882\n-1.3892\n-2.4549\n0.63856\n1.3028\n1.4628\n1.498\n1.6604\n1.5908\n1.8908\n1.8754\n2.284\n2.2368\n0.91044\n0.15815\n0.82873\n1.7408\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.87532\n-0.31698\n-0.10064\n-0.3514\n0.73497\n-0.076565\n-0.071287\n0.41893\n0.78375\n0.86083\n0.66913\n0.6479\n0.76815\n0.38965\n0.66109\n0.86092\n0.64296\n1.0748\n0.94162\n1.149\n0.87429\n0.67869\n1.0605\n0.78173\n0.47678\n0.82847\n1.2851\n1.1257\n0.6375\n0.20846\n0.2015\n-0.48719\n-1.1987\nNaN\nNaN\n-0.11885\n-0.67459\n-0.52847\n-0.63467\n-0.44758\n-0.22099\n0.042993\n-0.9461\n-1.4031\n-0.32724\n1.3957\n1.1343\n1.4904\n1.6049\n2.0661\n1.9342\n2.3936\n2.3425\n2.3851\n2.1727\n1.7724\n0.91551\n0.36551\n1.8616\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0251\n-0.72142\n-0.32675\n-0.33139\n0.14557\n-0.18501\n-0.073618\n-0.035117\n0.56493\n0.98283\n0.7557\n0.38012\n0.81504\n0.63312\n1.0501\n1.2597\n1.0614\n1.0949\n1.0788\n1.1553\n0.58855\n0.63156\n0.48552\n0.048013\n0.48057\n1.84\n1.6351\n1.2965\n0.89171\n0.79145\n0.21269\n-0.6205\nNaN\nNaN\nNaN\n-0.17381\n-0.47288\n-0.029395\n-0.86439\n-0.5408\n0.35139\n0.58534\n-0.78252\n-0.75955\n0.22828\n1.1024\n1.1671\n1.5146\n1.9776\n2.3008\n2.0589\n2.4999\n2.2816\n2.5418\nNaN\nNaN\n0.45889\n0.24078\n1.6017\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2592\n-1.2722\n-0.79089\n-0.34498\n-0.28486\n-0.11208\n0.24462\n0.51497\n0.74342\n0.85529\n0.48688\n0.58285\n0.93084\n0.66213\n1.0889\n1.1343\n1.3212\n1.0462\n0.67965\n0.59465\n0.033888\n0.17242\n0.15307\n-0.13244\n0.45307\n1.4075\n1.6145\n1.634\n0.98949\n1.0566\n0.44447\nNaN\nNaN\nNaN\nNaN\n-0.16724\n0.041559\n-0.14299\n-0.72743\n-0.23026\n0.60595\n-0.025133\n-0.2188\n0.25557\n0.72921\n1.1688\n1.574\n2.2376\n2.6258\n2.7307\n2.3885\n2.5731\n1.9751\nNaN\nNaN\nNaN\n0.53152\n0.23203\n0.35306\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24344\n-0.4859\n-0.22815\n-0.21923\n-0.092241\n0.20849\n0.40555\n0.34962\n0.66535\n0.64088\n0.60016\n0.75104\n0.56569\n0.85544\n1.1813\n1.2918\n1.4267\n0.98672\n0.41766\n0.32885\n0.10278\n0.15592\n-0.29368\n0.27484\n0.43777\n0.42174\n1.6983\n1.3182\n0.73101\n1.0115\n0.74441\nNaN\nNaN\nNaN\nNaN\n-0.5552\n-0.32549\n-0.010727\n-0.17701\n0.42775\n0.53911\n-0.65991\n-1.0239\n0.64687\n1.1371\n1.4709\n1.9621\n2.6027\n2.8769\n2.9311\n2.7799\n2.3813\n1.5732\nNaN\nNaN\nNaN\n-0.94529\n-1.1874\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31027\n0.033574\n-0.1231\n0.23836\n0.28265\n0.32114\n0.41059\n0.37515\n0.45866\n0.35609\n0.34022\n0.73501\n1.2861\n1.5857\n1.4339\n1.2976\n0.84823\n0.87965\n0.27233\n0.085718\n0.35647\n-0.54625\n-0.97954\n-0.56504\n-0.82797\n0.21222\n1.0601\n0.82691\n1.1957\n1.3751\n1.0171\nNaN\nNaN\nNaN\nNaN\n-0.78901\n-0.94367\n0.048495\n0.057161\n0.67687\n0.68906\n0.08252\n0.24891\n1.3754\n1.8369\n2.2828\n2.3498\n2.9081\n2.9806\n2.9349\n2.6954\n1.8563\n1.3867\nNaN\nNaN\nNaN\nNaN\n-0.62886\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.47253\n-0.15156\n0.33354\n0.75883\n0.50945\n0.28298\n0.4755\n0.31281\n0.20719\n0.27445\n0.15438\n0.67805\n1.4324\n1.6282\n1.6753\n1.1856\n1.3135\n1.2815\n0.76706\n0.24345\n-0.11898\n-1.3128\n-2.2224\n-1.3254\n-1.1714\n0.28385\n0.56043\n0.9326\n1.5218\n1.4158\n1.416\nNaN\n-0.28419\n-0.46792\n0.060488\n-0.2877\n-0.35304\n1.0364\n1.1754\n2.042\n1.8409\n0.47685\n0.57598\n2.945\nNaN\nNaN\nNaN\n3.2092\n3.6022\n3.2258\n2.3459\n2.4781\n1.8889\n0.4492\nNaN\nNaN\nNaN\n-1.5529\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.12906\n0.13259\n0.46535\n1.0305\n0.86108\n0.66813\n0.79131\n0.41964\n0.22838\n0.68894\n0.50485\n0.82048\n1.1832\n1.2236\n1.5471\n0.77795\n0.7094\n1.0743\n1.151\n0.48409\n-0.11287\n-0.47472\n-3.1249\n-2.4803\n-1.2707\n-0.12451\n0.66817\n1.9189\n1.8729\n1.3622\n0.095245\n-1.2295\n0.10265\n0.26721\n0.16956\n0.27555\n1.4015\n1.9712\n2.312\nNaN\nNaN\nNaN\n0.43586\nNaN\nNaN\nNaN\nNaN\n3.0437\n3.6727\n2.6706\n1.8434\n1.9379\n1.8431\n0.69494\n0.30521\n-0.72527\n-1.8768\n-1.8719\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14052\n0.12237\n0.44508\n1.1317\n0.6957\n0.81735\n0.60931\n0.36968\n0.44955\n0.28368\n0.39262\n0.71007\n1.0932\n1.1853\n1.4372\n0.52458\n0.22619\n0.54005\n0.84584\n0.58116\n-0.084221\n-0.63699\n-1.518\n-1.9903\n-0.61196\n0.403\n1.0646\n1.5903\n1.2485\n1.1371\n-0.38508\n-0.34139\n0.86832\n1.2759\n1.2587\n1.5142\n2.1672\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56219\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2207\nNaN\n1.1172\n1.6122\n0.62102\n0.34966\n-0.085776\n-1.556\n-1.6849\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30573\n-0.035347\n0.52085\n1.1369\n0.67901\n0.49522\n0.37669\n0.35345\n0.49446\n0.15789\n0.38089\n0.555\n0.94891\n0.94349\n1.0416\n0.4667\n0.78793\n0.48962\n0.43205\n0.021097\n-0.5711\n-1.337\n-1.8699\n-2.1138\n-0.35406\n0.51334\n1.332\n1.2222\n1.1504\n0.70654\n0.96494\n1.0496\n0.86192\n1.2671\n1.8256\n2.2753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14769\n0.29128\n-0.53008\n-1.538\n-1.3894\n-1.7802\n-1.6218\n-0.43139\n0.17271\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.042168\n0.1655\n0.72985\n1.0131\n0.67964\n0.010553\n0.26151\n0.32805\n0.14433\n0.17325\n0.061713\n0.15387\n0.52027\n0.84942\n0.94738\n0.50756\n0.16303\n-0.0029887\n0.053795\n-0.23026\n-0.64225\n-1.5637\n-1.8865\n-1.5839\n-0.26779\n0.53274\n0.85618\n0.95955\n1.4887\n1.1936\n0.74558\n1.5468\n1.8538\n1.6324\n2.0463\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65582\n-0.081026\n-0.40809\n-0.74709\n-0.12619\n-0.1424\n-0.23691\n0.52399\n0.4905\n-0.3942\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16457\n0.3494\n0.29633\n0.15848\n0.55051\n0.3719\n0.74382\n0.24518\n-0.30943\n-0.20041\n-0.082594\n-0.0050919\n0.32288\n0.81412\n0.78495\n0.14183\n-0.58383\n-0.93471\n-0.61929\n-0.3606\n-0.80129\n-1.7522\n-1.4985\n-0.83331\n0.51004\n1.1907\n0.78313\n0.99662\n1.7063\n1.359\n0.63296\n1.6754\n1.7267\n1.5072\n2.2809\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.575\n-0.95607\n-0.80209\n-0.1517\n0.81931\n1.1033\n0.21541\n0.047828\n0.3933\n-0.46785\n-0.17021\n0.67244\n1.1285\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24536\n0.51065\n0.1504\n-0.096843\n0.85953\n0.82615\n-0.17446\n-0.48971\n-0.64419\n-0.047068\n0.17676\n0.19317\n-0.082426\n0.64719\n0.40503\n-0.0017894\n-0.53035\n-0.56885\n-0.46564\n-1.0477\n-1.2382\n-2.1037\n-1.6695\n-0.21153\n0.66149\n1.7521\n1.9314\n1.918\n1.8467\n2.3625\n3.4139\n4.2262\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1024\n-0.47299\n-0.17696\n-0.31331\n-0.73682\n-0.15719\n1.1949\n1.444\n0.46887\n0.7046\n0.94555\n1.0297\n0.59223\n0.42578\n0.9773\n1.3076\n1.2608\n1.5838\n0.7205\n0.27643\n0.11998\n0.19218\n-0.069501\n-0.069738\n0.3947\n0.39837\n-0.44735\n-0.43866\n-0.65881\n-0.10984\n-0.048768\n-0.31629\n0.093404\n0.37841\n0.2446\n0.363\n0.10375\n-0.26872\n-0.12672\n-0.72288\n-0.38951\n-1.7698\n-1.9664\n0.017367\n1.2364\n2.8702\n2.4815\n2.6132\n2.9302\n3.393\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21337\n-0.053344\n0.14559\n0.70955\n0.44889\n0.21177\n-0.73051\n0.26373\n0.87564\n0.48093\n1.059\n1.2537\n0.79882\n0.4276\n0.087958\nNaN\nNaN\nNaN\nNaN\n0.50833\n0.50528\n0.13485\n0.063992\n-0.051199\n-0.45211\n-0.3869\n-0.43412\n-0.69726\n-0.53389\n-0.20838\n-0.30606\n-0.50041\n-0.41562\n0.066472\n0.17721\n0.18212\n0.53376\n0.10322\n-0.6323\n-1.4787\n-0.32729\n-0.40956\n-2.5289\n-2.0045\n-0.14096\n1.8403\n2.3297\n2.2345\n2.8425\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2623\n0.84604\n0.5881\n0.093775\n0.57683\n1.0666\n0.89821\n0.80638\n0.1429\n0.049681\n-0.074758\n0.50457\n1.0776\n1.5418\n0.88783\n0.85382\n0.95544\nNaN\nNaN\nNaN\nNaN\n-0.02649\n0.71598\n0.16509\n-0.21058\n-0.9377\n-0.68643\n-0.33064\n-0.48209\n-0.8479\n-0.29023\n0.31803\n-0.041421\n-0.32616\n0.082596\n0.20447\n0.17482\n0.2711\n1.2849\n0.82145\n-0.45405\n-1.667\n-2.3261\n-1.4192\n-1.4473\n-0.82346\n0.44897\n1.2079\n1.9279\n1.6946\n1.7903\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.83621\n1.2557\n1.023\n0.74161\n0.29159\n0.43519\n0.70152\n0.92736\n1.3667\n0.84943\n0.087701\n-0.34353\n0.1138\n0.53641\n1.3835\n0.36355\n0.84114\n1.079\nNaN\nNaN\n-1.147\n-0.2588\n0.096201\n0.019276\n-0.47667\n-0.59387\n-0.70312\n-0.48674\n-0.52387\n-0.045658\n-0.41373\n0.063063\n0.33327\n0.17037\n-0.22116\n-0.044952\n0.30268\n-0.12328\n-0.14597\n0.96017\n1.0278\n0.016757\n-1.3882\n-2.0475\n-1.0209\n-0.70356\n-0.43389\n0.86634\n1.5765\n1.805\n1.3808\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.096379\n0.31951\n1.9435\n1.0635\n1.0438\n0.63917\n0.41821\n0.37611\n0.86191\n1.6837\n1.5053\n0.45289\n0.29368\n0.50365\n0.086869\n-0.31365\n0.21783\n0.97457\n0.80292\n-0.044175\n-0.56282\n0.077135\n0.47882\n0.61074\n0.48497\n-0.82135\n-0.5998\n-0.31235\n-0.37706\n-0.74549\n0.11382\n-0.099581\n-0.10315\n0.10215\n-0.060518\n-0.28886\n0.19386\n0.32945\n0.4003\n0.78831\n0.90222\n1.4865\n0.093601\n-0.79899\n-0.86395\n-0.31326\n-0.42366\n-0.29125\n0.45329\n1.809\n2.433\n2.0409\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41058\n0.052457\n1.7411\n1.0082\n1.1902\n0.99046\n0.15141\n0.066091\n0.58094\n1.6956\n1.6629\n1.1795\n0.74014\n0.88292\n0.31589\n0.024446\n0.87545\n1.3923\n0.5624\n0.61821\n0.40762\n0.65977\n0.90295\n1.382\n0.76325\n-0.69699\n-0.31189\n-0.054368\n-0.19496\n-0.54079\n-0.20139\n-0.16852\n-0.30357\n-0.059126\n-0.34654\n-0.39966\n-0.2157\n0.22415\n0.63861\n0.99343\n0.97494\n0.92235\n-0.029075\n-0.051034\n-0.53941\n-0.43011\n-0.22601\n0.36926\n0.81374\n1.9395\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.34159\n0.41773\n0.74505\n1.6904\n-0.21039\n0.59357\n0.91924\n0.74927\n1.5339\n1.7756\n1.7906\n1.7221\n1.8935\n1.3921\n0.89069\n0.4349\n-0.26629\n1.2826\n1.0565\n1.1428\n1.0819\n0.96706\n1.0665\n1.249\n1.0467\n0.62059\n-0.4657\n-0.014887\n-0.13713\n-0.33781\n-0.49847\n-0.44307\n-0.5043\n-0.14177\n-0.066517\n-0.39154\n-0.31909\n-0.12392\n0.079998\n0.23\n0.88454\n0.95507\n0.47609\n-0.26649\n-0.48527\n-0.3322\n-0.30054\n-0.10308\n0.1902\n0.7948\n1.9546\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.412\nNaN\nNaN\nNaN\nNaN\n0.89237\n0.92304\n0.7568\n0.43474\n0.9528\n-0.23619\n0.52444\n1.0052\n2.2418\n1.9216\n1.8729\n1.5814\n0.041852\n1.6766\n0.93425\n0.49806\n-0.56439\n0.034646\n1.4338\n1.3568\n1.6146\n1.2607\n1.1901\n1.2678\n1.3388\n0.74211\n-0.31329\n-0.19462\n-0.043655\n-0.45321\n-0.4528\n-0.17628\n-0.33805\n-0.45244\n-0.30569\n-0.42081\n-0.23342\n-0.21659\n-0.13086\n-0.13773\n0.25037\n0.74199\n0.85832\n0.56171\n0.060482\n-0.5398\n-0.37488\n0.20019\n0.27835\n-0.22654\n0.84789\n2.6278\n3.7926\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1215\n1.5661\n1.7042\n1.2028\n0.40955\n0.09669\n0.51105\n0.3978\n0.16402\n1.0858\n2.1902\n1.3754\n1.4165\n0.67345\n-0.99644\n-0.46726\n0.16304\n0.047337\n-0.27063\n0.46971\n1.1293\n1.3135\n1.0834\n1.0609\n0.53921\n1.0643\n0.89989\n0.045854\n-1.9722\n-0.094033\n-0.27052\n-0.24837\n-0.42377\n-0.49376\n-0.36171\n0.046404\n-0.23408\n-0.68254\n-0.36056\n-0.30629\n-0.11408\n-0.251\n0.088274\n0.63575\n0.67461\n0.26813\n0.03225\n-0.11404\n-1.0368\n-1.0227\n0.078146\n-0.02024\n1.4285\n3.3622\n3.5781\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.426\nNaN\nNaN\n3.5795\n2.4945\n2.0231\n2.2348\n1.2774\n0.32739\n-0.15735\n-0.68064\n-0.42348\n-0.4231\n0.20417\n1.032\n0.33612\n0.1075\n0.1252\n-0.35322\n-0.77735\n-0.89537\n-0.31046\n-0.041453\n0.22741\n0.69561\n0.68157\n0.44843\n0.63084\n0.47934\n0.13089\n0.1129\n-0.06811\n-2.6113\n-0.28392\n-0.38435\n-0.51675\n-0.63658\n-0.97377\n-0.38597\n-0.19447\n-0.29766\n-0.42366\n-0.30565\n-0.27538\n-0.39135\n-0.55807\n-0.36732\n0.29979\n0.13911\n0.07379\n-0.43986\n-0.61779\n-0.92352\n-1.4009\n-0.55097\n0.17339\n2.2008\n3.4214\n3.9552\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.0235\n8.8255\n6.0133\n4.7047\n2.2024\n1.8645\n2.3551\n1.7637\n0.55633\n0.29525\n-0.04805\n-0.40341\n-0.35292\n-0.30459\n-0.16387\n-0.18641\n-0.30854\n-0.42822\n-0.65282\n-1.2018\n-1.2871\n-0.28563\n0.31288\n0.26035\n-0.012666\n-0.29099\n0.19355\n0.69764\n0.44794\n-0.35314\n0.11678\n-0.31062\n-1.3133\n-0.52395\n-0.46062\n-0.46676\n-0.28256\n-0.49751\n-0.47951\n-0.52572\n-0.3578\n-0.3745\n-0.62712\n-0.3082\n-0.77059\n-1.2551\n-0.83564\n-0.32233\n-0.15139\n-0.46171\n-1.0484\n-1.1439\n-0.92261\n-0.93947\n-0.75662\n0.2974\n1.6813\n3.1287\n4.7213\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.961\n8.3382\n5.6508\n4.2375\n1.755\n1.7637\n1.6253\n1.5803\n1.0128\n0.41689\n-0.22321\n-0.27019\n0.010827\n0.1249\n0.06063\n-0.9738\n-1.2136\n-0.74439\n-0.8738\n-1.732\n-2.1163\n-0.71644\n-0.0020014\n-0.0495\n-0.12376\n-0.52352\n0.057211\n0.10441\n0.55691\n-0.44403\n-0.70477\n-0.40444\n-0.21172\n-0.40876\n-0.33218\n-0.32639\n-0.053191\n-0.38539\n-0.45989\n-0.60532\n-0.58058\n-0.49722\n-0.70862\n-0.76632\n-0.82583\n-1.2864\n-0.93786\n-0.75136\n-0.4096\n-0.59826\n-1.6732\n-1.8777\n-0.75503\n-0.70015\n-1.0799\n-0.10397\n1.7226\n2.5991\n1.9627\n1.4854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.9587\n4.8764\n4.6619\n2.5713\n1.9194\n0.307\n0.90515\n1.1774\n0.50133\n0.1461\n-0.12872\n-0.085319\n0.098149\n0.0066196\n-0.29892\n-0.89975\n-1.9392\n-1.0469\n-0.63735\n-1.0072\n-1.7814\n-1.703\n-0.61792\n-0.27574\n-0.08346\n-0.20241\n-0.18591\n-0.3231\n-0.70384\n-1.0228\n-0.52473\n0.037671\n-0.033043\n-0.19861\n-0.383\n-0.43408\n-0.11704\n0.044181\n-0.10078\n-0.59143\n-0.60795\n-0.31521\n-0.43265\n-0.83085\n-0.87807\n-0.92431\n-0.81274\n-0.74367\n-0.44772\n-0.29275\n-0.71644\n-1.3926\n-1.2186\n-0.63371\n-1.1101\n-0.23335\n1.8076\n1.9053\n0.30605\n0.31172\n-0.52285\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.8125\n4.2031\n2.9329\n2.9554\n2.0706\n0.49684\n1.0766\n1.0324\n0.21961\n-0.71285\n-0.857\n0.082723\n0.059438\n-0.23333\n-1.0295\n-0.99093\n-1.0617\n-0.81994\n-0.32264\n-0.49166\n-0.66775\n-1.1832\n-1.0064\n-0.37781\n-0.43586\n-0.38913\n-0.54624\n-0.64538\n-0.99495\n-0.83346\n-0.48858\n-0.10731\n-0.22769\n-0.22223\n-0.60975\n-0.54514\n-0.29807\n0.18408\n0.041482\n-0.29158\n-0.26695\n-0.25714\n-0.52056\n-1.1504\n-0.81\n-0.60211\n-0.9374\n-1.1863\n-0.82359\n-0.16923\n-0.86705\n-0.81983\n-1.6899\n-1.5627\n-1.0705\n0.016558\n0.77712\n0.98706\n0.34213\n0.0045507\n0.45336\nNaN\nNaN\n2.0403\n2.8604\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.3718\n3.3153\n2.7395\n2.7599\n1.3272\n0.17615\n0.50123\n-0.062831\n-0.20837\n-1.4541\n-0.77554\n0.24446\n0.11054\n-0.37326\n-0.45942\n-0.61474\n-0.56572\n-0.4804\n-0.29811\n-0.53723\n-0.92269\n-1.0742\n-0.97152\n-0.7188\n-0.95021\n-1.1134\n-2.0865\n-2.3331\n-1.0679\n-0.59754\n-0.44544\n0.17896\n0.00091305\n-0.29473\n-0.34595\n-0.44472\n-0.54722\n0.21046\n0.50946\n0.16605\n0.045214\n-0.25806\n-0.46424\n-0.93606\n-0.52858\n-0.54206\n-0.88549\n-1.1086\n-0.4146\n-0.40092\n-1.4203\n-1.9418\n-1.7631\n-1.4571\n-0.92665\n-0.2607\n0.68584\n-0.26518\n-1.155\n-0.44817\n1.3144\n1.395\n2.3345\n2.8987\n2.3076\n2.5145\n2.9238\nNaN\nNaN\nNaN\nNaN\n3.4506\n3.56\n2.4845\n2.1228\n2.3873\n0.18872\n-1.1444\n-0.54397\n-0.36936\n-0.48435\n-1.1424\n-0.58456\n0.38405\n0.3063\n0.45917\n-0.13938\n-0.67897\n-0.63327\n-0.37093\n-0.40856\n-0.38047\n-0.49737\n-0.93189\n-1.0269\n-1.72\n-1.8236\n-1.4306\n-1.4212\n-1.4279\n-1.4887\n-1.174\n-0.58392\n0.29065\n-0.073596\n0.088278\n-0.12402\n-0.38473\n-0.36372\n-0.092611\n0.42483\n0.48268\n0.18526\n0.024603\n-0.31773\n-0.59627\n-0.27546\n-0.34727\n-0.58975\n-0.85495\n-0.79444\n-1.3104\n-2.197\n-2.131\n-1.6527\n-1.4198\n-0.29185\n0.16263\n0.53869\n1.6727\n0.83941\n0.99595\n1.3356\nNaN\n1.3574\n2.1136\n1.2273\n2.3749\n3.8041\nNaN\nNaN\nNaN\n2.4933\n2.9986\n2.7697\n2.1238\n0.76849\n0.96662\n0.25832\n-1.2558\n-0.91394\n0.36768\n-0.26718\n-1.1235\n-0.60202\n0.33112\n0.56596\n0.71813\n-0.28343\n-0.36754\n-0.48618\n-0.49837\n-0.46415\n-0.11164\n-0.28791\n-0.88274\n-1.2021\n-1.44\n-1.6129\n-1.2633\n-0.62788\n-0.7327\n-1.4672\n-1.0994\n-0.43895\n0.042794\n-0.43671\n-0.2457\n-0.071501\n-0.10818\n-0.057106\n0.10694\n0.4092\n0.4229\n0.052241\n-0.040619\n-0.27435\n-0.43244\n-0.3314\n-0.27732\n-0.32912\n-0.58638\n-1.0385\n-1.5123\n-2.3218\n-1.7856\n-1.7026\n-1.2394\n-0.71859\n0.4183\n-0.048679\n0.099737\n0.81631\n1.0811\nNaN\nNaN\n1.1132\n1.3935\n1.6592\n2.8105\n3.8987\nNaN\nNaN\nNaN\n2.0532\n2.1872\n2.4997\n1.659\n-0.031281\n-0.24913\n-0.59282\n-0.89085\n-0.15668\n0.37141\n0.16309\n-0.79822\n-0.41294\n0.11409\n0.75479\n0.76722\n0.25403\n-0.029516\n-0.17964\n-0.31948\n-0.32082\n0.035616\n-0.24616\n-0.60688\n-1.2973\n-1.5164\n-1.2328\n-0.66883\n-0.10269\n-0.1079\n-0.5144\n-0.43434\n-0.19754\n-0.30278\n-0.086746\n0.15022\n0.087535\n0.16193\n0.42645\n0.53299\n0.44563\n0.34157\n0.012969\n-0.27486\n-0.29593\n-0.21812\n-0.1069\n0.24593\n0.5633\n0.12398\n-0.92274\n-1.3018\n-2.2411\n-0.47273\n-0.31367\n-0.61239\n-0.17109\n0.19167\n-0.61639\n-0.73936\n0.25366\n0.6823\nNaN\nNaN\nNaN\n0.85362\n1.3366\n1.4277\n2.1903\nNaN\n2.326\n2.115\n1.3123\n1.3931\n2.2509\n0.45677\n-0.28531\n0.11246\n0.29409\n0.074538\n-0.31467\n-0.44363\n-0.49746\n-0.4894\n-0.028462\n0.35145\n0.68582\n0.45585\n-0.088206\n-0.15252\n-0.11818\n-0.023491\n0.27156\n0.29078\n0.18101\n-0.44236\n-1.0807\n-1.254\n-0.80571\n-0.3251\n0.1492\n0.24845\n0.026854\n-0.44315\n-0.65242\n-0.34774\n0.36072\n0.24782\n0.31387\n0.61252\n0.88614\n0.99268\n0.68837\n0.34669\n0.47945\n0.16431\n0.050045\n0.14381\n0.38331\n0.6075\n0.962\n1.085\n-0.2123\nNaN\nNaN\nNaN\n-0.41922\n-1.2602\n-0.41187\n-0.038943\n-1.6717\n-1.0001\n-0.029054\n0.40902\nNaN\nNaN\nNaN\n0.70893\n1.0867\n0.57005\n0.84591\n1.3548\n1.7547\n1.2048\n0.80874\n0.89267\n0.50222\n-0.39683\n-0.4583\n0.27767\n0.54611\n0.22311\n-0.16415\n-0.34683\n-0.29695\n-0.038118\n0.161\n0.38347\n0.32173\n-0.09407\n-0.52339\n-0.19898\n0.29941\n0.38664\n0.38431\n0.38151\n0.2593\n-0.30393\n-0.62646\n-0.71124\n-0.65781\n-0.23581\n0.031409\n0.42224\n0.42146\n-0.15296\n-0.48549\n-0.29647\n0.097254\n0.77041\n1.1411\n1.2593\n0.97167\n0.96933\n0.94731\n0.58925\n0.82349\n0.56238\n0.48295\n0.76004\n0.85808\n1.3485\n2.1967\n1.4771\n0.18045\nNaN\nNaN\nNaN\n-0.88109\n-1.3136\n-2.0018\n-1.3995\n-1.4857\n-0.45109\n-0.051319\n0.14601\nNaN\nNaN\nNaN\n0.035472\n0.42835\n0.52048\n0.94852\n2.0028\n1.531\n0.87994\n0.44432\n0.2113\n-0.076064\n-0.63168\n-0.65641\n0.18103\n0.63915\n0.49645\n0.19278\n-0.0021413\n-0.37648\n-0.22506\n0.18712\n0.39644\n0.17984\n-0.18318\n-0.047021\n0.40579\n0.51535\n0.066931\n0.21368\n0.32802\n0.4559\n0.002804\n-0.31358\n-0.38961\n-0.29694\n0.1293\n0.16435\n0.54008\n0.69325\n0.12519\n-0.19626\n0.1987\n0.15599\n1.1326\n1.3646\n1.3171\n1.1018\n1.2359\n1.2138\n1.1836\n0.87074\n0.84327\n0.85855\n1.1877\n1.464\n1.649\n1.7237\n1.3521\n1.1798\n-0.088419\nNaN\nNaN\n-1.338\n-1.1193\n-1.2643\n-1.1496\n-0.72012\n0.065821\n0.19581\n-0.054114\n0.17228\n-0.11828\n-0.18955\n-0.9897\n-0.27744\n0.28203\n0.84224\n1.7402\n1.3905\n1.1454\n0.75404\n0.35467\n0.33068\n-0.21741\n-0.24742\n0.38426\n0.50726\n0.5927\n0.11218\n-0.66047\n-1.1367\n-0.85053\n0.084627\n0.37284\n0.35303\n0.78243\n0.26585\n0.34101\n0.51237\n-0.081603\n0.062378\n0.19931\n0.59926\n0.25385\n-0.3665\n-0.15235\n0.19357\n0.47508\n0.35659\n0.42418\n0.80898\n0.033848\n0.24967\n0.80332\n0.41547\n0.90111\n1.105\n1.2317\n1.4021\n1.683\n1.7008\n1.5903\n1.1319\n1.1232\n1.1927\n1.1566\n1.4215\n1.5482\n1.6221\n1.0664\n0.96991\n0.32558\n-1.4348\n-1.9285\n-1.2401\n-0.96142\n-0.66985\n-0.5503\n-0.35015\n0.14184\n-0.0049579\n-0.10597\n0.019368\n0.10025\n-0.0018561\n-0.29884\n-0.21289\n0.10647\n0.49437\n1.2093\n1.1439\n0.91844\n1.264\n0.87671\n0.58024\n0.38728\n0.63796\n0.78219\n0.8422\n0.53714\n0.42352\n-0.60814\n-1.4945\n-1.9485\n-0.6894\n0.032144\n1.3676\n1.1444\n0.56208\n0.35079\n0.73631\n0.46248\n0.21688\n0.16843\n0.28584\n0.13976\n-0.16278\n0.4708\n0.58328\n0.48518\n0.42564\n0.99087\n1.0839\n0.26061\n0.42359\n0.31918\n0.18625\n0.68277\n1.1493\n1.4878\n1.7202\n2.1399\n2.3523\n2.0693\n1.9303\n1.398\n0.99319\n1.0708\n1.2998\n1.3014\nNaN\nNaN\nNaN\nNaN\n-0.26371\n-1.1601\n-1.0117\n-0.40386\n-0.49151\n-0.77242\n-0.44378\n0.032427\n-0.1998\n0.10128\n0.48208\n0.15677\n-0.12495\n-0.25858\n-0.32359\n-0.3188\n-0.2039\n-0.11543\n0.29497\n0.52782\n1.2703\n1.1111\n1.1109\n0.95692\n1.1048\n1.097\n0.31581\n-0.0012548\n0.58494\n0.17876\n-1.1138\n-1.0337\n-1.1381\n-0.44654\n0.26593\n0.55925\n0.20486\n0.28788\n0.86921\n0.28759\n0.1008\n0.31711\n0.027555\n0.027509\n0.4168\n0.95727\n0.49608\n0.058762\n0.44267\n1.4186\n1.2211\n0.68908\n0.16086\n-0.10713\n0.047501\n1.181\n1.3569\n1.7161\n2.0549\n2.2277\n2.3296\n2.4954\n2.5473\n2.1355\n1.679\n1.4095\n1.4432\n1.4829\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0473\n-0.76082\n-0.56173\n-0.46043\n-0.97903\n-0.71125\n-0.24581\n-0.1075\n0.10832\n0.26758\n-0.20568\n-0.33141\n-0.4954\n-0.4811\n0.019504\n-0.23928\n-0.24399\n0.26996\n0.69771\n1.6636\n1.7102\n1.586\n1.4675\n1.5338\n1.2077\n-0.63877\n-0.98091\n-0.49178\n0.53087\n-0.63604\n-0.50097\n-0.48022\n-0.41481\n-0.49188\n-0.48578\n-0.66839\n-0.19602\n0.20922\n-0.13512\n-0.068916\n0.54097\n0.53537\n0.25835\n0.66406\n0.79049\n0.51038\n0.056698\n0.88508\n1.8896\n1.265\n0.89583\n-0.11806\n-0.22096\n-0.23059\n1.5302\n1.4163\n1.7818\n2.4333\n2.5244\n3.2889\n3.2938\n3.2016\n2.583\n2.0412\n1.6253\n1.4882\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.89138\n-0.80927\n-0.90039\n-0.96591\n-1.0508\n-0.88508\n-0.97165\n-0.30711\n-0.15231\n-0.027598\n-0.26347\n-0.36256\n-0.1107\n0.039535\n-0.040066\n-0.034702\n0.22655\n0.29341\n1.273\n1.8579\n1.5968\n1.6119\n1.7878\n1.6292\n-0.65731\n-1.8593\n-1.5069\n0.17113\n-0.37403\n-0.2728\n-0.42076\n-0.46345\n-0.13952\n-0.67503\n-0.66183\n-0.26714\n0.1611\n-0.2856\n-0.22376\n0.69102\n0.19261\n-0.032539\n0.15986\n0.48365\n0.39141\n0.6624\n0.97464\n1.325\n1.0313\n0.95119\n-0.63169\n-0.77211\nNaN\n1.953\n1.5424\n2.3243\n2.9607\n3.1314\n3.9217\n4.5984\n4.1274\n2.5832\n2.1608\n1.8088\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3789\n-0.7153\n-0.7389\n-0.70707\n-0.96496\n-0.90707\n-0.72619\n-0.54728\n0.046073\n0.21381\n-0.16345\n-0.098772\n0.082753\n0.045058\n-0.015126\n-0.15808\n-0.42986\n-0.45562\n0.49794\n1.0684\n0.95929\n1.3249\n1.1968\n0.3434\n-0.75335\n-1.7211\n-1.8727\n-0.5693\n-0.41904\n-0.19863\n-0.53062\n-0.10439\n0.39312\n0.59772\n0.52626\n0.22724\n0.16088\n-0.36695\n-0.34641\n0.60544\n-0.51687\n-0.28023\n-0.74134\n-0.43799\n0.6421\nNaN\nNaN\n0.71966\n0.91022\n1.2237\nNaN\nNaN\nNaN\n0.7879\n0.014186\n-0.0072507\n-0.080746\n-0.071532\n-0.38154\n-0.85219\n-0.41667\n0.18154\n0.20954\n-0.30786\n-0.13757\n-0.4719\n-0.096185\n0.44293\n0.37419\n0.53136\n0.26786\n1.2053\n0.9985\n1.4805\n1.5843\n1.7487\n0.55757\n1.1028\n0.53386\n-0.40769\n-1.3569\n-0.80488\n-0.68678\n-1.6274\n-1.3563\n-0.47905\n-0.55943\n0.091696\n-0.59584\n-0.58619\n-0.26022\n-0.7454\n-0.96999\n-0.43217\n0.25203\n0.74293\n1.1068\n1.1673\n0.68605\n0.94009\n0.81169\n0.49991\n0.25915\n0.23064\n0.84775\n0.66197\n-0.13631\n0.85838\n0.31468\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.3448\n-0.23666\n-0.17641\n0.95648\n1.0626\n-1.0914\n-1.1229\n-0.64319\n0.27351\n0.37075\n0.47612\n0.3027\n-0.5083\n0.38475\n0.74634\n-0.15436\n-0.89456\n-0.53028\n0.3632\n0.74325\n2.6462\n1.4208\n1.2694\n1.8856\n1.2706\n0.91021\n0.32066\n-0.60862\n-0.026263\n-0.5198\n-0.29946\n-0.45874\n0.36623\n-0.049187\n0.038046\n-0.99767\n-0.92971\n-0.2229\n-0.46752\n0.46407\n0.45492\n0.38445\n0.83729\n1.4523\n1.2552\n0.83426\n0.82416\n0.51564\n0.27152\n0.19116\n0.22588\n0.58094\n0.58063\n0.84576\n2.1396\n0.82766\n-0.29323\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.61162\n-0.37906\n-0.18391\n-0.2656\n-1.0382\n-0.62546\n-0.95067\n-0.55153\n-0.19428\n0.96544\n1.052\n0.73809\n0.39119\n0.9482\n1.8614\n0.18245\n-0.084678\n0.31153\n0.24092\n1.5135\n1.618\n1.2781\n1.9664\n1.65\n1.5696\n1.7052\n1.1358\n0.198\n0.48414\n-0.53286\n0.0591\n-0.38904\n1.184\n0.93936\n-1.1732\n-1.4606\n-0.64316\n-0.32192\n0.095042\n0.12376\n0.29731\n0.68002\n0.54358\n1.245\n0.81572\n0.5985\n0.59418\n0.48096\n0.30405\n0.5219\n0.81308\n0.43814\n0.31494\n1.4628\n1.7065\n0.8197\n0.45991\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10848\n-0.31937\n-0.26104\n-0.15142\n-0.12153\n0.015673\n0.38369\n0.18752\n-0.046608\n0.18624\n0.51572\n-0.2024\n1.9477\n1.6724\n1.2624\n0.19638\n0.23064\n0.26843\n0.39769\n1.3901\n2.387\n0.99735\n1.6301\n1.3382\n1.9125\n1.9639\n1.1044\n0.63129\n0.077998\n-1.3538\n-0.16559\n0.19579\n1.3342\n1.0996\n-0.51634\n-0.98891\n-1.0853\n-0.41937\n0.25119\n-0.03543\n-0.038101\n0.34934\n0.094005\n0.012736\n0.31573\n0.37053\n0.27996\n0.69431\n1.0169\n1.805\n1.9843\n1.3776\n1.751\n2.0927\n1.6104\n1.0413\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.089623\n-0.078347\n-0.038573\n-0.041452\n0.30412\n0.086131\n0.39846\n0.55983\n0.0054628\n-0.65931\n-0.30767\n-0.73302\n-0.70939\n0.24943\n-0.66924\n-0.74961\n-0.37997\n0.23047\n-0.15302\n0.66663\n2.901\nNaN\nNaN\n-0.67147\n1.5141\n1.7348\n1.2261\n0.31556\n0.40618\n0.34147\n-0.17919\n0.55839\n0.82572\n0.85518\n0.70051\n-0.44034\n-1.443\n-0.66361\n0.057548\n-0.022822\n0.079127\n0.51473\n0.13163\n0.14857\n0.13166\n0.29725\n0.82912\n1.527\n1.4314\n1.3784\n1.8804\n1.6548\n1.2759\n2.1494\n1.4822\n1.4561\n2.8131\nNaN\nNaN\n-3.6416\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17674\n-0.20523\n-0.44197\n-0.018991\n0.533\n0.091899\n0.45131\n0.78441\n0.3934\n0.019311\n0.088955\n0.65644\n0.54913\n-0.42231\n-1.0711\n-0.088511\n-0.64304\n-0.57059\n-0.62655\nNaN\nNaN\nNaN\nNaN\n-0.00704\n0.7491\n1.6402\n1.4929\n1.1377\n0.82069\n1.0224\n0.5755\n0.38271\n-0.078965\n-0.062353\n0.59549\n-0.41928\n-1.3511\n-0.48579\n0.01494\n0.21868\n0.27775\n0.07098\n0.070333\n-0.25329\n-1.3556\n-0.32756\n0.86477\n1.5605\n1.323\n1.5103\n1.523\n1.4256\n1.2219\n1.1611\n0.84181\n0.30121\n1.1027\nNaN\nNaN\n-4.956\n-3.9778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27127\n-0.52136\n-0.31619\n0.50093\n0.84737\n0.24857\n0.87445\n0.86769\n0.86607\n0.55104\n0.40515\n0.28315\n0.24818\n-0.30729\n-0.60221\n-0.7088\n-0.81604\n-0.16625\n-0.23086\nNaN\nNaN\nNaN\nNaN\n-0.30331\n0.23674\n1.1127\n1.5221\n2.0074\n1.0006\n0.91184\n0.12903\n0.18689\n-0.99162\n-0.5392\n-0.35868\n-0.77798\n-1.1335\n-0.64096\n-0.20902\n0.28685\n0.50101\n0.12066\n-0.41306\n-1.0238\n-2.7127\n-0.59532\n0.80613\n1.5078\n1.7002\n1.7245\n1.2252\n1.4806\n1.8128\n1.5182\n0.79142\n-0.16926\n0.68307\nNaN\nNaN\n-2.323\n-3.994\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.047393\n0.089395\n-0.035327\n0.70364\n1.4886\n0.11215\n0.43879\n0.91218\n1.0224\n0.70621\n0.40142\n0.46373\n0.029204\n0.22732\n-0.31672\n-0.44307\n0.011829\n0.24306\n-0.078824\n-0.43546\n-0.28268\n0.11011\n-0.0090999\n0.4464\n0.59298\n1.1434\n1.4938\n1.7114\n0.91796\n0.73991\n0.46683\n-0.92851\n-1.9795\n-0.7614\n-0.44141\n-0.44006\n-0.52631\n-0.53281\n-0.42718\n-0.57486\n0.20403\n-0.0012906\n-0.46767\n-1.109\n-3.3851\n-0.51138\n1.1685\n1.3518\n1.2071\n1.7189\n1.4547\n1.7009\n2.0045\n1.6663\n1.5309\n0.77094\n0.16091\n1.3677\n0.98994\n-0.2861\n-2.4719\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.12831\n-0.42406\n-0.62706\n-0.1452\n0.72856\n-0.049309\n0.0032695\n0.96726\n1.1684\n0.98986\n0.5969\n0.61495\n0.6999\n0.38133\n0.343\n0.57281\n0.43317\n0.67203\n0.045164\n0.083918\n0.18642\n0.13187\n0.56876\n0.95358\n0.9047\n1.3231\n1.3159\n0.78115\n0.6149\n0.38673\n0.45961\n0.2969\n-1.1423\n-0.96435\n-0.54966\n-0.50166\n-0.43937\n-0.49723\n-0.45197\n-0.47187\n-0.48822\n-0.20943\n-0.7732\n-1.608\n-2.8623\n0.77248\n1.5778\n1.7657\n1.7982\n1.9718\n1.9182\n2.2737\n2.2713\n2.7574\n2.6968\n1.1096\n0.23153\n1.0245\n2.124\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0268\n-0.38331\n-0.13248\n-0.43189\n0.89435\n-0.072582\n-0.093484\n0.48439\n0.90125\n0.95287\n0.73407\n0.70818\n0.85081\n0.40159\n0.73045\n0.97859\n0.70279\n1.1989\n1.0475\n1.2949\n0.96914\n0.70199\n1.1303\n0.81661\n0.49113\n0.86921\n1.3939\n1.2085\n0.64948\n0.14722\n0.1369\n-0.66031\n-1.4922\nNaN\nNaN\n-0.17973\n-0.79413\n-0.60088\n-0.70589\n-0.49875\n-0.2392\n0.086603\n-1.0841\n-1.6376\n-0.36794\n1.6733\n1.3621\n1.7895\n1.9214\n2.4617\n2.3165\n2.876\n2.83\n2.8695\n2.6043\n2.1484\n1.1734\n0.48733\n2.2878\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2125\n-0.85011\n-0.38571\n-0.4011\n0.1807\n-0.22372\n-0.11139\n-0.078532\n0.63015\n1.1142\n0.83615\n0.36859\n0.89823\n0.67491\n1.1932\n1.4518\n1.1948\n1.2176\n1.1985\n1.2835\n0.5956\n0.62981\n0.46147\n-0.065309\n0.48671\n2.079\n1.7966\n1.3765\n0.94314\n0.81929\n0.12956\n-0.85673\nNaN\nNaN\nNaN\n-0.2521\n-0.58067\n-0.021477\n-1.0021\n-0.61612\n0.47107\n0.77597\n-0.88132\n-0.8605\n0.30438\n1.3306\n1.3811\n1.7882\n2.3468\n2.7504\n2.4511\n2.9704\n2.7233\n3.0302\nNaN\nNaN\n0.59091\n0.30643\n1.9526\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4736\n-1.4979\n-0.91793\n-0.40974\n-0.35032\n-0.16102\n0.25241\n0.56778\n0.81993\n0.94646\n0.49781\n0.59924\n1.028\n0.69763\n1.2384\n1.3044\n1.4905\n1.1531\n0.70596\n0.61341\n-0.045487\n0.092204\n0.034299\n-0.30071\n0.41772\n1.5577\n1.7752\n1.7739\n1.0236\n1.1159\n0.4181\nNaN\nNaN\nNaN\nNaN\n-0.23505\n0.024642\n-0.17539\n-0.82066\n-0.2251\n0.77681\n-0.0048984\n-0.21137\n0.36547\n0.90037\n1.4096\n1.8832\n2.6532\n3.1034\n3.2458\n2.8415\n3.0352\n2.3309\nNaN\nNaN\nNaN\n0.69381\n0.31796\n0.43902\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27281\n-0.56192\n-0.25085\n-0.2484\n-0.099636\n0.24064\n0.4515\n0.35907\n0.73982\n0.70071\n0.62671\n0.79008\n0.57733\n0.9329\n1.3454\n1.4735\n1.6274\n1.0918\n0.40146\n0.29178\n0.028314\n0.061519\n-0.49328\n0.22256\n0.4057\n0.36723\n1.863\n1.4078\n0.71427\n1.0901\n0.797\nNaN\nNaN\nNaN\nNaN\n-0.68327\n-0.40456\n-0.02164\n-0.15914\n0.5842\n0.73403\n-0.68277\n-1.1082\n0.84198\n1.3947\n1.7884\n2.3518\n3.0696\n3.3931\n3.4702\n3.2931\n2.8027\n1.8532\nNaN\nNaN\nNaN\n-1.0515\n-1.3542\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.39748\n0.059945\n-0.12959\n0.29268\n0.33574\n0.37335\n0.46435\n0.41497\n0.50372\n0.36357\n0.32608\n0.77369\n1.461\n1.8318\n1.6491\n1.4829\n0.93599\n0.96045\n0.23221\n0.006136\n0.30842\n-0.78881\n-1.2447\n-0.75843\n-1.1098\n0.098193\n1.0857\n0.83115\n1.2898\n1.518\n1.0979\nNaN\nNaN\nNaN\nNaN\n-0.9828\n-1.1581\n0.046619\n0.099986\n0.8393\n0.92375\n0.22884\n0.40576\n1.6974\n2.2339\n2.7543\n2.8011\n3.4347\n3.52\n3.4689\n3.1983\n2.2113\n1.6454\nNaN\nNaN\nNaN\nNaN\n-0.72151\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.60497\n-0.15858\n0.40724\n0.901\n0.59215\n0.3229\n0.54705\n0.34819\n0.20571\n0.28872\n0.11118\n0.71961\n1.6395\n1.8738\n1.9313\n1.3368\n1.4936\n1.428\n0.81303\n0.19076\n-0.27816\n-1.6439\n-2.6796\n-1.6525\n-1.5045\n0.17165\n0.4987\n0.96019\n1.6726\n1.5306\n1.4844\nNaN\n-0.39931\n-0.60012\n-0.0015349\n-0.45066\n-0.46326\n1.2373\n1.4411\n2.5239\n2.3059\n0.68353\n0.79468\n3.559\nNaN\nNaN\nNaN\n3.8093\n4.2714\n3.8283\n2.7881\n2.9255\n2.2124\n0.54345\nNaN\nNaN\nNaN\n-1.8271\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18818\n0.17909\n0.56127\n1.2258\n1.0262\n0.79405\n0.93445\n0.48389\n0.23438\n0.7753\n0.53035\n0.90587\n1.3415\n1.3857\n1.7799\n0.84493\n0.74975\n1.1614\n1.2532\n0.45798\n-0.24356\n-0.67589\n-3.7919\n-2.9882\n-1.6071\n-0.29578\n0.63602\n2.1158\n2.0684\n1.4693\n-0.076024\n-1.5531\n0.039278\n0.26832\n0.14213\n0.28844\n1.6576\n2.3539\n2.7798\nNaN\nNaN\nNaN\n0.60331\nNaN\nNaN\nNaN\nNaN\n3.6249\n4.3673\n3.1739\n2.1777\n2.2632\n2.163\n0.85843\n0.39\n-0.83142\n-2.2031\n-2.1851\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14652\n0.1659\n0.54116\n1.3454\n0.84345\n0.98242\n0.73039\n0.43306\n0.49436\n0.27298\n0.41106\n0.79276\n1.24\n1.3509\n1.6446\n0.53744\n0.1675\n0.51281\n0.88731\n0.58283\n-0.20727\n-0.84386\n-1.886\n-2.4017\n-0.82616\n0.3303\n1.0941\n1.7611\n1.3497\n1.2244\n-0.60582\n-0.55381\n0.89552\n1.4731\n1.4308\n1.7507\n2.5795\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.7204\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6397\nNaN\n1.2476\n1.889\n0.76077\n0.43444\n-0.091688\n-1.807\n-1.9502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.33478\n-0.013697\n0.63153\n1.3523\n0.82368\n0.60386\n0.45206\n0.41379\n0.53929\n0.12015\n0.40153\n0.59618\n1.0748\n1.0723\n1.1878\n0.46668\n0.83437\n0.47939\n0.40094\n-0.085527\n-0.79086\n-1.6558\n-2.2994\n-2.5766\n-0.51095\n0.45169\n1.4072\n1.3353\n1.2399\n0.72232\n0.9948\n1.1107\n0.91452\n1.4282\n2.0781\n2.6305\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15931\n0.31923\n-0.65833\n-1.8201\n-1.6309\n-2.1169\n-1.9211\n-0.49189\n0.23437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.014815\n0.21769\n0.8717\n1.2073\n0.81872\n0.018881\n0.31326\n0.37352\n0.13555\n0.15012\n0.026051\n0.11442\n0.55825\n0.95727\n1.0699\n0.53399\n0.1133\n-0.075398\n-0.023341\n-0.35874\n-0.82405\n-1.9185\n-2.2983\n-1.9714\n-0.43493\n0.48613\n0.87918\n1.0126\n1.6175\n1.29\n0.7292\n1.7182\n2.1459\n1.8605\n2.3261\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.83985\n-0.13383\n-0.5003\n-0.89167\n-0.14053\n-0.16303\n-0.28119\n0.62607\n0.59996\n-0.46788\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.22266\n0.43503\n0.35765\n0.19066\n0.66287\n0.44171\n0.8761\n0.26719\n-0.41164\n-0.27319\n-0.151\n-0.077306\n0.324\n0.92307\n0.89245\n0.11356\n-0.74873\n-1.1432\n-0.79087\n-0.49795\n-1.0342\n-2.1488\n-1.8566\n-1.0873\n0.47621\n1.269\n0.79628\n1.0439\n1.8667\n1.4681\n0.59351\n1.8754\n2.0012\n1.732\n2.5761\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8708\n-1.125\n-0.94932\n-0.17761\n0.98363\n1.3169\n0.25366\n0.02916\n0.50063\n-0.54137\n-0.17888\n0.81274\n1.3235\nNaN\nNaN\nNaN\nNaN\nNaN\n0.33122\n0.63028\n0.18701\n-0.12153\n1.0012\n0.98073\n-0.21517\n-0.61623\n-0.81816\n-0.10214\n0.17757\n0.19077\n-0.14075\n0.72731\n0.44528\n-0.055107\n-0.68152\n-0.70469\n-0.53501\n-1.287\n-1.576\n-2.5775\n-2.07\n-0.37954\n0.66735\n1.9424\n2.1442\n2.155\n2.0539\n2.7253\n3.9716\n4.954\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3478\n-0.54814\n-0.22686\n-0.3914\n-0.88484\n-0.18961\n1.4414\n1.7615\n0.56426\n0.82597\n1.1677\n1.2604\n0.72186\n0.51932\n1.1774\n1.5692\n1.4982\n1.897\n0.81622\n0.31135\n0.14268\n0.20974\n-0.064744\n-0.070507\n0.47808\n0.47541\n-0.53308\n-0.54805\n-0.81894\n-0.15551\n-0.083606\n-0.42308\n0.061173\n0.39395\n0.26262\n0.38162\n0.069228\n-0.35821\n-0.081861\n-0.89176\n-0.53735\n-2.1878\n-2.4188\n-0.10133\n1.3483\n3.2439\n2.7786\n2.9472\n3.3347\n3.9634\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24265\n-0.06573\n0.17136\n0.80705\n0.51045\n0.24261\n-0.89028\n0.30652\n1.0372\n0.54959\n1.2538\n1.5321\n0.95293\n0.49224\n0.092632\nNaN\nNaN\nNaN\nNaN\n0.61707\n0.58532\n0.15886\n0.087681\n-0.014301\n-0.51778\n-0.44873\n-0.52041\n-0.84976\n-0.65519\n-0.27149\n-0.38578\n-0.63937\n-0.54464\n0.026262\n0.1583\n0.16246\n0.5848\n0.064564\n-0.80795\n-1.7648\n-0.4259\n-0.54897\n-3.0823\n-2.4694\n-0.28391\n2.0489\n2.6004\n2.4777\n3.2639\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4923\n0.99448\n0.6854\n0.08387\n0.6569\n1.2481\n1.0478\n0.93988\n0.14438\n0.036069\n-0.12168\n0.57301\n1.2724\n1.8366\n1.05\n0.9688\n1.0999\nNaN\nNaN\nNaN\nNaN\n-0.0091971\n0.85431\n0.22383\n-0.23256\n-1.1168\n-0.80878\n-0.39063\n-0.59773\n-1.0283\n-0.36835\n0.35846\n-0.083661\n-0.43296\n0.051172\n0.18028\n0.15696\n0.28363\n1.4919\n0.93273\n-0.60144\n-2.0363\n-2.8089\n-1.7497\n-1.7816\n-1.0712\n0.39773\n1.2827\n2.1131\n1.8481\n2.0169\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.98564\n1.4763\n1.1847\n0.84421\n0.29803\n0.47324\n0.794\n1.0562\n1.5773\n0.97992\n0.074613\n-0.45839\n0.10054\n0.61986\n1.6099\n0.41385\n0.99592\n1.2551\nNaN\nNaN\n-1.3543\n-0.30792\n0.11962\n0.012072\n-0.52686\n-0.69716\n-0.82158\n-0.57696\n-0.63995\n-0.086461\n-0.51696\n0.046446\n0.37848\n0.17929\n-0.30664\n-0.10438\n0.30252\n-0.18944\n-0.21163\n1.1037\n1.1624\n-0.018324\n-1.7267\n-2.5065\n-1.2834\n-0.91607\n-0.60467\n0.91033\n1.7289\n1.9018\n1.431\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10965\n0.3485\n2.2631\n1.2128\n1.1813\n0.68932\n0.43214\n0.39699\n0.96221\n1.922\n1.7375\n0.49384\n0.2988\n0.56687\n0.075385\n-0.4151\n0.24309\n1.1659\n0.92133\n-0.094956\n-0.67163\n0.10806\n0.58752\n0.7362\n0.60103\n-0.9642\n-0.70132\n-0.35593\n-0.45596\n-0.91121\n0.12331\n-0.13784\n-0.15207\n0.10044\n-0.1064\n-0.39892\n0.18359\n0.35017\n0.42963\n0.89772\n1.0354\n1.7034\n0.057114\n-1.0178\n-1.1001\n-0.45819\n-0.59474\n-0.42501\n0.44204\n1.9739\n2.6657\n2.1949\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.49566\n0.011194\n2.0364\n1.164\n1.3796\n1.1095\n0.081991\n0.017663\n0.63976\n1.9417\n1.8954\n1.3369\n0.8213\n1.0088\n0.34493\n-0.0046108\n1.0238\n1.6496\n0.62845\n0.6976\n0.45868\n0.81186\n1.1094\n1.6632\n0.93794\n-0.8035\n-0.34593\n-0.054861\n-0.24059\n-0.64652\n-0.24999\n-0.22196\n-0.38443\n-0.095189\n-0.44971\n-0.52689\n-0.31241\n0.22017\n0.69994\n1.1417\n1.1319\n1.0473\n-0.093653\n-0.12109\n-0.70127\n-0.60101\n-0.36128\n0.32012\n0.83435\n2.1306\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.33279\n0.47684\n0.87574\n2.0165\n-0.15757\n0.72901\n1.0056\n0.78475\n1.7203\n2.0353\n2.0871\n1.9803\n2.1576\n1.5724\n1.0033\n0.47949\n-0.39112\n1.488\n1.1933\n1.3102\n1.2636\n1.1261\n1.2769\n1.4777\n1.2373\n0.7562\n-0.51401\n0.00050299\n-0.15484\n-0.40779\n-0.60856\n-0.55089\n-0.62844\n-0.18638\n-0.10767\n-0.51028\n-0.41955\n-0.18731\n0.051976\n0.21815\n1.0187\n1.1208\n0.53766\n-0.37179\n-0.64238\n-0.45978\n-0.43868\n-0.21311\n0.11499\n0.81846\n2.1654\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7303\nNaN\nNaN\nNaN\nNaN\n0.99781\n1.0552\n0.87212\n0.50848\n1.1178\n-0.19566\n0.59705\n1.1045\n2.5543\n2.1879\n2.156\n1.8615\n0.046338\n1.8922\n1.0437\n0.51898\n-0.76193\n-0.04458\n1.6344\n1.5469\n1.8684\n1.4671\n1.3975\n1.5029\n1.5864\n0.87068\n-0.38199\n-0.19967\n-0.034178\n-0.52417\n-0.5376\n-0.21626\n-0.41949\n-0.55824\n-0.36465\n-0.51012\n-0.29981\n-0.27699\n-0.168\n-0.17218\n0.25693\n0.86711\n1.0165\n0.65948\n0.031773\n-0.69078\n-0.53153\n0.14568\n0.24019\n-0.36863\n0.88401\n2.9707\n4.2937\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4845\n1.8177\n2.0048\n1.3989\n0.47148\n0.099009\n0.59107\n0.44934\n0.16876\n1.2214\n2.5099\n1.5918\n1.6347\n0.76826\n-1.2461\n-0.62385\n0.086439\n-0.08533\n-0.41655\n0.48472\n1.273\n1.4975\n1.2262\n1.2328\n0.61584\n1.2609\n1.0731\n0.058907\n-2.3269\n-0.070801\n-0.29329\n-0.27152\n-0.49887\n-0.60648\n-0.43984\n0.056016\n-0.27291\n-0.80901\n-0.43593\n-0.35592\n-0.12068\n-0.29714\n0.091109\n0.75469\n0.80598\n0.32076\n0.016542\n-0.1798\n-1.3025\n-1.3081\n-0.00082178\n-0.13245\n1.5532\n3.8276\n4.0495\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9878\nNaN\nNaN\n4.2062\n2.9333\n2.354\n2.615\n1.4748\n0.37537\n-0.2051\n-0.83228\n-0.53702\n-0.52225\n0.20393\n1.1665\n0.3431\n0.072823\n0.08505\n-0.498\n-0.98621\n-1.1368\n-0.50595\n-0.13074\n0.21869\n0.79695\n0.77501\n0.5011\n0.73417\n0.54276\n0.14992\n0.13883\n-0.061244\n-3.0781\n-0.28162\n-0.42372\n-0.60104\n-0.75003\n-1.1555\n-0.43905\n-0.20592\n-0.34944\n-0.50132\n-0.35367\n-0.30248\n-0.43731\n-0.65782\n-0.44254\n0.35132\n0.14114\n0.084473\n-0.53719\n-0.77812\n-1.1619\n-1.7438\n-0.73939\n0.10597\n2.4772\n3.9031\n4.4935\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.1557\n10.622\n7.2095\n5.652\n2.5922\n2.168\n2.7789\n2.0712\n0.62503\n0.31034\n-0.087335\n-0.51082\n-0.45692\n-0.3921\n-0.22461\n-0.24023\n-0.40653\n-0.56243\n-0.8287\n-1.4776\n-1.5894\n-0.42262\n0.3165\n0.27915\n-0.02605\n-0.36266\n0.22639\n0.82712\n0.51191\n-0.42748\n0.13942\n-0.33189\n-1.5382\n-0.56955\n-0.51056\n-0.52401\n-0.31575\n-0.57461\n-0.54457\n-0.60276\n-0.41146\n-0.44363\n-0.73765\n-0.3395\n-0.90089\n-1.4984\n-1.0044\n-0.39467\n-0.19659\n-0.55851\n-1.2563\n-1.3816\n-1.1364\n-1.1543\n-0.92771\n0.26894\n1.8812\n3.5672\n5.4197\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.142\n10.095\n6.7915\n5.0442\n2.0638\n2.0898\n1.9017\n1.8551\n1.1558\n0.44012\n-0.31106\n-0.35368\n-0.0094451\n0.13574\n0.047526\n-1.1855\n-1.4744\n-0.9352\n-1.0764\n-2.1035\n-2.6008\n-0.90505\n-0.055434\n-0.089918\n-0.16509\n-0.65419\n0.056862\n0.092248\n0.64621\n-0.5259\n-0.83956\n-0.47217\n-0.25148\n-0.42131\n-0.346\n-0.35013\n-0.037346\n-0.41671\n-0.52286\n-0.70525\n-0.67248\n-0.57801\n-0.83217\n-0.89006\n-0.96795\n-1.5366\n-1.114\n-0.8877\n-0.50088\n-0.72315\n-1.9704\n-2.1938\n-0.94224\n-0.90502\n-1.3233\n-0.19332\n1.916\n2.9299\n2.0875\n1.572\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.684\n5.8205\n5.5825\n3.0738\n2.2633\n0.32556\n1.0386\n1.374\n0.54367\n0.10301\n-0.17912\n-0.11755\n0.093073\n-0.013881\n-0.39691\n-1.0965\n-2.275\n-1.2526\n-0.77484\n-1.2344\n-2.1882\n-2.075\n-0.78733\n-0.36347\n-0.12722\n-0.28971\n-0.24728\n-0.39398\n-0.80953\n-1.205\n-0.63085\n0.033647\n-0.056425\n-0.16042\n-0.37926\n-0.45208\n-0.096501\n0.10854\n-0.079304\n-0.67594\n-0.69224\n-0.3485\n-0.49947\n-0.9751\n-1.0423\n-1.1082\n-0.96585\n-0.86055\n-0.53363\n-0.33496\n-0.81213\n-1.603\n-1.4548\n-0.82668\n-1.3626\n-0.3475\n2.0227\n2.1114\n0.31122\n0.31445\n-0.75734\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.09\n5.002\n3.4582\n3.4577\n2.4095\n0.57134\n1.2565\n1.1953\n0.21136\n-0.94321\n-1.0732\n0.080524\n0.046554\n-0.29446\n-1.2681\n-1.2084\n-1.2764\n-0.99611\n-0.4196\n-0.62015\n-0.8335\n-1.462\n-1.2425\n-0.4819\n-0.5358\n-0.49378\n-0.64668\n-0.76267\n-1.1857\n-1.0163\n-0.58196\n-0.15107\n-0.29072\n-0.19611\n-0.6623\n-0.58797\n-0.29489\n0.27663\n0.092873\n-0.31367\n-0.29147\n-0.27383\n-0.61907\n-1.3587\n-0.96212\n-0.71905\n-1.1019\n-1.3993\n-0.97692\n-0.18395\n-0.99395\n-0.94255\n-2.0338\n-1.8612\n-1.2835\n-0.029205\n0.83693\n1.0495\n0.31428\n-0.041774\n0.42226\nNaN\nNaN\n2.3749\n3.2997\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.1739\n3.8997\n3.2036\n3.2121\n1.5912\n0.2762\n0.56048\n-0.13078\n-0.27921\n-1.8384\n-0.97411\n0.28866\n0.11162\n-0.47709\n-0.58768\n-0.75207\n-0.69528\n-0.59887\n-0.3904\n-0.67616\n-1.1286\n-1.3211\n-1.1997\n-0.89237\n-1.1669\n-1.3589\n-2.4791\n-2.7653\n-1.2822\n-0.71631\n-0.53108\n0.20474\n0.0061069\n-0.28334\n-0.34783\n-0.46832\n-0.59295\n0.30563\n0.63697\n0.23187\n0.090538\n-0.27076\n-0.54616\n-1.1031\n-0.62339\n-0.63381\n-1.0497\n-1.3227\n-0.50527\n-0.48429\n-1.6947\n-2.3203\n-2.1234\n-1.7902\n-1.1517\n-0.37566\n0.7432\n-0.44742\n-1.5063\n-0.60897\n1.487\n1.5868\n2.6966\n3.3568\n2.6416\n2.8967\n3.438\nNaN\nNaN\nNaN\nNaN\n4.1012\n4.1988\n2.9293\n2.4776\n2.7733\n0.22679\n-1.3537\n-0.68876\n-0.47736\n-0.60302\n-1.3881\n-0.72524\n0.43643\n0.33515\n0.51492\n-0.20108\n-0.83392\n-0.77363\n-0.46333\n-0.52022\n-0.48515\n-0.62294\n-1.141\n-1.2471\n-2.0987\n-2.2137\n-1.7272\n-1.692\n-1.7065\n-1.7836\n-1.4067\n-0.69974\n0.3504\n-0.063539\n0.16239\n-0.094822\n-0.39217\n-0.38083\n-0.051609\n0.55333\n0.61175\n0.26134\n0.062318\n-0.36397\n-0.69661\n-0.32315\n-0.40713\n-0.72351\n-1.0317\n-0.95306\n-1.5584\n-2.5855\n-2.5542\n-1.9936\n-1.7286\n-0.40151\n0.14751\n0.57584\n1.9716\n1.0194\n1.17\n1.5355\nNaN\n1.6013\n2.4468\n1.4067\n2.7423\n4.4721\nNaN\nNaN\nNaN\n2.9568\n3.5367\n3.2492\n2.4921\n0.86522\n1.0847\n0.27912\n-1.4912\n-1.1065\n0.3939\n-0.3461\n-1.3799\n-0.76874\n0.3684\n0.65985\n0.83984\n-0.33328\n-0.45778\n-0.59246\n-0.60989\n-0.57307\n-0.14727\n-0.36517\n-1.0966\n-1.4718\n-1.7423\n-1.9426\n-1.5239\n-0.76613\n-0.8893\n-1.743\n-1.3033\n-0.51789\n0.052136\n-0.51452\n-0.25515\n-0.046391\n-0.07563\n-0.013218\n0.18138\n0.53663\n0.53946\n0.10928\n-0.0014647\n-0.2845\n-0.49426\n-0.37871\n-0.32656\n-0.44561\n-0.71426\n-1.2349\n-1.7891\n-2.7033\n-2.1356\n-2.0503\n-1.5044\n-0.88407\n0.46006\n-0.1061\n0.051815\n0.95884\n1.253\nNaN\nNaN\n1.3518\n1.6023\n1.9191\n3.3026\n4.6112\nNaN\nNaN\nNaN\n2.4071\n2.553\n2.9268\n1.939\n-0.053041\n-0.29061\n-0.7074\n-1.0627\n-0.19818\n0.40762\n0.17729\n-0.97156\n-0.52656\n0.12859\n0.89066\n0.8964\n0.29039\n-0.050169\n-0.21389\n-0.38014\n-0.38758\n0.038321\n-0.29741\n-0.73456\n-1.5662\n-1.8387\n-1.4855\n-0.79773\n-0.13658\n-0.14713\n-0.62896\n-0.52188\n-0.21852\n-0.36982\n-0.12401\n0.21524\n0.13427\n0.23834\n0.55635\n0.68167\n0.5833\n0.45318\n0.074565\n-0.26246\n-0.29497\n-0.22945\n-0.1041\n0.31117\n0.63491\n0.13083\n-1.0904\n-1.5573\n-2.6031\n-0.54981\n-0.3194\n-0.71965\n-0.22096\n0.18676\n-0.77015\n-0.95512\n0.27364\n0.81571\nNaN\nNaN\nNaN\n0.97265\n1.5479\n1.6612\n2.5791\nNaN\n2.7221\n2.4825\n1.524\n1.6036\n2.6325\n0.51931\n-0.35978\n0.12717\n0.33647\n0.077434\n-0.39546\n-0.56582\n-0.60615\n-0.58692\n-0.040451\n0.41231\n0.79951\n0.5238\n-0.099083\n-0.18687\n-0.14263\n-0.016985\n0.32144\n0.35043\n0.22271\n-0.53192\n-1.2901\n-1.5017\n-0.9639\n-0.38332\n0.1596\n0.27013\n0.0083131\n-0.55757\n-0.79957\n-0.42562\n0.42652\n0.32736\n0.39953\n0.7778\n1.1016\n1.2203\n0.85714\n0.46512\n0.63214\n0.27733\n0.12111\n0.20953\n0.48854\n0.72963\n1.1235\n1.273\n-0.23504\nNaN\nNaN\nNaN\n-0.43711\n-1.4881\n-0.52137\n-0.096111\n-2.0191\n-1.209\n-0.018935\n0.48029\nNaN\nNaN\nNaN\n0.79887\n1.2495\n0.64787\n0.96732\n1.5782\n2.0559\n1.3948\n0.90488\n1.0092\n0.55246\n-0.49935\n-0.56281\n0.31514\n0.63059\n0.23216\n-0.22028\n-0.43188\n-0.3703\n-0.065974\n0.17067\n0.43947\n0.36833\n-0.13116\n-0.66164\n-0.26002\n0.34858\n0.4582\n0.45535\n0.46475\n0.30919\n-0.36169\n-0.73349\n-0.8457\n-0.77486\n-0.28849\n0.013943\n0.48454\n0.48333\n-0.21219\n-0.60219\n-0.37673\n0.10423\n0.98073\n1.418\n1.5453\n1.2102\n1.1964\n1.1746\n0.75514\n1.0285\n0.73264\n0.62582\n0.94752\n1.0554\n1.6206\n2.5963\n1.7096\n0.20606\nNaN\nNaN\nNaN\n-1.0128\n-1.5766\n-2.4074\n-1.6765\n-1.7901\n-0.54219\n-0.048982\n0.1714\nNaN\nNaN\nNaN\n0.010105\n0.48442\n0.60373\n1.1033\n2.3487\n1.785\n0.97995\n0.46374\n0.20305\n-0.15065\n-0.78681\n-0.80517\n0.19304\n0.73536\n0.5521\n0.22792\n0.020452\n-0.42493\n-0.24973\n0.21078\n0.45387\n0.21709\n-0.23679\n-0.093872\n0.46168\n0.59875\n0.053814\n0.22983\n0.38916\n0.54342\n0.0090694\n-0.3759\n-0.4861\n-0.36817\n0.14206\n0.17402\n0.65139\n0.81387\n0.11847\n-0.27086\n0.21519\n0.16859\n1.4161\n1.6733\n1.6197\n1.3751\n1.5234\n1.5068\n1.4602\n1.0678\n1.0273\n1.0637\n1.46\n1.7889\n1.9967\n2.0605\n1.5846\n1.3785\n-0.12829\nNaN\nNaN\n-1.6017\n-1.3421\n-1.5097\n-1.3756\n-0.86727\n0.072346\n0.22973\n-0.067282\n0.21411\n-0.12605\n-0.21965\n-1.1991\n-0.34901\n0.32426\n0.97711\n2.0283\n1.5966\n1.275\n0.82685\n0.38422\n0.34899\n-0.30543\n-0.34126\n0.4393\n0.56094\n0.64989\n0.13376\n-0.74267\n-1.2799\n-0.93854\n0.077689\n0.42771\n0.4262\n0.9206\n0.28744\n0.39559\n0.59515\n-0.12613\n0.045626\n0.21588\n0.71011\n0.30902\n-0.45144\n-0.19377\n0.2221\n0.55655\n0.40511\n0.4972\n0.94373\n0.00043334\n0.26887\n0.94338\n0.47799\n1.1386\n1.3537\n1.511\n1.7292\n2.0678\n2.0865\n1.9374\n1.3803\n1.3707\n1.4621\n1.4266\n1.7387\n1.8807\n1.9402\n1.215\n1.0945\n0.40918\n-1.6751\n-2.2521\n-1.4582\n-1.1393\n-0.79648\n-0.67351\n-0.41332\n0.17083\n-0.0012927\n-0.12377\n0.031641\n0.13593\n0.0092393\n-0.36027\n-0.23115\n0.13857\n0.55598\n1.3904\n1.2958\n1.0227\n1.4426\n1.0045\n0.66638\n0.44678\n0.73326\n0.91631\n0.98888\n0.59517\n0.47062\n-0.68797\n-1.7178\n-2.2594\n-0.85391\n0.035659\n1.6285\n1.3583\n0.65846\n0.41452\n0.86752\n0.51601\n0.21688\n0.16926\n0.32686\n0.15853\n-0.21291\n0.56991\n0.70397\n0.5736\n0.49248\n1.1662\n1.2727\n0.26807\n0.47355\n0.34569\n0.17958\n0.86241\n1.403\n1.8114\n2.088\n2.6029\n2.8576\n2.4907\n2.3131\n1.7248\n1.2383\n1.334\n1.5984\n1.601\nNaN\nNaN\nNaN\nNaN\n-0.28754\n-1.3709\n-1.2017\n-0.46763\n-0.57036\n-0.90322\n-0.50753\n0.058315\n-0.23775\n0.12394\n0.59075\n0.201\n-0.13877\n-0.3066\n-0.39911\n-0.39887\n-0.27747\n-0.19303\n0.29015\n0.54754\n1.4371\n1.2809\n1.2924\n1.1122\n1.2921\n1.2903\n0.36969\n-0.023577\n0.66398\n0.20364\n-1.2729\n-1.2026\n-1.3163\n-0.50462\n0.32423\n0.67252\n0.23979\n0.31917\n0.99867\n0.32545\n0.091634\n0.35746\n0.010496\n-0.012676\n0.45683\n1.13\n0.58842\n0.031402\n0.49002\n1.6773\n1.4437\n0.81238\n0.15608\n-0.17924\n0.0083181\n1.4418\n1.6385\n2.0529\n2.4619\n2.6867\n2.7946\n2.9792\n3.0294\n2.5771\n2.0464\n1.7281\n1.754\n1.7932\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.272\n-0.91471\n-0.64236\n-0.51958\n-1.1319\n-0.80497\n-0.25591\n-0.11932\n0.13638\n0.32003\n-0.24263\n-0.39488\n-0.59815\n-0.60152\n-0.013305\n-0.3264\n-0.34388\n0.24929\n0.74457\n1.8911\n1.9797\n1.8501\n1.7243\n1.8197\n1.4321\n-0.77323\n-1.1879\n-0.61352\n0.56251\n-0.73614\n-0.59212\n-0.56778\n-0.48999\n-0.58766\n-0.58497\n-0.82548\n-0.27891\n0.21369\n-0.17739\n-0.11553\n0.61134\n0.59253\n0.2417\n0.74444\n0.92621\n0.58511\n-0.0032755\n1.0034\n2.236\n1.4944\n1.0736\n-0.14884\n-0.29649\n-0.30789\n1.8529\n1.7034\n2.1142\n2.8888\n3.0153\n3.9411\n3.9337\n3.7974\n3.0823\n2.4523\n1.9395\n1.7805\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0293\n-0.9425\n-1.0457\n-1.1234\n-1.2272\n-1.0149\n-1.1284\n-0.35369\n-0.17212\n-0.021043\n-0.31559\n-0.44552\n-0.16135\n-0.014425\n-0.11378\n-0.10365\n0.19544\n0.25266\n1.4246\n2.1568\n1.8718\n1.8865\n2.1139\n1.9571\n-0.7903\n-2.2307\n-1.824\n0.13515\n-0.44307\n-0.32248\n-0.50635\n-0.56691\n-0.16828\n-0.83422\n-0.83255\n-0.38311\n0.13438\n-0.37141\n-0.30423\n0.78623\n0.15702\n-0.097432\n0.16174\n0.53669\n0.42563\n0.7244\n1.0951\n1.5193\n1.2119\n1.1333\n-0.76901\n-0.92736\nNaN\n2.3526\n1.854\n2.7599\n3.5042\n3.7086\n4.6638\n5.4444\n4.8671\n3.0515\n2.5664\n2.1371\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.628\n-0.84117\n-0.86578\n-0.83062\n-1.1245\n-1.0596\n-0.86214\n-0.64139\n0.070824\n0.26995\n-0.21106\n-0.14685\n0.075069\n-0.021037\n-0.1045\n-0.27694\n-0.59951\n-0.63278\n0.53684\n1.2165\n1.0946\n1.5319\n1.4095\n0.41599\n-0.87929\n-2.0339\n-2.2675\n-0.73824\n-0.51464\n-0.23747\n-0.64206\n-0.12589\n0.45415\n0.69159\n0.58826\n0.22682\n0.14147\n-0.48656\n-0.4685\n0.67903\n-0.68106\n-0.37884\n-0.90684\n-0.55045\n0.72451\nNaN\nNaN\n0.8116\n1.0845\n1.4548\nNaN\nNaN\nNaN\n0.86603\n-0.041392\n-0.046879\n-0.15852\n-0.16768\n-0.51664\n-1.0603\n-0.58504\n0.11663\n0.18707\n-0.40437\n-0.20025\n-0.61897\n-0.18989\n0.47525\n0.37691\n0.55725\n0.22337\n1.4069\n1.141\n1.6783\n1.762\n1.9678\n0.65064\n1.2968\n0.57577\n-0.50592\n-1.6154\n-0.9697\n-0.81245\n-1.9635\n-1.6157\n-0.6133\n-0.74675\n0.047901\n-0.72536\n-0.71623\n-0.30512\n-0.91508\n-1.2052\n-0.55404\n0.19343\n0.80059\n1.2827\n1.3378\n0.7878\n1.0807\n0.94851\n0.58933\n0.3276\n0.30272\n0.99332\n0.80752\n-0.11074\n1.1123\n0.46163\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.44736\n-0.33606\n-0.23451\n1.0948\n1.1667\n-1.3833\n-1.3672\n-0.84038\n0.27207\n0.37891\n0.53969\n0.3626\n-0.65248\n0.37872\n0.84888\n-0.15975\n-1.1124\n-0.69496\n0.37934\n0.80363\n3.0224\n1.5738\n1.4147\n2.181\n1.5137\n1.0442\n0.34057\n-0.74294\n-0.074568\n-0.63528\n-0.42553\n-0.59992\n0.32618\n-0.16685\n-0.019983\n-1.2134\n-1.1442\n-0.25339\n-0.57422\n0.46062\n0.50236\n0.37358\n0.94998\n1.6259\n1.423\n0.95169\n0.94508\n0.60351\n0.33186\n0.24034\n0.27778\n0.71213\n0.71861\n1.0464\n2.5984\n1.0506\n-0.21952\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.7614\n-0.49217\n-0.26042\n-0.38425\n-1.3904\n-0.90324\n-1.2039\n-0.66335\n-0.25631\n1.0767\n1.2043\n0.79647\n0.34658\n1.0731\n2.2307\n0.28769\n-0.14859\n0.28002\n0.23515\n1.7081\n1.8156\n1.4652\n2.2528\n1.8823\n1.8444\n1.9549\n1.2645\n0.19614\n0.49141\n-0.69266\n-0.0065934\n-0.51025\n1.1901\n0.8776\n-1.4478\n-1.7583\n-0.81128\n-0.39082\n0.10116\n0.10778\n0.31826\n0.77505\n0.63832\n1.4283\n0.92694\n0.65249\n0.63165\n0.53711\n0.34064\n0.60004\n0.93525\n0.55436\n0.43385\n1.7265\n2.0467\n1.0687\n0.6475\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17416\n-0.4203\n-0.35128\n-0.2361\n-0.22064\n-0.042272\n0.38165\n0.15751\n-0.13562\n0.14749\n0.53949\n-0.35217\n2.0867\n1.9639\n1.4707\n0.20712\n0.19805\n0.21603\n0.3398\n1.531\n2.6932\n1.1829\n1.8724\n1.475\n2.1794\n2.2483\n1.2626\n0.69585\n0.067414\n-1.5217\n-0.26011\n0.17207\n1.3196\n1.0542\n-0.73061\n-1.2832\n-1.331\n-0.52111\n0.26491\n-0.045068\n-0.048961\n0.39837\n0.096792\n0.036013\n0.38168\n0.37201\n0.27416\n0.76552\n1.1343\n2.0622\n2.2888\n1.6212\n2.0598\n2.4336\n1.9509\n1.3233\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.052113\n-0.12773\n-0.084306\n-0.10442\n0.26848\n0.021033\n0.36139\n0.53398\n-0.08812\n-0.83376\n-0.4189\n-0.91945\n-0.84568\n0.24137\n-0.8157\n-0.90709\n-0.53844\n0.11376\n-0.39283\n0.62372\n3.2492\nNaN\nNaN\n-0.84371\n1.6965\n1.9915\n1.4657\n0.4028\n0.48784\n0.40902\n-0.29364\n0.51745\n0.76362\n0.8308\n0.67228\n-0.56087\n-1.7259\n-0.80554\n0.034175\n-0.032915\n0.081763\n0.55492\n0.12279\n0.15265\n0.15428\n0.30732\n0.91121\n1.7303\n1.6183\n1.5697\n2.1743\n1.9342\n1.533\n2.5181\n1.7886\n1.782\n3.3742\nNaN\nNaN\n-4.2139\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1401\n-0.29125\n-0.55033\n-0.096494\n0.51356\n0.0096835\n0.42364\n0.7927\n0.35983\n-0.058304\n0.037436\n0.68001\n0.56608\n-0.55968\n-1.2875\n-0.16187\n-0.83922\n-0.76663\n-0.7887\nNaN\nNaN\nNaN\nNaN\n-0.040313\n0.86503\n1.9224\n1.7802\n1.3351\n0.92632\n1.1488\n0.59496\n0.3187\n-0.15186\n-0.096291\n0.57974\n-0.59945\n-1.6469\n-0.63354\n-0.038222\n0.23396\n0.31668\n0.052921\n0.043084\n-0.31885\n-1.6135\n-0.43776\n0.94687\n1.8005\n1.5258\n1.7187\n1.7615\n1.669\n1.4673\n1.376\n1.0108\n0.302\n1.2534\nNaN\nNaN\n-5.7614\n-4.5123\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.37668\n-0.66558\n-0.43179\n0.48545\n0.88529\n0.19103\n0.9056\n0.91251\n0.90677\n0.58325\n0.41473\n0.25535\n0.23465\n-0.43454\n-0.74224\n-0.86184\n-0.98955\n-0.19987\n-0.32246\nNaN\nNaN\nNaN\nNaN\n-0.36955\n0.25278\n1.3021\n1.7927\n2.3054\n1.0909\n0.9916\n0.088944\n0.12419\n-1.1888\n-0.65879\n-0.48924\n-1.005\n-1.3809\n-0.80189\n-0.30843\n0.27134\n0.57319\n0.096035\n-0.53687\n-1.2157\n-3.1701\n-0.74727\n0.88137\n1.7156\n1.9452\n1.9641\n1.4023\n1.7156\n2.1061\n1.7907\n0.94439\n-0.20639\n0.78197\nNaN\nNaN\n-2.7084\n-4.6344\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12569\n0.02654\n-0.11214\n0.73043\n1.633\n0.064591\n0.43266\n0.97473\n1.1097\n0.77924\n0.42765\n0.47641\n0.019057\n0.24404\n-0.41122\n-0.57256\n-0.063623\n0.23001\n-0.15281\n-0.56595\n-0.35745\n0.10299\n-0.049122\n0.45749\n0.64259\n1.3169\n1.7477\n2.0227\n1.0043\n0.79051\n0.46547\n-1.1059\n-2.3\n-0.92877\n-0.56489\n-0.59173\n-0.64037\n-0.68421\n-0.56499\n-0.7073\n0.20779\n-0.05827\n-0.58739\n-1.3255\n-3.9427\n-0.64816\n1.2936\n1.5263\n1.368\n1.9527\n1.6448\n1.9406\n2.3339\n1.9793\n1.8181\n0.88178\n0.20326\n1.5837\n1.1675\n-0.35403\n-2.888\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.094797\n-0.55554\n-0.7824\n-0.23743\n0.77575\n-0.079818\n-0.036894\n1.0607\n1.2751\n1.0827\n0.62971\n0.66104\n0.75692\n0.38948\n0.32467\n0.58412\n0.45389\n0.7317\n-0.0056671\n0.056301\n0.1749\n0.087452\n0.58533\n1.0306\n1.0048\n1.5311\n1.5483\n0.93315\n0.67189\n0.38686\n0.46443\n0.26509\n-1.3546\n-1.1815\n-0.69129\n-0.62012\n-0.54125\n-0.64083\n-0.57525\n-0.58713\n-0.6185\n-0.29034\n-0.93359\n-1.9103\n-3.3603\n0.83936\n1.7786\n1.9822\n2.0243\n2.2227\n2.189\n2.6269\n2.6587\n3.2495\n3.1554\n1.258\n0.24969\n1.1988\n2.4652\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2449\n-0.51102\n-0.20658\n-0.56469\n0.97664\n-0.0982\n-0.1572\n0.4734\n0.94772\n1.0015\n0.77514\n0.75142\n0.92291\n0.4247\n0.81284\n1.0844\n0.75918\n1.3075\n1.1359\n1.4385\n1.0821\n0.77216\n1.2494\n0.87719\n0.52804\n0.98465\n1.6266\n1.4162\n0.7228\n0.12568\n0.14284\n-0.82396\n-1.8196\nNaN\nNaN\n-0.22464\n-0.87944\n-0.70263\n-0.86068\n-0.62856\n-0.31813\n0.060961\n-1.2917\n-1.9259\n-0.4553\n1.866\n1.5026\n1.9991\n2.1623\n2.7733\n2.624\n3.3212\n3.3091\n3.3777\n3.0333\n2.4583\n1.2975\n0.49348\n2.6454\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4899\n-1.0355\n-0.49042\n-0.52677\n0.15581\n-0.29683\n-0.19202\n-0.16633\n0.6353\n1.199\n0.91444\n0.39429\n1.0257\n0.74171\n1.3628\n1.66\n1.3053\n1.3255\n1.3251\n1.4505\n0.66039\n0.71298\n0.55276\n-0.13172\n0.50158\n2.3631\n2.0862\n1.5956\n1.0652\n0.92882\n0.13543\n-1.0392\nNaN\nNaN\nNaN\n-0.32393\n-0.70063\n-0.054174\n-1.2328\n-0.78121\n0.48544\n0.81675\n-1.0738\n-1.0541\n0.28258\n1.4461\n1.4819\n1.9706\n2.6418\n3.0624\n2.7393\n3.3762\n3.1441\n3.5511\nNaN\nNaN\n0.58155\n0.2582\n2.2264\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7772\n-1.7515\n-1.0809\n-0.5201\n-0.46326\n-0.24346\n0.21761\n0.56545\n0.85239\n1.0142\n0.52372\n0.63323\n1.155\n0.76764\n1.4274\n1.4932\n1.6347\n1.2766\n0.80419\n0.69747\n-0.071978\n0.11661\n0.070297\n-0.3804\n0.43602\n1.7648\n2.0583\n2.052\n1.192\n1.2997\n0.472\nNaN\nNaN\nNaN\nNaN\n-0.30698\n0.0024204\n-0.2535\n-1.0821\n-0.33781\n0.86602\n-0.037239\n-0.31238\n0.32677\n0.9137\n1.4988\n2.0535\n2.9342\n3.5083\n3.659\n3.1987\n3.4323\n2.6551\nNaN\nNaN\nNaN\n0.74625\n0.26026\n0.51498\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.39201\n-0.6871\n-0.33554\n-0.34756\n-0.16046\n0.23181\n0.45198\n0.34422\n0.77189\n0.73412\n0.67036\n0.85069\n0.64891\n1.0511\n1.551\n1.7066\n1.8767\n1.2653\n0.49257\n0.32812\n0.029887\n0.067561\n-0.61939\n0.20795\n0.45009\n0.40329\n2.1525\n1.6258\n0.82295\n1.2653\n0.92159\nNaN\nNaN\nNaN\nNaN\n-0.82191\n-0.50657\n-0.072652\n-0.28806\n0.57911\n0.78441\n-0.85886\n-1.3678\n0.87593\n1.5115\n1.9709\n2.6032\n3.4118\n3.8346\n3.9251\n3.7001\n3.1643\n2.0779\nNaN\nNaN\nNaN\n-1.3056\n-1.6367\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.38377\n0.014286\n-0.20249\n0.26651\n0.33711\n0.37431\n0.47097\n0.40198\n0.51957\n0.37412\n0.32729\n0.83465\n1.6354\n2.068\n1.8906\n1.743\n1.1384\n1.1223\n0.27824\n0.0090641\n0.34373\n-0.92851\n-1.5258\n-0.96242\n-1.34\n0.099307\n1.2573\n0.97652\n1.5119\n1.7813\n1.2852\nNaN\nNaN\nNaN\nNaN\n-1.1263\n-1.3721\n0.0098203\n0.0080753\n0.87001\n0.98995\n0.16972\n0.34968\n1.8691\n2.5282\n3.0958\n3.1195\n3.8365\n3.964\n3.9215\n3.5822\n2.4727\n1.8286\nNaN\nNaN\nNaN\nNaN\n-0.96823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.64842\n-0.24393\n0.39778\n0.97012\n0.61742\n0.31179\n0.55451\n0.33816\n0.2012\n0.29447\n0.083881\n0.776\n1.8407\n2.1144\n2.2018\n1.558\n1.7209\n1.6405\n0.93167\n0.19238\n-0.32088\n-1.9209\n-3.1849\n-2.0075\n-1.803\n0.17551\n0.58257\n1.1374\n1.9792\n1.8048\n1.7485\nNaN\n-0.53714\n-0.78289\n-0.035302\n-0.44591\n-0.56263\n1.3515\n1.5548\n2.8411\n2.5759\n0.70608\n0.78402\n3.9882\nNaN\nNaN\nNaN\n4.2657\n4.7999\n4.3085\n3.1122\n3.2723\n2.4594\n0.57317\nNaN\nNaN\nNaN\n-2.2013\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17071\n0.12486\n0.57133\n1.3584\n1.1349\n0.85891\n1.0139\n0.50675\n0.22197\n0.82476\n0.56179\n0.99991\n1.5193\n1.5785\n2.0247\n0.95044\n0.84424\n1.3273\n1.4545\n0.49958\n-0.28909\n-0.85669\n-4.5238\n-3.5844\n-1.9118\n-0.35021\n0.77283\n2.5148\n2.4531\n1.7117\n-0.046371\n-1.8411\n0.0022629\n0.2188\n0.087306\n0.38996\n1.8846\n2.6144\n3.1098\nNaN\nNaN\nNaN\n0.58392\nNaN\nNaN\nNaN\nNaN\n4.0516\n4.8879\n3.5305\n2.4188\n2.4683\n2.4082\n0.88497\n0.34534\n-1.0647\n-2.653\n-2.6068\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21387\n0.12408\n0.57568\n1.5148\n0.95493\n1.0906\n0.8133\n0.46709\n0.49824\n0.2804\n0.44119\n0.88219\n1.4194\n1.5475\n1.8434\n0.58447\n0.19156\n0.59224\n1.0302\n0.67348\n-0.27555\n-1.062\n-2.2602\n-2.8623\n-0.98298\n0.36097\n1.3092\n2.0833\n1.6106\n1.3905\n-0.67359\n-0.5424\n1.1063\n1.6175\n1.5964\n2.0954\n2.9012\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.74807\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9316\nNaN\n1.3595\n2.0769\n0.78413\n0.3983\n-0.20979\n-2.1923\n-2.3706\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41927\n-0.038294\n0.71474\n1.5326\n0.94652\n0.68524\n0.51344\n0.482\n0.55512\n0.11193\n0.43627\n0.6758\n1.23\n1.2471\n1.3655\n0.55952\n0.98757\n0.5536\n0.45353\n-0.11959\n-0.97367\n-1.9984\n-2.7163\n-3.0105\n-0.60732\n0.51143\n1.6398\n1.513\n1.4228\n0.82167\n1.2258\n1.3368\n1.1469\n1.7408\n2.4777\n3.0926\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26319\n0.25028\n-0.88506\n-2.1994\n-1.9342\n-2.5067\n-2.3125\n-0.60486\n0.19997\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.043295\n0.24619\n0.98664\n1.3655\n0.93943\n0.011804\n0.35791\n0.42907\n0.13168\n0.14498\n0.016969\n0.15215\n0.66219\n1.1248\n1.2653\n0.64926\n0.13496\n-0.11333\n-0.035759\n-0.44202\n-0.98758\n-2.3052\n-2.7183\n-2.29\n-0.52588\n0.54994\n1.0099\n1.1513\n1.8996\n1.5032\n0.95563\n2.0507\n2.553\n2.2191\n2.7503\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1371\n-0.27173\n-0.6884\n-1.121\n-0.21757\n-0.26005\n-0.4018\n0.71337\n0.73391\n-0.54686\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23568\n0.49186\n0.4001\n0.20202\n0.75888\n0.50683\n0.99947\n0.29166\n-0.50481\n-0.30847\n-0.17077\n-0.066446\n0.39407\n1.0684\n1.0313\n0.15484\n-0.845\n-1.3243\n-0.90668\n-0.61502\n-1.2106\n-2.5813\n-2.2001\n-1.2643\n0.52695\n1.4562\n0.92619\n1.2383\n2.2177\n1.8012\n0.82698\n2.2415\n2.3963\n2.098\n3.0498\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.2874\n-1.3734\n-1.1616\n-0.2822\n1.0571\n1.4539\n0.22044\n-0.029528\n0.62504\n-0.59336\n-0.12928\n0.96328\n1.4985\nNaN\nNaN\nNaN\nNaN\nNaN\n0.36004\n0.71502\n0.19981\n-0.15294\n1.1117\n1.1061\n-0.25084\n-0.72509\n-0.95361\n-0.099525\n0.22488\n0.24215\n-0.1465\n0.82472\n0.49825\n-0.060409\n-0.75794\n-0.78315\n-0.59615\n-1.4287\n-1.7999\n-3.0658\n-2.4269\n-0.43263\n0.77868\n2.2609\n2.4967\n2.5098\n2.43\n3.3211\n4.7562\n5.8196\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6808\n-0.75009\n-0.38472\n-0.55891\n-1.0902\n-0.30038\n1.5834\n1.9989\n0.60569\n0.94931\n1.3886\n1.4367\n0.85137\n0.66334\n1.3274\n1.7496\n1.6594\n2.0856\n0.84572\n0.29103\n0.13238\n0.1845\n-0.095481\n-0.076914\n0.57117\n0.54436\n-0.6016\n-0.62453\n-0.91576\n-0.15242\n-0.074584\n-0.46899\n0.095095\n0.44023\n0.28065\n0.43464\n0.051179\n-0.46063\n-0.090053\n-0.97643\n-0.58038\n-2.5322\n-2.8303\n-0.10031\n1.5914\n3.7945\n3.2262\n3.3855\n3.9787\n4.7999\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15149\n-0.2182\n0.075002\n0.82563\n0.50135\n0.20252\n-1.1386\n0.29191\n1.1729\n0.58768\n1.4642\n1.8217\n1.0629\n0.54532\n0.11865\nNaN\nNaN\nNaN\nNaN\n0.76791\n0.68841\n0.14804\n0.071852\n-0.0084983\n-0.59369\n-0.52453\n-0.59921\n-0.96263\n-0.75189\n-0.28732\n-0.41621\n-0.72421\n-0.61403\n0.035951\n0.19\n0.20561\n0.70593\n0.12217\n-0.97265\n-2.061\n-0.49736\n-0.60715\n-3.4827\n-2.8234\n-0.31379\n2.4001\n3.0122\n2.84\n3.7784\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5652\n1.0286\n0.66289\n-0.022359\n0.67051\n1.3551\n1.1249\n1.0143\n0.092464\n-0.015944\n-0.19483\n0.5981\n1.4559\n2.1456\n1.1663\n1.0774\n1.191\nNaN\nNaN\nNaN\nNaN\n0.022521\n1.0875\n0.21724\n-0.29432\n-1.3405\n-0.95938\n-0.46319\n-0.7006\n-1.1682\n-0.42194\n0.42951\n-0.082084\n-0.48734\n0.068921\n0.21844\n0.19704\n0.37512\n1.7704\n1.1292\n-0.681\n-2.3453\n-3.2186\n-2.0264\n-2.0498\n-1.2463\n0.46783\n1.5047\n2.4024\n2.0798\n2.3143\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.98238\n1.5431\n1.2083\n0.8311\n0.23317\n0.46677\n0.84383\n1.1458\n1.7587\n1.0736\n0.030032\n-0.56984\n0.078247\n0.65514\n1.8153\n0.43826\n1.1619\n1.4306\nNaN\nNaN\n-1.3621\n-0.27392\n0.16406\n0.088004\n-0.61886\n-0.85735\n-0.9871\n-0.69257\n-0.75586\n-0.11573\n-0.59642\n0.054261\n0.4466\n0.22656\n-0.33919\n-0.096797\n0.3872\n-0.16967\n-0.16309\n1.3425\n1.4082\n-0.0031539\n-2.0075\n-2.8841\n-1.4879\n-1.0592\n-0.7062\n1.0507\n1.98\n2.2533\n1.7498\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27893\n0.23432\n2.4462\n1.2397\n1.2391\n0.69941\n0.40896\n0.36172\n1.0615\n2.2049\n1.9695\n0.52496\n0.30832\n0.60673\n0.023548\n-0.52396\n0.27105\n1.3582\n1.0159\n-0.14816\n-0.81086\n0.20167\n0.7475\n0.90785\n0.75651\n-1.1833\n-0.85573\n-0.43897\n-0.54888\n-1.065\n0.14762\n-0.13159\n-0.17479\n0.13749\n-0.10901\n-0.42935\n0.25533\n0.45379\n0.54303\n1.0585\n1.264\n2.065\n0.078034\n-1.1802\n-1.2769\n-0.51125\n-0.67816\n-0.48063\n0.52097\n2.2603\n3.0461\n2.613\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.72555\n-0.16126\n2.1901\n1.1765\n1.4806\n1.1898\n-0.037244\n-0.10816\n0.66806\n2.2246\n2.1591\n1.5153\n0.91655\n1.1224\n0.35989\n-0.038731\n1.1619\n1.8971\n0.67829\n0.70684\n0.45103\n1.0079\n1.3912\n2.0154\n1.1604\n-0.97093\n-0.4363\n-0.10601\n-0.29272\n-0.73433\n-0.26743\n-0.22537\n-0.40858\n-0.09471\n-0.47409\n-0.59292\n-0.36432\n0.28517\n0.83655\n1.3443\n1.3887\n1.2883\n-0.088292\n-0.13564\n-0.81742\n-0.70129\n-0.40926\n0.35602\n0.9592\n2.4468\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.33823\n0.37193\n0.82154\n2.1774\n-0.41841\n0.67885\n1.0556\n0.77826\n1.8972\n2.2891\n2.3924\n2.2562\n2.4356\n1.764\n1.146\n0.51944\n-0.48191\n1.699\n1.3393\n1.4997\n1.3517\n1.2379\n1.5368\n1.7699\n1.5334\n0.97896\n-0.64349\n-0.042952\n-0.23605\n-0.48075\n-0.71469\n-0.644\n-0.72757\n-0.20456\n-0.13043\n-0.60293\n-0.48973\n-0.22363\n0.067894\n0.30211\n1.2156\n1.3569\n0.67135\n-0.40808\n-0.73711\n-0.53223\n-0.48967\n-0.23703\n0.11301\n0.91943\n2.4945\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9977\nNaN\nNaN\nNaN\nNaN\n1.1102\n1.0423\n0.85959\n0.45042\n1.1599\n-0.46834\n0.57115\n1.194\n2.871\n2.4547\n2.4466\n2.1441\n0.0081748\n2.1035\n1.1408\n0.56939\n-0.97571\n-0.10501\n1.8738\n1.7574\n2.1652\n1.6536\n1.6392\n1.8048\n1.8986\n1.0831\n-0.34408\n-0.26881\n-0.069865\n-0.6254\n-0.63375\n-0.26649\n-0.50212\n-0.65867\n-0.42241\n-0.59901\n-0.35479\n-0.29948\n-0.16221\n-0.1636\n0.34822\n1.019\n1.2175\n0.81201\n0.050142\n-0.80917\n-0.63028\n0.14244\n0.2533\n-0.45019\n1.0171\n3.4761\n5.0275\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8245\n2.0531\n2.1531\n1.4723\n0.45535\n0.017353\n0.56049\n0.44329\n0.13974\n1.3437\n2.8314\n1.7954\n1.8578\n0.86333\n-1.498\n-0.80899\n0.034563\n-0.13966\n-0.57176\n0.50838\n1.4661\n1.75\n1.4444\n1.4237\n0.7607\n1.5664\n1.3193\n0.14847\n-2.5767\n-0.12959\n-0.35351\n-0.30654\n-0.60452\n-0.74357\n-0.53533\n0.022893\n-0.34963\n-0.94805\n-0.47626\n-0.36546\n-0.12167\n-0.33303\n0.13714\n0.88082\n0.95822\n0.39236\n0.0025854\n-0.23175\n-1.5557\n-1.5503\n-0.014191\n-0.12474\n1.8408\n4.4831\n4.7493\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5636\nNaN\nNaN\n4.813\n3.383\n2.6839\n2.9779\n1.5771\n0.32167\n-0.34638\n-1.0888\n-0.74087\n-0.71271\n0.10789\n1.252\n0.28799\n-0.018927\n0.054529\n-0.61321\n-1.2325\n-1.4312\n-0.67943\n-0.23618\n0.22172\n0.94046\n0.91569\n0.59694\n0.86596\n0.67048\n0.29672\n0.24517\n0.0087988\n-3.4565\n-0.36643\n-0.51878\n-0.72143\n-0.89695\n-1.3405\n-0.51074\n-0.24393\n-0.41667\n-0.60496\n-0.39541\n-0.31568\n-0.48685\n-0.7275\n-0.48277\n0.3947\n0.10931\n0.039498\n-0.67748\n-0.95983\n-1.3859\n-2.0264\n-0.80691\n0.18958\n2.9539\n4.5895\n5.2823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.2399\n12.309\n8.3377\n6.5535\n3.0401\n2.5206\n3.0884\n2.3067\n0.62179\n0.25094\n-0.21827\n-0.73793\n-0.66424\n-0.59374\n-0.38046\n-0.38447\n-0.55834\n-0.73515\n-1.0185\n-1.7755\n-1.9558\n-0.53977\n0.33607\n0.32273\n-0.030657\n-0.44111\n0.23477\n0.95671\n0.60387\n-0.44583\n0.1996\n-0.31519\n-1.7113\n-0.72117\n-0.62509\n-0.65465\n-0.39669\n-0.69184\n-0.65302\n-0.72545\n-0.48576\n-0.52721\n-0.84702\n-0.37311\n-1.0427\n-1.7505\n-1.1609\n-0.4867\n-0.29183\n-0.72785\n-1.4978\n-1.6226\n-1.3745\n-1.3964\n-1.0836\n0.37261\n2.2354\n4.1656\n6.3269\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n15.152\n11.753\n7.9053\n5.862\n2.4385\n2.2938\n2.0984\n2.0264\n1.2139\n0.36192\n-0.52884\n-0.56176\n-0.15373\n0.041199\n-0.055016\n-1.4769\n-1.8193\n-1.185\n-1.3202\n-2.4978\n-3.0776\n-1.0936\n-0.089169\n-0.11461\n-0.19375\n-0.77469\n0.022038\n0.049765\n0.71843\n-0.6083\n-0.98688\n-0.5392\n-0.27172\n-0.50513\n-0.44732\n-0.4723\n-0.081451\n-0.51489\n-0.65111\n-0.85716\n-0.79527\n-0.68521\n-0.96842\n-1.0337\n-1.1354\n-1.7985\n-1.2865\n-1.0576\n-0.62331\n-0.91778\n-2.3628\n-2.641\n-1.1813\n-1.0977\n-1.5326\n-0.17794\n2.2395\n3.3901\n2.4185\n1.7877\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.317\n6.8225\n6.5557\n3.6261\n2.6547\n0.20442\n1.0631\n1.4453\n0.46774\n-0.047213\n-0.34223\n-0.26359\n-0.022106\n-0.13109\n-0.56743\n-1.3459\n-2.6873\n-1.5073\n-0.95112\n-1.4541\n-2.5541\n-2.4364\n-0.9524\n-0.42606\n-0.14925\n-0.3472\n-0.31735\n-0.4817\n-0.95803\n-1.387\n-0.72781\n0.039923\n-0.034689\n-0.2175\n-0.48476\n-0.57964\n-0.16313\n0.073045\n-0.12074\n-0.81559\n-0.82172\n-0.42303\n-0.60002\n-1.1633\n-1.2386\n-1.3076\n-1.1381\n-1.0275\n-0.64851\n-0.44975\n-1.011\n-1.929\n-1.8061\n-1.0141\n-1.5867\n-0.35691\n2.3544\n2.4925\n0.36041\n0.28389\n-0.95099\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.3409\n5.8373\n4.0291\n3.9763\n2.7436\n0.494\n1.2915\n1.2249\n0.02092\n-1.267\n-1.3728\n-0.038527\n-0.063621\n-0.43887\n-1.5813\n-1.455\n-1.5094\n-1.1934\n-0.52882\n-0.72063\n-0.94774\n-1.6542\n-1.4456\n-0.56375\n-0.63364\n-0.58008\n-0.75906\n-0.88487\n-1.3787\n-1.1397\n-0.60591\n-0.10313\n-0.27596\n-0.28354\n-0.82218\n-0.73911\n-0.39922\n0.27197\n0.097363\n-0.39897\n-0.36223\n-0.33976\n-0.72236\n-1.6033\n-1.147\n-0.85209\n-1.3061\n-1.6573\n-1.1747\n-0.2746\n-1.2221\n-1.1413\n-2.5119\n-2.3081\n-1.5502\n-0.02219\n1.0085\n1.2521\n0.39315\n-0.1212\n0.39036\nNaN\nNaN\n2.6982\n3.8739\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.0091\n4.5188\n3.6904\n3.6717\n1.6519\n0.12946\n0.48516\n-0.33128\n-0.56192\n-2.3062\n-1.2544\n0.23615\n0.033324\n-0.64205\n-0.77172\n-0.9285\n-0.85325\n-0.74525\n-0.50665\n-0.81671\n-1.295\n-1.4987\n-1.4015\n-1.0315\n-1.3537\n-1.5913\n-2.7924\n-3.102\n-1.442\n-0.77731\n-0.54308\n0.29322\n0.0696\n-0.39899\n-0.48908\n-0.59308\n-0.74872\n0.28246\n0.69978\n0.22581\n0.066011\n-0.35075\n-0.6541\n-1.3235\n-0.76218\n-0.76433\n-1.2823\n-1.6107\n-0.67174\n-0.6521\n-2.0432\n-2.7466\n-2.514\n-2.146\n-1.3788\n-0.44371\n0.8795\n-0.56627\n-1.8191\n-0.75431\n1.6652\n1.7676\n3.0783\n3.8043\n3.1161\n3.3993\n4.0813\nNaN\nNaN\nNaN\nNaN\n4.6779\n4.8184\n3.3382\n2.798\n3.0721\n0.1025\n-1.7389\n-0.95179\n-0.74186\n-0.89743\n-1.7146\n-0.93957\n0.4466\n0.3387\n0.53416\n-0.2789\n-1.0318\n-0.96516\n-0.59303\n-0.64597\n-0.58667\n-0.72171\n-1.3241\n-1.4631\n-2.4335\n-2.61\n-2.0337\n-1.964\n-1.9858\n-2.0342\n-1.6377\n-0.80029\n0.40518\n-0.068637\n0.077443\n-0.22578\n-0.55195\n-0.53451\n-0.1474\n0.60539\n0.65634\n0.24237\n-0.016734\n-0.47144\n-0.86632\n-0.42514\n-0.5269\n-0.93298\n-1.2924\n-1.1708\n-1.9246\n-3.1928\n-3.0644\n-2.3651\n-2.0253\n-0.46099\n0.17363\n0.6738\n2.4237\n1.2006\n1.3504\n1.7416\nNaN\n1.772\n2.7369\n1.5385\n3.2371\n5.2928\nNaN\nNaN\nNaN\n3.3343\n4.0331\n3.742\n2.859\n0.97402\n1.215\n0.20303\n-1.8648\n-1.406\n0.34105\n-0.48492\n-1.6526\n-0.94858\n0.38748\n0.73819\n0.92734\n-0.401\n-0.56019\n-0.73543\n-0.7544\n-0.69524\n-0.18167\n-0.42448\n-1.2849\n-1.7397\n-2.0614\n-2.311\n-1.8005\n-0.89936\n-1.05\n-2.0094\n-1.5269\n-0.62579\n0.022476\n-0.61746\n-0.43678\n-0.18777\n-0.18982\n-0.12967\n0.11868\n0.56778\n0.55261\n0.044583\n-0.099937\n-0.40248\n-0.64863\n-0.49331\n-0.43181\n-0.63151\n-0.92195\n-1.4903\n-2.1753\n-3.2942\n-2.5749\n-2.4365\n-1.7623\n-1.0428\n0.56989\n-0.073399\n0.12443\n1.1302\n1.3939\nNaN\nNaN\n1.4649\n1.7542\n2.1214\n3.7266\n5.2875\nNaN\nNaN\nNaN\n2.6931\n2.8715\n3.3734\n2.2138\n-0.1146\n-0.40969\n-0.91189\n-1.3368\n-0.29248\n0.41621\n0.15715\n-1.1601\n-0.68204\n0.11793\n1.0249\n0.97881\n0.26271\n-0.10625\n-0.29044\n-0.46418\n-0.44684\n0.048586\n-0.33532\n-0.84496\n-1.8227\n-2.1629\n-1.7651\n-0.94261\n-0.17832\n-0.19535\n-0.72107\n-0.59826\n-0.25766\n-0.42907\n-0.13573\n0.062177\n-0.0044644\n0.18188\n0.55606\n0.70157\n0.62164\n0.45528\n-0.0003404\n-0.37851\n-0.41465\n-0.34131\n-0.17765\n0.28254\n0.60423\n0.074951\n-1.3174\n-1.87\n-3.1573\n-0.70729\n-0.48711\n-0.94674\n-0.27231\n0.27903\n-0.7808\n-1.0716\n0.29334\n0.90075\nNaN\nNaN\nNaN\n0.99022\n1.7102\n1.8982\n2.9582\nNaN\n3.1241\n2.825\n1.6813\n1.7891\n3.0194\n0.63002\n-0.42335\n0.083163\n0.34247\n0.034208\n-0.51103\n-0.71182\n-0.7508\n-0.7077\n-0.067114\n0.46859\n0.9119\n0.55915\n-0.18706\n-0.27603\n-0.18991\n-0.016696\n0.38109\n0.41627\n0.26822\n-0.60655\n-1.4846\n-1.7461\n-1.1376\n-0.44603\n0.15733\n0.28319\n0.011703\n-0.66942\n-0.96499\n-0.48444\n0.51013\n0.21113\n0.28785\n0.77547\n1.1693\n1.3206\n0.92406\n0.45902\n0.64373\n0.26276\n0.074844\n0.19572\n0.52749\n0.77841\n1.248\n1.4815\n-0.29803\nNaN\nNaN\nNaN\n-0.59865\n-1.8183\n-0.64003\n-0.10457\n-2.3072\n-1.4021\n-0.044318\n0.54194\nNaN\nNaN\nNaN\n0.80175\n1.3742\n0.71033\n1.0945\n1.7914\n2.3398\n1.5827\n0.99243\n1.1157\n0.57296\n-0.60031\n-0.65227\n0.35036\n0.69566\n0.21795\n-0.29581\n-0.50627\n-0.41504\n-0.063743\n0.17368\n0.4902\n0.4059\n-0.17821\n-0.85444\n-0.36678\n0.37502\n0.50888\n0.53017\n0.56259\n0.37536\n-0.42037\n-0.84015\n-0.98287\n-0.91022\n-0.35113\n-0.016556\n0.5407\n0.55318\n-0.29866\n-0.76818\n-0.47054\n0.13981\n1.0118\n1.4882\n1.6254\n1.2808\n1.2766\n1.284\n0.75992\n1.0925\n0.79196\n0.65882\n1.0314\n1.1772\n1.8225\n2.954\n1.9716\n0.20746\nNaN\nNaN\nNaN\n-1.2521\n-1.9145\n-2.8808\n-1.9936\n-2.086\n-0.67476\n-0.11565\n0.12867\nNaN\nNaN\nNaN\n-0.0682\n0.47597\n0.6244\n1.2243\n2.7055\n2.0378\n1.0798\n0.48354\n0.1905\n-0.25872\n-0.92039\n-0.93502\n0.19633\n0.81517\n0.59687\n0.2332\n0.035483\n-0.46258\n-0.26709\n0.23218\n0.49699\n0.25434\n-0.27129\n-0.14734\n0.49684\n0.6471\n-0.004336\n0.27063\n0.47125\n0.63065\n-0.0094995\n-0.44643\n-0.57694\n-0.44329\n0.14288\n0.18663\n0.7956\n0.93914\n0.089309\n-0.35984\n0.22323\n0.21013\n1.5285\n1.8189\n1.7574\n1.4598\n1.6646\n1.6565\n1.5996\n1.1392\n1.1176\n1.147\n1.6137\n2.0156\n2.2565\n2.3118\n1.7288\n1.499\n-0.22109\nNaN\nNaN\n-1.968\n-1.6551\n-1.8368\n-1.6883\n-1.1008\n0.01131\n0.17673\n-0.14722\n0.19791\n-0.19571\n-0.28584\n-1.4491\n-0.49118\n0.29785\n1.0621\n2.3269\n1.822\n1.4319\n0.91624\n0.44484\n0.40651\n-0.36957\n-0.40467\n0.47374\n0.62966\n0.72937\n0.16311\n-0.83626\n-1.4811\n-1.0825\n0.072406\n0.47016\n0.48322\n1.0762\n0.31777\n0.44891\n0.65658\n-0.19813\n0.051532\n0.25156\n0.80188\n0.33914\n-0.55677\n-0.23861\n0.25346\n0.63398\n0.46264\n0.58035\n1.0899\n-0.034802\n0.26657\n1.0848\n0.55721\n1.1961\n1.437\n1.6317\n1.8711\n2.2685\n2.3032\n2.1359\n1.5014\n1.4794\n1.5835\n1.5599\n1.9276\n2.1066\n2.1709\n1.3339\n1.1821\n0.37373\n-2.0041\n-2.685\n-1.8097\n-1.4401\n-1.0265\n-0.89\n-0.55889\n0.13321\n-0.06514\n-0.19435\n-0.013066\n0.12961\n-0.012732\n-0.46334\n-0.32415\n0.075389\n0.54299\n1.5527\n1.4788\n1.1434\n1.6496\n1.1435\n0.75063\n0.50088\n0.8563\n1.0449\n1.133\n0.70457\n0.57572\n-0.78307\n-2.0167\n-2.6781\n-1.0222\n0.020072\n1.8637\n1.5802\n0.77288\n0.47226\n1.0002\n0.57739\n0.23639\n0.16977\n0.34377\n0.15301\n-0.28798\n0.65471\n0.8174\n0.6601\n0.54343\n1.3329\n1.462\n0.25706\n0.50729\n0.38087\n0.17287\n0.86752\n1.5071\n1.9996\n2.2887\n2.8896\n3.1901\n2.753\n2.5626\n1.9187\n1.3632\n1.4512\n1.7506\n1.7724\nNaN\nNaN\nNaN\nNaN\n-0.42732\n-1.7077\n-1.5412\n-0.64043\n-0.76677\n-1.1606\n-0.66506\n0.034883\n-0.30912\n0.091718\n0.63674\n0.19642\n-0.18938\n-0.38243\n-0.47482\n-0.5456\n-0.40045\n-0.26126\n0.2985\n0.59917\n1.6421\n1.4489\n1.4705\n1.267\n1.5\n1.4864\n0.4068\n-0.017435\n0.80588\n0.26089\n-1.4918\n-1.4369\n-1.5441\n-0.60014\n0.34104\n0.76803\n0.25776\n0.34239\n1.1408\n0.33506\n0.046089\n0.36395\n-0.029925\n-0.059863\n0.47579\n1.2599\n0.65828\n-0.00042991\n0.53785\n1.9192\n1.6557\n0.92798\n0.16179\n-0.22833\n0.0083264\n1.5344\n1.7731\n2.2427\n2.7053\n2.9921\n3.1239\n3.31\n3.4025\n2.9176\n2.316\n1.9048\n1.9085\n1.9883\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5936\n-1.1995\n-0.85742\n-0.71363\n-1.412\n-0.99648\n-0.31856\n-0.17306\n0.079022\n0.2919\n-0.33475\n-0.50385\n-0.72999\n-0.73432\n-0.074526\n-0.44435\n-0.46576\n0.22314\n0.82179\n2.1792\n2.2733\n2.1146\n1.9878\n2.1154\n1.6519\n-0.93091\n-1.3908\n-0.69979\n0.68974\n-0.88069\n-0.72099\n-0.68798\n-0.59001\n-0.71032\n-0.71589\n-0.96744\n-0.35334\n0.20542\n-0.26009\n-0.1876\n0.62056\n0.59818\n0.2134\n0.80306\n1.0424\n0.64969\n-0.081929\n1.0902\n2.5581\n1.7186\n1.2576\n-0.1495\n-0.33682\n-0.35384\n2.0316\n1.8278\n2.3146\n3.1995\n3.3851\n4.4853\n4.4482\n4.3119\n3.5255\n2.7901\n2.1237\n1.8929\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3408\n-1.2061\n-1.3392\n-1.4059\n-1.5141\n-1.2431\n-1.3873\n-0.50501\n-0.29011\n-0.090253\n-0.40927\n-0.58199\n-0.26932\n-0.10452\n-0.18611\n-0.18\n0.1473\n0.22913\n1.622\n2.5043\n2.1396\n2.175\n2.4526\n2.2649\n-0.95251\n-2.6274\n-2.1464\n0.16374\n-0.52899\n-0.39239\n-0.60813\n-0.69411\n-0.24496\n-1.0105\n-1.0095\n-0.49986\n0.1058\n-0.52892\n-0.45572\n0.79452\n0.042365\n-0.18145\n0.13552\n0.58022\n0.46175\n0.75358\n1.1809\n1.7098\n1.3898\n1.3151\n-0.90156\n-1.089\nNaN\n2.6336\n2.0075\n3.0841\n3.9431\n4.2155\n5.3353\n6.1799\n5.5592\n3.4931\n2.9214\n2.3665\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9794\n-1.0707\n-1.126\n-1.0628\n-1.3952\n-1.3161\n-1.099\n-0.83294\n-0.015103\n0.2306\n-0.31752\n-0.25771\n-0.0024261\n-0.087164\n-0.17618\n-0.37202\n-0.77614\n-0.80497\n0.57374\n1.3946\n1.2445\n1.7624\n1.6222\n0.45639\n-1.0334\n-2.4031\n-2.6988\n-0.9\n-0.64187\n-0.30374\n-0.79295\n-0.1893\n0.49364\n0.75853\n0.60591\n0.21135\n0.11317\n-0.67205\n-0.65635\n0.68283\n-0.90954\n-0.48971\n-1.1124\n-0.68006\n0.81927\nNaN\nNaN\n0.89307\n1.219\n1.6704\nNaN\nNaN\nNaN\n0.85751\n-0.17569\n-0.15966\n-0.33503\n-0.35426\n-0.6971\n-1.2847\n-0.86426\n-0.066862\n0.081956\n-0.58939\n-0.37324\n-0.85687\n-0.36694\n0.49987\n0.38534\n0.57344\n0.11497\n1.5058\n1.2497\n1.8554\n1.8655\n2.1725\n0.80533\n1.548\n0.62108\n-0.5982\n-1.8872\n-1.1567\n-0.93112\n-2.4187\n-1.9831\n-0.884\n-1.0883\n-0.096046\n-0.94701\n-0.93578\n-0.39675\n-1.124\n-1.4901\n-0.7107\n0.084032\n0.80375\n1.4311\n1.4775\n0.85141\n1.1906\n1.0621\n0.65168\n0.38315\n0.38586\n1.1113\n0.91736\n-0.11977\n1.3657\n0.5599\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65772\n-0.54943\n-0.41395\n1.171\n1.2077\n-1.7962\n-1.5824\n-1.1429\n0.16239\n0.30575\n0.48925\n0.28248\n-0.90034\n0.37741\n0.9882\n-0.13659\n-1.4411\n-0.9563\n0.32002\n0.78199\n3.2837\n1.6386\n1.5598\n2.5363\n1.8697\n1.2442\n0.38342\n-0.83882\n-0.10992\n-0.75465\n-0.63213\n-0.84847\n0.12785\n-0.41019\n-0.20221\n-1.5241\n-1.4519\n-0.3306\n-0.73513\n0.3833\n0.53006\n0.31229\n1.0104\n1.7143\n1.5414\n1.0435\n1.0595\n0.68238\n0.38974\n0.28524\n0.31616\n0.8251\n0.83351\n1.2329\n3.0806\n1.2292\n-0.19884\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0467\n-0.73884\n-0.45156\n-0.63387\n-2.0161\n-1.4557\n-1.7116\n-0.87547\n-0.40658\n1.1444\n1.2417\n0.68613\n0.13206\n1.2037\n2.6326\n0.39993\n-0.30395\n0.15675\n0.18192\n1.8794\n1.9591\n1.665\n2.573\n2.1686\n2.2502\n2.2888\n1.4463\n0.22806\n0.51324\n-0.86307\n-0.063712\n-0.69058\n1.0003\n0.60791\n-1.8328\n-2.1603\n-1.078\n-0.52682\n0.048281\n0.065067\n0.33318\n0.83308\n0.68358\n1.5732\n1.0124\n0.67399\n0.6394\n0.5792\n0.36543\n0.66779\n1.0254\n0.67314\n0.55898\n1.9577\n2.3501\n1.3348\n0.83433\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35982\n-0.65694\n-0.55333\n-0.43178\n-0.43236\n-0.21793\n0.24699\n0.0076168\n-0.36524\n-0.024467\n0.47067\n-0.69078\n1.9384\n2.2417\n1.6233\n0.11311\n0.060546\n0.068553\n0.2029\n1.6454\n2.9701\n1.4427\n2.1543\n1.5991\n2.513\n2.6163\n1.5155\n0.78604\n0.10707\n-1.5171\n-0.39029\n0.11989\n1.1152\n0.8237\n-1.1092\n-1.7402\n-1.6786\n-0.68265\n0.22105\n-0.075406\n-0.073209\n0.41412\n0.033334\n0.036356\n0.4272\n0.32543\n0.22062\n0.7964\n1.1984\n2.2734\n2.5546\n1.8453\n2.3467\n2.7305\n2.3126\n1.6605\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12566\n-0.28945\n-0.22781\n-0.26681\n0.12227\n-0.15431\n0.19608\n0.37453\n-0.30645\n-1.124\n-0.65798\n-1.2337\n-1.0693\n0.13157\n-1.0991\n-1.1709\n-0.82637\n-0.15671\n-0.79637\n0.58149\n3.6051\nNaN\nNaN\n-1.0906\n1.9033\n2.3437\n1.8657\n0.61051\n0.65719\n0.58465\n-0.47587\n0.33412\n0.5321\n0.69477\n0.44499\n-0.74937\n-2.0466\n-0.97941\n-0.022533\n-0.067369\n0.060844\n0.56015\n0.055397\n0.081636\n0.1342\n0.26147\n0.9303\n1.8616\n1.727\n1.6748\n2.4262\n2.1858\n1.7653\n2.8712\n2.0943\n2.1159\n3.9957\nNaN\nNaN\n-4.9181\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.0091538\n-0.47944\n-0.72762\n-0.26743\n0.38137\n-0.19277\n0.28516\n0.67356\n0.21857\n-0.22734\n-0.099504\n0.61942\n0.47301\n-0.81946\n-1.6307\n-0.37475\n-1.187\n-1.096\n-0.90327\nNaN\nNaN\nNaN\nNaN\n-0.054409\n1.0971\n2.3625\n2.2261\n1.6376\n1.1214\n1.3546\n0.61961\n0.10618\n-0.30539\n-0.18588\n0.39719\n-0.92533\n-2.0098\n-0.82933\n-0.122\n0.21495\n0.33698\n0.010675\n-0.038826\n-0.42542\n-1.9174\n-0.62856\n0.94735\n1.998\n1.6749\n1.8568\n1.9648\n1.889\n1.7037\n1.5708\n1.1545\n0.19666\n1.3157\nNaN\nNaN\n-6.8809\n-5.0953\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.57823\n-0.90571\n-0.62913\n0.3714\n0.82827\n0.020958\n0.84384\n0.8842\n0.87566\n0.58577\n0.37709\n0.15987\n0.16865\n-0.66686\n-0.97594\n-1.1259\n-1.2723\n-0.26341\n-0.50259\nNaN\nNaN\nNaN\nNaN\n-0.32863\n0.38476\n1.6369\n2.2332\n2.7272\n1.2871\n1.1559\n0.053011\n-0.056008\n-1.4777\n-0.85778\n-0.73751\n-1.3458\n-1.6979\n-1.0209\n-0.47297\n0.18245\n0.63023\n0.020245\n-0.73797\n-1.4692\n-3.689\n-0.98267\n0.87093\n1.8653\n2.1476\n2.1661\n1.5418\n1.9142\n2.3385\n2.0177\n1.0631\n-0.30415\n0.81051\nNaN\nNaN\n-3.224\n-5.3803\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.29555\n-0.12755\n-0.26293\n0.69033\n1.7083\n-0.044456\n0.37349\n0.99611\n1.1679\n0.85826\n0.46173\n0.4655\n0.0001161\n0.25257\n-0.51587\n-0.72802\n-0.18362\n0.22408\n-0.2511\n-0.69786\n-0.39604\n0.15518\n-0.010154\n0.56065\n0.82168\n1.6334\n2.1569\n2.5046\n1.225\n0.95728\n0.53481\n-1.2571\n-2.6311\n-1.1386\n-0.72171\n-0.78789\n-0.77351\n-0.92468\n-0.82317\n-0.94467\n0.15943\n-0.1919\n-0.80818\n-1.66\n-4.5993\n-0.86598\n1.3259\n1.6196\n1.4575\n2.148\n1.7555\n2.1089\n2.6098\n2.263\n2.0892\n0.92618\n0.21041\n1.7376\n1.336\n-0.48453\n-3.4523\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.0080164\n-0.74483\n-0.95941\n-0.37765\n0.79011\n-0.1325\n-0.10086\n1.1347\n1.3527\n1.1679\n0.67126\n0.72585\n0.82245\n0.41699\n0.3178\n0.5887\n0.49213\n0.83324\n-0.014445\n0.084686\n0.1986\n0.078643\n0.67502\n1.1843\n1.1995\n1.8883\n1.9597\n1.2751\n0.86119\n0.49007\n0.55482\n0.28565\n-1.4818\n-1.3854\n-0.81757\n-0.71741\n-0.66636\n-0.88965\n-0.82436\n-0.80157\n-0.82172\n-0.44734\n-1.1909\n-2.3398\n-4.0083\n0.82638\n1.8916\n2.1028\n2.1699\n2.4183\n2.3911\n2.9419\n3.0181\n3.7388\n3.5948\n1.3384\n0.17968\n1.3182\n2.7323\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5438\n-0.6993\n-0.31886\n-0.74636\n0.98043\n-0.16656\n-0.25478\n0.40114\n0.94913\n1.0535\n0.83236\n0.8165\n1.0232\n0.49415\n0.93981\n1.2073\n0.84972\n1.454\n1.2561\n1.6286\n1.2563\n0.94736\n1.4945\n1.0308\n0.63255\n1.2445\n2.062\n1.8214\n0.92043\n0.20423\n0.27736\n-0.93451\n-2.148\nNaN\nNaN\n-0.23513\n-0.94754\n-0.85658\n-1.1324\n-0.86268\n-0.48045\n-0.060015\n-1.6055\n-2.3044\n-0.61208\n1.9689\n1.5506\n2.1147\n2.3243\n3.0063\n2.8534\n3.72\n3.7593\n3.8936\n3.4533\n2.6847\n1.2422\n0.34452\n2.8912\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8671\n-1.2898\n-0.64784\n-0.7094\n0.068724\n-0.40401\n-0.30119\n-0.27766\n0.61284\n1.2786\n1.0313\n0.50062\n1.2381\n0.87832\n1.5923\n1.9152\n1.4364\n1.4726\n1.512\n1.7119\n0.84157\n0.94511\n0.81739\n-0.087898\n0.57405\n2.7626\n2.5924\n2.0495\n1.3257\n1.1899\n0.29391\n-1.1136\nNaN\nNaN\nNaN\n-0.37103\n-0.83029\n-0.13849\n-1.5794\n-1.0579\n0.36344\n0.66558\n-1.399\n-1.3764\n0.14176\n1.443\n1.4765\n2.0722\n2.8725\n3.2405\n2.9295\n3.7284\n3.5465\n4.1069\nNaN\nNaN\n0.40054\n0.076173\n2.3964\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1901\n-2.0554\n-1.3026\n-0.6814\n-0.61967\n-0.34536\n0.16305\n0.53753\n0.88466\n1.1042\n0.60952\n0.73675\n1.3594\n0.92424\n1.6885\n1.7309\n1.8065\n1.4704\n1.0302\n0.89748\n-0.0037528\n0.29977\n0.32767\n-0.30032\n0.57101\n2.0969\n2.5484\n2.571\n1.5821\n1.6874\n0.66402\nNaN\nNaN\nNaN\nNaN\n-0.37126\n-0.020421\n-0.38065\n-1.5406\n-0.59879\n0.84178\n-0.14387\n-0.55394\n0.10435\n0.75463\n1.4343\n2.0864\n3.1\n3.8647\n3.9857\n3.4703\n3.7904\n2.9651\nNaN\nNaN\nNaN\n0.65118\n0.029912\n0.5552\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.61273\n-0.87767\n-0.49923\n-0.52806\n-0.28263\n0.18672\n0.42448\n0.34032\n0.79406\n0.77686\n0.77974\n0.9912\n0.83296\n1.261\n1.8329\n2.0294\n2.218\n1.5523\n0.73913\n0.48841\n0.15214\n0.23365\n-0.60632\n0.28253\n0.62635\n0.59925\n2.661\n2.0661\n1.1419\n1.603\n1.1688\nNaN\nNaN\nNaN\nNaN\n-0.97512\n-0.63532\n-0.16295\n-0.59667\n0.371\n0.64093\n-1.2519\n-1.8726\n0.7109\n1.4692\n2.0047\n2.7166\n3.6588\n4.2303\n4.3198\n4.0237\n3.4933\n2.2639\nNaN\nNaN\nNaN\n-1.7537\n-2.0736\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.25508\n-0.11748\n-0.35615\n0.1542\n0.28721\n0.33055\n0.44478\n0.35432\n0.52961\n0.41977\n0.38702\n0.97506\n1.8551\n2.3362\n2.1953\n2.1132\n1.4926\n1.4097\n0.45371\n0.1399\n0.51995\n-0.90276\n-1.7896\n-1.1359\n-1.4595\n0.29576\n1.6722\n1.3459\n1.9346\n2.2296\n1.6392\nNaN\nNaN\nNaN\nNaN\n-1.2233\n-1.5935\n-0.055119\n-0.24048\n0.74899\n0.83286\n-0.16519\n0.023108\n1.8647\n2.7013\n3.2993\n3.3144\n4.1439\n4.3426\n4.3203\n3.8683\n2.649\n1.945\nNaN\nNaN\nNaN\nNaN\n-1.3867\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.58212\n-0.42224\n0.3013\n0.96869\n0.59347\n0.2579\n0.51256\n0.29626\n0.21299\n0.31061\n0.10876\n0.89617\n2.0807\n2.3944\n2.5299\n1.8935\n2.0417\n1.9738\n1.1754\n0.29808\n-0.18411\n-2.1144\n-3.7332\n-2.358\n-2.0173\n0.3821\n0.90072\n1.5443\n2.5179\n2.3209\n2.3155\nNaN\n-0.67746\n-1.0074\n-0.013598\n-0.24728\n-0.64998\n1.3806\n1.4971\n2.9478\n2.595\n0.48182\n0.49138\n4.2231\nNaN\nNaN\nNaN\n4.6031\n5.2175\n4.6897\n3.3338\n3.5473\n2.6607\n0.53227\nNaN\nNaN\nNaN\n-2.6976\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.057811\n-0.041767\n0.4927\n1.4304\n1.1863\n0.86546\n1.0389\n0.49792\n0.20968\n0.86425\n0.63398\n1.1436\n1.7595\n1.8487\n2.3241\n1.1417\n1.0448\n1.6366\n1.8168\n0.66906\n-0.2013\n-0.96511\n-5.2987\n-4.2601\n-2.1473\n-0.21736\n1.158\n3.2102\n3.1163\n2.1702\n0.27525\n-2.0595\n0.028959\n0.13779\n0.028314\n0.58205\n2.0863\n2.759\n3.2963\nNaN\nNaN\nNaN\n0.33715\nNaN\nNaN\nNaN\nNaN\n4.3404\n5.2615\n3.761\n2.5854\n2.5887\n2.6061\n0.76209\n0.15646\n-1.4494\n-3.2557\n-3.1715\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35604\n-0.016629\n0.54077\n1.6388\n1.0198\n1.1398\n0.8583\n0.47607\n0.48349\n0.33726\n0.51293\n1.01\n1.6692\n1.8136\n2.0771\n0.7134\n0.3497\n0.84503\n1.3359\n0.90667\n-0.24134\n-1.2547\n-2.6111\n-3.3701\n-1.043\n0.56869\n1.7968\n2.6289\n2.1008\n1.7018\n-0.52304\n-0.25049\n1.565\n1.7365\n1.7894\n2.5656\n3.1459\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.64575\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1131\nNaN\n1.501\n2.2012\n0.68294\n0.23289\n-0.44707\n-2.7455\n-2.9832\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.58121\n-0.12989\n0.76117\n1.6788\n1.037\n0.73122\n0.55787\n0.55945\n0.56777\n0.16495\n0.51208\n0.82743\n1.4476\n1.4985\n1.6059\n0.78827\n1.2979\n0.76476\n0.64601\n-0.030984\n-1.0714\n-2.3445\n-3.0978\n-3.4021\n-0.60604\n0.77124\n2.1239\n1.8206\n1.765\n1.0606\n1.7271\n1.7915\n1.603\n2.2341\n3.0677\n3.7014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.46892\n0.099926\n-1.2001\n-2.695\n-2.3286\n-2.9663\n-2.8163\n-0.79156\n0.055883\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15209\n0.23617\n1.0735\n1.4894\n1.0355\n-0.017864\n0.39084\n0.50172\n0.15164\n0.18681\n0.05484\n0.29798\n0.86365\n1.381\n1.563\n0.88446\n0.26202\n-0.08378\n0.054956\n-0.44603\n-1.1138\n-2.7069\n-3.1365\n-2.5116\n-0.48771\n0.79744\n1.3213\n1.444\n2.4146\n1.8973\n1.4927\n2.5984\n3.0972\n2.7444\n3.3727\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5191\n-0.47786\n-0.96941\n-1.4406\n-0.36713\n-0.43832\n-0.60233\n0.78169\n0.87459\n-0.63765\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1835\n0.50481\n0.41626\n0.18664\n0.83467\n0.56412\n1.1186\n0.32906\n-0.57174\n-0.2945\n-0.12053\n0.060354\n0.56184\n1.2731\n1.2212\n0.28624\n-0.85689\n-1.4713\n-0.95589\n-0.68768\n-1.3026\n-3.0283\n-2.5059\n-1.3286\n0.72669\n1.8298\n1.2438\n1.6543\n2.845\n2.4246\n1.3971\n2.8247\n2.9291\n2.6251\n3.7684\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8402\n-1.7198\n-1.4508\n-0.46892\n1.0402\n1.5166\n0.11549\n-0.11449\n0.74561\n-0.64265\n-0.042918\n1.1184\n1.6681\nNaN\nNaN\nNaN\nNaN\nNaN\n0.30325\n0.74595\n0.18025\n-0.19277\n1.2085\n1.2056\n-0.28661\n-0.81066\n-1.0343\n-0.023283\n0.33078\n0.3651\n-0.085508\n0.95786\n0.57689\n0.0020348\n-0.74382\n-0.79999\n-0.67537\n-1.4779\n-1.8751\n-3.5493\n-2.7183\n-0.31559\n1.0545\n2.786\n3.0763\n3.0572\n3.0536\n4.2032\n5.8263\n6.8875\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.0892\n-1.0887\n-0.64351\n-0.81054\n-1.3558\n-0.49079\n1.6183\n2.1388\n0.58884\n1.0792\n1.5876\n1.5417\n0.96793\n0.84546\n1.4176\n1.8498\n1.7495\n2.1491\n0.83408\n0.2205\n0.083093\n0.12303\n-0.17444\n-0.10149\n0.66417\n0.60051\n-0.66579\n-0.66436\n-0.94493\n-0.095795\n-0.017412\n-0.43968\n0.21266\n0.54118\n0.30927\n0.54321\n0.070794\n-0.56462\n-0.19888\n-0.98396\n-0.49664\n-2.7813\n-3.1835\n0.072603\n2.0286\n4.6228\n3.9247\n4.0258\n4.9402\n5.9541\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.051416\n-0.50856\n-0.1406\n0.78826\n0.43438\n0.096979\n-1.4671\n0.22374\n1.2854\n0.60553\n1.6925\n2.1033\n1.1283\n0.59029\n0.15373\nNaN\nNaN\nNaN\nNaN\n0.94119\n0.81633\n0.099932\n0.0054943\n-0.062139\n-0.70005\n-0.6307\n-0.68018\n-1.043\n-0.82601\n-0.25628\n-0.40033\n-0.74647\n-0.61035\n0.11248\n0.28912\n0.32867\n0.91454\n0.29175\n-1.1121\n-2.3851\n-0.53686\n-0.56895\n-3.7209\n-3.0497\n-0.18327\n2.9683\n3.6608\n3.422\n4.4662\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4967\n0.95852\n0.53037\n-0.21273\n0.6342\n1.4037\n1.1443\n1.0445\n0.0023808\n-0.093769\n-0.27993\n0.59256\n1.6329\n2.4688\n1.2433\n1.2071\n1.2495\nNaN\nNaN\nNaN\nNaN\n0.052891\n1.4086\n0.12956\n-0.41372\n-1.6155\n-1.1538\n-0.55999\n-0.7896\n-1.273\n-0.44997\n0.53336\n-0.029957\n-0.47879\n0.15061\n0.34158\n0.30967\n0.55281\n2.1335\n1.4243\n-0.67766\n-2.5926\n-3.5648\n-2.2393\n-2.2448\n-1.3184\n0.72024\n1.9503\n2.8945\n2.4791\n2.7464\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.83793\n1.4738\n1.1174\n0.7265\n0.12261\n0.43938\n0.87533\n1.2259\n1.9446\n1.1525\n-0.029047\n-0.65496\n0.061902\n0.65041\n2.0261\n0.44902\n1.3449\n1.6263\nNaN\nNaN\n-1.2031\n-0.16895\n0.22339\n0.24515\n-0.78387\n-1.0898\n-1.2182\n-0.8457\n-0.87468\n-0.12465\n-0.65035\n0.093798\n0.53874\n0.31514\n-0.30825\n-0.0082355\n0.57334\n-0.057004\n0.0024115\n1.6869\n1.7853\n0.066003\n-2.2156\n-3.1719\n-1.6171\n-1.1122\n-0.71114\n1.3402\n2.4022\n2.9767\n2.4235\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.60384\n-0.003773\n2.5306\n1.1802\n1.2557\n0.70916\n0.3844\n0.29945\n1.1966\n2.5809\n2.2383\n0.5732\n0.3498\n0.64178\n-0.05538\n-0.62213\n0.31117\n1.5539\n1.1104\n-0.18514\n-0.9889\n0.33833\n0.942\n1.1138\n0.92913\n-1.494\n-1.0799\n-0.57821\n-0.66302\n-1.2104\n0.18351\n-0.08348\n-0.16778\n0.21249\n-0.061719\n-0.36667\n0.4197\n0.64965\n0.75295\n1.2837\n1.597\n2.5951\n0.16893\n-1.2722\n-1.3768\n-0.44617\n-0.64764\n-0.43753\n0.72575\n2.7574\n3.6953\n3.396\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0969\n-0.43785\n2.2327\n1.0729\n1.5198\n1.2724\n-0.15181\n-0.27632\n0.69602\n2.5902\n2.508\n1.7569\n1.061\n1.2521\n0.37803\n-0.058856\n1.3071\n2.148\n0.73847\n0.67164\n0.39847\n1.2262\n1.7219\n2.4258\n1.4056\n-1.2241\n-0.60494\n-0.21997\n-0.35824\n-0.81863\n-0.26229\n-0.18315\n-0.38273\n-0.055517\n-0.41545\n-0.58575\n-0.35714\n0.43173\n1.0712\n1.6163\n1.7494\n1.6565\n0.00047626\n-0.079065\n-0.87562\n-0.70393\n-0.34212\n0.52442\n1.2437\n2.9778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.38975\n0.11729\n0.59596\n2.1839\n-1.0366\n0.43747\n1.1219\n0.79236\n2.1315\n2.5904\n2.7362\n2.595\n2.7901\n2.0195\n1.3507\n0.57572\n-0.49874\n1.9463\n1.5354\n1.7425\n1.3688\n1.3171\n1.8376\n2.1269\n1.9349\n1.2691\n-0.88637\n-0.1615\n-0.39443\n-0.56615\n-0.82327\n-0.72379\n-0.80267\n-0.20379\n-0.1298\n-0.65666\n-0.52173\n-0.22448\n0.13978\n0.49747\n1.4868\n1.6617\n0.87476\n-0.36734\n-0.75467\n-0.53423\n-0.43559\n-0.14826\n0.22701\n1.1526\n3.023\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.313\nNaN\nNaN\nNaN\nNaN\n1.2612\n0.91709\n0.74207\n0.27133\n1.0977\n-1.0911\n0.46601\n1.3285\n3.2659\n2.7879\n2.7953\n2.4463\n-0.07514\n2.376\n1.2672\n0.68713\n-1.1592\n-0.099455\n2.1965\n2.0305\n2.5366\n1.8454\n1.9233\n2.1733\n2.2774\n1.3793\n-0.21298\n-0.42963\n-0.16831\n-0.77801\n-0.75659\n-0.33521\n-0.58905\n-0.75805\n-0.4921\n-0.69603\n-0.40005\n-0.28838\n-0.12172\n-0.12178\n0.53289\n1.2\n1.4534\n1.0094\n0.119\n-0.891\n-0.64496\n0.22036\n0.34365\n-0.44096\n1.2986\n4.2216\n6.107\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.172\n2.3006\n2.175\n1.4479\n0.37185\n-0.13937\n0.43373\n0.39469\n0.091337\n1.4971\n3.2187\n2.0165\n2.1194\n0.97758\n-1.7314\n-0.99079\n0.063665\n-0.046322\n-0.6897\n0.58225\n1.7505\n2.111\n1.7737\n1.6552\n0.98735\n1.9812\n1.6351\n0.30367\n-2.7638\n-0.29888\n-0.47739\n-0.37999\n-0.75832\n-0.90667\n-0.65594\n-0.063655\n-0.47794\n-1.1179\n-0.4938\n-0.355\n-0.1384\n-0.37217\n0.2224\n1.0081\n1.1193\n0.47091\n-0.010991\n-0.26046\n-1.7829\n-1.7266\n0.064674\n0.036176\n2.3537\n5.4161\n5.7826\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2145\nNaN\nNaN\n5.444\n3.8713\n3.0468\n3.3528\n1.6135\n0.17509\n-0.57269\n-1.4395\n-1.0175\n-0.98533\n-0.057171\n1.3295\n0.20419\n-0.13786\n0.066328\n-0.66457\n-1.4877\n-1.7454\n-0.76518\n-0.31727\n0.26471\n1.1442\n1.1235\n0.75085\n1.0355\n0.87689\n0.56754\n0.4245\n0.12465\n-3.7935\n-0.57547\n-0.69795\n-0.89853\n-1.0952\n-1.5468\n-0.62483\n-0.33411\n-0.51551\n-0.75152\n-0.45028\n-0.34245\n-0.5698\n-0.78728\n-0.50011\n0.42394\n0.045592\n-0.070121\n-0.86668\n-1.1553\n-1.5818\n-2.236\n-0.73613\n0.45362\n3.6905\n5.5658\n6.4327\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.1839\n13.872\n9.4005\n7.4031\n3.5643\n2.949\n3.3078\n2.4894\n0.56665\n0.14053\n-0.42338\n-1.0654\n-0.95054\n-0.88935\n-0.61313\n-0.60833\n-0.74421\n-0.92152\n-1.2007\n-2.078\n-2.3619\n-0.6\n0.39912\n0.4068\n-0.020584\n-0.52057\n0.22103\n1.0898\n0.73609\n-0.40974\n0.29382\n-0.28951\n-1.8615\n-1.0118\n-0.83301\n-0.88414\n-0.5456\n-0.87019\n-0.82994\n-0.91563\n-0.60001\n-0.64133\n-0.97418\n-0.43813\n-1.2207\n-2.0268\n-1.3164\n-0.60396\n-0.44117\n-0.98017\n-1.7869\n-1.874\n-1.6326\n-1.6651\n-1.2305\n0.63081\n2.7927\n5.0059\n7.5582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.997\n13.267\n8.9833\n6.7088\n2.8907\n2.3888\n2.237\n2.1133\n1.2185\n0.21871\n-0.84694\n-0.87289\n-0.40425\n-0.14589\n-0.22966\n-1.8364\n-2.2362\n-1.471\n-1.5908\n-2.9034\n-3.5174\n-1.2611\n-0.078083\n-0.10798\n-0.19983\n-0.87107\n-0.040026\n-0.0056848\n0.78607\n-0.6957\n-1.1525\n-0.61656\n-0.27814\n-0.70156\n-0.66897\n-0.72133\n-0.2059\n-0.71143\n-0.86619\n-1.079\n-0.97002\n-0.84115\n-1.1397\n-1.2256\n-1.3519\n-2.0864\n-1.4744\n-1.2784\n-0.78316\n-1.186\n-2.884\n-3.2646\n-1.4656\n-1.2579\n-1.7118\n-0.044273\n2.7537\n4.0658\n3.0839\n2.2259\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.887\n7.8881\n7.5788\n4.2275\n3.1062\n-0.031802\n1.0021\n1.412\n0.30841\n-0.26024\n-0.59742\n-0.50874\n-0.22894\n-0.32924\n-0.7872\n-1.6374\n-3.2025\n-1.8128\n-1.1606\n-1.6546\n-2.856\n-2.7736\n-1.0898\n-0.44865\n-0.13358\n-0.3474\n-0.381\n-0.58283\n-1.1664\n-1.5824\n-0.81984\n0.059187\n0.035463\n-0.41451\n-0.74682\n-0.85867\n-0.34664\n-0.098092\n-0.25462\n-1.0368\n-1.0266\n-0.56504\n-0.75239\n-1.4142\n-1.4819\n-1.5332\n-1.3452\n-1.2711\n-0.80735\n-0.65399\n-1.3491\n-2.425\n-2.2877\n-1.1745\n-1.784\n-0.24809\n2.8619\n3.1207\n0.46893\n0.23865\n-1.0561\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.603\n6.7199\n4.6646\n4.5487\n3.1048\n0.27881\n1.2041\n1.1477\n-0.31637\n-1.6344\n-1.7297\n-0.25938\n-0.25482\n-0.65766\n-1.9504\n-1.7206\n-1.7593\n-1.4044\n-0.63097\n-0.77737\n-0.99571\n-1.7421\n-1.6002\n-0.60975\n-0.72077\n-0.63116\n-0.88534\n-1.018\n-1.5806\n-1.2001\n-0.57172\n0.039728\n-0.18249\n-0.52433\n-1.1301\n-1.0374\n-0.64778\n0.13778\n0.025836\n-0.5726\n-0.50382\n-0.48196\n-0.84008\n-1.9019\n-1.3771\n-1.0133\n-1.5713\n-1.9825\n-1.435\n-0.46199\n-1.5882\n-1.4559\n-3.1338\n-2.9237\n-1.8874\n0.045817\n1.3239\n1.648\n0.61246\n-0.22203\n0.40677\nNaN\nNaN\n3.0349\n4.6269\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.9016\n5.2015\n4.2329\n4.1811\n1.51\n-0.29837\n0.30146\n-0.62889\n-1.0328\n-2.8067\n-1.5911\n0.090598\n-0.11259\n-0.85265\n-0.99146\n-1.1355\n-1.0296\n-0.90543\n-0.62489\n-0.93945\n-1.4115\n-1.5931\n-1.5595\n-1.1207\n-1.4948\n-1.7971\n-3.0464\n-3.3723\n-1.5524\n-0.78579\n-0.49025\n0.44437\n0.18069\n-0.6794\n-0.80267\n-0.85488\n-1.0496\n0.11357\n0.68059\n0.12676\n-0.053588\n-0.52468\n-0.79919\n-1.6112\n-0.9582\n-0.95208\n-1.5973\n-1.9844\n-0.91972\n-0.91323\n-2.4845\n-3.2408\n-2.9491\n-2.5171\n-1.6038\n-0.44867\n1.1199\n-0.56707\n-2.0416\n-0.86099\n1.8826\n1.9703\n3.5216\n4.2942\n3.7725\n4.0645\n4.8738\nNaN\nNaN\nNaN\nNaN\n5.1919\n5.4469\n3.7281\n3.1102\n3.3288\n-0.18671\n-2.3118\n-1.3118\n-1.1404\n-1.3499\n-2.1107\n-1.2121\n0.42712\n0.33302\n0.53707\n-0.3544\n-1.2611\n-1.1993\n-0.74876\n-0.76749\n-0.66874\n-0.7797\n-1.4697\n-1.667\n-2.7086\n-2.9991\n-2.3442\n-2.2469\n-2.2738\n-2.2482\n-1.8709\n-0.88825\n0.45292\n-0.10462\n-0.19065\n-0.53737\n-0.89805\n-0.85265\n-0.40897\n0.55685\n0.59673\n0.10518\n-0.23079\n-0.65192\n-1.1174\n-0.59248\n-0.71724\n-1.2148\n-1.6394\n-1.4576\n-2.4244\n-4.0532\n-3.6778\n-2.7767\n-2.3115\n-0.46001\n0.25116\n0.85465\n3.0244\n1.3609\n1.5362\n1.9757\nNaN\n1.8706\n3.0184\n1.6422\n3.8921\n6.2895\nNaN\nNaN\nNaN\n3.6352\n4.5108\n4.2766\n3.2438\n1.1118\n1.3877\n0.043219\n-2.3869\n-1.8076\n0.23405\n-0.66979\n-1.9265\n-1.1155\n0.40294\n0.80977\n0.99275\n-0.49226\n-0.66739\n-0.90937\n-0.92453\n-0.82052\n-0.20792\n-0.4555\n-1.4283\n-1.9907\n-2.3885\n-2.7131\n-2.0888\n-1.0226\n-1.2139\n-2.283\n-1.7815\n-0.76693\n-0.044733\n-0.75432\n-0.80198\n-0.50609\n-0.47527\n-0.4311\n-0.10631\n0.48067\n0.44598\n-0.16651\n-0.35823\n-0.6516\n-0.90979\n-0.69036\n-0.60122\n-0.86139\n-1.2082\n-1.8188\n-2.6927\n-4.1462\n-3.1201\n-2.8721\n-2.019\n-1.1958\n0.75474\n0.060092\n0.3355\n1.325\n1.5159\nNaN\nNaN\n1.431\n1.8739\n2.2922\n4.1104\n5.9515\nNaN\nNaN\nNaN\n2.9323\n3.1719\n3.8684\n2.5022\n-0.21802\n-0.61603\n-1.2108\n-1.7185\n-0.43703\n0.41409\n0.10968\n-1.3609\n-0.86279\n0.085454\n1.1643\n1.0291\n0.18154\n-0.18905\n-0.40844\n-0.57308\n-0.49836\n0.06813\n-0.35814\n-0.93248\n-2.0616\n-2.4782\n-2.068\n-1.1071\n-0.22257\n-0.24521\n-0.78931\n-0.66791\n-0.32922\n-0.48006\n-0.118\n-0.3094\n-0.33191\n-0.028559\n0.40205\n0.57227\n0.53701\n0.32728\n-0.24158\n-0.65645\n-0.68513\n-0.57136\n-0.34224\n0.15093\n0.49636\n-0.037499\n-1.6194\n-2.2506\n-3.9571\n-0.96261\n-0.85458\n-1.3094\n-0.32813\n0.47612\n-0.64817\n-1.0681\n0.31437\n0.9263\nNaN\nNaN\nNaN\n0.92416\n1.8415\n2.1528\n3.3423\nNaN\n3.5569\n3.1625\n1.8015\n1.977\n3.4416\n0.79617\n-0.47455\n-0.019956\n0.31612\n-0.052716\n-0.65579\n-0.86761\n-0.93068\n-0.85719\n-0.10754\n0.52331\n1.0342\n0.57494\n-0.35148\n-0.41523\n-0.25853\n-0.028655\n0.45185\n0.48729\n0.31608\n-0.66603\n-1.6695\n-1.9912\n-1.3302\n-0.51905\n0.15295\n0.30255\n0.046785\n-0.7675\n-1.1407\n-0.52004\n0.61121\n-0.10337\n-0.020718\n0.58634\n1.0715\n1.2803\n0.87615\n0.3065\n0.48761\n0.081227\n-0.11799\n0.081939\n0.48311\n0.75367\n1.3502\n1.7187\n-0.41695\nNaN\nNaN\nNaN\n-0.94563\n-2.2714\n-0.76376\n-0.050227\n-2.5416\n-1.5873\n-0.12893\n0.58363\nNaN\nNaN\nNaN\n0.73929\n1.4812\n0.76507\n1.2411\n2.01\n2.6255\n1.7883\n1.1\n1.2403\n0.58741\n-0.69408\n-0.72754\n0.38613\n0.74917\n0.19604\n-0.38084\n-0.56528\n-0.42994\n-0.027177\n0.17833\n0.54441\n0.44203\n-0.22561\n-1.0768\n-0.50317\n0.38709\n0.54346\n0.61161\n0.67096\n0.45954\n-0.48247\n-0.9588\n-1.1309\n-1.0729\n-0.42261\n-0.048673\n0.6009\n0.63952\n-0.39541\n-0.96979\n-0.56644\n0.20824\n0.8417\n1.3337\n1.4901\n1.1656\n1.197\n1.2621\n0.58285\n0.99796\n0.71242\n0.55964\n0.9951\n1.2096\n1.9531\n3.2886\n2.2888\n0.18388\nNaN\nNaN\nNaN\n-1.6284\n-2.3341\n-3.4283\n-2.361\n-2.3787\n-0.85621\n-0.2686\n0.010444\nNaN\nNaN\nNaN\n-0.19029\n0.41161\n0.5857\n1.3225\n3.0933\n2.3104\n1.213\n0.53581\n0.19693\n-0.37114\n-1.0243\n-1.0442\n0.19799\n0.89173\n0.65134\n0.20582\n0.025736\n-0.50962\n-0.29334\n0.25542\n0.5358\n0.28961\n-0.27913\n-0.18546\n0.52785\n0.67456\n-0.088469\n0.34808\n0.57618\n0.71894\n-0.056755\n-0.52848\n-0.65561\n-0.51788\n0.13671\n0.21172\n0.9639\n1.0747\n0.054816\n-0.44591\n0.23531\n0.28904\n1.4448\n1.7878\n1.7157\n1.3366\n1.6455\n1.644\n1.5858\n1.0782\n1.1082\n1.0925\n1.6327\n2.1277\n2.4189\n2.48\n1.8012\n1.5587\n-0.35648\nNaN\nNaN\n-2.4436\n-2.063\n-2.2538\n-2.093\n-1.4212\n-0.11773\n0.034464\n-0.29894\n0.11392\n-0.34129\n-0.40185\n-1.7443\n-0.7032\n0.2045\n1.1104\n2.6597\n2.0983\n1.6635\n1.059\n0.55153\n0.52224\n-0.39261\n-0.42134\n0.49475\n0.73441\n0.85968\n0.1929\n-0.97495\n-1.7901\n-1.3293\n0.078256\n0.50868\n0.52293\n1.2579\n0.37396\n0.51\n0.71025\n-0.27928\n0.093714\n0.31762\n0.88043\n0.34224\n-0.67918\n-0.28439\n0.28968\n0.71392\n0.53995\n0.67732\n1.2581\n-0.051694\n0.26049\n1.2382\n0.66519\n1.0505\n1.3456\n1.5821\n1.8119\n2.2662\n2.334\n2.1767\n1.4887\n1.4419\n1.5436\n1.5377\n1.9705\n2.2124\n2.3154\n1.4539\n1.2669\n0.20918\n-2.4484\n-3.2627\n-2.3119\n-1.873\n-1.3638\n-1.1916\n-0.79089\n0.025538\n-0.20292\n-0.32504\n-0.12533\n0.065047\n-0.082907\n-0.61609\n-0.51211\n-0.093143\n0.47243\n1.7226\n1.7274\n1.3172\n1.9197\n1.3146\n0.84496\n0.55432\n1.0164\n1.1749\n1.2824\n0.88384\n0.74997\n-0.92159\n-2.4346\n-3.2485\n-1.1833\n-0.015575\n2.0845\n1.8184\n0.91336\n0.52738\n1.145\n0.67007\n0.29843\n0.18709\n0.34699\n0.12906\n-0.3802\n0.72098\n0.91856\n0.74847\n0.59041\n1.5048\n1.6655\n0.25362\n0.5456\n0.44413\n0.19165\n0.68303\n1.4552\n2.0459\n2.319\n2.989\n3.3384\n2.8599\n2.6874\n1.9569\n1.3435\n1.3986\n1.7375\n1.7933\nNaN\nNaN\nNaN\nNaN\n-0.69811\n-2.1804\n-2.0354\n-0.9299\n-1.0884\n-1.5526\n-0.92979\n-0.053862\n-0.41971\n-2.1376e-05\n0.60983\n0.13269\n-0.28786\n-0.49335\n-0.55081\n-0.7528\n-0.55884\n-0.29745\n0.34877\n0.72492\n1.9288\n1.6403\n1.664\n1.4367\n1.7401\n1.6945\n0.43009\n0.022953\n1.0227\n0.34955\n-1.8066\n-1.7581\n-1.8502\n-0.75196\n0.3133\n0.84251\n0.26037\n0.3731\n1.3226\n0.3304\n-0.01832\n0.35076\n-0.080222\n-0.0897\n0.49825\n1.3598\n0.70736\n-0.014979\n0.60977\n2.1606\n1.8687\n1.0425\n0.1963\n-0.22917\n0.072695\n1.4516\n1.7624\n2.2976\n2.7981\n3.1437\n3.3231\n3.5046\n3.6885\n3.154\n2.4713\n1.9235\n1.8992\n2.0632\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9995\n-1.6092\n-1.2203\n-1.0564\n-1.8407\n-1.3141\n-0.4605\n-0.27829\n-0.067616\n0.18389\n-0.48464\n-0.66059\n-0.88987\n-0.8695\n-0.1467\n-0.57357\n-0.58399\n0.22975\n0.97684\n2.5819\n2.6264\n2.4057\n2.2753\n2.4286\n1.8733\n-1.1089\n-1.5882\n-0.7465\n0.94456\n-1.0857\n-0.89367\n-0.84855\n-0.72134\n-0.86391\n-0.87858\n-1.0849\n-0.3967\n0.20586\n-0.37299\n-0.26616\n0.59392\n0.58489\n0.21143\n0.87066\n1.152\n0.71908\n-0.13782\n1.18\n2.8747\n1.9491\n1.4466\n-0.12073\n-0.32888\n-0.35478\n2.0649\n1.794\n2.4003\n3.3908\n3.6473\n4.9281\n4.8551\n4.7761\n3.9264\n3.0539\n2.1868\n1.8348\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8396\n-1.6119\n-1.7953\n-1.8315\n-1.9309\n-1.5972\n-1.7689\n-0.76755\n-0.51013\n-0.24142\n-0.54557\n-0.76669\n-0.41961\n-0.19726\n-0.22284\n-0.23079\n0.12278\n0.27475\n1.9188\n2.937\n2.4237\n2.5019\n2.8192\n2.5506\n-1.1425\n-3.0504\n-2.4693\n0.28882\n-0.63646\n-0.48598\n-0.72612\n-0.84018\n-0.36728\n-1.1912\n-1.174\n-0.58512\n0.10603\n-0.73873\n-0.65387\n0.74643\n-0.10498\n-0.25323\n0.10025\n0.64114\n0.52543\n0.79069\n1.2747\n1.9371\n1.5773\n1.5034\n-1.0256\n-1.2601\nNaN\n2.7983\n2.0067\n3.3159\n4.3075\n4.6819\n5.9636\n6.8579\n6.2605\n3.9375\n3.2423\n2.5176\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.442\n-1.4115\n-1.529\n-1.4129\n-1.7965\n-1.6932\n-1.4404\n-1.1289\n-0.21927\n0.08862\n-0.47625\n-0.41525\n-0.13604\n-0.11467\n-0.18559\n-0.39774\n-0.91439\n-0.92622\n0.64028\n1.6339\n1.4364\n2.0458\n1.8474\n0.46155\n-1.2306\n-2.8461\n-3.1545\n-1.0279\n-0.79483\n-0.39671\n-0.97877\n-0.29424\n0.52278\n0.8141\n0.60517\n0.20631\n0.10398\n-0.89391\n-0.87816\n0.647\n-1.1654\n-0.5929\n-1.3464\n-0.81197\n0.95196\nNaN\nNaN\n0.99182\n1.3178\n1.8817\nNaN\nNaN\nNaN\n0.77805\n-0.3701\n-0.33173\n-0.58831\n-0.60423\n-0.89833\n-1.5008\n-1.2236\n-0.33918\n-0.087175\n-0.85109\n-0.64882\n-1.1665\n-0.60354\n0.53349\n0.41922\n0.59838\n-0.033353\n1.5025\n1.339\n2.0316\n1.9229\n2.3893\n1.0236\n1.8552\n0.68571\n-0.67562\n-2.1665\n-1.3591\n-1.0384\n-2.988\n-2.4607\n-1.2862\n-1.5678\n-0.33374\n-1.267\n-1.2467\n-0.5413\n-1.3623\n-1.804\n-0.88601\n-0.04715\n0.76368\n1.5456\n1.5875\n0.87135\n1.2685\n1.1459\n0.67652\n0.40891\n0.46338\n1.1926\n0.96711\n-0.1923\n1.5723\n0.56261\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.96719\n-0.85952\n-0.70643\n1.1956\n1.2159\n-2.2973\n-1.744\n-1.5228\n-0.041017\n0.16842\n0.32477\n0.05126\n-1.2375\n0.40907\n1.1813\n-0.087183\n-1.8674\n-1.2955\n0.1985\n0.69798\n3.4544\n1.6438\n1.7298\n2.965\n2.3358\n1.5195\n0.46142\n-0.88555\n-0.11767\n-0.86886\n-0.89803\n-1.1874\n-0.20558\n-0.75519\n-0.49893\n-1.9288\n-1.8433\n-0.46651\n-0.94935\n0.25548\n0.54662\n0.21881\n1.0157\n1.729\n1.6166\n1.1099\n1.1674\n0.74521\n0.43265\n0.31299\n0.32848\n0.8982\n0.89973\n1.3736\n3.5455\n1.3203\n-0.28512\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4595\n-1.108\n-0.7447\n-0.99279\n-2.8646\n-2.2363\n-2.4502\n-1.1823\n-0.63687\n1.1848\n1.1655\n0.42174\n-0.22449\n1.3589\n3.0644\n0.50317\n-0.53831\n-0.03446\n0.096706\n2.0497\n2.0725\n1.891\n2.9451\n2.5285\n2.7934\n2.7268\n1.7082\n0.3065\n0.57511\n-1.0236\n-0.087205\n-0.91144\n0.66986\n0.19361\n-2.3116\n-2.6622\n-1.4324\n-0.73494\n-0.069493\n0.0045117\n0.34741\n0.85331\n0.66865\n1.6797\n1.0736\n0.67123\n0.63013\n0.60819\n0.37563\n0.72146\n1.0807\n0.77091\n0.65812\n2.1389\n2.5856\n1.5731\n0.97451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65573\n-1.0205\n-0.85756\n-0.72515\n-0.73481\n-0.49682\n-0.0018844\n-0.24627\n-0.71451\n-0.31093\n0.32963\n-1.1862\n1.5517\n2.5097\n1.7218\n-0.082935\n-0.16309\n-0.14626\n0.028811\n1.7667\n3.2493\n1.7794\n2.4922\n1.7417\n2.9393\n3.0932\n1.8842\n0.91788\n0.21017\n-1.3505\n-0.53716\n0.059848\n0.793\n0.47663\n-1.6166\n-2.3281\n-2.1172\n-0.90117\n0.1219\n-0.12758\n-0.11499\n0.39264\n-0.10376\n-0.0043714\n0.43947\n0.24224\n0.12764\n0.7931\n1.2166\n2.4466\n2.7852\n2.0366\n2.5971\n2.9763\n2.6648\n2.0122\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.43249\n-0.55791\n-0.46115\n-0.5158\n-0.11185\n-0.41877\n-0.068387\n0.11662\n-0.62451\n-1.5154\n-1.0112\n-1.6601\n-1.3755\n-0.068627\n-1.5177\n-1.5387\n-1.2205\n-0.53311\n-1.2977\n0.59561\n4.0168\nNaN\nNaN\n-1.396\n2.1636\n2.8124\n2.4289\n0.93565\n0.91695\n0.87319\n-0.70109\n0.043596\n0.18898\n0.49491\n0.058077\n-1.0156\n-2.4054\n-1.1837\n-0.10653\n-0.12722\n0.014804\n0.54013\n-0.070891\n-0.071059\n0.06028\n0.16325\n0.89525\n1.9268\n1.7629\n1.6963\n2.6335\n2.4001\n1.9495\n3.1969\n2.3711\n2.4227\n4.6478\nNaN\nNaN\n-5.7959\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.25799\n-0.76198\n-0.96896\n-0.51352\n0.16565\n-0.48984\n0.06319\n0.46104\n-0.002612\n-0.46571\n-0.30428\n0.50174\n0.29126\n-1.1867\n-2.0978\n-0.71433\n-1.6648\n-1.529\n-0.93916\nNaN\nNaN\nNaN\nNaN\n-0.038363\n1.4566\n2.9742\n2.8309\n2.054\n1.428\n1.6656\n0.67841\n-0.22374\n-0.53162\n-0.33352\n0.074085\n-1.3814\n-2.4271\n-1.0579\n-0.22274\n0.16373\n0.33762\n-0.053398\n-0.1731\n-0.57616\n-2.2686\n-0.89375\n0.87588\n2.1506\n1.7649\n1.9313\n2.1297\n2.0755\n1.9079\n1.7292\n1.2518\n-0.014051\n1.2865\nNaN\nNaN\n-8.3643\n-5.8017\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.86365\n-1.2319\n-0.89431\n0.18588\n0.70613\n-0.23464\n0.72393\n0.81414\n0.8055\n0.57939\n0.3117\n0.018941\n0.065708\n-0.98687\n-1.2955\n-1.4967\n-1.6592\n-0.35462\n-0.75542\nNaN\nNaN\nNaN\nNaN\n-0.16958\n0.64975\n2.1285\n2.8522\n3.3021\n1.6326\n1.4433\n0.045768\n-0.33436\n-1.8591\n-1.1374\n-1.0966\n-1.7848\n-2.0771\n-1.2878\n-0.6884\n0.033409\n0.67133\n-0.10167\n-1.0117\n-1.791\n-4.2853\n-1.2996\n0.78188\n1.9634\n2.3125\n2.341\n1.6435\n2.071\n2.5025\n2.1819\n1.1295\n-0.47248\n0.76128\nNaN\nNaN\n-3.8963\n-6.2596\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.53893\n-0.35137\n-0.47062\n0.6102\n1.749\n-0.1958\n0.28595\n1.0038\n1.2225\n0.95947\n0.51939\n0.4536\n-0.020113\n0.26135\n-0.61627\n-0.89196\n-0.32429\n0.24516\n-0.35488\n-0.81144\n-0.38795\n0.28087\n0.12685\n0.7841\n1.1603\n2.1109\n2.7328\n3.1656\n1.6244\n1.2831\n0.7115\n-1.3764\n-2.9793\n-1.382\n-0.90072\n-1.0069\n-0.9267\n-1.2443\n-1.1924\n-1.2885\n0.05769\n-0.39459\n-1.1305\n-2.1192\n-5.3805\n-1.1621\n1.2779\n1.6394\n1.4798\n2.3171\n1.7933\n2.2109\n2.8265\n2.4971\n2.3271\n0.899\n0.16632\n1.8205\n1.4828\n-0.68506\n-4.1881\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16517\n-0.97632\n-1.1438\n-0.54826\n0.79738\n-0.20121\n-0.17668\n1.2106\n1.4289\n1.2711\n0.74623\n0.83025\n0.91821\n0.48525\n0.35079\n0.61594\n0.56796\n0.99839\n0.042143\n0.18904\n0.27746\n0.13104\n0.87066\n1.4516\n1.5188\n2.4132\n2.5613\n1.8187\n1.216\n0.73358\n0.7664\n0.39336\n-1.5107\n-1.5551\n-0.91108\n-0.78316\n-0.81612\n-1.2359\n-1.1957\n-1.1184\n-1.0933\n-0.67676\n-1.5504\n-2.9061\n-4.8263\n0.74105\n1.9278\n2.145\n2.2528\n2.5794\n2.5324\n3.2245\n3.3428\n4.2123\n4.0085\n1.3531\n0.01519\n1.3667\n2.9185\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9158\n-0.93311\n-0.45809\n-0.96357\n0.92424\n-0.2785\n-0.37029\n0.29784\n0.94129\n1.1502\n0.93833\n0.93366\n1.1802\n0.62992\n1.127\n1.3685\n0.99975\n1.6732\n1.4407\n1.8959\n1.5138\n1.2509\n1.9009\n1.3143\n0.82936\n1.6746\n2.7193\n2.4389\n1.2662\n0.41053\n0.55756\n-0.97211\n-2.4525\nNaN\nNaN\n-0.20837\n-1.0223\n-1.073\n-1.5213\n-1.2\n-0.72872\n-0.27765\n-2.0346\n-2.7861\n-0.84213\n2.0006\n1.5254\n2.1577\n2.4261\n3.1864\n3.0225\n4.0801\n4.1753\n4.4066\n3.8642\n2.834\n1.015\n0.043522\n3.0185\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3296\n-1.6086\n-0.85268\n-0.93812\n-0.068478\n-0.53712\n-0.41937\n-0.38848\n0.60101\n1.3943\n1.216\n0.70658\n1.5522\n1.108\n1.8957\n2.2328\n1.6213\n1.6932\n1.788\n2.0921\n1.161\n1.3439\n1.2632\n0.094772\n0.73319\n3.3105\n3.3394\n2.7634\n1.7483\n1.6248\n0.61847\n-1.0688\nNaN\nNaN\nNaN\n-0.38921\n-0.97169\n-0.27655\n-2.0369\n-1.4404\n0.1106\n0.33302\n-1.8618\n-1.8296\n-0.10687\n1.3467\n1.3974\n2.1219\n3.0645\n3.3229\n3.0528\n4.052\n3.9402\n4.6956\nNaN\nNaN\n0.066389\n-0.22302\n2.4678\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7045\n-2.4199\n-1.5897\n-0.88788\n-0.80691\n-0.45038\n0.11303\n0.51602\n0.95658\n1.2524\n0.77954\n0.93835\n1.6661\n1.1928\n2.031\n2.0303\n2.0434\n1.7622\n1.3989\n1.2289\n0.17172\n0.64841\n0.80722\n-0.038054\n0.84745\n2.5813\n3.2678\n3.3624\n2.2142\n2.296\n1.0075\nNaN\nNaN\nNaN\nNaN\n-0.4256\n-0.046168\n-0.554\n-2.1781\n-1.0002\n0.70357\n-0.32928\n-0.93261\n-0.28856\n0.45217\n1.253\n2.0181\n3.1982\n4.2067\n4.2624\n3.6882\n4.1431\n3.2803\nNaN\nNaN\nNaN\n0.4163\n-0.35366\n0.54485\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.92197\n-1.1341\n-0.73904\n-0.78154\n-0.45932\n0.11676\n0.38883\n0.37396\n0.83577\n0.85679\n0.97952\n1.2419\n1.1472\n1.5851\n2.2\n2.4509\n2.6657\n1.9628\n1.1429\n0.78593\n0.40399\n0.57074\n-0.4338\n0.46625\n0.94685\n0.97438\n3.4181\n2.7567\n1.6926\n2.1205\n1.5516\nNaN\nNaN\nNaN\nNaN\n-1.151\n-0.79441\n-0.28647\n-1.0745\n-0.029052\n0.30662\n-1.8681\n-2.6267\n0.36176\n1.2903\n1.9144\n2.7272\n3.8602\n4.617\n4.6912\n4.3058\n3.822\n2.4359\nNaN\nNaN\nNaN\n-2.3859\n-2.6583\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.026387\n-0.32843\n-0.58531\n-0.030179\n0.19629\n0.25611\n0.40329\n0.29288\n0.5522\n0.51751\n0.52748\n1.2245\n2.15\n2.6665\n2.5776\n2.5963\n1.9931\n1.829\n0.76229\n0.40521\n0.85199\n-0.70088\n-2.0127\n-1.2536\n-1.4478\n0.7082\n2.3545\n1.9556\n2.5719\n2.873\n2.1698\nNaN\nNaN\nNaN\nNaN\n-1.2996\n-1.834\n-0.13409\n-0.63225\n0.49107\n0.45695\n-0.77165\n-0.55944\n1.7054\n2.7676\n3.3938\n3.4257\n4.4068\n4.6989\n4.7037\n4.1011\n2.7702\n2.0201\nNaN\nNaN\nNaN\nNaN\n-1.95\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.41426\n-0.68525\n0.13431\n0.91482\n0.53724\n0.17537\n0.44336\n0.23923\n0.25029\n0.34848\n0.20267\n1.1058\n2.3892\n2.7429\n2.9367\n2.3535\n2.4747\n2.4459\n1.5583\n0.52465\n0.13962\n-2.224\n-4.3138\n-2.6809\n-2.1297\n0.81425\n1.4707\n2.1927\n3.2975\n3.0895\n3.1996\nNaN\n-0.81393\n-1.2685\n0.059347\n0.1057\n-0.73421\n1.3441\n1.2877\n2.852\n2.3738\n0.015193\n-0.060989\n4.3064\nNaN\nNaN\nNaN\n4.8737\n5.5802\n5.0188\n3.4935\n3.7957\n2.856\n0.43358\nNaN\nNaN\nNaN\n-3.3072\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14422\n-0.30562\n0.34218\n1.4568\n1.1926\n0.8285\n1.0294\n0.47178\n0.21111\n0.91805\n0.76334\n1.3583\n2.083\n2.2174\n2.6989\n1.4378\n1.3702\n2.1095\n2.3538\n0.98631\n0.02547\n-0.97736\n-6.0952\n-4.9928\n-2.304\n0.11269\n1.7953\n4.2105\n4.0669\n2.8592\n0.88891\n-2.2074\n0.12498\n0.037836\n-0.025632\n0.82821\n2.2631\n2.8166\n3.3652\nNaN\nNaN\nNaN\n-0.11615\nNaN\nNaN\nNaN\nNaN\n4.5411\n5.5515\n3.9182\n2.7135\n2.6762\n2.7969\n0.52071\n-0.15223\n-1.9709\n-4.0028\n-3.8756\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56769\n-0.24585\n0.44478\n1.7251\n1.0405\n1.1421\n0.87477\n0.46961\n0.47191\n0.45599\n0.64115\n1.1937\n2.0031\n2.1647\n2.3726\n0.94412\n0.65131\n1.2848\n1.8166\n1.2931\n-0.091789\n-1.4004\n-2.924\n-3.9184\n-1.0012\n0.96958\n2.5597\n3.4051\n2.8222\n2.176\n-0.15977\n0.29071\n2.2549\n1.8496\n2.0232\n3.1336\n3.3378\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.45265\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2282\nNaN\n1.7018\n2.3044\n0.48596\n-0.033655\n-0.77808\n-3.4562\n-3.7727\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.82407\n-0.29295\n0.76941\n1.7971\n1.0919\n0.74189\n0.58559\n0.64323\n0.59743\n0.28861\n0.64053\n1.0602\n1.7385\n1.8325\n1.9198\n1.1543\n1.7674\n1.1231\n0.98959\n0.19076\n-1.0674\n-2.6813\n-3.4403\n-3.7581\n-0.50227\n1.245\n2.8749\n2.2819\n2.281\n1.4466\n2.4843\n2.4652\n2.254\n2.876\n3.8274\n4.4461\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.75481\n-0.095528\n-1.5717\n-3.2954\n-2.8194\n-3.495\n-3.4266\n-1.0556\n-0.18394\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34636\n0.18128\n1.1357\n1.5865\n1.1044\n-0.074391\n0.40846\n0.59035\n0.20348\n0.28732\n0.14259\n0.54859\n1.1622\n1.7268\n1.9617\n1.2334\n0.49377\n0.021616\n0.25214\n-0.3648\n-1.1994\n-3.111\n-3.5529\n-2.6443\n-0.31192\n1.2419\n1.8294\n1.9092\n3.1699\n2.4752\n2.3147\n3.3452\n3.7549\n3.4156\n4.1802\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9404\n-0.7186\n-1.3159\n-1.8329\n-0.57801\n-0.67999\n-0.8645\n0.83062\n1.0042\n-0.74382\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.061193\n0.46876\n0.4028\n0.14181\n0.88876\n0.60883\n1.2353\n0.38161\n-0.60747\n-0.23552\n-0.0023628\n0.29914\n0.82334\n1.5392\n1.4637\n0.50024\n-0.79502\n-1.5924\n-0.95131\n-0.71066\n-1.3125\n-3.4768\n-2.7716\n-1.2852\n1.0884\n2.4063\n1.7607\n2.2984\n3.7509\n3.3152\n2.2638\n3.6043\n3.5721\n3.2809\n4.7196\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.5116\n-2.1562\n-1.8079\n-0.71783\n0.9606\n1.5308\n-0.040872\n-0.20693\n0.84609\n-0.70872\n0.049892\n1.2715\n1.8447\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15424\n0.71665\n0.12632\n-0.24454\n1.3079\n1.2859\n-0.33072\n-0.87826\n-1.064\n0.11828\n0.48837\n0.55384\n0.033025\n1.13\n0.68161\n0.12869\n-0.6509\n-0.7728\n-0.79464\n-1.4681\n-1.8161\n-4.0235\n-2.9529\n-0.031712\n1.4992\n3.5316\n3.8994\n3.8128\n3.9293\n5.3258\n7.1382\n8.1472\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.545\n-1.5388\n-0.9728\n-1.1219\n-1.6706\n-0.73984\n1.5751\n2.1959\n0.52735\n1.2194\n1.7548\n1.5788\n1.0629\n1.048\n1.4554\n1.8914\n1.7874\n2.116\n0.80911\n0.10929\n-0.0037079\n0.036542\n-0.30222\n-0.15405\n0.74528\n0.64049\n-0.74241\n-0.67767\n-0.92336\n0.0019815\n0.074735\n-0.34792\n0.40114\n0.69814\n0.35024\n0.7061\n0.13087\n-0.66723\n-0.4259\n-0.94569\n-0.3029\n-2.9504\n-3.4899\n0.41061\n2.6609\n5.7452\n4.8967\n4.8999\n6.2029\n7.3687\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32966\n-0.90057\n-0.44231\n0.72975\n0.33505\n-0.053022\n-1.85\n0.11892\n1.3856\n0.62037\n1.9392\n2.3663\n1.1618\n0.63332\n0.18393\nNaN\nNaN\nNaN\nNaN\n1.1106\n0.95797\n0.019651\n-0.1107\n-0.18641\n-0.8505\n-0.77714\n-0.77439\n-1.1113\n-0.88988\n-0.19494\n-0.35783\n-0.72221\n-0.5485\n0.24441\n0.44566\n0.51818\n1.197\n0.55238\n-1.2287\n-2.7538\n-0.55387\n-0.44964\n-3.8387\n-3.1772\n0.098325\n3.7586\n4.5659\n4.2521\n5.3377\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3366\n0.82084\n0.32466\n-0.45732\n0.57553\n1.4251\n1.1342\n1.0559\n-0.10423\n-0.18033\n-0.36229\n0.57526\n1.81\n2.8026\n1.297\n1.3778\n1.3009\nNaN\nNaN\nNaN\nNaN\n0.069686\n1.7873\n-0.034388\n-0.59519\n-1.9385\n-1.399\n-0.68957\n-0.87242\n-1.3626\n-0.46364\n0.65872\n0.058882\n-0.42243\n0.28374\n0.53852\n0.48071\n0.79218\n2.5652\n1.8002\n-0.60702\n-2.8024\n-3.8833\n-2.4051\n-2.3892\n-1.2965\n1.1545\n2.6246\n3.6201\n3.0761\n3.3186\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59754\n1.3194\n0.96237\n0.57474\n-0.0014013\n0.41577\n0.91464\n1.3252\n2.1658\n1.2413\n-0.083482\n-0.70075\n0.06201\n0.6227\n2.263\n0.45975\n1.5465\n1.8552\nNaN\nNaN\n-0.94523\n-0.018845\n0.29086\n0.46066\n-1.0347\n-1.3935\n-1.5187\n-1.0423\n-1.0038\n-0.11622\n-0.68861\n0.15832\n0.64378\n0.43085\n-0.22891\n0.1434\n0.84027\n0.12285\n0.24895\n2.1124\n2.2714\n0.17015\n-2.3669\n-3.3971\n-1.6853\n-1.09\n-0.62962\n1.7795\n3.0088\n4.0732\n3.4304\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0472\n-0.32001\n2.5773\n1.0889\n1.2744\n0.75096\n0.3879\n0.24015\n1.392\n3.0739\n2.5711\n0.65978\n0.43984\n0.68913\n-0.14432\n-0.69928\n0.36962\n1.7553\n1.226\n-0.19178\n-1.1973\n0.49537\n1.1531\n1.3371\n1.0962\n-1.8904\n-1.3748\n-0.77843\n-0.805\n-1.3598\n0.22014\n-0.01137\n-0.14135\n0.30815\n0.01996\n-0.232\n0.65292\n0.91372\n1.0369\n1.5586\n2.0085\n3.2643\n0.31375\n-1.3087\n-1.4132\n-0.27952\n-0.51922\n-0.31194\n1.0475\n3.4827\n4.6457\n4.5312\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5725\n-0.77021\n2.2219\n0.90818\n1.5354\n1.3904\n-0.22011\n-0.44995\n0.74995\n3.0628\n2.9742\n2.087\n1.2742\n1.4194\n0.4126\n-0.053112\n1.4759\n2.4159\n0.83099\n0.63066\n0.32912\n1.4449\n2.0721\n2.8723\n1.6476\n-1.5676\n-0.85544\n-0.39682\n-0.4453\n-0.91758\n-0.2528\n-0.11565\n-0.33101\n0.0075442\n-0.29989\n-0.52305\n-0.30411\n0.64152\n1.3905\n1.9442\n2.1849\n2.1225\n0.1546\n0.035329\n-0.8885\n-0.62001\n-0.17368\n0.82531\n1.6874\n3.7415\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.50285\n-0.23563\n0.25608\n2.09\n-1.9535\n0.049439\n1.2444\n0.8731\n2.4677\n2.9763\n3.1402\n3.0261\n3.2612\n2.3707\n1.6308\n0.66187\n-0.42576\n2.2512\n1.8075\n2.055\n1.3571\n1.3881\n2.1633\n2.5364\n2.4177\n1.593\n-1.247\n-0.35461\n-0.62721\n-0.67338\n-0.94387\n-0.79958\n-0.86612\n-0.20159\n-0.11461\n-0.67759\n-0.5266\n-0.20065\n0.25562\n0.78138\n1.8172\n2.0129\n1.1229\n-0.26977\n-0.71068\n-0.47787\n-0.29776\n0.039512\n0.45758\n1.5259\n3.7668\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7497\nNaN\nNaN\nNaN\nNaN\n1.4675\n0.7361\n0.56648\n0.012191\n0.97778\n-1.9997\n0.32328\n1.5471\n3.7861\n3.2302\n3.234\n2.7827\n-0.19346\n2.7539\n1.453\n0.88859\n-1.2819\n-0.003827\n2.6221\n2.3904\n2.9944\n2.0659\n2.2475\n2.5923\n2.708\n1.7369\n-0.025492\n-0.68698\n-0.33309\n-0.99166\n-0.91754\n-0.42954\n-0.68708\n-0.86742\n-0.5879\n-0.81271\n-0.44657\n-0.26343\n-0.071185\n-0.072406\n0.78705\n1.3999\n1.7052\n1.2285\n0.22375\n-0.949\n-0.58091\n0.37685\n0.50665\n-0.34434\n1.7302\n5.2113\n7.542\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5516\n2.5827\n2.1249\n1.3716\n0.24764\n-0.34529\n0.24711\n0.3278\n0.042075\n1.7148\n3.7143\n2.2819\n2.4423\n1.124\n-1.9373\n-1.146\n0.20201\n0.21673\n-0.7416\n0.72897\n2.1407\n2.5922\n2.2208\n1.9395\n1.2896\n2.4795\n1.9991\n0.49622\n-2.9435\n-0.57869\n-0.676\n-0.51025\n-0.96892\n-1.0967\n-0.80862\n-0.20582\n-0.66218\n-1.3331\n-0.51136\n-0.35378\n-0.19274\n-0.43336\n0.32708\n1.1271\n1.2739\n0.54101\n-0.02919\n-0.26805\n-1.987\n-1.843\n0.22767\n0.33369\n3.0846\n6.6332\n7.158\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.9819\nNaN\nNaN\n6.1321\n4.4119\n3.4628\n3.7619\n1.6238\n-0.034518\n-0.85687\n-1.8539\n-1.3352\n-1.3124\n-0.24965\n1.4387\n0.12703\n-0.25354\n0.13644\n-0.64202\n-1.7279\n-2.0476\n-0.73011\n-0.34679\n0.36033\n1.4098\n1.4015\n0.96327\n1.2444\n1.1565\n0.92406\n0.64943\n0.25671\n-4.1411\n-0.91267\n-0.96876\n-1.1399\n-1.3524\n-1.7902\n-0.79579\n-0.49032\n-0.6573\n-0.9511\n-0.53697\n-0.40929\n-0.71059\n-0.86389\n-0.51632\n0.43223\n-0.044098\n-0.23886\n-1.1009\n-1.3596\n-1.7521\n-2.3901\n-0.55488\n0.87212\n4.6716\n6.835\n7.9532\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.9627\n15.339\n10.425\n8.2134\n4.1602\n3.454\n3.4796\n2.649\n0.4878\n0.010732\n-0.67022\n-1.4525\n-1.2753\n-1.2376\n-0.88848\n-0.88478\n-0.9414\n-1.0993\n-1.3617\n-2.3722\n-2.781\n-0.58831\n0.51749\n0.53344\n0.005414\n-0.59571\n0.19352\n1.2304\n0.90969\n-0.33771\n0.40652\n-0.28636\n-2.0214\n-1.4376\n-1.1382\n-1.2122\n-0.76659\n-1.116\n-1.0837\n-1.1787\n-0.76423\n-0.79691\n-1.1358\n-0.55766\n-1.4544\n-2.3427\n-1.4871\n-0.7499\n-0.63765\n-1.3077\n-2.1273\n-2.1413\n-1.9059\n-1.9581\n-1.38\n1.0198\n3.5418\n6.0996\n9.1361\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n18.735\n14.642\n10.033\n7.5955\n3.4103\n2.4108\n2.3493\n2.1542\n1.2088\n0.057109\n-1.2173\n-1.2452\n-0.72018\n-0.39147\n-0.44416\n-2.2411\n-2.7009\n-1.7668\n-1.8713\n-3.3124\n-3.9117\n-1.3983\n-0.013816\n-0.065454\n-0.18179\n-0.93969\n-0.11731\n-0.058954\n0.86092\n-0.79215\n-1.3425\n-0.71676\n-0.283\n-1.021\n-1.0119\n-1.0937\n-0.41132\n-1.0128\n-1.1688\n-1.3717\n-1.2045\n-1.0569\n-1.3618\n-1.4842\n-1.6319\n-2.4128\n-1.6959\n-1.5551\n-0.97679\n-1.5124\n-3.5352\n-4.0625\n-1.7784\n-1.3781\n-1.8761\n0.18543\n3.4601\n4.9758\n4.1047\n2.9064\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n15.447\n9.0098\n8.6379\n4.8676\n3.6136\n-0.33765\n0.89698\n1.3179\n0.11435\n-0.48476\n-0.90548\n-0.81664\n-0.48996\n-0.57545\n-1.0257\n-1.9538\n-3.8211\n-2.1608\n-1.3918\n-1.8332\n-3.095\n-3.086\n-1.189\n-0.43026\n-0.077971\n-0.28346\n-0.42957\n-0.69504\n-1.4352\n-1.8019\n-0.91594\n0.086327\n0.14105\n-0.75752\n-1.1706\n-1.2917\n-0.64659\n-0.40544\n-0.48699\n-1.3454\n-1.3178\n-0.78375\n-0.96195\n-1.7322\n-1.7782\n-1.7932\n-1.5952\n-1.5966\n-1.0115\n-0.93772\n-1.8226\n-3.0983\n-2.8807\n-1.2974\n-1.967\n-0.043915\n3.5496\n3.9945\n0.63535\n0.19613\n-1.0621\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.912\n7.6481\n5.365\n5.1945\n3.5136\n-0.030788\n1.0452\n1.012\n-0.73691\n-1.9975\n-2.1116\n-0.54491\n-0.49492\n-0.92645\n-2.3482\n-1.9943\n-2.0224\n-1.6208\n-0.7122\n-0.7903\n-0.98443\n-1.7403\n-1.7105\n-0.61988\n-0.79537\n-0.64342\n-1.0267\n-1.169\n-1.7981\n-1.2148\n-0.50625\n0.25407\n-0.032106\n-0.91635\n-1.5871\n-1.483\n-1.0386\n-0.12185\n-0.12968\n-0.83598\n-0.72285\n-0.70726\n-0.97789\n-2.2598\n-1.6561\n-1.2098\n-1.9029\n-2.3795\n-1.758\n-0.73808\n-2.0893\n-1.8928\n-3.8719\n-3.6793\n-2.2908\n0.16472\n1.7729\n2.2341\n0.96631\n-0.33149\n0.49658\nNaN\nNaN\n3.4028\n5.5479\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.8574\n5.9574\n4.8464\n4.7654\n1.2159\n-0.968\n0.057915\n-0.97275\n-1.6293\n-3.2963\n-1.9518\n-0.12291\n-0.30112\n-1.0859\n-1.2219\n-1.3606\n-1.2124\n-1.0646\n-0.7268\n-1.0346\n-1.4841\n-1.6166\n-1.6758\n-1.1636\n-1.5924\n-1.9754\n-3.2809\n-3.6267\n-1.6358\n-0.76446\n-0.39964\n0.64172\n0.31756\n-1.1175\n-1.2752\n-1.2522\n-1.4911\n-0.18674\n0.58571\n-0.060498\n-0.26697\n-0.79256\n-0.98181\n-1.9633\n-1.2106\n-1.2011\n-1.9876\n-2.4343\n-1.2338\n-1.2516\n-3.014\n-3.8064\n-3.4324\n-2.896\n-1.8247\n-0.39455\n1.4589\n-0.43578\n-2.157\n-0.92391\n2.1558\n2.2156\n4.0479\n4.8628\n4.5976\n4.887\n5.7967\nNaN\nNaN\nNaN\nNaN\n5.6668\n6.1056\n4.1174\n3.4392\n3.5936\n-0.60439\n-3.0429\n-1.731\n-1.6241\n-1.9103\n-2.554\n-1.517\n0.39553\n0.33368\n0.54449\n-0.41598\n-1.507\n-1.4599\n-0.91517\n-0.87199\n-0.72431\n-0.797\n-1.5809\n-1.8594\n-2.9312\n-3.3771\n-2.6576\n-2.5513\n-2.58\n-2.4474\n-2.1154\n-0.97199\n0.49265\n-0.17992\n-0.61493\n-1.0005\n-1.4124\n-1.3174\n-0.81698\n0.41528\n0.44329\n-0.13915\n-0.55952\n-0.89514\n-1.4394\n-0.81891\n-0.9702\n-1.5474\n-2.0525\n-1.8065\n-3.0409\n-5.1403\n-4.3842\n-3.228\n-2.5944\n-0.40561\n0.37562\n1.1159\n3.7371\n1.4876\n1.7253\n2.2488\nNaN\n1.916\n3.3235\n1.7385\n4.6939\n7.4384\nNaN\nNaN\nNaN\n3.8841\n4.9932\n4.866\n3.6573\n1.2849\n1.6185\n-0.16996\n-3.0369\n-2.2875\n0.10444\n-0.88029\n-2.1914\n-1.2522\n0.4278\n0.88403\n1.0514\n-0.60889\n-0.77346\n-1.1026\n-1.1087\n-0.94065\n-0.22325\n-0.45804\n-1.5249\n-2.2206\n-2.7178\n-3.1393\n-2.3854\n-1.1364\n-1.3823\n-2.5802\n-2.0732\n-0.93878\n-0.14054\n-0.92874\n-1.3116\n-0.96304\n-0.90787\n-0.88889\n-0.47078\n0.29062\n0.2388\n-0.5048\n-0.74974\n-1.0138\n-1.2608\n-0.9615\n-0.82572\n-1.1017\n-1.5514\n-2.2149\n-3.3291\n-5.2419\n-3.7591\n-3.3557\n-2.2812\n-1.3471\n1.0028\n0.2778\n0.66599\n1.5331\n1.6333\nNaN\nNaN\n1.2679\n1.9895\n2.46\n4.4883\n6.6279\nNaN\nNaN\nNaN\n3.1518\n3.4821\n4.4233\n2.8151\n-0.35633\n-0.89671\n-1.5875\n-2.1927\n-0.62182\n0.41478\n0.044227\n-1.5716\n-1.0514\n0.038187\n1.3136\n1.0664\n0.06748\n-0.28446\n-0.55661\n-0.70254\n-0.54493\n0.095976\n-0.36811\n-0.99808\n-2.2855\n-2.7812\n-2.388\n-1.2917\n-0.26602\n-0.29202\n-0.84122\n-0.73948\n-0.44127\n-0.52946\n-0.080941\n-0.83948\n-0.7996\n-0.37009\n0.11556\n0.31754\n0.34551\n0.091219\n-0.62699\n-1.0782\n-1.0886\n-0.90175\n-0.58493\n-0.063173\n0.35457\n-0.18501\n-1.9912\n-2.6915\n-4.9882\n-1.3007\n-1.3986\n-1.7853\n-0.38914\n0.76009\n-0.40849\n-0.9584\n0.3359\n0.89731\nNaN\nNaN\nNaN\n0.80891\n1.9632\n2.4318\n3.7421\nNaN\n4.0335\n3.5135\n1.9069\n2.1884\n3.9157\n1.0112\n-0.51566\n-0.16995\n0.26507\n-0.1759\n-0.82381\n-1.0247\n-1.142\n-1.0367\n-0.15952\n0.57785\n1.1729\n0.58603\n-0.57401\n-0.58768\n-0.34145\n-0.054538\n0.53243\n0.56195\n0.36603\n-0.71441\n-1.8542\n-2.2438\n-1.5421\n-0.60646\n0.15395\n0.33771\n0.11178\n-0.84848\n-1.32\n-0.53852\n0.72165\n-0.56364\n-0.47144\n0.24487\n0.83792\n1.128\n0.73752\n0.033144\n0.19151\n-0.25139\n-0.43919\n-0.12006\n0.36542\n0.67681\n1.4519\n1.9895\n-0.58784\nNaN\nNaN\nNaN\n-1.462\n-2.8349\n-0.88703\n0.065863\n-2.7416\n-1.7754\n-0.28095\n0.59919\nNaN\nNaN\nNaN\n0.64535\n1.5914\n0.81986\n1.4119\n2.2445\n2.9282\n2.0222\n1.2454\n1.4017\n0.61563\n-0.7814\n-0.797\n0.41987\n0.79513\n0.17537\n-0.46973\n-0.61343\n-0.42463\n0.036409\n0.18881\n0.60712\n0.48243\n-0.26634\n-1.3007\n-0.64821\n0.39583\n0.57079\n0.70009\n0.78391\n0.55971\n-0.54929\n-1.098\n-1.2961\n-1.2646\n-0.50056\n-0.073895\n0.67133\n0.74382\n-0.4862\n-1.1861\n-0.65659\n0.30279\n0.50747\n1.0021\n1.192\n0.90101\n0.99205\n1.138\n0.25948\n0.77862\n0.51315\n0.34844\n0.86013\n1.1676\n2.0348\n3.6284\n2.6768\n0.14393\nNaN\nNaN\nNaN\n-2.1302\n-2.8226\n-4.0422\n-2.7766\n-2.6746\n-1.0798\n-0.50166\n-0.17137\nNaN\nNaN\nNaN\n-0.3395\n0.31197\n0.50526\n1.4131\n3.5225\n2.6155\n1.3976\n0.63779\n0.23619\n-0.46531\n-1.1036\n-1.142\n0.198\n0.97166\n0.72582\n0.14731\n-0.020336\n-0.58341\n-0.34186\n0.2824\n0.57883\n0.32033\n-0.2618\n-0.19435\n0.56918\n0.69786\n-0.17631\n0.46138\n0.69953\n0.80909\n-0.12845\n-0.62182\n-0.72007\n-0.58838\n0.1299\n0.25426\n1.1424\n1.222\n0.029932\n-0.51668\n0.25978\n0.40208\n1.1985\n1.6191\n1.534\n1.0491\n1.503\n1.5024\n1.4527\n0.92217\n1.0276\n0.92756\n1.5438\n2.1478\n2.508\n2.5945\n1.8393\n1.5921\n-0.51195\nNaN\nNaN\n-3.0075\n-2.5467\n-2.7459\n-2.572\n-1.8072\n-0.29714\n-0.1766\n-0.50907\n-0.028804\n-0.55616\n-0.5683\n-2.0823\n-0.97145\n0.060733\n1.1417\n3.0397\n2.4396\n1.9899\n1.2709\n0.7022\n0.69466\n-0.3743\n-0.39281\n0.50679\n0.87967\n1.0487\n0.21348\n-1.1794\n-2.2266\n-1.696\n0.099197\n0.55031\n0.54771\n1.4662\n0.46286\n0.58269\n0.76825\n-0.35334\n0.17284\n0.41535\n0.95378\n0.32352\n-0.81183\n-0.32861\n0.33182\n0.80272\n0.64252\n0.78778\n1.4511\n-0.039847\n0.26459\n1.4087\n0.80551\n0.73864\n1.1218\n1.4024\n1.5981\n2.1062\n2.2213\n2.1021\n1.3808\n1.2978\n1.3793\n1.3906\n1.8972\n2.2243\n2.4049\n1.6055\n1.3822\n-0.055379\n-2.9985\n-3.9772\n-2.9411\n-2.4116\n-1.7835\n-1.5494\n-1.0906\n-0.13659\n-0.401\n-0.50686\n-0.2972\n-0.0579\n-0.20468\n-0.81666\n-0.79167\n-0.35145\n0.37228\n1.9207\n2.0539\n1.5603\n2.2658\n1.5242\n0.9533\n0.60685\n1.2092\n1.308\n1.4382\n1.128\n0.98345\n-1.1208\n-2.9856\n-3.9768\n-1.3319\n-0.068238\n2.3057\n2.0769\n1.0793\n0.58099\n1.306\n0.80429\n0.41005\n0.22996\n0.34739\n0.094932\n-0.47913\n0.77102\n1.0064\n0.84289\n0.64704\n1.6949\n1.8912\n0.27617\n0.60354\n0.54291\n0.25069\n0.34946\n1.2898\n1.9909\n2.2302\n2.9508\n3.3517\n2.8653\n2.7397\n1.8695\n1.2036\n1.2082\n1.5931\n1.6896\nNaN\nNaN\nNaN\nNaN\n-1.0768\n-2.7625\n-2.6501\n-1.3103\n-1.5082\n-2.0519\n-1.2856\n-0.20339\n-0.56353\n-0.13914\n0.52136\n0.015613\n-0.43454\n-0.64069\n-0.6318\n-1.0028\n-0.73436\n-0.29301\n0.45333\n0.94004\n2.3126\n1.8688\n1.8826\n1.6273\n2.0121\n1.9164\n0.43976\n0.090238\n1.3049\n0.45914\n-2.2309\n-2.1674\n-2.2438\n-0.96459\n0.24774\n0.89718\n0.25039\n0.41965\n1.5545\n0.32426\n-0.085012\n0.33253\n-0.12969\n-0.08897\n0.54375\n1.4496\n0.74109\n0.0019206\n0.72163\n2.4188\n2.0928\n1.1613\n0.26616\n-0.17498\n0.20517\n1.2389\n1.6548\n2.2743\n2.8019\n3.1918\n3.4416\n3.6229\n3.9435\n3.3243\n2.5392\n1.821\n1.771\n2.0546\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.458\n-2.1068\n-1.7034\n-1.519\n-2.3983\n-1.7494\n-0.68307\n-0.42922\n-0.28302\n0.018227\n-0.68007\n-0.85686\n-1.0724\n-1.0019\n-0.21449\n-0.69726\n-0.68139\n0.29105\n1.2284\n3.1163\n3.0544\n2.7386\n2.593\n2.7599\n2.0987\n-1.3076\n-1.7846\n-0.7634\n1.3221\n-1.3555\n-1.1093\n-1.0518\n-0.88642\n-1.0488\n-1.0687\n-1.1804\n-0.4027\n0.2265\n-0.50425\n-0.3392\n0.5591\n0.58223\n0.25766\n0.96862\n1.2663\n0.80375\n-0.14203\n1.3031\n3.207\n2.1966\n1.6411\n-0.072262\n-0.27582\n-0.31168\n1.9994\n1.6564\n2.4262\n3.5265\n3.8531\n5.3155\n5.2108\n5.2452\n4.3236\n3.2754\n2.1783\n1.6674\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4904\n-2.134\n-2.3848\n-2.3797\n-2.4611\n-2.0681\n-2.2591\n-1.1179\n-0.80913\n-0.45948\n-0.71562\n-0.98419\n-0.59168\n-0.26643\n-0.20631\n-0.23727\n0.14682\n0.41242\n2.3354\n3.4669\n2.7404\n2.8771\n3.2183\n2.8171\n-1.359\n-3.5002\n-2.7942\n0.51153\n-0.76861\n-0.6051\n-0.86207\n-1.0013\n-0.52616\n-1.3697\n-1.3197\n-0.62577\n0.14861\n-0.97695\n-0.87159\n0.67985\n-0.24154\n-0.29388\n0.071641\n0.73789\n0.63182\n0.86702\n1.4113\n2.2315\n1.7873\n1.7077\n-1.1415\n-1.4404\nNaN\n2.8905\n1.9046\n3.5075\n4.654\n5.1569\n6.5994\n7.5556\n7.0349\n4.4237\n3.5648\n2.6398\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.9967\n-1.8425\n-2.0465\n-1.8585\n-2.3108\n-2.1732\n-1.8617\n-1.5081\n-0.51772\n-0.13651\n-0.66998\n-0.59468\n-0.30204\n-0.083472\n-0.11327\n-0.33596\n-0.99026\n-0.97468\n0.75355\n1.943\n1.6806\n2.3919\n2.0895\n0.43343\n-1.4779\n-3.3661\n-3.6257\n-1.1139\n-0.96756\n-0.51386\n-1.1935\n-0.4354\n0.5489\n0.86921\n0.6072\n0.22604\n0.12809\n-1.1234\n-1.1024\n0.6039\n-1.4152\n-0.67865\n-1.5955\n-0.93522\n1.1363\nNaN\nNaN\n1.1329\n1.4014\n2.1055\nNaN\nNaN\nNaN\n0.65397\n-0.60574\n-0.54611\n-0.89365\n-0.89283\n-1.1127\n-1.7155\n-1.6323\n-0.66201\n-0.29347\n-1.1679\n-0.99736\n-1.531\n-0.88858\n0.55797\n0.45672\n0.61915\n-0.21677\n1.4346\n1.4106\n2.2014\n1.9514\n2.6201\n1.2849\n2.1902\n0.76355\n-0.74443\n-2.4573\n-1.5743\n-1.1402\n-3.6264\n-3.0114\n-1.7834\n-2.1356\n-0.64211\n-1.6581\n-1.6177\n-0.72367\n-1.6253\n-2.1386\n-1.0747\n-0.18434\n0.69936\n1.6296\n1.6752\n0.85347\n1.3221\n1.205\n0.66603\n0.39926\n0.52041\n1.2416\n0.96334\n-0.32368\n1.727\n0.48334\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3488\n-1.2351\n-1.0763\n1.1882\n1.2192\n-2.8502\n-1.8773\n-1.9513\n-0.30098\n-0.004722\n0.082947\n-0.28685\n-1.6446\n0.44997\n1.4008\n-0.0428\n-2.3713\n-1.6953\n0.028613\n0.56864\n3.5735\n1.6178\n1.9221\n3.4482\n2.865\n1.8438\n0.57215\n-0.90017\n-0.10547\n-0.98161\n-1.1922\n-1.5796\n-0.62825\n-1.164\n-0.87485\n-2.4067\n-2.2909\n-0.65232\n-1.2089\n0.10126\n0.55593\n0.10792\n0.9801\n1.698\n1.6617\n1.1519\n1.265\n0.79002\n0.45505\n0.31718\n0.31104\n0.93051\n0.91928\n1.4684\n3.9797\n1.3302\n-0.47084\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9588\n-1.5602\n-1.103\n-1.4188\n-3.8323\n-3.1473\n-3.3238\n-1.5511\n-0.9191\n1.2076\n1.011\n0.057846\n-0.67788\n1.5166\n3.5058\n0.5852\n-0.83113\n-0.27048\n-0.015092\n2.2223\n2.1674\n2.1374\n3.3556\n2.9448\n3.423\n3.2418\n2.0382\n0.42025\n0.67192\n-1.174\n-0.089324\n-1.1581\n0.26767\n-0.2844\n-2.8516\n-3.2393\n-1.8444\n-0.99974\n-0.24181\n-0.070095\n0.35462\n0.84496\n0.60443\n1.7569\n1.1132\n0.64814\n0.60749\n0.62021\n0.36694\n0.75964\n1.1101\n0.83808\n0.72103\n2.2846\n2.7617\n1.7666\n1.0578\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0262\n-1.472\n-1.234\n-1.0867\n-1.0981\n-0.8477\n-0.32437\n-0.56922\n-1.1415\n-0.67095\n0.1364\n-1.7822\n1.0322\n2.7554\n1.7653\n-0.35455\n-0.44503\n-0.4038\n-0.15016\n1.906\n3.5346\n2.1688\n2.8779\n1.9149\n3.4417\n3.6578\n2.3419\n1.0862\n0.35393\n-1.0993\n-0.69518\n-0.0022884\n0.41952\n0.08253\n-2.1964\n-2.9893\n-2.6159\n-1.165\n-0.021259\n-0.19857\n-0.17587\n0.34103\n-0.29784\n-0.091871\n0.41633\n0.13243\n0.0037574\n0.76029\n1.2011\n2.5995\n2.9988\n2.2012\n2.822\n3.1949\n3.0004\n2.3538\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.82524\n-0.90694\n-0.75986\n-0.82501\n-0.40518\n-0.74473\n-0.39434\n-0.19851\n-1.0084\n-1.9795\n-1.4497\n-2.1661\n-1.7461\n-0.33474\n-2.0389\n-1.9891\n-1.6885\n-0.96855\n-1.8375\n0.66751\n4.4767\nNaN\nNaN\n-1.7264\n2.4797\n3.3751\n3.1035\n1.3342\n1.2322\n1.2311\n-0.94571\n-0.30949\n-0.20758\n0.2621\n-0.42046\n-1.353\n-2.81\n-1.4221\n-0.21561\n-0.20931\n-0.056325\n0.50442\n-0.24147\n-0.28773\n-0.064203\n0.023395\n0.82264\n1.946\n1.7485\n1.6631\n2.8131\n2.5875\n2.0934\n3.5037\n2.6174\n2.7023\n5.3158\nNaN\nNaN\n-6.8257\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.57788\n-1.1182\n-1.2618\n-0.81038\n-0.099652\n-0.84805\n-0.21023\n0.19721\n-0.27322\n-0.75157\n-0.55796\n0.34773\n0.053715\n-1.6185\n-2.6541\n-1.1404\n-2.2293\n-2.0319\n-0.93638\nNaN\nNaN\nNaN\nNaN\n0.0030904\n1.9064\n3.7111\n3.545\n2.5497\n1.8124\n2.0492\n0.7701\n-0.62455\n-0.8149\n-0.53693\n-0.34033\n-1.9266\n-2.889\n-1.3135\n-0.33673\n0.084636\n0.31485\n-0.14133\n-0.34795\n-0.76638\n-2.6671\n-1.215\n0.75665\n2.2664\n1.8095\n1.9673\n2.2663\n2.2351\n2.0765\n1.8511\n1.3027\n-0.29598\n1.2036\nNaN\nNaN\n-10.128\n-6.6635\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2103\n-1.6223\n-1.2067\n-0.04242\n0.54837\n-0.54188\n0.57275\n0.72647\n0.71849\n0.56619\n0.22755\n-0.15106\n-0.065318\n-1.3608\n-1.6731\n-1.941\n-2.1201\n-0.47795\n-1.0595\nNaN\nNaN\nNaN\nNaN\n0.066283\n1.005\n2.7294\n3.5957\n3.9889\n2.088\n1.817\n0.058645\n-0.67975\n-2.3177\n-1.4852\n-1.542\n-2.2907\n-2.502\n-1.5875\n-0.93148\n-0.14982\n0.69186\n-0.26155\n-1.3399\n-2.1728\n-4.9599\n-1.6773\n0.64064\n2.0253\n2.4539\n2.5039\n1.7188\n2.1981\n2.6159\n2.291\n1.1448\n-0.69837\n0.66389\nNaN\nNaN\n-4.6998\n-7.2611\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.83153\n-0.61699\n-0.71408\n0.50877\n1.7745\n-0.37595\n0.18212\n1.0089\n1.2765\n1.0693\n0.58814\n0.44425\n-0.048911\n0.26355\n-0.71177\n-1.0578\n-0.4739\n0.28485\n-0.45891\n-0.90973\n-0.35335\n0.45542\n0.33504\n1.0945\n1.6148\n2.7027\n3.426\n3.9528\n2.1549\n1.7237\n0.96573\n-1.492\n-3.3612\n-1.6548\n-1.0967\n-1.2369\n-1.0999\n-1.6136\n-1.6287\n-1.7012\n-0.086208\n-0.64663\n-1.5271\n-2.6728\n-6.277\n-1.5174\n1.1789\n1.6109\n1.4578\n2.4752\n1.7851\n2.2736\n3.0017\n2.6857\n2.5297\n0.81772\n0.080805\n1.8598\n1.6083\n-0.94291\n-5.0568\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.36109\n-1.2375\n-1.3393\n-0.73903\n0.80075\n-0.28667\n-0.26431\n1.2902\n1.5094\n1.3908\n0.8454\n0.9614\n1.0348\n0.58448\n0.41886\n0.6685\n0.67533\n1.2069\n0.14592\n0.34679\n0.39742\n0.23013\n1.1495\n1.81\n1.9401\n3.0572\n3.2921\n2.4985\n1.6912\n1.0808\n1.0662\n0.56351\n-1.4807\n-1.7036\n-0.9784\n-0.82616\n-0.9894\n-1.6442\n-1.6415\n-1.5093\n-1.4156\n-0.96226\n-1.9949\n-3.5805\n-5.7829\n0.6037\n1.9128\n2.1401\n2.3033\n2.7291\n2.637\n3.4918\n3.6443\n4.6763\n4.4059\n1.3202\n-0.21752\n1.3625\n3.0565\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3428\n-1.2\n-0.62134\n-1.2071\n0.82994\n-0.42702\n-0.50167\n0.18234\n0.94208\n1.2907\n1.0877\n1.0977\n1.3881\n0.81554\n1.3584\n1.5657\n1.2015\n1.9465\n1.6721\n2.2236\n1.8313\n1.647\n2.432\n1.7034\n1.1033\n2.2265\n3.5297\n3.2053\n1.7182\n0.70698\n0.92824\n-0.97301\n-2.7496\nNaN\nNaN\n-0.16527\n-1.1211\n-1.343\n-1.9871\n-1.6124\n-1.0491\n-0.57298\n-2.5633\n-3.3615\n-1.1358\n1.9859\n1.4535\n2.1587\n2.4952\n3.3432\n3.1618\n4.4255\n4.5734\n4.9189\n4.2686\n2.931\n0.67741\n-0.35777\n3.074\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8541\n-1.9798\n-1.0956\n-1.2021\n-0.24245\n-0.6955\n-0.54546\n-0.49517\n0.61272\n1.5523\n1.4579\n0.98558\n1.94\n1.4096\n2.2528\n2.5977\n1.8589\n1.969\n2.126\n2.5568\n1.5802\n1.8606\n1.8357\n0.38851\n0.96657\n3.9718\n4.2561\n3.6578\n2.2841\n2.1787\n1.0443\n-0.96685\nNaN\nNaN\nNaN\n-0.39726\n-1.1394\n-0.46438\n-2.5753\n-1.9001\n-0.23556\n-0.12303\n-2.4381\n-2.388\n-0.43311\n1.1916\n1.2764\n2.1442\n3.2379\n3.3629\n3.1496\n4.3712\n4.3324\n5.3056\nNaN\nNaN\n-0.36336\n-0.59612\n2.4779\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.2978\n-2.8439\n-1.9334\n-1.1328\n-1.0191\n-0.55981\n0.067849\n0.50564\n1.0683\n1.4528\n1.0121\n1.2175\n2.0534\n1.5456\n2.4268\n2.37\n2.336\n2.1267\n1.8618\n1.6534\n0.42681\n1.1104\n1.4385\n0.3595\n1.2301\n3.1788\n4.1476\n4.3461\n3.0051\n3.0491\n1.4467\nNaN\nNaN\nNaN\nNaN\n-0.48305\n-0.090852\n-0.7733\n-2.9395\n-1.4991\n0.48182\n-0.58813\n-1.4197\n-0.80099\n0.055788\n0.99902\n1.8923\n3.269\n4.5494\n4.5229\n3.8867\n4.5055\n3.6002\nNaN\nNaN\nNaN\n0.090212\n-0.83352\n0.48073\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2955\n-1.4462\n-1.0361\n-1.0869\n-0.67701\n0.023426\n0.34573\n0.43088\n0.89565\n0.96941\n1.2435\n1.5751\n1.5492\n1.9917\n2.6215\n2.9337\n3.186\n2.4585\n1.6468\n1.1813\n0.74988\n1.0277\n-0.14103\n0.73084\n1.3655\n1.4737\n4.3503\n3.6219\n2.3989\n2.7574\n2.0216\nNaN\nNaN\nNaN\nNaN\n-1.3573\n-0.98945\n-0.44305\n-1.672\n-0.55993\n-0.16416\n-2.6554\n-3.5693\n-0.11592\n1.0191\n1.7407\n2.6785\n4.0479\n5.0103\n5.0619\n4.5772\n4.1589\n2.5993\nNaN\nNaN\nNaN\n-3.1469\n-3.3534\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27058\n-0.59855\n-0.87104\n-0.26426\n0.07055\n0.15293\n0.34599\n0.21894\n0.57993\n0.64815\n0.72449\n1.5547\n2.5071\n3.0525\n3.0134\n3.1535\n2.5855\n2.3396\n1.1588\n0.76231\n1.2964\n-0.38347\n-2.1964\n-1.3267\n-1.3507\n1.2661\n3.2154\n2.7238\n3.3518\n3.6456\n2.8144\nNaN\nNaN\nNaN\nNaN\n-1.3869\n-2.1015\n-0.22375\n-1.1282\n0.13183\n-0.077073\n-1.5734\n-1.3225\n1.439\n2.7569\n3.417\n3.4926\n4.6561\n5.0548\n5.0915\n4.3152\n2.8618\n2.0695\nNaN\nNaN\nNaN\nNaN\n-2.6164\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17217\n-1.0118\n-0.081038\n0.82888\n0.45888\n0.065133\n0.35385\n0.1694\n0.29657\n0.39752\n0.34301\n1.3823\n2.7572\n3.1506\n3.3998\n2.8978\n2.9898\n3.0184\n2.0416\n0.84126\n0.58768\n-2.2843\n-4.9168\n-2.9831\n-2.1787\n1.3941\n2.2025\n2.9952\n4.2333\n4.0197\n4.2841\nNaN\n-0.96415\n-1.5699\n0.13942\n0.5221\n-0.83059\n1.2616\n0.97387\n2.6156\n1.9893\n-0.62278\n-0.79009\n4.3048\nNaN\nNaN\nNaN\n5.119\n5.9331\n5.3353\n3.6266\n4.0461\n3.0565\n0.28614\nNaN\nNaN\nNaN\n-4.0173\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41175\n-0.63781\n0.14167\n1.4566\n1.1727\n0.76148\n0.99858\n0.43327\n0.22081\n0.98819\n0.92781\n1.6234\n2.4673\n2.6573\n3.1251\n1.8088\n1.7869\n2.699\n3.0135\n1.4129\n0.34519\n-0.9166\n-6.9099\n-5.7653\n-2.4168\n0.56267\n2.5853\n5.4081\n5.2019\n3.6951\n1.6696\n-2.3421\n0.24553\n-0.088464\n-0.093793\n1.0558\n2.4016\n2.8153\n3.3606\nNaN\nNaN\nNaN\n-0.70759\nNaN\nNaN\nNaN\nNaN\n4.7012\n5.8187\n4.0509\n2.8249\n2.758\n2.9996\n0.21296\n-0.54201\n-2.6015\n-4.8782\n-4.7022\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.83145\n-0.53613\n0.30644\n1.7869\n1.0308\n1.1126\n0.87148\n0.45471\n0.46951\n0.61549\n0.80859\n1.4187\n2.3936\n2.5764\n2.7185\n1.2483\n1.0437\n1.8514\n2.4206\n1.7903\n0.1404\n-1.5079\n-3.2144\n-4.5071\n-0.90087\n1.4962\n3.4877\n4.3283\n3.6835\n2.7514\n0.31055\n0.92771\n3.0467\n1.9452\n2.2683\n3.7047\n3.4876\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26784\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3149\nNaN\n1.9323\n2.4134\n0.23686\n-0.36381\n-1.1767\n-4.297\n-4.6986\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1355\n-0.51722\n0.74342\n1.8931\n1.1145\n0.72046\n0.59601\n0.7246\n0.64446\n0.45791\n0.80616\n1.3481\n2.079\n2.2216\n2.2872\n1.6057\n2.3381\n1.5811\n1.4366\n0.50857\n-0.98934\n-3.0129\n-3.7731\n-4.1174\n-0.33742\n1.8526\n3.7921\n2.8465\n2.9057\n1.9134\n3.3644\n3.2376\n2.9608\n3.5488\n4.6459\n5.2324\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0913\n-0.31847\n-1.984\n-3.9813\n-3.3958\n-4.0894\n-4.1244\n-1.3895\n-0.50151\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.61215\n0.080311\n1.1721\n1.66\n1.1419\n-0.16583\n0.40764\n0.68193\n0.2728\n0.42894\n0.2602\n0.86052\n1.5165\n2.1278\n2.4241\n1.6502\n0.7922\n0.17784\n0.51853\n-0.22126\n-1.2567\n-3.5194\n-3.9832\n-2.7454\n-0.057931\n1.8067\n2.4584\n2.4796\n4.0684\n3.1513\n3.2662\n4.1627\n4.4265\n4.1176\n5.0545\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3855\n-0.98044\n-1.7065\n-2.2832\n-0.83831\n-0.96624\n-1.1675\n0.85145\n1.1093\n-0.87762\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12008\n0.38849\n0.35815\n0.063792\n0.91401\n0.63233\n1.3461\n0.43823\n-0.62684\n-0.15178\n0.15378\n0.60085\n1.1341\n1.8427\n1.739\n0.76437\n-0.69148\n-1.711\n-0.91809\n-0.69284\n-1.2757\n-3.9386\n-3.0279\n-1.192\n1.5477\n3.1094\n2.3926\n3.0704\n4.8165\n4.3217\n3.2367\n4.4423\n4.224\n3.9545\n5.7711\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.2776\n-2.666\n-2.2225\n-1.0105\n0.84624\n1.5212\n-0.2301\n-0.31095\n0.91474\n-0.81334\n0.11464\n1.4093\n2.0199\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.063449\n0.63771\n0.039915\n-0.32149\n1.4051\n1.3513\n-0.39037\n-0.94265\n-1.0678\n0.29283\n0.67158\n0.77288\n0.17658\n1.3281\n0.80389\n0.2946\n-0.51323\n-0.73598\n-0.94373\n-1.437\n-1.6925\n-4.5122\n-3.1788\n0.33952\n2.0398\n4.4138\n4.8702\n4.6919\n4.9487\n6.5081\n8.5037\n9.4641\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.0422\n-2.0714\n-1.3546\n-1.4805\n-2.035\n-1.0262\n1.4888\n2.2013\n0.43368\n1.3621\n1.889\n1.5665\n1.1342\n1.2561\n1.4629\n1.897\n1.7904\n2.0284\n0.78308\n-0.028854\n-0.12268\n-0.070223\n-0.46982\n-0.24117\n0.80395\n0.66308\n-0.84076\n-0.68512\n-0.88121\n0.11625\n0.1763\n-0.23469\n0.6176\n0.88789\n0.39826\n0.9001\n0.21556\n-0.77478\n-0.72421\n-0.8939\n-0.05096\n-3.0971\n-3.7959\n0.83329\n3.4053\n7.0507\n6.0375\n5.9182\n7.6132\n8.8396\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65783\n-1.3604\n-0.80193\n0.65849\n0.21108\n-0.23942\n-2.275\n-0.020012\n1.4708\n0.63254\n2.1942\n2.6102\n1.1764\n0.67835\n0.21024\nNaN\nNaN\nNaN\nNaN\n1.258\n1.096\n-0.086577\n-0.26397\n-0.36676\n-1.0466\n-0.96208\n-0.88798\n-1.1916\n-0.96125\n-0.12719\n-0.31258\n-0.68483\n-0.46843\n0.39298\n0.62752\n0.7353\n1.5158\n0.85711\n-1.3395\n-3.1674\n-0.56382\n-0.28928\n-3.9235\n-3.2794\n0.45025\n4.6792\n5.628\n5.2341\n6.2813\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1247\n0.64353\n0.074104\n-0.74433\n0.4985\n1.4328\n1.1055\n1.052\n-0.23208\n-0.28435\n-0.45634\n0.54925\n1.9851\n3.1347\n1.3319\n1.5744\n1.3566\nNaN\nNaN\nNaN\nNaN\n0.068149\n2.1785\n-0.25198\n-0.82458\n-2.3019\n-1.6915\n-0.85313\n-0.96692\n-1.4643\n-0.48269\n0.78785\n0.15864\n-0.35106\n0.43451\n0.7675\n0.67548\n1.0506\n3.0308\n2.2197\n-0.50946\n-3.0124\n-4.2197\n-2.5632\n-2.5313\n-1.2384\n1.689\n3.433\n4.4924\n3.791\n3.9322\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.29703\n1.1224\n0.77736\n0.39716\n-0.14025\n0.38562\n0.95448\n1.4337\n2.4138\n1.3353\n-0.14262\n-0.73496\n0.06219\n0.58053\n2.5155\n0.45929\n1.7492\n2.0946\nNaN\nNaN\n-0.65356\n0.15993\n0.36146\n0.69764\n-1.3579\n-1.7498\n-1.8751\n-1.2798\n-1.1533\n-0.11056\n-0.72949\n0.23033\n0.74748\n0.55372\n-0.13307\n0.3168\n1.1432\n0.32755\n0.51876\n2.5723\n2.8139\n0.27921\n-2.5025\n-3.613\n-1.7383\n-1.0449\n-0.51286\n2.3039\n3.7296\n5.3696\n4.5899\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5769\n-0.68824\n2.621\n0.98916\n1.2965\n0.80642\n0.40143\n0.1751\n1.6256\n3.6506\n2.9506\n0.76911\n0.55636\n0.73869\n-0.24515\n-0.77427\n0.43202\n1.9532\n1.3548\n-0.17827\n-1.4199\n0.67047\n1.3898\n1.5716\n1.255\n-2.352\n-1.7248\n-1.0307\n-0.97867\n-1.5285\n0.24575\n0.063321\n-0.10969\n0.40539\n0.10866\n-0.069233\n0.91113\n1.2025\n1.3491\n1.8448\n2.4508\n4.0047\n0.47325\n-1.3287\n-1.4298\n-0.070951\n-0.35193\n-0.15549\n1.4282\n4.3474\n5.7884\n5.8363\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1225\n-1.1365\n2.1953\n0.71885\n1.538\n1.5262\n-0.26845\n-0.63701\n0.81774\n3.6135\n3.5314\n2.4823\n1.5329\n1.6105\n0.4499\n-0.038819\n1.6652\n2.7005\n0.94992\n0.60591\n0.26625\n1.6649\n2.4434\n3.343\n1.8807\n-1.9848\n-1.1718\n-0.62711\n-0.55879\n-1.0374\n-0.25271\n-0.043749\n-0.27416\n0.075134\n-0.16795\n-0.44231\n-0.23959\n0.87381\n1.7489\n2.2899\n2.6492\n2.6328\n0.32907\n0.16652\n-0.89152\n-0.50132\n0.040075\n1.1991\n2.2204\n4.6498\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.62616\n-0.64058\n-0.14248\n1.9506\n-3.0359\n-0.42118\n1.3994\n0.99208\n2.8716\n3.4233\n3.5953\n3.5311\n3.8237\n2.793\n1.9584\n0.76118\n-0.30062\n2.6025\n2.1395\n2.4193\n1.3534\n1.4719\n2.507\n2.9845\n2.9443\n1.9255\n-1.6982\n-0.60588\n-0.91461\n-0.80547\n-1.0814\n-0.88176\n-0.93317\n-0.20903\n-0.10134\n-0.68962\n-0.52657\n-0.17465\n0.38557\n1.1029\n2.1711\n2.3833\n1.3865\n-0.15563\n-0.64973\n-0.40031\n-0.12888\n0.27295\n0.75179\n1.9802\n4.6429\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2803\nNaN\nNaN\nNaN\nNaN\n1.6778\n0.52295\n0.35609\n-0.29998\n0.82907\n-3.0676\n0.16569\n1.8212\n4.397\n3.7481\n3.7344\n3.1438\n-0.34516\n3.2129\n1.6834\n1.1421\n-1.3693\n0.1488\n3.1209\n2.8207\n3.5184\n2.3206\n2.6031\n3.0444\n3.1679\n2.1227\n0.18486\n-1.0186\n-0.55149\n-1.2562\n-1.1146\n-0.55\n-0.80306\n-0.99742\n-0.71353\n-0.95306\n-0.50454\n-0.2442\n-0.034773\n-0.040496\n1.0657\n1.6017\n1.9589\n1.4504\n0.33841\n-1.0059\n-0.47484\n0.57865\n0.70907\n-0.20362\n2.2497\n6.3507\n9.2069\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9323\n2.8668\n2.0359\n1.2624\n0.088985\n-0.59301\n0.019746\n0.24085\n-0.020979\n1.9728\n4.2896\n2.5762\n2.8035\n1.2844\n-2.146\n-1.2945\n0.41239\n0.5879\n-0.75019\n0.92208\n2.5989\n3.1609\n2.7498\n2.2694\n1.6388\n3.021\n2.3832\n0.70006\n-3.1325\n-0.9405\n-0.93715\n-0.69268\n-1.2276\n-1.3167\n-0.99541\n-0.39201\n-0.8902\n-1.588\n-0.54378\n-0.37787\n-0.28913\n-0.53005\n0.42477\n1.2295\n1.4129\n0.59043\n-0.064627\n-0.27394\n-2.1896\n-1.9339\n0.43317\n0.70103\n3.9456\n8.0359\n8.7518\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.8667\nNaN\nNaN\n6.8564\n4.954\n3.8838\n4.1705\n1.6146\n-0.29094\n-1.1877\n-2.3192\n-1.6875\n-1.6843\n-0.46169\n1.5684\n0.046146\n-0.3774\n0.23297\n-0.58822\n-1.9653\n-2.3412\n-0.60873\n-0.33795\n0.48034\n1.7094\n1.7286\n1.216\n1.4831\n1.4785\n1.3029\n0.88493\n0.38517\n-4.5047\n-1.3479\n-1.3134\n-1.4334\n-1.662\n-2.0776\n-1.0167\n-0.70066\n-0.83661\n-1.1925\n-0.65728\n-0.52093\n-0.90873\n-0.97579\n-0.55312\n0.41314\n-0.15512\n-0.45293\n-1.3766\n-1.5842\n-1.925\n-2.5379\n-0.33168\n1.3663\n5.8006\n8.2954\n9.7136\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.7266\n16.805\n11.459\n9.0059\n4.7588\n3.9635\n3.6236\n2.7948\n0.38762\n-0.13523\n-0.94743\n-1.8792\n-1.6272\n-1.6205\n-1.1971\n-1.2031\n-1.1571\n-1.2801\n-1.5208\n-2.6736\n-3.2158\n-0.53114\n0.67365\n0.67801\n0.033809\n-0.66991\n0.15922\n1.3784\n1.1071\n-0.26348\n0.51755\n-0.31523\n-2.1968\n-1.9632\n-1.5185\n-1.6136\n-1.0465\n-1.4165\n-1.3961\n-1.4966\n-0.9695\n-0.98834\n-1.3346\n-0.73089\n-1.7449\n-2.7089\n-1.6891\n-0.93005\n-0.86747\n-1.6884\n-2.5156\n-2.4364\n-2.202\n-2.2793\n-1.5484\n1.4708\n4.4003\n7.3595\n10.95\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n20.506\n15.961\n11.073\n8.4956\n3.9302\n2.3811\n2.4435\n2.1754\n1.1936\n-0.11315\n-1.6224\n-1.6601\n-1.0788\n-0.67611\n-0.68569\n-2.689\n-3.2086\n-2.0733\n-2.1666\n-3.7424\n-4.3024\n-1.5236\n0.081978\n-0.0047585\n-0.15695\n-1.0003\n-0.20475\n-0.1135\n0.94343\n-0.90113\n-1.5607\n-0.84517\n-0.29523\n-1.4392\n-1.4448\n-1.554\n-0.67916\n-1.3957\n-1.5353\n-1.7168\n-1.4869\n-1.3216\n-1.6325\n-1.8053\n-1.9723\n-2.7876\n-1.9603\n-1.8824\n-1.1952\n-1.8752\n-4.2809\n-4.9818\n-2.1055\n-1.4791\n-2.0534\n0.45166\n4.2778\n6.034\n5.3352\n3.7239\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n17.074\n10.169\n9.6932\n5.5037\n4.1232\n-0.69666\n0.76753\n1.1929\n-0.10136\n-0.71674\n-1.2537\n-1.1688\n-0.78598\n-0.85279\n-1.279\n-2.2956\n-4.5288\n-2.5514\n-1.6456\n-2.013\n-3.3172\n-3.4015\n-1.2703\n-0.39074\n-0.0055665\n-0.18684\n-0.47517\n-0.82374\n-1.7386\n-2.0444\n-1.0208\n0.11109\n0.26252\n-1.2121\n-1.7141\n-1.8368\n-1.0296\n-0.81\n-0.79424\n-1.7202\n-1.6766\n-1.0615\n-1.2156\n-2.1068\n-2.1258\n-2.0931\n-1.887\n-1.9852\n-1.2487\n-1.2692\n-2.3818\n-3.8935\n-3.5412\n-1.4\n-2.1623\n0.19294\n4.3438\n5.0133\n0.83083\n0.14712\n-1.0349\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.285\n8.5898\n6.0863\n5.8769\n3.939\n-0.40168\n0.85297\n0.84677\n-1.2046\n-2.366\n-2.5219\n-0.87064\n-0.76652\n-1.2301\n-2.7699\n-2.2833\n-2.3067\n-1.8508\n-0.78566\n-0.78919\n-0.95196\n-1.7013\n-1.8111\n-0.61558\n-0.86999\n-0.63714\n-1.1868\n-1.3452\n-2.0295\n-1.2116\n-0.42869\n0.50822\n0.15037\n-1.4189\n-2.1563\n-2.0356\n-1.5292\n-0.46714\n-0.35106\n-1.1662\n-1.0034\n-0.99608\n-1.1361\n-2.669\n-1.9797\n-1.4416\n-2.2912\n-2.8368\n-2.1291\n-1.0672\n-2.6783\n-2.4129\n-4.6762\n-4.5058\n-2.7375\n0.30048\n2.2913\n2.929\n1.3923\n-0.45696\n0.62189\nNaN\nNaN\n3.7907\n6.532\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.844\n6.7497\n5.4951\n5.3947\n0.84082\n-1.7846\n-0.21756\n-1.3471\n-2.2992\n-3.7942\n-2.3364\n-0.37923\n-0.51754\n-1.3359\n-1.4608\n-1.604\n-1.4041\n-1.2256\n-0.8222\n-1.1233\n-1.543\n-1.6114\n-1.7821\n-1.19\n-1.6759\n-2.1497\n-3.5441\n-3.9161\n-1.721\n-0.7401\n-0.29691\n0.87008\n0.47458\n-1.6698\n-1.8585\n-1.7486\n-2.0335\n-0.57237\n0.43953\n-0.30988\n-0.54697\n-1.1274\n-1.1962\n-2.3663\n-1.5074\n-1.5001\n-2.4329\n-2.9389\n-1.5862\n-1.6352\n-3.6105\n-4.4342\n-3.9577\n-3.2861\n-2.0499\n-0.31657\n1.8489\n-0.23819\n-2.2282\n-0.98124\n2.4556\n2.4812\n4.6267\n5.4877\n5.4836\n5.7802\n6.7792\nNaN\nNaN\nNaN\nNaN\n6.1168\n6.787\n4.5054\n3.7837\n3.8819\n-1.1009\n-3.8883\n-2.1955\n-2.1592\n-2.5366\n-3.0407\n-1.8494\n0.3538\n0.33631\n0.55808\n-0.48047\n-1.7695\n-1.7401\n-1.0883\n-0.9719\n-0.77073\n-0.79774\n-1.6832\n-2.0573\n-3.1358\n-3.761\n-2.9856\n-2.886\n-2.9114\n-2.6572\n-2.3809\n-1.0592\n0.53277\n-0.27876\n-1.1383\n-1.5597\n-2.036\n-1.8792\n-1.317\n0.21063\n0.22785\n-0.45334\n-0.9565\n-1.1807\n-1.8104\n-1.0891\n-1.2715\n-1.9123\n-2.5077\n-2.2023\n-3.739\n-6.3829\n-5.1578\n-3.7173\n-2.8972\n-0.33108\n0.51775\n1.4168\n4.5095\n1.5801\n1.9049\n2.5393\nNaN\n1.9197\n3.6419\n1.814\n5.5488\n8.6493\nNaN\nNaN\nNaN\n4.0978\n5.4797\n5.4856\n4.0833\n1.4737\n1.8855\n-0.41747\n-3.7896\n-2.8328\n-0.041169\n-1.1133\n-2.4626\n-1.378\n0.45562\n0.96068\n1.1096\n-0.75316\n-0.88127\n-1.307\n-1.302\n-1.06\n-0.23476\n-0.44949\n-1.6034\n-2.4482\n-3.0578\n-3.5864\n-2.6954\n-1.2508\n-1.556\n-2.9014\n-2.3961\n-1.134\n-0.24944\n-1.1346\n-1.9069\n-1.5024\n-1.4323\n-1.44\n-0.91925\n0.035956\n-0.029978\n-0.92004\n-1.2171\n-1.4437\n-1.6669\n-1.2846\n-1.0937\n-1.3455\n-1.9299\n-2.6631\n-4.0489\n-6.5012\n-4.4613\n-3.8819\n-2.563\n-1.5154\n1.277\n0.52638\n1.0515\n1.7375\n1.7427\nNaN\nNaN\n1.017\n2.0946\n2.6179\n4.8625\n7.3132\nNaN\nNaN\nNaN\n3.3572\n3.7975\n5.0092\n3.1372\n-0.52556\n-1.2318\n-2.0272\n-2.7467\n-0.84462\n0.40907\n-0.041133\n-1.7975\n-1.2494\n-0.022893\n1.4684\n1.0987\n-0.064863\n-0.38126\n-0.71839\n-0.84499\n-0.59237\n0.12861\n-0.37418\n-1.0565\n-2.5151\n-3.0895\n-2.7243\n-1.4963\n-0.31105\n-0.33693\n-0.88826\n-0.81841\n-0.58854\n-0.58747\n-0.04201\n-1.4539\n-1.3458\n-0.79495\n-0.25167\n-0.010213\n0.088915\n-0.20602\n-1.0979\n-1.5905\n-1.5736\n-1.2922\n-0.87552\n-0.32465\n0.20411\n-0.3505\n-2.4152\n-3.18\n-6.173\n-1.6828\n-2.0395\n-2.3279\n-0.46475\n1.0804\n-0.1369\n-0.80831\n0.34197\n0.8239\nNaN\nNaN\nNaN\n0.65635\n2.0692\n2.7117\n4.139\nNaN\n4.5299\n3.8711\n2.0004\n2.4088\n4.4145\n1.2438\n-0.56344\n-0.3574\n0.18829\n-0.33584\n-1.0209\n-1.1956\n-1.3858\n-1.2436\n-0.22432\n0.62712\n1.3212\n0.59622\n-0.83077\n-0.77267\n-0.42816\n-0.087892\n0.62243\n0.64192\n0.41953\n-0.76201\n-2.0538\n-2.5163\n-1.7747\n-0.70906\n0.15705\n0.385\n0.19332\n-0.9201\n-1.5043\n-0.55771\n0.82556\n-1.1051\n-0.99879\n-0.18583\n0.52686\n0.91599\n0.55015\n-0.31134\n-0.18477\n-0.67791\n-0.83951\n-0.37739\n0.20389\n0.57342\n1.5639\n2.2895\n-0.79392\nNaN\nNaN\nNaN\n-2.075\n-3.4761\n-1.0187\n0.20885\n-2.956\n-1.9926\n-0.49806\n0.57906\nNaN\nNaN\nNaN\n0.52691\n1.6947\n0.86269\n1.5808\n2.4759\n3.2328\n2.2606\n1.4023\n1.5745\n0.63859\n-0.88618\n-0.88466\n0.43721\n0.8238\n0.14378\n-0.57352\n-0.67052\n-0.41739\n0.11094\n0.20182\n0.67463\n0.52517\n-0.30466\n-1.5202\n-0.79153\n0.40906\n0.59986\n0.79495\n0.89992\n0.67133\n-0.62093\n-1.2578\n-1.4809\n-1.4815\n-0.58178\n-0.091522\n0.75218\n0.86255\n-0.56684\n-1.4037\n-0.74959\n0.40503\n0.079278\n0.57723\n0.81139\n0.55514\n0.72226\n0.96076\n-0.14511\n0.49162\n0.24127\n0.067755\n0.66911\n1.0832\n2.0974\n3.995\n3.1278\n0.094145\nNaN\nNaN\nNaN\n-2.7124\n-3.3683\n-4.7249\n-3.2476\n-3.0013\n-1.3397\n-0.79443\n-0.4016\nNaN\nNaN\nNaN\n-0.52149\n0.18035\n0.3923\n1.4915\n3.9731\n2.9332\n1.6052\n0.75922\n0.28275\n-0.56017\n-1.1889\n-1.256\n0.17816\n1.0409\n0.80431\n0.059351\n-0.10153\n-0.68599\n-0.41192\n0.31176\n0.62806\n0.34162\n-0.23432\n-0.18423\n0.61941\n0.72392\n-0.25917\n0.5946\n0.83393\n0.90256\n-0.21094\n-0.72064\n-0.77544\n-0.65418\n0.13092\n0.31559\n1.3294\n1.3829\n0.018675\n-0.5731\n0.29198\n0.53465\n0.8617\n1.3795\n1.2809\n0.67984\n1.3025\n1.2935\n1.259\n0.7196\n0.90836\n0.69802\n1.399\n2.1242\n2.567\n2.6958\n1.8808\n1.6274\n-0.67826\nNaN\nNaN\n-3.6333\n-3.0865\n-3.2988\n-3.1116\n-2.2441\n-0.51214\n-0.43435\n-0.76552\n-0.21822\n-0.82978\n-0.78564\n-2.4775\n-1.2975\n-0.12789\n1.1555\n3.4468\n2.8165\n2.3703\n1.5169\n0.86141\n0.8849\n-0.34718\n-0.35221\n0.49826\n1.0369\n1.2666\n0.21817\n-1.4361\n-2.7574\n-2.1512\n0.13069\n0.59746\n0.56046\n1.6901\n0.56924\n0.65442\n0.82838\n-0.42151\n0.27375\n0.53554\n1.0307\n0.29806\n-0.94668\n-0.3687\n0.38595\n0.9085\n0.77041\n0.91149\n1.6662\n-0.0095287\n0.27775\n1.5965\n0.97457\n0.33438\n0.83366\n1.16\n1.3153\n1.8768\n2.0428\n1.9759\n1.2267\n1.1001\n1.1497\n1.1792\n1.7707\n2.1952\n2.4832\n1.7953\n1.5333\n-0.36989\n-3.6183\n-4.7855\n-3.654\n-3.0186\n-2.259\n-1.9483\n-1.441\n-0.33744\n-0.64485\n-0.73128\n-0.51594\n-0.23299\n-0.38144\n-1.0708\n-1.1486\n-0.67728\n0.2522\n2.1354\n2.4219\n1.8408\n2.6546\n1.7473\n1.0562\n0.6422\n1.4107\n1.4298\n1.5851\n1.4016\n1.2459\n-1.3727\n-3.6417\n-4.8234\n-1.475\n-0.1294\n2.5385\n2.3512\n1.2545\n0.62247\n1.4731\n0.96366\n0.55357\n0.29483\n0.35286\n0.060209\n-0.5756\n0.81763\n1.0925\n0.95064\n0.72101\n1.9069\n2.1378\n0.31649\n0.67877\n0.67023\n0.34757\n-0.065641\n1.0744\n1.8966\n2.0991\n2.8627\n3.3158\n2.8366\n2.7778\n1.7203\n0.99827\n0.94722\n1.3872\n1.5243\nNaN\nNaN\nNaN\nNaN\n-1.5185\n-3.418\n-3.3428\n-1.7457\n-1.9906\n-2.6294\n-1.7077\n-0.39714\n-0.73881\n-0.3146\n0.39119\n-0.14634\n-0.63247\n-0.83563\n-0.74358\n-1.2921\n-0.93128\n-0.27907\n0.5791\n1.2006\n2.7529\n2.116\n2.1107\n1.8215\n2.2963\n2.1368\n0.42096\n0.15816\n1.6201\n0.57474\n-2.7482\n-2.6443\n-2.7073\n-1.2217\n0.1579\n0.93926\n0.22575\n0.46958\n1.8155\n0.31788\n-0.14829\n0.3195\n-0.17437\n-0.063624\n0.61582\n1.5488\n0.77117\n0.046219\n0.86827\n2.7017\n2.3323\n1.2872\n0.36323\n-0.081881\n0.38665\n0.96086\n1.5131\n2.2369\n2.7881\n3.2101\n3.5425\n3.728\n4.2196\n3.4854\n2.576\n1.6672\n1.5956\n2.0187\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.9545\n-2.6611\n-2.262\n-2.0545\n-3.0468\n-2.2678\n-0.96538\n-0.61684\n-0.54261\n-0.18297\n-0.9139\n-1.0935\n-1.2886\n-1.1532\n-0.28921\n-0.82878\n-0.77911\n0.38024\n1.5337\n3.731\n3.5306\n3.1\n2.9213\n3.0935\n2.3185\n-1.5463\n-1.9995\n-0.77622\n1.7724\n-1.6866\n-1.3627\n-1.2946\n-1.0834\n-1.262\n-1.2812\n-1.274\n-0.39412\n0.25643\n-0.64817\n-0.41093\n0.52948\n0.59679\n0.33981\n1.0937\n1.3901\n0.90309\n-0.10377\n1.4649\n3.566\n2.4677\n1.8505\n-0.010924\n-0.19312\n-0.2328\n1.8947\n1.4829\n2.4491\n3.6692\n4.0602\n5.7102\n5.5766\n5.7622\n4.7547\n3.4982\n2.154\n1.4646\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.2387\n-2.7355\n-3.0603\n-3.0113\n-3.0694\n-2.6207\n-2.8244\n-1.5226\n-1.1581\n-0.72405\n-0.91801\n-1.2298\n-0.78409\n-0.32655\n-0.1666\n-0.22577\n0.19569\n0.60047\n2.8263\n4.0623\n3.0806\n3.2776\n3.6307\n3.0685\n-1.6148\n-3.9903\n-3.1405\n0.78859\n-0.92979\n-0.75312\n-1.0239\n-1.1814\n-0.71125\n-1.5554\n-1.4659\n-0.64565\n0.21605\n-1.2312\n-1.0969\n0.6161\n-0.35893\n-0.31334\n0.049746\n0.86544\n0.77118\n0.97687\n1.5923\n2.5942\n2.0304\n1.9392\n-1.2489\n-1.6173\nNaN\n2.9639\n1.7679\n3.709\n5.0311\n5.678\n7.2883\n8.3256\n7.9071\n4.9655\n3.9135\n2.7738\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.624\n-2.3372\n-2.6369\n-2.3657\n-2.8988\n-2.7207\n-2.3328\n-1.94\n-0.87059\n-0.41173\n-0.89011\n-0.78729\n-0.49101\n-0.024366\n0.00076644\n-0.23076\n-1.0392\n-0.98764\n0.89\n2.2905\n1.9518\n2.7714\n2.3333\n0.37096\n-1.7741\n-3.958\n-4.1296\n-1.1878\n-1.1634\n-0.65693\n-1.4399\n-0.60637\n0.5707\n0.92258\n0.61143\n0.25942\n0.17162\n-1.3534\n-1.3158\n0.56676\n-1.6548\n-0.75468\n-1.8539\n-1.054\n1.3593\nNaN\nNaN\n1.3238\n1.4996\n2.3564\nNaN\nNaN\nNaN\n0.5458\n-0.82798\n-0.75107\n-1.1867\n-1.1637\n-1.3141\n-1.92\n-2.0178\n-0.96062\n-0.48416\n-1.476\n-1.3387\n-1.889\n-1.1696\n0.57338\n0.48732\n0.63774\n-0.39566\n1.371\n1.4843\n2.3731\n1.9906\n2.8619\n1.55\n2.5176\n0.84969\n-0.80251\n-2.7391\n-1.7822\n-1.2422\n-4.242\n-3.55\n-2.2722\n-2.6764\n-0.93986\n-2.0398\n-1.9756\n-0.90142\n-1.881\n-2.4595\n-1.2558\n-0.30044\n0.65398\n1.7129\n1.7684\n0.83697\n1.3812\n1.2657\n0.65576\n0.38362\n0.56791\n1.2911\n0.95537\n-0.46215\n1.8607\n0.39067\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7191\n-1.5937\n-1.4343\n1.1898\n1.2455\n-3.3743\n-2.0049\n-2.3575\n-0.54545\n-0.16185\n-0.15281\n-0.62512\n-2.0496\n0.48416\n1.6103\n-0.020042\n-2.8661\n-2.083\n-0.13777\n0.44737\n3.7015\n1.6066\n2.1253\n3.9345\n3.3801\n2.1696\n0.70119\n-0.90738\n-0.08593\n-1.0932\n-1.4672\n-1.96\n-1.0364\n-1.5467\n-1.2378\n-2.8784\n-2.7238\n-0.84081\n-1.4659\n-0.035403\n0.56942\n0.012068\n0.94996\n1.6808\n1.7139\n1.194\n1.3646\n0.83553\n0.47736\n0.31958\n0.29081\n0.9571\n0.9334\n1.5532\n4.3902\n1.3242\n-0.67972\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4429\n-1.999\n-1.445\n-1.8204\n-4.733\n-4.0045\n-4.1472\n-1.9065\n-1.1897\n1.2388\n0.86036\n-0.29101\n-1.1158\n1.6619\n3.9286\n0.64861\n-1.117\n-0.49207\n-0.12172\n2.4029\n2.2698\n2.3881\n3.7697\n3.3667\n4.041\n3.7628\n2.3826\n0.53889\n0.77973\n-1.3099\n-0.090027\n-1.4008\n-0.094468\n-0.70576\n-3.374\n-3.8077\n-2.2398\n-1.2625\n-0.41872\n-0.13962\n0.36183\n0.83904\n0.53743\n1.8382\n1.156\n0.6308\n0.59504\n0.63467\n0.36155\n0.80287\n1.1464\n0.89814\n0.77372\n2.4346\n2.9276\n1.9358\n1.1175\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3826\n-1.9116\n-1.6007\n-1.4361\n-1.446\n-1.1887\n-0.6332\n-0.87955\n-1.5507\n-1.0167\n-0.054024\n-2.3544\n0.55773\n2.9826\n1.7927\n-0.62355\n-0.71569\n-0.64393\n-0.29414\n2.0662\n3.8273\n2.5569\n3.271\n2.1094\n3.9562\n4.2331\n2.8042\n1.261\n0.49724\n-0.87182\n-0.84747\n-0.054542\n0.095967\n-0.2574\n-2.7459\n-3.6179\n-3.0961\n-1.4254\n-0.16602\n-0.27119\n-0.23921\n0.28912\n-0.48947\n-0.18883\n0.38883\n0.033517\n-0.11097\n0.73373\n1.1962\n2.766\n3.2253\n2.3698\n3.0558\n3.4282\n3.3287\n2.6766\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2039\n-1.2515\n-1.0524\n-1.1216\n-0.68276\n-1.0576\n-0.69983\n-0.48944\n-1.3764\n-2.4339\n-1.8835\n-2.6595\n-2.1152\n-0.59451\n-2.5567\n-2.4353\n-2.1417\n-1.3746\n-2.3227\n0.77135\n4.9485\nNaN\nNaN\n-2.0338\n2.814\n3.9455\n3.7679\n1.7205\n1.5388\n1.5794\n-1.1711\n-0.63961\n-0.56263\n0.058705\n-0.86855\n-1.6909\n-3.2184\n-1.6643\n-0.32536\n-0.29357\n-0.13122\n0.47613\n-0.40634\n-0.50081\n-0.19221\n-0.11023\n0.76153\n1.9766\n1.7459\n1.6423\n3.0037\n2.7802\n2.2368\n3.818\n2.8577\n2.976\n5.9691\nNaN\nNaN\n-7.8612\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.88719\n-1.4686\n-1.5495\n-1.091\n-0.34243\n-1.1877\n-0.46497\n-0.041925\n-0.52668\n-1.0258\n-0.80368\n0.20552\n-0.17101\n-2.0286\n-3.2018\n-1.5545\n-2.7735\n-2.5179\n-0.94682\nNaN\nNaN\nNaN\nNaN\n0.051997\n2.3571\n4.4463\n4.2494\n3.0411\n2.1962\n2.4309\n0.87528\n-1.0036\n-1.0944\n-0.74685\n-0.73794\n-2.4542\n-3.3435\n-1.5642\n-0.44532\n0.0044711\n0.28592\n-0.22993\n-0.51661\n-0.95229\n-3.0612\n-1.5252\n0.65074\n2.383\n1.8589\n2.0166\n2.4101\n2.398\n2.2393\n1.9715\n1.3491\n-0.55409\n1.1523\nNaN\nNaN\n-11.885\n-7.5726\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5464\n-2.0019\n-1.505\n-0.25026\n0.41172\n-0.82868\n0.4415\n0.65809\n0.65077\n0.55752\n0.14876\n-0.31054\n-0.19189\n-1.7145\n-2.0386\n-2.3719\n-2.5688\n-0.60812\n-1.3547\nNaN\nNaN\nNaN\nNaN\n0.30137\n1.3621\n3.3271\n4.3319\n4.6777\n2.5498\n2.1899\n0.075794\n-1.0139\n-2.783\n-1.8356\n-1.9862\n-2.7826\n-2.917\n-1.8766\n-1.1583\n-0.3175\n0.70867\n-0.41598\n-1.6571\n-2.551\n-5.6362\n-2.0418\n0.5131\n2.0939\n2.6053\n2.6803\n1.8025\n2.3319\n2.7336\n2.3953\n1.155\n-0.91407\n0.59059\nNaN\nNaN\n-5.5038\n-8.27\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1096\n-0.86509\n-0.94111\n0.42446\n1.8166\n-0.54506\n0.090415\n1.0286\n1.3403\n1.1769\n0.6558\n0.44452\n-0.082259\n0.26334\n-0.79895\n-1.2115\n-0.60832\n0.33153\n-0.55129\n-0.99775\n-0.31783\n0.63172\n0.55188\n1.4139\n2.0762\n3.2959\n4.1174\n4.7352\n2.6894\n2.1664\n1.2238\n-1.6205\n-3.7616\n-1.9262\n-1.2874\n-1.456\n-1.2715\n-1.9665\n-2.0435\n-2.0966\n-0.22383\n-0.8863\n-1.9135\n-3.2181\n-7.1779\n-1.8606\n1.0957\n1.5939\n1.4468\n2.6473\n1.7908\n2.3512\n3.1808\n2.8676\n2.7256\n0.74348\n0.0023115\n1.9162\n1.7363\n-1.194\n-5.9213\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.54576\n-1.4867\n-1.5306\n-0.91782\n0.81199\n-0.37014\n-0.34804\n1.3781\n1.6018\n1.5203\n0.95366\n1.0997\n1.1564\n0.69108\n0.50049\n0.73678\n0.79302\n1.4202\n0.25726\n0.509\n0.52519\n0.34193\n1.447\n2.1844\n2.3758\n3.7015\n4.0171\n3.172\n2.1702\n1.4347\n1.3715\n0.73808\n-1.4579\n-1.8476\n-1.0367\n-0.86255\n-1.1596\n-2.0356\n-2.0662\n-1.8905\n-1.7267\n-1.2393\n-2.4355\n-4.2452\n-6.7353\n0.47777\n1.9085\n2.1516\n2.3735\n2.8983\n2.7545\n3.7697\n3.948\n5.1421\n4.8068\n1.2956\n-0.44092\n1.3666\n3.2093\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7589\n-1.4559\n-0.77909\n-1.4399\n0.74336\n-0.57361\n-0.62772\n0.084972\n0.9649\n1.4531\n1.2539\n1.2793\n1.613\n1.0086\n1.5944\n1.7741\n1.4164\n2.2282\n1.9108\n2.5616\n2.1557\n2.0511\n2.977\n2.1107\n1.3925\n2.7817\n4.3345\n3.97\n2.1736\n1.0063\n1.2908\n-0.98095\n-3.0436\nNaN\nNaN\n-0.12629\n-1.2345\n-1.615\n-2.4361\n-2.0148\n-1.3671\n-0.86481\n-3.0916\n-3.9372\n-1.4277\n1.9853\n1.3958\n2.1767\n2.5812\n3.5199\n3.3192\n4.7845\n4.9787\n5.4332\n4.6762\n3.0368\n0.3598\n-0.73212\n3.1473\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3622\n-2.3432\n-1.3323\n-1.4561\n-0.4108\n-0.85103\n-0.66375\n-0.58887\n0.64717\n1.7322\n1.7128\n1.2733\n2.3315\n1.7192\n2.611\n2.964\n2.1152\n2.2546\n2.4688\n3.0259\n2.0043\n2.3804\n2.4058\n0.70023\n1.2193\n4.6445\n5.1705\n4.5481\n2.8216\n2.7315\n1.4609\n-0.87983\nNaN\nNaN\nNaN\n-0.40555\n-1.3134\n-0.65596\n-3.0996\n-2.3475\n-0.57036\n-0.56085\n-3.011\n-2.9397\n-0.74697\n1.055\n1.1781\n2.1864\n3.4265\n3.4362\n3.2748\n4.7105\n4.7319\n5.9127\nNaN\nNaN\n-0.76125\n-0.93871\n2.5038\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.8797\n-3.2697\n-2.2743\n-1.3728\n-1.224\n-0.6619\n0.033488\n0.51159\n1.1999\n1.6719\n1.2541\n1.5115\n2.45\n1.9052\n2.8188\n2.7062\n2.6446\n2.4995\n2.3202\n2.0818\n0.68937\n1.5711\n2.0643\n0.77026\n1.6283\n3.7863\n5.0255\n5.3266\n3.7832\n3.7907\n1.8805\nNaN\nNaN\nNaN\nNaN\n-0.53935\n-0.13679\n-0.98884\n-3.6757\n-1.9815\n0.2673\n-0.84761\n-1.9004\n-1.2959\n-0.31761\n0.76757\n1.7924\n3.3685\n4.908\n4.8074\n4.1105\n4.8864\n3.9266\nNaN\nNaN\nNaN\n-0.21463\n-1.2818\n0.41024\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.656\n-1.7542\n-1.3242\n-1.3807\n-0.88814\n-0.067713\n0.30987\n0.49226\n0.96898\n1.0968\n1.5122\n1.9181\n1.9494\n2.4021\n3.0381\n3.4064\n3.7012\n2.9514\n2.1385\n1.5815\n1.101\n1.4881\n0.16889\n1.0092\n1.791\n1.9795\n5.2815\n4.4847\n3.1011\n3.387\n2.485\nNaN\nNaN\nNaN\nNaN\n-1.563\n-1.1805\n-0.59413\n-2.249\n-1.0688\n-0.62244\n-3.4335\n-4.5003\n-0.57622\n0.76423\n1.5811\n2.6535\n4.2623\n5.4213\n5.4526\n4.8732\n4.5091\n2.7708\nNaN\nNaN\nNaN\n-3.893\n-4.0409\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.55141\n-0.85889\n-1.1472\n-0.48622\n-0.053658\n0.050678\n0.29187\n0.15026\n0.61131\n0.78132\n0.92576\n1.8933\n2.8748\n3.4501\n3.4474\n3.7004\n3.1607\n2.8483\n1.5513\n1.119\n1.7485\n-0.06351\n-2.3557\n-1.3806\n-1.2439\n1.8263\n4.0687\n3.4836\n4.1253\n4.4098\n3.4493\nNaN\nNaN\nNaN\nNaN\n-1.4854\n-2.3573\n-0.30143\n-1.6075\n-0.21367\n-0.59918\n-2.36\n-2.0659\n1.1876\n2.7511\n3.4536\n3.583\n4.9307\n5.4313\n5.4981\n4.5541\n2.9687\n2.1298\nNaN\nNaN\nNaN\nNaN\n-3.2641\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.061531\n-1.3292\n-0.28437\n0.75761\n0.38923\n-0.044351\n0.26985\n0.10396\n0.34011\n0.44805\n0.4864\n1.6661\n3.1364\n3.5689\n3.8627\n3.4351\n3.5029\n3.5926\n2.5278\n1.1668\n1.0338\n-2.3452\n-5.5047\n-3.2676\n-2.2194\n1.9725\n2.9245\n3.7857\n5.1554\n4.9351\n5.3471\nNaN\n-1.1137\n-1.8689\n0.19835\n0.90044\n-0.91983\n1.1921\n0.67903\n2.3944\n1.6225\n-1.246\n-1.4953\n4.3326\nNaN\nNaN\nNaN\n5.393\n6.3193\n5.6804\n3.7831\n4.3225\n3.2719\n0.14136\nNaN\nNaN\nNaN\n-4.73\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.67249\n-0.95615\n-0.046448\n1.4688\n1.1643\n0.70098\n0.97501\n0.39862\n0.23377\n1.0696\n1.0927\n1.8908\n2.8523\n3.0961\n3.5483\n2.1796\n2.2055\n3.2893\n3.6709\n1.8496\n0.66494\n-0.83912\n-7.7058\n-6.5158\n-2.5244\n1.0061\n3.3567\n6.5845\n6.3168\n4.522\n2.421\n-2.4941\n0.35259\n-0.21097\n-0.16535\n1.2371\n2.5309\n2.829\n3.3777\nNaN\nNaN\nNaN\n-1.2763\nNaN\nNaN\nNaN\nNaN\n4.8901\n6.1267\n4.2174\n2.9533\n2.8665\n3.2237\n-0.069038\n-0.91339\n-3.2248\n-5.7538\n-5.5352\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0897\n-0.81328\n0.17707\n1.8568\n1.0261\n1.0881\n0.87004\n0.44414\n0.47913\n0.7749\n0.97768\n1.6462\n2.7784\n2.9851\n3.072\n1.5541\n1.4232\n2.4123\n3.0233\n2.2907\n0.38082\n-1.5957\n-3.4916\n-5.0835\n-0.79867\n2.0222\n4.3928\n5.2372\n4.5274\n3.3279\n0.75532\n1.5136\n3.7919\n2.0405\n2.5102\n4.2305\n3.6405\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12616\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4242\nNaN\n2.1553\n2.5492\n0.010611\n-0.67398\n-1.562\n-5.1322\n-5.615\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4482\n-0.74428\n0.71407\n1.9902\n1.1316\n0.69316\n0.60188\n0.79783\n0.70203\n0.62512\n0.97213\n1.6321\n2.4136\n2.6026\n2.6519\n2.0437\n2.8984\n2.0364\n1.8807\n0.83178\n-0.89919\n-3.3293\n-4.1059\n-4.4853\n-0.17168\n2.4527\n4.6954\n3.417\n3.5307\n2.3746\n4.2122\n3.9806\n3.6165\n4.1681\n5.4195\n5.9833\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4149\n-0.52202\n-2.3775\n-4.6594\n-3.9737\n-4.6795\n-4.8131\n-1.7263\n-0.81795\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.88101\n-0.030214\n1.2029\n1.7313\n1.1695\n-0.26967\n0.4005\n0.76363\n0.33761\n0.5705\n0.37465\n1.1583\n1.8538\n2.5174\n2.8765\n2.0536\n1.0867\n0.33477\n0.78207\n-0.070067\n-1.3052\n-3.9151\n-4.4104\n-2.8619\n0.19064\n2.365\n3.0829\n3.0485\n4.953\n3.8116\n4.1739\n4.9431\n5.0617\n4.7724\n5.8847\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8073\n-1.2221\n-2.0781\n-2.725\n-1.0922\n-1.2418\n-1.4556\n0.86431\n1.2015\n-1.0212\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.3038\n0.30362\n0.30742\n-0.023502\n0.92775\n0.64427\n1.4498\n0.48827\n-0.6476\n-0.075457\n0.30233\n0.88414\n1.4261\n2.1408\n2.0119\n1.0222\n-0.59358\n-1.8378\n-0.88564\n-0.66175\n-1.2401\n-4.3965\n-3.2857\n-1.1114\n2.0024\n3.8082\n3.0144\n3.8232\n5.8584\n5.284\n4.1464\n5.2363\n4.8419\n4.5811\n6.7861\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.0327\n-3.1698\n-2.6324\n-1.291\n0.7496\n1.5305\n-0.40281\n-0.4083\n0.96828\n-0.9355\n0.15039\n1.5346\n2.1942\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27744\n0.55756\n-0.049916\n-0.4107\n1.5\n1.4166\n-0.45577\n-1.0094\n-1.0773\n0.45497\n0.84453\n0.97433\n0.30613\n1.5278\n0.92853\n0.45843\n-0.38337\n-0.71762\n-1.0913\n-1.4193\n-1.5876\n-5.0008\n-3.4158\n0.69218\n2.567\n5.2869\n5.8291\n5.5597\n5.9471\n7.5421\n9.7239\n10.68\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.529\n-2.5937\n-1.7231\n-1.828\n-2.3997\n-1.2968\n1.421\n2.2185\n0.34954\n1.5033\n2.0129\n1.5569\n1.1987\n1.4523\n1.484\n1.9154\n1.8051\n1.9666\n0.77673\n-0.15232\n-0.23949\n-0.17239\n-0.63804\n-0.33928\n0.8528\n0.68451\n-0.94747\n-0.70314\n-0.85243\n0.21953\n0.26597\n-0.14101\n0.81405\n1.0739\n0.44863\n1.0904\n0.30293\n-0.88007\n-1.0134\n-0.85466\n0.1897\n-3.2632\n-4.113\n1.2379\n4.1323\n8.3387\n7.168\n6.9338\n8.9851\n10.144\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.96887\n-1.8\n-1.1445\n0.60112\n0.096545\n-0.42022\n-2.6887\n-0.15663\n1.5572\n0.65484\n2.4443\n2.8442\n1.1993\n0.731\n0.24199\nNaN\nNaN\nNaN\nNaN\n1.3913\n1.2284\n-0.18917\n-0.41409\n-0.55235\n-1.2516\n-1.1502\n-1.0051\n-1.2845\n-1.0427\n-0.070592\n-0.27908\n-0.66159\n-0.40742\n0.52355\n0.79964\n0.9382\n1.8209\n1.1457\n-1.4494\n-3.5854\n-0.57658\n-0.1357\n-4.0454\n-3.4065\n0.78307\n5.5834\n6.6805\n6.2158\n7.1508\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.93777\n0.48593\n-0.15535\n-1.0183\n0.43064\n1.454\n1.088\n1.0545\n-0.35983\n-0.39064\n-0.55255\n0.53231\n2.1605\n3.4545\n1.3691\n1.7742\n1.4301\nNaN\nNaN\nNaN\nNaN\n0.057436\n2.5427\n-0.46322\n-1.0515\n-2.6623\n-1.9861\n-1.0205\n-1.0699\n-1.5827\n-0.51153\n0.90807\n0.24633\n-0.2939\n0.57087\n0.98138\n0.85777\n1.2865\n3.4822\n2.627\n-0.42382\n-3.2348\n-4.5795\n-2.7322\n-2.6879\n-1.1909\n2.2127\n4.2288\n5.3682\n4.5129\n4.4921\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.018091\n0.95554\n0.62129\n0.24104\n-0.26974\n0.35743\n0.99731\n1.5451\n2.6694\n1.4336\n-0.20147\n-0.77398\n0.060714\n0.54939\n2.7697\n0.45256\n1.9371\n2.3263\nNaN\nNaN\n-0.40447\n0.33208\n0.43053\n0.91707\n-1.6856\n-2.0972\n-2.2284\n-1.5187\n-1.3077\n-0.11265\n-0.77942\n0.2942\n0.84234\n0.66623\n-0.05056\n0.47262\n1.4302\n0.51347\n0.75678\n3.01\n3.3356\n0.37529\n-2.6471\n-3.8495\n-1.8062\n-1.0127\n-0.4023\n2.8242\n4.4507\n6.639\n5.7177\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.094\n-1.0378\n2.6979\n0.91374\n1.3299\n0.86264\n0.41598\n0.11107\n1.8585\n4.2287\n3.3331\n0.87941\n0.67136\n0.79025\n-0.33938\n-0.85179\n0.4865\n2.1389\n1.4872\n-0.15971\n-1.6274\n0.84483\n1.6331\n1.8042\n1.41\n-2.8048\n-2.0688\n-1.2837\n-1.1562\n-1.7055\n0.26413\n0.12614\n-0.083133\n0.49308\n0.18439\n0.075405\n1.1501\n1.4714\n1.6403\n2.1124\n2.8688\n4.7155\n0.61761\n-1.3576\n-1.457\n0.12037\n-0.19778\n-0.0073441\n1.8027\n5.2093\n6.9334\n7.1137\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6561\n-1.4812\n2.1978\n0.55654\n1.548\n1.6611\n-0.31453\n-0.82224\n0.88549\n4.1668\n4.0961\n2.88\n1.7897\n1.8015\n0.4857\n-0.027565\n1.857\n2.9858\n1.0759\n0.60773\n0.22984\n1.8791\n2.8103\n3.8047\n2.1075\n-2.4\n-1.4852\n-0.85614\n-0.67605\n-1.1634\n-0.26228\n0.01811\n-0.22586\n0.13407\n-0.056452\n-0.37689\n-0.18721\n1.088\n2.0897\n2.6154\n3.0873\n3.1227\n0.48838\n0.28437\n-0.90201\n-0.39221\n0.24512\n1.5714\n2.75\n5.5566\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.74059\n-1.0114\n-0.50446\n1.8416\n-4.0581\n-0.86602\n1.5566\n1.1151\n3.2763\n3.8714\n4.0548\n4.0431\n4.3953\n3.2189\n2.2807\n0.85791\n-0.18109\n2.957\n2.479\n2.7864\n1.3837\n1.5797\n2.8449\n3.4293\n3.4519\n2.2407\n-2.1438\n-0.85129\n-1.1932\n-0.9394\n-1.2199\n-0.96674\n-1.0039\n-0.21962\n-0.095122\n-0.70958\n-0.53498\n-0.1582\n0.50228\n1.4024\n2.5064\n2.7382\n1.6431\n-0.051929\n-0.60268\n-0.32949\n0.028178\n0.50017\n1.0471\n2.4381\n5.5178\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.8314\nNaN\nNaN\nNaN\nNaN\n1.8775\n0.33586\n0.16676\n-0.59415\n0.70169\n-4.0718\n0.030602\n2.0993\n5.0122\n4.2671\n4.2317\n3.5018\n-0.49766\n3.6817\n1.9198\n1.3921\n-1.4527\n0.3017\n3.6202\n3.2599\n4.0458\n2.5906\n2.9598\n3.4871\n3.6171\n2.4919\n0.37745\n-1.3436\n-0.76706\n-1.5189\n-1.3109\n-0.66847\n-0.92027\n-1.1322\n-0.84478\n-1.0972\n-0.56717\n-0.23708\n-0.016793\n-0.029556\n1.3214\n1.7947\n2.2054\n1.6641\n0.44937\n-1.063\n-0.36852\n0.78541\n0.92143\n-0.055224\n2.7716\n7.4834\n10.865\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.3112\n3.1483\n1.9775\n1.1751\n-0.061804\n-0.83436\n-0.19459\n0.15558\n-0.088691\n2.2299\n4.8691\n2.8732\n3.1614\n1.4389\n-2.3635\n-1.4434\n0.62414\n0.95376\n-0.75101\n1.1163\n3.0501\n3.7249\n3.2731\n2.6065\n1.9788\n3.5363\n2.7479\n0.88463\n-3.3371\n-1.2946\n-1.1984\n-0.87865\n-1.4824\n-1.5352\n-1.1841\n-0.57333\n-1.1148\n-1.8429\n-0.58745\n-0.41873\n-0.39655\n-0.64171\n0.50361\n1.3248\n1.5445\n0.6283\n-0.10331\n-0.28031\n-2.3849\n-2.0192\n0.64219\n1.0602\n4.7987\n9.4392\n10.343\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.7984\nNaN\nNaN\n7.5866\n5.4825\n4.2963\n4.5774\n1.6222\n-0.53322\n-1.508\n-2.7742\n-2.0304\n-2.0476\n-0.66135\n1.7026\n-0.028771\n-0.49645\n0.32228\n-0.54431\n-2.1982\n-2.6192\n-0.47292\n-0.31821\n0.59213\n1.9965\n2.0515\n1.4676\n1.7238\n1.7892\n1.6371\n1.093\n0.49332\n-4.8761\n-1.7786\n-1.6547\n-1.7216\n-1.9709\n-2.3732\n-1.2412\n-0.91328\n-1.0156\n-1.4292\n-0.78482\n-0.64665\n-1.1188\n-1.1067\n-0.60668\n0.38757\n-0.25745\n-0.65751\n-1.6477\n-1.8057\n-2.099\n-2.6957\n-0.12776\n1.8411\n6.9194\n9.7535\n11.468\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.583\n18.317\n12.52\n9.8076\n5.3321\n4.4501\n3.7929\n2.9582\n0.29898\n-0.26909\n-1.2108\n-2.287\n-1.9654\n-1.9861\n-1.4946\n-1.5119\n-1.3706\n-1.4578\n-1.6829\n-2.9755\n-3.6396\n-0.47068\n0.83117\n0.80974\n0.053003\n-0.74109\n0.1322\n1.5288\n1.2993\n-0.21218\n0.61485\n-0.36072\n-2.3806\n-2.4804\n-1.8941\n-2.0075\n-1.3255\n-1.7146\n-1.7068\n-1.8114\n-1.1741\n-1.1781\n-1.5411\n-0.91425\n-2.0451\n-3.0865\n-1.9021\n-1.1133\n-1.0834\n-2.0526\n-2.9007\n-2.7339\n-2.4918\n-2.5899\n-1.7141\n1.9078\n5.2508\n8.6207\n12.76\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n22.356\n17.309\n12.122\n9.3937\n4.425\n2.3735\n2.56\n2.2281\n1.1998\n-0.26193\n-2.006\n-2.0566\n-1.4193\n-0.94724\n-0.91451\n-3.1323\n-3.7088\n-2.3695\n-2.4588\n-4.1761\n-4.7032\n-1.6496\n0.17531\n0.049867\n-0.14175\n-1.066\n-0.28601\n-0.16272\n1.0323\n-1.0133\n-1.7824\n-0.98064\n-0.31494\n-1.862\n-1.8718\n-2.0035\n-0.94415\n-1.7777\n-1.8963\n-2.0552\n-1.7675\n-1.5844\n-1.9075\n-2.1327\n-2.3179\n-3.1715\n-2.2336\n-2.2078\n-1.402\n-2.2198\n-5.0102\n-5.8757\n-2.4064\n-1.5703\n-2.2344\n0.70554\n5.0891\n7.0899\n6.54\n4.5373\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n18.76\n11.327\n10.732\n6.1226\n4.614\n-1.0299\n0.67177\n1.0996\n-0.29385\n-0.92746\n-1.5868\n-1.5041\n-1.0634\n-1.1137\n-1.5192\n-2.6333\n-5.24\n-2.9423\n-1.8972\n-2.2008\n-3.5549\n-3.7244\n-1.3521\n-0.35579\n0.056201\n-0.099944\n-0.52384\n-0.95587\n-2.0295\n-2.2892\n-1.127\n0.13146\n0.37386\n-1.6686\n-2.2556\n-2.3767\n-1.4047\n-1.2073\n-1.1005\n-2.0906\n-2.0338\n-1.3361\n-1.4662\n-2.4804\n-2.4753\n-2.3969\n-2.1787\n-2.3662\n-1.4748\n-1.5814\n-2.9175\n-4.6661\n-4.1661\n-1.49\n-2.3601\n0.41399\n5.1326\n6.0167\n1.0267\n0.1161\n-1.0017\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.689\n9.5261\n6.8019\n6.5634\n4.3685\n-0.74108\n0.69881\n0.71105\n-1.6364\n-2.7153\n-2.919\n-1.1743\n-1.0199\n-1.5196\n-3.1769\n-2.5698\n-2.595\n-2.0825\n-0.85907\n-0.80031\n-0.93643\n-1.6842\n-1.9267\n-0.61703\n-0.95092\n-0.63785\n-1.3529\n-1.5312\n-2.2586\n-1.222\n-0.36472\n0.7472\n0.32102\n-1.9162\n-2.7219\n-2.5818\n-2.0112\n-0.80373\n-0.57522\n-1.4916\n-1.2825\n-1.2817\n-1.2936\n-3.0769\n-2.3032\n-1.6745\n-2.6769\n-3.289\n-2.4902\n-1.375\n-3.2439\n-2.9129\n-5.429\n-5.2684\n-3.1547\n0.43473\n2.7996\n3.6159\n1.8127\n-0.56586\n0.76484\nNaN\nNaN\n4.2019\n7.5012\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.8336\n7.5433\n6.148\n6.0362\n0.51627\n-2.5516\n-0.46219\n-1.6977\n-2.9284\n-4.275\n-2.7042\n-0.61476\n-0.7173\n-1.5717\n-1.6844\n-1.843\n-1.592\n-1.3815\n-0.91498\n-1.2172\n-1.6147\n-1.6237\n-1.9019\n-1.2283\n-1.7737\n-2.335\n-3.8464\n-4.2472\n-1.8239\n-0.73567\n-0.21413\n1.0884\n0.62752\n-2.2153\n-2.4321\n-2.2423\n-2.5694\n-0.9447\n0.30099\n-0.55133\n-0.8195\n-1.4549\n-1.4077\n-2.7653\n-1.7994\n-1.7959\n-2.8654\n-3.4288\n-1.9156\n-1.9931\n-4.1906\n-5.0504\n-4.4661\n-3.6595\n-2.2597\n-0.23752\n2.2356\n-0.036555\n-2.2946\n-1.0347\n2.7666\n2.7629\n5.2216\n6.1393\n6.3509\n6.6634\n7.7422\nNaN\nNaN\nNaN\nNaN\n6.5932\n7.4853\n4.9102\n4.1525\n4.2079\n-1.5601\n-4.7086\n-2.6399\n-2.6617\n-3.1298\n-3.5166\n-2.1669\n0.32386\n0.34811\n0.58226\n-0.5469\n-2.0246\n-2.0097\n-1.2532\n-1.0717\n-0.8212\n-0.8077\n-1.7953\n-2.2616\n-3.3536\n-4.1483\n-3.3188\n-3.2302\n-3.248\n-2.8823\n-2.654\n-1.154\n0.57363\n-0.37563\n-1.6421\n-2.1005\n-2.6428\n-2.4253\n-1.7993\n0.013014\n0.023753\n-0.75409\n-1.3309\n-1.4553\n-2.1706\n-1.3502\n-1.5648\n-2.2564\n-2.9405\n-2.5844\n-4.4147\n-7.5931\n-5.9028\n-4.1916\n-3.2008\n-0.25714\n0.65916\n1.7159\n5.2638\n1.6771\n2.0904\n2.8408\nNaN\n1.9477\n3.9888\n1.9129\n6.3867\n9.8408\nNaN\nNaN\nNaN\n4.3412\n5.9876\n6.1108\n4.5152\n1.6715\n2.1643\n-0.63955\n-4.5284\n-3.3654\n-0.17054\n-1.3368\n-2.7297\n-1.4987\n0.4904\n1.0439\n1.1774\n-0.89849\n-0.98293\n-1.5016\n-1.4881\n-1.177\n-0.24765\n-0.44755\n-1.69\n-2.6792\n-3.3977\n-4.0261\n-3.0054\n-1.3696\n-1.7268\n-3.225\n-2.7194\n-1.3288\n-0.35315\n-1.3413\n-2.4762\n-2.0184\n-1.9382\n-1.9672\n-1.3477\n-0.207\n-0.28349\n-1.3142\n-1.656\n-1.851\n-2.054\n-1.5965\n-1.3554\n-1.5672\n-2.2875\n-3.0995\n-4.746\n-7.7239\n-5.1331\n-4.3917\n-2.8411\n-1.6834\n1.5427\n0.75688\n1.4225\n1.9446\n1.8684\nNaN\nNaN\n0.79941\n2.2255\n2.8017\n5.2629\n8.0196\nNaN\nNaN\nNaN\n3.5874\n4.1346\n5.5963\n3.4634\n-0.68173\n-1.5486\n-2.4539\n-3.2932\n-1.0634\n0.40684\n-0.12238\n-2.0177\n-1.4375\n-0.079619\n1.624\n1.1411\n-0.1844\n-0.46431\n-0.86851\n-0.98237\n-0.64196\n0.1598\n-0.38488\n-1.1211\n-2.7539\n-3.4018\n-3.056\n-1.7022\n-0.35588\n-0.37922\n-0.93965\n-0.90145\n-0.74121\n-0.65446\n-0.012193\n-2.0306\n-1.8616\n-1.2039\n-0.60005\n-0.31749\n-0.15393\n-0.48309\n-1.5443\n-2.0822\n-2.0367\n-1.6617\n-1.1509\n-0.56674\n0.081922\n-0.49791\n-2.8281\n-3.6546\n-7.32\n-2.0331\n-2.6308\n-2.8342\n-0.5364\n1.3825\n0.09525\n-0.6726\n0.35448\n0.76582\nNaN\nNaN\nNaN\n0.53749\n2.1957\n2.9981\n4.5429\nNaN\n5.0343\n4.2445\n2.1157\n2.6444\n4.9161\n1.4664\n-0.61245\n-0.53294\n0.11619\n-0.4932\n-1.2173\n-1.3653\n-1.6265\n-1.4466\n-0.28699\n0.6745\n1.4701\n0.61418\n-1.0754\n-0.94155\n-0.50686\n-0.11904\n0.71333\n0.72222\n0.47279\n-0.81459\n-2.2633\n-2.7957\n-2.0072\n-0.81403\n0.16109\n0.43606\n0.27136\n-0.99032\n-1.6859\n-0.58902\n0.91828\n-1.6135\n-1.4909\n-0.58947\n0.24066\n0.72796\n0.38169\n-0.63477\n-0.53472\n-1.085\n-1.2203\n-0.62039\n0.055179\n0.48688\n1.6914\n2.5954\n-0.99078\nNaN\nNaN\nNaN\n-2.6478\n-4.0944\n-1.1438\n0.3488\n-3.1884\n-2.2147\n-0.70967\n0.5631\nNaN\nNaN\nNaN\n0.43484\n1.8142\n0.91747\n1.7521\n2.712\n3.5434\n2.5005\n1.5643\n1.7527\n0.66689\n-0.99688\n-0.98203\n0.4497\n0.85074\n0.11127\n-0.68\n-0.73789\n-0.41873\n0.17991\n0.21749\n0.74375\n0.56947\n-0.34184\n-1.7275\n-0.92253\n0.42935\n0.6343\n0.89078\n1.0126\n0.78039\n-0.69143\n-1.4201\n-1.6679\n-1.6983\n-0.65922\n-0.10335\n0.83853\n0.98352\n-0.63818\n-1.6062\n-0.84288\n0.4968\n-0.31998\n0.19265\n0.47228\n0.23986\n0.48119\n0.80474\n-0.5197\n0.23411\n-0.011882\n-0.19486\n0.49813\n1.0132\n2.1782\n4.3853\n3.5963\n0.055115\nNaN\nNaN\nNaN\n-3.2699\n-3.9003\n-5.4019\n-3.7177\n-3.3344\n-1.587\n-1.0685\n-0.61303\nNaN\nNaN\nNaN\n-0.69\n0.068783\n0.30106\n1.5833\n4.4282\n3.2547\n1.8167\n0.88278\n0.33056\n-0.6468\n-1.2817\n-1.3795\n0.15197\n1.1064\n0.88127\n-0.02786\n-0.18897\n-0.7976\n-0.4886\n0.34368\n0.68327\n0.35682\n-0.21305\n-0.17191\n0.67362\n0.7575\n-0.33149\n0.7241\n0.96386\n0.99663\n-0.28501\n-0.81328\n-0.82792\n-0.71556\n0.1412\n0.38391\n1.5146\n1.548\n0.016398\n-0.62174\n0.3264\n0.66162\n0.55529\n1.1723\n1.0604\n0.34941\n1.1339\n1.1093\n1.092\n0.54597\n0.80949\n0.49126\n1.2791\n2.1237\n2.6496\n2.8261\n1.9596\n1.6924\n-0.82895\nNaN\nNaN\n-4.2338\n-3.6073\n-3.8379\n-3.6364\n-2.6643\n-0.70964\n-0.66903\n-1.0057\n-0.39345\n-1.092\n-0.99645\n-2.8693\n-1.6117\n-0.29981\n1.1836\n3.8591\n3.1951\n2.7515\n1.7622\n1.0108\n1.0638\n-0.32693\n-0.31824\n0.48681\n1.1877\n1.4807\n0.22005\n-1.6966\n-3.2894\n-2.6101\n0.1653\n0.64975\n0.57113\n1.907\n0.67191\n0.7189\n0.88932\n-0.48451\n0.37147\n0.65539\n1.1141\n0.28126\n-1.0708\n-0.40293\n0.44694\n1.0217\n0.90424\n1.0375\n1.8838\n0.021841\n0.29563\n1.7884\n1.1467\n-0.039267\n0.58044\n0.95051\n1.0742\n1.691\n1.9003\n1.8824\n1.1003\n0.93346\n0.9522\n0.99809\n1.679\n2.1953\n2.5928\n2.0049\n1.7042\n-0.65292\n-4.2191\n-5.5724\n-4.338\n-3.5976\n-2.7128\n-2.3277\n-1.7776\n-0.52363\n-0.87389\n-0.9431\n-0.72005\n-0.39984\n-0.55727\n-1.3213\n-1.4948\n-0.98329\n0.15395\n2.3607\n2.7877\n2.1239\n3.0417\n1.9665\n1.1531\n0.66981\n1.6045\n1.5451\n1.7238\n1.6652\n1.5001\n-1.6322\n-4.3035\n-5.6661\n-1.6147\n-0.18397\n2.7763\n2.6213\n1.4198\n0.65589\n1.6336\n1.1187\n0.69442\n0.36558\n0.36662\n0.033698\n-0.6601\n0.87163\n1.1841\n1.0642\n0.80511\n2.1247\n2.3873\n0.35974\n0.75875\n0.80103\n0.45454\n-0.45124\n0.89238\n1.8336\n2.0091\n2.8203\n3.3247\n2.848\n2.8573\n1.6018\n0.81793\n0.71928\n1.2184\n1.3902\nNaN\nNaN\nNaN\nNaN\n-1.9373\n-4.0511\n-4.0047\n-2.1558\n-2.4486\n-3.1893\n-2.1149\n-0.57957\n-0.90678\n-0.47616\n0.27833\n-0.29996\n-0.83069\n-1.034\n-0.86412\n-1.5693\n-1.1172\n-0.26631\n0.70373\n1.4561\n3.1889\n2.3642\n2.3367\n2.0111\n2.5753\n2.3501\n0.38818\n0.21202\n1.9229\n0.68342\n-3.2786\n-3.1211\n-3.1745\n-1.4761\n0.076119\n0.98411\n0.19958\n0.51568\n2.0707\n0.31557\n-0.2011\n0.31829\n-0.21136\n-0.032175\n0.69839\n1.6629\n0.80882\n0.09702\n1.0203\n2.9922\n2.5766\n1.4165\n0.46358\n0.013137\n0.56732\n0.71558\n1.4078\n2.2407\n2.8182\n3.2687\n3.6807\n3.8742\n4.538\n3.6813\n2.6441\n1.5522\n1.4636\n2.0183\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.435\n-3.1905\n-2.7943\n-2.5605\n-3.6764\n-2.7705\n-1.2384\n-0.79538\n-0.78058\n-0.36261\n-1.1377\n-1.3256\n-1.5048\n-1.3073\n-0.35824\n-0.95537\n-0.87492\n0.47393\n1.8355\n4.3389\n4.0055\n3.4623\n3.2417\n3.4172\n2.5311\n-1.8025\n-2.2248\n-0.80045\n2.2105\n-2.0288\n-1.6197\n-1.5409\n-1.2835\n-1.4739\n-1.4903\n-1.3731\n-0.38921\n0.28732\n-0.78595\n-0.47966\n0.51578\n0.62423\n0.42651\n1.223\n1.5191\n1.0069\n-0.055549\n1.6419\n3.9354\n2.7464\n2.0665\n0.046875\n-0.11345\n-0.15202\n1.8212\n1.35\n2.5121\n3.8553\n4.3024\n6.1417\n5.9813\n6.3149\n5.2154\n3.7512\n2.1734\n1.3151\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.9617\n-3.316\n-3.707\n-3.6204\n-3.6556\n-3.1539\n-3.3674\n-1.9015\n-1.4839\n-0.97253\n-1.1147\n-1.4642\n-0.96543\n-0.37767\n-0.1265\n-0.21386\n0.24961\n0.78755\n3.3133\n4.6536\n3.4212\n3.6711\n4.0337\n3.3175\n-1.8835\n-4.4895\n-3.4947\n1.0566\n-1.1008\n-0.90914\n-1.1938\n-1.3639\n-0.89162\n-1.7418\n-1.6152\n-0.66555\n0.28517\n-1.4711\n-1.307\n0.57286\n-0.4583\n-0.33052\n0.028431\n0.99676\n0.91284\n1.0953\n1.7872\n2.9755\n2.2877\n2.1814\n-1.3529\n-1.7838\nNaN\n3.0649\n1.6707\n3.9454\n5.4439\n6.2285\n8.0106\n9.1433\n8.8136\n5.5282\n4.286\n2.9479\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.2458\n-2.8154\n-3.1988\n-2.8482\n-3.4588\n-3.2417\n-2.7761\n-2.3473\n-1.1936\n-0.66348\n-1.0961\n-0.96149\n-0.66473\n0.037115\n0.11394\n-0.12978\n-1.0885\n-1.0013\n1.0252\n2.6323\n2.218\n3.1431\n2.5699\n0.3029\n-2.0776\n-4.5557\n-4.6387\n-1.267\n-1.362\n-0.80417\n-1.6901\n-0.77589\n0.59219\n0.97566\n0.62194\n0.29422\n0.21631\n-1.5688\n-1.508\n0.54487\n-1.8799\n-0.83081\n-2.1085\n-1.1728\n1.5825\nNaN\nNaN\n1.5336\n1.6214\n2.621\nNaN\nNaN\nNaN\n0.4992\n-0.99397\n-0.90541\n-1.4122\n-1.3715\n-1.4831\n-2.1113\n-2.3244\n-1.1775\n-0.61912\n-1.7211\n-1.6022\n-2.1824\n-1.3933\n0.59226\n0.51522\n0.66585\n-0.52727\n1.3711\n1.576\n2.5559\n2.0773\n3.1092\n1.774\n2.8052\n0.93411\n-0.86083\n-3.0036\n-1.9712\n-1.3518\n-4.7668\n-4.008\n-2.6609\n-3.1024\n-1.161\n-2.3481\n-2.2659\n-1.0448\n-2.1055\n-2.7428\n-1.4136\n-0.37976\n0.6531\n1.8203\n1.8861\n0.85386\n1.4661\n1.3471\n0.67289\n0.38654\n0.61586\n1.3671\n0.98341\n-0.56384\n2.0019\n0.34282\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.0091\n-1.8705\n-1.7079\n1.2311\n1.3086\n-3.8115\n-2.151\n-2.688\n-0.71719\n-0.26463\n-0.31036\n-0.86764\n-2.3809\n0.51201\n1.7882\n-0.018594\n-3.2757\n-2.3977\n-0.25275\n0.38162\n3.8969\n1.65\n2.325\n4.3735\n3.8145\n2.4495\n0.81648\n-0.93743\n-0.074813\n-1.2037\n-1.6895\n-2.2704\n-1.3352\n-1.8361\n-1.5106\n-3.2756\n-3.0875\n-0.993\n-1.6799\n-0.12203\n0.59407\n-0.046226\n0.9619\n1.7257\n1.8028\n1.2584\n1.4742\n0.89392\n0.51108\n0.33436\n0.28901\n1.0066\n0.97501\n1.6578\n4.7885\n1.3605\n-0.84011\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8285\n-2.343\n-1.7066\n-2.1296\n-5.4397\n-4.6681\n-4.7916\n-2.1914\n-1.3986\n1.2995\n0.78927\n-0.5249\n-1.4336\n1.7971\n4.3162\n0.70119\n-1.3377\n-0.64937\n-0.19531\n2.5943\n2.4055\n2.6251\n4.1555\n3.7468\n4.5633\n4.221\n2.6852\n0.63252\n0.87476\n-1.4352\n-0.10001\n-1.6082\n-0.32486\n-0.985\n-3.8163\n-4.2968\n-2.5631\n-1.475\n-0.55266\n-0.19024\n0.37452\n0.86084\n0.509\n1.95\n1.2208\n0.64093\n0.61011\n0.66639\n0.37301\n0.86114\n1.2119\n0.96899\n0.83518\n2.615\n3.1267\n2.1025\n1.1876\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6526\n-2.2535\n-1.8868\n-1.7044\n-1.7133\n-1.4458\n-0.8516\n-1.1063\n-1.8609\n-1.271\n-0.18378\n-2.7959\n0.27851\n3.2104\n1.854\n-0.81995\n-0.91341\n-0.81686\n-0.38561\n2.2392\n4.1342\n2.8887\n3.6304\n2.3097\n4.4184\n4.7431\n3.192\n1.4132\n0.60346\n-0.75928\n-0.9766\n-0.090056\n-0.098806\n-0.47134\n-3.1829\n-4.1318\n-3.5002\n-1.6417\n-0.27121\n-0.33132\n-0.29146\n0.26219\n-0.62853\n-0.26129\n0.38183\n-0.025808\n-0.18523\n0.74273\n1.236\n2.9686\n3.4817\n2.5611\n3.3151\n3.6984\n3.6509\n2.97\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4838\n-1.5142\n-1.2745\n-1.345\n-0.88001\n-1.2909\n-0.9151\n-0.68774\n-1.6558\n-2.8046\n-2.227\n-3.0616\n-2.4244\n-0.78348\n-2.976\n-2.8021\n-2.502\n-1.6769\n-2.6932\n0.87027\n5.4071\nNaN\nNaN\n-2.287\n3.1244\n4.4414\n4.313\n2.0197\n1.7811\n1.8487\n-1.351\n-0.87275\n-0.80161\n-0.0658\n-1.1816\n-1.9638\n-3.5892\n-1.8801\n-0.4121\n-0.36216\n-0.18874\n0.47264\n-0.52484\n-0.65337\n-0.28293\n-0.1986\n0.75049\n2.0645\n1.805\n1.6871\n3.2303\n2.9984\n2.406\n4.1515\n3.1076\n3.2527\n6.5781\nNaN\nNaN\n-8.7765\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.113\n-1.7409\n-1.7831\n-1.3012\n-0.5036\n-1.4402\n-0.64038\n-0.19246\n-0.70461\n-1.2353\n-0.989\n0.11967\n-0.32196\n-2.3522\n-3.6573\n-1.8712\n-3.2097\n-2.9125\n-0.99697\nNaN\nNaN\nNaN\nNaN\n0.087863\n2.725\n5.0622\n4.8367\n3.4529\n2.5108\n2.7484\n0.9711\n-1.2795\n-1.3133\n-0.91289\n-1.0194\n-2.8704\n-3.7447\n-1.7793\n-0.53215\n-0.051377\n0.27012\n-0.29739\n-0.64111\n-1.1007\n-3.4112\n-1.7706\n0.60746\n2.5334\n1.9528\n2.118\n2.585\n2.5834\n2.418\n2.1162\n1.4252\n-0.72344\n1.176\nNaN\nNaN\n-13.39\n-8.4143\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8098\n-2.3069\n-1.7401\n-0.38642\n0.34212\n-1.0345\n0.37477\n0.63883\n0.63146\n0.56754\n0.10049\n-0.42334\n-0.28206\n-1.9923\n-2.3367\n-2.7211\n-2.9368\n-0.71804\n-1.5886\nNaN\nNaN\nNaN\nNaN\n0.4636\n1.6376\n3.8182\n4.9423\n5.2706\n2.9274\n2.4929\n0.085479\n-1.2631\n-3.1805\n-2.1268\n-2.3426\n-3.1884\n-3.2771\n-2.122\n-1.338\n-0.43266\n0.73742\n-0.52861\n-1.9114\n-2.8744\n-6.2508\n-2.3367\n0.45161\n2.2056\n2.7898\n2.8853\n1.9193\n2.4975\n2.9\n2.5397\n1.2003\n-1.0727\n0.57908\nNaN\nNaN\n-6.1932\n-9.1832\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3201\n-1.0474\n-1.1113\n0.38844\n1.9036\n-0.66866\n0.036152\n1.0773\n1.4249\n1.2794\n0.7169\n0.45988\n-0.11091\n0.268\n-0.87873\n-1.3454\n-0.71075\n0.3731\n-0.62534\n-1.0843\n-0.30522\n0.76534\n0.71331\n1.6686\n2.443\n3.7855\n4.6958\n5.3913\n3.1172\n2.5166\n1.4252\n-1.765\n-4.1521\n-2.167\n-1.4529\n-1.6448\n-1.4243\n-2.2534\n-2.3702\n-2.4117\n-0.31878\n-1.0656\n-2.2209\n-3.67\n-7.9871\n-2.1369\n1.0812\n1.6367\n1.4898\n2.8484\n1.8574\n2.4826\n3.3999\n3.0759\n2.9394\n0.72525\n-0.040137\n2.0263\n1.88\n-1.3876\n-6.6621\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.67793\n-1.6904\n-1.7023\n-1.0584\n0.84473\n-0.43521\n-0.41267\n1.48\n1.7139\n1.6535\n1.0563\n1.2259\n1.2707\n0.78296\n0.5728\n0.80855\n0.89731\n1.6034\n0.33871\n0.63012\n0.62671\n0.42959\n1.6936\n2.5014\n2.7389\n4.2368\n4.613\n3.7076\n2.5476\n1.7082\n1.6102\n0.87121\n-1.4958\n-2.0013\n-1.1059\n-0.91212\n-1.3071\n-2.3495\n-2.3993\n-2.1942\n-1.9792\n-1.4551\n-2.7962\n-4.804\n-7.5605\n0.41448\n1.9679\n2.2305\n2.5014\n3.1059\n2.9225\n4.0737\n4.2715\n5.6096\n5.222\n1.3255\n-0.59413\n1.4212\n3.4191\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.1099\n-1.6633\n-0.90407\n-1.6318\n0.71047\n-0.68543\n-0.72871\n0.029346\n1.0164\n1.6114\n1.4072\n1.444\n1.8158\n1.1677\n1.7988\n1.9684\n1.6046\n2.4807\n2.1247\n2.8632\n2.4364\n2.3816\n3.4289\n2.448\n1.631\n3.2304\n4.9908\n4.592\n2.5371\n1.2304\n1.5615\n-1.0228\n-3.3318\nNaN\nNaN\n-0.10858\n-1.3533\n-1.8443\n-2.7974\n-2.3365\n-1.6163\n-1.0809\n-3.5315\n-4.4343\n-1.6623\n2.0497\n1.4048\n2.2602\n2.7235\n3.7499\n3.5314\n5.1737\n5.4034\n5.9414\n5.0905\n3.2044\n0.17224\n-0.98393\n3.3041\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.7898\n-2.6478\n-1.5242\n-1.662\n-0.53216\n-0.97825\n-0.76\n-0.66428\n0.69694\n1.9099\n1.9362\n1.5047\n2.6601\n1.9765\n2.9254\n3.2942\n2.3541\n2.5117\n2.7682\n3.4265\n2.3448\n2.7952\n2.8522\n0.93293\n1.4309\n5.2319\n5.9261\n5.2673\n3.2597\n3.1752\n1.7732\n-0.85884\nNaN\nNaN\nNaN\n-0.42498\n-1.4731\n-0.80513\n-3.5325\n-2.7082\n-0.80313\n-0.85781\n-3.4775\n-3.3885\n-0.97194\n1.0026\n1.1565\n2.2867\n3.6592\n3.6022\n3.4687\n5.0851\n5.1428\n6.4922\nNaN\nNaN\n-1.028\n-1.175\n2.6062\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.3736\n-3.6467\n-2.5625\n-1.5691\n-1.3933\n-0.74722\n0.014519\n0.53612\n1.3291\n1.8733\n1.4542\n1.7557\n2.7866\n2.1997\n3.16\n3.0061\n2.932\n2.8218\n2.6879\n2.428\n0.88629\n1.9218\n2.5375\n1.0708\n1.948\n4.3055\n5.754\n6.1288\n4.4014\n4.3859\n2.2188\nNaN\nNaN\nNaN\nNaN\n-0.59294\n-0.17289\n-1.1607\n-4.2615\n-2.3521\n0.13732\n-1.0456\n-2.273\n-1.654\n-0.56558\n0.64462\n1.7899\n3.5442\n5.2967\n5.149\n4.3931\n5.2911\n4.2626\nNaN\nNaN\nNaN\n-0.40886\n-1.5981\n0.37743\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9365\n-2.0063\n-1.5475\n-1.6096\n-1.0511\n-0.12998\n0.29716\n0.54655\n1.0508\n1.2189\n1.734\n2.2036\n2.2687\n2.7443\n3.4008\n3.814\n4.1472\n3.3618\n2.5198\n1.8949\n1.3686\n1.839\n0.38708\n1.2306\n2.1313\n2.3768\n6.0521\n5.1858\n3.6538\n3.8977\n2.8597\nNaN\nNaN\nNaN\nNaN\n-1.7439\n-1.3399\n-0.71168\n-2.6882\n-1.4265\n-0.93789\n-4.0461\n-5.244\n-0.89005\n0.62447\n1.5275\n2.7256\n4.5395\n5.8609\n5.8813\n5.2238\n4.8795\n2.9693\nNaN\nNaN\nNaN\n-4.4969\n-4.6129\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.7446\n-1.0504\n-1.3577\n-0.64181\n-0.13782\n-0.016244\n0.26497\n0.1095\n0.65049\n0.89258\n1.0858\n2.1747\n3.2061\n3.8188\n3.8347\n4.1667\n3.6264\n3.2677\n1.8546\n1.3876\n2.1034\n0.14893\n-2.5195\n-1.4486\n-1.2057\n2.2515\n4.7485\n4.0857\n4.7566\n5.0421\n3.9658\nNaN\nNaN\nNaN\nNaN\n-1.597\n-2.5888\n-0.36027\n-1.9662\n-0.4429\n-0.95634\n-2.9433\n-2.6103\n1.0625\n2.8307\n3.5847\n3.7582\n5.2674\n5.8485\n5.9373\n4.8571\n3.1333\n2.2364\nNaN\nNaN\nNaN\nNaN\n-3.7829\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21217\n-1.5734\n-0.4218\n0.74048\n0.35826\n-0.11897\n0.22227\n0.064254\n0.37708\n0.49486\n0.59572\n1.9043\n3.4834\n3.9545\n4.2816\n3.8881\n3.9425\n4.0782\n2.9251\n1.4207\n1.3576\n-2.4549\n-6.0508\n-3.5457\n-2.309\n2.4117\n3.4831\n4.416\n5.9128\n5.6807\n6.1979\nNaN\n-1.2452\n-2.1222\n0.23095\n1.1621\n-1.0012\n1.1849\n0.51257\n2.3289\n1.438\n-1.6877\n-1.9957\n4.4916\nNaN\nNaN\nNaN\n5.7449\n6.7748\n6.0885\n4.0082\n4.6437\n3.5147\n0.050188\nNaN\nNaN\nNaN\n-5.3461\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.8608\n-1.1881\n-0.16612\n1.5273\n1.1997\n0.6844\n0.99012\n0.38797\n0.24953\n1.1586\n1.2337\n2.1226\n3.1892\n3.4746\n3.9297\n2.4866\n2.5459\n3.7756\n4.2127\n2.1979\n0.89484\n-0.80815\n-8.4556\n-7.1982\n-2.6679\n1.3272\n3.9592\n7.5491\n7.2331\n5.2001\n2.9757\n-2.6772\n0.42692\n-0.29713\n-0.22161\n1.3732\n2.6866\n2.9271\n3.4993\nNaN\nNaN\nNaN\n-1.6808\nNaN\nNaN\nNaN\nNaN\n5.171\n6.5275\n4.4655\n3.1311\n3.0312\n3.4726\n-0.24919\n-1.1787\n-3.7303\n-6.5048\n-6.2564\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2897\n-1.013\n0.10248\n1.9646\n1.058\n1.1064\n0.89535\n0.45185\n0.50369\n0.90153\n1.1181\n1.8452\n3.1104\n3.34\n3.3982\n1.7998\n1.7063\n2.8494\n3.5098\n2.6903\n0.55313\n-1.6945\n-3.7752\n-5.6082\n-0.75288\n2.4299\n5.1127\n5.989\n5.214\n3.811\n1.0609\n1.92\n4.3736\n2.1634\n2.7426\n4.6793\n3.8487\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.06171\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6044\nNaN\n2.3568\n2.7212\n-0.13235\n-0.89216\n-1.8599\n-5.8345\n-6.3857\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6966\n-0.91566\n0.71556\n2.1158\n1.1759\n0.69348\n0.62191\n0.86603\n0.76444\n0.75149\n1.1091\n1.8648\n2.7016\n2.9259\n2.9678\n2.3864\n3.3504\n2.3953\n2.2268\n1.0709\n-0.86317\n-3.6321\n-4.4499\n-4.8644\n-0.066139\n2.919\n5.4263\n3.9004\n4.0523\n2.7512\n4.8831\n4.5747\n4.1314\n4.6741\n6.0691\n6.6385\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6693\n-0.66579\n-2.7015\n-5.2437\n-4.4741\n-5.2053\n-5.4072\n-2.0012\n-1.0526\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0842\n-0.10667\n1.2568\n1.8291\n1.2191\n-0.35172\n0.40574\n0.83492\n0.38626\n0.6782\n0.45971\n1.382\n2.1217\n2.8435\n3.2568\n2.3761\n1.3128\n0.44758\n0.97714\n0.030153\n-1.3728\n-4.2895\n-4.8233\n-3.0313\n0.35853\n2.8024\n3.5874\n3.5175\n5.6827\n4.3587\n4.8791\n5.5876\n5.6162\n5.3223\n6.5844\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.1639\n-1.4104\n-2.3778\n-3.0957\n-1.2887\n-1.4568\n-1.6827\n0.89585\n1.2962\n-1.1512\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.43274\n0.25708\n0.28146\n-0.086704\n0.95908\n0.66653\n1.5578\n0.53183\n-0.68239\n-0.03181\n0.40642\n1.0871\n1.651\n2.3993\n2.2522\n1.2217\n-0.54719\n-1.9792\n-0.88887\n-0.65504\n-1.2541\n-4.8313\n-3.5555\n-1.0971\n2.3613\n4.3837\n3.5137\n4.4296\n6.7155\n6.0595\n4.8423\n5.8957\n5.3812\n5.1093\n7.6403\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.6785\n-3.595\n-2.9794\n-1.5094\n0.71623\n1.5916\n-0.52088\n-0.4821\n1.028\n-1.049\n0.1687\n1.6584\n2.3742\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.42169\n0.5244\n-0.11088\n-0.4877\n1.6041\n1.5022\n-0.51401\n-1.0838\n-1.1187\n0.57041\n0.98012\n1.1271\n0.39459\n1.7069\n1.0416\n0.58078\n-0.31051\n-0.73371\n-1.2179\n-1.4528\n-1.573\n-5.4745\n-3.6787\n0.93699\n2.987\n6.0207\n6.6349\n6.2982\n6.7802\n8.2801\n10.657\n11.686\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.9561\n-3.0145\n-2.0145\n-2.1088\n-2.713\n-1.507\n1.4268\n2.3029\n0.30993\n1.6414\n2.1522\n1.5997\n1.2757\n1.6227\n1.5513\n1.9911\n1.8752\n1.9977\n0.80514\n-0.22968\n-0.32048\n-0.2424\n-0.76707\n-0.41753\n0.90955\n0.72137\n-1.0477\n-0.74061\n-0.86402\n0.29089\n0.32985\n-0.092212\n0.9595\n1.2273\n0.49785\n1.2482\n0.37205\n-0.97451\n-1.2293\n-0.85697\n0.35212\n-3.4788\n-4.4492\n1.5366\n4.7307\n9.4344\n8.1281\n7.8148\n10.149\n11.123\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1998\n-2.1385\n-1.4003\n0.58483\n0.026947\n-0.55095\n-3.0391\n-0.25143\n1.6624\n0.695\n2.6809\n3.081\n1.254\n0.79161\n0.2768\nNaN\nNaN\nNaN\nNaN\n1.519\n1.3527\n-0.25906\n-0.52214\n-0.69161\n-1.4257\n-1.3073\n-1.1112\n-1.3872\n-1.1314\n-0.038181\n-0.2678\n-0.67061\n-0.3874\n0.61758\n0.9349\n1.0963\n2.0732\n1.3662\n-1.5641\n-3.9708\n-0.60311\n-0.038364\n-4.2606\n-3.5992\n1.0218\n6.3437\n7.575\n7.0546\n7.8424\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.84875\n0.40231\n-0.30475\n-1.226\n0.40087\n1.5159\n1.1127\n1.0888\n-0.45723\n-0.47561\n-0.63502\n0.54116\n2.3417\n3.7636\n1.4336\n1.9588\n1.5289\nNaN\nNaN\nNaN\nNaN\n0.048006\n2.8511\n-0.61194\n-1.2287\n-2.9777\n-2.2386\n-1.1607\n-1.1721\n-1.7142\n-0.5488\n1.0125\n0.30736\n-0.27018\n0.67302\n1.1468\n1.0004\n1.4727\n3.8811\n2.972\n-0.38302\n-3.4811\n-4.9625\n-2.9289\n-2.8747\n-1.196\n2.6238\n4.8787\n6.1127\n5.1336\n4.9315\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16086\n0.88336\n0.54588\n0.15108\n-0.36176\n0.34798\n1.0542\n1.6639\n2.9191\n1.5416\n-0.24795\n-0.82616\n0.061362\n0.55119\n3.0199\n0.45562\n2.1101\n2.5441\nNaN\nNaN\n-0.26493\n0.45644\n0.48941\n1.084\n-1.9516\n-2.3821\n-2.5243\n-1.72\n-1.449\n-0.11949\n-0.83874\n0.34216\n0.92768\n0.75744\n-0.0022037\n0.58394\n1.6594\n0.64637\n0.92537\n3.3816\n3.7738\n0.44806\n-2.8231\n-4.1285\n-1.9109\n-1.0244\n-0.33917\n3.2549\n5.0636\n7.665\n6.624\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4989\n-1.2976\n2.8386\n0.89591\n1.392\n0.92236\n0.43521\n0.069228\n2.0652\n4.736\n3.6848\n0.97714\n0.76559\n0.84864\n-0.40883\n-0.93042\n0.53159\n2.3179\n1.6189\n-0.15153\n-1.8045\n0.98853\n1.8492\n2.0148\n1.5566\n-3.1821\n-2.3519\n-1.4868\n-1.3076\n-1.8752\n0.28321\n0.16816\n-0.068626\n0.56418\n0.23592\n0.1672\n1.3375\n1.6878\n1.8746\n2.3459\n3.2219\n5.3173\n0.72788\n-1.4181\n-1.5211\n0.24535\n-0.10374\n0.092058\n2.1068\n5.9363\n7.9019\n8.1612\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.0865\n-1.7505\n2.2722\n0.47163\n1.5996\n1.7957\n-0.35775\n-0.97229\n0.95391\n4.6563\n4.5921\n3.2273\n2.0099\n1.98\n0.52285\n-0.023257\n2.0395\n3.263\n1.1925\n0.63574\n0.2249\n2.0781\n3.1394\n4.2228\n2.3226\n-2.7423\n-1.7321\n-1.03\n-0.77271\n-1.2809\n-0.27804\n0.058259\n-0.20012\n0.17565\n0.0094011\n-0.34985\n-0.16121\n1.2561\n2.3705\n2.901\n3.46\n3.5372\n0.60534\n0.36754\n-0.9362\n-0.33268\n0.38904\n1.8684\n3.1862\n6.3245\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.82663\n-1.2757\n-0.74726\n1.8325\n-4.8335\n-1.1799\n1.7042\n1.2272\n3.6382\n4.2808\n4.4811\n4.5035\n4.9047\n3.5947\n2.5584\n0.94605\n-0.11234\n3.2799\n2.7794\n3.1132\n1.4585\n1.7097\n3.1539\n3.8278\n3.8848\n2.5129\n-2.4965\n-1.0332\n-1.4064\n-1.0538\n-1.3456\n-1.0508\n-1.0793\n-0.23002\n-0.095319\n-0.74677\n-0.55892\n-0.15495\n0.58929\n1.6336\n2.7955\n3.0504\n1.8659\n0.015649\n-0.59276\n-0.29241\n0.13392\n0.66859\n1.279\n2.8203\n6.2653\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.3361\nNaN\nNaN\nNaN\nNaN\n2.0534\n0.22843\n0.051525\n-0.80227\n0.64158\n-4.8338\n-0.049442\n2.344\n5.5666\n4.7347\n4.6822\n3.8409\n-0.61249\n4.1019\n2.1329\n1.5958\n-1.5522\n0.40952\n4.0579\n3.6512\n4.5181\n2.8531\n3.2863\n3.8827\n4.02\n2.8055\n0.51208\n-1.5916\n-0.92863\n-1.734\n-1.4758\n-0.762\n-1.0236\n-1.2553\n-0.9567\n-1.225\n-0.62587\n-0.24279\n-0.01155\n-0.030974\n1.5204\n1.9742\n2.4357\n1.8571\n0.5397\n-1.1277\n-0.3061\n0.95331\n1.1024\n0.051865\n3.2064\n8.4606\n12.292\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.6846\n3.4212\n2.0092\n1.1598\n-0.16213\n-1.0191\n-0.34049\n0.10076\n-0.14138\n2.4613\n5.3953\n3.1547\n3.4884\n1.5801\n-2.5819\n-1.5869\n0.77985\n1.2187\n-0.77257\n1.2768\n3.435\n4.2058\n3.7143\n2.9117\n2.2594\n3.9719\n3.0645\n1.0267\n-3.5724\n-1.561\n-1.4039\n-1.0254\n-1.6896\n-1.7228\n-1.3435\n-0.70567\n-1.2929\n-2.0636\n-0.63611\n-0.46293\n-0.48177\n-0.73993\n0.56326\n1.4261\n1.6775\n0.66858\n-0.13111\n-0.29107\n-2.5785\n-2.1282\n0.80907\n1.3329\n5.5096\n10.659\n11.713\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.6715\nNaN\nNaN\n8.2973\n5.9854\n4.6872\n4.9774\n1.6817\n-0.70511\n-1.7628\n-3.1576\n-2.318\n-2.3488\n-0.81261\n1.8392\n-0.079731\n-0.59045\n0.38511\n-0.53693\n-2.4182\n-2.8755\n-0.39253\n-0.31144\n0.67792\n2.2392\n2.3247\n1.6784\n1.937\n2.043\n1.8881\n1.252\n0.57234\n-5.2637\n-2.1112\n-1.9252\n-1.9578\n-2.2311\n-2.6405\n-1.4259\n-1.0791\n-1.1609\n-1.6229\n-0.89337\n-0.75302\n-1.2943\n-1.2328\n-0.66576\n0.38051\n-0.32753\n-0.8064\n-1.873\n-2.0042\n-2.2752\n-2.8802\n0.0011255\n2.2084\n7.8713\n11.018\n12.977\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.536\n19.902\n13.616\n10.64\n5.8566\n4.8898\n4.0307\n3.1632\n0.25249\n-0.36349\n-1.4188\n-2.6215\n-2.2473\n-2.2843\n-1.735\n-1.7588\n-1.5544\n-1.6203\n-1.8435\n-3.2665\n-4.0254\n-0.4439\n0.95983\n0.91371\n0.061847\n-0.80811\n0.12249\n1.6761\n1.463\n-0.19432\n0.6942\n-0.4053\n-2.5731\n-2.8906\n-2.1945\n-2.3228\n-1.5472\n-1.9594\n-1.9607\n-2.0708\n-1.3415\n-1.3349\n-1.7254\n-1.0645\n-2.3072\n-3.4326\n-2.1028\n-1.2678\n-1.2507\n-2.3455\n-3.239\n-3.013\n-2.7543\n-2.8644\n-1.8684\n2.2554\n5.9752\n9.7206\n14.341\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n24.303\n18.748\n13.19\n10.27\n4.8711\n2.4514\n2.726\n2.345\n1.2476\n-0.36278\n-2.315\n-2.3765\n-1.6834\n-1.1528\n-1.0912\n-3.5201\n-4.1496\n-2.6338\n-2.7254\n-4.5911\n-5.1161\n-1.7838\n0.23997\n0.083846\n-0.14118\n-1.1434\n-0.34527\n-0.19807\n1.1254\n-1.1182\n-1.9831\n-1.1013\n-0.34004\n-2.1968\n-2.2052\n-2.3538\n-1.1453\n-2.0807\n-2.1884\n-2.3358\n-2.0026\n-1.8018\n-2.1456\n-2.413\n-2.6157\n-3.5233\n-2.4824\n-2.4855\n-1.5738\n-2.5056\n-5.6316\n-6.6282\n-2.6661\n-1.6723\n-2.418\n0.89594\n5.7811\n8.0028\n7.5222\n5.2148\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n20.475\n12.441\n11.738\n6.7083\n5.0664\n-1.2698\n0.64547\n1.0848\n-0.42343\n-1.0902\n-1.8516\n-1.7667\n-1.2735\n-1.315\n-1.7207\n-2.9358\n-5.8692\n-3.2894\n-2.1201\n-2.3955\n-3.8276\n-4.0542\n-1.4483\n-0.34496\n0.09307\n-0.051827\n-0.57382\n-1.0733\n-2.2754\n-2.5195\n-1.2303\n0.14695\n0.45395\n-2.0195\n-2.6783\n-2.8005\n-1.6892\n-1.5005\n-1.3337\n-2.3938\n-2.3274\n-1.5533\n-1.6729\n-2.7996\n-2.7797\n-2.6701\n-2.4343\n-2.682\n-1.6613\n-1.8249\n-3.3442\n-5.3002\n-4.6865\n-1.5882\n-2.5585\n0.57158\n5.8103\n6.8575\n1.191\n0.11202\n-0.99869\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.079\n10.43\n7.4762\n7.2132\n4.781\n-0.97334\n0.63415\n0.65345\n-1.9598\n-3.0233\n-3.2651\n-1.4029\n-1.2119\n-1.7518\n-3.5364\n-2.8362\n-2.8664\n-2.2998\n-0.93461\n-0.83793\n-0.96229\n-1.7342\n-2.0684\n-0.63938\n-1.0369\n-0.66246\n-1.506\n-1.704\n-2.4757\n-1.2712\n-0.3373\n0.92236\n0.43875\n-2.2983\n-3.1734\n-3.0152\n-2.3841\n-1.0452\n-0.74027\n-1.7469\n-1.5023\n-1.5042\n-1.4343\n-3.4366\n-2.5851\n-1.878\n-3.0089\n-3.6816\n-2.7972\n-1.608\n-3.7002\n-3.3148\n-6.0615\n-5.8983\n-3.5068\n0.54066\n3.2197\n4.1788\n2.1463\n-0.64253\n0.89487\nNaN\nNaN\n4.6175\n8.3621\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.785\n8.2939\n6.7637\n6.6438\n0.34074\n-3.1107\n-0.62628\n-1.9743\n-3.4188\n-4.7168\n-3.0225\n-0.77925\n-0.86415\n-1.7696\n-1.8767\n-2.0549\n-1.7638\n-1.5254\n-1.0042\n-1.3193\n-1.716\n-1.6881\n-2.0454\n-1.2968\n-1.8993\n-2.5345\n-4.1772\n-4.6089\n-1.9541\n-0.76353\n-0.17371\n1.261\n0.74751\n-2.6378\n-2.8796\n-2.634\n-2.9966\n-1.2132\n0.22309\n-0.72203\n-1.0186\n-1.7093\n-1.5866\n-3.1098\n-2.0445\n-2.0423\n-3.2305\n-3.8485\n-2.1793\n-2.2765\n-4.6898\n-5.6004\n-4.9245\n-4.0071\n-2.4558\n-0.18807\n2.5617\n0.1004\n-2.4031\n-1.0948\n3.0635\n3.0424\n5.7823\n6.7698\n7.108\n7.4437\n8.6053\nNaN\nNaN\nNaN\nNaN\n7.1242\n8.182\n5.3356\n4.5339\n4.5663\n-1.8934\n-5.3805\n-3.0067\n-3.0624\n-3.605\n-3.9335\n-2.4362\n0.32029\n0.3711\n0.61875\n-0.60919\n-2.251\n-2.2439\n-1.3975\n-1.1709\n-0.88194\n-0.84322\n-1.928\n-2.4681\n-3.6056\n-4.5315\n-3.6419\n-3.5582\n-3.5694\n-3.1234\n-2.9177\n-1.2553\n0.61666\n-0.44927\n-2.0164\n-2.5148\n-3.1164\n-2.8523\n-2.1647\n-0.1132\n-0.10422\n-0.96855\n-1.6076\n-1.6746\n-2.4693\n-1.5589\n-1.8009\n-2.5424\n-3.3049\n-2.9067\n-4.9823\n-8.6048\n-6.5558\n-4.6216\n-3.4964\n-0.21301\n0.7754\n1.9684\n5.918\n1.7997\n2.2832\n3.1341\nNaN\n2.0401\n4.3542\n2.053\n7.1251\n10.918\nNaN\nNaN\nNaN\n4.6512\n6.5163\n6.7116\n4.9399\n1.8584\n2.4189\n-0.7951\n-5.1468\n-3.8143\n-0.25599\n-1.5228\n-2.987\n-1.6226\n0.53186\n1.1335\n1.2613\n-1.0201\n-1.076\n-1.6715\n-1.6537\n-1.2891\n-0.26434\n-0.4632\n-1.8025\n-2.9146\n-3.7253\n-4.4348\n-3.3017\n-1.4923\n-1.8904\n-3.5355\n-3.0149\n-1.4986\n-0.43276\n-1.5189\n-2.918\n-2.4123\n-2.3236\n-2.3654\n-1.6635\n-0.36901\n-0.45344\n-1.6046\n-1.9827\n-2.1646\n-2.3633\n-1.8451\n-1.5662\n-1.7533\n-2.5838\n-3.4748\n-5.3367\n-8.7422\n-5.7136\n-4.8505\n-3.1079\n-1.8426\n1.7635\n0.92696\n1.7068\n2.1459\n2.0183\nNaN\nNaN\n0.70027\n2.394\n3.0232\n5.7004\n8.7413\nNaN\nNaN\nNaN\n3.8611\n4.4933\n6.1533\n3.7842\n-0.7977\n-1.7964\n-2.8057\n-3.7509\n-1.2385\n0.41832\n-0.17813\n-2.2208\n-1.6022\n-0.1176\n1.776\n1.2072\n-0.26378\n-0.52715\n-0.98944\n-1.1006\n-0.69413\n0.18478\n-0.40568\n-1.2009\n-2.9989\n-3.7129\n-3.3654\n-1.8907\n-0.3972\n-0.41809\n-1.0037\n-0.98513\n-0.86691\n-0.72372\n0.0028084\n-2.4602\n-2.2479\n-1.5038\n-0.8402\n-0.522\n-0.31323\n-0.6724\n-1.875\n-2.46\n-2.3938\n-1.9465\n-1.3613\n-0.73548\n0.012833\n-0.60355\n-3.1804\n-4.0713\n-8.2747\n-2.3071\n-3.072\n-3.2356\n-0.59595\n1.6173\n0.2362\n-0.60773\n0.38037\n0.76141\nNaN\nNaN\nNaN\n0.49313\n2.3576\n3.283\n4.953\nNaN\n5.5249\n4.6275\n2.2671\n2.8884\n5.3965\n1.6526\n-0.66343\n-0.65972\n0.073803\n-0.61106\n-1.3839\n-1.5192\n-1.8329\n-1.6214\n-0.33654\n0.72494\n1.6124\n0.6473\n-1.2654\n-1.0751\n-0.57112\n-0.14151\n0.79586\n0.79757\n0.52216\n-0.87597\n-2.4738\n-3.067\n-2.2207\n-0.90851\n0.16766\n0.48354\n0.32907\n-1.067\n-1.8569\n-0.63469\n1.0016\n-1.9864\n-1.8477\n-0.86249\n0.069542\n0.63558\n0.28988\n-0.8588\n-0.77098\n-1.3774\n-1.4999\n-0.79292\n-0.036146\n0.45053\n1.8315\n2.8776\n-1.1456\nNaN\nNaN\nNaN\n-3.0829\n-4.6127\n-1.2568\n0.45092\n-3.4434\n-2.4269\n-0.86275\n0.57636\nNaN\nNaN\nNaN\n0.3991\n1.9561\n0.98937\n1.9208\n2.9525\n3.8581\n2.7351\n1.7206\n1.9257\n0.70721\n-1.1028\n-1.0801\n0.46906\n0.89246\n0.09111\n-0.77378\n-0.81095\n-0.43651\n0.22831\n0.2346\n0.81173\n0.61582\n-0.37741\n-1.9145\n-1.0332\n0.45765\n0.67734\n0.98179\n1.1169\n0.87484\n-0.75763\n-1.5699\n-1.8417\n-1.8923\n-0.72853\n-0.11272\n0.92328\n1.0939\n-0.70296\n-1.7846\n-0.93165\n0.5663\n-0.5815\n-0.038558\n0.27953\n0.051897\n0.34856\n0.73434\n-0.77037\n0.083369\n-0.17116\n-0.36605\n0.40856\n1.003\n2.3036\n4.7843\n4.0215\n0.036186\nNaN\nNaN\nNaN\n-3.7236\n-4.3616\n-6.0076\n-4.1381\n-3.6559\n-1.7906\n-1.2698\n-0.76033\nNaN\nNaN\nNaN\n-0.81527\n0.0069996\n0.26145\n1.6989\n4.8642\n3.5661\n2.0117\n0.99059\n0.37176\n-0.72136\n-1.3848\n-1.5064\n0.13557\n1.1795\n0.9559\n-0.085688\n-0.25489\n-0.8988\n-0.55636\n0.37555\n0.7418\n0.37408\n-0.20944\n-0.17029\n0.72979\n0.80396\n-0.3877\n0.82975\n1.0767\n1.0887\n-0.33841\n-0.89546\n-0.88657\n-0.77633\n0.15575\n0.44287\n1.6839\n1.7051\n0.016833\n-0.67135\n0.35802\n0.76134\n0.38318\n1.0847\n0.96045\n0.15773\n1.0704\n1.0304\n1.0255\n0.45961\n0.77628\n0.37947\n1.2485\n2.1963\n2.7931\n3.0089\n2.0846\n1.7973\n-0.94823\nNaN\nNaN\n-4.7461\n-4.0516\n-4.3032\n-4.0861\n-3.015\n-0.85313\n-0.83277\n-1.1865\n-0.5132\n-1.2914\n-1.1604\n-3.2112\n-1.861\n-0.41313\n1.2445\n4.2485\n3.5409\n3.0843\n1.9748\n1.1356\n1.2078\n-0.32756\n-0.3093\n0.49185\n1.3193\n1.6635\n0.22797\n-1.9212\n-3.7387\n-2.9916\n0.19365\n0.70515\n0.59304\n2.1027\n0.75633\n0.7785\n0.95486\n-0.54076\n0.44514\n0.75326\n1.2041\n0.28193\n-1.1799\n-0.43478\n0.50382\n1.129\n1.0212\n1.1526\n2.0841\n0.040597\n0.3156\n1.9695\n1.2947\n-0.27443\n0.4445\n0.85413\n0.96435\n1.6376\n1.8777\n1.89\n1.0603\n0.86348\n0.86406\n0.92147\n1.6842\n2.2738\n2.755\n2.2076\n1.8718\n-0.84703\n-4.7359\n-6.2507\n-4.9077\n-4.0767\n-3.0852\n-2.6417\n-2.0481\n-0.65616\n-1.0445\n-1.1045\n-0.86809\n-0.51604\n-0.68767\n-1.5234\n-1.7612\n-1.2044\n0.10242\n2.5833\n3.1104\n2.3742\n3.3862\n2.168\n1.2482\n0.70385\n1.7767\n1.6643\n1.8629\n1.8841\n1.7091\n-1.8563\n-4.8716\n-6.3893\n-1.755\n-0.22225\n3.0159\n2.8747\n1.5658\n0.69343\n1.7846\n1.2504\n0.80546\n0.42461\n0.38877\n0.01956\n-0.73125\n0.9375\n1.2832\n1.1727\n0.88755\n2.3327\n2.6235\n0.39623\n0.83278\n0.91066\n0.54081\n-0.70839\n0.81166\n1.8589\n2.0255\n2.8959\n3.4491\n2.9562\n3.0117\n1.5852\n0.73471\n0.60478\n1.1598\n1.3576\nNaN\nNaN\nNaN\nNaN\n-2.2645\n-4.5834\n-4.5504\n-2.4819\n-2.8153\n-3.6499\n-2.4414\n-0.71205\n-1.0418\n-0.59062\n0.22072\n-0.4062\n-0.98575\n-1.1967\n-0.97279\n-1.7958\n-1.2704\n-0.26788\n0.80478\n1.6636\n3.568\n2.5979\n2.5538\n2.1942\n2.8345\n2.5574\n0.36967\n0.24667\n2.1746\n0.77145\n-3.7349\n-3.5305\n-3.5799\n-1.6863\n0.028114\n1.045\n0.18743\n0.55863\n2.2971\n0.32408\n-0.23838\n0.33107\n-0.24049\n-0.012146\n0.77532\n1.7925\n0.86076\n0.13391\n1.1503\n3.2741\n2.8158\n1.5444\n0.54272\n0.074257\n0.70039\n0.58781\n1.3939\n2.3248\n2.9377\n3.4207\n3.8964\n4.1023\n4.9001\n3.9355\n2.783\n1.5401\n1.439\n2.0936\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.8532\n-3.6282\n-3.22\n-2.9604\n-4.1966\n-3.1789\n-1.451\n-0.93356\n-0.95232\n-0.48397\n-1.3161\n-1.5174\n-1.6935\n-1.4507\n-0.41449\n-1.0683\n-0.96686\n0.54873\n2.0847\n4.8644\n4.4365\n3.8037\n3.5454\n3.7274\n2.7423\n-2.037\n-2.4466\n-0.84646\n2.558\n-2.3226\n-1.8417\n-1.7522\n-1.456\n-1.6573\n-1.6719\n-1.4815\n-0.39923\n0.31591\n-0.8986\n-0.5392\n0.52654\n0.66206\n0.49415\n1.3427\n1.6504\n1.1057\n-0.025922\n1.8101\n4.2986\n3.0148\n2.2734\n0.084148\n-0.067751\n-0.10458\n1.8387\n1.3176\n2.6432\n4.1082\n4.6033\n6.625\n6.4422\n6.8806\n5.687\n4.0435\n2.2695\n1.2786\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.5513\n-3.7917\n-4.2359\n-4.1231\n-4.1439\n-3.5921\n-3.8179\n-2.1985\n-1.7345\n-1.1591\n-1.2767\n-1.6597\n-1.1114\n-0.42198\n-0.1067\n-0.21562\n0.29121\n0.92987\n3.7294\n5.1796\n3.7465\n4.0358\n4.4156\n3.5798\n-2.1264\n-4.9607\n-3.8392\n1.2584\n-1.2542\n-1.0453\n-1.347\n-1.5279\n-1.0362\n-1.9193\n-1.7644\n-0.69983\n0.3383\n-1.669\n-1.4809\n0.56615\n-0.53677\n-0.3553\n0.011044\n1.1112\n1.0315\n1.2049\n1.9706\n3.3219\n2.5321\n2.4091\n-1.4623\n-1.9438\nNaN\n3.2329\n1.6716\n4.232\n5.8912\n6.7877\n8.7392\n9.9737\n9.6867\n6.0715\n4.6685\n3.1668\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.786\n-3.2104\n-3.6549\n-3.2426\n-3.9201\n-3.6717\n-3.1413\n-2.6769\n-1.4293\n-0.83952\n-1.2601\n-1.0996\n-0.79418\n0.077619\n0.18862\n-0.072826\n-1.1585\n-1.0453\n1.1419\n2.9321\n2.4557\n3.477\n2.7989\n0.26296\n-2.3444\n-5.091\n-5.1197\n-1.3622\n-1.5374\n-0.92914\n-1.9096\n-0.91091\n0.62313\n1.0373\n0.64764\n0.32401\n0.25\n-1.7551\n-1.674\n0.54962\n-2.0836\n-0.90962\n-2.342\n-1.2902\n1.7742\nNaN\nNaN\n1.7248\n1.7602\n2.8774\nNaN\nNaN\nNaN\n0.53496\n-1.0843\n-0.98852\n-1.5405\n-1.4942\n-1.6115\n-2.2901\n-2.5267\n-1.284\n-0.67623\n-1.873\n-1.7471\n-2.3767\n-1.5244\n0.63146\n0.55088\n0.71585\n-0.58352\n1.4771\n1.7026\n2.7632\n2.2396\n3.3661\n1.9329\n3.0427\n1.0137\n-0.9275\n-3.2511\n-2.1345\n-1.465\n-5.1688\n-4.3489\n-2.8975\n-3.3714\n-1.2693\n-2.5503\n-2.46\n-1.1356\n-2.286\n-2.9768\n-1.5368\n-0.4148\n0.70639\n1.9675\n2.0399\n0.92124\n1.5864\n1.4579\n0.72748\n0.41713\n0.6667\n1.4792\n1.0633\n-0.61245\n2.1675\n0.368\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1865\n-2.0358\n-1.8613\n1.3282\n1.4143\n-4.1379\n-2.3302\n-2.9197\n-0.78467\n-0.29159\n-0.34669\n-0.95506\n-2.5948\n0.54367\n1.9334\n-0.026299\n-3.5609\n-2.6084\n-0.28462\n0.40289\n4.2073\n1.7779\n2.5187\n4.7455\n4.1407\n2.6612\n0.89612\n-1.0079\n-0.077625\n-1.306\n-1.8364\n-2.4716\n-1.4658\n-1.9992\n-1.6498\n-3.5605\n-3.3531\n-1.0832\n-1.8275\n-0.13869\n0.63989\n-0.054331\n1.0369\n1.8609\n1.947\n1.359\n1.5965\n0.9686\n0.55607\n0.36318\n0.31236\n1.0895\n1.0556\n1.7946\n5.1858\n1.4685\n-0.91594\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.0754\n-2.5509\n-1.8574\n-2.3161\n-5.9047\n-5.075\n-5.2018\n-2.3787\n-1.5209\n1.4026\n0.84317\n-0.58552\n-1.5726\n1.9363\n4.6739\n0.75795\n-1.4594\n-0.7134\n-0.21755\n2.8059\n2.5971\n2.845\n4.5032\n4.0654\n4.9536\n4.5831\n2.9185\n0.68963\n0.94915\n-1.5526\n-0.11533\n-1.7515\n-0.3697\n-1.0797\n-4.1446\n-4.6674\n-2.7851\n-1.6069\n-0.61038\n-0.2104\n0.40083\n0.92814\n0.54549\n2.1081\n1.3189\n0.69048\n0.65931\n0.72056\n0.40359\n0.9341\n1.3126\n1.0515\n0.90742\n2.8329\n3.3819\n2.2771\n1.2843\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8008\n-2.4545\n-2.0555\n-1.8575\n-1.8674\n-1.5794\n-0.9363\n-1.2106\n-2.0293\n-1.3905\n-0.21409\n-3.0487\n0.27712\n3.4657\n1.9913\n-0.90056\n-1.0017\n-0.89441\n-0.41934\n2.4241\n4.4697\n3.1392\n3.9394\n2.5074\n4.7972\n5.1507\n3.4693\n1.5338\n0.65815\n-0.80717\n-1.0665\n-0.099179\n-0.12271\n-0.52309\n-3.4617\n-4.4886\n-3.7994\n-1.7875\n-0.30565\n-0.36383\n-0.31959\n0.27908\n-0.68697\n-0.28882\n0.41\n-0.032134\n-0.20531\n0.80033\n1.3341\n3.2145\n3.7721\n2.7766\n3.597\n4.0081\n3.9599\n3.2228\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6207\n-1.6541\n-1.3919\n-1.4663\n-0.96538\n-1.4109\n-1.0047\n-0.75866\n-1.8082\n-3.0516\n-2.4307\n-3.3304\n-2.638\n-0.86167\n-3.2418\n-3.048\n-2.7264\n-1.8342\n-2.9274\n0.94301\n5.8505\nNaN\nNaN\n-2.4726\n3.3921\n4.8245\n4.6889\n2.199\n1.9366\n2.0098\n-1.4696\n-0.96364\n-0.88449\n-0.083632\n-1.299\n-2.1379\n-3.8984\n-2.0468\n-0.45526\n-0.39783\n-0.21026\n0.5075\n-0.57451\n-0.71565\n-0.31331\n-0.22162\n0.80724\n2.2306\n1.9487\n1.8216\n3.4993\n3.2479\n2.6077\n4.5009\n3.3689\n3.5292\n7.1318\nNaN\nNaN\n-9.52\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2188\n-1.8991\n-1.9398\n-1.4176\n-0.55588\n-1.5725\n-0.70581\n-0.22129\n-0.77572\n-1.3503\n-1.0834\n0.11839\n-0.36155\n-2.5563\n-3.9754\n-2.0421\n-3.4923\n-3.1737\n-1.092\nNaN\nNaN\nNaN\nNaN\n0.10128\n2.9672\n5.5025\n5.2571\n3.7521\n2.7267\n2.9792\n1.0512\n-1.4042\n-1.437\n-1.0021\n-1.125\n-3.1255\n-4.0669\n-1.9368\n-0.58255\n-0.06221\n0.28615\n-0.32809\n-0.7009\n-1.1965\n-3.7017\n-1.9263\n0.65056\n2.7373\n2.1099\n2.2896\n2.7986\n2.7973\n2.6185\n2.2913\n1.5416\n-0.78761\n1.2763\nNaN\nNaN\n-14.531\n-9.1325\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9713\n-2.5087\n-1.8919\n-0.4279\n0.36154\n-1.1308\n0.39577\n0.68492\n0.67633\n0.60787\n0.10046\n-0.46753\n-0.31461\n-2.1671\n-2.5392\n-2.9553\n-3.1904\n-0.78685\n-1.7314\nNaN\nNaN\nNaN\nNaN\n0.51416\n1.7868\n4.152\n5.3711\n5.7225\n3.1794\n2.7013\n0.089285\n-1.3834\n-3.4623\n-2.3189\n-2.558\n-3.468\n-3.5578\n-2.3046\n-1.4535\n-0.47386\n0.79298\n-0.57919\n-2.0779\n-3.1201\n-6.7792\n-2.538\n0.48168\n2.3816\n3.0182\n3.124\n2.0773\n2.7035\n3.1349\n2.7443\n1.2957\n-1.1641\n0.63077\nNaN\nNaN\n-6.7207\n-9.9592\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4393\n-1.1424\n-1.2092\n0.41406\n2.0527\n-0.73069\n0.033083\n1.1625\n1.5375\n1.38\n0.7722\n0.49468\n-0.12494\n0.28566\n-0.95197\n-1.4578\n-0.7734\n0.40417\n-0.67826\n-1.1741\n-0.32711\n0.83554\n0.78403\n1.8185\n2.662\n4.1173\n5.1053\n5.861\n3.388\n2.7335\n1.547\n-1.918\n-4.5077\n-2.3564\n-1.5799\n-1.789\n-1.5468\n-2.4485\n-2.5756\n-2.6176\n-0.35112\n-1.1609\n-2.4153\n-3.9868\n-8.6635\n-2.3225\n1.1625\n1.7648\n1.6071\n3.0836\n2.0056\n2.6843\n3.6768\n3.3269\n3.1809\n0.7804\n-0.042337\n2.197\n2.0382\n-1.5067\n-7.2274\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.74159\n-1.8365\n-1.8493\n-1.1515\n0.90817\n-0.47449\n-0.45072\n1.5995\n1.8509\n1.7878\n1.1442\n1.3289\n1.3739\n0.8493\n0.6224\n0.87645\n0.97513\n1.7397\n0.37097\n0.68778\n0.68458\n0.47172\n1.8475\n2.7214\n2.9835\n4.6075\n5.0173\n4.0382\n2.7734\n1.8606\n1.7514\n0.94587\n-1.6161\n-2.1692\n-1.1958\n-0.98525\n-1.4203\n-2.5542\n-2.6063\n-2.3859\n-2.1506\n-1.5841\n-3.0403\n-5.2151\n-8.2045\n0.44004\n2.1214\n2.4058\n2.7046\n3.3604\n3.1593\n4.4102\n4.6234\n6.0777\n5.6552\n1.4283\n-0.6516\n1.5382\n3.7022\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.376\n-1.8079\n-0.98502\n-1.7723\n0.75872\n-0.74717\n-0.79495\n0.025876\n1.0978\n1.7471\n1.5284\n1.5696\n1.9727\n1.2705\n1.9529\n2.1358\n1.7444\n2.6872\n2.3005\n3.1063\n2.6471\n2.5937\n3.7308\n2.6673\n1.7815\n3.5164\n5.4271\n4.9975\n2.7617\n1.3428\n1.702\n-1.1076\n-3.6123\nNaN\nNaN\n-0.11759\n-1.4685\n-2.0053\n-3.0375\n-2.5416\n-1.7624\n-1.1834\n-3.84\n-4.816\n-1.8107\n2.209\n1.5103\n2.4376\n2.9429\n4.053\n3.8174\n5.6019\n5.852\n6.4378\n5.5121\n3.46\n0.16854\n-1.0765\n3.5733\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.1135\n-2.8746\n-1.6569\n-1.8063\n-0.58534\n-1.0658\n-0.82832\n-0.72199\n0.75469\n2.0708\n2.1035\n1.6409\n2.8909\n2.1495\n3.175\n3.5723\n2.5556\n2.722\n3.0016\n3.7218\n2.5547\n3.0468\n3.1114\n1.0304\n1.5646\n5.6839\n6.4436\n5.7304\n3.5467\n3.4557\n1.9336\n-0.92771\nNaN\nNaN\nNaN\n-0.46126\n-1.6043\n-0.88307\n-3.838\n-2.9456\n-0.88548\n-0.94939\n-3.7853\n-3.6868\n-1.0673\n1.0717\n1.2407\n2.4669\n3.9548\n3.8912\n3.75\n5.5033\n5.568\n7.0334\nNaN\nNaN\n-1.1269\n-1.281\n2.8149\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.7489\n-3.9565\n-2.7806\n-1.7066\n-1.5159\n-0.81376\n0.011516\n0.57775\n1.4408\n2.0336\n1.5818\n1.912\n3.0283\n2.392\n3.4277\n3.2571\n3.1786\n3.063\n2.9223\n2.6456\n0.97564\n2.1028\n2.7764\n1.1865\n2.1313\n4.6825\n6.2574\n6.6652\n4.7877\n4.7692\n2.4163\nNaN\nNaN\nNaN\nNaN\n-0.64436\n-0.19294\n-1.2678\n-4.6369\n-2.5629\n0.13483\n-1.1479\n-2.4825\n-1.8126\n-0.63311\n0.67918\n1.9239\n3.8254\n5.7292\n5.5684\n4.7513\n5.7249\n4.6118\nNaN\nNaN\nNaN\n-0.45603\n-1.7446\n0.40108\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1089\n-2.1814\n-1.6841\n-1.7521\n-1.1467\n-0.14894\n0.31525\n0.58828\n1.136\n1.3217\n1.8822\n2.3949\n2.4671\n2.9814\n3.6872\n4.1344\n4.4971\n3.6516\n2.7432\n2.0707\n1.5018\n2.0157\n0.44468\n1.3496\n2.3292\n2.5975\n6.5808\n5.6433\n3.9812\n4.2372\n3.1084\nNaN\nNaN\nNaN\nNaN\n-1.8923\n-1.457\n-0.77981\n-2.9318\n-1.5666\n-1.0395\n-4.4116\n-5.7109\n-0.98824\n0.65584\n1.6344\n2.9365\n4.9042\n6.3406\n6.3627\n5.6487\n5.278\n3.2073\nNaN\nNaN\nNaN\n-4.8924\n-5.016\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.81884\n-1.1483\n-1.48\n-0.70644\n-0.16055\n-0.028306\n0.27827\n0.10903\n0.69914\n0.96731\n1.1798\n2.363\n3.4777\n4.1402\n4.156\n4.521\n3.9396\n3.5527\n2.0235\n1.5202\n2.3005\n0.18607\n-2.7153\n-1.5582\n-1.2887\n2.4635\n5.169\n4.4513\n5.1753\n5.4815\n4.3118\nNaN\nNaN\nNaN\nNaN\n-1.7286\n-2.8017\n-0.39511\n-2.1504\n-0.50061\n-1.0645\n-3.2224\n-2.8606\n1.1271\n3.0469\n3.8622\n4.0553\n5.6921\n6.3247\n6.4231\n5.2491\n3.3813\n2.411\nNaN\nNaN\nNaN\nNaN\n-4.1191\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24446\n-1.7167\n-0.46861\n0.79411\n0.38004\n-0.13967\n0.22952\n0.061865\n0.40499\n0.53415\n0.649\n2.0681\n3.7771\n4.2871\n4.6391\n4.221\n4.2779\n4.43\n3.1846\n1.5561\n1.4929\n-2.6455\n-6.5511\n-3.834\n-2.488\n2.6354\n3.7973\n4.8082\n6.4291\n6.1775\n6.7405\nNaN\n-1.3535\n-2.3093\n0.24157\n1.266\n-1.0829\n1.2703\n0.53336\n2.4967\n1.5275\n-1.8579\n-2.1947\n4.8428\nNaN\nNaN\nNaN\n6.2077\n7.3263\n6.584\n4.3292\n5.0221\n3.7968\n0.04079\nNaN\nNaN\nNaN\n-5.8135\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.94573\n-1.3012\n-0.19195\n1.6472\n1.2933\n0.73171\n1.0626\n0.41326\n0.2675\n1.2525\n1.3367\n2.3006\n3.457\n3.7674\n4.2556\n2.6999\n2.7683\n4.1046\n4.5813\n2.4012\n0.98612\n-0.86202\n-9.1578\n-7.8001\n-2.8795\n1.4589\n4.3133\n8.2071\n7.8622\n5.6557\n3.2468\n-2.9001\n0.46106\n-0.33109\n-0.25029\n1.4753\n2.8956\n3.1521\n3.7721\nNaN\nNaN\nNaN\n-1.8461\nNaN\nNaN\nNaN\nNaN\n5.5837\n7.0558\n4.8244\n3.3803\n3.2717\n3.7541\n-0.28527\n-1.294\n-4.0631\n-7.0713\n-6.8027\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4076\n-1.1082\n0.10249\n2.1224\n1.1407\n1.1888\n0.96265\n0.48562\n0.54334\n0.97864\n1.2138\n2.0006\n3.3707\n3.6203\n3.6817\n1.9557\n1.8552\n3.1027\n3.8205\n2.9341\n0.61426\n-1.8273\n-4.0835\n-6.0748\n-0.80253\n2.6529\n5.5629\n6.511\n5.6705\n4.1457\n1.1654\n2.0875\n4.7434\n2.3326\n2.9649\n5.0535\n4.1504\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.078533\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8896\nNaN\n2.5417\n2.9401\n-0.15688\n-0.98097\n-2.0312\n-6.3431\n-6.9422\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8507\n-1.0035\n0.76612\n2.2841\n1.2657\n0.74157\n0.66765\n0.93386\n0.82685\n0.81709\n1.2034\n2.0243\n2.9272\n3.1716\n3.2167\n2.5945\n3.6434\n2.6082\n2.4259\n1.1762\n-0.92131\n-3.9302\n-4.8175\n-5.2655\n-0.057702\n3.1821\n5.9015\n4.2434\n4.4101\n2.9954\n5.3047\n4.9635\n4.4722\n5.0534\n6.5693\n7.1826\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8208\n-0.73163\n-2.9368\n-5.6953\n-4.8604\n-5.6468\n-5.8714\n-2.1816\n-1.161\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1886\n-0.12651\n1.3511\n1.9711\n1.3103\n-0.3917\n0.43401\n0.8998\n0.4155\n0.73593\n0.50076\n1.5029\n2.2999\n3.0832\n3.535\n2.5833\n1.4341\n0.49162\n1.0686\n0.044233\n-1.4794\n-4.6448\n-5.2238\n-3.2777\n0.40168\n3.0548\n3.9061\n3.8292\n6.1802\n4.7375\n5.3019\n6.0548\n6.0791\n5.7543\n7.1254\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.4388\n-1.5352\n-2.5851\n-3.3664\n-1.4059\n-1.5884\n-1.83\n0.96139\n1.401\n-1.2549\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.48033\n0.26934\n0.29593\n-0.10623\n1.0278\n0.71378\n1.6808\n0.57193\n-0.73945\n-0.032829\n0.44499\n1.182\n1.7895\n2.6018\n2.4439\n1.3335\n-0.58123\n-2.1417\n-0.95332\n-0.69972\n-1.3484\n-5.2382\n-3.8494\n-1.1806\n2.5728\n4.7691\n3.826\n4.8199\n7.3013\n6.5785\n5.2541\n6.3854\n5.8281\n5.5271\n8.279\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-6.1697\n-3.9075\n-3.2381\n-1.6446\n0.76735\n1.7178\n-0.57103\n-0.52858\n1.1074\n-1.1425\n0.17761\n1.791\n2.566\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.46703\n0.55998\n-0.12804\n-0.53871\n1.7274\n1.6204\n-0.56003\n-1.1732\n-1.2089\n0.62139\n1.0644\n1.2202\n0.4292\n1.8522\n1.1322\n0.63718\n-0.32401\n-0.79151\n-1.3163\n-1.5627\n-1.6915\n-5.9338\n-3.9822\n1.0258\n3.2495\n6.544\n7.2103\n6.8428\n7.3696\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.2993\n-3.2853\n-2.197\n-2.2968\n-2.9505\n-1.6396\n1.535\n2.4841\n0.32866\n1.7758\n2.3241\n1.7224\n1.3766\n1.7611\n1.6782\n2.147\n2.0211\n2.1512\n0.86887\n-0.2506\n-0.35407\n-0.27032\n-0.83904\n-0.4608\n0.98048\n0.77805\n-1.1374\n-0.80278\n-0.93229\n0.31791\n0.3589\n-0.10026\n1.0397\n1.332\n0.54195\n1.3571\n0.40823\n-1.0561\n-1.3364\n-0.91943\n0.39606\n-3.7649\n-4.8171\n1.6782\n5.1413\n10.246\n8.8298\n8.4866\n11.017\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3157\n-2.3344\n-1.5328\n0.62394\n0.019852\n-0.60816\n-3.3035\n-0.28323\n1.7955\n0.75109\n2.9023\n3.3323\n1.3519\n0.85635\n0.30506\nNaN\nNaN\nNaN\nNaN\n1.6429\n1.4649\n-0.28702\n-0.57301\n-0.75881\n-1.552\n-1.4213\n-1.2056\n-1.504\n-1.2279\n-0.039818\n-0.28815\n-0.72468\n-0.41985\n0.66626\n1.0161\n1.1916\n2.2518\n1.4891\n-1.6926\n-4.3073\n-0.65092\n-0.029197\n-4.604\n-3.8883\n1.1197\n6.8915\n8.2285\n7.6661\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.90065\n0.42288\n-0.3436\n-1.3415\n0.42488\n1.6338\n1.1965\n1.1701\n-0.50658\n-0.52493\n-0.69548\n0.58121\n2.5324\n4.0704\n1.5417\n2.1179\n1.6528\nNaN\nNaN\nNaN\nNaN\n0.04685\n3.0891\n-0.67354\n-1.3384\n-3.2334\n-2.4326\n-1.2632\n-1.274\n-1.8606\n-0.59747\n1.0958\n0.33241\n-0.29217\n0.72952\n1.2443\n1.086\n1.5962\n4.2086\n3.2279\n-0.40895\n-3.7665\n-5.3751\n-3.1686\n-3.1086\n-1.2855\n2.8598\n5.3068\n6.6453\n5.5828\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19432\n0.94192\n0.57878\n0.15144\n-0.40208\n0.36778\n1.1341\n1.7955\n3.1584\n1.6642\n-0.27501\n-0.89788\n0.06309\n0.59241\n3.2656\n0.48195\n2.2769\n2.7481\nNaN\nNaN\n-0.27257\n0.50637\n0.53155\n1.1787\n-2.1263\n-2.5871\n-2.7427\n-1.8706\n-1.5745\n-0.1339\n-0.91143\n0.36863\n1.0025\n0.82007\n-0.0016359\n0.63422\n1.8042\n0.70495\n1.0053\n3.6665\n4.0973\n0.4878\n-3.0527\n-4.4693\n-2.068\n-1.1038\n-0.35634\n3.5419\n5.5034\n8.3297\n7.2017\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7327\n-1.4264\n3.0641\n0.95816\n1.4975\n0.9914\n0.46368\n0.064633\n2.2363\n5.1377\n3.9926\n1.0568\n0.82887\n0.9153\n-0.4485\n-1.0125\n0.5703\n2.5021\n1.7488\n-0.16449\n-1.9532\n1.0811\n2.0161\n2.1892\n1.6901\n-3.4588\n-2.5554\n-1.6199\n-1.4234\n-2.0362\n0.30316\n0.18129\n-0.074271\n0.61173\n0.25498\n0.18459\n1.4528\n1.8323\n2.0332\n2.5389\n3.4925\n5.7717\n0.79107\n-1.5314\n-1.6433\n0.27321\n-0.10281\n0.11095\n2.2966\n6.4506\n8.5887\n8.8652\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3648\n-1.9138\n2.448\n0.49664\n1.7204\n1.9372\n-0.39585\n-1.0661\n1.028\n5.0508\n4.9836\n3.5009\n2.1787\n2.1421\n0.56208\n-0.028463\n2.2088\n3.5327\n1.2909\n0.68623\n0.24544\n2.2571\n3.4139\n4.5807\n2.5202\n-2.9829\n-1.887\n-1.1271\n-0.84251\n-1.3894\n-0.30303\n0.064562\n-0.21237\n0.19137\n0.013964\n-0.37687\n-0.17408\n1.3626\n2.5702\n3.1402\n3.7512\n3.8439\n0.66131\n0.4014\n-1.0101\n-0.35252\n0.43407\n2.0403\n3.4698\n6.873\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.88461\n-1.3965\n-0.82625\n1.9686\n-5.2638\n-1.2984\n1.8404\n1.3238\n3.9403\n4.6366\n4.8591\n4.8857\n5.3194\n3.8973\n2.7748\n1.0227\n-0.11826\n3.5561\n3.0161\n3.3775\n1.5779\n1.8548\n3.4231\n4.1562\n4.2157\n2.7275\n-2.7194\n-1.13\n-1.5336\n-1.1449\n-1.4596\n-1.1396\n-1.1684\n-0.24714\n-0.10523\n-0.80923\n-0.60557\n-0.16947\n0.63743\n1.7732\n3.0285\n3.3083\n2.0328\n0.025293\n-0.63761\n-0.31054\n0.1561\n0.73847\n1.4011\n3.0726\n6.8094\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.7726\nNaN\nNaN\nNaN\nNaN\n2.2114\n0.23084\n0.041035\n-0.88503\n0.68091\n-5.2631\n-0.059033\n2.539\n6.0333\n5.1303\n5.0713\n4.1605\n-0.66933\n4.4472\n2.311\n1.7327\n-1.6818\n0.44736\n4.4031\n3.9641\n4.904\n3.0947\n3.5668\n4.2136\n4.3597\n3.0442\n0.56436\n-1.7356\n-1.0164\n-1.8859\n-1.6028\n-0.82845\n-1.1124\n-1.3643\n-1.0407\n-1.331\n-0.68071\n-0.26407\n-0.0165\n-0.039316\n1.6477\n2.1378\n2.6427\n2.0189\n0.59252\n-1.2152\n-0.32139\n1.0458\n1.2116\n0.073388\n3.494\n9.1941\n13.359\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.0622\n3.694\n2.1619\n1.2431\n-0.18718\n-1.1173\n-0.38416\n0.097785\n-0.16166\n2.6622\n5.8461\n3.4163\n3.7774\n1.709\n-2.8001\n-1.7229\n0.85001\n1.3314\n-0.83407\n1.3853\n3.7267\n4.5673\n4.0349\n3.1628\n2.4562\n4.3081\n3.322\n1.1159\n-3.8553\n-1.7054\n-1.5312\n-1.118\n-1.8367\n-1.8731\n-1.4631\n-0.77245\n-1.4082\n-2.2392\n-0.69038\n-0.50468\n-0.52889\n-0.80994\n0.60592\n1.5407\n1.8163\n0.72092\n-0.14404\n-0.31379\n-2.788\n-2.2941\n0.89325\n1.465\n5.9966\n11.585\n12.733\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.4198\nNaN\nNaN\n8.9846\n6.4703\n5.0629\n5.3792\n1.8108\n-0.7768\n-1.9224\n-3.4356\n-2.5236\n-2.5573\n-0.89271\n1.984\n-0.095581\n-0.64856\n0.41398\n-0.58157\n-2.6236\n-3.1147\n-0.41359\n-0.3343\n0.732\n2.4273\n2.526\n1.826\n2.1059\n2.22\n2.0457\n1.3565\n0.62094\n-5.6853\n-2.3038\n-2.0972\n-2.1277\n-2.4253\n-2.8673\n-1.5524\n-1.1766\n-1.2617\n-1.7614\n-0.97204\n-0.82251\n-1.4113\n-1.3416\n-0.72494\n0.40503\n-0.35803\n-0.87992\n-2.0358\n-2.1755\n-2.4635\n-3.1133\n0.015837\n2.4132\n8.5613\n11.975\n14.104\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.548\n21.573\n14.755\n11.522\n6.3331\n5.2834\n4.3561\n3.4206\n0.26409\n-0.40295\n-1.5484\n-2.8534\n-2.4475\n-2.4876\n-1.8927\n-1.9173\n-1.6935\n-1.762\n-2.0015\n-3.5432\n-4.3657\n-0.47496\n1.0433\n0.98793\n0.064553\n-0.87372\n0.13277\n1.8182\n1.5885\n-0.21149\n0.75285\n-0.44\n-2.7799\n-3.149\n-2.3898\n-2.529\n-1.687\n-2.1308\n-2.1333\n-2.2518\n-1.4579\n-1.4494\n-1.8745\n-1.1607\n-2.5085\n-3.7279\n-2.2828\n-1.3804\n-1.3584\n-2.5466\n-3.5137\n-3.2673\n-2.9861\n-3.102\n-2.0183\n2.4631\n6.4999\n10.563\n15.574\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n26.35\n20.312\n14.288\n11.121\n5.2676\n2.6424\n2.948\n2.536\n1.345\n-0.40251\n-2.5225\n-2.5895\n-1.8387\n-1.2627\n-1.1936\n-3.8263\n-4.5073\n-2.8591\n-2.9573\n-4.9794\n-5.5465\n-1.9325\n0.2624\n0.091581\n-0.15423\n-1.2378\n-0.37614\n-0.21701\n1.2202\n-1.2114\n-2.1518\n-1.1962\n-0.36844\n-2.3979\n-2.4048\n-2.5654\n-1.2537\n-2.2675\n-2.3822\n-2.5385\n-2.1757\n-1.9573\n-2.3302\n-2.6221\n-2.8424\n-3.8254\n-2.6943\n-2.6985\n-1.7061\n-2.7188\n-6.1089\n-7.1911\n-2.8865\n-1.8055\n-2.6139\n0.98549\n6.2853\n8.6927\n8.1755\n5.6735\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n22.2\n13.49\n12.711\n7.2616\n5.4805\n-1.3898\n0.69542\n1.1669\n-0.47047\n-1.1918\n-2.02\n-1.9274\n-1.3918\n-1.4351\n-1.8722\n-3.1885\n-6.373\n-3.5722\n-2.3021\n-2.5972\n-4.1472\n-4.3946\n-1.5687\n-0.37018\n0.10096\n-0.054462\n-0.62347\n-1.1667\n-2.4651\n-2.7289\n-1.3311\n0.15853\n0.49447\n-2.2092\n-2.9228\n-3.0534\n-1.8448\n-1.6426\n-1.4582\n-2.6028\n-2.5303\n-1.6899\n-1.8184\n-3.0416\n-3.0196\n-2.8994\n-2.6419\n-2.911\n-1.8011\n-1.9812\n-3.6288\n-5.7497\n-5.0806\n-1.7146\n-2.7668\n0.63187\n6.3141\n7.4537\n1.3017\n0.12711\n-1.068\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n17.429\n11.296\n8.0966\n7.8112\n5.173\n-1.0671\n0.67911\n0.697\n-2.1406\n-3.2843\n-3.5465\n-1.5311\n-1.3227\n-1.9068\n-3.8378\n-3.0766\n-3.1103\n-2.4956\n-1.0141\n-0.90842\n-1.0412\n-1.8739\n-2.2419\n-0.6913\n-1.1248\n-0.71716\n-1.6352\n-1.8504\n-2.6807\n-1.3728\n-0.35815\n1.0098\n0.48361\n-2.5108\n-3.4576\n-3.2848\n-2.5999\n-1.1485\n-0.81443\n-1.9035\n-1.6377\n-1.6386\n-1.5554\n-3.7297\n-2.8081\n-2.0395\n-3.2665\n-3.9947\n-3.0353\n-1.746\n-4.0147\n-3.5931\n-6.565\n-6.3866\n-3.7934\n0.59488\n3.5039\n4.5494\n2.3433\n-0.69154\n0.97911\nNaN\nNaN\n5.0144\n9.0666\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.682\n8.9844\n7.3259\n7.1972\n0.35685\n-3.389\n-0.68852\n-2.1536\n-3.7243\n-5.1181\n-3.2816\n-0.85192\n-0.94318\n-1.922\n-2.0363\n-2.2308\n-1.9144\n-1.6552\n-1.0903\n-1.4314\n-1.8568\n-1.8237\n-2.2173\n-1.4042\n-2.0581\n-2.7482\n-4.5287\n-4.9947\n-2.1158\n-0.82635\n-0.18374\n1.3732\n0.81951\n-2.8801\n-3.1425\n-2.8731\n-3.2653\n-1.332\n0.23084\n-0.79378\n-1.1151\n-1.8624\n-1.7233\n-3.3781\n-2.2232\n-2.2189\n-3.507\n-4.1765\n-2.3636\n-2.4696\n-5.087\n-6.0704\n-5.3325\n-4.3381\n-2.6538\n-0.19478\n2.789\n0.12251\n-2.5937\n-1.1804\n3.3243\n3.3006\n6.2745\n7.3444\n7.708\n8.0729\n9.3305\nNaN\nNaN\nNaN\nNaN\n7.7148\n8.8651\n5.7791\n4.9142\n4.9467\n-2.0632\n-5.8478\n-3.2701\n-3.3317\n-3.923\n-4.2714\n-2.646\n0.34508\n0.40165\n0.66769\n-0.66485\n-2.4427\n-2.4348\n-1.5175\n-1.2704\n-0.95618\n-0.91217\n-2.0881\n-2.6759\n-3.9036\n-4.9105\n-3.9484\n-3.8578\n-3.8679\n-3.3821\n-3.1637\n-1.3605\n0.66716\n-0.48779\n-2.2068\n-2.7473\n-3.3987\n-3.1101\n-2.364\n-0.13592\n-0.12553\n-1.0627\n-1.7538\n-1.8214\n-2.6846\n-1.6968\n-1.9588\n-2.7591\n-3.5854\n-3.1534\n-5.4079\n-9.3446\n-7.1038\n-5.0064\n-3.7884\n-0.2229\n0.84689\n2.144\n6.4298\n1.9525\n2.4775\n3.4014\nNaN\n2.2086\n4.7219\n2.2264\n7.7234\n11.836\nNaN\nNaN\nNaN\n5.0351\n7.0591\n7.2717\n5.3506\n2.0172\n2.6254\n-0.86879\n-5.5946\n-4.1466\n-0.28524\n-1.6576\n-3.2376\n-1.7592\n0.57592\n1.2278\n1.364\n-1.1088\n-1.1647\n-1.8127\n-1.7944\n-1.3969\n-0.28641\n-0.50142\n-1.9517\n-3.1579\n-4.0371\n-4.806\n-3.579\n-1.6176\n-2.0466\n-3.8284\n-3.2692\n-1.6284\n-0.47415\n-1.6513\n-3.1829\n-2.6346\n-2.5371\n-2.5825\n-1.8204\n-0.41474\n-0.50566\n-1.7528\n-2.1608\n-2.3548\n-2.5703\n-2.0079\n-1.7054\n-1.9029\n-2.8042\n-3.7698\n-5.7901\n-9.4878\n-6.1907\n-5.2552\n-3.3674\n-1.996\n1.9181\n1.0133\n1.8637\n2.3311\n2.1882\nNaN\nNaN\n0.75292\n2.5944\n3.2763\n6.1745\n9.4704\nNaN\nNaN\nNaN\n4.1797\n4.8668\n6.6657\n4.0969\n-0.86643\n-1.954\n-3.0527\n-4.0802\n-1.3503\n0.44859\n-0.19778\n-2.4064\n-1.7397\n-0.13038\n1.9235\n1.3033\n-0.29148\n-0.57066\n-1.0742\n-1.1943\n-0.75134\n0.20066\n-0.43898\n-1.3003\n-3.2502\n-4.0235\n-3.6471\n-2.0517\n-0.43197\n-0.45311\n-1.0851\n-1.0672\n-0.94516\n-0.78757\n0.0021805\n-2.6885\n-2.4562\n-1.6479\n-0.92765\n-0.58221\n-0.35354\n-0.74123\n-2.0466\n-2.6801\n-2.6064\n-2.119\n-1.4839\n-0.80569\n0.0070393\n-0.65747\n-3.451\n-4.4157\n-8.9776\n-2.4969\n-3.3304\n-3.5104\n-0.64489\n1.7602\n0.26426\n-0.64734\n0.41499\n0.82321\nNaN\nNaN\nNaN\n0.53044\n2.5529\n3.557\n5.3645\nNaN\n5.9862\n5.0128\n2.4545\n3.1298\n5.8448\n1.7927\n-0.71817\n-0.72113\n0.07413\n-0.67031\n-1.5062\n-1.6511\n-1.991\n-1.7587\n-0.36706\n0.78319\n1.7464\n0.69892\n-1.379\n-1.1667\n-0.61922\n-0.15338\n0.86401\n0.86503\n0.56616\n-0.9489\n-2.6818\n-3.3247\n-2.4076\n-0.98532\n0.17929\n0.52408\n0.3589\n-1.1546\n-2.0134\n-0.69035\n1.0813\n-2.174\n-2.0222\n-0.95411\n0.057913\n0.67428\n0.3028\n-0.94485\n-0.85071\n-1.5075\n-1.6388\n-0.86863\n-0.048061\n0.48045\n1.9823\n3.1226\n-1.2432\nNaN\nNaN\nNaN\n-3.3439\n-5.0035\n-1.3603\n0.49431\n-3.7291\n-2.6291\n-0.93821\n0.62397\nNaN\nNaN\nNaN\n0.42705\n2.1172\n1.0726\n2.0801\n3.1959\n4.1758\n2.9602\n1.8626\n2.085\n0.76126\n-1.1985\n-1.1733\n0.50376\n0.96062\n0.092756\n-0.84419\n-0.88285\n-0.47242\n0.24979\n0.25344\n0.87856\n0.66609\n-0.41033\n-2.0785\n-1.1215\n0.49484\n0.73274\n1.0646\n1.2109\n0.94925\n-0.82021\n-1.7018\n-1.9966\n-2.0529\n-0.7888\n-0.1221\n1.0018\n1.1876\n-0.76208\n-1.9349\n-1.0124\n0.61242\n-0.65136\n-0.062002\n0.2831\n0.038578\n0.36317\n0.78287\n-0.85092\n0.076476\n-0.19894\n-0.41048\n0.43056\n1.0763\n2.4884\n5.1847\n4.3682\n0.038986\nNaN\nNaN\nNaN\n-4.0407\n-4.7315\n-6.5171\n-4.489\n-3.9624\n-1.941\n-1.3791\n-0.82784\nNaN\nNaN\nNaN\n-0.88857\n0.0031228\n0.28053\n1.8388\n5.2704\n3.8619\n2.1787\n1.0723\n0.40122\n-0.78537\n-1.501\n-1.6342\n0.14036\n1.2714\n1.0318\n-0.09833\n-0.28087\n-0.97588\n-0.60369\n0.40687\n0.80303\n0.40156\n-0.2272\n-0.18554\n0.78817\n0.86753\n-0.4232\n0.90086\n1.1676\n1.1787\n-0.36694\n-0.96858\n-0.95845\n-0.83994\n0.17053\n0.48309\n1.8296\n1.8495\n0.017825\n-0.72681\n0.3867\n0.82668\n0.39593\n1.1582\n1.0236\n0.154\n1.1471\n1.1004\n1.0973\n0.48704\n0.82998\n0.39695\n1.3402\n2.3696\n3.0185\n3.2562\n2.2573\n1.9449\n-1.0313\nNaN\nNaN\n-5.1487\n-4.3971\n-4.67\n-4.4351\n-3.2739\n-0.92787\n-0.9073\n-1.2907\n-0.56042\n-1.4057\n-1.2621\n-3.4869\n-2.0252\n-0.45356\n1.344\n4.6032\n3.8374\n3.344\n2.1396\n1.2294\n1.308\n-0.35537\n-0.33421\n0.52667\n1.4277\n1.8029\n0.24686\n-2.0852\n-4.0596\n-3.2501\n0.21083\n0.76338\n0.63769\n2.2765\n0.81784\n0.83826\n1.0297\n-0.58886\n0.4843\n0.8186\n1.3029\n0.3046\n-1.2768\n-0.46902\n0.54962\n1.225\n1.1098\n1.2507\n2.2597\n0.042467\n0.33883\n2.1355\n1.4071\n-0.31782\n0.46507\n0.91005\n1.0299\n1.7601\n2.019\n2.0343\n1.1363\n0.92148\n0.92165\n0.98525\n1.8156\n2.4561\n2.9819\n2.3929\n2.0277\n-0.92536\n-5.139\n-6.7822\n-5.3274\n-4.4258\n-3.3504\n-2.8703\n-2.2274\n-0.71656\n-1.1373\n-1.2008\n-0.94455\n-0.56408\n-0.75244\n-1.6583\n-1.9173\n-1.3139\n0.106\n2.797\n3.3711\n2.5742\n3.6708\n2.3479\n1.3479\n0.75687\n1.9251\n1.798\n2.0132\n2.0441\n1.8568\n-2.0175\n-5.2915\n-6.9357\n-1.9007\n-0.241\n3.2641\n3.1125\n1.6932\n0.74534\n1.9294\n1.3529\n0.87425\n0.46219\n0.42013\n0.020157\n-0.79172\n1.0167\n1.3915\n1.2719\n0.96232\n2.5268\n2.842\n0.42489\n0.89949\n0.98873\n0.59088\n-0.78805\n0.86506\n2.0023\n2.1819\n3.1258\n3.725\n3.1905\n3.2556\n1.7055\n0.78322\n0.64178\n1.2459\n1.4614\nNaN\nNaN\nNaN\nNaN\n-2.4644\n-4.9784\n-4.9425\n-2.6965\n-3.0596\n-3.9695\n-2.6551\n-0.77634\n-1.1329\n-0.64416\n0.23477\n-0.44637\n-1.0762\n-1.3047\n-1.0597\n-1.9537\n-1.382\n-0.29151\n0.87128\n1.8039\n3.8687\n2.8133\n2.7643\n2.3749\n3.0715\n2.7666\n0.38999\n0.26588\n2.361\n0.83742\n-4.0628\n-3.8347\n-3.8879\n-1.8319\n0.026231\n1.1293\n0.19851\n0.60126\n2.4869\n0.34735\n-0.25987\n0.35784\n-0.26198\n-0.01212\n0.84045\n1.9408\n0.93126\n0.14655\n1.2469\n3.5455\n3.049\n1.6715\n0.58997\n0.0844\n0.76489\n0.61852\n1.4981\n2.5091\n3.1715\n3.6952\n4.2118\n4.4337\n5.3057\n4.2595\n3.0099\n1.6594\n1.5497\n2.2627\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.1856\n-3.9426\n-3.5\n-3.218\n-4.5609\n-3.4543\n-1.5783\n-1.0163\n-1.0381\n-0.53071\n-1.4323\n-1.6511\n-1.8412\n-1.5766\n-0.45311\n-1.1622\n-1.0525\n0.59391\n2.2607\n5.2761\n4.809\n4.1205\n3.8373\n4.0339\n2.9655\n-2.2206\n-2.654\n-0.91602\n2.7806\n-2.5291\n-2.0038\n-1.9061\n-1.5834\n-1.7999\n-1.8153\n-1.6043\n-0.43235\n0.34016\n-0.97775\n-0.58696\n0.56664\n0.7124\n0.53408\n1.4516\n1.7859\n1.1971\n-0.028274\n1.9604\n4.6545\n3.2671\n2.4658\n0.094396\n-0.069529\n-0.10692\n1.9778\n1.4144\n2.8554\n4.4421\n4.9789\n7.1721\n6.9725\n7.4528\n6.1621\n4.3789\n2.4533\n1.3771\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.9497\n-4.1213\n-4.6032\n-4.4789\n-4.499\n-3.9007\n-4.1449\n-2.3893\n-1.8865\n-1.2622\n-1.3886\n-1.8033\n-1.2097\n-0.4603\n-0.11608\n-0.23558\n0.31365\n1.0082\n4.0463\n5.6179\n4.0575\n4.3702\n4.7802\n3.8727\n-2.3163\n-5.3824\n-4.1635\n1.3687\n-1.3672\n-1.1405\n-1.4663\n-1.6605\n-1.1283\n-2.0832\n-1.9142\n-0.75812\n0.36621\n-1.8136\n-1.6092\n0.60698\n-0.58943\n-0.38887\n0.0059157\n1.203\n1.118\n1.3013\n2.1335\n3.6061\n2.7491\n2.6135\n-1.5819\n-2.102\nNaN\n3.4909\n1.798\n4.5774\n6.3766\n7.3507\n9.4668\n10.804\n10.497\n6.5784\n5.0558\n3.4282\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.2015\n-3.4897\n-3.9706\n-3.5209\n-4.2539\n-3.9845\n-3.4092\n-2.9065\n-1.5542\n-0.91591\n-1.3698\n-1.195\n-0.86558\n0.084963\n0.20673\n-0.077467\n-1.2577\n-1.1326\n1.237\n3.1787\n2.6595\n3.7661\n3.0278\n0.27562\n-2.5482\n-5.5277\n-5.5554\n-1.4781\n-1.6725\n-1.0134\n-2.0775\n-0.99281\n0.67119\n1.1175\n0.69587\n0.34766\n0.2685\n-1.9058\n-1.8145\n0.58964\n-2.2632\n-0.98927\n-2.5434\n-1.4021\n1.923\nNaN\nNaN\n1.8759\n1.9113\n3.1197\nNaN\nNaN\nNaN\n"
  },
  {
    "path": "tests/test_data/small_test/time_series/ts_cum_interp0_method2.csv",
    "content": "3.4447\n3.7754\n4.5259\n3.7308\n2.7042\n3.9566\n5.4718\n3.5406\n3.5083\n4.4488\n4.1143\n3.6699\n4.1961\n5.1891\n8.0604\n5.5408\n4.1915\n2.3362\n5.3474\n6.3871\n5.8625\n3.3138\n3.2961\n1.7841\n4.4894\n1.4115\n1.094\n0.057719\n1.7755\n5.9951\n-4.3606\n-2.4783\n-1.8241\n-4.6873\n-0.73396\n-1.9195\n-1.8821\n0.21731\n0.59691\n0.49463\n2.1917\n0.84194\n0.26784\n2.922\n2.5602\n1.7952\n1.4826\n1.8735\n1.2264\n1.4874\n2.5158\n2.081\n1.3209\n-0.2233\n3.2749\n0.84863\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3049\n3.7222\n4.0013\n5.4634\n3.9608\n2.9122\n7.1971\n3.6844\n5.3516\n4.7755\n4.1341\n4.3089\n4.5144\n7.5667\n8.7034\n8.6402\n2.921\n2.7762\n4.5446\n3.7016\n5.1788\n2.1425\n2.1005\n4.1194\n6.8345\n2.2846\n0.39345\n1.4485\n3.6354\n4.1664\n1.3485\n0.67651\n-0.33291\n-3.6576\n-2.4686\n-1.1935\n-0.54976\n1.2175\n0.48063\n0.36203\n3.0009\n1.0579\n1.4388\n0.67859\n2.1639\n2.3391\n2.399\n1.8168\n1.2011\n1.3471\n1.4158\n2.0337\n1.9219\n2.3315\n5.7934\n3.3138\n2.9024\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7237\n3.2545\n3.4623\n3.4508\n-1.0979\n-0.20597\n2.4791\n4.3697\n5.2934\n6.0395\n3.7042\n2.9963\n3.0904\n9.1397\n11.566\n9.2468\n3.7206\n2.8448\n3.762\n5.2694\n3.806\n4.0064\n3.9396\n3.3281\n7.0783\n3.5192\n1.4615\n2.4443\n2.4895\n1.0669\n3.387\n3.1756\n-1.2944\n-3.7782\n-1.8302\n-1.5315\n-0.28472\n0.88454\n1.3266\n1.3836\n2.8074\n2.1334\n2.0676\n1.7875\n2.1975\n1.3383\n0.50115\n1.9719\n2.237\n1.8598\n1.2659\n2.3063\n3.3365\n1.7753\n3.4452\n5.5092\n4.1186\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4885\n3.0545\n3.491\n3.1862\n3.1756\n3.4888\n4.2149\n4.2033\n3.2499\n3.0037\n4.593\n2.3351\n0.43783\n10.626\n8.6775\n4.0651\n2.217\n2.218\n-0.14958\n3.6604\n4.0139\n4.9732\n3.3996\n0.35102\n3.4015\n3.6981\n3.9093\n1.8001\n3.4506\n6.4047\n2.0397\n2.538\n-2.0453\n-2.0666\n-1.824\n-3.7576\n-1.7511\n0.97667\n1.4858\n1.8043\n2.7119\n2.2081\n0.32157\n2.324\n2.7605\n0.55251\n0.47323\n1.7806\n1.4547\n1.0759\n1.6939\n1.8471\n1.7934\n1.4238\n2.7767\n4.2274\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7248\n4.3277\n3.9153\n3.1539\n3.011\n3.3933\n2.5436\n2.2907\n2.5085\n2.392\n4.1443\n3.6146\n6.4072\n4.8\n3.9941\n2.3169\n0.53289\n1.9236\n-2.9933\n0.6627\n2.3913\nNaN\nNaN\n-2.2032\n1.5186\n3.5413\n5.9544\n4.4038\n5.1884\n6.1829\n0.92617\n-0.50285\n-1.9581\n0.030381\n-2.0781\n-2.3172\n0.18572\n1.6427\n1.8195\n1.9693\n2.4395\n1.5268\n0.085884\n-0.70966\n1.6061\n1.1709\n0.59032\n1.4478\n1.1265\n0.42448\n1.3111\n2.2042\n2.6364\n2.3482\n3.1286\n2.8692\n6.5639\nNaN\nNaN\n-3.4747\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0936\n3.3099\n3.747\n2.4967\n1.8427\n2.4125\n1.8302\n1.237\n2.4332\n3.0057\n3.7866\n3.9125\n3.5384\n1.0376\n2.0736\n1.9729\n0.81955\n3.737\n3.3707\nNaN\nNaN\nNaN\nNaN\n1.3953\n2.638\n5.2369\n5.2979\n4.537\n4.8183\n5.71\n2.8663\n-1.3626\n0.51069\n1.172\n-2.0463\n-4.4417\n-1.1816\n0.041096\n0.32857\n1.8548\n3.0927\n1.8724\n0.12131\n-0.019049\n-0.06835\n-0.17122\n0.21324\n2.8289\n1.7388\n1.1108\n2.0497\n2.8721\n3.5856\n3.2244\n2.8097\n-0.53265\n0.16291\nNaN\nNaN\n-7.001\n1.8362\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7039\n1.9061\n1.98\n2.0774\n2.4575\n1.9334\n2.9012\n2.675\n3.0051\n4.5025\n4.4332\n3.4356\n3.896\n1.507\n3.0063\n1.4297\n1.3021\n5.4887\n0.3475\nNaN\nNaN\nNaN\nNaN\n2.9118\n2.6649\n4.0899\n4.3487\n4.7083\n3.7733\n5.3772\n2.4874\n-1.558\n-0.064195\n0.40434\n-2.263\n-4.2522\n-2.8541\n-1.536\n-2.0175\n-1.1212\n3.1713\n1.1946\n-0.78795\n-0.45286\n-1.3734\n-0.86082\n0.15112\n1.8747\n1.774\n1.8218\n1.3341\n2.1729\n2.7028\n3.5256\n3.3592\n1.2055\n0.24752\nNaN\nNaN\n-3.9312\n-2.8875\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7539\n2.1098\n1.8923\n2.9199\n4.0685\n3.2075\n4.081\n3.6828\n4.2519\n5.7855\n5.0407\n3.6745\n4.858\n5.1554\n3.322\n2.125\n1.5838\n3.1909\n0.4931\n1.0007\n1.9515\n3.0272\n1.9294\n1.7789\n2.3639\n2.8267\n3.3251\n4.4491\n3.6602\n4.2935\n3.1145\n2.2039\n2.1071\n-0.28491\n-1.7892\n-1.89\n-1.8282\n-2.7478\n-3.2818\n-2.5358\n0.64328\n0.037245\n-0.32928\n-1.0879\n-2.9508\n-0.60163\n0.58134\n1.1986\n1.3443\n1.9146\n0.83492\n1.3796\n3.6718\n5.1187\n5.3325\n2.1455\n1.0242\n-0.25804\n2.539\n0.27012\n-3.5226\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6295\n2.7966\n4.7337\n2.8004\n4.3907\n4.2162\n4.437\n4.8768\n4.1816\n4.7298\n4.1163\n4.2804\n4.3162\n3.7213\n1.8701\n1.4522\n2.6956\n3.3831\n1.4208\n2.0982\n1.8176\n0.16451\n-0.9278\n0.82591\n1.1967\n2.8926\n4.0318\n4.5806\n3.3802\n3.0363\n3.3634\n1.2697\n1.9614\n-0.6004\n-1.5895\n-1.4223\n-2.2091\n-3.2884\n-3.5326\n-2.0123\n-1.0123\n-0.53923\n-0.61154\n-2.548\n-4.6975\n0.35011\n1.7725\n2.2014\n1.2937\n2.1665\n2.236\n3.4004\n4.8291\n6.5207\n5.5373\n2.2795\n-0.10515\n-0.19808\n1.6174\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0113\n2.4259\n3.8161\n2.527\n3.3414\n3.9127\n4.7056\n3.5035\n2.9214\n3.2135\n3.7853\n3.5257\n3.3616\n3.4106\n2.4468\n2.2015\n2.4685\n2.5123\n1.8387\n2.0783\n2.0945\n1.0991\n0.94175\n0.13381\n0.17478\n3.5663\n4.558\n2.7662\n2.334\n2.3457\n4.6566\n1.1001\n-1.2709\nNaN\nNaN\n-0.55132\n-0.47558\n-1.9294\n-3.7578\n-2.5405\n-1.1771\n-1.1644\n-0.61039\n-0.22643\n0.85039\n1.716\n1.6929\n2.2175\n1.8862\n2.3917\n2.0813\n4.1584\n5.7367\n6.8618\n5.6987\n2.3468\n-1.9685\n-3.0707\n1.0965\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11526\n1.2589\n2.324\n1.8959\n2.8045\n4.7262\n4.71\n3.6301\n2.9457\n3.452\n4.5382\n4.123\n5.6797\n4.2336\n3.6389\n3.9913\n1.5872\n1.3444\n1.8643\n3.18\n1.6861\n1.7021\n2.6395\n-1.0998\n-1.603\n1.7944\n4.3955\n4.3059\n2.2581\n2.6928\n2.8965\n1.1641\nNaN\nNaN\nNaN\n-1.1595\n-0.85739\n-1.9968\n-4.62\n-3.4538\n-2.0685\n-3.4326\n-1.2123\n-0.85875\n0.16383\n0.99505\n0.67638\n1.5738\n2.6709\n-0.52697\n0.38077\n3.5509\n5.4899\n7.7306\nNaN\nNaN\n-2.5838\n-1.598\n2.0854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6006\n2.0256\n2.0728\n2.7036\n3.2058\n4.6215\n4.8319\n3.9931\n3.3851\n2.7327\n3.3903\n2.9799\n5.2237\n5.5147\n4.1643\n4.4434\n2.0179\n2.1273\n3.5042\n2.6528\n0.27767\n1.6678\n1.377\n-1.8501\n-1.988\n0.4306\n3.5482\n5.3835\n5.3644\n4.5712\n2.0533\nNaN\nNaN\nNaN\nNaN\n-1.9877\n-0.84055\n-1.8675\n-7.1024\n-4.3563\n-1.4669\n-0.17121\n-1.7846\n-2.0752\n-1.7372\n-0.33763\n-0.0098281\n1.4535\n3.2763\n1.5255\n1.1478\n4.3063\n4.9676\nNaN\nNaN\nNaN\n-0.17468\n-2.5423\n4.1354\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5502\n2.0719\n1.5267\n1.7416\n3.2317\n4.4428\n3.7178\n4.801\n3.0116\n2.5498\n3.9555\n3.9486\n5.9615\n5.6091\n4.2245\n5.8699\n5.5708\n3.8763\n4.5677\n2.0926\n0.88418\n0.46231\n-2.9084\n-2.9361\n-1.4943\n0.30288\n5.3214\n4.6461\n3.8006\n4.3586\n2.8079\nNaN\nNaN\nNaN\nNaN\n-4.4757\n-2.9373\n-1.2311\n-5.4175\n-4.7584\n-3.0856\n-3.2566\n-4.4646\n-2.3178\n-0.64442\n0.76535\n0.37301\n1.5129\n3.027\n2.9262\n2.4019\n4.5151\n4.1367\nNaN\nNaN\nNaN\n-0.64136\n0.44899\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4559\n1.761\n1.5402\n1.5465\n3.4059\n3.6135\n3.2371\n2.6734\n2.5068\n2.8623\n3.1345\n3.3302\n3.4716\n3.1859\n3.7097\n5.6518\n6.499\n3.8449\n3.0708\n2.2558\n1.4381\n-0.92438\n-5.3572\n-4.0096\n-1.3383\n2.3303\n5.8495\n4.1192\n3.665\n3.4855\n2.6946\nNaN\nNaN\nNaN\nNaN\n-6.0169\n-7.1301\n0.02547\n-4.6547\n-3.5104\n-3.5954\n-4.5349\n-4.6263\n-0.97462\n1.9049\n2.8096\n1.1976\n1.8895\n3.2539\n3.5908\n2.1683\n2.8853\n3.5362\nNaN\nNaN\nNaN\nNaN\n-0.73926\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3194\n1.788\n2.0626\n1.7405\n2.2444\n3.1026\n3.0862\n2.3805\n2.7307\n1.9307\n1.9692\n2.0531\n2.6303\n2.4603\n3.2878\n4.8912\n5.3622\n4.0348\n2.3923\n0.95641\n2.4446\n-2.4607\n-6.9051\n-4.1332\n-1.6279\n3.1657\n5.1293\n4.4249\n4.6896\n4.572\n4.8863\nNaN\n-5.9744\n-7.0017\n-4.0168\n-4.3877\n-5.6202\n-1.3068\n-4.5481\n-3.7026\n-3.6918\n-3.995\n-5.4346\n-1.3322\nNaN\nNaN\nNaN\n1.5186\n1.8832\n2.3152\n1.6803\n2.0453\n3.9992\n3.3957\nNaN\nNaN\nNaN\n1.8699\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2991\n1.0279\n1.7366\n2.0063\n1.9638\n2.4832\n2.7608\n2.4842\n1.9053\n1.4594\n1.7399\n2.307\n3.0835\n3.6027\n3.0845\n2.9916\n3.5251\n3.4895\n3.3163\n0.99705\n2.1164\n-0.30297\n-8.7103\n-6.0305\n-1.5627\n2.2909\n4.4572\n6.1803\n5.9982\n3.5127\n4.2086\n-3.4608\n-3.9204\n-6.6118\n-4.4656\n0.85603\n-0.30083\n-2.4228\n-4.2809\nNaN\nNaN\nNaN\n-3.9813\nNaN\nNaN\nNaN\nNaN\n0.43643\n0.74074\n1.1706\n2.3444\n1.1818\n4.7685\n1.376\n1.2565\n1.0214\n0.60024\n2.0773\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1557\n1.1691\n2.4087\n2.3702\n2.7997\n2.7399\n3.3587\n2.3784\n0.63009\n2.8839\n2.6965\n2.5612\n3.1777\n3.0087\n1.3957\n2.1755\n4.4184\n3.3585\n2.8875\n2.2599\n1.1911\n-1.6036\n-3.2727\n-5.9353\n-0.17803\n1.2642\n5.1042\n4.6487\n4.4011\n0.080653\n2.178\n0.50027\n0.13644\n-6.199\n-1.603\n-1.3982\n-1.2317\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.0235\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9534\nNaN\n4.9139\n4.0988\n2.4767\n1.9081\n2.5999\n-0.026027\n0.080601\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9132\n2.7557\n3.941\n3.5435\n3.3308\n3.2297\n3.5941\n3.8565\n0.62508\n2.9578\n2.8328\n3.0693\n2.5006\n3.1534\n2.9893\n3.6135\n3.1823\n1.9895\n1.4393\n1.0695\n0.14316\n-2.9081\n-2.2212\n-2.637\n0.31053\n1.9493\n4.6955\n0.8324\n0.64783\n-1.3631\n1.2316\n1.2637\n-2.7611\n0.13033\n1.137\n-0.73614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0421\n2.4858\n1.147\n0.27617\n0.48453\n0.18055\n-0.45478\n2.2445\n3.2816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0448\n3.9375\n4.1646\n4.1374\n4.7777\n3.74\n3.2378\n3.9025\n3.0734\n3.0346\n2.3781\n4.0815\n4.3687\n3.9457\n4.0691\n2.6404\n0.43827\n-0.72006\n-0.24709\n-0.8186\n-0.74256\n-2.799\n-2.8383\n0.46082\n1.1149\n2.3682\n2.6033\n1.106\n3.5157\n-0.015494\n1.105\n-2.0739\n-4.3396\n-1.1971\n0.54004\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1778\n1.4643\n0.98721\n1.4464\n2.2957\n1.682\n1.7918\n4.9383\n4.7142\n4.9111\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2353\n3.3436\n3.2886\n3.5795\n5.88\n3.9647\n3.6286\n3.5443\n2.8668\n3.7472\n3.2915\n4.9076\n5.0104\n3.3612\n2.2167\n1.2798\n0.58689\n-0.7278\n-2.2819\n-3.0979\n-0.25388\n-3.3108\n-1.1797\n1.9525\n1.9031\n2.6858\n2.5815\n3.4798\n5.5007\n2\n-1.0663\n-3.8382\n-3.9155\n-3.1583\n-1.1061\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.97973\n1.281\n1.9577\n1.8954\n2.1102\n3.1333\n2.6952\n3.8551\n6.0596\n4.9459\n6.3689\n6.1153\n5.7504\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7124\n2.9128\n3.1619\n4.011\n4.9366\n3.1754\n3.3022\n3.2335\n3.4265\n4.134\n4.2456\n5.4534\n3.3175\n1.4971\n-0.39444\n-0.041694\n1.8955\n1.4006\n-2.3726\n-0.036699\n1.3228\n-2.8013\n-0.781\n3.7162\n2.9295\n3.7302\n3.868\n2.9431\n5.0189\n6.7053\n3.2316\n6.0588\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7984\n0.47677\n0.98537\n0.98057\n1.9641\n0.93493\n2.1558\n4.0529\n3.8268\n5.0368\n6.7355\n4.0544\n4.8793\n5.9622\n3.4516\n4.8226\n3.6075\n2.489\n0.99336\n-1.3944\n2.6882\n2.4843\n2.9876\n3.4421\n3.5286\n2.2291\n2.4141\n3.7576\n3.9081\n3.0734\n2.6007\n3.7545\n3.7353\n1.2491\n-1.2478\n-0.6668\n-0.80899\n-1.3393\n-2.5052\n0.46687\n2.0455\n0.3191\n-1.2375\n2.5517\n3.6442\n5.9994\n4.3705\n1.6166\n3.2071\n5.7433\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2289\n0.21455\n1.3692\n2.7015\n1.7956\n2.1502\n0.27022\n3.4068\n5.2915\n4.3741\n6.5971\n8.0558\n4.8508\n4.564\n2.3367\nNaN\nNaN\nNaN\nNaN\n5.3233\n3.8795\n2.6327\n1.6938\n2.075\n2.1843\n1.3418\n1.4916\n2.6369\n3.0134\n3.4981\n2.0597\n1.2313\n2.5012\n2.6111\n0.76585\n0.043653\n0.38357\n1.258\n-1.3558\n-1.5894\n-0.038622\n1.1859\n2.9837\n0.50233\n1.4646\n3.5712\n3.633\n1.9986\n-0.12001\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.3818\n0.47819\n0.11413\n0.86104\n2.7767\n2.6973\n1.9714\n3.0604\n2.5711\n3.4255\n4.3831\n3.553\n5.4276\n7.6351\n6.0019\n6.3372\n2.2426\nNaN\nNaN\nNaN\nNaN\n6.773\n10.473\n1.6353\n0.54292\n0.39\n0.81197\n1.0604\n2.0638\n3.3174\n2.6921\n3.0991\n1.9919\n1.0786\n1.4616\n1.15\n-0.20402\n0.40785\n1.0474\n1.8799\n-0.14487\n-1.5512\n-1.2293\n-0.84414\n-0.50538\n-0.73037\n1.6442\n3.3795\n0.90231\n-1.4934\n-2.4513\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21437\n-0.58084\n-0.6954\n0.030089\n1.4586\n3.0008\n3.1667\n2.9209\n4.2382\n3.4532\n3.3256\n4.7012\n3.8024\n2.1787\n5.1074\n6.2879\n7.0424\n6.8628\nNaN\nNaN\n10.356\n6.8335\n7.0681\n9.2442\n1.4726\n-0.91587\n0.041275\n0.67424\n1.2418\n2.2264\n2.819\n2.7952\n2.1073\n1.651\n1.4359\n1.4158\n0.76028\n-0.051767\n1.4385\n1.976\n2.0711\n-1.4912\n-2.5203\n-1.5446\n-0.78\n-0.57189\n-1.842\n0.17228\n0.51999\n0.29769\n-4.4954\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34589\n-0.07884\n0.58829\n0.34711\n1.5907\n2.1709\n2.6416\n1.5823\n3.6921\n6.2273\n5.0075\n4.054\n4.337\n3.6038\n2.4226\n3.5091\n6.2155\n6.7675\n5.2271\n4.3174\n2.7835\n7.015\n6.6264\n6.9321\n6.4515\n-0.41273\n-0.56903\n0.45456\n1.2972\n1.7549\n2.8258\n3.9474\n1.9026\n1.8157\n1.4739\n1.6485\n0.9457\n1.3675\n1.5447\n0.87276\n2.6233\n3.4952\n-0.85952\n-1.8235\n-1.326\n-0.1509\n-0.48529\n-1.0212\n-0.14593\n1.6239\n2.6586\n-5.7079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.63465\n-0.25562\n0.51795\n-0.59477\n0.99154\n2.4999\n1.5227\n0.13964\n2.1909\n5.9696\n6.1394\n5.3274\n4.5518\n4.3931\n3.817\n4.4486\n5.6124\n6.1684\n4.947\n3.1905\n2.9595\n7.3002\n8.5744\n8.8838\n6.4774\n-0.17149\n0.28184\n0.67246\n2.0752\n2.708\n3.441\n3.6218\n2.3379\n1.4131\n2.2133\n0.75283\n-0.62334\n1.2669\n2.2234\n1.8011\n3.2533\n1.5788\n-0.27811\n-0.35487\n-1.1227\n-1.4297\n-0.97782\n-0.65494\n0.41496\n2.2933\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.83149\n-1.4441\n-1.7489\n0.88998\n-5.5956\n-2.5226\n2.7321\n2.2242\n4.5018\n5.0074\n4.5771\n5.242\n5.8795\n5.0958\n5.6382\n3.7547\n5.413\n6.3041\n5.4127\n6.2952\n2.5743\n4.0457\n7.527\n8.4476\n10.267\n7.4613\n-0.061369\n1.3164\n-0.019071\n2.2476\n2.3604\n2.72\n2.2723\n1.0225\n1.0402\n1.2217\n0.28463\n-0.38858\n0.34926\n2.4802\n2.4347\n2.1908\n-0.48804\n-1.3572\n-1.0235\n-0.99894\n-2.3099\n-1.3771\n-1.1663\n-0.20184\n2.009\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.4456\nNaN\nNaN\nNaN\nNaN\n0.75234\n0.7439\n0.25708\n1.0077\n3.5728\n-3.5661\n1.1811\n4.3806\n5.2336\n5.5323\n6.5381\n5.7436\n2.3685\n5.0936\n4.7878\n5.4296\n2.5881\n4.5952\n6.695\n5.4128\n6.3534\n5.0774\n7.0141\n8.1513\n8.215\n7.5834\n5.072\n1.1884\n2.228\n1.3775\n1.1581\n1.0957\n2.1721\n2.1792\n1.1739\n0.55821\n0.34154\n0.87028\n0.99991\n0.91815\n2.7376\n0.71473\n0.63965\n0.58641\n-1.2314\n-3.4235\n-2.3302\n-3.8604\n-3.7204\n-2.2922\n0.46375\n3.7463\n6.1084\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.115\n1.7842\n-1.1639\n-0.1845\n1.4615\n2.0392\n1.8704\n3.5217\n4.3516\n5.2256\n5.9513\n6.4254\n6.8922\n4.8329\n1.412\n1.407\n4.7703\n6.0945\n2.5289\n4.6938\n7.1587\n7.3262\n7.3555\n6.3195\n8.4105\n10.457\n7.9684\n5.8791\n0.78878\n1.2769\n1.866\n1.5852\n0.51626\n1.054\n1.9252\n0.80338\n0.45045\n-0.39351\n1.4504\n1.4735\n0.35934\n0.58202\n1.8249\n0.71454\n1.7024\n2.1324\n0.1416\n-0.60893\n-4.0962\n-4.2657\n-2.8332\n-0.01916\n2.9295\n5.3047\n7.2434\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.40661\nNaN\nNaN\n15.394\n3.5236\n0.71816\n-0.47516\n0.75517\n0.31584\n0.14822\n-0.58414\n0.41766\n1.0173\n2.3883\n4.777\n3.5581\n2.7864\n3.9774\n2.7949\n0.26347\n-0.57692\n2.0613\n2.2836\n5.7784\n6.8687\n6.162\n6.1413\n6.5578\n8.7707\n11.232\n7.7755\n5.7058\n0.11638\n0.91345\n1.232\n1.0328\n1.3367\n2.1335\n1.8955\n1.217\n0.77919\n-0.88736\n1.3914\n1.3125\n0.23839\n0.76935\n0.87758\n0.55799\n-0.64866\n-0.84762\n-0.82441\n-0.58854\n-1.1831\n-1.7299\n0.8351\n2.2342\n4.8908\n5.6755\n9.6361\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.874\n14.256\n13.384\n9.8997\n2.7\n-0.30349\n-2.1083\n1.5314\n0.62615\n-0.11335\n-0.83939\n-1.4997\n0.91669\n0.96466\n1.6475\n0.33427\n1.4927\n1.3552\n1.6391\n1.4509\n-1.0372\n2.2621\n4.0669\n5.747\n4.8617\n3.5886\n3.8914\n6.1391\n7.1508\n7.6737\n5.0245\n4.6667\n3.168\n0.22906\n1.9716\n1.7071\n2.1456\n1.3442\n0.73839\n0.63611\n1.5172\n0.51715\n1.3745\n0.75131\n-0.0094748\n-0.52463\n-0.14473\n0.030366\n-1.3508\n-2.9926\n-1.3995\n0.86261\n-0.73895\n-2.6618\n-0.99028\n2.6222\n4.0868\n4.8623\n9.3436\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.051\n7.0668\n8.8833\n6.0796\n1.3953\n-4.3873\n-2.2069\n-1.2947\n-0.20239\n-0.74861\n-1.8246\n-1.5764\n-0.52655\n0.61273\n0.70548\n0.13351\n-0.70837\n0.033331\n0.32646\n1.2587\n1.9258\n1.5354\n3.4738\n4.0074\n4.2129\n4.0285\n3.1343\n3.318\n5.0541\n4.4204\n3.6226\n4.0325\n4.7248\n2.2728\n1.64\n0.63833\n2.5354\n1.1923\n0.40698\n0.42568\n1.3504\n0.47761\n0.35875\n-0.040391\n-0.26299\n-0.1321\n-0.40633\n-1.089\n-0.62654\n-1.9686\n-5.6955\n-5.1117\n-0.43162\n0.92558\n0.40737\n2.5205\n3.7407\n5.3021\n7.2482\n3.0912\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.941\n8.9454\n5.0657\n5.2806\n0.97455\n-6.3912\n-3.5695\n-2.1053\n-0.96497\n-0.011941\n-0.78286\n-1.2766\n-1.3074\n-0.74906\n-0.1527\n0.61284\n-1.2062\n-0.17692\n0.50697\n2.1857\n3.5499\n1.9358\n2.3249\n2.5595\n4.2048\n5.0448\n3.6695\n3.2439\n1.2266\n2.6354\n3.7263\n5.3692\n5.531\n2.5933\n1.1096\n0.15357\n0.89806\n0.24969\n1.2745\n0.28279\n0.52878\n0.28709\n0.4225\n-0.47908\n-0.20649\n0.29959\n-0.4145\n-1.9969\n-0.74138\n-2.0004\n-5.2999\n-6.0382\n-3.515\n1.055\n0.3959\n3.0227\n3.5603\n6.9788\n1.9076\n-1.4632\n-1.064\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.233\n5.1677\n0.97383\n1.0891\n-1.4267\n-6.812\n-4.0174\n-2.6284\n-2.6093\n-1.8625\n-3.0483\n-3.2078\n-2.5456\n-1.9918\n-2.1019\n-0.48762\n0.31146\n0.39593\n1.2368\n1.8514\n2.1384\n3.8333\n1.4197\n2.4791\n2.5589\n4.0156\n2.6947\n3.3798\n2.4121\n3.9908\n4.5859\n6.0371\n5.1162\n1.9781\n0.8021\n-0.15661\n0.030104\n1.3684\n2.4199\n1.0226\n1.3156\n0.63648\n1.5441\n-0.5051\n-0.13623\n0.44357\n-0.32132\n-0.87066\n-1.0134\n-1.9168\n-4.6145\n-4.2397\n-5.7523\n-3.7282\n-1.8301\n1.998\n4.3349\n7.0403\n5.5975\n-1.1542\n-0.19893\nNaN\nNaN\n1.079\n0.73826\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9741\n1.6517\n1.4148\n0.31423\n-8.1188\n-10.088\n-3.4199\n-2.8944\n-6.1948\n-3.9319\n-3.7992\n-3.2413\n-2.0587\n-0.77587\n-0.83986\n-1.2606\n-0.19984\n0.22201\n0.96663\n1.0982\n1.1608\n1.8729\n0.18447\n1.775\n1.7386\n2.3469\n4.5721\n4.7816\n5.2485\n4.9974\n4.862\n4.9614\n3.0633\n1.4697\n0.58864\n1.4245\n0.69245\n1.6452\n2.7896\n1.9802\n1.8104\n1.3506\n1.383\n0.15726\n0.805\n0.075188\n-0.43156\n-1.153\n-1.7468\n-1.7947\n-2.3641\n-1.7042\n-1.7193\n-1.5271\n-0.13389\n1.5902\n3.3081\n4.1731\n3.0944\n1.0835\n2.6304\n1.7591\n3.5652\n3.0113\n3.19\n5.4538\n8.2018\nNaN\nNaN\nNaN\nNaN\n-3.4555\n1.3285\n-0.30046\n-0.81463\n-2.6058\n-6.9376\n-7.9587\n-3.5583\n-4.9764\n-5.8212\n-4.2463\n-3.6391\n-0.52402\n0.58453\n0.47229\n0.39915\n-2.2526\n-2.1943\n-0.59055\n0.90942\n1.1268\n1.7057\n1.4146\n0.73546\n1.7144\n-0.44324\n0.57451\n1.9151\n1.772\n3.2753\n1.376\n2.2629\n2.7098\n0.81599\n1.4041\n1.0172\n1.0409\n1.1808\n1.7854\n4.1263\n2.8543\n2.3165\n0.80313\n1.2094\n0.80715\n1.3835\n0.936\n0.29534\n-0.59558\n-1.1305\n-3.46\n-8.8573\n-2.6473\n0.50559\n1.7829\n2.7524\n2.5978\n3.0686\n7.7538\n1.9339\n2.4684\n2.6989\nNaN\n0.11635\n2.2182\n-0.2026\n4.6907\n5.73\nNaN\nNaN\nNaN\n-3.6341\n-1.0165\n3.2326\n2.3357\n-0.39171\n0.35298\n-3.5911\n-5.1523\n-2.3113\n-0.91442\n-1.4065\n-1.706\n-0.54993\n1.0353\n1.744\n0.079507\n-1.295\n-2.3546\n-2.7173\n-1.9415\n-0.89633\n0.48581\n1.8574\n1.775\n0.34827\n-0.11143\n-0.9421\n1.1\n2.9418\n1.9976\n0.45527\n0.47542\n1.5562\n1.3379\n1.3521\n0.86317\n1.1894\n2.0854\n1.5564\n2.5243\n4.3245\n3.0952\n1.747\n0.33849\n0.52459\n0.6719\n1.1519\n1.8095\n1.1604\n-0.46625\n-1.028\n-3.2021\n-8.5308\n-2.3464\n0.23068\n2.727\n1.9591\n4.5124\n4.693\n6.6971\n2.9585\n1.0699\nNaN\nNaN\n-2.243\n1.5068\n0.87171\n1.4147\n1.2639\nNaN\nNaN\nNaN\n-1.831\n-0.89344\n3.7975\n2.6267\n-2.0472\n-1.1998\n-0.74536\n-2.3467\n-0.085433\n0.8374\n0.096176\n-1.1755\n-0.61806\n1.1582\n2.6716\n-0.23934\n-2.0934\n-2.295\n-2.2277\n-2.0089\n-1.3233\n-0.014674\n1.1323\n1.4476\n0.59361\n0.095908\n-0.070076\n1.7134\n2.4817\n2.2332\n2.443\n1.9396\n1.3767\n2.9749\n3.6245\n-0.46422\n0.54403\n3.2687\n2.8431\n3.1844\n4.3334\n3.3433\n1.6905\n1.2781\n0.7979\n0.41601\n1.0755\n-0.014213\n0.62692\n0.41369\n-0.90261\n-0.51046\n-7.448\n-0.68719\n-4.0748\n-1.7516\n2.3233\n5.9667\n6.9651\n6.1307\n1.6856\n0.66967\nNaN\nNaN\nNaN\n-0.57502\n1.6718\n3.9885\n3.2421\nNaN\n3.2598\n1.8575\n-1.0789\n-0.14858\n3.5539\n4.1386\n1.3053\n0.10345\n0.37607\n-1.8388\n-1.7372\n-1.3414\n-2.1536\n-1.5151\n1.4085\n2.1069\n2.1734\n0.29055\n-1.7506\n-2.1485\n-1.224\n-1.073\n-0.39927\n-0.25824\n-0.043026\n0.38387\n0.58167\n0.24178\n0.16746\n1.9132\n2.4036\n2.4976\n3.2042\n1.4782\n0.39613\n4.2665\n5.5336\n0.83704\n0.54595\n2.7776\n2.777\n3.1859\n3.9555\n3.4996\n2.7839\n2.2971\n1.6708\n1.5752\n1.8728\n1.5773\n2.7548\n3.7809\n0.21964\nNaN\nNaN\nNaN\n-2.9812\n-2.2919\n1.8089\n4.9199\n4.9923\n2.7561\n0.01892\n1.2656\nNaN\nNaN\nNaN\n-0.3236\n1.7384\n0.99397\n2.5881\n2.5203\n2.6579\n3.2668\n2.326\n2.0389\n0.89962\n0.62254\n1.1739\n1.6287\n1.0341\n-0.38223\n-0.29973\n1.0612\n0.27715\n0.26994\n0.65359\n1.0631\n0.78036\n0.47015\n-1.0847\n-1.2449\n-0.41546\n-0.10119\n-0.053487\n0.13701\n0.34418\n-0.31018\n0.39259\n0.76956\n0.85419\n1.3727\n1.5846\n2.0139\n2.5344\n1.1876\n0.28184\n3.0369\n5.2598\n1.7767\n0.98962\n1.999\n1.996\n1.9736\n3.575\n2.4569\n3.2109\n3.605\n2.4433\n2.1471\n2.7205\n3.0603\n3.4743\n4.9596\n1.6523\nNaN\nNaN\nNaN\n-1.3991\n-1.0749\n-1.2004\n0.73067\n3.3274\n1.6169\n-0.42178\n0.0078628\nNaN\nNaN\nNaN\n-0.21418\n0.15868\n-0.69798\n0.61559\n3.5689\n3.0701\n2.645\n2.204\n1.6191\n-0.52881\n0.29326\n0.58872\n1.2318\n1.5518\n0.91548\n-0.72947\n0.80995\n-0.22061\n-1.2698\n-0.34539\n-0.043163\n0.65117\n1.1875\n0.74274\n0.60089\n0.24426\n-0.2367\n1.3702\n1.4931\n1.1893\n-0.26258\n0.67137\n1.7201\n1.8084\n1.715\n1.8232\n2.725\n2.8449\n1.956\n1.5837\n3.3703\n4.7735\n2.0565\n2.6412\n2.8275\n1.1794\n2.42\n3.1398\n3.2012\n3.7931\n4.7224\n2.4594\n2.2386\n2.7097\n3.1137\n2.4147\n1.3064\n1.0771\n1.3537\nNaN\nNaN\n-1.5408\n-0.58268\n0.13125\n-0.027972\n0.41793\n1.6234\n0.53325\n1.1657\n1.3316\n0.23402\n0.12526\n-0.57115\n-1.2318\n-0.77707\n0.33482\n3.7711\n3.4209\n3.5323\n2.5787\n1.8395\n2.498\n1.8167\n2.0545\n1.0171\n2.4915\n2.0263\n0.71846\n0.44451\n-3.3642\n-4.2941\n-0.75402\n-1.1138\n0.12697\n1.9692\n1.6784\n2.2232\n1.4579\n0.66636\n1.894\n2.1801\n2.0926\n0.95635\n0.69126\n1.7074\n1.712\n2.8622\n2.9755\n3.0268\n3.8754\n4.1613\n2.8692\n3.6097\n4.3149\n1.881\n1.9805\n2.5473\n1.3088\n1.1997\n2.3701\n3.4205\n4.2268\n3.2803\n2.1624\n1.8639\n1.6376\n2.232\n2.6616\n3.8428\n3.5052\n0.042562\n-0.083325\n-1.399\n-2.0689\n-1.0872\n0.12977\n1.5916\n1.2631\n1.9003\n1.6956\n1.5367\n0.67374\n1.4138\n1.9764\n1.0488\n-1.1408\n-1.7754\n0.051842\n2.5731\n3.6556\n2.832\n2.9952\n1.64\n1.79\n1.7687\n3.2199\n2.3151\n2.8042\n3.7913\n3.7155\n1.1758\n-2.7556\n-5.496\n-1.532\n-1.9428\n0.51859\n2.0005\n2.5616\n2.933\n2.6943\n2.4331\n2.0518\n1.4137\n1.3848\n1.6319\n1.3809\n1.8071\n2.0476\n3.0619\n3.053\n4.4585\n4.7167\n4.7893\n4.0458\n3.8568\n3.1249\n2.1236\n3.0094\n3.8338\n2.5594\n1.6875\n2.1516\n3.3434\n4.5736\n3.4756\n3.4104\n2.2252\n1.1993\n1.0841\nNaN\nNaN\nNaN\nNaN\n-0.096323\n-0.84843\n-2.0046\n0.34446\n0.2303\n0.79914\n0.92793\n2.0032\n2.9385\n2.1502\n1.6287\n2.1326\n2.1308\n2.327\n2.8616\n0.57127\n1.0132\n2.3767\n2.6569\n2.9363\n3.6284\n2.2722\n2.2682\n2.0867\n3.3013\n3.2102\n3.0149\n3.553\n4.6189\n4.2967\n0.56801\n-1.3012\n-1.4804\n-1.4069\n-0.32952\n0.91396\n1.6475\n3.0929\n3.756\n1.1963\n0.17022\n0.45406\n0.94041\n2.5258\n2.988\n2.581\n2.5144\n3.0863\n4.2287\n5.5843\n5.0592\n4.4053\n3.9922\n4.0998\n4.8924\n3.1433\n3.9025\n4.3153\n3.6312\n3.158\n3.9847\n3.5116\n5.9934\n5.3235\n4.6832\n2.5691\n1.4609\n2.9088\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0806\n-0.532\n-0.49387\n0.061577\n0.12685\n0.41303\n1.7009\n2.5605\n1.4785\n1.6754\n2.2953\n2.0985\n2.433\n2.5287\n2.1129\n1.4387\n1.2482\n1.6763\n3.3998\n5.3474\n3.6518\n3.0235\n3.8051\n4.2778\n3.7737\n2.6647\n2.6489\n3.4314\n7.7489\n2.2491\n1.2163\n0.47427\n0.50674\n-0.055611\n0.07472\n1.7239\n2.571\n2.4757\n0.81323\n1.594\n1.7204\n1.9214\n3.5038\n4.1956\n3.6215\n3.9011\n4.1371\n4.2787\n6.1935\n5.5248\n5.2283\n3.9829\n4.6224\n4.0817\n4.6369\n3.593\n4.6449\n4.6976\n4.8018\n5.7958\n5.0165\n7.4078\n7.6541\n6.4358\n3.793\n1.0353\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4747\n-0.36285\n-0.093449\n-0.058567\n0.15201\n0.42418\n0.36748\n0.16076\n-0.10928\n0.6902\n1.7659\n1.0505\n0.18705\n1.0233\n2.2821\n1.8841\n1.5069\n3.5075\n4.8782\n5.6477\n3.4012\n4.8401\n4.9169\n3.4634\n2.4121\n1.8039\n1.9768\n6.3933\n3.2363\n2.464\n1.9927\n1.1778\n0.037484\n1.2551\n1.4941\n2.3292\n3.3785\n0.47918\n0.97793\n2.0974\n1.5658\n3.2108\n3.8518\n4.3335\n5.0034\n5.3621\n4.4257\n5.2339\n5.1639\n6.0283\n2.9666\n2.4683\nNaN\n5.9242\n3.7271\n5.8288\n6.1671\n7.1355\n7.6288\n6.9376\n9.5216\n9.5628\n8.381\n5.7691\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6426\n0.90602\n-0.31662\n0.27639\n0.16787\n0.29766\n0.51512\n-0.41588\n-1.4746\n-0.62109\n0.57704\n0.47178\n-0.61906\n1.7934\n2.8425\n3.4725\n2.5968\n2.7998\n2.985\n4.4557\n4.4025\n5.3524\n3.4749\n1.5964\n0.52935\n-0.39172\n0.67749\n2.7467\n1.3147\n1.8213\n0.84178\n0.31445\n1.4562\n2.2957\n2.1326\n3.462\n4.0343\n1.4167\n0.79452\n2.2314\n0.9961\n2.8672\n2.4578\n4.027\n6.6888\nNaN\nNaN\n4.2338\n4.6352\n6.4539\nNaN\nNaN\nNaN\n2.8591\n2.9887\n2.9777\n2.9589\n2.8035\n3.2091\n3.6276\n3.8982\n3.6017\n3.6553\n3.6295\n3.7388\n3.9928\n3.6652\n3.6744\n3.765\n3.8733\n3.6402\n3.2231\n2.3988\n2.4373\n2.4319\n2.8199\n3.1343\n2.5526\n3.0303\n3.0922\n3.3338\n3.5985\n3.8201\n3.1364\n3.0356\n2.8744\n2.9735\n3.2982\n2.7267\n2.4598\n2.405\n2.8867\n2.7885\n2.8845\n3.5718\n4.2505\n3.9254\n3.8038\n3.4837\n3.0006\n3.2692\n3.2183\n2.8169\n2.7114\n2.9955\n3.3278\n2.5357\n2.447\n3.0037\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9882\n3.1277\n2.9356\n3.0246\n2.4434\n3.2323\n3.7735\n3.9395\n3.9774\n3.7787\n3.3722\n3.5623\n4.0911\n3.3633\n3.447\n3.7772\n4.1597\n3.6112\n3.0286\n2.4975\n2.5239\n2.6905\n2.8001\n3.3464\n2.946\n2.9172\n3.0269\n3.2788\n4.1369\n3.4253\n3.4516\n3.069\n3.1746\n3.2535\n2.9232\n2.4488\n2.502\n2.5252\n2.8273\n2.6041\n2.5733\n3.2352\n3.6082\n3.7279\n3.7012\n3.4007\n3.3534\n3.2823\n3.1229\n3.0036\n2.9365\n3.1798\n3.7435\n3.3142\n2.8378\n3.4187\n3.1667\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0856\n3.1572\n3.0983\n3.0024\n2.5592\n3.0152\n3.6526\n3.5133\n3.9574\n3.7913\n2.9907\n3.0553\n3.3902\n3.1823\n2.8231\n3.2836\n3.4548\n2.894\n2.676\n2.8716\n3.0404\n2.6771\n3.138\n3.3549\n3.4553\n3.1357\n2.6587\n2.7955\n3.3893\n3.3369\n3.5118\n3.4012\n3.4588\n3.521\n3.0484\n1.9631\n2.4399\n2.4697\n2.4845\n2.4875\n2.8395\n2.9966\n3.2759\n3.5837\n3.5818\n3.2143\n3.214\n3.3875\n3.5503\n3.4473\n3.2766\n3.5646\n4.3133\n3.5973\n3.5695\n3.2953\n2.9684\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0293\n3.2543\n3.2094\n3.1369\n2.9538\n3.1625\n3.4148\n3.4648\n3.4499\n2.552\n2.4484\n2.8239\n2.9311\n2.8017\n2.427\n2.2803\n2.0255\n2.0907\n1.8225\n2.5564\n3.1819\n2.3819\n2.6865\n3.2381\n3.3912\n3.2172\n2.6656\n2.3217\n2.9816\n3.1265\n3.4642\n4.0972\n3.8606\n4.0209\n3.4583\n2.3989\n2.0345\n2.5432\n2.3862\n2.3783\n3.1473\n3.1282\n3.3928\n3.5437\n3.7698\n3.2815\n3.0465\n3.4958\n3.7013\n3.432\n3.378\n3.4223\n3.619\n3.7322\n3.5375\n3.0319\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3106\n3.3827\n3.2302\n3.2382\n3.1048\n3.0827\n2.9672\n3.0122\n3.0048\n2.6048\n3.0086\n3.1322\n2.8707\n2.6257\n2.4821\n1.415\n1.0171\n0.80548\n1.6889\n2.1437\n3.0312\nNaN\nNaN\n3.0397\n3.1609\n3.1427\n2.7281\n2.6001\n3.0481\n3.1731\n3.2997\n3.9524\n4.354\n4.2726\n3.3349\n3.1127\n3.0263\n2.9841\n2.5977\n2.8317\n3.4778\n3.5754\n3.3548\n3.2099\n3.6874\n3.5367\n3.3321\n3.9059\n3.8344\n3.2545\n3.1445\n3.0581\n3.1916\n3.6746\n3.895\n3.7412\n3.033\nNaN\nNaN\n2.3642\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5314\n3.2234\n3.0204\n3.0102\n2.8249\n2.9199\n2.6406\n2.4577\n2.6243\n2.7731\n2.9308\n2.9319\n2.7189\n2.2924\n2.4215\n2.0415\n1.2896\n1.3408\n1.765\nNaN\nNaN\nNaN\nNaN\n3.4931\n3.194\n2.9518\n2.8255\n2.8966\n3.1607\n3.1118\n2.7192\n3.6282\n4.0263\n3.7146\n2.9256\n3.0666\n3.3772\n3.332\n3.0281\n3.5848\n3.8671\n3.7178\n3.4152\n3.5065\n3.8022\n3.8021\n3.6556\n3.9749\n3.812\n3.3889\n3.0682\n3.0212\n3.0435\n3.3558\n3.5799\n3.4438\n2.8114\nNaN\nNaN\n2.1467\n2.4412\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1864\n2.7903\n2.5008\n2.4176\n2.535\n2.5425\n2.5486\n2.4032\n2.7244\n2.7639\n2.9662\n2.83\n2.6399\n2.6977\n3.069\n2.7596\n2.2595\n2.1586\n2.4171\nNaN\nNaN\nNaN\nNaN\n3.8689\n3.3599\n3.2274\n2.9263\n2.9899\n2.9709\n3.2208\n2.1355\n3.1467\n3.6861\n3.5033\n2.3979\n3.044\n3.4254\n3.5493\n3.6037\n3.606\n4.1093\n4.3022\n3.8283\n3.8792\n4.1313\n4.216\n3.9994\n3.6901\n3.5653\n3.5475\n3.1679\n3.0422\n2.9149\n3.1256\n3.1675\n2.6694\n2.5069\nNaN\nNaN\n2.3181\n2.7154\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9408\n2.5307\n2.2992\n2.3879\n2.2416\n2.3799\n2.7754\n2.7573\n2.976\n3.1709\n3.1977\n2.9965\n2.7117\n2.8227\n3.1126\n3.222\n3.0392\n3.3521\n2.8426\n2.8666\n2.7858\n2.7223\n3.4673\n3.9176\n3.569\n3.5199\n3.3062\n2.7933\n2.4956\n2.9112\n2.501\n3.2676\n3.8403\n3.5627\n3.0005\n3.2314\n3.4079\n3.7438\n3.868\n3.8527\n4.1755\n4.5053\n4.2879\n4.2768\n4.2793\n4.093\n3.7687\n3.4224\n3.2384\n3.5145\n3.1493\n2.9509\n2.9025\n3.1828\n3.3121\n2.6272\n2.0498\n2.3765\n2.9337\n3.2572\n3.0031\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8797\n2.7358\n2.8758\n2.6532\n2.0656\n2.3124\n2.948\n2.9273\n3.1583\n3.4737\n3.3801\n3.1146\n2.8875\n2.9043\n2.6406\n2.7428\n3.3503\n3.3058\n2.6606\n2.9231\n2.8986\n3.2344\n3.5195\n3.7333\n3.7772\n3.6401\n3.5109\n3.3276\n2.9469\n3.1182\n3.1815\n3.3486\n3.926\n3.6376\n3.3944\n3.4197\n3.2786\n3.7103\n3.8853\n4.0517\n4.2363\n4.1844\n3.9899\n4.0848\n4.1931\n3.8391\n3.5165\n3.5621\n3.25\n3.2145\n3.1728\n2.9098\n3.0356\n3.3429\n3.3671\n2.6319\n1.7613\n1.9598\n2.8887\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.744\n2.7487\n3.0132\n2.8009\n2.3102\n2.6522\n3.0865\n2.8951\n3.0648\n3.3972\n3.296\n3.1939\n3.0292\n2.8551\n2.7245\n2.9287\n3.4022\n2.9147\n2.4809\n2.9408\n3.3973\n3.356\n3.1416\n3.435\n3.8632\n4.0168\n3.6579\n3.4736\n3.3717\n3.35\n3.8075\n4.0189\n4.2866\nNaN\nNaN\n3.2439\n3.0275\n3.4447\n3.8755\n4.1246\n3.8958\n3.7637\n3.9507\n4.0467\n4.0398\n3.7687\n3.9165\n3.8184\n3.4384\n3.0293\n3.0586\n3.3268\n3.5608\n3.3489\n3.0714\n2.7162\n1.6916\n1.8688\n2.6986\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.197\n2.6185\n2.7189\n2.6123\n2.6044\n3.0046\n3.1333\n2.8855\n2.734\n2.7795\n2.9413\n2.76\n2.885\n3.0183\n2.9874\n3.1246\n3.4807\n2.9797\n2.9048\n3.1869\n3.462\n3.4479\n3.4493\n3.5758\n3.7318\n4.074\n3.8611\n3.7268\n3.4605\n3.4295\n3.9535\n4.4284\nNaN\nNaN\nNaN\n3.9572\n3.2793\n3.4553\n3.8766\n4.1149\n4.0901\n3.8015\n4.0675\n4.2241\n4.076\n3.992\n4.2696\n4.1839\n3.8095\n3.0009\n3.0096\n3.2719\n3.4796\n3.3\nNaN\nNaN\n2.4801\n2.6595\n2.8961\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3794\n2.6186\n2.8169\n2.7184\n2.703\n3.1345\n3.4549\n3.0852\n2.5804\n2.4393\n2.5294\n2.8321\n2.6838\n3.1607\n3.0517\n3.2729\n3.4414\n3.0985\n3.0469\n3.1672\n3.453\n3.6088\n4.1057\n3.5612\n3.6108\n4.2552\n4.2998\n4.0773\n4.0396\n3.7597\n3.9177\nNaN\nNaN\nNaN\nNaN\n4.1392\n3.44\n3.0216\n3.682\n3.9668\n4.0254\n3.9916\n4.2198\n4.4572\n4.3487\n4.2858\n4.3227\n4.3703\n3.9324\n3.3454\n3.3001\n3.4354\n3.4203\nNaN\nNaN\nNaN\n2.6257\n2.5968\n2.8125\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2133\n2.2048\n2.3144\n2.5455\n2.7335\n2.8541\n3.1518\n3.055\n2.6186\n2.519\n2.6532\n3.0307\n3.0722\n3.1429\n3.1555\n3.3077\n3.2173\n2.9448\n2.714\n3.0207\n3.3913\n3.8041\n4.05\n3.8595\n3.8449\n4.3723\n5.0519\n4.4754\n4.3915\n4.1725\n4.0668\nNaN\nNaN\nNaN\nNaN\n4.0926\n3.6071\n2.6979\n3.4932\n3.6352\n3.8951\n4.0581\n4.2086\n4.4772\n4.3817\n4.5728\n4.3208\n4.3456\n3.8184\n3.673\n3.8926\n3.868\n3.5374\nNaN\nNaN\nNaN\n3.0391\n3.1217\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7922\n1.9207\n2.1292\n2.2093\n2.3721\n2.4981\n2.6152\n2.7477\n2.6616\n2.6006\n2.7655\n2.9429\n3.0861\n3.0802\n3.1294\n3.0163\n2.993\n3.0779\n3.1546\n3.4182\n3.5455\n3.6469\n4.1182\n4.5448\n4.7809\n4.7284\n5.4255\n5.0094\n4.8559\n4.7232\n4.4835\nNaN\nNaN\nNaN\nNaN\n3.5601\n2.4921\n2.0061\n3.1678\n3.5365\n3.8754\n4.1142\n4.1428\n4.2524\n4.3048\n4.7194\n4.3397\n4.206\n3.9618\n3.831\n3.7702\n3.867\n3.6238\nNaN\nNaN\nNaN\nNaN\n3.3009\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7506\n2.0073\n2.1669\n2.1106\n2.2663\n2.3549\n2.4743\n2.6765\n2.5288\n2.2869\n2.7594\n2.6826\n2.6591\n2.6387\n2.9296\n2.7605\n3.1081\n3.0775\n3.4277\n3.8453\n3.8498\n3.5132\n3.6738\n4.8128\n4.7397\n4.866\n5.3107\n5.3067\n5.2382\n5.2174\n5.2983\nNaN\n5.0731\n4.572\n3.8921\n2.6137\n2.5256\n2.6559\n3.5202\n4.097\n4.282\n4.1375\n4.0653\n4.2458\nNaN\nNaN\nNaN\n3.9486\n3.621\n3.4408\n3.3339\n3.02\n3.3646\n3.3512\nNaN\nNaN\nNaN\n3.6515\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7997\n2.1191\n2.0909\n2.1277\n2.5101\n2.5278\n2.5\n2.3813\n2.3851\n2.4906\n2.8246\n2.5523\n2.2669\n2.2702\n2.599\n2.7976\n2.9841\n2.925\n3.5738\n3.7863\n4.0334\n4.1137\n3.2648\n4.6427\n4.3054\n4.665\n4.8333\n5.3095\n5.4767\n5.3409\n5.2608\n5.0146\n4.9232\n4.6988\n4.2754\n3.2761\n3.422\n3.9489\n4.0088\nNaN\nNaN\nNaN\n3.9246\nNaN\nNaN\nNaN\nNaN\n3.6735\n3.3181\n3.076\n3.1256\n3.0559\n3.3337\n3.2066\n3.4589\n3.3631\n3.3508\n3.5946\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8353\n2.302\n2.3485\n2.4511\n2.7943\n2.4097\n2.2706\n1.8607\n2.079\n2.7585\n2.7444\n2.582\n2.0424\n2.2919\n2.6686\n2.6237\n2.4602\n2.6976\n3.4114\n3.7707\n4.2454\n4.0845\n3.5218\n3.4605\n4.0034\n4.678\n4.7807\n5.5341\n5.5586\n5.1936\n5.0171\n4.7981\n4.4366\n4.6254\n4.6142\n4.4835\n3.8557\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2012\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4926\nNaN\n3.1769\n3.2426\n3.2904\n3.3612\n3.2645\n3.1574\n3.2925\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6739\n1.9296\n2.0632\n2.2591\n2.1207\n1.9587\n2.0833\n1.9841\n2.2278\n3.0768\n3.1493\n2.7386\n1.9531\n2.3685\n2.5971\n2.1556\n1.9982\n1.9442\n2.7091\n3.5127\n4.3605\n4.1767\n3.9019\n3.9252\n4.6588\n5.1521\n5.5606\n6.1056\n5.868\n5.112\n5.122\n5.3986\n4.7923\n4.8732\n4.7373\n4.5125\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3468\n3.1263\n3.0638\n3.2189\n2.7403\n2.7253\n3.0057\n3.3009\n4.3952\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6506\n1.8792\n1.922\n2.0486\n2.2085\n2.0315\n1.9856\n2.5126\n2.8887\n3.2442\n3.7181\n2.8053\n2.3367\n2.7913\n2.8412\n1.9843\n1.9496\n1.7439\n2.1166\n3.1278\n3.7746\n4.3365\n4.2728\n4.5272\n5.4033\n5.8546\n6.4124\n6.479\n6.4994\n5.4949\n5.3349\n5.984\n5.6944\n5.196\n4.9299\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2016\n3.1032\n3.3083\n3.567\n3.0624\n3.1043\n3.3384\n3.5571\n3.1066\n4.0243\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8189\n2.1235\n2.2216\n2.1968\n2.4254\n2.4113\n2.5813\n3.0724\n3.695\n3.3131\n3.9491\n2.5451\n2.5033\n3.2585\n3.2592\n2.2994\n2.1316\n2.1229\n1.8381\n2.7178\n3.7495\n4.4581\n4.7595\n5.0366\n5.693\n6.0444\n6.5514\n6.6384\n6.851\n6.2512\n5.7368\n6.1037\n6.1135\n5.4683\n5.245\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.448\n3.4343\n3.6255\n3.4845\n3.3019\n3.2982\n3.6727\n3.7212\n2.9432\n3.1493\n3.1664\n3.6085\n3.5397\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7181\n2.4277\n2.4668\n2.3998\n2.4975\n2.7484\n3.2382\n3.8749\n4.0008\n3.4085\n3.676\n2.6686\n2.6917\n3.1524\n2.833\n2.1537\n2.6821\n2.7967\n2.0404\n2.8647\n3.9395\n4.6116\n4.9839\n5.2524\n5.5907\n5.984\n6.4202\n6.6036\n6.9233\n6.8337\n6.2872\n6.3296\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6801\n3.5413\n3.5153\n3.1392\n3.2314\n3.4531\n3.2679\n3.2785\n3.7814\n3.9339\n3.6841\n3.6031\n3.5827\n3.4536\n4.0839\n6.1097\n6.7016\n7.469\n4.8963\n2.9049\n3.1456\n3.2178\n2.7407\n2.631\n2.8476\n2.8872\n3.2304\n3.6426\n3.6785\n3.1261\n3.2676\n3.1396\n3.2883\n2.7853\n2.2936\n2.1022\n2.8875\n3.3038\n2.518\n3.1647\n4.0481\n4.4989\n4.8825\n5.243\n5.368\n5.915\n6.232\n6.2963\n6.4491\n6.4181\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.119\n3.1488\n3.398\n3.4357\n2.7734\n2.6887\n2.8202\n3.2521\n3.5058\n3.826\n4.2756\n4.3685\n4.5615\n4.3965\n3.2689\nNaN\nNaN\nNaN\nNaN\n4.5585\n4.1799\n3.0522\n2.9924\n2.0274\n2.6559\n2.9225\n3.0371\n3.2411\n3.3228\n3.4791\n3.2394\n3.1603\n3.408\n3.6384\n2.6718\n2.1775\n2.1682\n2.7943\n3.3837\n3.2902\n3.6014\n3.899\n4.1053\n4.7549\n5.1782\n5.4703\n5.7411\n6.16\n6.1378\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4219\n3.2169\n3.0069\n2.7884\n2.957\n3.0209\n2.5949\n2.3451\n2.5681\n2.8145\n3.3787\n3.8282\n4.0658\n4.289\n4.6775\n4.004\n3.4172\nNaN\nNaN\nNaN\nNaN\n5.3887\n5.8119\n2.4501\n2.6762\n2.1605\n2.5949\n2.5754\n2.6205\n3.168\n3.2424\n3.495\n3.3032\n2.9766\n3.0855\n3.4133\n2.5911\n2.0867\n1.9756\n2.7767\n3.0896\n3.2262\n3.4458\n3.609\n4.0748\n4.9359\n5.086\n5.4461\n5.6835\n6.0966\n6.3912\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7597\n3.54\n3.0735\n2.7992\n2.5889\n2.5757\n2.4042\n2.2487\n2.1236\n2.4315\n2.638\n2.943\n2.9342\n3.2588\n3.4133\n3.6523\n3.0904\n3.6801\nNaN\nNaN\n5.9196\n6.6012\n6.436\n6.099\n2.0694\n2.2257\n2.3928\n2.4732\n2.5333\n2.2785\n3.0606\n3.3069\n3.2065\n3.1576\n3.1557\n3.1918\n3.5657\n2.3709\n2.1943\n2.391\n3.5927\n3.556\n3.1753\n3.1557\n3.5116\n4.139\n4.8833\n4.917\n5.0244\n6.1078\n6.7633\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.1599\n3.8099\n3.3401\n2.5318\n2.3935\n2.1618\n2.1848\n2.0525\n2.0573\n2.157\n2.4012\n2.4571\n2.6232\n2.5846\n2.9606\n3.2278\n3.4035\n3.0947\n4.2004\n4.2914\n5.3635\n6.7467\n7.3617\n6.7096\n5.8907\n2.1867\n2.0535\n2.0726\n2.5041\n2.8586\n2.7228\n3.4477\n3.5449\n3.3266\n3.3183\n3.2585\n3.4068\n3.4904\n2.3195\n2.5582\n3.0032\n4.0193\n4.1559\n3.9085\n3.7041\n3.815\n4.4534\n4.956\n5.1922\n5.651\n6.6747\n7.2527\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9\n3.2648\n2.8301\n2.3812\n1.9994\n2.13\n1.9346\n1.8691\n1.9641\n2.1502\n2.2166\n2.2372\n2.3684\n2.517\n2.7277\n2.9101\n3.3502\n3.3072\n3.8907\n3.8532\n5.3029\n7.1179\n7.6346\n6.744\n5.8168\n2.0684\n2.1451\n2.3592\n2.7626\n3.2338\n3.2591\n3.7231\n3.9835\n3.3236\n3.3469\n3.1878\n3.3166\n3.2739\n2.6786\n2.9257\n3.3157\n3.7381\n3.9965\n4.3693\n4.1508\n4.1523\n4.8113\n5.2714\n5.5843\n5.9534\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7173\n3.462\n3.1108\n2.5084\n1.913\n1.7895\n1.8588\n1.7855\n2.1543\n1.9715\n1.9304\n2.134\n2.2962\n2.3434\n2.5135\n2.4434\n2.5769\n3.5393\n3.6297\n4.2019\n4.0769\n5.5119\n6.5492\n6.8069\n6.449\n5.5438\n2.6056\n2.6489\n2.9055\n2.8787\n3.3145\n3.4157\n3.3276\n3.5483\n3.2531\n3.1771\n3.134\n3.2742\n3.1025\n2.7589\n2.9765\n3.4166\n3.3767\n3.2363\n3.8076\n4.3313\n4.7802\n5.1546\n5.1509\n5.6953\n6.1638\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4758\nNaN\nNaN\nNaN\nNaN\n4.3508\n3.7074\n3.32\n3.0142\n2.588\n2.0995\n1.9314\n1.8699\n1.8663\n2.2947\n2.0506\n1.801\n1.8736\n2.381\n2.536\n2.6113\n2.0982\n2.3829\n3.585\n4.3751\n4.7347\n4.4206\n5.2813\n5.861\n5.8596\n5.7014\n5.2084\n2.9083\n2.7951\n2.7759\n2.991\n3.2093\n3.2523\n3.1853\n2.9547\n3.0046\n3.0311\n3.1136\n3.2856\n3.2533\n2.9682\n3.0465\n3.6765\n3.7713\n3.0568\n2.8696\n4.0326\n4.6964\n5.2438\n5.0891\n5.6092\n6.4784\n7.8985\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.476\n4.3992\n3.8423\n3.4698\n3.5863\n3.2927\n2.8745\n2.4631\n2.2707\n2.28\n2.2971\n2.6486\n2.3027\n1.819\n1.8809\n2.0761\n2.4914\n2.8115\n2.4876\n2.7898\n4.0367\n4.6335\n4.7436\n4.9512\n4.811\n5.2787\n5.3948\n5.2238\n5.1572\n2.4632\n2.4553\n2.3861\n2.9655\n3.2799\n3.3474\n3.5061\n3.5339\n2.9366\n2.9614\n3.0564\n3.0132\n3.1835\n3.1298\n3.2838\n4.2249\n4.4467\n3.8328\n3.4228\n3.2632\n4.1392\n5.2155\n5.2908\n5.7917\n6.8658\n8.3114\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7923\nNaN\nNaN\n4.1957\n4.2571\n3.6349\n3.5348\n4.3624\n3.6637\n2.681\n1.8002\n1.7263\n2.1389\n2.6327\n2.7813\n2.8032\n2.5865\n1.8493\n1.804\n1.3354\n1.8763\n2.759\n3.1473\n3.4571\n3.7271\n4.2791\n5.0125\n5.0885\n5.0854\n5.6372\n5.4242\n4.9734\n5.1271\n1.9144\n2.4085\n2.7572\n2.7939\n2.8621\n3.2239\n3.4457\n3.5056\n3.0523\n2.9351\n2.8664\n2.8223\n2.9005\n2.978\n3.5688\n4.2212\n4.3804\n4.1691\n4.0475\n3.783\n4.5293\n5.1219\n5.3834\n5.9194\n6.3092\n8.2511\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9196\n0.28365\n1.8647\n2.8356\n3.2934\n3.1081\n3.7538\n4.89\n3.296\n1.7928\n0.81791\n0.76864\n1.9462\n2.3915\n2.5426\n2.2658\n1.868\n1.8577\n2.0318\n1.7191\n1.6533\n2.71\n3.4727\n3.3691\n2.9531\n3.9362\n4.548\n5.009\n5.0483\n5.4638\n5.0937\n4.8547\n4.7963\n1.6824\n2.659\n2.8112\n2.7693\n2.6339\n2.9145\n3.186\n3.2707\n3.0707\n2.9581\n2.8509\n2.7974\n2.7195\n2.8406\n3.4565\n3.8957\n3.9858\n4.0788\n4.1521\n4.4369\n4.9676\n5.1379\n5.2868\n5.7192\n6.0114\n7.1399\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3841\n-0.16408\n2.1409\n2.9419\n2.8645\n2.9153\n3.7898\n4.2872\n3.1314\n1.7839\n1.2188\n1.1364\n1.6014\n1.7447\n1.7558\n1.5994\n1.2901\n1.6525\n1.8812\n1.724\n1.4697\n1.7939\n2.5183\n2.5571\n2.2924\n3.0377\n3.9389\n3.9778\n4.5933\n4.9541\n4.8002\n4.6041\n4.434\n1.7515\n2.3098\n2.5515\n2.8642\n2.8917\n2.9378\n3.252\n3.3131\n3.1415\n2.9873\n2.9994\n2.8819\n2.6198\n2.6828\n3.0455\n3.5126\n3.8426\n3.9948\n4.6565\n5.2977\n5.6986\n5.6865\n5.3476\n5.6801\n6.0037\n6.4974\n7.6001\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2047\n2.1867\n3.0492\n3.2097\n2.8872\n2.6212\n3.6587\n3.8297\n2.8048\n2.2644\n0.9371\n0.74854\n1.0976\n1.339\n1.4033\n1.1306\n0.72305\n0.78088\n1.0624\n0.92631\n1.2944\n0.92067\n1.4948\n1.6605\n1.9001\n1.9261\n2.9533\n3.7956\n3.9814\n4.0854\n4.7116\n4.8706\n4.3607\n1.7755\n1.8689\n2.3166\n2.6142\n2.7878\n2.6983\n3.0816\n3.184\n3.1854\n3.057\n2.8018\n2.6784\n2.7311\n2.8011\n2.9224\n3.3925\n3.8934\n3.9204\n4.7089\n5.1392\n5.6939\n5.7646\n5.4117\n5.554\n6.0299\n6.2398\n6.9402\n8.5091\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9557\n4.5988\n4.292\n4.4092\n3.4598\n3.0217\n3.4311\n3.4475\n3.3582\n1.727\n0.43968\n0.31093\n0.8649\n1.1375\n0.96233\n0.053762\n0.16195\n0.39275\n0.44785\n-0.33361\n-0.4054\n0.096413\n-0.046148\n0.66176\n1.0741\n1.563\n2.4601\n3.6792\n4.1812\n3.7082\n4.4127\n4.6225\n4.1627\n1.8777\n1.7234\n2.0197\n2.2381\n2.2915\n2.3246\n2.8908\n3.2352\n3.2014\n2.9224\n2.5499\n2.5169\n2.803\n3.1905\n3.3799\n3.7656\n4.1537\n4.2863\n4.9228\n5.5685\n6.1367\n6.0435\n5.4685\n5.35\n6.2687\n6.4272\n6.7814\n7.7971\nNaN\nNaN\n6.5866\n7.1781\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.7899\n5.343\n4.9078\n4.7274\n3.6644\n3.1434\n3.3556\n3.2675\n1.9487\n0.97683\n0.28064\n0.761\n1.2093\n1.4933\n0.90503\n-0.20939\n-0.018827\n0.23233\n0.44777\n0.019241\n-0.50064\n-0.2148\n-0.46256\n0.3061\n0.5987\n1.3726\n2.7289\n3.4369\n4.2819\n3.6495\n3.6484\n3.8139\n3.6487\n2.0512\n1.8035\n2.0742\n2.2134\n2.2391\n2.267\n2.9376\n3.4581\n3.5629\n2.9613\n2.9265\n2.9719\n3.1548\n3.8082\n4.0415\n4.2958\n4.5945\n4.811\n5.0996\n5.3882\n5.7558\n5.9378\n5.4808\n5.4152\n6.3858\n6.4697\n6.5002\n7.049\n7.1052\n7.161\n7.2182\n7.8406\n7.1455\n5.2174\nNaN\nNaN\nNaN\nNaN\n6.2348\n6.2182\n5.7414\n5.3426\n4.6811\n3.7511\n3.5061\n3.0468\n2.0754\n0.97941\n-0.20547\n-0.084247\n1.3432\n1.7921\n1.2573\n0.28668\n-0.41181\n-0.083836\n0.18222\n0.60321\n0.43447\n0.41632\n0.73177\n0.70577\n1.8232\n1.6236\n1.8938\n3.1354\n3.5669\n3.4257\n2.8674\n2.3823\n2.9234\n2.6677\n1.9742\n1.6909\n2.6633\n2.5526\n2.7406\n2.9245\n3.3065\n3.7382\n3.7058\n3.1645\n3.3184\n3.651\n3.6973\n4.1396\n4.1009\n4.0589\n5.0212\n5.5258\n5.4588\n5.4838\n5.3169\n5.6031\n5.2895\n5.3544\n5.7496\n5.9691\n6.0841\n6.0676\nNaN\n6.8941\n7.9951\n8.0528\n7.3598\n7.1744\nNaN\nNaN\nNaN\n5.9681\n5.7058\n6.1269\n6.015\n5.3201\n4.9178\n4.1049\n4.0088\n2.9928\n1.6591\n0.29325\n-0.07395\n0.26216\n1.8014\n2.3269\n1.0844\n-0.14203\n-0.62275\n-0.39862\n-0.35759\n-0.21787\n0.28016\n0.77426\n1.1848\n1.2477\n1.4045\n1.9164\n2.2389\n2.8636\n3.4207\n3.0336\n2.927\n2.4654\n2.6786\n2.9943\n1.3242\n1.7461\n3.1107\n3.1634\n3.2532\n3.4379\n3.6112\n3.5735\n3.1975\n3.3443\n3.3277\n3.4917\n3.8759\n4.0469\n3.7205\n3.4299\n4.4503\n5.2584\n5.3565\n5.4947\n5.2763\n5.0532\n5.1251\n5.241\n5.5399\n5.8001\n5.6211\nNaN\nNaN\n6.3862\n7.6461\n7.7237\n7.7649\n8.0085\nNaN\nNaN\nNaN\n5.8264\n5.5354\n5.4962\n5.9483\n5.3505\n4.9763\n4.5063\n3.7833\n2.3471\n1.4333\n0.79456\n0.58928\n1.6375\n2.3082\n2.385\n0.98159\n0.38985\n0.0027046\n-0.13597\n-0.6364\n-0.88499\n-0.20505\n0.25712\n0.49889\n0.50992\n0.77996\n1.8063\n2.1493\n2.6118\n2.8183\n2.9204\n2.766\n2.2933\n3.0596\n3.9531\n0.98122\n2.1923\n3.5913\n3.7283\n3.679\n3.7196\n3.437\n3.299\n2.6996\n2.8383\n2.8647\n2.8469\n2.7876\n3.3919\n3.2183\n3.1212\n3.6707\n5.0811\n5.0202\n5.0904\n4.9716\n4.8026\n5.0321\n5.3179\n5.4908\n5.4853\n5.3037\nNaN\nNaN\nNaN\n6.6392\n6.6006\n6.5396\n7.4834\nNaN\n6.8344\n5.7955\n5.6228\n4.8721\n4.3784\n4.8302\n4.6697\n4.5877\n3.7422\n2.2144\n1.3411\n0.66447\n0.84134\n1.3258\n2.6003\n2.7001\n2.3603\n1.3269\n0.9842\n0.61761\n0.39821\n-0.16222\n-0.34377\n-0.35478\n-0.51756\n-0.070477\n0.44359\n0.81823\n1.1766\n1.7365\n2.3508\n2.6482\n2.9118\n2.6566\n1.7344\n2.5021\n3.5202\n1.4889\n2.8577\n4.1304\n4.0415\n4.0454\n4.1981\n3.7162\n3.3702\n2.8828\n2.6327\n2.5278\n2.7662\n2.689\n2.9277\n2.9993\n2.9118\nNaN\nNaN\nNaN\n4.7558\n4.614\n4.7481\n4.7186\n5.1876\n5.3251\n5.2014\n5.1712\nNaN\nNaN\nNaN\n5.2122\n5.1853\n5.265\n5.4989\n5.4492\n5.2414\n5.0746\n4.2999\n3.6905\n3.0877\n3.3209\n3.4927\n3.5857\n3.3968\n2.5115\n1.5444\n1.0271\n0.85795\n1.2064\n2.3887\n2.4828\n2.1037\n1.2108\n1.0201\n0.96164\n0.67594\n0.67939\n0\n-0.45239\n-0.44515\n0.20856\n0.99031\n1.4992\n1.5729\n2.2964\n2.5782\n3.1066\n3.2918\n2.5545\n1.8116\n2.551\n3.2976\n2.2815\n3.3014\n4.0249\n4.0635\n4.0962\n3.8832\n3.7021\n3.4658\n3.1014\n2.7595\n2.7488\n2.9495\n3.0706\n3.5702\n3.2514\n3.1159\nNaN\nNaN\nNaN\n4.1706\n4.2621\n4.4538\n4.7866\n5.0171\n5.0591\n5.051\n4.9847\nNaN\nNaN\nNaN\n4.5135\n4.6143\n4.5884\n4.34\n4.6416\n4.8563\n4.9646\n4.0056\n3.2462\n2.8085\n2.6073\n2.8962\n3.4909\n3.1816\n2.787\n2.0853\n1.6263\n0.83594\n0.85937\n1.5246\n1.7781\n0.82482\n0.60148\n0.62349\n0.7995\n0.87004\n0.8843\n0.51\n0.365\n0.47234\n0.84942\n1.5754\n1.8472\n1.9949\n2.7659\n2.5522\n3.346\n3.4224\n2.8011\n2.4958\n3.1798\n3.5973\n3.2152\n3.5585\n3.8635\n3.9949\n3.8873\n3.3179\n2.941\n3.1447\n3.1904\n3.0402\n3.1051\n3.1149\n3.221\n3.6626\n3.8341\n3.6745\n3.2515\nNaN\nNaN\n3.7389\n4.2239\n4.3882\n4.6166\n4.7691\n5.0238\n4.9293\n4.7403\n4.7873\n4.3506\n4.345\n4.4262\n4.4488\n4.3341\n4.267\n4.7534\n4.9118\n4.6392\n3.7843\n3.0563\n3.0764\n2.9531\n3.1227\n3.2372\n3.1017\n2.8123\n2.3964\n2.393\n1.5634\n0.86349\n1.2539\n0.68788\n0.54661\n0.40189\n0.39667\n0.67417\n1.0299\n0.95889\n1.1491\n1.3779\n1.5941\n1.2805\n1.9127\n2.0579\n1.9565\n2.5983\n2.5827\n3.1193\n3.467\n3.3683\n3.0118\n3.4878\n3.8864\n3.839\n3.7847\n4.033\n3.992\n3.8441\n3.3045\n3.0228\n3.3041\n3.1742\n3.4141\n3.4183\n3.3831\n3.514\n3.8314\n4.0845\n3.9768\n3.8443\n3.4428\n3.2554\n3.6219\n4.2209\n4.2758\n4.467\n4.6196\n4.83\n4.8237\n4.795\n4.7064\n4.778\n4.9634\n4.7635\n4.3314\n4.3303\n4.7557\n5.0251\n4.8977\n4.1287\n3.3194\n3.5884\n3.5803\n3.5539\n3.5957\n3.3566\n3.4516\n3.4037\n3.1656\n2.983\n2.7064\n2.3159\n1.439\n0.58067\n0.26677\n0.57476\n0.72907\n1.6809\n1.4539\n0.96679\n1.1596\n1.6064\n1.7927\n2.002\n2.2675\n2.2939\n2.4038\n2.4448\n2.2432\n2.8383\n3.348\n3.352\n3.2168\n3.6869\n3.5832\n4.0744\n4.0734\n4.136\n3.8721\n3.8096\n3.5283\n3.3948\n3.798\n4.0652\n4.0691\n3.8726\n3.8943\n3.7419\nNaN\nNaN\nNaN\nNaN\n4.2296\n4.0717\n3.9575\n4.0393\n4.1057\n4.2278\n4.3184\n4.47\n4.6102\n4.8093\n4.5901\n4.5911\n4.7245\n5.0439\n5.0378\n4.2731\n4.3863\n4.1659\n4.2417\n3.4325\n3.1634\n3.7314\n3.7457\n3.4854\n3.4514\n3.513\n3.4067\n3.7332\n3.8427\n3.4573\n3.5008\n2.8115\n2.3144\n1.1647\n1.4008\n1.8515\n2.1529\n2.7424\n2.0073\n1.3854\n1.2\n1.5863\n1.886\n2.6571\n2.8321\n2.99\n3.0652\n2.6402\n2.2245\n3.0763\n3.5188\n3.3307\n3.1669\n3.4076\n3.2693\n4.3811\n4.4852\n4.3588\n3.8054\n3.8547\n3.7873\n3.7556\n3.9202\n4.0743\n4.3785\n4.4568\n4.3169\n4.3991\nNaN\nNaN\nNaN\nNaN\nNaN\n3.961\n3.9591\n3.9086\n3.9661\n4.2265\n4.2898\n4.2763\n4.398\n4.5958\n4.4164\n4.4466\n4.0892\n4.21\n4.1182\n3.6134\n3.366\n3.3929\n3.4211\n3.1361\n3.2555\n3.852\n3.9107\n3.4127\n3.4754\n3.6472\n3.3215\n3.5242\n3.6631\n3.6047\n3.5812\n3.0383\n2.6328\n2.35\n2.5303\n2.7416\n3.0964\n3.0949\n2.8543\n2.1866\n2.1695\n2.1616\n2.1346\n2.8839\n3.2356\n2.932\n2.9277\n3.2922\n3.2126\n3.6462\n4.211\n3.9367\n3.4794\n3.475\n3.3582\n4.6702\n4.6275\n4.2964\n4.106\n3.985\n4.2595\n4.1855\n4.0595\n4.0906\n4.43\n4.6142\n4.9055\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7266\n3.8685\n4.0446\n4.1519\n4.4967\n4.3204\n4.0689\n3.9709\n4.0381\n4.149\n3.673\n3.525\n3.2285\n3.2626\n3.0927\n3.14\n3.3227\n3.2632\n3.3204\n3.6074\n3.783\n3.5898\n3.8969\n3.8857\n3.4314\n3.321\n3.3503\n3.5018\n3.3959\n3.2193\n2.924\n2.9114\n3.0825\n3.2773\n3.371\n3.3574\n3.7227\n3.4823\n3.4837\n2.7133\n2.698\n2.9207\n3.0432\n2.3309\n2.5819\n4.185\n4.3481\n4.3172\n4.7262\n4.1758\n3.5022\n3.6746\nNaN\n4.787\n4.5793\n4.223\n4.4439\n4.1828\n4.4673\n4.3408\n4.2441\n4.2119\n4.4401\n4.4923\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0103\n4.0707\n4.0413\n3.9667\n4.4224\n4.5359\n4.0052\n3.6002\n3.8399\n3.8741\n3.5919\n3.4542\n3.2701\n3.3096\n3.0998\n3.0154\n3.323\n3.2573\n3.3518\n3.4316\n3.6729\n3.6722\n3.8252\n3.4841\n3.3746\n3.2745\n3.1728\n3.4583\n3.662\n3.3872\n3.1121\n3.1486\n3.4117\n3.845\n3.9605\n3.8416\n4.2421\n4.3519\n3.9198\n3.0366\n2.9677\n2.9182\n3.5162\n3.3752\n3.5019\nNaN\nNaN\n4.3411\n5.0587\n4.5757\nNaN\nNaN\nNaN\n3.1139\n1.8602\n2.2476\n1.1578\n1.456\n2.3818\n2.7412\n2.354\n3.3543\n3.812\n2.1952\n1.8387\n1.3217\n2.3877\n4.9054\n4.2283\n4.3653\n1.6891\n7.6016\n6.1231\n5.308\n4.3427\n5.0836\n2.9231\n4.1164\n1.3018\n-0.13621\n-2.1349\n-0.094297\n4.1518\n-3.7631\n-2.7092\n-2.7846\n-4.9698\n-2.2\n-2.4885\n-2.0032\n-0.99157\n-1.7083\n-2.0474\n-0.34501\n1.0736\n1.3918\n2.9612\n3.3118\n2.0176\n1.7798\n1.9068\n0.69267\n0.3613\n0.94305\n1.8762\n1.0959\n-1.1622\n2.1231\n-0.063322\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3006\n1.2854\n0.93264\n3.8624\n4.4005\n1.456\n3.5908\n2.0574\n5.1614\n4.5673\n3.2916\n2.7258\n1.2033\n4.1661\n6.0633\n4.6539\n0.038058\n0.1073\n3.011\n2.7243\n7.7143\n3.7681\n3.1929\n6.1641\n6.2386\n2.3405\n0.72601\n0.39441\n2.9365\n2.8515\n1.7635\n0.74107\n-0.20363\n-3.7594\n-3.318\n-2.8475\n-1.815\n-0.65747\n-1.7909\n0.33549\n2.0993\n1.4199\n2.0383\n2.46\n3.1233\n2.7046\n2.7959\n1.8556\n0.79484\n0.49069\n0.795\n1.6324\n1.5235\n2.3783\n5.8643\n2.4111\n-0.15081\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3672\n1.3281\n1.6662\n1.7085\n-1.1581\n-0.37416\n1.0694\n2.3693\n3.5058\n4.6176\n3.24\n2.2281\n1.4095\n6.0056\n9.1133\n5.587\n1.803\n1.9143\n1.5127\n5.5772\n4.2266\n4.4376\n5.4944\n5.5262\n7.2234\n4.9915\n3.3459\n2.8797\n3.5086\n0.72244\n2.8439\n2.236\n1.294\n-0.51552\n-3.0053\n-3.4864\n-1.6544\n-0.94997\n-0.35241\n0.21828\n1.5695\n2.089\n1.4644\n2.3075\n2.3922\n1.9768\n1.8649\n2.2504\n1.7784\n1.8406\n2.037\n1.2306\n1.9067\n3.5559\n3.914\n3.1889\n1.782\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3724\n1.4539\n1.813\n1.6717\n1.5267\n1.9206\n3.319\n3.1568\n2.1896\n2.0605\n2.8651\n0.42506\n3.9809\n8.1061\n5.9081\n2.024\n1.3968\n1.7521\n0.873\n4.9736\n5.717\n4.7686\n5.4855\n3.5185\n5.9208\n6.1482\n4.8349\n3.2208\n1.9517\n1.1817\n1.4294\n2.1755\n1.2869\n1.1719\n-1.8143\n-3.2595\n-2.8656\n-0.69328\n0.48319\n0.4194\n1.629\n1.8682\n-0.35497\n0.49046\n1.6572\n1.2336\n0.8174\n2.2132\n2.3147\n3.1092\n3.9417\n2.7401\n3.4216\n4.4484\n3.5083\n3.1288\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7684\n2.3359\n2.5685\n2.0779\n2.3371\n2.1694\n2.2096\n2.4146\n1.4076\n0.39099\n1.4148\n0.86833\n3.2196\n3.1021\n1.0386\n-0.94094\n-0.57141\n2.3036\n-1.7846\n1.8993\n5.5806\nNaN\nNaN\n-1.4268\n4.6178\n6.36\n6.3865\n3.4133\n3.5302\n4.0442\n0.82299\n0.8391\n0.64742\n1.3984\n-0.94111\n-1.8455\n-2.5315\n-0.2934\n0.8131\n0.59792\n1.0296\n1.8775\n-0.16416\n-1.0424\n0.22429\n0.90186\n1.5933\n2.8592\n2.5061\n1.9246\n3.4241\n3.6066\n2.7167\n4.5136\n3.8899\n3.9245\n8.9745\nNaN\nNaN\n-8.0312\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.505\n1.7955\n2.1415\n2.0003\n2.7081\n2.061\n1.8961\n2.1603\n2.1242\n1.9704\n2.5385\n3.5328\n3.4138\n0.46367\n-0.5616\n0.35251\n-0.6586\n1.2442\n-1.7207\nNaN\nNaN\nNaN\nNaN\n1.5016\n3.3019\n6.5398\n6.2886\n4.6224\n3.8415\n4.7566\n3.1856\n-0.28442\n-0.35478\n-0.75364\n-1.6817\n-3.4344\n-3.3891\n-1.2126\n0.05848\n0.69707\n1.263\n0.96809\n-0.16379\n-1.1136\n-3.2062\n-0.92454\n1.4147\n3.7315\n2.7468\n2.9696\n3.3275\n3.7626\n3.2323\n3.3322\n2.373\n-0.62805\n1.8487\nNaN\nNaN\n-12.594\n-6.1349\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6866\n0.73647\n1.3977\n2.9276\n3.9936\n2.5637\n3.9469\n4.0545\n4.2575\n4.1124\n3.8781\n3.137\n3.1796\n1.019\n1.2437\n-0.29586\n-0.98407\n1.9775\n-1.6179\nNaN\nNaN\nNaN\nNaN\n1.6045\n1.7192\n4.5174\n5.6475\n5.1438\n3.1595\n4.8273\n1.4149\n-1.5964\n-2.6236\n-2.388\n-3.4784\n-4.0482\n-3.8018\n-2.1612\n-1.2774\n-0.28709\n1.6623\n0.36352\n-1.3587\n-2.4063\n-5.8535\n-1.2871\n1.3715\n3.007\n3.7606\n4.2395\n2.9364\n3.6145\n3.692\n3.4445\n2.5982\n0.22148\n1.5591\nNaN\nNaN\n-6.3945\n-8.9474\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2869\n2.4151\n2.3639\n4.1303\n5.7713\n3.1256\n4.7347\n5.4237\n5.2067\n5.0386\n4.2036\n3.6394\n2.89\n3.5062\n2.6819\n1.3818\n1.041\n2.4649\n0.49149\n0.21942\n0.54211\n2.1427\n1.6649\n2.0364\n2.6555\n3.6107\n4.4673\n5.4785\n3.4533\n4.1337\n3.2409\n-0.73958\n-2.6798\n-3.0596\n-3.4252\n-2.6515\n-2.3877\n-2.7277\n-2.8649\n-2.8604\n-0.10236\n-0.36868\n-0.79525\n-2.3413\n-7.7802\n-1.2498\n2.0319\n2.7783\n3.0327\n4.6529\n3.0481\n3.6988\n4.9986\n5.5354\n5.5598\n3.3758\n1.8369\n2.9302\n4.1326\n0.32425\n-5.1508\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7383\n2.3424\n3.4602\n3.1934\n4.8732\n3.3447\n4.2777\n6.1736\n5.5057\n5.3056\n4.0678\n4.1369\n4.2994\n3.9495\n2.6206\n2.6068\n2.8325\n3.0475\n1.4435\n1.6331\n1.5215\n0.26635\n0.02881\n2.4016\n3.051\n3.6571\n4.2427\n3.715\n3.2082\n2.8096\n3.5088\n1.166\n-0.5185\n-2.6993\n-2.4085\n-2.3794\n-2.8045\n-3.4473\n-3.566\n-3.204\n-2.4151\n-1.7263\n-2.9403\n-5.0344\n-8.1434\n1.3558\n3.6821\n4.7544\n4.9058\n5.9747\n4.7299\n5.5409\n6.4068\n8.8177\n8.075\n4.0977\n1.6815\n2.6649\n6.3259\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.34811\n2.042\n3.4462\n2.4465\n4.3532\n3.0854\n3.4295\n3.9804\n4.3938\n4.4348\n4.5455\n4.5637\n4.8115\n3.8822\n3.3405\n3.9403\n3.6621\n3.2808\n2.3994\n3.4354\n3.0594\n2.2775\n3.3072\n2.3368\n1.4835\n3.6604\n4.7593\n3.045\n2.4142\n1.5715\n2.8202\n-0.57153\n-3.1965\nNaN\nNaN\n-1.9916\n-2.917\n-3.6309\n-4.8082\n-3.9375\n-2.9134\n-2.6014\n-3.9701\n-4.2438\n-0.77135\n4.0112\n3.9418\n5.4341\n5.3725\n6.4548\n5.7708\n8.1351\n8.5812\n9.1407\n7.6956\n4.8366\n1.7939\n0.60478\n6.8886\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2281\n0.1357\n1.7381\n1.4743\n2.651\n3.5371\n4.1435\n3.8445\n3.9803\n5.069\n5.3823\n4.7468\n6.6505\n5.8448\n5.3677\n5.7033\n3.8677\n2.7268\n2.6309\n4.0563\n2.6829\n3.1434\n4.0806\n1.0377\n0.16332\n4.1495\n5.2813\n4.5819\n2.5797\n2.5672\n1.5917\n-0.85295\nNaN\nNaN\nNaN\n-2.2005\n-3.2752\n-2.9648\n-6.4118\n-4.8891\n-2.5867\n-3.0147\n-4.4719\n-3.5177\n0.053133\n2.9475\n3.7988\n5.4989\n6.4744\n5.0981\n5.401\n8.0807\n7.667\n9.4336\nNaN\nNaN\n0.48754\n0.66309\n6.4993\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0073\n-0.50512\n0.10614\n1.6417\n2.604\n4.2214\n5.273\n5.0442\n4.6039\n3.9488\n4.0858\n4.6526\n7.3741\n6.9207\n5.1741\n4.6808\n4.1026\n3.557\n3.5194\n3.294\n1.448\n2.4475\n2.7422\n-0.71702\n-0.57498\n2.6315\n4.987\n6.2507\n4.817\n4.2676\n1.7479\nNaN\nNaN\nNaN\nNaN\n-2.7367\n-2.6495\n-3.7741\n-8.0443\n-5.2321\n-2.4883\n-3.8801\n-4.6521\n-2.4291\n-0.53384\n1.9481\n3.6956\n6.2114\n7.6697\n6.807\n6.135\n8.4974\n6.9062\nNaN\nNaN\nNaN\n1.8172\n0.046548\n4.9866\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4518\n1.0411\n1.1866\n1.7211\n2.8851\n4.0439\n4.3017\n4.4695\n3.9524\n3.9065\n5.2537\n5.7101\n6.1294\n6.1755\n5.0298\n6.3832\n6.6065\n4.751\n4.025\n2.8824\n2.0138\n1.47\n-0.93241\n-1.3303\n-0.89157\n0.4245\n6.7349\n5.4947\n3.5852\n4.4765\n2.8631\nNaN\nNaN\nNaN\nNaN\n-5.1474\n-4.1927\n-2.8146\n-5.9221\n-4.6523\n-4.4861\n-7.9314\n-9.0707\n-2.6589\n0.72994\n2.9516\n4.3586\n6.9388\n8.2482\n8.2516\n7.6585\n8.2971\n5.7752\nNaN\nNaN\nNaN\n-1.8831\n-1.8423\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8935\n1.6632\n1.4105\n2.3866\n3.6097\n3.5291\n3.6584\n3.3169\n2.9666\n3.0807\n3.7361\n4.9595\n5.5819\n5.4908\n4.997\n6.4839\n6.1386\n4.6373\n3.307\n2.5995\n2.6533\n-1.0288\n-3.7551\n-3.097\n-3.2746\n1.2177\n5.7216\n3.8815\n4.004\n4.4414\n3.3643\nNaN\nNaN\nNaN\nNaN\n-6.4021\n-6.6777\n-0.98942\n-4.7105\n-3.441\n-4.6047\n-6.9629\n-6.272\n-0.76847\n3.0274\n5.5413\n5.7046\n7.4569\n8.7303\n9.0864\n7.1836\n5.7693\n5.1706\nNaN\nNaN\nNaN\nNaN\n-1.6597\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1393\n1.3895\n2.7996\n3.8404\n3.8642\n3.3446\n3.616\n3\n2.5793\n2.2627\n2.4813\n3.6522\n5.2522\n5.43\n5.2105\n5.7452\n6.5407\n5.8894\n3.9968\n2.317\n2.8453\n-2.7286\n-7.3379\n-4.6714\n-3.7702\n2.0961\n4.1729\n3.9991\n5.0157\n4.8415\n4.8758\nNaN\n-5.7538\n-6.725\n-4.306\n-4.6985\n-5.0071\n-0.22602\n-3.2247\n-1.7776\n-1.7279\n-5.2215\n-6.2226\n2.6141\nNaN\nNaN\nNaN\n7.2678\n8.9807\n8.7979\n6.0113\n6.667\n6.9497\n2.5853\nNaN\nNaN\nNaN\n-2.0304\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9868\n1.6182\n2.9516\n4.7492\n4.6117\n4.0562\n4.2871\n3.4765\n2.7753\n3.2226\n2.8048\n3.5134\n4.5192\n4.698\n4.4422\n4.1217\n5.0782\n5.2632\n5.6851\n3.0671\n3.048\n1.1922\n-10.429\n-8.51\n-3.8029\n0.36813\n3.2833\n6.5781\n6.3118\n3.725\n1.2445\n-6.6464\n-4.2656\n-5.662\n-4.3515\n-2.6166\n-0.30533\n-0.33953\n-1.0728\nNaN\nNaN\nNaN\n-5.2996\nNaN\nNaN\nNaN\nNaN\n5.5855\n8.0043\n6.4425\n5.317\n4.9872\n8.2495\n3.9476\n1.7554\n-1.273\n-4.8424\n-2.5951\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8673\n2.1133\n3.8039\n5.2555\n4.3197\n4.4129\n4.2795\n3.4101\n2.7013\n3.8026\n3.3961\n3.3843\n4.0223\n4.217\n3.8312\n3.4876\n4.4673\n3.917\n5.0125\n4.6806\n3.0443\n-0.14555\n-3.9935\n-8.5721\n-1.6578\n1.648\n4.1219\n4.9421\n3.4297\n0.30894\n-1.8812\n-2.1705\n-0.45776\n-4.4813\n-1.2955\n-1.2147\n-0.050967\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.3158\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.2085\nNaN\n4.8914\n7.5653\n4.6885\n2.8532\n2.024\n-4.7383\n-3.3038\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1059\n2.4061\n4.8882\n5.6883\n4.274\n3.765\n3.8417\n4.0096\n2.4082\n3.5316\n3.53\n3.7077\n3.2875\n3.618\n4.376\n3.8774\n3.776\n2.4584\n3.1862\n3.0748\n1.7215\n-2.5109\n-4.9036\n-7.2671\n-0.26657\n1.9033\n4.2603\n1.7385\n0.60766\n-2.0591\n0.39124\n0.66918\n-3.5483\n-0.45122\n0.90052\n-0.14746\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5897\n3.1157\n0.47407\n-1.9181\n-2.0522\n-4.4629\n-3.4681\n0.015309\n1.9107\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4687\n3.0948\n4.9056\n5.6136\n5.1752\n2.9988\n3.436\n4.1206\n3.0215\n3.3716\n2.0614\n3.0589\n3.6473\n3.8483\n4.2653\n2.703\n1.3893\n0.63558\n1.348\n1.0272\n0.053549\n-2.7948\n-4.945\n-3.883\n-0.65192\n1.4564\n1.1459\n0.16878\n2.5491\n-1.1626\n-0.86294\n-1.5074\n-2.0641\n-0.91357\n0.70092\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19848\n1.7793\n0.63634\n-0.13133\n0.78739\n0.7105\n1.4759\n3.7927\n3.6828\n2.116\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1131\n3.4196\n3.461\n3.537\n5.6644\n4.2569\n5.2198\n4.0403\n2.2926\n2.9115\n2.1344\n3.2886\n3.8459\n3.5324\n2.5957\n1.2282\n-0.26076\n-2.8339\n-1.3524\n-0.70038\n-0.67353\n-5.2125\n-3.7412\n-1.0099\n0.84988\n2.0823\n-0.37677\n0.42992\n3.3541\n0.70622\n-3.7925\n-2.9428\n-2.3294\n-1.6943\n-0.056174\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.282\n-0.47988\n-0.086006\n0.78683\n3.2328\n4.4278\n2.1379\n1.975\n3.5007\n0.45977\n1.1601\n4.4449\n5.8047\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2128\n3.8159\n3.0136\n2.8815\n5.7219\n4.341\n3.2115\n2.7707\n2.2858\n3.34\n3.2458\n3.8463\n2.2437\n2.4389\n0.63127\n-0.070688\n0.70418\n-0.66025\n0.037684\n-0.3461\n-1.4638\n-5.4971\n-4.2033\n0.61671\n1.0681\n3.0018\n2.4623\n1.7216\n2.9087\n4.1681\n2.3207\n5.0154\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.80088\n-0.30885\n0.72713\n0.52709\n-0.43902\n0.9271\n4.1537\n5.1049\n2.9024\n4.8375\n5.2084\n4.4029\n4.0123\n5.181\n5.4541\n5.1971\n4.2184\n4.4777\n2.5577\n0.77276\n2.5471\n3.1959\n2.3232\n2.0957\n2.4995\n2.1653\n1.6122\n2.7968\n2.4853\n1.9863\n1.539\n1.7713\n2.1115\n1.6292\n-0.46669\n-0.0097396\n-0.042997\n-0.92387\n2.0778\n0.76384\n1.377\n-3.6018\n-4.9228\n-0.58665\n2.0735\n5.6399\n3.4358\n1.2324\n2.1139\n2.6401\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5306\n0.43029\n1.6845\n3.9243\n2.4805\n1.8886\n-1.2379\n2.1673\n4.507\n3.395\n6.7676\n7.6843\n5.4386\n5.2999\n4.3855\nNaN\nNaN\nNaN\nNaN\n4.4702\n4.0966\n2.4873\n2.0082\n1.1449\n0.6423\n0.59627\n0.60488\n1.1196\n2.0249\n2.564\n1.1973\n0.20143\n1.1677\n1.5613\n0.39169\n-0.34147\n0.77839\n0.86691\n-1.6094\n-2.4641\n1.0717\n0.80864\n-4.2243\n-4.598\n-1.9432\n2.8728\n3.112\n1.3022\n-0.43332\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0764\n2.5802\n1.8157\n0.99405\n3.5099\n4.7735\n3.6541\n3.6669\n1.2181\n1.5585\n2.0967\n3.4058\n6.2563\n8.6674\n6.1017\n6.1513\n4.7063\nNaN\nNaN\nNaN\nNaN\n4.2757\n7.0159\n1.781\n0.59139\n-0.98877\n-0.085943\n0.67489\n0.96159\n0.95579\n1.3638\n2.9557\n1.604\n0.14055\n0.72971\n0.702\n-0.40837\n-0.3044\n2.2546\n2.6875\n-1.2332\n-4.5047\n-5.8181\n-3.416\n-4.3065\n-4.2793\n-0.70634\n1.4953\n0.85327\n-1.7366\n-3.8984\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.733\n3.1714\n3.0294\n2.6345\n1.7804\n2.9374\n3.8609\n4.0624\n6.0649\n3.9652\n2.1666\n1.8333\n2.3654\n2.9178\n6.0605\n4.8457\n6.5387\n6.1915\nNaN\nNaN\n4.6694\n5.4649\n5.2414\n6.2015\n-0.0082312\n-0.82324\n-0.55033\n0.26517\n0.66957\n1.7652\n1.6999\n2.6089\n2.3673\n1.3971\n0.41776\n0.33139\n0.31957\n-1.3525\n-0.86988\n1.9708\n2.7067\n-1.7279\n-4.9652\n-5.9068\n-3.2431\n-3.0973\n-4.1943\n-0.76437\n0.80837\n0.63089\n-2.4209\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.168\n0.88722\n6.4331\n4.6537\n4.4181\n3.2599\n3.0104\n2.4188\n5.0944\n9.0503\n7.1984\n3.9592\n3.5697\n3.1632\n1.4515\n1.4099\n5.147\n6.8804\n5.5174\n3.8273\n1.981\n6.3596\n7.7673\n6.7939\n6.4865\n-1.3686\n-0.72138\n0.26429\n0.94212\n0.92314\n2.6397\n3.0382\n2.1752\n1.6437\n0.67128\n0.4927\n0.057034\n0.34177\n0.75017\n0.85936\n2.5976\n4.2552\n-1.4192\n-3.871\n-3.6393\n-2.0755\n-2.6439\n-3.4323\n-1.5941\n1.694\n3.2452\n-2.4087\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3156\n1.1374\n6.1486\n4.5268\n5.0444\n4.338\n1.6608\n0.92795\n3.7218\n9.0664\n8.8113\n6.8955\n5.3266\n5.1608\n2.7709\n2.847\n6.9201\n8.313\n5.7625\n4.788\n4.5913\n8.1901\n10.156\n9.5616\n6.4127\n-1.0098\n-0.13468\n0.65423\n2.0401\n2.3732\n2.9192\n3.0314\n1.9481\n0.86566\n0.62656\n-0.40666\n-1.2339\n0.52199\n1.9553\n2.1897\n3.5116\n1.8672\n-1.266\n-1.3109\n-2.7445\n-3.0841\n-2.4601\n-1.1588\n0.055435\n2.6378\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0861\n0.92764\n1.6267\n6.3655\n1.2933\n3.6688\n4.6413\n3.7283\n6.9759\n7.9777\n9.2944\n9.0295\n9.2666\n7.8252\n7.211\n3.5709\n3.445\n8.31\n7.8503\n8.1657\n5.6732\n6.7739\n9.2911\n9.7816\n8.8894\n6.0013\n-0.66739\n1.0866\n0.91783\n2.131\n2.199\n2.3133\n1.5868\n1.0362\n0.66202\n0.33605\n-0.14952\n-0.49023\n-0.15204\n1.256\n2.6979\n3.3434\n0.024302\n-2.226\n-2.5943\n-1.9506\n-3.4355\n-2.4529\n-1.6661\n-0.39158\n2.1282\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.9356\nNaN\nNaN\nNaN\nNaN\n0.28184\n0.93406\n0.79124\n1.3171\n5.7002\n1.2954\n4.937\n6.6475\n9.7598\n8.791\n9.1842\n9.0745\n3.701\n8.6647\n7.0577\n6.6687\n1.7473\n4.1571\n8.9096\n8.5422\n9.2111\n7.3736\n9.1137\n9.8826\n8.6135\n6.1356\n2.8847\n0.70942\n1.9915\n1.0519\n0.97771\n0.78433\n1.6025\n1.4436\n0.72654\n0.28882\n0.28127\n0.25849\n-0.19935\n-0.63392\n1.372\n0.82642\n1.3462\n1.2541\n-0.87347\n-3.6244\n-2.5092\n-2.2131\n-2.1556\n-2.7837\n0.4497\n4.3101\n8.9492\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1998\n1.9645\n-0.080081\n0.1748\n0.41828\n1.1541\n2.6687\n3.6024\n3.9942\n7.0935\n10.462\n8.6466\n8.6674\n5.7876\n-0.31384\n1.5237\n5.8428\n6.6214\n3.3025\n5.3998\n8.2613\n9.8401\n8.6236\n7.7391\n7.8974\n9.7814\n6.7967\n4.0054\n-1.6434\n0.90542\n1.0459\n0.87568\n0.48946\n0.36617\n1.3148\n1.3846\n0.61273\n-0.27885\n0.54472\n-0.1733\n-0.80426\n-1.2168\n0.37937\n0.84102\n2.3087\n1.7335\n0.024091\n-0.72628\n-5.0352\n-4.8734\n-1.9595\n-0.72281\n3.1331\n7.6843\n9.2393\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7677\nNaN\nNaN\n15.255\n1.9279\n0.83129\n0.76431\n0.75218\n-0.35668\n-0.93444\n-1.7537\n0.047595\n0.50399\n2.999\n6.8929\n3.5639\n2.0275\n3.1039\n1.1563\n0.098409\n-0.07814\n3.3666\n3.8427\n5.225\n7.2293\n7.3121\n6.7777\n7.2735\n8.0404\n7.0857\n4.7021\n3.5263\n-3.4344\n-0.24591\n0.094237\n0.38457\n0.45679\n0.12414\n1.1451\n1.0421\n0.76385\n-0.26191\n0.79494\n-0.069631\n-1.4643\n-1.7037\n-1.241\n0.23551\n0.033988\n-0.42958\n-1.4147\n-1.8905\n-3.2547\n-4.7621\n-1.3948\n0.98774\n5.9449\n8.0731\n11.914\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.2548\n17.831\n15.03\n10.737\n-0.20265\n-1.8273\n-1.2353\n2.3729\n0.034335\n-0.42062\n-0.9771\n-1.497\n0.59699\n0.94962\n1.4335\n-0.16743\n0.35417\n0.59161\n0.53308\n-0.17838\n-2.1153\n2.8268\n5.2239\n5.3667\n4.2555\n3.0304\n4.3749\n7.1691\n6.9306\n4.5503\n2.7966\n1.8481\n0.28309\n-1.1417\n0.75352\n0.96184\n1.5922\n0.71897\n0.45578\n0.2977\n1.3365\n0.57633\n0.18386\n-0.38696\n-2.2681\n-3.9515\n-2.7986\n-1.4642\n-0.79167\n-2.205\n-2.4415\n-1.5624\n-2.2895\n-3.1611\n-2.0845\n1.4986\n4.2646\n7.0301\n13.478\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n21.598\n9.3678\n9.3686\n5.8279\n-1.492\n-4.7969\n-2.7009\n0.10224\n0.2341\n-0.82804\n-2.8198\n-2.6263\n-0.20977\n0.93735\n0.81642\n-2.039\n-2.8204\n-0.83399\n-0.65231\n-1.8854\n-2.599\n0.68762\n3.4331\n3.3964\n2.9616\n2.0705\n3.5051\n3.6978\n5.7472\n3.2502\n1.3766\n2.0284\n3.5249\n0.36326\n0.56422\n0.46192\n2.0892\n0.56129\n0.32873\n0.018604\n0.54614\n0.22965\n-0.90534\n-1.8038\n-2.3668\n-3.587\n-2.9177\n-3.039\n-0.7337\n-1.4939\n-6.0935\n-5.7424\n-0.12263\n-0.23584\n-2.1215\n0.41087\n4.0756\n6.8817\n5.6729\n1.6186\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.523\n7.697\n3.9853\n3.1406\n-0.71575\n-8.5963\n-4.2542\n-1.1269\n-1.3043\n-0.86174\n-2.6353\n-2.5839\n-1.4488\n-0.84096\n-1.1757\n-1.3545\n-5.0237\n-2.6923\n-0.71127\n-0.21608\n-0.81135\n-1.5244\n1.0388\n1.6886\n2.7915\n3.4811\n2.969\n2.3333\n0.78636\n0.91404\n2.4291\n4.4576\n4.86\n1.224\n0.38302\n-0.17567\n0.97652\n0.68586\n1.3958\n-0.084907\n0.17824\n0.1197\n-0.19985\n-2.184\n-2.4688\n-2.0451\n-2.2043\n-3.3649\n-0.76219\n-0.80195\n-3.9753\n-5.2932\n-3.1939\n0.20961\n-2.1526\n0.15508\n4.3437\n7.0574\n3.0396\n-0.64376\n-4.972\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.549\n5.2716\n1.2129\n2.7855\n0.75692\n-6.8901\n-2.9452\n-1.407\n-2.1026\n-4.2282\n-5.9321\n-3.252\n-2.2133\n-2.0487\n-3.7867\n-2.5429\n-2.209\n-1.7241\n-0.12964\n-0.27064\n-0.38068\n0.26816\n-0.92576\n1.2296\n1.5767\n2.5341\n1.3516\n1.5557\n1.0568\n1.9604\n3.1498\n4.9592\n4.5859\n1.1229\n-0.26961\n-0.58326\n-0.032451\n1.6431\n2.1204\n0.55509\n0.98524\n0.32419\n0.18152\n-2.6306\n-2.2737\n-1.0221\n-2.1143\n-3.2066\n-1.71\n-0.33134\n-3.8343\n-3.383\n-6.2786\n-3.3833\n-2.9805\n0.2267\n2.7826\n5.4209\n3.9481\n-1.2701\n-1.3096\nNaN\nNaN\n1.5445\n1.5309\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0296\n3.2241\n2.971\n3.273\n-4.2003\n-7.3425\n-3.2606\n-3.8985\n-5.5209\n-7.713\n-6.3148\n-3.0806\n-1.8372\n-1.5272\n-2.093\n-2.5428\n-1.5901\n-1.0739\n-0.41801\n-0.99738\n-1.4309\n-1.1911\n-2.0457\n0.02908\n-0.40602\n-0.093873\n-1.7106\n-1.9009\n1.9741\n2.5466\n3.0578\n5.1075\n3.9827\n0.37288\n-0.25798\n0.53788\n-0.39692\n1.9987\n3.3293\n1.9685\n1.9405\n1.1175\n0.15963\n-1.8203\n-0.30225\n-0.67117\n-1.1723\n-1.6729\n-0.37894\n-0.45838\n-3.4993\n-4.6735\n-3.9932\n-3.5167\n-2.0347\n-0.46741\n2.2165\n-0.0033107\n-2.9419\n-2.5732\n2.2486\n1.8386\n4.432\n4.644\n2.7959\n5.1152\n5.9377\nNaN\nNaN\nNaN\nNaN\n-3.1126\n2.4411\n0.26854\n-0.083053\n0.20602\n-5.9956\n-9.6011\n-5.473\n-5.49\n-6.0467\n-7.7587\n-6.1625\n-1.2982\n-0.29258\n0.70697\n-1.9627\n-3.7818\n-2.9856\n-1.044\n-0.44962\n-0.060958\n0.072203\n-1.054\n-1.4164\n-1.0445\n-2.9512\n-1.5224\n-0.46471\n-0.19258\n-0.16805\n-0.70809\n1.4494\n3.7674\n1.5396\n1.1843\n0.46893\n0.55053\n0.46621\n1.8493\n3.7861\n3.3848\n2.6807\n1.642\n0.40119\n-0.56198\n0.64193\n0.46284\n0.35131\n-0.54158\n-1.8086\n-4.0755\n-8.6242\n-4.6832\n-2.3739\n-2.5202\n0.41438\n1.2442\n1.9152\n8.0422\n1.4526\n1.7184\n2.4851\nNaN\n0.97251\n2.6504\n-2.1276\n4.1791\n5.2836\nNaN\nNaN\nNaN\n-4.2602\n-0.59913\n2.8896\n1.3932\n-1.91\n-0.66984\n-4.1106\n-8.6892\n-5.5684\n-0.99762\n-2.7603\n-5.6856\n-4.4437\n-0.26722\n1.298\n0.54521\n-3.2835\n-3.1893\n-2.8973\n-2.2291\n-1.5909\n0.21428\n0.67624\n-0.60298\n-1.9082\n-2.3176\n-2.4843\n-0.47915\n2.0914\n1.9154\n-0.97913\n-0.82978\n0.97448\n2.6243\n1.0071\n0.59368\n0.9993\n2.0828\n2.1688\n3.0862\n4.1501\n3.8747\n2.4193\n0.84523\n-0.024131\n-0.19865\n0.29862\n1.1972\n1.2632\n0.12458\n-1.9927\n-4.347\n-8.6826\n-3.5897\n-2.5376\n-0.56676\n-1.4075\n2.9329\n1.7418\n3.0425\n2.0328\n1.1056\nNaN\nNaN\n-0.33354\n0.81488\n0.42989\n3.3326\n3.4858\nNaN\nNaN\nNaN\n-2.1087\n-1.1173\n3.0013\n0.86744\n-4.2004\n-3.3234\n-3.724\n-5.7975\n-2.0102\n0.012601\n-0.65062\n-4.1591\n-2.629\n-0.079086\n2.3217\n0.33042\n-1.7935\n-1.8694\n-1.9307\n-2.2726\n-1.9369\n-0.017854\n0.042174\n-0.56787\n-2.5413\n-2.9062\n-1.3934\n0.81008\n2.6975\n2.7912\n1.8753\n1.516\n1.3811\n2.7153\n3.4569\n0.40748\n1.2208\n3.5405\n4.1496\n4.5684\n4.5784\n4.3538\n2.5494\n0.69469\n0.098849\n-0.033473\n0.76386\n1.2599\n2.745\n2.179\n-1.3303\n-2.6032\n-7.9089\n0.19992\n-1.1761\n-1.322\n0.35058\n3.7163\n2.1496\n0.42681\n-0.4599\n0.22329\nNaN\nNaN\nNaN\n-1.5492\n0.51688\n2.1784\n2.1999\nNaN\n2.2657\n1.533\n-1.7272\n-1.4957\n2.8374\n1.0424\n-2.1497\n-1.4614\n-0.95553\n-3.0233\n-3.2466\n-2.7911\n-3.3093\n-2.621\n0.12112\n1.3374\n2.1609\n0.53893\n-1.7114\n-1.7934\n-1.1393\n-1.0637\n0.13816\n0.19278\n-0.030866\n-1.1895\n-2.2975\n-2.4517\n-1.0903\n1.425\n2.5987\n3.2181\n3.3771\n1.6205\n0.36092\n2.6474\n4.5656\n1.544\n1.7433\n4.2765\n4.9581\n5.7271\n5.1246\n4.1743\n4.0745\n1.9044\n0.93931\n1.1393\n2.2492\n3.0634\n5.2313\n6.7054\n1.6506\nNaN\nNaN\nNaN\n-0.58108\n-3.4304\n0.60474\n2.777\n-1.9254\n-2.1539\n-2.2243\n-0.66959\nNaN\nNaN\nNaN\n-0.93212\n0.14667\n-1.4027\n-0.053138\n0.7992\n1.7141\n1.2748\n0.24947\n-0.092639\n-1.6917\n-3.2888\n-2.7655\n-0.37512\n0.099267\n-1.2562\n-1.6802\n-0.7037\n-0.4956\n0.31333\n0.97288\n1.2966\n1.0607\n-0.18898\n-1.9303\n-1.4629\n0.16061\n1.9161\n0.62165\n0.36125\n0.34724\n-1.1499\n-1.2904\n-0.45488\n-0.089928\n1.5963\n2.1909\n2.7356\n3.6123\n2.2005\n0.9656\n2.0665\n4.2576\n3.4726\n4.2512\n5.1322\n4.7762\n5.0988\n5.4294\n4.8721\n5.0484\n3.5781\n2.0868\n2.6082\n3.5354\n5.735\n8.8273\n8.175\n2.7244\nNaN\nNaN\nNaN\n-1.0649\n-2.9149\n-4.8226\n-2.6788\n-1.943\n-0.069231\n-1.2571\n-0.8403\nNaN\nNaN\nNaN\n-2.5358\n-1.7153\n-2.0745\n-0.60887\n3.5509\n2.4348\n1.3939\n0.3804\n-0.54009\n-3.1809\n-3.658\n-3.5166\n-0.99727\n0.83772\n0.58544\n-0.89919\n0.12915\n-0.80922\n-0.83789\n0.34979\n1.0032\n0.096529\n-0.11795\n0.16411\n0.55829\n1.1359\n1.0614\n1.2177\n1.6497\n2.1505\n0.41756\n0.57674\n1.5445\n2.1786\n3.0264\n2.7927\n3.4038\n4.5112\n3.4079\n2.5669\n3.6203\n4.385\n4.6023\n5.4952\n5.7981\n5.149\n5.9737\n6.0435\n6.3642\n5.7112\n5.3768\n3.2086\n4.4428\n5.8227\n6.7766\n7.2704\n6.1871\n5.0344\n1.8044\nNaN\nNaN\n-2.6795\n-1.6749\n-1.7862\n-2.0803\n-1.1096\n1.0842\n0.15136\n-0.37999\n-0.27969\n-2.2908\n-2.7187\n-5.2021\n-4.2105\n-2.6522\n-0.55473\n4.1587\n2.8835\n2.7618\n1.491\n-0.51969\n0.065953\n-0.92421\n-0.67555\n-0.069438\n1.1504\n1.3976\n0.33972\n-0.12164\n-3.6887\n-4.0504\n-0.049378\n0.19835\n0.19218\n2.4733\n1.1713\n0.87861\n1.466\n0.67924\n1.7178\n2.5446\n3.5608\n1.9585\n1.0049\n2.2611\n3.3957\n4.6804\n3.9316\n3.4867\n5.477\n4.5963\n3.6413\n5.1007\n5.0267\n4.2822\n5.4452\n6.3514\n6.4672\n7.1649\n7.5454\n7.5881\n6.3965\n5.4981\n4.2956\n4.4947\n5.6568\n6.5093\n8.0559\n7.9475\n7.1543\n2.9376\n-0.94696\n-2.9861\n-2.5425\n-1.3285\n-0.55667\n0.016507\n0.021347\n1.536\n0.51478\n-0.26112\n-0.77314\n-0.6601\n-0.8098\n-2.1522\n-3.4968\n-3.1547\n-0.30168\n2.6101\n2.4679\n2.1255\n2.9581\n0.8016\n0.58588\n0.43127\n2.502\n1.9942\n2.6654\n3.1101\n3.2485\n0.34054\n-4.0265\n-6.794\n-1.9588\n-1.1226\n2.3504\n3.0701\n2.0444\n1.6617\n2.2454\n2.4227\n2.3978\n2.3851\n2.7263\n2.4302\n2.1654\n3.0766\n3.7856\n4.4828\n3.9486\n6.0188\n6.418\n4.9414\n4.6553\n5.059\n4.6932\n4.3212\n6.4473\n8.0727\n8.2746\n8.8454\n9.4942\n9.1205\n9.1475\n6.3412\n4.9692\n4.7\n5.0076\n5.1384\nNaN\nNaN\nNaN\nNaN\n1.5357\n-1.8102\n-2.3963\n0.73815\n-0.093836\n-1.2144\n-0.43611\n1.575\n1.0038\n0.76201\n0.91769\n0.30772\n-0.72935\n-1.2882\n-1.3372\n-1.5803\n-0.96839\n-0.41248\n0.29869\n1.4199\n3.5649\n2.4774\n2.7003\n2.2073\n3.5692\n3.6663\n2.0216\n2.5537\n4.7685\n4.0769\n-1.1344\n-1.9337\n-2.7115\n-2.1136\n-1.1823\n1.205\n1.2446\n1.8865\n3.0977\n1.2066\n1.1338\n1.9602\n1.6607\n3.6452\n5.2556\n5.4863\n3.8339\n3.647\n4.9058\n7.6928\n6.6915\n5.1483\n4.9177\n5.1479\n6.0172\n6.3246\n8.121\n9.8186\n10.007\n10.385\n11.026\n10.487\n12.197\n10.298\n8.4279\n6.1773\n5.4839\n6.6708\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30162\n-0.94032\n-0.35818\n0.23041\n-0.97455\n-0.38144\n0.80871\n1.3063\n0.81952\n1.0894\n0.19973\n-0.55257\n-0.94032\n-0.69438\n0.91578\n-0.34311\n-0.82016\n0.57789\n2.5992\n5.589\n5.3591\n5.05\n4.6114\n5.3178\n4.71\n-0.26424\n0.28619\n2.4657\n7.2254\n0.70869\n0.63097\n-0.41468\n-0.75366\n-1.3247\n-0.61079\n-0.67012\n0.8511\n2.0179\n0.7077\n1.6666\n4.0669\n4.0545\n5.4151\n6.5026\n4.7102\n4.4212\n4.8707\n6.7387\n8.8357\n7.5132\n6.5306\n4.6482\n5.4576\n5.8464\n8.2902\n8.5552\n10.665\n11.98\n12.252\n14.971\n13.847\n15.337\n13.284\n10.82\n7.554\n5.3128\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0671\n-1.2607\n-0.93447\n-0.94546\n-1.0672\n-1.0534\n-1.5354\n-0.53393\n-0.71326\n-0.022756\n0.090432\n-0.29806\n-0.1982\n0.52553\n0.78998\n0.57937\n1.0557\n2.5634\n5.5121\n7.8946\n5.6319\n5.7992\n5.641\n5.1462\n-0.48536\n-2.3769\n-1.1894\n5.0971\n2.2499\n1.4454\n0.27711\n-0.039044\n-0.4968\n-0.51279\n-0.70626\n1.4231\n3.6948\n0.64922\n1.5177\n5.4555\n4.7982\n5.201\n5.4627\n5.587\n5.8572\n7.3737\n7.8412\n8.0789\n7.512\n7.9143\n3.806\n3.864\nNaN\n10.396\n9.0008\n12.697\n14.855\n15.19\n17.272\n17.025\n17.556\n14.064\n12.292\n9.2357\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.60116\n-0.010972\n-0.73683\n-0.036651\n-0.67583\n-0.71331\n-0.82735\n-1.3626\n-0.91462\n0.13321\n0.31858\n0.37257\n-0.15846\n0.85367\n1.4967\n1.6878\n0.58324\n1.1446\n3.3395\n5.3424\n4.9974\n5.8318\n3.7272\n1.0163\n-1.2977\n-3.8287\n-3.956\n0.31916\n0.33393\n0.60209\n-0.85016\n-0.0069594\n1.4282\n2.7935\n2.8349\n3.6392\n3.9853\n1.8304\n2.6149\n5.3901\n2.9527\n4.8017\n2.9996\n4.1387\n8.0032\nNaN\nNaN\n7.3379\n7.7466\n9.1323\nNaN\nNaN\nNaN\n3.523\n3.1738\n3.1711\n3.0589\n2.971\n3.9602\n4.8766\n3.4209\n4.0015\n4.6437\n3.4702\n3.0981\n3.8348\n4.8079\n7.2691\n5.4717\n4.3172\n2.7957\n3.1437\n4.4205\n5.8121\n4.2875\n4.8597\n2.1107\n4.5466\n2.3966\n1.5589\n-0.012831\n2.421\n6.219\n-2.2882\n-1.9331\n-1.909\n-2.9218\n0.51398\n-0.30108\n-0.39245\n0.69792\n1.24\n1.5229\n2.8463\n3.5881\n3.1063\n3.8921\n3.7659\n2.3725\n2.4454\n2.1914\n1.063\n0.68361\n1.3187\n2.2929\n1.0576\n-1.3124\n1.5676\n-0.46854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4322\n2.9412\n2.3292\n4.862\n4.888\n3.2743\n6.3713\n3.2445\n4.7523\n5.1471\n3.71\n3.0917\n3.9287\n7.3751\n7.5552\n6.0118\n2.27\n2.5144\n3.6961\n3.7008\n5.4041\n2.851\n3.3328\n5.0061\n6.3119\n3.4032\n2.2623\n2.5627\n5.0382\n4.6724\n2.9819\n1.1661\n0.78596\n-1.5229\n-1.631\n-0.11404\n0.60313\n1.5763\n0.93693\n2.3351\n4.0622\n3.4835\n3.218\n3.2251\n3.5684\n2.9041\n2.9405\n1.8635\n0.90034\n0.70064\n0.80403\n1.492\n1.4259\n1.649\n4.7429\n1.7914\n-0.59583\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9613\n2.4506\n2.8756\n3.3919\n0.51112\n1.6725\n2.2321\n2.9264\n4.6856\n7.3136\n4.0629\n3.4831\n4.1778\n7.8543\n7.701\n5.2481\n3.2323\n3.7731\n3.7407\n6.2904\n4.9337\n4.4308\n5.2859\n4.9734\n7.0608\n5.2124\n3.951\n4.1428\n4.7853\n2.9362\n4.7958\n3.3287\n2.7306\n1.3309\n-1.1624\n-1.0184\n0.98409\n1.4878\n1.6493\n2.4757\n3.8824\n3.4209\n2.5958\n3.2004\n2.9652\n2.357\n2.0121\n2.2904\n2.4144\n2.1645\n1.768\n1.362\n1.9375\n2.4682\n3.1629\n2.8954\n1.396\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9053\n2.052\n2.8369\n3.0753\n3.3627\n3.3188\n4.0898\n4.0601\n3.3908\n3.2952\n4.3386\n2.7932\n4.4764\n8.0931\n6.7972\n3.317\n2.741\n3.7397\n2.5035\n5.8954\n6.2433\n4.8236\n4.813\n3.0624\n5.5854\n5.8868\n5.3045\n3.5687\n3.9693\n4.8222\n3.0185\n2.8012\n2.6413\n3.0093\n-0.17835\n-1.3978\n-0.68165\n1.3668\n2.0763\n1.9358\n3.2038\n2.9927\n1.1109\n1.8316\n2.4305\n1.9007\n1.4865\n2.6291\n2.8616\n2.9996\n3.3982\n2.4652\n2.783\n3.1625\n1.8748\n2.0769\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1106\n2.9927\n3.0341\n2.8627\n3.4553\n3.5351\n3.5071\n3.629\n3.2072\n2.0719\n3.244\n2.7714\n4.3918\n4.849\n3.0545\n1.7422\n1.2265\n3.8799\n0.99598\n4.4303\n6.344\nNaN\nNaN\n-0.39592\n4.1818\n5.9606\n6.1906\n4.2133\n5.2944\n6.3922\n2.5346\n1.4856\n1.6124\n3.1673\n0.21513\n-0.051733\n0.5339\n1.8269\n2.2265\n2.0433\n2.7528\n3.06\n1.1604\n-0.15971\n1.4445\n1.8434\n2.0316\n3.2648\n2.7952\n1.6311\n3.129\n3.283\n2.367\n3.4318\n2.2789\n1.7023\n5.8766\nNaN\nNaN\n-6.2338\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0834\n2.636\n3.0069\n2.8143\n3.3233\n3.1882\n3.5398\n3.8093\n3.8279\n3.5386\n3.9346\n4.6018\n4.106\n2.1187\n1.5488\n1.8333\n1.0087\n3.8227\n1.2511\nNaN\nNaN\nNaN\nNaN\n2.9489\n4.0438\n6.3102\n5.8027\n5.0883\n5.7219\n6.6342\n4.9373\n0.83603\n1.1566\n0.62642\n-0.21624\n-1.671\n-0.51431\n0.98855\n1.4583\n2.258\n2.949\n2.5207\n1.2196\n0.42822\n-0.54225\n0.47395\n1.7791\n3.7751\n2.5766\n2.4071\n3.1015\n3.544\n3.2422\n2.7254\n2.3105\n0.70102\n1.9667\nNaN\nNaN\n-11.227\n-3.9201\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3245\n1.6367\n2.1832\n3.2379\n3.8818\n2.9141\n4.6821\n4.5366\n4.6763\n4.7678\n4.5053\n4.2056\n4.1511\n2.6801\n2.7076\n0.91917\n0.70542\n4.1361\n-0.53329\nNaN\nNaN\nNaN\nNaN\n3.5673\n3.4107\n4.9508\n5.3486\n6.3077\n5.0872\n5.9664\n3.3016\n0.24949\n-0.8526\n0.25949\n-1.028\n-2.0966\n-1.9549\n-0.87603\n-0.64685\n0.67257\n3.2926\n2.203\n0.37585\n-0.7598\n-3.3396\n0.11511\n1.93\n3.1926\n3.4143\n3.4961\n2.6712\n3.4346\n3.7149\n3.3832\n2.7102\n0.97808\n1.3108\nNaN\nNaN\n-5.4526\n-6.9771\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7124\n2.617\n2.7151\n4.0619\n5.5166\n3.3406\n4.843\n5.4508\n5.8303\n5.8401\n4.7823\n4.3763\n3.7751\n4.574\n3.573\n2.8942\n2.9704\n3.7887\n1.3547\n1.8406\n2.3004\n3.5088\n3.086\n3.593\n3.6847\n4.0087\n4.3752\n4.8277\n4.2509\n4.8574\n4.544\n1.3117\n-0.46806\n-0.39244\n-0.63751\n-0.23623\n-1.0422\n-1.6145\n-1.7661\n-1.4035\n1.8025\n1.1644\n-0.10646\n-1.5322\n-5.6233\n0.087259\n2.4468\n2.5163\n2.3629\n3.6792\n2.6169\n3.2105\n4.4307\n4.4308\n4.2613\n1.9624\n0.45388\n1.0738\n2.2657\n0.13326\n-4.9236\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1198\n2.7146\n4.3266\n3.2953\n4.732\n3.4553\n4.0266\n6.1003\n6.0328\n5.7415\n4.8755\n5.11\n5.2319\n4.657\n3.236\n3.4054\n3.8683\n4.3374\n2.4291\n2.5872\n2.2656\n1.4471\n1.1863\n3.1178\n2.8415\n3.8857\n4.4342\n4.2722\n4.0361\n3.8293\n4.6575\n2.4391\n1.4639\n-0.72471\n-0.74861\n-0.50356\n-1.2568\n-2.0485\n-2.4216\n-0.87556\n-0.042866\n-0.0093993\n-1.0915\n-3.0302\n-6.2453\n1.8836\n3.2546\n3.8753\n3.032\n4.2901\n3.9258\n4.6246\n5.1463\n6.712\n6.0151\n2.3617\n-0.71241\n0.036156\n2.4314\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2089\n2.6146\n4.0352\n2.9619\n3.9757\n3.3038\n4.0889\n4.6566\n4.9842\n5.3554\n5.2839\n5.1003\n5.0411\n4.2154\n3.8141\n4.1834\n4.1986\n4.5792\n3.6148\n3.7921\n3.1304\n2.5685\n3.3479\n2.1822\n1.2379\n4.1617\n5.1419\n3.9328\n3.5706\n2.9432\n4.3822\n0.71823\n-1.9968\nNaN\nNaN\n-0.44965\n-1.8975\n-2.489\n-3.2669\n-1.8276\n-0.57246\n-0.73834\n-1.4796\n-1.5187\n0.39276\n3.6404\n3.2672\n3.9227\n3.7942\n5.4719\n4.8088\n5.9781\n6.269\n7.0645\n6.1127\n3.302\n-1.6304\n-1.9698\n2.0136\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17479\n0.91716\n2.2959\n2.4485\n3.3018\n4.0547\n4.6713\n4.4279\n4.5907\n5.3406\n5.7311\n5.0017\n6.1131\n5.1696\n4.8191\n5.4578\n4.5319\n3.9134\n3.7337\n4.7274\n2.656\n2.8222\n2.9067\n0.54932\n0.22965\n4.3658\n5.5849\n5.2819\n3.6953\n3.8453\n3.0561\n1.0123\nNaN\nNaN\nNaN\n-0.86699\n-1.1737\n-1.9663\n-3.9878\n-2.7692\n-0.97887\n-2.0138\n-1.5799\n-1.0082\n0.65152\n2.6049\n3.1525\n4.0417\n5.0276\n4.1116\n4.3772\n6.7955\n7.0213\n8.0442\nNaN\nNaN\n-1.5324\n-0.12403\n3.2486\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.68874\n0.22754\n1.0137\n2.6986\n3.2449\n4.6016\n5.5408\n5.5447\n5.3606\n4.7974\n4.7497\n4.7694\n6.3056\n6.4118\n4.9999\n5.5657\n5.2285\n4.4186\n4.4706\n3.5985\n0.98075\n1.9521\n1.9785\n-0.81119\n0.08841\n2.7407\n4.8413\n6.2706\n5.3529\n4.981\n2.2123\nNaN\nNaN\nNaN\nNaN\n-1.5639\n-0.37132\n-1.22\n-5.2735\n-3.2116\n-0.55651\n0.088417\n-1.3162\n-0.54366\n0.64851\n1.9435\n2.9312\n4.928\n6.2851\n5.5115\n4.7534\n7.7568\n6.6871\nNaN\nNaN\nNaN\n-0.43468\n-1.4469\n3.0234\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9033\n1.5972\n1.8022\n2.4122\n3.4405\n4.5824\n4.8929\n5.4984\n5.0557\n4.7503\n5.292\n5.7764\n6.4628\n6.4102\n5.2367\n6.7752\n6.7962\n5.0524\n4.5791\n2.9982\n1.568\n1.3027\n-0.79811\n-1.1724\n-0.053599\n1.2331\n5.9041\n5.1046\n3.7804\n4.0635\n2.3418\nNaN\nNaN\nNaN\nNaN\n-3.9193\n-2.4155\n-0.6899\n-3.4598\n-2.3768\n-1.6624\n-2.7666\n-4.1936\n-0.4841\n1.5273\n2.6366\n3.4345\n5.7447\n6.8727\n6.7023\n6.5042\n7.4071\n5.7186\nNaN\nNaN\nNaN\n-0.83479\n-1.4165\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5862\n2.3051\n2.0096\n2.6973\n3.8562\n4.0105\n4.206\n4.0077\n3.828\n3.7109\n3.867\n4.8862\n5.39\n5.2749\n5.1675\n6.2607\n6.2053\n4.9759\n3.6975\n2.8439\n2.615\n-0.98001\n-4.3592\n-3.0946\n-1.2374\n2.3497\n5.8175\n3.5856\n3.63\n3.5693\n2.4955\nNaN\nNaN\nNaN\nNaN\n-6.0588\n-7.0458\n-0.46297\n-2.4627\n-1.0041\n-1.9814\n-3.3755\n-2.8148\n1.0553\n3.4269\n4.7998\n4.648\n6.4529\n7.2958\n7.2854\n6.1323\n5.1106\n5.0581\nNaN\nNaN\nNaN\nNaN\n-0.4358\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6669\n1.92\n2.9551\n3.4717\n3.482\n3.6688\n4.4303\n3.5429\n3.2479\n2.721\n2.7731\n3.667\n4.8409\n4.8517\n5.0832\n5.4213\n6.2302\n5.6727\n3.6714\n2.3052\n2.9101\n-3.4305\n-7.6758\n-3.9396\n-1.6128\n3.3581\n4.3894\n3.3864\n4.0364\n4.1919\n4.4745\nNaN\n-4.5662\n-4.8543\n-2.9428\n-5.5561\n-5.0427\n0.81725\n-1.2023\n-0.17588\n-0.21998\n-2.2236\n-2.8511\n3.0759\nNaN\nNaN\nNaN\n6.6244\n7.7171\n7.3404\n5.3457\n5.8763\n6.4142\n3.4184\nNaN\nNaN\nNaN\n-0.083374\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3493\n1.8672\n2.9791\n3.9772\n3.6384\n3.6413\n4.19\n3.5269\n2.9999\n3.576\n3.269\n3.9329\n4.6948\n4.7764\n4.7864\n4.0789\n4.7713\n5.0372\n4.3635\n2.5853\n2.8164\n1.4091\n-8.5706\n-6.2188\n-1.4052\n2.3121\n3.5362\n5.2083\n5.0288\n3.6202\n2.0223\n-5.2093\n-3.6486\n-4.4042\n-2.4758\n-0.2227\n1.5965\n1.1699\n0.088257\nNaN\nNaN\nNaN\n-1.6395\nNaN\nNaN\nNaN\nNaN\n5.688\n7.3128\n6.0279\n5.3934\n5.9769\n7.208\n2.9252\n2.2278\n0.71313\n-1.2393\n-0.77209\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1\n2.1785\n3.4259\n4.4301\n3.3514\n3.6522\n3.8528\n3.2533\n2.9587\n4.0108\n3.5262\n3.7532\n4.5768\n4.1645\n4.0138\n3.4631\n4.402\n4.2818\n3.8528\n3.3693\n2.6789\n-0.025218\n-3.1805\n-6.2969\n0.2883\n2.0134\n4.5137\n3.8033\n3.278\n1.072\n-0.36908\n-1.0684\n-0.043004\n-4.2407\n0.58085\n0.68765\n1.8055\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3431\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.7442\nNaN\n7.3961\n7.2336\n3.7085\n2.8391\n2.9126\n-1.3784\n-1.7692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4523\n2.3972\n4.1393\n4.9933\n3.5991\n3.0655\n3.679\n3.9614\n3.2058\n3.8977\n3.9585\n4.4899\n3.917\n3.8941\n4.2982\n3.7823\n4.0526\n2.8578\n2.4521\n2.4799\n1.5962\n-2.0835\n-2.9425\n-4.4592\n0.11489\n2.1556\n4.6577\n1.6135\n1.2785\n-1.2081\n1.0278\n1.9414\n-2.1287\n0.9207\n2.4863\n1.9171\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.761\n3.5805\n1.5744\n-0.93817\n-0.8864\n-1.1325\n-0.55147\n2.0143\n2.4194\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.922\n3.3031\n4.4055\n4.9795\n4.7769\n2.9134\n3.4136\n4.0634\n3.3274\n3.7528\n3.0626\n4.3576\n4.7662\n4.5273\n4.6855\n3.1236\n1.5817\n0.19761\n0.48165\n0.68088\n0.14147\n-2.3653\n-3.2949\n-1.0736\n0.88447\n2.5996\n2.3055\n0.93802\n2.9102\n-0.19015\n-0.37779\n-0.357\n-2.6039\n-0.24455\n2.0192\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.744\n2.4082\n1.4895\n1.0209\n1.8699\n1.7352\n2.14\n4.4481\n3.8441\n3.0481\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.545\n3.5498\n3.3538\n3.4247\n5.4647\n3.9573\n4.6073\n4.0329\n3.2505\n3.7096\n3.3883\n4.7025\n5.1171\n4.6364\n3.6774\n1.9264\n0.62341\n-1.3471\n-2.0649\n-1.1858\n-0.15095\n-3.1841\n-1.5776\n1.3959\n2.892\n3.7107\n1.6657\n1.4532\n3.6951\n1.109\n-2.6238\n-1.8226\n-2.4796\n-2.9941\n0.69052\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2153\n0.51405\n1.0191\n1.8715\n2.9517\n4.2309\n3.3462\n3.8318\n4.1846\n2.9588\n3.6945\n5.2214\n6.8457\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6591\n3.8282\n3.0757\n3.1204\n5.64\n4.3176\n3.1614\n3.3032\n3.2779\n4.0522\n4.1456\n5.1553\n3.4549\n3.6359\n1.8411\n0.9915\n1.6571\n0.53015\n-2.4664\n-1.2044\n-0.32747\n-3.0188\n-1.4269\n3.6978\n3.1874\n4.2753\n3.5217\n1.7544\n2.886\n4.968\n4.1449\n8.2338\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0604\n0.57178\n1.7763\n1.5829\n1.1257\n1.1801\n3.4668\n4.7879\n4.0429\n4.9731\n5.2766\n4.8044\n4.8889\n4.4316\n5.1212\n6.202\n5.7199\n5.8451\n3.7423\n1.2638\n3.2557\n3.513\n2.6515\n2.5633\n3.1167\n2.515\n1.8976\n3.177\n2.9034\n2.5407\n2.4036\n3.1898\n3.8302\n3.2134\n0.74121\n0.96006\n1.3446\n0.58393\n-2.1821\n-0.51311\n0.95752\n-1.4186\n-2.1961\n2.6399\n3.7584\n6.1152\n4.5289\n2.144\n3.134\n4.3383\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5959\n1.7345\n2.3872\n4.1127\n2.8809\n2.4945\n0.61657\n3.4973\n5.4587\n4.9599\n6.3461\n6.561\n5.547\n5.2314\n3.3586\nNaN\nNaN\nNaN\nNaN\n4.4099\n4.046\n2.957\n2.053\n1.4093\n1.1262\n0.73872\n0.92108\n1.7315\n2.458\n3.1461\n1.7359\n1.0607\n2.2054\n3.2635\n2.1862\n1.1213\n1.4498\n1.4831\n0.29056\n-1.7518\n0.29217\n0.63756\n-1.5914\n-1.9186\n1.1524\n3.955\n4.6246\n3.0514\n1.0879\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5611\n2.5928\n2.1616\n2.2798\n3.6574\n4.0935\n3.2851\n3.7407\n2.8728\n3.1895\n4.1867\n5.0403\n6.0306\n7.6067\n6.3381\n6.515\n4.6917\nNaN\nNaN\nNaN\nNaN\n4.8643\n7.6156\n2.0899\n0.82987\n-0.13586\n0.2452\n0.72996\n1.4818\n1.884\n2.5346\n3.5673\n2.3469\n1.3876\n2.5079\n2.0365\n0.8691\n0.89816\n2.4747\n2.6597\n0.22406\n-1.8684\n-3.0133\n-1.2946\n-1.4268\n-0.7271\n2.2457\n3.8249\n2.6393\n-0.16271\n-1.9697\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.004\n2.8776\n2.3841\n2.3747\n2.7663\n3.6799\n3.9002\n3.7023\n5.2148\n3.8896\n2.9347\n3.6578\n3.8118\n3.4733\n6.2837\n5.6832\n5.8824\n5.9913\nNaN\nNaN\n4.3532\n4.8178\n5.6151\n6.4339\n0.95864\n-0.33818\n-0.097614\n0.4858\n0.77665\n2.1686\n2.5179\n3.2061\n3.0689\n2.3026\n1.8807\n1.8666\n1.6798\n0.12627\n0.3412\n2.333\n3.0222\n-0.67408\n-1.9287\n-2.4665\n-1.0172\n-0.32708\n-0.9699\n2.0591\n3.1671\n2.4037\n-3.1555\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.87557\n2.5184\n4.8417\n3.1494\n3.7267\n3.5491\n3.5391\n2.4699\n4.1672\n6.6662\n5.4841\n3.9031\n4.1799\n4.0344\n3.0697\n3.1965\n4.9638\n5.7073\n5.2219\n3.8569\n1.7846\n5.4372\n6.0829\n5.9625\n5.3452\n-0.47015\n-0.43886\n0.25532\n1.2334\n1.5534\n3.2101\n3.8774\n2.669\n2.628\n2.1723\n1.8328\n1.4351\n1.7393\n2.2013\n1.857\n2.781\n3.9169\n-0.034954\n-1.1915\n-0.91847\n0.034339\n0.054971\n-0.24175\n1.2697\n4.2128\n5.2505\n-2.4125\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.46142\n2.331\n4.2368\n2.4123\n3.2128\n4.1633\n2.9975\n1.3256\n2.8222\n6.5121\n7.0269\n5.9224\n4.9325\n4.6634\n3.8734\n3.9537\n5.1379\n6.1629\n5.0474\n5.1051\n4.3152\n6.3124\n7.352\n7.5682\n5.2021\n-0.59704\n0.27731\n1.0061\n2.2914\n2.5044\n3.5257\n3.6482\n2.5698\n2.1433\n2.1234\n1.2226\n0.42246\n1.7092\n3.339\n2.9318\n3.0824\n2.0113\n0.1933\n0.30931\n-0.55668\n-0.65018\n-0.26068\n0.92046\n2.3796\n4.6053\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9531\n1.9315\n1.9914\n4.0265\n-2.7489\n0.031503\n4.167\n4.0733\n5.9107\n6.2002\n5.3084\n6.1711\n7.8051\n6.6313\n5.5991\n3.9165\n4.5834\n6.2902\n5.7369\n6.3209\n5.361\n6.0562\n6.9673\n7.9121\n8.0016\n5.301\n0.19846\n1.8506\n0.90287\n2.2175\n2.5651\n2.9567\n2.5787\n2.015\n1.9276\n1.8515\n1.2629\n0.74901\n1.2383\n2.8725\n3.022\n2.4508\n0.34197\n-0.91181\n-0.76774\n-0.43819\n-1.5104\n-0.61628\n0.28499\n1.715\n3.8823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.213\nNaN\nNaN\nNaN\nNaN\n4.1016\n4.2617\n2.84\n2.7003\n5.4486\n-1.2147\n1.5905\n4.697\n7.1697\n6.7977\n7.4011\n5.6508\n1.4658\n7.2397\n5.9326\n5.4861\n2.9924\n4.5989\n7.1509\n6.2327\n6.8818\n6.7287\n7.6877\n7.6901\n7.5872\n6.4126\n3.4574\n1.6441\n2.5154\n1.3703\n1.1643\n1.4683\n2.4832\n2.762\n1.6893\n1.1179\n1.3436\n1.4513\n1.2668\n0.94399\n2.9529\n1.6162\n1.4957\n1.046\n-0.61388\n-2.4775\n-1.2078\n-1.5454\n-1.3553\n-1.0626\n2.117\n5.393\n8.5047\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.3441\n5.291\n2.9535\n3.1203\n2.1355\n2.721\n3.5603\n3.7046\n3.4105\n5.3979\n7.2783\n6.2712\n6.9914\n4.3976\n0.60186\n1.565\n5.3036\n6.3222\n3.6799\n5.1672\n6.9335\n6.8814\n6.6516\n6.5668\n6.7917\n8.4092\n6.6153\n4.2394\n-1.672\n1.6642\n1.8129\n1.4608\n1.0175\n1.2355\n2.4979\n2.447\n1.4656\n0.23809\n1.6913\n1.3425\n1.012\n0.80859\n1.8069\n1.3902\n1.7907\n1.8881\n0.29209\n-0.085456\n-2.9519\n-3.2135\n-1.0932\n0.38702\n4.1853\n7.4931\n8.5532\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.2103\nNaN\nNaN\n21.102\n7.1269\n4.7885\n4.4392\n4.0349\n1.9411\n1.1985\n0.1831\n1.0561\n1.1033\n2.9522\n5.1896\n4.1276\n3.6606\n3.8051\n2.3012\n0.35005\n0.27107\n3.8671\n3.7177\n5.21\n5.5527\n5.3154\n5.2423\n5.8438\n7.1501\n7.2671\n5.5516\n3.9814\n-2.6311\n0.76017\n1.1944\n1.1679\n1.405\n1.2207\n1.9718\n1.7687\n1.5192\n0.51758\n1.7576\n1.2235\n0.54585\n0.33402\n0.43386\n1.2632\n1.201\n0.55721\n-0.4447\n0.0061584\n-0.77559\n-2.3063\n-0.13923\n1.5492\n5.7187\n7.3501\n10.419\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.425\n22.758\n19.662\n14.901\n5.3853\n3.666\n2.4814\n4.6456\n2.7589\n1.8779\n0.85061\n0.17885\n1.7088\n1.7209\n2.1872\n1.121\n1.9457\n1.8105\n1.2194\n0.60148\n-0.38888\n3.0368\n4.7172\n4.969\n3.6513\n2.9901\n3.6779\n5.7542\n6.2336\n5.1164\n4.0952\n3.2255\n1.4233\n0.051308\n1.6622\n1.8663\n2.2581\n1.5296\n1.095\n0.85416\n1.6349\n1.3435\n1.1127\n0.75714\n0.1802\n-0.51111\n-0.46553\n0.41718\n0.58402\n-1.0524\n-1.7076\n-0.39119\n-0.56402\n-2.6755\n-1.8755\n1.7636\n4.5105\n6.5533\n11.731\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n26.759\n15.368\n14.448\n11.151\n3.8983\n-0.63844\n1.3121\n2.6859\n3.0697\n2.1576\n0.84576\n0.6484\n1.1697\n1.667\n1.8977\n0.34962\n-0.35441\n0.85362\n0.12192\n0.082291\n0.83083\n1.6917\n3.9815\n3.7234\n3.4426\n3.1516\n3.1517\n3.8623\n5.389\n3.054\n3.2771\n3.7155\n4.4144\n1.4575\n1.8917\n1.753\n3.0213\n1.3032\n0.96499\n0.62544\n0.99801\n1.006\n0.51532\n0.048209\n0.019786\n-0.33085\n-0.81231\n-1.174\n-0.0024414\n-1.4119\n-5.061\n-4.4012\n1.1496\n1.2384\n-0.94453\n1.3805\n4.6472\n6.9236\n8.0819\n3.9962\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n24.49\n13.691\n9.4416\n7.3211\n3.4454\n-3.2057\n0.022666\n1.8188\n2.2398\n2.8411\n0.79844\n0.32414\n0.62392\n0.79264\n1.242\n0.70439\n-2.9789\n-0.7333\n0.32078\n1.1743\n1.8163\n0.5978\n2.2866\n2.4825\n3.7181\n4.464\n3.2431\n2.7427\n0.76981\n1.1855\n3.7298\n5.446\n5.2041\n2.2609\n1.4051\n0.85179\n1.8489\n1.2433\n1.6521\n0.33622\n0.45708\n0.81365\n0.75062\n-0.42036\n-0.014517\n0.36655\n-0.60837\n-2.1584\n-0.46357\n-1.5825\n-4.4104\n-4.9846\n-1.7167\n1.6956\n-0.76168\n1.7366\n4.8369\n7.322\n0.86518\n-0.86984\n-1.0031\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.543\n10.36\n5.8275\n6.3587\n3.6652\n-3.4238\n0.051896\n1.2317\n1.5822\n0.65825\n-1.7423\n-0.90773\n-0.67991\n-0.81594\n-1.126\n-0.5379\n-0.46263\n-0.026292\n1.3387\n0.94944\n0.87174\n1.8046\n0.56098\n2.1739\n2.1111\n3.4368\n1.6507\n2.0805\n1.5978\n2.576\n3.6085\n5.3163\n4.4359\n2.0578\n0.64793\n0.07263\n0.55258\n1.9179\n2.1725\n0.93168\n1.3099\n0.63222\n0.90839\n-1.0594\n0.14835\n0.83253\n-0.82187\n-1.5911\n-1.218\n-0.99958\n-3.936\n-3.4152\n-3.4097\n-1.3648\n-1.1741\n1.5638\n3.9843\n6.6538\n4.3882\n-0.69021\n1.653\nNaN\nNaN\n4.5224\n5.3651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.4686\n7.0052\n6.5193\n6.0026\n-3.0365\n-6.2612\n-0.11072\n-0.13809\n-1.5069\n-1.7639\n-2.3298\n-1.1442\n-0.30508\n0.39395\n0.35665\n-0.77706\n0.17214\n0.66981\n1.2119\n0.63871\n-0.31952\n0.254\n-0.47378\n0.71411\n0.37221\n1.2222\n0.29688\n0.018852\n3.3474\n3.6223\n3.5195\n4.7873\n2.749\n1.5197\n0.89443\n1.0692\n0.69079\n2.3204\n3.3585\n2.121\n1.8476\n1.052\n0.58583\n-0.31018\n1.2546\n0.29668\n-0.4706\n-1.0097\n-0.16781\n-0.21894\n-2.1846\n-2.3785\n-1.733\n-0.95527\n0.26044\n1.4856\n3.3417\n4.2959\n2.8685\n1.1252\n4.4597\n3.7043\n5.7537\n6.8921\n6.4542\n8.1954\n8.789\nNaN\nNaN\nNaN\nNaN\n1.4803\n6.157\n3.2271\n2.8259\n2.5738\n-3.5885\n-5.5694\n-1.355\n-1.3426\n-1.8597\n-3.1221\n-2.5146\n0.72066\n1.6212\n2.1638\n1.1781\n-1.4996\n-1.4227\n0.052385\n0.92914\n0.79238\n0.88578\n0.27815\n-0.1705\n0.11478\n-1.3012\n-0.46399\n0.23882\n0.59663\n1.5631\n0.70713\n1.6662\n3.652\n1.4874\n2.4433\n1.913\n1.7924\n1.8113\n2.2234\n3.7944\n3.2011\n2.5288\n1.6247\n0.78457\n0.67908\n1.8245\n0.67716\n0.77141\n-0.28695\n-0.61033\n-2.5312\n-7.0235\n-2.4626\n-0.16655\n0.53841\n2.0853\n2.1268\n3.1032\n5.7206\n1.3929\n2.7176\n4.2045\nNaN\n1.8717\n5.1521\n1.6507\n6.9974\n7.8463\nNaN\nNaN\nNaN\n0.7832\n3.253\n6.1261\n5.0511\n0.9267\n1.8528\n-1.2247\n-3.8595\n-0.67299\n2.0417\n0.27973\n-1.0818\n0.14482\n1.9241\n2.8297\n1.9609\n-1.0195\n-1.6925\n-2.0513\n-1.4168\n-0.80766\n0.46718\n1.3033\n0.9273\n-0.13029\n-0.78763\n-1.4933\n0.1104\n2.2155\n2.0659\n-0.39065\n0.1108\n1.6105\n2.1885\n1.6139\n2.103\n2.3087\n2.8547\n2.5132\n3.0749\n3.957\n3.3297\n2.0176\n0.65248\n0.33379\n1.0486\n1.2495\n1.3704\n1.9445\n-0.12609\n-1.3844\n-2.9892\n-8.0612\n-2.1008\n-0.31326\n1.5612\n1.0384\n3.3635\n2.6002\n4.969\n2.8086\n2.8633\nNaN\nNaN\n-0.21801\n3.7546\n3.2906\n4.5156\n4.4854\nNaN\nNaN\nNaN\n2.2667\n2.9898\n6.1834\n5.2703\n-0.94361\n-0.65926\n-0.28273\n-1.3853\n1.3416\n2.6651\n1.6041\n-0.06929\n0.85416\n1.8676\n3.4689\n1.9026\n-0.19978\n-0.89279\n-1.2305\n-1.5581\n-1.5422\n-0.03567\n0.37853\n0.025916\n-1.029\n-0.99396\n-0.56053\n1.1008\n2.6365\n2.6066\n1.9749\n1.6944\n1.5644\n2.6329\n3.633\n1.2617\n1.9304\n3.8884\n3.7029\n4.0191\n3.8139\n2.7823\n1.5447\n0.48343\n0.3803\n0.96509\n1.3332\n0.78564\n2.4671\n0.84623\n-1.6085\n-0.90407\n-7.5115\n-0.7113\n-2.8137\n-0.62625\n1.7967\n3.5245\n2.3323\n4.1668\n2.1727\n1.7348\nNaN\nNaN\nNaN\n2.1709\n3.7531\n4.7342\n4.3195\nNaN\n5.2498\n4.5565\n2.0695\n2.4066\n5.1805\n3.2913\n0.8603\n1.2316\n1.6648\n-0.61314\n-0.58785\n-0.24818\n-1.1551\n-0.6379\n1.9639\n2.5862\n3.1218\n1.9276\n-0.2573\n-0.65276\n-0.52047\n-1.073\n-0.049768\n-0.0018387\n-0.25252\n-0.43625\n-0.93764\n-0.91661\n-0.21549\n1.6014\n2.88\n3.1721\n3.1252\n1.587\n0.45656\n3.5676\n5.1426\n2.3042\n2.0593\n4.2883\n4.2333\n4.3457\n3.785\n2.6333\n2.3779\n1.3786\n1.1282\n1.497\n1.9249\n2.2228\n3.5309\n3.3558\n-1.3728\nNaN\nNaN\nNaN\n-2.4407\n-2.2863\n1.3058\n3.3702\n0.74767\n0.65176\n-0.1097\n1.595\nNaN\nNaN\nNaN\n1.7488\n3.4623\n1.7255\n3.0562\n3.7233\n4.1945\n4.0319\n3.2035\n2.7484\n1.6898\n0.31531\n0.67009\n2.5272\n2.5123\n1.1344\n0.58521\n0.90269\n-0.036878\n0.27721\n1.6529\n1.8736\n1.5905\n1.0745\n0.071015\n0.050877\n0.58879\n0.18092\n0.33201\n0.1534\n0.16279\n-0.48687\n-0.35505\n0.1852\n-0.030176\n1.4945\n2.4461\n3.0907\n3.3636\n1.8545\n1.0258\n2.9029\n4.4598\n3.0777\n3.3551\n4.6269\n3.3755\n3.0186\n3.3044\n2.6667\n3.2219\n2.7994\n2.1964\n2.7253\n3.024\n3.8897\n5.5017\n5.6373\n1.1422\nNaN\nNaN\nNaN\n-1.7034\n-1.6133\n-2.2964\n-1.3675\n0.1541\n1.0108\n0.22745\n0.94723\nNaN\nNaN\nNaN\n0.21663\n1.3306\n0.64409\n1.8159\n4.9641\n3.969\n3.7721\n2.4962\n1.3231\n0.45477\n-0.2126\n0.050997\n2.0438\n2.8295\n2.2266\n0.23829\n0.72094\n-0.98923\n-1.8451\n0.071253\n1.0208\n0.66876\n1.107\n1.435\n1.7046\n1.5393\n0.82229\n1.6535\n1.354\n1.5368\n0.41756\n0.8175\n1.6973\n1.6793\n2.4104\n2.7907\n3.0772\n4.0421\n3.0658\n2.4678\n3.6049\n4.302\n3.5978\n4.5346\n4.4275\n2.5807\n2.772\n3.2321\n3.5479\n3.4857\n4.1213\n2.9235\n3.2149\n3.5512\n4.1211\n4.2849\n4.3829\n3.2772\n0.86342\nNaN\nNaN\n-1.6942\n-1.1194\n-1.0035\n-1.0798\n-0.035031\n2.0061\n1.7319\n1.5808\n1.4139\n-0.16427\n-0.54202\n-1.6183\n-1.018\n-0.23994\n1.4222\n4.8328\n4.1567\n4.6421\n3.1543\n1.2739\n1.9841\n1.6521\n1.8239\n1.8055\n3.0272\n2.871\n0.9553\n-0.039529\n-4.1794\n-5.3724\n-0.1897\n0.09392\n0.71217\n2.5825\n2.1497\n2.3579\n2.3013\n1.4617\n2.2323\n2.4717\n3.0987\n1.9905\n1.372\n1.9046\n2.3736\n3.6659\n3.6501\n3.6416\n5.1689\n4.6672\n3.6733\n4.5498\n4.4913\n2.9966\n3.7351\n3.8542\n2.939\n2.9857\n3.6307\n4.3995\n4.2824\n3.6073\n3.4005\n3.0329\n3.2918\n3.72\n4.2815\n5.3133\n5.3372\n0.70218\n-1.9883\n-3.4808\n-2.3754\n-1.0643\n-0.2425\n1.4371\n0.73937\n1.9789\n1.6524\n1.2415\n0.54386\n0.87949\n1.0981\n0.52611\n-1.2818\n-1.0296\n1.6952\n4.0592\n4.0435\n3.7133\n3.8599\n2.2995\n2.0326\n1.9829\n3.6416\n3.0195\n3.2631\n3.6918\n3.4813\n0.49889\n-3.6595\n-6.1244\n-1.3999\n-1.0243\n2.6068\n3.0233\n2.4504\n2.9203\n3.2145\n3.0011\n2.6034\n2.476\n2.475\n2.7494\n2.3403\n2.3888\n2.9808\n3.7281\n3.753\n5.381\n5.9535\n5.5977\n5.1162\n4.6146\n3.9978\n2.8786\n4.0524\n4.5029\n4.2596\n3.7586\n4.1358\n5.1613\n5.9412\n4.127\n3.669\n3.0201\n2.8456\n2.7212\nNaN\nNaN\nNaN\nNaN\n-0.20479\n-1.3582\n-1.4833\n0.05612\n-0.12791\n0.041986\n0.24855\n1.2936\n1.8175\n1.9024\n1.971\n1.6503\n1.1625\n1.0749\n1.2086\n0.94027\n1.4664\n1.95\n2.5106\n3.0116\n4.4284\n3.3419\n3.187\n3.0341\n4.0593\n3.8963\n2.9402\n2.7086\n3.9391\n3.9965\n-0.50627\n-1.3402\n-2.3151\n-1.0839\n1.5048\n2.1198\n1.9718\n3.5977\n4.5827\n2.3313\n1.9135\n2.2843\n2.2002\n3.8426\n4.5389\n4.3972\n3.9865\n3.8944\n5.1291\n7.0212\n6.4197\n5.5165\n4.6787\n4.3867\n4.9403\n4.262\n5.0203\n5.9827\n5.9183\n5.1308\n5.8402\n5.7944\n7.681\n6.1021\n5.2427\n3.7359\n3.5085\n4.203\nNaN\nNaN\nNaN\nNaN\nNaN\n0.34599\n-0.5713\n-0.67193\n-0.17722\n-0.8837\n-0.80319\n0.49738\n1.8133\n1.867\n2.232\n1.5311\n1.0436\n0.85553\n1.3424\n2.2025\n1.4182\n1.263\n2.3117\n3.9718\n6.0356\n4.8431\n4.3616\n4.4982\n4.6749\n4.2861\n1.9316\n1.5335\n2.3296\n7.237\n1.808\n1.4124\n0.95955\n0.95068\n0.48906\n0.4259\n1.3278\n3.0121\n3.5035\n2.1047\n3.1568\n4.0373\n4.154\n5.0071\n5.9193\n5.4205\n5.2816\n5.5338\n6.4047\n8.5105\n6.921\n5.8637\n3.5777\n4.203\n4.0733\n5.392\n5.2753\n6.8291\n7.6443\n6.8022\n7.6879\n7.1635\n9.1385\n8.0946\n6.9987\n5.8963\n4.0548\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.461\n-0.72001\n-0.062278\n-0.57189\n-0.46955\n-0.52982\n-0.3981\n0.64035\n0.23256\n0.6826\n0.87221\n0.75264\n0.71867\n1.6805\n2.0641\n1.863\n2.4804\n4.2632\n5.4869\n6.0226\n4.5711\n5.6031\n5.4653\n4.1442\n1.721\n0.4647\n0.87139\n6.1942\n2.9281\n2.2879\n1.9602\n1.7178\n1.1856\n1.584\n1.9258\n3.293\n4.5467\n2.5337\n3.2098\n4.7554\n4.0733\n4.257\n4.5208\n5.4805\n5.6551\n7.0036\n6.6421\n7.2425\n6.2025\n6.0491\n2.3443\n2.0286\nNaN\n6.5855\n5.3501\n7.6314\n9.0021\n9.2374\n9.7506\n10.637\n11.601\n9.6951\n8.6489\n7.6632\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32508\n0.30674\n0.08767\n0.26453\n-0.42951\n-0.34163\n0.58777\n-0.21336\n-0.33912\n0.41064\n0.81333\n1.0051\n0.14239\n1.8669\n2.7939\n3.1793\n2.777\n2.8714\n3.5039\n5.1256\n5.0806\n6.0251\n4.0416\n1.6836\n-0.60798\n-1.715\n-0.064052\n2.9888\n1.6533\n1.9053\n1.175\n1.3205\n2.6678\n3.8307\n4.2128\n4.4681\n5.1667\n3.4175\n3.1908\n4.6383\n2.3493\n3.5297\n2.449\n3.784\n6.7444\nNaN\nNaN\n5.2207\n5.4346\n6.6574\nNaN\nNaN\nNaN\n2.9609\n2.3455\n2.9268\n2.3307\n1.495\n2.0045\n2.1987\n2.3158\n3.6913\n4.2032\n2.389\n2.0801\n2.1762\n3.3556\n5.6411\n3.9206\n3.4364\n0.87431\n7.4934\n6.8829\n5.7966\n4.8594\n5.3963\n1.8191\n3.1626\n1.268\n-0.2561\n-2.4469\n0.037926\n4.0438\n-5.9567\n-4.2176\n-3.2423\n-5.5902\n-3.4788\n-5.1715\n-4.1827\n-2.2718\n-2.3455\n-2.1673\n-0.030615\n0.19511\n-0.96127\n0.59012\n1.0614\n-0.66066\n-0.1755\n-0.037352\n-1.5384\n-2.1329\n-1.2006\n-0.053894\n-1.8698\n-3.9665\n-0.67797\n-3.3238\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.80059\n2.0988\n2.3387\n4.5919\n4.8899\n0.69574\n2.719\n2.0349\n5.3416\n4.7607\n3.2694\n2.9145\n2.182\n5.6115\n7.6846\n5.5905\n0.18448\n0.41654\n3.6127\n3.5048\n9.4893\n5.4372\n4.1251\n6.31\n5.7173\n1.8302\n0.58762\n-0.055531\n3.004\n2.6928\n1.8897\n1.5697\n0.34427\n-4.1673\n-3.3654\n-5.1563\n-3.2597\n-2.5808\n-2.946\n0.34128\n1.4704\n-0.20474\n-0.84574\n0.38325\n1.0958\n0.39266\n0.34797\n-0.76257\n-2.0643\n-1.9465\n-1.8078\n-1.2597\n-1.3922\n-1.1044\n2.8019\n-1.1238\n-2.2355\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.65997\n1.5883\n2.3253\n2.1652\n-0.45251\n0.12996\n1.824\n2.824\n3.8889\n4.8544\n3.0189\n3.3544\n2.6749\n7.9107\n12.268\n7.8312\n2.7525\n2.5607\n2.8731\n6.3956\n5.3669\n5.508\n5.9245\n5.5771\n7.224\n5.3927\n3.988\n2.4204\n3.5363\n0.17442\n2.5226\n3.4427\n2.4738\n0.75945\n-2.9456\n-5.1657\n-2.7681\n-2.5852\n-1.7401\n-0.57544\n-0.15627\n0.20514\n-0.54457\n0.24138\n0.26697\n-0.2806\n-0.93449\n-0.67512\n-1.1115\n-0.80116\n-0.35236\n-1.4331\n-0.90739\n0.25526\n0.6696\n0.90701\n-0.43059\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8379\n1.2923\n1.554\n1.225\n1.7899\n2.0829\n3.9809\n3.3386\n2.5925\n2.8494\n4.1899\n2.2464\n5.2224\n10.081\n6.8724\n2.8264\n1.9883\n1.9282\n2.0294\n5.6644\n6.3266\n5.7398\n6.1569\n4.1825\n6.6906\n7.1702\n6.4106\n3.2799\n3.1526\n1.5729\n1.3037\n3.3081\n2.4595\n2.2384\n-0.097876\n-3.2115\n-4.1883\n-1.9583\n-0.6621\n-0.18849\n-0.28617\n-0.10536\n-2.4618\n-2.3424\n-0.75032\n-1.1897\n-1.7553\n-0.50921\n-0.42132\n1.1463\n2.0872\n0.085467\n0.54881\n1.3859\n0.66481\n0.43093\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8211\n2.0603\n2.196\n1.5336\n1.9353\n2.0954\n2.3019\n2.8194\n1.7558\n0.59658\n2.1308\n1.696\n4.4441\n3.6378\n1.0022\n-0.95569\n-0.65939\n3.087\n-0.57477\n2.6799\n5.8899\nNaN\nNaN\n-0.26353\n5.6021\n7.0296\n7.071\n3.7399\n4.3653\n4.7245\n1.4277\n0.88914\n1.1141\n1.1963\n1.0682\n-7.5557\n-5.1799\n-2.2252\n-0.42622\n-0.0012465\n0.0016191\n0.39732\n-2.3752\n-3.7149\n-2.1953\n-1.35\n-0.93554\n0.085795\n-0.21273\n-0.13556\n0.50952\n0.70004\n0.22401\n1.4667\n0.91817\n0.62375\n6.1896\nNaN\nNaN\n-13.128\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4385\n0.56044\n0.98555\n1.2873\n1.957\n1.6689\n1.5217\n2.2025\n2.4236\n2.1526\n2.6734\n4.2698\n4.166\n0.83065\n-0.52238\n1.2733\n-0.2795\n2.2736\n1.9107\nNaN\nNaN\nNaN\nNaN\n1.9729\n3.7336\n7.9239\n6.6104\n5.9216\n5.7994\n6.5556\n4.0013\n-0.2932\n-1.6048\n-1.6957\n-1.0715\n-6.0042\n-3.8987\n-1.6727\n-0.87825\n0.090314\n0.86076\n-1.0328\n-2.5761\n-3.7579\n-5.4235\n-3.1521\n-0.98011\n0.88508\n-0.45108\n1.0747\n0.49565\n0.78018\n-0.039608\n0.33921\n-0.82387\n-3.4365\n-1.8438\nNaN\nNaN\n-17.461\n-13.383\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.65897\n-0.53269\n0.40005\n2.1612\n3.2643\n2.106\n3.8747\n4.0624\n4.1958\n4.4096\n4.4957\n3.339\n3.1078\n0.69044\n1.5913\n-0.10338\n-0.69934\n2.959\n0.17301\nNaN\nNaN\nNaN\nNaN\n2.0526\n2.734\n5.1965\n5.9512\n7.7454\n6.6332\n7.2687\n2.8948\n-1.6779\n-2.4768\n-2.5268\n-4.8646\n-5.5902\n-4.6866\n-2.5836\n-1.9223\n-1.158\n0.66941\n-1.4997\n-4.007\n-5.2502\n-9.0026\n-4.4635\n-1.3596\n0.88757\n1.0181\n2.0203\n-0.14358\n0.43224\n0.89612\n0.6394\n-0.47449\n-3.0111\n-1.8552\nNaN\nNaN\n-10.982\n-11.052\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6864\n1.9852\n1.0627\n3.5721\n5.8879\n2.6684\n4.1516\n4.3375\n4.234\n4.7871\n4.3572\n4.0451\n3.8426\n4.0088\n2.784\n1.682\n1.5778\n3.1521\n0.92562\n1.0635\n1.138\n3.113\n2.3943\n2.8515\n4.3336\n4.4712\n5.0786\n6.6569\n7.3525\n7.1449\n4.846\n-0.44872\n-1.3174\n-1.7458\n-2.7746\n-1.6252\n-3.7204\n-3.3733\n-3.6517\n-4.3092\n-2.3358\n-2.2745\n-3.368\n-5.1152\n-11.547\n-4.2032\n-0.26768\n0.38685\n0.20645\n2.0886\n0.19622\n0.96233\n2.58\n2.3308\n2.6337\n0.021478\n-2.0008\n-1.191\n0.33797\n-3.0249\n-8.9\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4669\n1.3748\n2.495\n2.0733\n5.1097\n2.8414\n3.3268\n4.9151\n4.6952\n5.2275\n4.2176\n3.951\n4.2565\n3.9118\n2.8885\n2.7861\n3.1665\n3.5754\n1.8686\n2.6844\n2.8517\n1.2378\n1.1363\n3.8546\n5.1347\n5.1928\n5.4204\n5.635\n5.984\n5.5843\n5.3377\n3.1614\n0.79773\n-0.84068\n-1.0189\n-1.4814\n-4.2501\n-3.965\n-3.789\n-4.9261\n-4.0365\n-3.5678\n-5.3528\n-8.216\n-12.027\n-1.9824\n1.0306\n2.3167\n2.1997\n3.211\n2.0499\n3.8767\n4.2274\n5.9081\n5.2471\n0.94566\n-2.1651\n-2.3017\n2.2306\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.57638\n0.92533\n1.9304\n1.0382\n4.3426\n2.0124\n3.7529\n4.3794\n4.8234\n4.8562\n5.1591\n4.9372\n4.8974\n3.8953\n2.8444\n3.7507\n4.123\n3.8806\n3.04\n4.3482\n3.5777\n2.6073\n4.1327\n3.7785\n3.3902\n5.9595\n6.5753\n4.4302\n3.8725\n3.8336\n4.2638\n0.26528\n-2.1352\nNaN\nNaN\n-1.4516\n-3.9986\n-4.2319\n-4.8173\n-4.9961\n-5.366\n-4.7321\n-6.6158\n-7.2905\n-3.5682\n0.94549\n1.1999\n2.719\n2.4484\n3.6351\n2.953\n5.7155\n6.0292\n6.3428\n5.3507\n2.0967\n-1.712\n-4.6365\n1.9655\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.2743\n-1.4807\n0.19475\n-0.072363\n1.9737\n2.7148\n3.7588\n3.3147\n5.1611\n6.1656\n6.1011\n4.1405\n6.3245\n5.6058\n4.7035\n5.3756\n4.1891\n2.7622\n2.9302\n4.6363\n3.604\n3.2237\n3.9073\n1.6922\n1.6203\n5.6978\n7.3046\n6.5426\n3.7679\n3.9263\n1.7415\n-2.2949\nNaN\nNaN\nNaN\n-2.7711\n-4.3182\n-3.9146\n-7.095\n-5.8606\n-4.1647\n-4.6405\n-7.078\n-6.2714\n-2.2355\n0.77113\n0.87429\n2.0439\n3.584\n2.453\n2.5639\n5.4375\n5.8357\n7.4559\nNaN\nNaN\n-3.6635\n-3.5668\n2.1414\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9326\n-2.6067\n-1.444\n0.17782\n1.5936\n3.3567\n4.3823\n4.5507\n5.1006\n4.3082\n3.5612\n4.1849\n7.1505\n6.8477\n4.301\n4.7322\n4.089\n3.3245\n3.47\n3.654\n1.6506\n2.3157\n1.6056\n0.3341\n0.047007\n3.5473\n6.379\n8.9506\n6.3453\n5.2708\n1.8823\nNaN\nNaN\nNaN\nNaN\n-3.7649\n-4.6203\n-5.2924\n-9.0864\n-6.0963\n-3.686\n-5.8524\n-6.3563\n-4.5285\n-2.6441\n0.18917\n1.1357\n4.4166\n5.4206\n4.5296\n3.7027\n6.1597\n4.797\nNaN\nNaN\nNaN\n-1.5548\n-3.8457\n-1.0672\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.33622\n-1.0482\n-0.81043\n-0.15366\n1.5216\n2.8699\n2.6785\n4.2194\n3.197\n2.9164\n4.1707\n4.9731\n5.823\n6.6113\n4.1527\n5.6464\n6.187\n4.2846\n3.7675\n2.9943\n2.0064\n1.4694\n-1.5068\n-1.3875\n-1.1192\n1.3808\n8.8256\n7.6366\n4.8554\n5.6879\n3.7294\nNaN\nNaN\nNaN\nNaN\n-7.8736\n-6.8062\n-3.9541\n-7.9016\n-6.4534\n-5.6785\n-9.6502\n-10.96\n-4.5634\n-1.6165\n0.51366\n1.8083\n4.4274\n5.3474\n5.8686\n5.4968\n5.8781\n3.7468\nNaN\nNaN\nNaN\n-4.0061\n-3.3442\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.22728\n-0.47489\n-0.84672\n0.33767\n1.7465\n2.054\n1.9341\n1.55\n1.4906\n2.0721\n3.1854\n4.397\n5.8505\n6.3671\n4.6378\n5.8766\n5.5049\n3.9374\n2.5292\n2.3472\n2.6317\n-1.3233\n-3.4595\n-2.4438\n-2.004\n2.7726\n7.1596\n5.343\n4.9166\n4.6875\n3.4113\nNaN\nNaN\nNaN\nNaN\n-11.455\n-11.902\n-0.95089\n-7.4403\n-6.358\n-6.5094\n-8.2976\n-7.8388\n-2.3664\n0.80259\n3.2431\n3.1729\n4.8756\n6.0818\n6.619\n5.3666\n3.9778\n3.1966\nNaN\nNaN\nNaN\nNaN\n-3.9254\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.63553\n-0.49433\n1.215\n1.578\n1.7316\n1.5044\n2.476\n1.497\n0.9471\n0.96717\n1.4055\n2.9679\n5.3728\n5.3655\n5.0522\n5.8153\n7.4276\n5.4335\n3.7299\n2.4477\n1.9706\n-3.3386\n-7.0251\n-3.7635\n-2.8661\n3.0971\n5.1298\n5.0553\n5.8808\n5.0902\n4.989\nNaN\n-8.3754\n-10.117\n-7.9113\n-11.58\n-9.9772\n-2.0032\n-5.5844\n-4.8884\n-4.4846\n-7.0656\n-7.0664\n1.536\nNaN\nNaN\nNaN\n5.0321\n6.6802\n6.1988\n4.0708\n5.3768\n5.177\n0.21688\nNaN\nNaN\nNaN\n-4.6626\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.45715\n-0.33561\n0.85796\n2.2265\n2.1233\n2.1895\n3.0342\n2.0743\n1.2432\n2.115\n0.62212\n2.7405\n4.4076\n5.2461\n4.3949\n4.1956\n4.7185\n4.7994\n4.9327\n2.8554\n1.6181\n0.46868\n-10.286\n-7.0491\n-4.0253\n0.38209\n3.0957\n7.8576\n7.2655\n3.7644\n0.81211\n-7.8151\n-5.7571\n-8.2831\n-7.4064\n-8.2171\n-3.6761\n-1.7821\n-3.1077\nNaN\nNaN\nNaN\n-5.5347\nNaN\nNaN\nNaN\nNaN\n3.3453\n6.3274\n4.8745\n3.3246\n2.992\n6.5811\n1.8807\n-0.83998\n-4.3409\n-7.4874\n-5.0777\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.34538\n-0.12401\n1.4497\n1.8818\n2.1331\n2.7457\n3.2524\n2.253\n1.1299\n2.0955\n2.6044\n3.2265\n3.648\n4.2746\n3.4406\n3.011\n3.2785\n3.2983\n4.2239\n3.856\n1.4144\n-1.4688\n-4.772\n-9.069\n-2.7451\n1.0709\n3.2777\n5.5825\n3.4056\n-0.18954\n-2.7634\n-2.8258\n-0.85298\n-5.9122\n-3.7102\n-3.0384\n-0.61741\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.523\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.4413\nNaN\n2.2686\n5.7321\n2.6731\n0.73417\n-0.42122\n-6.7123\n-5.1283\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4131\n-0.21552\n2.0355\n2.7751\n1.7279\n1.8355\n2.3476\n2.3904\n0.75636\n1.9923\n2.7932\n2.8599\n2.8243\n3.5169\n3.8976\n2.4702\n2.1285\n1.8549\n1.9972\n1.5281\n0.12938\n-4.173\n-6.0244\n-7.9383\n-1.1121\n1.1409\n4.1337\n2.4344\n0.35321\n-3.1611\n0.073143\n0.38916\n-4.0701\n-2.6174\n-0.73527\n-0.70593\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7676\n0.78374\n-1.8484\n-4.996\n-5.9028\n-6.327\n-6.4546\n-2.7512\n0.6433\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2393\n0.82392\n2.5605\n3.2821\n2.6534\n0.81215\n1.4738\n2.5769\n2.3843\n2.9202\n1.2715\n2.0169\n2.8407\n3.3065\n3.4529\n1.0496\n-0.59754\n-0.73583\n-0.57245\n-0.88001\n-1.7223\n-4.0903\n-6.5756\n-5.0174\n-1.0398\n1.1736\n2.0699\n0.99954\n2.4024\n-2.242\n-0.78409\n-1.8062\n-2.8749\n-1.8172\n-0.33682\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.236\n-0.6798\n-1.5799\n-2.3329\n-1.1154\n-0.37129\n0.098822\n1.6718\n0.76243\n1.1067\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76899\n0.7031\n0.78519\n0.67813\n3.631\n1.7771\n3.1191\n2.449\n1.3009\n1.9555\n1.4494\n2.4321\n2.6075\n2.4615\n1.4917\n-0.22788\n-1.6186\n-3.216\n-4.4674\n-2.8915\n-2.4561\n-6.0952\n-5.0539\n-2.5457\n0.41823\n2.2055\n1.0851\n1.6915\n3.6216\n0.35432\n-3.7083\n-3.2425\n-2.7525\n-2.9459\n-0.75221\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-6.6303\n-3.5257\n-2.3039\n-0.36008\n2.2478\n3.0217\n0.74844\n1.1463\n2.3726\n-1.0789\n0.27614\n4.0083\n4.6649\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.85976\n0.31676\n0.96132\n0.70311\n4.5962\n2.9305\n0.86505\n0.4575\n0.81682\n1.692\n2.3986\n2.9393\n0.44645\n0.72755\n-1.1491\n-1.3914\n0.055236\n-1.6462\n-3.1852\n-4.4135\n-3.3851\n-7.7615\n-6.859\n-0.94819\n0.80097\n3.7195\n3.794\n3.486\n3.9841\n4.6073\n2.3633\n6.6071\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8471\n-2.3732\n-1.5927\n-2.1408\n-2.3718\n-0.39455\n2.9495\n3.5433\n1.5624\n3.2545\n4.7155\n2.786\n2.3958\n4.1274\n3.1157\n5.213\n3.2584\n3.4406\n2.1566\n-1.2737\n0.27322\n0.96168\n1.1799\n0.34556\n0.73845\n0.67572\n-0.92542\n1.0861\n0.55091\n0.79191\n-0.096483\n-0.19087\n0.071195\n-0.2745\n-1.6469\n-1.5445\n-1.7574\n-2.9308\n-1.0003\n-3.4389\n-1.0203\n-5.7465\n-7.7151\n-1.9092\n1.8511\n6.7046\n4.5899\n2.8113\n3.5175\n3.7556\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.47531\n-1.7232\n-0.27432\n1.6804\n0.06408\n0.040605\n-2.3947\n0.54654\n2.7006\n2.0643\n5.1129\n6.9042\n4.0989\n3.2619\n-0.52402\nNaN\nNaN\nNaN\nNaN\n1.035\n2.0543\n1.0085\n0.24009\n0.21098\n-1.2409\n-1.4845\n-1.4164\n-2.2248\n-0.4163\n0.10103\n-1.3683\n-2.4298\n-1.3626\n-1.3464\n-2.2513\n-3.1509\n-1.786\n-2.1451\n-4.4361\n-5.7958\n-2.0567\n-1.8631\n-5.8961\n-7.0857\n-3.5232\n2.2252\n3.0932\n1.5095\n0.23652\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.87578\n0.031786\n-0.87262\n-2.1823\n0.90897\n2.6162\n1.3382\n1.9051\n-0.06769\n0.43746\n0.44766\n1.4504\n4.058\n6.1405\n4.5841\n4.8607\n2.4626\nNaN\nNaN\nNaN\nNaN\n4.504\n6.4689\n0.51395\n-1.5917\n-2.0938\n-2.4815\n-1.7764\n-1.6861\n-1.7643\n-0.91193\n0.68402\n-0.9315\n-2.3625\n-1.6068\n-1.8667\n-3.3286\n-3.6019\n-0.72899\n-0.19626\n-4.2639\n-7.9346\n-9.4967\n-6.394\n-7.1633\n-5.8854\n-2.2261\n0.63493\n0.66542\n-2.2597\n-4.9603\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.63922\n0.50385\n0.19321\n-0.2954\n-1.034\n0.30459\n1.6055\n1.9471\n4.3709\n2.864\n1.2966\n0.91603\n0.69021\n0.9193\n4.9336\n3.7516\n5.3849\n5.277\nNaN\nNaN\n3.4089\n5.7063\n6.0231\n6.2482\n-2.371\n-3.2369\n-2.8094\n-2.3964\n-2.019\n-0.8915\n-0.57067\n0.59147\n-0.14387\n-0.69647\n-1.8664\n-2.2883\n-2.4454\n-4.0097\n-3.9932\n-1.2524\n-0.62247\n-5.2046\n-8.4476\n-9.3471\n-6.1067\n-6.4828\n-6.8165\n-3.0276\n-1.2469\n-0.23362\n-4.2224\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.9277\n-2.0744\n3.1649\n1.6455\n1.4165\n0.2775\n0.37335\n-0.043638\n3.0762\n6.9679\n5.9732\n3.1219\n2.679\n1.8759\n0.20849\n0.38121\n4.5819\n6.2252\n5.1752\n2.8012\n1.1705\n6.2536\n8.2337\n6.3216\n5.0036\n-3.3446\n-3.207\n-2.2091\n-1.9859\n-2.6388\n-0.15757\n0.30937\n-0.48149\n-0.97604\n-1.6752\n-2.3262\n-2.7723\n-2.1637\n-2.3399\n-2.4385\n-0.86728\n0.74466\n-4.9667\n-7.1768\n-6.713\n-5.4507\n-6.3208\n-6.5282\n-4.3528\n-0.69491\n1.5969\n-4.2209\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3832\n-2.2886\n2.949\n1.3793\n1.7569\n1.3676\n-0.82858\n-1.5084\n1.561\n6.9782\n8.0934\n6.1577\n4.2971\n4.4203\n1.9115\n2.768\n7.2401\n8.1111\n5.7757\n4.5337\n4.0205\n7.312\n9.8052\n9.1444\n4.8456\n-3.8562\n-2.6879\n-2.0714\n-1.1579\n-0.81335\n-0.16721\n-0.12681\n-1.453\n-1.0955\n-1.8174\n-3.0844\n-4.4803\n-2.1881\n-0.90354\n-0.85633\n0.1759\n-1.8561\n-4.9089\n-4.7154\n-6.0326\n-6.6489\n-5.976\n-4.1533\n-3.4066\n0.43804\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.85789\n-1.5166\n-0.65683\n3.7264\n-1.8747\n0.056974\n1.8902\n1.1124\n5.3169\n6.4832\n6.897\n7.5224\n8.3101\n6.5019\n5.3732\n2.3524\n3.7721\n8.8461\n7.7168\n7.7635\n6.015\n5.8808\n8.1799\n9.0358\n8.4849\n4.6832\n-3.8458\n-1.3498\n-2.2915\n-0.88714\n-0.93415\n-0.59063\n-1.5538\n-2.486\n-1.597\n-1.6699\n-2.8041\n-3.3933\n-2.67\n-1.8608\n0.121\n0.3079\n-3.6991\n-6.2375\n-6.1945\n-5.3195\n-7.8572\n-5.9948\n-4.5795\n-3.1491\n0.32391\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.4272\nNaN\nNaN\nNaN\nNaN\n-0.49116\n-0.31793\n-0.54705\n-0.24047\n3.8358\n-0.64373\n2.6434\n4.3944\n7.4392\n7.1947\n7.4731\n6.74\n0.49398\n7.1982\n5.4785\n4.4549\n0.70437\n3.7593\n8.189\n8.183\n8.2339\n7.7561\n7.6441\n8.4705\n7.7966\n5.2804\n1.225\n-2.4367\n-0.78344\n-2.1021\n-2.3141\n-1.8493\n-1.1957\n-2.0027\n-2.3668\n-2.6027\n-2.7559\n-2.6626\n-3.2577\n-3.5369\n-1.651\n-1.2908\n-1.4115\n-2.0349\n-4.8737\n-7.8262\n-5.7808\n-6.2736\n-6.7904\n-6.5452\n-2.6974\n1.9642\n6.1113\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5574\n1.5372\n-0.89508\n-1.4191\n-0.90074\n-0.76517\n0.74778\n1.4319\n1.7314\n4.921\n8.022\n6.4699\n6.5513\n3.5833\n-3.3417\n-0.87434\n3.9344\n4.0575\n1.2962\n4.2897\n7.553\n9.8317\n8.5827\n8.7681\n8.4187\n9.3252\n6.6957\n3.8284\n-3.4839\n-1.7321\n-1.938\n-2.4696\n-3.115\n-2.4488\n-1.9429\n-1.9018\n-2.099\n-3.6135\n-2.4628\n-2.8857\n-3.6956\n-3.9045\n-2.2421\n-1.6361\n-0.80561\n-1.1259\n-3.4617\n-4.1326\n-9.0367\n-9.0035\n-6.5565\n-4.923\n-0.4527\n4.9239\n6.4864\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.6961\nNaN\nNaN\n15.898\n2.1701\n0.40437\n0.081733\n-1.1663\n-1.9926\n-2.7805\n-3.5024\n-1.9151\n-1.4965\n1.576\n4.8874\n1.4945\n-0.047317\n1.2325\n-1.128\n-1.6464\n-1.5575\n1.1912\n2.0752\n4.2122\n6.5625\n7.1514\n6.5102\n7.6033\n8.3761\n6.8477\n5.1679\n3.8902\n-3.6177\n-2.8267\n-2.7213\n-2.6054\n-2.8476\n-2.9883\n-1.8107\n-1.8658\n-2.2632\n-3.7423\n-2.212\n-2.8944\n-4.4451\n-4.7624\n-4.04\n-2.6113\n-3.2492\n-3.7474\n-4.7217\n-5.3365\n-6.86\n-8.9721\n-5.7753\n-3.2291\n1.8223\n5.5472\n9.4298\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3611\n19.727\n16.468\n11.488\n-0.17015\n-2.2644\n-2.3472\n0.42868\n-1.9746\n-2.1945\n-2.8332\n-2.8615\n-0.20985\n0.034231\n-0.045641\n-1.5183\n-1.325\n-1.3044\n-1.4007\n-1.8126\n-3.5021\n0.90464\n3.892\n4.4044\n4.0196\n3.0721\n4.4779\n7.0987\n7.2531\n4.8862\n3.4773\n2.0277\n0.82749\n-2.8005\n-1.6063\n-1.572\n-1.3041\n-2.1837\n-2.1882\n-1.7151\n-1.2105\n-2.8478\n-2.5699\n-3.5592\n-5.3094\n-6.8386\n-5.58\n-4.0912\n-4.2268\n-6.143\n-6.0565\n-4.4006\n-5.1984\n-6.3921\n-5.2747\n-2.7225\n0.43134\n5.0511\n11.928\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n22.434\n11.267\n10.617\n6.8137\n-1.6201\n-5.8511\n-3.8776\n-1.1609\n-1.1816\n-1.6943\n-3.5812\n-3.4853\n-1.2907\n0.026556\n-0.20106\n-3.2463\n-4.0192\n-2.2169\n-1.9773\n-3.472\n-4.5916\n-0.79088\n2.3112\n3.1161\n3.1369\n2.335\n3.5961\n3.1358\n6.1648\n3.9437\n1.4938\n1.9107\n3.3859\n-1.888\n-1.7889\n-2.5273\n-0.54061\n-2.1414\n-2.0246\n-2.0132\n-1.5743\n-3.1186\n-4.16\n-5.0307\n-5.3802\n-6.274\n-5.9586\n-5.4469\n-3.6525\n-3.6551\n-10.038\n-9.8225\n-3.477\n-3.2161\n-5.6863\n-3.6929\n0.61263\n4.3848\n3.4332\n-1.3887\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.813\n7.7502\n4.3863\n3.7995\n-0.7175\n-9.233\n-4.8629\n-2.1075\n-2.2123\n-1.5465\n-2.7133\n-2.7993\n-2.0866\n-1.5438\n-1.9031\n-2.203\n-5.7421\n-3.7072\n-1.6448\n-1.3721\n-2.3116\n-3.0594\n0.24185\n1.1953\n3.1374\n3.9386\n3.2809\n2.3749\n2.3726\n1.6673\n2.3171\n4.2497\n4.3608\n-1.1678\n-2.4919\n-3.0638\n-1.8152\n-2.1931\n-1.4953\n-2.7629\n-2.909\n-2.7426\n-2.154\n-4.1718\n-4.9073\n-4.8779\n-4.8427\n-5.698\n-4.079\n-3.1544\n-6.7668\n-10.119\n-6.4355\n-2.7581\n-5.6514\n-3.9293\n0.93582\n3.3835\n0.23837\n-3.5591\n-8.6511\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.393\n4.495\n0.445\n1.8422\n-0.18148\n-7.8221\n-2.865\n-2.0426\n-2.851\n-5.5145\n-6.9307\n-3.8129\n-3.1373\n-3.1981\n-4.7646\n-3.5611\n-2.857\n-2.1553\n-0.29608\n-0.87565\n-1.2174\n-0.71181\n-2.2944\n0.59164\n1.6509\n3.1245\n2.1599\n2.0087\n1.6241\n2.0297\n2.821\n4.7492\n4.0702\n-1.2382\n-2.9153\n-2.944\n-2.1621\n-0.29743\n-0.31316\n-1.5706\n-1.9488\n-2.1615\n-1.0824\n-4.2604\n-4.2761\n-3.7464\n-4.539\n-5.5661\n-4.9748\n-3.3499\n-6.8806\n-7.5568\n-9.1142\n-6.1808\n-5.8368\n-2.9095\n-0.29649\n2.5038\n1.252\n-4.0981\n-4.402\nNaN\nNaN\n-0.57549\n-0.55707\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1044\n2.3444\n1.8747\n1.9074\n-5.7939\n-9.0105\n-3.3182\n-4.1278\n-6.3103\n-9.3291\n-7.0177\n-3.8273\n-2.8947\n-2.5345\n-2.8048\n-3.2732\n-2.0191\n-1.1883\n0.047007\n-0.96821\n-1.6721\n-1.5687\n-2.7597\n-0.27291\n-0.10016\n0.027389\n-2.3301\n-3.0449\n1.8511\n2.5048\n2.8473\n5.222\n3.7514\n-1.536\n-1.4664\n-1.2491\n-2.0515\n0.63044\n1.3198\n0.20479\n-0.26751\n-0.88579\n-1.1578\n-2.8517\n-2.6953\n-3.4587\n-3.5849\n-4.3531\n-3.4489\n-3.6994\n-7.2542\n-8.0481\n-7.8511\n-6.5751\n-5.172\n-3.5994\n-0.70854\n-2.2512\n-4.7908\n-5.1387\n-0.74701\n-1.0684\n1.9755\n2.4877\n0.49331\n2.9649\n4.3729\nNaN\nNaN\nNaN\nNaN\n-3.9426\n0.8344\n-1.6701\n-2.1009\n-1.0612\n-7.5685\n-11.311\n-6.2417\n-6.2516\n-6.9198\n-8.3376\n-6.2805\n-1.6817\n-0.70165\n0.26147\n-2.514\n-4.2202\n-3.4758\n-1.4509\n-0.45253\n-0.28663\n0.0028399\n-1.1983\n-1.7405\n-1.4113\n-3.147\n-1.7257\n-0.89695\n-0.79736\n-0.58871\n-1.135\n1.2765\n3.7523\n1.5105\n0.63119\n0.45452\n-0.44036\n-0.74905\n0.93989\n2.6074\n1.76\n1.1351\n0.44729\n-0.57909\n-1.4186\n-1.6862\n-1.633\n-1.7257\n-2.6825\n-4.5966\n-6.876\n-12.444\n-8.298\n-5.701\n-5.4951\n-2.5974\n-1.5903\n-0.76604\n4.5563\n-1.1437\n-0.78025\n-0.36361\nNaN\n-2.3652\n-0.097653\n-4.9597\n1.8365\n3.2374\nNaN\nNaN\nNaN\n-5.2941\n-1.9399\n1.1026\n-0.29576\n-3.9573\n-2.0752\n-5.2536\n-10.051\n-6.6917\n-2.6158\n-3.9513\n-6.2407\n-4.0644\n-0.36302\n1.1699\n0.30059\n-3.8374\n-3.6842\n-3.1615\n-2.6363\n-1.5549\n-0.073748\n0.576\n-0.73055\n-2.2166\n-2.1509\n-2.341\n-0.65057\n1.6309\n1.0317\n-1.815\n-1.2059\n0.91863\n2.6839\n-0.10728\n0.65174\n1.4502\n1.4489\n1.5402\n2.2651\n3.7295\n2.7326\n1.2734\n0.65152\n-0.25294\n-0.97725\n-1.4962\n-0.78681\n-0.19811\n-1.9761\n-4.49\n-6.9448\n-11.672\n-6.8045\n-5.873\n-3.7966\n-3.8393\n0.22151\n-0.56206\n0.41012\n-0.74554\n-1.4463\nNaN\nNaN\n-4.0624\n-2.0288\n-2.1898\n1.0757\n1.6004\nNaN\nNaN\nNaN\n-3.7604\n-2.6921\n1.2479\n-0.42963\n-5.7506\n-4.2241\n-4.632\n-7.0982\n-3.3291\n-1.7379\n-2.2304\n-4.988\n-2.909\n-0.21305\n2.357\n0.1184\n-1.8623\n-1.8315\n-1.7956\n-2.4856\n-2.2384\n-0.18024\n0.41007\n0.32515\n-2.4096\n-2.8819\n-1.4468\n0.34476\n2.0238\n2.1207\n1.1614\n0.68887\n-0.1097\n1.8451\n2.0216\n2.1957\n1.9422\n2.3257\n2.4925\n3.4714\n4.1763\n3.7958\n1.5707\n0.25336\n-0.14448\n-0.76831\n-0.26392\n0.084303\n1.9756\n0.22333\n-3.6953\n-4.8168\n-10.42\n-1.4334\n-3.2028\n-3.6441\n-1.9002\n1.0112\n-0.12722\n-1.6837\n-3.3078\n-2.6954\nNaN\nNaN\nNaN\n-4.1223\n-2.1705\n-0.47452\n0.37506\nNaN\n0.42282\n-0.05456\n-3.542\n-3.1043\n1.3378\n0.42784\n-2.8058\n-2.4168\n-2.1081\n-4.5395\n-4.9093\n-4.6749\n-5.0068\n-3.9465\n-0.20328\n1.1016\n1.9921\n0.31192\n-1.3561\n-1.2867\n-0.76594\n-0.82498\n0.063931\n0.31498\n0.51295\n-1.0392\n-2.4812\n-3.0468\n-1.666\n0.83793\n2.3551\n3.0788\n2.924\n0.9935\n-0.3666\n2.6641\n3.7155\n2.1957\n2.5222\n3.1146\n3.7835\n4.7642\n5.1711\n3.9418\n3.4566\n1.8628\n0.9965\n0.40961\n1.2591\n2.0731\n3.872\n4.5864\n-0.21416\nNaN\nNaN\nNaN\n-2.4736\n-5.733\n-1.8417\n0.28791\n-3.6828\n-4.2478\n-5.5025\n-4.021\nNaN\nNaN\nNaN\n-3.0107\n-1.9245\n-3.3939\n-2.283\n-1.1813\n0.15595\n-0.2766\n-1.3199\n-1.292\n-2.764\n-4.525\n-3.9353\n-1.7939\n-1.4398\n-2.6113\n-2.8825\n-2.0559\n-2.0802\n-1.2102\n0.015937\n0.85002\n0.5662\n-0.34755\n-0.69373\n-0.45793\n0.79355\n1.8343\n0.62165\n0.55705\n0.76375\n-1.1129\n-1.4789\n-1.3718\n-0.51182\n1.2589\n1.8265\n2.7249\n3.1466\n2.2609\n1.1567\n1.7497\n3.9693\n3.2315\n3.8441\n4.9551\n4.3368\n4.6094\n5.9687\n4.0451\n4.3617\n3.3468\n1.7076\n2.3477\n2.7281\n4.8131\n7.3642\n6.3258\n0.69247\nNaN\nNaN\nNaN\n-3.0267\n-5.2435\n-7.836\n-5.0334\n-3.612\n-1.758\n-3.6993\n-3.2165\nNaN\nNaN\nNaN\n-4.7997\n-3.5671\n-3.7373\n-2.1703\n1.6692\n0.91766\n-0.31497\n-0.99228\n-1.5585\n-4.1715\n-4.8117\n-4.7116\n-2.4609\n-0.56713\n-0.53172\n-2.4409\n-1.1689\n-2.1705\n-2.1216\n-0.45522\n0.44789\n-0.35221\n-0.43045\n0.26674\n1.0024\n1.5149\n1.3588\n0.71949\n1.755\n1.792\n0.18208\n0.064562\n0.75034\n1.6629\n2.721\n2.4342\n2.9386\n3.9254\n3.0459\n1.7377\n3.5426\n4.663\n4.0409\n4.9844\n5.5939\n5.5703\n6.4192\n6.2974\n5.7509\n5.4134\n4.6411\n2.3738\n3.7569\n4.95\n5.6899\n5.523\n4.0246\n3.2099\n0.57761\nNaN\nNaN\n-4.7742\n-3.684\n-3.621\n-3.5909\n-2.2446\n-0.084768\n-1.443\n-2.0852\n-1.4767\n-3.4258\n-4.6658\n-7.8919\n-6.5279\n-4.1284\n-1.8112\n2.2364\n1.4911\n1.4163\n0.18052\n-1.9402\n-1.224\n-2.3472\n-2.118\n-1.5225\n-0.0077033\n0.3478\n-1.3149\n-1.5546\n-5.0391\n-5.0309\n-0.90372\n-0.65189\n-0.11961\n1.7866\n0.78975\n0.96922\n1.7334\n0.85079\n1.2979\n2.4575\n3.5581\n2.0462\n0.27303\n1.4916\n2.7365\n4.6817\n4.0143\n3.5387\n4.791\n3.7997\n3.5197\n5.3693\n6.2762\n4.2708\n4.7224\n5.9426\n6.8369\n6.9334\n7.2034\n7.0136\n5.9533\n4.7779\n3.4703\n3.5685\n4.2378\n4.8722\n6.1583\n5.6592\n4.9516\n1.638\n-1.8897\n-4.187\n-4.0927\n-2.7754\n-1.3959\n-0.59054\n-0.59958\n0.44732\n-0.60832\n-1.4186\n-1.9634\n-2.1597\n-2.8002\n-4.3394\n-5.2862\n-4.3291\n-1.3256\n1.0786\n1.2402\n0.69034\n1.8236\n-0.11314\n-0.44116\n-0.89004\n0.75695\n0.45987\n1.3554\n1.4782\n1.6649\n-1.0367\n-5.3\n-7.8224\n-2.7478\n-1.819\n2.9509\n2.8234\n1.6087\n1.2965\n2.6574\n2.6662\n2.1483\n2.2249\n2.9354\n2.6158\n1.6228\n2.9983\n3.3649\n4.5161\n4.5698\n6.4061\n6.0043\n4.7705\n4.8994\n5.163\n5.6049\n4.2727\n6.2763\n8.3309\n8.6203\n8.7317\n9.0932\n8.6353\n9.1892\n5.7601\n4.1211\n4.1223\n4.1382\n3.5022\nNaN\nNaN\nNaN\nNaN\n0.41783\n-2.6004\n-3.5392\n0.21263\n-0.31129\n-1.1926\n-1.0161\n0.15436\n0.0010084\n0.12749\n0.31469\n-0.27327\n-1.7828\n-2.2283\n-2.1261\n-2.6021\n-2.1402\n-1.8638\n-0.82917\n0.41083\n2.7306\n1.7194\n1.7611\n1.0992\n2.1292\n2.1177\n0.64554\n1.0465\n3.2779\n2.7638\n-2.3675\n-2.7309\n-3.5032\n-2.6266\n-1.1201\n0.68182\n0.58439\n2.2389\n3.9541\n1.7491\n1.0082\n2.1105\n1.8401\n3.2885\n5.0006\n5.8271\n3.3818\n3.7838\n5.0508\n8.0887\n6.5476\n4.9547\n4.5918\n4.7004\n6.1331\n5.4913\n8.0416\n9.926\n10.391\n9.7919\n9.9278\n10.03\n12.375\n10.484\n7.9256\n6.0954\n5.337\n6.3641\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65222\n-1.1264\n-0.61594\n0.18765\n-1.74\n-1.592\n-0.58691\n0.24987\n0.54149\n0.95181\n0.18905\n-0.72182\n-0.92679\n-1.3618\n-0.1163\n-1.2203\n-1.7688\n-0.19126\n1.7719\n4.9989\n4.4435\n4.1766\n3.5585\n4.0534\n3.4393\n-1.4202\n-0.90924\n0.39989\n5.7946\n-0.50162\n-0.28503\n-1.6622\n-1.4154\n-1.8254\n-0.86905\n-0.72404\n1.0032\n2.3261\n0.9165\n1.555\n4.1912\n4.2976\n4.6581\n6.1259\n4.8087\n4.457\n4.9722\n6.6077\n8.9607\n7.4573\n6.8463\n4.1443\n4.7897\n5.4417\n8.366\n7.9399\n9.4671\n11.582\n11.513\n13.776\n13.562\n15.393\n13.611\n10.367\n7.1892\n5.4721\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2509\n-1.6055\n-1.2492\n-1.7919\n-2.074\n-1.8823\n-1.87\n-0.51542\n-0.50958\n-0.028749\n-0.20527\n-0.68944\n-1.0532\n-0.20456\n0.15216\n0.019232\n0.27919\n1.586\n4.8274\n7.2253\n5.1297\n5.1338\n5.1915\n4.7839\n-0.95999\n-2.868\n-2.3342\n3.9523\n1.3012\n0.87879\n-0.10288\n-0.44954\n0.028726\n-0.46895\n-0.97846\n0.94369\n3.2156\n1.1574\n2.2066\n5.6132\n4.1508\n3.7326\n5.0521\n5.8283\n5.9029\n6.821\n7.4652\n9.0731\n7.8868\n9.3187\n3.5291\n3.3298\nNaN\n10.565\n8.0416\n11.126\n13.209\n14.059\n16.668\n18.433\n18.925\n14.049\n11.856\n8.8221\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.77081\n-0.7191\n-1.3792\n-0.83195\n-1.4868\n-1.2445\n-0.73677\n-1.2739\n-0.93492\n-0.17886\n-0.53731\n0.1085\n-0.75429\n0.045329\n0.66569\n0.8979\n-0.10124\n0.43298\n2.4055\n3.9013\n3.8121\n5.0916\n3.1455\n0.39369\n-1.7294\n-4.3248\n-4.019\n-0.56338\n-0.10313\n0.88099\n-0.7571\n-0.1272\n2.1345\n3.103\n3.0267\n3.5377\n3.8997\n2.487\n2.821\n4.9297\n2.3153\n2.735\n2.7777\n3.8895\n7.7918\nNaN\nNaN\n7.4341\n8.1668\n10.506\nNaN\nNaN\nNaN\n9.1048\n8.8713\n9.391\n10.299\n9.9919\n9.5213\n10.135\n10.637\n9.4171\n8.4109\n8.4733\n8.4851\n8.4132\n8.8683\n9.1796\n7.3603\n6.7979\n8.581\n7.9737\n7.6944\n7.8504\n6.7533\n5.1825\n2.8952\n4.9755\n4.7444\n3.1186\n2.5221\n3.4701\n4.1594\n2.2211\n3.1114\n5.101\n4.6351\n6.7519\n4.6886\n4.6988\n5.2804\n5.838\n6.0971\n7.1907\n7.142\n6.9192\n7.825\n7.9497\n7.5091\n7.0816\n7.3378\n7.1228\n6.7336\n6.694\n6.5506\n6.2629\n4.686\n5.5745\n5.4236\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.6469\n8.9532\n9.452\n10.846\n10.513\n10.249\n10.596\n10.634\n9.5431\n8.3009\n8.3959\n8.2205\n8.3223\n8.3039\n8.169\n7.1491\n6.6858\n7.164\n7.5843\n7.0985\n7.6221\n4.6945\n3.4894\n4.2959\n4.8596\n4.2991\n2.8285\n2.5448\n4.0986\n4.1626\n4.1253\n3.8472\n5.8783\n5.2411\n5.1628\n4.8411\n5.6427\n5.7703\n6.1857\n6.4189\n6.8869\n6.9596\n7.4314\n7.9323\n8.2751\n7.8228\n7.862\n7.6564\n7.1295\n6.688\n6.9876\n6.5403\n6.2866\n6.3019\n7.356\n7.177\n5.9244\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.3675\n8.8459\n8.5717\n9.3609\n9.6451\n10.546\n10.458\n9.0346\n8.7997\n9.0918\n8.7622\n8.1293\n8.4705\n8.7124\n8.8145\n6.5206\n6.108\n6.1178\n5.6654\n5.8616\n6.0061\n3.4787\n4.1524\n3.7463\n4.6165\n3.8756\n2.1526\n2.5026\n2.941\n3.2636\n5.2899\n4.5328\n6.7185\n5.4583\n3.5224\n3.9071\n6.1073\n5.9145\n6.2761\n6.2574\n7.3307\n6.9801\n7.2177\n7.8788\n7.8183\n7.436\n7.4408\n8.336\n8.2821\n7.7621\n7.4194\n6.5596\n6.6123\n6.7979\n7.786\n6.8515\n5.9563\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.674\n8.9101\n8.6392\n8.7435\n8.6903\n8.6449\n9.3868\n9.05\n8.4186\n7.2806\n8.1133\n7.6369\n7.7081\n9.1292\n8.8919\n5.8608\n5.2079\n5.6339\n3.5327\n4.7023\n6.6218\n2.1638\n2.6098\n3.0093\n3.312\n2.8133\n1.7707\n1.4608\n2.6996\n1.7817\n4.808\n5.4181\n7.8266\n6.7133\n4.3126\n3.1828\n4.5619\n5.9651\n6.2729\n5.4853\n6.4771\n6.7477\n6.7983\n7.4661\n7.6424\n7.4003\n7.4343\n8.7034\n9.036\n8.291\n7.8648\n7.14\n7.1893\n7.3579\n6.4979\n5.8953\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.753\n9.1297\n8.5241\n8.3456\n8.7962\n8.7673\n8.7607\n8.6485\n7.493\n6.257\n7.9901\n7.242\n5.6484\n5.8664\n5.9978\n4.6432\n4.6133\n5.5268\n4.1945\n3.1604\n6.1576\nNaN\nNaN\n0.77336\n1.9937\n1.522\n1.7216\n1.5207\n2.9867\n3.2101\n3.6219\n6.5612\n7.5594\n7.6722\n5.6405\n4.7604\n4.4253\n5.7525\n6.3661\n5.746\n6.6003\n6.9098\n6.9314\n7.3415\n7.7566\n7.9371\n8.1976\n9.5888\n9.4501\n8.2572\n8.0117\n7.6733\n7.2237\n7.7303\n7.0036\n7.1348\n7.4672\nNaN\nNaN\n3.5259\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.6786\n8.248\n6.9207\n7.1221\n7.4665\n7.9216\n7.1413\n6.5045\n6.4706\n6.3205\n6.6458\n7.0905\n6.1092\n3.3275\n4.8028\n5.506\n4.7731\n5.7905\n4.9382\nNaN\nNaN\nNaN\nNaN\n1.5167\n1.261\n1.741\n1.9819\n2.4499\n3.3455\n4.1544\n3.0756\n5.6411\n6.058\n5.9194\n5.6892\n4.1347\n3.2594\n4.7514\n5.3905\n6.4599\n7.1261\n7.3578\n7.2372\n7.4531\n7.1918\n7.6847\n8.4815\n9.7353\n9.2755\n8.2399\n8.1663\n7.8631\n7.9678\n7.9068\n7.6248\n7.6644\n7.0241\nNaN\nNaN\n4.2696\n5.963\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.6976\n6.6908\n5.7081\n6.5684\n6.907\n6.7103\n6.7853\n5.5575\n6.2363\n6.066\n5.8656\n5.6342\n5.5126\n4.3508\n4.9999\n4.8145\n4.5244\n5.2974\n4.9925\nNaN\nNaN\nNaN\nNaN\n1.6695\n2.1639\n2.5379\n2.1638\n3.883\n3.1178\n4.5403\n2.7742\n4.547\n4.1535\n4.5981\n4.5906\n3.0907\n2.2975\n3.6465\n4.1966\n5.5197\n7.7656\n7.9308\n7.4453\n7.509\n6.5185\n7.4916\n8.6454\n9.3078\n8.8486\n8.5058\n7.7629\n7.9966\n8.5627\n8.653\n8.3136\n6.8709\n5.9345\nNaN\nNaN\n5.1434\n3.8231\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.261\n6.3474\n5.155\n5.4618\n6.3647\n5.8195\n6.3219\n5.9773\n6.6038\n6.6154\n5.6977\n5.1384\n5.0226\n5.2118\n3.9893\n3.8432\n4.4096\n4.2467\n2.7838\n1.8005\n1.6219\n1.8224\n1.6578\n2.601\n2.2416\n2.6125\n2.5155\n2.5227\n2.1375\n3.0894\n2.4566\n3.0006\n2.4485\n3.085\n2.5688\n2.1377\n2.2628\n3.6364\n4.4062\n5.0078\n7.4942\n8.1991\n8.1582\n7.8077\n6.3384\n7.9326\n9.2827\n9.2094\n8.9401\n8.7673\n8.2203\n8.232\n8.8311\n9.3156\n9.4128\n7.6351\n5.2615\n6.1148\n6.9727\n6.1329\n4.2693\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.0636\n5.6306\n5.2902\n4.268\n4.6571\n4.7291\n5.8649\n6.4094\n6.5962\n6.5384\n5.589\n5.2591\n5.1557\n4.3454\n3.6916\n3.887\n4.4005\n4.4696\n2.3892\n2.191\n2.4314\n2.0662\n2.0677\n2.6486\n1.6226\n2.2582\n2.471\n2.0805\n1.9018\n2.1342\n2.5409\n2.4979\n1.5543\n2.1813\n0.78024\n0.40553\n1.9523\n3.5863\n4.0757\n6.2191\n7.343\n8.4235\n8.6257\n7.8155\n6.8598\n9.6336\n10.162\n10.334\n9.5671\n9.1374\n8.9855\n8.7254\n9.1202\n9.8859\n9.8617\n7.9484\n6.1516\n5.8711\n7.3981\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2825\n5.25\n5.4106\n4.075\n4.9749\n4.7967\n5.6221\n5.7814\n5.6155\n6.0324\n5.116\n4.5234\n4.3394\n3.8836\n3.5989\n3.7692\n3.7326\n4.4725\n3.9791\n3.7044\n3.4444\n1.9679\n1.6603\n1.8347\n1.5568\n2.2454\n2.1176\n1.5083\n2.0944\n1.9695\n3.2538\n3.1035\n2.9929\nNaN\nNaN\n0.83359\n1.2543\n3.0773\n3.8977\n6.074\n8.512\n9.067\n9.1286\n8.7671\n9.5212\n11.212\n10.657\n10.562\n10.234\n9.8061\n9.1054\n9.3122\n9.9911\n10.058\n9.7374\n9.156\n8.3985\n6.296\n7.7965\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0577\n4.405\n4.6572\n4.2731\n4.7344\n5.3763\n5.4807\n4.7473\n4.8791\n5.772\n5.1363\n3.8297\n4.4278\n3.8345\n4.2608\n4.6554\n3.9376\n4.1742\n3.8851\n3.6507\n1.6076\n1.1415\n0.91081\n0.24881\n1.3912\n3.0978\n2.2309\n1.9422\n2.4541\n2.1788\n2.9045\n3.4648\nNaN\nNaN\nNaN\n2.2376\n3.217\n3.9517\n4.262\n6.0141\n9.304\n10.053\n9.7829\n9.7685\n10.537\n11.289\n11.079\n10.731\n10.887\n9.5881\n8.7127\n9.7108\n10.15\n10.435\nNaN\nNaN\n10.343\n9.1656\n9.8463\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.6731\n4.0232\n4.5713\n4.8465\n4.3137\n4.9658\n5.8414\n5.5105\n5.0963\n4.842\n4.1027\n3.2926\n3.7825\n3.814\n4.3389\n4.8645\n4.7958\n3.6228\n2.7295\n2.0555\n0.38835\n0.43398\n-0.22391\n-0.81283\n0.83607\n2.4203\n2.5767\n2.5616\n3.1609\n2.8472\n2.8011\nNaN\nNaN\nNaN\nNaN\n2.5725\n4.3351\n4.1073\n5.1209\n6.5562\n8.9605\n9.7219\n10.255\n10.928\n11.611\n11.967\n11.752\n12.005\n11.382\n10.439\n9.6202\n10.4\n10.505\nNaN\nNaN\nNaN\n10.62\n10.04\n10.787\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.0801\n4.4502\n4.6241\n4.6046\n4.6504\n5.5866\n5.9906\n5.6806\n5.1785\n4.4691\n4.0886\n3.8875\n3.9712\n3.9142\n4.1233\n4.8531\n4.8988\n2.8031\n1.7574\n1.0833\n0.37293\n0.25947\n-0.9423\n0.71559\n1.1844\n1.5309\n3.8669\n2.9512\n3.0959\n3.7563\n3.421\nNaN\nNaN\nNaN\nNaN\n2.6749\n3.9249\n4.1947\n6.1012\n8.2235\n9.9124\n10.388\n11.004\n12.084\n12.357\n13.06\n12.36\n12.334\n11.379\n10.942\n11.136\n11.043\n10.954\nNaN\nNaN\nNaN\n10.413\n10.157\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.4652\n5.0606\n4.9005\n5.0443\n5.3399\n5.9016\n5.8808\n5.7605\n5.0465\n4.2462\n4.0251\n4.0088\n4.4904\n4.3282\n4.0344\n3.8525\n3.6229\n2.5317\n1.5305\n0.9896\n0.85496\n-0.02553\n-0.62673\n0.56048\n1.0648\n2.098\n4.1053\n3.2\n4.0425\n4.497\n4.0809\nNaN\nNaN\nNaN\nNaN\n2.006\n2.4661\n4.3931\n6.8682\n9.7859\n11.226\n11.826\n12.823\n13.702\n14.022\n15.395\n12.815\n12.779\n12.159\n11.175\n11.45\n11.045\n11.255\nNaN\nNaN\nNaN\nNaN\n11.725\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.34\n5.1583\n5.2659\n5.3134\n4.9602\n5.4421\n5.4844\n5.0303\n4.4447\n3.8437\n3.8859\n3.6944\n4.0284\n4.0327\n4.3009\n3.4616\n3.9045\n3.3308\n2.0716\n1.047\n0.13879\n-1.4764\n-1.8758\n0.24055\n0.6357\n2.7667\n3.7329\n3.8186\n4.6867\n4.8467\n4.2844\nNaN\n2.7872\n2.7825\n3.2484\n1.8513\n3.2567\n6.8064\n8.6741\n11.868\n12.514\n12.065\n12.521\n14.995\nNaN\nNaN\nNaN\n13.375\n12.792\n11.817\n11.22\n10.021\n11.492\n11.955\nNaN\nNaN\nNaN\n12.13\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.5854\n5.2623\n5.1217\n5.0542\n5.0781\n5.4888\n5.6175\n4.7108\n3.8526\n4.3427\n5.2214\n4.1665\n3.5861\n3.4136\n4.1862\n2.9266\n2.9019\n2.9348\n2.6775\n0.97074\n0.73008\n-0.30788\n-3.5457\n-0.6916\n0.61903\n2.1117\n3.3595\n4.4524\n5.2457\n5.2388\n3.505\n2.9135\n3.1836\n3.721\n4.2827\n6.5453\n7.3768\n8.4631\n9.1147\nNaN\nNaN\nNaN\n11.408\nNaN\nNaN\nNaN\nNaN\n13.599\n13.046\n11.925\n10.986\n10.451\n11.151\n11.312\n12.384\n12.682\n12.279\n12.509\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.3922\n4.7751\n4.8385\n5.4141\n5.8093\n5.6672\n5.0497\n3.8621\n3.3121\n3.7271\n4.2007\n4.0354\n3.4053\n3.2815\n3.6764\n2.6557\n3.3752\n2.2346\n1.7838\n0.88432\n0.67214\n0.5778\n-0.44523\n0.32254\n1.6023\n2.2938\n3.9613\n5.4955\n6.0149\n5.2128\n3.6614\n3.8278\n3.818\n5.069\n6.5301\n7.8809\n7.6968\nNaN\nNaN\nNaN\nNaN\nNaN\n9.6002\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.94\nNaN\n10.613\n10.758\n11.116\n12.103\n12.955\n11.938\n11.892\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.7508\n5.2042\n5.2893\n5.6242\n5.6192\n5.439\n4.4517\n3.8414\n3.1637\n3.2045\n3.4318\n2.9733\n2.9625\n2.8317\n3.1163\n2.4975\n2.0773\n1.4138\n0.54842\n-0.014462\n0.11336\n0.039736\n0.43577\n1.3093\n2.5873\n3.3929\n5.1618\n5.6398\n5.9631\n4.8918\n5.3332\n5.8922\n6.5992\n7.7038\n7.7462\n8.3622\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.277\n12.517\n11.235\n10.415\n9.5989\n8.0048\n7.7582\n8.4963\n10.836\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.211\n5.6293\n5.6746\n5.7243\n5.7541\n5.1796\n3.9496\n3.6531\n3.3309\n2.7736\n2.9632\n2.5847\n2.831\n3.1008\n3.153\n1.7896\n0.24741\n0.010661\n-0.53265\n-0.82885\n0.2064\n-0.11016\n0.52936\n2.1118\n3.5034\n4.378\n5.6576\n6.45\n6.9451\n5.5871\n5.5194\n7.7914\n8.3088\n8.8406\n8.5852\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.16\n11.499\n11.035\n10.954\n11.038\n9.6332\n8.4378\n9.2286\n8.2489\n7.9709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.3265\n5.5714\n5.1346\n5.3059\n5.7381\n4.7227\n4.0372\n3.4283\n2.8497\n2.6616\n2.9997\n2.3459\n2.7646\n3.6388\n3.4701\n1.0091\n-0.34714\n-0.95159\n-1.7018\n-1.9997\n0.026441\n0.98153\n1.5756\n2.7338\n4.1849\n5.1257\n6.2673\n7.3467\n7.8888\n7.3702\n6.8951\n8.4646\n8.8057\n8.027\n8.1574\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.048\n10.831\n10.601\n10.871\n11.528\n12.038\n10.583\n9.4348\n8.1843\n7.5401\n6.9355\n6.7241\n7.0847\nNaN\nNaN\nNaN\nNaN\nNaN\n5.6504\n5.8847\n4.908\n5.1573\n5.3439\n4.4621\n3.327\n2.7405\n2.2335\n2.8128\n3.2266\n3.3523\n2.2824\n2.8185\n1.8256\n0.39968\n-0.48016\n0.5845\n-1.6716\n-1.2061\n0.34552\n1.0702\n1.681\n3.412\n4.591\n5.9719\n7.5031\n8.2693\n8.3766\n8.8613\n9.5665\n11.908\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.049\n11.783\n11.44\n10.53\n9.5283\n9.4201\n11.89\n13.071\n11.315\n9.0524\n9.1543\n8.8784\n7.1021\n6.0089\n7.9201\n10.608\n9.8201\n10.678\n5.5479\n2.3942\n5.6243\n5.4195\n4.6872\n4.6753\n4.7677\n4.1725\n2.7619\n2.4377\n2.0634\n2.2178\n2.2758\n2.6268\n2.814\n1.7792\n0.67034\n0.25935\n0.3244\n1.1356\n-0.88826\n-0.11155\n1.5537\n1.0526\n1.1565\n3.0058\n4.8833\n7.8331\n8.2691\n8.7748\n9.6844\n10.552\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.24\n11.611\n11.886\n11.683\n10.446\n9.9633\n8.2982\n11.031\n12.396\n11.314\n9.8642\n9.8534\n9.4339\n7.5834\n5.1872\nNaN\nNaN\nNaN\nNaN\n6.5952\n3.7881\n5.177\n4.5392\n3.6256\n3.9122\n3.848\n3.1831\n2.9314\n2.774\n2.854\n1.7679\n0.87922\n1.4804\n2.6462\n1.2973\n0.80374\n0.79348\n1.0028\n0.75299\n0.4892\n1.2268\n1.6845\n-0.0052843\n0.38372\n2.9472\n6.084\n7.5052\n8.2615\n8.8851\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.796\n12.42\n11.99\n11.396\n11.576\n11.422\n10.709\n10.425\n9.6768\n9.852\n10.495\n10.071\n9.8557\n9.834\n10.925\n9.8778\n7.0908\nNaN\nNaN\nNaN\nNaN\n6.6199\n6.1234\n4.708\n3.8735\n2.7241\n3.4968\n3.206\n3.5272\n3.1885\n3.0434\n3.0744\n1.4377\n0.25899\n0.46217\n0.90843\n0.65153\n0.82865\n1.7741\n2.0123\n1.206\n-0.047761\n-0.052344\n0.80336\n0.94969\n2.075\n3.878\n5.7632\n7.1889\n7.8503\n9.0839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.026\n13.462\n12.225\n11.883\n11.018\n10.81\n10.536\n10.576\n10.126\n9.6112\n8.9058\n8.5309\n8.3156\n8.3101\n7.9075\n9.5717\n8.1441\n9.211\nNaN\nNaN\n4.6304\n3.0294\n6.1721\n4.4348\n4.3339\n3.197\n3.2879\n3.3045\n2.5978\n3.4587\n2.8592\n2.5346\n2.3837\n1.1992\n0.21268\n0.24852\n-0.024162\n-0.37785\n0.69189\n2.0317\n2.6119\n2.4605\n0.25782\n-0.17688\n1.1424\n2.4468\n2.9092\n4.2647\n5.419\n7.9631\n8.2583\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.527\n13.698\n14.125\n11.975\n11.484\n10.385\n9.7899\n9.5168\n9.1508\n9.2461\n9.7208\n8.4254\n7.5267\n8.0791\n7.4181\n6.1629\n7.2927\n7.4154\n7.9209\n8.0106\n10.221\n5.503\n3.5129\n5.5768\n5.1695\n4.3858\n3.3715\n3.6291\n3.223\n2.4974\n3.1374\n2.9571\n1.68\n1.0158\n0.41922\n-0.31211\n0.35645\n0.78013\n0.71006\n2.238\n2.5728\n3.0916\n3.5471\n2.3436\n1.8356\n2.5542\n3.0594\n3.5154\n4.2363\n6.8377\n8.9469\n8.8785\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.914\n12.976\n13.94\n11.689\n10.587\n10.466\n9.1341\n8.8872\n8.8209\n9.1843\n8.8698\n8.1415\n7.1565\n7.5343\n6.9637\n5.3056\n5.933\n6.4275\n5.9076\n9.1026\n9.0528\n6.0247\n5.1437\n6.295\n5.945\n4.6227\n4.3259\n4.427\n3.4398\n2.3758\n2.8261\n2.8139\n1.9384\n0.36195\n-0.32724\n-0.2672\n0.65835\n1.3088\n1.3668\n2.47\n2.8897\n2.765\n3.5496\n3.9093\n3.1791\n2.874\n3.3539\n4.271\n5.1188\n7.4048\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.118\n12.752\n13.165\n14.336\n11.407\n9.986\n10.266\n9.6519\n10.066\n10.254\n8.9168\n8.8338\n8.4385\n7.1878\n7.0561\n6.7361\n4.3132\n5.8464\n5.2409\n6.0306\n7.0368\n7.7684\n5.4191\n6.3019\n7.445\n5.7289\n5.6587\n4.7752\n4.576\n3.3155\n3.0501\n2.9455\n2.5616\n2.3971\n1.4767\n1.0262\n0.61098\n1.0094\n1.3872\n1.2628\n2.8399\n3.5793\n2.6931\n2.8829\n3.3694\n3.4381\n3.2898\n3.7702\n4.1234\n5.9235\n8.3182\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.9017\nNaN\nNaN\nNaN\nNaN\n14.204\n14.056\n13.122\n12.968\n13.313\n12.468\n10.307\n10.359\n10.52\n10.491\n10.619\n9.4701\n9.0759\n8.3323\n7.3762\n6.7878\n5.4076\n4.8155\n6.0448\n5.9209\n6.7342\n5.0294\n5.4891\n5.2919\n5.6408\n5.2255\n2.9007\n5.893\n4.8567\n3.8666\n3.293\n3.5013\n3.3343\n2.8441\n3.0679\n2.1515\n1.7533\n1.2521\n1.8465\n2.2213\n2.1183\n3.911\n4.6257\n4.2662\n4.1975\n2.934\n2.47\n2.8587\n4.0704\n4.3056\n5.9419\n9.5154\n10.676\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n15.73\n14.56\n14.49\n13.764\n12.414\n11.602\n11.154\n10.838\n10.901\n11.023\n11.334\n11.635\n10.607\n8.4153\n6.8122\n6.2237\n6.0881\n5.8475\n4.922\n5.4531\n6.578\n5.5266\n5.687\n4.2913\n4.1465\n4.7567\n4.3848\n2.0617\n-1.9643\n6.154\n4.3712\n3.3976\n3.707\n3.9073\n3.808\n4.0979\n3.6695\n1.4081\n1.5801\n1.879\n2.8136\n3.4926\n3.7953\n5.5004\n6.8682\n7.105\n7.4382\n5.7538\n2.7682\n2.0181\n4.1476\n4.5814\n6.6876\n10.348\n12.067\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.6282\nNaN\nNaN\n26.811\n15.903\n14.842\n14.394\n13.737\n11.569\n10.18\n8.5477\n8.3266\n9.2817\n10.836\n11.454\n10.918\n9.7598\n7.1125\n5.9213\n4.2204\n3.8135\n3.6642\n4.5017\n6.1996\n5.9815\n4.7975\n4.6218\n4.531\n4.6885\n4.4059\n3.4334\n0.92895\n-3.6289\n6.5181\n5.1035\n4.0579\n4.496\n4.0675\n4.2796\n4.1679\n3.3866\n2.1558\n2.9784\n2.8156\n3.3094\n4.0609\n4.6426\n6.1485\n7.2046\n8.8636\n8.7709\n7.6804\n5.4091\n3.7688\n4.7342\n5.5242\n8.1482\n9.6952\n12.89\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.93594\n24.993\n23.672\n21.753\n14.915\n13.947\n13.669\n13.475\n10.662\n8.886\n7.823\n7.0483\n8.1273\n8.9309\n9.1369\n7.6465\n7.1764\n6.3162\n5.644\n3.7845\n3.2676\n2.9758\n3.9732\n5.2446\n4.0981\n3.5916\n4.3215\n5.2813\n4.4253\n3.2519\n2.0202\n0.92241\n-1.5014\n5.8862\n5.9697\n6.0839\n5.6369\n4.7893\n4.0067\n3.6232\n3.8646\n3.6983\n3.4843\n3.7526\n3.9261\n3.9731\n4.543\n6.365\n7.1894\n7.4282\n7.7441\n7.7092\n7.0788\n5.9975\n5.7511\n6.7371\n8.7544\n9.7247\n12.576\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n24.197\n19.41\n20.244\n18.992\n13.676\n12.746\n12.243\n11.736\n10.358\n8.7385\n8.0004\n7.6531\n7.9953\n7.8442\n7.1736\n5.8519\n4.8781\n5.3065\n4.9427\n3.5259\n2.6833\n2.1855\n2.9394\n3.0139\n2.635\n2.2333\n3.3495\n3.4248\n3.6256\n2.1471\n0.99355\n0.88478\n0.71298\n6.3592\n6.4614\n6.2705\n6.291\n5.7514\n4.3437\n4.129\n4.4727\n4.5519\n4.2665\n4.4366\n4.5424\n4.3208\n4.596\n6.5699\n7.4955\n7.6084\n6.6264\n8.1977\n8.5392\n8.3628\n7.8454\n7.7433\n9.3616\n10.492\n11.038\n12.802\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n23.649\n16.991\n17.265\n16.358\n13.079\n10.919\n10.964\n10.96\n9.9078\n8.6293\n7.6878\n7.07\n6.568\n6.2734\n5.6055\n4.3012\n3.5268\n4.244\n3.9994\n2.6966\n2.2152\n1.3242\n1.8375\n1.2614\n1.6364\n0.947\n1.668\n1.7145\n-1.4452\n-0.66557\n0.2134\n1.6096\n0.40847\n7.3762\n6.7331\n6.0788\n6.1237\n6.562\n5.4721\n4.9538\n4.9805\n4.9859\n4.2291\n4.5755\n4.8175\n5.0373\n5.4228\n6.9269\n8.0116\n8.3615\n7.1648\n8.108\n8.5486\n8.3497\n8.0829\n8.3613\n9.4344\n10.371\n8.7105\n10.76\n12.373\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n21.563\n16.028\n13.459\n14.107\n11.656\n9.9069\n9.9426\n9.7578\n9.4972\n6.9496\n5.8693\n5.6549\n4.9696\n4.9478\n4.1663\n2.5934\n2.5835\n2.8284\n2.8007\n1.2801\n0.4118\n-0.65729\n-0.32806\n0.18538\n-0.41555\n-0.10813\n-0.10468\n0.57563\n-1.3673\n-1.7453\n-0.34848\n-0.33784\n-1.3608\n7.7802\n6.698\n5.3825\n5.8876\n7.2165\n6.089\n6.0867\n6.1401\n5.7456\n4.1947\n3.8731\n4.7199\n5.267\n6.4088\n7.1696\n8.6724\n8.9164\n7.9059\n8.453\n8.4411\n8.9988\n9.181\n9.0431\n9.1654\n10.482\n9.8398\n10.301\n12.22\nNaN\nNaN\n12.478\n15.133\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.957\n13.643\n13.536\n13.199\n9.8399\n8.579\n9.0503\n8.5624\n6.9462\n5.3165\n5.0223\n4.756\n4.6866\n4.5875\n4.3086\n2.0551\n2.129\n2.2472\n2.187\n1.3974\n-0.54871\n-1.6557\n-1.8168\n-1.8075\n-1.8733\n-0.45852\n-0.19519\n-0.59235\n-0.53895\n-0.77376\n-0.49314\n-0.34675\n-2.1935\n8.406\n7.6196\n6.6815\n6.9949\n8.5005\n7.5745\n7.3106\n7.4544\n7.1532\n5.9582\n5.287\n6.4748\n6.3987\n7.4464\n7.4164\n8.089\n8.9948\n8.8818\n8.3461\n7.9571\n8.1773\n9.2222\n9.2328\n9.59\n10.67\n10.301\n10.366\n12.97\n13.201\n14.322\n15.161\n15.798\n14.806\n13.834\nNaN\nNaN\nNaN\nNaN\n14.751\n14.359\n13.575\n12.853\n12.094\n8.6904\n7.5588\n7.748\n6.581\n5.4581\n4.0025\n4.4536\n4.9814\n4.8743\n3.8917\n3.2932\n1.153\n1.2946\n1.7865\n1.7422\n1.1179\n0.43384\n-0.11882\n-0.50717\n-2.5999\n-1.7415\n-0.42167\n0.40305\n-0.17194\n-1.5282\n-0.86913\n-0.52658\n0.57097\n-1.44\n9.4582\n8.4908\n8.6612\n8.9206\n9.3785\n9.8709\n8.7759\n8.9629\n8.6936\n8.0189\n7.1762\n8.3292\n8.237\n8.2219\n7.9597\n7.7006\n8.7255\n8.9001\n8.9351\n9.1411\n9.1723\n9.9116\n9.9217\n9.7667\n10.29\n11.05\n11.617\n12.359\nNaN\n14.58\n16.721\n15.923\n15.676\n17.261\nNaN\nNaN\nNaN\n13.717\n13.774\n14.072\n13.339\n10.703\n10.201\n8.9797\n8.2034\n7.3221\n6.4548\n4.2649\n2.688\n3.6268\n4.6949\n4.4738\n3.2124\n1.6159\n0.090604\n0.18644\n0.49698\n-0.030892\n0.48546\n0.74052\n0.22802\n-0.19104\n-0.70801\n-0.8857\n0.28912\n1.3437\n0.22747\n-1.9193\n-0.21571\n1.4413\n0.58251\n1.1104\n8.8597\n8.8688\n9.6802\n10.084\n10.145\n10.553\n10.191\n9.8871\n9.8493\n9.6392\n8.3771\n8.6365\n8.9277\n8.4246\n7.7629\n7.1634\n8.2549\n8.6634\n9.0387\n9.0749\n9.2463\n9.9149\n10.431\n9.8236\n9.9485\n10.82\n11.588\nNaN\nNaN\n13.76\n16.107\n15.911\n16.71\n17.573\nNaN\nNaN\nNaN\n13.046\n13.025\n13.416\n12.684\n9.9304\n9.3293\n8.6532\n7.4966\n5.4005\n5.194\n3.6782\n1.8486\n3.2503\n3.9008\n4.1097\n3.1777\n2.1517\n0.3469\n0.26194\n-0.15013\n-0.81893\n-0.015053\n0.31079\n0.17446\n0.10217\n-0.22375\n-0.13746\n1.1522\n1.7288\n1.3294\n0.20274\n0.20913\n1.6838\n0.03972\n0.34626\n9.0473\n9.6655\n11.138\n10.912\n10.594\n10.953\n11.075\n11.087\n10.905\n10.209\n8.6005\n8.0397\n7.8209\n8.254\n7.5111\n6.9005\n7.6262\n8.5999\n9.5945\n9.7305\n9.7801\n10.338\n10.821\n9.9711\n9.8195\n10.949\n11.684\nNaN\nNaN\nNaN\n14.615\n14.699\n14.465\n15.641\nNaN\n14.691\n13.103\n11.819\n11.782\n12.711\n9.786\n8.5233\n7.9533\n6.1417\n4.6022\n2.8863\n2.1159\n1.5696\n1.4808\n3.0992\n3.6313\n3.5962\n2.8165\n2.0658\n0.63115\n0.18818\n-0.37164\n-0.53962\n-0.39758\n-0.028064\n0.17501\n0.34079\n0.37333\n0.6385\n1.0463\n1.6378\n1.1896\n0.62968\n0.091299\n0.41919\n0.24766\n3.1153\n10.387\n11.117\n12.948\n11.396\n10.68\n10.92\n12.398\n12.895\n11.925\n10.801\n9.0405\n8.2832\n7.6887\n7.1856\n7.051\n6.6958\nNaN\nNaN\nNaN\n9.1946\n8.8694\n9.7965\n11.285\n10.262\n10.207\n11.302\n11.481\nNaN\nNaN\nNaN\n12.548\n12.854\n11.61\n12.273\n12.746\n12.541\n11.758\n10.846\n10.681\n9.7461\n7.3662\n6.2723\n5.5719\n5.0769\n3.5199\n2.7105\n1.7423\n0.81837\n0.9483\n2.048\n2.6565\n2.837\n1.9213\n1.1871\n0.97542\n0.51724\n0.30498\n-0.56915\n-0.78237\n-0.31329\n0.41243\n0.79063\n1.1952\n1.7543\n1.1176\n1.1807\n1.352\n1.1391\n0.85758\n1.0204\n2.2941\n2.0914\n11.007\n12.647\n13.525\n11.429\n10.824\n11.264\n12.939\n13.665\n12.742\n11.833\n10.348\n9.608\n8.9847\n8.2937\n7.581\n7.5456\nNaN\nNaN\nNaN\n8.3873\n8.5427\n8.4276\n10.145\n10.044\n9.5992\n9.9278\n10.509\nNaN\nNaN\nNaN\n11.218\n11.503\n10.904\n10.906\n11.809\n11.281\n10.116\n8.9325\n8.1356\n7.4257\n4.5315\n4.3715\n5.138\n5.0095\n3.8248\n3.3327\n2.157\n0.52409\n0.4411\n1.3944\n1.7463\n2.0898\n1.3156\n1.3725\n1.7804\n1.2432\n0.53962\n-0.070783\n-0.43788\n0.3821\n0.65729\n1.2498\n1.3765\n1.0044\n1.2013\n0.98423\n1.2409\n1.4932\n1.2851\n1.0472\n2.1474\n1.2447\n11.514\n12.367\n12.244\n10.872\n11.262\n11.874\n12.483\n13.381\n13.438\n12.808\n11.437\n10.565\n10.309\n9.7604\n8.3106\n8.5284\n8.1134\nNaN\nNaN\n7.7605\n8.6553\n8.8801\n9.3148\n9.2221\n9.4963\n9.8683\n9.9897\n9.7969\n9.7468\n9.9303\n10.264\n10.404\n10.041\n10.139\n11.152\n10.536\n9.2018\n7.5428\n6.4611\n6.2232\n5.0425\n4.9275\n5.3386\n4.7632\n3.7825\n2.4502\n0.3087\n-0.20078\n0.12621\n0.95407\n0.89655\n1.9919\n2.5086\n1.8378\n2.467\n1.887\n0.70991\n0.12794\n-0.17629\n0.4734\n0.079207\n1.3387\n1.5077\n0.1878\n0.99299\n1.3352\n1.6335\n1.9765\n2.3005\n1.6055\n0.81054\n-0.014904\n11.53\n11.964\n11.724\n11.06\n11.57\n12.367\n13.412\n14.323\n13.957\n13.759\n12.353\n11.397\n10.984\n10.246\n8.398\n8.1901\n8.968\n6.2338\n5.9627\n7.7742\n8.3606\n8.756\n9.4111\n9.0171\n9.1514\n9.4964\n9.2106\n8.8514\n9.1853\n10.064\n9.8128\n8.9367\n8.6277\n8.995\n10.308\n10.171\n8.2054\n6.613\n6.0715\n6.0791\n5.7719\n5.349\n5.5407\n5.2833\n3.9844\n3.0173\n1.4402\n0.9549\n0.39886\n0.44234\n0.33226\n2.6164\n2.9002\n2.8497\n3.6904\n2.1283\n0.814\n0.046351\n-0.75735\n-0.74007\n-0.33955\n1.0684\n1.7917\n0.90897\n1.2953\n2.1549\n3.1748\n3.1784\n3.1064\n1.7333\n-0.3016\n-1.2697\n12.114\n12.455\n12.311\n11.887\n11.932\n12.708\n13.602\n14.264\n14.173\n13.907\n13.441\n12.207\n11.338\nNaN\nNaN\nNaN\nNaN\n8.3967\n8.1699\n8.1902\n7.9419\n8.2751\n8.9871\n8.7187\n8.5746\n8.9011\n8.9668\n8.792\n8.256\n8.3181\n8.3978\n7.4905\n7.7466\n7.9445\n7.9243\n8.7099\n6.9577\n6.179\n6.1133\n6.0467\n5.2898\n5.3636\n5.7949\n5.2602\n4.3442\n4.1263\n2.5724\n2.5948\n1.3669\n1.0898\n0.81966\n2.4101\n2.6382\n3.0435\n2.8249\n1.8421\n0.22333\n-0.75829\n-1.2431\n-1.5758\n-0.73292\n0.9369\n1.9148\n1.5956\n1.427\n2.8281\n4.6528\n4.1288\n2.6149\n-0.016175\n-1.1151\n-1.8778\n14.666\n13.478\n13.442\n12.714\n12.53\n13.254\n13.509\n13.873\n13.403\n13.203\n13.238\n12.903\n12.855\nNaN\nNaN\nNaN\nNaN\nNaN\n7.906\n7.9155\n7.7151\n7.8364\n8.1971\n8.2424\n8.0686\n8.0906\n8.15\n8.0081\n7.3635\n6.5513\n6.1399\n5.9086\n6.0528\n5.9626\n5.9397\n5.9207\n5.6028\n6.5952\n6.4095\n6.2252\n5.9757\n6.1513\n5.7451\n4.4993\n3.3355\n3.5866\n3.6832\n3.9411\n2.1076\n1.7419\n1.5216\n1.0763\n1.2446\n1.104\n0.96141\n0.58849\n-0.37116\n-1.1234\n-0.34029\n-0.21652\n0.13069\n1.3411\n1.9007\n2.0031\n2.5314\n4.0392\n6.1541\n4.8987\n3.0656\n-0.78301\n-1.1024\n-2.4888\n15.11\n14.357\n13.731\n13.363\n12.908\n13.777\n13.497\n13.196\n13.11\n13.385\n14.019\n14.19\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.4732\n7.7018\n7.7542\n7.559\n7.7785\n7.3615\n6.6474\n6.4863\n6.4736\n5.9688\n5.8121\n5.3468\n4.8287\n4.9494\n4.9296\n4.8224\n4.5314\n4.4685\n5.9601\n6.1011\n5.9786\n6.0023\n6.3806\n5.2717\n3.7149\n2.5697\n2.6587\n3.531\n2.8409\n2.177\n1.5267\n1.5483\n0.98971\n0.98067\n0.99958\n0.60391\n0.59071\n-0.23436\n-0.56348\n0.62651\n0.39723\n0.44875\n1.1475\n1.5028\n2.0581\n4.2439\n4.0209\n4.4249\n4.7029\n2.5815\n-1.207\n-0.48016\nNaN\n14.953\n14.68\n14.842\n13.977\n13.642\n14.265\n14.346\n13.95\n13.708\n13.854\n14.26\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.6193\n7.6431\n7.2365\n6.6984\n6.8913\n6.7347\n6.1361\n5.4939\n5.4625\n5.1382\n5.104\n4.6658\n4.7151\n4.6666\n4.6056\n4.4225\n4.2327\n3.4121\n4.8053\n4.7216\n4.9414\n5.3255\n5.2394\n4.3267\n2.3567\n2.3835\n2.194\n2.8213\n2.2287\n1.5623\n1.1872\n1.0215\n0.43702\n1.2089\n1.4987\n1.2994\n1.3674\n0.019524\n-0.87408\n1.0862\n-0.32373\n-0.041833\n0.29306\n1.5141\n3.6543\nNaN\nNaN\n4.8402\n6.7269\n3.7402\nNaN\nNaN\nNaN\n2.5718\n1.158\n1.5714\n0.55021\n0.49876\n1.043\n0.95941\n0.66816\n2.025\n2.9627\n1.2617\n1.3524\n0.47112\n1.2946\n3.7268\n2.3908\n2.2127\n0.16428\n5.7687\n3.9533\n3.9395\n3.1284\n4.0249\n1.956\n3.4696\n0.65128\n-0.74513\n-2.997\n-0.47148\n3.4431\n-3.9725\n-2.9677\n-3.0935\n-4.771\n-1.7936\n-2.1414\n-1.7231\n-0.30411\n-1.7755\n-2.3613\n-0.30772\n0.66303\n1.6031\n3.3845\n3.4899\n1.8472\n2.1077\n2.323\n0.93997\n0.55443\n1.0451\n2.3182\n1.9339\n-0.49838\n3.6059\n1.659\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52174\n0.72989\n1.0561\n4.1586\n4.0425\n-0.25734\n1.4968\n0.40591\n4.0827\n3.7805\n3.2729\n3.2003\n0.58983\n3.0281\n4.9364\n3.4059\n-1.1995\n-0.94441\n1.9692\n1.7093\n6.5968\n2.7319\n2.3942\n5.3619\n5.4048\n1.9991\n0.63431\n-0.36079\n2.2405\n2.0412\n1.2334\n0.20769\n-0.67655\n-3.659\n-2.8045\n-2.9341\n-2.0504\n0.054634\n-1.52\n0.28529\n2.2051\n1.1628\n2.3505\n2.728\n3.1434\n2.2956\n2.416\n1.7361\n0.72205\n0.52055\n0.68669\n2.0666\n2.2957\n3.2691\n7.2765\n3.5434\n1.2086\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.58237\n0.88884\n1.4487\n1.1481\n-2.3431\n-1.355\n0.46154\n1.653\n3.0732\n4.7775\n3.5681\n2.4047\n0.95588\n4.9969\n8.3854\n4.9922\n1.0672\n1.1594\n1.0178\n4.8791\n3.7165\n3.9754\n5.0809\n4.6864\n6.3041\n4.4633\n2.7847\n2.278\n2.6681\n-0.30611\n1.952\n1.1737\n0.51754\n-0.99358\n-3.4804\n-3.9143\n-1.6554\n-0.4779\n0.24573\n0.3105\n1.6001\n2.3344\n1.8987\n2.7867\n2.242\n1.2602\n0.76656\n1.4448\n1.3896\n1.7005\n2.0541\n1.8021\n2.665\n4.5358\n5.2943\n4.3692\n3.0779\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6655\n0.83257\n1.1635\n1.0292\n0.78032\n1.3699\n2.7569\n2.4474\n1.4093\n1.6157\n2.2191\n-0.17176\n3.7031\n7.2978\n5.3095\n1.7586\n0.72144\n0.67631\n0.14329\n4.281\n5.2389\n4.4507\n4.9987\n2.7019\n5.2718\n5.6518\n4.3557\n2.7512\n1.4884\n0.17614\n0.91029\n1.1233\n-0.10137\n0.15404\n-2.3077\n-3.8021\n-3.2438\n-0.76098\n0.69204\n0.70165\n1.5956\n1.9903\n0.018973\n0.69167\n1.784\n0.48144\n0.15012\n1.5149\n2.1169\n3.573\n4.6741\n3.5609\n4.3854\n5.5688\n4.7832\n4.395\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1942\n1.634\n1.801\n1.2907\n1.4556\n1.2825\n1.4444\n1.6272\n0.76344\n-0.49147\n0.74975\n0.012974\n2.2237\n2.505\n0.55066\n-1.4038\n-1.6413\n0.66704\n-2.8732\n1.0186\n5.0116\nNaN\nNaN\n-2.0415\n4.0963\n5.9434\n5.9422\n3.0932\n3.3842\n3.9469\n0.50214\n0.27823\n-0.37291\n0.22696\n-1.0598\n-2.3571\n-3.3423\n-0.78002\n0.51934\n0.80145\n1.1337\n1.8119\n-0.011318\n-0.81854\n0.45844\n0.61047\n1.335\n2.9902\n2.6522\n2.3168\n4.1577\n4.4251\n3.8966\n5.4585\n4.7583\n5.0824\n9.8777\nNaN\nNaN\n-8.5147\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7178\n0.84828\n1.1895\n0.94946\n1.6269\n1.0501\n1.3376\n1.8042\n1.5369\n1.1611\n1.7468\n2.5446\n2.681\n0.21343\n-0.87511\n0.072688\n-1.5863\n0.25649\n-2.1392\nNaN\nNaN\nNaN\nNaN\n1.2426\n2.9253\n6.2058\n5.9974\n4.3819\n3.5417\n4.367\n2.7342\n-0.55575\n-0.37783\n-0.69772\n-1.6046\n-3.7515\n-3.8256\n-1.6335\n-0.39519\n0.84914\n1.4487\n0.68678\n-0.18421\n-1.1461\n-3.458\n-1.2373\n1.3263\n4.0462\n3.0967\n3.1851\n3.7054\n4.3425\n4.0599\n3.6651\n2.9341\n0.091002\n2.8069\nNaN\nNaN\n-13.006\n-7.2797\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.70145\n-0.27605\n0.3881\n1.7248\n2.7332\n1.3083\n2.7518\n3.0619\n3.1382\n3.0941\n2.9993\n2.1915\n2.2876\n0.64519\n0.87036\n-0.65572\n-1.3073\n1.6774\n-1.8597\nNaN\nNaN\nNaN\nNaN\n1.0586\n1.3099\n4.2535\n5.3677\n4.9179\n2.4294\n3.5875\n0.67201\n-1.6265\n-2.6204\n-1.7693\n-3.2297\n-4.189\n-3.8821\n-2.33\n-1.4603\n-0.20307\n1.9287\n0.30015\n-1.5645\n-2.7067\n-6.4698\n-1.5824\n1.4492\n3.2294\n4.0141\n4.1961\n3.0166\n4.0882\n4.6435\n4.3528\n2.9497\n0.1887\n2.058\nNaN\nNaN\n-6.9005\n-9.6731\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2627\n1.3665\n1.2254\n2.8408\n4.4977\n1.6939\n3.0931\n3.9556\n4.0667\n4.0918\n3.2511\n2.7312\n2.1736\n2.8827\n1.7685\n0.68238\n0.58149\n1.749\n-0.2043\n-0.3967\n-0.07176\n1.4244\n1.0396\n1.3189\n1.7845\n3.2292\n4.2436\n5.1283\n2.1768\n2.5597\n1.7572\n-1.6668\n-3.1988\n-2.5753\n-2.6816\n-2.261\n-2.1553\n-2.4937\n-2.3424\n-2.1634\n0.35918\n-0.43691\n-1.1119\n-2.6986\n-8.4819\n-1.4984\n2.0543\n2.7943\n2.9698\n4.3911\n3.1517\n4.0685\n5.8053\n5.9721\n5.6775\n2.8015\n1.4142\n3.0846\n3.6712\n-0.31314\n-5.9448\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8722\n0.9865\n1.8988\n1.7616\n3.3543\n2.1934\n2.7\n4.8033\n4.5667\n4.4409\n3.0258\n3.1711\n3.4612\n2.9361\n1.6131\n1.8197\n2.3947\n2.3883\n0.46762\n0.89884\n0.73525\n-0.47495\n-0.48139\n1.6446\n2.3773\n3.3053\n3.8195\n3.113\n2.1149\n1.5242\n2.0204\n0.011301\n-1.3602\n-3.1263\n-2.3015\n-1.9526\n-2.3608\n-2.7832\n-2.5299\n-2.3086\n-2.0514\n-1.6501\n-3.0104\n-5.1733\n-8.5229\n1.0618\n3.3326\n4.1999\n4.0733\n5.2334\n4.841\n6.0864\n7.1606\n9.4743\n8.5946\n3.3984\n0.73933\n2.2671\n5.9045\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.66152\n0.77312\n2.1573\n1.1217\n3.4929\n2.3402\n2.261\n2.9865\n3.6532\n3.4411\n3.4755\n3.571\n3.9247\n2.8909\n2.8245\n3.4601\n3.0676\n2.5131\n1.6691\n2.642\n2.144\n1.3348\n2.3457\n1.2894\n0.88604\n2.8549\n4.289\n3.179\n1.9306\n0.64505\n1.5598\n-2.2911\n-4.9792\nNaN\nNaN\n-1.6834\n-2.2194\n-2.9236\n-3.6905\n-3.1411\n-2.5108\n-2.1337\n-3.9129\n-4.3595\n-1.1092\n3.2611\n3.0268\n4.4505\n4.5143\n5.9862\n5.7201\n8.3449\n9.1895\n9.8075\n7.9298\n4.8589\n1.1105\n-0.48565\n6.2133\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4629\n-0.91221\n0.89606\n0.55302\n1.9771\n2.371\n2.673\n2.4142\n3.0592\n4.017\n4.3805\n3.569\n5.4659\n4.3944\n4.5257\n5.2059\n3.3972\n2.0118\n1.9808\n3.3028\n1.7296\n2.0522\n2.952\n-0.20043\n-0.46244\n3.6925\n4.774\n3.9531\n1.9036\n1.8959\n0.43734\n-2.6207\nNaN\nNaN\nNaN\n-2.0399\n-3.0769\n-2.5973\n-5.6749\n-4.2542\n-1.8038\n-2.2695\n-4.166\n-3.4585\n-0.39738\n2.0634\n2.4381\n4.0856\n5.5001\n4.7236\n5.0556\n7.8358\n7.98\n9.9115\nNaN\nNaN\n-0.47302\n-0.37141\n5.4041\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1256\n-1.5945\n-0.45876\n0.878\n1.5132\n2.8687\n3.735\n3.5486\n3.3302\n3.0791\n3.0375\n3.3832\n5.7671\n5.2983\n4.6514\n4.6951\n3.5073\n2.9279\n2.891\n2.4909\n0.58594\n1.4303\n1.9057\n-1.6886\n-1.1897\n2.0828\n4.231\n5.1635\n3.583\n3.371\n0.71033\nNaN\nNaN\nNaN\nNaN\n-2.4854\n-2.1765\n-3.2877\n-7.3931\n-4.2759\n-1.0577\n-2.5006\n-3.7683\n-2.0631\n-0.81956\n0.94861\n2.6553\n5.0674\n6.7747\n6.3331\n5.7563\n7.9591\n6.4434\nNaN\nNaN\nNaN\n1.1915\n-0.94877\n3.2989\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.29057\n0.16383\n0.61239\n1.0551\n2.1909\n3.0053\n3.1099\n3.0759\n2.9137\n2.9352\n3.778\n4.1638\n4.7945\n5.1564\n4.8113\n5.9562\n6.2072\n4.4722\n3.4187\n1.9114\n1.1627\n0.49288\n-1.9458\n-1.957\n-1.2414\n-0.21308\n5.4455\n4.0553\n2.1475\n3.2896\n1.8631\nNaN\nNaN\nNaN\nNaN\n-4.7585\n-3.901\n-2.4683\n-5.0673\n-3.3887\n-2.6743\n-5.7971\n-7.3146\n-1.7837\n0.64488\n2.2881\n3.4939\n5.7151\n7.3376\n7.4571\n6.9261\n7.3437\n4.9519\nNaN\nNaN\nNaN\n-2.7063\n-2.9432\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2988\n1.0597\n0.82022\n1.6605\n2.6426\n2.3649\n2.4652\n2.2423\n2.1519\n2.1826\n2.5193\n3.5414\n4.7832\n5.148\n4.9752\n6.3427\n6.0157\n4.4648\n2.6329\n1.7545\n1.5902\n-2.7458\n-5.2334\n-4.2849\n-4.1944\n0.26684\n4.1771\n2.486\n3.0921\n3.5969\n2.3293\nNaN\nNaN\nNaN\nNaN\n-6.0906\n-7.319\n-1.357\n-4.2912\n-2.9301\n-2.9939\n-4.9907\n-4.7701\n0.059836\n3.2581\n4.9439\n4.7724\n6.3376\n7.5878\n7.9766\n6.4115\n5.1689\n4.3206\nNaN\nNaN\nNaN\nNaN\n-3.152\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9058\n0.66123\n1.9856\n2.9291\n2.666\n2.0576\n2.6554\n2.0893\n1.7678\n1.5587\n1.4528\n2.6438\n4.6102\n4.8822\n4.9091\n5.2076\n6.0319\n5.2504\n3.2575\n1.3814\n1.3632\n-4.435\n-8.9484\n-5.9553\n-4.7293\n0.64706\n2.416\n2.5439\n4.1145\n3.6642\n3.3053\nNaN\n-5.9291\n-6.5219\n-4.2705\n-4.8065\n-5.3677\n-0.22675\n-2.2866\n0.1009\n0.10365\n-3.3041\n-4.568\n3.5125\nNaN\nNaN\nNaN\n6.5863\n8.2382\n7.9513\n5.2967\n5.9399\n5.6352\n1.7522\nNaN\nNaN\nNaN\n-3.8513\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6232\n0.80647\n2.03\n3.9875\n3.8608\n3.1374\n3.467\n2.6199\n1.8786\n2.5096\n1.8316\n2.891\n4.1414\n4.323\n4.228\n3.4096\n4.1071\n4.2877\n4.5603\n1.7694\n1.6712\n-0.57064\n-12.403\n-9.4954\n-4.6066\n-0.82532\n1.8181\n5.4448\n5.2096\n2.5288\n-0.54211\n-8.2348\n-4.8577\n-5.5578\n-4.2815\n-2.1789\n0.33184\n0.35045\n0.3889\nNaN\nNaN\nNaN\n-3.5899\nNaN\nNaN\nNaN\nNaN\n5.4356\n7.6665\n5.7747\n4.4871\n3.995\n6.8851\n2.6298\n0.58875\n-2.6505\n-6.3811\n-4.5498\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2897\n1.4234\n3.0751\n4.6477\n3.9053\n3.7081\n3.6667\n2.7897\n1.851\n2.833\n2.5691\n3.0112\n3.9475\n4.1061\n3.5756\n2.6084\n3.1872\n3.0678\n3.8811\n3.4094\n1.549\n-1.7823\n-5.5278\n-9.4632\n-2.4235\n0.27331\n2.7242\n3.9521\n2.6012\n-0.60268\n-3.4121\n-3.5668\n-1.182\n-4.4274\n-1.0305\n-0.33267\n0.95474\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.2701\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2986\nNaN\n3.6414\n6.3104\n3.3138\n1.3998\n0.26963\n-5.8987\n-5.2786\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.47959\n1.7414\n4.099\n5.0293\n3.8295\n3.1534\n3.3912\n3.7275\n1.9026\n2.7642\n3.1173\n3.5099\n3.391\n3.9631\n4.5169\n3.0951\n3.1672\n1.9252\n2.0783\n1.9278\n-0.023553\n-4.1002\n-6.1777\n-8.2298\n-1.5107\n0.4294\n2.8838\n0.7334\n-0.16764\n-2.8893\n-0.36609\n0.081705\n-3.8371\n-0.28877\n1.4855\n0.9585\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6833\n1.0646\n-1.6744\n-4.0379\n-3.353\n-5.1658\n-4.413\n-0.16115\n1.16\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1829\n2.6091\n4.1586\n4.8718\n4.4549\n2.1128\n3.05\n3.6926\n2.5227\n3.0041\n2.0836\n3.0558\n3.6985\n4.2486\n4.5737\n2.5393\n0.77667\n-0.31535\n0.30576\n0.32711\n-0.72639\n-4.4399\n-6.1305\n-4.7723\n-1.715\n0.36688\n0.3845\n-0.83914\n1.4994\n-1.7561\n-1.5478\n-1.8923\n-2.2665\n-0.94568\n1.1002\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3308\n-0.21666\n-1.0724\n-1.6045\n0.034945\n0.019095\n0.40743\n3.3743\n3.6734\n1.5866\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9468\n3.1031\n3.0122\n2.9839\n4.838\n3.6246\n4.7645\n3.4939\n1.8902\n2.9283\n2.2246\n3.1103\n3.728\n3.9163\n3.0584\n1.3389\n-0.54445\n-2.9899\n-1.9703\n-1.1487\n-1.5269\n-6.2747\n-4.6638\n-1.7193\n0.25483\n1.4687\n-0.84012\n-0.57112\n2.1577\n-0.27423\n-4.7483\n-3.3465\n-2.6959\n-1.8471\n0.39121\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.1511\n-1.972\n-1.3783\n-0.065278\n2.2889\n3.7006\n1.5837\n1.3395\n3.8048\n0.81076\n2.1208\n4.5936\n5.3652\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4815\n3.5522\n2.6539\n2.2059\n4.9405\n4.1905\n2.7607\n2.2047\n1.906\n3.1556\n3.4551\n3.767\n2.1323\n2.8578\n1.1945\n0.1556\n0.46392\n-0.80608\n-0.48373\n-0.9591\n-2.6134\n-6.7681\n-5.209\n0.24585\n0.86237\n2.6174\n1.8947\n0.89599\n1.7754\n3.2108\n1.5924\n5.0314\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.5596\n-1.9743\n-1.1547\n-1.2729\n-1.8262\n-0.14953\n3.2063\n4.9403\n2.6542\n4.5479\n5.6871\n4.6581\n4.573\n5.2775\n5.0569\n5.4796\n4.7536\n5.412\n2.5759\n0.26358\n2.4523\n2.5893\n2.1716\n2.08\n2.9575\n2.4871\n1.6621\n2.4687\n2.3107\n2.1743\n1.7636\n1.6415\n2.3893\n1.9629\n-0.15103\n0.18676\n0.0083579\n-1.1131\n1.5461\n0.054209\n0.46341\n-4.4692\n-6.0251\n-0.57645\n2.0826\n5.1993\n2.8133\n0.4832\n1.6816\n2.5864\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20532\n-1.2274\n-0.027589\n2.0345\n0.86221\n0.43332\n-2.6311\n1.1401\n3.9042\n2.9449\n6.5989\n8.0284\n5.2627\n5.1332\n3.9614\nNaN\nNaN\nNaN\nNaN\n5.0625\n4.092\n2.3753\n2.0671\n1.7438\n0.68549\n0.52389\n0.73501\n0.96398\n1.7024\n2.6589\n1.4677\n0.1561\n1.0025\n1.8463\n0.90413\n-0.0034218\n1.0923\n1.0423\n-1.6945\n-2.7139\n0.42445\n0.058877\n-5.1218\n-5.5424\n-2.1434\n2.5772\n2.6254\n0.45343\n-1.3309\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3964\n0.92954\n0.087018\n-0.88958\n1.572\n3.0482\n1.981\n2.1491\n0.052783\n0.47941\n0.95993\n2.8877\n5.9142\n8.3221\n5.5895\n5.4037\n3.962\nNaN\nNaN\nNaN\nNaN\n5.133\n8.0549\n1.8871\n0.73735\n-1.5211\n-0.48128\n0.51256\n0.55446\n0.83238\n1.6175\n3.4263\n2.0257\n0.50035\n1.2773\n1.1482\n0.30474\n0.60638\n3.115\n3.1712\n-1.1922\n-4.3829\n-5.7494\n-3.849\n-4.5468\n-4.3138\n-0.74628\n1.2529\n-0.12938\n-3.0157\n-5.0659\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.22551\n1.2346\n0.67509\n0.20394\n-0.32418\n1.0385\n1.9416\n2.0042\n4.2284\n2.5207\n0.73883\n0.47339\n1.4478\n2.3112\n5.3949\n3.9047\n5.8765\n5.3028\nNaN\nNaN\n5.8868\n6.4622\n5.9518\n6.621\n0.38343\n-1.1203\n-0.83127\n0.013122\n0.31533\n1.3172\n1.7471\n2.8489\n2.9653\n2.2701\n1.0449\n0.91663\n1.2752\n-0.26721\n0.024241\n2.9226\n3.4015\n-1.0912\n-4.7537\n-5.6732\n-3.4606\n-3.1744\n-3.899\n-0.33795\n0.97366\n-0.52016\n-4.3832\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.552\n-0.95316\n3.7804\n1.4683\n1.5441\n0.64535\n0.62442\n-0.040932\n2.8376\n6.7899\n5.1911\n2.2727\n2.2059\n2.07\n0.8079\n0.69258\n4.2087\n6.3205\n4.4999\n2.8721\n1.5359\n7.5726\n9.0934\n7.6861\n7.1265\n-1.8346\n-1.1287\n-0.01807\n0.68419\n0.63808\n2.92\n3.6005\n2.5163\n2.4249\n1.4185\n1.0243\n1.1003\n1.4873\n1.6318\n1.3594\n3.3565\n4.913\n-1.0133\n-3.2908\n-3.3729\n-2.0239\n-2.6342\n-2.9402\n-1.0563\n1.5214\n2.311\n-3.5346\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.0752\n-1.227\n3.2983\n1.2518\n1.9937\n1.7039\n-1.4715\n-2.0327\n1.2885\n6.781\n6.6696\n5.1099\n3.7238\n3.6802\n1.9565\n1.9789\n5.6214\n7.288\n4.5666\n4.0328\n3.9164\n8.8072\n11.178\n10.777\n7.3512\n-1.373\n-0.28876\n0.43197\n1.83\n2.5091\n3.3854\n3.597\n2.6494\n1.6979\n1.2304\n0.062975\n-0.81613\n1.1836\n2.5274\n2.7086\n4.1927\n2.4137\n-1.184\n-1.2189\n-2.5721\n-3.1058\n-2.4776\n-1.4569\n-0.050998\n2.2804\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.86287\n-0.9998\n-0.49615\n3.6348\n-2.4298\n-0.0014277\n1.3385\n0.26055\n3.7396\n5.4369\n6.7134\n6.5611\n7.0289\n5.7337\n5.0401\n2.3215\n1.8949\n6.8645\n6.0267\n6.8464\n5.1822\n6.2569\n9.4626\n10.144\n9.7451\n7.1559\n-0.54846\n1.1974\n0.68722\n1.8539\n2.0394\n2.3312\n1.6553\n1.652\n1.1841\n0.3575\n0.2052\n-0.027512\n0.55065\n2.008\n3.2234\n3.9486\n0.52945\n-2.2458\n-2.6984\n-1.8864\n-3.1185\n-2.4264\n-2.0303\n-0.79165\n1.7779\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.3441\nNaN\nNaN\nNaN\nNaN\n-0.58437\n-0.30018\n-0.58842\n-0.62478\n3.0529\n-2.1763\n1.0529\n2.9891\n6.1504\n5.4906\n6.6292\n6.5635\n0.64987\n5.8777\n4.6425\n4.1323\n-0.78564\n2.0891\n7.2452\n6.9287\n8.2108\n6.8755\n8.7838\n9.9231\n9.2137\n7.1567\n4.1663\n0.99907\n2.0959\n1.0687\n0.94969\n1.0699\n1.6202\n1.5304\n1.09\n0.63425\n0.78504\n1.0397\n0.90417\n0.65049\n2.2991\n1.6691\n2.3804\n1.7586\n-1.178\n-3.7913\n-2.4957\n-2.2333\n-2.4686\n-3.3758\n0.061337\n4.2432\n8.1929\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6793\n1.6812\n-0.3673\n-0.63411\n-0.72752\n-0.53595\n0.73393\n1.3823\n1.0619\n3.8754\n6.9895\n5.5841\n6.0678\n3.456\n-2.953\n-1.4834\n2.8613\n3.5903\n0.66923\n3.3415\n6.5515\n8.3168\n7.5953\n7.215\n7.2548\n10.18\n7.7608\n5.1352\n-0.40101\n1.0124\n1.3677\n1.4218\n0.52829\n0.095088\n1.4219\n1.6941\n1.0189\n-0.078673\n1.3764\n1.1404\n0.5473\n-0.14266\n1.2642\n1.5578\n2.6104\n1.9064\n-0.52605\n-1.3329\n-5.4128\n-5.3523\n-2.4735\n-1.4407\n2.7023\n7.2793\n8.0261\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.8224\nNaN\nNaN\n17.071\n2.7903\n0.84933\n0.95268\n0.25945\n-1.3116\n-2.4257\n-3.8339\n-2.5453\n-2.1221\n0.068466\n3.6097\n0.7266\n-0.21374\n1.2066\n-0.79228\n-2.5443\n-2.9299\n0.73592\n1.6419\n3.4115\n5.7918\n6.2012\n5.9867\n6.5891\n7.1806\n7.2461\n5.7566\n4.8486\n-1.8882\n-0.046768\n0.33565\n0.51212\n0.27216\n-0.10143\n1.5499\n1.6044\n1.1169\n0.035807\n1.412\n1.0537\n-0.11125\n-0.73006\n-0.505\n0.7084\n-0.062756\n-0.69056\n-2.093\n-2.5636\n-3.9116\n-5.4849\n-2.0563\n0.25294\n5.5026\n7.7144\n10.582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.534\n25.542\n20.219\n14.619\n1.1893\n-0.90288\n-0.20945\n2.45\n-0.68421\n-1.668\n-2.6506\n-3.7061\n-1.9445\n-1.6298\n-0.93194\n-1.8711\n-1.4615\n-1.2984\n-1.4464\n-2.42\n-4.4514\n0.98384\n3.9279\n4.0958\n3.0672\n2.1487\n3.6423\n6.3359\n6.0505\n4.3446\n3.716\n3.3906\n1.5133\n-1.2166\n0.77437\n0.8396\n1.3792\n0.60633\n0.70068\n0.48812\n1.4074\n0.7648\n0.43834\n0.46512\n-1.4065\n-3.3472\n-2.3499\n-1.2699\n-0.93529\n-2.6921\n-3.3974\n-2.4281\n-3.1121\n-4.3121\n-2.9712\n0.63821\n3.5169\n6.4312\n12.525\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n30.298\n17.582\n14.712\n9.0762\n-0.046412\n-3.5753\n-1.6834\n0.45698\n-0.33627\n-2.0914\n-4.2722\n-4.0042\n-2.1066\n-0.85283\n-0.8066\n-3.7679\n-4.5861\n-2.6274\n-2.5888\n-3.9624\n-4.876\n-0.80911\n2.3568\n2.4494\n2.0873\n1.0769\n2.4978\n2.4597\n4.9992\n2.7111\n1.4683\n2.498\n3.8534\n0.62482\n0.75898\n0.49165\n2.1429\n0.55252\n0.34002\n0.035299\n0.59296\n0.36257\n-0.6059\n-1.2729\n-1.8374\n-3.2426\n-2.4962\n-2.8862\n-1.1077\n-2.4005\n-6.7196\n-6.561\n-1.1919\n-1.2855\n-3.0324\n-0.44287\n3.0214\n5.5737\n3.6756\n-0.20609\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n26.116\n12.451\n8.3439\n5.5843\n0.53886\n-8.3408\n-3.8043\n-1.147\n-2.4004\n-2.4863\n-3.8505\n-3.6555\n-2.6608\n-2.0885\n-2.3693\n-2.6951\n-6.1531\n-3.8284\n-2.0459\n-1.7258\n-2.66\n-3.15\n-0.23276\n1.002\n1.9847\n2.1668\n1.7683\n1.6045\n1.067\n0.77387\n2.6769\n4.4805\n4.9466\n1.4243\n0.48148\n-0.072655\n1.1848\n0.98264\n1.4349\n-0.14264\n0.1129\n0.42745\n0.13294\n-2.0004\n-2.2928\n-1.9658\n-2.1432\n-3.1979\n-1.0391\n-1.4012\n-4.0876\n-5.6072\n-4.1251\n-0.8306\n-3.0599\n-0.62415\n3.4382\n5.7366\n1.0013\n-2.495\n-6.5712\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.37\n8.7401\n3.5582\n3.988\n1.0235\n-6.8982\n-2.8836\n-1.7946\n-3.5142\n-6.0707\n-7.2322\n-4.1449\n-3.2858\n-3.1333\n-5.145\n-3.716\n-3.1546\n-2.6958\n-1.1907\n-1.2852\n-1.2984\n-0.84083\n-2.1883\n0.39029\n0.55196\n1.6359\n0.75537\n1.1207\n1.073\n1.9324\n3.8471\n5.4229\n4.9277\n1.0139\n-0.57063\n-0.65315\n0.067246\n1.8366\n2.2038\n0.52525\n1.0069\n0.53717\n0.26165\n-2.6788\n-1.9803\n-0.90051\n-2.3467\n-3.4652\n-2.1716\n-0.7841\n-3.9986\n-3.4656\n-6.9175\n-4.0275\n-3.3047\n-0.10968\n2.266\n4.1705\n2.4409\n-2.7201\n-2.5207\nNaN\nNaN\n1.979\n2.2311\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.4054\n5.4496\n4.2168\n3.9876\n-4.2482\n-7.5505\n-3.6807\n-4.7698\n-6.4703\n-9.5293\n-7.3081\n-3.3191\n-2.5512\n-2.5085\n-3.0303\n-3.3768\n-2.2919\n-1.8101\n-1.4487\n-2.1112\n-2.3462\n-1.9442\n-3.0516\n-0.99391\n-1.4946\n-1.2031\n-2.1038\n-2.0885\n2.0153\n2.8962\n3.596\n5.6907\n4.6137\n0.2383\n-0.41071\n0.39738\n-0.39655\n1.8876\n3.2405\n1.9287\n1.9388\n1.1001\n-0.048035\n-1.923\n-0.29861\n-0.90189\n-2.0109\n-2.7035\n-1.2944\n-1.3094\n-4.3287\n-5.3036\n-4.5416\n-4.024\n-2.3778\n-0.91136\n1.7092\n-1.3321\n-4.3593\n-3.2746\n1.7902\n1.3093\n4.1307\n4.4993\n2.8724\n5.5532\n7.8962\nNaN\nNaN\nNaN\nNaN\n0.38965\n5.0313\n2.0537\n1.2056\n0.79903\n-6.0086\n-9.9686\n-6.1667\n-6.0966\n-6.7132\n-8.172\n-6.5584\n-1.4336\n-0.49987\n0.36933\n-2.2015\n-4.3551\n-3.7162\n-1.7151\n-1.4733\n-1.1075\n-0.77352\n-1.8452\n-2.2995\n-2.362\n-4.288\n-2.7386\n-1.3441\n-0.98682\n-0.35152\n-0.99682\n1.2575\n4.1361\n2.1787\n1.0455\n0.24413\n0.63898\n0.3562\n1.6176\n3.7192\n3.2194\n2.5182\n1.3062\n0.062226\n-0.66416\n0.42889\n-0.49937\n-1.0855\n-2.0556\n-2.4943\n-5.0771\n-10.088\n-5.95\n-3.2668\n-2.9061\n0.047816\n0.71218\n1.2384\n8.064\n1.7189\n1.7631\n2.1359\nNaN\n0.21412\n2.2449\n-2.086\n4.6448\n7.7845\nNaN\nNaN\nNaN\n-1.4034\n1.8029\n4.7301\n3.2077\n-0.9183\n-0.017421\n-3.9677\n-8.7573\n-5.841\n-1.6699\n-3.2584\n-5.7277\n-4.2895\n-0.21054\n1.6224\n0.85645\n-2.8786\n-3.2589\n-3.2699\n-2.7514\n-2.2399\n-0.27386\n0.082465\n-1.5176\n-2.9244\n-3.4544\n-3.7961\n-1.7292\n1.1465\n1.2057\n-1.1402\n-1.0174\n0.64647\n2.3656\n0.9021\n-0.050988\n0.40804\n1.7478\n1.724\n2.7449\n4.0113\n3.3583\n1.9841\n0.47415\n-0.14371\n-0.23471\n0.081055\n0.20374\n-0.636\n-1.6078\n-3.1045\n-5.3041\n-9.961\n-4.8112\n-3.4303\n-1.0847\n-1.7201\n2.4618\n0.84056\n2.4534\n2.1475\n0.94336\nNaN\nNaN\n-1.1783\n0.29154\n0.1344\n3.1566\n4.9119\nNaN\nNaN\nNaN\n-0.36104\n0.38454\n4.5009\n2.7051\n-3.2547\n-2.8704\n-3.7028\n-5.7716\n-1.9211\n-0.33922\n-1.0227\n-3.6578\n-2.6325\n0.16046\n2.7444\n0.5539\n-1.7639\n-1.8418\n-1.8333\n-2.2159\n-1.977\n0.022917\n-0.14299\n-1.0184\n-3.3839\n-4.023\n-2.5048\n0.04916\n2.0483\n2.069\n1.4655\n1.3312\n1.1555\n2.167\n3.187\n-0.68116\n0.20854\n2.9145\n3.6494\n4.1488\n4.267\n3.5027\n1.7952\n0.12336\n-0.089961\n-0.035882\n0.71619\n0.56633\n1.1159\n0.413\n-2.7447\n-3.4664\n-9.0322\n-0.78664\n-2.1408\n-2.1417\n-0.0024328\n2.8109\n0.95094\n-0.3911\n-0.56853\n0.10677\nNaN\nNaN\nNaN\n-1.946\n0.51216\n2.4619\n3.0998\nNaN\n3.6186\n2.795\n-0.64672\n-0.67384\n3.3731\n1.5919\n-1.5902\n-1.1519\n-0.60344\n-2.9383\n-3.4379\n-3.3958\n-3.653\n-2.5206\n0.41743\n1.6113\n2.3384\n0.52393\n-1.6578\n-1.6033\n-0.82489\n-0.65184\n0.53224\n0.62778\n0.24065\n-1.1895\n-2.5941\n-3.0183\n-1.738\n0.90564\n2.1011\n2.6113\n2.8546\n0.79097\n-0.75081\n2.2088\n4.4069\n0.38983\n0.55026\n3.5933\n4.4381\n5.119\n4.5174\n3.0593\n3.0348\n1.6231\n0.81926\n1.1238\n2.297\n2.3377\n4.0823\n5.2553\n-0.2665\nNaN\nNaN\nNaN\n-1.5839\n-4.5651\n-0.58258\n1.519\n-3.025\n-2.6619\n-2.2206\n-0.51488\nNaN\nNaN\nNaN\n-1.7029\n0.19048\n-0.96242\n0.29954\n1.3188\n2.338\n1.5568\n-0.11223\n-0.46071\n-2.0542\n-3.1957\n-2.3148\n0.19441\n0.4141\n-1.3617\n-1.9504\n-1.1531\n-0.87378\n-0.012834\n0.88404\n1.3751\n1.0106\n-0.30613\n-2.3503\n-1.4605\n0.4054\n1.216\n0.80036\n0.91039\n0.64369\n-1.0076\n-1.0503\n-0.7598\n-0.72278\n1.1936\n1.8503\n2.8489\n3.534\n1.2179\n-0.20874\n1.1315\n3.9235\n2.9674\n3.5908\n4.4379\n4.0783\n4.165\n4.5963\n3.2722\n3.7425\n3.0971\n1.9054\n2.7301\n3.5929\n5.1411\n7.8072\n6.9842\n1.3626\nNaN\nNaN\nNaN\n-2.3644\n-4.566\n-6.8457\n-4.5822\n-3.4058\n-1.085\n-1.5447\n-1.2154\nNaN\nNaN\nNaN\n-3.2161\n-1.9835\n-1.9131\n-0.43503\n3.9389\n2.4985\n0.91685\n-0.54622\n-1.4939\n-3.7751\n-3.5635\n-3.2275\n-0.72492\n0.70004\n0.19533\n-0.9379\n0.24529\n-0.86913\n-1.0807\n0.20007\n0.91598\n0.26135\n-0.11984\n-0.23716\n0.57162\n1.0023\n0.047896\n1.1471\n1.8266\n2.1244\n0.457\n0.48755\n0.97752\n1.4866\n2.8146\n2.8003\n4.3207\n4.689\n2.6188\n1.5849\n2.9422\n4.1753\n4.3976\n5.1413\n5.3306\n4.2734\n4.965\n4.9932\n4.9917\n4.0038\n4.189\n2.766\n4.1502\n5.4772\n6.1274\n6.0502\n4.9748\n3.7322\n0.11524\nNaN\nNaN\n-4.4819\n-3.5336\n-3.7728\n-4.0391\n-2.7601\n0.052242\n-0.54081\n-1.0121\n-0.5638\n-2.4828\n-2.7742\n-5.9121\n-4.8366\n-2.9411\n-0.71645\n4.0397\n2.6818\n2.103\n0.69867\n-0.78869\n-0.21282\n-1.3612\n-1.0711\n-0.318\n0.78208\n1.01\n0.37896\n0.068404\n-3.6467\n-4.1973\n-0.29022\n0.11956\n0.40214\n2.503\n0.8773\n0.98127\n1.4199\n0.019957\n1.5206\n2.4275\n3.5161\n2.1838\n0.6172\n1.8103\n3.1464\n4.572\n3.9804\n3.9909\n5.6167\n3.6037\n2.9739\n5.0748\n5.0676\n3.8597\n4.6651\n5.372\n5.4493\n6.0635\n6.2445\n6.0353\n4.8561\n4.1615\n3.6738\n4.0536\n5.0513\n5.6165\n6.4356\n5.7673\n5.0417\n1.3741\n-2.523\n-4.6541\n-4.4043\n-3.1717\n-2.3778\n-1.7595\n-1.2816\n0.57299\n-0.29378\n-0.80538\n-1.0604\n-0.68579\n-0.94906\n-2.5055\n-3.5972\n-3.1924\n-0.80495\n2.3561\n2.2647\n1.4018\n2.3798\n0.66613\n0.34977\n0.20925\n2.2797\n1.6901\n2.2227\n2.6156\n2.9705\n0.24043\n-4.4882\n-7.4527\n-2.483\n-1.0122\n2.9495\n3.4078\n2.021\n1.5743\n2.5974\n2.0625\n1.8655\n2.0627\n2.4457\n2.4036\n1.7462\n3.2301\n4.0403\n4.5505\n3.8269\n5.8971\n6.3474\n4.0318\n4.2525\n4.6233\n3.9805\n3.5019\n5.4852\n6.9069\n6.8923\n7.5587\n8.0881\n7.3365\n7.6423\n5.8137\n4.678\n4.3084\n4.4644\n4.4667\nNaN\nNaN\nNaN\nNaN\n-0.016753\n-3.545\n-4.2762\n-1.0602\n-1.7686\n-2.8673\n-1.5862\n0.89645\n0.25228\n0.24462\n0.91468\n0.16128\n-0.93904\n-1.4809\n-1.5817\n-2.294\n-1.7655\n-1.199\n-0.38877\n0.23185\n2.6394\n2.0484\n2.2714\n1.7914\n3.2836\n3.1994\n1.2019\n1.5205\n4.0059\n3.6458\n-1.5993\n-2.5754\n-2.9712\n-1.6936\n-0.10724\n1.9017\n1.1181\n1.8539\n3.2963\n1.2135\n0.7031\n1.8286\n1.5064\n2.8342\n4.2516\n5.104\n4.0586\n3.0672\n4.4521\n7.6144\n6.8034\n5.4062\n4.3306\n4.0038\n4.998\n5.1211\n6.7004\n8.0167\n8.223\n8.6563\n9.112\n8.4925\n10.479\n9.3444\n7.9454\n5.716\n4.8702\n5.8604\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6042\n-3.1543\n-2.105\n-1.3759\n-2.8257\n-1.7291\n0.26846\n0.64804\n0.2136\n0.64731\n-0.46784\n-1.2873\n-1.7225\n-1.6744\n-0.32926\n-1.5962\n-2.0447\n-0.64627\n1.2564\n4.4048\n4.5432\n4.3182\n3.9961\n4.7805\n4.1288\n-1.2357\n-0.99749\n1.1399\n6.1223\n-0.12085\n0.011141\n-0.61242\n-0.60298\n-1.0243\n-0.75741\n-0.71356\n0.59532\n1.7903\n0.55187\n1.2105\n3.038\n3.0643\n3.7961\n5.3137\n4.9153\n4.4075\n3.536\n5.8047\n9.0691\n7.719\n7.0898\n4.3549\n4.4763\n4.8241\n7.0962\n6.8311\n8.6783\n10.012\n10.392\n13.166\n11.865\n13.317\n11.939\n9.8549\n6.5805\n4.3368\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3504\n-2.9566\n-2.5484\n-2.6801\n-2.5997\n-2.008\n-2.4594\n-1.2466\n-1.3877\n-0.64378\n-0.86981\n-1.3974\n-1.5831\n-1.2107\n-0.88819\n-1.0207\n-0.50373\n0.80633\n3.7082\n6.4793\n4.7422\n5.1105\n5.4841\n5.1683\n-1.1019\n-3.6528\n-2.5696\n4.0454\n1.5236\n1.0164\n0.031374\n-0.34477\n-0.23472\n-0.7512\n-1.0244\n0.47289\n2.8424\n0.11678\n0.77852\n3.8186\n2.4527\n3.2048\n4.0811\n4.6451\n4.7926\n5.7115\n6.3568\n7.6314\n7.4381\n7.7953\n2.7774\n2.902\nNaN\n9.237\n7.2477\n10.577\n12.627\n13.379\n15.614\n15.496\n15.95\n12.381\n10.803\n8.0886\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.836\n-1.6422\n-2.2393\n-1.6624\n-2.135\n-1.9978\n-1.8276\n-2.1134\n-1.374\n-0.3789\n-0.88717\n-0.86172\n-1.5587\n-0.99664\n-0.5346\n-0.40314\n-1.4562\n-1.0104\n1.4061\n3.9364\n3.8755\n4.9535\n3.5159\n0.73345\n-1.6803\n-4.5736\n-5.0526\n-0.56939\n-0.27876\n0.40789\n-1.2035\n-0.068734\n1.8328\n3.0232\n2.4902\n2.9796\n3.3827\n0.82828\n1.303\n3.7379\n0.67837\n2.9396\n1.6055\n2.8498\n6.6604\nNaN\nNaN\n6.0966\n7.0732\n8.5272\nNaN\nNaN\nNaN\n4.5826\n4.3343\n4.3125\n4.0816\n3.4178\n3.3218\n3.6701\n4.5843\n4.5825\n4.2132\n4.4846\n4.782\n5.183\n5.8692\n6.3695\n5.8316\n5.8069\n5.5708\n5.3556\n5.692\n6.0812\n5.8655\n5.1459\n2.7695\n4.2247\n3.5578\n2.7702\n2.4017\n2.624\n2.947\n0.33984\n1.2285\n3.933\n2.9928\n4.7861\n2.2365\n1.3921\n1.7435\n2.4526\n2.1718\n2.4267\n3.2281\n4.2833\n5.2314\n4.8238\n4.5538\n3.8328\n3.7674\n3.9832\n3.6781\n3.5601\n4.1391\n4.2349\n2.7205\n3.5712\n3.2384\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.4944\n4.5932\n4.2201\n4.2324\n2.7761\n3.1053\n3.6131\n4.3547\n4.7547\n4.3076\n4.5653\n4.9714\n5.5403\n6.011\n5.4915\n5.7107\n5.9463\n5.5654\n5.4578\n5.43\n6.8154\n5.2533\n4.4093\n4.4182\n4.4085\n3.1206\n1.9353\n1.9495\n3.5318\n2.8128\n2.642\n2.5686\n5.2016\n3.9157\n3.9818\n2.6848\n1.9656\n2.783\n3.0758\n2.9683\n2.8075\n3.2745\n4.375\n5.0983\n5.0467\n4.5586\n4.1575\n3.5616\n3.3235\n3.4445\n3.4798\n4.116\n4.4498\n4.2674\n5.326\n5.2419\n3.922\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8252\n4.1115\n3.7241\n3.9153\n2.8135\n3.8552\n4.0781\n3.4275\n4.1465\n5.1025\n4.7849\n5.2648\n6.1803\n6.5322\n6.4412\n4.902\n5.2218\n4.8237\n4.4928\n5.4532\n5.9268\n4.2527\n5.0493\n3.9571\n4.5902\n3.5717\n2.0798\n1.9407\n3.0595\n2.4471\n3.6173\n3.3613\n5.3331\n4.3063\n2.5478\n1.9808\n2.3171\n2.8845\n3.4272\n2.5914\n3.3468\n3.4965\n3.9565\n4.6963\n4.545\n4.0328\n3.8282\n4.1193\n4.1518\n3.8805\n3.8139\n4.2678\n4.7825\n4.392\n5.6238\n5.0931\n4.3014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.3398\n4.4881\n4.3287\n4.3845\n4.2695\n4.6411\n4.9726\n4.9112\n4.8856\n4.085\n4.7624\n5.1162\n6.3739\n7.3799\n7.1859\n4.6999\n4.1201\n4.1091\n2.3094\n4.3099\n6.4016\n2.9644\n3.2767\n2.8865\n3.6803\n3.2594\n1.916\n1.8415\n2.7201\n1.8716\n3.4832\n4.2517\n5.7454\n5.2128\n3.3188\n1.6122\n1.5994\n2.9506\n3.3837\n2.1916\n3.0241\n3.3173\n3.516\n4.4844\n4.5684\n3.8674\n3.7829\n4.7592\n4.9775\n4.4754\n4.3369\n4.3478\n4.6197\n4.6466\n4.572\n4.0728\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.8845\n5.3753\n4.6194\n4.5338\n5.0449\n4.8106\n4.7298\n4.7731\n4.6029\n3.8968\n5.3203\n4.6917\n4.7307\n5.2975\n4.9011\n3.6716\n3.3288\n3.6443\n2.473\n2.536\n5.8908\nNaN\nNaN\n0.73131\n2.6715\n2.3644\n2.0245\n1.7855\n2.8415\n3.0448\n3.0518\n5.3608\n5.5475\n6.1349\n4.8526\n4.022\n3.4423\n3.4857\n3.2952\n2.7202\n3.5936\n3.5332\n3.4076\n3.7743\n4.5542\n4.3762\n4.363\n5.6212\n5.4943\n4.6284\n4.4209\n4.3296\n4.3369\n5.1785\n5.2185\n4.2908\n4.7082\nNaN\nNaN\n1.6681\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.3196\n5.2396\n4.2327\n4.4242\n4.5507\n4.5007\n4.2227\n4.0548\n4.3545\n4.4225\n4.761\n5.0743\n4.7363\n2.8014\n3.3165\n4.1059\n3.3163\n3.9899\n2.8931\nNaN\nNaN\nNaN\nNaN\n2.0716\n1.8486\n2.0074\n2.1024\n2.5079\n3.0165\n3.8045\n2.9499\n4.8063\n5.0478\n5.28\n4.9175\n3.5266\n3.0707\n3.3867\n3.0477\n3.8542\n4.4186\n4.0453\n3.556\n3.5138\n3.5522\n4.1175\n4.5564\n5.7386\n5.333\n4.6553\n4.6243\n4.4867\n4.8485\n5.2817\n5.1551\n4.4071\n4.113\nNaN\nNaN\n1.9134\n4.0084\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5335\n4.0111\n3.5601\n4.3363\n4.5052\n4.0346\n4.6361\n3.8464\n4.3341\n4.3197\n4.3791\n4.2396\n4.2859\n3.4577\n3.6045\n3.3275\n3.2192\n4.2348\n3.5585\nNaN\nNaN\nNaN\nNaN\n2.2169\n2.1844\n2.145\n1.8577\n3.2111\n2.6983\n3.8077\n2.7822\n4.2731\n4.0446\n4.4812\n3.7644\n2.6224\n2.4481\n2.7981\n2.4943\n3.1091\n4.94\n4.6404\n3.6519\n3.5496\n3.0491\n3.9189\n4.6512\n5.2597\n4.8901\n4.5686\n4.1524\n4.4039\n5.2812\n5.6383\n5.1021\n3.7966\n3.3651\nNaN\nNaN\n2.0596\n1.2061\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2265\n3.4839\n3.2671\n4.2705\n5.1754\n3.8559\n4.2484\n4.2438\n4.8496\n4.9727\n4.3966\n3.9061\n3.8112\n4.1581\n2.7576\n2.536\n3.1919\n3.7738\n2.697\n2.2683\n2.6373\n2.5124\n2.0188\n2.6964\n1.9286\n2.2726\n2.2685\n2.0149\n1.8964\n2.5789\n2.453\n3.5015\n3.0897\n3.275\n2.1903\n2.1179\n2.5952\n2.8233\n2.7231\n2.5744\n4.4097\n4.4926\n4.2335\n3.9184\n2.8094\n4.0375\n4.9891\n4.9168\n4.5182\n4.5305\n4.6177\n4.4946\n5.2401\n5.8119\n5.8117\n4.4857\n2.9771\n3.4639\n4.2096\n3.8637\n1.7676\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5012\n3.4138\n3.6423\n3.5917\n4.4202\n3.5669\n3.9306\n4.5104\n4.7693\n4.7414\n4.2877\n4.1134\n4.0287\n3.0875\n2.4342\n2.6575\n2.9053\n3.7907\n2.1759\n2.3528\n2.5086\n2.5405\n2.1284\n2.4574\n1.5604\n2.4951\n2.5814\n1.9295\n1.7542\n2.0582\n2.6361\n2.8832\n1.883\n2.2064\n1.6017\n1.7405\n2.8064\n2.8268\n2.483\n3.3984\n3.6126\n4.256\n4.6119\n3.8335\n2.9625\n5.0225\n5.5859\n5.7852\n4.7566\n4.7273\n5.1425\n4.8307\n5.3264\n5.8594\n5.9377\n4.9224\n3.3312\n3.6701\n4.9495\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2732\n3.5624\n4.1971\n3.5017\n4.4454\n3.8881\n4.3303\n3.9365\n3.6068\n3.7495\n3.4286\n3.083\n2.8604\n2.4513\n2.4982\n2.6203\n2.5326\n3.9059\n3.5471\n3.4262\n3.5139\n2.3202\n1.5582\n1.4608\n1.6954\n2.7509\n2.5565\n1.48\n1.9313\n2.0883\n3.4249\n3.6092\n3.1466\nNaN\nNaN\n2.9902\n2.6897\n3.1964\n2.6517\n3.7674\n4.4687\n4.7592\n4.892\n4.4822\n4.7591\n6.1003\n5.7679\n5.9708\n5.3258\n5.4245\n5.2075\n5.5123\n6.0807\n5.9561\n6.0298\n5.9443\n4.2527\n4.1021\n5.37\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8378\n3.3561\n3.4984\n3.4628\n3.9362\n4.4429\n4.1976\n3.276\n2.8162\n3.001\n2.682\n2.0278\n2.8593\n2.3815\n2.9733\n3.3541\n2.7763\n3.5559\n3.6529\n3.2975\n1.9739\n1.6553\n0.99345\n0.060146\n1.4885\n3.6027\n2.6596\n2.0982\n2.3816\n2.2921\n3.2376\n4.0392\nNaN\nNaN\nNaN\n3.6652\n4.1185\n4.317\n3.4766\n3.9291\n5.1997\n5.58\n5.4595\n5.2636\n5.5012\n6.1356\n5.9983\n6.1755\n6.2422\n5.1887\n4.83\n5.7781\n6.0522\n6.2024\nNaN\nNaN\n4.1024\n3.981\n5.4343\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5833\n3.3138\n3.427\n3.7457\n3.7143\n4.078\n4.4912\n4.1712\n3.1143\n2.5964\n2.2181\n2.0307\n2.4522\n2.6578\n3.5999\n3.9829\n3.7721\n3.062\n2.4775\n1.9492\n1.0193\n1.101\n0.16499\n-0.54524\n1.0063\n2.9816\n2.9679\n2.64\n3.0363\n3.0115\n3.1648\nNaN\nNaN\nNaN\nNaN\n3.8806\n4.8613\n4.7383\n4.4087\n4.2717\n5.3236\n5.8431\n5.9316\n6.1763\n6.2441\n6.6131\n6.5351\n6.9358\n6.9203\n6.1564\n5.6109\n6.2581\n6.0943\nNaN\nNaN\nNaN\n4.4446\n3.9611\n5.216\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.1131\n3.6779\n3.583\n3.6038\n3.9809\n4.6447\n4.5319\n4.2205\n3.6426\n2.9053\n2.7619\n2.7403\n2.8196\n3.1687\n3.9712\n4.339\n3.9087\n2.5193\n1.749\n1.1937\n0.70824\n0.78151\n-0.3922\n1.541\n1.8699\n2.068\n3.8388\n2.9154\n2.8246\n3.7254\n3.7208\nNaN\nNaN\nNaN\nNaN\n3.4629\n4.3778\n4.2985\n5.0521\n5.5285\n6.2025\n6.4025\n6.0972\n6.7484\n6.7568\n7.4402\n7.0682\n7.2065\n6.9212\n6.855\n6.8482\n6.6676\n5.9727\nNaN\nNaN\nNaN\n3.5619\n3.7091\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2901\n3.9733\n3.6903\n4.0084\n4.4275\n4.6514\n4.3705\n4.1713\n3.7557\n2.9943\n2.5045\n2.5656\n3.2682\n3.5355\n4.0644\n3.2872\n2.3858\n2.2264\n1.6698\n1.2729\n1.2173\n0.60609\n0.35375\n1.8937\n2.079\n2.4535\n3.9092\n3.2877\n3.6095\n3.8021\n3.6204\nNaN\nNaN\nNaN\nNaN\n2.226\n2.0233\n3.3867\n5.1802\n6.1666\n6.9596\n7.0002\n6.8593\n7.2651\n7.5249\n8.5885\n7.4272\n7.5622\n7.368\n7.0351\n6.7938\n6.3721\n5.9036\nNaN\nNaN\nNaN\nNaN\n4.6984\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.719\n4.0349\n4.2717\n4.0514\n3.9161\n4.4586\n4.2462\n3.7121\n3.3374\n2.7691\n2.1035\n2.1652\n2.962\n3.1682\n4.1491\n2.8075\n3.0505\n2.5743\n1.7857\n1.4203\n1.3626\n0.5317\n0.012976\n1.9607\n2.0285\n3.1596\n3.9089\n4.217\n4.3627\n4.3926\n4.4053\nNaN\n4.6485\n4.1584\n4.1723\n1.7278\n2.6678\n5.2456\n6.4074\n7.992\n7.876\n6.4903\n6.5572\n7.6576\nNaN\nNaN\nNaN\n7.5924\n7.1109\n6.7906\n6.1142\n5.4629\n5.8045\n5.3402\nNaN\nNaN\nNaN\n4.8687\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9655\n4.4489\n4.515\n4.0376\n3.8665\n4.2678\n4.1405\n3.3276\n2.5967\n2.8149\n2.845\n2.6149\n2.6114\n2.6393\n3.9805\n2.19\n1.7318\n1.8964\n2.2012\n1.5106\n1.8421\n1.7656\n-0.96617\n1.5478\n1.99\n3.1136\n4.0003\n4.5168\n4.8967\n4.8458\n4.4676\n4.7669\n4.8822\n5.2304\n4.9687\n6.6051\n7.0653\n7.4876\n7.14\nNaN\nNaN\nNaN\n5.455\nNaN\nNaN\nNaN\nNaN\n7.1128\n6.6595\n5.7313\n5.4354\n5.1582\n5.2245\n4.3741\n5.2613\n5.413\n5.364\n5.0083\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2914\n3.9522\n4.0252\n4.2562\n3.9452\n4.1652\n3.5031\n2.2889\n2.055\n2.3243\n2.2974\n2.3325\n2.3904\n2.5611\n3.2085\n1.6311\n1.5397\n0.92117\n1.6291\n1.5122\n1.9642\n2.1423\n1.3378\n1.8771\n2.4261\n3.0752\n4.6289\n5.0126\n5.2372\n5.2077\n5.2627\n5.6963\n5.4291\n6.5954\n6.6914\n7.898\n7.7604\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0494\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.9077\nNaN\n5.2089\n4.6556\n4.2827\n4.6792\n4.8517\n4.914\n4.6591\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6227\n3.9195\n4.0587\n4.5157\n3.7915\n3.6089\n3.1175\n2.614\n2.0516\n2.277\n2.3972\n1.8685\n2.0323\n2.2665\n2.3562\n1.4506\n1.4896\n0.76304\n1.1104\n0.91434\n1.6488\n1.9855\n2.0218\n2.2495\n3.0533\n3.8898\n5.4027\n5.324\n5.3929\n5.2616\n6.1194\n7.0381\n7.1739\n8.0796\n7.7174\n7.9493\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6427\n4.3228\n3.7188\n3.4615\n3.0394\n2.4787\n3.1773\n4.5495\n6.9568\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7656\n4.0004\n4.2917\n4.5558\n4.6412\n3.8093\n2.8685\n3.0265\n2.8118\n2.3466\n2.6281\n1.8709\n2.2867\n2.7769\n2.6775\n1.4632\n0.6818\n0.63539\n0.85982\n0.75637\n1.7055\n2.0284\n2.3873\n3.1252\n4.0991\n4.8267\n5.8304\n6.1418\n6.7665\n6.4647\n6.3448\n8.4638\n8.6491\n8.8048\n8.4035\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0649\n3.5246\n3.5747\n3.8557\n3.9378\n3.4939\n3.5344\n5.4491\n4.3346\n4.7721\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6285\n3.8465\n3.7498\n3.8288\n5.0023\n3.815\n3.4353\n3.3019\n2.8138\n2.183\n2.7573\n1.8726\n2.7441\n3.2997\n3.0243\n1.0731\n0.31955\n1.3332\n0.35748\n0.25464\n1.7744\n2.5769\n3.2089\n3.9979\n4.879\n5.6294\n6.4137\n7.0682\n8.0404\n8.4905\n8.4011\n9.03\n9.2469\n8.3754\n8.1318\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6377\n3.8164\n3.8679\n3.7881\n4.2\n4.442\n4.2282\n4.6405\n4.7401\n4.4211\n4.7366\n5.3637\n5.8382\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8512\n4.0059\n3.6475\n3.9263\n4.5964\n3.9911\n3.3441\n3.2072\n2.5394\n2.698\n3.2552\n3.0559\n2.403\n2.6482\n1.7406\n0.90357\n0.87841\n2.1904\n0.89224\n1.0955\n2.2734\n3.0132\n3.8942\n4.6736\n5.2696\n6.3361\n7.3197\n7.8403\n8.5003\n9.5384\n10.79\n11.732\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8079\n4.5782\n4.6653\n4.0965\n4.0478\n3.2883\n4.6751\n5.2676\n4.6865\n4.4563\n5.6858\n5.4288\n4.4532\n3.3414\n4.8225\n7.6237\n8.0701\n8.7681\n5.5063\n3.9394\n3.8663\n3.8439\n3.4768\n3.7588\n4.1493\n3.5617\n2.9204\n3.022\n2.3246\n2.576\n3.0821\n3.108\n3.4511\n2.2474\n1.562\n1.6221\n1.9169\n2.421\n1.3947\n1.8899\n2.7282\n3.0532\n3.6371\n4.875\n5.6863\n7.6169\n7.8902\n8.5308\n9.7426\n10.939\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5232\n4.4806\n4.8248\n4.8463\n4.0535\n4.1656\n3.0702\n4.782\n4.9762\n4.1649\n4.9675\n5.9585\n5.1911\n3.9169\n2.083\nNaN\nNaN\nNaN\nNaN\n5.7704\n5.699\n3.6671\n3.2711\n2.7384\n3.2116\n3.0367\n2.6241\n3.0488\n2.9852\n3.0964\n2.6836\n2.5287\n3.0865\n3.9663\n2.4062\n2.1253\n2.1494\n2.1798\n2.1266\n1.8246\n2.6025\n2.6254\n2.6101\n3.3937\n5.2122\n6.5382\n7.2885\n8.0378\n8.7557\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.5303\n4.5429\n4.3995\n4.4301\n4.5979\n4.4924\n4.1802\n4.2994\n3.9875\n4.0519\n4.4562\n4.2288\n4.9857\n6.3236\n6.1792\n5.1857\n4.0356\nNaN\nNaN\nNaN\nNaN\n5.2174\n7.8048\n3.4963\n2.7595\n2.0999\n2.8475\n2.9122\n2.8509\n3.0348\n3.2467\n3.6465\n3.0162\n2.52\n3.1526\n3.3989\n2.3814\n2.3781\n2.9544\n2.7101\n2.1084\n1.288\n1.497\n2.1952\n3.3832\n4.0238\n4.9584\n5.7608\n6.721\n7.5983\n9.083\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.6098\n4.9528\n4.3889\n4.2703\n4.1104\n4.3009\n4.0223\n4.0191\n4.118\n3.9175\n3.6133\n3.7266\n3.8089\n3.8114\n5.5502\n5.4091\n5.0005\n6.44\nNaN\nNaN\n3.2959\n3.7805\n5.5183\n6.407\n3.019\n2.0819\n2.4973\n2.796\n2.7113\n3.173\n2.769\n3.0884\n3.27\n2.8828\n2.7405\n3.2264\n3.2576\n1.8101\n2.4212\n3.3918\n3.4121\n2.8135\n1.1629\n1.0938\n2.7449\n3.6834\n3.8455\n4.8061\n5.3075\n6.9873\n7.7101\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.7828\n5.1428\n4.8412\n3.9576\n4.2376\n3.8147\n3.8483\n3.5945\n3.0313\n3.3103\n3.8004\n3.216\n3.2671\n3.9185\n3.7352\n3.7495\n4.2327\n4.8807\n5.4971\n3.5831\n2.8389\n3.8689\n4.0823\n5.6949\n5.5263\n2.6724\n2.3085\n2.895\n2.9878\n2.67\n3.1998\n3.1678\n2.738\n3.1149\n3.2081\n2.7318\n3.3032\n3.5521\n2.8711\n4.2032\n4.0218\n4.4519\n3.6311\n2.3124\n2.4801\n3.2929\n3.7846\n4.1881\n4.8011\n6.3276\n7.5354\n8.0687\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.8294\n3.9902\n4.3824\n3.4948\n3.7904\n3.9623\n3.7165\n3.4212\n2.9019\n3.2599\n2.7735\n2.667\n2.7961\n3.7804\n3.8891\n3.8404\n4.0983\n4.626\n4.239\n3.0353\n3.7339\n5.4586\n4.9919\n6.4046\n5.8349\n2.9377\n3.2892\n3.7996\n3.2466\n2.4985\n2.871\n3.008\n2.6638\n2.8214\n2.8298\n2.5849\n3.0109\n3.3956\n3.4139\n4.4167\n4.1136\n3.8963\n3.6317\n3.9454\n3.2982\n3.3455\n3.8238\n4.5068\n5.3128\n6.6216\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.0433\n3.9615\n3.9464\n4.3377\n2.4429\n2.8513\n3.8999\n3.9196\n4.2493\n3.6463\n2.7482\n2.7938\n3.2848\n3.1069\n3.3342\n3.4132\n2.6419\n4.2903\n3.9509\n4.6437\n3.6447\n4.5997\n5.741\n6.03\n6.4211\n5.5755\n3.7316\n4.0398\n3.4883\n2.9745\n2.7257\n2.7231\n2.633\n2.7922\n3.027\n2.682\n2.4436\n2.8278\n2.8357\n2.9508\n3.826\n3.4625\n2.9278\n2.5642\n3.0426\n3.2711\n3.4263\n3.9798\n4.2298\n5.4386\n6.8142\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.1741\nNaN\nNaN\nNaN\nNaN\n6.2118\n5.7934\n4.8784\n4.8271\n4.6921\n3.571\n3.1374\n3.6113\n4.1908\n4.5103\n4.1976\n3.5564\n3.2092\n3.623\n3.4251\n3.1116\n2.07\n2.6179\n4.4373\n4.1432\n4.7601\n4.471\n5.0621\n5.5961\n6.4515\n5.7645\n3.2183\n4.0446\n4.0582\n3.1414\n2.9919\n3.3156\n3.2763\n3.2301\n2.711\n2.4587\n2.5136\n2.5114\n3.1734\n3.2104\n3.2466\n3.478\n3.4122\n3.2526\n2.4906\n1.6493\n2.0884\n2.7166\n3.6955\n3.8864\n5.1633\n7.3985\n8.4625\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.7896\n6.676\n6.27\n5.6769\n5.0968\n4.8697\n4.7732\n4.4412\n4.2875\n4.2221\n4.5335\n4.9583\n4.5768\n3.4023\n2.1756\n2.6718\n2.4419\n2.1783\n2.1397\n3.5679\n4.8208\n3.9227\n4.0832\n4.5939\n4.657\n5.6866\n5.8931\n4.3784\n0.068926\n4.0707\n3.6273\n3.2353\n3.2217\n3.647\n3.7272\n3.9334\n3.3109\n2.0947\n2.328\n2.6739\n3.1212\n3.3905\n3.3102\n3.7574\n4.4332\n4.3718\n3.8598\n3.1174\n1.8358\n1.9953\n3.6833\n3.9013\n5.4098\n7.6222\n9.5063\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6846\nNaN\nNaN\n20.229\n8.9896\n7.3016\n6.7737\n6.4027\n5.1279\n4.0421\n2.9564\n3.2133\n3.7285\n4.4139\n4.9828\n4.9897\n4.4676\n3.119\n2.5546\n1.5775\n1.2618\n1.239\n2.4837\n4.5412\n4.452\n3.6555\n4.2151\n4.6708\n5.1378\n6.918\n5.2911\n4.3589\n-1.0475\n3.8041\n3.7002\n3.1012\n3.177\n2.9173\n3.2471\n3.3435\n3.0581\n2.2934\n2.5467\n2.8987\n2.841\n2.6003\n2.634\n4.0074\n3.6619\n4.1849\n3.7861\n3.6572\n3.1478\n3.4173\n4.136\n4.483\n6.1954\n7.1224\n9.7617\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.8323\n19.206\n17.084\n14.747\n7.939\n6.9489\n6.7004\n6.7848\n4.8066\n3.1928\n2.2972\n2.1028\n3.2355\n3.6544\n4.1481\n3.7045\n3.1973\n2.5334\n2.32\n1.8768\n1.49\n1.9187\n3.0585\n3.9523\n3.3835\n3.336\n4.4082\n5.2017\n5.2615\n5.8057\n5.1559\n4.5539\n1.8981\n3.3765\n3.7219\n3.5881\n3.7028\n2.9015\n2.7346\n2.7969\n2.9713\n2.5167\n2.4792\n3.0236\n2.6027\n2.2048\n2.3344\n3.4994\n3.0742\n2.9759\n2.9537\n3.2574\n3.5496\n3.9041\n4.3188\n4.9747\n6.4322\n7.1698\n8.9108\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.083\n13.624\n13.852\n11.862\n6.5096\n5.913\n5.8287\n5.4883\n4.591\n3.2175\n2.6913\n2.672\n3.3229\n3.4562\n3.1719\n2.7086\n1.9887\n1.7408\n1.7393\n1.7373\n1.5117\n1.5587\n2.2935\n2.4928\n2.6961\n3.1759\n4.1886\n4.6274\n5.0605\n4.4572\n4.7396\n4.7904\n4.1256\n3.6099\n3.6749\n3.551\n4.033\n3.6745\n3.0184\n2.8618\n2.9553\n2.6323\n2.4898\n2.7583\n2.6965\n2.1749\n2.1311\n2.5142\n2.3542\n2.4413\n1.7006\n2.6214\n3.3007\n3.9084\n4.4549\n5.1768\n6.6281\n7.548\n7.4426\n8.6928\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n18.328\n11.474\n11.388\n9.289\n6.0883\n4.5757\n4.8767\n5.1483\n4.5721\n4.0073\n3.062\n2.6889\n2.7902\n2.6472\n2.2961\n1.6434\n1.0633\n1.4307\n1.5046\n1.2252\n1.1549\n0.99515\n1.5601\n1.3301\n2.418\n2.6717\n3.5803\n4.2247\n2.8061\n2.9412\n3.853\n5.1762\n3.9814\n3.9892\n3.5\n3.4509\n3.5763\n4.0183\n3.647\n2.9798\n3.0275\n2.9763\n2.5217\n2.4296\n2.5496\n2.4116\n2.2398\n2.1091\n2.1097\n2.4901\n1.8186\n2.2783\n2.9654\n3.8594\n4.4034\n5.3615\n6.3758\n6.9128\n5.4574\n7.1139\n8.6257\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.024\n10.348\n7.2096\n7.1005\n5.4712\n4.4109\n4.4077\n4.7827\n4.9918\n3.1245\n1.8477\n1.8885\n2.2632\n2.0277\n1.1459\n0.34974\n0.26517\n0.59789\n0.97566\n0.52408\n0.17672\n0.10695\n0.43958\n1.2324\n1.6378\n1.9185\n2.7363\n3.8835\n2.8732\n2.6462\n2.505\n3.0055\n2.7058\n3.9007\n3.298\n3.1194\n3.1796\n3.5692\n3.4698\n3.3914\n3.6356\n3.3658\n2.2793\n1.8336\n2.1518\n2.3277\n2.6434\n2.5158\n2.4362\n2.7462\n2.2533\n2.5974\n2.5886\n3.064\n3.7113\n5.0627\n5.6256\n6.6013\n6.208\n6.7665\n8.1447\nNaN\nNaN\n8.0415\n10.668\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.5642\n7.8465\n7.1183\n6.4676\n4.8879\n4.0474\n4.2222\n4.3493\n3.1625\n2.0278\n1.3645\n2.1004\n2.3416\n2.0275\n1.3913\n0.29138\n0.22425\n0.40743\n1.1365\n0.95666\n-0.3152\n-0.4331\n0.032849\n0.89676\n0.71788\n1.2588\n2.5647\n2.9579\n3.688\n3.1914\n2.3954\n2.1714\n1.6852\n3.9527\n3.3554\n3.2901\n3.0624\n3.4697\n3.6189\n3.9857\n4.1565\n3.6821\n2.6287\n2.3907\n2.6325\n2.7234\n3.0991\n2.7712\n2.6977\n3.0747\n3.1155\n2.9932\n2.8329\n2.8542\n3.7408\n4.5093\n5.372\n6.7097\n6.5504\n6.7333\n8.4596\n8.6576\n9.2147\n9.8209\n11.094\n10.269\n8.9663\nNaN\nNaN\nNaN\nNaN\n9.5769\n9.1017\n7.7666\n6.7198\n5.9311\n4.303\n3.71\n3.7526\n2.7668\n1.8864\n0.60451\n0.92315\n2.5748\n2.6325\n2.0064\n1.6996\n0.079908\n0.13476\n0.37445\n1.1854\n0.98743\n0.5702\n0.69\n0.88217\n0.66807\n0.47827\n1.2186\n2.3683\n2.4029\n2.49\n2.1348\n1.5525\n2.0394\n1.796\n3.5194\n3.0332\n3.2431\n3.2569\n3.3915\n4.2354\n4.6134\n4.4663\n3.7554\n2.8717\n2.8348\n3.5314\n3.9282\n3.6247\n3.1897\n3.0741\n3.5519\n3.4469\n2.9901\n3.7916\n4.2636\n4.641\n4.939\n5.2757\n6.1741\n6.8518\n7.2078\n7.5251\nNaN\n9.2305\n10.593\n10.641\n10.829\n11.569\nNaN\nNaN\nNaN\n8.7725\n8.5106\n8.7082\n7.7383\n5.2641\n5.0389\n4.6047\n4.5898\n3.8083\n3.1094\n1.3696\n-0.069847\n0.73362\n2.6858\n2.8416\n1.8717\n0.72034\n-0.5914\n-0.68624\n-0.50504\n-0.24088\n0.53499\n1.0264\n1.0902\n0.78509\n0.67634\n0.7241\n1.5586\n2.4007\n2.2217\n1.1051\n1.7739\n2.1351\n2.1948\n2.8903\n2.3777\n2.8503\n3.6662\n3.5253\n3.8001\n4.4555\n4.5227\n3.904\n3.1637\n2.8876\n2.8027\n3.3615\n4.1917\n3.9865\n3.2322\n2.6854\n2.9793\n2.894\n2.9116\n3.7128\n4.2274\n4.6125\n5.0986\n5.0524\n5.6569\n6.4953\n6.8185\nNaN\nNaN\n8.7288\n10.061\n10.417\n11.2\n12.015\nNaN\nNaN\nNaN\n8.008\n7.7973\n8.2008\n7.077\n4.7496\n5.2245\n5.4607\n4.852\n3.4234\n2.9926\n2.3447\n0.44602\n1.9162\n2.833\n3.0927\n2.1037\n1.0887\n-0.30944\n-0.5244\n-0.81897\n-0.93236\n-0.20505\n0.31079\n0.42979\n0.30958\n0.29865\n0.99223\n2.0636\n2.544\n2.5079\n2.1061\n2.1638\n2.5064\n3.2389\n4.1707\n2.3885\n3.2079\n4.5116\n4.5966\n4.5275\n4.5574\n3.969\n3.6208\n3.1467\n2.7915\n2.6092\n2.7262\n3.0649\n4.0686\n3.3987\n2.6169\n2.7171\n2.6969\n3.1707\n3.5241\n4.0701\n4.5528\n4.9015\n4.922\n5.5118\n6.2648\n6.6197\nNaN\nNaN\nNaN\n8.8673\n9.1742\n9.0834\n10.457\nNaN\n9.449\n8.0443\n6.8633\n6.6247\n7.7554\n5.0034\n4.0717\n5.2341\n4.7752\n3.6202\n2.4555\n1.8267\n1.7007\n1.3003\n2.8736\n3.1671\n2.9129\n1.8885\n1.0765\n-0.067529\n-0.2807\n-0.4211\n-0.47013\n-0.40993\n-0.49202\n-0.12065\n0.27305\n0.49517\n1.0088\n1.7857\n2.7689\n2.845\n2.7469\n1.9883\n1.3022\n3.0412\n4.9612\n3.1953\n3.9574\n5.2335\n5.2896\n5.0778\n4.7558\n4.6446\n4.4058\n3.7491\n3.2677\n2.9785\n3.1947\n3.4818\n3.6213\n3.3001\n2.8512\nNaN\nNaN\nNaN\n3.2854\n3.5698\n4.0828\n4.7503\n5.3101\n5.6085\n6.3703\n6.4775\nNaN\nNaN\nNaN\n7.2284\n7.4981\n6.5631\n7.4587\n7.7891\n7.8994\n7.4452\n6.4207\n6.0721\n5.3996\n4.1587\n3.9293\n4.6652\n4.9522\n3.7861\n2.5589\n2.0654\n1.036\n1.0938\n2.3121\n2.5768\n2.0829\n1.0782\n0.31428\n0.24158\n0.49655\n0.85179\n0.37951\n-0.11925\n-0.3404\n-0.11664\n0.59672\n1.1177\n1.3795\n1.6467\n2.1686\n2.9079\n2.9628\n1.9692\n1.0638\n3.158\n3.9587\n4.1332\n4.6904\n5.3825\n5.2223\n4.9028\n4.8007\n4.7346\n4.885\n4.4408\n4.0774\n3.8536\n4.0581\n4.3547\n4.5469\n3.3967\n3.2581\nNaN\nNaN\nNaN\n3.2833\n3.803\n4.3262\n4.9707\n5.2956\n5.1606\n5.5713\n5.8806\nNaN\nNaN\nNaN\n6.3381\n6.619\n5.9815\n6.1583\n7.1316\n7.164\n6.5848\n5.6902\n4.9459\n4.2787\n3.2479\n3.6094\n5.1234\n5.1109\n4.13\n3.0573\n2.4749\n0.89896\n0.47823\n1.485\n1.7858\n1.3518\n0.58153\n0.75095\n1.3494\n1.5067\n1.0148\n1.0058\n0.74151\n1.0082\n0.44888\n0.7862\n1.1202\n1.3873\n1.889\n1.5457\n2.48\n3.0931\n2.3377\n1.8704\n3.5937\n3.5261\n4.8447\n5.063\n5.2126\n4.7427\n4.7599\n4.7166\n4.2975\n4.5331\n4.9439\n4.6534\n4.2838\n4.2801\n4.5615\n4.6483\n4.0079\n4.0314\n3.5365\nNaN\nNaN\n3.4684\n4.088\n4.5082\n4.9577\n5.2889\n5.6621\n5.9114\n5.8728\n6.0914\n5.841\n5.6918\n6.1512\n6.7425\n6.4905\n6.2368\n6.8332\n6.5799\n6.1554\n5.5408\n4.8562\n4.6622\n3.9488\n3.8882\n5.0143\n4.8235\n4.0961\n2.9589\n1.9467\n0.82928\n0.2642\n0.96114\n0.69828\n1.1253\n1.2288\n1.2012\n2.1286\n2.1464\n1.2864\n1.1744\n1.2928\n2.1176\n1.1249\n1.0013\n1.3589\n1.1136\n1.8933\n1.7759\n2.5476\n3.3105\n3.4452\n3.4168\n3.791\n3.252\n5.0983\n4.5988\n4.9344\n4.4189\n4.5619\n4.6253\n4.5554\n4.8033\n5.0091\n4.9151\n4.427\n4.2203\n4.4517\n4.3498\n3.9815\n4.0235\n3.9383\n2.6623\n2.5476\n3.5964\n4.1507\n4.6608\n5.4244\n5.4535\n5.696\n5.8974\n5.7905\n5.7539\n6.3666\n6.9092\n6.6428\n6.1887\n6.1399\n6.0387\n6.481\n6.2696\n5.6905\n5.2664\n5.2373\n5.3126\n5.1484\n4.8586\n5.286\n5.4622\n4.5619\n3.7729\n2.926\n2.1217\n1.1983\n0.45663\n0.20334\n0.87915\n1.4257\n1.9416\n3.2485\n2.7498\n1.9104\n1.344\n1.4292\n1.993\n1.929\n1.5973\n1.9952\n2.0918\n1.9012\n1.9163\n3.1645\n3.8895\n4.0708\n3.6506\n3.4548\n2.775\n4.9901\n4.5745\n4.5981\n4.2979\n4.4293\n4.6035\n4.8991\n4.8483\n4.9377\n4.8438\n4.5409\n4.309\n4.2562\nNaN\nNaN\nNaN\nNaN\n3.9466\n3.9581\n4.2869\n4.4426\n4.7839\n5.6967\n5.323\n5.1488\n5.5001\n5.8735\n5.8211\n6.1354\n6.5529\n6.9648\n7.0375\n5.6402\n5.5531\n5.3958\n5.5771\n4.973\n5.1359\n5.4239\n5.3971\n5.1692\n5.1193\n5.523\n5.4865\n4.4764\n4.2531\n3.8236\n3.7134\n2.2162\n1.732\n0.94498\n1.7225\n2.1843\n2.9798\n3.699\n3.4939\n2.1989\n1.5187\n1.7511\n2.0074\n2.4489\n2.5111\n3.035\n2.9149\n1.9896\n1.9802\n3.6953\n4.179\n3.6518\n2.9917\n2.9806\n2.7311\n5.5157\n4.7103\n4.4404\n4.3604\n4.5696\n4.8222\n5.2576\n4.8112\n4.5005\n4.6662\n4.627\n4.4623\n4.404\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2406\n4.5975\n4.5394\n4.7176\n5.148\n4.9094\n4.8623\n5.2184\n5.6812\n5.6238\n5.7725\n5.8495\n6.0093\n5.8468\n5.2359\n4.7779\n4.8524\n4.7625\n4.8464\n5.5513\n5.5197\n5.3678\n5.9114\n6.0106\n5.7545\n4.9657\n3.5861\n3.5912\n4.1114\n4.3215\n3.182\n2.9034\n2.4544\n2.3786\n2.374\n2.7339\n3.5158\n3.6985\n2.5179\n2.7055\n3.1578\n3.0083\n3.2779\n3.681\n3.3141\n3.0919\n2.8712\n3.101\n4.4986\n4.0918\n3.4934\n2.6742\n2.8475\n2.0311\n5.217\n4.7434\n4.331\n4.619\n4.6388\n5.3244\n5.6158\n5.1241\n4.6177\n4.9322\n4.9961\n4.7854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.387\n4.7001\n4.8489\n4.8265\n4.749\n4.791\n4.676\n4.7222\n4.9089\n5.3105\n4.9878\n4.6287\n4.5791\n4.7699\n4.6006\n4.533\n4.7679\n4.9378\n5.112\n5.3271\n5.2969\n6.0109\n6.5502\n6.0655\n4.7429\n3.0715\n3.0782\n4.1457\n3.8856\n3.8736\n3.5842\n2.934\n3.0164\n3.0034\n3.4808\n3.8535\n4.3093\n3.6272\n3.5346\n3.8563\n3.4218\n3.3073\n3.4063\n2.5312\n2.8288\n4.7845\n4.157\n3.2923\n3.5779\n3.8614\n2.2423\n1.478\nNaN\n5.2103\n4.8217\n4.7295\n4.9736\n4.7889\n5.3881\n6.0813\n5.7977\n5.5445\n5.6159\n5.1842\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5963\n4.842\n4.6509\n4.4491\n4.7928\n4.8341\n4.4746\n4.0668\n4.4901\n4.8349\n4.6105\n4.3615\n4.4928\n4.7557\n4.5237\n4.4572\n4.7045\n4.3144\n4.8173\n5.283\n5.4286\n5.8779\n5.8773\n5.0673\n3.6894\n3.1916\n3.1507\n4.0674\n3.9133\n4.0579\n3.75\n3.507\n3.8178\n4.5713\n4.9207\n4.583\n4.6957\n4.0529\n2.9766\n3.9453\n2.9103\n2.9644\n2.8344\n3.1228\n3.9184\nNaN\nNaN\n2.6996\n3.5744\n4.6691\nNaN\nNaN\nNaN\n2.8919\n2.5425\n2.4604\n2.6706\n2.3928\n2.5705\n2.5741\n2.5025\n2.5432\n2.6079\n2.6058\n2.7834\n3.4044\n4.0129\n5.4219\n5.6088\n5.3763\n4.6471\n2.8883\n3.1193\n4.365\n4.0813\n4.1755\n2.3742\n3.5919\n2.8565\n1.0791\n0.57048\n1.4329\n1.7261\n-1.2723\n-0.49494\n1.5168\n0.25298\n2.287\n0.50861\n-0.10889\n0.52868\n1.3174\n0.9929\n1.8036\n2.7199\n3.2197\n4.3556\n3.9916\n3.5554\n3.132\n3.3273\n3.2132\n2.9472\n3.1088\n3.4296\n3.3287\n1.9502\n2.7902\n2.1056\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9676\n1.9908\n1.7648\n3.2976\n2.382\n0.71058\n2.6459\n2.2293\n2.7352\n2.6778\n2.6528\n2.4746\n3.67\n5.5586\n5.3014\n4.7749\n2.7539\n2.5033\n3.2215\n3.0921\n4.7777\n3.4205\n3.526\n4.4688\n4.4328\n3.3466\n1.1353\n1.5203\n2.9947\n1.9986\n1.078\n1.1811\n2.8392\n1.5195\n1.7648\n0.59239\n0.026389\n1.1904\n1.7245\n2.0251\n2.2275\n2.6401\n3.3793\n4.1536\n4.1965\n4.0326\n3.8569\n3.2471\n2.7819\n2.7575\n2.9127\n3.3763\n3.607\n3.5312\n5.0857\n3.6588\n2.3672\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2151\n1.8545\n1.6497\n1.2174\n-2.5684\n-0.43602\n-1.946\n1.3217\n2.3777\n4.3658\n3.6437\n2.7067\n3.9637\n5.8734\n5.276\n3.1449\n2.7994\n2.659\n2.7724\n3.9263\n4.7025\n3.2674\n4.879\n4.6004\n5.3937\n4.341\n2.7485\n2.1781\n3.1043\n1.5745\n3.8057\n2.269\n3.0897\n1.5769\n0.17875\n-0.59214\n0.084856\n0.99849\n2.181\n1.9359\n2.797\n2.8761\n3.0939\n4.1448\n3.7767\n3.5737\n3.5671\n3.7255\n3.3139\n3.1761\n3.2909\n3.8077\n3.9385\n3.7091\n4.7492\n4.2218\n3.4876\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.889\n2.1444\n2.3993\n2.4916\n2.5372\n2.9682\n3.1357\n2.9702\n2.5398\n2.1238\n3.6583\n1.7156\n3.1012\n5.8761\n4.9641\n1.8971\n1.9378\n1.9491\n0.40208\n2.7396\n6.0576\n2.3646\n3.4236\n3.5216\n4.4514\n4.2964\n3.0561\n2.0886\n2.7588\n2.4286\n2.8139\n3.1368\n3.8219\n2.7863\n0.94019\n-0.81759\n-0.85705\n1.252\n2.2946\n1.4697\n2.2549\n2.6008\n2.4914\n3.1623\n3.3103\n3.0872\n2.9155\n4.0443\n4.2831\n4.5029\n4.3651\n4.0276\n4.0584\n4.0705\n4.2877\n3.8459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3919\n3.1602\n2.9035\n2.6065\n3.2709\n3.187\n3.0046\n3.1249\n2.8178\n1.626\n2.8073\n1.6359\n1.4382\n2.3512\n1.1037\n0.24216\n0.61767\n0.76557\n-0.37165\n1.8483\n7.0363\nNaN\nNaN\n0.13596\n3.2886\n3.8694\n3.5706\n2.643\n3.201\n3.3498\n1.46\n3.4041\n3.6215\n4.3408\n2.626\n2.5488\n1.6147\n2.0942\n2.3234\n1.979\n2.9929\n3.108\n2.5248\n2.5675\n3.23\n3.2054\n3.5858\n4.8394\n4.6771\n3.8124\n4.1385\n3.8678\n3.4615\n4.753\n4.4858\n3.8451\n5.1358\nNaN\nNaN\n-2.0898\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5628\n3.1289\n2.3186\n2.494\n2.9438\n2.8045\n2.9174\n2.7545\n2.8291\n2.6746\n2.937\n3.5692\n2.6893\n-0.15047\n0.17673\n1.1625\n-0.25817\n0.43413\n3.8554\nNaN\nNaN\nNaN\nNaN\n2.296\n3.3259\n3.9925\n3.859\n3.6876\n3.9844\n4.3855\n2.3078\n3.0152\n3.4363\n3.7084\n3.0888\n1.7865\n1.2897\n2.46\n2.4704\n3.1704\n3.8007\n3.4292\n2.6638\n2.3961\n1.6484\n2.5513\n3.7611\n5.2122\n4.423\n3.9672\n3.9881\n3.6643\n3.9018\n4.2155\n4.1539\n2.0348\n1.044\nNaN\nNaN\n-5.0242\n0.2203\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6567\n1.9903\n1.7263\n2.6014\n3.0448\n2.4511\n3.4934\n2.9201\n3.2761\n3.6678\n3.464\n2.9455\n2.9389\n0.81746\n1.0278\n0.50445\n0.1514\n2.2371\n1.7853\nNaN\nNaN\nNaN\nNaN\n3.1052\n3.2972\n3.9602\n4.217\n5.3413\n4.5205\n5.0507\n2.277\n3.098\n2.7404\n2.4296\n2.3086\n1.4622\n0.95438\n1.5152\n1.6649\n2.3003\n4.0436\n3.6885\n2.3329\n1.8803\n0.57874\n2.17\n3.7309\n4.8006\n4.4784\n4.4543\n3.7811\n3.7186\n4.3402\n4.7242\n4.1197\n1.5269\n0.73653\nNaN\nNaN\n-0.26856\n-1.4862\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5182\n2.1296\n1.7499\n3.0148\n3.9788\n2.5231\n3.2332\n3.602\n4.3644\n4.6813\n4.1085\n3.2609\n3.0734\n3.2185\n2.0063\n1.8754\n2.3583\n3.2961\n2.0915\n1.6423\n2.2562\n2.4327\n2.4424\n4.0456\n3.9203\n4.3397\n4.4082\n4.0913\n4.0716\n4.4326\n3.5202\n3.0917\n2.214\n2.1367\n1.9341\n2.0086\n1.8294\n1.2211\n0.95488\n0.72862\n3.0267\n3.2261\n2.3709\n1.6611\n0.039839\n2.2887\n3.9526\n4.0541\n3.61\n4.4462\n3.8633\n3.6292\n4.3518\n4.6082\n4.917\n3.0856\n0.90549\n2.0333\n3.2748\n2.373\n-0.49399\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9104\n2.0439\n2.4803\n2.3013\n3.3721\n2.2623\n3.0213\n4.1115\n4.5316\n4.5211\n4.0925\n3.8046\n3.8787\n3.1485\n2.703\n2.9468\n3.1174\n4.0432\n2.5244\n2.703\n2.5119\n2.7033\n3.1638\n4.072\n3.4719\n4.6861\n4.7497\n3.8173\n3.5144\n3.4323\n3.7735\n4.1054\n2.7428\n2.2669\n1.7414\n1.704\n1.6719\n1.1307\n0.63351\n1.985\n2.3936\n3.0752\n2.585\n1.0889\n0.16361\n3.8232\n4.6255\n4.5211\n4.1416\n4.2947\n4.4016\n4.4202\n4.6417\n4.8797\n5.3969\n3.6449\n1.1446\n1.9137\n3.3892\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2814\n2.2516\n3.0356\n2.0741\n3.5337\n2.2171\n3.0486\n3.0354\n3.4428\n4.1325\n3.684\n3.2229\n3.1113\n2.9547\n3.3731\n3.3787\n3.3305\n4.7637\n4.2438\n4.2266\n4.1281\n3.6243\n3.6368\n3.2873\n2.8942\n4.4933\n5.104\n4.1809\n3.6368\n3.3918\n4.415\n4.5096\n3.1848\nNaN\nNaN\n2.7003\n1.4045\n1.7893\n0.74357\n2.2518\n3.3294\n3.5425\n2.4763\n1.69\n2.9959\n4.9915\n4.8197\n4.8978\n4.7719\n5.0102\n4.6553\n5.1383\n5.1724\n5.1258\n5.4567\n4.932\n2.0881\n0.72079\n3.0841\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.60997\n1.6176\n2.264\n2.099\n3.0484\n2.7995\n3.0687\n2.6049\n2.8076\n3.5332\n3.4552\n2.6709\n3.4666\n3.3137\n4.3545\n4.7753\n4.0285\n4.829\n4.8273\n4.5948\n3.6738\n3.5884\n3.0753\n1.8677\n2.9016\n5.8066\n5.6719\n5.3544\n4.4586\n4.4221\n4.6995\n5.1888\nNaN\nNaN\nNaN\n3.1614\n2.621\n3.3589\n1.4303\n2.2481\n3.7156\n3.6473\n2.7582\n2.616\n3.6286\n4.769\n5.1522\n5.5238\n6.009\n4.8977\n4.5743\n5.6754\n5.9484\n6.25\nNaN\nNaN\n2.1408\n1.7663\n3.9184\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.65555\n1.0882\n1.5991\n2.3966\n2.344\n2.8808\n3.6728\n3.6325\n3.3687\n3.2804\n3.0748\n2.8939\n3.1999\n3.5787\n4.7467\n5.2425\n5.0401\n4.3344\n4.1486\n3.209\n2.1977\n2.8978\n3.2121\n1.9554\n3.024\n5.3982\n6.1295\n6.2322\n6.1017\n5.6992\n4.4977\nNaN\nNaN\nNaN\nNaN\n3.2737\n2.8997\n2.2992\n1.5419\n2.3517\n3.8068\n3.5295\n3.608\n3.9931\n4.7629\n5.6016\n5.7624\n6.6468\n7.3184\n6.4011\n5.6637\n6.5261\n6.325\nNaN\nNaN\nNaN\n2.4299\n1.9602\n3.844\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9538\n1.6698\n1.7921\n1.8919\n2.3841\n3.5035\n4.0083\n4.2868\n3.6857\n3.1671\n3.741\n3.9007\n4.0149\n4.0314\n4.9998\n5.6403\n5.2952\n3.8596\n3.328\n2.4952\n2.18\n2.9435\n1.8007\n3.0765\n3.5964\n4.0784\n7.0269\n6.0998\n5.1949\n5.3798\n4.912\nNaN\nNaN\nNaN\nNaN\n2.6568\n2.2143\n1.8987\n2.0933\n2.9333\n3.7001\n2.2727\n2.1425\n4.7485\n5.6466\n6.5474\n6.5027\n7.5588\n7.5048\n7.2735\n7.2207\n7.18\n6.0991\nNaN\nNaN\nNaN\n1.2929\n1.8001\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1272\n1.9889\n1.8848\n2.5159\n3.3692\n3.8732\n4.0755\n4.0523\n4.0222\n3.5998\n3.4938\n3.9842\n4.5796\n4.8018\n5.2161\n4.859\n4.3147\n3.5188\n2.9763\n2.7219\n2.6995\n2.3571\n0.90775\n2.553\n2.9233\n4.3189\n6.978\n6.2041\n6.1246\n6.2551\n5.7449\nNaN\nNaN\nNaN\nNaN\n2.2568\n-0.718\n0.57756\n2.6732\n4.3341\n4.3771\n3.6731\n4.1053\n5.9998\n7.0663\n7.9296\n7.1912\n8.0794\n7.8593\n7.5102\n7.1874\n6.3347\n5.6847\nNaN\nNaN\nNaN\nNaN\n2.9865\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7899\n1.9335\n2.7624\n3.0457\n3.2892\n3.8032\n3.9493\n3.6944\n3.6757\n3.1238\n3.2991\n3.7328\n4.3325\n4.6119\n5.6955\n4.5826\n4.8122\n4.3545\n3.7039\n2.9694\n2.7382\n1.1462\n-0.39189\n1.9658\n2.574\n5.1628\n6.266\n6.7162\n7.297\n7.4592\n8.3055\nNaN\n4.9915\n4.0867\n5.8864\n3.8739\n0.62976\n3.5088\n4.73\n6.406\n6.2572\n4.1812\n4.5807\n7.6215\nNaN\nNaN\nNaN\n8.0855\n7.8211\n7.1063\n6.3642\n5.9696\n6.2997\n4.6794\nNaN\nNaN\nNaN\n3.1196\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2563\n2.4421\n2.9823\n3.4007\n3.3213\n3.8183\n4.0308\n3.3888\n3.0678\n3.4572\n4.4519\n4.251\n4.2787\n4.3454\n5.5876\n3.9924\n3.6287\n4.1851\n4.6769\n3.1874\n2.6925\n1.7013\n-1.5306\n0.061062\n2.0686\n4.6728\n6.4389\n8.4035\n8.4683\n7.3934\n6.8614\n5.1945\n6.5318\n5.4955\n5.3018\n6.6744\n6.1935\n6.5993\n6.4206\nNaN\nNaN\nNaN\n3.8159\nNaN\nNaN\nNaN\nNaN\n7.6134\n7.339\n5.9548\n5.7621\n5.7427\n4.9704\n2.9859\n3.8783\n3.52\n2.8052\n2.8324\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.925\n2.3575\n2.9357\n3.9642\n3.4634\n3.8541\n3.4282\n2.4303\n2.6488\n3.4261\n3.5665\n3.886\n4.2785\n4.251\n4.7337\n3.3814\n3.5952\n3.5519\n3.8768\n3.2063\n2.5277\n1.3908\n0.24979\n0.34751\n2.6165\n4.9126\n7.3682\n7.7655\n7.5621\n6.6484\n5.8145\n5.8946\n7.9662\n7.6222\n7.1328\n8.1152\n7.6624\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8639\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.2593\nNaN\n6.7997\n4.0006\n2.8658\n3.5227\n3.6002\n2.5106\n2.0044\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1482\n2.6937\n3.399\n4.4121\n3.8577\n3.5694\n3.1648\n3.0485\n2.891\n3.4628\n3.635\n3.7229\n3.9031\n3.9537\n3.883\n3.437\n3.484\n2.5698\n3.0646\n2.1551\n1.9336\n0.84681\n1.1206\n1.6932\n3.2789\n6.1016\n8.1399\n7.1335\n7.0717\n6.564\n7.0307\n7.2542\n7.9311\n8.8917\n9.261\n9.1139\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6808\n3.9081\n2.8948\n1.8653\n1.6518\n0.84966\n1.1997\n3.3458\n5.6085\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6113\n3.4507\n4.06\n4.4103\n4.4023\n3.5944\n3.0111\n3.7102\n3.572\n3.3186\n3.7236\n3.5692\n4.0987\n4.3385\n4.226\n3.1417\n2.0514\n1.6898\n1.918\n1.2857\n1.2\n1.0156\n1.1029\n3.0451\n5.1314\n6.7658\n7.5722\n7.8471\n8.9919\n8.3611\n7.4215\n9.0222\n9.1298\n9.6043\n9.89\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0271\n3.3171\n2.8695\n3.3167\n3.4687\n3.1659\n2.7252\n5.2136\n4.1959\n4.128\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9334\n3.4661\n3.5176\n3.6032\n4.4812\n4.0956\n4.3581\n3.9387\n3.2213\n2.8106\n3.8995\n3.4938\n4.0898\n4.4336\n4.1574\n2.1764\n0.89211\n1.2013\n0.01759\n0.36929\n1.8609\n2.3191\n2.6148\n4.0987\n6.5375\n7.8394\n8.1999\n9.1659\n10.269\n9.591\n8.7669\n9.848\n8.7739\n8.8973\n9.388\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2676\n2.6065\n3.0701\n3.5368\n4.4105\n4.3859\n3.5938\n4.3203\n4.0363\n3.6019\n4.3008\n5.0223\n5.5967\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0209\n3.6236\n3.3068\n3.7336\n4.8185\n4.3236\n3.0366\n2.8225\n2.9104\n3.6301\n3.9919\n3.8759\n3.1725\n3.6623\n3.023\n1.8112\n1.4586\n2.7076\n-0.44599\n0.21002\n2.774\n1.9916\n3.1951\n5.6931\n7.1027\n8.7969\n10.028\n10.097\n10.473\n11.197\n12.729\n14.033\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8823\n3.9219\n4.2007\n3.6094\n3.5611\n3.1575\n4.8214\n5.0106\n4.3783\n4.8669\n5.1319\n4.9618\n4.3945\n3.5784\n4.2593\n7.3634\n7.9689\n8.2368\n4.7344\n2.1384\n3.6775\n4.0622\n3.0063\n3.5076\n4.0726\n3.5632\n2.5493\n3.1311\n2.6294\n3.0189\n3.4715\n3.7667\n4.489\n3.4577\n2.4289\n2.736\n2.6492\n2.7931\n-0.24572\n0.97184\n2.8312\n2.9324\n2.6893\n6.0766\n7.8665\n11.126\n10.721\n11.037\n11.862\n12.768\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9839\n3.5887\n4.1032\n5.1184\n4.3843\n4.1101\n2.8025\n4.6952\n5.3367\n4.4796\n5.5887\n5.7379\n5.0744\n3.9932\n1.8568\nNaN\nNaN\nNaN\nNaN\n4.9738\n4.4228\n3.5641\n3.21\n2.2828\n2.6938\n2.5578\n2.3717\n2.5969\n2.8116\n3.1301\n2.772\n2.9743\n3.8029\n4.828\n3.3417\n3.2647\n3.3678\n3.0002\n2.1973\n0.71018\n2.2737\n2.423\n1.728\n2.9578\n6.1677\n8.9928\n10.058\n10.033\n10.966\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.6761\n4.622\n4.1192\n3.9407\n4.6587\n4.967\n4.6831\n4.5297\n4.464\n4.3838\n4.5412\n4.7684\n5.605\n6.851\n6.2151\n5.4941\n4.1904\nNaN\nNaN\nNaN\nNaN\n4.5491\n6.8788\n2.8783\n2.5264\n1.4092\n2.1614\n2.5548\n2.828\n2.7928\n3.2867\n3.9831\n3.4877\n3.0378\n3.9225\n4.5216\n3.3514\n3.4689\n4.0908\n3.5825\n2.6865\n1.0328\n0.82762\n1.7532\n2.3349\n4.2791\n6.1626\n7.6834\n8.9542\n9.0598\n9.8982\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.547\n5.252\n4.6179\n4.5183\n4.4416\n4.7939\n4.7871\n5.2121\n4.8919\n4.6486\n4.0121\n3.8418\n4.1865\n4.0899\n6.0489\n5.338\n5.3111\n6.4372\nNaN\nNaN\n3.3557\n3.6654\n4.906\n5.2492\n2.0798\n1.4685\n1.7311\n2.3306\n2.4609\n3.3481\n2.9209\n3.6683\n3.8127\n3.4222\n3.202\n3.8845\n4.3016\n2.6555\n3.2582\n4.2875\n5.3207\n4.0467\n1.6378\n1.1258\n2.7763\n3.639\n4.2967\n6.0162\n7.2085\n9.3961\n8.5972\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2069\n5.2775\n5.8001\n4.6399\n5.3894\n5.2987\n4.7706\n4.7984\n4.6807\n4.9373\n5.3529\n4.0728\n4.0611\n4.2698\n3.6817\n3.7505\n4.4367\n5.3179\n5.603\n3.8507\n3.0298\n3.7653\n3.9711\n5.2093\n4.3334\n1.542\n1.5288\n2.3296\n2.7104\n2.5581\n3.2557\n3.3639\n3.1801\n3.3189\n3.601\n3.3605\n4.2759\n4.5044\n3.7341\n4.9041\n4.8451\n6.8149\n5.0224\n3.0332\n2.8995\n4.1074\n4.2303\n4.339\n5.554\n8.6097\n10.564\n9.9311\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0763\n4.1137\n5.4315\n4.1985\n5.3673\n5.8526\n5.0956\n4.8918\n4.5745\n4.8933\n4.5372\n4.3439\n4.3749\n4.9401\n4.2202\n3.8371\n4.5688\n5.2012\n4.4651\n3.4209\n4.0516\n5.7418\n5.2901\n6.3468\n4.8612\n1.797\n2.2206\n3.0724\n2.9788\n2.3067\n3.0204\n3.2442\n2.9181\n3.1446\n3.3489\n3.1865\n3.7661\n4.3078\n4.3535\n5.538\n5.199\n5.1836\n4.5333\n4.8719\n3.4765\n3.9721\n4.4482\n5.5865\n6.6255\n8.8254\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.7426\n3.6594\n3.9142\n4.6678\n-0.86454\n2.9085\n5.6555\n5.6959\n6.842\n6.4454\n4.7167\n4.7163\n5.3727\n5.1313\n4.6649\n4.2315\n3.2974\n5.2802\n4.9345\n5.434\n3.6999\n4.857\n6.1712\n6.381\n6.7121\n5.0628\n2.3124\n2.877\n2.7403\n2.4704\n2.4452\n2.6766\n2.4506\n2.845\n3.4485\n3.0785\n2.9357\n3.5437\n3.7863\n3.6588\n4.8933\n4.5031\n3.3333\n3.0419\n3.8371\n3.7176\n4.5904\n4.7366\n5.0182\n6.4075\n9.0349\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.9392\nNaN\nNaN\nNaN\nNaN\n6.288\n5.6751\n5.0594\n4.4198\n4.0126\n-0.78066\n3.0801\n5.1271\n6.9396\n7.1231\n6.5759\n4.8917\n3.3787\n5.6791\n4.4992\n4.2537\n2.9989\n3.533\n5.756\n5.5946\n6.0844\n4.826\n5.3805\n6.1573\n6.5419\n5.8741\n3.3112\n2.6057\n2.8492\n1.9658\n2.1565\n2.801\n2.754\n2.6718\n2.6156\n2.36\n2.6897\n2.9065\n3.665\n3.838\n3.921\n4.2653\n4.2218\n3.7829\n2.8762\n1.8403\n2.9865\n4.3521\n4.5389\n3.7938\n6.0646\n10.083\n12.904\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.5681\n6.8473\n6.4515\n5.9023\n4.9897\n4.4623\n4.5389\n4.2742\n4.1247\n5.4809\n6.865\n5.4552\n5.5359\n4.1803\n2.9035\n3.4408\n3.8098\n4.1386\n3.0613\n4.3239\n6.213\n6.06\n5.7648\n4.9964\n4.6787\n6.2843\n6.1325\n4.433\n-0.11836\n2.477\n2.1162\n1.9566\n2.1501\n2.9382\n2.9829\n3.3776\n2.9365\n1.5219\n2.3807\n2.6973\n2.9715\n3.5814\n3.9674\n4.3114\n4.7602\n4.7569\n4.0705\n3.2706\n1.3403\n2.1215\n4.6557\n4.8974\n7.2651\n10.4\n12.373\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0024\nNaN\nNaN\n20.909\n8.7305\n7.3198\n7.0793\n6.9498\n4.6042\n3.3364\n2.1438\n2.38\n2.6598\n4.1734\n5.8688\n5.041\n4.4804\n3.8104\n3.3484\n1.7579\n1.3895\n2.4936\n3.3494\n4.9914\n5.2334\n4.7065\n4.5823\n4.5684\n5.1232\n6.9826\n5.6872\n4.1156\n-1.6641\n2.0726\n1.9559\n1.6394\n2.0912\n2.0359\n2.5131\n2.9382\n2.7014\n1.8385\n2.3154\n2.5228\n2.4283\n2.8315\n3.1232\n4.3761\n3.8615\n3.9832\n3.574\n3.4069\n2.7659\n3.0145\n4.674\n5.6825\n8.0444\n10.009\n12.617\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.2604\n19.94\n18.008\n15.462\n7.717\n7.1333\n7.3663\n7.0578\n4.6681\n3.0762\n1.7228\n1.1513\n2.5393\n2.7683\n3.4902\n3.2306\n3.0542\n2.6381\n2.6066\n1.1893\n1.0653\n2.6779\n3.8426\n4.6641\n3.4546\n3.0428\n3.8527\n4.8784\n5.0758\n5.324\n5.1161\n3.6139\n0.9436\n1.5212\n1.9864\n2.0549\n2.6044\n1.8887\n1.882\n2.0691\n2.3123\n2.0585\n2.1054\n2.4623\n2.0972\n1.932\n2.3387\n3.4988\n2.7628\n2.4839\n2.3492\n2.3505\n2.8035\n2.8924\n3.0887\n5.3761\n7.535\n9.6253\n13.084\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n20.681\n13.844\n14.758\n12.982\n6.3915\n6.2351\n6.0359\n5.4429\n4.6407\n3.1341\n2.1928\n2.0051\n2.5667\n2.7834\n2.6419\n1.8859\n1.134\n1.6229\n1.8434\n0.71301\n0.73088\n1.5287\n2.8831\n3.0178\n2.932\n2.8476\n3.6675\n4.1164\n4.5468\n3.5892\n3.2763\n3.4538\n3.2888\n2.1562\n2.1266\n2.0931\n2.9124\n2.655\n2.0654\n1.9561\n2.1129\n1.97\n1.9547\n2.0627\n2.0008\n1.6969\n1.8081\n2.104\n1.9322\n2.0208\n0.72792\n0.15185\n1.7718\n3.4868\n3.5611\n4.9363\n8.1986\n9.8162\n12.304\n10.713\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.658\n12.363\n12.267\n9.8294\n6.3846\n4.2355\n4.2298\n4.901\n4.2084\n3.52\n2.1933\n1.6823\n2.0101\n1.9736\n1.7445\n0.93866\n-0.47997\n0.75926\n1.144\n0.85877\n0.64811\n-0.2732\n1.3689\n1.7214\n2.7909\n2.6137\n3.2348\n3.5226\n0.8807\n1.5176\n2.6717\n4.0506\n3.393\n2.5041\n2.062\n2.1241\n2.3956\n2.9657\n2.691\n1.9644\n1.984\n2.3029\n1.9426\n1.809\n1.8796\n1.8571\n1.6027\n1.4378\n1.5831\n2.0069\n1.2617\n0.92627\n0.75111\n3.3334\n3.6202\n5.1597\n8.2015\n8.9935\n5.6654\n6.3056\n8.159\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.855\n10.804\n7.6863\n7.8333\n5.8872\n3.8624\n4.1205\n4.7716\n4.0742\n2.2661\n0.85469\n1.0177\n1.5095\n1.2966\n0.31902\n-0.32441\n-0.14819\n0.41161\n1.1231\n0.68469\n0.37454\n-0.15789\n0.39305\n0.74371\n1.1182\n1.515\n2.0735\n3.0614\n1.3611\n2.0357\n2.423\n3.2418\n2.318\n2.3825\n1.8585\n1.8128\n2.0384\n2.5204\n2.6925\n2.4529\n2.8322\n2.6544\n1.7927\n1.0182\n1.6118\n1.8789\n1.6735\n1.4099\n1.7933\n2.1248\n1.3869\n1.9728\n-0.16276\n-1.6767\n1.3407\n4.617\n6.1718\n7.0082\n5.8957\n5.6297\n7.8384\nNaN\nNaN\n7.5435\n10.045\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.093\n8.7472\n7.5689\n7.1772\n3.6236\n1.4988\n3.884\n4.0346\n2.0346\n0.94105\n0.32\n1.0668\n1.5609\n1.1319\n0.60653\n-0.079009\n-0.10211\n0.16669\n1.1123\n0.82003\n-0.37706\n-0.43766\n-0.018821\n0.36579\n0.73231\n1.0256\n1.7749\n1.6674\n2.482\n2.702\n2.5288\n2.7002\n1.3518\n2.684\n2.3536\n2.2413\n2.0954\n2.6227\n2.9622\n3.1011\n3.2418\n2.8735\n2.2626\n1.6883\n1.8895\n2.097\n1.8601\n1.7181\n2.1847\n2.2595\n1.8353\n1.3999\n1.0007\n2.1553\n2.6992\n4.3877\n5.7806\n5.8718\n5.0611\n5.7257\n8.0394\n7.925\n8.8522\n9.5308\n10.133\n9.2326\n8.6254\nNaN\nNaN\nNaN\nNaN\n9.5643\n9.7601\n8.0742\n6.5624\n6.4827\n2.6937\n2.2641\n2.978\n1.6643\n0.74222\n-0.51095\n-0.090973\n2.0649\n2.2896\n1.9196\n1.5033\n-0.4909\n-0.48572\n-0.057482\n1.0212\n0.9497\n0.77615\n0.44501\n0.13492\n0.10486\n-0.063093\n0.66231\n1.4461\n1.3007\n1.1541\n1.2957\n1.3506\n2.203\n1.2329\n2.638\n2.2616\n2.2687\n2.3325\n2.4555\n3.5344\n3.6842\n3.4858\n2.8403\n2.3962\n2.1013\n2.7468\n3.0402\n2.2863\n1.7091\n2.1616\n2.41\n2.1814\n0.48241\n1.4699\n2.7576\n4.2487\n4.8056\n5.5988\n5.9418\n5.3532\n6.1374\n6.8045\nNaN\n6.9392\n8.9295\n8.4269\n9.4097\n10.726\nNaN\nNaN\nNaN\n7.7774\n8.5352\n9.1506\n8.0963\n5.1392\n4.9916\n3.4373\n2.9657\n2.4804\n2.7245\n0.62602\n-0.9164\n0.29023\n2.3288\n2.6458\n1.9775\n0.21831\n-0.92741\n-0.99594\n-0.74917\n-0.2998\n0.54586\n1.035\n0.9457\n0.40083\n-0.14137\n-0.45071\n0.51656\n1.8583\n1.3918\n-0.22988\n0.71979\n1.5749\n1.979\n1.9497\n1.5245\n2.248\n3.0392\n2.7597\n2.9405\n3.7626\n3.807\n2.7846\n2.0349\n1.8971\n1.7765\n2.4817\n3.3798\n2.942\n1.8263\n1.1925\n1.2483\n0.85411\n0.64943\n1.3432\n2.1143\n3.5996\n5.061\n5.4213\n4.7674\n5.3137\n5.8678\nNaN\nNaN\n5.4517\n7.9461\n8.5591\n10.139\n11.634\nNaN\nNaN\nNaN\n7.4033\n7.5452\n8.5058\n7.4326\n3.9915\n2.9254\n2.9629\n3.2229\n2.6433\n2.6178\n1.4783\n-0.70487\n1.0928\n2.2311\n3.1261\n2.2889\n1.1097\n-0.51741\n-0.83465\n-1.0215\n-0.84521\n0.031506\n0.22335\n0.13091\n-0.14443\n-0.40461\n-0.039576\n1.0312\n2.1193\n2.1257\n1.5672\n1.5102\n1.9268\n2.5451\n3.3811\n1.7234\n2.7937\n4.026\n3.9288\n3.7699\n3.8119\n3.014\n2.2691\n1.6467\n1.4491\n1.5433\n1.7901\n2.3401\n3.3314\n2.3313\n0.98991\n0.8452\n0.30247\n1.8018\n1.1357\n1.5455\n3.7174\n5.1483\n5.5683\n4.123\n4.7934\n4.7017\nNaN\nNaN\nNaN\n6.1991\n7.1322\n7.4358\n9.6724\nNaN\n8.9913\n7.3294\n6.0415\n6.1285\n7.5029\n4.9295\n3.5228\n3.7822\n3.7717\n2.747\n1.5349\n0.75297\n0.51619\n0.30859\n2.0124\n3.0148\n3.1738\n2.0071\n0.91686\n-0.27368\n-0.30542\n-0.44195\n-0.11016\n-0.074908\n-0.25017\n-0.29404\n-0.26585\n-0.22674\n0.17483\n1.0868\n2.4438\n2.5539\n2.4475\n1.4652\n0.66166\n2.2473\n3.9583\n2.4168\n3.5696\n4.6607\n4.6941\n4.5226\n4.3641\n3.5145\n2.9398\n2.2067\n1.9538\n1.9844\n2.2996\n2.7688\n3.1368\n2.9916\n1.5781\nNaN\nNaN\nNaN\n1.0043\n1.6281\n3.4078\n4.0103\n3.2701\n3.6356\n4.3392\n4.9232\nNaN\nNaN\nNaN\n5.1906\n5.7942\n4.7934\n6.1599\n6.8345\n7.1876\n6.5665\n5.5826\n5.258\n4.3533\n3.1841\n3.0641\n3.8832\n4.2043\n3.3119\n1.9255\n1.2166\n0.52692\n0.93351\n2.0581\n2.627\n2.3513\n1.2229\n0.68229\n0.60451\n0.77173\n0.87065\n0.41096\n0.064133\n0.0014746\n-0.32236\n-0.074653\n0.42855\n0.53798\n1.2944\n1.9603\n2.7961\n2.8592\n1.5934\n0.25567\n2.2255\n3.1542\n3.2188\n3.9931\n4.8653\n4.5751\n4.4699\n4.4652\n3.6229\n3.469\n3.0359\n2.9823\n2.9294\n3.2103\n3.69\n4.6134\n2.8701\n2.2883\nNaN\nNaN\nNaN\n1.0216\n2.1331\n2.2698\n3.0777\n3.0419\n3.0419\n3.2872\n3.7335\nNaN\nNaN\nNaN\n4.0345\n4.0079\n3.7628\n4.713\n6.4974\n6.4373\n6.1275\n4.8961\n3.8874\n3.1562\n2.0447\n2.2353\n4.0856\n4.4055\n3.7965\n2.4126\n1.376\n0.061227\n0.28209\n1.1731\n1.7397\n1.4699\n0.71704\n1.0171\n1.6984\n1.6163\n0.9044\n1.0144\n0.74151\n0.84859\n0.10307\n0.24842\n0.72339\n0.81108\n1.5373\n1.4912\n2.5435\n2.9843\n2.08\n1.4478\n2.8267\n2.9487\n4.0386\n4.649\n4.7317\n3.9602\n4.2873\n4.2603\n3.8237\n3.467\n3.7024\n3.6929\n3.5239\n3.4601\n3.6782\n3.7701\n2.8347\n3.4046\n2.3906\nNaN\nNaN\n0.69143\n1.8378\n2.0774\n2.5487\n3.0517\n3.778\n3.6492\n3.3914\n3.5789\n3.1463\n3.2058\n3.1053\n3.8723\n3.9118\n4.725\n6.1074\n6.3968\n6.348\n5.0331\n4.0715\n4.0043\n2.8954\n2.7551\n4.0661\n4.2974\n3.8817\n1.9058\n-0.032294\n-0.85082\n-0.46955\n0.90036\n0.8787\n1.2929\n1.8779\n1.4309\n2.1759\n2.2549\n1.1388\n1.2765\n1.1634\n1.5762\n0.55639\n0.41028\n0.96367\n0.86277\n1.771\n1.7483\n2.4761\n3.3443\n2.981\n2.872\n3.198\n2.8022\n4.3921\n4.223\n4.5581\n3.9837\n3.9312\n4.1643\n4.1496\n4.0524\n3.7041\n3.9683\n3.5232\n2.9768\n3.5086\n3.4667\n3.6916\n3.697\n2.0703\n-0.56562\n-1.4008\n0.3761\n1.3688\n2.3317\n2.9568\n3.4146\n3.7729\n3.5461\n3.3979\n3.2587\n3.6334\n4.428\n3.9883\n3.1728\n3.3147\n4.2219\n5.718\n6.3407\n5.2116\n5.0099\n4.9795\n4.7222\n4.4346\n4.098\n4.7399\n5.0306\n3.9446\n2.9204\n1.3318\n0.87792\n-0.21314\n0.21923\n0.11902\n2.0101\n2.3441\n2.3559\n3.0648\n3.1772\n2.3687\n1.7706\n1.1359\n1.3173\n1.1763\n0.88502\n1.7169\n1.8875\n1.8076\n1.7061\n3.2362\n3.946\n3.5616\n3.0811\n2.8494\n2.2008\n4.5713\n4.4202\n4.7801\n4.3814\n4.3066\n4.4635\n4.8222\n4.4057\n4.1411\n4.087\n3.5769\n3.4532\n3.4305\nNaN\nNaN\nNaN\nNaN\n2.2548\n1.5737\n1.4154\n1.7833\n2.1017\n2.7015\n2.7589\n2.8656\n3.2088\n3.5128\n3.357\n3.6523\n4.2512\n4.6829\n4.7978\n3.2209\n3.4925\n3.9627\n4.6952\n4.7757\n5.1901\n5.1296\n5.308\n5.1023\n4.6215\n5.1316\n4.9964\n4.0242\n4.1163\n2.5044\n2.5328\n1.3765\n0.73854\n0.15278\n1.3091\n2.1953\n2.6603\n3.7095\n3.9973\n1.987\n1.1597\n1.21\n1.179\n2.0053\n2.4411\n2.8647\n2.5103\n2.031\n2.217\n3.9363\n4.2149\n3.207\n2.1907\n2.0938\n2.0353\n5.4088\n4.9137\n4.9245\n4.8318\n4.8974\n5.2304\n5.8369\n4.9615\n4.439\n4.1354\n3.9603\n3.8909\n4.0374\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3164\n2.3129\n1.6457\n1.4121\n1.7141\n1.7751\n2.225\n2.8481\n2.934\n2.9769\n3.1227\n3.1114\n3.3178\n3.5463\n3.2185\n2.9348\n3.3308\n3.8029\n4.5558\n5.9258\n5.9712\n5.7354\n5.8011\n5.6229\n5.2445\n4.1805\n2.5033\n2.7934\n3.5188\n3.454\n2.4508\n2.1482\n1.8907\n1.6901\n1.8074\n2.4355\n3.3778\n3.383\n1.9712\n2.3408\n2.3727\n2.5159\n3.1557\n3.9682\n3.3257\n2.9308\n3.0065\n3.3844\n4.9643\n4.4217\n2.948\n1.8328\n1.8965\n1.21\n5.862\n5.1135\n5.0361\n5.6398\n5.7595\n6.2327\n6.7817\n6.16\n5.1494\n4.6846\n4.4286\n4.6138\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8241\n1.9524\n1.0249\n1.3047\n1.2365\n1.1051\n0.74577\n1.6033\n2.1949\n2.6767\n2.649\n2.1701\n2.7258\n3.3463\n3.3146\n3.4701\n3.8486\n4.1823\n5.4417\n5.5955\n5.7129\n6.0615\n6.6173\n5.5823\n3.7334\n1.5345\n1.7176\n3.3128\n3.0321\n3.0472\n2.7897\n2.4818\n2.6897\n2.4591\n2.9831\n3.4478\n3.8549\n2.7478\n2.76\n3.0553\n2.9196\n3.0421\n3.3098\n2.7502\n2.783\n4.9774\n5.0028\n4.3254\n3.8454\n2.8665\n1.0484\n0.27534\nNaN\n6.3704\n5.3155\n5.9793\n6.9007\n6.8608\n7.5225\n8.333\n7.895\n6.1559\n5.5227\n4.5677\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4195\n2.3844\n1.4238\n1.2845\n0.63647\n0.91092\n0.87024\n1.002\n1.7311\n2.2578\n2.3578\n2.1357\n2.8235\n3.7042\n3.5525\n3.4822\n3.5847\n3.2151\n4.3295\n4.8581\n5.0951\n5.7838\n5.7169\n4.1807\n2.3895\n1.537\n1.7334\n3.1729\n3.2574\n3.1702\n2.9697\n2.9624\n3.8988\n4.5498\n4.6267\n4.0543\n4.2408\n3.3572\n2.2866\n3.5671\n2.2429\n2.4758\n2.2219\n2.6216\n3.9237\nNaN\nNaN\n3.3293\n3.4324\n3.6687\nNaN\nNaN\nNaN\n3.6167\n3.2755\n3.5593\n3.2344\n2.8947\n3.5472\n4.4255\n3.2961\n3.4929\n3.9649\n3.3244\n3.4203\n4.1257\n5.2215\n6.9604\n6.1877\n5.6397\n5.5608\n4.3619\n6.3027\n7.4757\n6.6281\n7.6421\n6.0933\n7.538\n6.249\n5.1613\n3.1736\n3.7555\n5.2777\n0.25718\n0.94624\n2.6857\n2.868\n5.8732\n2.6074\n2.448\n4.0105\n4.2436\n3.8722\n4.8292\n5.5411\n6.1536\n6.7588\n6.7078\n5.1334\n5.1193\n4.773\n3.9197\n3.355\n3.5865\n3.785\n3.0412\n0.72308\n3.3809\n1.8889\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1539\n2.5115\n2.2061\n4.6224\n4.535\n1.9615\n5.5656\n3.021\n3.4462\n4.197\n3.4343\n3.3755\n3.9225\n7.0182\n7.2573\n6.0586\n3.3248\n4.3834\n5.0281\n5.4138\n7.2116\n5.8132\n7.0326\n8.5627\n8.5402\n7.3798\n5.5163\n4.6484\n5.861\n4.8076\n4.2227\n2.8937\n4.5104\n4.8756\n4.4244\n2.7839\n2.8475\n4.8322\n4.2788\n4.9665\n5.9481\n5.2648\n5.8304\n6.4435\n6.6584\n5.5653\n5.2864\n4.3676\n3.6934\n3.4939\n3.2138\n3.196\n3.2953\n3.5613\n5.9426\n3.3353\n0.48096\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.98565\n1.598\n1.9862\n2.2001\n-0.81484\n-0.010059\n0.8353\n1.6927\n2.8455\n5.8337\n3.3123\n3.4171\n2.8567\n7.2303\n7.6195\n4.8908\n3.8231\n4.3913\n4.66\n7.5818\n6.9343\n6.705\n8.3438\n7.4705\n9.1645\n8.2959\n6.5391\n4.975\n5.9753\n4.3722\n6.6857\n4.6536\n5.5449\n5.0856\n2.7767\n1.9382\n3.5523\n4.5504\n5.0294\n5.3261\n6.4473\n5.4027\n4.9068\n6.3483\n6.1865\n4.9386\n4.5396\n4.6381\n4.8372\n4.7088\n3.9153\n2.9049\n3.4503\n4.2416\n4.9715\n3.6885\n2.2911\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0744\n1.5439\n2.0312\n2.2723\n2.2587\n2.3309\n3.046\n3.0214\n2.4682\n2.1254\n3.6156\n2.4489\n2.648\n7.5174\n6.7136\n3.2738\n2.9592\n3.5896\n3.7598\n7.1533\n8.8111\n6.5473\n6.5486\n4.6678\n7.9182\n8.0451\n6.9294\n5.124\n5.6943\n5.3567\n5.3094\n6.4257\n6.0792\n6.2014\n2.9277\n1.4944\n2.288\n4.5911\n5.3562\n5.131\n5.5586\n5.3026\n4.147\n5.406\n5.8066\n4.6089\n4.1607\n4.9102\n5.1476\n5.1554\n4.9688\n3.829\n3.9586\n4.8409\n4.6002\n3.9309\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1536\n2.4808\n2.4123\n2.4825\n2.9253\n2.4308\n2.9234\n3.1584\n2.4423\n1.5447\n2.748\n2.2141\n2.6189\n4.0247\n2.4155\n1.7957\n1.3324\n1.9675\n2.1317\n6.8868\n10.178\nNaN\nNaN\n0.3673\n6.347\n7.5064\n7.8545\n6.2656\n6.7353\n7.722\n4.5773\n5.9598\n6.1825\n7.3409\n4.0017\n3.4979\n3.8407\n5.2466\n5.7125\n5.3795\n5.8452\n6.0817\n4.5957\n4.059\n5.3536\n4.968\n4.7157\n5.6714\n5.2564\n4.0019\n4.6341\n4.6933\n3.655\n5.1306\n4.6626\n4.04\n7.2899\nNaN\nNaN\n-4.9462\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5479\n2.1901\n1.9825\n2.1676\n2.5987\n1.9237\n2.702\n2.8512\n2.9091\n3.1098\n3.4794\n4.4969\n3.6385\n1.7543\n1.1627\n2.1716\n0.88493\n2.2586\n4.9429\nNaN\nNaN\nNaN\nNaN\n4.1442\n6.3689\n8.2397\n8.0065\n7.0484\n7.6397\n8.8195\n6.6323\n4.795\n5.5682\n5.6708\n4.2449\n2.6081\n3.6268\n5.0549\n5.6763\n6.0305\n6.7028\n5.8968\n4.8248\n4.6527\n3.9249\n3.9999\n4.6923\n6.8912\n5.7633\n4.8603\n4.7759\n4.9774\n4.4499\n4.0021\n3.5774\n2.0531\n2.8874\nNaN\nNaN\n-9.6666\n-2.7938\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9782\n1.1323\n1.1622\n2.3739\n3.0218\n1.5476\n3.2102\n3.3793\n3.9621\n4.316\n3.9369\n3.5557\n3.8272\n2.5776\n2.2102\n1.3253\n1.3636\n4.1925\n2.9732\nNaN\nNaN\nNaN\nNaN\n5.8187\n6.6262\n7.9378\n8.6368\n8.9811\n8.1229\n8.6312\n5.3158\n3.759\n3.0392\n5.1061\n3.4152\n2.6374\n3.2615\n4.2582\n4.2652\n4.9512\n7.6217\n5.857\n4.1182\n3.5295\n1.4531\n3.8012\n5.0474\n6.3692\n6.2889\n5.7123\n4.2913\n4.8153\n4.6293\n4.2591\n3.0623\n1.384\n1.9322\nNaN\nNaN\n-3.6987\n-4.8518\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9596\n1.4419\n1.1536\n2.6028\n4.4354\n1.9698\n3.1295\n3.939\n4.8796\n5.4674\n4.9018\n4.2833\n3.7251\n4.3338\n3.2306\n2.7741\n3.6842\n5.1316\n3.3078\n3.7411\n4.3025\n5.3471\n5.8486\n6.853\n7.2226\n8.0256\n8.3393\n8.4774\n7.9779\n7.9993\n6.664\n5.2029\n3.0713\n4.9166\n4.3012\n4.2697\n4.7185\n4.0347\n3.4578\n3.1211\n6.4326\n5.3145\n3.9055\n2.5874\n-0.62805\n4.1429\n5.6554\n5.3119\n4.7388\n5.6859\n4.2107\n4.6962\n5.2784\n5.1925\n4.667\n1.7546\n1.5205\n2.3135\n3.3855\n1.1776\n-4.3338\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0247\n1.2112\n1.9446\n1.5853\n3.4008\n2.1889\n2.6998\n4.2516\n5.0373\n5.4972\n4.9116\n5.1038\n5.1526\n4.4836\n3.9932\n4.0471\n4.97\n6.2528\n4.6558\n5.2891\n4.836\n5.2445\n6.5588\n6.9839\n6.4909\n8.3321\n8.8007\n8.4643\n7.6417\n7.1995\n7.1627\n6.4786\n5.64\n4.7586\n4.6436\n4.7497\n4.9813\n3.7716\n3.0293\n3.8903\n4.7329\n4.7938\n3.5532\n1.6777\n-1.0674\n5.9057\n6.1432\n6.4849\n4.9765\n5.9683\n5.3999\n5.9225\n6.1396\n7.5111\n6.5131\n2.736\n1.0764\n1.9592\n3.7492\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22961\n1.1568\n2.1871\n1.2493\n2.3709\n1.9935\n2.9886\n2.9856\n3.8023\n5.1412\n4.6505\n4.7152\n4.9807\n4.514\n4.7803\n4.7018\n5.2032\n6.8967\n6.3773\n6.7845\n6.5949\n6.7204\n7.7142\n6.5459\n5.5353\n8.3747\n9.9024\n9.1039\n7.7731\n6.8281\n7.9458\n5.1787\n3.5529\nNaN\nNaN\n5.8204\n4.7253\n3.995\n2.7854\n4.0451\n4.6928\n4.8034\n3.5039\n3.3685\n5.2373\n7.3373\n6.3384\n6.6799\n5.8615\n6.6958\n6.0238\n6.6278\n7.0159\n7.8248\n6.8504\n4.7969\n0.88223\n0.60676\n3.5943\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0166\n-0.053736\n0.80609\n0.91262\n1.7559\n2.5979\n2.9196\n2.4657\n3.2457\n4.4823\n4.6955\n4.5089\n5.7635\n5.0541\n5.2267\n5.5918\n5.4109\n6.5318\n6.978\n7.4677\n6.3948\n6.8425\n6.7961\n5.217\n5.1248\n9.2316\n10.595\n10.343\n8.002\n8.1282\n8.2782\n6.7303\nNaN\nNaN\nNaN\n6.3007\n6.0285\n5.0699\n3.1652\n4.0611\n5.2048\n4.3869\n4.1973\n4.1262\n5.457\n6.7043\n6.6129\n7.2493\n7.7284\n5.6776\n5.535\n7.3042\n7.6115\n8.7801\nNaN\nNaN\n0.22658\n1.1526\n4.0978\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.143\n-0.60185\n-0.21894\n1.1889\n1.6331\n2.8879\n3.8423\n3.7198\n4.0665\n4.3593\n4.2987\n4.8018\n5.5921\n5.6487\n5.9404\n6.0236\n6.606\n6.8574\n7.0326\n6.2398\n4.5358\n5.9663\n6.6992\n4.7929\n5.7641\n8.2715\n10.219\n10.937\n9.8493\n9.7544\n8.3118\nNaN\nNaN\nNaN\nNaN\n6.0008\n7.897\n6.4456\n2.683\n4.0067\n6.5083\n8.0811\n5.9714\n5.037\n5.8806\n6.5125\n6.7398\n8.5203\n9.2881\n7.8117\n6.4989\n8.3379\n7.5643\nNaN\nNaN\nNaN\n1.2076\n0.097952\n3.0001\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23631\n0.34427\n0.38199\n0.77497\n1.7364\n3.1613\n3.7815\n4.3284\n3.9006\n3.9652\n4.9994\n5.6551\n5.922\n6.37\n6.7723\n7.5235\n7.9956\n7.2542\n6.533\n5.3746\n4.763\n5.6927\n4.6122\n5.3847\n6.3642\n7.3839\n11.239\n10.147\n9.8186\n10.085\n8.8119\nNaN\nNaN\nNaN\nNaN\n4.7086\n6.379\n6.8075\n3.9626\n4.5604\n5.7908\n5.2831\n3.586\n5.8179\n6.6284\n7.1461\n7.355\n9.3446\n9.726\n9.045\n8.3832\n8.5244\n6.9506\nNaN\nNaN\nNaN\n0.3171\n-0.32627\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.82672\n0.90908\n0.63623\n1.5401\n2.612\n2.8612\n3.0297\n3.2677\n3.5738\n3.772\n4.3455\n5.4357\n5.8664\n6.2901\n7.117\n7.041\n6.8379\n7.1011\n5.7061\n5.1485\n6.0015\n4.4671\n1.4882\n3.4033\n5.2219\n8.6217\n11.257\n9.523\n10.495\n10.681\n10.015\nNaN\nNaN\nNaN\nNaN\n4.57\n4.6684\n6.8477\n4.8779\n6.4117\n5.698\n4.3905\n4.5874\n7.5277\n8.3908\n9.299\n8.4516\n9.7754\n9.9282\n9.4366\n8.0028\n6.8643\n6.5106\nNaN\nNaN\nNaN\nNaN\n1.3523\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1169\n0.71227\n1.5683\n2.1584\n2.2433\n2.5288\n2.986\n2.7546\n3.1563\n2.8874\n3.4903\n4.7162\n5.7517\n6.1146\n7.1041\n6.6021\n6.9749\n7.4869\n6.2865\n5.0912\n6.6547\n1.6617\n-1.9451\n2.0955\n4.0514\n9.1733\n10.43\n10.638\n11.795\n11.942\n13.402\nNaN\n5.8946\n5.3484\n7.2351\n7.327\n6.1169\n7.3466\n6.4473\n7.4247\n6.2612\n4.6499\n4.0566\n9.2879\nNaN\nNaN\nNaN\n9.8055\n10.009\n9.183\n7.0309\n7.3359\n7.7313\n4.8668\nNaN\nNaN\nNaN\n1.4855\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.66551\n0.85646\n1.6014\n2.4581\n2.4641\n2.5276\n2.9959\n2.4647\n2.5213\n3.4996\n3.8359\n4.7181\n5.6161\n6.1467\n6.8267\n5.5616\n5.7904\n7.1933\n7.7054\n5.9787\n6.2652\n5.1701\n-2.9787\n-0.53871\n3.4642\n7.717\n10.238\n13.289\n13.457\n11.799\n11.841\n5.6324\n7.3541\n6.8076\n7.5154\n9.4796\n9.7949\n9.3095\n9.0823\nNaN\nNaN\nNaN\n4.4888\nNaN\nNaN\nNaN\nNaN\n9.234\n9.5222\n7.6234\n6.9511\n7.2232\n8.3187\n4.1431\n3.3621\n1.8977\n0.34598\n0.64825\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.067876\n0.38893\n1.241\n2.6325\n2.3365\n2.4888\n2.2674\n1.5582\n2.2154\n3.7592\n3.5114\n3.8503\n5.3544\n5.901\n6.1321\n5.0801\n5.6992\n6.6593\n7.1921\n6.5393\n5.9922\n4.355\n2.7601\n-0.30994\n5.1377\n8.5476\n11.556\n12.009\n12.273\n10.748\n10.538\n8.8512\n9.9125\n8.5807\n10.331\n9.8032\n10.044\nNaN\nNaN\nNaN\nNaN\nNaN\n4.9586\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.8102\nNaN\n8.83\n8.1998\n4.8226\n3.6565\n3.5037\n-0.062162\n-0.55778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.52157\n0.19023\n1.8025\n2.8475\n1.7487\n1.7115\n1.8119\n2.1814\n2.7285\n3.9719\n4.0165\n4.4345\n4.8424\n5.6043\n5.8818\n5.2777\n6.0511\n5.3913\n5.7518\n5.3907\n4.9408\n2.5191\n2.7586\n1.6865\n6.3594\n9.7461\n12.674\n10.543\n11.29\n9.6839\n11.684\n11.837\n9.1751\n10.198\n11.811\n11.754\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3591\n3.9321\n1.9938\n-0.080813\n0.044716\n-0.3163\n-0.37189\n2.5299\n4.3386\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22457\n0.83263\n2.4208\n2.8161\n2.3923\n1.2434\n1.3297\n2.7129\n3.2724\n4.165\n3.8091\n4.6039\n5.0849\n5.8573\n5.9643\n4.608\n3.5041\n2.786\n4.0437\n4.0774\n3.6831\n1.728\n2.0588\n4.6348\n7.6295\n10.15\n11.468\n11.231\n13.682\n11.586\n11.719\n11.581\n9.3116\n10.587\n12.807\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3813\n2.7923\n2.0017\n1.5954\n2.2859\n2.1282\n1.8621\n4.6138\n3.914\n3.7382\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15318\n0.90089\n1.6251\n1.8908\n2.8184\n2.0498\n2.6128\n2.8606\n2.9377\n3.3683\n3.9655\n4.4562\n4.8694\n5.4465\n5.0394\n3.542\n1.8146\n0.88726\n1.9131\n2.6949\n3.9269\n1.7865\n3.7262\n6.6667\n9.5408\n11.397\n11.656\n12.817\n15.347\n12.789\n11.645\n11.121\n9.6161\n9.755\n14.079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18535\n1.3994\n1.7307\n2.3607\n3.2793\n4.4132\n3.4566\n4.0616\n4.0924\n2.8289\n3.9528\n5.1535\n5.8786\nNaN\nNaN\nNaN\nNaN\nNaN\n0.42651\n0.94394\n1.2697\n1.7973\n3.6188\n2.765\n1.878\n2.4566\n2.8527\n3.6134\n4.0851\n4.3298\n3.3358\n4.2694\n2.8118\n2.8487\n2.1824\n2.2777\n0.44247\n2.7074\n4.3101\n2.2413\n4.2543\n9.0866\n10.008\n12.648\n13.903\n13.654\n15.032\n16.367\n16.516\n17.437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4926\n2.0511\n3.0987\n2.4833\n1.7918\n1.688\n3.822\n4.6786\n4.0139\n5.2908\n5.1426\n4.2095\n4.1353\n3.777\n3.8948\n6.7146\n7.2763\n7.4856\n4.7401\n2.3317\n1.331\n1.5641\n0.63443\n1.3364\n2.3986\n2.176\n1.3692\n2.5205\n2.551\n2.8854\n3.0831\n3.6202\n4.5778\n3.9679\n2.1185\n3.2423\n3.1337\n2.4482\n-0.4204\n3.3534\n5.2855\n3.6415\n3.3031\n8.6754\n11.078\n15.85\n15.57\n15.432\n16.237\n18.125\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2704\n3.0241\n3.5505\n5.0207\n3.3235\n2.8926\n0.98895\n3.9919\n5.1708\n4.427\n6.232\n6.0793\n5.0307\n4.2869\n2.1728\nNaN\nNaN\nNaN\nNaN\n5.2822\n4.9519\n1.1891\n0.56343\n-0.26361\n0.19473\n0.48165\n0.96541\n1.6937\n1.9638\n2.9547\n2.425\n2.4457\n3.2156\n4.6827\n3.6161\n3.404\n4.1907\n4.6302\n2.7078\n0.55704\n3.6799\n4.7439\n3.0934\n3.2323\n7.8569\n12.738\n14.494\n15.127\n15.807\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.8392\n4.9432\n4.2689\n3.7275\n4.6242\n4.7134\n3.8963\n4.2309\n3.4426\n3.7798\n4.4135\n4.264\n5.4576\n7.1623\n6.0833\n6.5498\n4.4246\nNaN\nNaN\nNaN\nNaN\n5.0939\n7.7974\n0.14677\n-0.40873\n-1.3047\n-0.54909\n0.31464\n1.1159\n1.5325\n2.3993\n3.6337\n3.2839\n2.8439\n3.5741\n4.4314\n3.6871\n3.9992\n5.5818\n5.6539\n3.6248\n1.3288\n1.0568\n2.822\n3.6208\n5.5815\n9.7263\n12.281\n13.468\n13.189\n14.224\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.8584\n5.5572\n4.877\n4.5212\n4.0995\n4.5397\n4.5923\n4.5873\n5.716\n4.4263\n3.3349\n3.7458\n3.3445\n3.1213\n6.2747\n5.2769\n5.5853\n6.648\nNaN\nNaN\n4.4177\n4.69\n5.8649\n6.6631\n-1.0286\n-1.6997\n-1.3965\n-0.36985\n0.21131\n1.6935\n1.759\n2.834\n3.2306\n3.2463\n3.1408\n3.7361\n4.5345\n3.2235\n3.9515\n6.2485\n6.9177\n3.5955\n1.829\n1.7999\n3.9466\n5.3593\n6.1536\n9.8126\n11.679\n14.348\n11.753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.1182\n5.5454\n7.4156\n5.1896\n5.3435\n4.6241\n4.344\n3.1714\n4.6302\n7.0968\n5.8404\n4.0158\n3.8901\n3.706\n2.9749\n3.2035\n3.9991\n4.8228\n5.8123\n4.6717\n2.467\n5.2692\n5.7618\n6.4873\n5.7872\n-1.5957\n-1.6494\n-0.73352\n0.082052\n0.17076\n1.8797\n2.7257\n2.2989\n2.9778\n3.1674\n3.3907\n4.1688\n4.6209\n4.5142\n5.3504\n6.7166\n8.2434\n5.0477\n3.6637\n3.9721\n5.8914\n6.3012\n6.7873\n8.6389\n13.706\n16.528\n12.994\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.3981\n5.0274\n6.1495\n4.1112\n4.0514\n5.0474\n3.6928\n1.918\n3.1961\n6.767\n7.1238\n5.7172\n4.3717\n3.9526\n3.4635\n3.2144\n3.8324\n4.701\n4.5811\n4.6469\n3.9988\n5.5823\n6.4279\n8.3169\n6.0974\n-1.4941\n-0.75308\n0.28927\n0.6434\n0.13697\n1.4954\n2.399\n2.234\n2.5417\n2.7949\n2.841\n3.1618\n4.4686\n5.6134\n5.8057\n6.4321\n6.3334\n5.235\n5.5724\n4.6237\n5.544\n6.5601\n8.2801\n9.8296\n14.125\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.6573\n4.3175\n3.9813\n5.1382\n-1.792\n0.70675\n4.7911\n4.5778\n5.955\n5.3233\n4.6706\n5.9289\n7.2174\n5.651\n4.5059\n3.133\n3.3356\n5.0087\n4.898\n5.7801\n4.134\n5.0127\n6.1322\n7.6164\n8.8559\n6.6003\n-0.91569\n0.41705\n0.086854\n0.48783\n0.56686\n1.1915\n1.4083\n1.9321\n2.4497\n2.3733\n2.406\n2.9526\n3.9463\n5.5278\n5.7679\n5.5656\n4.2914\n3.8819\n4.444\n5.1887\n5.3212\n6.5992\n7.5991\n9.804\n13.627\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.84\nNaN\nNaN\nNaN\nNaN\n7.8077\n7.433\n6.0553\n4.6453\n6.0349\n-0.6438\n1.6295\n5.1133\n7.4051\n6.7136\n6.3116\n4.2983\n0.42955\n6.4195\n4.7481\n4.1903\n1.5003\n3.3313\n6.2791\n5.9891\n7.0029\n5.4066\n6.2037\n6.6943\n7.8369\n7.4812\n4.944\n0.12262\n0.8773\n0.10704\n0.31148\n1.1807\n1.6269\n1.7192\n1.1646\n1.1418\n1.9592\n2.4764\n3.1\n3.2826\n5.5768\n4.7722\n5.0713\n4.6\n3.7145\n2.5557\n4.6832\n5.0127\n5.5195\n6.1174\n9.8834\n14.917\n18.409\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.273\n8.9813\n8.2163\n7.1956\n5.9382\n4.8501\n4.481\n4.856\n4.3665\n6.2605\n7.6867\n6.2097\n6.28\n3.4777\n0.36241\n1.1997\n4.1031\n5.0001\n2.1018\n4.1441\n6.6703\n6.5612\n6.6776\n5.4815\n5.7529\n7.463\n6.8041\n4.9165\n-0.47741\n-0.047231\n0.44449\n0.54342\n0.27806\n1.1976\n1.7683\n1.7126\n1.1093\n-0.12953\n1.6692\n2.2658\n2.3786\n2.7861\n4.232\n4.5749\n5.3391\n5.2641\n4.3222\n4.2936\n2.7039\n3.1364\n5.5082\n7.1017\n11.856\n16.621\n19.186\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.968\nNaN\nNaN\n23.954\n11.816\n9.7017\n8.8873\n8.2536\n5.517\n3.9337\n2.0287\n2.6782\n2.6627\n4.0919\n5.8455\n4.6391\n4.0711\n3.9272\n2.8942\n0.14699\n-0.49214\n2.9516\n2.54\n4.4829\n5.1498\n4.6705\n4.9102\n4.9677\n6.1708\n7.0242\n5.508\n4.1175\n-2.5637\n-0.96742\n0.10165\n0.23336\n0.12992\n0.068477\n1.0816\n0.97918\n1.0768\n0.20401\n1.4167\n1.9752\n1.7613\n2.3674\n2.9728\n4.2653\n4.3599\n4.1522\n3.56\n4.1383\n3.9836\n3.8387\n6.1474\n8.0317\n13.396\n16.114\n20.902\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.711\n26.898\n23.365\n19.297\n10.802\n8.891\n9.3212\n9.3509\n6.3304\n4.549\n2.6818\n1.6108\n2.7813\n2.6906\n2.98\n1.8809\n2.344\n2.1129\n1.6013\n0.43545\n-0.93341\n2.3087\n3.682\n3.9535\n2.7105\n2.0876\n3.1705\n4.7687\n5.2995\n4.6664\n4.0307\n2.9027\n0.5916\n-1.5846\n0.10443\n0.045186\n0.74593\n0.15743\n0.38904\n0.25662\n0.85032\n0.96807\n1.0682\n1.6144\n1.2473\n0.89767\n1.8918\n3.0415\n3.4103\n2.4206\n2.3525\n3.6478\n3.8906\n3.2716\n4.1741\n8.0923\n11.872\n15.037\n20.347\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n29.4\n20.621\n19.819\n17.015\n8.9887\n7.4003\n8.554\n7.7373\n6.9228\n4.7035\n2.6088\n2.2015\n2.5248\n2.619\n2.7261\n0.60628\n-0.49478\n0.67265\n0.061868\n-0.36837\n0.055098\n0.96537\n2.8391\n2.2419\n1.884\n1.697\n2.3786\n3.164\n4.1571\n2.6232\n2.6108\n2.9716\n3.3216\n-1.1302\n-0.58614\n-0.42901\n1.4056\n-0.18717\n0.30262\n0.30331\n0.53265\n0.77417\n0.86695\n0.82988\n0.9895\n0.77295\n1.1315\n1.2861\n2.952\n2.2278\n-0.97752\n-0.35163\n5.4361\n6.4463\n5.0363\n7.5818\n12.068\n14.735\n15.509\n15.149\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n28.67\n18.605\n16.935\n13.367\n8.7846\n5.5232\n7.0034\n6.6499\n5.8606\n5.1017\n2.5881\n2.1673\n2.3923\n2.1351\n2.3114\n0.32909\n-3.3323\n-0.99173\n-0.048598\n0.15899\n0.43559\n-0.27963\n1.0429\n0.99714\n1.7832\n2.4657\n1.9029\n2.0399\n0.27324\n0.8794\n2.9475\n4.6976\n4.3755\n-1.1144\n-1.8555\n-1.4962\n0.044528\n-0.08853\n0.44869\n0.050762\n0.22483\n0.76743\n1.1943\n0.33685\n0.91351\n1.449\n1.2969\n0.56297\n2.6141\n2.2013\n-0.43613\n-1.3306\n2.5518\n6.6544\n5.0845\n7.8507\n11.905\n14.059\n7.4463\n8.0532\n10.416\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n25.496\n16.806\n11.698\n12.129\n8.5359\n4.9491\n5.9807\n6.1707\n4.7239\n3.8084\n1.5708\n1.6826\n1.8609\n1.3064\n0.1387\n-0.50844\n-0.88377\n-0.43468\n0.64474\n0.19331\n0.10428\n0.61161\n-0.60681\n0.86301\n0.3514\n1.4662\n0.20357\n1.2278\n1.0784\n2.302\n2.6998\n4.2111\n3.9242\n-1.4386\n-2.4872\n-2.3442\n-1.5297\n0.22984\n0.73989\n0.32707\n0.95767\n0.51204\n1.3682\n-0.3728\n0.99885\n1.9914\n1.1586\n0.95907\n2.0399\n2.5287\n-0.12208\n0.18489\n1.3522\n3.3801\n4.4512\n7.6114\n10.062\n12.66\n10.704\n7.4558\n11.165\nNaN\nNaN\n11.924\n15.019\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.522\n13.58\n12.24\n11.764\n5.4399\n1.3732\n5.3507\n4.9695\n1.8945\n2.27\n1.494\n1.6382\n2.0849\n1.9352\n1.28\n-0.7356\n-0.10159\n0.25711\n0.62452\n0.23751\n-1.1061\n-0.50774\n-0.84629\n0.26864\n-0.59771\n-0.16805\n-0.61167\n-0.34208\n2.6658\n2.6945\n2.5097\n3.5031\n2.6449\n-1.6555\n-2.0729\n-1.5398\n-1.7546\n0.056639\n1.5076\n1.1635\n1.2281\n0.58009\n1.0064\n0.48418\n2.1785\n1.671\n1.6536\n1.3715\n2.5699\n2.9363\n1.5387\n1.4167\n2.7619\n4.2899\n5.8437\n7.2594\n9.0982\n10.619\n9.4419\n8.3879\n12.303\n12.539\n15.087\n16.038\n15.012\n16.114\n16.501\nNaN\nNaN\nNaN\nNaN\n14.862\n16.061\n12.789\n12.622\n11.408\n4.6145\n1.1349\n3.8308\n2.7013\n1.0045\n0.29531\n1.2582\n2.8415\n3.3431\n3.2107\n2.0612\n-0.81572\n-0.9701\n0.15856\n0.61837\n0.50875\n0.29106\n-0.048675\n-0.36996\n-0.20943\n-1.7108\n-1.0093\n-0.40269\n-0.032354\n1.0369\n0.36781\n0.81051\n2.5452\n0.82762\n-1.0169\n-1.4135\n-1.4545\n-0.98317\n-0.43899\n1.7044\n2.1096\n1.7044\n1.0549\n1.3225\n1.4516\n2.9798\n2.4264\n3.069\n2.2816\n2.268\n1.0989\n-2.6348\n1.1743\n3.8915\n5.5275\n7.5734\n7.5674\n8.4928\n12.191\n8.3582\n9.6849\n11.165\nNaN\n11.349\n15.646\n13.548\n15.953\n20.069\nNaN\nNaN\nNaN\n13.211\n14.461\n15.376\n13.597\n10.416\n10.936\n6.991\n2.7126\n2.9547\n3.9735\n2.0048\n1.0162\n2.4425\n3.5757\n4.0315\n2.8108\n-0.1489\n-0.44338\n-1.0182\n-0.84454\n-0.80309\n0.21406\n0.83527\n0.69537\n-0.33613\n-1.0463\n-1.6498\n-0.081465\n1.7465\n1.3315\n-0.32874\n0.27497\n1.1727\n1.8318\n1.5967\n-1.7913\n-1.172\n-0.52576\n-0.42474\n0.50587\n1.9607\n2.3056\n1.0796\n0.39725\n0.74464\n1.6471\n2.3246\n3.174\n4.5233\n2.8122\n1.8686\n0.43185\n-4.2973\n1.2025\n3.6288\n6.0916\n5.8831\n8.5747\n7.902\n10.883\n9.4362\n9.4053\nNaN\nNaN\n8.9354\n13.79\n13.942\n15.534\n18.288\nNaN\nNaN\nNaN\n12.643\n13.07\n14.772\n12.996\n7.5808\n7.0844\n6.4606\n3.7631\n3.2657\n4.065\n2.6584\n1.3954\n2.8834\n3.3089\n4.4136\n2.5494\n0.60402\n0.16063\n-0.40708\n-1.047\n-1.3008\n-0.15303\n0.15047\n0.14956\n-1.0368\n-1.111\n-0.73075\n0.92521\n2.2659\n2.1119\n1.9371\n1.8141\n1.3813\n2.4986\n4.0296\n-2.0095\n-0.9594\n0.64902\n1.3217\n1.7977\n2.0803\n1.7765\n0.71204\n0.11669\n0.50948\n1.4162\n2.2214\n2.1297\n4.8042\n4.0239\n1.8438\n2.4446\n-4.131\n1.8925\n0.1512\n3.2578\n6.1947\n8.6721\n7.7759\n9.8953\n8.5881\n8.0791\nNaN\nNaN\nNaN\n11.145\n12.653\n13.487\n14.641\nNaN\n15.335\n13.283\n11.001\n11.236\n13.067\n9.6652\n7.2801\n6.6611\n6.2894\n4.1936\n2.8185\n2.5613\n1.3657\n1.332\n3.7392\n4.0083\n4.1977\n2.3757\n0.11178\n-0.079006\n0.13123\n-0.5483\n-0.056723\n-0.074908\n-0.25894\n-0.18419\n-0.84966\n-1.0421\n-0.36927\n0.77893\n2.6263\n2.8681\n3.0004\n2.0364\n0.81706\n2.7208\n5.0133\n-0.98792\n-0.17825\n1.4561\n2.0289\n2.7233\n2.7023\n1.9606\n2.0069\n1.1258\n1.2259\n2.0951\n3.0642\n3.4492\n5.3403\n6.2488\n2.1988\nNaN\nNaN\nNaN\n0.26286\n1.2382\n5.4606\n8.3731\n6.1649\n6.3909\n6.4877\n8.0154\nNaN\nNaN\nNaN\n9.7222\n11.242\n10.08\n11.366\n11.999\n12.374\n11.381\n9.829\n9.3408\n8.1482\n6.4001\n6.016\n6.6688\n6.6675\n5.2028\n3.5113\n3.0364\n2.3589\n2.6549\n3.5504\n3.7731\n3.051\n1.7351\n0.26207\n0.33337\n1.1063\n0.80948\n0.58507\n0.13412\n0.1966\n-0.22721\n-0.18316\n0.017\n0.017748\n1.1891\n2.3684\n3.3463\n3.6801\n2.2151\n1.0327\n2.6983\n4.5239\n-0.037543\n0.94759\n2.0138\n1.68\n2.0702\n2.6083\n1.5137\n3.0264\n2.7763\n2.4213\n3.1462\n4.1187\n5.2134\n7.2063\n7.9135\n4.0805\nNaN\nNaN\nNaN\n1.2686\n2.0661\n1.7392\n3.7578\n5.3532\n5.7012\n5.6927\n6.7475\nNaN\nNaN\nNaN\n8.4544\n9.2987\n8.6253\n9.538\n12.643\n11.658\n10.484\n8.9246\n7.4647\n6.7984\n5.4018\n5.4109\n6.799\n6.9619\n6.0617\n3.2711\n2.5772\n1.0076\n0.69391\n2.831\n3.0775\n1.9218\n1.721\n1.6935\n2.0254\n1.8083\n1.0716\n1.6535\n1.1741\n1.253\n0.16115\n0.65228\n1.3338\n1.2659\n2.3496\n2.7558\n3.4656\n4.2423\n2.9223\n2.4303\n4.1045\n4.6288\n1.1013\n2.21\n2.2648\n1.3164\n2.0503\n2.1983\n1.9433\n2.8828\n4.4496\n3.2224\n3.6919\n4.5988\n5.4014\n6.2753\n6.7612\n6.0221\n4.1718\nNaN\nNaN\n1.867\n2.8653\n3.0099\n3.5996\n4.6014\n6.1537\n6.406\n6.7036\n7.1716\n6.6478\n7.0024\n6.883\n7.5006\n7.7679\n8.8521\n12.363\n11.662\n11.115\n9.5422\n7.6437\n8.1166\n6.6065\n6.806\n6.2329\n6.8505\n6.8487\n3.843\n1.7809\n-1.964\n-2.6994\n2.4336\n1.9355\n1.8171\n3.2128\n2.3749\n2.3893\n2.347\n1.1682\n1.8493\n2.1606\n2.462\n1.1614\n0.70046\n1.226\n1.5196\n2.9106\n3.2413\n3.6783\n4.8815\n4.13\n3.771\n5.0011\n4.4957\n1.0213\n2.1508\n2.2724\n1.6854\n2.1658\n2.402\n2.9474\n3.7642\n4.0146\n3.6765\n3.4175\n4.1705\n4.8582\n6.1842\n7.8466\n7.9032\n3.7732\n1.4924\n0.20081\n1.4571\n2.7224\n3.5206\n5.3024\n4.9374\n6.2149\n6.3699\n6.4166\n6.3891\n7.1444\n7.4584\n7.2992\n6.1061\n6.3012\n8.1797\n10.939\n11.431\n9.9783\n9.7599\n8.2033\n7.3257\n6.6717\n7.8455\n6.7177\n6.6188\n7.5223\n6.8584\n2.2145\n-2.2278\n-4.265\n1.2149\n0.56034\n3.3704\n3.6106\n2.9526\n3.0004\n3.1521\n2.5981\n2.1437\n2.1109\n2.0039\n1.8973\n1.5414\n2.0255\n2.198\n2.6929\n2.8183\n4.5842\n5.247\n4.7825\n4.7467\n4.5277\n3.8133\n1.2663\n2.7044\n3.0527\n2.8331\n2.9588\n3.2548\n4.0031\n5.3627\n4.1283\n3.6173\n3.2169\n3.5813\n3.6662\nNaN\nNaN\nNaN\nNaN\n3.0568\n2.1068\n2.3784\n3.7557\n3.5547\n4.0349\n4.403\n5.7953\n6.4537\n6.8028\n7.1247\n6.8639\n6.6102\n7.0555\n7.7436\n6.5893\n7.2985\n8.4795\n9.4219\n9.0803\n9.9209\n8.48\n7.8776\n6.9361\n7.8638\n7.5332\n5.4977\n5.5641\n7.1349\n5.9232\n0.37447\n-0.53944\n-0.96027\n-0.22122\n2.1707\n2.6238\n2.6074\n3.6927\n4.4584\n1.6401\n0.76896\n1.5863\n1.9199\n2.8227\n3.284\n3.6993\n3.1186\n2.8145\n3.8012\n5.7516\n5.5173\n4.621\n4.1413\n4.0455\n4.1866\n2.7871\n3.363\n4.1021\n4.0929\n3.9478\n4.7457\n4.9398\n6.8292\n5.5611\n4.8577\n3.923\n4.125\n5.1442\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5668\n3.0585\n2.9517\n3.4179\n3.2342\n3.4609\n4.9145\n6.1425\n6.2974\n6.8515\n6.2364\n5.9982\n6.1781\n6.6165\n7.0647\n6.514\n6.9614\n8.1713\n9.1381\n11.171\n9.8559\n8.7388\n8.3097\n8.6056\n7.7163\n3.9948\n3.3083\n4.5884\n8.7795\n2.2201\n2.0168\n1.6897\n1.5695\n1.3271\n1.3036\n2.0266\n2.9392\n3.1379\n0.97768\n1.3123\n2.3123\n3.3157\n3.7754\n4.1049\n3.9263\n3.8779\n4.1132\n5.2307\n7.2307\n5.9522\n5.3283\n3.0822\n3.5746\n2.7973\n3.7652\n3.3193\n4.4417\n5.4414\n5.2497\n6.7235\n6.5418\n8.1851\n7.2313\n6.4351\n5.8224\n4.3583\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4499\n2.6246\n3.518\n3.1599\n3.5343\n3.5958\n3.5839\n4.6019\n4.8639\n5.2964\n5.3148\n5.1413\n5.134\n6.4424\n7.0138\n6.7194\n7.2688\n8.3527\n9.6728\n10.173\n8.1133\n8.8673\n8.96\n7.2835\n3.7512\n1.3872\n2.3304\n7.7642\n3.5417\n3.2282\n2.7254\n2.2658\n1.9213\n2.4765\n2.7415\n3.4705\n4.3548\n1.6716\n1.8563\n2.8969\n3.1048\n2.9394\n2.7459\n3.3916\n3.7582\n5.5029\n5.5518\n6.3036\n5.4353\n6.0586\n1.8274\n1.0856\nNaN\n4.7709\n3.3524\n5.3587\n6.6296\n7.4159\n8.7249\n10.16\n10.888\n8.9155\n8.1224\n7.4749\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1749\n3.1323\n3.0299\n3.268\n3.4622\n3.8143\n4.2583\n3.7415\n4.1255\n4.7635\n4.9857\n5.3519\n4.7274\n6.806\n7.2625\n7.2681\n6.4971\n6.3774\n6.8326\n8.5399\n7.8643\n8.6742\n7.4902\n5.1997\n2.392\n0.1706\n1.5897\n5.0944\n3.3188\n3.3776\n2.3272\n2.214\n3.4321\n4.8333\n4.7921\n4.6458\n5.0136\n2.7382\n2.0302\n3.0048\n1.41\n2.1442\n1.0244\n2.2849\n4.9683\nNaN\nNaN\n4.6217\n4.8783\n6.7742\nNaN\nNaN\nNaN\n3.6041\n3.2134\n3.2405\n1.9846\n2.1492\n2.7788\n3.5517\n2.4273\n2.2897\n2.2891\n1.2841\n0.84215\n1.4515\n2.5262\n4.6403\n3.7858\n3.2655\n2.4917\n1.9518\n3.9347\n4.7878\n3.6256\n4.6609\n3.2323\n4.2207\n2.6301\n1.5163\n-0.3741\n-0.19606\n1.2191\n-4.5618\n-3.8041\n-1.8347\n-1.9696\n0.94692\n-2.0484\n-2.0475\n-0.086772\n0.43806\n0.28754\n1.1792\n2.291\n3.0798\n4.1163\n4.36\n2.7812\n3.3557\n3.4696\n3.0717\n2.7628\n3.3396\n3.6688\n3.0677\n0.92742\n2.6061\n0.97329\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.762\n2.436\n2.1362\n4.6439\n4.4901\n1.3339\n4.5507\n2.2824\n2.464\n2.5684\n1.0532\n0.059535\n0.95035\n5.1229\n5.5333\n3.3826\n0.71642\n1.7129\n2.412\n2.345\n3.6237\n2.581\n4.1336\n5.6871\n5.9769\n4.0449\n2.1999\n1.1364\n2.1418\n0.77807\n0.031837\n-1.4697\n-0.39663\n0.21898\n-0.35119\n-1.7532\n-1.3223\n0.19415\n0.32654\n1.1895\n2.0804\n1.966\n2.4967\n3.2157\n4.0152\n3.2877\n4.0105\n3.3745\n3.0427\n3.1395\n2.7683\n3.3531\n3.5286\n3.795\n5.9297\n3.124\n0.4837\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.55145\n0.99961\n1.7212\n2.0501\n-0.25848\n-0.70965\n-0.05114\n0.87024\n2.0381\n4.5923\n1.3623\n0.74862\n0.91661\n5.745\n6.8983\n2.7655\n1.3655\n1.8563\n2.0203\n4.8534\n4.1233\n4.2542\n6.0471\n5.1345\n7.178\n5.9424\n3.6255\n1.6876\n2.7391\n0.61378\n3.005\n-0.50197\n0.71904\n0.61404\n-1.8214\n-2.7039\n-0.74511\n-0.17505\n0.55106\n1.3092\n2.7545\n1.8924\n1.2972\n3.7354\n3.633\n2.7446\n2.7391\n3.5\n3.9582\n4.0568\n3.7152\n4.2492\n4.4228\n5.2476\n5.1253\n4.7166\n3.358\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8047\n1.1496\n1.3765\n1.669\n1.7351\n1.4609\n1.9464\n1.9301\n1.4069\n0.71987\n1.8549\n0.3163\n0.68304\n6.0492\n4.2677\n0.56628\n0.71781\n1.0808\n1.7621\n4.9481\n6.0226\n4.3697\n4.459\n2.7119\n5.9915\n6.1183\n4.3128\n1.9999\n2.383\n1.9971\n1.7752\n1.8681\n2.2976\n2.1347\n-1.5933\n-3.0459\n-1.9265\n0.17694\n0.85623\n0.67628\n1.4134\n1.4611\n0.28514\n2.087\n3.0368\n2.3654\n2.4193\n3.6248\n4.4054\n5.1111\n5.638\n5.1222\n5.8352\n6.5589\n6.6108\n6.2172\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7235\n2.2161\n1.9797\n2.3225\n2.9644\n2.3184\n2.5215\n2.8292\n1.6566\n-0.093298\n1.1346\n0.70319\n1.1506\n1.5004\n-0.32219\n-1.0259\n-0.67858\n0.933\n0.96352\n4.9773\n7.3936\nNaN\nNaN\n-3.7346\n3.8527\n5.1778\n5.5101\n3.1351\n3.5226\n4.7319\n1.2104\n1.9776\n2.539\n3.4952\n-0.58236\n-0.653\n-0.66924\n0.70409\n1.4048\n0.84195\n1.2932\n2.2739\n1.0264\n0.82037\n2.1907\n2.5063\n3.2689\n4.5833\n4.3654\n3.5786\n5.3373\n5.3777\n4.8983\n7.1464\n6.0657\n5.933\n8.0609\nNaN\nNaN\n-5.5087\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7392\n2.3171\n2.1704\n2.4626\n2.859\n2.1444\n2.2373\n2.1578\n2.1559\n2.0061\n2.2376\n2.87\n1.758\n-0.62082\n-1.4022\n-0.17704\n-0.78172\n1.1486\n4.2261\nNaN\nNaN\nNaN\nNaN\n0.87645\n3.9602\n6.2994\n5.5486\n4.371\n4.8736\n5.8181\n3.3924\n0.82999\n1.8345\n1.851\n-0.37358\n-2.0749\n-1.001\n0.35584\n1.036\n1.4292\n2.2057\n2.1063\n1.4418\n1.5703\n1.1784\n1.5637\n3.0631\n5.2451\n4.8366\n4.3756\n5.3307\n5.4693\n5.5771\n5.893\n4.959\n4.0517\n6.1385\nNaN\nNaN\n-10.812\n-4.3688\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2666\n1.3336\n1.7662\n3.0367\n3.5617\n1.9734\n3.3786\n3.1022\n3.7421\n3.6519\n3.1523\n2.3272\n2.4397\n1.0848\n0.71921\n-0.43276\n-0.47794\n2.1663\n0.63202\nNaN\nNaN\nNaN\nNaN\n3.5333\n4.7136\n5.8073\n6.1742\n6.7061\n5.7354\n5.8896\n1.7392\n-0.33792\n-1.556\n0.24525\n-1.4656\n-2.1518\n-1.403\n-0.1153\n0.049424\n0.66827\n3.4568\n2.2525\n0.76285\n0.37716\n-1.3555\n1.1143\n2.9153\n4.614\n5.4597\n5.5342\n4.425\n5.1009\n5.7126\n5.6671\n4.8132\n3.3241\n4.477\nNaN\nNaN\n-3.9477\n-5.3072\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.714\n2.1156\n2.0698\n3.8326\n5.0983\n2.4455\n3.1517\n4.0087\n4.8967\n5.2609\n4.5984\n3.5736\n2.5433\n3.3302\n2.1493\n1.3991\n1.9748\n3.5713\n1.3785\n1.3739\n1.7954\n2.7638\n3.4586\n4.7608\n5.1164\n6.0513\n6.3087\n6.4281\n5.4677\n5.3938\n3.2948\n0.73725\n-1.7881\n0.30223\n0.0013156\n0.0085163\n0.20145\n-0.33488\n-0.85782\n-0.93429\n2.2977\n1.8609\n0.68434\n-0.75843\n-3.7098\n1.5981\n3.5478\n3.9684\n4.2109\n5.5836\n4.2404\n4.9452\n6.1636\n6.3567\n6.8096\n4.2577\n3.1692\n4.1724\n5.2772\n1.9807\n-5.1849\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8666\n2.321\n3.0757\n2.7269\n4.164\n2.7003\n3.0974\n4.7972\n5.3609\n5.9266\n5.1822\n5.0857\n4.7379\n3.8804\n3.4403\n3.1854\n3.8066\n4.8096\n2.849\n3.2822\n2.7166\n2.8575\n4.4279\n4.8188\n4.1474\n6.1065\n6.6921\n6.0527\n4.8121\n4.1758\n3.7859\n2.7234\n1.349\n0.82242\n0.89707\n1.0855\n0.58484\n-0.64287\n-1.1965\n-0.47707\n0.88722\n1.4307\n0.58003\n-1.3217\n-4.2762\n3.2532\n4.201\n4.9977\n4.788\n6.1175\n5.6302\n6.8307\n7.4457\n9.0859\n8.8095\n5.1878\n2.3\n3.8009\n5.5483\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2801\n2.133\n3.0591\n2.1761\n2.829\n2.3903\n3.4555\n3.7214\n4.5648\n6.2609\n5.2347\n5.1564\n5.3565\n4.1691\n3.9979\n3.7716\n4.1685\n5.507\n4.8041\n5.08\n4.8184\n4.7579\n5.6626\n4.4064\n3.2789\n6.1419\n7.7308\n6.781\n4.8007\n3.4591\n4.4592\n1.373\n-0.04232\nNaN\nNaN\n1.9031\n0.3149\n-0.27919\n-1.2891\n-0.17391\n0.83006\n1.2244\n0.27356\n0.12734\n1.9131\n4.8189\n4.3644\n5.2655\n5.2401\n6.3151\n6.1009\n7.7044\n9.0741\n9.856\n9.0513\n6.4533\n2.2586\n2.2294\n5.3124\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.11273\n0.96494\n2.1275\n2.1002\n2.3233\n3.4657\n3.7461\n3.3504\n4.2299\n5.2547\n5.2366\n5.2942\n6.2508\n5.0987\n4.7453\n5.0374\n4.5318\n5.1087\n5.4157\n5.8074\n4.5375\n5.0002\n4.7986\n2.8786\n2.7801\n6.8903\n8.3983\n8.292\n5.1861\n5.2452\n4.9571\n3.0217\nNaN\nNaN\nNaN\n2.9167\n1.7415\n0.85126\n-0.94382\n-0.11327\n1.2107\n0.63521\n0.37121\n0.58401\n2.4551\n4.1596\n4.6562\n5.9068\n6.6671\n5.0002\n5.5919\n8.0929\n8.5601\n10.495\nNaN\nNaN\n3.4364\n3.4089\n4.9618\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24744\n0.61685\n1.2271\n2.345\n2.8543\n4.193\n4.9057\n4.727\n5.0269\n5.3526\n5.0873\n5.3215\n5.761\n5.8769\n5.844\n5.6488\n5.7715\n5.6043\n5.4388\n4.6023\n2.7494\n4.0299\n4.9175\n3.1917\n3.5813\n5.9514\n7.8699\n8.8473\n7.5269\n7.1568\n5.1049\nNaN\nNaN\nNaN\nNaN\n2.6829\n3.6336\n2.3156\n-1.7357\n-0.34671\n2.1109\n3.261\n1.1566\n1.0951\n2.407\n3.5333\n4.6912\n6.84\n8.1742\n7.0462\n6.7566\n8.8766\n8.0656\nNaN\nNaN\nNaN\n4.0346\n2.784\n4.7866\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9185\n1.7698\n1.8335\n2.0385\n2.6888\n3.7867\n4.3584\n4.9771\n4.5302\n4.5542\n5.3429\n5.9731\n6.0869\n6.574\n6.4906\n6.6548\n6.9631\n5.8173\n4.8479\n3.8158\n3.0326\n3.7253\n2.5409\n3.076\n4.3437\n5.5123\n9.5635\n8.0354\n7.1789\n6.8261\n5.4686\nNaN\nNaN\nNaN\nNaN\n1.0216\n2.3078\n2.6565\n-0.22596\n0.1969\n0.79638\n-0.45333\n-1.8582\n1.777\n3.1483\n4.1432\n5.2886\n7.6331\n8.5511\n8.2411\n7.8628\n8.4595\n6.8924\nNaN\nNaN\nNaN\n-2.0799\n-0.63682\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5963\n2.566\n2.3399\n2.851\n3.3427\n3.4359\n3.7306\n3.8153\n4.1245\n4.492\n4.8372\n5.84\n5.9223\n6.0627\n6.6124\n6.5384\n6.2954\n5.9929\n4.1995\n3.4358\n4.2457\n2.6537\n-0.91323\n1.2099\n3.158\n6.7488\n9.3471\n7.5784\n7.8131\n7.5816\n6.4728\nNaN\nNaN\nNaN\nNaN\n0.83166\n0.76673\n3.2525\n0.57358\n1.755\n0.63796\n-0.83707\n-0.36236\n3.6588\n5.03\n6.6529\n6.4809\n7.995\n8.5409\n8.3942\n7.3618\n6.5347\n5.9499\nNaN\nNaN\nNaN\nNaN\n0.59041\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2501\n2.4362\n3.2808\n3.8085\n3.4993\n3.2099\n3.4578\n3.113\n3.6915\n3.2155\n3.8488\n4.9612\n5.6655\n5.9414\n6.5453\n6.0332\n6.1991\n6.6214\n5.0357\n3.4147\n4.3669\n-0.71948\n-4.788\n-0.14299\n1.9815\n7.02\n8.3227\n8.4104\n9.2608\n9.2955\n10.477\nNaN\n1.7684\n0.35401\n2.254\n3.8396\n2.2495\n3.3221\n2.1367\n3.1389\n1.9052\n-0.15142\n-0.68533\n5.6275\nNaN\nNaN\nNaN\n7.7776\n8.244\n7.7485\n6.2745\n7.0649\n7.2341\n4.234\nNaN\nNaN\nNaN\n-0.017682\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6648\n2.6636\n3.4311\n4.0896\n3.742\n3.4768\n3.6263\n2.8341\n2.7639\n3.6625\n4.2056\n4.7618\n5.2691\n5.667\n6.0272\n4.766\n5.0082\n6.2053\n6.4273\n4.2755\n3.6847\n2.2871\n-6.6268\n-3.6551\n1.3626\n5.4513\n7.9496\n10.819\n10.873\n9.0667\n8.4613\n0.99138\n2.7589\n2.1924\n2.7038\n4.6919\n5.1773\n4.9317\n4.9712\nNaN\nNaN\nNaN\n-0.079364\nNaN\nNaN\nNaN\nNaN\n6.8641\n7.4874\n6.1059\n5.5847\n5.4151\n7.304\n2.5825\n2.8354\n0.93252\n-0.73395\n-1.1301\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1372\n2.4032\n3.3277\n4.4967\n3.8972\n3.5761\n3.131\n2.1424\n2.268\n3.7726\n3.7414\n4.13\n4.8171\n5.2812\n5.3667\n4.1172\n4.6262\n5.5642\n5.8069\n4.5418\n3.4306\n1.6495\n-0.21655\n-2.7741\n2.8912\n6.1635\n9.4155\n9.4385\n9.5143\n7.3706\n6.7741\n4.9425\n5.2979\n3.8187\n6.3134\n5.4265\n5.4999\nNaN\nNaN\nNaN\nNaN\nNaN\n0.46524\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2699\nNaN\n5.6498\n6.225\n3.1121\n2.7617\n2.3368\n-1.1123\n-1.9246\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6695\n2.2279\n3.6947\n4.7031\n3.346\n2.9542\n2.8436\n2.9856\n2.7666\n3.856\n3.6148\n3.7654\n4.1187\n4.902\n4.9811\n4.4892\n5.3033\n4.6056\n4.3227\n3.6394\n2.4986\n-0.45221\n-0.49509\n-1.3104\n3.1333\n7.2757\n10.469\n7.4768\n7.8904\n6.3265\n8.1902\n8.1891\n5.034\n6.2557\n8.0249\n7.7147\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5729\n3.9261\n2.3128\n-0.65057\n-0.56973\n-0.38519\n-0.7038\n2.0735\n4.4491\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9458\n3.0144\n4.2731\n4.6481\n4.0236\n2.5538\n2.4917\n3.147\n3.2923\n4.0112\n3.4433\n4.057\n4.3251\n5.4095\n5.7017\n3.8806\n2.4174\n1.6524\n2.6175\n2.3636\n1.4954\n-1.0868\n-1.1469\n1.4818\n4.7566\n7.5652\n8.8923\n8.0178\n10.561\n8.3095\n8.3025\n7.1317\n4.8013\n6.9098\n9.0756\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7813\n3.4226\n2.2423\n1.615\n2.979\n2.3734\n2.2056\n5.1443\n4.318\n3.8852\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4567\n3.095\n3.488\n3.5448\n4.3923\n3.1742\n3.3508\n3.232\n3.1199\n3.1065\n3.9657\n4.034\n4.5139\n5.0108\n4.3553\n2.6445\n0.79448\n-0.36279\n0.82955\n1.1353\n2.0222\n-0.79196\n1.1102\n3.7718\n6.755\n8.8723\n8.9242\n9.7201\n12.427\n9.3745\n7.5631\n6.321\n4.6151\n5.4285\n10.371\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0031\n0.77188\n1.5846\n2.7313\n3.9062\n5.3654\n4.3104\n5.2051\n4.5916\n3.4161\n4.0805\n4.9769\n6.1183\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3954\n3.0445\n3.2358\n3.6142\n5.4025\n3.9617\n2.8573\n3.2595\n3.27\n3.8034\n4.2465\n4.0293\n2.8615\n3.7351\n2.1163\n2.007\n1.123\n0.44783\n-1.3229\n0.83031\n2.648\n-0.29702\n1.3188\n6.4694\n7.3179\n10.246\n11.779\n10.874\n12.192\n13.307\n12.806\n14.096\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4282\n0.65399\n2.152\n1.9593\n1.5353\n2.335\n3.964\n5.1961\n5.0039\n5.6696\n5.831\n4.6047\n4.5696\n4.3606\n4.3044\n7.0566\n7.4115\n8.1765\n5.738\n1.5278\n3.8429\n3.9751\n2.6628\n3.1502\n4.4738\n3.9337\n2.4341\n2.9962\n3.0888\n3.2579\n3.2476\n3.5137\n4.2688\n3.3172\n0.67167\n1.9329\n1.6726\n0.43424\n-2.299\n1.3813\n3.8448\n1.4965\n0.63741\n6.5043\n8.8725\n14.327\n13.769\n12.917\n13.519\n15.024\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3472\n1.5681\n2.4738\n4.664\n3.1777\n2.6587\n0.86237\n3.6116\n5.5091\n5.5114\n6.7977\n7.1391\n5.9332\n5.7662\n3.3892\nNaN\nNaN\nNaN\nNaN\n6.6546\n5.8069\n3.9477\n2.996\n1.4668\n1.9383\n2.0728\n2.7161\n3.153\n2.7372\n3.7281\n2.9699\n2.5458\n3.1184\n4.4374\n3.1874\n2.5772\n2.9612\n3.4475\n1.2011\n-1.1432\n2.0208\n3.1564\n0.84196\n1.11\n5.94\n10.701\n12.848\n13.139\n13.183\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3465\n2.9911\n2.7221\n2.6114\n4.058\n4.478\n3.8035\n4.1482\n2.9147\n3.224\n3.9654\n4.7337\n6.4155\n7.5512\n6.935\n8.3076\n5.5223\nNaN\nNaN\nNaN\nNaN\n5.8198\n10.196\n2.671\n1.8353\n0.72485\n1.4141\n2.2829\n3.1239\n3.0821\n3.7128\n4.5122\n3.7917\n2.8755\n3.3418\n4.4749\n3.4629\n3.1796\n4.8757\n4.7529\n2.3138\n-0.10915\n-1.0886\n0.66577\n1.3617\n3.0345\n7.8437\n10.184\n11.298\n10.394\n10.873\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8667\n3.1758\n3.0526\n2.9626\n3.2572\n3.9959\n4.0495\n4.3985\n6.1848\n4.4657\n3.2583\n4.1252\n4.1406\n4.2542\n7.1604\n5.6504\n6.001\n8.3383\nNaN\nNaN\n6.0356\n5.8593\n6.7154\n8.2323\n1.3973\n0.64116\n0.85323\n2.0673\n2.8767\n4.0168\n3.4518\n3.9977\n3.7149\n3.6797\n3.4456\n3.8726\n4.6191\n2.8695\n3.3522\n5.5072\n5.4825\n2.4412\n0.59878\n-0.0070225\n1.7321\n3.105\n3.5026\n7.5124\n9.5749\n12.553\n9.5048\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1537\n2.5531\n4.9797\n3.0727\n3.6065\n3.3232\n3.6457\n2.305\n4.3121\n7.857\n6.1659\n4.0341\n4.3365\n4.2531\n3.7961\n4.0474\n4.9069\n5.6969\n6.7641\n5.2864\n3.6445\n6.3575\n6.4303\n7.5429\n6.4915\n1.0139\n0.86352\n1.694\n2.7667\n2.7984\n4.027\n4.5492\n3.5963\n3.5569\n3.4779\n3.7575\n4.392\n4.6125\n4.2165\n4.5865\n5.7239\n7.0916\n3.2898\n2.42\n2.271\n3.8748\n3.9296\n3.9775\n6.1776\n11.724\n15.096\n11.575\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.71876\n2.2617\n3.6353\n1.6922\n1.667\n3.4858\n2.7221\n0.84621\n2.5361\n7.4867\n7.0812\n5.4498\n4.4621\n4.9023\n4.2078\n4.0679\n5.2403\n5.983\n5.7853\n5.3082\n5.3116\n6.1123\n6.7602\n9.5822\n7.2328\n1.0306\n1.7481\n2.8507\n3.3217\n2.8338\n3.8329\n4.5934\n3.8741\n3.1284\n2.8053\n2.8058\n3.0257\n4.173\n5.0993\n4.9489\n5.5641\n5.2875\n3.2802\n3.4016\n2.7537\n3.556\n4.2716\n6.1287\n8.1706\n12.316\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4236\n1.3196\n0.95886\n2.4004\n-5.3032\n-2.6738\n3.9644\n3.8643\n6.0678\n6.3294\n5.1459\n6.1628\n7.4157\n5.9903\n4.8657\n3.7833\n4.0278\n6.3956\n6.7406\n7.3422\n5.5935\n6.3269\n7.0806\n8.9218\n10.093\n7.6924\n1.6137\n3.4102\n2.8006\n3.0589\n3.1563\n3.6555\n3.4028\n3.169\n3.1316\n2.4617\n2.2331\n2.5593\n3.2897\n4.5755\n4.8071\n4.6334\n3.22\n2.2924\n2.399\n3.3248\n3.5616\n4.5954\n5.4749\n7.7052\n11.724\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.01\nNaN\nNaN\nNaN\nNaN\n4.0501\n4.176\n2.9251\n1.6452\n3.1134\n-3.1021\n2.0214\n5.4515\n7.4646\n6.916\n6.9351\n4.6462\n0.73567\n6.718\n4.8566\n4.6009\n2.2083\n4.2502\n7.8006\n7.2553\n8.3564\n6.4344\n6.8937\n7.7097\n8.912\n8.0176\n4.8098\n3.0616\n3.791\n2.6047\n2.895\n3.6586\n3.6455\n3.2034\n2.0076\n2.0272\n2.5492\n2.3975\n2.7922\n2.8002\n4.4924\n3.8362\n3.9044\n3.1384\n2.3778\n0.97382\n3.3222\n3.8405\n4.3994\n4.607\n8.0137\n13.492\n17.849\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.0009\n5.6896\n4.9448\n4.1292\n3.072\n2.0868\n2.306\n2.9344\n2.2983\n5.2659\n7.9675\n6.1224\n6.1705\n3.5624\n0.21413\n0.92506\n4.3884\n5.8693\n2.72\n4.8771\n7.3103\n7.3517\n7.7632\n6.5378\n6.4179\n8.1603\n7.4086\n4.8961\n-1.5091\n2.631\n2.8887\n2.9428\n2.7131\n3.5211\n3.4275\n3.125\n2.4132\n0.76943\n2.3011\n2.044\n1.8726\n2.1158\n3.2202\n3.6189\n4.2239\n3.2496\n2.8984\n2.822\n1.2907\n2.1057\n4.1578\n5.1497\n10.181\n15.844\n18.646\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.3525\nNaN\nNaN\n20.369\n8.4488\n6.5524\n5.8597\n5.5401\n2.5464\n0.97752\n-0.82195\n0.072344\n0.29479\n2.5307\n5.3503\n3.9096\n3.1908\n3.693\n2.9404\n0.11477\n-0.24847\n4.0019\n3.0951\n4.6465\n5.3771\n5.4796\n6.0484\n5.8258\n6.2437\n7.0846\n5.4645\n3.9686\n-3.0839\n1.7052\n2.3925\n2.6365\n2.1403\n1.7651\n2.1634\n1.8052\n2.1298\n0.91068\n1.6571\n1.4484\n0.88504\n1.1006\n1.8115\n3.2282\n2.8038\n2.1963\n1.7568\n2.5054\n2.0946\n1.9437\n4.1515\n6.0889\n12.558\n15.872\n20.504\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4323\n23.551\n19.628\n15.464\n7.4618\n5.8589\n6.6621\n6.8628\n3.5778\n1.802\n0.18646\n-0.96328\n0.50391\n0.92803\n1.8083\n0.90108\n1.3681\n1.5837\n1.3286\n0.19774\n-1.181\n2.8055\n4.1742\n3.7635\n2.4597\n2.6236\n3.785\n5.1877\n5.4153\n4.7127\n4.496\n2.8227\n0.28213\n0.61721\n2.2765\n2.0775\n2.4308\n1.6186\n1.058\n1.0553\n1.9281\n1.7198\n1.1914\n1.3149\n0.30407\n-0.35388\n0.84749\n2.1886\n1.868\n0.41411\n0.089197\n1.5903\n1.8819\n1.6326\n2.4072\n6.5887\n10.735\n14.616\n19.545\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n25.912\n16.767\n16.339\n14.161\n5.8097\n4.7362\n6.0498\n5.512\n4.275\n2.1704\n0.096912\n-0.21179\n0.58067\n1.0639\n1.4077\n-0.58191\n-1.2638\n0.30901\n-0.33657\n-1.0011\n-0.5598\n0.7908\n2.7903\n2.2091\n1.6701\n2.1898\n2.9094\n3.5762\n4.8074\n2.6065\n2.1313\n2.5013\n2.906\n1.0722\n1.6292\n1.3954\n2.9228\n1.0052\n1.0546\n1.5114\n1.7934\n1.2122\n0.68691\n0.18326\n0.11009\n-0.21811\n0.33949\n0.55653\n1.8429\n0.91111\n-3.3595\n-3.5097\n2.5773\n4.1484\n2.6599\n5.7083\n10.753\n13.661\n14.278\n12.71\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n25.094\n15.136\n13.834\n10.344\n5.5668\n1.8998\n4.27\n4.0621\n2.7608\n2.5236\n0.31408\n0.035946\n0.73325\n0.76186\n0.98731\n-0.45125\n-4.403\n-1.3967\n-0.46208\n-0.14646\n0.097218\n-0.3674\n1.5199\n1.341\n1.6407\n2.3074\n2.1981\n2.1665\n0.68532\n0.52033\n2.3221\n4.1259\n3.7925\n1.0103\n-0.35142\n-0.34903\n1.6191\n1.3403\n1.5093\n0.98625\n0.80695\n1.202\n1.1632\n0.10406\n0.31659\n0.78236\n0.77294\n-0.12091\n1.6647\n1.0381\n-1.6484\n-3.3949\n-0.23862\n4.2769\n2.649\n5.6207\n10.469\n12.667\n4.8878\n5.1579\n7.6993\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n21.752\n13.25\n8.7399\n9.1686\n5.2485\n1.2476\n2.8939\n3.1002\n1.9272\n1.2398\n-0.68364\n-0.36045\n0.39918\n-0.013557\n-0.83152\n-1.2484\n-1.6903\n-0.98937\n0.40419\n0.16742\n0.043545\n0.34928\n-0.73867\n1.0483\n-0.0058509\n1.3039\n0.48729\n1.1772\n1.3723\n2.0923\n1.9868\n3.5384\n3.2433\n1.0135\n-0.19113\n-0.30119\n0.21843\n1.7415\n2.0417\n1.7914\n2.3155\n1.8739\n2.1029\n-0.35326\n0.38872\n0.89996\n0.2867\n0.052691\n1.1707\n1.2984\n-1.0988\n-0.6348\n0.14849\n1.4099\n1.9193\n5.5728\n8.3039\n10.674\n8.1922\n4.6108\n8.4811\nNaN\nNaN\n8.6946\n11.793\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.438\n10.175\n8.8722\n8.6934\n1.4376\n-3.0106\n2.1716\n2.0411\n-0.9699\n-0.31361\n-0.6409\n0.24251\n0.87148\n0.8485\n0.76612\n-1.2713\n-0.75912\n-0.27089\n0.35425\n-0.0065241\n-0.91554\n-0.64261\n-1.2962\n0.17759\n-0.5976\n-0.45046\n-1.0884\n-0.95069\n2.1672\n1.9391\n1.7089\n2.7556\n1.9695\n0.79654\n0.042886\n0.4055\n0.049778\n1.6885\n3.485\n3.1073\n3.1334\n2.46\n2.2283\n0.435\n1.3602\n0.66222\n0.87239\n0.29978\n1.0496\n1.48\n0.31149\n0.09346\n0.97164\n2.1073\n3.6061\n5.2945\n7.2796\n8.4581\n6.6602\n5.722\n9.5917\n9.6611\n12.112\n12.987\n11.937\n13.212\n13.302\nNaN\nNaN\nNaN\nNaN\n10.897\n12.158\n9.3327\n9.0544\n8.2952\n0.49506\n-3.2746\n0.42408\n-0.26365\n-1.8651\n-2.4283\n-0.79127\n2.2746\n2.7822\n2.26\n1.0178\n-1.2815\n-1.2602\n-0.079704\n0.2959\n0.2589\n0.087972\n-0.2411\n-0.66404\n-1.0798\n-2.1863\n-1.5291\n-0.96683\n-0.69719\n0.027512\n-0.53542\n0.27129\n1.5387\n0.063162\n1.4358\n0.60754\n0.42569\n0.89182\n1.4659\n3.8559\n4.0217\n3.4492\n2.5562\n2.3429\n1.6462\n2.6258\n2.0239\n2.4355\n1.3584\n0.93976\n-0.72551\n-5.1375\n-0.063453\n2.3683\n3.383\n5.7028\n5.7146\n6.3478\n9.6672\n5.923\n7.0994\n8.4677\nNaN\n7.4281\n11.729\n10.039\n12.799\n16.04\nNaN\nNaN\nNaN\n9.2259\n10.678\n11.808\n9.4723\n6.696\n7.4453\n3.0373\n-1.7082\n-0.36773\n1.9131\n-0.16806\n-1.1013\n0.98278\n2.9194\n3.1811\n2.1958\n-0.57189\n-0.82377\n-1.4533\n-1.1824\n-1.0085\n0.21406\n0.71286\n0.52354\n-0.85456\n-1.8438\n-2.1373\n-0.68098\n1.0602\n0.73219\n-1.5059\n-1.0472\n0.01087\n0.87721\n0.82857\n0.68142\n0.9136\n1.3686\n1.4078\n2.3854\n4.1221\n3.9269\n2.6905\n2.0717\n1.8302\n1.9111\n2.2965\n2.6405\n3.7651\n1.4776\n0.029945\n-0.91121\n-5.2048\n0.30236\n2.0647\n3.9434\n3.8908\n6.2822\n5.028\n7.2965\n6.6919\n6.7606\nNaN\nNaN\n4.8924\n9.901\n10.348\n11.695\n14.559\nNaN\nNaN\nNaN\n8.654\n9.2568\n11.022\n8.1335\n3.416\n3.0655\n2.265\n0.11701\n1.0678\n2.249\n0.83832\n-0.71674\n0.83643\n1.9767\n3.3948\n2.132\n0.2642\n-0.40238\n-1.0616\n-1.3562\n-1.0458\n0.20156\n0.14749\n0.017507\n-1.3467\n-1.7171\n-1.3718\n0.11793\n1.753\n1.7463\n1.3009\n0.90035\n-0.046021\n1.495\n3.1882\n0.44166\n1.3644\n2.6805\n3.3386\n3.7368\n4.1985\n3.8166\n2.2127\n1.2305\n1.2564\n1.6595\n2.2392\n1.9213\n4.0875\n2.5875\n-0.19566\n0.62249\n-4.3709\n2.2933\n0.51481\n2.1542\n4.1985\n6.2401\n4.176\n5.6701\n5.3575\n4.9722\nNaN\nNaN\nNaN\n7.7323\n9.2366\n10.226\n11.703\nNaN\n12.01\n9.8158\n7.0372\n7.647\n10.002\n5.3342\n3.1119\n3.2401\n3.0055\n1.1017\n-0.018936\n0.14453\n-0.87295\n-0.78483\n1.8308\n2.4487\n3.0506\n1.597\n-0.8849\n-0.6826\n-0.3532\n-0.3943\n0.55112\n0.364\n0.095995\n-0.18626\n-1.1986\n-1.5354\n-0.96398\n0.19989\n2.2176\n2.6733\n2.561\n1.1507\n-0.29593\n1.4854\n4.1345\n1.7633\n2.4738\n3.6429\n4.3218\n4.9014\n4.8201\n3.7998\n3.5924\n1.9159\n2.0185\n2.3705\n3.2638\n3.7193\n5.2785\n5.3674\n1.0604\nNaN\nNaN\nNaN\n-0.16319\n-0.25303\n3.3094\n6.0201\n3.7167\n3.7613\n3.4107\n5.2467\nNaN\nNaN\nNaN\n6.3824\n7.9467\n6.7646\n7.9041\n8.2628\n8.8623\n8.0898\n6.931\n6.6551\n4.8343\n2.2249\n2.0071\n3.1091\n3.3066\n1.8913\n0.54254\n0.34477\n0.10111\n0.77573\n1.786\n2.3625\n1.6481\n0.55933\n-0.25641\n-0.086817\n0.66934\n0.65867\n0.89531\n0.44034\n0.53137\n-0.29484\n-0.42981\n-0.18399\n-0.33164\n0.98027\n2.0464\n3.0043\n3.0378\n1.401\n-0.001453\n2.0318\n3.552\n2.9043\n3.5352\n4.7042\n4.0535\n4.2181\n4.6318\n3.3466\n4.6692\n3.8749\n3.6008\n3.8574\n4.596\n5.5695\n7.6288\n8.0753\n3.4107\nNaN\nNaN\nNaN\n-0.32315\n0.29926\n-0.65269\n0.89921\n2.817\n3.0311\n3.0314\n3.9342\nNaN\nNaN\nNaN\n4.8013\n5.5794\n5.1595\n6.119\n9.0242\n8.1617\n7.222\n5.8579\n4.504\n3.3497\n1.7518\n1.5343\n3.013\n3.3355\n2.8414\n0.93305\n0.1035\n-1.3698\n-1.6025\n1.2622\n1.8397\n0.61561\n0.5553\n1.172\n1.547\n1.52\n0.3516\n1.8422\n1.3334\n1.5441\n0.14415\n0.65929\n1.316\n1.3628\n2.2906\n2.3241\n3.0445\n3.543\n2.2597\n1.7036\n3.1363\n3.559\n4.1163\n5.2418\n5.2301\n3.5961\n4.9693\n4.1239\n4.253\n5.206\n6.2418\n4.61\n4.465\n4.9884\n5.6711\n5.7474\n5.6963\n5.3562\n3.5909\nNaN\nNaN\n0.4581\n1.1113\n0.76021\n1.0538\n2.0262\n3.5281\n3.7499\n3.6721\n3.957\n3.0497\n3.5359\n3.0096\n3.7401\n4.6378\n5.7385\n8.7963\n8.3455\n8.0549\n6.281\n4.4429\n4.5137\n3.0281\n3.123\n2.8048\n4.177\n4.3762\n1.5075\n-1.6605\n-4.986\n-5.4238\n1.0568\n0.78462\n0.33706\n1.8107\n1.7415\n1.9809\n2.1702\n0.88459\n1.7674\n2.1731\n2.5421\n1.0103\n0.93608\n1.6508\n1.7234\n2.8391\n3.163\n3.3423\n4.308\n3.5386\n3.2143\n4.0199\n3.8518\n3.9207\n4.9088\n4.9905\n4.0936\n4.5736\n4.6485\n5.1569\n5.55\n5.896\n5.133\n4.5157\n4.7528\n4.8272\n5.5019\n5.9837\n5.6155\n2.5973\n-0.3924\n-1.7914\n-0.43797\n0.66018\n1.4268\n2.7975\n2.4284\n3.6394\n3.5456\n3.7249\n3.7755\n4.2568\n4.476\n3.9747\n2.4225\n3.2021\n5.0157\n7.4762\n8.0888\n6.7533\n6.4759\n4.7295\n3.9017\n2.9484\n4.0496\n3.5751\n3.7357\n4.8946\n4.348\n-0.99997\n-5.2233\n-6.8771\n-0.14945\n-0.31864\n1.4642\n2.3019\n2.4042\n2.3397\n2.8895\n2.5587\n2.1625\n2.0173\n1.8678\n1.6092\n1.782\n2.2152\n2.1942\n2.8948\n3.2407\n4.591\n5.0715\n4.3532\n3.9514\n3.9479\n3.327\n4.1952\n5.5719\n5.7881\n5.2763\n5.485\n5.9546\n6.5902\n7.8213\n5.4531\n5.2115\n4.7533\n4.3453\n3.6233\nNaN\nNaN\nNaN\nNaN\n1.1907\n0.11889\n0.16505\n1.5083\n1.4554\n1.1754\n1.7914\n3.4907\n4.1629\n4.2462\n4.4249\n4.1182\n3.7972\n4.1622\n4.6229\n3.0695\n3.7992\n4.9772\n5.7449\n5.5464\n6.3916\n5.2762\n4.6633\n3.8245\n4.5979\n4.283\n2.1652\n2.4098\n4.4056\n2.9061\n-2.9194\n-3.1616\n-3.0149\n-1.488\n0.72286\n1.9465\n2.0442\n3.0931\n4.417\n1.8302\n1.1365\n1.6068\n1.6422\n2.4999\n3.0105\n3.3593\n2.7839\n2.849\n3.7511\n5.503\n5.3942\n4.236\n3.5931\n3.5784\n3.6107\n6.0091\n6.3707\n6.8928\n6.4974\n6.2958\n7.2586\n7.8687\n9.6343\n7.7915\n6.9632\n5.7524\n5.3344\n5.7802\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3501\n0.92109\n0.3812\n1.2769\n-0.032177\n0.68463\n2.4966\n3.6134\n3.757\n4.3806\n3.5953\n3.199\n3.3985\n3.5806\n3.7175\n3.0486\n3.2892\n4.5505\n5.5551\n7.8176\n6.609\n5.5368\n5.1436\n4.9213\n4.2758\n0.64229\n0.17161\n1.8611\n6.3112\n-0.88604\n-0.3559\n-0.26574\n-0.10065\n0.39227\n0.46347\n1.6874\n2.8764\n3.1412\n1.0644\n1.399\n2.5483\n2.9216\n3.4969\n3.7906\n3.5683\n3.4938\n3.9536\n4.9596\n6.7843\n5.905\n4.9343\n2.5369\n3.1723\n2.4696\n6.862\n6.3747\n7.5327\n8.066\n7.8452\n9.6245\n9.5105\n10.891\n9.909\n8.6726\n7.6502\n6.0605\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6388\n0.11651\n0.29361\n0.10682\n0.37509\n0.87426\n1.1092\n2.1491\n2.3749\n2.6265\n2.5779\n2.4236\n2.2287\n3.42\n3.7347\n3.305\n3.9123\n5.1931\n6.5397\n6.967\n5.0409\n5.9986\n6.373\n4.8013\n0.88685\n-1.7887\n-0.51667\n5.2862\n0.61037\n0.89887\n0.6767\n0.37054\n0.51654\n1.2061\n1.8572\n2.9584\n4.1328\n1.3072\n1.3664\n2.8336\n2.8638\n2.8048\n2.5079\n3.1122\n3.6315\n5.6224\n5.672\n6.9079\n5.768\n5.5765\n1.6171\n1.1528\nNaN\n7.868\n6.8049\n9.0421\n9.7672\n10.606\n12.107\n13.669\n14.489\n11.571\n10.16\n9.2687\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.40186\n1.3847\n0.90154\n1.0166\n1.2581\n1.602\n2.1305\n1.4057\n1.9931\n2.2315\n2.5858\n3.0325\n2.0588\n3.977\n4.1088\n3.989\n3.3982\n3.2557\n3.2055\n5.1288\n5.1869\n6.0161\n4.8479\n2.0575\n-0.30358\n-2.3992\n-1.2082\n2.681\n0.77482\n1.0465\n0.19191\n0.47825\n2.385\n3.3394\n3.5913\n4.0422\n4.2917\n1.8719\n1.6417\n2.9628\n1.8808\n2.0868\n1.0862\n2.3467\n5.3789\nNaN\nNaN\n5.1687\n5.0337\n6.2311\nNaN\nNaN\nNaN\n5.1392\n2.8869\n3.1606\n2.3965\n2.0093\n2.5903\n2.7677\n2.9779\n4.4768\n5.2764\n3.6533\n3.3048\n2.2258\n2.723\n5.8801\n5.1128\n5.7602\n2.2305\n8.7277\n8.1866\n8.0395\n5.8682\n7.2232\n6.0575\n7.2002\n4.0811\n2.7793\n0.27446\n2.3567\n4.4768\n-3.4666\n-2.3167\n-1.6956\n-2.9226\n-0.099893\n-0.59236\n-0.746\n0.20295\n-0.76987\n-1.3953\n0.47182\n2.286\n3.9742\n5.1\n5.0743\n4.2341\n4.0532\n3.9151\n3.1739\n2.5166\n2.8286\n4.2123\n3.5843\n0.89364\n4.7501\n2.3727\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3647\n2.6457\n2.1296\n5.6169\n4.972\n0.78007\n3.3066\n2.7074\n6.092\n6.0104\n5.0825\n4.1679\n1.9659\n4.2644\n7.3392\n5.9368\n1.675\n1.7012\n4.3263\n4.7548\n9.6233\n5.1768\n5.266\n9.2369\n9.228\n5.5261\n4.9605\n2.9142\n5.372\n3.3236\n1.5904\n-0.234\n0.21349\n-1.9573\n-1.851\n-2.7165\n-1.5321\n0.36966\n-0.52069\n1.7991\n3.3193\n2.673\n4.0508\n4.1991\n4.7219\n4.4054\n4.7638\n3.7445\n3.3633\n2.6941\n2.7299\n3.9218\n4.048\n4.3146\n8.4921\n5.0287\n2.4038\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6103\n1.7565\n2.6857\n2.6641\n-0.58554\n-0.0087456\n2.8233\n4.2161\n4.967\n6.8154\n5.5821\n3.8569\n3.029\n6.7239\n12.122\n8.3991\n3.4443\n3.3564\n4.0485\n7.4086\n6.1542\n6.6872\n7.6064\n7.9276\n9.8584\n8.2538\n5.8688\n4.1958\n4.3579\n2.4744\n1.4381\n2.1865\n2.2165\n0.94938\n-3.0334\n-4.0899\n-0.84798\n-0.020351\n0.11219\n1.4914\n2.2671\n3.8863\n3.7635\n4.5987\n4.1509\n3.4229\n3.613\n3.9293\n3.5395\n3.9695\n4.1073\n3.9391\n4.8791\n5.337\n6.1003\n6.1273\n4.1119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.946\n1.8239\n2.3639\n2.4163\n2.2668\n2.6543\n3.9756\n3.936\n2.7949\n2.6416\n3.3335\n0.70425\n4.9716\n9.4561\n6.8937\n3.7692\n2.2197\n3.1528\n2.2526\n6.1368\n7.037\n7.8297\n7.8756\n6.3278\n9.0437\n9.3301\n7.0199\n3.8269\n4.1967\n4.9681\n0.41872\n4.4177\n2.2369\n2.3225\n-1.5092\n-3.8124\n-2.3795\n-0.31687\n0.43201\n1.072\n2.4964\n3.0806\n1.9188\n2.2934\n3.5191\n2.5675\n1.9863\n3.9813\n4.0385\n5.6242\n6.4538\n5.8119\n7.6345\n6.6228\n6.3076\n5.5409\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2511\n2.8107\n2.9595\n2.8208\n2.7571\n2.613\n2.427\n2.4268\n1.3704\n0.53636\n1.2868\n1.272\n3.2715\n4.24\n1.058\n0.53448\n-0.9504\n0.79666\n-2.0375\n1.2747\n6.0699\nNaN\nNaN\n2.7444\n6.7508\n8.7241\n9.018\n5.6257\n5.9884\n5.6587\n0.77951\n-0.46121\n0.23326\n2.18\n0.1091\n-1.2973\n-2.7955\n-0.89503\n0.70872\n1.3775\n2.3391\n2.8375\n1.3188\n0.66296\n1.905\n2.2099\n2.9755\n5.3798\n4.7706\n4.0898\n6.1579\n5.8338\n5.8272\n7.7447\n6.7285\n6.7943\n11.119\nNaN\nNaN\n-8.3823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5487\n1.8298\n2.263\n2.6912\n2.8977\n2.0535\n2.0085\n2.1814\n2.1951\n1.7779\n2.5456\n4.0907\n3.7218\n1.2657\n-0.36491\n1.1144\n-0.86442\n-0.63543\n-4.7005\nNaN\nNaN\nNaN\nNaN\n4.0779\n6.4681\n9.658\n9.5374\n7.7479\n5.821\n5.3473\n3.3735\n-1.1695\n-0.96728\n-0.44524\n-1.2442\n-3.4815\n-3.6402\n-1.1874\n0.51757\n2.1211\n2.8634\n1.9354\n0.92334\n0.49295\n-2.6115\n-0.10883\n2.6527\n5.1609\n4.4449\n4.6575\n5.3995\n5.5073\n5.5826\n5.4392\n4.5322\n1.3516\n4.145\nNaN\nNaN\n-13.109\n-6.8278\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5226\n0.92113\n1.6166\n2.8678\n3.832\n2.5488\n3.9696\n4.0839\n4.0836\n4.0288\n3.6429\n3.2201\n3.127\n0.94763\n1.8098\n1.1971\n0.19348\n2.6947\n-0.70824\nNaN\nNaN\nNaN\nNaN\n3.7677\n4.1075\n7.1452\n8.0583\n9.0895\n4.9016\n4.5721\n2.0151\n-0.9361\n-3.1327\n-2.2799\n-4.2286\n-4.7421\n-3.7965\n-1.6871\n-0.46285\n1.1421\n3.2208\n1.9882\n-0.30795\n-1.2494\n-5.6734\n-0.4165\n2.6726\n4.3732\n5.1229\n5.7692\n4.5531\n5.3118\n5.2233\n5.0959\n4.2807\n1.8035\n3.9468\nNaN\nNaN\n-5.9857\n-9.3831\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0452\n2.2642\n2.3524\n3.8719\n5.4044\n3.3456\n4.7466\n5.4212\n5.1302\n4.7072\n3.8376\n4.0171\n3.5589\n4.0775\n3.0413\n2.2392\n2.0374\n3.6511\n1.6098\n0.91609\n2.4537\n4.1996\n3.7833\n4.1862\n5.1826\n6.5269\n7.9743\n9.6094\n4.9086\n4.6417\n3.6907\n-0.72824\n-3.0741\n-2.7453\n-2.7171\n-2.9702\n-1.9639\n-2.3849\n-2.059\n-1.0342\n1.6437\n1.3344\n-0.014211\n-1.7716\n-7.709\n-0.6864\n3.3399\n3.9046\n3.6827\n5.7974\n4.1489\n4.8675\n6.1303\n6.8921\n6.9672\n3.8329\n2.7785\n4.9732\n5.349\n1.4292\n-4.6641\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8515\n1.9068\n2.1712\n2.4504\n4.2735\n3.7548\n4.1557\n6.0844\n5.1189\n5.09\n4.7319\n5.0166\n4.2734\n4.2023\n2.8564\n3.1742\n3.9359\n4.5077\n2.1679\n2.6124\n3.563\n2.3822\n2.9591\n4.8782\n6.1287\n7.3566\n8.2865\n7.7665\n5.2287\n4.3567\n4.859\n2.2789\n-0.24815\n-2.3541\n-1.5522\n-1.2737\n-2.3501\n-3.0203\n-2.1449\n-1.5564\n-0.87547\n-0.016391\n-1.5197\n-3.6638\n-7.6981\n2.3601\n4.7103\n5.2739\n5.9002\n6.0333\n5.5679\n6.9042\n7.3772\n10.532\n9.2607\n3.655\n0.6305\n3.515\n7.4784\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.037266\n1.6953\n3.0889\n1.9637\n3.8503\n3.557\n3.3518\n3.7078\n4.1358\n4.5887\n5.5726\n5.4844\n5.4122\n4.5174\n4.1709\n4.9144\n4.8172\n3.8853\n2.8334\n5.1573\n5.525\n4.8306\n5.2388\n4.7195\n4.6816\n6.8071\n8.0009\n6.8074\n4.6511\n3.6205\n5.5958\n0.92376\n-2.7484\nNaN\nNaN\n-0.73002\n-2.0593\n-2.5844\n-2.9961\n-2.5679\n-1.2941\n-0.82219\n-2.8594\n-3.5244\n0.039473\n4.7459\n4.1243\n5.3309\n5.9567\n6.2991\n5.9064\n9.2453\n9.8592\n10.689\n8.7377\n4.7449\n-0.12273\n-0.14604\n7.1094\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3046\n0.24375\n1.4394\n1.1109\n2.2363\n3.0648\n3.3938\n3.1702\n3.924\n5.4328\n6.2131\n5.4997\n7.1764\n6.0381\n6.5311\n6.8191\n5.2582\n3.5216\n3.8722\n6.6017\n5.0205\n5.6955\n6.3108\n3.1042\n3.6795\n8.662\n9.4503\n7.9999\n6.0514\n5.9161\n4.3423\n-0.00031471\nNaN\nNaN\nNaN\n-1.4934\n-3.1898\n-2.2411\n-5.2857\n-3.8016\n-0.89861\n-1.3932\n-3.5391\n-2.666\n0.48723\n3.1705\n3.57\n4.9034\n6.5413\n5.6457\n5.6225\n8.4053\n9.0941\n10.768\nNaN\nNaN\n-0.87524\n0.40573\n6.6226\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5306\n-0.7764\n0.78392\n1.278\n1.4913\n2.9562\n4.1236\n4.6008\n4.702\n4.5566\n4.2627\n5.0893\n7.5943\n6.8417\n5.9881\n5.4732\n5.1492\n4.6227\n4.8429\n6.0297\n3.5307\n5.1694\n5.145\n2.7656\n3.9166\n7.2499\n9.7248\n10.388\n7.9656\n7.3408\n4.3498\nNaN\nNaN\nNaN\nNaN\n-1.9089\n-1.5496\n-3.0817\n-7.5101\n-3.856\n-0.34586\n-2.575\n-3.4475\n-1.3026\n-0.24872\n1.9807\n3.8989\n5.7999\n8.1928\n7.5366\n6.6905\n8.3669\n7.4729\nNaN\nNaN\nNaN\n0.48627\n-1.2193\n4.1346\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.62475\n0.52402\n1.0107\n0.91092\n2.0014\n3.232\n3.3463\n3.6869\n3.9259\n3.8971\n4.3766\n5.5739\n6.4788\n6.7688\n5.4611\n6.9477\n7.0907\n5.7304\n5.3818\n5.6867\n4.7198\n5.0817\n2.2079\n2.2463\n3.8244\n4.9187\n10.931\n9.9743\n7.4734\n7.3223\n5.2845\nNaN\nNaN\nNaN\nNaN\n-4.0229\n-3.0763\n-2.3438\n-5.142\n-3.0888\n-2.5313\n-6.6468\n-7.8421\n-1.4718\n1.3354\n3.36\n4.889\n6.5945\n8.6101\n8.9599\n8.1267\n8.2436\n5.6163\nNaN\nNaN\nNaN\n-2.7784\n-3.3959\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4398\n0.92243\n0.58919\n0.95931\n1.9661\n2.4552\n2.6099\n1.6\n2.0403\n2.6239\n3.0058\n4.726\n6.1691\n6.4225\n5.3667\n7.2646\n6.8956\n5.9588\n5.0603\n5.0352\n6.1588\n3.0907\n-0.94878\n-0.43429\n0.53428\n5.3899\n9.5928\n8.6069\n8.6891\n8.5736\n6.5358\nNaN\nNaN\nNaN\nNaN\n-5.7831\n-5.7328\n-0.95757\n-4.6922\n-2.2215\n-2.987\n-5.7177\n-5.1573\n0.46706\n4.2938\n6.0495\n6.0041\n7.5521\n8.9288\n9.4721\n7.4168\n5.3479\n4.8247\nNaN\nNaN\nNaN\nNaN\n-3.0597\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3831\n0.0062008\n1.3392\n2.97\n2.4814\n1.9766\n2.0175\n1.8387\n1.8272\n1.8607\n2.1024\n3.273\n5.5857\n5.8267\n5.6732\n6.9099\n7.2735\n7.0771\n6.0522\n5.0421\n5.2462\n-0.71593\n-6.1772\n-2.7863\n-0.9057\n6.1146\n8.1498\n8.8696\n9.9455\n9.5139\n9.07\nNaN\n-4.5905\n-6.7429\n-5.0365\n-5.4971\n-4.1193\n0.30596\n-1.968\n0.62826\n0.064908\n-3.7605\n-4.6929\n4.3955\nNaN\nNaN\nNaN\n7.9424\n9.6325\n9.2626\n6.2008\n6.8809\n5.9103\n2.1596\nNaN\nNaN\nNaN\n-4.1899\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.54748\n0.062343\n1.5399\n3.8855\n3.7657\n2.8568\n2.9962\n2.3501\n1.8775\n2.5772\n2.3522\n3.0275\n4.2783\n4.6213\n4.5855\n4.214\n5.2545\n6.4723\n7.4918\n6.1049\n4.6715\n2.4335\n-10.077\n-7.7295\n-1.3978\n4.9493\n8.0974\n12.21\n11.587\n9.0033\n5.1954\n-4.1436\n-2.3528\n-4.651\n-4.4788\n-2.3882\n0.68121\n1.0365\n0.637\nNaN\nNaN\nNaN\n-3.4785\nNaN\nNaN\nNaN\nNaN\n6.2159\n8.6344\n6.8863\n5.2709\n4.687\n6.9265\n2.485\n1.0311\n-2.2575\n-5.8127\n-4.8365\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2489\n0.97228\n2.6664\n4.7189\n3.8577\n3.2422\n3.383\n2.7409\n1.9606\n2.9301\n3.2669\n3.3814\n3.7235\n3.9124\n3.8783\n2.9703\n3.1284\n4.5311\n6.5117\n6.7871\n4.2764\n0.62279\n-3.5433\n-5.9764\n1.3966\n5.2521\n8.8824\n10.413\n8.9101\n6.3082\n2.399\n1.6792\n3.3245\n-2.4634\n0.53345\n0.55099\n1.4504\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.3152\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.9804\nNaN\n3.6214\n6.2967\n2.9591\n2.0576\n1.0665\n-4.6704\n-4.6702\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.51538\n1.0717\n3.2205\n4.7137\n3.3531\n2.3458\n2.9044\n2.7497\n1.7859\n2.6063\n3.1188\n3.6248\n2.8322\n3.0719\n3.5073\n3.9121\n5.2183\n3.3469\n3.4371\n3.5456\n2.3476\n-2.053\n-4.3617\n-4.5678\n3.1888\n5.4208\n9.0726\n7.6633\n7.356\n3.9296\n6.1527\n5.2672\n0.93841\n1.7921\n3.6211\n3.255\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9141\n2.2829\n-0.31487\n-3.3138\n-3.2719\n-3.9428\n-4.4236\n-0.62573\n0.14297\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20636\n1.7902\n3.0753\n3.9523\n3.828\n1.2892\n2.4898\n2.6904\n1.6685\n1.9768\n2.1917\n2.9523\n2.9046\n3.6448\n5.009\n3.7352\n2.7594\n0.61265\n1.2972\n1.0108\n0.45672\n-2.8797\n-4.2346\n-1.9617\n2.9388\n6.0469\n7.0605\n7.0797\n10.014\n5.4057\n5.6746\n4.7897\n4.5435\n1.9822\n3.0828\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7661\n1.0838\n0.090645\n-1.3048\n0.78306\n0.24654\n1.367\n3.6108\n4.7169\n1.4785\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.78443\n2.4661\n1.7285\n0.4596\n3.8988\n2.7161\n3.573\n2.6053\n1.2927\n1.9779\n2.5262\n2.495\n2.9426\n4.185\n4.0766\n2.773\n1.5245\n-1.74\n-1.06\n-0.81734\n0.14418\n-6.1041\n-3.0372\n0.91516\n5.1521\n8.1249\n6.8313\n8.2666\n11.879\n8.556\n3.6686\n4.083\n6.4064\n2.4599\n4.5192\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.9021\n-1.3971\n-0.50282\n0.74658\n3.7105\n5.0478\n2.4094\n1.8562\n4.2856\n1.4567\n2.687\n5.3935\n6.3773\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5555\n3.3951\n1.6767\n0.40457\n3.2416\n3.5162\n2.075\n1.8516\n1.1918\n3.0334\n3.5846\n3.2401\n2.1404\n3.3012\n1.9289\n1.2147\n2.988\n1.0915\n0.12983\n0.20828\n-0.04275\n-6.4431\n-2.9179\n2.7888\n5.5116\n10.199\n10.42\n10.197\n12.085\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4273\n-1.7671\n-0.16173\n-0.26077\n-0.82206\n0.77884\n4.6561\n6.1255\n3.4689\n5.0963\n6.0817\n4.811\n4.2761\n6.2398\n7.0212\n6.3933\n5.1945\n5.109\n2.8864\n2.1289\n1.48\n1.2751\n1.207\n0.78789\n2.4435\n2.1163\n0.59803\n1.5915\n1.4785\n2.1293\n1.984\n1.0053\n1.8968\n1.8177\n1.3322\n1.4555\n1.4727\n-0.051392\n0.62966\n1.2699\n3.5704\n-3.362\n-3.801\n3.4587\n7.4142\n13.547\n11.817\n10.418\n10.22\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.56466\n-0.7421\n0.75073\n3.3189\n2.3033\n1.5649\n-2.0434\n2.3449\n5.8669\n4.6039\n6.9117\n7.9578\n4.8977\n4.2035\n4.0907\nNaN\nNaN\nNaN\nNaN\n4.4516\n4.3574\n1.315\n0.55545\n0.12829\n-0.3653\n-0.12052\n-0.23309\n-0.22636\n0.53768\n2.1199\n0.98172\n-0.29928\n0.14452\n0.50276\n1.1059\n0.79862\n2.3609\n2.3101\n-1.4382\n-4.5115\n0.51066\n2.9175\n-1.8995\n-1.4388\n2.7083\n9.2128\n10.62\n10.141\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5485\n2.4043\n1.3857\n0.33435\n3.2082\n4.6381\n3.5669\n3.4422\n0.82499\n1.3635\n2.2085\n3.7162\n6.008\n8.0813\n4.4493\n4.5981\n3.8868\nNaN\nNaN\nNaN\nNaN\n3.2445\n7.6627\n0.40273\n-0.59628\n-2.7676\n-1.5938\n-0.6951\n-0.89144\n-1.0119\n0.06058\n2.3834\n0.38639\n-0.75654\n0.75916\n0.53909\n-0.40389\n-0.28723\n3.6348\n4.2399\n-0.25142\n-3.9563\n-6.2151\n-2.6172\n-1.9409\n0.038113\n4.3966\n7.7743\n8.9518\n7.5816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.076\n2.8862\n2.4234\n1.9139\n1.2683\n2.3797\n3.4033\n3.8778\n5.7751\n4.113\n2.1003\n1.9893\n2.8123\n2.9619\n5.6853\n2.9374\n4.6671\n5.033\nNaN\nNaN\n5.5281\n6.4152\n5.5145\n6.609\n-1.7546\n-1.8692\n-2.0085\n-1.6213\n-1.5705\n-0.046957\n-0.2213\n1.0874\n1.5342\n0.68883\n-0.42094\n0.18538\n1.5146\n-0.83882\n-0.6347\n2.6057\n4.8013\n-0.39283\n-4.2613\n-6.299\n-2.8033\n-0.82638\n0.36628\n4.5019\n6.283\n8.7499\n8.1493\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7256\n-1.0754\n5.6507\n2.9265\n3.5211\n2.8624\n2.2773\n1.5473\n5.0695\n9.2239\n7.7075\n4.1663\n3.8481\n3.697\n1.4122\n0.65321\n3.7327\n5.1135\n4.725\n2.7126\n2.1302\n7.1987\n9.0867\n8.0495\n7.3298\n-3.4428\n-1.9976\n-1.3794\n-1.2446\n-1.8221\n1.1538\n1.4619\n0.75796\n1.3736\n0.10574\n-0.10801\n0.67707\n1.361\n1.06\n1.2709\n3.0055\n7.1354\n0.22637\n-2.759\n-2.5494\n-0.093155\n0.53488\n1.3513\n3.8036\n7.3331\n10.87\n7.2274\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.743\n-1.5443\n4.9663\n2.4789\n4.1647\n3.9988\n1.0987\n-0.079519\n3.2495\n9.2297\n9.7767\n7.516\n5.3865\n5.0318\n2.9303\n2.7063\n6.6729\n7.9756\n4.8536\n3.5438\n4.8167\n9.473\n12.287\n10.304\n7.8034\n-3.0089\n-1.4301\n-1.1695\n0.16563\n0.37865\n1.1147\n1.7066\n1.4861\n0.74859\n1.0757\n-0.74871\n-1.3612\n0.79956\n2.0924\n2.4255\n4.219\n5.7186\n1.2411\n0.64338\n-0.9694\n-0.60896\n1.5854\n3.8758\n5.4711\n8.656\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5247\n0.69809\n1.2453\n5.3574\n-1.8396\n1.4279\n4.4151\n3.5789\n7.2986\n7.8293\n9.8438\n9.7938\n9.6326\n7.5002\n7.5264\n3.9133\n3.3833\n8.5968\n7.5319\n8.3039\n5.0277\n7.2751\n10.433\n11.254\n9.9002\n7.257\n-2.6529\n-0.47691\n-1.3546\n0.58862\n0.35548\n0.36088\n0.42854\n1.1705\n-0.086333\n-0.2186\n-0.72671\n-1.1459\n-0.69387\n1.326\n3.0376\n4.3919\n4.2313\n0.74432\n-0.69415\n-0.048893\n0.1713\n2.1307\n3.2275\n5.5062\n9.4259\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.4413\nNaN\nNaN\nNaN\nNaN\n2.4761\n1.5238\n1.3941\n1.8854\n4.9566\n-1.0482\n4.5065\n6.2617\n10.198\n9.499\n9.0775\n9.4701\n4.0787\n8.6978\n6.6041\n6.5571\n1.0589\n3.964\n9.4435\n9.2889\n10.429\n8.2497\n10.28\n10.936\n9.7624\n7.2629\n4.0526\n-0.80615\n-0.13885\n-0.93438\n-0.54991\n-0.014709\n0.028491\n-0.39008\n-0.20448\n-1.2029\n-1.0197\n-0.39942\n-1.0047\n-1.7512\n1.0526\n1.9891\n3.871\n4.4873\n2.0663\n-1.3354\n-0.82911\n0.9652\n3.3247\n2.6261\n6.6563\n13.616\n20.578\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.2272\n4.4714\n2.6315\n2.3485\n1.7523\n1.543\n2.1428\n3.0255\n4.1443\n6.4012\n10.122\n9.3141\n8.8763\n5.7682\n-0.31248\n1.1288\n5.0785\n5.7986\n2.7537\n5.322\n8.8372\n10.442\n9.4742\n9.6499\n9.2702\n10.145\n7.6787\n4.6465\n0.11234\n-0.84711\n-0.85064\n-0.6566\n-1.1235\n-1.649\n-0.86475\n0.10747\n-0.94007\n-1.5704\n-0.31574\n-0.58567\n-1.2117\n-2.0213\n-0.051868\n1.4065\n3.6294\n2.6166\n1.1272\n0.47629\n-3.601\n-2.5341\n3.2192\n5.1137\n10.521\n18.555\n21.737\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.659\nNaN\nNaN\n22.211\n6.8637\n3.8496\n4.4715\n3.1581\n0.88493\n-0.32757\n-2.4586\n-0.688\n0.087329\n2.0507\n5.5122\n3.395\n2.1126\n2.4892\n1.1424\n-1.0213\n-0.18838\n3.1157\n3.342\n4.7789\n7.3106\n8.0372\n7.4498\n8.9013\n9.3616\n7.5388\n5.2467\n3.9528\n-1.1691\n-1.9413\n-1.4253\n-1.1977\n-1.3819\n-1.6139\n-0.30242\n0.18192\n0.10995\n-0.83716\n0.0079553\n-0.71793\n-2.155\n-1.9558\n-1.1036\n0.11716\n0.7693\n0.67354\n-1.0747\n-1.7284\n-1.9459\n-2.2161\n3.0188\n6.7055\n14.992\n18.774\n23.534\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n18.828\n35.897\n26.675\n20.141\n5.5505\n2.5119\n3.4359\n5.8654\n1.4127\n0.26018\n-1.1786\n-2.3276\n-0.53103\n-0.055065\n0.35033\n-0.25208\n-0.18877\n-0.50287\n-0.42054\n-1.6389\n-2.6961\n2.7028\n4.9057\n4.9986\n4.0072\n3.7191\n5.0593\n8.3903\n8.1107\n4.9089\n4.4688\n3.2125\n1.785\n-2.8764\n-1.0276\n-1.0914\n-0.19631\n-0.65639\n-1.2434\n-1.216\n0.49676\n-0.09348\n-0.9053\n-0.9304\n-2.9463\n-4.8062\n-2.759\n-1.458\n-0.14783\n-1.552\n-2.0252\n-1.5582\n-1.6498\n-1.4055\n1.1399\n7.4685\n12.599\n16.476\n23.413\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n41.484\n27.462\n20.971\n15.016\n4.0915\n-0.21038\n2.3613\n4.0363\n2.4286\n-0.075065\n-2.9828\n-2.8838\n-1.093\n0.073093\n-0.0079912\n-3.0194\n-4.0195\n-1.6155\n-1.1268\n-3.0103\n-4.1639\n0.15039\n3.3334\n3.5955\n3.3238\n2.7631\n3.8576\n3.8118\n6.8305\n3.9051\n1.8539\n2.7314\n3.8708\n-1.7155\n-1.3297\n-1.6244\n0.034595\n-0.8063\n-1.7211\n-1.65\n-0.59941\n-0.47086\n-1.4383\n-2.0834\n-2.6526\n-4.3517\n-2.8758\n-2.4023\n-0.26898\n-1.682\n-5.6978\n-5.1525\n0.50745\n1.3833\n0.96657\n5.8145\n11.327\n13.656\n12.507\n10.673\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n35.788\n20.738\n14.725\n10.2\n4.7032\n-5.1241\n0.67239\n1.9659\n0.034178\n-0.37995\n-1.9818\n-2.0547\n-1.472\n-0.97046\n-1.4325\n-2.1703\n-5.9637\n-2.8893\n-0.75787\n-0.61952\n-2.0139\n-3.056\n0.40823\n2.2829\n3.4511\n3.8288\n3.1111\n2.3217\n1.0995\n1.2199\n3.159\n4.9398\n4.8506\n-1.2247\n-1.9185\n-2.3038\n-0.96023\n-1.0261\n-0.35789\n-1.5249\n-1.0584\n-0.34331\n-0.88804\n-2.7895\n-2.8149\n-2.694\n-2.2062\n-2.3886\n-0.48027\n-0.61527\n-3.0255\n-3.8978\n-2.1866\n1.5689\n0.6697\n5.2251\n10.689\n13.422\n7.3026\n4.7857\n3.3459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n26.666\n13.722\n7.8779\n8.0041\n4.6665\n-3.5457\n0.62124\n0.63781\n-2.1015\n-3.7651\n-4.8819\n-2.6201\n-2.0993\n-2.1931\n-4.0783\n-2.7589\n-2.6015\n-1.7896\n0.23149\n-0.2562\n-0.41654\n-0.15143\n-1.6186\n1.2784\n1.5095\n2.3646\n1.1782\n1.4741\n0.69645\n1.752\n4.2207\n6.4294\n4.7995\n-1.3038\n-2.5879\n-2.859\n-1.9627\n-0.19023\n0.44866\n-0.77534\n-0.52806\n-0.55253\n-0.081044\n-3.3206\n-2.7672\n-1.1493\n-2.1318\n-2.9086\n-2.1052\n0.073089\n-3.0183\n-1.4091\n-3.8748\n-1.5164\n0.66795\n4.4616\n8.1726\n11.78\n9.5084\n3.7931\n5.0219\nNaN\nNaN\n11.194\n11.642\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.308\n9.6508\n8.4068\n8.0151\n-0.6749\n-4.3955\n-1.0869\n-3.819\n-6.1978\n-7.1816\n-5.067\n-1.5094\n-1.1574\n-1.2854\n-1.5555\n-2.4798\n-1.7587\n-1.1466\n0.013584\n-0.73889\n-1.1751\n-0.86249\n-2.7018\n-0.50803\n-1.158\n-1.1464\n-1.9306\n-1.8571\n1.8165\n2.5217\n3.5558\n5.5933\n4.8291\n-2.0833\n-3.0043\n-2.2926\n-2.7332\n-0.10535\n2.0031\n0.73089\n0.42762\n-0.42137\n-0.39033\n-2.9527\n-1.4105\n-0.71699\n-1.7019\n-2.5985\n0.036333\n-0.028042\n-3.6672\n-4.0754\n-2.6208\n-2.6222\n0.59681\n3.1283\n7.5201\n5.5477\n2.1134\n2.63\n7.6644\n7.269\n11.435\n12.777\n12.081\n12.798\n13.384\nNaN\nNaN\nNaN\nNaN\n6.0247\n9.7777\n6.1696\n5.42\n4.5563\n-3.4355\n-7.9735\n-4.4821\n-5.2154\n-6.568\n-6.8795\n-4.7007\n1.0153\n1.6327\n1.261\n-1.1364\n-3.5369\n-2.998\n-1.719\n-0.2028\n0.1096\n0.34107\n-1.0725\n-2.0217\n-2.1299\n-3.9965\n-2.3726\n-0.8384\n-0.42189\n0.031127\n-1.2571\n0.73827\n3.2455\n1.6277\n-1.4938\n-2.5559\n-2.7243\n-2.2317\n-0.84607\n2.394\n2.0559\n0.9832\n0.18393\n-0.33546\n-1.7841\n-0.042149\n0.38161\n-0.082421\n-1.0069\n-1.2837\n-4.003\n-10.072\n-3.8649\n-1.5871\n-1.6403\n3.3882\n4.5956\n7.0479\n13.656\n6.5888\n7.0402\n8.1401\nNaN\n7.0907\n10.198\n6.0983\n11.481\n14.645\nNaN\nNaN\nNaN\n3.8213\n6.7214\n8.4755\n6.5044\n2.3103\n2.9061\n-1.857\n-7.2676\n-4.5933\n-0.031669\n-2.466\n-4.3959\n-3.041\n1.8201\n3.0319\n1.5664\n-1.9376\n-2.3038\n-3.0839\n-2.9191\n-1.4487\n0.35671\n0.78182\n-0.77535\n-2.1256\n-2.6078\n-3.121\n-1.3787\n1.4363\n1.4914\n-1.257\n-1.3086\n0.47071\n1.0588\n0.01711\n-2.8801\n-1.9748\n-0.58316\n-0.74451\n0.24669\n2.1454\n1.7997\n0.45789\n-0.42028\n-0.48057\n-1.3615\n-0.47308\n0.63395\n0.79037\n-0.23663\n-1.8447\n-4.2196\n-8.6258\n-2.6317\n-1.8891\n-0.49136\n0.72285\n6.4122\n6.1295\n8.2874\n7.2018\n5.8836\nNaN\nNaN\n5.6112\n7.4031\n7.5945\n10.366\n11.619\nNaN\nNaN\nNaN\n4.0034\n4.9418\n7.8073\n5.2531\n-0.6167\n-1.286\n-2.9234\n-4.9687\n-0.791\n1.3654\n0.75021\n-2.4906\n-1.7562\n1.2823\n3.9887\n1.1295\n-1.3762\n-1.245\n-2.1253\n-2.5025\n-1.8333\n-0.14882\n0.23701\n-0.47772\n-2.7487\n-3.21\n-1.7035\n0.22768\n2.1345\n2.4491\n1.7336\n1.3139\n1.04\n1.6735\n2.9524\n-2.6752\n-1.5807\n1.1956\n1.6948\n2.0267\n2.6876\n2.226\n0.50785\n-0.49976\n-0.84488\n-0.84511\n-0.38617\n0.74093\n2.0009\n1.9805\n-1.2961\n-2.6347\n-7.2771\n2.1187\n1.2954\n-0.49494\n2.7461\n6.576\n5.8403\n5.4034\n4.5282\n4.8549\nNaN\nNaN\nNaN\n3.87\n6.2092\n7.2337\n8.2061\nNaN\n7.927\n6.792\n3.3195\n3.9065\n7.2701\n4.6005\n1.0703\n0.80689\n1.1206\n-1.4749\n-2.279\n-2.4869\n-2.4194\n-1.316\n1.2012\n2.6058\n3.5675\n1.4896\n-1.8726\n-1.6743\n-1.2036\n-0.95033\n0.13813\n0.23358\n0.053586\n-1.2112\n-2.4195\n-2.5789\n-0.9234\n1.7481\n2.2963\n3.2143\n3.4672\n1.1376\n-0.44617\n2.2863\n4.7941\n-1.3961\n-0.78284\n2.2482\n2.9799\n3.7456\n3.803\n2.5664\n2.3656\n1.3717\n0.2405\n0.78079\n1.2446\n2.0854\n4.493\n7.1366\n1.9169\nNaN\nNaN\nNaN\n1.2787\n-2.1201\n2.4106\n5.0853\n0.60034\n1.1567\n2.4225\n4.0037\nNaN\nNaN\nNaN\n2.549\n4.5703\n3.2383\n4.2544\n5.1553\n5.6937\n4.9561\n3.7636\n3.7514\n1.4729\n-0.49214\n-0.2723\n1.8299\n1.8429\n0.0077696\n-0.98602\n-0.48833\n-0.062797\n0.93282\n1.3733\n1.9885\n1.7749\n0.12356\n-2.9437\n-1.63\n0.2553\n1.3288\n0.91788\n0.75027\n0.51021\n-0.79654\n-1.0072\n-0.63017\n-0.32501\n1.4815\n1.8248\n3.4734\n4.1383\n1.3204\n0.012564\n1.7417\n4.1292\n0.97536\n2.3776\n3.3078\n2.9465\n3.3499\n4.1582\n3.011\n3.8682\n3.0457\n1.4926\n2.0762\n2.6224\n4.9187\n9.0316\n9.2348\n3.0325\nNaN\nNaN\nNaN\n-0.43565\n-2.1069\n-4.0488\n-0.85997\n0.24816\n2.6701\n2.1702\n2.3222\nNaN\nNaN\nNaN\n0.60844\n2.0732\n2.0392\n3.5733\n7.7377\n5.994\n4.2867\n2.8747\n2.0025\n-0.78418\n-1.2024\n-1.0522\n0.94495\n2.5016\n2.0006\n0.33505\n1.107\n-0.261\n-0.2887\n0.60301\n1.1757\n0.33485\n-0.021566\n-0.22754\n0.81648\n0.93767\n0.11305\n1.3735\n1.7384\n1.7588\n0.457\n0.63808\n1.0351\n1.3773\n2.5313\n2.6164\n4.8541\n4.897\n2.4269\n1.5198\n3.5073\n4.4879\n2.6284\n3.788\n3.9279\n3.2013\n4.444\n4.3407\n4.777\n4.5692\n4.0863\n2.5371\n3.6531\n5.0393\n6.2977\n7.419\n5.7705\n4.6929\n1.0429\nNaN\nNaN\n-2.6918\n-1.8096\n-1.3241\n-1.1321\n0.12723\n3.5973\n2.9098\n2.2905\n3.0754\n1.0313\n0.54539\n-2.5591\n-1.1038\n0.97263\n2.7104\n7.1032\n5.9085\n5.4335\n3.6645\n2.1745\n2.3408\n1.0274\n1.4044\n1.9042\n3.227\n3.0206\n2.0985\n0.27331\n-3.5807\n-3.5969\n0.0027388\n0.21359\n0.29802\n2.6955\n1.0191\n0.99591\n1.2293\n-0.069228\n1.6357\n2.2664\n2.9104\n1.7401\n0.6911\n1.8529\n3.0849\n3.9195\n3.5725\n3.6696\n5.4295\n3.1855\n2.5613\n5.6582\n5.3882\n1.7756\n2.6607\n4.0059\n4.3166\n5.2433\n5.5665\n6.0482\n5.1224\n3.8287\n3.5185\n3.5657\n5.1759\n6.4443\n7.7425\n6.6794\n5.9357\n2.2606\n-2.5647\n-4.446\n-2.8637\n-1.3904\n0.19361\n0.80094\n1.1903\n3.2188\n2.801\n2.4116\n2.0919\n2.456\n1.9401\n0.43625\n-0.3133\n-0.21012\n2.0076\n4.9292\n5.1109\n4.8313\n5.6118\n3.7868\n2.7621\n2.4714\n4.9417\n4.0158\n4.5381\n4.7928\n4.9137\n0.47633\n-4.2495\n-6.729\n-2.4449\n-0.77212\n3.0146\n3.5022\n2.0219\n2.1885\n2.6197\n1.9235\n1.9256\n1.9429\n2.0275\n1.9599\n1.3382\n3.4913\n4.1062\n3.8723\n3.3207\n5.5553\n6.2425\n3.3258\n3.5596\n4.2883\n4.2788\n0.98485\n3.6225\n5.8399\n5.8916\n6.9718\n7.7605\n7.1402\n7.96\n5.7804\n4.0791\n3.6106\n4.5642\n5.1649\nNaN\nNaN\nNaN\nNaN\n0.1295\n-2.9427\n-2.803\n1.0952\n0.42713\n-1.0571\n0.62715\n2.7777\n2.4294\n3.0921\n3.6604\n2.6073\n1.4881\n1.0604\n1.276\n-0.07621\n0.61579\n1.5221\n2.6214\n3.6208\n6.2016\n4.8749\n4.9733\n4.845\n6.4275\n5.6102\n2.7158\n3.8685\n6.5013\n5.0375\n-2.2569\n-2.1351\n-3.2304\n-1.7004\n0.24099\n1.9572\n1.4995\n2.4163\n3.7154\n1.0154\n0.6936\n1.5406\n0.82319\n2.61\n3.985\n5.079\n3.6822\n2.7068\n3.7336\n7.1905\n6.6189\n4.8015\n3.8763\n3.703\n4.9559\n3.316\n5.3366\n6.9883\n7.265\n7.9419\n8.611\n8.2545\n10.659\n9.5119\n8.0674\n5.6011\n5.0894\n6.7386\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8077\n-1.3803\n-0.12361\n0.31721\n-1.1324\n0.22302\n1.8052\n2.4801\n2.4792\n2.4203\n1.7509\n1.09\n0.773\n0.94079\n2.1779\n0.72691\n0.14325\n2.2095\n4.4966\n8.6355\n7.8091\n7.0754\n6.75\n7.3987\n6.1447\n-0.73145\n0.89181\n2.9916\n8.2075\n0.48416\n0.051862\n-0.65508\n-0.56067\n-1.1249\n-0.71911\n0.13722\n1.7435\n2.3266\n0.4057\n1.3803\n3.1\n2.2084\n3.3568\n4.7108\n4.6207\n4.2549\n3.0508\n5.1885\n8.5663\n7.5391\n6.8315\n3.8509\n3.8946\n4.2579\n5.6465\n5.3784\n7.3898\n8.9677\n9.3987\n12.697\n11.862\n13.103\n12.22\n10.222\n7.1759\n5.358\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4426\n-1.2533\n-1.29\n-0.82272\n-0.50915\n-0.24659\n-0.81542\n0.51764\n0.57644\n1.6911\n1.4539\n0.69185\n0.75451\n1.5674\n2.2578\n1.8137\n2.1724\n3.4919\n8.0851\n10.54\n7.4775\n7.8186\n7.9915\n7.0898\n-1.0426\n-2.6307\n-1.2467\n5.8358\n1.5623\n1.0332\n0.33339\n0.080954\n0.065446\n-0.80513\n-0.49837\n1.7624\n3.2912\n0.37593\n1.0197\n3.7876\n0.98337\n2.2391\n2.8796\n4.443\n4.9204\n5.1191\n5.7478\n8.1201\n8.0562\n7.6952\n2.0701\n1.9545\nNaN\n7.7103\n5.6586\n9.179\n11.393\n12.157\n14.916\n15.682\n15.976\n12.617\n11.082\n8.6662\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4687\n-0.52134\n-0.67954\n0.38103\n0.028688\n-0.036182\n-0.12663\n-0.37397\n0.84835\n1.6582\n1.1592\n0.97296\n1.1291\n2.5982\n3.2063\n2.8735\n1.0313\n1.5892\n5.3162\n7.3598\n6.239\n7.5872\n5.6135\n1.9324\n-1.1231\n-4.3681\n-4.3208\n0.6368\n0.33375\n1.0743\n-0.74675\n0.75666\n2.0764\n2.5354\n2.7801\n2.9103\n3.3132\n1.094\n1.3628\n3.6748\n-0.66966\n1.6094\n-0.088436\n1.6\n6.6912\nNaN\nNaN\n6.9726\n8.197\n8.927\nNaN\nNaN\nNaN\n"
  },
  {
    "path": "tests/test_data/small_test/time_series/ts_error_interp0_method1.csv",
    "content": "0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\n0.27227\n0.27227\n0.27614\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\n0.27614\n0.27614\n0.27614\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\n0.27614\n0.27614\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\n0.27614\n0.27614\n0.27614\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.288\n0.288\n0.288\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27261\n0.27261\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.28532\n0.288\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27261\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.28909\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.28909\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\nNaN\nNaN\nNaN\nNaN\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\nNaN\nNaN\n0.31026\n0.27614\n0.27614\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\n0.31026\n0.31026\n0.31026\n0.27614\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31026\n0.31026\n0.31026\n0.31026\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31026\n0.31026\n0.31026\n0.31026\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31026\n0.31026\n0.27614\n0.27614\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27614\n0.27614\n0.27614\n0.27614\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\n0.27614\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\n0.27227\n0.27227\n0.27227\n0.27614\n0.27614\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\n0.27227\nNaN\nNaN\n0.27227\n0.27227\n0.27227\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\n0.18451\n0.18451\n0.18437\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\n0.18437\n0.18437\n0.18437\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\n0.18437\n0.18437\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\n0.18437\n0.18437\n0.18437\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18823\n0.18823\n0.18823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18503\n0.18503\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18592\n0.18823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18503\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.1882\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.1882\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\nNaN\nNaN\nNaN\nNaN\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\nNaN\nNaN\n0.20434\n0.18437\n0.18437\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\n0.20434\n0.20434\n0.20434\n0.18437\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20434\n0.20434\n0.20434\n0.20434\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20434\n0.20434\n0.20434\n0.20434\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20434\n0.20434\n0.18437\n0.18437\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18437\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18437\n0.18437\n0.18437\n0.18437\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\n0.18437\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\n0.18451\n0.18451\n0.18451\n0.18437\n0.18437\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.14606\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.14606\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\n0.18451\nNaN\nNaN\n0.18451\n0.18451\n0.18451\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.14766\n0.14766\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.14766\nNaN\n0.14839\n0.14839\nNaN\nNaN\n0.14839\n0.14839\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14766\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.14839\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\n0.14839\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\nNaN\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14944\n0.14944\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.15243\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\n0.21648\n0.21648\n0.21519\n0.21648\nNaN\nNaN\nNaN\nNaN\n0.15243\n0.21519\n0.15243\n0.14839\nNaN\nNaN\n0.14839\n0.14839\n0.21648\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\n0.21519\n0.21519\n0.21519\n0.21519\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\nNaN\nNaN\n0.14839\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.14944\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\n0.21519\n0.21519\n0.21519\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.1382\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\n0.21519\n0.21519\n0.21519\n0.21519\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14766\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.14839\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21452\n0.21452\n0.21452\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15325\n0.14766\n0.14839\nNaN\n0.14839\n0.14839\n0.14839\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21899\n0.21899\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21615\n0.21452\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.14839\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.21648\n0.21899\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21478\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21478\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21519\nNaN\nNaN\nNaN\nNaN\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.14839\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15243\nNaN\nNaN\nNaN\n0.21519\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15243\n0.21519\nNaN\nNaN\n0.23825\n0.21519\n0.21519\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15243\n0.16719\n0.16719\n0.16392\n0.21519\n0.23825\n0.23825\n0.23825\n0.21519\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23825\n0.23825\n0.23825\n0.23825\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23825\n0.23825\n0.23825\n0.23825\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23825\n0.23825\n0.21519\n0.21519\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21519\n0.15243\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21519\n0.21519\n0.21519\n0.21519\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\n0.21519\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\n0.21648\n0.21648\n0.21648\n0.21519\n0.21519\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.1765\n0.14839\n0.14839\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.1765\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\n0.21648\nNaN\nNaN\n0.21648\n0.21648\n0.21648\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.5928\n0.5928\n0.5928\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.5928\n0.5928\n0.5928\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.5928\n0.5928\n0.5928\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.17079\n0.17079\n0.17079\n0.17079\n0.5928\n0.5928\n0.5928\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.18122\n0.18122\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.18122\n0.18122\n0.18122\n0.18122\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.20459\n0.20459\n0.16743\n0.16743\n0.17079\n0.17079\n0.17079\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.18122\n0.18122\n0.18122\n0.18122\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.20459\n0.20459\n0.16743\nNaN\n0.17079\n0.17079\n0.5928\n0.5928\n0.17079\n0.17079\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.16743\nNaN\nNaN\n0.5928\n0.5928\n0.5928\n0.17079\n0.17079\n0.17079\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\nNaN\nNaN\nNaN\n0.5928\n0.5928\n0.5928\n0.5928\n0.17079\n0.17079\n0.5928\n0.5928\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5928\n0.5928\n0.17079\n0.5928\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5928\nNaN\n0.5928\n0.5928\n0.17078\n0.17078\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.75363\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\n0.18311\n0.17448\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\n0.20459\n0.20459\n0.20294\n0.20459\n0.75363\nNaN\nNaN\nNaN\n0.17448\n0.20294\n0.17448\n0.17079\n0.18122\n0.18122\n0.17079\n0.17079\n0.20459\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\n0.20294\n0.20294\n0.20294\n0.20294\n0.75363\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.52686\n0.17079\n0.18122\n0.18122\n0.17079\n0.17079\n0.17079\n0.17079\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.17078\n0.5928\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\n0.20294\n0.20294\n0.20294\n0.75363\n0.75363\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.52686\n0.17079\n0.18122\n0.18122\n0.18122\n0.17079\n0.17079\n0.17079\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.16324\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\n0.20294\n0.20294\n0.20294\n0.20294\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.52686\n0.16743\n0.18122\n0.18122\n0.18122\n0.17079\n0.17079\n0.17079\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\nNaN\n0.52686\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.19849\n0.19849\n0.19849\n0.52686\n0.52686\n0.52686\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.5928\n0.17258\n0.16743\n0.17079\n0.18122\n0.17079\n0.17079\n0.17079\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20814\n0.20814\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20468\n0.19849\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.5928\n0.18311\n0.1816\n0.17079\n0.17079\n0.17079\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.20459\n0.20814\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.19876\n0.52686\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18311\n0.1816\n0.17079\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.19876\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18311\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17794\n0.17794\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18311\n0.17794\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\n0.52686\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5928\n0.17794\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20294\n0.52686\nNaN\n0.52686\n0.52686\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.17079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.17448\n0.17794\n0.17794\n0.17794\n0.20294\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17448\n0.20294\n0.17794\n0.17794\n0.22804\n0.20294\n0.20294\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75363\n0.17448\n0.18793\n0.18793\n0.18594\n0.20294\n0.22804\n0.22804\n0.22804\n0.20294\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75363\n0.75363\nNaN\nNaN\nNaN\nNaN\n0.22804\n0.22804\n0.22804\n0.22804\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.52686\n0.75363\n0.75363\nNaN\nNaN\n0.52686\n0.52686\n0.22804\n0.22804\n0.22804\n0.22804\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\n0.75363\n0.75363\n0.52686\n0.52686\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.52686\n0.22804\n0.22804\n0.20294\n0.20294\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.20459\n0.20294\n0.17448\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52686\n0.20294\n0.20294\n0.20294\n0.20294\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\n0.20294\nNaN\nNaN\nNaN\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.20459\n0.20459\n0.20459\n0.20294\n0.20294\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.19808\n0.17079\n0.17079\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.19808\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.75363\n0.75363\n0.75363\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.52686\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.20459\n0.18122\n0.18122\n0.20459\n0.20459\n0.20459\n0.52686\n0.52686\n0.52686\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.18417\n0.18417\n0.18417\n0.18417\nNaN\nNaN\nNaN\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.195\n0.195\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.195\n0.195\n0.195\n0.195\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.17119\n0.17119\n0.17952\n0.17952\n0.18417\n0.18417\n0.18417\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.195\n0.195\n0.195\n0.195\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.17119\n0.17119\n0.17952\nNaN\n0.18417\n0.18417\nNaN\nNaN\n0.18417\n0.18417\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17952\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18417\n0.18417\n0.18417\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18417\n0.18417\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18417\nNaN\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1843\n0.1843\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.753\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\n0.19772\n0.18785\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\n0.17119\n0.17119\n0.17105\n0.17119\n0.753\nNaN\nNaN\nNaN\n0.18785\n0.17105\n0.18785\n0.18417\n0.195\n0.195\n0.18417\n0.18417\n0.17119\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\n0.17105\n0.17105\n0.17105\n0.17105\n0.753\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.52633\n0.18417\n0.195\n0.195\n0.18417\n0.18417\n0.18417\n0.18417\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.1843\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\n0.17105\n0.17105\n0.17105\n0.753\n0.753\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.52633\n0.18417\n0.195\n0.195\n0.195\n0.18417\n0.18417\n0.18417\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.17822\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\n0.17105\n0.17105\n0.17105\n0.17105\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.52633\n0.17952\n0.195\n0.195\n0.195\n0.18417\n0.18417\n0.18417\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\nNaN\n0.52633\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.16653\n0.16653\n0.16653\n0.52633\n0.52633\n0.52633\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\nNaN\n0.18478\n0.17952\n0.18417\n0.195\n0.18417\n0.18417\n0.18417\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17347\n0.17347\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.175\n0.16653\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\nNaN\n0.19772\n0.19392\n0.18417\n0.18417\n0.18417\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.17119\n0.17347\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.16607\n0.52633\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19772\n0.19392\n0.18417\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.16607\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19772\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19335\n0.19335\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19772\n0.19335\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\n0.52633\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19335\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17105\n0.52633\nNaN\n0.52633\n0.52633\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.18785\n0.19335\n0.19335\n0.19335\n0.17105\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18785\n0.17105\n0.19335\n0.19335\n0.19607\n0.17105\n0.17105\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.753\n0.18785\n0.19494\n0.19494\n0.19925\n0.17105\n0.19607\n0.19607\n0.19607\n0.17105\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.753\n0.753\nNaN\nNaN\nNaN\nNaN\n0.19607\n0.19607\n0.19607\n0.19607\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.52633\n0.753\n0.753\nNaN\nNaN\n0.52633\n0.52633\n0.19607\n0.19607\n0.19607\n0.19607\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\n0.753\n0.753\n0.52633\n0.52633\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.52633\n0.19607\n0.19607\n0.17105\n0.17105\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.17119\n0.17105\n0.18785\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52633\n0.17105\n0.17105\n0.17105\n0.17105\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\n0.17105\nNaN\nNaN\nNaN\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.17119\n0.17119\n0.17119\n0.17105\n0.17105\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.18249\n0.18417\n0.18417\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.18249\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.753\n0.753\n0.753\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.52633\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.17119\n0.195\n0.195\n0.17119\n0.17119\n0.17119\n0.52633\n0.52633\n0.52633\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.20223\n0.20223\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.20223\n0.20223\n0.20223\n0.20223\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.1502\n0.16457\n0.16457\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.20223\n0.20223\n0.20223\n0.20223\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.1502\n0.16457\nNaN\n0.16767\n0.16767\nNaN\nNaN\n0.16767\n0.16767\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16457\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16767\n0.16767\n0.16767\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16767\n0.16767\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16767\nNaN\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16768\n0.16768\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\n0.20584\n0.17167\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\n0.1502\n0.1502\n0.15273\n0.1502\nNaN\nNaN\nNaN\nNaN\n0.17167\n0.15273\n0.17167\n0.16767\n0.20223\n0.20223\n0.16767\n0.16767\n0.1502\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\n0.15273\n0.15273\n0.15273\n0.15273\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16767\n0.20223\n0.20223\n0.16767\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.16768\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\n0.15273\n0.15273\n0.15273\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16767\n0.20223\n0.20223\n0.20223\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.16018\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\n0.15273\n0.15273\n0.15273\n0.15273\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16457\n0.20223\n0.20223\n0.20223\n0.16767\n0.16767\n0.16767\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15605\n0.15605\n0.15605\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17081\n0.16457\n0.16767\n0.20223\n0.16767\n0.16767\n0.16767\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15025\n0.15025\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1599\n0.15605\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20584\n0.20205\n0.16767\n0.16767\n0.16767\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.1502\n0.15025\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1562\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20584\n0.20205\n0.16767\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1562\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20584\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2003\n0.2003\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20584\n0.2003\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2003\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15273\nNaN\nNaN\nNaN\nNaN\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.16767\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17167\n0.2003\n0.2003\n0.2003\n0.15273\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17167\n0.15273\n0.2003\n0.2003\n0.1731\n0.15273\n0.15273\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17167\n0.17676\n0.17676\n0.18422\n0.15273\n0.1731\n0.1731\n0.1731\n0.15273\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1731\n0.1731\n0.1731\n0.1731\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1731\n0.1731\n0.1731\n0.1731\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1731\n0.1731\n0.15273\n0.15273\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.15273\n0.17167\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15273\n0.15273\n0.15273\n0.15273\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\n0.15273\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\n0.1502\n0.1502\n0.1502\n0.15273\n0.15273\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.15889\n0.16767\n0.16767\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.15889\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.1502\n0.20223\n0.20223\n0.1502\n0.1502\n0.1502\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.19161\n0.19161\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.19161\n0.19161\n0.19161\n0.19161\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.16722\n0.15617\n0.15617\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.19161\n0.19161\n0.19161\n0.19161\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.16722\n0.15617\nNaN\n0.15651\n0.15651\nNaN\nNaN\n0.15651\n0.15651\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15617\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15651\n0.15651\n0.15651\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15651\n0.15651\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15651\nNaN\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15732\n0.15732\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\n0.19497\n0.15995\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\n0.16722\n0.16722\n0.17014\n0.16722\nNaN\nNaN\nNaN\nNaN\n0.15995\n0.17014\n0.15995\n0.15651\n0.19161\n0.19161\n0.15651\n0.15651\n0.16722\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\n0.17014\n0.17014\n0.17014\n0.17014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15651\n0.19161\n0.19161\n0.15651\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.15732\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\n0.17014\n0.17014\n0.17014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15651\n0.19161\n0.19161\n0.19161\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.14667\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\n0.17014\n0.17014\n0.17014\n0.17014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15617\n0.19161\n0.19161\n0.19161\n0.15651\n0.15651\n0.15651\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.18228\n0.18228\n0.18228\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16204\n0.15617\n0.15651\n0.19161\n0.15651\n0.15651\n0.15651\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16903\n0.16903\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17797\n0.18228\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19497\n0.19302\n0.15651\n0.15651\n0.15651\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.16722\n0.16903\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.18509\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19497\n0.19302\n0.15651\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.18509\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19497\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18789\n0.18789\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19497\n0.18789\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18789\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17014\nNaN\nNaN\nNaN\nNaN\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.15651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15995\n0.18789\n0.18789\n0.18789\n0.17014\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15995\n0.17014\n0.18789\n0.18789\n0.18175\n0.17014\n0.17014\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15995\n0.17141\n0.17141\n0.17232\n0.17014\n0.18175\n0.18175\n0.18175\n0.17014\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18175\n0.18175\n0.18175\n0.18175\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18175\n0.18175\n0.18175\n0.18175\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18175\n0.18175\n0.17014\n0.17014\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.17014\n0.15995\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17014\n0.17014\n0.17014\n0.17014\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\n0.17014\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\n0.16722\n0.16722\n0.16722\n0.17014\n0.17014\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16338\n0.15651\n0.15651\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16338\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.16722\n0.19161\n0.19161\n0.16722\n0.16722\n0.16722\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.19381\n0.19381\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.19381\n0.19381\n0.19381\n0.19381\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.20582\n0.17743\n0.17743\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.19381\n0.19381\n0.19381\n0.19381\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.20582\n0.17743\nNaN\n0.17692\n0.17692\nNaN\nNaN\n0.17692\n0.17692\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17743\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17692\n0.17692\n0.17692\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17692\n0.17692\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17692\nNaN\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18034\n0.18034\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\n0.19435\n0.17842\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\n0.20582\n0.20582\n0.20712\n0.20582\nNaN\nNaN\nNaN\nNaN\n0.17842\n0.20712\n0.17842\n0.17692\n0.19381\n0.19381\n0.17692\n0.17692\n0.20582\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\n0.20712\n0.20712\n0.20712\n0.20712\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17692\n0.19381\n0.19381\n0.17692\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.18034\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\n0.20712\n0.20712\n0.20712\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17692\n0.19381\n0.19381\n0.19381\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.16887\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\n0.20712\n0.20712\n0.20712\n0.20712\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17743\n0.19381\n0.19381\n0.19381\n0.17692\n0.17692\n0.17692\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.21657\n0.21657\n0.21657\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18037\n0.17743\n0.17692\n0.19381\n0.17692\n0.17692\n0.17692\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.212\n0.212\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.21259\n0.21657\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19435\n0.19583\n0.17692\n0.17692\n0.17692\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.20582\n0.212\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.22157\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19435\n0.19583\n0.17692\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.22157\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19435\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18785\n0.18785\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19435\n0.18785\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18785\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20712\nNaN\nNaN\nNaN\nNaN\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.17692\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17842\n0.18785\n0.18785\n0.18785\n0.20712\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17842\n0.20712\n0.18785\n0.18785\n0.21134\n0.20712\n0.20712\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17842\n0.19437\n0.19437\n0.18725\n0.20712\n0.21134\n0.21134\n0.21134\n0.20712\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21134\n0.21134\n0.21134\n0.21134\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21134\n0.21134\n0.21134\n0.21134\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21134\n0.21134\n0.20712\n0.20712\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.20712\n0.17842\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20712\n0.20712\n0.20712\n0.20712\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\n0.20712\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\n0.20582\n0.20582\n0.20582\n0.20712\n0.20712\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.19727\n0.17692\n0.17692\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.19727\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.20582\n0.19381\n0.19381\n0.20582\n0.20582\n0.20582\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.58983\n0.58983\n0.58983\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.58983\n0.58983\n0.58983\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.58983\n0.58983\n0.58983\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.21113\n0.21113\n0.21113\n0.21113\n0.58983\n0.58983\n0.58983\n0.21113\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21497\n0.21497\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21497\n0.21497\n0.21497\n0.21497\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.23689\n0.23689\n0.21118\n0.21118\n0.21113\n0.21113\n0.21113\n0.21113\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21497\n0.21497\n0.21497\n0.21497\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.23689\n0.23689\n0.21118\n0.62411\n0.21113\n0.21113\n0.58983\n0.58983\n0.21113\n0.21113\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21118\n0.62411\nNaN\n0.58983\n0.58983\n0.58983\n0.21113\n0.21113\n0.21113\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.62411\n0.62411\nNaN\n0.58983\n0.58983\n0.58983\n0.58983\n0.21113\n0.21113\n0.58983\n0.58983\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\n0.62411\n0.62411\nNaN\nNaN\nNaN\n0.58983\n0.58983\n0.21113\n0.58983\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\nNaN\nNaN\nNaN\nNaN\nNaN\n0.58983\nNaN\n0.58983\n0.58983\n0.21659\n0.21659\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.21113\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.75366\n0.23689\n0.21113\n0.62411\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.23689\n0.21113\n0.62411\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\nNaN\n0.2141\n0.2121\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.62411\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\n0.23689\n0.23689\n0.23778\n0.23689\n0.75366\nNaN\nNaN\nNaN\n0.2121\n0.23778\n0.2121\n0.21113\n0.21497\n0.21497\n0.21113\n0.21113\n0.23689\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.62411\n0.62411\n1.2289\n1.2289\n1.2289\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\n0.23778\n0.23778\n0.23778\n0.23778\n0.75366\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.52356\n0.21113\n0.21497\n0.21497\n0.21113\n0.21113\n0.21113\n0.21113\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.21659\n0.58983\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\n0.23778\n0.23778\n0.23778\n0.75366\n0.75366\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.52356\n0.21113\n0.21497\n0.21497\n0.21497\n0.21113\n0.21113\n0.21113\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.20687\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\n0.23778\n0.23778\n0.23778\n0.23778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.52356\n0.21118\n0.21497\n0.21497\n0.21497\n0.21113\n0.21113\n0.21113\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\nNaN\n0.52356\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.22772\n0.22772\n0.22772\n0.52356\n0.52356\n0.52356\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.58983\n0.21259\n0.21118\n0.21113\n0.21497\n0.21113\n0.21113\n0.21113\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.24562\n0.24562\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.24105\n0.22772\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.58983\n0.2141\n0.21645\n0.21113\n0.21113\n0.21113\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.23689\n0.24562\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23239\n0.52356\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2141\n0.21645\n0.21113\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23239\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2141\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20972\n0.20972\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2141\n0.20972\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\n0.52356\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.58983\n0.20972\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23778\n0.52356\nNaN\n0.52356\n0.52356\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21113\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.2121\n0.20972\n0.20972\n0.20972\n0.23778\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2121\n0.23778\n0.20972\n0.20972\n0.2373\n0.23778\n0.23778\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75366\n0.2121\n0.21622\n0.21622\n0.21783\n0.23778\n0.2373\n0.2373\n0.2373\n0.23778\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n1.2289\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75366\n0.75366\nNaN\nNaN\nNaN\nNaN\n0.2373\n0.2373\n0.2373\n0.2373\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n1.2289\n1.2289\nNaN\nNaN\n0.52356\n0.52356\n0.75366\n0.75366\nNaN\nNaN\n0.52356\n0.52356\n0.2373\n0.2373\n0.2373\n0.2373\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n1.2289\n0.75366\n0.75366\n0.52356\n0.52356\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.52356\n0.2373\n0.2373\n0.23778\n0.23778\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.23689\n0.23778\n0.2121\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52356\n0.23778\n0.23778\n0.23778\n0.23778\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\n0.23778\nNaN\nNaN\nNaN\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.23689\n0.23689\n0.23689\n0.23778\n0.23778\nNaN\nNaN\nNaN\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23118\n0.21113\n0.21113\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23118\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.75366\n0.75366\n0.75366\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\n0.62411\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.62411\n0.62411\n0.62411\n0.62411\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\nNaN\nNaN\nNaN\n1.2289\n1.2289\n1.2289\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.52356\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n1.2289\nNaN\nNaN\nNaN\n1.2289\n1.2289\n1.2289\n0.62411\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.23689\n0.21497\n0.21497\n0.23689\n0.23689\n0.23689\n0.52356\n0.52356\n0.52356\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.59032\n0.59032\n0.59032\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.59032\n0.59032\n0.59032\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.59032\n0.59032\n0.59032\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.23097\n0.23097\n0.23097\n0.23097\n0.59032\n0.59032\n0.59032\n0.23097\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23549\n0.23549\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23549\n0.23549\n0.23549\n0.23549\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.24364\n0.24364\n0.23089\n0.23089\n0.23097\n0.23097\n0.23097\n0.23097\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23549\n0.23549\n0.23549\n0.23549\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.24364\n0.24364\n0.23089\n0.62471\n0.23097\n0.23097\n0.59032\n0.59032\n0.23097\n0.23097\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23089\n0.62471\nNaN\n0.59032\n0.59032\n0.59032\n0.23097\n0.23097\n0.23097\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.62471\n0.62471\nNaN\n0.59032\n0.59032\n0.59032\n0.59032\n0.23097\n0.23097\n0.59032\n0.59032\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\n0.62471\n0.62471\nNaN\nNaN\nNaN\n0.59032\n0.59032\n0.23097\n0.59032\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59032\nNaN\n0.59032\n0.59032\n0.23578\n0.23578\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.23097\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.23097\n0.62471\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.24364\n0.23097\n0.62471\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\n0.23483\n0.23218\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.62471\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\n0.24364\n0.24364\n0.24454\n0.24364\nNaN\nNaN\nNaN\nNaN\n0.23218\n0.24454\n0.23218\n0.23097\n0.23549\n0.23549\n0.23097\n0.23097\n0.24364\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.62471\n0.62471\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\n0.24454\n0.24454\n0.24454\n0.24454\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.52504\n0.23097\n0.23549\n0.23549\n0.23097\n0.23097\n0.23097\n0.23097\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.23578\n0.59032\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\n0.24454\n0.24454\n0.24454\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.52504\n0.23097\n0.23549\n0.23549\n0.23549\n0.23097\n0.23097\n0.23097\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.22794\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\n0.24454\n0.24454\n0.24454\n0.24454\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.52504\n0.23089\n0.23549\n0.23549\n0.23549\n0.23097\n0.23097\n0.23097\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\nNaN\n0.52504\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.19737\n0.19737\n0.19737\n0.52504\n0.52504\n0.52504\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.59032\n0.23231\n0.23089\n0.23097\n0.23549\n0.23097\n0.23097\n0.23097\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.25072\n0.25072\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24703\n0.19737\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.59032\n0.23483\n0.2366\n0.23097\n0.23097\n0.23097\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.24364\n0.25072\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.20085\n0.52504\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23483\n0.2366\n0.23097\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.20085\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23483\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2315\n0.2315\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23483\n0.2315\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\n0.52504\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59032\n0.2315\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24454\n0.52504\nNaN\n0.52504\n0.52504\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23097\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.23218\n0.2315\n0.2315\n0.2315\n0.24454\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23218\n0.24454\n0.2315\n0.2315\n0.24356\n0.24454\n0.24454\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23218\n0.20576\n0.20576\n0.23734\n0.24454\n0.24356\n0.24356\n0.24356\n0.24454\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.24356\n0.24356\n0.24356\n0.24356\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.52504\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.52504\n0.24356\n0.24356\n0.24356\n0.24356\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.52504\n0.52504\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.52504\n0.24356\n0.24356\n0.24454\n0.24454\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.24364\n0.24454\n0.23218\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52504\n0.24454\n0.24454\n0.24454\n0.24454\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\n0.24454\nNaN\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\n0.24364\n0.24364\n0.24364\n0.24454\n0.24454\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24345\n0.23097\n0.23097\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24345\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\n0.62471\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.62471\n0.62471\n0.62471\n0.62471\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.52504\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.62471\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.24364\n0.23549\n0.23549\n0.24364\n0.24364\n0.24364\n0.52504\n0.52504\n0.52504\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.59232\n0.59232\n0.59232\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.59232\n0.59232\n0.59232\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.59232\n0.59232\n0.59232\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20734\n0.20734\n0.20734\n0.20734\n0.59232\n0.59232\n0.59232\n0.20734\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.22275\n0.22275\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.22275\n0.22275\n0.22275\n0.22275\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.20444\n0.20444\n0.20747\n0.20747\n0.20734\n0.20734\n0.20734\n0.20734\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.22275\n0.22275\n0.22275\n0.22275\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.20444\n0.20444\n0.20747\n0.62556\n0.20734\n0.20734\n0.59232\n0.59232\n0.20734\n0.20734\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20747\n0.62556\nNaN\n0.59232\n0.59232\n0.59232\n0.20734\n0.20734\n0.20734\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.62556\n0.62556\nNaN\n0.59232\n0.59232\n0.59232\n0.59232\n0.20734\n0.20734\n0.59232\n0.59232\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\n0.62556\n0.62556\nNaN\nNaN\nNaN\n0.59232\n0.59232\n0.20734\n0.59232\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59232\nNaN\n0.59232\n0.59232\n0.21058\n0.21058\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20734\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.75521\n0.20444\n0.20734\n0.62556\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20444\n0.20734\n0.62556\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\nNaN\n0.22326\n0.20943\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.62556\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\n0.20444\n0.20444\n0.2059\n0.20444\n0.75521\nNaN\nNaN\nNaN\n0.20943\n0.2059\n0.20943\n0.20734\n0.22275\n0.22275\n0.20734\n0.20734\n0.20444\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.62556\n0.62556\n1.2297\n1.2297\n1.2297\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\n0.2059\n0.2059\n0.2059\n0.2059\n0.75521\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.52638\n0.20734\n0.22275\n0.22275\n0.20734\n0.20734\n0.20734\n0.20734\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.21058\n0.59232\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\n0.2059\n0.2059\n0.2059\n0.75521\n0.75521\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.52638\n0.20734\n0.22275\n0.22275\n0.22275\n0.20734\n0.20734\n0.20734\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20276\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\n0.2059\n0.2059\n0.2059\n0.2059\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.52638\n0.20747\n0.22275\n0.22275\n0.22275\n0.20734\n0.20734\n0.20734\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\nNaN\n0.52638\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.14564\n0.14564\n0.14564\n0.52638\n0.52638\n0.52638\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.59232\n0.21015\n0.20747\n0.20734\n0.22275\n0.20734\n0.20734\n0.20734\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20819\n0.20819\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2093\n0.14564\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.59232\n0.22326\n0.22389\n0.20734\n0.20734\n0.20734\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20444\n0.20819\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.14698\n0.52638\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.22326\n0.22389\n0.20734\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.14698\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.22326\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21947\n0.21947\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.22326\n0.21947\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\n0.52638\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59232\n0.21947\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2059\n0.52638\nNaN\n0.52638\n0.52638\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20734\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.20943\n0.21947\n0.21947\n0.21947\n0.2059\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20943\n0.2059\n0.21947\n0.21947\n0.20878\n0.2059\n0.2059\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75521\n0.20943\n0.17197\n0.17197\n0.21682\n0.2059\n0.20878\n0.20878\n0.20878\n0.2059\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n1.2297\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75521\n0.75521\nNaN\nNaN\nNaN\nNaN\n0.20878\n0.20878\n0.20878\n0.20878\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n1.2297\n1.2297\nNaN\nNaN\n0.52638\n0.52638\n0.75521\n0.75521\nNaN\nNaN\n0.52638\n0.52638\n0.20878\n0.20878\n0.20878\n0.20878\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n1.2297\n0.75521\n0.75521\n0.52638\n0.52638\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.52638\n0.20878\n0.20878\n0.2059\n0.2059\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.20444\n0.2059\n0.20943\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52638\n0.2059\n0.2059\n0.2059\n0.2059\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\n0.2059\nNaN\nNaN\nNaN\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.20444\n0.20444\n0.20444\n0.2059\n0.2059\nNaN\nNaN\nNaN\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.21058\n0.20734\n0.20734\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.21058\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.75521\n0.75521\n0.75521\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\n0.62556\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.62556\n0.62556\n0.62556\n0.62556\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\nNaN\nNaN\nNaN\n1.2297\n1.2297\n1.2297\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.52638\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n1.2297\nNaN\nNaN\nNaN\n1.2297\n1.2297\n1.2297\n0.62556\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.20444\n0.22275\n0.22275\n0.20444\n0.20444\n0.20444\n0.52638\n0.52638\n0.52638\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.18699\n0.18699\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.18699\n0.18699\n0.18699\n0.18699\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.13858\n0.1549\n0.1549\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.18699\n0.18699\n0.18699\n0.18699\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.13858\n0.1549\nNaN\n0.1551\n0.1551\nNaN\nNaN\n0.1551\n0.1551\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1549\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1551\n0.1551\n0.1551\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1551\n0.1551\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1551\nNaN\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15669\n0.15669\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\n0.18922\n0.15885\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\n0.13858\n0.13858\n0.14079\n0.13858\nNaN\nNaN\nNaN\nNaN\n0.15885\n0.14079\n0.15885\n0.1551\n0.18699\n0.18699\n0.1551\n0.1551\n0.13858\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\n0.14079\n0.14079\n0.14079\n0.14079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1551\n0.18699\n0.18699\n0.1551\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.15669\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\n0.14079\n0.14079\n0.14079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1551\n0.18699\n0.18699\n0.18699\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.14665\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\n0.14079\n0.14079\n0.14079\n0.14079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1549\n0.18699\n0.18699\n0.18699\n0.1551\n0.1551\n0.1551\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16014\n0.1549\n0.1551\n0.18699\n0.1551\n0.1551\n0.1551\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13912\n0.13912\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1465\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18922\n0.18814\n0.1551\n0.1551\n0.1551\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.13858\n0.13912\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18922\n0.18814\n0.1551\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18922\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18386\n0.18386\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18922\n0.18386\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18386\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14079\nNaN\nNaN\nNaN\nNaN\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.1551\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15885\n0.18386\n0.18386\n0.18386\n0.14079\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15885\n0.14079\n0.18386\n0.18386\n0.15535\n0.14079\n0.14079\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15885\nNaN\nNaN\n0.17096\n0.14079\n0.15535\n0.15535\n0.15535\n0.14079\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15535\n0.15535\n0.15535\n0.15535\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15535\n0.15535\n0.15535\n0.15535\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15535\n0.15535\n0.14079\n0.14079\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.14079\n0.15885\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14079\n0.14079\n0.14079\n0.14079\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\n0.14079\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\n0.13858\n0.13858\n0.13858\n0.14079\n0.14079\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.15036\n0.1551\n0.1551\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.15036\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.13858\n0.18699\n0.18699\n0.13858\n0.13858\n0.13858\nNaN\nNaN\nNaN\n"
  },
  {
    "path": "tests/test_data/small_test/time_series/ts_incr_interp0_method1.csv",
    "content": "0.10794\n-0.14015\n-0.12985\n-0.20832\n-0.19689\n-0.21865\n-0.32379\n-0.34774\n-0.15873\n-0.076641\n-0.26196\n-0.24276\n-0.33314\n-0.19914\n0.12157\n0.11318\n0.14037\n-0.051849\n0.24641\n0.28141\n0.44836\n0.37849\n0.54384\n0.30076\n0.47134\n0.17309\n-0.13072\n-0.48569\n-0.31384\n-0.21342\n-0.76913\n-0.64933\n-0.41751\n-0.4844\n-0.16789\n-0.37402\n-0.36123\n-0.16726\n-0.3327\n-0.43268\n-0.21723\n-0.030492\n0.13567\n0.31751\n0.33175\n0.15787\n0.25732\n0.23372\n0.12103\n0.068302\n0.10349\n0.23651\n0.16687\n-0.096049\n0.31609\n0.045421\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30957\n-0.28435\n-0.26568\n0.22012\n0.24349\n-0.59394\n-0.33312\n-0.41072\n-0.092056\n-0.018136\n-0.033772\n-0.13108\n-0.36742\n0.11245\n0.31245\n0.0042441\n-0.51284\n-0.36721\n-0.018175\n0.090556\n0.67904\n0.30548\n0.40916\n0.73893\n0.63335\n0.41562\n0.14624\n-0.14411\n0.0056973\n-0.1851\n-0.25364\n-0.35238\n-0.1842\n-0.27009\n-0.22646\n-0.52306\n-0.49068\n-0.15848\n-0.26433\n0.0063023\n0.11254\n0.019646\n0.17882\n0.31666\n0.3227\n0.22694\n0.26041\n0.15892\n0.091339\n0.061028\n0.055111\n0.1707\n0.16445\n0.27369\n0.78406\n0.22091\n-0.15643\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.44432\n-0.36406\n-0.26123\n-0.32404\n-0.84814\n-0.71875\n-0.75737\n-0.34695\n-0.21235\n0.23895\n0.15028\n-0.057794\n-0.19699\n0.31836\n0.71453\n0.10934\n-0.19793\n-0.076971\n-0.013245\n0.45508\n0.42809\n0.44919\n0.70832\n0.63978\n0.76177\n0.71378\n0.46262\n0.11789\n0.16926\n-0.21523\n0.0098651\n-0.24413\n0.0051661\n-0.10096\n-0.60719\n-0.69284\n-0.40339\n-0.23295\n-0.080396\n-0.016514\n0.077426\n0.15738\n0.095024\n0.34151\n0.22077\n0.12922\n0.12806\n0.1291\n0.077337\n0.15619\n0.2158\n0.16165\n0.13543\n0.44249\n0.52103\n0.33463\n0.18191\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24908\n-0.34837\n-0.28865\n-0.25561\n-0.25362\n-0.21227\n-0.11105\n-0.15452\n-0.2753\n-0.18263\n-0.0041224\n-0.42369\n0.10868\n0.54489\n0.32367\n-0.11956\n-0.12467\n-0.10255\n-0.030068\n0.40145\n0.72036\n0.47857\n0.61548\n0.4105\n0.75147\n0.80175\n0.53712\n0.24819\n0.10901\n-0.14002\n-0.13739\n0.0080518\n0.05414\n-0.011147\n-0.48882\n-0.64822\n-0.55912\n-0.25653\n-0.02914\n-0.0481\n-0.03807\n0.055994\n-0.090484\n-0.036125\n0.072669\n0.021953\n-0.0066221\n0.14774\n0.23163\n0.51512\n0.5958\n0.43334\n0.5584\n0.62914\n0.60038\n0.47683\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21874\n-0.22728\n-0.18816\n-0.19651\n-0.11157\n-0.18187\n-0.11403\n-0.072714\n-0.24001\n-0.43896\n-0.34162\n-0.48041\n-0.386\n-0.1057\n-0.47243\n-0.44876\n-0.38363\n-0.23138\n-0.39068\n0.18355\n0.93971\nNaN\nNaN\n-0.3618\n0.54004\n0.74813\n0.70883\n0.32845\n0.2956\n0.30735\n-0.19688\n-0.09884\n-0.071118\n0.043021\n-0.14993\n-0.30079\n-0.5762\n-0.29487\n-0.051547\n-0.051323\n-0.019376\n0.099454\n-0.069264\n-0.091756\n-0.034689\n-0.010956\n0.15088\n0.37045\n0.32735\n0.30259\n0.55322\n0.50891\n0.40029\n0.70001\n0.51561\n0.53295\n1.083\nNaN\nNaN\n-1.4537\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15324\n-0.2614\n-0.27503\n-0.18561\n-0.044509\n-0.20255\n-0.070248\n0.0080566\n-0.082294\n-0.17449\n-0.13621\n0.052584\n-0.023314\n-0.36646\n-0.58816\n-0.28626\n-0.50226\n-0.44977\n-0.15157\nNaN\nNaN\nNaN\nNaN\n0.026169\n0.45556\n0.83853\n0.79543\n0.57352\n0.42639\n0.47075\n0.18421\n-0.16927\n-0.19102\n-0.1339\n-0.13008\n-0.43852\n-0.59757\n-0.27135\n-0.067051\n0.006018\n0.056751\n-0.03104\n-0.08624\n-0.16671\n-0.54894\n-0.26826\n0.12936\n0.44106\n0.34365\n0.37522\n0.4455\n0.43991\n0.40419\n0.36021\n0.24366\n-0.096098\n0.2149\nNaN\nNaN\n-2.2115\n-1.4105\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27233\n-0.35679\n-0.263\n-0.028101\n0.094522\n-0.13562\n0.10182\n0.14018\n0.14097\n0.11882\n0.041273\n-0.041531\n-0.022936\n-0.30304\n-0.36848\n-0.43378\n-0.47008\n-0.10972\n-0.244\nNaN\nNaN\nNaN\nNaN\n0.082519\n0.27782\n0.63392\n0.81709\n0.88441\n0.50351\n0.43544\n0.03266\n-0.17708\n-0.51004\n-0.33662\n-0.36221\n-0.49768\n-0.52343\n-0.33209\n-0.19903\n-0.047682\n0.13636\n-0.064696\n-0.29241\n-0.46043\n-1.0256\n-0.36216\n0.10341\n0.39076\n0.48607\n0.50347\n0.33771\n0.43023\n0.49753\n0.43161\n0.20837\n-0.16439\n0.11034\nNaN\nNaN\n-1.0166\n-1.5226\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18971\n-0.14419\n-0.15692\n0.09768\n0.35336\n-0.084044\n0.03733\n0.21234\n0.26864\n0.23431\n0.13861\n0.1008\n-0.0065666\n0.058524\n-0.12855\n-0.20354\n-0.09267\n0.081769\n-0.083476\n-0.16246\n-0.040618\n0.13594\n0.12926\n0.29448\n0.41709\n0.63328\n0.77922\n0.88872\n0.53098\n0.43605\n0.25799\n-0.28126\n-0.68516\n-0.34515\n-0.22654\n-0.25115\n-0.22404\n-0.35122\n-0.36882\n-0.38289\n-0.034104\n-0.15049\n-0.34423\n-0.58711\n-1.315\n-0.32939\n0.21073\n0.29852\n0.27139\n0.49919\n0.33502\n0.43602\n0.57974\n0.51854\n0.49644\n0.13972\n0.0009082\n0.35187\n0.32287\n-0.21223\n-1.0922\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.087472\n-0.25519\n-0.25966\n-0.14855\n0.16842\n-0.057221\n-0.046849\n0.2745\n0.31755\n0.30264\n0.20035\n0.22551\n0.23379\n0.15031\n0.11837\n0.16023\n0.16713\n0.28479\n0.073487\n0.11728\n0.11752\n0.089996\n0.30019\n0.43673\n0.4671\n0.70666\n0.76292\n0.60857\n0.43242\n0.29893\n0.28704\n0.16704\n-0.24143\n-0.31663\n-0.17089\n-0.14223\n-0.20427\n-0.36789\n-0.37928\n-0.34564\n-0.3069\n-0.21864\n-0.44317\n-0.7759\n-1.2365\n0.09905\n0.35829\n0.40629\n0.4489\n0.54943\n0.5119\n0.69501\n0.72116\n0.93948\n0.88109\n0.24458\n-0.079656\n0.24923\n0.58836\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.49447\n-0.24986\n-0.12541\n-0.24555\n0.14595\n-0.09942\n-0.098796\n0.036952\n0.20246\n0.30153\n0.26008\n0.26404\n0.32428\n0.20969\n0.31323\n0.34656\n0.28593\n0.43933\n0.37819\n0.4974\n0.42148\n0.40858\n0.58556\n0.42474\n0.28259\n0.5439\n0.82738\n0.75744\n0.42924\n0.21545\n0.26642\n-0.15085\n-0.52795\nNaN\nNaN\n-0.011604\n-0.23104\n-0.29871\n-0.44866\n-0.36696\n-0.24681\n-0.15712\n-0.56589\n-0.71905\n-0.2553\n0.37908\n0.27191\n0.4152\n0.48835\n0.66249\n0.62063\n0.88307\n0.91201\n0.99488\n0.86159\n0.5602\n0.063505\n-0.12827\n0.57481\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.60189\n-0.41849\n-0.23261\n-0.25159\n-0.064361\n-0.14192\n-0.10002\n-0.082979\n0.146\n0.34781\n0.34175\n0.26284\n0.45332\n0.34514\n0.49979\n0.56254\n0.41745\n0.44517\n0.48179\n0.5839\n0.39881\n0.47011\n0.47064\n0.16686\n0.25397\n0.88601\n0.98462\n0.87392\n0.54981\n0.53319\n0.30088\n-0.12602\nNaN\nNaN\nNaN\n-0.057879\n-0.23196\n-0.11753\n-0.56335\n-0.42496\n-0.10418\n-0.10546\n-0.55149\n-0.53524\n-0.12745\n0.2101\n0.24188\n0.42682\n0.64892\n0.64941\n0.61983\n0.88178\n0.87659\n1.0886\nNaN\nNaN\n-0.12738\n-0.15558\n0.4653\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.70136\n-0.59359\n-0.41258\n-0.23972\n-0.20776\n-0.099567\n0.033094\n0.12301\n0.25229\n0.3373\n0.25972\n0.31158\n0.47947\n0.38251\n0.53616\n0.51307\n0.51833\n0.48941\n0.45324\n0.40909\n0.1528\n0.31719\n0.41252\n0.17991\n0.33377\n0.72918\n0.95861\n1.0199\n0.73181\n0.72938\n0.37532\nNaN\nNaN\nNaN\nNaN\n-0.083911\n-0.014391\n-0.17289\n-0.66705\n-0.36188\n0.044679\n-0.15454\n-0.34752\n-0.23326\n-0.043054\n0.16123\n0.35143\n0.64673\n0.92453\n0.90244\n0.77331\n0.92084\n0.73834\nNaN\nNaN\nNaN\n-0.035249\n-0.22187\n0.082325\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.29112\n-0.31418\n-0.23545\n-0.24195\n-0.15226\n0.0025283\n0.079678\n0.11827\n0.20488\n0.22936\n0.30922\n0.3889\n0.38794\n0.4698\n0.5763\n0.64437\n0.70085\n0.56358\n0.41378\n0.31809\n0.22826\n0.30542\n0.072123\n0.21716\n0.35896\n0.39962\n1.0121\n0.86512\n0.60967\n0.6524\n0.48146\nNaN\nNaN\nNaN\nNaN\n-0.27599\n-0.20556\n-0.099611\n-0.40946\n-0.19828\n-0.12401\n-0.64246\n-0.83722\n-0.10429\n0.15066\n0.30298\n0.50693\n0.81441\n1.0213\n1.0242\n0.91973\n0.85317\n0.5291\nNaN\nNaN\nNaN\n-0.71168\n-0.73695\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.092201\n-0.14931\n-0.20161\n-0.076391\n0.0029427\n0.024661\n0.073058\n0.04955\n0.13374\n0.16703\n0.19876\n0.38279\n0.55531\n0.6567\n0.65153\n0.69454\n0.59471\n0.54484\n0.30836\n0.23122\n0.35333\n0.025295\n-0.39927\n-0.21919\n-0.18996\n0.37619\n0.79304\n0.67816\n0.79058\n0.83891\n0.66195\nNaN\nNaN\nNaN\nNaN\n-0.26377\n-0.41809\n-0.04343\n-0.28731\n-0.032083\n-0.12043\n-0.44772\n-0.38574\n0.22138\n0.50879\n0.6471\n0.68066\n0.93595\n1.0251\n1.0349\n0.86057\n0.56356\n0.40983\nNaN\nNaN\nNaN\nNaN\n-0.58069\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.0085996\n-0.23502\n-0.039111\n0.15476\n0.089222\n0.0084089\n0.068403\n0.037583\n0.080442\n0.099466\n0.11536\n0.3361\n0.60137\n0.67985\n0.73026\n0.65015\n0.66461\n0.68708\n0.49202\n0.24483\n0.22547\n-0.40529\n-0.98814\n-0.56631\n-0.37138\n0.40709\n0.58094\n0.73233\n0.97864\n0.94251\n1.0274\nNaN\n-0.18046\n-0.32312\n0.054517\n0.17776\n-0.15393\n0.23041\n0.13035\n0.4312\n0.2873\n-0.24093\n-0.28009\n0.80443\nNaN\nNaN\nNaN\n1.0168\n1.1883\n1.0679\n0.71635\n0.81911\n0.62889\n0.039007\nNaN\nNaN\nNaN\n-0.85321\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.11938\n-0.16388\n0.0055579\n0.28338\n0.22692\n0.14273\n0.19461\n0.088413\n0.061733\n0.21912\n0.22643\n0.37161\n0.54489\n0.58955\n0.67119\n0.425\n0.43351\n0.63577\n0.70481\n0.37493\n0.1536\n-0.11415\n-1.3808\n-1.1685\n-0.43248\n0.22277\n0.65281\n1.2446\n1.1951\n0.86867\n0.48634\n-0.42987\n0.089233\n-0.016646\n-0.0082428\n0.22784\n0.47169\n0.53252\n0.62794\nNaN\nNaN\nNaN\n-0.23702\nNaN\nNaN\nNaN\nNaN\n0.92004\n1.1517\n0.79917\n0.56449\n0.55881\n0.61997\n0.0040117\n-0.15124\n-0.57813\n-1.043\n-1.0061\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1933\n-0.13877\n0.043258\n0.35303\n0.19541\n0.2098\n0.16858\n0.09209\n0.10947\n0.16574\n0.201\n0.32161\n0.52676\n0.56534\n0.58793\n0.31237\n0.28791\n0.47435\n0.58627\n0.45121\n0.10493\n-0.25699\n-0.60956\n-0.91324\n-0.11777\n0.41195\n0.84456\n0.99278\n0.86231\n0.64738\n0.17262\n0.29112\n0.71043\n0.39467\n0.48392\n0.76844\n0.67825\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.033955\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.65153\nNaN\n0.42723\n0.49709\n0.019766\n-0.10493\n-0.26664\n-0.93268\n-1.0192\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26285\n-0.13375\n0.13748\n0.37575\n0.21111\n0.13143\n0.1163\n0.15223\n0.15078\n0.13788\n0.19799\n0.31844\n0.45755\n0.48997\n0.50218\n0.39526\n0.55412\n0.39826\n0.37468\n0.18493\n-0.12629\n-0.5806\n-0.72679\n-0.80355\n-0.0030444\n0.4924\n0.90612\n0.66506\n0.68432\n0.46763\n0.79363\n0.75033\n0.66476\n0.76314\n0.99901\n1.0991\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24232\n-0.065757\n-0.40646\n-0.83672\n-0.72106\n-0.84692\n-0.86851\n-0.31099\n-0.13821\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16146\n-0.0037118\n0.22873\n0.32738\n0.21996\n-0.044018\n0.078549\n0.14923\n0.077686\n0.12542\n0.085389\n0.22814\n0.35339\n0.47443\n0.54075\n0.39028\n0.21846\n0.082135\n0.16629\n0.013025\n-0.2165\n-0.68919\n-0.78578\n-0.50129\n0.069183\n0.47455\n0.60477\n0.59789\n0.9475\n0.73065\n0.78155\n0.91741\n0.92475\n0.87636\n1.0879\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.47447\n-0.19289\n-0.35577\n-0.48005\n-0.18801\n-0.21343\n-0.24934\n0.16747\n0.21993\n-0.17816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.054106\n0.058809\n0.062723\n0.0030016\n0.1768\n0.12473\n0.27518\n0.10285\n-0.10004\n-0.0018909\n0.071661\n0.17777\n0.27527\n0.40655\n0.38189\n0.19903\n-0.09729\n-0.32951\n-0.14931\n-0.098875\n-0.20346\n-0.77816\n-0.57588\n-0.17698\n0.40427\n0.73877\n0.58882\n0.7368\n1.1132\n0.98692\n0.76725\n0.96624\n0.88298\n0.83398\n1.2573\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.90188\n-0.5667\n-0.46747\n-0.21995\n0.15464\n0.2982\n-0.055539\n-0.053423\n0.17259\n-0.17318\n0.018002\n0.2836\n0.4183\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.050485\n0.10567\n-0.0027749\n-0.066788\n0.2917\n0.26939\n-0.074628\n-0.16876\n-0.179\n0.096771\n0.1654\n0.18865\n0.066862\n0.29487\n0.18209\n0.096384\n-0.059433\n-0.12709\n-0.20189\n-0.25354\n-0.26719\n-0.88744\n-0.60032\n0.15847\n0.50127\n1.006\n1.1084\n1.0544\n1.1262\n1.3616\n1.7587\n1.9497\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.61768\n-0.45701\n-0.28967\n-0.31093\n-0.42345\n-0.21902\n0.2761\n0.41229\n0.077558\n0.28848\n0.36489\n0.28875\n0.22139\n0.26546\n0.28391\n0.36798\n0.35108\n0.38315\n0.1699\n-0.011849\n-0.033428\n-0.013776\n-0.1121\n-0.060101\n0.15772\n0.1312\n-0.16943\n-0.11782\n-0.14594\n0.047215\n0.056058\n-0.014421\n0.16025\n0.21276\n0.09191\n0.21199\n0.072461\n-0.14527\n-0.19351\n-0.15007\n0.053273\n-0.57679\n-0.72922\n0.25512\n0.78599\n1.5693\n1.3588\n1.3167\n1.6646\n1.8241\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15206\n-0.30524\n-0.186\n0.1405\n0.040866\n-0.058663\n-0.47022\n-0.012092\n0.29913\n0.13992\n0.45888\n0.51928\n0.23494\n0.15102\n0.055028\nNaN\nNaN\nNaN\nNaN\n0.25576\n0.23544\n-0.023572\n-0.068817\n-0.10828\n-0.22941\n-0.20704\n-0.17836\n-0.22782\n-0.18156\n-0.0049485\n-0.045424\n-0.1104\n-0.062191\n0.10966\n0.1582\n0.18158\n0.34204\n0.21965\n-0.2471\n-0.65047\n-0.091272\n-0.00797\n-0.7316\n-0.60624\n0.1705\n1.057\n1.2683\n1.1895\n1.3258\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20132\n0.11459\n-0.0020187\n-0.1596\n0.10481\n0.29121\n0.22237\n0.2137\n-0.046494\n-0.053859\n-0.080524\n0.11923\n0.41042\n0.64191\n0.26927\n0.35092\n0.28812\nNaN\nNaN\nNaN\nNaN\n0.012418\n0.46604\n-0.080455\n-0.18875\n-0.47822\n-0.35794\n-0.18148\n-0.18513\n-0.28329\n-0.084783\n0.17431\n0.054471\n-0.043918\n0.11507\n0.19424\n0.16675\n0.2379\n0.64211\n0.48895\n-0.064282\n-0.58208\n-0.83377\n-0.48495\n-0.47858\n-0.19526\n0.43832\n0.81211\n1.0348\n0.87759\n0.84446\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.031718\n0.20772\n0.14908\n0.077906\n-0.019005\n0.09175\n0.2079\n0.30977\n0.51631\n0.28399\n-0.017074\n-0.1196\n0.029779\n0.11961\n0.52929\n0.10067\n0.36384\n0.4451\nNaN\nNaN\n-0.087699\n0.064676\n0.087831\n0.17485\n-0.31281\n-0.37628\n-0.40283\n-0.27123\n-0.22961\n-0.0076368\n-0.13291\n0.064802\n0.16113\n0.12776\n0.00027543\n0.096396\n0.27193\n0.09691\n0.13694\n0.55388\n0.62034\n0.076537\n-0.46845\n-0.69349\n-0.31461\n-0.16636\n-0.051529\n0.54744\n0.85413\n1.26\n1.0766\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.36052\n-0.15637\n0.53664\n0.20753\n0.27988\n0.19119\n0.10741\n0.048573\n0.36813\n0.80581\n0.63713\n0.18408\n0.14543\n0.16432\n-0.042749\n-0.13613\n0.10307\n0.39872\n0.29626\n-0.0053173\n-0.28112\n0.15715\n0.30355\n0.33659\n0.25989\n-0.50465\n-0.37234\n-0.23113\n-0.20319\n-0.30145\n0.055194\n0.029583\n-0.0040722\n0.095982\n0.041757\n0.024016\n0.21731\n0.27466\n0.30549\n0.39447\n0.52921\n0.87201\n0.1259\n-0.23306\n-0.25008\n0.040164\n-0.015087\n0.017326\n0.35424\n1.0016\n1.3335\n1.3372\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.46035\n-0.23265\n0.4406\n0.13926\n0.31299\n0.33676\n-0.016304\n-0.11797\n0.18862\n0.7937\n0.78323\n0.55462\n0.35215\n0.3522\n0.10642\n0.012554\n0.35781\n0.5613\n0.22213\n0.14139\n0.0701\n0.34666\n0.51322\n0.69835\n0.38459\n-0.4362\n-0.26886\n-0.14943\n-0.11479\n-0.2078\n-0.042751\n0.010541\n-0.034707\n0.030979\n-0.0052833\n-0.058539\n-0.020902\n0.20779\n0.39416\n0.48716\n0.56693\n0.57858\n0.10302\n0.067966\n-0.14895\n-0.049806\n0.067696\n0.32064\n0.53944\n1.0661\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15705\n-0.15172\n-0.059385\n0.36611\n-0.72328\n-0.13478\n0.32416\n0.24866\n0.64251\n0.74289\n0.76691\n0.77117\n0.84313\n0.62316\n0.44172\n0.17606\n-0.0073558\n0.56442\n0.48471\n0.53575\n0.28229\n0.3161\n0.52851\n0.63976\n0.63725\n0.40673\n-0.3909\n-0.14937\n-0.20782\n-0.16345\n-0.21234\n-0.16504\n-0.17224\n-0.03294\n-0.0077076\n-0.11617\n-0.086923\n-0.017948\n0.10157\n0.26445\n0.46535\n0.50355\n0.30586\n0.0022164\n-0.094852\n-0.0426\n0.023405\n0.11481\n0.22358\n0.48426\n1.0552\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.93634\nNaN\nNaN\nNaN\nNaN\n0.36076\n0.097926\n0.060484\n-0.083225\n0.15841\n-0.7228\n0.03784\n0.4246\n0.96341\n0.82268\n0.80673\n0.65878\n-0.075135\n0.71681\n0.38394\n0.28452\n-0.23026\n0.087063\n0.69256\n0.62877\n0.76611\n0.49825\n0.55757\n0.64741\n0.67021\n0.46126\n0.068079\n-0.24257\n-0.13462\n-0.27344\n-0.23308\n-0.11372\n-0.15724\n-0.19686\n-0.15057\n-0.19437\n-0.0954\n-0.039452\n-0.0026013\n-0.0068214\n0.24669\n0.3332\n0.40512\n0.30701\n0.093821\n-0.1812\n-0.044221\n0.17092\n0.19767\n0.020436\n0.54321\n1.4107\n2.0463\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.80677\n0.5931\n0.39214\n0.24472\n0.0096203\n-0.13073\n-0.009992\n0.050881\n0.0053582\n0.44246\n0.93392\n0.55501\n0.60553\n0.28261\n-0.40867\n-0.2335\n0.15414\n0.21948\n-0.099598\n0.23515\n0.58661\n0.70838\n0.6233\n0.49712\n0.37546\n0.64944\n0.50447\n0.1602\n-0.62264\n-0.2347\n-0.21894\n-0.16249\n-0.26401\n-0.26681\n-0.20654\n-0.094994\n-0.19649\n-0.33301\n-0.1065\n-0.081996\n-0.076556\n-0.11791\n0.093738\n0.24617\n0.28697\n0.11948\n-0.0057919\n-0.033977\n-0.41457\n-0.34291\n0.14579\n0.22158\n0.91512\n1.7749\n1.951\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4556\nNaN\nNaN\n1.4214\n1.0117\n0.79968\n0.85045\n0.32819\n-0.075291\n-0.25474\n-0.48526\n-0.34577\n-0.35163\n-0.088827\n0.34804\n0.028679\n-0.060393\n0.083464\n-0.074425\n-0.37467\n-0.44548\n-0.038848\n-0.022659\n0.13244\n0.38141\n0.39352\n0.28686\n0.33105\n0.34211\n0.298\n0.19797\n0.084197\n-0.90615\n-0.32872\n-0.30215\n-0.30976\n-0.35391\n-0.42997\n-0.226\n-0.16686\n-0.18034\n-0.25559\n-0.14374\n-0.12548\n-0.21223\n-0.20687\n-0.1114\n0.07762\n-0.029565\n-0.10595\n-0.28576\n-0.30927\n-0.36246\n-0.47243\n-0.0043054\n0.3597\n1.3006\n1.8288\n2.1579\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8869\n3.3528\n2.3024\n1.7923\n0.9728\n0.81585\n0.71761\n0.56673\n0.079429\n-0.024988\n-0.19965\n-0.39298\n-0.33074\n-0.33401\n-0.24619\n-0.25569\n-0.22825\n-0.24156\n-0.28538\n-0.52261\n-0.63785\n-0.056004\n0.17652\n0.16348\n0.020076\n-0.1207\n0.039179\n0.2942\n0.25374\n-0.032745\n0.11528\n-0.075831\n-0.44291\n-0.45497\n-0.34488\n-0.36249\n-0.23741\n-0.30931\n-0.31073\n-0.32831\n-0.21078\n-0.20966\n-0.28079\n-0.17264\n-0.37692\n-0.56278\n-0.34506\n-0.19448\n-0.18165\n-0.35886\n-0.51771\n-0.48646\n-0.4348\n-0.45164\n-0.29791\n0.37081\n0.9929\n1.6218\n2.3917\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.1215\n3.1295\n2.2066\n1.7136\n0.80319\n0.45091\n0.49053\n0.43402\n0.24934\n-0.016532\n-0.33776\n-0.35158\n-0.23466\n-0.15222\n-0.14491\n-0.55357\n-0.65827\n-0.40817\n-0.42965\n-0.74522\n-0.83468\n-0.28188\n0.05204\n0.022895\n-0.016664\n-0.18033\n-0.035771\n-0.0075234\n0.20711\n-0.17609\n-0.31764\n-0.17585\n-0.052653\n-0.34899\n-0.34298\n-0.36226\n-0.16793\n-0.32532\n-0.34224\n-0.36929\n-0.31945\n-0.28598\n-0.34748\n-0.39214\n-0.424\n-0.57688\n-0.40816\n-0.39893\n-0.24227\n-0.38747\n-0.90796\n-1.0659\n-0.41035\n-0.2563\n-0.39144\n0.14886\n0.96975\n1.3469\n1.2611\n0.88945\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4709\n2.067\n1.9383\n1.1035\n0.8405\n-0.1625\n0.15033\n0.22899\n-0.02051\n-0.13248\n-0.26905\n-0.25688\n-0.17296\n-0.18336\n-0.25497\n-0.46601\n-0.96056\n-0.52997\n-0.33507\n-0.39016\n-0.63191\n-0.66609\n-0.22841\n-0.053608\n0.020398\n-0.001761\n-0.080222\n-0.16302\n-0.36817\n-0.41576\n-0.19789\n0.035146\n0.076637\n-0.31365\n-0.42014\n-0.43847\n-0.25524\n-0.22178\n-0.20139\n-0.37932\n-0.37079\n-0.24289\n-0.26481\n-0.45035\n-0.4481\n-0.4319\n-0.39349\n-0.43098\n-0.25943\n-0.2769\n-0.52851\n-0.85056\n-0.73857\n-0.2404\n-0.41284\n0.095025\n0.977\n1.1433\n0.21431\n0.056404\n-0.13413\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7159\n1.7366\n1.247\n1.2179\n0.81441\n-0.11176\n0.1555\n0.15956\n-0.25928\n-0.45666\n-0.51039\n-0.19671\n-0.16714\n-0.26167\n-0.55917\n-0.45831\n-0.4691\n-0.37214\n-0.14369\n-0.13926\n-0.1661\n-0.30102\n-0.34331\n-0.10352\n-0.16635\n-0.1057\n-0.2428\n-0.27528\n-0.40573\n-0.21726\n-0.067567\n0.14162\n0.063596\n-0.3538\n-0.50138\n-0.47511\n-0.36994\n-0.14822\n-0.10782\n-0.27011\n-0.23116\n-0.23298\n-0.23103\n-0.55931\n-0.41647\n-0.30048\n-0.48469\n-0.59562\n-0.44454\n-0.23937\n-0.58676\n-0.52638\n-0.96228\n-0.93333\n-0.55716\n0.098627\n0.5374\n0.699\n0.36326\n-0.072414\n0.18883\nNaN\nNaN\n0.79811\n1.3893\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8054\n1.3916\n1.1413\n1.128\n0.11836\n-0.45655\n-0.055094\n-0.27632\n-0.50289\n-0.74261\n-0.47241\n-0.10078\n-0.11454\n-0.26806\n-0.28799\n-0.32778\n-0.28191\n-0.24116\n-0.15293\n-0.21144\n-0.29168\n-0.29308\n-0.34029\n-0.21685\n-0.31659\n-0.41668\n-0.71198\n-0.7883\n-0.33007\n-0.13465\n-0.041877\n0.20152\n0.11371\n-0.40694\n-0.4452\n-0.413\n-0.4715\n-0.17025\n0.059856\n-0.096139\n-0.14538\n-0.26203\n-0.2505\n-0.49937\n-0.32137\n-0.32247\n-0.51032\n-0.61043\n-0.32957\n-0.34343\n-0.74912\n-0.90831\n-0.79748\n-0.64167\n-0.38834\n-0.019304\n0.43347\n0.038056\n-0.37422\n-0.15831\n0.54237\n0.54275\n0.99348\n1.168\n1.182\n1.2422\n1.422\nNaN\nNaN\nNaN\nNaN\n1.2205\n1.3909\n0.91803\n0.78606\n0.80736\n-0.26475\n-0.84476\n-0.45594\n-0.45809\n-0.54724\n-0.63079\n-0.38241\n0.072159\n0.078498\n0.12253\n-0.089188\n-0.36031\n-0.35694\n-0.21805\n-0.18298\n-0.13961\n-0.14051\n-0.32112\n-0.4062\n-0.6008\n-0.74427\n-0.59635\n-0.5859\n-0.58651\n-0.52466\n-0.48008\n-0.20777\n0.10921\n-0.070917\n-0.2948\n-0.37838\n-0.48106\n-0.43874\n-0.32436\n0.0051592\n0.011417\n-0.13045\n-0.23145\n-0.25647\n-0.38742\n-0.23521\n-0.2731\n-0.38782\n-0.5146\n-0.45684\n-0.78731\n-1.3681\n-1.0548\n-0.74494\n-0.56709\n-0.025644\n0.14069\n0.3379\n0.96794\n0.31289\n0.39856\n0.54797\nNaN\n0.38356\n0.77194\n0.38505\n1.1852\n1.8051\nNaN\nNaN\nNaN\n0.81142\n1.1159\n1.1396\n0.84452\n0.32601\n0.42156\n-0.093399\n-0.81391\n-0.59755\n-0.0073073\n-0.23068\n-0.48862\n-0.25988\n0.1022\n0.20084\n0.22494\n-0.16644\n-0.17495\n-0.2682\n-0.26482\n-0.2084\n-0.040243\n-0.075404\n-0.29665\n-0.47706\n-0.60984\n-0.72257\n-0.53768\n-0.23936\n-0.30357\n-0.58827\n-0.49345\n-0.23734\n-0.055748\n-0.23978\n-0.44059\n-0.35736\n-0.34669\n-0.35024\n-0.23853\n-0.03219\n-0.040948\n-0.23125\n-0.29289\n-0.33119\n-0.36487\n-0.28211\n-0.23376\n-0.2522\n-0.39497\n-0.55383\n-0.85198\n-1.4007\n-0.91527\n-0.78158\n-0.50163\n-0.29155\n0.29752\n0.15368\n0.27915\n0.36839\n0.36636\nNaN\nNaN\n0.1663\n0.44554\n0.55038\n0.99922\n1.4946\nNaN\nNaN\nNaN\n0.68097\n0.78323\n1.0444\n0.65139\n-0.1102\n-0.26989\n-0.43333\n-0.58904\n-0.18385\n0.089584\n-0.012814\n-0.36355\n-0.24758\n-0.006654\n0.30377\n0.21978\n-0.02397\n-0.077425\n-0.15546\n-0.17948\n-0.119\n0.029214\n-0.069985\n-0.20244\n-0.4988\n-0.61183\n-0.54756\n-0.30473\n-0.054098\n-0.056433\n-0.1639\n-0.16003\n-0.13498\n-0.11229\n0.0080884\n-0.35345\n-0.32228\n-0.2095\n-0.099428\n-0.047258\n-0.021847\n-0.078191\n-0.27444\n-0.37757\n-0.36876\n-0.2955\n-0.20244\n-0.091258\n0.047109\n-0.068915\n-0.50561\n-0.653\n-1.3291\n-0.35372\n-0.46603\n-0.49676\n-0.083138\n0.26684\n0.023537\n-0.10121\n0.083076\n0.15494\nNaN\nNaN\nNaN\n0.13472\n0.4324\n0.57224\n0.85375\nNaN\n0.94542\n0.79899\n0.41148\n0.50916\n0.92098\n0.27767\n-0.10172\n-0.082937\n0.033467\n-0.081109\n-0.21336\n-0.23671\n-0.29033\n-0.26078\n-0.04567\n0.12959\n0.27812\n0.12338\n-0.19015\n-0.16488\n-0.090029\n-0.02546\n0.12913\n0.12925\n0.08304\n-0.14987\n-0.4152\n-0.50901\n-0.36223\n-0.14563\n0.041241\n0.094481\n0.060683\n-0.16707\n-0.29581\n-0.10202\n0.17417\n-0.27663\n-0.2498\n-0.092198\n0.05804\n0.14812\n0.082497\n-0.10512\n-0.087884\n-0.19947\n-0.22096\n-0.10941\n0.015232\n0.10375\n0.33064\n0.49065\n-0.17155\nNaN\nNaN\nNaN\n-0.47428\n-0.73242\n-0.18826\n0.085705\n-0.57122\n-0.3922\n-0.12201\n0.11506\nNaN\nNaN\nNaN\n0.1128\n0.35877\n0.18727\n0.34071\n0.51642\n0.66878\n0.47807\n0.31211\n0.34453\n0.14333\n-0.17187\n-0.17371\n0.090484\n0.16704\n0.034416\n-0.11454\n-0.13001\n-0.073854\n0.037749\n0.050417\n0.14555\n0.11284\n-0.054526\n-0.29845\n-0.15674\n0.083863\n0.12217\n0.16328\n0.17924\n0.14001\n-0.12669\n-0.26281\n-0.30265\n-0.30759\n-0.11226\n-0.0053312\n0.16514\n0.19115\n-0.098734\n-0.27626\n-0.13986\n0.098304\n-0.049195\n0.051479\n0.10801\n0.058758\n0.10449\n0.15917\n-0.079288\n0.057928\n0.0021936\n-0.030149\n0.097752\n0.19217\n0.41257\n0.82655\n0.68349\n0.025749\nNaN\nNaN\nNaN\n-0.58667\n-0.69291\n-0.96606\n-0.6628\n-0.59426\n-0.27301\n-0.18185\n-0.094284\nNaN\nNaN\nNaN\n-0.10284\n0.03572\n0.074485\n0.30865\n0.82909\n0.61621\n0.36148\n0.18865\n0.081835\n-0.093573\n-0.22523\n-0.24609\n0.038355\n0.21577\n0.17757\n0.00068488\n-0.038678\n-0.15344\n-0.094727\n0.069359\n0.13506\n0.064799\n-0.035389\n-0.018608\n0.13267\n0.14842\n-0.045222\n0.13814\n0.17545\n0.18415\n-0.049253\n-0.14367\n-0.14079\n-0.12058\n0.037181\n0.081699\n0.27677\n0.2932\n0.021307\n-0.094938\n0.074377\n0.13055\n0.11075\n0.22966\n0.20937\n0.079122\n0.22231\n0.21321\n0.21301\n0.11894\n0.16467\n0.10097\n0.24381\n0.39704\n0.49697\n0.53803\n0.38906\n0.33826\n-0.12858\nNaN\nNaN\n-0.75153\n-0.6371\n-0.68152\n-0.64334\n-0.46443\n-0.10924\n-0.10142\n-0.16613\n-0.05878\n-0.18985\n-0.17301\n-0.50806\n-0.27491\n-0.036478\n0.23734\n0.72853\n0.60976\n0.53588\n0.349\n0.19732\n0.20815\n-0.044665\n-0.043285\n0.10091\n0.23317\n0.28953\n0.042969\n-0.31903\n-0.61712\n-0.49433\n0.03895\n0.12594\n0.10442\n0.35129\n0.13145\n0.13515\n0.17106\n-0.075025\n0.076264\n0.12752\n0.21123\n0.054969\n-0.18463\n-0.064112\n0.08951\n0.19688\n0.17639\n0.19684\n0.3567\n0.024277\n0.071641\n0.33859\n0.22122\n0.00446\n0.12577\n0.19208\n0.21363\n0.32588\n0.36258\n0.36226\n0.22009\n0.18989\n0.18986\n0.19452\n0.32039\n0.4156\n0.49702\n0.40135\n0.34762\n-0.10008\n-0.76205\n-1.0116\n-0.77493\n-0.6356\n-0.47444\n-0.39927\n-0.30692\n-0.077938\n-0.14329\n-0.15765\n-0.11878\n-0.064543\n-0.093017\n-0.22811\n-0.26551\n-0.16631\n0.054906\n0.45869\n0.53545\n0.41626\n0.57898\n0.37551\n0.22367\n0.13223\n0.30472\n0.29325\n0.3259\n0.31911\n0.28487\n-0.30352\n-0.79606\n-1.0428\n-0.2836\n-0.030684\n0.51054\n0.48122\n0.26246\n0.12429\n0.30315\n0.21611\n0.13998\n0.0799\n0.077901\n0.015188\n-0.1068\n0.1638\n0.22009\n0.20239\n0.15951\n0.40148\n0.45028\n0.088491\n0.15682\n0.16399\n0.10422\n-0.065814\n0.18376\n0.35421\n0.38993\n0.53558\n0.62726\n0.54698\n0.55047\n0.3042\n0.15927\n0.14305\n0.23702\n0.26572\nNaN\nNaN\nNaN\nNaN\n-0.33802\n-0.72096\n-0.7078\n-0.37484\n-0.42867\n-0.56499\n-0.37313\n-0.095627\n-0.15173\n-0.071703\n0.062443\n-0.044917\n-0.14303\n-0.17873\n-0.14649\n-0.26684\n-0.18087\n-0.025781\n0.15312\n0.29389\n0.6088\n0.45217\n0.44375\n0.38192\n0.48279\n0.44131\n0.080154\n0.049626\n0.36362\n0.13194\n-0.60567\n-0.56935\n-0.58516\n-0.27415\n0.015646\n0.18216\n0.043234\n0.10618\n0.39112\n0.067631\n-0.022132\n0.070915\n-0.026341\n0.014438\n0.14973\n0.31981\n0.15719\n0.036111\n0.20406\n0.56172\n0.4842\n0.26949\n0.10192\n0.024112\n0.1227\n0.15384\n0.2837\n0.44141\n0.54615\n0.62232\n0.70028\n0.74096\n0.86297\n0.69219\n0.49696\n0.2999\n0.2887\n0.38902\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.6003\n-0.55665\n-0.49397\n-0.45099\n-0.6583\n-0.49798\n-0.22165\n-0.13337\n-0.12537\n-0.048108\n-0.19356\n-0.22957\n-0.26197\n-0.22192\n-0.043622\n-0.15239\n-0.13584\n0.11552\n0.36508\n0.82255\n0.75614\n0.65371\n0.60653\n0.63398\n0.4732\n-0.31974\n-0.39537\n-0.13382\n0.42513\n-0.36752\n-0.28916\n-0.27691\n-0.23157\n-0.26425\n-0.26381\n-0.24057\n-0.05454\n0.068832\n-0.13173\n-0.071998\n0.11679\n0.13861\n0.10683\n0.2483\n0.29022\n0.19809\n0.021491\n0.32839\n0.73663\n0.51691\n0.38478\n0.015649\n-0.0055507\n-0.012015\n0.35654\n0.27747\n0.49489\n0.74238\n0.81616\n1.1495\n1.125\n1.1899\n0.97822\n0.70637\n0.42732\n0.27351\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.70595\n-0.58951\n-0.65977\n-0.64629\n-0.65315\n-0.56787\n-0.60413\n-0.3312\n-0.25503\n-0.16458\n-0.19092\n-0.25024\n-0.15472\n-0.03951\n0.0034203\n-0.013175\n0.076411\n0.1784\n0.6379\n0.87407\n0.64466\n0.68825\n0.74911\n0.61215\n-0.3354\n-0.80962\n-0.62475\n0.21541\n-0.1945\n-0.16011\n-0.21185\n-0.23922\n-0.15459\n-0.30417\n-0.2781\n-0.096879\n0.076789\n-0.2471\n-0.21467\n0.13353\n-0.045369\n-0.032483\n0.024822\n0.20186\n0.18674\n0.23465\n0.36223\n0.57416\n0.43611\n0.41092\n-0.23409\n-0.31328\nNaN\n0.58356\n0.33588\n0.75634\n1.0346\n1.1734\n1.4974\n1.7158\n1.6538\n1.0418\n0.81107\n0.57283\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76022\n-0.49686\n-0.56487\n-0.50211\n-0.61631\n-0.57648\n-0.48811\n-0.41405\n-0.20295\n-0.10736\n-0.18094\n-0.15217\n-0.099777\n0.036646\n0.052292\n0.0074516\n-0.1655\n-0.14963\n0.21294\n0.50383\n0.42707\n0.59469\n0.48166\n0.061866\n-0.37817\n-0.82909\n-0.83117\n-0.2096\n-0.23616\n-0.13965\n-0.29909\n-0.13358\n0.11837\n0.19231\n0.13604\n0.075695\n0.063197\n-0.25787\n-0.24401\n0.12832\n-0.30929\n-0.12896\n-0.36629\n-0.19573\n0.31145\nNaN\nNaN\n0.30883\n0.31594\n0.49597\nNaN\nNaN\nNaN\n0.13766\n0.0090557\n-0.0020751\n-0.0089499\n0.003817\n-0.046782\n-0.12497\n-0.050775\n0.046851\n0.042173\n-0.049241\n-0.024917\n-0.07014\n-0.004966\n0.079632\n0.081134\n0.10957\n0.068196\n0.18573\n0.1639\n0.25594\n0.28262\n0.31411\n0.10372\n0.18656\n0.10644\n-0.055678\n-0.21451\n-0.12565\n-0.1122\n-0.25318\n-0.22124\n-0.074649\n-0.073022\n0.027376\n-0.092228\n-0.090778\n-0.046011\n-0.11241\n-0.14245\n-0.061421\n0.073848\n0.14789\n0.18843\n0.20365\n0.11938\n0.16262\n0.1357\n0.082418\n0.036893\n0.030334\n0.14016\n0.10037\n-0.037235\n0.10803\n0.020693\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.056618\n-0.036831\n-0.0405\n0.15363\n0.19098\n-0.15965\n-0.16893\n-0.091297\n0.045902\n0.068433\n0.069071\n0.027492\n-0.081349\n0.080281\n0.12693\n-0.037878\n-0.14007\n-0.07586\n0.06235\n0.13187\n0.44408\n0.25262\n0.23366\n0.32796\n0.21163\n0.16699\n0.070813\n-0.083371\n0.012755\n-0.078334\n-0.032018\n-0.067403\n0.077632\n0.019821\n0.016547\n-0.1554\n-0.14239\n-0.04256\n-0.072716\n0.097227\n0.082526\n0.088006\n0.14903\n0.26163\n0.22182\n0.1474\n0.14652\n0.088929\n0.045195\n0.029668\n0.035568\n0.088127\n0.086057\n0.12525\n0.32681\n0.10866\n-0.095688\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10227\n-0.064893\n-0.032105\n-0.0377\n-0.14418\n-0.07742\n-0.15902\n-0.10093\n-0.03619\n0.16965\n0.16832\n0.12184\n0.079318\n0.15928\n0.27846\n-0.0060788\n-0.011331\n0.067564\n0.045488\n0.26567\n0.28513\n0.22067\n0.34545\n0.29742\n0.26994\n0.30616\n0.21786\n0.050719\n0.10931\n-0.062788\n0.03655\n-0.055736\n0.24208\n0.21186\n-0.1792\n-0.23365\n-0.095699\n-0.05429\n0.013399\n0.030721\n0.059975\n0.11844\n0.086651\n0.21588\n0.14527\n0.1163\n0.12341\n0.091871\n0.058903\n0.093781\n0.14169\n0.063419\n0.035387\n0.24102\n0.26641\n0.1007\n0.039789\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.017433\n-0.054939\n-0.04272\n-0.01927\n-0.0091886\n0.0069055\n0.067216\n0.035347\n0.0012285\n0.035954\n0.090066\n-0.021265\n0.35548\n0.26015\n0.1947\n0.023136\n0.048027\n0.065617\n0.099522\n0.25816\n0.4245\n0.16494\n0.28627\n0.25455\n0.34514\n0.35197\n0.2016\n0.12359\n0.024243\n-0.23006\n-0.0084637\n0.04701\n0.28471\n0.24409\n-0.060993\n-0.13534\n-0.16711\n-0.064757\n0.045486\n-0.0074669\n-0.0042353\n0.060491\n0.017218\n-0.0040197\n0.048989\n0.082458\n0.064574\n0.1314\n0.18708\n0.31619\n0.34055\n0.22874\n0.291\n0.35497\n0.25186\n0.14583\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.013989\n-0.01675\n-0.0065306\n-0.00019075\n0.066438\n0.026631\n0.085511\n0.11714\n0.016887\n-0.10369\n-0.050807\n-0.11966\n-0.12988\n0.042454\n-0.11952\n-0.12952\n-0.047161\n0.070189\n0.030542\n0.16461\n0.5322\nNaN\nNaN\n-0.096496\n0.28116\n0.31271\n0.20838\n0.052938\n0.069774\n0.061891\n-0.0082395\n0.125\n0.19049\n0.19016\n0.14345\n-0.051168\n-0.22574\n-0.10148\n0.017726\n-0.0046436\n0.014967\n0.10082\n0.030916\n0.029286\n0.020614\n0.060942\n0.15601\n0.27022\n0.25371\n0.23846\n0.32241\n0.27731\n0.19898\n0.36026\n0.23247\n0.22161\n0.44686\nNaN\nNaN\n-0.62439\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.039525\n-0.028497\n-0.070221\n0.011882\n0.11433\n0.034463\n0.098087\n0.15863\n0.08577\n0.018334\n0.025088\n0.12575\n0.10126\n-0.061912\n-0.18248\n-0.014543\n-0.09679\n-0.082533\n-0.090883\nNaN\nNaN\nNaN\nNaN\n0.014102\n0.14287\n0.28647\n0.25308\n0.19653\n0.15332\n0.18922\n0.12154\n0.093628\n0.0013148\n-0.0073757\n0.12095\n-0.038911\n-0.20604\n-0.061758\n0.020558\n0.040542\n0.045972\n0.022633\n0.024199\n-0.033414\n-0.21645\n-0.039612\n0.16217\n0.26707\n0.22678\n0.26337\n0.26063\n0.23789\n0.19278\n0.18856\n0.13299\n0.062345\n0.19403\nNaN\nNaN\n-0.85959\n-0.70588\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.035474\n-0.077229\n-0.039544\n0.10703\n0.16671\n0.062834\n0.17388\n0.17033\n0.17139\n0.10601\n0.078647\n0.063436\n0.052098\n-0.036529\n-0.099738\n-0.12004\n-0.13768\n-0.036163\n-0.034444\nNaN\nNaN\nNaN\nNaN\n-0.0321\n0.060283\n0.20034\n0.2676\n0.35678\n0.19892\n0.18004\n0.039505\n0.052858\n-0.16467\n-0.088583\n-0.045027\n-0.1017\n-0.17139\n-0.091338\n-0.015085\n0.065879\n0.086226\n0.033818\n-0.052234\n-0.16464\n-0.45161\n-0.082689\n0.15191\n0.26382\n0.29771\n0.30494\n0.21603\n0.25281\n0.30286\n0.24475\n0.1247\n-0.02663\n0.11898\nNaN\nNaN\n-0.39275\n-0.68331\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.0053246\n0.031188\n0.012127\n0.14119\n0.27407\n0.033539\n0.093739\n0.17959\n0.19628\n0.13112\n0.079083\n0.095139\n0.0040248\n0.040181\n-0.040415\n-0.057689\n0.023269\n0.058355\n0.0060433\n-0.05293\n-0.033889\n0.03224\n0.021686\n0.10832\n0.12998\n0.21351\n0.26592\n0.29417\n0.18396\n0.15537\n0.11015\n-0.14564\n-0.33853\n-0.1191\n-0.058607\n-0.049836\n-0.074976\n-0.072187\n-0.055944\n-0.088743\n0.043672\n0.015841\n-0.06918\n-0.18118\n-0.57012\n-0.069901\n0.21368\n0.23849\n0.21244\n0.30615\n0.25808\n0.29639\n0.33274\n0.26515\n0.24466\n0.13247\n0.024372\n0.23515\n0.16648\n-0.040525\n-0.41816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.030618\n-0.055873\n-0.09054\n-0.0046784\n0.14053\n-0.0047583\n0.01139\n0.18312\n0.22251\n0.1879\n0.12246\n0.12339\n0.13687\n0.084369\n0.084938\n0.12499\n0.092093\n0.13389\n0.032923\n0.033676\n0.048456\n0.050237\n0.13311\n0.19746\n0.17961\n0.24164\n0.23448\n0.14106\n0.12881\n0.094067\n0.10838\n0.081779\n-0.17471\n-0.14102\n-0.071173\n-0.066743\n-0.059713\n-0.068645\n-0.067985\n-0.071274\n-0.06788\n-0.023092\n-0.12449\n-0.26328\n-0.47738\n0.14802\n0.27922\n0.31444\n0.32198\n0.35497\n0.33395\n0.38597\n0.37427\n0.45074\n0.44757\n0.19479\n0.042387\n0.17378\n0.35907\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16382\n-0.047566\n-0.0071806\n-0.05429\n0.16199\n-0.012384\n-0.0060735\n0.10376\n0.18059\n0.19712\n0.15135\n0.14623\n0.16963\n0.087294\n0.14229\n0.18692\n0.143\n0.23528\n0.20742\n0.246\n0.1863\n0.14796\n0.22922\n0.17484\n0.10629\n0.16924\n0.25327\n0.22202\n0.13385\n0.04903\n0.039952\n-0.08557\n-0.22081\nNaN\nNaN\n-0.019149\n-0.14344\n-0.10198\n-0.11367\n-0.073899\n-0.03031\n0.022154\n-0.17942\n-0.27359\n-0.056079\n0.30392\n0.25149\n0.3232\n0.34451\n0.44101\n0.4103\n0.49023\n0.46884\n0.47107\n0.43659\n0.36962\n0.20715\n0.10186\n0.38745\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18561\n-0.13181\n-0.054131\n-0.050356\n0.042421\n-0.030018\n-0.0018999\n0.010104\n0.13436\n0.21796\n0.16288\n0.083808\n0.16625\n0.13675\n0.21766\n0.26015\n0.23485\n0.24231\n0.23286\n0.24108\n0.12586\n0.13288\n0.094754\n0.025618\n0.11412\n0.38303\n0.32606\n0.25802\n0.18546\n0.16272\n0.045307\n-0.11249\nNaN\nNaN\nNaN\n-0.024374\n-0.086855\n0.0049547\n-0.15249\n-0.088552\n0.090109\n0.14376\n-0.14193\n-0.13588\n0.06629\n0.24906\n0.26954\n0.33629\n0.42259\n0.50201\n0.447\n0.52519\n0.46489\n0.50345\nNaN\nNaN\n0.12556\n0.079518\n0.33944\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.23828\n-0.25191\n-0.15435\n-0.058032\n-0.043956\n-0.010202\n0.066502\n0.12552\n0.17156\n0.19118\n0.1104\n0.1327\n0.19524\n0.14155\n0.22225\n0.23244\n0.29022\n0.22645\n0.13967\n0.12302\n0.013673\n0.034113\n0.027592\n-0.01681\n0.10635\n0.29622\n0.32474\n0.32589\n0.19415\n0.20894\n0.092667\nNaN\nNaN\nNaN\nNaN\n-0.02085\n0.018265\n-0.013508\n-0.10847\n-0.022774\n0.13428\n0.0042279\n-0.025069\n0.080038\n0.1835\n0.27228\n0.35462\n0.48971\n0.55507\n0.58144\n0.51085\n0.54309\n0.41108\nNaN\nNaN\nNaN\n0.12633\n0.080427\n0.075489\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.029099\n-0.087783\n-0.032715\n-0.02747\n-0.0070752\n0.052769\n0.099962\n0.085406\n0.15503\n0.14911\n0.13455\n0.16728\n0.11659\n0.17704\n0.241\n0.26021\n0.28916\n0.19927\n0.076216\n0.069365\n0.023612\n0.035004\n-0.04163\n0.072664\n0.097342\n0.091569\n0.34123\n0.26402\n0.14604\n0.2008\n0.14941\nNaN\nNaN\nNaN\nNaN\n-0.096162\n-0.050366\n0.011276\n-0.0034901\n0.117\n0.12921\n-0.11375\n-0.18249\n0.15964\n0.25978\n0.32597\n0.43139\n0.56592\n0.60949\n0.61974\n0.5941\n0.50382\n0.33546\nNaN\nNaN\nNaN\n-0.16783\n-0.2237\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.083059\n0.022258\n-0.0095914\n0.068385\n0.070539\n0.078942\n0.10004\n0.096219\n0.10938\n0.083538\n0.080312\n0.16328\n0.27317\n0.3327\n0.2949\n0.25546\n0.15644\n0.17596\n0.053843\n0.01802\n0.077066\n-0.10406\n-0.1715\n-0.089349\n-0.15366\n0.044301\n0.21076\n0.16143\n0.23619\n0.27245\n0.20264\nNaN\nNaN\nNaN\nNaN\n-0.14977\n-0.17134\n0.018897\n0.04483\n0.16825\n0.16415\n0.044364\n0.084283\n0.30469\n0.38849\n0.48701\n0.51031\n0.62717\n0.63415\n0.62068\n0.57824\n0.39903\n0.30007\nNaN\nNaN\nNaN\nNaN\n-0.093326\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10885\n-0.014639\n0.08681\n0.17459\n0.12219\n0.073154\n0.11543\n0.080492\n0.051929\n0.066477\n0.043098\n0.15184\n0.30387\n0.3435\n0.34745\n0.23574\n0.26494\n0.26254\n0.15896\n0.058356\n-0.022458\n-0.25689\n-0.42372\n-0.24165\n-0.22096\n0.06103\n0.10908\n0.18034\n0.29595\n0.27761\n0.27785\nNaN\n-0.028987\n-0.061583\n0.029501\n-0.063647\n-0.054237\n0.23305\n0.27309\n0.44214\n0.40412\n0.12286\n0.15449\n0.63229\nNaN\nNaN\nNaN\n0.68898\n0.76915\n0.68772\n0.50617\n0.53151\n0.4072\n0.10439\nNaN\nNaN\nNaN\n-0.29434\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.038179\n0.049348\n0.11583\n0.2274\n0.19154\n0.15221\n0.17777\n0.099259\n0.060114\n0.15903\n0.11775\n0.17855\n0.24703\n0.25177\n0.32136\n0.16358\n0.14924\n0.22171\n0.2337\n0.10813\n-0.017813\n-0.074141\n-0.59379\n-0.46848\n-0.2421\n-0.023322\n0.12543\n0.36945\n0.36221\n0.27306\n0.0073752\n-0.23409\n0.038115\n0.086981\n0.06182\n0.0488\n0.29785\n0.4314\n0.50268\nNaN\nNaN\nNaN\n0.11893\nNaN\nNaN\nNaN\nNaN\n0.65591\n0.78863\n0.57845\n0.39988\n0.43193\n0.39595\n0.16905\n0.089537\n-0.12083\n-0.35586\n-0.36189\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.016762\n0.044834\n0.1057\n0.24513\n0.14919\n0.17875\n0.13153\n0.084253\n0.11222\n0.067804\n0.088324\n0.15215\n0.22466\n0.24314\n0.30724\n0.11578\n0.04517\n0.10926\n0.17225\n0.12097\n-0.003705\n-0.10293\n-0.28451\n-0.37782\n-0.11434\n0.089052\n0.20465\n0.30991\n0.24012\n0.23962\n-0.085158\n-0.079508\n0.17019\n0.28832\n0.27554\n0.30764\n0.46277\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.11335\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.48214\nNaN\n0.24597\n0.35322\n0.15015\n0.097056\n0.0074432\n-0.29132\n-0.31442\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.051971\n0.00056957\n0.11189\n0.24174\n0.14184\n0.10487\n0.079371\n0.071563\n0.12038\n0.040286\n0.08504\n0.11673\n0.19565\n0.189\n0.21365\n0.090125\n0.1555\n0.099685\n0.092693\n0.013615\n-0.096382\n-0.24432\n-0.36193\n-0.4187\n-0.064832\n0.10859\n0.26611\n0.25714\n0.23848\n0.15088\n0.18844\n0.21179\n0.17226\n0.25167\n0.36491\n0.46317\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.011373\n0.087759\n-0.077205\n-0.28718\n-0.26711\n-0.34701\n-0.304\n-0.076812\n0.05156\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.00044195\n0.035662\n0.15438\n0.2146\n0.14099\n0.005265\n0.055842\n0.06824\n0.035424\n0.042336\n0.017463\n0.026301\n0.10054\n0.16765\n0.18564\n0.097041\n0.036335\n0.009417\n0.017636\n-0.034055\n-0.11617\n-0.29015\n-0.36249\n-0.31725\n-0.047164\n0.11218\n0.17457\n0.19989\n0.29514\n0.24284\n0.13584\n0.31728\n0.38139\n0.33194\n0.41407\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.091872\n0.012714\n-0.055146\n-0.12923\n-0.012535\n-0.012059\n-0.02881\n0.10875\n0.091449\n-0.080628\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.041904\n0.076201\n0.065834\n0.038679\n0.11321\n0.078539\n0.15699\n0.055814\n-0.054429\n-0.041889\n-0.016809\n-0.0080941\n0.060004\n0.16635\n0.1619\n0.026058\n-0.12045\n-0.18704\n-0.11977\n-0.05535\n-0.15417\n-0.32739\n-0.28715\n-0.16514\n0.11107\n0.24516\n0.15555\n0.19189\n0.32967\n0.26298\n0.11292\n0.34486\n0.35288\n0.30431\n0.4624\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28647\n-0.17454\n-0.14683\n-0.012841\n0.18553\n0.24028\n0.061166\n0.022823\n0.065989\n-0.10476\n-0.056825\n0.12842\n0.23589\nNaN\nNaN\nNaN\nNaN\nNaN\n0.060521\n0.11175\n0.036316\n-0.016195\n0.18469\n0.17639\n-0.031693\n-0.092873\n-0.127\n-0.012533\n0.031679\n0.032383\n-0.018975\n0.13941\n0.091896\n0.0024718\n-0.11315\n-0.121\n-0.09035\n-0.2188\n-0.25375\n-0.40018\n-0.32508\n-0.043317\n0.13317\n0.35115\n0.38532\n0.38246\n0.35845\n0.45209\n0.67267\n0.84349\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19508\n-0.066766\n-0.0050375\n-0.036142\n-0.13345\n-0.011209\n0.26314\n0.30183\n0.10552\n0.14412\n0.17959\n0.21511\n0.11671\n0.070403\n0.21048\n0.28084\n0.27637\n0.35238\n0.1739\n0.080568\n0.035166\n0.056142\n-0.0083167\n-0.014278\n0.077234\n0.084606\n-0.090295\n-0.0892\n-0.13907\n-0.027379\n-0.012536\n-0.067709\n0.015039\n0.084583\n0.060271\n0.080066\n0.033863\n-0.036796\n-0.015894\n-0.15159\n-0.084633\n-0.35488\n-0.3849\n0.0015449\n0.24326\n0.56855\n0.49752\n0.53409\n0.57325\n0.65202\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.074748\n0.024947\n0.059949\n0.17059\n0.11392\n0.06185\n-0.12188\n0.067445\n0.18246\n0.10774\n0.21069\n0.23974\n0.1719\n0.094432\n0.023157\nNaN\nNaN\nNaN\nNaN\n0.093897\n0.10374\n0.03774\n0.023393\n-0.011221\n-0.090738\n-0.073618\n-0.085252\n-0.14189\n-0.10655\n-0.045413\n-0.064749\n-0.1002\n-0.084293\n0.01659\n0.03964\n0.038404\n0.10668\n0.015003\n-0.11174\n-0.28735\n-0.057927\n-0.084437\n-0.53278\n-0.41005\n-0.029042\n0.36679\n0.47092\n0.45937\n0.58383\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.29705\n0.20362\n0.15337\n0.049978\n0.13912\n0.23865\n0.20454\n0.18033\n0.045353\n0.021549\n-0.0043036\n0.11873\n0.22323\n0.30799\n0.19071\n0.18113\n0.21681\nNaN\nNaN\nNaN\nNaN\n-0.016076\n0.11783\n0.045613\n-0.032078\n-0.17683\n-0.12959\n-0.060149\n-0.0925\n-0.17464\n-0.056854\n0.064676\n-0.0071602\n-0.064366\n0.019394\n0.044616\n0.038182\n0.0497\n0.2557\n0.15987\n-0.089502\n-0.33332\n-0.47249\n-0.28125\n-0.29052\n-0.16065\n0.09194\n0.24044\n0.40246\n0.36105\n0.3771\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.20987\n0.29845\n0.25063\n0.18852\n0.087193\n0.10766\n0.1599\n0.20602\n0.29152\n0.18498\n0.028939\n-0.063876\n0.031419\n0.12586\n0.29107\n0.080161\n0.16514\n0.22053\nNaN\nNaN\n-0.28467\n-0.072104\n0.011\n-0.017013\n-0.091415\n-0.10279\n-0.12942\n-0.088119\n-0.098336\n-0.0024962\n-0.081505\n0.015967\n0.070414\n0.034585\n-0.043855\n-0.0095821\n0.0593\n-0.031149\n-0.044821\n0.18403\n0.19969\n0.0076763\n-0.27118\n-0.41276\n-0.20063\n-0.13619\n-0.079195\n0.18174\n0.32949\n0.36777\n0.28387\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.01807\n0.1082\n0.43983\n0.25801\n0.24427\n0.15425\n0.1068\n0.10061\n0.18647\n0.34415\n0.31298\n0.10044\n0.066142\n0.11284\n0.032851\n-0.054309\n0.04338\n0.19246\n0.17285\n-0.00069654\n-0.10553\n-0.0024128\n0.082758\n0.11148\n0.085978\n-0.14698\n-0.10605\n-0.052601\n-0.066745\n-0.1432\n0.025848\n-0.023411\n-0.015615\n0.021291\n-0.010446\n-0.060792\n0.035401\n0.060749\n0.075349\n0.16104\n0.17242\n0.28506\n0.024276\n-0.1538\n-0.16759\n-0.06178\n-0.080966\n-0.054357\n0.096328\n0.37683\n0.50511\n0.42526\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.045227\n0.055577\n0.39683\n0.2497\n0.2727\n0.22464\n0.062614\n0.045966\n0.13572\n0.34768\n0.34174\n0.24376\n0.15636\n0.18788\n0.071871\n0.010146\n0.18036\n0.28333\n0.12397\n0.14843\n0.10317\n0.11982\n0.15944\n0.25841\n0.13899\n-0.12693\n-0.050266\n0.0036736\n-0.03117\n-0.10766\n-0.041556\n-0.0369\n-0.063558\n-0.010693\n-0.076463\n-0.079191\n-0.035441\n0.043598\n0.12765\n0.20137\n0.18395\n0.17618\n-0.0032145\n-0.0040861\n-0.10068\n-0.07809\n-0.039921\n0.085757\n0.17404\n0.40153\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.089466\n0.13171\n0.201\n0.38288\n0.022448\n0.16715\n0.21256\n0.18327\n0.33161\n0.37384\n0.36946\n0.35636\n0.39605\n0.29439\n0.18335\n0.095797\n-0.051668\n0.26259\n0.22284\n0.23445\n0.24635\n0.21426\n0.20453\n0.2398\n0.18503\n0.10122\n-0.077232\n0.011337\n-0.0076963\n-0.061721\n-0.09339\n-0.083725\n-0.097042\n-0.023949\n-0.0069332\n-0.072178\n-0.057492\n-0.016358\n0.019934\n0.039115\n0.17474\n0.18509\n0.093485\n-0.051027\n-0.092597\n-0.059719\n-0.0546\n-0.014428\n0.050935\n0.1745\n0.40251\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52411\nNaN\nNaN\nNaN\nNaN\n0.2001\n0.22963\n0.18955\n0.12105\n0.22368\n0.014385\n0.1366\n0.22229\n0.47383\n0.40592\n0.38844\n0.32248\n0.021591\n0.35939\n0.20476\n0.10806\n-0.093337\n0.016897\n0.29337\n0.28285\n0.32715\n0.26585\n0.23799\n0.24306\n0.25649\n0.13202\n-0.084567\n-0.025427\n0.002255\n-0.082254\n-0.083162\n-0.026754\n-0.060161\n-0.083322\n-0.056186\n-0.077259\n-0.038919\n-0.042802\n-0.02817\n-0.030891\n0.042615\n0.15125\n0.17061\n0.10955\n0.017267\n-0.09739\n-0.063929\n0.059066\n0.075596\n-0.030786\n0.18023\n0.52737\n0.75955\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.44496\n0.33532\n0.38668\n0.28009\n0.10402\n0.042741\n0.13365\n0.097917\n0.044544\n0.23467\n0.46034\n0.28785\n0.2927\n0.14165\n-0.18712\n-0.072301\n0.047846\n0.018488\n-0.0321\n0.10673\n0.2285\n0.26052\n0.21148\n0.21412\n0.094412\n0.1874\n0.16251\n-0.0096614\n-0.42049\n-0.0039464\n-0.047399\n-0.047065\n-0.072702\n-0.0854\n-0.061861\n0.026333\n-0.033186\n-0.12783\n-0.07419\n-0.068129\n-0.021419\n-0.048527\n0.014159\n0.13188\n0.13635\n0.053906\n0.01637\n-0.01132\n-0.18917\n-0.18774\n0.031185\n-0.0021712\n0.28525\n0.67305\n0.71353\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.70917\nNaN\nNaN\n0.73853\n0.51166\n0.42726\n0.47357\n0.29176\n0.09457\n-0.0050494\n-0.10748\n-0.057031\n-0.059781\n0.070632\n0.231\n0.093042\n0.04586\n0.034183\n-0.063151\n-0.13468\n-0.1516\n-0.038143\n0.014304\n0.051657\n0.13449\n0.13258\n0.086438\n0.12288\n0.083595\n-0.0076864\n5.3155e-05\n-0.033112\n-0.54933\n-0.044836\n-0.067279\n-0.093699\n-0.11724\n-0.19294\n-0.072813\n-0.03216\n-0.05089\n-0.071435\n-0.059824\n-0.059021\n-0.078264\n-0.11658\n-0.077431\n0.068749\n0.048176\n0.03573\n-0.070031\n-0.10557\n-0.1712\n-0.27385\n-0.11575\n0.026048\n0.43113\n0.67965\n0.7829\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0042\n1.7736\n1.2132\n0.9489\n0.44099\n0.38461\n0.51093\n0.38173\n0.13856\n0.086423\n0.018953\n-0.046268\n-0.040666\n-0.02929\n-0.0048938\n-0.01158\n-0.041806\n-0.066301\n-0.11768\n-0.22797\n-0.23124\n-0.043461\n0.071943\n0.051517\n-0.0043313\n-0.05409\n0.046152\n0.14122\n0.086074\n-0.086023\n0.014069\n-0.078743\n-0.28116\n-0.089529\n-0.082652\n-0.079863\n-0.046175\n-0.090144\n-0.086731\n-0.095416\n-0.065449\n-0.065941\n-0.12426\n-0.061555\n-0.15097\n-0.24608\n-0.16554\n-0.05385\n-0.0079379\n-0.064871\n-0.1955\n-0.22309\n-0.16769\n-0.16781\n-0.14374\n0.051887\n0.33201\n0.62793\n0.94379\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2227\n1.6659\n1.131\n0.85423\n0.34939\n0.3907\n0.3568\n0.3529\n0.23647\n0.11957\n-0.0052223\n-0.018561\n0.036546\n0.052554\n0.038319\n-0.17267\n-0.21882\n-0.12567\n-0.15854\n-0.33632\n-0.41401\n-0.13308\n0.0059343\n-0.0087185\n-0.026737\n-0.10385\n0.022587\n0.035971\n0.11955\n-0.090515\n-0.13834\n-0.082556\n-0.046693\n-0.077327\n-0.053494\n-0.045344\n0.0013459\n-0.065973\n-0.077527\n-0.10858\n-0.1099\n-0.090628\n-0.13668\n-0.14867\n-0.15853\n-0.25236\n-0.186\n-0.14086\n-0.067453\n-0.096778\n-0.30869\n-0.34566\n-0.12487\n-0.12514\n-0.21267\n-0.026578\n0.34829\n0.52908\n0.39601\n0.31194\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8188\n0.96413\n0.92472\n0.50669\n0.38597\n0.10612\n0.21944\n0.27661\n0.1408\n0.068393\n0.0038429\n0.01173\n0.051\n0.029008\n-0.03524\n-0.16494\n-0.38051\n-0.19884\n-0.11634\n-0.19945\n-0.35855\n-0.33799\n-0.11615\n-0.054878\n-0.019053\n-0.041749\n-0.031379\n-0.058951\n-0.13649\n-0.20864\n-0.10518\n0.0079893\n-0.013211\n-0.031226\n-0.062958\n-0.069477\n-0.0068168\n0.02706\n-0.0087749\n-0.10682\n-0.11262\n-0.053387\n-0.078417\n-0.1563\n-0.16647\n-0.1776\n-0.15572\n-0.13839\n-0.07774\n-0.038608\n-0.11851\n-0.25105\n-0.20915\n-0.10998\n-0.21688\n-0.053527\n0.36773\n0.37666\n0.067506\n0.090091\n-0.080996\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3833\n0.84413\n0.59634\n0.61288\n0.44088\n0.14583\n0.26004\n0.25013\n0.1003\n-0.1025\n-0.14266\n0.049161\n0.041922\n-0.021968\n-0.1807\n-0.18785\n-0.20893\n-0.15796\n-0.057086\n-0.10136\n-0.14163\n-0.25198\n-0.20262\n-0.076132\n-0.086365\n-0.07977\n-0.10931\n-0.13059\n-0.19888\n-0.17746\n-0.11365\n-0.03826\n-0.059116\n-0.029671\n-0.10723\n-0.09225\n-0.04256\n0.051371\n0.013406\n-0.047188\n-0.043521\n-0.041976\n-0.10251\n-0.22294\n-0.15318\n-0.11328\n-0.17884\n-0.22775\n-0.15097\n-0.012344\n-0.14915\n-0.14254\n-0.2952\n-0.27135\n-0.19436\n0.0048596\n0.15049\n0.19156\n0.063686\n0.025513\n0.12191\nNaN\nNaN\n0.43094\n0.58422\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.89027\n0.67901\n0.56769\n0.58001\n0.32398\n0.091895\n0.14284\n0.031393\n0.019278\n-0.25118\n-0.12668\n0.075374\n0.047561\n-0.053213\n-0.071324\n-0.11056\n-0.10397\n-0.086348\n-0.049907\n-0.10331\n-0.19193\n-0.22713\n-0.19429\n-0.14722\n-0.19371\n-0.22309\n-0.44352\n-0.49737\n-0.22709\n-0.13392\n-0.10712\n0.023509\n-0.011608\n-0.041401\n-0.048518\n-0.076285\n-0.094035\n0.060696\n0.11369\n0.0459\n0.021744\n-0.040081\n-0.087539\n-0.17708\n-0.094342\n-0.097877\n-0.15916\n-0.2004\n-0.055993\n-0.051872\n-0.2607\n-0.37269\n-0.33631\n-0.27129\n-0.17118\n-0.046551\n0.13761\n-0.042185\n-0.21668\n-0.075505\n0.28415\n0.30412\n0.48659\n0.61034\n0.46845\n0.50737\n0.56799\nNaN\nNaN\nNaN\nNaN\n0.72175\n0.73539\n0.52074\n0.45142\n0.52166\n0.084133\n-0.18318\n-0.07107\n-0.02632\n-0.046907\n-0.20573\n-0.095202\n0.092035\n0.073655\n0.10793\n-0.018717\n-0.12137\n-0.11074\n-0.061696\n-0.073924\n-0.0724\n-0.10245\n-0.18957\n-0.20407\n-0.34773\n-0.35738\n-0.28131\n-0.28575\n-0.28478\n-0.30839\n-0.23403\n-0.11987\n0.060553\n-0.012769\n0.043539\n0.0010422\n-0.053642\n-0.0506\n0.0023779\n0.093438\n0.11158\n0.053741\n0.028669\n-0.051817\n-0.10573\n-0.040828\n-0.054289\n-0.093428\n-0.14582\n-0.13934\n-0.22913\n-0.38395\n-0.39854\n-0.31666\n-0.2795\n-0.057091\n0.035363\n0.11034\n0.30726\n0.16979\n0.20643\n0.28294\nNaN\n0.30124\n0.45543\n0.27567\n0.47788\n0.75242\nNaN\nNaN\nNaN\n0.5319\n0.62643\n0.56758\n0.43865\n0.16594\n0.20747\n0.085104\n-0.21669\n-0.15344\n0.10403\n-0.033464\n-0.21574\n-0.11133\n0.075451\n0.12055\n0.15689\n-0.053368\n-0.066157\n-0.085363\n-0.088599\n-0.086697\n-0.019995\n-0.059256\n-0.17675\n-0.2351\n-0.28132\n-0.3108\n-0.24708\n-0.12403\n-0.14074\n-0.29566\n-0.21603\n-0.082032\n0.019518\n-0.08023\n-0.018191\n0.015076\n0.0024215\n0.016051\n0.043408\n0.093587\n0.10307\n0.031325\n0.015478\n-0.038184\n-0.068716\n-0.052132\n-0.041531\n-0.037802\n-0.09242\n-0.19174\n-0.27507\n-0.41899\n-0.33018\n-0.32512\n-0.2438\n-0.13919\n0.077139\n-0.02142\n0.0038912\n0.16421\n0.23699\nNaN\nNaN\n0.25874\n0.31063\n0.36486\n0.59745\n0.80699\n0.71399\nNaN\nNaN\n0.4433\n0.46708\n0.51058\n0.3446\n0.010602\n-0.030695\n-0.096701\n-0.15228\n-0.015004\n0.08975\n0.045996\n-0.15271\n-0.06631\n0.030391\n0.15364\n0.17032\n0.070368\n0.0069188\n-0.025109\n-0.058093\n-0.065029\n0.0062666\n-0.05372\n-0.12733\n-0.26256\n-0.30032\n-0.23816\n-0.13044\n-0.014872\n-0.014135\n-0.10428\n-0.087222\n-0.0358\n-0.059284\n-0.016389\n0.071234\n0.053868\n0.055302\n0.10897\n0.12941\n0.10124\n0.084445\n0.023628\n-0.039128\n-0.043425\n-0.025547\n-0.0073842\n0.070615\n0.14657\n0.046409\n-0.16998\n-0.24484\n-0.40933\n-0.074255\n-0.026763\n-0.091225\n-0.029024\n0.023373\n-0.15362\n-0.16037\n0.060544\n0.15354\nNaN\nNaN\nNaN\n0.21007\n0.29372\n0.29673\n0.4524\n0.34221\n0.47947\n0.44239\n0.29018\n0.30075\n0.46205\n0.08353\n-0.05631\n0.040262\n0.073107\n0.031659\n-0.048111\n-0.07402\n-0.085645\n-0.089198\n-0.00051669\n0.073431\n0.14223\n0.10433\n0.00044522\n-0.015473\n-0.017459\n-0.0050143\n0.053456\n0.056551\n0.033055\n-0.093268\n-0.22237\n-0.25233\n-0.15714\n-0.064583\n0.037149\n0.058162\n0.0058861\n-0.082128\n-0.1211\n-0.073855\n0.068739\n0.087226\n0.10347\n0.15474\n0.20634\n0.22421\n0.15421\n0.087011\n0.1168\n0.044872\n0.023485\n0.03977\n0.087045\n0.13967\n0.20843\n0.21959\n-0.033818\nNaN\nNaN\nNaN\n-0.055382\n-0.22703\n-0.072317\n-0.010312\n-0.34916\n-0.20182\n0.0046603\n0.090676\nNaN\nNaN\nNaN\n0.176\n0.23885\n0.12695\n0.17857\n0.2842\n0.36588\n0.25154\n0.17674\n0.19231\n0.11659\n-0.075142\n-0.092487\n0.061418\n0.1215\n0.060079\n-0.021957\n-0.068315\n-0.061528\n-0.0082287\n0.040784\n0.083557\n0.07143\n-0.012755\n-0.086326\n-0.024944\n0.067551\n0.084817\n0.077747\n0.071278\n0.047305\n-0.061332\n-0.12902\n-0.14195\n-0.12975\n-0.042304\n0.015751\n0.091968\n0.089128\n-0.017254\n-0.08089\n-0.05258\n0.013872\n0.18471\n0.26882\n0.29446\n0.2267\n0.22363\n0.20787\n0.14663\n0.18872\n0.12433\n0.11134\n0.16831\n0.18397\n0.28629\n0.45991\n0.30161\n0.046094\nNaN\nNaN\nNaN\n-0.15474\n-0.24104\n-0.37906\n-0.26916\n-0.2994\n-0.078387\n0.0086192\n0.05035\nNaN\nNaN\nNaN\n0.028393\n0.10875\n0.12588\n0.20634\n0.40962\n0.31735\n0.19213\n0.10234\n0.052389\n0.0046296\n-0.12557\n-0.13185\n0.044934\n0.13998\n0.11253\n0.049932\n-0.0011797\n-0.080802\n-0.047374\n0.043051\n0.089246\n0.035849\n-0.038752\n-0.0014781\n0.090268\n0.11542\n0.029865\n0.04234\n0.060999\n0.092504\n0.0068443\n-0.059564\n-0.07468\n-0.055206\n0.033263\n0.038509\n0.10198\n0.14376\n0.038404\n-0.026744\n0.046356\n0.027047\n0.25658\n0.30494\n0.29489\n0.25572\n0.27351\n0.26526\n0.26057\n0.19668\n0.1845\n0.19295\n0.25883\n0.31031\n0.34803\n0.36982\n0.30376\n0.26579\n-0.00021394\nNaN\nNaN\n-0.24043\n-0.19967\n-0.2329\n-0.20841\n-0.12148\n0.032255\n0.06391\n0.0074192\n0.048058\n-0.0111\n-0.028937\n-0.18387\n-0.032505\n0.07776\n0.18854\n0.35826\n0.28797\n0.24225\n0.16235\n0.071816\n0.065689\n-0.040181\n-0.047723\n0.088235\n0.10792\n0.12632\n0.023984\n-0.13561\n-0.22455\n-0.16774\n0.023771\n0.08418\n0.074325\n0.15553\n0.056178\n0.068971\n0.11054\n-0.004493\n0.013097\n0.039995\n0.12684\n0.056905\n-0.064177\n-0.025951\n0.042389\n0.099643\n0.073936\n0.085476\n0.16637\n0.015705\n0.061736\n0.16538\n0.08243\n0.21197\n0.25607\n0.27843\n0.31659\n0.37322\n0.37193\n0.34672\n0.24946\n0.25121\n0.26734\n0.25692\n0.3107\n0.33312\n0.34976\n0.23573\n0.21899\n0.092949\n-0.27412\n-0.37017\n-0.21893\n-0.16287\n-0.10962\n-0.085908\n-0.051451\n0.045417\n0.015208\n-0.0082838\n0.017658\n0.028567\n0.0067278\n-0.047636\n-0.026386\n0.045098\n0.12632\n0.25979\n0.2361\n0.19594\n0.26084\n0.18315\n0.12335\n0.083794\n0.12895\n0.16414\n0.17492\n0.10602\n0.079582\n-0.12312\n-0.29039\n-0.37297\n-0.12921\n0.014623\n0.28154\n0.22934\n0.10981\n0.071502\n0.14853\n0.096918\n0.047337\n0.041555\n0.067294\n0.036578\n-0.021339\n0.097914\n0.11996\n0.099715\n0.091332\n0.20365\n0.22274\n0.064228\n0.094899\n0.070237\n0.046594\n0.16928\n0.26038\n0.32359\n0.3793\n0.46516\n0.50654\n0.45007\n0.41615\n0.30126\n0.21737\n0.2392\n0.28927\n0.28661\nNaN\nNaN\nNaN\nNaN\n-0.027998\n-0.20478\n-0.1663\n-0.057138\n-0.073769\n-0.13028\n-0.071096\n0.015865\n-0.032945\n0.032939\n0.10971\n0.04041\n-0.018118\n-0.045938\n-0.063352\n-0.043704\n-0.021622\n-0.014834\n0.067616\n0.11384\n0.26138\n0.23396\n0.23252\n0.20057\n0.2243\n0.22566\n0.069978\n-0.0015213\n0.11061\n0.031287\n-0.21794\n-0.19469\n-0.22224\n-0.084149\n0.062915\n0.11744\n0.047048\n0.063438\n0.17712\n0.067943\n0.036505\n0.078124\n0.017091\n0.017953\n0.098394\n0.20672\n0.10818\n0.020301\n0.094783\n0.29092\n0.2509\n0.14273\n0.037815\n-0.015649\n0.0099404\n0.27138\n0.30351\n0.37845\n0.44992\n0.48097\n0.49965\n0.53899\n0.53823\n0.44625\n0.35223\n0.30858\n0.32255\n0.32147\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18442\n-0.12015\n-0.085485\n-0.066061\n-0.17311\n-0.12678\n-0.042943\n-0.013211\n0.041077\n0.07277\n-0.030083\n-0.057247\n-0.093341\n-0.089497\n0.018055\n-0.032955\n-0.033611\n0.069863\n0.14945\n0.33681\n0.35154\n0.32926\n0.2998\n0.30933\n0.24732\n-0.12098\n-0.19438\n-0.10023\n0.096327\n-0.12026\n-0.090675\n-0.086886\n-0.076639\n-0.090134\n-0.087076\n-0.13227\n-0.032611\n0.052575\n-0.012927\n0.00047806\n0.132\n0.13021\n0.069638\n0.14954\n0.16765\n0.1098\n0.028971\n0.19592\n0.3887\n0.25892\n0.17759\n-0.027679\n-0.045101\n-0.045388\n0.33507\n0.32152\n0.39319\n0.52734\n0.53716\n0.6861\n0.6929\n0.66617\n0.52973\n0.42331\n0.35762\n0.34266\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14338\n-0.13576\n-0.15124\n-0.16907\n-0.1889\n-0.16168\n-0.17715\n-0.038827\n-0.008564\n0.010876\n-0.043504\n-0.057676\n-0.0012298\n0.029335\n0.0036138\n0.006431\n0.065081\n0.073431\n0.26254\n0.37352\n0.33083\n0.32809\n0.36034\n0.3315\n-0.12472\n-0.36662\n-0.29651\n0.03101\n-0.071074\n-0.049764\n-0.07931\n-0.083423\n-0.014499\n-0.12532\n-0.1222\n-0.039442\n0.04596\n-0.031968\n-0.018758\n0.16934\n0.074718\n0.012312\n0.045468\n0.10649\n0.085028\n0.15442\n0.21939\n0.27876\n0.21216\n0.18961\n-0.12348\n-0.15002\nNaN\n0.41464\n0.3462\n0.49754\n0.62831\n0.65256\n0.80914\n0.95784\n0.8482\n0.52732\n0.44561\n0.3896\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.25841\n-0.12122\n-0.1185\n-0.1175\n-0.17146\n-0.16109\n-0.12321\n-0.088633\n0.035238\n0.065698\n-0.014023\n0.0016208\n0.040299\n0.023988\n0.0088292\n-0.021814\n-0.068858\n-0.076187\n0.11256\n0.22023\n0.20012\n0.27016\n0.24529\n0.076972\n-0.14729\n-0.33567\n-0.36274\n-0.10458\n-0.072123\n-0.032863\n-0.094646\n-0.0080769\n0.088647\n0.13268\n0.12627\n0.058972\n0.04524\n-0.046456\n-0.039679\n0.14992\n-0.073083\n-0.040728\n-0.13382\n-0.078749\n0.13277\nNaN\nNaN\n0.15725\n0.19335\n0.24887\nNaN\nNaN\nNaN\n0.19268\n0.062443\n0.046265\n0.058014\n0.066056\n-0.011972\n-0.10613\n0.023364\n0.12367\n0.094258\n0.0081654\n0.044797\n-0.0056648\n0.058582\n0.097379\n0.090447\n0.12487\n0.11939\n0.24046\n0.18788\n0.2777\n0.33737\n0.3291\n0.062193\n0.15377\n0.1082\n-0.058244\n-0.20451\n-0.11083\n-0.11526\n-0.18374\n-0.15894\n0.012214\n0.023987\n0.094565\n-0.036476\n-0.038269\n-0.019755\n-0.082945\n-0.10382\n-0.033826\n0.11186\n0.18195\n0.20542\n0.22218\n0.14234\n0.18058\n0.14713\n0.096727\n0.042661\n0.022203\n0.15508\n0.11603\n-0.022953\n0.097228\n0.037297\n-0.03403\n-0.17333\n-0.39795\n-0.42841\n-0.35244\n-0.089853\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.012767\n0.037131\n0.034625\n0.1918\n0.23162\n-0.07864\n-0.17595\n-0.021329\n0.11061\n0.11842\n0.1365\n0.10875\n-0.0088462\n0.095362\n0.114\n-0.0483\n-0.06403\n-0.007392\n0.11472\n0.19012\n0.51775\n0.3117\n0.23937\n0.29052\n0.14409\n0.12878\n0.053983\n-0.10478\n0.0076747\n-0.072722\n0.019\n0.00011419\n0.18257\n0.10314\n0.096578\n-0.091496\n-0.08174\n-0.01364\n-0.035108\n0.14783\n0.097735\n0.12389\n0.18107\n0.31635\n0.25377\n0.16474\n0.1509\n0.092121\n0.043137\n0.029587\n0.041582\n0.094403\n0.09333\n0.12569\n0.30372\n0.12622\n-0.076024\n-0.31175\n-0.29955\n-0.19879\n-0.15652\n0.023177\n0.15462\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.018805\n0.018973\n0.038577\n0.045503\n0.025675\n0.096378\n-0.0087961\n-0.043107\n0.016571\n0.2016\n0.23851\n0.2279\n0.19493\n0.16924\n0.25754\n-0.014913\n0.049784\n0.13201\n0.081681\n0.28646\n0.32638\n0.2148\n0.32878\n0.26629\n0.19217\n0.25672\n0.18748\n0.032177\n0.10596\n-0.051449\n0.04254\n-0.014415\n0.37337\n0.34818\n-0.10719\n-0.16385\n-0.031737\n-0.011027\n0.053044\n0.051439\n0.069305\n0.14165\n0.11546\n0.23978\n0.16235\n0.13957\n0.14534\n0.10195\n0.066594\n0.098504\n0.15834\n0.056605\n0.023133\n0.25231\n0.28129\n0.081445\n0.030809\n-0.32318\n-0.23993\n0.0015185\n-0.22519\n0.040176\n0.20984\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.057461\n0.030014\n0.028809\n0.05326\n0.066838\n0.084285\n0.15203\n0.11239\n0.092277\n0.12231\n0.15621\n0.10433\n0.53862\n0.27599\n0.23827\n0.090478\n0.11968\n0.13372\n0.15437\n0.28116\n0.45338\n0.11699\n0.26129\n0.26293\n0.30525\n0.30094\n0.14462\n0.10963\n-0.0020939\n-0.33134\n0.02068\n0.067249\n0.41592\n0.36956\n0.045149\n-0.03037\n-0.10143\n-0.023889\n0.085705\n0.0061303\n0.0049301\n0.080784\n0.056722\n0.010157\n0.060136\n0.11561\n0.097525\n0.15949\n0.22234\n0.343\n0.35999\n0.23456\n0.29519\n0.37312\n0.22736\n0.10131\nNaN\nNaN\nNaN\n-0.50115\n-0.35605\n0.072742\n0.28487\nNaN\nNaN\nNaN\n-0.0094805\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10123\n0.055831\n0.056281\n0.064209\n0.14182\n0.10181\n0.17223\n0.20786\n0.10212\n-0.025913\n0.036215\n-0.034991\n-0.072701\n0.1114\n-0.02982\n-0.05749\n0.043648\n0.17981\n0.14505\n0.17858\n0.54354\nNaN\nNaN\n-0.055519\n0.26618\n0.25288\n0.10761\n-0.021719\n0.017864\n-0.00020517\n0.037834\n0.22144\n0.30617\n0.27235\n0.27277\n-0.0032738\n-0.19257\n-0.074613\n0.043757\n0.011326\n0.03148\n0.12443\n0.067516\n0.075202\n0.047235\n0.097848\n0.19711\n0.31549\n0.3027\n0.28941\n0.3442\n0.28959\n0.2055\n0.3622\n0.22252\n0.20162\n0.39712\nNaN\nNaN\n-0.54628\n-0.44943\n-0.0012784\n0.32834\n-0.11372\n-0.26174\n-0.096331\n-0.28356\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11376\n0.040233\n-0.024156\n0.074384\n0.19089\n0.116\n0.17481\n0.24571\n0.16089\n0.083155\n0.08529\n0.18628\n0.17454\n0.014594\n-0.10152\n0.076034\n0.0042649\n0.012777\n-0.099837\nNaN\nNaN\nNaN\nNaN\n0.0017577\n0.071035\n0.18747\n0.15193\n0.13152\n0.10444\n0.14988\n0.12545\n0.19335\n0.057364\n0.032676\n0.23251\n0.057961\n-0.15645\n-0.024196\n0.044919\n0.063925\n0.06013\n0.041523\n0.059935\n-0.006946\n-0.18346\n0.013073\n0.21403\n0.29365\n0.25822\n0.30492\n0.28026\n0.24881\n0.19362\n0.19714\n0.14449\n0.1256\n0.2409\nNaN\nNaN\n-0.67432\n-0.67357\n-0.095182\n-0.14494\n-0.45565\n-0.15869\n-0.048039\n-0.16121\nNaN\nNaN\nNaN\nNaN\nNaN\n0.028466\n-0.013537\n0.015106\n0.17479\n0.23194\n0.1384\n0.24074\n0.22237\n0.22341\n0.13198\n0.11422\n0.11235\n0.091455\n0.031896\n-0.041087\n-0.052348\n-0.068687\n-0.010921\n0.024965\nNaN\nNaN\nNaN\nNaN\n-0.095121\n-0.007147\n0.11092\n0.16205\n0.28611\n0.14495\n0.14099\n0.046276\n0.13325\n-0.10271\n-0.033876\n0.03753\n-0.024689\n-0.12533\n-0.053522\n0.024869\n0.11126\n0.098244\n0.068679\n-0.0020806\n-0.12577\n-0.41153\n-0.030412\n0.20522\n0.30135\n0.32331\n0.32711\n0.23767\n0.27248\n0.33866\n0.27064\n0.14404\n0.0088545\n0.15427\nNaN\nNaN\n-0.31432\n-0.61901\n-0.50138\nNaN\n-0.62529\n-0.40283\nNaN\nNaN\n0.13666\n-0.0083267\nNaN\nNaN\nNaN\n0.066465\n0.091441\n0.061704\n0.18931\n0.32614\n0.077264\n0.13324\n0.21201\n0.22327\n0.13624\n0.081399\n0.11563\n0.014122\n0.050364\n-0.031695\n-0.038796\n0.056582\n0.059753\n0.024657\n-0.047224\n-0.051443\n-0.00028954\n-0.02459\n0.05758\n0.054921\n0.12678\n0.16911\n0.1861\n0.11157\n0.095389\n0.07828\n-0.152\n-0.32659\n-0.087325\n-0.032959\n-0.016102\n-0.061813\n-0.01739\n0.01562\n-0.02908\n0.076603\n0.064618\n-0.0076431\n-0.11006\n-0.50929\n-0.018339\n0.27264\n0.28895\n0.25547\n0.32911\n0.30659\n0.3387\n0.36305\n0.27897\n0.25099\n0.17144\n0.039734\n0.26496\n0.16686\n-0.0035105\n-0.33236\n-0.48196\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10944\n-0.14536\n-0.33176\nNaN\nNaN\n0.077772\n-0.015769\n-0.069956\n0.030175\n0.16992\n0.011972\n0.030079\n0.20152\n0.2489\n0.19974\n0.12421\n0.11869\n0.13958\n0.077971\n0.083785\n0.13494\n0.084349\n0.11497\n0.013218\n0.0039889\n0.029875\n0.033138\n0.09225\n0.15483\n0.1201\n0.15039\n0.12432\n0.023945\n0.053496\n0.038323\n0.064874\n0.059784\n-0.22107\n-0.14259\n-0.077943\n-0.078261\n-0.043558\n-0.0044902\n0.0039236\n-0.010987\n-0.021287\n0.02592\n-0.062105\n-0.18482\n-0.38595\n0.20014\n0.33581\n0.37612\n0.37091\n0.38998\n0.37608\n0.40721\n0.3873\n0.44434\n0.45385\n0.23717\n0.093798\n0.2008\n0.3952\n0.25069\n-0.40079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26638\n-0.15243\nNaN\nNaN\n-0.11088\n-0.0050066\n0.022174\n-0.019411\n0.21598\n0.014898\n0.020653\n0.14808\n0.2145\n0.20358\n0.14714\n0.137\n0.15571\n0.062159\n0.11821\n0.17869\n0.12431\n0.22369\n0.19957\n0.22402\n0.15233\n0.08648\n0.15569\n0.11969\n0.062034\n0.083063\n0.13087\n0.10007\n0.061281\n-0.0064324\n-0.032001\n-0.10781\n-0.21068\n-0.25304\n-0.17083\n-0.03662\n-0.1563\n-0.070922\n-0.046275\n-0.0088621\n0.025273\n0.084514\n-0.10951\n-0.21428\n-0.010805\n0.36451\n0.313\n0.38426\n0.39487\n0.49401\n0.46031\n0.51463\n0.47939\n0.45897\n0.44056\n0.4229\n0.3159\n0.19527\n0.44618\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22459\nNaN\n-0.23477\n-0.065064\n-0.11818\n-0.08577\n-0.018309\n-0.010017\n0.086297\n-0.0039528\n0.022534\n0.030871\n0.15723\n0.22736\n0.14297\n0.036584\n0.11562\n0.096141\n0.18271\n0.23354\n0.22602\n0.22913\n0.20586\n0.18852\n0.055971\n0.04166\n-0.015857\n-0.044292\n0.077694\n0.31502\n0.19505\n0.11758\n0.10492\n0.07442\n-0.04134\n-0.16594\n-0.34929\n-0.092786\n-0.1862\n-0.035461\n-0.073442\n0.040959\n-0.078527\n-0.017125\n0.17691\n0.26488\n-0.053876\n-0.051625\n0.14398\n0.32849\n0.34243\n0.39234\n0.46761\n0.58922\n0.51064\n0.56113\n0.47695\n0.48\nNaN\nNaN\n0.23217\n0.16363\n0.40021\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.069702\n0.17003\n-0.16469\n-0.21877\n-0.11778\n-0.021687\n-0.011268\n0.0078667\n0.086105\n0.14972\n0.18039\n0.18461\n0.07932\n0.093867\n0.14962\n0.091861\n0.18106\n0.20762\n0.28218\n0.19026\n0.066394\n0.052195\n-0.047756\n-0.06677\n-0.11451\n-0.1184\n0.034299\n0.22071\n0.19859\n0.18144\n0.063779\n0.089115\n0.012132\nNaN\nNaN\nNaN\n-0.11467\n-0.0222\n0.025506\n0.025116\n0.020597\n0.070986\n0.20571\n0.058121\n0.069701\n0.19915\n0.29888\n0.37702\n0.44677\n0.56985\n0.5929\n0.64095\n0.56757\n0.57478\n0.42669\nNaN\nNaN\nNaN\n0.21555\n0.18841\n0.098569\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.039923\n-0.043567\n0.018813\n0.026487\n0.034626\n0.082166\n0.12571\n0.089643\n0.17051\n0.15064\n0.10174\n0.12497\n0.052593\n0.12522\n0.19858\n0.2107\n0.23746\n0.13145\n-0.017779\n-0.0080706\n-0.055183\n-0.066819\n-0.13097\n0.017345\n0.015875\n-0.0064337\n0.20841\n0.12963\n0.020014\n0.10399\n0.076506\nNaN\nNaN\nNaN\n-0.10746\n-0.085615\n-0.029344\n0.043041\n0.11377\n0.2469\n0.25334\n0.027915\n-0.023724\n0.28942\n0.36601\n0.42583\n0.52419\n0.63605\n0.64653\n0.66434\n0.65375\n0.53271\n0.36827\nNaN\nNaN\nNaN\n-0.044533\n-0.12431\n-0.28669\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.16048\n0.0808\n0.044584\n0.12789\n0.11003\n0.116\n0.13006\n0.12809\n0.12228\n0.069147\n0.050766\n0.12117\n0.25015\n0.31708\n0.26108\n0.1888\n0.062447\n0.098819\n-0.026141\n-0.062521\n-0.012045\n-0.20683\n-0.18605\n-0.10776\n-0.21845\n-0.073755\n0.065255\n0.024742\n0.11278\n0.15821\n0.10118\nNaN\nNaN\nNaN\nNaN\n-0.17918\n-0.16813\n0.040834\n0.15114\n0.27272\n0.30881\n0.22189\n0.26066\n0.42259\n0.47049\n0.58132\n0.59564\n0.69786\n0.68481\n0.66194\n0.64784\n0.45901\n0.34949\nNaN\nNaN\nNaN\nNaN\n0.024077\n-0.28781\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18287\n0.048839\n0.14751\n0.22378\n0.15943\n0.1095\n0.15665\n0.10814\n0.049311\n0.065832\n0.01625\n0.11867\n0.28491\n0.32414\n0.31898\n0.1677\n0.21007\n0.19038\n0.081218\n-0.0076238\n-0.13372\n-0.31106\n-0.39964\n-0.24261\n-0.26892\n-0.05817\n-0.030878\n0.040329\n0.15272\n0.1307\n0.097823\nNaN\n-0.01971\n-0.026953\n0.0075674\n-0.18028\n-0.060547\n0.29468\n0.38904\n0.5782\n0.56442\n0.28054\n0.3381\n0.76106\n0.42738\n0.57126\n0.23489\n0.77462\n0.84801\n0.75747\n0.57853\n0.58455\n0.447\n0.15655\nNaN\nNaN\nNaN\n-0.20606\n-0.33143\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10222\n0.12608\n0.17832\n0.26908\n0.22919\n0.19427\n0.21873\n0.12482\n0.06559\n0.17174\n0.10051\n0.15529\n0.21491\n0.21031\n0.29668\n0.11509\n0.086286\n0.13659\n0.13395\n0.027897\n-0.10135\n-0.1172\n-0.56213\n-0.41585\n-0.28089\n-0.13361\n-0.023859\n0.18716\n0.19147\n0.14368\n-0.16427\n-0.26559\n0.0084488\n0.11665\n0.076883\n0.0071359\n0.32597\n0.51567\n0.59998\nNaN\nNaN\n-0.055516\n0.2667\n0.25599\n0.67556\n0.68604\n0.37178\n0.75334\n0.89041\n0.6659\n0.45602\n0.49871\n0.43266\n0.26512\n0.18667\n-0.014829\n-0.24512\n-0.26126\n-0.69266\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.033773\n0.1102\n0.15209\n0.27594\n0.17767\n0.2195\n0.1584\n0.10223\n0.12928\n0.042191\n0.066696\n0.13299\n0.1876\n0.20512\n0.2929\n0.070015\n-0.031507\n0.0060058\n0.070922\n0.029284\n-0.067975\n-0.117\n-0.29573\n-0.34476\n-0.17104\n-0.016525\n0.042823\n0.17184\n0.10044\n0.1556\n-0.2104\n-0.25027\n0.023042\n0.31647\n0.27154\n0.23485\n0.52503\nNaN\nNaN\nNaN\nNaN\n-0.093478\n-0.19837\n0.15212\n0.32159\nNaN\nNaN\n0.33905\n0.27679\n0.56031\nNaN\n0.24778\n0.39978\n0.23055\n0.18362\n0.091442\n-0.17446\n-0.18484\n-0.75613\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.0011726\n0.046177\n0.13827\n0.26777\n0.16442\n0.13057\n0.092603\n0.068201\n0.12831\n0.0065107\n0.062637\n0.075691\n0.16276\n0.14491\n0.17753\n0.0094933\n0.061633\n0.018097\n0.0082379\n-0.063864\n-0.14879\n-0.24094\n-0.37826\n-0.43942\n-0.12516\n-0.009517\n0.11839\n0.17895\n0.14329\n0.070734\n0.023085\n0.074292\n0.037009\n0.14574\n0.25458\n0.37947\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.28039\nNaN\nNaN\n0.39146\n0.14731\n0.077993\n0.22846\nNaN\nNaN\nNaN\n0.055269\n0.14919\n-0.011286\n-0.20712\n-0.20524\n-0.29677\n-0.22577\n-0.024769\n0.12926\nNaN\nNaN\n-0.15501\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.055358\n0.064776\n0.17825\n0.2424\n0.16191\n0.025567\n0.065589\n0.061113\n0.025325\n0.016529\n-0.01\n-0.037325\n0.046056\n0.11599\n0.12343\n0.024507\n-0.028248\n-0.029953\n-0.045016\n-0.085145\n-0.14153\n-0.27918\n-0.36027\n-0.37058\n-0.12178\n0.0043607\n0.067798\n0.10959\n0.15594\n0.1391\n-0.056198\n0.18621\n0.29353\n0.2347\n0.30078\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1165\nNaN\nNaN\nNaN\n0.0068625\n-0.17631\n-0.18743\nNaN\nNaN\nNaN\n-0.024061\n0.064743\n0.010299\n-0.064824\n0.0382\n0.043941\n0.024643\n0.12635\n0.0865\n-0.069154\n-0.14761\n-0.096842\n-0.091277\n-0.16283\n-0.35122\nNaN\nNaN\nNaN\n0.086515\n0.10679\n0.086453\n0.061636\n0.13247\n0.087857\n0.16292\n0.051372\n-0.067697\n-0.072303\n-0.058942\n-0.074745\n0.0059491\n0.13246\n0.13161\n-0.031148\n-0.17512\n-0.20605\n-0.16446\n-0.08621\n-0.20643\n-0.31005\n-0.30462\n-0.229\n0.025752\n0.13626\n0.046047\n0.061482\n0.16491\n0.08539\n-0.093584\n0.21238\n0.26229\n0.20229\n0.31543\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.090815\nNaN\nNaN\nNaN\n0.061685\n-0.56792\n-0.41076\nNaN\nNaN\nNaN\n-0.19053\n-0.10763\n-0.09329\n0.043825\n0.24568\n0.28965\n0.11023\n0.047889\n0.068858\n-0.10255\n-0.077252\n0.12801\n0.24688\n0.043119\n-0.077528\nNaN\nNaN\nNaN\n0.11371\n0.1468\n0.059578\n-0.0039228\n0.20258\n0.19622\n-0.031406\n-0.10691\n-0.15992\n-0.05706\n-0.0042414\n-0.0068811\n-0.057284\n0.11875\n0.075942\n-0.039634\n-0.17252\n-0.15843\n-0.089906\n-0.2839\n-0.34297\n-0.39404\n-0.35816\n-0.13951\n0.038068\n0.22905\n0.25269\n0.26919\n0.21062\n0.29315\n0.51587\n0.74086\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.54007\n0.32555\n0.32321\nNaN\n0.10108\n-0.038994\n-0.53705\nNaN\n-0.13403\n0.028899\n0.069949\n0.02712\n-0.086463\n0.041803\n0.33365\n0.36368\n0.14554\n0.14148\n0.19226\n0.2611\n0.12474\n0.040247\n0.24522\n0.33432\n0.32564\n0.43358\n0.2051\n0.11435\n0.06256\n0.08434\n0.02343\n0.00011079\n0.078918\n0.093277\n-0.094611\n-0.11162\n-0.18309\n-0.061572\n-0.041959\n-0.10941\n-0.031607\n0.054673\n0.054636\n0.04792\n0.012478\n-0.032547\n0.031457\n-0.20687\n-0.16675\n-0.40789\n-0.41402\n-0.097211\n0.12909\n0.40529\n0.35247\n0.42129\n0.379\n0.47515\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27996\n0.030458\n0.31174\n-0.16263\n-0.31434\n0.15684\n0.12321\n0.14501\n0.21674\n0.15986\n0.11234\n-0.060903\n0.11032\n0.2023\n0.12489\n0.19911\n0.2405\n0.20091\n0.098119\n0.0099919\n-0.15885\n-0.0083389\n0.0066229\n-0.049334\n0.076834\n0.088675\n0.063926\n0.055548\n0.024551\n-0.070949\n-0.055237\n-0.08514\n-0.16161\n-0.11963\n-0.075493\n-0.095175\n-0.13726\n-0.12552\n-0.018134\n0.0013061\n-0.0073314\n0.053919\n-0.055182\n-0.12407\n-0.27079\n-0.079935\n-0.14782\n-0.63161\n-0.48562\n-0.12006\n0.23984\n0.33536\n0.33601\n0.50025\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19568\n-0.17653\n0.082063\n0.39919\n0.27806\n0.23255\n0.11613\n0.18097\n0.28293\n0.24955\n0.21798\n0.082038\n0.048891\n0.01421\n0.14445\n0.22941\n0.30099\n0.22159\n0.17581\n0.24216\n-0.13807\n-0.13122\n-0.055974\n-0.068335\n-0.01901\n0.061032\n0.098285\n0.0036936\n-0.14077\n-0.10042\n-0.043891\n-0.09704\n-0.19404\n-0.070041\n0.044781\n-0.037103\n-0.1006\n-0.01583\n-0.0032447\n-0.0038245\n-0.00085529\n0.20413\n0.096441\n-0.13906\n-0.37035\n-0.51013\n-0.31785\n-0.33228\n-0.21856\n-0.01335\n0.10985\n0.2889\n0.26955\n0.31514\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32525\n-0.23402\n0.31256\n0.39448\n0.33367\n0.25861\n0.13046\n0.13425\n0.18305\n0.22422\n0.29879\n0.20323\n0.047454\n-0.06964\n0.035918\n0.15541\n0.29671\n0.098806\n0.15995\n0.21755\n-0.018652\n-0.066296\n-0.4064\n-0.13218\n-0.0049028\n-0.072417\n-0.044307\n-0.056708\n-0.087182\n-0.062524\n-0.093681\n-0.0089916\n-0.095131\n-0.0018297\n0.056463\n0.0091177\n-0.080782\n-0.056143\n-0.0028129\n-0.088458\n-0.11573\n0.11966\n0.11724\n-0.025371\n-0.31233\n-0.46366\n-0.24133\n-0.18632\n-0.13504\n0.09525\n0.23183\n0.14331\n0.057987\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13138\n0.20286\n0.51321\n0.32585\n0.2856\n0.17104\n0.12343\n0.13251\n0.17376\n0.29153\n0.29688\n0.097695\n0.054332\n0.12447\n0.057925\n-0.051968\n0.040391\n0.19382\n0.17956\n-0.0082809\n-0.087855\n-0.04701\n0.039938\n0.077976\n0.063427\n-0.085118\n-0.060905\n-0.015141\n-0.05019\n-0.14525\n0.020373\n-0.049854\n-0.034648\n-0.0029727\n-0.039023\n-0.11657\n-0.020772\n0.0044303\n0.018862\n0.12764\n0.10878\n0.17901\n-0.016228\n-0.1944\n-0.21067\n-0.12994\n-0.14687\n-0.11302\n0.021593\n0.25797\n0.35239\n0.19505\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.057433\n0.13762\n0.47786\n0.33394\n0.32399\n0.23947\n0.082079\n0.091255\n0.14677\n0.30101\n0.29373\n0.21012\n0.13278\n0.18595\n0.078201\n0.0091354\n0.17697\n0.28157\n0.12137\n0.17957\n0.12864\n0.090169\n0.10528\n0.20815\n0.1125\n-0.070224\n0.0012092\n0.047852\n-0.020555\n-0.11272\n-0.056869\n-0.067762\n-0.09954\n-0.035038\n-0.12786\n-0.12179\n-0.067425\n-0.0041705\n0.072105\n0.1642\n0.11813\n0.094048\n-0.052464\n-0.044448\n-0.13347\n-0.13334\n-0.10886\n0.0078952\n0.080494\n0.27852\n0.76685\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.069577\n0.23642\n0.31952\n0.4876\n0.24506\n0.29302\n0.22063\n0.19222\n0.31316\n0.35634\n0.34605\n0.32064\n0.35715\n0.26334\n0.14964\n0.093143\n-0.088767\n0.24147\n0.19424\n0.20183\n0.29151\n0.23104\n0.16642\n0.18751\n0.11062\n0.048596\n-0.0037356\n0.061718\n0.042768\n-0.053876\n-0.092458\n-0.091953\n-0.11202\n-0.038358\n-0.018991\n-0.092345\n-0.078776\n-0.035157\n-0.011142\n-0.026838\n0.13114\n0.13786\n0.044423\n-0.098499\n-0.13656\n-0.10189\n-0.11892\n-0.08362\n-0.017512\n0.097926\n0.2869\n0.92032\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.51961\nNaN\nNaN\nNaN\nNaN\n0.18621\n0.31944\n0.27045\n0.21032\n0.30097\n0.23578\n0.19238\n0.208\n0.44005\n0.3791\n0.3641\n0.3126\n0.054257\n0.33305\n0.19505\n0.072814\n-0.098153\n-0.014117\n0.24505\n0.24363\n0.27759\n0.26167\n0.20485\n0.19144\n0.20836\n0.074414\n-0.15354\n0.033353\n0.043965\n-0.050334\n-0.067366\n-0.014967\n-0.055618\n-0.081182\n-0.048692\n-0.071945\n-0.043784\n-0.063863\n-0.048592\n-0.04977\n-0.012496\n0.13421\n0.14377\n0.074917\n-0.01589\n-0.12018\n-0.1153\n0.0080454\n0.025352\n-0.086595\n0.090744\n0.38186\n0.5462\n0.50805\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.47561\nNaN\nNaN\nNaN\n0.45217\n0.33716\n0.48185\n0.35766\n0.15758\n0.10212\n0.20724\n0.13644\n0.067704\n0.22825\n0.43166\n0.28454\n0.27771\n0.13677\n-0.18656\n-0.061503\n0.0080607\n-0.062739\n-0.044612\n0.084348\n0.17622\n0.18974\n0.13932\n0.18402\n0.03836\n0.11208\n0.1139\n-0.058258\n-0.48126\n0.067898\n-0.0064419\n-0.023844\n-0.042307\n-0.064595\n-0.042717\n0.061894\n-0.00051735\n-0.11087\n-0.090093\n-0.082691\n-0.011512\n-0.043234\n-0.0077831\n0.13192\n0.1274\n0.048025\n0.01806\n-0.022908\n-0.20178\n-0.22724\n-0.027439\n-0.098518\n0.14602\n0.49944\n0.50617\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.70038\n0.65831\nNaN\nNaN\n0.73249\n0.50088\n0.4192\n0.47602\n0.34915\n0.16459\n0.06102\n-0.033599\n0.0031955\n0.0042244\n0.12848\n0.25014\n0.12732\n0.079211\n0.020347\n-0.090436\n-0.11735\n-0.12954\n-0.083458\n0.0062682\n0.035389\n0.098161\n0.086755\n0.045017\n0.092917\n0.031147\n-0.085978\n-0.049234\n-0.069184\n-0.5994\n0.036089\n-0.012613\n-0.058169\n-0.082893\n-0.17769\n-0.047292\n-0.0015579\n-0.032767\n-0.046672\n-0.053456\n-0.052328\n-0.057605\n-0.12164\n-0.090442\n0.080712\n0.070276\n0.077873\n-0.035926\n-0.090207\n-0.18195\n-0.31477\n-0.20193\n-0.088118\n0.26569\n0.49679\n0.55753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.66397\n0.75406\n0.7687\n0.45089\n1.0326\n1.8433\n1.2512\n0.98165\n0.40423\n0.34873\n0.58178\n0.42388\n0.1843\n0.13263\n0.079739\n0.032161\n0.024067\n0.043293\n0.057112\n0.05047\n-0.0065532\n-0.045098\n-0.11203\n-0.21515\n-0.19391\n-0.073079\n0.047415\n0.030513\n-0.011532\n-0.054494\n0.057444\n0.13457\n0.059058\n-0.12038\n-0.0093866\n-0.091636\n-0.30902\n0.0020363\n-0.022609\n-0.015983\n-0.0011169\n-0.051108\n-0.044941\n-0.054417\n-0.042795\n-0.048454\n-0.11392\n-0.043271\n-0.1263\n-0.22499\n-0.16288\n-0.033384\n0.027156\n-0.011782\n-0.16493\n-0.21462\n-0.15094\n-0.15045\n-0.15245\n-0.047006\n0.20483\n0.47837\n0.74452\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3161\n1.7512\n1.1533\n0.84388\n0.30857\n0.47426\n0.40498\n0.41326\n0.28351\n0.17766\n0.077636\n0.064137\n0.11807\n0.12281\n0.095781\n-0.11521\n-0.15927\n-0.091254\n-0.1331\n-0.31816\n-0.42012\n-0.13902\n-0.017276\n-0.025026\n-0.03705\n-0.11461\n0.040682\n0.048875\n0.12207\n-0.090837\n-0.12613\n-0.076984\n-0.058223\n-0.0037362\n0.024839\n0.037354\n0.054844\n-0.0032501\n-0.022226\n-0.065139\n-0.07914\n-0.061991\n-0.11511\n-0.11827\n-0.12561\n-0.23026\n-0.17479\n-0.10739\n-0.044936\n-0.050664\n-0.23073\n-0.23919\n-0.097002\n-0.14362\n-0.23274\n-0.10132\n0.23401\n0.40303\n0.20786\n0.18735\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.853\n0.92421\n0.89909\n0.48021\n0.3546\n0.19877\n0.28448\n0.35294\n0.21158\n0.12881\n0.079794\n0.08949\n0.12268\n0.091727\n0.0089669\n-0.12773\n-0.30881\n-0.15776\n-0.088245\n-0.20322\n-0.38647\n-0.34304\n-0.1257\n-0.07713\n-0.037416\n-0.070084\n-0.030548\n-0.047909\n-0.10683\n-0.20571\n-0.11164\n-0.00048285\n-0.046416\n0.06022\n0.037802\n0.027684\n0.068121\n0.11331\n0.049818\n-0.056602\n-0.06743\n-0.013197\n-0.047125\n-0.11497\n-0.13281\n-0.1567\n-0.13283\n-0.092116\n-0.05339\n0.012958\n-0.039012\n-0.15358\n-0.12989\n-0.12513\n-0.23403\n-0.12625\n0.26301\n0.22783\n0.025832\n0.10121\n-0.12459\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.378\n0.81996\n0.55883\n0.58571\n0.42984\n0.24605\n0.34723\n0.33026\n0.213\n-0.050668\n-0.087406\n0.12645\n0.10464\n0.034068\n-0.13304\n-0.16657\n-0.19109\n-0.14152\n-0.05277\n-0.12013\n-0.17555\n-0.31068\n-0.2236\n-0.09341\n-0.088599\n-0.096918\n-0.099036\n-0.12283\n-0.19792\n-0.21948\n-0.15932\n-0.10547\n-0.11608\n0.070716\n-0.010475\n0.0022081\n0.049045\n0.13349\n0.061374\n0.0084828\n0.00085235\n0.0043063\n-0.094814\n-0.18777\n-0.12108\n-0.093755\n-0.1402\n-0.18522\n-0.1134\n0.042718\n-0.069907\n-0.079462\n-0.20763\n-0.17443\n-0.15714\n-0.034084\n0.062346\n0.073899\n-0.024359\n0.040551\n0.10196\nNaN\nNaN\n0.42458\n0.47303\n0.59719\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.85617\n0.6418\n0.53697\n0.55329\n0.45654\n0.27017\n0.22414\n0.11176\n0.15726\n-0.20383\n-0.074205\n0.1417\n0.099004\n-0.015383\n-0.038563\n-0.082774\n-0.08488\n-0.070092\n-0.039045\n-0.10418\n-0.21748\n-0.27423\n-0.21193\n-0.17216\n-0.21567\n-0.2327\n-0.47817\n-0.53963\n-0.25946\n-0.16875\n-0.15385\n-0.024092\n-0.054625\n0.067389\n0.065683\n0.012759\n0.00014317\n0.1546\n0.16668\n0.10522\n0.080793\n0.017252\n-0.064292\n-0.1323\n-0.056228\n-0.061889\n-0.1066\n-0.14566\n-0.006539\n0.004533\n-0.20254\n-0.33054\n-0.3112\n-0.26086\n-0.17403\n-0.084608\n0.073115\n-0.10387\n-0.25735\n-0.091488\n0.26686\n0.29741\n0.45313\n0.59194\n0.35596\n0.40796\n0.47676\nNaN\nNaN\nNaN\nNaN\n0.76744\n0.73637\n0.53818\n0.46211\n0.55776\n0.19448\n-0.053329\n0.0062709\n0.074413\n0.070107\n-0.1461\n-0.045161\n0.11782\n0.085962\n0.12548\n-0.0075021\n-0.092228\n-0.076818\n-0.039097\n-0.06815\n-0.077555\n-0.12282\n-0.2074\n-0.20547\n-0.38045\n-0.35522\n-0.27383\n-0.27848\n-0.28032\n-0.33228\n-0.23108\n-0.12927\n0.060923\n0.00021315\n0.16506\n0.12225\n0.066726\n0.057043\n0.10827\n0.15725\n0.17787\n0.12783\n0.11315\n-0.0055643\n-0.052743\n0.0021131\n-0.0079421\n-0.045123\n-0.090157\n-0.095126\n-0.14291\n-0.22061\n-0.33476\n-0.29174\n-0.28233\n-0.094958\n0.0031466\n0.059597\n0.20067\n0.17612\n0.20188\n0.26945\nNaN\n0.34912\n0.46856\n0.30203\n0.3786\n0.6399\nNaN\nNaN\nNaN\n0.5874\n0.64549\n0.54895\n0.43393\n0.15104\n0.18607\n0.14616\n-0.10994\n-0.074426\n0.15213\n0.0077755\n-0.20335\n-0.10612\n0.083949\n0.12744\n0.17558\n-0.033462\n-0.058939\n-0.062161\n-0.067714\n-0.080156\n-0.022342\n-0.073103\n-0.19781\n-0.23868\n-0.27098\n-0.28654\n-0.23677\n-0.1292\n-0.14094\n-0.29688\n-0.19813\n-0.06038\n0.042512\n-0.058592\n0.10626\n0.13483\n0.11336\n0.1346\n0.14645\n0.16838\n0.17955\n0.12239\n0.11462\n0.042006\n-0.00075451\n0.00080548\n0.0015311\n-0.0023625\n-0.041012\n-0.14532\n-0.1924\n-0.25842\n-0.26664\n-0.29446\n-0.24373\n-0.13985\n0.028671\n-0.084103\n-0.082729\n0.14584\n0.25235\nNaN\nNaN\n0.34983\n0.3396\n0.39356\n0.63001\n0.81891\n0.6854\n0.45254\n0.18045\n0.48198\n0.48854\n0.48923\n0.34351\n0.039078\n0.026887\n-0.028556\n-0.071687\n0.026069\n0.1073\n0.071725\n-0.13871\n-0.040838\n0.047881\n0.15218\n0.19743\n0.11103\n0.025918\n0.0022177\n-0.040662\n-0.068397\n0.00080782\n-0.062142\n-0.13929\n-0.2678\n-0.29874\n-0.22101\n-0.11702\n-0.01236\n-0.012906\n-0.12123\n-0.093604\n-0.015795\n-0.063458\n-0.034547\n0.21954\n0.18138\n0.15566\n0.20648\n0.22372\n0.17727\n0.16412\n0.12597\n0.061368\n0.049535\n0.048854\n0.049886\n0.13587\n0.2023\n0.083307\n-0.12335\n-0.20031\n-0.26115\n-0.021539\n0.086393\n-0.0084889\n-0.025805\n-0.043792\n-0.2445\n-0.23013\n0.06238\n0.19116\nNaN\nNaN\nNaN\n0.27295\n0.32197\n0.292\n0.45499\n0.32554\n0.46718\n0.45178\n0.32057\n0.30915\n0.45077\n0.050544\n-0.061976\n0.082154\n0.099622\n0.067783\n-0.02015\n-0.056627\n-0.056203\n-0.068688\n0.010575\n0.07654\n0.13865\n0.12173\n0.053173\n0.017467\n-0.0043001\n0.00082558\n0.045209\n0.051832\n0.030554\n-0.10109\n-0.22537\n-0.24823\n-0.14328\n-0.058615\n0.041469\n0.05604\n-0.01454\n-0.090099\n-0.11196\n-0.085262\n0.05995\n0.22243\n0.236\n0.27534\n0.31171\n0.31222\n0.2247\n0.17806\n0.22032\n0.14659\n0.11469\n0.10198\n0.13779\n0.18741\n0.22332\n0.19625\n-0.0038784\nNaN\nNaN\nNaN\n0.048717\n-0.15031\n-0.068039\n-0.047774\n-0.37843\n-0.20346\n0.042372\n0.10503\nNaN\nNaN\nNaN\n0.22812\n0.25984\n0.13677\n0.17416\n0.28919\n0.37339\n0.24991\n0.17615\n0.1931\n0.13425\n-0.070892\n-0.095391\n0.067036\n0.13638\n0.07481\n-0.0069513\n-0.070924\n-0.078405\n-0.029642\n0.041487\n0.08297\n0.074035\n-0.0086691\n-0.059133\n-0.0017206\n0.077779\n0.09391\n0.073256\n0.062962\n0.035322\n-0.059295\n-0.12275\n-0.13521\n-0.11285\n-0.038335\n0.017665\n0.088765\n0.076593\n-0.0091319\n-0.056258\n-0.046034\n-0.0058692\n0.31484\n0.41194\n0.43325\n0.3436\n0.3229\n0.28853\n0.26019\n0.28783\n0.21001\n0.19556\n0.24424\n0.23969\n0.33018\n0.47095\n0.26213\n0.059953\nNaN\nNaN\nNaN\n-0.073769\n-0.18532\n-0.32338\n-0.23252\n-0.29648\n-0.047542\n0.062084\n0.1001\nNaN\nNaN\nNaN\n0.065663\n0.15233\n0.16817\n0.22697\n0.39558\n0.31209\n0.1821\n0.093631\n0.050776\n0.021894\n-0.13622\n-0.13766\n0.055058\n0.1511\n0.11524\n0.074445\n0.014097\n-0.077765\n-0.045774\n0.041722\n0.093117\n0.039372\n-0.051978\n-0.0042181\n0.098575\n0.13297\n0.051037\n0.022023\n0.045873\n0.091382\n0.022883\n-0.054838\n-0.082545\n-0.056871\n0.034131\n0.028123\n0.078377\n0.13353\n0.042977\n-0.026684\n0.045741\n0.0021507\n0.37992\n0.41715\n0.40779\n0.38318\n0.37031\n0.3652\n0.3552\n0.28032\n0.24938\n0.28167\n0.34208\n0.37801\n0.40508\n0.41811\n0.34723\n0.30439\n0.029729\nNaN\nNaN\n-0.16791\n-0.13591\n-0.17199\n-0.14508\n-0.061513\n0.077055\n0.12314\n0.056426\n0.092463\n0.039964\n0.005575\n-0.14486\n0.022517\n0.12732\n0.21926\n0.34196\n0.2635\n0.20697\n0.13997\n0.050951\n0.037618\n-0.05731\n-0.069709\n0.10458\n0.094201\n0.10273\n0.023061\n-0.11407\n-0.16423\n-0.11022\n0.017569\n0.088017\n0.087708\n0.14208\n0.04645\n0.071377\n0.12156\n0.0075849\n-0.0073588\n0.020883\n0.13482\n0.071658\n-0.05455\n-0.027627\n0.034331\n0.093811\n0.058561\n0.073378\n0.15023\n0.0079942\n0.067833\n0.15557\n0.05995\n0.3399\n0.36638\n0.38392\n0.43762\n0.49389\n0.48465\n0.44457\n0.33444\n0.34493\n0.36982\n0.35241\n0.39692\n0.40311\n0.398\n0.23576\n0.22404\n0.17075\n-0.20569\n-0.28264\n-0.12244\n-0.07747\n-0.03864\n-0.024436\n0.0069948\n0.09123\n0.064131\n0.031624\n0.06104\n0.066198\n0.040922\n-0.0093198\n0.03735\n0.11627\n0.17009\n0.26214\n0.20342\n0.17219\n0.22955\n0.17038\n0.12438\n0.090737\n0.10922\n0.16928\n0.17741\n0.066106\n0.036234\n-0.09956\n-0.21776\n-0.27938\n-0.13134\n0.026517\n0.29284\n0.22094\n0.098111\n0.079304\n0.14605\n0.084154\n0.024948\n0.032254\n0.076465\n0.04798\n-0.011025\n0.10476\n0.12212\n0.094476\n0.092855\n0.19975\n0.21361\n0.066631\n0.095923\n0.052125\n0.029153\n0.29043\n0.3572\n0.4086\n0.48207\n0.57489\n0.61466\n0.54679\n0.49244\n0.39375\n0.30466\n0.34174\n0.38765\n0.37485\nNaN\nNaN\nNaN\nNaN\n0.05232\n-0.11517\n-0.062952\n0.016676\n0.0074572\n-0.037878\n-0.00084328\n0.054643\n-0.0066202\n0.072028\n0.15514\n0.079609\n0.016746\n-0.017353\n-0.05554\n0.0030911\n0.009723\n-0.025119\n0.051535\n0.079475\n0.22018\n0.22629\n0.22869\n0.1972\n0.20711\n0.22287\n0.087198\n-0.019931\n0.062449\n0.0094189\n-0.15677\n-0.13994\n-0.17453\n-0.049932\n0.093753\n0.13072\n0.060124\n0.065568\n0.16028\n0.082386\n0.053154\n0.092401\n0.025743\n0.010261\n0.097801\n0.22391\n0.11921\n0.011344\n0.082299\n0.29111\n0.25002\n0.14299\n0.020001\n-0.045344\n-0.031765\n0.38371\n0.39302\n0.46099\n0.54296\n0.572\n0.57735\n0.62446\n0.58909\n0.50305\n0.41454\n0.40106\n0.4211\n0.39109\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12207\n-0.033892\n0.0090968\n0.028076\n-0.076942\n-0.049218\n0.0030373\n0.019451\n0.099233\n0.1259\n0.010148\n-0.021367\n-0.068795\n-0.079041\n0.03342\n-0.015724\n-0.023651\n0.061066\n0.11314\n0.27553\n0.3196\n0.31662\n0.29095\n0.30181\n0.24994\n-0.096005\n-0.19324\n-0.12404\n0.01838\n-0.077694\n-0.059977\n-0.05796\n-0.053995\n-0.068263\n-0.06522\n-0.14396\n-0.044852\n0.05381\n0.01086\n0.011264\n0.16046\n0.14998\n0.060535\n0.14934\n0.17351\n0.10884\n0.022313\n0.19806\n0.39254\n0.25171\n0.16706\n-0.05201\n-0.078103\n-0.079146\n0.42545\n0.41991\n0.46403\n0.60182\n0.60056\n0.73998\n0.75506\n0.69281\n0.54777\n0.46153\n0.43179\n0.44903\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.020188\n-0.041628\n-0.045064\n-0.076815\n-0.10658\n-0.084217\n-0.099595\n0.033173\n0.058111\n0.064132\n-0.012685\n-0.022\n0.033516\n0.042727\n-0.0073972\n0.001859\n0.06296\n0.044852\n0.21034\n0.32249\n0.32406\n0.31035\n0.34756\n0.34514\n-0.097006\n-0.34167\n-0.28928\n-0.023849\n-0.054055\n-0.031135\n-0.063728\n-0.066557\n0.017308\n-0.11655\n-0.12169\n-0.049753\n0.034744\n0.0079513\n0.017883\n0.21192\n0.10732\n0.012015\n0.055722\n0.099839\n0.071348\n0.15791\n0.21933\n0.25441\n0.1988\n0.17773\n-0.1336\n-0.15273\nNaN\n0.48445\n0.44173\n0.55339\n0.67264\n0.67396\n0.82777\n0.99164\n0.8404\n0.5231\n0.46385\n0.43854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1811\n-0.046704\n-0.025243\n-0.04169\n-0.090677\n-0.086322\n-0.055019\n-0.021295\n0.1094\n0.13039\n0.023309\n0.036671\n0.081973\n0.011187\n-0.017605\n-0.05163\n-0.076811\n-0.093863\n0.10264\n0.18844\n0.18073\n0.24434\n0.24258\n0.10383\n-0.11856\n-0.28371\n-0.33432\n-0.11487\n-0.050437\n-0.011968\n-0.065277\n0.021391\n0.098602\n0.14485\n0.14592\n0.059176\n0.039959\n-0.015917\n-0.013542\n0.18187\n-0.046399\n-0.041107\n-0.11294\n-0.07379\n0.11097\nNaN\nNaN\n0.14169\n0.20576\n0.24469\nNaN\nNaN\nNaN\n0.19724\n0.065662\n0.056232\n0.066926\n0.061336\n-0.025523\n-0.12522\n0.018275\n0.12204\n0.097524\n0.018584\n0.063677\n-0.0016064\n0.056858\n0.087382\n0.064911\n0.099638\n0.10128\n0.26708\n0.18914\n0.2655\n0.32445\n0.30291\n0.039946\n0.13885\n0.088266\n-0.071141\n-0.21217\n-0.11426\n-0.1198\n-0.17867\n-0.14305\n0.030194\n0.03128\n0.099541\n-0.025574\n-0.0282\n-0.0073946\n-0.087864\n-0.11516\n-0.039334\n0.085784\n0.16622\n0.20502\n0.21622\n0.14258\n0.17896\n0.15033\n0.1026\n0.051934\n0.028875\n0.16075\n0.13393\n0.00080712\n0.13592\n0.082565\n0.031605\n-0.12721\n-0.32187\n-0.36525\n-0.36046\n-0.11852\n0.1985\n-0.96474\n-0.59063\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.027683\n0.051186\n0.064997\n0.20522\n0.22299\n-0.08537\n-0.20445\n-0.023986\n0.12438\n0.12197\n0.16456\n0.15533\n0.003077\n0.072186\n0.099834\n-0.043962\n-0.056146\n-0.0061882\n0.12155\n0.19134\n0.52585\n0.30516\n0.21314\n0.26259\n0.12204\n0.1009\n0.033639\n-0.12889\n-0.012756\n-0.081832\n0.012788\n0.0075274\n0.18954\n0.096031\n0.10797\n-0.085622\n-0.078546\n0.0012147\n-0.03032\n0.13826\n0.092673\n0.10614\n0.17956\n0.30841\n0.24664\n0.15828\n0.14067\n0.089907\n0.044081\n0.033091\n0.046036\n0.10717\n0.10998\n0.14543\n0.32995\n0.16557\n-0.014466\n-0.22861\n-0.25298\n-0.18845\n-0.14499\n0.017645\n0.14008\n-0.60696\n-0.10589\n-0.12123\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.0022796\n0.041139\n0.057634\n0.058052\n0.038855\n0.11139\n0.032978\n-0.018285\n0.033599\n0.19717\n0.26318\n0.25507\n0.20567\n0.16064\n0.27862\n0.019579\n0.060232\n0.12823\n0.082265\n0.27152\n0.31494\n0.20178\n0.30078\n0.23055\n0.15756\n0.21709\n0.14719\n0.0072554\n0.071579\n-0.076884\n0.009946\n-0.016227\n0.35538\n0.32301\n-0.10468\n-0.15553\n-0.026659\n0.0018178\n0.0656\n0.044985\n0.058089\n0.14188\n0.12815\n0.23662\n0.15539\n0.12562\n0.12255\n0.090082\n0.058931\n0.092522\n0.15653\n0.067627\n0.042101\n0.26065\n0.30618\n0.11726\n0.071455\n-0.21913\n-0.17275\n0.012624\n-0.20163\n0.032286\n0.16466\n-0.53862\n0.029016\n0.14091\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.074117\n0.052236\n0.04439\n0.063211\n0.071292\n0.096878\n0.1672\n0.12487\n0.10344\n0.13667\n0.16315\n0.11986\n0.54913\n0.29036\n0.25776\n0.11606\n0.12446\n0.12453\n0.13064\n0.25339\n0.42456\n0.10437\n0.23662\n0.23021\n0.26346\n0.25834\n0.10853\n0.08462\n-0.02261\n-0.35359\n0.0048509\n0.055238\n0.37724\n0.33306\n0.046188\n-0.033421\n-0.097714\n-0.019394\n0.090393\n0.011239\n0.004623\n0.083509\n0.067557\n0.020972\n0.067214\n0.1003\n0.085234\n0.14754\n0.21089\n0.33308\n0.35475\n0.23906\n0.30035\n0.37331\n0.24185\n0.12224\n-0.67426\n-1.2824\n-1.7283\n-0.42506\n-0.31076\n0.093561\n0.21004\n0.09189\n-0.13977\n-0.15786\n0.041461\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1227\n0.075485\n0.069818\n0.071179\n0.14006\n0.10392\n0.1698\n0.20098\n0.10334\n-0.014961\n0.054034\n-0.018399\n-0.048856\n0.12443\n-0.00044409\n-0.035522\n0.045436\n0.16588\n0.11103\n0.11865\n0.48456\n0.255\n0.37021\n-0.055811\n0.22984\n0.20999\n0.074725\n-0.04234\n0.00056955\n-0.024475\n0.025766\n0.20854\n0.27516\n0.23716\n0.27653\n-0.014038\n-0.20091\n-0.079563\n0.039147\n0.016262\n0.033491\n0.11349\n0.068442\n0.083838\n0.056142\n0.094226\n0.18725\n0.30916\n0.29815\n0.29354\n0.34074\n0.29173\n0.22475\n0.3619\n0.23802\n0.22321\n0.40683\n-1.0473\n-1.7501\n-0.49984\n-0.40129\n0.010602\n0.21734\n-0.11628\n-0.25203\n-0.077614\n-0.26016\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11881\n0.048382\n-0.018546\n0.071427\n0.17896\n0.11248\n0.16624\n0.23447\n0.15447\n0.079446\n0.085528\n0.17877\n0.17891\n0.025957\n-0.076191\n0.097411\n0.016389\n0.017402\n-0.12522\n-0.39829\n-0.13927\n0.13025\n0.074035\n-0.016033\n0.032974\n0.14541\n0.11949\n0.10557\n0.070332\n0.11334\n0.09526\n0.18498\n0.055057\n0.038587\n0.23642\n0.050908\n-0.16371\n-0.036414\n0.030232\n0.065296\n0.063234\n0.033528\n0.056096\n-0.00838\n-0.18396\n0.010892\n0.2073\n0.29102\n0.25912\n0.30243\n0.27734\n0.25097\n0.20604\n0.20449\n0.15638\n0.13185\n0.24525\n-0.11363\n-1.1135\n-0.57834\n-0.61634\n-0.12241\n-0.14523\n-0.41409\n-0.12304\n-0.052126\n-0.16793\nNaN\nNaN\nNaN\nNaN\nNaN\n0.031586\n-0.0095546\n0.012829\n0.16226\n0.216\n0.13086\n0.22164\n0.20321\n0.20218\n0.11776\n0.10656\n0.10317\n0.085508\n0.032926\n-0.026722\n-0.031455\n-0.048459\n0.00081444\n0.034874\n-0.15487\n-0.05244\n-0.13041\n0.045602\n-0.13128\n-0.048136\n0.069995\n0.11736\n0.23949\n0.090595\n0.092258\n0.024603\n0.13005\n-0.086667\n-0.020454\n0.0423\n-0.033225\n-0.12932\n-0.058566\n0.016135\n0.10515\n0.097256\n0.062879\n-0.0057631\n-0.12006\n-0.3979\n-0.032752\n0.20023\n0.29549\n0.3129\n0.31215\n0.23\n0.27065\n0.34619\n0.28548\n0.15636\n0.017624\n0.1612\n-1.1081\n-1.3701\n-0.28259\n-0.57702\n-0.47728\nNaN\n-0.57339\n-0.36927\n-0.39841\n0.33859\n0.024961\n-0.21324\nNaN\nNaN\nNaN\n0.06464\n0.086376\n0.052413\n0.17064\n0.30367\n0.068095\n0.11552\n0.18479\n0.19589\n0.11646\n0.06343\n0.09755\n0.014014\n0.046572\n-0.044504\n-0.051829\n0.040729\n0.03806\n0.0081253\n-0.067556\n-0.071005\n-0.025442\n-0.061747\n0.0061681\n-0.00026547\n0.078076\n0.12457\n0.14671\n0.052959\n0.037844\n0.028915\n-0.1662\n-0.31367\n-0.086216\n-0.040261\n-0.031496\n-0.069469\n-0.017711\n0.022511\n-0.015251\n0.076159\n0.061076\n0.00051107\n-0.092587\n-0.48115\n-0.020288\n0.26626\n0.28609\n0.25294\n0.31145\n0.30115\n0.33463\n0.37048\n0.29405\n0.2614\n0.17547\n0.047397\n0.26687\n0.16454\n-0.00055014\n-0.29685\n-0.47407\nNaN\nNaN\n-1.2417\n-1.1678\n-0.16008\n-0.22324\n-0.32068\n-0.40903\nNaN\nNaN\n0.076379\n-0.024478\n-0.084568\n0.014405\n0.14967\n0.0090962\n0.018727\n0.17641\n0.21956\n0.17233\n0.095107\n0.090298\n0.11311\n0.049874\n0.050184\n0.10278\n0.059176\n0.082793\n-0.021814\n-0.027009\n0.004991\n-0.0024427\n0.040398\n0.10049\n0.073772\n0.10273\n0.077764\n-0.019591\n0.0016402\n-0.012314\n0.014282\n0.015358\n-0.25065\n-0.16396\n-0.099408\n-0.096259\n-0.051094\n0.0011509\n0.021307\n0.0013639\n-0.021053\n0.027296\n-0.049053\n-0.16515\n-0.3551\n0.19136\n0.32952\n0.36596\n0.35619\n0.36607\n0.36891\n0.40258\n0.3927\n0.44946\n0.45425\n0.23389\n0.10162\n0.20493\n0.3982\n0.30553\n-0.35884\nNaN\nNaN\nNaN\n-1.1799\n-1.2313\n-0.71539\n-0.43696\n-0.35317\n-0.24752\n0.012281\n-0.43161\n-0.10615\n-0.014544\n0.0097781\n-0.032146\n0.21105\n0.020341\n0.012929\n0.13013\n0.1862\n0.15861\n0.11056\n0.10063\n0.11854\n0.03051\n0.087352\n0.14875\n0.089732\n0.17649\n0.15644\n0.18163\n0.11418\n0.035663\n0.090017\n0.062466\n0.025871\n0.032267\n0.073568\n0.046153\n0.013126\n-0.049585\n-0.072871\n-0.14297\n-0.23924\n-0.30192\n-0.22755\n-0.051475\n-0.14382\n-0.056857\n-0.026062\n0.0021415\n0.030856\n0.09344\n-0.09127\n-0.19622\n-0.0050545\n0.34821\n0.29789\n0.36778\n0.37715\n0.46856\n0.44293\n0.50566\n0.48226\n0.46021\n0.43396\n0.41972\n0.32896\n0.19665\n0.45313\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.7659\n-1.046\n-0.23722\n0.37956\n-0.21545\n-0.051113\n-0.11939\n-0.085348\n-0.021698\n-0.019432\n0.081215\n-0.0091215\n0.0057645\n0.0068873\n0.12735\n0.1897\n0.10809\n-0.0031152\n0.079854\n0.055095\n0.1499\n0.20345\n0.18311\n0.17828\n0.15832\n0.14183\n0.0079068\n-0.013088\n-0.064015\n-0.10017\n0.034785\n0.25595\n0.12938\n0.046979\n0.051536\n0.021121\n-0.092155\n-0.21605\n-0.4008\n-0.18509\n-0.23303\n-0.056092\n-0.080627\n0.042225\n-0.070019\n-0.010166\n0.18854\n0.28216\n-0.035215\n-0.036801\n0.14546\n0.31473\n0.31323\n0.35912\n0.4385\n0.56012\n0.48138\n0.53183\n0.46321\n0.46979\n-0.76241\n-0.84996\n0.22855\n0.15322\n0.39674\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3313\nNaN\n-0.59299\n-0.50733\n0.042012\n0.17274\n-0.15487\n-0.20793\n-0.10618\n-0.025547\n-0.021876\n-0.010176\n0.058917\n0.11672\n0.13919\n0.1422\n0.03744\n0.04471\n0.10651\n0.046209\n0.14938\n0.18115\n0.23049\n0.14006\n0.020348\n0.010346\n-0.08483\n-0.11211\n-0.17252\n-0.17714\n-0.021345\n0.16143\n0.13252\n0.10677\n-0.00024822\n0.029185\n-0.035644\n-0.36244\n-1.879\n-0.56731\n-0.14188\n-0.040278\n0.012178\n0.01829\n0.027501\n0.08341\n0.22128\n0.067058\n0.084096\n0.20964\n0.28988\n0.35826\n0.42117\n0.53126\n0.55335\n0.60588\n0.53673\n0.53442\n0.39904\n-1.1087\n-1.2631\n-0.91129\n0.22488\n0.18506\n0.096675\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.036857\n-0.040371\n0.021199\n0.023705\n0.032473\n0.071031\n0.1002\n0.056301\n0.13495\n0.11176\n0.054637\n0.069886\n0.00857\n0.083384\n0.16538\n0.17657\n0.19923\n0.092408\n-0.054556\n-0.050531\n-0.093911\n-0.11769\n-0.19321\n-0.032329\n-0.0344\n-0.063008\n0.13655\n0.059453\n-0.044716\n0.054264\n0.037033\n-0.17122\n-2.2931\n-2.0172\n-0.12484\n-0.097439\n-0.040216\n0.034567\n0.12218\n0.26213\n0.28057\n0.068376\n0.019496\n0.30209\n0.36064\n0.41611\n0.49961\n0.5863\n0.5996\n0.62277\n0.61232\n0.49162\n0.34039\n-1.0877\n-1.5319\n-1.4582\n-0.021257\n-0.10241\n-0.24533\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15894\n0.079823\n0.043518\n0.11848\n0.099135\n0.10153\n0.10744\n0.10129\n0.093268\n0.036372\n0.010379\n0.067776\n0.20742\n0.27923\n0.22643\n0.15876\n0.034631\n0.060025\n-0.063738\n-0.101\n-0.061873\n-0.26066\n-0.22272\n-0.14874\n-0.2659\n-0.13452\n-0.0089583\n-0.03741\n0.056148\n0.10552\n0.05132\n0.043512\n-0.84077\n-0.80858\n-0.77232\n-0.19629\n-0.18611\n0.032193\n0.1485\n0.26798\n0.33653\n0.26398\n0.28971\n0.4267\n0.46916\n0.56741\n0.56316\n0.64715\n0.63655\n0.6173\n0.6087\n0.43471\n0.32732\n-1.0955\n-1.484\n-1.621\n-1.2911\n0.02107\n-0.24118\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1894\n0.04926\n0.13833\n0.2057\n0.13861\n0.09192\n0.13501\n0.086591\n0.025504\n0.04268\n-0.020328\n0.071447\n0.24227\n0.28066\n0.27858\n0.13198\n0.17392\n0.14145\n0.034868\n-0.05211\n-0.18827\n-0.33956\n-0.41093\n-0.27487\n-0.31013\n-0.1261\n-0.098716\n-0.020399\n0.094485\n0.064953\n0.012955\n-0.27373\n-0.055039\n-0.056268\n-0.031098\n-0.22154\n-0.084321\n0.2783\n0.38294\n0.59046\n0.58504\n0.31439\n0.36348\n0.74725\n0.47222\n0.61614\n0.25338\n0.72884\n0.79671\n0.7127\n0.54484\n0.54296\n0.40581\n0.14925\n-1.307\n-1.7002\n-1.6415\n-0.19932\n-0.30914\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10803\n0.12103\n0.16564\n0.2506\n0.21343\n0.17892\n0.20019\n0.10715\n0.040947\n0.13905\n0.060156\n0.11503\n0.17642\n0.17195\n0.25781\n0.074285\n0.040365\n0.080269\n0.078581\n-0.026867\n-0.14731\n-0.16923\n-0.58822\n-0.42746\n-0.31525\n-0.19035\n-0.086212\n0.11772\n0.12414\n0.076767\n-0.23421\n-0.29999\n-0.033147\n0.080219\n0.039099\n-0.0082256\n0.30599\n0.4916\n0.58142\nNaN\n0.26829\n-0.043677\n0.28726\n0.29972\n0.70908\n0.74435\n0.40854\n0.7144\n0.84191\n0.6271\n0.42299\n0.44843\n0.39454\n0.25676\n0.18025\n-0.011486\n-0.23286\n-0.24265\n-0.60293\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.035768\n0.1061\n0.14403\n0.25763\n0.17343\n0.20929\n0.1508\n0.091104\n0.098583\n0.0079424\n0.0366\n0.10332\n0.15417\n0.17167\n0.24914\n0.026401\n-0.075392\n-0.049558\n0.016397\n-0.020302\n-0.11747\n-0.16007\n-0.32821\n-0.3545\n-0.20881\n-0.081473\n-0.027418\n0.11581\n0.045595\n0.094505\n-0.26214\n-0.30274\n-0.035336\n0.27641\n0.22768\n0.2033\n0.5011\n-0.43124\nNaN\nNaN\nNaN\n-0.075649\n-0.21652\n0.087673\n0.35432\n0.23138\n0.025171\n0.36323\n0.28951\n0.52673\n-0.71812\n0.19618\n0.36209\n0.22056\n0.17391\n0.081978\n-0.15754\n-0.16643\n-0.65075\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.0079242\n0.05166\n0.13322\n0.2516\n0.16164\n0.12835\n0.088412\n0.061456\n0.094998\n-0.026792\n0.03522\n0.04414\n0.13294\n0.11962\n0.14825\n-0.028183\n0.016671\n-0.02642\n-0.043567\n-0.11358\n-0.19964\n-0.27111\n-0.40289\n-0.45208\n-0.16102\n-0.078127\n0.041386\n0.12104\n0.084346\n0.017291\n-0.040219\n0.013201\n-0.012112\n0.10659\n0.20709\n0.33363\n0.44292\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.716\n-0.16379\n0.30937\n0.20014\n0.1223\n0.40822\n0.15686\n0.081773\n0.21367\n-0.67394\n-0.7549\n-1.1295\n0.050734\n0.12009\n-0.035126\n-0.20698\n-0.19595\n-0.28954\n-0.22347\n-0.018816\n0.13009\n0.31\n0.45853\n-0.15628\n-0.23378\nNaN\nNaN\nNaN\nNaN\nNaN\n0.063495\n0.068775\n0.16849\n0.22876\n0.15677\n0.023739\n0.061526\n0.049469\n0.0058937\n-0.011034\n-0.03114\n-0.063248\n0.020286\n0.091354\n0.09757\n-0.0042659\n-0.063521\n-0.064588\n-0.085111\n-0.12409\n-0.16805\n-0.30521\n-0.37796\n-0.39477\n-0.16804\n-0.058345\n0.0090422\n0.052175\n0.090088\n0.081047\n-0.11561\n0.12586\n0.25412\n0.18938\n0.24358\n0.18137\n1.103\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1909\n-0.029513\n0.13341\n-0.071079\n-0.059925\n-0.08909\n0.0083982\n-0.16023\n-0.18776\n-0.90205\n-1.2052\n-1.4371\n-0.06542\n0.034403\n-0.007475\n-0.072985\n0.036151\n0.039143\n0.016603\n0.12142\n0.092626\n-0.066256\n-0.12497\n-0.089642\n-0.097538\n-0.15814\n-0.35224\nNaN\nNaN\nNaN\n0.09026\n0.1076\n0.081321\n0.055161\n0.12804\n0.080773\n0.14874\n0.03515\n-0.087259\n-0.084332\n-0.078504\n-0.10003\n-0.018342\n0.10876\n0.10954\n-0.052106\n-0.19096\n-0.21211\n-0.18575\n-0.12017\n-0.23722\n-0.33659\n-0.33083\n-0.26218\n-0.031054\n0.070501\n-0.0072844\n0.0064442\n0.098454\n0.023684\n-0.15362\n0.15188\n0.22857\n0.16665\n0.24582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.77386\n-0.13004\n0.026799\n-0.11569\n-0.30388\n-0.32626\n0.056457\n-0.54126\n-0.40001\n-1.5341\n-1.4522\n-1.3426\n-0.19609\n-0.1072\n-0.094501\n0.037273\n0.23345\n0.27513\n0.099551\n0.030538\n0.085867\n-0.087357\n-0.054137\n0.13242\n0.22746\n0.055952\n-0.067646\nNaN\n-0.41548\nNaN\n0.12161\n0.14643\n0.057283\n-0.0099372\n0.18056\n0.18414\n-0.036734\n-0.12117\n-0.17827\n-0.074247\n-0.016076\n-0.020982\n-0.07303\n0.094161\n0.055111\n-0.061011\n-0.18524\n-0.16233\n-0.083489\n-0.29147\n-0.37428\n-0.42207\n-0.38589\n-0.18717\n-0.011021\n0.16585\n0.185\n0.21198\n0.15151\n0.25564\n0.46666\n0.69216\n0.84462\n0.49377\n1.2622\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10061\n0.70185\n0.40831\n0.3005\n0.39845\n-0.32322\n0.10457\n-0.091651\n-0.5238\n-1.3043\n-0.15561\n0.021887\n0.047795\n0.0066402\n-0.093466\n0.03123\n0.32201\n0.36621\n0.14025\n0.13051\n0.2088\n0.26476\n0.12939\n0.049667\n0.23769\n0.32445\n0.30774\n0.41469\n0.17161\n0.093352\n0.055687\n0.065471\n0.027487\n0.0045302\n0.080829\n0.089287\n-0.093012\n-0.12002\n-0.19071\n-0.068104\n-0.050331\n-0.12475\n-0.050276\n0.026395\n0.037786\n0.023026\n-0.015048\n-0.054109\n0.051219\n-0.21435\n-0.1914\n-0.43021\n-0.43825\n-0.14209\n0.078102\n0.32705\n0.27271\n0.34107\n0.31328\n0.4417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.037361\n1.3983\n0.016636\n-0.17963\n0.35823\n0.12315\n0.26941\n-0.20067\n-0.29593\n0.13384\n0.10374\n0.12664\n0.18172\n0.13424\n0.096249\n-0.077511\n0.098052\n0.19176\n0.10839\n0.19033\n0.25419\n0.19106\n0.084022\n-0.00021838\n-0.15466\n0.020601\n0.019562\n-0.039362\n0.081843\n0.077432\n0.056757\n0.053869\n0.043755\n-0.061012\n-0.051009\n-0.085373\n-0.16594\n-0.12616\n-0.082524\n-0.10071\n-0.15254\n-0.14362\n-0.041648\n-0.021935\n-0.030527\n0.031123\n-0.076251\n-0.14939\n-0.27008\n-0.098153\n-0.16933\n-0.63288\n-0.50256\n-0.16236\n0.1767\n0.2551\n0.24966\n0.43263\n1.8027\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22854\n-0.72779\n-0.077762\n-0.13252\n0.064707\n0.36473\n0.24977\n0.2042\n0.08727\n0.15193\n0.25383\n0.22175\n0.19437\n0.062\n0.0331\n-0.0041404\n0.12216\n0.2145\n0.29091\n0.20626\n0.14597\n0.20835\n-0.15318\n-0.10293\n-0.039442\n-0.079684\n-0.0038226\n0.071072\n0.10165\n0.0065568\n-0.14188\n-0.098483\n-0.045118\n-0.10743\n-0.19593\n-0.078548\n0.03426\n-0.051628\n-0.11728\n-0.036034\n-0.031144\n-0.026288\n-0.015642\n0.18299\n0.076189\n-0.1612\n-0.38122\n-0.50969\n-0.33517\n-0.34595\n-0.24899\n-0.067936\n0.04551\n0.20174\n0.1864\n0.25358\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15037\n-0.24937\n-0.17825\n0.28205\n0.35509\n0.28965\n0.21657\n0.09294\n0.10153\n0.15066\n0.18735\n0.26009\n0.17723\n0.028381\n-0.090415\n0.016684\n0.13553\n0.26638\n0.083907\n0.15221\n0.19585\n-0.030147\n-0.051321\n-0.36822\n-0.1192\n0.0022728\n-0.066145\n-0.028132\n-0.058096\n-0.083694\n-0.064865\n-0.10225\n-0.026533\n-0.10418\n-0.015877\n0.045256\n-0.0010903\n-0.096795\n-0.075623\n-0.025728\n-0.10058\n-0.12236\n0.10261\n0.090522\n-0.042086\n-0.33619\n-0.47761\n-0.26433\n-0.21469\n-0.16812\n0.041912\n0.16103\n0.033964\n-0.037659\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.098713\n0.0179\n0.11469\n0.16481\n0.45382\n0.27207\n0.23406\n0.12269\n0.080566\n0.094425\n0.13355\n0.2422\n0.25833\n0.070676\n0.027774\n0.10202\n0.038842\n-0.071246\n0.030995\n0.18957\n0.15424\n-0.02988\n-0.088306\n-0.030591\n0.05257\n0.084693\n0.075671\n-0.084608\n-0.060511\n-0.013478\n-0.056933\n-0.15559\n0.012409\n-0.055899\n-0.048813\n-0.012151\n-0.052805\n-0.13552\n-0.038075\n-0.01038\n0.00059359\n0.10516\n0.091817\n0.15046\n-0.040349\n-0.21774\n-0.23562\n-0.16171\n-0.18074\n-0.1412\n-0.018871\n0.17257\n0.24199\n0.083441\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24676\n-0.070824\n0.037565\n0.09192\n0.42582\n0.28534\n0.28051\n0.18959\n0.023022\n0.046841\n0.10983\n0.25316\n0.24417\n0.17101\n0.098845\n0.15689\n0.059397\n-0.0073894\n0.16032\n0.26616\n0.094936\n0.14883\n0.10571\n0.10312\n0.12502\n0.21709\n0.12717\n-0.063637\n0.00603\n0.043532\n-0.028448\n-0.1126\n-0.060215\n-0.074401\n-0.10576\n-0.044374\n-0.13693\n-0.14014\n-0.091929\n-0.023069\n0.044689\n0.1407\n0.10592\n0.073538\n-0.076415\n-0.070466\n-0.15631\n-0.16887\n-0.14493\n-0.045028\n0.019758\n0.1934\n0.52156\n0.98753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.53626\n0.11776\n0.0255\n0.20132\n0.28391\n0.45382\n0.24538\n0.26817\n0.16189\n0.12511\n0.24657\n0.30248\n0.30816\n0.27392\n0.29716\n0.21116\n0.11598\n0.069897\n-0.1185\n0.21411\n0.1547\n0.17077\n0.26175\n0.20566\n0.16701\n0.18197\n0.11382\n0.064046\n0.0061669\n0.061432\n0.035626\n-0.058766\n-0.10028\n-0.10235\n-0.12301\n-0.046526\n-0.032884\n-0.11085\n-0.095896\n-0.054454\n-0.030364\n-0.04673\n0.11331\n0.12856\n0.03232\n-0.11918\n-0.16127\n-0.128\n-0.15042\n-0.11984\n-0.066807\n0.038116\n0.20995\n0.67935\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.4319\n1.1274\nNaN\n-0.082735\n-0.12072\n0.1453\n0.27603\n0.23632\n0.18659\n0.26975\n0.23644\n0.15761\n0.1503\n0.36453\n0.31394\n0.31365\n0.28754\n0.041139\n0.2673\n0.1505\n0.032669\n-0.14264\n-0.055196\n0.20281\n0.20151\n0.2438\n0.2349\n0.18971\n0.18592\n0.20376\n0.074417\n-0.14326\n0.040029\n0.044745\n-0.047188\n-0.06919\n-0.020838\n-0.065028\n-0.091077\n-0.050244\n-0.07724\n-0.055315\n-0.07047\n-0.051496\n-0.050251\n-0.026438\n0.12332\n0.13882\n0.070239\n-0.034717\n-0.14103\n-0.15142\n-0.03785\n-0.020273\n-0.1296\n0.033712\n0.3079\n0.44046\n0.32787\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4288\n0.35012\n0.39816\n0.32516\n0.26719\n0.41758\n0.30047\n0.44356\n0.32031\n0.13833\n0.082556\n0.18015\n0.11256\n0.046415\n0.18046\n0.36431\n0.24802\n0.24061\n0.11242\n-0.2141\n-0.09995\n-0.047013\n-0.12789\n-0.094318\n0.043485\n0.13797\n0.15484\n0.10927\n0.16563\n0.030976\n0.11533\n0.11901\n-0.046431\n-0.44785\n0.076712\n0.0022581\n-0.014975\n-0.044752\n-0.076955\n-0.050594\n0.053171\n-0.0038878\n-0.11084\n-0.089784\n-0.073479\n-0.0045884\n-0.041332\n-0.011841\n0.12578\n0.12388\n0.046713\n0.0036116\n-0.045836\n-0.23126\n-0.26485\n-0.07139\n-0.14113\n0.08214\n0.41487\n0.40744\n-0.48097\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.57341\n0.60289\n0.94228\n0.74806\n0.68706\n0.47024\n0.37697\n0.43472\n0.30825\n0.14352\n0.041425\n-0.054309\n-0.023877\n-0.015907\n0.093884\n0.20277\n0.087076\n0.042822\n-0.012797\n-0.12521\n-0.15064\n-0.16876\n-0.15001\n-0.039366\n0.0079245\n0.081545\n0.068711\n0.030117\n0.083993\n0.022485\n-0.073441\n-0.035891\n-0.050011\n-0.55645\n0.05354\n-0.002301\n-0.055124\n-0.082533\n-0.17317\n-0.039863\n0.0061069\n-0.033669\n-0.049963\n-0.048639\n-0.038548\n-0.043246\n-0.11299\n-0.088043\n0.072706\n0.05022\n0.066138\n-0.048142\n-0.11273\n-0.20791\n-0.33987\n-0.22898\n-0.12424\n0.20335\n0.41616\n0.45688\n-0.47602\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19471\n0.56249\n0.69177\n0.71841\n0.45784\n1.0998\n1.8558\n1.2465\n0.98187\n0.38438\n0.31529\n0.54477\n0.39137\n0.15403\n0.10119\n0.052908\n0.0036843\n-0.0055734\n0.015424\n0.030106\n0.030391\n-0.031924\n-0.075262\n-0.13774\n-0.2361\n-0.22409\n-0.11309\n0.016997\n0.014834\n-0.016879\n-0.061707\n0.050774\n0.12764\n0.049067\n-0.114\n-0.003185\n-0.064413\n-0.28019\n0.018515\n-0.010472\n-0.0084282\n0.0021449\n-0.046949\n-0.037106\n-0.047582\n-0.03878\n-0.050444\n-0.10814\n-0.03073\n-0.1164\n-0.22129\n-0.16216\n-0.040616\n0.01104\n-0.026195\n-0.17031\n-0.21972\n-0.16917\n-0.16957\n-0.16252\n-0.078288\n0.15161\n0.40063\n0.64127\n-0.46653\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11114\n0.59554\nNaN\nNaN\nNaN\nNaN\n2.3004\n1.7917\n1.1599\n0.82582\n0.29381\n0.44781\n0.37296\n0.38012\n0.24348\n0.13618\n0.042142\n0.035811\n0.090871\n0.10176\n0.071442\n-0.13235\n-0.17728\n-0.1193\n-0.15251\n-0.33226\n-0.44754\n-0.16246\n-0.042699\n-0.03865\n-0.043311\n-0.12473\n0.029713\n0.02709\n0.10818\n-0.08659\n-0.12267\n-0.069047\n-0.054149\n0.021293\n0.03945\n0.043858\n0.058551\n0.009158\n-0.0179\n-0.062308\n-0.072097\n-0.058621\n-0.10935\n-0.10724\n-0.1177\n-0.22695\n-0.16891\n-0.10418\n-0.054946\n-0.063344\n-0.22579\n-0.22697\n-0.12281\n-0.17509\n-0.24308\n-0.12493\n0.17057\n0.32014\n0.097654\n0.096697\nNaN\nNaN\nNaN\nNaN\n0.57562\n1.0943\n0.83227\n0.95117\nNaN\nNaN\n2.4503\n2.1846\n1.816\n0.92111\n0.89979\n0.4809\n0.33831\n0.16461\n0.2509\n0.31887\n0.16946\n0.081377\n0.056696\n0.070344\n0.097426\n0.06924\n-0.017678\n-0.14107\n-0.28931\n-0.16032\n-0.097695\n-0.21439\n-0.40444\n-0.35585\n-0.14767\n-0.090124\n-0.04739\n-0.08882\n-0.043758\n-0.05323\n-0.092348\n-0.19268\n-0.11002\n-0.0049817\n-0.050052\n0.086043\n0.062299\n0.046174\n0.076893\n0.12559\n0.059565\n-0.048686\n-0.057111\n-0.0057328\n-0.0423\n-0.10923\n-0.13069\n-0.15811\n-0.13069\n-0.082182\n-0.057156\n0.0097962\n-0.030401\n-0.13742\n-0.14097\n-0.15821\n-0.24637\n-0.1486\n0.19991\n0.15751\n-0.0015996\n0.064019\n-0.18314\nNaN\n-1.778\n-1.5236\n0.16617\n1.6753\nNaN\nNaN\nNaN\nNaN\n-0.13078\n1.3397\n1.3353\n0.80233\n0.53063\n0.53886\n0.38549\n0.21671\n0.31384\n0.29242\n0.1656\n-0.10302\n-0.11654\n0.10382\n0.080016\n0.016243\n-0.15656\n-0.17821\n-0.19255\n-0.14832\n-0.069097\n-0.13092\n-0.18448\n-0.31954\n-0.23691\n-0.10475\n-0.094551\n-0.10674\n-0.095096\n-0.11668\n-0.19242\n-0.21927\n-0.14805\n-0.1052\n-0.11609\n0.090521\n0.0093267\n0.02001\n0.065379\n0.14744\n0.074523\n0.017235\n0.006882\n0.013518\n-0.092206\n-0.1804\n-0.11926\n-0.094588\n-0.13367\n-0.17772\n-0.11468\n0.039769\n-0.061231\n-0.071451\n-0.22482\n-0.18355\n-0.16186\n-0.052843\n0.026871\n0.0226\n-0.060455\n0.0109\n0.040659\n-1.1873\n-1.2095\n0.38667\n0.41382\n0.47101\nNaN\nNaN\nNaN\nNaN\nNaN\n0.40682\n0.82002\n0.60287\n0.49353\n0.49853\n0.42837\n0.27064\n0.18934\n0.070333\n0.11798\n-0.25649\n-0.10225\n0.12817\n0.07852\n-0.036607\n-0.061549\n-0.093631\n-0.094956\n-0.082805\n-0.056227\n-0.1183\n-0.2216\n-0.27977\n-0.22502\n-0.18256\n-0.22423\n-0.24091\n-0.45287\n-0.50777\n-0.25125\n-0.16022\n-0.14259\n-0.021981\n-0.046566\n0.086218\n0.082089\n0.031801\n0.018174\n0.16541\n0.16923\n0.11107\n0.08806\n0.026798\n-0.061906\n-0.12731\n-0.05664\n-0.059831\n-0.10941\n-0.15214\n-0.0225\n-0.010153\n-0.20796\n-0.33025\n-0.31811\n-0.28328\n-0.1931\n-0.11023\n0.041645\n-0.15718\n-0.3067\n-0.12287\n0.221\n0.25075\n0.40126\n0.52839\n0.30121\n0.35698\n0.45704\nNaN\nNaN\nNaN\n-1.0082\n0.74086\n0.69736\n0.50752\n0.42317\n0.5005\n0.17486\n-0.063123\n-0.02323\n0.040632\n0.039697\n-0.15979\n-0.061793\n0.10203\n0.068186\n0.10322\n-0.023973\n-0.10506\n-0.088773\n-0.052085\n-0.083502\n-0.090906\n-0.13159\n-0.2138\n-0.21112\n-0.39106\n-0.36676\n-0.27911\n-0.27104\n-0.27626\n-0.32332\n-0.2288\n-0.127\n0.05996\n0.0098761\n0.17448\n0.13107\n0.083244\n0.068575\n0.1211\n0.16898\n0.18181\n0.13413\n0.11424\n-0.0038762\n-0.050377\n-0.0015349\n-0.011938\n-0.063373\n-0.10436\n-0.10314\n-0.15103\n-0.22442\n-0.34287\n-0.29932\n-0.29088\n-0.11415\n-0.016566\n0.030847\n0.19687\n0.1806\n0.18907\n0.23524\n-1.7257\n0.32348\n0.41765\n0.26451\n0.3332\n0.60661\nNaN\nNaN\nNaN\n0.56255\n0.6107\n0.51359\n0.4067\n0.1255\n0.15152\n0.12046\n-0.11524\n-0.088531\n0.11883\n-0.010816\n-0.2158\n-0.12469\n0.069518\n0.11713\n0.16072\n-0.03016\n-0.067489\n-0.070459\n-0.077233\n-0.088895\n-0.02906\n-0.080143\n-0.21154\n-0.25127\n-0.27788\n-0.29303\n-0.24177\n-0.13529\n-0.14745\n-0.28644\n-0.19176\n-0.0592\n0.036512\n-0.058108\n0.10682\n0.13596\n0.12273\n0.14248\n0.15561\n0.17943\n0.18123\n0.12977\n0.12218\n0.053017\n0.0019024\n0.0020363\n-0.003564\n-0.036755\n-0.057977\n-0.14764\n-0.19281\n-0.24365\n-0.27347\n-0.30143\n-0.25024\n-0.148\n0.014966\n-0.096831\n-0.10058\n0.13787\n0.22536\n-1.5534\n-2.0178\n0.3383\n0.2977\n0.3504\n0.58386\n0.77812\n0.66111\n0.44808\n0.18909\n0.44694\n0.44836\n0.45554\n0.31953\n0.029236\n0.024574\n-0.034234\n-0.077847\n0.016104\n0.084778\n0.058184\n-0.14326\n-0.058202\n0.042473\n0.1452\n0.17968\n0.096608\n0.015072\n-0.0012872\n-0.041245\n-0.068392\n-0.00067302\n-0.060311\n-0.13783\n-0.26818\n-0.30553\n-0.22608\n-0.11664\n-0.021364\n-0.024427\n-0.125\n-0.093485\n-0.010957\n-0.067743\n-0.043898\n0.21289\n0.17457\n0.16046\n0.21042\n0.22712\n0.18897\n0.17119\n0.13781\n0.080474\n0.066719\n0.054072\n0.053041\n0.13071\n0.16732\n0.063179\n-0.12379\n-0.20364\n-0.24158\n-0.023219\n0.092732\n-0.015913\n-0.033121\n-0.054746\n-0.24181\n-0.24765\n0.047663\n0.18266\n-1.4304\n-2.133\n-2.141\n0.23588\n0.28847\n0.26668\n0.42917\n0.30813\n0.43398\n0.4218\n0.29006\n0.27408\n0.41711\n0.045026\n-0.065297\n0.072982\n0.087899\n0.056205\n-0.033052\n-0.076276\n-0.065283\n-0.070735\n0.0071486\n0.071893\n0.12682\n0.10641\n0.048323\n0.010373\n-0.0063871\n0.0061568\n0.043769\n0.053147\n0.034362\n-0.098129\n-0.21779\n-0.2444\n-0.14306\n-0.056268\n0.029341\n0.039772\n-0.025175\n-0.10386\n-0.12355\n-0.086604\n0.057869\n0.2148\n0.2242\n0.27464\n0.31005\n0.30813\n0.22696\n0.18674\n0.23022\n0.17231\n0.13283\n0.11147\n0.14325\n0.17667\n0.19961\n0.17852\n-0.0030522\nNaN\nNaN\nNaN\n0.061723\n-0.15041\n-0.083254\n-0.066561\n-0.37291\n-0.20263\n0.045922\n0.09826\n-1.4701\n-1.9432\n-1.7268\n0.192\n0.22928\n0.11906\n0.15246\n0.265\n0.3467\n0.22531\n0.14374\n0.16273\n0.10804\n-0.078927\n-0.096714\n0.058728\n0.12119\n0.053803\n-0.020704\n-0.077579\n-0.08316\n-0.037997\n0.028311\n0.071387\n0.063428\n-0.01812\n-0.079478\n-0.015582\n0.07022\n0.085746\n0.070024\n0.068035\n0.036664\n-0.056605\n-0.11189\n-0.13143\n-0.10761\n-0.042903\n0.0033241\n0.076369\n0.064589\n-0.027842\n-0.072089\n-0.057992\n-0.0090532\n0.32005\n0.40888\n0.42355\n0.34261\n0.31831\n0.29174\n0.26173\n0.28901\n0.22584\n0.2062\n0.24974\n0.24225\n0.31942\n0.43933\n0.22987\n0.048649\nNaN\nNaN\nNaN\n-0.065913\n-0.19434\n-0.33332\n-0.23504\n-0.29557\n-0.052146\n0.059827\n0.089848\n-1.5435\n-1.4284\n-1.2471\n0.044252\n0.13155\n0.15194\n0.20656\n0.3685\n0.28539\n0.14423\n0.059698\n0.026303\n-0.0090141\n-0.14467\n-0.1408\n0.042678\n0.1323\n0.091106\n0.067714\n0.02362\n-0.064469\n-0.037189\n0.032986\n0.079016\n0.03982\n-0.057058\n-0.022716\n0.08428\n0.11854\n0.031252\n0.011173\n0.045699\n0.08787\n0.02233\n-0.055507\n-0.091592\n-0.064285\n0.024727\n0.016024\n0.082951\n0.12276\n0.022498\n-0.047897\n0.032228\n-0.0037608\n0.38533\n0.41288\n0.40502\n0.38381\n0.36975\n0.3701\n0.35483\n0.27481\n0.24471\n0.28295\n0.343\n0.37862\n0.39892\n0.39769\n0.31202\n0.27138\n0.010646\nNaN\nNaN\n-0.17817\n-0.14665\n-0.17786\n-0.1528\n-0.072696\n0.065752\n0.11017\n0.048167\n0.090539\n0.042705\n0.0068272\n-0.1529\n0.0074546\n0.11343\n0.1971\n0.31141\n0.2293\n0.16028\n0.10273\n0.034576\n0.019223\n-0.075252\n-0.086706\n0.090531\n0.071957\n0.07412\n0.022163\n-0.091748\n-0.13084\n-0.078246\n0.0043368\n0.074706\n0.086575\n0.13354\n0.031766\n0.065512\n0.10921\n-0.0096699\n-0.019623\n0.010918\n0.12637\n0.07032\n-0.063146\n-0.034661\n0.027344\n0.084749\n0.047703\n0.068484\n0.13569\n-0.014129\n0.048461\n0.14378\n0.051867\n0.34478\n0.35683\n0.37733\n0.4343\n0.49003\n0.4816\n0.43672\n0.3279\n0.33721\n0.36563\n0.35271\n0.39353\n0.39636\n0.37737\n0.19352\n0.17926\n0.16196\n-0.19297\n-0.26406\n-0.12382\n-0.085482\n-0.047146\n-0.040692\n0.0012282\n0.083134\n0.058997\n0.028339\n0.059447\n0.070026\n0.043511\n-0.013776\n0.041664\n0.11141\n0.14305\n0.22871\n0.16893\n0.13404\n0.19461\n0.14766\n0.10884\n0.080518\n0.095071\n0.15552\n0.16397\n0.045916\n0.022839\n-0.081941\n-0.19028\n-0.2533\n-0.14525\n0.021688\n0.2827\n0.21293\n0.091693\n0.075686\n0.13857\n0.065298\n0.0046115\n0.014722\n0.064177\n0.040018\n-0.023611\n0.10433\n0.1211\n0.088603\n0.081936\n0.18598\n0.19726\n0.04126\n0.075945\n0.032828\n0.0062818\n0.28887\n0.34799\n0.40139\n0.46886\n0.56426\n0.60379\n0.52546\n0.47127\n0.39879\n0.31188\n0.34676\n0.38588\n0.37421\nNaN\nNaN\nNaN\nNaN\n0.04999\n-0.11919\n-0.074644\n0.011443\n0.0034724\n-0.039277\n0.0012869\n0.057546\n-0.0085128\n0.068012\n0.15479\n0.081671\n0.019449\n-0.016565\n-0.058209\n-0.011344\n-0.011132\n-0.049694\n0.022695\n0.04061\n0.17996\n0.19864\n0.20595\n0.17723\n0.19059\n0.20715\n0.07848\n-0.029428\n0.04825\n0.0061156\n-0.13343\n-0.12972\n-0.15615\n-0.038306\n0.093618\n0.12892\n0.054457\n0.052691\n0.14069\n0.069632\n0.03327\n0.075667\n0.011062\n-0.015145\n0.070881\n0.20682\n0.11151\n-0.0089935\n0.061526\n0.27486\n0.23602\n0.13388\n0.001128\n-0.070252\n-0.053378\n0.37208\n0.37665\n0.43529\n0.51585\n0.55242\n0.55232\n0.59098\n0.55705\n0.49405\n0.41525\n0.39997\n0.41086\n0.38128\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14047\n-0.050135\n0.0086274\n0.028543\n-0.07068\n-0.037277\n0.015742\n0.01963\n0.093384\n0.11702\n0.007817\n-0.023223\n-0.071293\n-0.090643\n0.011651\n-0.038205\n-0.05088\n0.023511\n0.070041\n0.22873\n0.2829\n0.28646\n0.27024\n0.28871\n0.23725\n-0.10204\n-0.19791\n-0.13369\n-0.0089697\n-0.07057\n-0.06115\n-0.058471\n-0.052611\n-0.069235\n-0.069665\n-0.1516\n-0.064021\n0.034006\n-0.0013247\n-0.00866\n0.13172\n0.11656\n0.021347\n0.11689\n0.15911\n0.093658\n-0.016077\n0.16271\n0.3717\n0.23745\n0.1664\n-0.054023\n-0.092201\n-0.094039\n0.41315\n0.39736\n0.42968\n0.56173\n0.57056\n0.71328\n0.72084\n0.6527\n0.52725\n0.44996\n0.40852\n0.42299\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.021868\n-0.042369\n-0.044317\n-0.073737\n-0.10222\n-0.071318\n-0.090767\n0.029747\n0.053168\n0.061979\n-0.016355\n-0.032643\n0.011736\n0.0069825\n-0.039702\n-0.029817\n0.0221\n-0.0032753\n0.16224\n0.28785\n0.29727\n0.28524\n0.33079\n0.3404\n-0.10018\n-0.34139\n-0.29634\n-0.051444\n-0.054401\n-0.031799\n-0.065865\n-0.074253\n0.01226\n-0.12899\n-0.13984\n-0.08107\n0.0036058\n-0.014482\n-0.0082135\n0.17622\n0.05595\n-0.024383\n0.033846\n0.075458\n0.048292\n0.11542\n0.1737\n0.21763\n0.18425\n0.17293\n-0.14052\n-0.15608\n-0.39333\n0.47032\n0.41854\n0.517\n0.62519\n0.63145\n0.78734\n0.9331\n0.78504\n0.49096\n0.44025\n0.40787\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17916\n-0.050518\n-0.030274\n-0.045766\n-0.086515\n-0.083171\n-0.059851\n-0.023302\n0.10439\n0.12508\n0.0082035\n0.015105\n0.060258\n-0.026763\n-0.058642\n-0.092083\n-0.11869\n-0.13594\n0.06981\n0.15585\n0.15137\n0.21574\n0.22725\n0.10073\n-0.10933\n-0.27264\n-0.34447\n-0.14025\n-0.060318\n-0.014146\n-0.071606\n0.015871\n0.087502\n0.12789\n0.11803\n0.033397\n0.012483\n-0.046711\n-0.049179\n0.14533\n-0.088101\n-0.069433\n-0.12829\n-0.089722\n0.086915\n-0.075927\n0.064075\n0.11189\n0.19517\n0.2342\n-0.74627\n-0.86125\n-0.79433\n0.15238\n0.01717\n0.022179\n0.011585\n-0.005854\n-0.078615\n-0.17209\n-0.059791\n0.047706\n0.052229\n-0.023403\n0.021635\n-0.061347\n-0.007523\n0.056969\n0.024527\n0.056912\n0.030843\n0.26561\n0.17617\n0.23296\n0.26139\n0.25872\n0.050961\n0.15229\n0.05786\n-0.091915\n-0.23998\n-0.1403\n-0.1261\n-0.2427\n-0.18379\n-0.029296\n-0.057283\n0.038101\n-0.067547\n-0.067707\n-0.019795\n-0.12948\n-0.17587\n-0.080357\n0.011027\n0.1112\n0.19037\n0.19346\n0.12388\n0.16061\n0.1448\n0.09713\n0.059358\n0.045739\n0.15523\n0.14476\n0.019118\n0.20112\n0.1287\n0.077723\n-0.09975\n-0.27972\n-0.32731\n-0.36219\n-0.13254\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.019057\n-0.003791\n0.030153\n0.18569\n0.1735\n-0.17382\n-0.24046\n-0.095863\n0.084674\n0.080062\n0.13976\n0.14221\n-0.053756\n0.024468\n0.093122\n-0.028465\n-0.12147\n-0.073629\n0.082755\n0.13937\n0.47951\n0.24587\n0.17413\n0.26562\n0.15945\n0.09792\n0.015979\n-0.14748\n-0.039634\n-0.10181\n-0.045592\n-0.046605\n0.10068\n0.0019036\n0.043405\n-0.1421\n-0.13636\n-0.0094314\n-0.065048\n0.074455\n0.069448\n0.046782\n0.14881\n0.24921\n0.21029\n0.13688\n0.12567\n0.085769\n0.047765\n0.037789\n0.047581\n0.12054\n0.12681\n0.1757\n0.39509\n0.2063\n0.049382\n-0.18229\n-0.23011\n-0.18139\n-0.13267\n0.019453\n0.14316\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.048506\n-0.01022\n0.013218\n-0.0074086\n-0.11045\n-0.037046\n-0.058464\n-0.042264\n0.0040926\n0.15807\n0.23176\n0.19108\n0.10826\n0.14068\n0.33225\n0.074524\n0.014568\n0.060704\n0.044731\n0.23476\n0.26341\n0.19161\n0.28307\n0.216\n0.18818\n0.21144\n0.12067\n-0.010041\n0.028033\n-0.12651\n-0.039802\n-0.058538\n0.20804\n0.15726\n-0.17499\n-0.21475\n-0.085677\n-0.025467\n0.043395\n0.013126\n0.032517\n0.12068\n0.1183\n0.21126\n0.13194\n0.087786\n0.074822\n0.067948\n0.042283\n0.080909\n0.14073\n0.088841\n0.078889\n0.26636\n0.33163\n0.18567\n0.13594\n-0.15783\n-0.13525\n0.023448\n-0.18259\n0.032566\n0.14536\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.026452\n0.0016894\n-0.0028645\n0.0069917\n0.0031483\n0.039871\n0.1083\n0.069445\n0.031751\n0.073945\n0.11042\n0.018368\n0.39576\n0.30105\n0.24797\n0.086268\n0.063138\n0.047103\n0.043234\n0.19596\n0.36422\n0.13247\n0.23047\n0.17999\n0.24719\n0.25093\n0.11254\n0.065253\n-0.030555\n-0.29881\n-0.045264\n0.018245\n0.20219\n0.16404\n-0.057868\n-0.14157\n-0.15993\n-0.054803\n0.058744\n0.0027678\n-0.0053485\n0.068567\n0.042992\n0.021752\n0.066725\n0.050213\n0.039246\n0.10814\n0.16498\n0.29764\n0.3332\n0.24186\n0.30602\n0.36216\n0.28892\n0.19513\n-0.67322\n-1.2839\n-1.7322\n-0.38314\n-0.28605\n0.10868\n0.17095\nNaN\nNaN\nNaN\n0.074011\nNaN\nNaN\nNaN\nNaN\nNaN\n0.070449\n0.034364\n0.030021\n0.019858\n0.06737\n0.035631\n0.084947\n0.10657\n0.023121\n-0.075783\n-0.0054931\n-0.079562\n-0.071953\n0.076844\n-0.047021\n-0.078319\n-0.038262\n0.045967\n-0.048973\n0.02125\n0.40096\n0.22038\n0.33141\n-0.10184\n0.19684\n0.21111\n0.12653\n-0.0017653\n0.022367\n-0.0030971\n-0.037673\n0.10225\n0.12501\n0.11248\n0.15768\n-0.071067\n-0.24758\n-0.11308\n0.0084641\n0.0055573\n0.018566\n0.076533\n0.034017\n0.052002\n0.042356\n0.055188\n0.13788\n0.26167\n0.2495\n0.25445\n0.31981\n0.28723\n0.24634\n0.36499\n0.2736\n0.2767\n0.47926\n-1.0468\n-1.7531\n-0.51738\n-0.38047\n0.017181\n0.15487\n-0.10646\n-0.24536\n-0.067423\n-0.25334\nNaN\nNaN\nNaN\nNaN\nNaN\n0.057884\n-0.0039412\n-0.054017\n0.0089227\n0.093327\n0.031514\n0.082427\n0.13754\n0.074568\n0.012866\n0.029262\n0.11306\n0.11773\n-0.034496\n-0.12278\n0.038844\n-0.06464\n-0.06847\n-0.15905\n-0.4749\n-0.18407\n0.081605\n0.04174\n-0.033036\n0.046664\n0.18236\n0.17296\n0.13061\n0.066209\n0.099179\n0.049035\n0.080027\n-0.001679\n0.007657\n0.13569\n-0.050713\n-0.22737\n-0.092077\n-0.013717\n0.0429\n0.051664\n0.0043356\n0.016343\n-0.037845\n-0.22285\n-0.043647\n0.1519\n0.26772\n0.23526\n0.26434\n0.25927\n0.24798\n0.2253\n0.21071\n0.16428\n0.077513\n0.20757\n-0.10902\n-1.1121\n-0.63227\n-0.5715\n-0.14141\n-0.1453\n-0.38486\n-0.095092\n-0.055693\n-0.17864\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.023515\n-0.064252\n-0.041582\n0.084951\n0.1382\n0.0521\n0.13636\n0.13159\n0.12812\n0.076475\n0.064442\n0.045725\n0.042049\n-0.032545\n-0.066181\n-0.071173\n-0.091136\n-0.010269\n-0.01225\n-0.17083\n-0.09927\n-0.18263\n-0.0077487\n-0.12732\n-0.046073\n0.097548\n0.15802\n0.2406\n0.062587\n0.063111\n-0.014014\n0.047811\n-0.12753\n-0.059664\n-0.031279\n-0.12068\n-0.18399\n-0.10545\n-0.035911\n0.052239\n0.082924\n0.019982\n-0.060577\n-0.15289\n-0.42608\n-0.087313\n0.14535\n0.25638\n0.28023\n0.27679\n0.2038\n0.25443\n0.32755\n0.28572\n0.15794\n-0.0047183\n0.13829\n-1.1092\n-1.3727\n-0.31677\n-0.59208\n-0.46871\nNaN\n-0.54048\n-0.34621\nNaN\nNaN\n-0.046692\n-0.3437\nNaN\nNaN\nNaN\n0.0058867\n0.024583\n-0.0046542\n0.10482\n0.23137\n0.017294\n0.058965\n0.12345\n0.13834\n0.088086\n0.038899\n0.054615\n0.0036093\n0.031676\n-0.071559\n-0.091213\n-0.016081\n0.0051247\n-0.034174\n-0.10529\n-0.085727\n-0.032337\n-0.073713\n-0.020149\n-0.008748\n0.091788\n0.155\n0.19571\n0.038491\n0.015256\n-0.0084997\n-0.18341\n-0.31554\n-0.1236\n-0.083044\n-0.091481\n-0.096005\n-0.074306\n-0.040547\n-0.058897\n0.041696\n0.0076634\n-0.047121\n-0.13809\n-0.50945\n-0.073464\n0.20518\n0.23974\n0.2149\n0.27298\n0.25385\n0.2951\n0.35854\n0.30956\n0.27737\n0.15184\n0.048501\n0.24883\n0.16918\n-0.029277\n-0.33232\n-0.46279\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30255\n-0.44091\n-0.46952\nNaN\nNaN\n0.031012\n-0.072749\n-0.12234\n-0.036548\n0.10002\n-0.0083974\n-0.010078\n0.1317\n0.15991\n0.12724\n0.05477\n0.057052\n0.076549\n0.0188\n0.0057203\n0.049866\n0.030421\n0.05559\n-0.05265\n-0.044021\n-0.014419\n-0.039057\n0.0028142\n0.064069\n0.064115\n0.12166\n0.11647\n0.027163\n-0.0014618\n-0.032282\n-0.01496\n-0.027068\n-0.25446\n-0.20015\n-0.13025\n-0.11817\n-0.080732\n-0.057362\n-0.02994\n-0.045333\n-0.071094\n-0.020919\n-0.09438\n-0.2188\n-0.40743\n0.13392\n0.275\n0.30286\n0.30023\n0.31139\n0.3273\n0.38294\n0.39582\n0.47342\n0.46004\n0.19917\n0.073384\n0.19579\n0.38315\n0.35051\n-0.33668\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.42123\n-0.31479\nNaN\nNaN\n-0.15145\n-0.066324\n-0.031843\n-0.080487\n0.15938\n0.0039834\n-0.022198\n0.065461\n0.1175\n0.09204\n0.064939\n0.060281\n0.08266\n0.011937\n0.069362\n0.11767\n0.059831\n0.12408\n0.10583\n0.14583\n0.094849\n0.023301\n0.0698\n0.03488\n0.014355\n0.040748\n0.10885\n0.082771\n0.011978\n-0.061241\n-0.064606\n-0.17312\n-0.29349\n-0.34132\n-0.26384\n-0.060878\n-0.11953\n-0.072408\n-0.071219\n-0.051177\n-0.018209\n0.04361\n-0.13802\n-0.23443\n-0.040704\n0.27758\n0.22785\n0.29904\n0.31654\n0.39566\n0.38234\n0.48245\n0.48752\n0.48433\n0.43156\n0.37594\n0.25791\n0.12183\n0.42627\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.25421\nNaN\n-0.19147\n-0.030261\n-0.18742\n-0.12869\n-0.058964\n-0.069705\n0.035125\n-0.038712\n-0.037774\n-0.043416\n0.065215\n0.13141\n0.080454\n-0.011524\n0.083191\n0.04179\n0.14313\n0.19216\n0.13341\n0.12268\n0.11969\n0.12812\n0.0070524\n-0.0017474\n-0.024052\n-0.11332\n0.006142\n0.23901\n0.16152\n0.080038\n0.051432\n0.027842\n-0.083132\n-0.23623\n-0.43549\n-0.241\n-0.26294\n-0.07829\n-0.10779\n0.0079183\n-0.13767\n-0.075321\n0.11968\n0.19064\n-0.098797\n-0.10095\n0.076095\n0.22823\n0.21398\n0.2736\n0.36915\n0.44963\n0.39227\n0.47053\n0.44163\n0.48843\n-0.76247\n-0.85045\n0.13202\n0.065645\n0.3509\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.036098\n0.18792\n-0.21441\n-0.22567\n-0.12704\n-0.064763\n-0.065456\n-0.048943\n0.0077872\n0.052805\n0.076505\n0.091168\n0.010932\n0.01639\n0.097195\n0.035501\n0.14954\n0.17012\n0.16928\n0.10696\n0.026308\n0.018758\n-0.079376\n-0.080215\n-0.11878\n-0.16827\n-0.035354\n0.15018\n0.16071\n0.13993\n0.034095\n0.059325\n-0.026367\n-0.36316\n-1.8819\n-0.56791\n-0.15438\n-0.06781\n-0.016917\n-0.032405\n-0.093233\n0.0051539\n0.17086\n0.020235\n0.0074279\n0.1099\n0.17116\n0.24081\n0.30918\n0.41563\n0.47755\n0.51512\n0.45302\n0.46208\n0.35572\n-1.1118\n-1.2665\n-0.91196\n0.16229\n0.085935\n0.085959\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.029362\n-0.076024\n-0.022698\n-0.029166\n-0.0073953\n0.03214\n0.045951\n0.0094485\n0.074468\n0.059832\n0.026553\n0.039047\n0.011645\n0.077467\n0.16412\n0.18163\n0.20071\n0.10505\n-0.016197\n-0.037072\n-0.074466\n-0.094401\n-0.19959\n-0.052282\n-0.032074\n-0.05451\n0.1647\n0.089575\n-0.01674\n0.0786\n0.05259\n-0.17051\n-2.2955\n-2.0187\n-0.12971\n-0.12807\n-0.079067\n-0.010913\n0.017873\n0.15646\n0.19492\n-0.022855\n-0.084232\n0.19512\n0.25762\n0.31749\n0.3897\n0.46688\n0.51615\n0.5391\n0.51323\n0.42144\n0.27998\n-1.0939\n-1.5399\n-1.4651\n-0.10618\n-0.1668\n-0.22677\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.087204\n0.026371\n-0.0064837\n0.054317\n0.053097\n0.052214\n0.053755\n0.039815\n0.045055\n0.0074845\n-0.014133\n0.038681\n0.17499\n0.24605\n0.21516\n0.18529\n0.08776\n0.080801\n-0.040115\n-0.079582\n-0.04805\n-0.24256\n-0.26511\n-0.19339\n-0.28187\n-0.11403\n0.025631\n0.0042406\n0.094092\n0.14293\n0.080775\n0.046014\n-0.84065\n-0.81023\n-0.77329\n-0.19379\n-0.21438\n-0.0018759\n0.042825\n0.16243\n0.23469\n0.14632\n0.15685\n0.32205\n0.39694\n0.4715\n0.45136\n0.52657\n0.53935\n0.534\n0.50294\n0.35501\n0.25868\n-1.1011\n-1.4925\n-1.6294\n-1.2966\n-0.092648\n-0.22336\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13244\n-0.0070158\n0.073692\n0.14217\n0.082697\n0.039919\n0.071552\n0.035381\n-0.0014739\n0.014266\n-0.043199\n0.041558\n0.20706\n0.24563\n0.256\n0.15119\n0.18005\n0.14657\n0.045961\n-0.052695\n-0.15918\n-0.33114\n-0.45718\n-0.32701\n-0.33315\n-0.11219\n-0.061731\n0.027583\n0.15077\n0.11485\n0.068406\n-0.27453\n-0.11511\n-0.1322\n-0.062023\n-0.16295\n-0.11022\n0.20082\n0.26567\n0.48191\n0.46506\n0.20668\n0.2187\n0.61399\n0.49185\n0.63622\n0.25859\n0.60007\n0.66925\n0.60252\n0.44225\n0.44734\n0.32352\n0.094258\n-1.3117\n-1.706\n-1.6489\n-0.27421\n-0.31716\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.059118\n0.046502\n0.095926\n0.19532\n0.16515\n0.12592\n0.14314\n0.064251\n0.0059971\n0.08636\n0.0255\n0.085397\n0.15821\n0.16214\n0.23286\n0.066975\n0.040346\n0.08703\n0.10216\n-0.026104\n-0.1307\n-0.20117\n-0.66697\n-0.50789\n-0.33642\n-0.17127\n-0.032154\n0.19689\n0.19553\n0.10716\n-0.17127\n-0.32353\n-0.063371\n0.00111\n-0.027425\n0.012896\n0.25605\n0.38268\n0.46775\nNaN\n0.27891\n0.0013168\n0.16745\n0.31787\n0.72451\n0.7684\n0.42216\n0.58125\n0.69461\n0.50323\n0.33431\n0.32536\n0.3199\n0.16349\n0.08479\n-0.10615\n-0.32627\n-0.31322\n-0.57401\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.0060037\n0.043534\n0.096081\n0.21368\n0.14775\n0.16506\n0.12108\n0.063381\n0.044808\n-0.010702\n0.01844\n0.082694\n0.14679\n0.16564\n0.20737\n0.01286\n-0.058689\n-0.027244\n0.041467\n0.0016667\n-0.12305\n-0.20687\n-0.36801\n-0.41138\n-0.2142\n-0.072706\n0.029516\n0.17072\n0.1012\n0.087329\n-0.22074\n-0.21241\n0.0272\n0.19723\n0.17213\n0.2365\n0.41239\n-0.43376\nNaN\nNaN\nNaN\n-0.0050888\n-0.1582\n0.072099\n0.36596\n0.27329\n0.049053\n0.36727\n0.29014\n0.41897\n-0.71854\n0.13045\n0.27685\n0.13975\n0.084782\n-0.0059115\n-0.25105\n-0.26525\n-0.60516\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.029048\n0.02165\n0.11068\n0.21542\n0.14467\n0.10864\n0.075371\n0.060342\n0.044831\n-0.037735\n0.020643\n0.041184\n0.12588\n0.12878\n0.14619\n-1.6435e-05\n0.046447\n-0.010238\n-0.031107\n-0.10662\n-0.21976\n-0.3188\n-0.42954\n-0.46284\n-0.15689\n-0.061658\n0.075228\n0.11313\n0.08942\n0.015784\n0.029855\n0.061062\n0.052607\n0.1611\n0.25251\n0.35518\n0.44293\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7196\n-0.16676\n0.32227\n0.25121\n0.14519\n0.40608\n0.15342\n0.080946\n0.20833\n-0.67396\n-0.75374\n-1.1296\n-0.011621\n0.027945\n-0.12825\n-0.28206\n-0.24153\n-0.33669\n-0.29935\n-0.0605\n0.061668\n0.3077\n0.45459\n-0.16592\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.027353\n0.052185\n0.14185\n0.19418\n0.13909\n0.0083285\n0.05175\n0.045471\n-0.0087781\n-0.023134\n-0.035662\n-0.039444\n0.037982\n0.10784\n0.12252\n0.026432\n-0.049729\n-0.072409\n-0.077137\n-0.12847\n-0.1818\n-0.35476\n-0.41178\n-0.38754\n-0.16714\n-0.046613\n0.022997\n0.053092\n0.12879\n0.096328\n-0.016378\n0.17149\n0.2921\n0.22807\n0.27979\n0.18012\n1.1046\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1955\n-0.03181\n0.13486\n-0.018863\n-0.0059346\n-0.07241\n-0.00042976\n-0.15615\n-0.19374\n-0.90101\n-1.2012\n-1.4404\n-0.18403\n-0.052808\n-0.092202\n-0.14458\n-0.014338\n-0.020627\n-0.044279\n0.10208\n0.10946\n-0.073685\n-0.12083\n-0.095782\n-0.11394\n-0.16667\n-0.35996\nNaN\nNaN\nNaN\n0.058092\n0.085631\n0.061316\n0.032183\n0.11236\n0.069812\n0.13228\n0.022007\n-0.10221\n-0.072772\n-0.068409\n-0.072214\n0.0011137\n0.10895\n0.1075\n-0.028273\n-0.1649\n-0.20848\n-0.17159\n-0.13734\n-0.23287\n-0.39666\n-0.35807\n-0.25402\n-0.033834\n0.078346\n0.013157\n0.047284\n0.16039\n0.10917\n-0.039453\n0.20006\n0.27449\n0.22476\n0.29524\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.77696\n-0.13289\n-0.0095179\n-0.074539\n-0.26107\n-0.29622\n0.046511\n-0.53414\n-0.40442\n-1.5334\n-1.4469\n-1.3474\n-0.29579\n-0.16891\n-0.14723\n-0.025914\n0.16432\n0.21363\n0.038252\n-0.018668\n0.10732\n-0.073524\n-0.0086649\n0.1403\n0.19502\n0.058054\n-0.064248\nNaN\n-0.41883\nNaN\n0.085861\n0.11964\n0.03661\n-0.024691\n0.14168\n0.15458\n-0.040706\n-0.12651\n-0.17397\n-0.05507\n0.00080868\n-0.0023948\n-0.058319\n0.08012\n0.040249\n-0.053318\n-0.15117\n-0.13584\n-0.069377\n-0.23924\n-0.33783\n-0.47379\n-0.40055\n-0.16802\n0.0058596\n0.19037\n0.21274\n0.23694\n0.20716\n0.36282\n0.55767\n0.72778\n0.84469\n0.49376\n1.2604\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10346\nNaN\n0.33271\n0.28911\n0.44516\n-0.26793\n0.10349\n-0.125\n-0.53729\n-1.312\n-0.24545\n-0.075154\n-0.049896\n-0.078091\n-0.14801\n-0.032422\n0.24646\n0.31752\n0.095387\n0.12137\n0.22213\n0.23064\n0.12963\n0.093546\n0.20008\n0.26157\n0.23738\n0.31318\n0.095724\n0.034929\n0.022691\n0.017564\n0.0047565\n-0.00076854\n0.083374\n0.077035\n-0.085728\n-0.10939\n-0.16013\n-0.045675\n-0.034838\n-0.10679\n-0.032231\n0.015541\n0.018018\n0.018617\n-0.034526\n-0.089489\n0.044862\n-0.16888\n-0.14784\n-0.41803\n-0.45242\n-0.1187\n0.11184\n0.37374\n0.29713\n0.33405\n0.40456\n0.5704\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.040517\nNaN\n-0.10896\n-0.19089\n0.40393\n0.17413\n0.24046\n-0.24844\n-0.30453\n0.02928\n-0.012386\n0.025771\n0.097501\n0.061564\n0.030837\n-0.15976\n0.042786\n0.16161\n0.068658\n0.19484\n0.27843\n0.15411\n0.064641\n0.0046743\n-0.15043\n0.021295\n0.017026\n-0.035177\n0.10874\n0.080037\n0.02401\n0.023689\n0.036898\n-0.065676\n-0.061822\n-0.086286\n-0.15251\n-0.1213\n-0.063115\n-0.07972\n-0.13896\n-0.12902\n-0.04021\n-0.018919\n-0.019661\n0.051032\n-0.038657\n-0.17565\n-0.28608\n-0.098614\n-0.13941\n-0.55345\n-0.46492\n-0.14295\n0.20858\n0.27065\n0.24321\n0.4214\n1.8045\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.38298\n-0.74807\n-0.027721\n-0.12285\n0.042827\n0.22996\n0.14844\n0.0973\n-0.0099052\n0.080062\n0.18149\n0.14961\n0.1335\n0.0014798\n-0.013612\n-0.046923\n0.068441\n0.19481\n0.29485\n0.16215\n0.11498\n0.14442\n-0.17051\n-0.097974\n-0.025593\n-0.079941\n0.017293\n0.13833\n0.058742\n-0.021986\n-0.17909\n-0.12235\n-0.059999\n-0.11563\n-0.18045\n-0.078128\n0.040429\n-0.04224\n-0.10679\n-0.031424\n-0.024192\n-0.01786\n0.012522\n0.20702\n0.11128\n-0.1474\n-0.36936\n-0.48286\n-0.33053\n-0.33424\n-0.24769\n-0.051244\n0.074773\n0.18517\n0.1535\n0.22665\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.2344\n-0.22756\n-0.16502\n0.14944\n0.22055\n0.16171\n0.1026\n0.0064431\n0.038056\n0.092488\n0.12886\n0.21063\n0.13048\n-0.013088\n-0.11486\n-0.013264\n0.083443\n0.2264\n0.050306\n0.15478\n0.17608\n-0.045252\n-0.04448\n-0.20728\n-0.049123\n0.023424\n-0.0072042\n-0.050194\n-0.10328\n-0.11846\n-0.090214\n-0.11608\n-0.040804\n-0.10323\n-0.016617\n0.045216\n0.0089147\n-0.085485\n-0.059427\n-0.00016892\n-0.066159\n-0.065657\n0.14352\n0.13466\n-0.03508\n-0.33859\n-0.45903\n-0.26249\n-0.21251\n-0.17078\n0.043991\n0.15246\n0.096792\n0.050207\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.086862\n-0.00012287\n-0.013271\n0.028988\n0.31958\n0.14939\n0.13751\n0.050156\n0.013935\n0.020875\n0.1003\n0.23835\n0.23216\n0.04095\n0.0051203\n0.063219\n-0.011485\n-0.10145\n0.025257\n0.19131\n0.11841\n-0.050781\n-0.10882\n0.030928\n0.1087\n0.12546\n0.11606\n-0.14285\n-0.10152\n-0.043584\n-0.078903\n-0.16572\n0.009484\n-0.038262\n-0.04892\n-0.0017107\n-0.045882\n-0.11005\n-0.010272\n0.02072\n0.029326\n0.10941\n0.13318\n0.21682\n-0.036487\n-0.21883\n-0.23612\n-0.14493\n-0.17108\n-0.13376\n-0.01125\n0.16495\n0.23267\n0.15392\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35225\n-0.094874\n-0.085077\n-0.041263\n0.2953\n0.15578\n0.1894\n0.11906\n-0.06942\n-0.048428\n0.058819\n0.24618\n0.2325\n0.15738\n0.081151\n0.12591\n0.029041\n-0.029057\n0.1483\n0.25721\n0.066043\n0.079389\n0.051052\n0.15209\n0.20643\n0.28123\n0.17469\n-0.10651\n-0.034035\n-0.00049264\n-0.045634\n-0.10573\n-0.048599\n-0.053441\n-0.080862\n-0.036063\n-0.10317\n-0.12723\n-0.096716\n-0.0039823\n0.061337\n0.14831\n0.15699\n0.12493\n-0.064578\n-0.070053\n-0.16186\n-0.1709\n-0.13527\n-0.049138\n0.020607\n0.19101\n0.38925\n0.98996\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.090755\n-0.0088027\n0.059104\n0.13069\n0.32615\n0.052814\n0.13544\n0.086345\n0.035482\n0.18649\n0.2597\n0.29648\n0.25817\n0.26416\n0.18035\n0.11257\n0.044594\n-0.12484\n0.20543\n0.13682\n0.1674\n0.18169\n0.15905\n0.21044\n0.22869\n0.19055\n0.13561\n-0.048314\n0.01539\n-0.017719\n-0.069978\n-0.11009\n-0.10782\n-0.12414\n-0.044612\n-0.041153\n-0.11874\n-0.10046\n-0.063391\n-0.028022\n-0.011848\n0.13412\n0.16575\n0.061573\n-0.1053\n-0.15711\n-0.12758\n-0.13814\n-0.11003\n-0.075209\n0.023659\n0.21075\n0.55765\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31834\n1.12\nNaN\n-0.091793\n-0.12773\n0.10544\n0.13218\n0.11532\n0.073738\n0.16502\n0.040523\n0.072613\n0.099288\n0.31245\n0.26625\n0.28303\n0.28006\n0.0044861\n0.21569\n0.10947\n0.020915\n-0.19754\n-0.079227\n0.20063\n0.19014\n0.25378\n0.20648\n0.20734\n0.23503\n0.24757\n0.12857\n-0.068698\n-0.0050478\n0.0094768\n-0.070952\n-0.084808\n-0.039972\n-0.081439\n-0.1058\n-0.058954\n-0.089311\n-0.066389\n-0.060404\n-0.037139\n-0.034451\n0.0065652\n0.12512\n0.15818\n0.097776\n-0.028708\n-0.15098\n-0.15665\n-0.05451\n-0.038159\n-0.14208\n0.036116\n0.34287\n0.5011\n0.23202\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4238\n0.26202\n0.28602\n0.21286\n0.19432\n0.363\n0.25166\n0.30052\n0.19614\n0.061927\n0.0023195\n0.08002\n0.051549\n0.0047379\n0.13556\n0.31964\n0.21637\n0.21818\n0.094808\n-0.24968\n-0.15659\n-0.076597\n-0.13267\n-0.14592\n0.015013\n0.14369\n0.18401\n0.1428\n0.17186\n0.076633\n0.19668\n0.17324\n0.013052\n-0.3547\n0.023231\n-0.022771\n-0.023148\n-0.0751\n-0.11273\n-0.078124\n0.009612\n-0.038833\n-0.12647\n-0.075365\n-0.049625\n-0.0066037\n-0.046144\n0.0028359\n0.11894\n0.13137\n0.052628\n-0.015708\n-0.065757\n-0.2657\n-0.28541\n-0.078968\n-0.11221\n0.12465\n0.46538\n0.47139\n-0.48709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.48753\n0.5618\n0.90763\n0.70301\n0.62668\n0.4388\n0.33084\n0.38028\n0.19743\n0.047983\n-0.047754\n-0.15164\n-0.11354\n-0.099152\n-0.00024121\n0.13458\n0.0069796\n-0.034677\n-0.040146\n-0.14478\n-0.20886\n-0.2414\n-0.19549\n-0.089283\n-0.00872\n0.10134\n0.093441\n0.052673\n0.10334\n0.06342\n0.019025\n0.025937\n0.0068661\n-0.46676\n0.0023021\n-0.039374\n-0.084285\n-0.11345\n-0.18176\n-0.053082\n-0.011446\n-0.051775\n-0.077657\n-0.048013\n-0.027105\n-0.045965\n-0.099746\n-0.075224\n0.051529\n0.0020371\n0.010683\n-0.09733\n-0.16033\n-0.23837\n-0.34288\n-0.18842\n-0.067424\n0.27641\n0.48169\n0.53834\n-0.47617\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18983\n0.49612\n0.64336\n0.68721\n0.46109\n1.1322\n1.7963\n1.1962\n0.94731\n0.38977\n0.30353\n0.42378\n0.3075\n0.068696\n0.01509\n-0.039285\n-0.10741\n-0.104\n-0.087515\n-0.060745\n-0.053821\n-0.09799\n-0.13421\n-0.17587\n-0.27579\n-0.30227\n-0.13699\n0.0036227\n0.018797\n-0.013384\n-0.071668\n0.032837\n0.12948\n0.063964\n-0.074332\n0.022638\n-0.021268\n-0.22488\n-0.0456\n-0.049947\n-0.057248\n-0.033198\n-0.0771\n-0.065059\n-0.077035\n-0.05366\n-0.069128\n-0.11053\n-0.031297\n-0.1303\n-0.24326\n-0.16874\n-0.072337\n-0.045199\n-0.096801\n-0.20785\n-0.23767\n-0.21384\n-0.21488\n-0.17109\n-0.028462\n0.19983\n0.4385\n0.69839\n-0.46634\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10636\n0.59191\nNaN\nNaN\nNaN\nNaN\n2.1815\n1.7564\n1.1407\n0.80666\n0.30883\n0.32612\n0.27641\n0.27477\n0.14297\n0.02323\n-0.087851\n-0.083494\n-0.020273\n0.010839\n-0.013104\n-0.21174\n-0.26078\n-0.19081\n-0.20259\n-0.37153\n-0.48449\n-0.18861\n-0.053433\n-0.040419\n-0.041327\n-0.13067\n-0.00034937\n-0.012165\n0.089296\n-0.081873\n-0.13479\n-0.067732\n-0.03976\n-0.012553\n-0.013822\n-0.023744\n0.015845\n-0.031325\n-0.062975\n-0.099936\n-0.091904\n-0.080784\n-0.12355\n-0.12375\n-0.14212\n-0.25013\n-0.17618\n-0.13633\n-0.091278\n-0.12489\n-0.29725\n-0.31611\n-0.18721\n-0.20487\n-0.24338\n-0.089345\n0.19336\n0.33074\n0.1248\n0.08654\nNaN\nNaN\nNaN\nNaN\n0.57016\n1.0889\n0.82829\n0.94859\nNaN\nNaN\n2.4435\n2.179\n1.7257\n0.94405\n0.92056\n0.50253\n0.34394\n0.018558\n0.13346\n0.19663\n0.042331\n-0.043089\n-0.050401\n-0.032234\n-0.0050761\n-0.020501\n-0.097994\n-0.19674\n-0.33576\n-0.20575\n-0.13749\n-0.22717\n-0.40683\n-0.37206\n-0.1694\n-0.087727\n-0.043762\n-0.087296\n-0.06137\n-0.070872\n-0.10569\n-0.18222\n-0.10612\n-0.0040239\n-0.023383\n0.038193\n0.0037355\n-0.017998\n0.020543\n0.06436\n0.021476\n-0.084508\n-0.084287\n-0.03329\n-0.066822\n-0.14425\n-0.16419\n-0.18391\n-0.15311\n-0.11688\n-0.085913\n-0.042208\n-0.095697\n-0.21039\n-0.23627\n-0.19297\n-0.25249\n-0.11416\n0.2151\n0.20612\n0.0051667\n0.0027276\n-0.23448\nNaN\n-1.7874\n-1.5307\n0.1586\n1.6677\nNaN\nNaN\nNaN\nNaN\n-0.1372\n1.333\n1.2775\n0.79895\n0.52538\n0.5023\n0.33884\n0.074505\n0.1799\n0.16291\n-0.0082547\n-0.23036\n-0.21616\n-0.0021991\n-0.012884\n-0.061128\n-0.23864\n-0.21751\n-0.21474\n-0.17618\n-0.096956\n-0.12849\n-0.16574\n-0.27876\n-0.23605\n-0.10409\n-0.099933\n-0.10465\n-0.10044\n-0.11729\n-0.19075\n-0.18283\n-0.093375\n-0.043765\n-0.063031\n0.026119\n-0.052546\n-0.042836\n0.0031815\n0.092547\n0.051391\n-0.02209\n-0.024519\n-0.016694\n-0.098517\n-0.20832\n-0.15212\n-0.11694\n-0.16453\n-0.213\n-0.15333\n-0.014724\n-0.12689\n-0.12272\n-0.34384\n-0.29856\n-0.21297\n-0.045764\n0.059819\n0.062394\n-0.027857\n-0.046325\n-0.031097\n-1.1938\n-1.2155\n0.33462\n0.43932\n0.40387\nNaN\nNaN\nNaN\nNaN\nNaN\n0.39843\n0.80201\n0.58445\n0.46408\n0.45217\n0.26391\n0.10005\n0.059251\n-0.067945\n-0.070837\n-0.38433\n-0.19857\n0.044198\n0.0010724\n-0.10383\n-0.12826\n-0.13732\n-0.12956\n-0.11847\n-0.092291\n-0.13894\n-0.2059\n-0.24687\n-0.22818\n-0.17357\n-0.21666\n-0.24548\n-0.3926\n-0.43222\n-0.21434\n-0.11878\n-0.08564\n0.025779\n0.0051938\n0.01139\n-0.0018867\n-0.0236\n-0.045724\n0.095171\n0.12751\n0.065817\n0.045324\n-0.012704\n-0.081925\n-0.16699\n-0.09481\n-0.09175\n-0.16425\n-0.21412\n-0.090674\n-0.08337\n-0.2744\n-0.37849\n-0.36029\n-0.33314\n-0.22503\n-0.11497\n0.057351\n-0.18224\n-0.3513\n-0.16081\n0.1726\n0.19175\n0.36218\n0.45816\n0.33401\n0.38226\n0.51424\nNaN\nNaN\nNaN\n-1.0215\n0.65065\n0.63872\n0.44482\n0.35482\n0.38599\n0.038069\n-0.20928\n-0.1448\n-0.108\n-0.11867\n-0.2457\n-0.14068\n0.052378\n0.028847\n0.055745\n-0.061699\n-0.15495\n-0.14036\n-0.092406\n-0.11167\n-0.10468\n-0.12557\n-0.20915\n-0.22026\n-0.3786\n-0.39009\n-0.29665\n-0.27086\n-0.27858\n-0.29497\n-0.23275\n-0.11582\n0.05975\n0.010058\n0.074111\n0.029198\n-0.0074457\n-0.017107\n0.041002\n0.1285\n0.12907\n0.076085\n0.037715\n-0.046242\n-0.10034\n-0.047697\n-0.059857\n-0.13377\n-0.17673\n-0.15862\n-0.24804\n-0.38841\n-0.42315\n-0.34098\n-0.30885\n-0.10966\n-0.015122\n0.037155\n0.29887\n0.18\n0.17402\n0.1999\n-1.73\n0.24393\n0.3332\n0.17942\n0.3674\n0.66802\nNaN\nNaN\nNaN\n0.46354\n0.53812\n0.47947\n0.36828\n0.096731\n0.11807\n0.020804\n-0.23539\n-0.19255\n0.026219\n-0.078918\n-0.25641\n-0.16672\n0.037279\n0.09389\n0.12171\n-0.049848\n-0.090244\n-0.10628\n-0.11153\n-0.10892\n-0.035629\n-0.077261\n-0.21388\n-0.2697\n-0.30227\n-0.32967\n-0.26057\n-0.13825\n-0.1566\n-0.2757\n-0.20389\n-0.078943\n0.0093424\n-0.077813\n-0.009453\n0.02511\n0.03255\n0.043888\n0.07444\n0.12743\n0.11656\n0.057037\n0.039154\n-0.010141\n-0.061823\n-0.047304\n-0.049239\n-0.11649\n-0.12788\n-0.19637\n-0.27683\n-0.38151\n-0.35009\n-0.34772\n-0.26504\n-0.16548\n0.041764\n-0.057423\n-0.047922\n0.14253\n0.17191\n-1.557\n-2.0199\n0.2386\n0.20881\n0.25989\n0.4921\n0.71256\n0.64596\n0.43823\n0.18337\n0.35392\n0.36583\n0.42706\n0.27994\n-0.02176\n-0.041488\n-0.11458\n-0.17188\n-0.0415\n0.03621\n0.014201\n-0.17333\n-0.11363\n0.014501\n0.13587\n0.12918\n0.036357\n-0.020653\n-0.034253\n-0.060662\n-0.066757\n0.0027056\n-0.051248\n-0.12768\n-0.26884\n-0.32226\n-0.25266\n-0.12891\n-0.03389\n-0.039232\n-0.11456\n-0.087537\n-0.020983\n-0.06704\n-0.037261\n0.065019\n0.046734\n0.076406\n0.1299\n0.14868\n0.13766\n0.11161\n0.061596\n0.012402\n0.00095515\n-0.011333\n0.0027964\n0.065235\n0.071606\n0.0068509\n-0.16771\n-0.25547\n-0.36201\n-0.077076\n-0.0057335\n-0.10726\n-0.049868\n-0.0049157\n-0.15376\n-0.21576\n0.019981\n0.1334\n-1.4353\n-2.1393\n-2.1509\n0.11903\n0.21133\n0.2335\n0.38874\n0.29179\n0.39602\n0.36756\n0.21172\n0.21049\n0.38157\n0.062542\n-0.074477\n0.014712\n0.04238\n0.0028964\n-0.080793\n-0.12219\n-0.10869\n-0.097526\n-0.011988\n0.060859\n0.11369\n0.067948\n-0.010878\n-0.034356\n-0.024458\n0.0065063\n0.049875\n0.059644\n0.041702\n-0.089565\n-0.20934\n-0.24774\n-0.15818\n-0.058221\n0.010395\n0.021673\n-0.018541\n-0.11442\n-0.14715\n-0.077879\n0.0658\n0.079539\n0.085664\n0.16528\n0.21548\n0.22765\n0.16877\n0.11843\n0.15269\n0.11302\n0.071066\n0.06572\n0.10522\n0.12213\n0.16149\n0.18795\n-0.022737\nNaN\nNaN\nNaN\n-0.017893\n-0.22797\n-0.1095\n-0.057168\n-0.34736\n-0.20893\n0.010119\n0.071264\n-1.4763\n-1.9523\n-1.7349\n0.089941\n0.16273\n0.077813\n0.12141\n0.22341\n0.30115\n0.18993\n0.096136\n0.11653\n0.050246\n-0.10252\n-0.1045\n0.037469\n0.084482\n0.0090539\n-0.056125\n-0.085052\n-0.073353\n-0.027856\n0.0096721\n0.056007\n0.046597\n-0.037093\n-0.13825\n-0.061037\n0.049165\n0.071563\n0.071036\n0.083236\n0.049884\n-0.057766\n-0.10702\n-0.13446\n-0.11705\n-0.052684\n-0.017466\n0.062301\n0.061876\n-0.059227\n-0.11669\n-0.080269\n0.0069771\n0.21033\n0.27687\n0.28599\n0.23851\n0.22703\n0.22724\n0.16589\n0.20504\n0.17025\n0.14287\n0.18748\n0.19728\n0.27217\n0.39959\n0.23252\n0.025611\nNaN\nNaN\nNaN\n-0.13173\n-0.26295\n-0.40554\n-0.27694\n-0.30438\n-0.091105\n0.0023371\n0.025389\n-1.5496\n-1.4351\n-1.2524\n-0.025367\n0.056069\n0.083254\n0.1548\n0.34592\n0.25392\n0.10001\n0.019422\n-0.0082566\n-0.07459\n-0.15513\n-0.14876\n0.012017\n0.096206\n0.055651\n0.035145\n0.022593\n-0.048452\n-0.024669\n0.02366\n0.057427\n0.037255\n-0.053608\n-0.046852\n0.055884\n0.083405\n-0.013117\n0.016156\n0.061138\n0.087515\n0.0062654\n-0.062321\n-0.096491\n-0.071235\n0.012755\n0.0096671\n0.1113\n0.12063\n-0.0067145\n-0.074595\n0.016491\n0.012598\n0.28348\n0.30864\n0.30262\n0.2733\n0.2875\n0.29302\n0.27658\n0.19708\n0.18407\n0.20519\n0.27224\n0.32494\n0.3477\n0.33683\n0.23257\n0.19871\n-0.039867\nNaN\nNaN\n-0.26367\n-0.22282\n-0.24544\n-0.22596\n-0.14715\n0.0065252\n0.033921\n-0.013169\n0.041827\n-0.0077693\n-0.030104\n-0.20941\n-0.071564\n0.042229\n0.13486\n0.28814\n0.20605\n0.12958\n0.072801\n0.029553\n0.018314\n-0.088019\n-0.093834\n0.055045\n0.053686\n0.057195\n0.021579\n-0.082206\n-0.14318\n-0.088011\n-0.0069386\n0.054863\n0.073173\n0.13817\n0.021596\n0.054577\n0.082784\n-0.044524\n-0.016753\n0.016564\n0.11084\n0.055165\n-0.084939\n-0.04142\n0.028528\n0.081472\n0.048523\n0.073027\n0.13474\n-0.033415\n0.019198\n0.14006\n0.062519\n0.23752\n0.24864\n0.27928\n0.32705\n0.3848\n0.3857\n0.34715\n0.24844\n0.24744\n0.26949\n0.27004\n0.31712\n0.3325\n0.3181\n0.14864\n0.12458\n0.083602\n-0.24029\n-0.32358\n-0.21812\n-0.17791\n-0.12663\n-0.12321\n-0.063173\n0.028983\n0.0036652\n-0.017796\n0.012274\n0.035678\n0.011095\n-0.061424\n-0.018267\n0.032108\n0.061615\n0.1811\n0.15188\n0.10423\n0.17865\n0.12779\n0.086139\n0.0595\n0.095299\n0.13412\n0.14668\n0.058024\n0.047094\n-0.079825\n-0.22328\n-0.3109\n-0.16451\n0.0035148\n0.26083\n0.21383\n0.096376\n0.063737\n0.13121\n0.053539\n2.8615e-06\n0.00083029\n0.041024\n0.018764\n-0.050129\n0.099109\n0.12069\n0.088417\n0.066841\n0.17535\n0.18881\n0.0074579\n0.049963\n0.026506\n-0.0066638\n0.17964\n0.25366\n0.32357\n0.36782\n0.46297\n0.50532\n0.42141\n0.38278\n0.3268\n0.24515\n0.26328\n0.29857\n0.29961\nNaN\nNaN\nNaN\nNaN\n-0.02383\n-0.21077\n-0.19\n-0.063771\n-0.07885\n-0.13079\n-0.063753\n0.025887\n-0.037943\n0.02266\n0.10867\n0.044225\n-0.013823\n-0.048014\n-0.075525\n-0.080069\n-0.073572\n-0.077605\n-0.0048248\n0.019718\n0.16682\n0.16982\n0.18144\n0.1553\n0.18735\n0.1933\n0.053876\n-0.022322\n0.079047\n0.024878\n-0.15906\n-0.16885\n-0.17824\n-0.058088\n0.058292\n0.11328\n0.03493\n0.031287\n0.12946\n0.037855\n-0.0091642\n0.040354\n-0.017058\n-0.040185\n0.040022\n0.17274\n0.09234\n-0.027359\n0.047349\n0.25867\n0.22255\n0.1233\n-0.0047811\n-0.072106\n-0.039183\n0.26076\n0.28159\n0.33676\n0.40704\n0.45899\n0.46499\n0.48375\n0.48203\n0.44155\n0.3674\n0.31862\n0.31078\n0.31029\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22478\n-0.15389\n-0.080632\n-0.059149\n-0.15284\n-0.093724\n-0.010095\n-0.011825\n0.028056\n0.052445\n-0.036951\n-0.06347\n-0.10275\n-0.12043\n-0.032809\n-0.08712\n-0.099891\n-0.020676\n0.046859\n0.22751\n0.26953\n0.26405\n0.2568\n0.28588\n0.22441\n-0.13446\n-0.20697\n-0.12173\n0.031638\n-0.10009\n-0.091155\n-0.087558\n-0.075174\n-0.095775\n-0.099193\n-0.15709\n-0.08289\n0.0044627\n-0.042266\n-0.046609\n0.070372\n0.05716\n-0.016654\n0.080373\n0.13572\n0.074728\n-0.059973\n0.11828\n0.34642\n0.22939\n0.17774\n-0.030777\n-0.07553\n-0.077304\n0.32268\n0.28716\n0.33245\n0.4555\n0.4909\n0.65218\n0.63993\n0.59588\n0.49928\n0.41115\n0.31427\n0.29227\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.13794\n-0.13324\n-0.14535\n-0.15749\n-0.17634\n-0.1298\n-0.15675\n-0.046581\n-0.019802\n0.0065553\n-0.05212\n-0.082957\n-0.050646\n-0.05396\n-0.073715\n-0.068946\n-0.031111\n-0.040747\n0.15162\n0.29883\n0.27497\n0.27455\n0.3261\n0.32789\n-0.13299\n-0.3714\n-0.3171\n-0.035982\n-0.069044\n-0.049674\n-0.085589\n-0.10346\n-0.028763\n-0.15919\n-0.17072\n-0.11596\n-0.026718\n-0.085818\n-0.080471\n0.095215\n-0.035597\n-0.064893\n0.0018856\n0.053042\n0.034224\n0.061999\n0.12046\n0.1943\n0.18062\n0.18215\n-0.13732\n-0.15525\n-0.39105\n0.39958\n0.31161\n0.43564\n0.54348\n0.57728\n0.74211\n0.84595\n0.73964\n0.46838\n0.40562\n0.3283\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24916\n-0.12587\n-0.12688\n-0.12355\n-0.15952\n-0.15251\n-0.13595\n-0.094104\n0.024751\n0.056145\n-0.047604\n-0.048079\n-0.0076844\n-0.066095\n-0.08937\n-0.11887\n-0.16965\n-0.17717\n0.038895\n0.14811\n0.13529\n0.20693\n0.21268\n0.072588\n-0.12594\n-0.31274\n-0.39481\n-0.16894\n-0.0956\n-0.038848\n-0.11144\n-0.021496\n0.061025\n0.093866\n0.061992\n-0.00041778\n-0.019413\n-0.1196\n-0.12209\n0.073583\n-0.16419\n-0.098615\n-0.1655\n-0.11246\n0.082408\n-0.11398\n0.028926\n0.091941\n0.17431\n0.23111\n-0.74562\n-0.86142\n-0.79317\n0.078126\n-0.055578\n-0.039628\n-0.077775\n-0.096147\n-0.1351\n-0.2081\n-0.16837\n-0.064913\n-0.022472\n-0.09651\n-0.06268\n-0.14707\n-0.093704\n0.032316\n0.0027202\n0.025886\n-0.044496\n0.20157\n0.14246\n0.19786\n0.17768\n0.2191\n0.093063\n0.19397\n0.041905\n-0.09823\n-0.25854\n-0.16482\n-0.12568\n-0.33606\n-0.25939\n-0.13425\n-0.18732\n-0.043794\n-0.12952\n-0.13004\n-0.044906\n-0.16968\n-0.23522\n-0.12188\n-0.058595\n0.057665\n0.17599\n0.1705\n0.10175\n0.14061\n0.13683\n0.089423\n0.068452\n0.072088\n0.14558\n0.14555\n0.025572\n0.2539\n0.14696\n0.086159\n-0.08699\n-0.26208\n-0.30726\n-0.33482\n-0.10937\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10256\n-0.099403\n-0.058105\n0.13834\n0.10412\n-0.29187\n-0.24433\n-0.19718\n-0.0014384\n0.0081675\n0.063571\n0.059903\n-0.14418\n-0.0060267\n0.10254\n-0.0053895\n-0.21782\n-0.16468\n0.01614\n0.060381\n0.37617\n0.15294\n0.14526\n0.29535\n0.24317\n0.13395\n0.019909\n-0.13433\n-0.048305\n-0.11548\n-0.12607\n-0.14118\n-0.040046\n-0.11766\n-0.058028\n-0.21573\n-0.21447\n-0.030495\n-0.1067\n-0.0034527\n0.047438\n-0.010871\n0.11269\n0.17369\n0.16777\n0.11743\n0.12092\n0.08787\n0.060339\n0.049177\n0.0519\n0.13118\n0.13798\n0.20062\n0.45875\n0.22295\n0.073712\n-0.17159\n-0.21087\n-0.15786\n-0.10559\n0.036934\n0.16742\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14979\n-0.11311\n-0.076511\n-0.11866\n-0.35211\n-0.27779\n-0.25322\n-0.11182\n-0.062036\n0.11126\n0.15223\n0.058375\n-0.044605\n0.12489\n0.36928\n0.10524\n-0.063917\n-0.031512\n-0.0057706\n0.1946\n0.19762\n0.18711\n0.28637\n0.23228\n0.27474\n0.2497\n0.12864\n-0.0018536\n0.0072733\n-0.1598\n-0.065693\n-0.1212\n0.0060663\n-0.061756\n-0.27454\n-0.29773\n-0.16811\n-0.0689\n0.0061222\n-0.015977\n0.02095\n0.095033\n0.094734\n0.18327\n0.11122\n0.054\n0.037463\n0.056152\n0.036589\n0.078132\n0.12216\n0.11621\n0.11891\n0.26365\n0.34017\n0.24901\n0.18759\n-0.14708\n-0.12051\n0.041369\n-0.16165\n0.043547\n0.16388\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.065678\n-0.10093\n-0.090246\n-0.08468\n-0.099113\n-0.057945\n-0.0020458\n-0.030015\n-0.089016\n-0.038753\n0.023772\n-0.14977\n0.13901\n0.2915\n0.20835\n0.010733\n-0.032585\n-0.052399\n-0.057894\n0.14094\n0.30623\n0.18553\n0.24223\n0.13679\n0.26686\n0.28433\n0.15821\n0.064559\n-0.010584\n-0.16784\n-0.094524\n-0.023724\n-0.014618\n-0.045374\n-0.21427\n-0.2943\n-0.24566\n-0.10174\n0.013725\n-0.009638\n-0.01086\n0.049028\n0.0027869\n0.023276\n0.065951\n0.0014767\n-0.0057983\n0.071209\n0.11742\n0.2572\n0.3045\n0.24365\n0.30881\n0.34088\n0.34052\n0.28192\nNaN\nNaN\nNaN\n-0.36659\n-0.27\n0.11281\n0.18773\nNaN\nNaN\nNaN\n0.071033\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.03751\n-0.049387\n-0.045733\n-0.062969\n-0.035632\n-0.065098\n-0.03707\n-0.025857\n-0.093583\n-0.17445\n-0.11123\n-0.18642\n-0.13629\n-0.0080591\n-0.14646\n-0.15748\n-0.15847\n-0.11671\n-0.23981\n-0.042903\n0.34819\n0.20104\n0.30713\n-0.17224\n0.18247\n0.25664\n0.23966\n0.087238\n0.08166\n0.067551\n-0.11445\n-0.040943\n-0.0621\n-0.024372\n-0.028228\n-0.12053\n-0.28289\n-0.14193\n-0.023372\n-0.010093\n0.0026353\n0.040193\n-0.0088371\n0.0040747\n0.022619\n0.010067\n0.08209\n0.20329\n0.18693\n0.19125\n0.29392\n0.2794\n0.25716\n0.36871\n0.30633\n0.32587\n0.56113\nNaN\nNaN\n-0.57227\n-0.36348\n0.029266\n0.17507\n-0.085263\n-0.22071\n-0.061743\n-0.25744\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.036643\n-0.086023\n-0.10836\n-0.077503\n-0.019437\n-0.082216\n-0.027673\n0.0082898\n-0.033578\n-0.077615\n-0.05152\n0.023574\n0.016959\n-0.13737\n-0.21637\n-0.073362\n-0.19618\n-0.19604\n-0.16215\n-0.49522\n-0.19413\n0.061349\n0.028944\n-0.033273\n0.11593\n0.2822\n0.28733\n0.19738\n0.10563\n0.1264\n0.019458\n-0.064011\n-0.072895\n-0.033938\n-0.015756\n-0.18017\n-0.29575\n-0.14775\n-0.053163\n0.015283\n0.038931\n-0.018058\n-0.027249\n-0.065553\n-0.25784\n-0.1102\n0.082106\n0.23994\n0.20281\n0.2084\n0.23852\n0.24347\n0.24539\n0.2149\n0.16899\n0.00078577\n0.15071\nNaN\nNaN\n-0.80536\n-0.53451\n-0.12882\n-0.1361\n-0.36376\n-0.071712\n-0.047823\n-0.173\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10542\n-0.14422\n-0.1156\n-0.015479\n0.037923\n-0.057534\n0.031149\n0.044824\n0.040699\n0.032211\n0.0095879\n-0.027802\n-0.013522\n-0.12725\n-0.14003\n-0.15304\n-0.17351\n-0.033616\n-0.0916\n-0.1749\n-0.11195\n-0.19716\n-0.025153\n-0.066241\n0.016037\n0.18938\n0.27057\n0.29805\n0.090294\n0.079766\n-0.040086\n-0.062699\n-0.19718\n-0.11959\n-0.13055\n-0.227\n-0.2474\n-0.16093\n-0.099408\n-0.015509\n0.072176\n-0.024627\n-0.12381\n-0.19189\n-0.45738\n-0.15195\n0.075242\n0.2078\n0.24498\n0.23966\n0.17708\n0.23502\n0.29327\n0.2725\n0.15297\n-0.037133\n0.098901\nNaN\nNaN\n-0.38545\n-0.64037\n-0.46243\nNaN\n-0.51679\n-0.3287\nNaN\nNaN\n-0.030516\n-0.31044\nNaN\nNaN\nNaN\n-0.078296\n-0.062855\n-0.076815\n0.026789\n0.14436\n-0.047556\n-0.0061357\n0.062554\n0.08729\n0.073028\n0.026227\n0.01268\n-0.010147\n0.016723\n-0.094496\n-0.12949\n-0.075453\n-0.013051\n-0.07399\n-0.13049\n-0.074766\n-0.0071157\n-0.040022\n0.011088\n0.049604\n0.1735\n0.25388\n0.31129\n0.086369\n0.050598\n-0.0013649\n-0.17736\n-0.32047\n-0.16737\n-0.12348\n-0.15167\n-0.11406\n-0.15139\n-0.13781\n-0.13244\n0.0037631\n-0.05698\n-0.11973\n-0.21644\n-0.55763\n-0.13678\n0.12509\n0.17446\n0.16084\n0.23378\n0.19012\n0.23973\n0.32937\n0.31307\n0.28725\n0.11084\n0.042344\n0.21601\n0.17757\n-0.067933\n-0.4161\n-0.44633\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28521\n-0.42119\n-0.45939\nNaN\nNaN\n-0.033512\n-0.13148\n-0.15533\n-0.09223\n0.047191\n-0.030509\n-0.040164\n0.09345\n0.10671\n0.092865\n0.032804\n0.046093\n0.057027\n0.0081594\n-0.018331\n0.011316\n0.020726\n0.059668\n-0.050831\n-0.027617\n-0.011514\n-0.044419\n0.01657\n0.076983\n0.10015\n0.20799\n0.23232\n0.152\n0.056988\n0.00012748\n0.0048205\n-0.031808\n-0.21227\n-0.21715\n-0.14163\n-0.11845\n-0.10189\n-0.1436\n-0.12328\n-0.11526\n-0.13028\n-0.080909\n-0.16039\n-0.30229\n-0.49797\n0.066879\n0.20073\n0.21652\n0.22608\n0.25089\n0.27082\n0.35317\n0.38743\n0.49205\n0.45856\n0.1484\n0.018159\n0.17432\n0.34122\n0.34808\n-0.32631\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41667\n-0.29315\nNaN\nNaN\n-0.21811\n-0.12771\n-0.074096\n-0.1328\n0.082289\n-0.025618\n-0.06372\n-0.010991\n0.046462\n0.048641\n0.041068\n0.043238\n0.0721\n0.023114\n0.08239\n0.10582\n0.056385\n0.10867\n0.088482\n0.14365\n0.11298\n0.070169\n0.11915\n0.060581\n0.036906\n0.11543\n0.23264\n0.20773\n0.07332\n-0.021545\n0.0059453\n-0.16365\n-0.32747\n-0.3411\n-0.25169\n-0.044912\n-0.085308\n-0.10175\n-0.15479\n-0.12981\n-0.078932\n-0.025642\n-0.20755\n-0.2883\n-0.087358\n0.19268\n0.14048\n0.20959\n0.24092\n0.31158\n0.3075\n0.4452\n0.47904\n0.50819\n0.42905\n0.30989\n0.12403\n0.0061461\n0.35757\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.25107\nNaN\n-0.1657\n-0.012805\n-0.27738\n-0.18538\n-0.1047\n-0.12567\n-0.024889\n-0.073108\n-0.080633\n-0.0878\n0.0051561\n0.084762\n0.078291\n0.025696\n0.12743\n0.066797\n0.16964\n0.20818\n0.11045\n0.10797\n0.12661\n0.16704\n0.064787\n0.083167\n0.091297\n-0.066414\n0.014876\n0.28407\n0.28959\n0.21911\n0.12205\n0.10952\n0.005869\n-0.18243\n-0.41678\n-0.22273\n-0.24978\n-0.071832\n-0.11996\n-0.032697\n-0.23078\n-0.16509\n0.014368\n0.040776\n-0.19247\n-0.19365\n-0.021797\n0.11551\n0.1008\n0.18245\n0.29503\n0.31198\n0.28814\n0.40579\n0.42084\n0.52085\nNaN\nNaN\n-0.0093654\n-0.048223\n0.27379\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.058897\n0.20596\n-0.30355\n-0.2536\n-0.16301\n-0.11036\n-0.11294\n-0.082443\n-0.034795\n-0.0023302\n0.032463\n0.067743\n0.025914\n0.033986\n0.12696\n0.070013\n0.18897\n0.18882\n0.14419\n0.12343\n0.098231\n0.084066\n-0.02649\n0.024402\n0.035998\n-0.07969\n0.018308\n0.20705\n0.28315\n0.27807\n0.16838\n0.18374\n0.053894\nNaN\nNaN\nNaN\n-0.1373\n-0.071931\n-0.022221\n-0.078104\n-0.26139\n-0.1127\n0.089208\n-0.03234\n-0.10101\n-0.038698\n0.01333\n0.089156\n0.17028\n0.28101\n0.40495\n0.41317\n0.3572\n0.39712\n0.32424\nNaN\nNaN\nNaN\n0.052439\n-0.057697\n0.075967\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1192\n-0.12518\n-0.084696\n-0.099162\n-0.060826\n-0.0088284\n0.00047534\n-0.014856\n0.032072\n0.033411\n0.043652\n0.060602\n0.071574\n0.11823\n0.20563\n0.23309\n0.24926\n0.17349\n0.091111\n0.036337\n0.001573\n0.0060428\n-0.12611\n-0.014605\n0.044388\n0.036056\n0.28951\n0.21801\n0.10868\n0.1752\n0.12459\nNaN\nNaN\nNaN\n-0.11307\n-0.13864\n-0.10202\n-0.051012\n-0.12892\n-0.0050948\n0.050377\n-0.17609\n-0.25968\n0.033942\n0.11681\n0.18252\n0.2514\n0.34225\n0.44152\n0.45492\n0.40697\n0.3616\n0.2247\nNaN\nNaN\nNaN\n-0.25413\n-0.28256\n-0.22303\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.013713\n-0.045659\n-0.072902\n-0.026167\n0.0013689\n0.00095836\n0.0066238\n-0.012988\n0.015856\n0.010548\n0.001205\n0.060956\n0.17433\n0.2362\n0.24149\n0.26012\n0.20244\n0.1619\n0.046029\n0.0029281\n0.035305\n-0.1397\n-0.28118\n-0.20399\n-0.23014\n0.0011147\n0.17157\n0.14537\n0.22207\n0.26329\n0.18729\nNaN\nNaN\nNaN\nNaN\n-0.14347\n-0.214\n-0.036799\n-0.091911\n0.030709\n0.066204\n-0.059118\n-0.056081\n0.17164\n0.2943\n0.34149\n0.31833\n0.40181\n0.44402\n0.45265\n0.38386\n0.26133\n0.18317\nNaN\nNaN\nNaN\nNaN\n-0.24672\n-0.22423\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.043448\n-0.085348\n-0.0094537\n0.069118\n0.025272\n-0.011104\n0.0074657\n-0.010031\n-0.0045128\n0.0057488\n-0.0273\n0.056392\n0.20122\n0.24063\n0.27053\n0.22123\n0.22729\n0.21244\n0.11865\n0.0016217\n-0.042726\n-0.27694\n-0.50526\n-0.35501\n-0.29848\n0.0038555\n0.083872\n0.17725\n0.30667\n0.27415\n0.26412\nNaN\n-0.13784\n-0.18278\n-0.033767\n0.0047499\n-0.099374\n0.1142\n0.11375\n0.3172\n0.26996\n0.022545\n-0.010662\n0.42918\n0.48073\n0.62265\n0.25287\n0.4564\n0.52848\n0.48022\n0.32404\n0.34686\n0.24702\n0.029714\nNaN\nNaN\nNaN\n-0.37419\n-0.32448\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.017467\n-0.054232\n0.010056\n0.13267\n0.10868\n0.064856\n0.079449\n0.022855\n-0.012415\n0.049465\n0.031445\n0.094035\n0.17783\n0.19282\n0.24479\n0.10551\n0.094491\n0.1659\n0.20126\n0.041599\n-0.045522\n-0.1808\n-0.73187\n-0.59628\n-0.30471\n-0.054427\n0.13681\n0.39903\n0.38465\n0.24241\n0.029653\n-0.28806\n-0.037016\n-0.049515\n-0.054829\n0.10152\n0.22702\n0.26049\n0.33005\nNaN\n0.31977\n0.070472\n-0.019393\n0.30982\n0.71283\n0.7514\n0.41381\n0.42666\n0.52067\n0.35663\n0.24107\n0.20502\n0.24514\n0.026543\n-0.04466\n-0.23328\n-0.44991\n-0.4217\n-0.59273\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.067353\n-0.041822\n0.034516\n0.16935\n0.11148\n0.10814\n0.082909\n0.034037\n0.0038771\n0.0074179\n0.030134\n0.089433\n0.17941\n0.19656\n0.19881\n0.047029\n0.024065\n0.079435\n0.14292\n0.090648\n-0.068284\n-0.21812\n-0.37419\n-0.46065\n-0.15682\n0.030674\n0.2151\n0.32221\n0.26091\n0.16605\n-0.067773\n0.011404\n0.21082\n0.14443\n0.16555\n0.3447\n0.3217\nNaN\nNaN\nNaN\nNaN\n0.093363\n-0.027678\n0.12453\n0.36482\n0.29697\n0.066414\n0.36268\n0.28266\n0.29193\nNaN\n0.11186\n0.18788\n0.023363\n-0.036141\n-0.11811\n-0.3853\n-0.42041\n-0.61494\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.084494\n-0.024597\n0.083206\n0.18035\n0.12285\n0.081381\n0.061383\n0.068212\n0.015824\n-0.0082248\n0.034738\n0.079622\n0.15522\n0.17486\n0.1777\n0.092836\n0.15319\n0.074216\n0.052587\n-0.034062\n-0.18281\n-0.34259\n-0.41688\n-0.43394\n-0.096377\n0.059742\n0.2326\n0.17764\n0.18295\n0.099346\n0.23103\n0.22616\n0.23237\n0.31254\n0.39963\n0.46207\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.32918\n0.2827\n0.16097\n0.39847\n0.14878\n0.079401\n0.20323\nNaN\nNaN\nNaN\n-0.10388\n-0.068945\n-0.22674\n-0.37936\n-0.30326\n-0.38973\n-0.39138\n-0.11297\n-0.034405\nNaN\nNaN\n-0.16456\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.02848\n0.028508\n0.11493\n0.15815\n0.1207\n-0.007077\n0.044653\n0.05555\n-0.0038676\n-0.00514\n-0.0090819\n0.037725\n0.10394\n0.16752\n0.19537\n0.11527\n0.021661\n-0.037933\n-0.012418\n-0.083284\n-0.16353\n-0.38673\n-0.41997\n-0.3186\n-0.090947\n0.063813\n0.13067\n0.13866\n0.28217\n0.21319\n0.22643\n0.33245\n0.40707\n0.35864\n0.42417\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.14627\n0.016033\n0.024857\n-0.058803\n-0.0048074\n-0.15556\n-0.19693\nNaN\nNaN\nNaN\n-0.29722\n-0.13789\n-0.1881\n-0.22929\n-0.077042\n-0.097019\n-0.12062\n0.087307\n0.13395\n-0.078977\n-0.12749\n-0.1056\n-0.12079\n-0.16836\n-0.36078\nNaN\nNaN\nNaN\n0.01302\n0.056821\n0.042452\n0.011363\n0.096013\n0.065121\n0.12337\n0.024469\n-0.093176\n-0.035284\n-0.019762\n0.010861\n0.070074\n0.14533\n0.13885\n0.041277\n-0.096276\n-0.18113\n-0.1158\n-0.11708\n-0.17641\n-0.43246\n-0.3435\n-0.17695\n0.050742\n0.1872\n0.12991\n0.19437\n0.35104\n0.33304\n0.23347\n0.36609\n0.39513\n0.36597\n0.47367\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.012454\n-0.0443\n-0.22707\n-0.27465\n0.045471\n-0.52703\n-0.40734\nNaN\nNaN\nNaN\n-0.41663\n-0.24845\n-0.21223\n-0.10458\n0.073441\n0.13701\n-0.033227\n-0.058688\n0.12442\n-0.051987\n0.049592\n0.15054\n0.1749\n0.056553\n-0.057178\nNaN\nNaN\nNaN\n0.028822\n0.084736\n0.0128\n-0.03141\n0.11044\n0.12536\n-0.035669\n-0.10887\n-0.13544\n0.0026143\n0.047308\n0.051373\n-0.0057595\n0.097408\n0.052964\n-0.0053012\n-0.076418\n-0.078455\n-0.061132\n-0.14178\n-0.2239\n-0.48832\n-0.35693\n-0.05309\n0.11133\n0.31846\n0.35257\n0.35483\n0.37609\n0.59573\n0.7846\n0.86567\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.35592\n0.30022\n0.43771\n-0.23796\n0.10288\n-0.11309\n-0.54117\nNaN\n-0.33297\n-0.20195\n-0.15786\n-0.16751\n-0.2054\n-0.11076\n0.14204\n0.2374\n0.041436\n0.12334\n0.22094\n0.17632\n0.12951\n0.14402\n0.15004\n0.18044\n0.16123\n0.18863\n0.029497\n-0.020325\n-0.010299\n-0.02524\n-0.030737\n-0.0064072\n0.093096\n0.068953\n-0.068518\n-0.076483\n-0.096817\n0.0030945\n0.0090217\n-0.04591\n0.033922\n0.046282\n0.018034\n0.053024\n-0.018049\n-0.10242\n-0.0081927\n-0.084669\n-0.04303\n-0.34435\n-0.41151\n0.0010244\n0.24311\n0.55061\n0.44758\n0.43826\n0.64393\n0.83651\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12656\n-0.18509\n0.39527\n0.16571\n0.24028\n-0.24621\n-0.31039\n-0.09116\n-0.15247\n-0.096358\n0.018583\n-0.009097\n-0.040086\n-0.24828\n-0.014606\n0.13569\n0.038086\n0.21032\n0.2896\n0.10999\n0.053088\n0.026018\n-0.13761\n0.0037848\n0.0094624\n-0.027923\n0.15084\n0.10308\n-0.010816\n-0.015829\n0.0058023\n-0.075906\n-0.0758\n-0.078804\n-0.11287\n-0.096706\n-0.015828\n-0.030431\n-0.084837\n-0.069391\n0.0096887\n0.03171\n0.043146\n0.12114\n0.057609\n-0.1647\n-0.29618\n-0.071462\n-0.058181\n-0.40043\n-0.35396\n-0.02988\n0.3512\n0.41182\n0.3623\n0.51451\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.38159\n-0.74012\n-0.046635\n-0.13595\n0.038261\n0.072985\n0.03407\n-0.022507\n-0.10623\n0.013614\n0.10699\n0.077128\n0.074396\n-0.051912\n-0.052013\n-0.073146\n0.025089\n0.18357\n0.30899\n0.11636\n0.10863\n0.09113\n-0.16956\n-0.11835\n-0.022871\n-0.069384\n0.031718\n0.23318\n-0.006591\n-0.061755\n-0.22367\n-0.1506\n-0.072559\n-0.10287\n-0.13981\n-0.053583\n0.07105\n0.0015763\n-0.054383\n0.017749\n0.038166\n0.04008\n0.091494\n0.2785\n0.19651\n-0.079555\n-0.30898\n-0.40967\n-0.27661\n-0.26827\n-0.17518\n0.0701\n0.22204\n0.28932\n0.23172\n0.2974\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.23025\n-0.23586\n-0.17513\n-0.0032682\n0.066831\n0.023607\n-0.013113\n-0.064859\n-0.006476\n0.049826\n0.089582\n0.18137\n0.093689\n-0.044581\n-0.11145\n-0.022289\n0.035289\n0.2054\n0.024408\n0.16595\n0.17546\n-0.055129\n-0.047304\n-0.0078722\n0.03401\n0.044433\n0.075932\n-0.092001\n-0.16019\n-0.16552\n-0.11562\n-0.11591\n-0.02927\n-0.079459\n0.0078153\n0.068121\n0.047276\n-0.032552\n0.0075816\n0.084687\n0.019767\n0.048533\n0.2388\n0.24577\n0.01517\n-0.28078\n-0.37754\n-0.20447\n-0.14315\n-0.10154\n0.14035\n0.25108\n0.35153\n0.31882\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10267\n0.022045\n-0.16928\n-0.11418\n0.1831\n0.026894\n0.057805\n0.010084\n-0.023182\n-0.035265\n0.099306\n0.28285\n0.23198\n0.031126\n0.0095217\n0.039868\n-0.051837\n-0.10886\n0.027957\n0.19231\n0.094589\n-0.053204\n-0.13923\n0.093608\n0.15998\n0.17165\n0.15548\n-0.21911\n-0.15441\n-0.083034\n-0.092917\n-0.1538\n0.024311\n0.0062532\n-0.02272\n0.037052\n-0.0026116\n-0.030431\n0.071742\n0.10361\n0.11341\n0.1608\n0.22857\n0.36167\n0.02092\n-0.16236\n-0.17684\n-0.053059\n-0.083425\n-0.055619\n0.078934\n0.28636\n0.38042\n0.4181\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35465\n-0.080942\n-0.22989\n-0.17246\n0.15371\n0.012466\n0.101\n0.080263\n-0.11924\n-0.12583\n0.028301\n0.28285\n0.26374\n0.17838\n0.095254\n0.11358\n0.014959\n-0.03412\n0.13814\n0.24754\n0.049845\n0.0092418\n-0.0076456\n0.19607\n0.28181\n0.35216\n0.22245\n-0.16743\n-0.090375\n-0.051148\n-0.052125\n-0.087814\n-0.017441\n-0.0034085\n-0.024154\n0.00047836\n-0.024375\n-0.066026\n-0.051905\n0.064998\n0.13661\n0.20259\n0.2568\n0.24103\n0.0053615\n-0.014555\n-0.11614\n-0.10028\n-0.047978\n0.035896\n0.12485\n0.31622\n0.43921\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10334\n0.0054377\n-0.1049\n-0.054194\n0.16085\n-0.26083\n-0.050158\n0.050058\n-0.0064909\n0.17682\n0.25387\n0.30533\n0.27597\n0.27791\n0.19158\n0.14278\n0.039943\n-0.090789\n0.21102\n0.14596\n0.18947\n0.088098\n0.11175\n0.25988\n0.29222\n0.29618\n0.22276\n-0.12947\n-0.043455\n-0.081202\n-0.072964\n-0.10614\n-0.093105\n-0.099129\n-0.018172\n-0.022763\n-0.092646\n-0.070184\n-0.036326\n0.015917\n0.083959\n0.19691\n0.23604\n0.13369\n-0.036292\n-0.09473\n-0.07245\n-0.050994\n-0.023914\n-0.0019819\n0.10097\n0.32912\n0.61149\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.26737\nNaN\nNaN\nNaN\nNaN\n0.11234\n-0.012953\n-0.012538\n-0.058059\n0.042114\n-0.27268\n-0.025904\n0.089473\n0.31674\n0.26685\n0.2907\n0.28261\n-0.038163\n0.21123\n0.097117\n0.05041\n-0.21378\n-0.060426\n0.23939\n0.21053\n0.29679\n0.18644\n0.24171\n0.30199\n0.31222\n0.21245\n0.037906\n-0.069145\n-0.035687\n-0.10124\n-0.096141\n-0.050235\n-0.082626\n-0.10043\n-0.057759\n-0.088885\n-0.054986\n-0.022485\n0.0057862\n0.0085843\n0.091286\n0.15193\n0.20099\n0.15253\n0.018368\n-0.11839\n-0.098753\n-0.0032354\n0.013105\n-0.081565\n0.13309\n0.50539\n0.73386\n0.2826\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.2645\n0.25595\n0.1857\n0.18139\n0.34002\n0.23538\n0.14838\n0.073337\n-0.016127\n-0.081656\n-0.030582\n-0.0060572\n-0.029017\n0.12228\n0.32158\n0.20357\n0.22304\n0.095073\n-0.25188\n-0.18514\n-0.051877\n-0.054333\n-0.15521\n0.023658\n0.19308\n0.25249\n0.2182\n0.1909\n0.14486\n0.30543\n0.24616\n0.089562\n-0.24975\n-0.058788\n-0.060225\n-0.035019\n-0.10565\n-0.13708\n-0.095491\n-0.033124\n-0.076711\n-0.13904\n-0.040336\n-0.0095375\n-0.00099052\n-0.03589\n0.046029\n0.12613\n0.15224\n0.071604\n-0.013957\n-0.051947\n-0.25319\n-0.24211\n-0.013369\n0.0077159\n0.28765\n0.6555\n0.69973\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.492\n0.57582\n0.89201\n0.6872\n0.6068\n0.44972\n0.32992\n0.36288\n0.10229\n-0.053702\n-0.14128\n-0.2565\n-0.20385\n-0.19046\n-0.096045\n0.085423\n-0.055108\n-0.091751\n-0.030521\n-0.11521\n-0.2463\n-0.29447\n-0.17348\n-0.10544\n0.0030307\n0.14351\n0.14068\n0.095839\n0.13179\n0.12772\n0.1468\n0.10634\n0.070043\n-0.37844\n-0.084813\n-0.095062\n-0.12039\n-0.14692\n-0.18494\n-0.071686\n-0.03801\n-0.067225\n-0.10364\n-0.041742\n-0.0132\n-0.049535\n-0.069681\n-0.040229\n0.043381\n-0.031838\n-0.044975\n-0.14029\n-0.18171\n-0.224\n-0.28259\n-0.067528\n0.083606\n0.47669\n0.68635\n0.78883\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.50711\n0.64476\n0.69879\n0.46481\n1.0842\n1.6875\n1.1281\n0.90153\n0.44788\n0.3526\n0.30951\n0.23552\n-0.0032371\n-0.059404\n-0.13093\n-0.22712\n-0.20733\n-0.20164\n-0.15585\n-0.14424\n-0.15181\n-0.17272\n-0.18979\n-0.29786\n-0.36647\n-0.11715\n0.019576\n0.043588\n-0.0046074\n-0.078453\n0.0083782\n0.12959\n0.091959\n-0.018355\n0.060184\n0.016698\n-0.1731\n-0.15162\n-0.11453\n-0.13064\n-0.080933\n-0.11723\n-0.10845\n-0.1227\n-0.074299\n-0.083577\n-0.10937\n-0.033615\n-0.14179\n-0.2521\n-0.1565\n-0.092038\n-0.09524\n-0.16935\n-0.24148\n-0.24109\n-0.2381\n-0.24209\n-0.15589\n0.10367\n0.35426\n0.59835\n0.90722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0101\n1.6584\n1.1138\n0.81781\n0.37466\n0.20399\n0.19671\n0.17133\n0.058159\n-0.078191\n-0.21778\n-0.20808\n-0.14428\n-0.094537\n-0.10254\n-0.29137\n-0.34487\n-0.24982\n-0.24377\n-0.39427\n-0.47676\n-0.18858\n-0.033734\n-0.024696\n-0.028663\n-0.1205\n-0.034824\n-0.042482\n0.072224\n-0.082397\n-0.14731\n-0.067031\n-0.020242\n-0.083816\n-0.10132\n-0.12217\n-0.044104\n-0.098176\n-0.12824\n-0.15191\n-0.12279\n-0.10721\n-0.13625\n-0.14366\n-0.16745\n-0.26197\n-0.17251\n-0.16993\n-0.12243\n-0.19463\n-0.39235\n-0.44719\n-0.23906\n-0.19271\n-0.20929\n0.015376\n0.32352\n0.46024\n0.33102\n0.21573\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6325\n1.002\n0.97322\n0.55232\n0.39142\n-0.12114\n0.024467\n0.071232\n-0.07593\n-0.15022\n-0.16311\n-0.14604\n-0.11518\n-0.11721\n-0.17052\n-0.24941\n-0.41235\n-0.2547\n-0.17628\n-0.21975\n-0.3659\n-0.36134\n-0.16507\n-0.062598\n-0.022031\n-0.057494\n-0.070078\n-0.087721\n-0.1485\n-0.18199\n-0.09696\n0.0062758\n0.021737\n-0.057083\n-0.1055\n-0.12756\n-0.066631\n-0.035496\n-0.041439\n-0.13965\n-0.12948\n-0.074531\n-0.10055\n-0.18821\n-0.19637\n-0.19937\n-0.17228\n-0.16691\n-0.11488\n-0.11479\n-0.19888\n-0.32604\n-0.35128\n-0.18745\n-0.22405\n-0.0094086\n0.33161\n0.38104\n0.049193\n-0.03056\n-0.19366\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2509\n0.83525\n0.5709\n0.51866\n0.33413\n-0.077343\n0.034965\n0.029618\n-0.19044\n-0.32376\n-0.2996\n-0.11905\n-0.11017\n-0.14441\n-0.31322\n-0.24654\n-0.23301\n-0.19732\n-0.10922\n-0.10047\n-0.11424\n-0.19223\n-0.20314\n-0.081849\n-0.097841\n-0.086299\n-0.11238\n-0.1222\n-0.19299\n-0.12344\n-0.023953\n0.047941\n0.014769\n-0.087426\n-0.15988\n-0.15113\n-0.10433\n-0.0046525\n0.0044902\n-0.085304\n-0.07076\n-0.065926\n-0.10328\n-0.24452\n-0.18489\n-0.13303\n-0.20419\n-0.25794\n-0.19774\n-0.090644\n-0.22818\n-0.19876\n-0.47814\n-0.44683\n-0.26672\n0.0070152\n0.17154\n0.20267\n0.078872\n-0.079428\n-0.031905\nNaN\nNaN\n0.32326\n0.57421\n0.44099\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.8353\n0.61905\n0.48678\n0.45966\n0.060753\n-0.14674\n-0.075321\n-0.20051\n-0.28272\n-0.46779\n-0.28028\n-0.05251\n-0.078291\n-0.16496\n-0.18404\n-0.17643\n-0.15797\n-0.14638\n-0.11625\n-0.14055\n-0.16642\n-0.1776\n-0.20181\n-0.13913\n-0.18687\n-0.23248\n-0.31329\n-0.3367\n-0.15974\n-0.060999\n-0.011999\n0.088485\n0.063493\n-0.11565\n-0.14124\n-0.12475\n-0.15578\n-0.023172\n0.062805\n-0.0060568\n-0.024527\n-0.079982\n-0.10793\n-0.22044\n-0.13879\n-0.13051\n-0.23255\n-0.28792\n-0.16647\n-0.16781\n-0.34851\n-0.4263\n-0.39063\n-0.35576\n-0.2271\n-0.068046\n0.1363\n-0.11886\n-0.31288\n-0.14533\n0.17819\n0.18083\n0.38169\n0.44741\n0.47447\n0.50257\n0.64325\nNaN\nNaN\nNaN\nNaN\n0.57666\n0.6196\n0.40894\n0.32044\n0.29885\n-0.12429\n-0.3852\n-0.26302\n-0.2645\n-0.29441\n-0.32649\n-0.21433\n0.010175\n0.0035485\n0.019246\n-0.07782\n-0.19788\n-0.19154\n-0.12969\n-0.12575\n-0.10152\n-0.098766\n-0.18303\n-0.21595\n-0.33483\n-0.39633\n-0.30644\n-0.27191\n-0.27932\n-0.25062\n-0.23092\n-0.10055\n0.054784\n-0.0050981\n-0.084946\n-0.13095\n-0.15978\n-0.15368\n-0.095794\n0.05206\n0.044589\n-0.018978\n-0.079052\n-0.10747\n-0.16971\n-0.10199\n-0.11977\n-0.20947\n-0.26071\n-0.21773\n-0.36619\n-0.60731\n-0.51023\n-0.3715\n-0.29669\n-0.059481\n0.026115\n0.097956\n0.45205\n0.1812\n0.18039\n0.20612\nNaN\n0.17061\n0.29009\n0.13178\n0.49473\n0.82074\nNaN\nNaN\nNaN\n0.37755\n0.49641\n0.49285\n0.36689\n0.1088\n0.13026\n-0.0761\n-0.37363\n-0.29952\n-0.052849\n-0.13882\n-0.27269\n-0.17984\n0.019075\n0.078342\n0.087504\n-0.067728\n-0.10241\n-0.14296\n-0.1445\n-0.12217\n-0.034397\n-0.059309\n-0.1883\n-0.26787\n-0.31911\n-0.36839\n-0.2766\n-0.13323\n-0.1607\n-0.26646\n-0.2236\n-0.1079\n-0.02966\n-0.10293\n-0.18163\n-0.14137\n-0.11419\n-0.11646\n-0.062693\n0.03115\n0.013152\n-0.064695\n-0.098472\n-0.11798\n-0.15437\n-0.11461\n-0.10525\n-0.1859\n-0.20769\n-0.2554\n-0.38619\n-0.59092\n-0.43924\n-0.38615\n-0.25785\n-0.15871\n0.10983\n0.032702\n0.072612\n0.17136\n0.14097\nNaN\nNaN\n0.11314\n0.15197\n0.20234\n0.42399\n0.67626\n0.67228\n0.45996\n0.20359\n0.28603\n0.31846\n0.44663\n0.27486\n-0.061555\n-0.11907\n-0.20449\n-0.27408\n-0.094294\n0.0085932\n-0.020138\n-0.18857\n-0.15547\n-0.010666\n0.13421\n0.082413\n-0.027676\n-0.056085\n-0.076548\n-0.084041\n-0.059261\n0.010265\n-0.037914\n-0.1104\n-0.25654\n-0.32419\n-0.2796\n-0.14488\n-0.041737\n-0.048214\n-0.09211\n-0.076383\n-0.039138\n-0.059252\n-0.011719\n-0.15306\n-0.13873\n-0.056454\n-0.00029743\n0.019899\n0.038344\n0.0020973\n-0.074906\n-0.11606\n-0.11967\n-0.11186\n-0.073548\n-0.028631\n-0.030679\n-0.05588\n-0.22695\n-0.31278\n-0.5541\n-0.15748\n-0.16771\n-0.22709\n-0.051349\n0.092273\n-0.010652\n-0.11652\n0.019698\n0.085043\nNaN\nNaN\nNaN\n0.017561\n0.16234\n0.23704\n0.37914\n0.3117\n0.40201\n0.34248\n0.15726\n0.18549\n0.38696\n0.11071\n-0.063564\n-0.044012\n0.0059986\n-0.043226\n-0.11557\n-0.146\n-0.14465\n-0.12078\n-0.026663\n0.056281\n0.11239\n0.03535\n-0.087972\n-0.08916\n-0.047277\n0.00028963\n0.059654\n0.065841\n0.045507\n-0.074633\n-0.19452\n-0.24436\n-0.17372\n-0.062715\n-0.0022643\n0.013061\n0.0033899\n-0.11185\n-0.16542\n-0.058825\n0.083605\n-0.11623\n-0.11168\n-0.0023335\n0.067726\n0.10026\n0.066923\n-0.0060961\n0.011585\n-0.014566\n-0.046267\n-0.013808\n0.038952\n0.048783\n0.12454\n0.2085\n-0.062993\nNaN\nNaN\nNaN\n-0.16153\n-0.3302\n-0.11866\n-0.0084545\n-0.28816\n-0.19309\n-0.025383\n0.061655\nNaN\nNaN\nNaN\n0.0028808\n0.12472\n0.06246\n0.12721\n0.21322\n0.28387\n0.18797\n0.087554\n0.10648\n0.020498\n-0.10097\n-0.089467\n0.035222\n0.065068\n-0.014209\n-0.075536\n-0.074386\n-0.044735\n0.0022311\n0.0030051\n0.05073\n0.03757\n-0.047048\n-0.1928\n-0.10676\n0.026444\n0.050673\n0.074818\n0.097843\n0.066172\n-0.058678\n-0.10666\n-0.13717\n-0.13536\n-0.062636\n-0.030499\n0.056162\n0.069846\n-0.086472\n-0.166\n-0.093806\n0.035574\n0.031047\n0.070219\n0.080157\n0.070638\n0.080257\n0.10946\n0.0047798\n0.064001\n0.059322\n0.032994\n0.083881\n0.12182\n0.20184\n0.35764\n0.26199\n0.0014011\nNaN\nNaN\nNaN\n-0.23931\n-0.33792\n-0.47349\n-0.31719\n-0.29588\n-0.13256\n-0.066667\n-0.042737\nNaN\nNaN\nNaN\n-0.078304\n-0.0084541\n0.020668\n0.12094\n0.35683\n0.25284\n0.099815\n0.019806\n-0.012547\n-0.10806\n-0.13358\n-0.12985\n0.0032833\n0.07981\n0.044778\n0.0052765\n0.015031\n-0.037652\n-0.017357\n0.021404\n0.043125\n0.037242\n-0.034502\n-0.05347\n0.035166\n0.048344\n-0.05815\n0.040796\n0.082096\n0.087232\n-0.018569\n-0.070524\n-0.090844\n-0.07512\n0.00081792\n0.012611\n0.14422\n0.12526\n-0.029162\n-0.088979\n0.0080397\n0.041539\n0.11243\n0.1456\n0.13773\n0.084698\n0.14127\n0.1497\n0.13938\n0.07141\n0.090215\n0.083228\n0.15373\n0.22666\n0.25975\n0.25133\n0.14413\n0.12046\n-0.0928\nNaN\nNaN\n-0.36626\n-0.31291\n-0.32712\n-0.31272\n-0.23354\n-0.061036\n-0.053001\n-0.079938\n-0.016196\n-0.069664\n-0.06619\n-0.25003\n-0.14217\n-0.026412\n0.08501\n0.29863\n0.22543\n0.15694\n0.089393\n0.060616\n0.057515\n-0.064139\n-0.063414\n0.034433\n0.068714\n0.079471\n0.029358\n-0.093589\n-0.20113\n-0.14399\n-0.0052821\n0.042451\n0.057014\n0.15563\n0.030332\n0.053323\n0.061431\n-0.072007\n0.0059059\n0.035683\n0.091778\n0.030122\n-0.10533\n-0.044839\n0.031355\n0.077426\n0.057533\n0.083146\n0.14614\n-0.035236\n-0.0023009\n0.14146\n0.079225\n0.057516\n0.083338\n0.12067\n0.14192\n0.20064\n0.21669\n0.19848\n0.12106\n0.10868\n0.12138\n0.13327\n0.18894\n0.22589\n0.23063\n0.11892\n0.087608\n-0.035449\n-0.329\n-0.43296\n-0.35144\n-0.3008\n-0.23003\n-0.21649\n-0.14557\n-0.037616\n-0.063848\n-0.070583\n-0.044707\n-0.0063176\n-0.021971\n-0.10308\n-0.092993\n-0.063184\n-0.01299\n0.16224\n0.18307\n0.12075\n0.20691\n0.13902\n0.084254\n0.054099\n0.12304\n0.12855\n0.14413\n0.10941\n0.10511\n-0.095104\n-0.29889\n-0.41875\n-0.16832\n-0.015587\n0.23528\n0.22192\n0.11442\n0.057742\n0.13271\n0.061375\n0.019507\n0.00051183\n0.016907\n-0.0055207\n-0.075073\n0.084799\n0.11343\n0.086499\n0.050954\n0.16664\n0.18933\n-0.011014\n0.033744\n0.035184\n-0.0067142\n0.0051118\n0.10407\n0.18823\n0.20069\n0.2867\n0.33253\n0.26226\n0.24949\n0.19389\n0.12481\n0.11713\n0.1522\n0.17142\nNaN\nNaN\nNaN\nNaN\n-0.13979\n-0.33686\n-0.33949\n-0.17281\n-0.1964\n-0.25742\n-0.15753\n-0.023431\n-0.071369\n-0.032217\n0.045987\n-0.0045773\n-0.05061\n-0.075831\n-0.075713\n-0.14674\n-0.12298\n-0.068222\n0.0083498\n0.051628\n0.20493\n0.16797\n0.17814\n0.15476\n0.20785\n0.19608\n0.037115\n0.0061415\n0.1419\n0.057249\n-0.21892\n-0.23438\n-0.22781\n-0.095512\n0.016812\n0.095507\n0.017966\n0.023219\n0.14213\n0.0096169\n-0.045545\n0.0064921\n-0.040421\n-0.047187\n0.018962\n0.12987\n0.069857\n-0.031832\n0.047825\n0.24192\n0.21206\n0.11561\n0.0057105\n-0.049085\n8.2859e-06\n0.092668\n0.13463\n0.18977\n0.24335\n0.30545\n0.3293\n0.33086\n0.3731\n0.34056\n0.26958\n0.17672\n0.1545\n0.19513\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32153\n-0.28482\n-0.21506\n-0.19405\n-0.28014\n-0.19151\n-0.062648\n-0.05374\n-0.057355\n-0.028131\n-0.092118\n-0.10897\n-0.13185\n-0.13279\n-0.061221\n-0.11795\n-0.12188\n-0.026141\n0.077216\n0.28812\n0.29353\n0.26447\n0.26351\n0.29566\n0.21979\n-0.15768\n-0.20293\n-0.08627\n0.12723\n-0.14455\n-0.12887\n-0.1202\n-0.10003\n-0.12267\n-0.13092\n-0.14196\n-0.07443\n-0.0082655\n-0.0827\n-0.072079\n0.0092245\n0.0056554\n-0.028302\n0.058624\n0.1162\n0.06458\n-0.078653\n0.086888\n0.32206\n0.22423\n0.18403\n-0.00065819\n-0.040339\n-0.045951\n0.17868\n0.12441\n0.20039\n0.31075\n0.36973\n0.54421\n0.51442\n0.51441\n0.44321\n0.33773\n0.18416\n0.11248\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.31144\n-0.2636\n-0.29341\n-0.28247\n-0.28687\n-0.22819\n-0.25892\n-0.15132\n-0.11799\n-0.06921\n-0.093681\n-0.13648\n-0.10797\n-0.090096\n-0.072329\n-0.076356\n-0.048137\n-0.023527\n0.19741\n0.34756\n0.26778\n0.28853\n0.33868\n0.30783\n-0.16221\n-0.39674\n-0.32239\n0.02859\n-0.085921\n-0.069914\n-0.10178\n-0.1272\n-0.076681\n-0.17628\n-0.177\n-0.11675\n-0.028577\n-0.15751\n-0.15149\n0.0082853\n-0.11465\n-0.08402\n-0.026227\n0.043522\n0.036123\n0.029186\n0.085769\n0.19056\n0.17791\n0.18176\n-0.13255\n-0.16161\nNaN\n0.281\n0.15349\n0.32418\n0.43891\n0.50688\n0.67152\n0.73555\n0.69213\n0.44152\n0.35499\n0.22934\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35132\n-0.2295\n-0.26024\n-0.23214\n-0.27069\n-0.25652\n-0.23687\n-0.19155\n-0.085927\n-0.039358\n-0.10646\n-0.11086\n-0.077495\n-0.066127\n-0.071685\n-0.09508\n-0.17662\n-0.17219\n0.036902\n0.17817\n0.1499\n0.2305\n0.21273\n0.040396\n-0.15407\n-0.3692\n-0.43129\n-0.16176\n-0.12723\n-0.066271\n-0.15089\n-0.063409\n0.039495\n0.066944\n0.017651\n-0.01547\n-0.028301\n-0.18549\n-0.18785\n0.0038034\n-0.22847\n-0.11087\n-0.20555\n-0.12961\n0.094756\n-0.13167\n0.014373\n0.081472\n0.13443\n0.21557\nNaN\nNaN\nNaN\n-0.0085182\n-0.1343\n-0.11278\n-0.17651\n-0.18658\n-0.18046\n-0.22438\n-0.27922\n-0.18349\n-0.10512\n-0.18502\n-0.17299\n-0.2379\n-0.17705\n0.024625\n0.0084216\n0.016197\n-0.1084\n0.098995\n0.10879\n0.17713\n0.10351\n0.20473\n0.15469\n0.2512\n0.045309\n-0.092279\n-0.27182\n-0.18696\n-0.11866\n-0.45521\n-0.36738\n-0.2707\n-0.34151\n-0.14395\n-0.22165\n-0.21955\n-0.091625\n-0.2089\n-0.28486\n-0.15666\n-0.1094\n0.003158\n0.14832\n0.13977\n0.063609\n0.10991\n0.11363\n0.062349\n0.055548\n0.083131\n0.11796\n0.10984\n-0.0090283\n0.2534\n0.09827\n0.03165\n-0.11927\n-0.31401\n-0.35072\n-0.31729\n-0.077467\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21036\n-0.21337\n-0.17944\n0.076237\n0.041029\n-0.41287\n-0.21515\n-0.30255\n-0.10968\n-0.073161\n-0.050439\n-0.08012\n-0.24786\n-0.0013106\n0.13932\n0.023156\n-0.32871\n-0.26134\n-0.059321\n-0.021645\n0.26125\n0.064794\n0.14508\n0.35537\n0.35593\n0.20005\n0.042855\n-0.095876\n-0.035357\n-0.11936\n-0.20661\n-0.24855\n-0.19833\n-0.24334\n-0.18223\n-0.31065\n-0.30777\n-0.077206\n-0.1609\n-0.077317\n0.027701\n-0.061297\n0.060385\n0.088321\n0.11838\n0.091805\n0.1144\n0.078864\n0.057882\n0.0449\n0.038381\n0.11297\n0.1149\n0.18655\n0.48221\n0.17862\n0.020674\n-0.23291\n-0.23759\n-0.15745\n-0.10571\n0.04584\n0.18129\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28527\n-0.24667\n-0.19114\n-0.24961\n-0.62574\n-0.55248\n-0.50767\n-0.21213\n-0.15027\n0.06772\n0.037462\n-0.11034\n-0.21452\n0.13065\n0.40192\n0.11224\n-0.15535\n-0.12328\n-0.053233\n0.17132\n0.14347\n0.19985\n0.32021\n0.28629\n0.40585\n0.33387\n0.18183\n0.031918\n0.021827\n-0.17041\n-0.057118\n-0.18034\n-0.18978\n-0.26969\n-0.38503\n-0.40197\n-0.26675\n-0.136\n-0.052883\n-0.042714\n0.014918\n0.058031\n0.045267\n0.14491\n0.085412\n0.021493\n0.0077572\n0.042087\n0.024794\n0.067749\n0.090158\n0.11878\n0.12513\n0.23117\n0.30335\n0.26604\n0.18683\n-0.22628\n-0.16732\n0.035574\n-0.17437\n0.050641\n0.20171\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18566\n-0.23664\n-0.20205\n-0.19567\n-0.21172\n-0.17566\n-0.13465\n-0.14989\n-0.22962\n-0.17195\n-0.068818\n-0.3386\n-0.14825\n0.27777\n0.15262\n-0.094009\n-0.1375\n-0.14748\n-0.1369\n0.11438\n0.2769\n0.25986\n0.28192\n0.12415\n0.33362\n0.36809\n0.2529\n0.090192\n0.039653\n0.0045534\n-0.13017\n-0.052182\n-0.20438\n-0.23054\n-0.37856\n-0.45694\n-0.34763\n-0.16153\n-0.043865\n-0.030338\n-0.024248\n0.015751\n-0.063458\n0.00034382\n0.045521\n-0.046579\n-0.053537\n0.030877\n0.064017\n0.21119\n0.26584\n0.2241\n0.28695\n0.29688\n0.36174\n0.33725\nNaN\nNaN\nNaN\n-0.42097\n-0.2997\n0.093669\n0.24933\nNaN\nNaN\nNaN\n0.023023\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17777\n-0.16172\n-0.1435\n-0.16239\n-0.14621\n-0.17534\n-0.16531\n-0.15944\n-0.21833\n-0.29022\n-0.23908\n-0.31429\n-0.22357\n-0.1098\n-0.28335\n-0.26376\n-0.28794\n-0.27048\n-0.40354\n-0.04223\n0.35596\n0.21802\n0.32731\n-0.24694\n0.20679\n0.35221\n0.40002\n0.20771\n0.16935\n0.17564\n-0.18223\n-0.18332\n-0.23152\n-0.13604\n-0.22729\n-0.18851\n-0.32073\n-0.17387\n-0.056708\n-0.034454\n-0.020919\n0.005233\n-0.067394\n-0.071011\n-0.020079\n-0.04585\n0.019093\n0.13133\n0.10864\n0.10507\n0.25193\n0.25163\n0.23231\n0.35312\n0.30575\n0.33399\n0.62148\nNaN\nNaN\n-0.70422\n-0.39315\n0.022987\n0.26836\n-0.08143\n-0.2198\n-0.075645\n-0.2768\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14925\n-0.18819\n-0.17729\n-0.17094\n-0.13219\n-0.20246\n-0.13848\n-0.11915\n-0.14125\n-0.16903\n-0.13694\n-0.060594\n-0.093073\n-0.25978\n-0.34323\n-0.21288\n-0.34779\n-0.32935\n-0.11457\n-0.45495\n-0.16596\n0.091691\n0.04655\n-0.014096\n0.23211\n0.44005\n0.44584\n0.30252\n0.19513\n0.20581\n0.024656\n-0.21252\n-0.15353\n-0.089586\n-0.18254\n-0.32588\n-0.36291\n-0.19578\n-0.083777\n-0.019012\n0.020301\n-0.042246\n-0.081909\n-0.10658\n-0.30394\n-0.1908\n0.00048165\n0.19756\n0.14909\n0.13809\n0.2033\n0.21995\n0.23641\n0.19477\n0.14373\n-0.10534\n0.062304\nNaN\nNaN\n-1.1195\n-0.58301\n-0.10225\n-0.13491\n-0.39271\n-0.095752\n-0.042665\n-0.16387\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20155\n-0.24012\n-0.19735\n-0.11406\n-0.057026\n-0.17007\n-0.061752\n-0.028311\n-0.031113\n0.0025199\n-0.03764\n-0.095481\n-0.066003\n-0.23231\n-0.23371\n-0.26403\n-0.28275\n-0.06354\n-0.18013\n-0.16707\n-0.083744\n-0.16585\n0.0045712\n0.040923\n0.13198\n0.33484\n0.44053\n0.42181\n0.19629\n0.1643\n-0.035933\n-0.1802\n-0.28891\n-0.19899\n-0.24827\n-0.34085\n-0.31701\n-0.219\n-0.16454\n-0.088887\n0.057047\n-0.07579\n-0.20111\n-0.25348\n-0.51892\n-0.2354\n-0.010442\n0.1497\n0.20244\n0.20196\n0.1395\n0.19859\n0.23247\n0.22696\n0.11867\n-0.097759\n0.028539\nNaN\nNaN\n-0.51554\n-0.74587\n-0.47828\nNaN\n-0.55181\n-0.35218\nNaN\nNaN\n0.066303\n-0.13018\nNaN\nNaN\nNaN\n-0.16986\n-0.15409\n-0.15078\n-0.040105\n0.075359\n-0.10905\n-0.059163\n0.021374\n0.058176\n0.079026\n0.034084\n-0.010912\n-0.018941\n0.0085349\n-0.10466\n-0.15546\n-0.11999\n-0.0059312\n-0.098287\n-0.13191\n-0.038594\n0.052191\n0.038968\n0.10316\n0.17909\n0.31645\n0.40918\n0.48185\n0.22067\n0.16677\n0.069338\n-0.15127\n-0.33112\n-0.20982\n-0.15682\n-0.19617\n-0.13314\n-0.24048\n-0.25818\n-0.23737\n-0.04836\n-0.13363\n-0.22079\n-0.33452\n-0.65664\n-0.21782\n0.032296\n0.093394\n0.08953\n0.19539\n0.11074\n0.16829\n0.27593\n0.28363\n0.2711\n0.044398\n0.0071573\n0.1539\n0.16848\n-0.1305\n-0.56436\n-0.453\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18277\n-0.26657\n-0.39025\nNaN\nNaN\n-0.10281\n-0.18929\n-0.17702\n-0.14022\n0.014352\n-0.052681\n-0.063965\n0.074032\n0.077553\n0.085144\n0.041555\n0.064806\n0.065524\n0.0275\n-0.0068639\n0.004581\n0.038235\n0.10153\n-0.0087779\n0.028385\n0.02369\n-0.0088091\n0.089688\n0.15377\n0.19469\n0.35724\n0.41138\n0.342\n0.1893\n0.10322\n0.090385\n0.020564\n-0.12715\n-0.20392\n-0.12627\n-0.097297\n-0.12511\n-0.24882\n-0.24911\n-0.21445\n-0.20322\n-0.157\n-0.25733\n-0.42953\n-0.64802\n-0.012985\n0.11301\n0.12057\n0.14565\n0.19556\n0.2021\n0.31499\n0.3594\n0.48927\n0.43948\n0.080386\n-0.070014\n0.11939\n0.2671\n0.2921\n-0.35558\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34019\n-0.20827\nNaN\nNaN\n-0.29892\n-0.18828\n-0.11229\n-0.18167\n0.0037878\n-0.068355\n-0.097571\n-0.072255\n0.0014189\n0.052007\n0.057224\n0.065075\n0.10026\n0.069451\n0.12697\n0.12286\n0.090544\n0.14643\n0.12012\n0.19007\n0.17423\n0.1752\n0.24502\n0.15357\n0.10451\n0.2598\n0.43546\n0.40527\n0.19762\n0.078557\n0.13452\n-0.11055\n-0.32838\n-0.29232\n-0.1944\n-0.010488\n-0.068102\n-0.15395\n-0.2717\n-0.23413\n-0.16232\n-0.12098\n-0.31384\n-0.37856\n-0.15678\n0.10293\n0.048009\n0.11564\n0.16195\n0.23297\n0.22937\n0.39877\n0.45024\n0.51596\n0.42001\n0.22643\n-0.055255\n-0.14897\n0.24582\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.23773\nNaN\n-0.17976\n-0.025555\n-0.37722\n-0.25426\n-0.15742\n-0.18263\n-0.087084\n-0.10717\n-0.10917\n-0.11133\n-0.022463\n0.079607\n0.1169\n0.10633\n0.21246\n0.13661\n0.22944\n0.25516\n0.1311\n0.14708\n0.18687\n0.26142\n0.18118\n0.23213\n0.26462\n0.043826\n0.072467\n0.39954\n0.50619\n0.45385\n0.26048\n0.26113\n0.15848\n-0.074394\n-0.36017\n-0.13534\n-0.20002\n-0.047107\n-0.12966\n-0.084317\n-0.34656\n-0.27665\n-0.122\n-0.15117\n-0.32518\n-0.32224\n-0.14082\n-0.0031463\n-0.005396\n0.10157\n0.23074\n0.17815\n0.19026\n0.35213\n0.4024\n0.55583\nNaN\nNaN\n-0.18101\n-0.18203\n0.17002\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.085024\n0.20299\n-0.41291\n-0.30398\n-0.22165\n-0.1613\n-0.15641\n-0.1019\n-0.054562\n-0.027916\n0.03227\n0.09001\n0.085796\n0.10352\n0.20437\n0.1566\n0.26111\n0.23767\n0.17179\n0.19382\n0.22604\n0.20001\n0.068225\n0.18317\n0.25738\n0.080077\n0.13498\n0.33209\n0.49011\n0.51899\n0.39016\n0.38773\n0.19202\nNaN\nNaN\nNaN\n-0.10618\n-0.064286\n-0.022841\n-0.12716\n-0.4585\n-0.26098\n-0.024235\n-0.10663\n-0.24156\n-0.22243\n-0.15907\n-0.064418\n0.032964\n0.16582\n0.35638\n0.3267\n0.27162\n0.35807\n0.30998\nNaN\nNaN\nNaN\n-0.095073\n-0.23035\n0.040216\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22072\n-0.19057\n-0.16368\n-0.1805\n-0.12217\n-0.045086\n-0.027503\n-0.0038946\n0.022163\n0.042733\n0.10938\n0.14051\n0.18405\n0.20988\n0.28187\n0.32284\n0.34132\n0.287\n0.24656\n0.1603\n0.12225\n0.16609\n0.013074\n0.074576\n0.17626\n0.19596\n0.50852\n0.44029\n0.31892\n0.33771\n0.24722\nNaN\nNaN\nNaN\n-0.091806\n-0.15321\n-0.12875\n-0.090296\n-0.30861\n-0.20811\n-0.14348\n-0.39305\n-0.50476\n-0.16502\n-0.042333\n0.033775\n0.11341\n0.24699\n0.39571\n0.39468\n0.32361\n0.32896\n0.18601\nNaN\nNaN\nNaN\n-0.44805\n-0.43689\n-0.25864\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12868\n-0.13176\n-0.15367\n-0.11231\n-0.049901\n-0.043763\n-0.026198\n-0.047662\n0.01003\n0.04565\n0.059726\n0.14042\n0.21973\n0.26826\n0.30468\n0.37021\n0.35421\n0.2873\n0.17547\n0.13084\n0.17622\n0.02575\n-0.26376\n-0.17352\n-0.1195\n0.19645\n0.41489\n0.36936\n0.42278\n0.44824\n0.35401\nNaN\nNaN\nNaN\nNaN\n-0.097009\n-0.22144\n-0.064939\n-0.24855\n-0.12103\n-0.15709\n-0.33491\n-0.32657\n-0.0043945\n0.17311\n0.20346\n0.19494\n0.30736\n0.3786\n0.39876\n0.28613\n0.17634\n0.11645\nNaN\nNaN\nNaN\nNaN\n-0.4185\n-0.26376\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.066293\n-0.17831\n-0.096479\n-0.00143\n-0.023949\n-0.053896\n-0.041958\n-0.041897\n0.011796\n0.01614\n0.024876\n0.12017\n0.23996\n0.27999\n0.32804\n0.33548\n0.32083\n0.33336\n0.2437\n0.1057\n0.13678\n-0.19349\n-0.54835\n-0.35055\n-0.21426\n0.20659\n0.31815\n0.4069\n0.5387\n0.51612\n0.56694\nNaN\n-0.14032\n-0.22447\n0.021705\n0.19863\n-0.087349\n0.029156\n-0.057769\n0.10667\n0.019079\n-0.22426\n-0.29264\n0.23486\n0.43753\n0.57704\n0.23316\n0.3374\n0.41757\n0.38117\n0.22159\n0.27499\n0.20125\n-0.040894\nNaN\nNaN\nNaN\n-0.4963\n-0.34029\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1129\n-0.16662\n-0.078627\n0.072007\n0.051393\n0.0065522\n0.024969\n-0.0088309\n-0.012285\n0.039487\n0.072187\n0.14369\n0.24023\n0.27013\n0.29935\n0.19128\n0.20053\n0.30932\n0.36235\n0.16948\n0.087787\n-0.10842\n-0.77495\n-0.67564\n-0.23547\n0.13285\n0.38518\n0.69534\n0.66323\n0.45843\n0.32162\n-0.2184\n0.026696\n-0.081016\n-0.058991\n0.1921\n0.20175\n0.14468\n0.18649\nNaN\n0.35441\n0.087297\n-0.24677\n0.27148\n0.67631\n0.69558\n0.37865\n0.28879\n0.37356\n0.23055\n0.16665\n0.12044\n0.19788\n-0.12288\n-0.18888\n-0.38469\n-0.60268\n-0.56469\n-0.6655\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14217\n-0.14071\n-0.034905\n0.12402\n0.064902\n0.049266\n0.044998\n0.0089809\n-0.014748\n0.056866\n0.07174\n0.1278\n0.24985\n0.26609\n0.23367\n0.12894\n0.15814\n0.25279\n0.30567\n0.23319\n0.034214\n-0.19276\n-0.35092\n-0.50778\n-0.060001\n0.20772\n0.4876\n0.54563\n0.49021\n0.31129\n0.15055\n0.29191\n0.45865\n0.11901\n0.19308\n0.47022\n0.24469\nNaN\nNaN\nNaN\nNaN\n0.11174\n0.10233\n0.19904\n0.3377\n0.27303\n0.052479\n0.34097\n0.26713\n0.1815\nNaN\n0.1415\n0.12433\n-0.10119\n-0.16541\n-0.23728\n-0.55314\n-0.61266\n-0.70019\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16194\n-0.091592\n0.046436\n0.14615\n0.090493\n0.04598\n0.044426\n0.077448\n0.012654\n0.053019\n0.075808\n0.15163\n0.21757\n0.25137\n0.24045\n0.22875\n0.31034\n0.21116\n0.19249\n0.088606\n-0.097749\n-0.3461\n-0.38146\n-0.39155\n0.0012872\n0.25981\n0.48407\n0.30768\n0.34217\n0.23888\n0.50122\n0.45467\n0.45612\n0.49328\n0.58996\n0.60887\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.30764\n0.25428\n0.14619\n0.38217\n0.13914\n0.073654\n0.20503\nNaN\nNaN\nNaN\n-0.20573\n-0.15035\n-0.31506\n-0.49558\n-0.39446\n-0.45965\n-0.50385\n-0.1867\n-0.14409\nNaN\nNaN\n-0.15674\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1088\n-0.010027\n0.086901\n0.12391\n0.096073\n-0.029669\n0.032932\n0.072642\n0.01996\n0.041827\n0.037871\n0.14583\n0.20146\n0.25625\n0.29776\n0.2352\n0.12705\n0.02955\n0.090715\n-0.0040154\n-0.12621\n-0.40166\n-0.41829\n-0.22161\n0.03817\n0.24751\n0.31147\n0.29267\n0.51492\n0.3941\n0.53708\n0.54769\n0.54429\n0.52533\n0.62242\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13896\n-0.013418\n-0.0066791\n-0.068524\n-0.0068286\n-0.16902\n-0.20246\nNaN\nNaN\nNaN\n-0.38206\n-0.20613\n-0.28101\n-0.31963\n-0.14956\n-0.17827\n-0.20052\n0.068316\n0.14067\n-0.090796\n-0.14499\n-0.11215\n-0.11435\n-0.16739\n-0.35401\nNaN\nNaN\nNaN\n-0.052183\n0.012957\n0.016165\n-0.015383\n0.075782\n0.057288\n0.11916\n0.0374\n-0.066929\n0.013969\n0.050236\n0.1268\n0.16777\n0.20467\n0.1899\n0.1314\n-0.011884\n-0.14696\n-0.049211\n-0.072657\n-0.091997\n-0.44703\n-0.30585\n-0.064274\n0.19975\n0.37356\n0.31764\n0.416\n0.62733\n0.62336\n0.5701\n0.58321\n0.53271\n0.52715\n0.71855\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.067454\n-0.064956\n-0.24687\n-0.28888\n0.049014\n-0.54454\n-0.41977\nNaN\nNaN\nNaN\n-0.55282\n-0.34639\n-0.28923\n-0.18672\n-0.016886\n0.062695\n-0.10495\n-0.084963\n0.12057\n-0.049294\n0.086366\n0.15516\n0.16966\n0.045915\n-0.062088\nNaN\nNaN\nNaN\n-0.056792\n0.030927\n-0.01956\n-0.039829\n0.096822\n0.099476\n-0.035778\n-0.085574\n-0.080712\n0.076241\n0.1059\n0.12296\n0.060997\n0.13315\n0.078639\n0.062443\n0.014121\n-0.016847\n-0.079222\n-0.049159\n-0.075145\n-0.48351\n-0.29133\n0.11704\n0.27582\n0.52514\n0.57952\n0.54735\n0.62359\n0.88215\n1.0701\n1.0678\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.4594\n0.31731\n0.36793\n-0.26513\n0.096363\n-0.065555\n-0.54943\nNaN\n-0.40837\n-0.33858\n-0.25879\n-0.25163\n-0.26552\n-0.19041\n0.034925\n0.13987\n-0.016851\n0.12986\n0.19901\n0.10505\n0.11657\n0.18212\n0.090184\n0.10021\n0.090022\n0.063502\n-0.011639\n-0.070523\n-0.049284\n-0.061468\n-0.078959\n-0.024577\n0.093\n0.056153\n-0.064196\n-0.039835\n-0.02917\n0.056624\n0.057172\n0.02931\n0.11756\n0.10095\n0.028614\n0.10856\n0.019615\n-0.10399\n-0.10883\n-0.0075271\n0.083742\n-0.2491\n-0.35323\n0.17291\n0.43725\n0.82822\n0.69856\n0.64026\n0.96155\n1.1541\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.04056\n-0.17818\n0.32369\n0.086139\n0.27102\n-0.20401\n-0.32287\n-0.20291\n-0.29036\n-0.21561\n-0.037371\n-0.066972\n-0.10555\n-0.32855\n-0.068175\n0.11246\n0.017859\n0.22834\n0.28154\n0.065425\n0.044965\n0.035083\n-0.14075\n-0.023272\n-0.0018529\n-0.033088\n0.17328\n0.12792\n-0.048113\n-0.066358\n-0.053641\n-0.10636\n-0.10617\n-0.080963\n-0.080377\n-0.074112\n0.031042\n0.015881\n-0.022265\n0.0036813\n0.076529\n0.099115\n0.12306\n0.2086\n0.16958\n-0.13945\n-0.32413\n-0.039494\n0.038207\n-0.2382\n-0.2263\n0.13052\n0.56814\n0.6486\n0.58199\n0.6878\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24924\n-0.73083\n-0.14479\n-0.17338\n0.05155\n-0.068527\n-0.070035\n-0.13252\n-0.19037\n-0.036308\n0.048637\n0.01934\n0.030243\n-0.090083\n-0.077825\n-0.085108\n-0.0055438\n0.17693\n0.32315\n0.076974\n0.12967\n0.058495\n-0.15561\n-0.14648\n-0.039186\n-0.05911\n0.03037\n0.3211\n-0.087685\n-0.11941\n-0.27508\n-0.1944\n-0.096796\n-0.089\n-0.10488\n-0.028037\n0.10385\n0.052128\n0.0085507\n0.081687\n0.12313\n0.11263\n0.17769\n0.36309\n0.29507\n0.0033421\n-0.24725\n-0.34615\n-0.21289\n-0.19499\n-0.072033\n0.25241\n0.44562\n0.49213\n0.3993\n0.43203\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15612\n-0.29234\n-0.21841\n-0.14444\n-0.069339\n-0.090955\n-0.1046\n-0.11057\n-0.027392\n0.0315\n0.080054\n0.18583\n0.078859\n-0.059079\n-0.085123\n-0.016344\n-0.0047332\n0.21084\n0.010765\n0.18307\n0.19575\n-0.048486\n-0.062813\n0.15906\n0.10496\n0.059336\n0.15715\n-0.16501\n-0.23245\n-0.23112\n-0.15313\n-0.11882\n-0.0089208\n-0.053932\n0.039537\n0.092139\n0.088577\n0.030945\n0.088562\n0.18614\n0.11267\n0.16551\n0.34444\n0.37709\n0.069157\n-0.20803\n-0.28779\n-0.12926\n-0.05293\n-0.0049393\n0.28949\n0.42216\n0.72332\n0.67364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.12684\n0.055675\n-0.32491\n-0.23809\n0.08443\n-0.059517\n0.016553\n0.0097505\n-0.024559\n-0.062278\n0.1351\n0.37602\n0.26883\n0.048236\n0.041473\n0.035042\n-0.078927\n-0.098177\n0.040123\n0.19567\n0.094468\n-0.036978\n-0.17805\n0.13666\n0.1945\n0.20593\n0.17261\n-0.31073\n-0.22414\n-0.13925\n-0.11414\n-0.14539\n0.035892\n0.048109\n0.0070056\n0.075002\n0.047293\n0.062681\n0.16436\n0.19586\n0.20992\n0.22518\n0.33302\n0.53004\n0.090895\n-0.091971\n-0.099832\n0.06508\n0.030526\n0.043102\n0.20478\n0.49714\n0.64919\n0.783\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26757\n-0.049287\n-0.37136\n-0.27659\n0.042548\n-0.10355\n0.039257\n0.082595\n-0.11456\n-0.16815\n0.027962\n0.36559\n0.34889\n0.2416\n0.14442\n0.12964\n0.018147\n-0.020126\n0.14522\n0.25092\n0.060176\n-0.035207\n-0.052562\n0.21831\n0.33072\n0.4104\n0.24526\n-0.25319\n-0.16864\n-0.11396\n-0.065524\n-0.084302\n0.0051447\n0.04222\n0.025848\n0.039194\n0.058637\n0.0071718\n0.0071788\n0.14657\n0.23467\n0.27202\n0.36069\n0.3682\n0.088768\n0.056578\n-0.058198\n-0.0026422\n0.067135\n0.1684\n0.28451\n0.531\n0.65144\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13486\n0.051523\n-0.25464\n-0.22559\n0.0064615\n-0.61821\n-0.24138\n0.066245\n0.014099\n0.23437\n0.30125\n0.34378\n0.33881\n0.35457\n0.25557\n0.2047\n0.056288\n-0.016823\n0.24729\n0.19618\n0.24285\n0.017119\n0.079198\n0.30078\n0.35696\n0.40147\n0.29009\n-0.24288\n-0.11855\n-0.15838\n-0.0854\n-0.10858\n-0.079787\n-0.0751\n0.00076349\n0.00063284\n-0.053738\n-0.031992\n-0.00084758\n0.071884\n0.19537\n0.27121\n0.30484\n0.2034\n0.040738\n-0.017561\n-0.0019934\n0.054079\n0.088765\n0.11401\n0.23322\n0.52855\n0.81758\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.31535\nNaN\nNaN\nNaN\nNaN\n0.15101\n-0.12517\n-0.11752\n-0.17909\n-0.062277\n-0.62273\n-0.10513\n0.13459\n0.39493\n0.3332\n0.34861\n0.30219\n-0.083315\n0.27256\n0.12635\n0.11774\n-0.18352\n0.0055518\n0.32266\n0.2731\n0.37133\n0.19181\n0.2841\n0.36843\n0.3788\n0.29617\n0.13111\n-0.16082\n-0.098441\n-0.15261\n-0.12285\n-0.068723\n-0.08693\n-0.099384\n-0.069698\n-0.09702\n-0.045253\n0.011098\n0.040492\n0.041823\n0.18467\n0.18098\n0.23586\n0.19743\n0.068859\n-0.081839\n-0.014681\n0.077924\n0.090351\n0.0092373\n0.28149\n0.74548\n1.0795\n0.44544\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.36938\n0.32273\n0.25529\n0.22615\n0.34746\n0.24754\n0.021831\n-0.024355\n-0.083503\n-0.15672\n-0.12676\n-0.048597\n-0.048405\n0.15343\n0.38724\n0.22116\n0.2616\n0.11425\n-0.23339\n-0.1818\n0.029102\n0.093341\n-0.11794\n0.073867\n0.28444\n0.36104\n0.32935\n0.23158\n0.22665\n0.41489\n0.3158\n0.1552\n-0.1871\n-0.16929\n-0.12387\n-0.073458\n-0.1538\n-0.16311\n-0.12061\n-0.086548\n-0.12831\n-0.16987\n-0.017539\n0.010459\n-0.016726\n-0.039131\n0.085259\n0.12729\n0.16104\n0.078547\n-0.013576\n-0.028715\n-0.22723\n-0.17633\n0.078865\n0.16091\n0.51286\n0.93297\n1.0334\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59517\n0.65089\n0.89771\n0.70237\n0.63101\n0.48834\n0.36288\n0.37491\n0.036402\n-0.14658\n-0.22631\n-0.35073\n-0.27663\n-0.27262\n-0.16506\n0.077522\n-0.083803\n-0.11893\n0.011799\n-0.051354\n-0.25523\n-0.31421\n-0.085752\n-0.081095\n0.042986\n0.20373\n0.20777\n0.15391\n0.16955\n0.20642\n0.27083\n0.17933\n0.11585\n-0.33697\n-0.20903\n-0.17917\n-0.1771\n-0.19827\n-0.20631\n-0.1141\n-0.090175\n-0.098848\n-0.14656\n-0.05487\n-0.026762\n-0.08295\n-0.059775\n-0.017338\n0.029241\n-0.063714\n-0.10962\n-0.1892\n-0.19548\n-0.19593\n-0.2096\n0.070785\n0.26404\n0.73658\n0.97638\n1.1503\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.59177\n0.70326\n0.74802\n0.45895\n0.94404\n1.5627\n1.0629\n0.84957\n0.52422\n0.42834\n0.21946\n0.18267\n-0.055139\n-0.11041\n-0.20511\n-0.32743\n-0.28629\n-0.29561\n-0.23268\n-0.22386\n-0.18588\n-0.18637\n-0.18227\n-0.30254\n-0.40609\n-0.060227\n0.063045\n0.084065\n0.010073\n-0.079462\n-0.013731\n0.13309\n0.13222\n0.036094\n0.094215\n0.025684\n-0.1502\n-0.29066\n-0.20792\n-0.22949\n-0.14891\n-0.17835\n-0.17692\n-0.19018\n-0.11426\n-0.11412\n-0.12716\n-0.065019\n-0.17806\n-0.27629\n-0.15555\n-0.11725\n-0.14934\n-0.25232\n-0.28916\n-0.25134\n-0.258\n-0.2687\n-0.14686\n0.2582\n0.55728\n0.84027\n1.2312\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8446\n1.5139\n1.0781\n0.84675\n0.45226\n0.094967\n0.13859\n0.08688\n0.004517\n-0.14322\n-0.3181\n-0.31113\n-0.25052\n-0.18709\n-0.17465\n-0.35951\n-0.4169\n-0.28602\n-0.27063\n-0.40565\n-0.43978\n-0.16749\n0.011086\n0.0066388\n-0.0060804\n-0.096379\n-0.062063\n-0.05545\n0.067638\n-0.087406\n-0.16562\n-0.077358\n-0.0064164\n-0.19643\n-0.22165\n-0.24903\n-0.12445\n-0.19654\n-0.21508\n-0.22181\n-0.17475\n-0.15593\n-0.17129\n-0.19191\n-0.21648\n-0.28786\n-0.1879\n-0.22074\n-0.15985\n-0.26822\n-0.52121\n-0.62359\n-0.28435\n-0.1602\n-0.17923\n0.13367\n0.51423\n0.67564\n0.66545\n0.43824\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5705\n1.0656\n1.0231\n0.60139\n0.45146\n-0.23622\n-0.060976\n-0.033321\n-0.15933\n-0.21303\n-0.25519\n-0.24515\n-0.20683\n-0.19815\n-0.21977\n-0.29151\n-0.51524\n-0.30548\n-0.20952\n-0.20046\n-0.30186\n-0.33721\n-0.13737\n-0.022584\n0.015679\n-0.00019652\n-0.063648\n-0.10113\n-0.2084\n-0.19535\n-0.092026\n0.019265\n0.070152\n-0.19701\n-0.26206\n-0.27903\n-0.18351\n-0.17114\n-0.13388\n-0.22125\n-0.20492\n-0.14201\n-0.15237\n-0.25084\n-0.24324\n-0.22565\n-0.20708\n-0.24369\n-0.15884\n-0.20423\n-0.33809\n-0.49598\n-0.48161\n-0.16034\n-0.19737\n0.10883\n0.50752\n0.62822\n0.10853\n-0.045238\n-0.10507\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2622\n0.88262\n0.63552\n0.57238\n0.36126\n-0.21519\n-0.087375\n-0.077173\n-0.33729\n-0.36741\n-0.35696\n-0.22085\n-0.1912\n-0.21879\n-0.36907\n-0.26562\n-0.24986\n-0.21099\n-0.10215\n-0.056744\n-0.04797\n-0.087933\n-0.15456\n-0.046002\n-0.087131\n-0.051079\n-0.12628\n-0.13314\n-0.20191\n-0.060421\n0.03419\n0.14286\n0.093471\n-0.24079\n-0.30787\n-0.29824\n-0.24855\n-0.1342\n-0.071527\n-0.17363\n-0.14159\n-0.14221\n-0.11772\n-0.29863\n-0.23014\n-0.16121\n-0.26517\n-0.3253\n-0.26029\n-0.1874\n-0.36606\n-0.31457\n-0.62193\n-0.6156\n-0.33719\n0.068007\n0.31538\n0.39585\n0.21931\n-0.10082\n0.016413\nNaN\nNaN\n0.33673\n0.75295\n0.55323\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.89245\n0.68279\n0.54255\n0.50936\n-0.14195\n-0.42784\n-0.1837\n-0.29761\n-0.47088\n-0.5005\n-0.3367\n-0.14555\n-0.14591\n-0.2106\n-0.21974\n-0.20704\n-0.17637\n-0.16018\n-0.11824\n-0.12273\n-0.1165\n-0.094383\n-0.15803\n-0.089203\n-0.1411\n-0.20577\n-0.25395\n-0.27032\n-0.11041\n-0.0084748\n0.052833\n0.15115\n0.11109\n-0.2804\n-0.31359\n-0.2618\n-0.30089\n-0.16888\n-0.019186\n-0.099056\n-0.1196\n-0.17393\n-0.14509\n-0.28775\n-0.19602\n-0.18775\n-0.31505\n-0.37374\n-0.24798\n-0.26113\n-0.44124\n-0.49422\n-0.43512\n-0.3711\n-0.22502\n-0.0049564\n0.24036\n-0.00079884\n-0.22247\n-0.10669\n0.21744\n0.20272\n0.44323\n0.48993\n0.65639\n0.66519\n0.7925\nNaN\nNaN\nNaN\nNaN\n0.514\n0.62857\n0.3899\n0.31223\n0.25667\n-0.28921\n-0.57288\n-0.36\n-0.3985\n-0.45244\n-0.39613\n-0.27254\n-0.019482\n-0.0056807\n0.0029104\n-0.075504\n-0.22928\n-0.23411\n-0.15573\n-0.12151\n-0.082072\n-0.057989\n-0.14563\n-0.20395\n-0.27512\n-0.38908\n-0.31051\n-0.28292\n-0.288\n-0.21392\n-0.23324\n-0.087967\n0.047739\n-0.035986\n-0.2681\n-0.31159\n-0.3461\n-0.31814\n-0.26157\n-0.048536\n-0.059609\n-0.13718\n-0.21405\n-0.18048\n-0.2511\n-0.16734\n-0.19035\n-0.28178\n-0.347\n-0.28678\n-0.49981\n-0.86041\n-0.61342\n-0.4116\n-0.28616\n0.00098083\n0.07753\n0.18085\n0.60074\n0.16033\n0.18583\n0.2341\nNaN\n0.098675\n0.28154\n0.10373\n0.65503\n0.99662\nNaN\nNaN\nNaN\n0.30082\n0.47769\n0.53456\n0.38479\n0.13779\n0.17271\n-0.15981\n-0.5221\n-0.40161\n-0.107\n-0.18487\n-0.27386\n-0.16692\n0.015468\n0.071586\n0.065402\n-0.09126\n-0.1072\n-0.17395\n-0.17013\n-0.12528\n-0.026254\n-0.031028\n-0.14337\n-0.25102\n-0.3271\n-0.40204\n-0.28839\n-0.12323\n-0.16386\n-0.27354\n-0.25465\n-0.14114\n-0.067209\n-0.13687\n-0.3652\n-0.31832\n-0.28545\n-0.30142\n-0.22499\n-0.087118\n-0.10662\n-0.21109\n-0.25829\n-0.24913\n-0.26116\n-0.19705\n-0.16941\n-0.22988\n-0.28624\n-0.32847\n-0.51746\n-0.85204\n-0.5452\n-0.43561\n-0.25666\n-0.15304\n0.18485\n0.13349\n0.21107\n0.19477\n0.122\nNaN\nNaN\n-0.033909\n0.11969\n0.17074\n0.3838\n0.66396\n0.70952\n0.47284\n0.20915\n0.23916\n0.30042\n0.49496\n0.28836\n-0.10342\n-0.20635\n-0.29887\n-0.38172\n-0.14455\n-0.002119\n-0.047472\n-0.2008\n-0.18075\n-0.032472\n0.13943\n0.050291\n-0.081177\n-0.082794\n-0.118\n-0.1089\n-0.051517\n0.019543\n-0.022822\n-0.087527\n-0.23887\n-0.31532\n-0.30292\n-0.1645\n-0.044244\n-0.049868\n-0.068244\n-0.069643\n-0.071565\n-0.050995\n0.017726\n-0.37157\n-0.32745\n-0.21044\n-0.154\n-0.1293\n-0.084627\n-0.128\n-0.24124\n-0.27794\n-0.27049\n-0.23005\n-0.16459\n-0.13161\n-0.10787\n-0.11245\n-0.30202\n-0.38054\n-0.79981\n-0.25532\n-0.36748\n-0.3627\n-0.055828\n0.19708\n0.13263\n0.0035078\n0.021023\n0.025548\nNaN\nNaN\nNaN\n-0.066056\n0.13122\n0.25457\n0.38412\n0.34008\n0.43283\n0.3375\n0.12022\n0.18785\n0.42217\n0.16615\n-0.051203\n-0.10312\n-0.026349\n-0.086924\n-0.14475\n-0.15579\n-0.17988\n-0.14948\n-0.040422\n0.054716\n0.12233\n0.01579\n-0.16442\n-0.1392\n-0.068616\n-0.011959\n0.070762\n0.071019\n0.047862\n-0.05947\n-0.18494\n-0.2451\n-0.19253\n-0.073022\n-0.0043799\n0.019361\n0.035082\n-0.098077\n-0.17573\n-0.03559\n0.10108\n-0.3145\n-0.30857\n-0.18913\n-0.097855\n-0.040291\n-0.047914\n-0.15252\n-0.15611\n-0.18154\n-0.19284\n-0.11378\n-0.044375\n-0.024738\n0.10221\n0.23719\n-0.11892\nNaN\nNaN\nNaN\n-0.34698\n-0.45309\n-0.12372\n0.054338\n-0.23436\n-0.1852\n-0.084609\n0.041685\nNaN\nNaN\nNaN\n-0.062461\n0.10699\n0.05474\n0.14661\n0.21861\n0.28576\n0.20552\n0.10757\n0.1246\n0.014455\n-0.093764\n-0.075267\n0.035768\n0.053511\n-0.021913\n-0.085025\n-0.059009\n-0.014901\n0.036566\n0.0046509\n0.054207\n0.036134\n-0.047402\n-0.22232\n-0.13639\n0.01207\n0.034579\n0.081443\n0.10837\n0.084184\n-0.062102\n-0.11865\n-0.14802\n-0.16269\n-0.07148\n-0.032117\n0.0602\n0.086337\n-0.096746\n-0.20161\n-0.095898\n0.068438\n-0.17008\n-0.15449\n-0.13528\n-0.11521\n-0.079624\n-0.021927\n-0.17708\n-0.094575\n-0.079538\n-0.099181\n-0.036296\n0.032381\n0.13059\n0.3346\n0.31718\n-0.023581\nNaN\nNaN\nNaN\n-0.3763\n-0.41959\n-0.54747\n-0.36738\n-0.29271\n-0.18146\n-0.15295\n-0.11822\nNaN\nNaN\nNaN\n-0.12209\n-0.06436\n-0.038701\n0.09821\n0.38781\n0.27258\n0.13321\n0.052262\n0.0064358\n-0.11242\n-0.10387\n-0.10921\n0.0016615\n0.07656\n0.054469\n-0.027376\n-0.0097469\n-0.047037\n-0.02625\n0.023241\n0.038805\n0.035277\n-0.0078412\n-0.038118\n0.031003\n0.02746\n-0.084134\n0.077446\n0.10492\n0.08829\n-0.047255\n-0.082055\n-0.078664\n-0.074586\n-0.0061658\n0.025089\n0.16829\n0.13561\n-0.034493\n-0.086076\n0.012082\n0.078908\n-0.083658\n-0.03108\n-0.041717\n-0.1232\n-0.019191\n-0.012541\n-0.013773\n-0.061069\n-0.0094026\n-0.054488\n0.019005\n0.11216\n0.16246\n0.16816\n0.07246\n0.059722\n-0.13539\nNaN\nNaN\n-0.47566\n-0.40794\n-0.41698\n-0.40474\n-0.32034\n-0.12904\n-0.14226\n-0.15172\n-0.083995\n-0.14558\n-0.11601\n-0.29517\n-0.21202\n-0.093344\n0.048268\n0.33281\n0.27627\n0.2316\n0.1428\n0.10669\n0.11573\n-0.023042\n-0.016667\n0.021017\n0.10476\n0.13032\n0.02979\n-0.13869\n-0.30901\n-0.24678\n0.0058499\n0.038527\n0.039712\n0.18163\n0.05618\n0.061086\n0.053673\n-0.08115\n0.042182\n0.066059\n0.07855\n0.0031046\n-0.12241\n-0.045781\n0.036224\n0.079946\n0.077303\n0.096975\n0.16823\n-0.016892\n-0.0060779\n0.1534\n0.10798\n-0.1456\n-0.091436\n-0.049587\n-0.059178\n-0.0022484\n0.030812\n0.040784\n-0.012645\n-0.037494\n-0.039953\n-0.022215\n0.042941\n0.1058\n0.14455\n0.11994\n0.084774\n-0.16455\n-0.44424\n-0.57768\n-0.50221\n-0.43283\n-0.33731\n-0.30164\n-0.232\n-0.10767\n-0.13778\n-0.13068\n-0.11227\n-0.064562\n-0.070175\n-0.15274\n-0.18796\n-0.16853\n-0.070562\n0.16989\n0.24855\n0.17376\n0.27017\n0.17104\n0.094325\n0.053441\n0.16013\n0.13007\n0.14935\n0.17927\n0.17425\n-0.13852\n-0.41792\n-0.57039\n-0.16103\n-0.035647\n0.22073\n0.23826\n0.14048\n0.055112\n0.14478\n0.092679\n0.062042\n0.017311\n0.0032189\n-0.023944\n-0.092222\n0.066268\n0.10116\n0.088372\n0.046983\n0.17197\n0.20346\n-0.0034336\n0.038308\n0.06326\n0.018776\n-0.18449\n-0.051879\n0.0463\n0.030314\n0.099415\n0.14831\n0.10695\n0.12482\n0.03819\n-0.01963\n-0.052535\n-0.013113\n0.020933\nNaN\nNaN\nNaN\nNaN\n-0.27079\n-0.47265\n-0.49416\n-0.28947\n-0.32165\n-0.39192\n-0.26474\n-0.088745\n-0.11059\n-0.09174\n-0.026904\n-0.063727\n-0.098476\n-0.11092\n-0.075981\n-0.20719\n-0.1584\n-0.036191\n0.050276\n0.12575\n0.28669\n0.19142\n0.19352\n0.16972\n0.24007\n0.2081\n0.023289\n0.040388\n0.21686\n0.088662\n-0.31478\n-0.32113\n-0.30604\n-0.15182\n-0.02774\n0.074474\n0.002612\n0.030719\n0.18175\n-0.0046643\n-0.064409\n-0.013194\n-0.050298\n-0.029837\n0.022464\n0.099921\n0.049083\n-0.014549\n0.071927\n0.24139\n0.21291\n0.11453\n0.034504\n-0.0008435\n0.064369\n-0.082786\n-0.010737\n0.05489\n0.092806\n0.1516\n0.19926\n0.19462\n0.28604\n0.23637\n0.15532\n0.018615\n-0.0093036\n0.074875\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.40596\n-0.40971\n-0.3629\n-0.34273\n-0.42865\n-0.31767\n-0.14194\n-0.10523\n-0.14664\n-0.10801\n-0.1499\n-0.15674\n-0.15988\n-0.13519\n-0.072179\n-0.12922\n-0.11823\n0.0066043\n0.15505\n0.40265\n0.35318\n0.2911\n0.28744\n0.31324\n0.22135\n-0.17803\n-0.19734\n-0.046706\n0.25482\n-0.20497\n-0.17267\n-0.16058\n-0.13133\n-0.15359\n-0.1627\n-0.11741\n-0.043351\n0.00044277\n-0.1129\n-0.078556\n-0.026644\n-0.013286\n-0.0019714\n0.067603\n0.10961\n0.069393\n-0.055893\n0.089779\n0.31665\n0.23047\n0.18899\n0.028767\n0.007949\n-0.00093861\n0.033353\n-0.033783\n0.085661\n0.19126\n0.26226\n0.44284\n0.40691\n0.46423\n0.40092\n0.26382\n0.063119\n-0.058136\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.49882\n-0.40575\n-0.45616\n-0.42567\n-0.41685\n-0.35411\n-0.38159\n-0.26254\n-0.22002\n-0.15117\n-0.1363\n-0.1847\n-0.15029\n-0.09274\n-0.036727\n-0.050782\n-0.024521\n0.045615\n0.29675\n0.4327\n0.28417\n0.32694\n0.36661\n0.28574\n-0.19\n-0.42301\n-0.32292\n0.12508\n-0.10747\n-0.093588\n-0.118\n-0.14607\n-0.12232\n-0.18071\n-0.16441\n-0.085259\n0.00022684\n-0.2098\n-0.19815\n-0.048094\n-0.14734\n-0.071783\n-0.035266\n0.060928\n0.063675\n0.037109\n0.09386\n0.22726\n0.18747\n0.1883\n-0.12404\n-0.17114\nNaN\n0.16479\n-0.00075367\n0.23183\n0.36435\n0.46634\n0.62827\n0.67797\n0.70126\n0.44442\n0.32092\n0.1511\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.46265\n-0.34087\n-0.40297\n-0.35009\n-0.40132\n-0.37708\n-0.34136\n-0.29592\n-0.20417\n-0.14198\n-0.15873\n-0.15754\n-0.13361\n-0.02751\n-0.0094071\n-0.02572\n-0.13825\n-0.12125\n0.066542\n0.23923\n0.19195\n0.28346\n0.22519\n0.005161\n-0.19726\n-0.44306\n-0.45568\n-0.12794\n-0.15296\n-0.092963\n-0.18582\n-0.10494\n0.029136\n0.055567\n-0.00073441\n-0.0050448\n-0.0091808\n-0.22186\n-0.22181\n-0.03583\n-0.25589\n-0.10319\n-0.23403\n-0.13191\n0.13269\n-0.10906\n0.038623\n0.098744\n0.098802\n0.21135\nNaN\nNaN\nNaN\n-0.079458\n-0.19441\n-0.17207\n-0.25329\n-0.24997\n-0.20123\n-0.21615\n-0.35939\n-0.27232\n-0.16913\n-0.2617\n-0.27558\n-0.30962\n-0.2366\n0.033616\n0.033884\n0.024937\n-0.14832\n-0.003303\n0.089204\n0.1762\n0.05735\n0.21675\n0.2183\n0.30722\n0.064632\n-0.077418\n-0.27927\n-0.20243\n-0.1073\n-0.56935\n-0.47758\n-0.40215\n-0.47955\n-0.2377\n-0.32\n-0.3109\n-0.14455\n-0.23834\n-0.31396\n-0.17531\n-0.13118\n-0.040065\n0.1146\n0.10995\n0.019944\n0.077922\n0.083803\n0.024839\n0.025759\n0.077524\n0.081342\n0.049751\n-0.072535\n0.20662\n0.0027026\n-0.062052\n-0.18449\n-0.41768\n-0.44239\n-0.32112\n-0.051953\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30946\n-0.31009\n-0.29248\n0.024505\n0.0082171\n-0.50116\n-0.16159\n-0.37988\n-0.20341\n-0.13733\n-0.16448\n-0.23122\n-0.33711\n0.031657\n0.19313\n0.04941\n-0.42628\n-0.3392\n-0.12152\n-0.084004\n0.17078\n0.0052298\n0.17003\n0.42864\n0.46609\n0.27531\n0.077994\n-0.046734\n-0.0077428\n-0.11422\n-0.2659\n-0.33896\n-0.33343\n-0.345\n-0.29673\n-0.40475\n-0.39138\n-0.13591\n-0.21423\n-0.12783\n0.016563\n-0.093477\n0.0053589\n0.014759\n0.075227\n0.066429\n0.10792\n0.062834\n0.04291\n0.027751\n0.012321\n0.073096\n0.066219\n0.14072\n0.46492\n0.091056\n-0.086277\n-0.34341\n-0.30278\n-0.18241\n-0.13616\n0.041487\n0.18035\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41285\n-0.36919\n-0.29314\n-0.35893\n-0.84848\n-0.78057\n-0.73868\n-0.30685\n-0.23029\n0.040336\n-0.076199\n-0.26439\n-0.35655\n0.15513\n0.43184\n0.10325\n-0.23436\n-0.19121\n-0.085211\n0.17025\n0.11344\n0.226\n0.3721\n0.35986\n0.54323\n0.43799\n0.26193\n0.078438\n0.061879\n-0.16051\n-0.023493\n-0.22085\n-0.33047\n-0.4143\n-0.47876\n-0.50193\n-0.35438\n-0.20812\n-0.11777\n-0.060555\n0.014225\n0.020229\n-0.014933\n0.10651\n0.061214\n-0.002762\n-0.0092706\n0.028998\n0.010202\n0.053672\n0.055314\n0.097771\n0.099138\n0.18128\n0.23557\n0.23836\n0.14018\n-0.36575\n-0.25927\n0.0080854\n-0.21728\n0.051099\n0.2442\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.29591\n-0.36354\n-0.30423\n-0.29337\n-0.30245\n-0.27889\n-0.24888\n-0.25388\n-0.34927\n-0.28647\n-0.14104\n-0.49541\n-0.38669\n0.26799\n0.098505\n-0.19604\n-0.22364\n-0.21481\n-0.17409\n0.12128\n0.27914\n0.33668\n0.33788\n0.14256\n0.42635\n0.47684\n0.3687\n0.13183\n0.1031\n0.16662\n-0.14687\n-0.060039\n-0.3222\n-0.34707\n-0.50748\n-0.58791\n-0.43862\n-0.21853\n-0.099146\n-0.052178\n-0.041785\n-0.021483\n-0.1371\n-0.040728\n0.012261\n-0.083196\n-0.092977\n-0.0032939\n0.018205\n0.17318\n0.23053\n0.19126\n0.25041\n0.24586\n0.35221\n0.35171\nNaN\nNaN\nNaN\n-0.52705\n-0.36549\n0.060095\n0.33164\nNaN\nNaN\nNaN\n-0.043916\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30683\n-0.26846\n-0.23335\n-0.24899\n-0.23412\n-0.26446\n-0.26446\n-0.25791\n-0.31806\n-0.39139\n-0.35319\n-0.42633\n-0.30628\n-0.20019\n-0.41869\n-0.36782\n-0.39416\n-0.37639\n-0.50132\n0.014113\n0.41167\n0.2636\n0.38327\n-0.30538\n0.26033\n0.46871\n0.56317\n0.32514\n0.25976\n0.28854\n-0.22522\n-0.29053\n-0.34313\n-0.19986\n-0.38691\n-0.26621\n-0.35877\n-0.2043\n-0.084002\n-0.059853\n-0.04604\n-0.020027\n-0.12629\n-0.15269\n-0.073919\n-0.098218\n-0.035051\n0.065136\n0.035904\n0.021534\n0.20732\n0.21431\n0.18416\n0.32573\n0.27685\n0.30674\n0.65213\nNaN\nNaN\n-0.87779\n-0.4616\n-0.0009523\n0.3971\n-0.09668\n-0.2469\n-0.10218\n-0.29943\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24884\n-0.28254\n-0.24134\n-0.24608\n-0.21572\n-0.29707\n-0.22197\n-0.21252\n-0.22119\n-0.23837\n-0.20477\n-0.11768\n-0.18176\n-0.36721\n-0.46703\n-0.33957\n-0.47784\n-0.43303\n-0.035886\n-0.37714\n-0.11642\n0.15557\n0.084741\n0.016046\n0.35949\n0.61172\n0.60485\n0.41638\n0.30656\n0.31103\n0.058794\n-0.32992\n-0.22623\n-0.14765\n-0.32311\n-0.45611\n-0.41729\n-0.22853\n-0.10074\n-0.05122\n0.00063957\n-0.064074\n-0.13428\n-0.15073\n-0.35119\n-0.26519\n-0.071476\n0.15257\n0.090007\n0.074541\n0.16486\n0.18649\n0.2042\n0.15839\n0.097293\n-0.21071\n-0.029218\nNaN\nNaN\n-1.4834\n-0.70642\n-0.074773\n-0.14269\n-0.46181\n-0.16018\n-0.041133\n-0.15383\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28542\n-0.32621\n-0.26518\n-0.18552\n-0.12214\n-0.25559\n-0.11991\n-0.070062\n-0.070163\n-0.0063793\n-0.065399\n-0.14092\n-0.10294\n-0.32001\n-0.31952\n-0.37082\n-0.38686\n-0.09121\n-0.25283\n-0.15189\n-0.031802\n-0.10802\n0.064825\n0.15905\n0.26499\n0.49156\n0.61894\n0.57487\n0.34541\n0.28735\n-0.0072433\n-0.27835\n-0.38141\n-0.27966\n-0.35907\n-0.43893\n-0.37926\n-0.2669\n-0.21543\n-0.14904\n0.041101\n-0.12191\n-0.27374\n-0.3218\n-0.59633\n-0.31696\n-0.089044\n0.098053\n0.16483\n0.17493\n0.10173\n0.15684\n0.164\n0.1642\n0.066467\n-0.16833\n-0.049231\nNaN\nNaN\n-0.67234\n-0.87929\n-0.51033\nNaN\n-0.63347\n-0.40763\nNaN\nNaN\n0.20423\n0.12314\nNaN\nNaN\nNaN\n-0.24338\n-0.22382\n-0.20769\n-0.080132\n0.040666\n-0.15135\n-0.087546\n0.0077293\n0.054574\n0.10121\n0.057656\n-0.011899\n-0.020229\n0.0087713\n-0.1004\n-0.16395\n-0.14067\n0.021086\n-0.10378\n-0.11358\n0.0080943\n0.12569\n0.13701\n0.22345\n0.33859\n0.47749\n0.57588\n0.66102\n0.39935\n0.32579\n0.17669\n-0.11929\n-0.34819\n-0.24337\n-0.179\n-0.21901\n-0.15319\n-0.31961\n-0.36922\n-0.34379\n-0.10174\n-0.20269\n-0.32229\n-0.45925\n-0.78121\n-0.29613\n-0.048005\n0.019776\n0.022254\n0.16905\n0.037791\n0.10205\n0.21663\n0.23414\n0.23785\n-0.027177\n-0.044096\n0.08294\n0.14684\n-0.20053\n-0.73574\n-0.47917\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.037453\n-0.039933\n-0.28754\nNaN\nNaN\n-0.15715\n-0.23149\n-0.18438\n-0.1706\n0.0072712\n-0.068708\n-0.075818\n0.075898\n0.076171\n0.10327\n0.074969\n0.1044\n0.095762\n0.068266\n0.032986\n0.02724\n0.075835\n0.16515\n0.056588\n0.10436\n0.078864\n0.052392\n0.19564\n0.26728\n0.31926\n0.5249\n0.60167\n0.54352\n0.35483\n0.24351\n0.21158\n0.10771\n-0.028934\n-0.16972\n-0.093511\n-0.065745\n-0.14976\n-0.34628\n-0.37134\n-0.31686\n-0.27161\n-0.22942\n-0.3595\n-0.56632\n-0.81797\n-0.085325\n0.03622\n0.042187\n0.082817\n0.1611\n0.14128\n0.28265\n0.32475\n0.47357\n0.41363\n0.014723\n-0.16449\n0.048485\n0.18619\n0.20971\n-0.4145\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22095\n-0.09303\nNaN\nNaN\n-0.37196\n-0.23381\n-0.13922\n-0.21722\n-0.056184\n-0.11195\n-0.11551\n-0.1033\n-0.0078442\n0.096672\n0.10597\n0.11716\n0.157\n0.13576\n0.18722\n0.16118\n0.15003\n0.21927\n0.1846\n0.26731\n0.25747\n0.30356\n0.4064\n0.28349\n0.19682\n0.43014\n0.65731\n0.61741\n0.34577\n0.2063\n0.2802\n-0.037596\n-0.30451\n-0.21576\n-0.11789\n0.026756\n-0.074797\n-0.21644\n-0.3889\n-0.33735\n-0.24826\n-0.21763\n-0.42914\n-0.48169\n-0.23005\n0.031711\n-0.025209\n0.042983\n0.10182\n0.18015\n0.16917\n0.36007\n0.41603\n0.51296\n0.41085\n0.14932\n-0.22718\n-0.30099\n0.12728\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21763\nNaN\n-0.22667\n-0.059344\n-0.46256\n-0.31888\n-0.20484\n-0.22872\n-0.1372\n-0.13312\n-0.11818\n-0.11082\n-0.011832\n0.11572\n0.18463\n0.20596\n0.31408\n0.22971\n0.30343\n0.31764\n0.18494\n0.22055\n0.27599\n0.38019\n0.31942\n0.3988\n0.44577\n0.18267\n0.15914\n0.5479\n0.74704\n0.71387\n0.42264\n0.43485\n0.32456\n0.04473\n-0.29343\n-0.017157\n-0.13666\n-0.018176\n-0.1414\n-0.13806\n-0.45746\n-0.38255\n-0.25283\n-0.33256\n-0.4628\n-0.4532\n-0.24863\n-0.096305\n-0.07906\n0.049659\n0.19201\n0.08238\n0.12327\n0.32365\n0.39366\n0.58866\nNaN\nNaN\n-0.33415\n-0.29919\n0.071384\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10602\n0.18539\n-0.51441\n-0.36448\n-0.28706\n-0.20647\n-0.18724\n-0.10502\n-0.050014\n-0.021515\n0.071917\n0.14819\n0.17002\n0.2016\n0.3067\n0.26858\n0.34256\n0.29936\n0.23689\n0.29181\n0.36863\n0.33137\n0.17547\n0.34864\n0.47955\n0.26226\n0.27644\n0.48443\n0.71937\n0.79146\n0.6321\n0.60862\n0.34344\nNaN\nNaN\nNaN\n-0.075932\n-0.054338\n-0.025747\n-0.17335\n-0.6376\n-0.40145\n-0.13821\n-0.18541\n-0.37867\n-0.39291\n-0.30246\n-0.18132\n-0.068297\n0.098217\n0.34197\n0.27675\n0.21786\n0.35266\n0.31522\nNaN\nNaN\nNaN\n-0.23488\n-0.38357\n-0.010352\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30924\n-0.25643\n-0.23981\n-0.25349\n-0.17668\n-0.069965\n-0.035643\n0.033634\n0.041712\n0.079937\n0.19977\n0.25072\n0.31422\n0.32413\n0.3671\n0.42147\n0.44775\n0.41056\n0.40379\n0.29751\n0.25185\n0.33709\n0.17252\n0.18373\n0.32051\n0.37513\n0.75707\n0.6906\n0.55077\n0.51756\n0.38277\nNaN\nNaN\nNaN\n-0.075888\n-0.1759\n-0.15909\n-0.12353\n-0.47788\n-0.40005\n-0.33431\n-0.61623\n-0.75406\n-0.34914\n-0.17889\n-0.090231\n0.010583\n0.20139\n0.38671\n0.37147\n0.28208\n0.3287\n0.17202\nNaN\nNaN\nNaN\n-0.63227\n-0.58468\n-0.31715\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22869\n-0.21096\n-0.22916\n-0.18438\n-0.090926\n-0.074437\n-0.04149\n-0.061432\n0.022599\n0.097738\n0.14046\n0.24939\n0.29488\n0.33031\n0.38233\n0.48311\n0.50043\n0.41937\n0.30859\n0.2653\n0.33204\n0.20188\n-0.22312\n-0.11768\n0.011635\n0.41243\n0.68229\n0.60971\n0.63722\n0.64344\n0.5306\nNaN\nNaN\nNaN\nNaN\n-0.07634\n-0.24054\n-0.078974\n-0.39177\n-0.25792\n-0.37591\n-0.60647\n-0.58255\n-0.1593\n0.066305\n0.094502\n0.11134\n0.26294\n0.3563\n0.38343\n0.23277\n0.1212\n0.075109\nNaN\nNaN\nNaN\nNaN\n-0.56328\n-0.32385\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.16787\n-0.26301\n-0.16699\n-0.053866\n-0.056227\n-0.082525\n-0.069195\n-0.057029\n0.037298\n0.037867\n0.09391\n0.20962\n0.30858\n0.3485\n0.40684\n0.46001\n0.43298\n0.47211\n0.38292\n0.22658\n0.32372\n-0.10961\n-0.58056\n-0.32287\n-0.11241\n0.43215\n0.57001\n0.64833\n0.77955\n0.76862\n0.8841\nNaN\n-0.13647\n-0.26109\n0.072944\n0.35298\n-0.084234\n-0.036534\n-0.20932\n-0.095738\n-0.2212\n-0.46662\n-0.55237\n0.083327\n0.38446\n0.52319\n0.21058\n0.27063\n0.3627\n0.32905\n0.15977\n0.24841\n0.19535\n-0.098692\nNaN\nNaN\nNaN\n-0.60954\n-0.35715\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20203\n-0.26385\n-0.15052\n0.026386\n0.0062982\n-0.036962\n-0.0094889\n-0.026134\n0.001426\n0.053807\n0.12936\n0.2147\n0.32344\n0.36873\n0.37489\n0.29609\n0.32543\n0.47293\n0.53699\n0.31724\n0.22677\n-0.012249\n-0.79651\n-0.73275\n-0.15668\n0.33005\n0.63724\n1.0003\n0.95055\n0.68901\n0.61367\n-0.14789\n0.096025\n-0.099953\n-0.053946\n0.24615\n0.17673\n0.057596\n0.06889\nNaN\n0.36745\n0.045615\n-0.4533\n0.22229\n0.63522\n0.62794\n0.33401\n0.20069\n0.28997\n0.15713\n0.12806\n0.087531\n0.19082\n-0.24138\n-0.30869\n-0.52149\n-0.74708\n-0.70403\n-0.76134\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21165\n-0.22922\n-0.095993\n0.08633\n0.020676\n0.0022451\n0.01647\n-0.0064614\n-0.011582\n0.11873\n0.12822\n0.18368\n0.33381\n0.35116\n0.29557\n0.23072\n0.3016\n0.43976\n0.48074\n0.38639\n0.14955\n-0.14567\n-0.31286\n-0.54826\n0.041788\n0.40089\n0.76293\n0.77621\n0.72142\n0.47422\n0.36328\n0.54121\n0.6899\n0.11307\n0.23378\n0.568\n0.19191\nNaN\nNaN\nNaN\nNaN\n0.042316\n0.19309\n0.26646\n0.2983\n0.21751\n0.01813\n0.31255\n0.25328\n0.11506\nNaN\n0.20089\n0.10318\n-0.19698\n-0.26655\n-0.33101\n-0.71074\n-0.78947\n-0.82467\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24287\n-0.16306\n0.0082353\n0.11828\n0.054906\n0.010675\n0.027716\n0.083787\n0.029662\n0.12367\n0.12845\n0.23274\n0.29095\n0.33398\n0.31383\n0.36599\n0.46947\n0.35835\n0.34358\n0.22174\n0.0040563\n-0.33685\n-0.34258\n-0.35597\n0.10377\n0.47376\n0.75102\n0.46125\n0.51604\n0.38604\n0.75724\n0.67372\n0.65101\n0.64194\n0.75971\n0.74466\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.26855\n0.18294\n0.11245\n0.36623\n0.13077\n0.068404\n0.21826\nNaN\nNaN\nNaN\n-0.28589\n-0.19545\n-0.37158\n-0.60035\n-0.4908\n-0.52865\n-0.61023\n-0.26404\n-0.23982\nNaN\nNaN\n-0.14611\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19427\n-0.054885\n0.062208\n0.097154\n0.068854\n-0.056526\n0.017621\n0.08863\n0.051841\n0.10051\n0.087746\n0.25062\n0.29859\n0.34578\n0.39866\n0.34898\n0.23176\n0.1054\n0.19718\n0.081231\n-0.085574\n-0.40414\n-0.41633\n-0.1327\n0.17579\n0.44442\n0.50808\n0.46524\n0.75536\n0.57794\n0.822\n0.74686\n0.65764\n0.67116\n0.80755\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11612\n-0.090537\n-0.081717\n-0.092841\n-0.0036579\n-0.18936\n-0.20331\nNaN\nNaN\nNaN\n-0.4213\n-0.24074\n-0.34647\n-0.39233\n-0.21088\n-0.24167\n-0.26217\n0.048928\n0.1296\n-0.10616\n-0.16522\n-0.11007\n-0.095748\n-0.16586\n-0.34391\nNaN\nNaN\nNaN\n-0.12231\n-0.036058\n-0.013468\n-0.044834\n0.054097\n0.04471\n0.11671\n0.052554\n-0.03573\n0.05898\n0.11817\n0.23879\n0.2615\n0.26611\n0.24251\n0.214\n0.061864\n-0.12115\n0.0045813\n-0.02298\n-0.0099266\n-0.44845\n-0.26574\n0.043406\n0.36176\n0.57646\n0.51684\n0.64415\n0.90588\n0.89068\n0.86672\n0.77953\n0.64299\n0.65578\n0.95123\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.13526\n-0.12316\n-0.30907\n-0.32882\n0.057099\n-0.58004\n-0.43209\nNaN\nNaN\nNaN\n-0.67138\n-0.43634\n-0.3571\n-0.24891\n-0.079582\n0.014252\n-0.15636\n-0.092444\n0.10048\n-0.066075\n0.09281\n0.15308\n0.17664\n0.031387\n-0.077947\nNaN\nNaN\nNaN\n-0.14901\n-0.029303\n-0.053931\n-0.05177\n0.099398\n0.080339\n-0.044111\n-0.067598\n-0.029653\n0.14157\n0.15759\n0.18874\n0.11853\n0.17211\n0.10473\n0.12665\n0.092915\n0.027188\n-0.11928\n0.0098264\n0.058982\n-0.47417\n-0.23461\n0.28388\n0.44467\n0.74559\n0.82314\n0.75567\n0.87573\n1.1225\n1.3119\n1.2597\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.60338\n0.33717\n0.26846\n-0.33218\n0.088422\n-0.003475\n-0.55606\nNaN\n-0.4558\n-0.45016\n-0.32929\n-0.31138\n-0.31484\n-0.24906\n-0.043188\n0.057064\n-0.061491\n0.14025\n0.16714\n0.037075\n0.094999\n0.20255\n0.037789\n0.041591\n0.037955\n-0.033081\n-0.02497\n-0.11121\n-0.086801\n-0.086491\n-0.12778\n-0.052554\n0.081112\n0.039973\n-0.076619\n-0.013302\n0.021562\n0.097777\n0.092147\n0.09176\n0.18848\n0.15695\n0.040972\n0.1629\n0.06008\n-0.10261\n-0.22701\n0.03827\n0.19374\n-0.16919\n-0.3064\n0.33801\n0.63225\n1.1224\n0.97199\n0.87412\n1.2627\n1.4147\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10782\n-0.16888\n0.22307\n-0.030288\n0.32286\n-0.14163\n-0.3351\n-0.27824\n-0.39201\n-0.3017\n-0.058513\n-0.099333\n-0.15\n-0.38289\n-0.10481\n0.10017\n0.014838\n0.24666\n0.26301\n0.033426\n0.04303\n0.030196\n-0.15693\n-0.045571\n-0.011533\n-0.049762\n0.1694\n0.14164\n-0.080281\n-0.11619\n-0.12427\n-0.15046\n-0.14644\n-0.094213\n-0.068328\n-0.063876\n0.061344\n0.042499\n0.024264\n0.061849\n0.13193\n0.15654\n0.18951\n0.2825\n0.26063\n-0.11656\n-0.36868\n-0.017018\n0.11931\n-0.11776\n-0.12759\n0.28159\n0.79031\n0.90508\n0.83004\n0.8715\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.051747\n-0.72151\n-0.27953\n-0.21761\n0.07959\n-0.16012\n-0.13768\n-0.20571\n-0.24459\n-0.058675\n0.021346\n-0.010082\n0.011411\n-0.10661\n-0.086563\n-0.082358\n-0.0173\n0.17713\n0.33386\n0.053675\n0.17071\n0.051392\n-0.13343\n-0.16616\n-0.065075\n-0.052579\n0.016795\n0.37872\n-0.16394\n-0.18147\n-0.32291\n-0.24523\n-0.12958\n-0.082824\n-0.089548\n-0.013669\n0.12536\n0.088839\n0.056354\n0.13314\n0.19694\n0.17103\n0.23937\n0.43166\n0.37592\n0.07064\n-0.20986\n-0.31856\n-0.16584\n-0.14437\n0.021874\n0.43424\n0.67431\n0.72555\n0.59698\n0.57226\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.04542\n-0.37384\n-0.27621\n-0.2404\n-0.15438\n-0.15503\n-0.15176\n-0.12401\n-0.023609\n0.039305\n0.099372\n0.22126\n0.088786\n-0.054436\n-0.04579\n0.00010801\n-0.027714\n0.2369\n0.010725\n0.20156\n0.22886\n-0.02572\n-0.080981\n0.25786\n0.15011\n0.067469\n0.21551\n-0.25085\n-0.30371\n-0.30048\n-0.19664\n-0.12914\n0.008429\n-0.03826\n0.064521\n0.10504\n0.11571\n0.079342\n0.15163\n0.26693\n0.17985\n0.24653\n0.42547\n0.48611\n0.10415\n-0.15131\n-0.2252\n-0.068217\n0.022162\n0.081525\n0.43937\n0.60666\n1.0966\n1.007\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.15186\n0.087006\n-0.44337\n-0.31623\n0.04671\n-0.091328\n0.018687\n0.041804\n0.0034938\n-0.059293\n0.19541\n0.493\n0.33277\n0.086581\n0.090045\n0.047359\n-0.088938\n-0.07715\n0.058446\n0.20143\n0.11561\n-0.0066417\n-0.20836\n0.15704\n0.21114\n0.22332\n0.16707\n-0.39631\n-0.29488\n-0.20022\n-0.14198\n-0.14937\n0.036627\n0.07211\n0.026433\n0.095662\n0.081679\n0.13467\n0.23322\n0.26407\n0.28396\n0.27488\n0.41153\n0.66922\n0.14482\n-0.036528\n-0.03647\n0.16665\n0.12842\n0.12559\n0.32176\n0.72524\n0.95043\n1.1352\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.13301\n-0.012148\n-0.47563\n-0.33236\n-0.010763\n-0.16475\n0.015582\n0.11806\n-0.068301\n-0.17363\n0.053923\n0.47258\n0.46624\n0.33011\n0.21324\n0.16738\n0.034564\n0.0057449\n0.16882\n0.26793\n0.092526\n-0.040976\n-0.069352\n0.21871\n0.35016\n0.44646\n0.24195\n-0.34344\n-0.2505\n-0.17685\n-0.087054\n-0.098946\n0.0094918\n0.067497\n0.051725\n0.063061\n0.11556\n0.062699\n0.053029\n0.20979\n0.3193\n0.32789\n0.43546\n0.46601\n0.15413\n0.11439\n-0.012887\n0.083917\n0.16844\n0.30089\n0.44367\n0.76374\n0.93938\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.17229\n0.1131\n-0.35292\n-0.33988\n-0.093844\n-0.91687\n-0.38803\n0.1225\n0.080738\n0.33621\n0.38591\n0.40408\n0.43109\n0.47103\n0.35114\n0.28008\n0.08615\n0.07298\n0.30485\n0.27205\n0.31247\n-0.011704\n0.071023\n0.32573\n0.40955\n0.48279\n0.32399\n-0.36064\n-0.19311\n-0.23278\n-0.10723\n-0.1206\n-0.075796\n-0.063445\n0.0022072\n0.015184\n-0.020922\n-0.0048751\n0.023832\n0.11584\n0.28391\n0.33037\n0.35117\n0.24811\n0.097573\n0.043993\n0.056354\n0.13783\n0.18777\n0.23057\n0.37328\n0.7438\n1.093\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.43672\nNaN\nNaN\nNaN\nNaN\n0.20636\n-0.18099\n-0.17559\n-0.25914\n-0.11988\n-0.90863\n-0.14273\n0.21853\n0.5202\n0.44225\n0.43872\n0.33643\n-0.11832\n0.37785\n0.18579\n0.20146\n-0.12263\n0.095628\n0.42562\n0.35986\n0.45782\n0.22047\n0.3242\n0.41904\n0.43062\n0.35759\n0.18748\n-0.25735\n-0.16478\n-0.21365\n-0.16095\n-0.094328\n-0.098036\n-0.10937\n-0.095796\n-0.11669\n-0.046523\n0.024944\n0.050534\n0.04937\n0.25415\n0.19992\n0.25187\n0.21908\n0.10474\n-0.057993\n0.064055\n0.15649\n0.163\n0.096613\n0.43163\n0.98972\n1.435\n0.6532\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.53604\n0.45383\n0.3872\n0.3073\n0.37965\n0.28202\n-0.050083\n-0.076253\n-0.12421\n-0.20592\n-0.18662\n-0.066892\n-0.049262\n0.2177\n0.49557\n0.26535\n0.32298\n0.14643\n-0.20587\n-0.15517\n0.13835\n0.26305\n-0.051906\n0.14672\n0.39021\n0.48116\n0.44707\n0.28431\n0.30229\n0.49826\n0.36405\n0.19255\n-0.17969\n-0.27981\n-0.19861\n-0.13026\n-0.2106\n-0.19001\n-0.15268\n-0.14217\n-0.18424\n-0.21517\n-0.017561\n0.0012149\n-0.054349\n-0.061197\n0.10468\n0.11895\n0.15465\n0.070109\n-0.018199\n-0.0075926\n-0.20413\n-0.1164\n0.163\n0.29752\n0.73096\n1.2171\n1.3753\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.75931\n0.76739\n0.92603\n0.74445\n0.68806\n0.54059\n0.41607\n0.40907\n0.010357\n-0.20961\n-0.28419\n-0.41441\n-0.31771\n-0.32703\n-0.19247\n0.10922\n-0.077162\n-0.11568\n0.070108\n0.022548\n-0.24016\n-0.30216\n0.035066\n-0.029518\n0.095622\n0.26563\n0.27806\n0.21242\n0.20888\n0.27958\n0.35652\n0.22493\n0.13206\n-0.34759\n-0.3372\n-0.27081\n-0.24138\n-0.25722\n-0.2434\n-0.17096\n-0.15621\n-0.14179\n-0.19958\n-0.086689\n-0.066841\n-0.1408\n-0.076617\n-0.016216\n0.0082929\n-0.089689\n-0.16873\n-0.23426\n-0.20432\n-0.17025\n-0.15411\n0.18125\n0.4185\n0.9811\n1.2692\n1.5205\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.71841\n0.7933\n0.81495\n0.45\n0.77881\n1.4671\n1.0242\n0.81032\n0.59592\n0.50501\n0.17171\n0.15959\n-0.078853\n-0.1298\n-0.24684\n-0.38715\n-0.32478\n-0.34822\n-0.27534\n-0.27646\n-0.19719\n-0.17781\n-0.16092\n-0.2942\n-0.41905\n0.01169\n0.11837\n0.12665\n0.025998\n-0.075134\n-0.027513\n0.14062\n0.17361\n0.072024\n0.1127\n0.0031485\n-0.15997\n-0.4258\n-0.30521\n-0.32811\n-0.22099\n-0.24581\n-0.25372\n-0.26312\n-0.16421\n-0.15558\n-0.1616\n-0.11953\n-0.23362\n-0.31587\n-0.17067\n-0.14595\n-0.19649\n-0.3275\n-0.34033\n-0.26729\n-0.27331\n-0.29301\n-0.14952\n0.38901\n0.74904\n1.0937\n1.5779\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7384\n1.3755\n1.0493\n0.88673\n0.51962\n0.022002\n0.11236\n0.040876\n-0.0096276\n-0.1616\n-0.37031\n-0.37229\n-0.31593\n-0.24558\n-0.2145\n-0.40471\n-0.46471\n-0.29576\n-0.28049\n-0.40894\n-0.39436\n-0.13713\n0.064267\n0.042521\n0.018047\n-0.068617\n-0.077284\n-0.053269\n0.07485\n-0.096445\n-0.18997\n-0.10021\n-0.0048648\n-0.31948\n-0.34289\n-0.37233\n-0.20542\n-0.30137\n-0.30259\n-0.29278\n-0.23446\n-0.21576\n-0.22205\n-0.25859\n-0.28001\n-0.32644\n-0.22148\n-0.27671\n-0.19363\n-0.32643\n-0.65125\n-0.79787\n-0.3128\n-0.12018\n-0.16427\n0.2297\n0.70635\n0.91002\n1.0207\n0.68043\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5601\n1.1217\n1.0591\n0.64009\n0.50742\n-0.30585\n-0.10512\n-0.094029\n-0.19406\n-0.22452\n-0.30806\n-0.3079\n-0.26102\n-0.24621\n-0.23854\n-0.31636\n-0.61856\n-0.34794\n-0.23116\n-0.17863\n-0.23908\n-0.3124\n-0.099214\n0.018393\n0.055604\n0.063944\n-0.04857\n-0.11221\n-0.26875\n-0.21954\n-0.096105\n0.027139\n0.10559\n-0.34301\n-0.42379\n-0.43299\n-0.29995\n-0.30735\n-0.23237\n-0.30853\n-0.29116\n-0.21871\n-0.20956\n-0.31803\n-0.29635\n-0.25998\n-0.24998\n-0.32543\n-0.2042\n-0.28374\n-0.47348\n-0.67324\n-0.59296\n-0.12293\n-0.18297\n0.20417\n0.68771\n0.87383\n0.16641\n-0.042517\n-0.006038\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3085\n0.92818\n0.70033\n0.64579\n0.40876\n-0.30959\n-0.15894\n-0.13568\n-0.42054\n-0.36309\n-0.38186\n-0.28553\n-0.2401\n-0.26879\n-0.39775\n-0.27374\n-0.2631\n-0.21642\n-0.081229\n-0.012933\n0.011283\n0.0018088\n-0.1103\n-0.010134\n-0.074599\n-0.012259\n-0.14135\n-0.15096\n-0.21753\n-0.014628\n0.065472\n0.21434\n0.15038\n-0.39202\n-0.45707\n-0.44562\n-0.39079\n-0.25962\n-0.15551\n-0.26337\n-0.21903\n-0.2253\n-0.13781\n-0.35791\n-0.27891\n-0.19646\n-0.33163\n-0.3969\n-0.32307\n-0.27609\n-0.50113\n-0.4369\n-0.73804\n-0.75562\n-0.40335\n0.11891\n0.44904\n0.58608\n0.35384\n-0.10947\n0.089812\nNaN\nNaN\n0.36793\n0.92102\n0.69763\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.95585\n0.75584\n0.61353\n0.58428\n-0.29408\n-0.66963\n-0.24355\n-0.34386\n-0.59653\n-0.4896\n-0.36074\n-0.21351\n-0.18852\n-0.23323\n-0.23044\n-0.22502\n-0.18283\n-0.15913\n-0.10191\n-0.095203\n-0.072539\n-0.023516\n-0.11629\n-0.042861\n-0.09752\n-0.17831\n-0.2345\n-0.2544\n-0.083466\n0.021327\n0.090609\n0.19735\n0.13687\n-0.4381\n-0.47251\n-0.39731\n-0.44154\n-0.30031\n-0.094876\n-0.18726\n-0.21339\n-0.26789\n-0.18262\n-0.35204\n-0.25239\n-0.24902\n-0.39022\n-0.44994\n-0.31408\n-0.33836\n-0.52949\n-0.56561\n-0.48323\n-0.37888\n-0.22093\n0.054114\n0.33903\n0.13129\n-0.11538\n-0.062915\n0.27321\n0.24523\n0.52639\n0.56858\n0.82508\n0.8225\n0.92297\nNaN\nNaN\nNaN\nNaN\n0.47494\n0.65868\n0.38923\n0.32897\n0.26479\n-0.41768\n-0.73114\n-0.41923\n-0.48369\n-0.56044\n-0.44323\n-0.30487\n-0.031585\n0.00066277\n0.0074212\n-0.061578\n-0.24588\n-0.26067\n-0.16641\n-0.1045\n-0.055566\n-0.017308\n-0.11115\n-0.19239\n-0.22257\n-0.37802\n-0.31338\n-0.30438\n-0.30627\n-0.19922\n-0.24452\n-0.083737\n0.03973\n-0.075295\n-0.42428\n-0.46309\n-0.51437\n-0.4648\n-0.40801\n-0.14157\n-0.15344\n-0.24433\n-0.32873\n-0.24321\n-0.32196\n-0.22643\n-0.25296\n-0.33266\n-0.41314\n-0.34893\n-0.61648\n-1.0872\n-0.70635\n-0.45123\n-0.28292\n0.054392\n0.12446\n0.2613\n0.71272\n0.12666\n0.18909\n0.2731\nNaN\n0.045352\n0.30509\n0.09631\n0.80178\n1.1489\nNaN\nNaN\nNaN\n0.24895\n0.48242\n0.58936\n0.41356\n0.17308\n0.23082\n-0.21318\n-0.64997\n-0.47992\n-0.12962\n-0.2105\n-0.26493\n-0.13668\n0.024853\n0.074253\n0.058637\n-0.11663\n-0.10607\n-0.19326\n-0.1842\n-0.12013\n-0.015328\n-0.0025359\n-0.096623\n-0.22988\n-0.32928\n-0.42627\n-0.29654\n-0.11378\n-0.16847\n-0.29721\n-0.29165\n-0.17185\n-0.095802\n-0.17442\n-0.50958\n-0.45695\n-0.4326\n-0.45779\n-0.36447\n-0.19005\n-0.20718\n-0.33829\n-0.39151\n-0.36223\n-0.35097\n-0.27114\n-0.2245\n-0.24029\n-0.3432\n-0.39616\n-0.63639\n-1.0956\n-0.63907\n-0.48359\n-0.26222\n-0.15126\n0.2481\n0.2177\n0.3305\n0.20811\n0.11734\nNaN\nNaN\n-0.16311\n0.11557\n0.16779\n0.3779\n0.67648\n0.74791\n0.47311\n0.19701\n0.21949\n0.31016\n0.55492\n0.31292\n-0.1383\n-0.28067\n-0.3767\n-0.4742\n-0.18479\n0.00068572\n-0.065454\n-0.21068\n-0.18859\n-0.047267\n0.1493\n0.037308\n-0.11406\n-0.095415\n-0.14817\n-0.12946\n-0.046568\n0.027846\n-0.0099634\n-0.065598\n-0.22391\n-0.30305\n-0.31997\n-0.18459\n-0.043453\n-0.046804\n-0.051908\n-0.07157\n-0.11204\n-0.049394\n0.037059\n-0.53008\n-0.46769\n-0.34153\n-0.28649\n-0.25473\n-0.19151\n-0.23607\n-0.38541\n-0.42177\n-0.4035\n-0.33039\n-0.24269\n-0.2141\n-0.14179\n-0.14751\n-0.37178\n-0.44092\n-1.0312\n-0.33809\n-0.54402\n-0.47582\n-0.061008\n0.28397\n0.23968\n0.10973\n0.021537\n-0.028989\nNaN\nNaN\nNaN\n-0.11525\n0.12178\n0.27902\n0.39975\n0.36828\n0.47658\n0.35101\n0.1054\n0.2114\n0.47406\n0.21505\n-0.041106\n-0.14999\n-0.051054\n-0.12319\n-0.16802\n-0.1571\n-0.21135\n-0.17953\n-0.051984\n0.054541\n0.13863\n0.011088\n-0.22253\n-0.17245\n-0.082928\n-0.025884\n0.080577\n0.074667\n0.049948\n-0.048389\n-0.18472\n-0.25263\n-0.21197\n-0.087406\n0.00099772\n0.035158\n0.064995\n-0.08098\n-0.17924\n-0.018483\n0.11044\n-0.46027\n-0.45073\n-0.34147\n-0.23357\n-0.15229\n-0.13862\n-0.27336\n-0.29611\n-0.33261\n-0.3212\n-0.202\n-0.1177\n-0.076864\n0.10171\n0.27089\n-0.17089\nNaN\nNaN\nNaN\n-0.51641\n-0.56347\n-0.12327\n0.11609\n-0.2\n-0.18807\n-0.15202\n0.015564\nNaN\nNaN\nNaN\n-0.093938\n0.11019\n0.054792\n0.17081\n0.23447\n0.30271\n0.23393\n0.14535\n0.16143\n0.028213\n-0.087319\n-0.069456\n0.033745\n0.045958\n-0.020672\n-0.088897\n-0.048151\n0.0053098\n0.063586\n0.010485\n0.062713\n0.040402\n-0.040732\n-0.22399\n-0.14504\n0.0087357\n0.027328\n0.08848\n0.11295\n0.10016\n-0.066813\n-0.13922\n-0.16525\n-0.19171\n-0.077959\n-0.025222\n0.070424\n0.10431\n-0.090792\n-0.21632\n-0.090154\n0.094549\n-0.33424\n-0.33163\n-0.2981\n-0.26459\n-0.20494\n-0.12412\n-0.32337\n-0.21934\n-0.19927\n-0.2112\n-0.13497\n-0.041966\n0.081745\n0.33986\n0.38795\n-0.039948\nNaN\nNaN\nNaN\n-0.50177\n-0.48853\n-0.61392\n-0.41558\n-0.29595\n-0.22356\n-0.23306\n-0.18181\nNaN\nNaN\nNaN\n-0.14922\n-0.099641\n-0.08044\n0.090626\n0.42919\n0.30508\n0.18462\n0.10199\n0.039254\n-0.094172\n-0.079371\n-0.097739\n1.5056e-05\n0.079935\n0.07448\n-0.058508\n-0.046071\n-0.073795\n-0.048525\n0.026982\n0.043031\n0.030712\n0.017325\n-0.0088919\n0.041339\n0.023302\n-0.087845\n0.1133\n0.12335\n0.090153\n-0.071698\n-0.093335\n-0.064462\n-0.070503\n-0.0068121\n0.042534\n0.17851\n0.14727\n-0.024884\n-0.070764\n0.024464\n0.11304\n-0.24638\n-0.16869\n-0.1817\n-0.28757\n-0.14242\n-0.14154\n-0.1331\n-0.15599\n-0.080541\n-0.16491\n-0.088862\n0.020029\n0.089095\n0.11456\n0.038026\n0.033345\n-0.15547\nNaN\nNaN\n-0.56389\n-0.48375\n-0.49206\n-0.47897\n-0.38606\n-0.17941\n-0.21106\n-0.21013\n-0.14272\n-0.21487\n-0.16645\n-0.33795\n-0.26825\n-0.14377\n0.031332\n0.37995\n0.34136\n0.32637\n0.21186\n0.15067\n0.17242\n0.018311\n0.028528\n0.012038\n0.14526\n0.18898\n0.020575\n-0.20445\n-0.43652\n-0.36668\n0.020941\n0.041623\n0.024782\n0.20831\n0.088904\n0.072688\n0.057993\n-0.074059\n0.07913\n0.097729\n0.073348\n-0.018727\n-0.13265\n-0.044223\n0.042139\n0.0888\n0.10257\n0.11045\n0.19302\n0.011848\n0.0041028\n0.17051\n0.14032\n-0.31191\n-0.22379\n-0.17972\n-0.21384\n-0.15997\n-0.11262\n-0.074555\n-0.10794\n-0.14406\n-0.16431\n-0.14709\n-0.073331\n0.011951\n0.089462\n0.15162\n0.11528\n-0.26455\n-0.5501\n-0.71445\n-0.62924\n-0.53863\n-0.41967\n-0.35776\n-0.29971\n-0.16213\n-0.19808\n-0.18182\n-0.17187\n-0.12295\n-0.12177\n-0.20058\n-0.27956\n-0.2583\n-0.10015\n0.19818\n0.32654\n0.24317\n0.3461\n0.20963\n0.10834\n0.052526\n0.19279\n0.13308\n0.15585\n0.24414\n0.23348\n-0.19923\n-0.55103\n-0.72829\n-0.1486\n-0.052663\n0.22123\n0.25847\n0.16594\n0.053613\n0.16103\n0.13422\n0.11161\n0.042875\n0.00040568\n-0.034131\n-0.09893\n0.050044\n0.087801\n0.094415\n0.056625\n0.19005\n0.22571\n0.022545\n0.057936\n0.098783\n0.059039\n-0.33357\n-0.16534\n-0.055026\n-0.088792\n-0.038132\n0.013285\n0.005339\n0.052258\n-0.087393\n-0.13989\n-0.19042\n-0.14436\n-0.10371\nNaN\nNaN\nNaN\nNaN\n-0.37869\n-0.58212\n-0.61474\n-0.38036\n-0.41977\n-0.49937\n-0.35584\n-0.14953\n-0.14382\n-0.13912\n-0.088474\n-0.11708\n-0.14669\n-0.14734\n-0.080999\n-0.25005\n-0.17552\n0.0044348\n0.10456\n0.21512\n0.38387\n0.22851\n0.2186\n0.19057\n0.27201\n0.2219\n0.0096725\n0.067286\n0.28218\n0.10959\n-0.42436\n-0.40935\n-0.39363\n-0.21264\n-0.065559\n0.054671\n-0.0099767\n0.046546\n0.23199\n-0.0061424\n-0.066693\n-0.018232\n-0.049467\n0.00073\n0.045495\n0.089784\n0.033723\n0.016899\n0.11186\n0.25817\n0.22416\n0.11875\n0.069859\n0.054185\n0.13248\n-0.21271\n-0.10755\n-0.02328\n0.0037866\n0.048051\n0.11848\n0.11827\n0.25499\n0.17028\n0.067935\n-0.10248\n-0.12817\n-0.0085875\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.45842\n-0.49754\n-0.48306\n-0.46268\n-0.55769\n-0.43521\n-0.22257\n-0.15093\n-0.21541\n-0.16566\n-0.19543\n-0.19627\n-0.18255\n-0.13243\n-0.067784\n-0.12368\n-0.097391\n0.061306\n0.25152\n0.53438\n0.42795\n0.33296\n0.31767\n0.33127\n0.22545\n-0.19866\n-0.19648\n-0.016902\n0.37752\n-0.26986\n-0.21559\n-0.20329\n-0.16508\n-0.18492\n-0.19014\n-0.095549\n-0.0060046\n0.02064\n-0.13126\n-0.073039\n-0.034819\n-0.0026605\n0.046233\n0.09796\n0.11432\n0.084667\n-0.0042057\n0.1231\n0.33225\n0.24749\n0.19455\n0.048469\n0.053058\n0.043102\n-0.06557\n-0.13763\n0.025904\n0.13576\n0.20575\n0.38742\n0.35577\n0.46914\n0.39718\n0.2215\n-0.0085525\n-0.16742\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.6508\n-0.52213\n-0.58952\n-0.54812\n-0.53019\n-0.47097\n-0.49019\n-0.35033\n-0.299\n-0.21806\n-0.17005\n-0.21749\n-0.17207\n-0.069173\n0.016525\n-0.0064868\n0.024033\n0.13767\n0.41664\n0.52992\n0.31669\n0.37511\n0.3991\n0.2665\n-0.21647\n-0.44976\n-0.32491\n0.22272\n-0.13214\n-0.11912\n-0.13594\n-0.16115\n-0.15889\n-0.17852\n-0.14571\n-0.040653\n0.042586\n-0.23823\n-0.21772\n-0.066578\n-0.13656\n-0.04065\n-0.028609\n0.096743\n0.10639\n0.076325\n0.13658\n0.29436\n0.21\n0.20432\n-0.11587\n-0.18028\nNaN\n0.092161\n-0.10214\n0.19162\n0.34649\n0.47501\n0.63579\n0.69775\n0.77443\n0.48624\n0.32246\n0.12224\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.55466\n-0.43098\n-0.51749\n-0.44562\n-0.51426\n-0.48007\n-0.42133\n-0.37926\n-0.29844\n-0.22513\n-0.19373\n-0.17942\n-0.16601\n0.031201\n0.072318\n0.061784\n-0.07587\n-0.048462\n0.11327\n0.3091\n0.24415\n0.34607\n0.24209\n-0.028116\n-0.24726\n-0.51995\n-0.47125\n-0.085999\n-0.17273\n-0.11716\n-0.21474\n-0.14116\n0.02612\n0.055109\n0.0020249\n0.019731\n0.024109\n-0.2295\n-0.22423\n-0.043102\n-0.24982\n-0.085751\n-0.24905\n-0.12325\n0.18432\n-0.057235\n0.089438\n0.14107\n0.083677\n0.22372\nNaN\nNaN\nNaN\n-0.12408\n-0.23563\n-0.21438\n-0.30533\n-0.2886\n-0.21435\n-0.21465\n-0.40867\n-0.32283\n-0.20629\n-0.31677\n-0.34854\n-0.36456\n-0.28504\n0.024482\n0.037501\n0.020764\n-0.18342\n-0.067976\n0.071635\n0.16978\n0.028519\n0.23087\n0.26129\n0.33507\n0.077844\n-0.068813\n-0.29079\n-0.21521\n-0.10177\n-0.63832\n-0.55071\n-0.49727\n-0.56778\n-0.30837\n-0.39114\n-0.37098\n-0.18237\n-0.26301\n-0.33455\n-0.18868\n-0.13719\n-0.064329\n0.083938\n0.087769\n-0.017882\n0.053566\n0.059029\n-0.010489\n-0.009652\n0.05703\n0.048931\n-0.003776\n-0.13138\n0.15474\n-0.079262\n-0.13942\n-0.25242\n-0.53033\n-0.54071\n-0.34938\n-0.044635\n0.19552\n-0.96669\n-0.59117\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.38163\n-0.37558\n-0.36989\n-0.0074041\n0.0032301\n-0.55286\n-0.13338\n-0.42844\n-0.25996\n-0.17314\n-0.24182\n-0.33811\n-0.40716\n0.040902\n0.21944\n0.044383\n-0.50387\n-0.39984\n-0.16988\n-0.12934\n0.11906\n-0.025969\n0.19228\n0.4832\n0.52922\n0.3243\n0.11073\n-0.014621\n0.012198\n-0.11275\n-0.29413\n-0.39218\n-0.42267\n-0.40878\n-0.37592\n-0.47793\n-0.4476\n-0.18581\n-0.25959\n-0.15421\n0.0093121\n-0.11089\n-0.035624\n-0.030994\n0.045082\n0.041985\n0.097578\n0.044809\n0.022401\n0.0041908\n-0.017441\n0.032319\n0.019548\n0.094761\n0.43415\n0.0099513\n-0.18572\n-0.45527\n-0.38455\n-0.22199\n-0.17876\n0.029566\n0.17583\n-0.60772\n-0.10273\n-0.12086\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.49932\n-0.45214\n-0.35829\n-0.426\n-0.96773\n-0.91098\n-0.8736\n-0.36877\n-0.28223\n0.022819\n-0.1545\n-0.36389\n-0.45339\n0.15769\n0.44135\n0.082031\n-0.29283\n-0.23602\n-0.1118\n0.17267\n0.094928\n0.24641\n0.41057\n0.41634\n0.62958\n0.51503\n0.32994\n0.11375\n0.096805\n-0.15044\n-0.0021185\n-0.24663\n-0.40219\n-0.47801\n-0.54\n-0.57709\n-0.41204\n-0.2648\n-0.17231\n-0.074607\n0.0072192\n-0.008346\n-0.064218\n0.077219\n0.039631\n-0.023084\n-0.02264\n0.01202\n-0.0086958\n0.038186\n0.029351\n0.067176\n0.062906\n0.14565\n0.17612\n0.1935\n0.08326\n-0.49966\n-0.35622\n-0.023144\n-0.26712\n0.045278\n0.27586\n-0.53756\n0.03151\n0.13997\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.37047\n-0.45151\n-0.37641\n-0.36158\n-0.36326\n-0.35088\n-0.32249\n-0.32296\n-0.42703\n-0.36002\n-0.19322\n-0.59606\n-0.51956\n0.24571\n0.043448\n-0.27161\n-0.28193\n-0.25754\n-0.17897\n0.13931\n0.28529\n0.38937\n0.38574\n0.17324\n0.50239\n0.56461\n0.45769\n0.16832\n0.14376\n0.25116\n-0.15802\n-0.062137\n-0.37349\n-0.3941\n-0.57973\n-0.66128\n-0.49868\n-0.26381\n-0.14316\n-0.070987\n-0.060875\n-0.051611\n-0.19408\n-0.087499\n-0.023131\n-0.10981\n-0.12389\n-0.032817\n-0.015412\n0.15292\n0.21363\n0.16466\n0.22485\n0.21861\n0.33552\n0.34161\n-0.6703\n-1.2819\n-1.731\n-0.63886\n-0.44091\n0.027\n0.39662\n0.092984\n-0.14106\n-0.15912\n-0.090804\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.39275\n-0.34902\n-0.2987\n-0.30921\n-0.29333\n-0.32596\n-0.32595\n-0.31513\n-0.38391\n-0.46416\n-0.43857\n-0.506\n-0.37055\n-0.26611\n-0.52118\n-0.45043\n-0.46793\n-0.43545\n-0.53976\n0.071906\n0.45995\n0.31651\n0.45414\n-0.3304\n0.31607\n0.56271\n0.67454\n0.39852\n0.31523\n0.35794\n-0.24462\n-0.35309\n-0.39656\n-0.23281\n-0.47854\n-0.33744\n-0.40467\n-0.23835\n-0.10908\n-0.082087\n-0.071129\n-0.035708\n-0.17058\n-0.21667\n-0.12448\n-0.13985\n-0.07261\n0.019199\n-0.014399\n-0.033204\n0.17956\n0.18744\n0.14393\n0.30681\n0.24626\n0.27962\n0.66804\n-1.0448\n-1.7516\n-1.0298\n-0.54832\n-0.038888\n0.4972\n-0.11434\n-0.29808\n-0.13146\n-0.32201\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.31989\n-0.35623\n-0.29286\n-0.29686\n-0.2653\n-0.35821\n-0.27342\n-0.26383\n-0.2706\n-0.28586\n-0.25368\n-0.15402\n-0.23754\n-0.43186\n-0.55635\n-0.42611\n-0.56443\n-0.50292\n0.0027808\n-0.3173\n-0.084775\n0.21822\n0.12216\n0.041454\n0.44973\n0.73692\n0.71403\n0.49571\n0.38438\n0.38359\n0.091697\n-0.4008\n-0.28328\n-0.2034\n-0.41441\n-0.54518\n-0.46194\n-0.25565\n-0.11399\n-0.079096\n-0.022769\n-0.087934\n-0.17485\n-0.19022\n-0.39852\n-0.32126\n-0.11923\n0.11585\n0.044573\n0.035992\n0.13662\n0.1596\n0.16857\n0.12192\n0.050851\n-0.28193\n-0.082815\n-0.10692\n-1.113\n-1.7641\n-0.86177\n-0.078534\n-0.1716\n-0.53693\n-0.23144\n-0.047046\n-0.15936\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34668\n-0.39037\n-0.31235\n-0.2283\n-0.15776\n-0.30724\n-0.15118\n-0.087671\n-0.08701\n-0.013204\n-0.084144\n-0.17\n-0.13103\n-0.37394\n-0.37762\n-0.44434\n-0.46094\n-0.12333\n-0.3041\n-0.14445\n0.0041334\n-0.068564\n0.11943\n0.23586\n0.35525\n0.60086\n0.74353\n0.68679\n0.45547\n0.37376\n0.012877\n-0.34539\n-0.45862\n-0.34771\n-0.44538\n-0.50591\n-0.42486\n-0.29969\n-0.24308\n-0.18323\n0.020521\n-0.15988\n-0.32815\n-0.3818\n-0.67454\n-0.37771\n-0.14124\n0.061949\n0.14148\n0.16294\n0.075249\n0.12702\n0.1134\n0.10913\n0.015304\n-0.22589\n-0.097392\n-1.1083\n-1.3724\n-0.8035\n-1.0015\n-0.56269\n-1.3078\n-0.72558\n-0.46918\n-0.40099\n0.33771\n0.29993\n0.30357\nNaN\nNaN\nNaN\n-0.2926\n-0.26563\n-0.24346\n-0.10143\n0.025465\n-0.18015\n-0.10383\n0.0050236\n0.05402\n0.10986\n0.068754\n-0.009346\n-0.028798\n0.0022086\n-0.095491\n-0.16587\n-0.14962\n0.039689\n-0.10403\n-0.098287\n0.034602\n0.17455\n0.20819\n0.31043\n0.45457\n0.59185\n0.6933\n0.78718\n0.53055\n0.44059\n0.25423\n-0.11555\n-0.38196\n-0.27286\n-0.19597\n-0.22995\n-0.17321\n-0.36926\n-0.43636\n-0.41277\n-0.1439\n-0.25204\n-0.3966\n-0.55353\n-0.89644\n-0.35528\n-0.098972\n-0.028488\n-0.021977\n0.15809\n-0.0082593\n0.062717\n0.17519\n0.18862\n0.20264\n-0.081283\n-0.085513\n0.039259\n0.1255\n-0.25785\n-0.86872\n-0.52051\n-0.72111\nNaN\n-1.2452\n-1.1711\n-0.16267\n0.058475\n0.11885\n-0.2223\nNaN\nNaN\n-0.19592\n-0.26115\n-0.19555\n-0.19077\n0.0033764\n-0.08546\n-0.087636\n0.079517\n0.080484\n0.1197\n0.099172\n0.13115\n0.1166\n0.099228\n0.06807\n0.052554\n0.10737\n0.20847\n0.10378\n0.15774\n0.11996\n0.099098\n0.27884\n0.35834\n0.4213\n0.64396\n0.73081\n0.6798\n0.47518\n0.34722\n0.29985\n0.17015\n0.029978\n-0.14848\n-0.067324\n-0.042998\n-0.17328\n-0.40824\n-0.44575\n-0.39082\n-0.32226\n-0.2855\n-0.44448\n-0.67444\n-0.95663\n-0.13735\n-0.015005\n-0.004867\n0.050522\n0.14973\n0.10469\n0.26731\n0.30146\n0.46397\n0.39745\n-0.032927\n-0.23271\n-0.004195\n0.13806\n0.14927\n-0.48585\n-0.67535\n-0.16941\nNaN\n-1.1833\n-1.2365\n-0.71867\n-0.43856\n-0.14405\n-0.021722\n0.010227\n-0.43308\n-0.42702\n-0.26693\n-0.16325\n-0.24356\n-0.094301\n-0.14851\n-0.13138\n-0.1155\n0.00078874\n0.14049\n0.14939\n0.16408\n0.20796\n0.18563\n0.23142\n0.19723\n0.20171\n0.27323\n0.23145\n0.3277\n0.31749\n0.39613\n0.5311\n0.38919\n0.27394\n0.55188\n0.81033\n0.76641\n0.45201\n0.29645\n0.37068\n-0.00090517\n-0.2971\n-0.16387\n-0.065059\n0.043101\n-0.098777\n-0.26996\n-0.46578\n-0.4124\n-0.32035\n-0.29533\n-0.52871\n-0.57535\n-0.29366\n-0.014709\n-0.071945\n0.00098275\n0.069072\n0.15682\n0.13922\n0.34541\n0.39806\n0.51233\n0.40439\n0.097013\n-0.33761\n-0.4013\n0.055438\n-0.0716\n-0.26945\n-1.3514\n-1.2365\nNaN\nNaN\nNaN\n-0.76797\n-1.0479\n-0.21677\n0.37697\n-0.27883\n-0.085294\n-0.52446\n-0.37119\n-0.24295\n-0.26403\n-0.17397\n-0.15838\n-0.12608\n-0.10669\n0.011711\n0.15794\n0.24192\n0.279\n0.38779\n0.30154\n0.35712\n0.36489\n0.23761\n0.27588\n0.33801\n0.46468\n0.41921\n0.51669\n0.57254\n0.29374\n0.23338\n0.6613\n0.91665\n0.89442\n0.53579\n0.55389\n0.4258\n0.10198\n-0.26585\n0.063715\n-0.098404\n-0.00805\n-0.16771\n-0.18783\n-0.53847\n-0.45973\n-0.34617\n-0.45605\n-0.57634\n-0.55844\n-0.32624\n-0.1551\n-0.12104\n0.022374\n0.17336\n0.03997\n0.096772\n0.31919\n0.3922\n0.61004\n-0.75883\n-0.84866\n-0.42975\n-0.3731\n0.010148\n0.27544\n0.48797\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3325\nNaN\n-0.5959\n-0.50848\n0.11586\n0.17716\n-0.59331\n-0.42399\n-0.34373\n-0.24495\n-0.2122\n-0.10943\n-0.045185\n-0.010381\n0.11171\n0.20041\n0.23261\n0.27919\n0.38737\n0.35277\n0.39575\n0.33975\n0.29268\n0.36445\n0.46293\n0.42458\n0.25509\n0.46199\n0.63125\n0.39755\n0.38267\n0.5975\n0.87983\n0.98367\n0.79087\n0.75309\n0.43929\n-0.36175\n-1.887\n-0.56903\n-0.062445\n-0.057448\n-0.044683\n-0.2193\n-0.7614\n-0.4989\n-0.22175\n-0.25885\n-0.48707\n-0.51243\n-0.39639\n-0.25401\n-0.12584\n0.070772\n0.34275\n0.26042\n0.19857\n0.3624\n0.31992\n-1.1092\n-1.2634\n-0.91025\n-0.32609\n-0.47986\n-0.064121\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.37356\n-0.31208\n-0.29707\n-0.30538\n-0.21769\n-0.093331\n-0.043108\n0.056925\n0.059882\n0.11261\n0.26403\n0.33319\n0.40206\n0.40651\n0.42153\n0.48278\n0.52021\n0.4957\n0.50392\n0.39541\n0.34589\n0.45694\n0.29276\n0.26459\n0.41865\n0.49931\n0.93229\n0.86515\n0.70624\n0.63682\n0.47004\n-0.17003\n-2.3021\n-2.0242\n-0.07135\n-0.20632\n-0.19504\n-0.15658\n-0.5975\n-0.53088\n-0.47078\n-0.7873\n-0.94268\n-0.47768\n-0.27119\n-0.1737\n-0.048686\n0.18773\n0.39322\n0.37069\n0.2714\n0.3369\n0.16335\n-1.0906\n-1.5374\n-1.4656\n-0.76102\n-0.69506\n-0.38538\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.29697\n-0.27012\n-0.28572\n-0.23408\n-0.12574\n-0.10318\n-0.057293\n-0.073943\n0.02773\n0.13064\n0.19702\n0.33022\n0.35712\n0.386\n0.43576\n0.5572\n0.59243\n0.51057\n0.39654\n0.3571\n0.44438\n0.31741\n-0.18368\n-0.073048\n0.097135\n0.55788\n0.86093\n0.76817\n0.77996\n0.7726\n0.64459\n0.047422\n-0.83691\n-0.80833\n-0.77304\n-0.087287\n-0.26747\n-0.089655\n-0.49593\n-0.35923\n-0.53402\n-0.80173\n-0.76308\n-0.26638\n-0.010696\n0.02325\n0.0669\n0.24933\n0.35592\n0.38784\n0.21415\n0.091633\n0.049383\n-1.0981\n-1.4903\n-1.63\n-1.2961\n-0.66641\n-0.39532\n0.25412\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24208\n-0.3266\n-0.21535\n-0.085936\n-0.07836\n-0.11024\n-0.089506\n-0.069833\n0.046282\n0.049043\n0.14035\n0.27648\n0.36793\n0.40766\n0.46311\n0.54432\n0.51512\n0.57248\n0.48329\n0.3166\n0.44806\n-0.060339\n-0.60299\n-0.30221\n-0.048986\n0.57986\n0.73179\n0.80253\n0.93584\n0.9302\n1.0845\n-0.27039\n-0.15022\n-0.30147\n0.080078\n0.4164\n-0.096374\n-0.082455\n-0.31387\n-0.2364\n-0.38442\n-0.63797\n-0.7291\n-0.0015596\n0.33423\n0.47475\n0.18382\n0.2453\n0.35293\n0.3165\n0.13307\n0.25035\n0.2005\n-0.14744\n-1.3104\n-1.7049\n-1.6478\n-0.71018\n-0.40006\n-0.63132\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26753\n-0.3322\n-0.20051\n-0.00021518\n-0.019863\n-0.067012\n-0.030795\n-0.038513\n0.0097031\n0.070137\n0.16448\n0.26511\n0.38436\n0.43992\n0.42619\n0.37104\n0.41674\n0.58949\n0.6597\n0.42656\n0.31972\n0.060762\n-0.81472\n-0.77243\n-0.11285\n0.44998\n0.79004\n1.1976\n1.1351\n0.83592\n0.78072\n-0.13464\n0.12054\n-0.1263\n-0.068161\n0.22756\n0.13851\n-0.0013744\n-0.0045903\nNaN\n0.28702\n-0.056436\n-0.59144\n0.1754\n0.60052\n0.5642\n0.28601\n0.16011\n0.26726\n0.1327\n0.11139\n0.081764\n0.20276\n-0.30775\n-0.38979\n-0.63067\n-0.87545\n-0.82667\n-0.86965\n-1.6294\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26376\n-0.29028\n-0.13834\n0.06177\n-0.0097482\n-0.029442\n-0.0032854\n-0.0149\n-0.0023931\n0.1595\n0.16744\n0.22508\n0.39052\n0.41166\n0.34586\n0.3042\n0.39238\n0.56665\n0.60398\n0.49729\n0.23219\n-0.10746\n-0.29036\n-0.58874\n0.10032\n0.5266\n0.92793\n0.92318\n0.86134\n0.57538\n0.47032\n0.63699\n0.79177\n0.095612\n0.2451\n0.57102\n0.14979\n-0.43244\nNaN\nNaN\nNaN\n-0.097776\n0.18482\n0.26987\n0.25613\n0.16033\n-0.019396\n0.2739\n0.2362\n0.086697\n-0.71516\n0.23041\n0.10902\n-0.24911\n-0.33015\n-0.3986\n-0.84081\n-0.9259\n-0.95251\n-2.0467\n-2.0251\n-1.7304\n-1.5713\n-0.25665\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.3114\n-0.22427\n-0.025987\n0.095993\n0.022597\n-0.02143\n0.010425\n0.081366\n0.047031\n0.16929\n0.16564\n0.28794\n0.34043\n0.38911\n0.36742\n0.45144\n0.57073\n0.45801\n0.44699\n0.31782\n0.078024\n-0.3316\n-0.3328\n-0.35938\n0.16485\n0.60757\n0.91722\n0.56458\n0.6247\n0.46682\n0.88013\n0.77238\n0.70683\n0.67281\n0.81854\n0.78625\n0.44335\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7139\n-0.15505\n0.22783\n0.10459\n0.077847\n0.34113\n0.11249\n0.054258\n0.23221\n-0.67194\n-0.75231\n-1.1306\n-0.33649\n-0.22295\n-0.41226\n-0.68593\n-0.57638\n-0.59445\n-0.69785\n-0.33386\n-0.31757\n0.29969\n0.44798\n-0.15219\n-0.23803\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26579\n-0.10097\n0.036362\n0.073501\n0.037526\n-0.091444\n-0.00081948\n0.091586\n0.069314\n0.14162\n0.11761\n0.31193\n0.35427\n0.401\n0.46239\n0.41672\n0.29842\n0.15623\n0.2664\n0.14355\n-0.057382\n-0.40842\n-0.43032\n-0.10112\n0.25399\n0.56486\n0.62905\n0.57037\n0.8985\n0.67612\n0.95151\n0.8175\n0.67162\n0.70206\n0.87427\n0.18263\n1.1028\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1867\n-0.018891\n0.075864\n-0.18105\n-0.16433\n-0.12019\n-0.015966\n-0.22243\n-0.21077\n-0.89909\n-1.1996\n-1.4407\n-0.44504\n-0.26185\n-0.39066\n-0.45023\n-0.2603\n-0.28625\n-0.303\n0.020827\n0.10508\n-0.13381\n-0.18857\n-0.10983\n-0.08734\n-0.18072\n-0.3447\nNaN\nNaN\nNaN\n-0.18128\n-0.080267\n-0.044642\n-0.078013\n0.025244\n0.023494\n0.11079\n0.056617\n-0.019371\n0.083741\n0.15614\n0.30171\n0.3108\n0.30349\n0.27529\n0.26413\n0.10354\n-0.11859\n0.033217\n0.017822\n0.036831\n-0.46184\n-0.25624\n0.093172\n0.45926\n0.70315\n0.63191\n0.77196\n1.0656\n1.0065\n0.97285\n0.83802\n0.65197\n0.67357\n1.0515\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76153\n-0.11978\n0.16532\n-0.19161\n-0.38677\n-0.37818\n0.046017\n-0.64027\n-0.45392\n-1.532\n-1.4456\n-1.3465\n-0.76603\n-0.50982\n-0.41466\n-0.29269\n-0.11436\n-0.0096665\n-0.18923\n-0.10401\n0.068649\n-0.10462\n0.064746\n0.1378\n0.17518\n0.015814\n-0.10283\nNaN\n-0.4183\nNaN\n-0.21769\n-0.078939\n-0.086408\n-0.076943\n0.097273\n0.065413\n-0.059642\n-0.064388\n-0.0038041\n0.17454\n0.18321\n0.21904\n0.14356\n0.19816\n0.12228\n0.16591\n0.13767\n0.036823\n-0.14909\n0.031033\n0.1236\n-0.48868\n-0.22593\n0.37123\n0.54067\n0.88216\n0.97085\n0.87909\n1.0194\n1.1824\n1.3656\n1.3169\n0.85245\n0.5039\n1.2709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.089134\n0.70464\n0.7178\n0.34034\n0.18376\n-0.40512\n0.062569\n0.01849\n-0.58443\n-1.3118\n-0.49724\n-0.53258\n-0.38179\n-0.35857\n-0.36435\n-0.28635\n-0.08636\n0.0054136\n-0.093671\n0.14273\n0.13425\n-0.01225\n0.071312\n0.20806\n0.0075512\n0.0056325\n0.0030092\n-0.087626\n-0.026034\n-0.13815\n-0.11897\n-0.10677\n-0.1676\n-0.087129\n0.058663\n0.022592\n-0.098349\n-0.0074559\n0.042155\n0.11427\n0.10156\n0.11323\n0.21646\n0.18975\n0.04802\n0.194\n0.084687\n-0.10755\n-0.29831\n0.051787\n0.25194\n-0.14669\n-0.30599\n0.42269\n0.74443\n1.3056\n1.1408\n1.0183\n1.4103\n1.4709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.028032\n1.3994\n0.21221\n-0.17148\n0.13609\n-0.14047\n0.35163\n-0.13158\n-0.36875\n-0.32818\n-0.45984\n-0.35963\n-0.071256\n-0.12397\n-0.18639\n-0.42505\n-0.13893\n0.085226\n0.012171\n0.25501\n0.2439\n0.014604\n0.045026\n0.026312\n-0.17696\n-0.071764\n-0.029176\n-0.074266\n0.14745\n0.13804\n-0.10623\n-0.15328\n-0.18036\n-0.1961\n-0.18495\n-0.11359\n-0.080274\n-0.07137\n0.067746\n0.045253\n0.03738\n0.080068\n0.14857\n0.18186\n0.21711\n0.31873\n0.30473\n-0.1108\n-0.41359\n-0.0099431\n0.16035\n-0.084743\n-0.10213\n0.35192\n0.92061\n1.0622\n0.98205\n0.94367\n1.8165\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.02242\n-0.74514\n-0.41292\n-0.27149\n0.086088\n-0.21188\n-0.17731\n-0.25056\n-0.287\n-0.077029\n0.0076738\n-0.028697\n-0.0038919\n-0.12785\n-0.10401\n-0.094047\n-0.026006\n0.17514\n0.33205\n0.034952\n0.19657\n0.055702\n-0.12541\n-0.17759\n-0.078956\n-0.04203\n-0.0015368\n0.39122\n-0.21759\n-0.22939\n-0.36348\n-0.29245\n-0.16355\n-0.0945\n-0.10169\n-0.019042\n0.12913\n0.099754\n0.071379\n0.15077\n0.22899\n0.19478\n0.25846\n0.4656\n0.41952\n0.097558\n-0.20996\n-0.33637\n-0.15807\n-0.14211\n0.058125\n0.5345\n0.80837\n0.87231\n0.71496\n0.61363\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.016334\n-0.4725\n-0.34873\n-0.30051\n-0.19697\n-0.18501\n-0.17758\n-0.13885\n-0.030144\n0.039845\n0.1085\n0.24801\n0.094021\n-0.059134\n-0.034203\n0.00017965\n-0.042164\n0.25255\n-0.00045756\n0.20266\n0.23945\n-0.010526\n-0.092532\n0.29167\n0.17878\n0.070602\n0.23698\n-0.32319\n-0.35632\n-0.35636\n-0.23746\n-0.14948\n0.0056624\n-0.040882\n0.072009\n0.1037\n0.12288\n0.095835\n0.17341\n0.3029\n0.2047\n0.26981\n0.45994\n0.54251\n0.10906\n-0.13559\n-0.21593\n-0.052977\n0.045074\n0.11676\n0.52433\n0.72078\n1.2963\n1.1595\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.12446\n0.055175\n-0.52965\n-0.36824\n0.043723\n-0.099742\n0.022097\n0.055457\n0.013534\n-0.065052\n0.23356\n0.57667\n0.37957\n0.10933\n0.11652\n0.049555\n-0.10083\n-0.074991\n0.062399\n0.19796\n0.12881\n0.013506\n-0.22263\n0.1751\n0.23668\n0.23448\n0.15884\n-0.46161\n-0.35005\n-0.25222\n-0.17367\n-0.16871\n0.025617\n0.074691\n0.031658\n0.097241\n0.088698\n0.16277\n0.25822\n0.28875\n0.31222\n0.28627\n0.44231\n0.74043\n0.1595\n-0.02002\n-0.016528\n0.20857\n0.16729\n0.15645\n0.38064\n0.86477\n1.1427\n1.305\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.07851\n-0.026123\n-0.54993\n-0.3663\n-0.02661\n-0.18933\n0.002559\n0.13574\n-0.048335\n-0.18706\n0.067796\n0.55073\n0.5572\n0.39533\n0.25873\n0.1911\n0.037298\n0.014293\n0.18926\n0.2846\n0.11893\n-0.024755\n-0.062867\n0.21992\n0.3713\n0.47072\n0.2331\n-0.4172\n-0.31635\n-0.2303\n-0.11349\n-0.11981\n8.2918e-05\n0.071905\n0.056846\n0.06759\n0.13194\n0.08074\n0.064524\n0.23228\n0.35836\n0.34564\n0.46437\n0.51029\n0.17447\n0.13119\n-0.0030152\n0.1187\n0.21376\n0.37383\n0.53298\n0.90828\n1.1621\n1.0046\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52894\n0.16081\n0.12331\n-0.40496\n-0.39856\n-0.13945\n-1.0824\n-0.47062\n0.15501\n0.11898\n0.40385\n0.44704\n0.45504\n0.50497\n0.56252\n0.42232\n0.32761\n0.099304\n0.12514\n0.35129\n0.33204\n0.36429\n-0.0037225\n0.083859\n0.34372\n0.44802\n0.5266\n0.33244\n-0.45116\n-0.25126\n-0.2874\n-0.13209\n-0.13748\n-0.082173\n-0.067052\n-0.0074447\n0.01327\n-0.012033\n3.351e-05\n0.025994\n0.12995\n0.3215\n0.35394\n0.37047\n0.26366\n0.11414\n0.060951\n0.077565\n0.16888\n0.23344\n0.29421\n0.45426\n0.87603\n1.3115\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.53055\n1.1243\nNaN\n-0.091864\n-0.12442\n0.21028\n-0.21314\n-0.21039\n-0.31217\n-0.14871\n-1.0679\n-0.15759\n0.27417\n0.61084\n0.51794\n0.50043\n0.36108\n-0.1517\n0.45905\n0.23039\n0.25348\n-0.087483\n0.15263\n0.49883\n0.43031\n0.52399\n0.25473\n0.35566\n0.45206\n0.45988\n0.38583\n0.21035\n-0.33158\n-0.2184\n-0.26454\n-0.19703\n-0.12046\n-0.11598\n-0.12999\n-0.12563\n-0.14034\n-0.05797\n0.019235\n0.036412\n0.031911\n0.27868\n0.20171\n0.25364\n0.22184\n0.11466\n-0.056913\n0.10606\n0.20179\n0.20242\n0.14072\n0.51951\n1.1394\n1.6649\n0.80058\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4412\n0.67749\n0.55483\n0.48015\n0.35517\n0.38066\n0.28415\n-0.088955\n-0.1092\n-0.15865\n-0.24772\n-0.22736\n-0.086952\n-0.063054\n0.25798\n0.57534\n0.29435\n0.36113\n0.16043\n-0.20876\n-0.14855\n0.21038\n0.37117\n-0.0085854\n0.19311\n0.45819\n0.56877\n0.529\n0.32983\n0.3492\n0.54154\n0.38409\n0.20383\n-0.18902\n-0.36181\n-0.26115\n-0.18242\n-0.25869\n-0.21999\n-0.18679\n-0.18618\n-0.22802\n-0.25487\n-0.032416\n-0.024087\n-0.096387\n-0.09669\n0.097692\n0.10245\n0.13899\n0.049418\n-0.035437\n-0.0058852\n-0.20259\n-0.090933\n0.20549\n0.36734\n0.86095\n1.4027\n1.5938\n-0.47951\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.91245\n0.88477\n0.96875\n0.79056\n0.72433\n0.54212\n0.42098\n0.40863\n-0.0092621\n-0.25642\n-0.33083\n-0.46528\n-0.35233\n-0.37195\n-0.21204\n0.12972\n-0.08088\n-0.12386\n0.096529\n0.053803\n-0.23738\n-0.29362\n0.12139\n0.0088456\n0.12001\n0.29962\n0.32706\n0.25275\n0.23874\n0.32207\n0.37879\n0.2355\n0.12847\n-0.36358\n-0.4352\n-0.34459\n-0.29349\n-0.30956\n-0.28744\n-0.22091\n-0.21034\n-0.17931\n-0.24135\n-0.12031\n-0.11164\n-0.19813\n-0.1119\n-0.036801\n-0.01909\n-0.11102\n-0.21408\n-0.27569\n-0.22458\n-0.17298\n-0.14782\n0.2232\n0.49416\n1.129\n1.4605\n1.7604\n-0.46811\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19274\n0.83363\n0.87323\n0.87744\n0.47198\n0.76389\n1.4653\n1.0343\n0.79243\n0.59864\n0.50956\n0.14403\n0.14577\n-0.10017\n-0.14596\n-0.2772\n-0.42671\n-0.35191\n-0.38295\n-0.30861\n-0.31827\n-0.21571\n-0.18073\n-0.15918\n-0.30141\n-0.43479\n0.057166\n0.15616\n0.14456\n0.028395\n-0.074203\n-0.034305\n0.14797\n0.19739\n0.074231\n0.11103\n-0.028871\n-0.17534\n-0.52555\n-0.38028\n-0.40135\n-0.27996\n-0.30048\n-0.31247\n-0.31785\n-0.20528\n-0.19143\n-0.19882\n-0.17323\n-0.29054\n-0.36624\n-0.202\n-0.18014\n-0.22982\n-0.38071\n-0.38833\n-0.29515\n-0.29609\n-0.32116\n-0.16837\n0.45096\n0.8586\n1.2599\n1.8137\n-0.45765\n0.20155\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10942\n0.59782\nNaN\nNaN\nNaN\nNaN\n1.7712\n1.3181\n1.0403\n0.90016\n0.51984\n-0.029671\n0.094212\n0.021255\n-0.015258\n-0.17026\n-0.40514\n-0.41495\n-0.35861\n-0.28464\n-0.24153\n-0.44783\n-0.5077\n-0.30649\n-0.29536\n-0.43009\n-0.3907\n-0.12532\n0.095794\n0.060696\n0.02484\n-0.060631\n-0.087444\n-0.054547\n0.082504\n-0.10898\n-0.21828\n-0.12841\n-0.012228\n-0.4182\n-0.43295\n-0.46036\n-0.26784\n-0.38287\n-0.36653\n-0.34509\n-0.28245\n-0.2647\n-0.27071\n-0.3211\n-0.34043\n-0.37473\n-0.2644\n-0.32732\n-0.21844\n-0.36278\n-0.74572\n-0.91929\n-0.32702\n-0.10096\n-0.17733\n0.26624\n0.81776\n1.0582\n1.2306\n0.81752\n-0.87788\n2.172\nNaN\nNaN\n0.586\n1.1033\n0.8346\n0.95531\nNaN\nNaN\n2.4652\n2.1977\n1.6269\n1.1589\n1.0553\n0.63609\n0.50957\n-0.35902\n-0.12945\n-0.12503\n-0.21571\n-0.23199\n-0.34819\n-0.35212\n-0.29602\n-0.27734\n-0.25326\n-0.34183\n-0.70765\n-0.39059\n-0.25376\n-0.1798\n-0.22218\n-0.31555\n-0.081281\n0.039514\n0.072405\n0.096614\n-0.045598\n-0.1287\n-0.30345\n-0.24252\n-0.10488\n0.024766\n0.12147\n-0.45453\n-0.54347\n-0.54512\n-0.38303\n-0.40456\n-0.30725\n-0.37481\n-0.35879\n-0.2777\n-0.25363\n-0.37458\n-0.34763\n-0.29985\n-0.29184\n-0.38861\n-0.23712\n-0.3315\n-0.5592\n-0.79528\n-0.66053\n-0.10255\n-0.19528\n0.23685\n0.79426\n1.0188\n0.19548\n-0.049017\n0.027163\n-0.57465\n-1.7841\n-1.5295\n0.17213\n1.6855\nNaN\nNaN\nNaN\nNaN\n-0.13632\n1.345\n1.3731\n0.94173\n0.72136\n0.68241\n0.42541\n-0.37089\n-0.19218\n-0.16526\n-0.46774\n-0.36854\n-0.41037\n-0.32573\n-0.2716\n-0.30361\n-0.42173\n-0.28899\n-0.2843\n-0.22999\n-0.073461\n0.0011096\n0.032462\n0.039079\n-0.10058\n0.0043059\n-0.074625\n0.006281\n-0.16011\n-0.17626\n-0.23141\n0.0031666\n0.077558\n0.25415\n0.18247\n-0.50253\n-0.56913\n-0.55258\n-0.49063\n-0.34529\n-0.22139\n-0.33026\n-0.28054\n-0.28882\n-0.15824\n-0.40917\n-0.32367\n-0.23185\n-0.38832\n-0.45739\n-0.37105\n-0.32914\n-0.58896\n-0.52015\n-0.80429\n-0.82648\n-0.44673\n0.13575\n0.51839\n0.69497\n0.426\n-0.12546\n0.1253\n-1.1904\n-1.214\n0.38785\n0.98413\n0.79992\nNaN\nNaN\nNaN\nNaN\nNaN\n0.40524\n0.98659\n0.79235\n0.64865\n0.62935\n-0.37506\n-0.81664\n-0.27547\n-0.3744\n-0.66988\n-0.49788\n-0.38461\n-0.25632\n-0.21642\n-0.25\n-0.23891\n-0.24342\n-0.19163\n-0.16101\n-0.095402\n-0.088671\n-0.058972\n0.0052019\n-0.10626\n-0.026407\n-0.083559\n-0.17425\n-0.26327\n-0.28944\n-0.085133\n0.024358\n0.10272\n0.22836\n0.15702\n-0.55226\n-0.58332\n-0.49641\n-0.54238\n-0.38563\n-0.14618\n-0.24938\n-0.28\n-0.33483\n-0.21438\n-0.40304\n-0.29678\n-0.299\n-0.44532\n-0.50455\n-0.35238\n-0.38359\n-0.59657\n-0.62783\n-0.52532\n-0.39008\n-0.22518\n0.077977\n0.38997\n0.19759\n-0.071243\n-0.057332\n0.2998\n0.26569\n0.57879\n0.62492\n0.88607\n0.89321\n0.98248\nNaN\nNaN\nNaN\n-1.0255\n0.44993\n0.6814\n0.38809\n0.34452\n0.28831\n-0.49646\n-0.84544\n-0.46444\n-0.53519\n-0.62632\n-0.48674\n-0.33241\n-0.041729\n0.0026337\n0.013588\n-0.064492\n-0.26257\n-0.2802\n-0.17317\n-0.099915\n-0.046424\n-0.00073949\n-0.10231\n-0.19788\n-0.20464\n-0.38381\n-0.32799\n-0.33479\n-0.33133\n-0.20977\n-0.26546\n-0.08725\n0.040118\n-0.098845\n-0.52337\n-0.5592\n-0.62356\n-0.56172\n-0.50006\n-0.20465\n-0.21544\n-0.31419\n-0.39697\n-0.28557\n-0.37098\n-0.27018\n-0.30134\n-0.36492\n-0.45517\n-0.39577\n-0.69811\n-1.2425\n-0.77362\n-0.4893\n-0.30278\n0.074531\n0.14213\n0.30086\n0.77239\n0.092478\n0.17962\n0.29048\n-1.7295\n0.0037018\n0.31839\n0.075503\n0.85491\n1.2109\nNaN\nNaN\nNaN\n0.2137\n0.48648\n0.61965\n0.42602\n0.18884\n0.26699\n-0.24751\n-0.75278\n-0.54524\n-0.1456\n-0.23297\n-0.27125\n-0.12578\n0.027819\n0.076658\n0.058234\n-0.14426\n-0.10781\n-0.20436\n-0.1933\n-0.11934\n-0.011508\n0.0085482\n-0.078485\n-0.22757\n-0.34004\n-0.44705\n-0.30999\n-0.11445\n-0.17362\n-0.3212\n-0.32296\n-0.19522\n-0.1089\n-0.20589\n-0.59533\n-0.5394\n-0.52444\n-0.55114\n-0.44846\n-0.25466\n-0.26878\n-0.41525\n-0.46738\n-0.42987\n-0.40618\n-0.32308\n-0.268\n-0.24377\n-0.37856\n-0.44816\n-0.71981\n-1.2593\n-0.70219\n-0.52622\n-0.2818\n-0.16832\n0.27419\n0.24859\n0.38554\n0.20446\n0.10944\n-1.5556\n-2.0206\n-0.25093\n0.10508\n0.15791\n0.37416\n0.68524\n0.76096\n0.44972\n0.16069\n0.20539\n0.31542\n0.58589\n0.32212\n-0.16923\n-0.3351\n-0.43979\n-0.55397\n-0.22281\n-0.0057064\n-0.085359\n-0.2259\n-0.19801\n-0.06108\n0.15482\n0.0323\n-0.13234\n-0.096799\n-0.16178\n-0.14245\n-0.047446\n0.032635\n-0.0060758\n-0.058454\n-0.22959\n-0.30831\n-0.33629\n-0.20462\n-0.045036\n-0.044915\n-0.047044\n-0.078936\n-0.14728\n-0.058018\n0.038931\n-0.61439\n-0.54615\n-0.42486\n-0.36723\n-0.32775\n-0.25659\n-0.29724\n-0.47087\n-0.51225\n-0.48496\n-0.39046\n-0.29059\n-0.26148\n-0.15046\n-0.16549\n-0.42402\n-0.48848\n-1.1847\n-0.38207\n-0.64094\n-0.54267\n-0.075612\n0.32034\n0.27159\n0.15009\n0.0060648\n-0.073414\n-1.4336\n-2.1396\n-2.1508\n-0.15256\n0.10593\n0.27991\n0.39697\n0.3718\n0.49638\n0.35763\n0.09351\n0.22039\n0.49881\n0.23257\n-0.047784\n-0.18745\n-0.076778\n-0.15993\n-0.1971\n-0.17091\n-0.24377\n-0.20692\n-0.064801\n0.049267\n0.14837\n0.010193\n-0.25676\n-0.18499\n-0.086706\n-0.033353\n0.090002\n0.079961\n0.053502\n-0.047595\n-0.19952\n-0.27246\n-0.23257\n-0.1026\n0.0031\n0.047291\n0.081544\n-0.071617\n-0.18434\n-0.019192\n0.10391\n-0.54142\n-0.52735\n-0.43069\n-0.31106\n-0.21203\n-0.18738\n-0.34448\n-0.37628\n-0.42653\n-0.40032\n-0.25733\n-0.16153\n-0.10339\n0.11196\n0.29997\n-0.20608\n0.1504\n0.3341\n0.0047355\n-0.613\n-0.64123\n-0.13163\n0.14299\n-0.21444\n-0.21718\n-0.21711\n-0.020135\n-1.4763\n-1.9533\n-1.7363\n-0.11844\n0.10337\n0.042829\n0.1689\n0.2314\n0.30455\n0.23841\n0.15693\n0.17284\n0.022964\n-0.10478\n-0.087661\n0.017341\n0.02867\n-0.031589\n-0.10379\n-0.057088\n0.0072332\n0.074528\n0.013009\n0.067503\n0.042733\n-0.038316\n-0.21942\n-0.14332\n0.013234\n0.029077\n0.094865\n0.11601\n0.11162\n-0.071648\n-0.15983\n-0.18475\n-0.21683\n-0.081219\n-0.017627\n0.080853\n0.11873\n-0.080642\n-0.21754\n-0.092995\n0.10224\n-0.42819\n-0.42486\n-0.38065\n-0.34587\n-0.2698\n-0.17721\n-0.40459\n-0.287\n-0.27187\n-0.28068\n-0.19102\n-0.084433\n0.062589\n0.36655\n0.45101\n-0.049784\n0.2352\n0.50218\n-0.34185\n-0.58221\n-0.54572\n-0.68265\n-0.47101\n-0.32668\n-0.25989\n-0.29276\n-0.23023\n-1.5531\n-1.4384\n-1.2553\n-0.18198\n-0.13162\n-0.11296\n0.078385\n0.45056\n0.31775\n0.20758\n0.12143\n0.046566\n-0.094857\n-0.085279\n-0.11399\n-0.019847\n0.069255\n0.078481\n-0.087962\n-0.081193\n-0.10258\n-0.070056\n0.029353\n0.049235\n0.021293\n0.02748\n0.01012\n0.050225\n0.026057\n-0.082858\n0.13322\n0.13441\n0.093463\n-0.082487\n-0.098825\n-0.055374\n-0.065802\n0.0010207\n0.061332\n0.18702\n0.16092\n-0.011257\n-0.056418\n0.032201\n0.13257\n-0.33676\n-0.23959\n-0.25311\n-0.36921\n-0.20055\n-0.20886\n-0.1937\n-0.20258\n-0.11926\n-0.22955\n-0.14483\n-0.023547\n0.058952\n0.10122\n0.041563\n0.035365\n-0.16631\n-0.52809\n-0.75241\n-0.62578\n-0.5398\n-0.55295\n-0.53954\n-0.43693\n-0.215\n-0.25775\n-0.25646\n-0.18941\n-0.27363\n-0.21734\n-0.3952\n-0.32605\n-0.18863\n0.013806\n0.40708\n0.37687\n0.38045\n0.246\n0.1592\n0.19025\n0.02712\n0.040596\n-0.0085362\n0.15726\n0.21795\n0.0046931\n-0.25665\n-0.53079\n-0.4552\n0.031491\n0.047152\n0.012746\n0.22388\n0.10638\n0.071735\n0.060131\n-0.068163\n0.1009\n0.12019\n0.076881\n-0.025462\n-0.13485\n-0.040086\n0.05413\n0.10578\n0.12789\n0.12371\n0.21511\n0.030318\n0.013153\n0.18777\n0.16905\n-0.40426\n-0.28813\n-0.24244\n-0.28283\n-0.22946\n-0.17855\n-0.12623\n-0.15406\n-0.19769\n-0.22958\n-0.21139\n-0.1265\n-0.029115\n0.078364\n0.1898\n0.1511\n-0.31451\n-0.61985\n-0.80836\n-0.71287\n-0.60698\n-0.4755\n-0.39891\n-0.35042\n-0.20084\n-0.24385\n-0.22442\n-0.21874\n-0.17508\n-0.17676\n-0.25416\n-0.35696\n-0.32584\n-0.12007\n0.21464\n0.36798\n0.2805\n0.38878\n0.22313\n0.10287\n0.035353\n0.20145\n0.12176\n0.14686\n0.27363\n0.26246\n-0.25189\n-0.65613\n-0.84661\n-0.14316\n-0.061164\n0.23284\n0.27426\n0.17524\n0.041482\n0.16705\n0.15937\n0.14353\n0.064866\n0.0054701\n-0.034723\n-0.096463\n0.046603\n0.086148\n0.10775\n0.073966\n0.21201\n0.24661\n0.040319\n0.075233\n0.12732\n0.096889\n-0.4151\n-0.21542\n-0.094267\n-0.13111\n-0.088194\n-0.035884\n-0.02868\n0.038084\n-0.14919\n-0.20537\n-0.26099\n-0.20588\n-0.16531\n0.2741\n0.73239\n0.76405\n-0.081741\n-0.44168\n-0.65551\n-0.69273\n-0.43541\n-0.48242\n-0.5775\n-0.42211\n-0.19375\n-0.17529\n-0.17546\n-0.13016\n-0.16196\n-0.19793\n-0.19494\n-0.11178\n-0.28922\n-0.19692\n0.013945\n0.12577\n0.26056\n0.4403\n0.24726\n0.22808\n0.19421\n0.2842\n0.2204\n-0.018801\n0.067918\n0.31523\n0.1156\n-0.51724\n-0.47684\n-0.46351\n-0.25714\n-0.089841\n0.042083\n-0.024643\n0.049925\n0.26098\n-0.0063747\n-0.063276\n-0.013031\n-0.044684\n0.025346\n0.072078\n0.099227\n0.030088\n0.044299\n0.14664\n0.2829\n0.23947\n0.12592\n0.097073\n0.093103\n0.18148\n-0.27807\n-0.1417\n-0.03735\n-0.013769\n0.018276\n0.10088\n0.10507\n0.27612\n0.16108\n0.036794\n-0.1538\n-0.17543\n-0.035928\n0.10977\n0.86104\n0.7911\n0.8524\n0.0452\n-0.49655\n-0.55433\n-0.5586\n-0.53546\n-0.64845\n-0.51847\n-0.28232\n-0.18762\n-0.25958\n-0.2012\n-0.23383\n-0.23669\n-0.21617\n-0.15123\n-0.074721\n-0.13153\n-0.097721\n0.089188\n0.3053\n0.61472\n0.47625\n0.36135\n0.32835\n0.33366\n0.21975\n-0.23873\n-0.21485\n-0.012827\n0.45037\n-0.33105\n-0.25343\n-0.24277\n-0.197\n-0.21312\n-0.2125\n-0.093603\n0.0085765\n0.02993\n-0.14391\n-0.071731\n-0.029616\n0.01456\n0.082152\n0.12505\n0.12377\n0.099336\n0.038254\n0.16177\n0.35908\n0.27114\n0.20937\n0.061338\n0.082701\n0.078883\n-0.10466\n-0.17348\n0.02293\n0.14261\n0.20713\n0.39467\n0.36577\n0.51692\n0.43114\n0.22287\n-0.024222\n-0.20281\nNaN\nNaN\nNaN\n-0.45315\n1.4086\n0.24787\n-0.55338\n-0.74831\n-0.60151\n-0.67541\n-0.63164\n-0.60828\n-0.55257\n-0.56525\n-0.40477\n-0.34896\n-0.26457\n-0.20239\n-0.24566\n-0.19241\n-0.060115\n0.039715\n0.011503\n0.048873\n0.18805\n0.49088\n0.59532\n0.34013\n0.40059\n0.41239\n0.25136\n-0.25586\n-0.4901\n-0.34629\n0.27706\n-0.16118\n-0.14802\n-0.16183\n-0.18006\n-0.18509\n-0.18565\n-0.14624\n-0.019875\n0.067431\n-0.25422\n-0.22529\n-0.063751\n-0.11739\n-0.019453\n-0.021896\n0.12756\n0.13936\n0.10986\n0.18102\n0.36275\n0.24313\n0.23153\n-0.10742\n-0.17691\n-0.39261\n0.073367\n-0.1367\n0.20142\n0.37715\n0.52111\n0.68894\n0.77002\n0.87218\n0.54178\n0.34872\n0.13404\n0.49905\nNaN\nNaN\nNaN\n-0.20718\n0.34748\n0.073821\n-0.70041\n-0.62735\n-0.49465\n-0.59043\n-0.50723\n-0.58807\n-0.54748\n-0.47112\n-0.43189\n-0.35288\n-0.27522\n-0.22012\n-0.19262\n-0.18896\n0.059107\n0.11404\n0.1052\n-0.048919\n-0.012961\n0.13645\n0.34755\n0.27117\n0.37955\n0.24383\n-0.062471\n-0.29623\n-0.59194\n-0.50388\n-0.073907\n-0.19589\n-0.14306\n-0.24642\n-0.17097\n0.021804\n0.053371\n0.0042362\n0.033378\n0.043524\n-0.22995\n-0.2134\n-0.03714\n-0.23958\n-0.076032\n-0.25841\n-0.11878\n0.22303\n0.00042067\n0.14796\n0.19088\n0.09819\n0.25093\n-0.75233\n-0.86976\n-0.79805\n-0.10817\n-0.22225\n-0.20496\n-0.29308\n-0.27091\n-0.20142\n-0.20453\n-0.38549\n-0.29861\n-0.19069\n-0.30818\n-0.34136\n-0.35796\n-0.28107\n0.015407\n0.030599\n0.018598\n-0.17889\n-0.063607\n0.073703\n0.17166\n0.039227\n0.24181\n0.26511\n0.32733\n0.086133\n-0.058079\n-0.28178\n-0.20794\n-0.10205\n-0.61562\n-0.53858\n-0.48883\n-0.54085\n-0.29775\n-0.38167\n-0.35798\n-0.17775\n-0.25564\n-0.32094\n-0.18107\n-0.11609\n-0.045373\n0.083322\n0.093173\n-0.016501\n0.059088\n0.060755\n-0.01027\n-0.015631\n0.047496\n0.049584\n-0.0079678\n-0.13847\n0.13367\n-0.092672\n-0.14979\n-0.26668\n-0.55899\n-0.56386\n-0.362\n-0.043739\n0.19508\n-0.96632\n-0.58864\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.37024\n-0.35862\n-0.35801\n0.0016644\n0.026376\n-0.5241\n-0.12759\n-0.40622\n-0.24447\n-0.15713\n-0.23576\n-0.33827\n-0.40495\n0.034187\n0.20954\n0.022758\n-0.49481\n-0.38761\n-0.16638\n-0.12128\n0.12805\n-0.011216\n0.20316\n0.48629\n0.51516\n0.32577\n0.12904\n-0.0072062\n0.019539\n-0.11159\n-0.27508\n-0.38034\n-0.40812\n-0.38275\n-0.36295\n-0.4717\n-0.43288\n-0.18849\n-0.25699\n-0.13666\n0.013489\n-0.095851\n-0.03014\n-0.017196\n0.052184\n0.042107\n0.099632\n0.04551\n0.022316\n0.0023977\n-0.020225\n0.026583\n0.014123\n0.084769\n0.41052\n-0.0060309\n-0.20889\n-0.47894\n-0.40567\n-0.23279\n-0.18992\n0.028497\n0.18059\n-0.60642\n-0.09823\n-0.11538\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.48406\n-0.4388\n-0.34197\n-0.40165\n-0.90068\n-0.85727\n-0.82339\n-0.35543\n-0.27055\n0.031221\n-0.15068\n-0.34885\n-0.43793\n0.1453\n0.42284\n0.063412\n-0.28582\n-0.22159\n-0.10663\n0.18061\n0.10234\n0.25066\n0.41409\n0.42186\n0.61798\n0.52104\n0.34445\n0.11863\n0.10781\n-0.13591\n-0.00070284\n-0.24268\n-0.36214\n-0.42136\n-0.52244\n-0.56838\n-0.39534\n-0.26277\n-0.17692\n-0.069527\n0.0072046\n-0.0059266\n-0.066998\n0.081267\n0.042808\n-0.017345\n-0.012457\n0.014457\n-0.005386\n0.04323\n0.036375\n0.060058\n0.052696\n0.15004\n0.16584\n0.16918\n0.059709\n-0.52777\n-0.37787\n-0.027783\n-0.27613\n0.045776\n0.28733\n-0.53425\n0.035456\n0.14427\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.35638\n-0.43957\n-0.36678\n-0.34933\n-0.34792\n-0.34098\n-0.30883\n-0.31033\n-0.40921\n-0.3457\n-0.19043\n-0.57215\n-0.47445\n0.22721\n0.027433\n-0.269\n-0.27066\n-0.24012\n-0.14398\n0.16019\n0.29271\n0.38815\n0.39313\n0.1945\n0.51447\n0.57535\n0.46232\n0.17482\n0.14331\n0.22751\n-0.15229\n-0.052254\n-0.32355\n-0.33993\n-0.54954\n-0.62856\n-0.48022\n-0.26042\n-0.14476\n-0.072623\n-0.063344\n-0.051908\n-0.19163\n-0.096959\n-0.027508\n-0.098912\n-0.11472\n-0.026559\n-0.0049\n0.16651\n0.22647\n0.16853\n0.23378\n0.23329\n0.32837\n0.32273\nNaN\nNaN\nNaN\n-0.66031\n-0.45543\n0.023002\n0.41415\n0.095064\n-0.13964\n-0.15544\n-0.082127\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.37869\n-0.34453\n-0.29253\n-0.29657\n-0.27758\n-0.31288\n-0.30549\n-0.29093\n-0.36802\n-0.45439\n-0.43377\n-0.49348\n-0.36915\n-0.25976\n-0.51773\n-0.4462\n-0.45326\n-0.40605\n-0.48527\n0.10384\n0.47173\n0.33418\n0.4746\n-0.30735\n0.33424\n0.57045\n0.66445\n0.38636\n0.30665\n0.34828\n-0.22535\n-0.33012\n-0.35505\n-0.20339\n-0.44808\n-0.33785\n-0.40838\n-0.24223\n-0.10975\n-0.084257\n-0.074891\n-0.028284\n-0.16487\n-0.21308\n-0.128\n-0.13363\n-0.061105\n0.030685\n-0.0026323\n-0.020813\n0.19063\n0.19264\n0.14335\n0.31432\n0.24031\n0.27367\n0.65329\nNaN\nNaN\n-1.0356\n-0.56854\n-0.050364\n0.52135\n-0.11102\n-0.3093\n-0.12687\n-0.31159\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30931\n-0.35042\n-0.28772\n-0.28062\n-0.24278\n-0.33967\n-0.25474\n-0.23913\n-0.25347\n-0.27427\n-0.24572\n-0.14221\n-0.22473\n-0.41012\n-0.5477\n-0.41407\n-0.54425\n-0.486\n-0.010442\n-0.29696\n-0.079217\n0.22816\n0.13357\n0.048907\n0.45076\n0.73514\n0.70446\n0.49134\n0.38381\n0.38173\n0.10518\n-0.37905\n-0.27948\n-0.20992\n-0.39761\n-0.52753\n-0.45446\n-0.25073\n-0.1086\n-0.080165\n-0.028938\n-0.088596\n-0.16866\n-0.18592\n-0.39408\n-0.31023\n-0.1059\n0.1166\n0.04941\n0.049257\n0.14375\n0.16289\n0.16277\n0.12038\n0.046415\n-0.2581\n-0.051381\nNaN\nNaN\n-1.757\n-0.90909\n-0.085639\n-0.17928\n-0.54733\n-0.24622\n-0.040196\n-0.15536\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.33611\n-0.37962\n-0.29833\n-0.20784\n-0.13665\n-0.2868\n-0.13126\n-0.068374\n-0.067721\n-0.0086669\n-0.078795\n-0.15948\n-0.12658\n-0.35372\n-0.3655\n-0.43092\n-0.44872\n-0.13017\n-0.29523\n-0.14021\n0.011227\n-0.062881\n0.13433\n0.23509\n0.35705\n0.59769\n0.73622\n0.68875\n0.46177\n0.37291\n0.017149\n-0.33415\n-0.46526\n-0.35047\n-0.44426\n-0.49197\n-0.41504\n-0.28916\n-0.22686\n-0.16768\n0.016814\n-0.15443\n-0.31723\n-0.37819\n-0.67628\n-0.3645\n-0.12754\n0.068591\n0.15136\n0.17638\n0.083697\n0.13388\n0.11769\n0.10434\n0.010173\n-0.21569\n-0.073298\nNaN\nNaN\n-0.80394\n-1.0089\n-0.57182\n-1.3058\n-0.7399\n-0.47498\n-0.39847\n0.34063\n0.32143\n0.33715\nNaN\nNaN\nNaN\n-0.27806\n-0.2481\n-0.22703\n-0.084308\n0.042106\n-0.16911\n-0.091706\n0.019773\n0.063791\n0.10753\n0.067663\n0.00026854\n-0.033348\n-0.00021892\n-0.087184\n-0.15363\n-0.13441\n0.046681\n-0.092383\n-0.088019\n0.035516\n0.1763\n0.21683\n0.31939\n0.46142\n0.5932\n0.69132\n0.78245\n0.53447\n0.44278\n0.25806\n-0.12852\n-0.40036\n-0.2714\n-0.19067\n-0.21914\n-0.17159\n-0.35292\n-0.41474\n-0.39535\n-0.13762\n-0.23967\n-0.38639\n-0.54534\n-0.90097\n-0.3432\n-0.083209\n-0.01708\n-0.010946\n0.17211\n0.005703\n0.077572\n0.17918\n0.18192\n0.19594\n-0.074234\n-0.078493\n0.056453\n0.128\n-0.25113\n-0.86449\n-0.52415\n-0.71926\nNaN\n-1.2425\n-1.1699\n-0.15992\n0.080974\n0.1477\n-0.20248\nNaN\nNaN\n-0.18467\n-0.24924\n-0.19123\n-0.17879\n0.011235\n-0.08347\n-0.083726\n0.087983\n0.092423\n0.12949\n0.10826\n0.13831\n0.12155\n0.1066\n0.081635\n0.068284\n0.11769\n0.2133\n0.11134\n0.16222\n0.12778\n0.11179\n0.29746\n0.37446\n0.43572\n0.64431\n0.72495\n0.67356\n0.47898\n0.35395\n0.30524\n0.17457\n0.022759\n-0.14403\n-0.058281\n-0.03639\n-0.17018\n-0.39145\n-0.42475\n-0.38124\n-0.31113\n-0.27703\n-0.44062\n-0.66468\n-0.95237\n-0.12592\n-0.0042901\n0.011483\n0.0702\n0.16921\n0.11743\n0.27793\n0.30376\n0.46583\n0.40091\n-0.024597\n-0.22341\n0.0041218\n0.15272\n0.14423\n-0.50217\n-0.67462\n-0.16812\nNaN\n-1.1815\n-1.2367\n-0.7161\n-0.43686\n-0.12655\n0.0013298\n0.010563\n-0.43248\n-0.41608\n-0.25587\n-0.15776\n-0.23281\n-0.086586\n-0.14659\n-0.12605\n-0.097366\n0.022823\n0.16241\n0.16618\n0.1816\n0.22487\n0.19302\n0.23599\n0.20845\n0.21495\n0.28174\n0.23869\n0.33795\n0.32443\n0.4041\n0.54504\n0.40724\n0.28922\n0.55522\n0.80486\n0.76476\n0.45535\n0.29928\n0.36258\n-0.0079346\n-0.29392\n-0.15617\n-0.059021\n0.038978\n-0.11335\n-0.27199\n-0.44901\n-0.40234\n-0.31801\n-0.29183\n-0.52828\n-0.57575\n-0.29189\n-0.00059474\n-0.057655\n0.018006\n0.086054\n0.17661\n0.15742\n0.35899\n0.4053\n0.51433\n0.40758\n0.10581\n-0.31761\n-0.37434\n0.073384\n-0.071037\n-0.2712\n-1.3541\n-1.2379\nNaN\nNaN\nNaN\n-0.76477\n-1.0454\n-0.21081\n0.37996\n-0.28538\n-0.081864\n-0.50806\n-0.36343\n-0.23671\n-0.254\n-0.16836\n-0.15553\n-0.11829\n-0.093698\n0.034452\n0.17991\n0.25486\n0.28773\n0.39155\n0.30966\n0.35821\n0.36632\n0.25624\n0.2856\n0.34279\n0.46914\n0.42413\n0.51982\n0.57013\n0.31172\n0.25276\n0.67269\n0.91444\n0.8903\n0.53754\n0.55276\n0.41664\n0.087021\n-0.27107\n0.072203\n-0.096975\n-0.0082897\n-0.17399\n-0.19158\n-0.5243\n-0.44736\n-0.3348\n-0.43782\n-0.57294\n-0.55169\n-0.31387\n-0.13662\n-0.098273\n0.042124\n0.18859\n0.073336\n0.12529\n0.3393\n0.39954\n0.60711\nNaN\nNaN\n-0.39789\n-0.34259\n0.025857\n0.27599\n0.48738\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3275\nNaN\n-0.59214\n-0.50621\n0.11809\n0.17658\n-0.58186\n-0.42582\n-0.34093\n-0.24001\n-0.20493\n-0.10208\n-0.034361\n0.0059543\n0.13163\n0.2191\n0.24198\n0.29391\n0.39653\n0.35961\n0.39205\n0.33615\n0.30856\n0.37286\n0.45842\n0.42837\n0.26255\n0.46075\n0.62588\n0.41077\n0.39816\n0.60749\n0.87789\n0.98047\n0.77808\n0.74154\n0.4338\nNaN\nNaN\nNaN\n-0.063482\n-0.056296\n-0.045934\n-0.21554\n-0.73615\n-0.48232\n-0.21452\n-0.25948\n-0.48077\n-0.4949\n-0.37339\n-0.23145\n-0.09991\n0.099529\n0.35851\n0.28454\n0.22378\n0.38091\n0.32637\nNaN\nNaN\nNaN\n-0.30484\n-0.44824\n-0.070484\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.36046\n-0.30805\n-0.28804\n-0.29374\n-0.21113\n-0.091139\n-0.035858\n0.061383\n0.073332\n0.12739\n0.26869\n0.343\n0.40013\n0.41043\n0.41656\n0.47277\n0.51527\n0.49284\n0.49168\n0.40013\n0.35115\n0.46037\n0.30992\n0.2784\n0.42549\n0.50585\n0.93116\n0.86289\n0.70223\n0.62966\n0.46338\nNaN\nNaN\nNaN\n-0.071596\n-0.20568\n-0.19102\n-0.15107\n-0.57697\n-0.50889\n-0.45828\n-0.77806\n-0.93092\n-0.4603\n-0.2549\n-0.15962\n-0.024993\n0.21437\n0.41105\n0.3907\n0.29595\n0.35019\n0.17156\nNaN\nNaN\nNaN\n-0.7461\n-0.6875\n-0.39957\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28083\n-0.26034\n-0.27615\n-0.22196\n-0.12421\n-0.10225\n-0.05412\n-0.068677\n0.03138\n0.13317\n0.20126\n0.33864\n0.36768\n0.39756\n0.43408\n0.54695\n0.57519\n0.50872\n0.39249\n0.35665\n0.45216\n0.31996\n-0.15929\n-0.053916\n0.10679\n0.56019\n0.85333\n0.7598\n0.77351\n0.76418\n0.63489\nNaN\nNaN\nNaN\nNaN\n-0.098539\n-0.25585\n-0.077685\n-0.47934\n-0.3455\n-0.52211\n-0.78664\n-0.74335\n-0.25137\n-0.0058061\n0.036516\n0.090408\n0.27461\n0.37642\n0.40661\n0.23891\n0.10691\n0.060296\nNaN\nNaN\nNaN\nNaN\n-0.64771\n-0.41205\n0.25486\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.2337\n-0.31732\n-0.20333\n-0.071273\n-0.069656\n-0.10948\n-0.084004\n-0.065439\n0.043538\n0.050531\n0.14338\n0.2838\n0.37927\n0.41836\n0.4629\n0.53733\n0.51306\n0.5742\n0.48618\n0.32558\n0.44611\n-0.060838\n-0.58797\n-0.28453\n-0.040763\n0.5784\n0.72198\n0.79052\n0.92209\n0.91541\n1.063\nNaN\n-0.14954\n-0.29893\n0.058923\n0.37835\n-0.08924\n-0.069492\n-0.29484\n-0.22122\n-0.36684\n-0.62321\n-0.70524\n0.027791\n0.31809\n0.45965\n0.17387\n0.27398\n0.38617\n0.34516\n0.1565\n0.27643\n0.21539\n-0.14478\nNaN\nNaN\nNaN\n-0.71267\n-0.41618\n-0.63183\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26075\n-0.31833\n-0.18812\n0.012178\n-0.0083891\n-0.060508\n-0.023574\n-0.034649\n0.012961\n0.081411\n0.16492\n0.26737\n0.38497\n0.43881\n0.42317\n0.37078\n0.41858\n0.59025\n0.65739\n0.43674\n0.31975\n0.077474\n-0.79588\n-0.75052\n-0.10752\n0.44344\n0.77139\n1.1764\n1.1149\n0.82692\n0.75138\n-0.15202\n0.10706\n-0.12251\n-0.071558\n0.18129\n0.12934\n0.01372\n0.017133\nNaN\n0.22854\n-0.10494\n-0.5687\n0.1613\n0.58692\n0.54208\n0.26776\n0.18897\n0.30797\n0.16658\n0.12843\n0.10849\n0.22408\n-0.28199\n-0.37138\n-0.62323\n-0.87555\n-0.83293\n-0.89647\n-1.6306\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.25829\n-0.27715\n-0.12937\n0.069945\n-0.0046566\n-0.02454\n-0.0014428\n-0.010577\n0.0096213\n0.15941\n0.16909\n0.22743\n0.38482\n0.40868\n0.35357\n0.30582\n0.37956\n0.56081\n0.60266\n0.50038\n0.24043\n-0.087832\n-0.27724\n-0.57637\n0.10221\n0.52603\n0.90508\n0.9089\n0.84386\n0.57656\n0.44477\n0.58593\n0.74526\n0.095255\n0.24187\n0.52587\n0.15288\nNaN\nNaN\nNaN\nNaN\n-0.16126\n0.14167\n0.24486\n0.24134\n0.14165\n-0.031081\n0.25521\n0.22785\n0.10936\nNaN\n0.22308\n0.13573\n-0.22625\n-0.31018\n-0.38532\n-0.83521\n-0.91642\n-0.97715\n-2.0458\n-2.0233\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.31273\n-0.22706\n-0.029347\n0.097179\n0.017086\n-0.027303\n0.0058723\n0.073225\n0.057565\n0.16721\n0.16596\n0.28398\n0.33465\n0.38102\n0.36471\n0.438\n0.56031\n0.45525\n0.44416\n0.32321\n0.090149\n-0.31637\n-0.33273\n-0.36784\n0.16575\n0.60014\n0.90325\n0.57057\n0.62501\n0.46117\n0.84773\n0.74298\n0.65562\n0.61925\n0.77352\n0.75093\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7132\n-0.15384\n0.2131\n0.077387\n0.06679\n0.32583\n0.10095\n0.041992\n0.23243\nNaN\nNaN\nNaN\n-0.32363\n-0.20354\n-0.39355\n-0.67807\n-0.57791\n-0.59005\n-0.68865\n-0.33688\n-0.31644\nNaN\nNaN\n-0.15911\n-0.23749\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26886\n-0.11053\n0.030831\n0.071272\n0.027661\n-0.10384\n-0.0071383\n0.081697\n0.064814\n0.14156\n0.11445\n0.29778\n0.33727\n0.38964\n0.45243\n0.4035\n0.29451\n0.15693\n0.26353\n0.15119\n-0.048504\n-0.39568\n-0.42719\n-0.11644\n0.24857\n0.55829\n0.6245\n0.56894\n0.88459\n0.66029\n0.90771\n0.78032\n0.63514\n0.65475\n0.83015\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1859\n-0.017363\n0.055791\n-0.21154\n-0.19183\n-0.1316\n-0.029848\n-0.23846\n-0.21049\nNaN\nNaN\nNaN\n-0.42182\n-0.24166\n-0.37153\n-0.44188\n-0.25388\n-0.27561\n-0.2881\n0.012867\n0.09226\n-0.14355\n-0.19089\n-0.10334\n-0.082114\n-0.18895\n-0.34613\nNaN\nNaN\nNaN\n-0.18372\n-0.084863\n-0.050732\n-0.087294\n0.013744\n0.011948\n0.10371\n0.050039\n-0.02076\n0.076323\n0.14855\n0.28329\n0.29194\n0.29815\n0.27289\n0.25783\n0.097904\n-0.1268\n0.032452\n0.031089\n0.035552\n-0.45793\n-0.25778\n0.080599\n0.45473\n0.69876\n0.62177\n0.75279\n1.0419\n0.96222\n0.90974\n0.79403\n0.61785\n0.62665\n1.015\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.75792\n-0.11798\n0.16319\n-0.21535\n-0.41676\n-0.3995\n0.026334\n-0.66515\n-0.45409\nNaN\nNaN\nNaN\n-0.75505\n-0.50388\n-0.40982\n-0.28045\n-0.09664\n0.0093424\n-0.17271\n-0.097352\n0.053546\n-0.12215\n0.035755\n0.1253\n0.17429\n0.016361\n-0.11008\nNaN\n-0.41764\nNaN\n-0.21399\n-0.080149\n-0.089831\n-0.089214\n0.094892\n0.065262\n-0.065405\n-0.06678\n-0.0095072\n0.16215\n0.17295\n0.20145\n0.12955\n0.19967\n0.12464\n0.16383\n0.12986\n0.018357\n-0.14756\n0.017741\n0.10487\n-0.48856\n-0.23702\n0.35266\n0.52712\n0.8731\n0.95881\n0.86781\n0.99847\n1.034\n1.2202\n1.216\n0.85098\n0.503\n1.2734\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.086457\n0.70237\n0.7394\n0.3336\n0.15519\n-0.43388\n0.036716\n-0.0082882\n-0.58727\nNaN\n-0.48674\n-0.5223\n-0.36847\n-0.34753\n-0.36475\n-0.27066\n-0.067761\n0.017189\n-0.084142\n0.14118\n0.12388\n-0.0096513\n0.064458\n0.19624\n0.021057\n0.018365\n0.014707\n-0.061761\n-0.006344\n-0.12346\n-0.11681\n-0.10217\n-0.16822\n-0.098101\n0.048857\n0.021432\n-0.10671\n-0.018013\n0.028785\n0.10327\n0.089675\n0.093678\n0.19645\n0.18604\n0.050366\n0.19029\n0.087369\n-0.10529\n-0.28921\n0.039241\n0.24066\n-0.16602\n-0.31711\n0.40465\n0.72694\n1.288\n1.1304\n1.0156\n1.372\n1.3044\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.026175\n1.3975\n0.23802\n-0.16852\n0.11079\n-0.17924\n0.33801\n-0.15325\n-0.37934\n-0.31104\n-0.43955\n-0.34256\n-0.05737\n-0.11454\n-0.1808\n-0.41367\n-0.13662\n0.086378\n0.022292\n0.2501\n0.23402\n0.022942\n0.052653\n0.031744\n-0.17657\n-0.075073\n-0.03364\n-0.078277\n0.13327\n0.13235\n-0.10259\n-0.15012\n-0.18559\n-0.20497\n-0.18808\n-0.1171\n-0.092888\n-0.081467\n0.056598\n0.033497\n0.023241\n0.061009\n0.13057\n0.17213\n0.2029\n0.30515\n0.28861\n-0.10991\n-0.41804\n-0.012759\n0.15358\n-0.12195\n-0.12717\n0.33282\n0.90416\n1.0525\n0.98163\n0.8695\n1.8176\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.020573\n-0.73974\n-0.44337\n-0.2905\n0.074208\n-0.18694\n-0.1576\n-0.22945\n-0.27393\n-0.067854\n0.021227\n-0.017551\n0.0024677\n-0.12774\n-0.1063\n-0.096211\n-0.016942\n0.17541\n0.31983\n0.037146\n0.19981\n0.07352\n-0.1224\n-0.1714\n-0.073687\n-0.033144\n-0.010713\n0.36418\n-0.21124\n-0.22693\n-0.36037\n-0.2946\n-0.16736\n-0.10293\n-0.11843\n-0.028847\n0.12023\n0.087695\n0.057161\n0.13636\n0.21388\n0.18229\n0.23584\n0.45143\n0.40728\n0.085637\n-0.22243\n-0.35981\n-0.16907\n-0.15662\n0.047483\n0.52376\n0.79578\n0.87578\n0.72187\n0.55988\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.02231\n-0.49957\n-0.37207\n-0.27894\n-0.1669\n-0.15607\n-0.15612\n-0.12949\n-0.028193\n0.042832\n0.11134\n0.25556\n0.098285\n-0.058851\n-0.03902\n-0.0014762\n-0.031142\n0.25419\n-0.0067286\n0.18798\n0.23169\n-0.0055661\n-0.088784\n0.24909\n0.17215\n0.069068\n0.21943\n-0.32773\n-0.34738\n-0.35334\n-0.23887\n-0.15444\n-0.0020863\n-0.049924\n0.063875\n0.094867\n0.11251\n0.082512\n0.15582\n0.28701\n0.18592\n0.23802\n0.43762\n0.52163\n0.09608\n-0.14468\n-0.23646\n-0.067875\n0.032229\n0.11056\n0.52036\n0.72107\n1.2694\n1.1278\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10056\n0.025398\n-0.51718\n-0.34953\n0.076899\n-0.075415\n0.033423\n0.056222\n0.01455\n-0.064031\n0.23289\n0.57817\n0.38242\n0.1103\n0.115\n0.051561\n-0.094231\n-0.077513\n0.05448\n0.18565\n0.13241\n0.018561\n-0.20747\n0.17436\n0.24331\n0.23267\n0.15495\n-0.45287\n-0.34398\n-0.25301\n-0.17758\n-0.17706\n0.018374\n0.062819\n0.02656\n0.087687\n0.075728\n0.14464\n0.239\n0.26889\n0.29113\n0.26755\n0.41794\n0.71078\n0.14436\n-0.028893\n-0.027226\n0.19132\n0.15416\n0.14814\n0.37456\n0.8619\n1.1449\n1.2775\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.082059\n-0.045783\n-0.53365\n-0.34471\n0.0025098\n-0.16231\n0.010041\n0.13495\n-0.046089\n-0.18523\n0.067746\n0.5533\n0.56469\n0.39774\n0.25681\n0.19095\n0.035801\n0.011254\n0.19184\n0.28528\n0.12597\n0.0018267\n-0.036412\n0.21422\n0.36688\n0.46176\n0.22677\n-0.41521\n-0.31344\n-0.22903\n-0.11727\n-0.12598\n-0.0095697\n0.06186\n0.048305\n0.05894\n0.1115\n0.065418\n0.052381\n0.21415\n0.34077\n0.32556\n0.43807\n0.48992\n0.15931\n0.11785\n-0.010492\n0.10911\n0.20504\n0.37223\n0.52962\n0.90677\n1.205\n1.0067\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52506\n0.14111\n0.11443\n-0.3708\n-0.36198\n-0.10899\n-1.0222\n-0.44483\n0.15724\n0.12298\n0.40469\n0.44808\n0.45955\n0.51203\n0.57161\n0.42586\n0.32227\n0.096735\n0.11953\n0.35453\n0.33944\n0.36713\n0.030291\n0.10775\n0.33783\n0.44487\n0.50764\n0.31517\n-0.44565\n-0.24541\n-0.27855\n-0.13393\n-0.13853\n-0.08498\n-0.070691\n-0.010594\n0.006223\n-0.019958\n-0.0084105\n0.016454\n0.11671\n0.29947\n0.33527\n0.35482\n0.25653\n0.10371\n0.047049\n0.070815\n0.15706\n0.22721\n0.29534\n0.45791\n0.87492\n1.3544\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.55112\n1.1278\nNaN\n-0.090622\n-0.12358\n0.19972\n-0.18709\n-0.18933\n-0.29417\n-0.12738\n-1.0042\n-0.13509\n0.27802\n0.61523\n0.51897\n0.4973\n0.35799\n-0.1525\n0.46876\n0.23642\n0.25002\n-0.083364\n0.1529\n0.4993\n0.43924\n0.52748\n0.27005\n0.35663\n0.44275\n0.44918\n0.36915\n0.19259\n-0.325\n-0.21557\n-0.2627\n-0.19636\n-0.11847\n-0.11721\n-0.13474\n-0.13125\n-0.14411\n-0.062636\n0.007117\n0.01798\n0.01094\n0.25567\n0.19304\n0.24654\n0.21375\n0.11096\n-0.05706\n0.10632\n0.20677\n0.21236\n0.14839\n0.52185\n1.1327\n1.6578\n0.82905\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4491\n0.70986\n0.57805\n0.49737\n0.36257\n0.37894\n0.28151\n-0.058388\n-0.087354\n-0.15079\n-0.24136\n-0.21434\n-0.085261\n-0.067713\n0.25709\n0.57948\n0.297\n0.3579\n0.15447\n-0.21747\n-0.14886\n0.21175\n0.36586\n-0.00082133\n0.19426\n0.45118\n0.56398\n0.52337\n0.33718\n0.33997\n0.51527\n0.36471\n0.18457\n-0.20459\n-0.35414\n-0.26125\n-0.18597\n-0.25481\n-0.21851\n-0.18867\n-0.18133\n-0.22459\n-0.25497\n-0.043668\n-0.040859\n-0.10742\n-0.11166\n0.078838\n0.095326\n0.13156\n0.037871\n-0.038683\n-0.0063728\n-0.1953\n-0.085292\n0.20902\n0.35914\n0.85309\n1.4033\n1.5907\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.95817\n0.93176\n0.99321\n0.81102\n0.73025\n0.52848\n0.41247\n0.40684\n0.0076069\n-0.24228\n-0.32025\n-0.45498\n-0.34282\n-0.36334\n-0.19966\n0.13422\n-0.074917\n-0.11905\n0.089318\n0.043901\n-0.23291\n-0.27798\n0.1358\n0.019735\n0.11179\n0.28702\n0.3229\n0.25157\n0.24067\n0.31061\n0.33428\n0.20802\n0.10815\n-0.37143\n-0.43076\n-0.3414\n-0.28819\n-0.30889\n-0.29553\n-0.22452\n-0.21262\n-0.17902\n-0.23676\n-0.12753\n-0.12572\n-0.2101\n-0.13091\n-0.053554\n-0.025574\n-0.10233\n-0.20458\n-0.27104\n-0.22151\n-0.17397\n-0.15774\n0.20392\n0.47477\n1.1188\n1.4581\n1.7544\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.86882\n0.8631\n0.87099\n0.48667\n0.85639\n1.5121\n1.0612\n0.80169\n0.57328\n0.48656\n0.16928\n0.16343\n-0.088644\n-0.13387\n-0.26341\n-0.40776\n-0.33812\n-0.36557\n-0.29756\n-0.3088\n-0.21349\n-0.17772\n-0.16208\n-0.30188\n-0.42385\n0.06046\n0.15752\n0.13173\n0.019195\n-0.071178\n-0.027018\n0.15043\n0.19217\n0.051303\n0.097295\n-0.045483\n-0.1838\n-0.51721\n-0.37558\n-0.39391\n-0.27891\n-0.29809\n-0.31063\n-0.31483\n-0.20461\n-0.18973\n-0.20655\n-0.18337\n-0.30021\n-0.37761\n-0.21302\n-0.18324\n-0.2159\n-0.36427\n-0.3851\n-0.29748\n-0.28982\n-0.31059\n-0.16577\n0.43703\n0.85042\n1.2612\n1.8099\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8501\n1.3484\n1.0493\n0.89807\n0.49478\n-0.0076272\n0.11643\n0.052697\n0.0062689\n-0.14878\n-0.38361\n-0.39649\n-0.34052\n-0.27113\n-0.22882\n-0.44336\n-0.50016\n-0.29617\n-0.29215\n-0.43367\n-0.40073\n-0.12604\n0.093336\n0.054625\n0.015192\n-0.065704\n-0.081257\n-0.049218\n0.088901\n-0.11213\n-0.22171\n-0.13547\n-0.019708\n-0.42273\n-0.427\n-0.44945\n-0.26499\n-0.38208\n-0.36099\n-0.33837\n-0.28054\n-0.26282\n-0.275\n-0.32741\n-0.34555\n-0.3839\n-0.2733\n-0.32545\n-0.20674\n-0.34456\n-0.72927\n-0.89394\n-0.30097\n-0.091216\n-0.18101\n0.25387\n0.81125\n1.0559\n1.2047\n0.81341\nNaN\nNaN\nNaN\nNaN\n0.59252\n1.109\nNaN\nNaN\nNaN\nNaN\n2.4767\n2.2068\n1.6854\n1.1585\n1.0387\n0.61886\n0.49083\n-0.33319\n-0.095757\n-0.093262\n-0.19249\n-0.21072\n-0.33314\n-0.33538\n-0.27738\n-0.26091\n-0.24019\n-0.33768\n-0.71129\n-0.39099\n-0.25163\n-0.18777\n-0.23771\n-0.32286\n-0.08188\n0.034953\n0.061767\n0.086899\n-0.048668\n-0.13212\n-0.29088\n-0.2448\n-0.10612\n0.020367\n0.11134\n-0.45651\n-0.54154\n-0.53991\n-0.37506\n-0.39733\n-0.30629\n-0.37039\n-0.35717\n-0.27465\n-0.25059\n-0.37361\n-0.34946\n-0.3038\n-0.29163\n-0.38104\n-0.22612\n-0.31215\n-0.5357\n-0.7726\n-0.62489\n-0.090051\n-0.19779\n0.22105\n0.78874\n1.0034\n0.19591\n-0.03102\n0.03322\nNaN\nNaN\nNaN\n0.18035\n1.6959\nNaN\nNaN\nNaN\nNaN\n-0.13208\n1.3523\n1.4047\n0.93629\n0.7156\n0.68649\n0.4295\n-0.3394\n-0.15416\n-0.13572\n-0.43171\n-0.34928\n-0.3971\n-0.30368\n-0.2534\n-0.28953\n-0.40701\n-0.28647\n-0.28833\n-0.23163\n-0.073411\n-0.011118\n0.015537\n0.017081\n-0.11563\n-0.0014548\n-0.08093\n-0.00071566\n-0.1661\n-0.18595\n-0.22905\n-0.010415\n0.063965\n0.23898\n0.17066\n-0.49734\n-0.56561\n-0.54628\n-0.48197\n-0.3366\n-0.22416\n-0.32531\n-0.2791\n-0.2856\n-0.1575\n-0.40793\n-0.32351\n-0.23294\n-0.38567\n-0.45213\n-0.36112\n-0.30781\n-0.56566\n-0.5\n-0.75283\n-0.76259\n-0.41723\n0.13425\n0.50827\n0.68689\n0.42039\n-0.1089\n0.14295\nNaN\nNaN\n0.41119\n0.96914\n0.82531\nNaN\nNaN\nNaN\nNaN\nNaN\n0.41043\n0.98958\n0.79354\n0.65289\n0.6415\n-0.32455\n-0.76694\n-0.24463\n-0.35055\n-0.62924\n-0.48083\n-0.36777\n-0.23553\n-0.19976\n-0.23578\n-0.22358\n-0.23898\n-0.18796\n-0.15592\n-0.092774\n-0.093847\n-0.071684\n-0.012264\n-0.11977\n-0.038299\n-0.097788\n-0.18536\n-0.30229\n-0.33111\n-0.10297\n0.004434\n0.082787\n0.21836\n0.15294\n-0.54552\n-0.5736\n-0.49374\n-0.53589\n-0.37233\n-0.13855\n-0.24145\n-0.27252\n-0.32746\n-0.21155\n-0.39894\n-0.29206\n-0.29582\n-0.4325\n-0.48991\n-0.32943\n-0.35795\n-0.5801\n-0.61616\n-0.50841\n-0.37345\n-0.20975\n0.079055\n0.38676\n0.20164\n-0.066393\n-0.053422\n0.31098\n0.28169\n0.59487\n0.65164\n0.86726\n0.88318\n0.96293\nNaN\nNaN\nNaN\nNaN\n0.47642\n0.69827\n0.40473\n0.36875\n0.32606\n-0.45927\n-0.82024\n-0.44447\n-0.50242\n-0.59317\n-0.47589\n-0.31753\n-0.029947\n0.011796\n0.024182\n-0.066424\n-0.25509\n-0.2696\n-0.16482\n-0.099763\n-0.05047\n-0.0099524\n-0.11213\n-0.2043\n-0.2178\n-0.38737\n-0.33325\n-0.34417\n-0.3366\n-0.22512\n-0.27315\n-0.094794\n0.040863\n-0.096863\n-0.50378\n-0.54083\n-0.6068\n-0.54613\n-0.48225\n-0.19762\n-0.2041\n-0.30074\n-0.37445\n-0.27459\n-0.36026\n-0.26115\n-0.29324\n-0.34404\n-0.43281\n-0.38216\n-0.67575\n-1.2102\n-0.74501\n-0.47431\n-0.30362\n0.073941\n0.14141\n0.29906\n0.75432\n0.097001\n0.18548\n0.30152\nNaN\n0.028027\n0.3469\n0.098897\n0.83792\n1.1916\nNaN\nNaN\nNaN\n0.24339\n0.50789\n0.62523\n0.43184\n0.19775\n0.2788\n-0.22208\n-0.7388\n-0.5326\n-0.12937\n-0.22358\n-0.26702\n-0.12073\n0.034787\n0.083255\n0.067772\n-0.14533\n-0.10166\n-0.19466\n-0.18612\n-0.117\n-0.012899\n0.001944\n-0.086603\n-0.23104\n-0.33991\n-0.43969\n-0.31002\n-0.11874\n-0.17084\n-0.32361\n-0.32322\n-0.1948\n-0.10371\n-0.20666\n-0.56933\n-0.51593\n-0.50592\n-0.52718\n-0.42843\n-0.24295\n-0.25351\n-0.39418\n-0.43892\n-0.40734\n-0.38707\n-0.3119\n-0.26164\n-0.22179\n-0.35758\n-0.43639\n-0.69705\n-1.2227\n-0.67172\n-0.50977\n-0.27813\n-0.16803\n0.26567\n0.2305\n0.37098\n0.20701\n0.12573\nNaN\nNaN\n-0.21756\n0.13096\n0.18377\n0.40042\n0.7064\n0.76955\n0.45073\n0.15382\n0.23019\n0.33707\n0.58717\n0.32618\n-0.15617\n-0.31681\n-0.42661\n-0.54647\n-0.21879\n-0.0022331\n-0.081251\n-0.2202\n-0.18813\n-0.056726\n0.15562\n0.042398\n-0.11954\n-0.083047\n-0.15013\n-0.13739\n-0.049586\n0.031192\n-0.010701\n-0.064611\n-0.23882\n-0.31224\n-0.3317\n-0.20585\n-0.044823\n-0.042285\n-0.051388\n-0.083035\n-0.15267\n-0.066983\n0.029817\n-0.57678\n-0.5159\n-0.40893\n-0.34838\n-0.30728\n-0.24285\n-0.27707\n-0.44645\n-0.49169\n-0.46311\n-0.3695\n-0.27538\n-0.24209\n-0.12219\n-0.14741\n-0.41287\n-0.47457\n-1.147\n-0.35037\n-0.59128\n-0.50632\n-0.071642\n0.30211\n0.23215\n0.13572\n0.012513\n-0.058075\nNaN\nNaN\nNaN\n-0.11886\n0.12655\n0.28639\n0.40387\n0.37549\n0.50448\n0.37339\n0.1153\n0.23568\n0.50158\n0.22257\n-0.049012\n-0.17554\n-0.072099\n-0.15736\n-0.19636\n-0.16963\n-0.24073\n-0.20295\n-0.062665\n0.047386\n0.14885\n0.017954\n-0.24462\n-0.16888\n-0.078696\n-0.031151\n0.090902\n0.080303\n0.053257\n-0.052585\n-0.2095\n-0.27948\n-0.23252\n-0.10496\n0.0040419\n0.051059\n0.078034\n-0.070227\n-0.18158\n-0.031305\n0.092718\n-0.50844\n-0.49206\n-0.40365\n-0.2862\n-0.18803\n-0.16846\n-0.32344\n-0.34995\n-0.40708\n-0.38083\n-0.243\n-0.14871\n-0.086541\n0.12752\n0.3059\n-0.19686\n0.15165\n0.33606\n0.0065382\n-0.57278\n-0.61828\n-0.12514\n0.13995\n-0.23238\n-0.22212\n-0.21161\n-0.015952\nNaN\nNaN\nNaN\n-0.092067\n0.11949\n0.054784\n0.17125\n0.23605\n0.31065\n0.23995\n0.16203\n0.17815\n0.028302\n-0.1107\n-0.097371\n0.012492\n0.026937\n-0.032511\n-0.10647\n-0.067368\n-0.0013331\n0.068976\n0.015674\n0.06912\n0.044307\n-0.037177\n-0.20734\n-0.131\n0.020292\n0.034435\n0.095828\n0.11272\n0.10906\n-0.070495\n-0.16229\n-0.187\n-0.21684\n-0.077431\n-0.011832\n0.086348\n0.12097\n-0.071342\n-0.20256\n-0.093298\n0.091769\n-0.39926\n-0.38458\n-0.3391\n-0.31528\n-0.24107\n-0.15602\n-0.37459\n-0.25751\n-0.25315\n-0.26261\n-0.17098\n-0.069985\n0.080815\n0.39029\n0.46855\n-0.039031\n0.23662\n0.50396\n-0.33978\n-0.55751\n-0.53203\n-0.677\n-0.47012\n-0.3331\n-0.2473\n-0.2741\n-0.21143\nNaN\nNaN\nNaN\n-0.16851\n-0.11157\n-0.091241\n0.091829\n0.45513\n0.32145\n0.21148\n0.12357\n0.047804\n-0.086637\n-0.092816\n-0.12349\n-0.026182\n0.0655\n0.076963\n-0.087211\n-0.087445\n-0.11161\n-0.076674\n0.031921\n0.055201\n0.015199\n0.021275\n0.012326\n0.054212\n0.033578\n-0.072321\n0.1295\n0.12993\n0.094068\n-0.074071\n-0.092637\n-0.052481\n-0.061378\n0.010286\n0.06832\n0.18517\n0.16507\n-0.0022769\n-0.048639\n0.03442\n0.12697\n-0.30641\n-0.20722\n-0.2205\n-0.33043\n-0.16864\n-0.18423\n-0.16697\n-0.17363\n-0.098866\n-0.20675\n-0.11994\n-0.00054793\n0.082605\n0.13034\n0.078761\n0.064975\n-0.15069\n-0.52462\n-0.74961\n-0.60053\n-0.52077\n-0.53906\n-0.52488\n-0.42016\n-0.1975\n-0.23468\n-0.24022\n-0.17524\n-0.26224\n-0.2108\n-0.39181\n-0.3142\n-0.17192\n0.028118\n0.41229\n0.37864\n0.38119\n0.24532\n0.14938\n0.17891\n0.020251\n0.033969\n-0.01145\n0.15077\n0.21412\n0.0018774\n-0.26052\n-0.53203\n-0.45895\n0.034617\n0.052287\n0.010677\n0.21698\n0.10267\n0.064481\n0.060939\n-0.063007\n0.097723\n0.11986\n0.083387\n-0.016797\n-0.12409\n-0.034231\n0.060989\n0.11321\n0.13383\n0.12602\n0.21755\n0.03137\n0.017885\n0.1919\n0.1721\n-0.37365\n-0.25322\n-0.20944\n-0.24102\n-0.18577\n-0.14248\n-0.093521\n-0.12645\n-0.16665\n-0.19747\n-0.18108\n-0.091678\n3.4346e-05\n0.10952\n0.20964\n0.17097\n-0.28303\n-0.6008\n-0.78688\n-0.684\n-0.57908\n-0.45378\n-0.37934\n-0.33659\n-0.18619\n-0.22904\n-0.21182\n-0.20411\n-0.16686\n-0.17583\n-0.25051\n-0.34614\n-0.306\n-0.098253\n0.22528\n0.3658\n0.28301\n0.38711\n0.21918\n0.096964\n0.027606\n0.19385\n0.11536\n0.13877\n0.26356\n0.25414\n-0.25949\n-0.66176\n-0.84268\n-0.13963\n-0.054571\n0.23774\n0.27014\n0.16523\n0.033419\n0.16048\n0.15505\n0.14084\n0.070749\n0.013759\n-0.02651\n-0.084508\n0.054009\n0.091564\n0.11354\n0.084103\n0.21787\n0.24948\n0.043259\n0.079978\n0.1308\n0.10696\n-0.3856\n-0.18204\n-0.063016\n-0.089997\n-0.042368\n0.0088969\n0.011384\n0.079535\n-0.11845\n-0.18034\n-0.22793\n-0.16882\n-0.13409\n0.27757\n0.73433\n0.76709\n-0.079958\n-0.41883\n-0.63309\n-0.66189\n-0.41016\n-0.45802\n-0.55987\n-0.40712\n-0.18243\n-0.16797\n-0.16156\n-0.11286\n-0.15362\n-0.19821\n-0.19839\n-0.12053\n-0.27727\n-0.18593\n0.012757\n0.12463\n0.2555\n0.436\n0.24815\n0.22597\n0.18963\n0.27907\n0.21335\n-0.032786\n0.053862\n0.30278\n0.10868\n-0.53044\n-0.47685\n-0.46714\n-0.25433\n-0.081779\n0.044847\n-0.026171\n0.046101\n0.25517\n-0.0023146\n-0.052814\n-0.00121\n-0.036989\n0.031449\n0.082568\n0.11409\n0.037644\n0.050801\n0.15204\n0.29055\n0.24428\n0.12934\n0.10035\n0.095018\n0.18068\n-0.24528\n-0.10537\n0.003748\n0.030125\n0.058623\n0.13824\n0.14627\n0.31842\n0.19595\n0.06813\n-0.11496\n-0.13193\n-0.00037709\n0.11361\n0.86302\n0.79472\n0.85467\n0.045635\n-0.48051\n-0.52942\n-0.53231\n-0.50601\n-0.62964\n-0.50271\n-0.27297\n-0.17854\n-0.23797\n-0.17964\n-0.22379\n-0.23206\n-0.21621\n-0.15411\n-0.069033\n-0.12659\n-0.095808\n0.093685\n0.30184\n0.60786\n0.47489\n0.36232\n0.32038\n0.32368\n0.21264\n-0.25619\n-0.22528\n-0.024229\n0.43803\n-0.3422\n-0.25702\n-0.24629\n-0.20008\n-0.21194\n-0.20909\n-0.099136\n0.0049165\n0.030892\n-0.13779\n-0.068726\n-0.0137\n0.027437\n0.086695\n0.12935\n0.12898\n0.10376\n0.048225\n0.17701\n0.36936\n0.2787\n0.21599\n0.057799\n0.079665\n0.080776\n-0.073458\n-0.13298\n0.062953\n0.18611\n0.2422\n0.43154\n0.40466\n0.55271\n0.4607\n0.25298\n0.019347\n-0.14946\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.55274\n-0.723\n-0.58048\n-0.64671\n-0.60906\n-0.58624\n-0.53315\n-0.54308\n-0.37884\n-0.32585\n-0.24848\n-0.19674\n-0.23439\n-0.18134\n-0.051117\n0.040095\n0.011912\n0.053918\n0.18708\n0.48699\n0.59139\n0.34066\n0.3935\n0.40302\n0.24894\n-0.26866\n-0.4992\n-0.35425\n0.26804\n-0.17099\n-0.15601\n-0.16991\n-0.18247\n-0.18037\n-0.18645\n-0.14926\n-0.019901\n0.069129\n-0.2399\n-0.21015\n-0.043237\n-0.099368\n-0.017179\n-0.021315\n0.13131\n0.14167\n0.1184\n0.19487\n0.38124\n0.25728\n0.24217\n-0.10398\n-0.16649\n-0.39157\n0.10101\n-0.097164\n0.23644\n0.41277\n0.55048\n0.72229\n0.81763\n0.90653\n0.56269\n0.37251\n0.17406\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.70053\n-0.62178\n-0.47819\n-0.56191\n-0.48252\n-0.55999\n-0.52101\n-0.4433\n-0.40733\n-0.32304\n-0.25175\n-0.20599\n-0.1742\n-0.17372\n0.061481\n0.11318\n0.10098\n-0.049348\n-0.013684\n0.13525\n0.34181\n0.26623\n0.37169\n0.23657\n-0.068056\n-0.30348\n-0.59769\n-0.50912\n-0.07912\n-0.19855\n-0.14724\n-0.25017\n-0.16951\n0.021489\n0.053086\n0.010507\n0.034806\n0.044695\n-0.21548\n-0.19218\n-0.021886\n-0.22506\n-0.07613\n-0.25461\n-0.11882\n0.22322\n0.01696\n0.16041\n0.20988\n0.12174\n0.26459\n-0.75119\n-0.86881\n-0.79728\n-0.046597\n-0.16599\n-0.15434\n-0.22549\n-0.20772\n-0.16905\n-0.19125\n-0.30656\n-0.21692\n-0.13496\n-0.2451\n-0.26347\n-0.29335\n-0.22367\n0.018886\n0.027897\n0.02811\n-0.13161\n0.00012142\n0.091704\n0.18285\n0.086663\n0.24729\n0.22398\n0.28757\n0.084425\n-0.058321\n-0.26456\n-0.18893\n-0.1096\n-0.52482\n-0.45807\n-0.38866\n-0.42598\n-0.22115\n-0.30831\n-0.29027\n-0.14335\n-0.22456\n-0.28333\n-0.15783\n-0.079327\n-0.00088347\n0.10742\n0.11771\n0.01689\n0.084941\n0.081404\n0.017127\n0.0029105\n0.047951\n0.075951\n0.028038\n-0.10168\n0.14122\n-0.047852\n-0.10334\n-0.23144\n-0.50361\n-0.51573\n-0.35634\n-0.052955\n0.19703\n-0.96285\n-0.58588\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.29008\n-0.27674\n-0.27357\n0.041285\n0.063033\n-0.4372\n-0.14603\n-0.33055\n-0.17174\n-0.10278\n-0.15755\n-0.24252\n-0.3313\n0.027852\n0.17788\n0.0014479\n-0.40968\n-0.31478\n-0.11498\n-0.065751\n0.19532\n0.043365\n0.19977\n0.43906\n0.43433\n0.27993\n0.11528\n-0.030052\n0.011117\n-0.11053\n-0.22225\n-0.31049\n-0.29881\n-0.28936\n-0.27279\n-0.39713\n-0.36368\n-0.15219\n-0.21392\n-0.086627\n0.02465\n-0.058293\n0.011936\n0.044894\n0.088947\n0.06435\n0.10957\n0.058389\n0.033715\n0.014783\n-0.0018067\n0.049529\n0.041606\n0.10461\n0.39834\n0.036297\n-0.16039\n-0.41904\n-0.36734\n-0.21796\n-0.17342\n0.032566\n0.18377\n-0.6043\n-0.096605\n-0.11229\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.38558\n-0.34404\n-0.26163\n-0.30918\n-0.70667\n-0.66356\n-0.64438\n-0.28491\n-0.20899\n0.060652\n-0.071088\n-0.23389\n-0.31775\n0.13526\n0.38758\n0.052574\n-0.22075\n-0.1573\n-0.073596\n0.19137\n0.13576\n0.23698\n0.38579\n0.3801\n0.52232\n0.45814\n0.3026\n0.09363\n0.095028\n-0.12532\n-0.0099865\n-0.20745\n-0.23039\n-0.27924\n-0.44231\n-0.48904\n-0.32332\n-0.21244\n-0.13394\n-0.05062\n0.012689\n0.021806\n-0.028434\n0.11178\n0.06476\n0.010137\n0.015074\n0.031719\n0.011457\n0.058267\n0.065419\n0.070843\n0.061455\n0.18038\n0.19913\n0.16676\n0.070156\n-0.45497\n-0.32836\n-0.014177\n-0.25182\n0.047721\n0.26924\n-0.53245\n0.037134\n0.1477\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27004\n-0.34197\n-0.28605\n-0.26835\n-0.26728\n-0.25717\n-0.21841\n-0.22677\n-0.31011\n-0.25431\n-0.12976\n-0.44154\n-0.27922\n0.22782\n0.061229\n-0.1964\n-0.19772\n-0.17293\n-0.09147\n0.17299\n0.30688\n0.33176\n0.35935\n0.20023\n0.46221\n0.50991\n0.38774\n0.15221\n0.10622\n0.11254\n-0.12913\n-0.035514\n-0.19477\n-0.21395\n-0.43699\n-0.51388\n-0.40406\n-0.21627\n-0.10519\n-0.060125\n-0.052243\n-0.026929\n-0.13906\n-0.072457\n-0.0069986\n-0.059325\n-0.074263\n0.0089981\n0.039702\n0.20256\n0.25647\n0.19129\n0.25936\n0.2702\n0.32217\n0.29347\n-0.66983\n-1.2803\n-1.7281\n-0.60147\n-0.4168\n0.04236\n0.37706\n0.096247\n-0.13682\n-0.15254\n-0.042032\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27991\n-0.26273\n-0.22211\n-0.22338\n-0.19726\n-0.23328\n-0.21527\n-0.1983\n-0.27936\n-0.37069\n-0.34353\n-0.40202\n-0.30921\n-0.18898\n-0.41939\n-0.36678\n-0.3603\n-0.30232\n-0.37051\n0.098923\n0.45861\n0.31085\n0.44249\n-0.25318\n0.31046\n0.49582\n0.54513\n0.29917\n0.24228\n0.26933\n-0.17994\n-0.23314\n-0.23899\n-0.1245\n-0.31309\n-0.27289\n-0.37082\n-0.21582\n-0.086734\n-0.068596\n-0.057525\n-0.0034952\n-0.1185\n-0.15255\n-0.090721\n-0.088373\n-0.011044\n0.087867\n0.059115\n0.044786\n0.22659\n0.21824\n0.16923\n0.33349\n0.24984\n0.27668\n0.60898\n-1.0439\n-1.7484\n-0.91523\n-0.52692\n-0.033887\n0.46376\n-0.10083\n-0.28711\n-0.10519\n-0.28727\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22577\n-0.27227\n-0.23358\n-0.21019\n-0.16117\n-0.25249\n-0.17541\n-0.15053\n-0.17792\n-0.20946\n-0.18531\n-0.085846\n-0.15095\n-0.32359\n-0.45553\n-0.31667\n-0.43612\n-0.39456\n-0.050153\n-0.32643\n-0.098255\n0.19453\n0.11529\n0.035866\n0.36791\n0.6159\n0.58729\n0.41181\n0.31455\n0.31746\n0.095818\n-0.27593\n-0.21895\n-0.16604\n-0.28146\n-0.41625\n-0.40126\n-0.21505\n-0.08683\n-0.055848\n-0.015795\n-0.067462\n-0.12449\n-0.1484\n-0.34995\n-0.24537\n-0.043285\n0.15037\n0.093904\n0.10146\n0.17496\n0.18542\n0.17869\n0.14471\n0.07608\n-0.16935\n0.023723\n-0.10563\n-1.1102\n-1.5043\n-0.84169\n-0.090314\n-0.16549\n-0.50718\n-0.21166\n-0.033723\n-0.15029\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26336\n-0.30497\n-0.23507\n-0.13616\n-0.069599\n-0.20583\n-0.066724\n-0.019257\n-0.019304\n0.010026\n-0.048271\n-0.1128\n-0.090163\n-0.27775\n-0.29808\n-0.34911\n-0.36794\n-0.10992\n-0.23387\n-0.14473\n-0.0091966\n-0.085426\n0.1073\n0.16223\n0.27553\n0.49115\n0.61038\n0.59293\n0.37762\n0.30298\n0.009685\n-0.24917\n-0.39747\n-0.29116\n-0.3564\n-0.40578\n-0.36003\n-0.24538\n-0.17967\n-0.11516\n0.028754\n-0.11263\n-0.25427\n-0.32341\n-0.61464\n-0.29485\n-0.061494\n0.11174\n0.18444\n0.20494\n0.11683\n0.16559\n0.16641\n0.14433\n0.04534\n-0.15868\n-0.011513\n-1.1043\n-1.3687\n-0.68943\n-0.91316\n-0.54309\n-1.3042\n-0.69038\n-0.43999\n-0.3959\n0.34255\n0.26454\n0.22652\nNaN\nNaN\nNaN\n-0.21048\n-0.18229\n-0.17015\n-0.03602\n0.087053\n-0.1236\n-0.054263\n0.048663\n0.084591\n0.1025\n0.061094\n0.015361\n-0.028654\n0.0046647\n-0.079782\n-0.13393\n-0.10243\n0.041571\n-0.074045\n-0.086585\n0.012616\n0.13362\n0.16143\n0.25466\n0.36679\n0.48955\n0.57844\n0.65608\n0.42782\n0.35021\n0.20141\n-0.14451\n-0.39046\n-0.24073\n-0.16557\n-0.18885\n-0.15279\n-0.28696\n-0.32668\n-0.31516\n-0.094954\n-0.17927\n-0.30748\n-0.4519\n-0.80915\n-0.27631\n-0.014529\n0.042828\n0.042961\n0.20105\n0.066583\n0.13138\n0.21908\n0.20824\n0.21381\n-0.018229\n-0.042449\n0.11011\n0.14364\n-0.19351\n-0.74077\n-0.50089\n-0.71763\nNaN\n-1.2389\n-1.1669\n-0.15722\n0.023954\n0.050578\n-0.23805\nNaN\nNaN\n-0.13217\n-0.20373\n-0.17171\n-0.14061\n0.032737\n-0.065075\n-0.064628\n0.10188\n0.11211\n0.13318\n0.10267\n0.12624\n0.11434\n0.091874\n0.072305\n0.07177\n0.10429\n0.18325\n0.081451\n0.12111\n0.10152\n0.087661\n0.24668\n0.31702\n0.36308\n0.53528\n0.59589\n0.5356\n0.37743\n0.27344\n0.2387\n0.13313\n-0.037815\n-0.15366\n-0.069197\n-0.049575\n-0.14749\n-0.31388\n-0.3331\n-0.30371\n-0.25246\n-0.21582\n-0.36062\n-0.55879\n-0.82523\n-0.063288\n0.059387\n0.078921\n0.12787\n0.20759\n0.16801\n0.30395\n0.32341\n0.46744\n0.41519\n0.029937\n-0.1532\n0.054544\n0.20986\n0.18411\n-0.46496\n-0.67268\n-0.16657\nNaN\n-1.1779\n-1.2323\n-0.71294\n-0.43467\n-0.17139\n-0.038917\n0.014056\n-0.42886\n-0.35099\n-0.20741\n-0.12498\n-0.19185\n-0.03289\n-0.11183\n-0.10099\n-0.055625\n0.051524\n0.15831\n0.1533\n0.16465\n0.20277\n0.15915\n0.20433\n0.19427\n0.1882\n0.25244\n0.21386\n0.3016\n0.28067\n0.33044\n0.45186\n0.33732\n0.2385\n0.44867\n0.65626\n0.62198\n0.36353\n0.22416\n0.27067\n-0.041872\n-0.28821\n-0.18999\n-0.096592\n0.017718\n-0.11879\n-0.22935\n-0.36133\n-0.3217\n-0.24926\n-0.21609\n-0.43988\n-0.49706\n-0.23465\n0.064386\n0.008957\n0.083541\n0.14229\n0.23005\n0.21218\n0.38925\n0.42469\n0.50816\n0.41436\n0.16758\n-0.18755\n-0.25181\n0.15673\n-0.070708\n-0.27117\n-1.3536\n-1.2378\nNaN\nNaN\nNaN\n-0.76118\n-1.0418\n-0.20814\n0.38274\n-0.25581\n-0.061858\n-0.42768\n-0.30455\n-0.19189\n-0.20586\n-0.12135\n-0.12722\n-0.096255\n-0.075413\n0.049766\n0.17773\n0.22344\n0.23143\n0.32861\n0.25725\n0.31442\n0.33014\n0.23894\n0.25707\n0.29945\n0.40055\n0.34045\n0.41482\n0.44637\n0.2327\n0.21162\n0.58742\n0.7556\n0.71927\n0.43802\n0.44372\n0.31228\n0.020991\n-0.29944\n0.01558\n-0.12724\n-0.019425\n-0.15972\n-0.14917\n-0.4329\n-0.36073\n-0.23277\n-0.29696\n-0.46643\n-0.44875\n-0.22497\n-0.052395\n-0.021577\n0.10033\n0.23272\n0.16596\n0.19386\n0.37461\n0.41086\n0.57946\n-0.75728\n-0.84657\n-0.26676\n-0.23625\n0.10244\n0.27695\n0.48755\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3231\nNaN\n-0.58911\n-0.50416\n0.11263\n0.1841\n-0.49391\n-0.37697\n-0.28816\n-0.19631\n-0.16927\n-0.085324\n-0.018969\n0.024532\n0.12914\n0.20144\n0.20005\n0.24429\n0.33665\n0.29447\n0.34113\n0.29998\n0.28735\n0.32225\n0.3677\n0.34615\n0.19692\n0.35068\n0.47311\n0.30054\n0.31968\n0.51919\n0.72848\n0.80225\n0.61817\n0.59521\n0.33821\n-0.36591\n-1.8858\n-0.57187\n-0.079374\n-0.053596\n-0.036102\n-0.17181\n-0.58582\n-0.37059\n-0.12999\n-0.19798\n-0.37251\n-0.35806\n-0.24797\n-0.12295\n-0.0025053\n0.17567\n0.38876\n0.34164\n0.28255\n0.40475\n0.33594\n-1.1075\n-1.261\n-0.90833\n-0.19423\n-0.31632\n-0.032811\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28053\n-0.25203\n-0.22333\n-0.22898\n-0.16291\n-0.062263\n-0.012708\n0.054283\n0.081793\n0.12212\n0.22173\n0.28548\n0.31934\n0.34224\n0.36272\n0.40755\n0.44601\n0.41046\n0.38132\n0.31343\n0.26753\n0.35092\n0.21819\n0.22136\n0.34029\n0.39723\n0.7706\n0.70109\n0.55274\n0.51072\n0.37472\n-0.17483\n-2.3005\n-2.0238\n-0.081012\n-0.1809\n-0.15943\n-0.11755\n-0.4392\n-0.3577\n-0.31546\n-0.61263\n-0.74375\n-0.31383\n-0.13976\n-0.053612\n0.072047\n0.27726\n0.43958\n0.4287\n0.35068\n0.37037\n0.19845\n-1.0881\n-1.5323\n-1.4606\n-0.6039\n-0.57203\n-0.36332\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19318\n-0.19155\n-0.2105\n-0.15559\n-0.084162\n-0.066922\n-0.026904\n-0.040764\n0.039181\n0.11126\n0.16005\n0.28135\n0.33132\n0.36873\n0.38731\n0.46622\n0.46567\n0.41938\n0.30328\n0.26867\n0.35487\n0.21244\n-0.16381\n-0.068028\n0.038207\n0.42524\n0.6798\n0.60218\n0.63122\n0.63236\n0.5165\n0.041041\n-0.84211\n-0.8113\n-0.77544\n-0.11153\n-0.23146\n-0.058841\n-0.35865\n-0.22923\n-0.35716\n-0.58332\n-0.54448\n-0.12508\n0.079604\n0.13113\n0.17516\n0.33668\n0.41729\n0.43914\n0.30294\n0.16455\n0.10657\n-1.096\n-1.4857\n-1.6242\n-1.2936\n-0.51874\n-0.37463\n0.25453\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15064\n-0.24426\n-0.13743\n-0.017131\n-0.030967\n-0.07462\n-0.047584\n-0.039705\n0.036965\n0.046813\n0.10932\n0.23824\n0.34696\n0.38559\n0.41892\n0.45297\n0.43962\n0.48557\n0.39735\n0.25382\n0.32383\n-0.1097\n-0.54603\n-0.27807\n-0.089543\n0.43924\n0.55866\n0.63026\n0.75738\n0.74552\n0.85078\n-0.27646\n-0.13153\n-0.25335\n0.032602\n0.26168\n-0.081343\n-0.0071947\n-0.16646\n-0.06551\n-0.18452\n-0.44167\n-0.50039\n0.15901\n0.34238\n0.48414\n0.18468\n0.35188\n0.45554\n0.40806\n0.22505\n0.32115\n0.24272\n-0.091171\n-1.308\n-1.7015\n-1.6437\n-0.61606\n-0.39767\n-0.63283\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1883\n-0.23198\n-0.11967\n0.058469\n0.035328\n-0.016582\n0.015114\n-0.010654\n0.015755\n0.088976\n0.14101\n0.23184\n0.33689\n0.3785\n0.38134\n0.30698\n0.34036\n0.48629\n0.54183\n0.34826\n0.2299\n0.030977\n-0.74979\n-0.68245\n-0.14353\n0.32104\n0.6025\n0.9646\n0.91628\n0.67812\n0.55465\n-0.18308\n0.074333\n-0.086159\n-0.056263\n0.13617\n0.1557\n0.098099\n0.1216\nNaN\n0.21445\n-0.10094\n-0.40455\n0.18422\n0.60259\n0.57281\n0.28816\n0.28087\n0.4008\n0.24803\n0.17785\n0.16473\n0.24893\n-0.18015\n-0.26527\n-0.50556\n-0.75106\n-0.72124\n-0.83799\n-1.6318\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19991\n-0.19976\n-0.074595\n0.10772\n0.031946\n0.018302\n0.025317\n0.0077141\n0.024556\n0.12663\n0.14045\n0.19904\n0.332\n0.35493\n0.32615\n0.24571\n0.2831\n0.4372\n0.48649\n0.39961\n0.17231\n-0.098798\n-0.28356\n-0.52471\n0.045783\n0.40769\n0.71998\n0.75179\n0.68664\n0.48301\n0.30558\n0.40632\n0.58164\n0.12295\n0.23242\n0.44877\n0.20822\n-0.43601\nNaN\nNaN\nNaN\n-0.14975\n0.064455\n0.20068\n0.25893\n0.16362\n-0.017107\n0.26803\n0.23369\n0.1802\n-0.71865\n0.20144\n0.17204\n-0.14296\n-0.21818\n-0.29788\n-0.70224\n-0.77068\n-0.90472\n-2.0457\n-2.0221\n-1.7294\n-1.5712\n-0.25903\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24844\n-0.17138\n0.0014888\n0.12553\n0.044325\n0.00031906\n0.020023\n0.068206\n0.062414\n0.12637\n0.13698\n0.23268\n0.28799\n0.32332\n0.31589\n0.34267\n0.45196\n0.35896\n0.34609\n0.23909\n0.036027\n-0.30285\n-0.34402\n-0.37918\n0.10554\n0.46627\n0.73096\n0.48334\n0.52156\n0.37661\n0.67091\n0.59413\n0.51493\n0.50602\n0.64962\n0.65522\n0.43814\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7184\n-0.16175\n0.22865\n0.10659\n0.079426\n0.33197\n0.10504\n0.043223\n0.22177\n-0.6741\n-0.75443\n-1.1308\n-0.25436\n-0.14378\n-0.32404\n-0.58432\n-0.50041\n-0.52582\n-0.59411\n-0.27482\n-0.23462\n0.3025\n0.45163\n-0.15726\n-0.23521\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.20318\n-0.076459\n0.053894\n0.097814\n0.049555\n-0.082046\n0.0052398\n0.07129\n0.04865\n0.10771\n0.085058\n0.2237\n0.26795\n0.32601\n0.3803\n0.32244\n0.22609\n0.11281\n0.19508\n0.10022\n-0.067546\n-0.3744\n-0.41295\n-0.16937\n0.1679\n0.43742\n0.50445\n0.46896\n0.72966\n0.54707\n0.70519\n0.64455\n0.55457\n0.54988\n0.69974\n0.17769\n1.0976\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1924\n-0.026156\n0.066297\n-0.17748\n-0.15992\n-0.12338\n-0.031502\n-0.22746\n-0.20841\n-0.90184\n-1.2044\n-1.4396\n-0.35665\n-0.18832\n-0.29968\n-0.37063\n-0.19649\n-0.21499\n-0.22709\n0.031535\n0.094635\n-0.12999\n-0.17538\n-0.097994\n-0.084499\n-0.185\n-0.3467\nNaN\nNaN\nNaN\n-0.12894\n-0.046546\n-0.025964\n-0.063202\n0.031328\n0.022257\n0.10792\n0.043564\n-0.034792\n0.043647\n0.10409\n0.20301\n0.22493\n0.25851\n0.24036\n0.19955\n0.04639\n-0.14136\n-0.003231\n0.0067071\n-0.013972\n-0.43477\n-0.26982\n0.014254\n0.3589\n0.57551\n0.49939\n0.60644\n0.85712\n0.77558\n0.6959\n0.65936\n0.53929\n0.52823\n0.85424\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76702\n-0.12672\n0.13437\n-0.19109\n-0.3903\n-0.38335\n0.019936\n-0.64322\n-0.44334\n-1.5348\n-1.4514\n-1.3449\n-0.64578\n-0.42514\n-0.34708\n-0.21847\n-0.033368\n0.061058\n-0.11807\n-0.073795\n0.059743\n-0.11347\n0.018302\n0.12377\n0.17996\n0.027707\n-0.099108\nNaN\n-0.41696\nNaN\n-0.14426\n-0.033157\n-0.060962\n-0.077003\n0.10403\n0.085664\n-0.058234\n-0.074371\n-0.041387\n0.11543\n0.13559\n0.15272\n0.088454\n0.17911\n0.11303\n0.12235\n0.072858\n-0.016089\n-0.12663\n-0.033515\n0.014606\n-0.47377\n-0.26285\n0.24481\n0.42003\n0.73379\n0.80585\n0.73841\n0.83304\n0.73799\n0.93318\n1.0061\n0.8432\n0.49447\n1.2645\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.095739\n0.69739\n0.66778\n0.32142\n0.19528\n-0.4072\n0.036797\n-0.043264\n-0.57348\n-1.3095\n-0.4271\n-0.42082\n-0.29141\n-0.28076\n-0.3133\n-0.2102\n0.0058127\n0.084434\n-0.039608\n0.13803\n0.13928\n0.0428\n0.076998\n0.17038\n0.0673\n0.07571\n0.07003\n0.031097\n0.028406\n-0.07736\n-0.080989\n-0.070011\n-0.12903\n-0.078251\n0.056748\n0.036854\n-0.10021\n-0.03747\n-0.011592\n0.07136\n0.06388\n0.048801\n0.14545\n0.15335\n0.049226\n0.15781\n0.069122\n-0.094436\n-0.21587\n-0.0023089\n0.16242\n-0.21568\n-0.33612\n0.29868\n0.59842\n1.0956\n0.96012\n0.88099\n1.164\n0.97913\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.035315\n1.3927\n0.17282\n-0.16901\n0.15376\n-0.12732\n0.30828\n-0.17648\n-0.36449\n-0.23093\n-0.3385\n-0.25579\n-0.016294\n-0.069598\n-0.13073\n-0.35035\n-0.094797\n0.10525\n0.040167\n0.23663\n0.23682\n0.054716\n0.060611\n0.034812\n-0.16438\n-0.056596\n-0.023931\n-0.065577\n0.12773\n0.12436\n-0.069893\n-0.10805\n-0.13927\n-0.17414\n-0.15711\n-0.10613\n-0.10268\n-0.088659\n0.032411\n0.011279\n-0.0090192\n0.020023\n0.094023\n0.13526\n0.15811\n0.25225\n0.22045\n-0.11475\n-0.38538\n-0.026533\n0.097332\n-0.21515\n-0.19269\n0.2387\n0.76037\n0.89443\n0.83882\n0.69154\n1.8077\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.064539\n-0.72646\n-0.37142\n-0.26643\n0.061108\n-0.08902\n-0.083616\n-0.1494\n-0.20778\n-0.02977\n0.06187\n0.024746\n0.034264\n-0.097401\n-0.084964\n-0.082469\n0.0088527\n0.18117\n0.30906\n0.064567\n0.18461\n0.098835\n-0.12656\n-0.15539\n-0.060302\n-0.035799\n-0.0094296\n0.30839\n-0.14872\n-0.17724\n-0.31538\n-0.25251\n-0.14019\n-0.10222\n-0.1315\n-0.037267\n0.1044\n0.061031\n0.023718\n0.10215\n0.16542\n0.1426\n0.18625\n0.39887\n0.345\n0.040807\n-0.24629\n-0.38299\n-0.19668\n-0.18672\n-0.0051324\n0.41102\n0.64992\n0.74454\n0.62067\n0.43936\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.069774\n-0.451\n-0.33788\n-0.17895\n-0.072177\n-0.075401\n-0.089957\n-0.092025\n-0.009446\n0.056868\n0.11879\n0.2497\n0.108\n-0.04648\n-0.052179\n0.0006476\n0.0017943\n0.25013\n0.0030576\n0.17297\n0.21783\n-0.011682\n-0.077556\n0.13954\n0.12436\n0.058873\n0.16694\n-0.26594\n-0.28491\n-0.29591\n-0.20131\n-0.14121\n-0.0068399\n-0.059325\n0.047956\n0.085333\n0.091207\n0.048357\n0.11132\n0.22925\n0.13291\n0.16859\n0.37162\n0.43826\n0.072769\n-0.17598\n-0.27902\n-0.10472\n-0.011706\n0.063128\n0.43067\n0.61295\n1.026\n0.90632\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.084856\n0.0052322\n-0.4049\n-0.25978\n0.1407\n-0.017831\n0.062102\n0.059724\n0.019229\n-0.041841\n0.20672\n0.50725\n0.35175\n0.097727\n0.094229\n0.058392\n-0.069451\n-0.078633\n0.045099\n0.17903\n0.1317\n0.0081777\n-0.17712\n0.1437\n0.21603\n0.21059\n0.14664\n-0.37731\n-0.28312\n-0.20317\n-0.15131\n-0.16961\n0.019083\n0.042024\n0.014507\n0.071102\n0.051536\n0.09179\n0.18741\n0.21648\n0.23435\n0.2335\n0.35314\n0.60181\n0.11028\n-0.060493\n-0.064098\n0.12498\n0.094036\n0.099402\n0.30409\n0.72694\n0.96859\n1.0475\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14094\n-0.067121\n-0.43037\n-0.26931\n0.074434\n-0.084903\n0.051547\n0.13454\n-0.043213\n-0.15005\n0.068425\n0.48951\n0.49597\n0.34727\n0.22019\n0.17853\n0.037155\n0.0043085\n0.18245\n0.27719\n0.11658\n0.028006\n-0.0049332\n0.19898\n0.32913\n0.41804\n0.21515\n-0.34228\n-0.24685\n-0.17391\n-0.096661\n-0.11751\n-0.015758\n0.040149\n0.025735\n0.041573\n0.065853\n0.027043\n0.025999\n0.16819\n0.28082\n0.28557\n0.37269\n0.41444\n0.11696\n0.083165\n-0.03419\n0.059526\n0.14392\n0.29705\n0.43625\n0.76796\n1.0667\n0.9971\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52335\n0.11973\n0.086043\n-0.26431\n-0.24279\n-0.0090562\n-0.77547\n-0.31392\n0.14751\n0.11215\n0.36193\n0.40937\n0.42621\n0.46038\n0.50937\n0.37585\n0.27767\n0.088139\n0.068747\n0.32289\n0.30038\n0.32679\n0.074789\n0.12998\n0.30902\n0.39842\n0.43282\n0.27223\n-0.35272\n-0.18195\n-0.21321\n-0.11438\n-0.12571\n-0.084099\n-0.075401\n-0.010392\n-0.0001967\n-0.037192\n-0.023945\n0.003252\n0.087006\n0.2312\n0.28918\n0.31228\n0.22285\n0.067577\n0.0099212\n0.03708\n0.10574\n0.16842\n0.23184\n0.38223\n0.74748\n1.2207\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.50473\n1.1229\nNaN\n-0.091884\n-0.12694\n0.1759\n-0.10743\n-0.11524\n-0.20812\n-0.060112\n-0.76199\n-0.080044\n0.24471\n0.55438\n0.46756\n0.4505\n0.33915\n-0.11483\n0.42023\n0.21316\n0.20372\n-0.099456\n0.10782\n0.43771\n0.39125\n0.47223\n0.26249\n0.32653\n0.39558\n0.4029\n0.31367\n0.13463\n-0.248\n-0.16157\n-0.21511\n-0.16487\n-0.093531\n-0.10335\n-0.12316\n-0.11192\n-0.12782\n-0.058695\n-0.0057099\n0.0052429\n-0.0014177\n0.199\n0.17954\n0.23028\n0.193\n0.090337\n-0.064751\n0.062423\n0.1679\n0.18099\n0.10709\n0.43481\n0.97723\n1.4273\n0.7357\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4364\n0.62871\n0.51823\n0.43684\n0.32699\n0.37337\n0.27293\n0.031699\n-0.015309\n-0.10032\n-0.18476\n-0.1459\n-0.054827\n-0.052686\n0.23137\n0.52623\n0.28148\n0.32704\n0.1412\n-0.21837\n-0.14349\n0.15571\n0.26492\n-0.021565\n0.16047\n0.38494\n0.48088\n0.44114\n0.30513\n0.28055\n0.43562\n0.31653\n0.14205\n-0.23526\n-0.26641\n-0.20548\n-0.14674\n-0.20721\n-0.18766\n-0.15937\n-0.13234\n-0.17812\n-0.22066\n-0.04866\n-0.044199\n-0.085223\n-0.098223\n0.059657\n0.10131\n0.13303\n0.040275\n-0.027798\n-0.010755\n-0.19357\n-0.10904\n0.16688\n0.27276\n0.71093\n1.2197\n1.3702\n-0.48045\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.87983\n0.87309\n0.98241\n0.79352\n0.71063\n0.50287\n0.39093\n0.40007\n0.059508\n-0.1719\n-0.25481\n-0.38344\n-0.2876\n-0.30112\n-0.15126\n0.13653\n-0.05096\n-0.094007\n0.062827\n0.0073817\n-0.22002\n-0.25628\n0.080388\n0.0067695\n0.08579\n0.24276\n0.27321\n0.21079\n0.21316\n0.2538\n0.25099\n0.15901\n0.079016\n-0.38758\n-0.33258\n-0.27042\n-0.23622\n-0.26017\n-0.26738\n-0.18473\n-0.16586\n-0.14528\n-0.19363\n-0.10855\n-0.10638\n-0.17544\n-0.12611\n-0.059082\n-0.0070534\n-0.070085\n-0.14888\n-0.22532\n-0.19845\n-0.17621\n-0.18455\n0.12888\n0.36738\n0.95184\n1.2642\n1.509\n-0.47194\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19058\n0.80932\n0.77346\n0.79648\n0.47727\n0.95256\n1.5857\n1.0959\n0.832\n0.52443\n0.43969\n0.23787\n0.205\n-0.046484\n-0.094393\n-0.20798\n-0.3345\n-0.28192\n-0.2982\n-0.24038\n-0.24695\n-0.18379\n-0.1625\n-0.16061\n-0.291\n-0.38579\n0.026784\n0.12866\n0.10397\n0.0088433\n-0.067022\n-0.0097113\n0.14731\n0.16372\n0.017863\n0.079347\n-0.044581\n-0.19251\n-0.41025\n-0.30044\n-0.31528\n-0.22171\n-0.24488\n-0.25398\n-0.25942\n-0.16737\n-0.15681\n-0.18429\n-0.15021\n-0.26206\n-0.34607\n-0.20071\n-0.15448\n-0.16729\n-0.29287\n-0.33833\n-0.27912\n-0.26249\n-0.27454\n-0.15426\n0.34757\n0.72446\n1.0999\n1.5816\n-0.46239\n0.2004\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10736\n0.59376\nNaN\nNaN\nNaN\nNaN\n1.947\n1.4391\n1.0678\n0.87613\n0.44614\n0.077985\n0.16602\n0.11684\n0.047734\n-0.10085\n-0.30896\n-0.31986\n-0.26405\n-0.20557\n-0.17669\n-0.38773\n-0.44086\n-0.26432\n-0.26657\n-0.41502\n-0.41294\n-0.13416\n0.064655\n0.033979\n0.00057404\n-0.077365\n-0.059263\n-0.035353\n0.093026\n-0.10499\n-0.20063\n-0.12062\n-0.025106\n-0.33483\n-0.33338\n-0.35027\n-0.20113\n-0.30299\n-0.29207\n-0.2806\n-0.2351\n-0.21739\n-0.23814\n-0.28023\n-0.29787\n-0.3518\n-0.24879\n-0.27768\n-0.1718\n-0.28586\n-0.6214\n-0.75247\n-0.25964\n-0.10203\n-0.18354\n0.1904\n0.69205\n0.91287\n0.98221\n0.67746\n-0.87919\n2.1687\nNaN\nNaN\n0.58311\n1.0995\n0.83068\n0.95044\nNaN\nNaN\n2.4627\n2.1941\n1.7153\n1.1142\n1.0057\n0.58579\n0.45236\n-0.23991\n-0.026303\n-0.014842\n-0.12958\n-0.16271\n-0.26477\n-0.26253\n-0.21012\n-0.20131\n-0.20155\n-0.30255\n-0.62915\n-0.34706\n-0.2229\n-0.19466\n-0.27268\n-0.32984\n-0.096135\n0.010827\n0.036869\n0.048117\n-0.04998\n-0.11745\n-0.24587\n-0.23027\n-0.10335\n0.015494\n0.080098\n-0.35091\n-0.42273\n-0.42385\n-0.28455\n-0.29314\n-0.23317\n-0.30318\n-0.29365\n-0.21715\n-0.2067\n-0.31925\n-0.30444\n-0.27324\n-0.25568\n-0.31576\n-0.18648\n-0.24356\n-0.42672\n-0.63406\n-0.52037\n-0.098192\n-0.19844\n0.15759\n0.67768\n0.84082\n0.16424\n-0.0040725\n0.003031\n-0.57662\n-1.7837\n-1.5291\n0.17225\n1.6851\nNaN\nNaN\nNaN\nNaN\n-0.13591\n1.3428\n1.3892\n0.90409\n0.6743\n0.64981\n0.41251\n-0.23226\n-0.064667\n-0.057602\n-0.32345\n-0.30797\n-0.3461\n-0.22857\n-0.19203\n-0.23223\n-0.35947\n-0.26639\n-0.27134\n-0.21731\n-0.07554\n-0.037618\n-0.025861\n-0.050011\n-0.14173\n-0.022349\n-0.085957\n-0.024601\n-0.15312\n-0.17279\n-0.21711\n-0.049224\n0.027424\n0.17516\n0.11773\n-0.3821\n-0.4515\n-0.43333\n-0.3729\n-0.2415\n-0.16505\n-0.25534\n-0.21982\n-0.22253\n-0.14064\n-0.35967\n-0.28187\n-0.20347\n-0.33202\n-0.39266\n-0.30705\n-0.23294\n-0.45621\n-0.40184\n-0.63248\n-0.62997\n-0.35208\n0.10594\n0.42019\n0.56289\n0.33358\n-0.07667\n0.13003\n-1.1906\n-1.2139\n0.4156\n0.86096\n0.75477\nNaN\nNaN\nNaN\nNaN\nNaN\n0.4047\n0.95109\n0.75062\n0.61567\n0.60759\n-0.17553\n-0.55908\n-0.1641\n-0.27665\n-0.49033\n-0.44176\n-0.31825\n-0.1645\n-0.14685\n-0.19794\n-0.19235\n-0.21191\n-0.17178\n-0.14388\n-0.089268\n-0.10211\n-0.10125\n-0.06443\n-0.14349\n-0.068487\n-0.12561\n-0.19951\n-0.33079\n-0.36168\n-0.13019\n-0.027856\n0.04042\n0.17258\n0.11999\n-0.42257\n-0.4475\n-0.39165\n-0.42721\n-0.26849\n-0.077901\n-0.1707\n-0.19914\n-0.2544\n-0.17886\n-0.34456\n-0.24504\n-0.24635\n-0.36515\n-0.41968\n-0.26373\n-0.28339\n-0.49918\n-0.54997\n-0.45838\n-0.34759\n-0.19617\n0.049445\n0.32611\n0.13695\n-0.10846\n-0.060135\n0.29686\n0.27946\n0.56066\n0.6305\n0.75713\n0.78036\n0.86311\nNaN\nNaN\nNaN\n-1.0217\n0.53099\n0.69677\n0.42545\n0.38142\n0.35833\n-0.33329\n-0.67192\n-0.36674\n-0.4007\n-0.47521\n-0.41689\n-0.26931\n-0.0035678\n0.022989\n0.036481\n-0.062293\n-0.22642\n-0.23414\n-0.14438\n-0.099275\n-0.060741\n-0.035527\n-0.13273\n-0.20653\n-0.25203\n-0.3832\n-0.32311\n-0.32798\n-0.32146\n-0.2411\n-0.26371\n-0.10128\n0.043025\n-0.073646\n-0.37431\n-0.41428\n-0.47363\n-0.42706\n-0.36537\n-0.12621\n-0.12798\n-0.21446\n-0.2767\n-0.21932\n-0.2987\n-0.20867\n-0.23614\n-0.28603\n-0.36434\n-0.32224\n-0.5676\n-1.0118\n-0.65302\n-0.42998\n-0.29557\n0.044132\n0.11624\n0.25256\n0.65415\n0.12264\n0.19283\n0.29331\n-1.729\n0.092392\n0.36545\n0.14004\n0.73844\n1.0774\nNaN\nNaN\nNaN\n0.31002\n0.52876\n0.60073\n0.42472\n0.1869\n0.25466\n-0.15555\n-0.61835\n-0.44896\n-0.085448\n-0.18599\n-0.25732\n-0.12395\n0.041453\n0.089594\n0.08394\n-0.12164\n-0.093069\n-0.16986\n-0.16558\n-0.11208\n-0.016686\n-0.015652\n-0.11253\n-0.23542\n-0.32753\n-0.40876\n-0.29628\n-0.12274\n-0.16357\n-0.31051\n-0.2955\n-0.16983\n-0.079612\n-0.17757\n-0.44179\n-0.39393\n-0.38533\n-0.39821\n-0.31587\n-0.16201\n-0.16994\n-0.29043\n-0.32666\n-0.31354\n-0.30932\n-0.24859\n-0.21081\n-0.18602\n-0.29631\n-0.37532\n-0.59072\n-1.0183\n-0.58058\n-0.45887\n-0.26683\n-0.15913\n0.22081\n0.17008\n0.28432\n0.20134\n0.14981\n-1.5554\n-2.0195\n-0.099143\n0.16844\n0.22152\n0.43746\n0.7217\n0.75513\n0.45838\n0.1663\n0.27369\n0.35878\n0.55696\n0.32083\n-0.11597\n-0.24783\n-0.35185\n-0.45771\n-0.17505\n0.011476\n-0.055751\n-0.2031\n-0.16473\n-0.037982\n0.15192\n0.066074\n-0.079375\n-0.062846\n-0.12093\n-0.11819\n-0.052167\n0.02498\n-0.020795\n-0.079774\n-0.245\n-0.31112\n-0.30943\n-0.18854\n-0.041327\n-0.038869\n-0.06403\n-0.083684\n-0.1257\n-0.069268\n0.015002\n-0.42959\n-0.38629\n-0.29995\n-0.24015\n-0.20451\n-0.15929\n-0.18931\n-0.33071\n-0.37789\n-0.35715\n-0.28481\n-0.21036\n-0.16874\n-0.069089\n-0.10565\n-0.35236\n-0.41677\n-0.9547\n-0.27392\n-0.44112\n-0.40137\n-0.059553\n0.23479\n0.14095\n0.064866\n0.025891\n-0.0044082\n-1.4332\n-2.1378\n-2.1477\n-0.044359\n0.16192\n0.28488\n0.41013\n0.36641\n0.49054\n0.38299\n0.15145\n0.24391\n0.48045\n0.18624\n-0.050976\n-0.12678\n-0.042388\n-0.11786\n-0.16666\n-0.15398\n-0.20639\n-0.17485\n-0.049552\n0.050435\n0.1423\n0.033123\n-0.19003\n-0.13351\n-0.064267\n-0.022471\n0.082528\n0.075351\n0.049372\n-0.061374\n-0.21049\n-0.27129\n-0.2135\n-0.094485\n0.0065651\n0.047485\n0.057713\n-0.07669\n-0.17108\n-0.045675\n0.083276\n-0.37289\n-0.35684\n-0.27301\n-0.17112\n-0.09238\n-0.091815\n-0.22402\n-0.23625\n-0.2924\n-0.27958\n-0.17253\n-0.091325\n-0.036352\n0.14002\n0.28218\n-0.1548\n0.15092\n0.33524\n0.0059\n-0.43508\n-0.51833\n-0.11304\n0.10212\n-0.25502\n-0.21221\n-0.15308\n0.013261\n-1.4746\n-1.9507\n-1.734\n-0.035737\n0.14191\n0.071893\n0.1687\n0.24058\n0.31463\n0.23456\n0.15634\n0.17302\n0.040321\n-0.10589\n-0.098049\n0.019352\n0.04172\n-0.020158\n-0.093783\n-0.073062\n-0.017779\n0.048397\n0.017104\n0.067987\n0.046346\n-0.035575\n-0.18703\n-0.11065\n0.028294\n0.043048\n0.091002\n0.10424\n0.094456\n-0.066199\n-0.14979\n-0.17378\n-0.19405\n-0.069312\n-0.0093704\n0.084752\n0.11038\n-0.064772\n-0.17837\n-0.088769\n0.069494\n-0.26152\n-0.23121\n-0.19276\n-0.18796\n-0.13263\n-0.070394\n-0.25067\n-0.15074\n-0.15927\n-0.17119\n-0.089577\n-0.010147\n0.12536\n0.39901\n0.4252\n-0.018928\n0.2351\n0.50255\n-0.34081\n-0.45372\n-0.46128\n-0.60567\n-0.4204\n-0.32151\n-0.20367\n-0.20124\n-0.1473\n-1.5507\n-1.4363\n-1.254\n-0.12527\n-0.061783\n-0.039609\n0.11556\n0.43598\n0.31149\n0.19501\n0.1078\n0.041204\n-0.074561\n-0.10302\n-0.12693\n-0.0164\n0.073067\n0.074633\n-0.057828\n-0.065912\n-0.10119\n-0.067764\n0.031867\n0.058537\n0.017262\n0.0036068\n0.001613\n0.056171\n0.046466\n-0.056205\n0.10565\n0.1128\n0.092028\n-0.053399\n-0.082182\n-0.058649\n-0.060771\n0.014546\n0.058966\n0.16926\n0.15713\n0.00043536\n-0.049618\n0.031621\n0.099725\n-0.17211\n-0.087615\n-0.09995\n-0.19168\n-0.063454\n-0.078941\n-0.066472\n-0.08636\n-0.033208\n-0.11179\n-0.03057\n0.072611\n0.14357\n0.18274\n0.125\n0.10495\n-0.11928\n-0.52621\n-0.75091\n-0.5123\n-0.44428\n-0.46535\n-0.44966\n-0.35068\n-0.14349\n-0.16374\n-0.18075\n-0.11975\n-0.19941\n-0.16393\n-0.34194\n-0.2493\n-0.11332\n0.060883\n0.38945\n0.34576\n0.33275\n0.21257\n0.12477\n0.14395\n-0.00063818\n0.0089428\n0.0050425\n0.13157\n0.18278\n0.0079241\n-0.22459\n-0.44926\n-0.38144\n0.02835\n0.0554\n0.021902\n0.19567\n0.08442\n0.059596\n0.065541\n-0.056246\n0.073668\n0.097865\n0.090073\n0.00067023\n-0.10913\n-0.03185\n0.056884\n0.10725\n0.11696\n0.1151\n0.20031\n0.018756\n0.019966\n0.18113\n0.14805\n-0.23516\n-0.13594\n-0.096382\n-0.10988\n-0.053435\n-0.022574\n0.0076632\n-0.04001\n-0.069981\n-0.088135\n-0.076626\n0.0051663\n0.078529\n0.16224\n0.20269\n0.16756\n-0.19411\n-0.51678\n-0.67835\n-0.56972\n-0.47909\n-0.37242\n-0.31404\n-0.27052\n-0.13253\n-0.1706\n-0.1614\n-0.14804\n-0.1162\n-0.1304\n-0.20208\n-0.26638\n-0.22114\n-0.05153\n0.22266\n0.32273\n0.25037\n0.34451\n0.20152\n0.095094\n0.034038\n0.1722\n0.11914\n0.13909\n0.21895\n0.20909\n-0.22415\n-0.56807\n-0.72324\n-0.14031\n-0.038272\n0.23962\n0.25341\n0.14601\n0.037537\n0.15102\n0.13174\n0.11104\n0.059032\n0.022152\n-0.014138\n-0.071144\n0.065863\n0.099157\n0.1085\n0.082438\n0.20799\n0.23618\n0.036486\n0.074036\n0.10964\n0.086275\n-0.25715\n-0.080712\n0.025367\n0.016378\n0.075607\n0.12438\n0.10826\n0.15445\n-0.016677\n-0.08322\n-0.11451\n-0.058591\n-0.032601\n0.27672\n0.73308\n0.76506\n-0.081591\n-0.32716\n-0.53229\n-0.54567\n-0.32607\n-0.36668\n-0.46059\n-0.32651\n-0.13248\n-0.13501\n-0.11446\n-0.057607\n-0.10623\n-0.15506\n-0.16269\n-0.10867\n-0.22647\n-0.15319\n-0.0015716\n0.10105\n0.20747\n0.37906\n0.2337\n0.21717\n0.18313\n0.2592\n0.20723\n-0.018511\n0.034656\n0.25167\n0.088031\n-0.45625\n-0.40939\n-0.40541\n-0.2102\n-0.048004\n0.060851\n-0.012152\n0.042955\n0.22643\n0.0085117\n-0.037275\n0.012784\n-0.029128\n0.020029\n0.076923\n0.12961\n0.051946\n0.036889\n0.12998\n0.28187\n0.23919\n0.12791\n0.079139\n0.06112\n0.13307\n-0.12777\n-0.013842\n0.084138\n0.11947\n0.15198\n0.21563\n0.22808\n0.36202\n0.25414\n0.13885\n-0.012078\n-0.024631\n0.075299\n0.11319\n0.86204\n0.79298\n0.85204\n0.043372\n-0.41814\n-0.43764\n-0.42575\n-0.39992\n-0.52019\n-0.40838\n-0.21262\n-0.13818\n-0.17174\n-0.12136\n-0.17843\n-0.19177\n-0.18872\n-0.14339\n-0.056247\n-0.11291\n-0.091945\n0.074803\n0.24923\n0.52559\n0.43102\n0.3414\n0.30367\n0.31015\n0.21122\n-0.23452\n-0.2218\n-0.04601\n0.34749\n-0.29388\n-0.22204\n-0.21132\n-0.17249\n-0.18336\n-0.18162\n-0.10831\n-0.010024\n0.028588\n-0.11265\n-0.059543\n0.010754\n0.037827\n0.06764\n0.11969\n0.13134\n0.098835\n0.029627\n0.16819\n0.36319\n0.2684\n0.20694\n0.037273\n0.045701\n0.047444\n0.017451\n-0.032385\n0.13111\n0.25298\n0.30087\n0.48325\n0.46094\n0.56574\n0.47162\n0.29232\n0.096113\n-0.036473\nNaN\nNaN\nNaN\n-0.45475\n1.4044\n0.2447\n-0.55447\n-0.58965\n-0.47573\n-0.52895\n-0.50277\n-0.48829\n-0.43821\n-0.45043\n-0.29697\n-0.25055\n-0.18661\n-0.16193\n-0.19542\n-0.14595\n-0.044311\n0.0198\n-0.0017667\n0.041601\n0.14232\n0.4161\n0.52595\n0.32532\n0.36464\n0.38186\n0.26235\n-0.24288\n-0.47122\n-0.34445\n0.20175\n-0.15341\n-0.13613\n-0.15319\n-0.16402\n-0.14456\n-0.17747\n-0.14922\n-0.03428\n0.05313\n-0.19791\n-0.17385\n-0.0067053\n-0.078471\n-0.024785\n-0.017387\n0.1144\n0.11864\n0.10966\n0.18344\n0.34649\n0.24436\n0.22763\n-0.10943\n-0.16001\n-0.39128\n0.168\n0.00086039\n0.28663\n0.44724\n0.55929\n0.7286\n0.83039\n0.87306\n0.54327\n0.38243\n0.21885\n0.50011\nNaN\nNaN\nNaN\n-0.20824\n0.345\n0.070818\n-0.7024\n-0.54023\n-0.395\n-0.45613\n-0.39441\n-0.46128\n-0.42998\n-0.36514\n-0.32952\n-0.23562\n-0.17604\n-0.16397\n-0.13806\n-0.12945\n0.040504\n0.074681\n0.056955\n-0.070013\n-0.043934\n0.11666\n0.29978\n0.23768\n0.33383\n0.229\n-0.039941\n-0.26683\n-0.53529\n-0.48096\n-0.095243\n-0.17545\n-0.12497\n-0.21952\n-0.13502\n0.030939\n0.061631\n0.025699\n0.029786\n0.033687\n-0.18626\n-0.16599\n0.0047469\n-0.20371\n-0.078808\n-0.23351\n-0.11735\n0.19172\n-0.0081158\n0.13214\n0.19111\n0.13879\n0.2564\n-0.74797\n-0.86442\n-0.79511\n0.035761\n-0.09032\n-0.083116\n-0.12827\n-0.12278\n-0.12839\n-0.17887\n-0.20229\n-0.10648\n-0.057108\n-0.15184\n-0.1449\n-0.19434\n-0.13111\n0.039195\n0.035661\n0.049994\n-0.056248\n0.10599\n0.12662\n0.2073\n0.1623\n0.25689\n0.15892\n0.23757\n0.079592\n-0.066669\n-0.24745\n-0.16333\n-0.11316\n-0.40201\n-0.34089\n-0.23657\n-0.26894\n-0.10826\n-0.20215\n-0.19404\n-0.090855\n-0.18044\n-0.234\n-0.12317\n-0.035032\n0.053293\n0.14716\n0.1538\n0.067386\n0.12031\n0.11075\n0.054596\n0.030592\n0.050847\n0.11212\n0.079867\n-0.048613\n0.16555\n0.02518\n-0.031559\n-0.17449\n-0.40693\n-0.43296\n-0.34609\n-0.075756\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17736\n-0.16532\n-0.15338\n0.097051\n0.10574\n-0.3264\n-0.17925\n-0.23166\n-0.067485\n-0.026955\n-0.036327\n-0.087427\n-0.21392\n0.031657\n0.14517\n-0.0077051\n-0.28517\n-0.21068\n-0.031868\n0.021268\n0.31039\n0.1279\n0.19362\n0.37194\n0.32621\n0.21168\n0.079645\n-0.070484\n-0.0028124\n-0.10231\n-0.14695\n-0.2012\n-0.13065\n-0.16316\n-0.13925\n-0.28497\n-0.26562\n-0.09016\n-0.14766\n-0.016662\n0.045817\n-0.0081052\n0.07504\n0.13519\n0.14416\n0.10061\n0.12236\n0.074682\n0.044987\n0.028815\n0.023356\n0.082851\n0.080584\n0.1368\n0.39729\n0.10804\n-0.075826\n-0.32151\n-0.30445\n-0.19448\n-0.14762\n0.032824\n0.17522\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.24692\n-0.20788\n-0.1508\n-0.18651\n-0.46502\n-0.40691\n-0.41015\n-0.18724\n-0.12226\n0.10317\n0.0539\n-0.060616\n-0.13903\n0.13916\n0.35773\n0.056762\n-0.1217\n-0.064034\n-0.022241\n0.21164\n0.19154\n0.21994\n0.34771\n0.31858\n0.39026\n0.36218\n0.23322\n0.05711\n0.074396\n-0.11739\n-0.015318\n-0.14331\n-0.044841\n-0.094719\n-0.32828\n-0.37069\n-0.22199\n-0.13194\n-0.057721\n-0.020158\n0.026315\n0.067297\n0.036493\n0.15807\n0.098171\n0.049544\n0.049201\n0.054168\n0.030582\n0.072963\n0.10071\n0.082489\n0.072241\n0.21792\n0.25522\n0.17453\n0.096615\n-0.33477\n-0.24671\n0.0053076\n-0.21833\n0.045177\n0.22586\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.14819\n-0.20099\n-0.16868\n-0.15311\n-0.15414\n-0.13357\n-0.084692\n-0.10431\n-0.16847\n-0.11957\n-0.030303\n-0.25281\n-0.0013878\n0.25529\n0.13736\n-0.080608\n-0.088304\n-0.077557\n-0.03373\n0.1849\n0.33555\n0.25053\n0.30899\n0.19775\n0.37874\n0.40763\n0.27736\n0.12054\n0.054691\n-0.047894\n-0.089926\n-0.0091236\n-0.023908\n-0.051749\n-0.27883\n-0.35678\n-0.29928\n-0.14583\n-0.034439\n-0.032513\n-0.02813\n0.01689\n-0.058432\n-0.027531\n0.028167\n-0.0063261\n-0.020079\n0.057603\n0.098163\n0.24595\n0.29038\n0.21556\n0.28185\n0.3097\n0.30901\n0.25275\nNaN\nNaN\nNaN\n-0.51011\n-0.35983\n0.0697\n0.30094\nNaN\nNaN\nNaN\n-0.0020583\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.13689\n-0.13988\n-0.11738\n-0.12136\n-0.085361\n-0.12\n-0.089591\n-0.070923\n-0.15241\n-0.24702\n-0.20369\n-0.26887\n-0.21356\n-0.078192\n-0.26579\n-0.24592\n-0.22434\n-0.15724\n-0.23422\n0.072741\n0.44345\n0.27146\n0.39275\n-0.18563\n0.26773\n0.38312\n0.37584\n0.17932\n0.15551\n0.16105\n-0.11856\n-0.090894\n-0.082873\n-0.017832\n-0.1174\n-0.17417\n-0.30917\n-0.16671\n-0.043166\n-0.035672\n-0.021522\n0.034859\n-0.049671\n-0.062285\n-0.030379\n-0.023019\n0.056747\n0.1661\n0.14375\n0.13451\n0.26898\n0.24944\n0.20171\n0.3494\n0.26136\n0.2765\n0.55369\nNaN\nNaN\n-0.74351\n-0.46181\n-0.0080847\n0.34887\n-0.098406\n-0.26155\n-0.0901\n-0.27779\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1058\n-0.15818\n-0.15663\n-0.11638\n-0.052278\n-0.13231\n-0.065434\n-0.02883\n-0.071114\n-0.115\n-0.09441\n-0.0012781\n-0.039586\n-0.20411\n-0.31802\n-0.17093\n-0.2826\n-0.26118\n-0.095017\n-0.38832\n-0.13058\n0.14757\n0.085706\n0.013416\n0.24218\n0.44032\n0.42041\n0.29922\n0.21598\n0.23075\n0.080077\n-0.12471\n-0.12366\n-0.089203\n-0.10558\n-0.2551\n-0.32218\n-0.15753\n-0.050393\n-0.010833\n0.016029\n-0.030704\n-0.059789\n-0.095851\n-0.29052\n-0.15567\n0.043095\n0.20386\n0.15705\n0.17155\n0.21359\n0.2139\n0.20058\n0.17508\n0.11646\n-0.064172\n0.10031\nNaN\nNaN\n-1.1418\n-0.71819\n-0.10054\n-0.1495\n-0.45309\n-0.15875\n-0.040917\n-0.16048\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1615\n-0.20184\n-0.15185\n-0.041478\n0.019414\n-0.096334\n0.020996\n0.046085\n0.044863\n0.040329\n-2.4857e-05\n-0.044196\n-0.032558\n-0.17483\n-0.2025\n-0.23421\n-0.2536\n-0.068809\n-0.14284\n-0.15402\n-0.043677\n-0.12226\n0.06132\n0.050556\n0.1492\n0.33375\n0.42884\n0.45194\n0.25201\n0.20836\n0.0038066\n-0.12035\n-0.28181\n-0.19211\n-0.21534\n-0.27957\n-0.28075\n-0.18263\n-0.11544\n-0.041203\n0.055557\n-0.050575\n-0.16656\n-0.24575\n-0.52841\n-0.2013\n0.030072\n0.176\n0.22843\n0.23871\n0.158\n0.20598\n0.23484\n0.20463\n0.095355\n-0.091355\n0.051688\nNaN\nNaN\n-0.52756\n-0.77603\n-0.50672\nNaN\n-0.62329\n-0.39763\nNaN\nNaN\n0.14866\n0.015093\nNaN\nNaN\nNaN\n-0.11918\n-0.094992\n-0.097962\n0.025624\n0.14906\n-0.062024\n-0.0030687\n0.085192\n0.11262\n0.10067\n0.055298\n0.034791\n-0.014028\n0.017658\n-0.073234\n-0.11235\n-0.062653\n0.031065\n-0.052921\n-0.089764\n-0.02189\n0.070196\n0.070721\n0.1499\n0.21895\n0.33179\n0.40946\n0.46974\n0.27081\n0.21687\n0.12179\n-0.15301\n-0.3556\n-0.18943\n-0.127\n-0.1442\n-0.12255\n-0.1951\n-0.20545\n-0.2059\n-0.03234\n-0.095346\n-0.19438\n-0.31676\n-0.6764\n-0.18563\n0.081307\n0.12807\n0.11733\n0.2353\n0.14826\n0.20173\n0.27687\n0.25102\n0.24141\n0.055148\n-0.0021999\n0.17061\n0.15824\n-0.11917\n-0.56536\n-0.47598\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.097431\n-0.13276\n-0.3245\nNaN\nNaN\n-0.063657\n-0.14608\n-0.14697\n-0.093052\n0.063446\n-0.039283\n-0.03805\n0.1195\n0.13696\n0.13432\n0.087822\n0.10292\n0.10315\n0.066338\n0.0496\n0.067898\n0.077819\n0.13627\n0.032256\n0.057658\n0.057871\n0.042126\n0.15387\n0.21998\n0.24455\n0.37067\n0.40431\n0.33062\n0.22575\n0.15239\n0.14121\n0.074664\n-0.1203\n-0.16793\n-0.089896\n-0.073126\n-0.11324\n-0.20473\n-0.20697\n-0.1917\n-0.17139\n-0.12895\n-0.24417\n-0.41111\n-0.64394\n0.025559\n0.15349\n0.1753\n0.20327\n0.25446\n0.23682\n0.33651\n0.35199\n0.46813\n0.43316\n0.10276\n-0.057472\n0.11702\n0.28309\n0.24792\n-0.40775\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26456\n-0.13847\nNaN\nNaN\n-0.2661\n-0.14461\n-0.080949\n-0.1405\n0.048251\n-0.061734\n-0.066243\n-0.0034704\n0.081341\n0.13571\n0.12123\n0.12565\n0.15694\n0.10282\n0.15412\n0.16738\n0.13978\n0.20654\n0.17581\n0.24315\n0.21069\n0.21211\n0.30192\n0.21926\n0.15044\n0.28603\n0.43634\n0.40547\n0.22465\n0.1124\n0.14048\n-0.084737\n-0.28053\n-0.24599\n-0.15584\n-0.0090115\n-0.11524\n-0.16101\n-0.24014\n-0.20518\n-0.14604\n-0.10246\n-0.3085\n-0.38169\n-0.14838\n0.15928\n0.10556\n0.17736\n0.21939\n0.30312\n0.28606\n0.4282\n0.44864\n0.49638\n0.42162\n0.25559\n-0.0036986\n-0.092541\n0.26925\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22298\nNaN\n-0.22353\n-0.047721\n-0.32362\n-0.22678\n-0.13268\n-0.14434\n-0.053185\n-0.087525\n-0.068324\n-0.05771\n0.057753\n0.16089\n0.16729\n0.1362\n0.23071\n0.17304\n0.24952\n0.27812\n0.20147\n0.2103\n0.23332\n0.29531\n0.20996\n0.2516\n0.25916\n0.097505\n0.13364\n0.45199\n0.51749\n0.46305\n0.28707\n0.2805\n0.16044\n-0.068869\n-0.34151\n-0.07497\n-0.17343\n-0.036288\n-0.13121\n-0.077938\n-0.3055\n-0.23741\n-0.082354\n-0.091581\n-0.30781\n-0.29831\n-0.095372\n0.069161\n0.084145\n0.18019\n0.29559\n0.28903\n0.28128\n0.41813\n0.42519\n0.54124\nNaN\nNaN\n-0.098903\n-0.10603\n0.20869\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.089564\n0.18692\n-0.37531\n-0.30982\n-0.21811\n-0.13746\n-0.12255\n-0.066535\n-0.0030037\n0.04163\n0.11178\n0.16024\n0.12761\n0.15631\n0.24168\n0.19237\n0.26774\n0.25095\n0.24668\n0.24124\n0.23433\n0.21762\n0.089349\n0.18097\n0.23896\n0.11567\n0.18335\n0.37698\n0.50342\n0.53643\n0.38636\n0.38328\n0.19751\nNaN\nNaN\nNaN\n-0.10243\n-0.051421\n-0.020048\n-0.10712\n-0.37534\n-0.21087\n-0.0024882\n-0.1023\n-0.20953\n-0.15861\n-0.067532\n0.034566\n0.13399\n0.28118\n0.43246\n0.41934\n0.35821\n0.43383\n0.34926\nNaN\nNaN\nNaN\n-0.047169\n-0.14655\n0.023653\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17238\n-0.17515\n-0.13659\n-0.1425\n-0.095642\n-0.018965\n0.018092\n0.041734\n0.085274\n0.10281\n0.1482\n0.19135\n0.19835\n0.2371\n0.28643\n0.32046\n0.34985\n0.28981\n0.22333\n0.17583\n0.1332\n0.17672\n0.057599\n0.11905\n0.19788\n0.2207\n0.52869\n0.45751\n0.32738\n0.33949\n0.24867\nNaN\nNaN\nNaN\n-0.09471\n-0.14833\n-0.11705\n-0.068134\n-0.2436\n-0.14013\n-0.10157\n-0.36548\n-0.46688\n-0.098192\n0.031375\n0.10685\n0.2109\n0.36465\n0.47966\n0.48134\n0.42484\n0.39857\n0.23798\nNaN\nNaN\nNaN\n-0.3955\n-0.40312\n-0.30681\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.074243\n-0.09784\n-0.12232\n-0.06463\n-0.022726\n-0.012061\n0.0133\n-0.00046953\n0.048643\n0.074728\n0.093963\n0.18831\n0.27156\n0.32133\n0.3212\n0.35433\n0.31328\n0.285\n0.16893\n0.13259\n0.19716\n0.037139\n-0.19579\n-0.1096\n-0.083017\n0.21194\n0.42043\n0.36553\n0.4187\n0.43942\n0.34601\nNaN\nNaN\nNaN\nNaN\n-0.13158\n-0.2129\n-0.034839\n-0.1842\n-0.057708\n-0.10812\n-0.27908\n-0.25026\n0.064524\n0.21618\n0.27755\n0.29714\n0.42471\n0.47618\n0.48583\n0.39207\n0.24803\n0.17464\nNaN\nNaN\nNaN\nNaN\n-0.33625\n-0.31309\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.032291\n-0.14323\n-0.046814\n0.053626\n0.021777\n-0.020704\n0.0072506\n-0.0023892\n0.027916\n0.039283\n0.053278\n0.16377\n0.29368\n0.33258\n0.35749\n0.33285\n0.33538\n0.35178\n0.25949\n0.13548\n0.13526\n-0.19063\n-0.50031\n-0.28829\n-0.17904\n0.22364\n0.31414\n0.3922\n0.51632\n0.49684\n0.54259\nNaN\n-0.10829\n-0.18706\n0.010622\n0.10392\n-0.081681\n0.085329\n0.020798\n0.16778\n0.089546\n-0.17024\n-0.19894\n0.35122\n0.39896\n0.54063\n0.21308\n0.46283\n0.55146\n0.49556\n0.321\n0.37841\n0.28216\n-0.0093976\nNaN\nNaN\nNaN\n-0.46745\n-0.36401\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.084936\n-0.11304\n-0.025827\n0.11996\n0.093643\n0.047317\n0.072494\n0.025296\n0.017975\n0.093909\n0.10293\n0.17793\n0.26777\n0.29274\n0.32598\n0.21332\n0.22238\n0.32903\n0.36856\n0.20332\n0.091278\n-0.053876\n-0.70218\n-0.60188\n-0.21165\n0.1317\n0.35408\n0.658\n0.62913\n0.45557\n0.27112\n-0.22299\n0.034138\n-0.033957\n-0.028675\n0.1021\n0.20894\n0.225\n0.27272\nNaN\n0.2546\n-0.047072\n-0.16529\n0.23498\n0.64779\n0.64604\n0.33997\n0.41272\n0.5283\n0.35893\n0.24919\n0.24047\n0.2815\n-0.036076\n-0.11537\n-0.33274\n-0.56647\n-0.54626\n-0.73615\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.1179\n-0.095124\n9.0657e-06\n0.15786\n0.082612\n0.0824\n0.067298\n0.033772\n0.039649\n0.077104\n0.09569\n0.15542\n0.26032\n0.28032\n0.28348\n0.15584\n0.14889\n0.25322\n0.31078\n0.24372\n0.061131\n-0.13276\n-0.30836\n-0.46659\n-0.049641\n0.22296\n0.45015\n0.52202\n0.45651\n0.33479\n0.10446\n0.16757\n0.36982\n0.16917\n0.22229\n0.37423\n0.30168\nNaN\nNaN\nNaN\nNaN\n-0.072657\n-0.016823\n0.16326\n0.30217\n0.21289\n0.013971\n0.30823\n0.25513\n0.28515\nNaN\n0.1849\n0.21888\n-0.024529\n-0.088813\n-0.17129\n-0.50859\n-0.55647\n-0.78797\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.15407\n-0.087811\n0.050561\n0.16833\n0.089744\n0.048093\n0.045749\n0.067826\n0.062405\n0.065598\n0.09425\n0.15957\n0.22561\n0.24567\n0.24889\n0.20809\n0.293\n0.2129\n0.19904\n0.10531\n-0.058143\n-0.29807\n-0.36763\n-0.40105\n0.0084368\n0.2631\n0.47513\n0.34306\n0.35782\n0.24422\n0.42163\n0.38881\n0.34085\n0.37933\n0.50024\n0.54406\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.26785\n0.17203\n0.10929\n0.36093\n0.12519\n0.060666\n0.21558\nNaN\nNaN\nNaN\n-0.15154\n-0.065839\n-0.2352\n-0.4516\n-0.38632\n-0.44153\n-0.46421\n-0.18039\n-0.10842\nNaN\nNaN\n-0.15656\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.10438\n-0.019838\n0.094258\n0.142\n0.091232\n-0.039986\n0.028263\n0.064881\n0.029237\n0.057725\n0.041052\n0.12093\n0.1782\n0.23976\n0.27817\n0.20723\n0.12131\n0.044043\n0.091429\n0.01408\n-0.10657\n-0.35533\n-0.40051\n-0.24643\n0.043146\n0.25242\n0.31868\n0.3117\n0.49754\n0.37882\n0.42281\n0.4672\n0.4629\n0.43198\n0.54101\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.099497\n-0.10498\n-0.091178\n-0.099388\n-0.016828\n-0.19549\n-0.20223\nNaN\nNaN\nNaN\n-0.27481\n-0.12473\n-0.20739\n-0.27077\n-0.11723\n-0.13155\n-0.14737\n0.065544\n0.10484\n-0.10371\n-0.15573\n-0.099562\n-0.096449\n-0.17514\n-0.35083\nNaN\nNaN\nNaN\n-0.047592\n0.012258\n0.014469\n-0.019529\n0.068672\n0.047245\n0.12303\n0.040098\n-0.057051\n-0.0010198\n0.038576\n0.094823\n0.13847\n0.20242\n0.19166\n0.11179\n-0.034048\n-0.16252\n-0.064449\n-0.044675\n-0.09436\n-0.40692\n-0.29396\n-0.083467\n0.21147\n0.38541\n0.31229\n0.39026\n0.58581\n0.51899\n0.41184\n0.48969\n0.4469\n0.4177\n0.6387\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.091733\n-0.13617\n-0.32849\n-0.34175\n0.034687\n-0.59208\n-0.42727\nNaN\nNaN\nNaN\n-0.49118\n-0.31253\n-0.25868\n-0.13518\n0.051119\n0.12626\n-0.050149\n-0.046484\n0.079412\n-0.093538\n0.0089137\n0.13262\n0.19186\n0.039273\n-0.082098\nNaN\nNaN\nNaN\n-0.045333\n0.035576\n-0.017158\n-0.051006\n0.12331\n0.11819\n-0.04602\n-0.089434\n-0.090195\n0.050985\n0.084293\n0.093155\n0.034614\n0.14531\n0.09063\n0.056398\n-0.013499\n-0.057795\n-0.098359\n-0.10987\n-0.11846\n-0.45931\n-0.30352\n0.088776\n0.26256\n0.52336\n0.57536\n0.54466\n0.58945\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5495\n0.31569\n0.29157\n-0.34378\n0.0675\n-0.063216\n-0.55678\nNaN\n-0.34321\n-0.27081\n-0.18255\n-0.188\n-0.23746\n-0.13255\n0.10816\n0.18121\n0.018732\n0.13439\n0.17196\n0.12273\n0.10086\n0.13839\n0.12692\n0.15587\n0.14597\n0.15346\n0.063729\n-0.020919\n-0.033595\n-0.027915\n-0.071967\n-0.04327\n0.070928\n0.056689\n-0.089741\n-0.062176\n-0.068273\n0.027028\n0.029043\n-0.0080463\n0.08018\n0.10475\n0.0441\n0.10893\n0.036176\n-0.081584\n-0.10712\n-0.062459\n0.043932\n-0.28607\n-0.36795\n0.14159\n0.41066\n0.81149\n0.7017\n0.67179\n0.86762\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.063424\n-0.17289\n0.24949\n-0.0099758\n0.28609\n-0.19514\n-0.33817\n-0.11594\n-0.19596\n-0.13255\n0.039108\n-0.0070949\n-0.057215\n-0.26443\n-0.0318\n0.13307\n0.056084\n0.22137\n0.25132\n0.097902\n0.06474\n0.028262\n-0.15418\n-0.03005\n-0.0095249\n-0.051579\n0.12389\n0.1122\n-0.027959\n-0.050864\n-0.067194\n-0.12628\n-0.11399\n-0.094406\n-0.11685\n-0.096552\n-0.0016364\n-0.020344\n-0.054071\n-0.032447\n0.048688\n0.081209\n0.095317\n0.17865\n0.1229\n-0.12851\n-0.33657\n-0.047812\n0.0091669\n-0.34345\n-0.28907\n0.09789\n0.54776\n0.65357\n0.61147\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.17921\n-0.7259\n-0.2406\n-0.21167\n0.057124\n0.0519\n0.020564\n-0.038857\n-0.11551\n0.024008\n0.11797\n0.083802\n0.081307\n-0.049353\n-0.049324\n-0.060457\n0.040049\n0.19067\n0.30682\n0.10806\n0.15905\n0.12384\n-0.14231\n-0.13983\n-0.048762\n-0.051936\n-0.0011565\n0.23796\n-0.0616\n-0.10964\n-0.25569\n-0.19398\n-0.10248\n-0.10189\n-0.14638\n-0.048676\n0.0833\n0.025051\n-0.021993\n0.056501\n0.097461\n0.085623\n0.12346\n0.32749\n0.25591\n-0.025938\n-0.28535\n-0.41258\n-0.23966\n-0.23395\n-0.089535\n0.23604\n0.42805\n0.53259\n0.44919\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12768\n-0.362\n-0.26915\n-0.033458\n0.058565\n0.032893\n0.00035632\n-0.040314\n0.019798\n0.079874\n0.13168\n0.23934\n0.1227\n-0.027058\n-0.071728\n0.0017284\n0.041225\n0.24573\n0.026324\n0.16683\n0.204\n-0.027802\n-0.067371\n-0.0076397\n0.049933\n0.042144\n0.094649\n-0.17476\n-0.20493\n-0.21837\n-0.15057\n-0.12557\n-0.014416\n-0.072685\n0.026468\n0.074814\n0.062629\n0.00056779\n0.050278\n0.14479\n0.058572\n0.079881\n0.28494\n0.32352\n0.039741\n-0.22954\n-0.34078\n-0.15708\n-0.079437\n-0.017171\n0.28701\n0.43973\n0.66468\n0.5777\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.090383\n0.0098689\n-0.23378\n-0.1288\n0.22541\n0.062256\n0.10552\n0.069031\n0.028471\n-0.004595\n0.1711\n0.40175\n0.30779\n0.079705\n0.063279\n0.066654\n-0.03967\n-0.082091\n0.038703\n0.18421\n0.12988\n-0.012956\n-0.14871\n0.09261\n0.1669\n0.17435\n0.13349\n-0.27661\n-0.20344\n-0.13306\n-0.11589\n-0.16107\n0.01995\n0.013131\n-0.0056459\n0.047541\n0.019061\n0.017395\n0.11523\n0.14442\n0.15863\n0.19301\n0.27059\n0.45439\n0.063183\n-0.11333\n-0.12222\n0.027855\n0.00093113\n0.018892\n0.18984\n0.51436\n0.68671\n0.70392\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.21638\n-0.073128\n-0.27826\n-0.16324\n0.1758\n0.025003\n0.12086\n0.14148\n-0.0381\n-0.093773\n0.074051\n0.39449\n0.39145\n0.27359\n0.1688\n0.16208\n0.039223\n-0.0052059\n0.1693\n0.26967\n0.098431\n0.050494\n0.020537\n0.179\n0.27449\n0.35788\n0.19757\n-0.24064\n-0.15487\n-0.09702\n-0.069803\n-0.10853\n-0.024995\n0.0063025\n-0.012248\n0.015725\n0.0045628\n-0.027029\n-0.012877\n0.10641\n0.19975\n0.23922\n0.29123\n0.30672\n0.055968\n0.033869\n-0.073939\n-0.019839\n0.045025\n0.17187\n0.28354\n0.54847\n0.82237\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1144\n0.05798\n-0.12076\n-0.078997\n0.13607\n-0.43022\n-0.1185\n0.13629\n0.096587\n0.30209\n0.35579\n0.37809\n0.38224\n0.41471\n0.30254\n0.21647\n0.076628\n-0.0059143\n0.27622\n0.2367\n0.26427\n0.11948\n0.14508\n0.26927\n0.32841\n0.33099\n0.21465\n-0.2229\n-0.096763\n-0.1272\n-0.091121\n-0.11398\n-0.088775\n-0.08918\n-0.017119\n-0.0099159\n-0.062461\n-0.046642\n-0.014522\n0.048143\n0.13965\n0.23291\n0.25791\n0.16689\n0.0096442\n-0.044849\n-0.01813\n0.022184\n0.069882\n0.1221\n0.25231\n0.54414\n0.98334\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.43646\nNaN\nNaN\nNaN\nNaN\n0.15799\n0.0024068\n-0.01049\n-0.08276\n0.039335\n-0.42935\n-0.0095906\n0.19507\n0.46667\n0.39564\n0.3891\n0.31963\n-0.056843\n0.34532\n0.17801\n0.13691\n-0.12963\n0.037836\n0.34514\n0.31293\n0.38591\n0.24154\n0.28051\n0.33089\n0.33973\n0.23868\n0.052282\n-0.14409\n-0.087774\n-0.15191\n-0.12695\n-0.066453\n-0.088807\n-0.109\n-0.084005\n-0.10603\n-0.054845\n-0.021278\n-0.0049501\n-0.0083421\n0.12726\n0.16362\n0.20697\n0.16183\n0.052816\n-0.087428\n-0.015286\n0.092494\n0.10913\n0.021523\n0.28757\n0.73351\n1.0673\n0.56169\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.48911\n0.42219\n0.34512\n0.27614\n0.3776\n0.2727\n0.15267\n0.083299\n-0.025052\n-0.098208\n-0.043669\n-0.0029733\n-0.020281\n0.20097\n0.45077\n0.26162\n0.28904\n0.12885\n-0.21828\n-0.13607\n0.070158\n0.11272\n-0.061492\n0.10847\n0.29166\n0.36147\n0.32065\n0.25116\n0.19687\n0.33621\n0.25754\n0.089209\n-0.28293\n-0.14431\n-0.12732\n-0.092599\n-0.14705\n-0.15025\n-0.11959\n-0.066777\n-0.11529\n-0.17558\n-0.054272\n-0.041748\n-0.047117\n-0.070009\n0.042659\n0.11457\n0.13876\n0.052343\n-0.012927\n-0.022721\n-0.20951\n-0.16589\n0.084174\n0.13205\n0.48697\n0.92626\n1.0203\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.73037\n0.74824\n0.95165\n0.757\n0.68732\n0.48488\n0.37569\n0.40171\n0.12909\n-0.071682\n-0.15968\n-0.278\n-0.20567\n-0.20849\n-0.080103\n0.14483\n-0.01585\n-0.058107\n0.028865\n-0.044632\n-0.20544\n-0.23918\n-0.021056\n-0.022862\n0.054077\n0.18807\n0.20133\n0.14758\n0.16893\n0.17708\n0.15753\n0.10454\n0.048601\n-0.4216\n-0.19254\n-0.17208\n-0.16994\n-0.19424\n-0.22673\n-0.12645\n-0.097468\n-0.10075\n-0.13858\n-0.078675\n-0.06949\n-0.11707\n-0.10873\n-0.059182\n0.024521\n-0.030494\n-0.073527\n-0.16277\n-0.17131\n-0.18827\n-0.23311\n0.014711\n0.20477\n0.69\n0.95731\n1.1266\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.69395\nNaN\nNaN\n0.46163\n1.0126\n1.671\n1.1388\n0.882\n0.47658\n0.39366\n0.32539\n0.25738\n0.011595\n-0.039467\n-0.12959\n-0.23188\n-0.20019\n-0.20333\n-0.15768\n-0.15849\n-0.13907\n-0.14168\n-0.15795\n-0.2767\n-0.3403\n-0.03106\n0.083496\n0.074219\n0.0027063\n-0.065607\n0.010286\n0.14203\n0.12548\n-0.017177\n0.058656\n-0.034702\n-0.20685\n-0.25834\n-0.19526\n-0.20617\n-0.13979\n-0.1714\n-0.17258\n-0.18098\n-0.11646\n-0.11452\n-0.14908\n-0.096266\n-0.20133\n-0.29535\n-0.17996\n-0.11266\n-0.10775\n-0.20107\n-0.27464\n-0.25433\n-0.23182\n-0.23759\n-0.14991\n0.20766\n0.52469\n0.84231\n1.2329\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0462\n1.5641\n1.098\n0.8516\n0.39648\n0.19098\n0.22205\n0.191\n0.097431\n-0.039731\n-0.20749\n-0.21305\n-0.15536\n-0.10987\n-0.10241\n-0.30622\n-0.35772\n-0.22534\n-0.23199\n-0.38825\n-0.43041\n-0.14869\n0.022435\n0.0077357\n-0.013051\n-0.094449\n-0.030863\n-0.018939\n0.094849\n-0.093183\n-0.16874\n-0.094901\n-0.028394\n-0.20112\n-0.19965\n-0.21164\n-0.10844\n-0.1868\n-0.19386\n-0.20275\n-0.17309\n-0.15545\n-0.18462\n-0.20917\n-0.22667\n-0.30216\n-0.2119\n-0.21301\n-0.13229\n-0.21321\n-0.47732\n-0.56292\n-0.22045\n-0.13314\n-0.1959\n0.089548\n0.50415\n0.6899\n0.65335\n0.45877\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7255\n1.0483\n0.97337\n0.55329\n0.4141\n-0.12002\n0.049955\n0.082157\n-0.047038\n-0.10167\n-0.16838\n-0.16072\n-0.11831\n-0.12011\n-0.15145\n-0.25271\n-0.5038\n-0.28278\n-0.18199\n-0.20179\n-0.31953\n-0.34041\n-0.1204\n-0.025219\n0.0078915\n-0.0026348\n-0.04965\n-0.093363\n-0.18976\n-0.2094\n-0.10082\n0.01158\n0.040516\n-0.18977\n-0.24446\n-0.25285\n-0.15559\n-0.14215\n-0.12449\n-0.20906\n-0.2029\n-0.13668\n-0.14554\n-0.24195\n-0.23984\n-0.22927\n-0.20755\n-0.22899\n-0.13981\n-0.15631\n-0.28462\n-0.44949\n-0.3941\n-0.12637\n-0.20831\n0.060286\n0.50382\n0.59621\n0.11076\n0.015084\n-0.069285\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3501\n0.86604\n0.62039\n0.59796\n0.39195\n-0.093723\n0.04496\n0.043552\n-0.18079\n-0.26109\n-0.28138\n-0.12822\n-0.11077\n-0.15499\n-0.30143\n-0.24039\n-0.24391\n-0.1958\n-0.07951\n-0.07049\n-0.078918\n-0.1397\n-0.17343\n-0.051917\n-0.08791\n-0.054704\n-0.1292\n-0.14647\n-0.20501\n-0.10152\n-0.02085\n0.087407\n0.044857\n-0.21248\n-0.28422\n-0.26965\n-0.21582\n-0.10331\n-0.07416\n-0.1566\n-0.13535\n-0.13434\n-0.12111\n-0.2931\n-0.22297\n-0.16152\n-0.25754\n-0.31305\n-0.23801\n-0.13804\n-0.31452\n-0.27837\n-0.50349\n-0.48829\n-0.2866\n0.054219\n0.28417\n0.3706\n0.19703\n-0.049013\n0.084248\nNaN\nNaN\n0.39689\n0.70448\n0.63017\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.89767\n0.69052\n0.56225\n0.55339\n0.016115\n-0.27833\n-0.062244\n-0.17926\n-0.3055\n-0.40127\n-0.25913\n-0.072669\n-0.079028\n-0.15243\n-0.15959\n-0.17591\n-0.15055\n-0.12981\n-0.086026\n-0.11217\n-0.14087\n-0.13557\n-0.1719\n-0.10742\n-0.15878\n-0.21368\n-0.35145\n-0.38576\n-0.1617\n-0.062827\n-0.010034\n0.11221\n0.072007\n-0.24225\n-0.26294\n-0.23913\n-0.26865\n-0.11881\n0.0077544\n-0.071745\n-0.096511\n-0.15318\n-0.13672\n-0.26829\n-0.17871\n-0.17662\n-0.27645\n-0.32797\n-0.1843\n-0.19311\n-0.39716\n-0.47\n-0.40802\n-0.33098\n-0.19801\n-0.0067006\n0.22722\n0.022118\n-0.19068\n-0.085641\n0.26085\n0.25825\n0.49227\n0.57459\n0.59993\n0.62916\n0.7252\nNaN\nNaN\nNaN\nNaN\n0.59062\n0.68308\n0.44347\n0.38034\n0.38044\n-0.16983\n-0.46735\n-0.26343\n-0.26937\n-0.31796\n-0.33795\n-0.20982\n0.024787\n0.030553\n0.048945\n-0.055664\n-0.19169\n-0.19088\n-0.11997\n-0.099501\n-0.074234\n-0.068942\n-0.16003\n-0.20782\n-0.29795\n-0.37898\n-0.30646\n-0.2996\n-0.29846\n-0.25872\n-0.24597\n-0.10518\n0.050505\n-0.038516\n-0.19037\n-0.23256\n-0.28229\n-0.25774\n-0.19934\n-0.022719\n-0.021308\n-0.094203\n-0.14618\n-0.14683\n-0.21526\n-0.13788\n-0.15784\n-0.21672\n-0.28055\n-0.24675\n-0.42551\n-0.73975\n-0.54802\n-0.38487\n-0.29204\n-0.0098892\n0.071488\n0.17557\n0.51187\n0.15277\n0.19425\n0.26729\nNaN\n0.16846\n0.36766\n0.17341\n0.59832\n0.91728\nNaN\nNaN\nNaN\n0.38387\n0.54278\n0.56013\n0.41065\n0.15886\n0.20641\n-0.073691\n-0.44779\n-0.33232\n-0.029252\n-0.13478\n-0.25059\n-0.13657\n0.04406\n0.094287\n0.10265\n-0.088679\n-0.088655\n-0.14117\n-0.14069\n-0.10783\n-0.022067\n-0.038214\n-0.14916\n-0.24329\n-0.31186\n-0.37121\n-0.27729\n-0.12528\n-0.15621\n-0.29292\n-0.25432\n-0.12976\n-0.041392\n-0.13243\n-0.26489\n-0.22235\n-0.21354\n-0.21712\n-0.15689\n-0.045727\n-0.052227\n-0.14814\n-0.17811\n-0.19025\n-0.20692\n-0.16285\n-0.13919\n-0.14967\n-0.22033\n-0.29499\n-0.45337\n-0.7456\n-0.47701\n-0.40464\n-0.25946\n-0.15345\n0.15461\n0.086297\n0.15686\n0.18518\n0.16998\nNaN\nNaN\n0.052649\n0.20041\n0.25308\n0.47409\n0.7291\n0.71969\n0.45969\n0.18164\n0.31861\n0.37344\n0.51236\n0.3127\n-0.068731\n-0.15751\n-0.24702\n-0.32926\n-0.11189\n0.03027\n-0.019644\n-0.18564\n-0.13748\n-0.012774\n0.14756\n0.096086\n-0.02771\n-0.043509\n-0.084794\n-0.093732\n-0.057212\n0.015876\n-0.033305\n-0.09943\n-0.2513\n-0.3106\n-0.28168\n-0.16096\n-0.034766\n-0.035022\n-0.081408\n-0.082034\n-0.078241\n-0.063844\n-0.00062786\n-0.22829\n-0.20822\n-0.14405\n-0.087448\n-0.060205\n-0.04031\n-0.068829\n-0.17156\n-0.22004\n-0.21259\n-0.17245\n-0.12268\n-0.070207\n-0.0057938\n-0.053914\n-0.27059\n-0.34436\n-0.70285\n-0.18988\n-0.2585\n-0.27479\n-0.048943\n0.14285\n0.028059\n-0.039607\n0.034618\n0.061793\nNaN\nNaN\nNaN\n0.037309\n0.1953\n0.27403\n0.4115\n0.34407\n0.46134\n0.38531\n0.18734\n0.24141\n0.44824\n0.14012\n-0.054744\n-0.061403\n0.0003268\n-0.059252\n-0.12229\n-0.13188\n-0.1581\n-0.13721\n-0.030522\n0.058246\n0.13405\n0.051619\n-0.1136\n-0.091626\n-0.048102\n-0.011869\n0.068145\n0.067458\n0.043999\n-0.072934\n-0.208\n-0.25771\n-0.18693\n-0.076807\n0.011636\n0.040539\n0.029831\n-0.087632\n-0.15648\n-0.055661\n0.079793\n-0.18763\n-0.17454\n-0.091622\n-0.011629\n0.0387\n0.01292\n-0.086056\n-0.079736\n-0.13015\n-0.13892\n-0.07571\n-0.011914\n0.029921\n0.15084\n0.24502\n-0.097612\nNaN\nNaN\nNaN\n-0.26101\n-0.39075\n-0.10343\n0.043386\n-0.28562\n-0.20223\n-0.075459\n0.047604\nNaN\nNaN\nNaN\n0.027951\n0.16104\n0.083221\n0.1593\n0.24332\n0.3177\n0.22513\n0.14199\n0.1593\n0.054047\n-0.095691\n-0.093195\n0.034699\n0.068161\n0.0016459\n-0.070411\n-0.071898\n-0.035913\n0.021484\n0.01884\n0.066831\n0.050267\n-0.032919\n-0.16399\n-0.08834\n0.037188\n0.055391\n0.082833\n0.093977\n0.074409\n-0.06258\n-0.13184\n-0.15494\n-0.16056\n-0.060272\n-0.0093778\n0.078546\n0.093729\n-0.059127\n-0.15029\n-0.080758\n0.046122\n-0.069854\n-0.023444\n0.0035732\n-0.013319\n0.014615\n0.048528\n-0.080551\n-0.0068928\n-0.027779\n-0.044436\n0.022001\n0.073276\n0.18487\n0.40036\n0.34666\n0.0027999\nNaN\nNaN\nNaN\n-0.31706\n-0.36991\n-0.50955\n-0.35087\n-0.30645\n-0.15038\n-0.10931\n-0.067514\nNaN\nNaN\nNaN\n-0.073301\n-0.0038768\n0.019083\n0.13993\n0.40622\n0.29577\n0.16703\n0.081665\n0.029453\n-0.064002\n-0.11627\n-0.12781\n0.004787\n0.091882\n0.075911\n-0.012642\n-0.025981\n-0.077084\n-0.047336\n0.031328\n0.061229\n0.027475\n-0.017759\n-0.015246\n0.05838\n0.063572\n-0.035502\n0.071117\n0.090953\n0.090057\n-0.028527\n-0.073121\n-0.071877\n-0.063606\n0.014781\n0.040214\n0.14569\n0.14441\n0.0009916\n-0.055459\n0.028683\n0.065337\n0.01275\n0.073566\n0.063168\n-0.0037229\n0.076667\n0.070007\n0.071755\n0.027433\n0.0537\n0.017478\n0.091696\n0.17327\n0.22538\n0.24735\n0.17274\n0.14755\n-0.083064\nNaN\nNaN\n-0.40253\n-0.34553\n-0.36675\n-0.34899\n-0.25892\n-0.074745\n-0.074528\n-0.10422\n-0.047219\n-0.11432\n-0.10169\n-0.27566\n-0.16421\n-0.040429\n0.09948\n0.3547\n0.29647\n0.2598\n0.16484\n0.093892\n0.1002\n-0.027807\n-0.024911\n0.034817\n0.10841\n0.13938\n0.018892\n-0.164\n-0.32092\n-0.25852\n0.017177\n0.058234\n0.044652\n0.17379\n0.061511\n0.059762\n0.074875\n-0.048101\n0.039161\n0.065347\n0.098751\n0.022674\n-0.096923\n-0.034241\n0.045794\n0.096055\n0.088591\n0.098072\n0.17563\n0.0018701\n0.023228\n0.16593\n0.11243\n-0.043398\n0.020575\n0.055914\n0.06555\n0.12251\n0.1413\n0.14429\n0.07597\n0.058001\n0.057593\n0.063786\n0.13136\n0.18233\n0.22692\n0.18532\n0.15593\n-0.078334\n-0.40307\n-0.53143\n-0.41967\n-0.34902\n-0.26522\n-0.22859\n-0.1793\n-0.060404\n-0.092829\n-0.096275\n-0.076459\n-0.048045\n-0.064765\n-0.13486\n-0.15617\n-0.10944\n0.0035749\n0.21371\n0.2607\n0.19997\n0.28455\n0.17989\n0.099627\n0.053026\n0.14835\n0.13376\n0.1503\n0.16\n0.14765\n-0.16111\n-0.41989\n-0.5464\n-0.14572\n-0.018759\n0.24819\n0.23777\n0.12739\n0.051914\n0.14479\n0.10245\n0.068799\n0.037582\n0.031362\n0.00059741\n-0.06047\n0.079181\n0.10831\n0.099242\n0.074774\n0.19408\n0.21851\n0.028664\n0.066708\n0.078064\n0.050064\n-0.079658\n0.053393\n0.14333\n0.15645\n0.22989\n0.27592\n0.23427\n0.24386\n0.12037\n0.048516\n0.037\n0.0861\n0.10373\nNaN\nNaN\nNaN\nNaN\n-0.19997\n-0.39504\n-0.39217\n-0.21462\n-0.2443\n-0.31957\n-0.21375\n-0.064294\n-0.091081\n-0.053542\n0.014051\n-0.040172\n-0.090425\n-0.10802\n-0.086913\n-0.1579\n-0.11159\n-0.023632\n0.066499\n0.14036\n0.30067\n0.21542\n0.21047\n0.18065\n0.237\n0.20929\n0.020325\n0.01921\n0.18641\n0.065971\n-0.32796\n-0.30421\n-0.30804\n-0.14564\n-0.0018835\n0.084383\n0.011084\n0.042631\n0.18977\n0.02327\n-0.021493\n0.026766\n-0.021487\n2.6007e-05\n0.065139\n0.14829\n0.070496\n0.012644\n0.096603\n0.27136\n0.23328\n0.1271\n0.047251\n0.010143\n0.064499\n0.03071\n0.10421\n0.18425\n0.23379\n0.27448\n0.31537\n0.33132\n0.40561\n0.32404\n0.22696\n0.11925\n0.11069\n0.16917\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.33241\n-0.31439\n-0.27995\n-0.25762\n-0.36429\n-0.27539\n-0.12734\n-0.082752\n-0.085791\n-0.046743\n-0.1162\n-0.13373\n-0.14763\n-0.12588\n-0.038622\n-0.093899\n-0.085646\n0.045175\n0.17595\n0.4117\n0.37243\n0.31683\n0.29194\n0.30652\n0.22313\n-0.18351\n-0.20743\n-0.069555\n0.22265\n-0.20645\n-0.16204\n-0.15392\n-0.12745\n-0.14266\n-0.14333\n-0.12289\n-0.03312\n0.024249\n-0.079151\n-0.047759\n0.040104\n0.050347\n0.039929\n0.10885\n0.1355\n0.091382\n-0.0023521\n0.15025\n0.35593\n0.25233\n0.19237\n0.010247\n-0.0017784\n-0.0023461\n0.13911\n0.096867\n0.21222\n0.33386\n0.37562\n0.54714\n0.53034\n0.57216\n0.47507\n0.33532\n0.1838\n0.098488\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.3984\n-0.32961\n-0.36729\n-0.35579\n-0.35507\n-0.30858\n-0.32707\n-0.1908\n-0.15205\n-0.10308\n-0.11196\n-0.14363\n-0.09836\n-0.038324\n-0.009374\n-0.019957\n0.02244\n0.078286\n0.31694\n0.43832\n0.311\n0.3344\n0.3646\n0.29293\n-0.18998\n-0.42166\n-0.32433\n0.11034\n-0.11297\n-0.095238\n-0.11934\n-0.1326\n-0.092124\n-0.16385\n-0.14985\n-0.058291\n0.027909\n-0.14458\n-0.1283\n0.040822\n-0.052665\n-0.033568\n-0.0051281\n0.091877\n0.086547\n0.096347\n0.16287\n0.28418\n0.21704\n0.2044\n-0.11956\n-0.15818\nNaN\n0.25805\n0.12647\n0.34535\n0.4854\n0.56292\n0.72762\n0.83032\n0.8106\n0.50694\n0.38736\n0.26139\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.4155\n-0.27934\n-0.3157\n-0.27823\n-0.33383\n-0.31281\n-0.26795\n-0.22963\n-0.12494\n-0.07639\n-0.10977\n-0.09543\n-0.071398\n0.0073444\n0.018106\n-0.0046406\n-0.099188\n-0.087384\n0.095093\n0.24656\n0.20383\n0.28916\n0.22893\n0.012659\n-0.20383\n-0.4367\n-0.43574\n-0.11593\n-0.13509\n-0.084282\n-0.16785\n-0.081902\n0.048057\n0.080218\n0.048225\n0.023656\n0.018497\n-0.15068\n-0.14058\n0.040025\n-0.17962\n-0.079649\n-0.20144\n-0.11196\n0.14875\n-0.05434\n0.087613\n0.1511\n0.15118\n0.24233\nNaN\nNaN\nNaN\n"
  },
  {
    "path": "tests/test_data/small_test/time_series/ts_incr_interp0_method2.csv",
    "content": "3.4447\n3.7754\n4.5259\n3.7308\n2.7042\n3.9566\n5.4718\n3.5406\n3.5083\n4.4488\n4.1143\n3.6699\n4.1961\n5.1891\n8.0604\n5.5408\n4.1915\n2.3362\n5.3474\n6.3871\n5.8625\n3.3138\n3.2961\n1.7841\n4.4894\n1.4115\n1.094\n0.057719\n1.7755\n5.9951\n-4.3606\n-2.4783\n-1.8241\n-4.6873\n-0.73396\n-1.9195\n-1.8821\n0.21731\n0.59691\n0.49463\n2.1917\n0.84194\n0.26784\n2.922\n2.5602\n1.7952\n1.4826\n1.8735\n1.2264\n1.4874\n2.5158\n2.081\n1.3209\n-0.2233\n3.2749\n0.84863\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.3049\n3.7222\n4.0013\n5.4634\n3.9608\n2.9122\n7.1971\n3.6844\n5.3516\n4.7755\n4.1341\n4.3089\n4.5144\n7.5667\n8.7034\n8.6402\n2.921\n2.7762\n4.5446\n3.7016\n5.1788\n2.1425\n2.1005\n4.1194\n6.8345\n2.2846\n0.39345\n1.4485\n3.6354\n4.1664\n1.3485\n0.67651\n-0.33291\n-3.6576\n-2.4686\n-1.1935\n-0.54976\n1.2175\n0.48063\n0.36203\n3.0009\n1.0579\n1.4388\n0.67859\n2.1639\n2.3391\n2.399\n1.8168\n1.2011\n1.3471\n1.4158\n2.0337\n1.9219\n2.3315\n5.7934\n3.3138\n2.9024\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7237\n3.2545\n3.4623\n3.4508\n-1.0979\n-0.20597\n2.4791\n4.3697\n5.2934\n6.0395\n3.7042\n2.9963\n3.0904\n9.1397\n11.566\n9.2468\n3.7206\n2.8448\n3.762\n5.2694\n3.806\n4.0064\n3.9396\n3.3281\n7.0783\n3.5192\n1.4615\n2.4443\n2.4895\n1.0669\n3.387\n3.1756\n-1.2944\n-3.7782\n-1.8302\n-1.5315\n-0.28472\n0.88454\n1.3266\n1.3836\n2.8074\n2.1334\n2.0676\n1.7875\n2.1975\n1.3383\n0.50115\n1.9719\n2.237\n1.8598\n1.2659\n2.3063\n3.3365\n1.7753\n3.4452\n5.5092\n4.1186\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.4885\n3.0545\n3.491\n3.1862\n3.1756\n3.4888\n4.2149\n4.2033\n3.2499\n3.0037\n4.593\n2.3351\n0.43783\n10.626\n8.6775\n4.0651\n2.217\n2.218\n-0.14958\n3.6604\n4.0139\n4.9732\n3.3996\n0.35102\n3.4015\n3.6981\n3.9093\n1.8001\n3.4506\n6.4047\n2.0397\n2.538\n-2.0453\n-2.0666\n-1.824\n-3.7576\n-1.7511\n0.97667\n1.4858\n1.8043\n2.7119\n2.2081\n0.32157\n2.324\n2.7605\n0.55251\n0.47323\n1.7806\n1.4547\n1.0759\n1.6939\n1.8471\n1.7934\n1.4238\n2.7767\n4.2274\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7248\n4.3277\n3.9153\n3.1539\n3.011\n3.3933\n2.5436\n2.2907\n2.5085\n2.392\n4.1443\n3.6146\n6.4072\n4.8\n3.9941\n2.3169\n0.53289\n1.9236\n-2.9933\n0.6627\n2.3913\nNaN\nNaN\n-2.2032\n1.5186\n3.5413\n5.9544\n4.4038\n5.1884\n6.1829\n0.92617\n-0.50285\n-1.9581\n0.030381\n-2.0781\n-2.3172\n0.18572\n1.6427\n1.8195\n1.9693\n2.4395\n1.5268\n0.085884\n-0.70966\n1.6061\n1.1709\n0.59032\n1.4478\n1.1265\n0.42448\n1.3111\n2.2042\n2.6364\n2.3482\n3.1286\n2.8692\n6.5639\nNaN\nNaN\n-3.4747\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0936\n3.3099\n3.747\n2.4967\n1.8427\n2.4125\n1.8302\n1.237\n2.4332\n3.0057\n3.7866\n3.9125\n3.5384\n1.0376\n2.0736\n1.9729\n0.81955\n3.737\n3.3707\nNaN\nNaN\nNaN\nNaN\n1.3953\n2.638\n5.2369\n5.2979\n4.537\n4.8183\n5.71\n2.8663\n-1.3626\n0.51069\n1.172\n-2.0463\n-4.4417\n-1.1816\n0.041096\n0.32857\n1.8548\n3.0927\n1.8724\n0.12131\n-0.019049\n-0.06835\n-0.17122\n0.21324\n2.8289\n1.7388\n1.1108\n2.0497\n2.8721\n3.5856\n3.2244\n2.8097\n-0.53265\n0.16291\nNaN\nNaN\n-7.001\n1.8362\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7039\n1.9061\n1.98\n2.0774\n2.4575\n1.9334\n2.9012\n2.675\n3.0051\n4.5025\n4.4332\n3.4356\n3.896\n1.507\n3.0063\n1.4297\n1.3021\n5.4887\n0.3475\nNaN\nNaN\nNaN\nNaN\n2.9118\n2.6649\n4.0899\n4.3487\n4.7083\n3.7733\n5.3772\n2.4874\n-1.558\n-0.064195\n0.40434\n-2.263\n-4.2522\n-2.8541\n-1.536\n-2.0175\n-1.1212\n3.1713\n1.1946\n-0.78795\n-0.45286\n-1.3734\n-0.86082\n0.15112\n1.8747\n1.774\n1.8218\n1.3341\n2.1729\n2.7028\n3.5256\n3.3592\n1.2055\n0.24752\nNaN\nNaN\n-3.9312\n-2.8875\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.7539\n2.1098\n1.8923\n2.9199\n4.0685\n3.2075\n4.081\n3.6828\n4.2519\n5.7855\n5.0407\n3.6745\n4.858\n5.1554\n3.322\n2.125\n1.5838\n3.1909\n0.4931\n1.0007\n1.9515\n3.0272\n1.9294\n1.7789\n2.3639\n2.8267\n3.3251\n4.4491\n3.6602\n4.2935\n3.1145\n2.2039\n2.1071\n-0.28491\n-1.7892\n-1.89\n-1.8282\n-2.7478\n-3.2818\n-2.5358\n0.64328\n0.037245\n-0.32928\n-1.0879\n-2.9508\n-0.60163\n0.58134\n1.1986\n1.3443\n1.9146\n0.83492\n1.3796\n3.6718\n5.1187\n5.3325\n2.1455\n1.0242\n-0.25804\n2.539\n0.27012\n-3.5226\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6295\n2.7966\n4.7337\n2.8004\n4.3907\n4.2162\n4.437\n4.8768\n4.1816\n4.7298\n4.1163\n4.2804\n4.3162\n3.7213\n1.8701\n1.4522\n2.6956\n3.3831\n1.4208\n2.0982\n1.8176\n0.16451\n-0.9278\n0.82591\n1.1967\n2.8926\n4.0318\n4.5806\n3.3802\n3.0363\n3.3634\n1.2697\n1.9614\n-0.6004\n-1.5895\n-1.4223\n-2.2091\n-3.2884\n-3.5326\n-2.0123\n-1.0123\n-0.53923\n-0.61154\n-2.548\n-4.6975\n0.35011\n1.7725\n2.2014\n1.2937\n2.1665\n2.236\n3.4004\n4.8291\n6.5207\n5.5373\n2.2795\n-0.10515\n-0.19808\n1.6174\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0113\n2.4259\n3.8161\n2.527\n3.3414\n3.9127\n4.7056\n3.5035\n2.9214\n3.2135\n3.7853\n3.5257\n3.3616\n3.4106\n2.4468\n2.2015\n2.4685\n2.5123\n1.8387\n2.0783\n2.0945\n1.0991\n0.94175\n0.13381\n0.17478\n3.5663\n4.558\n2.7662\n2.334\n2.3457\n4.6566\n1.1001\n-1.2709\nNaN\nNaN\n-0.55132\n-0.47558\n-1.9294\n-3.7578\n-2.5405\n-1.1771\n-1.1644\n-0.61039\n-0.22643\n0.85039\n1.716\n1.6929\n2.2175\n1.8862\n2.3917\n2.0813\n4.1584\n5.7367\n6.8618\n5.6987\n2.3468\n-1.9685\n-3.0707\n1.0965\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.11526\n1.2589\n2.324\n1.8959\n2.8045\n4.7262\n4.71\n3.6301\n2.9457\n3.452\n4.5382\n4.123\n5.6797\n4.2336\n3.6389\n3.9913\n1.5872\n1.3444\n1.8643\n3.18\n1.6861\n1.7021\n2.6395\n-1.0998\n-1.603\n1.7944\n4.3955\n4.3059\n2.2581\n2.6928\n2.8965\n1.1641\nNaN\nNaN\nNaN\n-1.1595\n-0.85739\n-1.9968\n-4.62\n-3.4538\n-2.0685\n-3.4326\n-1.2123\n-0.85875\n0.16383\n0.99505\n0.67638\n1.5738\n2.6709\n-0.52697\n0.38077\n3.5509\n5.4899\n7.7306\nNaN\nNaN\n-2.5838\n-1.598\n2.0854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6006\n2.0256\n2.0728\n2.7036\n3.2058\n4.6215\n4.8319\n3.9931\n3.3851\n2.7327\n3.3903\n2.9799\n5.2237\n5.5147\n4.1643\n4.4434\n2.0179\n2.1273\n3.5042\n2.6528\n0.27767\n1.6678\n1.377\n-1.8501\n-1.988\n0.4306\n3.5482\n5.3835\n5.3644\n4.5712\n2.0533\nNaN\nNaN\nNaN\nNaN\n-1.9877\n-0.84055\n-1.8675\n-7.1024\n-4.3563\n-1.4669\n-0.17121\n-1.7846\n-2.0752\n-1.7372\n-0.33763\n-0.0098281\n1.4535\n3.2763\n1.5255\n1.1478\n4.3063\n4.9676\nNaN\nNaN\nNaN\n-0.17468\n-2.5423\n4.1354\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5502\n2.0719\n1.5267\n1.7416\n3.2317\n4.4428\n3.7178\n4.801\n3.0116\n2.5498\n3.9555\n3.9486\n5.9615\n5.6091\n4.2245\n5.8699\n5.5708\n3.8763\n4.5677\n2.0926\n0.88418\n0.46231\n-2.9084\n-2.9361\n-1.4943\n0.30288\n5.3214\n4.6461\n3.8006\n4.3586\n2.8079\nNaN\nNaN\nNaN\nNaN\n-4.4757\n-2.9373\n-1.2311\n-5.4175\n-4.7584\n-3.0856\n-3.2566\n-4.4646\n-2.3178\n-0.64442\n0.76535\n0.37301\n1.5129\n3.027\n2.9262\n2.4019\n4.5151\n4.1367\nNaN\nNaN\nNaN\n-0.64136\n0.44899\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4559\n1.761\n1.5402\n1.5465\n3.4059\n3.6135\n3.2371\n2.6734\n2.5068\n2.8623\n3.1345\n3.3302\n3.4716\n3.1859\n3.7097\n5.6518\n6.499\n3.8449\n3.0708\n2.2558\n1.4381\n-0.92438\n-5.3572\n-4.0096\n-1.3383\n2.3303\n5.8495\n4.1192\n3.665\n3.4855\n2.6946\nNaN\nNaN\nNaN\nNaN\n-6.0169\n-7.1301\n0.02547\n-4.6547\n-3.5104\n-3.5954\n-4.5349\n-4.6263\n-0.97462\n1.9049\n2.8096\n1.1976\n1.8895\n3.2539\n3.5908\n2.1683\n2.8853\n3.5362\nNaN\nNaN\nNaN\nNaN\n-0.73926\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3194\n1.788\n2.0626\n1.7405\n2.2444\n3.1026\n3.0862\n2.3805\n2.7307\n1.9307\n1.9692\n2.0531\n2.6303\n2.4603\n3.2878\n4.8912\n5.3622\n4.0348\n2.3923\n0.95641\n2.4446\n-2.4607\n-6.9051\n-4.1332\n-1.6279\n3.1657\n5.1293\n4.4249\n4.6896\n4.572\n4.8863\nNaN\n-5.9744\n-7.0017\n-4.0168\n-4.3877\n-5.6202\n-1.3068\n-4.5481\n-3.7026\n-3.6918\n-3.995\n-5.4346\n-1.3322\nNaN\nNaN\nNaN\n1.5186\n1.8832\n2.3152\n1.6803\n2.0453\n3.9992\n3.3957\nNaN\nNaN\nNaN\n1.8699\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2991\n1.0279\n1.7366\n2.0063\n1.9638\n2.4832\n2.7608\n2.4842\n1.9053\n1.4594\n1.7399\n2.307\n3.0835\n3.6027\n3.0845\n2.9916\n3.5251\n3.4895\n3.3163\n0.99705\n2.1164\n-0.30297\n-8.7103\n-6.0305\n-1.5627\n2.2909\n4.4572\n6.1803\n5.9982\n3.5127\n4.2086\n-3.4608\n-3.9204\n-6.6118\n-4.4656\n0.85603\n-0.30083\n-2.4228\n-4.2809\nNaN\nNaN\nNaN\n-3.9813\nNaN\nNaN\nNaN\nNaN\n0.43643\n0.74074\n1.1706\n2.3444\n1.1818\n4.7685\n1.376\n1.2565\n1.0214\n0.60024\n2.0773\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1557\n1.1691\n2.4087\n2.3702\n2.7997\n2.7399\n3.3587\n2.3784\n0.63009\n2.8839\n2.6965\n2.5612\n3.1777\n3.0087\n1.3957\n2.1755\n4.4184\n3.3585\n2.8875\n2.2599\n1.1911\n-1.6036\n-3.2727\n-5.9353\n-0.17803\n1.2642\n5.1042\n4.6487\n4.4011\n0.080653\n2.178\n0.50027\n0.13644\n-6.199\n-1.603\n-1.3982\n-1.2317\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.0235\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9534\nNaN\n4.9139\n4.0988\n2.4767\n1.9081\n2.5999\n-0.026027\n0.080601\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9132\n2.7557\n3.941\n3.5435\n3.3308\n3.2297\n3.5941\n3.8565\n0.62508\n2.9578\n2.8328\n3.0693\n2.5006\n3.1534\n2.9893\n3.6135\n3.1823\n1.9895\n1.4393\n1.0695\n0.14316\n-2.9081\n-2.2212\n-2.637\n0.31053\n1.9493\n4.6955\n0.8324\n0.64783\n-1.3631\n1.2316\n1.2637\n-2.7611\n0.13033\n1.137\n-0.73614\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0421\n2.4858\n1.147\n0.27617\n0.48453\n0.18055\n-0.45478\n2.2445\n3.2816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0448\n3.9375\n4.1646\n4.1374\n4.7777\n3.74\n3.2378\n3.9025\n3.0734\n3.0346\n2.3781\n4.0815\n4.3687\n3.9457\n4.0691\n2.6404\n0.43827\n-0.72006\n-0.24709\n-0.8186\n-0.74256\n-2.799\n-2.8383\n0.46082\n1.1149\n2.3682\n2.6033\n1.106\n3.5157\n-0.015494\n1.105\n-2.0739\n-4.3396\n-1.1971\n0.54004\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1778\n1.4643\n0.98721\n1.4464\n2.2957\n1.682\n1.7918\n4.9383\n4.7142\n4.9111\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.2353\n3.3436\n3.2886\n3.5795\n5.88\n3.9647\n3.6286\n3.5443\n2.8668\n3.7472\n3.2915\n4.9076\n5.0104\n3.3612\n2.2167\n1.2798\n0.58689\n-0.7278\n-2.2819\n-3.0979\n-0.25388\n-3.3108\n-1.1797\n1.9525\n1.9031\n2.6858\n2.5815\n3.4798\n5.5007\n2\n-1.0663\n-3.8382\n-3.9155\n-3.1583\n-1.1061\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.97973\n1.281\n1.9577\n1.8954\n2.1102\n3.1333\n2.6952\n3.8551\n6.0596\n4.9459\n6.3689\n6.1153\n5.7504\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7124\n2.9128\n3.1619\n4.011\n4.9366\n3.1754\n3.3022\n3.2335\n3.4265\n4.134\n4.2456\n5.4534\n3.3175\n1.4971\n-0.39444\n-0.041694\n1.8955\n1.4006\n-2.3726\n-0.036699\n1.3228\n-2.8013\n-0.781\n3.7162\n2.9295\n3.7302\n3.868\n2.9431\n5.0189\n6.7053\n3.2316\n6.0588\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7984\n0.47677\n0.98537\n0.98057\n1.9641\n0.93493\n2.1558\n4.0529\n3.8268\n5.0368\n6.7355\n4.0544\n4.8793\n5.9622\n3.4516\n4.8226\n3.6075\n2.489\n0.99336\n-1.3944\n2.6882\n2.4843\n2.9876\n3.4421\n3.5286\n2.2291\n2.4141\n3.7576\n3.9081\n3.0734\n2.6007\n3.7545\n3.7353\n1.2491\n-1.2478\n-0.6668\n-0.80899\n-1.3393\n-2.5052\n0.46687\n2.0455\n0.3191\n-1.2375\n2.5517\n3.6442\n5.9994\n4.3705\n1.6166\n3.2071\n5.7433\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2289\n0.21455\n1.3692\n2.7015\n1.7956\n2.1502\n0.27022\n3.4068\n5.2915\n4.3741\n6.5971\n8.0558\n4.8508\n4.564\n2.3367\nNaN\nNaN\nNaN\nNaN\n5.3233\n3.8795\n2.6327\n1.6938\n2.075\n2.1843\n1.3418\n1.4916\n2.6369\n3.0134\n3.4981\n2.0597\n1.2313\n2.5012\n2.6111\n0.76585\n0.043653\n0.38357\n1.258\n-1.3558\n-1.5894\n-0.038622\n1.1859\n2.9837\n0.50233\n1.4646\n3.5712\n3.633\n1.9986\n-0.12001\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.3818\n0.47819\n0.11413\n0.86104\n2.7767\n2.6973\n1.9714\n3.0604\n2.5711\n3.4255\n4.3831\n3.553\n5.4276\n7.6351\n6.0019\n6.3372\n2.2426\nNaN\nNaN\nNaN\nNaN\n6.773\n10.473\n1.6353\n0.54292\n0.39\n0.81197\n1.0604\n2.0638\n3.3174\n2.6921\n3.0991\n1.9919\n1.0786\n1.4616\n1.15\n-0.20402\n0.40785\n1.0474\n1.8799\n-0.14487\n-1.5512\n-1.2293\n-0.84414\n-0.50538\n-0.73037\n1.6442\n3.3795\n0.90231\n-1.4934\n-2.4513\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21437\n-0.58084\n-0.6954\n0.030089\n1.4586\n3.0008\n3.1667\n2.9209\n4.2382\n3.4532\n3.3256\n4.7012\n3.8024\n2.1787\n5.1074\n6.2879\n7.0424\n6.8628\nNaN\nNaN\n10.356\n6.8335\n7.0681\n9.2442\n1.4726\n-0.91587\n0.041275\n0.67424\n1.2418\n2.2264\n2.819\n2.7952\n2.1073\n1.651\n1.4359\n1.4158\n0.76028\n-0.051767\n1.4385\n1.976\n2.0711\n-1.4912\n-2.5203\n-1.5446\n-0.78\n-0.57189\n-1.842\n0.17228\n0.51999\n0.29769\n-4.4954\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.34589\n-0.07884\n0.58829\n0.34711\n1.5907\n2.1709\n2.6416\n1.5823\n3.6921\n6.2273\n5.0075\n4.054\n4.337\n3.6038\n2.4226\n3.5091\n6.2155\n6.7675\n5.2271\n4.3174\n2.7835\n7.015\n6.6264\n6.9321\n6.4515\n-0.41273\n-0.56903\n0.45456\n1.2972\n1.7549\n2.8258\n3.9474\n1.9026\n1.8157\n1.4739\n1.6485\n0.9457\n1.3675\n1.5447\n0.87276\n2.6233\n3.4952\n-0.85952\n-1.8235\n-1.326\n-0.1509\n-0.48529\n-1.0212\n-0.14593\n1.6239\n2.6586\n-5.7079\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.63465\n-0.25562\n0.51795\n-0.59477\n0.99154\n2.4999\n1.5227\n0.13964\n2.1909\n5.9696\n6.1394\n5.3274\n4.5518\n4.3931\n3.817\n4.4486\n5.6124\n6.1684\n4.947\n3.1905\n2.9595\n7.3002\n8.5744\n8.8838\n6.4774\n-0.17149\n0.28184\n0.67246\n2.0752\n2.708\n3.441\n3.6218\n2.3379\n1.4131\n2.2133\n0.75283\n-0.62334\n1.2669\n2.2234\n1.8011\n3.2533\n1.5788\n-0.27811\n-0.35487\n-1.1227\n-1.4297\n-0.97782\n-0.65494\n0.41496\n2.2933\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.83149\n-1.4441\n-1.7489\n0.88998\n-5.5956\n-2.5226\n2.7321\n2.2242\n4.5018\n5.0074\n4.5771\n5.242\n5.8795\n5.0958\n5.6382\n3.7547\n5.413\n6.3041\n5.4127\n6.2952\n2.5743\n4.0457\n7.527\n8.4476\n10.267\n7.4613\n-0.061369\n1.3164\n-0.019071\n2.2476\n2.3604\n2.72\n2.2723\n1.0225\n1.0402\n1.2217\n0.28463\n-0.38858\n0.34926\n2.4802\n2.4347\n2.1908\n-0.48804\n-1.3572\n-1.0235\n-0.99894\n-2.3099\n-1.3771\n-1.1663\n-0.20184\n2.009\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.4456\nNaN\nNaN\nNaN\nNaN\n0.75234\n0.7439\n0.25708\n1.0077\n3.5728\n-3.5661\n1.1811\n4.3806\n5.2336\n5.5323\n6.5381\n5.7436\n2.3685\n5.0936\n4.7878\n5.4296\n2.5881\n4.5952\n6.695\n5.4128\n6.3534\n5.0774\n7.0141\n8.1513\n8.215\n7.5834\n5.072\n1.1884\n2.228\n1.3775\n1.1581\n1.0957\n2.1721\n2.1792\n1.1739\n0.55821\n0.34154\n0.87028\n0.99991\n0.91815\n2.7376\n0.71473\n0.63965\n0.58641\n-1.2314\n-3.4235\n-2.3302\n-3.8604\n-3.7204\n-2.2922\n0.46375\n3.7463\n6.1084\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.115\n1.7842\n-1.1639\n-0.1845\n1.4615\n2.0392\n1.8704\n3.5217\n4.3516\n5.2256\n5.9513\n6.4254\n6.8922\n4.8329\n1.412\n1.407\n4.7703\n6.0945\n2.5289\n4.6938\n7.1587\n7.3262\n7.3555\n6.3195\n8.4105\n10.457\n7.9684\n5.8791\n0.78878\n1.2769\n1.866\n1.5852\n0.51626\n1.054\n1.9252\n0.80338\n0.45045\n-0.39351\n1.4504\n1.4735\n0.35934\n0.58202\n1.8249\n0.71454\n1.7024\n2.1324\n0.1416\n-0.60893\n-4.0962\n-4.2657\n-2.8332\n-0.01916\n2.9295\n5.3047\n7.2434\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.40661\nNaN\nNaN\n15.394\n3.5236\n0.71816\n-0.47516\n0.75517\n0.31584\n0.14822\n-0.58414\n0.41766\n1.0173\n2.3883\n4.777\n3.5581\n2.7864\n3.9774\n2.7949\n0.26347\n-0.57692\n2.0613\n2.2836\n5.7784\n6.8687\n6.162\n6.1413\n6.5578\n8.7707\n11.232\n7.7755\n5.7058\n0.11638\n0.91345\n1.232\n1.0328\n1.3367\n2.1335\n1.8955\n1.217\n0.77919\n-0.88736\n1.3914\n1.3125\n0.23839\n0.76935\n0.87758\n0.55799\n-0.64866\n-0.84762\n-0.82441\n-0.58854\n-1.1831\n-1.7299\n0.8351\n2.2342\n4.8908\n5.6755\n9.6361\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.874\n14.256\n13.384\n9.8997\n2.7\n-0.30349\n-2.1083\n1.5314\n0.62615\n-0.11335\n-0.83939\n-1.4997\n0.91669\n0.96466\n1.6475\n0.33427\n1.4927\n1.3552\n1.6391\n1.4509\n-1.0372\n2.2621\n4.0669\n5.747\n4.8617\n3.5886\n3.8914\n6.1391\n7.1508\n7.6737\n5.0245\n4.6667\n3.168\n0.22906\n1.9716\n1.7071\n2.1456\n1.3442\n0.73839\n0.63611\n1.5172\n0.51715\n1.3745\n0.75131\n-0.0094748\n-0.52463\n-0.14473\n0.030366\n-1.3508\n-2.9926\n-1.3995\n0.86261\n-0.73895\n-2.6618\n-0.99028\n2.6222\n4.0868\n4.8623\n9.3436\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.051\n7.0668\n8.8833\n6.0796\n1.3953\n-4.3873\n-2.2069\n-1.2947\n-0.20239\n-0.74861\n-1.8246\n-1.5764\n-0.52655\n0.61273\n0.70548\n0.13351\n-0.70837\n0.033331\n0.32646\n1.2587\n1.9258\n1.5354\n3.4738\n4.0074\n4.2129\n4.0285\n3.1343\n3.318\n5.0541\n4.4204\n3.6226\n4.0325\n4.7248\n2.2728\n1.64\n0.63833\n2.5354\n1.1923\n0.40698\n0.42568\n1.3504\n0.47761\n0.35875\n-0.040391\n-0.26299\n-0.1321\n-0.40633\n-1.089\n-0.62654\n-1.9686\n-5.6955\n-5.1117\n-0.43162\n0.92558\n0.40737\n2.5205\n3.7407\n5.3021\n7.2482\n3.0912\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.941\n8.9454\n5.0657\n5.2806\n0.97455\n-6.3912\n-3.5695\n-2.1053\n-0.96497\n-0.011941\n-0.78286\n-1.2766\n-1.3074\n-0.74906\n-0.1527\n0.61284\n-1.2062\n-0.17692\n0.50697\n2.1857\n3.5499\n1.9358\n2.3249\n2.5595\n4.2048\n5.0448\n3.6695\n3.2439\n1.2266\n2.6354\n3.7263\n5.3692\n5.531\n2.5933\n1.1096\n0.15357\n0.89806\n0.24969\n1.2745\n0.28279\n0.52878\n0.28709\n0.4225\n-0.47908\n-0.20649\n0.29959\n-0.4145\n-1.9969\n-0.74138\n-2.0004\n-5.2999\n-6.0382\n-3.515\n1.055\n0.3959\n3.0227\n3.5603\n6.9788\n1.9076\n-1.4632\n-1.064\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.233\n5.1677\n0.97383\n1.0891\n-1.4267\n-6.812\n-4.0174\n-2.6284\n-2.6093\n-1.8625\n-3.0483\n-3.2078\n-2.5456\n-1.9918\n-2.1019\n-0.48762\n0.31146\n0.39593\n1.2368\n1.8514\n2.1384\n3.8333\n1.4197\n2.4791\n2.5589\n4.0156\n2.6947\n3.3798\n2.4121\n3.9908\n4.5859\n6.0371\n5.1162\n1.9781\n0.8021\n-0.15661\n0.030104\n1.3684\n2.4199\n1.0226\n1.3156\n0.63648\n1.5441\n-0.5051\n-0.13623\n0.44357\n-0.32132\n-0.87066\n-1.0134\n-1.9168\n-4.6145\n-4.2397\n-5.7523\n-3.7282\n-1.8301\n1.998\n4.3349\n7.0403\n5.5975\n-1.1542\n-0.19893\nNaN\nNaN\n1.079\n0.73826\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9741\n1.6517\n1.4148\n0.31423\n-8.1188\n-10.088\n-3.4199\n-2.8944\n-6.1948\n-3.9319\n-3.7992\n-3.2413\n-2.0587\n-0.77587\n-0.83986\n-1.2606\n-0.19984\n0.22201\n0.96663\n1.0982\n1.1608\n1.8729\n0.18447\n1.775\n1.7386\n2.3469\n4.5721\n4.7816\n5.2485\n4.9974\n4.862\n4.9614\n3.0633\n1.4697\n0.58864\n1.4245\n0.69245\n1.6452\n2.7896\n1.9802\n1.8104\n1.3506\n1.383\n0.15726\n0.805\n0.075188\n-0.43156\n-1.153\n-1.7468\n-1.7947\n-2.3641\n-1.7042\n-1.7193\n-1.5271\n-0.13389\n1.5902\n3.3081\n4.1731\n3.0944\n1.0835\n2.6304\n1.7591\n3.5652\n3.0113\n3.19\n5.4538\n8.2018\nNaN\nNaN\nNaN\nNaN\n-3.4555\n1.3285\n-0.30046\n-0.81463\n-2.6058\n-6.9376\n-7.9587\n-3.5583\n-4.9764\n-5.8212\n-4.2463\n-3.6391\n-0.52402\n0.58453\n0.47229\n0.39915\n-2.2526\n-2.1943\n-0.59055\n0.90942\n1.1268\n1.7057\n1.4146\n0.73546\n1.7144\n-0.44324\n0.57451\n1.9151\n1.772\n3.2753\n1.376\n2.2629\n2.7098\n0.81599\n1.4041\n1.0172\n1.0409\n1.1808\n1.7854\n4.1263\n2.8543\n2.3165\n0.80313\n1.2094\n0.80715\n1.3835\n0.936\n0.29534\n-0.59558\n-1.1305\n-3.46\n-8.8573\n-2.6473\n0.50559\n1.7829\n2.7524\n2.5978\n3.0686\n7.7538\n1.9339\n2.4684\n2.6989\nNaN\n0.11635\n2.2182\n-0.2026\n4.6907\n5.73\nNaN\nNaN\nNaN\n-3.6341\n-1.0165\n3.2326\n2.3357\n-0.39171\n0.35298\n-3.5911\n-5.1523\n-2.3113\n-0.91442\n-1.4065\n-1.706\n-0.54993\n1.0353\n1.744\n0.079507\n-1.295\n-2.3546\n-2.7173\n-1.9415\n-0.89633\n0.48581\n1.8574\n1.775\n0.34827\n-0.11143\n-0.9421\n1.1\n2.9418\n1.9976\n0.45527\n0.47542\n1.5562\n1.3379\n1.3521\n0.86317\n1.1894\n2.0854\n1.5564\n2.5243\n4.3245\n3.0952\n1.747\n0.33849\n0.52459\n0.6719\n1.1519\n1.8095\n1.1604\n-0.46625\n-1.028\n-3.2021\n-8.5308\n-2.3464\n0.23068\n2.727\n1.9591\n4.5124\n4.693\n6.6971\n2.9585\n1.0699\nNaN\nNaN\n-2.243\n1.5068\n0.87171\n1.4147\n1.2639\nNaN\nNaN\nNaN\n-1.831\n-0.89344\n3.7975\n2.6267\n-2.0472\n-1.1998\n-0.74536\n-2.3467\n-0.085433\n0.8374\n0.096176\n-1.1755\n-0.61806\n1.1582\n2.6716\n-0.23934\n-2.0934\n-2.295\n-2.2277\n-2.0089\n-1.3233\n-0.014674\n1.1323\n1.4476\n0.59361\n0.095908\n-0.070076\n1.7134\n2.4817\n2.2332\n2.443\n1.9396\n1.3767\n2.9749\n3.6245\n-0.46422\n0.54403\n3.2687\n2.8431\n3.1844\n4.3334\n3.3433\n1.6905\n1.2781\n0.7979\n0.41601\n1.0755\n-0.014213\n0.62692\n0.41369\n-0.90261\n-0.51046\n-7.448\n-0.68719\n-4.0748\n-1.7516\n2.3233\n5.9667\n6.9651\n6.1307\n1.6856\n0.66967\nNaN\nNaN\nNaN\n-0.57502\n1.6718\n3.9885\n3.2421\nNaN\n3.2598\n1.8575\n-1.0789\n-0.14858\n3.5539\n4.1386\n1.3053\n0.10345\n0.37607\n-1.8388\n-1.7372\n-1.3414\n-2.1536\n-1.5151\n1.4085\n2.1069\n2.1734\n0.29055\n-1.7506\n-2.1485\n-1.224\n-1.073\n-0.39927\n-0.25824\n-0.043026\n0.38387\n0.58167\n0.24178\n0.16746\n1.9132\n2.4036\n2.4976\n3.2042\n1.4782\n0.39613\n4.2665\n5.5336\n0.83704\n0.54595\n2.7776\n2.777\n3.1859\n3.9555\n3.4996\n2.7839\n2.2971\n1.6708\n1.5752\n1.8728\n1.5773\n2.7548\n3.7809\n0.21964\nNaN\nNaN\nNaN\n-2.9812\n-2.2919\n1.8089\n4.9199\n4.9923\n2.7561\n0.01892\n1.2656\nNaN\nNaN\nNaN\n-0.3236\n1.7384\n0.99397\n2.5881\n2.5203\n2.6579\n3.2668\n2.326\n2.0389\n0.89962\n0.62254\n1.1739\n1.6287\n1.0341\n-0.38223\n-0.29973\n1.0612\n0.27715\n0.26994\n0.65359\n1.0631\n0.78036\n0.47015\n-1.0847\n-1.2449\n-0.41546\n-0.10119\n-0.053487\n0.13701\n0.34418\n-0.31018\n0.39259\n0.76956\n0.85419\n1.3727\n1.5846\n2.0139\n2.5344\n1.1876\n0.28184\n3.0369\n5.2598\n1.7767\n0.98962\n1.999\n1.996\n1.9736\n3.575\n2.4569\n3.2109\n3.605\n2.4433\n2.1471\n2.7205\n3.0603\n3.4743\n4.9596\n1.6523\nNaN\nNaN\nNaN\n-1.3991\n-1.0749\n-1.2004\n0.73067\n3.3274\n1.6169\n-0.42178\n0.0078628\nNaN\nNaN\nNaN\n-0.21418\n0.15868\n-0.69798\n0.61559\n3.5689\n3.0701\n2.645\n2.204\n1.6191\n-0.52881\n0.29326\n0.58872\n1.2318\n1.5518\n0.91548\n-0.72947\n0.80995\n-0.22061\n-1.2698\n-0.34539\n-0.043163\n0.65117\n1.1875\n0.74274\n0.60089\n0.24426\n-0.2367\n1.3702\n1.4931\n1.1893\n-0.26258\n0.67137\n1.7201\n1.8084\n1.715\n1.8232\n2.725\n2.8449\n1.956\n1.5837\n3.3703\n4.7735\n2.0565\n2.6412\n2.8275\n1.1794\n2.42\n3.1398\n3.2012\n3.7931\n4.7224\n2.4594\n2.2386\n2.7097\n3.1137\n2.4147\n1.3064\n1.0771\n1.3537\nNaN\nNaN\n-1.5408\n-0.58268\n0.13125\n-0.027972\n0.41793\n1.6234\n0.53325\n1.1657\n1.3316\n0.23402\n0.12526\n-0.57115\n-1.2318\n-0.77707\n0.33482\n3.7711\n3.4209\n3.5323\n2.5787\n1.8395\n2.498\n1.8167\n2.0545\n1.0171\n2.4915\n2.0263\n0.71846\n0.44451\n-3.3642\n-4.2941\n-0.75402\n-1.1138\n0.12697\n1.9692\n1.6784\n2.2232\n1.4579\n0.66636\n1.894\n2.1801\n2.0926\n0.95635\n0.69126\n1.7074\n1.712\n2.8622\n2.9755\n3.0268\n3.8754\n4.1613\n2.8692\n3.6097\n4.3149\n1.881\n1.9805\n2.5473\n1.3088\n1.1997\n2.3701\n3.4205\n4.2268\n3.2803\n2.1624\n1.8639\n1.6376\n2.232\n2.6616\n3.8428\n3.5052\n0.042562\n-0.083325\n-1.399\n-2.0689\n-1.0872\n0.12977\n1.5916\n1.2631\n1.9003\n1.6956\n1.5367\n0.67374\n1.4138\n1.9764\n1.0488\n-1.1408\n-1.7754\n0.051842\n2.5731\n3.6556\n2.832\n2.9952\n1.64\n1.79\n1.7687\n3.2199\n2.3151\n2.8042\n3.7913\n3.7155\n1.1758\n-2.7556\n-5.496\n-1.532\n-1.9428\n0.51859\n2.0005\n2.5616\n2.933\n2.6943\n2.4331\n2.0518\n1.4137\n1.3848\n1.6319\n1.3809\n1.8071\n2.0476\n3.0619\n3.053\n4.4585\n4.7167\n4.7893\n4.0458\n3.8568\n3.1249\n2.1236\n3.0094\n3.8338\n2.5594\n1.6875\n2.1516\n3.3434\n4.5736\n3.4756\n3.4104\n2.2252\n1.1993\n1.0841\nNaN\nNaN\nNaN\nNaN\n-0.096323\n-0.84843\n-2.0046\n0.34446\n0.2303\n0.79914\n0.92793\n2.0032\n2.9385\n2.1502\n1.6287\n2.1326\n2.1308\n2.327\n2.8616\n0.57127\n1.0132\n2.3767\n2.6569\n2.9363\n3.6284\n2.2722\n2.2682\n2.0867\n3.3013\n3.2102\n3.0149\n3.553\n4.6189\n4.2967\n0.56801\n-1.3012\n-1.4804\n-1.4069\n-0.32952\n0.91396\n1.6475\n3.0929\n3.756\n1.1963\n0.17022\n0.45406\n0.94041\n2.5258\n2.988\n2.581\n2.5144\n3.0863\n4.2287\n5.5843\n5.0592\n4.4053\n3.9922\n4.0998\n4.8924\n3.1433\n3.9025\n4.3153\n3.6312\n3.158\n3.9847\n3.5116\n5.9934\n5.3235\n4.6832\n2.5691\n1.4609\n2.9088\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0806\n-0.532\n-0.49387\n0.061577\n0.12685\n0.41303\n1.7009\n2.5605\n1.4785\n1.6754\n2.2953\n2.0985\n2.433\n2.5287\n2.1129\n1.4387\n1.2482\n1.6763\n3.3998\n5.3474\n3.6518\n3.0235\n3.8051\n4.2778\n3.7737\n2.6647\n2.6489\n3.4314\n7.7489\n2.2491\n1.2163\n0.47427\n0.50674\n-0.055611\n0.07472\n1.7239\n2.571\n2.4757\n0.81323\n1.594\n1.7204\n1.9214\n3.5038\n4.1956\n3.6215\n3.9011\n4.1371\n4.2787\n6.1935\n5.5248\n5.2283\n3.9829\n4.6224\n4.0817\n4.6369\n3.593\n4.6449\n4.6976\n4.8018\n5.7958\n5.0165\n7.4078\n7.6541\n6.4358\n3.793\n1.0353\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4747\n-0.36285\n-0.093449\n-0.058567\n0.15201\n0.42418\n0.36748\n0.16076\n-0.10928\n0.6902\n1.7659\n1.0505\n0.18705\n1.0233\n2.2821\n1.8841\n1.5069\n3.5075\n4.8782\n5.6477\n3.4012\n4.8401\n4.9169\n3.4634\n2.4121\n1.8039\n1.9768\n6.3933\n3.2363\n2.464\n1.9927\n1.1778\n0.037484\n1.2551\n1.4941\n2.3292\n3.3785\n0.47918\n0.97793\n2.0974\n1.5658\n3.2108\n3.8518\n4.3335\n5.0034\n5.3621\n4.4257\n5.2339\n5.1639\n6.0283\n2.9666\n2.4683\nNaN\n5.9242\n3.7271\n5.8288\n6.1671\n7.1355\n7.6288\n6.9376\n9.5216\n9.5628\n8.381\n5.7691\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6426\n0.90602\n-0.31662\n0.27639\n0.16787\n0.29766\n0.51512\n-0.41588\n-1.4746\n-0.62109\n0.57704\n0.47178\n-0.61906\n1.7934\n2.8425\n3.4725\n2.5968\n2.7998\n2.985\n4.4557\n4.4025\n5.3524\n3.4749\n1.5964\n0.52935\n-0.39172\n0.67749\n2.7467\n1.3147\n1.8213\n0.84178\n0.31445\n1.4562\n2.2957\n2.1326\n3.462\n4.0343\n1.4167\n0.79452\n2.2314\n0.9961\n2.8672\n2.4578\n4.027\n6.6888\nNaN\nNaN\n4.2338\n4.6352\n6.4539\nNaN\nNaN\nNaN\n-0.58552\n-0.7867\n-1.5482\n-0.77191\n0.099251\n-0.74751\n-1.8443\n0.35769\n0.093442\n-0.79349\n-0.48481\n0.068918\n-0.20333\n-1.5239\n-4.3859\n-1.7758\n-0.31814\n1.3041\n-2.1243\n-3.9883\n-3.4253\n-0.8819\n-0.47622\n1.3501\n-1.9368\n1.6187\n1.9981\n3.2761\n1.823\n-2.1751\n7.497\n5.5139\n4.6985\n7.6608\n4.0322\n4.6462\n4.3419\n2.1877\n2.2898\n2.2939\n0.69278\n2.7299\n3.9826\n1.0034\n1.2436\n1.6885\n1.518\n1.3957\n1.9919\n1.3295\n0.19562\n0.91442\n2.0069\n2.759\n-0.82786\n2.155\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.31672\n-0.59452\n-1.0657\n-2.4388\n-1.5173\n0.32001\n-3.4236\n0.25512\n-1.3743\n-0.99674\n-0.76193\n-0.74664\n-0.4233\n-4.2035\n-5.2564\n-4.863\n1.2387\n0.83496\n-1.516\n-1.2041\n-2.6549\n0.54803\n0.69966\n-0.77302\n-3.8885\n0.63254\n2.6334\n1.8302\n0.50145\n-0.74114\n2.1031\n2.3925\n3.5075\n6.911\n5.3918\n3.6422\n3.0517\n1.3076\n2.3466\n2.2421\n-0.42764\n2.1773\n2.1694\n3.0493\n1.5373\n1.0616\n0.95435\n1.4655\n1.9218\n1.6565\n1.5206\n1.1461\n1.8216\n0.98268\n-2.9555\n0.10483\n0.26427\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.3619\n-0.09735\n-0.36404\n-0.44841\n3.6571\n3.2212\n1.1734\n-0.85641\n-1.336\n-2.2482\n-0.71344\n0.058932\n0.29978\n-5.9575\n-8.7432\n-5.9632\n-0.26581\n0.049205\n-1.086\n-2.3978\n-0.76567\n-1.3293\n-0.80168\n0.026775\n-3.6229\n-0.3835\n1.1972\n0.35123\n0.89971\n2.27\n0.12481\n0.22561\n4.7532\n7.2992\n4.8786\n3.4946\n2.7246\n1.5852\n1.1579\n1.1039\n0.032067\n0.86326\n1.2083\n1.7962\n1.3843\n1.876\n2.7128\n1.4155\n1.3133\n1.5875\n2.0107\n1.2583\n0.97678\n1.822\n0.12427\n-2.2139\n-1.1502\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.45923\n0.19977\n-0.28159\n-0.049329\n-0.22178\n-0.32631\n-0.80014\n-0.73853\n0.19995\n-0.45164\n-2.1445\n0.48881\n2.4933\n-7.8246\n-6.2504\n-1.7848\n-0.19155\n-0.1273\n1.972\n-1.104\n-0.83206\n-2.5912\n-0.71314\n2.887\n-0.010321\n-0.4809\n-1.2437\n0.52164\n-0.469\n-3.2782\n1.4245\n1.5592\n5.9059\n6.0875\n5.2823\n6.1565\n3.7856\n1.5665\n0.90033\n0.57401\n0.43537\n0.92005\n3.0713\n1.2197\n1.0093\n2.729\n2.5733\n1.7153\n2.2466\n2.3561\n1.6841\n1.5752\n1.8257\n2.3084\n0.76075\n-1.1955\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41422\n-0.94505\n-0.68506\n0.084224\n0.09387\n-0.31063\n0.42352\n0.72155\n0.49629\n0.21285\n-1.1357\n-0.48247\n-3.5365\n-2.1743\n-1.512\n-0.9019\n0.48418\n-1.1181\n4.6822\n1.481\n0.63989\nNaN\nNaN\n5.2429\n1.6424\n-0.39859\n-3.2263\n-1.8037\n-2.1403\n-3.0098\n2.3735\n4.4553\n6.3121\n4.2422\n5.4129\n5.4299\n2.8405\n1.3415\n0.77825\n0.86233\n1.0383\n2.0485\n3.2689\n3.9196\n2.0813\n2.3658\n2.7418\n2.4582\n2.7079\n2.83\n1.8334\n0.85391\n0.5552\n1.3264\n0.7664\n0.87205\n-3.5308\nNaN\nNaN\n5.8389\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.43778\n-0.086444\n-0.72662\n0.51354\n0.98219\n0.50744\n0.81034\n1.2207\n0.19111\n-0.23264\n-0.85578\n-0.98055\n-0.81955\n1.2549\n0.34795\n0.068576\n0.47001\n-2.3962\n-1.6057\nNaN\nNaN\nNaN\nNaN\n2.0978\n0.55593\n-2.2851\n-2.4724\n-1.6405\n-1.6576\n-2.5982\n-0.14711\n4.9908\n3.5156\n2.5426\n4.972\n7.5082\n4.5588\n3.2909\n2.6995\n1.73\n0.77445\n1.8454\n3.2939\n3.5256\n3.8705\n3.9733\n3.4424\n1.1461\n2.0732\n2.2781\n1.0185\n0.14917\n-0.54211\n0.13141\n0.77019\n3.9764\n2.6485\nNaN\nNaN\n9.1477\n0.60497\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.4825\n0.88421\n0.5208\n0.34011\n0.077461\n0.60903\n-0.3526\n-0.2718\n-0.28062\n-1.7386\n-1.467\n-0.60557\n-1.2562\n1.1907\n0.062702\n1.3299\n0.95745\n-3.3301\n2.0696\nNaN\nNaN\nNaN\nNaN\n0.95711\n0.69502\n-0.86247\n-1.4224\n-1.7184\n-0.80245\n-2.1564\n-0.35189\n4.7047\n3.7503\n3.099\n4.6609\n7.2962\n6.2795\n5.0853\n5.6212\n4.7272\n0.93797\n3.1075\n4.6162\n4.3321\n5.5047\n5.0768\n3.8483\n1.8153\n1.7913\n1.7257\n1.8338\n0.86921\n0.21206\n-0.39999\n-0.19175\n1.4639\n2.2594\nNaN\nNaN\n6.2493\n5.6028\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18685\n0.42089\n0.40686\n-0.53201\n-1.8268\n-0.82754\n-1.3056\n-0.9255\n-1.2758\n-2.6147\n-1.8431\n-0.678\n-2.1463\n-2.3327\n-0.20944\n1.097\n1.4554\n0.16115\n2.3495\n1.8658\n0.83438\n-0.30488\n1.5379\n2.1387\n1.2051\n0.69322\n-0.018868\n-1.6558\n-1.1646\n-1.3823\n-0.61359\n1.0638\n1.7332\n3.8476\n4.7897\n5.1214\n5.2361\n6.4916\n7.1498\n6.3885\n3.5322\n4.4681\n4.6172\n5.3647\n7.2301\n4.6946\n3.1873\n2.2239\n1.8941\n1.5999\n2.3144\n1.5713\n-0.76936\n-1.9359\n-2.0204\n0.48174\n1.0256\n2.6345\n0.3947\n2.9871\n6.5257\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.74976\n-0.060812\n-1.8579\n-0.14719\n-2.3251\n-1.9038\n-1.4891\n-1.9494\n-1.0233\n-1.2561\n-0.73618\n-1.1658\n-1.4287\n-0.81706\n0.77049\n1.2906\n0.65473\n-0.077266\n1.2398\n0.82485\n1.081\n3.0699\n4.4473\n2.9074\n2.5805\n0.74751\n-0.52082\n-1.2531\n-0.43333\n0.081887\n-0.18195\n2.0789\n1.9646\n4.238\n4.9839\n4.8421\n5.4877\n6.9987\n7.4179\n6.064\n5.2487\n4.7237\n4.6015\n6.6328\n8.8906\n3.4889\n1.7439\n1.3606\n1.9562\n1.0481\n0.93684\n-0.49067\n-1.7934\n-3.1779\n-2.1702\n0.35248\n1.8664\n2.1579\n1.2713\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.73272\n0.32278\n-0.80288\n0.27396\n-1.0312\n-1.2605\n-1.6191\n-0.60843\n0.14341\n0.18366\n-0.48933\n-0.33181\n-0.33241\n-0.55546\n0.27765\n0.72714\n0.93368\n0.40243\n0.64216\n0.86246\n1.3027\n2.257\n2.1998\n3.3012\n3.6884\n0.45054\n-0.9001\n0.70737\n1.0377\n1.0043\n-0.84908\n2.9188\n5.5575\nNaN\nNaN\n3.7953\n3.5031\n5.3741\n7.6333\n6.6651\n5.0729\n4.9282\n4.5611\n4.2732\n3.1895\n2.0526\n2.2236\n1.6009\n1.5522\n0.63759\n0.97729\n-0.83164\n-2.1759\n-3.5128\n-2.6273\n0.36937\n3.6601\n4.9395\n1.6021\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0817\n1.3596\n0.39492\n0.7164\n-0.20004\n-1.7216\n-1.5766\n-0.74456\n-0.21165\n-0.67251\n-1.5969\n-1.363\n-2.7947\n-1.2153\n-0.65148\n-0.86667\n1.8935\n1.6352\n1.0404\n0.0069554\n1.7759\n1.7458\n0.80978\n4.6756\n5.3348\n2.2796\n-0.53441\n-0.57906\n1.2024\n0.73668\n1.0569\n3.2643\nNaN\nNaN\nNaN\n5.1167\n4.1367\n5.4521\n8.4966\n7.5688\n6.1586\n7.234\n5.2798\n5.0828\n3.9122\n2.9969\n3.5932\n2.6101\n1.1386\n3.5278\n2.6289\n-0.27896\n-2.0103\n-4.4305\nNaN\nNaN\n5.0639\n4.2576\n0.81069\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.7788\n0.59304\n0.74412\n0.014819\n-0.50278\n-1.487\n-1.3769\n-0.90798\n-0.80468\n-0.29345\n-0.86092\n-0.14775\n-2.5399\n-2.354\n-1.1126\n-1.1705\n1.4235\n0.97124\n-0.45723\n0.51441\n3.1753\n1.9411\n2.7286\n5.4112\n5.5989\n3.8246\n0.75158\n-1.3062\n-1.3248\n-0.81156\n1.8644\nNaN\nNaN\nNaN\nNaN\n6.1269\n4.2806\n4.8891\n10.784\n8.3231\n5.4923\n4.1628\n6.0045\n6.5324\n6.086\n4.6234\n4.3326\n2.9167\n0.65613\n1.8199\n2.1522\n-0.87085\n-1.5474\nNaN\nNaN\nNaN\n2.8004\n5.139\n-1.3229\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.66306\n0.13297\n0.78774\n0.80385\n-0.49823\n-1.5888\n-0.56599\n-1.746\n-0.393\n-0.030785\n-1.3023\n-0.91789\n-2.8893\n-2.4662\n-1.0691\n-2.5621\n-2.3535\n-0.93147\n-1.8537\n0.92808\n2.5071\n3.3418\n6.9584\n6.7956\n5.3392\n4.0694\n-0.26945\n-0.1707\n0.59085\n-0.18614\n1.259\nNaN\nNaN\nNaN\nNaN\n8.5684\n6.5444\n3.929\n8.9107\n8.3936\n6.9807\n7.3147\n8.6732\n6.795\n5.0261\n3.8075\n3.9478\n2.8328\n0.79141\n0.74676\n1.4907\n-0.64713\n-0.5993\nNaN\nNaN\nNaN\n3.6805\n2.6727\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.33629\n0.15969\n0.58901\n0.66277\n-1.0338\n-1.1154\n-0.6219\n0.074318\n0.15477\n-0.2617\n-0.36904\n-0.38723\n-0.38554\n-0.10565\n-0.58023\n-2.6355\n-3.506\n-0.76709\n0.083737\n1.1625\n2.1074\n4.5712\n9.4754\n8.5544\n6.1192\n2.398\n-0.42405\n0.89019\n1.1909\n1.2377\n1.789\nNaN\nNaN\nNaN\nNaN\n9.5771\n9.6222\n1.9806\n7.8225\n7.0468\n7.4708\n8.6491\n8.769\n5.227\n2.3999\n1.9098\n3.1421\n2.3164\n0.70784\n0.2402\n1.6019\n0.98173\n0.087662\nNaN\nNaN\nNaN\nNaN\n4.0401\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56877\n0.21932\n0.10428\n0.37008\n0.021889\n-0.74771\n-0.61188\n0.29592\n-0.20196\n0.35621\n0.79018\n0.62948\n0.028829\n0.17844\n-0.35817\n-2.1308\n-2.2541\n-0.95732\n1.0355\n2.8889\n1.4052\n5.9738\n10.579\n8.9459\n6.3676\n1.7004\n0.18135\n0.88183\n0.54858\n0.64541\n0.41208\nNaN\n11.047\n11.574\n7.9089\n7.0014\n8.1459\n3.9627\n8.0682\n7.7996\n7.9737\n8.1325\n9.4999\n5.5779\nNaN\nNaN\nNaN\n2.43\n1.7378\n1.1256\n1.6536\n0.97471\n-0.63459\n-0.044478\nNaN\nNaN\nNaN\n1.7816\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.49944\n1.0913\n0.35431\n0.12142\n0.54626\n0.044628\n-0.26074\n-0.10292\n0.47984\n1.0312\n1.0846\n0.24537\n-0.81659\n-1.3325\n-0.48546\n-0.19392\n-0.541\n-0.56453\n0.25747\n2.7892\n1.917\n4.4167\n11.975\n10.673\n5.868\n2.3741\n0.3761\n-0.87085\n-0.52149\n1.8282\n1.0522\n8.4754\n8.8436\n11.311\n8.741\n2.42\n3.7229\n6.3716\n8.2896\nNaN\nNaN\nNaN\n7.9059\nNaN\nNaN\nNaN\nNaN\n3.2371\n2.5774\n1.9054\n0.78121\n1.8741\n-1.4348\n1.8306\n2.2024\n2.3417\n2.7506\n1.5173\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.32039\n1.1328\n-0.060191\n0.080853\n-0.0054602\n-0.33018\n-1.088\n-0.51761\n1.4489\n-0.1254\n0.047852\n0.020747\n-1.1353\n-0.71674\n1.2729\n0.44818\n-1.9582\n-0.66092\n0.5239\n1.5107\n3.0544\n5.6881\n6.7944\n9.3958\n4.1815\n3.4139\n-0.32355\n0.88536\n1.1575\n5.1129\n2.8391\n4.2978\n4.3002\n10.824\n6.2172\n5.8817\n5.0874\nNaN\nNaN\nNaN\nNaN\nNaN\n8.2246\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5392\nNaN\n-1.737\n-0.85624\n0.81369\n1.4531\n0.66462\n3.1835\n3.2119\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.23929\n-0.82613\n-1.8777\n-1.2844\n-1.2101\n-1.271\n-1.5108\n-1.8724\n1.6027\n0.11896\n0.31653\n-0.33064\n-0.54749\n-0.78491\n-0.39225\n-1.4579\n-1.1841\n-0.045262\n1.2698\n2.4431\n4.2174\n7.0848\n6.1231\n6.5622\n4.3482\n3.2028\n0.86502\n5.2732\n5.2202\n6.4751\n3.8904\n4.1349\n7.5534\n4.7429\n3.6003\n5.2487\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.30464\n0.64055\n1.9168\n2.9428\n2.2558\n2.5448\n3.4605\n1.0564\n1.1136\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.39419\n-2.0583\n-2.2427\n-2.0888\n-2.5692\n-1.7086\n-1.2522\n-1.3899\n-0.18467\n0.20958\n1.34\n-1.2762\n-2.032\n-1.1544\n-1.2279\n-0.65611\n1.5113\n2.4639\n2.3637\n3.9464\n4.5172\n7.1355\n7.1111\n4.0664\n4.2884\n3.4864\n3.8091\n5.373\n2.9837\n5.5104\n4.2298\n8.0578\n10.034\n6.3931\n4.3899\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0238\n1.6389\n2.321\n2.1206\n0.76667\n1.4223\n1.5466\n-1.3812\n-1.6076\n-0.8868\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.41632\n-1.2201\n-1.067\n-1.3827\n-3.4546\n-1.5534\n-1.0473\n-0.47185\n0.82819\n-0.43409\n0.65752\n-2.3626\n-2.507\n-0.1027\n1.0425\n1.0196\n1.5447\n2.8507\n4.12\n5.8157\n4.0033\n7.7689\n5.9392\n3.0841\n3.79\n3.3586\n3.9699\n3.1586\n1.3503\n4.2512\n6.8031\n9.942\n10.029\n8.6266\n6.3511\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.4277\n2.1533\n1.6678\n1.5891\n1.1917\n0.1649\n0.97754\n-0.13393\n-3.1164\n-1.7966\n-3.2024\n-2.5068\n-2.2108\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0056\n-0.48517\n-0.69508\n-1.6112\n-2.439\n-0.427\n-0.063943\n0.6414\n0.57425\n-0.72543\n-0.56962\n-2.7848\n-0.62575\n1.6552\n3.2274\n2.1954\n0.78657\n1.3961\n4.413\n2.9014\n2.6167\n7.4128\n5.7649\n1.5363\n2.6612\n2.2538\n2.5522\n3.6605\n1.9043\n0.12845\n3.0556\n0.27075\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8817\n3.0645\n2.5299\n2.1586\n1.2672\n2.5182\n1.112\n-0.77435\n-0.04538\n-1.1029\n-3.0514\n-0.45128\n-1.2966\n-2.5085\n0.63226\n1.287\n3.0942\n4.98\n3.9029\n4.2994\n0.45739\n0.7336\n-0.24695\n-0.81114\n-0.68104\n0.65805\n0.81629\n-0.11496\n-0.22962\n0.052722\n0.66684\n-0.61495\n-0.44701\n1.5363\n3.5414\n2.769\n3.6965\n4.6431\n5.0232\n2.6979\n2.0026\n4.1798\n6.1201\n2.6912\n1.7238\n-0.084394\n1.8615\n4.6797\n3.2419\n0.67477\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8901\n2.9343\n2.0288\n0.73421\n0.97787\n0.53844\n2.55\n-0.15468\n-1.7857\n-0.54808\n-2.3215\n-3.6873\n-0.28926\n-0.16752\n0.93223\nNaN\nNaN\nNaN\nNaN\n-0.76477\n0.30042\n0.41954\n1.2986\n-0.047593\n0.47154\n1.5807\n1.5455\n0.60422\n0.30938\n-0.019007\n1.1797\n1.9289\n0.90679\n1.0273\n1.9059\n2.1339\n1.7846\n1.5362\n4.7395\n4.8797\n3.64\n2.7131\n1.1216\n4.2526\n3.7136\n1.8991\n2.108\n4.1614\n6.2578\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0401\n2.7387\n2.8928\n1.9274\n0.18031\n0.32366\n0.62348\n-0.71534\n-0.0030096\n-0.61106\n-1.0043\n0.2752\n-1.3618\n-3.3461\n-1.3245\n-2.3333\n1.1746\nNaN\nNaN\nNaN\nNaN\n-1.3843\n-4.6607\n0.81482\n2.1332\n1.7705\n1.7829\n1.515\n0.55669\n-0.14937\n0.55023\n0.39588\n1.3112\n1.898\n1.6238\n2.2632\n2.7951\n1.6788\n0.92827\n0.89681\n3.2345\n4.7774\n4.6751\n4.4531\n4.5802\n5.6663\n3.4417\n2.0666\n4.7812\n7.59\n8.8425\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5453\n4.1208\n3.7689\n2.7691\n1.1302\n-0.42513\n-0.76254\n-0.67214\n-2.1146\n-1.0217\n-0.68763\n-1.7582\n-0.86824\n1.0801\n-1.6942\n-2.6356\n-3.9519\n-3.1827\nNaN\nNaN\n-4.4369\n-0.23226\n-0.63203\n-3.1452\n0.59679\n3.1416\n2.3515\n1.799\n1.2915\n0.052068\n0.24161\n0.51168\n1.0992\n1.5066\n1.7198\n1.776\n2.8054\n2.4227\n0.75577\n0.41507\n1.5216\n5.0472\n5.6955\n4.7003\n4.2916\n4.7109\n6.7253\n4.7447\n4.5044\n5.8101\n11.259\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5058\n3.8887\n2.7518\n2.1846\n0.80274\n-0.0090492\n-0.45677\n0.47019\n-1.6348\n-4.0703\n-2.6064\n-1.5969\n-1.7138\n-1.0192\n0.53801\n-0.28134\n-2.812\n-3.6727\n-1.0268\n-0.025942\n2.5799\n-0.26832\n0.73525\n-0.2225\n-0.56084\n2.5994\n2.6225\n1.6181\n1.2068\n1.1037\n-0.10306\n-0.49978\n1.6424\n1.511\n1.8444\n1.6101\n2.4611\n2.1229\n0.77483\n1.6855\n0.37989\n0.5241\n5.0154\n5.732\n5.0301\n3.9659\n4.9387\n5.9773\n5.3382\n4.027\n4.0161\n12.961\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5346\n3.5205\n2.3122\n2.9759\n1.0079\n-0.36984\n0.41186\n1.7295\n-0.22686\n-3.8194\n-3.9227\n-3.0902\n-2.1834\n-1.8761\n-1.0893\n-1.5385\n-2.2622\n-2.8612\n-1.0563\n0.66278\n2.3434\n-0.18238\n-0.93981\n-2.1397\n-0.66062\n2.2398\n1.8632\n1.6868\n0.68739\n0.52577\n-0.18188\n0.10131\n1.6456\n1.9105\n1.1336\n2.435\n3.9399\n2.007\n0.45513\n1.1247\n0.062415\n2.1593\n4.2746\n4.7242\n5.2736\n5.582\n5.7891\n5.9264\n5.1693\n3.6602\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8858\n4.9061\n4.8597\n1.6184\n7.5086\n4.3121\n-0.87329\n-0.4387\n-2.3475\n-3.0359\n-2.6467\n-3.108\n-3.5833\n-2.7523\n-3.1246\n-1.3113\n-2.8362\n-2.7648\n-1.783\n-2.0933\n1.5025\n1.4662\n-0.97781\n-1.6408\n-3.8184\n-1.9175\n2.667\n1.3324\n2.9246\n0.63111\n0.95415\n0.69567\n1.0553\n2.5258\n2.2129\n1.9554\n2.8494\n3.6628\n2.7532\n0.27865\n0.54184\n1.2258\n3.8647\n4.5934\n4.8311\n5.3302\n7.0901\n6.5317\n6.3172\n5.8972\n4.1548\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.96976\nNaN\nNaN\nNaN\nNaN\n3.5985\n2.9635\n3.0629\n2.0065\n-0.98482\n5.6655\n0.75038\n-2.5108\n-3.3673\n-3.2375\n-4.4876\n-3.9426\n-0.49487\n-2.7126\n-2.2518\n-2.8183\n-0.48994\n-2.2123\n-3.11\n-1.0377\n-1.6188\n-0.65689\n-1.7329\n-2.2903\n-2.3554\n-1.8821\n0.13634\n1.7199\n0.5671\n1.3984\n1.8328\n2.1136\n1.0802\n1.0061\n1.7808\n2.4463\n2.6896\n2.2433\n2.2857\n2.3351\n0.23067\n2.3318\n3.0368\n3.1849\n4.2882\n6.293\n6.3628\n8.5568\n8.9642\n7.3814\n5.1455\n2.7321\n1.7901\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.361\n2.615\n5.0062\n3.6543\n2.1248\n1.2535\n1.004\n-1.0586\n-2.0809\n-2.9456\n-3.6543\n-3.7768\n-4.5895\n-3.0139\n0.4689\n0.6691\n-2.2789\n-3.283\n-0.04126\n-1.9039\n-3.1221\n-2.6927\n-2.6119\n-1.3684\n-3.5995\n-5.1787\n-2.5737\n-0.65525\n4.3684\n1.1863\n0.58929\n0.80085\n2.4492\n2.2259\n1.4222\n2.7027\n3.0834\n3.3301\n1.511\n1.5829\n2.6539\n2.6014\n1.3049\n2.5693\n2.5225\n2.3143\n3.6912\n4.0317\n7.3595\n8.4049\n8.0488\n5.31\n2.8622\n1.5611\n1.068\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.1989\nNaN\nNaN\n-11.198\n0.73343\n2.9167\n4.01\n3.6072\n3.3478\n2.5327\n2.3843\n1.3087\n1.1216\n0.2444\n-1.9957\n-0.75489\n-0.19995\n-2.1281\n-0.99089\n1.0719\n2.4533\n0.69762\n0.86368\n-2.3214\n-3.1416\n-1.8829\n-1.1288\n-1.4693\n-3.6853\n-5.5949\n-2.3513\n-0.73249\n5.0107\n1.0009\n1.1765\n1.7244\n1.4572\n0.72857\n1.3284\n2.2287\n2.7264\n3.9397\n1.5436\n1.5539\n2.5839\n2.1312\n2.1004\n3.0108\n4.8698\n5.228\n4.9935\n4.636\n4.9661\n6.2593\n4.2868\n3.1492\n1.0286\n0.63371\n-1.385\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.793\n-13.972\n-11.519\n-7.0641\n0.59339\n3.4116\n5.8621\n3.3586\n2.6698\n1.9062\n1.6573\n2.2684\n1.0295\n1.4269\n0.89508\n1.9315\n0.37527\n0.5025\n0.3927\n0.26827\n2.6904\n0.44785\n-0.59421\n-2.3779\n-1.9085\n0.34756\n0.65667\n-1.1301\n-2.1024\n-2.2099\n0.069162\n0.188\n1.6283\n1.4533\n0.68746\n1.1041\n0.62369\n1.2898\n2.1761\n2.5499\n1.7535\n2.5535\n1.5836\n2.0996\n2.8069\n3.2441\n2.9854\n3.4261\n5.2465\n6.9784\n5.4783\n3.2895\n5.1759\n7.6294\n6.1282\n2.6646\n1.6324\n1.1491\n-2.2038\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-11.667\n-7.2309\n-6.7425\n-3.1377\n1.4693\n7.3026\n5.9968\n5.5818\n3.3338\n2.5326\n3.0433\n2.7128\n2.1279\n1.132\n1.0503\n1.4659\n1.9985\n1.6192\n1.5548\n0.46531\n-0.45603\n0.25852\n-0.95548\n-1.4504\n-1.9204\n-0.99083\n0.80455\n0.65975\n-0.46078\n0.5337\n1.1777\n0.57163\n-0.29075\n-0.52133\n0.66985\n1.9131\n0.32873\n1.6995\n2.5308\n2.8263\n1.9627\n2.6639\n2.6286\n3.0398\n3.1449\n2.7519\n3.0891\n4.1345\n4.1392\n5.8112\n9.6903\n9.7682\n5.7293\n4.773\n5.2791\n2.8271\n1.9394\n0.70158\n-0.75081\n4.5089\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.736\n-6.7587\n-2.0165\n-2.0709\n1.9127\n9.0124\n7.2282\n5.935\n3.7697\n2.2763\n1.72\n2.0251\n2.405\n2.088\n1.556\n0.51778\n1.9292\n0.9578\n0.55539\n-1.2594\n-2.2555\n-1.0151\n-0.83005\n-0.89896\n-2.3047\n-3.1188\n-0.7162\n0.55167\n2.7549\n1.45\n0.98532\n-0.4986\n-1.1704\n-0.81789\n0.75924\n2.163\n1.7161\n2.5381\n1.4238\n2.7988\n2.6552\n2.8983\n2.6345\n3.2809\n2.8849\n2.4315\n3.2156\n4.9193\n4.1339\n5.8938\n9.2203\n10.747\n8.6543\n4.639\n5.3686\n2.389\n1.9938\n-0.94886\n4.3322\n8.4034\n9.5731\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.2775\n-0.56894\n3.3182\n3.3201\n4.8865\n9.8337\n7.4485\n6.0759\n5.9676\n3.5895\n3.488\n3.5187\n3.4105\n3.1293\n3.0642\n0.54138\n-0.14952\n-0.0031804\n-0.78894\n-2.185\n-2.5438\n-3.7369\n-1.4659\n-1.8173\n-1.4848\n-2.4526\n-0.23455\n0.29944\n1.7691\n-0.28265\n-0.1732\n-1.4146\n-0.9535\n-0.10041\n0.92127\n2.1764\n2.208\n0.92309\n-0.095276\n1.8682\n1.9195\n2.5649\n1.3783\n3.055\n2.6531\n2.3595\n3.5119\n4.2506\n4.779\n6.0705\n8.9007\n9.1625\n11.321\n9.8648\n7.8735\n3.4704\n1.0151\n-0.77152\n0.82974\n7.9355\n7.996\nNaN\nNaN\n5.5076\n6.4398\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8158\n3.6913\n3.493\n4.4132\n11.783\n13.231\n6.7755\n6.1619\n8.1435\n4.9087\n4.0798\n4.0023\n3.268\n2.2691\n1.7449\n1.0512\n0.18101\n0.010323\n-0.51886\n-1.0789\n-1.6614\n-2.0877\n-0.64703\n-1.4689\n-1.1399\n-0.97435\n-1.8432\n-1.3446\n-0.96659\n-1.348\n-1.2136\n-1.1475\n0.58538\n0.58151\n1.2149\n0.6497\n1.5209\n0.59383\n-0.52264\n0.95734\n1.6477\n2.2124\n1.5784\n2.7692\n2.1669\n3.0796\n4.2398\n5.1945\n6.0426\n6.3892\n7.1751\n6.8039\n7.1074\n7.2829\n6.0717\n3.8906\n2.1071\n2.2127\n3.3753\n5.4167\n4.4185\n5.3461\n3.5958\n4.2069\n4.6506\n1.6917\n-2.9844\nNaN\nNaN\nNaN\nNaN\n9.6904\n4.8897\n6.0419\n6.1572\n7.2869\n10.689\n11.465\n6.6051\n7.0518\n6.8006\n4.0409\n3.5549\n1.8673\n1.2076\n0.78499\n-0.11247\n1.8408\n2.1105\n0.77277\n-0.3062\n-0.69234\n-1.2894\n-0.68282\n-0.029688\n0.10879\n2.0669\n1.3193\n1.2203\n1.7949\n0.15041\n1.4913\n0.11935\n0.21365\n1.8517\n0.57015\n0.67374\n1.6224\n1.3719\n0.95522\n-1.2018\n0.45225\n1.4217\n2.9027\n1.9551\n2.5113\n2.2675\n2.7613\n3.8443\n4.6965\n5.1894\n8.4812\n14.383\n8.106\n4.9782\n3.534\n2.8507\n2.6917\n2.2857\n-2.0042\n4.0352\n3.6157\n3.3688\nNaN\n6.7778\n5.7768\n8.2554\n2.669\n1.4443\nNaN\nNaN\nNaN\n9.6022\n6.7223\n2.8943\n3.6794\n5.7118\n4.5649\n7.696\n9.1612\n5.3041\n2.5735\n1.6998\n1.6321\n0.81209\n0.76609\n0.58288\n1.0049\n1.153\n1.7319\n2.3187\n1.5839\n0.67846\n-0.20565\n-1.0832\n-0.59026\n0.89948\n1.516\n2.8585\n1.1389\n-0.078121\n1.4232\n2.5783\n2.4516\n0.90912\n1.3407\n1.6422\n0.46104\n0.55665\n1.0252\n1.607\n0.72896\n-0.88656\n0.51596\n1.8266\n2.859\n2.8197\n2.6558\n2.3398\n2.0664\n2.8865\n4.1868\n4.4579\n7.6525\n13.789\n7.7029\n5.264\n2.5493\n3.0942\n0.61264\n0.54801\n-1.1572\n2.8416\n4.5511\nNaN\nNaN\n8.6291\n6.1394\n6.852\n6.3502\n6.7446\n5.2072\nNaN\nNaN\n7.6574\n6.4288\n1.6987\n3.3215\n7.3977\n6.1761\n5.2517\n6.13\n2.4326\n0.5959\n0.69838\n1.7648\n2.2556\n1.1499\n-0.28659\n1.2209\n2.4833\n2.2977\n2.0917\n1.3725\n0.43828\n-0.19037\n-0.87522\n-0.94875\n-0.08369\n0.68405\n1.8764\n0.43586\n0.13008\n0.58506\n0.47733\n0.82633\n0.91661\n0.084623\n0.32854\n1.4454\n1.6483\n0.32264\n0.88518\n0.49466\n-0.61381\n0.093737\n1.6085\n1.4215\n2.0404\n2.4487\n1.7714\n2.8018\n2.765\n2.8046\n4.0238\n4.1812\n12.529\n5.7074\n9.1651\n6.7232\n2.4792\n-0.93455\n-1.6472\n-0.63991\n3.7997\n4.6341\nNaN\nNaN\nNaN\n7.2142\n4.9288\n2.5511\n4.2413\n4.1305\n3.5746\n3.938\n6.7017\n5.0206\n0.82448\n0.69157\n3.3644\n4.4843\n3.3661\n4.0532\n3.0783\n2.0058\n2.9949\n2.8409\n1.1917\n0.59323\n0.18692\n1.0363\n2.7348\n2.7661\n1.6222\n0.91082\n0.055494\n-0.096539\n-0.47453\n-0.45435\n-0.13808\n0.57646\n1.0091\n-0.17669\n-0.05281\n0.15062\n-0.29241\n1.1784\n1.3383\n-1.7644\n-2.0134\n0.65182\n2.3118\n1.3528\n1.2645\n0.85948\n0.2426\n0.21657\n0.58625\n0.58565\n0.96188\n0.95263\n0.89347\n1.1117\n0.17289\n-0.78164\n2.6922\nNaN\nNaN\nNaN\n7.737\n6.9059\n2.9392\n-0.20129\n0.19527\n2.569\n5.1825\n3.9056\nNaN\nNaN\nNaN\n5.5358\n3.4468\n4.271\n2.9108\n2.9289\n2.5835\n1.8078\n1.9739\n1.6516\n2.1881\n2.6984\n2.3188\n1.957\n2.3627\n2.8937\n1.8441\n-0.034108\n0.5808\n0.93643\n1.7351\n1.4197\n1.3233\n0.74065\n2.1049\n2.2066\n1.0914\n0.78058\n0.053487\n-0.58939\n-0.78933\n0.51874\n0.59773\n0.72967\n0.71875\n0.92376\n0.99364\n1.0927\n0.75746\n1.367\n1.5297\n-0.48589\n-1.9622\n0.50476\n2.3118\n2.0259\n2.0675\n2.1226\n0.30818\n1.2451\n0.25493\n-0.50352\n0.31619\n0.60174\n0.22901\n0.010356\n0.095916\n-1.7082\n1.4636\nNaN\nNaN\nNaN\n5.5697\n5.337\n5.6542\n4.0559\n1.6897\n3.4423\n5.4728\n4.9769\nNaN\nNaN\nNaN\n4.7277\n4.4556\n5.2864\n3.7244\n1.0727\n1.7862\n2.3195\n1.8015\n1.6271\n3.3373\n2.3141\n2.3075\n2.2591\n1.6298\n1.8715\n2.8147\n0.81637\n1.0566\n2.1292\n1.87\n1.8213\n0.17365\n-0.58603\n-0.11925\n0.19862\n0.62578\n1.121\n-0.86024\n-1.1281\n-0.71699\n1.112\n0.90401\n0.12717\n0.18649\n1.051\n0.72894\n0.621\n0.57748\n0.84511\n0.91212\n-0.19045\n-1.1762\n1.1587\n0.91731\n1.036\n2.8155\n1.4673\n0.17814\n-0.2602\n-0.64838\n-1.5321\n0.58084\n0.86651\n0.40524\n0.10733\n1.248\n2.5277\n2.5974\n1.8978\nNaN\nNaN\n5.2797\n4.8066\n4.257\n4.6445\n4.3512\n3.4004\n4.3961\n3.5746\n3.4557\n4.1165\n4.2198\n4.9973\n5.6806\n5.1112\n3.9322\n0.98228\n1.4909\n1.1069\n1.2056\n1.2168\n0.57843\n1.1364\n1.0681\n2.2201\n0.61019\n0.78598\n1.6779\n1.9485\n4.9275\n5.1576\n2.0079\n1.8017\n0.41964\n-1.5673\n-1.2817\n-1.5491\n-0.42805\n0.29253\n-0.74491\n-0.80219\n-0.49857\n0.32418\n1.2214\n0.35055\n0.24444\n-0.2639\n-0.39282\n0.092528\n-0.4084\n-0.79309\n0.14267\n-0.12189\n-0.42851\n1.958\n1.8041\n1.4857\n2.6832\n2.6444\n0.93443\n-0.39769\n-0.92266\n-0.10611\n1.2516\n1.5544\n1.7455\n1.2819\n1.1698\n0.24171\n0.47161\n3.8018\n3.5262\n4.6545\n5.6908\n5.3081\n4.146\n2.8755\n3.3565\n2.9297\n3.1281\n3.2583\n4.0327\n3.3642\n2.987\n3.7147\n5.4721\n6.1057\n4.7038\n2.452\n1.2421\n1.2968\n0.32415\n1.9484\n1.7904\n1.7852\n0.37579\n1.0414\n0.64745\n-0.38755\n-0.54995\n1.8072\n5.462\n7.8119\n2.971\n2.5234\n-0.25182\n-1.4258\n-1.8326\n-1.2521\n-1.2404\n-1.4663\n-0.89217\n0.19266\n0.40782\n0.37001\n0.88653\n0.48686\n0.35618\n-0.61711\n-0.80978\n-1.6203\n-1.3686\n-1.4373\n-0.829\n-0.16988\n0.45831\n1.9508\n1.0641\n0.30228\n1.3127\n2.1221\n1.3766\n0.051487\n-0.77556\n0.58961\n0.65865\n1.6474\n2.695\n2.6578\nNaN\nNaN\nNaN\nNaN\n4.326\n4.9201\n5.9622\n3.6948\n3.8754\n3.4286\n3.3905\n2.4668\n1.6717\n2.6591\n2.9614\n2.4585\n2.5936\n2.7169\n2.1761\n3.7018\n3.3731\n1.7892\n1.5847\n0.4962\n-0.46509\n1.4592\n1.4775\n1.3987\n0.15011\n0.30277\n0.39179\n0.18021\n-0.77621\n-0.83945\n2.9328\n4.1127\n3.7949\n2.5717\n1.7304\n0.93756\n0.50542\n-0.35051\n-1.7487\n0.18911\n1.0298\n1.1322\n0.9456\n0.13125\n-0.15588\n0.40904\n0.5508\n-0.44608\n-2.0042\n-2.508\n-1.5404\n-1.0746\n-0.8253\n-0.69217\n-1.6231\n1.2378\n0.5827\n0.043531\n0.17417\n0.69672\n-0.19741\n0.24397\n-2.0732\n-1.2492\n-0.30468\n1.8877\n2.856\n1.4903\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8805\n4.4911\n4.4024\n3.9046\n4.0996\n3.8768\n2.5754\n1.8375\n3.1173\n2.741\n2.1512\n1.9907\n1.7771\n1.5895\n1.5004\n1.9273\n2.1447\n1.7448\n-0.26368\n-2.0918\n0.20021\n0.88728\n-0.39235\n-0.80245\n-0.12655\n0.65685\n0.8753\n0.23164\n-4.1442\n1.3321\n1.822\n2.1585\n1.8433\n2.5859\n2.6669\n1.3725\n0.52387\n0.37859\n1.3733\n0.57556\n0.44119\n0.21318\n-0.61988\n-0.96001\n-0.68947\n-0.97337\n-0.84486\n-1.0661\n-2.5472\n-1.3138\n-1.2916\n-0.50346\n-1.1475\n-0.72345\n0.033366\n1.0345\n-0.34845\n-0.59163\n-0.8168\n-1.5363\n-0.83094\n-3.3483\n-3.5635\n-2.0057\n0.82114\n3.8702\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2013\n4.2313\n4.138\n4.2105\n4.3447\n3.8962\n3.7014\n3.8102\n4.1473\n3.4588\n1.907\n2.4745\n3.0414\n2.2393\n0.81055\n1.2558\n1.8158\n-0.24433\n-1.5578\n-2.0403\n0.38185\n-1.2502\n-1.02\n0.42227\n1.0193\n1.5171\n1.3735\n-2.8914\n0.15961\n0.75528\n0.9313\n1.7336\n3.045\n2.0222\n1.8769\n1.0282\n0.34414\n3.0032\n2.5057\n0.61591\n1.1322\n-0.2901\n-0.80854\n-2.0027\n-2.4216\n-1.1771\n-0.077609\n-0.91678\n-0.43775\n-1.8525\n0.53553\n1.2063\nNaN\n-1.1372\n0.85223\n-1.6058\n-1.7232\n-2.9527\n-3.1615\n-2.5968\n-5.2776\n-5.3509\n-3.9409\n-1.2768\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3677\n3.1646\n4.3579\n3.6903\n4.2545\n4.2382\n3.4901\n4.0161\n5.3144\n4.4952\n3.0148\n2.9824\n3.8892\n1.5162\n0.25735\n-0.4571\n0.72618\n0.45746\n0.36684\n-1.0241\n-0.72956\n-1.6802\n0.35026\n1.8877\n2.8452\n3.6662\n2.4953\n0.71151\n2.3472\n1.5658\n2.2703\n2.8341\n1.9555\n1.5494\n1.8279\n0.37963\n0.20777\n2.9351\n3.1253\n0.80525\n1.9716\n0.051\n1.0585\n-0.65181\n-3.1869\nNaN\nNaN\n0.10723\n0.42356\n-1.8783\nNaN\nNaN\nNaN\n0.25478\n-1.1285\n-0.73007\n-1.8011\n-1.3475\n-0.82726\n-0.88636\n-1.5442\n-0.24746\n0.15664\n-1.4344\n-1.9001\n-2.6711\n-1.2775\n1.231\n0.46331\n0.49201\n-1.9511\n4.3785\n3.7243\n2.8707\n1.9109\n2.2637\n-0.21121\n1.5638\n-1.7285\n-3.2284\n-5.4687\n-3.6928\n0.33178\n-6.8995\n-5.7448\n-5.659\n-7.9433\n-5.4982\n-5.2152\n-4.463\n-3.3965\n-4.595\n-4.8359\n-3.2295\n-2.4983\n-2.8587\n-0.96421\n-0.492\n-1.4661\n-1.2208\n-1.3624\n-2.5256\n-2.4556\n-1.7683\n-1.1192\n-2.2319\n-3.6979\n-0.32397\n-3.067\n-4.0128\n-5.6654\n-8.7598\n-7.3931\n-5.4129\n-4.677\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6876\n-1.8423\n-2.003\n0.83777\n1.957\n-1.7763\n-0.18271\n-1.8821\n1.184\n0.78861\n-0.080608\n-0.83652\n-2.8878\n0.80286\n2.6163\n0.87679\n-4.1217\n-3.5039\n-0.017563\n0.22677\n5.1904\n1.0777\n0.39281\n2.8177\n3.2926\n-0.57669\n-2.3009\n-2.8843\n-1.2004\n-0.57383\n-1.6881\n-2.3279\n-3.3782\n-7.0128\n-6.2412\n-5.2963\n-4.317\n-3.1826\n-4.6182\n-2.2687\n-0.47395\n-1.8153\n-1.5699\n-1.2679\n-0.57792\n-0.69607\n-0.55748\n-1.4267\n-2.3281\n-2.5129\n-2.1415\n-1.5474\n-2.22\n-0.93592\n3.0265\n-1.0075\n-3.3175\n-7.4911\n-8.4882\n-5.9195\n-5.9887\n-3.1992\n-2.6976\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7184\n-1.8291\n-1.4321\n-1.2939\n-3.7174\n-3.3894\n-2.5832\n-1.144\n-0.45161\n0.82632\n0.24926\n-0.82716\n-1.9807\n2.8233\n6.2901\n2.3034\n-1.6518\n-0.97975\n-1.1633\n2.7056\n1.1862\n1.7605\n2.3564\n2.1714\n3.7681\n1.8558\n0.68721\n0.084189\n0.11935\n-2.6144\n-0.66793\n-1.1653\n-2.1648\n-4.0366\n-6.0538\n-5.4495\n-4.0944\n-3.4197\n-2.8369\n-2.2692\n-1.27\n-0.90761\n-1.8114\n-1.2762\n-1.1896\n-1.2375\n-1.3491\n-1.1371\n-1.7719\n-1.6067\n-1.2396\n-2.334\n-2.4066\n-0.04143\n0.34454\n-0.1064\n-1.1865\n-7.4791\n-7.4776\n-3.0441\n-6.3813\n-1.1228\n-0.011989\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.65686\n-1.8004\n-1.3963\n-1.4652\n-1.4271\n-1.2419\n-0.095832\n-0.30794\n-1.2603\n-0.49149\n0.41664\n-2.3989\n1.0498\n5.3044\n3.4811\n-0.25624\n-0.62871\n-0.33867\n-0.94947\n2.4171\n2.5351\n2.3867\n2.799\n0.28045\n2.5296\n2.931\n2.1693\n0.89914\n-1.0299\n-1.9448\n-2.0348\n-1.9217\n-2.5737\n-2.849\n-5.2726\n-5.6584\n-4.9\n-3.2365\n-1.903\n-1.9589\n-1.5183\n-1.26\n-3.7478\n-3.0532\n-2.1126\n-2.0479\n-2.2291\n-1.2826\n-1.3866\n-0.32282\n0.56369\n-0.68218\n-0.19742\n0.71619\n-0.029228\n0.096898\nNaN\nNaN\nNaN\n-7.7476\n-6.5876\n-0.21881\n1.7892\nNaN\nNaN\nNaN\n-4.2873\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.54219\n-1.0468\n-0.66177\n-1.1602\n-0.76769\n-0.9133\n-0.75756\n-0.5977\n-1.5972\n-2.2138\n-1.5938\n-2.2638\n0.34895\n0.47642\n-1.4435\n-2.356\n-1.5885\n1.4981\n-3.4735\n-0.24433\n2.5494\nNaN\nNaN\n-4.4665\n1.4568\n3.2174\n3.6584\n0.81325\n0.48208\n0.87107\n-2.4767\n-3.1133\n-3.7066\n-2.8742\n-4.276\n-4.9582\n-5.5577\n-3.2775\n-1.7846\n-2.2338\n-2.4481\n-1.6978\n-3.519\n-4.2523\n-3.4631\n-2.6348\n-1.7388\n-1.0467\n-1.3283\n-1.3299\n0.27964\n0.54846\n-0.47488\n0.839\n-0.0051222\n0.18331\n5.9414\nNaN\nNaN\n-10.395\n-6.0257\n-1.1854\n2.1368\n-3.3196\n-5.7584\n-5.0862\n-6.6689\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0264\n-1.4279\n-0.87887\n-1.0099\n-0.11678\n-0.85894\n-0.74445\n-0.29736\n-0.50008\n-0.80267\n-0.39234\n0.60084\n0.69496\n-1.8288\n-2.9831\n-1.689\n-1.9482\n-0.096582\n-3.4857\nNaN\nNaN\nNaN\nNaN\n-1.9915\n0.10797\n3.588\n3.4631\n1.7259\n0.68079\n1.6447\n0.46638\n-3.9127\n-4.381\n-4.4682\n-4.6073\n-6.501\n-6.7663\n-4.5447\n-2.9696\n-2.8877\n-2.6041\n-2.7498\n-3.579\n-4.6202\n-7.0083\n-4.7266\n-2.2409\n-0.2434\n-1.0652\n-0.41928\n0.25935\n0.74141\n0.18882\n-0.023637\n-1.2069\n-4.0718\n-0.96271\nNaN\nNaN\n-14.741\n-8.5761\n-0.78877\n-1.3298\n-6.0083\n-4.0899\n-3.6578\n-4.9685\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4998\n-2.0539\n-1.1031\n0.51009\n1.4586\n0.021231\n1.3983\n1.6513\n1.533\n1.3485\n0.91191\n0.307\n0.53977\n-1.6787\n-1.8253\n-3.0555\n-3.2436\n-0.18111\n-4.035\nNaN\nNaN\nNaN\nNaN\n-2.2644\n-1.6408\n1.29\n2.7212\n2.1539\n0.18864\n1.6065\n-0.72054\n-4.7431\n-6.3098\n-5.8913\n-5.8763\n-7.0922\n-7.2272\n-5.7105\n-4.8811\n-3.8931\n-2.447\n-3.9386\n-5.187\n-6.2855\n-9.9848\n-5.5031\n-2.6279\n-0.68307\n0.19536\n0.69207\n-0.23145\n0.57235\n0.77711\n0.31893\n-0.56921\n-2.448\n-0.94781\nNaN\nNaN\n-8.7125\n-11.663\n-5.8528\nNaN\n-8.0197\n-5.8791\nNaN\nNaN\n-0.6\n-1.8315\nNaN\nNaN\nNaN\n-0.6539\n-0.11558\n0.064716\n1.7424\n3.5297\n0.74561\n1.9593\n2.6664\n2.2306\n1.8678\n1.0059\n0.64293\n0.17832\n0.68343\n-0.43069\n-1.8402\n-1.9982\n-0.88712\n-2.3511\n-2.6472\n-2.2437\n-0.57964\n-1.8023\n-1.8813\n-0.91346\n0.090842\n1.1611\n2.6852\n0.95775\n1.2225\n0.73991\n-4.0072\n-6.5201\n-6.6223\n-6.4257\n-5.8829\n-5.7957\n-6.4715\n-6.7329\n-6.713\n-4.2778\n-4.874\n-5.0832\n-6.6181\n-12.059\n-5.3428\n-1.7368\n-0.64411\n-0.20573\n1.1385\n-0.10127\n0.74785\n2.0961\n2.3526\n2.2477\n0.74856\n-0.21288\n0.55372\n1.1989\n-2.933\n-8.1539\n-3.5219\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.5529\n-1.9113\n-4.276\nNaN\nNaN\n-0.14142\n-0.39334\n0.58438\n0.54018\n2.8076\n1.0323\n1.3297\n3.2463\n2.3474\n1.8318\n0.68763\n1.0223\n1.4119\n1.0453\n-0.019981\n-0.13599\n-0.51784\n-0.25834\n-1.2171\n-1.29\n-1.3772\n-2.9681\n-3.4907\n-1.3318\n-0.72616\n0.016997\n0.73174\n0.38742\n0.26131\n-0.30863\n0.32734\n-2.1826\n-4.4445\n-6.3369\n-5.8029\n-5.7991\n-6.0831\n-7.1577\n-7.4513\n-7.2557\n-6.6515\n-5.9107\n-6.9302\n-9.1192\n-12.337\n-2.4832\n0.16564\n1.1923\n1.6558\n2.7602\n1.5571\n2.6311\n3.3712\n5.4748\n4.7079\n1.4658\n-0.079738\n0.7051\n3.4372\n2.7713\n-4.42\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.5999\n-3.2174\nNaN\nNaN\n-2.3959\n-0.70673\n0.43301\n-0.3544\n2.043\n0.43321\n0.34302\n1.0853\n1.329\n1.0377\n1.2496\n1.3698\n1.7823\n1.027\n0.61607\n1.0117\n0.25986\n0.36607\n-0.081471\n0.49462\n-0.33783\n-1.0785\n0.16567\n-1.0982\n-2.3797\n-0.35647\n1.1014\n-0.42858\n-0.95751\n-1.7785\n-0.98734\n-4.5904\n-7.4831\n-6.1225\n-6.3087\n-5.2355\n-5.9445\n-7.0756\n-8.6837\n-8.0621\n-6.8092\n-6.3651\n-7.9208\n-8.2905\n-4.8112\n0.24256\n0.025235\n1.6157\n1.9341\n3.4255\n2.7122\n4.8084\n5.0204\n5.7917\n4.6242\n2.1204\n0.10239\n-1.264\n4.19\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.1035\nNaN\n-5.1474\n-4.8657\n-3.4251\n-2.4828\n-0.9808\n-1.138\n0.04654\n0.53254\n1.0102\n0.95901\n1.2463\n2.2895\n2.441\n1.9868\n3.7654\n2.8265\n2.3802\n2.5787\n0.38699\n-0.25285\n-0.27388\n0.86934\n-0.7791\n-0.30448\n0.63125\n-2.5381\n-3.5685\n0.075588\n1.4202\n0.85515\n-0.8808\n-0.86229\n-2.3617\n-5.2814\n-7.7711\n-5.5632\n-7.3596\n-6.1577\n-6.5545\n-6.42\n-10.288\n-9.0041\n-6.6768\n-6.8162\n-8.5394\n-7.7417\n-4.0229\n-1.0445\n-0.47077\n1.3149\n2.665\n2.0973\n2.3914\n4.8088\n4.1874\n6.1335\nNaN\nNaN\n-1.9926\n-1.9964\n3.6032\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.8589\n-3.7329\n-3.3866\n-3.1238\n-2.7108\n-1.0768\n-0.098944\n1.0869\n1.818\n1.9591\n2.0235\n1.5096\n1.5564\n1.8205\n4.6903\n3.76\n2.1224\n1.4079\n0.66124\n0.45848\n0.47248\n0.12679\n-2.005\n-1.1613\n-1.3635\n-4.2782\n-4.1858\n-1.6237\n0.68722\n2.1734\n0.77736\n0.50795\n-2.1698\nNaN\nNaN\nNaN\n-6.7854\n-6.8759\n-6.0896\n-6.7957\n-11.726\n-9.1988\n-6.5137\n-7.8717\n-8.8719\n-6.8863\n-4.8826\n-2.3377\n-0.62714\n1.8412\n3.7373\n3.4617\n2.835\n5.062\n3.4859\nNaN\nNaN\nNaN\n-0.80855\n-2.5502\n2.174\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.76154\n-1.1637\n-1.1278\n-0.82436\n0.15154\n1.1898\n1.1499\n1.4146\n1.3338\n1.3876\n2.6005\n2.6793\n3.0571\n3.0325\n1.8743\n3.0755\n3.3892\n1.8061\n1.311\n-0.1383\n-1.3775\n-2.3341\n-4.9824\n-5.1899\n-4.7365\n-3.9478\n1.683\n1.0193\n-0.80632\n0.30403\n-1.2038\nNaN\nNaN\nNaN\n-6.7108\n-9.24\n-7.7998\n-5.5125\n-9.4153\n-8.2875\n-8.3812\n-11.99\n-13.279\n-7.1362\n-3.6517\n-1.6212\n0.03777\n2.5932\n4.4298\n4.5787\n3.7659\n4.4291\n2.2378\nNaN\nNaN\nNaN\n-4.9222\n-4.964\n-0.72823\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.10129\n-0.25746\n-0.71869\n0.17736\n1.2376\n1.031\n1.0432\n0.56926\n0.30504\n0.48007\n0.97063\n2.0166\n2.4958\n2.4106\n1.8675\n3.4677\n3.1455\n1.5595\n0.1524\n-0.8187\n-0.89216\n-4.6756\n-7.8733\n-7.6418\n-8.0555\n-3.5107\n0.29611\n-1.128\n-0.85187\n-0.28179\n-1.1193\nNaN\nNaN\nNaN\nNaN\n-9.9622\n-9.1698\n-2.9955\n-7.8784\n-6.9775\n-8.48\n-11.077\n-10.415\n-5.0209\n-1.2775\n0.82191\n1.365\n3.251\n4.7685\n5.2554\n3.4135\n1.9022\n1.5468\nNaN\nNaN\nNaN\nNaN\n-4.9606\n-1.0387\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.38865\n-0.61784\n0.63275\n1.7298\n1.5979\n0.9897\n1.1417\n0.32355\n0.050555\n-0.024183\n-0.27816\n0.96955\n2.593\n2.7913\n2.2809\n2.9847\n3.4326\n2.8119\n0.56906\n-1.5283\n-1.0045\n-6.2417\n-11.012\n-9.4842\n-8.5099\n-2.77\n-1.1378\n-1.3076\n-0.22251\n-0.37593\n-0.42253\nNaN\n-10.827\n-11.297\n-8.1981\n-7.3123\n-7.5327\n-2.8819\n-6.7449\n-5.8746\n-6.0099\n-9.359\n-10.288\n-1.6316\n2.1463\n4.1189\n2.2261\n3.3192\n5.3597\n5.357\n2.6774\n3.647\n3.5851\n-0.76589\nNaN\nNaN\nNaN\n-5.682\n-1.4472\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.1871\n-0.50089\n0.8607\n2.6215\n2.1016\n1.5283\n1.7871\n1.0952\n0.39016\n0.73196\n-0.01976\n0.96101\n2.2523\n2.4279\n1.8432\n1.3241\n2.0941\n2.3382\n2.1114\n-0.71917\n-0.98543\n-2.9215\n-13.694\n-13.153\n-8.1083\n-4.2969\n-1.55\n1.2686\n0.83508\n-1.6158\n-4.0163\n-11.661\n-9.1888\n-10.361\n-8.6268\n-5.8927\n-3.7274\n-4.2884\n-5.0815\nNaN\nNaN\n-10.523\n-9.2242\n-1.2876\n4.9563\n4.6511\n3.1145\n1.912\n4.6862\n3.3665\n2.1913\n1.9313\n4.9158\n0.74098\n-1.7035\n-4.6361\n-8.1932\n-6.1897\n-4.055\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.032006\n-0.18862\n1.4554\n2.8044\n1.5254\n2.0031\n2.0089\n1.5494\n0.62222\n1.0441\n0.65176\n0.80235\n1.9798\n1.9251\n1.1627\n0.8639\n2.0071\n1.2194\n1.6012\n0.90995\n-1.2011\n-4.2301\n-7.5153\n-12.033\n-5.6612\n-3.03\n-0.65878\n-0.59195\n-2.129\n-4.8846\n-6.8983\n-6.9686\n-4.8944\n-9.1067\n-5.9097\n-5.6983\n-3.9067\nNaN\nNaN\nNaN\nNaN\n-13.66\n-9.5169\n-6.3729\n-0.74446\nNaN\nNaN\n2.7126\n3.4282\n2.7159\nNaN\n1.7145\n4.3227\n1.3981\n-0.50802\n-1.2405\n-7.8957\n-6.5963\n-6.1854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.56802\n0.47644\n2.825\n3.4293\n2.1533\n1.8063\n1.7585\n2.0255\n0.18041\n0.45484\n0.38068\n0.96911\n1.3344\n1.2494\n1.779\n1.7219\n1.7778\n0.51414\n0.47709\n-0.43781\n-2.639\n-6.6875\n-8.8055\n-11.192\n-4.9253\n-3.2489\n-1.3002\n-4.3671\n-5.2604\n-7.1711\n-4.7307\n-4.7294\n-8.3406\n-5.3245\n-3.8368\n-4.66\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.546\nNaN\nNaN\n3.1074\n1.7183\n1.933\n5.8913\nNaN\nNaN\nNaN\n0.24291\n-0.010593\n-2.5897\n-5.137\n-4.7925\n-7.1882\n-6.4738\n-3.2855\n-2.4845\nNaN\nNaN\n-3.493\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.18187\n1.2156\n2.9836\n3.565\n2.9667\n0.96738\n1.4504\n1.6081\n0.13276\n0.12742\n-1.6567\n0.25362\n1.3107\n1.057\n1.4241\n0.71868\n-0.56029\n-1.1083\n-0.76859\n-2.1006\n-3.7211\n-7.1313\n-9.2178\n-8.4103\n-6.0552\n-4.3982\n-5.2665\n-6.3102\n-3.9503\n-6.6575\n-6.1978\n-7.4913\n-7.7585\n-6.1096\n-4.229\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.7783\nNaN\nNaN\nNaN\n1.2785\n-0.34852\n1.9097\nNaN\nNaN\nNaN\n-3.0032\n-1.324\n-2.6719\n-3.6983\n-2.275\n-2.3938\n-1.8626\n0.23563\n0.57619\n-1.9083\n-2.5929\n-1.8565\n-1.2972\n-1.7516\n-4.0894\nNaN\nNaN\nNaN\n0.29411\n1.2962\n1.2395\n1.3402\n3.239\n1.8457\n2.6385\n0.96784\n-1.4023\n-0.40164\n-1.8147\n0.74355\n1.3426\n0.2739\n-0.66352\n-1.0712\n-2.3924\n-4.9568\n-3.1905\n-3.4181\n-4.423\n-9.6706\n-8.5007\n-6.0465\n-4.8432\n-3.9621\n-6.9281\n-6.2085\n-3.497\n-5.545\n-9.5293\n-9.0465\n-8.4429\n-7.1625\n-5.3011\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6867\nNaN\nNaN\nNaN\n2.4884\n-2.9146\n-0.20249\nNaN\nNaN\nNaN\n-6.7299\n-3.9142\n-3.7115\n-2.6977\n-0.069146\n1.1296\n-1.5349\n-1.7462\n0.55748\n-2.6895\n-2.0063\n0.83646\n2.2651\n-0.29506\n-1.7852\nNaN\nNaN\nNaN\n-0.50522\n1.3882\n0.54678\n0.48164\n3.2244\n1.5926\n-0.026763\n-1.1042\n-1.7149\n-0.06854\n-0.43025\n1.1777\n-0.448\n-0.71343\n-2.2017\n-2.2244\n-1.9779\n-3.457\n-2.0027\n-3.2108\n-5.4033\n-10.109\n-9.1873\n-4.6357\n-4.5226\n-2.9822\n-3.9579\n-4.8821\n-4.0145\n-2.6656\n-3.9664\n-1.3142\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.0409\n1.2087\n2.4643\nNaN\n4.4086\n3.0856\n-3.3315\nNaN\n-4.481\n-3.8501\n-2.7882\n-2.6121\n-3.6704\n-2.526\n0.88587\n1.8264\n-0.87901\n0.90361\n1.5243\n0.79982\n0.42957\n1.7274\n1.3702\n-0.91261\n-2.4832\n-2.9913\n-2.3385\n-2.1322\n-0.59846\n-0.021952\n-0.41747\n-0.53521\n-0.34806\n-0.72188\n-1.6182\n-0.84585\n-1.1932\n-1.1398\n-1.7286\n-1.3682\n-1.1768\n-1.1562\n-2.7603\n-2.1119\n-2.9305\n-4.2276\n-0.44022\n-2.4009\n-2.6711\n-8.1007\n-9.8053\n-5.8296\n-3.2945\n-0.27514\n-2.7962\n-5.0639\n-4.3352\n-3.778\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.76452\n-0.1116\n6.23\n-1.2363\n-2.2738\n-1.5884\n-2.7185\n-1.7135\n0.48861\n-0.29289\n-0.80005\n-4.0582\n-1.0848\n1.0012\n-0.43104\n2.492\n3.3157\n0.87709\n0.90341\n1.1166\n-1.0197\n0.61217\n0.37472\n-2.1694\n-0.088295\n-0.083335\n-0.56497\n-0.98424\n-0.88253\n-2.0136\n-2.3262\n-2.4322\n-2.1215\n-1.2979\n-0.91508\n-2.0421\n-2.9588\n-2.2403\n-2.0771\n-2.2801\n-2.519\n-1.3898\n-1.9274\n-4.9931\n-5.7543\n-2.5297\n-3.0904\n-8.3296\n-9.3529\n-7.1215\n-2.5975\n-2.6291\n-4.8578\n-6.5711\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.4627\n-0.53185\n2.6002\n-0.34551\n-0.63664\n-1.1912\n-1.7944\n0.55293\n1.7526\n1.0592\n1.3219\n-1.35\n-1.256\n-1.282\n-0.42234\n2.1905\n4.3784\n1.4242\n2.1473\n1.289\n-0.76595\n-0.90897\n-1.3721\n-2.0592\n-1.113\n1.204\n-0.66917\n-2.0848\n-3.1493\n-2.6808\n-1.9006\n-1.6589\n-2.2122\n-1.8786\n-0.5393\n-1.6992\n-2.8361\n-2.3558\n-2.7113\n-2.9995\n-2.3911\n0.27894\n-0.089238\n-4.3228\n-7.7309\n-9.2639\n-7.025\n-8.3813\n-9.2153\n-5.7923\n-3.9508\n-4.8302\n-7.8332\n-10.29\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-6.2194\n-2.7376\n-2.0267\n-0.36853\n-0.044106\n-0.16466\n-0.80846\n0.36169\n1.4567\n1.8136\n3.9414\n1.5337\n-0.47132\n-1.1097\n-0.56875\n-0.34096\n2.6472\n1.1934\n3.4483\n2.5114\n0.96703\n-0.99192\n-1.2502\n-1.1364\n-1.1947\n0.10248\n-2.0776\n-3.049\n-2.9431\n-2.2081\n-1.8637\n-0.51328\n-1.3607\n-0.69799\n-0.83917\n-1.7604\n-2.738\n-2.8604\n-3.2461\n-3.7234\n-3.0642\n-0.42026\n-0.88599\n-5.2839\n-8.1405\n-9.0626\n-6.7547\n-7.2363\n-9.0776\n-5.6814\n-4.216\n-5.4769\n-9.1842\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.3279\n-2.9227\n3.093\n2.1219\n2.0246\n1.0981\n0.82559\n0.3663\n3.0371\n6.8933\n4.7973\n1.5021\n0.94651\n0.57864\n-1.5091\n-1.8178\n1.7434\n3.7857\n1.3171\n-0.46407\n-3.3825\n-0.38703\n0.40565\n0.084285\n0.59577\n-3.5553\n-2.7748\n-1.8083\n-1.5619\n-1.9355\n-0.083058\n-0.40949\n-1.3698\n-1.6829\n-2.647\n-2.7658\n-3.3498\n-3.1486\n-1.5694\n-1.6989\n-0.40562\n0.2359\n-5.5751\n-7.7795\n-7.3433\n-5.8905\n-7.0972\n-8.3884\n-6.7864\n-3.957\n-3.4295\n-9.6615\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.2156\n-2.1274\n3.3185\n2.1456\n3.045\n2.208\n-0.27379\n-0.94114\n1.7578\n6.9162\n6.5947\n4.6583\n2.9582\n2.6438\n0.043205\n-0.063066\n3.5699\n5.0058\n1.8718\n0.93475\n-0.7116\n1.0722\n2.5219\n2.8176\n0.59584\n-3.0781\n-2.2797\n-1.705\n-0.72248\n-0.86061\n-0.33996\n-0.6917\n-2.0355\n-2.4579\n-2.7203\n-3.5944\n-4.5505\n-2.752\n-0.72322\n-0.73601\n0.19587\n-1.8709\n-5.2625\n-5.6802\n-6.8954\n-7.2364\n-7.2714\n-6.4302\n-5.5288\n-3.3156\n-0.50305\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6312\n-2.5344\n-1.4841\n3.8572\n-0.61977\n1.8793\n2.7824\n1.9428\n4.8215\n6.0062\n7.364\n6.8955\n6.9704\n5.4818\n4.6975\n1.1275\n0.86817\n4.7707\n4.2206\n3.9638\n1.5964\n1.262\n2.7419\n2.9748\n2.4403\n0.45748\n-3.273\n-1.5623\n-1.9877\n-0.7477\n-1.1155\n-1.1023\n-1.7408\n-2.5121\n-2.5911\n-2.8411\n-3.2835\n-3.7645\n-3.2545\n-1.5028\n-0.27861\n-0.073187\n-3.3524\n-5.4623\n-6.4019\n-6.2819\n-8.2157\n-7.6075\n-6.8169\n-6.0869\n-4.0356\n0.66809\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4598\nNaN\nNaN\nNaN\nNaN\n-4.069\n-2.7733\n-2.5288\n-1.6971\n3.1122\n-0.80412\n3.0056\n4.7776\n7.8935\n6.4963\n7.1336\n7.2736\n1.8274\n6.2837\n4.5217\n4.0574\n-0.3509\n1.7742\n5.3246\n4.1671\n4.4764\n2.9531\n3.8324\n4.0217\n2.7539\n0.43426\n-2.3237\n-2.1989\n-0.80361\n-1.724\n-2.0133\n-2.425\n-1.6498\n-1.7417\n-2.2282\n-2.7157\n-2.7499\n-2.8551\n-3.485\n-3.8872\n-1.5962\n-2.2201\n-2.3302\n-2.5172\n-3.9302\n-6.494\n-6.5419\n-6.9095\n-7.3994\n-7.8729\n-5.1595\n-2.1683\n1.0507\n-6.1218\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5564\nNaN\nNaN\nNaN\n-2.2762\n-2.4347\n-3.9223\n-3.295\n-3.168\n-2.1386\n-0.20576\n1.1393\n1.7235\n4.8135\n8.1654\n5.998\n6.3647\n3.9686\n-2.1947\n-0.55234\n3.3514\n3.8099\n0.8149\n2.61\n4.2246\n5.2065\n3.8801\n2.788\n3.0865\n4.5027\n1.4019\n-1.2184\n-6.8006\n-1.5577\n-1.4094\n-1.5104\n-2.476\n-2.9137\n-2.0326\n-2.1215\n-2.9211\n-3.2154\n-2.4167\n-3.2297\n-3.8175\n-4.4003\n-2.7504\n-2.4428\n-1.9162\n-2.7132\n-3.8087\n-4.149\n-8.2984\n-9.0126\n-7.175\n-6.0136\n-2.6586\n0.81849\n0.92792\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8251\n-1.0246\nNaN\nNaN\n11.059\n-2.3291\n-2.8036\n-2.7705\n-3.6102\n-4.0204\n-3.6154\n-3.5538\n-1.6787\n-1.6349\n0.36633\n4.1116\n0.76068\n-0.55901\n1.2546\n-0.6477\n-1.237\n-1.9545\n0.60759\n0.69545\n1.768\n3.5022\n3.0329\n1.7651\n2.1851\n2.955\n1.4486\n-0.72212\n-1.447\n-8.5615\n-2.1603\n-2.3143\n-2.3726\n-2.3371\n-2.738\n-2.0788\n-2.4035\n-2.7417\n-3.3143\n-2.1401\n-2.936\n-4.2867\n-4.6042\n-4.219\n-3.3333\n-4.1872\n-4.81\n-5.5838\n-5.938\n-7.0377\n-9.2914\n-6.5167\n-4.3956\n0.025552\n1.7638\n3.6632\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3713\n5.1756\n2.0898\n-2.0781\n-6.1744\n17.547\n13.166\n7.9019\n-3.4961\n-4.9354\n-4.9891\n-2.5171\n-3.2616\n-2.2134\n-1.795\n-2.2656\n-1.3492\n-1.4419\n-1.1091\n-2.4332\n-1.5138\n-1.266\n-1.4987\n-1.8975\n-3.7685\n0.11677\n1.7512\n1.9976\n1.3024\n-0.90577\n-0.17316\n2.1601\n1.8823\n-0.91347\n-2.2971\n-3.0066\n-4.5132\n-2.8241\n-1.9055\n-1.8493\n-1.177\n-1.915\n-2.4587\n-2.8883\n-1.9342\n-2.4943\n-2.7743\n-3.2379\n-5.0656\n-6.671\n-5.6392\n-4.9207\n-4.6874\n-6.1908\n-6.5203\n-5.7145\n-6.7265\n-8.1287\n-7.2225\n-3.7882\n-1.4546\n1.0187\n6.3382\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.214\n9.5318\n7.2278\n2.886\n-4.3565\n-7.7122\n-6.4907\n-4.1849\n-2.8973\n-2.612\n-4.0385\n-3.7628\n-1.8111\n-0.80738\n-0.93939\n-3.6385\n-4.1105\n-2.4865\n-2.5336\n-3.6095\n-4.0688\n-1.1063\n0.91479\n0.83933\n0.66916\n-0.96722\n-0.43372\n-0.28\n1.1539\n-1.7038\n-3.4236\n-2.5757\n-0.90916\n-1.3883\n-1.7456\n-2.0895\n-0.77496\n-2.3304\n-2.609\n-3.2334\n-2.767\n-2.9119\n-3.8927\n-4.8031\n-5.2488\n-6.2068\n-5.6005\n-6.0845\n-4.2463\n-5.3365\n-10.088\n-10.399\n-5.4203\n-5.9345\n-7.808\n-4.9368\n-1.6044\n0.87797\n-0.82455\n-5.9815\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.319\n5.5103\n0.93613\n-0.069178\n-3.603\n-11.217\n-7.9129\n-4.9566\n-4.1091\n-3.1261\n-3.5724\n-3.3325\n-2.5464\n-2.18\n-2.579\n-2.4851\n-5.7467\n-3.4732\n-1.7736\n-1.1424\n-2.1057\n-2.4451\n-0.456\n0.02804\n0.89142\n1.555\n0.01567\n-1.4623\n-3.1951\n-3.1713\n-2.2824\n-0.41293\n0.49935\n-0.55143\n-1.4858\n-2.4923\n-1.6377\n-2.102\n-1.3026\n-3.1665\n-3.0057\n-3.0657\n-3.2569\n-4.9858\n-5.1472\n-4.7761\n-5.0053\n-6.2873\n-4.1547\n-4.6954\n-7.8957\n-10.002\n-8.3331\n-5.4843\n-7.9172\n-5.2566\n-1.2103\n1.0275\n-3.2001\n-7.584\n-13.481\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.593\n0.67282\n-3.0792\n-1.6237\n-2.7028\n-9.9119\n-6.3762\n-4.8546\n-5.4609\n-5.9552\n-6.3718\n-3.5629\n-3.0782\n-3.1863\n-4.749\n-2.5967\n-2.371\n-2.1169\n-0.5775\n0.062967\n0.024717\n0.17175\n-0.87961\n0.56784\n0.50263\n0.9711\n-1.1085\n-2.1235\n-3.1245\n-1.7478\n-1.2629\n0.33665\n0.42316\n-0.75485\n-1.993\n-2.603\n-2.2705\n-0.64841\n-0.20415\n-2.3357\n-2.2499\n-2.8772\n-2.7409\n-5.1805\n-4.7906\n-3.8251\n-5.3049\n-6.5865\n-5.4756\n-4.4851\n-8.1205\n-8.3059\n-11.847\n-9.5199\n-9.0239\n-5.2418\n-2.5673\n-0.84786\n-2.4791\n-8.0514\n-9.1067\nNaN\nNaN\n-5.0422\n-5.6472\n0.08687\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7603\n-2.1189\n-1.9368\n-1.4544\n-7.8647\n-10.486\n-6.6162\n-7.1661\n-7.4696\n-8.6899\n-6.5954\n-3.8416\n-3.0465\n-3.0205\n-2.998\n-2.3334\n-1.5713\n-1.3062\n-0.86578\n-1.0166\n-0.93029\n-0.97633\n-1.5831\n-0.27702\n-1.0047\n-1.4665\n-4.4395\n-5.3378\n-2.3078\n-1.1029\n-0.59058\n1.2936\n0.33402\n-1.6783\n-2.0615\n-1.5363\n-2.6103\n-0.24034\n1.0623\n-0.96904\n-1.5176\n-2.4455\n-2.8017\n-4.7468\n-3.2742\n-3.826\n-4.9806\n-5.7145\n-4.6747\n-5.0528\n-8.3103\n-9.7731\n-9.3814\n-9.2725\n-7.9726\n-5.9483\n-3.1987\n-6.3891\n-9.4116\n-9.0733\n-4.8004\n-5.2666\n-2.729\n-2.5742\n-5.0447\n-2.0303\n0.72025\nNaN\nNaN\nNaN\nNaN\n-9.3474\n-3.7771\n-5.4729\n-5.4256\n-4.4751\n-9.7467\n-13.107\n-8.5198\n-7.5654\n-7.0261\n-7.5533\n-6.0783\n-2.6415\n-2.0847\n-0.5503\n-2.2494\n-3.37\n-2.9018\n-1.2262\n-1.0528\n-0.49542\n-0.34412\n-1.7857\n-2.1222\n-2.8677\n-4.5748\n-3.4162\n-3.6001\n-3.7595\n-3.5937\n-3.5755\n-0.93289\n0.84402\n-1.1282\n-0.78988\n-1.222\n-2.1128\n-2.0864\n-0.89137\n0.86162\n0.078272\n-1.0575\n-2.0638\n-2.7633\n-3.8804\n-3.0091\n-3.2344\n-3.7883\n-4.6425\n-5.8675\n-9.0967\n-14.15\n-10.142\n-7.8577\n-7.8371\n-5.1887\n-4.0453\n-3.4392\n2.2926\n-4.5165\n-4.3657\n-3.5825\nNaN\n-5.9216\n-5.3447\n-10.18\n-3.1807\n-1.8908\nNaN\nNaN\nNaN\n-10.228\n-6.305\n-3.2373\n-4.6218\n-7.2301\n-5.5877\n-8.2155\n-12.698\n-8.5612\n-2.6567\n-3.0535\n-5.6117\n-4.7059\n-2.0686\n-1.0289\n-0.5392\n-3.1415\n-2.5666\n-2.4986\n-1.8715\n-1.373\n-0.065884\n-0.098018\n-1.7877\n-3.156\n-3.7222\n-4.4007\n-2.718\n-0.77222\n-1.5054\n-4.0127\n-3.7568\n-1.4909\n-0.054292\n-1.9872\n-0.73053\n-0.74677\n-1.0279\n-0.99456\n-0.16701\n0.71218\n0.26352\n-1.1542\n-2.3523\n-3.3684\n-3.5264\n-3.1931\n-2.6786\n-2.7837\n-3.596\n-5.4226\n-8.7974\n-13.941\n-8.9462\n-8.0323\n-5.8431\n-6.4607\n-2.1922\n-3.4992\n-2.4973\n-3.7673\n-4.5154\nNaN\nNaN\n-6.7197\n-6.8312\n-7.2938\n-4.4323\n-4.5227\n-5.6179\n-8.8635\n-9.871\n-7.9351\n-6.6527\n-2.4949\n-5.0808\n-9.5508\n-8.2997\n-8.2303\n-9.5808\n-4.3573\n-1.4207\n-1.4452\n-4.7484\n-4.2665\n-2.3873\n-0.063348\n-0.65117\n-2.1834\n-1.8721\n-1.7948\n-1.6362\n-1.052\n0.18719\n-0.21494\n-1.0668\n-3.0512\n-3.6861\n-3.1997\n-1.3392\n0.085703\n-0.027101\n-1.0451\n-1.25\n-0.9122\n-0.34426\n-0.49614\n-0.57374\n-0.97155\n-0.050855\n0.4213\n0.88937\n0.8588\n0.91672\n-0.74954\n-2.0049\n-2.7394\n-2.8981\n-2.0831\n-1.5277\n-0.64691\n-1.0392\n-4.4515\n-6.2739\n-12.99\n-4.8203\n-6.2665\n-6.2936\n-4.452\n-1.3158\n-3.1684\n-5.064\n-5.9451\n-5.0804\nNaN\nNaN\nNaN\n-8.1884\n-6.0837\n-4.3612\n-5.2836\n-5.6811\n-4.5687\n-4.2625\n-7.35\n-6.3678\n-1.541\n-3.7878\n-6.8194\n-6.0491\n-4.6977\n-5.2377\n-4.5877\n-3.4556\n-4.1506\n-3.9469\n-2.4792\n-1.3627\n-0.19943\n-0.78794\n-2.6956\n-2.411\n-1.5375\n-0.90147\n0.48193\n0.54755\n0.48669\n-1.1191\n-2.7411\n-3.27\n-2.2669\n-0.3115\n0.24785\n0.5699\n0.46532\n-1.0361\n-1.3735\n0.14533\n1.0454\n0.055181\n-1.1144\n0.14614\n0.91661\n1.6816\n0.92646\n0.45818\n0.70438\n-0.97835\n-1.6934\n-1.3885\n-0.51702\n0.37441\n2.3035\n3.7062\n-1.2613\nNaN\nNaN\nNaN\n-5.3369\n-8.0443\n-4.1434\n-1.9416\n-7.113\n-7.479\n-7.4257\n-5.8408\nNaN\nNaN\nNaN\n-6.1444\n-5.0386\n-6.6677\n-5.5521\n-4.65\n-3.5274\n-3.7999\n-4.0504\n-3.7831\n-4.7794\n-6.6098\n-6.2583\n-3.9608\n-3.2975\n-3.7677\n-3.2245\n-1.7308\n-1.3535\n-0.89303\n-1.4158\n-1.1862\n-1.043\n-1.3998\n-2.9504\n-2.4245\n-0.51532\n1.2367\n0.62165\n0.81364\n0.79239\n-1.3585\n-2.2807\n-1.9541\n-1.6629\n-0.70018\n-0.38731\n-0.37099\n0.3205\n-0.354\n-0.84595\n-0.48451\n0.96\n1.1911\n0.94982\n1.1073\n0.71267\n1.0026\n1.5462\n1.17\n1.5826\n0.47665\n-0.67273\n-0.14059\n0.5859\n2.6644\n5.2571\n4.9236\n-0.39149\nNaN\nNaN\nNaN\n-5.2354\n-7.1769\n-9.2764\n-7.4654\n-6.9601\n-5.1283\n-6.3081\n-5.825\nNaN\nNaN\nNaN\n-7.0493\n-6.3296\n-6.6629\n-4.9488\n-1.0908\n-2.4216\n-3.5707\n-3.6252\n-3.7863\n-5.9894\n-6.2653\n-6.4129\n-4.4882\n-2.3439\n-2.2016\n-2.9845\n-1.4972\n-1.6452\n-1.6973\n-1.1748\n-0.77499\n-0.72829\n-0.71943\n-0.45938\n-0.24121\n0.26585\n0.17708\n0.70773\n1.2847\n1.6782\n-0.43187\n-0.99863\n-0.30268\n0.18367\n0.26047\n0.24055\n0.057775\n1.0888\n0.60679\n0.071131\n0.44049\n0.7877\n1.3871\n1.9367\n1.9346\n1.1541\n2.0864\n2.7256\n3.4232\n2.5665\n2.1864\n0.16833\n1.3377\n2.7077\n3.5556\n3.6078\n2.353\n1.3599\n-1.4472\nNaN\nNaN\n-6.4183\n-5.8988\n-6.1744\n-6.6969\n-5.8787\n-3.9396\n-4.778\n-5.1203\n-5.067\n-6.6414\n-7.0637\n-9.6283\n-8.6593\n-6.9863\n-4.8217\n-0.59468\n-2.0283\n-1.8774\n-2.2933\n-3.576\n-3.0105\n-3.8773\n-3.7982\n-3.3066\n-1.9513\n-1.4147\n-2.0567\n-2.5147\n-5.252\n-4.9139\n-1.3033\n-0.48953\n-0.35443\n2.0714\n0.77465\n0.20444\n0.43616\n-0.27965\n0.56871\n1.1666\n1.9667\n0.67798\n-0.9078\n0.20322\n1.4393\n2.0821\n1.3489\n0.36731\n2.01\n1.228\n0.62942\n1.6129\n1.1403\n0.44318\n1.6606\n2.3184\n2.4753\n3.3208\n4.2409\n4.5652\n3.0924\n2.3239\n0.88158\n1.0764\n2.2737\n2.9953\n4.2245\n3.863\n3.1775\n-0.90674\n-4.3898\n-6.2415\n-6.1644\n-5.5494\n-4.8325\n-4.4505\n-4.5983\n-3.294\n-4.3089\n-5.0561\n-5.4795\n-5.4381\n-5.7732\n-6.9157\n-7.8282\n-7.485\n-5.0574\n-2.4149\n-2.4298\n-2.0033\n-0.36132\n-2.7868\n-2.9945\n-3.1227\n-1.0937\n-1.3624\n-0.78623\n-0.29359\n0.082934\n-2.6424\n-6.7329\n-9.11\n-3.3978\n-1.7032\n2.0836\n2.4953\n1.3153\n-0.019197\n0.79152\n1.456\n1.2383\n0.77873\n0.93364\n0.42824\n-0.10208\n0.78262\n1.3817\n2.038\n1.7054\n3.1805\n3.0699\n1.5894\n1.4385\n1.3721\n1.1101\n0.24673\n2.3738\n3.9367\n4.4024\n5.0357\n5.9659\n5.7257\n5.3494\n2.276\n0.90012\n0.82746\n1.1133\n1.3965\nNaN\nNaN\nNaN\nNaN\n-2.6939\n-5.8819\n-6.3538\n-3.3011\n-4.1995\n-5.4422\n-4.7545\n-2.8951\n-3.6064\n-4.0473\n-3.6724\n-4.2834\n-5.4538\n-6.332\n-6.375\n-5.8534\n-5.3547\n-4.5784\n-3.943\n-2.0126\n0.40156\n-1.254\n-1.0453\n-1.2781\n0.1178\n0.15332\n-1.385\n-1.1795\n0.92573\n0.61958\n-4.6352\n-4.7452\n-5.026\n-3.2784\n-2.5831\n-0.64652\n-0.90835\n-0.85592\n1.0905\n-0.17883\n-0.066209\n0.37391\n-0.22536\n0.98811\n2.4236\n2.4963\n0.76874\n1.0068\n2.6814\n4.6165\n3.1727\n1.8176\n1.7508\n1.7403\n2.7479\n1.9436\n3.6358\n5.4598\n6.2014\n6.5299\n7.239\n6.7319\n8.2772\n6.2234\n4.0494\n1.7205\n1.167\n2.2717\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.2626\n-4.8994\n-4.2667\n-3.7357\n-5.201\n-4.6713\n-3.4676\n-3.0916\n-3.7763\n-3.3269\n-4.2468\n-4.6418\n-5.1504\n-4.8126\n-2.6976\n-3.7091\n-4.213\n-2.8432\n-0.53691\n2.3334\n1.5071\n1.1393\n1.1987\n1.8424\n1.0628\n-3.5858\n-3.238\n-1.1974\n3.6207\n-2.8725\n-2.4073\n-3.0475\n-3.1037\n-3.855\n-3.3524\n-3.7665\n-2.2438\n-0.83634\n-1.4789\n-0.50292\n1.9053\n1.9199\n2.5312\n3.267\n1.7782\n1.4935\n1.5785\n3.5261\n5.1895\n3.3022\n2.5939\n1.1688\n1.9827\n2.4881\n3.6199\n3.9277\n6.3686\n7.8744\n8.2675\n10.711\n9.6617\n11.277\n9.1936\n6.3901\n2.9398\n0.40731\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.7937\n-5.1292\n-4.9791\n-5.0974\n-5.5639\n-5.3737\n-5.6043\n-4.5049\n-4.7513\n-4.1717\n-3.5825\n-3.823\n-3.4266\n-2.737\n-2.3027\n-2.5606\n-2.2669\n-0.69976\n2.1916\n4.2872\n1.8488\n2.2094\n1.7441\n1.2605\n-3.9168\n-5.6979\n-4.5397\n1.5953\n-1.146\n-1.7739\n-2.6469\n-2.9504\n-3.5793\n-3.7901\n-4.0772\n-1.9343\n-0.027858\n-2.8331\n-1.966\n2.7422\n2.1001\n2.2804\n2.4195\n3.2562\n3.2753\n3.1888\n3.4931\n3.7617\n2.7859\n3.7385\n0.30389\n0.1894\nNaN\n5.6092\n4.4214\n8.4739\n10.411\n11.007\n12.804\n12.685\n13.312\n9.8524\n7.8518\n4.7434\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.6115\n-4.0816\n-4.7781\n-4.0034\n-5.0982\n-5.2492\n-4.8325\n-4.9628\n-4.7545\n-3.7409\n-3.2733\n-3.0816\n-3.4286\n-2.4559\n-1.6031\n-1.3276\n-2.7398\n-2.1127\n-0.012373\n1.9108\n1.3245\n2.1596\n-0.097948\n-2.4677\n-4.6723\n-7.1032\n-7.1288\n-3.1391\n-3.3281\n-2.7851\n-3.9622\n-3.1555\n-1.9835\n-1.0516\n-1.1256\n-0.20238\n-0.25679\n-2.5215\n-1.305\n2.3535\n-0.015037\n1.8835\n-0.51658\n0.76346\n4.5013\nNaN\nNaN\n2.9968\n2.6879\n4.5567\nNaN\nNaN\nNaN\n0.4091\n1.3136\n0.92347\n1.9011\n1.515\n1.5783\n2.1354\n1.0669\n0.64726\n0.83177\n1.2751\n1.2594\n2.5131\n2.4202\n2.3637\n1.2434\n-0.048113\n1.1065\n-4.4579\n-1.7026\n0.50407\n-0.055235\n-0.22389\n-0.81238\n0.43019\n1.0948\n1.6952\n2.1221\n2.5153\n2.0672\n1.4749\n0.77617\n0.87563\n2.048\n2.714\n2.1874\n1.6107\n1.6895\n2.9483\n3.5703\n3.1913\n2.5145\n1.7146\n0.93089\n0.45408\n0.35491\n0.66556\n0.28452\n0.37034\n0.32231\n0.37569\n0.41665\n-0.038328\n-0.15016\n-0.55548\n-0.40521\n-0.39668\n1.2293\n2.179\n1.641\n1.2859\n1.4558\n1.3071\n-0.87701\n-1.4632\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1317\n1.6558\n1.3965\n0.99965\n0.48758\n1.8183\n2.7805\n1.1871\n-0.40914\n0.57974\n0.41841\n0.36596\n2.7254\n3.209\n1.4918\n1.3578\n2.232\n2.4071\n0.68507\n0.9765\n-2.3101\n-0.91711\n0.13988\n-1.158\n0.073267\n1.0627\n1.5363\n2.1683\n2.1017\n1.821\n1.2184\n0.42507\n0.98959\n2.2364\n1.687\n2.7335\n2.4181\n2.2338\n2.7278\n1.9996\n1.9629\n2.0635\n1.1797\n0.76518\n0.4451\n0.19954\n0.14459\n0.0078945\n0.10549\n0.20995\n0.0090237\n-0.14046\n-0.09767\n-0.72923\n-1.1214\n-0.61976\n-0.44502\n1.4154\n2.5614\n2.0211\n1.5761\n0.4669\n-0.19679\n-0.98178\n-2.9391\n-2.1093\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5941\n1.1225\n1.2094\n1.6834\n1.6693\n2.0466\n1.1626\n0.55712\n1.1798\n2.696\n0.82287\n1.255\n2.7683\n1.8487\n-1.4122\n-0.33888\n1.4293\n1.8588\n2.228\n0.71319\n0.70717\n-0.0067997\n-0.20847\n-0.55284\n-0.16258\n0.22093\n0.60513\n1.2631\n1.2767\n2.2137\n1.9519\n1.0928\n1.4366\n1.8465\n1.843\n2.468\n2.6385\n2.4377\n2.0017\n2.2574\n2.3129\n1.3319\n1.1313\n0.89286\n0.57306\n0.3802\n0.14724\n0.040026\n0.63601\n0.32387\n-0.26907\n0.13136\n0.030886\n-1.0877\n-0.75114\n-0.29356\n-0.38599\n1.0561\n1.8918\n0.80888\n0.44838\n-0.84826\n-0.34068\n-2.1346\n-2.3775\n-1.0406\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.53288\n0.59806\n1.0239\n1.4036\n1.836\n1.3982\n0.77088\n0.90325\n1.2012\n1.2347\n1.4736\n2.3681\n0.49545\n-0.013048\n0.8891\n1.293\n1.3442\n1.9876\n1.6305\n0.92187\n0.52633\n0.05497\n-0.67251\n-0.45614\n-0.33536\n-0.26145\n0.46959\n0.34785\n2.0176\n3.6405\n1.5891\n0.62574\n1.3545\n1.8373\n1.636\n1.8617\n2.1839\n2.0601\n1.5931\n1.5164\n1.5748\n1.1245\n1.4659\n1.3412\n0.77329\n0.66704\n0.66908\n0.4159\n0.54689\n-0.10963\n-0.54343\n-0.27488\n-0.63862\n-1.2859\n-1.6335\n-1.0519\n-1.5808\n-0.81048\n-0.0094624\n1.7496\n1.3683\n-1.0325\n-0.30114\n-1.1367\n-0.43597\n-0.93401\n-1.3626\nNaN\nNaN\nNaN\nNaN\nNaN\n0.34221\n0.65678\n0.46565\n0.78477\n1.1182\n1.3657\n1.2975\n1.2144\n1.7996\n1.6809\n1.8292\n1.9031\n1.1722\n1.7469\n2.0158\n2.6832\n1.7979\n1.5763\n2.7806\n2.5309\n0.76339\n-0.30821\n-1.6036\n1.0308\n-0.436\n-0.39947\n-0.19597\n0.79992\n1.7642\n2.348\n1.7116\n0.64654\n0.96493\n1.7689\n1.1562\n1.7937\n3.0654\n2.1203\n1.4134\n1.4454\n1.7232\n1.1824\n1.3246\n0.8827\n1.2202\n0.94155\n0.43824\n0.40555\n0.28908\n-0.29343\n-0.29508\n-0.32357\n-0.34965\n-1.0818\n-1.611\n-2.2222\n-3.0979\n-1.6842\n-0.71747\n1.7974\n2.5675\n0.26514\n0.18361\n-1.1808\n0.46224\n-0.23241\n-0.26548\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5784\n0.8405\n0.86538\n0.81396\n0.61525\n1.1273\n1.6436\n1.649\n1.7037\n1.5682\n1.3961\n1.069\n0.69217\n1.655\n2.1104\n1.4808\n1.6673\n2.5785\n2.9718\n3.5998\n0.90398\n-0.51189\n0.77895\n1.4472\n0.7419\n-0.22953\n-0.48592\n0.4659\n1.8804\n1.8776\n1.7518\n1.1205\n1.5113\n1.3801\n1.4654\n1.7634\n2.8748\n2.2012\n1.3998\n1.5609\n1.686\n1.5527\n1.3834\n1.5419\n2.6639\n1.3985\n0.36443\n0.043558\n-0.17018\n-0.56255\n-0.22604\n-0.21865\n0.0098972\n-0.60678\n-0.06251\n1.3291\n0.11801\n-3.443\n-2.6484\n1.3668\n2.2148\n0.52327\n-0.7437\n-0.054527\n0.073938\n0.15358\n0.39293\nNaN\nNaN\nNaN\nNaN\nNaN\n0.63791\n0.90019\n0.78546\n0.31027\n-0.11176\n0.35044\n0.73514\n0.48204\n0.41883\n0.65539\n0.62726\n1.0686\n0.97144\n1.6611\n1.464\n1.215\n1.6895\n2.1587\n1.0846\n1.1377\n1.5077\n2.0964\n1.687\n1.9628\n1.6916\n0.4334\n-0.29883\n1.1639\n1.9277\n1.1391\n1.8867\n1.8459\n1.7711\n2.6475\n2.4504\n1.9517\n1.8469\n1.2852\n0.63055\n0.95966\n1.6303\n1.8395\n1.7346\n1.6465\n2.5139\n1.4022\n0.55849\n0.18561\n-0.34632\n-0.74345\n-0.26527\n-0.17989\n0.022947\n-0.061373\n0.112\n0.7566\n-0.24828\n-1.4427\n-0.68195\n0.94187\n1.9703\n0.60192\nNaN\n0.53127\n-0.12232\n-0.21703\n-0.25039\n0.38481\n1.9599\nNaN\nNaN\nNaN\n0.42553\n0.20198\n0.35115\n-0.068388\n-0.25473\n0.21504\n0.10829\n0.027025\n0.62363\n0.80144\n0.57872\n0.73691\n0.88503\n1.0678\n0.89115\n1.5124\n1.9294\n1.3238\n0.86323\n1.6212\n1.7583\n1.3661\n1.421\n1.5566\n1.0291\n0.39799\n-0.092115\n-0.65078\n0.79758\n0.72373\n1.3031\n2.0513\n2.2117\n2.6672\n2.7877\n2.4152\n1.3456\n1.1132\n1.0989\n1.4569\n1.9049\n1.5331\n0.68879\n0.80905\n2.1569\n1.3371\n0.41484\n-0.26206\n-0.66976\n-0.97368\n-0.43122\n-0.48823\n-0.56783\n-1.1046\n-1.2985\n-1.4133\n-1.383\n-1.8564\n-1.8669\n-0.19099\n0.2272\n-1.2782\nNaN\nNaN\n-0.86238\n-0.45733\n-0.047398\n1.7979\n2.363\n1.9338\nNaN\nNaN\n0.38148\n0.37216\n0.86645\n0.10196\n-0.14113\n0.11055\n-0.25107\n-0.073328\n0.52709\n0.4359\n0.80773\n0.97314\n0.93251\n0.70749\n0.61535\n0.79863\n1.0359\n1.2899\n0.98565\n0.95415\n0.74413\n1.1807\n1.1575\n0.71621\n-0.20955\n0.22856\n0.19153\n0.55718\n0.82789\n1.0197\n1.1486\n1.2731\n1.9824\n1.9746\n1.6598\n1.8758\n1.5477\n1.3988\n1.1444\n2.3285\n2.3723\n1.7169\n1.8488\n2.0042\n1.8981\n0.52778\n-0.42749\n-0.87908\n-1.8738\n-1.6846\n-0.80404\n-0.91629\n-1.2605\n-2.1057\n-2.0598\n-1.736\n-2.3939\n-2.6288\n-3.8946\n-3.7752\n0.3475\nNaN\nNaN\nNaN\n-0.68598\n0.37281\n-0.33434\n-0.48744\n1.5023\n2.3728\n0.1548\n-0.66171\n0.86083\n0.57264\n0.58899\n0.51537\n-0.37752\n0.21835\n0.65932\n0.67618\n0.59047\n0.92062\n0.73834\n0.53666\n0.2296\n0.33326\n0.47357\n0.24308\n0.5365\n1.2985\n1.2154\n0.3567\n0.070925\n0.29098\n0.040655\n-0.15458\n-0.24559\n0.50138\n0.3826\n0.88778\n1.1564\n1.3717\n1.5621\n1.2898\n1.1998\n1.6019\n1.6724\n1.5419\n1.0194\n1.1419\n1.5413\n2.1099\n2.3409\n1.863\n2.4905\n2.7251\n1.1641\n-0.37081\n-0.67461\n-1.5114\n-1.5783\n-0.98295\n-0.96195\n-2.157\n-2.3121\n-2.0761\n-1.5829\n-1.5346\n-3.4244\n-2.5746\n-4.875\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.204\n-1.4461\n0.76618\n0.42439\n-2.0514\n-1.8972\n1.4029\n0.78146\n0.55779\n0.97417\n0.65082\n0.51764\n0.52778\n0.58337\n0.61039\n0.27162\n0.34878\n0.25485\n-0.53734\n-0.6752\n-0.54853\n-0.2455\n0.66421\n1.1866\n1.1028\n0.67109\n-0.026869\n-0.32121\n-1.1738\n-0.48837\n0.066332\n0.21629\n0.30357\n0.69996\n1.1156\n1.2782\n1.4644\n1.8653\n1.2449\n1.0257\n1.2404\n1.3335\n2.1015\n0.9985\n2.4239\n2.1199\n1.6078\n1.001\n2.892\n2.5094\n0.59839\n-0.34262\n-0.64635\n-1.4572\n-1.4468\n-0.98658\n-1.0239\n-1.2853\n-0.64569\n-1.3894\n-1.2647\n-1.1364\n-2.0199\n-0.78712\n-3.2507\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7719\nNaN\n-0.60076\n-0.86404\n-1.2411\n-1.4834\n1.696\n0.73266\n0.90754\n1.057\n0.64086\n0.38022\n0.26785\n0.50044\n0.75669\n0.84854\n0.66385\n0.11674\n-1.0685\n-0.50883\n-0.17418\n0.8849\n1.1259\n0.86165\n0.95119\n0.30447\n-0.46725\n-0.49541\n-0.76366\n-0.094164\n0.66339\n0.10918\n-0.14564\n0.019831\n0.53592\n0.71332\n0.46443\n1.0571\n-0.089693\n0.56285\n0.31004\n1.1728\n2.2782\n2.5541\n2.7708\n2.0205\n1.9318\n3.9685\n3.3359\n1.8855\n1.1824\n-0.0045567\n-0.7644\n-1.2834\n-1.3846\n-1.2956\n-1.3816\n-0.74064\n-0.21902\n0.30398\n0.19939\n-1.0369\n-2.2519\n-1.4935\n-1.9632\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.45153\n0.55608\n0.6156\n0.69108\n0.55544\n0.53856\n0.59118\n1.0289\n1.1034\n0.84373\n0.038292\n0.066347\n0.33345\n0.23467\n0.20685\n0.392\n0.18972\n0.30145\n0.55407\n0.11583\n-0.44585\n-0.16724\n0.1343\n0.15793\n0.83797\n0.80862\n-0.83085\n-0.39016\n0.19527\n-0.41297\n-0.52125\n0.54091\n-0.86605\n-0.89449\n-0.11069\n1.2281\n1.7772\n2.1247\n2.4623\n2.2755\n2.8237\n5.1648\n4.8772\n2.1748\n0.79737\n-0.31503\n-0.92404\n-1.1941\n-1.3755\n-1.5493\n-1.1543\n-0.88997\n-0.056562\n2.116\n2.1494\n1.7046\n1.0483\n0.42577\n-0.49969\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.69271\n0.64193\n0.59907\n0.3107\n0.24654\n0.48137\n0.54761\n0.69082\n0.86134\n0.63022\n0.13091\n-0.073345\n-0.19191\n-0.21587\n0.1705\n-0.22325\n0.066702\n0.33857\n0.39056\n0.24441\n-0.038347\n0.048754\n-0.60402\n0.0023174\n2.0372\n1.132\n0.09586\n-0.29591\n-0.37402\n-0.87211\n-0.8688\n-0.032173\n0.19961\n0.8846\n0.42411\n0.34327\n-0.36813\n0.52645\n2.2478\n2.4369\n2.6233\n3.5874\n3.4572\n1.8238\n0.39954\n-0.74151\n-1.0567\n-1.0041\n-1.4345\n-1.801\n-1.0513\n-0.65862\n-0.11253\n1.8232\n2.6865\n2.3137\n1.5442\n1.2239\n-0.195\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.52762\n0.53053\n0.15549\n-0.36861\n-0.38217\n0.32416\n0.81423\n0.54292\n0.66853\n0.45835\n0.29184\n0.014853\n-0.41124\n-0.57825\n-0.12731\n-0.32393\n-0.31053\n-0.21671\n-0.32538\n-0.011858\n0.064762\n-0.70189\n-0.33788\n0.73187\n2.1574\n1.262\n0.21646\n-0.61279\n-0.9793\n-0.64956\n-0.40135\n1.5422\n1.1877\n1.8707\n1.3632\n-0.85758\n-0.035643\n1.0433\n2.0224\n1.6017\n1.508\n2.9979\n3.3715\n0.46171\n-0.56204\n-0.87467\n-0.57166\n-0.64339\n-1.2636\n-1.4575\n-0.66559\n-0.7907\n-0.53554\n0.83313\n1.0053\n1.0693\n2.022\n1.9471\n1.4732\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.3625\n0.24895\n0.02746\n-0.772\n-0.9733\n-0.4149\n-0.097073\n0.050432\n0.22466\n0.35341\n0.4642\n0.41957\n0.1756\n0.078411\n0.34418\n-0.042805\n-0.30694\n-0.22606\n-1.3216\n-0.48179\n-0.23156\n0.21685\n1.8582\n2.2912\n2.3978\n1.944\n0.25287\n-1.3698\n-1.2829\n-0.10483\n0.77782\n1.4371\n0.61706\n1.2578\n1.8757\n2.3939\n1.9018\n1.5094\n1.161\nNaN\n1.602\n1.7631\n3.6601\n0.39232\n-0.74744\n-0.16353\n-0.20375\n0.10257\n-0.69158\n-0.41459\n0.07649\n0.98971\n-1.0415\n-1.0224\n0.47238\n1.9861\n3.6031\n1.823\n0.76973\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.23262\n0.065165\n-0.37799\n-0.8254\n-0.96821\n-0.76068\n-0.42672\n-0.15677\n0.25747\n0.20818\n0.13002\n0.36891\n0.55449\n-0.05249\n0.18252\n-0.024527\n-0.065332\n0.36479\n-1.1597\n-1.3113\n-0.36546\n0.12034\n0.81303\n2.2751\n1.9461\n0.36536\n0.39175\n-1.1388\n-0.15169\n0.76308\n1.5121\n1.102\n0.41476\n0.2406\n1.8764\n1.9024\n1.8564\n1.9889\nNaN\nNaN\nNaN\n0.048506\n1.9726\n2.0565\n1.5471\n1.4016\n1.0103\n0.89814\n-0.069544\n-0.46432\n0.25606\n2.5046\n-0.33165\n-0.98\n-0.01404\n0.88866\n3.3599\n1.5347\n0.57096\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.34642\n-0.0088825\n-0.74897\n-0.69503\n-0.67488\n-0.69944\n-0.16274\n-0.048218\n0.79757\n0.36607\n0.42852\n0.78218\n0.62948\n0.27609\n-0.077795\n-0.095148\n0.27664\n0.39948\n-0.73401\n-0.59489\n-0.12533\n0.42744\n1.9611\n2.8079\n0.38146\n0.25233\n0.39736\n-0.12501\n0.67084\n0.85097\n0.63659\n1.2722\n1.4195\n1.3719\n1.5858\n2.0646\n1.6994\nNaN\nNaN\nNaN\nNaN\nNaN\n0.80003\n1.8566\n2.0671\n3.1398\n1.7018\n1.2151\n0.56088\n-0.83044\n-2.9786\n-0.21766\n-1.0335\n-0.92938\n-0.82863\n0.46479\n1.1004\n0.97994\n1.1658\n3.3304\n2.9166\n1.999\n0.50871\n1.4351\n2.3512\n1.5426\n1.2364\nNaN\nNaN\nNaN\nNaN\nNaN\n0.45328\n0.20835\n-0.50007\n-0.63417\n-0.39831\n-0.085409\n-0.022352\n-0.057276\n0.30593\n0.38116\n1.0012\n1.2987\n1.1188\n0.67899\n0.42018\n0.42062\n0.1924\n-0.43797\n-0.86638\n-0.34635\n0.087925\n0.42949\n1.65\n2.8094\n1.5364\n1.1433\n1.1597\n0.76924\n0.36107\n0.97246\n0.48516\n1.1504\n-0.53978\n0.66902\n1.3183\n2.0764\n1.4968\nNaN\nNaN\nNaN\nNaN\nNaN\n1.8684\n1.5584\n2.6728\n4.2616\n3.2182\n0.84212\n0.20253\n-0.46131\n-0.8501\n-0.96447\n-2.6458\n0.39757\n1.5455\n0.62892\n0.85317\n1.1522\n1.0825\n1.0247\n0.66411\n0.65536\n0.16135\n0.93211\n0.87044\n1.0763\n1.7654\n1.7066\n0.99671\nNaN\nNaN\nNaN\n0.43193\n0.13015\n-0.10723\n-0.1123\n-0.19966\n-0.29965\n-0.6125\n-0.0073338\n0.95782\n0.79814\n1.2539\n1.4138\n1.2712\n1.1041\n1.0818\n0.69817\n0.88416\n1.4868\n-0.71253\n-0.48547\n0.52259\n2.0283\n2.1636\n2.4058\n2.0422\n1.6284\n2.0424\n1.0233\n0.34102\n0.4028\n1.1687\n1.1202\n-0.15021\n-1.2998\n0.74669\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.64463\n1.7014\n0.14262\n1.4762\n2.1455\n0.36595\n-0.47971\n0.065144\n0.231\n-1.3504\n-3.4874\n1.2942\n2.0667\n0.99393\n1.1051\n1.0847\n-0.28114\n-0.19692\n1.2083\n1.8568\n0.68396\n2.499\n2.5344\n0.77643\n1.0409\n1.153\n0.73292\nNaN\n1.3205\nNaN\n0.4463\n0.012243\n0.062098\n0.23892\n-0.081942\n-0.023468\n-0.0501\n0.53245\n0.99207\n0.71219\n0.89982\n1.309\n1.2111\n1.197\n1.2098\n1.0622\n0.9529\n1.1904\n-2.5041\n-0.85828\n1.1364\n2.4783\n2.7765\n3.0811\n2.1193\n1.2735\n1.0594\n0.032839\n-0.022783\n0.79986\n1.8241\n3.2184\n1.9348\n1.4663\n2.3994\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.501\n0.92129\n0.57857\n-1.001\n-2.0376\n-0.75461\n-2.0212\n-1.1555\n2.6148\n3.008\n2.8613\n0.88063\n1.0492\n1.0558\n1.5647\n0.25301\n-0.68697\n-0.31693\n1.1405\n0.13568\n0.068197\n0.40149\n0.87662\n-0.7494\n-0.33289\n1.0049\n1.5015\n1.3674\n1.1845\n0.49104\n0.70862\n0.31715\n0.32828\n0.46758\n0.61712\n0.34973\n0.28545\n0.38021\n0.418\n0.55435\n0.86466\n1.4185\n1.7187\n1.5843\n1.2079\n0.9698\n1.3876\n1.5078\n-4.2598\n-1.2769\n-0.41948\n2.1832\n2.7267\n3.2266\n1.6848\n0.47533\n1.093\n0.91159\n1.0201\n1.6982\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0545\n2.3381\n1.7413\n-0.21824\n-1.6697\n-1.4264\n-1.4552\n3.5486\n2.0695\n1.0653\n1.3042\n0.70265\n0.18841\n0.40036\n0.60589\n1.8545\n1.33\n0.95168\n1.5649\n-0.42149\n-1.1233\n0.10841\n-0.068554\n-1.0269\n0.57475\n0.91157\n0.47977\n0.4851\n-0.060314\n-0.050657\n0.46974\n0.044828\n0.26438\n0.48391\n0.14246\n0.31621\n0.61187\n0.43308\n0.58213\n0.53858\n0.85923\n1.0378\n1.7022\n1.7945\n1.4627\n0.67141\n0.61621\n1.9\n0.71234\n-0.77952\n-0.17108\n2.6329\n2.6794\n3.0956\n1.0822\n1.5126\n1.7492\n1.5212\n1.1825\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.7424\n2.273\n0.7265\n0.55799\n0.34827\n0.48469\n0.012535\n0.34596\n1.2858\n0.1475\n-0.67995\n-0.36897\n0.073772\n1.6547\n1.631\n2.0899\n1.6344\n-0.22565\n-1.0607\n0.23645\n0.36371\n-0.014585\n0.99648\n0.14889\n-0.40094\n0.067438\n0.58858\n0.5997\n0.3089\n0.23847\n0.85291\n0.33114\n0.055071\n0.5202\n0.92819\n1.1708\n0.61161\n0.74291\n1.2471\n1.7782\n1.3345\n1.2775\n1.2026\n0.22009\n-0.027803\n1.4572\n2.6363\n2.8047\n2.1214\n2.8796\n3.5522\n2.9521\n2.3296\n1.7861\n1.5739\n1.9286\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2966\n2.6643\n1.842\n1.2711\n-0.2938\n-0.64525\n-0.25981\n0.98589\n0.74253\n0.039301\n-0.36009\n-0.85016\n-0.075541\n0.76806\n1.8245\n1.4464\n0.5555\n0.22318\n0.83754\n-0.65627\n-0.20013\n-0.49956\n-0.13743\n-0.31625\n-0.64703\n0.37378\n0.23245\n0.96688\n0.48506\n0.45272\n0.22062\n0.10708\n0.40344\n0.81808\n0.59717\n0.70157\n0.90543\n1.4629\n1.5352\n1.3602\n1.4787\n1.2111\n0.36227\n0.31545\n1.0538\n3.0366\n3.4403\n2.2259\n2.7702\n3.2244\n2.8234\n2.3587\n1.7728\n-0.73453\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0659\n2.7249\n2.0435\n1.6311\n-1.5913\n-1.5043\n-0.69145\n0.28919\n0.52866\n0.051125\n-0.92724\n-2.3841\n-1.7144\n-0.056074\n0.61018\n0.87116\n1.6182\n1.7866\n-0.1832\n-1.1732\n-0.29549\n0.029585\n-0.19632\n-0.9224\n-1.6845\n-0.83133\n-1.1413\n0.89842\n0.28252\n-0.0089664\n0.29127\n0.63024\n0.5704\n0.83922\n0.49378\n0.98425\n1.501\n1.3401\n1.378\n1.3976\n1.4512\n0.9976\n0.18338\n-0.33838\n1.3843\n2.6795\n2.7208\n2.1098\n2.6988\n3.1906\n2.8638\n2.5189\n2.0053\n-0.0037327\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2435\n1.8419\n1.777\n1.1935\n-1.9118\n-2.1145\n-1.8316\n-0.17477\n1.3366\n0.39764\n-0.89963\n-2.5544\n-1.7844\n-0.97312\n-0.39405\n-0.49742\n1.1025\n1.1066\n-1.7822\n-2.1501\n-0.71511\n0.31716\n-0.27618\n-1.8777\n-2.8045\n-1.9935\n-1.2106\n0.41272\n0.41199\n0.35191\n0.25125\n0.13123\n0.6065\n0.61686\n0.62179\n1.2776\n1.4968\n1.6293\n1.6564\n1.1872\n1.3837\n0.74202\n-0.42916\n0.14409\n1.4593\n1.6202\n2.1878\n2.434\n2.1994\n2.0793\n2.3241\n1.9675\n0.71188\n-0.43693\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.3291\n3.3122\n1.867\n1.0038\n0.36465\n-2.3391\n-4.0421\n-3.6373\n-0.47421\n0.34503\n-1.0651\n-1.7776\n-3.986\n-2.8584\n-1.4615\n-1.1939\n-1.6119\n0.34559\n1.1383\n-2.0198\n-2.1134\n-1.8448\n-0.31227\n-0.71771\n-2.3237\n-1.8695\n-0.8878\n-0.70025\n0.86584\n0.76403\n-0.014956\n0.086557\n0.36604\n0.64341\n0.9919\n0.97878\n1.2655\n1.5154\n1.4124\n1.2392\n1.3903\n1.6165\n0.32415\n-0.89257\n0.31767\n1.3142\n1.8265\n1.5124\n1.9252\n1.8366\n1.951\n2.1066\n1.7541\n-0.27693\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.2773\n5.1714\nNaN\n5.2907\n4.2439\n3.8198\n3.3277\n2.0487\n1.3831\n-0.25154\n-2.51\n-3.3464\n-1.9505\n-2.5901\n-1.9933\n-1.7831\n-3.4238\n-2.2352\n-1.4249\n-1.1251\n-1.1826\n1.2451\n0.44188\n-1.7587\n-2.3095\n-2.3293\n-0.64494\n-1.426\n-2.1926\n-1.0263\n0.277\n0.57272\n0.93469\n0.52391\n0.31836\n0.18659\n0.68398\n0.88075\n1.3184\n0.96276\n0.82908\n1.0624\n1.1928\n1.4661\n1.5779\n1.5809\n0.78983\n0.14947\n-0.20816\n0.25958\n1.1469\n1.3014\n0.66773\n0.80027\n1.7211\n1.6673\n1.0829\n-0.44444\n1.4321\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.1802\n3.8147\n4.1202\n5.0994\n6.1623\n5.1443\n3.3264\n3.0336\n2.9455\n1.7173\n1.5669\n0.89164\n0.10219\n-0.58364\n-1.6956\n-3.1842\n-2.3754\n-1.6759\n-1.39\n0.9157\n0.041229\n-0.53918\n-0.2992\n0.37733\n-0.23259\n-1.3278\n-2.9586\n-1.9721\n-1.1723\n-1.1058\n-1.3722\n-0.18137\n0.23399\n-0.028669\n0.75876\n0.76698\n0.58513\n0.52801\n0.86936\n1.1831\n1.0624\n0.8529\n0.51694\n1.1465\n1.5158\n1.8163\n2.0254\n1.4276\n0.54921\n-0.51801\n0.15458\n0.268\n0.64083\n2.0833\n1.6599\n0.86627\n1.1098\n1.0522\n-0.19126\n-0.68613\n3.2895\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0939\n4.4426\n4.5595\n5.1091\n5.847\n5.1989\n3.9572\n3.6748\n3.2827\n2.2978\n2.1329\n1.9368\n1.0085\n0.59933\n-0.046835\n-1.7033\n0.56373\n1.6331\n0.70125\n1.1448\n0.25164\n0.34921\n0.50055\n-0.12502\n-0.015099\n-1.6767\n-1.9966\n-1.5353\n-1.4297\n-0.89029\n0.18135\n0.84955\n0.45508\n0.80337\n1.0061\n1.1002\n0.78335\n0.94821\n1.0965\n0.82669\n0.7266\n0.75539\n0.77949\n0.9627\n1.2931\n2.0102\n2.0377\n1.6748\n1.0277\n1.167\n0.9868\n0.97002\n1.8967\n2.4791\n2.4558\n1.2556\n0.5615\n-0.22619\n-0.72293\n-1.4952\n0.20296\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.691\n4.4558\n3.7889\n3.2995\n3.7229\n3.6798\n4.9269\n4.6314\n4.1634\n5.588\n5.4933\n3.7167\n2.2728\n2.7246\n2.2986\n1.8277\n1.6758\n1.1118\n0.7713\n0.75373\n1.2885\n1.5916\n1.2189\n0.68627\n0.77986\n1.7264\n0.21\n-0.50669\n-0.39777\n-0.60427\n-0.040335\n-0.697\n-1.4149\n-0.69699\n0.56603\n1.2986\n1.3775\n1.1402\n1.193\n0.90868\n0.90444\n0.66584\n0.81059\n0.63918\n0.55646\n0.29841\n0.7672\n0.92887\n1.1441\n2.4483\n3.4404\n2.333\n1.8813\n1.3757\n1.1526\n0.73393\n1.1712\n1.7255\n0.4856\n0.20898\n0.26502\n0.24587\n-0.47677\n-1.7475\n0.1946\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5078\n3.6313\nNaN\nNaN\nNaN\nNaN\n5.1611\n5.9998\n5.0795\n5.3233\n5.3903\n4.1584\n4.013\n2.5837\n2.8356\n2.9857\n3.6655\n3.2747\n1.3795\n0.72967\n1.0813\n2.3886\n2.466\n1.6876\n0.77423\n1.9677\n3.4299\n1.0041\n0.54838\n0.32703\n0.481\n1.0811\n-0.35349\n0.16452\n-0.35816\n-0.19618\n1.9005\n1.6871\n0.88951\n1.0942\n1.3275\n1.2911\n0.93205\n0.74196\n0.63627\n0.60684\n0.45187\n0.77636\n1.4207\n1.852\n2.3866\n3.2562\n2.1054\n1.865\n0.73126\n0.082033\n1.0324\n1.3411\n1.2722\n1.4742\n1.177\n0.96968\n0.57158\n0.04191\n2.4091\n2.3776\nNaN\nNaN\nNaN\nNaN\n2.6446\n3.4365\n4.0359\n3.4882\nNaN\nNaN\n4.2967\n3.8999\n4.9671\n5.9939\n5.4563\n4.1806\n4.1612\n5.3906\n4.2769\n2.9457\n3.5441\n3.7029\n3.4338\n2.9081\n2.0727\n1.6336\n2.4177\n2.0589\n2.0448\n1.959\n1.0321\n1.3904\n2.6277\n2.1222\n1.2477\n0.79397\n0.92658\n0.98291\n0.27409\n0.4094\n-0.016542\n0.27147\n1.3007\n0.98838\n0.34411\n1.0369\n1.0221\n1.0275\n0.87236\n0.55747\n0.25634\n0.42113\n0.27884\n0.69395\n0.95046\n1.7636\n2.4543\n2.4116\n1.5959\n1.2065\n0.29861\n-0.78058\n-0.43505\n0.30861\n1.4771\n1.486\n1.3909\n1.5816\n0.49313\n0.2646\n-2.1745\n-0.22608\n3.969\nNaN\n3.9013\n2.8043\n2.9767\n4.051\nNaN\nNaN\nNaN\nNaN\n3.1068\n4.1665\n4.994\n5.0888\n4.6146\n3.5732\n2.9083\n3.4664\n2.9971\n2.6388\n3.6848\n4.8864\n4.1899\n2.3443\n1.5334\n1.2328\n2.6607\n2.005\n1.7464\n1.6978\n1.4683\n1.2201\n1.2524\n1.5365\n1.4867\n0.94433\n0.53435\n0.90272\n0.29915\n0.52488\n0.54107\n0.61558\n0.45867\n0.35716\n-0.15001\n0.93495\n0.91754\n0.65589\n0.58503\n0.27482\n0.052023\n0.37659\n0.32462\n0.30802\n0.72686\n1.5712\n2.4221\n1.8546\n1.2925\n1.6155\n0.49205\n-0.66824\n-0.10171\n-0.032154\n2.8689\n2.0185\n1.8064\n1.3371\n1.2016\n1.2329\n0.44004\n0.57985\n2.9626\n2.8362\n2.5398\n2.978\n3.8342\n3.8618\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5613\n4.4389\n3.7812\n3.5483\n2.7297\n1.1638\n1.0813\n3.1499\n3.7605\n4.014\n5.9492\n3.9849\n1.9363\n1.5322\n1.9211\n2.4496\n1.7657\n1.7622\n1.7437\n1.6299\n1.6361\n1.1114\n1.4451\n1.5719\n0.68503\n0.77822\n1.3161\n2.0075\n1.9197\n1.3734\n1.0757\n0.46171\n-0.32019\n-1.2337\n1.1468\n1.1524\n0.53135\n1.0877\n0.32168\n0.029181\n0.15253\n-0.092886\n-0.065498\n0.42621\n1.5101\n1.5568\n0.96784\n0.70172\n0.66324\n0.21113\n0.23944\n1.3147\n2.295\n2.2602\n2.5614\n2.2952\n1.953\n1.1252\n4.2992\n5.8103\n3.6983\n2.211\n1.8657\n1.3218\n2.2482\n3.6583\n3.0802\n2.8513\nNaN\nNaN\nNaN\n6.6596\n4.5929\n3.7158\n2.9585\n2.909\n2.3678\n2.4071\n4.0317\n4.1181\n4.1474\n4.187\n4.6366\n3.6479\n2.0189\n1.9138\n1.4568\n3.1408\n2.2822\n1.563\n1.0964\n1.3788\n0.85334\n0.81358\n1.3321\n1.2459\n1.1593\n1.65\n1.0584\n0.70354\n0.78921\n1.7311\n1.4152\n0.21676\n-0.1154\n-0.052107\n1.259\n1.444\n1.2419\n1.3451\n0.37408\n0.0083256\n-0.18367\n-0.15191\n-0.017286\n0.38338\n1.2411\n1.1826\n0.21432\n0.42009\n0.25464\n1.1983\n1.5442\n1.6008\n2.2206\n2.2074\n3.0586\n1.6709\n0.8826\n1.1881\n-2.3216\n-0.059671\n0.99923\n1.7194\n0.83476\n0.89916\n2.5017\n3.7783\n2.8183\n2.5627\nNaN\nNaN\nNaN\n5.0434\n3.8521\n3.2365\n3.6579\n2.8367\n2.5227\n2.8859\n4.8298\n4.8954\n3.0393\n3.04\n4.6038\n4.5885\n2.1913\n1.5317\n1.4156\n2.264\n1.4968\n0.84594\n0.81229\n0.78322\n0.2529\n0.62709\n1.5303\n1.7779\n1.53\n0.99099\n0.58955\n0.12406\n0.15055\n0.58848\n0.94058\n0.636\n-0.43582\n0.60679\n1.5093\n1.3094\n0.77197\n0.3444\n-0.011286\n-0.19312\n-0.54497\n-0.40169\n-0.19275\n0.35792\n1.2472\n0.9509\n0.17319\n0.68129\n-0.25067\n0.60826\n1.3578\n0.62146\n1.4889\n2.2244\n2.128\n2.446\n0.43064\n0.85837\n1.9265\n0.77581\n1.7577\n0.62474\n-0.89101\n0.11554\n2.9397\n2.8607\n1.183\n0.99969\n2.1224\n3.5305\n4.5537\n4.3754\n4.1072\n3.1821\n4.4028\n3.2567\n2.6641\n3.4412\n4.4122\n3.3518\n2.6525\n2.2548\n4.0898\n3.4831\n1.9467\n1.1473\n1.5722\n1.5938\n0.97662\n0.7002\n0.71449\n0.39478\n-0.017817\n0.33636\n0.59379\n1.5123\n1.9122\n0.83291\n0.29071\n-0.06098\n-0.18457\n0.099596\n0.17841\n0.18336\n-0.082354\n0.1761\n0.85419\n0.70967\n0.34792\n-0.44674\n-0.54928\n-0.76452\n-1.5715\n-1.0048\n-0.21127\n0.28145\n0.99857\n0.56929\n-0.47425\n-0.27794\n-1.3328\n-0.27811\n1.6991\n0.39742\n-0.91122\n-1.6376\n0.6958\n1.4461\n-0.19183\n0.18274\n3.74\n2.6326\n1.5115\n1.4828\n1.4696\n3.3841\n3.72\n3.2362\n2.5557\n2.1196\n2.4375\n2.9841\n3.0235\n3.7967\n3.9023\n2.3431\n2.2489\n3.01\n2.693\n2.6203\n2.4102\n2.6587\n2.5429\n2.1542\n1.9831\n1.8428\n1.2488\n0.96086\n1.3887\n1.4541\n1.1407\n0.61878\n-0.0093479\n-0.18793\n-0.19461\n-0.22165\n0.75328\n1.3599\n1.5351\n0.8748\n0.1764\n0.2813\n-0.046022\n-0.25195\n-0.033567\n0.09564\n0.92015\n0.57699\n0.76011\n0.316\n0.011808\n-0.72481\n-1.3813\n-1.3396\n-1.541\n-1.6966\n-0.5258\n0.18887\n0.35775\n-0.32437\n-0.84057\n-1.7004\n-3.3496\n-3.0233\nNaN\nNaN\nNaN\n-1.8596\n1.1441\n0.70106\n0.59321\n2.6731\n2.8057\n2.1146\n2.2646\n2.0386\n3.2578\n2.9334\n2.6809\n3.3156\n3.1282\n3.1094\n2.9241\n2.4804\n2.7571\n2.9541\n2.8411\n3.3815\n3.6041\n3.4356\n2.9023\n2.4131\n2.3906\n2.2654\n1.6064\n0.45872\n-0.036125\n0.68002\n0.57697\n0.5298\n1.2635\n2.0013\n1.5138\n0.42818\n-1.7352\n-0.28964\n-0.20785\n-0.18445\n0.66302\n0.93538\n0.64008\n0.059752\n-0.10174\n0.25518\n0.3551\n-0.24869\n-0.34602\n0.060175\n0.83645\n0.20218\n-0.39493\n-0.89616\n-0.50534\n-1.4006\n-2.0802\n-2.125\n-2.2054\n-1.8265\n-0.77869\n0.1096\n0.11709\n-0.51135\n-1.8453\n-3.3257\n-2.5377\n-1.5822\nNaN\nNaN\nNaN\n-0.63851\n1.3016\n2.5262\n1.3114\n2.0971\n1.08\n1.4845\n1.7875\n1.8941\n2.482\n1.9676\n2.7524\n3.0459\n2.7186\n2.4248\n1.4132\n1.5342\n2.3782\n2.1158\n1.8632\n3.6356\n3.4454\n3.5676\n3.041\n1.9917\n1.6412\n1.1375\n0.59179\n-0.18001\n-1.0073\n-0.27854\n0.017649\n0.57223\n1.225\n1.2709\n1.1463\n0.40339\n-0.23909\n0.43577\n-0.2957\n-0.6137\n9.0237e-17\n0.24076\n0.1528\n-0.49924\n-0.61601\n-0.0020084\n-0.32651\n-0.46912\n-0.34207\n-0.099134\n-0.015471\n-0.083044\n-1.0044\n-0.96061\n-1.3706\n-2.5683\n-3.2017\n-2.8114\n-2.8164\n-2.2255\n-1.2554\n-0.28509\n-1.2279\n-2.2715\n-2.6556\n-2.9855\n-1.8042\n-1.7572\n-0.94095\nNaN\nNaN\n0.98522\n0.55545\n0.7827\n1.0005\n1.0745\n0.92196\n1.5805\n1.9607\n1.6936\n2.1266\n2.1767\n3.5839\n3.1925\n2.4123\n1.9769\n0.67413\n1.2732\n1.8802\n1.6633\n1.7935\n1.9181\n2.5763\n2.4995\n1.875\n1.8768\n1.4733\n0.61558\n0.082111\n-0.49071\n-1.322\n-0.14032\n-0.10443\n0.51999\n0.10926\n0.97837\n1.4793\n0.8353\n0.78243\n0.51446\n-0.072857\n-0.46205\n0.032015\n0.36711\n-0.35652\n-1.0221\n-1.0145\n-0.28152\n0.15493\n-0.30804\n0.070932\n0.032078\n-0.55088\n-0.53536\n-1.2856\n-1.7102\n-2.4972\n-3.5283\n-4.1792\n-3.9148\n-3.1885\n-2.1141\n-1.8908\n-0.8951\n-1.4617\n-2.365\n-2.7893\n-3.7744\n-2.6343\n-1.8171\n-2.2354\n-1.0414\n-0.49477\n0.1671\n0.26415\n0.31417\n1.4206\n0.71802\n0.44288\n1.1377\n1.5026\n1.317\n1.5396\n1.9079\n2.6783\n2.215\n2.1251\n1.9969\n1.4491\n1.5756\n1.5878\n0.90189\n1.4979\n1.4467\n1.5516\n1.1396\n1.0253\n0.59775\n0.58169\n0.23285\n0.15835\n0.36705\n0.66957\n0.55894\n0.098227\n0.25641\n-0.046827\n0.40599\n1.2586\n0.96905\n0.57836\n0.2055\n0.090834\n-0.25134\n0.31917\n0.17494\n-0.68778\n-0.80479\n-0.75465\n-0.19559\n-0.63782\n-0.46449\n0.65632\n0.46086\n-0.44435\n-0.69538\n-1.4426\n-2.3949\n-3.5698\n-4.0149\n-5.0867\n-5.3584\n-3.9592\n-3.2063\n-2.2142\n-1.3002\n-1.6799\n-2.162\n-2.4172\nNaN\nNaN\nNaN\nNaN\n-1.7405\n0.452\n0.91297\n-0.68203\n-0.034071\n1.2564\n0.68465\n-0.28137\n0.8137\n1.1404\n1.0534\n1.3425\n1.8919\n2.3631\n2.5458\n2.5205\n2.4348\n2.3625\n2.2119\n1.5917\n0.86349\n0.86454\n0.48662\n0.82678\n0.49018\n0.22998\n0.91858\n0.15487\n-0.82936\n-0.080374\n0.62809\n0.59352\n0.39645\n1.0297\n2.687\n0.91482\n0.7272\n1.7112\n1.4849\n1.1247\n0.77972\n0.32417\n0.53959\n0.19744\n-0.71679\n-1.0891\n0.15263\n0.24734\n0.2232\n-0.67163\n-0.27175\n0.36819\n-0.23891\n-0.76114\n-1.0768\n-2.0626\n-3.1007\n-3.8359\n-4.0885\n-5.2538\n-5.186\n-4.6931\n-4.5165\n-4.1956\n-3.1852\n-2.4413\n-1.9755\n-2.4678\nNaN\nNaN\nNaN\nNaN\nNaN\n0.6476\n0.36902\n-0.31375\n-0.40762\n0.090851\n-0.42175\n-0.31134\n0.50696\n1.0475\n1.1425\n1.3314\n1.5962\n1.7959\n2.0368\n1.2868\n1.7613\n2.0831\n1.7339\n1.3725\n0.44661\n-0.51601\n-0.6884\n-0.1132\n-0.64283\n-0.42388\n2.1959\n1.2473\n-0.13608\n0.011637\n1.0993\n0.78144\n1.3742\n1.7043\n1.8138\n1.0367\n1.9979\n2.161\n1.4856\n1.397\n1.4902\n-0.029623\n0.099512\n-0.40803\n-0.58334\n0.71024\n0.86043\n0.66307\n-0.33405\n-0.32524\n-0.59216\n-0.66692\n-1.0706\n-1.2547\n-1.7731\n-2.8982\n-3.2799\n-3.836\n-4.336\n-5.4503\n-7.2829\n-6.6837\n-6.1982\n-5.1896\n-3.8214\n-1.6577\n-1.258\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.39392\n0.54065\n0.87219\n0.37356\n0.59768\n0.52354\n1.1373\n1.1743\n0.94582\n0.70535\n0.78178\n1.0507\n0.91687\n1.155\n1.2741\n1.2837\n1.4247\n1.6998\n-0.025152\n-1.872\n-1.0608\n-0.19608\n-0.17567\n-1.0019\n2.2064\n2.8416\n2.0608\n1.0971\n0.67822\n0.84245\n1.6831\n1.7569\n1.6824\n2.0968\n2.6321\n1.87\n0.85193\n1.8845\n1.6921\n-0.70005\n-0.72487\n-0.94399\n-0.94197\n-0.10652\n-0.20204\n-0.37019\n-1.1991\n-0.83645\n-1.3095\n-1.8652\n-1.4618\n-1.8354\n-2.0427\n-3.8108\n-3.6507\n-5.0656\n-5.8525\n-5.9524\n-7.521\n-6.3889\n-5.9545\n-4.3692\n-3.643\n-1.5725\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.27608\n0.31771\n0.8245\n0.30118\n0.24633\n0.37168\n1.4151\n1.1492\n0.57549\n0.27743\n0.49475\n0.63248\n0.30085\n1.0133\n1.2972\n1.4915\n2.1938\n1.7268\n0.16442\n-0.21678\n0.083197\n0.1933\n0.31435\n0.66727\n0.68973\n2.1137\n3.8919\n2.6697\n1.3194\n1.3032\n2.0251\n1.3274\n1.2396\n1.0372\n1.3779\n0.82881\n1.1814\n1.5871\n0.57591\n-0.75189\n-0.60338\n-1.2721\n-0.55061\n-0.35466\n-1.2588\n-2.3949\n-3.1824\n-2.1172\n-2.312\n-2.475\n-1.5863\n-1.2329\n-1.7843\n-0.56207\n-0.8283\n-0.24427\n-0.72817\n-1.476\n-1.9557\n-2.6779\n-1.1051\n-0.31026\n-0.4405\n-1.0812\n-1.018\n-1.6586\n-1.4523\n-1.6281\n-1.5511\n-0.88083\n-1.9213\n4.3497\n2.4624\n-0.015474\n0.57188\n0.53656\n-0.29155\n-1.384\n-1.1287\n-1.815\n-2.4341\n-2.3831\n-2.1752\n-3.6685\n-2.2845\n-1.3333\n-2.6684\n-3.9928\n-4.8704\n-3.7902\n-2.9697\n-3.5855\n-3.6901\n-2.8769\n-3.393\n-4.0676\n-3.3019\n-2.7045\n-3.0332\n-2.6209\n-2.2287\n-2.6014\n-2.8165\n-2.5193\n-2.3467\n-2.9274\n-2.6541\n-2.2456\n-2.8553\n-1.8177\n-3.7855\n-6.1357\n-6.2761\n-5.4218\n-3.7615\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6317\n-0.8424\n0.009512\n-0.27014\n0.0018997\n-2.5785\n-3.6523\n-1.2095\n0.58931\n-0.38633\n-0.44062\n-0.17729\n-1.7467\n-1.7636\n0.12944\n-0.42129\n-2.0855\n-2.0979\n-0.083406\n-0.19597\n4.0851\n2.5861\n0.79227\n1.3039\n-0.59455\n-1.5729\n-1.6746\n-2.6183\n-2.0343\n-1.9796\n-1.0923\n0.40354\n-0.44168\n-2.6443\n-1.7344\n-5.0422\n-3.8628\n-4.1571\n-3.8829\n-1.9938\n-2.5918\n-3.6882\n-4.0637\n-2.8419\n-2.4726\n-2.5115\n-2.5925\n-2.6261\n-2.9646\n-2.6471\n-2.6118\n-2.7517\n-2.8181\n-2.7535\n-1.9411\n-2.9152\n-1.6397\n-4.4\n-5.9619\n-5.7799\n-5.3559\n-2.5658\n-2.9028\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3013\n-0.86234\n-0.55034\n-1.2267\n-0.96364\n-1.5425\n-0.40808\n-0.10242\n-0.79677\n-2.4591\n-1.044\n-0.12877\n-1.5028\n0.056351\n4.5674\n2.583\n-0.47976\n-1.2124\n-0.86756\n0.10521\n0.43314\n1.0772\n0.6386\n0.60368\n0.16317\n0.18027\n0.036987\n-1.7223\n-1.2489\n-2.7618\n-2.2732\n0.11399\n-0.25676\n-0.57149\n-1.7832\n-4.1473\n-3.7522\n-4.0729\n-3.3893\n-3.0511\n-4.0387\n-3.2158\n-3.1403\n-2.959\n-2.6983\n-2.6376\n-2.9466\n-2.9656\n-3.5259\n-2.9656\n-2.1203\n-2.7951\n-2.8449\n-2.2129\n-2.4933\n-1.9883\n-1.8266\n-4.3613\n-4.9872\n-4.0364\n-3.6261\n-0.96012\n-1.8348\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0674\n-0.75969\n-1.283\n-1.8503\n-1.5728\n-1.2359\n-0.10897\n-0.72149\n-0.79828\n-0.44586\n-0.14871\n-0.54681\n0.74606\n1.9882\n0.075207\n-0.49063\n-0.75262\n-1.8114\n-0.47409\n-0.23104\n0.083273\n0.91623\n1.3439\n1.1201\n1.1052\n1.2834\n1.1061\n-0.28878\n-0.81674\n-3.2493\n-1.7148\n0.50693\n-0.18184\n-0.77088\n0.080471\n-1.8137\n-3.5067\n-3.3252\n-2.7384\n-2.1243\n-3.4899\n-3.098\n-3.5728\n-4.174\n-3.1808\n-3.0904\n-3.2418\n-3.1383\n-3.2829\n-1.8533\n-1.311\n-2.3798\n-2.2342\n-1.7766\n-1.21\n-1.6459\n-0.79063\n-2.4007\n-3.7796\n-6.1897\n-4.8292\n-0.75815\n-1.3519\nNaN\nNaN\nNaN\n1.0321\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.28949\n-0.93237\n-0.83806\n-1.3291\n-1.52\n-1.4397\n-1.2052\n-0.80951\n-1.4514\n-1.4753\n-1.1132\n-1.0754\n0.052319\n-1.2112\n-2.0522\n-2.6979\n-1.8859\n-0.79296\n-1.5708\n-1.7504\n-0.45412\n1.3028\n2.3741\n0.13239\n1.4203\n1.069\n0.88049\n-0.47335\n-0.92904\n-1.6677\n-1.1068\n-0.5965\n-0.49829\n-1.971\n0.85306\n-7.504\n-5.7137\n-4.0521\n-2.6528\n-2.0445\n-2.7512\n-2.6627\n-3.5356\n-3.5552\n-3.6398\n-3.1934\n-2.9671\n-3.179\n-3.0079\n-1.7667\n-2.6195\n-2.583\n-2.143\n-1.9651\n-1.3607\n-1.0786\n0.31297\n-1.1482\n-3.3\n-6.8944\n-7.0207\n-3.2337\n-1.5704\n-1.9566\n-4.7851\n-0.45071\n0.9956\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6449\n-2.0756\n-2.0213\n-1.527\n-1.3663\n-1.5193\n-2.0181\n-1.6068\n-1.4043\n-1.386\n-1.2612\n-0.332\n0.059982\n-1.288\n-2.0712\n-0.56\n-1.2882\n-1.5492\n0.65964\n-2.8231\n-0.43938\n1.3526\n-0.36328\n-0.97596\n-0.31027\n1.6137\n0.80769\n0.83323\n0.077496\n-0.078583\n-0.93604\n-1.1292\n-2.7614\n-2.3221\n-0.85527\n-4.3331\n-3.3844\n-2.6612\n-2.3365\n-2.1676\n-2.0883\n-3.5535\n-3.7957\n-4.1861\n-4.8813\n-3.6261\n-2.7592\n-2.89\n-3.0277\n-1.3324\n-2.6058\n-2.7638\n-3.2818\n-2.3862\n-3.1344\n-4.1375\n-3.8105\n2.0571\n0.29188\n-6.2333\n-9.4629\n-4.6665\n-3.2475\n-4.6005\n-4.8185\n-1.0873\n-1.1663\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6656\n-2.1693\n-1.7832\n-1.0768\n-0.6175\n-0.80817\n-0.80736\n-0.47421\n-0.48047\n-0.35823\n-0.0096817\n-0.86655\n-1.0432\n-1.9897\n-1.1164\n-1.0226\n-1.4048\n-1.1772\n0.7063\n-0.61647\n-0.39571\n-0.62679\n-1.375\n-1.5147\n-0.67677\n0.24569\n0.60257\n1.4377\n1.546\n1.3023\n-0.40685\n-1.9274\n-1.6242\n-2.7863\n-3.8366\n-3.4936\n-2.7317\n-1.7076\n-1.2754\n-1.8306\n-2.6232\n-3.7027\n-4.3829\n-4.4904\n-5.663\n-4.5786\n-3.2897\n-2.305\n-2.3962\n-1.4758\n-2.8148\n-3.0024\n-2.8188\n-2.7438\n-3.1847\n-3.9892\n-3.166\n-1.3635\n-2.318\n-5.5292\n-4.0747\n-5.5291\nNaN\n-5.8617\n-3.6724\nNaN\nNaN\n-1.3036\n-4.2312\nNaN\nNaN\nNaN\n-1.026\n-0.6318\n-1.6523\n-0.4898\n0.37133\n-0.67219\n-0.69141\n-1.1133\n-1.5963\n-1.053\n-0.42507\n-0.33125\n0.067478\n-0.56519\n-0.78904\n-1.2122\n-1.3926\n-0.63662\n-0.4291\n-0.77709\n-1.1624\n-0.39581\n-0.69163\n-0.74145\n0.64891\n0.46246\n0.70346\n1.8291\n3.1016\n2.2874\n0.30206\n-1.7605\n-0.84937\n-1.3534\n-2.1371\n-1.389\n-2.6783\n-1.7588\n-1.8857\n-2.9056\n-4.1383\n-3.4389\n-3.2616\n-3.583\n-5.9237\n-4.2904\n-2.7144\n-2.1294\n-2.1564\n-1.5906\n-2.4206\n-2.2482\n-1.8507\n-2.1\n-1.6276\n-1.9409\n-2.4547\n-2.2649\n-1.9277\n-3.1581\n-3.9764\n-5.8622\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7027\n-3.3932\n-3.307\nNaN\nNaN\n-0.65288\n-1.3398\n-1.8317\n-1.222\n0.37766\n-0.61388\n-0.69983\n-1.1852\n-1.3375\n-0.51401\n-0.65788\n-1.159\n-0.97541\n-0.74524\n-0.34753\n-0.61931\n-0.70181\n-0.76201\n-0.56053\n0.097115\n0.5861\n-0.20933\n-0.050039\n0.73679\n2.2932\n1.3071\n0.98621\n1.3628\n1.948\n1.755\n0.68027\n0.7223\n-0.66615\n-0.11597\n-0.27033\n-0.97784\n-2.9933\n-1.9164\n-1.3674\n-4.0506\n-3.9937\n-3.5584\n-4.2614\n-5.1858\n-5.7821\n-3.866\n-2.2241\n-1.5586\n-0.8323\n-1.0791\n-1.876\n-0.74791\n-0.9189\n-0.80388\n-0.76805\n-1.416\n-1.4526\n-2.3378\n-0.20081\n1.7489\n-3.6175\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.0255\n-5.3825\nNaN\nNaN\n-1.7853\n-1.6893\n-2.1048\n-1.9237\n0.36688\n-1.2913\n-0.336\n-0.27713\n-0.16082\n-0.49919\n-0.12479\n-0.16317\n-0.14372\n-0.32015\n-0.9697\n-0.43271\n-0.075546\n-0.69867\n-0.57475\n0.55607\n0.44735\n0.038765\n0.78484\n1.5963\n2.1523\n1.7978\n1.4335\n0.49738\n0.30191\n0.89036\n-0.11842\n-0.45295\n-0.13842\n0.79788\n-0.43886\n-1.0019\n-2.1011\n-1.7429\n-1.5504\n-3.1685\n-4.7936\n-3.9938\n-5.1361\n-5.7718\n-3.961\n-2.6949\n-2.0672\n-1.2037\n-1.3458\n-1.8368\n-1.8558\n-0.26259\n-0.23983\n-0.72171\n-0.76195\n-1.2053\n-0.081621\n-2.6667\n-0.048151\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6082\nNaN\n-2.9569\n-0.77873\n-2.4491\n-2.3979\n-2.1011\n-2.5209\n-1.3281\n-1.34\n-0.91254\n-1.1132\n0.57037\n0.82499\n0.36996\n-0.86119\n0.21138\n0.43623\n-0.1156\n-0.082235\n-0.34281\n-1.1512\n-0.80343\n-0.091087\n0.94801\n0.40158\n1.0005\n1.1429\n1.3906\n1.3319\n1.7197\n1.2607\n0.072674\n0.080971\n-1.3147\n-3.3073\n-1.8416\n-0.4862\n-0.30047\n-1.9041\n-3.1445\n-1.9484\n-3.1072\n-3.0914\n-3.1858\n-2.6267\n-5.498\n-5.2632\n-2.887\n-1.8338\n-2.2782\n-1.9978\n-1.4436\n-1.6586\n-1.8133\n-1.3579\n-1.1856\n-0.58828\n-1.521\n-1.2817\n-2.1312\n-3.4427\n-1.1072\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.98512\n-0.54222\n-2.6213\n-2.8343\n-2.4577\n-2.5208\n-1.6513\n-1.2449\n-1.1585\n-0.99394\n-0.26\n-0.48922\n-1.1885\n-0.58447\n0.84483\n0.4359\n-0.69899\n-0.83351\n-1.1395\n-1.0942\n-1.0006\n0.05547\n0.66981\n0.36361\n-0.3729\n1.1453\n-0.041403\n0.80666\n1.5376\n2.6801\n0.99243\n0.28981\n-0.32998\n-1.7194\n-1.9976\n-1.0122\n-0.74578\n-2.201\n-4.2489\n-4.0724\n-3.8129\n-2.8847\n-3.1295\n-5.9409\n-5.0401\n-3.9848\n-3.2926\n-1.7543\n-1.7955\n-0.51136\n-0.86456\n-0.98185\n-1.0507\n-1.5971\n-1.8902\n-3.2301\n-3.6225\n-1.4475\n-1.1201\n-2.3988\n-4.0906\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.2395\n-2.6454\n-2.6126\n-2.5658\n-1.9188\n-1.7125\n-2.2144\n-1.279\n-1.8587\n-1.8339\n-1.1214\n-0.80335\n-0.6398\n0.20115\n-1.084\n-1.1288\n-0.60918\n-0.76786\n-0.81161\n-0.0039482\n0.43845\n0.1667\n-0.70869\n-0.21506\n-1.0656\n0.14766\n2.9215\n2.532\n1.075\n1.6243\n1.3875\n-0.59175\n-1.6941\n-1.384\n-0.60693\n-3.9543\n-4.3908\n-3.2642\n-4.4418\n-4.0766\n-4.0161\n-6.8835\n-6.7666\n-4.0793\n-3.1438\n-2.1229\n-1.6262\n-1.3173\n-1.5253\n-0.83376\n-1.0074\n-1.529\n-1.9718\n-5.0247\n-5.8387\n-4.407\n-3.1713\n-1.9277\n-4.0446\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3589\n-2.78\n-2.8563\n-2.3596\n-2.1097\n-1.9565\n-2.2719\n-2.4577\n-2.3374\n-1.6388\n-0.68159\n-0.48917\n0.46047\n1.0922\n-0.52971\n-0.38411\n-0.70038\n-1.0385\n-1.1683\n-0.49677\n0.016727\n-0.34327\n0.89965\n0.65084\n-0.7666\n0.42297\n1.3422\n1.7574\n1.2865\n1.1182\n0.91588\n0.14121\n-2.4892\n-2.6341\n-1.7134\n-5.3959\n-4.8561\n-0.48791\n-4.9776\n-5.3539\n-4.5281\n-4.9221\n-5.0239\n-3.4218\n-2.6243\n-1.5566\n-1.4751\n-1.5773\n-1.214\n-0.66643\n-0.76567\n-1.1329\n-1.8615\n-4.6701\n-6.0915\n-5.2671\n-4.075\n-3.4896\n-3.7211\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.0314\n-2.4143\n-1.7402\n-1.8937\n-1.7504\n-2.1644\n-1.9542\n-2.046\n-2.3008\n-1.7539\n-1.3676\n-0.69915\n0.53186\n0.51377\n-0.030962\n0.39401\n1.1974\n-0.23921\n0.058542\n0.14257\n-0.93953\n0.091862\n0.65078\n0.17611\n-1.2533\n-0.26094\n0.74047\n1.669\n1.8444\n0.89833\n0.5145\n-2.6494\n-3.8092\n-5.2627\n-4.9685\n-6.0237\n-4.9345\n-2.8204\n-4.3821\n-4.7125\n-4.2646\n-4.842\n-4.2153\n-1.5398\n-0.75486\n-0.40171\n-1.1696\n-1.5922\n-1.0369\n-1.1416\n-1.275\n-0.4995\n-1.2372\n-3.2016\n-3.849\n-4.6717\n-5.4526\n-4.5792\n-3.8466\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8921\n-2.2028\n-2.1211\n-1.7507\n-1.5152\n-1.4518\n-1.1559\n-1.4527\n-1.7567\n-1.461\n-2.6469\n-1.1924\n-0.28719\n0.46966\n-0.39144\n0.11667\n-0.052765\n-0.23776\n0.56921\n0.27007\n-1.1984\n-0.94042\n-1.7158\n-0.83028\n-2.6201\n-1.93\n-0.44046\n2.6493\n2.2367\n0.14414\n-1.2102\n-2.6059\n-2.1085\n-3.8788\n-4.9306\n-7.9944\n-5.2726\n-2.952\n-3.1959\nNaN\n-5.1485\n-4.9544\n-3.8953\n-1.0023\n-0.49103\n-0.90567\n-1.8854\n-2.3427\n-0.98534\n-1.1534\n-2.0688\n-2.9849\n-0.62684\n-1.0445\n-3.0678\n-5.0541\n-6.248\n-4.3056\n-2.7857\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7546\n-2.3025\n-1.9762\n-2.5483\n-1.2183\n-0.90652\n-0.60045\n-1.0004\n-1.8288\n-1.9153\n-0.92175\n-0.52675\n-0.92875\n0.11013\n-0.57318\n-0.45206\n-1.1235\n-0.98353\n0.37115\n0.48668\n-1.2645\n-1.4436\n-1.5915\n-2.7721\n-3.0334\n-0.9425\n-1.236\n1.7792\n0.12764\n-1.2616\n-2.3943\n-1.7574\n-0.80998\n-1.6715\n-4.2911\n-3.7261\n-2.4229\n-2.6715\nNaN\nNaN\nNaN\n-5.6743\n-4.1799\n-3.4128\n-3.1661\n-3.0205\n-3.1186\n-3.1613\n-1.4307\n-1.3029\n-2.3199\n-5.1275\n-1.5015\n-1.0354\n-2.105\n-3.3339\n-5.334\n-3.3592\n-3.5819\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8654\n-2.6127\n-2.1037\n-2.2182\n-1.8712\n-1.23\n-1.3314\n-1.571\n-2.4494\n-1.9054\n-1.1654\n-1.63\n-1.0927\n-0.37719\n-0.40064\n-1.3121\n-1.9241\n-1.0029\n-0.4549\n-0.95182\n-1.4668\n-2.0896\n-3.0819\n-3.4791\n-1.2269\n-1.0147\n-0.52402\n0.82093\n-0.92529\n-1.953\n-0.95469\n-1.5522\n-1.9414\n-3.5381\n-3.2216\n-2.623\n-0.40278\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.9597\n-4.3601\n-4.2408\n-4.6871\n-3.263\n-2.8004\n-2.2562\n-1.2553\n0.85313\n-1.5187\n-0.78852\n-0.99999\n-0.99341\n-2.7968\n-3.4229\n-4.0578\n-5.0164\n-5.1944\n-5.9032\n-4.7654\n-1.7761\n1.5203\n0.52785\n-0.98644\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.1613\n-2.4792\n-1.845\n-1.6973\n-2.1234\n-2.1013\n-1.9399\n-1.4864\n-0.94314\n-0.83257\n-1.7911\n-2.3407\n-1.9254\n-1.2208\n-1.2326\n-2.074\n-2.1792\n-0.93344\n-1.0541\n-1.5609\n-1.8637\n-1.725\n-3.2807\n-3.9438\n-1.9243\n-1.426\n-0.23561\n0.061516\n-0.50778\n-2.0518\n-0.4063\n-1.4492\n-0.27096\n-1.5726\n-2.356\n-1.8586\n1.6521\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.619\n-4.106\n-5.7206\n-5.8442\n-4.4554\n-2.7525\n-2.0637\n-2.3815\n-2.1751\n-1.2255\n-0.10299\n-3.0143\n-3.9799\n-3.088\n-3.0694\n-3.3538\n-2.9853\n-2.1065\n-2.0412\n-2.7762\n-3.0817\n-1.9414\n-0.44333\n0.16123\n-1.0754\n-2.0748\n-0.0072784\nNaN\nNaN\nNaN\n-3.314\n-2.8467\n-2.5686\n-2.7466\n-1.8337\n-2.1802\n-1.4882\n-1.584\n-1.9495\n-1.7541\n-1.9389\n-2.2704\n-2.5096\n-2.1749\n-2.1858\n-2.1543\n-2.242\n-1.8689\n-2.4026\n-1.7056\n-2.3052\n-2.9111\n-3.4763\n-3.9416\n-2.4738\n-1.5052\n-0.58062\n0.23837\n-0.073521\n-0.75471\n-1.0844\n-1.4198\n-0.27291\n0.048157\n-1.4427\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.4518\n-4.4862\n-6.4399\n-4.6091\n-5.5251\n-2.8795\n-2.2221\n-4.8213\n-3.6811\n-1.8934\n0.34245\n-3.9973\n-5.4151\n-4.0397\n-3.3229\n-2.2316\n-0.70389\n-1.2092\n-2.5978\n-2.6855\n-1.812\n-4.0376\n-3.4184\n-1.213\n-2.1808\n-1.5686\n-2.4713\nNaN\n-2.1645\nNaN\n-3.5189\n-3.5114\n-2.1143\n-2.4173\n-1.0438\n-1.3871\n-2.2963\n-2.8457\n-2.4611\n-2.3602\n-1.747\n-2.216\n-3.0084\n-2.9084\n-2.9901\n-2.3829\n-1.6018\n-2.1763\n-0.71877\n-3.2091\n-3.0577\n-4.7427\n-5.4321\n-4.646\n-2.3864\n-0.55579\n0.27235\n1.7315\n1.0982\n-0.36072\n-1.7815\n-1.6267\n-1.3527\n-2.2112\n-1.7663\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.5713\nNaN\n-7.0899\n-3.9838\n-0.78598\n-2.3011\n-1.9409\n-2.9543\n-5.2567\n-5.2798\n-4.9075\n-2.945\n-3.369\n-3.7236\n-3.4975\n-1.5747\n-0.51726\n-1.2447\n-2.4805\n-1.7186\n-0.56118\n-2.0184\n-2.4931\n-0.30425\n-2.0055\n-0.98898\n-2.4614\n-2.4045\n-1.5857\n-2.5375\n-2.9825\n-2.5514\n-1.4716\n-2.2178\n-2.3782\n-1.8393\n-2.823\n-2.0909\n-2.3524\n-1.7488\n-2.5001\n-3.3807\n-3.759\n-3.4879\n-2.3882\n-2.5046\n-3.102\n-3.5148\n1.1818\n-2.9258\n-1.9778\n-4.328\n-5.5191\n-4.5491\n-1.9072\n0.58936\n0.060991\n0.66731\n0.38351\n-0.58269\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.4002\nNaN\n-8.7233\n-4.7679\n-0.74269\n-1.5335\n-1.4141\n-5.6588\n-4.0075\n-3.0712\n-3.4577\n-2.6615\n-2.4322\n-2.8168\n-2.4539\n-3.0113\n-2.9507\n-2.7581\n-2.8956\n-1.2332\n0.34321\n-1.4482\n-1.9694\n-3.8827\n-3.988\n-0.4389\n-0.73611\n-1.8927\n-3.3749\n-1.9916\n-1.9485\n-1.8129\n-1.1983\n-2.3671\n-2.2232\n-2.3375\n-3.9563\n-2.8743\n-3.0451\n-3.1042\n-3.4904\n-3.5681\n-4.6099\n-4.4375\n-4.2722\n-3.2358\n-3.6282\n-4.7266\n-4.044\n-2.3489\n-2.5006\n-4.3046\n-5.1672\n-4.6756\n-1.7298\n-1.5314\n-1.5419\n-0.85139\n0.029783\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-12.565\n-7.5238\n-2.2361\n-2.1434\n-2.2307\n-2.6853\n-2.561\n-3.0343\n-4.4621\n-2.7485\n-1.4773\n-1.9469\n-1.8356\n-2.9405\n-2.752\n-3.739\n-3.5898\n-1.9726\n-1.4663\n-1.754\n-1.6543\n-2.2291\n-1.9316\n1.9175\n-0.38345\n-0.77495\n-0.3603\n-1.1467\n-1.5759\n-2.4215\n-1.958\n-2.7267\n-2.5063\n-3.1679\n-3.6483\n-3.4465\n-2.8833\n-3.2784\n-3.7501\n-4.1147\n-3.9031\n-4.1977\n-4.5001\n-3.2036\n-2.8559\n-4.488\n-6.0662\n-6.4833\n-5.0994\n-5.7365\n-5.1583\n-4.4718\n-3.19\n-1.9739\n-2.097\n-2.9905\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.9157\n-4.4929\n-3.6128\n-3.6432\n-2.3738\n-2.1909\n-2.6701\n-3.8003\n-3.3753\n-2.2947\n-1.7552\n-0.84387\n-1.0256\n-1.6381\n-2.7418\n-3.1216\n-2.554\n-1.3501\n-1.9316\n-0.49751\n-0.71434\n1.1346\n-0.4347\n-0.94423\n0.88845\n0.40798\n-0.18571\n-3.3296\n-2.8987\n-2.7118\n-2.8822\n-2.7956\n-3.0601\n-3.0886\n-2.6146\n-3.2127\n-2.999\n-3.7471\n-4.1549\n-4.1251\n-4.136\n-4.3344\n-3.5854\n-3.6446\n-4.5306\n-6.5189\n-6.8806\n-5.0895\n-6.1557\n-5.8466\n-5.0867\n-4.4139\n-2.6374\n-1.0669\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3775\n-4.4734\n-3.8032\n-4.5928\n-1.6769\n-1.5039\n-2.3101\n-3.2716\n-3.1657\n-2.5136\n-1.091\n0.30174\n0.48912\n-0.78119\n-1.5009\n-2.1585\n-2.8612\n-2.8153\n-0.38191\n0.51788\n-0.046743\n-1.0558\n-0.6141\n0.81642\n2.1509\n0.35909\n-0.34157\n-2.8745\n-2.7682\n-2.4645\n-3.2193\n-4.1922\n-3.3677\n-3.568\n-3.1504\n-3.604\n-3.8474\n-4.159\n-4.2074\n-3.903\n-4.5413\n-4.2954\n-3.6483\n-3.1722\n-4.9317\n-5.9853\n-5.7945\n-5.4851\n-6.3758\n-6.2864\n-5.6224\n-4.9077\n-3.6536\n-1.8085\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-8.3527\n-3.5107\n-3.8446\n-4.6196\n-1.2878\n-1.033\n-1.456\n-2.7956\n-3.826\n-2.834\n-1.2612\n0.46619\n1.0665\n0.2353\n-0.63539\n-0.24313\n-1.9619\n-1.1856\n2.1021\n1.9482\n0.72828\n-0.57144\n-0.29463\n0.99965\n2.4532\n1.5762\n-0.35653\n-3.2592\n-2.9652\n-3.0776\n-3.4493\n-3.3177\n-3.6929\n-3.7751\n-4.0229\n-3.2388\n-3.9408\n-4.307\n-4.9028\n-3.8973\n-4.2425\n-3.7881\n-2.9065\n-3.8675\n-5.1022\n-5.0247\n-5.4759\n-5.9987\n-5.7154\n-5.0738\n-5.7862\n-4.1673\n-2.3309\n-0.90179\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.3696\n-3.811\n-3.4481\n-2.6482\n-0.30001\n0.87417\n0.025471\n-2.2769\n-2.961\n-0.59385\n0.28305\n1.5886\n1.3513\n0.50496\n-0.12936\n-0.22592\n-1.5641\n-0.8113\n2.5559\n1.9798\n1.4426\n0.65408\n-0.17546\n1.2126\n1.1237\n0.4833\n-0.61785\n-4.0443\n-3.2003\n-3.1943\n-3.1047\n-3.4992\n-3.5474\n-4.1325\n-4.501\n-3.5246\n-3.5214\n-4.067\n-4.1424\n-3.9083\n-4.7333\n-2.901\n-2.1429\n-4.0411\n-5.3257\n-5.4268\n-4.8813\n-6.3468\n-5.3785\n-4.8645\n-4.8641\n-3.5584\n-1.8755\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7857\n-3.1557\nNaN\n-4.6136\n-4.4819\n-4.5927\n-4.5796\n-3.387\n-2.9407\n-1.6129\n0.57092\n1.0529\n-0.30256\n0.26949\n0.39694\n0.071934\n1.0893\n-0.9718\n-0.041515\n-0.45415\n-1.0312\n-2.288\n-0.8396\n1.0381\n1.9503\n1.3521\n1.0274\n-0.043608\n0.78043\n0.20942\n-1.1322\n-2.2324\n-4.0808\n-3.2989\n-3.4724\n-3.4784\n-3.3176\n-3.679\n-4.7647\n-4.0561\n-3.7206\n-4.0995\n-4.1139\n-4.5244\n-4.4809\n-4.604\n-2.907\n-2.9072\n-3.0809\n-4.2598\n-5.3487\n-4.573\n-4.7283\n-5.4351\n-5.4826\n-4.8144\n-3.4288\n-2.3934\n-3.3709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8205\n-2.5491\n-2.608\n-3.3062\n-5.0744\n-4.7867\n-3.7537\n-3.8486\n-4.5394\n-3.0363\n-3.4861\n-2.8126\n-2.2727\n-1.6791\n-0.47691\n0.74378\n0.19872\n-0.44018\n-0.81423\n-3.9435\n-2.4393\n-1.3693\n-2.2646\n-2.3837\n-0.87754\n0.61949\n2.9503\n1.9311\n2.2013\n1.627\n0.91602\n0.080414\n-0.41096\n-1.8118\n-3.3963\n-3.7509\n-3.9304\n-4.1325\n-3.6844\n-4.4409\n-4.3487\n-3.5646\n-3.8516\n-4.1541\n-4.2282\n-4.7077\n-4.7131\n-4.049\n-3.0263\n-2.5963\n-3.014\n-3.7538\n-4.0472\n-6.0848\n-5.79\n-5.4633\n-5.31\n-4.638\n-2.5692\n-2.0668\n-5.9388\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6476\n-2.5142\n-3.0726\n-3.6951\n-5.2037\n-4.9568\n-4.3841\n-4.3574\n-5.2011\n-3.9338\n-3.979\n-3.6855\n-2.9712\n-2.5998\n-1.3762\n-0.30219\n-2.6331\n-3.7079\n-2.5726\n-3.4291\n-1.9964\n-1.8286\n-2.6759\n-1.6425\n-0.99771\n1.0098\n1.836\n1.2679\n1.7595\n1.226\n-0.41942\n-0.38369\n-0.091167\n-0.98667\n-3.5868\n-3.9157\n-3.7733\n-4.2526\n-4.2089\n-3.7825\n-3.6346\n-3.7825\n-4.2599\n-3.9696\n-4.1179\n-4.991\n-5.0964\n-4.4739\n-3.8745\n-4.4502\n-4.3047\n-4.277\n-5.3427\n-6.0844\n-6.6658\n-5.6361\n-4.7783\n-3.8964\n-1.8029\n-0.9893\n-3.1937\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.5486\n-3.9601\n-3.2667\n-2.6942\n-2.3693\n-1.7861\n-3.0305\n-3.1937\n-3.4133\n-5.5555\n-5.9305\n-4.8285\n-4.217\n-4.7336\n-4.0725\n-3.6838\n-3.0403\n-1.9187\n-1.6867\n-2.2328\n-2.6393\n-3.2708\n-3.1149\n-2.6201\n-2.4141\n-3.1132\n-2.1321\n-0.82523\n-0.56459\n0.36835\n0.082087\n0.79999\n1.3445\n1.0195\n-0.23012\n-0.61795\n-1.1978\n-0.59582\n-2.8519\n-3.2685\n-3.4383\n-3.5622\n-3.7133\n-3.2832\n-2.5692\n-2.8454\n-4.1913\n-3.6826\n-4.3164\n-5.4896\n-6.3275\n-5.1145\n-4.5084\n-4.8108\n-5.0906\n-4.3489\n-4.0094\n-4.6344\n-3.7166\n-3.3992\n-4.4861\n-4.0792\n-1.5022\n0.1975\n-3.2911\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.5963\n-3.5069\nNaN\nNaN\nNaN\nNaN\n-4.3256\n-4.1003\n-3.831\n-4.3375\n-5.5184\n-5.2127\n-5.1897\n-3.8468\n-4.2513\n-3.8519\n-4.427\n-4.1337\n-2.4604\n-1.6405\n-2.0988\n-3.5959\n-3.6648\n-3.0705\n-2.0992\n-3.5543\n-5.4224\n-2.4826\n-1.6704\n-0.60729\n-0.30567\n-0.81657\n0.44441\n-0.72651\n0.7758\n0.88968\n-1.7833\n-1.8048\n-1.0285\n-3.3454\n-3.6806\n-4.2804\n-3.5619\n-3.4446\n-2.9896\n-2.6387\n-2.5723\n-4.1246\n-4.6754\n-5.0789\n-5.3999\n-5.9432\n-5.1463\n-4.2729\n-3.6501\n-2.2433\n-4.977\n-5.4213\n-4.6265\n-4.4545\n-4.7417\n-5.0735\n-4.0346\n-2.5388\n-4.6487\n-5.3849\nNaN\nNaN\nNaN\nNaN\n-4.851\n-4.1247\n-3.5465\n-2.9059\nNaN\nNaN\n-4.0948\n-3.4491\n-4.6769\n-5.9408\n-5.0553\n-3.5216\n-4.1629\n-6.0274\n-4.8856\n-3.9262\n-4.4521\n-4.3876\n-3.5118\n-3.1234\n-2.7105\n-2.3364\n-3.1451\n-2.9074\n-2.7632\n-2.9739\n-1.9656\n-2.5464\n-4.1279\n-3.6572\n-2.0447\n-1.2873\n-0.58071\n-0.52541\n0.037796\n-0.36774\n1.6028\n0.48184\n-1.4127\n-1.1964\n-0.8433\n-3.4287\n-3.897\n-3.9156\n-3.6641\n-3.4364\n-3.1474\n-3.0991\n-3.3661\n-3.5563\n-2.9046\n-3.7514\n-4.8927\n-5.2444\n-4.2344\n-3.5396\n-3.6154\n-1.5719\n-2.3564\n-5.1349\n-4.7187\n-4.4538\n-4.8897\n-5.666\n-3.9011\n-3.9386\n-0.62681\n-2.6892\n-7.648\nNaN\n-7.7037\n-5.6562\n-5.4593\n-4.5304\nNaN\nNaN\nNaN\nNaN\n-2.9014\n-3.6865\n-5.1502\n-5.8654\n-5.3825\n-4.5165\n-3.8467\n-4.3983\n-2.9169\n-3.2743\n-4.4332\n-6.1727\n-5.1884\n-2.9052\n-2.4574\n-2.3822\n-3.6386\n-3.0232\n-2.3944\n-2.129\n-1.6348\n-1.8251\n-2.0892\n-2.5164\n-2.8554\n-1.5823\n-0.46016\n-0.31228\n0.50914\n-0.071789\n0.026272\n-0.54635\n-0.78742\n-0.56712\n-0.3657\n-3.296\n-3.5632\n-3.0166\n-2.7147\n-2.2154\n-2.4856\n-2.5023\n-3.2586\n-2.7937\n-1.9908\n-3.2009\n-4.4245\n-4.5789\n-3.7172\n-3.975\n-3.7568\n-2.3503\n-2.9446\n-4.1416\n-5.7045\n-4.816\n-4.6627\n-4.4732\n-4.2808\n-4.15\n-3.1362\n-3.4079\n-6.055\n-5.6725\n-4.9086\n-5.0979\n-5.9222\n-5.4371\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.6469\n-5.3641\n-4.6608\n-4.6446\n-4.0952\n-2.7574\n-2.7493\n-3.2075\n-3.9898\n-4.8034\n-7.5653\n-4.6879\n-2.6831\n-2.5896\n-2.9284\n-3.1615\n-2.4961\n-2.1913\n-1.8581\n-1.1649\n-1.6069\n-1.3526\n-1.8227\n-2.2859\n-0.98703\n-0.47237\n-1.1948\n-2.627\n-3.0638\n-1.4964\n-1.1175\n-0.67223\n0.43471\n1.0024\n-3.0557\n-2.3608\n-2.3184\n-2.7422\n-1.69\n-2.0387\n-1.9163\n-2.1151\n-1.9377\n-1.7436\n-2.5415\n-3.9499\n-3.7554\n-3.1143\n-3.3434\n-3.2811\n-3.4805\n-5.0697\n-5.6696\n-6.1181\n-5.6198\n-5.4325\n-5.085\n-4.0502\n-6.5471\n-7.6593\n-6.2639\n-5.2067\n-4.7727\n-3.7782\n-4.4045\n-5.9609\n-5.2305\n-4.4161\nNaN\nNaN\nNaN\n-6.7328\n-5.423\n-5.3226\n-4.8972\n-4.9269\n-3.635\n-3.98\n-5.7413\n-4.8867\n-4.909\n-5.0602\n-5.2155\n-3.7659\n-2.4023\n-2.3229\n-1.9023\n-3.6921\n-2.7206\n-2.0531\n-1.5032\n-1.3817\n-1.079\n-0.88294\n-1.4764\n-1.57\n-1.526\n-1.8458\n-1.2617\n-1.1358\n-1.394\n-2.1518\n-1.8422\n-0.38963\n0.10026\n0.023026\n-1.8121\n-1.4584\n-2.2327\n-2.5604\n-1.2835\n-1.187\n-1.4412\n-1.3937\n-1.1774\n-1.3637\n-2.0977\n-3.5107\n-2.3102\n-2.4971\n-2.3955\n-3.9862\n-4.3448\n-5.4209\n-5.8355\n-5.5344\n-6.0335\n-4.6827\n-3.7171\n-3.8693\n-1.1643\n-2.5366\n-3.4979\n-4.5681\n-4.1257\n-4.2369\n-5.2498\n-6.6104\n-5.1609\n-4.6089\nNaN\nNaN\nNaN\n-6.0773\n-5.1929\n-5.0235\n-5.3468\n-4.884\n-3.9281\n-4.0289\n-6.191\n-6.0187\n-4.6576\n-4.231\n-5.1589\n-4.2092\n-2.2871\n-1.6597\n-1.6603\n-2.8178\n-1.9917\n-1.1102\n-1.2195\n-0.74727\n-0.54092\n-0.72733\n-1.6578\n-2.0864\n-1.3633\n-0.84765\n-0.76097\n-0.58456\n-1.0342\n-1.4243\n-1.3167\n-0.69184\n0.49543\n-1.7211\n-1.4513\n-0.8585\n-1.4058\n-0.97301\n-0.80985\n-0.22755\n-0.59712\n-0.74426\n-0.00096131\n-0.58673\n-2.0258\n-2.7457\n-2.1572\n-2.1426\n-1.85\n-3.1056\n-3.9556\n-3.6108\n-4.7037\n-5.5598\n-5.3578\n-4.8778\n-3.142\n-3.1622\n-4.5589\n-3.5542\n-4.3096\n-3.8705\n-2.8851\n-3.8443\n-5.7834\n-5.4804\n-3.4399\n-2.885\n-3.7467\n-5.5388\n-6.4576\n-6.0271\n-5.6819\n-4.9356\n-5.6999\n-4.807\n-3.5648\n-4.3492\n-5.7129\n-4.6707\n-4.4029\n-3.8345\n-4.9188\n-3.7632\n-2.0807\n-1.112\n-1.7842\n-1.6625\n-0.93874\n-0.56502\n-0.9275\n-0.69621\n-0.14457\n0.031532\n0.29923\n-1.3806\n-1.8879\n-0.88626\n-0.75604\n-0.61272\n-0.4859\n-0.81349\n-1.0055\n-1.6741\n-0.78789\n-1.6114\n0.93404\n0.011799\n-1.5627\n-1.2104\n-0.54776\n0.36244\n1.0135\n0.026068\n-0.23007\n-0.52478\n-1.7334\n-1.5971\n-0.70133\n-0.49144\n-0.6229\n-2.0868\n-3.9127\n-2.9081\n-0.72209\n-0.38901\n-3.0179\n-3.6969\n-2.5133\n-2.4595\n-5.8506\n-5.4805\n-4.4301\n-4.4765\n-5.3048\n-7.1868\n-6.2932\n-5.9235\n-5.2087\n-3.9444\n-4.3535\n-4.827\n-4.611\n-5.6116\n-5.5109\n-3.8426\n-2.8634\n-3.6661\n-3.6484\n-3.7728\n-3.9264\n-4.3214\n-4.4267\n-3.8517\n-3.3086\n-2.1672\n-1.4846\n-1.1297\n-1.6157\n-1.0988\n-0.63398\n-0.24547\n0.24805\n0.1137\n0.31682\n0.76546\n-0.60299\n-1.5435\n-2.1302\n-1.4505\n-0.76348\n-0.52485\n-0.0933\n-0.20121\n-0.59345\n-0.82316\n-0.90351\n-1.427\n-0.10843\n0.46283\n-1.1738\n-0.44979\n0.41846\n1.3861\n1.3085\n1.0787\n0.48415\n-0.13168\n-1.0874\n-0.66581\n-0.14974\n0.34111\n1.2306\n1.1586\nNaN\nNaN\nNaN\n-0.032856\n-3.4467\n-3.1475\n-3.0823\n-4.4305\n-4.8995\n-5.3928\n-5.616\n-4.6293\n-6.3539\n-5.5136\n-4.7595\n-5.3868\n-5.1194\n-5.3392\n-4.9045\n-4.0385\n-4.3085\n-4.5234\n-4.0405\n-4.4538\n-4.8403\n-4.6054\n-4.3211\n-3.9521\n-3.7457\n-3.4677\n-2.9586\n-2.0433\n-1.4874\n-1.637\n-1.0236\n-1.0243\n-1.4221\n-0.76474\n-0.50881\n0.20475\n1.6534\n0.28964\n0.40365\n0.60095\n-0.62607\n-1.1238\n-1.557\n-0.48164\n-0.23566\n-0.61961\n-0.36579\n-0.21699\n0.40635\n0.13096\n-1.1532\n-0.49052\n0.15383\n0.48907\n0.32827\n0.96122\n1.5908\n2.6643\n1.3784\n1.1398\n0.54739\n-0.48874\n-0.37764\n-0.29589\n0.92344\n1.8625\n0.68846\n-0.44973\nNaN\nNaN\nNaN\n-1.3233\n-3.6302\n-5.5396\n-3.6659\n-3.7661\n-2.7688\n-3.9267\n-4.1638\n-3.6494\n-3.9879\n-3.2823\n-5.0163\n-4.8977\n-4.3814\n-3.9862\n-3.2949\n-3.0513\n-4.0871\n-3.4884\n-2.8816\n-4.6263\n-4.5991\n-4.7626\n-4.5047\n-3.3966\n-2.7584\n-2.6792\n-1.8898\n-1.1813\n-0.27646\n-0.52647\n-0.57291\n-1.021\n-1.5375\n-1.1683\n-0.70229\n-0.024403\n0.53655\n-0.93401\n0.40096\n0.25516\n-0.23547\n-0.75294\n-0.947\n-0.016453\n0.31063\n-0.35646\n-0.13861\n-0.11668\n-0.019876\n-0.73004\n-0.062222\n0.36098\n0.4431\n0.44976\n1.1664\n2.9897\n3.6471\n3.0653\n2.203\n1.9277\n0.51979\n-0.54963\n0.542\n1.3988\n1.5689\n1.2381\n-0.35823\n-0.067278\n-0.28581\nNaN\nNaN\n-3.08\n-2.5645\n-2.6175\n-2.5111\n-2.2096\n-2.0909\n-3.1749\n-3.666\n-2.8907\n-3.2615\n-4.1238\n-6.2737\n-5.5099\n-3.8885\n-3.2333\n-2.5964\n-2.6656\n-3.2257\n-2.9738\n-3.2141\n-3.2081\n-3.9993\n-3.9419\n-3.3281\n-3.0349\n-2.5232\n-2.2702\n-1.515\n-0.85972\n0.34149\n-0.71402\n-0.74581\n-0.83178\n-0.79595\n-1.3599\n-1.3887\n-0.56791\n-0.61088\n-0.93434\n-0.014179\n0.45941\n0.055702\n-1.099\n-0.41301\n0.36287\n1.0158\n0.3642\n-0.10289\n-0.37793\n-0.86754\n-0.15367\n0.81945\n1.7849\n1.2741\n0.98735\n2.0884\n3.8979\n3.9476\n3.5727\n2.6141\n1.6708\n1.1706\n0.069733\n0.53563\n0.94599\n1.1522\n1.8768\n0.34593\n-0.3856\n0.93585\n0.098644\n-0.7062\n-1.7173\n-1.7111\n-1.1534\n-2.0276\n-1.3389\n-1.5316\n-2.2608\n-2.6601\n-2.5073\n-3.0392\n-3.8984\n-4.8655\n-4.0043\n-3.2996\n-3.0209\n-2.9806\n-2.8033\n-3.023\n-2.0363\n-2.4126\n-2.4738\n-2.873\n-2.8847\n-2.5596\n-1.9077\n-2.2136\n-1.8164\n-1.5356\n-1.6405\n-1.6979\n-1.3479\n-0.79469\n0.34409\n-0.19989\n-0.84173\n-1.6239\n-0.55707\n-0.33489\n-0.45507\n-0.25106\n0.46045\n-0.13356\n-0.71756\n0.60952\n0.38408\n0.78797\n0.81678\n1.0252\n0.050787\n-0.82719\n-0.21676\n0.54834\n1.607\n1.3941\n2.224\n3.828\n4.3606\n4.9731\n4.9574\n3.474\n3.248\n1.6331\n0.45209\n1.1022\n1.2927\n0.78095\nNaN\nNaN\nNaN\nNaN\n0.62261\n-1.2422\n-2.0558\n0.15651\n-0.18338\n-1.2345\n-1.2646\n-1.1392\n-1.8165\n-1.7749\n-1.6564\n-1.9235\n-2.9453\n-3.3032\n-3.3347\n-3.5423\n-3.6065\n-3.8138\n-3.3398\n-2.6008\n-1.6978\n-1.6225\n-1.4258\n-1.9349\n-1.9302\n-1.7785\n-2.2947\n-1.6621\n-0.66119\n-1.2327\n-1.8613\n-1.3907\n-1.1881\n-1.5427\n-2.6249\n-1.438\n-1.3874\n-1.3588\n-0.62855\n-0.58221\n-0.90526\n-0.1738\n-0.36017\n-0.55414\n0.4617\n1.4299\n-0.60476\n-0.11059\n-0.078215\n1.0675\n0.1279\n-0.56182\n-0.086983\n0.31367\n1.1928\n1.2293\n3.0213\n3.9433\n4.473\n4.6611\n4.0876\n4.2357\n4.6938\n4.3821\n2.6829\n2.3595\n1.8285\n2.1611\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.99821\n-0.55506\n0.05599\n0.36487\n-0.8563\n-0.78879\n-1.0843\n-1.5634\n-1.3255\n-1.2801\n-1.3421\n-1.7654\n-1.7823\n-2.7042\n-2.3188\n-2.6385\n-3.0318\n-2.503\n-2.1999\n-1.0367\n-0.39961\n-0.18502\n-0.93965\n-0.62155\n-0.84677\n-3.3518\n-2.4427\n-1.9297\n-1.4424\n-2.3096\n-1.6974\n-2.6218\n-2.366\n-2.3144\n-1.295\n-2.0518\n-2.0089\n-1.1774\n-1.1882\n-1.6019\n0.15384\n0.14359\n-0.34899\n0.20661\n-0.61174\n-0.82465\n-0.56153\n0.20303\n0.45022\n0.53629\n0.98267\n0.56664\n0.58673\n1.3684\n2.9741\n2.6646\n2.638\n3.9378\n4.7109\n6.088\n6.3989\n6.2549\n5.5167\n3.368\n1.2929\n1.4173\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.21011\n-0.88545\n-1.1869\n-1.22\n-1.6044\n-1.3525\n-1.4719\n-1.1558\n-0.74213\n-0.71135\n-1.0775\n-1.4421\n-1.7719\n-1.8851\n-1.9119\n-1.8438\n-2.2012\n-2.6772\n-0.65948\n1.2027\n0.55867\n-0.46936\n-0.27386\n0.63971\n-2.681\n-3.3327\n-3.2056\n-2.2419\n-1.6269\n-1.4091\n-2.0631\n-2.1674\n-1.1568\n-2.053\n-2.9043\n-2.3493\n-1.3311\n-1.3764\n-1.0032\n0.85781\n0.077469\n-0.52447\n0.53139\n0.34778\n0.24775\n-0.18251\n0.82307\n1.8307\n1.6842\n3.2696\n1.1848\n1.3012\n1.402\n3.9795\n2.6915\n3.4946\n4.2064\n4.8218\n6.9171\n7.7964\n7.3239\n4.3537\n3.2075\n1.1589\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.44573\n-1.0258\n-1.4669\n-1.0965\n-1.0573\n-0.90284\n-1.3245\n-1.0605\n-0.5958\n-0.5895\n-1.3506\n-0.89655\n-0.89668\n-1.8216\n-2.1282\n-2.2814\n-2.8783\n-2.4385\n-1.0984\n-1.2243\n-1.2686\n-0.93345\n-0.89607\n-1.2899\n-1.1214\n-2.6098\n-3.9549\n-3.5522\n-1.7565\n-1.0243\n-1.932\n-1.4477\n-0.53327\n-0.72771\n-1.1861\n-0.9304\n-1.2671\n-0.93048\n-0.36977\n0.29141\n-0.03406\n-0.79469\n0.32868\n0.10545\n1.0474\n1.5664\n1.431\n2.2134\n2.7322\n3.849\n1.4645\n1.2814\n1.1644\n6.1439\n6.5258\n6.4641\n7.9678\n8.4968\n7.5168\n7.9362\n8.3217\n5.7259\n4.2077\n6.0843\n6.4049\n6.237\n5.5126\n3.5386\n3.4397\n3.3615\n7.7067\n0.48038\n0.81147\n2.0538\n1.8939\n-0.21376\n1.0761\n1.8129\n3.4764\n3.3747\n4.969\n3.4322\n0.1156\n8.1778\n7.329\n8.3433\n10.225\n10.231\n9.8601\n8.8815\n7.5522\n8.1836\n8.2644\n7.2214\n6.9469\n7.8805\n7.2348\n6.8883\n8.1697\n7.2571\n7.3752\n8.6612\n8.8665\n7.8946\n6.6045\n8.1327\n8.6525\n6.2525\n8.7475\n7.8575\n10.384\n15.559\n14.478\n9.5961\n6.961\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.8463\n6.8544\n7.1134\n6.2537\n5.6228\n9.5531\n7.8772\n8.5993\n4.2016\n3.5402\n5.1265\n5.3061\n6.1403\n2.6924\n0.48442\n1.5586\n6.5013\n6.7475\n3.9716\n3.5937\n-1.8671\n-0.74262\n-0.63568\n-2.0141\n-0.85772\n2.4688\n2.2409\n2.6004\n1.0946\n1.4697\n2.2356\n2.2775\n5.534\n9.4084\n8.5283\n9.9973\n8.9024\n8.3511\n9.1316\n6.0776\n5.4165\n7.1643\n8.2772\n7.5491\n7.1793\n7.4302\n7.514\n8.4189\n9.1938\n8.6345\n8.7954\n7.8001\n7.6788\n7.4064\n4.5542\n8.3008\n8.1599\n12.904\n14.806\n12.076\n9.9941\n5.7376\n7.6913\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.7075\n7.2576\n6.2464\n7.1957\n10.098\n10.416\n8.6345\n6.2106\n4.9108\n4.2374\n5.7433\n4.775\n5.7956\n0.80176\n-3.4539\n-1.3106\n3.3555\n3.5571\n2.7923\n-0.534\n0.63926\n-2.0293\n-1.7721\n-1.8308\n-2.6075\n-1.5171\n-1.8354\n0.082189\n-0.59534\n3.0892\n2.7673\n1.0901\n4.2447\n4.6988\n6.468\n9.0728\n8.8754\n8.4997\n8.0162\n6.8329\n7.4869\n6.7749\n7.7622\n7.6374\n7.5513\n7.7166\n8.3753\n9.0111\n9.3936\n8.5632\n7.7718\n7.9927\n7.5197\n6.5427\n7.1164\n5.9445\n6.3869\n12.367\n12.838\n8.6807\n9.8131\n4.2815\n3.8329\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.8361\n7.6179\n7.0853\n7.5185\n6.9004\n6.562\n5.406\n5.7114\n5.8261\n4.4312\n3.9234\n5.3905\n2.4857\n-0.95202\n2.0195\n3.0344\n3.2196\n3.7056\n1.5034\n-0.96207\n0.29518\n-3.576\n-3.547\n-1.1732\n-3.3786\n-4.3568\n-4.64\n-1.8191\n-0.45296\n0.20874\n3.5042\n2.11\n5.3671\n4.4749\n4.4105\n6.3943\n8.7502\n7.9235\n6.935\n5.6738\n6.7632\n6.8531\n9.2601\n9.8085\n8.3927\n8.59\n9.1896\n9.2126\n9.4573\n7.1447\n5.7776\n7.0545\n6.6405\n5.972\n5.8331\n5.4644\nNaN\nNaN\nNaN\n14.037\n11.602\n5.0049\n2.0399\nNaN\nNaN\nNaN\n8.244\nNaN\nNaN\nNaN\nNaN\nNaN\n5.9319\n7.0694\n6.328\n6.812\n6.8609\n6.6719\n6.4589\n5.8291\n5.7372\n5.6604\n5.8593\n5.5459\n1.2042\n2.2286\n4.9955\n5.5989\n5.2726\n2.4398\n4.7693\n0.48054\n0.26771\n-4.9463\n-7.5898\n1.0369\n-3.6083\n-5.5076\n-5.3495\n-2.2192\n-1.3786\n-1.5144\n2.1941\n5.672\n6.4453\n6.4759\n4.5723\n12.316\n9.6051\n7.9776\n6.7923\n5.7472\n6.5987\n6.5125\n9.3065\n11.056\n9.9519\n9.287\n9.1331\n9.503\n9.6628\n8.3928\n7.5022\n6.9733\n6.9997\n6.2636\n6.0854\n6.5111\n1.2776\nNaN\nNaN\n16.654\n13.316\n7.8117\n1.4113\n5.4626\n11.626\n6.8331\n8.1632\nNaN\nNaN\nNaN\nNaN\nNaN\n7.2401\n7.6876\n5.9351\n5.8348\n5.5094\n6.2528\n5.6196\n4.302\n4.047\n4.1679\n3.9723\n2.8207\n1.9432\n2.4968\n5.3252\n4.2327\n5.0526\n3.5169\n3.0275\n2.7698\n2.5858\n-4.61\n-3.2023\n-0.45624\n-2.4726\n-6.183\n-4.6286\n-3.4717\n-2.4539\n-2.4012\n-0.92567\n5.9343\n7.6627\n7.6151\n6.7607\n10.139\n7.1581\n6.4241\n6.2688\n6.3696\n6.2653\n8.3906\n9.8133\n11.211\n12.615\n10.837\n9.4616\n8.8502\n9.7266\n7.1653\n7.6707\n7.0829\n8.0074\n7.5676\n8.4486\n11.101\n8.8679\nNaN\nNaN\n21.73\n19.346\n9.1963\n9.7233\n10.844\n10.162\n6.2912\n9.1751\nNaN\nNaN\nNaN\nNaN\nNaN\n7.0387\n7.2235\n5.308\n4.4072\n3.6427\n4.6043\n2.9106\n1.4951\n2.0405\n1.6565\n1.3699\n2.2952\n2.4048\n3.6603\n3.4087\n4.9179\n5.2237\n2.3385\n4.8195\n1.6326\n0.69563\n0.43655\n-1.9971\n-0.3831\n-0.57004\n-2.6585\n-3.7874\n-3.8624\n-3.5153\n-2.7284\n-0.12056\n6.2249\n6.6303\n7.1249\n9.4551\n8.6809\n6.984\n6.2301\n6.1189\n6.6777\n7.0962\n9.4305\n11.452\n12.759\n15.521\n11.955\n10.005\n8.4202\n7.8305\n6.4855\n7.9065\n7.5644\n7.6666\n8.0136\n8.7881\n9.882\n7.7897\nNaN\nNaN\n16.125\n14.875\n13.914\nNaN\n14.273\n10.514\nNaN\nNaN\n5.2818\n10.02\nNaN\nNaN\nNaN\n5.5746\n4.3621\n4.0923\n1.8897\n0.47673\n3.1511\n2.1704\n1.6398\n2.3698\n1.8283\n1.3405\n1.0933\n1.18\n1.203\n1.2054\n2.1612\n2.8318\n1.0946\n1.8581\n0.73704\n0.48383\n-1.2906\n-0.73654\n-0.25054\n-2.092\n-1.8587\n-2.5632\n-4.1342\n-5.2151\n-4.0554\n-2.3895\n3.4493\n3.7659\n4.8308\n5.3434\n3.7629\n5.9833\n7.0096\n8.0579\n9.317\n9.8299\n10.474\n11.526\n12.923\n17.885\n12.136\n9.5503\n8.8225\n8.7336\n6.6787\n8.0241\n7.2697\n6.2511\n6.9848\n6.7792\n7.6136\n7.2624\n7.3058\n6.6347\n9.1578\n13.169\n13.334\nNaN\nNaN\nNaN\nNaN\nNaN\n8.2984\n9.3514\n11.941\nNaN\nNaN\n4.5966\n4.2558\n2.7953\n2.1947\n-0.45264\n1.8877\n2.5381\n1.4943\n1.901\n1.3109\n1.3714\n1.308\n0.89917\n0.43365\n0.80309\n1.1009\n1.234\n0.89425\n0.52059\n-0.49331\n-0.42025\n0.82845\n0.93137\n-1.206\n-3.512\n-2.9345\n-2.9495\n-3.5545\n-4.0822\n-3.4501\n-2.7969\n-0.66356\n0.75654\n3.022\n1.7992\n1.8869\n6.2024\n7.5513\n7.8647\n11.145\n11.38\n11.991\n13.979\n16.031\n18.887\n11.616\n9.1311\n8.0173\n7.3674\n5.9264\n6.9357\n4.8487\n4.8928\n3.9778\n4.6146\n7.0028\n8.3167\n8.1728\n5.1675\n2.9581\n10.092\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.4861\n13.106\nNaN\nNaN\n5.8589\n4.3247\n3.4801\n3.0368\n0.63226\n2.7842\n1.8692\n1.402\n0.79205\n1.1761\n-0.043077\n-0.41373\n-0.55801\n-0.011694\n0.75453\n0.01853\n-0.39047\n0.59194\n0.93911\n-0.64376\n-0.13331\n-0.63936\n-2.4725\n-1.9438\n-1.8334\n-3.7141\n-4.4577\n-2.9219\n-1.778\n-1.8641\n-1.01\n2.8382\n5.1281\n2.4579\n1.8771\n2.2852\n5.2529\n7.3092\n8.715\n11.07\n13.878\n13.799\n15.744\n16.058\n13.089\n10.267\n9.4571\n7.8433\n7.7855\n6.1711\n6.1524\n3.5967\n3.9619\n3.7156\n4.3867\n7.0594\n10.111\n10.932\n5.831\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.5802\nNaN\n13.235\n9.8983\n6.3319\n5.8858\n4.4624\n4.3454\n2.7607\n2.6616\n1.7219\n1.4326\n-0.282\n-0.39359\n-0.9648\n-0.31073\n-1.8967\n-1.7713\n-0.44272\n-0.72013\n-0.2515\n1.412\n0.95482\n-0.98554\n-1.9964\n-2.0823\n-2.9965\n-1.4434\n-0.22905\n-2.6\n-5.0737\n-4.6004\n-1.3139\n-1.7475\n1.163\n5.7598\n6.8854\n3.4082\n3.9588\n5.0087\n7.5352\n7.8663\n11.357\n11.875\n13.469\n14.694\n16.861\n16.04\n12.773\n10.518\n10.205\n8.6872\n7.3029\n7.1351\n6.1488\n4.2733\n4.3142\n2.9791\nNaN\nNaN\n14.006\n12.732\n7.7049\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.668\n10.14\n6.6056\n6.63\n6.0153\n4.6687\n2.7202\n1.6091\n1.4591\n0.95981\n-0.004272\n0.53381\n0.5415\n-0.89226\n-3.368\n-3.0337\n0.03799\n0.13231\n0.7068\n0.29834\n-0.74049\n-1.5985\n-1.2622\n-1.8817\n-1.8295\n-1.1469\n0.78907\n-1.127\n-3.8023\n-6.3891\n-3.1845\n-2.4235\n0.91876\nNaN\nNaN\nNaN\n5.1934\n6.3374\n8.9554\n9.3997\n14.207\n12.653\n12.647\n15.574\n16.611\n15.457\n14.255\n11.778\n10.616\n7.5879\n5.961\n5.9092\n5.9175\n4.2399\n5.7077\nNaN\nNaN\nNaN\n12.175\n13.886\n11.854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.4163\n5.4984\n5.4345\n4.7582\n3.1288\n2.7166\n3.312\n1.4612\n1.9815\n1.5528\n-0.082122\n-1.0856\n-1.8518\n-2.6971\n-0.029378\n-0.79333\n-1.2881\n-1.4814\n-2.01\n-1.911\n-1.6335\n-1.21\n0.5645\n2.1031\n2.3036\n0.15015\n-4.9587\n-4.6854\n-1.7596\n-1.9316\n-0.30832\nNaN\nNaN\nNaN\n5.6283\n10.548\n10.731\n8.1488\n14.003\n14.677\n15.591\n20.038\n21.964\n16.648\n13.973\n12.546\n10.552\n7.9068\n6.0314\n5.0734\n5.6394\n5.1648\n7.2073\nNaN\nNaN\nNaN\n14.419\n13.501\n14.057\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.2379\n5.5355\n5.7472\n4.7066\n3.5934\n3.8477\n3.9466\n4.2105\n3.5559\n2.1741\n0.83967\n-0.38821\n-1.36\n-2.0389\n-0.60339\n-2.0241\n-1.882\n-1.4057\n-0.99874\n-1.3576\n-1.7768\n1.2977\n2.8328\n3.0043\n3.0688\n-0.67461\n-3.0544\n-2.143\n-0.87406\n-0.1905\n0.66952\nNaN\nNaN\nNaN\nNaN\n13.461\n14.368\n5.344\n14.308\n16.144\n17.736\n20.124\n20.662\n16.068\n13.219\n12.152\n9.6424\n7.9038\n6.0769\n4.5564\n6.0831\n7.0669\n8.0586\nNaN\nNaN\nNaN\nNaN\n15.65\n14.629\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.7044\n5.6526\n4.0509\n3.7354\n3.2286\n3.9377\n3.0084\n3.5334\n3.4976\n2.8765\n2.4804\n0.72656\n-1.3444\n-1.3328\n-0.75131\n-2.3537\n-3.5231\n-2.1027\n-1.6583\n-1.4008\n-1.8318\n1.8622\n5.1492\n4.004\n3.5018\n-0.3304\n-1.3969\n-1.2367\n-1.1941\n-0.24348\n-0.70459\nNaN\n11.163\n12.9\n11.16\n13.431\n13.234\n8.8096\n14.259\n16.757\n16.999\n19.13\n19.588\n13.459\n11.588\n10.584\n9.0245\n8.3429\n6.1116\n5.6182\n7.1488\n4.6439\n6.3149\n11.738\nNaN\nNaN\nNaN\n16.792\n14.737\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.1282\n5.5979\n4.2637\n2.8277\n2.9548\n3.2993\n2.5833\n2.6365\n2.6094\n2.2277\n4.5993\n1.4259\n-0.82152\n-1.8325\n-0.20878\n-1.269\n-1.8166\n-1.8646\n-2.2552\n-1.8846\n-0.88799\n-0.77656\n6.7407\n6.3575\n4.6443\n1.7296\n0.26379\n-3.4052\n-2.0198\n1.4744\n2.6929\n10.729\n8.9407\n12.004\n11.689\n14.762\n11.053\n10.245\n12.222\nNaN\n23.057\n20.509\n16.943\n11.664\n9.7519\n11.381\n11.19\n10.254\n6.7187\n7.0501\n7.6617\n7.4589\n4.5699\n9.4311\n13.224\n17.023\n19.767\n17.587\n15.722\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0468\n4.8992\n3.3888\n3.5323\n3.6762\n2.9215\n1.7973\n1.6092\n2.1822\n1.6316\n1.5963\n0.80893\n-0.24272\n-0.99318\n0.23583\n-0.35534\n0.096713\n-1.0636\n-2.4401\n-2.9717\n-0.74223\n2.0466\n4.3268\n9.3915\n4.3474\n1.223\n0.68363\n-0.087044\n2.6093\n5.4024\n6.4248\n6.6536\n4.671\n10.981\n10.24\n10.919\n8.3142\nNaN\nNaN\nNaN\nNaN\n25.078\n17.123\n15.401\n12.701\n13.728\n12.956\n11.341\n7.9013\n7.499\nNaN\n8.3444\n5.0258\n8.4432\n11.368\n13.376\n18.65\n17.021\n16.547\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.1639\n5.4197\n3.2537\n2.8491\n3.8912\n3.6035\n2.1041\n1.451\n2.4073\n1.2122\n0.63867\n0.11332\n0.13825\n-0.68522\n-0.78127\n0.027303\n-0.051175\n-0.44111\n-1.4488\n-1.5426\n-0.01602\n4.2128\n6.4602\n9.2476\n3.6994\n2.2521\n1.0281\n3.2054\n5.6099\n8.0529\n5.2601\n5.503\n10.669\n10.321\n8.4815\n9.0682\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n13.82\n15.976\n12.802\n10.753\n9.6315\n8.466\n4.4987\nNaN\nNaN\nNaN\n9.5092\n11.734\n13.083\n15.411\n15.502\n14.332\n14.213\n11.247\n10.192\nNaN\nNaN\n9.3633\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.4503\n4.8054\n3.114\n2.4422\n3.1007\n4.3675\n2.4758\n1.0762\n0.94664\n-0.14663\n1.6917\n0.56776\n-0.0096879\n-0.20572\n-0.29988\n0.74007\n0.84495\n0.74649\n0.039794\n0.051164\n1.9287\n3.9802\n7.105\n7.1292\n4.5432\n3.2044\n3.5877\n5.4504\n4.5428\n7.8291\n6.3035\n9.5975\n11.184\n10.658\n8.9221\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n16.918\n17.967\n16.026\n12.65\n10.259\n11.335\n9.7504\nNaN\nNaN\nNaN\n13.396\n12.178\n12.615\n13.287\n12.153\n10.005\n8.339\n7.5568\n7.4865\n6.8642\n4.7025\n3.3529\n4.0518\n4.8704\n5.6214\nNaN\nNaN\nNaN\n6.0955\n4.8683\n4.3494\n4.6277\n2.107\n2.9455\n0.9181\n0.97932\n1.5488\n0.7061\n1.5503\n-0.086222\n0.15709\n1.1774\n1.9785\n1.237\n1.2714\n2.2644\n2.7656\n0.8918\n2.4825\n7.0767\n6.6295\n5.2795\n3.7667\n2.9203\n5.1823\n5.6551\n4.2672\n7.0158\n10.603\n11.707\n11.558\n10.973\n8.9096\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n18.454\n17.173\n17.69\n14.503\n10.949\n15.831\n12.449\nNaN\nNaN\nNaN\n17.678\n14.356\n12.904\n11.231\n9.2801\n9.0161\n9.8342\n8.2885\n5.8117\n8.619\n6.6594\n2.7158\n2.4199\n3.5349\n5.9782\nNaN\nNaN\nNaN\n6.5101\n5.568\n3.9467\n4.4542\n0.74769\n1.5316\n2.4619\n2.283\n1.4167\n1.1209\n0.82808\n0.41298\n1.836\n2.0909\n2.9747\n1.7911\n-0.5354\n2.2307\n1.5136\n3.2074\n3.7306\n8.8316\n8.54\n4.3602\n3.7901\n2.2524\n3.7091\n4.7833\n4.3925\n4.254\n7.2032\n5.3006\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.5417\n15.033\n11.769\n14.77\n12.158\n11.138\n15.074\nNaN\n13.896\n14.156\n13.032\n12.671\n11.9\n9.8147\n8.9401\n9.5277\n9.7522\n5.7978\n4.4388\n6.0924\n4.7062\n1.8816\n4.8044\n5.3953\n6.5617\n7.2377\n3.3914\n3.6679\n5.3511\n4.4578\n3.5074\n4.3297\n4.0293\n3.4968\n3.6873\n1.3517\n1.5125\n1.4259\n2.3723\n2.8177\n2.7428\n2.0537\n2.3173\n1.8039\n2.0818\n4.0665\n0.11206\n3.3273\n2.574\n6.7991\n8.8717\n4.915\n3.0322\n1.1286\n3.6792\n5.9635\n6.1669\n6.7965\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.038\n18.169\n13.111\n14.044\n9.4357\n15.42\n13.909\n12.716\n13.334\n12.161\n10.003\n10.382\n9.9227\n10.693\n10.484\n9.6956\n9.2495\n4.7514\n2.9492\n5.335\n4.3214\n5.7113\n8.1425\n6.6265\n4.7669\n4.9074\n5.5602\n1.7338\n4.1685\n4.2991\n3.4146\n5.1531\n5.3325\n4.5995\n5.1562\n3.1903\n2.753\n3.1362\n3.309\n2.843\n3.9926\n3.5486\n3.9547\n2.5795\n3.1479\n5.1891\n6.285\n3.2836\n3.5476\n5.8908\n7.4695\n6.4704\n3.8588\n4.412\n6.7519\n8.6486\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n25.622\n24.361\n17.632\n12.42\n10.532\n12.92\n12.388\n12.863\n13.578\n10.667\n8.8056\n9.371\n8.5198\n9.7445\n9.4145\n10.048\n8.6202\n5.7977\n3.6935\n6.3411\n5.0172\n4.6282\n5.5774\n2.3933\n2.79\n0.60855\n2.1159\n-0.34546\n4.1941\n5.4652\n4.8179\n5.9783\n4.9823\n5.2133\n4.9528\n3.9554\n2.3904\n2.3692\n2.6215\n2.069\n2.7751\n3.9801\n4.4306\n2.5031\n2.2085\n5.4699\n7.8868\n9.4443\n7.1974\n8.113\n7.9604\n6.1041\n5.1282\n6.5235\n10.11\n14.044\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n20.751\n18.5\n14.975\n14.665\n12.958\n12.032\n12.179\n12.052\n10.506\n8.9309\n8.6284\n5.755\n6.7471\n7.6092\n7.6149\n7.6254\n7.3908\n2.974\n5.82\n2.7592\n3.934\n2.2426\n5.0732\n1.2215\n-2.6768\n0.14894\n-1.8134\n6.7048\n6.434\n6.0973\n5.7009\n4.6168\n4.3502\n3.4299\n1.9431\n2.5276\n1.8957\n2.0791\n2.5368\n2.4212\n3.6319\n4.6851\n3.2841\n3.2344\n7.6651\n8.7054\n9.1702\n7.2491\n8.9296\n9.7257\n7.2923\n6.6658\n8.1967\n12.481\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n15.34\n17.097\n16.455\n15.772\n10.961\n10.33\n10.068\n10.107\n9.4166\n9.5604\n6.0746\n2.2781\n3.7476\n5.3034\n4.8476\n6.2032\n7.2096\n5.7817\n2.7108\n1.1903\n2.7458\n5.2094\n9.0508\n-0.75061\n-4.7209\n-0.74485\n0.16594\n7.7305\n6.5785\n5.8383\n5.2089\n5.1362\n3.295\n2.6477\n2.1615\n1.9918\n2.0944\n2.014\n3.1287\n2.9438\n3.05\n4.6765\n3.4401\n2.347\n8.5138\n9.5203\n8.5486\n8.0049\n9.3802\n10.044\n8.5891\n7.5326\n7.35\n13.099\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n19.084\n14.632\n16.297\n15.264\n10.991\n10.309\n8.8303\n9.0979\n9.9627\n10.396\n7.2599\n2.206\n0.77639\n1.9838\n2.8593\n3.114\n5.0522\n2.5375\n-1.3071\n-1.6836\n0.13197\n4.5689\n5.0323\n-1.2873\n-4.6614\n-2.8494\n1.0995\n8.479\n7.0137\n6.4984\n4.5976\n3.1892\n2.9933\n2.9407\n3.3914\n1.4575\n1.4902\n2.8172\n5.1387\n3.497\n2.2704\n3.3263\n2.7138\n4.6211\n8.4585\n8.6247\n9.2117\n9.5229\n9.3299\n8.4243\n8.5254\n6.9668\n4.7733\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n14.523\n13.976\n14.269\n13.822\n10.609\n13.282\n9.9291\n8.3761\n8.5395\n4.7487\n3.7707\n2.0198\n1.3114\n0.12847\n0.68588\n1.683\n4.3837\n0.54115\n-2.9997\n-2.4759\n-1.7329\n1.0217\n1.8877\n-2.7608\n-2.7339\n-1.0399\n1.0457\n9.5045\n6.125\n6.8674\n4.2027\n3.9843\n3.5361\n4.1155\n4.8831\n3.0737\n2.6961\n3.415\n4.4027\n4.0572\n3.1235\n2.7189\n3.2714\n6.3922\n9.1203\n9.5639\n8.7576\n11.147\n9.765\n8.7029\n9.0726\n7.9942\n4.212\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4745\nNaN\nNaN\nNaN\nNaN\n14.695\n14.374\n13.669\n13.208\n9.4772\n13.112\n7.6631\n5.9644\n3.0804\n3.2964\n3.1463\n2.7301\n8.5819\n1.1341\n1.8977\n2.3329\n4.7033\n1.0562\n-2.1443\n-2.2621\n-1.4997\n-2.7267\n-2.155\n-3.1786\n-2.1557\n-0.054949\n1.6757\n8.3297\n5.6401\n5.9687\n5.6071\n5.3506\n4.5301\n4.8467\n5.4346\n4.7542\n4.5092\n3.9147\n5.1042\n5.7582\n3.7693\n5.2018\n6.0372\n6.3012\n9.0712\n10.76\n8.2508\n9.1324\n10.861\n10.851\n8.6392\n7.5512\n4.5645\n10.384\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26392\n5.5197\n6.7804\n9.0508\n12.172\n13.022\n15.385\n15.183\n13.314\n12.367\n10.406\n9.4065\n9.1695\n6.1021\n3.3116\n5.1654\n4.0554\n4.832\n10.154\n7.0981\n2.1537\n1.79\n3.6258\n1.1635\n-0.97493\n-4.3051\n-2.8956\n-4.4768\n-4.2722\n-4.5685\n-2.3109\n-1.7668\n1.5195\n7.8861\n6.3092\n5.8672\n6.822\n6.3561\n5.7509\n5.9996\n5.7684\n5.0216\n4.0429\n4.7647\n6.5093\n7.3971\n6.0374\n7.1364\n7.6738\n8.2309\n10.9\n9.8864\n11.805\n11.022\n10.704\n9.5044\n7.1403\n5.4244\n5.5811\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.27602\n1.9321\n5.267\n6.3885\n10.913\n13.733\n14.438\n14.312\n14.904\n13.561\n12.96\n12.05\n10.242\n10.778\n9.2604\n6.5669\n9.4233\n9.8071\n5.88\n7.0493\n5.8667\n5.371\n2.473\n2.4264\n1.9873\n-0.58097\n-2.3538\n-1.8884\n-3.0723\n-3.6876\n-2.4417\n-1.7346\n-2.9613\n-0.011187\n9.3447\n7.8247\n6.6633\n7.3436\n7.0557\n6.0904\n6.0337\n5.6498\n5.8981\n5.1903\n5.71\n7.7545\n8.8233\n8.6826\n8.7598\n10.454\n12.611\n13.493\n13.017\n12.269\n12.741\n10.509\n8.7533\n6.3259\n4.148\n3.4605\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5948\n-0.834\n1.1692\n2.0719\n2.297\n5.2659\n7.2038\n10.265\n15.085\n16.212\n16.016\n13.046\n12.637\n11.081\n10.656\n9.9098\n8.3372\n8.8967\n9.1826\n9.1648\n8.5015\n7.6206\n7.0447\n5.5971\n6.7697\n2.0712\n0.081236\n0.84027\n0.078456\n0.51944\n-0.15637\n-1.8173\n-2.8278\n-1.6343\n-1.457\n-1.1053\n-2.3289\n8.6868\n7.576\n7.6559\n6.9411\n6.973\n6.1949\n5.3383\n5.0751\n6.546\n6.0543\n7.3118\n9.2355\n10.812\n10.123\n10.456\n11.416\n13.571\n13.801\n12.11\n12.277\n12.39\n11.026\n9.4596\n8.3231\n4.6735\n0.64834\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.763\n8.1428\n9.627\n12.179\n15.296\n18.597\n16.12\n12.897\n11.54\n10.433\n11.582\n11.138\n9.286\n7.8177\n7.3747\n9.0982\n8.8973\n7.5233\n6.92\n6.9979\n7.2748\n2.9764\n0.62825\n-0.10226\n-0.50187\n-0.10173\n-0.24656\n0.28898\n-2.5393\n-1.7967\n-0.50022\n-1.0259\n-2.6729\n8.2471\n8.2503\n8.7978\n6.8316\n7.8927\n6.3683\n6.1423\n6.047\n7.6704\n8.4265\n9.4673\n9.9226\n10.595\n10.555\n12.017\n11.148\n11.263\n16.664\n18.02\n12.016\n11.579\n13.532\n11.436\n8.749\n6.107\n7.6048\n14.19\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8351\n9.2408\n12.878\n12.559\n13.797\n20.152\n15.827\n13.068\n12.12\n10.176\n10.401\n9.8693\n8.6546\n7.8172\n7.5086\n6.5041\n9.2689\n7.9512\n5.6442\n4.0686\n4.5268\n4.3836\n1.5956\n0.066094\n-1.5009\n-2.9916\n-1.6129\n-0.66042\n-3.8178\n-2.3329\n-2.1037\n-2.6401\n-3.9524\n8.544\n9.225\n9.1426\n7.9389\n8.7551\n6.9674\n7.7167\n7.8895\n7.7286\n6.383\n8.7473\n9.7247\n9.9152\n10.265\n12.625\n12.091\n11.516\n13.932\n18.227\n14.984\n11.108\n13.734\n12.291\n8.4986\n6.9879\n8.4722\n14.319\n21.024\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.1696\n11.533\n13.014\n12.265\n11.838\n17.729\n12.808\n11.8\n12.348\n12.464\n12.8\n9.4678\n8.1069\n8.146\n8.9309\n6.1546\n5.4404\n4.9838\n3.0968\n2.1558\n1.6292\n0.054521\n1.9664\n-0.40626\n-2.0665\n-3.2326\n-2.2646\n-1.4331\n-2.9914\n-3.775\n-3.1695\n-5.0871\n-5.431\n9.0185\n9.6133\n8.3266\n8.0497\n7.514\n6.4022\n7.6573\n8.0889\n7.907\n5.2771\n8.1334\n8.996\n9.0134\n10.948\n12.736\n13.647\n12.266\n14.787\n16.01\n17.555\n15.18\n15.018\n11.953\n9.4619\n7.978\n8.5878\n14.399\n16.622\nNaN\nNaN\n13.053\n15.69\n7.3863\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n11.852\n11.299\n11.661\n11.292\n15.634\n17.589\n12.369\n12.69\n13.257\n14.646\n12.04\n8.5833\n7.5813\n7.122\n7.1134\n5.3283\n4.1481\n3.4355\n2.14\n2.3656\n1.1234\n-0.087085\n0.94289\n-1.5345\n-1.7731\n-0.48591\n2.1349\n2.4526\n-2.39\n-3.2785\n-3.3404\n-5.5688\n-5.9449\n9.9421\n9.086\n7.9307\n9.0464\n7.87\n6.2547\n7.1059\n7.722\n8.0389\n7.116\n8.1387\n9.1701\n9.8574\n11.031\n11.769\n11.538\n12.694\n16.136\n16.394\n15.808\n14.752\n14.394\n12.832\n10.299\n12.921\n15.091\n15.505\n13.717\n14.269\n12.346\n12.673\n15.305\n11.841\n9.4612\nNaN\nNaN\nNaN\nNaN\n18.694\n13.524\n15.245\n14.954\n13.155\n16.259\n18.87\n13.99\n12.833\n12.378\n12.34\n10.734\n6.6631\n5.5759\n3.6303\n5.8072\n5.3732\n4.7704\n3.2374\n2.1948\n1.4046\n0.431\n1.0795\n1.2333\n-1.1887\n1.4055\n1.304\n1.3\n0.62543\n-0.93945\n0.2659\n-1.8031\n-3.1813\n-2.9505\n8.827\n8.0363\n9.1016\n9.6696\n8.4386\n7.2635\n7.0159\n7.8278\n8.2464\n8.598\n8.5948\n10.015\n9.87\n9.9476\n10.642\n12.297\n15.602\n21.344\n17.233\n14.842\n14.667\n12.509\n11.512\n10.533\n5.7335\n12.194\n12.397\n12.722\nNaN\n16.945\n16.819\n20.883\n13.84\n14.024\nNaN\nNaN\nNaN\n19.011\n15.714\n12.97\n13.635\n14.66\n12.277\n14.233\n18.254\n14.014\n9.0706\n8.2162\n8.9286\n7.6912\n5.0579\n3.3039\n2.9118\n5.4532\n3.7748\n3.348\n3.1333\n1.524\n0.55921\n0.16452\n0.95856\n2.0256\n1.4429\n1.4553\n0.93969\n-0.28722\n-0.80427\n-0.10435\n0.99018\n0.52271\n-2.1014\n1.2177\n8.208\n7.4186\n8.2313\n8.5436\n7.8801\n6.8236\n7.4579\n8.6137\n9.1978\n9.8921\n9.3543\n10.133\n9.7145\n8.6227\n9.739\n11.653\n15.2\n20.335\n15.843\n14.948\n13.043\n13.754\n10.209\n10.386\n9.5383\n11.565\n13.034\nNaN\nNaN\n17.822\n18.136\n18.101\n15.634\n15.972\n17.618\n20.047\n18.9\n16.806\n15.718\n12.168\n13.113\n15.681\n13.553\n13.285\n14.595\n8.7296\n6.9319\n5.9086\n6.8366\n6.1593\n4.1138\n1.7528\n3.0593\n4.014\n2.1784\n2.0575\n2.3355\n1.4194\n0.16518\n-0.09928\n-0.15069\n2.5118\n2.6581\n1.3093\n0.80742\n-0.29496\n-0.79132\n-0.95862\n-0.47975\n1.7935\n-1.8053\n-1.6753\n6.8516\n7.7233\n8.8124\n8.42\n7.1221\n6.7762\n7.2788\n9.5159\n10.651\n10.353\n9.3688\n8.3037\n7.7366\n6.2784\n7.2878\n10.596\n12.443\n19.019\n11.028\n12.933\n13.424\n12.238\n9.8103\n10.098\n11.503\n14.256\n14.379\nNaN\nNaN\nNaN\n18.737\n16.869\n14.94\n15.266\n15.849\n14.268\n13.157\n15.361\n14.887\n11.373\n9.3581\n11.329\n10.37\n8.2497\n9.1417\n7.7956\n6.7908\n6.5764\n5.4274\n3.3025\n2.5297\n1.6042\n2.5046\n3.4219\n1.9179\n0.95413\n0.45335\n-0.60356\n-0.71256\n-0.54101\n1.2143\n2.822\n3.4201\n2.3045\n0.20834\n-0.71732\n-1.8892\n-2.2943\n-0.90221\n0.78579\n-2.4164\n-0.60026\n8.1913\n8.5949\n9.8337\n7.6122\n5.9153\n5.7489\n8.4558\n9.4382\n10.062\n9.8049\n8.6308\n7.0241\n5.6156\n3.3135\n2.4645\n6.91\nNaN\nNaN\nNaN\n11.668\n14.602\n11.638\n10.997\n13.945\n14.454\n16.805\n15.502\nNaN\nNaN\nNaN\n15.558\n14.778\n15.004\n14.556\n13.927\n12.385\n12.035\n12.166\n11.973\n12.51\n11.891\n10.208\n7.3658\n6.5167\n6.1312\n5.593\n3.7983\n2.8986\n2.1585\n2.0321\n1.8065\n2.2708\n2.2689\n1.8809\n1.4333\n-0.27631\n-1.5294\n-1.1908\n-1.3394\n-1.077\n1.5254\n2.2695\n2.567\n2.2661\n-0.14126\n-0.64576\n-1.373\n-2.0075\n-1.4033\n-0.13634\n0.54439\n-1.8779\n7.7752\n8.8024\n8.5703\n7.0924\n6.2145\n5.2953\n8.894\n9.3031\n9.3955\n10.125\n8.0002\n6.8799\n4.1716\n0.92949\n1.2552\n6.8531\nNaN\nNaN\nNaN\n11.414\n13.786\n16.264\n15.178\n13.656\n11.357\n13.627\n13.726\nNaN\nNaN\nNaN\n16.018\n15.07\n14.642\n13.076\n10.139\n10.364\n10.43\n9.9248\n9.6941\n11.597\n9.3432\n9.083\n7.5989\n5.5766\n4.3565\n5.7736\n3.3259\n2.6946\n2.5627\n1.8496\n1.2984\n2.442\n1.746\n1.1057\n0.77809\n-0.27168\n-0.81923\n-0.79027\n-2.1929\n-1.4099\n0.47521\n1.1853\n0.62615\n-0.65842\n-1.5198\n-1.45\n-1.6977\n-2.4322\n-1.7608\n-0.69056\n-1.3953\n-3.4183\n7.4732\n7.3828\n6.6498\n5.3016\n4.8424\n5.5764\n6.7323\n7.9676\n8.7966\n10.434\n7.6796\n5.615\n4.6189\n4.2374\n4.286\n5.3185\n7.5358\nNaN\nNaN\n12.535\n12.339\n12.501\n12.906\n11.467\n9.5811\n11.311\n12.075\n11.274\n13.173\n14.596\n18.155\n16.932\n14.169\n11.95\n8.9152\n9.0448\n7.7854\n7.3623\n8.4013\n7.4472\n7.3897\n7.0455\n6.8611\n4.7709\n3.4347\n3.765\n1.8633\n4.8383\n5.1571\n1.8578\n1.5484\n2.1116\n0.72202\n1.0481\n1.4978\n0.15359\n-0.14088\n-1.17\n-2.6338\n-3.0847\n-1.967\n1.0657\n0.016089\n-2.5487\n-3.6887\n-2.6791\n-1.9052\n-2.8145\n-1.4991\n-1.9142\n-4.5587\n-6.2911\n7.2595\n7.2412\n5.7814\n4.2231\n4.637\n5.1637\n6.3984\n8.37\n9.1786\n10.289\n8.7847\n7.1588\n6.112\n4.0877\n2.7388\n3.2385\n7.3299\n8.1235\n10.15\n11.867\n11.136\n10.152\n10.002\n9.6166\n8.7041\n10.105\n10.629\n10.815\n11.345\n12.864\n14.152\n14.223\n12.957\n10.321\n9.2294\n8.9307\n7.5151\n4.7894\n6.1847\n6.5202\n6.6619\n4.592\n5.0808\n3.9278\n2.5062\n1.3524\n2.477\n6.2549\n8.2212\n3.1901\n2.1513\n-0.33447\n0.076818\n1.241\n2.394\n-0.5291\n-1.8522\n-2.1019\n-2.9823\n-3.6755\n-2.9554\n-0.55441\n-1.2066\n-2.4559\n-3.2208\n-2.4149\n-3.2313\n-2.8258\n-1.6641\n-3.166\n-5.4645\n-6.8746\n7.8414\n6.1785\n3.98\n3.2672\n3.1999\n3.6144\n4.9671\n5.075\n8.4132\n9.7854\n9.3185\n8.0685\n7.836\nNaN\nNaN\nNaN\nNaN\n7.9789\n10.77\n11.729\n7.7292\n8.5864\n10.18\n9.7348\n8.4202\n8.9001\n8.8393\n8.4773\n8.5293\n10.101\n10.626\n9.6166\n10.349\n10.085\n9.7881\n9.5391\n6.5469\n3.4483\n4.3938\n4.2856\n4.1906\n3.2344\n3.6772\n4.6147\n3.2977\n0.84841\n-0.19138\n4.9623\n4.0978\n4.5929\n3.4462\n3.5302\n1.9564\n2.4591\n0.58603\n-2.1121\n-1.5258\n-1.7665\n-3.3536\n-3.4159\n-4.0214\n-4.0637\n-3.9124\n-1.7862\n-2.3568\n-2.2227\n-3.4359\n-2.4189\n-2.3398\n-4.6079\n-5.8155\n-8.0109\n9.1746\n5.4367\n3.5159\n2.3225\n2.7378\n3.3261\n3.4788\n1.498\n2.9184\n5.2773\n7.1422\n7.5663\n6.4905\nNaN\nNaN\nNaN\nNaN\nNaN\n8.5583\n9.0418\n8.331\n7.6487\n9.9371\n9.8344\n8.6555\n7.8408\n7.6085\n7.0563\n7.1745\n7.2731\n7.0667\n7.2704\n6.1691\n7.1828\n7.7084\n6.112\n3.8309\n1.5963\n1.966\n2.0485\n2.4171\n2.0979\n2.3057\n5.9195\n4.2448\n3.1867\n-2.1114\n4.4427\n2.3926\n3.4041\n2.9369\n2.9017\n2.1136\n1.8281\n-0.041805\n-1.7377\n-1.2877\n-2.6784\n-4.5315\n-4.5141\n-4.5274\n-4.7848\n-2.908\n-2.4539\n-2.4408\n-2.5684\n-2.8065\n-2.5587\n-3.7807\n-4.9273\n-5.8921\n-7.9304\n6.7436\n6.417\n4.2643\n1.7806\n1.3947\n0.0011114\n-0.065425\n-2.1974\n-0.50158\n3.0184\n6.8298\n8.718\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.7241\n9.3073\n9.0034\n9.3509\n9.8525\n9.2438\n8.5174\n7.0017\n6.9832\n5.9976\n6.0174\n6.0363\n5.8819\n5.1539\n4.7774\n4.8031\n4.2522\n2.8824\n1.1326\n-1.1241\n0.84886\n0.86857\n1.1892\n0.48779\n4.6749\n5.4377\n4.9929\n-0.42133\n1.5397\n1.2982\n1.6296\n1.9978\n0.96098\n1.4496\n1.978\n-0.33978\n-2.6249\n-1.3917\n-2.7701\n-4.9867\n-3.7535\n-3.2838\n-3.9046\n-4.3255\n-3.8448\n-2.5772\n-3.4442\n-4.6482\n-3.1838\n-6.7372\n-4.7361\n-3.8099\nNaN\n4.3884\n6.638\n3.7155\n0.76836\n-0.41707\n-2.4031\n-4.0866\n-4.9748\n-0.34092\n1.9975\n5.4374\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.3901\n8.3622\n8.6157\n7.5303\n8.3782\n7.9792\n6.8728\n6.7678\n6.3974\n5.3171\n5.6413\n4.5573\n5.4694\n4.6212\n3.9399\n3.5246\n4.3339\n2.9791\n2.3999\n0.8203\n1.1293\n0.23388\n2.0939\n3.933\n4.0861\n6.7083\n6.213\n3.3846\n2.3318\n0.68132\n1.9443\n1.1487\n-1.6975\n-1.894\n-1.5279\n-2.2383\n-2.5322\n-2.4675\n-3.6951\n-3.8434\n-2.639\n-2.7768\n-2.4846\n-2.3753\n-4.1374\n-4.9863\n-5.0252\n-2.5939\n-1.4399\n-6.7663\nNaN\nNaN\nNaN\n-6.533\n-7.7133\n-7.8195\n-9.7483\n-9.4931\n-8.4783\n-9.1755\n-9.9693\n-7.3921\n-5.4482\n-7.2116\n-7.1327\n-7.942\n-7.5737\n-5.4528\n-4.9695\n-4.5852\n-8.4167\n-2.2051\n-3.7411\n-3.9109\n-3.6249\n-1.1576\n-0.93925\n-1.5059\n-4.0931\n-3.8637\n-5.5191\n-3.9416\n-0.71637\n-6.1936\n-6.0791\n-8.1945\n-9.4061\n-8.5454\n-6.83\n-6.4219\n-5.5845\n-7.6136\n-8.4584\n-7.4985\n-6.479\n-5.3161\n-4.4405\n-4.4598\n-5.6619\n-4.9739\n-5.0149\n-6.1828\n-6.1792\n-5.649\n-4.2324\n-4.329\n-5.1844\n-1.9686\n-3.7646\n-4.2502\n-7.2591\n-11.316\n-10.008\n-6.6369\n-4.4965\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-8.1252\n-8.2233\n-8.396\n-6.687\n-6.4703\n-10.506\n-9.0994\n-10.228\n-5.4605\n-4.5204\n-5.123\n-5.0202\n-7.7325\n-5.2759\n-3.2327\n-3.7433\n-7.8853\n-8.1084\n-5.6151\n-5.3891\n-1.0253\n-1.9626\n-1.0952\n1.066\n0.54519\n-2.3\n-2.1942\n-2.9056\n-1.858\n-2.1214\n-2.8919\n-3.6395\n-6.5548\n-8.9001\n-7.9673\n-7.7752\n-7.6931\n-5.7156\n-7.7057\n-6.1336\n-4.6818\n-5.7968\n-5.0809\n-5.2043\n-5.1317\n-5.5273\n-5.4459\n-5.9203\n-6.4075\n-6.1674\n-6.3009\n-4.4738\n-3.9909\n-3.0328\n-0.07951\n-3.6336\n-4.7158\n-9.2927\n-11.01\n-8.0226\n-5.5938\n-3.0542\n-3.8527\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.7851\n-7.957\n-7.123\n-8.2128\n-11.988\n-11.901\n-9.9969\n-7.3816\n-5.7264\n-4.3143\n-5.194\n-5.7247\n-7.5146\n-3.7155\n-0.42909\n-1.5283\n-5.0408\n-4.9585\n-4.6475\n-0.9825\n-2.2896\n0.49668\n0.92846\n0.94007\n1.6876\n0.58775\n0.63214\n-0.22462\n-0.27287\n-3.5697\n-3.3379\n-3.3591\n-6.201\n-6.4518\n-7.0028\n-7.8214\n-7.7627\n-6.3924\n-6.0304\n-5.947\n-5.7305\n-4.6457\n-5.319\n-5.0921\n-5.5762\n-6.1758\n-6.6743\n-6.8912\n-6.8925\n-6.0616\n-5.3653\n-4.7575\n-3.9473\n-2.2621\n-2.4917\n-2.4823\n-2.8783\n-8.0076\n-8.7885\n-4.6094\n-6.304\n-2.722\n-1.5212\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.0085\n-8.0776\n-7.4757\n-7.7143\n-7.91\n-7.2749\n-6.63\n-6.6026\n-7.0094\n-5.6648\n-5.8942\n-7.8087\n-4.0051\n-1.8315\n-3.5824\n-4.1022\n-4.4865\n-4.9575\n-3.3894\n-0.4213\n-1.3829\n2.2869\n2.3889\n-0.30738\n1.9598\n2.8385\n2.585\n1.2904\n-1.2112\n-1.6055\n-3.8977\n-4.2948\n-7.9279\n-6.5593\n-6.6203\n-6.9849\n-7.8056\n-6.7261\n-5.5809\n-4.7837\n-4.8814\n-4.7574\n-6.7793\n-6.7744\n-5.8584\n-6.9189\n-7.2842\n-7.1885\n-6.9191\n-4.718\n-3.1907\n-3.579\n-2.8039\n-1.7891\n-1.7147\n-1.5003\nNaN\nNaN\nNaN\n-9.592\n-8.1728\n-3.3472\n-0.59955\nNaN\nNaN\nNaN\n-6.8933\nNaN\nNaN\nNaN\nNaN\nNaN\n-6.5588\n-7.4957\n-6.7231\n-7.0549\n-7.3406\n-7.4847\n-7.3164\n-7.0213\n-6.7295\n-6.7485\n-7.2403\n-7.229\n-3.4247\n-3.3614\n-5.4471\n-6.0471\n-6.2546\n-4.8597\n-7.0678\n-2.1418\n-1.146\n3.6516\n6.5\n-2.8149\n2.1026\n4.4213\n4.2206\n1.5725\n0.39744\n0.73683\n-3.1197\n-6.2829\n-7.9323\n-7.4452\n-6.7002\n-7.1175\n-7.7676\n-6.5325\n-5.8468\n-4.9445\n-5.4667\n-5.0979\n-6.9427\n-8.1601\n-7.2981\n-7.3266\n-6.8626\n-6.5986\n-6.7979\n-5.9404\n-3.8541\n-3.2482\n-3.3272\n-2.2717\n-2.2453\n-2.0525\n2.4105\nNaN\nNaN\n-12.041\n-9.398\n-5.192\n-0.51928\n-2.4234\n-8.0667\n-5.9597\n-9.2003\nNaN\nNaN\nNaN\nNaN\nNaN\n-6.9608\n-7.3997\n-5.7312\n-6.1726\n-5.8396\n-6.8715\n-5.8036\n-4.7003\n-4.9337\n-5.1594\n-4.8989\n-4.5459\n-3.4282\n-3.1141\n-5.678\n-5.4333\n-6.3595\n-5.534\n-7.0775\n-4.5588\n-4.1418\n3.1077\n2.2078\n-0.27402\n1.6643\n4.4649\n4.0155\n1.9321\n0.19619\n0.21254\n-0.34138\n-6.1969\n-6.4358\n-6.6171\n-7.2938\n-7.8863\n-7.085\n-6.3849\n-5.7857\n-5.6108\n-5.6773\n-6.671\n-7.4214\n-8.5992\n-10.65\n-8.922\n-7.1552\n-5.6891\n-6.1788\n-5.0549\n-4.4609\n-3.5206\n-3.9078\n-4.2417\n-4.6907\n-7.5734\n-4.2173\nNaN\nNaN\n-17.276\n-13.243\n-5.7041\n-6.6673\n-6.9531\n-5.1787\n-5.3258\n-8.8141\nNaN\nNaN\nNaN\nNaN\nNaN\n-6.9962\n-6.9668\n-5.32\n-4.8436\n-4.1738\n-5.402\n-4.0335\n-2.4956\n-3.0981\n-2.972\n-2.8663\n-3.4427\n-3.2251\n-3.7056\n-4.1296\n-5.4702\n-5.8317\n-3.62\n-6.8522\n-2.8451\n-2.4869\n-2.7573\n0.9949\n-0.61097\n-0.85406\n1.7156\n3.2038\n1.0349\n-0.68848\n-0.9528\n-2.1022\n-6.1734\n-6.7739\n-6.3673\n-7.8203\n-7.2796\n-6.1796\n-5.9765\n-5.657\n-5.7228\n-5.8369\n-7.6307\n-9.0098\n-10.216\n-12.988\n-9.0741\n-7.1962\n-6.0784\n-4.8345\n-4.3097\n-4.7463\n-3.9084\n-3.9192\n-4.3001\n-5.3639\n-6.6822\n-3.8764\nNaN\nNaN\n-12.044\n-13.496\n-9.6137\nNaN\n-10.187\n-7.6286\nNaN\nNaN\n-5.8795\n-10.417\nNaN\nNaN\nNaN\n-5.9983\n-4.9809\n-3.9296\n-2.621\n-1.867\n-4.1256\n-3.2288\n-2.0217\n-2.5372\n-2.5236\n-2.4467\n-2.4071\n-2.849\n-2.3291\n-2.2208\n-3.1608\n-3.8281\n-2.4977\n-2.9881\n-2.1972\n-1.6936\n-0.398\n-0.61821\n-1.2821\n-0.45712\n0.6167\n1.7282\n2.6056\n0.039377\n-0.52976\n-0.69936\n-4.6674\n-5.6472\n-5.6603\n-5.2505\n-4.3987\n-4.4181\n-6.13\n-6.7486\n-7.1713\n-7.135\n-8.6361\n-9.2701\n-10.506\n-14.82\n-9.431\n-7.2284\n-6.4151\n-5.9703\n-4.3762\n-5.0686\n-4.1635\n-3.0258\n-3.3435\n-3.7353\n-4.8336\n-3.8474\n-3.0302\n-3.3015\n-6.4461\n-10.214\n-7.5396\nNaN\nNaN\nNaN\nNaN\nNaN\n-9.2184\n-11.518\n-12.857\nNaN\nNaN\n-5.1914\n-4.6441\n-3.3914\n-2.5064\n-1.3027\n-2.5357\n-3.1648\n-1.6061\n-2.0295\n-2.0975\n-2.5632\n-2.088\n-1.6945\n-1.4093\n-2.0785\n-2.0673\n-2.0059\n-2.0813\n-1.9216\n-1.2922\n-1.6962\n-2.5412\n-2.549\n-1.0039\n0.75464\n1.0471\n1.3486\n1.0325\n0.21308\n-0.61001\n-0.52046\n-2.4866\n-2.9145\n-5.3076\n-3.0817\n-2.3581\n-4.3131\n-6.3695\n-6.6056\n-8.5278\n-9.3945\n-10.074\n-11.636\n-12.989\n-15.383\n-8.5718\n-6.829\n-6.1341\n-5.4939\n-3.904\n-4.1445\n-2.639\n-1.9595\n-0.4116\n-1.2671\n-4.55\n-5.4123\n-3.6041\n-1.4936\n-1.0954\n-8.3957\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-11.96\n-12.09\nNaN\nNaN\n-5.944\n-4.4769\n-3.2532\n-2.9533\n-1.4819\n-2.4565\n-3.3611\n-2.7949\n-1.9623\n-2.5913\n-1.6405\n-0.95242\n-0.41471\n-0.99272\n-0.77442\n-0.30912\n-0.66493\n-1.9594\n-2.31\n-1.0624\n-1.3003\n-0.63315\n0.68547\n-0.54527\n-0.67075\n0.60955\n2.1714\n1.6707\n-0.16386\n-1.3244\n-1.694\n-5.3946\n-7.9721\n-6.047\n-3.4949\n-2.517\n-3.4737\n-6.0009\n-7.5882\n-9.2151\n-11.023\n-11.201\n-13.042\n-13.127\n-10.63\n-7.951\n-7.6303\n-6.1118\n-5.7196\n-3.8199\n-3.3852\n-0.96726\n-0.80158\n-0.25096\n-1.8076\n-4.2971\n-7.288\n-6.7816\n-1.5831\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.272\nNaN\n-8.9702\n-6.6218\n-6.5205\n-5.3172\n-3.7611\n-3.72\n-2.7572\n-3.0053\n-2.8077\n-2.333\n-1.8198\n-1.755\n-0.75582\n-0.2607\n1.0381\n0.55986\n0.26491\n0.55041\n-0.54034\n-2.1624\n-1.9042\n-0.34794\n0.122\n0.91071\n2.0412\n-0.44924\n-1.8537\n0.59471\n2.5431\n2.0109\n-0.55043\n-0.28285\n-2.4671\n-6.0855\n-8.0862\n-5.2598\n-5.3602\n-4.2775\n-6.2939\n-6.549\n-9.9369\n-10.268\n-11.108\n-12.323\n-13.949\n-13.227\n-10.935\n-9.2253\n-8.6414\n-6.6455\n-5.3867\n-4.8644\n-3.657\n-1.875\n-2.1699\n-0.52356\nNaN\nNaN\n-10.816\n-9.537\n-4.4423\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-9.2315\n-6.132\n-6.7987\n-5.6177\n-5.03\n-3.9685\n-2.8005\n-2.0971\n-2.1064\n-1.9619\n-1.7661\n-1.7629\n-1.0652\n0.090523\n1.9846\n1.4843\n0.31244\n-0.1694\n-1.2885\n-0.69494\n0.16151\n0.43546\n0.19759\n0.99637\n2.1296\n-0.8758\n-2.0258\n-0.33759\n1.6543\n2.6019\n0.42209\n0.52381\n-2.0908\nNaN\nNaN\nNaN\n-4.6941\n-5.0579\n-6.5117\n-7.395\n-12.514\n-10.832\n-10.018\n-12.223\n-14.023\n-12.992\n-12.43\n-11.018\n-9.0966\n-6.9372\n-4.6069\n-4.1056\n-3.8639\n-2.4405\n-4.0613\nNaN\nNaN\nNaN\n-9.4287\n-10.989\n-7.4878\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.7895\n-4.2864\n-4.0117\n-3.5495\n-2.4595\n-2.5813\n-2.8807\n-2.6048\n-2.2648\n-1.5339\n-0.31059\n0.27633\n0.82329\n1.2423\n0.68797\n1.1031\n1.3084\n1.669\n1.6613\n0.82816\n0.78975\n0.23341\n-1.0035\n-2.6726\n-2.4257\n-1.744\n1.5786\n1.1041\n-0.94831\n-0.46677\n-1.5579\nNaN\nNaN\nNaN\n-4.7307\n-7.4334\n-7.8259\n-6.6631\n-11.168\n-11.612\n-12.587\n-16.185\n-18.318\n-13.868\n-11.712\n-10.772\n-8.8664\n-6.6191\n-4.0412\n-3.4849\n-4.2102\n-3.6992\n-6.0023\nNaN\nNaN\nNaN\n-13.119\n-13.1\n-11.489\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.1664\n-4.0009\n-4.0803\n-3.3838\n-2.6974\n-3.5368\n-3.4156\n-3.5182\n-2.8947\n-2.0636\n-1.5058\n-0.46742\n0.29281\n0.81979\n0.94083\n2.4902\n2.3927\n1.9331\n1.1024\n0.7649\n0.73525\n-2.7202\n-4.6067\n-4.8454\n-5.2592\n-1.8312\n0.07181\n-0.71396\n-0.95044\n-0.90011\n-1.7515\nNaN\nNaN\nNaN\nNaN\n-8.0967\n-9.7851\n-5.7501\n-11.159\n-12.716\n-14.22\n-16.817\n-17.593\n-13.642\n-10.764\n-10.451\n-8.0428\n-6.4418\n-4.5709\n-3.1988\n-5.0383\n-5.8758\n-6.9346\nNaN\nNaN\nNaN\nNaN\n-14.877\n-12.721\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.4342\n-4.4971\n-3.2802\n-2.3844\n-2.2942\n-3.3844\n-2.829\n-2.9411\n-2.6769\n-2.2849\n-2.4331\n-1.0506\n0.58179\n0.8495\n0.60821\n1.746\n2.1274\n1.9196\n1.1858\n0.33447\n1.2244\n-2.9585\n-7.0725\n-6.1959\n-5.365\n-2.1197\n-1.3169\n-1.2748\n-0.57216\n-1.1825\n-0.97906\nNaN\n-8.7163\n-9.3044\n-7.5189\n-6.6578\n-8.6245\n-7.0332\n-10.961\n-11.767\n-12.411\n-15.369\n-17.089\n-11.483\n-9.5826\n-8.3877\n-7.5473\n-6.7887\n-4.5535\n-3.8658\n-5.9229\n-4.0808\n-5.8566\n-10.203\nNaN\nNaN\nNaN\n-15.981\n-14.773\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.9622\n-4.4559\n-3.0917\n-1.0667\n-1.2173\n-2.3514\n-2.1504\n-2.0909\n-1.9741\n-1.8331\n-3.3898\n-1.2755\n0.5553\n0.90941\n0.041872\n0.48301\n1.2052\n1.3529\n1.8828\n0.79864\n0.94115\n-0.26276\n-8.8569\n-8.8038\n-5.2257\n-2.937\n-1.5415\n0.9924\n-0.036148\n-2.7099\n-4.0471\n-11.148\n-8.0413\n-9.2787\n-8.5642\n-8.7242\n-7.0449\n-8.1126\n-8.7258\nNaN\n-17.431\n-15.67\n-14.998\n-10.003\n-7.2233\n-8.8155\n-8.8097\n-8.1636\n-5.3796\n-6.1498\n-6.4992\n-6.4558\n-4.2659\n-8.682\n-11.795\n-15.333\n-18.66\n-17.059\n-16.746\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.1025\n-3.3518\n-1.7634\n-0.76638\n-1.904\n-1.9591\n-1.383\n-1.0725\n-1.4611\n-0.89411\n-1.6316\n-1.0242\n0.54219\n0.82463\n-0.10077\n-0.047257\n-0.18798\n0.83317\n2.0973\n2.5251\n0.8769\n-2.3601\n-5.0826\n-9.7858\n-4.0259\n-2.0205\n-1.2371\n-1.5433\n-3.4137\n-5.8155\n-7.0735\n-7.3946\n-5\n-9.4964\n-7.5606\n-8.2135\n-6.742\nNaN\nNaN\nNaN\nNaN\n-17.48\n-14.87\n-13.357\n-9.6785\n-9.9953\n-10.057\n-8.7461\n-6.5942\n-6.6417\nNaN\n-6.9716\n-4.4475\n-7.8024\n-10.703\n-12.685\n-17.837\n-17.171\n-15.877\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.2712\n-3.4629\n-1.1902\n-0.5949\n-1.7896\n-2.2856\n-1.0605\n-0.11393\n-1.261\n-0.44038\n-0.31453\n0.53669\n0.42842\n1.1315\n1.4006\n0.59764\n1.0899\n0.51139\n1.5299\n1.9423\n-0.13692\n-4.14\n-6.6135\n-9.539\n-4.098\n-2.9635\n-2.278\n-4.9064\n-6.1308\n-7.7811\n-5.6993\n-5.8105\n-10.436\n-7.9925\n-6.2608\n-7.4037\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-9.5188\n-11.433\n-9.693\n-8.5794\n-8.4238\n-7.6279\n-4.0745\nNaN\nNaN\nNaN\n-9.5935\n-11.453\n-12.909\n-14.453\n-12.952\n-13.171\n-12.171\n-8.6575\n-9.6755\nNaN\nNaN\n-10.368\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.0281\n-3.0201\n-1.5159\n-0.85251\n-1.2992\n-3.0668\n-0.89964\n0.039518\n-0.80817\n0.2305\n-0.87958\n0.47113\n0.86745\n1.1479\n1.4207\n0.74961\n0.52926\n-0.32601\n0.83841\n1.156\n-0.93279\n-4.3297\n-6.6598\n-6.8841\n-5.2184\n-4.0111\n-5.2731\n-7.2891\n-5.4457\n-7.3432\n-7.0673\n-9.6836\n-10.575\n-9.7863\n-7.485\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-12.81\n-13.882\n-12.593\n-10.332\n-9.847\n-10.441\n-9.1961\nNaN\nNaN\nNaN\n-13.491\n-11.715\n-12.107\n-12.559\n-11.003\n-9.6141\n-8.0304\n-5.8544\n-4.5755\n-6.3842\n-5.3271\n-4.9567\n-5.3614\n-5.3304\n-7.4389\nNaN\nNaN\nNaN\n-3.3797\n-2.4683\n-2.1224\n-2.322\n-0.90006\n-1.0981\n0.72724\n0.065581\n-0.95955\n0.26669\n-0.77514\n0.76449\n0.96334\n0.27744\n-0.41171\n0.32977\n-0.19732\n-2.0383\n-0.26849\n0.85097\n-1.5533\n-7.2562\n-6.2395\n-4.4531\n-3.9301\n-3.657\n-7.1074\n-7.9178\n-5.7311\n-7.6444\n-11.643\n-11.811\n-11.502\n-9.8741\n-7.7662\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-12.474\n-13.18\n-13.771\n-12.461\n-9.9853\n-14.582\n-11.844\nNaN\nNaN\nNaN\n-16.199\n-12.803\n-11.979\n-10.936\n-9.239\n-8.3371\n-8.9989\n-8.0952\n-4.3795\n-6.7294\n-4.8148\n-2.1305\n-1.7195\n-2.734\n-4.3973\nNaN\nNaN\nNaN\n-3.1689\n-2.3325\n-2.2541\n-2.9513\n-0.4034\n-0.27159\n-0.56625\n-0.53584\n-0.3275\n0.34275\n0.22846\n0.41477\n-0.15016\n0.039352\n-0.63116\n-0.24408\n0.94408\n-1.3906\n1.1879\n0.24699\n-2.9589\n-7.8383\n-6.8899\n-3.1661\n-3.7287\n-3.3545\n-5.6084\n-7.3733\n-6.6012\n-5.6505\n-7.9742\n-6.8763\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4085\n-9.6615\n-8.2509\n-12.145\n-10.498\n-10.437\n-15.142\nNaN\n-13.608\n-13.757\n-12.594\n-11.803\n-11.355\n-9.5696\n-8.6833\n-8.1307\n-8.6603\n-4.5045\n-3.4671\n-4.2203\n-2.5291\n-0.73146\n-2.8632\n-5.1287\n-5.0665\n-5.2663\n-2.972\n-2.1306\n-3.172\n-2.8302\n-2.5157\n-2.5953\n-1.8102\n-1.6854\n-1.0998\n0.030948\n0.24733\n-0.043491\n-0.51218\n-0.98531\n-0.42465\n0.1837\n-0.82137\n-0.072583\n-0.31604\n-2.2488\n2.4344\n0.16576\n-1.0903\n-5.5217\n-7.1817\n-3.5823\n-2.8007\n-2.6339\n-5.4558\n-8.2916\n-8.0028\n-7.9657\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.9309\n-13.01\n-9.9482\n-11.075\n-8.5209\n-15.691\n-14.027\n-12.446\n-12.838\n-11.914\n-9.6486\n-9.5842\n-9.5299\n-10.929\n-9.8905\n-8.492\n-8.369\n-3.2654\n-1.825\n-4.1712\n-2.4502\n-1.2259\n-5.4293\n-7.7511\n-4.9382\n-3.3373\n-1.5328\n0.30388\n-2.8017\n-2.4722\n-1.8818\n-3.2267\n-3.3241\n-2.4481\n-1.9674\n-1.0716\n-0.19517\n-0.3002\n-0.72312\n-0.47789\n-0.79989\n-0.39316\n-0.80716\n0.29884\n0.039469\n-2.4475\n-3.2031\n-0.8024\n-1.6256\n-5.1165\n-5.9261\n-5.0905\n-3.5068\n-4.8798\n-7.808\n-10.216\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-18.741\n-19.731\n-16.342\n-12.185\n-10.383\n-12.4\n-11.491\n-11.903\n-12.285\n-10.004\n-8.3736\n-8.7282\n-8.2758\n-9.624\n-9.3726\n-9.5356\n-7.183\n-3.9415\n-1.5118\n-5.3358\n-4.4741\n-3.1288\n-5.685\n-4.976\n-1.4305\n0.5943\n-1.4869\n1.9315\n-2.8209\n-3.1362\n-4.2452\n-3.9781\n-2.6934\n-2.9727\n-2.3561\n-1.4259\n0.35187\n0.588\n0.24136\n0.81511\n0.23978\n-0.34679\n-0.22227\n1.3409\n1.1589\n-2.3981\n-4.3352\n-5.697\n-4.6523\n-5.4964\n-6.3887\n-4.6243\n-4.5103\n-7.3183\n-10.866\n-14.15\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-17.001\n-17.315\n-14.309\n-13.801\n-12.227\n-11.55\n-11.679\n-11.342\n-9.7717\n-8.5948\n-8.5713\n-5.8974\n-7.0905\n-8.167\n-8.0575\n-6.8678\n-5.9989\n-2.5127\n-5.667\n-2.2676\n-3.9082\n-3.972\n-4.4343\n1.2564\n3.4328\n-0.22026\n2.1862\n-3.9505\n-4.3174\n-4.1191\n-3.2914\n-2.2825\n-2.1415\n-1.1121\n0.31434\n0.58155\n1.0709\n0.83223\n0.66811\n1.2994\n0.11064\n-0.66765\n0.89089\n0.78964\n-3.5517\n-5.0116\n-5.4963\n-4.6029\n-5.6212\n-6.8082\n-4.6026\n-4.4453\n-8.4833\n-12.641\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-15.654\n-16.317\n-16.079\n-14.651\n-10.345\n-10.507\n-9.9401\n-9.7394\n-9.1655\n-9.5577\n-6.3132\n-2.4561\n-4.5297\n-6.1527\n-5.3208\n-6.0091\n-6.6102\n-5.4703\n-3.084\n-1.0949\n-3.421\n-5.1385\n-8.6854\n2.0696\n5.5805\n2.1093\n1.9569\n-6.2205\n-4.5002\n-3.6472\n-2.5388\n-1.8593\n-0.21744\n0.64345\n0.83627\n1.4091\n0.99929\n1.3364\n0.74389\n0.70712\n0.92171\n-0.87865\n0.78372\n1.8214\n-4.5604\n-5.6344\n-5.2085\n-4.578\n-5.6936\n-6.4556\n-5.2926\n-5.3163\n-6.6359\n-12.413\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-15.577\n-14.497\n-15.989\n-14.203\n-10.641\n-10.437\n-8.5935\n-8.7616\n-10.606\n-10.92\n-7.5324\n-2.4033\n-2.2001\n-3.0316\n-3.4327\n-3.8541\n-5.0072\n-3.3267\n-0.3116\n0.86051\n-1.341\n-5.0698\n-5.1364\n2.7825\n6.0341\n4.4821\n1.4062\n-5.9958\n-4.6147\n-3.995\n-1.6098\n0.13329\n0.55925\n0.78312\n0.71107\n1.3359\n1.5576\n0.33017\n-1.4745\n-0.12527\n1.1605\n0.23863\n1.303\n-0.35127\n-4.7336\n-5.1282\n-5.7512\n-5.9798\n-5.8315\n-5.7279\n-5.1698\n-5.1244\n-4.0851\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-13.929\n-13.981\n-13.752\n-13.661\n-10.701\n-13.837\n-9.9875\n-8.9278\n-9.3913\n-6.326\n-4.8169\n-2.2035\n-2.2728\n-1.4097\n-1.4541\n-2.0161\n-4.4146\n-2.4183\n1.0181\n0.78587\n0.81583\n-1.8546\n-1.5115\n4.0435\n3.8424\n2.3001\n1.427\n-6.2071\n-3.5778\n-3.8887\n-1.4616\n-1.0107\n-0.61432\n-0.9063\n-0.74509\n-0.2926\n-0.66871\n-0.40578\n-1.0369\n-0.83656\n0.74524\n0.38345\n0.36929\n-2.1637\n-5.1287\n-6.0679\n-5.3245\n-6.4083\n-6.1966\n-6.1537\n-6.7152\n-6.5402\n-2.6255\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.5577\nNaN\nNaN\nNaN\nNaN\n-14.788\n-14.356\n-13.711\n-13.592\n-10.26\n-14.645\n-9.2536\n-7.3697\n-4.3693\n-5.0005\n-3.9902\n-2.9066\n-8.4261\n-2.4546\n-2.7337\n-2.6555\n-6.1933\n-2.7264\n1.2004\n1.0078\n1.4767\n1.8461\n3.2946\n4.6313\n3.5728\n1.9312\n1.2655\n-4.8939\n-2.7607\n-2.7979\n-2.3433\n-2.4314\n-1.7142\n-1.3137\n-1.9778\n-1.5172\n-0.96828\n-0.21243\n-0.94237\n-1.5708\n0.18082\n-2.2419\n-2.2453\n-2.5076\n-5.3756\n-6.7253\n-4.9657\n-5.092\n-6.539\n-7.6814\n-5.8805\n-5.2722\n-2.483\n-9.1246\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.7317\n-6.689\n-8.3196\n-9.6445\n-12.05\n-12.878\n-14.857\n-14.398\n-13.141\n-12.138\n-10.42\n-9.4561\n-9.839\n-7.1477\n-4.3441\n-6.0512\n-4.5389\n-4.9593\n-9.7652\n-7.7072\n-3.2268\n-2.2572\n-4.2527\n-2.1116\n-0.02657\n2.7902\n1.9082\n2.9237\n3.1082\n5.4231\n3.3761\n3.0735\n1.5633\n-5.1417\n-3.0036\n-1.9758\n-3.1788\n-3.8122\n-2.3861\n-2.4037\n-2.6505\n-1.4868\n-0.20365\n-0.73861\n-2.2663\n-3.6353\n-2.5311\n-3.9426\n-4.2578\n-5.1986\n-7.9642\n-7.0867\n-8.181\n-7.3703\n-6.6211\n-6.0221\n-3.9853\n-3.0689\n-4.0414\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.48023\n-0.80581\n-2.9551\n-4.7969\n-9.7406\n-13.112\n-13.993\n-13.441\n-13.478\n-12.88\n-12.606\n-12.382\n-10.872\n-11.404\n-10.768\n-7.8445\n-10.191\n-9.9735\n-5.9059\n-6.7136\n-6.7647\n-6.7435\n-2.9283\n-2.8598\n-2.7881\n-0.18967\n1.4037\n1.3649\n2.058\n2.4922\n2.8402\n2.3233\n3.9196\n1.7407\n-6.5648\n-4.7678\n-3.5458\n-4.2238\n-4.1689\n-2.7297\n-2.5635\n-2.2697\n-2.1199\n-1.5664\n-1.7619\n-3.4207\n-4.791\n-5.1476\n-5.4401\n-7.2674\n-9.5542\n-10.864\n-10.244\n-9.3207\n-9.2537\n-6.7905\n-5.2712\n-2.6456\n-1.9808\n-2.3086\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.30543\n2.9005\n2.2613\n2.5209\n6.5981\n0.54887\n-3.4529\n-7.1333\n-13.726\n-14.85\n-13.878\n-11.025\n-11.346\n-10.554\n-10.474\n-10.754\n-10.072\n-10.561\n-10.069\n-9.5176\n-8.6379\n-7.6146\n-7.0904\n-6.2045\n-7.7191\n-1.992\n-0.045275\n-1.1488\n-1.0309\n-1.4429\n-0.67917\n1.0545\n1.6252\n1.0927\n1.6958\n2.4682\n3.0147\n-7.1028\n-5.1954\n-5.2443\n-4.2577\n-4.1829\n-3.306\n-3.1351\n-2.4572\n-2.9335\n-3.046\n-3.2875\n-5.3326\n-7.3203\n-6.8929\n-7.6349\n-8.1247\n-10.12\n-11.141\n-10.137\n-10.191\n-10.31\n-8.7223\n-6.0989\n-5.2375\n-3.2934\n-0.051941\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.1017\n-1.8279\n-5.5323\n-9.9162\n-13.722\n-16.321\n-13.926\n-11.279\n-10.695\n-10.83\n-12.273\n-11.657\n-10.102\n-8.697\n-7.9802\n-9.6198\n-9.4643\n-7.9339\n-7.5315\n-7.4883\n-7.5592\n-2.9946\n-0.58257\n-0.56446\n-0.54775\n-1.1564\n-0.85172\n-0.96503\n1.3737\n0.56402\n0.47476\n1.6132\n3.1404\n-5.7344\n-5.7024\n-5.7788\n-4.1481\n-5.1988\n-4.0037\n-4.0937\n-3.8798\n-4.1893\n-4.8724\n-5.7095\n-6.3798\n-7.5634\n-7.0922\n-9.4561\n-8.6032\n-10.009\n-13.346\n-14.759\n-9.7311\n-9.6483\n-10.878\n-8.1862\n-6.3403\n-4.9181\n-7.3625\n-13.008\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.4677\n-4.5401\n-8.9209\n-10.774\n-12.54\n-19.259\n-14.768\n-12.107\n-12.308\n-11.116\n-11.538\n-10.726\n-9.2288\n-8.3619\n-7.9748\n-6.9962\n-9.68\n-8.0724\n-6.0453\n-4.4223\n-4.8752\n-4.4742\n-2.0702\n-0.25933\n0.34829\n1.2198\n0.10029\n-0.11006\n2.5122\n1.4394\n2.4635\n2.8709\n4.5381\n-5.9519\n-6.2516\n-6.1514\n-4.9389\n-5.5794\n-4.0372\n-5.0964\n-4.8676\n-4.5585\n-4.0961\n-6.5759\n-7.1102\n-7.0031\n-7.5659\n-10.125\n-9.0507\n-9.7627\n-11.252\n-13.715\n-12.674\n-9.1803\n-11.143\n-8.9855\n-5.9962\n-4.6348\n-7.7093\n-13.255\n-18.944\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1929\n-7.2883\n-9.9009\n-10.119\n-10.633\n-16.805\n-12.826\n-11.552\n-13.011\n-13.02\n-13.102\n-9.7998\n-8.2554\n-8.0811\n-9.3113\n-6.3094\n-5.738\n-5.5242\n-3.9914\n-2.5653\n-1.7102\n-0.18353\n-1.8603\n0.20491\n0.96751\n1.744\n0.86005\n0.54505\n2.4403\n3.6778\n4.1956\n5.7608\n6.2885\n-6.7664\n-7.2687\n-6.0357\n-5.8203\n-5.38\n-3.8852\n-5.5614\n-5.1333\n-5.2084\n-3.933\n-6.5519\n-6.7002\n-6.1675\n-8.7554\n-10.635\n-10.844\n-9.7005\n-11.905\n-11.919\n-15.359\n-13.026\n-12.486\n-9.1527\n-6.8994\n-6.3114\n-7.3989\n-13.021\n-14.741\nNaN\nNaN\n-10.498\n-12.902\n-5.0156\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.5516\n-8.1938\n-9.319\n-9.2119\n-14.088\n-16.129\n-12.731\n-13.332\n-13.416\n-14.846\n-12.33\n-8.075\n-7.2378\n-7.096\n-7.3389\n-5.4319\n-4.4209\n-4.0573\n-3.6357\n-3.5086\n-1.7975\n-0.28844\n-1.2347\n0.81354\n0.37875\n-0.7446\n-1.9086\n-1.4962\n2.5542\n3.67\n4.0892\n6.0375\n6.8072\n-8.1677\n-8.0303\n-6.2842\n-7.3915\n-6.6129\n-4.3339\n-5.3819\n-5.5156\n-6.0531\n-6.0062\n-7.21\n-6.7734\n-7.3006\n-9.4573\n-10.12\n-9.3833\n-10.304\n-13.21\n-13.65\n-12.499\n-12.201\n-11.6\n-10.144\n-7.8808\n-12.002\n-14.66\n-13.641\n-11.18\n-11.892\n-10.191\n-10.661\n-12.926\n-9.253\n-5.9379\nNaN\nNaN\nNaN\nNaN\n-14.362\n-9.3274\n-11.521\n-11.647\n-11.295\n-14.699\n-17.527\n-13.915\n-12.678\n-12.171\n-12.174\n-11.012\n-6.415\n-5.3741\n-3.5224\n-5.4947\n-5.5081\n-5.0108\n-3.5016\n-3.2155\n-2.2254\n-1.2074\n-1.7264\n-1.7924\n0.23787\n-2.5466\n-2.3169\n-1.7472\n-0.81488\n1.1766\n-0.1277\n1.7841\n3.5651\n3.6187\n-8.4126\n-8.2467\n-8.0222\n-8.5644\n-7.7608\n-6.1517\n-5.5565\n-6.4448\n-7.3874\n-7.9567\n-7.8404\n-7.9003\n-8.7364\n-9.3074\n-10.015\n-10.195\n-13.803\n-18.988\n-14.885\n-12.408\n-12.078\n-9.8638\n-9.2095\n-8.5283\n-2.2258\n-9.3316\n-9.8535\n-10.223\nNaN\n-14.366\n-14.476\n-18.009\n-11.031\n-9.4769\nNaN\nNaN\nNaN\n-15.121\n-11.971\n-9.342\n-10.132\n-11.621\n-10.219\n-12.947\n-16.961\n-13.163\n-8.1247\n-7.5234\n-8.4157\n-7.9163\n-4.9054\n-2.8514\n-2.3559\n-4.4945\n-3.3495\n-3.4564\n-3.2484\n-2.209\n-0.75932\n-0.65805\n-1.7456\n-2.7334\n-2.7464\n-2.9104\n-2.0184\n-0.19719\n0.9782\n0.77916\n-0.80166\n-0.79487\n1.7831\n-0.20829\n-8.9107\n-8.4607\n-7.9324\n-8.3598\n-7.4004\n-6.5418\n-6.8322\n-7.9029\n-9.3752\n-9.7829\n-8.6118\n-8.5554\n-8.724\n-9.0606\n-9.3708\n-10.268\n-13.559\n-18.624\n-13.85\n-12.505\n-10.331\n-11.635\n-7.9691\n-8.983\n-7.495\n-8.6723\n-10.645\nNaN\nNaN\n-14.938\n-15.816\n-15.777\n-13.553\n-12.661\n-13.666\n-15.504\n-14.597\n-13.407\n-12.641\n-8.9147\n-9.9785\n-13.185\n-12.2\n-12.356\n-13.268\n-7.3216\n-5.5332\n-4.7009\n-5.5064\n-5.8827\n-3.7403\n-1.3653\n-2.6238\n-3.9156\n-2.1887\n-2.0953\n-2.0657\n-1.158\n0.03797\n-0.45377\n-1.1929\n-3.486\n-3.7992\n-2.3673\n-1.103\n0.31944\n0.73956\n1.2628\n1.1221\n-0.52826\n2.1272\n2.8408\n-9.7285\n-9.4569\n-8.2236\n-7.263\n-6.4447\n-6.6855\n-7.572\n-9.2914\n-10.781\n-10.299\n-8.6364\n-7.3235\n-7.2545\n-7.1381\n-7.0981\n-9.6453\n-11.093\n-17.632\n-10.381\n-11.871\n-11.922\n-10.34\n-8.0105\n-9.0201\n-10.211\n-11.517\n-11.577\nNaN\nNaN\nNaN\n-16.561\n-14.187\n-12.003\n-12.541\n-12.734\n-11.072\n-10.308\n-12.466\n-12.456\n-9.3376\n-8.1941\n-10.114\n-9.1052\n-6.7451\n-7.5405\n-6.3242\n-5.5116\n-5.2225\n-4.0015\n-2.6817\n-2.02\n-1.2579\n-2.2926\n-3.7236\n-2.2344\n-1.0131\n-0.2802\n1.0719\n1.0254\n0.26871\n-1.3645\n-2.9349\n-3.3916\n-2.3765\n-0.14064\n0.46327\n1.4217\n2.2249\n0.69968\n-1.17\n1.9612\n1.2917\n-9.9972\n-10.567\n-9.355\n-6.9576\n-5.5605\n-6.4026\n-9.3384\n-9.86\n-10.302\n-9.9822\n-7.9167\n-5.9862\n-5.351\n-3.1033\n-1.7957\n-6.9623\nNaN\nNaN\nNaN\n-10.778\n-13.434\n-10.379\n-9.7659\n-13.287\n-12.869\n-13.523\n-11.996\nNaN\nNaN\nNaN\n-14.251\n-12.663\n-12.573\n-11.974\n-11.427\n-10.203\n-10.201\n-10.959\n-11.141\n-11.8\n-10.562\n-8.5871\n-5.3775\n-4.6628\n-4.8816\n-4.6609\n-2.8954\n-1.6921\n-0.96113\n-1.164\n-1.2814\n-1.8264\n-2.2274\n-3.5375\n-2.4359\n-0.11184\n0.91102\n1.3695\n1.6928\n0.95698\n-1.42\n-1.8409\n-1.955\n-2.4771\n0.076003\n0.6696\n1.497\n2.3949\n0.36033\n-1.2291\n-1.1626\n1.8321\n-8.0394\n-9.0558\n-9.0875\n-7.3509\n-6.6589\n-6.6676\n-9.6668\n-9.9223\n-9.6452\n-9.9275\n-7.6177\n-6.0151\n-3.8437\n-0.48649\n-0.59679\n-6.1831\nNaN\nNaN\nNaN\n-10.752\n-13.109\n-15.273\n-14.727\n-13.45\n-10.684\n-11.473\n-11.725\nNaN\nNaN\nNaN\n-14.434\n-13.487\n-12.818\n-11.341\n-7.8697\n-8.7827\n-9.1987\n-9.4787\n-9.6296\n-11.201\n-8.095\n-7.5989\n-5.8629\n-4.3094\n-3.6295\n-4.2706\n-1.9117\n-1.3932\n-1.5218\n-1.1943\n-0.83027\n-1.8284\n-1.4354\n-1.6096\n-1.2088\n-0.24089\n-0.49172\n1.2179\n2.2645\n1.7423\n-0.20029\n-0.76228\n-0.39897\n0.48216\n1.6134\n1.816\n3.0798\n3.1958\n1.3337\n0.53777\n0.7948\n2.9306\n-7.1166\n-7.2259\n-6.9132\n-6.5986\n-6.2965\n-6.8805\n-7.4915\n-9.3772\n-9.2487\n-10.042\n-7.2863\n-5.0877\n-4.1814\n-3.7102\n-3.3358\n-4.7962\n-7.9982\nNaN\nNaN\n-12.242\n-12.189\n-12.653\n-13.354\n-11.982\n-9.4441\n-10.409\n-11.002\n-10.361\n-12.23\n-12.705\n-16.176\n-15.241\n-12.982\n-10.856\n-7.1119\n-7.854\n-7.0987\n-6.8442\n-7.2498\n-6.436\n-6.4037\n-5.9986\n-5.6566\n-3.9811\n-2.7725\n-2.0712\n-0.2403\n-3.4459\n-4.3235\n-1.2443\n-0.77699\n-1.5898\n-0.0055829\n-0.96053\n-1.4857\n-0.46711\n-0.68996\n1.3927\n2.6038\n3.0427\n2.1046\n-0.72151\n0.3026\n2.9586\n3.579\n2.6453\n2.3574\n3.6402\n1.3032\n1.3684\n4.2643\n5.0825\n-7.6705\n-7.2985\n-6.352\n-5.6107\n-5.5068\n-6.1226\n-7.3768\n-9.4672\n-9.795\n-10.085\n-8.2997\n-6.3453\n-5.3677\n-3.8104\n-2.6307\n-3.1485\n-7.5939\n-8.7569\n-10.617\n-12.178\n-11.532\n-11.134\n-11.171\n-10.299\n-8.5784\n-9.7902\n-10.016\n-9.9117\n-9.871\n-11.013\n-12.318\n-12.534\n-11.82\n-9.8\n-7.9518\n-7.9063\n-6.8036\n-4.2333\n-5.4054\n-5.7293\n-5.5626\n-3.0693\n-3.8506\n-3.0605\n-1.3687\n-0.046825\n-1.1998\n-5.4431\n-7.8516\n-2.9253\n-1.3445\n0.33315\n0.50759\n-0.82871\n-2.1161\n0.46911\n1.2485\n1.8191\n2.8201\n3.1858\n2.7432\n0.67782\n1.4385\n3.1314\n3.2552\n1.672\n2.7224\n3.1689\n0.92539\n2.5192\n4.9249\n5.2502\n-8.6122\n-6.9696\n-5.404\n-4.9952\n-4.3729\n-4.6195\n-6.266\n-6.6219\n-8.3597\n-9.2286\n-9.1324\n-7.7423\n-6.8715\nNaN\nNaN\nNaN\nNaN\n-8.4134\n-11.715\n-12.466\n-9.0021\n-10.044\n-11.854\n-10.305\n-7.6781\n-8.6488\n-8.7221\n-7.8773\n-8.0948\n-9.2571\n-9.8787\n-9.0723\n-10.041\n-9.7101\n-9.1232\n-9.0987\n-6.7259\n-3.5396\n-4.0649\n-3.7753\n-3.4984\n-2.0799\n-2.5956\n-4.0584\n-2.8237\n-0.12039\n1.0735\n-4.1941\n-3.9423\n-4.0609\n-2.5133\n-2.5173\n-0.73652\n-1.9254\n-0.97101\n1.4542\n0.9902\n1.4614\n3.0717\n3.0823\n3.5671\n3.3147\n3.1893\n2.463\n1.6403\n1.624\n2.9617\n2.6746\n2.7913\n4.3467\n5.1189\n6.8758\n-9.5448\n-6.778\n-5.4252\n-4.4907\n-3.8734\n-4.1419\n-5.0164\n-3.3937\n-4.0581\n-5.2575\n-7.5216\n-8.0331\n-6.9943\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.51\n-11.07\n-9.8201\n-9.2122\n-11.023\n-9.9715\n-7.8002\n-7.4426\n-7.9364\n-7.3608\n-7.8314\n-7.8386\n-7.8624\n-7.583\n-6.382\n-7.5588\n-7.9844\n-6.567\n-4.3464\n-2.1904\n-1.8664\n-1.907\n-1.9795\n-1.3707\n-1.6163\n-5.735\n-4.333\n-2.4467\n2.4391\n-4.0619\n-2.0964\n-2.3543\n-2.1245\n-2.1006\n-2.002\n-1.8176\n-0.36609\n1.2018\n0.92303\n2.3339\n3.3783\n3.2808\n3.6654\n3.9726\n3.0146\n2.4044\n1.0045\n1.7655\n2.915\n2.8204\n4.0242\n5.138\n5.5786\n7.3129\n-8.0134\n-7.5258\n-5.053\n-3.3507\n-2.5162\n-0.61124\n-1.6315\n0.12133\n-1.171\n-3.5302\n-7.4385\n-9.8533\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.824\n-10.658\n-10.303\n-10.239\n-10.378\n-9.3695\n-9.1068\n-7.7329\n-7.8614\n-6.6126\n-6.6819\n-6.7443\n-6.4118\n-6.16\n-5.8178\n-5.843\n-5.0351\n-3.6621\n-2.2519\n0.37813\n-1.2364\n-0.89182\n-0.89659\n-0.10342\n-4.8168\n-6.2225\n-5.2283\n0.51443\n-1.3173\n-1.1606\n-1.4953\n-1.8931\n-1.2244\n-1.7319\n-2.024\n-0.13102\n2.2517\n0.35114\n1.342\n3.1921\n2.0554\n2.756\n2.9336\n3.1423\n2.7346\n1.4676\n2.3358\n3.2065\n2.7352\n5.2138\n3.9844\n3.3822\nNaN\n-5.7163\n-7.4319\n-4.2642\n-1.3502\n-0.26343\n1.3493\n1.1499\n1.9997\n-1.3264\n-3.051\n-6.1709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.455\n-9.2853\n-9.4758\n-8.3608\n-9.0263\n-8.7325\n-7.9637\n-7.6073\n-6.8365\n-5.5171\n-5.9911\n-5.5275\n-6.2738\n-5.6632\n-5.1402\n-4.8257\n-5.6889\n-4.4226\n-3.3992\n-0.78527\n-1.0659\n-0.37201\n-1.7235\n-3.5932\n-4.037\n-6.9572\n-7.2466\n-3.3907\n-2.5075\n-1.1544\n-2.3907\n-1.0903\n1.3957\n1.8143\n0.99147\n1.6802\n2.0152\n0.80876\n2.1771\n2.6517\n1.0021\n2.9814\n1.3124\n1.3357\n3.0061\n3.8653\n5.2043\n1.2564\n0.3463\n4.787\nNaN\nNaN\nNaN\n2.0108\n3.1764\n2.7411\n3.5314\n2.919\n2.2788\n2.7107\n3.9161\n2.5575\n1.2505\n3.223\n3.4296\n4.7119\n4.5746\n2.6427\n3.4409\n3.5941\n5.4065\n-0.4131\n1.7386\n2.1417\n2.7371\n1.121\n0.81355\n0.75514\n2.9065\n3.5154\n5.3987\n3.0955\n-0.49602\n4.3123\n4.1962\n7.0266\n7.7639\n6.5797\n4.3779\n3.1152\n2.0476\n4.2282\n4.5331\n2.7345\n2.565\n2.6802\n1.8469\n1.3339\n2.7066\n1.7251\n1.4445\n3.0432\n3.1237\n2.515\n1.8209\n2.301\n3.2189\n-0.034645\n1.5794\n2.1038\n4.9925\n8.3928\n6.7748\n5.7318\n3.7138\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.9726\n3.8633\n3.164\n0.073836\n-1.2664\n3.3626\n2.1163\n3.9488\n0.672\n0.52705\n1.2924\n1.7711\n4.9505\n2.9829\n0.55514\n2.3048\n7.1458\n6.5098\n3.4886\n3.7207\n0.2186\n2.5214\n2.0151\n-0.94371\n-0.99636\n1.1215\n1.301\n2.3103\n1.2913\n0.77161\n1.4085\n2.3609\n5.8782\n7.5747\n6.7863\n5.6189\n4.016\n2.7284\n4.5958\n2.683\n0.60239\n2.1118\n2.0245\n2.3704\n1.9033\n2.263\n1.7414\n1.8256\n2.6015\n2.924\n2.7931\n2.0495\n2.1541\n0.99835\n-1.9505\n1.6985\n2.7133\n7.3463\n8.4869\n5.0277\n4.7532\n2.6632\n1.5483\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2428\n3.2227\n2.2754\n2.7672\n5.1566\n5.2102\n3.6166\n1.7745\n1.0732\n0.32501\n1.2168\n2.8601\n5.2244\n1.5353\n-1.9442\n-0.090266\n4.1546\n3.6643\n3.475\n0.57408\n2.2103\n0.27733\n-0.031557\n-0.72932\n-1.7139\n-0.89159\n-0.70489\n-0.33734\n0.39138\n2.7532\n1.6652\n2.1876\n4.8156\n5.2999\n6.0282\n5.8951\n3.9725\n3.3624\n3.1815\n2.2809\n1.7466\n1.1621\n2.0578\n1.9096\n2.303\n2.7726\n3.0616\n2.6745\n2.7622\n2.1801\n1.7598\n2.4656\n2.1175\n-0.1438\n0.32942\n0.72394\n1.2235\n6.8686\n7.0634\n2.4216\n5.2723\n2.0361\n0.52772\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6744\n3.6555\n3.1651\n3.3553\n3.4891\n3.2712\n2.2157\n2.4638\n3.4764\n2.4693\n2.5433\n5.288\n2.6709\n0.082146\n1.8763\n2.9413\n3.3986\n3.4328\n2.1661\n0.028869\n1.1627\n-1.4863\n-1.722\n0.18458\n-1.5915\n-2.3924\n-2.4397\n-0.90963\n1.2316\n1.6955\n2.5729\n3.1284\n5.8468\n5.0587\n5.6265\n5.4143\n4.8432\n3.7116\n2.6917\n1.4899\n1.4285\n1.327\n3.497\n3.7927\n2.7844\n3.3859\n3.6328\n3.2443\n2.8605\n0.9024\n-0.33714\n0.78689\n0.23432\n-0.92217\n-0.21122\n-0.32219\nNaN\nNaN\nNaN\n7.6844\n6.6997\n1.9621\n-0.48916\nNaN\nNaN\nNaN\n2.5901\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6903\n3.7412\n2.8184\n3.2431\n3.5892\n3.5281\n3.2854\n3.1459\n3.8395\n4.3883\n4.5706\n4.6787\n2.507\n2.7924\n4.3504\n5.0754\n4.9701\n2.9773\n5.3462\n1.5174\n0.8792\n-2.4967\n-5.2459\n2.7728\n-1.4249\n-3.579\n-3.9177\n-1.3077\n-0.5427\n-0.90217\n2.5496\n5.0825\n5.9204\n5.908\n5.9124\n6.3791\n6.7846\n4.2658\n2.7759\n1.9188\n2.46\n1.7213\n3.4189\n4.5928\n4.0957\n3.7658\n3.028\n2.631\n2.8421\n2.3116\n0.26323\n-0.095437\n0.44032\n-0.27997\n0.46028\n-0.79163\n-5.1695\nNaN\nNaN\n10.183\n7.5554\n3.7039\n-0.35091\n2.587\n7.9756\n5.3635\n5.922\nNaN\nNaN\nNaN\nNaN\nNaN\n3.6018\n4.3913\n3.0432\n3.4748\n2.9238\n3.4507\n2.885\n2.2506\n2.8176\n3.2614\n3.0142\n2.5298\n2.0553\n2.5879\n4.1916\n4.0332\n4.9027\n3.7335\n5.0324\n3.816\n5.0038\n-1.4069\n-0.75247\n0.82893\n-1.0767\n-4.1984\n-3.8949\n-1.874\n-0.52515\n-0.56246\n0.2157\n5.362\n5.4256\n5.9778\n6.5221\n7.2781\n6.8963\n5.0203\n3.4429\n3.0051\n2.9699\n3.3585\n3.7402\n4.6599\n7.0102\n5.3549\n3.2301\n1.6923\n2.2363\n1.4702\n0.91885\n0.14426\n0.78856\n1.6167\n2.221\n4.3161\n1.3062\nNaN\nNaN\n14.919\n11.288\n4.0563\n4.5609\n6.0591\n4.6898\n3.9715\n5.7562\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8321\n4.2872\n3.172\n2.6115\n1.7719\n2.7263\n1.8843\n0.78453\n1.1959\n1.2257\n1.3798\n2.0481\n1.9983\n2.8125\n2.7341\n3.9832\n4.5265\n2.5573\n5.4182\n2.9053\n4.0893\n4.0537\n-0.39888\n1.1583\n0.87457\n-2.1085\n-3.51\n-1.7068\n0.26896\n0.22015\n2.1102\n5.8996\n6.6651\n6.2504\n6.9941\n6.8114\n6.3303\n5.1281\n3.9547\n3.3122\n3.0112\n4.3403\n5.2163\n6.2563\n9.5189\n5.5013\n3.2019\n2.0304\n0.87604\n0.37256\n1.1358\n0.31574\n0.63769\n1.2854\n2.1525\n3.6079\n1.307\nNaN\nNaN\n8.9601\n10.879\n7.0636\nNaN\n8.0249\n6.0272\nNaN\nNaN\n4.0519\n4.4085\nNaN\nNaN\nNaN\n2.9637\n2.1174\n2.0417\n1.4297\n0.67771\n2.162\n1.1553\n0.2882\n0.78294\n0.88089\n1.1455\n1.1749\n1.6376\n1.2754\n0.98904\n1.8536\n2.6104\n2.0248\n2.9013\n2.665\n2.709\n1.088\n0.97927\n1.3776\n0.14405\n-0.95659\n-1.9751\n-3.1134\n-0.28047\n0.019182\n0.69584\n5.1682\n6.2885\n5.8503\n4.872\n4.3789\n4.7504\n5.3169\n5.0656\n4.7379\n4.0505\n4.9295\n5.3455\n6.6169\n11.291\n5.5359\n2.9348\n2.1225\n1.5484\n0.13933\n1.466\n0.42611\n-0.56516\n-0.16015\n0.13417\n1.6842\n1.563\n0.37927\n0.53841\n4.1768\n7.7124\n4.527\nNaN\nNaN\nNaN\nNaN\nNaN\n6.1313\n5.1109\n5.6793\nNaN\nNaN\n2.629\n2.4273\n1.7435\n1.8302\n1.0659\n1.3735\n1.2306\n-0.29289\n0.20255\n0.30043\n1.2619\n0.94229\n0.56756\n0.15138\n0.82113\n0.8378\n0.51063\n1.4024\n1.7082\n1.454\n1.7733\n3.0155\n2.6098\n0.81278\n-0.8169\n-0.81016\n-1.2381\n-1.1834\n-0.36066\n0.53396\n0.61568\n2.8719\n3.2432\n5.3327\n3.9032\n3.693\n5.1672\n5.6101\n5.0129\n5.7071\n5.6641\n5.9061\n7.6223\n9.0068\n11.485\n3.9607\n2.2533\n1.5853\n0.68336\n-0.50611\n0.30147\n-1.2557\n-1.8342\n-3.6149\n-2.6569\n1.524\n2.5918\n1.403\n-0.95499\n-0.5344\n6.3888\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.7545\n5.7031\nNaN\nNaN\n3.9347\n2.7893\n2.0398\n2.38\n0.95247\n1.5479\n2.0692\n0.94996\n-0.046386\n0.30837\n-0.046894\n-0.48798\n-1.0642\n-0.43956\n-0.32636\n-0.83978\n-0.53507\n1.3928\n1.878\n0.78421\n1.3699\n0.98544\n-0.78751\n0.1714\n0.80937\n-0.10402\n-1.7325\n-1.699\n0.0007373\n1.4432\n1.8651\n5.9003\n8.1258\n5.7532\n4.6719\n4.6736\n4.9091\n6.12\n6.3421\n6.9084\n6.9794\n6.8929\n8.8049\n8.8417\n5.8683\n2.8392\n2.7411\n1.5203\n0.81157\n-0.56176\n-0.51259\n-2.8326\n-3.1088\n-3.8513\n-1.8999\n1.0854\n3.1422\n4.5878\n-0.84335\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.907\nNaN\n4.7851\n2.0299\n5.3007\n4.2683\n2.6023\n2.9098\n1.959\n2.072\n1.5247\n0.86176\n-0.24308\n-1.016\n-1.6984\n-1.5413\n-2.6066\n-2.0128\n-1.5525\n-1.8517\n-0.62092\n1.5442\n1.672\n-0.0052318\n0.24429\n-0.39687\n-1.9585\n0.26057\n1.9509\n-0.089825\n-2.1143\n-1.8549\n0.47799\n0.39614\n2.8003\n6.6599\n8.5561\n5.5933\n6.0956\n5.7051\n7.1954\n6.9143\n9.1515\n8.1833\n7.0035\n7.8495\n9.6255\n8.722\n5.8986\n4.0721\n3.5602\n2.0899\n0.74209\n0.46509\n-0.22561\n-2.0578\n-1.9278\n-3.709\nNaN\nNaN\n4.5754\n4.3524\n0.030278\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1514\n-0.25266\n5.7089\n4.9083\n3.8858\n2.8677\n2.2012\n1.2093\n0.75626\n0.6226\n-0.21585\n-0.48263\n-0.81935\n-1.3525\n-3.3149\n-2.6405\n-1.0515\n-0.71219\n0.26479\n0.13413\n-0.41357\n-0.54175\n0.43337\n-0.32939\n-1.7407\n1.1434\n2.1959\n0.89879\n-1.2631\n-2.5235\n-0.54664\n-0.35959\n2.4544\nNaN\nNaN\nNaN\n5.5209\n6.366\n7.0378\n8.026\n11.802\n8.5476\n6.3813\n8.3438\n9.7\n8.2393\n7.0637\n5.6645\n3.8798\n1.8684\n0.1456\n-0.17678\n-0.14543\n-1.701\n-0.34912\nNaN\nNaN\nNaN\n3.2531\n4.9099\n1.9171\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.8225\n3.5141\n2.9706\n2.5488\n1.79\n1.6394\n1.422\n1.1447\n0.72889\n-0.029925\n-1.0161\n-1.4235\n-1.975\n-1.9878\n-0.84005\n-1.6172\n-2.2985\n-1.9529\n-1.6697\n-0.71769\n-0.45444\n0.28863\n1.5535\n3.4981\n3.1112\n2.2811\n-1.6067\n-1.1398\n0.67701\n0.43581\n1.8577\nNaN\nNaN\nNaN\n5.2581\n8.2213\n8.2788\n6.7668\n10.119\n8.9173\n8.8768\n12.2\n13.412\n8.532\n6.1119\n5.1521\n3.5743\n1.4914\n-0.41633\n-0.60209\n-0.077862\n-0.67611\n1.0208\nNaN\nNaN\nNaN\n6.2682\n6.6523\n4.4478\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.9913\n2.9136\n2.8701\n2.3479\n1.785\n2.2865\n1.9053\n1.9291\n1.6038\n0.81163\n-0.014791\n-0.97577\n-1.515\n-1.6125\n-0.91077\n-3.0555\n-3.6298\n-2.2385\n-0.9631\n-0.48164\n-0.37296\n3.3519\n5.5872\n6.1786\n6.2734\n2.1866\n-0.26786\n0.80165\n0.51741\n0.20522\n1.2911\nNaN\nNaN\nNaN\nNaN\n8.3166\n9.3423\n4.7437\n9.4713\n9.0967\n9.9535\n11.991\n11.629\n7.2052\n4.2668\n3.6446\n2.6547\n1.2246\n-0.21978\n-0.9415\n0.3823\n1.2032\n1.583\nNaN\nNaN\nNaN\nNaN\n7.8504\n5.1787\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8132\n3.3737\n2.2861\n1.1223\n1.2501\n2.401\n1.5908\n1.6228\n1.5696\n1.2104\n0.65072\n-0.47863\n-1.6482\n-1.714\n-0.76006\n-2.4001\n-2.9813\n-2.6761\n-1.4718\n0.038907\n-0.00055818\n4.9667\n8.9613\n7.916\n6.7578\n2.5125\n1.4928\n1.6731\n0.24817\n0.72841\n1.1\nNaN\n10.578\n10.68\n8.4428\n6.5343\n8.0355\n5.4723\n8.694\n7.8911\n7.7724\n9.7943\n11.125\n4.145\n1.5877\n0.37628\n1.3734\n1.006\n-1.1273\n-1.1606\n0.81759\n-0.47704\n0.16921\n3.588\nNaN\nNaN\nNaN\n8.72\n6.4268\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3424\n3.6424\n2.485\n0.050057\n0.005738\n1.1304\n0.67348\n0.70778\n0.71818\n0.30532\n1.0134\n-0.27614\n-1.53\n-1.6837\n-0.24754\n-1.2196\n-2.3752\n-2.3913\n-2.359\n-0.25881\n0.17085\n2.3363\n11.436\n11.043\n6.5967\n3.9389\n2.1822\n-0.928\n-0.31291\n2.317\n5.0098\n13.002\n9.7399\n10.788\n9.2502\n8.784\n6.7334\n7.1371\n6.7511\nNaN\n13.126\n9.8807\n9.0449\n2.7943\n-1.0152\n0.80038\n1.4329\n1.6772\n-1.007\n-0.043433\n0.94825\n1.1632\n-1.6606\n1.7442\n4.6725\n8.0635\n11.745\n9.5581\n8.8258\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0017\n2.5289\n0.95007\n-0.39151\n0.039941\n0.45706\n-0.16365\n-0.50072\n0.20399\n-0.50877\n-0.27176\n-0.67867\n-1.5571\n-1.545\n-0.36711\n-0.97732\n-1.6475\n-2.1466\n-2.252\n-1.8972\n0.41514\n3.9245\n6.8656\n11.34\n4.8496\n2.8019\n1.9047\n1.0605\n2.6359\n5.8104\n8.6748\n9.2631\n6.6111\n11.023\n7.7219\n8.2306\n6.8056\nNaN\nNaN\nNaN\nNaN\n11.987\n9.3196\n6.5917\n2.2623\n2.2377\n2.112\n1.6444\n-0.019254\n0.60906\nNaN\n1.5675\n-1.6548\n0.96892\n3.2795\n4.5821\n10.813\n9.9377\n8.9015\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.1431\n2.1781\n-0.040326\n-0.51358\n-0.038058\n0.45554\n-0.2737\n-1.1135\n0.14895\n-0.48716\n-0.72007\n-1.6414\n-1.3587\n-1.6967\n-2.1607\n-1.6445\n-1.6776\n-1.1622\n-0.96787\n-1.0135\n1.6724\n6.0858\n8.1995\n10.479\n4.564\n3.4604\n2.5189\n4.5906\n5.5606\n8.1509\n6.4855\n6.9564\n11.011\n8.3684\n6.232\n6.9908\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9739\n3.4052\n1.2536\n0.82844\n1.4043\n1.0046\n-2.2683\nNaN\nNaN\nNaN\n1.9593\n3.2582\n5.3932\n7.4994\n6.3924\n7.6445\n7.5903\n4.7106\n5.7968\nNaN\nNaN\n4.5758\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.5827\n1.3913\n0.13303\n-0.316\n0.18629\n1.6964\n-0.18147\n-0.66611\n0.28911\n-0.65745\n0.54456\n-1.1849\n-1.4117\n-1.4718\n-1.8961\n-1.076\n-0.094865\n0.95073\n0.55406\n0.42926\n2.4318\n6.4683\n8.5178\n7.8975\n5.814\n4.4598\n5.4459\n6.9809\n5.2671\n8.2208\n7.8926\n10.356\n10.916\n9.7505\n7.3033\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.5487\n5.2669\n3.9699\n1.7073\n2.1467\n3.2146\n1.2131\nNaN\nNaN\nNaN\n5.3958\n3.7412\n4.6471\n5.4601\n3.9029\n3.4748\n3.1269\n2.0748\n0.66124\n3.1854\n3.1793\n2.2363\n2.5329\n3.7458\n4.3262\nNaN\nNaN\nNaN\n1.6817\n0.74339\n0.73752\n0.84498\n0.16424\n0.19038\n-1.3292\n-0.19192\n0.9236\n-0.74527\n0.53269\n-1.2377\n-0.98388\n-0.61654\n-0.034093\n-0.26583\n0.864\n4.3231\n2.3278\n1.4034\n3.3013\n8.8516\n7.8727\n5.7172\n4.6242\n4.1607\n7.2539\n7.6393\n5.8827\n8.7647\n13.149\n12.377\n11.943\n10.223\n7.7406\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.0576\n4.5055\n5.2126\n3.8192\n1.6113\n6.7084\n3.062\nNaN\nNaN\nNaN\n8.7888\n5.7884\n5.2462\n3.8534\n1.9112\n0.74133\n2.6445\n3.301\n0.93526\n3.6103\n2.6158\n0.77014\n0.47302\n0.97712\n2.6825\nNaN\nNaN\nNaN\n1.3697\n0.45368\n0.99362\n1.7204\n-0.34416\n-0.19936\n0.58339\n1.0025\n0.63336\n-0.45755\n-0.19994\n-0.71115\n0.27071\n-0.20962\n0.54614\n0.74797\n0.4145\n2.9965\n1.376\n2.0546\n4.8868\n9.7813\n9.1032\n4.4278\n4.4072\n3.7188\n5.425\n6.9443\n6.725\n6.3276\n9.1978\n6.7006\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.7859\n1.4861\n-0.049484\n3.5923\n1.121\n1.9544\n6.3795\nNaN\n6.3675\n6.5525\n5.82\n5.3694\n5.8741\n3.4378\n1.4688\n0.32731\n2.0323\n-0.0916\n-0.0013261\n0.77069\n-0.11983\n-1.936\n-0.23439\n2.1441\n3.3165\n3.3561\n2.9304\n3.6758\n1.4139\n1.2546\n1.3052\n1.6788\n1.1918\n1.0746\n1.2583\n0.55335\n0.013926\n0.40171\n1.3184\n1.4665\n1.0617\n0.28448\n1.713\n1.4354\n1.9085\n3.5341\n-0.15143\n1.8357\n2.2648\n7.5223\n9.6622\n5.4515\n3.6037\n2.4176\n5.0768\n8.0476\n8.061\n8.3525\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1922\n4.8683\n1.6584\n2.9073\n-1.0219\n6.6758\n5.8764\n4.7285\n5.708\n4.8524\n2.8118\n3.1913\n3.7323\n5.7013\n3.6419\n1.072\n1.22\n-1.6314\n-2.0699\n-0.071621\n-1.2163\n-1.8784\n0.98587\n2.4753\n2.3123\n3.0637\n0.70798\n1.607\n1.2918\n1.204\n0.99454\n2.5262\n2.5128\n1.8891\n2.0848\n1.2828\n0.4375\n1.2159\n2.3726\n2.084\n2.12\n1.5021\n2.1287\n1.0571\n1.1376\n3.8211\n4.5385\n2.1781\n2.5665\n7.7319\n8.936\n7.3556\n3.9609\n4.6631\n7.5843\n10.087\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n12.644\n11.852\n8.6133\n4.244\n1.6485\n4.1339\n3.6134\n4.3125\n5.3197\n3.0259\n1.4442\n2.1993\n2.1502\n3.9347\n3.5725\n3.4962\n1.3411\n-0.92846\n-1.9985\n0.58978\n-0.21799\n0.07357\n2.2368\n1.2207\n-0.68992\n-0.46601\n0.08442\n-0.25006\n1.6092\n2.0221\n3.6209\n3.3288\n2.3997\n2.2965\n2.2024\n1.6292\n0.2202\n0.99042\n2.0196\n1.8753\n2.2507\n2.0767\n1.7717\n-0.16059\n-0.46105\n3.3006\n5.6709\n7.2464\n6.0441\n7.93\n8.3376\n5.7047\n4.5079\n6.8503\n10.614\n14.149\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.1185\n9.7296\n6.4495\n5.3843\n3.7181\n3.7138\n4.0663\n4.4346\n3.2625\n2.0807\n2.0149\n-0.11042\n1.3968\n2.8744\n3.2532\n2.3611\n1.5002\n0.15528\n1.5044\n-0.876\n1.1372\n1.3314\n0.17189\n-2.5909\n-2.6817\n-0.43354\n-0.21404\n2.6356\n3.2023\n3.3285\n2.7829\n2.396\n1.8558\n1.0219\n0.23945\n0.30474\n0.61266\n1.6956\n2.3098\n1.9824\n2.0773\n2.397\n0.46918\n0.010577\n3.9047\n5.9167\n6.767\n6.2054\n6.8578\n7.7446\n5.1441\n4.3338\n7.5075\n12.093\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.7561\n8.6137\n8.3347\n6.096\n1.0608\n2.4894\n2.6935\n3.1694\n3.2239\n3.6354\n0.19377\n-3.4796\n-1.3907\n0.94331\n1.0612\n1.8484\n2.9273\n3.057\n0.024016\n-1.4398\n0.99713\n0.71106\n1.303\n-3.7038\n-5.0111\n-1.9912\n-1.6002\n4.507\n3.4372\n2.9131\n2.3036\n2.032\n0.27984\n-0.43272\n0.2217\n0.69002\n1.7896\n1.7075\n2.2029\n2.0649\n1.2393\n2.8439\n0.66525\n-0.46111\n4.6444\n5.6033\n5.853\n5.3167\n6.4188\n7.1283\n5.8574\n4.8063\n5.2244\n11.603\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.9372\n6.718\n7.9045\n5.2172\n1.0841\n2.243\n1.7967\n2.2584\n5.188\n5.4539\n1.6134\n-3.5211\n-3.8961\n-2.4429\n-0.92764\n0.10028\n1.9326\n1.8615\n-1.5231\n-2.662\n-0.32761\n-0.99743\n-0.18256\n-3.3486\n-6.186\n-4.3726\n-1.5163\n4.3107\n3.578\n3.3677\n1.4167\n-0.010632\n-0.51441\n-0.58899\n0.01439\n1.1236\n1.5994\n2.5219\n3.827\n2.212\n0.88653\n1.7081\n-0.079059\n1.4825\n4.8157\n5.1644\n5.8702\n6.4513\n6.3014\n5.9637\n5.3638\n4.3412\n2.4422\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.4388\n5.9061\n4.9613\n4.4425\n0.70292\n4.8727\n2.8527\n2.5614\n3.6591\n0.50977\n-1.7906\n-3.9651\n-3.7673\n-3.7441\n-2.6269\n-1.7058\n1.0916\n0.74701\n-2.5742\n-2.0758\n-2.2027\n-1.5375\n-1.6572\n-3.7216\n-4.1143\n-3.324\n-1.5804\n4.2801\n2.8424\n2.801\n1.1206\n0.68626\n0.39192\n0.97765\n1.1401\n1.8429\n2.3245\n2.2384\n2.8553\n2.285\n0.94276\n0.60259\n-0.48607\n2.3984\n4.81\n5.741\n5.1575\n6.5448\n6.4061\n6.2601\n6.2302\n5.0363\n0.61477\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.83003\nNaN\nNaN\nNaN\nNaN\n6.7962\n6.0936\n5.4668\n5.4519\n1.6392\n5.7472\n2.0845\n0.62224\n-1.9596\n-0.98031\n-2.4316\n-3.007\n2.5593\n-2.2547\n-1.2174\n-1.0207\n2.8556\n0.52872\n-2.8079\n-2.7855\n-3.4507\n-2.4045\n-3.7216\n-4.327\n-2.7622\n-1.3922\n-0.94799\n3.0456\n1.9622\n2.0727\n2.0423\n2.2458\n1.6561\n1.6997\n1.621\n1.8244\n1.7286\n1.4717\n2.2693\n2.5599\n0.94753\n1.8088\n1.0318\n1.494\n3.6686\n5.4406\n4.5841\n4.9499\n6.1641\n7.2622\n5.102\n3.1553\n0.26959\n7.1567\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7219\n1.9602\n3.3347\n3.6977\n5.1103\n4.9948\n6.6373\n6.3111\n5.8243\n5.4056\n4.0393\n3.0589\n3.2256\n0.34663\n-2.456\n-0.62585\n-1.4909\n-0.053722\n5.1286\n4.1552\n-0.41943\n-1.412\n1.4705\n0.22644\n-1.7307\n-4.3941\n-3.512\n-2.6211\n-2.5978\n-4.4932\n-1.8677\n-0.75679\n0.46994\n3.0584\n2.2597\n1.8135\n2.6934\n3.5519\n2.3053\n2.2393\n2.2919\n2.1733\n0.95156\n1.5335\n2.5739\n3.5331\n2.046\n2.1996\n1.8227\n2.4654\n4.3859\n4.4503\n7.2486\n7.3475\n6.1568\n5.3421\n2.7075\n0.34285\n1.4802\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.0943\n-3.1378\n-2.4517\n-1.3081\n3.1587\n6.1993\n6.4523\n5.821\n6.1432\n6.4396\n6.4678\n6.7903\n5.7586\n5.8506\n4.3455\n1.3731\n4.2631\n4.6813\n1.9124\n3.3469\n4.1218\n4.1918\n0.50308\n0.84183\n1.1297\n-1.3399\n-2.5458\n-1.7716\n-1.9183\n-2.0428\n-0.32813\n-0.46555\n-0.48972\n0.8407\n3.8509\n3.3646\n2.5891\n2.9049\n3.0187\n1.6972\n1.7391\n1.9411\n2.2576\n1.1347\n1.845\n2.9522\n3.3304\n3.139\n3.299\n3.7247\n4.8754\n5.8791\n6.2209\n7.0595\n8.9022\n6.1923\n4.2301\n0.69278\n-0.59196\n-0.82004\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4309\n-6.5136\n-5.885\n-6.5661\n-11.366\n-6.3359\n-3.1348\n0.12796\n6.7497\n7.8517\n6.9099\n4.3348\n5.4908\n4.8607\n4.9477\n5.8089\n5.18\n5.2842\n5.08\n5.5756\n4.6588\n3.8318\n3.7664\n4.2968\n5.9414\n0.93487\n-0.86938\n-0.14352\n0.31634\n1.1873\n0.76583\n-1.1342\n-0.78904\n1.4611\n1.4399\n1.1633\n0.38475\n4.5931\n2.9476\n2.7485\n2.3236\n2.2952\n2.0339\n2.3087\n1.5638\n1.7519\n2.0409\n2.5585\n4.0092\n5.5519\n4.6843\n4.7694\n4.0095\n5.668\n6.351\n5.6855\n6.6617\n8.2162\n7.29\n4.3365\n2.9152\n0.73857\n-3.6137\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-11.216\n-3.9582\n-0.85936\n2.7857\n6.556\n9.4883\n7.5121\n5.0313\n4.9273\n5.309\n6.9635\n6.6763\n5.4295\n4.309\n3.9785\n6.4765\n6.5748\n4.3682\n4.328\n5.6997\n6.3877\n2.3678\n-0.063348\n0.043374\n0.60878\n2.099\n1.6908\n2.1677\n0.061294\n1.7461\n3.2713\n2.2925\n0.27223\n2.9851\n2.916\n3.0594\n1.8901\n3.122\n2.6784\n2.8265\n2.3624\n2.2698\n3.0957\n4.0312\n4.5339\n5.4175\n4.6274\n5.4004\n3.4619\n4.8419\n8.4202\n9.1824\n4.4926\n5.1939\n7.4873\n5.6197\n3.6068\n1.9743\n3.767\n8.8988\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.7885\n-0.97649\n3.0442\n3.7048\n5.5495\n12.917\n8.681\n6.2953\n6.9725\n6.4936\n6.9125\n6.3444\n5.4509\n4.7356\n4.6654\n4.3384\n7.2164\n5.2591\n3.5505\n2.9509\n3.8149\n4.1452\n1.7929\n0.32804\n0.43326\n0.50486\n1.8121\n2.6203\n1.7391\n2.1673\n1.176\n0.69562\n-0.96518\n2.5649\n3.0185\n3.5236\n2.3915\n3.0357\n2.2122\n3.1225\n2.9146\n2.5489\n2.3887\n4.43\n4.8424\n4.3773\n4.383\n5.307\n3.1488\n3.8913\n5.9061\n7.8855\n7.0905\n4.69\n7.4633\n5.9856\n2.9376\n1.1761\n4.4562\n9.609\n15.197\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3458\n1.6082\n3.6514\n3.1126\n4.4476\n11.309\n7.2913\n6.5773\n8.506\n9.1953\n9.0799\n6.0334\n5.549\n5.161\n6.2909\n4.0657\n3.4198\n3.2937\n2.1664\n1.8093\n1.4751\n0.94777\n2.6279\n0.84212\n1.0858\n0.28254\n1.9809\n2.7628\n1.8002\n0.71378\n-1.3421\n-2.4174\n-2.2219\n2.8869\n3.8686\n3.7726\n3.1123\n1.7327\n1.266\n2.8662\n2.6287\n2.8287\n2.0176\n4.5124\n4.1321\n3.2282\n4.9901\n5.981\n4.6078\n3.5303\n6.252\n6.063\n9.5061\n7.0915\n7.016\n5.1724\n3.3596\n2.4309\n3.7671\n9.4867\n10.665\nNaN\nNaN\n6.0625\n8.4367\n1.2375\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1588\n2.3969\n2.9016\n2.48\n9.1361\n11.598\n7.9029\n9.1191\n9.6328\n11.557\n8.6726\n5.4194\n4.8928\n4.5359\n4.4216\n3.6682\n2.5161\n2.2175\n2.5852\n3.0678\n2.031\n1.5111\n3.0844\n1.8907\n2.2124\n2.4619\n4.6686\n5.0465\n1.6727\n0.29522\n-1.2006\n-3.5194\n-2.9286\n3.7144\n3.7662\n2.8928\n3.4589\n1.5821\n0.37834\n2.057\n2.2177\n2.582\n2.6767\n4.3137\n2.9311\n3.6253\n5.1101\n5.4747\n3.992\n4.3841\n7.4442\n8.2968\n7.3745\n6.8782\n6.1186\n5.4206\n3.6628\n8.0417\n10.91\n10.008\n6.6694\n7.3483\n5.084\n5.3216\n8.222\n4.7161\n1.0701\nNaN\nNaN\nNaN\nNaN\n9.1873\n4.0704\n5.713\n5.5142\n5.1321\n10.312\n13.679\n9.9193\n8.8634\n8.5995\n8.7765\n7.4815\n4.0084\n3.1324\n1.6371\n3.9011\n4.435\n3.851\n2.0895\n2.6587\n2.0949\n1.3437\n2.5352\n3.1817\n3.0301\n4.7663\n3.9572\n3.7124\n3.3897\n2.8416\n3.1316\n0.29505\n-2.0967\n-0.38269\n2.4739\n2.7891\n2.6041\n2.9007\n1.7738\n0.51617\n1.394\n1.9482\n2.4492\n2.8094\n3.4989\n3.1026\n4.4276\n4.7103\n5.2454\n5.5683\n8.629\n13.535\n8.9401\n7.0584\n7.1697\n4.5932\n4.2268\n4.0373\n-1.8899\n5.1328\n5.4447\n5.3891\nNaN\n9.0163\n8.3478\n12.727\n6.1844\n3.7843\nNaN\nNaN\nNaN\n10.176\n6.7077\n3.9781\n4.5307\n6.1824\n5.0563\n8.5724\n13.347\n9.6493\n4.7793\n4.6281\n5.6579\n5.0232\n2.8963\n1.2192\n1.0153\n3.5989\n2.6675\n2.5837\n2.2464\n1.999\n0.80885\n0.94397\n2.6078\n3.7095\n4.1308\n4.5202\n3.2878\n1.2542\n1.0161\n2.2452\n2.7913\n1.4886\n-0.17078\n1.9882\n2.4287\n2.4423\n1.9184\n1.8013\n1.0553\n0.44422\n1.1644\n1.9198\n2.6895\n3.0313\n3.0375\n3.2805\n3.988\n4.6225\n4.8401\n5.7899\n8.2834\n12.855\n7.7227\n7.1431\n5.3121\n6.3326\n2.6368\n4.2118\n3.2035\n4.3477\n5.8751\nNaN\nNaN\n9.9071\n9.7692\n10.283\n8.0438\n7.103\n8.0043\n10.17\n9.8188\n8.369\n7.4128\n3.6999\n4.3719\n8.0043\n8.0949\n9.1635\n10.624\n5.3446\n3.3318\n3.3674\n4.1039\n4.5487\n2.6725\n0.34828\n1.5498\n2.8525\n1.5323\n1.3089\n1.3969\n1.0446\n-0.22796\n0.45377\n1.4482\n3.6935\n4.3216\n3.497\n2.0144\n0.4957\n0.4389\n0.64063\n0.83265\n1.3509\n1.0719\n0.98371\n3.0697\n2.9994\n1.597\n0.94724\n0.37865\n0.29034\n0.46633\n1.8256\n3.0234\n2.8815\n2.6451\n2.01\n2.4985\n2.9527\n2.9857\n5.3617\n6.1835\n11.729\n3.9574\n5.6649\n6.2118\n4.5552\n2.0906\n3.971\n5.9029\n6.8333\n6.5129\nNaN\nNaN\nNaN\n10.813\n8.6621\n6.6215\n7.3573\n7.4363\n5.8304\n5.2493\n7.51\n7.2985\n4.3823\n3.4115\n5.662\n6.386\n5.3786\n6.5585\n5.8934\n5.2225\n5.3536\n3.8209\n2.4562\n1.5558\n0.57454\n1.3645\n2.7343\n1.5357\n0.5442\n0.23074\n-1.0024\n-1.0377\n-0.73267\n1.0689\n2.8672\n3.5134\n2.7468\n0.88005\n0.66784\n0.23371\n-0.10769\n1.1973\n2.053\n0.83237\n0.55426\n2.8055\n3.4071\n1.6402\n0.85146\n-0.041268\n0.23845\n1.5853\n1.3709\n2.126\n2.4484\n1.8548\n0.89767\n1.1441\n-0.46103\n-1.9552\n3.1177\nNaN\nNaN\nNaN\n4.8694\n8.135\n4.6654\n3.2313\n8.3351\n8.2704\n8.5909\n6.9924\nNaN\nNaN\nNaN\n8.9313\n7.3077\n7.5255\n7.1592\n6.4704\n5.5614\n5.8884\n6.533\n6.5328\n7.4539\n7.3544\n6.2441\n4.4708\n4.5381\n5.1478\n4.5092\n3.2185\n1.9098\n1.1066\n1.4281\n1.2016\n1.0724\n1.3843\n2.6646\n1.702\n0.091151\n-0.36421\n-0.42084\n-1.0296\n-0.98409\n0.89092\n1.647\n1.8775\n2.1023\n0.45314\n0.31832\n0.058998\n-0.5712\n0.75128\n1.2725\n2.0265\n0.035204\n1.1658\n1.0996\n0.94457\n1.144\n0.73778\n0.20442\n1.4625\n1.1425\n1.3437\n2.172\n1.1235\n0.46518\n-0.78641\n-3.2603\n-3.5874\n1.8956\nNaN\nNaN\nNaN\n5.6478\n8.369\n11.172\n9.5529\n8.7015\n6.2456\n7.116\n7.096\nNaN\nNaN\nNaN\n9.5542\n8.6026\n7.8946\n6.5934\n3.1927\n4.6654\n5.668\n6.2365\n6.4398\n8.0538\n6.8114\n6.8368\n5.8483\n4.4108\n3.9346\n3.9952\n2.2296\n1.7681\n1.559\n1.285\n0.86983\n1.0904\n0.70138\n0.98811\n0.77779\n0.50443\n0.96692\n-0.1413\n-1.0851\n-1.1162\n-0.008121\n0.29865\n0.14271\n-0.099315\n-0.92564\n-1.2545\n-1.8407\n-1.5959\n-0.2811\n0.28549\n0.65153\n-0.64915\n0.44716\n-0.07826\n-0.11802\n0.46936\n-0.20514\n-0.27661\n-0.69421\n0.52932\n0.75487\n1.8874\n0.13361\n-1.1971\n-1.566\n-1.4019\n-0.96695\n0.29923\n3.4213\nNaN\nNaN\n7.9503\n7.6217\n8.2809\n8.9968\n8.049\n5.6098\n6.4522\n6.8849\n6.6552\n8.3238\n8.466\n12.063\n11.579\n9.4315\n6.9532\n2.7934\n3.8981\n4.0524\n4.8422\n5.6449\n4.875\n5.31\n4.9593\n5.3323\n4.0414\n3.0862\n2.5799\n1.8783\n4.4759\n4.4615\n1.2514\n0.57872\n0.72321\n-1.2742\n0.32387\n1.1473\n0.7265\n1.2665\n-0.34623\n-1.1348\n-1.3986\n-1.059\n0.38412\n-0.45136\n-2.0328\n-2.6787\n-2.2045\n-1.4433\n-2.3062\n-0.15847\n0.44294\n-1.2839\n-1.8155\n1.2386\n-0.066336\n-0.43759\n-1.0303\n-1.5017\n-1.6192\n-1.4799\n-0.052743\n0.84763\n1.2413\n0.37338\n-0.831\n-1.1648\n-2.0858\n-1.7858\n-1.0182\n2.5642\n5.1853\n7.2017\n8.0006\n7.3224\n7.0386\n7.1839\n6.7351\n5.123\n6.1912\n6.5959\n6.8143\n7.0523\n7.8582\n9.1483\n9.7859\n9.3323\n6.8436\n4.1249\n4.0049\n4.2887\n2.8867\n4.5712\n4.9628\n4.9391\n2.579\n3.596\n3.2395\n1.9463\n0.80239\n2.6856\n6.6099\n8.651\n2.9396\n1.2155\n-2.0704\n-1.9821\n-0.079415\n1.6742\n0.1524\n-0.1521\n-0.52141\n-0.63353\n-0.45271\n-0.47456\n-0.14884\n-1.235\n-1.9485\n-2.6493\n-1.9106\n-2.7326\n-2.4578\n0.03898\n-0.60192\n-1.1684\n-1.2055\n1.4882\n-0.91071\n-2.3088\n-2.5944\n-3.1294\n-3.4846\n-2.4374\n-2.794\n-0.876\n0.1658\n0.23243\n-0.15544\n-0.21055\nNaN\nNaN\nNaN\nNaN\n3.9634\n7.503\n8.5631\n5.5028\n6.5525\n8.564\n6.9093\n4.2524\n5.2478\n5.6289\n4.9065\n5.9741\n7.492\n8.4457\n8.6192\n7.9342\n7.3186\n6.5947\n5.9659\n4.7411\n2.4965\n3.3755\n3.1256\n3.3779\n1.8357\n2.3236\n4.2846\n2.956\n0.24716\n0.17777\n5.3126\n4.7917\n4.7031\n2.6386\n1.8297\n0.28257\n1.8617\n1.8451\n0.19758\n0.98537\n0.81562\n-0.077524\n0.50093\n-0.38526\n-1.7405\n-2.069\n-1.1437\n-1.0776\n-2.4719\n-3.9191\n-2.6243\n-1.7544\n-1.3388\n-1.0232\n-2.2669\n0.39453\n-1.9901\n-3.5763\n-3.8626\n-4.0866\n-4.2898\n-3.2349\n-5.6678\n-4.844\n-3.2792\n-1.0891\n-0.40784\n-1.4564\nNaN\nNaN\nNaN\nNaN\nNaN\n6.8448\n7.7519\n6.6443\n6.0934\n7.9736\n6.6384\n4.5939\n4.5704\n5.4676\n4.9764\n6.2403\n7.1368\n7.7318\n7.5212\n5.5651\n6.3742\n6.8971\n5.4087\n3.59\n1.1465\n0.9765\n1.0496\n1.9153\n1.23\n1.6257\n6.2015\n4.5836\n2.4514\n-2.0109\n4.4423\n3.1709\n3.5159\n3.0574\n3.4029\n3.1314\n3.4474\n2.9205\n1.9082\n1.966\n1.495\n0.1198\n-0.056007\n-0.51823\n-1.6327\n-1.6012\n-1.3156\n-0.66479\n-2.7037\n-4.5705\n-3.6272\n-3.5964\n-1.6807\n-1.6288\n-2.7929\n-1.8793\n-2.0878\n-4.3474\n-5.3931\n-5.7528\n-7.8413\n-6.2496\n-8.1932\n-7.3209\n-4.9227\n-1.5843\n0.44854\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.7374\n7.6567\n7.3973\n7.5066\n7.3487\n6.799\n7.1354\n5.9687\n6.2967\n5.9543\n5.8576\n6.0261\n6.1622\n5.9806\n5.4888\n5.5537\n5.2716\n4.1315\n1.4038\n-1.1521\n0.55467\n0.9004\n1.0662\n0.89717\n5.8448\n6.7243\n5.6478\n0.10029\n2.3621\n2.8572\n3.5528\n3.2788\n3.2512\n3.7546\n4.5053\n3.3806\n1.4669\n3.5104\n2.7561\n0.037682\n0.96913\n0.10256\n-0.67473\n-2.1139\n-1.9638\n-0.92701\n-2.1998\n-4.3391\n-3.8603\n-3.9339\n-0.53508\n-1.424\nNaN\n-4.0268\n-2.426\n-5.8478\n-7.6531\n-8.5898\n-10.226\n-9.415\n-10.152\n-6.8369\n-5.187\n-2.9044\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n7.4323\n6.4842\n6.8902\n6.1115\n6.9278\n6.8318\n6.3023\n6.1803\n5.8642\n5.2138\n5.4977\n5.2232\n6.0515\n5.7524\n5.0583\n4.8603\n6.1608\n5.3248\n3.4112\n1.3466\n1.5531\n0.92436\n2.3614\n4.3339\n5.3697\n7.7653\n8.2033\n4.6368\n4.1921\n3.65\n4.9536\n3.5757\n1.985\n1.548\n2.4305\n1.6034\n1.313\n3.2246\n1.6736\n0.2074\n2.232\n0.02485\n1.2289\n0.27298\n-2.742\n-3.0651\n-4.2762\n-3.397\n-3.4988\n-3.8581\nNaN\nNaN\nNaN\n-1.6907\n-1.7918\n-1.8521\n-1.411\n-1.0249\n-0.75129\n-1.0961\n-2.0818\n-2.0393\n-1.6052\n-1.8788\n-1.9986\n-1.7786\n-1.8564\n-0.94753\n-0.22278\n-0.4306\n-0.92373\n-2.4673\n-2.5727\n-1.7162\n-1.7842\n-0.97035\n-0.39537\n-0.63285\n-0.70132\n-1.6911\n-1.8312\n-1.1912\n-1.2209\n-1.6121\n-1.7234\n-2.4162\n-2.7399\n-2.4991\n-1.7279\n-1.501\n-1.2148\n-1.1352\n-1.1789\n-0.62315\n-0.50814\n-1.0635\n-0.87584\n-0.83226\n-0.99836\n-0.7008\n-0.44011\n-0.76995\n-0.73089\n-0.45134\n-0.7095\n-0.90611\n-0.77029\n-0.78109\n-1.1328\n-0.75902\n-0.83638\n-1.3904\n-1.6281\n-0.59006\n0.006073\n0.099041\n-0.8925\n-1.1959\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.5268\n-2.6024\n-2.4553\n-0.93481\n-0.39405\n-2.3947\n-0.9672\n-2.1254\n-2.0195\n-1.6297\n-1.9125\n-2.4968\n-1.8703\n-0.45245\n-0.19007\n-0.93579\n-3.1924\n-3.0621\n-2.2363\n-2.338\n-2.0377\n-1.8328\n-0.88334\n0.050579\n0.024345\n0.22596\n-0.80008\n-0.4292\n-0.5371\n-0.81424\n-1.564\n-1.3875\n-2.3624\n-2.3962\n-2.217\n-2.0924\n-1.9392\n-1.5926\n-1.3513\n-0.94323\n-0.58001\n-0.6344\n-0.99569\n-0.94471\n-0.85023\n-0.52591\n-0.30055\n-0.31457\n-0.54161\n-0.68702\n-0.56715\n-0.73968\n-0.84275\n-0.7362\n-0.24027\n-1.5832\n-1.5548\n-1.7345\n-1.1444\n-0.50949\n-0.67837\n-0.33792\n0.28418\n-0.8906\n-0.90321\n-1.6203\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6101\n-2.257\n-2.0743\n-2.6979\n-5.3819\n-4.2912\n-6.0241\n-2.1058\n-1.7688\n-0.73673\n-1.1412\n-2.558\n-2.2166\n-0.65875\n-1.1652\n-1.7571\n-2.4224\n-2.1647\n-1.7204\n-1.5269\n-1.2243\n-0.98539\n-0.17033\n0.64332\n0.80352\n0.76929\n0.66864\n0.23747\n0.044758\n-0.87263\n0.18842\n-1.0923\n-2.2434\n-2.7294\n-2.3691\n-2.5729\n-2.2323\n-1.886\n-1.2462\n-0.65557\n-0.54974\n-0.62034\n-0.86263\n-0.55149\n-0.76831\n-0.45909\n-0.26104\n-0.3938\n-0.83789\n-0.70443\n-0.52296\n-0.46009\n-0.84396\n-0.6829\n-0.87457\n-0.87136\n-0.81378\n-1.9821\n-1.5098\n-0.83835\n-1.0826\n-0.45714\n0.014809\n-1.2843\n-0.68156\n-1.1168\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4508\n-2.3437\n-1.9294\n-1.893\n-1.7322\n-1.6729\n-1.8369\n-1.941\n-2.3458\n-1.9613\n-1.1041\n-3.4006\n-3.2727\n-1.5038\n-2.2218\n-2.8029\n-2.1822\n-2.16\n-1.9073\n-1.5702\n-0.34398\n-0.59975\n0.14687\n0.63514\n0.77111\n1.037\n1.1401\n0.24705\n0.038769\n0.55695\n-0.66928\n-1.1149\n-1.9235\n-2.4264\n-2.3786\n-2.4298\n-2.4564\n-1.6986\n-1.0891\n-0.72183\n-0.76921\n-0.71654\n-1.0245\n-1.3221\n-1.2581\n-0.78023\n-0.86743\n-0.71493\n-0.69439\n0.0275\n0.028156\n-0.32028\n-0.56131\n-0.57613\n-0.2843\n-0.22691\n-0.16135\n-1.1243\n-1.9473\n-2.8079\n-1.966\n-0.86081\n0.48564\n-0.27548\n-0.45647\n-1.3357\n-1.1074\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4926\n-2.2151\n-1.7159\n-1.9273\n-1.7739\n-1.6236\n-1.7252\n-1.6482\n-1.7851\n-2.2708\n-2.513\n-3.0559\n-3.2925\n-2.9463\n-3.7974\n-3.4294\n-2.7111\n-2.8788\n-2.8447\n-0.68778\n1.1455\n0.13817\n1.138\n-0.59535\n0.61719\n1.505\n1.5462\n0.85749\n0.3595\n0.305\n-1.5918\n-1.9567\n-1.9259\n-1.7941\n-2.2266\n-1.4732\n-1.8276\n-1.3915\n-0.97188\n-0.7412\n-0.60077\n-0.42514\n-0.88282\n-1.2068\n-1.3242\n-1.1708\n-0.77718\n-0.78186\n-0.81721\n-0.81606\n-0.28244\n-0.46187\n-0.87536\n-0.42554\n-0.73274\n-0.44561\n0.42761\n-0.80494\n-2.0019\n-3.7578\n-2.4844\n-0.57565\n0.98033\n-1.3259\n-2.0527\n-1.7243\n-1.1821\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7568\n-2.1107\n-1.9141\n-1.9303\n-1.6069\n-1.6962\n-1.3053\n-1.3003\n-1.5254\n-1.7478\n-1.8241\n-1.5052\n-2.047\n-2.9518\n-3.1398\n-2.9434\n-3.5745\n-3.5558\n0.96231\n-1.1488\n-0.17753\n1.72\n-0.35154\n0.22441\n1.4773\n1.9851\n1.7566\n1.1797\n0.96786\n0.58096\n-0.64217\n-1.7911\n-1.6115\n-1.5716\n-1.8287\n-1.7401\n-1.781\n-0.9267\n-0.57738\n-0.68381\n-0.61793\n-0.61611\n-0.89215\n-1.1177\n-1.9038\n-1.5662\n-0.79539\n-0.52639\n-0.90994\n-0.68806\n-0.63621\n-0.82245\n-0.94672\n-1.0662\n-1.0012\n-2.3723\n-3.069\n-0.0050354\n-1.8771\n-6.9376\n-3.7881\n-0.50823\n-1.0342\n-2.7239\n-1.7667\n-1.0466\n-0.58749\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8768\n-2.0208\n-1.8338\n-1.7349\n-1.4603\n-1.5835\n-1.1427\n-0.92635\n-1.058\n-0.65195\n-0.91504\n-1.2942\n-1.347\n-2.6403\n-2.5767\n-2.823\n-3.0678\n-1.9977\n-1.7733\n-1.1729\n-0.13645\n0.41165\n0.19863\n0.88829\n1.1128\n1.8152\n2.3593\n2.1302\n1.8221\n1.243\n-0.50523\n-1.175\n-1.3043\n-2.0516\n-1.4557\n-1.1602\n-1.4937\n-1.2828\n-0.82946\n-0.80883\n-0.8964\n-0.95188\n-1.319\n-1.6693\n-2.4703\n-1.7489\n-0.9203\n-0.45913\n-0.41172\n-0.11435\n-0.37134\n-0.68532\n-0.94104\n-0.91408\n-0.9825\n-2.2697\n-2.6285\n-1.6674\n-1.9708\n-2.3281\n-2.6923\n-2.105\n-2.3163\n-2.8242\n-2.33\n-1.4616\n-0.635\n0.41945\n2.5951\nNaN\nNaN\nNaN\n-1.7083\n-1.3543\n-1.5172\n-1.2557\n-1.1966\n-1.3328\n-1.0152\n-0.64176\n-0.48521\n-0.29141\n-0.28811\n-0.64524\n-0.7378\n-0.93966\n-0.75123\n-0.6606\n-0.83361\n-0.47762\n-0.60547\n-0.62607\n-0.38105\n-0.079662\n0.4236\n1.3492\n1.9918\n2.0671\n2.1398\n2.0764\n2.1753\n1.8537\n1.0672\n-0.40976\n-0.87573\n-1.1382\n-0.25624\n-0.10933\n-0.76574\n-1.6022\n-1.7683\n-1.8458\n-1.383\n-1.2665\n-1.8626\n-2.2573\n-2.7696\n-1.7487\n-1.0365\n-0.86271\n-0.90819\n-0.084301\n-0.75444\n-0.86542\n-0.88837\n-1.2037\n-0.89467\n-1.4002\n-2.0716\n-1.4305\n-0.93481\n-1.4907\n-2.2616\n-2.2501\n-1.6571\nNaN\n-2.3872\n-1.7923\n-1.2957\n0.19199\n1.9736\n0.079494\nNaN\nNaN\n-1.5908\n-1.3699\n-1.1619\n-1.2905\n-1.0481\n-1.3046\n-0.9093\n-0.39896\n-0.2377\n-0.22025\n-0.19522\n-0.30882\n-0.15005\n0.061039\n0.26881\n0.28933\n0.21213\n0.25249\n0.3485\n0.35025\n0.0033684\n0.16279\n1.0354\n1.6146\n1.9115\n2.1909\n2.1683\n1.8878\n1.7602\n1.3741\n1.1374\n1.2222\n0.85984\n0.060486\n0.13961\n-0.036453\n-1.1346\n-1.6961\n-1.8495\n-1.4134\n-1.219\n-1.1808\n-2.0269\n-2.7446\n-2.7989\n-1.1993\n-0.9604\n-1.2641\n-0.61503\n-0.43252\n-0.74086\n-0.41053\n-0.68472\n-0.97976\n-0.54077\n-1.2775\n-2.1866\n-1.7564\n-1.5603\n-1.1597\n-1.5182\n-1.034\n-0.81945\nNaN\n-1.9713\n-1.4499\n-1.8363\n-1.0421\n0.25956\n-0.41653\n0.078358\n-0.32285\n-1.9917\n-1.3108\n-1.1615\n-1.4276\n-0.91168\n-1.671\n-1.2817\n-0.90106\n-0.16402\n0.38302\n0.25534\n0.13985\n0.25093\n0.50341\n0.87494\n0.75833\n0.79799\n0.85788\n0.69667\n0.80034\n0.61415\n1.3041\n2.0786\n1.8265\n1.1988\n1.7424\n2.5475\n2.7009\n1.7055\n1.3035\n0.99005\n0.90043\n0.038258\n0.25465\n0.49994\n-0.2899\n-1.2852\n-1.4072\n-1.9081\n-1.5156\n-1.1393\n-1.2167\n-2.4158\n-2.7923\n-1.7632\n-1.1088\n-0.94827\n-1.073\n-0.55396\n-0.41426\n-0.5522\n-0.37397\n-0.90835\n-0.83029\n-0.57318\n-1.0123\n-2.1646\n-3.3814\n-2.2859\n-0.35901\n0.63649\n0.0094948\n-0.50705\nNaN\nNaN\nNaN\n-1.8068\n-1.7641\n-0.592\n-0.82995\n-0.37654\n0.58179\n-2.2278\n-1.7385\n-1.2345\n-1.3637\n-0.88783\n-1.6434\n-1.1289\n-0.67114\n-0.0086098\n0.53217\n0.77317\n0.64309\n0.60736\n0.93221\n1.3812\n1.4212\n1.2521\n1.273\n1.1744\n1.2973\n1.6999\n1.933\n2.0818\n1.8076\n1.4131\n2.2039\n3.0122\n3.2562\n2.077\n2.13\n1.4618\n1.1497\n0.19294\n1.1137\n0.58761\n-0.50381\n-1.4975\n-0.95808\n-2.0462\n-1.6809\n-1.4841\n-1.9327\n-2.7012\n-2.6476\n-1.8726\n-1.3666\n-0.84607\n-0.65169\n-0.2332\n-0.29096\n-0.25572\n-0.10271\n-0.10376\n0.047543\n-0.27694\n-0.85742\n-1.9616\n-2.2147\n-1.5159\n0.0020256\n0.79525\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.7156\nNaN\n-2.1235\n-1.262\n0.026245\n1.6085\n-2.9278\n-2.2256\n-1.8279\n-1.3491\n-1.3703\n-1.1972\n-0.81839\n-0.53876\n0.25438\n0.68399\n0.8567\n0.86322\n0.74763\n0.92096\n1.1468\n1.2596\n1.268\n1.2724\n1.6711\n1.2599\n1.1784\n1.7968\n3.0471\n2.5007\n2.0177\n2.4166\n3.1617\n3.5922\n3.0654\n2.6877\n1.3329\n0.7542\n-3.1585\n-0.25071\n-0.13921\n-0.60684\n-1.9616\n-2.439\n-2.8668\n-1.9199\n-1.5168\n-2.3137\n-2.3237\n-2.1832\n-1.4813\n-1.0115\n-0.77266\n-0.289\n0.39809\n0.2447\n0.052818\n0.26802\n0.2307\n-0.8105\n-0.98122\n-0.902\n-2.0147\n-2.0009\n-1.3719\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1593\n-2.0081\n-1.791\n-1.712\n-1.5968\n-1.1413\n-0.52365\n0.066273\n0.043125\n0.2618\n0.97909\n1.1604\n1.1954\n0.86268\n1.0286\n1.3013\n1.3865\n1.3403\n1.579\n1.3015\n1.4717\n2.162\n2.1929\n1.5354\n1.7265\n2.0104\n3.1881\n3.1843\n2.3703\n1.6544\n1.1912\n0.83845\n-3.9536\n-3.2521\n-0.53827\n-0.80606\n-2.1635\n-2.3999\n-2.9587\n-2.5953\n-2.5024\n-4.1298\n-3.9548\n-1.9998\n-1.1102\n-0.8928\n-0.56548\n0.35223\n0.58359\n0.41856\n0.37249\n0.51237\n0.12642\n-0.71734\n-1.8013\n-2.3777\n-2.2689\n-1.9089\n-1.441\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1629\n-1.9844\n-1.8055\n-1.4925\n-1.0584\n-0.77824\n-0.29499\n-0.11903\n0.26656\n0.60556\n0.98932\n1.4186\n1.3114\n1.2663\n1.1517\n1.5718\n1.9288\n1.2924\n1.3065\n1.4491\n1.4822\n1.751\n0.554\n0.65933\n0.84427\n1.8655\n3.0688\n2.9164\n2.5151\n2.453\n2.1245\n1.5201\n0.95754\n0.20345\n-0.2398\n0.030796\n-2.7413\n-2.8091\n-2.5069\n-1.8325\n-2.5825\n-3.3271\n-2.754\n-1.2653\n-0.45861\n-0.65883\n-0.23596\n0.51722\n0.49131\n0.47505\n0.39361\n-0.037434\n-0.21895\n-0.72862\n-1.7083\n-2.6984\n-1.5694\n-1.712\n-1.2848\n-0.13261\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9291\n-2.1014\n-1.5094\n-1.0057\n-0.62685\n-0.65541\n-0.29691\n-0.017643\n0.3383\n0.35463\n1.1956\n1.5676\n1.3704\n1.4437\n1.5465\n1.7751\n1.7616\n1.7802\n1.9182\n1.5491\n1.3755\n0.61446\n-0.40487\n0.0051079\n0.54553\n2.0032\n2.3572\n2.4992\n2.9343\n3.0666\n3.9002\n1.7926\n0.34302\n-0.071785\n1.7141\n2.1461\n-2.038\n-1.7368\n-1.6774\n-1.586\n-1.6188\n-2.3091\n-1.9765\n-0.036091\n0.69023\n0.79187\n0.14819\n0.49311\n0.71017\n0.31569\n0.24994\n0.50677\n0.49527\n-0.66078\n-1.4228\n-2.0049\n-2.0268\n-1.7491\n-0.52445\n-0.37838\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7092\n-2.0068\n-1.5327\n-0.63685\n-0.54522\n-0.44956\n-0.10973\n0.06115\n0.47102\n0.64228\n1.6069\n1.6362\n1.6673\n1.7061\n1.6071\n1.8024\n1.8968\n2.2886\n2.4757\n1.6769\n0.85044\n-0.064293\n-0.5644\n-1.4868\n0.07851\n1.5592\n2.4386\n3.8867\n3.5717\n2.5476\n2.3938\n0.42762\n1.6495\n0.26514\n0.3331\n0.069279\n-0.87178\n-0.88833\n-0.71936\nNaN\n-1.4959\n-1.9184\n-1.6391\n0.71063\n1.4091\n0.90195\n0.43611\n0.50064\n0.67952\n0.22349\n0.32676\n0.58458\n-0.25409\n-1.3882\n-1.3829\n-1.893\n-2.5588\n-2.176\n-2.2606\n-1.0501\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3665\n-1.5948\n-1.0895\n-0.292\n-0.48187\n-0.31107\n-0.074844\n0.14133\n0.59384\n1.1018\n1.2691\n1.5534\n1.8881\n1.6899\n1.5252\n1.7503\n2.0555\n2.6307\n2.2477\n1.6941\n0.56354\n-0.75141\n-1.088\n-1.5296\n0.1904\n1.8375\n2.7392\n2.7529\n2.3249\n1.4407\n0.55187\n0.19833\n2.5372\n1.0268\n0.44143\n0.21722\n-0.097988\n0.58805\nNaN\nNaN\nNaN\n-2.3039\n-0.18551\n0.94252\n0.84834\n0.83754\n0.65869\n0.65992\n0.55528\n0.35161\n0.68201\n1.5908\n-0.65493\n-1.4169\n-1.1566\n-1.2516\n-2.4034\n-2.6547\n-2.9632\n-2.5182\n-2.9219\n-2.1642\n-1.5871\n0.80981\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.4745\n-1.2258\n-0.6597\n-0.10357\n0.066254\n-0.03949\n0.047279\n0.43451\n0.83944\n1.1858\n1.2377\n1.8543\n1.8709\n1.6872\n1.5267\n1.9863\n1.9944\n1.8067\n1.9541\n1.2408\n0.28474\n-1.1387\n-0.90123\n-0.55627\n0.22556\n2.2118\n2.7371\n1.8094\n1.6788\n1.3024\n0.9113\n0.21616\n0.75723\n0.81208\n1.5435\n1.1646\n1.5329\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1107\n1.3359\n1.2791\n0.71701\n1.0099\n1.057\n0.77526\n1.1447\n0.53701\n0.1733\n-0.063797\n-1.2955\n-0.96193\n-0.41467\n-0.82398\n-1.5962\n-1.3876\n-1.629\n-1.9776\n-1.2037\n-1.3483\n-1.8998\n-1.5812\n-1.0904\n-0.92693\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1542\n-0.5497\n-0.2317\n-0.14554\n-0.23889\n-0.21483\n0.14257\n0.68365\n0.76012\n0.97199\n1.0954\n1.6983\n1.8119\n1.5617\n1.5485\n1.6785\n1.3695\n1.0545\n1.0581\n0.52935\n-0.5055\n-1.0129\n-1.2844\n-0.080143\n1.0324\n1.9392\n1.7418\n1.7053\n2.2254\n1.8964\n1.0767\n0.55841\n0.48061\n0.79949\n1.4864\n1.6917\n1.7056\nNaN\nNaN\nNaN\nNaN\nNaN\n0.038132\n1.4781\n1.3124\n0.035015\n0.28936\n1.2944\n1.0805\n0.43528\n-1.066\n0.017509\n-0.033741\n-1.6549\n-0.037823\n-0.20741\n-0.70523\n-0.53895\n-0.46912\n-0.32798\n-0.8092\n-0.23553\n-0.13869\n-0.64407\n-0.58301\n-0.47901\n-0.45406\n-0.37078\n-0.57534\nNaN\nNaN\nNaN\n-0.69511\n-0.38045\n-0.23216\n-0.22568\n-0.52108\n0.28061\n0.92286\n0.63681\n0.40755\n0.62762\n1.1423\n1.6212\n1.3458\n1.1339\n1.1331\n1.1034\n0.57256\n-0.1319\n-0.33989\n0.11464\n0.086475\n-0.25783\n-0.59417\n0.10072\n1.6585\n2.21\n1.7862\n2.0977\n2.229\n1.1005\n0.3658\n0.81797\n-0.47306\n0.52183\n1.2562\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.6029\n1.3657\n0.92581\n0.24831\n0.406\n1.2003\n1.8905\n-0.61083\n-1.9781\n-0.73005\n-0.18803\n-1.3627\n-1.3701\n-1.2098\n-0.79789\n-0.2513\n0.21051\n-0.056004\n-0.63447\n-0.32016\n-0.70386\n-0.81916\n-0.43575\n-0.34145\n-0.2415\n-0.46459\n-0.29936\nNaN\n-0.19925\nNaN\n-0.83033\n-0.38228\n-0.34074\n-0.19269\n0.22213\n0.33249\n-0.3075\n-0.38472\n0.371\n0.9321\n0.73675\n0.82006\n0.76957\n1.0141\n1.2824\n0.90764\n0.58023\n0.51714\n-1.3382\n-0.88552\n0.50065\n-1.0216\n-0.69912\n1.0195\n1.8331\n2.4608\n2.7084\n2.2569\n1.9727\n1.6589\n1.9388\n2.3012\n2.1369\n2.093\n1.6691\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2892\n1.2786\n1.3981\n0.71406\n1.0471\n1.0634\n2.0204\n3.7458\n-2.0949\n-1.5249\n-0.92568\n-0.65636\n-0.46457\n-0.48715\n-0.48668\n-0.1308\n0.1463\n-0.257\n-0.30817\n0.4106\n-0.55391\n-0.46697\n-0.058643\n0.23696\n-0.56324\n-0.26025\n-0.1012\n-0.53129\n-0.77186\n-1.801\n-0.18874\n0.21835\n-0.47046\n-0.25129\n-0.076771\n0.0015259\n-0.3711\n0.10911\n0.30474\n0.44289\n0.38939\n0.6587\n1.0379\n1.2103\n0.86689\n1.1139\n0.73234\n0.37212\n-1.6404\n-0.91802\n0.10303\n-0.12076\n-0.94779\n1.2016\n2.1802\n3.509\n2.8307\n2.506\n2.1192\n1.8295\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2117\n1.5111\n-0.25315\n-0.28399\n0.31844\n0.88785\n3.1075\n2.2488\n-0.68467\n-0.53926\n-0.89184\n-0.72162\n0.27209\n0.33084\n-0.055489\n-0.26765\n-0.086826\n0.36054\n0.31474\n0.62126\n-0.22062\n-0.11667\n0.076283\n-0.22618\n-0.45913\n-0.41679\n-0.46297\n-0.8133\n-0.79663\n-1.2762\n-0.103\n-0.061092\n-0.45555\n-0.51786\n-0.47893\n-0.2524\n-0.45188\n-0.17363\n0.033768\n0.088432\n0.4456\n0.7164\n0.86176\n0.9355\n1.1395\n1.2184\n0.82038\n0.070721\n-1.1144\n-0.32877\n-0.20235\n-0.88213\n-0.43583\n0.95552\n2.4546\n2.7696\n1.9957\n2.2102\n2.7864\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.6072\n-3.1451\n-2.2476\n-0.41848\n1.6996\n0.14579\n0.079098\n-0.28031\n-0.48938\n0.060787\n0.47458\n0.5029\n0.23032\n0.47649\n0.33186\n0.085087\n0.53962\n0.61931\n0.52742\n0.035851\n0.30833\n0.15477\n-0.15413\n-0.40377\n-0.24449\n-0.10565\n-0.66826\n-0.92603\n-0.61795\n-0.23311\n-0.69062\n-0.68611\n-0.35741\n-0.02293\n-0.24202\n0.039959\n0.33663\n0.47158\n0.51786\n0.76994\n1.1227\n0.96994\n1.0908\n1.1364\n0.87243\n0.57813\n-0.25527\n-0.66938\n-0.44196\n-1.0483\n0.25532\n1.2042\n1.9226\n2.2333\n1.4614\n0.81522\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.2887\n-1.4772\n-0.58653\n-0.06287\n0.29926\n0.229\n0.24799\n0.33124\n0.49297\n0.76475\n1.193\n0.77391\n0.73113\n0.39888\n0.11518\n0.37766\n0.27855\n0.49874\n-0.071083\n0.31061\n-0.0028038\n-0.095119\n-0.014545\n0.059757\n-0.11512\n-0.61229\n-1.1578\n-0.9392\n-0.61345\n-0.76614\n-0.46536\n-0.25037\n0.1751\n0.15188\n0.57996\n0.54263\n0.53938\n0.46146\n0.65805\n1.044\n0.84545\n0.83695\n0.89573\n1.9086\n1.2331\n0.47488\n0.031986\n0.031425\n-0.044388\n0.45118\n1.2101\n1.901\n2.4088\n0.88709\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.62458\n-0.93639\n-0.57588\n0.13468\n0.95894\n0.68224\n1.1518\n1.484\n0.92228\n1.204\n1.6493\n1.6269\n1.5525\n0.8568\n0.79406\n0.35133\n-0.053455\n0.00096512\n0.20405\n0.43718\n0.10598\n0.2676\n0.19089\n-0.10356\n-0.11119\n-0.48566\n-1.1929\n-1.1304\n-0.77974\n-0.56542\n-0.27742\n-0.1119\n0.055882\n0.19606\n0.44214\n0.20402\n0.39294\n0.62873\n0.97272\n0.95229\n0.86303\n0.7008\n0.82328\n2.363\n1.3913\n0.72074\n0.41944\n0.81454\n0.44571\n0.15085\n0.75294\n2.2821\n3.0284\n1.8623\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.71139\n-0.68128\n-0.75303\n0.12352\n1.0491\n0.70366\n1.5769\n1.8903\n1.3791\n1.4706\n1.6726\n1.6334\n1.7637\n1.6769\n1.5787\n1.1597\n0.33107\n-0.0032387\n0.47051\n0.57522\n0.22609\n0.38556\n0.31778\n0.2832\n0.29816\n-0.057766\n-0.97362\n-1.1407\n-1.0686\n-0.72718\n-0.26782\n-0.19182\n0.14946\n0.23613\n0.25432\n0.3232\n0.51907\n0.60163\n0.75528\n0.9122\n0.93959\n1.1213\n1.0854\n1.2874\n0.90163\n0.92647\n0.1784\n0.62661\n0.62442\n1.0796\n1.3127\n2.2038\n3.4008\n2.3299\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19662\n-0.036442\n-0.30063\n-0.30206\n-0.032169\n0.33011\n-3.3074\n0.057201\n1.7556\n1.7763\n2.5927\n2.7991\n1.9684\n1.9226\n2.0879\n2.0244\n1.3306\n0.81833\n0.65546\n0.98987\n0.98351\n0.79031\n0.055195\n0.25729\n0.4302\n0.35094\n0.29107\n-0.51273\n-1.4192\n-1.1628\n-0.74792\n-0.50409\n-0.2805\n-0.046528\n-0.18233\n0.052799\n0.42155\n0.39655\n0.49207\n0.71591\n0.95062\n0.70801\n1.0673\n1.0406\n0.40549\n0.47774\n0.79451\n0.44654\n1.164\n0.75686\n0.78843\n0.96895\n2.2207\n4.2128\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.76511\n0.52044\nNaN\n-0.24972\n0.29533\n0.076191\n-0.11829\n0.18105\n-0.40732\n-0.67943\n-4.3516\n-0.057335\n1.5158\n2.7488\n2.6128\n2.3783\n1.3352\n0.16951\n2.0562\n1.0741\n1.1421\n0.92889\n0.91517\n1.3187\n1.4514\n1.3243\n0.35501\n0.31836\n0.56117\n0.090405\n0.10965\n0.092937\n-1.4389\n-1.209\n-1.1756\n-0.83541\n-0.51457\n-0.52227\n-0.55831\n-0.095402\n-0.098621\n0.17612\n0.39506\n0.49158\n0.62767\n0.67433\n0.78734\n0.8096\n0.53033\n0.38566\n0.19099\n0.8981\n1.6355\n0.84338\n-0.092571\n0.90134\n2.6842\n4.4411\n2.7724\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1073\n1.4463\n1.1298\n1.007\n0.56345\n-0.22152\n0.17139\n0.1815\n0.22534\n-0.10711\n-0.40736\n-0.23429\n-0.16692\n-0.16282\n1.2589\n2.3315\n0.49694\n0.9591\n0.77799\n0.72792\n0.76899\n1.3679\n1.9603\n0.92159\n0.75602\n1.3922\n2.1374\n1.6816\n0.40246\n0.021744\n0.59774\n0.2394\n0.054646\n-0.18729\n-1.5937\n-1.5111\n-1.2787\n-1.0716\n-0.70876\n-0.74429\n-0.5558\n-0.37437\n-0.57281\n0.052769\n0.023411\n-0.14968\n0.19091\n0.6572\n0.55403\n0.32708\n0.38509\n0.21062\n0.15313\n-0.49555\n0.1262\n0.97242\n0.99601\n1.8554\n2.7774\n2.8668\n1.446\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7293\n1.3179\n1.1812\n1.337\n0.67931\n-0.25911\n0.018177\n0.30561\n0.54712\n-0.52374\n-0.70562\n-0.81258\n-0.83331\n-1.0687\n-0.24055\n0.88599\n0.051338\n0.012867\n0.69139\n0.79377\n0.18039\n0.12769\n1.2546\n0.86567\n0.45013\n0.78144\n1.0511\n0.36721\n-0.10235\n-0.014606\n0.064632\n0.39609\n-0.24328\n-0.61663\n-1.7315\n-1.7443\n-1.4618\n-1.0859\n-0.88145\n-0.73401\n-0.40528\n-0.35668\n-0.45485\n-0.23131\n-0.37592\n-0.41269\n0.23114\n0.48922\n0.36872\n0.1996\n-0.20166\n-0.21207\n-0.25039\n-0.38195\n-0.40284\n0.53798\n1.1995\n1.849\n2.8861\n2.8551\n1.9907\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.3299\n1.4482\n1.753\n1.4563\n1.2945\n1.572\n0.73426\n0.92373\n0.71471\n-0.22196\n0.18442\n0.66588\n0.27299\n-0.13848\n-0.11657\n-0.57436\n-0.95152\n-0.69616\n-0.88606\n-0.65786\n-0.47391\n-0.14307\n0.10472\n0.28659\n-0.68743\n-0.42468\n0.75917\n0.78405\n0.71176\n0.071056\n-0.2932\n-0.55548\n-0.32327\n-0.18572\n-0.48179\n-0.039768\n-0.94002\n-0.95446\n-1.8554\n-1.7356\n-1.5332\n-1.0984\n-1.0128\n-0.85258\n-0.72773\n-0.65898\n-0.4582\n-0.37381\n-0.56131\n-0.50553\n-0.27273\n0.004303\n-0.00060272\n-0.31136\n-0.49201\n-0.60447\n-0.90685\n-0.74608\n-1.0118\n-1.2301\n0.40139\n1.1028\n2.4555\n4.1727\n2.3013\n0.71475\nNaN\nNaN\nNaN\nNaN\nNaN\n1.2557\n2.7116\nNaN\nNaN\nNaN\nNaN\n1.5985\n0.21972\n0.90578\n1.1202\n-0.11805\n0.32212\n0.2072\n-0.045361\n0.049732\n-0.083389\n-0.49851\n-0.66697\n-0.75623\n-0.67276\n-0.53003\n-0.82276\n-0.85468\n-0.11791\n0.10411\n-1.0243\n-0.78082\n-0.030025\n0.58963\n0.52497\n0.23592\n-0.32828\n-0.52105\n-0.51103\n-0.51374\n-0.86798\n-1.4633\n-1.3367\n-0.83681\n-1.4537\n-1.5483\n-1.458\n-1.1207\n-1.0195\n-0.953\n-0.90564\n-0.84244\n-0.66232\n-0.53517\n-0.69553\n-0.69564\n-0.47799\n-0.32307\n-0.41026\n-0.42196\n-0.42057\n-0.97267\n-2.4695\n-1.5289\n-0.42157\n-0.89375\n-0.24052\n1.5704\n2.2682\n4.8618\n2.0203\n-0.28756\n3.6598\nNaN\nNaN\n1.0166\n1.4648\n3.0158\n3.3802\nNaN\nNaN\n1.764\n1.7696\n1.3303\n0.88903\n0.87931\n0.54039\n0.29626\n-0.34024\n-0.64685\n-0.24728\n-0.36369\n-0.48732\n-0.86863\n-1.0066\n-0.78007\n-0.67352\n-0.55159\n-0.7047\n-1.5433\n-0.67144\n-0.36062\n-0.36641\n-0.50682\n-1.2684\n-0.19124\n0.39134\n0.3729\n-0.057964\n-0.34549\n-0.70211\n-1.9254\n-1.4236\n-1.1812\n-1.1256\n-0.58847\n-1.4851\n-1.438\n-1.3268\n-1.1807\n-1.0526\n-0.95603\n-1.0154\n-1.0435\n-0.67347\n-0.57906\n-0.62057\n-0.67\n-0.55449\n-0.63705\n-0.6713\n-0.5266\n-0.48315\n-0.5569\n-1.3521\n-2.2143\n-0.52608\n-0.78323\n-0.20176\n1.8257\n2.0808\n0.20793\n-0.80832\n-0.46672\n0.31114\n-0.97855\n-1.1933\n-0.32539\n0.97485\nNaN\nNaN\nNaN\nNaN\n-1.8459\n0.50087\n0.83113\n0.45528\n0.47678\n0.73278\n0.41599\n-0.54847\n-0.28714\n-0.011112\n-0.9176\n-0.8584\n-0.99305\n-0.87085\n-0.75368\n-0.73109\n-0.82692\n-0.67415\n-0.41336\n-0.18629\n0.1474\n0.16061\n0.19783\n-0.26484\n-0.046528\n-0.4887\n-0.51954\n-0.40348\n-0.6628\n-0.82202\n-1.5121\n-0.61051\n-0.081997\n0.23625\n-0.38784\n-1.5182\n-1.4394\n-1.3066\n-1.1411\n-1.0489\n-0.77736\n-0.93856\n-0.80334\n-0.71147\n-0.48655\n-0.81547\n-0.53998\n-0.44879\n-0.96991\n-1.106\n-0.64295\n-0.62136\n-0.86644\n-0.62463\n-2.7514\n-4.7407\n-2.3706\n-0.44577\n0.54614\n0.40681\n-0.31228\n-1.1369\n-0.30632\n-0.29168\n-0.79575\n-0.49796\n-0.6232\n-0.2408\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26856\n0.52883\n0.90069\n0.45058\n0.70965\n-1.2642\n-2.5486\n-0.33818\n-0.31468\n-1.1278\n-1.0868\n-1.0445\n-1.0336\n-0.78075\n-0.8956\n-0.78477\n-0.37039\n-0.32635\n-0.24074\n-0.02422\n-0.13663\n-0.061855\n-0.0045509\n-0.05167\n-0.53096\n0.014431\n-0.23319\n-0.78986\n-1.2905\n-1.206\n-0.48942\n0.13345\n0.52887\n-0.33342\n-1.2686\n-1.0018\n-1.0488\n-0.96701\n-0.84693\n-0.65669\n-0.88456\n-0.91468\n-0.80864\n-0.36605\n-0.70242\n-0.743\n-0.62642\n-1.239\n-1.0532\n-0.513\n-0.81518\n-1.2801\n-1.5933\n-1.8322\n-0.69893\n-1.0416\n-0.12156\n0.40859\n-0.8379\n-1.4893\n-1.0076\n-0.42017\n-0.73256\n-0.36251\n-0.29012\n-0.96167\n-1.0367\n-0.34089\nNaN\nNaN\nNaN\n-2.3436\n-0.012669\n0.65837\n0.3075\n-0.15749\n0.55159\n-1.6092\n-1.4459\n-0.77466\n-1.1025\n-1.1441\n-1.1155\n-1.0141\n-0.50987\n-0.3429\n-0.086826\n-0.19629\n-0.57081\n-0.62049\n-0.43193\n-0.16422\n-0.037731\n0.20595\n-0.24498\n-0.74726\n-0.56321\n-0.54136\n-0.55633\n-0.92221\n-1.1022\n-1.336\n-0.83908\n-0.20189\n0.16358\n-0.56308\n-0.88144\n-0.77169\n-0.97436\n-0.9244\n-0.93597\n-0.701\n-0.92918\n-0.98056\n-0.91515\n-0.47545\n-0.73348\n-0.78466\n-0.88808\n-1.3384\n-1.4807\n-0.91246\n-1.1419\n-1.2655\n-2.5077\n-2.3217\n-1.506\n-0.39227\n-0.13336\n0.32307\n-0.23232\n-1.4986\n-1.0703\n-0.72052\n-1.6523\n-2.2913\n-1.6632\n-2.2139\n-1.4195\n-0.84264\nNaN\nNaN\nNaN\n-0.99506\n0.024609\n0.44246\n0.35797\n-0.1249\n-0.047306\n-1.1674\n-1.6241\n-1.3279\n-0.38483\n-0.7436\n-0.84655\n-0.44339\n-0.35693\n-0.1958\n0.10577\n-0.50203\n-0.33601\n-0.3097\n-0.24413\n-0.058918\n0.010872\n0.0085487\n-0.14453\n-0.38427\n-0.81771\n-1.1748\n-1.042\n-0.54242\n-0.82991\n-1.3349\n-1.0542\n-0.56022\n-0.21582\n-0.94058\n-0.85319\n-0.6023\n-0.62702\n-0.76563\n-0.8596\n-0.69294\n-0.71572\n-1.1193\n-1.1287\n-0.99054\n-1.0262\n-0.87982\n-0.81194\n-1.0445\n-1.4059\n-1.4929\n-1.731\n-2.0399\n-2.2621\n-2.3696\n-2.1131\n-1.0129\n-0.037525\n0.3689\n-0.88956\n-1.1816\n-0.95067\n-1.2231\n-2.3171\n-3.277\n-2.1146\n-1.858\n-1.0613\n-0.3808\n-0.52868\n-1.0726\n-0.77343\n-0.60469\n-0.25214\n0.30494\n0.35563\n-0.75811\n-2.2991\n-2.4978\n-1.6291\n-0.78011\n-0.37473\n-0.86645\n-1.1509\n-0.82338\n-0.60186\n0.033436\n0.18517\n0.021015\n-0.20797\n-0.31025\n-0.20255\n0.087151\n0.23655\n-0.087433\n-0.29888\n-0.45401\n-0.70326\n-1.0318\n-1.0324\n-0.42471\n-0.38211\n-0.53897\n-0.65369\n-0.57955\n-0.69378\n-0.78967\n-0.66512\n-0.41418\n-0.48557\n-0.66787\n-0.75757\n-0.74546\n-0.955\n-1.3517\n-1.5\n-1.3424\n-1.0659\n-0.93616\n-0.72473\n-0.73719\n-1.0674\n-1.627\n-1.8719\n-2.3944\n-1.3689\n-2.3884\n-2.5246\n-0.83544\n0.24672\n0.64629\n-1.3888\n-1.4713\n-1.9181\n-1.0686\n-2.4092\n-2.4944\n-2.6681\n-2.0421\n-1.6475\n-0.78472\n-0.68972\n-0.45771\n-0.71489\n-0.82177\n-0.49622\n-0.25252\n-0.073921\n-0.54895\n-1.452\n-1.0034\n-0.87323\n-0.92063\n-1.0738\n-1.1845\n-0.9917\n-0.86119\n-0.15234\n0.26088\n0.1186\n-0.15966\n-0.20615\n-0.024719\n-0.020855\n0.35997\n0.33503\n0.24185\n-0.17339\n-0.53891\n-0.72191\n-0.834\n-0.69892\n-0.32509\n-0.29111\n-0.29947\n-0.52313\n-0.64055\n-0.79391\n-1.0029\n-0.77855\n-0.38784\n-0.57275\n-0.59545\n-0.55522\n-0.39172\n-1.1301\n-1.4659\n-1.5424\n-1.3139\n-0.99411\n-0.89508\n-0.71295\n-0.4845\n-0.30844\n-1.2731\n-0.50147\n-0.68525\n-0.93272\n-2.2812\n-1.9418\n-0.67495\n-0.73993\n-2.04\n-1.9729\n-2.0311\n-1.5543\n-1.6612\n-2.455\n-2.2974\n-2.0378\n-1.7039\n-1.7697\n-1.2988\n-0.95465\n-0.7118\n-0.87861\n-0.83808\n-0.81407\n-1.0464\n-0.97458\n-0.86523\n-0.78207\n-0.74791\n-0.47424\n-0.63338\n-0.84875\n-0.50913\n-0.16027\n-0.25401\n0.050182\n0.26833\n0.14476\n0.36802\n0.36293\n0.27518\n0.018864\n0.031452\n0.18338\n0.34187\n-0.20572\n-0.67138\n-0.68917\n-0.84152\n-0.35231\n-0.20831\n-0.11179\n-0.10363\n-0.3758\n-0.80811\n-0.93245\n-0.80455\n-0.91441\n-0.6973\n-0.51717\n-0.64722\n-0.43288\n-0.3355\n-1.1117\n-1.416\n-1.4049\n-1.0951\n-0.9242\n-0.84778\n-0.66468\n0.066441\n-0.52664\n-0.96983\n-0.51638\n-0.42826\n-1.4228\n-2.2617\n-1.6699\n-2.0564\n-1.893\n-2.2537\n-2.1187\n-2.2841\n-2.1471\n-2.6447\n-2.4106\n-1.9947\n-2.3036\n-2.6111\n-2.2187\n-1.4454\n-0.63422\n-0.72666\n-0.45732\n-0.79416\n-1.0584\n-1.1225\n-1.2032\n-1.3741\n-1.0377\n-0.70539\n-0.33345\n-0.64466\n-1.0989\n-0.83773\n-0.19615\n-0.31195\n-0.04612\n0.11813\n0.1355\n0.2662\n0.34901\n0.10952\n-0.11042\n0.0085564\n-1.6351e-16\n-0.15959\n-0.34581\n-0.53778\n-0.39685\n-0.57621\n-0.35167\n-0.054543\n0.063503\n-0.10883\n-0.25768\n-0.42261\n-0.76701\n-0.57742\n-0.80607\n-0.41397\n-0.48081\n-0.78257\n-0.47262\n-0.45628\n-0.47379\n-1.0661\n-1.2415\n-0.96043\n-0.75995\n-0.82002\n-0.8833\n-0.87819\n-1.1732\n-0.62685\n-1.146\n-2.3346\n-2.2122\n-2.777\n-2.2503\n-2.4308\n-2.409\n-2.2372\n-1.884\n-2.2622\n-2.4815\n-2.5125\n-2.6947\n-2.486\n-3.0458\n-2.8702\n-2.5787\n-1.5118\n-0.72578\n-0.18303\n0.19257\n-0.50772\n-0.78462\n-0.65788\n-1.0534\n-1.133\n-0.94822\n-0.5261\n-0.21443\n-1.053\n-1.979\n-1.6801\n-0.73375\n-0.060776\n0.18042\n0.1676\n0.64907\n0.22969\n0.047306\n0.10854\n-0.14766\n0.10215\n-0.12941\n-0.54139\n-0.56847\n-0.59105\n-0.39526\n-0.25082\n-0.12228\n-0.027573\n-0.071484\n0.033741\n-0.46424\n-0.54485\n-0.59299\n-0.4498\n-0.70621\n-0.37574\n-0.37636\n-0.43527\n-0.63069\n-0.461\n-0.40571\n-0.7509\n-1.305\n-0.94687\n-0.90378\n-1.2435\n-0.9431\n-0.88307\n-0.28988\n-0.32645\n-1.868\n-3.2279\n-3.9483\n-3.2203\n-2.7819\n-2.3291\n-2.4676\n-2.0389\n-1.9231\n-2.3513\n-2.3927\n-2.4952\n-2.7332\n-2.4812\n-2.6545\n-3.0159\n-2.8253\n-1.8168\n-0.76295\n0.071091\n-0.4789\n-0.25651\n-0.25779\n-0.59045\n-0.71379\n-0.76069\n-0.54619\n-0.43155\n-0.61733\n-0.85245\n-1.5943\n-1.2438\n-1.4115\n-0.2374\n-0.084316\n1.1309\n0.91843\n0.41431\n-0.18372\n0.42739\n0.45829\n0.42657\n-0.2933\n-0.67571\n-0.75272\n-0.71232\n-0.27832\n-0.2043\n-0.093624\n-0.21022\n0.071728\n0.056416\n-0.50913\n-0.5695\n-0.60543\n-0.57418\n-0.41874\n-0.15425\n0.18206\n0.083488\n-0.12274\n-0.14009\n-0.076931\n-0.44261\n-0.79659\n-0.75675\n-0.96393\n-0.85576\n-0.82562\n-1.523\n-0.28547\n-0.82649\n-1.0196\n-1.6918\n-2.3844\n-2.8715\n-2.6593\n-2.6822\n-2.9952\n-2.5641\n-2.2833\n-2.2913\n-2.3608\n-2.4641\n-2.4832\n-2.3018\n-2.2819\n-2.2397\n-2.4193\n-2.0606\n-1.4331\n-0.88191\n-0.19725\n0.05418\n-0.29428\n-0.089069\n-0.066898\n-0.49778\n-0.39145\n-0.49003\n-0.45217\n-0.13678\n-1.3192\n-1.1806\n-0.83972\n-0.99342\n-0.79219\n-0.41343\n0.011005\n-0.3195\n0.01049\n0.50347\n-0.2119\n-0.35905\n-0.54105\n-0.82837\n-0.4436\n-0.069996\n-0.17029\n-0.40461\n0.041397\n0.23683\n0.24094\n0.035892\n-0.44477\n-0.80102\n-0.88685\n-0.69588\n-0.10685\n0.20343\n0.48414\n0.47132\n0.32779\n0.4082\n0.57924\n0.15032\n-0.061451\n-0.53086\n-0.66668\n-0.57148\n-0.36652\n-1.877\n-0.1684\n-1.0955\n-0.34775\n-0.2034\n-1.9242\n-2.2847\n-2.8936\n-3.3054\n-3.4339\n-3.1343\n-2.6373\n-2.3703\n-2.7472\n-2.6468\n-2.6498\n-2.738\n-2.6915\n-2.3005\n-2.0174\n-1.8431\n-1.5215\n-0.95956\n-0.29066\n0.37451\n0.45159\n0.36762\n-0.11029\n-0.38762\n-0.51004\n-0.78529\n-1.0829\n-0.79782\n-0.59259\n-0.86743\n-0.73124\n-0.75528\n-0.56367\n-0.68848\n-0.56658\n-0.29832\n-0.13801\n-0.31549\n-0.54666\n-0.36469\n-0.78511\n-0.49231\n-0.12223\n0.28723\n0.011597\n-0.16117\n0.13528\n0.28345\n0.46569\n0.32985\n-0.54538\n-0.84142\n-0.95099\n-0.82117\n0.64508\n0.37011\n0.70516\n1.0209\n1.1207\n0.90825\n1.1659\n1.0359\n0.53164\n-0.24753\n-0.5675\n-0.17158\nNaN\nNaN\nNaN\n0.26588\n3.3123\n1.6729\n-0.90425\n-2.5629\n-2.7477\n-3.824\n-3.5218\n-3.5126\n-3.6859\n-3.9302\n-3.1188\n-2.714\n-2.6337\n-2.3387\n-2.4586\n-1.8534\n-1.4236\n-1.286\n-1.0629\n-0.91936\n-0.75554\n0.32968\n0.26836\n0.41602\n0.050617\n0.067112\n-0.48318\n-1.0095\n-1.537\n-1.3605\n-0.83286\n-0.85349\n-0.82636\n-0.79447\n-0.45226\n-0.32669\n-0.54433\n-0.49776\n-0.40577\n-0.45444\n-0.8794\n-0.77462\n-0.801\n-0.50219\n-0.26526\n-0.096493\n0.21899\n-0.045856\n0.19292\n0.84582\n1.0331\n0.2675\n-0.9949\n-1.1939\n-1.2026\n-1.3206\n1.1602\n0.49382\n1.2499\n1.9271\n2.072\n2.1344\n2.2517\n2.0973\n0.61141\n-0.093235\n-0.61648\n0.024433\nNaN\nNaN\nNaN\n0.26772\n1.4628\n1.4233\n-0.65585\n-2.1768\n-2.4577\n-3.2271\n-3.1646\n-4.1563\n-3.9232\n-3.6044\n-3.0648\n-2.759\n-2.5771\n-2.2528\n-2.2257\n-1.6693\n-1.0515\n-0.97114\n-0.97502\n-1.1198\n-1.0993\n-0.48781\n-0.42492\n-0.33352\n-0.094112\n-0.16042\n-0.88663\n-1.2999\n-1.6546\n-1.4173\n-0.89449\n-0.65589\n-0.8877\n-0.78032\n-0.54455\n0.081024\n-0.021511\n-0.29397\n-0.52869\n-0.4549\n-0.69573\n-0.69007\n-0.37819\n-0.6674\n-0.48859\n-0.61255\n-0.50122\n0.0052681\n0.82296\n1.6908\n0.62976\n-0.142\n-1.0004\n-2.4994\n-2.7261\n-2.1249\n0.72481\n0.733\n1.099\n0.56382\n0.5019\n0.97672\n1.8515\n0.79356\n0.94967\n1.3569\n0.71864\n0.6369\n0.72136\n1.2087\n1.5385\n0.57883\n0.26346\n0.91372\n1.4735\n3.1834\n3.1107\n2.5468\n3.4666\n3.7191\n3.9461\n3.3926\n4.0821\n2.6031\n2.3226\n3.5516\n1.5295\n1.4412\n1.1689\n2.615\n3.5862\n2.0988\n2.5569\n3.4818\n2.9262\n2.8794\n3.0256\n2.8212\n2.9339\n2.4032\n2.7162\n1.5779\n1.9873\n1.4457\n0.70647\n0.40774\n0.47775\n0.35537\n-0.28759\n-1.2271\n0.5907\n-0.21671\n-1.2896\n-0.90225\n-1.7649\n-0.67522\n-0.81339\n0.38806\n-0.73644\n-3.3435\n-1.5857\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18628\n0.5207\n0.4413\n1.3248\n2.153\n1.2509\n2.9198\n0.79166\n0.71095\n1.5192\n0.78149\n0.90092\n0.25251\n1.4597\n1.9559\n1.2837\n0.57088\n1.88\n1.8066\n2.3217\n2.4339\n2.3927\n3.5067\n4.0939\n4.1074\n4.0332\n4.3811\n3.128\n2.8662\n2.809\n3.1447\n1.7126\n1.6712\n3.3561\n2.6596\n2.1915\n2.8211\n3.6418\n2.5543\n2.9414\n3.7206\n2.6247\n2.4511\n2.2898\n2.4619\n1.5327\n1.4295\n1.1205\n0.91145\n0.73634\n0.30115\n-0.18031\n-0.31174\n0.0301\n0.85684\n-0.32346\n-1.8862\n-2.2344\n-1.2377\n0.47743\n0.079175\n0.25777\n-0.3361\n-1.6078\n-0.39139\n-0.10102\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.22943\n-0.25652\n0.33651\n0.98265\n1.7536\n0.42596\n2.7813\n0.37096\n0.46785\n1.468\n-0.33148\n0.71038\n-1.107\n1.3568\n2.3435\n1.7459\n1.0237\n1.7323\n1.8876\n3.6556\n2.2318\n3.4377\n3.4649\n2.8701\n3.7708\n3.9549\n3.7907\n2.7969\n2.871\n2.7977\n2.8801\n2.3846\n2.4552\n3.5087\n2.598\n2.5304\n3.4675\n3.5519\n2.8484\n3.3902\n3.6503\n2.5265\n1.813\n2.2035\n2.4098\n1.3649\n0.97251\n0.91263\n1.5232\n1.5327\n0.62433\n-0.90278\n-0.48825\n0.53247\n0.22233\n-0.53325\n-1.1965\n-2.978\n-1.1963\n1.7191\n-0.46041\n0.26016\n1.1934\n-0.80551\n-0.35583\n-0.31047\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.18535\n-0.60046\n-0.36806\n-0.21929\n-0.27855\n-0.63732\n-0.089751\n0.051135\n-0.0716\n0.0016489\n-0.042798\n0.73324\n-0.45316\n1.6412\n1.7495\n1.3767\n1.0214\n1.6405\n3.3577\n4.4137\n2.7535\n4.1827\n3.1251\n1.1462\n3.4668\n3.7487\n3.8734\n3.0354\n2.9355\n2.9281\n2.4955\n3.2889\n2.2573\n3.415\n1.9875\n2.312\n3.145\n3.3391\n3.0616\n3.6612\n3.3037\n2.7018\n1.6555\n2.2437\n2.4963\n1.5217\n1.2452\n0.86589\n0.86455\n0.65252\n0.60376\n-0.1986\n-0.099793\n0.77046\n0.31251\n0.084996\nNaN\nNaN\nNaN\n-1.1861\n-0.92669\n-0.31139\n1.6148\n-0.3114\n-1.5401\n-0.31808\n-0.91436\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.23826\n-0.67935\n-0.49125\n-0.12394\n-0.3456\n-0.75622\n-0.08126\n0.03348\n-0.37554\n-0.081367\n-0.059325\n0.57821\n1.1808\n1.6735\n1.3118\n1.5535\n0.71476\n1.202\n2.5034\n5.0385\n3.142\n3.7882\n3.7957\n0.23134\n3.0583\n3.637\n4.2839\n3.6226\n3.5344\n4.3722\n3.1173\n2.5557\n2.5609\n3.0001\n1.3757\n0.94909\n2.2261\n3.1524\n3.3891\n3.4004\n2.8524\n2.9737\n2.0709\n1.4915\n2.1236\n1.7626\n1.1299\n0.83203\n0.57928\n0.18954\n0.49568\n0.82552\n0.19351\n0.37764\n0.17685\n0.19487\n2.154\nNaN\nNaN\n-2.8564\n-0.91482\n-1.4358\n1.5524\n0.30143\n-1.0823\n-0.23025\n-2.0223\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0149\n-0.93881\n-0.33611\n-0.32641\n-0.34516\n-0.88081\n-0.21541\n0.096702\n0.079962\n0.43517\n0.54245\n0.92772\n0.94911\n1.9048\n0.98596\n1.0091\n1.1431\n1.8245\n1.0875\n4.8863\n2.1469\n1.8644\n3.2163\n1.8482\n3.043\n4.2472\n4.1475\n3.3608\n3.6553\n4.434\n4.3245\n1.7798\n2.132\n1.9623\n1.1561\n0.82157\n2.3371\n2.5949\n3.206\n2.8601\n2.9021\n2.4676\n2.161\n2.2566\n2.2765\n1.4486\n0.93123\n1.679\n1.3403\n0.89304\n0.78779\n1.3131\n0.54813\n-0.21344\n-0.57651\n0.018222\n1.8434\nNaN\nNaN\n-4.6424\n-3.0141\n-1.6403\n-2.4745\n-0.67626\n-0.70579\n0.29875\n-0.8438\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.67847\n-0.85803\n-0.56408\n-0.22752\n-0.023035\n-0.90349\n-0.28324\n0.4592\n0.68595\n0.64826\n0.47283\n0.61027\n0.88835\n1.7601\n1.1823\n0.82083\n1.2122\n1.9554\n1.1879\n2.643\n2.2701\n1.1685\n3.3057\n2.7135\n3.329\n3.9776\n4.4198\n3.6399\n3.6024\n3.5805\n3.0388\n0.66092\n0.29888\n2.6766\n1.1066\n1.1753\n2.3071\n2.743\n2.6003\n2.651\n3.5782\n2.1684\n1.7853\n1.6492\n0.8744\n1.6312\n1.3165\n1.5686\n1.8105\n1.2581\n0.5102\n1.0967\n0.2891\n-0.46509\n-1.0573\n-0.14287\n1.1956\nNaN\nNaN\n-3.4302\n-3.3656\n-2.2932\n-1.5105\n-1.9541\n-0.86883\n-0.35836\n0.52254\n1.4911\n1.8044\nNaN\nNaN\nNaN\n-0.55859\n-0.68768\n-0.59627\n-0.41198\n0.45666\n-0.5533\n-0.1037\n0.337\n0.51518\n0.78609\n0.79332\n1.0224\n0.65171\n1.1153\n1.2243\n0.89867\n1.3259\n1.8355\n1.2163\n2.0988\n2.0463\n2.9144\n3.4061\n2.8074\n3.3023\n3.6859\n3.9311\n4.386\n3.9062\n3.5667\n3.1438\n2.1112\n0.85732\n2.7798\n2.3671\n2.2611\n2.8891\n2.8136\n2.5029\n2.3925\n3.4059\n2.0884\n1.5346\n0.92637\n-0.66789\n1.8542\n1.7028\n1.2578\n1.1288\n1.2397\n0.34747\n1.067\n0.92665\n0.58425\n-0.24995\n-1.331\n0.61498\n0.28015\n0.11076\n-1.1954\n-3.8399\n-1.6429\n-1.0299\nNaN\n-1.7213\n-2.0167\n-0.098765\n1.3346\n2.1114\n0.86867\nNaN\nNaN\n-0.88567\n-0.83272\n-0.53577\n-0.71597\n0.028631\n-0.073421\n-0.32151\n0.14014\n0.50571\n0.97605\n0.81917\n1.2992\n1.2739\n1.335\n1.2902\n1.1003\n1.8526\n2.2096\n2.1315\n2.586\n2.3241\n2.5412\n3.3949\n2.9119\n3.019\n3.646\n4.051\n4.647\n4.1273\n3.7672\n3.3892\n2.3732\n2.8972\n2.4917\n2.9022\n3.0456\n3.3094\n2.6409\n2.3957\n1.9053\n2.3393\n1.7186\n0.96816\n0.58887\n-1.231\n2.0825\n1.5176\n1.9638\n0.83494\n1.6736\n0.99833\n1.5023\n1.4979\n2.6314\n1.1162\n-0.90889\n-0.068206\n0.045495\n0.36008\n-0.47565\n-2.7986\n-1.9582\n-0.75565\nNaN\n-2.1496\n-3.499\n-0.93934\n-0.92087\n1.3627\n1.6007\n-2.3619\n-3.0168\n-1.511\n-1.0948\n-0.84853\n-0.82482\n-1.1628\n-0.22355\n-0.060039\n-0.049779\n0.35946\n1.0088\n0.96652\n1.4923\n1.8694\n1.5593\n1.4072\n1.3232\n1.8727\n2.133\n2.1335\n2.5579\n2.4668\n3.096\n4.0774\n3.2586\n2.6411\n3.8814\n4.7984\n4.923\n4.1362\n3.4363\n3.5308\n0.6691\n0.36801\n2.6828\n3.0597\n3.1201\n3.3207\n2.2057\n2.0418\n1.7933\n1.3634\n1.2609\n1.0276\n1.6786\n2.2414\n2.3458\n1.5187\n1.7822\n1.0896\n1.6856\n1.3685\n1.4895\n1.8435\n2.6989\n1.3938\n-0.13507\n-1.2059\n-0.11403\n0.51023\n-0.013597\n-1.3953\n-3.4157\n-2.2413\nNaN\nNaN\nNaN\n-1.5337\n-2.3292\n-1.0338\n0.48653\n-3.3246\n-2.2889\n-1.6266\n-1.6714\n-1.4579\n-1.1864\n-1.2924\n-0.20158\n-0.14918\n-0.1392\n0.43818\n0.9491\n1.2403\n1.838\n2.2969\n1.7404\n0.87218\n0.81651\n1.3824\n1.7028\n2.1507\n2.8729\n2.721\n3.2541\n3.7209\n3.3493\n2.2232\n3.425\n4.923\n4.9888\n3.5434\n3.7061\n3.5787\n1.5414\n1.0054\n3.1928\n2.2103\n3.1392\n3.4075\n1.7111\n1.7349\n1.8129\n1.4892\n0.73958\n1.4391\n1.5102\n1.8284\n1.9353\n1.4608\n1.7255\n1.7194\n0.77984\n0.96067\n1.6288\n1.663\n2.5301\nNaN\nNaN\n-1.9142\n-0.61372\n0.1794\n-0.24337\n-0.039629\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.3131\nNaN\n-0.14505\n-0.7172\n-1.1307\n-1.7538\n-1.7985\n-1.69\n-1.818\n-1.2076\n-0.71099\n0.0071516\n0.16949\n0.087364\n0.6978\n1.0789\n1.2239\n1.9079\n2.3922\n2.07\n1.1937\n0.78105\n1.5659\n2.5231\n2.884\n3.0308\n2.3382\n3.0685\n3.4871\n2.8374\n2.7401\n2.8734\n4.0891\n4.7048\n3.7476\n4.0552\n3.8141\nNaN\nNaN\nNaN\n2.7787\n2.727\n4.9973\n4.1464\n1.1412\n1.655\n2.7016\n4.5517\n2.3634\n1.0439\n1.1178\n0.91091\n0.97739\n1.8734\n1.9696\n1.4106\n0.83523\n1.8118\n1.2393\nNaN\nNaN\nNaN\n-1.2223\n-1.8622\n-0.84388\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7174\n-1.3255\n-1.4101\n-1.1169\n-0.64768\n-0.34215\n-0.22684\n0.0416\n0.21489\n0.79815\n1.2584\n1.7544\n1.9071\n2.3387\n1.7725\n1.8833\n2.7004\n3.3946\n3.205\n2.8794\n2.5831\n2.7492\n2.8115\n2.3082\n2.7678\n3.3055\n4.212\n4.047\n4.6238\n4.7057\n3.8999\nNaN\nNaN\nNaN\n3.3222\n2.0518\n4.1647\n4.9089\n1.8693\n1.6271\n2.0907\n3.0104\n1.4436\n1.0693\n0.98176\n0.59878\n0.85228\n1.7859\n2.2212\n1.7715\n1.1625\n1.3444\n0.8515\nNaN\nNaN\nNaN\n-0.97585\n-2.1264\n-1.0846\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.3005\n-1.0798\n-1.2486\n-0.97581\n-0.7572\n-1.0119\n-1.0458\n-0.7846\n-0.4484\n0.17223\n0.8517\n1.4515\n1.2868\n1.4883\n1.9009\n2.182\n2.5232\n3.5823\n2.7298\n2.4265\n3.302\n2.11\n0.58049\n0.85032\n2.2986\n4.3027\n4.2786\n3.3189\n4.3704\n4.4258\n4.2702\nNaN\nNaN\nNaN\nNaN\n2.3132\n5.3864\n6.2701\n2.2047\n2.0777\n1.3209\n0.7174\n0.4821\n1.5279\n1.3244\n1.3694\n1.2604\n1.696\n2.0689\n1.9264\n0.81542\n0.52964\n0.82597\nNaN\nNaN\nNaN\nNaN\n-1.6341\n-1.1071\n1.0144\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.6729\n-1.2212\n-1.194\n-0.88732\n-1.046\n-1.2744\n-0.96323\n-0.93984\n-0.51945\n-0.23633\n0.19118\n0.98337\n1.4193\n1.5027\n1.4085\n2.0195\n2.1627\n3.1324\n2.5826\n2.1218\n3.9166\n0.51557\n-1.5532\n0.12971\n1.4774\n4.0105\n4.1641\n3.9222\n4.4976\n4.4828\n5.0967\nNaN\n0.90308\n1.2617\n1.3487\n3.4531\n5.4872\n3.8378\n1.7173\n1.0187\n0.003953\n0.46871\n-0.52409\n1.6664\n1.8443\n1.7108\n1.8807\n1.72\n2.1876\n2.0767\n0.66673\n1.3663\n1.4316\n0.1874\nNaN\nNaN\nNaN\n-1.6341\n-0.97115\n-0.052431\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.5908\n-1.5856\n-1.3809\n-0.94261\n-0.85721\n-1.2906\n-1.0349\n-0.92411\n-0.54648\n0.042419\n-0.61597\n0.46707\n1.3374\n1.8013\n1.239\n1.5692\n2.1617\n3.0082\n3.0286\n2.7913\n3.5727\n3.4687\n-1.4481\n-0.59977\n1.3956\n3.0442\n3.7987\n4.8859\n4.9891\n4.4052\n4.9798\n0.43786\n0.82232\n1.312\n2.2136\n2.8052\n3.6014\n2.7103\n2.6616\nNaN\n-0.86166\n0.72595\n0.67291\n1.7469\n2.3314\n1.6138\n1.7624\n1.6206\n2.1833\n1.6686\n1.189\n1.4805\n3.3483\n1.1572\n-0.5162\n-1.6223\n-2.4592\n-2.1841\n-1.8842\n-1.2519\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8571\n-1.9685\n-1.6947\n-1.3316\n-1.1268\n-1.3653\n-1.1608\n-0.87209\n-0.43338\n0.33314\n-0.055055\n-0.035693\n1.0758\n1.65\n1.3984\n1.6986\n2.104\n3.1074\n3.3152\n3.333\n3.4645\n2.9642\n2.5103\n-0.65744\n2.5211\n3.635\n4.1877\n4.2431\n4.7105\n4.0993\n4.7234\n2.9565\n1.9463\n0.95841\n3.1979\n1.688\n2.3814\nNaN\nNaN\nNaN\nNaN\n-0.15991\n1.0947\n1.4677\n2.2458\n1.4506\n1.6135\n1.7433\n1.9028\n1.5509\nNaN\n2.0303\n4.1991\n1.9568\n0.13379\n-0.096424\n-2.5728\n-2.5622\n-2.0604\n-1.6142\n-2.0252\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6697\n-2.5034\n-1.5965\n-1.5646\n-2.109\n-1.8579\n-1.3529\n-0.8671\n-0.16255\n0.5091\n0.38155\n0.71164\n0.93929\n1.6506\n1.9989\n1.8408\n2.5671\n2.8216\n2.6872\n3.2355\n3.0072\n1.6723\n1.638\n-0.0067835\n3.0805\n3.6445\n4.5344\n3.4094\n4.2182\n3.1199\n4.653\n4.5829\n1.2441\n1.3063\n2.5503\n2.6398\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.48166\n1.6068\n2.2714\n1.1904\n1.9685\n2.2513\n1.4021\n0.51088\n3.0186\nNaN\nNaN\nNaN\n0.67832\n0.024023\n-0.90101\n-1.9461\n-1.6071\n-1.166\n-1.5716\n-0.81594\n-1.2699\nNaN\nNaN\n-0.78918\n-0.37328\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.8359\n-2.6181\n-1.6391\n-1.5942\n-2.01\n-2.351\n-1.6814\n-0.99722\n-0.29952\n0.84639\n0.085479\n1.0348\n0.98623\n1.5188\n1.7383\n1.4662\n1.4528\n1.0962\n2.1258\n2.7917\n2.4832\n0.71244\n0.95593\n1.5897\n2.498\n3.3842\n3.8963\n3.3841\n4.6901\n3.2251\n4.2979\n2.5586\n0.18185\n0.9825\n2.9174\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.5823\n2.076\n1.5772\n1.0887\n0.92434\n1.2672\n1.1196\n0.18965\n3.7573\nNaN\nNaN\nNaN\n-0.6458\n-0.52485\n-0.86775\n-1.7213\n-1.1828\n-1.0377\n-0.86312\n-0.59972\n-0.28186\n-0.38981\n-0.63531\n0.34104\n0.76881\n-0.2697\n-0.78082\nNaN\nNaN\nNaN\n-2.7803\n-2.5652\n-1.8925\n-1.7123\n-1.6628\n-2.0458\n-1.7453\n-1.0781\n-0.28366\n0.55764\n0.065996\n0.96235\n0.77956\n1.0129\n0.88204\n1.3656\n0.92254\n-0.31399\n1.8955\n2.3256\n2.066\n-0.53259\n1.1114\n2.568\n3.0033\n3.5576\n3.4558\n3.6516\n5.0774\n3.198\n2.8777\n1.2733\n0.84229\n0.85768\n4.6915\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4166\n1.9841\n2.6254\n1.5561\n0.24311\n-0.45791\n0.49873\n-0.50333\n4.0982\nNaN\nNaN\nNaN\n-2.0823\n-1.2071\n-1.3393\n-1.1761\n-1.1312\n0.027298\n-0.13717\n-0.25878\n0.05613\n-0.77309\n-0.34803\n0.13124\n0.28189\n-0.24196\n-0.73217\nNaN\n-0.93272\nNaN\n-2.5944\n-2.6797\n-2.0371\n-1.9363\n-1.1997\n-1.5587\n-1.1586\n-0.36589\n-0.057645\n-0.016728\n0.093156\n0.45384\n0.16326\n0.60716\n-0.21116\n1.0375\n0.72378\n-0.42986\n0.88846\n2.4974\n1.5361\n0.24967\n1.0592\n3.3935\n2.9053\n3.8508\n3.8751\n3.5567\n4.5586\n5.1693\n3.7873\n3.4033\n3.1354\n2.4921\n5.3763\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.3467\n3.0846\n7.9465\n4.2183\n1.1817\n-1.3094\n-0.20483\n-1.2678\n2.8973\nNaN\n0.61033\n-1.8707\n-1.102\n-1.126\n-1.7694\n-1.4695\n-0.99935\n-0.33206\n-0.36438\n0.42389\n0.010755\n-0.7523\n-0.25925\n0.19861\n-0.36448\n-0.64883\n-0.69263\n-0.75117\n0.0057411\n0.19327\n-2.3465\n-2.4981\n-2.3719\n-2.1712\n-1.674\n-1.3872\n-1.1801\n-0.61064\n-0.078362\n-0.13355\n-0.38835\n-0.14651\n0.088853\n0.5102\n-0.31039\n0.50623\n0.48448\n-0.34485\n-0.17468\n2.3815\n2.4542\n0.70908\n0.61379\n2.5988\n3.212\n4.724\n4.8488\n4.3951\n4.3753\n5.3565\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.8389\n4.5227\n6.8439\n3.6871\n2.1506\n-1.2615\n0.87751\n-1.1366\n0.63915\n0.28652\n-0.56459\n-0.55272\n-0.09772\n-1.0608\n-1.2175\n-1.8136\n-0.70322\n-0.1659\n-0.052541\n0.64326\n0.34139\n-0.043775\n0.29372\n0.31595\n-0.40771\n-0.49301\n-0.27564\n-0.75924\n0.30842\n0.52907\n-2.375\n-2.6465\n-2.5464\n-2.4991\n-2.0761\n-1.4063\n-0.9032\n-0.84782\n-0.1754\n-0.34698\n-0.5286\n-0.58732\n-0.1453\n0.27437\n0.13931\n0.82291\n1.63\n0.51053\n-0.15314\n1.4062\n2.3208\n1.3654\n0.27445\n1.6892\n3.745\n4.4363\n5.0935\n4.8409\n5.1132\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n5.0815\n3.1906\n0.86863\n1.3034\n0.98122\n0.16315\n0.32116\n0.14976\n-0.21321\n-0.034452\n-0.25361\n-0.78684\n-0.29885\n-1.0214\n-0.60393\n-0.1277\n-0.5044\n-0.14743\n0.31131\n-0.13175\n1.0558\n0.23422\n-0.35698\n-0.56208\n-0.096709\n0.12059\n0.54472\n0.91857\n-2.7315\n-2.9351\n-2.7139\n-2.7105\n-2.2402\n-1.7121\n-1.2602\n-0.88743\n-0.34945\n-0.20383\n-0.1939\n-0.34839\n-0.090178\n0.33569\n0.5303\n1.491\n2.0714\n0.93829\n0.2961\n0.22914\n1.0688\n1.2858\n1.3023\n3.5636\n4.5978\n4.5141\n4.129\n4.3258\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.2697\n0.2655\n0.055972\n0.31147\n0.30515\n0.25908\n0.0029678\n-0.3421\n-0.25418\n-0.19483\n-0.62486\n0.82413\n-0.2223\n-0.67723\n-0.095952\n-0.84204\n-0.96865\n0.22575\n-0.061131\n0.27413\n0.21086\n0.065174\n0.089766\n1.0621\n1.0247\n0.95897\n1.4139\n-3.1085\n-3.1682\n-3.1276\n-2.7005\n-2.2496\n-1.6546\n-1.162\n-0.83436\n-0.58205\n-0.17589\n-0.061159\n-0.14831\n0.23284\n0.56796\n0.6933\n1.961\n1.5969\n-0.45123\n0.19116\n0.67411\n1.1703\n1.7203\n1.8569\n3.7964\n4.4707\n4.9517\n3.1559\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5777\n1.4563\n-0.088666\n0.26793\n1.6155\n0.54977\n-0.045896\n-0.67461\n-0.42659\n-1.6271\n-0.050434\n2.1595\n0.48751\n-0.056996\n-0.17108\n-0.56383\n-0.70681\n-0.547\n-0.43767\n-0.49511\n0.20924\n0.82097\n-0.56276\n1.5039\n1.7907\n1.278\n1.4538\n-3.1377\n-3.1782\n-3.0631\n-2.6283\n-2.3874\n-1.376\n-0.63822\n-0.88122\n-0.34115\n-0.4336\n0.030206\n-0.10708\n0.11645\n0.78005\n0.44634\n1.8715\n1.4286\n0.025266\n0.63052\n1.0726\n1.784\n2.0709\n2.4483\n3.0849\n5.0963\n5.9644\n3.0628\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1915\n2.0489\n0.32175\n0.9137\n0.71792\n-0.087255\n-1.3159\n-0.80522\n-1.4028\n-2.9738\n-1.3784\n1.8737\n2.5866\n1.3732\n-0.0031767\n-0.98752\n-0.75675\n-0.62271\n-0.73643\n-0.50026\n0.11596\n1.226\n-0.052811\n-0.15955\n1.1379\n1.9701\n1.2362\n-3.2911\n-2.9737\n-2.7832\n-2.3354\n-2.1697\n-1.525\n-0.84512\n-0.68413\n-0.60297\n-0.55396\n-0.34558\n-0.60428\n0.16081\n1.2599\n0.26772\n1.233\n1.1497\n0.70173\n0.70047\n1.1471\n1.5719\n2.1119\n2.6936\n3.2041\n5.2995\n7.0843\n3.0988\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n3.0315\n1.959\n1.9147\n0.65806\n0.06705\n0.47032\n-0.92746\n-2.2017\n-0.8644\n-1.1181\n-0.887\n-1.1221\n-0.046028\n1.2126\n1.8448\n0.51974\n-0.15899\n-1.0985\n0.038247\n-0.27154\n-0.036485\n0.34612\n0.43416\n0.15571\n-0.038998\n1.2355\n2.1438\n1.5375\n-3.2281\n-2.46\n-2.6535\n-1.9825\n-1.8783\n-1.4851\n-1.0423\n-0.9129\n-0.99882\n-0.70528\n-0.52966\n-0.59109\n0.15996\n1.8691\n0.87464\n1.0625\n0.95813\n0.83997\n0.60694\n1.4711\n0.73086\n1.8625\n2.5808\n3.3965\n4.5919\n5.694\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.9004\n5.4591\nNaN\n1.9806\n1.8021\n1.5197\n1.7579\n0.99587\n0.2255\n2.0222\n0.13687\n-1.4506\n-0.013835\n0.46543\n-0.40942\n-0.26425\n-0.59342\n-2.9492\n0.74036\n0.24887\n-0.063384\n-1.4986\n-0.20172\n0.52307\n0.3945\n0.91851\n0.58061\n0.82317\n0.53702\n1.2951\n1.6071\n1.6328\n-2.4831\n-1.9719\n-1.8587\n-1.845\n-1.6203\n-1.127\n-0.95264\n-1.4511\n-1.2183\n-0.73048\n-0.43012\n-0.56508\n-0.55546\n1.6559\n0.50689\n0.84948\n0.81709\n0.83829\n0.71534\n1.6967\n0.66061\n0.98065\n2.3236\n3.8188\n4.8338\n5.5054\n3.2824\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.4578\n8.6152\n6.8805\n6.0997\n3.5956\n2.7052\n2.134\n1.7648\n1.2934\n0.94853\n0.38773\n-0.057942\n0.58179\n0.24187\n0.77961\n0.8217\n0.75454\n0.74407\n-0.70266\n-2.5411\n-2.2411\n0.29333\n0.8615\n-0.95959\n-0.17987\n0.45727\n0.50124\n0.91279\n0.48517\n1.0742\n1.1786\n0.6716\n0.48349\n-0.35905\n-2.5242\n-1.6717\n-1.4132\n-1.8721\n-1.7406\n-1.2146\n-1.665\n-1.8272\n-1.6514\n-0.71155\n-0.43151\n-0.59296\n-0.79529\n0.26462\n0.26348\n0.57891\n0.50719\n0.25173\n1.0231\n1.3636\n1.015\n0.8525\n2.2043\n4.5913\n6.2219\n6.8132\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n9.5961\n8.9656\n6.5276\n5.6309\n3.0454\n3.0858\n2.3819\n1.808\n1.3038\n0.9128\n0.59721\n-0.11509\n0.29827\n0.0028658\n-0.08145\n-0.023257\n-0.40191\n-0.4093\n0.11687\n-0.45417\n-1.6109\n-1.8817\n0.45801\n-0.80942\n-0.50848\n-0.083573\n-0.036052\n0.32791\n0.39926\n1.0475\n0.041532\n-0.17914\n0.001935\n-0.89954\n-3.04\n-1.8543\n-1.406\n-1.9613\n-1.9674\n-1.4316\n-1.9591\n-1.6245\n-1.6345\n-0.89869\n-0.54761\n-0.66702\n-0.46404\n-0.1504\n-0.11082\n0.49836\n0.16897\n-0.013946\n0.73144\n1.2178\n0.82418\n1.4734\n2.3492\n5.3516\n6.1053\n8.2857\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n6.9402\n10.145\n10.217\n8.4455\n6.9714\n6.9579\n5.3568\n3.8352\n3.0851\n1.7577\n1.9549\n2.2931\n1.6624\n1.4728\n0.95901\n0.45949\n0.24202\n-0.077737\n-0.51025\n-1.3497\n-0.71023\n-0.52519\n-1.0053\n-0.7539\n-1.9987\n-0.36916\n-0.16061\n-0.71061\n-0.74415\n-0.95514\n-0.68219\n-0.1097\n0.22369\n-0.65754\n-1.0854\n-0.71117\n-0.352\n-3.1057\n-1.8819\n-2.0098\n-1.8585\n-1.7313\n-1.493\n-1.8125\n-1.462\n-1.0904\n-1.0373\n-0.84791\n-0.84992\n-1.0344\n-0.44687\n-0.45738\n0.64748\n-0.06331\n0.003314\n1.2973\n1.087\n0.37923\n1.0854\n2.7162\n4.3372\n5.4115\n7.2633\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n8.7187\n6.7771\n5.0611\n4.0325\n2.5972\n1.1652\n2.5182\n2.2944\n2.2821\n1.5693\n0.41603\n0.19647\n-0.041854\n-0.1644\n0.084137\n-1.2796\n-1.6288\n-0.95029\n-1.7815\n-1.0814\n-0.67578\n-0.56328\n-0.044038\n-0.77585\n-1.048\n-1.1507\n-1.2889\n-0.95234\n-0.38968\n-0.966\n-0.6655\n-0.48212\n0.032758\n-3.2864\n-2.7127\n-2.5221\n-1.5068\n-2.8422\n-1.7628\n-1.6528\n-1.5802\n-1.1958\n-1.0877\n-1.2329\n-1.0114\n-0.92393\n-0.6766\n-0.81784\n1.0198\n0.20703\n-1.7054\n-0.50348\n3.6644\n2.9595\n1.4752\n2.6455\n3.8692\n4.9189\n3.2042\n4.4362\nNaN\nNaN\nNaN\nNaN\n4.5069\n5.3523\nNaN\nNaN\nNaN\nNaN\n10.413\n9.1871\n9.0123\n6.2415\n4.6671\n3.5373\n2.4\n1.2877\n2.7735\n1.749\n1.6522\n1.5817\n0.39473\n0.48499\n0.38221\n0.16148\n0.56689\n-0.60956\n-2.8523\n-1.751\n-1.1926\n-0.69978\n-0.21252\n-0.0064268\n-0.32596\n-0.72427\n-1.0077\n-0.14795\n-1.3319\n-1.4827\n-0.60745\n-0.6382\n0.27582\n0.64708\n0.98254\n-3.6185\n-3.9175\n-3.6203\n-2.3511\n-3.0542\n-2.2423\n-1.9136\n-1.7592\n-1.5354\n-0.74827\n-1.4722\n-0.96609\n-0.40806\n-0.30586\n-0.87486\n1.031\n0.19442\n-1.6978\n-2.2569\n1.8007\n3.321\n1.4643\n2.691\n3.7038\n5.0651\n1.7809\n1.7476\n2.2566\nNaN\nNaN\nNaN\n5.1159\n8.157\nNaN\nNaN\nNaN\nNaN\n5.5114\n7.8633\n8.6411\n6.0026\n4.0119\n4.2958\n2.6487\n1.0867\n1.8602\n1.3991\n0.64971\n1.5423\n0.71609\n0.66493\n0.35139\n0.009798\n-0.18032\n-0.18403\n-0.73558\n-0.84629\n-0.47833\n-0.49138\n-0.27026\n0.7695\n-0.99986\n0.1193\n-0.76682\n-0.048781\n-1.8699\n-1.8337\n-0.28263\n0.26629\n0.27679\n0.96935\n1.6063\n-3.8211\n-4.3458\n-4.1571\n-3.5681\n-2.2905\n-1.9526\n-2.1258\n-1.8746\n-2.1423\n-0.42452\n-1.391\n-0.61295\n0.11249\n-0.51491\n-0.45079\n0.24667\n0.4039\n-1.509\n-1.7879\n1.5149\n5.0568\n3.1105\n2.9945\n3.8899\n5.6521\n4.8088\n1.8261\n3.3264\nNaN\nNaN\n4.3805\n4.9748\n7.6978\nNaN\nNaN\nNaN\nNaN\nNaN\n5.1384\n6.4286\n4.8327\n4.6714\n4.5868\n1.8163\n-0.12556\n1.4667\n0.93492\n-0.14016\n1.329\n1.174\n0.57142\n0.524\n0.80332\n0.67348\n-0.6566\n0.00051403\n0.090423\n-0.48777\n-0.58251\n-0.72909\n-0.070083\n-0.82747\n-0.097148\n-1.33\n-1.1936\n-2.3866\n-2.0095\n0.18381\n-0.0075254\n-0.019168\n0.80287\n1.2932\n-4.3395\n-4.4265\n-3.7811\n-3.85\n-2.5661\n-1.4546\n-1.9376\n-2.0137\n-2.2934\n-1.2563\n-1.2041\n0.28898\n-0.42602\n-0.20649\n-0.34655\n0.38525\n0.67677\n-0.29667\n0.016772\n1.7612\n2.1346\n3.1445\n2.8716\n3.3176\n4.7471\n4.3808\n2.6622\n4.2632\n4.6142\n6.235\n6.5075\n4.8788\n6.8815\n7.876\nNaN\nNaN\nNaN\nNaN\n5.2981\n6.301\n4.7153\n6.0594\n4.9248\n1.9207\n-1.1292\n0.85281\n1.037\n0.26225\n0.80626\n1.3492\n0.77651\n1.0535\n1.2911\n0.55791\n-0.32482\n-0.48438\n0.21604\n-0.40283\n-0.44095\n-0.48509\n-0.49369\n-0.50488\n-0.31429\n-1.6478\n-1.6716\n-1.8488\n-1.3331\n-0.11722\n-0.92789\n-0.54013\n0.34218\n-0.4053\n-3.6549\n-3.6751\n-3.7233\n-3.3157\n-2.8945\n-1.83\n-1.5746\n-1.7814\n-1.7853\n-1.0737\n-0.64971\n0.23305\n-0.61376\n0.78266\n0.57255\n0.10633\n-1.3111\n-4.8162\n0.69186\n2.4217\n2.7699\n3.3247\n2.7618\n2.894\n6.2487\n3.005\n3.5475\n4.3605\nNaN\n4.4099\n6.7165\n5.1215\n6.5431\n9.3429\nNaN\nNaN\nNaN\n5.4337\n5.9254\n6.2251\n5.5006\n5.277\n5.9445\n3.5537\n-0.25318\n0.47429\n1.249\n1.3787\n1.9326\n2.1522\n1.2469\n1.3858\n0.83331\n-0.36721\n0.48402\n-0.022259\n-0.095372\n-0.50329\n-0.3318\n-0.19971\n-0.25032\n-0.73696\n-0.90491\n-1.1991\n-0.59802\n-0.11179\n-0.060322\n-0.09886\n-0.44482\n-0.40216\n-0.14716\n-0.35302\n-3.3158\n-3.42\n-3.5649\n-3.1845\n-2.4347\n-1.8019\n-1.5015\n-1.705\n-1.6377\n-1.1524\n-0.12943\n-0.1571\n-0.20581\n1.5814\n0.98592\n0.67616\n-0.81646\n-5.1514\n0.55302\n2.2857\n3.9772\n2.2835\n3.5136\n2.4807\n6.1161\n4.1225\n3.5375\nNaN\nNaN\n3.4836\n5.8435\n5.3831\n5.395\n6.6536\n6.9928\n5.6937\n4.065\n5.2399\n5.5247\n6.2659\n5.5633\n3.5893\n4.159\n3.4977\n0.54013\n0.62234\n1.4471\n1.1802\n2.1002\n1.7906\n1.0777\n1.2875\n0.26056\n-0.50565\n0.67804\n0.42757\n-0.025531\n-0.45555\n-0.18454\n-0.072886\n0.018644\n-0.89241\n-0.70637\n-0.69118\n-0.10596\n0.14658\n-0.013852\n0.36991\n0.30394\n-0.54551\n-0.046529\n0.64849\n-3.7329\n-3.7531\n-3.377\n-2.6071\n-1.9722\n-1.7316\n-1.2375\n-1.557\n-1.5301\n-0.93961\n-0.12715\n0.43132\n-0.21043\n1.4728\n1.6925\n0.85388\n1.5994\n-4.4335\n0.090667\n-0.98446\n1.7122\n2.4773\n3.5239\n2.2076\n5.7723\n3.7946\n3.3774\nNaN\nNaN\nNaN\n4.9459\n5.5209\n6.0514\n4.9681\n5.6071\n6.3439\n5.9531\n4.9598\n5.108\n5.5641\n4.7357\n3.7573\n2.8789\n2.5177\n1.4466\n1.2836\n1.8083\n0.84952\n1.0234\n1.7267\n0.99344\n1.0239\n0.36858\n-0.80508\n0.19467\n0.43665\n-0.10635\n0.053435\n-4.269e-16\n-0.0087709\n0.10986\n-0.58381\n-0.81536\n-0.5441\n-0.30784\n0.18246\n0.31424\n0.55292\n0.57119\n0.1554\n0.47358\n1.055\n-3.4047\n-3.7478\n-3.2046\n-2.6652\n-1.7993\n-1.6618\n-1.554\n-0.93294\n-1.081\n-0.72784\n0.11063\n0.76464\n0.68042\n2.2035\n3.2572\n0.62065\n1.5901\n2.2939\n1.7087\n-0.74141\n-0.38991\n2.0527\n4.3628\n2.8948\n2.7552\n2.1486\n3.0922\nNaN\nNaN\nNaN\n4.5316\n5.4478\n5.2862\n5.2059\n5.1645\n5.1865\n4.8142\n4.2464\n4.0827\n3.7949\n3.216\n2.9519\n2.7856\n2.4632\n1.8909\n1.5858\n1.8198\n1.832\n1.7213\n1.4922\n1.1462\n0.69974\n0.51216\n-0.42022\n-0.27114\n0.33457\n-0.061176\n0.17411\n0.069987\n0.19513\n0.095148\n-0.10851\n-0.41155\n-0.52023\n-0.10533\n0.40811\n0.55012\n0.8209\n0.6217\n0.77703\n0.47279\n1.3697\n-3.2563\n-3.0455\n-2.8515\n-2.8951\n-2.3998\n-1.8569\n-2.1092\n-0.44263\n-0.25956\n-0.56092\n0.21675\n0.90837\n1.5234\n2.593\n5.0434\n1.7922\n2.4972\n2.8753\n1.8825\n0.247\n-0.067024\n-0.53065\n0.68009\n2.3113\n2.6593\n2.4054\n3.014\nNaN\nNaN\nNaN\n4.4199\n5.2908\n4.8625\n4.825\n6.1455\n5.2206\n4.3563\n4.0285\n3.5773\n3.6421\n3.3571\n3.1756\n2.7134\n2.5564\n2.2651\n0.85845\n1.2013\n0.94634\n0.41183\n1.658\n1.3378\n0.45187\n1.004\n0.67636\n0.32695\n0.192\n0.16716\n0.63912\n0.4326\n0.40439\n0.058085\n0.40386\n0.6104\n0.45485\n0.81223\n1.2646\n0.92204\n1.258\n0.84224\n0.9825\n1.2778\n1.6801\n-2.9374\n-2.4391\n-2.4669\n-2.6437\n-2.2369\n-2.062\n-1.8805\n-0.58424\n0.74722\n-0.47054\n0.16802\n1.1388\n1.7232\n2.5052\n3.9264\n2.6175\n1.7812\n3.0752\n2.1721\n1.1755\n1.0276\n0.9325\n1.0509\n1.5497\n2.3756\n2.7568\n3.3123\n3.5927\n3.5015\n3.7967\n3.7776\n3.6283\n3.8561\n4.1272\n6.2559\n5.265\n4.7666\n4.5091\n3.5721\n4.1123\n3.7112\n4.0508\n2.1668\n2.5531\n2.967\n1.9372\n1.8132\n-1.1132\n-2.2299\n1.5332\n1.0568\n0.52417\n1.335\n0.94409\n0.2134\n0.092109\n0.029384\n0.57282\n0.99721\n0.88579\n0.60503\n0.29018\n0.26234\n0.65684\n1.1396\n1.493\n1.2023\n1.5372\n1.149\n0.89902\n1.8031\n1.6935\n-3.3708\n-2.0723\n-2.2857\n-2.2983\n-1.7654\n-1.7623\n-1.2022\n-0.28823\n0.31055\n-0.29174\n-0.10573\n1.1937\n1.3497\n2.7175\n4.1549\n4.2061\n1.703\n2.058\n1.6016\n1.081\n1.3536\n1.1889\n2.3456\n1.5228\n2.442\n2.8238\n3.0187\n3.1305\n3.511\n3.0304\n3.311\n2.9333\n2.9865\n3.9579\n5.2205\n5.0901\n4.7667\n4.75\n3.2237\n2.6035\n2.2371\n3.7476\n1.9779\n1.5881\n3.5777\n3.9379\n0.88269\n-3.1058\n-4.0519\n0.99564\n0.44132\n1.3603\n1.2664\n0.5967\n-0.06434\n-0.025132\n0.22943\n0.37309\n0.97495\n0.68658\n0.72096\n0.65635\n0.30861\n0.31054\n0.88527\n1.1123\n1.3479\n1.3011\n1.2209\n1.6656\n1.6783\n1.6124\n-3.3051\n-1.7158\n-1.7275\n-1.5482\n-1.3477\n-1.2086\n-0.81916\n0.957\n-0.01274\n-0.46973\n-0.36006\n0.12807\n0.2357\n3.2055\n3.0741\n4.5002\n2.5042\n0.802\n0.53313\n0.96299\n1.9724\n1.453\n1.3334\n1.6441\n2.9297\n3.245\n3.2901\n3.7677\n3.2116\n2.359\n2.3726\n2.9457\n3.3684\n3.806\n4.5169\n4.7267\n4.3046\n4.7308\n3.3504\n2.5696\n1.8337\n3.2422\n2.4017\n0.50122\n1.5399\n3.0185\n3.4188\n-2.1583\n-1.9159\n-1.6988\n-0.374\n0.8616\n0.42851\n-0.052955\n-0.016807\n0.46103\n-0.34686\n-0.39071\n0.37633\n0.7409\n0.8174\n0.84291\n0.8346\n0.6083\n0.7835\n1.5842\n1.8154\n1.3024\n1.4139\n1.9506\n1.9517\n2.1514\n-2.6217\n-1.5508\n-0.82241\n-0.73888\n-0.94959\n-0.48468\n-0.89706\n1.8677\n1.1221\n0.72235\n-0.037305\n0.2341\n1.1067\n2.8922\n2.9785\n4.6189\n4.6345\n2.3519\n1.2505\n0.74563\n1.3059\n2.0057\n1.5201\n1.6858\n2.6895\n3.2944\n3.3634\n3.8745\n3.1137\n2.8867\n2.8603\n3.0702\n3.8462\n3.5791\n3.6305\n4.3684\n4.5823\n5.245\n3.8847\n3.0034\n2.5086\n2.9826\n2.4718\n-0.18561\n0.80504\n1.795\n5.2607\n-1.2339\n-0.43404\n-0.45843\n-0.3212\n-0.36308\n-0.50383\n-0.40894\n-0.4386\n-0.24512\n-0.99352\n-1.0285\n-0.060386\n0.79978\n0.61968\n0.13666\n0.60062\n0.94715\n1.1068\n1.8463\n2.2664\n1.5305\n2.3803\n1.2494\n1.6781\n1.5873\n-2.0968\n-1.7941\n-0.59437\n-0.19846\n-0.50988\n0.4908\n-0.23995\n2.025\n2.082\n1.7505\n1.3937\n-0.25544\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.4021\n-0.37419\n0.67214\n2.4931\n1.8552\n2.2978\n2.4907\n2.8382\n2.9986\n2.669\n2.6197\n2.6658\n2.9712\n2.4083\n3.0961\n3.6992\n3.2492\n3.4203\n4.1704\n4.2311\n4.5777\n2.4004\n2.8057\n2.3427\n1.7012\n0.017804\n-0.14727\n0.61279\n4.4513\n0.50953\n0.18102\n-0.064285\n-0.21599\n-0.76847\n0.017422\n-0.24156\n0.022696\n0.49997\n-1.0762\n-0.90365\n-0.15838\n0.18525\n-0.1027\n-0.5639\n0.64143\n0.9752\n0.52549\n0.54896\n1.9782\n1.5899\n3.1922\n0.77906\n0.81028\n0.75196\n-1.5996\n-1.9631\n-0.62062\n-0.27115\n0.55503\n1.2024\n1.8268\n2.9934\n2.7596\n2.5998\n2.9072\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.90182\n-0.2446\n0.74798\n1.6061\n1.9835\n2.8257\n2.9034\n3.3881\n2.7395\n2.3944\n2.5057\n2.6279\n3.2161\n1.904\n3.1017\n3.7099\n3.7859\n2.9125\n3.1623\n2.5031\n3.6818\n2.7692\n2.8905\n1.7733\n1.019\n0.0025501\n-1.3665\n-0.14371\n1.9215\n0.061406\n0.20735\n-0.6425\n-0.74845\n-0.46666\n0.28353\n0.1654\n0.59148\n0.77281\n-0.61897\n-0.25639\n-0.56228\n-0.83299\n-0.3316\n-1.1975\n-0.33668\n1.0446\n0.47648\n0.23663\n1.2924\n1.4459\n3.1054\n0.54598\n0.1269\n0.27724\n-0.012601\n-0.062094\n-0.31885\n-1.2498\n-0.74557\n-0.76839\n-0.87383\n-0.86872\n-1.2031\n-1.6757\n-2.0403\n-2.5781\n-2.6742\n-2.6953\n-2.3201\n-2.4019\n-2.3742\n-3.0691\n-2.4101\n-2.368\n-2.6878\n-3.0025\n-2.9812\n-2.861\n-3.3173\n-3.619\n-3.6449\n-3.5477\n-3.9515\n-4.0587\n-4.819\n-4.7504\n-4.5204\n-4.8376\n-4.9263\n-4.6558\n-4.4955\n-4.0973\n-3.8055\n-3.5847\n-3.65\n-3.2501\n-3.0738\n-2.6425\n-2.3478\n-2.3521\n-1.7636\n-1.3034\n-0.848\n-0.59212\n-0.24693\n-0.11619\n0.026525\n0.20434\n-0.77479\n-0.9156\n-0.31085\n-1.6249\n-3.3992\n-3.542\n-1.9043\n-0.23865\n1.0959\n2.1559\n2.4619\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.39192\n-0.075418\n-0.069877\n0.021461\n-0.044833\n-0.62759\n-1.015\n-0.73853\n-0.98218\n-1.6286\n-2.3812\n-3.316\n-2.9722\n-1.8954\n-1.7241\n-2.676\n-2.6084\n-2.6705\n-2.6161\n-3.0687\n-3.5879\n-3.2322\n-2.899\n-2.8756\n-2.5633\n-3.3349\n-3.3164\n-3.5119\n-3.7192\n-4.0295\n-4.1909\n-4.3634\n-4.907\n-4.6567\n-4.7756\n-4.5371\n-4.1698\n-4.6381\n-3.9522\n-3.7771\n-3.8677\n-3.2988\n-3.3337\n-3.2278\n-2.6433\n-2.2776\n-1.276\n-0.99309\n-0.65069\n-0.35437\n-0.44553\n0.15703\n0.23332\n0.23372\n-0.0129\n-0.21129\n0.0027447\n-1.2363\n-2.0839\n-2.1867\n-1.8091\n0.12016\n0.99417\n1.5491\n3.4\n3.8871\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.4342\n-0.59837\n-0.26504\n-0.14994\n0.55636\n-0.69959\n-0.88644\n-0.82245\n-0.8074\n-1.2415\n-1.9499\n-2.6685\n-1.9401\n-1.4852\n-0.72124\n-2.1253\n-2.4576\n-2.5349\n-2.6397\n-2.7285\n-2.811\n-2.4508\n-2.2967\n-2.3359\n-1.9865\n-2.3535\n-2.9137\n-3.2874\n-3.2362\n-3.7584\n-3.6807\n-5.1555\n-4.8259\n-4.4715\n-4.5981\n-4.6422\n-4.2975\n-4.7255\n-4.4783\n-4.0169\n-3.6928\n-3.5103\n-3.6096\n-2.6128\n-2.5535\n-2.194\n-1.8006\n-1.1381\n-0.87901\n-0.65197\n-0.20006\n1.3443\n0.97254\n1.006\n0.15379\n1.028\n1.0669\n-0.89437\n-1.7593\n-1.2201\n-0.48779\n0.57778\n1.7669\n2.1694\n3.2709\n3.8924\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.26968\n-0.39434\n-0.65469\n-0.60324\n-0.52361\n-0.86996\n-1.0995\n-1.0912\n-1.0614\n-1.4056\n-1.7607\n-2.1326\n-1.965\n-1.4682\n-2.4459\n-2.7075\n-2.2414\n-2.5088\n-1.9977\n-2.2052\n-2.7885\n-2.1776\n-2.0896\n-1.9559\n-1.9267\n-1.9268\n-2.6166\n-3.1241\n-3.3113\n-3.3596\n-3.5342\n-4.5577\n-3.7817\n-4.0666\n-4.521\n-4.5404\n-4.2145\n-4.4142\n-4.5\n-4.4547\n-4.1452\n-3.8415\n-3.8618\n-3.319\n-2.7698\n-2.2435\n-1.7414\n-1.2853\n-0.74226\n-0.044322\n0.66917\n1.2932\n1.8766\n1.718\n2.0106\n2.2864\n1.1952\n0.18966\n-0.38204\n-0.97855\n-0.33983\n0.65213\n2.7475\n2.0438\n2.595\n2.8112\n3.152\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.43011\n-0.26471\n-0.43257\n-0.16007\n0.039061\n-0.11239\n-0.40192\n-0.3292\n-0.7857\n-1.638\n-1.6134\n-1.5109\n-1.4683\n-2.5243\n-2.7377\n-2.8216\n-2.011\n-1.0345\n-1.1682\n-1.9094\n-2.7847\n-2.4481\n-2.4232\n-4.1019\n-2.4943\n-2.3285\n-2.3444\n-3.1305\n-3.2128\n-2.9901\n-3.3669\n-3.9821\n-3.6434\n-3.8457\n-4.584\n-4.1509\n-4.51\n-4.5425\n-4.3077\n-4.5375\n-4.5521\n-3.8078\n-3.5693\n-3.2386\n-3.1629\n-2.4617\n-1.4468\n-1.0881\n-0.89102\n-0.42334\n0.70313\n0.68442\n1.2433\n2.0157\n1.403\n1.893\n0.77102\n0.64514\n0.35526\n-0.5625\n-0.73955\n0.8469\n4.1779\n1.3326\n1.3455\n2.2266\n4.3327\nNaN\nNaN\nNaN\nNaN\nNaN\n0.19134\n0.12708\n0.18798\n0.29504\n0.26036\n0.22073\n-0.46465\n-0.69342\n-0.75316\n-1.1037\n-1.2418\n-1.6269\n-1.8805\n-2.3752\n-2.5649\n-2.3486\n-1.6666\n-1.1101\n-0.71687\n-1.856\n-2.4844\n-2.6399\n-2.8281\n-3.2678\n-2.4087\n-1.9402\n-2.4579\n-2.6774\n-2.7661\n-3.0014\n-3.2399\n-3.965\n-3.7337\n-3.8197\n-4.6185\n-4.683\n-4.6278\n-4.6991\n-4.6403\n-4.6013\n-4.497\n-3.7905\n-3.383\n-3.0824\n-2.7465\n-2.4362\n-1.6292\n-1.646\n-0.9267\n-0.48464\n0.55486\n0.49191\n1.1272\n1.8909\n1.3816\n1.9987\n3.2511\n3.6413\n1.5929\n-1.1454\n-1.575\n0.71431\n1.0804\n0.31337\n0.069933\n1.9775\n2.9922\nNaN\nNaN\nNaN\nNaN\nNaN\n0.28836\n0.20139\n0.60397\n0.66282\n0.53988\n0.42582\n0.16844\n-0.27707\n-0.21993\n-0.66416\n-0.78457\n-1.2286\n-1.3875\n-1.4928\n-1.4909\n-1.758\n-1.8416\n-2.0262\n-2.3412\n-2.5753\n-2.4977\n-2.3591\n-2.8325\n-2.2853\n-1.9126\n-2.1305\n-2.4626\n-2.2751\n-2.3875\n-2.7416\n-3.5765\n-4.0969\n-4.5953\n-4.8609\n-4.8808\n-4.7892\n-4.6645\n-4.3735\n-4.2158\n-4.283\n-4.1649\n-3.6045\n-3.3553\n-3.1523\n-2.8086\n-2.6868\n-2.1321\n-1.7552\n-0.82929\n-0.17811\n0.13368\n0.28556\n1.0833\n1.408\n1.7509\n1.9401\n2.5448\n2.2653\n0.94937\n-0.24901\n-0.45539\n-0.020505\n0.97305\n0.32165\n0.60071\n1.5469\n2.3239\n4.3097\n6.0096\nNaN\nNaN\nNaN\n0.75444\n0.6737\n0.91619\n1.2298\n0.66284\n0.4757\n0.022159\n0.069646\n0.01715\n-0.20645\n-0.30342\n-0.70972\n-1.1818\n-1.0035\n-1.0813\n-1.3749\n-1.7093\n-1.5603\n-1.9293\n-2.3671\n-2.5071\n-2.5833\n-2.39\n-2.0922\n-2.1062\n-1.9743\n-2.0306\n-2.0493\n-2.5102\n-2.6054\n-3.3692\n-4.4656\n-4.8594\n-4.6144\n-4.2998\n-4.2612\n-4.517\n-4.3695\n-4.3156\n-4.0554\n-4.1349\n-3.4535\n-3.2212\n-3.3459\n-3.0817\n-2.5448\n-2.1076\n-1.3435\n-0.52785\n-0.10228\n0.029654\n0.24907\n0.88517\n1.1643\n2.1425\n2.5031\n1.6487\n1.8589\n1.8917\n0.80308\n-0.85106\n-0.20763\n1.5643\nNaN\n1.4824\n0.86225\n1.936\n4.5271\n4.0724\n3.3365\nNaN\nNaN\n1.8419\n1.1099\n1.1311\n1.1416\n0.76323\n0.5114\n0.39755\n0.54562\n0.32363\n0.42948\n0.27062\n-0.018109\n-0.41464\n-0.6032\n-0.55285\n-0.8617\n-1.1634\n-1.4432\n-1.8069\n-2.0068\n-2.1194\n-2.387\n-2.1309\n-2.1651\n-2.3435\n-2.2255\n-2.1086\n-2.4116\n-2.8296\n-3.0237\n-3.3768\n-3.7552\n-4.2909\n-3.9362\n-3.7465\n-3.6642\n-4.3965\n-4.4145\n-4.2258\n-4.3674\n-3.8456\n-3.3631\n-2.9731\n-2.9994\n-3.2088\n-2.6526\n-1.9421\n-1.4872\n-0.18849\n0.14926\n0.23024\n0.90823\n1.3062\n1.5748\n2.2965\n2.4518\n1.2237\n1.8417\n1.7991\n-0.062946\n-0.19467\n1.8242\n1.9362\nNaN\n1.465\n1.4881\n1.6033\n1.4589\n1.9125\n3.9666\n3.1091\n3.0764\n1.5097\n0.97613\n0.872\n0.92683\n0.4581\n0.39673\n0.46697\n0.73571\n0.76257\n1.1197\n0.58423\n0.44122\n0.37579\n-0.34489\n-0.7824\n-0.93022\n-1.0347\n-1.3898\n-1.5732\n-1.7045\n-1.7765\n-1.9624\n-2.0516\n-2.1395\n-2.2564\n-2.2328\n-2.1716\n-2.323\n-2.9723\n-3.369\n-3.4866\n-3.8057\n-3.5952\n-3.2833\n-3.7783\n-3.9174\n-4.4104\n-4.2741\n-4.0745\n-4.219\n-3.8627\n-3.579\n-3.2304\n-3.2412\n-3.3241\n-2.5184\n-1.974\n-1.4144\n-0.62137\n-0.38072\n0.077081\n1.0766\n2.0582\n2.0313\n2.2009\n1.6564\n1.3763\n1.6226\n1.7181\n0.41704\n-0.4947\n-1.1697\n-1.2808\nNaN\nNaN\nNaN\n2.5954\n2.3747\n1.9645\n2.5946\n2.81\n2.9741\n0.9039\n1.0187\n1.3214\n1.1876\n0.56739\n0.86776\n0.82656\n0.8847\n0.98412\n0.77244\n0.54111\n0.78531\n0.48723\n0.044642\n-0.48144\n-0.55439\n-0.8791\n-1.4231\n-1.5623\n-1.6603\n-1.8572\n-1.8423\n-1.9976\n-2.3384\n-2.3447\n-2.3413\n-2.1966\n-2.0513\n-2.8158\n-2.883\n-3.3211\n-3.7086\n-3.4357\n-3.5999\n-3.6963\n-3.384\n-4.287\n-4.2187\n-4.1091\n-4.1743\n-3.9941\n-3.7517\n-3.8261\n-3.5422\n-3.0019\n-2.5447\n-1.9567\n-1.3425\n-1.0612\n-0.67735\n0.056918\n0.78875\n0.94869\n1.7152\n1.8462\n1.4075\n3.2098\n2.2563\n0.86395\n1.5407\n0.61478\nNaN\nNaN\nNaN\nNaN\nNaN\n3.5484\nNaN\n1.8099\n1.468\n2.9713\n3.0907\n1.3904\n1.2187\n1.446\n1.156\n1.2212\n1.3051\n1.0634\n1.0071\n0.96043\n0.99324\n0.78861\n0.51975\n0.16892\n0.22825\n-0.096408\n-0.3748\n-0.83449\n-1.2531\n-1.5938\n-1.6375\n-1.7864\n-1.9364\n-1.7817\n-1.6011\n-2.1828\n-2.3202\n-2.3488\n-2.0897\n-2.3224\n-2.5976\n-3.2068\n-3.8064\n-3.7787\n-3.6941\n-3.5467\n-3.3178\n-4.2634\n-4.13\n-4.4187\n-4.3535\n-4.3975\n-4.8201\n-4.8148\n-3.942\n-3.4736\n-2.9792\n-2.0486\n-1.6803\n-1.1139\n-0.76553\n0.25768\n0.53864\n0.50136\n0.027931\n0.48556\n1.1038\n2.827\n2.686\n1.7864\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.6822\n1.4255\n1.4515\n1.2636\n0.95243\n0.6254\n0.57694\n0.64871\n0.62956\n0.58897\n0.34344\n0.31801\n0.1649\n0.20397\n-0.28176\n-0.86872\n-1.0325\n-1.4369\n-1.6851\n-1.5588\n-1.7305\n-1.9674\n-2.0712\n-2.3087\n-2.0205\n-1.8716\n-1.6754\n-2.1113\n-2.6397\n-3.2593\n-3.3433\n-3.9149\n-4.1875\n-4.1962\n-3.7773\n-3.687\n-4.0712\n-4.151\n-4.1885\n-4.3635\n-4.9944\n-5.7365\n-5.4442\n-4.0408\n-3.48\n-3.003\n-2.0665\n-1.7115\n-1.1749\n-0.8039\n-0.52038\n-0.064882\n-0.05824\n-0.34699\n0.54397\n-0.099395\n-2.397\n-0.31055\n0.30528\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.7696\n1.657\n1.7037\n1.311\n0.73072\n0.57468\n0.7009\n0.54762\n0.55071\n0.71993\n0.49166\n0.40427\n0.055921\n-0.22739\n-0.50454\n-0.50254\n-0.54254\n-1.1082\n-1.5066\n-1.7127\n-1.7558\n-1.8134\n-2.4015\n-2.1934\n-2.0639\n-1.8729\n-1.9095\n-1.9445\n-2.6819\n-3.0993\n-3.5423\n-4.0245\n-4.0308\n-3.4148\n-3.183\n-3.7383\n-3.9017\n-3.5952\n-4.3043\n-4.6568\n-5.0601\n-5.2276\n-4.9498\n-3.8689\n-3.3607\n-2.6461\n-1.9707\n-1.7804\n-1.3873\n-1.0424\n-0.64095\n-0.3296\n-0.56069\n-0.53787\n-0.18114\n-0.1797\n-1.1276\n-0.76191\n-0.23659\n-0.039718\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1332\n1.7239\n1.7125\n1.6501\n1.256\n0.68114\n0.47176\n0.3584\n0.53527\n0.32804\n0.35844\n0.24505\n-0.086286\n-0.17315\n-0.55876\n-0.5689\n-0.77573\n-0.86549\n-1.2507\n-1.6765\n-2.2878\n-2.3812\n-2.8429\n-2.2385\n-2.0698\n-2.1533\n-2.1075\n-2.228\n-2.5338\n-2.6465\n-2.9256\n-4.459\n-4.1261\n-4.9944\n-4.9811\n-3.4874\n-3.8674\n-4.0245\n-4.3106\n-4.2858\n-4.356\n-4.8013\n-4.7419\n-3.6604\n-3.1711\n-2.3454\n-2.0021\n-2.0279\n-1.7647\n-1.4345\n-0.7564\n-0.27108\n-0.49715\n-0.6328\n-0.64585\n-0.6813\n-0.70681\n-1.5032\n-0.4301\n-1.9398\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.9993\n1.8071\n1.8296\n1.6315\n1.2779\n0.94912\n0.63035\n0.36938\n0.24263\n0.16293\n0.36972\n0.043658\n-0.34702\n-0.47964\n-0.79947\n-0.79567\n-0.78221\n-0.988\n-1.2782\n-1.7032\n-2.5805\n-2.8829\n-3.6481\n-3.1164\n-2.1015\n-2.2656\n-2.2879\n-2.4707\n-2.5848\n-2.732\n-3.3799\n-4.641\n-4.5952\n-4.6152\n-4.8115\n-4.7877\n-4.6176\n-4.3778\n-4.1111\nNaN\n-5.4581\n-5.1161\n-4.5682\n-3.4702\n-2.9961\n-2.8465\n-2.781\n-2.3699\n-2.0348\n-1.5175\n-1.3664\n-1.8081\n-1.0147\n-1.5605\n-0.52672\n-0.96514\n-1.0799\n-1.7783\n-1.8392\n-3.3038\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.0693\n2.0143\n2.0868\n1.8642\n1.5607\n1.0873\n0.8636\n0.58426\n0.052579\n0.013352\n0.23\n0.27972\n-0.53727\n-0.61985\n-0.76543\n-0.96284\n-1.0729\n-1.0951\n-1.3851\n-1.9975\n-2.5616\n-2.7055\n-2.9766\n-2.4642\n-2.2465\n-2.3841\n-2.1404\n-2.5702\n-2.7583\n-3.377\n-3.7639\n-3.9086\n-4.6146\n-4.762\n-4.0173\n-4.3767\n-4.5439\n-4.1226\nNaN\nNaN\nNaN\n-6.1465\n-4.4934\n-3.7392\n-3.8514\n-3.6734\n-3.4532\n-2.9171\n-2.1537\n-2.5403\n-2.7561\n-3.1802\n-1.9747\n-1.7105\n-0.89478\n-1.1669\n-1.0502\n-1.3668\n-1.4021\n-1.7156\n-0.24769\n-0.54594\n-1.528\n-3.168\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.191\n2.0377\n1.8922\n1.8556\n1.5973\n1.2426\n1.0317\n0.80417\n0.038123\n-0.1159\n-0.40168\n-0.66913\n-0.72369\n-0.70232\n-0.90068\n-0.78847\n-0.74771\n-0.78572\n-1.4291\n-1.7513\n-2.4421\n-2.9713\n-3.2537\n-2.9968\n-3.2261\n-2.4704\n-2.2057\n-3.066\n-3.3994\n-3.3574\n-3.4934\n-3.648\n-4.1411\n-3.9423\n-3.7863\n-4.039\n-3.9667\nNaN\nNaN\nNaN\nNaN\nNaN\n-5.4115\n-3.6521\n-3.9536\n-4.1738\n-3.506\n-2.93\n-2.5968\n-2.7759\n-2.3647\n-1.8602\n-1.6368\n-1.7156\n-1.7862\n-0.0060463\n0.31898\n-0.56976\n-0.61445\n-0.068895\n-0.3319\n-0.45636\n0.11049\n-0.98965\n-0.28088\n0.61129\n0.53297\nNaN\nNaN\nNaN\nNaN\nNaN\n2.1704\n2.1818\n1.8523\n1.8321\n1.6313\n1.3104\n1.162\n0.43402\n0.019899\n-0.15378\n-0.36578\n-0.54687\n-0.7598\n-0.44783\n-0.26259\n-0.72732\n-1.0867\n-1.1336\n-1.4262\n-1.7137\n-2.1878\n-2.8148\n-3.2057\n-3.153\n-2.8729\n-2.5849\n-2.5761\n-3.2134\n-3.121\n-3.2767\n-3.4168\n-4.449\n-4.5103\n-3.6769\n-3.7318\n-3.7717\n-3.4741\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.9551\n-3.6857\n-4.2217\n-4.6598\n-4.3996\n-3.6834\n-3.3961\n-3.2974\n-3.0867\n-2.213\n-3.1342\n-1.9134\n-0.60006\n0.63028\n0.24058\n0.019547\n0.69319\n0.24515\n0.34357\n0.53051\n0.40393\n0.14698\n0.57281\n0.36528\n0.064939\n-0.9727\n-0.35784\nNaN\nNaN\nNaN\n2.3035\n2.1942\n1.8629\n1.654\n1.5739\n1.1244\n0.73798\n0.37134\n0.1822\n-0.26175\n0.00020313\n-0.42221\n-0.35554\n-0.43574\n-0.68412\n-0.89754\n-1.0202\n-1.25\n-1.0835\n-1.5596\n-1.9047\n-2.5785\n-2.6159\n-2.8949\n-2.7858\n-2.5247\n-2.7315\n-3.0974\n-2.9199\n-3.4146\n-4.0814\n-4.8004\n-5.001\n-4.3265\n-3.7087\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.5636\n-3.7535\n-3.7679\n-4.82\n-4.8276\n-4.0516\n-3.7787\n-4.3136\n-3.4586\n-3.211\n-3.8407\n-1.6384\n-1.1884\n-0.62754\n-0.14618\n0.37054\n0.62693\n0.95214\n0.85382\n1.1436\n0.49923\n0.58725\n0.12773\n-0.17661\n0.23969\n1.0554\n0.1472\nNaN\n-0.198\nNaN\n1.9689\n2.1005\n1.966\n1.8169\n1.7837\n1.1967\n0.97925\n0.80292\n0.41724\n0.18997\n0.16142\n-0.3005\n-0.47426\n-0.53436\n-0.69552\n-0.84168\n-1.0595\n-1.8299\n-1.7654\n-1.8771\n-1.6621\n-2.5383\n-2.9355\n-2.6172\n-2.69\n-2.4019\n-2.1247\n-2.7795\n-2.8395\n-3.0598\n-3.7104\n-3.3405\n-3.9074\n-3.8674\n-3.5527\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.6462\n-3.8723\n-3.7429\n-4.7628\n-5.1712\n-4.004\n-3.5941\n-4.5905\n-2.8971\n-2.3963\n-2.0644\n-1.3971\n-0.9467\n-0.52398\n-0.25651\n0.64701\n0.142\n0.5175\n0.98991\n0.37876\n0.6884\n0.39515\n0.43429\n0.58363\n0.40957\n0.342\n0.13528\n0.69086\n0.99784\n-0.80388\n2.5119\n2.411\n2.0284\n1.8138\n2.0752\n1.7578\n1.0649\n0.47569\n0.53776\n0.37259\n0.16447\n-0.10647\n-0.30899\n-0.65072\n-1.4468\n-1.3094\n-1.4611\n-2.014\n-1.8786\n-1.9721\n-1.4407\n-2.1449\n-2.6657\n-2.1711\n-2.206\n-1.5231\n-1.8007\n-2.5146\n-2.7177\n-3.1011\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.4841\n-3.6821\n-3.8896\n-4.6462\n-5.3519\n-4.3811\n-2.7156\n-2.4325\n-2.2265\n-1.9233\n-1.4561\n-1.0767\n-0.35668\n-0.14583\n-0.23396\n-0.12658\n-0.38038\n0.33826\n1.0843\n0.56567\n1.0598\n0.90252\n1.4792\n1.2164\n0.42784\n0.83331\n1.0454\n1.5088\n1.3724\n0.85503\n2.7585\n2.4326\n1.7305\n1.7436\n1.5911\n1.7507\n1.4593\n0.77342\n0.77336\n0.54487\n0.10013\n-0.097152\n-0.24534\n-0.42866\n-0.82681\n-1.2295\n-1.1828\n-1.5067\n-1.7003\n-1.6591\n-1.5875\n-2.2514\n-2.1223\n-1.9169\n-2.0364\n-1.6467\n-1.9875\n-2.6237\n-2.4744\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.3517\n-4.2419\n-3.9958\n-3.3537\n-2.8425\n-2.4927\n-1.9521\n-1.5468\n-1.1162\n-0.56619\n-0.23542\n-0.092757\n-0.082694\n-0.52787\n-0.55585\n-0.44814\n0.46972\n0.95789\n0.3889\n0.85164\n1.7578\n1.0977\n1.4322\n1.1672\n1.1565\n1.1366\n0.72594\n2.3984\n2.5242\n2.244\n2.0295\n1.9632\n1.9682\n2.0079\n1.5496\n1.3135\n0.87851\n0.50784\n0.031634\n-0.23235\n0.043521\n-0.22416\n-0.81962\n-0.70605\n-0.90101\n-1.311\n-1.438\n-2.1453\n-2.1562\n-2.2591\n-2.547\n-1.8826\n-2.0968\n-2.1705\n-2.795\n-3.3513\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.7118\n-3.5211\n-3.1875\n-2.9917\n-2.3813\n-1.8244\n-1.5587\n-0.84228\n-0.54378\n-0.54276\n-0.18883\n0.46872\n0.039414\n-0.076662\n0.37939\n0.79608\n1.133\n0.88571\n0.3735\n0.41576\n1.6903\n1.65\n0.84834\n1.6179\n1.1693\n0.85045\n1.5693\n2.426\n2.3409\n2.2497\n2.4371\n2.6654\n2.3234\n1.6928\n1.1638\n0.48428\n0.43344\n0.30477\n0.13643\n0.084623\n-0.35402\n-0.59925\n-0.74129\n-1.4352\n-1.1543\n-1.2302\n-1.8069\n-2.2145\n-2.2543\n-2.651\n-2.3002\n-2.1043\n-1.7948\n-2.2484\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.6768\n-3.7457\n-3.9645\n-2.9924\n-2.4359\n-2.1169\n-1.7371\n-1.3009\n-0.69828\n-0.86643\n-0.3181\n0.76022\n0.32547\n0.018313\n0.44648\n0.54711\n0.82125\n0.84389\n0.90779\n0.87409\n0.95182\n0.61472\n1.1774\n1.0883\n0.66851\n1.0556\n0.70433\n2.6096\n2.513\n2.4275\n2.6847\n2.6276\n2.1474\n1.8236\n1.2973\n0.57912\n0.3105\n0.36675\n0.22316\n-0.0083895\n-0.29769\n-0.76388\n-0.99272\n-1.1518\n-1.7578\n-1.2437\n-1.7012\n-2.0166\n-2.3716\n-2.8098\n-2.4613\n-1.9817\n-1.4326\n-1.4192\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.1595\n-3.9616\n-3.6793\n-2.7657\n-2.5141\n-2.419\n-2.3844\n-1.5616\n-0.97072\n-1.0718\n-0.65998\n0.71977\n-0.042568\n-0.26739\n0.090384\n0.94974\n0.74436\n0.85349\n1.4079\n1.2821\n1.2042\n0.66132\n1.3128\n0.52998\n0.33223\n1.2654\n1.1354\n2.5246\n2.5012\n2.5614\n2.6783\n2.6968\n2.3375\n2.1944\n1.6401\n0.58678\n0.010339\n-0.035132\n-0.13618\n-0.2956\n-0.51418\n-0.85682\n-0.868\n-1.0459\n-1.9548\n-2.1708\n-1.87\n-1.9881\n-2.2885\n-2.1514\n-1.659\n-1.8094\n-1.1351\n-1.43\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.8613\n-4.4122\n-3.2337\n-2.9979\n-3.0224\n-2.7377\n-3.5112\n-3.3805\n-0.82672\n-0.71343\n0.11285\n1.0061\n0.47529\n0.23386\n0.1983\n0.3393\n0.35983\n0.65031\n0.69221\n1.387\n1.8426\n1.5621\n1.4595\n1.3142\n0.9484\n1.3054\n1.2375\n1.0921\n2.5294\n2.9932\n2.7138\n2.5711\n2.5894\n2.464\n1.9945\n1.237\n0.68184\n0.088484\n-0.17292\n-0.39324\n-0.65656\n-0.9523\n-0.96079\n-0.93222\n-1.0714\n-1.5895\n-2.045\n-1.8639\n-1.7596\n-2.0038\n-2.1242\n-2.0988\n-1.9025\n-0.71701\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.8295\n-3.6803\nNaN\n-3.6791\n-3.8368\n-3.7575\n-3.257\n-3.1302\n-3.0001\n-2.9214\n-2.4583\n0.39194\n0.33824\n0.059493\n0.20238\n0.62344\n0.34791\n0.30612\n0.29847\n0.10847\n0.41064\n0.70796\n0.91889\n1.5216\n1.2662\n1.3534\n1.0278\n0.69006\n1.0154\n1.0751\n0.53645\n-0.13413\n2.939\n2.9137\n2.4977\n2.5835\n2.4779\n2.0185\n1.4842\n0.84308\n0.8854\n0.58999\n-0.078901\n-0.30774\n-0.48238\n-1.0844\n-0.93598\n-1.1669\n-1.4616\n-1.3367\n-1.5818\n-1.361\n-1.1722\n-1.1201\n-1.5104\n-1.8697\n-1.4241\n-0.56026\n-1.2487\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.5377\n-3.6043\n-3.8937\n-3.9136\n-3.4726\n-3.2724\n-3.2917\n-3.2716\n-3.0664\n-2.8662\n-2.7633\n-2.175\n-1.9217\n-2.0683\n-0.99466\n0.28079\n-0.087375\n-0.10948\n0.084718\n-0.14828\n-0.2746\n0.28524\n0.86927\n0.6183\n0.73308\n0.63999\n0.79045\n1.0856\n1.0563\n0.66496\n0.69736\n0.60444\n-0.020366\n-1.0317\n2.6782\n2.4442\n2.3993\n2.435\n2.3235\n1.6592\n1.4124\n1.304\n0.89896\n0.63186\n-0.2218\n-0.50593\n-0.67033\n-1.0118\n-0.95601\n-1.1152\n-2.0145\n-1.4238\n-1.4716\n-1.4132\n-1.0307\n-1.3504\n-1.9519\n-1.6751\n-0.77765\n-0.54017\n-1.1439\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.3839\n-3.6155\n-3.5777\n-3.611\n-3.5855\n-3.3675\n-3.1492\n-3.0276\n-2.7135\n-2.9706\n-2.9561\n-2.8507\n-2.6059\n-2.3679\n-1.5612\n-0.49524\n-0.7295\n-0.88033\n-0.23421\n0.046187\n-0.032217\n0.24367\n1.0503\n0.5551\n0.16365\n0.22727\n0.80912\n1.1381\n0.8581\n0.072939\n0.060475\n-0.043571\n-0.14889\n-0.52017\n2.6726\n2.2909\n2.4032\n2.0103\n1.6966\n1.0819\n0.82599\n1.053\n0.70666\n0.24037\n-0.52679\n-0.87624\n-1.2668\n-1.1613\n-1.0371\n-1.5561\n-1.9559\n-1.8033\n-1.6329\n-1.889\n-1.895\n-1.9959\n-1.9428\n-0.83795\n-0.24181\n-0.39837\n-1.058\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.6733\n-2.8782\n-3.2184\n-3.1937\n-3.5305\n-3.2787\n-3.3469\n-3.7371\n-3.8333\n-3.3403\n-3.0321\n-2.6591\n-2.4881\n-2.7526\n-2.747\n-2.4953\n-2.5741\n-2.2774\n-1.7626\n-1.1717\n-0.97984\n-0.97594\n-0.52919\n-0.27271\n-0.23771\n-0.2476\n0.49678\n0.49219\n-0.18995\n-0.25077\n0.53599\n0.61452\n0.41901\n0.1158\n0.046253\n0.46532\n-0.080027\n-0.30947\n2.2018\n2.1721\n2.0323\n1.6849\n1.4612\n0.66896\n0.79869\n1.0778\n0.75176\n0.12326\n-0.29941\n-0.9432\n-1.2515\n-1.0444\n-0.85283\n-1.5424\n-2.0065\n-2.2633\n-2.0575\n-2.0086\n-1.639\n-1.7669\n-1.5036\n-1.1374\n-0.42048\n-0.80152\n-1.4827\n-1.1995\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.5722\n-2.3638\nNaN\nNaN\nNaN\nNaN\n-3.488\n-3.8539\n-3.4801\n-2.8537\n-3.179\n-2.6641\n-2.5042\n-2.2252\n-2.6479\n-2.5331\n-2.5119\n-2.4133\n-1.9442\n-1.5551\n-1.3183\n-1.1882\n-0.76902\n-0.36364\n-0.39844\n-0.63276\n-0.61489\n-0.17457\n-0.04878\n-0.032855\n-0.21392\n0.49279\n0.53075\n0.41216\n0.65032\n-0.016684\n-0.47952\n-0.47038\n-0.41554\n2.2024\n2.2153\n1.8244\n1.5172\n1.1923\n0.75196\n1.2081\n1.2607\n0.43804\n-0.18004\n-0.64662\n-0.8794\n-0.99106\n-0.79198\n-0.7296\n-1.1091\n-1.3167\n-2.382\n-3.158\n-2.8588\n-2.2979\n-2.3764\n-1.8735\n-1.3153\n-1.0738\n-1.2304\n-2.4397\n-2.4894\n-1.8898\nNaN\nNaN\n-2.7326\n-2.9913\n-2.0054\n-2.0084\nNaN\nNaN\n-2.9807\n-3.1672\n-3.5761\n-3.4685\n-3.1006\n-3.0223\n-3.2178\n-3.6234\n-2.7333\n-2.5878\n-3.0998\n-2.5781\n-2.274\n-2.1313\n-1.6591\n-1.3732\n-1.3241\n-0.78034\n-1.0708\n-0.40494\n-0.41349\n-0.30544\n-0.33837\n-0.087771\n0.47701\n0.34391\n-0.14245\n-0.15836\n0.29518\n0.12656\n0.41207\n-0.35907\n-0.62545\n-0.57171\n-0.58297\n2.1247\n1.5041\n1.1472\n1.5746\n1.4288\n1.0606\n0.93548\n0.58212\n0.43459\n-0.031102\n-0.23279\n-0.59692\n-0.66666\n-0.52392\n-0.68388\n-0.94939\n-1.1633\n-1.2123\n-2.0643\n-2.7904\n-2.3774\n-2.4355\n-2.23\n-1.4364\n-1.392\n-2.5585\n-2.8953\n-2.7163\n-2.9928\n-4.0712\n-3.6832\n-3.265\n-2.506\nNaN\nNaN\nNaN\nNaN\n-5.1956\n-4.1698\n-3.7445\n-3.5565\n-2.9583\n-2.9606\n-3.2873\n-3.7015\n-3.0869\n-3.0705\n-2.7967\n-2.5686\n-2.2544\n-2.0431\n-1.4617\n-1.32\n-0.97022\n-0.73999\n-0.8065\n-0.55469\n-0.24054\n-0.025884\n-0.060735\n-0.26233\n-0.13186\n0.18528\n-0.35725\n-0.16228\n0.28372\n-0.050528\n0.29385\n-0.20968\n-0.71302\n-0.67278\n-0.68089\n2.452\n2.2961\n2.043\n1.7481\n1.5117\n1.3018\n1.4643\n1.3578\n1.3618\n0.7347\n0.019538\n-0.61012\n-1.0915\n-0.87189\n-0.90638\n-0.86923\n-1.2304\n-0.9767\n-0.81969\n-1.2037\n-1.9703\n-2.5319\n-2.0386\n-1.7577\n-1.9865\n-2.5123\n-2.8449\n-2.6837\n-2.8298\n-3.063\n-3.2295\n-3.226\n-2.698\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.2125\n-4.0839\n-3.4047\n-3.3681\n-3.0707\n-4.0023\n-4.3838\n-3.1791\n-2.9284\n-2.8644\n-2.5836\n-2.1349\n-1.3957\n-1.2134\n-1.0867\n-0.51389\n-0.5357\n-0.65753\n-0.528\n-0.27027\n-0.24404\n0.19061\n-0.13487\n-0.44989\n-0.091058\n0.00011349\n-0.28241\n-0.47673\n-0.60862\n-0.49858\n-0.75542\n-0.80075\n-0.74752\n-0.67546\n2.452\n2.1158\n1.9453\n1.8044\n1.6319\n1.9774\n1.9438\n1.9053\n1.8799\n1.2219\n-0.049188\n-0.81833\n-1.0087\n-0.78119\n-1.0717\n-1.5203\n-1.4563\n-1.2272\n-1.3233\n-1.7903\n-2.1826\n-2.2376\n-1.9649\n-1.8187\n-2.1608\n-2.7817\n-2.6659\n-2.711\n-2.8781\n-2.9747\n-3.0516\n-3.0742\n-2.9018\n-3.1989\nNaN\nNaN\nNaN\n-4.4924\n-3.9655\n-3.9035\n-3.4567\n-3.5674\n-3.1123\n-4.1194\n-4.4095\n-3.4067\n-2.965\n-2.8695\n-2.7236\n-2.0495\n-0.56684\n-0.56093\n-0.95063\n-1.0434\n-0.46576\n-0.29006\n-0.23826\n-0.32247\n-0.24984\n-0.20309\n-0.19243\n-0.29407\n-0.87032\n-0.47546\n-0.51974\n-0.56414\n-0.66483\n-1.0093\n-0.90323\n-0.53922\n-1.0065\n-0.76445\n2.4527\n2.0211\n1.8802\n1.875\n1.9049\n2.1515\n1.9121\n1.7448\n1.5012\n1.0204\n0.19464\n-0.35398\n-0.4025\n-0.63354\n-0.92323\n-1.3282\n-1.8244\n-2.5027\n-1.2377\n-1.5233\n-2.1445\n-1.8706\n-1.8528\n-2.145\n-2.5234\n-2.4352\n-2.5855\n-2.6973\n-2.809\n-3.9211\n-3.9173\n-3.5097\n-3.1538\n-4.0294\nNaN\nNaN\nNaN\n-3.9852\n-3.7828\n-3.5678\n-4.1246\n-3.7202\n-3.4908\n-3.9537\n-4.4208\n-3.3224\n-2.0604\n-2.1728\n-2.1175\n-1.4597\n-0.65635\n-0.85038\n-0.61503\n-0.42299\n-0.38038\n-0.43508\n-0.33784\n-0.20538\n-7.9074e-17\n-0.12241\n-0.17183\n-0.51842\n-0.79749\n-0.48749\n-0.59952\n-0.68629\n-0.59932\n-1.1771\n-1.3222\n-1.1618\n-0.9546\n-0.76809\n2.4728\n2.0856\n1.8944\n1.8325\n1.8795\n2.1614\n1.6213\n1.6109\n1.6745\n1.0856\n0.26404\n-0.028102\n-0.53345\n-0.75822\n-1.3347\n-1.8387\n-1.3431\n-0.90754\n-0.90009\n-1.5641\n-2.1481\n-1.9923\n-2.2924\n-2.8741\n-3.587\n-2.7443\n-2.6447\n-2.3614\n-2.17\n-4.043\n-3.8885\n-3.5941\n-3.839\n-3.7288\n-3.3687\n-3.9702\n-3.9607\n-3.9892\n-3.8132\n-3.7499\n-4.8624\n-4.1648\n-4.0189\n-4.1956\n-3.6461\n-2.1979\n-1.816\n-1.8201\n-2.1121\n-2.047\n-1.3322\n-1.0188\n-0.41741\n-0.33982\n-0.56301\n-0.65456\n-0.30915\n0.25494\n0.35459\n-0.0029726\n-0.13205\n-0.30989\n-0.60607\n-0.64105\n-0.80728\n-0.51288\n-0.36557\n-0.63613\n-0.91374\n-1.4273\n-1.0035\n-0.8414\n2.4512\n2.3238\n2.0315\n2.017\n1.9392\n2.1182\n2.0401\n1.5007\n1.1138\n0.74689\n0.24332\n0.017787\n-0.20835\n-0.71664\n-1.4364\n-2.0394\n-1.8221\n-0.23989\n0.40084\n0.36361\n-1.1036\n-1.9962\n-2.4321\n-3.5999\n-4.2252\n-3.2306\n-3.1069\n-2.4553\n-3.2515\n-3.4939\n-3.4127\n-3.4164\n-3.2609\n-2.9371\n-3.5034\n-3.3256\n-3.4668\n-3.9642\n-3.5895\n-3.0645\n-4.331\n-4.1682\n-3.421\n-3.2839\n-3.0919\n-2.8375\n-2.4168\n-2.2387\n-2.1168\n-1.9084\n-1.5595\n-1.1471\n-0.77869\n-0.99668\n-0.6036\n-0.48444\n0.15401\n0.60785\n0.43891\n0.35493\n-0.0020704\n-0.34893\n-0.49331\n-0.59471\n-0.57904\n-0.40863\n-0.19485\n-0.43942\n-0.88564\n-1.113\n-1.2354\n-0.87879\n2.7513\n2.652\n2.1867\n2.2929\n2.1781\n2.1177\n1.8393\n1.5855\n0.79014\n0.79252\n0.2754\n0.19954\n0.27002\n-0.061816\n-0.88144\n-1.1383\n-0.56546\n-0.3788\n-0.51367\n-0.42605\n-1.4912\n-2.1512\n-2.353\n-2.4482\n-2.6296\n-3.077\n-2.7687\n-2.5574\n-3.765\n-3.5594\n-3.3398\n-3.2952\n-3.315\n-3.4616\n-3.7361\n-3.5118\n-3.2909\n-2.898\n-2.6857\n-3.3139\n-4.1751\n-4.0088\n-3.5597\n-3.3608\n-3.3115\n-2.9687\n-2.6917\n-2.2578\n-1.8791\n-1.7643\n-1.4106\n-1.403\n-1.1758\n-0.51849\n-0.42019\n-0.43696\n-0.15081\n0.31024\n0.30622\n0.33477\n-0.067628\n-0.24664\n-0.20099\n-0.34939\n-0.20882\n-0.322\n-0.34193\n-0.64229\n-0.81405\n-1.0342\n-0.66654\n-0.97189\n2.9419\n2.5876\n2.6904\n2.3735\n2.1479\n2.0235\n1.8329\n1.6428\n1.0986\n1.1795\n0.71125\n0.47734\n0.35615\n0.4225\n0.16174\n-0.6698\n-1.4297\n-0.96327\n-1.3016\n-1.5918\n-1.7668\n-2.3919\n-2.8586\n-2.5362\n-2.6701\n-2.6613\n-2.8134\n-3.1464\n-3.4855\n-3.311\n-3.6531\n-3.7193\n-3.4658\n-3.419\n-3.6187\n-3.4962\n-3.2618\n-3.0667\n-2.9607\n-3.4487\n-3.65\n-3.8766\n-3.786\n-3.6264\n-3.2202\n-2.3381\n-2.4737\n-2.3774\n-2.2964\n-1.5688\n-1.2378\n-1.3062\n-1.1658\n-0.52152\n-0.47836\n-0.28823\n-0.71995\n0.18871\n0.15929\n0.29112\n-0.017005\n0.0070162\n-0.017817\n0.096866\n-0.059016\n-0.43172\n-0.42107\n-0.69929\n-0.66259\n-0.72674\n-0.96816\n-1.0697\n3.015\n3.0318\n2.9653\n2.2797\n2.9189\n1.9256\n2.3097\n2.3232\n1.7922\n1.3876\n0.77313\n0.38958\n0.26973\n-0.52798\n-1.0649\n-0.6659\n-0.58087\n-1.973\n-1.9378\n-1.4089\n-1.754\n-2.2496\n-2.5458\n-2.5752\n-2.6256\n-2.6561\n-3.0315\n-3.2146\n-3.5981\n-3.4666\n-3.8734\n-3.7605\n-3.13\n-3.1136\n-3.567\n-3.3164\n-3.0597\n-3.2612\n-3.2008\n-3.6029\n-3.5785\n-3.6829\n-3.4281\n-2.6735\n-2.4725\n-2.3355\n-3.4415\n-3.022\n-2.7244\n-1.3768\n-1.1509\n-1.4801\n-1.4021\n-0.63344\n-0.40846\n-0.17682\n-0.28357\n-0.081903\n0.012505\n0.080083\n-0.15111\n0.23562\n0.42475\n0.20375\n-0.071497\n-0.078301\n-0.336\n-0.57354\n-0.59135\n-0.55666\n-0.98112\n-0.64395\n2.8994\n2.758\n2.7181\n2.4082\n2.4079\n2.2465\n2.2095\n1.7858\n1.8813\n1.4565\n1.0982\n0.58231\n-0.031035\n-0.6823\n-1.8628\n-2.2876\n-1.176\n-1.8848\n-1.9922\n-1.8951\n-2.0622\n-2.0937\n-2.505\n-2.5091\n-2.5755\n-2.8243\n-2.6916\n-2.6136\n-2.8876\n-2.9824\n-3.3246\n-3.6836\n-3.0991\n-3.164\n-3.4623\n-3.342\n-3.2249\n-3.284\n-3.4737\n-3.424\n-3.7233\n-3.7959\n-3.1427\n-2.8831\n-2.6277\n-2.5104\n-3.2144\n-2.9955\n-2.6121\n-1.3643\n-0.87899\n-1.9063\n-1.3087\n-0.54837\n-0.66078\n-0.2626\n-0.03942\n0.018786\n-0.093565\n-0.13611\n-0.28813\n0.2406\n0.18978\n-0.0038452\n0.20196\n0.42239\n0.0068703\n-0.17557\n-0.42938\n-0.79532\n-0.57981\n-0.48621\n2.9289\n2.8674\n2.7355\n2.4432\n2.5261\n2.6998\n2.5871\n2.4586\n1.3247\n1.5942\n1.5365\n0.76401\n-0.042922\n-0.25244\n-0.51609\n-1.294\n-1.8294\n-1.8661\n-1.9879\n-2.2133\n-2.2474\n-2.0993\n-2.8595\n-2.6116\n-2.3047\n-2.2908\n-2.5566\n-2.6997\n-2.7457\n-2.8129\n-2.8933\n-3.1207\n-3.5198\n-3.4993\n-3.5024\n-3.677\n-3.5339\n-3.5293\n-3.2038\n-3.2143\n-3.1116\n-3.2659\n-3.2502\n-3.3325\n-3.1543\n-2.7293\n-3.0171\n-3.2938\n-2.6221\n-2.0546\n-1.2668\n-1.4478\n-0.67731\n-0.56318\n-0.59957\n-0.041358\n0.19009\n0.36751\n0.020424\n-0.27773\n-0.32278\n-0.27353\n-0.33999\n-0.33474\n0.034522\n-0.050151\n-0.24864\n-0.12304\n-0.38498\n-0.54817\n-0.46707\n-0.57598\n3.222\n3.0077\n2.7907\n2.4045\n2.348\n2.5129\n2.9289\n2.8051\n2.2305\n2.1055\n1.8294\n1.2094\n0.63601\n0.16676\n-0.051751\n-0.80804\n-2.0433\n-2.6788\n-2.2168\n-2.1374\n-2.5705\n-2.1409\n-3.2664\n-2.7763\n-2.4179\n-2.5292\n-2.5405\n-2.4708\n-2.6411\n-2.7991\n-2.7796\n-3.0359\n-3.3472\n-3.4654\n-3.6722\n-3.6208\n-3.583\n-3.3533\n-3.2469\n-3.202\n-3.1662\n-3.6843\n-3.4404\n-3.3526\n-3.1367\n-2.7273\n-2.4683\n-3.1062\n-2.3727\n-1.9555\n-1.6702\n-0.93478\n-0.84013\n-0.33916\n-0.062824\n0.0033369\n0.086705\n0.086782\n0.23599\n-0.39411\n-0.27844\n-0.31425\n-0.35802\n-0.38414\n-0.15967\n-0.27118\n-0.44636\n-0.047185\n-0.39397\n-0.54536\n-0.40227\n-0.32765\n3.0968\n3.0554\n3.0909\n2.6246\n2.5955\n2.9011\n2.9687\n2.7061\n2.6777\n2.2375\n1.8278\n1.7022\nNaN\nNaN\nNaN\n-2.416\n-3.7883\n-3.6424\n-2.5849\n-3.0888\n-2.5081\n-3.2244\n-3.0531\n-3.1592\n-2.7216\n-2.4748\n-2.4528\n-2.489\n-2.6699\n-2.7369\n-2.7177\n-2.9053\n-3.0224\n-3.2791\n-3.4143\n-3.3566\n-3.1596\n-3.1331\n-3.2061\n-3.0724\n-2.8687\n-2.587\n-2.4822\n-2.8643\n-3.1759\n-2.8471\n-2.478\n-2.9313\n-2.3293\n-2.0487\n-1.8952\n-1.4047\n-1.2704\n-0.88434\n-0.51201\n-0.22201\n-0.36442\n-0.48987\n-0.063346\n-0.24106\n-0.13457\n-0.23809\n-0.27947\n-0.12669\n0.11947\n0.12021\n0.60434\n0.33277\n-0.48216\n-0.21039\n0.067204\n-0.21212\n3.0971\n3.4525\n3.6834\n3.1376\n3.1903\n3.382\n3.5094\n3.6002\n2.6551\n2.0376\n1.7937\n1.7947\nNaN\nNaN\nNaN\n-1.5036\n-2.6974\n-3.6102\n-3.0229\n-2.5767\n-1.7476\n-2.1284\n-2.2514\n-2.2041\n-2.2123\n-2.1278\n-2.3358\n-2.1324\n-2.532\n-2.3999\n-2.3194\n-2.6686\n-2.829\n-3.1536\n-3.2791\n-3.099\n-3.1217\n-3.6271\n-3.411\n-2.6775\n-2.6581\n-2.6422\n-3.1423\n-2.6956\n-2.5698\n-2.7979\n-2.4133\n-2.544\n-2.3311\n-2.1353\n-1.7357\n-1.0471\n-1.4939\n-1.2008\n-0.60356\n-0.72189\n-0.86636\n-0.38846\n-0.042088\n0.47082\n-0.057475\n0.061811\n0.061816\n0.41066\n0.69882\n0.41779\n0.54703\n0.15546\n-0.54303\n-0.32556\n-0.19467\n-0.51673\n1.5351\n-0.32651\n-0.079923\n0.41181\n-0.13984\n-0.18846\n-0.78401\n0.55053\n2.1871\n2.9873\n2.3692\n2.4626\n0.77432\n0.19683\n1.2398\n1.327\n2.4946\n-0.26114\n6.7759\n4.2519\n3.2517\n2.2426\n2.5623\n2.8251\n2.9795\n1.4511\n1.2629\n0.64855\n2.5527\n3.2578\n1.0952\n1.4875\n0.13909\n-0.95298\n-1.0468\n1.456\n1.3015\n0.28972\n-1.2079\n-1.6829\n-0.70741\n-0.0050035\n0.89435\n0.98369\n0.7143\n1.4529\n0.69754\n0.44547\n0.1022\n-0.24626\n-0.51099\n0.54355\n0.51667\n-0.033776\n2.1441\n1.3994\n1.5844\n0.38026\n0.30716\n0.78315\n-0.40044\n-1.9826\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n0.60277\n0.20965\n-0.0066328\n0.97305\n0.48189\n-0.55382\n-1.2441\n0.42497\n3.6281\n3.442\n4.0294\n4.1084\n1.0156\n-0.85845\n1.8059\n2.5541\n0.9586\n-0.01165\n1.9143\n2.4097\n5.9995\n2.5958\n1.1324\n3.5498\n3.2511\n1.4812\n2.7605\n1.7778\n3.2303\n2.5455\n1.5585\n1.2357\n0.61013\n-2.1763\n-1.4998\n-0.96331\n-0.20976\n0.17552\n-0.84723\n0.60966\n1.2389\n0.707\n1.554\n0.98337\n0.70677\n1.1176\n0.75329\n0.37003\n0.32058\n-0.44536\n-0.038423\n0.56872\n0.51936\n0.51957\n2.5624\n1.9047\n1.9201\n-0.54864\n-1.6461\n-1.1792\n-0.51024\n-1.0522\n1.4\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0588\n0.7569\n0.96445\n0.61394\n-0.32706\n0.70091\n2.8744\n3.3458\n2.9289\n2.2231\n4.2197\n3.1083\n2.1124\n0.97889\n5.2233\n5.6335\n2.0788\n1.5001\n2.0282\n2.5553\n2.0309\n2.4329\n1.5592\n2.7931\n2.6804\n2.3114\n2.2434\n2.5081\n1.6188\n1.8607\n-1.5669\n2.6884\n1.4975\n0.33534\n-1.212\n-1.386\n-0.10287\n0.1547\n-0.43888\n0.18226\n-0.48745\n1.994\n2.4663\n0.8633\n0.5179\n0.6783\n0.87389\n0.42925\n-0.41862\n-0.087322\n0.39207\n-0.31011\n0.45631\n0.089387\n0.97499\n1.4107\n0.75391\n-0.24381\n-1.0427\n-1.4366\n-2.1266\n-0.98609\n-1.1121\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.1413\n0.67434\n0.98741\n0.74723\n0.5317\n1.1934\n2.0292\n2.0058\n1.388\n1.9217\n1.4786\n0.38795\n4.2886\n3.407\n2.626\n3.2029\n1.5018\n2.0721\n0.49057\n1.1887\n1.0144\n3.46\n3.4166\n3.6159\n3.0522\n3.2118\n2.7071\n1.8269\n1.8137\n2.971\n-1.3565\n2.5496\n-0.060625\n0.1878\n0.084059\n-0.76651\n-0.45296\n-0.49381\n-0.42422\n0.39573\n1.083\n1.6195\n1.6336\n0.20643\n0.4823\n0.20214\n-0.43304\n0.35647\n-0.36691\n0.51314\n0.81575\n0.68978\n1.7993\n0.063871\n-0.30321\n-0.67634\nNaN\nNaN\nNaN\n-2.419\n-2.6961\n0.26452\n-2.4303\nNaN\nNaN\nNaN\n-1.7262\nNaN\nNaN\nNaN\nNaN\nNaN\n1.5276\n0.59457\n0.97981\n0.49834\n-0.20727\n0.29453\n-0.094463\n-0.40241\n-0.28614\n0.62965\n0.15224\n0.56877\n2.1209\n2.7396\n1.3802\n1.5604\n-0.27182\n-0.13635\n-3.001\n-3.7027\n-1.3237\n4.0115\n5.0444\n6.4789\n2.8981\n3.5463\n3.5078\n2.4906\n2.4658\n0.92687\n-0.43085\n-2.4388\n-2.3058\n-1.3152\n0.69146\n-0.64434\n-2.1263\n-1.5991\n-0.69607\n0.53557\n1.0459\n0.56356\n0.29245\n-0.15741\n-0.28576\n-0.29639\n-0.29344\n0.79651\n0.4052\n0.51119\n0.82068\n0.45606\n0.9289\n0.59835\n0.66283\n0.86131\n3.0586\nNaN\nNaN\n-2.8736\n-2.9809\n-0.96313\n-4.1847\n-1.8416\n-4.4751\n-3.3607\n-5.4052\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.19054\n-0.48739\n0.092505\n0.22863\n0.038653\n-0.09089\n-0.22881\n0.023614\n0.039204\n-0.22818\n0.30797\n1.2207\n1.9638\n1.8865\n1.0373\n1.2914\n-0.082695\n-1.784\n-8.9266\n-6.668\n-2.375\n1.6832\n3.2795\n3.2014\n2.508\n3.3586\n3.9887\n3.3769\n0.94742\n-0.47078\n-0.018975\n-1.9995\n-2.8018\n-2.2963\n-0.87064\n-1.4065\n-2.6392\n-1.5432\n-0.51847\n0.69186\n0.65767\n-0.17094\n-0.51845\n-1.0774\n-3.7899\n-1.6726\n-0.41044\n-0.084276\n-0.39165\n0.28189\n0.068722\n0.038035\n0.0054403\n-0.45372\n-0.42678\n-2.7001\n-1.9935\nNaN\nNaN\n-2.2972\n-2.459\n-1.7251\n-0.18571\n-2.4136\n-1.5195\n-3.3671\n-6.538\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.74404\n-0.41251\n-0.14961\n-0.16893\n0.27031\n0.57537\n0.59094\n0.98166\n0.34149\n0.37688\n0.49064\n0.89292\n0.68731\n-0.13715\n1.0906\n1.6299\n0.67142\n0.52843\n-1.3403\n-0.34604\n-1.1266\n-0.8663\n2.5149\n0.23437\n-0.6061\n1.3379\n1.8841\n2.3834\n-0.83379\n-1.3174\n0.27582\n-0.59818\n-1.5766\n-2.5251\n-2.7629\n-2.5904\n-2.3935\n-1.5718\n-0.51228\n0.47382\n-0.23598\n-0.26431\n-1.0708\n-1.6266\n-4.318\n-1.5308\n-0.24262\n-0.2408\n-0.33677\n0.23502\n0.12811\n0.21094\n-0.4893\n-0.57122\n-0.53246\n-1.5206\n-0.53013\nNaN\nNaN\n-2.038\n-4.0759\n-2.0844\nNaN\n-2.1555\n-1.6026\nNaN\nNaN\n-8.2057\n-14.275\nNaN\nNaN\nNaN\n-0.66885\n0.14859\n0.28265\n0.039356\n0.30609\n0.90007\n1.5949\n1.4125\n0.23347\n-0.55371\n-0.76072\n0.44357\n1.0156\n0.74723\n0.89199\n0.84007\n0.06255\n0.079806\n0.23132\n-0.45784\n0.65826\n1.4357\n0.32478\n-0.5746\n0.066221\n0.47562\n1.6657\n3.1814\n-0.55909\n-0.75212\n0.39593\n-1.4655\n-1.286\n-3.0476\n-2.7184\n-2.9787\n-2.1654\n-2.05\n-1.2012\n-0.099958\n-0.65399\n-0.52659\n-0.69855\n-1.0131\n-3.9992\n-2.2845\n-0.20793\n-0.063784\n-0.5282\n0.21375\n-0.091548\n-0.077763\n-0.033303\n0.53537\n0.15764\n-0.4248\n-0.39069\n0.80086\n0.071771\n-0.55145\n0.52079\n0.82365\nNaN\nNaN\nNaN\nNaN\nNaN\n-10.805\n-12.625\n-8.7129\nNaN\nNaN\n-1.0151\n-0.41422\n-0.90449\n-0.2765\n0.1095\n1.0545\n1.0584\n1.2872\n-0.24205\n-0.83667\n-0.45035\n-0.069042\n-0.46452\n0.32195\n-0.58398\n-0.011122\n0.12924\n-0.30189\n-0.68106\n-0.66988\n0.84642\n-0.47531\n-1.4688\n0.059352\n1.9812\n1.2501\n1.5944\n1.7138\n0.41654\n0.1809\n1.0731\n-0.44453\n-1.5972\n-3.1765\n-2.4493\n-2.3592\n-2.935\n-2.3774\n-0.94841\n-1.0793\n-1.7627\n-1.4471\n-2.0997\n-2.3421\n-3.4219\n-0.89309\n0.50931\n0.27615\n1.1121\n-0.084288\n-0.062256\n0.073545\n-0.068474\n1.4463\n0.45118\n-1.5328\n-1.6695\n-0.28597\n1.9301\n5.5839\n0.6214\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.3253\n-7.5296\nNaN\nNaN\n-1.3173\n-0.43764\n0.029845\n-0.21244\n1.0213\n1.1668\n-0.10373\n-0.013574\n-0.42902\n-1.6722\n0.33789\n0.32802\n0.055729\n0.3483\n0.17297\n1.1428\n0.64873\n-1.6217\n-1.9707\n0.077287\n0.70661\n0.072655\n-0.42379\n0.31313\n1.4028\n0.66519\n0.27003\n0.026374\n-0.14969\n0.16139\n1.1367\n-0.44925\n-2.7061\n-3.7221\n-3.076\n-2.6331\n-2.3742\n-2.3052\n-1.707\n-2.394\n-2.1242\n-2.0466\n-3.133\n-3.6518\n-1.8736\n-0.07308\n-0.2401\n0.065344\n0.71665\n-0.015928\n-0.19451\n1.5409\n0.78509\n0.83304\n-0.31367\n-1.7084\n-2.3813\n-2.3754\n1.7969\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.9445\nNaN\n-2.0146\n-2.2231\n-1.1919\n-0.72118\n-0.6881\n-0.98929\n-0.086967\n-0.40089\n-0.35234\n-0.18018\n-0.30588\n0.17807\n0.97656\n0.20548\n0.92567\n0.93936\n1.7858\n1.7816\n0.72642\n-1.5871\n-1.5435\n0.79427\n0.483\n0.69534\n1.5123\n0.22556\n0.8994\n1.7718\n1.052\n-0.29211\n0.86522\n0.67097\n-0.61477\n-3.022\n-4.0014\n-3.5489\n-3.4687\n-4.41\n-4.9313\n-3.0924\n-4.3419\n-3.6883\n-2.1093\n-2.0284\n-3.9103\n-3.25\n-1.9679\n-0.98916\n-1.0862\n-1.0034\n-0.12584\n0.64546\n0.030573\n0.31235\n0.53396\n0.27278\nNaN\nNaN\n-4.3116\n-3.0031\n1.6608\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.02\n-2.8035\n-1.778\n-1.3933\n-0.44318\n-1.0669\n-1.363\n-1.2368\n-0.78212\n-0.1262\n-0.32494\n-0.79591\n-0.82459\n-0.23224\n1.8333\n0.96479\n0.14414\n-0.17553\n-0.62226\n-0.98164\n-0.59596\n1.4274\n0.78123\n1.1395\n0.22755\n-0.42612\n0.33531\n1.2985\n1.855\n1.5402\n0.43864\n0.18408\n-0.75515\nNaN\nNaN\nNaN\n-3.3957\n-4.5918\n-5.1833\n-5.3973\n-5.7744\n-3.5093\n-2.4567\n-5.8361\n-4.6041\n-2.3976\n-2.6557\n-1.5525\n-0.79226\n-1.0401\n0.018624\n0.49044\n-0.06608\n-0.50971\n-0.5927\nNaN\nNaN\nNaN\n-3.5483\n-4.0032\n-0.65198\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.2938\n-1.2458\n-0.82279\n-1.1276\n-0.68747\n-0.55467\n-1.0121\n-1.2902\n-0.60431\n-0.65709\n-0.96628\n-0.39918\n0.39192\n0.19482\n-1.0294\n0.29291\n0.12759\n-0.086942\n0.53396\n1.8709\n1.6872\n1.3564\n-0.33306\n-0.82971\n-0.51934\n-0.59359\n1.368\n1.9389\n0.29452\n0.49617\n-0.18402\nNaN\nNaN\nNaN\n-3.0125\n-5.0446\n-5.384\n-5.0003\n-4.9161\n-3.2857\n-3.3277\n-6.1935\n-5.9839\n-3.2488\n-1.813\n-0.78318\n-0.39957\n-1.0386\n0.059043\n0.71878\n0.26386\n-0.21592\n-1.2761\nNaN\nNaN\nNaN\n-0.69852\n-2.7591\n-1.6794\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.1566\n-1.6436\n-1.7507\n-1.8917\n-1.3766\n-0.9807\n-1.1207\n-2.2153\n-2.0842\n-1.8681\n-1.8314\n-1.114\n0.24674\n0.35983\n-1.2457\n0.72613\n0.60019\n-0.034126\n0.86089\n1.5994\n1.9131\n0.43699\n-0.035552\n-1.6442\n-2.6237\n-1.3589\n0.24566\n1.0285\n0.87595\n0.99201\n0.06298\nNaN\nNaN\nNaN\nNaN\n-6.6147\n-6.4995\n-4.2101\n-5.2658\n-3.9765\n-3.6249\n-4.8807\n-4.795\n-3.1918\n-0.73627\n-0.60343\n-0.47672\n-0.44292\n0.38789\n1.0779\n0.054959\n-1.1868\n-1.1252\nNaN\nNaN\nNaN\nNaN\n-3.6501\n-1.9925\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.867\n-2.43\n-1.9416\n-0.83849\n-1.018\n-1.2333\n-1.4403\n-1.2743\n-1.8644\n-1.3548\n-1.7463\n-1.6882\n-0.07978\n-0.11473\n-0.87214\n0.8767\n1.0743\n0.4557\n1.0165\n1.6274\n0.87928\n0.0035443\n-1.3892\n-2.6434\n-2.8872\n-0.90544\n-0.1729\n0.45917\n0.68475\n0.21836\n-1.4067\nNaN\n-6.3589\n-7.0969\n-7.2904\n-9.3367\n-6.3688\n-3.0162\n-4.1047\n-2.5106\n-1.8403\n-3.609\n-4.0076\n-1.232\n0.55195\n1.1324\n-0.86797\n0.16486\n1.3884\n1.5141\n-0.073719\n-0.18396\n-1.3239\n-2.0744\nNaN\nNaN\nNaN\n-4.1722\n-4.5284\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1173\n-2.6012\n-1.8911\n-0.20416\n0.023731\n-0.61994\n-0.6301\n-0.48395\n-0.88643\n-1.0853\n-1.8534\n-1.7343\n-0.99082\n-1.0458\n-1.4417\n-0.55194\n0.24634\n0.26702\n1.0645\n1.8294\n0.9868\n0.14633\n-3.4506\n-4.0744\n-2.7604\n-0.50198\n0.14783\n1.3916\n0.71409\n-0.063407\n-3.2659\n-5.1349\n-5.1117\n-6.8434\n-7.1827\n-7.0801\n-4.4961\n-3.8952\n-4.3342\nNaN\n-4.9535\n-3.3079\n-3.3991\n-0.68648\n1.9261\n1.077\n-0.30198\n-0.64823\n1.147\n0.78038\n-0.31383\n-0.72816\n-0.37751\n-0.097567\n-1.8044\n-3.19\n-5.0788\n-3.7065\n-3.6364\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.8883\n-1.4309\n-0.66136\n0.2222\n-0.039544\n-0.33395\n0.252\n0.5984\n-0.30741\n-0.84245\n-0.4745\n-0.74856\n-1.0936\n-1.3687\n-1.4883\n-1.1469\n-1.4978\n-1.0331\n0.7048\n2.2453\n0.84574\n-1.0267\n-3.3268\n-3.2023\n-1.4946\n-0.91146\n-0.53308\n0.97417\n-0.60419\n-1.0624\n-4.3751\n-3.2634\n-1.9734\n-6.2821\n-5.78\n-4.8755\n-4.0494\nNaN\nNaN\nNaN\nNaN\n-2.7198\n-4.7804\n-4.0938\n-0.040941\n-0.14869\n-0.39442\n-0.70812\n0.35338\n0.71045\nNaN\n-2.0285\n0.071697\n-0.15306\n-0.70412\n-1.2703\n-3.5581\n-2.7456\n-2.715\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1848\n-1.1562\n-0.4742\n0.010593\n0.0070634\n-0.60831\n0.060811\n-0.23582\n-0.98071\n-1.2497\n-0.49606\n-0.14053\n-1.2865\n-1.8301\n-1.4738\n-0.5771\n-0.085071\n-1.2587\n-0.88557\n-0.093777\n-0.15103\n-1.6008\n-3.8666\n-3.2574\n0.055529\n-1.8549\n-1.3961\n0.18642\n-0.53446\n-2.3969\n-2.0376\n-2.9219\n-4.0956\n-4.4636\n-4.4038\n-4.4597\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.0357\n-1.3936\n-0.56966\n-1.0887\n-0.53231\n0.83305\n1.2545\nNaN\nNaN\nNaN\n0.34128\n-1.6432\n-2.6277\n-2.6632\n-2.7022\n-3.5576\n-3.7198\n-2.6992\n-4.3061\nNaN\nNaN\n-0.97388\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.1522\n-1.2242\n-1.1979\n-0.69582\n-0.19566\n-1.2646\n-0.0019573\n-0.45651\n-1.6238\n-2.0344\n-1.2516\n-1.1047\n-1.4205\n-1.7647\n-0.69268\n-0.14547\n0.34194\n-1.0398\n-1.3203\n-1.3528\n-1.0386\n-1.7929\n-3.0877\n-3.4435\n-1.8178\n-1.5183\n-1.8318\n-0.93802\n-0.54749\n-2.9038\n-2.628\n-2.342\n-0.25775\n-4.9276\n-5.9927\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.4966\n-2.9457\n-2.1664\n-1.3029\n-0.81648\n0.011525\n-0.70847\nNaN\nNaN\nNaN\n-3.5474\n-2.3388\n-2.1516\n-2.9198\n-2.196\n-2.1268\n-0.8386\n-1.5335\n0.39894\n-2.4067\n-2.1736\n-0.86217\n-1.3655\n-1.5174\n-2.7764\nNaN\nNaN\nNaN\n-1.6723\n-0.62895\n-1.7595\n-3.0852\n-0.49351\n-0.45811\n0.22216\n-0.62664\n-1.8272\n-1.1286\n-1.4395\n-1.539\n-1.5712\n-0.82575\n-0.27873\n0.12852\n0.73002\n-1.3773\n-1.8896\n-1.9526\n-1.8781\n-5.3121\n-4.1475\n-2.8566\n-1.6029\n-0.74744\n-2.0929\n-1.4535\n-0.54827\n-0.81847\n-3.8946\n-2.2379\n1.7913\n-2.9686\n-5.8516\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.9448\n-1.9634\n-2.71\n-1.738\n-0.57105\n-0.84758\n-1.7378\nNaN\nNaN\nNaN\n-3.899\n-2.1689\n-2.0874\n-1.9847\n-0.19572\n-0.3176\n-1.9011\n-3.3489\n-0.30605\n-1.9594\n-1.3935\n0.41662\n0.259\n-0.25162\n-1.2205\nNaN\nNaN\nNaN\n-0.83993\n0.35061\n-1.5591\n-3.2096\n-2.161\n-0.44552\n-0.78225\n-1.4079\n-2.0781\n-0.76994\n-0.6619\n-0.78919\n-0.72111\n-0.4339\n-0.1874\n-0.79234\n1.865\n0.6437\n1.4527\n-0.62204\n-2.6907\n-6.1461\n-4.2368\n-3.6806\n-1.8063\n-0.047049\n-1.358\n-0.67699\n-0.10702\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n1.0643\n-0.90543\n2.4448\n-1.1482\n-0.20698\n-0.31564\n-3.322\nNaN\n-3.8555\n-2.4211\n-2.3137\n-2.2201\n-2.3573\n-1.5561\n0.69207\n0.92941\n-1.535\n-0.57322\n0.25069\n0.20637\n-0.29346\n1.8791\n2.7168\n-0.66335\n-2.217\n-3.0675\n-2.8516\n0.60109\n-2.3629\n-2.6999\n-1.4558\n-2.3623\n-2.0303\n-1.8174\n-1.8361\n-1.4047\n-1.6103\n-1.1287\n-1.2636\n-2.5084\n-2.3721\n-1.4995\n0.66057\n-0.47736\n-0.19991\n-0.48563\n2.9286\n-0.11137\n-0.27436\n-4.8585\n-4.4385\n-3.0457\n-1.4582\n-0.78002\n-1.9522\n-2.4995\n-3.2989\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-1.7786\n-2.7554\n1.3153\n-0.016182\n-0.72245\n-4.8571\n-2.1938\n-1.7825\n-2.3102\n-1.7231\n-1.3451\n-0.87436\n-1.0938\n-2.9057\n-1.2667\n0.35783\n-0.90747\n0.11409\n0.81872\n-1.0354\n-1.5627\n0.70155\n1.102\n-1.4828\n-2.7605\n-2.6256\n-2.203\n-1.4495\n-2.6327\n-2.4406\n-1.3386\n-2.3036\n-2.1933\n-2.9492\n-3.3794\n-2.1995\n-1.6082\n-1.9882\n-2.8451\n-2.9739\n-3.9346\n-2.0816\n-1.7786\n-0.60028\n-1.1373\n-2.6393\n-3.3682\n-1.5101\n-0.23885\n-2.7414\n-2.5488\n-3.2317\n-1.4886\n-2.2277\n-2.9982\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-7.025\n-5.8309\n-2.9275\n-3.0229\n-1.1362\n-0.79805\n-0.58681\n-1.3364\n-2.277\n-0.84987\n0.16009\n-0.23667\n-0.70597\n-2.0897\n-1.8605\n-1.7569\n-1.0175\n-0.40748\n0.53004\n-2.4857\n-3.7095\n-1.6356\n-2.628\n-0.82412\n-0.42362\n-0.45004\n-2.5753\n-2.5331\n-2.2682\n-2.4316\n-3.4925\n-3.0078\n-2.978\n-4.0153\n-4.0941\n-3.6522\n-2.1288\n-3.4053\n-3.6321\n-2.5826\n-3.9358\n-3.8668\n-3.4668\n-1.2409\n-0.51303\n-2.5652\n-3.8471\n-5.1265\n-3.283\n-3.3026\n-2.9964\n-3.4471\n-2.41\n-2.3461\n-2.8122\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.1842\n-4.285\n-2.4823\n-1.7908\n-0.2896\n-0.62921\n-1.0487\n-1.989\n-1.6162\n-0.64616\n-0.52065\n-0.40962\n-0.35272\n-1.1579\n-2.1359\n-1.3283\n-1.2924\n-1.4751\n-2.713\n-1.3339\n-3.3053\n-3.0951\n-1.4242\n-0.50752\n0.55588\n-1.2009\n-1.6234\n-3.152\n-2.5103\n-2.8617\n-3.6886\n-4.4472\n-4.0638\n-3.6731\n-2.9103\n-2.1807\n-2.9909\n-3.8665\n-3.6872\n-3.1044\n-3.7083\n-3.9869\n-2.9015\n-0.68119\n-2.834\n-4.8601\n-6.292\n-4.5354\n-3.9314\n-3.1363\n-3.0106\n-3.2919\n-3.8031\n-1.3554\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.407\n-3.8242\n-3.8793\n-3.6285\n0.67101\n-0.14617\n-0.085373\n-0.46084\n-1.3684\n-0.75764\n0.75739\n1.367\n1.5417\n0.13222\n-0.48841\n-0.55612\n-2.384\n-3.3942\n-1.1741\n-0.58333\n-2.0391\n-2.5738\n-1.5143\n0.84122\n2.6563\n0.50654\n0.83833\n-4.4567\n-2.8611\n-3.0734\n-4.0114\n-4.6205\n-2.8732\n-3.0873\n-2.8383\n-2.1833\n-3.3722\n-3.8655\n-3.7149\n-3.2515\n-3.1564\n-3.3156\n-2.7184\n0.043778\n-3.0634\n-5.1789\n-4.8204\n-3.968\n-3.3947\n-2.6262\n-2.3741\n-4.3911\n-4.2258\n-4.3472\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.1461\n-2.3238\n-3.4617\n-3.8061\n1.3309\n0.78671\n2.4977\n0.51297\n-1.6234\n-0.92573\n0.71344\n1.743\n2.6955\n2.0662\n0.92439\n0.12949\n-1.2775\n-1.3617\n1.4326\n1.9925\n-0.93171\n-1.7644\n-0.49488\n3.3608\n5.5266\n0.72219\n0.57059\n-4.0394\n-3.1782\n-4.0202\n-3.1561\n-2.4551\n-2.7182\n-2.8868\n-2.388\n-2.3798\n-1.7295\n-3.5545\n-4.3869\n-3.3735\n-3.0068\n-2.5234\n-1.345\n0.4311\n-2.0391\n-2.7582\n-3.7231\n-4.1649\n-2.6862\n-2.2529\n-2.6994\n-3.6595\n-2.2352\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.81485\n-1.8989\n-0.62152\n0.2864\n2.957\n3.4636\n4.1017\n0.4507\n-0.28539\n1.2308\n1.4999\n4.6978\n3.631\n2.2169\n1.5099\n2.6607\n0.12992\n-0.64453\n2.2011\n0.79135\n0.96165\n-0.56576\n0.94816\n3.3526\n2.3322\n-0.19325\n-0.43543\n-4.2666\n-3.8871\n-4.1552\n-2.4703\n-2.8008\n-3.2946\n-2.9743\n-1.9986\n-3.2179\n-2.6804\n-2.9598\n-3.7053\n-3.9836\n-3.2496\n-1.7695\n-0.24155\n1.0112\n-1.548\n-3.0932\n-3.3737\n-3.3903\n-2.4646\n-2.2473\n-2.1991\n-2.2983\n1.4465\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-4.5688\nNaN\nNaN\nNaN\nNaN\n-1.574\n-2.6521\n-1.531\n0.2402\n1.8431\n2.0539\n2.4851\n0.8102\n2.7338\n2.583\n2.1424\n4.8239\n3.343\n1.9798\n1.7475\n1.9562\n-1.1493\n-0.28619\n1.6429\n2.0337\n2.0727\n1.8153\n3.386\n3.2266\n0.8504\n-0.75469\n-0.75725\n-3.8678\n-3.9299\n-3.5391\n-3.4449\n-3.6733\n-3.617\n-3.5934\n-2.2121\n-3.2301\n-3.5689\n-2.7969\n-3.7969\n-4.5514\n-3.4398\n-1.8471\n-0.033416\n1.3489\n-0.31152\n-2.3092\n-4.1514\n-2.8753\n-1.0747\n-1.9809\n-1.3575\n0.12394\n2.7294\n0.19949\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.40237\n-1.4911\n-2.3797\n0.87609\n-0.77365\n-1.2182\n-2.3133\n-1.7807\n-1.3197\n-0.54379\n-0.16317\n0.091123\n1.8461\n1.1353\n2.1546\n3.1917\n2.7058\n2.2059\n-0.52661\n0.20378\n0.69015\n-0.070715\n0.033689\n0.44482\n1.527\n3.0905\n1.711\n3.1121\n2.8523\n1.9842\n0.27012\n-0.24962\n1.6214\n-3.4781\n-3.7393\n-3.5994\n-3.8366\n-5.1701\n-4.2922\n-3.0175\n-3.3533\n-2.3398\n-2.6168\n-2.6296\n-3.0844\n-4.1371\n-3.272\n-2.2124\n-0.59452\n-0.63298\n-1.7713\n-2.3457\n-4.8917\n-4.6398\n-0.93856\n-0.035988\n0.33952\n2.7115\n3.0913\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n2.6501\n3.3069\n6.449\n4.981\n1.8426\n-1.5852\n-2.7029\n-1.3882\n-2.3819\n-1.6614\n-1.3051\n-1.6367\n-0.76034\n-0.20746\n-0.48\n0.1619\n-0.51456\n-1.0782\n-1.2038\n-1.798\n-1.1361\n0.060088\n-0.88617\n0.24694\n0.13238\n1.9335\n2.5576\n1.4015\n3.0755\n3.1179\n0.45412\n-0.21773\n-0.015807\n1.9148\n-3.6465\n-3.8178\n-3.8342\n-3.5221\n-3.379\n-2.4658\n-1.6232\n-2.0199\n-1.7478\n-1.6491\n-2.1663\n-3.04\n-3.0564\n-2.9151\n-3.111\n-2.0345\n-1.5227\n-2.8314\n-4.2338\n-4.0406\n-4.1598\n-1.1327\n0.61658\n2.4339\n2.9024\n3.03\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.7436\nNaN\nNaN\n-0.26623\n17.395\n12.345\n7.0473\n4.6769\n-1.9113\n-3.347\n-3.2262\n-0.99739\n-2.1651\n-1.5418\n-1.3651\n-1.3643\n-1.0349\n-0.98309\n-1.4579\n-1.1532\n-1.5568\n-2.0866\n-1.7491\n-1.8366\n-1.5151\n-0.10267\n0.73156\n1.2351\n1.5475\n1.0955\n1.2743\n3.2025\n2.6954\n0.19626\n-0.027208\n0.38974\n1.5028\n-3.4937\n-3.3041\n-3.1689\n-2.6271\n-2.275\n-2.3014\n-2.2713\n-1.4314\n-1.8133\n-2.0967\n-2.2454\n-3.2503\n-4.4523\n-3.6065\n-3.6466\n-2.0158\n-1.9661\n-2.1144\n-3.1484\n-3.5317\n-3.0382\n-1.2673\n0.87979\n1.8643\n1.8595\n3.8677\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n15.572\n10.695\n4.6322\n0.85515\n-1.7182\n-4.9466\n-3.6885\n-1.4757\n-1.8463\n-2.2454\n-3.0797\n-2.672\n-1.6737\n-0.99079\n-1.4157\n-2.4375\n-2.7557\n-1.9245\n-0.7902\n-2.0092\n-3.6041\n-0.6404\n0.54313\n1.3865\n1.6537\n0.57329\n0.94825\n0.23564\n2.0231\n1.2986\n-0.27736\n0.23019\n0.9648\n-2.7877\n-2.9589\n-3.0199\n-2.8882\n-1.8115\n-2.7757\n-3.1614\n-2.3928\n-1.6831\n-2.1252\n-2.2666\n-2.7626\n-4.1336\n-3.2153\n-2.9589\n-2.1119\n-2.5931\n-2.3382\n-1.6428\n-2.0699\n-2.7651\n-1.6934\n0.10617\n0.57484\n-0.005388\n-1.7709\n-2.0365\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n10.694\n5.6016\n0.89115\n-0.14413\n-0.86366\n-7.0239\n-3.5976\n-2.0962\n-2.7266\n-2.9036\n-2.2958\n-2.0907\n-2.2052\n-1.7323\n-2.4198\n-1.7191\n-1.5607\n-1.4927\n-0.29579\n-0.47307\n-2.1111\n-2.6886\n-1.1117\n0.94185\n1.8104\n1.5214\n0.91305\n0.1552\n0.41418\n0.69959\n0.83687\n0.81388\n1.058\n-2.2349\n-1.5671\n-1.9548\n-2.5793\n-2.3664\n-1.8672\n-2.5112\n-1.8654\n-1.5453\n-2.0513\n-2.8936\n-3.1315\n-3.4764\n-2.9791\n-2.2677\n-2.145\n-1.6533\n-1.3771\n-0.50283\n-1.9479\n-2.708\n-1.9793\n-0.39558\n0.22035\n0.75483\n2.4147\n-0.37221\n-4.3534\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n4.9146\n0.47242\n-0.86209\n-1.1645\n-0.58201\n-4.7933\n-2.2726\n-2.4624\n-4.0287\n-5.0048\n-4.1982\n-2.2597\n-2.4985\n-2.1795\n-3.2468\n-1.5105\n-0.91124\n-0.80019\n-0.17271\n-0.42362\n-0.46009\n-0.50071\n-0.87993\n0.23012\n1.5154\n1.0607\n0.69092\n0.29691\n-0.67585\n-0.34029\n2.234\n2.891\n1.5561\n-2.3172\n-2.3968\n-2.5578\n-2.1812\n-1.9317\n-1.593\n-2.5667\n-2.8435\n-2.4264\n-2.184\n-2.9673\n-3.1559\n-2.0493\n-2.4185\n-2.9613\n-3.276\n-1.2253\n-1.9195\n-0.77431\n-4.0233\n-2.9263\n-1.2513\n-1.1112\n-0.1314\n1.106\n1.3162\n-0.81774\n-3.4592\nNaN\nNaN\n2.4995\n-0.15122\n1.8465\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.12959\n-0.52437\n-0.46545\n-0.67828\n-2.1125\n-1.3849\n-3.2585\n-5.8602\n-5.2279\n-6.868\n-4.4261\n-1.752\n-2.0289\n-2.1339\n-2.3216\n-1.2085\n-0.99961\n-0.87569\n-0.34067\n-0.73236\n-0.25959\n-0.21988\n-1.4056\n-0.68562\n-0.56044\n-0.69597\n-0.84217\n-0.90642\n-0.35072\n0.58264\n1.8469\n2.8377\n2.8596\n-2.8798\n-3.0472\n-2.6981\n-2.7829\n-1.7939\n-1.4819\n-2.3764\n-2.7058\n-2.8814\n-2.6186\n-3.3877\n-2.7707\n-1.3792\n-2.5742\n-2.8983\n-1.0133\n-1.508\n-3.9787\n-4.1688\n-3.5924\n-4.7295\n-3.0093\n-2.1662\n0.24052\n-2.9104\n-4.5468\n-3.092\n-1.9273\n-2.3921\n-0.67707\n-0.21006\n0.1437\n-0.41411\n0.081672\nNaN\nNaN\nNaN\nNaN\n-4.8721\n-2.3799\n-3.163\n-3.6343\n-3.7389\n-3.9305\n-4.6989\n-4.9062\n-4.9518\n-4.7029\n-4.4512\n-3.9095\n-1.2593\n-1.1495\n-0.999\n-2.1543\n-2.2554\n-1.7378\n-1.6393\n-0.4987\n-0.1493\n0.2531\n-0.83145\n-1.3577\n-1.0502\n-1.8102\n-0.84351\n0.12844\n0.2753\n0.0036144\n-0.7217\n0.46698\n1.7068\n1.5646\n-2.9296\n-3.1634\n-3.15\n-3.1235\n-2.312\n-1.4619\n-1.9657\n-2.466\n-2.3722\n-2.6784\n-3.4304\n-2.668\n-1.6423\n-2.5179\n-2.3652\n-2.2235\n-3.2775\n-4.9347\n-3.8015\n-3.9554\n-5.0233\n-2.3146\n-1.119\n0.70014\n3.9893\n0.66579\n-0.059175\n-0.32762\nNaN\n-0.33738\n-1.5306\n-3.9403\n-1.3179\n-1.3944\nNaN\nNaN\nNaN\n-5.4046\n-3.9564\n-3.3325\n-2.9679\n-4.3857\n-4.5391\n-4.8943\n-5.5594\n-4.2255\n-1.9447\n-2.298\n-3.2946\n-4.0238\n-1.0993\n-0.14922\n-0.62933\n-1.3657\n-1.48\n-1.6307\n-1.7367\n-0.44027\n0.14265\n0.068964\n-1.2989\n-1.271\n-0.76401\n-0.98367\n-0.69771\n0.37605\n0.75925\n0.24883\n-0.26134\n0.45984\n0.18155\n-0.81146\n-3.5615\n-2.8884\n-1.9518\n-2.1523\n-2.1387\n-1.9767\n-2.1272\n-2.2326\n-2.492\n-2.3108\n-3.2727\n-2.7696\n-2.0066\n-2.9747\n-1.7142\n-1.8746\n-3.3084\n-3.4209\n-2.9341\n-3.9538\n-4.4348\n-3.1679\n0.13001\n1.1016\n0.99091\n0.50987\n-0.877\nNaN\nNaN\n0.71876\n-2.4979\n-2.7536\n-1.3289\n-2.9394\n-2.8468\n-2.8975\n-4.1927\n-4.6506\n-4.3149\n-3.2145\n-2.8804\n-4.0327\n-4.3515\n-5.1883\n-5.0857\n-1.8588\n-0.88359\n-0.088107\n-1.7739\n-2.5926\n-0.69435\n0.59389\n-1.0025\n-1.6404\n-0.84265\n-1.0637\n-1.1463\n-0.78746\n-0.35038\n0.089512\n-0.49523\n-1.402\n-1.4929\n-0.33173\n0.10975\n0.38152\n0.70281\n0.4326\n0.41359\n1.086\n0.17853\n-0.23578\n-3.1168\n-2.9451\n-1.4849\n-1.6439\n-1.7101\n-1.5109\n-1.5907\n-1.7049\n-1.7302\n-2.1013\n-2.5046\n-2.6253\n-1.1804\n-2.0866\n-0.60696\n-1.1004\n-3.2572\n-2.9062\n-0.17457\n0.78056\n-2.6492\n-1.4524\n0.33595\n1.6643\n-0.26667\n-0.82928\n-0.11732\nNaN\nNaN\nNaN\n-3.8624\n-3.0274\n-2.9927\n-3.4973\n-3.2553\n-4.0827\n-3.0237\n-3.7177\n-3.7404\n-2.7323\n-0.73373\n-2.0416\n-2.4332\n-1.8849\n-2.5766\n-2.2601\n-2.6314\n-1.5465\n-0.53115\n-0.62955\n0.1571\n0.51695\n-0.10736\n-0.98772\n-0.9917\n-0.85043\n-0.55603\n-0.41299\n-0.13042\n-0.042409\n-1.025\n-1.2209\n-1.0435\n0.040581\n1.5482\n0.078634\n0.54098\n0.9062\n-0.013078\n-0.15024\n0.80083\n0.65961\n-3.1594\n-3.2566\n-1.3947\n-1.3419\n-1.1558\n-1.0171\n-1.2335\n-1.2267\n-0.54419\n-1.778\n-1.5897\n-2.0192\n-1.6338\n-0.78548\n1.7692\n0.85644\nNaN\nNaN\nNaN\n1.4419\n-1.8671\n-0.89879\n-0.93486\n-3.1164\n-2.6046\n-0.98824\n-1.243\nNaN\nNaN\nNaN\n-3.8334\n-3.3764\n-3.5264\n-3.6497\n-3.1075\n-3.1685\n-3.1337\n-3.1674\n-2.9037\n-3.3614\n-2.7171\n-2.2794\n-1.2792\n-1.4637\n-1.8835\n-1.5286\n-0.8331\n-0.1639\n0.15709\n-0.4127\n-0.37405\n0.12679\n-0.43576\n-2.6873\n-1.5432\n-0.41404\n0.67015\n0.022573\n0.30993\n-0.021161\n-0.5017\n-0.57739\n-0.44619\n0.0066304\n0.50127\n-0.22166\n0.46902\n1.1005\n-0.080598\n0.014017\n-0.29005\n0.57725\n-1.9289\n-1.1576\n-1.3963\n-1.107\n-0.86816\n-0.4736\n-0.33563\n-0.80108\n-0.8292\n-2.1082\n-1.7812\n-1.9737\n-0.6508\n1.4028\n1.1595\n-0.37817\nNaN\nNaN\nNaN\n-0.1125\n-2.4062\n-3.3961\n-1.7592\n-2.5689\n-0.36098\n-0.8612\n-1.612\nNaN\nNaN\nNaN\n-4.1928\n-3.5062\n-3.1203\n-2.5457\n-1.2865\n-2.1678\n-2.9353\n-2.9832\n-2.5015\n-4.1339\n-2.9542\n-2.5865\n-2.0681\n-0.83388\n-0.84085\n-0.59799\n1.0036\n1.1088\n1.3138\n-0.65924\n-0.66409\n-0.28077\n-0.57686\n-1.3995\n-0.73053\n-0.58236\n-0.23855\n-0.46868\n0.40501\n0.21473\n0.31285\n-0.021214\n-0.28089\n0.014535\n0.24078\n0.29228\n1.8096\n1.354\n0.16726\n-0.18377\n0.37103\n0.92889\n-1.4879\n-1.4537\n-1.3022\n-0.39485\n-0.52524\n0.21675\n0.52405\n-0.6368\n-2.1556\n-2.073\n-0.81197\n0.050898\n0.62663\n1.6716\n0.074252\n-0.66327\n-2.548\nNaN\nNaN\n-3.1499\n-2.9208\n-2.0843\n-2.1859\n-1.899\n0.069164\n-0.84008\n-1.3816\n-0.88155\n-2.0185\n-2.9905\n-5.5686\n-4.8439\n-3.6652\n-3.0281\n-1.6931\n-2.437\n-2.6214\n-2.6165\n-2.2684\n-2.1729\n-2.0006\n-1.7186\n-0.90057\n-0.94998\n-1.3555\n0.59098\n1.9339\n1.4053\n1.8269\n-1.0541\n-0.57103\n-0.039036\n0.88473\n-0.72237\n-0.98495\n-0.94089\n-0.95382\n-0.13177\n0.093307\n0.36833\n0.72975\n-0.24498\n0.20214\n1.3616\n1.0804\n0.40949\n0.32724\n1.1215\n-0.3531\n-0.65306\n1.6382\n1.5364\n-2.1451\n-2.2481\n-0.98455\n0.22296\n0.6697\n0.91799\n0.89129\n-0.42757\n-2.0672\n-1.6145\n-0.94997\n0.42318\n1.6171\n2.2405\n0.69567\n0.3202\n-0.33664\n-2.1723\n-2.6546\n-2.4258\n-2.0506\n-1.2332\n-1.9965\n-1.238\n-0.42056\n-0.74455\n-1.3133\n-1.6836\n-1.8008\n-2.5359\n-3.5384\n-2.7358\n-3.4122\n-3.0081\n-2.547\n-2.9779\n-1.922\n-0.86411\n-0.94274\n-1.1396\n-0.47696\n0.89214\n0.44077\n0.80248\n-0.10184\n0.56567\n1.4763\n0.97384\n0.14809\n-2.2954\n-0.45347\n1.5505\n1.2004\n-0.38226\n-0.15116\n-0.26975\n-0.63524\n-0.23686\n-0.074385\n0.15977\n0.35076\n-0.44381\n1.2761\n1.912\n0.97753\n0.079988\n0.96426\n1.171\n-1.0274\n-0.39181\n0.34042\n0.95178\n-3.2103\n-1.9493\n0.051717\n0.6153\n1.4869\n1.8059\n0.55003\n0.13866\n0.32735\n-1.1324\n-1.1428\n0.21888\n1.5416\nNaN\nNaN\nNaN\nNaN\n-1.0613\n-3.0615\n-2.9681\n-0.41307\n-1.0282\n-2.2326\n-1.1642\n-0.71294\n-1.7336\n-1.1542\n-0.76459\n-1.5109\n-2.3091\n-3.1018\n-3.3469\n-3.1457\n-3.1834\n-3.4551\n-3.1235\n-1.9257\n-0.19001\n-0.40124\n0.30999\n1.0205\n1.8296\n1.3272\n0.55057\n1.4587\n2.0957\n2.1314\n0.66249\n1.0265\n-0.21551\n-0.21244\n-0.48187\n0.010691\n-0.54465\n-0.67682\n-0.70156\n-0.81487\n-0.44287\n-0.066174\n-0.81896\n0.11002\n0.97448\n1.7196\n0.89828\n-0.14227\n-0.017526\n1.6875\n1.2247\n0.5655\n0.2832\n0.12459\n1.3453\n-2.6931\n-1.0341\n0.095471\n0.76763\n1.6461\n1.3524\n0.38583\n1.0244\n1.7204\n1.1042\n-0.15122\n-0.24501\n0.95847\nNaN\nNaN\nNaN\nNaN\nNaN\n-3.1578\n-2.3014\n-0.50481\n-0.95973\n-1.1002\n-0.46161\n-0.6914\n-1.1333\n-1.2778\n-1.9603\n-1.8444\n-2.109\n-2.6255\n-2.6398\n-1.5396\n-2.3217\n-3.1459\n-2.341\n-1.0585\n0.81785\n1.2001\n1.5387\n1.6064\n2.4774\n1.8689\n-1.3737\n0.7202\n1.1305\n1.8963\n1.3702\n0.40776\n-0.38934\n-0.46003\n-1.5171\n-1.1826\n-1.5502\n-1.1329\n-0.81469\n-0.65869\n-0.01878\n0.5517\n-0.71325\n-0.14012\n0.92018\n1.0524\n0.76113\n-0.90276\n0.22898\n1.782\n1.634\n1.8971\n1.3141\n0.72232\n1.7883\n-1.2154\n-0.99637\n-0.14289\n0.90172\n1.5535\n3.0723\n2.3511\n2.2114\n2.3109\n1.5489\n-0.47429\n-0.70248\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-0.80372\n-1.3698\n-1.5836\n-0.92953\n-0.88424\n-1.1208\n-1.9246\n-1.6315\n-1.7984\n-0.93537\n-1.124\n-1.7318\n-1.4742\n-1.8526\n-1.4769\n-1.4913\n-1.7399\n-1.7012\n1.5454\n3.5729\n2.4365\n1.82\n1.6185\n2.2884\n-1.9295\n-0.84204\n-0.73007\n0.54964\n0.95188\n0.13433\n-0.3433\n-0.28958\n-0.4511\n-2.0112\n-2.3555\n-1.1961\n-0.84166\n-0.93123\n-0.34673\n0.95397\n-1.8804\n-0.5657\n0.37177\n1.3309\n1.2889\n-0.5033\n0.075822\n1.2122\n2.2881\n2.1187\n0.45301\n0.80169\nNaN\n-0.1577\n-1.1462\n0.13686\n1.6257\n1.5505\n2.8089\n2.0132\n1.4876\n1.0463\n0.92199\n-0.60244\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n-2.0668\n-1.9061\n-1.5811\n-0.63556\n-1.2294\n-1.6382\n-2.2572\n-1.7796\n-1.1448\n-0.57336\n-1.4266\n-2.0595\n-0.92972\n-1.3787\n-0.90251\n-1.1155\n-2.3668\n-1.6666\n2.1107\n2.231\n1.0521\n1.5711\n0.76561\n-0.12504\n-0.81955\n-1.969\n-3.1126\n-2.0442\n-0.44106\n0.027772\n-0.93866\n0.27841\n-0.30859\n-0.80398\n-0.81114\n-1.132\n-0.97849\n-0.77787\n-0.27889\n0.71204\n-2.5504\n-0.47738\n-1.1746\n-0.74675\n1.3123\n1.0387\n1.3892\n1.8039\n3.1633\n2.6958\nNaN\nNaN\nNaN\n"
  },
  {
    "path": "tests/test_data/small_test/vcm/alpha.csv",
    "content": "0.14375\n1.9182\n1.1942\n1.6152\n1.1953\n0.72399\n1.5768\n0.65021\n1.505\n0.94893\n0.85524\n0.66165\n0.79467\n0.71407\n1.8778\n0.78962\n0.38688\n"
  },
  {
    "path": "tests/test_data/small_test/vcm/vcmt.csv",
    "content": "15.416,0,-11.506,-3.1605,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,2.8583,0,0,1.5086,0,0,-3.2655,-1.9155,0,0,0,0,0,0,0,0\n-11.506,0,34.349,4.7177,0,0,0,0,0,0,0,-11.16,-15.881,0,0,0,0\n-3.1605,0,4.7177,2.5919,0,0,0,0,0,0,0,3.0655,0,0,-1.1563,0,0\n0,1.5086,0,0,3.1851,1.6956,1.1441,-3.4471,-2.022,0,0,0,0,0,0,0,0\n0,0,0,0,1.6956,3.6105,1.2182,0,0,-2.4828,-3.1459,0,0,0,0,0,0\n0,0,0,0,1.1441,1.2182,1.644,0,0,1.6753,0,0,0,-1.8137,0,0,0\n0,-3.2655,0,0,-3.4471,0,0,14.923,4.3767,0,0,0,0,0,0,4.5978,-4.5822\n0,-1.9155,0,0,-2.022,0,0,4.3767,5.1345,0,0,0,0,0,0,0,2.6878\n0,0,0,0,0,-2.4828,1.6753,0,0,6.829,4.3266,0,0,-3.6965,0,0,0\n0,0,0,0,0,-3.1459,0,0,0,4.3266,10.964,0,0,4.6839,0,0,0\n0,0,-11.16,3.0655,0,0,0,0,0,0,0,14.503,10.319,0,-2.7351,0,0\n0,0,-15.881,0,0,0,0,0,0,0,0,10.319,29.371,0,3.8923,-6.4505,0\n0,0,0,0,0,0,-1.8137,0,0,-3.6965,4.6839,0,0,8.0036,0,0,0\n0,0,0,-1.1563,0,0,0,0,0,0,0,-2.7351,3.8923,0,2.0633,-1.7097,0\n0,0,0,0,0,0,0,4.5978,0,0,0,0,-6.4505,0,-1.7097,5.6666,-2.8236\n0,0,0,0,0,0,0,-4.5822,2.6878,0,0,0,0,0,0,-2.8236,5.628\n"
  },
  {
    "path": "tests/test_data/system/gamma/20060619-20061002_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20060619_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 06 19 8 28 59.6906\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\n"
  },
  {
    "path": "tests/test_data/system/gamma/20060619_utm_dem.par",
    "content": "Gamma DIFF&GEO DEM/MAP parameter file\ntitle: GAMMA DEM par file created from ROI_PAC rsc files MCG 28/01/2016\nDEM_projection:     EQA\ndata_format:        REAL*4\nDEM_hgt_offset:          0.00000\nDEM_scale:               1.00000\nwidth:                47\nnlines:               72\ncorner_lat:    -34.1700000  decimal degrees\ncorner_lon:     150.9100000  decimal degrees\npost_lat:   -8.33333e-04  decimal degrees\npost_lon:    8.33333e-04  decimal degrees\n\nellipsoid_name: WGS 84\nellipsoid_ra:        6378137.000   m\nellipsoid_reciprocal_flattening:  298.2572236\n\ndatum_name: WGS 1984\ndatum_shift_dx:              0.000   m\ndatum_shift_dy:              0.000   m\ndatum_shift_dz:              0.000   m\ndatum_scale_m:         0.00000e+00\ndatum_rotation_alpha:  0.00000e+00   arc-sec\ndatum_rotation_beta:   0.00000e+00   arc-sec\ndatum_rotation_gamma:  0.00000e+00   arc-sec\ndatum_country_list Global Definition, WGS84, World\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20060828-20061211_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20060828_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 08 28 12 18 9.6906\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20061002-20070219_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061002-20070430_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061002_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 10 02 13 5 19.0003\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20061106-20061211_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061106-20070115_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061106-20070326_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061106_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 11 06 22 58 23.1246\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20061211-20070709_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061211-20070813_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20061211_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 12 11 23 18 53.1241\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070115-20070326_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070115-20070917_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070115_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 01 15 21 48 23.124\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070219-20070430_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070219-20070604_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070219_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 02 19 1 18 3.124\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070326-20070917_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070326_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 03 26 2 32 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070430-20070604_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070430_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 04 30 5 32 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070604-20070709_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070604_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 06 04 9 31 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070709-20070813_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/gamma/20070709_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 07 09 19 01 43.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070813_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 08 13 16 23 43.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/20070917_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 09 17 12 41 25.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/gamma/baseline_list.txt",
    "content": "tests/test_data/system/gamma/20060619-20061002_base.par\ntests/test_data/system/gamma/20060828-20061211_base.par\ntests/test_data/system/gamma/20061002-20070219_base.par\ntests/test_data/system/gamma/20061002-20070430_base.par\ntests/test_data/system/gamma/20061106-20061211_base.par\ntests/test_data/system/gamma/20061106-20070115_base.par\ntests/test_data/system/gamma/20061106-20070326_base.par\ntests/test_data/system/gamma/20061211-20070709_base.par\ntests/test_data/system/gamma/20061211-20070813_base.par\ntests/test_data/system/gamma/20070115-20070326_base.par\ntests/test_data/system/gamma/20070115-20070917_base.par\ntests/test_data/system/gamma/20070219-20070430_base.par\ntests/test_data/system/gamma/20070219-20070604_base.par\ntests/test_data/system/gamma/20070326-20070917_base.par\ntests/test_data/system/gamma/20070430-20070604_base.par\ntests/test_data/system/gamma/20070604-20070709_base.par\ntests/test_data/system/gamma/20070709-20070813_base.par\n"
  },
  {
    "path": "tests/test_data/system/gamma/header_list.txt",
    "content": "tests/test_data/system/gamma/20060619_slc.par\ntests/test_data/system/gamma/20060828_slc.par\ntests/test_data/system/gamma/20061002_slc.par\ntests/test_data/system/gamma/20061106_slc.par\ntests/test_data/system/gamma/20061211_slc.par\ntests/test_data/system/gamma/20070115_slc.par\ntests/test_data/system/gamma/20070219_slc.par\ntests/test_data/system/gamma/20070326_slc.par\ntests/test_data/system/gamma/20070430_slc.par\ntests/test_data/system/gamma/20070604_slc.par\ntests/test_data/system/gamma/20070709_slc.par\ntests/test_data/system/gamma/20070813_slc.par\ntests/test_data/system/gamma/20070917_slc.par\n"
  },
  {
    "path": "tests/test_data/system/gamma/input_parameters.conf",
    "content": "# PyRate configuration file for GAMMA-format interferograms\n#\n#------------------------------------\n# input/output parameters\n\n# File containing the list of interferograms to use.\nifgfilelist:  tests/test_data/system/gamma/interferogram_list.txt\n\n# The DEM file used in the InSAR processing\ndemfile:      tests/test_data/system/gamma/20060619_utm.dem\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/system/gamma/20060619_utm_dem.par\n\n# File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) \nhdrfilelist: tests/test_data/system/gamma/header_list.txt\n\n# File listing the pool of available coherence files.\ncohfilelist:\n\n# File listing the pool of available baseline files.\nbasefilelist:  tests/test_data/system/gamma/baseline_list.txt\n\n# Look-up table containing radar-coded row and column for lat/lon pixels\nltfile:       tests/test_data/system/gamma/cropped_lookup_table.lt\n\n# Directory to write the outputs to\noutdir:       tests/test_data/system/gamma/out\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1, GEOTIFF = 2\nprocessor:    1\n\n# No data averaging threshold for prepifg\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/prepifg\n# gamma prepifg runs in parallel on a single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done in parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  0\nprocesses: 8\n\n#------------------------------------\n# Coherence masking options: used by process\n# cohmask: 1 = ON, 0 = OFF\n# cohthresh: coherence threshold value, between 0 and 1\ncohmask:   0\ncohthresh:  0.1\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   2\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:\nrefy:\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.01\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n#------------------------------------\n# APS correction using spatio-temporal filter\n# apsest: ON = 1, OFF = 0\n# Spatial low-pass filter parameters\n# slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters\n# slpnanfill: 1 for interpolation, 0 for zero fill\n# slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1\n# Temporal low-pass filter parameters\n# tlpfcutoff: cutoff t0 for gaussian filter in days;\n# tlpfpthr: valid pixel threshold;\napsest:         1\nslpfcutoff:     0.001\nslpnanfill:     1\nslpnanfill_method:  cubic\ntlpfcutoff:   12\ntlpfpthr:     1\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    0\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor)\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      2\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacking calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativel least squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n[correct]\nsteps =\n    orbfit\n    refphase\n    demerror\n    mst\n    apscorrect\n    maxvar\n"
  },
  {
    "path": "tests/test_data/system/gamma/interferogram_list.txt",
    "content": "tests/test_data/system/gamma/20060619-20061002_utm.unw\ntests/test_data/system/gamma/20060828-20061211_utm.unw\ntests/test_data/system/gamma/20061002-20070219_utm.unw\ntests/test_data/system/gamma/20061002-20070430_utm.unw\ntests/test_data/system/gamma/20061106-20061211_utm.unw\ntests/test_data/system/gamma/20061106-20070115_utm.unw\ntests/test_data/system/gamma/20061106-20070326_utm.unw\ntests/test_data/system/gamma/20061211-20070709_utm.unw\ntests/test_data/system/gamma/20061211-20070813_utm.unw\ntests/test_data/system/gamma/20070115-20070326_utm.unw\ntests/test_data/system/gamma/20070115-20070917_utm.unw\ntests/test_data/system/gamma/20070219-20070430_utm.unw\ntests/test_data/system/gamma/20070219-20070604_utm.unw\ntests/test_data/system/gamma/20070326-20070917_utm.unw\ntests/test_data/system/gamma/20070430-20070604_utm.unw\ntests/test_data/system/gamma/20070604-20070709_utm.unw\ntests/test_data/system/gamma/20070709-20070813_utm.unw"
  },
  {
    "path": "tests/test_data/system/geotiff/20060619-20061002_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20060619_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 06 19 8 28 59.6906\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20060619_utm_dem.par",
    "content": "Gamma DIFF&GEO DEM/MAP parameter file\ntitle: GAMMA DEM par file created from ROI_PAC rsc files MCG 28/01/2016\nDEM_projection:     EQA\ndata_format:        REAL*4\nDEM_hgt_offset:          0.00000\nDEM_scale:               1.00000\nwidth:                47\nnlines:               72\ncorner_lat:    -34.1700000  decimal degrees\ncorner_lon:     150.9100000  decimal degrees\npost_lat:   -8.33333e-04  decimal degrees\npost_lon:    8.33333e-04  decimal degrees\n\nellipsoid_name: WGS 84\nellipsoid_ra:        6378137.000   m\nellipsoid_reciprocal_flattening:  298.2572236\n\ndatum_name: WGS 1984\ndatum_shift_dx:              0.000   m\ndatum_shift_dy:              0.000   m\ndatum_shift_dz:              0.000   m\ndatum_scale_m:         0.00000e+00\ndatum_rotation_alpha:  0.00000e+00   arc-sec\ndatum_rotation_beta:   0.00000e+00   arc-sec\ndatum_rotation_gamma:  0.00000e+00   arc-sec\ndatum_country_list Global Definition, WGS84, World\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20060828-20061211_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20060828_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 08 28 12 18 9.6906\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20061002-20070219_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061002-20070430_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061002_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 10 02 13 5 19.0003\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20061106-20061211_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061106-20070115_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061106-20070326_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061106_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 11 06 22 58 23.1246\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20061211-20070709_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061211-20070813_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20061211_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2006 12 11 23 18 53.1241\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070115-20070326_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070115-20070917_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070115_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 01 15 21 48 23.124\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070219-20070430_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070219-20070604_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070219_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 02 19 1 18 3.124\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070326-20070917_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070326_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 03 26 2 32 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070430-20070604_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070430_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 04 30 5 32 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070604-20070709_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070604_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 06 04 9 31 13.1244\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070709-20070813_base.par",
    "content": "initial_baseline(TCN):          0.1585592       15.3525463        9.7718029   m   m   m\ninitial_baseline_rate:          0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nprecision_baseline(TCN):        0.1585592       15.3525463        9.7718029   m   m   m\nprecision_baseline_rate:        0.0000000       -0.0439410        0.0177195   m/s m/s m/s\nunwrap_phase_constant:            0.00000     radians\n\n"
  },
  {
    "path": "tests/test_data/system/geotiff/20070709_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 07 09 19 01 43.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070813_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 08 13 16 23 43.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/20070917_slc.par",
    "content": "# GAMMA SLC par file created  from ROI_PAC rsc files MCG 28/01/2016\ndate: 2007 09 17 12 41 25.444\nradar_frequency: 5.334694994e+09 Hz\nincidence_angle: 22.9671 degrees\nheading:                 193.1522256   degrees\nazimuth_angle:               90.0000   degrees\nrange_samples:                  8630\nazimuth_lines:                  8571\nrange_looks:                       8\nazimuth_looks:                     2\nrange_pixel_spacing:       18.635856   m\nazimuth_pixel_spacing:     28.136512   m\nnear_range_slc:           802867.7247  m\nprf:                      486.4863103  Hz\nsar_to_earth_center:             7080600.3965   m\nearth_radius_below_sensor:       6371577.2590   m\nearth_semi_major_axis:           6378137.0000   m\nearth_semi_minor_axis:           6356752.3141   m"
  },
  {
    "path": "tests/test_data/system/geotiff/baseline_list.txt",
    "content": "tests/test_data/system/geotiff/20060619-20061002_base.par\ntests/test_data/system/geotiff/20060828-20061211_base.par\ntests/test_data/system/geotiff/20061002-20070219_base.par\ntests/test_data/system/geotiff/20061002-20070430_base.par\ntests/test_data/system/geotiff/20061106-20061211_base.par\ntests/test_data/system/geotiff/20061106-20070115_base.par\ntests/test_data/system/geotiff/20061106-20070326_base.par\ntests/test_data/system/geotiff/20061211-20070709_base.par\ntests/test_data/system/geotiff/20061211-20070813_base.par\ntests/test_data/system/geotiff/20070115-20070326_base.par\ntests/test_data/system/geotiff/20070115-20070917_base.par\ntests/test_data/system/geotiff/20070219-20070430_base.par\ntests/test_data/system/geotiff/20070219-20070604_base.par\ntests/test_data/system/geotiff/20070326-20070917_base.par\ntests/test_data/system/geotiff/20070430-20070604_base.par\ntests/test_data/system/geotiff/20070604-20070709_base.par\ntests/test_data/system/geotiff/20070709-20070813_base.par\n"
  },
  {
    "path": "tests/test_data/system/geotiff/header_list.txt",
    "content": "tests/test_data/system/geotiff/20060619_slc.par\ntests/test_data/system/geotiff/20060828_slc.par\ntests/test_data/system/geotiff/20061002_slc.par\ntests/test_data/system/geotiff/20061106_slc.par\ntests/test_data/system/geotiff/20061211_slc.par\ntests/test_data/system/geotiff/20070115_slc.par\ntests/test_data/system/geotiff/20070219_slc.par\ntests/test_data/system/geotiff/20070326_slc.par\ntests/test_data/system/geotiff/20070430_slc.par\ntests/test_data/system/geotiff/20070604_slc.par\ntests/test_data/system/geotiff/20070709_slc.par\ntests/test_data/system/geotiff/20070813_slc.par\ntests/test_data/system/geotiff/20070917_slc.par"
  },
  {
    "path": "tests/test_data/system/geotiff/input_parameters.conf",
    "content": "# PyRate configuration file for GAMMA-format interferograms\n#\n#------------------------------------\n# input/output parameters\n\n# File containing the list of interferograms to use.\nifgfilelist:  tests/test_data/system/geotiff/interferogram_list.txt\n\n# The DEM file used in the InSAR processing\ndemfile:      tests/test_data/system/geotiff/20060619_utm_dem.tif\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/system/geotiff/20060619_utm_dem.par\n\n# File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) \nhdrfilelist: tests/test_data/system/geotiff/header_list.txt\n\n# File listing the pool of available coherence files.\ncohfilelist:\n\n# File listing the pool of available baseline files.\nbasefilelist:  tests/test_data/system/gamma/baseline_list.txt\n\n# Look-up table containing radar-coded row and column for lat/lon pixels\nltfile:       tests/test_data/system/gamma/cropped_lookup_table.lt\n\n# Directory to write the outputs to\noutdir:       tests/test_data/system/geotiff/out/\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1, geotiff = 2\nprocessor:    2\n\n# No data averaging threshold for prepifg\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/prepifg\n# gamma prepifg runs in parallel on a single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done in parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  0\nprocesses: 1\n\n#------------------------------------\n# Coherence masking options: used by process\n# cohmask: 1 = ON, 0 = OFF\n# cohthresh: coherence threshold value, between 0 and 1\ncohmask:   0\ncohthresh:  0.1\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   2\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:\nrefy:\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.01\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n#------------------------------------\n# APS correction using spatio-temporal filter\n# apsest: ON = 1, OFF = 0\n# Spatial low-pass filter parameters\n# slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters\n# slpnanfill: 1 for interpolation, 0 for zero fill\n# slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1\n# Temporal low-pass filter parameters\n# tlpfcutoff: cutoff t0 for gaussian filter in days;\n# tlpfpthr: valid pixel threshold;\napsest:         1\nslpfcutoff:     0.001\nslpnanfill:     1\nslpnanfill_method:  cubic\ntlpfcutoff:   12\ntlpfpthr:     1\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    0\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor)\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      2\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacking calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativel least squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n[correct]\nsteps =\n    orbfit\n    refphase\n    demerror\n    mst\n    apscorrect\n    maxvar\n"
  },
  {
    "path": "tests/test_data/system/geotiff/interferogram_list.txt",
    "content": "tests/test_data/system/geotiff/20060619-20061002_utm_unw.tif\ntests/test_data/system/geotiff/20060828-20061211_utm_unw.tif\ntests/test_data/system/geotiff/20061002-20070219_utm_unw.tif\ntests/test_data/system/geotiff/20061002-20070430_utm_unw.tif\ntests/test_data/system/geotiff/20061106-20061211_utm_unw.tif\ntests/test_data/system/geotiff/20061106-20070115_utm_unw.tif\ntests/test_data/system/geotiff/20061106-20070326_utm_unw.tif\ntests/test_data/system/geotiff/20061211-20070709_utm_unw.tif\ntests/test_data/system/geotiff/20061211-20070813_utm_unw.tif\ntests/test_data/system/geotiff/20070115-20070326_utm_unw.tif\ntests/test_data/system/geotiff/20070115-20070917_utm_unw.tif\ntests/test_data/system/geotiff/20070219-20070430_utm_unw.tif\ntests/test_data/system/geotiff/20070219-20070604_utm_unw.tif\ntests/test_data/system/geotiff/20070326-20070917_utm_unw.tif\ntests/test_data/system/geotiff/20070430-20070604_utm_unw.tif\ntests/test_data/system/geotiff/20070604-20070709_utm_unw.tif\ntests/test_data/system/geotiff/20070709-20070813_utm_unw.tif"
  },
  {
    "path": "tests/test_data/system/roipac/dem/roipac_test_trimmed.dem.rsc",
    "content": "WIDTH\t\t47\nFILE_LENGTH\t72\nX_FIRST\t\t150.91\nY_FIRST\t\t-34.17\nX_STEP\t\t0.000833333\nY_STEP\t\t-0.000833333\nX_UNIT\t\tdegrees\nY_UNIT\t\tdegrees\nZ_OFFSET\t0\nZ_SCALE\t\t1\nPROJECTION\tLATLON\nDATUM\t\tWGS84\n"
  },
  {
    "path": "tests/test_data/system/roipac/header_list.txt",
    "content": "tests/test_data/system/roipac/headers/geo_060619-061002.unw.rsc\ntests/test_data/system/roipac/headers/geo_060828-061211.unw.rsc\ntests/test_data/system/roipac/headers/geo_061002-070219.unw.rsc\ntests/test_data/system/roipac/headers/geo_061002-070430.unw.rsc\ntests/test_data/system/roipac/headers/geo_061106-061211.unw.rsc\ntests/test_data/system/roipac/headers/geo_061106-070115.unw.rsc\ntests/test_data/system/roipac/headers/geo_061106-070326.unw.rsc\ntests/test_data/system/roipac/headers/geo_061211-070709.unw.rsc\ntests/test_data/system/roipac/headers/geo_061211-070813.unw.rsc\ntests/test_data/system/roipac/headers/geo_070115-070326.unw.rsc\ntests/test_data/system/roipac/headers/geo_070115-070917.unw.rsc\ntests/test_data/system/roipac/headers/geo_070219-070430.unw.rsc\ntests/test_data/system/roipac/headers/geo_070219-070604.unw.rsc\ntests/test_data/system/roipac/headers/geo_070326-070917.unw.rsc\ntests/test_data/system/roipac/headers/geo_070430-070604.unw.rsc\ntests/test_data/system/roipac/headers/geo_070604-070709.unw.rsc\ntests/test_data/system/roipac/headers/geo_070709-070813.unw.rsc"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_060619-061002.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060619\nDATE12            060619-061002\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_060828-061211.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060828\nDATE12            060828-061211\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061002-070219.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061002\nDATE12            061002-070219\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061002-070430.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061002\nDATE12            061002-070430\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061106-061211.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-061211\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061106-070115.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-070115\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061106-070326.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-070326\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061211-070709.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061211\nDATE12            061211-070709\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_061211-070813.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061211\nDATE12            061211-070813\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070115-070326.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070115\nDATE12            070115-070326\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070115-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070115\nDATE12            070115-070917\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070219-070430.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070219\nDATE12            070219-070430\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070219-070604.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070219\nDATE12            070219-070604\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070326-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070326\nDATE12            070326-070917\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070430-070604.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070430\nDATE12            070430-070604\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070604-070709.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070604\nDATE12            070604-070709\n"
  },
  {
    "path": "tests/test_data/system/roipac/headers/geo_070709-070813.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070709\nDATE12            070709-070813\n"
  },
  {
    "path": "tests/test_data/system/roipac/input_parameters.conf",
    "content": "#------------------------------------\n# input/output parameters\n\n# File containing the list of (raw) interferograms.\nifgfilelist:  tests/test_data/system/roipac/interferogram_list.txt\n\n# The DEM used by the processing software\ndemfile:      tests/test_data/system/roipac/dem/roipac_test_trimmed.dem\n\n# The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc).\ndemHeaderFile: tests/test_data/system/roipac/dem/roipac_test_trimmed.dem.rsc\n\n# File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc)\nhdrfilelist: tests/test_data/system/roipac/header_list.txt\n\n# File listing the pool of available coherence files.\ncohfilelist:    \n\n# Where to write the outputs\noutdir:       tests/test_data/system/roipac/out\n\n# InSAR processing software: ROI_PAC = 0, GAMMA = 1\nprocessor:    0\n\n# No data averaging threshold for prepifg\nnoDataAveragingThreshold: 0.5\n\n# The no data value in the interferograms\nnoDataValue:  0.0\n\n# Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan\nnan_conversion: 1\n\n\n#-----------------------------------\n# Multi-threading parameters: used by stacking/timeseries/gamma prepifg\n# gamma prepifg runs in parallel in single machine if parallel != 0\n# parallel = 1, stacking/timeseries computation is done parallel\n# parallel = 0, stacking/timeseries computation is done in serial\nparallel:  0\nprocesses: 1\n\n\n#------------------------------------\n# Coherence masking options: used by process\n# cohmask: 1 = ON, 0 = OFF\n# cohthresh: coherence threshold value, between 0 and 1\ncohmask:   0\ncohthresh:  0.1\n\n#------------------------------------\n# Interferogram multi-look and crop options\n# ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size\n# ifglksx/y: multi-look/subsampling factor in east and north direction respectively\n# ifgxfirst,ifgyfirst: x,y of top-left corner\n# ifgxlast,ifgylast: x,y of bottom-right corner\nifgcropopt:   1\nifglksx:      1\nifglksy:      1\nifgxfirst:    150.92\nifgxlast:     150.94\nifgyfirst:    -34.18\nifgylast:     -34.22\n\n#------------------------------------\n# Reference pixel search options\n# refx/y: coordinate of reference pixel. If <= 0 then search for pixel will be performed\n# refnx/y: number of search grid points in x/y direction\n# refchipsize: chip size of the data window at each search grid point\n# refminfrac: minimum fraction of valid (non-NaN) pixels in the data window\nrefx:\nrefy:\nrefnx:         5\nrefny:         5\nrefchipsize:   5\nrefminfrac:    0.8\n\n#------------------------------------\n# Orbital error correction\n# orbfit: ON = 1, OFF = 0\n# orbfitmethod = 1: independent method; 2: network method\n# orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic)\n# orbfitlksx/y: additional multi-look factor for orbital correction\norbfit:        1\norbfitmethod:  1\norbfitdegrees: 1\norbfitlksx:    1\norbfitlksy:    1\n\n#------------------------------------\n# Reference phase calculation method\n# refest: 1 = median of the whole interferogram\n# refest: 2 = median within the window surrounding the chosen reference pixel\nrefest:        2\n\n#------------------------------------\n# APS Atmospheric Phase Screen correction\n# NOT CURRENTLY IMPLEMENTED\n# apsmethod 1: scene centre incidence angle used from GAMMA header files\n# apsmethod 2: uses GAMMA incidence angle map (lv_theta file)\n# incidencemap  or elevationmap is only used for method 2\n# one of incidencemap or elevationmap must be provided for method 2.\n# if both incidencemap and elevationmap is provided, only incidencemap is used\napscorrect:     1\napsmethod:      2\nincidencemap:   tests/test_data/small_test/gamma_obs/20060619_utm.inc\nelevationmap:   tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta\n\n#------------------------------------\n# APS correction using spatio-temporal filter\n# apsest: ON = 1, OFF = 0\n# Spatial low-pass filter parameters\n# slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters\n# slpnanfill: 1 for interpolation, 0 for zero fill\n# slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1\n# Temporal low-pass filter parameters\n# tlpfcutoff: cutoff t0 for gaussian filter in days;\n# tlpfpthr: valid pixel threshold;\napsest:         1\nslpfcutoff:     0.001\nslpnanfill:     1\nslpnanfill_method:  cubic\ntlpfcutoff:   12\ntlpfpthr:     1\n\n#------------------------------------\n# # DEM error correction\n# demerror: 1 = ON, 0 = OFF\n# de_pthr: valid observations threshold\ndemerror:    1\nde_pthr:     20\n\n#------------------------------------\n# Time Series Calculation\n# tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD)\n# smorder: order of Laplacian smoothing operator (1 =  first-order difference; 2 = second-order difference)\n# smfactor: smoothing factor for Laplacian smoothing\n# ts_pthr: valid observations threshold for time series inversion\ntsmethod:      1\nsmorder:       2\nsmfactor:     -0.25\nts_pthr:       10\n\n#------------------------------------\n# Stacked Rate calculation\n# pthr: minimum number of coherent ifg connections for each pixel\n# nsig: n-sigma used as residuals threshold for iterativelLeast squares stacking\n# maxsig: maximum residual used as a threshold for values in the rate map\nnsig:          3\npthr:          5\nmaxsig:        2\n\n# validate file list values\nvalidate: False\n\n[correct]\nsteps =\n    orbfit\n    refphase\n    demerror\n    mst\n    apscorrect\n    maxvar"
  },
  {
    "path": "tests/test_data/system/roipac/interferogram_list.txt",
    "content": "tests/test_data/system/roipac/interferograms/geo_060619-061002.unw\ntests/test_data/system/roipac/interferograms/geo_060828-061211.unw\ntests/test_data/system/roipac/interferograms/geo_061002-070219.unw\ntests/test_data/system/roipac/interferograms/geo_061002-070430.unw\ntests/test_data/system/roipac/interferograms/geo_061106-061211.unw\ntests/test_data/system/roipac/interferograms/geo_061106-070115.unw\ntests/test_data/system/roipac/interferograms/geo_061106-070326.unw\ntests/test_data/system/roipac/interferograms/geo_061211-070709.unw\ntests/test_data/system/roipac/interferograms/geo_061211-070813.unw\ntests/test_data/system/roipac/interferograms/geo_070115-070326.unw\ntests/test_data/system/roipac/interferograms/geo_070115-070917.unw\ntests/test_data/system/roipac/interferograms/geo_070219-070430.unw\ntests/test_data/system/roipac/interferograms/geo_070219-070604.unw\ntests/test_data/system/roipac/interferograms/geo_070326-070917.unw\ntests/test_data/system/roipac/interferograms/geo_070430-070604.unw\ntests/test_data/system/roipac/interferograms/geo_070604-070709.unw\ntests/test_data/system/roipac/interferograms/geo_070709-070813.unw"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_060619-061002.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060619\nDATE12            060619-061002\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_060828-061211.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              060828\nDATE12            060828-061211\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061002-070219.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061002\nDATE12            061002-070219\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061002-070430.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061002\nDATE12            061002-070430\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061106-061211.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-061211\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061106-070115.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-070115\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061106-070326.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061106\nDATE12            061106-070326\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061211-070709.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061211\nDATE12            061211-070709\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_061211-070813.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              061211\nDATE12            061211-070813\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070115-070326.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070115\nDATE12            070115-070326\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070115-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070115\nDATE12            070115-070917\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070219-070430.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070219\nDATE12            070219-070430\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070219-070604.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070219\nDATE12            070219-070604\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070326-070917.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070326\nDATE12            070326-070917\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070430-070604.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070430\nDATE12            070430-070604\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070604-070709.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070604\nDATE12            070604-070709\n"
  },
  {
    "path": "tests/test_data/system/roipac/interferograms/geo_070709-070813.unw.rsc",
    "content": "WIDTH             47\nFILE_LENGTH       72\nX_FIRST           150.910000000   \nX_STEP            0.000833333     \nY_FIRST           -34.170000000   \nY_STEP            -0.000833333   \nWAVELENGTH        0.0562356424 \nDATE              070709\nDATE12            070709-070813\n"
  },
  {
    "path": "tests/test_dem_error.py",
    "content": "import shutil\nimport glob\nimport os\nfrom os.path import join\nfrom pathlib import Path\nfrom scipy.interpolate import griddata\nimport math\nimport pytest\nimport numpy as np\n\nimport pyrate.constants as C\nfrom tests import common\nfrom pyrate.configuration import Configuration, MultiplePaths\nfrom pyrate import prepifg, correct\nfrom pyrate.core.geometry import get_lonlat_coords\nfrom pyrate.core.dem_error import dem_error_calc_wrapper, _calculate_bperp_wrapper\nfrom pyrate.core.ref_phs_est import ref_phase_est_wrapper\nfrom pyrate.core.shared import Ifg, Geometry, DEM, save_numpy_phase\n\ngeometry_path = common.MEXICO_CROPA_DIR_GEOMETRY\ndem_error_path = common.MEXICO_CROPA_DIR_DEM_ERROR\n\n\n@pytest.fixture(params=list(range(200)))\ndef point():\n    x, y = np.random.randint(0, 60), np.random.randint(0, 100)\n    return x, y\n\n\nclass TestPyRateGammaBperp:\n\n    @classmethod\n    def setup_class(cls):\n        cls.params = Configuration(common.MEXICO_CROPA_CONF).__dict__\n        # run prepifg\n        prepifg.main(cls.params)\n        # copy IFGs to temp folder\n        correct._copy_mlooked(cls.params)\n        # read radar azimuth, range and dem tif files\n        geom_files = Configuration.geometry_files(cls.params)\n        rdc_az_file = geom_files['rdc_azimuth']\n        geom_az = Geometry(rdc_az_file)\n        cls.az = geom_az.data\n        rdc_rg_file = geom_files['rdc_range']\n        geom_rg = Geometry(rdc_rg_file)\n        cls.rg = geom_rg.data\n        dem_file = join(cls.params[C.GEOMETRY_DIR], 'dem.tif')\n        dem_data = DEM(dem_file)\n        cls.dem = dem_data.data\n        # calc bperp using pyrate funcs\n        cls.pbperp = cls.pyrate_bperp()\n\n    def gamma_bperp(self, x, y):\n        \"\"\"\n        Calculate Bperp for specified pixel from GAMMA out files (interpolation required)\n        x0, y0 is the interpolation location in azimuth and range\n        \"\"\"\n        # round azimuth and range coordinates to closest step (500 for az, 200 for rg)\n        azstep = 500\n        rgstep = 200\n        az = self.az[x, y]\n        rg = self.rg[x, y]\n\n        az1 = azstep * math.floor(az / azstep)\n        rg1 = rgstep * math.floor(rg / rgstep)\n        az2 = azstep * math.ceil(az / azstep)\n        rg2 = rgstep * math.ceil(rg / rgstep)\n\n        # four coordinates for bi-linear interpolation\n        teststr1 = str(az1).rjust(6, ' ') + str(rg1).rjust(7, ' ')\n        teststr2 = str(az1).rjust(6, ' ') + str(rg2).rjust(7, ' ')\n        teststr3 = str(az2).rjust(6, ' ') + str(rg1).rjust(7, ' ')\n        teststr4 = str(az2).rjust(6, ' ') + str(rg2).rjust(7, ' ')\n\n        # loop through all corresponding bperp.par files in base.par list\n        bperp_files = sorted(list(glob.glob(os.path.join(geometry_path, '*_bperp.par'))))\n        bperp_int = np.empty(shape=(len(bperp_files)))\n        for i, bperp_file in enumerate(bperp_files):\n            # read Mexico city bperp file\n            with open(bperp_file, 'r') as f:\n                for line in f.readlines():\n                    if teststr1 in line:\n                        bperp1 = line.split()[7]\n                    if teststr2 in line:\n                        bperp2 = line.split()[7]\n                    if teststr3 in line:\n                        bperp3 = line.split()[7]\n                    if teststr4 in line:\n                        bperp4 = line.split()[7]\n\n            # setup numpy array for bi-linear interpolation\n            n = np.array([(az1, rg1, bperp1),\n                          (az1, rg2, bperp2),\n                          (az2, rg1, bperp3),\n                          (az2, rg2, bperp4)])\n            # interpolate using scipy function \"griddata\"\n            bperp_int[i] = griddata(n[:, 0:2], n[:, 2], [(az, rg)], method='linear')\n\n        return bperp_int\n\n    @classmethod\n    def pyrate_bperp(cls):\n        \"\"\"\n        Calculate Bperp image for each ifg using PyRate functions\n        \"\"\"\n        multi_paths = cls.params[C.INTERFEROGRAM_FILES]\n        tmp_paths = [ifg_path.tmp_sampled_path for ifg_path in multi_paths]\n        # keep only ifg files in path list (i.e. remove coherence and dem files)\n        ifg_paths = [item for item in tmp_paths if 'ifg.tif' in item]\n        # read and open the first IFG in list\n        ifg0_path = ifg_paths[0]\n        ifg0 = Ifg(ifg0_path)\n        ifg0.open(readonly=True)\n        # size of ifg dataset\n        # calculate per-pixel lon/lat\n        lon, lat = get_lonlat_coords(ifg0)\n        bperp = _calculate_bperp_wrapper(ifg_paths, cls.az, cls.rg, lat.data, lon.data, cls.dem)[0]\n        return np.moveaxis(bperp, (0, 1, 2), (2, 0, 1))\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR], ignore_errors=True)\n\n    def test_pyrate_bperp_matches_gamma_bperp(self, point):\n        x, y = point\n        az = self.az[x, y]\n        rg = self.rg[x, y]\n\n        if az < 0 or rg < 0:\n            pytest.skip('skipped due to -ve az or rg')\n\n        res = self.pbperp[x, y, :]\n        exp = self.gamma_bperp(*point)\n        np.testing.assert_array_almost_equal(exp, res, 2)  # max difference < 1cm\n\n    def test_avg_bperp_calculation(self):\n        # TODO - improve this test by reading bperp *.npy files\n        res = np.mean(self.pbperp, axis=(0, 1), dtype=np.float64)\n        # assuming array centre is a good proxy for average value\n        # TODO - use interpolation to calculate actual Gamma array average\n        exp = self.gamma_bperp(30, 50)\n        np.testing.assert_array_almost_equal(exp, res, 2)\n\n\nclass TestDEMErrorFilesReusedFromDisc:\n\n    @classmethod\n    def setup_class(cls):\n        cls.conf = common.MEXICO_CROPA_CONF\n        cls.params = Configuration(cls.conf).__dict__\n        prepifg.main(cls.params)\n        cls.params = Configuration(cls.conf).__dict__\n        multi_paths = cls.params[C.INTERFEROGRAM_FILES]\n        cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_dem_error_used_from_disc_on_rerun(self):\n        correct._copy_mlooked(self.params)\n        correct._update_params_with_tiles(self.params)\n        times_written = self.__run_once()\n        assert len(times_written) == len(self.ifg_paths)\n        times_written_1 = self.__run_once()\n        np.testing.assert_array_equal(times_written_1, times_written)\n\n    def __run_once(self):\n        dem_files = [MultiplePaths.dem_error_path(i, self.params) for i in self.ifg_paths]\n        correct._copy_mlooked(self.params)\n        correct._update_params_with_tiles(self.params)\n        correct._create_ifg_dict(self.params)\n        save_numpy_phase(self.ifg_paths, self.params)\n        dem_error_calc_wrapper(self.params)\n        assert all(m.exists() for m in dem_files)\n        return [os.stat(o).st_mtime for o in dem_files]\n\n\nclass TestDEMErrorResults:\n\n    @classmethod\n    def setup_class(cls):\n        cls.conf = common.MEXICO_CROPA_CONF\n        cls.params = Configuration(cls.conf).__dict__\n        prepifg.main(cls.params)\n        cls.params = Configuration(cls.conf).__dict__\n        multi_paths = cls.params[C.INTERFEROGRAM_FILES]\n        cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n        cls.params[C.REFX_FOUND] = 8  # this is the pixel of the location given in the pyrate_mexico_cropa.conf file\n        cls.params[C.REFY_FOUND] = 33  # however, the median of the whole interferogram is used for this validation\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_calc_dem_errors(self):\n        # validate output of current version of the code with saved files from an independent test run\n        # only the reference phase and dem_error are used in this test\n\n        # saved dem_error.tif (expected)\n        dem_error_tif_exp = join(dem_error_path, 'dem_error.tif')\n        dem = DEM(dem_error_tif_exp)\n        dem_error_exp = dem.data\n        # run relevant parts of the 'correct' step\n        correct._copy_mlooked(self.params)\n        correct._update_params_with_tiles(self.params)\n        correct._create_ifg_dict(self.params)\n        save_numpy_phase(self.ifg_paths, self.params)\n        # subtract the reference phase to enable comparison with a 'normal' pyrate run\n        ref_phase_est_wrapper(self.params)\n        dem_error_calc_wrapper(self.params)\n        # dem_error.tif from this run (result)\n        dem_error_tif_res = join(self.params[C.DEM_ERROR_DIR], 'dem_error.tif')\n        dem = DEM(dem_error_tif_res)\n        dem_error_res = dem.data\n        # check equality\n        np.testing.assert_allclose(dem_error_exp, dem_error_res)\n\n        # ifg correction files in subdirectory out/dem_error/\n        # three different ifgs:\n        # ifg1 -> short_baseline_ifg: 20180106-20180319 (ca. 3 m)\n        # ifg2 -> long_baseline_ifg: 20180130-20180412(ca. 108 m)\n        # ifg3 -> medium_baseline_ifg: 20180412-20180518 (ca. 48 m)\n        # load saved files\n        dem_error_ifg1_path = join(dem_error_path, '20180106-20180319_ifg_20_dem_error.npy')\n        dem_error_ifg1_exp = np.load(dem_error_ifg1_path)\n        dem_error_ifg2_path = join(dem_error_path, '20180130-20180412_ifg_20_dem_error.npy')\n        dem_error_ifg2_exp = np.load(dem_error_ifg2_path)\n        dem_error_ifg3_path = join(dem_error_path, '20180412-20180518_ifg_20_dem_error.npy')\n        dem_error_ifg3_exp = np.load(dem_error_ifg3_path)\n        # load correction values saved from this run (result)\n        dem_error_ifg1_path = Path(self.params[C.DEM_ERROR_DIR]).joinpath('20180106-20180319_ifg_20_dem_error.npy')\n        dem_error_ifg1_res = np.load(dem_error_ifg1_path)\n        dem_error_ifg2_path = Path(self.params[C.DEM_ERROR_DIR]).joinpath('20180130-20180412_ifg_20_dem_error.npy')\n        dem_error_ifg2_res = np.load(dem_error_ifg2_path)\n        dem_error_ifg3_path = Path(self.params[C.DEM_ERROR_DIR]).joinpath('20180412-20180518_ifg_20_dem_error.npy')\n        dem_error_ifg3_res = np.load(dem_error_ifg3_path)\n        # check equality\n        np.testing.assert_allclose(dem_error_ifg1_exp, dem_error_ifg1_res)\n        np.testing.assert_allclose(dem_error_ifg2_exp, dem_error_ifg2_res)\n        np.testing.assert_allclose(dem_error_ifg3_exp, dem_error_ifg3_res)\n"
  },
  {
    "path": "tests/test_gamma.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the gamma.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nimport tempfile\nfrom datetime import date, time\nfrom os.path import join\nimport pytest\nfrom pathlib import Path\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal\nfrom osgeo import gdal\n\nimport pyrate.configuration\n\nimport pyrate.core.ifgconstants as ifc\nfrom pyrate.core import shared, gamma\nfrom pyrate import prepifg, conv2tif\nfrom pyrate.core.shared import write_fullres_geotiff, GeotiffException\nimport pyrate.constants as C\nfrom pyrate.constants import PYRATEPATH, IFG_FILE_LIST, PROCESSOR, OUT_DIR, DEM_HEADER_FILE, \\\n    NO_DATA_VALUE, BASE_FILE_LIST\n\nfrom tests.common import manipulate_test_conf\nfrom pyrate.configuration import Configuration\nfrom tests.common import GAMMA_TEST_DIR, WORKING_DIR\nfrom tests.common import TEMPDIR\nfrom tests.common import small_data_setup\n\ngdal.UseExceptions()\n\nLIGHTSPEED = 3e8  # approx\n\n\nclass TestGammaCommandLineTests:\n\n    def setup_method(self):\n        self.base = join(PYRATEPATH, 'tests', 'test_data', 'gamma')\n        self.hdr = join(self.base, 'dem16x20raw.dem.par')\n        temp_text = tempfile.mktemp()\n        self.confFile = os.path.join(TEMPDIR,'{}/gamma_test.cfg'.format(temp_text))\n        self.ifgListFile = os.path.join(TEMPDIR, '{}/gamma_ifg.list'.format(temp_text))\n        self.baseListFile = os.path.join(TEMPDIR, '{}/gamma_base.list'.format(temp_text))\n        self.base_dir = os.path.dirname(self.confFile)\n        shared.mkdir_p(self.base_dir)\n\n    def teardown_method(self):\n        try:\n            os.remove(self.exp_path)\n        except:\n            pass\n        shutil.rmtree(self.base_dir, ignore_errors=True)\n\n    def makeInputFiles(self, data):\n        with open(self.confFile, 'w') as conf:\n            conf.write('{}: {}\\n'.format(DEM_HEADER_FILE, self.hdr))\n            conf.write('{}: {}\\n'.format(NO_DATA_VALUE, '0.0'))\n            conf.write('{}: {}\\n'.format(WORKING_DIR, self.base_dir))\n            conf.write('{}: {}\\n'.format(IFG_FILE_LIST, self.ifgListFile))\n            conf.write('{}: {}\\n'.format(BASE_FILE_LIST, self.baseListFile))\n            conf.write('{}: {}\\n'.format(PROCESSOR, '1'))\n            conf.write('{}: {}\\n'.format(OUT_DIR, self.base_dir))\n        with open(self.ifgListFile, 'w') as ifgl:\n            ifgl.write(data)\n\n\nclass TestGammaToGeoTiff:\n    \"\"\"Tests conversion of GAMMA rasters to custom PyRate GeoTIFF\"\"\"\n\n    @classmethod\n    def setup_method(cls):\n        # create common combined header obj so the headers are only read once\n        # tricker: needs both ifg headers, and DEM one for the extents\n        filenames = ['r20090713_VV.slc.par', 'r20090817_VV.slc.par']\n        hdr_paths = [join(GAMMA_TEST_DIR, f) for f in filenames]\n        hdrs = [gamma.parse_epoch_header(p) for p in hdr_paths]\n        dem_hdr_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par')\n        base_hdr_path = join(GAMMA_TEST_DIR, '20160114-20160126_base.par')\n        cls.DEM_HDR = gamma.parse_dem_header(dem_hdr_path)\n        cls.BASE_HDR = gamma.parse_baseline_header(base_hdr_path)\n        cls.COMBINED = gamma.combine_headers(*hdrs, dem_hdr=cls.DEM_HDR, base_hdr=cls.BASE_HDR)\n\n    def teardown_method(self):\n        if os.path.exists(self.dest):\n            os.remove(self.dest)\n\n    def test_to_geotiff_dem(self):\n        hdr_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par')\n        hdr = gamma.parse_dem_header(hdr_path)\n        data_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem')\n        self.dest = os.path.join(TEMPDIR, \"tmp_gamma_dem.tif\")\n\n        write_fullres_geotiff(hdr, data_path, self.dest, nodata=0)\n        exp_path = join(GAMMA_TEST_DIR, 'dem16x20_subset_from_gamma.tif')\n        exp_ds = gdal.Open(exp_path)\n        ds = gdal.Open(self.dest)\n\n        # compare data and geographic headers\n        # HACK: round  expected to nearest integer\n        assert_array_almost_equal(np.rint(exp_ds.ReadAsArray()),\n                                  ds.ReadAsArray())\n        self.compare_rasters(exp_ds, ds)\n        md = ds.GetMetadata()\n        assert md['AREA_OR_POINT'] == 'Area'\n\n    def test_to_geotiff_ifg(self):\n        self.dest = os.path.join(TEMPDIR, 'tmp_gamma_ifg.tif')\n        data_path = join(GAMMA_TEST_DIR,\n                         '16x20_20090713-20090817_VV_4rlks_utm.unw')\n        write_fullres_geotiff(self.COMBINED, data_path, self.dest, nodata=0)\n\n        ds = gdal.Open(self.dest)\n        exp_path = join(GAMMA_TEST_DIR, '16x20_20090713-20090817_VV_4rlks_utm.tif')\n        exp_ds = gdal.Open(exp_path)\n\n        # compare data and geographic headers\n        assert_array_almost_equal(exp_ds.ReadAsArray(), ds.ReadAsArray())\n        self.compare_rasters(ds, exp_ds)\n\n        md = ds.GetMetadata()\n        assert len(md) == 32 # 32 metadata items\n        assert md[ifc.FIRST_DATE] == str(date(2009, 7, 13))\n        assert md[ifc.SECOND_DATE] == str(date(2009, 8, 17))\n        assert md[ifc.PYRATE_TIME_SPAN] == str(35 / ifc.DAYS_PER_YEAR)\n        # TODO test failing\n        # self.assertTrue(md[ifc.FIRST_DATE] == str(12))\n        # TODO test failing\n        # self.assertTrue(md[ifc.SECOND_DATE] == str(time(12)))\n\n        wavelen = float(md[ifc.PYRATE_WAVELENGTH_METRES])\n        assert wavelen == pytest.approx(0.05627457792190739)\n\n    def test_to_geotiff_wrong_input_data(self):\n        # use TIF, not UNW for data\n        self.dest = os.path.join(TEMPDIR, 'tmp_gamma_ifg.tif')\n        data_path = join(GAMMA_TEST_DIR, '16x20_20090713-20090817_VV_4rlks_utm.tif')\n        with pytest.raises(GeotiffException):\n            write_fullres_geotiff(self.COMBINED, data_path, self.dest, nodata=0)\n\n    def compare_rasters(self, ds, exp_ds):\n        band = ds.GetRasterBand(1)\n        exp_band = exp_ds.GetRasterBand(1)\n\n        nodata = band.GetNoDataValue()\n        assert ~(nodata is None)\n        assert exp_band.GetNoDataValue() == nodata\n\n        pj = ds.GetProjection()\n        assert 'WGS 84' in pj\n        assert exp_ds.GetProjection() == pj\n        for exp, act in zip(exp_ds.GetGeoTransform(), ds.GetGeoTransform()):\n            assert exp == pytest.approx(act, abs=0.0001)\n\n    def test_bad_projection(self):\n        hdr = self.DEM_HDR.copy()\n        hdr[ifc.PYRATE_DATUM] = 'nonexistent projection'\n        data_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem')\n        self.dest = os.path.join(TEMPDIR, 'tmp_gamma_dem2.tif')\n        with pytest.raises(GeotiffException):\n            write_fullres_geotiff(hdr, data_path, self.dest, nodata=0)\n\nclass TestGammaHeaderListRaiseException:\n    'Test to make sure PyRate raises exception when IFG header list contains more than two files'\n\n    def setup_method(self):\n        self.demHeaderString = f'{GAMMA_TEST_DIR}/dem16x20raw.dem.par'\n        self.headerList = [f'{GAMMA_TEST_DIR}/r20090713_VV.slc.par', f'{GAMMA_TEST_DIR}/r20090817_VV.slc.par', f'{GAMMA_TEST_DIR}/r20090713_VV.slc.par', f'{GAMMA_TEST_DIR}/r20090817_VV.slc.par']\n\n    def test_exception(self):\n        with pytest.raises(gamma.GammaException):\n            gamma.manage_headers(self.demHeaderString, self.headerList)\n\n\nclass TestGammaHeaderParsingTests:\n    'Tests conversion of GAMMA headers to Py dicts'\n\n    def test_parse_gamma_epoch_header(self):\n        # minimal required headers are:\n        # date:      2009  7 13\n        # radar_frequency:        5.3310040e+09   Hz\n        path = join(GAMMA_TEST_DIR, 'r20090713_VV.slc.par')\n        hdrs = gamma.parse_epoch_header(path)\n\n        exp_date = date(2009, 7, 13)\n        assert hdrs[ifc.FIRST_DATE] == exp_date\n\n        exp_wavelen = LIGHTSPEED / 5.3310040e+09\n        assert hdrs[ifc.PYRATE_WAVELENGTH_METRES] == exp_wavelen\n\n        incidence_angle = 22.9671\n        assert hdrs[ifc.PYRATE_INCIDENCE_DEGREES] == incidence_angle\n\n    def test_parse_gamma_dem_header(self):\n        path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par')\n        hdrs = gamma.parse_dem_header(path)\n\n        self.assert_equal(hdrs[ifc.PYRATE_NCOLS], 16)\n        self.assert_equal(hdrs[ifc.PYRATE_NROWS], 20)\n        self.assert_equal(hdrs[ifc.PYRATE_LAT], -33.3831945)\n        self.assert_equal(hdrs[ifc.PYRATE_LONG], 150.3870833)\n        self.assert_equal(hdrs[ifc.PYRATE_X_STEP], 6.9444445e-05)\n        self.assert_equal(hdrs[ifc.PYRATE_Y_STEP], -6.9444445e-05)\n\n    @staticmethod\n    def assert_equal(arg1, arg2):\n        assert arg1 == arg2\n\n\n# Test data for the epoch header combination\nH0 = {ifc.FIRST_DATE: date(2009, 7, 13),\n      ifc.FIRST_TIME: time(12),\n      ifc.PYRATE_WAVELENGTH_METRES: 1.8,\n      ifc.PYRATE_INCIDENCE_DEGREES: 35.565,\n      }\n\nH1 = {ifc.FIRST_DATE: date(2009, 8, 17),\n      ifc.FIRST_TIME: time(12, 10, 10),\n      ifc.PYRATE_WAVELENGTH_METRES: 1.8,\n      ifc.PYRATE_INCIDENCE_DEGREES: 35.56,\n      }\n\nH1_ERR1 = {ifc.FIRST_DATE: date(2009, 8, 17),\n           ifc.FIRST_TIME: time(12),\n           ifc.PYRATE_WAVELENGTH_METRES: 2.4,\n           ifc.PYRATE_INCIDENCE_DEGREES: 35.56,\n           }\n\nH1_ERR2 = {ifc.FIRST_DATE: date(2009, 8, 17),\n           ifc.FIRST_TIME: time(12),\n           ifc.PYRATE_WAVELENGTH_METRES: 1.8,\n           ifc.PYRATE_INCIDENCE_DEGREES: 35.76,\n           }\n\n\nclass TestHeaderCombination:\n    'Tests GAMMA epoch and DEM headers can be combined into a single Py dict'\n\n    def setup_method(self):\n        self.err = gamma.GammaException\n        dem_hdr_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par')\n        self.dh = gamma.parse_dem_header(dem_hdr_path)\n        base_hdr_path = join(GAMMA_TEST_DIR, '20160114-20160126_base.par')\n        self.bh = gamma.parse_baseline_header(base_hdr_path)\n\n    @staticmethod\n    def assert_equal(arg1, arg2):\n        assert arg1 == arg2\n\n    def test_combine_headers(self):\n        filenames = ['r20090713_VV.slc.par', 'r20090817_VV.slc.par']\n        paths = [join(GAMMA_TEST_DIR, p) for p in filenames]\n        hdr0, hdr1 = [gamma.parse_epoch_header(p) for p in paths]\n\n        chdr = gamma.combine_headers(hdr0, hdr1, self.dh, self.bh)\n\n        exp_timespan = (18 + 17) / ifc.DAYS_PER_YEAR\n        self.assert_equal(chdr[ifc.PYRATE_TIME_SPAN], exp_timespan)\n\n        exp_date = date(2009, 7, 13)\n        self.assert_equal(chdr[ifc.FIRST_DATE], exp_date)\n        exp_date2 = date(2009, 8, 17)\n        self.assert_equal(chdr[ifc.SECOND_DATE], exp_date2)\n\n        exp_wavelen = LIGHTSPEED / 5.3310040e+09\n        self.assert_equal(chdr[ifc.PYRATE_WAVELENGTH_METRES], exp_wavelen)\n\n    def test_fail_non_dict_header(self):\n        self.assertRaises(gamma.combine_headers, H0, '', self.dh, self.bh)\n        self.assertRaises(gamma.combine_headers, '', H0, self.dh, self.bh)\n        self.assertRaises(gamma.combine_headers, H0, H1, None, self.bh)\n        self.assertRaises(gamma.combine_headers, H0, H1, '', self.bh)\n\n    def test_fail_mismatching_wavelength(self):\n        self.assertRaises(gamma.combine_headers, H0, H1_ERR1, self.dh, self.bh)\n        \n    def test_fail_mismatching_incidence(self):\n        self.assertRaises(gamma.combine_headers, H0, H1_ERR2, self.dh, self.bh)\n\n    def test_fail_same_date(self):\n        self.assertRaises(gamma.combine_headers, H0, H0, self.dh, self.bh)\n\n    def test_fail_bad_date_order(self):\n        with pytest.raises(self.err):\n            gamma.combine_headers(H1, H0, self.dh, self.bh)\n\n    def assertRaises(self, func, * args):\n        with pytest.raises(self.err):\n            func(* args)\n\n\nifg_glob_suffix = \"*_ifg.tif\"\ncoh_glob_suffix = \"*_coh.tif\"\n\n\n@pytest.fixture(scope='module')\ndef parallel_ifgs(gamma_conf):\n\n    tdir = Path(tempfile.mkdtemp())\n\n    params_p = manipulate_test_conf(gamma_conf, tdir)\n    params_p[C.PARALLEL] = 1\n\n    output_conf_file = 'conf.conf'\n    output_conf = tdir.joinpath(output_conf_file)\n    pyrate.configuration.write_config_file(params=params_p, output_conf_file=output_conf)\n\n    params_p = Configuration(output_conf).__dict__\n\n    conv2tif.main(params_p)\n    prepifg.main(params_p)\n\n    parallel_df = list(Path(params_p[C.INTERFEROGRAM_DIR]).glob(ifg_glob_suffix))\n    parallel_coh_files = list(Path(params_p[C.COHERENCE_DIR]).glob(coh_glob_suffix))\n\n    p_ifgs = small_data_setup(datafiles=parallel_df + parallel_coh_files)\n    yield p_ifgs\n\n\n@pytest.fixture(scope='module')\ndef series_ifgs(gamma_conf):\n\n    print('======================setup series==========================')\n    tdir = Path(tempfile.mkdtemp())\n\n    params_s = manipulate_test_conf(gamma_conf, tdir)\n    params_s[C.PARALLEL] = 0\n\n    output_conf_file = 'conf.conf'\n    output_conf = tdir.joinpath(output_conf_file)\n    pyrate.configuration.write_config_file(params=params_s, output_conf_file=output_conf)\n\n    params_s = Configuration(output_conf).__dict__\n\n    conv2tif.main(params_s)\n\n    prepifg.main(params_s)\n\n    serial_ifgs = list(Path(params_s[C.INTERFEROGRAM_DIR]).glob(ifg_glob_suffix))\n    coh_files = list(Path(params_s[C.COHERENCE_DIR]).glob(coh_glob_suffix))\n\n    s_ifgs = small_data_setup(datafiles=serial_ifgs + coh_files)\n    yield s_ifgs\n\n\ndef test_equality(series_ifgs, parallel_ifgs):\n    for s, p in zip(series_ifgs, parallel_ifgs):\n        np.testing.assert_array_almost_equal(s.phase_data, p.phase_data)\n\n\ndef test_meta_data_exists(series_ifgs, parallel_ifgs):\n    for i, (s, p) in enumerate(zip(series_ifgs, parallel_ifgs)):\n        # all metadata equal\n        assert s.meta_data == p.meta_data\n        # test that DATA_TYPE exists in metadata\n        assert ifc.DATA_TYPE in s.meta_data.keys()\n        # test that DATA_TYPE is MULTILOOKED\n        assert (s.meta_data[ifc.DATA_TYPE] == ifc.MULTILOOKED) or \\\n               (s.meta_data[ifc.DATA_TYPE] == ifc.MULTILOOKED_COH)\n    assert i + 1 == 34\n\n\nclass TestGammaBaselineRead:\n    \"\"\"Tests the reading of initial and precise baselines\"\"\"\n\n    def setup_method(self):\n        init_path = join(GAMMA_TEST_DIR, '20160114-20160126_base_init.par')\n        self.init = gamma.parse_baseline_header(init_path)\n        prec_path = join(GAMMA_TEST_DIR, '20160114-20160126_base.par')\n        self.prec = gamma.parse_baseline_header(prec_path)\n\n    def test_prec_baseline_read(self):\n        \"\"\"Test that the Precise baseline values are being read\"\"\"\n        exp_i = {'BASELINE_T': -0.0000026, 'BASELINE_C': -103.7427072,\n                 'BASELINE_N': 2.8130731, 'BASELINE_RATE_T': 0.0,\n                 'BASELINE_RATE_C': -0.0173538,\n                 'BASELINE_RATE_N': -0.0055098}\n\n        exp_p = {'BASELINE_T': 0.0, 'BASELINE_C': -103.8364725,\n                 'BASELINE_N': 2.8055662, 'BASELINE_RATE_T': 0.0,\n                 'BASELINE_RATE_C': -0.0182215,\n                 'BASELINE_RATE_N': -0.0065402}\n\n        # Precise values are read\n        assert self.prec != exp_i\n        assert self.prec == exp_p\n\n        # Initial values are ignored\n        assert self.init != exp_i\n        assert self.init != exp_p\n\n\n    def test_init_baseline_read(self):\n        \"\"\"Test that the Initial baseline values are being read\"\"\"\n        exp_i = {'BASELINE_T': 0.6529765, 'BASELINE_C': -103.9065694,\n                 'BASELINE_N': 2.9253896, 'BASELINE_RATE_T': 0.0,\n                 'BASELINE_RATE_C': -0.0231786,\n                 'BASELINE_RATE_N': -0.0038703}\n\n        exp_p = {'BASELINE_T': 0.0, 'BASELINE_C': 0.0,\n                 'BASELINE_N': 0.0, 'BASELINE_RATE_T': 0.0,\n                 'BASELINE_RATE_C': 0.0, 'BASELINE_RATE_N': 0.0}\n\n        # Precise values are ignored\n        assert self.prec != exp_i\n        assert self.prec != exp_p\n\n        # Initial values are read\n        assert self.init == exp_i\n        assert self.init != exp_p\n\n"
  },
  {
    "path": "tests/test_gamma_vs_roipac.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests that compare GAMMA and ROI_PAC\nfunctionality in PyRate.\n\"\"\"\nimport os\nimport shutil\nimport pytest\nfrom pathlib import Path\n\nimport pyrate.configuration\nimport pyrate.constants as C\nfrom pyrate.core.shared import DEM\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.core.prepifg_helper import _is_number\nfrom pyrate import prepifg, conv2tif, configuration\nfrom tests.common import SML_TEST_DIR, small_data_setup, copytree, TEST_CONF_ROIPAC, TEST_CONF_GAMMA, working_dirs, \\\n    WORKING_DIR\n\n\nSMLNEY_GAMMA_TEST = os.path.join(SML_TEST_DIR, \"gamma_obs\")\n\n\ndef test_files_are_same(tempdir, get_config):\n    roipac_params = get_config(TEST_CONF_ROIPAC)\n    roipac_tdir = Path(tempdir())\n    roipac_params[WORKING_DIR] = working_dirs[Path(TEST_CONF_ROIPAC).name]\n    roipac_params = __workflow(roipac_params, roipac_tdir)\n\n    gamma_params = get_config(TEST_CONF_GAMMA)\n    gamma_tdir = Path(tempdir())\n    gamma_params[WORKING_DIR] = working_dirs[Path(TEST_CONF_GAMMA).name]\n    gamma_params = __workflow(gamma_params, gamma_tdir)\n\n    # conv2tif output equal\n    __assert_same_files_produced(roipac_params[C.INTERFEROGRAM_DIR], gamma_params[C.INTERFEROGRAM_DIR], \"*_unw.tif\", 17)\n\n    # prepifg output equal\n    __assert_same_files_produced(roipac_params[C.INTERFEROGRAM_DIR], gamma_params[C.INTERFEROGRAM_DIR], f\"*_ifg.tif\", 17)\n\n    __assert_same_files_produced(roipac_params[C.GEOMETRY_DIR], gamma_params[C.GEOMETRY_DIR], \"dem.tif\", 1)\n\n    # clean up\n    shutil.rmtree(roipac_params[WORKING_DIR])\n    shutil.rmtree(gamma_params[WORKING_DIR])\n\n\ndef __workflow(params, tdir):\n    copytree(params[WORKING_DIR], tdir)\n    # manipulate params\n    outdir = tdir.joinpath('out')\n    outdir.mkdir(exist_ok=True)\n    params[C.OUT_DIR] = outdir.as_posix()\n\n    params[C.DEM_FILE] = tdir.joinpath(Path(params[C.DEM_FILE]).name).as_posix()\n    params[C.DEM_HEADER_FILE] = tdir.joinpath(Path(params[C.DEM_HEADER_FILE]).name).as_posix()\n    params[C.HDR_FILE_LIST] = tdir.joinpath(Path(params[C.HDR_FILE_LIST]).name).as_posix()\n    params[C.IFG_FILE_LIST] = tdir.joinpath(Path(params[C.IFG_FILE_LIST]).name).as_posix()\n    params[C.TMPDIR] = tdir.joinpath(Path(params[C.TMPDIR]).name).as_posix()\n    output_conf = tdir.joinpath('roipac_temp.conf')\n    pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n    params = configuration.Configuration(output_conf).__dict__\n    conv2tif.main(params)\n    prepifg.main(params)\n    params[WORKING_DIR] = tdir.as_posix()\n    return params\n\n\ndef __assert_same_files_produced(dir1, dir2, ext, num_files):\n    dir1_files = list(Path(dir1).glob(ext))\n    dir2_files = list(Path(dir2).glob(ext))\n    dir1_files.sort()\n    dir2_files.sort()\n    # 17 unwrapped geotifs\n    # 17 cropped multilooked tifs + 1 dem\n    assert len(dir1_files) == num_files\n    assert len(dir2_files) == num_files\n    c = 0\n\n    all_roipac_ifgs = [f for f in small_data_setup(dir1_files) if not isinstance(f, DEM)]\n    all_gamma_ifgs = [f for f in small_data_setup(dir2_files) if not isinstance(f, DEM)]\n\n    for c, (i, j) in enumerate(zip(all_roipac_ifgs, all_gamma_ifgs)):\n        mdi = i.meta_data\n        mdj = j.meta_data\n        for k in mdi:  # all key values equal\n            if k == \"INCIDENCE_DEGREES\":\n                pass  # incidence angle not implemented for roipac\n            elif _is_number(mdi[k]):\n                assert pytest.approx(float(mdj[k]), 0.00001) == float(mdi[k])\n            elif mdi[k] == \"ROIPAC\" or \"GAMMA\":\n                pass  # INSAR_PROCESSOR can not be equal\n            else:\n                assert mdj[k] == mdi[k]\n\n        if i.data_path.__contains__(\"_ifg.tif\"):\n            # these are multilooked tifs\n            # test that DATA_STEP is MULTILOOKED\n            assert mdi[ifc.DATA_TYPE] == ifc.MULTILOOKED\n            assert mdj[ifc.DATA_TYPE] == ifc.MULTILOOKED\n        else:\n            assert mdi[ifc.DATA_TYPE] == ifc.ORIG\n            assert mdj[ifc.DATA_TYPE] == ifc.ORIG\n    if not all_gamma_ifgs:  # checking for dem\n        return\n    assert c + 1 == len(all_gamma_ifgs)\n"
  },
  {
    "path": "tests/test_gdal_python.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the gdal_python.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nimport subprocess\nimport tempfile\nfrom copy import copy\nfrom pathlib import Path\n\nimport numpy as np\nfrom osgeo import gdal, gdalconst, osr\n\nimport pyrate.core\nfrom pyrate import constants as c, conv2tif\n\nfrom pyrate.configuration import MultiplePaths\nfrom pyrate.core import gdal_python, ifgconstants as ifc\nfrom pyrate.core.shared import Ifg\nfrom tests import common\n\n\nclass TestResample(common.UnitTestAdaptation):\n\n    def test_small_data_resampling(self):\n        small_test_ifgs = common.small_data_setup()\n        # minX, minY, maxX, maxY = extents\n        extents = [150.91, -34.229999976, 150.949166651, -34.17]\n        extents_str = [str(e) for e in extents]\n        resolutions = [0.001666666, .001, 0.002, 0.0025, .01]\n\n        for res in resolutions:\n            res = [res, -res]\n            self.check_same_resampled_output(extents, extents_str, res,\n                                             small_test_ifgs)\n\n    def check_same_resampled_output(self, extents, extents_str, res,\n                                    small_test_ifgs):\n        cmd = ['gdalwarp', '-overwrite', '-srcnodata', 'None',\n               '-q', '-r', 'near', '-te'] \\\n              + extents_str\n\n        if res[0]:\n            new_res_str = [str(r) for r in res]\n            cmd += ['-tr'] + new_res_str\n        for s in small_test_ifgs:\n            temp_tif = tempfile.mktemp(suffix='.tif')\n            t_cmd = cmd + [s.data_path, temp_tif]\n            subprocess.check_call(t_cmd)\n            resampled_ds = gdal.Open(temp_tif)\n            resampled_ref = resampled_ds.ReadAsArray()\n\n            resampled_temp_tif = tempfile.mktemp(suffix='.tif',\n                                                 prefix='resampled_')\n            resampled = gdal_python.resample_nearest_neighbour(s.data_path,\n                                                               extents, res,\n                                                               resampled_temp_tif)\n            np.testing.assert_array_almost_equal(resampled_ref, resampled[0, :, :])\n            try:\n                os.remove(temp_tif)\n            except PermissionError:\n                print(\"File opened by another process.\")\n\n            try:\n                os.remove(resampled_temp_tif)  # also proves file was written\n            except PermissionError:\n                print(\"File opened by another process.\")\n\n    def test_none_resolution_output(self):\n        small_test_ifgs = common.small_data_setup()\n        # minX, minY, maxX, maxY = extents\n        extents = [150.91, -34.229999976, 150.949166651, -34.17]\n        extents_str = [str(e) for e in extents]\n\n        self.check_same_resampled_output(extents, extents_str, [None, None],\n                                         small_test_ifgs)\n\n    def test_output_file_written(self):\n        small_test_ifgs = common.small_data_setup()\n        extents = [150.91, -34.229999976, 150.949166651, -34.17]\n        resolutions = [0.001666666, .001, 0.002, 0.0025, .01]\n        for res in resolutions:\n            for s in small_test_ifgs:\n                resampled_temp_tif = tempfile.mktemp(suffix='.tif',\n                                                    prefix='resampled_')\n                gdal_python.resample_nearest_neighbour(s.data_path, extents,\n                                                       [res, -res],\n                                                       resampled_temp_tif)\n                self.assertTrue(os.path.exists(resampled_temp_tif))\n                os.remove(resampled_temp_tif)\n\n    def test_resampled_tif_has_metadata(self):\n        small_test_ifgs = common.small_data_setup()\n\n        # minX, minY, maxX, maxY = extents\n        extents = [150.91, -34.229999976, 150.949166651, -34.17]\n        for s in small_test_ifgs:\n\n            resampled_temp_tif = tempfile.mktemp(suffix='.tif',\n                                                prefix='resampled_')\n            gdal_python.resample_nearest_neighbour(\n                s.data_path, extents, [None, None], resampled_temp_tif)\n            dst_ds = gdal.Open(resampled_temp_tif)\n            md = dst_ds.GetMetadata()\n            self.assertDictEqual(md, s.meta_data)\n            try:\n                os.remove(resampled_temp_tif)\n            except PermissionError:\n                print(\"File opened by another process.\")\n\n\nclass TestBasicReampleTests(common.UnitTestAdaptation):\n\n    def test_reproject_with_no_data(self):\n\n        data = np.array([[2, 7],\n                         [2, 7]])\n        src_ds = gdal.GetDriverByName('MEM').Create('', 2, 2)\n        src_ds.GetRasterBand(1).WriteArray(data)\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 1, 1)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[7]])\n        np.testing.assert_array_equal(got_data, expected_data)\n\n    def test_reproject_with_no_data_2(self):\n\n        data = np.array([[2, 7, 7, 7],\n                         [2, 7, 7, 2]])\n        height, width = data.shape\n        src_ds = gdal.GetDriverByName('MEM').Create('', width, height)\n        src_ds.GetRasterBand(1).WriteArray(data)\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 1)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[7, 3]])\n        np.testing.assert_array_equal(got_data, expected_data)\n\n    def test_reproject_with_no_data_3(self):\n\n        data = np.array([[2, 7, 7, 7],\n                         [2, 7, 7, 7],\n                         [2, 7, 7, 7],\n                         [2, 7, 7, 2],\n                         [2, 7, 7, 2]])\n        src_ds = gdal.GetDriverByName('MEM').Create('', 4, 5)\n        src_ds.GetRasterBand(1).WriteArray(data)\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 2)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[7, 7],\n                                  [7, 3]])\n        np.testing.assert_array_equal(got_data, expected_data)\n\n    def test_reproject_with_no_data_4(self):\n\n        data = np.array([[2, 7, 7, 7, 2],\n                         [2, 7, 7, 7, 2],\n                         [2, 7, 7, 7, 2],\n                         [2, 7, 7, 2, 2],\n                         [2, 7, 7, 2, 2]])\n        src_ds = gdal.GetDriverByName('MEM').Create('', 5, 5)\n        src_ds.GetRasterBand(1).WriteArray(data)\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 2)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[7, 7],\n                                  [7, 3]])\n        np.testing.assert_array_equal(got_data, expected_data)\n\n\n    def test_reproject_with_no_data_5(self):\n\n        data = np.array([[2, 7, 7, 7, 2],\n                         [2, 7, 7, 7, 2],\n                         [2, 7, 7, 7, 2],\n                         [2, 7, 7, 2, 2],\n                         [2, 7, 7, 2, 2],\n                         [2, 7, 7, 2, 2]])\n        src_ds = gdal.GetDriverByName('MEM').Create('', 5, 6)\n        src_ds.GetRasterBand(1).WriteArray(data)\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 3)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[7, 7],\n                                  [7, 3],\n                                  [7, 3]])\n        np.testing.assert_array_equal(got_data, expected_data)\n\n\n    def test_reproject_average_resampling(self):\n\n        data = np.array([[4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 2, 2, 7.],\n                         [4, 7, 7, 2, 2, 7.],\n                         [4, 7, 7, 10, 2, 7.]], dtype=np.float32)\n        src_ds = gdal.GetDriverByName('MEM').Create('', 6, 6, 1,\n                                                    gdalconst.GDT_Float32)\n        src_ds.GetRasterBand(1).WriteArray(data)\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 1,\n                                                    gdalconst.GDT_Float32)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_Average)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[5.5, 7, 7],\n                                  [5.5, 7, 7],\n                                  [5.5, 8, 7]])\n        np.testing.assert_array_equal(got_data, expected_data)\n\n    def test_reproject_average_resampling_with_2bands(self):\n\n        data = np.array([[[4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 2, 2, 7.],\n                         [4, 7, 7, 2, 2, 7.],\n                         [4, 7, 7, 10, 2, 7.]],\n                        [[2, 0, 0, 0, 0, 0.],\n                         [2, 0, 0, 0, 0, 2.],\n                         [0, 1., 0, 0, 0, 1.],\n                         [0, 0, 0, 0, 0, 2],\n                         [0, 0, 0, 0, 0, 0.],\n                         [0, 0, 0, 0, 0, 0.]]], dtype=np.float32)\n        src_ds = gdal.GetDriverByName('MEM').Create('', 6, 6, 2,\n                                                    gdalconst.GDT_Float32)\n\n        src_ds.GetRasterBand(1).WriteArray(data[0, :, :])\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.GetRasterBand(2).WriteArray(data[1, :, :])\n        # src_ds.GetRasterBand(1).SetNoDataValue()\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 2,\n                                                    gdalconst.GDT_Float32)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_Average)\n        got_data = dst_ds.GetRasterBand(1).ReadAsArray()\n        expected_data = np.array([[5.5, 7, 7],\n                                  [5.5, 7, 7],\n                                  [5.5, 8, 7]])\n        np.testing.assert_array_equal(got_data, expected_data)\n        band2 = dst_ds.GetRasterBand(2).ReadAsArray()\n        np.testing.assert_array_equal(band2, np.array([[1., 0., 0.5],\n                                                       [0.25, 0., 0.75],\n                                                       [0., 0., 0.]]))\n\n\nclass TestMEMVsGTiff(common.UnitTestAdaptation):\n\n    @staticmethod\n    def check(driver_type):\n\n        temp_tif = tempfile.mktemp(suffix='.tif')\n        data = np.array([[[4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 7, 2, 7.],\n                         [4, 7, 7, 2, 2, 7.],\n                         [4, 7, 7, 2, 2, 7.],\n                         [4, 7, 7, 10, 2, 7.]],\n                        [[2, 0, 0, 0, 0, 0.],\n                         [2, 0, 0, 0, 0, 2.],\n                         [0, 1., 0, 0, 0, 1.],\n                         [0, 0, 0, 0, 0, 2],\n                         [0, 0, 0, 0, 0, 0.],\n                         [0, 0, 0, 0, 0, 0.]]], dtype=np.float32)\n        src_ds = gdal.GetDriverByName(driver_type).Create(temp_tif, 6, 6, 2,\n                                                    gdalconst.GDT_Float32)\n\n        src_ds.GetRasterBand(1).WriteArray(data[0, :, :])\n        src_ds.GetRasterBand(1).SetNoDataValue(2)\n        src_ds.GetRasterBand(2).WriteArray(data[1, :, :])\n        src_ds.GetRasterBand(2).SetNoDataValue(3)\n        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])\n        src_ds.FlushCache()\n\n        dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 2,\n                                                    gdalconst.GDT_Float32)\n        dst_ds.GetRasterBand(1).SetNoDataValue(3)\n        dst_ds.GetRasterBand(1).Fill(3)\n        dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])\n\n        gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_Average)\n        band1 = dst_ds.GetRasterBand(1).ReadAsArray()\n        np.testing.assert_array_equal(band1, np.array([[5.5, 7, 7],\n                                                       [5.5, 7, 7],\n                                                       [5.5, 8, 7]]))\n        band2 = dst_ds.GetRasterBand(2).ReadAsArray()\n        np.testing.assert_array_equal(band2, np.array([[1., 0., 0.5],\n                                                       [0.25, 0., 0.75],\n                                                       [0., 0., 0.]]))\n        if os.path.exists(temp_tif):\n            try:\n                os.remove(temp_tif)\n            except PermissionError:\n                print(\"File opened by another process.\")\n\n    def test_mem(self):\n        self.check('MEM')\n\n    def test_gtiff(self):\n        self.check('GTiff')\n\n\ndef test_coherence_files_not_converted():\n    # define constants\n    NO_DATA_VALUE = 0\n    driver = gdal.GetDriverByName('GTiff')\n\n    # create a sample gdal dataset\n\n    # sample gdal dataset\n    sample_gdal_filename = \"dataset_01122000.tif\"\n    options = ['PROFILE=GeoTIFF']\n    sample_gdal_dataset = driver.Create(sample_gdal_filename, 5, 5, 1, gdal.GDT_Float32, options=options)\n    srs = osr.SpatialReference()\n    wkt_projection = srs.ExportToWkt()\n    sample_gdal_dataset.SetProjection(wkt_projection)\n\n    sample_gdal_band = sample_gdal_dataset.GetRasterBand(1)\n    sample_gdal_band.SetNoDataValue(NO_DATA_VALUE)\n    sample_gdal_band.WriteArray(np.arange(25).reshape(5, 5))\n    sample_gdal_dataset.SetMetadataItem(ifc.FIRST_DATE, '2019-10-20')\n    sample_gdal_dataset.SetMetadataItem(ifc.SECOND_DATE, '2019-11-01')\n    sample_gdal_dataset.SetMetadataItem(ifc.PYRATE_WAVELENGTH_METRES, '10.05656')\n    sample_gdal_dataset.FlushCache()\n    sample_gdal_dataset = None\n    ifg = Ifg(sample_gdal_filename)\n    ifg.open()\n\n    # create a coherence mask dataset\n    tmpdir = tempfile.mkdtemp()\n    out_dir = Path(tmpdir)  # we won't be creating any output coherence mask files as there are already GeoTIFFs\n    params = common.min_params(out_dir)\n    coherence_mask_filename = MultiplePaths(Path(\"mask_dataset_01122000-02122000.tif\").as_posix(), params)\n    coherence_mask_dataset = driver.Create(coherence_mask_filename.converted_path, 5, 5, 1, gdal.GDT_Float32)\n    srs = osr.SpatialReference()\n    wkt_projection = srs.ExportToWkt()\n    coherence_mask_dataset.SetProjection(wkt_projection)\n    coherence_mask_band = coherence_mask_dataset.GetRasterBand(1)\n    coherence_mask_band.SetNoDataValue(NO_DATA_VALUE)\n    arr = np.arange(0, 75, 3).reshape(5, 5) / 100.0\n    arr[3, 4] = 0.25  # insert some random lower than threshold number\n    arr[4, 2] = 0.20  # insert some random lower than threshold number\n\n    coherence_mask_band.WriteArray(arr)\n    # del the tmp handler datasets created\n    del coherence_mask_dataset\n    # create an artificial masked dataset\n    expected_result_array = np.nan_to_num(\n        np.array(\n            [\n                [np.nan, np.nan, np.nan, np.nan, np.nan],\n                [np.nan, np.nan, np.nan, np.nan, np.nan],\n                [10.0, 11.0, 12.0, 13.0, 14.0],\n                [15.0, 16.0, 17.0, 18.0, np.nan],\n                [20.0, 21.0, np.nan, 23.0, 24.0],\n            ]\n        )\n    )\n\n    # use the gdal_python.coherence_masking to find the actual mask dataset\n    coherence_thresh = 0.3\n\n    gdal_python.coherence_masking(ifg.dataset, coherence_mask_filename.converted_path, coherence_thresh)\n\n    sample_gdal_array = np.nan_to_num(ifg.phase_data)\n\n    # compare the artificial masked and actual masked datasets\n    np.testing.assert_array_equal(sample_gdal_array, expected_result_array)\n\n    # del the tmp datasets created\n    os.remove(coherence_mask_filename.converted_path)\n\n    ifg.close()\n    os.remove(sample_gdal_filename)\n\n\ndef test_small_data_coherence(gamma_or_mexicoa_conf):\n    work_dir = Path(tempfile.mkdtemp())\n    params = common.manipulate_test_conf(conf_file=gamma_or_mexicoa_conf, work_dir=work_dir)\n\n    params[c.COH_MASK] = 1\n\n    ifg_multilist = copy(params[c.INTERFEROGRAM_FILES])\n    conv2tif.main(params)\n\n    for i in ifg_multilist:\n        p = Path(i.converted_path)\n        p.chmod(0o664)  # assign write permission as conv2tif output is readonly\n        ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix())\n        if not isinstance(ifg, Ifg):\n            continue\n        ifg.open()\n        # now do coherence masking and compare\n        ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix())\n        ifg.open()\n        converted_coh_file_path = pyrate.core.prepifg_helper.coherence_paths_for(p, params, tif=True)\n        gdal_python.coherence_masking(ifg.dataset,\n                                      coh_file_path=converted_coh_file_path,\n                                      coh_thr=params[c.COH_THRESH]\n                                      )\n        nans = np.isnan(ifg.phase_data)\n        coherence_path = pyrate.core.prepifg_helper.coherence_paths_for(p, params, tif=True)\n        cifg = Ifg(coherence_path)\n        cifg.open()\n        cifg_below_thrhold = cifg.phase_data < params[c.COH_THRESH]\n        np.testing.assert_array_equal(nans, cifg_below_thrhold)\n    shutil.rmtree(work_dir)\n"
  },
  {
    "path": "tests/test_geometry.py",
    "content": "import shutil\nfrom typing import Tuple\nimport numpy as np\nfrom os.path import join\nimport pytest\n\nimport pyrate.constants as C\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.core.geometry import get_lonlat_coords, get_sat_positions, vincinv\nfrom pyrate.core.refpixel import convert_pixel_value_to_geographic_coordinate\nfrom tests import common\nfrom pyrate.configuration import Configuration\nfrom subprocess import run, PIPE\nfrom pyrate import prepifg, correct\nfrom pyrate.core.shared import Ifg, Geometry\n\n\ndef get_lonlat_coords_slow(ifg: Ifg) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Function to get longitude and latitude coordinates for each pixel in the multi-looked.\n    interferogram dataset. Coordinates are identical for each interferogram in the stack.\n    :param ifg: pyrate.core.shared.Ifg Class object.\n    :return: lon: Longitude for each pixel (decimal degrees)\n    :return: lat: Latitude for each pixel (decimal degrees)\n    \"\"\"\n    # assume all interferograms have same projection and will share the same transform\n    transform = ifg.dataset.GetGeoTransform()\n    # number of rows and columns in dataset\n    nrows, ncols = ifg.shape\n    lon = np.zeros((nrows, ncols))  # pre-allocate 2D numpy array\n    lat = np.zeros((nrows, ncols))  # pre-allocate 2D numpy array\n    for i in range(0, nrows):  # rows are y-direction\n        for j in range(0, ncols):  # cols are x-direction\n            lon[i, j], lat[i, j] = convert_pixel_value_to_geographic_coordinate(j, i, transform)\n\n    return lon, lat\n\n\ndef test_get_lonlat_coords_vectorised(dem):\n    lon, lat = get_lonlat_coords_slow(dem)\n    lon_v, lat_v = get_lonlat_coords(dem)\n    np.testing.assert_array_almost_equal(lon, lon_v.data)\n    np.testing.assert_array_almost_equal(lat, lat_v.data)\n\n\n@pytest.fixture(params=[(29, 50, -2.94634866714478), (94, 58, -2.94684600830078)])\ndef point_azimuth(request):\n    return request.param\n\n\n@pytest.fixture(params=[(29, 50, 1.02217936515808), (94, 58, 1.0111095905304)])\ndef point_incidence(request):\n    return request.param\n\n\nclass TestPyRateAngleFiles:\n\n    @classmethod\n    def setup_class(cls):\n        cls.params = Configuration(common.MEXICO_CROPA_CONF).__dict__\n        # run prepifg\n        prepifg.main(cls.params)\n        # copy IFGs to temp folder\n        correct._copy_mlooked(cls.params)\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR], ignore_errors=True)\n\n    # the xy position in the original GAMMA geometry (before cropping and further multi-looking is required for\n    # comparison of PyRate with GAMMA angle values. To get this position for a particular coordinate, the following\n    # commands can be applied on Gadi:\n    # cd /g/data/dg9/INSAR_ANALYSIS/MEXICO/S1/UNIT-TEST-DATA/CROP-A/GEOTIFF\n    # gdallocationinfo -wgs84 cropA_20180106-20180130_VV_8rlks_eqa_unw.tif -99.06 19.37\n    # -> outputs the cropped pixel coordinates: (94P,58L) -> x0 = 94, y0 = 58\n    # cd /g/data/dg9/INSAR_ANALYSIS/MEXICO/S1/GAMMA/T005A/geotiff_files/unw_ifg_geotiffs/\n    # gdallocationinfo -wgs84 20180106-20180130_VV_8rlks_eqa_unw.tif -99.06 19.37\n    # -> outputs the original pixel coordinates: (940P,3271L)\n    # cd /g/data/dg9/INSAR_ANALYSIS/MEXICO/S1/GAMMA/T005A/DEM/\n    # gdallocationinfo 20180106_VV_8rlks_eqa_lv_phi.tif 940 3271\n    # -> outputs the GAMMA azimuth angle: -2.94684600830078\n    # gdallocationinfo 20180106_VV_8rlks_eqa_lv_theta.tif 940 3271\n    # > outputs the GAMMA incidence angle: 1.0111095905304\n\n    def get_pyrate_angle(self, x0, y0, tif_file):\n        \"\"\"\n        Get angle at particular pixel in the azimuth/incidence tif file\n        \"\"\"\n        # get azimuth angle value of PyRate file azimuth_angle.tif\n        temp = run(['gdallocationinfo', tif_file, str(x0), str(y0)], universal_newlines=True, stdout=PIPE)\n        out = temp.stdout\n        angle_value = float(out.split('Value:')[1].strip('\\n'))\n\n        return angle_value\n\n    def test_pyrate_azimuth_matches_gamma_azimuth(self, point_azimuth):\n        x0, y0, exp = point_azimuth\n        # azimuth angle validation value extracted from GAMMA azimuth file for pixel location corresponding\n        # to (29,50) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_phi.tif 613 3228\n        # exp = -2.94634866714478  # GAMMMA azimuth angle at pixel location\n        # azimuth angle validation value extracted from GAMMA azimuth file for pixel location corresponding\n        # to (94,58) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_phi.tif 940 3271\n        # exp = -2.94684600830078\n        # GAMMA azimuth is defined towards satellite in an anti-clockwise angle system, with East being zero\n        # PyRate azimuth angle is defined towards the satellite in a clockwise angle system with North being zero\n        tif_file = join(self.params[C.GEOMETRY_DIR], 'azimuth_angle.tif')\n        azimuth_angle_pyrate = self.get_pyrate_angle(x0, y0, tif_file)\n        # convert PyRate azimuth into GAMMA azimuth\n        res = -(azimuth_angle_pyrate - np.pi / 2)\n        np.testing.assert_array_almost_equal(exp, res, decimal=2)  # max difference < 0.01 rad\n        # screen output of difference if need be:\n        # print(exp - res)\n\n    def test_pyrate_incidence_matches_gamma_incidence(self, point_incidence):\n        x0, y0, exp = point_incidence\n        # incidence angle validation value extracted from GAMMA incidence file for pixel location corresponding\n        # to (29,50) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_theta.tif 613 3228\n        # exp = 1.02217936515808\n        # incidence angle validation value extracted from GAMMA incidence file for pixel location corresponding\n        # to (94,58) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_theta.tif 940 3271\n        # exp = 1.0111095905304\n        # GAMMA angle is defined from the horizontal plane with the zenith direction being pi / 2 radians (i.e.90 deg)\n        # PyRate angle is defined from the vertical axis with the zenith direction being zero\n        tif_file = join(self.params[C.GEOMETRY_DIR], 'incidence_angle.tif')\n        incidence_angle_pyrate = self.get_pyrate_angle(x0, y0, tif_file)\n        # convert PyRate incidence into GAMMA incidence\n        res = np.pi / 2 - incidence_angle_pyrate\n        # convert PyRate incidence into GAMMA incidence\n        np.testing.assert_array_almost_equal(exp, res, decimal=3)  # max difference < 0.001 rad\n        # screen output of difference if need be:\n        # print(exp - res)\n\n    def test_azimuth_angle_calculation(self):\n        \"\"\"\n         Calculate local azimuth angle using a spherical model and compare to result using Vincenty's equations\n        \"\"\"\n        # get first IFG in stack to calculate lon/lat values\n        multi_paths = self.params[C.INTERFEROGRAM_FILES]\n        tmp_paths = [ifg_path.tmp_sampled_path for ifg_path in multi_paths]\n        # keep only ifg files in path list (i.e. remove coherence and dem files)\n        ifg_paths = [item for item in tmp_paths if 'ifg.tif' in item]\n        # read and open the first IFG in list\n        ifg0_path = ifg_paths[0]\n        ifg0 = Ifg(ifg0_path)\n        ifg0.open(readonly=True)\n        lon, lat = get_lonlat_coords(ifg0)\n\n        # read incidence and look angle files\n        tif_file = join(self.params[C.GEOMETRY_DIR], 'incidence_angle.tif')\n        geom = Geometry(tif_file)\n        incidence_angle = geom.data\n        tif_file = join(self.params[C.GEOMETRY_DIR], 'look_angle.tif')\n        geom = Geometry(tif_file)\n        look_angle = geom.data\n        # get metadata\n        a = float(ifg0.meta_data[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES])\n        b = float(ifg0.meta_data[ifc.PYRATE_SEMI_MINOR_AXIS_METRES])\n        heading = float(ifg0.meta_data[ifc.PYRATE_HEADING_DEGREES])\n        azimuth = float(ifg0.meta_data[ifc.PYRATE_AZIMUTH_DEGREES])\n\n        # convert all angles from deg to radians\n        lon = np.radians(lon.data)\n        lat = np.radians(lat.data)\n        heading = np.radians(heading)\n        azimuth = np.radians(azimuth)\n\n        # calculate satellite positions\n        sat_lat, sat_lon = get_sat_positions(lat, lon, look_angle, incidence_angle, heading, azimuth)\n\n        # calculate azimuth angle from pixel to satellite using spherical trigonometry\n        # see Eq. 86 on page 4-11 in EARTH-REFERENCED AIRCRAFT NAVIGATION AND SURVEILLANCE ANALYSIS\n        # (https://ntlrepository.blob.core.windows.net/lib/59000/59300/59358/DOT-VNTSC-FAA-16-12.pdf)\n        azimuth_angle_spherical = np.arctan2(np.cos(sat_lat) * np.sin(sat_lon - lon), \\\n                                             np.sin(sat_lat) * np.cos(lat) - np.cos(sat_lat) * np.sin(lat) * \\\n                                             np.cos(sat_lon - lon))\n        # add 2 pi in case an angle is below zero\n        for azi in np.nditer(azimuth_angle_spherical, op_flags=['readwrite']):\n            if azi < 0:\n                azi[...] = azi + 2 * np.pi\n\n        azimuth_angle_elliposidal = vincinv(lat, lon, sat_lat, sat_lon, a, b)\n\n        # the difference between Vincenty's azimuth calculation and the spherical approximation is ~0.001 radians\n        azimuth_angle_diff = azimuth_angle_spherical - azimuth_angle_elliposidal\n        # max difference < 0.01 rad\n        np.testing.assert_array_almost_equal(azimuth_angle_spherical, azimuth_angle_elliposidal, decimal=2)\n"
  },
  {
    "path": "tests/test_merge.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the Merge step of PyRate.\n\"\"\"\nimport shutil\nimport os\nfrom subprocess import check_call\nimport itertools\nimport tempfile\nimport pytest\nfrom pathlib import Path\n\nimport numpy as np\n\nimport pyrate.constants as C\nimport pyrate.core.ifgconstants as ifc\nfrom pyrate.merge import create_png_and_kml_from_tif, los_projection_out_types\nfrom pyrate.merge import _merge_stack, _merge_linrate, _merge_timeseries, los_projection_divisors\nfrom pyrate.core.shared import DEM\nfrom pyrate.core.ifgconstants import LOS_PROJECTION_OPTION\nfrom pyrate.configuration import Configuration, write_config_file\nfrom tests.common import manipulate_test_conf, PY37GDAL302, MEXICO_CROPA_CONF, assert_same_files_produced\n\n\n@pytest.fixture(params=list(LOS_PROJECTION_OPTION.keys()))\ndef los_projection(request):\n    return request.param\n\n\n@pytest.fixture(params=[-1, 1])\ndef signal_polarity(request):\n    return request.param\n\n\ndef test_los_conversion_divisors():\n    \"\"\"\n    Unit test to check the LOS conversions for specific incidence angles\n    \"\"\"\n    inc = [0, 30, 45, 90]  # incidence angles in degrees\n\n    # Test pseudo-vertical\n    res = [los_projection_divisors[ifc.PSEUDO_VERTICAL](np.radians(x)) for x in inc]\n    exp = [1.0, 0.8660254037844387, 0.7071067811865476, 6.123233995736766e-17]\n    np.testing.assert_almost_equal(res, exp, decimal=6)\n\n    # Test pseudo-horizontal\n    res = [los_projection_divisors[ifc.PSEUDO_HORIZONTAL](np.radians(x)) for x in inc]\n    exp = [0.0, 0.49999999999999994, 0.7071067811865475, 1.0]\n    np.testing.assert_almost_equal(res, exp, decimal=6)\n\n    # Test line-of-sight\n    res = [los_projection_divisors[ifc.LINE_OF_SIGHT](np.radians(x)) for x in inc]\n    exp = [1.0, 1.0, 1.0, 1.0]\n    np.testing.assert_almost_equal(res, exp, decimal=1)\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL302, reason=\"Only run in one CI env\")\nclass TestLOSConversion:\n    @classmethod\n    def setup_class(cls):\n        tdir = Path(tempfile.mkdtemp())\n        params = manipulate_test_conf(MEXICO_CROPA_CONF, tdir)\n        output_conf_file = tdir.joinpath('conf.cfg')\n        output_conf = tdir.joinpath(output_conf_file)\n        write_config_file(params=params, output_conf_file=output_conf)\n        check_call(f\"mpirun -n 3 pyrate conv2tif -f {output_conf}\", shell=True)\n        check_call(f\"mpirun -n 3 pyrate prepifg -f {output_conf}\", shell=True)\n        check_call(f\"mpirun -n 3 pyrate correct -f {output_conf}\", shell=True)\n        check_call(f\"mpirun -n 3 pyrate timeseries -f {output_conf}\", shell=True)\n        check_call(f\"mpirun -n 3 pyrate stack -f {output_conf}\", shell=True)\n\n        params = Configuration(output_conf).__dict__\n        cls.params = params\n        cls.tdir = tdir\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.tdir, ignore_errors=True)\n\n    def test_los_conversion_comparison(self):\n        \"\"\"\n        compare outputs in each of the los projection types\n        compares sine and cosine components are larger than LOS component\n        assert metadata equal except\n        \"\"\"\n        params = self.params\n        all_dirs = {}\n        params[C.SIGNAL_POLARITY] = -1\n        for k in LOS_PROJECTION_OPTION.keys():\n            params[C.LOS_PROJECTION] = k\n            k_dir = Path(params[C.OUT_DIR]).joinpath(ifc.LOS_PROJECTION_OPTION[k])\n            k_dir.mkdir(exist_ok=True)\n            all_dirs[k] = k_dir\n            self.run_with_new_params(k_dir, params)\n\n        signal_dir = Path(params[C.OUT_DIR]).joinpath('signal_polarity_dir')\n        signal_dir.mkdir(exist_ok=True)\n        all_dirs[C.SIGNAL_POLARITY] = signal_dir\n\n        params[C.SIGNAL_POLARITY] = 1\n        self.run_with_new_params(signal_dir, params)\n\n        los_proj_dir = all_dirs[ifc.LINE_OF_SIGHT]\n        pseudo_ver = all_dirs[ifc.PSEUDO_VERTICAL]\n        pseudo_hor = all_dirs[ifc.PSEUDO_HORIZONTAL]\n        num_files = 24 if params[C.PHASE_CLOSURE] else 26  # phase closure removes 1 interferogram\n        assert len(list(los_proj_dir.glob('*.tif'))) == num_files  # 12 tsincr, 12 tscuml + 1 stack rate + 1 linear rate\n        signal_polarity_reversed_pseudo_hor = all_dirs[C.SIGNAL_POLARITY]\n\n        for tif in los_proj_dir.glob('*.tif'):\n            ds = DEM(tif)\n            ds_ver = DEM(pseudo_ver.joinpath(tif.name))\n            ds_hor = DEM(pseudo_hor.joinpath(tif.name))\n            ds_hor_sig = DEM(signal_polarity_reversed_pseudo_hor.joinpath(tif.name))\n            ds.open()\n            ds_ver.open()\n            ds_hor.open()\n            ds_hor_sig.open()\n\n            non_nans_indices = ~np.isnan(ds.data)\n            # assert division by sine and cosine always yields larger components in vertical and horizontal directions\n            assert np.all(np.abs(ds.data[non_nans_indices]) <= np.abs(ds_ver.data[non_nans_indices]))\n            assert np.all(np.abs(ds.data[non_nans_indices]) <= np.abs(ds_hor.data[non_nans_indices]))\n            assert np.all(np.abs(ds.data[non_nans_indices]) <= np.abs(ds_hor_sig.data[non_nans_indices]))\n            assert np.all(ds_hor.data[non_nans_indices] == -ds_hor_sig.data[non_nans_indices])\n            ds_md = ds.dataset.GetMetadata()\n            assert ds_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.LINE_OF_SIGHT]\n            ds_ver_md = ds_ver.dataset.GetMetadata()\n            assert ds_ver_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.PSEUDO_VERTICAL]\n            assert ds_md == ds_ver_md\n            assert ds_md.pop(C.SIGNAL_POLARITY.upper()) == '-1'\n            ds_hor_md = ds_hor.dataset.GetMetadata()\n            ds_hor_sig_md = ds_hor_sig.dataset.GetMetadata()\n            assert ds_hor_sig_md.pop(C.SIGNAL_POLARITY.upper()) != ds_hor_md.pop(C.SIGNAL_POLARITY.upper())\n            assert ds_hor_sig_md == ds_hor_md\n            assert ds_hor_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.PSEUDO_HORIZONTAL]\n            assert ds_hor_sig_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.PSEUDO_HORIZONTAL]\n            assert ds_md == ds_hor_md\n            assert ds_md == ds_hor_sig_md\n\n    def run_with_new_params(self, k_dir, params):\n        _merge_stack(params)\n        _merge_linrate(params)\n        _merge_timeseries(params, 'tscuml')\n        _merge_timeseries(params, 'tsincr')\n\n        for out_type in los_projection_out_types:\n            for tif in itertools.chain(Path(params[C.VELOCITY_DIR]).glob(out_type + '*.tif'),\n                                       Path(params[C.TIMESERIES_DIR]).glob(out_type + '*.tif')):\n                shutil.move(tif, k_dir.joinpath(tif.name))\n\n    def test_file_creation(self, los_projection):\n        params = self.params\n        params[C.LOS_PROJECTION] = los_projection\n        _merge_stack(params)\n        _merge_linrate(params)\n        _merge_timeseries(params, 'tscuml')\n        _merge_timeseries(params, 'tsincr')\n        # check if color map is created\n        for ot in ['stack_rate', 'stack_error', 'linear_rate', 'linear_error', 'linear_rsquared']:\n            create_png_and_kml_from_tif(params[C.VELOCITY_DIR], output_type=ot)\n            output_color_map_path = os.path.join(params[C.VELOCITY_DIR], f\"colourmap_{ot}.txt\")\n            assert Path(output_color_map_path).exists(), \"Output color map file not found at: \" + output_color_map_path\n\n        # check if merged files are created\n        for _type, ot in itertools.product(['stack_rate', 'stack_error', 'linear_rate',\n                                            'linear_error', 'linear_rsquared'], ['.tif', '.png', '.kml']):\n            output_image_path = os.path.join(params[C.VELOCITY_DIR], _type + ot)\n            print(f\"checking {output_image_path}\")\n            assert Path(output_image_path).exists(), f\"Output {ot} file not found at {output_image_path}\"\n\n        # check los_projection metadata\n        for out_type in los_projection_out_types:\n            for tif in Path(params[C.TIMESERIES_DIR]).glob(out_type + '*.tif'):\n                self.__check_md(los_projection, tif.as_posix())\n\n    @staticmethod\n    def __check_md(los_projection, output_image_path):\n        ifg = DEM(output_image_path)\n        ifg.open()\n        assert ifg.dataset.GetMetadataItem(C.LOS_PROJECTION.upper()) == LOS_PROJECTION_OPTION[los_projection]\n"
  },
  {
    "path": "tests/test_mpi.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for mpi operations in PyRate.\nTun this module as 'mpirun -n 4 pytest tests/test_mpi.py'\n\"\"\"\nimport shutil\nimport numpy as np\nimport os\nfrom pathlib import Path\nimport pytest\n\nimport pyrate.configuration\nimport pyrate.constants as C\nimport pyrate.core.covariance\nimport pyrate.core.orbital\nimport pyrate.core.ref_phs_est\nimport pyrate.core.refpixel\nfrom pyrate import correct, prepifg, conv2tif, configuration\nfrom pyrate.core import mpiops\nfrom tests import common\nfrom tests.common import SML_TEST_DIR\nfrom tests.test_covariance import legacy_maxvar\n\n\n@pytest.mark.mpi\ndef test_vcm_legacy_vs_mpi(mpisync, tempdir, roipac_or_gamma_conf):\n\n    params = configuration.Configuration(roipac_or_gamma_conf).__dict__\n    LEGACY_VCM_DIR = os.path.join(SML_TEST_DIR, 'vcm')\n    legacy_vcm = np.genfromtxt(os.path.join(LEGACY_VCM_DIR, 'vcmt.csv'), delimiter=',')\n    tmpdir = Path(mpiops.run_once(tempdir))\n    params[C.OUT_DIR] = tmpdir.joinpath('out')\n    params[C.PARALLEL] = 0\n    output_conf = Path(tmpdir).joinpath('conf.cfg')\n    pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n    params = configuration.Configuration(output_conf).__dict__\n\n    # dest_paths = [p.sampled_path for p in params[cf.INTERFEROGRAM_FILES]]\n    # run conv2tif and prepifg, create the dest_paths files\n    conv2tif.main(params)\n    params = configuration.Configuration(output_conf).__dict__\n    prepifg.main(params)\n    params = configuration.Configuration(output_conf).__dict__\n    params[C.ORBFIT_OFFSET] = True\n    correct._copy_mlooked(params=params)\n    correct._update_params_with_tiles(params)\n    correct._create_ifg_dict(params=params)\n    pyrate.core.refpixel.ref_pixel_calc_wrapper(params)\n    pyrate.core.orbital.orb_fit_calc_wrapper(params)\n    pyrate.core.ref_phs_est.ref_phase_est_wrapper(params)\n\n    maxvar, vcmt = pyrate.core.covariance.maxvar_vcm_calc_wrapper(params)\n\n    # phase data after ref pixel has changed due to commit bf2f7ebd\n    # Legacy tests won't match anymore\n    np.testing.assert_array_almost_equal(maxvar, legacy_maxvar, decimal=4)\n    np.testing.assert_array_almost_equal(legacy_vcm, vcmt, decimal=3)\n    mpiops.run_once(shutil.rmtree, tmpdir)\n"
  },
  {
    "path": "tests/test_mpi_vs_multiprocess_vs_single_process.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains regression tests for comparing output from serial,\nparallel and MPI PyRate runs.\n\"\"\"\nimport shutil\nimport pytest\nfrom pathlib import Path\nfrom subprocess import check_call, CalledProcessError, run\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.configuration import Configuration, write_config_file\n\nfrom tests.common import (\n    assert_same_files_produced,\n    assert_two_dirs_equal,\n    manipulate_test_conf,\n    MEXICO_CROPA_CONF,\n    TEST_CONF_GAMMA,\n    PY37GDAL304,\n    PY37GDAL302,\n    PYTHON3P8,\n    PYTHON3P7,\n    WORKING_DIR\n)\n\n\n@pytest.fixture(params=[0, 1])\ndef parallel(request):\n    return request.param\n\n\n@pytest.fixture(params=[1, 4])\ndef local_crop(request):\n    return request.param\n\n\n@pytest.fixture()\ndef modified_config(tempdir, get_lks, get_crop, orbfit_lks, orbfit_method, orbfit_degrees, ref_est_method):\n    def modify_params(conf_file, parallel_vs_serial, output_conf_file):\n        tdir = Path(tempdir())\n        params = manipulate_test_conf(conf_file, tdir)\n\n        if params[C.PROCESSOR] == 1:  # turn on coherence for gamma\n            params[C.COH_MASK] = 1\n\n        params[C.PARALLEL] = parallel_vs_serial\n        params[C.PROCESSES] = 4\n        params[C.APSEST] = 1\n        params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks\n        params[C.REFNX], params[C.REFNY] = 2, 2\n\n        params[C.IFG_CROP_OPT] = get_crop\n        params[C.ORBITAL_FIT_LOOKS_X], params[C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks\n        params[C.ORBITAL_FIT] = 1\n        params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        params[C.REF_EST_METHOD] = ref_est_method\n        params[C.MAX_LOOP_LENGTH] = 3\n        params[C.LR_MAXSIG] = 0 # turn off pixel masking for these tests\n        params[\"rows\"], params[\"cols\"] = 3, 2\n        params[\"savenpy\"] = 1\n        params[\"notiles\"] = params[\"rows\"] * params[\"cols\"]  # number of tiles\n\n        print(params)\n        # write new temp config\n        output_conf = tdir.joinpath(output_conf_file)\n        write_config_file(params=params, output_conf_file=output_conf)\n\n        return output_conf, params\n    return modify_params\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif(not PYTHON3P8, reason=\"Only run in one CI env\")\ndef test_pipeline_parallel_vs_mpi(modified_config, gamma_or_mexicoa_conf=TEST_CONF_GAMMA):\n    \"\"\"\n    Tests proving single/multiprocess/mpi produce same output\n    \"\"\"\n    gamma_conf = gamma_or_mexicoa_conf\n    if np.random.rand() > 0.1:  # skip 90% of tests randomly\n        pytest.skip(\"Randomly skipping as part of 85 percent\")\n        if gamma_conf == MEXICO_CROPA_CONF:  # skip cropA conf 95% time\n            if np.random.rand() > 0.5:\n                pytest.skip('skipped in mexicoA')\n\n    print(\"\\n\\n\")\n    print(\"===x===\"*10)\n\n    mpi_conf, params = modified_config(gamma_conf, 0, 'mpi_conf.conf')\n\n    run(f\"mpirun -n 3 pyrate conv2tif -f {mpi_conf}\", shell=True, check=True)\n    run(f\"mpirun -n 3 pyrate prepifg -f {mpi_conf}\", shell=True, check=True)\n    try:\n        run(f\"mpirun -n 3 pyrate correct -f {mpi_conf}\", shell=True, check=True)\n        run(f\"mpirun -n 3 pyrate timeseries -f {mpi_conf}\", shell=True, check=True)\n        run(f\"mpirun -n 3 pyrate stack -f {mpi_conf}\", shell=True, check=True)\n        run(f\"mpirun -n 3 pyrate merge -f {mpi_conf}\", shell=True, check=True)\n    except CalledProcessError as e:\n        print(e)\n        pytest.skip(\"Skipping as part of correction error\")\n\n    mr_conf, params_m = modified_config(gamma_conf, 1, 'multiprocess_conf.conf')\n\n    run(f\"pyrate workflow -f {mr_conf}\", shell=True, check=True)\n\n    sr_conf, params_s = modified_config(gamma_conf, 0, 'singleprocess_conf.conf')\n\n    run(f\"pyrate workflow -f {sr_conf}\", shell=True, check=True)\n\n    # convert2tif tests, 17 interferograms\n    if not gamma_conf == MEXICO_CROPA_CONF:\n        assert_same_files_produced(params[C.INTERFEROGRAM_DIR],\n                                   params_m[C.INTERFEROGRAM_DIR], params_s[C.INTERFEROGRAM_DIR], \"*_unw.tif\", 17)\n        # dem\n        assert_same_files_produced(params[C.GEOMETRY_DIR],\n                                   params_m[C.GEOMETRY_DIR], params_s[C.GEOMETRY_DIR], \"*_dem.tif\", 1)\n        # if coherence masking, comprare coh files were converted\n        if params[C.COH_FILE_LIST] is not None:\n            assert_same_files_produced(params[C.COHERENCE_DIR], params_m[C.COHERENCE_DIR], params_s[C.COHERENCE_DIR],\n                                       \"*_cc.tif\", 17)\n            print(\"coherence files compared\")\n\n    # prepifg checks\n    num_of_ifgs = 30 if gamma_conf == MEXICO_CROPA_CONF else 17\n    num_of_coh = 30 if gamma_conf == MEXICO_CROPA_CONF else 17\n\n    # check geom files\n    if params[C.DEMERROR]:\n        # check files required by dem error correction are produced\n        assert_same_files_produced(\n            params[C.GEOMETRY_DIR], params_m[C.GEOMETRY_DIR], params_s[C.GEOMETRY_DIR],\n            [ft + '.tif' for ft in C.GEOMETRY_OUTPUT_TYPES] + ['*dem.tif'],\n            7 if gamma_conf == MEXICO_CROPA_CONF else 8\n        )\n\n    # ifgs\n    assert_same_files_produced(params[C.INTERFEROGRAM_DIR], params_m[C.INTERFEROGRAM_DIR],\n                               params_s[C.INTERFEROGRAM_DIR], [\"*_ifg.tif\"], num_of_ifgs)\n\n    # coherence\n    assert_same_files_produced(params[C.COHERENCE_DIR], params_m[C.COHERENCE_DIR],\n                               params_s[C.COHERENCE_DIR], [\"*_coh.tif\"], num_of_coh)\n\n    # coherence stats\n    assert_same_files_produced(params[C.COHERENCE_DIR], params_m[C.COHERENCE_DIR],\n                               params_s[C.COHERENCE_DIR], [\"coh_*.tif\"], 3)\n\n    num_files = 30 if gamma_conf == MEXICO_CROPA_CONF else 17\n    # cf.TEMP_MLOOKED_DIR will contain the temp files that can be potentially deleted later\n    assert_same_files_produced(params[C.TEMP_MLOOKED_DIR], params_m[C.TEMP_MLOOKED_DIR],\n                               params_s[C.TEMP_MLOOKED_DIR], \"*_ifg.tif\", num_files)\n\n    # prepifg + correct steps that overwrite tifs test\n    # ifg phase checking in the previous step checks the correct pipeline upto APS correction\n\n    # 2 x because of aps files\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"tsincr_*.npy\", params['notiles'] * 2)\n\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"tscuml_*.npy\", params['notiles'])\n\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"linear_rate_*.npy\", params['notiles'])\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"linear_error_*.npy\", params['notiles'])\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"linear_intercept_*.npy\", params['notiles'])\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"linear_rsquared_*.npy\", params['notiles'])\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"linear_samples_*.npy\", params['notiles'])\n\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"stack_rate_*.npy\", params['notiles'])\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"stack_error_*.npy\", params['notiles'])\n    assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[\n        C.TMPDIR], \"stack_samples_*.npy\", params['notiles'])\n\n    # compare merge step\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"stack*.tif\", 3)\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"stack*.kml\", 2)\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"stack*.png\", 2)\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"stack*.npy\", 3)\n    \n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"linear_*.tif\", 5)\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"linear_*.kml\", 3)\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"linear_*.png\", 3)\n    assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[\n        C.VELOCITY_DIR], \"linear_*.npy\", 5)\n\n    if params[C.PHASE_CLOSURE]:  # only in cropA\n        __check_equality_of_phase_closure_outputs(mpi_conf, sr_conf)\n        __check_equality_of_phase_closure_outputs(mpi_conf, mr_conf)\n        assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[\n            C.TIMESERIES_DIR], \"tscuml*.tif\", 11)  # phase closure removes one tif\n        assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[\n            C.TIMESERIES_DIR], \"tsincr*.tif\", 11)\n    else:\n        assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[\n            C.TIMESERIES_DIR], \"tscuml*.tif\", 12)\n        assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[\n            C.TIMESERIES_DIR], \"tsincr*.tif\", 12)\n        \n    print(\"==========================xxx===========================\")\n\n    shutil.rmtree(params[WORKING_DIR])\n    shutil.rmtree(params_m[WORKING_DIR])\n    shutil.rmtree(params_s[WORKING_DIR])\n\n\ndef __check_equality_of_phase_closure_outputs(mpi_conf, sr_conf):\n    m_config = Configuration(mpi_conf)\n    s_config = Configuration(sr_conf)\n    m_close = m_config.closure()\n    s_close = s_config.closure()\n    m_closure = np.load(m_close.closure)\n    s_closure = np.load(s_close.closure)\n    # loops\n    m_loops = np.load(m_close.loops, allow_pickle=True)\n    s_loops = np.load(s_close.loops, allow_pickle=True)\n    m_weights = [m.weight for m in m_loops]\n    s_weights = [m.weight for m in s_loops]\n    np.testing.assert_array_equal(m_weights, s_weights)\n    for i, (m, s) in enumerate(zip(m_loops, s_loops)):\n        assert all(m_e == s_e for m_e, s_e in zip(m.edges, s.edges))\n    # closure\n    np.testing.assert_array_almost_equal(np.abs(m_closure), np.abs(s_closure), decimal=4)\n    # num_occurrences_each_ifg\n    m_num_occurences_each_ifg = np.load(m_close.num_occurences_each_ifg, allow_pickle=True)\n    s_num_occurences_each_ifg = np.load(s_close.num_occurences_each_ifg, allow_pickle=True)\n    np.testing.assert_array_equal(m_num_occurences_each_ifg, s_num_occurences_each_ifg)\n    # check ps\n    m_ifgs_breach_count = np.load(m_close.ifgs_breach_count)\n    s_ifgs_breach_count = np.load(s_close.ifgs_breach_count)\n    np.testing.assert_array_equal(m_ifgs_breach_count, s_ifgs_breach_count)\n\n\n@pytest.fixture(params=[0, 1])\ndef coh_mask(request):\n    return request.param\n\n\n@pytest.fixture()\ndef modified_config_short(tempdir, local_crop, get_lks, coh_mask):\n    orbfit_lks = 1\n    orbfit_method = 1\n    orbfit_degrees = 1\n    ref_est_method = 1\n    ref_pixel = (150.941666654, -34.218333314)\n\n    def modify_params(conf_file, parallel, output_conf_file, largetifs):\n        tdir = Path(tempdir())\n        params = manipulate_test_conf(conf_file, tdir)\n        params[C.COH_MASK] = coh_mask\n        params[C.PARALLEL] = parallel\n        params[C.PROCESSES] = 4\n        params[C.APSEST] = 1\n        params[C.LARGE_TIFS] = largetifs\n        params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks\n        params[C.REFX], params[C.REFY] = ref_pixel\n        params[C.REFNX], params[C.REFNY] = 4, 4\n\n        params[C.IFG_CROP_OPT] = local_crop\n        params[C.ORBITAL_FIT_LOOKS_X], params[\n            C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks\n        params[C.ORBITAL_FIT] = 1\n        params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        params[C.REF_EST_METHOD] = ref_est_method\n        params[\"rows\"], params[\"cols\"] = 3, 2\n        params[\"savenpy\"] = 1\n        params[\"notiles\"] = params[\"rows\"] * params[\"cols\"]  # number of tiles\n\n        # print(params)\n        # write new temp config\n        output_conf = tdir.joinpath(output_conf_file)\n        write_config_file(params=params, output_conf_file=output_conf)\n\n        return output_conf, params\n\n    return modify_params\n\n\n@pytest.fixture\ndef create_mpi_files():\n\n    def _create(modified_config_short, gamma_conf):\n\n        mpi_conf, params = modified_config_short(gamma_conf, 0, 'mpi_conf.conf', 1)\n\n        check_call(f\"mpirun -n 3 pyrate conv2tif -f {mpi_conf}\", shell=True)\n        check_call(f\"mpirun -n 3 pyrate prepifg -f {mpi_conf}\", shell=True)\n\n        try:\n            check_call(f\"mpirun -n 3 pyrate correct -f {mpi_conf}\", shell=True)\n            check_call(f\"mpirun -n 3 pyrate timeseries -f {mpi_conf}\", shell=True)\n            check_call(f\"mpirun -n 3 pyrate stack -f {mpi_conf}\", shell=True)\n        except CalledProcessError as c:\n            print(c)\n            pytest.skip(\"Skipping as we encountered a process error during CI\")\n        check_call(f\"mpirun -n 3 pyrate merge -f {mpi_conf}\", shell=True)\n        return params\n\n    return _create\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\ndef test_stack_and_ts_mpi_vs_parallel_vs_serial(modified_config_short, gamma_conf, create_mpi_files, parallel):\n    \"\"\"\n    Checks performed:\n    1. mpi vs single process pipeline\n    2. mpi vs parallel (python multiprocess) pipeline.\n    3. Doing 1 and 2 means we have checked single vs parallel python multiprocess pipelines\n    4. This also checks the entire pipeline using largetifs (new prepifg) vs old perpifg (python based)\n    \"\"\"\n    if np.random.randint(0, 1000) > 300:  # skip 90% of tests randomly\n        pytest.skip(\"Randomly skipping as part of 60 percent\")\n\n    print(\"\\n\\n\")\n\n    print(\"===x===\"*10)\n\n    params = create_mpi_files(modified_config_short, gamma_conf)\n\n    sr_conf, params_p = modified_config_short(gamma_conf, parallel, 'parallel_conf.conf', 0)\n\n    check_call(f\"pyrate workflow -f {sr_conf}\", shell=True)\n\n    # convert2tif tests, 17 interferograms\n    assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], \"*_unw.tif\", 17)\n\n    # if coherence masking, compare coh files were converted\n    if params[C.COH_FILE_LIST] is not None:\n        assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], \"*_cc.tif\", 17)\n        print(\"coherence files compared\")\n    assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], [\"*_ifg.tif\"], 17)\n\n    # one original dem, another multilooked dem\n    assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], ['*dem.tif'], 2)\n    assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR],\n                          [t + \"*.tif\" for t in C.GEOMETRY_OUTPUT_TYPES], 6)  # 2 dems, 6 geom\n\n    assert_two_dirs_equal(params[C.TEMP_MLOOKED_DIR], params_p[C.TEMP_MLOOKED_DIR], \"*_ifg.tif\", 17)\n\n    # ifg phase checking in the previous step checks the correct pipeline upto APS correction\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"tsincr_*.npy\", params['notiles'] * 2)\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"tscuml_*.npy\", params['notiles'])\n\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"linear_rate_*.npy\", params['notiles'])\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"linear_error_*.npy\", params['notiles'])\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"linear_samples_*.npy\", params['notiles'])\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"linear_intercept_*.npy\", params['notiles'])\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"linear_rsquared_*.npy\", params['notiles'])\n\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"stack_rate_*.npy\", params['notiles'])\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"stack_error_*.npy\", params['notiles'])\n    assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], \"stack_samples_*.npy\", params['notiles'])\n\n    # compare merge step\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"stack*.tif\", 3)\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"stack*.kml\", 2)\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"stack*.png\", 2)\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"stack*.npy\", 3)\n\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"linear*.tif\", 5)\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"linear*.kml\", 3)\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"linear*.png\", 3)\n    assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], \"linear*.npy\", 5)\n\n    assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], \"tscuml*.tif\")\n    assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], \"tsincr*.tif\")\n    assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], \"tscuml*.npy\")\n    assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], \"tsincr*.npy\")\n\n    print(\"==========================xxx===========================\")\n\n    shutil.rmtree(params[WORKING_DIR])\n    shutil.rmtree(params_p[WORKING_DIR])\n"
  },
  {
    "path": "tests/test_mst.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis module contains tests for the mst.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nfrom itertools import product\nfrom numpy import empty, array, nan, isnan, sum as nsum\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core import algorithm, mst\nfrom pyrate.core.shared import IfgPart, Tile, Ifg, save_numpy_phase\nfrom pyrate.configuration import Configuration\nfrom pyrate import conv2tif, prepifg, correct\nfrom tests import common\nfrom tests.common import UnitTestAdaptation, TEST_CONF_GAMMA, MockIfg, small5_mock_ifgs, small_data_setup\n\n\nclass TestMST(UnitTestAdaptation):\n    \"\"\"Basic verification of minimum spanning tree (MST) functionality.\"\"\"\n\n    def setup_method(self):\n        self.ifgs = small_data_setup()\n\n    def test_mst_matrix_as_array(self):\n        # Verifies MST matrix func returns array with dict/trees in each cell\n        for i in self.ifgs[3:]:\n            i.phase_data[0, 1] = 0  # partial stack of NODATA to one cell\n\n        for i in self.ifgs:\n            i.convert_to_nans() # zeros to NaN/NODATA\n\n        epochs = algorithm.get_epochs(self.ifgs)[0]\n        res = mst._mst_matrix_as_array(self.ifgs)\n        ys, xs = res.shape\n\n        for y, x in product(range(ys), range(xs)):\n            r = res[y, x]\n            num_nodes = len(r)\n            self.assertTrue(num_nodes < len(epochs.dates))\n\n            stack = array([i.phase_data[y, x] for i in self.ifgs])  # 17 ifg stack\n            self.assertTrue(0 == nsum(stack == 0))  # all 0s should be converted\n            nc = nsum(isnan(stack))\n            exp_count = len(epochs.dates) - 1\n\n            if nc == 0:\n                self.assertEqual(num_nodes, exp_count)\n            elif nc > 5:\n                # rough test: too many nans must reduce the total tree size\n                self.assertTrue(num_nodes <= (17-nc))\n\n    def test_mst_matrix_as_ifgs(self):\n        # ensure only ifgs are returned, not individual MST graphs\n        ifgs = small5_mock_ifgs()\n        nifgs = len(ifgs)\n        ys, xs = ifgs[0].shape\n        result = mst._mst_matrix_ifgs_only(ifgs)\n\n        for coord in product(range(ys), range(xs)):\n            stack = (i.phase_data[coord] for i in self.ifgs)\n            nc = nsum([isnan(n) for n in stack])\n            check = len(result[coord]) <= (nifgs - nc)\n            self.assertTrue(check)\n\n            # HACK: type testing here is a bit grubby\n            self.assertTrue(all([isinstance(i, MockIfg) for i in ifgs]))\n\n    def test_partial_nan_pixel_stack(self):\n        # Ensure a limited # of coherent cells results in a smaller MST tree\n        num_coherent = 3\n\n        def assert_equal():\n            res = mst._mst_matrix_as_array(mock_ifgs)\n            self.assertEqual(len(res[0,0]), num_coherent)\n\n        mock_ifgs = [MockIfg(i, 1, 1) for i in self.ifgs]\n        for m in mock_ifgs[num_coherent:]:\n            m.phase_data[:] = nan\n        assert_equal()\n\n        # fill in more nans leaving only one ifg\n        for m in mock_ifgs[1:num_coherent]:\n            m.phase_data[:] = nan\n        num_coherent = 1\n        assert_equal()\n\n    def test_all_nan_pixel_stack(self):\n        # ensure full stack of NaNs in an MST pixel classifies to NaN\n        mock_ifgs = [MockIfg(i, 1, 1) for i in self.ifgs]\n        for m in mock_ifgs:\n            m.phase_data[:] = nan\n\n        res = mst._mst_matrix_as_array(mock_ifgs)\n        exp = empty((1, 1))  # , dtype=object)\n        exp[:] = nan\n\n        shape = (mock_ifgs[0].nrows, mock_ifgs[0].ncols)\n        self.assertTrue(res.shape == shape)\n        self.assertTrue(res.shape == exp.shape)\n        self.assertTrue(isnan(res[0][0]) and isnan(exp[0][0]))\n\n\nclass TestDefaultMST(UnitTestAdaptation):\n\n    def test_default_mst(self):\n        # default MST from full set of Ifgs shouldn't drop any nodes\n        ifgs = small5_mock_ifgs()\n        dates = [(i.first, i.second) for i in ifgs]\n\n        res = mst.mst_from_ifgs(ifgs)[0]\n        num_edges = len(res)\n        self.assertEqual(num_edges, len(ifgs))\n\n        # test edges, note node order can be reversed\n        for edge in res:\n            self.assertTrue(edge in dates or (edge[1], edge[0]) in dates)\n\n        # check all nodes exist in this default tree\n        mst_dates = set(res)\n        mst_dates = list(sum(mst_dates, ()))\n        for i in ifgs:\n            for node in (i.first, i.second):\n                self.assertIn(node, mst_dates)\n\n\nclass TestNetworkxMSTTreeCheck(UnitTestAdaptation):\n\n    @classmethod\n    def setup_class(cls):\n        cls.ifgs = small_data_setup()\n\n    def test_assert_is_not_tree(self):\n        non_overlapping = [1, 2, 5, 6, 12, 13, 14, 15, 16, 17]\n        ifgs_non_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if i + 1 in non_overlapping]\n        edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_non_overlapping)\n        self.assertFalse(is_tree)\n        self.assertEqual(4, ntrees)\n\n    def test_small_data_tree(self):\n        self.assertTrue(mst.mst_from_ifgs(self.ifgs)[1])\n\n    def test_assert_is_tree(self):\n        overlapping = [1, 2, 3, 4, 6, 7, 10, 11, 16, 17]\n\n        ifgs_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if (i + 1 in overlapping)]\n        edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_overlapping)\n        self.assertFalse(is_tree)\n        self.assertEqual(4, ntrees)\n\n    def test_assert_two_trees_overlapping(self):\n        overlapping = [3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17]\n\n        ifgs_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if (i + 1 in overlapping)]\n        edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_overlapping)\n        self.assertFalse(is_tree)\n        self.assertEqual(2, ntrees)\n\n    def test_assert_two_trees_non_overlapping(self):\n        non_overlapping = [2, 5, 6, 12, 13, 15]\n        ifgs_non_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if i + 1 in non_overlapping]\n        edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_non_overlapping)\n        self.assertFalse(is_tree)\n        self.assertEqual(2, ntrees)\n\n\nclass TestIfgPart(UnitTestAdaptation):\n\n    def setup_method(self):\n        self.ifgs = small_data_setup()\n        self.params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n\n    def test_ifg_part_shape_and_slice(self):\n        r_start = 0\n        r_end = 10\n        for i in self.ifgs:\n            tile = Tile(0, top_left=(r_start, 0), bottom_right=(r_end, i.ncols))\n            ifg_part = IfgPart(i.data_path, tile, params=self.params)\n            self.assertEqual(ifg_part.phase_data.shape, (r_end-r_start, i.phase_data.shape[1]))\n            np.testing.assert_array_equal(ifg_part.phase_data, i.phase_data[r_start:r_end, :])\n\n    def test_mst_multiprocessing_serial(self):\n        self.params[C.PARALLEL] = False\n        original_mst = mst.mst_boolean_array(self.ifgs)\n        parallel_mst = mst.mst_parallel(self.ifgs, params=self.params)\n        np.testing.assert_array_equal(original_mst, parallel_mst)\n\n    def test_mst_multiprocessing(self):\n        self.params[C.PARALLEL] = True\n        original_mst = mst.mst_boolean_array(self.ifgs)\n        parallel_mst = mst.mst_parallel(self.ifgs, params=self.params)\n        np.testing.assert_array_equal(original_mst, parallel_mst)\n\n\nclass TestMSTFilesReusedFromDisc:\n\n    @classmethod\n    def setup_class(cls):\n        cls.conf = TEST_CONF_GAMMA\n        cls.params = Configuration(cls.conf).__dict__\n        conv2tif.main(cls.params)\n        cls.params = Configuration(cls.conf).__dict__\n        prepifg.main(cls.params)\n        cls.params = Configuration(cls.conf).__dict__\n        multi_paths = cls.params[C.INTERFEROGRAM_FILES]\n        cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_mst_used_from_disc_on_rerun(self):\n        correct._copy_mlooked(self.params)\n        correct._update_params_with_tiles(self.params)\n        times_written = self.__run_once()\n        times_written_1 = self.__run_once()\n\n        np.testing.assert_array_equal(times_written_1, times_written)\n\n    def __run_once(self):\n        tiles = self.params[C.TILES]\n        mst_files = [Configuration.mst_path(self.params, t.index) for t in tiles]\n        correct._copy_mlooked(self.params)\n        correct._create_ifg_dict(self.params)\n        save_numpy_phase(self.ifg_paths, self.params)\n        mst.mst_calc_wrapper(self.params)\n        assert all(m.exists() for m in mst_files)\n        return [os.stat(o).st_mtime for o in mst_files]\n"
  },
  {
    "path": "tests/test_orbital.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the orbital.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nimport tempfile\nimport pytest\nfrom itertools import product\nfrom numpy import empty, dot, concatenate, float32\nfrom numpy import nan, isnan, array\nfrom os.path import join\nfrom pathlib import Path\nfrom datetime import date\n\nimport numpy as np\nfrom numpy.linalg import pinv, inv\nfrom numpy.testing import assert_array_equal, assert_array_almost_equal, assert_allclose\nfrom scipy.linalg import lstsq\nfrom osgeo import gdal, gdalconst\n\nimport pyrate.constants as C\nimport pyrate.core.orbital\nfrom tests.common import small5_mock_ifgs, MockIfg\nfrom pyrate.core.algorithm import first_second_ids, get_all_epochs\nfrom pyrate.core.orbital import INDEPENDENT_METHOD, NETWORK_METHOD, PLANAR, \\\n    QUADRATIC, PART_CUBIC\nfrom pyrate.core.orbital import OrbitalError, __orb_correction, __orb_inversion\nfrom pyrate.core.orbital import get_design_matrix, get_network_design_matrix, orb_fit_calc_wrapper\nfrom pyrate.core.orbital import _get_num_params, remove_orbital_error, network_orbital_correction\nfrom pyrate.core.orbital import calc_network_orb_correction\nfrom pyrate.core.shared import Ifg, mkdir_p\nfrom pyrate.core.shared import nanmedian\nfrom pyrate.core import roipac\nfrom pyrate import correct, conv2tif, prepifg\nfrom pyrate.configuration import Configuration, MultiplePaths\nfrom pyrate.constants import ORB_ERROR_DIR\nfrom tests import common\nfrom tests.common import IFMS16, TEST_CONF_GAMMA\nfrom tests.common import SML_TEST_LEGACY_ORBITAL_DIR\nfrom tests.common import SML_TEST_TIF, PY37GDAL302\nfrom tests.common import small_ifg_file_list\n\n# TODO: Purpose of this variable? Degrees are 1, 2 and 3 respectively\nDEG_LOOKUP = {\n    2: PLANAR,\n    5: QUADRATIC,\n    6: PART_CUBIC}\n\nNUM_COEF_LOOKUP = {\n    PLANAR: 2,\n    QUADRATIC: 5,\n    PART_CUBIC: 6}\n\n\nclass TestSingleDesignMatrixTests:\n    \"\"\"\n    Tests to verify correctness of basic planar & quadratic design matrices or\n    DMs. This class serves two purposes, ensuring the independent method DMs are\n    produced correctly. Secondly, these indivdual DMs are subsets of the larger\n    DM 'grid' required for the networked orbital correction method.\n    \"\"\"\n\n    def setup_class(cls):\n        # faked cell sizes\n        cls.xs = 0.75\n        cls.ys = 0.8\n        cls.ifg = Ifg(join(SML_TEST_TIF, 'geo_060619-061002_unw.tif'))\n        cls.ifg.open()\n        cls.ifg.nodata_value = 0\n\n        cls.m = MockIfg(cls.ifg, 3, 4)\n        cls.m.x_size = cls.xs\n        cls.m.y_size = cls.ys\n\n    # tests for planar model\n\n    def test_create_planar_dm(self):\n        act = get_design_matrix(self.m, PLANAR, intercept=False, scale=100)\n        assert act.shape == (self.m.num_cells, 2)\n        exp = unittest_dm(self.m, INDEPENDENT_METHOD, PLANAR, offset=False)\n        assert_array_equal(act, exp)\n\n    def test_create_planar_dm_offsets(self):\n        act = get_design_matrix(self.m, PLANAR, intercept=True, scale=100)\n        assert act.shape == (self.m.num_cells, 3)\n        exp = unittest_dm(self.m, INDEPENDENT_METHOD, PLANAR, offset=True)\n        assert_array_almost_equal(act, exp)\n\n    # tests for quadratic model\n\n    def test_create_quadratic_dm(self):\n        act = get_design_matrix(self.m, QUADRATIC, intercept=False, scale=100)\n        assert act.shape == (self.m.num_cells, 5)\n        exp = unittest_dm(self.m, INDEPENDENT_METHOD, QUADRATIC, offset=False)\n        assert_array_equal(act, exp)\n\n    def test_create_quadratic_dm_offsets(self):\n        act = get_design_matrix(self.m, QUADRATIC, intercept=True, scale=100)\n        assert act.shape == (self.m.num_cells, 6)\n        exp = unittest_dm(self.m, INDEPENDENT_METHOD, QUADRATIC, offset=True)\n        assert_array_equal(act, exp)\n\n    # tests for partial cubic model\n\n    def test_create_partcubic_dm(self):\n        act = get_design_matrix(self.m, PART_CUBIC, intercept=False, scale=100)\n        assert act.shape == (self.m.num_cells, 6)\n        exp = unittest_dm(self.m, INDEPENDENT_METHOD, PART_CUBIC, offset=False)\n        assert_array_equal(act, exp)\n\n    def test_create_partcubic_dm_offsets(self):\n        act = get_design_matrix(self.m, PART_CUBIC, intercept=True, scale=100)\n        assert act.shape == (self.m.num_cells, 7)\n        exp = unittest_dm(self.m, INDEPENDENT_METHOD, PART_CUBIC, offset=True)\n        assert_array_equal(act, exp)\n\n    # tests for unittest_dm() assuming network method\n\n    def test_create_planar_dm_network(self):\n        # networked method planar version should not have offsets col\n        ncol_exp = 2\n        exp = unittest_dm(self.m, NETWORK_METHOD, PLANAR, False)\n        assert exp.shape == (self.m.num_cells, ncol_exp)\n        exp2 = unittest_dm(self.m, NETWORK_METHOD, PLANAR, True)\n        assert exp2.shape == (self.m.num_cells, ncol_exp)\n        assert_array_equal(exp, exp2)\n\n    def test_create_quadratic_dm_network(self):\n        # quadratic version with networked method does not have offsets col\n        ncol_exp = 5\n        exp = unittest_dm(self.m, NETWORK_METHOD, QUADRATIC, False)\n        assert exp.shape == (self.m.num_cells, ncol_exp)\n        exp2 = unittest_dm(self.m, NETWORK_METHOD, QUADRATIC, True)\n        assert exp2.shape == (self.m.num_cells, ncol_exp)\n        assert_array_equal(exp, exp2)\n\n    def test_create_partcubic_dm_network(self):\n        # partial cubic version with networked method does not have offsets col\n        ncol_exp = 6\n        exp = unittest_dm(self.m, NETWORK_METHOD, PART_CUBIC, False)\n        assert exp.shape == (self.m.num_cells, ncol_exp)\n        exp2 = unittest_dm(self.m, NETWORK_METHOD, PART_CUBIC, True)\n        assert exp2.shape == (self.m.num_cells, ncol_exp)\n        assert_array_equal(exp, exp2)\n\n\nclass TestIndependentCorrection:\n    \"\"\"Test cases for the orbital correction component of PyRate.\"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        cls.ifgs = small5_mock_ifgs()\n        _add_nodata(cls.ifgs)\n\n        for ifg in cls.ifgs:\n            ifg.x_size = 90.0\n            ifg.y_size = 89.5\n            ifg.open()\n\n    def alt_orbital_correction(self, ifg, deg, offset, scale):\n        data = ifg.phase_data.reshape(ifg.num_cells)\n        dm = get_design_matrix(ifg, deg, intercept=True, scale=scale)[~isnan(data)]\n        fd = data[~isnan(data)].reshape((dm.shape[0], 1))\n\n        dmt = dm.T\n        invNbb = inv(dmt.dot(dm))\n        orbparams = invNbb.dot(dmt.dot(fd))\n        alt_params = lstsq(dm, fd)[0]\n        # FIXME: precision\n        assert_array_almost_equal(orbparams, alt_params, decimal=1)\n\n        dm2 = get_design_matrix(ifg, deg, intercept=True, scale=scale)\n        fullorb = np.reshape(np.dot(dm2, orbparams), ifg.phase_data.shape)\n        if offset:\n            offset_removal = nanmedian(np.ravel(ifg.phase_data - fullorb))\n        else:\n            offset_removal = 0\n\n        fwd_correction = fullorb - offset_removal\n        # ifg.phase_data -= (fullorb - offset_removal)\n        return ifg.phase_data - fwd_correction\n\n    def check_correction(self, degree, method, offset, decimal=2):\n        orig = array([c.phase_data.copy() for c in self.ifgs])\n        exp = [self.alt_orbital_correction(i, degree, offset, scale=100) for i in self.ifgs]\n        params = dict()\n        params[C.ORBITAL_FIT_METHOD] = method\n        params[C.ORBITAL_FIT_DEGREE] = degree\n        params[C.ORBFIT_OFFSET] = offset\n        params[C.ORBFIT_INTERCEPT] = 1\n        params[C.ORBFIT_SCALE] = 100\n        params[C.PARALLEL] = False\n        params[C.NO_DATA_VALUE] = 0\n        params[C.NAN_CONVERSION] = False\n        params[C.OUT_DIR] = tempfile.mkdtemp()\n        params[C.ORBITAL_FIT_LOOKS_X] = 1\n        params[C.ORBITAL_FIT_LOOKS_Y] = 1\n        params[C.TEMP_MLOOKED_DIR] = tempfile.mkdtemp()\n        for i in self.ifgs:\n            i.mm_converted = True\n        remove_orbital_error(self.ifgs, params)\n        corrected = array([c.phase_data for c in self.ifgs])\n\n        assert ~(orig == corrected).all()\n        self.check_results(self.ifgs, orig)  # test shape, data is non zero\n\n        # FIXME: is decimal=2 close enough?\n        for i, (e, a) in enumerate(zip(exp, corrected)):\n            assert_array_almost_equal(e, a, decimal=decimal)\n\n    def check_results(self, ifgs, corrections):\n        \"\"\"Helper method for result verification\"\"\"\n        for i, c in zip(ifgs, corrections):\n            ys, xs = c.shape\n            assert i.nrows == ys\n            assert i.ncols == xs\n\n            # ensure there is real data\n            assert ~ isnan(i.phase_data).all()\n            assert ~ isnan(c).all()\n            assert c.ptp() != 0  # ensure range of values in grid\n\n    def test_independent_correction_planar(self):\n        self.check_correction(PLANAR, INDEPENDENT_METHOD, False)\n\n    def test_independent_correction_planar_offsets(self):\n        self.check_correction(PLANAR, INDEPENDENT_METHOD, True)\n\n    def test_independent_correction_quadratic(self):\n        self.check_correction(QUADRATIC, INDEPENDENT_METHOD, False)\n\n    def test_independent_correction_quadratic_offsets(self):\n        self.check_correction(QUADRATIC, INDEPENDENT_METHOD, True)\n\n    def test_independent_correction_partcubic(self):\n        self.check_correction(PART_CUBIC, INDEPENDENT_METHOD, False)\n\n    def test_independent_correction_partcubic_offsets(self):\n        self.check_correction(PART_CUBIC, INDEPENDENT_METHOD, True, decimal=1)\n\n\nclass TestError:\n    \"\"\"Tests for the networked correction method\"\"\"\n\n    @classmethod\n    def setup_method(cls):\n        out_dir = tempfile.mkdtemp()\n        cls.params = common.min_params(out_dir)\n        cls.ifgs = small5_mock_ifgs()\n\n    def test_invalid_ifgs_arg(self):\n        # min requirement is 1 ifg, can still subtract one epoch from the other\n        with pytest.raises(OrbitalError):\n            get_network_design_matrix([], PLANAR, 100, True)\n\n    def test_invalid_degree_arg(self):\n        # test failure of a few different args for 'degree'\n        for d in range(-5, 1):\n            with pytest.raises(OrbitalError):\n                get_network_design_matrix(self.ifgs, d, 100, True)\n        for d in range(4, 7):\n            with pytest.raises(OrbitalError):\n                get_network_design_matrix(self.ifgs, d, 100, True)\n\n    def test_invalid_method(self):\n        # test failure of a few different args for 'method'\n        for m in [None, 5, -1, -3, 45.8]:\n            self.params[C.ORBITAL_FIT_METHOD] = m\n            with pytest.raises(OrbitalError):\n                remove_orbital_error(self.ifgs, self.params)\n\n    def test_different_looks_raise(self):\n        # different x/y looks factors should be accepted\n        self.params[C.ORBITAL_FIT_LOOKS_X] = 1\n        self.params[C.ORBITAL_FIT_LOOKS_Y] = 5\n        try:\n            remove_orbital_error(self.ifgs, self.params)\n        except:\n            pytest.fail\n\n    def test_looks_as_int(self):\n        self.params[C.ORBITAL_FIT_LOOKS_X] = 1.1\n        self.params[C.ORBITAL_FIT_LOOKS_Y] = 5\n        with pytest.raises(OrbitalError):\n            remove_orbital_error(self.ifgs, self.params)\n        self.params[C.ORBITAL_FIT_LOOKS_X] = 1\n        self.params[C.ORBITAL_FIT_LOOKS_Y] = '5'\n        with pytest.raises(OrbitalError):\n            remove_orbital_error(self.ifgs, self.params)\n\n\nclass TestNetworkDesignMatrixTests:\n    \"\"\"Contains tests verifying creation of sparse network design matrix.\"\"\"\n\n    def setup_class(self):\n        self.ifgs = small5_mock_ifgs()\n        _add_nodata(self.ifgs)\n        self.nifgs = len(self.ifgs)\n        self.ncells = self.ifgs[0].num_cells\n        self.date_ids = get_date_ids(self.ifgs)\n        self.nepochs = len(self.date_ids)\n        assert self.nepochs == 6\n\n        for ifg in self.ifgs:\n            ifg.X_SIZE = 90.0\n            ifg.Y_SIZE = 89.5\n\n    def test_planar_network_dm(self):\n        ncoef = 2\n        offset = False\n        act = get_network_design_matrix(self.ifgs, PLANAR, 100, intercept=offset)\n        assert act.shape == (self.ncells * self.nifgs, ncoef * self.nepochs)\n        assert act.ptp() != 0\n        self.check_equality(ncoef, act, self.ifgs, offset)\n\n    def test_planar_network_dm_offset(self):\n        ncoef = 2  # NB: doesn't include offset col\n        offset = True\n        act = get_network_design_matrix(self.ifgs, PLANAR, 100, intercept=offset)\n        assert act.shape[0] == self.ncells * self.nifgs\n        assert act.shape[1] == (self.nepochs * (ncoef + offset))\n        assert act.ptp() != 0\n        self.check_equality(ncoef, act, self.ifgs, offset)\n\n    def test_quadratic_network_dm(self):\n        ncoef = 5\n        offset = False\n        act = get_network_design_matrix(self.ifgs, QUADRATIC, 100, intercept=offset)\n        assert act.shape == (self.ncells * self.nifgs, ncoef * self.nepochs)\n        assert act.ptp() != 0\n        self.check_equality(ncoef, act, self.ifgs, offset)\n\n    def test_quadratic_network_dm_offset(self):\n        ncoef = 5\n        offset = True\n        act = get_network_design_matrix(self.ifgs, QUADRATIC, 100, intercept=offset)\n        assert act.shape[0] == self.ncells * self.nifgs\n        assert act.shape[1] == (self.nepochs * (ncoef + offset))\n        assert act.ptp() != 0\n        self.check_equality(ncoef, act, self.ifgs, offset)\n\n    def test_partcubic_network_dm(self):\n        ncoef = 6\n        offset = False\n        act = get_network_design_matrix(self.ifgs, PART_CUBIC, 100, intercept=offset)\n        assert act.shape == (self.ncells * self.nifgs, ncoef * self.nepochs)\n        assert act.ptp() != 0\n        self.check_equality(ncoef, act, self.ifgs, offset)\n\n    def test_partcubic_network_dm_offset(self):\n        ncoef = 6\n        offset = True\n        act = get_network_design_matrix(self.ifgs, PART_CUBIC, 100, intercept=offset)\n        assert act.shape[0] == self.ncells * self.nifgs\n        assert act.shape[1] == (self.nepochs * (ncoef + offset))\n        assert act.ptp() != 0\n        self.check_equality(ncoef, act, self.ifgs, offset)\n\n    def check_equality(self, ncoef, dm, ifgs, offset):\n        \"\"\"\n        Internal test function to check subsets against network design matrix\n        ncoef - base number of coefficients, without extra col for offsets\n        dm - network design matrix to check the results\n        ifgs - sequence of Ifg objs\n        offset - boolean to include extra parameters for model offsets\n        \"\"\"\n        deg = DEG_LOOKUP[ncoef]\n        np = ncoef * self.nepochs  # index of 1st offset col\n\n        for i, ifg in enumerate(ifgs):\n            exp = unittest_dm(ifg, NETWORK_METHOD, deg, offset)\n            assert exp.shape == (ifg.num_cells, ncoef)\n\n            ib1, ib2 = [x * self.ncells for x in (i, i + 1)]  # row start/end\n            jbm = (ncoef + offset) * self.date_ids[ifg.first]  # starting col index for first image\n            jbs = (ncoef + offset) * self.date_ids[ifg.second]  # col start for second image\n            assert_array_almost_equal(-exp, dm[ib1:ib2, jbm:jbm + ncoef])\n            assert_array_almost_equal(exp, dm[ib1:ib2, jbs:jbs + ncoef])\n\n            # ensure remaining rows/cols are zero for this ifg NOT inc offsets\n            assert_array_equal(0, dm[ib1:ib2, :jbm])  # all cols leading up to first image\n            assert_array_equal(0, dm[ib1:ib2, jbm + ncoef + offset:jbs])  # cols btwn mas/slv\n            assert_array_equal(0, dm[ib1:ib2, jbs + ncoef + offset:np])  # to end of non offsets\n\n\n# components for network correction testing\ndef network_correction(ifgs, deg, intercept, ml_ifgs=None, tol=1e-6):\n    \"\"\"\n    Compares results of orbital_correction() to alternate implementation.\n    deg - PLANAR, QUADRATIC or PART_CUBIC\n    off - True/False to calculate correction with offsets\n    \"\"\"\n    ncells = ifgs[0].num_cells\n\n    if ml_ifgs:\n        ml_nc = ml_ifgs[0].num_cells\n        ml_data = concatenate([i.phase_data.reshape(ml_nc) for i in ml_ifgs])\n        dm = get_network_design_matrix(ml_ifgs, deg, 100, intercept)[~isnan(ml_data)]\n        fd = ml_data[~isnan(ml_data)].reshape((dm.shape[0], 1))\n    else:\n        data = concatenate([i.phase_data.reshape(ncells) for i in ifgs])\n        dm = get_network_design_matrix(ifgs, deg, 100, intercept)[~isnan(data)]\n        fd = data[~isnan(data)].reshape((dm.shape[0], 1))\n\n    params = pinv(dm, tol).dot(fd)\n    assert params.shape == (dm.shape[1], 1)\n\n    # calculate forward correction\n    sdm = unittest_dm(ifgs[0], NETWORK_METHOD, deg)\n    ncoef = _get_num_params(deg, intercept=False)  # NB: ignore offsets for network method\n    assert sdm.shape == (ncells, ncoef)\n    orbs = _expand_corrections(ifgs, sdm, params, ncoef, intercept)\n\n    # tricky: get expected result before orbital_correction() modifies ifg phase\n    return [i.phase_data - orb for i, orb in zip(ifgs, orbs)]\n\n\ndef _expand_corrections(ifgs, dm, params, ncoef, offset):\n    \"\"\"\n    Convenience func returns model converted to data points.\n    dm: design matrix (do not filter/remove nan cells)\n    params: model parameters array from pinv() * dm\n    ncoef: number of model coefficients (2 planar, 5 quadratic)\n    offsets: True/False to calculate correction with offsets\n    \"\"\"\n    # NB: cannot work on singular ifgs due to date ID id/indexing requirement\n    date_ids = get_date_ids(ifgs)\n\n    corrections = []\n    for ifg in ifgs:\n        jbm = date_ids[ifg.first] * ncoef  # starting row index for first image\n        jbs = date_ids[ifg.second] * ncoef  # row start for second image\n        par = params[jbs:jbs + ncoef] - params[jbm:jbm + ncoef]\n\n        # estimate orbital correction effects\n        # corresponds to \"fullorb = B*parm + offset\" in orbfwd.m\n        cor = dm.dot(par).reshape(ifg.phase_data.shape)\n\n        if offset:\n            off = np.ravel(ifg.phase_data - cor)\n            # bring all ifgs to same base level\n            cor -= nanmedian(off)\n\n        corrections.append(cor)\n    return corrections\n\n\nclass TestNetworkCorrectionTests:\n    \"\"\"Verifies orbital correction using network method and no multilooking\"\"\"\n\n    def setup_class(cls):\n        # fake some real ifg data by adding nans\n        cls.ifgs = small5_mock_ifgs()\n        _add_nodata(cls.ifgs)\n\n        # use different sizes to differentiate axes results\n        for ifg in cls.ifgs:\n            ifg.X_SIZE = 90.0\n            ifg.Y_SIZE = 89.5\n\n        cls.nc_tol = 1e-6\n\n    \"\"\"\n    this test checks that the network orbital fit will return the same\n    parameters if we add a constant to every interferogram. The current\n    network method actually uses the constant parameters, which are\n    assigned per epoch rather than per interferogram, so this test will\n    fail (and should fail).\n    \"\"\"\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_offset_inversion(self):\n        \"\"\"\n        Ensure pinv(DM)*obs gives equal results given constant change to fd\n        \"\"\"\n\n        def get_orbital_params():\n            \"\"\"Returns pseudo-inverse of the DM\"\"\"\n            ncells = self.ifgs[0].num_cells\n            data = concatenate([i.phase_data.reshape(ncells) for i in self.ifgs])\n            dm = get_network_design_matrix(self.ifgs, PLANAR, 100, True)[~isnan(data)]\n            fd = data[~isnan(data)].reshape((dm.shape[0], 1))\n            return dot(pinv(dm, self.nc_tol), fd)\n\n        tol = 1e-5\n        nifgs = len(self.ifgs)\n        params0 = get_orbital_params()\n\n        # apply constant change to the observed values (fd)\n        for value in [5.2, -23.5]:\n            for i in self.ifgs:  # change ifgs in place\n                i.phase_data += value\n                assert isnan(i.phase_data).any()\n\n            params = get_orbital_params()\n            diff = params - params0\n            assert (diff[:-nifgs] < tol).all()\n            assert_array_almost_equal(diff[-nifgs:], value, decimal=5)\n\n            # reset back to orig data\n            for i in self.ifgs:\n                i.phase_data -= value\n\n    # These functions test full size data for orbital correction. The options\n    # are separated as the ifg.phase_data arrays are modified in place, allowing\n    # setUp() reset phase data between tests.\n\n    def test_network_correction_planar(self):\n        deg, intercept = PLANAR, False\n        exp = network_correction(self.ifgs, deg, intercept)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    \"\"\"\n    the test_network_correction_{DEGREE}_offset tests check against a method\n    that fits a constant offset to each interferogram but doesn't remove it.\n    This differs from the current implementation of the network correction\n    so these tests will fail.\n    \"\"\"\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_network_correction_planar_offset(self):\n        deg, intercept = PLANAR, True\n        exp = network_correction(self.ifgs, deg, intercept)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    def test_network_correction_quadratic(self):\n        deg, intercept = QUADRATIC, False\n        offset = intercept\n        exp = network_correction(self.ifgs, deg, intercept)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_network_correction_quadratic_offset(self):\n        deg, intercept = QUADRATIC, True\n        exp = network_correction(self.ifgs, deg, intercept)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    def test_network_correction_partcubic(self):\n        deg, intercept = PART_CUBIC, False\n        exp = network_correction(self.ifgs, deg, intercept)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_network_correction_partcubic_offset(self):\n        deg, intercept = PART_CUBIC, True\n        exp = network_correction(self.ifgs, deg, intercept)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    @staticmethod\n    def verify_corrections(ifgs, exp, deg, intercept):\n        # checks orbital correction against unit test version\n        params = dict()\n        params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD\n        params[C.ORBITAL_FIT_DEGREE] = deg\n        params[C.ORBITAL_FIT_LOOKS_X] = 1\n        params[C.ORBITAL_FIT_LOOKS_Y] = 1\n        params[C.PARALLEL] = False\n        params[C.OUT_DIR] = tempfile.mkdtemp()\n        params[C.ORBFIT_OFFSET] = intercept\n        params[C.ORBFIT_INTERCEPT] = intercept\n        params[C.ORBFIT_SCALE] = 100\n        params[C.PREREAD_IFGS] = None\n        mkdir_p(Path(params[C.OUT_DIR]).joinpath(C.ORB_ERROR_DIR))\n        network_orbital_correction(ifgs, params)\n        act = [i.phase_data for i in ifgs]\n        assert_array_almost_equal(act, exp, decimal=5)\n\n\nclass TestNetworkCorrectionTestsMultilooking:\n    'Verifies orbital correction with multilooking and network method'\n\n    @classmethod\n    def setup_class(cls):\n        # fake some real ifg data by adding nans\n        cls.ml_ifgs = small5_mock_ifgs()\n        # 2x data of default Small mock\n        cls.ifgs = small5_mock_ifgs(xs=6, ys=8)\n\n        # use different sizes to differentiate axes results\n        for ifg in cls.ifgs:\n            ifg.X_SIZE = 90.0\n            ifg.Y_SIZE = 89.5\n\n        # add common nodata to all ifgs\n        for i in cls.ifgs + cls.ml_ifgs:\n            i.phase_data[0, :] = nan\n\n    # These functions test multilooked data for orbital correction. The options\n    # are separated as the ifg.phase_data arrays are modified in place, allowing\n    # setUp() refresh phase data between tests.\n\n    def test_mlooked_network_correction_planar(self):\n        deg, intercept = PLANAR, False\n        exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_mlooked_network_correction_planar_offset(self):\n        deg, intercept = PLANAR, True\n        exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    def test_mlooked_network_correction_quadratic(self):\n        deg, intercept = QUADRATIC, False\n        exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_mlooked_network_correction_quadratic_offset(self):\n        deg, intercept = QUADRATIC, True\n        exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    def test_mlooked_network_correction_partcubic(self):\n        deg, intercept = PART_CUBIC, False\n        exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    @pytest.mark.skip(reason=\"legacy test against old network method\")\n    def test_mlooked_network_correction_partcubic_offset(self):\n        deg, intercept = PART_CUBIC, True\n        exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs)\n        self.verify_corrections(self.ifgs, exp, deg, intercept)\n\n    def verify_corrections(self, ifgs, exp, deg, intercept):\n        # checks orbital correction against unit test version\n        params = dict()\n        params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD\n        params[C.ORBITAL_FIT_DEGREE] = deg\n        params[C.ORBITAL_FIT_LOOKS_X] = 1\n        params[C.ORBITAL_FIT_LOOKS_Y] = 1\n        params[C.PARALLEL] = False\n        params[C.ORBFIT_OFFSET] = intercept\n        params[C.ORBFIT_INTERCEPT] = intercept\n        params[C.ORBFIT_SCALE] = 100\n        params[C.PREREAD_IFGS] = None\n        params[C.OUT_DIR] = tempfile.mkdtemp()\n        mkdir_p(Path(params[C.OUT_DIR]).joinpath(C.ORB_ERROR_DIR))\n        network_orbital_correction(ifgs, params, self.ml_ifgs)\n        act = [i.phase_data for i in ifgs]\n        assert_array_almost_equal(act, exp, decimal=4)\n\n\ndef unittest_dm(ifg, method, degree, offset=False, scale=100.0):\n    '''Helper/test func to create design matrix segments. Includes handling for\n    making quadratic DM segments for use in network method.\n    ifg - source interferogram to model design matrix on\n    method - INDEPENDENT_METHOD or NETWORK_METHOD\n    degree - PLANAR, QUADRATIC or PART_CUBIC\n    offset - True/False to include additional cols for offsets\n    '''\n    assert method in [INDEPENDENT_METHOD, NETWORK_METHOD]\n\n    xlen = ncoef = NUM_COEF_LOOKUP[degree]\n    if offset and method == INDEPENDENT_METHOD:\n        ncoef += 1\n    else:\n        offset = False  # prevent offsets in DM sections for network method\n\n    # NB: avoids meshgrid to prevent copying production implementation\n    data = empty((ifg.num_cells, ncoef), dtype=float32)\n    rows = iter(data)\n    yr = range(1, ifg.nrows + 1)  # simulate meshgrid starting from 1\n    xr = range(1, ifg.ncols + 1)\n\n    xsz, ysz = [i / scale for i in [ifg.x_size, ifg.y_size]]\n\n    if degree == PLANAR:\n        for y, x in product(yr, xr):\n            row = next(rows)\n            row[:xlen] = [x * xsz, y * ysz]\n    elif degree == QUADRATIC:\n        for y, x in product(yr, xr):\n            ys = y * ysz\n            xs = x * xsz\n            row = next(rows)\n            row[:xlen] = [xs ** 2, ys ** 2, xs * ys, xs, ys]\n    else:\n        for y, x in product(yr, xr):\n            ys = y * ysz\n            xs = x * xsz\n            row = next(rows)\n            row[:xlen] = [xs * ys ** 2, xs ** 2, ys ** 2, xs * ys, xs, ys]\n\n    if offset:\n        data[:, -1] = 1\n\n    return data\n\n\ndef get_date_ids(ifgs):\n    '''\n    Returns unique epoch date IDs from the given Ifgs.\n    '''\n\n    dates = []\n    for ifg in ifgs:\n        dates += [ifg.first, ifg.second]\n    return first_second_ids(dates)\n\n\ndef _add_nodata(ifgs):\n    \"\"\"Adds some NODATA/nan cells to the small mock ifgs\"\"\"\n    ifgs[0].phase_data[0, :] = nan  # 3 error cells\n    ifgs[1].phase_data[2, 1:3] = nan  # 2 error cells\n    ifgs[2].phase_data[3, 2:3] = nan  # 1 err\n    ifgs[3].phase_data[1, 2] = nan  # 1 err\n    ifgs[4].phase_data[1, 1:3] = nan  # 2 err\n\n\nclass TestLegacyComparisonTestsOrbfitMethod1:\n    \"\"\"\n    This is the legacy comparison test of orbital correction functionality.\n    Tests use the following config\n    orbfit:        1\n    orbfitmethod:  1\n    orbfitdegrees: 1\n    orbfitlksx:    1\n    orbfitlksy:    1\n\n    \"\"\"\n\n    @classmethod\n    @pytest.fixture(autouse=True)\n    def setup_class(cls, roipac_params):\n        cls.params = roipac_params\n        cls.BASE_DIR = cls.params[C.OUT_DIR]\n        # change to orbital error correction method 1\n        cls.params[C.ORBITAL_FIT_METHOD] = INDEPENDENT_METHOD\n        cls.params[C.ORBITAL_FIT_LOOKS_X] = 1\n        cls.params[C.ORBITAL_FIT_LOOKS_Y] = 1\n        cls.params[C.PARALLEL] = False\n        cls.params[C.ORBFIT_OFFSET] = True\n\n        data_paths = [os.path.join(SML_TEST_TIF, p) for p in IFMS16]\n        cls.ifg_paths = [os.path.join(cls.BASE_DIR, os.path.basename(d)) for d in data_paths]\n\n        for d in data_paths:\n            shutil.copy(d, os.path.join(cls.BASE_DIR, os.path.basename(d)))\n\n    @classmethod\n    def teardown_class(cls):\n        \"roipac_params fixture auto cleans\"\n        pass\n\n    @pytest.mark.skipif(True, reason=\"Does not work anymore\")\n    def test_orbital_correction_legacy_equality(self):\n        from pyrate import correct\n        from pyrate.configuration import MultiplePaths\n\n        multi_paths = [MultiplePaths(p, params=self.params) for p in self.ifg_paths]\n        for m in multi_paths:  # cheat\n            m.sampled_path = m.converted_path\n\n        self.params[C.INTERFEROGRAM_FILES] = multi_paths\n        self.params['rows'], self.params['cols'] = 2, 3\n        self.params[C.ORBFIT_OFFSET] = False\n        Path(self.BASE_DIR).joinpath('tmpdir').mkdir(exist_ok=True, parents=True)\n        correct._copy_mlooked(self.params)\n        correct._update_params_with_tiles(self.params)\n        correct._create_ifg_dict(self.params)\n        correct._copy_mlooked(self.params)\n        pyrate.core.orbital.orb_fit_calc_wrapper(self.params)\n\n        onlyfiles = [f for f in os.listdir(SML_TEST_LEGACY_ORBITAL_DIR)\n                     if os.path.isfile(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f))\n                     and f.endswith('.csv') and f.__contains__('_method1_')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            ifg_data = np.genfromtxt(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f), delimiter=',')\n            for k, j in enumerate([m.tmp_sampled_path for m in multi_paths]):\n                ifg = Ifg(j)\n                ifg.open()\n                if os.path.basename(j).split('_ifg.')[0] == os.path.basename(f).split(\n                        '_orb_planar_1lks_method1_geo_')[1].split('.')[0]:\n                    count += 1\n                    # all numbers equal\n                    np.testing.assert_array_almost_equal(ifg_data, ifg.phase_data, decimal=2)\n\n                    # means must also be equal\n                    assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(ifg.phase_data), abs=1e-2)\n\n                    # number of nans must equal\n                    assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(ifg.phase_data))\n                ifg.close()\n\n        # ensure that we have expected number of matches\n        assert count == len(self.ifg_paths)\n\n    def test_orbfit_treats_process_inputs_as_read_only(self):\n        pass\n\n\nclass TestLegacyComparisonTestsOrbfitMethod2:\n    \"\"\"\n    This is the legacy comparison test of orbital correction functionality.\n    Tests use the following config\n    orbfit:        1\n    orbfitmethod:  2\n    orbfitdegrees: 1\n    orbfitlksx:    1\n    orbfitlksy:    1\n\n    \"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        # change to orbital error correction method 2\n        cls.params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        cls.BASE_DIR = cls.params[C.OUT_DIR]\n        cls.params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD\n        cls.params[C.ORBITAL_FIT_LOOKS_X] = 1\n        cls.params[C.ORBITAL_FIT_LOOKS_Y] = 1\n        cls.params[C.ORBFIT_OFFSET] = True\n        cls.params[C.OUT_DIR] = cls.BASE_DIR\n        data_paths = [os.path.join(SML_TEST_TIF, p) for p in small_ifg_file_list()]\n        cls.new_data_paths = [os.path.join(cls.BASE_DIR, os.path.basename(d)) for d in data_paths]\n        cls.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(file_name=d, params=cls.params) for d in data_paths]\n        for p in cls.params[C.INTERFEROGRAM_FILES]:\n            p.sampled_path = p.converted_path\n\n        # copy the files from the dir into temp dir\n        for d in data_paths:\n            d_copy = os.path.join(cls.BASE_DIR, os.path.basename(d))\n            shutil.copy(d, d_copy)\n            os.chmod(d_copy, 0o660)\n\n        cls.headers = [roipac.roipac_header(i, cls.params) for i in cls.new_data_paths]\n        cls.orb_error_dir = Path(cls.params[C.OUT_DIR]).joinpath(ORB_ERROR_DIR)\n        cls.orb_error_dir.mkdir(parents=True, exist_ok=True)\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.BASE_DIR, ignore_errors=True)\n\n    def test_orbital_correction_legacy_equality_orbfit_method_2(self):\n        correct._copy_mlooked(self.params)\n        correct._create_ifg_dict(self.params)\n        remove_orbital_error(self.new_data_paths, self.params)\n\n        onlyfiles = [f for f in os.listdir(SML_TEST_LEGACY_ORBITAL_DIR)\n                     if os.path.isfile(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f))\n                     and f.endswith('.csv') and f.__contains__('_method2_')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            legacy_phase_data = np.genfromtxt(os.path.join(\n                SML_TEST_LEGACY_ORBITAL_DIR, f), delimiter=',')\n            for k, j in enumerate(self.new_data_paths):\n                if os.path.basename(j).split('_unw.')[0] == os.path.basename(f).split('_method2_')[1].split('.')[0]:\n                    count += 1\n                    ifg = Ifg(j)\n                    ifg.open()\n                    # all numbers equal\n                    # Note this changed as the nodata mask in the gdal_python.gdal_average changed to nan from 0\n                    # np.testing.assert_array_almost_equal(legacy_phase_data, ifg.phase_data, decimal=3)\n                    # number of nans must equal\n                    assert np.sum(np.isnan(legacy_phase_data)) == np.sum(np.isnan(ifg.phase_data))\n\n        # ensure that we have expected number of matches\n        assert count == len(self.new_data_paths)\n\n    def test_orbital_error_method2_dummy(self):\n        \"\"\"\n        does not test anything except that the method is working\n        \"\"\"\n        # change to orbital error correction method 2\n        self.params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD\n        self.params[C.ORBITAL_FIT_LOOKS_X] = 2\n        self.params[C.ORBITAL_FIT_LOOKS_Y] = 2\n        correct._copy_mlooked(self.params)\n        correct._create_ifg_dict(self.params)\n        remove_orbital_error(self.new_data_paths, self.params)\n\n        onlyfiles = [f for f in os.listdir(SML_TEST_LEGACY_ORBITAL_DIR)\n                     if os.path.isfile(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f))\n                     and f.endswith('.csv') and f.__contains__('_method2_')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            legacy_phase_data = np.genfromtxt(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f), delimiter=',')\n            for k, j in enumerate(self.new_data_paths):\n                if os.path.basename(j).split('_unw.')[0] == os.path.basename(f).split('_method2_')[1].split('.')[0]:\n                    count += 1\n                    ifg = Ifg(j)\n                    ifg.open()\n                    # number of nans must equal\n                    assert np.sum(np.isnan(legacy_phase_data)) == np.sum(np.isnan(ifg.phase_data))\n\n        # ensure that we have expected number of matches\n        assert count == len(self.new_data_paths)\n\n\n# TODO: Write tests for various looks and degree combinations\n# TODO: write mpi tests\n\n\nclass TestOrbErrorCorrectionsOnDiscReused:\n\n    @classmethod\n    def setup_class(cls):\n        cls.conf = TEST_CONF_GAMMA\n        params = Configuration(cls.conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(cls.conf).__dict__\n        prepifg.main(params)\n        cls.params = Configuration(cls.conf).__dict__\n        correct._copy_mlooked(cls.params)\n        correct._create_ifg_dict(cls.params)\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_orb_error(self, orbfit_method, orbfit_degrees):\n        self.params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        self.params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        multi_paths = self.params[C.INTERFEROGRAM_FILES]\n        self.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n        remove_orbital_error(self.ifg_paths, self.params)\n\n        # test_orb_errors_written\n        orb_error_files = [MultiplePaths.orb_error_path(i, self.params) for i in self.ifg_paths]\n        assert all(p.exists() for p in orb_error_files)\n\n        last_mod_times = np.array([os.stat(o).st_mtime for o in orb_error_files])\n\n        # run orbit removal again\n        remove_orbital_error(self.ifg_paths, self.params)\n        orb_error_files2 = [MultiplePaths.orb_error_path(i, self.params) for i in self.ifg_paths]\n        # if files are written again - times will change\n        last_mod_times_2 = np.array([os.stat(o).st_mtime for o in orb_error_files2])\n\n        # test_orb_error_reused_if_params_unchanged\n        assert all(a == b for a, b in zip(last_mod_times, last_mod_times_2))\n\n        # change one of the params\n        _degrees = set(C.ORB_DEGREE_NAMES.keys())\n        _degrees.discard(orbfit_degrees)\n\n        # test_orb_errors_recalculated_if_params_change\n        self.params[C.ORBITAL_FIT_DEGREE] = _degrees.pop()\n        import time\n        time.sleep(0.1)\n        remove_orbital_error(self.ifg_paths, self.params)\n        orb_error_files3 = [MultiplePaths.orb_error_path(i, self.params) for i in self.ifg_paths]\n        last_mod_times_3 = np.array([os.stat(o).st_mtime for o in orb_error_files3])\n        assert all(a != b for a, b in zip(last_mod_times, last_mod_times_3))\n\n\nclass TestOrbErrorCorrectionsReappliedDoesNotChangePhaseData:\n\n    @classmethod\n    def setup_method(cls):\n        cls.conf = TEST_CONF_GAMMA\n        params = Configuration(cls.conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(cls.conf).__dict__\n        prepifg.main(params)\n        cls.params = Configuration(cls.conf).__dict__\n        correct._copy_mlooked(cls.params)\n        correct._create_ifg_dict(cls.params)\n        multi_paths = cls.params[C.INTERFEROGRAM_FILES]\n        cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n\n    @classmethod\n    def teardown_method(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_orb_error_multiple_run_does_not_change_phase_data(self, orbfit_method, orbfit_degrees):\n        self.params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        self.params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        remove_orbital_error(self.ifg_paths, self.params)\n        ifgs = [Ifg(i) for i in self.ifg_paths]\n        for i in ifgs:\n            i.open()\n\n        phase_prev = [i.phase_data for i in ifgs]\n\n        # orb correct once more\n        correct._copy_mlooked(self.params)\n        remove_orbital_error(self.ifg_paths, self.params)\n\n        # and again\n        correct._copy_mlooked(self.params)\n        remove_orbital_error(self.ifg_paths, self.params)\n        ifgs = [Ifg(i) for i in self.ifg_paths]\n        for i in ifgs:\n            i.open()\n        phase_now = [i.phase_data for i in ifgs]\n        np.testing.assert_array_equal(phase_now, phase_prev)\n\n\n@pytest.fixture(params=[2, 3, 4])\ndef orbfit_looks(request):\n    x_lk = request.param\n    y_lk = np.random.choice([2, 3, 4])\n    return x_lk, y_lk\n\n\nclass TestOrbfitIndependentMethodWithMultilooking:\n\n    @classmethod\n    def setup_class(cls):\n        cls.conf = TEST_CONF_GAMMA\n        params = Configuration(cls.conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(cls.conf).__dict__\n        prepifg.main(params)\n        cls.params = Configuration(cls.conf).__dict__\n        correct._copy_mlooked(cls.params)\n        correct._create_ifg_dict(cls.params)\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_independent_method_works_with_multilooking(self, orbfit_looks, orbfit_degrees, orbfit_method=1):\n        \"\"\"\n        tests when multilooking is used in orbfit method 1 correction\n        also tests that multilooking factors in x and y can be different\n        \"\"\"\n        xlks, ylks = orbfit_looks\n        self.params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        self.params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        self.params[C.ORBITAL_FIT_LOOKS_Y] = int(ylks)\n        self.params[C.ORBITAL_FIT_LOOKS_X] = int(xlks)\n        multi_paths = self.params[C.INTERFEROGRAM_FILES]\n        self.ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n        remove_orbital_error(self.ifg_paths, self.params)\n        ifgs = [Ifg(p) for p in self.ifg_paths]\n        for i in ifgs:\n            i.open()\n            assert i.shape == (72, 47)  # shape should not change\n\n\nfrom pyrate.core.shared import cell_size\n\n\nclass SyntheticIfg:\n    \"\"\"\n    This class will generate a mock interferogram whose signal consists entirely\n    of a synthetic orbital error. The orbital error is generated as a 2D\n    polynomial signal with zero noise.\n    \"\"\"\n\n    def __init__(self, orbfit_degrees):\n        self.x_step = 0.001388888900000  # pixel size - same as cropA\n        self.y_step = 0.001388888900000\n        self.nrows = 100\n        self.ncols = 100\n        self.num_cells = self.nrows * self.ncols\n        self.is_open = False\n        self.orbfit_degrees = orbfit_degrees\n        self.first = None\n        self.second = None\n        self._phase_data = None\n        self._phase_data_first = None\n        self._phase_data_second = None\n        self.y_first = 0\n        self.x_first = 0\n        self.add_geographic_data()\n\n    def add_geographic_data(self):\n        \"\"\"\n        Determine and add geographic data to object\n        \"\"\"\n        # add some geographic data\n        self.x_centre = int(self.ncols / 2)\n        self.y_centre = int(self.nrows / 2)\n        self.lat_centre = self.y_first + (self.y_step * self.y_centre)\n        self.long_centre = self.x_first + (self.x_step * self.x_centre)\n        # use cell size from centre of scene\n        self.x_size, self.y_size = cell_size(self.lat_centre, self.long_centre, self.x_step, self.y_step)\n\n    @property\n    def phase_data(self):\n        \"\"\"\n        Returns phase band as an array.\n        \"\"\"\n        if self._phase_data is None:\n            self.open()\n        return self._phase_data\n\n    def open(self):\n        x, y = np.meshgrid(np.arange(self.nrows) * self.x_step, np.arange(self.ncols) * self.y_step)\n        x += self.x_step\n        y += self.y_step\n\n        # define some random coefficients, different for each date\n        x_slope, y_slope, x2_slope, y2_slope, x_y_slope, x_y2_slope, const = np.ravel(np.random.rand(1, 7))\n        x_slope_, y_slope_, x2_slope_, y2_slope_, x_y_slope_, x_y2_slope_, const_ = np.ravel(np.random.rand(1, 7))\n\n        # compute the 2D polynomial separately for first and second dates\n        self._phase_data_first = x_slope * x + y_slope * y + const  # planar\n        self._phase_data_second = x_slope_ * x + y_slope_ * y + const_  # planar\n        if self.orbfit_degrees == QUADRATIC:\n            self._phase_data_first += x2_slope * x ** 2 + y2_slope * y ** 2 + x_y_slope * x * y\n            self._phase_data_second += x2_slope_ * x ** 2 + y2_slope_ * y ** 2 + x_y_slope_ * x * y\n        elif self.orbfit_degrees == PART_CUBIC:\n            self._phase_data_first += x2_slope * x ** 2 + y2_slope * y ** 2 + x_y_slope * x * y + \\\n                                      x_y2_slope * x * (y ** 2)\n            self._phase_data_second += x2_slope_ * x ** 2 + y2_slope_ * y ** 2 + x_y_slope_ * x * y + \\\n                                       x_y2_slope_ * x * (y ** 2)\n\n        # combine orbit error for first and second dates to give synthetic phase data for this ifg\n        self._phase_data = self._phase_data_first - self._phase_data_second\n        self.is_open = True\n\n\nfrom pyrate.core.gdal_python import _gdalwarp_width_and_height\nfrom pyrate.core.orbital import __orb_inversion\n\n\n# helper function to multilook a synthetic ifg with gdal\ndef mlk_ifg(ifg, nlooks):\n    src = gdal.GetDriverByName('MEM').Create('', ifg.ncols, ifg.nrows, 1, gdalconst.GDT_Float32)\n    gt = (0, ifg.x_step, 0, 0, 0, ifg.y_step)\n    src.SetGeoTransform(gt)\n    src.GetRasterBand(1).WriteArray(ifg.phase_data)\n    resampled_gt = (0, ifg.x_step * nlooks, 0, 0, 0, ifg.y_step * nlooks)\n    min_x, min_y = 0, 0\n    max_x, max_y = ifg.x_step * ifg.ncols, ifg.y_step * ifg.nrows\n\n    px_height, px_width = _gdalwarp_width_and_height(max_x, max_y, min_x, min_y, resampled_gt)\n\n    dst = gdal.GetDriverByName('MEM').Create('', px_height, px_width, 1, gdalconst.GDT_Float32)\n    dst.SetGeoTransform(resampled_gt)\n\n    gdal.ReprojectImage(src, dst, '', '', gdal.GRA_Average)\n\n    mlooked = Ifg(dst)\n    mlooked.first = ifg.first\n    mlooked.second = ifg.second\n    return mlooked\n\n\n@pytest.fixture(params=[1, 2, 3, 4])\ndef orb_lks(request):\n    return request.param\n\n\ndef test_single_synthetic_ifg_independent_method(orbfit_degrees, orb_lks, ifg=None):\n    \"\"\"\n    These tests are checking that perfect orbital errors, those matching the assumed orbital error model, can be\n    completely removed by the independent orbital correction with and without multilooking.\n\n    These tests also prove that orbital error estimates using orbfit multilooking is a valid approach and matches the\n    modelled error with acceptable numerical accuracy, with the accuracy depending on the multilooking factor used.\n\n    These tests also prove that the orbital error parameters can be approximated using multilooking in the\n    independent method.\n    \"\"\"\n    if ifg is None:\n        ifg = SyntheticIfg(orbfit_degrees)\n    fullres_dm = get_design_matrix(ifg, orbfit_degrees, intercept=True, scale=1)\n\n    m_looked_ifg = mlk_ifg(ifg, orb_lks)\n\n    mlooked_phase = np.reshape(m_looked_ifg.phase_data, m_looked_ifg.num_cells)\n    mlooked_dm = get_design_matrix(m_looked_ifg, orbfit_degrees, intercept=True, scale=1)\n\n    orb_corr = __orb_correction(fullres_dm, mlooked_dm, ifg.phase_data, mlooked_phase, offset=True)\n    if orb_lks == 1:\n        assert_array_almost_equal(fullres_dm, mlooked_dm)\n        decimal = 4\n    else:\n        decimal = 2\n    assert_array_almost_equal(ifg.phase_data, orb_corr, decimal=decimal)\n\n\n@pytest.mark.slow\n@pytest.mark.skipif((not PY37GDAL302), reason=\"Only run in one CI env\")\ndef test_set_synthetic_ifgs_independent_method(mexico_cropa_params, orbfit_degrees, orb_lks):\n    \"\"\"\n    Test that the independent method can generate a set of orbital corrections\n    that matches a set of synthetic ifg for a range of multi-look factors and\n    polynomial degrees.\n    \"\"\"\n    # Use the CropA ifg network configuration\n    ifgs = [Ifg(i.converted_path) for i in mexico_cropa_params[C.INTERFEROGRAM_FILES]]\n    for i in ifgs:\n        i.open()\n        test_ifg = SyntheticIfg(orbfit_degrees)\n        test_single_synthetic_ifg_independent_method(orbfit_degrees, orb_lks, test_ifg)\n\n\n# an in-memory \"open\" interferogram that we can pass to top-level orb correction methods\nclass FakeIfg:\n    def __init__(self, orbfit_deg, model_params, date_first, date_second):\n        self.x_step = 0.001388888900000  # pixel size - same as cropA\n        self.y_step = 0.001388888900000\n        self.nrows = 100\n        self.ncols = 100\n        self.num_cells = self.nrows * self.ncols\n        self.is_open = False\n        self.orbfit_degrees = orbfit_deg\n        self.model_params = model_params\n        self.first = date_first\n        self.second = date_second\n        self._phase_data = None\n        self.y_first = 0\n        self.x_first = 0\n        self.nan_fraction = 0\n        self.add_geographic_data()\n\n    def add_geographic_data(self):\n        \"\"\"\n        Determine and add geographic data to object\n        \"\"\"\n        # add some geographic data\n        self.x_centre = int(self.ncols / 2)\n        self.y_centre = int(self.nrows / 2)\n        self.lat_centre = self.y_first + (self.y_step * self.y_centre)\n        self.long_centre = self.x_first + (self.x_step * self.x_centre)\n        # use cell size from centre of scene\n        self.x_size, self.y_size = cell_size(self.lat_centre, self.long_centre, self.x_step, self.y_step)\n\n    @property\n    def phase_data(self):\n        \"\"\"\n        Returns phase band as an array.\n        \"\"\"\n        if self._phase_data is None:\n            self.open()\n        return self._phase_data\n\n    def open(self):\n        x, y = np.meshgrid(np.arange(self.nrows) * self.x_step, np.arange(self.ncols) * self.y_step)\n        x += self.x_step\n        y += self.y_step\n\n        # use provided coefficients\n        if self.orbfit_degrees == PLANAR:\n            mx, my = self.model_params\n            self._phase_data = mx * x + my * y\n        elif self.orbfit_degrees == QUADRATIC:\n            mx, my, mx2, my2, mxy = self.model_params\n            self._phase_data = mx * x + my * y + mx2 * x ** 2 + my2 * y ** 2 + mxy * x * y\n        else:\n            mx, my, mx2, my2, mxy, mxy2 = self.model_params\n            self._phase_data = mx * x + my * y + mx2 * x ** 2 + my2 * y ** 2 + mxy * x * y + mxy2 * x * y ** 2\n\n        self.is_open = True\n\n\n# tests for network method to recover synthetic orbital error\nclass SyntheticNetwork:\n    \"\"\"\n    This class will generate a network of synthetic ifgs, based on\n    orbital errors for each epoch. The signal will be purely from the synthetic\n    orbital error with no noise.\n    \"\"\"\n\n    def __init__(self, orbfit_deg, epochs, network, model_params):\n        \"\"\"\n        orbfit_deg: synthesise ifgs with planar, quadratic, or part cubic\n        orbit error models.\n        epochs: list of epoch dates in the network\n        network: list of lists, spec of the ifgs to generate for each epoch as\n        primary\n        model_params: list of iterable - model parameters of correct degree for\n        each epoch\n        \"\"\"\n        ifgs = []\n        for i, e1 in enumerate(epochs):\n            for j in network[i]:\n                ifg_err_model = [model_params[j][k] - model_params[i][k] for k in range(len(model_params[0]))]\n                ifgs.append(FakeIfg(orbfit_deg, ifg_err_model, e1, epochs[j]))\n        self.ifgs = ifgs\n        self.epochs = epochs\n\n@pytest.mark.skip(reason=\"test is non-deterministic due to float calculation errors in array comparison\")\ndef test_synthetic_network_correction(orbfit_degrees, orb_lks):\n    epochs = [\n        date(2000, 1, 1),\n        date(2000, 1, 13),\n        date(2000, 1, 25),\n        date(2000, 2, 6),\n        date(2000, 2, 18),\n        date(2000, 3, 1)\n        ]\n    # start with the network as a connected tree so mst does nothing\n    network = [[2], [2], [3], [4, 5], [], []]\n    # six sets of model parameters - one for each epoch\n    model_params = [[-1, 1, -1, 1, -1, 1],\n                    [0, 1, 2, 3, 4, 5],\n                    [5, 4, 3, 2, 1, 0],\n                    [3, 6, 9, 6, 3, 0],\n                    [9, 4, 1, 0, 1, 4],\n                    [1, 1, 1, 1, 1, 1]]\n    if orbfit_degrees == PLANAR:\n        nparam = 2\n    elif orbfit_degrees == QUADRATIC:\n        nparam = 5\n    else:\n        nparam = 6\n    model_params = [mi[:nparam] for mi in model_params]\n\n    # network method uses a hard coded scale of 100\n    scale = 100\n\n    syn_data = SyntheticNetwork(orbfit_degrees, epochs, network, model_params)\n#    id_dict = {date: i for date, i in enumerate(epochs)}\n    nepochs = len(epochs)\n\n    mlk_ifgs = [mlk_ifg(ifg, orb_lks) for ifg in syn_data.ifgs]\n\n    coeffs = calc_network_orb_correction(mlk_ifgs, orbfit_degrees, scale, nepochs, intercept=True)\n\n    # reconstruct correction\n    reconstructed = []\n    # ifgs are built with lat/long metadata,\n    # orbfit modelling is done with metres coordinates\n    csx = syn_data.ifgs[0].x_size\n    csy = syn_data.ifgs[0].y_size\n    x, y = (coord + 1 for coord in np.meshgrid(np.arange(100, dtype=float), np.arange(100, dtype=float)))\n    x *= csx\n    y *= csy\n    x /= scale\n    y /= scale\n\n    for i, js in enumerate(network):\n        for j in js:\n            cpair = [cj - ci for ci, cj in zip(coeffs[i], coeffs[j])]\n            if orbfit_degrees == PLANAR:\n                reconstructed.append(cpair[0] * x + cpair[1] * y)\n            elif orbfit_degrees == QUADRATIC:\n                reconstructed.append(cpair[0] * x ** 2 + cpair[1] * y ** 2 + cpair[2] * x * y \\\n                                     + cpair[3] * x + cpair[4] * y)\n            else:\n                reconstructed.append(cpair[0] * x * y ** 2 + cpair[1] * x ** 2 + cpair[2] * y ** 2 + \\\n                                     cpair[3] * x * y + cpair[4] * x + cpair[5] * y)\n\n    for orig, recon in zip(syn_data.ifgs, reconstructed):\n        assert_array_almost_equal(orig.phase_data, recon, decimal=2)\n\n\ndef test_orbital_inversion():\n    \"\"\"Small unit to test the application of numpy pseudoinverse\"\"\"\n    A = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1]])\n    d = np.array([2, 4, 3])\n    exp = np.array([1.5, 0.5, 2.5])\n    res = __orb_inversion(A, d)\n    assert_array_almost_equal(res, exp, decimal=9)\n"
  },
  {
    "path": "tests/test_prepifg.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the prepifg.py PyRate module.\n\"\"\"\nimport shutil\nimport os\nfrom os.path import exists, join\nimport sys\nimport subprocess\nimport tempfile\nfrom math import floor\nfrom itertools import product\nfrom pathlib import Path\nimport pytest\n\nimport numpy as np\nfrom numpy import isnan, nanmax, nanmin, nanmean, ones, nan, reshape, sum as npsum\nfrom numpy.testing import assert_array_almost_equal, assert_array_equal\n\nfrom osgeo import gdal\n\nimport pyrate.configuration\nimport pyrate.constants as C\nfrom pyrate.core.logger import pyratelogger as log\nfrom pyrate.core.shared import Ifg, DEM, dem_or_ifg\nfrom pyrate.core.shared import InputTypes\nfrom pyrate.core.prepifg_helper import CUSTOM_CROP, MAXIMUM_CROP, MINIMUM_CROP, ALREADY_SAME_SIZE\nfrom pyrate.core import roipac\nfrom pyrate.core.prepifg_helper import prepare_ifg, _resample, PreprocessError, CustomExts\nfrom pyrate.core.prepifg_helper import get_analysis_extent\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.configuration import Configuration, MultiplePaths\nfrom pyrate import conv2tif, prepifg\n\nfrom tests import common\nfrom tests.common import SML_TEST_LEGACY_PREPIFG_DIR, PY37GDAL302, BASE_TEST, WORKING_DIR\nfrom tests.common import PREP_TEST_TIF, PREP_TEST_OBS, MEXICO_CROPA_CONF, assert_two_dirs_equal\nfrom tests.common import SML_TEST_DEM_TIF, SML_TEST_DEM_HDR, manipulate_test_conf, UnitTestAdaptation\n\ngdal.UseExceptions()\nDUMMY_SECTION_NAME = 'pyrate'\n\nif not exists(PREP_TEST_TIF):\n    sys.exit(\"ERROR: Missing 'prepifg' dir for unittests\\n\")\n\n\ndef test_prepifg_treats_inputs_and_outputs_read_only(gamma_conf, tempdir, coh_mask):\n    tdir = Path(tempdir())\n    params = common.manipulate_test_conf(gamma_conf, tdir)\n    params[C.COH_MASK] = coh_mask\n    output_conf = tdir.joinpath('conf.cfg')\n    pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n\n    params = Configuration(output_conf.as_posix()).__dict__\n    conv2tif.main(params)\n\n    tifs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_unw.tif'))\n    assert len(tifs) == 17\n\n    params = Configuration(output_conf.as_posix()).__dict__\n    prepifg.main(params)\n    cropped_ifgs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_ifg.tif'))\n    cropped_cohs = list(Path(params[C.COHERENCE_DIR]).glob('*_coh.tif'))\n    cropped_dem = list(Path(params[C.GEOMETRY_DIR]).glob('*_dem.tif'))\n\n    if params[C.COH_FILE_LIST] is not None:  # 17 + 1 dem + 17 coh files\n        assert len(cropped_ifgs) + len(cropped_cohs) + len(cropped_dem) == 35\n    else:  # 17 + 1 dem\n        assert len(cropped_ifgs) + len(cropped_cohs) + len(cropped_dem) == 18\n    # check all tifs from conv2tif are still readonly\n    for t in tifs:\n        assert t.stat().st_mode == 33060\n\n    # check all prepifg outputs are readonly\n    for c in cropped_cohs + cropped_ifgs + cropped_dem:\n        assert c.stat().st_mode == 33060\n\n\ndef test_prepifg_file_types(tempdir, gamma_conf, coh_mask):\n    tdir = Path(tempdir())\n    params = manipulate_test_conf(gamma_conf, tdir)\n    params[C.COH_MASK] = coh_mask\n    params[C.PARALLEL] = 0\n    output_conf_file = 'conf.conf'\n    output_conf = tdir.joinpath(output_conf_file)\n    pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n    params_s = Configuration(output_conf).__dict__\n    conv2tif.main(params_s)\n    # reread params from config\n    params_s = Configuration(output_conf).__dict__\n    prepifg.main(params_s)\n    ifg_files = list(Path(tdir.joinpath(params_s[C.INTERFEROGRAM_DIR])).glob('*_unw.tif'))\n    assert len(ifg_files) == 17\n    mlooked_files = list(Path(tdir.joinpath(params_s[C.INTERFEROGRAM_DIR])).glob('*_ifg.tif'))\n    assert len(mlooked_files) == 17\n    coh_files = list(Path(tdir.joinpath(params_s[C.COHERENCE_DIR])).glob('*_cc.tif'))\n    mlooked_coh_files = list(Path(tdir.joinpath(params_s[C.COHERENCE_DIR])).glob('*_coh.tif'))\n    if coh_mask:\n        assert len(coh_files) == 17\n        assert len(mlooked_coh_files) == 17\n    dem_file = list(Path(tdir.joinpath(params_s[C.GEOMETRY_DIR])).glob('*_dem.tif'))[0]\n    mlooked_dem_file = list(Path(tdir.joinpath(params_s[C.GEOMETRY_DIR])).glob('dem.tif'))[0]\n    import itertools\n\n    # assert coherence and ifgs have correct metadata\n    for i in itertools.chain(*[ifg_files, mlooked_files, coh_files, mlooked_coh_files]):\n        ifg = Ifg(i)\n        ifg.open()\n        md = ifg.meta_data\n        if i.name.endswith('_unw.tif'):\n            assert md[ifc.DATA_TYPE] == ifc.ORIG\n            assert ifc.IFG_LKSX not in md\n            assert ifc.IFG_LKSY not in md\n            assert ifc.IFG_CROP not in md\n            continue\n        if i.name.endswith('_cc.tif'):\n            assert md[ifc.DATA_TYPE] == ifc.COH\n            assert ifc.IFG_LKSX not in md\n            assert ifc.IFG_LKSY not in md\n            assert ifc.IFG_CROP not in md\n            continue\n        if i.name.endswith('_coh.tif'):\n            assert md[ifc.DATA_TYPE] == ifc.MULTILOOKED_COH\n            assert md[ifc.IFG_LKSX] == '1'\n            assert md[ifc.IFG_LKSY] == '1'\n            assert md[ifc.IFG_CROP] == '1'\n            continue\n        if i.name.endswith('_ifg.tif'):\n            if coh_mask:\n                assert md[ifc.DATA_TYPE] == ifc.MLOOKED_COH_MASKED_IFG\n                assert md[ifc.IFG_LKSX] == '1'\n                assert md[ifc.IFG_LKSY] == '1'\n                assert md[ifc.IFG_CROP] == '1'\n            else:\n                assert md[ifc.DATA_TYPE] == ifc.MULTILOOKED\n                assert md[ifc.IFG_LKSX] == '1'\n                assert md[ifc.IFG_LKSY] == '1'\n                assert md[ifc.IFG_CROP] == '1'\n            continue\n\n    # assert dem has correct metadata\n    dem = DEM(dem_file.as_posix())\n    dem.open()\n    md = dem.dataset.GetMetadata()\n    assert md[ifc.DATA_TYPE] == ifc.DEM\n    assert ifc.IFG_LKSX not in md\n    assert ifc.IFG_LKSY not in md\n    assert ifc.IFG_CROP not in md\n\n    dem = DEM(mlooked_dem_file.as_posix())\n    dem.open()\n    md = dem.dataset.GetMetadata()\n    assert md[ifc.DATA_TYPE] == ifc.MLOOKED_DEM\n    assert ifc.IFG_LKSX in md\n    assert ifc.IFG_LKSY in md\n    assert ifc.IFG_CROP in md\n    shutil.rmtree(tdir)\n\n\n# convenience ifg creation funcs\ndef diff_exts_ifgs():\n    \"\"\"Returns pair of test Ifgs with different extents\"\"\"\n    bases = ['geo_060619-061002_unw.tif', 'geo_070326-070917_unw.tif']\n    headers = ['geo_060619-061002.unw.rsc', 'geo_070326-070917.unw.rsc']\n    random_dir = tempfile.mkdtemp()\n    for p, h in zip(bases, headers):\n        shutil.copy(src=os.path.join(PREP_TEST_TIF, p),\n                    dst=os.path.join(random_dir, p))\n        shutil.copy(src=os.path.join(PREP_TEST_OBS, h),\n                    dst=os.path.join(random_dir, h))\n    return [Ifg(join(random_dir, p)) for p in bases], random_dir\n\n\ndef same_exts_ifgs():\n    \"\"\"Return pair of Ifgs with same extents\"\"\"\n    return [Ifg(join(PREP_TEST_TIF, f)) for f in ('geo_060619-061002.tif', 'geo_070326-070917.tif')]\n\n\ndef extents_from_params(params):\n    \"\"\"Custom extents from supplied parameters\"\"\"\n    keys = (\n    C.IFG_XFIRST, C.IFG_YFIRST, C.IFG_XLAST, C.IFG_YLAST)\n    return CustomExts(*[params[k] for k in keys])\n\n\ndef test_extents_from_params():\n    xf, yf = 1.0, 2.0\n    xl, yl = 5.0, 7.0\n    pars = {C.IFG_XFIRST: xf, C.IFG_XLAST: xl,\n            C.IFG_YFIRST: yf, C.IFG_YLAST: yl}\n\n    assert extents_from_params(pars) == CustomExts(xf, yf, xl, yl)\n\n\nclass TestPrepifgOutput(UnitTestAdaptation):\n    \"\"\"Tests aspects of the prepifg.py script, such as resampling.\"\"\"\n\n    @staticmethod\n    def assert_geotransform_equal(files):\n        \"\"\"\n        Asserts geotransforms for the given files are equivalent. Files can be paths\n        to datasets, or GDAL dataset objects.\n        \"\"\"\n        assert len(files) > 1, \"Need more than 1 file to compare\"\n        if not all([hasattr(f, \"GetGeoTransform\") for f in files]):\n            datasets = [gdal.Open(f) for f in files]\n            assert all(datasets)\n        else:\n            datasets = files\n\n        transforms = [ds.GetGeoTransform() for ds in datasets]\n        head = transforms[0]\n        for t in transforms[1:]:\n            assert_array_almost_equal(t, head, decimal=6, err_msg=\"Extents do not match!\")\n\n    @classmethod\n    def setup_class(cls):\n        cls.xs = 0.000833333\n        cls.ys = -cls.xs\n        cls.ifgs, cls.random_dir = diff_exts_ifgs()\n        cls.ifg_paths = [i.data_path for i in cls.ifgs]\n\n        cls.params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        cls.params[C.OUT_DIR] = cls.random_dir\n        cls.params[C.GEOMETRY_DIR] = Path(cls.random_dir).joinpath(C.GEOMETRY_DIR)\n        cls.params[C.GEOMETRY_DIR].mkdir(exist_ok=True)\n        cls.params[C.INTERFEROGRAM_DIR] = Path(cls.random_dir).joinpath(C.INTERFEROGRAM_DIR)\n        cls.params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True)\n\n        cls.headers = [roipac.roipac_header(i.data_path, cls.params) for i in cls.ifgs]\n        paths = [\"060619-061002_ifg.tif\",\n                 \"060619-061002_ifg.tif\",\n                 \"060619-061002_ifg.tif\",\n                 \"060619-061002_ifg.tif\",\n                 \"070326-070917_ifg.tif\",\n                 \"070326-070917_ifg.tif\",\n                 \"070326-070917_ifg.tif\",\n                 \"070326-070917_ifg.tif\"]\n        cls.exp_files = [join(cls.random_dir, C.INTERFEROGRAM_DIR,  p) for p in paths]\n\n    @staticmethod\n    def test_mlooked_paths():\n        test_mlooked_path()\n\n    @staticmethod\n    def test_extents_from_params():\n        test_extents_from_params()\n\n    @classmethod\n    def teardown_class(cls):\n        for exp_file in cls.exp_files:\n            if exists(exp_file):\n                os.remove(exp_file)\n        for ifg in cls.ifgs:\n            ifg.close()\n        shutil.rmtree(cls.random_dir)\n\n    def _custom_ext_latlons(self):\n        return [150.91 + (7 * self.xs),  # xfirst\n                -34.17 + (16 * self.ys),  # yfirst\n                150.91 + (27 * self.xs),  # 20 cells from xfirst\n                -34.17 + (44 * self.ys)]  # 28 cells from yfirst\n\n    def _custom_extents_tuple(self):\n        return CustomExts(*self._custom_ext_latlons())\n\n    def assert_projection_equal(self, files):\n        \"\"\"\n        Asserts preojections for the given files are equivalent.\n        Files can be paths to datasets, or GDAL dataset objects.\n        \"\"\"\n        assert len(files) > 1, \"Need more than 1 file to compare\"\n        if not all([hasattr(f, \"GetGeoTransform\") for f in files]):\n            datasets = [gdal.Open(f) for f in files]\n            assert all(datasets)\n        else:\n            datasets = files\n\n        projections = [ds.GetProjection() for ds in datasets]\n        head = projections[0]\n        for t in projections[1:]:\n            self.assertEqual(t, head)\n\n    def test_multilooked_projection_same_as_geotiff(self):\n        xlooks = ylooks = 1\n        exts = get_analysis_extent(crop_opt=MAXIMUM_CROP, rasters=self.ifgs, xlooks=xlooks, ylooks=ylooks,\n                                   user_exts=None)\n        out_dir = tempfile.mkdtemp()\n        params = common.min_params(out_dir)\n        params[C.IFG_LKSX] = xlooks\n        params[C.IFG_LKSY] = ylooks\n        params[C.IFG_CROP_OPT] = MAXIMUM_CROP\n        params[C.GEOMETRY_DIR] = Path(out_dir).joinpath(C.GEOMETRY_DIR)\n        params[C.GEOMETRY_DIR].mkdir(exist_ok=True)\n        params[C.INTERFEROGRAM_DIR] = Path(out_dir).joinpath(C.INTERFEROGRAM_DIR)\n        params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True)\n\n        mlooked_paths = [mlooked_path(f, params, input_type=InputTypes.IFG)\n                         for f in self.ifg_paths]\n        for i, h, m in zip(self.ifg_paths, self.headers, mlooked_paths):\n            prepare_ifg(i, xlooks, ylooks, exts, thresh=0.5, crop_opt=MAXIMUM_CROP, header=h, write_to_disk=True,\n                        out_path=m)\n        self.assert_projection_equal(self.ifg_paths + mlooked_paths)\n\n    def test_default_max_extents(self):\n        \"\"\"Test ifgcropopt=2 crops datasets to max bounding box extents.\"\"\"\n        xlooks = ylooks = 1\n        prepare_ifgs(self.ifg_paths, MAXIMUM_CROP, xlooks, ylooks, self.headers, params=self.params)\n        for f in [self.exp_files[1], self.exp_files[5]]:\n            self.assertTrue(exists(f), msg=\"Output files not created\")\n\n        # output files should have same extents\n        # NB: also verifies gdalwarp correctly copies geotransform across\n        ifg = Ifg(self.exp_files[1])\n        ifg.open()\n        gt = ifg.dataset.GetGeoTransform()\n\n        # copied from gdalinfo output\n        exp_gt = (150.91, 0.000833333, 0, -34.17, 0, -0.000833333)\n        for i, j in zip(gt, exp_gt):\n            self.assertAlmostEqual(i, j)\n\n        self.assert_geotransform_equal([self.exp_files[1], self.exp_files[5]])\n\n        ifg.close()\n        for i in self.ifgs:\n            i.close()\n\n    def test_min_extents(self):\n        \"\"\"Test ifgcropopt=1 crops datasets to min extents.\"\"\"\n        xlooks = ylooks = 1\n        prepare_ifgs(self.ifg_paths, MINIMUM_CROP, xlooks, ylooks, headers=self.headers, params=self.params)\n        ifg = Ifg(self.exp_files[0])\n        ifg.open()\n\n        # output files should have same extents\n        # NB: also verifies gdalwarp correctly copies geotransform across\n        # NB: expected data copied from gdalinfo output\n        gt = ifg.dataset.GetGeoTransform()\n        exp_gt = (150.911666666, 0.000833333, 0, -34.172499999, 0, -0.000833333)\n        for i, j in zip(gt, exp_gt):\n            self.assertAlmostEqual(i, j)\n        self.assert_geotransform_equal([self.exp_files[0], self.exp_files[4]])\n\n        ifg.close()\n        for i in self.ifgs:\n            i.close()\n\n    def test_custom_extents(self):\n        xlooks = ylooks = 1\n        cext = self._custom_extents_tuple()\n        prepare_ifgs(self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, headers=self.headers,\n                     user_exts=cext, params=self.params)\n\n        ifg = Ifg(self.exp_files[2])\n        ifg.open()\n\n        gt = ifg.dataset.GetGeoTransform()\n        exp_gt = (cext.xfirst, self.xs, 0, cext.yfirst, 0, self.ys)\n\n        for i, j in zip(gt, exp_gt):\n            self.assertAlmostEqual(i, j)\n        self.assert_geotransform_equal([self.exp_files[2], self.exp_files[6]])\n\n        # close ifgs\n        ifg.close()\n        for i in self.ifgs:\n            i.close()\n\n    def test_exception_without_all_4_crop_parameters(self):\n        \"\"\"Test misaligned cropping extents raise errors.\"\"\"\n        xlooks = ylooks = 1\n        # empty string and none raises exceptio\n        for i in [None, '']:\n            cext = (150.92, -34.18, 150.94, i)\n            self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths,\n                              CUSTOM_CROP, xlooks, ylooks, self.headers, user_exts=cext, params=self.params)\n        # three parameters provided\n        self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths,\n                          CUSTOM_CROP, xlooks, ylooks, self.headers, params=self.params,\n                          user_exts=(150.92, -34.18, 150.94))\n        # close ifgs\n        for i in self.ifgs:\n            i.close()\n\n    def test_custom_extents_misalignment(self):\n        \"\"\"Test misaligned cropping extents raise errors.\"\"\"\n        xlooks = ylooks = 1\n        latlons = tuple(self._custom_ext_latlons())\n        for i, _ in enumerate(['xfirst', 'yfirst', 'xlast', 'ylast']):\n            # error = step / pi * [1000 100]\n            for error in [0.265258, 0.026526]:\n                tmp_latlon = list(latlons)\n                tmp_latlon[i] += error\n                cext = CustomExts(*tmp_latlon)\n\n                self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths, CUSTOM_CROP, xlooks, ylooks,\n                                  user_exts=cext, headers=self.headers, params=self.params)\n        # close ifgs\n        for i in self.ifgs:\n            i.close()\n\n    def test_nodata(self):\n        \"\"\"Verify NODATA value copied correctly (amplitude band not copied)\"\"\"\n        xlooks = ylooks = 1\n        prepare_ifgs(self.ifg_paths, MINIMUM_CROP, xlooks, ylooks, self.headers, self.params)\n\n        for ex in [self.exp_files[0], self.exp_files[4]]:\n            ifg = Ifg(ex)\n            ifg.open()\n            # NB: amplitude band doesn't have a NODATA value\n            self.assertTrue(isnan(ifg.dataset.GetRasterBand(1).GetNoDataValue()))\n            ifg.close()\n        for i in self.ifgs:\n            i.close()\n\n    def test_nans(self):\n        \"\"\"Verify NaNs replace 0 in the multilooked phase band\"\"\"\n        xlooks = ylooks = 1\n        prepare_ifgs(self.ifg_paths, MINIMUM_CROP, xlooks, ylooks, self.headers, self.params)\n\n        for ex in [self.exp_files[0], self.exp_files[4]]:\n            ifg = Ifg(ex)\n            ifg.open()\n\n            phase = ifg.phase_band.ReadAsArray()\n            self.assertFalse((phase == 0).any())\n            self.assertTrue((isnan(phase)).any())\n            ifg.close()\n\n        self.assertAlmostEqual(nanmax(phase), 4.247, 3)  # copied from gdalinfo\n        self.assertAlmostEqual(nanmin(phase), 0.009, 3)  # copied from gdalinfo\n        for i in self.ifgs:\n            i.close()\n\n    def test_multilook(self):\n        \"\"\"Test resampling method using a scaling factor of 4\"\"\"\n        scale = 4  # assumes square cells\n        self.ifgs.append(DEM(SML_TEST_DEM_TIF))\n        self.ifg_paths = [i.data_path for i in self.ifgs]\n        # append the dem header\n        self.headers.append(SML_TEST_DEM_HDR)\n        cext = self._custom_extents_tuple()\n        xlooks = ylooks = scale\n        prepare_ifgs(self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, thresh=1.0, user_exts=cext,\n                     headers=self.headers, params=self.params)\n\n        for n, ipath in enumerate([self.exp_files[3], self.exp_files[7]]):\n            i = Ifg(ipath)\n            i.open()\n            self.assertEqual(i.dataset.RasterXSize, 20 / scale)\n            self.assertEqual(i.dataset.RasterYSize, 28 / scale)\n\n            # verify resampling\n            path = join(PREP_TEST_TIF, \"geo_%s.tif\" % Path(ipath).name.split('_ifg')[0])\n            ds = gdal.Open(path)\n            src_data = ds.GetRasterBand(2).ReadAsArray()\n            exp_resample = multilooking(src_data, scale, scale, thresh=0)\n            self.assertEqual(exp_resample.shape, (7, 5))\n            assert_array_almost_equal(exp_resample, i.phase_band.ReadAsArray())\n            ds = None\n            i.close()\n            os.remove(ipath)\n\n        # verify DEM has been correctly processed\n        # ignore output values as resampling has already been tested for phase\n        exp_dem_path = join(self.params[C.GEOMETRY_DIR], 'dem.tif')\n        self.assertTrue(exists(exp_dem_path))\n        orignal_dem = DEM(SML_TEST_DEM_TIF)\n        orignal_dem.open()\n        dem_dtype = orignal_dem.dataset.GetRasterBand(1).DataType\n        orignal_dem.close()\n        dem = DEM(exp_dem_path)\n        dem.open()\n\n        # test multilooked dem is of the same datatype as the original dem tif\n        self.assertEqual(dem_dtype, dem.dataset.GetRasterBand(1).DataType)\n\n        self.assertEqual(dem.dataset.RasterXSize, 20 / scale)\n        self.assertEqual(dem.dataset.RasterYSize, 28 / scale)\n        data = dem.data\n        self.assertTrue(data.ptp() != 0)\n        # close ifgs\n        dem.close()\n        for i in self.ifgs:\n            i.close()\n        os.remove(exp_dem_path)\n\n    def test_output_datatype(self):\n        \"\"\"Test resampling method using a scaling factor of 4\"\"\"\n        scale = 4  # assumes square cells\n        self.ifgs.append(DEM(SML_TEST_DEM_TIF))\n        ifg_paths = [i.data_path for i in self.ifgs]\n        data_types = [InputTypes.IFG] * len(self.ifg_paths)\n        ifg_paths.append(SML_TEST_DEM_TIF)\n        data_types.append(InputTypes.DEM)\n        self.headers.append(SML_TEST_DEM_HDR)\n\n        cext = self._custom_extents_tuple()\n        xlooks = ylooks = scale\n        prepare_ifgs(ifg_paths, CUSTOM_CROP, xlooks, ylooks, thresh=1.0, user_exts=cext, headers=self.headers,\n                     params=self.params)\n        self.params[C.IFG_LKSX] = xlooks\n        self.params[C.IFG_LKSY] = ylooks\n        self.params[C.IFG_CROP_OPT] = CUSTOM_CROP\n        for i, t in zip(ifg_paths, data_types):\n            mlooked_ifg = mlooked_path(i, self.params, input_type=t)\n            ds1 = DEM(mlooked_ifg)\n            ds1.open()\n            ds2 = DEM(i)\n            ds2.open()\n            self.assertEqual(ds1.dataset.GetRasterBand(1).DataType,\n                             ds2.dataset.GetRasterBand(1).DataType)\n            ds1 = ds2 = None\n\n    def test_invalid_looks(self):\n        \"\"\"Verify only numeric values can be given for multilooking\"\"\"\n        values = [0, -1, -10, -100000.6, \"\"]\n        for v in values:\n            self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths,\n                              CUSTOM_CROP, xlooks=v, ylooks=1, headers=self.headers, params=self.params)\n\n            self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths,\n                              CUSTOM_CROP, xlooks=1, ylooks=v, headers=self.headers, params=self.params)\n\n\nclass TestThresholdTests(UnitTestAdaptation):\n    \"\"\"Tests for threshold of data -> NaN during resampling.\"\"\"\n\n    def test_nan_threshold_inputs(self):\n        data = ones((1, 1))\n        for thresh in [-10, -1, -0.5, 1.000001, 10]:\n            self.assertRaises(ValueError, _resample, data, 2, 2, thresh)\n\n    @staticmethod\n    def test_nan_threshold():\n        # test threshold based on number of NaNs per averaging tile\n        data = ones((2, 10))\n        data[0, 3:] = nan\n        data[1, 7:] = nan\n\n        # key: NaN threshold as a % of pixels, expected result\n        expected = [(0.0, [1, nan, nan, nan, nan]),\n                    (0.25, [1, nan, nan, nan, nan]),\n                    (0.5, [1, 1, nan, nan, nan]),\n                    (0.75, [1, 1, 1, nan, nan]),\n                    (1.0, [1, 1, 1, 1, nan])]\n\n        for thresh, exp in expected:\n            res = _resample(data, xscale=2, yscale=2, thresh=thresh)\n            assert_array_equal(res, reshape(exp, res.shape))\n\n    @staticmethod\n    def test_nan_threshold_alt():\n        # test threshold on odd numbers\n        data = ones((3, 6))\n        data[0] = nan\n        data[1, 2:5] = nan\n\n        expected = [(0.4, [nan, nan]), (0.5, [1, nan]), (0.7, [1, 1])]\n        for thresh, exp in expected:\n            res = _resample(data, xscale=3, yscale=3, thresh=thresh)\n            assert_array_equal(res, reshape(exp, res.shape))\n\n\nclass TestSameSizeTests(UnitTestAdaptation):\n    \"\"\"Tests aspects of the prepifg.py script, such as resampling.\"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        import datetime\n        cls.xs = 0.000833333\n        cls.ys = -cls.xs\n        cls.headers = [\n            {'NCOLS': 47, 'NROWS': 72, 'LAT': -34.17, 'LONG': 150.91, 'X_STEP': 0.000833333, 'Y_STEP': -0.000833333,\n             'WAVELENGTH_METRES': 0.0562356424, 'FIRST_DATE': datetime.date(2007, 3, 26),\n             'SECOND_DATE': datetime.date(2007, 9, 17), 'TIME_SPAN_YEAR': 0.4791238877481177,\n             'DATA_UNITS': 'RADIANS', 'INSAR_PROCESSOR': 'ROIPAC', 'X_LAST': 150.94916665099998,\n             'Y_LAST': -34.229999976, 'DATUM': 'WGS84', 'DATA_TYPE': 'ORIGINAL_IFG'},\n            {'NCOLS': 47, 'NROWS': 72, 'LAT': -34.17, 'LONG': 150.91, 'X_STEP': 0.000833333, 'Y_STEP': -0.000833333,\n             'WAVELENGTH_METRES': 0.0562356424, 'FIRST_DATE': datetime.date(2007, 3, 26),\n             'SECOND_DATE': datetime.date(2007, 9, 17), 'TIME_SPAN_YEAR': 0.4791238877481177,\n             'DATA_UNITS': 'RADIANS', 'INSAR_PROCESSOR': 'ROIPAC', 'X_LAST': 150.94916665099998,\n             'Y_LAST': -34.229999976, 'DATUM': 'WGS84', 'DATA_TYPE': 'ORIGINAL_IFG'}\n        ]\n        out_dir = tempfile.mkdtemp()\n        cls.params = common.min_params(out_dir)\n        cls.params[C.GEOMETRY_DIR] = Path(cls.params[C.OUT_DIR]).joinpath(C.GEOMETRY_DIR)\n        cls.params[C.GEOMETRY_DIR].mkdir(exist_ok=True)\n        cls.params[C.INTERFEROGRAM_DIR] = Path(cls.params[C.OUT_DIR]).joinpath(C.INTERFEROGRAM_DIR)\n        cls.params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True)\n\n\n    # TODO: check output files for same extents?\n    # TODO: make prepifg dir readonly to test output to temp dir\n    # TODO: move to class for testing same size option?\n    def test_already_same_size(self):\n        # should do nothing as layers are same size & no multilooking required\n        ifgs = same_exts_ifgs()\n        ifg_data_paths = [d.data_path for d in ifgs]\n        res_tup = prepare_ifgs(ifg_data_paths, ALREADY_SAME_SIZE, 1, 1, self.headers, self.params)\n        res = [r[1] for r in res_tup]\n        self.assertTrue(all(res))\n\n    def test_already_same_size_mismatch(self):\n        ifgs, random_dir = diff_exts_ifgs()\n        ifg_data_paths = [d.data_path for d in ifgs]\n        self.assertRaises(PreprocessError, prepare_ifgs, ifg_data_paths, ALREADY_SAME_SIZE, 1, 1, self.headers,\n                          self.params)\n        for i in ifgs:\n            i.close()\n        shutil.rmtree(random_dir)\n\n    # TODO: ensure multilooked files written to output dir\n    def test_same_size_multilooking(self):\n        ifgs = same_exts_ifgs()\n        ifg_data_paths = [d.data_path for d in ifgs]\n        xlooks = ylooks = 2\n        out_dir = tempfile.mkdtemp()\n        params = common.min_params(out_dir)\n        params[C.IFG_LKSX] = xlooks\n        params[C.IFG_LKSY] = ylooks\n        params[C.IFG_CROP_OPT] = ALREADY_SAME_SIZE\n        params[C.GEOMETRY_DIR] = Path(params[C.OUT_DIR]).joinpath(C.GEOMETRY_DIR)\n        params[C.GEOMETRY_DIR].mkdir(exist_ok=True)\n        params[C.INTERFEROGRAM_DIR] = Path(params[C.OUT_DIR]).joinpath(C.INTERFEROGRAM_DIR)\n        params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True)\n\n        prepare_ifgs(ifg_data_paths, ALREADY_SAME_SIZE, xlooks, ylooks, self.headers, params)\n        looks_paths = [mlooked_path(d, params, input_type=InputTypes.IFG) for d in ifg_data_paths]\n        mlooked = [Ifg(i) for i in looks_paths]\n        for m in mlooked:\n            m.open()\n        self.assertEqual(len(mlooked), 2)\n\n        for ifg in mlooked:\n            self.assertAlmostEqual(ifg.x_step, xlooks * self.xs)\n            self.assertAlmostEqual(ifg.x_step, ylooks * self.xs)\n            os.remove(ifg.data_path)\n\n\ndef mlooked_path(path, params, input_type):\n    m = MultiplePaths(path, params=params, input_type=input_type)\n    return m.sampled_path\n\n\ndef test_mlooked_path():\n    path = 'geo_060619-061002_unw.tif'\n    for xlks, ylks, cr, input_type in product([2, 4, 8], [4, 2, 5], [1, 2, 3, 4], [InputTypes.IFG, InputTypes.COH]):\n        out_dir = tempfile.mkdtemp()\n        params = common.min_params(out_dir)\n        params[C.IFG_LKSX] = xlks\n        params[C.IFG_LKSY] = ylks\n        params[C.IFG_CROP_OPT] = cr\n        m = mlooked_path(path, params, input_type=input_type)\n        assert Path(m).name == f'060619-061002_{input_type.value}.tif'\n\n\n# class LineOfSightTests(unittest.TestCase):\n# def test_los_conversion(self):\n# TODO: needs LOS matrix\n# TODO: this needs to work from config and incidence files on disk\n# TODO: is convflag (see 'ifgconv' setting) used or just defaulted?\n# TODO: los conversion has 4 options: 1: ignore, 2: vertical, 3: N/S, 4: E/W\n# also have a 5th option of arbitrary azimuth angle (PyRate doesn't have this)\n#    params = _default_extents_param()\n#    params[IFG_CROP_OPT] = MINIMUM_CROP\n#    params[PROJECTION_FLAG] = None\n#    prepare_ifgs(params)\n\n\n# def test_phase_conversion(self):\n# TODO: check output data is converted to mm from radians (in prepifg??)\n# raise NotImplementedError\n\n\nclass TestLocalMultilookTests:\n    \"\"\"Tests for local testing functions\"\"\"\n\n    @staticmethod\n    def test_multilooking_thresh():\n        data = ones((3, 6))\n        data[0] = nan\n        data[1, 2:5] = nan\n        expected = [(6, [nan, nan]),\n                    (5, [1, nan]),\n                    (4, [1, 1])]\n        scale = 3\n        for thresh, exp in expected:\n            res = multilooking(data, scale, scale, thresh)\n            assert_array_equal(res, reshape(exp, res.shape))\n\n\ndef multilooking(src, xscale, yscale, thresh=0):\n    \"\"\"\n    src: numpy array of phase data\n    thresh: min number of non-NaNs required for a valid tile resampling\n    \"\"\"\n    thresh = int(thresh)\n    num_cells = xscale * yscale\n    if thresh > num_cells or thresh < 0:\n        msg = \"Invalid threshold: %s (need 0 <= thr <= %s\" % (thresh, num_cells)\n        raise ValueError(msg)\n\n    rows, cols = src.shape\n    rows_lowres = int(floor(rows / yscale))\n    cols_lowres = int(floor(cols / xscale))\n    dest = ones((rows_lowres, cols_lowres)) * nan\n\n    size = xscale * yscale\n    for row in range(rows_lowres):\n        for col in range(cols_lowres):\n            ys = row * yscale\n            ye = ys + yscale\n            xs = col * xscale\n            xe = xs + xscale\n\n            patch = src[ys:ye, xs:xe]\n            num_values = num_cells - npsum(isnan(patch))\n\n            if num_values >= thresh and num_values > 0:\n                # nanmean() only works on 1g axis\n                reshaped = patch.reshape(size)\n                dest[row, col] = nanmean(reshaped)\n\n    return dest\n\n\nclass TestLegacyEqualityTestRoipacSmallTestData(UnitTestAdaptation):\n    \"\"\"\n    Legacy roipac prepifg equality test for small test data\n    \"\"\"\n\n    def setup_class(cls):\n        from tests.common import small_data_setup\n        cls.ifgs = small_data_setup()\n        cls.ifg_paths = [i.data_path for i in cls.ifgs]\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        cls.headers = [roipac.roipac_header(i.data_path, params) for i in cls.ifgs]\n        params[C.IFG_LKSX], params[C.IFG_LKSY], params[\n            C.IFG_CROP_OPT] = 1, 1, 1\n        prepare_ifgs(cls.ifg_paths, crop_opt=1, xlooks=1, ylooks=1, headers=cls.headers, params=params)\n        looks_paths = [mlooked_path(d, params, t) for d, t in zip(cls.ifg_paths, [InputTypes.IFG]*len(cls.ifgs))]\n        cls.ifgs_with_nan = [Ifg(i) for i in looks_paths]\n        for ifg in cls.ifgs_with_nan:\n            ifg.open()\n\n    @classmethod\n    def teardown_class(cls):\n        for i in cls.ifgs_with_nan:\n            if os.path.exists(i.data_path):\n                i.close()\n                os.remove(i.data_path)\n\n    def test_legacy_prepifg_equality_array(self):\n        \"\"\"\n        Legacy prepifg equality test\n        \"\"\"\n        # path to csv folders from legacy output\n        onlyfiles = [\n            fln for fln in os.listdir(SML_TEST_LEGACY_PREPIFG_DIR)\n            if os.path.isfile(os.path.join(SML_TEST_LEGACY_PREPIFG_DIR, fln))\n            and fln.endswith('.csv') and fln.__contains__('_rad_')\n            ]\n\n        for fln in onlyfiles:\n            ifg_data = np.genfromtxt(os.path.join(\n                SML_TEST_LEGACY_PREPIFG_DIR, fln), delimiter=',')\n            for k, j in enumerate(self.ifgs):\n                if fln.split('_rad_')[-1].split('.')[0] == \\\n                        os.path.split(j.data_path)[-1].split('.')[0]:\n                    np.testing.assert_array_almost_equal(ifg_data,\n                                                         self.ifgs_with_nan[\n                                                             k].phase_data,\n                                                         decimal=2)\n\n    def test_legacy_prepifg_and_convert_phase(self):\n        \"\"\"\n        Legacy data prepifg equality test\n        \"\"\"\n        # path to csv folders from legacy output\n        for i in self.ifgs_with_nan:\n            if not i.mm_converted:\n                i.convert_to_mm()\n        onlyfiles = [\n            f for f in os.listdir(SML_TEST_LEGACY_PREPIFG_DIR)\n            if os.path.isfile(os.path.join(SML_TEST_LEGACY_PREPIFG_DIR, f))\n            and f.endswith('.csv') and f.__contains__('_mm_')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            ifg_data = np.genfromtxt(os.path.join(\n                SML_TEST_LEGACY_PREPIFG_DIR, f), delimiter=',')\n            for k, j in enumerate(self.ifgs):\n                if f.split('_mm_')[-1].split('.')[0] == \\\n                        os.path.split(j.data_path)[-1].split('_unw.')[0]:\n                    count += 1\n                    # all numbers equal\n                    np.testing.assert_array_almost_equal(\n                        ifg_data, self.ifgs_with_nan[k].phase_data, decimal=2)\n\n                    # means must also be equal\n                    self.assertAlmostEqual(\n                        nanmean(ifg_data),\n                        nanmean(self.ifgs_with_nan[k].phase_data),\n                        places=4)\n\n                    # number of nans must equal\n                    self.assertEqual(\n                        np.sum(np.isnan(ifg_data)),\n                        np.sum(np.isnan(self.ifgs_with_nan[k].phase_data)))\n\n        # ensure we have the correct number of matches\n        self.assertEqual(count, len(self.ifgs))\n\n\nclass TestOneIncidenceOrElevationMap(UnitTestAdaptation):\n\n    @classmethod\n    def setup_class(cls):\n        cls.base_dir = tempfile.mkdtemp()\n        cls.conf_file = tempfile.mktemp(suffix='.conf', dir=cls.base_dir)\n        cls.ifgListFile = os.path.join(common.GAMMA_SML_TEST_DIR, 'ifms_17')\n        cls.baseListFile = os.path.join(common.GAMMA_SML_TEST_DIR, 'baseline_17')\n\n    @classmethod\n    def teardown_class(cls):\n        params = Configuration(cls.conf_file).__dict__\n        shutil.rmtree(cls.base_dir)\n        common.remove_tifs(params[WORKING_DIR])\n\n    def make_input_files(self, inc='', ele=''):\n        with open(self.conf_file, 'w') as conf:\n            conf.write('{}: {}\\n'.format(C.NO_DATA_VALUE, '0.0'))\n            conf.write('{}: {}\\n'.format(WORKING_DIR, common.GAMMA_SML_TEST_DIR))\n            conf.write('{}: {}\\n'.format(C.OUT_DIR, self.base_dir))\n            conf.write('{}: {}\\n'.format(C.IFG_FILE_LIST, self.ifgListFile))\n            conf.write('{}: {}\\n'.format(C.BASE_FILE_LIST, self.baseListFile))\n            conf.write('{}: {}\\n'.format(C.PROCESSOR, '1'))\n            conf.write('{}: {}\\n'.format(\n                C.DEM_HEADER_FILE, os.path.join(\n                    common.GAMMA_SML_TEST_DIR, '20060619_utm_dem.par')))\n            conf.write('{}: {}\\n'.format(C.IFG_LKSX, '1'))\n            conf.write('{}: {}\\n'.format(C.IFG_LKSY, '1'))\n            conf.write('{}: {}\\n'.format(C.IFG_CROP_OPT, '1'))\n            conf.write('{}: {}\\n'.format(C.NO_DATA_AVERAGING_THRESHOLD, '0.5'))\n            conf.write('{}: {}\\n'.format(C.HDR_FILE_LIST,\n                                         common.SML_TEST_GAMMA_HEADER_LIST))\n            conf.write('{}: {}\\n'.format(C.DEM_FILE, common.SML_TEST_DEM_GAMMA))\n            conf.write('{}: {}\\n'.format(C.APS_INCIDENCE_MAP, inc))\n            conf.write('{}: {}\\n'.format(C.APS_ELEVATION_MAP, ele))\n            conf.write('{}: {}\\n'.format(C.APS_CORRECTION, '1'))\n            conf.write('{}: {}\\n'.format(C.APS_METHOD, '2'))\n            conf.write('{}: {}\\n'.format(C.TIME_SERIES_SM_ORDER, 1))\n\n    def common_check(self, ele, inc):\n        import glob\n        from pyrate.configuration import Configuration\n        assert os.path.exists(self.conf_file)\n\n        params = Configuration(self.conf_file).__dict__\n\n        conv2tif.main(params)\n        sys.argv = ['dummy', self.conf_file]\n        prepifg.main(params)\n        # test 17 geotiffs created\n        geotifs = glob.glob(os.path.join(params[C.OUT_DIR], '*_unw.tif'))\n        self.assertEqual(17, len(geotifs))\n        # test dem geotiff created\n        demtif = glob.glob(os.path.join(params[C.OUT_DIR], '*_dem.tif'))\n        self.assertEqual(1, len(demtif))\n        # mlooked tifs\n        mlooked_tifs = glob.glob(os.path.join(self.base_dir, '*_ifg.tif'))\n        mlooked_tifs.append(os.path.join(self.base_dir, 'dem.tif'))\n        # 19 including 17 ifgs, 1 dem and one incidence\n        self.assertEqual(18, len(mlooked_tifs))\n        inc = glob.glob(os.path.join(self.base_dir, '*utm_{inc}.tif'.format(inc=inc)))\n        self.assertEqual(0, len(inc))\n\n\ndef prepare_ifgs(raster_data_paths, crop_opt, xlooks, ylooks, headers, params, thresh=0.5, user_exts=None,\n                 write_to_disc=True):\n    \"\"\"\n    Wrapper function to prepare a sequence of interferogram files for\n    PyRate analysis. See prepifg.prepare_ifg() for full description of\n    inputs and returns.\n\n    Note: function need refining for crop options\n\n    :param list raster_data_paths: List of interferogram file paths\n    :param int crop_opt: Crop option\n    :param int xlooks: Number of multi-looks in x; 5 is 5 times smaller, 1 is no change\n    :param int ylooks: Number of multi-looks in y\n    :param float thresh: see thresh in prepare_ifgs()\n    :param tuple user_exts: Tuple of user defined georeferenced extents for\n        new file: (xfirst, yfirst, xlast, ylast)cropping coordinates\n    :param bool write_to_disc: Write new data to disk\n\n    :return: resampled_data: output cropped and resampled image\n    :rtype: ndarray\n    :return: out_ds: destination gdal dataset object\n    :rtype: List[gdal.Dataset]\n    \"\"\"\n    if xlooks != ylooks:\n        log.warning('X and Y multi-look factors are not equal')\n\n    # use metadata check to check whether it's a dem or ifg\n    rasters = [dem_or_ifg(r) for r in raster_data_paths]\n    exts = get_analysis_extent(crop_opt, rasters, xlooks, ylooks, user_exts)\n    out_paths = []\n    for r, t in zip(raster_data_paths, rasters):\n        if isinstance(t, DEM):\n            input_type = InputTypes.DEM\n        else:\n            input_type = InputTypes.IFG\n        out_path = MultiplePaths(r, params, input_type).sampled_path\n        out_paths.append(out_path)\n    return [prepare_ifg(d, xlooks, ylooks, exts, thresh, crop_opt, h, write_to_disc, p) for d, h, p\n            in zip(raster_data_paths, headers, out_paths)]\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL302, reason=\"Only run in one CI env\")\ndef test_coh_stats_equality(mexico_cropa_params):\n    subprocess.run(f\"mpirun -n 3 pyrate prepifg -f {MEXICO_CROPA_CONF.as_posix()}\", shell=True)\n    params = mexico_cropa_params\n    mexico_cropa_coh_stats_dir = Path(BASE_TEST).joinpath(\"cropA\", 'coherence_stats')\n    assert_two_dirs_equal(params[C.COHERENCE_DIR], mexico_cropa_coh_stats_dir, ext=\"coh*.tif\", num_files=3)\n\n    # assert metadata was written\n    from pyrate.prepifg import out_type_md_dict\n    for stat_tif in mexico_cropa_coh_stats_dir.glob(\"coh*.tif\"):\n        ds = gdal.Open(stat_tif.as_posix())\n        md = ds.GetMetadata()\n        expected_md = out_type_md_dict[stat_tif.stem.upper()]\n        assert ifc.DATA_TYPE in md.keys()\n        assert expected_md in md.values()\n"
  },
  {
    "path": "tests/test_prepifg_system_vs_python.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests that compare system vs python prepifg methods.\n\"\"\"\nimport shutil\nimport pytest\nfrom pathlib import Path\nimport numpy as np\n\nimport pyrate.configuration\nimport pyrate.constants as C\nfrom pyrate import conv2tif, prepifg\nfrom pyrate.configuration import Configuration\nfrom tests.common import (\n    assert_two_dirs_equal,\n    manipulate_test_conf,\n    GITHUB_ACTIONS,\n    PY37GDAL302\n)\n\n\n@pytest.fixture(params=[1, 2, 3, 4])\ndef local_crop(request):\n    return request.param\n\n\n@pytest.fixture()\ndef modified_config_short(tempdir, local_crop, get_lks, coh_mask):\n    orbfit_lks = 1\n    orbfit_method = 1\n    orbfit_degrees = 1\n    ref_est_method = 1\n\n    def modify_params(conf_file, parallel, output_conf_file):\n\n        tdir = Path(tempdir())\n\n        params = manipulate_test_conf(conf_file, tdir)\n        params[C.COH_MASK] = coh_mask\n        params[C.PARALLEL] = parallel\n        params[C.PROCESSES] = 4\n        params[C.APSEST] = 1\n        params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks\n        params[C.REFNX], params[C.REFNY] = 4, 4\n\n        params[C.IFG_CROP_OPT] = local_crop\n        params[C.ORBITAL_FIT_LOOKS_X], params[\n            C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks\n        params[C.ORBITAL_FIT] = 1\n        params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        params[C.REF_EST_METHOD] = ref_est_method\n        params[\"rows\"], params[\"cols\"] = 3, 2\n        params[\"notiles\"] = params[\"rows\"] * params[\"cols\"]  # number of tiles\n\n        print(params)\n        # write new temp config\n        output_conf = tdir.joinpath(output_conf_file)\n        pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n\n        return output_conf, params\n\n    return modify_params\n\n\n@pytest.fixture\ndef create_mpi_files(modified_config_short):\n\n    def _create(conf):\n\n        mpi_conf, params = modified_config_short(conf, 0, 'mpi_conf.conf')\n        params = Configuration(mpi_conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(mpi_conf).__dict__\n        prepifg.main(params)\n\n        return params  # don't need the reamining params\n    return _create\n\n\n@pytest.fixture()\ndef modified_config_largetifs(tempdir, local_crop, get_lks, coh_mask):\n    orbfit_lks = 1\n    orbfit_method = 1\n    orbfit_degrees = 1\n    ref_est_method = 1\n\n    def modify_params(conf_file, parallel, output_conf_file):\n        tdir = Path(tempdir())\n        params = manipulate_test_conf(conf_file, tdir)\n        params[C.COH_MASK] = coh_mask\n        params[C.LARGE_TIFS] = 1\n        params[C.PARALLEL] = parallel\n        params[C.PROCESSES] = 4\n        params[C.APSEST] = 1\n        params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks\n        params[C.REFNX], params[C.REFNY] = 4, 4\n\n        params[C.IFG_CROP_OPT] = local_crop\n        params[C.ORBITAL_FIT_LOOKS_X], params[\n            C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks\n        params[C.ORBITAL_FIT] = 1\n        params[C.ORBITAL_FIT_METHOD] = orbfit_method\n        params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees\n        params[C.REF_EST_METHOD] = ref_est_method\n        params[\"rows\"], params[\"cols\"] = 3, 2\n        params[\"notiles\"] = params[\"rows\"] * params[\"cols\"]  # number of tiles\n\n        print(params)\n        # write new temp config\n        output_conf = tdir.joinpath(output_conf_file)\n        pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n\n        return output_conf, params\n\n    return modify_params\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL302, reason=\"Only run in one CI env\")\ndef test_prepifg_largetifs_vs_python(modified_config_largetifs, gamma_conf, create_mpi_files):\n\n    print(\"\\n\\n\")\n    print(\"===x===\"*10)\n    if GITHUB_ACTIONS and np.random.randint(0, 1000) > 899:  # skip 90% of tests randomly\n        pytest.skip(\"Randomly skipping as part of 50 percent\")\n\n    params = create_mpi_files(gamma_conf)\n    sr_conf, params_p = modified_config_largetifs(gamma_conf, 1, 'parallel_conf.conf')\n    params_p = Configuration(sr_conf).__dict__\n    conv2tif.main(params_p)\n    params_p = Configuration(sr_conf).__dict__\n    prepifg.main(params_p)\n    params_p = Configuration(sr_conf).__dict__\n    # convert2tif tests, 17 interferograms\n    assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], \"*_unw.tif\", 17)\n\n    # if coherence masking, compare coh files were converted\n    if params[C.COH_FILE_LIST] is not None:\n        assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], \"*_cc.tif\", 17)\n        # 17 ifgs + 1 dem + 17 mlooked file\n        assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], \"*_coh.tif\", 17)\n        assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], [\"coh_*.tif\"], 3)\n\n    assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], \"*_dem.tif\", 1)\n    assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR],\n                          [t + '.tif' for t in C.GEOMETRY_OUTPUT_TYPES], 6)\n    # prepifg\n    # 17 ifgs + 1 dem\n    assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], \"*_ifg.tif\", 17)\n    assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], \"dem.tif\", 1)\n\n    print(\"==========================xxx===========================\")\n    shutil.rmtree(params[C.OUT_DIR])\n    shutil.rmtree(params_p[C.OUT_DIR])\n"
  },
  {
    "path": "tests/test_pyrate.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains system integration tests for the PyRate workflow.\n\"\"\"\nimport glob\nimport os\nimport shutil\nimport tempfile\nimport pytest\nfrom os.path import join\nfrom pathlib import Path\nimport numpy as np\n\nimport pyrate.configuration\nimport pyrate.constants as C\nimport pyrate.core.prepifg_helper\nimport pyrate.core.shared\nimport pyrate.main\nimport tests.common\nfrom pyrate.core import shared, prepifg_helper\nfrom pyrate.core.shared import dem_or_ifg\nfrom pyrate import correct\nfrom pyrate.configuration import MultiplePaths, Configuration\nfrom tests import common\nfrom tests.common import PY37GDAL304\n\n# taken from\n# http://stackoverflow.com/questions/6260149/os-symlink-support-in-windows\nif os.name == \"nt\":\n    def symlink_ms(source, link_name):\n        import ctypes\n        csl = ctypes.windll.kernel32.CreateSymbolicLinkW\n        csl.argtypes = (ctypes.c_wchar_p, ctypes.c_wchar_p, ctypes.c_uint32)\n        csl.restype = ctypes.c_ubyte\n        flags = 1 if os.path.isdir(source) else 0\n        try:\n            if csl(link_name, source.replace('/', '\\\\'), flags) == 0:\n                raise ctypes.WinError()\n        except:\n            pass\n    os.symlink = symlink_ms\n\nCURRENT_DIR = os.getcwd()\n\n\ndef test_transform_params():\n    params = {C.IFG_LKSX: 3, C.IFG_LKSY: 2, C.IFG_CROP_OPT: 1}\n    assert pyrate.core.prepifg_helper.transform_params(params) == (3, 2, 1)\n\n\ndef test_warp_required():\n    nocrop = prepifg_helper.ALREADY_SAME_SIZE\n    assert shared.warp_required(xlooks=2, ylooks=1, crop=nocrop)\n    assert shared.warp_required(xlooks=1, ylooks=2, crop=nocrop)\n    assert shared.warp_required(xlooks=1, ylooks=1, crop=nocrop)\n    assert not shared.warp_required(xlooks=1, ylooks=1, crop=None)\n\n    for c in prepifg_helper.CROP_OPTIONS[:-1]:\n        assert shared.warp_required(xlooks=1, ylooks=1, crop=c)\n\n\ndef test_original_ifg_paths():\n    ifgdir = common.SML_TEST_TIF\n    ifglist_path = join(ifgdir, 'ifms_17')\n    paths = tests.common.original_ifg_paths(ifglist_path, ifgdir)\n    assert paths[0] == join(ifgdir, 'geo_060619-061002_unw.tif'), str(paths[0])\n    assert paths[-1] == join(ifgdir, 'geo_070709-070813_unw.tif')\n\n\ndef dest_ifg_paths(ifg_paths, outdir):\n    \"\"\"\n    Returns paths to out/dest ifgs.\n    \"\"\"\n\n    bases = [os.path.basename(p) for p in ifg_paths]\n    return [join(outdir, p) for p in bases]\n\n\ndef test_dest_ifg_paths():\n    # given source ifgs to process, get paths of ifgs in out dir\n    src_paths = ['tif/ifg0.tif', 'tif/ifg1.tif']\n    dest_paths = dest_ifg_paths(src_paths, outdir='out')\n    assert dest_paths == [os.path.join('out', i) for i in ['ifg0.tif',\n                                                           'ifg1.tif']]\n\n\n# FIXME: change to read output ifgs\ndef get_ifgs(out_dir, _open=True):\n    paths = glob.glob(join(out_dir, 'geo_*-*_unw.tif'))\n    ifgs = [shared.Ifg(p) for p in paths]\n    assert len(ifgs) == 17, 'Got %s' % ifgs\n\n    if _open:\n        for i in ifgs:\n            i.open(readonly=False)\n    return ifgs\n\n\nclass TestPyRate:\n    # Initialise & run workflow from class setup, ignoring multilooking as it is\n    # a separate step. Unit tests verify different steps have completed\n\n    @classmethod\n    def setup_class(cls):\n\n        # testing constants2\n        cls.BASE_DIR = tempfile.mkdtemp()\n        cls.BASE_OUT_DIR = join(cls.BASE_DIR, 'out')\n        cls.BASE_DEM_DIR = join(cls.BASE_DIR, 'dem')\n        cls.BASE_DEM_FILE = join(cls.BASE_DEM_DIR, 'roipac_test_trimmed.tif')\n\n        try:\n            # copy source data (treat as prepifg already run)\n            os.makedirs(cls.BASE_OUT_DIR)\n            for path in glob.glob(join(common.SML_TEST_TIF, '*')):\n                dest = join(cls.BASE_OUT_DIR, os.path.basename(path))\n                shutil.copy(path, dest)\n                os.chmod(dest, 0o660)\n\n            config = Configuration(common.TEST_CONF_ROIPAC)\n            params = config.__dict__\n            params['correct'] = ['orbfit', 'refphase', 'mst', 'apscorrect', 'maxvar']\n            params[C.OUT_DIR] = cls.BASE_OUT_DIR\n            params[C.PROCESSOR] = 0  # roipac\n            params[C.APS_CORRECTION] = 0\n            paths = glob.glob(join(cls.BASE_OUT_DIR, 'geo_*-*.tif'))\n            paths = sorted(paths)\n            params[C.PARALLEL] = False\n            params[C.ORBFIT_OFFSET] = True\n            params[C.TEMP_MLOOKED_DIR] = cls.BASE_OUT_DIR.join(C.TEMP_MLOOKED_DIR)\n            params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, params) for p in paths]\n            for p in params[C.INTERFEROGRAM_FILES]:  # cheat\n                p.sampled_path = p.converted_path\n                p.tmp_sampled_path = p.converted_path\n            params[\"rows\"], params[\"cols\"] = 2, 2\n            params[C.REF_PIXEL_FILE] = Configuration.ref_pixel_path(params)\n            Path(params[C.OUT_DIR]).joinpath(C.APS_ERROR_DIR).mkdir(exist_ok=True, parents=True)\n            Path(params[C.OUT_DIR]).joinpath(C.MST_DIR).mkdir(exist_ok=True, parents=True)\n            correct.correct_ifgs(config)\n\n            if not hasattr(cls, 'ifgs'):\n                cls.ifgs = get_ifgs(out_dir=cls.BASE_OUT_DIR)\n        except:\n            # revert working dir & avoid paths busting other tests\n            os.chdir(CURRENT_DIR)\n            raise\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.BASE_DIR, ignore_errors=True)\n        os.chdir(CURRENT_DIR)\n\n    def key_check(self, ifg, key, value):\n        'Helper to check for metadata flags'\n        md = ifg.dataset.GetMetadata()\n        assert key in md, 'Missing %s in %s' % (key, ifg.data_path)\n        assert md[key] == value\n\n    def test_basic_outputs(self):\n        assert os.path.exists(self.BASE_OUT_DIR)\n\n        for i in self.ifgs:\n            assert ~i.is_read_only\n\n        # log_path = self.get_logfile_path()\n        # st = os.stat(log_path)\n        # self.assertTrue(st.st_size > 0)\n\n    def test_phase_conversion(self):\n        # ensure phase has been converted from radians to millimetres\n        key = 'DATA_UNITS'\n        value = 'MILLIMETRES'\n\n        for i in self.ifgs:\n            self.key_check(i, key, value)\n\n    def test_orbital_correction(self):\n        key = 'ORBITAL_ERROR'\n        value = 'REMOVED'\n\n        for i in self.ifgs:\n            self.key_check(i, key, value)\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\nclass TestParallelPyRate:\n    \"\"\"\n    parallel vs serial pyrate tests verifying results from all steps equal\n    \"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        gamma_conf = common.TEST_CONF_GAMMA\n        from tests.common import manipulate_test_conf\n        rate_types = ['stack_rate', 'stack_error', 'stack_samples']\n        cls.tif_dir = Path(tempfile.mkdtemp())\n        params = manipulate_test_conf(gamma_conf, cls.tif_dir)\n\n        from pyrate.configuration import Configuration\n        # change the required params\n        params[C.PROCESSES] = 4\n        params[C.PROCESSOR] = 1  # gamma\n        params[C.IFG_FILE_LIST] = os.path.join(common.GAMMA_SML_TEST_DIR, 'ifms_17')\n        params[C.PARALLEL] = 1\n        params[C.APS_CORRECTION] = 0\n        params[C.REFX], params[C.REFY] = -1, -1\n        rows, cols = params[\"rows\"], params[\"cols\"]\n\n        output_conf_file = 'gamma.conf'\n        output_conf = cls.tif_dir.joinpath(output_conf_file).as_posix()\n        pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n\n        config = Configuration(output_conf)\n        params = config.__dict__\n\n        common.sub_process_run(f\"pyrate conv2tif -f {output_conf}\")\n        common.sub_process_run(f\"pyrate prepifg -f {output_conf}\")\n\n        cls.sampled_paths = [p.tmp_sampled_path for p in params[C.INTERFEROGRAM_FILES]]\n\n        ifgs = common.small_data_setup()\n        correct._copy_mlooked(params)\n        tiles = pyrate.core.shared.get_tiles(cls.sampled_paths[0], rows, cols)\n        correct.correct_ifgs(config)\n        pyrate.main.timeseries(config)\n        pyrate.main.stack(config)\n        cls.refpixel_p, cls.maxvar_p, cls.vcmt_p = \\\n            (params[C.REFX], params[C.REFY]), params[C.MAXVAR], params[\n                C.VCMT]\n        cls.mst_p = common.reconstruct_mst(ifgs[0].shape, tiles, params[C.OUT_DIR])\n        cls.rate_p, cls.error_p, cls.samples_p = \\\n            [common.reconstruct_stack_rate(ifgs[0].shape, tiles, params[C.TMPDIR], t) for t in rate_types]\n        \n        common.remove_tifs(params[C.OUT_DIR])\n\n        # now create the non parallel version\n        cls.tif_dir_s = Path(tempfile.mkdtemp())\n        params = manipulate_test_conf(gamma_conf, cls.tif_dir_s)\n        params[C.PROCESSES] = 4\n        params[C.PROCESSOR] = 1  # gamma\n        params[C.IFG_FILE_LIST] = os.path.join(common.GAMMA_SML_TEST_DIR, 'ifms_17')\n        params[C.PARALLEL] = 0\n        params[C.APS_CORRECTION] = 0\n        params[C.REFX], params[C.REFY] = -1, -1\n        output_conf_file = 'gamma.conf'\n        output_conf = cls.tif_dir_s.joinpath(output_conf_file).as_posix()\n        pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n        config = Configuration(output_conf)\n        params = config.__dict__\n\n        common.sub_process_run(f\"pyrate conv2tif -f {output_conf}\")\n        common.sub_process_run(f\"pyrate prepifg -f {output_conf}\")\n\n        correct._copy_mlooked(params)\n        correct.correct_ifgs(config)\n        pyrate.main.timeseries(config)\n        pyrate.main.stack(config)\n        cls.refpixel, cls.maxvar, cls.vcmt = \\\n            (params[C.REFX], params[C.REFY]), params[C.MAXVAR], params[\n                C.VCMT]\n        cls.mst = common.reconstruct_mst(ifgs[0].shape, tiles, params[C.OUT_DIR])\n        cls.rate, cls.error, cls.samples = \\\n            [common.reconstruct_stack_rate(ifgs[0].shape, tiles, params[C.TMPDIR], t) for t in rate_types]\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_orbital_correction(self):\n        key = 'ORBITAL_ERROR'\n        value = 'REMOVED'\n        ifgs = [dem_or_ifg(i) for i in self.sampled_paths]\n        for i in ifgs:\n            i.open()\n            i.nodata_value = 0\n            self.key_check(i, key, value)\n\n    def key_check(self, ifg, key, value):\n        'Helper to check for metadata flags'\n        md = ifg.dataset.GetMetadata()\n        assert key in md, 'Missing %s in %s' % (key, ifg.data_path)\n        assert md[key] == value\n\n    def test_phase_conversion(self):\n        # ensure phase has been converted from radians to millimetres\n        key = 'DATA_UNITS'\n        value = 'MILLIMETRES'\n        ifgs = [dem_or_ifg(i) for i in self.sampled_paths]\n\n        for i in ifgs:\n            i.open()\n            i.nodata_value = 0\n            self.key_check(i, key, value)\n\n    def test_mst_equal(self):\n        np.testing.assert_array_equal(self.mst, self.mst_p)\n\n    def test_refpixel_equal(self):\n        np.testing.assert_array_equal(self.refpixel, self.refpixel_p)\n\n    def test_maxvar_equal(self):\n        np.testing.assert_array_almost_equal(self.maxvar, self.maxvar_p, decimal=4)\n\n    def test_vcmt_equal(self):\n        np.testing.assert_array_almost_equal(self.vcmt, self.vcmt_p, decimal=4)\n\n    def test_stack_rate_equal(self):\n        np.testing.assert_array_almost_equal(self.rate, self.rate_p, decimal=4)\n        np.testing.assert_array_almost_equal(self.error, self.error_p, decimal=4)\n        np.testing.assert_array_almost_equal(self.samples, self.samples_p, decimal=4)\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\nclass TestPrePrepareIfgs:\n\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        cls.tmp_dir = tempfile.mkdtemp()\n        common.copytree(common.SML_TEST_TIF, cls.tmp_dir)\n        tifs = glob.glob(os.path.join(cls.tmp_dir, \"*.tif\"))\n        for t in tifs:\n            os.chmod(t, 0o644)\n        small_ifgs = common.small_data_setup(datafiles=tifs)\n        ifg_paths = [i.data_path for i in small_ifgs]\n\n        cls.ifg_ret = common.pre_prepare_ifgs(ifg_paths, params=params)\n        for i in cls.ifg_ret:\n            i.close()\n\n        nan_conversion = params[C.NAN_CONVERSION]\n\n        # prepare a second set\n        cls.tmp_dir2 = tempfile.mkdtemp()\n        common.copytree(common.SML_TEST_TIF, cls.tmp_dir2)\n        tifs = glob.glob(os.path.join(cls.tmp_dir2, \"*.tif\"))\n        for t in tifs:\n            os.chmod(t, 0o644)\n        small_ifgs = common.small_data_setup(datafiles=tifs)\n        ifg_paths = [i.data_path for i in small_ifgs]\n\n        cls.ifgs = [shared.Ifg(p) for p in ifg_paths]\n\n        for i in cls.ifgs:\n            i.open(readonly=False)\n            if nan_conversion:  # nan conversion happens here in networkx mst\n                i.nodata_value = params[C.NO_DATA_VALUE]\n                i.convert_to_nans()\n            if not i.mm_converted:\n                i.convert_to_mm()\n            i.close()\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.tmp_dir2)\n        shutil.rmtree(cls.tmp_dir)\n\n    def test_small_data_prep_phase_equality(self):\n        for i, j in zip(self.ifgs, self.ifg_ret):\n            np.testing.assert_array_almost_equal(i.phase_data, j.phase_data)\n            assert ~(i.phase_data == 0).any()\n            # if there was any 0 still present\n            i.phase_data[4, 2] = 0\n            assert (i.phase_data == 0).any()\n\n    def test_small_data_prep_metadata_equality(self):\n        for i, j in zip(self.ifgs, self.ifg_ret):\n            assert i.meta_data == j.meta_data\n"
  },
  {
    "path": "tests/test_ref_phs_est.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module contains tests for the ref_phs_est.py PyRate module.\n\"\"\"\nimport glob\nimport os\nfrom pathlib import Path\nimport shutil\nimport tempfile\nimport pytest\nimport numpy as np\n\nimport pyrate.constants as C\nfrom pyrate.core import ifgconstants as ifc\nfrom pyrate.core.ref_phs_est import ReferencePhaseError, ref_phase_est_wrapper\nfrom pyrate.core.refpixel import ref_pixel_calc_wrapper\nfrom pyrate.core.orbital import remove_orbital_error\nfrom pyrate.core.shared import CorrectionStatusError, Ifg\nfrom pyrate import prepifg, correct, conv2tif\nfrom pyrate.configuration import MultiplePaths, Configuration\nfrom tests import common\nfrom tests.common import TEST_CONF_GAMMA\n\nlegacy_ref_phs_method1 = [-18.2191658020020,\n                          27.7119445800781,\n                          -18.4944229125977,\n                          -2.92210483551025,\n                          31.1168708801270,\n                          21.2123012542725,\n                          9.01810073852539,\n                          6.08130645751953,\n                          -3.79313516616821,\n                          -11.3826837539673,\n                          -7.28352737426758,\n                          17.6365375518799,\n                          -12.8673439025879,\n                          5.46325922012329,\n                          -35.4149475097656,\n                          -13.5371961593628,\n                          -12.7864856719971]\n\n\nlegacy_ref_phs_method2 = [-21.4459648132324,\n                          27.1714553833008,\n                          -20.8264484405518,\n                          -3.47468209266663,\n                          30.4519863128662,\n                          22.3201427459717,\n                          9.58487224578857,\n                          4.81979084014893,\n                          -3.89160847663879,\n                          -12.0131330490112,\n                          -8.64702987670898,\n                          19.2060871124268,\n                          -9.92049789428711,\n                          4.38952684402466,\n                          -34.9590339660645,\n                          -14.3167810440063,\n                          -11.9066228866577]\n\n\nclass TestRefPhsTests:\n    \"\"\"Basic reference phase estimation tests\"\"\"\n\n    def setup_method(self):\n        self.params = Configuration(common.TEST_CONF_GAMMA).__dict__\n        self.tmp_dir = tempfile.mkdtemp()\n        self.params[C.OUT_DIR] = self.tmp_dir\n        self.params[C.REF_EST_METHOD] = 1\n        self.params[C.PARALLEL] = False\n        self.params[C.TMPDIR] = self.tmp_dir\n        common.copytree(common.SML_TEST_TIF, self.tmp_dir)\n        self.small_tifs = glob.glob(os.path.join(self.tmp_dir, \"*.tif\"))\n        for s in self.small_tifs:\n            os.chmod(s, 0o644)\n        self.ifgs = common.small_data_setup(self.tmp_dir, is_dir=True)\n        self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs]\n        for p in self.params[C.INTERFEROGRAM_FILES]:\n            p.sampled_path = p.converted_path\n            p.tmp_sampled_path = p.sampled_path\n        for ifg in self.ifgs:\n            ifg.close()\n\n        self.params[C.REFX], self.params[C.REFY] = -1, -1\n        self.params[C.REFNX], self.params[C.REFNY] = 10, 10\n        self.params[C.REF_CHIP_SIZE], self.params[C.REF_MIN_FRAC] = 21, 0.5\n        self.params['rows'], self.params['cols'] = 3, 2\n        self.params[C.REF_PIXEL_FILE] = Configuration.ref_pixel_path(self.params)\n        correct._update_params_with_tiles(self.params)\n        correct.ref_pixel_calc_wrapper(self.params)\n       \n    def teardown_method(self):\n        shutil.rmtree(self.params[C.OUT_DIR])\n\n    def test_need_at_least_two_ifgs(self):\n        self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs[:1]]\n        for p in self.params[C.INTERFEROGRAM_FILES]:\n            p.sampled_path = p.converted_path\n            p.tmp_sampled_path = p.sampled_path\n\n        with pytest.raises(ReferencePhaseError):\n            ref_phase_est_wrapper(self.params)\n\n    def test_metadata(self):\n        for ifg in self.ifgs:\n            ifg.open()\n            assert ifc.PYRATE_REF_PHASE not in ifg.dataset.GetMetadata()\n            ifg.close()\n        ref_phase_est_wrapper(self.params)\n        for ifg in self.ifgs:\n            ifg.open()\n            assert ifg.dataset.GetMetadataItem(ifc.PYRATE_REF_PHASE) == ifc.REF_PHASE_REMOVED\n            ifg.close()\n    \n    def test_mixed_metadata_raises(self):\n\n        # change config to 5 ifgs\n        self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs[:5]]\n        for p in self.params[C.INTERFEROGRAM_FILES]:\n            p.sampled_path = p.converted_path\n            p.tmp_sampled_path = p.sampled_path\n\n        # correct reference phase for some of the ifgs\n        ref_phase_est_wrapper(self.params)\n        for ifg in self.ifgs:\n            ifg.open()\n\n        # change config to all ifgs\n        self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs]\n        for p in self.params[C.INTERFEROGRAM_FILES]:\n            p.sampled_path = p.converted_path\n            p.tmp_sampled_path = p.sampled_path\n\n        # now it should raise exception if we want to correct refernece phase again on all of them\n        with pytest.raises(CorrectionStatusError):\n            ref_phase_est_wrapper(self.params)\n        \n\nclass TestRefPhsEstimationLegacyTestMethod1Serial:\n    \"\"\"\n    Reference phase estimation method 1 is tested vs legacy output\n    \"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        # start with a clean output dir\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        conv2tif.main(params)\n        prepifg.main(params)\n        for p in params[C.INTERFEROGRAM_FILES]:  # hack\n            p.tmp_sampled_path = p.sampled_path\n            Path(p.sampled_path).chmod(0o664)  # assign write permission as conv2tif output is readonly\n        params[C.REF_EST_METHOD] = 1\n        params[C.PARALLEL] = False\n        params[C.ORBFIT_OFFSET] = True\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n        # start run_pyrate copy\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n        mst_grid = common.mst_calculation(dest_paths, params)\n        # Estimate reference pixel location\n        refx, refy = ref_pixel_calc_wrapper(params)\n\n        # Estimate and remove orbit errors\n        remove_orbital_error(ifgs, params)\n\n        for i in ifgs:\n            i.close()\n\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n\n        for ifg in ifgs:\n            ifg.close()\n\n        for p in params[C.INTERFEROGRAM_FILES]:\n            p.tmp_sampled_path = p.sampled_path\n        params[C.REFX], params[C.REFY] = refx, refy\n        params['rows'], params['cols'] = 3, 2\n        correct._update_params_with_tiles(params)\n        cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params)\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    @pytest.mark.skip(True, reason='Orbfit correction update')\n    def test_estimate_reference_phase(self):\n        np.testing.assert_array_almost_equal(legacy_ref_phs_method1, self.ref_phs, decimal=3)\n\n    def test_ifgs_after_ref_phs_est(self):\n        for ifg in self.ifgs:\n            if not ifg.is_open:\n                ifg.open()\n\n        LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est')\n\n        onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR)\n                if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f))\n                and f.endswith('.csv') and f.__contains__('_ref_phase_')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            ifg_data = np.genfromtxt(os.path.join(LEGACY_REF_PHASE_DIR, f), delimiter=',')\n            for k, j in enumerate(self.ifgs):\n                if f.split('_correctedgeo_')[-1].split('.')[0] == \\\n                        os.path.split(j.data_path)[-1].split('_ifg.tif')[0]:\n                    count += 1\n                    # all numbers equal\n                    np.testing.assert_array_almost_equal(ifg_data,\n                        self.ifgs[k].phase_data, decimal=3)\n\n                    # means must also be equal\n                    assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001)\n\n                    # number of nans must equal\n                    assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data))\n\n        # ensure we have the correct number of matches\n        assert count == len(self.ifgs)\n\n\nclass TestRefPhsEstimationLegacyTestMethod1Parallel:\n    \"\"\"\n    Reference phase estimation method 1 is tested vs legacy output\n    \"\"\"\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        conv2tif.main(params)\n        prepifg.main(params)\n        for p in params[C.INTERFEROGRAM_FILES]:  # hack\n            p.tmp_sampled_path = p.sampled_path\n            Path(p.sampled_path).chmod(0o664)  # assign write permission as conv2tif output is readonly\n\n\n        params[C.REF_EST_METHOD] = 1\n        params[C.PARALLEL] = True\n        params[C.ORBFIT_OFFSET] = True\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n\n        # start run_pyrate copy\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n        mst_grid = common.mst_calculation(dest_paths, params)\n        # Estimate reference pixel location\n        refx, refy = ref_pixel_calc_wrapper(params)\n\n        # Estimate and remove orbit errors\n        remove_orbital_error(ifgs, params)\n\n        for i in ifgs:\n            i.close()\n\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n\n        for i in ifgs:\n            i.close()\n        for p in params[C.INTERFEROGRAM_FILES]:\n            p.tmp_sampled_path = p.sampled_path\n        params[C.REFX], params[C.REFY] = refx, refy\n        params['rows'], params['cols'] = 3, 2\n        correct._update_params_with_tiles(params)\n        cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params)\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    @pytest.mark.skip(True, reason='Orbfit correction update')\n    def test_estimate_reference_phase(self):\n        np.testing.assert_array_almost_equal(legacy_ref_phs_method1, self.ref_phs, decimal=3)\n\n    def test_ifgs_after_ref_phs_est(self):\n        for ifg in self.ifgs:\n            ifg.open()\n        LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est')\n\n        onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR)\n                if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f))\n                and f.endswith('.csv') and f.__contains__('_ref_phase_')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            ifg_data = np.genfromtxt(os.path.join(\n                LEGACY_REF_PHASE_DIR, f), delimiter=',')\n            for k, j in enumerate(self.ifgs):\n                if f.split('_correctedgeo_')[-1].split('.')[0] == os.path.split(j.data_path)[-1].split(\n                        '_ifg.tif')[0]:\n                    count += 1\n                    # all numbers equal\n                    np.testing.assert_array_almost_equal(\n                        ifg_data,\n                        self.ifgs[k].phase_data,\n                        decimal=3)\n\n                    # means must also be equal\n                    assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001)\n\n                    # number of nans must equal\n                    assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data))\n\n        # ensure we have the correct number of matches\n        assert count == len(self.ifgs)\n\n\nclass TestRefPhsEstimationLegacyTestMethod2Serial:\n    \"\"\"\n    Reference phase estimation method 2 is tested vs legacy output\n    \"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        conv2tif.main(params)\n        prepifg.main(params)\n        for p in params[C.INTERFEROGRAM_FILES]:  # hack\n            p.tmp_sampled_path = p.sampled_path\n            Path(p.sampled_path).chmod(0o664)  # assign write permission as conv2tif output is readonly\n\n        params[C.REF_EST_METHOD] = 2\n        params[C.PARALLEL] = False\n        params[C.ORBFIT_OFFSET] = True\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n\n        # start run_pyrate copy\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n        mst_grid = common.mst_calculation(dest_paths, params)\n        # Estimate reference pixel location\n        refx, refy = ref_pixel_calc_wrapper(params)\n\n        # Estimate and remove orbit errors\n        remove_orbital_error(ifgs, params)\n\n        for i in ifgs:\n            i.close()\n\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n        \n        for i in ifgs:\n            i.close()\n        for p in params[C.INTERFEROGRAM_FILES]:\n            p.tmp_sampled_path = p.sampled_path\n        params[C.REFX], params[C.REFY] = refx, refy\n        params['rows'], params['cols'] = 3, 2\n        correct._update_params_with_tiles(params)\n\n        cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params)\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_ifgs_after_ref_phs_est(self):\n        for ifg in self.ifgs:\n            ifg.open()\n        LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est')\n\n        onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR)\n                if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f))\n                and f.endswith('.csv') and f.__contains__('_ref_phase_')\n                     and f.__contains__('method2')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            ifg_data = np.genfromtxt(os.path.join(LEGACY_REF_PHASE_DIR, f), delimiter=',')\n            for k, j in enumerate(self.ifgs):\n                if f.split('_corrected_method2geo_')[-1].split('.')[0] == \\\n                        os.path.split(j.data_path)[-1].split('_ifg.tif')[0]:\n                    count += 1\n                    # all numbers equal\n                    np.testing.assert_array_almost_equal(ifg_data,\n                        self.ifgs[k].phase_data, decimal=3)\n\n                    # means must also be equal\n                    assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001)\n\n                    # number of nans must equal\n                    assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data))\n\n\n        # ensure we have the correct number of matches\n        assert count == len(self.ifgs)\n\n    @pytest.mark.skip(True, reason='Orbfit correction update')\n    def test_estimate_reference_phase_method2(self):\n        np.testing.assert_array_almost_equal(legacy_ref_phs_method2, self.ref_phs, decimal=3)\n\n\nclass TestRefPhsEstimationLegacyTestMethod2Parallel:\n    \"\"\"\n    Reference phase estimation method 2 is tested vs legacy output\n\n    \"\"\"\n    # TODO: Improve the parallel tests to remove duplication from serial tests\n\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        conv2tif.main(params)\n        prepifg.main(params)\n        for p in params[C.INTERFEROGRAM_FILES]:  # hack\n            p.tmp_sampled_path = p.sampled_path\n            Path(p.sampled_path).chmod(0o664)  # assign write permission as conv2tif output is readonly\n\n        params[C.REF_EST_METHOD] = 2\n        params[C.PARALLEL] = 1\n        params[C.ORBFIT_OFFSET] = True\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n\n        # start run_pyrate copy\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n        # Estimate reference pixel location\n        refx, refy = ref_pixel_calc_wrapper(params)\n\n        # Estimate and remove orbit errors\n        remove_orbital_error(ifgs, params)\n\n        for i in ifgs:\n            i.close()\n\n        ifgs = common.pre_prepare_ifgs(dest_paths, params)\n\n        for i in ifgs:\n            i.close()\n\n        for p in params[C.INTERFEROGRAM_FILES]:\n            p.tmp_sampled_path = p.sampled_path\n        params[C.REFX], params[C.REFY] = refx, refy\n        params['rows'], params['cols'] = 3, 2\n        correct._update_params_with_tiles(params)\n        cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params)\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_ifgs_after_ref_phs_est(self):\n        for ifg in self.ifgs:\n            ifg.open()\n        LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est')\n\n        onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR)\n                     if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f))\n                     and f.endswith('.csv') and f.__contains__('_ref_phase_')\n                     and f.__contains__('method2')]\n\n        count = 0\n        for i, f in enumerate(onlyfiles):\n            ifg_data = np.genfromtxt(os.path.join(LEGACY_REF_PHASE_DIR, f), delimiter=',')\n            for k, j in enumerate(self.ifgs):\n                if f.split('_corrected_method2geo_')[-1].split('.')[0] == \\\n                        os.path.split(j.data_path)[-1].split('_ifg.tif')[0]:\n                    count += 1\n                    # all numbers equal\n                    np.testing.assert_array_almost_equal(\n                        ifg_data, self.ifgs[k].phase_data, decimal=3)\n\n                    # means must also be equal\n                    assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001)\n\n                    # number of nans must equal\n                    assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data))\n\n        # ensure we have the correct number of matches\n        assert count == len(self.ifgs)\n\n    @pytest.mark.skip(True, reason='Orbfit correction update')\n    def test_estimate_reference_phase_method2(self):\n        np.testing.assert_array_almost_equal(legacy_ref_phs_method2, self.ref_phs, decimal=3)\n\n\nclass TestRefPhsEstReusedFromDisc:\n\n    @classmethod\n    def setup_class(cls):\n        cls.conf = TEST_CONF_GAMMA\n        params = Configuration(cls.conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(cls.conf).__dict__\n        prepifg.main(params)\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_ref_phase_used_from_disc_on_rerun(self, ref_est_method):\n        self.params = Configuration(self.conf).__dict__\n        self.params[C.REF_EST_METHOD] = ref_est_method\n        correct._copy_mlooked(self.params)\n        correct._update_params_with_tiles(self.params)\n\n        phase_prev, time_written = self.__run_once()\n\n        # run again\n        phase_now, time_written_1 = self.__run_once()\n\n        # and once more\n        phase_again, time_written_2 = self.__run_once()\n\n        # assert no new file was written\n        assert time_written_1 == time_written\n        assert time_written_2 == time_written\n\n        # assert phase data is unchanged after applying ref_ph correction from disc\n        np.testing.assert_array_equal(phase_now, phase_prev)\n        np.testing.assert_array_equal(phase_now, phase_again)\n\n    def __run_once(self):\n        ref_phs_file = Configuration.ref_phs_file(self.params)\n        correct._copy_mlooked(self.params)\n        multi_paths = self.params[C.INTERFEROGRAM_FILES]\n        ifg_paths = [p.tmp_sampled_path for p in multi_paths]\n        ifgs = [Ifg(i) for i in ifg_paths]\n        self.params[C.REFX_FOUND], self.params[C.REFY_FOUND] = ref_pixel_calc_wrapper(self.params)\n        correct._create_ifg_dict(self.params)\n        ref_phase_est_wrapper(self.params)\n        for i in ifgs:\n            i.open()\n        phase_prev = [i.phase_data for i in ifgs]\n        # assert ref_ph_file present\n        assert ref_phs_file.exists()\n        time_written = os.stat(ref_phs_file).st_mtime\n        for i in ifgs:\n            i.close()\n        return phase_prev, time_written\n"
  },
  {
    "path": "tests/test_refpixel.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the refpixel.py PyRate module.\n\"\"\"\nimport os\nimport copy\nimport shutil\nfrom subprocess import run, PIPE\nfrom pathlib import Path\nimport pytest\nimport itertools\nimport numpy as np\nfrom numpy import nan, mean, std, isnan\n\nimport pyrate.configuration\nimport pyrate.constants as C\nimport pyrate.core.refpixel\nfrom pyrate.core.refpixel import ref_pixel, _step, RefPixelError, ref_pixel_calc_wrapper, \\\n    convert_geographic_coordinate_to_pixel_value, convert_pixel_value_to_geographic_coordinate\nfrom pyrate.core import shared, ifgconstants as ifc\nfrom pyrate import correct, conv2tif, prepifg\nfrom pyrate.configuration import Configuration, ConfigException\nfrom tests.common import TEST_CONF_ROIPAC, TEST_CONF_GAMMA, SML_TEST_DEM_TIF\nfrom tests.common import small_data_setup, MockIfg, copy_small_ifg_file_list, \\\n    copy_and_setup_small_data, manipulate_test_conf, assert_two_dirs_equal, PY37GDAL304\n\n\n# TODO: figure out how  editing  resource.setrlimit fixes the error\n# to fix the open to many files error\n# https://stackoverflow.com/questions/18280612/ioerror-errno-24-too-many-open-files\n\n# default testing values\nREFNX = 5\nREFNY = 7\nMIN_FRAC = 0.7\nCHIPSIZE = 3\nPARALLEL = False\n\n\nclass TestReferencePixelInputTests:\n    '''\n    Verifies error checking capabilities of the reference pixel function\n    '''\n\n    @classmethod\n    def setup_method(cls):\n        cls.ifgs = small_data_setup()\n        cls.params = Configuration(TEST_CONF_ROIPAC).__dict__\n        cls.params[C.REFNX] = REFNX\n        cls.params[C.REFNY] = REFNY\n        cls.params[C.REF_CHIP_SIZE] = CHIPSIZE\n        cls.params[C.REF_MIN_FRAC] = MIN_FRAC\n        cls.params[C.PARALLEL] = PARALLEL\n\n    def test_missing_chipsize(self):\n        self.params[C.REF_CHIP_SIZE] = None\n        with pytest.raises(ConfigException):\n            ref_pixel(self.ifgs, self.params)\n\n    def test_chipsize_valid(self):\n        for illegal in [0, -1, -15, 1, 2, self.ifgs[0].ncols+1, 4, 6, 10, 20]:\n            self.params[C.REF_CHIP_SIZE] = illegal\n            with pytest.raises(RefPixelError):\n                ref_pixel(self.ifgs, self.params)\n\n    def test_minimum_fraction_missing(self):\n        self.params[C.REF_MIN_FRAC] = None\n        with pytest.raises(ConfigException):\n            ref_pixel(self.ifgs, self.params)\n\n    def test_minimum_fraction_threshold(self):\n        for illegal in [-0.1, 1.1, 1.000001, -0.0000001]:\n            self.params[C.REF_MIN_FRAC] = illegal\n            with pytest.raises(RefPixelError):\n                ref_pixel(self.ifgs, self.params)\n\n    def test_search_windows(self):\n        # 45 is max # cells a width 3 sliding window can iterate over\n        for illegal in [-5, -1, 0, 46, 50, 100]:\n            self.params[C.REFNX] = illegal\n            with pytest.raises(RefPixelError):\n                ref_pixel(self.ifgs, self.params)\n\n        # 40 is max # cells a width 3 sliding window can iterate over\n        for illegal in [-5, -1, 0, 71, 85, 100]:\n            self.params[C.REFNY] = illegal\n            with pytest.raises(RefPixelError):\n                ref_pixel(self.ifgs, self.params)\n\n    def test_missing_search_windows(self):\n        self.params[C.REFNX] = None\n        with pytest.raises(ConfigException):\n            ref_pixel(self.ifgs, self.params)\n\n        self.params[C.REFNX] = REFNX\n        self.params[C.REFNY] = None\n\n        with pytest.raises(ConfigException):\n            ref_pixel(self.ifgs, self.params)\n\n\nclass TestReferencePixelTests:\n    \"\"\"\n    Tests reference pixel search\n    \"\"\"\n\n    @classmethod\n    def setup_method(cls):\n        cls.params = Configuration(TEST_CONF_ROIPAC).__dict__\n        cls.params[C.OUT_DIR], cls.ifgs = copy_and_setup_small_data()\n        cls.params[C.REFNX] = REFNX\n        cls.params[C.REFNY] = REFNY\n        cls.params[C.REF_CHIP_SIZE] = CHIPSIZE\n        cls.params[C.REF_MIN_FRAC] = MIN_FRAC\n        cls.params[C.PARALLEL] = PARALLEL\n\n    def test_all_below_threshold_exception(self):\n        # test failure when no valid stacks in dataset\n\n        # rig mock data to be below threshold\n        mock_ifgs = [MockIfg(i, 6, 7) for i in self.ifgs]\n        for m in mock_ifgs:\n            m.phase_data[:1] = nan\n            m.phase_data[1:5] = 0.1\n            m.phase_data[5:] = nan\n\n        self.params[C.REFNX] = 2\n        self.params[C.REFNY] = 2\n        self.params[C.REF_CHIP_SIZE] = CHIPSIZE\n        self.params[C.REF_MIN_FRAC] = MIN_FRAC\n        self.params[C.PARALLEL] = PARALLEL\n        with pytest.raises(ValueError):\n            ref_pixel(mock_ifgs, self.params)\n\n    def test_refnxy_step_1(self):\n        # test step of 1 for refnx|y gets the reference pixel for axis centre\n        mock_ifgs = [MockIfg(i, 47, 72) for i in self.ifgs]\n        for m in mock_ifgs:\n            m.phase_data[:1] = 0.2\n            m.phase_data[1:5] = 0.1\n            m.phase_data[5:] = 0.3\n        exp_refpx = (1, 1)\n        self.params[C.REFNX] = 1\n        self.params[C.REFNY] = 1\n        self.params[C.REF_CHIP_SIZE] = CHIPSIZE\n        self.params[C.REF_MIN_FRAC] = MIN_FRAC\n        self.params[C.PARALLEL] = PARALLEL\n        res = ref_pixel(mock_ifgs, self.params)\n        assert exp_refpx == res\n\n    def test_large_window(self):\n        # 5x5 view over a 5x5 ifg with 1 window/ref pix search\n        chps = 5\n        mockifgs = [MockIfg(i, chps, chps) for i in self.ifgs]\n        self.params[C.REFNX] = 1\n        self.params[C.REFNY] = 1\n        self.params[C.REF_CHIP_SIZE] = chps\n        self.params[C.REF_MIN_FRAC] = MIN_FRAC\n        self.params[C.PARALLEL] = PARALLEL\n        res = ref_pixel(mockifgs, self.params)\n        assert (2, 2) == res\n\n    def test_step(self):\n        # test different search windows to verify x/y step calculation\n\n        # convenience testing function\n        def assert_equal(actual, expected):\n            for a, e in zip(actual, expected):\n                assert a == e\n\n        # start with simple corner only test\n        width = 47\n        radius = 2\n        refnx = 2\n        exp = [2, 25, 44]\n        act = _step(width, refnx, radius)\n        assert_equal(act, exp)\n\n        # test with 3 windows\n        refnx = 3\n        exp = [2, 17, 32]\n        act = _step(width, refnx, radius)\n        assert_equal(act, exp)\n\n        # test 4 search windows\n        refnx = 4\n        exp = [2, 13, 24, 35]\n        act = _step(width, refnx, radius)\n        assert_equal(act, exp)\n\n    def test_ref_pixel(self):\n        exp_refpx = (2, 25)\n        self.params[C.REFNX] = 2\n        self.params[C.REFNY] = 2\n        self.params[C.REF_CHIP_SIZE] = 5\n        self.params[C.REF_MIN_FRAC] = MIN_FRAC\n        self.params[C.PARALLEL] = PARALLEL\n        res = ref_pixel(self.ifgs, self.params)\n        assert res == exp_refpx\n\n        # Invalidate first data stack, get new refpix coods & retest\n        for i in self.ifgs:\n            i.phase_data[:30, :50] = nan\n\n        exp_refpx = (38, 2)\n        res = ref_pixel(self.ifgs, self.params)\n        assert res == exp_refpx\n\n\ndef _expected_ref_pixel(ifgs, cs):\n    \"\"\"Helper function for finding reference pixel when refnx/y=2\"\"\"\n\n    # calculate expected data\n    data = [i.phase_data for i in ifgs]  # len 17 list of arrays\n    ul = [i[:cs, :cs] for i in data]  # upper left corner stack\n    ur = [i[:cs, -cs:] for i in data]\n    ll = [i[-cs:, :cs] for i in data]\n    lr = [i[-cs:, -cs:] for i in data]\n\n    ulm = mean([std(i[~isnan(i)]) for i in ul])  # mean std of all the layers\n    urm = mean([std(i[~isnan(i)]) for i in ur])\n    llm = mean([std(i[~isnan(i)]) for i in ll])\n    lrm = mean([std(i[~isnan(i)]) for i in lr])\n    assert isnan([ulm, urm, llm, lrm]).any() is False\n\n    # coords of the smallest mean is the result\n    mn = [ulm, urm, llm, lrm]\n\n\nclass TestLegacyEqualityTest:\n\n    @classmethod\n    def setup_method(cls):\n        cls.params = Configuration(TEST_CONF_ROIPAC).__dict__\n        cls.params[C.PARALLEL] = 0\n        cls.params[C.OUT_DIR], cls.ifg_paths = copy_small_ifg_file_list()\n        conf_file = Path(cls.params[C.OUT_DIR], 'conf_file.conf')\n        pyrate.configuration.write_config_file(params=cls.params, output_conf_file=conf_file)\n        cls.params = Configuration(conf_file).__dict__\n        cls.params_alt_ref_frac = copy.copy(cls.params)\n        cls.params_alt_ref_frac[C.REF_MIN_FRAC] = 0.5\n        cls.params_all_2s = copy.copy(cls.params)\n        cls.params_all_2s[C.REFNX] = 2\n        cls.params_all_2s[C.REFNY] = 2\n        cls.params_chipsize_15 = copy.copy(cls.params_all_2s)\n        cls.params_chipsize_15[C.REF_CHIP_SIZE] = 15\n        cls.params_all_1s = copy.copy(cls.params)\n        cls.params_all_1s[C.REFNX] = 1\n        cls.params_all_1s[C.REFNY] = 1\n        cls.params_all_1s[C.REF_MIN_FRAC] = 0.7\n\n        for p, q in zip(cls.params[C.INTERFEROGRAM_FILES], cls.ifg_paths):  # hack\n            p.sampled_path = q\n            p.tmp_sampled_path = q\n\n    @classmethod\n    def teardown_method(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_small_test_data_ref_pixel_lat_lon_provided(self):\n        self.params[C.REFX], self.params[C.REFY] = 150.941666654, -34.218333314\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params)\n        assert refx == 38\n        assert refy == 58\n        assert 0.8 == pytest.approx(self.params[C.REF_MIN_FRAC])\n\n    def test_small_test_data_ref_pixel(self):\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params)\n        assert refx == 38\n        assert refy == 58\n        assert 0.8 == pytest.approx(self.params[C.REF_MIN_FRAC])\n\n    def test_small_test_data_ref_chipsize_15(self):\n\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_chipsize_15)\n        assert refx == 7\n        assert refy == 7\n        assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC])\n\n    def test_metadata(self):\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_chipsize_15)\n        for i in self.ifg_paths:\n            ifg = shared.Ifg(i)\n            ifg.open(readonly=True)\n            md = ifg.meta_data\n            for k, v in zip([ifc.PYRATE_REFPIX_X, ifc.PYRATE_REFPIX_Y, ifc.PYRATE_REFPIX_LAT,\n                            ifc.PYRATE_REFPIX_LON, ifc.PYRATE_MEAN_REF_AREA, ifc.PYRATE_STDDEV_REF_AREA],\n                            [str(refx), str(refy), 0, 0, 0, 0]):\n                assert k in md  # metadata present\n                # assert values\n            ifg.close()\n\n    def test_small_test_data_ref_all_1(self):\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_all_1s)\n        assert 0.7 == pytest.approx(self.params_all_1s[C.REF_MIN_FRAC])\n        assert 1 == self.params_all_1s[C.REFNX]\n        assert 1 == self.params_all_1s[C.REFNY]\n        assert refx == 2\n        assert refy == 2\n\n\nclass TestLegacyEqualityTestMultiprocessParallel:\n\n    @classmethod\n    def setup_method(cls):\n        cls.params = Configuration(TEST_CONF_ROIPAC).__dict__\n        cls.params[C.PARALLEL] = 1\n        cls.params[C.OUT_DIR], cls.ifg_paths = copy_small_ifg_file_list()\n        conf_file = Path(cls.params[C.OUT_DIR], 'conf_file.conf')\n        pyrate.configuration.write_config_file(params=cls.params, output_conf_file=conf_file)\n        cls.params = Configuration(conf_file).__dict__\n        cls.params_alt_ref_frac = copy.copy(cls.params)\n        cls.params_alt_ref_frac[C.REF_MIN_FRAC] = 0.5\n        cls.params_all_2s = copy.copy(cls.params)\n        cls.params_all_2s[C.REFNX] = 2\n        cls.params_all_2s[C.REFNY] = 2\n        cls.params_chipsize_15 = copy.copy(cls.params_all_2s)\n        cls.params_chipsize_15[C.REF_CHIP_SIZE] = 15\n        cls.params_all_1s = copy.copy(cls.params)\n        cls.params_all_1s[C.REFNX] = 1\n        cls.params_all_1s[C.REFNY] = 1\n        cls.params_all_1s[C.REF_MIN_FRAC] = 0.7\n\n        for p, q in zip(cls.params[C.INTERFEROGRAM_FILES], cls.ifg_paths):  # hack\n            p.sampled_path = q\n            p.tmp_sampled_path = q\n\n    @classmethod\n    def teardown_method(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_small_test_data_ref_pixel(self):\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params)\n        assert refx == 38\n        assert refy == 58\n        assert 0.8 == pytest.approx(self.params[C.REF_MIN_FRAC])\n\n    def test_more_small_test_data_ref_pixel(self):\n\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_alt_ref_frac)\n        assert refx == 38\n        assert refy == 58\n        assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC])\n\n    def test_small_test_data_ref_pixel_all_2(self):\n\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_all_2s)\n        assert refx == 25\n        assert refy == 2\n        assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC])\n\n    def test_small_test_data_ref_chipsize_15(self):\n\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_chipsize_15)\n        assert refx == 7\n        assert refy == 7\n        assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC])\n\n    def test_small_test_data_ref_all_1(self):\n\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_all_1s)\n\n        assert 0.7 == pytest.approx(self.params_all_1s[C.REF_MIN_FRAC])\n        assert 1 == self.params_all_1s[C.REFNX]\n        assert 1 == self.params_all_1s[C.REFNY]\n        assert refx == 2\n        assert refy == 2\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\ndef test_error_msg_refpixel_out_of_bounds(tempdir, gamma_conf):\n    \"check correct latitude/longitude refpixel error is raised when specified refpixel is out of bounds\"\n    for x, (refx, refy) in zip(['longitude', 'latitude', 'longitude and latitude'],\n                               [(150., -34.218333314), (150.941666654, -34.), (150, -34)]):\n        _, err = _get_mlooked_files(gamma_conf, Path(tempdir()), refx=refx, refy=refy)\n        msg = \"Supplied {} value is outside the bounds of the interferogram data\"\n        assert msg.format(x) in err\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\ndef test_gamma_ref_pixel_search_vs_lat_lon(tempdir, gamma_conf):\n    params_1, _ = _get_mlooked_files(gamma_conf, Path(tempdir()), refx=-1, refy=-1)\n    params_2, _ = _get_mlooked_files(gamma_conf, Path(tempdir()), refx=150.941666654, refy=-34.218333314)\n    assert_two_dirs_equal(params_1[C.COHERENCE_DIR], params_2[C.COHERENCE_DIR], ['*_coh.tif', '*_cc.tif'], 34)\n    assert_two_dirs_equal(params_1[C.INTERFEROGRAM_DIR], params_2[C.INTERFEROGRAM_DIR], [\"*_ifg.tif\", '*_unw.tif'], 34)\n    assert_two_dirs_equal(params_1[C.GEOMETRY_DIR], params_2[C.GEOMETRY_DIR], [\"*.tif\"], 8)\n\n\ndef _get_mlooked_files(gamma_conf, tdir, refx, refy):\n    params = manipulate_test_conf(gamma_conf, tdir)\n    params[C.REFX] = refx\n    params[C.REFY] = refy\n    output_conf_file = 'config.conf'\n    output_conf = tdir.joinpath(output_conf_file)\n    pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf)\n    params = Configuration(output_conf).__dict__\n    conv2tif.main(params)\n    params = Configuration(output_conf).__dict__\n    prepifg.main(params)\n    err = run(f\"pyrate correct -f {output_conf}\", shell=True, universal_newlines=True, stderr=PIPE).stderr\n    return params, err\n\n\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL304, reason=\"Only run in one CI env\")\nclass TestRefPixelReuseLoadsSameFileAndPixels:\n\n    @classmethod\n    def setup_method(cls):\n        cls.conf = TEST_CONF_GAMMA\n        params = Configuration(cls.conf).__dict__\n        conv2tif.main(params)\n        params = Configuration(cls.conf).__dict__\n        prepifg.main(params)\n        params = Configuration(cls.conf).__dict__\n        correct._copy_mlooked(params)\n        cls.params = params\n\n    @classmethod\n    def teardown_method(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_ref_pixel_multiple_runs_reuse_from_disc(self, ref_pixel):\n        params = self.params\n        params[C.REFX], params[C.REFY] = ref_pixel\n        params[C.REF_PIXEL_FILE] = Configuration.ref_pixel_path(params)\n        ref_pixel_calc_wrapper(params)\n\n        ref_pixel_file = self.params[C.REF_PIXEL_FILE]\n        time_written = os.stat(ref_pixel_file).st_mtime\n        assert self.params[C.REFX_FOUND] == 38\n        assert self.params[C.REFY_FOUND] == 58\n        # run again\n        ref_pixel_calc_wrapper(self.params)\n        ref_pixel_file = self.params[C.REF_PIXEL_FILE]\n        time_written_1 = os.stat(ref_pixel_file).st_mtime\n        assert self.params[C.REFX_FOUND] == 38\n        assert self.params[C.REFY_FOUND] == 58\n\n        # run a third time\n        ref_pixel_calc_wrapper(self.params)\n        ref_pixel_file = self.params[C.REF_PIXEL_FILE]\n        time_written_2 = os.stat(ref_pixel_file).st_mtime\n        assert time_written == time_written_2 == time_written_1\n        assert self.params[C.REFX], self.params[C.REFY] == ref_pixel\n        assert self.params[C.REFX_FOUND] == 38\n        assert self.params[C.REFY_FOUND] == 58\n\n\n@pytest.fixture(scope='module')\ndef x_y_pixel():\n    dem = shared.DEM(SML_TEST_DEM_TIF)\n    dem.open()\n    Y = dem.nrows\n    X = dem.ncols\n    x = np.random.choice(range(X), 5)\n    y = np.random.choice(range(Y), 5)\n    return itertools.product(x, y)  # returns a matrix of 5x5 random x, y pairs\n\n\ndef test_convert_pixel_value_to_geographic_coordinate(x_y_pixel):\n    transform = dem_transform()\n    for x, y in x_y_pixel:\n        lon, lat = convert_pixel_value_to_geographic_coordinate(x, y, transform)\n        out = run(f\"gdallocationinfo -geoloc {SML_TEST_DEM_TIF} {lon} {lat}\", shell=True, universal_newlines=True,\n                  stdout=PIPE).stdout\n        xs = (x, x+1, x-1)\n        ys = (y, y+1, y-1)\n        assert any(f\"({xx}P,{yy}L)\" in out for xx, yy in itertools.product(xs, ys))\n\n\ndef dem_transform():\n    dem = shared.DEM(SML_TEST_DEM_TIF)\n    dem.open()\n    transform = dem.dataset.GetGeoTransform()\n    return transform\n\n\ndef test_convert_geographic_coordinate_to_pixel_value(x_y_pixel):\n    transform = dem_transform()\n    for x, y in x_y_pixel:\n        lon, lat = convert_pixel_value_to_geographic_coordinate(x, y, transform)\n        xp, yp = convert_geographic_coordinate_to_pixel_value(lon, lat, transform)\n        assert (xp == x) & (yp == y)\n"
  },
  {
    "path": "tests/test_roipac.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the roipac.py PyRate module.\n\"\"\"\nimport os\nimport sys\nfrom datetime import date\nfrom os.path import exists, join\n\nfrom numpy.testing import assert_array_almost_equal\nfrom osgeo import gdal\n\nimport pyrate.core.ifgconstants as ifc\nfrom pyrate.core import roipac\nfrom pyrate.core.shared import GeotiffException\nfrom pyrate.core.shared import write_fullres_geotiff\nfrom tests.common import HEADERS_TEST_DIR, PREP_TEST_OBS, PREP_TEST_TIF\nfrom tests.common import SML_TEST_DEM_DIR, ROIPAC_SML_TEST_DIR, TEMPDIR, UnitTestAdaptation\nfrom tests.common import SML_TEST_DEM_ROIPAC, SML_TEST_DEM_HDR\n\ngdal.UseExceptions()\n\n\n#  sanity checking\nif not exists(HEADERS_TEST_DIR):\n    sys.exit(\"ERROR: Missing the 'headers' data for unittests\\n\")\n\n# constants\nSHORT_HEADER_PATH = join(ROIPAC_SML_TEST_DIR, 'geo_060619-061002.unw.rsc')\nFULL_HEADER_PATH = join(HEADERS_TEST_DIR, \"geo_060619-060828.unw.rsc\")\n\n\nclass TestRoipacToGeoTiff(UnitTestAdaptation):\n    \"\"\"Tests conversion of GAMMA rasters to custom PyRate GeoTIFF\"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        hdr_path = join(PREP_TEST_OBS, 'geo_060619-061002.unw.rsc')\n        cls.HDRS = roipac.parse_header(hdr_path)\n\n    @classmethod\n    def teardown_class(cls):\n        pass\n\n    def test_to_geotiff_dem(self):\n        hdr = roipac.parse_header(SML_TEST_DEM_HDR)        \n        self.dest = os.path.join(TEMPDIR, \"tmp_roipac_dem.tif\")\n\n        write_fullres_geotiff(hdr, SML_TEST_DEM_ROIPAC, self.dest, nodata=0)\n        exp_path = join(SML_TEST_DEM_DIR, 'roipac_test_trimmed.tif')\n        exp_ds = gdal.Open(exp_path)\n        ds = gdal.Open(self.dest)\n\n        # compare data and geographic headers\n        assert_array_almost_equal(exp_ds.ReadAsArray(), ds.ReadAsArray())\n        self.compare_rasters(ds, exp_ds)\n        self.assertIsNotNone(ds.GetMetadata())\n\n    def test_to_geotiff_ifg(self):\n        # tricker: needs ifg header, and DEM one for extents\n        hdrs = self.HDRS.copy()\n        hdrs[ifc.PYRATE_DATUM] = 'WGS84'\n        hdrs[ifc.DATA_TYPE] = ifc.ORIG\n\n        self.dest = os.path.join('tmp_roipac_ifg.tif')\n        data_path = join(PREP_TEST_OBS, 'geo_060619-061002.unw')\n        write_fullres_geotiff(hdrs, data_path, self.dest, nodata=0)\n\n        ds = gdal.Open(self.dest)\n        band = ds.GetRasterBand(1)\n        self.assertEqual(ds.RasterCount, 1)\n\n        exp_path = join(PREP_TEST_TIF, 'geo_060619-061002_unw.tif')\n        exp_ds = gdal.Open(exp_path)\n        exp_band = exp_ds.GetRasterBand(1)\n\n        # compare data and geographic headers\n        assert_array_almost_equal(exp_band.ReadAsArray(), band.ReadAsArray())\n        self.compare_rasters(ds, exp_ds)\n\n        md = ds.GetMetadata()\n        date1 = date(2006, 6, 19)\n        date2 = date(2006, 10, 2)\n        diff = (date2 - date1).days\n        self.assertTrue(md[ifc.FIRST_DATE] == str(date1))\n        self.assertTrue(md[ifc.SECOND_DATE] == str(date2))\n        self.assertTrue(md[ifc.PYRATE_TIME_SPAN] == str(diff / ifc.DAYS_PER_YEAR))\n\n        wavelen = float(md[ifc.PYRATE_WAVELENGTH_METRES])\n        self.assertAlmostEqual(wavelen, 0.0562356424)\n\n    def test_to_geotiff_wrong_input_data(self):\n        # ensure failure if TIF/other file used instead of binary UNW data\n        self.dest = os.path.join(TEMPDIR, 'tmp_roipac_ifg.tif')\n        data_path = join(PREP_TEST_TIF, 'geo_060619-061002_unw.tif')\n        self.assertRaises(\n            GeotiffException,\n            write_fullres_geotiff,\n            self.HDRS,\n            data_path,\n            self.dest,\n            nodata=0)\n\n    def test_bad_projection(self):\n        hdrs = self.HDRS.copy()\n        hdrs[ifc.PYRATE_DATUM] = 'bad datum string'\n        hdrs[ifc.DATA_TYPE] = ifc.ORIG\n        self.dest = os.path.join(TEMPDIR, 'tmp_roipac_ifg2.tif')\n        data_path = join(PREP_TEST_OBS, 'geo_060619-061002.unw')\n        self.assertRaises(GeotiffException, write_fullres_geotiff, hdrs, data_path, self.dest, 0)\n\n    def compare_rasters(self, ds, exp_ds):\n        band = ds.GetRasterBand(1)\n        exp_band = exp_ds.GetRasterBand(1)\n\n        nodata = band.GetNoDataValue()\n        self.assertFalse(nodata is None)\n        self.assertEqual(exp_band.GetNoDataValue(), nodata)\n\n        pj = ds.GetProjection()\n        self.assertTrue('WGS 84' in pj)\n        self.assertEqual(exp_ds.GetProjection(), pj)\n        for exp, act in zip(exp_ds.GetGeoTransform(), ds.GetGeoTransform()):\n            self.assertAlmostEqual(exp, act, places=4)\n\n\nclass TestDateParsing(UnitTestAdaptation):\n\n    def test_parse_short_date_pre2000(self):\n        dstr = \"980416\"\n        self.assertEqual(date(1998, 4, 16), roipac.parse_date(dstr))\n\n    def test_parse_short_date_post2000(self):\n        dstr = \"081006\"\n        self.assertEqual(date(2008, 10, 6), roipac.parse_date(dstr))\n\n    def test_parse_date_range(self):\n        dstr = \"980416-081006\"\n        exp = (date(1998, 4, 16), date(2008, 10, 6))\n        self.assertEqual(exp, roipac.parse_date(dstr))\n\n\nclass TestHeaderParsingTests(UnitTestAdaptation):\n    \"\"\"Verifies ROIPAC headers are parsed correctly.\"\"\"\n\n    # short format header tests\n    def test_parse_short_roipac_header(self):\n        hdrs = roipac.parse_header(SHORT_HEADER_PATH)\n        self.assertEqual(hdrs[ifc.PYRATE_NCOLS], 47)\n        self.assertEqual(hdrs[ifc.PYRATE_NROWS], 72)\n        self.assertAlmostEqual(hdrs[ifc.PYRATE_LONG], 150.910)\n        self.assertEqual(hdrs[ifc.PYRATE_X_STEP], 0.000833333)\n        self.assertEqual(hdrs[ifc.PYRATE_LAT], -34.170000000)\n        self.assertEqual(hdrs[ifc.PYRATE_Y_STEP], -0.000833333)\n        self.assertEqual(hdrs[ifc.PYRATE_WAVELENGTH_METRES], 0.0562356424)\n\n    def test_parse_short_header_has_timespan(self):\n        # Ensures TIME_SPAN_YEAR field is added during parsing\n        hdrs = roipac.parse_header(SHORT_HEADER_PATH)\n        self.assertIn(roipac.TIME_SPAN_YEAR, hdrs.keys())\n\n        # check time span calc\n        first = date(2006, 6, 19)\n        second = date(2006, 10, 2)\n        diff = (second - first).days / ifc.DAYS_PER_YEAR\n        self.assertEqual(diff, hdrs[roipac.TIME_SPAN_YEAR])\n\n    # long format header tests\n    def test_parse_full_roipac_header(self):\n        # Ensures \"long style\" original header can be parsed correctly\n        hdrs = roipac.parse_header(FULL_HEADER_PATH)\n\n        # check DATE/ DATE12 fields are parsed correctly\n        date0 = date(2006, 6, 19) # from \"DATE 060619\" header\n        date2 = date(2006, 8, 28) # from DATE12 060619-060828\n        self.assertEqual(hdrs[ifc.FIRST_DATE], date0)\n        self.assertEqual(hdrs[ifc.SECOND_DATE], date2)\n\n    def test_read_full_roipac_header2(self):\n        # Tests header from cropped original dataset is parsed correctly\n        hdrs = roipac.parse_header(FULL_HEADER_PATH)\n        self.assertTrue(len(hdrs) is not None)\n\n    def test_xylast(self):\n        # Test the X_LAST and Y_LAST header elements are calculated\n        hdrs = roipac.parse_header(FULL_HEADER_PATH)\n        self.assertAlmostEqual(hdrs[roipac.X_LAST], 151.8519444445)\n        self.assertAlmostEqual(hdrs[roipac.Y_LAST], -34.625)\n"
  },
  {
    "path": "tests/test_shared.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis Python module contains tests for the shared.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nimport sys\nimport random\nimport string\nimport tempfile\nimport pytest\nfrom pathlib import Path\nfrom itertools import product\nfrom numpy import isnan, where, nan\nfrom os.path import join, basename, exists\nfrom stat import S_IRGRP, S_IWGRP, S_IWOTH, S_IROTH, S_IRUSR, S_IWUSR\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nfrom osgeo import gdal\nfrom osgeo.gdal import Open, Dataset, UseExceptions\n\nimport pyrate.constants as C\nimport tests.common\nfrom tests.common import SML_TEST_TIF, SML_TEST_DEM_TIF, TEMPDIR, WORKING_DIR\nfrom pyrate.core import shared, ifgconstants as ifc, gamma\nfrom pyrate.core.shared import dem_or_ifg\nfrom pyrate.core import mpiops\nfrom pyrate import prepifg, conv2tif\nfrom pyrate.configuration import Configuration, MultiplePaths\nfrom pyrate.core.shared import Ifg, DEM, RasterException\nfrom pyrate.core.shared import cell_size, _utm_zone, tiles_split\n\nfrom tests import common\n\nUseExceptions()\n\nif not exists(SML_TEST_TIF):\n    sys.exit(\"ERROR: Missing small_test data for unit tests\\n\")\n\n\nclass TestIfgTests:\n    \"\"\"Unit tests for the Ifg/interferogram class.\"\"\"\n\n    def setup_class(cls):\n        cls.ifg = Ifg(join(SML_TEST_TIF, 'geo_060619-061002_unw.tif'))\n        cls.ifg.open()\n        cls.ifg.nodata_value = 0\n\n    def test_headers_as_attr(self):\n        for a in ['ncols', 'nrows', 'x_first', 'x_step',\n                  'y_first', 'y_step', 'wavelength', 'first', 'second']:\n            assert getattr(self.ifg, a) is not None\n\n    def test_convert_to_nans(self):\n        self.ifg.convert_to_nans()\n        assert self.ifg.nan_converted\n\n    def test_xylast(self):\n        # ensure the X|Y_LAST header element has been created\n        assert self.ifg.x_last == pytest.approx(150.9491667)\n        assert self.ifg.y_last == pytest.approx(-34.23)\n\n    def test_num_cells(self):\n        # test cell size from header elements\n        data = self.ifg.phase_band.ReadAsArray()\n        ys, xs = data.shape\n        exp_ncells = ys * xs\n        assert exp_ncells == self.ifg.num_cells\n\n    def test_shape(self):\n        assert self.ifg.shape == self.ifg.phase_data.shape\n\n    def test_nan_count(self):\n        num_nan = 0\n        for row in self.ifg.phase_data:\n            for v in row:\n                if isnan(v):\n                    num_nan += 1\n        if self.ifg.nan_converted:\n            assert num_nan == self.ifg.nan_count\n        else:\n            assert num_nan == 0\n\n    def test_phase_band(self):\n        data = self.ifg.phase_band.ReadAsArray()\n        assert data.shape == (72, 47)\n\n    def test_nan_fraction(self):\n        # NB: source data lacks 0 -> NaN conversion\n        data = self.ifg.phase_data\n        data = where(data == 0, nan, data) # fake 0 -> nan for the count below\n\n        # manually count # nan cells\n        nans = 0\n        ys, xs = data.shape\n        for y, x in product(range(ys), range(xs)):\n            if isnan(data[y, x]):\n                nans += 1\n        del data\n\n        num_cells = float(ys * xs)\n        assert nans > 0\n        assert nans <= num_cells\n        assert nans / num_cells == self.ifg.nan_fraction\n\n    def test_xy_size(self):\n        assert ~ (self.ifg.ncols is None)\n        assert ~ (self.ifg.nrows is None)\n\n        # test with tolerance from base 90m cell\n        # within 2% of cells over small?\n        assert self.ifg.y_size > 88.0\n        assert self.ifg.y_size < 92.0, 'Got %s' % self.ifg.y_size\n\n        width = 76.9 # from nearby PyRate coords\n        assert self.ifg.x_size > 0.97 * width  # ~3% tolerance\n        assert  self.ifg.x_size < 1.03 * width\n\n    def test_centre_latlong(self):\n        lat_exp = self.ifg.y_first + \\\n                  (int(self.ifg.nrows / 2) * self.ifg.y_step)\n        long_exp = self.ifg.x_first + \\\n                   (int(self.ifg.ncols / 2) * self.ifg.x_step)\n        assert lat_exp == self.ifg.lat_centre\n        assert long_exp == self.ifg.long_centre\n\n    def test_centre_cell(self):\n        assert self.ifg.x_centre == 23\n        assert self.ifg.y_centre == 36\n\n    def test_time_span(self):\n        assert self.ifg.time_span == pytest.approx(0.287474332649)\n\n    def test_wavelength(self):\n        assert self.ifg.wavelength == pytest.approx(0.0562356424)\n\n\nclass TestIfgIOTests:\n\n    def setup_method(self):\n        self.ifg = Ifg(join(SML_TEST_TIF, 'geo_070709-070813_unw.tif'))\n        self.header = join(common.ROIPAC_SML_TEST_DIR, 'geo_070709-070813_unw.rsc')\n\n    def test_open(self):\n        assert self.ifg.dataset is None\n        assert self.ifg.is_open is False\n        self.ifg.open(readonly=True)\n        assert self.ifg.dataset is not None\n        assert self.ifg.is_open is True\n        assert isinstance(self.ifg.dataset, Dataset)\n\n        # ensure open cannot be called twice\n        with pytest.raises(RasterException):\n            self.ifg.open(True)\n\n    def test_open_ifg_from_dataset(self):\n        \"\"\"\n        Test showing open() can not be used for Ifg created with\n        gdal.Dataset object as Dataset has already been read in\n        \"\"\"\n        self.ifg.open()\n        dataset = self.ifg.dataset\n        new_ifg = Ifg(dataset)\n        with pytest.raises(RasterException):\n            new_ifg.open()\n\n    def test_write(self):\n        base = TEMPDIR\n        src = self.ifg.data_path\n        dest = join(base, basename(self.ifg.data_path))\n\n        # shutil.copy needs to copy writeable permission from src\n        os.chmod(src, S_IRGRP | S_IWGRP | S_IWOTH | S_IROTH |\n                 S_IRUSR | S_IWUSR)\n        shutil.copy(src, dest)\n        os.chmod(src, S_IRGRP | S_IROTH | S_IRUSR)  # revert\n\n        i = Ifg(dest)\n        i.open()\n        i.phase_data[0, 1:] = nan\n        i.write_modified_phase()\n        del i\n\n        # reopen to ensure data/nans can be read back out\n        i = Ifg(dest)\n        i.open(readonly=True)\n        assert_array_equal(True, isnan(i.phase_data[0, 1:]))\n        i.close()\n        os.remove(dest)\n\n    def test_write_fails_on_readonly(self):\n        # check readonly status is same before\n        # and after open() for readonly file\n        assert self.ifg.is_read_only\n        self.ifg.open(readonly=True)\n        assert self.ifg.is_read_only\n        with pytest.raises(IOError):\n            self.ifg.write_modified_phase()\n\n    def test_phase_band_unopened_ifg(self):\n        try:\n            _ = self.ifg.phase_band\n            self.fail(\"Should not be able to access band without open dataset\")\n        except RasterException:\n            pass\n\n    def test_nan_fraction_unopened(self):\n        try:\n            # NB: self.assertRaises doesn't work here (as it is a property?)\n            _ = self.ifg.nan_fraction\n            self.fail(\"Shouldn't be able to \"\n                      \"call nan_fraction() with unopened Ifg\")\n        except RasterException:\n            pass\n\n    def test_phase_data_properties(self):\n        # Use raw GDAL to isolate raster reading from Ifg functionality\n        ds = Open(self.ifg.data_path)\n        data = ds.GetRasterBand(1).ReadAsArray()\n        del ds\n\n        self.ifg.open()\n\n        # test full array and row by row access\n        assert_array_equal(data, self.ifg.phase_data)\n        for y, row in enumerate(self.ifg.phase_rows):\n            assert_array_equal(data[y], row)\n\n        # test the data is cached if changed\n        crd = (5, 4)\n        orig = self.ifg.phase_data[crd]\n        self.ifg.phase_data[crd] *= 2\n        nv = self.ifg.phase_data[crd]  # pull new value out again\n        assert nv == 2 * orig\n\n\n# FIXME:\n# class IncidenceFileTests():\n#     'Unit tests to verify operations on GeoTIFF format Incidence rasters'\n#\n#     def setUp(self):\n#         raise NotImplementedError\n#         self.inc = Incidence(join(INCID_TEST_DIR, '128x2.tif'))\n#         self.inc.open()\n#\n#\n#     def test_incidence_data(self):\n#         # check incidences rises while traversing the scene\n#         data = self.inc.incidence_data\n#         diff = data.ptp()\n#         self.assertTrue(diff > 0.5, \"Got ptp() diff of %s\" % diff)\n#\n#         # ascending pass, values should increase from W->E across scene\n#         for i in range(2):\n#             d = data[i]\n#             self.assertFalse((d == 0).any()) # ensure no NODATA\n#             self.assertFalse((isnan(d)).any())\n#\n#             diff = array([d[i+1] - d[i] for i in range(len(d)-1)])\n#             res = abs(diff[diff < 0])\n# TODO: check if this is normal\n#             self.assertTrue((res < 1e-4).all())\n#\n#\n#     def test_azimuth_data(self):\n#         # ensure azimuth is fairly constant\n#\n#         az = self.inc.azimuth_data\n#         self.assertFalse((az == 0).all())\n#         az = az[az != 0] # filter NODATA cells\n#\n#         # azimuth should be relatively constant\n#         ptp = az.ptp()\n#         self.assertTrue(ptp < 0.1, msg=\"min -> max diff is %s\" % ptp)\n\n\nclass TestDEMTests:\n    'Unit tests to verify operations on GeoTIFF format DEMs'\n\n    def setup_method(self):\n        self.ras = DEM(SML_TEST_DEM_TIF)\n\n    def test_create_raster(self):\n        # validate header path\n        assert os.path.exists(self.ras.data_path)\n\n    def test_headers_as_attr(self):\n        self.ras.open()\n        attrs = ['ncols', 'nrows', 'x_first', 'x_step', 'y_first', 'y_step' ]\n\n        # TODO: are 'projection' and 'datum' attrs needed?\n        for a in attrs:\n            assert getattr(self.ras, a) is not None\n\n    def test_is_dem(self):\n        self.ras = DEM(join(SML_TEST_TIF, 'geo_060619-061002_unw.tif'))\n        assert  ~hasattr(self.ras, 'datum')\n\n    def test_open(self):\n        assert self.ras.dataset is None\n        self.ras.open()\n        assert self.ras.dataset is not None\n        assert isinstance(self.ras.dataset, Dataset)\n\n        # ensure open cannot be called twice\n        with pytest.raises(RasterException):\n            self.ras.open()\n\n    # def test_band_fails_with_unopened_raster(self):  # now opening if not open\n    #     # test accessing bands with open and unopened datasets\n    #     with pytest.raises(RasterException):\n    #         self.ras.band\n\n    def test_band_read_with_open_raster(self):\n        data = self.ras.band.ReadAsArray()\n        assert data.shape == (72, 47)\n\n\nclass TestWriteUnw:\n\n    @classmethod\n    @pytest.fixture(autouse=True)\n    def setup_class(cls, gamma_params):\n        # change the required params\n        shared.mkdir_p(gamma_params[C.OUT_DIR])\n        from copy import deepcopy\n        cls.params = deepcopy(gamma_params)\n        cls.params[WORKING_DIR] = common.GAMMA_SML_TEST_DIR\n        cls.params[C.PROCESSOR] = 1  # gamma\n        cls.params[C.PARALLEL] = 0\n        cls.params[C.REF_EST_METHOD] = 1\n        cls.params[C.DEM_FILE] = common.SML_TEST_DEM_GAMMA\n        cls.params[C.BASE_FILE_LIST] = common.GAMMA_SML_TEST_DIR\n        # base_unw_paths need to be geotiffed and multilooked by run_prepifg\n        cls.base_unw_paths = tests.common.original_ifg_paths(cls.params[C.IFG_FILE_LIST], cls.params[WORKING_DIR])\n        cls.base_unw_paths.append(common.SML_TEST_DEM_GAMMA)\n\n        # dest_paths are tifs that have been geotif converted and multilooked\n        conv2tif.main(cls.params)\n        prepifg.main(cls.params)\n\n        cls.dest_paths = [Path(cls.params[C.INTERFEROGRAM_DIR]).joinpath(Path(c.sampled_path).name).as_posix()\n                          for c in gamma_params[C.INTERFEROGRAM_FILES]]\n        cls.dest_paths += [Path(cls.params[C.COHERENCE_DIR]).joinpath(Path(c.sampled_path).name).as_posix()\n                          for c in gamma_params[C.COHERENCE_FILE_PATHS]]\n\n        cls.ifgs = [dem_or_ifg(i) for i in cls.dest_paths]\n        for i in cls.ifgs:\n            i.open()\n            i.nodata_value = 0\n\n    @classmethod\n    def teardown_class(cls):\n        \"\"\"auto cleaning on\"\"\"\n\n    def test_unw_contains_same_data_as_numpy_array(self):\n        from datetime import time\n        temp_unw = tempfile.mktemp(suffix='.unw')\n        temp_tif = tempfile.mktemp(suffix='.tif')\n\n        # setup some header files for use in write_geotif\n        dem_header_file = common.SML_TEST_DEM_HDR_GAMMA\n        dem_header = gamma.parse_dem_header(dem_header_file)\n\n        header = gamma.parse_epoch_header(\n            os.path.join(common.GAMMA_SML_TEST_DIR, '20060828_slc.par'))\n        header.update(dem_header)\n             \n        base_header = gamma.parse_baseline_header(\n            os.path.join(common.GAMMA_SML_TEST_DIR, '20060828-20061211_base.par'))\n        header.update(base_header)\n                \n        # insert some dummy data so we are the dem in write_fullres_geotiff is not\n        # not activated and ifg write_fullres_geotiff operation works\n        header[ifc.PYRATE_TIME_SPAN] = 0\n        header[ifc.SECOND_DATE] = 0\n        header[ifc.DATA_UNITS] = 'degrees'\n        header[ifc.DATA_TYPE] = ifc.ORIG\n        header[ifc.SECOND_TIME] = time(10)\n        \n        # now create arbitrary data\n        data = np.random.rand(dem_header[ifc.PYRATE_NROWS], dem_header[ifc.PYRATE_NCOLS])\n\n        # convert numpy array to .unw\n        shared.write_unw_from_data_or_geotiff(geotif_or_data=data, dest_unw=temp_unw, ifg_proc=1)\n        # convert the .unw to geotif\n        shared.write_fullres_geotiff(header=header, data_path=temp_unw, dest=temp_tif, nodata=np.nan)\n\n        # now compare geotiff with original numpy array\n        ds = gdal.Open(temp_tif, gdal.GA_ReadOnly)\n        data_lv_theta = ds.ReadAsArray()\n        ds = None\n        np.testing.assert_array_almost_equal(data, data_lv_theta)\n        try:\n            os.remove(temp_tif)\n        except PermissionError:\n            print(\"File opened by another process.\")\n\n        try:\n            os.remove(temp_unw)\n        except PermissionError:\n            print(\"File opened by another process.\")\n\n    def test_multilooked_tiffs_converted_to_unw_are_same(self):\n        # Get multilooked geotiffs\n        geotiffs = list(set(self.dest_paths))\n        geotiffs = [g for g in geotiffs if 'dem' not in g]\n\n        # Convert back to .unw\n        dest_unws = []\n        for g in set(geotiffs):\n            dest_unw = os.path.join(self.params[C.OUT_DIR], Path(g).stem + '.unw')\n            shared.write_unw_from_data_or_geotiff(geotif_or_data=g, dest_unw=dest_unw, ifg_proc=1)\n            dest_unws.append(dest_unw)\n\n        dest_unws_ = []\n\n        for d in dest_unws:\n            dest_unws_.append(MultiplePaths(d, self.params))\n\n        # Convert back to tiff\n        new_geotiffs_ = conv2tif.do_geotiff(dest_unws_, self.params)\n        new_geotiffs = [gt for gt, b in new_geotiffs_]\n\n        # Ensure original multilooked geotiffs and \n        #  unw back to geotiff are the same\n        geotiffs.sort(key=lambda x: Path(x).name)\n        new_geotiffs.sort(key=lambda x: Path(x).name)\n\n        for g, u in zip(geotiffs, new_geotiffs):\n            g_ds = gdal.Open(g)\n            u_gs = gdal.Open(u)\n            np.testing.assert_array_almost_equal(u_gs.ReadAsArray(), g_ds.ReadAsArray())\n            u_gs = None\n            g_ds = None\n\n    def test_roipac_raises(self):\n        geotiffs = [os.path.join(\n            self.params[C.OUT_DIR], os.path.basename(b).split('.')[0] + '_'\n                                                   + os.path.basename(b).split('.')[1] + '.tif')\n            for b in self.base_unw_paths]\n\n        for g in geotiffs[:1]:\n            dest_unw = os.path.join(self.params[C.OUT_DIR], os.path.splitext(g)[0] + '.unw')\n            with pytest.raises(NotImplementedError):\n                shared.write_unw_from_data_or_geotiff(geotif_or_data=g, dest_unw=dest_unw, ifg_proc=0)\n\n\nclass TestGeodesy:\n\n    def test_utm_zone(self):\n        # test some different zones (collected manually)\n        for lon in [174.0, 176.5, 179.999, 180.0]:\n            assert 60 == _utm_zone(lon)\n\n        for lon in [144.0, 144.1, 146.3456, 149.9999]:\n            assert 55 == _utm_zone(lon)\n\n        for lon in [-180.0, -179.275, -176.925]:\n            assert 1 == _utm_zone(lon)\n\n        for lon in [-72.0, -66.1]:\n            assert 19 == _utm_zone(lon)\n\n        for lon in [0.0, 0.275, 3.925, 5.999]:\n            assert 31 == _utm_zone(lon)\n\n    def test_cell_size_polar_region(self):\n        # Can't have polar area zones: see http://www.dmap.co.uk/utmworld.htm\n        for lat in [-80.1, -85.0, -90.0, 84.1, 85.0, 89.9999, 90.0]:\n            with pytest.raises(ValueError):\n                cell_size(lat, 0, 0.1, 0.1)\n\n    def test_cell_size_calc(self):\n        # test conversion of X|Y_STEP to X|Y_SIZE\n        x_deg = 0.000833333\n        y_deg = -x_deg\n\n        approx = 90.0 # x_deg is approx 90m\n        exp_low = approx - (.15 * approx) # assumed tolerance\n        exp_high = approx + (.15 * approx)\n\n        latlons = [(10.0, 15.0), (-10.0, 15.0), (10.0, -15.0), (-10.0, -15.0),\n            (178.0, 33.0), (-178.0, 33.0), (178.0, -33.0), (-178.0, -33.0) ]\n\n        for lon, lat in latlons:\n            xs, ys = cell_size(lat, lon, x_deg, y_deg)\n            for s in (xs, ys):\n                assert s > 0, \"size=%s\" % s\n                assert s > exp_low, \"size=%s\" % s\n                assert s < exp_high, \"size=%s\" % s\n\n\n@pytest.fixture(params=[0, 1])\ndef parallel(request):\n    return request.param\n\n\ndef get_random_string(length):\n    letters = string.ascii_lowercase\n    result_str = ''.join(random.choice(letters) for _ in range(length))\n    return result_str\n\n\ndef _data_types():\n    return {\n        'str': [get_random_string(np.random.randint(2, 10)) for _ in range(20)],\n        'int': np.arange(20),\n        'ndarray': [np.random.randint(low=0, high=10, size=(2, 3), dtype=np.uint8) for _ in range(20)]\n        }\n\n\ndata_types = mpiops.run_once(_data_types)  # required otherwise differnt arrays are generated in each mpi process\n\n\n@pytest.fixture(params=list(data_types.keys()))\ndef data_type(request):\n    return request.param\n\n\ndef test_tiles_split(parallel, data_type):\n    params = {\n        C.TILES: data_types[data_type],\n        C.PARALLEL: parallel,\n        C.LOG_LEVEL: 'INFO',\n        'multiplier': 2,\n        C.PROCESSES: 4\n    }\n\n    def func(tile, params):\n        return tile * params['multiplier']\n\n    ret = tiles_split(func, params)\n\n    expected_ret = np.array([item*params['multiplier'] for item in data_types[data_type]], dtype=object)\n    np.testing.assert_array_equal(ret, expected_ret)\n\n\ndef test_convert_to_radians():\n    import math\n    data = np.random.randint(1, 10, (4, 5))\n    wavelength = 10.5\n    ret = shared.convert_mm_to_radians(data, wavelength)\n    expected = data * (4 * math.pi) / wavelength /ifc.MM_PER_METRE\n    np.testing.assert_array_almost_equal(ret, expected)\n\n\ndef test_convert_to_radians_ifg(ten_geotiffs):\n    for g in ten_geotiffs[:2]:\n        ifg = Ifg(g)\n        ifg.open()\n        md = ifg.dataset.GetMetadata()\n        assert ifc.DATA_TYPE in md\n        assert md[ifc.DATA_TYPE] == ifc.ORIG\n        assert md[ifc.DATA_UNITS] == shared.RADIANS\n        rad_data = ifg.phase_data\n        ifg.convert_to_mm()\n        assert ifg.meta_data[ifc.DATA_UNITS] == shared.MILLIMETRES\n        ifg.convert_to_radians()\n        assert md[ifc.DATA_UNITS] == shared.RADIANS\n        np.testing.assert_array_almost_equal(rad_data, ifg.phase_data, decimal=4)\n"
  },
  {
    "path": "tests/test_stackrate.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module contains tests for the stack.py PyRate module.\n\"\"\"\nimport os\nimport shutil\n\nfrom numpy import eye, array, ones, nan\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal, assert_array_equal\n\nimport pyrate.constants as C\nimport pyrate.core.orbital\nimport pyrate.core.prepifg_helper\nimport pyrate.core.ref_phs_est\nimport pyrate.core.refpixel\nimport tests.common\nfrom pyrate.core import covariance as vcm_module\nfrom pyrate.core.stack import stack_rate_pixel, mask_rate\nfrom pyrate import correct, prepifg, conv2tif\nfrom pyrate.configuration import Configuration\nfrom tests import common\nfrom tests.common import SML_TEST_DIR, prepare_ifgs_without_phase, pre_prepare_ifgs\n\n\ndef default_params():\n    return {'pthr': 3, 'nsig': 3, 'maxsig': 2, 'parallel': 1, 'processes': 8}\n\n\nclass SinglePixelIfg(object):\n\n    def __init__(self, timespan, phase):\n        self.time_span = timespan\n        self.phase_data = array([[phase]])\n\n\nclass TestStackRatePixel:\n    \"\"\"\n    Tests the weighted least squares algorithm for determining\n    the best fitting velocity\n    \"\"\"\n\n    def setup_method(self):\n        self.phase = array([0.5, 3.5, 4, 2.5, 3.5, 1])\n        self.timespan = array([[0.1, 0.7, 0.8, 0.5, 0.7, 0.2]])\n        self.vcmt = eye(6, 6)\n        self.mst = ones((6, 1, 1))\n        self.mst[4] = 0\n        self.params = default_params()\n\n    def test_stack_rate_pixel(self):\n        # Simple test with one pixel and equal weighting\n        exprate = array([[5.0]])\n        experr = array([[0.836242010007091]])\n        expsamp = array([[5]])\n        rate, error, samples = stack_rate_pixel(self.phase, self.mst, self.vcmt,\n                self.timespan, self.params['nsig'], self.params['pthr'])\n        assert_array_almost_equal(rate, exprate)\n        assert_array_almost_equal(error, experr)\n        assert_array_almost_equal(samples, expsamp)\n\n\nclass TestMaskRate:\n    \"\"\"\n    Test the maxsig threshold masking algorithm\n    \"\"\"\n\n    def setup_method(self):\n        self.r = array([5.0, 4.5]) # rates for 2 pixels\n        self.e = array([1.1, 2.1]) # errors for 2 pixels\n\n    def test_mask_rate_maxsig1(self):\n        # both rate and error values masked\n        rate, error = mask_rate(self.r, self.e, 1)\n        assert_array_equal(rate, array([nan, nan]))\n        assert_array_equal(error, array([nan, nan]))\n\n    def test_mask_rate_maxsig2(self):\n        # one rate and one error masked\n        rate, error = mask_rate(self.r, self.e, 2)\n        assert_array_equal(rate, array([5.0, nan]))\n        assert_array_equal(error, array([1.1, nan]))\n\n    def test_mask_rate_maxsig3(self):\n        # No values masked in rate or error\n        rate, error = mask_rate(self.r, self.e, 3)\n        assert_array_equal(rate, self.r)\n        assert_array_equal(error, self.e)\n\n\nclass TestLegacyEquality:\n    \"\"\"\n    Tests equality with legacy data\n    \"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        params[C.TEMP_MLOOKED_DIR] = os.path.join(params[C.OUT_DIR], C.TEMP_MLOOKED_DIR)\n\n        # force error maps to 1-sigma to match legacy\n        params[C.VELERROR_NSIG] = 1\n        conv2tif.main(params)\n        prepifg.main(params)\n\n        params[C.REF_EST_METHOD] = 2\n\n        xlks, _, crop = pyrate.core.prepifg_helper.transform_params(params)\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n        correct._copy_mlooked(params)\n        copied_dest_paths = [os.path.join(params[C.TEMP_MLOOKED_DIR], os.path.basename(d)) for d in dest_paths]\n        del dest_paths\n        # start run_pyrate copy\n        ifgs = pre_prepare_ifgs(copied_dest_paths, params)\n        mst_grid = tests.common.mst_calculation(copied_dest_paths, params)\n\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(params)\n\n        params[C.REFX] = refx\n        params[C.REFY] = refy\n        params[C.ORBFIT_OFFSET] = True\n\n        # Estimate and remove orbit errors\n        pyrate.core.orbital.remove_orbital_error(ifgs, params)\n        ifgs = prepare_ifgs_without_phase(copied_dest_paths, params)\n        for ifg in ifgs:\n            ifg.close()\n        correct._update_params_with_tiles(params)\n        _, ifgs = pyrate.core.ref_phs_est.ref_phase_est_wrapper(params)\n        ifgs[0].open()\n        r_dist = vcm_module.RDist(ifgs[0])()\n        ifgs[0].close()\n        maxvar = [vcm_module.cvd(i, params, r_dist)[0] for i in copied_dest_paths]\n        for ifg in ifgs:\n            ifg.open()\n        vcmt = vcm_module.get_vcmt(ifgs, maxvar)    \n        for ifg in ifgs:\n            ifg.close()     \n            ifg.open()\n        \n        # Calculate stacked rate map\n        params[C.PARALLEL] = 1\n        cls.rate, cls.error, cls.samples = tests.common.calculate_stack_rate(ifgs, params, vcmt, mst_mat=mst_grid)\n\n        # Calculate stacked rate map\n        params[C.PARALLEL] = 0\n        cls.rate_s, cls.error_s, cls.samples_s = tests.common.calculate_stack_rate(ifgs, params, vcmt, mst_mat=mst_grid)\n\n        stackrate_dir = os.path.join(SML_TEST_DIR, 'stackrate')\n\n        cls.rate_container = np.genfromtxt(os.path.join(stackrate_dir, 'stackmap.csv'), delimiter=',')\n        cls.error_container = np.genfromtxt(os.path.join(stackrate_dir, 'errormap.csv'), delimiter=',')\n        cls.samples_container = np.genfromtxt(os.path.join(stackrate_dir, 'coh_sta.csv'), delimiter=',')\n    \n        for ifg in ifgs:\n            ifg.close()\n\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_stack_rate_full_parallel(self):\n        \"\"\"\n        python multiprocessing by rows vs serial\n        \"\"\"\n        assert_array_almost_equal(self.rate, self.rate_s, decimal=3)\n\n    def test_stackrate_error_parallel(self):\n        \"\"\"\n        python multiprocessing by rows vs serial\n        \"\"\"\n        assert_array_almost_equal(self.error, self.error_s, decimal=3)\n\n    def test_stackrate_samples_parallel(self):\n        \"\"\"\n        python multiprocessing by rows vs serial\n        \"\"\"\n        assert_array_almost_equal(self.samples, self.samples_s, decimal=3)\n\n    def test_stack_rate(self):\n        \"\"\"\n        Compare with legacy data\n        \"\"\"\n        assert_array_almost_equal(self.rate_s, self.rate_container, decimal=3)\n\n    def test_stackrate_error(self):\n        \"\"\"\n        Compare with legacy data\n        \"\"\"\n        assert_array_almost_equal(self.error_s, self.error_container, decimal=3)\n\n    def test_stackrate_samples(self):\n        \"\"\"\n        Compare with legacy data\n        \"\"\"\n        assert_array_almost_equal(self.samples_s, self.samples_container, decimal=3)\n"
  },
  {
    "path": "tests/test_system.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\"\"\"\npyrate basic workflow for all supported input datasets\n\n\"\"\"\nimport shutil\nfrom subprocess import check_call\nfrom pathlib import Path\nimport pytest\nimport numpy as np\nimport pyrate.constants as C\nfrom pyrate.core.mpiops import MPI_INSTALLED\nfrom pyrate.configuration import Configuration\nfrom tests.common import MEXICO_CROPA_CONF, PY37GDAL302\n\n\n@pytest.mark.mpi\n@pytest.mark.slow\n@pytest.mark.skipif(not PY37GDAL302, reason=\"Only run in one CI env\")\ndef test_workflow(system_conf):\n    \"\"\"check the handlers are working as expected\"\"\"\n    check_call(f\"mpirun -n 3 pyrate conv2tif -f {system_conf}\", shell=True)\n    check_call(f\"mpirun -n 3 pyrate prepifg -f {system_conf}\", shell=True)\n    check_call(f\"mpirun -n 3 pyrate correct -f {system_conf}\", shell=True)\n    check_call(f\"mpirun -n 3 pyrate timeseries -f {system_conf}\", shell=True)\n    check_call(f\"mpirun -n 3 pyrate stack -f {system_conf}\", shell=True)\n    check_call(f\"mpirun -n 3 pyrate merge -f {system_conf}\", shell=True)\n\n    # assert logs generated in the outdir\n    params = Configuration(system_conf).__dict__\n    for stage in ['conv2tif', 'prepifg', 'correct', 'timeseries', 'stack', 'merge']:\n        log_file_name = 'pyrate.log.' + stage\n        files = list(Path(params[C.OUT_DIR]).glob(log_file_name + '.*'))\n        assert len(files) == 1\n    shutil.rmtree(params[C.OUT_DIR])\n\n\ndef test_single_workflow(gamma_or_mexicoa_conf):\n    if MPI_INSTALLED:\n        check_call(f\"mpirun -n 4 pyrate workflow -f {gamma_or_mexicoa_conf}\", shell=True)\n    else:\n        check_call(f\"pyrate workflow -f {gamma_or_mexicoa_conf}\", shell=True)\n\n    params = Configuration(gamma_or_mexicoa_conf).__dict__\n\n    log_file_name = 'pyrate.log.' + 'workflow'\n    files = list(Path(params[C.OUT_DIR]).glob(log_file_name + '.*'))\n    assert len(files) == 1\n\n    # ref pixel file generated\n    ref_pixel_file = params[C.REF_PIXEL_FILE]\n    assert Path(ref_pixel_file).exists()\n    ref_pixel = np.load(ref_pixel_file)\n    if gamma_or_mexicoa_conf == MEXICO_CROPA_CONF:\n        np.testing.assert_array_equal(ref_pixel, [42, 2])\n        for f in C.GEOMETRY_OUTPUT_TYPES:\n            assert Path(params[C.GEOMETRY_DIR]).joinpath(f + '.tif').exists()\n    else:\n        np.testing.assert_array_equal(ref_pixel, [38, 58])\n\n    # assert orbfit exists on disc\n    from pyrate.core import shared\n    looked_files = [p.sampled_path for p in params[C.INTERFEROGRAM_FILES]]\n    ifgs = [shared.Ifg(ifg) for ifg in looked_files]\n    orbfits_on_disc = [Path(params[C.OUT_DIR], C.ORB_ERROR_DIR, Path(ifg.data_path).stem + '_orbfit.npy')\n                       for ifg in ifgs]\n    assert all(orbfits_on_disc)\n    shutil.rmtree(params[C.OUT_DIR])\n"
  },
  {
    "path": "tests/test_timeseries.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n# coding: utf-8\n\"\"\"\nThis Python module contains tests for the timeseries.py PyRate module.\n\"\"\"\nimport os\nimport shutil\nfrom copy import deepcopy\nimport pytest\nfrom datetime import date, timedelta\nfrom numpy import nan, asarray, where, array\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal\n\nimport pyrate.constants as C\nimport pyrate.core.orbital\nimport pyrate.core.prepifg_helper\nimport pyrate.core.ref_phs_est\nimport pyrate.core.refpixel\nimport tests.common as common\nfrom pyrate.core import mst, covariance\nfrom pyrate import correct, prepifg, conv2tif\nfrom pyrate.configuration import Configuration\nfrom pyrate.core.timeseries import time_series, linear_rate_pixel, linear_rate_array, TimeSeriesError\n\n\ndef default_params():\n    return {C.TIME_SERIES_METHOD: 1,\n            C.TIME_SERIES_PTHRESH: 0,\n            C.TIME_SERIES_SM_ORDER: 2,\n            C.TIME_SERIES_SM_FACTOR: -0.25,\n            C.PARALLEL: 0,\n            C.PROCESSES: 1,\n            C.NAN_CONVERSION: 1,\n            C.NO_DATA_VALUE: 0}\n\n\nclass SinglePixelIfg(object):\n    \"\"\"\n    A single pixel ifg (interferogram) solely for unit testing\n    \"\"\"\n\n    def __init__(self, first, second, phase, nan_fraction):\n        self.phase_data = asarray([[phase]])\n        self.first = first\n        self.second = second\n        self.nrows = 1\n        self.ncols = 1\n        self.nan_fraction = asarray([nan_fraction])\n\n    def convert_to_nans(self, val=0):\n        \"\"\"\n        Converts given values in phase data to NaNs\n        val - value to convert, default is 0\n        \"\"\"\n        self.phase_data = where(self.phase_data == val, nan, self.phase_data)\n        self.nan_converted = True\n\n\nclass TestTimeSeries:\n    \"\"\"Verifies error checking capabilities of the time_series function\"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        cls.ifgs = common.small_data_setup()\n        cls.params = default_params()\n        cls.mstmat = mst.mst_boolean_array(cls.ifgs)\n        r_dist = covariance.RDist(cls.ifgs[0])()\n        cls.maxvar = [covariance.cvd(i.data_path, cls.params, r_dist)[0]\n                      for i in cls.ifgs]\n        cls.vcmt = covariance.get_vcmt(cls.ifgs, cls.maxvar)\n\n    def test_time_series_unit(self):\n        \"\"\"\n        Checks that the code works the same as the calculated example\n        \"\"\"\n        ifirst = asarray([1, 1, 2, 2, 3, 3, 4, 5])\n        isecond = asarray([2, 4, 3, 4, 5, 6, 6, 6])\n        timeseries = asarray([0.0, 0.1, 0.6, 0.8, 1.1, 1.3])\n        phase = asarray([0.5, 4, 2.5, 3.5, 2.5, 3.5, 2.5, 1])\n        nan_fraction = asarray([0.5, 0.4, 0.2, 0.3, 0.1, 0.3, 0.2, 0.1])\n\n        now = date.today()\n\n        dates = [now + timedelta(days=(t*365.25)) for t in timeseries]\n        dates.sort()\n        first = [dates[m_num - 1] for m_num in ifirst]\n        second = [dates[s_num - 1] for s_num in isecond]\n\n        self.ifgs = [SinglePixelIfg(m, s, p, n) for m, s, p, n in\n                     zip(first, second, phase, nan_fraction)]\n\n        tsincr, tscum, tsvel = time_series(\n            self.ifgs, params=self.params, vcmt=self.vcmt, mst=None)\n        expected = asarray([[[0.50, 3.0, 4.0, 5.5, 6.5]]])\n        assert_array_almost_equal(tscum, expected, decimal=2)\n\n\nclass TestLegacyTimeSeriesEquality:\n\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        params[C.TEMP_MLOOKED_DIR] = os.path.join(params[C.OUT_DIR],\n                                                                 C.TEMP_MLOOKED_DIR)\n        conv2tif.main(params)\n        prepifg.main(params)\n\n        params[C.REF_EST_METHOD] = 2\n\n        xlks, _, crop = pyrate.core.prepifg_helper.transform_params(params)\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n        correct._copy_mlooked(params)\n        copied_dest_paths = [os.path.join(params[C.TEMP_MLOOKED_DIR], os.path.basename(d)) for d in dest_paths]\n        del dest_paths\n        # start run_pyrate copy\n        ifgs = common.pre_prepare_ifgs(copied_dest_paths, params)\n        mst_grid = common.mst_calculation(copied_dest_paths, params)\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(params)\n\n        params[C.REFX] = refx\n        params[C.REFY] = refy\n        params[C.ORBFIT_OFFSET] = True\n        # Estimate and remove orbit errors\n        pyrate.core.orbital.remove_orbital_error(ifgs, params)\n        ifgs = common.prepare_ifgs_without_phase(copied_dest_paths, params)\n        for ifg in ifgs:\n            ifg.close()\n        correct._update_params_with_tiles(params)\n        _, ifgs = pyrate.core.ref_phs_est.ref_phase_est_wrapper(params)\n        ifgs[0].open()\n        r_dist = covariance.RDist(ifgs[0])()\n        ifgs[0].close()\n        maxvar = [covariance.cvd(i, params, r_dist)[0] for i in copied_dest_paths]\n        for ifg in ifgs:\n            ifg.open()\n        vcmt = covariance.get_vcmt(ifgs, maxvar)\n\n        for ifg in ifgs:\n            ifg.close()\n            ifg.open()\n            ifg.nodata_value = 0.0\n\n        params[C.TIME_SERIES_METHOD] = 1\n        params[C.PARALLEL] = 0\n        # Calculate time series\n        cls.tsincr_0, cls.tscum_0, _ = common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid)\n\n        params[C.PARALLEL] = 1\n        cls.tsincr_1, cls.tscum_1, cls.tsvel_1 = common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid)\n\n        # load the legacy data\n        ts_dir = os.path.join(common.SML_TEST_DIR, 'time_series')\n        tsincr_path = os.path.join(ts_dir, 'ts_incr_interp0_method1.csv')\n        ts_incr = np.genfromtxt(tsincr_path)\n\n        tscum_path = os.path.join(ts_dir, 'ts_cum_interp0_method1.csv')\n        ts_cum = np.genfromtxt(tscum_path)\n        cls.ts_incr = np.reshape(ts_incr, newshape=cls.tsincr_0.shape, order='F')\n        cls.ts_cum = np.reshape(ts_cum, newshape=cls.tscum_0.shape, order='F')\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_time_series_equality_parallel_by_rows(self):\n        \"\"\"\n        check time series parallel by rows jobs\n        \"\"\"\n\n        self.assertEqual(self.tsincr_1.shape, self.tscum_1.shape)\n        self.assertEqual(self.tsvel_1.shape, self.tsincr_1.shape)\n\n        np.testing.assert_array_almost_equal(\n            self.ts_incr, self.tsincr_1, decimal=3)\n\n        np.testing.assert_array_almost_equal(\n            self.ts_cum, self.tscum_1, decimal=3)\n\n    def test_time_series_equality_serial_by_the_pixel(self):\n        \"\"\"\n        check time series\n        \"\"\"\n\n        self.assertEqual(self.tsincr_0.shape, self.tscum_0.shape)\n\n        np.testing.assert_array_almost_equal(\n            self.ts_incr, self.tsincr_0, decimal=3)\n\n        np.testing.assert_array_almost_equal(\n            self.ts_cum, self.tscum_0, decimal=3)\n\n    @staticmethod\n    def assertEqual(val1, val2):\n        assert val1 == val2\n\n\nclass TestLegacyTimeSeriesEqualityMethod2Interp0:\n\n    @classmethod\n    def setup_class(cls):\n        params = Configuration(common.TEST_CONF_ROIPAC).__dict__\n        params[C.TEMP_MLOOKED_DIR] = os.path.join(params[C.OUT_DIR],\n                                                                 C.TEMP_MLOOKED_DIR)\n        conv2tif.main(params)\n        prepifg.main(params)\n\n        params[C.REF_EST_METHOD] = 2\n\n        xlks, _, crop = pyrate.core.prepifg_helper.transform_params(params)\n\n        dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params)\n        correct._copy_mlooked(params)\n        copied_dest_paths = [os.path.join(params[C.TEMP_MLOOKED_DIR], os.path.basename(d)) for d in dest_paths]\n        del dest_paths\n        # start run_pyrate copy\n        ifgs = common.pre_prepare_ifgs(copied_dest_paths, params)\n        mst_grid = common.mst_calculation(copied_dest_paths, params)\n        refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(params)\n\n        params[C.REFX] = refx\n        params[C.REFY] = refy\n        params[C.ORBFIT_OFFSET] = True\n\n        # Estimate and remove orbit errors\n        pyrate.core.orbital.remove_orbital_error(ifgs, params)\n        ifgs = common.prepare_ifgs_without_phase(copied_dest_paths, params)\n        for ifg in ifgs:\n            ifg.close()\n\n        correct._update_params_with_tiles(params)\n        _, ifgs = pyrate.core.ref_phs_est.ref_phase_est_wrapper(params)\n        ifgs[0].open()\n        r_dist = covariance.RDist(ifgs[0])()\n        ifgs[0].close()\n        # Calculate interferogram noise\n        maxvar = [covariance.cvd(i, params, r_dist)[0] for i in copied_dest_paths]\n        for ifg in ifgs:\n            ifg.open()\n        vcmt = covariance.get_vcmt(ifgs, maxvar)\n        for ifg in ifgs:\n            ifg.close()\n            ifg.open()\n            ifg.nodata_value = 0.0\n\n        params[C.TIME_SERIES_METHOD] = 2\n        params[C.PARALLEL] = 1\n        # Calculate time series\n        cls.tsincr, cls.tscum, _ = common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid)\n\n        params[C.PARALLEL] = 0\n        # Calculate time series serailly by the pixel\n        cls.tsincr_0, cls.tscum_0, _ = common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid)\n\n        # copy legacy data\n        SML_TIME_SERIES_DIR = os.path.join(common.SML_TEST_DIR, 'time_series')\n        tsincr_path = os.path.join(SML_TIME_SERIES_DIR, 'ts_incr_interp0_method2.csv')\n        ts_incr = np.genfromtxt(tsincr_path)\n\n        tscum_path = os.path.join(SML_TIME_SERIES_DIR, 'ts_cum_interp0_method2.csv')\n        ts_cum = np.genfromtxt(tscum_path)\n\n        cls.ts_incr = np.reshape(ts_incr, newshape=cls.tsincr_0.shape, order='F')\n        cls.ts_cum = np.reshape(ts_cum, newshape=cls.tscum_0.shape, order='F')\n        cls.params = params\n\n    @classmethod\n    def teardown_class(cls):\n        shutil.rmtree(cls.params[C.OUT_DIR])\n\n    def test_time_series_equality_parallel_by_rows(self):\n\n        assert self.tsincr.shape == self.tscum.shape\n\n        np.testing.assert_array_almost_equal(self.ts_incr, self.tsincr, decimal=1)\n\n        np.testing.assert_array_almost_equal(self.ts_cum, self.tscum, decimal=1)\n\n    def test_time_series_equality_serial_by_the_pixel(self):\n\n        assert self.tsincr_0.shape == self.tscum_0.shape\n\n        np.testing.assert_array_almost_equal(self.ts_incr, self.tsincr_0, decimal=3)\n\n        np.testing.assert_array_almost_equal(self.ts_cum, self.tscum_0, decimal=3)\n\n\nclass TestLinearRatePixel:\n    \"\"\"\n    Tests the linear regression algorithm for determining the best\n    fitting velocity from a cumulative time series\n    \"\"\"\n\n    def test_linear_rate_pixel_clean(self):\n        y = array([0, 2, 4, 6, 8, 10])\n        t = array([0, 1, 2, 3, 4, 5])\n        exp = (2.0, 0.0, 1.0, 0.0, 6)\n        res = linear_rate_pixel(y, t)\n        assert res == exp\n\n    def test_linear_rate_pixel_neg_rate(self):\n        y = array([0, -2, -4, -6, -8, -10])\n        t = array([0, 1, 2, 3, 4, 5])\n        exp = (-2.0, 0.0, 1.0, 0.0, 6)\n        res = linear_rate_pixel(y, t)\n        assert res == exp\n\n    def test_linear_rate_pixel_outlier(self):\n        y = array([0, 2, 4, 6, 8, 20])\n        t = array([0, 1, 2, 3, 4, 5])\n        exp = (3.428571, -1.904761, 0.812030, 0.824786, 6)\n        res = linear_rate_pixel(y, t)\n        assert res == pytest.approx(exp, rel=1e-6)\n\n    def test_linear_rate_pixel_noise(self):\n        y = array([0, 2, 4, 6, 8, 10])\n        r = y + np.random.rand(6) # add different uniform noise each time\n        t = array([0, 1, 2, 3, 4, 5])\n        exprate = 2.0\n        explsqd = 1.0\n        experr = 0.0\n        rate, _, lsqd, err, _ = linear_rate_pixel(y, t)\n        assert exprate == pytest.approx(rate, rel=1e-1)\n        assert explsqd == pytest.approx(lsqd, rel=1e-1)\n        assert experr == pytest.approx(err, rel=1e-1)\n\n    def test_linear_rate_pixel_exception(self):\n        # input vectors should be equal length\n        y = array([2, 4, 6, 8, 10])\n        t = array([0, 1, 2, 3, 4, 5])\n        with pytest.raises(TimeSeriesError):\n            res = linear_rate_pixel(y, t)\n\n    def test_linear_rate_pixel_nans(self):\n        # at least two obs are required for line fitting\n        y = array([0, nan, nan, nan, nan, nan])\n        t = array([0, 1, 2, 3, 4, 5])\n        exp = (nan, nan, nan, nan, nan)\n        res = linear_rate_pixel(y, t)\n        assert res == exp\n\n\nclass TestLinearRateArray:\n    \"\"\"\n    Tests the array loop wrapper for the linear regression algorithm using real data\n    \"\"\"\n\n    @classmethod\n    @pytest.fixture(autouse=True)\n    def setup_class(cls, roipac_params):\n        cls.params = roipac_params\n        cls.ifgs = common.small_data_setup()\n\n        # read in input (tscuml) and expected output arrays\n        tscuml_path = os.path.join(common.SML_TEST_LINRATE, \"tscuml_0.npy\")\n        cls.tscuml0 = np.load(tscuml_path)\n        # add zero epoch to tscuml 3D array\n        cls.tscuml = np.insert(cls.tscuml0, 0, 0, axis=2) \n\n        linrate_path = os.path.join(common.SML_TEST_LINRATE, \"linear_rate.npy\")\n        cls.linrate = np.load(linrate_path)\n\n        error_path = os.path.join(common.SML_TEST_LINRATE, \"linear_error.npy\")\n        cls.error = np.load(error_path)\n\n        icpt_path = os.path.join(common.SML_TEST_LINRATE, \"linear_intercept.npy\")\n        cls.icpt = np.load(icpt_path)\n\n        samp_path = os.path.join(common.SML_TEST_LINRATE, \"linear_samples.npy\")\n        cls.samp = np.load(samp_path)\n\n        rsq_path = os.path.join(common.SML_TEST_LINRATE, \"linear_rsquared.npy\")\n        cls.rsq = np.load(rsq_path)\n\n    def test_linear_rate_array(self):\n        \"\"\"\n        Input and expected output are on disk. This test only tests the linear_rate_array\n        and linear_rate_pixel functions using real data.\n        \"\"\"\n        l, i, r, e, s = linear_rate_array(self.tscuml, self.ifgs, self.params)\n        # test to 20 decimal places\n        assert_array_almost_equal(self.linrate, l, 1e-20)\n        assert_array_almost_equal(self.icpt, i, 1e-20)\n        assert_array_almost_equal(self.rsq, r, 1e-20)\n        assert_array_almost_equal(self.error, e, 1e-20)\n        assert_array_almost_equal(self.samp, s, 1e-20)\n\n    def test_linear_rate_array_two_sigma(self):\n        \"\"\"\n        Check that the \"nsigma\" switch in the config dictionary\n        actually results in a change in the error map.\n        \"\"\"\n        # make a deep copy of the params dict to avoid changing\n        # state for other tests if this one fails\n        params = deepcopy(self.params)\n        params[C.VELERROR_NSIG] = 2\n        _, _, _, e, _ = linear_rate_array(self.tscuml, self.ifgs, params)\n        assert_array_almost_equal(self.error*2, e, 1e-20)\n\n    def test_linear_rate_array_exception(self):\n        # depth of tscuml should equal nepochs\n        with pytest.raises(TimeSeriesError):\n            res = linear_rate_array(self.tscuml0, self.ifgs, self.params)\n\n"
  },
  {
    "path": "utils/.coveragerc",
    "content": "# .coveragerc to control coverage.py\n[run]\nbranch = True\nsource = pyrate\nomit =\n    # omit everything in\n    # pyrate/tasks/*\n    # omit these files\n    pyrate/pyratelog.py\n    pyrate/scripts/main.py\n\n[report]\n# Regexes for lines to exclude from consideration\nexclude_lines =\n    # Have to re-enable the standard pragma\n    pragma: no cover\n\n    # Don't complain about missing debug-only code:\n    def __repr__\n    def __str__\n    if self\\.debug\n\n    # Don't complain if tests don't hit defensive assertion code:\n    raise AssertionError\n    raise NotImplementedError\n    raise ValueError\n    raise RuntimeError\n    raise IOError\n    except OSError\n    except ValueError\n    except IndexError\n    raise ImportError\n    except ImportError\n    raise ConfigException\n    raise ReferencePhaseError\n    raise RefPixelError\n    raise RoipacException\n    raise GeotiffException\n    raise RasterException\n    raise IfgException\n    raise GammaException\n    raise PreprocessError\n    raise TimeSeriesError\n    raise OrbitalError\n    raise CorrectionStatusError\n\n    # Don't complain if non-runnable code isn't run:\n    if 0:\n    if __name__ == .__main__.:\n\nignore_errors = True\n\n[html]\ndirectory = coverage_html_report\n"
  },
  {
    "path": "utils/__init__.py",
    "content": ""
  },
  {
    "path": "utils/create_lv_theta.py",
    "content": "# This Python module is part of the PyRate software package\n#\n# Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n'''\nThis is used to create the dummy incidence map file .inc file\nThis is used to create the dummy elevation map file .lv_theta file\n'''\n\nimport os\n\nimport numpy as np\nfrom osgeo import gdal\n\nfrom pyrate.core import shared, ifgconstants as ifc, gamma\nfrom tests import common\n\nelevation_file = os.path.join(common.GAMMA_SML_TEST_DIR,\n                              os.path.splitext(common.SML_TEST_DEM_GAMMA)[0]\n                              + '.lv_theta')\n\ninc_file = os.path.join(common.GAMMA_SML_TEST_DIR,\n                        os.path.splitext(common.SML_TEST_DEM_GAMMA)[0]\n                        + '.inc')\n\ndest_lv_theta = os.path.splitext(elevation_file)[0] + '_lv_theta.tif'\ndest_inc = os.path.splitext(elevation_file)[0] + '_inc.tif'\n\ndem_header_file = common.SML_TEST_DEM_HDR_GAMMA\n\ndem_header = gamma.parse_dem_header(dem_header_file)\n\nheader = gamma.parse_epoch_header(\n    os.path.join(common.GAMMA_SML_TEST_DIR, '20060828_slc.par'))\n\n\nincidence_angle = header[ifc.PYRATE_INCIDENCE_DEGREES]\nincidence_data = np.ones(shape=(dem_header[ifc.PYRATE_NROWS],\n                                dem_header[ifc.PYRATE_NCOLS])\n                         ) * incidence_angle\n\nelevation_data = np.ones(shape=(dem_header[ifc.PYRATE_NROWS],\n                                dem_header[ifc.PYRATE_NCOLS])\n                         ) * (90.0 - incidence_angle)\n\nshared.write_unw_from_data_or_geotiff(geotif_or_data=incidence_data,\n                                      dest_unw=inc_file,\n                                      ifg_proc=1)\n\nshared.write_unw_from_data_or_geotiff(geotif_or_data=elevation_data,\n                                      dest_unw=elevation_file,\n                                      ifg_proc=1)\n\nheader.update(dem_header)\nheader[ifc.PYRATE_TIME_SPAN] = 0\nheader[ifc.SECOND_DATE] = 0\nheader[ifc.DATA_UNITS] = 'degrees'\nheader[ifc.DATA_TYPE] = ifc.INCIDENCE\nheader[ifc.SECOND_TIME] = 0\nshared.write_geotiff(header=header, data_path=elevation_file,\n                     dest=dest_lv_theta, nodata=np.nan)\n\nshared.write_geotiff(header=header, data_path=inc_file,\n                     dest=dest_inc, nodata=np.nan)\n\n\nds = gdal.Open(dest_lv_theta, gdal.GA_ReadOnly)\ndata_elevation = ds.ReadAsArray()\nds = None\n\n\nds = gdal.Open(dest_inc, gdal.GA_ReadOnly)\ndata_inc = ds.ReadAsArray()\nds = None\n\nnp.testing.assert_array_almost_equal(90 - incidence_data, data_elevation,\n                                     decimal=4)\nnp.testing.assert_array_almost_equal(incidence_data, data_inc,\n                                     decimal=4)\n"
  },
  {
    "path": "utils/crop_ifgs.py",
    "content": "# This Python module is part of the PyRate software package\n#\n# Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\npython utility to crop an interferogram\n\nexample usage:\npython pyrate/utils/crop_ifgs.py -i tests/test_data/small_test/tif/geo_060619-061002_unw.tif\n-o out.tif -e '150.91 -34.229999976 150.949166651  -34.17'\n\n\"\"\"\nfrom optparse import OptionParser\nimport subprocess\n\n\ndef crop_using_gdalwarp(input_file, output_file, extents):\n    # TODO: add extents checking between input_file and extents\n    extents_str = [str(e) for e in extents]\n    cmd = ['gdalwarp', '-overwrite', '-srcnodata', 'None', '-q', '-te'] \\\n          + extents_str\n    cmd += [input_file, output_file]\n    subprocess.check_call(cmd)\n\n\nif __name__ == '__main__':\n    parser = OptionParser(usage='%prog -i input_file -o output_file'\n                                ' -e extents\\n'\n                                'Crop a larger interferogram into '\n                                'smaller ones')\n    parser.add_option('-i', '--input', type=str, dest='input_file',\n                      help='name of input interferogram')\n\n    parser.add_option('-o', '--out', type=str, dest='output_file',\n                      help='name of cropped output interferogram')\n\n    parser.add_option('-e', '--extents', type=str, dest='extents',\n                      help='extents to be used for the cropped file.\\n'\n                           'needs to be a list of 4 floats with spaces\\n'\n                           'example: '\n                           \"-e '150.91 -34.229999976 150.949166651 -34.17'\")\n\n    options, args = parser.parse_args()\n\n    if not options.input_file:  # if filename is not given\n        parser.error('Input filename not given.')\n\n    if not options.output_file:  # if filename is not given\n        parser.error('Output filename not given.')\n\n    if not options.extents:  # if filename is not given\n        parser.error('Crop extents must be provided')\n\n    extents = [float(t) for t in options.extents.split()]\n\n    if len(extents) != 4:\n        raise AttributeError('extents to be used for the cropped file.\\n'\n                             'needs to be a list or tuples of 4 floats\\n'\n                             \"example:\"\n                             \"--extents \"\n                             \"'150.91 -34.229999976 150.949166651 -34.17'\")\n\n    crop_using_gdalwarp(input_file=options.input_file,\n                        output_file=options.output_file,\n                        extents=extents)\n"
  },
  {
    "path": "utils/developer_hints.txt",
    "content": "# publish package to PyPI\n\nfollow the steps at: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56\n\nSummary:\n\n1) create new 'release' in Github. this will tag a commit hash.\n2) copy link to tar.gz Source Code from github release, add to download_url in setup.py\n3) update version number in setup.py (to match the Github release)\n4) `python3 setup.py sdist bdist_wheel` - prepare package\n5) `python3 -m twine upload --repository testpypi dist/*` - test upload that does not affect real PyPI\n5) `python3 -m twine upload dist/*` - upload package to PyPI\n\ncheck out here also: https://packaging.python.org/tutorials/packaging-projects/\n\n---------------------------------------------\n# Clone Github repo\ncd ~\ngit clone git@github.com:GeoscienceAustralia/PyRate.git\nor\ngit clone https://github.com/GeoscienceAustralia/PyRate.git \n\n# build PyRate package\ncd ~/PyRate\npip install -r requirements.txt\npip install -r requirements-dev.txt\npip install -r requirements-test.txt\npython3 setup.py install\n\n# Run workflow, one step at a time\npyrate conv2tif -f input_parameters.conf\npyrate prepifg -f input_parameters.conf\npyrate correct -f input_parameters.conf\npyrate timeseries -f input_parameters.conf\npyrate stack -f input_parameters.conf\npyrate merge -f input_parameters.conf\n\n# or run all workflow steps in order\npyrate workflow -f input_parameters.conf\n\n# alternatively run commands via python, e.g.\npython3 pyrate/main.py workflow -f input_parameters.conf\n\n---------------------------------------\n# Build Sphinx docs\npip install -r requirements-dev.txt\ncd /PyRate/docs && make html\n\n---------------------------------------\n# Run unit tests, avoiding those marked as \"slow\"\npip install -r requirements-test.txt\ncd /PyRate\n# file permission change required for a test:\nchmod 444 tests/test_data/small_test/tif/geo_070709-070813_unw.tif\npytest tests/ -m \"not slow\"\n\n---------------------------------------\nNCI Gadi supercomputer\n\nssh <username>@gadi.nci.org.au\n\nrm -rf ~/PyRate\nrm -rf ~/PyRateVenv\n\ngit clone git@github.com:GeoscienceAustralia/PyRate.git\nsource PyRate/scripts/nci_load_modules.sh\npython3 -m venv ~/PyRateVenv\nsource ~/PyRateVenv/bin/activate\ncd ~/PyRate\npip install -r requirements-dev.txt\npip install -r requirements-test.txt\npip install -r requirements.txt\n\npython3 setup.py install\n\n# run with debug messages\npyrate -v DEBUG workflow -f input_parameters.conf\n\n# run using MPI\nmpirun -n 6 pyrate workflow -f input_parameters.conf\n"
  },
  {
    "path": "utils/docker_install.txt",
    "content": "# Docker cleanup, see: https://docs.docker.com/ee/dtr/user/manage-images/delete-images/\n\n# Docker Setup\n# first clone the PyRate repository\ngit clone git@github.com:GeoscienceAustralia/PyRate.git\ncd PyRate\n\n# the docker image built will be called 'pyrate-image', but name it what you want\ndocker build -t pyrate-image .\ndocker run -it pyrate-image \n\n# once the docker container is running:\nsource /usr/local/bin/virtualenvwrapper.sh\nworkon pyrate\ncd PyRate\npython3 setup.py install\npyrate --help\n\n# Run full workflow\npyrate workflow -f input_parameters.conf\n\n# or run each step in turn\npyrate conv2tif -f input_parameters.conf\npyrate prepifg -f input_parameters.conf\npyrate correct -f input_parameters.conf\npyrate timeseries -f input_parameters.conf\npyrate stack -f input_parameters.conf\npyrate merge -f input_parameters.conf\n\n# The pyrate executable is built during the 'docker build' step above\n# If 'pyrate' is not found then re-install the PyRate package:\ncd PyRate\npython3 setup.py install\n\n"
  },
  {
    "path": "utils/gdaldem.py",
    "content": "# This Python module is part of the PyRate software package\n#\n# Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nBased on: http://gis.stackexchange.com/questions/130199/changing-color-of-raster-images-based-on-their-data-values-gdal\nUtility script to convert grayscale geotiff into colour.\n================================================================================\nUsage:\n-------------\nCustom color:\npython gdaldem.py input.tif color.txt output_color.tif\nAuto color:\npython gdaldem.py input.tif auto output_color.tif\n\nExamples:\n-------------\nCustom color:\npython utils/gdaldem.py tests/test_data/small_test/dem/roipac_test_trimmed.tif utils/tiff_colour_map.txt out.tif\n\nAuto color:\npython utils/gdaldem.py tests/test_data/small_test/dem/roipac_test_trimmed.tif auto out.tif\n\n\"\"\"\nimport subprocess\nimport sys\nimport os\nimport tempfile\nimport numpy as np\nfrom pyrate.core.shared import DEM\n\n\ndef main(input_file, color_file, output_file):\n    cmd = \"gdaldem color-relief \" + input_file \\\n          + ' ' + color_file + ' ' + output_file\n    subprocess.check_call(cmd, shell=True)\n\n\ndef gen_color_file(input_file):\n    fp, temp_file = tempfile.mkstemp(suffix='.txt')\n\n    dem = DEM(input_file)\n    dem.open()\n    phase_data = dem._band.ReadAsArray()\n\n    max_ph = np.nanmax(phase_data)\n    min_ph = np.nanmin(phase_data)\n    range_ph = max_ph-min_ph\n    colors = ['black', 'blue', 'yellow', 'orange', 'red', 'white']\n    with open(temp_file, 'w') as f:\n        for i, c in enumerate(colors[:-1]):\n            f.write(str(int(min_ph + (i + 1)*range_ph/len(colors))) +\n                    ' ' + c + '\\n')\n        f.write(str(int(max_ph - range_ph/len(colors))) +\n                ' ' + colors[-1] + '\\n')\n    os.close(fp)\n    return temp_file\n\n\nif __name__ == '__main__':\n    input_file = sys.argv[1]\n    color_file = sys.argv[2]\n    output_file = sys.argv[3]\n    if color_file == 'auto':\n        print('\\nauto generating color file')\n        color_file = gen_color_file(input_file)\n    with open(color_file, 'r') as f:\n        print('\\ncolor file contents')\n        for l in f.readlines():\n            print(l)\n    main(input_file, color_file, output_file)\n"
  },
  {
    "path": "utils/list_creator.sh",
    "content": "\"\"\"\nThis script generates input lists for PyRate baseed on the PyGAMMA workflow for descending frame S1 data in Australia.\nSmall modifications are necessary if using ascending frame data\nUsage: . list_creator.sh\n\"\"\"\n\n# Provide path to the Gamma folder titled with the frame name\nDIR=\"/path/to/frame/T045D\"\nls -d \"$DIR\"/*/*/*unw.tif > ifgs.list\n# \"coh\" is \"cc\" in asending frame data\nls -d \"$DIR\"/*/*/*flat*coh.tif > cohfiles.list\nls -d \"$DIR\"/*/*/*base.par > baseline.list\n# Change \"VV\" depending on polarisation of data.\nls -d \"$DIR\"/*/*/*VV*mli.par > headers.list\n\nwc -l *.list\n"
  },
  {
    "path": "utils/make_tscuml_animation.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis python script can be used to make an animated gif of PyRate cumulative time series products.\n\nUsage: python3 utils/make_tscuml_animation.py <path to PyRate outdir>\n\"\"\"\nimport rasterio\nimport matplotlib.pyplot as plt\nimport matplotlib.backend_bases\nimport numpy as np\nimport os, sys, re\nimport xarray as xr\nfrom datetime import datetime as dt\nimport matplotlib.animation as animation\n\nif len(sys.argv) != 2:\n    print('Exiting: Provide abs path to <outdir> as command line argument')\n    exit()\nelse:\n    path = sys.argv[1]\n    print(f\"Looking for PyRate products in: {path}\")\n#################################\n\n# Reading velocity data\nwith rasterio.open(os.path.join(path, 'velocity_dir', 'linear_rate.tif')) as src2:\n    vel = src2.read()\n    bounds2 = src2.bounds\n    x_coord2 = np.linspace(bounds2[0], bounds2[2], src2.width)\n    y_coord2 = np.linspace(bounds2[1], bounds2[3], src2.height)\n    # grab list of time series dates from metadata\n    ed = src2.tags()['EPOCH_DATE']\n\n# convert metadata string to list of strings\ndate_str = re.findall(r'\\'(.+?)\\'', ed)\n\n# make velocity xarray (also apply deramp to the velocity map directly)\ndac2 = xr.DataArray(vel[0,:,:], coords={'lon': x_coord2, 'lat': y_coord2}, dims=['lat', 'lon'])\nvs = xr.Dataset()\nvs['vel'] = dac2\nlongitude = vs.coords['lon']\nlatitude = vs.coords['lat']\n\n# pre-allocate a 3D numpy array to read the tscuml tiff raster bands to\n# include first 'zero' time slice, which PyRate does not save to disk\ntscuml = np.zeros((len(date_str), vel.shape[1], vel.shape[2])) # commented Chandra\n\n# reading *tif files and generate cumulative variable\nprint('Reading tscuml files:')\nfor i, d in enumerate(date_str[1:]):\n    print(i+1, 'tscuml_' + d + '.tif')\n    with rasterio.open(os.path.join(path, 'timeseries_dir', 'tscuml_' + d + '.tif')) as src:\n        data = src.read()\n        bounds = src.bounds\n        x_coord = np.linspace(bounds[0], bounds[2], src.width)\n        y_coord = np.linspace(bounds[1], bounds[3], src.height)\n    tscuml[i+1, :, :] = np.squeeze(data, axis=(0,)) # commented chandra\n\n# copy Nans in first time slice to zero epoch\nzeroepoch = np.zeros((vel.shape[1], vel.shape[2]))\nzeroepoch[np.isnan(tscuml[1,:,:])] = np.nan\ntscuml[0,:,:] = zeroepoch\n\n# convert date strings to datetime objects\nimdates_dt = [dt.strptime(x, '%Y-%m-%d') for x in date_str]\n\n# make tscuml xarray\ndac = xr.DataArray(tscuml, coords={'time': imdates_dt, 'lon': x_coord, 'lat': y_coord}, dims=['time', 'lat', 'lon'])\nds = xr.Dataset()\nds['tscuml'] = dac\nn_im, length, width = tscuml.shape\n\n# Add max and min displacement range\n#refx1 = int(len(x_coord2)/ 2)\n#refx2 = int(len(x_coord2)/ 2) + 1\n#refy1 = int(len(y_coord2)/ 2)\n#refy2 = int(len(y_coord2)/ 2) + 1\n#refvalue_lastepoch = np.nanmean(tscuml[-1, refy1:refy2, refx1:refx2]) # reference values\n\nauto_crange: float = 100\ndmin_auto = np.nanpercentile(tscuml, 100 - auto_crange)\ndmax_auto = np.nanpercentile(tscuml, auto_crange)\n# find the absolute max displacement; round to nearest 10 units, for colour bar limits\nlim = np.round(np.amax(np.array([np.abs(dmin_auto), np.abs(dmax_auto)])), decimals=-1)\n#dmin = dmin_auto - refvalue_lastepoch\n#dmax = dmax_auto - refvalue_lastepoch\n\n####  ANIMATION of LOS Cumulative displacement TS\nfig = plt.figure('PyRate Cumulative Displacement Animation', figsize=(5,5))\nfaxv = fig.add_axes([0.15,0.15,0.75,0.75])\ncmap = matplotlib.cm.Spectral_r\ncmap.set_bad('grey',1.) # filled grey color to nan value\nims = [] # pre-allocate list for appending time slices\n\n# loop over all timeslices, including zero epoch\nfor ii in range(0,len(imdates_dt)):\n    im = faxv.imshow(ds.tscuml[ii], cmap=cmap, alpha=1, origin='upper',extent=[ds.coords['lon'].min(),\n         ds.coords['lon'].max(), ds.coords['lat'].min(), ds.coords['lat'].max()], clim=[-lim, lim])\n    title = fig.text(0.40, 0.90, \"Date: {}\".format(imdates_dt[ii].date()), fontsize=12, va='bottom' )\n    ims.append([im, title])\n\nfcbr = fig.colorbar(im, orientation='horizontal')\nfcbr.set_label('LOS Displacement [mm]')\nani = animation.ArtistAnimation(fig, ims, interval=500, blit=False)\n#plt.show()\nfile = path + '/timeseries_dir/' +'tscuml_animation.gif'\nani.save(file, writer='imagemagick', fps=10, dpi=100)\nprint('Animation saved to ' + file)\n"
  },
  {
    "path": "utils/plot_correction_files.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis script plots the original interferogram, the corresponding correction file, \nand the final corrected interferogram from a PyRate directory with already,\nprocessed data. directories are given as user arguments to the script, and the\nnumber of plots is determined by a number range given by user. \n\nUsage: python3 plot_correction_files.py <IFG_DIR> <CORRECTION_DIR> <CORRECTED_DIR> <SAVE_DIR> <FIRST_IFG> <LAST_IFG>\n\nCommand-line arguments:\n           IFG_DIR       - full path to uncorrected interferograms in PyRate.\n    CORRECTION_DIR       - full path to correction files in PyRate.\n     CORRECTED_DIR       - full path to corrected interferograms in PyRate.\n          SAVE_DIR       - full path to directory where images will be saved (needs to exist).\n         FIRST_IFG       - first IFG in range of IFGs to plot (e.g. 1 to start plotting at 1st IFG in directory).\n          LAST_IFG       - last IFG in range of IFGs to plot (e.g. 37 will plot up until the 37th IFG in directory).\n         NORMALISE       - Switch to subtract median to from figures (0=YES, 1=No, default is 0). \n\"\"\"\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\nimport glob\nimport re\nimport math\nimport argparse\nimport os\nfrom pyrate.core.shared import Ifg\n\n# Arguments\nparser = argparse.ArgumentParser(description=\"Script to plot correction files with uncorrected and corrected interferogram\")\nparser.add_argument(\"IFG_DIR\", type=str, help=\"full path to uncorrected interferograms in PyRate\")\nparser.add_argument(\"CORRECTION_FILE_DIR\", type=str, help=\"full path to correction files in PyRate\")\nparser.add_argument(\"CORRECTED_IFG_DIR\", type=str, help=\"full path to corrected interferograms in PyRate\")\nparser.add_argument(\"SAVE_DIR\", type=str, help=\"full path to directory where images will be saved\")\nparser.add_argument(\"FIRST_IFG\", type=int, help=\"first IFG in range of IFGs to plot (e.g. 1 to start plotting at first IFG in directory)\")\nparser.add_argument(\"LAST_IFG\", type=int, help=\"last IFG in range of IFGs to plot (e.g. 37 will plot up until the 37th IFG in directory)\")\nparser.add_argument(\"-sm\", \"--subtract_median\", type=int, default=1, help=\"Switch to subtract median to from figures (1=YES, 0=No, default is 1)\")\nargs = parser.parse_args()\n\n# Directories and variable from user agruments\nifg_dir = os.path.abspath(args.IFG_DIR)\ncorr_dir = os.path.abspath(args.CORRECTION_FILE_DIR)\ntempml_dir = os.path.abspath(args.CORRECTED_IFG_DIR)\nsave_dir = os.path.abspath(args.SAVE_DIR)\n\n# Create Lists\nifg_list = []\nfor file in glob.glob(f'{ifg_dir}/*ifg.tif'):\n    ifg_list.append(file)\n\ncorr_list = []\nfor file in glob.glob(f'{corr_dir}/*.npy'):\n    corr_list.append(file)\n\ntempml_list = []\nfor file in glob.glob(f'{tempml_dir}/*ifg.tif'):\n    tempml_list.append(file)\n\n# Sort\nifg_list.sort()\ncorr_list.sort()\ntempml_list.sort()\n\n# define colour map\ncmap = cm.Spectral_r\ncmap.set_bad(color='grey')\n\n# loop over each ifg in turn\nfor i in range(args.FIRST_IFG - 1, args.LAST_IFG):    \n    # Read data\n    orig_ifg = Ifg(ifg_list[i])\n    orig_ifg.open()\n    orig_ifg.convert_to_mm() # force mm conversion\n    ifg = orig_ifg.phase_data\n    orig_ifg.close()\n\n    corr =  np.load(corr_list[i])\n    \n    corr_ifg = Ifg(tempml_list[i])\n    corr_ifg.open()\n    corr_ifg.convert_to_mm() # force mm conversion\n    ifg_corr = corr_ifg.phase_data\n    corr_ifg.close()\n    \n    # Identify Date Pair\n    date_pair_list_ifg = re.findall(r'\\d{8}-\\d{8}', ifg_list[i])\n    date_pair_string_ifg = date_pair_list_ifg[0]\n\n    date_pair_list_corr = re.findall(r'\\d{8}-\\d{8}', corr_list[i])\n    date_pair_string_corr = date_pair_list_corr[0]\n\n    date_pair_list_ifgcorr = re.findall(r'\\d{8}-\\d{8}', tempml_list[i])\n    date_pair_string_ifgcorr = date_pair_list_ifgcorr[0]\n\n    # Check the Date-pairs are the same in case of mismatched files saved into the directories\n    if date_pair_string_ifg == date_pair_string_corr and date_pair_string_ifg == date_pair_string_ifgcorr:\n        print(f'\\nPlotting for {date_pair_string_ifg}...\\n')\n        pass\n    else:\n        print(f'\\nERROR: Interferogram datepair mismatch at {date_pair_string_ifg}, check that directories have the same interferograms.\\n')\n        break\n    \n    # PLOTTING\n#    ifg_quant = np.nanquantile(ifg, [0.05, 0.95])\n#    climit = np.nanmax(np.abs(ifg_quant))\n    climit = 30\n    \n    # subtract median of image\n    if args.subtract_median == 1:\n        ifg -= np.nanmedian(ifg)\n        corr -= np.nanmedian(corr)\n        ifg_corr -= np.nanmedian(ifg_corr)\n\n    # three sub-plots\n    fig, ax = plt.subplots(1,3, figsize=(6, 3))\n     \n    # ORIGINAL IFG\n    s0 = ax[0].imshow(ifg, cmap=cmap, clim=(-1*climit, climit))\n    ax[0].set_title('Original Ifg', fontsize=8)\n    ax[0].set_axis_off()\n\n    # CORRECTION FILE\n    s1 =ax[1].imshow(corr, cmap=cmap, clim=(-1*climit, climit))\n    ax[1].set_title('Correction', fontsize=8)\n    ax[1].set_axis_off()\n\n    # CORRECTED IFG\n    s2 = ax[2].imshow(ifg_corr, cmap=cmap, clim=(-1*climit,climit))\n    ax[2].set_title('Corrected Ifg', fontsize=8)\n    ax[2].set_axis_off()\n\n    # Colorbar\n    cbar = fig.colorbar(s1, ax=ax.ravel().tolist(), orientation='horizontal', label='mm', \\\n            ticks=[-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50])\n    cbar.ax.tick_params(labelsize=8)\n\n    # set figure window colour to grey\n    fig.set_facecolor('grey')\n    \n    # Title\n    fig.suptitle(f'{date_pair_string_ifg}', y=0.75, fontsize=10, fontweight='bold')\n\n    plt.savefig(f'{save_dir}/{date_pair_string_ifg}.png', dpi=300)\n    \n    plt.close()\n\n"
  },
  {
    "path": "utils/plot_linear_rate_profile.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis python script can be used to make a profile through the PyRate linear rate product.\n\nUsage: python3 utils/plot_linear_rate_profile.py <path to PyRate outdir>\n\"\"\"\nimport rasterio\nimport matplotlib.pyplot as plt\nimport matplotlib.backend_bases\nimport numpy as np\nimport os, sys, re\nimport xarray as xr\nfrom datetime import datetime as dt\nfrom pylab import plot, ginput, show, axis # for velocity profile\n\nif len(sys.argv) != 2:\n    print('Exiting: Provide abs path to <outdir> as command line argument')\n    exit()\nelse:\n    path = sys.argv[1]\n    print(f\"Looking for PyRate products in: {path}\")\n\n# ----- Reading linear velocity file --------------------------------\nwith rasterio.open(os.path.join(path, 'velocity_dir', 'linear_rate.tif')) as src2:\n    vel = src2.read()\n    bounds2 = src2.bounds\n    x_coord2 = np.linspace(bounds2[0], bounds2[2], src2.width)\n    y_coord2 = np.linspace(bounds2[1], bounds2[3], src2.height)\n    # grab list of time series dates from metadata\n    ed = src2.tags()['EPOCH_DATE']\n\n# convert metadata string to list of strings\ndate_str = re.findall(r'\\'(.+?)\\'', ed)\nimdates_dt = [dt.strptime(x, '%Y-%m-%d') for x in date_str]\n\n# make velocity xarray \ndac2 = xr.DataArray(vel[0,:,:], coords={'lon': x_coord2, 'lat': y_coord2}, dims=['lat', 'lon'])\nvs = xr.Dataset()\nvs['vel'] = dac2\nlongitude = vs.coords['lon']\nlatitude = vs.coords['lat']\n\n# ------ Masking the velocity file using linear sample --------------\n# # linear resample *tif file and mask out velocity\nsrc3 = rasterio.open(os.path.join(path, 'velocity_dir', 'linear_samples.tif'))\nlsample = src3.read()\nlsamp_val = lsample[0,:,:]\n[mask_x,mask_y] = np.where((lsamp_val >= (len(imdates_dt)-2)))\nvel_masked = np.empty((vel.shape[1],vel.shape[2],)) * np.nan\nvel_masked[mask_x,mask_y] = vs.vel.data[mask_x,mask_y]\n\n# -------- Plot velocity profile across two points -------------------\ndef get_profile(pts, img):\n    '''\n    Extract values from image at points along the profile line, using nearest-neighbor interpolation\n    '''\n    num = 500 # hardcoded 500 points per profile\n    # generate 500 points between the provided points\n    x, y = np.linspace(pts[0][0], pts[1][0], num), np.linspace(pts[0][1], pts[1][1], num)\n    # pull z value from nearest whole pixel\n    z = img[y.astype(int), x.astype(int)]\n    return x, y, z\n\n# vmin = -50; vmax = 50\n#refx1 = int(len(x_coord2)/ 2)\n#refx2 = int(len(x_coord2)/ 2) + 1\n#refy1 = int(len(y_coord2)/ 2)\n#refy2 = int(len(y_coord2)/ 2) + 1\n#refvalue_vel = np.nanmean(vel[0, refy1:refy2 + 1, refx1:refx2 + 1])\n\nauto_crange: float = 100\nvmin_auto = np.nanpercentile(vel[0, :, :], 100 - auto_crange)\nvmax_auto = np.nanpercentile(vel[0, :, :], auto_crange)\n# find the absolute max displacement; round to nearest 10 units, for colour bar limits\nlim = np.round(np.amax(np.array([np.abs(vmin_auto), np.abs(vmax_auto)])), decimals=-1)\n#vmin = vmin_auto - refvalue_vel\n#vmax = vmax_auto - refvalue_vel\n\ncmap = matplotlib.cm.Spectral_r\ncmap.set_bad('grey',1.)\nfig = plt.figure('PyRate Linear Rate Profile', figsize=(7,10))\naxes = fig.subplots(2, 1, gridspec_kw={'height_ratios': [2, 1]})\ncax = axes[0].imshow(vel_masked, clim=[-lim, lim], cmap = cmap)\ncbr = fig.colorbar(cax,ax=axes[0], orientation='vertical')\ncbr.set_label('LOS velocity [mm/yr]')\n\nprint(\"Please click any two points\")\nig = 1\nwhile ig!=2:\n    pts = ginput(2) # it will wait for two clicks\n    [x,y,z] = get_profile(pts, vel_masked)\n    if axes[0].lines:\n        del axes[0].lines[0]\n        axes[1].cla()\n    axes[0].plot([pts[0][0], pts[1][0]], [pts[0][1], pts[1][1]], 'ro-') # axes[0].plot([x0, x1], [y0, y1], 'ro-')\n    axes[1].plot(x, z, 'gray')\n    axes[1].plot(x, z, 'r.')\n    # axes[1].set_ylim(-5,5)\n    axes[1].set_ylabel('LOS velocity [mm/yr]')\n    axes[1].set_xlabel('X coordinate')\n    axes[1].grid(zorder=0)\n    plt.pause(0.1)\n    fig.canvas.draw()\n    fig.canvas.flush_events()\n\nplt.show()\n"
  },
  {
    "path": "utils/plot_sbas_network.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis python script can be used to generate a baseline-time plot of the \ninterferograms used in the PyRate SBAS (Small Baseline Subset) network.\nThe functions 'plot_baseline_time_sbas' and 'epoch_baselines' are copies of the corresponding\nfunctions in 'calc_baselines_functions.py' in GA's gamma-insar repository, see\nhttps://github.com/GeoscienceAustralia/gamma_insar/blob/develop/calc_baselines_functions.py\n\nUsage: python3 utils/plot_sbas_network.py <path to PyRate outdir> \n\"\"\"\nimport rasterio\nimport glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport os, sys\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom datetime import datetime, timedelta\n\n\nprint('')\nif len(sys.argv) != 2:\n    print('Exiting: Provide path to <PyRate outdir> as command line argument')\n    print('')\n    print('Usage: python3 utils/plot_time_series.py <path to PyRate outdir>')\n    exit()\nelse:\n    path = sys.argv[1]\n    print(f\"Looking for PyRate products in: {path}\")\n\n\ndef readtif(tifname: str):\n    \"\"\"\n    wrapper for rasterio tif reading\n    \"\"\"\n    print(f\"Reading file: {tifname}\")\n    with rasterio.open(tifname) as src:\n        md = src.tags()\n\n    return md\n\n\ndef plot_baseline_time_sbas(epochs, Bperps, epoch1, epoch2, filename):\n    \"\"\"\n    Make a baseline time plot including IFG connections and save to disk\n    \"\"\"\n\n    fig = plt.figure()\n    ax1 = fig.add_subplot(111)\n    divider = make_axes_locatable(ax1)\n\n    # plot interferograms as lines\n    for n, m in zip(epoch1, epoch2):\n        #print n, m\n        x = [epochs[n], epochs[m]]\n        y = [Bperps[n], Bperps[m]]\n        #  baselines[x]\n        ax1.plot_date(x, y, xdate=True, ydate=False, linestyle='-',\n        color = 'r', linewidth=1.0)\n\n    # plot epochs as filled circles\n    ax1.plot_date(epochs, Bperps, xdate=True, ydate=False, marker=\"o\",\n                  markersize=14, markerfacecolor=\"black\", linestyle=\"None\")\n\n    # plot epoch numbers as symbols\n    labels = [i+1 for i in range(len(Bperps))]\n    for a, b, c in zip(epochs, Bperps, labels):\n        ax1.text(a, b, c, color=\"white\", ha=\"center\", va=\"center\", size=9,\n                 weight=\"bold\")\n\n    #format the time axis ticks\n    years   = mdates.MonthLocator(bymonth=[1, 7])   # every 0.5 year\n    months   = mdates.MonthLocator()  # every month\n    yearsFmt = mdates.DateFormatter(\"%Y-%m-%d\")\n    ax1.xaxis.set_major_locator(years)\n    ax1.xaxis.set_major_formatter(yearsFmt)\n    ax1.xaxis.set_minor_locator(months)\n\n    #set the time axis range\n    date_min = epochs.min()\n    date_max = epochs.max()\n    date_range = date_max - date_min\n    date_add = date_range.days/15\n    ax1.set_xlim(date_min - timedelta(days=date_add), date_max +\n                 timedelta(days=date_add))\n\n    # set the Bperp axis range\n    Bperp_min = min(Bperps)\n    Bperp_max = max(Bperps)\n    Bperp_range = Bperp_max - Bperp_min\n    ax1.set_ylim(Bperp_min - Bperp_range/15, Bperp_max + Bperp_range/15)\n\n    #set axis titles\n    ax1.set_xlabel(\"Date (YYYY-MM-DD)\")\n    ax1.set_ylabel(\"Perpendicular Baseline (m)\")\n    ax1.grid(True)\n\n    #rotates and right aligns the date labels\n    fig.autofmt_xdate()\n\n    # Save plot to PNG file\n    plt.savefig(filename, orientation=\"landscape\", transparent=False,\n                format=\"png\")\n    return\n\n\ndef epoch_baselines(epochs, bperp, masidx, slvidx, supermaster):\n    '''\n    Determine relative perpendicular baselines of epochs from\n    interferometric baselines\n\n    INPUT:\n    epochs    list of epoch dates\n    bperp    list of interferogram absolute perpendicular baselines\n    masidx    list of master indices from get_index()\n    slvidx    list of slave indices from get_index()\n    supermaster    epoch to set relative bperp to zero (integer)\n\n    OUTPUT:\n    epochbperp    list of epoch relative perpendicular baselines\n    '''\n\n    # Count number of ifgs and epochs\n    nifgs = len(bperp)\n    nepochs = len(epochs)\n    print(nifgs, \"interferograms and\", nepochs, \"epochs in the network.\")\n\n    # Initialise design matrix 'A'\n    A = np.zeros((nifgs+1,nepochs))\n\n    # assign super-master epoch to constrain relative baselines\n    A[0,supermaster] = 1\n    b = np.zeros(nifgs+1)\n    b[1:nifgs+1] = bperp\n\n    # Construct design matrix\n    for i in range(nifgs):\n        imas = masidx[i]\n        islv = slvidx[i]\n        A[i+1,imas] = -1\n        A[i+1,islv] = 1\n\n    # Do overdetermined linear inversion x=A\\b\n    x = np.linalg.lstsq(A, b, rcond=None)\n    return x[:][0]\n\n\n###########\n# Main code\nprint('')\nprint('Generating the baseline-time plot from tif-files in temp_mlooked_dir')\n\n# reading metadata from tif file\npath_to_tif = os.path.join(path, 'temp_mlooked_dir/*.tif')\n# some empty lists\nBperps_ifg = []\nepoch1 = []\nepoch2 = []\nfor tif_file in glob.glob(path_to_tif):\n    md = readtif(tif_file)\n    # look for perpendicular baseline in geotiff metadata; skip ifg if not there\n    if 'BASELINE_PERP_METRES' in md:\n        Bperps_ifg.append(float(md['BASELINE_PERP_METRES']))\n        epoch1.append(md['FIRST_DATE'])\n        epoch2.append(md['SECOND_DATE'])\nprint('')\n\n# Quit if no ifg has a perpendicular baseline value\nif len(Bperps_ifg) == 0:\n    print('No perpendicular baseline values in ifg metadata. ' +\n          'First run the DEM error correction to calculate baselines.')\n    quit()\n\n# create date vector containing all epochs in the network\nepochs= list(set(epoch1+epoch2))\nepochs.sort()\n\n# get epoch indices for all interferograms\nepoch1_ix = []\nfor e in epoch1:\n    epoch1_ix.append(epochs.index(e))\nepoch2_ix = []\nfor e in epoch2:\n    epoch2_ix.append(epochs.index(e))\n# convert epochs to datetime object\nepochs[:] = [datetime.strptime(epo, \"%Y-%m-%d\") for epo in epochs]\n\n# convert IFG Bperps into single-master Bperps referenced to the first epoch\nBperps_epoch = epoch_baselines(epochs,Bperps_ifg,epoch1_ix,epoch2_ix, 0)\n\n# filename of output image\nfilename = os.path.join(path, 'temp_mlooked_dir/baseline_time_plot.png')\n# call the function to create the plot\nplot_baseline_time_sbas(np.array(epochs), Bperps_epoch, epoch1_ix, epoch2_ix, filename)\nprint('Network plot saved to ' + filename)\nprint('')\n\n"
  },
  {
    "path": "utils/plot_time_series.py",
    "content": "#   This Python module is part of the PyRate software package.\n#\n#   Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis python script can be used to plot PyRate linear-rate and cumulative time series\nproducts.\nThis script is a version of a script in the LiCSBAS package; see\nhttps://github.com/yumorishita/LiCSBAS/blob/master/bin/LiCSBAS_plot_ts.py\n\nUsage: python3 utils/plot_time_series.py <path to PyRate outdir> \n\"\"\"\nimport rasterio\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Slider, RadioButtons, RectangleSelector, CheckButtons\nimport matplotlib.dates as mdates\nimport matplotlib.backend_bases\nimport numpy as np\nimport fnmatch\nimport os, sys, re\nimport statsmodels.api as sm\nimport xarray as xr\nfrom datetime import datetime as dt\nimport warnings\n\nif len(sys.argv) != 2:\n    print('Exiting: Provide path to <PyRate outdir> as command line argument')\n    print('')\n    print('Usage: python3 utils/plot_time_series.py <path to PyRate outdir>')\n    exit()\nelse:\n    path = sys.argv[1]\n    print(f\"Looking for PyRate products in: {path}\")\n\n###############################\ndef readtif(tifname: str):\n    \"\"\"\n    wrapper for rasterio tif reading\n    \"\"\"\n    print(f\"Reading file: {tifname}\")\n    with rasterio.open(tifname) as src:\n        img = src.read()\n        bounds = src.bounds\n        md = src.tags()\n\n    x_coord = np.linspace(bounds[0], bounds[2], src.width)\n    y_coord = np.linspace(bounds[1], bounds[3], src.height)\n\n    return img, x_coord, y_coord, md\n###############################\n\n# reading velocity data from linear_rate product\nvel, x_coord, y_coord, md = readtif(os.path.join(path, 'velocity_dir', 'linear_rate.tif'))\n\n# read regression intercept from linear_intercept product\nintercept, _, _, _ = readtif(os.path.join(path, 'velocity_dir', 'linear_intercept.tif'))\n\n# convert time series dates from metadata string to list of strings\ndate_str = re.findall(r'\\'(.+?)\\'', md['EPOCH_DATE'])\n\n# convert date strings to datetime objects\nimdates_dt = [dt.strptime(x, '%Y-%m-%d') for x in date_str]\nimdates_ordinal = [x.toordinal() for x in imdates_dt]\n\n# make velocity xarray\ndac2 = xr.DataArray(vel[0,:,:], coords={'lon': x_coord, 'lat': y_coord}, dims=['lat', 'lon'])\nvs = xr.Dataset()\nvs['vel'] = dac2\nlongitude = vs.coords['lon']\nlatitude = vs.coords['lat']\n\n# pre-allocate a 3D numpy array to read the tscuml tiff raster bands to\n# include first 'zero' time slice, which PyRate does not save to disk\ntscuml = np.zeros((len(date_str), vel.shape[1], vel.shape[2]))\n\n# reading tscuml*tif files and add to tscuml variable\nfor i, d in enumerate(date_str[1:]):\n    data, x_coord, y_coord, _ = readtif(os.path.join(path, 'timeseries_dir', 'tscuml_' + d + '.tif'))\n    tscuml[i+1, :, :] = np.squeeze(data, axis=(0,))\n\n# make tscuml xarray\ndac = xr.DataArray(tscuml, coords={'time': imdates_dt, 'lon': x_coord, 'lat': y_coord}, dims=['time', 'lat', 'lon'])\nds = xr.Dataset()\nds['tscuml'] = dac\nn_im, length, width = tscuml.shape\n\n# choose final time slice\ntime_slice = len(imdates_dt)-1\n\n# set reference area and initial point (scene centre)\nrefx1 = int(len(x_coord)/ 2)\nrefx2 = int(len(x_coord)/ 2) + 1\nrefy1 = int(len(y_coord)/ 2)\nrefy2 = int(len(y_coord)/ 2) + 1\nrefarea = (refx1,refx2,refy1,refy2)\npoint_x = refx1\npoint_y = refy1\n\n\ndef get_range(arr, refarea):\n    \"\"\"\n    determine plot scale range\n    \"\"\"\n    auto_crange: float = 99.8\n    refvalue = np.nanmean(arr[refarea[0]:refarea[1], refarea[2]:refarea[3]]) # reference values\n    if str(refvalue) == 'nan':\n        refvalue = 0\n    dmin_auto = np.nanpercentile(arr, 100 - auto_crange)\n    dmax_auto = np.nanpercentile(arr, auto_crange)\n    dmin = dmin_auto - refvalue\n    dmax = dmax_auto - refvalue\n\n    return dmin, dmax\n\n# range from last tscuml epoch\ndmin, dmax = get_range(tscuml[-1, :, :], refarea)\ndmax = abs(max([dmin,dmax], key=abs))\ndmin = -dmax\n\n# range from velocity\nvmin, vmax = get_range(vel[0, :, :], refarea)\nvmax = abs(max([vmin,vmax], key=abs))\nvmin = -vmax\n\n# Plot figure of Velocity and Cumulative displacement\nfigsize = (7,7)\npv = plt.figure('PyRate: linear_rate / tscuml map viewer', figsize)\naxv = pv.add_axes([0.15,0.15,0.75,0.83])\naxv.set_title('linear_rate')\naxt2 = pv.text(0.01, 0.99, 'Left-doubleclick:\\n Plot time series\\nRight-drag:\\n Change ref area', fontsize=8, va='top')\naxt = pv.text(0.01, 0.78, 'Ref area:\\n X {}:{}\\n Y {}:{}\\n (start from 0)'.format(refx1, refx2, refy1, refy2),\n              fontsize=8, va='bottom')\n\n# create masked array for NaNs\nmvel = np.ma.array(vs.vel, mask=np.isnan(vs.vel))\ncmap = matplotlib.cm.Spectral_r\ncmap.set_bad('grey')\ncax = axv.imshow(mvel, clim=[vmin, vmax], cmap=cmap)\ncbr = pv.colorbar(cax, orientation='vertical')\ncbr.set_label('mm/yr')\n\n# Radio buttom for velocity selection\nmapdict_data = {}\nmapdict_unit = {}\n\nnames = ['Velocity', 'Error', 'R squared']\nunits = ['mm/yr', 'mm/yr', '']\n\nvelfile = os.path.join(path, 'velocity_dir', 'linear_rate.tif')\nErrfile = os.path.join(path, 'velocity_dir', 'linear_error.tif')\nRsqrfile = os.path.join(path, 'velocity_dir', 'linear_rsquared.tif')\nfiles = [velfile, Errfile, Rsqrfile]\n\nfor i, name in enumerate(names):\n    try:\n        pp = rasterio.open(files[i])\n        data = pp.read()\n        mapdict_data[name] = data[0, :, :]\n        mapdict_unit[name] = units[i]\n        print('Reading {}'.format(os.path.basename(files[i])))\n    except:\n        print('No {} found, not use.'.format(files[i]))\n\naxrad_vel = pv.add_axes([0.01, 0.3, 0.13, len(mapdict_data)*0.025+0.04])\n# Radio buttons\nradio_vel = RadioButtons(axrad_vel, tuple(mapdict_data.keys()))\nfor label in radio_vel.labels:\n    label.set_fontsize(10)\n\ntscuml_disp_flag = False\nclimauto = True\n\ndef line_select_callback(eclick, erelease):\n    \"\"\"\n    Set ref function\n    \"\"\"\n    global refx1, refx2, refy1, refy2, dmin, dmax   ## global cannot change existing values... why?\n\n    x1, y1 = eclick.xdata, eclick.ydata\n    x2, y2 = erelease.xdata, erelease.ydata\n\n    if x1 <= x2:\n        refx1, refx2 = [int(np.round(x1)), int(np.round(x2))]\n    elif x1 > x2:\n        refx1, refx2 = [int(np.round(x1)), int(np.round(x2))]\n    if y1 <= y2:\n        refy1, refy2 = [int(np.round(y1)), int(np.round(y2))]\n    elif y1 > y2:\n        refy1, refy2 = [int(np.round(y1)), int(np.round(y2))]\n    refarea = (refx1,refx2,refy1,refy2)\n\n    axt.set_text('Ref area:\\n X {}:{}\\n Y {}:{}\\n (start from 0)'.format(refx1, refx2, refy1, refy2))\n    pv.canvas.draw()\n\n    ### Change clim\n    if climauto:  ## auto\n        dmin, dmax = get_range(tscuml[-1, :, :], refarea)\n        dmax = abs(max([dmin, dmax], key=abs))\n        dmin = -dmax\n\n    ### Update draw\n    if not tscuml_disp_flag:  ## vel or noise indice # Chandra\n        val_selected = radio_vel.value_selected\n        val_ind = list(mapdict_data.keys()).index(val_selected)\n        radio_vel.set_active(val_ind)\n    else:  ## cumulative displacement\n        time_selected = tslider.val\n        tslider.set_val(time_selected)\n\n    if lastevent:  ## Time series plot\n        printcoords(lastevent)\n\nRS = RectangleSelector(axv, line_select_callback, drawtype='box', useblit=True, button=[3], spancoords='pixels', interactive=False)\n\nplt.connect('key_press_event', RS)\n\n# auto_crange: float = 99.8\nvlimauto = True\ndef show_vel(val_ind):\n    global vmin, vmax, tscuml_disp_flag\n    tscuml_disp_flag = False\n\n    if 'Velocity' in val_ind:  ## Velocity\n        data = mapdict_data[val_ind]\n        # if vlimauto:  ## auto\n        #     vmin = np.nanpercentile(data, 100 - auto_crange)\n        #     vmax = np.nanpercentile(data, auto_crange)\n        cax.set_cmap(cmap)\n        cax.set_clim(vmin, vmax)\n        cbr.set_label('mm/yr')\n    else:\n        data = mapdict_data[val_ind]\n        cmap2 = matplotlib.cm.Reds # cmap2 = 'Reds'\n        cmap2.set_bad('grey', 1.)  # filled grey color to nan value\n        if val_ind == 'Error':\n            cax.set_clim(0, 20)\n        elif val_ind == 'R squared':\n            cax.set_clim(0, 1)\n        cax.set_cmap(cmap2)\n\n    cbr.set_label(mapdict_unit[val_ind])\n    cax.set_data(data)\n    axv.set_title(val_ind)\n\n    pv.canvas.draw()\n\nradio_vel.on_clicked(show_vel)\n\n# Slider for cumulative displacement\naxtim = pv.add_axes([0.1, 0.08, 0.8, 0.05], yticks=[])\nmdt = mdates.date2num(imdates_dt)\ntslider = Slider(axtim, 'year', mdates.date2num(imdates_dt[0]), mdates.date2num(imdates_dt[-1]))\ntslider.ax.bar(mdt, np.ones(len(mdt)), facecolor='black', width=4)\ntslider.ax.bar(mdt[0], 1, facecolor='red', width=12)\n\nloc_tslider = tslider.ax.xaxis.set_major_locator(mdates.AutoDateLocator())\ntry:  # Only support from Matplotlib 3.1!\n    tslider.ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc_tslider))\n    for label in tslider.ax.get_xticklabels():\n        label.set_rotation(20)\n        label.set_horizontalalignment('right')\nexcept:\n    tslider.ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d'))\n    for label in tslider.ax.get_xticklabels():\n        label.set_rotation(20)\n        label.set_horizontalalignment('right')\n\ndstr_ref = imdates_dt[0].strftime('%Y/%m/%d') # reference image date\ndef tim_slidupdate(val):\n    global tscuml_disp_flag\n    timein = tslider.val\n    timenearest = np.argmin(np.abs(mdates.date2num(imdates_dt) - timein))\n\n    dstr = imdates_dt[timenearest].strftime('%Y/%m/%d')\n    axv.set_title('tscuml: %s (Ref: %s)' % (dstr, dstr_ref))\n\n    newv = (tscuml[timenearest, :, :])\n    cax.set_data(newv)\n    cax.set_cmap(cmap)\n    cax.set_clim(dmin, dmax)\n    cbr.set_label('mm')\n    tscuml_disp_flag = True\n\n    pv.canvas.draw()\n\ntslider.on_changed(tim_slidupdate)\n\n##### Plot figure of time-series at a point\npts = plt.figure('PyRate: pixel time-series graph', (9,5))\naxts = pts.add_axes([0.12, 0.14, 0.7, 0.8])\n\naxts.scatter(imdates_dt, np.zeros(len(imdates_dt)), c='b', alpha=0.6)\naxts.grid()\n\nloc_ts = axts.xaxis.set_major_locator(mdates.AutoDateLocator())\ntry:  # Only support from Matplotlib 3.1\n    axts.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc_ts))\nexcept:\n    axts.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d'))\n    for label in axts.get_xticklabels():\n        label.set_rotation(20)\n        label.set_horizontalalignment('right')\n\n### Ref info at side\naxtref = pts.text(0.83, 0.95, 'Ref area:\\n X {}:{}\\n Y {}:{}\\n (start from 0)\\nRef date:\\n {}'.format(refx1, refx2, refy1, refy2, dstr_ref), fontsize=8, va='top')\n\n### Fit function for time series\nfitbox = pts.add_axes([0.83, 0.10, 0.16, 0.25])\nmodels = ['PyRate linear rate', 'Annual+L model', 'Quad model', 'Annual+Q model']\nvisibilities = [True, False, False, False]\nfitcheck = CheckButtons(fitbox, models, visibilities)\nfor label in fitcheck.labels:\n    label.set_fontsize(8)\n\ndef fitfunc(label):\n    index = models.index(label)\n    visibilities[index] = not visibilities[index]\n    lines1[index].set_visible(not lines1[index].get_visible())\n\n    pts.canvas.draw()\n\nfitcheck.on_clicked(fitfunc)\n\n### First show of selected point in image window\npax, = axv.plot([point_y], [point_x], 'k', linewidth=3)\npax2, = axv.plot([point_y], [point_x], 'Pk')\n\n### Plot time series at clicked point\nlastevent = []\ngeocod_flag = False\nlabel1 = 'tscuml'\nlabel2 = 'PyRate linear_rate'\n\nylen = []\n\n\ndef calc_model(dph, imdates_ordinal, xvalues, model, vel1p, intercept1p):\n    \"\"\"\n    Function to calculate model to fit cumulative time series data for one pixel\n    \"\"\"\n    imdates_years = np.divide(imdates_ordinal,365.25) # imdates_ordinal / 365.25\n    xvalues_years = xvalues / 365.25\n\n    A = sm.add_constant(imdates_years)  # [1, t]\n    An = sm.add_constant(xvalues_years)  # [1, t]\n    if model == 0:  # PyRate linear fit results\n        # print(\" M = \", vel1p, \" & C = \", intercept1p )\n        A = np.dot(A, vel1p) + intercept1p\n        An = np.dot(An, vel1p) + intercept1p\n        # pass\n    if model == 1:  # Annual+L\n        sin = np.sin(2 * np.pi * imdates_years)\n        cos = np.cos(2 * np.pi * imdates_years)\n        A = np.concatenate((A, sin[:, np.newaxis], cos[:, np.newaxis]), axis=1)\n        sin = np.sin(2 * np.pi * xvalues_years)\n        cos = np.cos(2 * np.pi * xvalues_years)\n        An = np.concatenate((An, sin[:, np.newaxis], cos[:, np.newaxis]), axis=1)\n    if model == 2:  # Quad\n        A = np.concatenate((A, (imdates_years ** 2)[:, np.newaxis]), axis=1)\n        An = np.concatenate((An, (xvalues_years ** 2)[:, np.newaxis]), axis=1)\n    if model == 3:  # Annual+Q\n        sin = np.sin(2 * np.pi * imdates_years)\n        cos = np.cos(2 * np.pi * imdates_years)\n        A = np.concatenate((A, (imdates_years ** 2)[:, np.newaxis], sin[:, np.newaxis], cos[:, np.newaxis]), axis=1)\n        sin = np.sin(2 * np.pi * xvalues_years)\n        cos = np.cos(2 * np.pi * xvalues_years)\n        An = np.concatenate((An, (xvalues_years ** 2)[:, np.newaxis], sin[:, np.newaxis], cos[:, np.newaxis]), axis=1)\n\n    result = sm.OLS(dph, A, missing='drop').fit()\n    return result.predict(An)\n#################################\n\ndef printcoords(event):\n    global dph, lines1, lines2, lastevent, imdates_ordinal, imdates_dt\n    # outputting x and y coords to console\n    if event.inaxes != axv:\n        return\n    elif event.button != 1:\n        return\n    elif not event.dblclick:  ## Only double click\n        return\n    else:\n        lastevent = event  ## Update last event\n\n    ii = np.int(np.round(event.ydata))\n    jj = np.int(np.round(event.xdata))\n\n    ### Plot on image window\n    ii1h = ii - 0.5; ii2h = ii + 1 - 0.5\n    jj1h = jj - 0.5; jj2h = jj + 1 - 0.5\n\n    pax.set_data([jj1h, jj2h, jj2h, jj1h, jj1h], [ii1h, ii1h, ii2h, ii2h, ii1h])\n    pax2.set_data(jj, ii)\n    pv.canvas.draw()\n\n    axts.cla()\n    axts.grid(zorder=0)\n    axts.set_axisbelow(True)\n    axts.set_xlabel('Date')\n    axts.set_ylabel('Cumulative Displacement (mm)')\n    axts.set_ylim(dmin, dmax)\n\n    ### Get values of noise indices and incidence angle\n    noisetxt = ''\n    for key in mapdict_data:\n        # val_temp = mapdict_data[key]\n        val = mapdict_data[key][ii, jj]\n        # val = val_temp[0,ii,jj]\n        unit = mapdict_unit[key]\n        if key.startswith('Velocity'):\n            continue\n        elif key.startswith('n_') or key == 'mask':\n            noisetxt = noisetxt + '{}: {:d} {}\\n'.format(key, int(val), unit)\n        else:\n            noisetxt = noisetxt + '{}: {:.2f} {}\\n'.format(key, float(val), unit)\n\n    try:  # Only support from Matplotlib 3.1!\n        axts.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc_ts))\n    except:\n        axts.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d'))\n        for label in axts.get_xticklabels():\n            label.set_rotation(20)\n            label.set_horizontalalignment('right')\n\n    ### If not masked\n    ### tscuml file\n    vel1p = vel[0, ii, jj]\n    intercept1p = intercept[0,ii,jj]\n    dph = tscuml[:,ii,jj]\n    err = np.ones(dph.shape) * np.std(dph) # using constant err value\n    errp = mapdict_data['Error'][ii,jj] # for error reading\n\n    ## fit function\n    lines1 = [0, 0, 0, 0]\n    xvalues = np.arange(imdates_ordinal[0], imdates_ordinal[-1], 10)\n    xdates = [dt.fromordinal(pp) for pp in xvalues]\n    # Mask to exclude nan elements\n    mask = ~np.isnan(dph)\n    # remove nan elements from both arrays\n    imo = np.asarray(imdates_ordinal)\n    imo = imo[mask]\n    imt = np.asarray(imdates_dt)\n    imt = imt[mask]\n    dph = dph[mask]\n\n    err = err[mask]\n\n    for model, vis in enumerate(visibilities):\n        if len(dph) > 1:\n            yvalues = calc_model(dph, imo, xvalues, model, vel1p, intercept1p)\n            if model == 0:\n                lines1[model], = axts.plot(xdates, yvalues, 'r-', label=label2, visible=vis, alpha=0.6, zorder=3)\n                axts.legend()\n            else:\n                lines1[model], = axts.plot(xdates, yvalues, 'r-', visible=vis, alpha=0.6, zorder=3)\n    axts.scatter(imt, dph, label=label1, c='b', alpha=0.6, zorder=5)\n    # axts.errorbar(imt, dph, yerr=err, label=label1, fmt='.', color='black', ecolor='blue', elinewidth=0.5, capsize=2)\n    axts.set_title('Velocity = {:.1f} +/- {:.1f} [mm/yr] @({}, {})'.format(vel1p, errp, ii, jj), fontsize=10)\n    # axts.set_ylim(-100,100)\n\n    ### Y axis\n    if ylen:\n        vlim = [np.nanmedian(dph) - ylen / 2, np.nanmedian(dph) + ylen / 2]\n        axts.set_ylim(vlim)\n    ### Legend\n    axts.legend()\n\n    pts.canvas.draw()\n\n\n#%% First show of time series window\nevent = matplotlib.backend_bases.LocationEvent\nevent.xdata = point_x\nevent.ydata = point_y\nevent.inaxes = axv\nevent.button = 1\nevent.dblclick = True\nlastevent = event\nprintcoords(lastevent)\n\n\n#%% Final linking of the canvas to the plots.\ncid = pv.canvas.mpl_connect('button_press_event', printcoords)\nwith warnings.catch_warnings(): ## To silence user warning\n    warnings.simplefilter('ignore', UserWarning)\n    plt.show()\npv.canvas.mpl_disconnect(cid)\n\n"
  },
  {
    "path": "utils/pyrate_pycallgraph.py",
    "content": "# This Python module is part of the PyRate software package\n#\n# Copyright 2022 Geoscience Australia\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\"\"\"\nThis script creates the pycallgraph file output_file='pyrate_with_roipac.png'.\nThis script can be run from the 'PyRate' directory.\nChange the name of the config file as required.\n\"\"\"\n\nimport sys\nfrom pycallgraph import PyCallGraph\nfrom pycallgraph import Config\nfrom pycallgraph import GlobbingFilter\nfrom pycallgraph.output import GraphvizOutput\nfrom pyrate import correct\n\nconfig = Config()\nconfig.trace_filter = GlobbingFilter(exclude=[\n    'pycallgraph.*',\n    '*.secret_function',\n])\n\ngraphviz = GraphvizOutput(output_file='pyrate_with_roipac.png')\nconfig = Config(max_depth=6, groups=False, threaded=True)\n\n# sys.argv[0]: name of this script\n# sys.argv[1]: name of the config file\nsys.argv = ['pyrate_profile.py', 'pyrate.conf']\n\nwith PyCallGraph(output=graphviz, config=config):\n    correct.main()\n"
  }
]